25 research outputs found

    A Survey on Causal Discovery Methods for Temporal and Non-Temporal Data

    Full text link
    Causal Discovery (CD) is the process of identifying the cause-effect relationships among the variables from data. Over the years, several methods have been developed primarily based on the statistical properties of data to uncover the underlying causal mechanism. In this study we introduce the common terminologies in causal discovery, and provide a comprehensive discussion of the approaches designed to identify the causal edges in different settings. We further discuss some of the benchmark datasets available for evaluating the performance of the causal discovery algorithms, available tools to perform causal discovery readily, and the common metrics used to evaluate these methods. Finally, we conclude by presenting the common challenges involved in CD and also, discuss the applications of CD in multiple areas of interest

    MAtch, eXpand and Improve: Unsupervised Finetuning for Zero-Shot Action Recognition with Language Knowledge

    Full text link
    Large scale Vision-Language (VL) models have shown tremendous success in aligning representations between visual and text modalities. This enables remarkable progress in zero-shot recognition, image generation & editing, and many other exciting tasks. However, VL models tend to over-represent objects while paying much less attention to verbs, and require additional tuning on video data for best zero-shot action recognition performance. While previous work relied on large-scale, fully-annotated data, in this work we propose an unsupervised approach. We adapt a VL model for zero-shot and few-shot action recognition using a collection of unlabeled videos and an unpaired action dictionary. Based on that, we leverage Large Language Models and VL models to build a text bag for each unlabeled video via matching, text expansion and captioning. We use those bags in a Multiple Instance Learning setup to adapt an image-text backbone to video data. Although finetuned on unlabeled video data, our resulting models demonstrate high transferability to numerous unseen zero-shot downstream tasks, improving the base VL model performance by up to 14\%, and even comparing favorably to fully-supervised baselines in both zero-shot and few-shot video recognition transfer. The code will be released later at \url{https://github.com/wlin-at/MAXI}.Comment: Accepted at ICCV 202

    Visuell individbestemmelse av gaupe (Lynx lynx) ved hjelp av viltkamerabilder

    Get PDF
    Bestandsestimering av store rovdyr er vanskelig, ettersom store rovdyr er skye dyr med store leveområder. Det brukes flere ulike metoder for å estimere bestandene av store rovdyr, som DNA-innsamling, sporing på snø og viltkamera-overvåkning. Bilder fra viltkamera benyttes også til individbestemmelse for enkelte arter. Dette forutsetter at arten har permanente markører som er unike for hvert individ, ikke endrer seg igjennom dyrets levetid og er synlige på viltkamerabilder. Denne metodikken benyttes ofte til å estimere bestander av mønstrete kattedyr, for eksempel gaupe (Lynx lynx). Forskere individbestemmer fotograferte gauper ut ifra pelsmønster og estimerer bestand og tetthet ut ifra antall individbestemte dyr. Denne studien undersøker nøyaktigheten av individbestemmelse av gauper fotografert med viltkamera. Dette ble testet med viltkamerabilder av 40 kjente gauper fra 13 dyreparker i Norge, Sverige, Danmark, Finland, Tyskland og England. Bilder av de ulike individene ble sammenliknet i en nettbasert undersøkelse hvor deltakerne skulle avgjøre om to gaupe-observasjoner var av samme individ, av ulike individer eller uidentifiserbare. Resultatene fra undersøkelsen viste at sammenlikninger med minst en uniform gaupe hadde høyest sannsynlighet for å bli regnet som uidentifiserbare. Deltakerne mente at nesten halvparten av alle sammenlikninger med to uniforme gauper var uidentifiserbare. Sammenlikninger med minst en IR-natt-observasjon hadde signifikant høyere sannsynlighet for å være uidentifiserbare enn sammenlikninger med to dag-observasjoner. Deltakers erfaring så derimot ikke ut til å påvirke sannsynligheten for å besvare en sammenlikning. Deltakere med erfaring fra visuell individgjenkjenning av gaupe har derimot høy sannsynlighet for å svare rett >95%. Sammenlikninger med to uniforme gauper viste signifikant høyere sannsynlighet for å bli feilidentifisert enn sammenlikninger med minst en mønstret gaupe. To IR-natt-observasjoner viste også en tendens til å gi høyere sannsynlighet for feil svar sammenliknet med to dag-observasjoner. Selv om andelen feil var lav, kan feilene gi store utslag ved en reell bestandsestimering. Feil som innebærer at deltakeren svarer at to observasjoner av samme individ er to ulike individer skaper ikke-eksisterende «spøkelses-individer». Dette kan derfor føre til store overestimat av en bestand. Metodikken bør fungere til å skille familiegrupper, eller til å estimere bestander med lav andel uniforme gauper. Dette forutsetter at individbestemmelsen blir utført av to eller flere erfarne personer.To estimate the population of large carnivores is difficult. Large carnivores are elusive animals with large home-ranges. Several different methods are used to estimate the populations of large carnivores, including collection of DNA samples, tracking in snow, and trail camera monitoring. Photos from trail cameras are used for individual identification of some species. This method works if the species has permanent markers that are individually unique and do not change throughout the animal's lifetime and that the markers are visible on trail camera photos. This method is used to estimate the population of patterned felids, like lynx (Lynx lynx). Researchers identify photographed lynx individuals based on their fur pattern, and estimate population and density based on the number of identified animals. This study examines the accuracy of individual identification of lynx photographed with trail cameras. The study used trail camera photos of 40 lynx from 13 zoos in Norway, Sweden, Denmark, Finland, Germany, and England. Photos of the different individuals were then compared in an online survey where participants were asked whether there were two observations of the same lynx, two different lynxes, or if the observations were unidentifiable. The results of the survey showed that comparisons with at least one uniform lynx had the highest likelihood of being considered unidentifiable. Participants thought that almost half of all comparisons with two uniform lynxes were unidentifiable. Comparisons with at least one IR-night observation had a significantly higher likelihood of being unidentifiable than comparisons with two daytime observations. However, participants experience did not seem to affect the likelihood of answering a comparison. However, participants with experience in visual lynx identification, had a high likelihood of answering correctly (>95%). Comparisons with two uniform lynxes showed a significantly higher likelihood of being misidentified than comparisons with at least one patterned lynx. Two IR-night observations also showed a tendency to give a higher likelihood of incorrect answers compared to two daytime observations. Although the proportion of errors was low, errors that result in the participant answering that two observations of the same individual are two different individuals create non-existent “ghost individuals." This can therefore lead to overestimation of a population. The methodology should work to distinguish family groups or estimate populations with a low proportion of uniform lynx. This assumes that individual identification is performed by two or more experienced persons

