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Found 32 papers, 17 papers with code
Is “My Favorite New Movie” My Favorite Movie? Probing the Understanding of Recursive Noun Phrases
We introduce the Recursive Noun Phrase Challenge (RNPC), a dataset of three textual inference tasks involving textual entailment and event plausibility comparison, precisely targeting the understanding of recursive NPs.
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios
We introduce RESIN-11, a new schema-guided event extraction&prediction framework that can be applied to a large variety of newsworthy scenarios.
Calibrating Large Language Models with Sample Consistency
Accurately gauging the confidence level of Large Language Models' (LLMs) predictions is pivotal for their reliable application.
Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge
The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients.
LEOD: Label-Efficient Object Detection for Event Cameras
On 1Mpx, RVT-S with 10% labels even surpasses its fully-supervised counterpart using 100% labels.
Interpretable-by-Design Text Understanding with Iteratively Generated Concept Bottleneck
Black-box deep neural networks excel in text classification, yet their application in high-stakes domains is hindered by their lack of interpretability.
Head-Neck Dual-energy CT Contrast Media Reduction Using Diffusion Models
Iodinated contrast media is essential for dual-energy computed tomography (DECT) angiography.
Representation Of Lexical Stylistic Features In Language Models' Embedding Space
The representation space of pretrained Language Models (LMs) encodes rich information about words and their relationships (e. g., similarity, hypernymy, polysemy) as well as abstract semantic notions (e. g., intensity).
Explanation-based Finetuning Makes Models More Robust to Spurious Cues
Large Language Models (LLMs) are so powerful that they sometimes learn correlations between labels and features that are irrelevant to the task, leading to poor generalization on out-of-distribution data.
Specialty-Oriented Generalist Medical AI for Chest CT Screening
Modern medical records include a vast amount of multimodal free text clinical data and imaging data from radiology, cardiology, and digital pathology.
Translating Radiology Reports into Plain Language using ChatGPT and GPT-4 with Prompt Learning: Promising Results, Limitations, and Potential
The large language model called ChatGPT has drawn extensively attention because of its human-like expression and reasoning abilities.
Faithful Chain-of-Thought Reasoning
While Chain-of-Thought (CoT) prompting boosts Language Models' (LM) performance on a gamut of complex reasoning tasks, the generated reasoning chain does not necessarily reflect how the model arrives at the answer (aka.
Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20$\times$ Speedup
Low-dose computed tomography (LDCT) is an important topic in the field of radiology over the past decades.
Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models
To address this challenge, computational conversion is a viable approach between MRI and CT images, especially from MRI to CT images.
Towards Faithful Model Explanation in NLP: A Survey
In this survey, we review over 110 model explanation methods in NLP through the lens of faithfulness.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Show Me More Details: Discovering Hierarchies of Procedures from Semi-structured Web Data
To this end, we develop a simple and efficient method that links steps (e. g., "purchase a camera") in an article to other articles with similar goals (e. g., "how to choose a camera"), recursively constructing the KB.
SoftDropConnect (SDC) -- Effective and Efficient Quantification of the Network Uncertainty in Deep MR Image Analysis
Recently, deep learning has achieved remarkable successes in medical image analysis.
Is "My Favorite New Movie" My Favorite Movie? Probing the Understanding of Recursive Noun Phrases
We introduce the Recursive Noun Phrase Challenge (RNPC), a dataset of three textual inference tasks involving textual entailment and event plausibility comparison, precisely targeting the understanding of recursive NPs.
A transformer-based deep learning approach for classifying brain metastases into primary organ sites using clinical whole brain MRI
Treatment decisions for brain metastatic disease rely on knowledge of the primary organ site, and currently made with biopsy and histology.
Zero-shot Event Extraction via Transfer Learning: Challenges and Insights
Event extraction has long been a challenging task, addressed mostly with supervised methods that require expensive annotation and are not extensible to new event ontologies.
Goal-Oriented Script Construction
The knowledge of scripts, common chains of events in stereotypical scenarios, is a valuable asset for task-oriented natural language understanding systems.
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System
We present a new information extraction system that can automatically construct temporal event graphs from a collection of news documents from multiple sources, multiple languages (English and Spanish for our experiment), and multiple data modalities (speech, text, image and video).
Visual Goal-Step Inference using wikiHow
Understanding what sequence of steps are needed to complete a goal can help artificial intelligence systems reason about human activities.
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Suppression of Correlated Noise with Similarity-based Unsupervised Deep Learning
Limited by the independent noise assumption, current unsupervised denoising methods cannot process correlated noises as in CT images.
Reasoning about Goals, Steps, and Temporal Ordering with WikiHow
We propose a suite of reasoning tasks on two types of relations between procedural events: goal-step relations ("learn poses" is a step in the larger goal of "doing yoga") and step-step temporal relations ("buy a yoga mat" typically precedes "learn poses").
Intent Detection with WikiHow
Modern task-oriented dialog systems need to reliably understand users' intents.
Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network
As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' discomfort.
Multi-Contrast Super-Resolution MRI Through a Progressive Network
Our experimental results demonstrate that the proposed networks can produce MRI super-resolution images with good image quality and outperform other multi-contrast super-resolution methods in terms of structural similarity and peak signal-to-noise ratio.
MRI Super-Resolution with Ensemble Learning and Complementary Priors
Finally, a convolutional neural network is used for ensemble learning that synergizes the outputs of GANs into the final MR super-resolution images.
Learning color space adaptation from synthetic to real images of cirrus clouds
With our adapted synthetic data for training the semantic segmentation, we achieve an improvement of 6:59% when applied to real images, superior to alternative methods.
Super-resolution MRI through Deep Learning
Magnetic resonance imaging (MRI) is extensively used for diagnosis and image-guided therapeutics.
Medical Physics
