This paper surveys studies on the use of neural networks for optimization in the training-data-free setting. Specifically, we examine the dataless application of neural network architectures in optimization by re-parameterizing problems using fully connected (or MLP), convolutional, graph, and quadratic neural networks. Although MLPs have been used to solve linear programs a few decades ago, this approach has recently gained increasing attention due to its promising results across diverse applications, including those based on combinatorial optimization, inverse problems, and partial differential equations. The motivation for this setting stems from two key (possibly over-lapping) factors: (i) data-driven learning approaches are still underdeveloped and have yet to demonstrate strong results, as seen in combinatorial optimization, and (ii) the availability of training data is inherently limited, such as in medical image reconstruction and other scientific applications. In this paper, we define the dataless setting and categorize it into two variants based on how a problem instance—defined by a single datum—is encoded onto the neural network: (i) architecture-agnostic methods and (ii) architecture-specific methods. Additionally, we discuss similarities and clarify distinctions between the dataless neural network (dNN) settings and related concepts such as zero-shot learning, one-shot learning, lifting in optimization, and over-parameterization.

Recent advances in reinforcement learning (RL) have renewed focus on the design of reward functions that shape agent behavior. Manually crafting such functions is often tedious and error-prone. A more principled alternative is to specify behavioral requirements using a formal, unambiguous language that can be automatically translated into a reward function. Omega-regular languages are a natural choice for this purpose, given their established role in formal verification and synthesis. However, existing approaches using omega-regular specifications typically rely on discounted reward RL in an episodic setting, where the environment is periodically reset to an initial state during learning. This setup is misaligned with the semantics of omega-regular specifications, which describe properties over infinite behavior traces. In such cases, the average reward criterion and the continuing setting—where the agent interacts with the environment over a single, uninterrupted lifetime—are more appropriate. To address the challenges of infinite-horizon, continuing tasks, we restrict our focus to the subclass of omega-regular languages known as absolute liveness specifications. These specifications cannot be violated by any finite prefix of the agent’s behavior, aligning naturally with the continuing setting. We present the first model-free RL framework that translates absolute liveness specifications to average-reward objectives. In contrast to prior work, our approach enables learning in communicating Markov Decision Processes without episodic resetting. We further introduce a reward structure for lexicographic multi-objective optimization, where the goal is to maximize an external average-reward objective among the policies that also maximize the satisfaction probability of a given absolute liveness omega-regular specification. Our method guarantees convergence in unknown communicating MDPs and supports on-the-fly reductions that do not require full knowledge of the environment, thus enabling model-free RL. Empirical results across various benchmarks demonstrate that our average-reward approach in the continuing setting is more effective than competing methods based on discounting.

Standard Reinforcement Learning (RL) algorithms are typically designed to maximize the expected accumulative reward, which may be inadequate in scenarios where risk sensitivity is critical. In this work, the problem of risk-sensitive RL with Iterated Conditional Value at Risk is studied, where the objective is to optimize outcomes under a specified risk level τ at each step. This work provides the first minimax optimal sample complexity analysis for this problem with a generative model. Specifically, the sample complexity is firstly characterized as a function of the number of statesS, actionsA, and effective horizon (1−γ)−1(resp. horizonHin the finite-horizon setting), and is further shown to be minimax optimal via a novel minimax lower bound analysis when the risk level 0Hin the finite horizon setting). For the case when the risk level is small, the limiting case of τ → 0, termed worst-path RL, is then studied, and the minimax optimal sample complexity is also theoretically characterized.

With the widespread deployment of Multimodal Large Language Models (MLLMs) for visual-reasoning tasks, improving their safety has become crucial. Recent research indicates that despite training-time safety alignment, these models remain vulnerable to jailbreak attacks. In this work, we first highlight an important safety gap to describe that alignment achieved solely through safety training may be insufficient against jailbreak attacks. To address this vulnerability, we propose Immune, an inference-time defense framework that leverages a safety reward model through controlled decoding to defend against jailbreak attacks. Additionally, we provide a mathematical characterization of Immune, offering insights on why it improves safety against jailbreaks. Extensive evaluations on diverse jailbreak benchmarks using recent MLLMs reveal that Immune effectively enhances model safety while preserving the model’s original capabilities. For instance, against text-based jailbreak attacks on LLaVA-1.6, Immune reduces the attack success rate by 57.82% and 16.78% compared to the base MLLM and state-of-the-art defense strategy, respectively.

Planning in non-Markovian environments often requires inferring task structures, such as reward machines, through interactions with the environment. Traditional active grammatical inference methods, like Angluin’s L*algorithm, depend on continuous querying to learn task structures for the underlying planning objectives. In contrast, we propose a hybrid approach that combines passive grammatical inference, using the Regular Positive and Negative Inference (RPNI) algorithm, with online planning. By leveraging pre-collected positive and negative trajectories, RPNI learns a deterministic finite automaton (DFA) that captures the task structure, significantly reducing the need for real-time interactions. Subsequently, online planning is conducted over the product MDP, which integrates the environment with the learned DFA. This hybrid methodology minimizes the cost of online interactions and improves learning efficiency in complex environments. Our approach outperforms baseline algorithms in terms of runtime and sample complexity, and is well-suited for real-world scenarios where task structures are implicit, and interactions with the environment are expensive.

