New Measures for Offline Evaluation of Learning Path Recommenders

International audienceRecommending students useful and effective learning paths is highly valuable to improve their learning experience. The evaluation of the effectiveness of this recommendation is a challenging task that can be performed online or offline. Online evaluation is highly popular but it relies on actual path recommendations to students, which may have dramatic implications. Offline evaluation relies on static datasets of students' learning activities and simulates paths recommendations. Although easier to run, it is difficult to accurately evaluate offline the effectiveness of a learning path recommendation. To tackle this issue, this work proposes simple offline evaluation measures. We show that they actually allow to characterise and differentiate the algorithms

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research

Generative models for natural images

Nous traitons de modèles génératifs construits avec des réseaux de neurones dans le contexte de la modélisation d’images. De nos jours, trois types de modèles sont particulièrement prédominants: les modèles à variables latentes, tel que l’auto-encodeur variationnel (VAE), les modèles autorégressifs, tel que le réseau de neurones récurrent pixel (PixelRNN), et les modèles génératifs antagonistes (GANs), qui sont des modèles à transformation de bruit entrainés à l’aide d’un adversaire. Cette thèse traite de chacun de ces modèles. Le premier chapitre couvre la base des modèles génératifs, ainsi que les réseaux de neurones pro- fonds, qui constituent la technologie principalement utilisée à l’heure actuelle pour l’implémentation de modèles statistiques puissants. Dans le deuxième chapitre, nous implémentons un auto-encodeur variationnel avec un décodeur auto-régressif. Cela permet de se libérer de l’hypothèse d’indépendance des dimensions de sortie du décodeur variationnel, en modélisant une distribution jointe traçable à la place, et de doter le modèle auto-régressif d’un code latent. De plus, notre implémentation a un coût computationnel significativement réduit, si on le compare à un modèle purement auto-régressif ayant les mêmes hypothèses de modélisation et la même performance. Nous décrivons l’espace latent de façon hiérarchique, et montrons de manière qualitative la décomposition sémantique des causes latente induites par ce design. Finalement, nous présentons des résultats obtenus avec des jeux de données standards et démontrant que la performance de notre implémentation est fortement compétitive. Dans le troisième chapitre, nous présentons une procédure d’entrainement améliorée pour une variante récente de modèles génératifs antagoniste. Le «Wasserstein GAN» minimise la distance, mesurée avec la métrique de Wasserstein, entre la distribution réelle et celle générée par le modèle, ce qui le rend plus facile à entrainer qu’un GAN avec un objectif minimax. Cependant, en fonction des paramètres, il présente toujours des cas d’échecs avec certain modes d’entrainement. Nous avons découvert que le coupable est le coupage des poids, et nous le remplaçons par une pénalité sur la norme des gradients. Ceci améliore et stabilise l’entrainement, et ce sur différents types du paramètres (incluant des modèles de langue sur des données discrètes), et permet de générer des échantillons de haute qualités sur CIFAR-10 et LSUN bedrooms. Finalement, dans le quatrième chapitre, nous considérons l’usage de modèles génératifs modernes comme modèles de normalité dans un cadre de détection hors-distribution «zero-shot». Nous avons évalué certains des modèles précédemment présentés dans la thèse, et avons trouvé que les VAEs sont les plus prometteurs, bien que leurs performances laissent encore un large place à l’amélioration. Cette partie de la thèse constitue un travail en cours. Nous concluons en répétant l’importance des modèles génératifs dans le développement de l’intelligence artificielle et mentionnons quelques défis futurs.We discuss modern generative modelling of natural images based on neural networks. Three varieties of such models are particularly predominant at the time of writing: latent variable models such as variational autoencoders (VAE), autoregressive models such as pixel recurrent neural networks (PixelRNN), and generative adversarial networks (GAN), which are noise-transformation models trained with an adversary. This thesis touches on all three kinds. The first chapter covers background on generative models, along with relevant discussions about deep neural networks, which are currently the dominant technology for implementing powerful statistical models. In the second chapter, we implement variational autoencoders with autoregressive decoders. This removes the strong assumption of output dimensions being conditionally independent in variational autoencoders, instead tractably modelling a joint distribution, while also endowing autoregressive models with a latent code. Additionally, this model has significantly reduced computational cost compared to that of a purely autoregressive model with similar modelling assumptions and performance. We express the latent space as a hierarchy, and qualitatively demonstrate the semantic decomposition of latent causes induced by this design. Finally, we present results on standard datasets that demonstrate strongly competitive performance. In the third chapter, we present an improved training procedure for a recent variant on generative adversarial networks. Wasserstein GANs minimize the Earth-Mover’s distance between the real and generated distributions and have been shown to be much easier to train than with the standard minimax objective of GANs. However, they still exhibit some failure modes in training for some settings. We identify weight clipping as a culprit and replace it with a penalty on the gradient norm. This improves training further, and we demonstrate stability on a wide variety of settings (including language models over discrete data), and samples of high quality on the CIFAR-10 and LSUN bedrooms datasets. Finally, in the fourth chapter, we present work in development, where we consider the use of modern generative models as normality models in a zero-shot out-of-distribution detection setting. We evaluate some of the models we have discussed previously in the thesis, and find that VAEs are the most promising, although their overall performance leaves a lot of room for improvement. We conclude by reiterating the significance of generative modelling in the development of artificial intelligence, and mention some of the challenges ahead

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

Proceedings of the tenth international conference Models in developing mathematics education: September 11 - 17, 2009, Dresden, Saxony, Germany

