Transformer Models for Type Inference in the Simply Typed Lambda Calculus: A Case Study in Deep Learning for Code

no code implementations • 15 Mar 2023 • Brando Miranda, Avi Shinnar, Vasily Pestun, Barry Trager

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

1 code implementation • 12 Feb 2022 • Koundinya Vajjha, Barry Trager, Avraham Shinnar, Vasily Pestun

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

1 code implementation • 23 Sep 2020 • Koundinya Vajjha, Avraham Shinnar, Vasily Pestun, Barry Trager, Nathan Fulton

Reinforcement learning algorithms solve sequential decision-making problems in probabilistic environments by optimizing for long-term reward.

Decision Making reinforcement-learning +1