PDF-Recurrent Convolutional Neural Networks for Scene Labeling Pedro O

Pinheiro PEDRO PINHEIRO IDIAP CH Ronan Collobert RONAN COLLOBERT COM Ecole Polytechnique F ed erale de Lausanne EPFL Lausanne Switzerland Idiap Research Institute Martigny Switzerland Abstract The goal of the scene labeling task is to assign a class. We propose a method that uses a multiscale convolutional network traine d from raw pixels to extract dense feature vectors that encod e regions of multiple sizes centered on each pixel The method alleviate s the need for engineered features and prod using Convolutional Neural Network and Simple Logistic Classifier. Hurieh. . Khalajzadeh. Mohammad . Mansouri. Mohammad . Teshnehlab. Table of Contents. Convolutional Neural . Networks. Proposed CNN structure for face recognition. Kong Da, Xueyu Lei & Paul McKay. Digit Recognition. Convolutional Neural Network. Inspired by the visual cortex. Our example: Handwritten digit recognition. Reference: . LeCun. et al. . Back propagation Applied to Handwritten Zip Code Recognition. 1. Recurrent Networks. Some problems require previous history/context in order to be able to give proper output (speech recognition, stock forecasting, target tracking, etc.. One way to do that is to just provide all the necessary context in one "snap-shot" and use standard learning. 2015/10/02. 陳柏任. Outline. Neural Networks. Convolutional Neural Networks. Some famous CNN structure. Applications. Toolkit. Conclusion. Reference. 2. Outline. Neural Networks. Convolutional Neural Networks. Abhinav . Podili. , Chi Zhang, Viktor . Prasanna. Ming Hsieh Department of Electrical Engineering. University of Southern California. {. podili. , zhan527, . prasanna. }@usc.edu. fpga.usc.edu. ASAP, July 2017. Last time. Linear classifiers on pixels bad, need non-linear classifiers. Multi-layer . perceptrons. . overparametrized. Reduce parameters by local connections and shift invariance => Convolution. 1. Table of contents. Recurrent models. Partially recurrent neural networks. . Elman networks. Jordan networks. Recurrent neural networks. BackPropagation Through Time. Dynamics of a neuron with feedback. Convolutions. Reduce parameters. Capture shift-invariance: location of patch in image should not matter. Subsampling. Allows greater invariance to deformations. Allows the capture of large patterns with small filters. Article and Work by. : Justin . Salamon. and Juan Pablo Bello. Presented by . : . Dhara. Rana. Overall Goal of Paper. Create a way to classify environmental sound given an audio clip. Other methods of sound classification: (1) dictionary learning and (2) wavelet filter banks . Abigail See, Peter J. Liu, Christopher D. Manning. Presented by: Matan . Eyal. Agenda. Introduction. Word Embeddings. RNNs. Sequence-to-Sequence. Attention. Pointer Networks. Coverage Mechanism. Introduction . . 循环神经网络. Neural Networks. Recurrent Neural Networks. Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence.. Models and applications. Outline. Sequence Data. Recurrent Neural Networks Variants. Handling Long Term Dependencies. Attention Mechanisms. Properties of RNNs. Applications of RNNs. Hands-on LSTM-supported timeseries prediction. An overview and applications. Outline. Overview of Convolutional Neural Networks. The Convolution operation. A typical CNN model architecture. Properties of CNN models. Applications of CNN models. Notable CNN models.