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Found 8 papers, 4 papers with code
WatChat: Explaining perplexing programs by debugging mental models
Often, a good explanation for a program's unexpected behavior is a bug in the programmer's code.
How to guess a gradient
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
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
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.
Beyond Laurel/Yanny: An Autoencoder-Enabled Search for Polyperceivable Audio
Our results suggest that polyperceivable examples are surprisingly prevalent in natural language, existing for {\textgreater}2{\%} of English words.
Gradient Descent: The Ultimate Optimizer
This allows us to easily apply the method to other optimizers and hyperparameters (e. g. momentum coefficients).
SPoC: Search-based Pseudocode to Code
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.
Ranked #2 on
Program Synthesis
on SPoC TestP
