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).
A Two Sigma research scientist provides an overview of some of the most interesting research presented at ICML 2016.