Unsupervised Deep Embedding for Clustering Analysis

TPOT: A Tree-based Pipeline Optimization Tool for ...

JMLR: Workshop and Conference Proceedings 64:66{74, 2016 ICML 2016 AutoML Workshop TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine …

  Automating, Machine, Tool, Pipeline, Optimization, Pipeline optimization tool for automating machine

Ensembles for Time Series Forecasting

Ensembles for Time Series Forecasting set of real world time series. Our results clearly indicate that this is a promising research direction. In Section2we provide a brief description of the tasks being tackled in this paper.

  Series, Time, Time series, Forecasting, Beslenme, Ensembles for time series forecasting

Show, Attend and Tell: Neural Image CaptionGeneration …

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention Kelvin Xu? [email protected] Jimmy Lei Bay [email protected] Ryan Kirosy [email protected] Kyunghyun Cho?

  Image, Attention, Neural, Tell, And tell, Neural image captiongeneration, Captiongeneration

Wasserstein Generative Adversarial Networks

Wasserstein Generative Adversarial Networks Figure 1: These plots show ˆ(P ;P 0) as a function of when ˆis the EM distance (left plot) or the JS divergence (right plot).The EM plot is continuous and provides a usable gradient everywhere.

  Network, Adversarial, Generative, Wasserstein generative adversarial networks, Wasserstein

Self-Attention Generative Adversarial Networks

Self-Attention Generative Adversarial Networks Figure 1. The proposed SAGAN generates images by leveraging complementary features in distant portions of the image rather than local regions of fixed shape to generate consistent objects/scenarios. In each row, the first image shows five representative query locations with color coded dots.

  Network, Self, Attention, Adversarial, Generative, Self attention generative adversarial networks

Generative Adversarial Text to Image Synthesis

deep convolutional decoder networks to generate realistic images.Dosovitskiy et al.(2015) trained a deconvolutional network (several layers of convolution and upsampling) to generate 3D chair renderings conditioned on a set of graph-ics codes indicating shape, position and lighting.Yang et al. (2015) added an encoder network as well as actions ...

  Image, Texts, Decoder, Synthesis, Deep, Encoder, Convolutional, Text to image synthesis, Deep convolutional decoder

On the di culty of training recurrent neural networks

On the di culty of training recurrent neural networks @Et+1 @xt+1 Et Et+1 Et 1 xt 1 xt +1 ut +11 u tu @Et @xt @Et1 @xt1 @ xt +2 @xt +1 @x +1 x @xt1 @xt1 @xt2 Figure 2. Unrolling recurrent neural networks in time by creating a copy of the model for each time step.

Deep Gaussian Processes

representational power of a Gaussian process in the same role is significantly greater than that of an RBM. For the GP the corresponding likelihood is over a continuous vari-able, but it is a nonlinear function of the inputs, p(yjx) = N yjf(x);˙2; where N j ;˙2 is a Gaussian density with mean and variance ˙2. In this case the likelihood is ...

  Process, Gaussian, Gaussian process

Noise-contrastive estimation: A new estimation principle ...

ated noise y. The estimation principle thus relies on noise with which the data is contrasted, so that we will refer to the new method as “noise-contrastive estima-tion”. In Section 2, we formally define noise-contrastive es-timation, establish fundamental statistical properties, and make the connection to supervised learning ex-plicit.

  Into, Noise, Estimation, Contrastive, Noise contrastive estimation, Noise contrastive estima tion, Estima, Timation

Gender Shades: Intersectional Accuracy Disparities in ...

117 million Americans are included in law en-forcement face recognition networks. A year-long research investigation across 100 police de-partments revealed that African-American indi-viduals are more likely to be stopped by law enforcement and be subjected to face recogni-tion searches than individuals of other ethnici-ties (Garvie et al.,2016).

