activity
20192022
most citedNeural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents

48 citations · 156 across the 13 of their papers we have counts for

collaborators

22 papers

cs.RO20227 cited

SE(3)-Equivariant Relational Rearrangement with Neural Descriptor Fields

Anthony Simeonov, Yilun Du, Lin Yen-Chen +4

We present a method for performing tasks involving spatial relations between novel object instances initialized in arbitrary poses directly from point cloud observations. Our frame…

cs.CL202221 cited

Self-conditioned Embedding Diffusion for Text Generation

Robin Strudel, Corentin Tallec, Florent Altché +8

Can continuous diffusion models bring the same performance breakthrough on natural language they did for image generation? To circumvent the discrete nature of text data, we can si…

cs.CV20227 cited

Composing Ensembles of Pre-trained Models via Iterative Consensus

Shuang Li, Yilun Du, Joshua B. Tenenbaum +2

Large pre-trained models exhibit distinct and complementary capabilities dependent on the data they are trained on. Language models such as GPT-3 are capable of textual reasoning b…

cs.CV20225 cited

Kubric: A scalable dataset generator

Klaus Greff, Francois Belletti, Lucas Beyer +32

Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and trainin…

cs.CV202111 cited

Unsupervised Learning of Compositional Energy Concepts

Yilun Du, Shuang Li, Yash Sharma +2

Humans are able to rapidly understand scenes by utilizing concepts extracted from prior experience. Such concepts are diverse, and include global scene descriptors, such as the wea…

cs.LG20211 cited

The Neural MMO Platform for Massively Multiagent Research

Joseph Suarez, Yilun Du, Clare Zhu +2

Neural MMO is a computationally accessible research platform that combines large agent populations, long time horizons, open-ended tasks, and modular game systems. Existing environ…