Results 81 to 90 of about 129,225 (287)
Learning Local Feature Aggregation Functions with Backpropagation
, 2017 This paper introduces a family of local feature aggregation functions and a novel method to estimate their parameters, such that they generate optimal representations for classification (or any task that can be expressed as a cost function minimization ...
csurka +6 more
core +1 more source
, 2023 This paper presents a concise mathematical framework for investigating both feed-forward and backward process, during the training to learn model weights, of an artificial neural network (ANN). Inspired from the idea of the two-step rule for backpropagation, we define a notion of F_adjoint which is aimed at a better description of the backpropagation ...
openaire +2 more sources
Advanced Electronic Materials, EarlyView.A biocompatible graphene/ZnO optical charge trap memory (CTM) is reported with over 54 h retention, enabled by interfacial photodoping. Using transient absorption spectroscopy and electrical analysis, charge transfer quenching is elucidated and reveal that a large energy barrier at the interface is responsible for long‐term memory retention.
Seungmin Shin +10 more
wiley +1 more source
Regular Cycles of Forward and Backward Signal Propagation in Prefrontal Cortex and in Consciousness
Frontiers in Systems Neuroscience, 2016 This paper addresses two fundamental questions:(1) Is it possible to develop mathematical neural network models which can explain and replicate the way in which higher-order capabilities like intelligence, consciousness, optimization and prediction ...
Paul John Werbos +1 more
doaj +1 more source
The Prediction of Yarn Elongation of Kenyan Ring-Spun Yarn using Extreme Learning Machines (ELM) [PDF]
Tekstilec, 2017 The optimization of the manufacture of cotton yarns involves several processes, while the prediction of yarn quality parameters forms an important area of investigation. This research work concentrated on the prediction of cotton yarn elongation.
Josphat Igadwa Mwasiagi
doaj +1 more source
Updatable Closed‐Form Evaluation of Arbitrarily Complex Multiport Network Connections
Advanced Electronic Materials, EarlyView.The inverse design of electrically large wave devices often uses reduced‐order multiport models with discrete optimization, requiring many evaluations of complex interconnections between subsystems that differ only in a few blocks. This paper introduces a closed‐form framework enabling efficient Woodbury low‐rank updates of related, previous ...
Hugo Prod'homme, Philipp del Hougne
wiley +1 more source
Advanced Electronic Materials, EarlyView.A lead‐free perovskite memristive solar cell structure that call emulate both synaptic and neuronal functions controlled by light and electric fields depending on top electrode type. ABSTRACT Memristive devices based on halide perovskites hold strong promise to provide energy‐efficient systems for the Internet of Things (IoT); however, lead (Pb ...
Michalis Loizos +4 more
wiley +1 more source
Unbiasing Truncated Backpropagation Through Time
, 2017 Truncated Backpropagation Through Time (truncated BPTT) is a widespread method for learning recurrent computational graphs. Truncated BPTT keeps the computational benefits of Backpropagation Through Time (BPTT) while relieving the need for a complete ...
Ollivier, Yann, Tallec, Corentin
core
A practical Bayesian framework for backpropagation networks [PDF]
, 1992 A quantitative and practical Bayesian framework is described for learning of mappings in feedforward networks. The framework makes possible (1) objective comparisons between solutions using alternative network architectures, (2) objective stopping rules ...
MacKay, David J. C.
core
Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference
Advanced Electronic Materials, EarlyView.This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho +6 more
wiley +1 more source