1,720,957 research outputs found
Learning with uncertainty via Hyperbolic Neural Networks
Full text linkThis thesis explores the application of hyperbolic geometry and hyperbolic neural networks across various domains, with a focus on leveraging uncertainty estimation to improve the learning process and performance in complex tasks. We begin with a brief introduction to hyperbolic neural networks, providing the theoretical foundation and key concepts that underpin our subsequent research. This work then spans three main areas: self-supervised representation learning for skeleton-based action recognition, active domain adaptation for semantic segmentation, and multimodal large language models.
First, this thesis investigates self-supervised learning in the context of skeleton-based action recognition, where effective representation learning remains challenging due to the hierarchical nature of human motion data. We introduce hyperbolic neural networks to address this challenge through uncertainty-aware learning, developing a novel Hyperbolic Self-Paced learning model (HYSP). This approach leverages the hyperbolic radius as an uncertainty metric to adaptively pace the learning process, scaling the gradient determined by each sample by the norm of the hyperbolic embedding. When evaluated on standard action recognition benchmarks, HYSP demonstrates superior performance while eliminating the need for computationally expensive negative mining procedures.
Next, we explore active learning for semantic segmentation under domain shift, where efficient label acquisition is crucial for adapting to new environments while keeping labeling costs down. For this challenge, we develop a hyperbolic approach named HALO (Hyperbolic Active Learning Optimization), which interprets the hyperbolic radius as an indicator of data scarcity. By combining the hyperbolic radius with prediction entropy, we obtain an estimator of epistemic uncertainty, which we use for selective annotation of pixels in the image. HALO achieves state-of-the-art results on domain adaptation benchmarks while requiring only a small fraction of target labels, surpassing even fully supervised domain adaptation methods.
Finally, this thesis examines large-scale vision-language modeling, where uncertainty estimation becomes particularly challenging due to the scale and multimodal nature of the data. By developing a novel training strategy for a hyperbolic version of BLIP-2, we demonstrate that hyperbolic learning can be successfully scaled to billion-parameter architectures without compromising stability or performance. Our approach achieves results comparable to its Euclidean counterpart while providing meaningful uncertainty estimates thanks to hyperbolic embeddings, offering a new perspective on uncertainty quantification in large multimodal models.
Throughout these studies, we demonstrate that learning in hyperbolic space offers unique advantages in estimating uncertainty and improving model performance and efficiency across diverse machine learning tasks. This work contributes to the broader understanding of hyperbolic neural networks and their potential to advance the field of deep learning
HYperbolic Self-Paced Learning for Self-Supervised Skeleton-based Action Representations
Full text linkSelf-paced learning has been beneficial for tasks where some initial
knowledge is available, such as weakly supervised learning and domain
adaptation, to select and order the training sample sequence, from easy to
complex. However its applicability remains unexplored in unsupervised learning,
whereby the knowledge of the task matures during training. We propose a novel
HYperbolic Self-Paced model (HYSP) for learning skeleton-based action
representations. HYSP adopts self-supervision: it uses data augmentations to
generate two views of the same sample, and it learns by matching one (named
online) to the other (the target). We propose to use hyperbolic uncertainty to
determine the algorithmic learning pace, under the assumption that less
uncertain samples should be more strongly driving the training, with a larger
weight and pace. Hyperbolic uncertainty is a by-product of the adopted
hyperbolic neural networks, it matures during training and it comes with no
extra cost, compared to the established Euclidean SSL framework counterparts.
When tested on three established skeleton-based action recognition datasets,
HYSP outperforms the state-of-the-art on PKU-MMD I, as well as on 2 out of 3
downstream tasks on NTU-60 and NTU-120. Additionally, HYSP only uses positive
pairs and bypasses therefore the complex and computationally-demanding mining
procedures required for the negatives in contrastive techniques. Code is
available at https://github.com/paolomandica/HYSP.Comment: Accepted at ICLR 202
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
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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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