Open AccessProceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
Reads0
Chats0
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
Proceedings ArticleDOI
Deep Closest Point: Learning Representations for Point Cloud Registration
Yue Wang,Justin Solomon +1 more
TL;DR: This work proposes a learning-based method, titled Deep Closest Point (DCP), inspired by recent techniques in computer vision and natural language processing, that provides a state-of-the-art registration technique and evaluates the suitability of the learned features transferred to unseen objects.
Posted Content
Lookahead Optimizer: k steps forward, 1 step back
TL;DR: Lookahead improves the learning stability and lowers the variance of its inner optimizer with negligible computation and memory cost, and can significantly improve the performance of SGD and Adam, even with their default hyperparameter settings.
Posted Content
Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models.
TL;DR: This work builds upon previous results that incorporated GANs and RL in order to generate sequence data and test this model in several settings for the generation of molecules encoded as text sequences and in the context of music generation, showing for each case that it can effectively bias the generation process towards desired metrics.
Journal ArticleDOI
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
TL;DR: In this paper, a convolutional recurrent neural network (CRNN) was proposed for polyphonic sound event detection task and compared with CNN, RNN and other established methods, and observed a considerable improvement for four different datasets consisting of everyday sound events.
Posted Content
Variable Rate Image Compression with Recurrent Neural Networks
George Toderici,Sean M. O'Malley,Sung Jin Hwang,Damien Vincent,David Minnen,Shumeet Baluja,Michele Covell,Rahul Sukthankar +7 more
TL;DR: A general framework for variable-rate image compression and a novel architecture based on convolutional and deconvolutional LSTM recurrent networks are proposed, which provide better visual quality than (headerless) JPEG, JPEG2000 and WebP, with a storage size reduced by 10% or more.
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.