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Lightweight deep learning for resource-constrained environments: A survey
Over the past decade, the dominance of deep learning has prevailed across various
domains of artificial intelligence, including natural language processing, computer vision …
domains of artificial intelligence, including natural language processing, computer vision …
Deep architectures for image compression: a critical review
Deep learning architectures are now pervasive and filled almost all applications under
image processing, computer vision, and biometrics. The attractive property of feature …
image processing, computer vision, and biometrics. The attractive property of feature …
A survey on model compression for large language models
Abstract Large Language Models (LLMs) have transformed natural language processing
tasks successfully. Yet, their large size and high computational needs pose challenges for …
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
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 …
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
This chapter provides approaches to the problem of quantizing the numerical values in deep
Neural Network computations, covering the advantages/disadvantages of current methods …
Neural Network computations, covering the advantages/disadvantages of current methods …
Making ai forget you: Data deletion in machine learning
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 …
when their data can and cannot be used---the EU's Right To Be Forgotten regulation is an …
Hawq-v3: Dyadic neural network quantization
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 …
and forth from floating point to quantized integer values. This hidden cost limits the latency …
An introduction to neural data compression
Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …
methods to data compression. Recent advances in statistical machine learning have opened …
Wireless network intelligence at the edge
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 …
based machine learning (ML) have transformed every aspect of our lives from face …
HuBERT: How much can a bad teacher benefit ASR pre-training?
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 …
ASR are challenged by three unique problems:(1) There are multiple sound units in each …