Deep Active Learning in the Presence of Label Noise: A Survey

Deep active learning has emerged as a powerful tool for training deep learning models within a predefined labeling budget. These models have achieved performances comparable to those trained in an offline setting. However, deep active learning faces substantial issues when dealing with classification datasets containing noisy labels. In this literature review, we discuss the current state of deep active learning in the presence of label noise, highlighting unique approaches, their strengths, and weaknesses. With the recent success of vision transformers in image classification tasks, we provide a brief overview and consider how the transformer layers and attention mechanisms can be used to enhance diversity, importance, and uncertainty-based selection in queries sent to an oracle for labeling. We further propose exploring contrastive learning methods to derive good image representations that can aid in selecting high-value samples for labeling in an active learning setting. We also highlight the need for creating unified benchmarks and standardized datasets for deep active learning in the presence of label noise for image classification to promote the reproducibility of research. The review concludes by suggesting avenues for future research in this area.Comment: 20 pages, PhD literature revie

Stokes, Gauss, and Bayes Walk into a Bar...

This thesis consists of three distinct projects. The first is a study of microbial aggregate fragmentation, in which we develop a dynamical model of aggregate deformation and breakage and use it to obtain a post-fragmentation density function. The second and third projects deal with dimensionality reduction in machine learning problems. In the second project, we derive a one-pass sparsified Gaussian mixture model to perform clustering analysis on high-dimensional streaming data. The model estimates parameters in dense space while storing and performing computations in a compressed space. In the final project, we build an expert system classifier with a Bayesian network for use on high-volume streaming data. Our approach is specialized to reduce the number of observations while obtaining sufficient labeled training data in a regime of extreme class-imbalance and expensive oracle queries

Stokes, Gauss, and Bayes walk into a bar...

This thesis consists of three distinct projects. The first is a study of microbial aggregate fragmentation, in which we develop a dynamical model of aggregate deformation and breakage and use it to obtain a post-fragmentation density function. The second and third projects deal with dimensionality reduction in machine learning problems. In the second project, we derive a one-pass sparsified Gaussian mixture model to perform clustering analysis on high-dimensional streaming data. The model estimates parameters in dense space while storing and performing computations in a compressed space. In the final project, we build an expert system classifier with a Bayesian network for use on high-volume streaming data. Our approach is specialized to reduce the number of observations while obtaining sufficient labeled training data in a regime of extreme class-imbalance and expensive oracle queries

Human-Machine Collaboration for Fast Land Cover Mapping

We propose incorporating human labelers in a model fine-tuning system that provides immediate user feedback. In our framework, human labelers can interactively query model predictions on unlabeled data, choose which data to label, and see the resulting effect on the model's predictions. This bi-directional feedback loop allows humans to learn how the model responds to new data. Our hypothesis is that this rich feedback allows human labelers to create mental models that enable them to better choose which biases to introduce to the model. We compare human-selected points to points selected using standard active learning methods. We further investigate how the fine-tuning methodology impacts the human labelers' performance. We implement this framework for fine-tuning high-resolution land cover segmentation models. Specifically, we fine-tune a deep neural network -- trained to segment high-resolution aerial imagery into different land cover classes in Maryland, USA -- to a new spatial area in New York, USA. The tight loop turns the algorithm and the human operator into a hybrid system that can produce land cover maps of a large area much more efficiently than the traditional workflows. Our framework has applications in geospatial machine learning settings where there is a practically limitless supply of unlabeled data, of which only a small fraction can feasibly be labeled through human efforts.Comment: To appear in AAAI 202

Lucian’s Alexander: technoprophecy, thaumatology and the poetics of wonder

This is the final version of the chapter. Available from De Gruyter via the DOI in this record.Trends in Classics - Supplementary Volumes number 53This paper focuses on Lucian’s critique of the wonder-working of the second century CE prophet of Asclepius, Alexander of Abonouteichos, in Alexander or the False Prophet. It explores meta-literary depths of the essay which have not been scrutinized before. The analysis unfolds in three sections. In the first, Alexander emerges from an intertextual reading with Hippolytus’ polemic against magic (Ref. 4.28-42) as a creative innovator of the common magician’s repertoire, making his magic a cypher for Lucian’s own literary techniques. In the second section, I argue that Alexander’s ‘autophone’ oracles dramatize Lucian’s poetics in a particularly pointed way, embroiling author and subject in a dialogue of mutual exposure. Overlaps emerge between Lucian’s technoprophet and the discourse of Orakelkritik, which sharpen and lend nuance to Lucian’s attack, whilst comparison with Hero of Alexander’s mechanical wonders opens up a more ambivalent interpretation of the professed scepticism of both Lucian and his readers. Having examined the ways in which Lucian implicates himself in Alexander’s fraud, connections are explored with other Lucianic works-of-wonder such as Lover of lies, True Stories and the prolaliai, showing that magic and religious fraud are deeply connected with fiction in Lucian’s oeuvre. This lends uniquely rich complexity to Lucian’s thaumatology, since he meditates not only on the nature of wonders, but on the nature of reading about wonders as well.This article was written whilst I was a Marie Curie research fellow at the Aarhus Institute of Advanced Studies, and I gratefully acknowledge both the funding and the resources of AIAS and Aarhus Universit

Deconstructing meaning: Industrial design as Adornment and Wit

The catalog has ISBN nr 978-91-979541-5-0 and refers to the full length academic papers onlineInternational audienceIn this paper we present new theoretical perspectives about industrial design. First, we establish that antinomies about function, form and meaning cannot offer a theory of industrial design. Then we bear on advances in Design theory in the literature of engineering design to find out universal features of design which are common to industrial design, Architecture and Engineering. Taking into account social and cognitive contexts, we identify the dilemma that is specific of industrial design. This dilemma can be solved in two ways that we define as "adornement" and "wit" which differ by how the identity of objects is maintained or challenged by design. Each way corresponds to different types of rhetoric -classic and conceptist- that we identify. The combination of adornment and wit explains the generative power of industrial design and its paradoxical situation: neither Art, neither engineering. Moreover, the academic identity of industrial design research can be clarified within the traditions of Design theory, anthropology and rhetoric

In response to 'Celebrate citation: flipping the pedagogy of plagiarism in Qatar'

In her article (http://uobrep.openrepository.com/uobrep/handle/10547/335947) Molly McHarg makes several points that I agree with, particularly that for the majority of students the plagiarism is not deliberate but is due to a lack of understanding of how to reference correctly

‘Sand’s Way’: The Voices of George Sand’s François the Waif in Marcel Proust’s Remembrance of Things Past

This paper traces one of the origins of Marcel Proust’s artistic vocation in his fascination for a novel by George Sand, François le Champi (François the Waif). In Remembrance of Things Past, the adult writer explores the gradual recognition of this early phase of his formation: Sand’s novel appears in the ‘Combray’ section in Swann’s Way and it reappears at the moment of apparent illumination regarding his future as a writer in Time Regained. Leaving deliberately aside the psychoanalytic implications of the story, this article will instead emphasise the ‘vocality’ of the story, taken from the oral tradition of the provincial Berry countryside and imbued with colloquial texture, to show how the retelling of this moment in time when the novel was encountered includes a definition of what artistic vocation is for the narrator