Sparsity-Constrained Optimal Transport

Regularized optimal transport (OT) is now increasingly used as a loss or as a matching layer in neural networks. Entropy-regularized OT can be computed using the Sinkhorn algorithm but it leads to fully-dense transportation plans, meaning that all sources are (fractionally) matched with all targets. To address this issue, several works have investigated quadratic regularization instead. This regularization preserves sparsity and leads to unconstrained and smooth (semi) dual objectives, that can be solved with off-the-shelf gradient methods. Unfortunately, quadratic regularization does not give direct control over the cardinality (number of nonzeros) of the transportation plan. We propose in this paper a new approach for OT with explicit cardinality constraints on the transportation plan. Our work is motivated by an application to sparse mixture of experts, where OT can be used to match input tokens such as image patches with expert models such as neural networks. Cardinality constraints ensure that at most $k$ tokens are matched with an expert, which is crucial for computational performance reasons. Despite the nonconvexity of cardinality constraints, we show that the corresponding (semi) dual problems are tractable and can be solved with first-order gradient methods. Our method can be thought as a middle ground between unregularized OT (recovered in the limit case $k=1$) and quadratically-regularized OT (recovered when $k$ is large enough). The smoothness of the objectives increases as $k$ increases, giving rise to a trade-off between convergence speed and sparsity of the optimal plan

Propuesta de intervención para un caso de distonía focal del músico

La distonía focal del músico es un desorden motor caracterizado por contracciones musculares involuntarias sostenidas que interfieren con el control motor voluntario durante la ejecución de un instrumento musical. El músico suele presentar estrés por la pérdida del desempeño práctico y el alejamiento de la comunidad profesional como consecuencia de un tratamiento prolongado. Los tratamientos para este trastorno se basan fundamentalmente en el reentrenamiento pedagógico, cuya finalidad es la de crear nuevos hábitos de ejecución musical. Sin embargo, diversos autores señalan que la terapia psicológica tiene un papel fundamental en la recuperación de las personas con esta condición, aunque los tratamientos psicológicos aplicados a esta área son en realidad escasos. Por tanto, en este estudio sobre un caso de distonía focal de la embocadura se realiza una evaluación a través de un análisis funcional y una propuesta de intervención desde un enfoque psicológico, cuyo objetivo terapéutico es mitigar y reducir la sintomatología característica de los trastornos de depresión y ansiedad, los cuales son fruto de las consecuencias del diagnóstico de distonía focal de la embocadura. Para ello, se aplican tratamientos psicológicos para este trastorno con la intención de que pudieran contribuir a la recuperación parcial o total del problema principal.Musician's focal dystonia is a motor disorder characterized by sustained involuntary muscle contractions that interfere with voluntary motor control during the playing of a musical instrument. The musician is often stressed by the loss of practical performance and withdrawal from the professional community as a result of prolonged treatment. The treatments for this disorder are mainly based on pedagogical retraining, the purpose of which is to create new musical performance habits. However, several authors point out that psychological therapy has a fundamental role in the recovery of people with this condition, although psychological treatments applied to this area are actually scarce. Therefore, in this study on a case of focal embouchure dystonia, an evaluation is made through a functional analysis and an intervention proposal from a psychological approach, whose therapeutic objective is to mitigate and reduce the characteristic symptomatology of depression and anxiety disorders, which are the result of the consequences of the diagnosis of focal embouchure dystonia. For this purpose, psychological treatments for this disorder are applied with the intention that they could contribute to the partial or total recovery of the main problem

Diseño de un remolque para el trasporte de pequeñas embarcaciones

[ES] El trabajo se centra en el diseño y análisis de un remolque dedicado al transporte por carretera de pequeñas embarcaciones de recreo. El propósito de éste, es diseñar un remolque para el transporte de embarcaciones que cumpla tanto su función como las normas impuestas para este tipo de vehículos no motorizados. Para ello, primeramente se realizará un estudio de los diferentes tipos de remolques para este uso que existen en el mercado. A continuación, se estudiarán diferentes alternativas de diseño (tipo de remolque, materiales, etc.). Finalmente se procederá a proponer el diseño del remolque y la justificación de las soluciones adoptadas. El trabajo se completará con un anejo de cálculos, el presupuesto y planos del diseño final.[EN] The project focuses on the design and analysis of a trailer dedicated to road transport of small recreational boats. The purpose of this, is to design a trailer for the transport of boats that fulfills both its function and the standards imposed for this type of non-motorized vehicles. For this, first a study of the different types of trailers for this use that exist in the market will be carried out. Next, different design alternatives will be studied (type of trailer, materials, etc.). Finally we will proceed to propose the design of the trailer and the justification of the adopted solutions. The project will be completed with an annex of calculations, the budget and final design plans.Forqués Puigcerver, J. (2019). Diseño de un remolque para el trasporte de pequeñas embarcaciones. http://hdl.handle.net/10251/129158TFG

