Search Results for author:
Found 10 papers, 4 papers with code
An Attention-driven Two-stage Clustering Method for Unsupervised Person Re-Identification
By iteratively learning with the two strategies, the attentive regions are gradually shifted from the background to the foreground and the features become more discriminative.
CombiBench: Benchmarking LLM Capability for Combinatorial Mathematics
CombiBench is suitable for testing IMO solving capabilities since it includes all IMO combinatorial problems since 2000 (except IMO 2004 P3 as its statement contain an images).
Kimina-Prover Preview: Towards Large Formal Reasoning Models with Reinforcement Learning
We introduce Kimina-Prover Preview, a large language model that pioneers a novel reasoning-driven exploration paradigm for formal theorem proving, as showcased in this preview release.
Ranked #1 on
Automated Theorem Proving
on miniF2F-test
(using extra training data)
ProtTeX: Structure-In-Context Reasoning and Editing of Proteins with Large Language Models
To address these challenges, we introduce a novel framework, ProtTeX, which tokenizes the protein sequences, structures, and textual information into a unified discrete space.
FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving
In this paper, we propose FVEL, an interactive Formal Verification Environment with LLMs.
ATG: Benchmarking Automated Theorem Generation for Generative Language Models
We conduct extensive experiments to investigate whether current LMs can generate theorems in the library and benefit the problem theorems proving.
MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data
Recent large language models (LLMs) have witnessed significant advancement in various tasks, including mathematical reasoning and theorem proving.
Invertible Coarse Graining with Physics-Informed Generative Artificial Intelligence
Multiscale molecular modeling is widely applied in scientific research of molecular properties over large time and length scales.
DSDP: A Blind Docking Strategy Accelerated by GPUs
In this study, we take the advantage of both traditional and machine-learning based methods, and present a method Deep Site and Docking Pose (DSDP) to improve the performance of blind docking.
Spatiotemporal Information Processing with a Reservoir Decision-making Network
Spatiotemporal information processing is fundamental to brain functions.
