Results 111 to 120 of about 36,894 (295)
Advanced Materials, EarlyView.ABSTRACT Accurately knowing the frontier orbital energies of the structurally disordered small‐molecule organic semiconductors that are used in optoelectronic devices such as organic light‐emitting diodes is required to rationally improve their performance. Here, we show that these energies can be deduced with a large accuracy from the peak energies of
Christian B. McDonald +7 more
wiley +1 more source
Improvement of Supervised Shape Retrieval by Learning the Manifold Space
International Journal of Information and Communication Technology Research, 2012 Manifold learning is the technique that aims for finding a constructive way to embed the data from a highdimensional space into a low-dimensional one based on non-linear approaches.
Mohammad Ali Zare Chahooki +1 more
doaj
Manifold Mixup: Better Representations by Interpolating Hidden States
, 2019 Deep neural networks excel at learning the training data, but often provide incorrect and confident predictions when evaluated on slightly different test examples. This includes distribution shifts, outliers, and adversarial examples.
Verma, Vikas +6 more
core
Gaussian Process Manifold Learning
, 2021 Gaussian Process Manifold Learning is a novel model based machine learning method that uses a probabilistic approach to represent a set of data as a manifold.
Adams, Larin Cole
core
Phase Engineering of Nanomaterials (PEN): Evolution, Current Challenges, and Future Opportunities
Advanced Materials, EarlyView.This review summarizes the synthesis, phase transition, advanced characterization spanning ex situ to in situ and operando techniques, and diverse applications of phase engineering of nanomaterials (PEN). It further outlines key challenges and future opportunities, such as phase stability, architecture control, and artificial intelligence (AI)‐driven ...
Ye Chen +7 more
wiley +1 more source
Charting the Right Manifold: Manifold Mixup for Few-shot Learning [PDF]
, 2019 Few-shot learning algorithms aim to learn model parameters capable of adapting to unseen classes with the help of only a few labeled examples. A recent regularization technique - Manifold Mixup focuses on learning a generalpurpose representation, robust ...
Singh, Mayank +3 more
core
Nanomaterial Integration at Liquid–Liquid Interfaces for Green Catalysis
Advanced Materials, EarlyView.Functional nanomaterials assembled at liquid–liquid interfaces create dual‐role platforms serving as emulsion stabilizers and catalytic sites, offering enhanced reaction kinetics with improved catalyst recovery and recyclability. This review examines design strategies, structure‐performance relationships, and industrial implementation prospects of ...
Bokgi Seo +6 more
wiley +1 more source
Dimension-adaptive bounds on compressive FLD Classification
, 2013 Efficient dimensionality reduction by random projections (RP) gains popularity, hence the learning guarantees achievable in RP spaces are of great interest.
Kabán, Ata +3 more
core +1 more source
Manifold learning in metric spaces
Applied and Computational Harmonic AnalysisLaplacian-based methods are popular for the dimensionality reduction of data lying in $\mathbb{R}^N$. Several theoretical results for these algorithms depend on the fact that the Euclidean distance locally approximates the geodesic distance on the underlying submanifold which the data are assumed to lie on. However, for some applications, other metrics,
Liane Xu, Amit Singer
openaire +3 more sources
Manifold Learning: The Price of Normalization
J. Mach. Learn. Res., 2008 We analyze the performance of a class of manifold-learning algorithms that find their output by minimizing a quadratic form under some normalization constraints. This class consists of Locally Linear Embedding (LLE), Laplacian Eigenmap, Local Tangent Space Alignment (LTSA), Hessian Eigenmaps (HLLE), and Diffusion maps.
Yair Goldberg +3 more
openaire +3 more sources