Beidi Chen | DeepAI

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∙ 07/14/2023

Fast Algorithms for a New Relaxation of Optimal Transport

We introduce a new class of objectives for optimal transport computation...

0 Moses Charikar, et al. ∙

∙ 06/24/2023

H_2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models

Large Language Models (LLMs), despite their recent impressive accomplish...

0 Zhenyu Zhang, et al. ∙

∙ 06/20/2023

InRank: Incremental Low-Rank Learning

The theory of greedy low-rank learning (GLRL) aims to explain the impres...

10 Jiawei Zhao, et al. ∙

∙ 05/25/2023

Scan and Snap: Understanding Training Dynamics and Token Composition in 1-layer Transformer

Transformer architecture has shown impressive performance in multiple re...

20 Yuandong Tian, et al. ∙

∙ 05/17/2023

Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt

Large Language Models (LLMs), armed with billions of parameters, exhibit...

9 Zhaozhuo Xu, et al. ∙

∙ 03/13/2023

FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU

The high computational and memory requirements of large language model (...

0 Ying Sheng, et al. ∙

∙ 06/02/2022

Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees

Communication compression is a crucial technique for modern distributed ...

1 Jue Wang, et al. ∙

∙ 06/02/2022

Decentralized Training of Foundation Models in Heterogeneous Environments

Training foundation models, such as GPT-3 and PaLM, can be extremely exp...

8 Binhang Yuan, et al. ∙

∙ 11/30/2021

Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models

Overparameterized neural networks generalize well but are expensive to t...

4 Beidi Chen, et al. ∙

∙ 11/12/2021

Satellite Images and Deep Learning to Identify Discrepancy in Mailing Addresses with Applications to Census 2020 in Houston

The accuracy and completeness of population estimation would significant...

0 Zhaozhuo Xu, et al. ∙

∙ 10/28/2021

Scatterbrain: Unifying Sparse and Low-rank Attention Approximation

Recent advances in efficient Transformers have exploited either the spar...

5 Beidi Chen, et al. ∙

∙ 12/31/2020

A Constant-time Adaptive Negative Sampling

Softmax classifiers with a very large number of classes naturally occur ...

13 Shabnam Daghaghi, et al. ∙

∙ 08/30/2020

SOLAR: Sparse Orthogonal Learned and Random Embeddings

Dense embedding models are commonly deployed in commercial search engine...

11 Tharun Medini, et al. ∙

∙ 07/02/2020

Climbing the WOL: Training for Cheaper Inference

Efficient inference for wide output layers (WOLs) is an essential yet ch...

0 Zichang Liu, et al. ∙

∙ 12/04/2019

Angular Visual Hardness

Although convolutional neural networks (CNNs) are inspired by the mechan...

66 Beidi Chen, et al. ∙

∙ 10/30/2019

Lsh-sampling Breaks the Computation Chicken-and-egg Loop in Adaptive Stochastic Gradient Estimation

Stochastic Gradient Descent or SGD is the most popular optimization algo...

14 Beidi Chen, et al. ∙

∙ 03/07/2019

SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems

Deep Learning (DL) algorithms are the central focus of modern machine le...

0 Beidi Chen, et al. ∙

∙ 10/07/2017

Unique Entity Estimation with Application to the Syrian Conflict

Entity resolution identifies and removes duplicate entities in large, no...

0 Beidi Chen, et al. ∙

∙ 12/06/2016

Revisiting Winner Take All (WTA) Hashing for Sparse Datasets

WTA (Winner Take All) hashing has been successfully applied in many larg...

0 Beidi Chen, et al. ∙

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