MLIA - Conference papers

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

Publications MLIA	2017	2018	2019	2020	2021	Total
Books	0	0	0	0	0	0
Edited books	0	2	2	0	0	4
Journal articles	4	12	10	6	6	38
Book chapters	0	0	0	0	0	0
Conference papers	23	33	43	26	40	165
Habilitations	0	0	0	1	1	2
Theses	7	4	9	9	5	34

LIP6

W. Aissa, L. Soulier, L. Denoyer : “Modèle de compréhension du besoin en information pour la RI conversationnelle”, CORIA 2019 - 16ème COnférence en Recherche d’Information et Applications, Lyon, France [Aissa 2019]
E. Arnaud, A. Dapogny, K. Bailly : “Tree-gated deep regressor ensemble for face alignment in the wild”, 2019 14^th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), Lille, France, pp. 1-7 [Arnaud 2019]
I. Ayed, N. Cedilnik, P. Gallinari, M. Sermesant : “EP-Net: Learning Cardiac Electrophysiology Models for Physiology-based Constraints in Data-Driven Predictions”, FIMH 2019 - 10^th International Conference on Functional Imaging of the Hearth, Bordeaux, France, pp. 55-63, (Springer) [Ayed 2019b]
N. Baskiotis, L. Becirspahic, R. Brault, S. Duguet, V. Guigue, V. Guiguet, S. Lorin, V. Thouvenot : “A Deep Approach of Affluence Forecasting in Subway Networks”, WCRR 2019 - 12^th World Congress on Railway Research, Tokyo, Japan [Baskiotis 2019]
H. Ben‑younes, R. Cadene, N. Thome, M. Cord : “BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection”, AAAI 2019 - 33rd AAAI Conference on Artificial Intelligence, Honolulu, United States [Ben-younes 2019]
P. Bordes, É. Zablocki, L. Soulier, B. Piwowarski : “Un modèle multimodal d’apprentissage de représentations de phrases qui préserve la sémantique visuelle”, COnférence en Recherche d'Informations et Applications, COnférence en Recherche d'Informations et Applications - CORIA 2019, 16^th French Information Retrieval Conference. Lyon, France, May 25-29, 2019. Proceedings, Lyon, France [Bordes 2019a]
P. Bordes, É. Zablocki, L. Soulier, B. Piwowarski, P. Gallinari : “Incorporating Visual Semantics into Sentence Representations within a Grounded Space”, 2019 Conference on Empirical Methods in Natural Language Processing and 9^th International Joint Conference on Natural Language Processing, Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9^th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, pp. 696-707, (Association for Computational Linguistics) [Bordes 2019b]
D. Brooks, O. Schwander, F. Barbaresco, J.‑Y. Schneider, M. Cord : “A Hermitian Positive Definite neural network for micro-Doppler complex covariance processing”, International Radar Conference, Toulon, France [Brooks 2019a]
D. Brooks, O. Schwander, F. Barbaresco, J.‑Y. Schneider, M. Cord : “Complex-valued neural networks for fully-temporal micro-Doppler classification”, 2019 20^th International Radar Symposium (IRS), 2019 20^th International Radar Symposium (IRS), Ulm, Germany, (IEEE) [Brooks 2019b]
D. Brooks, O. Schwander, F. Barbaresco, J.‑Y. Schneider, M. Cord : “Exploring Complex Time-series Representations for Riemannian Machine Learning of Radar Data”, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton, United Kingdom, pp. 3672-3676, (IEEE) [Brooks 2019c]
D. Brooks, O. Schwander, F. Barbaresco, J.‑Y. Schneider, M. Cord : “Riemannian batch normalization for SPD neural networks”, Thirty-third Annual Conference on Neural Information Processing Systems., Vancouver, Canada [Brooks 2019d]
D. Brooks, O. Schwander, F. Barbaresco, J.‑Y. Schneider, M. Cord : “Second-order networks in PyTorch”, GSI 2019 - 4^th International Conference on Geometric Science of Information, vol. 11712, Lecture Notes in Computer Science, Toulouse, France, pp. 751-758, (Springer) [Brooks 2019e]
