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2020 – today
- 2022
[c49]Alexander Levine, Soheil Feizi:
Provable Adversarial Robustness for Fractional Lp Threat Models. AISTATS 2022: 9908-9942- [i65]Mazda Moayeri, Phillip Pope, Yogesh Balaji, Soheil Feizi:
A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes. CoRR abs/2201.10766 (2022) - [i64]Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi:
Certifying Model Accuracy under Distribution Shifts. CoRR abs/2201.12440 (2022) - [i63]Wenxiao Wang, Alexander Levine, Soheil Feizi:
Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation. CoRR abs/2202.02628 (2022) - [i62]Neha Mukund Kalibhat, Kanika Narang, Liang Tan, Hamed Firooz, Maziar Sanjabi, Soheil Feizi:
Understanding Failure Modes of Self-Supervised Learning. CoRR abs/2203.01881 (2022) - [i61]Alexander Levine, Soheil Feizi:
Provable Adversarial Robustness for Fractional Lp Threat Models. CoRR abs/2203.08945 (2022) - [i60]Sahil Singla, Mazda Moayeri, Soheil Feizi:
Core Risk Minimization using Salient ImageNet. CoRR abs/2203.15566 (2022) - 2021
- [c48]Neha Mukund Kalibhat, Yogesh Balaji, Soheil Feizi:
Winning Lottery Tickets in Deep Generative Models. AAAI 2021: 8038-8046 - [c47]Mucong Ding, Constantinos Daskalakis, Soheil Feizi:
GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences. AISTATS 2021: 3709-3717 - [c46]Vedant Nanda, Samuel Dooley, Sahil Singla, Soheil Feizi, John P. Dickerson:
Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning. FAccT 2021: 466-477 - [c45]Mazda Moayeri, Soheil Feizi:
Sample Efficient Detection and Classification of Adversarial Attacks via Self-Supervised Embeddings. ICCV 2021: 7657-7666 - [c44]Vasu Singla, Sahil Singla, Soheil Feizi, David Jacobs:
Low Curvature Activations Reduce Overfitting in Adversarial Training. ICCV 2021: 16403-16413 - [c43]Alexander Levine, Soheil Feizi:
Deep Partition Aggregation: Provable Defenses against General Poisoning Attacks. ICLR 2021 - [c42]Sahil Singla, Soheil Feizi:
Fantastic Four: Differentiable and Efficient Bounds on Singular Values of Convolution Layers. ICLR 2021 - [c41]Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi:
Understanding Over-parameterization in Generative Adversarial Networks. ICLR 2021 - [c40]Samyadeep Basu, Phillip Pope, Soheil Feizi:
Influence Functions in Deep Learning Are Fragile. ICLR 2021 - [c39]Cassidy Laidlaw, Sahil Singla, Soheil Feizi:
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models. ICLR 2021 - [c38]Alexander Levine, Soheil Feizi:
Improved, Deterministic Smoothing for L1 Certified Robustness. ICML 2021: 6254-6264 - [c37]Sahil Singla, Soheil Feizi:
Skew Orthogonal Convolutions. ICML 2021: 9756-9766 - [c36]Aya Abdelsalam Ismail, Héctor Corrada Bravo, Soheil Feizi:
Improving Deep Learning Interpretability by Saliency Guided Training. NeurIPS 2021: 26726-26739 - [c35]Gowthami Somepalli, Yexin Wu, Yogesh Balaji, Bhanukiran Vinzamuri, Soheil Feizi:
Unsupervised anomaly detection with adversarial mirrored autoencoders. UAI 2021: 365-375 - [i59]Vasu Singla, Sahil Singla, David Jacobs, Soheil Feizi:
Low Curvature Activations Reduce Overfitting in Adversarial Training. CoRR abs/2102.07861 (2021) - [i58]Alexander Levine, Soheil Feizi:
Improved, Deterministic Smoothing for L1 Certified Robustness. CoRR abs/2103.10834 (2021) - [i57]Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi:
Understanding Overparameterization in Generative Adversarial Networks. CoRR abs/2104.05605 (2021) - [i56]Sahil Singla, Soheil Feizi:
Skew Orthogonal Convolutions. CoRR abs/2105.11417 (2021) - [i55]Aounon Kumar, Alexander Levine, Soheil Feizi:
Policy Smoothing for Provably Robust Reinforcement Learning. CoRR abs/2106.11420 (2021) - [i54]Sahil Singla, Surbhi Singla, Soheil Feizi:
Householder Activations for Provable Robustness against Adversarial Attacks. CoRR abs/2108.04062 (2021) - [i53]Mazda Moayeri, Soheil Feizi:
Sample Efficient Detection and Classification of Adversarial Attacks via Self-Supervised Embeddings. CoRR abs/2108.13797 (2021) - [i52]Priyatham Kattakinda, Soheil Feizi:
FOCUS: Familiar Objects in Common and Uncommon Settings. CoRR abs/2110.03804 (2021) - [i51]Sahil Singla, Soheil Feizi:
Causal ImageNet: How to discover spurious features in Deep Learning? CoRR abs/2110.04301 (2021) - [i50]Samyadeep Basu, Amr Sharaf, Nicolò Fusi, Soheil Feizi:
On Hard Episodes in Meta-Learning. CoRR abs/2110.11190 (2021) - [i49]Aya Abdelsalam Ismail, Héctor Corrada Bravo, Soheil Feizi:
Improving Deep Learning Interpretability by Saliency Guided Training. CoRR abs/2111.14338 (2021) - [i48]Jiang Liu, Alexander Levine, Chun Pong Lau, Rama Chellappa, Soheil Feizi:
Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection. CoRR abs/2112.04532 (2021) - [i47]Jiang Liu, Chun Pong Lau, Hossein Souri, Soheil Feizi, Rama Chellappa:
Mutual Adversarial Training: Learning together is better than going alone. CoRR abs/2112.05005 (2021) - [i46]Chun Pong Lau, Jiang Liu, Hossein Souri, Wei-An Lin, Soheil Feizi, Rama Chellappa:
Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses. CoRR abs/2112.06323 (2021) - 2020
- [j10]Soheil Feizi
, Farzan Farnia, Tony Ginart, David Tse:
Understanding GANs in the LQG Setting: Formulation, Generalization and Stability. IEEE J. Sel. Areas Inf. Theory 1(1): 304-311 (2020) - [j9]Soheil Feizi, Gerald T. Quon, Mariana Recamonde Mendoza, Muriel Médard, Manolis Kellis, Ali Jadbabaie:
Spectral Alignment of Graphs. IEEE Trans. Netw. Sci. Eng. 7(3): 1182-1197 (2020) - [c34]Micah Goldblum, Liam Fowl, Soheil Feizi, Tom Goldstein:
Adversarially Robust Distillation. AAAI 2020: 3996-4003 - [c33]Alexander Levine, Soheil Feizi:
Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation. AAAI 2020: 4585-4593 - [c32]Luke J. O'Connor, Muriel Médard, Soheil Feizi:
Maximum Likelihood Embedding of Logistic Random Dot Product Graphs. AAAI 2020: 5289-5297 - [c31]Phillip Pope, Yogesh Balaji, Soheil Feizi:
Adversarial Robustness of Flow-Based Generative Models. AISTATS 2020: 3795-3805 - [c30]Alexander Levine, Soheil Feizi:
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks. AISTATS 2020: 3938-3947 - [c29]Neehar Peri, Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, John P. Dickerson:
Deep k-NN Defense Against Clean-Label Data Poisoning Attacks. ECCV Workshops (1) 2020: 55-70 - [c28]Samyadeep Basu, Xuchen You, Soheil Feizi:
