arXiv:2506.03227v2 Announce Type: replace Abstract: A neural ordinary differential equation (neural ODE) is a machine learning model that is commonly described as a continuous-depth generalization of a residual network (ResNet) with a single residual block, or conversely, the ResNet can…
arXiv:2510.09684v1 Announce Type: new Abstract: We propose a simple yet effective use of LLM-powered AI tools to improve causal estimation. In double machine learning, the accuracy of causal estimates of the effect of a treatment on an outcome in the…
arXiv:2508.03501v2 Announce Type: replace Abstract: Research on applications of reinforcement learning (RL) to large language models has mostly been focused on single-turn problems, such as mathematical reasoning or single-shot code generation. While these problems can be viewed as token-level multi-turn…
arXiv:2510.09685v1 Announce Type: new Abstract: Deep learning has become a pivotal technology in fields such as computer vision, scientific computing, and dynamical systems, significantly advancing these disciplines. However, neural Networks persistently face challenges related to theoretical understanding, interpretability, and generalization.…
arXiv:2510.03134v2 Announce Type: replace Abstract: Explainable Artificial Intelligence has become a crucial area of research, aiming to demystify the decision-making processes of deep learning models. Among various explainability techniques, counterfactual explanations have been proven particularly promising, as they offer insights…
arXiv:2510.09687v1 Announce Type: new Abstract: This study delves into the plasticity of neural networks, offering empirical support for the notion that critical learning periods and warm-starting performance loss can be avoided through simple adjustments to learning hyperparameters. The critical learning…
arXiv:2409.10023v2 Announce Type: replace-cross Abstract: We propose a reinforcement learning-based search strategy to explore new physics beyond the Standard Model. The reinforcement learning, which is one of machine learning methods, is a powerful approach to find model parameters with phenomenological…
arXiv:2506.24000v2 Announce Type: replace Abstract: Test-time adaptation (TTA) methods have gained significant attention for enhancing the performance of vision-language models (VLMs) such as CLIP during inference, without requiring additional labeled data. However, current TTA researches generally suffer from major limitations…
arXiv:2509.18171v2 Announce Type: replace Abstract: Federated Graph Learning (FGL) under domain skew — as observed on platforms such as emph{Twitch Gamers} and multilingual emph{Wikipedia} networks — drives client models toward incompatible representations, rendering naive aggregation both unstable and ineffective. We…
arXiv:2502.01755v4 Announce Type: replace Abstract: Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) optimize federated training by reducing computational and communication costs. We propose RoLoRA, a federated framework using alternating optimization to fine-tune LoRA adapters. Our approach emphasizes the importance…
