[PDF] Top 20 Lexicosyntactic Inference in Neural Models
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Lexicosyntactic Inference in Neural Models
... For each sentence generated in this way, 10 differ- ent annotators are asked to answer the question did that thing happen?: yes, maybe or maybe not, no. There are two important aspects of these con- texts to note. First, ... See full document
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Multi-Source Neural Variational Inference
... including neural networks (Ngiam et al. 2011), probabilistic graphical models (Srivastava and Salakhutdinov 2014), and canonical correla- tion analysis (Andrew et ... See full document
8
Connectionist Inference Models
... both neural networks and Von-Neumann type physical symbol systems are both universal Turing machines (Franklin and Garzon, 1990), at some level of abstraction there is no distinction between ...systems. ... See full document
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Interpreting Recurrent and Attention Based Neural Models: a Case Study on Natural Language Inference
... deep models in their intermediate layers, specifically, by examining the saliency of the at- tention and the gating ...NLI models employ complex neural archi- tectures involving key mechanisms, such ... See full document
6
Benchmarking Approximate Inference Methods for Neural Structured Prediction
... a neural network (an “infer- ence network”) to output a structure in the relaxed space that has high score under the structured scoring function (Tu and Gimpel, ...and inference networks for three sequence ... See full document
12
WEATHER FORECASTING USING SOFT COMPUTING TECHNIQUES
... Neuro-Fuzzy Inference System (ANFIS) and Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) models were used to analyze metrological data sets obtained from the metrological ...both ... See full document
8
Comparative study of ANN and ANFIS prediction models for thermal error compensation on CNC machine tools
... Artificial Neural Networks [9], fuzzy logic [10], adaptive network fuzzy inference system [11] and a combination of several different modelling methods ... See full document
11
TIGS: An Inference Algorithm for Text Infilling with Gradient Search
... approximate inference algorithms. In this paper, we propose an iterative inference algorithm based on gradient search, which is the first inference algorithm that can be broadly applied to any ... See full document
11
Neural Network based Video Quality via Adaptive FEC in Wireless Environment
... Artificial Neural Network (ANN) has proven capability in wireless communications in building wireless intelligent ...applying neural networks is to change from the lengthy analysis and design cycles ... See full document
5
Adaptive Neural Fuzzy Inference System Models for Predicting the Shear Strength of Reinforced Concrete Deep Beams
... neuro-fuzzy inference system (ANFIS) proved to be simple and powerful tool for predicting the shear strength of reinforced concrete is (RC) deep ...neuro-fuzzy inference system (ANFIS) is a fuzzy ... See full document
10
On Lifted Inference Using Neural Embeddings
... using neural embeddings to represent symmetries in the ...relational models such as PSL (Bach et ...lifted inference (Poole 2003) algorithms have been proposed that exploit symmetries in the ... See full document
8
Sieg at MEDIQA 2019: Multi task Neural Ensemble for Biomedical Inference and Entailment
... The initial work (Ben Abacha and Demner- Fushman, 2017), in addition to creating the work- ing dataset for RQE, uses handcrafted lexical and semantic features as an input to traditional ma- chine learning models ... See full document
9
Comparison of Artificial Intelligence Techniques for river flow forecasting
... Fuzzy Inference Sys- tem (ANFIS) and Artificial Neural Network (ANN) methods, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN), and Auto-Regressive (AR) ... See full document
17
Virtual Sensors for Safe Operation of Electrolyser and Hydrogen powered Car
... predictive models including Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are presented as virtual sensors for the accurate estimation of hydrogen parameters ... See full document
14
Neural Natural Language Inference Models Enhanced with External Knowledge
... guage Inference (SNLI) dataset (Bowman et ...our models (trained on the SNLI training set) on a new test set (Glockner et ...lexical inference abilities of NLI systems and consists of 8,193 sam- ... See full document
12
Effective Inference for Generative Neural Parsing
... ative models for constituency parsing (Henderson, 2003) and dependency parsing (Titov and Hen- derson, 2010; Buys and Blunsom, 2015), among other ...for neural generative constituency parsers in which ... See full document
6
Structured prediction models for RNN based sequence labeling in clinical text
... Sequence labeling is a widely used method for named entity recognition and information extraction from unstructured natural language data. In the clinical domain one major ap- plication of sequence labeling involves ex- ... See full document
10
Inference with Distributional Semantic Models
... We just observed that SVM and BDSM have similar lexical entailment performance, especially in count space. However, the two models are radically different in their struc- ture. SVM fits a 2nd order polynomial ... See full document
73
Probabilistic Inference in Piecewise Graphical Models
... last inference tool that unlike the pre- vious two contributions, is based on Hamiltonian Monte ...experimental models, regardless of the tuning parameters, RHMC outperformed the baseline and it was much ... See full document
165
Qml inference for volatility models with covariates
... GARCH-X models, the coefficients are generally positively constrained, and tests of nullity of some components of ϑ 0 help to find a parsimonious GARCH-X ...GARCH models is the quasi-maximum likelihood ... See full document
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