13 papers
How Hyper-Datafication Impacts the Sustainability Costs in Frontier AI
Sophia N. Wilson, Sebastian Mair, Mophat Okinyi +3
Large-scale data has fuelled the success of frontier artificial intelligence (AI) models over the past decade. This expansion has relied on sustained efforts by large technology co…
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…
Performance-Carbon Trade-Offs across Architectural Biases in Shear Flow Forecasting
Sophia N. Wilson, Jens Hesselbjerg Christensen, Raghavendra Selvan
Development of modern deep learning methods has been driven primarily by the push for improving model efficacy (accuracy metrics), leading to large-scale models that require massiv…
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…
deCIFer: Crystal Structure Prediction from Powder Diffraction Data using Autoregressive Language Models
Frederik Lizak Johansen, Ulrik Friis-Jensen, Erik Bjørnager Dam +3
Novel materials drive advancements in fields ranging from energy storage to electronics, with crystal structure characterization forming a crucial yet challenging step in materials…
CoDeQ: End-to-End Joint Model Compression with Dead-Zone Quantizer for High-Sparsity and Low-Precision Networks
Jonathan Wenshøj, Tong Chen, Bob Pepin +1
While joint pruning--quantization is theoretically superior to sequential application, current joint methods rely on auxiliary procedures outside the training loop for finding comp…