Grounding inductive biases in natural images: invariance stems from variations in data

von Bouchacourt, Diane
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 24 of 36 (2022)

ONE TICKET TO WIN THEM ALL: GENERALIZING LOTTERY TICKET INITIALIZATIONS ACROSS DATASETS AND OPTIMIZERS

von Morcos, Ari
Veröffentlicht in NeurIPS (33. : 2019 : Vancouver, British Columbia) 32nd Conference on Neural Information Processing Systems (NeurIPS 2019) ; Volume 7 of 20 (2020)

Joslim: Joint Widths and Weights Optimization for Slimmable Neural Networks

von Chin, Ting-Wu
Veröffentlicht in ECML PKDD (2021 : Online) Machine Learning and Knowledge Discovery in Databases ; Part 3: Research Track (2021)

THE GENERALIZATION-STABILITY TRADEOFF IN NEURAL NETWORK PRUNING

von Bartoldson, Brian
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 25 of 27 (2021)

Beyond neural scaling laws: beating power law scaling via data pruning

von Sorscher, Ben
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 26 of 50 (2023)

CURI: A BENCHMARK FOR PRODUCTIVE CONCEPT LEARNING UNDER UNCERTAINTY

von VEDANTAM, RAMAKRISHNA
Veröffentlicht in International Conference on Machine Learning (38. : 2021 : Online) International Conference on Machine Learning (ICML 2021 ; Part 13 of 16 (2022)

COAT: Measuring Object Compositionality in Emergent Representations

von Xie, Sirui
Veröffentlicht in International Conference on Machine Learning (39. : 2022 : Baltimore, Md.; Online) International Conference on Machine Learning (ICML 2022) ; Part 30 of 33 (2023)

CONVIT: IMPROVING VISION TRANSFORMERS WITH SOFT CONVOLUTIONAL INDUCTIVE BIASES

von D’ASCOLI, STÉPHANE
Veröffentlicht in International Conference on Machine Learning (38. : 2021 : Online) International Conference on Machine Learning (ICML 2021 ; Part 3 of 16 (2022)

Model Soups: Averaging Weights of Multiple Fine-Tuned Models Improves Accuracy Without Increasing Inference Time

von Wortsman, Mitchell
Veröffentlicht in International Conference on Machine Learning (39. : 2022 : Baltimore, Md.; Online) International Conference on Machine Learning (ICML 2022) ; Part 29 of 33 (2023)