19 citations · 32 across the 2 of their papers we have counts for
5 papers
Evaluating the Performance of Reinforcement Learning Algorithms
Scott M. Jordan, Yash Chandak, Daniel Cohen +2
Performance evaluations are critical for quantifying algorithmic advances in reinforcement learning. Recent reproducibility analyses have shown that reported performance results ar…
Distributed Evaluations: Ending Neural Point Metrics
Daniel Cohen, Scott M. Jordan, W. Bruce Croft
With the rise of neural models across the field of information retrieval, numerous publications have incrementally pushed the envelope of performance for a multitude of IR tasks. H…
WikiPassageQA: A Benchmark Collection for Research on Non-factoid Answer Passage Retrieval
Daniel Cohen, Liu Yang, W. Bruce Croft
With the rise in mobile and voice search, answer passage retrieval acts as a critical component of an effective information retrieval system for open domain question answering. Cur…
Cross Domain Regularization for Neural Ranking Models Using Adversarial Learning
Daniel Cohen, Bhaskar Mitra, Katja Hofmann +1
Unlike traditional learning to rank models that depend on hand-crafted features, neural representation learning models learn higher level features for the ranking task by training…
Adaptability of Neural Networks on Varying Granularity IR Tasks
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. Deep neural networks (DNN) are capable of learni…