New AI method captures long-range atomic interactions in complex molecules

Researchers from Google DeepMind in Berlin, BIFOLD, and the Technical University of Berlin have introduced a new machine ...
IEEE

Somatic Mutation Driven Cancer Stage Classification via Autoencoder Clustering and Explainable Deep Learning

Abstract: The identification of the site of origin, stage and driver genes of cancer is important for the improved prognosis of cancer patients. Publicly available somatic mutation data and machine ...
IEEE

Deep Learning-Based Inpainting for Sparse Arrays in Ultrafast Ultrasound Imaging

Abstract: Sparse arrays offer a promising solution for reducing the data volume required to reconstruct ultrasound images, making them well-suited for portable and wireless devices. However, the ...
The New York Times

Trump Administration Approves Ultra-Deepwater Oil Drilling Plan

The $5 billion project in the Gulf of Mexico is expected to help produce up to 10 billion barrels of oil by the end of this decade. Critics say it could endanger people and marine life. By Lisa ...
EurekAlert!

Improving AI models’ ability to explain their predictions

Cambridge, MA — In high-stakes settings like medical diagnostics, users often want to know what led a computer vision model to make a certain prediction, so they can determine whether to trust its ...
Frontiers

Adaptive multi-mode locomotion for bipedal wheel-legged robots via sparse mixture-of-experts deep reinforcement learning

The bipedal wheel-legged robot combines the high energy efficiency of wheeled movement with the terrain adaptability of legged locomotion. However, achieving a smooth transition between these two ...
Scientific Research Publishing

Vahdat, A. and Kautz, J. (2020) NVAE: A Deep Hierarchical Variational Autoencoder. Advances in Neural Information Processing Systems, 33, 19667-19679.

ABSTRACT: Treatment response prediction remains one of the most pressing challenges in precision psychiatry, where patient heterogeneity and complex biomarker interactions limit the reliability of ...
GitHub

Variational autoencoder with Kolmogorov-Arnold Network for spatial domain detection.

Experiments were executed on NVIDIA A40 of 46068MiB memory in linux with torch==2.1.0+cu121, torch_geometric==2.3.1, torch-sparse==0.6.18+pt21cu121, and torchvision==0.16.0+cu121. The stkan is an ...
blockchain

List of Flash News about sparse autoencoder

According to @StanfordAILab, researchers optimized the K-SVD algorithm to match sparse autoencoder performance for interpreting transformer and LLM embeddings, as highlighted in its latest blog update ...