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 recency
| Inference from Real-World Sparse Measurements, , and , in: Transactions on Machine Learning Research, 2024 |
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| Benefits of Max Pooling in Neural Networks: Theoretical and Experimental Evidence, , and , in: Transactions on Machine Learning Research, 2023 |
| 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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| 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 |
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| Efficient Training of Low-Curvature Neural Networks, , , and , in: NeurIPS 2022, 2022 |
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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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| HyperMixer: An MLP-based Green AI Alternative to Transformers, , , , , , and , in: arxiv, 2022 |
| Borrowing from yourself: Faster future video segmentation with partial channel update, and , in: International Conference on Pattern Recognition, 2022 |
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| 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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| Efficient Wind Speed Nowcasting with GPU-Accelerated Nearest Neighbors Algorithm, , and , in: Proceedings of SIAM Data Mining, Virginia US and Virtual, 2022 |
| 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 |
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| Efficient Wind Speed Nowcasting with GPU-Accelerated Nearest Neighbors Algorithm, , and , Idiap-RR-05-2022 |
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| 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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| Test time Adaptation through Perturbation Robustness, and , Idiap-RR-17-2021 |
| Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability, and , in: International Conference on Learning Representations, 2021 |
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| 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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| 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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| Real-Time Segmentation Networks should be Latency Aware, and , in: Asian Conference on Computer Vision, 2020 |
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| Fast Transformers with Clustered Attention, , and , in: Proceedings of the International Conference on Neural Information Processing Systems, 2020 |
| Taming GANs with Lookahead, , , and , Idiap-RR-20-2020 |
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| Deep Generative Models and Applications, and , EPFL, 2020 |
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| Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention, , , and , in: Proceedings of International Conference on Machine Learning, 2020 |
| 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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| Gradient Alignment in Deep Neural Networks, and , Idiap-RR-14-2020 |
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| Learning an event sequence embedding for event-based deep stereo, , , , and , in: Proceedings of the IEEE International Conference on Computer Vision, 2019 |
| Reducing Noise in GAN Training with Variance Reduced Extragradient, , , and , in: Proceedings of the international conference on Neural Information Processing Systems, 2019 |
| On the Tunability of Optimizers in Deep Learning, , , , and , Idiap-RR-19-2019 |
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| Full-Gradient Representation for Neural Network Visualization, and , in: Advances in Neural Information Processing Systems, 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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| Processing Megapixel Images with Deep Attention-Sampling Models, and , Idiap-RR-07-2019 |
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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 |
| Geodesic Convolutional Shape Optimization, , , and , in: Proceedings of the International Conference on Machine Learning, 2018 |
| Kronecker Recurrent Units, , and , in: Proceedings of the International Conference on Machine Learning, 2018 |
| 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 |
| Knowledge Transfer with Jacobian Matching, and , in: Proceedings of the International Conference on Machine Learning, 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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