scholarly journals Deep Ensemble of Weighted Viterbi Decoders for Tail-Biting Convolutional Codes

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 93
Author(s):  
Tomer Raviv ◽  
Asaf Schwartz ◽  
Yair Be’ery
Keyword(s):  
Viterbi Algorithm ◽  
State Of The Art ◽  
Convolutional Codes ◽  
Specific Region ◽  
Short Length ◽  
High Signal ◽  
Learning Approach ◽  
Tail Biting ◽  
Machine Learning Approach ◽  
Viterbi Decoders

Tail-biting convolutional codes extend the classical zero-termination convolutional codes: Both encoding schemes force the equality of start and end states, but under the tail-biting each state is a valid termination. This paper proposes a machine learning approach to improve the state-of-the-art decoding of tail-biting codes, focusing on the widely employed short length regime as in the LTE standard. This standard also includes a CRC code. First, we parameterize the circular Viterbi algorithm, a baseline decoder that exploits the circular nature of the underlying trellis. An ensemble combines multiple such weighted decoders, and each decoder specializes in decoding words from a specific region of the channel words’ distribution. A region corresponds to a subset of termination states; the ensemble covers the entire states space. A non-learnable gating satisfies two goals: it filters easily decoded words and mitigates the overhead of executing multiple weighted decoders. The CRC criterion is employed to choose only a subset of experts for decoding purpose. Our method achieves FER improvement of up to 0.75 dB over the CVA in the waterfall region for multiple code lengths, adding negligible computational complexity compared to the circular Viterbi algorithm in high signal-to-noise ratios (SNRs).

scholarly journals Deep Ensemble of Weighted Viterbi Decoders for Tail-Biting Convolutional Codes

2020 ◽  
Author(s):  
Tomer Raviv ◽  
Asaf Schwartz ◽  
Yair Be'ery
Keyword(s):  
Machine Learning ◽  
Viterbi Algorithm ◽  
State Of The Art ◽  
Convolutional Codes ◽  
Specific Region ◽  
Short Length ◽  
Learning Approach ◽  
Tail Biting ◽  
Machine Learning Approach ◽  
Viterbi Decoders

Tail-biting convolutional codes extend the classical zero-termination convolutional codes: Both encoding schemes force the equality of start and end states, but under the tail-biting each state is a valid termination. This paper proposes a machine-learning approach to improve the state-of-the-art decoding of tail-biting codes, focusing on the widely employed short length regime as in the LTE standard. This standard also includes a CRC code. First, we parameterize the circular Viterbi algorithm, a baseline decoder that exploits the circular nature of the underlying trellis. An ensemble combines multiple such weighted decoders, each decoder specializes in decoding words from a specific region of the channel words' distribution. A region corresponds to a subset of termination states; the ensemble covers the entire states space. A non-learnable gating satisfies two goals: it filters easily decoded words and mitigates the overhead of executing multiple weighted decoders. The CRC criterion is employed to choose only a subset of experts for decoding purpose. Our method achieves FER improvement of up to 0.75dB over the CVA in the waterfall region for multiple code lengths, adding negligible computational complexity compared to the circular Viterbi algorithm in high SNRs.

scholarly journals A Machine Learning Approach to Coreference Resolution of Noun Phrases

2001 ◽  
Vol 27 (4) ◽  
pp. 521-544 ◽  
Author(s):  
Wee Meng Soon ◽  
Hwee Tou Ng ◽  
Daniel Chung Yong Lim
Keyword(s):  
Machine Learning ◽  
Noun Phrase ◽  
State Of The Art ◽  
Noun Phrases ◽  
Learning Approach ◽  
Data Sets ◽  
Coreference Resolution ◽  
Machine Learning Approach

In this paper, we present a learning approach to coreference resolution of noun phrases in unrestricted text. The approach learns from a small, annotated corpus and the task includes resolving not just a certain type of noun phrase (e.g., pronouns) but rather general noun phrases. It also does not restrict the entity types of the noun phrases; that is, coreference is assigned whether they are of “organization,” “person,” or other types. We evaluate our approach on common data sets (namely, the MUC-6 and MUC-7 coreference corpora) and obtain encouraging results, indicating that on the general noun phrase coreference task, the learning approach holds promise and achieves accuracy comparable to that of nonlearning approaches. Our system is the first learning-based system that offers performance comparable to that of state-of-the-art nonlearning systems on these data sets.