We address the problem of video question answering (video QA) with temporal grounding in a weakly supervised setup, without any temporal annotations. Given a video and a question, we generate an open-ended answer grounded with the start and end time. For this task, we propose TOGA: a vision-language model for Temporally Grounded Open-Ended Video QA with Weak Supervision. We instruct-tune TOGA to jointly generate the answer and the temporal grounding. We operate in a weakly supervised setup where the temporal grounding annotations are not available. We generate pseudo labels for temporal grounding and ensure the validity of these labels by imposing a consistency constraint between the question of a grounding response and the response generated by a question referring to the same temporal segment. We notice that jointly generating the answers with the grounding improves performance on question answering as well as grounding. We evaluate TOGA on grounded QA and open-ended QA tasks. For grounded QA, we consider the NExT-GQA benchmark which is designed to evaluate weakly supervised grounded question answering. For open-ended QA, we consider the MSVD-QA and ActivityNet-QA benchmarks. We achieve state-of-the-art performance for both tasks on these benchmarks.

Combinatorial Optimization (CO) addresses many important problems, including the challenging Maximum Independent Set (MIS) problem. Alongside exact and heuristic solvers, differentiable approaches have emerged, often using continuous relaxations of quadratic objectives. Noting that an MIS in a graph is a Maximum Clique (MC) in its complement, we propose a new quadratic formulation for MIS by incorporating an MC term, improving convergence and exploration. We show that every maximal independent set corresponds to a local minimizer, derive conditions with respect to the MIS size, and characterize stationary points. To tackle the non-convexity of the objective, we propose optimizing several initializations in parallel using momentum-based gradient descent, complemented by an efficient MIS checking criterion derived from our theory. We dub our method as parallelized Clique-Informed Quadratic Optimization for MIS (pCQO-MIS). Our experimental results demonstrate the effectiveness of the proposed method compared to exact, heuristic, sampling, and data-centric approaches. Notably, our method avoids the out-of-distribution tuning and reliance on (un)labeled data required by data-centric methods, while achieving superior MIS sizes and competitive run-time relative to their inference time. Additionally, a key advantage of pCQO-MIS is that, unlike exact and heuristic solvers, the run-time scales only with the number of nodes in the graph, not the number of edges. Our code is available at the GitHub repository: https://github.com/ledenmat/pCQO-mis-benchmark/tree/refactor.

Actor-critic methods for decentralized multi-agent reinforcement learning (MARL) facilitate collaborative optimal decision making without centralized coordination, thus enabling a wide range of applications in practice. To date, however, most theoretical convergence studies for existing actor-critic decentralized MARL methods are limited to the guarantee of a stationary solution under the linear function approximation. This leaves a significant gap between the highly successful use of deep neural actor-critic for decentralized MARL in practice and the current theoretical understanding. To bridge this gap, in this paper, we make the first attempt to develop a deep neural actor-critic method for decentralized MARL, where both the actor and critic components are inherently non-linear. We show that our proposed method enjoys a global optimality guarantee with a finite-time convergence rate of $\mathcal{O}(1/T)$, where $T$ is the total iteration times. This marks the first global convergence result for deep neural actor-critic methods in the MARL literature. We also conduct extensive numerical experiments, which verify our theoretical results.

With wireless devices increasingly forming a unified smart network for seamless, user-friendly operations, random access (RA) medium access control (MAC) design is considered a key solution for handling unpredictable data traffic from multiple terminals. However, it remains challenging to design an effective RA-based MAC protocol to minimize collisions and ensure transmission fairness across the devices. While existing multi-agent reinforcement learning (MARL) approaches with centralized training and decentralized execution (CTDE) have been proposed to optimize RA performance, their reliance on centralized training and the significant overhead required for information collection can make real-world applications unrealistic. In this work, we adopt a fully decentralized MARL architecture, where policy learning does not rely on centralized tasks but leverages consensus-based information exchanges across devices. We design our MARL algorithm over an actor-critic (AC) network and propose exchanging only local rewards to minimize communication overhead. Furthermore, we provide a theoretical proof of convergence for our approach. Numerical experiments show that our proposed MARL algorithm can significantly improve RA network performance compared to other baselines.

Current Retrieval-Augmented Generation systems use uniform processing, causing inefficiency as simple queries consume resources similar to complex multi-hop tasks. We present SymRAG, a framework that introduces adaptive query routing via real-time complexity and load assessment to select symbolic, neural, or hybrid pathways. SymRAG’s neuro-symbolic approach adjusts computational pathways based on both query characteristics and system load, enabling efficient resource allocation across diverse query types. By combining linguistic and structural query properties with system load metrics, SymRAG allocates resources proportional to reasoning requirements. Evaluated on 2,000 queries across HotpotQA (multi-hop reasoning) and DROP (discrete reasoning) using Llama-3.2-3B and Mistral-7B models, SymRAG achieves competitive accuracy (97.6–100.0% exact match) with efficient resource utilization (3.6–6.2% CPU utilization, 0.985–3.165s processing). Disabling adaptive routing increases processing time by 169–1151%, showing its significance for complex models. These results suggest adaptive computation strategies are more sustainable and scalable for hybrid AI systems that use dynamic routing and neuro-symbolic frameworks.

