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Found 7 papers, 1 papers with code
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition
In this study, based on the knowledge distillation framework and multi-task learning, we introduce the similarity metric model as an auxiliary task to improve the cross-lingual NER performance on the target domain.
RoboEXP: Action-Conditioned Scene Graph via Interactive Exploration for Robotic Manipulation
Robots need to explore their surroundings to adapt to and tackle tasks in unknown environments.
D$^3$Fields: Dynamic 3D Descriptor Fields for Zero-Shot Generalizable Robotic Manipulation
These fields capture the dynamics of the underlying 3D environment and encode both semantic features and instance masks.
Beyond Conservatism: Diffusion Policies in Offline Multi-agent Reinforcement Learning
We present a novel Diffusion Offline Multi-agent Model (DOM2) for offline Multi-Agent Reinforcement Learning (MARL).
Sensing User's Channel and Location with Terahertz Extra-Large Reconfigurable Intelligent Surface under Hybrid-Field Beam Squint Effect
Specifically, we first propose a joint channel and location sensing scheme, which consists of a location-assisted generalized multiple measurement vector orthogonal matching pursuit (LA-GMMV-OMP) algorithm for channel estimation (CE) and a complete dictionary based localization (CDL) scheme, where a frequency selective polar-domain redundant dictionary is proposed to overcome the hybrid field beam squint effect.
Time Series Analysis in American Stock Market Recovering in Post COVID-19 Pandemic Period
Every financial crisis has caused a dual shock to the global economy.
Network Topology Optimization via Deep Reinforcement Learning
A2C-GS consists of three novel components, including a verifier to validate the correctness of a generated network topology, a graph neural network (GNN) to efficiently approximate topology rating, and a DRL actor layer to conduct a topology search.
