Generative Adversarial Imitation Learning

Prototypical Networks for Few-shot Learning

˚: RD!RMwith learnable parameters ˚. Each prototype is the mean vector of the embedded support points belonging to its class: c k= 1 jS kj X (x i;y i)2S k f ˚(x i) (1) Given a distance function d: R M R ![0;+1), Prototypical Networks produce a distribution over classes for a query point x based on a softmax over distances to the prototypes ...

  Parameters, Prototype

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

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

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

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

Time Series: Autoregressive models AR, MA, ARMA, ARIMA

Gaussian White Noise {A particular useful white noise is Gaussian white noise, wherein the w ... -20 0 20 40 60 80 12/77. Time Series Analysis The procedure of using known data values to t a time series ... Measures of Dependence A complete description of a time series, observed as a

  Measure, Gaussian

Types of Data Descriptive Statistics

The Normal (Gaussian) Distribution Measures of Dispersion (contd) Coefficient of Variation • Measure of the relative spread in data ... 20 2.7%, and 2.4%. These values indicate relatively good reproducibility of the assay, because the variation as measured by

  Statistics, Descriptive, Measure, Descriptive statistics, Gaussian

LOAM: Lidar Odometry and Mapping in Real-time

velocity in [18], [19] and with Gaussian processes in [20], [21]. Our method uses a similar linear motion model as [18], [19] in the odometry algorithm, but with different types of features. The methods [18]–[21] involve visual features from ... and an encoder that measures the

  Measure, Gaussian

Chapter 3

2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 For all 4 of them, the slope of the regression line is 0.500 (to three decimal places) and …