Methods > General > Feedforward Networks

Feedforward Network

 
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A Feedforward Network, or a Multilayer Perceptron (MLP), is a neural network with solely densely connected layers. This is the classic neural network architecture of the literature. It consists of inputs $x$ passed through units $h$ (of which there can be many layers) to predict a target $y$. Activation functions are generally chosen to be non-linear to allow for flexible functional approximation.

Image Source: Deep Learning, Goodfellow et al

Latest Papers

PAPER DATE
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# 4 SEMI-SUPERVISED IMAGE CLASSIFICATION ON IMAGENET - 10% LABELED DATA
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2019-12-12
Object Recognition with Human in the Loop Intelligent Frameworks
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# 19 SEMI-SUPERVISED IMAGE CLASSIFICATION ON IMAGENET - 1% LABELED DATA
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Initialized Equilibrium Propagation for Backprop-Free Training
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IB-GAN: Disentangled Representation Learning with Information Bottleneck GAN
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Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?
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AlphaStar: An Evolutionary Computation Perspective
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Heartbeat Anomaly Detection using Adversarial Oversampling
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Sensor Adaptation for Improved Semantic Segmentation of Overhead Imagery
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Adaptive Extreme Learning Machine for Recurrent Beta-basis Function Neural Network Training
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Mask Propagation Network for Video Object Segmentation
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Skin Lesion Segmentation Using Atrous Convolution via DeepLab v3
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In pixels we trust: From Pixel Labeling to Object Localization and Scene Categorization
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Auto-Keras: An Efficient Neural Architecture Search System
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Vortex Pooling: Improving Context Representation in Semantic Segmentation
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On Kernel Method-Based Connectionist Models and Supervised Deep Learning Without Backpropagation
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