Convolutional LSTM Network: A Machine Learning Approach ...
2.2 Long Short-Term Memory for Sequence Modeling For general-purpose sequence modeling, LSTM as a special RNN structure has proven stable and powerful for modeling long-range dependencies in various previous studies [12, 11, 17, 23]. The major innovation of LSTM is its memory cell ct which essentially acts as an accumulator of the state ...
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Generative Adversarial Imitation Learning
proceedings.neurips.ccnetworks [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
Prototypical Networks for Few-shot Learning
proceedings.neurips.cc˚: 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 
Inductive Representation Learning on Large Graphs
proceedings.neurips.ccnode 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 ...
Bootstrap Your Own Latent A New Approach to Self ...
proceedings.neurips.ccmining 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 …
Spatial Transformer Networks - NeurIPS
proceedings.neurips.ccConvolutional 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 ...
Semi-supervised Learning with Deep Generative Models
proceedings.neurips.ccapproximately 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 ...
Unsupervised Learning of Visual Features by Contrasting ...
proceedings.neurips.ccpseudo-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.
PyTorch: An Imperative Style, High-Performance Deep ...
proceedings.neurips.ccFacebook 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
Visualizing the Loss Landscape of Neural Nets
proceedings.neurips.cctask 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].
InfoGAN: Interpretable Representation Learning by ...
proceedings.neurips.ccof 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.
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Generating Sequences With Recurrent Neural Networks - …
arxiv.orgLong Short-term Memory (LSTM) [16] is an RNN architecture designed to be better at storing and accessing information than standard RNNs. LSTM has recently given state-of-the-art results in a variety of sequence processing tasks, including speech and handwriting recognition [10, 12]. The main goal of this
Using LSTM in Stock prediction and Quantitative Trading
cs230.stanford.eduLong-Short Term Memory (LSTM) model is an updated version of RNN. It can overcome the drawback of RNN in capturing long term influences. LSTM introduces the memory cell that enables long-term dependency between time lags. The
Sequence to Sequence Learning with Neural Networks
cs224d.stanford.eduHowever, th e Long Short-Term Memory (LSTM) [16] is known to learn problems with long range temporal dependencies, so an LSTM may succeed in this setting. The goal of the LSTM is to estimate the conditional probability p(y
Abstract - arXiv
arxiv.org2.2 Long Short-Term Memory for Sequence Modeling For general-purpose sequence modeling, LSTM as a special RNN structure has proven stable and powerful for modeling long-range dependencies in various previous studies [12, 11, 17, 23]. The major innovation of LSTM is its memory cell ct which essentially acts as an accumulator of the state ...
Recurrent Neural Network for Text Classification with ...
www.ijcai.orgLong short-term memory network (LSTM) was proposed by [Hochreiter and Schmidhuber, 1997] to specifically ad-dress this issue of learning long-term dependencies. The LSTM maintains a separate memory cell inside it that up-dates and exposes its content only when deemed necessary. A number of minor modifications to the standard LSTM unit have ...
Stock Market Prediction using CNN and LSTM
cs230.stanford.eduand Long-Short Term Memory. The difference in performance was attributed to the fact that CNN does not rely on historical data as is the case with time sequence based models. On the other hand, Sutskever et al. [13] argues for the use of …
Social LSTM: Human Trajectory Prediction in Crowded Spaces
cvgl.stanford.eduLong Short-Term Memory (LSTM) networks have been shown to successfully learn and generalize the properties of isolated sequences like handwriting [20] and speech [21]. Inspired by this, we develop a LSTM based model for our trajectory prediction problem as well. In particular, we have one LSTM for each person in a scene. This LSTM learns