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• Incorporating Interpretable Output Constraints in Bayesian Neural Networks
  Domains where supervised models are deployed often come with task-specific constraints, such as prior expert knowledge on the ground-truth function, or desiderata like safety and fairness. We introduce a novel probabilistic framework for reasoning with such co…
  openalex Wanqian Yang, Lars Lorch, Moritz A. Graule, Himabindu Lakkaraju 等 2020-10-21 置信度 0.72 InferenceMachine learningArtificial intelligenceComputer scienceProbabilistic logic
• Monthly climate prediction using deep convolutional neural network and long short-term memory
  Climate change affects plant growth, food production, ecosystems, sustainable socio-economic development, and human health. The different artificial intelligence models are proposed to simulate climate parameters of Jinan city in China, include artificial neur…
  openalex Qingchun Guo, Zhenfang He, Zhaosheng Wang 2024-07-31 置信度 0.72 Mean squared errorClimate changeRecurrent neural networkConvolutional neural networkComputer science
• The role of the cingulate cortex as neural generator of the N200 and P300 in a tactile response inhibition task
  Both the N200 and P300, which are, for example, evoked by Go/Nogo or Stop-Signal tasks, have long been interpreted as indicators for inhibition processes. Such interpretations have recently been challenged, and interest in the exact neural generators of these …
  openalex René J. Huster, René Westerhausen, Christo Pantev, Carsten Konrad 2010-01-08 置信度 0.72 NeuroscienceFunctional magnetic resonance imagingPsychologyBrain mappingComputer science
• Applications of machine learning in cancer prediction and prognosis.
  Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to "learn" from past examples and to detect hard-to-discern patterns from large, noisy or complex dat…
  openalex Joseph A. Cruz, David S. Wishart 2007-02-11 置信度 0.72 Machine learningArtificial intelligenceComputer scienceArtificial neural networkProbabilistic logic
• Causal interactions between fronto-parietal central executive and default-mode networks in humans
  Information processing during human cognitive and emotional operations is thought to involve the dynamic interplay of several large-scale neural networks, including the fronto-parietal central executive network (CEN), cingulo-opercular salience network (SN), a…
  openalex Ashley C. Chen, Desmond J. Oathes, Catie Chang, Travis Bradley 等 2013-11-18 置信度 0.72 Default mode networkSalience (neuroscience)NeuroscienceNeuroimagingPsychology
• Vehicle Lateral Dynamics-Inspired Hybrid Model Using Neural Network for Parameter Identification and Error Characterization
  Autonomous vehicle requires a high-precision lateral dynamics model for path following and lateral stability control. However, existing physical models suffer from low accuracy due to modeling simplification and inaccurate model parameters, while data-driven m…
  openalex Zhisong Zhou, Yafei Wang, Guofeng Zhou, Xulei Liu 等 2024-06-20 置信度 0.72 Artificial neural networkIdentification (biology)Estimation theoryComputer scienceSystem identification
• Lorentz Group Equivariant Neural Network for Particle Physics
  We present a neural network architecture that is fully equivariant with respect to transformations under the Lorentz group, a fundamental symmetry of space and time in physics. The architecture is based on the theory of the finite-dimensional representations o…
  openalex Alexander Bogatskiy, Brandon Anderson, Jan T. Offermann, Marwah Roussi 等 2020-06-08 置信度 0.72 Equivariant mapPhysicsGroup (periodic table)Lorentz transformationArtificial neural network
• Exploring the Computational Effects of Advanced Deep Neural Networks on Logical and Activity Learning for Enhanced Thinking Skills
  The Logical and Activity Learning for Enhanced Thinking Skills (LAL) method is an educational approach that fosters the development of critical thinking, problem-solving, and decision-making abilities in students using practical, experiential learning activiti…
  openalex Deming Li, Kellyt D. Ortegas, Marvin White 2023-06-22 置信度 0.72 Experiential learningCognitionLogical reasoningComputer scienceArtificial intelligence
• Noise injection for training artificial neural networks: A comparison with weight decay and early stopping
  The purpose of this study was to investigate the effect of a noise injection method on the "overfitting" problem of artificial neural networks (ANNs) in two-class classification tasks. The authors compared ANNs trained with noise injection to ANNs trained with…
  openalex Richard M. Zur, Yulei Jiang, Lorenzo L. Pesce, Karen Drukker 2009-09-25 置信度 0.72 OverfittingEarly stoppingNoise (video)Artificial neural networkReceiver operating characteristic
• A hybrid deep neural network for classification of schizophrenia using EEG Data
  Schizophrenia is a serious mental illness that causes great harm to patients, so timely and accurate detection is essential. This study aimed to identify a better feature to represent electroencephalography (EEG) signals and improve the classification accuracy…
  openalex Jie Sun, Rui Cao, Mengni Zhou, Waqar Hussain 等 2021-02-25 置信度 0.72 Computer scienceArtificial intelligencePattern recognition (psychology)ElectroencephalographyFast Fourier transform
• Coarse-to-Fine Decoding for Neural Semantic Parsing
  Semantic parsing aims at mapping natural language utterances into structured meaning representations. In this work, we propose a structure-aware neural architecture which decomposes the semantic parsing process into two stages. Given an input utterance, we fir…
  openalex Li Dong, Mirella Lapata 2018-01-01 置信度 0.72 Computer scienceNatural language processingParsingUtteranceArtificial intelligence
• Geodesic Convolutional Neural Networks on Riemannian Manifolds
  Feature descriptors play a crucial role in a wide range of geometry analysis and processing applications, including shape correspondence, retrieval, and segmentation. In this paper, we introduce Geodesic Convolutional Neural Networks (GCNN), a generalization o…
