Exploring chemical space to find novel molecules that simultaneously satisfy multiple properties is crucial in drug discovery. However, existing methods often struggle with trading off multiple properties due to the conflicting or correlated nature of chemical properties. To tackle this issue, we introduce InversionGNN framework, an effective yet sample-efficient dual-path graph neural network (GNN) for multi-objective drug discovery. In the direct prediction path of InversionGNN, we train the model for multi-property prediction to acquire knowledge of the optimal combination of functional groups. Then the learned chemical knowledge helps the inversion generation path to generate molecules with required properties. In order to decode the complex knowledge of multiple properties in the inversion path, we propose a gradient-based Pareto search method to balance conflicting properties and generate Pareto optimal molecules. Additionally, InversionGNN is able to search the full Pareto front approximately in discrete chemical space. Comprehensive experimental evaluations show that InversionGNN is both effective and sample-efficient in various discrete multi-objective settings including drug discovery.

Circuit discovery has recently attracted attention as a potential research direction to explain the non-trivial behaviors of language models. It aims to find the computational subgraphs, also known as circuits, within the model that are responsible for solving specific tasks. However, most existing studies overlook the holistic nature of these circuits and require designing specific corrupted activations for different tasks, which is inaccurate and inefficient. In this work, we propose an end-to-end approach based on the principle of Information Bottleneck, called IBCircuit, to holistically identify informative circuits. In contrast to traditional causal interventions, IBCircuit is an optimization framework for holistic circuit discovery and can be applied to any given task without tediously corrupted activation design. In both the Indirect Object Identification (IOI) and Greater-Than tasks, IBCircuit identifies more faithful and minimal circuits in terms of critical node components and edge components compared to recent related work.

The LifeTime Value (LTV) prediction, which endeavors to forecast the cumulative purchase contribution of a user to a particular item, remains a vital challenge that advertisers are keen to resolve. A precise LTV prediction system enhances the alignment of user interests with meticulously designed advertisements, thereby generating substantial profits for advertisers. Nonetheless, this issue is complicated by the paucity of data typically observed in real-world advertising scenarios. The purchase rate among registered users is often as critically low as 0.1%, resulting in a dataset where the majority of users make only several purchases. Consequently, there is insufficient supervisory signal for effectively training the LTV prediction model. An additional challenge emerges from the interdependencies among tasks with high correlation. It is a common practice to estimate a user's contribution to a game over a specified temporal interval. Varying the lengths of these intervals corresponds to distinct predictive tasks, which are highly correlated. For instance, predictions over a 7-day period are heavily reliant on forecasts made over a 3-day period, where exceptional cases can adversely affect the accuracy of both tasks. In order to comprehensively address the aforementioned challenges, we introduce an innovative framework denoted as Graph-Represented Pareto-Optimal LifeTime Value prediction (GRePO-LTV). Graph representation learning is initially employed to address the issue of data scarcity. Subsequently, Pareto-Optimization is utilized to manage the interdependence of prediction tasks. Our method is evaluated using a proprietary offline mini-game recommendation dataset in conjunction with an online A/B test. The implementation of our method results in a significant enhancement within the offline dataset. Moreover, the A/B test demonstrates encouraging outcomes, increasing average Gross Merchandise Value (GMV) by 8.4%.

Recently, generative models based on the diffusion process have emerged as a promising direction for automating the design of molecules. However, directly adding continuous Gaussian noise to discrete graphs leads to the problem that the generated data do not conform to the discrete graph data distribution in the training set. Current graph diffusion models either corrupt discrete data through a transition matrix or relax the discrete data to continuous space for the diffusion process. These approaches make it hard to perform extensible conditional generation, such as adapting to text-based conditions, due to the lack of embedding representations and require significant computation resources due to the diffusion process of the bond type matrix. This paper introduces the Hierarchical Graph Latent Diffusion Model (HGLDM), a novel variant of latent diffusion models that overcomes the problem of applying continuous diffusion models directly to discrete graph data. Meanwhile, based on the latent diffusion framework, HGLDM avoids the issues of computational consumption and lack of embeddings for extensible conditional generation. In addition, by comparing the HGLDM with its variant, the Graph Latent Diffusion Model (GLDM), which only has graph-level embeddings, we validate the advantage of the hierarchical graph structure for capturing the relationship between structure information and molecular properties. We evaluate the performance of our model through various conditional generation tasks, demonstrating its superior performance.

