402 citations · 1.1k across the 59 of their papers we have counts for
12 papers · 1 filter
Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains
Haojie Pan, Chengyu Wang, Minghui Qiu +3
Pre-trained language models have been applied to various NLP tasks with considerable performance gains. However, the large model sizes, together with the long inference time, limit…
EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applications
Minghui Qiu, Peng Li, Chengyu Wang +8
The literature has witnessed the success of leveraging Pre-trained Language Models (PLMs) and Transfer Learning (TL) algorithms to a wide range of Natural Language Processing (NLP)…
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
Wayne Xin Zhao, Shanlei Mu, Yupeng Hou +16
In recent years, there are a large number of recommendation algorithms proposed in the literature, from traditional collaborative filtering to deep learning algorithms. However, th…
Scalable Graph Neural Networks via Bidirectional Propagation
Ming Chen, Zhewei Wei, Bolin Ding +4
Graph Neural Networks (GNN) is an emerging field for learning on non-Euclidean data. Recently, there has been increased interest in designing GNN that scales to large graphs. Most…
Simple and Deep Graph Convolutional Networks
Ming Chen, Zhewei Wei, Zengfeng Huang +2
Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-structured data. Recently, GCNs and subsequent variants have shown superior performance in vario…
FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data
Yuexiang Xie, Zhen Wang, Yaliang Li +6
High-order interactive features capture the correlation between different columns and thus are promising to enhance various learning tasks on ubiquitous tabular data. To automate t…
Sequential Recommendation with Self-Attentive Multi-Adversarial Network
Ruiyang Ren, Zhaoyang Liu, Yaliang Li +4
Recently, deep learning has made significant progress in the task of sequential recommendation. Existing neural sequential recommenders typically adopt a generative way trained wit…
Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents
Daoyuan Chen, Yaliang Li, Kai Lei +1
Distant supervision based methods for entity and relation extraction have received increasing popularity due to the fact that these methods require light human annotation efforts.…
Practical Data Poisoning Attack against Next-Item Recommendation
Hengtong Zhang, Yaliang Li, Bolin Ding +1
Online recommendation systems make use of a variety of information sources to provide users the items that users are potentially interested in. However, due to the openness of the…
A Survey on Causal Inference
Liuyi Yao, Zhixuan Chu, Sheng Li +3
Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy and economics, for decades. Nowadays, estimating c…
Automated Relational Meta-learning
Huaxiu Yao, Xian Wu, Zhiqiang Tao +4
In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones. However, a critical challenge i…
AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search
Daoyuan Chen, Yaliang Li, Minghui Qiu +7
Large pre-trained language models such as BERT have shown their effectiveness in various natural language processing tasks. However, the huge parameter size makes them difficult to…