Search Results - Computer Learning AND Pattern Recognition::Evolutionary Computing

Interpolation consistency training for semi-supervised learning by Verma, Vikas, Kawaguchi, Kenji, Lamb, Alex, Kannala, Juho, Solin, Arno, Bengio, Yoshua, Lopez-Paz, David

“…We introduce Interpolation Consistency Training (ICT), a simple and computation efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm…”
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Artificial Intelligence and Data Analytics for Energy Exploration and Production by Fred Aminzadeh, Cenk Temizel, Yasin Hajizadeh

“…, evolutionary computing, expert systems, artificial intelligence machine learning, human-computer interface, natural…”
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Explainability in deep reinforcement learning by Heuillet, Alexandre, Couthouis, Fabien, Díaz-Rodríguez, Natalia

“… for reinforcement learning (RL), has not been extensively studied. We review recent works in the direction to attain Explainable Reinforcement Learning (XRL…”
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Deep Learning for Classification of Hyperspectral Data: A Comparative Review by Audebert, Nicolas, Le Saux, Bertrand, Lefevre, Sebastien

“…In recent years, deep-learning techniques revolutionized the way remote sensing data are processed…”
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Hand-Gesture Recognition Based on EMG and Event-Based Camera Sensor Fusion: A Benchmark in Neuromorphic Computing by Ceolini, Enea, Frenkel, Charlotte, Shrestha, Sumit Bam, Taverni, Gemma, Khacef, Lyes, Payvand, Melika, Donati, Elisa

“… Nowadays, with the increasing use of technology, hand-gesture recognition is considered to be an important aspect of Human-Machine Interaction (HMI…”
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Learning Accurate Performance Predictors for Ultrafast Automated Model Compression by Wang, Ziwei, Lu, Jiwen, Xiao, Han, Liu, Shengyu, Zhou, Jie

“…In this paper, we propose an ultrafast automated model compression framework called SeerNet for flexible network deployment. Conventional non-differen-tiable…”
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Learning to compare image patches via convolutional neural networks by Zagoruyko, Sergey, Komodakis, Nikos

“…) a general similarity function for comparing image patches, which is a task of fundamental importance for many computer vision problems…”
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Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms by Moebel, Emmanuel, Martinez-Sanchez, Antonio, Lamm, Lorenz, Righetto, Ricardo D, Wietrzynski, Wojciech, Albert, Sahradha, Larivière, Damien, Fourmentin, Eric, Pfeffer, Stefan, Ortiz, Julio, Baumeister, Wolfgang, Peng, Tingying, Engel, Benjamin D, Kervrann, Charles

“…s. Once trained, the inference stage of DeepFinder is faster than template matching and performs better than other competitive deep learning methods at identifying macromolecules of various sizes…”
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Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy by Ben naceur, Mostefa, Akil, Mohamed, Saouli, Rachida, Kachouri, Rostom

“…•A novel fully automatic Deep Convolutional Neural Networks model for brain tumor segmentation.•The model is inspired by the Occipito-Temporal Pathway…”
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Unsupervised visual feature learning with spike-timing-dependent plasticity: How far are we from traditional feature learning approaches? by Falez, Pierre, Tirilly, Pierre, Bilasco, Ioan Marius, Devienne, Philippe, Boulet, Pierre

“…•We compare the performance of spiking neural networks (SNNs) with auto-encoders for visual feature learning…”
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Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images by naceur, Mostefa Ben, Saouli, Rachida, Akil, Mohamed, Kachouri, Rostom

“…•A new fully automatic end-to-end deep learning model for brain tumor segmentation…”
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Deep Representation-Based Feature Extraction and Recovering for Finger-Vein Verification by Qin, Huafeng, El-Yacoubi, Mounim A.

“… This paper proposes a deep learning model to extract and recover vein features using limited a priori knowledge…”
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Oscillatory neural network learning for pattern recognition: an on-chip learning perspective and implementation by Abernot, Madeleine, Azemard, Nadine, Todri-Sanial, Aida

“…In the human brain, learning is continuous, while currently in AI, learning algorithms are pre-trained, making the model non-evolutive and predetermined…”
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Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks by Audebert, Nicolas, Le Saux, Bertrand, Lefèvre, Sébastien

“… Our results indicate that late fusion make it possible to recover errors steaming from ambiguous data, while early fusion allows for better joint-feature learning…”
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Efficient evolutionary curriculum learning for scalable multi-agent reinforcement learning by Li, Chao, Liu, Yanfei, Wang, Jieling, Wang, Zhong, Wang, Chengjin, Tian, Qi

“… Addressing this critical challenge, we introduce the Efficient Evolutionary Curriculum Learning (E2CL…”
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A Deep learning method for accurate and fast identification of coral reef fishes in underwater images by Villon, Sébastien, Mouillot, David, Chaumont, Marc, Darling, Emily S., Subsol, Gérard, Claverie, Thomas, Villéger, Sébastien

“…Identifying and counting fish individuals on photos and videos is a crucial task to cost-effectively monitor marine biodiversity, yet it remains difficult and…”
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From artificial neural networks to deep learning for music generation: history, concepts and trends by Briot, Jean-Pierre

“…, for weather prediction and pattern recognition), but has already conquered other areas, such as translation…”
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High-level prior-based loss functions for medical image segmentation: A survey by El Jurdi, Rosana, Petitjean, Caroline, Honeine, Paul, Cheplygina, Veronika, Abdallah, Fahed

“…Today, deep convolutional neural networks (CNNs) have demonstrated state of the art performance for supervised medical image segmentation, across various…”
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Effective training of convolutional neural networks for face-based gender and age prediction by Antipov, Grigory, Baccouche, Moez, Berrani, Sid-Ahmed, Dugelay, Jean-Luc

“…•Distribution label encoding as an optimal way to represent age in a CNN.•Role of pretraining in selection of the CNN architecture and the training…”
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Reducing environment exposure to COVID-19 by IoT sensing and computing with deep learning by Ma, Chendong, Song, Jun, Xu, Yibo, Fan, Hongwei, Liu, Xiaoran, Wu, Xing, Luo, Yang, Sun, Tuo, Xie, Jiemin

“… in: Computer vision and pattern recognition, 2015). We achieved…”
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