Evidential Nearest Neighbours in Active Learning . . . . . . . . . . . . . . . . . . . . . Daniel Zhu, Arnaud Martin, Yolande Le Gall, Jean-Christophe Dubois and Vincent Lemaire SLAYER: A Semi-supervised Learning Approach for Drifting Data Streams under Extreme Verification Latency . . . . . . . . . . . . . . . . . . . . . . . . . Maria Arostegi, Jesus Lobo and Javier Del Ser A Concept for Highly Automated Pre-Labeling via Cross-Domain Label Transfer for Perception in Autonomous Driving . . . . . . . . . . . . . . . . . Maarten Bieshaar, Marek Herde, Denis Huselijc and Bernhard Sick Active Class Selection with Uncertain Class Proportions . . . . . . . . . . . . . . . Mirko Bunse and Katharina Morik Sample Noise Impact on Active Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alexandre Abraham and L´eo Dreyfus-Schmidt Contrastive Representations for Label Noise Require Fine-Tuning . . . . . . . 89 Pierre Nodet, Vincent Lemaire, Alexis Bondu and Antoine Cornu´ejols Combining Gaussian Processes with Neural Networks for Active Learning in Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Jiˇr´ı R˚uˇziˇcka, Jan Koza, Jiˇr´ı Tumpach, Zbynˇek Pitra and Martin Holena Stochastic Adversarial Gradient Embedding for Active Domain Adaptation 121 Victor Bouvier, Philippe Very, Cl´ement Chastagnol, Myriam Tami and Hudelot C´eline
21 35 50 65 70 80