MLIA - Conference papers

Current teams :	ALMASTY ALSOC APR BD CIAN ComplexNetworks DECISION DELYS LFI MOCAH MoVe NPA PEQUAN PolSys QI RO SMA SYEL
Former team :	ACASA

LIP6

E. Belilovsky, M. Eickenberg, E. Oyallon : “Decoupled Greedy Learning of CNNs”, Proceedings of the 37^th International Conference on Machine Learning, Vienna (virtual), Austria, pp. 5368-5377 [Belilovsky 2020]
V. Besnier, H. Jain, A. Bursuc, M. Cord, P. Pérez : “This dataset does not exist: training models from generated images”, ICASSP 2020 - 45^th International Conference on Acoustics, Speech, and Signal Processing, Barcelona, Spain [Besnier 2020]
D. Brooks, O. Schwander, F. Barbaresco, J.‑Y. Schneider, M. Cord : “Deep Learning and Information Geometry for Drone Micro-Doppler Radar Classification”, 2020 IEEE Radar Conference (RadarConf20), Florence, Italy, pp. 1-6, (IEEE) [Brooks 2020]
P. Cribier‑Delande, R. Puget, V. Guigue, L. Denoyer : “Time Series Prediction using Disentangled Latent Factors”, ESANN 2020 - 28^th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium [Cribier-Delande 2020]
A. Dapogny, M. Cord, P. Pérez : “The Missing Data Encoder: Cross-Channel Image Completion with Hide-And-Seek Adversarial Network”, AAAI-20 - Thirty-Fourth AAAI Conference on Artificial Intelligence, New York, New York, United States [Dapogny 2020]
M. Déchelle, J. Donà, K. Plessis‑Fraissard, P. Gallinari, M. Levy : “Bridging dynamical models and deep networks to solve forward and inverse problems”, NeurIPS 2020, Paris (virtual event), France [Déchelle 2020]
A. Douillard, M. Cord, Ch. Ollion, Th. Robert, E. Valle : “PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning”, ECCV 2020 - 16^th European Conference on Computer Vision, vol. 12365, Lecture Notes in Computer Science, Glasgow, United Kingdom, pp. 86-102, (Springer) [Douillard 2020]
J.‑Y. Franceschi, E. Delasalles, M. Chen, S. Lamprier, P. Gallinari : “Stochastic Latent Residual Video Prediction”, Proceedings of the 37^th International Conference on Machine Learning, vol. 119, Proceedings of Machine Learning Research, Vienne, Austria, pp. 3233-3246, (PMLR) [Franceschi 2020]
C. Gainon de Forsan de Gabriac, V. Guigue, P. Gallinari : “Resume: A Robust Framework for Professional Profile Learning & Evaluation”, ESANN 2020 - 28^th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium [Gainon de Forsan de Gabriac 2020]
S. Gidaris, A. Bursuc, N. Komodakis, P. Pérez, M. Cord : “Learning Representations by Predicting Bags of Visual Words”, CVPR2020 - Conference on Computer Vision and Pattern Recognition, CVPR, Seattle, WA, United States [Gidaris 2020]
V. Grari, S. Lamprier, M. Detyniecki : “Fairness-Aware Neural Rényi Minimization for Continuous Features”, Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}, Yokohama, Japan, pp. 2262-2268, (International Joint Conferences on Artificial Intelligence Organization) [Grari 2020b]
S. Karkar, I. Ayed, E. De Bézenac, P. Gallinari : “A Principle of Least Action for the Training of Neural Networks”, ECML PKDD, Ghent, Belgium [karkar 2020]
N. Miolane, N. Guigui, H. Zaatiti, Ch. Shewmake, H. Hajri, D. Brooks, A. Le Brigant, J. Mathe, B. Hou, Y. Thanwerdas, S. Heyder, O. Peltre, N. Koep, Y. Cabanes, Th. Gerald, P. Chauchat, B. Kainz, C. Donnat, S. Holmes, X. Pennec : “Introduction to Geometric Learning in Python with Geomstats”, SciPy 2020 - 19^th Python in Science Conference, Austin, Texas, United States, pp. 48-57 [Miolane 2020b]