We address the problem of detecting out-of-context (OOC) objects in a scene. Given an image, we aim to detect whether the image has objects that are not present in their usual context and localize such OOC objects. Existing approaches for OOC detection rely on defining the common context in terms of the manually constructed features, such as the co-occurrence of objects, spatial relations between objects, and shape and size of the objects, and then learning such context for a given dataset. But context is often nu-anced ranging from very common to very surprising. Further, learned context from specific datasets may not be generalized as datasets may not truly represent the human notion of what is in context. Motivated by the success of large language models and more generally, foundation models (FMs) in common sense reasoning, we investigate the FM's ability to capture a more generalized notion of context. We find that a pre-trained FM, such as GPT-4, provides a more nuanced notion of OOC and enables zero-shot OOC detection when coupled with other pre-trained FMs for caption generation such as BLIP-2, and image in-painting with Sta-ble Diffusion 2.0. Our approach does not need any dataset-specific training. We demonstrate the efficacy of our approach on two OOC object detection datasets, achieving 90.8% zero-shot accuracy on the MIT-OOC dataset and 87.26% on the IJCAI22-COCO-OOC dataset.

MOTIVATION: Integrating Large Language Models (LLMs) with research tools presents technical and reproducibility challenges for biomedical research. While commercial artificial intelligence (AI) systems are easy to adopt, they obscure data provenance, lack transparency, and can generates false information, making them unfit for many research problems. To address these challenges, we developed the Bioinformatics Retrieval Augmented Digital (BRAD) agent software system. RESULTS: Here, we introduce BRAD, an agentic system that integrates LLMs with external tools and data to streamline research workflows. BRAD's modular agents retrieve information from literature, custom software, and online databases while maintaining transparent protocols to increase the reliability of AI generated results. We apply BRAD to a biomarker discovery pipeline, automating both execution and the generation of enrichment reports. This workflow contextualizes user data within the literature, enabling a level of interpretation and automation that surpasses conventional research tools. Beyond the workflow we highlight here, BRAD is a flexible system that has been deployed in other applications including a chatbot, video RAG, and analysis of single cell data. AVAILABILITY AND IMPLEMENTATION: The source code for BRAD is available at https://github.com/Jpickard1/BRAD; Information for pip installation, tutorials, documentation, and further information can be found at: ReadTheDocs.

Database migration is a ubiquitous need faced by enterprises that generate and use vast amounts of data. This is because of database software updates, or it is from changes to hardware, project standards, and other business factors. Migrating a large collection of databases is a way more challenging task than migrating a single database because of the presence of additional constraints. These constraints include capacities of shifts and sizes of databases. In this paper, we present a comprehensive framework that can be used to model database migration problems of different enterprises with customized constraints by appropriately instantiating the parameters of the framework. These parameters are the size of each database, the size of each shift, and the cost of testing each application. Each of these parameters can be either constant or arbitrary. Additionally, the cost of testing an application can be proportional to the number of databases that the application uses. We establish the computational complexities of a number of instantiations of this framework. We present fixed-parameter intractability results for various relevant parameters of the database migration problem. We also provide approximability and inapproximability results as well as lower bounds for the running time of any exact algorithm for the database migration problem. We show that the database migration problem is equivalent to a variation of the classical hypergraph partitioning problem. Our theoretical results also imply new theoretical results for the hypergraph partitioning problem that are interesting in their own right. Finally, we adapt heuristic algorithms devised for the hypergraph partitioning problem to the database migration problem, and we also give experimental results for the adapted heuristics. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: B. Caskurlu and U. U. Acikalin are supported by The Scientific and Technological Research Council of Türkiye [Grant 122E599]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0021 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0021 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

We present a modular approach to reinforcement learning (RL) in environments consisting of simpler components evolving in parallel. A monolithic view of such modular environments may be prohibitively large to learn, or may require unrealizable communication between the components in the form of a centralized controller. Our proposed approach is based on the assume-guarantee paradigm where the optimal control for the individual components is synthesized in isolation by making assumptions about the behaviors of neighboring components, and providing guarantees about their own behavior. We express these assume-guarantee contracts as regular languages and provide automatic translations to scalar rewards to be used in RL. By combining local probabilities of satisfaction for each component, we provide a lower bound on the probability of satisfaction of the complete system. By solving a Markov game for each component, RL can produce a controller for each component that maximizes this lower bound. The controller utilizes the information it receives through communication, observations, and any knowledge of a coarse model of other agents. We experimentally demonstrate the efficiency of the proposed approach on a variety of case studies.

