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Found 11 papers, 5 papers with code
Rapidly Bootstrapping a Question Answering Dataset for COVID-19
We present CovidQA, the beginnings of a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge.
Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset
We present Covidex, a search engine that exploits the latest neural ranking models to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institute for AI.
Rapidly Deploying a Neural Search Engine for the COVID-19 Open Research Dataset: Preliminary Thoughts and Lessons Learned
We present the Neural Covidex, a search engine that exploits the latest neural ranking architectures to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institute for AI.
Language Control Diffusion: Efficiently Scaling through Space, Time, and Tasks
Training generalist agents is difficult across several axes, requiring us to deal with high-dimensional inputs (space), long horizons (time), and generalization to novel tasks.
Rapidly Deploying a Neural Search Engine for the COVID-19 Open Research Dataset
The Neural Covidex is a search engine that exploits the latest neural ranking architectures to provide information access to the COVID-19 Open Research Dataset (CORD-19) curated by the Allen Institute for AI.
Cydex: Neural Search Infrastructure for the Scholarly Literature
Cydex is a platform that provides neural search infrastructure for domain-specific scholarly literature.
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators
Behavior constrained policy optimization has been demonstrated to be a successful paradigm for tackling Offline Reinforcement Learning.
Towards a Pretrained Model for Restless Bandits via Multi-arm Generalization
Restless multi-arm bandits (RMABs), a class of resource allocation problems with broad application in areas such as healthcare, online advertising, and anti-poaching, have recently been studied from a multi-agent reinforcement learning perspective.
Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping
Inverse reinforcement learning (IRL) is computationally challenging, with common approaches requiring the solution of multiple reinforcement learning (RL) sub-problems.
A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health
Efforts to reduce maternal mortality rate, a key UN Sustainable Development target (SDG Target 3. 1), rely largely on preventative care programs to spread critical health information to high-risk populations.
Social Environment Design
Artificial Intelligence (AI) holds promise as a technology that can be used to improve government and economic policy-making.