  Enforcement, Gender, Shades, Stopped, Forcement, Stopped by law enforcement, Law en forcement, Gender shades

SPECTRAL ANALYSIS OF SIGNALS - Uppsala University

Spectral Analysis of Signals/Petre Stoica and Randolph Moses p. cm. Includes bibliographical references index. ISBN 0-13-113956-8 1. Spectral theory (Mathematics) I. Moses, Randolph II. Title 512’{dc21 2005 QA814.G27 00-055035 CIP Acquisitions Editor: Tom Robbins Editor-in-Chief: ? Assistant Vice President of Production and Manufacturing: ?

  Analysis, Signal, Spectral analysis of signals, Spectral

Resolutions of Remote Sensing - Environmental Data Center

Spectral Cover of Landsat Sensors Hyperspectral Data Example: Hyperion hyperspectral sensor is capable of resolving 220 spectral bands at 10 nm interval (from 0.4 to 2.5 µm) with a 30 meter spatial resolution. The shown image, acquired April 6, 2004, is displayed as--640.50 µm in Red color--548.92 µm in Green color--457.34 µm in Blue color

  Remote, Sensing, Spectral, Remote sensing

Topic: Spectrogram, Cepstrum and Mel-Frequency Analysis

Spectral Envelope Spectrum Spectral details A pseudo-frequency axis IFFT log X[k] = log H[k] + log E[k] log H[k] log E[k] x[k] = h[k] + e[k] • x[k] is referred to as Cepstrum • h[k] is obtained by considering the low frequency region of x[k]. • h[k] represents the spectral envelope and is widely used as feature for speech recognition

  Analysis, Spectrum, Frequency, Spectrograms, Spectral, Cepstrum and mel frequency analysis

Introduction to Time Series Analysis. Lecture 1.

Spectral analysis 4. State space models(?) (a) ARMAX models. (b) Forecasting, Kalman filter. (c) Parameter estimation. 28. Time Series Models A time series model specifies the joint distribution of the se-quence {Xt} of random variables. For example:

  Analysis, Spectral, Spectral analysis

A Tutorial on Spectral Clustering - arXiv

di erent approaches. Advantages and disadvantages of the di erent spectral clustering algorithms are discussed. Keywords: spectral clustering; graph Laplacian 1 Introduction Clustering is one of the most widely used techniques for …

  Spectral, Clustering, Spectral clustering

FUNCTIONAL ANALYSIS - ETH Z

functional analysis for many of the relevant applications. The manuscript is addressed primarily to third year students of mathe-matics or physics, and the reader is assumed to be familiar with rst year analysis and linear algebra, as well as complex analysis and the basics of point set topology and measure and integration.

  Analysis, Functional, Functional analysis

[TS] Time Series - Stata

Parametric spectral density estimation after arima, arfima, and ucm 419 ... [ST] Stata Survival Analysis and Epidemiological Tables Reference Manual [TS] Stata Time-Series Reference Manual [TE] Stata Treatment-Effects Reference Manual: …

  Analysis, Spectral

Spectral Analysis in R - McMaster University

Spectral Analysis in R Helen J. Wearing June 8, 2010 Contents 1 Motivation 1 2 What is spectral analysis? 2 3 Assessing periodicity of model output 7 4 Assessing periodicity of real data 11 5 Other details and extensions 12 1 Motivation Cyclic dynamics are the rule rather than the exception in infectious disease data, which may be due

  Analysis, Spectral, Spectral analysis

On Spectral Clustering: Analysis and an algorithm

One line of analysis makes the link to spectral graph partitioning, in which the sec-ond eigenvector of a graph's Laplacian is used to define a semi-optimal cut. Here, the eigenvector is seen as a solving a relaxation of an NP-hard discrete graph parti­ ...

  Analysis, Spectral, Clustering, Spectral clustering

MACBETH’S SOLILOQUY: ACT 2 SCENE 1- A CRITICAL …

EVIDENCE ANALYSIS This soliloquy of Macbeth’s, just prior to the murder of King Duncan is an exemplary piece in revealing the troubled state of mind and in unravelling the hidden thoughts and feelings. Like all other soliloquies in Shakespearean ...

  Analysis, Scene, Act 1 scene 2