Industrial automation in smart logistics and factories requires simulation platforms that support rapid environment building before costly physical deployment. Yet existing tools often require substantial expertise, complex setup, and long configuration times, hindering agile prototyping. We present RAPID, a simulation platform with two components: layout design, which enables intuitive visual configuration of factory layouts, and behavior simulation and validation, which allows users to attach behavior models and evaluate system performance. RAPID lowers the entry barrier to industrial simulation, letting users apply existing behavior models or trained reinforcement learning (RL) agents to new layouts with minimal effort. This approach lets practitioners prototype facilities in minutes rather than weeks and gives researchers a standardized environment for benchmarking multi-agent RL and coordination algorithms. By combining rapid design with simulation-based validation, RAPID accelerates automation development from concept to implementation.

Reinforcement learning (RL) has evolved beyond monolithic training, yet existing frameworks remain limited to single algorithms or simple offline-to-online transitions. We present multi-phase RL, a framework that orchestrates multiple learning phases for continual policy improvement. It enables efficient fine-tuning of pretrained policies with new data and smooth adaptation from simulation to real-world environments. To support this paradigm, we introduce RL-Studio, a platform that addresses key implementation barriers, including neural architecture mismatches, parameter transfer complexities, and experiment management overhead. It provides phase orchestration, transition-point monitoring, and full experiment lineage tracking. We demonstrate the effectiveness of multi-phase RL through representative scenarios and highlight RL-Studio’s capabilities.

Anomaly detection platforms in real-world environments require continuous interaction between automated systems and domain experts, as anomalies evolve dynamically and their definitions vary across contexts. Therefore, an effective platform must collaborate with experts and incorporate their feedback to update the system. This paper introduces AEGIS, an anomaly detection platform that aims to support interaction between domain experts and data-driven agents through three core capabilities: (1) data-driven insights through real-time monitoring, explanations, and distribution shift detection, which invoke customized tools to generate appropriate responses, (2) an expert feedback interface for labeling and direct updates via chat-based interaction, and (3) autonomous model construction that leverages expert-labeled data with LLM-driven hyperparameter optimization. Through this design, AEGIS fosters continuous interaction in which the platform provides insights while experts guide model improvement, ensuring user intent is reflected and robustness is maintained under evolving data distributions.

Causal agents have emerged as promising tools for automating causal analysis based on user queries. However, existing causal agent systems are often limited to a single causal task, limiting their ability to handle complex queries. In addition, they accept only numerical data as input, preventing the integration of domain knowledge expressed in natural language. To overcome these limitations, we propose the OrcheCause agent, a causal agent leveraging textual knowledge for end-to-end causal inference. Specifically, OrcheCause is designed to orchestrate a sequence of interrelated causal tasks in response to user queries. Furthermore, OrcheCause supports diverse data types—numerical as well as textual data—by extracting cause-effect pairs from the relevant sources and incorporating them into causal discovery (CD), thereby improving the performance of CD. OrcheCause also introduces a metric-based hyperparameter optimization framework for CD when ground-truth graphs are not available.

Tabular data, widely used across industries, remains underexplored in deep learning. Self-supervised learning (SSL) shows promise for pre-training deep neural networks (DNNs) on tabular data, but its potential is hindered by challenges in designing suitable augmentations. Unlike image and text data, where SSL leverages inherent spatial or semantic structures, tabular data lacks such explicit structure. This makes traditional input-level augmentations, like modifying or removing features, less effective due to difficulties in balancing critical information preservation with variability. To address these challenges, we propose RaTab, a novel method that shifts augmentation from input-level to representation-level using matrix factorization, specifically truncated SVD. This approach preserves essential data structures while generating diverse representations by applying dropout at various stages of the representation, thereby significantly enhancing SSL performance for tabular data.

Vision Transformers (ViTs) have achieved remarkable success in various computer vision tasks. However, ViTs have a huge computational cost due to their inherent reliance on multi-head self-attention (MHSA), prompting efforts to accelerate ViTs for practical applications. To this end, recent works aim to reduce the number of tokens, mainly focusing on how to effectively prune or merge them. Nevertheless, since ViT tokens are generated from non-overlapping grid patches, they usually do not convey sufficient semantics, making it incompatible with efficient ViTs. To address this, we propose ImagePiece, a novel re-tokenization strategy for Vision Transformers. Following the MaxMatch strategy of NLP tokenization, ImagePiece groups semantically insufficient yet locally coherent tokens until they convey meaning. This simple retokenization is highly compatible with previous token reduction methods, being able to drastically narrow down relevant tokens, enhancing the inference speed of DeiT-S by 54% (nearly 1.5x faster) while achieving a 0.39% improvement in ImageNet classification accuracy. For hyper-speed inference scenarios (with 251% acceleration), our approach surpasses other baselines by an accuracy over 8%.

