Search Results for author:
Found 9 papers, 3 papers with code
Economics of Sourcing Human Data
Progress in AI has relied on human-generated data, from annotator marketplaces to the wider Internet.
The in-context inductive biases of vision-language models differ across modalities
These results help to reveal how vision-language models represent different types of inputs in context, and may have practical implications for the use of vision-language models.
Scaling 4D Representations
Scaling has not yet been convincingly demonstrated for pure self-supervised learning from video.
Diffusion Generative Inverse Design
Inverse design refers to the problem of optimizing the input of an objective function in order to enact a target outcome.
Improving Dual-Encoder Training through Dynamic Indexes for Negative Mining
First, we introduce an algorithm that uses a tree structure to approximate the softmax with provable bounds and that dynamically maintains the tree.
Residual Policy Learning
In these tasks, reinforcement learning from scratch remains data-inefficient or intractable, but learning a residual on top of the initial controller can yield substantial improvements.
End-to-End Differentiable Physics for Learning and Control
We present a differentiable physics engine that can be integrated as a module in deep neural networks for end-to-end learning.
Relational inductive biases, deep learning, and graph networks
As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.
