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11/09/2022
Large Language Models with Controllable Working Memory
Large language models (LLMs) have led to a series of breakthroughs in na...
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11/01/2022
Preserving In-Context Learning ability in Large Language Model Fine-tuning
Pretrained large language models (LLMs) are strong in-context learners t...
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10/12/2022
Large Models are Parsimonious Learners: Activation Sparsity in Trained Transformers
This paper studies the curious phenomenon for machine learning models wi...
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07/20/2022
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning
Federated learning (FL) has recently attracted increasing attention from...
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03/09/2022
Correlated quantization for distributed mean estimation and optimization
We study the problem of distributed mean estimation and optimization und...
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03/08/2021
InFillmore: Neural Frame Lexicalization for Narrative Text Infilling
We propose a structured extension to bidirectional-context conditional l...
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12/01/2020
Modifying Memories in Transformer Models
Large Transformer models have achieved impressive performance in many na...
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07/27/2020
Learning discrete distributions: user vs item-level privacy
Much of the literature on differential privacy focuses on item-level pri...
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04/23/2020
Doubly-stochastic mining for heterogeneous retrieval
Modern retrieval problems are characterised by training sets with potent...
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03/31/2020
Take the Scenic Route: Improving Generalization in Vision-and-Language Navigation
In the Vision-and-Language Navigation (VLN) task, an agent with egocentr...
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07/24/2019
Sampled Softmax with Random Fourier Features
The computational cost of training with softmax cross entropy loss grows...
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10/16/2018
Stochastic Negative Mining for Learning with Large Output Spaces
We consider the problem of retrieving the most relevant labels for a giv...
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05/27/2018
cpSGD: Communication-efficient and differentially-private distributed SGD
Distributed stochastic gradient descent is an important subroutine in di...
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11/15/2017
