Papers

2024⌗

• Amazon AGI, The Amazon Nova family of models: Technical report and model card, Technical Report, 2024.

2023⌗

• Peerat Limkonchotiwat, Weiwei Cheng, Christos Christodoulopoulos, Amir Saffari, Jens Lehmann, mReFinED: An Efficient End-to-End Multilingual Entity Linking System, EMNLP 2023.
• Priyanka Sen, Sandeep Mavadia, Amir Saffari, Knowledge Graph-Augmented Language Models for Complex Question Answering ACL Natural Language Reasoning and Structured Explanations Workshop 2023.
• Jinheon Baek, Alham Fikri Aji, Amir Saffari, Knowledge-Augmented Language Model Prompting or Zero-Shot Knowledge Graph Question Answering ACL Matching Workshop 2023.

2022⌗

• Andy Rosenbaum, Saleh Soltan, Wael Hamza, Amir Saffari, Macro Damonte, Isabel Groves, CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing, arXiv, AACL 2022
• Priyanka Sen, Alham Fikri Aji, Amir Saffari, Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering, arXiv, COLING 2022. Dataset

2021⌗

• Priyanka Sen, Amir Saffari, Armin Oliya, Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection, EMNLP 2021.
• Armin Oliya, Amir Saffari, Priyanka Sen, Tom Ayoola, End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs, EMNLP 2021.
• Gaurav Singh, Siffi Singh, Joshua Wong, Amir Saffari, Relation Extraction from Tables using Artificially Generated Metadata, arXiv

2020⌗

• Priyanka Sen, Amir Saffari, What do Models Learn from Question Answering Datasets?, EMNLP 2020. arXiv
• Hamza Harkous, Isabel Groves, Amir Saffari, Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity, COLING 2020. arXiv
• Monika Myszczynska, Poojitha Ojamies, Alix Lacoste, Daniel Neil, Amir Saffari, Richard Mead, Guillaume Hautbergue, Joanna Holbrook, Laura Ferraiuolo, Applications of Machine Learning to Diagnosis and Treatment of Neurodegenerative Diseases, Nature Reviews Neurology, 2020.

2018⌗

• Rim Assouel, Mohamed Ahmed, Marwin H Segler, Amir Saffari, Yoshua Bengio, DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation, Machine Learning for Molecules and Materials Workshop, NIPS 2018. arXiv
• Daniel Neil, Joss Briody, Alix Lacoste, Aaron Sim, Paidi Creed, Amir Saffari, Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs, Machine Learning for Healthcare Workshop, NIPS 2018. arXiv

2016⌗

• Sam Hare, Stuart Golodetz, Amir Saffari, Vibhav Vineet, Ming-Ming Cheng, Stephen L. Hicks, and Philip Torr, Struck: Structured Output Tracking with Kernels, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2016. Code

2013⌗

• Samuel Schulter, Paul Wohlhart, Christian Leistner, Amir Saffari, Peter M. Roth, Horst Bischof, Alternating Decision Forests, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.

2012⌗

• Sam Hare, Amir Saffari, Philip Torr, Efficient Online Structured Output Learning for Keypoint-Based Object Tracking, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.

2011⌗

• Isabelle Guyon, Gavin Cawley, Gideon Dror, and Amir Saffari, Hands-On Pattern Recognition, Microtome Publishing, 2011.
• Ziming Zhang, Lubor Ladicky, Philip Torr, Amir Saffari, Learning Anchor Planes for Classification, Proceedings of Neural Information Processing Systems (NIPS), 2011.
• Sam Hare, Amir Saffari, Phil Torr, Struck: Structured Output Tracking with Kernels, Proceedings of IEEE International Conference on Computer Vision (ICCV), 2011. Project and code.
• Christian Leistner, Martin Godec, Samuel Schulter, Amir Saffari, Manuel Werlberger, Horst Bischof, Improving Classifiers with Unlabeled Weakly-Related Videos, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.

