Search Results for author:
Found 6 papers, 2 papers with code
Graph2Tac: Learning Hierarchical Representations of Math Concepts in Theorem proving
G2T is an online model that is deeply integrated into the users' workflow and can adapt in real time to new Coq projects and their definitions.
Transformer Models for Type Inference in the Simply Typed Lambda Calculus: A Case Study in Deep Learning for Code
Despite a growing body of work at the intersection of deep learning and formal languages, there has been relatively little systematic exploration of transformer models for reasoning about typed lambda calculi.
Formalization of a Stochastic Approximation Theorem
Stochastic approximation algorithms are iterative procedures which are used to approximate a target value in an environment where the target is unknown and direct observations are corrupted by noise.
CertRL: Formalizing Convergence Proofs for Value and Policy Iteration in Coq
Reinforcement learning algorithms solve sequential decision-making problems in probabilistic environments by optimizing for long-term reward.
Language as a matrix product state
We propose a statistical model for natural language that begins by considering language as a monoid, then representing it in complex matrices with a compatible translation invariant probability measure.
Tensor network language model
We propose a new statistical model suitable for machine learning of systems with long distance correlations such as natural languages.
