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01/25/2023
When to Trust Aggregated Gradients: Addressing Negative Client Sampling in Federated Learning
Federated Learning has become a widely-used framework which allows learn...
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10/11/2022
From Mimicking to Integrating: Knowledge Integration for Pre-Trained Language Models
Investigating better ways to reuse the released pre-trained language mod...
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06/04/2022
Rethinking the Openness of CLIP
Contrastive Language-Image Pre-training (CLIP) has demonstrated great po...
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12/14/2021
Model Uncertainty-Aware Knowledge Amalgamation for Pre-Trained Language Models
As many fine-tuned pre-trained language models (PLMs) with promising per...
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10/13/2021
Well-classified Examples are Underestimated in Classification with Deep Neural Networks
The conventional wisdom behind learning deep classification models is to...
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10/08/2021
Topology-Imbalance Learning for Semi-Supervised Node Classification
The class imbalance problem, as an important issue in learning node repr...
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05/28/2021
Learning Relation Alignment for Calibrated Cross-modal Retrieval
Despite the achievements of large-scale multimodal pre-training approach...
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05/16/2020
Layer-Wise Cross-View Decoding for Sequence-to-Sequence Learning
In sequence-to-sequence learning, the attention mechanism has been a gre...
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12/25/2019
Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection
Self-attention based Transformer has demonstrated the state-of-the-art p...
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11/17/2019
MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning
In sequence to sequence learning, the self-attention mechanism proves to...
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11/16/2019
Understanding and Improving Layer Normalization
Layer normalization (LayerNorm) is a technique to normalize the distribu...
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08/23/2018