M. Bucher, T.‑H. Vu, M. Cord, P. Pérez : “Zero-Shot Semantic Segmentation”, NeurIPS, vancouver, Canada [bucher 2019]
R. Cadene, C. Dancette, H. Ben‑younes, M. Cord, D. Parikh : “RUBi: Reducing Unimodal Biases for Visual Question Answering”, Neural Information Processing Systems, vol. 32, Advances in Neural Information Processing Systems, Vancouver, Canada, pp. 841-852, (Curran Associates, Inc.) [Cadene 2019a]
R. Cadene, H. Ben‑younes, M. Cord, N. Thome : “MUREL: Multimodal Relational Reasoning for Visual Question Answering”, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, United States [Cadene 2019b]
Y. Chen, A. Saporta, A. Dapogny, M. Cord : “Delving Deep into Interpreting Neural Nets with Piece-Wise Affine Representation”, 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, Province of China, pp. 609-613, (IEEE) [Chen 2019]
M. Chen, Th. Artières, L. Denoyer : “Unsupervised Object Segmentation by Redrawing”, Advances in Neural Information Processing Systems 32 (NIPS 2019), Vancouver, Canada, pp. 12705-12716, (Curran Associates, Inc.) [Chen 2019]
O. Chuquimia, Th. Garbay, W. Xu, A. Pinna, X. Dray, H. Sahbi, B. Granado : “Study to integrate CNN inside a WCE to realize a screening tool”, Journées d'Etude sur la TéléSanté, Paris, France [Chuquimia 2019c]
Ch. Corbière, N. Thome, A. Bar‑Hen, M. Cord, P. Pérez : “Addressing Failure Prediction by Learning Model Confidence”, Advances in Neural Information Processing Systems 32, Vancouver, Canada, pp. 2898-2909, (Curran Associates, Inc.) [Corbière 2019]
A. Dapogny, B. Kevin, M. Cord : “DeCaFA: Deep Convolutional Cascade for Face Alignment In The Wild”, The IEEE International Conference on Computer Vision (ICCV), Seoul, Korea, Republic of [Dapogny 2019]
E. Delasalles, S. Lamprier, L. Denoyer : “Dynamic Neural Language Models”, ICONIP 2019 - 26^th International Conference on Neural Information Processing, vol. 11955, Lecture Notes in Computer Science, Sydney, Australia, pp. 282-294 [Delasalles 2019b]
E. Delasalles, S. Lamprier, L. Denoyer : “Learning Dynamic Author Representations with Temporal Language Models”, 2019 IEEE International Conference on Data Mining (ICDM), Beijing, China, pp. 120-129, (IEEE) [Delasalles 2019c]
Ch. Dias , V. Guigue, P. Gallinari : “Filtrage collaboratif explicite par analyse de sentiments à l’aveugle”, CAp 2019 - 21ème Conférence sur l'Apprentissage automatique, Toulouse, France [Dias 2019b]
M. Engilberge, L. Chevallier, P. Pérez, M. Cord : “SoDeep: a Sorting Deep net to learn ranking loss surrogates”, CVPR 2019 - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, United States [Engilberge 2019]
J.‑Y. Franceschi, A. Dieuleveut, M. Jaggi : “Unsupervised Scalable Representation Learning for Multivariate Time Series”, Thirty-third Conference on Neural Information Processing Systems, vol. 32, Advances in Neural Information Processing Systems, Vancouver, Canada, pp. 4650-4661, (Curran Associates, Inc.) [Franceschi 2019a]
Th. Garbay, O. Chuquimia, A. Pinna, H. Sahbi, X. Dray, B. Granado : “Distilling the knowledge in CNN for WCE screening tool”, 2019 Conference on Design and Architectures for Signal and Image Processing (DASIP), Montreal, Canada, pp. 19-22, (IEEE) [Garbay 2019]
Th. Gerald, N. Baskiotis : “Joint Label/Example Hyperbolic Representation for Extreme Classification”, Conférence sur l’Apprentissage automatique 2019, Toulouse, France [Gerald 2019]
V. Guiguet, P. Cribier‑Delande, N. Baskiotis, V. Guigue : “Prédiction de séries temporelles multi-variées stationnaires: modélisation du contexte pour l'analyse des données de transports”, GRETSI 2019, Lille, France [Guiguet 2019]
