Reading

Here’s a list of everything I have been reading recently.

Academic Reading

• Roscher, R., Bohn, B., Duarte, M. F., & Garcke, J. (2020). Explainable machine learning for scientific insights and discoveries. Ieee Access, 8, 42200-42216.

• Gu, A., & Dao, T. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752.

• Akbari, H., Yuan, L., Qian, R., Chuang, W. H., Chang, S. F., Cui, Y., & Gong, B. (2021). Vatt: Transformers for multimodal self-supervised learning from raw video, audio and text. Advances in Neural Information Processing Systems, 34, 24206-24221.

• Morioka, H., & Hyvarinen, A. (2023, April). Connectivity-contrastive learning: Combining causal discovery and representation learning for multimodal data. In International conference on artificial intelligence and statistics (pp. 3399-3426). PMLR.

• Hsu, Y. C., Shen, Y., Jin, H., & Kira, Z. (2020). Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10951-10960).

• Hendrycks, D., Basart, S., Mu, N., Kadavath, S., Wang, F., Dorundo, E., … & Gilmer, J. (2021). The many faces of robustness: A critical analysis of out-of-distribution generalization. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 8340-8349).