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Found 8 papers, 1 papers with code
Unlocking Insights: Semantic Search in Jupyter Notebooks
We demonstrate a semantic search framework that achieves a comprehensive semantic understanding of the entire notebook's contents, enabling it to effectively handle various types of user queries.
An objective comparison of methods for augmented reality in laparoscopic liver resection by preoperative-to-intraoperative image fusion
A total of 6 teams from 4 countries participated, whose proposed methods were evaluated on 16 images and two preoperative 3D models from two patients.
Twice Class Bias Correction for Imbalanced Semi-Supervised Learning
Differing from traditional semi-supervised learning, class-imbalanced semi-supervised learning presents two distinct challenges: (1) The imbalanced distribution of training samples leads to model bias towards certain classes, and (2) the distribution of unlabeled samples is unknown and potentially distinct from that of labeled samples, which further contributes to class bias in the pseudo-labels during training.
CLAF: Contrastive Learning with Augmented Features for Imbalanced Semi-Supervised Learning
For each pseudo-labeled sample, we select positive and negative samples from labeled data instead of unlabeled data to compute contrastive loss.
KAER: A Knowledge Augmented Pre-Trained Language Model for Entity Resolution
Entity resolution has been an essential and well-studied task in data cleaning research for decades.
Optimal Boxes: Boosting End-to-End Scene Text Recognition by Adjusting Annotated Bounding Boxes via Reinforcement Learning
Text detection and recognition are essential components of a modern OCR system.
Preliminary Steps Towards Federated Sentiment Classification
Automatically mining sentiment tendency contained in natural language is a fundamental research to some artificial intelligent applications, where solutions alternate with challenges.
I-BERT: Inductive Generalization of Transformer to Arbitrary Context Lengths
The model inductively generalizes on a variety of algorithmic tasks where state-of-the-art Transformer models fail to do so.