S. Lamprier : “A Recurrent Neural Cascade-based Model for Continuous-Time Diffusion”, ICML 2019 - 36^th International Conference on Machine Learning, vol. 97, Proceedings of Machine Learning Research, Long Beach, CA, United States, pp. 3632-3641, (PMLR) [Lamprier 2019a]
Ah. Mazari, H. Sahbi : “Deep Temporal Pyramid Design For Action Recognition”, IEEE International Conference on Acoustic, Speech and Signal Processing, ICASSP, Brighton, United Kingdom, pp. 2077-2081, (IEEE) [Mazari 2019a]
Ah. Mazari, H. Sahbi : “MLGCN: Multi-Laplacian Graph Convolutional Networks for Human Action Recognition”, The British Machine Vision Conference (BMVC), Cardiff, United Kingdom [Mazari 2019b]
H. Sahbi : “Deep Total Variation Support Vector Networks”, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea, Republic of, pp. 3028-3038, (IEEE) [Sahbi 2019a]
H. Sahbi : “Learning CCA Representations for Misaligned Data”, European Conference on Computer Vision, ECCV - Workshop, vol. 11132, Munich, Germany, pp. 468-485, (Springer) [Sahbi 2019b]
A. Saporta, Y. Chen, M. Blot, M. Cord : “REVE: Regularizing Deep Learning with Variational Entropy Bound”, 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, Province of China, pp. 1610-1614, (IEEE) [Saporta 2019]
Th. Scialom, B. Piwowarski, J. Staiano : “Architecture basée sur les mécanismes d'attention: le cas de la génération de questions neuronales”, COnférence en Recherche d'Informations et Applications - CORIA 2019, 16^th French Information Retrieval Conference, COnférence en Recherche d'Informations et Applications - CORIA 2019, 16^th French Information Retrieval Conference. Lyon, France, May 25-29, 2019. Proceedings, Lyon, France [Scialom 2019a]
Th. Scialom, B. Piwowarski, J. Staiano : “Self-Attention Architectures for Answer-Agnostic Neural Question Generation”, ACL 2019 - Annual Meeting of the Association for Computational Linguistics, Proceedings of the 57^th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, pp. 6027-6032, (Association for Computational Linguistics) [Scialom 2019b]
Th. Scialom, S. Lamprier, B. Piwowarski, J. Staiano : “Answers Unite! Unsupervised Metrics for Reinforced Summarization Models”, 2019 Conference on Empirical Methods in Natural Language Processing and the 9^th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), ACL Anthology, Hong Kong, China, pp. 3237-3247, (Association for Computational Linguistics) [Scialom 2019c]
É. Simon, V. Guigue, B. Piwowarski : “Extraction d'information non supervisée avec des modèles discriminants”, CAp 2019 - 21ème Conférence sur l'Apprentissage automatique, Toulouse, France [Simon 2019a]
É. Simon, V. Guigue, B. Piwowarski : “Unsupervised Information Extraction: Regularizing Discriminative Approaches with Relation Distribution Losses”, Proceedings of the 57^th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, pp. 1378-1387, (Association for Computational Linguistics) [Simon 2019b]
B. Taillé, V. Guigue, P. Gallinari : “Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization”, EurNLP, London, United Kingdom [Taillé 2019a]
B. Taillé, V. Guigue, P. Gallinari : “Une Etude Empirique de la Capacité de Généralisation des Plongements de Mots Contextuels en Extraction d'Entités”, CAp 2019 - 21ème Conférence sur l'Apprentissage automatique, Toulouse, France [Taillé 2019b]
T. Véniat, O. Schwander, L. Denoyer : “STOCHASTIC ADAPTIVE NEURAL ARCHITECTURE SEARCH FOR KEYWORD SPOTTING”, ICASSP 2019 - International Conference on Acoustics, Speech, and Signal Processing, Brighton, United Kingdom [Véniat 2019]
É. Zablocki, P. Bordes, B. Piwowarski, L. Soulier, P. Gallinari : “Context-Aware Zero-Shot Learning for Object Recognition”, Thirty-sixth International Conference on Machine Learning (ICML), Long Beach, CA, United States [Zablocki 2019]

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