Lightweight deep learning for resource-constrained environments: A survey

HI Liu, M Galindo, H **e, LK Wong, HH Shuai… - ACM Computing …, 2024‏ - dl.acm.org
Over the past decade, the dominance of deep learning has prevailed across various
domains of artificial intelligence, including natural language processing, computer vision …

Deep architectures for image compression: a critical review

D Mishra, SK Singh, RK Singh - Signal Processing, 2022‏ - Elsevier
Deep learning architectures are now pervasive and filled almost all applications under
image processing, computer vision, and biometrics. The attractive property of feature …

A survey on model compression for large language models

X Zhu, J Li, Y Liu, C Ma, W Wang - Transactions of the Association for …, 2024‏ - direct.mit.edu
Abstract Large Language Models (LLMs) have transformed natural language processing
tasks successfully. Yet, their large size and high computational needs pose challenges for …

Hubert: Self-supervised speech representation learning by masked prediction of hidden units

WN Hsu, B Bolte, YHH Tsai, K Lakhotia… - … ACM transactions on …, 2021‏ - ieeexplore.ieee.org
Self-supervised approaches for speech representation learning are challenged by three
unique problems:(1) there are multiple sound units in each input utterance,(2) there is no …

A survey of quantization methods for efficient neural network inference

A Gholami, S Kim, Z Dong, Z Yao… - Low-power computer …, 2022‏ - taylorfrancis.com
This chapter provides approaches to the problem of quantizing the numerical values in deep
Neural Network computations, covering the advantages/disadvantages of current methods …

Making ai forget you: Data deletion in machine learning

A Ginart, M Guan, G Valiant… - Advances in neural …, 2019‏ - proceedings.neurips.cc
Intense recent discussions have focused on how to provide individuals with control over
when their data can and cannot be used---the EU's Right To Be Forgotten regulation is an …

Hawq-v3: Dyadic neural network quantization

Z Yao, Z Dong, Z Zheng, A Gholami… - International …, 2021‏ - proceedings.mlr.press
Current low-precision quantization algorithms often have the hidden cost of conversion back
and forth from floating point to quantized integer values. This hidden cost limits the latency …

An introduction to neural data compression

Y Yang, S Mandt, L Theis - Foundations and Trends® in …, 2023‏ - nowpublishers.com
Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

Wireless network intelligence at the edge

J Park, S Samarakoon, M Bennis… - Proceedings of the …, 2019‏ - ieeexplore.ieee.org
Fueled by the availability of more data and computing power, recent breakthroughs in cloud-
based machine learning (ML) have transformed every aspect of our lives from face …

HuBERT: How much can a bad teacher benefit ASR pre-training?

WN Hsu, YHH Tsai, B Bolte… - ICASSP 2021-2021 …, 2021‏ - ieeexplore.ieee.org
Compared to vision and language applications, self-supervised pre-training approaches for
ASR are challenged by three unique problems:(1) There are multiple sound units in each …