Machine Learning Model
Machine learning models aim to create systems that can learn from data and make predictions or decisions without explicit programming. Current research emphasizes improving model accuracy, interpretability, and robustness, focusing on architectures like deep neural networks, decision tree ensembles, and transformer models, as well as exploring decentralized learning and techniques for mitigating biases and vulnerabilities. These advancements are crucial for diverse applications, ranging from optimizing resource management (e.g., smart irrigation) to improving healthcare diagnostics and enhancing the security and trustworthiness of AI systems.
Papers
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures
Yongji Wu, Matthew Lentz, Danyang Zhuo, Yao Lu
Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey
Julian Wörmann, Daniel Bogdoll, Christian Brunner, Etienne Bührle, Han Chen, Evaristus Fuh Chuo, Kostadin Cvejoski, Ludger van Elst, Philip Gottschall, Stefan Griesche, Christian Hellert, Christian Hesels, Sebastian Houben, Tim Joseph, Niklas Keil, Johann Kelsch, Mert Keser, Hendrik Königshof, Erwin Kraft, Leonie Kreuser, Kevin Krone, Tobias Latka, Denny Mattern, Stefan Matthes, Franz Motzkus, Mohsin Munir, Moritz Nekolla, Adrian Paschke, Stefan Pilar von Pilchau, Maximilian Alexander Pintz, Tianming Qiu, Faraz Qureishi, Syed Tahseen Raza Rizvi, Jörg Reichardt, Laura von Rueden, Alexander Sagel, Diogo Sasdelli, Tobias Scholl, Gerhard Schunk, Gesina Schwalbe, Hao Shen, Youssef Shoeb, Hendrik Stapelbroek, Vera Stehr, Gurucharan Srinivas, Anh Tuan Tran, Abhishek Vivekanandan, Ya Wang, Florian Wasserrab, Tino Werner, Christian Wirth, Stefan Zwicklbauer
AdaCap: Adaptive Capacity control for Feed-Forward Neural Networks
Katia Meziani, Karim Lounici, Benjamin Riu
On Designing Data Models for Energy Feature Stores
Gregor Cerar, Blaž Bertalanič, Anže Pirnat, Andrej Čampa, Carolina Fortuna
Btech thesis report on adversarial attack detection and purification of adverserially attacked images
Dvij Kalaria
Visualization of Decision Trees based on General Line Coordinates to Support Explainable Models
Alex Worland, Sridevi Wagle, Boris Kovalerchuk
Human-in-the-loop online multi-agent approach to increase trustworthiness in ML models through trust scores and data augmentation
Gusseppe Bravo-Rocca, Peini Liu, Jordi Guitart, Ajay Dholakia, David Ellison, Miroslav Hodak
A Framework for Constructing Machine Learning Models with Feature Set Optimisation for Evapotranspiration Partitioning
Adam Stapleton, Elke Eichelmann, Mark Roantree
Local Explanation of Dimensionality Reduction
Avraam Bardos, Ioannis Mollas, Nick Bassiliades, Grigorios Tsoumakas