[HTML][HTML] A state-of-the-art survey on deep learning theory and architectures
In recent years, deep learning has garnered tremendous success in a variety of application
domains. This new field of machine learning has been growing rapidly and has been …
domains. This new field of machine learning has been growing rapidly and has been …
The history began from alexnet: A comprehensive survey on deep learning approaches
Deep learning has demonstrated tremendous success in variety of application domains in
the past few years. This new field of machine learning has been growing rapidly and applied …
the past few years. This new field of machine learning has been growing rapidly and applied …
The case for 4-bit precision: k-bit inference scaling laws
Quantization methods reduce the number of bits required to represent each parameter in a
model, trading accuracy for smaller memory footprints and inference latencies. However, the …
model, trading accuracy for smaller memory footprints and inference latencies. However, the …
Pruning and quantization for deep neural network acceleration: A survey
Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …
abilities in the field of computer vision. However, complex network architectures challenge …
8-bit optimizers via block-wise quantization
Stateful optimizers maintain gradient statistics over time, eg, the exponentially smoothed
sum (SGD with momentum) or squared sum (Adam) of past gradient values. This state can …
sum (SGD with momentum) or squared sum (Adam) of past gradient values. This state can …
[BOEK][B] Neural networks and deep learning
CC Aggarwal - 2018 - Springer
“Any AI smart enough to pass a Turing test is smart enough to know to fail it.”–*** Ian
McDonald Neural networks were developed to simulate the human nervous system for …
McDonald Neural networks were developed to simulate the human nervous system for …
Hermes: an efficient federated learning framework for heterogeneous mobile clients
Federated learning (FL) has been a popular method to achieve distributed machine learning
among numerous devices without sharing their data to a cloud server. FL aims to learn a …
among numerous devices without sharing their data to a cloud server. FL aims to learn a …
Deep convolutional neural networks for image classification: A comprehensive review
Convolutional neural networks (CNNs) have been applied to visual tasks since the late
1980s. However, despite a few scattered applications, they were dormant until the mid …
1980s. However, despite a few scattered applications, they were dormant until the mid …
Demystifying parallel and distributed deep learning: An in-depth concurrency analysis
Deep Neural Networks (DNNs) are becoming an important tool in modern computing
applications. Accelerating their training is a major challenge and techniques range from …
applications. Accelerating their training is a major challenge and techniques range from …
Fedmask: Joint computation and communication-efficient personalized federated learning via heterogeneous masking
Recent advancements in deep neural networks (DNN) enabled various mobile deep
learning applications. However, it is technically challenging to locally train a DNN model due …
learning applications. However, it is technically challenging to locally train a DNN model due …