Automated Ensemble Multimodal Machine Learning for Healthcare

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Copyright © 2024 Meystre, Jacquemier, Burri, Zsolnai, Frank, Vieira, Shi, Perin, Keller and Markram.Nissl histology underpins our understanding of brain anatomy and architecture. Despite its importance, no high-resolution datasets are currently available i ...

Novice students in law courses or students who encounter legal education face the challenge of acquiring specialized and highly concept-oriented knowledge. Structured and persuasive writing combined with the necessary domain knowledge is challenging for ma ...

Sharpness-aware minimization (SAM) has been shown to improve the generalization of neural networks. However, each SAM update requires sequentially computing two gradients, effectively doubling the per-iteration cost compared to base optimizers like SGD. We ...

The increasing availability of Massive Open Online Courses (MOOCs) has created a necessity for personalized course recommendation systems. These systems often combine neural networks with Knowledge Graphs (KGs) to achieve richer representations of learners ...

The advent of the "generative age"marks a pivotal moment in AI evolution, with models like OpenAI's GPT-4 and DALL-E transforming content creation across various fields. These advancements bring unprecedented efficiency and new applications but also presen ...

The use of immersive representations for digital media has been gaining growing attention fueled by novel applications such as virtual and augmented reality. Point clouds, which represent shapes as a collection of unconnected points in space with geometric ...

Graph Neural Networks (GNNs) have become a popular tool for learning on graphs, but their widespread use raises privacy concerns as graph data can contain personal or sensitive information. Differentially private GNN models have been recently proposed to p ...

We propose to learn non-convex regularizers with a prescribed upper bound on their weak-convexity modulus. Such regularizers give rise to variational denoisers that minimize a convex energy. They rely on few parameters (less than 15,000) and offer a signal ...

Weight-based model editing methods update the parametric knowledge of language models post-training. However, these methods can unintentionally alter unrelated parametric knowledge representations, potentially increasing the risk of harm. In this work, we ...

Automated Ensemble Multimodal Machine Learning for Healthcare

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Cell density quantification of high resolution Nissl images of the juvenile rat brain

LegalWriter: An Intelligent Writing Support System for Structured and Persuasive Legal Case Writing for Novice Law Students

SAMPa: Sharpness-aware Minimization Parallelized

Finding Paths for Explainable MOOC Recommendation: A Learner Perspective

Déjà Vu: Uncanny and Serendipitous Re-encountering of Audiovisual Archives

On quality assessment and learning-based compression of point clouds for conventional and DNA-based storage

ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees

Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms

“Flex Tape Can't Fix That”: Bias and Misinformation in Edited Language Models