Keywords:
- accelerated exact k-means
- Adaboost
- adaptive exploration/exploitation trade-off
- algorithm
- Annulus
- Automatic Speech Recognition
- batch
- Bayesian framework
- Benchmarking
- BERT
- bioinformatics
- boosting
- Bootstrapping
- clustering
- computational efficiency
- Computer Vision
- content-based image retrieval
- Convolutional Neural Networks
- cue integration
- Deep Generative Models
- deep learning
- Elkan
- exact
- exact k-means
- Exponion
- feature combination
- feature selection
- Gaussian Mixture Model
- Gaussian Mixture Models (GMM)
- Generative Adversarial Networks
- genomics
- Greedy Edge Expectation Maximization (GEEM)
- Hamerly
- Hard Mining
- Hyperparameter optimization
- Image classification
- Importance Sampling
- Initialisation
- interactive
- iterative
- iterative relevance feedback
- K-means
- k-medoids
- k-nearest neighbors
- large-scale
- large-scale iterative relevance feedback
- localization
- log-based similarity learning
- lookahead
- machine learning
- medoid
- Metric learning
- Mini-batch
- minimax
- multimodal textual-based and visual content-based features
- nested
- nesting
- object detection
- Off-target effect
- optimization
- orthonormal regularizer
- parallel
- pattern classification
- person re-identifcation
- premature fine-tuning
- query-free
- query-free interactive content-based image retrieval
- query-free interactive image retrieval
- Random forest
- ranking
- redundancy
- relevance feedback
- Sampling
- scalable
- Seeding
- self-attention
- semantic segmentation
- sparsity
- Stochastic Variance Reduced Gradient
- sub-quadratic
- SVM
- tensorial operations
- tracking
- trajectories
- Transformer
- Tree
- user-based evaluation
- Variance Reduced Gradient
- Vector quantization
- visual content-based features
- Wind Speed Nowcasting
- Yinyany
Publications of Francois Fleuret sorted by first author
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| Macro-Action Discovery Based on Change Point Detection and Boosting, and , in: International Conference on Machine Learning and Applications, 2012 |
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| Joint Cascade Optimization Using a Product Of Boosted Classifiers, and , in: Proceedings of the Neural Information Processing Systems Conference, pages 1315–1323, 2010 |
| treeKL: A distance between high dimension empirical distributions, and , in: Pattern Recognition Letters, 34(2):140-145, 2013 |
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| A tree-based distance between distributions: application to classification of neurons, and , in: ICASSP 2012 : IEEE International Conference on Acoustics, Speech and Signal Processing, 2012 |
| Machine learning techniques to analyse complex, computer vision-extracted, dynamic cellular phenotypes, , , , , and , in: 1st International SystemsX.ch Conference on Systems Biology, 2011 |
| Machine learning-based tools to model and to remove the off-target effect, , , and , in: Pattern Analysis and Applications, 20(1):87-100, 2017 |
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| HyperMixer: An MLP-based Low Cost Alternative to Transformers, , , , , , and , in: Proc. of the 61st Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Toronto, Canada, pages 15632-15654, 2023 |
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| HyperMixer: An MLP-based Green AI Alternative to Transformers, , , , , , and , in: arxiv, 2022 |
| Non-Markovian Globally Consistent Multi-Object Tracking, , , and , in: Proceedings of the IEEE International Conference on Computer Vision, 2017 |
| Benefits of Max Pooling in Neural Networks: Theoretical and Experimental Evidence, , and , in: Transactions on Machine Learning Research, 2023 |
| Exact Preimages of Neural Network Aircraft Collision Avoidance Systems, and , in: Machine Learning for Engineering Modeling, Simulation, and Design Workshop at Neural Information Processing Systems 2020, 2020 |
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| A Sub-Quadratic Exact Medoid Algorithm, and , Idiap-RR-19-2017 |
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| A Sub-Quadratic Exact Medoid Algorithm, and , in: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017 |
| K-Medoids For K-Means Seeding, and , in: Proceedings of the international conference on Neural Information Processing Systems, 2017 |
| Fast K-Means with Accurate Bounds, and , Idiap-RR-17-2016 |
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| Fast K-Means with Accurate Bounds, and , in: Proceedings of the International Conference on Machine Learning (ICML), New York, 2016 |
| Nested Mini-Batch K-Means, and , in: Proceedings of NIPS, 2016 |
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| Inference from Real-World Sparse Measurements, , and , in: Transactions on Machine Learning Research, 2024 |
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| Efficient Wind Speed Nowcasting with GPU-Accelerated Nearest Neighbors Algorithm, , and , Idiap-RR-05-2022 |
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| Efficient Wind Speed Nowcasting with GPU-Accelerated Nearest Neighbors Algorithm, , and , in: Proceedings of SIAM Data Mining, Virginia US and Virtual, 2022 |
| LETHA: Learning from High Quality Inputs for 3D Pose Estimation in Low Quality Images., , , and , Idiap-RR-22-2014 |
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| LETHA: Learning from High Quality Inputs for 3D Pose Estimation in Low Quality Images, , , and , in: Proceedings of the International Conference on 3D vision, pages 517–524, 2014 |
| Improving Object Classification using Pose Information, , , and , Idiap-RR-30-2012 |
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| Classification-based Probabilistic Modeling of Texture Transition for Fast Line Search Tracking and Delineation, , , and , in: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008 |
| Uncertainty Reduction for Model Adaptation in Semantic Segmentation, and , in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021 |
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| Test time Adaptation through Perturbation Robustness, and , Idiap-RR-17-2021 |
| Test time Adaptation through Perturbation Robustness, and , in: Workshop on Distribution Shifts, 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021 |
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| Optimizer Benchmarking Needs to Account for Hyperparameter Tuning, , , , and , in: Proceedings of the 37th International Conference on Machine Learning, Vienna, Austria, 2020 |
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| On the Tunability of Optimizers in Deep Learning, , , , and , Idiap-RR-19-2019 |
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| Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability, and , in: International Conference on Learning Representations, 2021 |
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| Gradient Alignment in Deep Neural Networks, and , Idiap-RR-14-2020 |
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