With the rapid advancement of autonomous driving technology, there is an increasingly urgent demand for faster and more accurate object detection frameworks. Recently, numerous deep learning-based object detectors have shown impressive performance in real-time driving applications. However, the detection of small objects such as various traffic signs remains challenging due to the complex nature of these objects. This paper proposes a transformer-based detector TS-DETR to improve the accuracy of small object detection in autonomous driving systems. We introduce a constrained decoder structure to focus the model's attention on the predicted boxes. Additionally, a content-aware deformable cross-attention mechanism is proposed to obtain more comprehensive attention weights. Experimental results on challenging public datasets such as TT100K and CCTSDB2021 demonstrate that our method demonstrates substantial performance enhancements while introducing only a slight increment in parameter count compared to current algorithms.

As the complexity of very-large-scale integration (VLSI) increases, empirically determining the design constraints necessary to achieve the optimal performance, power, and area (PPA) within the electronic design automation (EDA) workflow becomes more challenging. Design space exploration is capable of effectively and automatically identifying the design constraints required to attain the optimal PPA in VLSI designs. However, the absence of prior knowledge can lead to less efficient explorations. This paper proposes a novel fast constraint tuning framework via transfer learning and multi-objective Bayesian optimization (MOBO) to find the optimal design constraints. Firstly, we introduce transfer learning into multi-objective Bayesian optimization by Gaussian Copula and transform the PPA data into residual observations. We propose to transfer the prior information of the implemented technologies to the advanced technology to optimize the parameter design space under the advanced technology. Secondly, we propose Gaussian process regression with an auto-encoder-based deep kernel as a surrogate model in MOBO. The auto-encoder-based deep kernel can extract more input features to make the surrogate model more precise. We employ the batch uncertainty-aware search acquisition function to improve exploration efficiency. Using this surrogate model and this acquisition function in MOBO can reduce the amount that EDA tools need to run. The average EDA tools running times of the proposed model is 204, and the average ADRS is 0.0373. Compared to state-of-the-art approaches, experiments on a CPU design reveal that a higher-quality Pareto frontier can be provided with a shorter running time.

Graph neural networks (GNNs) are popular weapons for modeling relational data. Existing GNNs are not specified for attribute-incomplete graphs, making missing attribute imputation a burning issue. Until recently, many works notice that GNNs are coupled with spectral concentration, which means the spectrum obtained by GNNs concentrates on a local part in spectral domain, e.g., low-frequency due to oversmoothing issue. As a consequence, GNNs may be seriously flawed for reconstructing graph attributes as graph spectral concentration tends to cause a low imputation precision. In this work, we present a regularized graph autoencoder for graph attribute imputation, named MEGAE, which aims at mitigating spectral concentration problem by maximizing the graph spectral entropy. Notably, we first present the method for estimating graph spectral entropy without the eigen-decomposition of Laplacian matrix and provide the theoretical upper error bound. A maximum entropy regularization then acts in the latent space, which directly increases the graph spectral entropy. Extensive experiments show that MEGAE outperforms all the other state-of-the-art imputation methods on a variety of benchmark datasets.

With increasing appealing to privacy issues in face recognition, federated learning has emerged as one of the most prevalent approaches to study the unconstrained face recognition problem with private decentralized data. However, conventional decentralized federated algorithm sharing whole parameters of networks among clients suffers from privacy leakage in face recognition scene. In this work, we introduce a framework, FedGC, to tackle federated learning for face recognition and guarantees higher privacy. We explore a novel idea of correcting gradients from the perspective of backward propagation and propose a softmax-based regularizer to correct gradients of class embeddings by precisely injecting a cross-client gradient term. Theoretically, we show that FedGC constitutes a valid loss function similar to standard softmax. Extensive experiments have been conducted to validate the superiority of FedGC which can match the performance of conventional centralized methods utilizing full training dataset on several popular benchmark datasets.