Integración de Redes Neuronales en tareas de Reconocimiento de Escritura Manuscrita

[ES] Este proyecto consiste en adaptar la librería de redes neuronales desarrollada por el director en un sistema de reconocimiento de escritura manuscrita basada en modelos ocultos de Markov. El objetivo principal es integrar una red neuronal con el objetivo de mejorar los resultados anteriores. La función de la red neuronal es estimar las probabilidades de emisión de los modelos de Markov, anteriormente modeladas con mixturas de gaussianas. El proyecto incluye un análisis del problema y el diseño del sistema de reconocimiento. También se expone el desarrollo de los programas que implementan dicho sistema de reconocimiento. Por último, contiene los detalles de los múltiples experimentos realizados para obtener resultados que permitan comparar la técnica anterior con la nueva.[EN] This project consists in adapt a neuronal network library developed by the director in a handwritten text recognition text based on Markov’s hidden models The main goal is adapt a neural network in order to improve the previoys results. The function of the neural network is providing the emission probabilities of the states in the Markov’s model, previously obtained by a Gaussian mixture. This project includes an analysis of the problem and the handwritten recognition system design. Also, the developments of the programs that implement this recognition system are exposed. At the end, multiple experiments are performed in order to obtain results for comparing this technique with the previous one.Puigcerver Ibañez, J. (2016). Integración de Redes Neuronales en tareas de Reconocimiento de Escritura Manuscrita. http://hdl.handle.net/10251/68994.TFG

Querying out-of-vocabulary words in lexicon-based keyword spotting

The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-016-2197-8[EN] Lexicon-based handwritten text keyword spotting (KWS) has proven to be a faster and more accurate alternative to lexicon-free methods. Nevertheless, since lexicon-based KWS relies on a predefined vocabulary, fixed in the training phase, it does not support queries involving out-of-vocabulary (OOV) keywords. In this paper, we outline previous work aimed at solving this problem and present a new approach based on smoothing the (null) scores of OOV keywords by means of the information provided by ``similar'' in-vocabulary words. Good results achieved using this approach are compared with previously published alternatives on different data sets.This work was partially supported by the Spanish MEC under FPU Grant FPU13/06281, by the Generalitat Valenciana under the Prometeo/2009/014 Project Grant ALMA-MATER, and through the EU Projects: HIMANIS (JPICH programme, Spanish grant Ref. PCIN-2015-068) and READ (Horizon-2020 programme, grant Ref. 674943).Puigcerver, J.; Toselli, AH.; Vidal, E. (2016). Querying out-of-vocabulary words in lexicon-based keyword spotting. Neural Computing and Applications. 1-10. https://doi.org/10.1007/s00521-016-2197-8S110Almazan J, Gordo A, Fornes A, Valveny E (2013) Handwritten word spotting with corrected attributes. In: 2013 IEEE international conference on computer vision (ICCV), pp 1017–1024. doi: 10.1109/ICCV.2013.130Amengual JC, Vidal E (2000) On the estimation of error-correcting parameters. In: Proceedings 15th international conference on pattern recognition, 2000, vol 2, pp 883–886Fernández D, Lladós J, Fornés A (2011) Handwritten word spotting in old manuscript images using a pseudo-structural descriptor organized in a hash structure. In: Vitri'a J, Sanches JM, Hern'andez M (eds) Pattern recognition and image analysis: Proceedings of 5th Iberian Conference, IbPRIA 2011, Las Palmas de Gran Canaria, Spain, June 8–10. Springer, Berlin, Heidelberg, pp 628–635. doi: 10.1007/978-3-642-21257-4_78Fischer A, Keller A, Frinken V, Bunke H (2012) Lexicon-free handwritten word spotting using character HMMs. Pattern Recognit Lett 33(7):934–942. doi: 10.1016/j.patrec.2011.09.009 Special Issue on Awards from ICPR 2010Fornés A, Frinken V, Fischer A, Almazán J, Jackson G, Bunke H (2011) A keyword spotting approach using blurred shape model-based descriptors. In: Proceedings of the 2011 workshop on historical document imaging and processing, pp 83–90. ACMFrinken V, Fischer A, Manmatha R, Bunke H (2012) A novel word spotting method based on recurrent neural networks. IEEE Trans Pattern Anal Mach Intell 34(2):211–224. doi: 10.1109/TPAMI.2011.113Gatos B, Pratikakis I (2009) Segmentation-free word spotting in historical printed documents. In: 10th International conference on document analysis and recognition, 2009. ICDAR’09, pp 271–275. IEEEJelinek F (1998) Statistical methods for speech recognition. 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In: 7th Iberian conference on pattern recognition and image analysis. SpringerRath T, Manmatha R (2007) Word spotting for historical documents. Int J Doc Anal Recognit 9:139–152Robertson S. (2008) A new interpretation of average precision. In: Proceedings of the international. ACM SIGIR conference on research and development in information retrieval (SIGIR ’08), pp 689–690. ACM, New York, NY, USA. doi: http://doi.acm.org/10.1145/1390334.1390453Rodriguez-Serrano JA, Perronnin F (2009) Handwritten word-spotting using hidden markov models and universal vocabularies. Pattern Recognit 42(9):2106–2116. doi: 10.1016/j.patcog.2009.02.005 . http://www.sciencedirect.com/science/article/pii/S0031320309000673Rusinol M, Aldavert D, Toledo R, Llados J (2011) Browsing heterogeneous document collections by a segmentation-free word spotting method. 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A Probabilistic Formulation of Keyword Spotting

