Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …

Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …

Deep learning (DL) techniques have obtained remarkable achievements on various tasks,
such as image recognition, object detection, and language modeling. However, building a …

In this paper, we present a thorough evaluation of the efficacy of knowledge distillation and
its dependence on student and teacher architectures. Starting with the observation that more …

Green AI Page 1 54 COMMUNICATIONS OF THE ACM | DECEMBER 2020 | VOL. 63 | NO.
12 contributed articles ILL US TRA TION B Y LIS A SHEEHAN DOI:10.1145/3381831 …

Structural pruning of neural network parameters reduces computational, energy, and
memory transfer costs during inference. We propose a novel method that estimates the …

Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis proximity to where data are captured based on artificial …

Existing popular methods for semi-supervised learning with Graph Neural Networks (such
as the Graph Convolutional Network) provably cannot learn a general class of …