Essays about: "explainable ai"
Showing result 1 - 5 of 59 essays containing the words explainable ai.
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1. Evaluating and optimizing Transformer models for predicting chemical reactions
University essay from Göteborgs universitet/Institutionen för data- och informationsteknikAbstract : In this thesis, we assess the effectiveness of a transformer model specifically trained to predict chemical reactions. The model, named Chemformer, is a sequence-tosequence model that uses the transformer’s encoder and decoder stacks. READ MORE
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2. Computationally Efficient Explainable AI: Bayesian Optimization for Computing Multiple Counterfactual Explanantions
University essay from KTH/Matematik (Avd.)Abstract : In recent years, advanced machine learning (ML) models have revolutionized industries ranging from the healthcare sector to retail and E-commerce. However, these models have become increasingly complex, making it difficult for even domain experts to understand and retrace the model's decision-making process. READ MORE
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3. Unsupervised Online Anomaly Detection in Multivariate Time-Series
University essay from Uppsala universitet/DatorteknikAbstract : This research aims to identify a method for unsupervised online anomaly detection in multivariate time series in dynamic systems in general and on the case study of Devwards IoT-system in particular. A requirement of the solution is its explainability, online learning and low computational expense. READ MORE
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4. Categorization of Historical Photographs using Convolutional Neural Networks
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : The goal of this project is to explore the possibilities of using Convolutional Neural Networks (CNN) on historical photographs taken in Sweden to determine a feasible way of automatically detecting the studio that the photograph was taken in. Photographs supplied by The City Faces Project were used for this purpose. READ MORE
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5. Towards gradient faithfulness and beyond
University essay from Högskolan i Halmstad/Akademin för informationsteknologiAbstract : The riveting interplay of industrialization, informalization, and exponential technological growth of recent years has shifted the attention from classical machine learning techniques to more sophisticated deep learning approaches; yet its intrinsic black-box nature has been impeding its widespread adoption in transparency-critical operations. In this rapidly evolving landscape, where the symbiotic relationship between research and practical applications has never been more interwoven, the contribution of this paper is twofold: advancing gradient faithfulness of CAM methods and exploring new frontiers beyond it. READ MORE