Navigate
421 papers with code • 0 benchmarks • 1 datasets
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Most implemented papers
Goal Misgeneralization in Deep Reinforcement Learning
We study goal misgeneralization, a type of out-of-distribution generalization failure in reinforcement learning (RL).
WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings
Machine learning models often learn latent embedding representations that capture the domain semantics of their training data.
Jelly Bean World: A Testbed for Never-Ending Learning
eaplatanios/jelly-bean-world
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ICLR 2020
Never-ending learning is a machine learning paradigm that aims to bridge this gap, with the goal of encouraging researchers to design machine learning systems that can learn to perform a wider variety of inter-related tasks in more complex environments.
Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments
In this paper we present our proof of concept for autonomous self-learning robot navigation in an unknown environment for a real robot without a map or planner.
HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units
HOLMES is tested on risk prediction task on pediatric cardio ICU data with above 95% prediction accuracy and sub-second latency on 64-bed simulation.
AutoTrans: Automating Transformer Design via Reinforced Architecture Search
Though the transformer architectures have shown dominance in many natural language understanding tasks, there are still unsolved issues for the training of transformer models, especially the need for a principled way of warm-up which has shown importance for stable training of a transformer, as well as whether the task at hand prefer to scale the attention product or not.
Extracting a Knowledge Base of Mechanisms from COVID-19 Papers
dwadden/dygiepp
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NAACL 2021
The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge.
Towards mental time travel: a hierarchical memory for reinforcement learning agents
deepmind/deepmind-research
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NeurIPS 2021
Agents with common memory architectures struggle to recall and integrate across multiple timesteps of a past event, or even to recall the details of a single timestep that is followed by distractor tasks.
ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models
Personalized content-based recommender systems have become indispensable tools for users to navigate through the vast amount of content available on platforms like daily news websites and book recommendation services.
Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation
m5823779/MotionPlannerUsingDDPG
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We present a learning-based mapless motion planner by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as input and the continuous steering commands as output.
