As some of you may know, one of my side interests is approximate nearest neighbor algorithms. I’m the author of Annoy, a library with 3,500+ stars on Github as of today. It offers fast approximate search for nearest neighbors with the additional benefit that you can load data super fast from disk using mmap.Read Full Story
We’re organizing a NIPS workshop on approximate inference. It is together with Tamara Broderick, Stephan Mandt, and James McInerney—and alongside an incredible cast of seminal researchers: David Blei, Andrew Gelman, Mike Jordan, and Kevin Murphy. [Workshop homepage]
This year, we set a theme based on what we believe are some of the most important challenges.
• EntropyDB: A Probabilistic Approach to Approximate Query Processing• RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers• TENER: Adapting Transformer Encoder for Name Entity Recognition• A Computing Kernel for Network Binarization on PyTorch• Making Good on LSTMs Unfulfilled Promise• A Simple Differentiable Programming Language• Explainable Artificial IntellRead Full Story
EntropyDB: A Probabilistic Approach to Approximate Query Processing
We present EntropyDB, an interactive data exploration system that uses a probabilistic approach to generate a small, query-able summary of a dataset.
This year we’re organizing the third NIPS workshop on approximate inference. It is together with Francisco Ruiz, Stephan Mandt, Cheng Zhang, and James Mclnerney—and alongside our amazing committee of Tamara Broderick, Michalis Titsias, David Blei, and Max Welling.
Call for papers below.
Note: We have a lot of funding for awards this year.