271 citations · 603 across the 6 of their papers we have counts for
6 papers
A Soft Robotic System Automatically Learns Precise Agile Motions Without Model Information
Simon Bachhuber, Alexander Pawluchin, Arka Pal +2
Many application domains, e.g., in medicine and manufacturing, can greatly benefit from pneumatic Soft Robots (SRs). However, the accurate control of SRs has remained a significant…
Giraffe: Adventures in Expanding Context Lengths in LLMs
Arka Pal, Deep Karkhanis, Manley Roberts +3
Modern large language models (LLMs) that rely on attention mechanisms are typically trained with fixed context lengths which enforce upper limits on the length of input sequences t…
Understanding disentangling in -VAE
Christopher P. Burgess, Irina Higgins, Arka Pal +4
We present new intuitions and theoretical assessments of the emergence of disentangled representation in variational autoencoders. Taking a rate-distortion theory perspective, we s…
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning
Irina Higgins, Arka Pal, Andrei A. Rusu +6
Domain adaptation is an important open problem in deep reinforcement learning (RL). In many scenarios of interest data is hard to obtain, so agents may learn a source policy in a s…
SCAN: Learning Hierarchical Compositional Visual Concepts
Irina Higgins, Nicolas Sonnerat, Loic Matthey +7
The seemingly infinite diversity of the natural world arises from a relatively small set of coherent rules, such as the laws of physics or chemistry. We conjecture that these rules…
Early Visual Concept Learning with Unsupervised Deep Learning
Irina Higgins, Loic Matthey, Xavier Glorot +5
Automated discovery of early visual concepts from raw image data is a major open challenge in AI research. Addressing this problem, we propose an unsupervised approach for learning…