Deep neural networks rival the representation of primate IT cortex for core visual object recognition. - Test2 - elhamkhani - TriplyDB

Deep neural networks rival the representation of primate IT cortex for core visual object recognition.

Deep neural networks rival the representation of primate IT cortex for core visual object recognition.

http://www.wikidata.org/entity/Q34732320

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Deep supervised, but not unsupervised, models may explain IT cortical representation Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence Generic decoding of seen and imagined objects using hierarchical visual features.Shape Selectivity of Middle Superior Temporal Sulcus Body Patch Neurons.Correlated activity supports efficient cortical processing BrainLiner: A Neuroinformatics Platform for Sharing Time-Aligned Brain-Behavior Data.Comparison of Object Recognition Behavior in Human and Monkey Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance.Visual features as stepping stones toward semantics: Explaining object similarity in IT and perception with non-negative least squares Typicality sharpens category representations in object-selective cortex Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder.What the success of brain imaging implies about the neural code.Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models.Using goal-driven deep learning models to understand sensory cortex.Reward-based training of recurrent neural networks for cognitive and value-based tasks Models of visual categorization.Inferring brain-computational mechanisms with models of activity measurements.Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration.Comparison of IT Neural Response Statistics with Simulations Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition.Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback.Modeling the Dynamics of Human Brain Activity with Recurrent Neural Networks.Perception Science in the Age of Deep Neural Networks.Perceptual category learning of photographic and painterly stimuli in rhesus macaques (Macaca mulatta) and humans ECG data compression using a neural network model based on multi-objective optimization.Random synaptic feedback weights support error backpropagation for deep learning Invariant visual object recognition: biologically plausible approaches.On the partnership between neural representations of object categories and visual features in the ventral visual pathway.A Developmental Approach to Machine Learning?A Deep-Dream Virtual Reality Platform for Studying Altered Perceptual Phenomenology.Deep Learning Predicts Correlation between a Functional Signature of Higher Visual Areas and Sparse Firing of Neurons.Inferential Learning of Serial Order of Perceptual Categories by Rhesus Monkeys (Macaca mulatta).Invariant object recognition is a personalized selection of invariant features in humans, not simply explained by hierarchical feed-forward vision models.Towards deep learning with segregated dendrites.Statistics of Visual Responses to Image Object Stimuli from Primate AIT Neurons to DNN Neurons.Big data and the industrialization of neuroscience: A safe roadmap for understanding the brain?Neural Quadratic Discriminant Analysis: Nonlinear Decoding with V1-Like Computation.Linear readout of object manifolds.Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior.Sharpening of Hierarchical Visual Feature Representations of Blurred Images.

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Deep neural networks rival the representation of primate IT cortex for core visual object recognition.

http://www.wikidata.org/entity/Q34732320

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2014 nî lūn-bûn

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2014 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած

@hyw

2014 թվականի դեկտեմբերին հրատարակված գիտական հոդված

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2014年の論文

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2014年論文

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2014年論文

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2014年論文

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2014年論文

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2014年論文

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2014年论文

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Deep neural networks rival the ...... ore visual object recognition.

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Deep neural networks rival the ...... ore visual object recognition.

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Deep neural networks rival the ...... ore visual object recognition.

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Deep neural networks rival the ...... ore visual object recognition.

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Charles F Cadieu

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Daniel L K Yamins

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Diego Ardila

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Ethan A Solomon

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Ha Hong

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Nicolas Pinto

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P2860

Why is real-world visual object recognition hard?

Receptive fields, binocular interaction and functional architecture in the cat's visual cortex

A high-throughput screening approach to discovering good forms of biologically inspired visual representation

Detecting meaning in RSVP at 13 ms per picture

Learning representations by back-propagating errors

Gradient-based learning applied to document recognition

Unsupervised natural visual experience rapidly reshapes size-invariant object representation in inferior temporal cortex.

Neural population dynamics during reaching.

Clustering by passing messages between data points.

Performance-optimized hierarchical models predict neural responses in higher visual cortex.

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12

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10

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2014-12-18T00:00:00Z

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263091712

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25521294

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1406.3284

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deep neural network

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4270441

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