Zobrazeno 1 - 10
of 390
pro vyhledávání: '"Lu Haiping"'
Autor:
Bai, Peizhen, Miljković, Filip, Liu, Xianyuan, De Maria, Leonardo, Croasdale-Wood, Rebecca, Rackham, Owen, Lu, Haiping
Inverse protein folding generates valid amino acid sequences that can fold into a desired protein structure, with recent deep-learning advances showing significant potential and competitive performance. However, challenges remain in predicting highly
Externí odkaz:
http://arxiv.org/abs/2412.07815
Autor:
Pukowski, Pawel, Lu, Haiping
In the AutoML domain, test accuracy is heralded as the quintessential metric for evaluating model efficacy, underpinning a wide array of applications from neural architecture search to hyperparameter optimization. However, the reliability of test acc
Externí odkaz:
http://arxiv.org/abs/2409.14401
The increase in high-dimensional multiomics data demands advanced integration models to capture the complexity of human diseases. Graph-based deep learning integration models, despite their promise, struggle with small patient cohorts and high-dimens
Externí odkaz:
http://arxiv.org/abs/2408.02845
Lateralization is a fundamental feature of the human brain, where sex differences have been observed. Conventional studies in neuroscience on sex-specific lateralization are typically conducted on univariate statistical comparisons between male and f
Externí odkaz:
http://arxiv.org/abs/2404.05781
Autor:
Tripathi, Prasun C, Tabakhi, Sina, Suvon, Mohammod N I, Schöb, Lawrence, Alabed, Samer, Swift, Andrew J, Zhou, Shuo, Lu, Haiping
Pulmonary Arterial Wedge Pressure (PAWP) is an essential cardiovascular hemodynamics marker to detect heart failure. In clinical practice, Right Heart Catheterization is considered a gold standard for assessing cardiac hemodynamics while non-invasive
Externí odkaz:
http://arxiv.org/abs/2404.04718
Autor:
Suvon, Mohammod N. I., Tripathi, Prasun C., Fan, Wenrui, Zhou, Shuo, Liu, Xianyuan, Alabed, Samer, Osmani, Venet, Swift, Andrew J., Chen, Chen, Lu, Haiping
Recent advancements in non-invasive detection of cardiac hemodynamic instability (CHDI) primarily focus on applying machine learning techniques to a single data modality, e.g. cardiac magnetic resonance imaging (MRI). Despite their potential, these a
Externí odkaz:
http://arxiv.org/abs/2403.13658
Autor:
Fan, Wenrui, Suvon, Mohammod Naimul Islam, Zhou, Shuo, Liu, Xianyuan, Alabed, Samer, Osmani, Venet, Swift, Andrew, Chen, Chen, Lu, Haiping
Vision-language pre-training (VLP) models have shown significant advancements in the medical domain. Yet, most VLP models align raw reports to images at a very coarse level, without modeling fine-grained relationships between anatomical and pathologi
Externí odkaz:
http://arxiv.org/abs/2403.10635
Molecular property prediction with deep learning has gained much attention over the past years. Owing to the scarcity of labeled molecules, there has been growing interest in self-supervised learning methods that learn generalizable molecular represe
Externí odkaz:
http://arxiv.org/abs/2309.00483
Autor:
Pukowski, Pawel, Lu, Haiping
Despite their limited interpretability, weights and biases are still the most popular encoding of the functions learned by ReLU Neural Networks (ReLU NNs). That is why we introduce SkelEx, an algorithm to extract a skeleton of the membership function
Externí odkaz:
http://arxiv.org/abs/2305.05562