[PDF] Top 20 Learning and Representing Temporal Knowledge in Recurrent Networks
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Learning and Representing Temporal Knowledge in Recurrent Networks
... takes temporal knowledge as input (in the form of temporal logic rules) to produce a NARX ...of temporal logic used is an extension of the logic used in [14] with a richer language containing ... See full document
14
Learning Text Similarity with Siamese Recurrent Networks
... The task of job title normalization is often framed as a classification task (Javed et al., 2014; Malherbe et al., 2014). Given the large number of classes (often in the thousands), multi-stage clas- sifiers have shown ... See full document
10
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 groups, ... See full document
10
Segregated Temporal Assembly Recurrent Networks for Weakly Supervised Multiple Action Detection
... labels (i.e., action categories). Correspondingly, we address the weakly supervised action detection task in the following two steps. Step 1: Action assembling for multi-instance pat- tern generation. In order to ... See full document
9
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 ...discover temporal ... See full document
10
Self training improves Recurrent Neural Networks performance for Temporal Relation Extraction
... for temporal infor- mation extraction are able to take advantage of ...for temporal relation ex- traction operates on primitive features, models the sentence structure well, and is highly scalable and ... See full document
12
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 generative ... See full document
24
auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks
... representation learning from acoustic data, we train sequence to sequence autoencoders built of long short-term memory cells or gated recurrent units on spectrograms, which are viewed as time dependent ... See full document
5
NETWORK ANALYSIS OF KNOWLEDGE CONSTRUCTION IN ASYNCHRONOUS LEARNING NETWORKS
... Also note that the “-1” actor who represents the external entity (to whom all initial messages respond) cannot be analyzed in the same way as other actors in the network: It has a fixed location in the thread response ... See full document
23
Bidirectional Learning in Recurrent Neural Networks Using Equilibrium Propagation
... transfer learning to the untrained task, while restricting training to a subset of weights worsens the ...unrollable recurrent neural networks with asymmetric connection ...network’s recurrent ... See full document
55
Learning to Adaptively Scale Recurrent Neural Networks
... fit the temporal dynamics throughout the time. Although pat- terns in different scale levels require distinct frequencies to update, they do not always stick to a certain scale and could vary at different time ... See full document
8
MoL 2013 18: Learning and Knowledge in Social Networks
... Furthermore, the essential facts about a situation might be about this kind of access to information. Consider a motivating example: Two children sit down for a test knowing the same things individually, but one child ... See full document
62
On The Use Of Machine Learning For Temporal Performance Prediction In Lte Advanced Networks
... machine learning models which incorporate temporal features such as seasonality combined with temporal correlations in sub seasonal time ...machine learning models were explored, these are: ... See full document
6
Learning to skip state updates in recurrent neural networks
... the temporal domain, either by learning how many times an input needs to be pondered before moving to the next one [18] or building RNNs whose number of layers depends on the input data ...lags. ... See full document
55
Knowledge Extraction and Recurrent Neural Networks: An Analysis of an Elman Network trained on a Natural Language Learning Task
... Knowledge Extraction and Recurrent Neural Networks: A n Analysis of an Elman Network trained on a Natural Language Learning.. We present results of experiments with Elman recurrent neura[r] ... See full document
6
representing fuzzy temporal knowledge KBCS2000
... fuzzy temporal logic where a formula has a truth-value at each instant in time (computed from the given underlying temporal databases) and is useful to characterize time dependent ...fuzzy temporal ... See full document
10
Learning Sequence Encoders for Temporal Knowledge Graph Completion
... in knowledge graphs has mainly focused on static multi- relational ...poral knowledge graphs where relations be- tween entities may only hold for a time in- terval or a specific point in ...static ... See full document
6
Representing Compositionality based on Multiple Timescales Gated Recurrent Neural Networks with Adaptive Temporal Hierarchy for Character Level Language Models
... the temporal hierarchies with the adaptive timescale approach can represent the compositonality better and increases the capabil- ity of the model to handle longer sequences for the ...The temporal ... See full document
8
Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling
... neural networks: (i) When training, the injected noise encourages model-parameter trajectories to better explore the parameter ...authors’ knowledge, RNN training using SG-MCMC has not been in- vestigated ... See full document
11
Skip RNN: learning to skip state updates in recurrent neural networks
... the temporal domain, such as in learning how many times an input needs to be ”pondered” before moving to the next one (Graves, 2016) or designing RNN architectures whose number of layers depend on the input ... See full document
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