ICLR 2020

A few highlights and takeaways

William of Ockham (image via Wikipedia)

I recently had the pleasure to attend #ICLR2020, sort of a formal academic conference gathering of machine learning researchers sharing their work. The experience was quite ‘with the times’, as the original meeting space in Ethiopia was only a few short weeks ago set aside in the interest of social distancing, and conference halls and meeting spaces were traded in aggregate for chat sessions, Zoom meetings, pre-recorded poster summaries, oh and not too mention this sort of 8bit avatar space with built in proximity video conferencing — pretty neat stuff.

I’d like to use this forum to quickly highlight a few of the papers I found of interest. I tried to approach the explorations with a novelty filter, mostly turning attention towards unrecognized points within proximity of my interests, which are admittedly somewhat varied. I’ll structure this as a power point presentation of sorts, loosely inspired by seeing Warren Buffett’s understated approach to slide presentation at the recent shareholder’s meeting. Yeah so without further ado.

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References

Ba, J., Erdogdu, M., Suzuki, T., Wu, D., and Zhang, T. Generalization of two-layer neural networks: An asymptotic viewpoint. In International Conference on Learning Representations, 2020. URL https://openreview.net/forum?id=H1gBsgBYwH.

Bengio, Y., Deleu, T., Rahaman, N., Ke, R., Lachapelle, S., Bilaniuk, O., Goyal, A., and Pal, C. A meta-transfer objective for learning to disentangle causal mechanisms, 2019. URL https://arxiv.org/abs/1901.10912.

Dyer, E. and Gur-Ari, G. Asymptotics of wide networks from feynman diagrams. In International Conference on Learning Representations, 2020. URL https://openreview.net/forum?id=S1gFvANKDS.

Ermon, S. Measuring economic development from space with machine learning, April 2020.

Flatter, G. C. Why the climate change ai community should care about weather: A new approach for africa, April 2020.

Gissin, D., Shalev-Shwartz, S., and Daniely, A. The implicit bias of depth: How incremental learning drives generalization, 2019. URL https://arxiv.org/abs/1909.12051.

Goyal, A., Sodhani, S., Binas, J., Peng, X. B., Levine, S., and Bengio, Y. Reinforcement learning with competitive ensembles of information-constrained primitives, 2019. URL https://arxiv.org/abs/1906.10667.

Jia, J., Cao, X., Wang, B., and Gong, N. Z. Certified robustness for top-k predictions against adversarial perturbations via randomized smoothing, 2019. URL https://arxiv.org/abs/1912.09899.

Jiang, Y., Neyshabur, B., Mobahi, H., Krishnan, D., and Bengio, S. Fantastic generalization measures and where to find them, 2019. URL https://arxiv.org/abs/1912.02178.

Kong, L., d’Autume, C. d. M., Ling, W., Yu, L., Dai, Z., and Yogatama, D. A mutual information maximization perspective of language representation learning, 2019. URL https://arxiv.org/abs/1910.08350.

Lee, C., Cho, K., and Kang, W. Mixout: Effective regularization to finetune large-scale pretrained language models, 2019. URL https://arxiv.org/abs/1909.11299.

Li, H., Chaudhari, P., Yang, H., Lam, M., Ravichandran, A., Bhotika, R., and Soatto, S. Rethinking the hyperparameters for fine-tuning, 2020a. URL https://arxiv.org/abs/2002.11770.

Li, J., Socher, R., and Hoi, S. C. H. Dividemix: Learning with noisy labels as semi-supervised learning. 2020b. doi: 10.48550/ARXIV.2002.07394. URL https://arxiv.org/abs/2002.07394.

Long, Q., Zhou, Z., Gupta, A., Fang, F., Wu, Y., and Wang, X. Evolutionary population curriculum for scaling multi-agent reinforcement learning, 2020. URL https://arxiv.org/abs/2003.10423.

Nakkiran, P., Kaplun, G., Bansal, Y., Yang, T., Barak, B., and Sutskever, I. Deep double descent: Where bigger models and more data hurt, 2019. URL https://arxiv.org/abs/1912.02292.

Oktay, D., Balle ́, J., Singh, S., and Shrivastava, A. Scalable model compression by entropy penalized reparameterization. In International Conference on Learning Representations, 2020. URL https://openreview.net/forum?id=HkgxW0EYDS.

Oner, M. U., Lee, H. K., and Sung, W.-K. Weakly supervised clustering by exploiting unique class count. In International Conference on Learning Representations, 2020. URL https://openreview.net/forum?id=B1xIj3VYvr.

Park, S., Kim, K., Kim, S., Lee, J., Lee, J., Lee, J., and Choo, J. Hurricane nowcasting with irregular time-step using neural-ode and video prediction. In ICLR 2020 Workshop on Tackling Climate Change with Machine Learning, 2020. URL https://www.climatechange.ai/papers/iclr2020/21.

Popov, S., Morozov, S., and Babenko, A. Neural oblivious decision ensembles for deep learning on tabular data, 2019. URL https://arxiv.org/abs/1909.06312.

Rahaman, N., Wolf, S., Goyal, A., Remme, R., and Bengio, Y. Learning the arrow of time for problems in reinforcement learning. In International Conference on Learning Representations, 2020. URL https://openreview.net/forum?id=rylJkpEtwS.

Sinitsin, A., Plokhotnyuk, V., Pyrkin, D., Popov, S., and Babenko, A. Editable neural networks, 2020. URL https: //arxiv.org/abs/2004.00345.

Toth, P., Rezende, D. J., Jaegle, A., Racaniére, S., Botev, A., and Higgins, I. Hamiltonian generative networks, 2019. URL https://arxiv.org/abs/1909.13789.

Wei, J., Goyal, M., Durrett, G., and Dillig, I. Lambdanet: Probabilistic type inference using graph neural networks. In International Conference on Learning Representations, 2020. URL https://openreview.net/forum?id=Hkx6hANtwH.

Wu, Z., Liu, Z., Lin, J., Lin, Y., and Han, S. Lite transformer with long-short range attention, 2020. URL https: //arxiv.org/abs/2004.11886.

Xiong, W., Du, J., Wang, W. Y., and Stoyanov, V. Pretrained encyclopedia: Weakly supervised knowledge-pretrained language model, 2019. URL https://arxiv.org/abs/1912.09637.

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Nicholas Teague

Nicholas Teague

Writing for fun and because it helps me organize my thoughts. I also write software to prepare data for machine learning at automunge.com. Consistently unique.