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

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

Abstract arXiv:1611.05431v2 [cs.CV] 11 Apr 2017

1000-d fc, softmax 1000-d fc, softmax # params. 25.5 6106 25.0 10 FLOPs 4.1 9109 4.2 10 Table 1. (Left) ResNet-50. (Right) ResNeXt-50 with a 32 4d template (using the reformulation in Fig.3(c)). Inside the brackets are the shape of a residual block, and outside the brackets is the number of stacked blocks on a stage. “C=32” suggests grouped

Organic Chemistry 32-235 Practice Questions for Exam #2 ONE

(D) (E) Go by definition or look for plane of symmetry/point of inversion. The answer is B. 7. The relationship between (A) and (B) structures in the previous question is: A) identical B) structural isomers C) diastereomers D) enantiomers E) None of the above Again need to know terms and how to apply them. The answer is C.

Authoritarian vs. Authoritative Parenting

2 Commonly cited problems are trends for the children to conform easily and have strong relational dependence associate obedience and success with love struggle with self-control and making choices have a strong sense of failure and low self-esteem have difficulty in social situations (e.g., lack social competence, display inappropriate

  Parenting, Authoritative, Authoritarian, Authoritative parenting, Authoritarian vs

Abstract arXiv:2104.00681v1 [cs.CV] 1 Apr 2021

Jiaming Sun 1;2 Yiming Xie Linghao Chen Xiaowei Zhou 1Hujun Bao y 1Zhejiang University 2SenseTime Research Abstract We present a novel framework named NeuralRecon for real-time 3D scene reconstruction from a monocular video. Unlike previous methods that estimate single-view depth maps separately on each key-frame and fuse them later, we