Xavier Boix - RankMe

63 papers A* 9A 2Journal 42Unranked 9

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Emergent Neural Network Mechanisms for Generalization to Objects in Novel Orientations. Trans. Mach. Learn. Res. Avi Cooper, Daniel Harari, Tomotake Sasaki, Spandan Madan, Hanspeter Pfister, Pawan Sinha, Xavier Boix
2025	J	`jnl`	In-distribution adversarial attacks on object recognition models using gradient-free search. Trans. Mach. Learn. Res. Spandan Madan, Tomotake Sasaki, Hanspeter Pfister, Tzu-Mao Li, Xavier Boix
2025	J	`jnl`	Merge to Mix: Mixing Datasets via Model Merging. CoRR Zhixu Silvia Tao, Kasper Vinken, Hao-Wei Yeh, Avi Cooper, Xavier Boix
2025	J	`jnl`	Task Arithmetic Through The Lens Of One-Shot Federated Learning. Trans. Mach. Learn. Res. Zhixu Tao, Ian Mason, Sanjeev R. Kulkarni, Xavier Boix
2024	J	`jnl`	Configural processing as an optimized strategy for robust object recognition in neural networks. CoRR Hojin Jang, Pawan Sinha, Xavier Boix
2024	J	`jnl`	D3: Data Diversity Design for Systematic Generalization in Visual Question Answering. Trans. Mach. Learn. Res. Amir Rahimi, Vanessa D'Amario, Moyuru Yamada, Kentaro Takemoto, Tomotake Sasaki, Xavier Boix
2024	J	`jnl`	Rethinking VLMs and LLMs for Image Classification. CoRR Avi Cooper, Keizo Kato, Chia-Hsien Shih, Hiroaki Yamane, Kasper Vinken, Kentaro Takemoto, Taro Sunagawa, Hao-Wei Yeh, Jin Yamanaka, Ian Mason, Xavier Boix
2024	J	`jnl`	Task Arithmetic Through The Lens Of One-Shot Federated Learning. CoRR Zhixu Tao, Ian Mason, Sanjeev R. Kulkarni, Xavier Boix
2024	J	`jnl`	Transformer Module Networks for Systematic Generalization in Visual Question Answering. IEEE Trans. Pattern Anal. Mach. Intell. Moyuru Yamada, Vanessa D'Amario, Kentaro Takemoto, Xavier Boix, Tomotake Sasaki
2023	J	`jnl`	D3: Data Diversity Design for Systematic Generalization in Visual Question Answering. CoRR Amir Rahimi, Vanessa D'Amario, Moyuru Yamada, Kentaro Takemoto, Tomotake Sasaki, Xavier Boix
2023	J	`jnl`	Deephys: Deep Electrophysiology, Debugging Neural Networks under Distribution Shifts. CoRR Anirban Sarkar, Matthew Groth, Ian Mason, Tomotake Sasaki, Xavier Boix
2023	J	`jnl`	Modularity Trumps Invariance for Compositional Robustness. CoRR Ian Mason, Anirban Sarkar, Tomotake Sasaki, Xavier Boix
2023	J	`jnl`	Multi-domain improves out-of-distribution and data-limited scenarios for medical image analysis. CoRR Ece Ozkan, Xavier Boix
2023	J	`jnl`	Robustness to Transformations Across Categories: Is Robustness Driven by Invariant Neural Representations? Neural Comput. Hojin Jang, Syed Suleman Abbas Zaidi, Xavier Boix, Neeraj Prasad, Sharon Gilad-Gutnick, Shlomit Ben-Ami, Pawan Sinha
2022	J	`jnl`	Do Neural Networks for Segmentation Understand Insideness? CoRR Kimberly Villalobos, Vilim Stih, Amineh Ahmadinejad, Shobhita Sundaram, Jamell Dozier, Andrew Francl, Frederico Azevedo, Tomotake Sasaki, Xavier Boix
2022	J	`jnl`	The Data Efficiency of Deep Learning Is Degraded by Unnecessary Input Dimensions. Frontiers Comput. Neurosci. Vanessa D'Amario, Sanjana Srivastava, Tomotake Sasaki, Xavier Boix
2022	J	`jnl`	Three approaches to facilitate invariant neurons and generalization to out-of-distribution orientations and illuminations. Neural Networks Akira Sakai, Taro Sunagawa, Spandan Madan, Kanata Suzuki, Takashi Katoh, Hiromichi Kobashi, Hanspeter Pfister, Pawan Sinha, Xavier Boix, Tomotake Sasaki
2022	J	`jnl`	Transformer Module Networks for Systematic Generalization in Visual Question Answering. CoRR Moyuru Yamada, Vanessa D'Amario, Kentaro Takemoto, Xavier Boix, Tomotake Sasaki
2022	J	`jnl`	When and how convolutional neural networks generalize to out-of-distribution category-viewpoint combinations. Nat. Mach. Intell. Spandan Madan, Timothy Henry, Jamell Dozier, Helen Ho, Nishchal Bhandari, Tomotake Sasaki, Frédo Durand, Hanspeter Pfister, Xavier Boix
