[PDF] Top 20 Recursive Neural Networks Can Learn Logical Semantics
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Recursive Neural Networks Can Learn Logical Semantics
... to learn the foundations of natural lan- guage inference by training them to reproduce the behavior of the natural logic of MacCartney and Manning (2009) on artificial ...bare logical relations (§3), the ... See full document
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Quantifying the Vanishing Gradient and Long Distance Dependency Problem in Recursive Neural Networks and Recursive LSTMs
... Recursive neural networks (RNN) and their recently proposed extension recur- sive long short term memory networks (RLSTM) are models that compute rep- resentations for sentences, by ... See full document
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A Dependency Based Neural Network for Relation Classification
... a neural network ...two neural networks are used to model shortest dependency paths and dependency subtrees ...convolutional neural network (CNN) is applied over the shortest dependency path, ... See full document
6
Demographic Inference on Twitter using Recursive Neural Networks
... Graph Recursive Neural Networks RNNs are deep learning models that recursively compose the vector of a parent unit from those of child units over a given structure in topological or- der (Pollack, ... See full document
7
Political Ideology Detection Using Recursive Neural Networks
... which refers to sentiment carried by sentence struc- ture and not word choice. They use syntactic depen- dency relation features combined with lexical infor- mation to achieve then state-of-the-art performance on ... See full document
10
Better Word Representations with Recursive Neural Networks for Morphology
... combine recursive neural networks (RNNs), where each mor- pheme is a basic unit, with neural language models (NLMs) to consider contextual information in learning morphologically- aware word ... See full document
10
Fractal Unfolding: A Metamorphic Approach to Learning to Parse Recursive Structure
... rent neural network models discover the structure of grammatical systems by sequentially processing the corpus data, attempting to predict after each word, what word will come next (Elman, 1990; Elman, ...the ... See full document
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Transition based Dependency Parsing Using Two Heterogeneous Gated Recursive Neural Networks
... Recently, neural network based depen- dency parsing has attracted much interest, which can effectively alleviate the prob- lems of data sparsity and feature engineer- ing by using the dense ...in ... See full document
11
Rumor Detection on Twitter with Tree structured Recursive Neural Networks
... why can such neural model do better for the task? Analysis has generally found that Twit- ter could “self-correct” some inaccurate informa- tion as users share opinions, conjectures and evi- dences (Zubiaga ... See full document
10
Simple Customization of Recursive Neural Networks for Semantic Relation Classification
... In this paper, we present a recursive neural network (RNN) model that works on a syn- tactic tree. Our model differs from previous RNN models in that the model allows for an explicit weighting of important ... See full document
5
Image Captioning using Multimodal Embedding
... visual semantics of a given image but also on combination techniques from the domain of natural language ...visual semantics of two different images being fed ...context can be used to generate a ... See full document
6
Using inductive types for ensuring correctness of neuro-symbolic computations
... of neural networks; and used to insure correct- ness of neuro-symbolic ...the neural networks to implement recursive computation, and cascading of neural layers to implement ... See full document
11
Combining heterogeneous classifiers for stock selection
... probabililistic neural network (a kernel density ...of logical rules, represented in our study by a recursive partitioning algorithm, and the RIPPER rule induction ... See full document
31
Combined Distributional and Logical Semantics
... the semantics of phrases or sentences (Mitchell and Lapata, ...a neural network—however their method requires annotated ...represent logical relations such as quantification in vector-space ...that ... See full document
14
MoL 2018 13: Recognizing Logical Entailment: Reasoning with Recursive and Recurrent Neural Networks
... it is remarkable that the tensor model only deteriorates this little. The matrix model suffers much more from the new data. Average testing accuracy is below 82%, while the tRNTN used to only slightly outperform the ... See full document
114
Online Self Tuning PID Control Using Neural Network for Tracking Control of a Pneumatic Cylinder Using Pulse Width Modulation Piloted Digital Valves
... The neural networks are capable of generalizing and learning dynamic relationships between the inputs and outputs of the ...the neural networks can constantly update their connection ... See full document
14
Image Description using Deep Neural Networks
... Convolutional Neural Networks (CNNs) are deployed for visual feature extraction and recursive neural network based architectures, either a simple recursive network or a Long-Short Term ... See full document
97
Segment Level Sequence Modeling using Gated Recursive Semi Markov Conditional Random Fields
... to learn fix-length representations of the whole source sen- tence in neural machine ...to learn hierarchical represen- tations ...Gated Recursive Neural Net- works (GRNNs), a variant ... See full document
11
Causal pattern inference from neural spike train data
... which can therefore be linked to the neural ...and neural activity can be used for a validation of the SSS on real single-unit data: Depending on the rat’s location, different dynamics are ... See full document
227
Generate Faces Using Ladder Variational Autoencoder with Maximum Mean Discrepancy (MMD)
... It has also been observed that the evidence lower bound (ELBO) used in tradi- tional variational autoencoders suffers from uninformative latent feature prob- lem [4] where these models tend to under-use the latent ... See full document
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