Two Sigma researchers highlight several papers from ICML 2018 that they found particularly novel, practical, or otherwise compelling.
In the machine learning research community, it is generally believed that there is a tension between memorization and generalization. This paper examines the extent to which this tension exists, by exploring whether it is possible to generalize by memorizing alone.
Two Sigma’s Co-Founder discusses algorithmic investing, self-driving cars, blockchain, education, and more.
Two Sigma researchers share highlights from NIPS 2017.
An overview of best practices derived from building a machine-learning based starter bot for Halite, Two Sigma's public artificial intelligence programming challenge.
A senior Two Sigma researcher provides an overview of some of the most interesting Deep Learning research from ICML 2017.
Two Sigma Co-founder and Co-chairman David Siegel offers his views on these topics and more at Bloomberg’s Sooner Than You Think conference.
Writing in the Wall Street Journal, Two Sigma co-founder David Siegel argues that embracing the scientific method in investment management brings much-needed rigor to the process, while helping to counteract common but harmful biases.
Speed and repeatability are crucial in machine learning, but the latter is not guaranteed in TensorFlow. A Two Sigma researcher demonstrates a workaround to attain repeatable results.
Two Sigma researchers discuss notable advances in deep learning, optimization algorithms, Bayesian techniques, and time-series analysis presented at 2016's Conference on Neural Information Processing Systems (NIPS).