432 citations · 1.2k across the 38 of their papers we have counts for
5 papers · 1 filter
Learning Generalized Gumbel-max Causal Mechanisms
Guy Lorberbom, Daniel D. Johnson, Chris J. Maddison +2
To perform counterfactual reasoning in Structural Causal Models (SCMs), one needs to know the causal mechanisms, which provide factorizations of conditional distributions into nois…
Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models
Daniel D. Johnson, Jacob Austin, Rianne van den Berg +1
Denoising diffusion probabilistic models (DDPMs) have shown impressive results on sequence generation by iteratively corrupting each example and then learning to map corrupted vers…
Structured Denoising Diffusion Models in Discrete State-Spaces
Jacob Austin, Daniel D. Johnson, Jonathan Ho +2
Denoising diffusion probabilistic models (DDPMs) (Ho et al. 2020) have shown impressive results on image and waveform generation in continuous state spaces. Here, we introduce Disc…
Learning to Combine Per-Example Solutions for Neural Program Synthesis
Disha Shrivastava, Hugo Larochelle, Daniel Tarlow
The goal of program synthesis from examples is to find a computer program that is consistent with a given set of input-output examples. Most learning-based approaches try to find a…
Learning to Extend Program Graphs to Work-in-Progress Code
Xuechen Li, Chris J. Maddison, Daniel Tarlow
Source code spends most of its time in a broken or incomplete state during software development. This presents a challenge to machine learning for code, since high-performing model…