  openalex Jonathan Masci, Davide Boscaini, Michael M. Bronstein, Pierre Vandergheynst 2015-12-01 置信度 0.72 GeodesicConvolutional neural networkInvariant (physics)Euclidean geometryComputer science
• A solution to the learning dilemma for recurrent networks of spiking neurons
  Recurrently connected networks of spiking neurons underlie the astounding information processing capabilities of the brain. Yet in spite of extensive research, how they can learn through synaptic plasticity to carry out complex network computations remains unc…
  openalex Guillaume Bellec, Franz Scherr, Anand Subramoney, Elias Hajek 等 2020-07-17 置信度 0.72 Computer scienceArtificial intelligenceReinforcement learningArtificial neural networkSpiking neural network
• Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task
  Deep learning techniques have proven highly effective in image classification, but their deployment in resource-constrained environments remains challenging due to high computational demands. Furthermore, their interpretability is of high importance which dema…
  openalex Alireza Maleki, Mahsa Lavaei, Mohsen Bagheritabar, Salar Beigzad 等 2024-12-06 置信度 0.72 Computer scienceArtificial intelligenceScheme (mathematics)Artificial neural networkDeep learning
• Sign Language Interpreting System Using Recursive Neural Networks
  According to the World Health Organization (WHO), 5% of people around the world have hearing disabilities, which limits their capacity to communicate with others. Recently, scientists have proposed systems based on deep learning techniques to create a sign lan…
  openalex Erick A. Borges-Galindo, Nayely Morales-Ramírez, Mario González-Lee, José R. García‐Martínez 等 2024-09-23 置信度 0.72 Computer science
• Adaptive networks for fault diagnosis and process control
  The use of adaptive (artificial neural) networks for fault diagnosis and process control is explored. Adaptive networks can be used as fault recognition systems, as adaptive nonlinear process models, and as controllers. Connection strengths representing correl…
  openalex Lyle Ungar, Benjamin Powell, S.N. Kamens 1990-05-01 置信度 0.72 Artificial neural networkController (irrigation)Fault detection and isolationContext (archaeology)Adaptive control
• Compositional Attention Networks for Machine Reasoning
  We present the MAC network, a novel fully differentiable neural network architecture, designed to facilitate explicit and expressive reasoning. MAC moves away from monolithic black-box neural architectures towards a design that encourages both transparency and…
  openalex Drew A. Hudson, Christopher D. Manning 2018-03-08 置信度 0.72 InterpretabilityComputer scienceRobustness (evolution)Artificial intelligenceArtificial neural network
• Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation
  Several recent trends in machine learning theory and practice, from the design of state-of-the-art Gaussian Process to the convergence analysis of deep neural nets (DNNs) under stochastic gradient descent (SGD), have found it fruitful to study wide random neur…
  openalex Greg Yang 2019-02-13 置信度 0.72 Artificial neural networkInitializationGradient descentGaussian processComputer science
• Deep neural networks for choice analysis: Extracting complete economic information for interpretation
  openalex Shenhao Wang, Qingyi Wang, Jinhua Zhao 2020-07-10 置信度 0.72 Robustness (evolution)EconometricsSample size determinationHyperparameterComputer science
• Explainability of deep neural networks for MRI analysis of brain tumors
  PURPOSE: Artificial intelligence (AI), in particular deep neural networks, has achieved remarkable results for medical image analysis in several applications. Yet the lack of explainability of deep neural models is considered the principal restriction before a…
  openalex Ramy A. Zeineldin, Mohamed Esmail Karar, Ziad Elshaer, Jan Coburger 等 2022-04-23 置信度 0.72 Computer scienceDeep learningArtificial intelligenceVisualizationSegmentation
• Bearing Remaining Useful Life Prediction Based on Regression Shapalet and Graph Neural Network
  Remaining useful life (RUL) prediction of bearing is essential to guarantee its safe operation. In recent years, deep learning (DL)-based methods attract a lot of research attention for accurate RUL prediction. However, the weak interpretability of the DL mode…
  openalex Xiaoyu Yang, Ying Zheng, Yong Zhang, David Shan‐Hill Wong 等 2022-01-01 置信度 0.72 InterpretabilityAdjacency matrixGraphComputer scienceArtificial neural network
• Detection of pneumonia using convolutional neural networks and deep learning
  The objective and automated detection of pneumonia represents a serious challenge in medical imaging, because the signs of the illness are not obvious in CT or X-ray scans. Further on, it is also an important task, since millions of people die of pneumonia eve…
  openalex Patrik Szepesi, László Szilágyi 2022-07-01 置信度 0.72 Convolutional neural networkDeep learningComputer scienceArtificial intelligenceNovelty
• Interpretable deep learning for automatic diagnosis of 12-lead electrocardiogram
  Electrocardiogram (ECG) is a widely used reliable, non-invasive approach for cardiovascular disease diagnosis. With the rapid growth of ECG examinations and the insufficiency of cardiologists, accurate and automatic diagnosis of ECG signals has become a hot re…
  openalex Dongdong Zhang, Samuel Yang, Xiaohui Yuan, Ping Zhang 2021-03-29 置信度 0.72 Deep learningArtificial intelligenceMachine learningComputer scienceArtificial neural network
• OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks
  The OmicsFootPrint framework addresses the need for advanced multi-omics data analysis methodologies by transforming data into intuitive two-dimensional circular images and facilitating the interpretation of complex diseases. Utilizing deep neural networks and…
