1,720,965 research outputs found
Improving the capabilities of Variational Autoencoder Models by exploring their latent space
No full textA fundamental goal in developing of Machine Learning is to build systems that are able to mimic the capabilities of humans and animals. This drives the field towards the creation of algorithms and architectures capable of mastering a vast array of tasks, from basic pattern recognition to complex decision-making under and drug design uncertain conditions. Recent advancements in deep learning have shown that neural networks can make significant advances by using large amount of data and computing power. The last decade deep generative models, have achieve great achievements in fields like computer vision, natural language processing, that we could not even imagine two decades ago. These achievements highlight the potential of deep learning and underscore the ongoing effort to narrow the gap between human intelligence and machine intelligence. This thesis delves into the advancement of deep generative modeling, particularly focusing on Variational Autoencoders (VAEs), to tackle significant challenges such as out-ofdistribution (OOD) generation, catastrophic forgetting, and the learning of multi-modal probabilistic structures. Inspired by human cognitive abilities to learn from minimal observations and adapt to new environments, our work seeks to learn similar capabilities within machine learning models, thereby narrowing the gap between human intelligence and artificial intelligence. Through three main contributions, we address limitations of current generative modeling approaches and propose solutions to improve their performance. We explore first, the ability of VAEs to achieve OOD conditional generations. Although conditional generation is already a challenging task because the model might ignore these conditions, our research goes further into a more complex task. As humans’ brains are able to understand and produce new combinations of familiar elements, we develop a novel framework that is capable of generating data with desired property values combinations not included in the training data. Our method, leveraging conditional VAEs with a back-translation mechanism, can handle a diverse range of input–attribute pairs that may not be present in the training data, thus enhancing its capability to handle OOD data. Moreover, the back-translation procedure preserves the content of the input data while manipulating their attribute values, enabling style transfer. Then, we examine another challenging task for ML, namely, continual classification learning. In this thesis, we tackle this challenge by introducing a joint generative model approach, combining naturally a generative model with a classifier in the latent space, relying on the joint generative model to replicate the data distribution with the corresponding labels of the previously seen tasks. Finally, we study the limitations of VAEs, focusing on their inability to produce generations from the individual modalities of data originating from mixture distributions, reflecting humans’ ability to understand and process complex, heterogeneous information. To address this, we propose a 2-level hierarchical latent variable model, which introduces both continuous and categorical latent variables, thereby offering a richer representation of data. By integrating a more flexible variational posterior and an informative conditional prior, mirroring the same structure, our method substantially improves the model’s capacity for capturing and generating the complex probabilistic structures.</p
Back translation variational autoencoders for OOD generation
No full textHumans are able to quickly adapt to new situations, learn effectively with limited data, and create unique combinations of basic concepts. In contrast generalizing out-of-distribution (OOD) data and achieving combinatorial generalizations are fundamental challenges for the machine learning models. To address these challenges, we propose BtVAE, a method that employs supervised conditional VAE models to achieve combinatorial generalization in certain scenarios and consequently to generate out-of-distribution (OOD) data. Unlike previous approaches that use new factors of variation during testing, our method uses only existing attributes from the training data, but in ways that were not seen during training (e.g., small objects during training and large objects during testing). We first learn a latent representation of the in-distribution inputs and we passing this representation in a conditional decoder, conditioning on some OOD attribute values, to generate implicit OOD samples. These generated samples are then translated back to the original in-distribution inputs, conditioning on the actual attribute values. To ensure that the generated OOD samples have the specified OOD attribute values, a predictor is introduced. By training with OOD attribute values the decoder learns to produce the correct output for unseen combinations, resulting in a model that not only is able to reconstruct OOD data but also to manipulate the OOD data and to generate samples conditioning on unseen combinations of attribute values
Δυναμικός πραγραμματισμός στην επιχειρησιακή έρευνα
Full text link113 σ.Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Μαθηματική Προτυποποίηση σε Σύγχρονες Τεχνολογίες στην Οικονομία”Προβλήματα Δυναμικού Πραγραμματισμού με εφαρμογές στους στην Επιχειρησιακή ΈρευναΦραντζέσκα Ι. Λάβδ
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Improving VAE Generations of Multimodal Data Through Data-Dependent Conditional Priors
No full textOne of the major shortcomings of variational autoencoders is the inability to produce generations from the individual modalities of data originating from mixture distributions. This is primarily due to the use of a simple isotropic Gaussian as the prior for the latent code in the ancestral sampling procedure for the data generations. We propose a novel formulation of variational autoencoders, conditional prior VAE (CP-VAE), which learns to differentiate between the individual mixture components and therefore allows for generations from the distributional data clusters. We assume a two-level generative process with a continuous (Gaussian) latent variable sampled conditionally on a discrete (categorical) latent component. The new variational objective naturally couples the learning of the posterior and prior conditionals, and the learning of the latent categories encoding the multimodality of the original data in an unsupervised manner. The data-dependent conditional priors are then used to sample the continuous latent code when generating new samples from the individual mixture components corresponding to the multimodal structure of the original data. Our experimental results illustrate the generative performance of our new model comparing to multiple baselines
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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