60 citations · 137 across the 8 of their papers we have counts for
8 papers
MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation
Jinpeng Wang, Ziyun Zeng, Yunxiao Wang +7
The goal of sequential recommendation (SR) is to predict a user's potential interested items based on her/his historical interaction sequences. Most existing sequential recommender…
Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking
Yinghui Li, Yong Jiang, Yangning Li +4
Entity Linking (EL) is a fundamental task for Information Extraction and Knowledge Graphs. The general form of EL (i.e., end-to-end EL) aims to first find mentions in the given inp…
Locality Preserving Multiview Graph Hashing for Large Scale Remote Sensing Image Search
Wenyun Li, Guo Zhong, Xingyu Lu +1
Hashing is very popular for remote sensing image search. This article proposes a multiview hashing with learnable parameters to retrieve the queried images for a large-scale remote…
A framework for massive scale personalized promotion
Yitao Shen, Yue Wang, Xingyu Lu +6
Technology companies building consumer-facing platforms may have access to massive-scale user population. In recent years, promotion with quantifiable incentive has become a popula…
Active Deep Learning on Entity Resolution by Risk Sampling
Youcef Nafa, Qun Chen, Zhaoqiang Chen +4
While the state-of-the-art performance on entity resolution (ER) has been achieved by deep learning, its effectiveness depends on large quantities of accurately labeled training da…
Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning
Xingyu Lu, Kimin Lee, Pieter Abbeel +1
Despite the significant progress of deep reinforcement learning (RL) in solving sequential decision making problems, RL agents often overfit to training environments and struggle t…
Predictive Coding for Boosting Deep Reinforcement Learning with Sparse Rewards
Xingyu Lu, Stas Tiomkin, Pieter Abbeel
While recent progress in deep reinforcement learning has enabled robots to learn complex behaviors, tasks with long horizons and sparse rewards remain an ongoing challenge. In this…
Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?
Ofir Nachum, Haoran Tang, Xingyu Lu +3
Hierarchical reinforcement learning has demonstrated significant success at solving difficult reinforcement learning (RL) tasks. Previous works have motivated the use of hierarchy…