12 code implementations • 11 Dec 2020 • Xin Huang, Ashish Khetan, Milan Cvitkovic, Zohar Karnin
We propose TabTransformer, a novel deep tabular data modeling architecture for supervised and semi-supervised learning.
1 code implementation • 6 Feb 2020 • Milan Cvitkovic
The majority of data scientists and machine learning practitioners use relational data in their work [State of ML and Data Science 2017, Kaggle, Inc.].
1 code implementation • 28 Dec 2019 • Keng Wah Loon, Laura Graesser, Milan Cvitkovic
We introduce SLM Lab, a software framework for reproducible reinforcement learning (RL) research.
no code implementations • ICLR 2020 • Jiahao Su, Milan Cvitkovic, Furong Huang
Bayesian learning of model parameters in neural networks is important in scenarios where estimates with well-calibrated uncertainty are important.
1 code implementation • 19 May 2019 • Milan Cvitkovic, Günther Koliander
We introduce Minimal Achievable Sufficient Statistic (MASS) Learning, a training method for machine learning models that attempts to produce minimal sufficient statistics with respect to a class of functions (e. g. deep networks) being optimized over.
1 code implementation • NeurIPS 2018 • Joseph Marino, Milan Cvitkovic, Yisong Yue
We introduce the variational filtering EM algorithm, a simple, general-purpose method for performing variational inference in dynamical latent variable models using information from only past and present variables, i. e. filtering.
no code implementations • 25 Oct 2018 • Milan Cvitkovic
Based on 46 in-depth interviews with scientists, engineers, and CEOs, this document presents a list of concrete machine research problems, progress on which would directly benefit tech ventures in East Africa.
3 code implementations • ICLR 2019 • Milan Cvitkovic, Badal Singh, Anima Anandkumar
Machine learning models that take computer program source code as input typically use Natural Language Processing (NLP) techniques.