  openalex Xiaojia Tang, Naresh Prodduturi, Kevin J. Thompson, Richard M. Weinshilboum 等 2024-10-06 置信度 0.72 InterpretabilityBreast cancerBiologyRobustness (evolution)Artificial neural network
• Towards Interpretable Anomaly Detection: Unsupervised Deep Neural Network Approach using Feedback Loop
  As telecom networks generate high-dimensional data, it becomes important to support large numbers of co-existing network attributes and to provide an interpretable and eXplainable Artificial Intelligence (XAI) anomaly detection system. Most state-of-the-art te…
  openalex Ashima Chawla, Paul Jacob, Paddy Farrell, Erik Aumayr 等 2022-04-25 置信度 0.72 Anomaly detectionComputer scienceArtificial intelligenceArtificial neural networkLoop (graph theory)
• Computer-Aided Diagnosis in Histopathological Images of the Endometrium Using a Convolutional Neural Network and Attention Mechanisms
  Uterine cancer (also known as endometrial cancer) can seriously affect the female reproductive system, and histopathological image analysis is the gold standard for diagnosing endometrial cancer. Due to the limited ability to model the complicated relationship…
  openalex Hao Sun, Xianxu Zeng, Tao Xu, Gang Peng 等 2019-10-01 置信度 0.72 InterpretabilityConvolutional neural networkArtificial intelligenceEndometriumComputer science
• Collective chaos in pulse-coupled neural networks
  We study the dynamics of two symmetrically coupled populations of identical leaky integrate-and-fire neurons characterized by an excitatory coupling. Upon varying the coupling strength, we find symmetry-breaking transitions that lead to the onset of various ch…
  openalex Simona Olmi, Antonio Politi, Alessandro Torcini 2010-12-01 置信度 0.72 Lyapunov exponentPhysicsStatistical physicsComputationAmplitude
• I2HGNN: Iterative Interpretable HyperGraph Neural Network for semi-supervised classification
  openalex Hongwei Zhang, Saizhuo Wang, Zixin Hu, Qi Yuan 等 2024-11-22 置信度 0.72 HypergraphArtificial neural networkArtificial intelligencePattern recognition (psychology)Computer science
• Transparent Operator Network: A Fully Interpretable Network Incorporating Learnable Wavelet Operator for Intelligent Fault Diagnosis
  The advent of Industry 4.0 has heightened the demand for the interpretability of intelligent diagnostics, especially for high-risk industrial assets. However, the comprehensive interpretation of neural networks remains inadequately explored. To address this ch…
  openalex Qi Li, Hua Li, Wenyang Hu, Shilin Sun 等 2024-03-20 置信度 0.72 Operator (biology)WaveletComputer scienceArtificial intelligenceFault (geology)
• MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification
  openalex Diponkor Bala, Md. Shamim Hossain, Md. Alamgir Hossain, Md. Ibrahim Abdullah 等 2023-02-21 置信度 0.72 MonkeypoxDeep learningArtificial intelligenceComputer scienceConvolutional neural network
• Antagonistic neural networks underlying differentiated leadership roles
  The emergence of two distinct leadership roles, the task leader and the socio-emotional leader, has been documented in the leadership literature since the 1950s. Recent research in neuroscience suggests that the division between task-oriented and socio-emotion…
  openalex Richard E. Boyatzis, Kylie Rochford, Anthony I. Jack 2014-03-04 置信度 0.72 PsychologyOpenness to experienceContext (archaeology)CreativityTask (project management)
• VAMPnets for deep learning of molecular kinetics
  There is an increasing demand for computing the relevant structures, equilibria, and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations. Current methods employ transformation of …
  openalex Andreas Mardt, Luca Pasquali, Hao Wu, Frank Noé 2017-12-27 置信度 0.72 Computer scienceMarkov chainMarkov modelPipeline (software)Molecular dynamics
• A neural-embedded discrete choice model: Learning taste representation with strengthened interpretability
  openalex Yafei Han, Francisco Camara Pereira, Moshe Ben‐Akiva, Christopher Zegras 2022-07-15 置信度 0.72 InterpretabilityTasteRepresentation (politics)Discrete choiceArtificial intelligence
• Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks
  openalex Zhiyu Zhang, Wenchong Tian, Chenkaixiang Lu, Zhenliang Liao 等 2024-07-26 置信度 0.72 Surrogate modelDrainageComputer scienceArtificial neural networkInterpretability
• The ultrametric organization of memories in a neural network
  In the original formulation of Hopfield's memory model, the learning rule setting the interaction strengths is best suited for orthogonal words. From the point of view of categorization, this feature is not convenient unless we reinterpret these words as primo…
  openalex Néstor Parga, M. A. Virasoro 1986-01-01 置信度 0.72 Ultrametric spaceArtificial neural networkComputer scienceCognitive scienceArtificial intelligence
• Perceptual and Neural Olfactory Similarity in Honeybees
  The question of whether or not neural activity patterns recorded in the olfactory centres of the brain correspond to olfactory perceptual measures remains unanswered. To address this question, we studied olfaction in honeybees Apis mellifera using the olfactor…
  openalex Fernando J. Guerrieri, Marco Schubert, Jean‐Christophe Sandoz, Martín Giurfa 2005-02-16 置信度 0.72 Carbon chainBiologyOlfactionAntennal lobeOdor
• Rumination and the default mode network: Meta-analysis of brain imaging studies and implications for depression
  Rumination is strongly and consistently correlated with depression. Although multiple studies have explored the neural correlates of rumination, findings have been inconsistent and the mechanisms underlying rumination remain elusive. Functional brain imaging s…
  openalex Huixia Zhou, Xiao Chen, Yangqian Shen, Le Li 等 2019-10-23 置信度 0.72 RuminationDefault mode networkPsychologyNeuroscienceMeta-analysis
• Defining epileptogenic networks: Contribution of SEEG and signal analysis
  Epileptogenic networks are defined by the brain regions involved in the production and propagation of epileptic activities. In this review we describe the historical, methodologic, and conceptual bases of this model in the analysis of electrophysiologic intrac…
