Semi-supervised Learning with Deep Generative Models

approximately invariant to local perturbations along the manifold. The idea of manifold learning ... We show for the first time how variational inference can be brought to bear upon the prob- ... probabilities are formed by a non-linear transformation, with parameters , of a set of latent vari-ables z. This non-linear transformation is ...

  With, Linear, Model, Time, Learning, Deep, Supervised, Generative, Invariant, Supervised learning with deep generative models

Learning Structured Output Representation using Deep ...

posterior inference. However, the parameters of the VAE can be estimated efficiently in the stochas-tic gradient variational Bayes (SGVB) [16] framework, where the variational lower bound of the log-likelihood is used as a surrogate objective function. The variational lower bound is written as: logp (x) = KL(q ˚(zjx)kp (zjx))+E q ˚(zjx) logq ...

  Output, Stochas tic, Stochas

Generative Adversarial Imitation Learning

networks [8], a technique from the deep learning community that has led to recent successes in modeling distributions of natural images: our algorithm harnesses generative adversarial training to fit distributions of states and actions defining expert behavior. We test our algorithm in Section 6, where

  Network, Learning, Adversarial, Generative, Imitation, Generative adversarial, Generative adversarial imitation learning

Inductive Representation Learning on Large Graphs

node classification, clustering, and link prediction [11, 28, 35]. ... (e.g., citation data with text attributes, biological data with functional/molecular markers), our approach can also make use of structural features that are present in all graphs (e.g., node degrees). ... through theoretical analysis, that GraphSAGE is capable of learning ...

  Large, Learning, Through, Representation, Prediction, Marker, Molecular, Inductive, Graph, Molecular markers, Inductive representation learning on large graphs

Bootstrap Your Own Latent A New Approach to Self ...

mining strategies [14, 15] to retrieve the nega-tive pairs. In addition, their performance criti-cally depends on the choice of image augmenta- ... to prevent collapsing while preserving high performance. To prevent collapse, a straightforward solution …

  Strategies, Collapsing

Spatial Transformer Networks - NeurIPS

Convolutional Neural Networks define an exceptionally powerful class of models, ... localisation, semantic segmentation, and action recognition tasks, amongst others. ... can take any form, such as a fully-connected network or a convolutional network, but should include a final regression layer to produce the transformation ...

  Network, Fully, Segmentation, Spatial, Convolutional, Semantics, Semantic segmentation

Unsupervised Learning of Visual Features by Contrasting ...

pseudo-labels to learn visual representations. This method scales to large uncurated dataset and can be used for pre-training of supervised networks [7]. However, their formulation is not principled and recently, Asano et al. [2] show how to cast the pseudo-label assignment problem as an instance of the optimal transport problem.

  Visual, Representation, Visual representation

PyTorch: An Imperative Style, High-Performance Deep ...

Facebook AI Research benoitsteiner@fb.com Lu Fang Facebook lufang@fb.com Junjie Bai Facebook jbai@fb.com Soumith Chintala Facebook AI Research soumith@gmail.com Abstract Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals

  Research, Machine, Learning, Machine learning, Pytorch

Visualizing the Loss Landscape of Neural Nets

task that is hard in theory, but sometimes easy in practice. Despite the NP-hardness of training general neural loss functions [3], simple gradient methods often find global minimizers (parameter configurations with zero or near-zero training loss), even when data and labels are randomized before training [43].

  Practices, Theory, Loss, Landscapes, Nets, Neural, Visualizing, Visualizing the loss landscape of neural nets

InfoGAN: Interpretable Representation Learning by ...

of the digit (0-9), and chose to have two additional continuous variables that represent the digit’s angle and thickness of the digit’s stroke. It would be useful if we could recover these concepts without any supervision, by simply specifying that an MNIST digit is generated by an 1-of-10 variable and two continuous variables.

  Digit

Modeling Mechanical and Hydraulic Systems in Simscape

prototypes because mistakes are caught earlier ... because model parameters are incorrect Solution:Use Simulink Design Optimization to automatically tune model parameters Model: Area A Area B Area V 0.01760.025 0.01060.02 200175 …

  System, Mechanical, Modeling, Hydraulic, Parameters, Prototype, Modeling mechanical and hydraulic systems

Random Number Generation C++

function parameters so that the contents of variables change between the function and the caller of the function. This is called call by reference. As an example, consider the following changes to the previous ... Also note that without checking the function prototypes or the function definitions, it is not possible to tell from the calls alone ...

  Parameters, Prototype

Random Number Generation C++

function parameters so that the contents of variables change between the function and the caller of the function. This is called call by reference. As an example, consider the following changes to the previous ... Also note that without checking the function prototypes or the function definitions, it is not possible to tell from the calls alone ...

  Generation, Number, Parameters, Random, Prototype, Random number generation c

PANet: Few-Shot Image Semantic Segmentation With …

The prototypes are optimized to be compact and robust rep-resentations for each semantic class and the non-parametric ... ditioning branch to generate a set of parameters θ from the support set, which is then used to tune the segmen-tation process of the query set. Rakelly et al. [16] con-

  Parameters, Prototype

Chapter 52 Prototyping Tools and Techniques

respect to pre-defined benchmarks and, by systematically varying parameters, fine-tune the system. Designers in creative fields, such as typography or graphic design, create prototypes to express ideas and reflect on them. This approach is intuitive, oriented more to discovery and generation of new ideas than to evaluation of existing ideas.

  Parameters, Prototype

What is Simulink?

rapid construction of virtual prototypes to explore design concepts at any level of detail with minimal effort. For modeling, Simulink provides a graphical user interface (GUI) for building models as block diagrams. It includes a comprehensive library of pre-defined blocks to be used to construct graphical models of systems using drag-and-drop

  What, Simulink, Prototype, What is simulink

Mixture Models - Carnegie Mellon University

are all from the same parametric family, but with different parameters — for instance they might all be Gaussians with different centers and variances, or all Poisson dis-tributions with different means, or all power laws with different exponents. (It’s not strictly necessary that they all be of the same kind.) We’ll write the parameter, or

  Parameters

Software Component Template - AUTOSAR

Software Component Template V4.5.0 R4.1 Rev 3 Document Title Software Component Template Document Owner AUTOSAR Document Responsibility AUTOSAR Document Identification No 062 Document Classification Standard Document Version 4.5.0 Document Status Final Part of Release 4.1 Revision 3 Document Change History