45 citations · 97 across the 32 of their papers we have counts for
11 papers · 1 filter
Discovering New Theorems via LLMs with In-Context Proof Learning in Lean
Kazumi Kasaura, Naoto Onda, Yuta Oriike +3
Large Language Models (LLMs) have demonstrated significant promise in formal theorem proving. In this study, we investigate the ability of LLMs to discover novel theorems and produ…
Decomposition of Equivariant Maps via Invariant Maps: Application to Universal Approximation under Symmetry
Akiyoshi Sannai, Yuuki Takai, Matthieu Cordonnier
In this paper, we develop a theory about the relationship between invariant and equivariant maps with regard to a group . We then leverage this theory in the context of deep neu…
Unification of Symmetries Inside Neural Networks: Transformer, Feedforward and Neural ODE
Koji Hashimoto, Yuji Hirono, Akiyoshi Sannai
Understanding the inner workings of neural networks, including transformers, remains one of the most challenging puzzles in machine learning. This study introduces a novel approach…
Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach
Masayuki Takayama, Tadahisa Okuda, Thong Pham +4
In practical statistical causal discovery (SCD), embedding domain expert knowledge as constraints into the algorithm is important for reasonable causal models reflecting the broad…
A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC Guarantees
Toshinori Kitamura, Tadashi Kozuno, Masahiro Kato +6
We study a primal-dual (PD) reinforcement learning (RL) algorithm for online constrained Markov decision processes (CMDPs). Despite its widespread practical use, the existing theor…
Equivariant and Invariant Reynolds Networks
Akiyoshi Sannai, Makoto Kawano, Wataru Kumagai
Invariant and equivariant networks are useful in learning data with symmetry, including images, sets, point clouds, and graphs. In this paper, we consider invariant and equivariant…