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Found 8 papers, 4 papers with code
Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner
Our model is able to explain a given hypothesis by systematically generating a step-by-step explanation from textual premises.
Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making
In the dominant paradigm for designing equitable machine learning systems, one works to ensure that model predictions satisfy various fairness criteria, such as parity in error rates across race, gender, and other legally protected traits.
End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems
Our model comprises a single transformer-based encoder-decoder network that is trained end-to-end to generate both answers and questions.
The Ingredients of Real World Robotic Reinforcement Learning
The success of reinforcement learning in the real world has been limited to instrumented laboratory scenarios, often requiring arduous human supervision to enable continuous learning.
The Ingredients of Real-World Robotic Reinforcement Learning
In this work, we discuss the elements that are needed for a robotic learning system that can continually and autonomously improve with data collected in the real world.
ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots
ROBEL introduces two robots, each aimed to accelerate reinforcement learning research in different task domains: D'Claw is a three-fingered hand robot that facilitates learning dexterous manipulation tasks, and D'Kitty is a four-legged robot that facilitates learning agile legged locomotion tasks.
Soft Actor-Critic Algorithms and Applications
A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost
Dexterous multi-fingered robotic hands can perform a wide range of manipulation skills, making them an appealing component for general-purpose robotic manipulators.
