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Found 10 papers, 3 papers with code
A Unified Framework for Multi-Domain CTR Prediction via Large Language Models
Click-Through Rate (CTR) prediction is a crucial task in online recommendation platforms as it involves estimating the probability of user engagement with advertisements or items by clicking on them.
Co-Speech Gesture Synthesis by Reinforcement Learning With Contrastive Pre-Trained Rewards
There is a growing demand of automatically synthesizing co-speech gestures for virtual characters.
The ReprGesture entry to the GENEA Challenge 2022
This paper describes the ReprGesture entry to the Generation and Evaluation of Non-verbal Behaviour for Embodied Agents (GENEA) challenge 2022.
Learning Pseudometric-based Action Representations for Offline Reinforcement Learning
Offline reinforcement learning is a promising approach for practical applications since it does not require interactions with real-world environments.
Online Ad Hoc Teamwork under Partial Observability
Autonomous agents often need to work together as a team to accomplish complex cooperative tasks.
CMML: Contextual Modulation Meta Learning for Cold-Start Recommendation
Meta learning, especially gradient based one, can be adopted to tackle this problem by learning initial parameters of the model and thus allowing fast adaptation to a specific task from limited data examples.
Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation
Aiming to alleviate data sparsity and cold-start problems of traditional recommender systems, incorporating knowledge graphs (KGs) to supplement auxiliary information has recently gained considerable attention.
Event-Triggered Multi-agent Reinforcement Learning with Communication under Limited-bandwidth Constraint
Then the design of the event-triggered strategy is formulated as a constrained Markov decision problem, and reinforcement learning finds the best communication protocol that satisfies the limited bandwidth constraint.
Multiagent Systems
Dynamic Horizon Value Estimation for Model-based Reinforcement Learning
Existing model-based value expansion methods typically leverage a world model for value estimation with a fixed rollout horizon to assist policy learning.
Vehicle Traffic Driven Camera Placement for Better Metropolis Security Surveillance
Security surveillance is one of the most important issues in smart cities, especially in an era of terrorism.
