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04/26/2023
Self-Supervised Multi-Modal Sequential Recommendation
With the increasing development of e-commerce and online services, perso...
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02/19/2023
Pseudo Contrastive Learning for Graph-based Semi-supervised Learning
Pseudo Labeling is a technique used to improve the performance of semi-s...
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12/16/2022
Convolution-enhanced Evolving Attention Networks
Attention-based neural networks, such as Transformers, have become ubiqu...
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11/10/2022
MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation
Responding with multi-modal content has been recognized as an essential ...
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10/19/2022
Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering
Recent self-supervised pre-training methods on Heterogeneous Information...
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08/13/2022
Entropy Induced Pruning Framework for Convolutional Neural Networks
Structured pruning techniques have achieved great compression performanc...
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08/02/2022
Binary Classification with Positive Labeling Sources
To create a large amount of training labels for machine learning models ...
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03/16/2022
Privacy-preserving Online AutoML for Domain-Specific Face Detection
Despite the impressive progress of general face detection, the tuning of...
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12/22/2021
SkipNode: On Alleviating Over-smoothing for Deep Graph Convolutional Networks
Over-smoothing is a challenging problem, which degrades the performance ...
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10/16/2021
Multimodal Dialogue Response Generation
Responsing with image has been recognized as an important capability for...
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10/07/2021
Creating Training Sets via Weak Indirect Supervision
Creating labeled training sets has become one of the major roadblocks in...
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10/03/2021
Graph Pointer Neural Networks
Graph Neural Networks (GNNs) have shown advantages in various graph-base...
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09/23/2021
WRENCH: A Comprehensive Benchmark for Weak Supervision
Recent Weak Supervision (WS) approaches have had widespread success in e...
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09/05/2021
Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Recommendation
To alleviate data sparsity and cold-start problems of traditional recomm...
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08/06/2021
AceNAS: Learning to Rank Ace Neural Architectures with Weak Supervision of Weight Sharing
Architecture performance predictors have been widely used in neural arch...
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03/07/2021
Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees
Pre-trained language models like BERT achieve superior performances in v...
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10/16/2020
How Does Supernet Help in Neural Architecture Search?
With the success of Neural Architecture Search (NAS), weight sharing, as...
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10/14/2020
AutoADR: Automatic Model Design for Ad Relevance
Large-scale pre-trained models have attracted extensive attention in the...
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05/27/2020
Interpretable and Efficient Heterogeneous Graph Convolutional Network
Graph Convolutional Network (GCN) has achieved extraordinary success in ...
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04/08/2020
LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression
BERT is a cutting-edge language representation model pre-trained by a la...
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04/08/2020
Improving BERT with Self-Supervised Attention
One of the most popular paradigms of applying large, pre-trained NLP mod...
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01/06/2020
Deeper Insights into Weight Sharing in Neural Architecture Search
With the success of deep neural networks, Neural Architecture Search (NA...
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12/23/2019
TextNAS: A Neural Architecture Search Space tailored for Text Representation
Learning text representation is crucial for text classification and othe...
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10/10/2019
