Results 71 to 80 of about 60,295 (249)

Toward Capacitive In‐Memory‐Computing: A Device to Systems Level Perspective on the Future of Artificial Intelligence Hardware

Advanced Intelligent Discovery, EarlyView.
Capacitive, charge‐domain compute‐in‐memory (CIM) stores weights as capacitance,eliminating DC sneak paths and IR‐drop, yielding near‐zero standbypower. In this perspective, we present a device to systems level performance analysis of most promising architectures and predict apathway for upscaling capacitive CIM for sustainable edge computing ...
Kapil Bhardwaj, Ella Paasio, Sayani Majumdar   +2 more
wiley   +1 more source

Revealing Protein–Protein Interactions Using a Graph Theory‐Augmented Deep Learning Approach

Advanced Intelligent Discovery, EarlyView.
This study presents a fast, cost‐efficient approach for classifying protein–protein interactions by integrating graph‐theory parametrization with deep learning (DL). Multiscale features extracted from graph‐encoded polarized‐light microscopy (PLM) images enable accurate prediction of binding strengths.
Bahar Dadfar, Gözde Kabay, Matthias Franzreb, Kody Whisnant, Nicholas A. Kotov, Joerg Lahann   +5 more
wiley   +1 more source

Unsupervised Kernel Learning Vector Quantization [PDF]

Advanced Engineering Forum, 2012
In this paper, we propose an unsupervised kernel learning vector quantization (UKLVQ) algorithm that combines the concepts of the kernel method and traditional unsupervised learning vector quantization (ULVQ). We first use the definition of the shadow kernel to give a general representation of the UKLVQ method and then easily implement the UKLVQ ...
openaire   +1 more source

Adaptive Metric Learning Vector Quantization for Ordinal Classification [PDF]

Neural Computation, 2012
Many pattern analysis problems require classification of examples into naturally ordered classes. In such cases, nominal classification schemes will ignore the class order relationships, which can have a detrimental effect on classification accuracy.
Fouad, Shereen, Tino, Peter
openaire   +2 more sources

VAE+DDPG: An Attention‐Enhanced Variational Autoencoder for Deep Reinforcement Learning‐Based Autonomous Navigation in Low‐Light Environments

Advanced Intelligent Systems, EarlyView.
Variational Autoencoder+Deep Deterministic Policy Gradient addresses low‐light failures of infrared depth sensing for indoor robot navigation. Stage 1 pretrains an attention‐enhanced Variational Autoencoder (Convolutional Block Attention Module+Feature Pyramid Network) to map dark depth frames to a well‐lit reconstruction, yielding a 128‐D latent code ...
Uiseok Lee, Chanhong Hwang, Hyunseok Kang, Yechan Park, Hangwoo Jin, Tuan Anh Nguyen, Eunmi Choi, Dugki Min   +7 more
wiley   +1 more source

Video data compression using artificial neural network differential vector quantization [PDF]

An artificial neural network vector quantizer is developed for use in data compression applications such as Digital Video. Differential Vector Quantization is used to preserve edge features, and a new adaptive algorithm, known as Frequency-Sensitive ...
Ahalt, Stanley C., Bibyk, Steven B., Krishnamurthy, Ashok K.   +2 more
core   +1 more source

Large‐Scale and Highly Reliable Hopfield Neural Networks Using Vertical NAND Flash Memory for the In‐Memory Associative Computing

Advanced Intelligent Systems, EarlyView.
Large‐scale Hopfield neural networks (HNNs) for associative computing are implemented using vertical NAND (VNAND) flash memory. The proposed VNAND HNN with the asynchronous update scenario achieve robust image restoration performance despite fabrication variations, while significantly reducing chip area (≈117× smaller than resistive random‐access ...
Jin Ho Chang, Jae Seung Woo, Suk‐Kang Sung, Ki‐Whan Song, Woo Young Choi   +4 more
wiley   +1 more source

Generalized Derivative Based Kernelized Learning Vector Quantization [PDF]

, 2010
We derive a novel derivative based version of kernelized Generalized Learning Vector Quantization (KGLVQ) as an effective, easy to interpret, prototype based and kernelized classifier. It is called D-KGLVQ and we provide generalization error bounds, experimental results on real world data, showing that D-KGLVQ is competitive with KGLVQ and the SVM on ...
Schleif, Frank-Michael, Villmann, T., Hammer, Barbara, Schneider, P., Biehl, M., Fyfe, Colin, Tino, Peter, Charles, Darryl, Garcia-Osorio, Cesar, Yin, Hujun   +9 more
openaire   +3 more sources

A CRDNet‐Based Watermarking Algorithm for Fused Visible–Infrared Images

Advanced Intelligent Systems, EarlyView.
CRDnet includes encoders and decoders based on residual and dense structures, a fusion network robust to 12 visible and infrared image fusion algorithms, and predictors for predicting watermarked infrared images. The encoder and decoder incorporate preprocessing steps, attention mechanisms, and activation functions suitable for infrared images.
Yu Bai, Li Li, Shanqing Zhang, Ting Luo, Chin‐Chen Chang   +4 more
wiley   +1 more source

Diagnosa Penyakit dengan Gejala Demam Menggunakan Learning Vector Quantization (LVQ)

Jurnal Informatika dan Multimedia, 2017
Terdapat beberapa penyakit dengan gejala demam yang sulit untuk dibedakan yaitu Demam tifoid (tifus), Demam berdarah dengue (DBD), dan Malaria. Kesulitan dalam mendeteksi lebih awal ketiga penyakit ini karena memiliki gejala klinis yang hampir sama ...
Dian Eka Ratnawati, Marji Marji
doaj   +1 more source