Search Results for author: Daniel Cohen

Found 7 papers, 2 papers with code

A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models

1 code implementation25 Jun 2021 Oleg Lesota, Navid Rekabsaz, Daniel Cohen, Klaus Antonius Grasserbauer, Carsten Eickhoff, Markus Schedl

In contrast to the matching paradigm, the probabilistic nature of generative rankers readily offers a fine-grained measure of uncertainty.

Passage Re-Ranking Passage Retrieval +2

Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models

no code implementations10 May 2021 Daniel Cohen, Bhaskar Mitra, Oleg Lesota, Navid Rekabsaz, Carsten Eickhoff

In any ranking system, the retrieval model outputs a single score for a document based on its belief on how relevant it is to a given search query.

Machine Learning for Mechanical Ventilation Control

no code implementations12 Feb 2021 Daniel Suo, Cyril Zhang, Paula Gradu, Udaya Ghai, Xinyi Chen, Edgar Minasyan, Naman Agarwal, Karan Singh, Julienne LaChance, Tom Zajdel, Manuel Schottdorf, Daniel Cohen, Elad Hazan

We consider the problem of controlling an invasive mechanical ventilator for pressure-controlled ventilation: a controller must let air in and out of a sedated patient's lungs according to a trajectory of airway pressures specified by a clinician.

Evaluating the Performance of Reinforcement Learning Algorithms

1 code implementation ICML 2020 Scott M. Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip S. Thomas

Performance evaluations are critical for quantifying algorithmic advances in reinforcement learning.

Cross Domain Regularization for Neural Ranking Models Using Adversarial Learning

no code implementations9 May 2018 Daniel Cohen, Bhaskar Mitra, Katja Hofmann, W. Bruce Croft

We use an adversarial discriminator and train our neural ranking model on a small set of domains.

Information Retrieval

Adaptability of Neural Networks on Varying Granularity IR Tasks

no code implementations24 Jun 2016 Daniel Cohen, Qingyao Ai, W. Bruce Croft

Recent work in Information Retrieval (IR) using Deep Learning models has yielded state of the art results on a variety of IR tasks.

Information Retrieval

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