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
2018
| 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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| Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching, , and , in: Proceedings of the international conference on Neural Information Processing Systems, 2018 |
| 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] |
| Stochastic Variance Reduced Gradient Optimization of Generative Adversarial Networks, , , and , in: International Conference on Machine Learning (ICML) workshop on Theoretical Foundations and Applications of Deep Generative Models, 2018 |
| WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection, , , , , , , , and , in: Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, pages 5030-5039, 2018 |
[DOI] |
2017
| 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 |
| Boosted Exudate Segmentation in Retinal Images using Residual Nets, , , and , in: Proceedings of the MICCAI Workshop on Ophthalmic Medical Image Analysis, 2017 |
| Deep Multi-Camera People Detection, and , in: Proceedings of the IEEE International Conference on Machine Learning and Applications, 2017 |
| Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection, , and , in: Proceedings of the IEEE International Conference on Computer Vision, 2017 |
| Exploratory Study on Direct Prediction of Diabetes using Deep Residual Networks, , , and , in: Proceedings of the thematic conference on computational vision and medical image processing, 2017 |
| K-Medoids For K-Means Seeding, and , in: Proceedings of the international conference on Neural Information Processing Systems, 2017 |
| Machine learning-based tools to model and to remove the off-target effect, , , and , in: Pattern Analysis and Applications, 20(1):87-100, 2017 |
[DOI] |
| Multi-Modal Mean-Fields via Cardinality-Based Clamping, , and , in: Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition, 2017 |
| Non-Markovian Globally Consistent Multi-Object Tracking, , , and , in: Proceedings of the IEEE International Conference on Computer Vision, 2017 |
| 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 |
| Weakly Supervised Learning of Deep Metrics for Stereo Reconstruction, , and , in: Proceedings of the IEEE International Conference on Computer Vision, 2017 |
2016
| Adaptive relevance feedback for large-scale image retrieval, and , in: Multimedia Tools and Applications, 75(12):6777-6807, 2016 |
[DOI] |
| 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] |
| 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 |
| Importance Sampling Tree for Large-scale Empirical Expectation, , and , in: Proceedings of the International Conference on Machine Learning (ICML), New-York, 2016 |
| Jointly Informative Feature Selection, and , in: Journal of Machine Learning Research, 2016 |
| Large Scale Hard Sample Mining with Monte Carlo Tree Search, and , in: Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition, 2016 |
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| Nested Mini-Batch K-Means, and , in: Proceedings of NIPS, 2016 |
| Principled Parallel Mean-Field Inference for Discrete Random Fields, , , and , in: Proceedings of the IEEE international conference on Computer Vision and Pattern Recognition, 2016 |
| Scalable Metric Learning via Weighted Approximate Rank Component Analysis, and , in: ECCV 2016, 2016 |
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| Tracking Interacting Objects Using Intertwined Flows, , , and , in: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016 |
2015
| 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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| 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 |
2014
| Adaptive Sampling for Large Scale Boosting, and , in: Journal of Machine Learning Research, 15:1431-1453, 2014 |
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