[ES] La detección de palabras clave (Keyword Spotting, en inglés), aplicada a documentos de texto manuscrito, tiene como objetivo recuperar los documentos, o partes de ellos, que sean relevantes para una cierta consulta (query, en inglés), indicada por el usuario, entre una gran colección de documentos. La temática ha recogido un gran interés en los últimos 20 años entre investigadores en Reconocimiento de Formas (Pattern Recognition), así como bibliotecas y archivos digitales. Esta tesis, en primer lugar, define el objetivo de la detección de palabras clave a partir de una perspectiva basada en la Teoría de la Decisión y una formulación probabilística adecuada. Más concretamente, la detección de palabras clave se presenta como un caso particular de Recuperación de la Información (Information Retrieval), donde el contenido de los documentos es desconocido, pero puede ser modelado mediante una distribución de probabilidad. Además, la tesis también demuestra que, bajo las distribuciones de probabilidad correctas, el marco de trabajo desarrollada conduce a la solución óptima del problema, según múltiples medidas de evaluación utilizadas tradicionalmente en el campo. Más tarde, se utilizan distintos modelos estadísticos para representar las distribuciones necesarias: Redes Neuronales Recurrentes o Modelos Ocultos de Markov. Los parámetros de estos son estimados a partir de datos de entrenamiento, y las respectivas distribuciones son representadas mediante Transductores de Estados Finitos con Pesos (Weighted Finite State Transducers). Con el objetivo de hacer que el marco de trabajo sea práctico en grandes colecciones de documentos, se presentan distintos algoritmos para construir índices de palabras a partir de modelos probabilísticos, basados tanto en un léxico cerrado como abierto. Estos índices son muy similares a los utilizados por los motores de búsqueda tradicionales. Además, se estudia la relación que hay entre la formulación probabilística presentada y otros métodos de gran influencia en el campo de la detección de palabras clave, destacando cuáles son las limitaciones de los segundos. Finalmente, todas la aportaciones se evalúan de forma experimental, no sólo utilizando pruebas académicas estándar, sino también en colecciones con decenas de miles de páginas provenientes de manuscritos históricos. Los resultados muestran que el marco de trabajo presentado permite construir sistemas de detección de palabras clave muy rápidos y precisos, con una sólida base teórica.[CA] La detecció de paraules clau (Keyword Spotting, en anglès), aplicada a documents de text manuscrit, té com a objectiu recuperar els documents, o parts d'ells, que siguen rellevants per a una certa consulta (query, en anglès), indicada per l'usuari, dintre d'una gran col·lecció de documents. La temàtica ha recollit un gran interés en els últims 20 anys entre investigadors en Reconeixement de Formes (Pattern Recognition), així com biblioteques i arxius digitals. Aquesta tesi defineix l'objectiu de la detecció de paraules claus a partir d'una perspectiva basada en la Teoria de la Decisió i una formulació probabilística adequada. Més concretament, la detecció de paraules clau es presenta com un cas concret de Recuperació de la Informació (Information Retrieval), on el contingut dels documents és desconegut, però pot ser modelat mitjançant una distribució de probabilitat. A més, la tesi també demostra que, sota les distribucions de probabilitat correctes, el marc de treball desenvolupat condueix a la solució òptima del problema, segons diverses mesures d'avaluació utilitzades tradicionalment en el camp. Després, diferents models estadístics s'utilitzen per representar les distribucions necessàries: Xarxes Neuronal Recurrents i Models Ocults de Markov. Els paràmetres d'aquests són estimats a partir de dades d'entrenament, i les corresponents distribucions són representades mitjançant Transductors d'Estats Finits amb Pesos (Weighted Finite State Transducers). Amb l'objectiu de fer el marc de treball útil per a grans col·leccions de documents, es presenten distints algorismes per construir índexs de paraules a partir dels models probabilístics, tan basats en un lèxic tancat com en un obert. Aquests índexs són molt semblants als utilitzats per motors de cerca tradicionals. A més a més, s'estudia la relació que hi ha entre la formulació probabilística presentada i altres mètodes de gran influència en el camp de la detecció de paraules clau, destacant algunes limitacions dels segons. Finalment, totes les aportacions s'avaluen de forma experimental, no sols utilitzant proves acadèmics estàndard, sinó també en col·leccions amb desenes de milers de pàgines provinents de manuscrits històrics. Els resultats mostren que el marc de treball presentat permet construir sistemes de detecció de paraules clau molt acurats i ràpids, amb una sòlida base teòrica.[EN] Keyword Spotting, applied to handwritten text documents, aims to retrieve the documents, or parts of them, that are relevant for a query, given by the user, within a large collection of documents. The topic has gained a large interest in the last 20 years among Pattern Recognition researchers, as well as digital libraries and archives. This thesis, first defines the goal of Keyword Spotting from a Decision Theory perspective. Then, the problem is tackled following a probabilistic formulation. More precisely, Keyword Spotting is presented as a particular instance of Information Retrieval, where the content of the documents is unknown, but can be modeled by a probability distribution. In addition, the thesis also proves that, under the correct probability distributions, the framework provides the optimal solution, under many of the evaluation measures traditionally used in the field. Later, different statistical models are used to represent the probability distribution over the content of the documents. These models, Hidden Markov Models or Recurrent Neural Networks, are estimated from training data, and the corresponding distributions over the transcripts of the images can be efficiently represented using Weighted Finite State Transducers. In order to make the framework practical for large collections of documents, this thesis presents several algorithms to build probabilistic word indexes, using both lexicon-based and lexicon-free models. These indexes are very similar to the ones used by traditional search engines. Furthermore, we study the relationship between the presented formulation and other seminal approaches in the field of Keyword Spotting, highlighting some limitations of the latter. Finally, all the contributions are evaluated experimentally, not only on standard academic benchmarks, but also on collections including tens of thousands of pages of historical manuscripts. The results show that the proposed framework and algorithms allow to build very accurate and very fast Keyword Spotting systems, with a solid underlying theory.Puigcerver I Pérez, J. (2018). A Probabilistic Formulation of Keyword Spotting [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/116834TESI

