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"Region-Aware Hierarchical Latent Feature Representation Learning-Guide" by Jun Wang, Chang Tang et al.

Hyperspectral band selection aims to identify an optimal subset of bands for hyperspectral images (HSIs). For most existing clustering-based band selection methods, they directly stretch each band into a single feature vector and employ the pixelwise features to address band redundancy. In this way, they do not take full consideration of the spatial information and deal with the importance of different regions in HSIs, which leads to a nonoptimal selection. To address these issues, a region-aware hierarchical latent feature representation learning-guided clustering (HLFC) method is proposed. Specifically, in order to fully preserve the spatial information of HSIs, the superpixel segmentation algorithm is adopted to segment HSIs into multiple regions first. For each segmented region, the similarity graph is constructed to reflect the bands-wise similarity, and its corresponding Laplacian matrix is generated for learning low-dimensional latent features in a hierarchical way. All latent f ....

Clustering Algorithms , Clustering Methods , Feature Extraction , Feature Fusion , Ierarchical Latent Feature Learning , Yperspectral Band Selection , Hyperspectral Imaging , Information Entropy , Aplace Equations , Representation Learning ,

Contrastive Representation Learning

The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ones are far apart. Contrastive learning can be applied to both supervised and unsupervised settings. When working with unsupervised data, contrastive learning is one of the most powerful approaches in self-supervised learning.
Contrastive Training Objectives In early versions of loss functions for contrastive learning, only one positive and one negative sample are involved. ....

Prannay Khosla , Wang Isola , Salakhutdinov Hinton , Jason Wei , Ekind Cubuk , Lajanugen Logeswaran , Phillip Isola , Dmitry Kalenichenko , Tianyu Gao , Yazhe Li Oriol Vinyals , Joshua Robinson , Logeswaran Lee , Ching Yao Chuang , Monte Carlo , Geoffrey Hinton , Mathilde Caron , Florian Schroff , Iryna Gurevych , Geoff Hinton , Cutmix Yun , Nicolas Papernot , Aapo Hyv , Bohan Li , Dinghan Shen , Sumit Chopra , Wei Zou ,

China Tech Digest: Baidu's Biocomputing Research Resut Published In Sub-journal Of Nature; SenseTime To Put AI Servers Under The Sea – China Money Network

Baidu's biocomputing research resut published in sub-journal of Nature Baidu's latest research resut "Geometry Enhanced Molecular Representation Learning . Alibaba, Artificial Intelligence, Baidu, Beijing, SenseTime, Sequoia, Sequoia Capital China, shenzhen, Tencent, Tencent Music ....

Laboratory Service Guarantee Center , Alibaba Cloud Computing Co Ltd , Academy Of Artificial Intelligence , Enhanced Molecular Representation Learning , Geometry Enhanced Molecular , Representation Learning , Beijing Academy , Artificial Intelligence , Beijing Laboratory Service Guarantee , Tiger Global , Sequoia Capital China , Linear Capital , Xianghe Capital , Unicorn Capital ,

Seeking the cellular mechanisms of disease, with help from machine learning

Credits:
Image: Adam Glanzman
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Caroline Uhler’s research blends machine learning and statistics with biology to better understand gene regulation, health, and disease. Despite this lofty mission, Uhler remains dedicated to her original career passion: teaching. “The students at MIT are amazing,” says Uhler. “That’s what makes it so fun to work here.”
Uhler recently received tenure in the Department of Electrical Engineering and Computer Science. She is also an associate member of the Broad Institute of MIT and Harvard, and a researcher at the MIT Institute for Data, Systems, and Society, and the Laboratory for Information and Decision Systems. ....

United States , University Of California At Berkeley , Caroline Uhler , Bernd Sturmfels , Laboratory For Information , Broad Institute , Institute For Data , Department Of Electrical Engineering , Wendy Schmidt Center , Electrical Engineering , Lake Zurich , Representation Learning , Gene Regulation , ஒன்றுபட்டது மாநிலங்களில் , பல்கலைக்கழகம் ஆஃப் கலிஃபோர்னியா இல் பெர்க்லி , கரோலின் ஊலேர் , ஆய்வகம் க்கு தகவல் , பரந்த நிறுவனம் , நிறுவனம் க்கு தகவல்கள் , துறை ஆஃப் மின் பொறியியல் , வெண்டி ஶ்மிட் மையம் , மின் பொறியியல் , ஏரி ஸுரி , பிரதிநிதித்துவம் கற்றல் , கீந் ஒழுங்குமுறை ,