Sökning: "explainable AI"
Visar resultat 1 - 5 av 63 uppsatser innehållade orden explainable AI.
1. Evaluating and optimizing Transformer models for predicting chemical reactions
Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknikSammanfattning : 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. LÄS MER
2. Computationally Efficient Explainable AI: Bayesian Optimization for Computing Multiple Counterfactual Explanantions
Master-uppsats, KTH/Matematik (Avd.)Sammanfattning : 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. LÄS MER
3. Unsupervised Online Anomaly Detection in Multivariate Time-Series
Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/DatorteknikSammanfattning : 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. LÄS MER
4. Categorization of Historical Photographs using Convolutional Neural Networks
Master-uppsats, Uppsala universitet/Institutionen för informationsteknologiSammanfattning : 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. LÄS MER
5. Towards gradient faithfulness and beyond
Master-uppsats, Högskolan i Halmstad/Akademin för informationsteknologiSammanfattning : 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. LÄS MER