Summary of Fastlrnr and Sparse Physics Informed Backpropagation, by Woojin Cho et al.
FastLRNR and Sparse Physics Informed Backpropagationby Woojin Cho, Kookjin Lee, Noseong Park, Donsub Rim, Gerrit…
FastLRNR and Sparse Physics Informed Backpropagationby Woojin Cho, Kookjin Lee, Noseong Park, Donsub Rim, Gerrit…
Discovering Message Passing Hierarchies for Mesh-Based Physics Simulationby Huayu Deng, Xiangming Zhu, Yunbo Wang, Xiaokang…
P1-KAN: an effective Kolmogorov-Arnold network with application to hydraulic valley optimizationby Xavier WarinFirst submitted to…
The Smart Buildings Control Suite: A Diverse Open Source Benchmark to Evaluate and Scale HVAC…
Linear Transformer Topological Masking with Graph Random Featuresby Isaac Reid, Kumar Avinava Dubey, Deepali Jain,…
On the Hardness of Learning One Hidden Layer Neural Networksby Shuchen Li, Ilias Zadik, Manolis…
Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learningby Nils Lehmann, Jakob…
Optimization Proxies using Limited Labeled Data and Training Time – A Semi-Supervised Bayesian Neural Network…
A Training-Free Conditional Diffusion Model for Learning Stochastic Dynamical Systemsby Yanfang Liu, Yuan Chen, Dongbin…
Spatial-aware decision-making with ring attractors in reinforcement learning systemsby Marcos Negre Saura, Richard Allmendinger, Wei…