While deep-learning-enabled recommender systems demonstrate strong performance benchmarks, many struggle to adapt effectively in real-world environments due to limited use of user-item relationship data and insufficient transparency in recommendation generation. Traditional collaborative filtering approaches fail to integrate multifaceted item attributes, and although Factorization Machines account for item-specific details, they overlook broader relational patterns. Collaborative knowledge graph-based models have progressed by embedding user-item interactions with item-attribute relationships, offering a holistic perspective on interconnected entities. However, these models frequently aggregate attribute and interaction data in an implicit manner, leaving valuable relational nuances underutilized.This study introduces the Knowledge Graph Attention Network with Information Fusion (KGIF), a specialized framework designed to merge entity and relation embeddings explicitly through a tailored self-attention mechanism. The KGIF framework integrates reparameterization via dynamic projection vectors, enabling embeddings to adaptively represent intricate relationships within knowledge graphs. This explicit fusion enhances the interplay between user-item interactions and item-attribute relationships, providing a nuanced balance between user-centric and item-centric representations. An attentive propagation mechanism further optimizes knowledge graph embeddings, capturing multi-layered interaction patterns. The contributions of this work include an innovative method for explicit information fusion, improved robustness for sparse knowledge graphs, and the ability to generate explainable recommendations through interpretable path visualization. The implementation and datasets for this study are publicly available1.

We present a new xor-based attention function for efficient hardware implementation of transformers. While the standard attention mechanism relies on matrix multiplication between the key and the transpose of the query, we propose replacing the computation of this attention function with bitwise xor operations. We mathematically analyze the information-theoretic properties of the standard multiplication-based attention, demonstrating that it preserves input entropy, and then computationally show that the xor-based attention approximately preserves the entropy of its input despite small variations in correlations between the inputs. Across various admittedly simple tasks, including arithmetic, sorting, and text generation, we show comparable performance to baseline methods using scaled GPT models. The xor-based computation of the attention function shows substantial improvement in power consumption, latency, and circuit area compared to the corresponding multiplication-based attention function. This hardware efficiency makes xor-based attention more compelling for the deployment of transformers under tight resource constraints, opening new application domains in sustainable energy-efficient computing. Additional optimizations to the xor-based attention function can further improve efficiency of transformers.

Semantic reasoning and dynamic planning capabilities are crucial for an autonomous agent to perform complex navigation tasks in unknown environments. It requires a large amount of common-sense knowledge, that humans possess, to succeed in these tasks. We present SayNav, a new approach that leverages human knowledge from Large Language Models (LLMs) for efficient generalization to complex navigation tasks in unknown large-scale environments. SayNav uses a novel grounding mechanism, that incrementally builds a 3D scene graph of the explored environment as inputs to LLMs, for generating feasible and contextually appropriate high-level plans for navigation. The LLM-generated plan is then executed by a pre-trained low-level planner, that treats each planned step as a short-distance point-goal navigation sub-task. SayNav dynamically generates step-by-step instructions during navigation and continuously refines future steps based on newly perceived information. We evaluate SayNav on multi-object navigation (MultiON) task, that requires the agent to utilize a massive amount of human knowledge to efficiently search multiple different objects in an unknown environment. We also introduce a benchmark dataset for MultiON task employing ProcTHOR framework that provides large photo-realistic indoor environments with variety of objects. SayNav achieves state-of-the-art results and even outperforms an oracle based baseline with strong ground-truth assumptions by more than 8% in terms of success rate, highlighting its ability to generate dynamic plans for successfully locating objects in large-scale new environments. The code, benchmark dataset and demonstration videos are accessible at https://www.sri.com/ics/computer-vision/saynav.

Reinforcement Learning (RL) has made significant strides in enabling artificial agents to learn diverse behaviors. However, learning an effective policy often requires a large number of environment interactions. To mitigate sample complexity issues, recent approaches have used high-level task specifications, such as Linear Temporal Logic (LTLf) formulas or Reward Machines (RM), to guide the learning progress of the agent. In this work, we propose a novel approach, called Logical Specifications-guided Dynamic Task Sampling (LSTS), that learns a set of RL policies to guide an agent from an initial state to a goal state based on a high-level task specification, while minimizing the number of environmental interactions. Unlike previous work, LSTS does not assume information about the environment dynamics or the Reward Machine, and dynamically samples promising tasks that lead to successful goal policies. We evaluate LSTS on a gridworld and show that it achieves improved time-to-threshold performance on complex sequential decision-making problems compared to state-of-the-art RM and Automaton-guided RL baselines, such as Q-Learning for Reward Machines and Compositional RL from logical Specifications (DIRL). Moreover, we demonstrate that our method outperforms RM and Automaton-guided RL baselines in terms of sample-efficiency, both in a partially observable robotic task and in a continuous control robotic manipulation task.

