Results 101 to 110 of about 36,894 (295)
K-anonymous privacy preserving manifold learning
, 2023 In this modern world of digitalization, abundant amount of data is being generated. This often leads to data of high dimension, making data points far-away from each other.
Garg, Sonakshi +3 more
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Advanced Materials, EarlyView.The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj +8 more
wiley +1 more source
Alignment of vector fields on manifolds via contraction mappings
Учёные записки Казанского университета: Серия Физико-математические науки, 2018 According to the manifold hypothesis, high-dimensional data can be viewed and meaningfully represented as a lower-dimensional manifold embedded in a higher dimensional feature space. Manifold learning is a part of machine learning where an intrinsic data
O.N. Kachan +2 more
doaj
Advanced Materials, EarlyView.The energetic offset between the donor and the acceptor components in organic photoactive layers is central to the tradeoff between photovoltage and photocurrent losses. This Perspective covers the most important issues surrounding this topic in non‐fullerene acceptor blends, from the difficulty of accurately determining state energies and driving ...
Dieter Neher, Manasi Pranav
wiley +1 more source
Semi-Supervised Manifold Learning for Hyperspectral Data
, 2020 S.15-23There are real world data sets where a linear approximation like the principal components might not capture the intrinsic characteristics of the data. Nonlinear dimensionality reduction or manifold learning uses a graph-based approach to model the
Becker, Florian
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Advanced Materials, EarlyView.Path‐decoupled III–V van der Waals memtransistors spatially separate ionic and electronic transport to overcome the conventional trade‐off between accuracy and energy in neuromorphic hardware. Mobile K+ ions in the vdW gaps set a wide conductance window, Gmax/Gmin, while gate‐tunable hole conduction lowers programming energy, enabling reliable ...
Jihong Bae +13 more
wiley +1 more source
Multi-Manifold Semi-Supervised Learning
, 2018 We study semi-supervised learning when the data consists of multiple intersecting manifolds. We give a finite sample analysis to quantify the potential gain of using unlabeled data in this multi-manifold setting.
Aarti Singh (5358575) +4 more
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Measuring the Hall Effect in Hysteretic Materials
Advanced Materials, EarlyView.The authors highlight common pitfalls in measuring the Hall effect: in hysteretic magnets, improper data processing can create signals that look exotic but are not real. This Perspective explains the origin of these artifacts and presents practical measurement strategies that help researchers identify reliable Hall responses in complex magnetic ...
Jaime M. Moya +6 more
wiley +1 more source
Deep Nets for Local Manifold Learning
Frontiers in Applied Mathematics and Statistics, 2018 The problem of extending a function f defined on a training data C on an unknown manifold 𝕏 to the entire manifold and a tubular neighborhood of this manifold is considered in this paper. For 𝕏 embedded in a high dimensional ambient Euclidean space ℝD, a
Charles K. Chui, Hrushikesh N. Mhaskar
doaj +1 more source
Manifold learning by a deep Gaussian process autoencoder
, 2023 The paper presents a novel manifold learning algorithm, the deep Gaussian process autoencoder (DPGA), based on deep Gaussian processes. Deep Gaussian process autoencoder algorithm has the following two main characteristics.
Camastra F., Iannuzzo G., Casolaro A.
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