On Discriminative vs. Generative Classifiers: A comparison ...

SAGA: A Fast Incremental Gradient Method With Support for ...

SAGA is preferred over SVRG both theoretically and in practice. For neural networks, where no theory is available for either method, the storage of gradients is generally more expensive than the

  With, Methods, Support, Fast, Saga, Derating, Incremental, A fast incremental gradient method with support

Thinking Fast and Slow with Deep Learning and Tree Search

System 1 is a fast, unconscious and automatic mode of thought, also known as intuition or heuristic process. System 2, an evolutionarily recent process unique to humans, is a slow, conscious, explicit

  With, Learning, Search, Tree, Thinking, Deep, Fast, Slow, Thinking fast and slow with deep learning and tree search

A Growing Neural Gas Network Learns Topologies

A Growing Neural Gas Network Learns Topologies 627 a) Delaunay triangulation b) induced Delaunay triangulation Figure 1: Two ways of defining closeness among a set of points.

  Network, Neural, Topologies, Neural gas network

Attention is All you Need - Neural Information Processing ...

Attention Is All You Need Ashish Vaswani Google Brain avaswani@google.com Noam Shazeer Google Brain noam@google.com Niki Parmar Google Research nikip@google.com

  Needs, Attention, Neural, Attention is all you need

ImageNet Classification with Deep Convolutional Neural ...

Challenge, an annual competition called the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) has been held. ILSVRC uses a subset of ImageNet with roughly 1000 images in each of 1000 categories. In all, there are roughly 1.2 million training images, 50,000 validation images, and 150,000 testing images. ILSVRC-2010 is the only version ...

  Challenges, Scale, Visual, Recognition, Ilsvrc, Scale visual recognition challenge

Generative Adversarial Nets - NIPS

Generative adversarial networks has been sometimes confused with the related concept of “adversar-ial examples” [28]. Adversarial examples are examples found by using gradient-based optimization directly on the input to a classification network, in order to find examples that are similar to the data yet misclassified.

  Network, Adversarial, Generative, Generative adversarial, Generative adversarial networks, Adversar ial, Adversar

Time-series Generative Adversarial Networks

A good generative model for time-series data should preserve temporal dynamics, in the sense that new sequences respect the original relationships between variables across time. Existing methods that bring generative adversarial networks (GANs) into the sequential setting do not adequately attend to the temporal correlations unique to time ...

  Network, Adversarial, Generative, Generative adversarial networks

Hidden Technical Debt in Machine Learning Systems

account for in system design. These include boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, configuration issues, changes in the external world, and a variety of system-level anti-patterns. 1 Introduction As the machine learning (ML) community continues to accumulate years of experience with live

  System, Design, Machine, Technical, Learning, Debt, Hidden, Hidden technical debt in machine learning systems

Character-level Convolutional Networks for Text Classification

Applying convolutional networks to text classification or natural language processing at large was explored in literature. It has been shown that ConvNets can be directly applied to distributed [6] [16] or discrete [13] embedding of words, without any knowledge on the syntactic or semantic structures of a language.

  Network, Convolutional, Convolutional networks, Semantics

InfoGAN: Interpretable Representation Learning by ...

30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain. ... a higher-order extension of the spike-and-slab restricted Boltzmann machine that can disentangle emotion from identity on the Toronto Face Dataset ... we want PG(cjx) to have a small entropy. In other words, the information in the latent code cshould not ...

  Information, Neural, Entropy, Spike, Neural information

Maximum Likelihood, Logistic Regression, and Stochastic ...

Maximum Likelihood, Logistic Regression, and Stochastic Gradient Training Charles Elkan elkan@cs.ucsd.edu January 10, 2014 1 Principle of maximum likelihood

  Logistics, Regression, Logistic regression

European Heart Journal (2003) 24,1 2 - euroSCORE.org

sophisticated risk tools in this rapidly evolving field. References 1. Nashef SAM, Roques F, Michel P et al. European system for cardiac operative

Lecture 10: Logistical Regression II— Multinomial Data

Classical vs. Logistic Regression Data Structure: continuous vs. discrete Logistic/Probit regression is used when the dependent variable is binary or dichotomous. Different assumptions between traditional regression and logistic regression

  Data, Logistics, Regression, Multinomial, Logistical, Logistical regression ii multinomial data

LOGISTIC QUANTITIES FOR VARIOUS PACKINGS - Elburg …

Packing sizes vegetable oils Number of bottles/cans per carton Number of cartons/cans per pallet (100x120/ Europallet 80x120) Number of cartons/cans/

  Logistics, Quantities, Logistic quantities

Pseudo R Indices A Comparison of Logistic Regression ...

Smith & McKenna 18 Multiple Linear Regression Viewpoints, 2013, Vol. 39(2) McFadden (1974) outlines perhaps the most straightforward of such pseudo R2 indices, in the sense of reflecting both the criterion being minimized in logistic regression estimation and the variance-accounted-

  Comparison, Logistics, Regression, A comparison of logistic regression

Multilevel Logistic Regression Analysis Applied to Binary ...

Multilevel Logistic Regression Analysis 95 Because of cost, time and efficiency considerations, stratified multistage samples are the norm for sociological and demographic surveys.

  Analysis, Applied, Logistics, Regression, Multilevel, Multilevel logistic regression analysis applied

ALLIED JOINT LOGISTIC DOCTRINE AJP-4(A)

AJP-4(A) Original xi TABLE OF CONTENT CHAPTER 1 - THE ALLIANCE’S CONCEPT OF LOGISTIC SUPPORT Section I - Introduction Page Purpose 1-1 Applicability 1-1

  Logistics