The design of complex cyber-physical systems involves balancing multiple, often conflicting performance objectives. In practice, some design requirements remain implicit, embedded in the intuition and expertise of seasoned designers who have worked on similar systems for years. These designers rely on their experience to explore a limited set of promising design candidates, evaluating or simulating them with detailed but computationally slow scientific models. The typical goal is to produce a diverse array of high-performing configurations that offer flexibility in trade-offs and avoid premature commitment to a specific design. In this invited talk, we describe an AI assistant that leverages neuro-symbolic machine learning to automate parts of the system design process. Our approach extends oracle-guided inductive synthesis by integrating a hierarchy of oracles, ranging from slow, detailed scientific models to faster but lower-fidelity deep neural network surrogates and symbolic rules. This approach accelerates design iterations, especially during early design phases. We employ deep generative models in the form of fine-tuned large language models to learn the valid design space, followed by joint exploration and optimization across this learned manifold. This allows the generation of a diverse set of optimal designs based on specified performance objectives.

Robust Markov decision processes (MDPs) aim to find a policy that optimizes the worst-case performance over an uncertainty set of MDPs. Existing studies mostly have focused on the robust MDPs under the discounted reward criterion, leaving the ones under the average-reward criterion largely unexplored. In this paper, we develop the first comprehensive and systematic study of robust average-reward MDPs, where the goal is to optimize the long-term average performance under the worst case. Our contributions are four-folds: (1) we prove the uniform convergence of the robust discounted value function to the robust average-reward function as the discount factor γ goes to 1; (2) we derive the robust average-reward Bellman equation, characterize the structure of its solution set, and prove the equivalence between solving the robust Bellman equation and finding the optimal robust policy; (3) we design robust dynamic programming algorithms, and theoretically characterize their convergence to the optimal policy; and (4) we design two model-free algorithms unitizing the multi-level Monte-Carlo approach, and prove their asymptotic convergence

In robust Markov decision processes (MDPs), the uncertainty in the transition kernel is addressed by finding a policy that optimizes the worst-case performance over an uncertainty set of MDPs. While much of the literature has focused on discounted MDPs, robust average-reward MDPs remain largely unexplored. In this paper, we focus on robust average-reward MDPs, where the goal is to find a policy that optimizes the worst-case average reward over an uncertainty set. We first take an approach that approximates average-reward MDPs using discounted MDPs. We prove that the robust discounted value function converges to the robust average-reward as the discount factor goes to 1, and moreover when it is large, any optimal policy of the robust discounted MDP is also an optimal policy of the robust average-reward. We further design a robust dynamic programming approach, and theoretically characterize its convergence to the optimum. Then, we investigate robust average-reward MDPs directly without using discounted MDPs as an intermediate step. We derive the robust Bellman equation for robust average-reward MDPs, prove that the optimal policy can be derived from its solution, and further design a robust relative value iteration algorithm that provably finds its solution, or equivalently, the optimal robust policy.

Robust Markov decision processes (MDPs) address the challenge of model uncertainty by optimizing the worst-case performance over an uncertainty set of MDPs. In this paper, we focus on the robust average-reward MDPs under the model-free setting. We first theoretically characterize the structure of solutions to the robust average-reward Bellman equation, which is essential for our later convergence analysis. We then design two model-free algorithms, robust relative value iteration (RVI) TD and robust RVI Q-learning, and theoretically prove their convergence to the optimal solution. We provide several widely used uncertainty sets as examples, including those defined by the contamination model, total variation, Chi-squared divergence, Kullback-Leibler (KL) divergence, and Wasserstein distance.

The evaluation of classifiers' robustness against adversarial attacks is typically performed through metrics based on the minimal perturbation required for misclassification. The conventional method of generating these perturbations relies on setting a limit on the maximum allowed perturbation (restricted attack method) and a non-targeted attack formulation. This approach, however, disregards any relationships between classes. Our paper introduces a novel, non-targeted, bound-restricted method for achieving coarse misclassification, so that the perturbed feature is classified outside its true coarse class. We present an efficient, single-step solution to the coarse misclassification problem and analyze its computational requirements. Our experiments showcase the superiority of our method, surpassing state-of-the-art in terms of both the perceptibility of adversarial examples and runtime.

Neural SDEs with Brownian motion as noise lead to smoother attributions than traditional ResNets. Various attribution methods such as saliency maps, integrated gradients, DeepSHAP and DeepLIFT have been shown to be more robust for neural SDEs than for ResNets using the recently proposed sensitivity metric. In this paper, we show that neural SDEs with adaptive attribution-driven noise lead to even more robust attributions and smaller sensitivity metrics than traditional neural SDEs with Brownian motion as noise. In particular, attribution-driven shaping of noise leads to 6.7%, 6.9% and 19.4% smaller sensitivity metric for integrated gradients computed on three discrete approximations of neural SDEs with standard Brownian motion noise: stochastic ResNet-50, WideResNet-101 and ResNeXt-101 models respectively. The neural SDE model with adaptive attribution-driven noise leads to 25.7% and 4.8% improvement in the SIC metric over traditional ResNets and Neural SDEs with Brownian motion as noise. To the best of our knowledge, we are the first to propose the use of attributions for shaping the noise injected in neural SDEs, and demonstrate that this process leads to more robust attributions than traditional neural SDEs with standard Brownian motion as noise.