Out-of-distribution (OOD) detection, determining whether a given sample is part of the in-distribution (ID) or not, has been newly explored by a generative model-based outlier synthesizing approach, especially with diffusion models. Nonetheless, existing diffusion models often produce outliers that are considerably distant from the ID in pixel-space, showing limited efficacy for capturing subtle distinctions between ID and OOD. To address these issues, we propose a novel framework, Semantic Outlier generation via Nuisance Awareness (SONA), which directly utilizes informative pixel-space ID images in diffusion models. Thereby, the generated outliers achieve two crucial properties: (i) they closely resemble the ID mainly in nuisances, while (ii) represent discriminative semantic information. To facilitate the separate effect on semantics and nuisances, we introduce SONA guidance, providing region-specific guidance. Extensive experiments demonstrate the effectiveness of our framework, achieving an impressive AUROC of 87% on near-OOD datasets, which surpasses the performance of baseline methods by a significant margin of approximately 6%.

Thematic investing, which aims to construct portfolios aligned with structural trends, remains a challenging endeavor due to overlapping sector boundaries and evolving market dynamics. A promising direction is to build semantic representations of investment themes from textual data. However, despite their power, general-purpose LLM embedding models are not well-suited to capture the nuanced characteristics of financial assets, since the semantic representation of investment assets may differ fundamentally from that of general financial text. To address this, we introduce THEME, a framework that fine-tunes embeddings using hierarchical contrastive learning. THEME aligns themes and their constituent stocks using their hierarchical relationship, and subsequently refines these embeddings by incorporating stock returns. This process yields representations effective for retrieving thematically aligned assets with strong return potential. Empirical results demonstrate that THEME excels in two key areas. For thematic asset retrieval, it significantly outperforms leading large language models. Furthermore, its constructed portfolios demonstrate compelling performance. By jointly modeling thematic relationships from text and market dynamics from returns, THEME generates stock embeddings specifically tailored for a wide range of practical investment applications.

Multi-Agent Reinforcement Learning (MARL) struggles with coordination in sparse reward environments. Macro-actions —sequences of actions executed as single decisions— facilitate long-term planning but introduce asynchrony, complicating Centralized Training with Decentralized Execution (CTDE). Existing CTDE methods use padding to handle asynchrony, risking misaligned asynchronous experiences and spurious correlations. We propose the Agent-Centric Actor-Critic (ACAC) algorithm to manage asynchrony without padding. ACAC uses agent-centric encoders for independent trajectory processing, with an attention-based aggregation module integrating these histories into a centralized critic for improved temporal abstractions. The proposed structure is trained via a PPO-based algorithm with a modified Generalized Advantage Estimation for asynchronous environments. Experiments show ACAC accelerates convergence and enhances performance over baselines in complex MARL tasks.

Reinforcement learning with offline data suffers from Q-value extrapolation errors. To address this issue, we first demonstrate that linear extrapolation of the Q-function beyond the data range is particularly problematic. To mitigate this, we propose guiding the gradual decrease of Q-values outside the data range, which is achieved through reward scaling with layer normalization (RS-LN) and a penalization mechanism for infeasible actions (PA). By combining RS-LN and PA, we develop a new algorithm called PARS. We evaluate PARS across a range of tasks, demonstrating superior performance compared to state-of-the-art algorithms in both offline training and online fine-tuning on the D4RL benchmark, with notable success in the challenging AntMaze Ultra task.

Offline-to-online reinforcement learning (RL) aims to integrate the complementary strengths of offline and online RL by pre-training an agent offline and subsequently fine-tuning it through online interactions. However, recent studies reveal that offline pre-trained agents often underperform during online fine-tuning due to inaccurate value estimation caused by distribution shift, with random initialization proving more effective in certain cases. In this work, we propose a novel method, Online Pre-Training for Offline-to-Online RL (OPT), explicitly designed to address the issue of inaccurate value estimation in offline pre-trained agents. OPT introduces a new learning phase, Online Pre-Training, which allows the training of a new value function tailored specifically for effective online fine-tuning. Implementation of OPT on TD3 and SPOT demonstrates an average 30% improvement in performance across a wide range of D4RL environments, including MuJoCo, Antmaze, and Adroit.