  openalex Fabrice Bartoloméi, Stanislas Lagarde, Fabrice Wendling, Aileen McGonigal 等 2017-05-20 置信度 0.72 StereoelectroencephalographyIctalNeuroscienceEpilepsyContext (archaeology)
• Interpretable Complex-Valued Neural Networks for Privacy Protection
  Previous studies have found that an adversary attacker can often infer unintended input information from intermediate-layer features. We study the possibility of preventing such adversarial inference, yet without too much accuracy degradation. We propose a gen…
  openalex Liyao Xiang, Haotian Ma, Hao Zhang, Yifan Zhang 等 2019-01-28 置信度 0.72 Adversarial systemComputer scienceAdversaryArtificial neural networkKey (lock)
• AI applications to medical images: From machine learning to deep learning
  PURPOSE: Artificial intelligence (AI) models are playing an increasing role in biomedical research and healthcare services. This review focuses on challenges points to be clarified about how to develop AI applications as clinical decision support systems in th…
  openalex Isabella Castiglioni, Leonardo Rundo, Marina Codari, Giovanni Di Leo 等 2021-03-01 置信度 0.72 Artificial intelligenceInterpretabilityComputer scienceDeep learningConvolutional neural network
• FastGCN: Fast Learning with Graph Convolutional Networks via Importance\n Sampling
  The graph convolutional networks (GCN) recently proposed by Kipf and Welling\nare an effective graph model for semi-supervised learning. This model, however,\nwas originally designed to be learned with the presence of both training and\ntest data. Moreover, th…
  openalex Jie Chen, Tengfei Ma, Cao Xiao 2018-01-30 置信度 0.72 Computer scienceEmbeddingInferenceGraphImportance sampling
• Explainable artificial intelligence enhances the ecological interpretability of black‐box species distribution models
  Species distribution models (SDMs) are widely used in ecology, biogeography and conservation biology to estimate relationships between environmental variables and species occurrence data and make predictions of how their distributions vary in space and time. D…
  openalex Masahiro Ryo, Boyan Angelov, Stefano Mammola, Jamie M. Kass 等 2020-11-17 置信度 0.72 InterpretabilityArtificial intelligenceComputer scienceMachine learningEnvironmental niche modelling
• Towards Self-Explainable Graph Neural Network
  Graph Neural Networks (GNNs), which generalize the deep neural networks to graph-structured data, have achieved great success in modeling graphs. However, as an extension of deep learning for graphs, GNNs lack explainability, which largely limits their adoptio…
  openalex Enyan Dai, Suhang Wang 2021-10-26 置信度 0.72 Computer scienceGraphArtificial intelligenceNode (physics)Similarity (geometry)
• Toward an interpretation of dynamic neural activity in terms of chaotic dynamical systems
  Using the concepts of chaotic dynamical systems, we present an interpretation of dynamic neural activity found in cortical and subcortical areas. The discovery of chaotic itinerancy in high-dimensional dynamical systems with and without a noise term has motiva…
  openalex Ichiro Tsuda 2001-10-01 置信度 0.72 AttractorChaoticComputer scienceCoding (social sciences)Neural coding
• Integrative analysis of 111 reference human epigenomes
  The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest colle…
  openalex Anshul Kundaje, Wouter Meuleman, Jason Ernst, Misha Bilenky 等 2015-02-17 置信度 0.72 EpigenomicsDNA methylationComputational biologyBiologyHuman genome
• Applications of Artificial Neural Networks in Greenhouse Technology and Overview for Smart Agriculture Development
  This article reviews the applications of artificial neural networks (ANNs) in greenhouse technology, and also presents how this type of model can be developed in the coming years by adapting to new technologies such as the internet of things (IoT) and machine …
  openalex Axel Escamilla-García, Genaro M. Soto-Zarazúa, Manuel Toledano‐Ayala, Edgar A. Rivas-Araiza 等 2020-05-31 置信度 0.72 GreenhouseComputer scienceArtificial neural networkProcess (computing)Agriculture
• IMU-Based Gait Recognition Using Convolutional Neural Networks and Multi-Sensor Fusion
  The wide spread usage of wearable sensors such as in smart watches has provided continuous access to valuable user generated data such as human motion that could be used to identify an individual based on his/her motion patterns such as, gait. Several methods …
  openalex Omid Dehzangi, Mojtaba Taherisadr, Raghu Changalvala 2017-11-27 置信度 0.72 Discriminative modelArtificial intelligenceGaitComputer scienceInertial measurement unit
• Interpretable Chirality-Aware Graph Neural Network for Quantitative Structure Activity Relationship Modeling in Drug Discovery
  Abstract In computer-aided drug discovery, quantitative structure activity relation models are trained to predict biological activity from chemical structure. Despite the recent success of applying graph neural network to this task, important chemical informat…
  openalex Yunchao Liu, Yu Wang, Oanh Vu, Rocco Moretti 等 2022-08-26 置信度 0.72 InterpretabilityComputer scienceSimilarity (geometry)GraphArtificial intelligence
• Facial expression recognition using deep convolutional neural networks
  Facial expressions convey non-verbal information between humans in face-to-face interactions. Automatic facial expression recognition, which plays a vital role in human-machine interfaces, has attracted increasing attention from researchers since the early nin…
  openalex Dinh Viet Sang, Nguyen Van Dat, Do Phan Thuan 2017-10-01 置信度 0.72 Computer scienceConvolutional neural networkArtificial intelligenceDeep learningFeature extraction
• Representation of Linguistic Form and Function in Recurrent Neural Networks
  We present novel methods for analyzing the activation patterns of recurrent neural networks from a linguistic point of view and explore the types of linguistic structure they learn. As a case study, we use a standard standalone language model, and a multi-task…