    Continual learning from stationary and non-stationary data

    Get PDF
    Continual learning aims at developing models that are capable of working on constantly evolving problems over a long-time horizon. In such environments, we can distinguish three essential aspects of training and maintaining machine learning models - incorporating new knowledge, retaining it and reacting to changes. Each of them poses its own challenges, constituting a compound problem with multiple goals. Remembering previously incorporated concepts is the main property of a model that is required when dealing with stationary distributions. In non-stationary environments, models should be capable of selectively forgetting outdated decision boundaries and adapting to new concepts. Finally, a significant difficulty can be found in combining these two abilities within a single learning algorithm, since, in such scenarios, we have to balance remembering and forgetting instead of focusing only on one aspect. The presented dissertation addressed these problems in an exploratory way. Its main goal was to grasp the continual learning paradigm as a whole, analyze its different branches and tackle identified issues covering various aspects of learning from sequentially incoming data. By doing so, this work not only filled several gaps in the current continual learning research but also emphasized the complexity and diversity of challenges existing in this domain. Comprehensive experiments conducted for all of the presented contributions have demonstrated their effectiveness and substantiated the validity of the stated claims

    Systems for AutoML Research

    Get PDF

    Algorithmic Reason

    Get PDF
    Are algorithms ruling the world today? Is artificial intelligence making life-and-death decisions? Are social media companies able to manipulate elections? As we are confronted with public and academic anxieties about unprecedented changes, this book offers a different analytical prism to investigate these transformations as more mundane and fraught. Aradau and Blanke develop conceptual and methodological tools to understand how algorithmic operations shape the government of self and other. While disperse and messy, these operations are held together by an ascendant algorithmic reason. Through a global perspective on algorithmic operations, the book helps us understand how algorithmic reason redraws boundaries and reconfigures differences. The book explores the emergence of algorithmic reason through rationalities, materializations, and interventions. It traces how algorithmic rationalities of decomposition, recomposition, and partitioning are materialized in the construction of dangerous others, the power of platforms, and the production of economic value. The book shows how political interventions to make algorithms governable encounter friction, refusal, and resistance. The theoretical perspective on algorithmic reason is developed through qualitative and digital methods to investigate scenes and controversies that range from mass surveillance and the Cambridge Analytica scandal in the UK to predictive policing in the US, and from the use of facial recognition in China and drone targeting in Pakistan to the regulation of hate speech in Germany. Algorithmic Reason offers an alternative to dystopia and despair through a transdisciplinary approach made possible by the authors’ backgrounds, which span the humanities, social sciences, and computer sciences