2010⌗

• Isabelle Guyon, Amir Saffari, Gideon Dror, Gavin Cawley, Model Selection: Beyond the Bayesian/Frequentist Divide, Journal of Machine Learning Research (JMLR), Vol. 11, Pages 61-87, 2010.
• Amir Saffari, Martin Godec, Thomas Pock, Christian Leistner, Horst Bischof, Online Multi-Class LPBoost, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010. Code.
• Bernhard Zeisl, Christian Leistner , Amir Saffari , Horst Bischof, Online Semi-Supervised Multiple-Instance Boosting, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.
• Jakob Santner, Christian Leistner, Amir Saffari, Thomas Pock, Horst Bischof, PROST: Parallel Robust Online Simple Tracking, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010. Code.
• Martin Godec, Christian Leistner, Amir Saffari, and Horst Bischof, Online Random Naive Bayes for Tracking, IEEE International Conference on Pattern Recognition (ICPR), 2010.
• Amir Saffari, Multi-Class Semi-Supervised and Online Boosting, PhD Thesis, Institute for Computer Graphics and Vision, Graz University of Technology, Austria, May 2010. Presentation Video.
• Amir Saffari, Christian Leistner, Martin Godec, Horst Bischof, Robust Multi-View Boosting with Priors, Proceedings of European Conference on Computer Vision (ECCV), 2010.
• Christian Leistner, Amir Saffari, Horst Bischof, MILForests: Multiple-Instance Learning with Randomized Trees, Oral Presentation, Proceedings of European Conference on Computer Vision (ECCV), 2010.
• Christian Leistner, Martin Godec, Amir Saffari, Horst Bischof, Online Multi-View Forests for Tracking,Proceedings of Symposium of the German Association for Pattern Recognition (DAGM), 2010.

2009⌗

• Amir Saffari, Christian Leistner, Horst Bischof, Regularized Multi-Class Semi-Supervised Boosting, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Oral Presentation, Slides.
• Christian Leistner, Amir Saffari, Jakob Santner, Horst Bischof, Semi-Supervised Random Forests, Proceedings of IEEE International Conference on Computer Vision (ICCV), 2009.
• Jakob Santner, Markus Unger, Thomas Pock, Christian Leistner, Amir Saffari, Horst Bischof, Interactive Texture Segmentation using Random Forests and Total Variation, British Machine Vision Conference (BMVC), 2009.
• Amir Saffari, Christian Leistner, Jakob Santner, Martin Godec, Horst Bischof, On-line Random Forests, 3rd IEEE ICCV Workshop on On-line Learning for Computer Vision, 2009. Slides, Code.
• Christian Leistner,  Amir Saffari, Peter Roth, Horst Bischof, On Robustness of On-line Boosting: A Competitive Study, 3rd IEEE ICCV Workshop on On-line Learning for Computer Vision, 2009.
• Inayatullah Khan, Amir Saffari, Horst Bischof, TVGraz: Multi-Modal Learning of Object Categories by Combining Textual and Visual Features, Proc. 33rd Workshop of the Austrian Association for Pattern Recognition, AAPR / OAGM, 2009. Dataset

2008⌗

• Amir Saffari, Helmut Grabner, Horst Bischof, SERBoost: Semi-supervised Boosting with Expectation Regularization, Proceedings of European Conference on Computer Vision (ECCV), 2008.
• Isabelle Guyon, Amir Saffari, Gideon Dror, Gavin Cawley, Analysis of the IJCNN 2007 agnostic learning vs. prior knowledge challenge, Neural Networks, Vol. 21, Pages 544-550, 2008.
• Amir Saffari, Horst Bischof, Boosting for Model-Based Data Clustering, Proc. of 30th Symposium of the German Association for Pattern Recognition (DAGM 2008), Oral Presentation, Pages 51-60, 2008.

2007⌗

• Amir Saffari, Horst Bischof, Clustering in a Boosting Framework, Proc. of Computer Vision Winter Workshop (CVWW), St. Lambrecht, Austria, Pages 75-82, 2007.
• Isabelle Guyon, Amir Saffari, Gideon Dror, Gavin Cawley, Agnostic Learning vs. Prior Knowledge Challenge, Proc. of IEEE International Joint Conference on Neural Networks (IJCNN), Orlando, Florida, USA, Pages 829-834, 2007.
• Isabelle Guyon, Amir Saffari, Hugo Escalante, Gokhan Bakir, Gavin Cawley, CLOP: a Matlab Learning Object Package, NIPS 2007 Demonstrations, Vancouver, British Columbia, Canada, 2007. Code.