Adversarial attacks can drastically reduce the accuracy and confidence level of classifiers while being imperceptible. Existing studies on the topic have largely focused on one-stage classifiers. In this paper, we study the robustness of two Two-Stage Hierarchical Classifier models, the flat and top-down hierarchical classifiers, termed FHC and TDHC respectively, to targeted and confidence reduction attacks. We formulate feasibility programs based on similarity and distance measures for the one-shot synthesis of adversarial examples, and devise a generative approach to the solution. In this approach, the adjustable parameters of a generative network are iteratively updated by optimizing loss functions for the dual objective of (i) low attack perceptibility and (ii) small distance from desired soft predictions. We demonstrate the performance of the proposed approach in terms of imperceptibilty and measures of attack success, and show it compares favorably with state-of-the-art techniques.

Responsible integration of deep neural networks into the design of trustworthy systems requires the ability to explain decisions made by these models. Explainability and transparency are critical for system analysis, certification, and human-machine teaming. We have recently demonstrated that neural stochastic differential equations (SDEs) present an explanation-friendly DNN architecture. In this paper, we present ExplainIt, an online tool for explaining AI decisions that uses neural SDEs to create visually sharper and more robust attributions than traditional residual neural networks. Our tool shows that the injection of noise in every layer of a residual network often leads to less noisy and less fragile integrated gradient attributions. The discrete neural stochastic differential equation model is trained on the ImageNet data set with a million images, and the demonstration produces robust attributions on images in the ImageNet validation library and on a variety of images in the wild. Our online tool is hosted publicly for educational purposes.

Deep Learning (DL) is being applied in various domains, especially in safety-critical applications such as autonomous driving. Consequently, it is of great significance to ensure the robustness of these methods and thus counteract uncertain behaviors caused by adversarial attacks. In this paper, we use gradient heatmaps to analyze the response characteristics of the VGG-16 model when the input images are mixed with adversarial noise and statistically similar Gaussian random noise. In particular, we compare the network response layer by layer to determine where errors occurred. Several interesting findings are derived. First, compared to Gaussian random noise, intentionally generated adversarial noise causes severe behavior deviation by distracting the area of concentration in the networks. Second, in many cases, adversarial examples only need to compromise a few intermediate blocks to mislead the final decision. Third, our experiments revealed that specific blocks are more vulnerable and easier to exploit by adversarial examples. Finally, we demonstrate that the layers Block4_conv1 and Block5_ cov1 of the VGG-16 model are more susceptible to adversarial attacks. Our work could potentially provide useful insights into developing more reliable Deep Neural Network (DNN) models.

Despite the recent state-of-the-art performance of Deep Learning (DL), imbalanced graph-structured data remains an open challenge in social science, traffic networks, and biomedical informatics. Recently, a surge in research on Representation Learning has significantly improved the performance of DL algorithms on imbalanced non-graph-structured data. In addition, Graph Neural Networks (GNNs) already in widespread use for representing graph-structured data in DL models with more advanced techniques in neural message-passing and deep graph embedding. However, most existing works are based on assumptions that oversimplify the complexity of real-world problems. In this paper, we propose Reinforced Contrastive GNNs (RCGNN), a novel graph representation learning model for anomaly detection with multi-relational graph-structured data. The proposed model produces a neighbor selection with Reinforcement Learning (RL) based on the similarity of neighborhoods in multi-relational structured graphs. In addition, the graph representation is learned by an adaptive AutoEncoder (AE) with Triplet Loss (TL) in Contrastive Learning. By aggregating the nodes with the highest similarities in their features and the importance of each node, our model is able to construct the multi-relational graphs by keeping the complexity of the graph structure as well as the relation-dependency representations. Experiments on multiple benchmark data sets demonstrate the advantage of RCGNN in learning better representations for multi-relational graphs. Furthermore, compared to other GNN models, our model shows better performance in accuracy, F1, and PR AUC scores.

Reinforcement learning and planning have been revolutionized in recent years, due in part to the mass adoption of deep convolutional neural networks and the resurgence of powerful methods to refine decision-making policies. However, the problem of sparse reward signals and their representation remains pervasive in many domains. While various rewardshaping mechanisms and imitation learning approaches have been proposed to mitigate this problem, the use of humanaided artificial rewards introduces human error, sub-optimal behavior, and a greater propensity for reward hacking. In this paper, we mitigate this by representing objectives as automata in order to define novel reward shaping functions over this structured representation. In doing so, we address the sparse rewards problem within a novel implementation of Monte Carlo Tree Search (MCTS) by proposing a reward shaping function which is updated dynamically to capture statistics on the utility of each automaton transition as it pertains to satisfying the goal of the agent. We further demonstrate that such automaton-guided reward shaping can be utilized to facilitate transfer learning between different environments when the objective is the same.