Two Methods to Improve Confidence Scores for Lexicon-Free Word Spotting in Handwritten Text

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Two methods are presented to improve word confidence scores for Line-Level Query-by-String Lexicon-Free Keyword Spotting (KWS) in handwritten text images. The first one approaches true relevance probabilities by means of computations directly carried out on character lattices obtained from the lines images considered. The second method uses the same character lattices, but it obtains relevance scores by first computing frame-level character sequence scores which resemble the word posteriorgrams used in previous approaches for lexicon-based KWS. The first method results from a formal probabilistic derivation, which allow us to better understand and further develop the underlying ideas. The second one is less formal but, according with experiments presented in the paper, it obtains almost identical results with much lower computational cost. Moreover, in contrast with the first method, the second one allows to directly obtain accurate bounding boxes for the spotted words.This work was partially supported by the Spanish MEC under FPU grant FPU13/06281, by the Generalitat Valenciana under the Prometeo/2009/014 project grant ALMAMATER, and through the EU projects: HIMANIS (JPICH programme, Spanish grant Ref. PCIN-2015-068) and READ (Horizon-2020 programme, grant Ref. 674943).Toselli, AH.; Puigcerver, J.; Vidal, E. (2016). Two Methods to Improve Confidence Scores for Lexicon-Free Word Spotting in Handwritten Text. IEEE. https://doi.org/10.1109/ICFHR.2016.0072

