Deep Learning
LRM-Zero: Training Large Reconstruction Models with Synthesized Data
Unlike previous 3D datasets (e.g., Objaverse) which are often captured or crafted by humans to approximate real 3D data, Zeroverse completely ignores realistic global semantics but is
Injecting Multimodal Information into Rigid Protein Docking via Bi-level Optimization
The structure of protein-protein complexes is critical for understanding binding dynamics, biological mechanisms, and intervention strategies.
Abstract Reasoning with Distracting Features
Kecheng Zheng, Zheng-Jun Zha, Wei Wei
Sometraditional models [4,24,25,26,27,29,30] rely on the assumptions and heuristics rules about various measurements of image similarity to perform abstract reasoning.
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments
Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence.
WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking
Thus, in this paper, we seek to establish a new gold standard for small molecule drug discovery benchmarking, W elQrate .
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction
Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan, Lane McIntosh, Stephen Baccus, Surya Ganguli
For eachstimulus,ourextractedcomputational mechanisms areconsistent withprior scientific literature, and in one case yields a new mechanistic hypothesis.
GradOrth: A Simple yet Efficient Out-of-Distribution Detection with Orthogonal Projection of Gradients
Detecting out-of-distribution (OOD) data is crucial for ensuring the safe deployment of machine learning models in real-world applications.