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07/15/2023
Transformers are Universal Predictors
We find limits to the Transformer architecture for language modeling and...
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05/17/2023
Equivariant Few-Shot Learning from Pretrained Models
Efficient transfer learning algorithms are key to the success of foundat...
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10/13/2022
Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models
We introduce equi-tuning, a novel fine-tuning method that transforms (po...
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05/21/2022
Equivariant Mesh Attention Networks
Equivariance to symmetries has proven to be a powerful inductive bias in...
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04/06/2022
Hypergraph-based Source Codes for Function Computation Under Maximal Distortion
This work investigates functional source coding problems with maximal di...
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04/10/2021
Group Equivariant Neural Architecture Search via Group Decomposition and Reinforcement Learning
Recent works show that including group equivariance as an inductive bias...
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11/08/2020
The Twelvefold Way of Non-Sequential Lossless Compression
Many information sources are not just sequences of distinguishable symbo...
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07/29/2020
Mirostat: A Perplexity-Controlled Neural Text Decoding Algorithm
Neural text decoding is important for generating high-quality texts usin...
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10/18/2019
Functional Epsilon Entropy
We consider the problem of coding for computing with maximal distortion,...
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10/25/2018
Coding for Computing Arbitrary Functions Unknown to the Encoder
In this paper we consider point-to-point and distributed source coding p...
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04/09/2018
