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Found 7 papers, 4 papers with code
Self-Adaptive Reconstruction with Contrastive Learning for Unsupervised Sentence Embeddings
However, due to the token bias in pretrained language models, the models can not capture the fine-grained semantics in sentences, which leads to poor predictions.
Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference
Intriguingly, the unified objective can be seen as the sum of the vanilla fine-tuning objective, which learns new knowledge from target data, and the causal objective, which preserves old knowledge from PLMs.
Self-Learning Symmetric Multi-view Probabilistic Clustering
Then, SLS-MPC proposes a novel self-learning probability function without any prior knowledge and hyper-parameters to learn each view's individual distribution.
Pair-Based Joint Encoding with Relational Graph Convolutional Networks for Emotion-Cause Pair Extraction
Emotion-cause pair extraction (ECPE) aims to extract emotion clauses and corresponding cause clauses, which have recently received growing attention.
MPC: Multi-View Probabilistic Clustering
Despite the promising progress having been made, the two challenges of multi-view clustering (MVC) are still waiting for better solutions: i) Most existing methods are either not qualified or require additional steps for incomplete multi-view clustering and ii) noise or outliers might significantly degrade the overall clustering performance.
Regression via Arbitrary Quantile Modeling
In the regression problem, L1 and L2 are the most commonly used loss functions, which produce mean predictions with different biases.
Relief R-CNN : Utilizing Convolutional Features for Fast Object Detection
R-CNN style methods are sorts of the state-of-the-art object detection methods, which consist of region proposal generation and deep CNN classification.