This volume contains the papers presented at the International Conference on “Models in Developing Mathematics Education” held from September 11-17, 2009 at The University of Applied Sciences, Dresden, Germany. The Conference was organized jointly by The University of Applied Sciences and The Mathematics Education into the 21st Century Project - a non-commercial international educational project founded in 1986. The Mathematics Education into the 21st Century Project is dedicated to the improvement of mathematics education world-wide through the publication and dissemination of innovative ideas. Many prominent mathematics educators have supported and contributed to the project, including the late Hans Freudental, Andrejs Dunkels and Hilary Shuard, as well as Bruce Meserve and Marilyn Suydam, Alan Osborne and Margaret Kasten, Mogens Niss, Tibor Nemetz, Ubi D’Ambrosio, Brian Wilson, Tatsuro Miwa, Henry Pollack, Werner Blum, Roberto Baldino, Waclaw Zawadowski, and many others throughout the world. Information on our project and its future work can be found on Our Project Home Page http://math.unipa.it/~grim/21project.htm It has been our pleasure to edit all of the papers for these Proceedings. Not all papers are about research in mathematics education, a number of them report on innovative experiences in the classroom and on new technology. We believe that “mathematics education” is fundamentally a “practicum” and in order to be “successful” all new materials, new ideas and new research must be tested and implemented in the classroom, the real “chalk face” of our discipline, and of our profession as mathematics educators. These Proceedings begin with a Plenary Paper and then the contributions of the Principal Authors in alphabetical name order. We sincerely thank all of the contributors for their time and creative effort. It is clear from the variety and quality of the papers that the conference has attracted many innovative mathematics educators from around the world. These Proceedings will therefore be useful in reviewing past work and looking ahead to the future

Approximate computing: An integrated cross-layer framework

A new design approach, called approximate computing (AxC), leverages the flexibility provided by intrinsic application resilience to realize hardware or software implementations that are more efficient in energy or performance. Approximate computing techniques forsake exact (numerical or Boolean) equivalence in the execution of some of the application’s computations, while ensuring that the output quality is acceptable. While early efforts in approximate computing have demonstrated great potential, they consist of ad hoc techniques applied to a very narrow set of applications, leaving in question the applicability of approximate computing in a broader context. The primary objective of this thesis is to develop an integrated cross-layer approach to approximate computing, and to thereby establish its applicability to a broader range of applications. The proposed framework comprises of three key components: (i) At the circuit level, systematic approaches to design approximate circuits, or circuits that realize a slightly modified function with improved efficiency, (ii) At the architecture level, utilize approximate circuits to build programmable approximate processors, and (iii) At the software level, methods to apply approximate computing to machine learning classifiers, which represent an important class of applications that are being utilized across the computing spectrum. Towards this end, the thesis extends the state-of-the-art in approximate computing in the following important directions. Synthesis of Approximate Circuits: First, the thesis proposes a rigorous framework for the automatic synthesis of approximate circuits , which are the hardware building blocks of approximate computing platforms. Designing approximate circuits involves making judicious changes to the function implemented by the circuit such that its hardware complexity is lowered without violating the specified quality constraint. Inspired by classical approaches to Boolean optimization in logic synthesis, the thesis proposes two synthesis tools called SALSA and SASIMI that are general, i.e., applicable to any given circuit and quality specification. The framework is further extended to automatically design quality configurable circuits , which are approximate circuits with the capability to reconfigure their quality at runtime. Over a wide range of arithmetic circuits, complex modules and complete datapaths, the circuits synthesized using the proposed framework demonstrate significant benefits in area and energy. Programmable AxC Processors: Next, the thesis extends approximate computing to the realm of programmable processors by introducing the concept of quality programmable processors (QPPs). A key principle of QPPs is that the notion of quality is explicitly codified in their HW/SW interface i.e., the instruction set. Instructions in the ISA are extended with quality fields, enabling software to specify the accuracy level that must be met during their execution. The micro-architecture is designed with hardware mechanisms to understand these quality specifications and translate them into energy savings. As a first embodiment of QPPs, the thesis presents a quality programmable 1D/2D vector processor QP-Vec, which contains a 3-tiered hierarchy of processing elements. Based on an implementation of QP-Vec with 289 processing elements, energy benefits up to 2.5X are demonstrated across a wide range of applications. Software and Algorithms for AxC: Finally, the thesis addresses the problem of applying approximate computing to an important class of applications viz. machine learning classifiers such as deep learning networks. To this end, the thesis proposes two approaches—AxNN and scalable effort classifiers. Both approaches leverage domain- specific insights to transform a given application to an energy-efficient approximate version that meets a specified application output quality. In the context of deep learning networks, AxNN adapts backpropagation to identify neurons that contribute less significantly to the network’s accuracy, approximating these neurons (e.g., by using lower precision), and incrementally re-training the network to mitigate the impact of approximations on output quality. On the other hand, scalable effort classifiers leverage the heterogeneity in the inherent classification difficulty of inputs to dynamically modulate the effort expended by machine learning classifiers. This is achieved by building a chain of classifiers of progressively growing complexity (and accuracy) such that the number of stages used for classification scale with input difficulty. Scalable effort classifiers yield substantial energy benefits as a majority of the inputs require very low effort in real-world datasets. In summary, the concepts and techniques presented in this thesis broaden the applicability of approximate computing, thus taking a significant step towards bringing approximate computing to the mainstream. (Abstract shortened by ProQuest.