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Found 11 papers, 2 papers with code
The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity Typing
The Knowledge Graph Entity Typing (KGET) task aims to predict missing type annotations for entities in knowledge graphs.
An Information Theoretic Approach to Interaction-Grounded Learning
Reinforcement learning (RL) problems where the learner attempts to infer an unobserved reward from some feedback variables have been studied in several recent papers.
Provably Efficient CVaR RL in Low-rank MDPs
We study CVaR RL in low-rank MDPs with nonlinear function approximation.
Beyond the Gates of Euclidean Space: Temporal-Discrimination-Fusions and Attention-based Graph Neural Network for Human Activity Recognition
Human activity recognition (HAR) through wearable devices has received much interest due to its numerous applications in fitness tracking, wellness screening, and supported living.
Exploiting Semantic Epsilon Greedy Exploration Strategy in Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) can model many real world applications.
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering
Visual question answering aims to answer the natural language question about a given image.
The Evolutionary Dynamics of Independent Learning Agents in Population Games
Understanding the evolutionary dynamics of reinforcement learning under multi-agent settings has long remained an open problem.
Modelling the Dynamics of Multiagent Q-Learning in Repeated Symmetric Games: a Mean Field Theoretic Approach
Modelling the dynamics of multi-agent learning has long been an important research topic, but all of the previous works focus on 2-agent settings and mostly use evolutionary game theoretic approaches.
A Boundary-aware Neural Model for Nested Named Entity Recognition
We propose a boundary-aware neural model for nested NER which leverages entity boundaries to predict entity categorical labels.
Ranked #11 on
Named Entity Recognition (NER)
on GENIA
Exploring Topic Discriminating Power of Words in Latent Dirichlet Allocation
Latent Dirichlet Allocation (LDA) and its variants have been widely used to discover latent topics in textual documents.