E. Moschos, O. Schwander, A. Stegner, P. Gallinari : “DEEP-SST-EDDIES: A Deep Learning framework to detect oceanic eddies in Sea Surface Temperature images”, ICASSP 2020 - 45^th International Conference on Acoustics, Speech, and Signal Processing, Barcelona, Spain, pp. 4307-4311 [Moschos 2020b]
A. Mustar, S. Lamprier, B. Piwowarski : “Using BERT and BART for Query Suggestion”, Joint Conference of the Information Retrieval Communities in Europe, vol. 2621, CEUR Workshop Proceedings, Samatan, France, (CEUR-WS.org) [Mustar 2020]
E. Oyallon : “Interferometric Graph Transform: a Deep Unsupervised Graph Representation”, 37^th International Conference on Machine Learning (ICML 2020), Online, Austria [Oyallon 2020]
B. Piwowarski : “Experimaestro and Datamaestro”, SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval, Virtual Event China, China, pp. 2173-2176, (ACM) [Piwowarski 2020]
C. Rebuffel, L. Soulier, G. Scoutheeten, P. Gallinari : “A Hierarchical Model for Data-to-Text Generation”, 42^nd European Conference on IR Research, ECIR 2020, vol. 12035, Lecture Notes in Computer Science, Lisbon, Portugal, pp. 65-80, (Springer) [Rebuffel 2020a]
C. Rebuffel, L. Soulier, G. Scoutheeten, P. Gallinari : “Capturing Entity Hierarchy in Data-to-Text Generative Models”, First Joint Conference of the Information Retrieval Communities in Europe (CIRCLE 2020), vol. 2621, CEUR Workshop Proceedings, Online, France, (CEUR-WS.org) [Rebuffel 2020b]
C. Rebuffel, L. Soulier, G. Scoutheeten, P. Gallinari : “PARENTing via Model-Agnostic Reinforcement Learning to Correct Pathological Behaviors in Data-to-Text Generation”, Proceedings of the 13^th International Conference on Natural Language Generation, INLG 2020, Dublin, Ireland [Rebuffel 2020c]
H. Sahbi : “Kernel-based Graph Convolutional Networks”, IAPR ICPR, Milan / Virtuel, Italy [Sahbi 2020]
A. Saporta, T.‑H. Vu, M. Cord, P. Pérez : “ESL: Entropy-guided Self-supervised Learning for Domain Adaptation in Semantic Segmentation”, Workshop on Scalability in Autonomous Driving at IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Washington (virtual), United States [Saporta 2020]
Th. Scialom, P.‑A. Dray, S. Lamprier, B. Piwowarski, J. Staiano : “ColdGANs: Taming Language GANs with Cautious Sampling Strategies”, Advances in Neural Information Processing Systems, vol. 33, NeurIPS Proceedings, Virtual, Åland Islands, pp. 18978-18989, (Curran Associates, Inc.) [Scialom 2020b]
Th. Scialom, P.‑A. Dray, S. Lamprier, B. Piwowarski, J. Staiano : “Discriminative Adversarial Search for Abstractive Summarization”, 37^th International Conference on Machine Learning, vol. 119, Proceedings of Machine Learning Research, Virtual, Åland Islands, pp. 8555-8564, (PMLR) [Scialom 2020d]
Th. Scialom, P.‑A. Dray, S. Lamprier, B. Piwowarski, J. Staiano : “MLSUM: The Multilingual Summarization Corpus”, 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, France, pp. 8051-8067, (Association for Computational Linguistics) [Scialom 2020f]
B. Taillé, V. Guigue, G. Scoutheeten, P. Gallinari : “Let’s Stop Incorrect Comparisons in End-to-end Relation Extraction!”, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Punta Cana (Online), Dominican Republic, pp. 3689-3701, (Association for Computational Linguistics) [Taillé 2020a]
B. Taillé, V. Guigue, P. Gallinari : “Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization”, ECIR 2020 - 42^nd European Conference on Information Retrieval, Lisbon, Portugal [Taillé 2020b]