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08/28/2023
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
Deep Neural Networks (DNNs) have been a large driver and enabler for AI ...
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02/15/2023
The Expressive Power of Tuning Only the Norm Layers
Feature normalization transforms such as Batch and Layer-Normalization h...
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01/30/2023
Looped Transformers as Programmable Computers
We present a framework for using transformer networks as universal compu...
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06/14/2022
LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks
Fine-tuning pretrained language models (LMs) without making any architec...
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05/23/2022
Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment
Word translation without parallel corpora has become feasible, rivaling ...
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10/20/2021
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond
In distributed learning, local SGD (also known as federated averaging) a...
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10/18/2021
Finding Everything within Random Binary Networks
A recent work by Ramanujan et al. (2020) provides significant empirical ...
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06/14/2021
An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks
It is well known that modern deep neural networks are powerful enough to...
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02/19/2021
Permutation-Based SGD: Is Random Optimal?
A recent line of ground-breaking results for permutation-based SGD has c...
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07/09/2020
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
Due to its decentralized nature, Federated Learning (FL) lends itself to...
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06/14/2020
Optimal Lottery Tickets via SubsetSum: Logarithmic Over-Parameterization is Sufficient
The strong lottery ticket hypothesis (LTH) postulates that one can appro...
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02/24/2020
Closing the convergence gap of SGD without replacement
Stochastic gradient descent without replacement sampling is widely used ...
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07/29/2019
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation
To improve the resilience of distributed training to worst-case, or Byza...
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05/22/2019
Convergence and Margin of Adversarial Training on Separable Data
Adversarial training is a technique for training robust machine learning...
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05/08/2019