scholarly journals Avoiding the Inherent Limitations in Datasets Used for Measuring Aesthetics When Using a Machine Learning Approach

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Adrian Carballal ◽  
Carlos Fernandez-Lozano ◽  
Nereida Rodriguez-Fernandez ◽  
Luz Castro ◽  
Antonino Santos
Keyword(s):  
State Of The Art ◽  
Learning Approach ◽  
Aesthetic Value ◽  
Experimental Conditions ◽  
Generative Art ◽  
Machine Learning Approach ◽  
Evolutionary Art ◽  
The Aesthetic ◽  
Psychological Experiments

An important topic in evolutionary art is the development of systems that can mimic the aesthetics decisions made by human begins, e.g., fitness evaluations made by humans using interactive evolution in generative art. This paper focuses on the analysis of several datasets used for aesthetic prediction based on ratings from photography websites and psychological experiments. Since these datasets present problems, we proposed a new dataset that is a subset of DPChallenge.com. Subsequently, three different evaluation methods were considered, one derived from the ratings available at DPChallenge.com and two obtained under experimental conditions related to the aesthetics and quality of images. We observed different criteria in the DPChallenge.com ratings, which had more to do with the photographic quality than with the aesthetic value. Finally, we explored learning systems other than state-of-the-art ones, in order to predict these three values. The obtained results were similar to those using state-of-the-art procedures.

Exploring patterns in dictionary definitions for synonym extraction

2011 ◽  
Vol 18 (3) ◽  
pp. 313-342 ◽  
Author(s):  
TONG WANG ◽  
GRAEME HIRST
Keyword(s):  
Language Processing ◽  
State Of The Art ◽  
Learning Approach ◽  
Automatic Determination ◽  
Rule Based ◽  
Machine Learning Approach ◽  
Pros And Cons ◽  
Machine Readable ◽  
One Machine

AbstractAutomatic determination of synonyms and/or semantically related words has various applications in Natural Language Processing. Two mainstream paradigms to date, lexicon-based and distributional approaches, both exhibit pros and cons with regard to coverage, complexity, and quality. In this paper, we propose three novel methods—two rule-based methods and one machine learning approach—to identify synonyms from definition texts in a machine-readable dictionary. Extracted synonyms are evaluated in two extrinsic experiments and one intrinsic experiment. Evaluation results show that our pattern-based approach achieves best performance in one of the experiments and satisfactory results in the other, comparable to corpus-based state-of-the-art results.

scholarly journals Machine Learning Approach to Dysphonia Detection

2018 ◽  
Vol 8 (10) ◽  
pp. 1927 ◽  
Author(s):  
Zuzana Dankovičová ◽  
Dávid Sovák ◽  
Peter Drotár ◽  
Liberios Vokorokos
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Nearest Neighbors ◽  
Classification Model ◽  
Support Vector ◽  
Learning Approach ◽  
K Nearest Neighbors ◽  
Machine Learning Methods ◽  
Machine Learning Approach ◽  
Speech Features

This paper addresses the processing of speech data and their utilization in a decision support system. The main aim of this work is to utilize machine learning methods to recognize pathological speech, particularly dysphonia. We extracted 1560 speech features and used these to train the classification model. As classifiers, three state-of-the-art methods were used: K-nearest neighbors, random forests, and support vector machine. We analyzed the performance of classifiers with and without gender taken into account. The experimental results showed that it is possible to recognize pathological speech with as high as a 91.3% classification accuracy.

scholarly journals Predicting materials properties without crystal structure: deep representation learning from stoichiometry

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Rhys E. A. Goodall ◽  
Alpha A. Lee
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
State Of The Art ◽  
Computational Cost ◽  
Weighted Graph ◽  
Representation Learning ◽  
Learning Approach ◽  
Materials Properties ◽  
Machine Learning Approach ◽  
Low Computational Cost

AbstractMachine learning has the potential to accelerate materials discovery by accurately predicting materials properties at a low computational cost. However, the model inputs remain a key stumbling block. Current methods typically use descriptors constructed from knowledge of either the full crystal structure — therefore only applicable to materials with already characterised structures — or structure-agnostic fixed-length representations hand-engineered from the stoichiometry. We develop a machine learning approach that takes only the stoichiometry as input and automatically learns appropriate and systematically improvable descriptors from data. Our key insight is to treat the stoichiometric formula as a dense weighted graph between elements. Compared to the state of the art for structure-agnostic methods, our approach achieves lower errors with less data.

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