  openalex Ákos Kádár, Grzegorz Chrupała, Afra Alishahi 2017-09-11 置信度 0.72 Computer scienceSentenceNatural language processingArtificial intelligenceRepresentation (politics)
• Spectral Temporal Graph Neural Network for Multivariate Time-series\n Forecasting
  Multivariate time-series forecasting plays a crucial role in many real-world\napplications. It is a challenging problem as one needs to consider both\nintra-series temporal correlations and inter-series correlations\nsimultaneously. Recently, there have been m…
  openalex Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang 等 2021-03-13 置信度 0.72 Series (stratigraphy)Multivariate statisticsComputer scienceGraphConvolution (computer science)
• Channel selection and classification of electroencephalogram signals: An artificial neural network and genetic algorithm-based approach
  OBJECTIVE: An electroencephalogram-based (EEG-based) brain-computer-interface (BCI) provides a new communication channel between the human brain and a computer. Amongst the various available techniques, artificial neural networks (ANNs) are well established in…
  openalex Jianhua Yang, Harsimrat Singh, Evor L. Hines, Friederike Schlaghecken 等 2012-04-12 置信度 0.72 Computer scienceArtificial neural networkSelection (genetic algorithm)Artificial intelligenceGenetic algorithm
• A review on convolutional neural network in rolling bearing fault diagnosis
  Abstract The health condition of rolling bearings has a direct impact on the safe operation of rotating machinery. And their working environment is harsh and the working condition is complex, which brings challenges to fault diagnosis. With the development of …
  openalex Xin Li, Zengqiang Ma, Zonghao Yuan, Tianming Mu 等 2024-03-19 置信度 0.72 InterpretabilityConvolutional neural networkComputer scienceArtificial intelligenceDeep learning
• Multi-hazard susceptibility mapping based on Convolutional Neural Networks
  Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of a…
  openalex Kashif Ullah, Yi Wang, Zhice Fang, Lizhe Wang 等 2022-06-17 置信度 0.72 Computer scienceConvolutional neural networkHazardFlash floodLandslide
• Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization
  Overfitting is one of the most critical challenges in deep neural networks, and there are various types of regularization methods to improve generalization performance. Injecting noises to hidden units during training, e.g., dropout, is known as a successful r…
  openalex Hyeonwoo Noh, Tackgeun You, Jonghwan Mun, Bohyung Han 2017-10-14 置信度 0.72 OverfittingDeep neural networksRegularization (linguistics)Computer scienceStochastic gradient descent
• A Deep Neural Network for the Rapid Prediction of X-ray Absorption Spectra
  X-ray spectroscopy delivers strong impact across the physical and biological sciences by providing end users with highly detailed information about the electronic and geometric structure of matter. To decode this information in challenging cases, e.g., in oper…
  openalex Conor D. Rankine, Marwah M. M. Madkhali, Thomas J. Penfold 2020-05-05 置信度 0.72 Focus (optics)Artificial neural networkAbsorption (acoustics)Enhanced Data Rates for GSM EvolutionComputer science
• Neural Document Summarization by Jointly Learning to Score and Select Sentences
  Sentence scoring and sentence selection are two main steps in extractive document summarization systems. However, previous works treat them as two separated subtasks. In this paper, we present a novel end-to-end neural network framework for extractive document…
  openalex Qingyu Zhou, Nan Yang, Furu Wei, Shaohan Huang 等 2018-01-01 置信度 0.72 Automatic summarizationComputer scienceSentenceArtificial intelligenceSelection (genetic algorithm)
• Guided Soft Attention Network for Classification of Breast Cancer Histopathology Images
  An attention guided convolutional neural network (CNN) for the classification of breast cancer histopathology images is proposed. Neural networks are generally applied as black box models and often the network's decisions are difficult to interpret. Making the…
  openalex Heechan Yang, Ji‐Ye Kim, Hyongsuk Kim, Shyam Prasad Adhikari 2019-10-17 置信度 0.72 Convolutional neural networkArtificial intelligenceComputer sciencePattern recognition (psychology)Margin (machine learning)
• Meta-neural-network for real-time and passive deep-learning-based object recognition
  Abstract Analyzing scattered wave to recognize object is of fundamental significance in wave physics. Recently-emerged deep learning technique achieved great success in interpreting wave field such as in ultrasound non-destructive testing and disease diagnosis…
  openalex Jingkai Weng, Yujiang Ding, Chengbo Hu, Xuefeng Zhu 等 置信度 0.72 Computer scienceDeep learningArtificial intelligenceArtificial neural networkCognitive neuroscience of visual object recognition
• DeepXplainer: An interpretable deep learning based approach for lung cancer detection using explainable artificial intelligence
  BACKGROUND AND OBJECTIVE: Artificial intelligence (AI) has several uses in the healthcare industry, some of which include healthcare management, medical forecasting, practical making of decisions, and diagnosis. AI technologies have reached human-like performa…
  openalex Niyaz Ahmad Wani, Ravinder Kumar, Jatin Bedi 2023-10-24 置信度 0.72 Artificial intelligenceComputer scienceDeep learningCancer detectionMachine learning
• Global crustal thickness from neural network inversion of surface wave data
  We present a neural network approach to invert surface wave data for a global model of crustal thickness with corresponding uncertainties. We model the a posteriori probability distribution of Moho depth as a mixture of Gaussians and let the various parameters…
  openalex Ueli Meier, Andrew Curtis, Jeannot Trampert 2007-04-29 置信度 0.72 Artificial neural networkGeologyCrustInversion (geology)A priori and a posteriori
• Predicting stress, strain and deformation fields in materials and structures with graph neural networks
