38 citations · 87 across the 5 of their papers we have counts for
8 papers
Open-domain clarification question generation without question examples
Julia White, Gabriel Poesia, Robert Hawkins +2
An overarching goal of natural language processing is to enable machines to communicate seamlessly with humans. However, natural language can be ambiguous or unclear. In cases of u…
Calibrate your listeners! Robust communication-based training for pragmatic speakers
Rose E. Wang, Julia White, Jesse Mu +1
To be good conversational partners, natural language processing (NLP) systems should be trained to produce contextually useful utterances. Prior work has investigated training NLP…
Temperature as Uncertainty in Contrastive Learning
Oliver Zhang, Mike Wu, Jasmine Bayrooti +1
Contrastive learning has demonstrated great capability to learn representations without annotations, even outperforming supervised baselines. However, it still lacks important prop…
Inducing Interpretable Representations with Variational Autoencoders
N. Siddharth, Brooks Paige, Alban Desmaison +5
We develop a framework for incorporating structured graphical models in the \emph{encoders} of variational autoencoders (VAEs) that allows us to induce interpretable representation…
Deep Amortized Inference for Probabilistic Programs
Daniel Ritchie, Paul Horsfall, Noah D. Goodman
Probabilistic programming languages (PPLs) are a powerful modeling tool, able to represent any computable probability distribution. Unfortunately, probabilistic program inference i…
Practical optimal experiment design with probabilistic programs
Long Ouyang, Michael Henry Tessler, Daniel Ly +1
Scientists often run experiments to distinguish competing theories. This requires patience, rigor, and ingenuity - there is often a large space of possible experiments one could ru…