Search Results for author: Kartik Chandra

Found 8 papers, 4 papers with code

How to guess a gradient

no code implementations7 Dec 2023 Utkarsh Singhal, Brian Cheung, Kartik Chandra, Jonathan Ragan-Kelley, Joshua B. Tenenbaum, Tomaso A. Poggio, Stella X. Yu

We study how to narrow the gap in optimization performance between methods that calculate exact gradients and those that use directional derivatives.

Differentiating Metropolis-Hastings to Optimize Intractable Densities

1 code implementation13 Jun 2023 Gaurav Arya, Ruben Seyer, Frank Schäfer, Kartik Chandra, Alexander K. Lew, Mathieu Huot, Vikash K. Mansinghka, Jonathan Ragan-Kelley, Christopher Rackauckas, Moritz Schauer

We develop an algorithm for automatic differentiation of Metropolis-Hastings samplers, allowing us to differentiate through probabilistic inference, even if the model has discrete components within it.

Designing Perceptual Puzzles by Differentiating Probabilistic Programs

no code implementations26 Apr 2022 Kartik Chandra, Tzu-Mao Li, Joshua Tenenbaum, Jonathan Ragan-Kelley

We design new visual illusions by finding "adversarial examples" for principled models of human perception -- specifically, for probabilistic models, which treat vision as Bayesian inference.

Color Constancy Probabilistic Programming

Beyond Laurel/Yanny: An Autoencoder-Enabled Search for Polyperceivable Audio

no code implementations ACL 2021 Kartik Chandra, Chuma Kabaghe, Gregory Valiant

Our results suggest that polyperceivable examples are surprisingly prevalent in natural language, existing for {\textgreater}2{\%} of English words.

Gradient Descent: The Ultimate Optimizer

2 code implementations29 Sep 2019 Kartik Chandra, Audrey Xie, Jonathan Ragan-Kelley, Erik Meijer

This allows us to easily apply the method to other optimizers and hyperparameters (e. g. momentum coefficients).

BIG-bench Machine Learning Hyperparameter Optimization

SPoC: Search-based Pseudocode to Code

1 code implementation NeurIPS 2019 Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alex Aiken, Percy Liang

Given test cases as a mechanism to validate programs, we search over the space of possible translations of the pseudocode to find a program that passes the validation.

Program Synthesis Translation

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