Search Results for author:
Found 7 papers, 4 papers with code
Offline Trajectory Generalization for Offline Reinforcement Learning
Then we propose four strategies to use World Transformers to generate high-rewarded trajectory simulation by perturbing the offline data.
On the Effectiveness of Unlearning in Session-Based Recommendation
On the one hand, these approaches cannot achieve satisfying unlearning effects due to the collaborative correlations and sequential connections between the unlearning item and the remaining items in the session.
Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related Features
To address this limitation, recent studies enable generalization to an unseen target domain with only a few labeled examples using data augmentation techniques.
FIS-ONE: Floor Identification System with One Label for Crowdsourced RF Signals
To build a prediction model to identify the floor number of a new RF signal upon its measurement, conventional approaches using the crowdsourced RF signals assume that at least few labeled signal samples are available on each floor.
Interpretation of Time-Series Deep Models: A Survey
Deep learning models developed for time-series associated tasks have become more widely researched nowadays.
Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation
More specifically, we propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning (CKML) framework to learn shared and behavior-specific interests for different behaviors.
Robust Binary Models by Pruning Randomly-initialized Networks
In this paper, we introduce an approach to obtain robust yet compact models by pruning randomly-initialized binary networks.