2006⌗

• Amir Saffari, Isabelle Guyon, Quick Start Guide for CLOP, Technical Report, Institute for Computer Graphics and Vision, Graz University of Technology and Clopinet, 2006. Code.
• Amir Saffari, Horst Bischof, Video Tracking Red Light Enforcement, Technical Report, Institute for Computer Graphics and Vision, Graz University of Technology, 2006.
• Isabelle Guyon, Amir Saffari, Gideon Dror, Gavin Cawley, Olivier Guyon, NIPS 2006 Model Selection Game, NIPS Workshop on Multi-level Inference, Vancouver, BC, Canada, 2006.
• Amir Saffari, Variable Selection using Correlation and Single Variable Classifier Methods: Applications, Book Chapter, Feature Extraction: Foundations and Applications, Editors: Isabelle Guyon, Steve Gunn, Masoud Nikravesh, Lotfi Zadeh, Springer-Verlag, Pages 343-358, 2006. Code.
• Isabelle Guyon, Amir Saffari, Gideon Dror, Joachim Buhmann, Performance Prediction Challenge, Proc. of International Joint Conference on Neural Networks (IJCNN), IEEE World Congress on Computational Intelligence (WCCI), Vancouver, British Columbia, Canada, Pages 2958-2965, 2006.
• Amir Saffari, Book Review: Complex Worlds from Simpler Nervous Systems, International Journal of Computational Intelligence and Applications (IJCIA), Vol. 6, Pages 569-572, 2006.

2005⌗

• Michael Pfeiffer, Amir Saffari, Andreas Juffinger, Predicting Text Relevance from Sequential Reading Behavior, Proc. of the NIPS Workshop on Machine Learning for Implicit Feedback and User Modeling, Whistler, British Columbia, Canada, 2005. Challenge Winner.
• Amir Saffari, Unknown Environment Representation for Mobile Robot Using Spiking Neural Networks, Proc. of WEC, Transactions on Engineering, Computing and Technology, Istanbul, Turkey, 2005.

2003⌗

• Amir Saffari, NIPS Feature Selection Challenge, NIPS 2003, Feature Extraction Workshop, Whistler, British Columbia, Canada, 2003.
• Amir Saffari, S. Ashkboos, T. Emami, BCI Competitions 2003, Technical Report, Sahand University of Technology, Tabriz, Iran, 2003.
• Amir Saffari, Spiking Neural Networks: Dynamical Systems Approach, Technical Report, Sahand University of Technology, Tabriz, Iran, 2003.

2001⌗

• Amir Saffari, Analyzing Information Processing Models of Biological Neural Networks: A Time-Coding Approach,  Master Thesis, Biomedical Eng. Dept., Tehran Polytechnic (Amirkabir University of Technology), Tehran, Iran, 2001.
• Amir Saffari, Mehdi Azizian, A New Modeling View of Mind-Brain Interaction Using Chaotic Dynamics and Quantum Mechanics, Proc. of 1st International Conference of Cognitive Sciences, Tehran, Iran, 2001.
• Amir Saffari, Mehdi Azizian, An Information Compression Method Based on Chaotic Dynamics and Neural Networks, Proc. of 1st International Conference of Cognitive Sciences, Tehran, Iran, 2001.

1999⌗

• Amir Saffari, A Review on OGY Algorithm for Chaos Control, Proc. of 1st Symposium of Intelligent Systems, Tehran University, Tehran, Iran, 1999.
• Amir Saffari, Designing of a Stand-Alone Bedside Monitoring System using 80C196KC Micro-Controller and Graphic LCD, Bachelor Thesis, Biomedical Eng. Dept., Tehran Polytechnic (Amirkabir University of Technology), Tehran, Iran, 1999.