Search Results - computer learning and pattern recognition::(evolutionary OR evolution) computing

Q-learning enhanced differential evolution for feature selection in high-dimensional medical data analysis by Huang, Chenliang, Wang, Mingjing, Asghar, Heidari Ali, Wang, Zhilin, Chen, Huiling

“… In this study, we propose a hybrid optimization algorithm named QDEHHO, where Differential Evolution (DE…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

How evolution learns to generalise: Using the principles of learning theory to understand the evolution of developmental organisation by Kouvaris, Kostas, Clune, Jeff, Kounios, Loizos, Brede, Markus, Watson, Richard A.

“…One of the most intriguing questions in evolution is how organisms exhibit suitable phenotypic variation to rapidly adapt in novel selective environments…”
Get full text

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…”
Get full text

Fast and Accurate Terrain Image Classification for ASTER Remote Sensing by Data Stream Mining and Evolutionary-EAC Instance-Learning-Based Algorithm by Hu, Shimin, Fong, Simon, Yang, Lili, Yang, Shuang-Hua, Dey, Nilanjan, Millham, Richard C., Fiaidhi, Jinan

“… Traditional data mining requires all the data to be available prior to inducing a model by supervised learning, for automatic image recognition or classification…”
Get full text

Parallel alternatives for evolutionary multi-objective optimization in unsupervised feature selection by Kimovski, Dragi, Ortega, Julio, Ortiz, Andrés, Baños, Raúl

“… Many machine learning and pattern recognition applications require reducing dimensionality to improve learning accuracy while irrelevant inputs are removed…”
Get full text

Increasing pattern recognition accuracy for chemical sensing by evolutionary based drift compensation by Di Carlo, S., Falasconi, M., Sanchez, E., Scionti, A., Squillero, G., Tonda, A.

“… Artificial olfaction systems, which mimic human olfaction by using arrays of gas chemical sensors combined with pattern recognition methods, represent a potentially low-cost tool in many areas…”
Get full text

Deep Optimisation: Transitioning the Scale of Evolutionary Search by Inducing and Searching in Deep Representations by Caldwell, Jamie, Knowles, Joshua, Thies, Christoph, Kubacki, Filip, Watson, Richard

“… We call the algorithm Deep Optimisation (DO) to recognise both its use of deep learning methods and the multi-level rescaling of biological evolutionary processes…”
Get full text

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…”
Get full text

ImaGene: a convolutional neural network to quantify natural selection from genomic data by Torada, Luis, Lorenzon, Lucrezia, Beddis, Alice, Isildak, Ulas, Pattini, Linda, Mathieson, Sara, Fumagalli, Matteo

“… An alternative approach to classic association studies to determining such genetic bases is an evolutionary framework…”
Get full text

SimilCatch: Enhanced social spammers detection on Twitter using Markov Random Fields by El-Mawass, Nour, Honeine, Paul, Vercouter, Laurent

“…highlights•Social spam evolution is leading to a marked deterioration in the performance of state-of-the-art supervised classifiers…”
Get full text

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…”
Get full text

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…”
Get full text

Self-adaptive attribute weighting for Naive Bayes classification by Wu, Jia, Pan, Shirui, Zhu, Xingquan, Cai, Zhihua, Zhang, Peng, Zhang, Chengqi

“…) for attribute weighting.•Seamlessly integrating learning objective and AIS affinity function for attribute weighting…”
Get full text

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…”
Get full text

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…”
Get full text