Search
A Single-Loop Accelerated Extra-Gradient Difference Algorithm with Improved Complexity Bounds for Constrained Minimax Optimization
Then momentum acceleration is also introduced into our dual accelerating update step.
A Single-Loop Accelerated Extra-Gradient Difference Algorithm with Improved Complexity Bounds for Constrained Minimax Optimization
Then momentum acceleration is also introduced into our dual accelerating update step.
GRAND-SLAMIN' Interpretable Additive Modeling with Structural Constraints
It is desirable to restrict the number of components (i.e., encourage sparsity) for
Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletability
Furthermore, we explore the potential of language models in generating co-crystals. Finally, we present numerous previously unknown co-crystals predicted by GEM-CODE and discuss its potential in accelerating drug development.
Appendix
We provide more information on AIPS' deductive engine and the training process for the value network. To highlight the reasoning ability and maintain readability of proofs, we avoid using brute-force methods such as augmentation-substitution and Wu's method Wu [1978].
Proving Olympiad Algebraic Inequalities without Human Demonstrations
Solving Olympiad-level mathematical problems represents a significant advancement in machine intelligence and automated reasoning.
CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework
This work presents a novel approach to neural architecture search (NAS) that aims to increase carbon efficiency for the model design process.
What Planning Problems Can A Relational Neural Network Solve?
Goal-conditioned policies are generally understood to be "feed-forward" circuits,