    AdaCC: Cumulative Cost-Sensitive Boosting for Imbalanced Classification

    Get PDF
    Class imbalance poses a major challenge for machine learning as most supervised learning models might exhibit bias towards the majority class and under-perform in the minority class. Cost-sensitive learning tackles this problem by treating the classes differently, formulated typically via a user-defined fixed misclassification cost matrix provided as input to the learner. Such parameter tuning is a challenging task that requires domain knowledge and moreover, wrong adjustments might lead to overall predictive performance deterioration. In this work, we propose a novel cost-sensitive boosting approach for imbalanced data that dynamically adjusts the misclassification costs over the boosting rounds in response to model's performance instead of using a fixed misclassification cost matrix. Our method, called AdaCC, is parameter-free as it relies on the cumulative behavior of the boosting model in order to adjust the misclassification costs for the next boosting round and comes with theoretical guarantees regarding the training error. Experiments on 27 real-world datasets from different domains with high class imbalance demonstrate the superiority of our method over 12 state-of-the-art cost-sensitive boosting approaches exhibiting consistent improvements in different measures, for instance, in the range of [0.3%-28.56%] for AUC, [3.4%-21.4%] for balanced accuracy, [4.8%-45%] for gmean and [7.4%-85.5%] for recall.Comment: 30 page

    Artificial Intelligence and Ambient Intelligence

    Get PDF
    This book includes a series of scientific papers published in the Special Issue on Artificial Intelligence and Ambient Intelligence at the journal Electronics MDPI. The book starts with an opinion paper on “Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules”, presenting relations between information society, electronics and artificial intelligence mainly through twenty-four IS laws. After that, the book continues with a series of technical papers that present applications of Artificial Intelligence and Ambient Intelligence in a variety of fields including affective computing, privacy and security in smart environments, and robotics. More specifically, the first part presents usage of Artificial Intelligence (AI) methods in combination with wearable devices (e.g., smartphones and wristbands) for recognizing human psychological states (e.g., emotions and cognitive load). The second part presents usage of AI methods in combination with laser sensors or Wi-Fi signals for improving security in smart buildings by identifying and counting the number of visitors. The last part presents usage of AI methods in robotics for improving robots’ ability for object gripping manipulation and perception. The language of the book is rather technical, thus the intended audience are scientists and researchers who have at least some basic knowledge in computer science

    Analysis and Modular Approach for Text Extraction from Scientific Figures on Limited Data

    Get PDF
    Scientific figures are widely used as compact, comprehensible representations of important information. The re-usability of these figures is however limited, as one can rarely search directly for them, since they are mostly indexing by their surrounding text (e. g., publication or website) which often does not contain the full-message of the figure. In this thesis, the focus is on making the content of scientific figures accessible by extracting the text from these figures. A modular pipeline for unsupervised text extraction from scientific figures, based on a thorough analysis of the literature, was built to address the problem. This modular pipeline was used to build several unsupervised approaches, to evaluate different methods from the literature and new methods and method combinations. Some supervised approaches were built as well for comparison. One challenge, while evaluating the approaches, was the lack of annotated data, which especially needed to be considered when building the supervised approach. Three existing datasets were used for evaluation as well as two datasets of 241 scientific figures which were manually created and annotated. Additionally, two existing datasets for text extraction from other types of images were used for pretraining the supervised approach. Several experiments showed the superiority of the unsupervised pipeline over common Optical Character Recognition engines and identified the best unsupervised approach. This unsupervised approach was compared with the best supervised approach, which, despite of the limited amount of training data available, clearly outperformed the unsupervised approach.Infografiken sind ein viel verwendetes Medium zur kompakten Darstellung von Kernaussagen. Die Nachnutzbarkeit dieser Abbildungen ist jedoch häufig limitiert, da sie schlecht auffindbar sind, da sie meist über die umschließenden Medien, wie beispielsweise Publikationen oder Webseiten, und nicht über ihren Inhalt indexiert sind. Der Fokus dieser Arbeit liegt auf der Extraktion der textuellen Inhalte aus Infografiken, um deren Inhalt zu erschließen. Ausgehend von einer umfangreichen Analyse verwandter Arbeiten, wurde ein generalisierender, modularer Ansatz für die unüberwachte Textextraktion aus wissenschaftlichen Abbildungen entwickelt. Mit diesem modularen Ansatz wurden mehrere unüberwachte Ansätze und daneben auch noch einige überwachte Ansätze umgesetzt, um diverse Methoden aus der Literatur sowie neue und bisher noch nicht genutzte Methoden zu vergleichen. Eine Herausforderung bei der Evaluation war die geringe Menge an annotierten Abbildungen, was insbesondere beim überwachten Ansatz Methoden berücksichtigt werden musste. Für die Evaluation wurden drei existierende Datensätze verwendet und zudem wurden zusätzlich zwei Datensätze mit insgesamt 241 Infografiken erstellt und mit den nötigen Informationen annotiert, sodass insgesamt 5 Datensätze für die Evaluation verwendet werden konnten. Für das Pre-Training des überwachten Ansatzes wurden zudem zwei Datensätze aus verwandten Textextraktionsbereichen verwendet. In verschiedenen Experimenten wird gezeigt, dass der unüberwachte Ansatz besser funktioniert als klassische Texterkennungsverfahren und es wird aus den verschiedenen unüberwachten Ansätzen der beste ermittelt. Dieser unüberwachte Ansatz wird mit dem überwachten Ansatz verglichen, der trotz begrenzter Trainingsdaten die besten Ergebnisse liefert
    corecore