  Developing accurate yet fast computational tools to simulate complex physical phenomena is a long-standing problem. Recent advances in machine learning have revolutionized the way simulations are approached, shifting from a purely physics- to AI-based paradigm…
  openalex M Maurizi, Chao Gao, Filippo Berto 2022-12-17 置信度 0.72 Computer scienceArtificial neural networkNonlinear systemArtificial intelligenceGraph
• Networks in Cognitive Science
  openalex Andrea Baronchelli, Ramon Ferrer‐i‐Cancho, Romualdo Pastor‐Satorras, Nick Chater 等 2013-05-30 置信度 0.72 Cognitive scienceCognitionPsychologyNetwork scienceSemantic network
• Boredom, sustained attention and the default mode network
  openalex James Danckert, Colleen Merrifield 2016-03-15 置信度 0.72 Default mode networkBoredomPsychologyMind-wanderingNeuroscience
• Optimizing the Latent Space of Generative Networks
  Generative Adversarial Networks (GANs) have achieved remarkable results in the task of generating realistic natural images. In most successful applications, GAN models share two common aspects: solving a challenging saddle point optimization problem, interpret…
  openalex Piotr Bojanowski, Armand Joulin, David López-Paz, Arthur Szlam 2018-02-15 置信度 0.72 DiscriminatorGenerator (circuit theory)Computer scienceArtificial intelligenceGenerative grammar
• Neural processes underlying conscious perception: Experimental findings and a global neuronal workspace framework
  openalex Claire Sergent, Stanislas Dehaene 2004-07-01 置信度 0.72 PerceptionWorkspaceNeural correlates of consciousnessProperty (philosophy)Sensory system
• Soil properties: Their prediction and feature extraction from the LUCAS spectral library using deep convolutional neural networks
  openalex Liang Zhong, Guo Xi, Zhe Xu, Meng Ding 2021-07-28 置信度 0.72 Convolutional neural networkComputer scienceArtificial intelligenceSoil carbonPattern recognition (psychology)
• Short-term PV power forecasting using variational mode decomposition integrated with Ant colony optimization and neural network
  In this paper, Artificial Neural Network (ANN) is integrated with data processing, input variable selection, and external optimization techniques to forecast the day ahead output power of a PV system. Variational mode decomposition (VMD) is used to decompose t…
  openalex Solomon Netsanet, Dehua Zheng, Wei Zhang, Girmaw Teshager 2022-01-28 置信度 0.72 Ant colony optimization algorithmsArtificial neural networkOrthogonalityComputer sciencePower (physics)
• Interpretable and Accurate Fine-grained Recognition via Region Grouping
  We present an interpretable deep model for fine-grained visual recognition. At the core of our method lies the integration of region-based part discovery and attribution within a deep neural network. Our model is trained using image-level object labels, and pr…
  openalex Zixuan Huang, Yin Li 2020-06-01 置信度 0.72 Artificial intelligenceComputer scienceObject (grammar)Pattern recognition (psychology)Cognitive neuroscience of visual object recognition
• High Fidelity Neural Audio Compression
  We introduce a state-of-the-art real-time, high-fidelity, audio codec leveraging neural networks. It consists in a streaming encoder-decoder architecture with quantized latent space trained in an end-to-end fashion. We simplify and speed-up the training by usi…
  openalex Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi 2022-10-24 置信度 0.72 Computer scienceHigh fidelityEncoderCodecSound quality
• Judgment Prediction via Injecting Legal Knowledge into Neural Networks
  Legal Judgment Prediction (LJP) is a key problem in legal artificial intelligence, which is aimed to predict a law case's judgment based on a given text describing the facts of the law case. Most of the previous work treats LJP as a text classification task an…
  openalex Leilei Gan, Kun Kuang, Yi Yang, Fei Wu 2021-05-18 置信度 0.72 Artificial intelligenceComputer scienceArtificial neural networkTask (project management)Set (abstract data type)
• Feature selection for time series prediction – A combined filter and wrapper approach for neural networks
  openalex Sven F. Crone, Nikolaos Kourentzes 2010-03-15 置信度 0.72 Computer scienceTime seriesBenchmark (surveying)Series (stratigraphy)Artificial neural network
• An interpretable deep learning framework for classifying neuronal morphologies using topology and graph neural networks
  Abstract Neuronal shape determines how neurons process and integrate information, yet a consistent and objective classification of neuronal morphologies remains elusive. Current approaches rely heavily on subjective expert views or on predefined features, limi…
  openalex Lida Kanari, Stanislav Schmidt, Francesco Casalegno, Émilie Delattre 等 2024-09-15 置信度 0.72 Artificial intelligenceComputer scienceArtificial neural networkGraphVisual cortex
• Artificial intelligence in retina
  Major advances in diagnostic technologies are offering unprecedented insight into the condition of the retina and beyond ocular disease. Digital images providing millions of morphological datasets can fast and non-invasively be analyzed in a comprehensive mann…
  openalex Ursula Schmidt‐Erfurth, Amir Sadeghipour, Bianca S. Gerendas, Sebastian M. Waldstein 等 2018-08-01 置信度 0.72 Artificial intelligenceInterpretabilityComputer scienceMachine learningConvolutional neural network
• Distributed ARTMAP: a neural network for fast distributed supervised learning
  Distributed coding at the hidden layer of a multi-layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off-line learning to avoid catastrophic forgetting in an open input en…
  openalex Gail A. Carpenter, Boriana L. Milenova, Benjamin W. Noeske 1998-07-01 置信度 0.72 Computer scienceAdaptive resonance theoryArtificial intelligenceArtificial neural networkPerceptron
• Exploring the unity and diversity of the neural substrates of executive functioning
  Previous studies exploring the neural substrates of executive functioning used task-specific analyses, which might not be the most appropriate approach due to the difficulty of precisely isolating executive functions. Consequently, the aim of this study was to…
