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Found 11 papers, 5 papers with code
CASPR: Automated Evaluation Metric for Contrastive Summarization
Summarizing comparative opinions about entities (e. g., hotels, phones) from a set of source reviews, often referred to as contrastive summarization, can considerably aid users in decision making.
STRUM-LLM: Attributed and Structured Contrastive Summarization
Users often struggle with decision-making between two options (A vs B), as it usually requires time-consuming research across multiple web pages.
Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning
Although supervised learning has enabled high performance for image segmentation, it requires a large amount of labeled training data, which can be difficult to obtain in the medical imaging field.
Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction
Unrolled neural networks have enabled state-of-the-art reconstruction performance and fast inference times for the accelerated magnetic resonance imaging (MRI) reconstruction task.
Data-Efficient Information Extraction from Form-Like Documents
Automating information extraction from form-like documents at scale is a pressing need due to its potential impact on automating business workflows across many industries like financial services, insurance, and healthcare.
VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction
Deep neural networks have enabled improved image quality and fast inference times for various inverse problems, including accelerated magnetic resonance imaging (MRI) reconstruction.
SSFD: Self-Supervised Feature Distance as an MR Image Reconstruction Quality Metric
Self-supervised learning (SSL) has become a popular pre-training tool due to its ability to capture generalizable and domain-specific feature representations of the underlying data for downstream tasks.
Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning
Our proposed fine-tuning objective leads to models that are more robust to different levels of noise in the fine-tuning training data, and can generalize better to related tasks with limited labeled data.
Self-training Improves Pre-training for Natural Language Understanding
Unsupervised pre-training has led to much recent progress in natural language understanding.
Mind The Facts: Knowledge-Boosted Coherent Abstractive Text Summarization
In this work, we propose a novel architecture that extends Transformer encoder-decoder architecture in order to improve on these shortcomings.
Learning Mixed-Curvature Representations in Product Spaces
The quality of the representations achieved by embeddings is determined by how well the geometry of the embedding space matches the structure of the data.
