16 citations · 18 across the 5 of their papers we have counts for
5 papers
Understanding Trade offs When Conditioning Synthetic Data
Brandon Trabucco, Qasim Wani, Benjamin Pikus +1
Learning robust object detectors from only a handful of images is a critical challenge in industrial vision systems, where collecting high quality training data can take months. Sy…
AnyMorph: Learning Transferable Polices By Inferring Agent Morphology
Brandon Trabucco, Mariano Phielipp, Glen Berseth
The prototypical approach to reinforcement learning involves training policies tailored to a particular agent from scratch for every new morphology. Recent work aims to eliminate t…
Discovering Non-monotonic Autoregressive Orderings with Variational Inference
Xuanlin Li, Brandon Trabucco, Dong Huk Park +4
The predominant approach for language modeling is to process sequences from left to right, but this eliminates a source of information: the order by which the sequence was generate…
Synthetic Datasets for Neural Program Synthesis
Richard Shin, Neel Kant, Kavi Gupta +4
The goal of program synthesis is to automatically generate programs in a particular language from corresponding specifications, e.g. input-output behavior. Many current approaches…
Inferring the Optimal Policy using Markov Chain Monte Carlo
Brandon Trabucco, Albert Qu, Simon Li +1
This paper investigates methods for estimating the optimal stochastic control policy for a Markov Decision Process with unknown transition dynamics and an unknown reward function.…