[PDF] Top 20 State representation learning with recurrent capsule networks
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State representation learning with recurrent capsule networks
... (2018). State representation learning with recurrent capsule ...Perception, Learning, and Control, NeurIPS 2018 - 32nd Conference on Neural Information Processing Systems, 07 Dec ... See full document
6
Learning and Representing Temporal Knowledge in Recurrent Networks
... B. Towards Validating the Model Using Knowledge Extraction Several approaches to knowledge extraction have been proposed in the literature [35], [36], [37], [5]. In our work, extraction is used as a was of validating our ... See full document
14
Learning Topic Representation for SMT with Neural Networks
... Deep learning is an active topic in recent years which has triumphed in many machine learning research ...deep learning is able to achieve state-of-the-art performance in var- ious research ... See full document
11
Learning Morphological Transformations with Recurrent Neural Networks
... neural networks are structurally similar to Multilayer Perceptrons (MLP) with the distinction that there are connections between hidden units, which introduce feedback in the ...the recurrent weights, in ... See full document
10
Generating Energy Data for Machine Learning with Recurrent Generative Adversarial Networks
... Machine learning (ML) has been applied for tasks that are important for smart grid operation including energy consumption and generation forecasting, anomaly detection, and state ...a recurrent ... See full document
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Efficient Sequence Learning with Group Recurrent Networks
... sequence learning which consists of group recur- rent layers and representation rearrangement lay- ...a recurrent layer, we split both the input of the sequence and the hidden states into disjoint ... See full document
10
Statistical Script Learning with Recurrent Neural Networks
... While the count-based event co-occurrence sys- tem we investigated in Pichotta and Mooney (2014) treats events as atomic—for example, the plane flew and the plane flew over land are unrelated events with completely ... See full document
6
Learning Text Similarity with Siamese Recurrent Networks
... for learning a similarity metric on variable- length character ...a representation that is selective to differences in the input that reflect seman- tic differences ... See full document
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Multi domain Dialog State Tracking using Recurrent Neural Networks
... dialog state tracking ...domain, learning domain-specific behaviour while retaining the cross-domain dialog patterns learned during the initial training ... See full document
6
Learning text representation using recurrent convolutional neural network with highway layers
... neural networks has brought new inspiration to various NLP and IR ...combining Recurrent Convolutional Neural Networks (RCNN) with highway ...rectional Recurrent Neural Network (Bi-RNN) module ... See full document
5
Hyperparameter optimisation for Capsule Networks
... of capsule routing by recursive updates to the weighted assignment coefficient matrix and clustering probabilities(that are relatively ...of capsule layers, enabling effective representation of ... See full document
8
Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning
... We evaluate our approach using two downstream applications. First, we employ our model to facil- itate transfer learning between multiple languages using English as the pivot language. For this, we do an extensive ... See full document
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Word Based Dialog State Tracking with Recurrent Neural Networks
... One key issue in applying machine learning to the task of dialog state tracking is being able to deal with states which have not been seen in training. For example, the system should be able to recog- nise ... See full document
8
gl2vec: Learning feature representation using graphlets for directed networks
... feature representation to classify and compare networks of varying sizes and periods of time with high ...machine learning models along with our graph feature repre- sentation for network ... See full document
6
Continual State Representation Learning for Reinforcement Learning using Generative Replay
... of learning over extended periods of time in the real world is a long standing challenge of Reinforcement Learning (RL) research, with direct applications in ...use state features to learn to solve ... See full document
9
Fine-grained Visual Representation Learning with Deep Neural Networks
... deep learning that learns rep- resentations of data with multiple levels of abstraction has been successfully used in general visual object recognition [83, ...achieve state-of-the-art results [14, 102, 97, ... See full document
137
Multi Module Recurrent Neural Networks with Transfer Learning
... We fed the presented model with training data from the VUAMC corpus. The model has been used in two settings: standalone, to directly pre- dict the output labels, and in another mode, where we used the extracted logits ... See full document
5
Learning to Adaptively Scale Recurrent Neural Networks
... other state-of-the-art results of sin- gle layer RNNs in the third ...the state- of-the-art performances by taking dilated convolutions with those pixels that more spatially related to the current ... See full document
8
Online Representation Learning in Recurrent Neural Language Models
... While the system of Le and Mikolov (2014) uses a basic feedforward language model, we ex- tend the idea to recurrent neural network language models, as they are currently used in state-of- the-art language ... See full document
6
From phonemes to images: levels of representation in a recurrent neural model of visually grounded language learning
... neural networks, a visual object recognition model and a word recognition model, and an embedding alignment model that learns to map recognized words and objects into the same high-dimensional ... See full document
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