Deep Learning | Shayan Fazeli

PaperView - Toolformer: Language models can teach themselves to use tools

PaperView - going over the paper -> "Schick, Timo, et al. 'Toolformer- Language models can teach themselves to use tools.' arXiv preprint arXiv:2302.04761 (2023)."

PaperView - Deep learning on a data die - Finding important examples early in training

PaperView - going over the paper -> "Paul, Mansheej, Surya Ganguli, and Gintare Karolina Dziugaite. "Deep learning on a data diet - Finding important examples early in training." Advances in Neural Information Processing Systems 34 (2021) - 20596-20607."

PaperView - When Are Graph Neural Networks Better Than Structure-Agnostic Methods?

PaperView - going over the paper -> "Gomes, Diana, et al. When Are Graph Neural Networks Better Than Structure-Agnostic Methods?. I Can't Believe It's Not Better Workshop - Understanding Deep Learning Through Empirical Falsification."

PaperView - Mish: A self regularized non-monotonic activation function.

PaperView - going over the paper -> "Misra, Diganta. "Mish - A self regularized non-monotonic activation function." arXiv preprint arXiv:1908.08681 (2019)."

PaperView - Self-supervised multi-modal alignment for whole body medical imaging

PaperView - going over the paper -> "Windsor, R., Jamaludin, A., Kadir, T. and Zisserman, A., 2021. Self-supervised multi-modal alignment for whole body medical imaging. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2021, 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part II 24 (pp. 90-101). Springer International Publishing."

PaperView - CNT (Conditioning on Noisy Targets): A new Algorithm for Leveraging Top-Down Feedback

PaperView - going over the paper -> "Jolicoeur-Martineau, Alexia, et al. "CNT (Conditioning on Noisy Targets) - A new Algorithm for Leveraging Top-Down Feedback." arXiv preprint arXiv:2210.09505 (2022)."

PaperView - Denoising diffusion probabilistic models

PaperView - going over the paper -> "Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems. 2020;33:6840-51."

PaperView - Learning to Learn from Noisy Labeled Data

PaperView - going over the paper -> "Li J, Wong Y, Zhao Q, Kankanhalli MS. Learning to learn from noisy labeled data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2019 (pp. 5051-5059)."

PaperView - Which strategies matter for noisy label classification? Loss and Uncertainty

PaperView - going over the paper -> "Shin W, Ha JW, Li S, Cho Y, Song H, Kwon S. Which strategies matter for noisy label classification? insight into loss and uncertainty. arXiv preprint arXiv:2008.06218. 2020 Aug 14."

PaperView - Coverage-centric Coreset Selection for High Pruning Rates

PaperView - going over the paper -> "Zheng H, Liu R, Lai F, Prakash A. Coverage-centric Coreset Selection for High Pruning Rates. arXiv preprint arXiv:2210.15809. 2022 Oct 28."