Transfer learning is a crucial technique for handling a small amount of data that is potentially related to other abundant data. However, most of the existing methods are focused on classification tasks using images and language datasets. Therefore, in order to expand the transfer learning scheme to regression tasks, we propose a novel transfer technique based on differential geometry, namely the Geometrically Aligned Transfer Encoder (${\it GATE}$). In this method, we interpret the latent vectors from the model to exist on a Riemannian curved manifold. We find a proper diffeomorphism between pairs of tasks to ensure that every arbitrary point maps to a locally flat coordinate in the overlapping region, allowing the transfer of knowledge from the source to the target data. This also serves as an effective regularizer for the model to behave in extrapolation regions. In this article, we demonstrate that ${\it GATE}$ outperforms conventional methods and exhibits stable behavior in both the latent space and extrapolation regions for various molecular graph datasets.

The ability of deep networks to learn superior representations hinges on leveraging the proper inductive biases, considering the inherent properties of datasets. In tabular domains, it is critical to effectively handle heterogeneous features (both categorical and numerical) in a unified manner and to grasp irregular functions like piecewise constant functions. To address the challenges in the self-supervised learning framework, we propose a novel pretext task based on the classical binning method. The idea is straightforward: reconstructing the bin indices (either orders or classes) rather than the original values. This pretext task provides the encoder with an inductive bias to capture the irregular dependencies, mapping from continuous inputs to discretized bins, and mitigates the feature heterogeneity by setting all features to have category-type targets. Our empirical investigations ascertain several advantages of binning: capturing the irregular function, compatibility with encoder architecture and additional modifications, standardizing all features into equal sets, grouping similar values within a feature, and providing ordering information. Comprehensive evaluations across diverse tabular datasets corroborate that our method consistently improves tabular representation learning performance for a wide range of downstream tasks. The codes are available in https://github.com/kyungeun-lee/tabularbinning.

In time-series forecasting, signals such as traffic volume collected in the real world are noisy and irregularly sampled due to sensor malfunctions, so it is difficult to make accurate prediction. To resolve such difficulty, downsampling can be used to reduce noise and allow capturing slow trend of signals. In addition, upsampling can fill the missing data of irregularly sampled signals to catch fine details. Although extracting multi-resolution temporal features such as down or upsampling can improve prediction accuracy, the existing time-series forecasting approaches have used the original and/or downsampled signals only, so they cannot detect fine details of upsampled one. Moreover, these methods merge multi-resolution inputs without carefully concern to chronological order of time-series, which is very important in the time-series. To overcome this challenge, we propose a framework that can fully utilize multi-resolution time-series signals in up, original, and downscale, and sequentially aggregate them, named multi-resolution sequence aggregation and model-agnostic (MAMA) framework. Note that i) MAMA aggregates the multi-resolution signals without breaking its sequential characteristics, whose effectiveness was verified by the experiment results, and ii) it can adopt any existing forecasting algorithms. From experiments with the real-world datasets, it was observed that the prediction accuracy of the well-known forecasting models (i.e., LSTNet, TCN, and Informer) were improved by 11.5% on average when the proposed architecture is used. In ablation study, we showed that a performance improvement of 1.5% was achieved with the help of sequential aggregation module.

Automatic speech recognition (ASR) is crucial in virtual personal assistant (VPA) services such as Apple Siri, Amazon Alexa, Google Now and SKT NUGU. Recently, ASR has been showing a remarkable advance in accuracy by applying deep learning. However, with the explosive increase of the user utterances and growing complexities in ASR, the demands for the custom accelerators in datacenters are highly increasing in order to process them in real time with low power consumption. This paper evaluates a custom inference accelerator for ASR enhanced by a deep neural network, called AIX (Artificial Intelligence aXellerator). AIX is developed on a Xilinx FPGA and deployed to SKT NUGU since 2018. Owing to the full exploitation of DSP slices and memory bandwidth provided by FPGA, AIX outperforms the cutting-edge CPUs by 10.2 times and even a state-of-the-art GPU by 20.1 times with real time workloads of ASR in performance and power consumption wise. This improvement achieves faster response time in ASR, and in turn reduces the number of required machines in datacenters to a third.

