15 citations · 15 across the 4 of their papers we have counts for
4 papers
Characterizing Learning in Deep Neural Networks using Tractable Algorithmic Complexity Analysis
Pedram Bakhtiarifard, Sophia N. Wilson, Mahmoud Afifi +2
Training large-scale deep neural networks (DNNs) is resource-intensive, making model compression a practical necessity. The widely accepted ''learning as compression'' hypothesis p…
Algorithmic Simplification of Neural Networks with Mosaic-of-Motifs
Pedram Bakhtiarifard, Tong Chen, Jonathan Wenshøj +2
Large-scale deep learning models are well-suited for compression. Across a variety of tasks, methods like pruning, quantization, and knowledge distillation have been used to achiev…
Position: Neglecting the Sustainability of AI is Fuelling a Global AI Arms Race
Pedram Bakhtiarifard, Pınar Tözün, Christian Igel +1
Sustainability encompasses three key facets: economic, environmental, and social. However, the nascent discourse on sustainable artificial intelligence (AI) predominantly focuses o…
EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture Search
Pedram Bakhtiarifard, Christian Igel, Raghavendra Selvan
Energy consumption from the selection, training, and deployment of deep learning models has seen a significant uptick recently. This work aims to facilitate the design of energy-ef…