Results 51 to 60 of about 129,225 (287)

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

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, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

A Scalable Perovskite Platform With Multi‐State Photoresponsivity for In‐Sensor Saliency Detection

Advanced Materials, EarlyView.
A scalable in‐sensor computing platform (32 × 32 array) with ultra‐low variability is developed by incorporating ferroelectric copolymers into halide perovskite thin films. These devices achieve 1000 programmable photoresponsivity states and high thermal reliability.
Xuechao Xing, Anubhab Tripathi, Si En Ng, Natalia Yantara, Yue Gong, Qinjie Wu, Yeow Boon Tay, Wen Siang Lew, Yang Chai, Arindam Basu, Nripan Mathews   +10 more
wiley   +1 more source

Towards New Generation, Biologically Plausible Deep Neural Network Learning

Sci, 2022
Artificial neural networks in their various different forms convincingly dominate machine learning of the present day. Nevertheless, the manner in which these networks are trained, in particular by using end-to-end backpropagation, presents a major ...
Anirudh Apparaju, Ognjen Arandjelović
doaj   +1 more source

Backpropagation Through Soft Body: Investigating Information Processing in Brain–Body Coupling Systems

Advanced Robotics Research, EarlyView.
This study explores how information processing is distributed between brains and bodies through a codesign approach. Using the “backpropagation through soft body” framework, brain–body coupling agents are developed and analyzed across several tasks in which output is generated through the agents’ physical dynamics.
Hiroki Tomioka, Katsuma Inoue, Yasuo Kuniyoshi, Kohei Nakajima   +3 more
wiley   +1 more source

Stochastic Digital Backpropagation with Residual Memory Compensation

, 2015
Stochastic digital backpropagation (SDBP) is an extension of digital backpropagation (DBP) and is based on the maximum a posteriori principle. SDBP takes into account noise from the optical amplifiers in addition to handling deterministic linear and ...
Agrell, Erik, Irukulapati, Naga V., Johannisson, Pontus, Marsella, Domenico, Secondini, Marco, Wymeersch, Henk   +5 more
core   +1 more source

Robust Implicit Backpropagation

CoRR, 2018
Arguably the biggest challenge in applying neural networks is tuning the hyperparameters, in particular the learning rate. The sensitivity to the learning rate is due to the reliance on backpropagation to train the network. In this paper we present the first application of Implicit Stochastic Gradient Descent (ISGD) to train neural networks, a method ...
Francois Fagan, Garud Iyengar
openaire   +2 more sources

AutomataGPT: Transformer‐Based Forecasting and Ruleset Inference for Two‐Dimensional Cellular Automata

Advanced Science, EarlyView.
We introduce AutomataGPT, a generative pretrained transformer (GPT) trained on synthetic spatiotemporal data from 2D cellular automata to learn symbolic rules. Demonstrating strong performance on both forward and inverse tasks, AutomataGPT establishes a scalable, domain‐agnostic framework for interpretable modeling, paving the way for future ...
Jaime A. Berkovich, Noah S. David, Markus J. Buehler   +2 more
wiley   +1 more source

Small-variance asymptotics for Bayesian neural networks [PDF]

, 2018
Bayesian neural networks (BNNs) are a rich and flexible class of models that have several advantages over standard feedforward networks, but are typically expensive to train on large-scale data.
Sankarapandian, Sivaramakrishnan
core  

Neural Fields for Highly Accelerated 2D Cine Phase Contrast MRI

Advanced Science, EarlyView.
ABSTRACT 2D cine phase contrast (CPC) MRI provides quantitative information on blood velocity and flow within the human vasculature. However, data acquisition is time‐consuming, motivating the reconstruction of the velocity field from undersampled measurements to reduce scan times. In this work, neural fields are proposed as a continuous spatiotemporal
Pablo Arratia, Martin J. Graves, Mary McLean, Carolin Pirkl, Carola‐Bibiane Schönlieb, Timo Schirmer, Florian Wiesinger, Matthias J. Ehrhardt   +7 more
wiley   +1 more source

Recunoașterea unei cifre scrise de mână folosind o rețea neuronală convoluțională și biblioteca TensorFlow unei cifre scrise de mână folosind o rețea neuronală convoluțională și biblioteca TensorFlow [PDF]

Revista Română de Informatică și Automatică, 2019
In this paper it is proposed to solve a visual problem of recognizing a handwritten figure. A machine learning technique will be used in which a result is produced based on previous experience.
Paul TEODORESCU
doaj   +1 more source