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08/30/2023
Emergence of Segmentation with Minimalistic White-Box Transformers
Transformer-like models for vision tasks have recently proven effective ...
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06/01/2023
White-Box Transformers via Sparse Rate Reduction
In this paper, we contend that the objective of representation learning ...
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03/31/2022
Efficient Maximal Coding Rate Reduction by Variational Forms
The principle of Maximal Coding Rate Reduction (MCR^2) has recently been...
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02/11/2022
Incremental Learning of Structured Memory via Closed-Loop Transcription
This work proposes a minimal computational model for learning a structur...
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11/12/2021
Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction
This work proposes a new computational framework for learning an explici...
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06/17/2021
How Low Can We Go: Trading Memory for Error in Low-Precision Training
Low-precision arithmetic trains deep learning models using less energy, ...
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01/01/2021
TenIPS: Inverse Propensity Sampling for Tensor Completion
Tensors are widely used to represent multiway arrays of data. The recove...
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11/30/2020
Incremental Learning via Rate Reduction
Current deep learning architectures suffer from catastrophic forgetting,...
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06/07/2020
Efficient AutoML Pipeline Search with Matrix and Tensor Factorization
Data scientists seeking a good supervised learning model on a new datase...
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09/10/2019
PARN: Position-Aware Relation Networks for Few-Shot Learning
Few-shot learning presents a challenge that a classifier must quickly ad...
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02/19/2019