On Second-Order Group Influence Functions for Black-Box Predictions. ICML 2020: 715-724 - [c27]Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi:
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness. ICML 2020: 5458-5467 - [c26]Sahil Singla, Soheil Feizi:
Second-Order Provable Defenses against Adversarial Attacks. ICML 2020: 8981-8991 - [c25]Alexander Levine, Soheil Feizi:
(De)Randomized Smoothing for Certifiable Defense against Patch Attacks. NeurIPS 2020 - [c24]Yogesh Balaji, Rama Chellappa, Soheil Feizi:
Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation. NeurIPS 2020 - [c23]Aya Abdelsalam Ismail, Mohamed K. Gunady, Héctor Corrada Bravo, Soheil Feizi:
Benchmarking Deep Learning Interpretability in Time Series Predictions. NeurIPS 2020 - [c22]Aounon Kumar, Alexander Levine, Soheil Feizi, Tom Goldstein:
Certifying Confidence via Randomized Smoothing. NeurIPS 2020 - [c21]Wei-An Lin, Chun Pong Lau, Alexander Levine, Rama Chellappa, Soheil Feizi:
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks. NeurIPS 2020 - [i45]Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi:
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness. CoRR abs/2002.03239 (2020) - [i44]Alexander Levine, Soheil Feizi:
(De)Randomized Smoothing for Certifiable Defense against Patch Attacks. CoRR abs/2002.10733 (2020) - [i43]Mucong Ding, Constantinos Daskalakis, Soheil Feizi:
Subadditivity of Probability Divergences on Bayes-Nets with Applications to Time Series GANs. CoRR abs/2003.00652 (2020) - [i42]Yexin Wu, Yogesh Balaji, Bhanukiran Vinzamuri, Soheil Feizi:
Mirrored Autoencoders with Simplex Interpolation for Unsupervised Anomaly Detection. CoRR abs/2003.10713 (2020) - [i41]Sahil Singla, Soheil Feizi:
Second-Order Provable Defenses against Adversarial Attacks. CoRR abs/2006.00731 (2020) - [i40]Vedant Nanda, Samuel Dooley, Sahil Singla, Soheil Feizi, John P. Dickerson:
Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning. CoRR abs/2006.12621 (2020) - [i39]Cassidy Laidlaw, Sahil Singla, Soheil Feizi:
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models. CoRR abs/2006.12655 (2020) - [i38]Samyadeep Basu, Phillip Pope, Soheil Feizi:
Influence Functions in Deep Learning Are Fragile. CoRR abs/2006.14651 (2020) - [i37]Alexander Levine, Soheil Feizi:
Deep Partition Aggregation: Provable Defense against General Poisoning Attacks. CoRR abs/2006.14768 (2020) - [i36]Wei-An Lin, Chun Pong Lau, Alexander Levine, Rama Chellappa, Soheil Feizi:
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks. CoRR abs/2009.02470 (2020) - [i35]Aounon Kumar, Alexander Levine, Soheil Feizi, Tom Goldstein:
Certifying Confidence via Randomized Smoothing. CoRR abs/2009.08061 (2020) - [i34]Pirazh Khorramshahi, Hossein Souri, Rama Chellappa, Soheil Feizi:
GANs with Variational Entropy Regularizers: Applications in Mitigating the Mode-Collapse Issue. CoRR abs/2009.11921 (2020) - [i33]Neha Mukund Kalibhat, Yogesh Balaji, Soheil Feizi:
Winning Lottery Tickets in Deep Generative Models. CoRR abs/2010.02350 (2020) - [i32]Yogesh Balaji, Rama Chellappa, Soheil Feizi:
Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation. CoRR abs/2010.05862 (2020) - [i31]Alexander Levine, Aounon Kumar, Thomas A. Goldstein, Soheil Feizi:
Tight Second-Order Certificates for Randomized Smoothing. CoRR abs/2010.10549 (2020) - [i30]Aya Abdelsalam Ismail, Mohamed K. Gunady, Héctor Corrada Bravo, Soheil Feizi:
Benchmarking Deep Learning Interpretability in Time Series Predictions. CoRR abs/2010.13924 (2020)
2010 – 2019
- 2019
- [j8]Soheil Feizi, Muriel Médard, Gerald T. Quon, Manolis Kellis, Ken Duffy:
Network Infusion to Infer Information Sources in Networks. IEEE Trans. Netw. Sci. Eng. 6(3): 402-417 (2019) - [c20]Yogesh Balaji, Rama Chellappa, Soheil Feizi:
Normalized Wasserstein for Mixture Distributions With Applications in Adversarial Learning and Domain Adaptation. ICCV 2019: 6499-6507 - [c19]Ali Shafahi, W. Ronny Huang, Christoph Studer, Soheil Feizi, Tom Goldstein:
Are adversarial examples inevitable? ICLR (Poster) 2019 - [c18]Yogesh Balaji, Hamed Hassani, Rama Chellappa, Soheil Feizi:
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs. ICML 2019: 414-423 - [c17]Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi:
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation. ICML 2019: 5848-5856 - [c16]Shouvanik Chakrabarti, Yiming Huang, Tongyang Li, Soheil Feizi, Xiaodi Wu:
Quantum Wasserstein Generative Adversarial Networks. NeurIPS 2019: 6778-6789 - [c15]Cassidy Laidlaw, Soheil Feizi:
Functional Adversarial Attacks. NeurIPS 2019: 10408-10418 - [c14]Aya Abdelsalam Ismail, Mohamed K. Gunady, Luiz Pessoa, Héctor Corrada Bravo, Soheil Feizi:
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks. NeurIPS 2019: 10813-10823 - [i29]Angeline Aguinaldo, Ping-Yeh Chiang, Alexander Gain, Ameya Patil, Kolten Pearson, Soheil Feizi:
Compressing GANs using Knowledge Distillation. CoRR abs/1902.00159 (2019) - [i28]Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi:
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation. CoRR abs/1902.00407 (2019) - [i27]Yogesh Balaji, Rama Chellappa, Soheil Feizi:
Normalized Wasserstein Distance for Mixture Distributions with Applications in Adversarial Learning and Domain Adaptation. CoRR abs/1902.00415 (2019) - [i26]Sahil Singla, Soheil Feizi:
Robustness Certificates Against Adversarial Examples for ReLU Networks. CoRR abs/1902.01235 (2019) - [i25]Micah Goldblum, Liam Fowl, Soheil Feizi, Tom Goldstein:
Adversarially Robust Distillation. CoRR abs/1905.09747 (2019) - [i24]Alexander Levine, Sahil Singla, Soheil Feizi:
Certifiably Robust Interpretation in Deep Learning. CoRR abs/1905.12105 (2019) - [i23]Samuel Barham, Soheil Feizi:
Interpretable Adversarial Training for Text. CoRR abs/1905.12864 (2019) - [i22]Cassidy Laidlaw, Soheil Feizi:
Functional Adversarial Attacks. CoRR abs/1906.00001 (2019) - [i21]Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, John P. Dickerson:
Strong Baseline Defenses Against Clean-Label Poisoning Attacks. CoRR abs/1909.13374 (2019) - [i20]Alexander Levine, Soheil Feizi:
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks. CoRR abs/1910.10783 (2019) - [i19]Aya Abdelsalam Ismail, Mohamed K. Gunady, Luiz Pessoa, Héctor Corrada Bravo, Soheil Feizi:
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks. CoRR abs/1910.12370 (2019) - [i18]Shouvanik Chakrabarti, Yiming Huang, Tongyang Li, Soheil Feizi, Xiaodi Wu:
Quantum Wasserstein Generative Adversarial Networks. CoRR abs/1911.00111 (2019) - [i17]Samyadeep Basu, Xuchen You, Soheil Feizi:
Second-Order Group Influence Functions for Black-Box Predictions. CoRR abs/1911.00418 (2019) - [i16]Phillip Pope, Yogesh Balaji, Soheil Feizi:
Adversarial Robustness of Flow-Based Generative Models. CoRR abs/1911.08654 (2019) - [i15]Alexander Levine, Soheil Feizi:
Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation. CoRR abs/1911.09272 (2019) - [i14]Sahil Singla, Soheil Feizi:
Bounding Singular Values of Convolution Layers. CoRR abs/1911.10258 (2019) - [i13]Cassidy Laidlaw, Soheil Feizi:
Playing it Safe: Adversarial Robustness with an Abstain Option. CoRR abs/1911.11253 (2019) - 2018
- [c13]Soheil Feizi, Hamid Javadi, Jesse M. Zhang, David Tse:
Porcupine Neural Networks: Approximating Neural Network Landscapes. NeurIPS 2018: 4836-4846 - [i12]Ali Shafahi, W. Ronny Huang, Christoph Studer, Soheil Feizi, Tom Goldstein:
Are adversarial examples inevitable? CoRR abs/1809.02104 (2018) - [i11]Yogesh Balaji, Hamed Hassani, Rama Chellappa, Soheil Feizi:
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs. CoRR abs/1810.04147 (2018) - 2017
- [j7]Soheil Feizi, Ali Makhdoumi, Ken Duffy, Manolis Kellis, Muriel Médard:
Network Maximal Correlation. IEEE Trans. Netw. Sci. Eng. 4(4): 229-247 (2017) - [c12]Saeed Haghiri, Ali Nemati, Soheil Feizi, Amirali Amirsoleimani, Arash Ahmadi, Majid Ahmadi:
A memristor based binary multiplier. CCECE 2017: 1-4 - [c11]Soheil Feizi, Hamid Javadi, David Tse:
Tensor Biclustering. NIPS 2017: 1311-1320 - [i10]Soheil Feizi, David Tse:
Maximally Correlated Principal Component Analysis. CoRR abs/1702.05471 (2017) - [i9]Soheil Feizi, Hamid Javadi, Jesse M. Zhang, David Tse:
Porcupine Neural Networks: (Almost) All Local Optima are Global. CoRR abs/1710.02196 (2017) - [i8]Soheil Feizi, Changho Suh, Fei Xia, David Tse:
Understanding GANs: the LQG Setting. CoRR abs/1710.10793 (2017) - 2016
- [b1]Soheil Feizi-Khankandi:
On the analysis of complex networks: fundamental limits, scalable algorithms, and applications. Massachusetts Institute of Technology, Cambridge, USA, 2016 - [i7]Soheil Feizi, Gerald T. Quon, Mariana Recamonde Mendoza, Muriel Médard, Manolis Kellis, Ali Jadbabaie:
Spectral Alignment of Networks. CoRR abs/1602.04181 (2016) - [i6]Soheil Feizi, Muriel Médard, Gerald T. Quon, Manolis Kellis, Ken R. Duffy:
Network Infusion to Infer Information Sources in Networks. CoRR abs/1606.07383 (2016) - 2015
- [j6]Roadmap Epigenomics Consortium, Anshul Kundaje, Wouter Meuleman, Jason Ernst, Misha Bilenky, Angela Yen, Alireza Heravi Moussavi, Pouya Kheradpour, ZhiZhuo Zhang, Jianrong Wang, Michael J. Ziller, Viren Amin, John W. Whitaker, Matthew D. Schultz, Lucas D. Ward, Abhishek Sarkar, Gerald T. Quon, Richard S. Sandstrom, Matthew L. Eaton, Yi-Chieh Wu, Andreas R. Pfenning, Xinchen Wang, Melina Claussnitzer, Yaping Liu, Cristian Coarfa, R. Alan Harris, Noam Shoresh, Charles B. Epstein, Elizabeta Gjoneska, Danny Leung, Wei Xie, R. David Hawkins, Ryan Lister, Chibo Hong, Philippe Gascard, Andrew J. Mungall, Richard A. Moore, Eric Chuah, Angela