Several methods have recently been developed for computing attributions of a neural network's prediction over the input features. However, these existing approaches for computing attributions are noisy and not robust to small perturbations of the input. This paper uses the recently identified connection between dynamical systems and residual neural networks to show that the attributions computed over neural stochastic differential equations (SDEs) are less noisy, visually sharper, and quantitatively more robust. Using dynamical systems theory, we theoretically analyze the robustness of these attributions. We also experimentally demonstrate the efficacy of our approach in providing smoother, visually sharper and quantitatively robust attributions by computing attributions for ImageNet images using ResNet-50, WideResNet-101 models and ResNeXt-101 models.

Quantum algorithms are notoriously hard to design and require significant human ingenuity and insight. We present a new methodology called Quantum Automated Synthesizer (QUASH) that can automatically synthesize quantum circuits using decision procedures that perform symbolic reasoning for combinatorial search. Our automated synthesis approach constructs finite symbolic abstract models of the quantum gates automatically and discovers a quantum circuit as a composition of quantum gates using these symbolic models. Our key insight is that most current quantum algorithms work on a finite number of classical inputs, and hence, their correctness proof relies only on reasoning about a finite set of quantum states that can be represented using finite symbolic systems. We demonstrate the potential of our approach by automatically synthesizing four quantum circuits and re-discovering the Bernstein-Vazirani quantum algorithm using state-of-the-art decision procedures. Our synthesis approach only requires distinguishing between a finite set of symbolic quantum states; for example, the synthesis of the Bernstein-Vazirani quantum algorithm only requires reasoning about the following qubit states: |0, |1, -i|0, i|1, |+, |-, e1/2|1i, eiπ/4|1 and a remaining symbolic state representing all other possible quantum states. Our approach leverages decision procedures and theorem provers to assist in the discovery of new quantum algorithms and is a step towards the automation of quantum algorithm design.

The planning domain has experienced increased interest in the formal synthesis of decision-making policies. This formal synthesis typically entails finding a policy which satisfies formal specifications in the form of some well-defined logic. While many such logics have been proposed with varying degrees of expressiveness and complexity in their capacity to capture desirable agent behavior, their value is limited when deriving decision-making policies which satisfy certain types of asymptotic behavior in general system models. In particular, we are interested in specifying constraints on the steady-state behavior of an agent, which captures the proportion of time an agent spends in each state as it interacts for an indefinite period of time with its environment. This is sometimes called the average or expected behavior of the agent and the associated planning problem is faced with significant challenges unless strong restrictions are imposed on the underlying model in terms of the connectivity of its graph structure. In this paper, we explore this steady-state planning problem that consists of deriving a decision-making policy for an agent such that constraints on its steady-state behavior are satisfied. A linear programming solution for the general case of multichain Markov Decision Processes (MDPs) is proposed and we prove that optimal solutions to the proposed programs yield stationary policies with rigorous guarantees of behavior.

Trust in predictions made by machine learning models is increased if the model generalizes well on previously unseen samples and when inference is accompanied by cogent explanations of the reasoning behind predictions. In the image classification domain, generalization can be assessed through accuracy, sensitivity, and specificity. Explainability can be assessed by how well the model localizes the object of interest within an image. However, both generalization and explainability through localization are degraded in scenarios with significant overlap between classes. We propose a method based on binary expert networks that enhances the explainability of image classifications through better localization by mitigating the model uncertainty induced by class overlap. Our technique performs discriminative localization on images that contain features with significant class overlap, without explicitly training for localization. Our method is particularly promising in real-world class overlap scenarios, such as COVID-19 and pneumonia, where expertly labeled data for localization is not readily available. This can be useful for early, rapid, and trustworthy screening for COVID-19.

The formal synthesis of automated or autonomous agents has elicited strong interest from the artificial intelligence community in recent years. This problem space broadly entails the derivation of decision-making policies for agents acting in an environment such that a formal specification of behavior is satisfied. Popular formalisms for such specifications include the quintessential Linear Temporal Logic (LTL) and Computation Tree Logic (CTL) which reason over infinite sequences and trees, respectively, of states. However, the related and relevant problem of reasoning over the frequency with which states are visited infinitely and enforcing behavioral specifications on the same has received little attention. That problem, known as Steady-State Policy Synthesis (SSPS) or steady-state control, is the focus of this paper. Prior related work has been mostly confined to unichain Markov Decision Processes (MDPs), while a tractable solution to the general multichain setting heretofore remains elusive. In this paper, we provide a solution to the latter within the context of multichain MDPs over a class of policies that account for all possible transitions in the given MDP. The solution policy is derived from a novel linear program (LP) that encodes constraints on the limiting distributions of the Markov chain induced by said policy. We establish a one-to-one correspondence between the feasible solutions of the LP and the stationary distributions of the induced Markov chains. The derived policy is shown to maximize the reward among the constrained class of stationary policies and to satisfy the specification constraints even when it does not exercise all possible transitions.

Machine learning implements backpropagation via abundant training samples. We demonstrate a multi-stage learning system realized by a promising non-volatile memory device, the domain-wall magnetic tunnel junction (DW-MTJ). The system consists of unsupervised (clustering) as well as supervised sub-systems, and generalizes quickly (with few samples). We demonstrate interactions between physical properties of this device and optimal implementation of neuroscience-inspired plasticity learning rules, and highlight performance on a suite of tasks. Our energy analysis confirms the value of the approach, as the learning budget stays below 20μJ even for large tasks used typically in machine learning.

