Results 91 to 100 of about 842,651 (374)

Confidence Calibration for Incremental Learning

IEEE Access, 2020
Class incremental learning is an online learning paradigm wherein the classes to be recognized are gradually increased with limited memory, storing only a partial set of examples of past tasks.
Dongmin Kang, Yeonsik Jo, Yeongwoo Nam, Jonghyun Choi   +3 more
doaj   +1 more source

DILRS: Domain-Incremental Learning for Semantic Segmentation in Multi-Source Remote Sensing Data

Remote Sensing, 2023
With the exponential growth in the speed and volume of remote sensing data, deep learning models are expected to adapt and continually learn over time.
Xue Rui, Ziqiang Li, Yang Cao, Ziyang Li, Weiguo Song   +4 more
doaj   +1 more source

DeepWalk: Online Learning of Social Representations

, 2014
We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models.
Al-Rfou R., Bottou L., Dean J., Hinton G. E., Kondor R. I., Krizhevsky A., Macskassy S. A., Mikolov T., Mikolov T., Morin F., Neville J., Recht B., Vishwanathan S.   +12 more
core   +1 more source

DS-AL: A Dual-Stream Analytic Learning for Exemplar-Free Class-Incremental Learning [PDF]

AAAI Conference on Artificial Intelligence
Class-incremental learning (CIL) under an exemplar-free constraint has presented a significant challenge. Existing methods adhering to this constraint are prone to catastrophic forgetting, far more so than replay-based techniques that retain access to ...
Huiping Zhuang, Run He, Kai Tong, Ziqian Zeng, Cen Chen, Zhiping Lin   +5 more
semanticscholar   +1 more source

Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones

Advanced Engineering Materials, EarlyView.
A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell, Martha Leach, Hongtao Song, Carolyn Seepersad   +3 more
wiley   +1 more source

Numerical Modeling of Tank Cars Carrying Hazardous Materials With and Without Composite Metal Foam

Advanced Engineering Materials, EarlyView.
Large‐scale puncture models consisting of hazardous materials (HAZMATs) tank car with protective steel–steel composite metal foam (S–S CMF) are solved numerically. Tank car plate with added 10.91–13.33 mm thick S–S CMF layer does not puncture. Protective S–S CMF absorbs impact energy, reduces plate deformation, and prevents shear bands formation ...
Aman Kaushik, Afsaneh Rabiei
wiley   +1 more source

Catalyst Acceleration for Gradient-Based Non-Convex Optimization [PDF]

, 2017
We introduce a generic scheme to solve nonconvex optimization problems using gradient-based algorithms originally designed for minimizing convex functions.
Drusvyatskiy, Dmitriy, Harchaoui, Zaid, Lin, Hongzhou, Mairal, Julien, Paquette, Courtney   +4 more
core   +1 more source

DeeSIL: Deep-Shallow Incremental Learning

, 2018
Incremental Learning (IL) is an interesting AI problem when the algorithm is assumed to work on a budget. This is especially true when IL is modeled using a deep learning approach, where two com- plex challenges arise due to limited memory, which induces
AL Ginsca, B Thomee, M Mccloskey, O Russakovsky, Z Li   +4 more
core   +2 more sources

Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization

Advanced Engineering Materials, EarlyView.
Magnetocaloric cooling (MCE) is an environmentally friendly refrigeration method with great potential. Optimizing MCE materials involves the preparation and screening of large quantities of samples, which in turn generates a large amount of data. A digitalization approach is presented that uses ontologies, knowledge graphs, and digital workflows to ...
Simon Bekemeier, Moritz Blum, Luana Caron, Alisa Chirkova, Philipp Cimiano, Basil Ell, Inga Ennen, Michael Feige, Maik Gaerner, Thomas Hilbig, Andreas Hütten, Günter Reiss, Tapas Samanta, Sonja Schöning, Christian Schröder, Lennart Schwan, Chris Taake, Martin Wortmann   +17 more
wiley   +1 more source

Multimodal Data‐Driven Microstructure Characterization

Advanced Engineering Materials, EarlyView.
A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang, Santiago Benito, Özge Özgün, Sebastian Weber, Markus Stricker   +4 more
wiley   +1 more source