Downloads
Implementation of SPIBB-DQN
May 2019
This project can be used to reproduce the DQN implementation presented in the ICML2019 paper: Safe Policy Improvement with Baseline Bootstrapping, by Romain Laroche, Paul Trichelair, and Rémi Tachet des Combes. For the finite MDPs experiments, please refer to git…
Implementation of Safe Policy Improvement with Baseline Bootstrapping
May 2019
This project can be used to reproduce the finite MDPs experiments presented in the ICML2019 paper: Safe Policy Improvement with Baseline Bootstrapping, by Romain Laroche, Paul Trichelair, and Rémi Tachet des Combes. For the DQN implementation, please refer to git…
NewsQA Dataset
March 2018
The purpose of Microsoft Montreal’s NewsQA dataset is to help the research community build algorithms that are capable of answering questions requiring human-level comprehension and reasoning skills. Leveraging CNN articles from the DeepMind Q&A Dataset, we prepared a crowd-sourced machine…
TREC Tip-of-the-Tongue Track
April 2024
Tip-of-the-tongue (ToT) known-item retrieval is defined as “an item identification task in which the searcher has previously experienced an item but cannot recall a reliable identifier” (i.e., “It’s on the tip of my tongue…”). The TREC ToT track aims to…
Conformer-Kernel Model with Query Term Independence (TREC Deep Learning Quick Start)
March 2021
This is a quick start guide for the document ranking task in the TREC Deep Learning (TREC-DL) benchmark. If you are new to TREC-DL, then this repository may make it more convenient for you to download all the required datasets…
Tip of the Tongue Known Item Retrieval Dataset for Movie Identification
August 2021
The Tip of the Tongue (ToT) dataset is from the paper Tip of the Tongue Known-Item Retrieval: A Case Study in Movie Identification. It is comprised of 758 question/answer pairs scraped from the website iRememberThisMovie.com between 2013 and 2018. These…
Sepsis Cohort from MIMIC III
December 2020
This repo provides code for generating the sepsis cohort from MIMIC III dataset. Our main goal is to facilitate reproducibility of results in the literature.
AMDIM – Augmented Multiscale Deep InfoMax
June 2019
AMDIM (Augmented Multiscale Deep InfoMax) is an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context.