1,720,979 research outputs found
Latent communication in artificial neural networks
Full text linkAs NNs (Neural Networks) permeate various scientific and industrial domains, understanding the universality and reusability of their representations becomes crucial. At their core, these networks create intermediate neural representations, indicated as latent spaces, of the input data and subsequently leverage them to perform specific downstream tasks. This dissertation focuses on the universality and reusability of neural representations. Do the latent representations crafted by a NN remain exclusive to a particular trained instance, or can they generalize across models, adapting to factors such as randomness during training, model architecture, or even data domain? This adaptive quality introduces the notion of Latent Communication – a phenomenon that describes when representations can be unified or reused across neural spaces. A salient observation from our research is the emergence of similarities in latent representations, even when these originate from distinct or seemingly unrelated NNs. By exploiting a partial correspondence between the two data distributions that establishes a semantic link, we found that these representations can either be projected into a universal representation (Moschella* , Maiorca* , et al., 2023), coined as Relative Representation, or be directly translated from one space to another (Maiorca* et al., 2023). Intriguingly, this holds even when the transformation relating the spaces is unknown (Cannistraci, Moschella, Fumero, et al., 2024) and when the semantic bridge between them is minimal (Cannistraci, Moschella, Maiorca, et al., 2023). Latent Communication allows for a bridge between independently trained NN, irrespective of their training regimen, architecture, or the data modality they were trained on – as long as the data semantic content stays the same (e.g., images and their captions). This holds true for both generation, classification and retrieval downstream tasks; in supervised, weakly supervised, and unsupervised settings; and spans various data modalities including images, text, audio, and graphs – showcasing the universality of the Latent Communication phenomenon. From a practical standpoint, our research offers the potential to repurpose and reuse models, circumventing the need for resource-intensive retraining; enables the transfer of knowledge across them; and allows for downstream performance evaluation directly in the latent space. Indeed, several works leveraged the insights from our Latent Communication research (Kiefer and Buckley, 2024; Z. Wu, Y. Wu, and Mou, 2024; Jian et al., 2023; Norelli, Fumero, et al., 2023; G. Wang et al., 2023). For example, relative representations have been instrumental in attaining state-of-the-art results in Weakly Supervised Vision-and-Language Pretraining (C. Chen et al., 2023). Reflecting its significance, (Moschella* , Maiorca* , et al., 2023) has been presented orally at ICLR 2023 and Latent Communication has been a central theme in the UniReps: Unifying Representations in Neural Models Workshop at NeurIPS 2023, co-organized by our team
Performance variations of the Bayesian model of peer-assessment implemented in OpenAnswer response to modifications of the number of peers assessed and of the quality of the class
No full textThe paper presents a study of the performance
variationsoftheBayesianmodelofpeerassessmentimplementedin
OpenAnswer, in terms of the grades prediction accuracy.
OpenAnswer (OA)modelsapeerassessmentsessionasaBayesian
network. For each student, a subnetwork contains variables
describingrelevantaspectsofboththeindividualcognitivestateand
the state of the current assessment session. Subnetworks are
interconnected to each other to obtain the final one. Evidence
propagated through the global network is represented by all the
gradesgivenbystudentstotheirpeers,togetherwithasubsetofthe
teacher’scorrections.Amongthepossibleaffectingfactors,thepaper
reportsabouttheinvestigationofthedependenceofgradesprediction
performance on the quality of the class, i.e., the average level of
proficiency of itsstudents,andon thenumberofpeersassessedby
eachstudent.Theresultsshowthatbothfactorsaffecttheaccuracyof
the inferred marks produced by the Bayesian network, when
comparedwiththeavailablegroundtruthproducedbyteachers
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
Latent Spectral Regularization for Continual Learning
Full text linkWhile biological intelligence grows organically as new knowledge is gathered throughout life, Artificial Neural Networks forget catastrophically whenever they face a changing training data distribution. Rehearsal-based Continual Learning (CL) approaches have been established as a versatile and reliable solution to overcome this limitation; however, sudden input disruptions and memory constraints are known to alter the consistency of their predictions. We study this phenomenon by investigating the geometric characteristics of the learner\u27s latent space and find that replayed data points of different classes increasingly mix up, interfering with classification. Hence, we propose a geometric regularizer that enforces weak requirements on the Laplacian spectrum of the latent space, promoting a partitioning behavior. Our proposal, called Continual Spectral Regularizer for Incremental Learning (CaSpeR-IL), can be easily combined with any rehearsal-based CL approach and improves the performance of SOTA methods on standard benchmarks.14 pages, 4 figures, , to appear in Pattern Recognition Letters, Volume 184, August 2024, Pages 119-12
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
- …
