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ORCIDPKDD/ECML 2023: Turin, Italy - Workshops
- Rosa Meo
, Fabrizio Silvestri:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023, Turin, Italy, September 18-22, 2023, Revised Selected Papers, Part V. Communications in Computer and Information Science 2137, Springer 2025, ISBN 978-3-031-74642-0
Challenges and Opportunities of Large Language Models in Real-World Machine Learning Applications
- Dustin Axman, Avik Ray, Shubham Garg, Jing Huang:
Contextual Data Augmentation for Task-Oriented Dialog Systems. 3-12 - Yashar Deldjoo:
Fairness of ChatGPT and the Role of Explainable-Guided Prompts. 13-22
Deep Learning Meets Neuromorphic Hardware
- Alessio Gravina, Claudio Gallicchio, Davide Bacciu:
Non-dissipative Propagation by Randomized Anti-symmetric Deep Graph Networks. 25-36 - Diego Argüello Ron:
On the Noise Robustness of Analog Complex-Valued Neural Networks. 37-50 - Vittorio Fra, Andrea Pignata, Riccardo Pignari, Enrico Macii, Gianvito Urgese:
Neu-BrAuER: A Neuromorphic Braille Letters Audio-Reader for Commercial Edge Devices. 51-60
Discovery Challenge
- Chao Yi, Xu-Yang Chen, Lu Ren, Han-Jia Ye, De-Chuan Zhan:
Transductive Fire-Affected Area Segmentation Using False-Color Composite Data. 63-72 - Sagar Verma, Kavya Gupta:
Post Wildfire Burnt-up Detection Using Siamese UNet. 73-81 - Andrew Sweet:
Predicting Exoplanetary Features with a Residual Model for Uniform and Gaussian Distributions. 82-99 - Eyup B. Unlu, Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva:
Reproducing Bayesian Posterior Distributions for Exoplanet Atmospheric Parameter Retrievals with a Machine Learning Surrogate Model. 100-112 - Mayeul Aubin, Carolina Cuesta-Lázaro, Ethan Tregidga, Javier Viaña, Cecilia Garraffo, Iouli E. Gordon, Mercedes López-Morales, Robert J. Hargreaves, Vladimir Yu. Makhnev, Jeremy J. Drake, Douglas P. Finkbeiner, Phillip Cargile:
Simulation-Based Inference for Exoplanet Atmospheric Retrieval: Insights from Winning the Ariel Data Challenge 2023 Using Normalizing Flows. 113-131
ITEM: IoT, Edge, and Mobile for Embedded Machine Learning
- Yannick Emonds, Kai Xi, Holger Fröning:
Implications of Noise in Resistive Memory on Deep Neural Networks for Image Classification. 135-149 - Manfred Mücke, Christoph Gratl:
Evaluating Custom-Precision Operator Support in MLIR for ARM CPUs. 150-162 - Mark Deutel, Christopher Mutschler, Jürgen Teich:
μYOLO: Towards Single-Shot Object Detection on Microcontrollers. 163-169 - Martin Lechner, Axel Jantsch:
OptiSim: A Hardware-Aware Optimization Space Exploration Tool for CNN Architectures. 170-182 - Lisa Kuhn, Bernhard Klein, Holger Fröning:
On the Non-associativity of Analog Computations. 183-195 - Nitish Satya Murthy, Francky Catthoor, Marian Verhelst, Peter Vrancx:
Quantized Dynamics Models for Hardware-Efficient Control and Planning in Model-Based RL. 196-209
LIMBO - LearnIng and Mining for BlOckchains
- Rafael Ramos Tubino, Rémy Cazabet, Natkamon Tovanich, Céline Robardet:
Temporal and Geographical Analysis of Real Economic Activities in the Bitcoin Blockchain. 213-225
Machine Learning for Cybersecurity (MLCS 2023)
- Corentin Larroche:
A Source Separation Approach to Temporal Graph Modelling for Computer Networks. 229-244 - Grégoire Barrué, Tony Quertier:
Quantum Machine Learning for Malware Classification. 245-260 - Arthur Grisel-Davy, Goksen U. Guler, Julian Dickert, Philippe Vibien, Waleed Khan, Jack Morgan, Carlos Moreno, Sebastian Fischmeister:
Side-Channel Based Runtime Intrusion Detection for Network Equipment. 261-276 - Raz Lapid, Moshe Sipper:
I See Dead People: Gray-Box Adversarial Attack on Image-to-Text Models. 277-289 - Ayush K. Varshney, Vicenç Torra:
Concept Drift Detection Using Ensemble of Integrally Private Models. 290-304
MIDAS - The 8th Workshop on MIning DAta for Financial applicationS
- Furkan Pala, Mehmet Yasin Akpinar, Onur Deniz, Gülsen Eryigit:
ViBERTgrid BiLSTM-CRF: Multimodal Key Information Extraction from Unstructured Financial Documents. 307-322 - Eric Benhamou, Jean-Jacques Ohana, Beatrice Guez, David Saltiel, Rida Laraki, Jamal Atif:
Comparing Deep RL and Traditional Financial Portfolio Methods. 323-338 - Julian Tritscher, Alexander Roos, Daniel Schlör, Andreas Hotho, Anna Krause:
Occupational Fraud Detection Through Agent-Based Data Generation. 339-356 - Marco Gregnanin, Johannes De Smedt, Giorgio Gnecco, Maurizio Parton:
Stock Price Time Series Forecasting Using Dynamic Graph Neural Networks and Attention Mechanism in Recurrent Neural Networks. 357-373 - Tennessee Hickling, Dennis Prangle:
Flexible Tails for Normalising Flows, with Application to the Modelling of Financial Return Data. 374-386 - Lucas Manchado-Marcos, Ariel Duarte-López, Argimiro Arratia:
Exploring Alternative Data for Nowcasting: A Case Study on US GDP Using Topic Attention. 387-401 - Haseeb Tariq, Marwan Hassani:
Topology-Agnostic Detection of Temporal Money Laundering Flows in Billion-Scale Transactions. 402-419 - Elena Tiukhova, Adriano Salcuni, Can Oguz, Marcella Niglio, Giuseppe Storti, Fabio Forte, Bart Baesens, Monique Snoeck:
Boosting Credit Risk Data Quality Using Machine Learning and eXplainable AI Techniques. 420-429 - Jorge Miguel Bravo:
Ensemble Methods for Stock Market Prediction. 430-448
Workshop on Advancements in Federated Learning
- Urszula Chajewska, Harsh Shrivastava:
Federated Learning with Neural Graphical Models. 451-460 - Thomas Tsouparopoulos, Iordanis Koutsopoulos:
On Improving Accuracy in Federated Learning Using GANs-Based Pre-training and Ensemble Learning. 461-469 - Mohamed Suliman, Douglas J. Leith, Anisa Halimi:
Re-evaluating the Privacy Benefit of Federated Learning. 470-477 - Bart Cox, Jeroen Galjaard, Aditya Shankar, Jérémie Decouchant, Lydia Y. Chen:
Parameterizing Federated Continual Learning for Reproducible Research. 478-486
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