Comparing Hidden Markov Models and Long Short Term Memory Neural Networks for Learning Action Representations

Panzner M, Cimiano P. Comparing Hidden Markov Models and Long Short Term Memory Neural Networks for Learning Action Representations. In: Pardalos PM, Conca P, Giuffrida G, Nicosia G, eds. Machine Learning, Optimization, and Big Data : Second International Workshop, MOD 2016, Volterra, Italy, August 26-29, 2016. Revised Selected Papers. Lecture Notes in Computer Science. Vol 10122. Cham: Springer International Publishing; 2016: 94-105

Comparing Hidden Markov Models and Long Short Term Memory Neural Networks for Learning Action Representations

Panzner M, Cimiano P. Comparing Hidden Markov Models and Long Short Term Memory Neural Networks for Learning Action Representations. In: Pardalos PM, Conca P, Giuffrida G, Nicosia G, eds. Machine Learning, Optimization, and Big Data : Second International Workshop, MOD 2016, Volterra, Italy, August 26-29, 2016. Revised Selected Papers. Lecture Notes in Computer Science. Vol 10122. Cham: Springer International Publishing; 2016: 94-105

Система розпізнавання української мови

Дана дипломна робота містить 78 ст., 4 ч., 9 табл., 15 рис., 2 дод., 18 джерел.Об‟єкт дослідження – процес розпізнавання української мови. Мета та цілі роботи – розглянути теоретичне підґрунтя процесу розпізнавання мовлення, провести огляд існуючих методів та засобів розпізнавання, розробка засобів програмного забезпечення для автоматичного розпізнавання української мови. Методи дослідження – аналіз процесу розпізнавання мовлення, експеримент, результатом якого є програмний продукт, аналіз отриманих результатів. Результатом роботи є система розпізнавання української мови, а саме система голосового локального управління комп‟ютером. Новизною роботи є створення способу розпізнавання української мови на основі Прихованих Марківських моделей та з використанням JSGF- граматик, а також розробка програмного забезпечення з використанням знайденого способу. Результати даної роботи можна застосовувати для полегшення та пришвидшення роботи з операційною системою Mac OS за допомогою голосового управління. Також, створений модуль розпізнавання української мови є універсальним та може бути використаний у будь-якому додатку, де необхідно розпізнавати злитне мовлення.This thesis contains 78 p., 4 sections, 9 tabl., 16 fig., 2 appendixes, 18 sources. The object of this work is the recognition process of the Ukrainian language. The purpose and aims of this work is to consider the theoretical basis of the speech recognition process, to conduct an overview of existing methods and means of recognition, development of software tools for automatic recognition of the Ukrainian language. Research methods - analysis of speech recognition process, experiment, the result of which is a software product, analysis of the results. The result of the work is the recognition system of the Ukrainian language, namely the system of voice local control of the computer. The novelty of the work is to create a way to recognize the Ukrainian language based on Hidden Markov models and using JSGF-grammar, as well as software development using the found method. The results of this work can be used to facilitate and speed up the operation of the Mac OS. Also, the Ukrainian language recognition module is universal and can be used in any application where it is necessary to recognize interlaced speech

Statistical methods for fine-grained retail product recognition

In recent years, computer vision has become a major instrument in automating retail processes with emerging smart applications such as shopper assistance, visual product search (e.g., Google Lens), no-checkout stores (e.g., Amazon Go), real-time inventory tracking, out-of-stock detection, and shelf execution. At the core of these applications lies the problem of product recognition, which poses a variety of new challenges in contrast to generic object recognition. Product recognition is a special instance of fine-grained classification. Considering the sheer diversity of packaged goods in a typical hypermarket, we are confronted with up to tens of thousands of classes, which, particularly if under the same product brand, tend to have only minute visual differences in shape, packaging texture, metric size, etc., making them very difficult to discriminate from one another. Another challenge is the limited number of available datasets, which either have only a few training examples per class that are taken under ideal studio conditions, hence requiring cross-dataset generalization, or are captured from the shelf in an actual retail environment and thus suffer from issues like blur, low resolution, occlusions, unexpected backgrounds, etc. Thus, an effective product classification system requires substantially more information in addition to the knowledge obtained from product images alone. In this thesis, we propose statistical methods for a fine-grained retail product recognition. In our first framework, we propose a novel context-aware hybrid classification system for the fine-grained retail product recognition problem. In the second framework, state-of-the-art convolutional neural networks are explored and adapted to fine-grained recognition of products. The third framework, which is the most significant contribution of this thesis, presents a new approach for fine-grained classification of retail products that learns and exploits statistical context information about likely product arrangements on shelves, incorporates visual hierarchies across brands, and returns recognition results as "confidence sets" that are guaranteed to contain the true class at a given confidence leve

Learning From Almost No Data

The tremendous recent growth in the fields of artificial intelligence and machine learning has largely been tied to the availability of big data and massive amounts of compute. The increasingly popular approach of training large neural networks on large datasets has provided great returns, but it leaves behind the multitude of researchers, companies, and practitioners who do not have access to sufficient funding, compute power, or volume of data. This thesis aims to rectify this growing imbalance by probing the limits of what machine learning and deep learning methods can achieve with small data. What knowledge does a dataset contain? At the highest level, a dataset is just a collection of samples: images, text, etc. Yet somehow, when we train models on these datasets, they are able to find patterns, make inferences, detect similarities, and otherwise generalize to samples that they have previously never seen. This suggests that datasets may contain some kind of intrinsic knowledge about the systems or distributions from which they are sampled. Moreover, it appears that this knowledge is somehow distributed and duplicated across the samples; we intuitively expect that removing an image from a large training set will have virtually no impact on the final model performance. We develop a framework to explain efficient generalization around three principles: information sharing, information repackaging, and information injection. We use this framework to propose `less than one'-shot learning, an extreme form of few-shot learning where a learner must recognize N classes from M < N training examples. To achieve this extreme level of efficiency, we develop new framework-consistent methods and theory for lost data restoration, for dataset size reduction, and for few-shot learning with deep neural networks and other popular machine learning models