Open AccessProceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TLDR
This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.Abstract:
We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.read more
Citations
More filters
Posted Content
In Search of Lost Domain Generalization
Ishaan Gulrajani,David Lopez-Paz +1 more
TL;DR: This paper implements DomainBed, a testbed for domain generalization including seven multi-domain datasets, nine baseline algorithms, and three model selection criteria, and finds that, when carefully implemented, empirical risk minimization shows state-of-the-art performance across all datasets.
Book ChapterDOI
Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network
TL;DR: This paper introduces a new method based on a deep neural network combining convolutional and gated recurrent networks that is able to capture both word sequence and order information in short texts and sets new benchmark by outperforming on 6 out of 7 datasets by between 1 and 13% in F1.
Proceedings ArticleDOI
CamemBERT: a Tasty French Language Model
Louis Martin,Benjamin Muller,Pedro Javier Ortiz Suárez,Yoann Dupont,Laurent Romary,Éric Villemonte de la Clergerie,Djamé Seddah,Benoît Sagot +7 more
TL;DR: This paper investigates the feasibility of training monolingual Transformer-based language models for other languages, taking French as an example and evaluating their language models on part-of-speech tagging, dependency parsing, named entity recognition and natural language inference tasks.
Proceedings ArticleDOI
TALL: Temporal Activity Localization via Language Query
TL;DR: A novel Cross-modal Temporal Regression Localizer (CTRL) is proposed to jointly model text query and video clips, output alignment scores and action boundary regression results for candidate clips, and Experimental results show that CTRL outperforms previous methods significantly on both datasets.
Journal ArticleDOI
A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT
TL;DR: A weakly-supervised deep learning model can accurately predict the COVID-19 infectious probability and discover lesion regions in chest CT without the need for annotating the lesions for training.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Auto-Encoding Variational Bayes
Diederik P. Kingma,Max Welling +1 more
TL;DR: A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced.
Journal ArticleDOI
Reducing the Dimensionality of Data with Neural Networks
TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
Journal ArticleDOI
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
Geoffrey E. Hinton,Li Deng,Dong Yu,George E. Dahl,Abdelrahman Mohamed,Navdeep Jaitly,Andrew W. Senior,Vincent Vanhoucke,Patrick Nguyen,Tara N. Sainath,Brian Kingsbury +10 more
TL;DR: This article provides an overview of progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.
Proceedings Article
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.
TL;DR: Adaptive subgradient methods as discussed by the authors dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradient-based learning, which allows us to find needles in haystacks in the form of very predictive but rarely seen features.