Summary of Re-examining Learning Linear Functions in Context, by Omar Naim et al.
Re-examining learning linear functions in contextby Omar Naim, Guilhem Fouilhé, Nicholas AsherFirst submitted to arxiv…
Re-examining learning linear functions in contextby Omar Naim, Guilhem Fouilhé, Nicholas AsherFirst submitted to arxiv…
Physics meets Topology: Physics-informed topological neural networks for learning rigid body dynamicsby Amaury Wei, Olga…
Physics Encoded Blocks in Residual Neural Network Architectures for Digital Twin Modelsby Muhammad Saad Zia,…
Graph Neural Networks for Quantifying Compatibility Mechanisms in Traditional Chinese Medicineby Jingqi Zeng, Xiaobin JiaFirst…
Alien Recombination: Exploring Concept Blends Beyond Human Cognitive Availability in Visual Artby Alejandro Hernandez, Levin…
Efficient Sample-optimal Learning of Gaussian Tree Models via Sample-optimal Testing of Gaussian Mutual Informationby Sutanu…
A Pre-Trained Graph-Based Model for Adaptive Sequencing of Educational Documentsby Jean Vassoyan, Anan Schütt, Jill-Jênn…
SeqProFT: Applying LoRA Finetuning for Sequence-only Protein Property Predictionsby Shuo Zhang, Jian K. LiuFirst submitted…
Real-Time Fitness Exercise Classification and Counting from Video Framesby Riccardo RiccioFirst submitted to arxiv on:…
Robust Causal Analysis of Linear Cyclic Systems With Hidden Confoundersby Boris Lorbeer, Axel KüpperFirst submitted…