2021	J	`jnl`	A Robust Optimization Approach to Deep Learning. CoRR Dimitris Bertsimas, Xavier Boix, Kimberly Villalobos Carballo, Dick den Hertog
2021	J	`jnl`	Do Neural Networks for Segmentation Understand Insideness? Neural Comput. Kimberly Villalobos, Vilim Stih, Amineh Ahmadinejad, Shobhita Sundaram, Jamell Dozier, Andrew Francl, Frederico Azevedo, Tomotake Sasaki, Xavier Boix
2021	A*	`conf`	Frivolous Units: Wider Networks Are Not Really That Wide. AAAI Stephen Casper, Xavier Boix, Vanessa D'Amario, Ling Guo, Martin Schrimpf, Kasper Vinken, Gabriel Kreiman
2021	J	`jnl`	How Modular Should Neural Module Networks Be for Systematic Generalization? CoRR Vanessa D'Amario, Tomotake Sasaki, Xavier Boix
2021	A*	`conf`	How Modular should Neural Module Networks Be for Systematic Generalization? NeurIPS Vanessa D'Amario, Tomotake Sasaki, Xavier Boix
2021	J	`jnl`	Small in-distribution changes in 3D perspective and lighting fool both CNNs and Transformers. CoRR Spandan Madan, Tomotake Sasaki, Tzu-Mao Li, Xavier Boix, Hanspeter Pfister
2021	J	`jnl`	Symmetry Perception by Deep Networks: Inadequacy of Feed-Forward Architectures and Improvements with Recurrent Connections. CoRR Shobhita Sundaram, Darius Sinha, Matthew Groth, Tomotake Sasaki, Xavier Boix
2021	J	`jnl`	The Foes of Neural Network's Data Efficiency Among Unnecessary Input Dimensions. CoRR Vanessa D'Amario, Sanjana Srivastava, Tomotake Sasaki, Xavier Boix
2021	J	`jnl`	Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations: late-stopping, tuning batch normalization and invariance loss. CoRR Akira Sakai, Taro Sunagawa, Spandan Madan, Kanata Suzuki, Takashi Katoh, Hiromichi Kobashi, Hanspeter Pfister, Pawan Sinha, Xavier Boix, Tomotake Sasaki
2021	J	`jnl`	To Which Out-Of-Distribution Object Orientations Are DNNs Capable of Generalizing? CoRR Avi Cooper, Xavier Boix, Daniel Harari, Spandan Madan, Hanspeter Pfister, Tomotake Sasaki, Pawan Sinha
2020	J	`jnl`	Is Robustness To Transformations Driven by Invariant Neural Representations? CoRR Syed Suleman Abbas Zaidi, Xavier Boix, Neeraj Prasad, Sharon Gilad-Gutnick, Shlomit Ben-Ami, Pawan Sinha
2020	J	`jnl`	On the Capability of Neural Networks to Generalize to Unseen Category-Pose Combinations. CoRR Spandan Madan, Timothy Henry, Jamell Dozier, Helen Ho, Nishchal Bhandari, Tomotake Sasaki, Frédo Durand, Hanspeter Pfister, Xavier Boix
2019	—	`conf`	Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images. ICLR (Poster) Sanjana Srivastava, Guy Ben-Yosef, Xavier Boix
2019	J	`jnl`	Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images. CoRR Sanjana Srivastava, Guy Ben-Yosef, Xavier Boix
2019	J	`jnl`	Removable and/or Repeated Units Emerge in Overparametrized Deep Neural Networks. CoRR Stephen Casper, Xavier Boix, Vanessa D'Amario, Ling Guo, Martin Schrimpf, Kasper Vinken, Gabriel Kreiman
2018	J	`jnl`	Theory IIIb: Generalization in Deep Networks. CoRR Tomaso A. Poggio, Qianli Liao, Brando Miranda, Andrzej Banburski, Xavier Boix, Jack Hidary
2018	J	`jnl`	Theory of Deep Learning III: explaining the non-overfitting puzzle. CoRR Tomaso A. Poggio, Kenji Kawaguchi, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Xavier Boix, Jack Hidary, Hrushikesh N. Mhaskar
2017	A*	`conf`	Active Video Summarization: Customized Summaries via On-line Interaction with the User. AAAI Ana Garcia del Molino, Xavier Boix, Joo-Hwee Lim, Ah-Hwee Tan
2017	—	`conf`	Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision. AAAI Spring Symposia Francis X. Chen, Gemma Roig, Leyla Isik, Xavier Boix, Tomaso A. Poggio
