Search Results for author:
Found 8 papers, 6 papers with code
TabTransformer: Tabular Data Modeling Using Contextual Embeddings
We propose TabTransformer, a novel deep tabular data modeling architecture for supervised and semi-supervised learning.
Supervised Learning on Relational Databases with Graph Neural Networks
The majority of data scientists and machine learning practitioners use relational data in their work [State of ML and Data Science 2017, Kaggle, Inc.].
SLM Lab: A Comprehensive Benchmark and Modular Software Framework for Reproducible Deep Reinforcement Learning
We introduce SLM Lab, a software framework for reproducible reinforcement learning (RL) research.
Sampling-Free Learning of Bayesian Quantized Neural Networks
Bayesian learning of model parameters in neural networks is important in scenarios where estimates with well-calibrated uncertainty are important.
Minimal Achievable Sufficient Statistic Learning
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.
A General Method for Amortizing Variational Filtering
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.
Some Requests for Machine Learning Research from the East African Tech Scene
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.
Open Vocabulary Learning on Source Code with a Graph-Structured Cache
Machine learning models that take computer program source code as input typically use Natural Language Processing (NLP) techniques.
