Most current speech recognition systems use hidden Markov models (HMMs) to deal with the
temporal variability of speech and Gaussian mixture models (GMMs) to determine how well …

Supervised learning on molecules has incredible potential to be useful in chemistry, drug
discovery, and materials science. Luckily, several promising and closely related neural …

We propose a novel context-dependent (CD) model for large-vocabulary speech recognition
(LVSR) that leverages recent advances in using deep belief networks for phone recognition…

Gaussian mixture models are currently the dominant technique for modeling the emission
distribution of hidden Markov models for speech recognition. We show that better phone …

Recently, pre-trained deep neural networks (DNNs) have outperformed traditional acoustic
models based on Gaussian mixture models (GMMs) on a variety of large vocabulary speech …

Neural networks were widely used for quantitative structure–activity relationships (QSAR) in
the 1990s. Because of various practical issues (eg, slow on large problems, difficult to train, …

Each year, the treatment decisions for more than 230,000 breast cancer patients in the US
hinge on whether the cancer has metastasized away from the breast. Metastasis detection is …

Automatically generated malware is a significant problem for computer users. Analysts are
able to manually investigate a small number of unknown files, but the best large-scale …

We investigate the impact of choosing regressors and molecular representations for the
construction of fast machine learning (ML) models of 13 electronic ground-state properties of …

Techniques such as ensembling and distillation promise model quality improvements when
paired with almost any base model. However, due to increased test-time cost (for ensembles…