Results 141 to 150 of about 24,126 (306)

GReAT: A Graph Regularized Adversarial Training Method

IEEE Access
This paper presents GReAT (Graph Regularized Adversarial Training), a novel regularization method designed to enhance the robust classification performance of deep learning models.
Samet Bayram, Kenneth Barner
doaj   +1 more source

Interval-regular graphs

Discrete Mathematics, 1982
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +2 more sources

Exploiting Ferroelectric and Spintronic Dynamics for Neural Network Computation

Advanced Intelligent Systems, EarlyView.
Ferroelectric and spintronic devices, relying on the control of polarization and magnetization, offer intrinsically fast, durable, energy‐efficient, and low‐latency building blocks for analog in‐memory computing. The hysteretic dynamics of an order parameter are leveraged to provide nonvolatile, multistate memory and nonlinear switching. Brain‐inspired
Dashiell Harrison, Kevin Nadaud, Josquin Harrison, Guillaume F. Nataf, Laura Bégon‐Lours   +4 more
wiley   +1 more source

Learning on graph with Laplacian regularization

, 2007
We consider a general form of transductive learning on graphs with Laplacian regularization, and derive margin-based generalization bounds using appropriate geometric properties of the graph.
Ando, Rie Kubota, Zhang, Tong
core  

Dynamic Graph Regularization for Multi-Stream Concept Drift Self-adaptation

Concept drift is an inevitable problem in non-stationary data stream environments, due to changes in data distribution over time. In practical applications, multi-stream data is more common and complex than single-stream data, yet they have received ...
Zhang, G, Lu, P, Lu, J, Zhou, M
core   +1 more source

EC-PGMGR: Ensemble Clustering Based on Probability Graphical Model With Graph Regularization for Single-Cell RNA-seq Data. [PDF]

Front Genet, 2020
Zhu Y, Zhang DX, Zhang XF, Yi M, Ou-Yang L, Wu M.   +5 more
europepmc   +1 more source

Talk to Your Data: An Agentic Artificial Intelligence‐Driven Decision‐Support Framework for Prosumer Energy Optimization and Recommendations

Advanced Intelligent Systems, EarlyView.
An agentic AI‐driven decision‐support framework for prosumers is proposed, integrating PV generation, load profiling, and multihorizon optimization within a four‐agent architecture. The approach significantly reduces grid dependence, enhances self‐sufficiency and prevents system oversizing.
Adela BÂRA, Simona‐Vasilica OPREA
wiley   +1 more source

Multiclass SVM with graph path coding regularization for face classification

, 2016
International audienceWe consider the problem of learning graphs in a sparse multiclass support vector machines framework. For such a problem, sparse graph penalty is useful to select the significant features and interpret the results.
Nelly Pustelnik, Meriam Chebre, Philippe Ricoux, Ricoux, Philippe, Chebre, Meriam, Jiu, Mingyuan, Mingyuan Jiu, Stefan Janaqv, Pustelnik, Nelly, Janaqi, Stefan   +9 more
core   +1 more source

Blind Remote Sensing Image Deblurring Based on Local Maximum High-Frequency Coefficient Prior and Graph Regularization

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In satellite remote sensing imaging, factors such as optical axis shift, image plane jitter, movement of the target object, and Earth's rotation can induce image blur.
Zhidan Cai, Ming Fang, Zhe Li, Jinyi Ming, Huimin Wang   +4 more
doaj   +1 more source

Unveiling Endotypes in Systemic Lupus Erythematosus Through Multiomic Analysis: Insights Into Cardiovascular and Renal Complications

Arthritis &Rheumatology, EarlyView.
Objective Systemic lupus erythematosus (SLE) shows clinical and molecular heterogeneity, and cardiovascular (CV) complications and lupus nephritis (LN) remain leading causes of morbidity and mortality. This study investigated whether omic profiling can reveal molecular endotypes linked to these outcomes.
Tomás Cerdó, Laurel Woodridge, Sagrario Corrales, Juan Rafael Muñoz‐Castañeda, Ana Isabel Torralbo, Anisur Rahman, Filipa Farinha, Rafaela Ortega Castro, Pedro Segui, Ismael Sanchez‐Pareja, Laura Muñoz‐Barrera, Christian Merlo, Desiree Ruiz‐Vilchez, M. Carmen Ábalos‐Aguilera, Pilar Font, Nuria Barbarroja Puerto, PRECISESADS Clinical Consortium, Lorenzo Beretta, Barbara Vigone, Jacques‐Olivier Pers, Alain Saraux, Valérie Devauchelle‐Pensec, Divi Cornec, Sandrine Jousse‐Joulin, Bernard Lauwerys, Julie Ducreux, Anne‐Lise Maudoux, Carlos Vasconcelos, Ana Tavares, Esmeralda Neves, Raquel Faria, Mariana Brandão, Ana Campar, António Marinho, Fátima Farinha, Isabel Almeida, Miguel Angel Gonzalez‐Gay Mantecón, Ricardo Blanco Alonso, Alfonso Corrales Martínez, Ricard Cervera, Ignasi Rodríguez‐Pintó, Gerard Espinosa, Rik Lories, Ellen De Langhe, Nicolas Hunzelmann, Doreen Belz, Torsten Witte, Niklas Baerlecken, Georg Stummvoll, Michael Zauner, Michaela Lehner, Eduardo Collantes, Ma Carmen Castro‐Villegas, Norberto Ortego, María Concepción Fernández Roldán, Enrique Raya, Inmaculada Jiménez Moleón, Enrique de Ramon, Isabel Díaz Quintero, Pier Luigi Meroni, Maria Gerosa, Tommaso Schioppo, Carolina Artusi, Carlo Chizzolini, Aleksandra Zuber, Donatienne Wynar, Laszló Kovács, Attila Balog, Magdolna Deák, Márta Bocskai, Sonja Dulic, Gabriella Kádár, Falk Hiepe, Velia Gerl, Silvia Thiel, Manuel Rodriguez Maresca, Antonio López‐Berrio, Rocío Aguilar‐Quesada, Héctor Navarro‐Linares, Marta Alarcón‐Riquelme, Alejandro Escudero‐Contreras, M. Ángeles Aguirre‐Zamorano, Carlos Perez‐Sanchez, Elizabeth C. Jury, Chary Lopez‐Pedrera   +84 more
wiley   +1 more source