In this paper, we propose a novel approach to feature compensation performed in the cepstral domain. We apply the linear approximation method in the cepstral domain to simplify the relationship among clean speech, noise and noisy speech. Conventional log-spectral domain feature compensation methods usually assume that each log-spectral coefficient is independent, which is far from real observations. Processing in the cepstral domain has the advantage that the spectral correlation among different frequencies are taken into consideration. By using the diagonal covariance approximation, we can easily modify the conventional log-spectral domain feature compensation technique to fit to the cepstral domain. The proposed approach shows significant improvements in the AURORA2 speech recognition task.

In this paper, we propose a novel approach to feature compensation for robust speech recognition in noisy environments. We analyze the statistics of the modeling error in the log mel magnitude spectrum domain, and model it as a Gaussian distribution. The mean and variance of the distribution are Gaussian functions of the SNR, which enables us to use the SNR dependency of the modeling error efficiently. The proposed feature compensation approach, which is based on the interacting multiple model (IMM) technique, incorporates the statistics of the modeling error and shows significant improvement in the AURORA2 speech recognition task.

In this letter, we propose a novel approach to feature compensation for robust speech recognition in noisy environments. We analyze the error distribution of speech corruption model in the log spectral domain and represent the statistics as functions with respect to the signal-to-noise ratio. The proposed algorithm incorporates modeling error statistics into the interacting multiple model technique and shows a performance improvement over the AURORA2 speech recognition task.

Abstract In this paper, an improved preprocessor for low-bit-rate speech coding employing the perceptual weightingfilter is proposed. Speech modification in the proposedapproach is performed according to a criterion whichmakes a compromise between the modification and per-ceptual weighted quantization errors. For this, the per-ceptual weighting filter is expressed in terms of a trans-form domain matrix. The proposed approach is effec-tive in enhancing the speech signal at coder-decoder(CODEC) output through a number of listening tests. 1. Introduction Ingeneral,theperformanceofalow-bit-ratespeechcoderdegrades seriously under the presence of various inter-fering signals such as background noise, acoustic echo,music sounds or interfering speaker’s speech. This phe-nomenon is mainly due to the deviation from the as-sumed speech production model which is used in thecodebook training since a number of codebooks used inthe coder are trained based on a large amount of speechdata and the ranges for parameter search are specified tofit the pure speech signals. One of the successful appli-cations of the unwanted distortion reduction technique tolow-bit-rate coding is the speech enhancement technique[2, 3, 8, 1]. Even though aforementioned enhancementtechniques have been found effective in the presence of astationary background noise, they are not capable of han-dling such interfering signals as the acoustic echoes, mu-sicsoundsorco-talkers’speech. Thisismainlyduetothefactthattheconventionalapproachesadopttheopenloopanalysis which can not take advantage of speech codercharacteristics. An alternative method is the generalized

In this paper, we present a new approach to feature compensation for robust speech recognition in noisy environments. We employ the switching linear dynamic model (SLDM) as a parametric model for the clean speech distribution, which enables us to utilize temporal correlations in speech signals. Both the background noise and clean speech components are simultaneously estimated by means of the interacting multiple model (IMM) algorithm. Moreover, we combine the SLDM algorithm with the spectral subtraction (SS) approach based on a soft decision. Performance of the presented compensation technique is evaluated through the experiments on AURORA 2 database.

In this letter, we propose a novel approach to feature compensation for robust speech recognition in noisy environments. We employ the switching linear dynamic model (SLDM) as a parametric model for the clean speech distribution, which enables us to exploit temporal correlations inherent in speech signals. Both the background noise and clean speech components are simultaneously estimated by means of the interacting multiple model (IMM) algorithm.

In this letter, we propose an approach to robust unsupervised speaker adaptation. Usually, recognition errors made on the adaptation utterances mislead parameter estimation when a speaker adaptation algorithm is operated in an unsupervised mode. In order to alleviate this problem, we first adapt a Gaussian mixture model (GMM) and then transform the hidden Markov model (HMM) parameters according to the information extracted from GMM adaptation.

This paper presents a new approach to online speaker adaptation based on transformation space model evolution. This approach extends the previous idea of speaker space model evolution by applying the a priori knowledge of training speakers to the speaker-dependent maximum likelihood linear regression (MLLR) matrix parameters. A quasi-Bayes (QB) estimation algorithm is devised to incrementally update the hyperparameters of the transformation space model and the regression matrices simultaneously. Experiments on supervised speaker adaptation demonstrate that the proposed approach is more effective compared with the conventional quasi-Bayes linear regression (QBLR) technique when a small amount of adaptation data is available.