Sequence to Sequence Learning with Neural Networks

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

arXiv:1301.3781v3 [cs.CL] 7 Sep 2013

the NNLM. Thus, the word vectors are learned even without constructing the full NNLM. In this work, we directly extend this architecture, and focus just on the first step where the word vectors are learned using a simple model. It was later shown that the word vectors can be used to significantly improve and simplify many NLP applications [4 ...

  Without, Words

Interpretation

Translation and Nation: A Cultural Politics of Englishness Roger Ellis and Liz Oakley-Brown (eds) The Interpreter’s Resource Mary Phelan Annotated Texts for Translation: English–German Christina Schäffner with Uwe Wiesemann Contemporary Translation Theories (2nd Edition) Edwin Gentzler Literary Translation: A Practical Guide Clifford E ...

  Translation

Woodcock-Johnson® IV

Price Data, 2014 $2,176.90 per Complete Battery Plus (Achievement Form A or Form B, Cognitive Abilities, Oral ... and may not be otherwise duplicated or distributed without written permission. Please refer to the Buros website ... clusters were adapted for Spanish administration using parallel form development, not translation.

  Data, Without, Translation, Parallel

Hands-On Machine Learning with Scikit-Learn and TensorFlow

Aurélien Géron Hands-On Machine Learning with Scikit-Learn and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems Beijing Boston Farnham Sebastopol Tokyo Download from finelybook www.finelybook.com

1 Transformers in Vision: A Survey

1 Transformers in Vision: A Survey Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah Abstract—Astounding results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems.

Sequence to Sequence Learning with Neural Networks

where α,β,γ is the translation of a,b,c. This way, ais in close proximity to α, bis fairly close to β, and so on, a fact that makes it easy for SGD to “establish communication”between the input and the output. We found this simple data transformation to greatly improve the performance of the LSTM. 3 Experiments

  Data, Learning, Sequence, Translation, Sequence to sequence learning

A NEW VERSE TRANSLATION

A NEW VERSE TRANSLATION Poems 1965-1975 Sweeney Astray: A Version from the Irish Station Island ... Beowulf without recourse to this immense body of commentary ... ends of the Nordic peoples which would parallel or foreshadow by the strangeness of the names and the immediate lack of

  Without, Translation, Parallel

Deep Contextualized Word Representations

pivot word itself in the representation and are computed with the encoder of either a supervised neural machine translation (MT) system (CoVe; McCann et al. , 2017 ) or an unsupervised lan-guage model ( Peters et al. , 2017 ). Both of these approaches beneÞt from large datasets, although the MT approach is limited by the size of parallel corpora.

  Words, Translation, Parallel

PCT: Point Cloud Transformer - Tsinghua University

cloud data and conduct feature learning through the atten-tion mechanism. As shown in Figure 1, the distribution of ... machine translation; it is based solely on self-attention, without any recurrence or convolution operators. Devlin ... use a parallel framework to extend CNN from the conven-tional domain to a curved two-dimensional manifold. How-

  Cloud, Data, Without, Points, Translation, Parallel, Transformers, Point cloud transformer