Generative Adversarial Imitation Learning - NeurIPS

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

Piaget’s Theory - Psychology and Neuroscience

demonstrated by “deferred imitation,” the repetition of others’ behaviors minutes, hours, or days after it has occurred. Preoperational Stage •Symbolic representations - the use of one object to stand for another. •Egocentrism: Looking at the world only from one’s own point of view. •Centration: Focusing on one dimension of

  Theory, Piaget, Imitation, Piaget s theory

éduscol - Education

4.1. L’imitation pour apprendre à construire 9 4.2. Motivations exogène et endogène : du jeu vers le projet 10 4.3. Du jeu libre au jeu structuré 10 5. Jeux de construction et programme 12 Mobiliser le langage dans toutes ses dimensions 12 Agir, s’exprimer, comprendre à …

  Imitation, 233 duscol, Duscol

The History of Artificial Intelligence

of the Imitation Game. The original game upon which Turings idea was based required a man, a woman and an interrogator. The goal was for the interrogator to identify which of the participants was a man and which was a woman. Since the . Is intelligence possible without passing the …

  Intelligence, Artificial, Artificial intelligence, Imitation, The imitation

DIGITAL ACCOUNTABILITY AND TRANSPARENCY ACT OF …

PUBLIC LAW 113–101—MAY 9, 2014 DIGITAL ACCOUNTABILITY AND TRANSPARENCY ACT OF 2014 dkrause on DSKHT7XVN1PROD with LOCATORS VerDate Mar 15 2010 15:07 May 13, 2014 Jkt 039139 PO 00101 Frm 00001 Fmt 6579 Sfmt 6579 E:\PUBLAW\PUBL101.113 PUBL101

  Accountability, Transparency, Digital, Digital accountability and transparency

TITLE XII—COMMITTEE ON FINANCE Subtitle A—Universal ...

GOE21E33 08S S.L.C. 1 TITLE XII—COMMITTEE ON 2 FINANCE 3 Subtitle A—Universal 4 Comprehensive Paid Leave 5 SEC. 120001. COMPREHENSIVE PAID LEAVE. 6 The Social Security Act is amended by adding at the 7 end the following: 8 ‘‘TITLE XXII—COMPREHENSIVE 9 PAID LEAVE BENEFITS 10 ‘‘SEC. 2201. ENTITLEMENT TO COMPREHENSIVE PAID

FIREARMS REGISTRY Licence Categories and Firearm Types

What about imitation firearms? Imitation firearms are defined in section 4D of the Firearms Act 1996. A person must obtain a firearms permit to possess or use imitation firearms. For more information on imitation firearms, see the FACT Sheet 'Imitation Firearms Permit' available on the Firearms Permit page on the Firearms Registry Internet site.

  Licence, Imitation