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Found 9 papers, 1 papers with code
Contrastive Training Improves Zero-Shot Classification of Semi-structured Documents
The standard classification setting where categories are fixed during both training and testing falls short in dynamic environments where new document categories could potentially emerge.
Label Semantics for Few Shot Named Entity Recognition
We study the problem of few shot learning for named entity recognition.
Robust Multi-Agent Reinforcement Learning with Model Uncertainty
In contrast, we model the problem as a robust Markov game, where the goal of all agents is to find policies such that no agent has the incentive to deviate, i. e., reach some equilibrium point, which is also robust to the possible uncertainty of the MARL model.
REPAINT: Knowledge Transfer in Deep Reinforcement Learning
Accelerating learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when the similarity between source and target tasks is low.
REPAINT: Knowledge Transfer in Deep Actor-Critic Reinforcement Learning
Accelerating the learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when the similarity between source and target tasks is low or unknown.
Zero-Shot Reinforcement Learning with Deep Attention Convolutional Neural Networks
Simulation-to-simulation and simulation-to-real world transfer of neural network models have been a difficult problem.
SAVEHR: Self Attention Vector Representations for EHR based Personalized Chronic Disease Onset Prediction and Interpretability
Chronic disease progression is emerging as an important area of investment for healthcare providers.
DeepRacer: Educational Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learning
DeepRacer is a platform for end-to-end experimentation with RL and can be used to systematically investigate the key challenges in developing intelligent control systems.
Effectiveness of LSTMs in Predicting Congestive Heart Failure Onset
In this paper we present a Recurrent neural networks (RNN) based architecture that achieves an AUCROC of 0. 9147 for predicting the onset of Congestive Heart Failure (CHF) 15 months in advance using a 12-month observation window on a large cohort of 216, 394 patients.
