Dataset Groups Activity Stream Broken Neural Scaling Laws A smoothly broken power law functional form that accurately models and extrapolates the scaling behaviors of deep neural networks for various architectures and tasks. BibTex: @dataset{Ethan_Caballero_and_Kshitij_Gupta_and_Irina_Rish_and_David_Krueger_2024, abstract = {A smoothly broken power law functional form that accurately models and extrapolates the scaling behaviors of deep neural networks for various architectures and tasks.}, author = {Ethan Caballero and Kshitij Gupta and Irina Rish and David Krueger}, doi = {10.57702/vmz9zwm4}, institution = {No Organization}, keyword = {'Deep Learning', 'Machine Learning', 'Neural Networks', 'Scaling Laws'}, month = {dec}, publisher = {TIB}, title = {Broken Neural Scaling Laws}, url = {https://service.tib.eu/ldmservice/dataset/broken-neural-scaling-laws}, year = {2024} }
