Search Results for author: Marko Vasic

Found 5 papers, 1 papers with code

Programming and Training Rate-Independent Chemical Reaction Networks

no code implementations20 Sep 2021 Marko Vasic, Cameron Chalk, Austin Luchsinger, Sarfraz Khurshid, David Soloveichik

Embedding computation in biochemical environments incompatible with traditional electronics is expected to have wide-ranging impact in synthetic biology, medicine, nanofabrication and other fields.

Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks

no code implementations ICML 2020 Marko Vasic, Cameron Chalk, Sarfraz Khurshid, David Soloveichik

Embedding computation in molecular contexts incompatible with traditional electronics is expected to have wide ranging impact in synthetic biology, medicine, nanofabrication and other fields.

Transfer Learning

A Study of the Learnability of Relational Properties: Model Counting Meets Machine Learning (MCML)

no code implementations25 Dec 2019 Muhammad Usman, Wenxi Wang, Kaiyuan Wang, Marko Vasic, Haris Vikalo, Sarfraz Khurshid

However, MCML metrics based on model counting show that the performance can degrade substantially when tested against the entire (bounded) input space, indicating the high complexity of precisely learning these properties, and the usefulness of model counting in quantifying the true performance.

MoET: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning

no code implementations16 Jun 2019 Marko Vasic, Andrija Petrovic, Kaiyuan Wang, Mladen Nikolic, Rishabh Singh, Sarfraz Khurshid

By training MoET models using an imitation learning procedure on deep RL agents we outperform the previous state-of-the-art technique based on decision trees while preserving the verifiability of the models.

Game of Go Imitation Learning +2

Neural Program Repair by Jointly Learning to Localize and Repair

2 code implementations ICLR 2019 Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, Rishabh Singh

We show that it is beneficial to train a model that jointly and directly localizes and repairs variable-misuse bugs.

Variable misuse

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