A faster problem-solving tool that guarantees feasibility
The FSNet system, developed at MIT, could help power grid operators rapidly find feasible solutions for optimizing the flow of electricity.
Archives AI News
Is Limited Participant Diversity Impeding EEG-based Machine Learning?
arXiv:2503.13497v3 Announce Type: replace-cross Abstract: The application of machine learning (ML) to electroencephalography (EEG) has great potential to advance both neuroscientific research and clinical applications. However, the generalisability and robustness of EEG-based ML models often hinge on the amount and…
Smooth Flow Matching
arXiv:2508.13831v2 Announce Type: replace-cross Abstract: Functional data, i.e., smooth random functions observed over a continuous domain, are increasingly available in areas such as biomedical research, health informatics, and epidemiology. However, effective statistical analysis for functional data is often hindered by…
Hankel Singular Value Regularization for Highly Compressible State Space Models
arXiv:2510.22951v2 Announce Type: replace Abstract: Deep neural networks using state space models as layers are well suited for long-range sequence tasks but can be challenging to compress after training. We use that regularizing the sum of Hankel singular values of…
Data-Driven Stochastic Optimal Control in Reproducing Kernel Hilbert Spaces
arXiv:2407.16407v2 Announce Type: replace-cross Abstract: This paper proposes a fully data-driven approach for optimal control of nonlinear control-affine systems represented by a stochastic diffusion. The focus is on the scenario where both the nonlinear dynamics and stage cost functions are…
SC-LoRA: Balancing Efficient Fine-tuning and Knowledge Preservation via Subspace-Constrained LoRA
arXiv:2505.23724v3 Announce Type: replace Abstract: Parameter-Efficient Fine-Tuning (PEFT) methods, particularly Low-Rank Adaptation (LoRA), are indispensable for efficiently customizing Large Language Models (LLMs). However, vanilla LoRA suffers from slow convergence speed and knowledge forgetting problems. Recent studies have leveraged the power…
Discrete Diffusion Models: Novel Analysis and New Sampler Guarantees
arXiv:2509.16756v2 Announce Type: replace Abstract: Discrete diffusion models have recently gained significant prominence in applications involving natural language and graph data. A key factor influencing their effectiveness is the efficiency of discretized samplers. Among these, $tau$-leaping samplers have become particularly…
MM-OPERA: Benchmarking Open-ended Association Reasoning for Large Vision-Language Models
arXiv:2510.26937v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) have exhibited remarkable progress. However, deficiencies remain compared to human intelligence, such as hallucination and shallow pattern matching. In this work, we aim to evaluate a fundamental yet underexplored intelligence: association,…
Robust Offline Reinforcement Learning with Linearly Structured f-Divergence Regularization
arXiv:2411.18612v2 Announce Type: replace Abstract: The Robust Regularized Markov Decision Process (RRMDP) is proposed to learn policies robust to dynamics shifts by adding regularization to the transition dynamics in the value function. Existing methods mostly use unstructured regularization, potentially leading…
Integrating Ontologies with Large Language Models for Enhanced Control Systems in Chemical Engineering
arXiv:2510.26898v1 Announce Type: new Abstract: This work presents an ontology-integrated large language model (LLM) framework for chemical engineering that unites structured domain knowledge with generative reasoning. The proposed pipeline aligns model training and inference with the COPE ontology through a…