From Sparse to Soft Mixtures of Experts

Sparse mixture of expert architectures (MoEs) scale model capacity without large increases in training or inference costs. Despite their success, MoEs suffer from a number of issues: training instability, token dropping, inability to scale the number of experts, or ineffective finetuning. In this work, we proposeSoft MoE, a fully-differentiable sparse Transformer that addresses these challenges, while maintaining the benefits of MoEs. Soft MoE performs an implicit soft assignment by passing different weighted combinations of all input tokens to each expert. As in other MoE works, experts in Soft MoE only process a subset of the (combined) tokens, enabling larger model capacity at lower inference cost. In the context of visual recognition, Soft MoE greatly outperforms standard Transformers (ViTs) and popular MoE variants (Tokens Choice and Experts Choice). For example, Soft MoE-Base/16 requires 10.5x lower inference cost (5.7x lower wall-clock time) than ViT-Huge/14 while matching its performance after similar training. Soft MoE also scales well: Soft MoE Huge/14 with 128 experts in 16 MoE layers has over 40x more parameters than ViT Huge/14, while inference time cost grows by only 2%, and it performs substantially better

Out of vocabulary queries for word graph-based keyword spotting

[EN] In this master thesis several approaches are presented to support out of vocabulary queries in a Word Graph (WG)-based Keyword Spotting (KWS) application for handwritten text lines. Generally, KWS assigns a score that estimates how likely is that a given keyword is present in a certain line image. WGbased KWS offers very fast search times but assumes a closed vocabulary and assigns null scores to any word not included in such vocabulary. This work tries to provide to the WG-based KWS the flexibility of non-restricted searches and the speed achieved by the usage of WG.[ES] En este trabajo fin de máster se presentan distintas alternativas para dar soporte a búsquedas con palabras fuera del vocabulario en Keyword Spotting (KWS) sobre líneas de texto manuscrito usando Word Graphs (WG). En general, en KWS se asigna una puntuación que indica cuán probable es que una palabra aparezca en una imagen de una línea de texto. El KWS basado en WG ofrece tiempos de búsqueda muy rápidos pero asume un vocabulario cerrado y asigna puntuaciones nulas a las palabras no incluidas en él. Con éste trabajo se pretende proporcionar al KWS basado en WG de la flexibilidad de búsquedas no restringidas al vocabulario de entrenamiento, junto a la velocidad que se consigue con el uso de WG.Puigcerver I Pérez, J. (2014). Out of vocabulary queries for word graph-based keyword spotting. http://hdl.handle.net/10251/53360Archivo delegad

Quantificació de les millores en la segmentació del cos central del text manuscrit utilitzant aprenentatge supervisat

Tot i el progrés aconseguit en els últims anys pel Reconeixement de Text Manuscrit, aquest encara té molts problemes per resoldre. Molts d'ells causats perquè la gran variabilitat que presenta el text, en quant a l'estil d'escriptura, no aporta cap informació rellevant per a la classificació dels símbols representats a les imatges i dificulta el seu reconeixement.. Per això, un dels components fonamentals de qualsevol sistema de reconeixement de l'escriptura és la normalització d'aquest text, un procés que tracta de reduir aquesta variabilitat. Aquest projecte compara i quantifica les diferències entre dues alternatives per solucionar un dels problemes que forma part d'aquest procés de normalització: la segmentació del cos central del text manuscrit. El cos central d'una línia de text manuscrit és aquella porció de la línia on resideix el cos central de cadascun dels símbols que formen el text. Les dues alternatives estudiades per a aquesta segmentació del cos central estan basades en un enfocament heurístic del problema, on un algorisme amb unes regles pre-establertes determina quina és la regió del cos central, i una altra basada en tècniques d'aprenentatge supervisat, on un humà ha segmentat manualment el cos central d'un conjunt d'imatges de mostra i ha entrenat el sistema per a que intente segmentar de manera semblant les noves imatges.Puigcerver I Pérez, J. (2012). Quantificació de les millores en la segmentació del cos central del text manuscrit utilitzant aprenentatge supervisat. http://hdl.handle.net/10251/18229.Archivo delegad