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08/28/2023
When hard negative sampling meets supervised contrastive learning
State-of-the-art image models predominantly follow a two-stage strategy:...
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08/27/2023
Label Denoising through Cross-Model Agreement
Learning from corrupted labels is very common in real-world machine-lear...
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05/23/2023
BAND: Biomedical Alert News Dataset
Infectious disease outbreaks continue to pose a significant threat to hu...
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05/22/2023
Biomedical Named Entity Recognition via Dictionary-based Synonym Generalization
Biomedical named entity recognition is one of the core tasks in biomedic...
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03/31/2023
LaCViT: A Label-aware Contrastive Training Framework for Vision Transformers
Vision Transformers have been incredibly effective when tackling compute...
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03/25/2023
COFFEE: A Contrastive Oracle-Free Framework for Event Extraction
Event extraction is a complex information extraction task that involves ...
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11/07/2022
Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces
Knowledge graph embedding (KGE) is a increasingly popular technique that...
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10/12/2022
Can Pretrained Language Models (Yet) Reason Deductively?
Acquiring factual knowledge with Pretrained Language Models (PLMs) has a...
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09/10/2021
Mixture-of-Partitions: Infusing Large Biomedical Knowledge Graphs into BERT
Infusing factual knowledge into pre-trained models is fundamental for ma...
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08/27/2021
Few-Shot Table-to-Text Generation with Prototype Memory
Neural table-to-text generation models have achieved remarkable progress...
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07/08/2021
Graph Neural Pre-training for Enhancing Recommendations using Side Information
Leveraging the side information associated with entities (i.e. users and...
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05/20/2021
Probabilistic and Variational Recommendation Denoising
Learning from implicit feedback is one of the most common cases in the a...
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10/22/2020
Self-alignment Pre-training for Biomedical Entity Representations
Despite the widespread success of self-supervised learning via masked la...
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07/26/2020
Exploring Data Splitting Strategies for the Evaluation of Recommendation Models
Effective methodologies for evaluating recommender systems are critical,...
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10/31/2019
Semi-supervisedly Co-embedding Attributed Networks
Deep generative models (DGMs) have achieved remarkable advances. Semi-su...
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09/17/2019
Variational Bayesian Context-aware Representation for Grocery Recommendation
Grocery recommendation is an important recommendation use-case, which ai...
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10/12/2018