Generation of Geodesics with Actor-Critic Reinforcement Learning to Predict Midpoints arXiv:2407.01991v4 Announce Type: replace Abstract: To find the shortest paths for all pairs on manifolds with infinitesimally defined metrics, we introduce a framework to generate them by predicting midpoints recursively.…
Differentiable multiphase flow model for physics-informed machine learning in reservoir pressure management arXiv:2508.19419v1 Announce Type: new Abstract: Accurate subsurface reservoir pressure control is extremely challenging due to geological heterogeneity and multiphase fluid-flow dynamics. Predicting behavior in this setting relies on…
Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence arXiv:2412.18164v3 Announce Type: replace Abstract: Diffusion models have emerged as powerful tools for generative modeling, demonstrating exceptional capability in capturing target data distributions from large datasets. However, fine-tuning these massive…
MS-ConTab: Multi-Scale Contrastive Learning of Mutation Signatures for Pan Cancer Representation and Stratification arXiv:2508.19424v1 Announce Type: new Abstract: Motivation. Understanding the pan-cancer mutational landscape offers critical insights into the molecular mechanisms underlying tumorigenesis. While patient-level machine learning techniques have been…
R-TPT: Improving Adversarial Robustness of Vision-Language Models through Test-Time Prompt Tuning arXiv:2504.11195v2 Announce Type: replace Abstract: Vision-language models (VLMs), such as CLIP, have gained significant popularity as foundation models, with numerous fine-tuning methods developed to enhance performance on downstream tasks.…
Data-Augmented Few-Shot Neural Stencil Emulation for System Identification of Computer Models arXiv:2508.19441v1 Announce Type: new Abstract: Partial differential equations (PDEs) underpin the modeling of many natural and engineered systems. It can be convenient to express such models as neural PDEs…
ProARD: progressive adversarial robustness distillation: provide wide range of robust students arXiv:2506.07666v3 Announce Type: replace Abstract: Adversarial Robustness Distillation (ARD) has emerged as an effective method to enhance the robustness of lightweight deep neural networks against adversarial attacks. Current ARD…
Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization arXiv:2508.19443v1 Announce Type: new Abstract: Producing large complex simulation datasets can often be a time and resource consuming task. Especially when these experiments are very expensive, it is becoming more reasonable…
GTPO: Trajectory-Based Policy Optimization in Large Language Models arXiv:2508.03772v3 Announce Type: replace Abstract: Policy-based optimizations are widely adopted today for the training and alignment of language models, where one of the most recent and effective approaches is Group-relative Policy Optimization…
On Surjectivity of Neural Networks: Can you elicit any behavior from your model? arXiv:2508.19445v1 Announce Type: new Abstract: Given a trained neural network, can any specified output be generated by some input? Equivalently, does the network correspond to a function…
