Activity
The Machine Learning Group investigates the development of novel machine learning methods with a particular interest for their algorithmic cost and sample efficiency.
It was created in July 2020, and parts of its activities are conducted with research assistants from the Machine Learning group at the Idiap Research Institute and the EPFL.
It is part of the Computer Science department of the Faculty of Science, and located on the Battelle campus.
Recent publications
E. Courdier and F. Fleuret. Borrowing from yourself: Faster future video segmentation with partial channel update. In Proceedings of the IEEE International Conference on Pattern Recognition (ICPR), 2022. To appear. bib · pre
M. Johari, Y. Lepoittevin, and F. Fleuret. GeoNeRF: Generalizing NeRF with Geometry Priors. In Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition (CVPR), 2022. To appear. bib · pre
A. Pannatier, R. Picatoste, and F. Fleuret. Efficient Wind Speed Nowcasting with GPU-Accelerated Nearest Neighbors Algorithm. In Proceedings of the SIAM International Conference on Data Mining (SDM), 2022. To appear. bib · pre
T. Prabhu and F. Fleuret. Test time Adaptation through Perturbation Robustness. In Proceedings of the NeurIPS DistShift Workshop (NeurIPS DistShift), 2021. bib · pdf
V. Micheli and F. Fleuret. Language Models are Few-Shot Butlers. In Proceedings of the international conference on Empirical Methods in Natural Language Processing (EMLNP), 2021. (to appear). bib
M. Johari, C. Carta, and F. Fleuret. DepthInSpace: Exploitation and Fusion of Multiple Video Frames for Structured-Light Depth Estimation. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2021. (to appear). bib
T. Prabhu and F. Fleuret. Uncertainty Reduction for Model Adaptation in Semantic Segmentation. In Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition (CVPR), pages 9613–9623, 2021. bib · pdf
S. Srinivas and F. Fleuret. Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability. In Proceedings of the International Conference on Learning Representations (ICLR), 2021. bib · pdf
T. Chavdarova, M. Pagliardini, S. Stich, F. Fleuret, and M. Jaggi. Taming GANs with Lookahead-Minmax. In Proceedings of the International Conference on Learning Representations (ICLR), 2021. bib · pdf
A. Vyas, A. Katharopoulos, and F. Fleuret. Fast Transformers with Clustered Attention. In Proceedings of the international conference on Neural Information Processing Systems (NeurIPS), pages 21665–21674, 2020. bib · pdf
V. Micheli, M. d'Hoffschmidt, and F. Fleuret. On the importance of pre-training data volume for compact language models. In Proceedings of the international Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7853–7858, 2020. bib · pdf
E. Courdier and F. Fleuret. Real-Time Segmentation Networks should be Latency Aware. In Proceedings of the Asian Conference on Computer Vision (ACCV), pages 603–619, 2020. (to appear). bib · pdf
T. Prabhu, F. Mai, T. Vogels, M. Jaggi, and F. Fleuret. Optimizer Benchmarking Needs to Account for Hyperparameter Tuning. In Proceedings of the International Conference on Machine Learning (ICML), pages 8837–8846, 2020. bib · pdf
A. Katharopoulos, A. Vyas, N. Pappas, and F. Fleuret. Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention. In Proceedings of the International Conference on Machine Learning (ICML), pages 5294–5303, 2020. bib · pdf