Keywords:
- accelerated exact k-means
- Adaboost
- adaptive exploration/exploitation trade-off
- algorithm
- Annulus
- Automatic Speech Recognition
- Autoregressive models
- 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
- Permutations
- person re-identifcation
- premature fine-tuning
- query-free
- query-free interactive content-based image retrieval
- query-free interactive image retrieval
- Random forest
- ranking
- redundancy
- Rejection Sampling
- relevance feedback
- Sampling
- scalable
- Seeding
- self-attention
- semantic segmentation
- sparsity
- Stochastic Variance Reduced Gradient
- sub-quadratic
- SVM
- tensorial operations
- tracking
- trajectories
- Transformer
- transformers
- Tree
- user-based evaluation
- Variance Reduced Gradient
- Vector quantization
- visual content-based features
- Wind Speed Nowcasting
- Yinyany
Publications of Francois Fleuret sorted by title
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| 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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| Macro-Action Discovery Based on Change Point Detection and Boosting, and , in: International Conference on Machine Learning and Applications, 2012 |
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| Morphodynamic profiling to explore spatio-temporal signaling networks regulating neurite outgrowth, , , , , , and , in: 1st International SystemsX.ch Conference on Systems Biology, 2011 |
| Multi-Camera People Tracking with a Probabilistic Occupancy Map, , , and , in: IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2), 2008 |
| Multi-Camera Tracking and Atypical Motion Detection with Behavioral Maps, , and , in: proceedings of the European Conference on Computer Vision, 2008 |
| Multi-Commodity Network Flow for Tracking Multiple People, , , and , in: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013 |
| Multi-layer Boosting for Pattern Recognition, , Idiap-RR-76-2008 |
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| Multi-layer Boosting for Pattern Recognition, , in: Pattern Recognition Letter, 30, 2009 |
| Multi-Modal Mean-Fields via Cardinality-Based Clamping, , and , in: Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition, 2017 |
| Multiple Object Tracking using Flow Linear Programming, , and , Idiap-RR-10-2009 |
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| Multiple Object Tracking using K-Shortest Paths Optimization, , , and , in: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011 |
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| Nested Mini-Batch K-Means, and , in: Proceedings of NIPS, 2016 |
| Non-Markovian Globally Consistent Multi-Object Tracking, , , and , in: Proceedings of the IEEE International Conference on Computer Vision, 2017 |
| Not All Samples Are Created Equal: Deep Learning with Importance Sampling, and , Idiap-RR-12-2018 |
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| Not All Samples Are Created Equal: Deep Learning with Importance Sampling, and , in: Proceedings of International Conference on Machine Learning, 2018 |
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| On the Tunability of Optimizers in Deep Learning, , , , and , Idiap-RR-19-2019 |
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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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| Paumer: Patch Pausing Transformer for Semantic Segmentation, , and , in: 33th British Machine Vision Conference 2022, London, UK, 21 - 24 November 2022, 2022 |
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| Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching, , and , in: Proceedings of the international conference on Neural Information Processing Systems, 2018 |
| Principled Detection-by-classification from Multiple Views, , and , in: proceedings of the International Conference on Computer Vision Theory and Applications, 2008 |
| Principled Parallel Mean-Field Inference for Discrete Random Fields, , , and , in: Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition, 2016 |
| Probability Occupancy Maps for Occluded Depth Images, , and , in: Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition, pages 2829-2837, 2015 |
| Processing Megapixel Images with Deep Attention-Sampling Models, and , Idiap-RR-07-2019 |
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| Processing Megapixel Images with Deep Attention-Sampling Models, and , in: Proceedings of International Conference on Machine Learning, 2019 |
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| Re-Identification for Improved People Tracking, , and , in: Person Re-Identification, pages 311-336, Springer, 2014 |
| Real-Time Segmentation Networks should be Latency Aware, and , in: Asian Conference on Computer Vision, 2020 |
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| Reducing Noise in GAN Training with Variance Reduced Extragradient, , , and , in: Proceedings of the international conference on Neural Information Processing Systems, 2019 |
| Reservoir Boosting : Between Online and Offline Ensemble Learning, and , in: Proceedings of the international conference on Neural Information Processing Systems, 2013 |
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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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| σ-GPTs: A New Approach to Autoregressive Models., , and , in: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2024 |
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S
| Sample Distillation for Object Detection and Image Classification, , and , in: Proceedings of the 6th Asian Conference on Machine Learning (ACML), Nha Trang, Vietnam, 2014 |
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| Scalable Metric Learning via Weighted Approximate Rank Component Analysis, and , in: ECCV 2016, 2016 |
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| SGAN: An Alternative Training of Generative Adversarial Networks, and , in: Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, pages 9407-9415, IEEE, 2018 |
[DOI] |
| Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition, , , , and , in: Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition, 2017 |
| Stationary Features and Cat Detection, and , Idiap-RR-56-2007 |
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| Stationary Features and Cat Detection, and , in: Journal of Machine Learning Research, 9, 2008 |