In this paper, we introduce the Steady-State Policy Synthesis (SSPS) problem which consists of finding a stochastic decision-making policy that maximizes expected rewards while satisfying a set of asymptotic behavioral specifications. These specifications are determined by the steady-state probability distribution resulting from the Markov chain induced by a given policy. Since such distributions necessitate recurrence, we propose a solution which finds policies that induce recurrent Markov chains within possibly non-recurrent Markov Decision Processes (MDPs). The SSPS problem functions as a generalization of steady-state control, which has been shown to be in PSPACE. We improve upon this result by showing that SSPS is in P via linear programming. Our results are validated using CPLEX simulations on MDPs with over 10000 states. We also prove that the deterministic variant of SSPS is NP-hard.

Compact in-memory circuits for implementing n-bit addition with varying degrees of destructive operations are presented. The non-destructive, semi-destructive, and fully-destructive adder variants are posed as bounded model checking procedures for design synthesis. The resulting designs are shown to be state-of-the-art in the number of execution steps required for computation when compared to other serial adders.

The memory-processor bottleneck and scaling difficulties of the CMOS transistor have given rise to a plethora of research initiatives to overcome these challenges. Popular among these is in-memory crossbar computing. In this paper, we propose a framework for synthesizing logic-in-memory circuits based on the behavior of paths of electric current throughout the memory. Limitations of using only bidirectional components with this approach are also established. We demonstrate the effectiveness of our approach by generating η-bit addition circuits that can compute using a constant number of read and write cycles.

A novel in-memory computing design that can compute single-source reachability and transitive closure of graphs is introduced. The proposed design leverages the parallel flow of information in three-dimensional crosspoint memories and can be implemented using memories with two layers of 1-diode 1-resistor (1D1R) interconnects. Our logic-in-memory designs mitigate the infamous memory-processor bottleneck characteristic of John von Neumann architectures and have runtime complexities of O(n) and O(n2) using O(n2) memory cells for the single-source reachability and transitive closure problems, respectively, where n is the number of nodes in the graph. This work builds upon preliminary results presented in [1].

The infamous memory-processor bottleneck has motivated the search for logic-in-memory architectures. In this paper, we demonstrate how the transitive closure problem can be solved through in-memory computing within a 3D crosspoint memory. The proposed architecture requires only two layers of 1-diode 1-resistor (1D1R) interconnects and external feedback loops.

Energy concerns, the infamous memory wall, and the enormous data deluge of the current big-data age have made the integration of processing and memory elements into a very appealing paradigm. In this paper, we focus on a computation-in-memory solution to the problem of multiplying a set of Boolean matrices, also known as Boolean matrix chain multiplication (BMCM). This is a fundamental computational task with applications in graph theory, group testing, data compression, and digital signal processing. In particular, we propose a framework for mapping arbitrary instances of BMCM to a 3-dimensional (3D) crossbar memory architecture consisting of 1-diode 1-resistor (1D1R) structures.

We present the design and implementation of a full adder circuit that exploits the natural flow of current through nanowires and More-than-Moore nano-devices in two dimensional crossbars. We evaluate the speed and energy efficiency of our design and compare it to equivalent one-bit adder designs using CMOS and nanoscale memristors. Our memristive full adder circuit has been shown to be an order of magnitude faster and more energy-efficient than equivalent CMOS designs. Our circuit is an order of magnitude more compact that equivalent CMOS designs. We also argue that our design occupies less area and is faster than competing memristor designs.

Boolean matrix multiplication (BMM) is a fundamental problem with applications in graph theory, group testing, data compression, and digital signal processing (DSP). The search for efficient BMM algorithms has produced several fast, albeit impractical, algorithms with sub-cubic time complexity. In this paper, we propose a memristor-crossbar framework for computing BMM at the hardware level in linear time. Our design leverages the diode-like characteristics of recently studied rectifying memristors to resolve the pervasive sneak paths constraint that is ubiquitous in crossbar computing.

In this paper, we present two new problems and a theoretical framework that can be used to route information in heterogeneous communication networks. These problems are the cardinality-constrained and interval-constrained paths problems and they consist of finding paths in a network such that cardinality constraints on the number of nodes belonging to different sets of labels are satisfied. We propose a novel algorithm for finding said paths and demonstrate the effectiveness of our approach on networks of various sizes.

There has been a surge of interest in the effective storage and computation of data using nanoscale crossbars. In this paper, we present a new method for automating the design of fault-tolerant crossbars that can effectively compute Boolean formula. Our approach leverages recent advances in Satisfiability Modulo Theories (SMT) solving for quantified bit-vector formula (QBVF). We demonstrate that our method is well-suited for fault-tolerant computation and can perform Boolean computations despite stuck-open and stuck-closed interconnect defects as well as wire faults. We employ our framework to generate various arithmetic and logical circuits that compute correctly despite the presence of stuck-at faults as well as broken wires.