  openalex Fabienne Collette, Martial Van der Linden, Steven Laureys, Guy Delfiore 等 2005-04-25 置信度 0.72 PsychologyPrefrontal cortexIntraparietal sulcusExecutive functionsNeuroscience
• A study on the Interpretability of Neural Retrieval Models using DeepSHAP
  A recent trend in IR has been the usage of neural networks to learn retrieval models for text based adhoc search. While various approaches and architectures have yielded significantly better performance than traditional retrieval models such as BM25, it is sti…
  openalex Zeon Trevor Fernando, Jaspreet Singh, Avishek Anand 2019-07-18 置信度 0.72 InterpretabilityComputer scienceArtificial intelligenceRobustness (evolution)Artificial neural network
• Layered neural networks
  Exact solutions for the dynamics of layered feedforward neural networks are presented. These networks are expected to respond to an input by going through a sequence of preassigned states on the various layers. The family of networks considered has a variety o…
  openalex Eytan Domany, Wolfgang Kinzel, Ron Meir 1989-06-21 置信度 0.72 Hebbian theoryArtificial neural networkMoore–Penrose pseudoinverseComputer scienceBoltzmann machine
• Establishing and Interpreting Graded Sonic Hedgehog Signaling during Vertebrate Neural Tube Patterning: The Role of Negative Feedback
  The secreted protein Sonic Hedgehog (SHH) acts in graded fashion to pattern the dorsal-ventral axis of the vertebrate neural tube. This is a dynamic process in which increasing concentrations and durations of exposure to SHH generate neurons with successively …
  openalex Vanessa Ribes, James Briscoe 2009-07-15 置信度 0.72 MorphogenSonic hedgehogBiologyNeural tubeVertebrate
• SNAS: Stochastic Neural Architecture Search
  We propose Stochastic Neural Architecture Search (SNAS), an economical end-to-end solution to Neural Architecture Search (NAS) that trains neural operation parameters and architecture distribution parameters in same round of back-propagation, while maintaining…
  openalex Sirui Xie, Hehui Zheng, Chunxiao Liu, Liang Lin 2018-12-24 置信度 0.72 Computer scienceReinforcement learningDifferentiable functionLeverage (statistics)Search cost
• Classification of volcanic ash particles using a convolutional neural network and probability
  Analyses of volcanic ash are typically performed either by qualitatively classifying ash particles by eye or by quantitatively parameterizing its shape and texture. While complex shapes can be classified through qualitative analyses, the results are subjective…
  openalex Daigo Shoji, Rina Noguchi, Shizuka Otsuki, Hideitsu Hino 2018-05-18 置信度 0.72 Convolutional neural networkPattern recognition (psychology)Artificial intelligenceComputer scienceCluster analysis
• Transparency and Explanation in Deep Reinforcement Learning Neural Networks
  Autonomous AI systems will be entering human society in the near future to provide services and work alongside humans. For those systems to be accepted and trusted, the users should be able to understand the reasoning process of the system, i.e. the system sho…
  openalex Rahul Iyer, Yuezhang Li, Huao Li, Michael Lewis 等 2018-12-27 置信度 0.72 Transparency (behavior)Computer scienceReinforcement learningArtificial intelligenceDebugging
• HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images
  Contains fulltext : 231089.pdf (Publisher’s version ) (Open Access)
  openalex Mart van Rijthoven, Maschenka Balkenhol, Karīna Siliņa, Jeroen van der Laak 等 2021-01-01 置信度 0.72 Computer scienceSegmentationArtificial intelligenceConvolutional neural networkPattern recognition (psychology)
• A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network
  Cardiovascular diseases (CVDs) are the leading cause of death today. The current identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a medical monitoring technology recording cardiac activity. Unfortunately, looking for exp…
  openalex Mengze Wu, Yongdi Lu, Wenli Yang, Shen Yuong Wong 2021-01-04 置信度 0.72 HeartbeatComputer scienceConvolutional neural networkArtificial intelligenceRobustness (evolution)
• Variational Denoising Network: Toward Blind Noise Modeling and Removal
  Blind image denoising is an important yet very challenging problem in computer vision due to the complicated acquisition process of real images. In this work we propose a new variational inference method, which integrates both noise estimation and image denois…
  openalex Zongsheng Yue, Hongwei Yong, Qian Zhao, Lei Zhang 等 2019-08-29 置信度 0.72 Noise reductionArtificial intelligenceNoise (video)Computer sciencePattern recognition (psychology)
• IDS-INT: Intrusion detection system using transformer-based transfer learning for imbalanced network traffic
  A network intrusion detection system is critical for cyber security against illegitimate attacks. In terms of feature perspectives, the network traffic may include a variety of elements such as attack reference, attack type, a sub-category of attack, host info…
  openalex Farhan Ullah, Shamsher Ullah, Gautam Srivastava, Jerry Chun‐Wei Lin 2023-03-17 置信度 0.72 Computer scienceIntrusion detection systemTransfer of learningAttack modelArtificial intelligence
• A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope
  This paper presents a comprehensive study of Convolutional Neural Networks (CNN) and transfer learning in the context of medical imaging. Medical imaging plays a critical role in the diagnosis and treatment of diseases, and CNN-based models have demonstrated s…
  openalex Ahmad Waleed Salehi, Shakir Khan, Gaurav Gupta, Bayan Alabduallah 等 2023-03-29 置信度 0.72 InterpretabilityTransfer of learningComputer scienceConvolutional neural networkDeep learning
• Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework
  Semi-supervised learning on graphs is an important problem in the machine learning area. In recent years, state-of-the-art classification methods based on graph neural networks (GNNs) have shown their superiority over traditional ones such as label propagation…
