InfoGAN: Interpretable Representation Learning by ...

On Discriminative vs. Generative Classifiers: A …

On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes Andrew Y. Ng Computer Science Division University of California, Berkeley

  Comparison, Logistics, Regression, Logistic regression

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

Hands-On Machine Learning with Scikit-Learn and TensorFlow

Aurélien Géron Hands-On Machine Learning with Scikit-Learn and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems Beijing Boston Farnham Sebastopol Tokyo Download from finelybook www.finelybook.com

Autoencoders - Deep Learning

The denoising autoencoder (DAE) is an autoencoder that receives a corrupted data point as input and is trained to predict the original, uncorrupted data point as its output. The DAE training procedure is illustrated in figure 14.3. We introduce a ...

  Autoencoder

Lecture 13: Generative Models

Variational Markov Chain Fully Visible Belief Nets - NADE - MADE - PixelRNN/CNN Change of variables models (nonlinear ICA) Variational Autoencoder Boltzmann Machine GSN GAN Figure copyright and adapted from Ian Goodfellow, Tutorial on Generative Adversarial Networks, 2017.

  Generative, Variational, Autoencoder, Variational autoencoder

PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and ...

denoising autoencoder [40]. The networks are pre-trained on a large synthetic FlyingChairs dataset but can surpris-ingly capture the motion of fast moving objects on the Sin-tel dataset. The raw output of the network, however, con-tains large errors in smooth background regions and re-quires variational refinement [10]. Mayer et al. [35] apply

  Variational, Autoencoder

Graph Representation Learning - McGill University School ...

tral graph theory, harmonic analysis, variational inference, and the theory of graph isomorphism. This book is my attempt to synthesize and summarize these methodological threads in a practical way. My hope is to introduce the reader to the current practice of …

  Variational

2020-21 PLACEMENT BROCHURE - Indian Statistical Institute

(Variational Autoencoder) Entropy Analysis - Biometric Key Generation System Searchable Symmetric Encryption Implementation and attack on A5/1 stream cipher Quantum computation Academic Projects ISI Placement Brochure 2020-21 | 11

  Variational, Autoencoder, Variational autoencoder

Self-Prediction and Contrastive Learning

Denoising autoencoder (Vincent et al. 2008) Add noise = Randomly mask some pixels Only reconstruction loss Context autoencoder (Pathak et al. 2016) Mask a random region in the image Reconstruction loss + adversarial loss Vision Pretext Tasks: Masked Prediction 50

  Autoencoder