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07/24/2023
On the Connection between Pre-training Data Diversity and Fine-tuning Robustness
Pre-training has been widely adopted in deep learning to improve model p...
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06/16/2023
Neural Priming for Sample-Efficient Adaptation
We propose Neural Priming, a technique for adapting large pretrained mod...
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04/27/2023
DataComp: In search of the next generation of multimodal datasets
Large multimodal datasets have been instrumental in recent breakthroughs...
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01/10/2023
Neural Radiance Field Codebooks
Compositional representations of the world are a promising step towards ...
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05/26/2022
Matryoshka Representations for Adaptive Deployment
Learned representations are a central component in modern ML systems, se...
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12/06/2021
Forward Compatible Training for Representation Learning
In visual retrieval systems, updating the embedding model requires recom...
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06/02/2021
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes
Learning binary representations of instances and classes is a classical ...
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10/19/2020
Parameter Norm Growth During Training of Transformers
The capacity of neural networks like the widely adopted transformer is k...
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06/26/2020
Supermasks in Superposition
We present the Supermasks in Superposition (SupSup) model, capable of se...
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02/08/2020
Soft Threshold Weight Reparameterization for Learnable Sparsity
Sparsity in Deep Neural Networks (DNNs) is studied extensively with the ...
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11/29/2019
What's Hidden in a Randomly Weighted Neural Network?
Training a neural network is synonymous with learning the values of the ...
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08/13/2018