107 citations · 1k across the 45 of their papers we have counts for
5 papers · 2 filters
Latent Programmer: Discrete Latent Codes for Program Synthesis
Joey Hong, David Dohan, Rishabh Singh +2
In many sequence learning tasks, such as program synthesis and document summarization, a key problem is searching over a large space of possible output sequences. We propose to lea…
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration
Hanjun Dai, Rishabh Singh, Bo Dai +2
Discrete structures play an important role in applications like program language modeling and software engineering. Current approaches to predicting complex structures typically co…
Scaling Symbolic Methods using Gradients for Neural Model Explanation
Subham Sekhar Sahoo, Subhashini Venugopalan, Li Li +2
Symbolic techniques based on Satisfiability Modulo Theory (SMT) solvers have been proposed for analyzing and verifying neural network properties, but their usage has been fairly li…
Towards Modular Algorithm Induction
Daniel A. Abolafia, Rishabh Singh, Manzil Zaheer +1
We present a modular neural network architecture Main that learns algorithms given a set of input-output examples. Main consists of a neural controller that interacts with a variab…
Towards a Kernel based Uncertainty Decomposition Framework for Data and Models
Rishabh Singh, Jose C. Principe
This paper introduces a new framework for quantifying predictive uncertainty for both data and models that relies on projecting the data into a Gaussian reproducing kernel Hilbert…