  openalex Cheng Yang, Jiawei Liu, Chuan Shi 2021-04-19 置信度 0.72 Computer scienceMachine learningArtificial intelligenceBenchmark (surveying)Parameterized complexity
• Face Recognition Using Neural Network: A Review
  Face recognition from the real data, capture images, sensor images and database images is challenging problem due to the wide variation of face appearances, illumination effect and the complexity of the image background. Face recognition is one of the most eff…
  openalex Manisha Kasar, Debnath Bhattacharyya, Tai-hoon Kim 2016-03-31 置信度 0.72 Computer scienceArtificial neural networkArtificial intelligencePattern recognition (psychology)Facial recognition system
• Deep Convolutional Neural Networks for Unconstrained Ear Recognition
  This paper employs state-of-the-art Deep Convolutional Neural Networks (CNNs), namely AlexNet, VGGNet, Inception, ResNet and ResNeXt in a first experimental study of ear recognition on the unconstrained EarVN1.0 dataset. As the dataset size is still insufficie…
  openalex Hammam Alshazly, Christoph Linse, Erhardt Barth, Thomas Martinetz 2020-01-01 置信度 0.72 Computer scienceConvolutional neural networkSpeech recognitionArtificial intelligencePattern recognition (psychology)
• Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
  We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. Our models are decision lists, which consist of a series of if…then…statements (e.g., if high blood pressure, then stroke) that discretize a high-dimen…
  openalex Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, David Madigan 2015-09-01 置信度 0.72 Machine learningComputer scienceArtificial intelligenceBayesian probabilityFeature (linguistics)
• Graph Convolutional Neural Networks as “General-Purpose” Property Predictors: The Universality and Limits of Applicability
  Nowadays the development of new functional materials/chemical compounds using machine learning (ML) techniques is a hot topic and includes several crucial steps, one of which is the choice of chemical structure representation. The classical approach of rigorou…
  openalex Vadim Korolev, Artem Mitrofanov, Alexandru Korotcov, Valery Tkachenko 2019-12-20 置信度 0.72 InterpretabilityComputer scienceUniversality (dynamical systems)Convolutional neural networkArtificial intelligence
• Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data
  For a long time, researchers have tried to find a way to analyze tropical cyclone (TC) intensity in real-time. Since there is no standardized method for estimating TC intensity and the most widely used method is a manual algorithm using satellite-based cloud i…
  openalex Juhyun Lee, Jungho Im, Dong‐Hyun Cha, Haemi Park 等 2019-12-28 置信度 0.72 Geostationary orbitInitializationConvolutional neural networkComputer scienceTropical cyclone
• Review of the application of Artificial Neural Networks in ocean engineering
  Artificial Neural Networks (ANNs) were firstly used to model ocean engineering problems in the decade of 1990s. Since then, this soft-modelling technique has proved several advantages against traditional approaches. In this article, the application of ANNs in …
  openalex Nerea Portillo Juan, Vicente Negro Valdecantos 2022-07-11 置信度 0.72 Artificial neural networkComputer scienceArtificial intelligenceEngineering
• Evolution of the neural language network
  The evolution of language correlates with distinct changes in the primate brain. The present article compares language-related brain regions and their white matter connectivity in the developing and mature human brain with the respective structures in the nonh…
  openalex Angela D. Friederici 2016-07-01 置信度 0.72 PsychologyBroca's areaHuman brainNeuroscienceHierarchy
• Training Robust Neural Networks Using Lipschitz Bounds
  Due to their susceptibility to adversarial perturbations, neural networks (NNs) are hardly used in safety-critical applications. One measure of robustness to such perturbations in the input is the Lipschitz constant of the input-output map defined by an NN. In…
  openalex Patricia Pauli, Anne Koch, Julian Berberich, Paul Köhler 等 2021-01-13 置信度 0.72 Lipschitz continuityTraining (meteorology)Artificial neural networkComputer scienceArtificial intelligence
• Identification of gene regulatory networks associated with breast cancer patient survival using an interpretable deep neural network model
  Artificial neural networks have recently gained significant attention in biomedical research. However, their utility in survival analysis still faces many challenges. In addition to designing models for high accuracy, it is essential to optimize models that pr…
  openalex Xue Wang, Vivekananda Sarangi, Daniel P. Wickland, Shaoyu Li 等 2024-11-04 置信度 0.72 Breast cancerIdentification (biology)Artificial neural networkComputer scienceArtificial intelligence
• Neuro-fuzzy methods for nonlinear system identification
  openalex Robert Babuška, H.B. Verbruggen 2003-01-01 置信度 0.72 Nonlinear system identificationNeuro-fuzzyInterpretabilityIdentification (biology)Nonlinear system
• Toward a Neural Basis of Music Perception – A Review and Updated Model
  Music perception involves acoustic analysis, auditory memory, auditory scene analysis, processing of interval relations, of musical syntax and semantics, and activation of (pre)motor representations of actions. Moreover, music perception potentially elicits em…
  openalex Stefan Koelsch 2011-01-01 置信度 0.72 PsychologyPerceptionCognitive psychologyFeelingAuditory scene analysis
• CloudScout: A Deep Neural Network for On-Board Cloud Detection on Hyperspectral Images
  The increasing demand for high-resolution hyperspectral images from nano and microsatellites conflicts with the strict bandwidth constraints for downlink transmission. A possible approach to mitigate this problem consists in reducing the amount of data to tran…
  openalex Gianluca Giuffrida, Lorenzo Diana, Francesco de Gioia, Gionata Benelli 等 2020-07-10 置信度 0.72 Hyperspectral imagingComputer sciencePayload (computing)Convolutional neural networkRemote sensing