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

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

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

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

A New Approach to Self-Supervised Learning ... Generative approaches to representation learning build a distribution over data and latent embedding and ... 2 consistency loss between the softmax predictions of the teacher and the student is added to the classification loss. While [20] demonstrates the effectiveness of MT in the semi-supervised ...

  Your, Approach, Distribution, Talent, Loss, Bootstrap, Bootstrap your own latent

Probability of Default Ratings and Loss Given Default ...

analyses can be useful in estimating the expected value of firm assets available for distribution to creditors in bankruptcy. Our methodology further extends our existing expected loss approach to rating corporate obligations with varying levels of seniority and security.

  Approach, Distribution, Loss, Default, Loss approach

A Discriminative Feature Learning Approach for Deep Face ...

A Discriminative Feature Learning Approach for Deep Face Recognition 501 ... distribution, we propose the center loss to improve the discriminative power of the deeply learned features, followed by some discussions. 3.1 A Toy Example Inthissection,atoyexampleonMNIST[20]datasetispresented.Wemodifythe ...

  Approach, Distribution, Loss

Guidelines on loss-absorbing capacity of technical ...

approach based on average tax rates, provided they are able to demonstrate that those average tax rates are determined at an appropriate level, and that such an approach avoids a material misstatement of the adjustment. Guideline 8 - Loss attribution 1.24. Where undertakings use an approach based on average tax rates, they should

  Approach, Technical, Capacity, Loss, Absorbing, Loss absorbing capacity of technical

8. Air Distribution Systems - Energy Star

ing the efficiency of distribution system components. This chapter will describe the opportunities in each of these areas, but first, it is important to gain an understanding of the types of systems that are commonly encountered and the various components of air distribution systems. Figure 8.1 The staged approach to building upgrades

  System, Approach, Distribution, Energy, Star, Energy star, Air distribution systems

Electric Power Distribution Systems - EOLSS

There are three major types of distribution networking: Single-end radial fed Single-end radial fed refers to a number of customer substations or pole-mounted substations are connected to the primary substation. The supply security is the lowest as any single point failure will result in the loss of supply to the customer substation.

  Distribution, Loss

Probability

This is reproduced from the Faculty handbook. Schedules All this material will be covered in lectures, but in a slightly di erent order. Basic concepts: Classical probability, equally …