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Found 5 papers, 4 papers with code
Report on the First HIPstIR Workshop on the Future of Information Retrieval
The vision of HIPstIR is that early stage information retrieval (IR) researchers get together to develop a future for non-mainstream ideas and research agendas in IR.
Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement Learning
We show that TOYBOX enables a wide range of experiments and analyses that are impossible in other environments.
Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments
Reproducibility in reinforcement learning is challenging: uncontrolled stochasticity from many sources, such as the learning algorithm, the learned policy, and the environment itself have led researchers to report the performance of learned agents using aggregate metrics of performance over multiple random seeds for a single environment.
ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents
It is a widely accepted principle that software without tests has bugs.
Named Entity Recognition with Extremely Limited Data
Traditional information retrieval treats named entity recognition as a pre-indexing corpus annotation task, allowing entity tags to be indexed and used during search.
