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
D
| Exact Acceleration of Linear Object Detectors, and , in: Proceedings of the European Conference on Computer Vision, 2012 |
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| Tasting Families of Features for Image Classification, and , in: International Conference on Computer Vision, 2011 |
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| Boosting with Maximum Adaptive Sampling, and , in: Proceedings of the Neural Information Processing Systems Conference, 2011 |
F
| Multi-layer Boosting for Pattern Recognition, , in: Pattern Recognition Letter, 30, 2009 |
| Multi-layer Boosting for Pattern Recognition, , Idiap-RR-76-2008 |
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| The MASH Project, , , and , in: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2011 |
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| Re-Identification for Improved People Tracking, , and , in: Person Re-Identification, pages 311-336, Springer, 2014 |
| Multi-Camera People Tracking with a Probabilistic Occupancy Map, , , and , in: IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2), 2008 |
| Stationary Features and Cat Detection, and , in: Journal of Machine Learning Research, 9, 2008 |
| Stationary Features and Cat Detection, and , Idiap-RR-56-2007 |
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| Comparing machines and humans on a visual categorization test, , , , , and , in: Proceedings of the National Academy of Sciences, 2011 |
| Computer vision profiling of neurite outgrowth dynamics reveals spatio-temporal modularity of Rho GTPase signaling, , , , , , , , , and , in: Journal of Cell Biology, 212(1):91-111, 2016 |
[DOI] |
| Morphodynamic profiling to explore spatio-temporal signaling networks regulating neurite outgrowth, , , , , , and , in: 1st International SystemsX.ch Conference on Systems Biology, 2011 |
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| Steerable Features for Statistical 3D Dendrite Detection, , , , and , in: Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention, 2009 |
| Learning Rotational Features for Filament Detection, , and , in: Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition, 2009 |
| Automated Delineation of Dendritic Networks in Noisy Image Stacks, , and , in: proceedings of the European Conference on Computer Vision, 2008 |
| Delineating Trees in Noisy 2D Images and 3D Image Stacks, , , and , in: Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition, pages 2799–2806, 2010 |
J
| ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields, , and , in: Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition (CVPR), pages 17408-17419, 2023 |
[DOI] |
| DepthInSpace: Exploitation and Fusion of Multiple Video Frames for Structured-Light Depth Estimation, , and , in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 6039-6048, 2021 |
[URL] |
| GeoNeRF: Generalizing NeRF with Geometry Priors, , and , in: Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition (CVPR), 2022 |
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| Kronecker Recurrent Units, , and , in: Proceedings of the International Conference on Machine Learning, 2018 |
| Scalable Metric Learning via Weighted Approximate Rank Component Analysis, and , in: ECCV 2016, 2016 |
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K
| Processing Megapixel Images with Deep Attention-Sampling Models, and , Idiap-RR-07-2019 |
[URL] |
| Processing Megapixel Images with Deep Attention-Sampling Models, and , in: Proceedings of International Conference on Machine Learning, 2019 |
[URL] |
| 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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| Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention, , , and , in: Proceedings of International Conference on Machine Learning, 2020 |
| Kullback-Leibler Proximal Variational Inference, , , and , in: Proceedings of the international conference on Neural Information Processing Systems, pages 3402-3410, 2015 |
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L
| Jointly Informative Feature Selection, and , in: Journal of Machine Learning Research, 2016 |
| Jointly Informative Feature Selection, and , in: International Conference on Artificial Intelligence and Statistics, pages 567–575, 2014 |
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| Dynamic Programming Boosting for Discriminative Macro-Action Discovery, and , in: International Conference on Machine Learning, 2014 |
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| 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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