Results 91 to 100 of about 6,372,670 (283)

A General Approach for Achieving Supervised Subspace Learning in Sparse Representation

IEEE Access, 2019
Over the past few decades, a large family of subspace learning algorithms based on dictionary learning have been designed to provide different solutions to learn subspace feature.
Jianshun Sang, Dezhong Peng, Yongsheng Sang   +2 more
doaj   +1 more source

Atomic Defects in Layered Transition Metal Dichalcogenides for Sustainable Energy Storage and the Intelligent Trends in Data Analytics

Advanced Science, EarlyView.
This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo, Ying Yang, Zizhen Fu, Fengqi Liu, Susu Fang, Kele Xu, Shanshan Wang   +6 more
wiley   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

Unsupervised Representation Learning with Adaptive Multi-Order Structural Graph Fusion

Applied Sciences
High-dimensional unlabeled data often contain complex latent structures that are easily obscured by redundant features, noise, and unreliable neighborhood relationships.
Canyu Zhang, Yunjing Zhang, Jiawen Sun, Chen Ding, Pengfei Wan, Shaojun Shi, Sisi Wang, Shenfei Pei, Yanping Chen   +8 more
doaj   +1 more source

Astrocytic LMP2 Coordinates NF‐κB and TGF‐β1/Smad3 Signaling to Drive Neuroinflammation after Cerebral Ischemia/Reperfusion

Advanced Science, EarlyView.
ABSTRACT Astrocyte reactivity critically shapes neuroinflammatory outcomes after ischemic stroke, yet the upstream regulators governing astrocyte state transitions remain incompletely defined. Here, we identify the immunoproteasome subunit low molecular weight protein 2 (LMP2) as an important modulator of astrocyte functional remodeling following ...
Yanguang Mao, Rulan Ma, Zejing Lin, Huiying Zhao, Yuexian Liu, Yonghe Su, Xu Zhang, Xingyong Chen   +7 more
wiley   +1 more source

Unsupervised end-to-end training with a self-defined target

Neuromorphic Computing and Engineering
Designing algorithms for versatile AI hardware that can learn on the edge using both labeled and unlabeled data is challenging. Deep end-to-end training methods incorporating phases of self-supervised and supervised learning are accurate and adaptable to
Dongshu Liu, Jérémie Laydevant, Adrien Pontlevy, Damien Querlioz, Julie Grollier   +4 more
doaj   +1 more source

GBS-Assisted Quantum Unsupervised Machine Learning on a Universal Programmable Integrated Quantum Chip

Research
Quantum machine learning stands poised as a forefront application for near-term quantum devices, addressing scalability challenges posed by classical computers in handling large datasets.
Huihui Zhu, Wei Luo, Rudai Yan, Chao Ren, Jia Guo, Zichao Zhao, Haoran Ma, Tian Chen, Feng Gao, Leong Chuan Kwek, Hong Cai, Yuehai Wang, Jianyi Yang, Ai-Qun Liu   +13 more
doaj   +1 more source

Ferroelectric Devices for In‐Memory and In‐Sensor Computing

Advanced Science, EarlyView.
Inspired by biological systems, in‐memory and in‐sensor computing overcome von Neumann bottlenecks. Ferroelectric devices can mimic synaptic functions and sense stimuli like light or force, therefore are ideal for these paradigms. This review introduces the ferroelectric devices applied for in‐memory and in‐sensor computing, covering their structures ...
Hong Fang, Ronghuan Xie, Qiang Cao, Shishen Yan, Xiaolin Wang, Limei Zheng   +5 more
wiley   +1 more source

Investigating Contrastive Pair Learning’s Frontiers in Supervised, Semisupervised, and Self-Supervised Learning

Journal of Imaging
In recent years, contrastive learning has been a highly favored method for self-supervised representation learning, which significantly improves the unsupervised training of deep image models. Self-supervised learning is a subset of unsupervised learning
Bihi Sabiri, Amal Khtira, Bouchra El Asri, Maryem Rhanoui   +3 more
doaj   +1 more source

Unsupervised learning by program synthesis

, 2015
We introduce an unsupervised learning algorithmthat combines probabilistic modeling with solver-based techniques for program synthesis.We apply our techniques to both a visual learning domain and a language learning problem,showing that our algorithm can
Tenenbaum, Joshua B, Solar Lezama, Armando, Ellis, Kevin M.   +2 more
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