2017	—	`conf`	On Active Video Summarization: Customized Summaries via On-Line Interaction with the User. AAAI Spring Symposia Ana Garcia del Molino, Xavier Boix, Joo-Hwee Lim, Ah-Hwee Tan
2017	—	`conf`	Predicting When Eye Fixations Are Consistent. AAAI Spring Symposia Anna Volokitin, Michael Gygli, Xavier Boix
2017	J	`jnl`	Reports on the 2017 AAAI Spring Symposium Series. AI Mag. Jeannette Bohg, Xavier Boix, Nancy Chang, Elizabeth F. Churchill, Vivian Chu, Fei Fang, Jerome Feldman, Avelino J. Gonzalez, Takashi Kido, William F. Lawless, José L. Montaña, Santiago Ontañón, Jivko Sinapov, Donald A. Sofge, Luc Steels, Molly Wright Steenson, Keiki Takadama, Amulya Yadav
2016	J	`jnl`	Herding Generalizes Diverse M -Best Solutions. CoRR Ece Ozkan, Gemma Roig, Orcun Goksel, Xavier Boix
2016	J	`jnl`	Learning to Predict Sequences of Human Visual Fixations. IEEE Trans. Neural Networks Learn. Syst. Ming Jiang, Xavier Boix, Gemma Roig, Juan Xu, Luc Van Gool, Qi Zhao
2016	A*	`conf`	Predicting When Saliency Maps are Accurate and Eye Fixations Consistent. CVPR Anna Volokitin, Michael Gygli, Xavier Boix
2015	—	`—`	Computational commonalities of image understanding algorithms. Xavier Boix
2015	J	`jnl`	Foveation-based Mechanisms Alleviate Adversarial Examples. CoRR Yan Luo, Xavier Boix, Gemma Roig, Tomaso A. Poggio, Qi Zhao
2015	A*	`conf`	SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks. ICCV Xun Huang, Chengyao Shen, Xavier Boix, Qi Zhao
2015	J	`jnl`	SEEDS: Superpixels Extracted Via Energy-Driven Sampling. Int. J. Comput. Vis. Michael Van den Bergh, Xavier Boix, Gemma Roig, Luc Van Gool
2015	A	`conf`	Saliency Prediction with Active Semantic Segmentation. BMVC Ming Jiang, Xavier Boix, Juan Xu, Gemma Roig, Luc Van Gool, Qi Zhao
2014	J	`jnl`	Comment on "Ensemble Projection for Semi-supervised Image Classification". CoRR Xavier Boix, Gemma Roig, Luc Van Gool
2014	—	`conf`	Reconstruction of Inextensible Surfaces on a Budget via Bootstrapping. 3DV Alex Locher, Lennart Elsen, Xavier Boix, Luc Van Gool
2014	—	`conf`	Self-Adaptable Templates for Feature Coding. NIPS Xavier Boix, Gemma Roig, Salomon Diether, Luc Van Gool
2013	A*	`conf`	Active MAP Inference in CRFs for Efficient Semantic Segmentation. ICCV Gemma Roig, Xavier Boix, Roderick de Nijs, Sebastian Ramos, Kolja Kühnlenz, Luc Van Gool
2013	A*	`conf`	Online Video SEEDS for Temporal Window Objectness. ICCV Michael Van den Bergh, Gemma Roig, Xavier Boix, Santiago Manen, Luc Van Gool
2013	J	`jnl`	Random Binary Mappings for Kernel Learning and Efficient SVM. CoRR Gemma Roig, Xavier Boix, Luc Van Gool
2013	J	`jnl`	SEEDS: Superpixels Extracted via Energy-Driven Sampling. CoRR Michael Van den Bergh, Xavier Boix, Gemma Roig, Luc Van Gool
2013	A*	`conf`	Sparse Quantization for Patch Description. CVPR Xavier Boix, Michael Gygli, Gemma Roig, Luc Van Gool
2012	J	`jnl`	Harmony Potentials - Fusing Global and Local Scale for Semantic Image Segmentation. Int. J. Comput. Vis. Xavier Boix, Josep M. Gonfaus, Joost van de Weijer, Andrew D. Bagdanov, Joan Serrat Gual, Jordi Gonzàlez
2012	—	`conf`	Nested Sparse Quantization for Efficient Feature Coding. ECCV (2) Xavier Boix, Gemma Roig, Christian Leistner, Luc Van Gool
2012	A	`conf`	On-line semantic perception using uncertainty. IROS Roderick de Nijs, Sebastian Ramos, Gemma Roig, Xavier Boix, Luc Van Gool, Kolja Kühnlenz
2012	—	`conf`	SEEDS: Superpixels Extracted via Energy-Driven Sampling. ECCV (7) Michael Van den Bergh, Xavier Boix, Gemma Roig, Benjamin de Capitani, Luc Van Gool
2011	A*	`conf`	Conditional Random Fields for multi-camera object detection. ICCV Gemma Roig, Xavier Boix, Horesh Ben Shitrit, Pascal Fua
2009	—	`conf`	Optimal feature selection for subspace image matching. ICCV Workshops Gemma Roig, Xavier Boix, Fernando De la Torre