Tam, Theresa K. Canfield, R. Scott Hansen, Rajinder Kaul, Peter J. Sabo, Mukul S. Bansal, Annaick Carles, Jesse R. Dixon, Kyle Kai-How Farh, Soheil Feizi, Rosa Karlic, Ah-Ram Kim, Ashwinikumar Kulkarni, Daofeng Li, Rebecca F. Lowdon, GiNell Elliott, Tim R. Mercer, Shane J. Neph, Vitor Onuchic, Paz Polak, Nisha Rajagopal, Pradipta Ray, Richard C. Sallari, Kyle T. Siebenthall, Nicholas A. Sinnott-Armstrong, Michael Stevens, Robert E. Thurman, Jie Wu, Bo Zhang, Xin Zhou, Arthur E. Beaudet, Laurie A. Boyer, Philip L. De Jager, Peggy J. Farnham, Susan J. Fisher, David Haussler, Steven J. M. Jones, Wei Li, Marco A. Marra, Michael T. McManus, Shamil R. Sunyaev, James A. Thomson, Thea D. Tlsty, Li-Huei Tsai, Wei Wang, Robert A. Waterland, Michael Q. Zhang, Lisa H. Chadwick, Bradley E. Bernstein, Joseph F. Costello, Joseph R. Ecker, Martin Hirst, Alexander Meissner, Aleksandar Milosavljevic, Bing Ren, John A. Stamatoyannopoulos, Ting Wang, Manolis Kellis:
Integrative analysis of 111 reference human epigenomes Open. Nat. 518(7539): 317-330 (2015) - [i5]Jeffrey G. Andrews, Alexandros G. Dimakis, Lara Dolecek, Michelle Effros, Muriel Médard, Olgica Milenkovic, Andrea Montanari, Sriram Vishwanath, Edmund M. Yeh, Randall Berry, Ken R. Duffy, Soheil Feizi, Saul Kato, Manolis Kellis, Stuart Licht, Jon Sorenson, Lav R. Varshney, Haris Vikalo:
A Perspective on Future Research Directions in Information Theory. CoRR abs/1507.05941 (2015) - 2014
- [j5]Soheil Feizi, Muriel Médard:
On Network Functional Compression. IEEE Trans. Inf. Theory 60(9): 5387-5401 (2014) - [j4]Soheil Feizi, Georgios Angelopoulos, Vivek K. Goyal, Muriel Médard:
Backward Adaptation for Power Efficient Sampling. IEEE Trans. Signal Process. 62(16): 4327-4338 (2014) - [c10]Soheil Feizi, Daniel E. Lucani, Chres W. Sørensen, Ali Makhdoumi, Muriel Médard:
Tunable sparse network coding for multicast networks. NetCod 2014: 1-6 - [c9]Luke J. O'Connor, Soheil Feizi:
Biclustering Usinig Message Passing. NIPS 2014: 3617-3625 - 2012
- [j3]Soheil Feizi, Vivek K. Goyal, Muriel Médard:
Time-Stampless Adaptive Nonuniform Sampling for Stochastic Signals. IEEE Trans. Signal Process. 60(10): 5440-5450 (2012) - [c8]Muriel L. Rambeloarison, Soheil Feizi, Georgios Angelopoulos, Muriel Médard:
Empirical rate-distortion study of compressive sensing-based joint source-channel coding. ACSCC 2012: 1224-1228 - [c7]Soheil Feizi, Vivek K. Goyal, Muriel Médard:
Time-stampless adaptive nonuniform sampling for stochastic signals. ICASSP 2012: 3809-3812 - 2011
- [c6]Soheil Feizi, Muriel Médard:
A power efficient sensing/communication scheme: Joint source-channel-network coding by using compressive sensing. Allerton 2011: 1048-1054 - [i4]Soheil Feizi-Khankandi, Muriel Médard:
A Power Efficient Sensing/Communication Scheme: Joint Source-Channel-Network Coding by Using Compressive Sensing. CoRR abs/1110.0428 (2011) - [i3]Soheil Feizi-Khankandi, Vivek K. Goyal, Muriel Médard:
Time-Stampless Adaptive Nonuniform Sampling for Stochastic Signals. CoRR abs/1110.3774 (2011) - 2010
- [c5]Soheil Feizi, Muriel Médard:
Cases where finding the minimum entropy coloring of a characteristic graph is a polynomial time problem. ISIT 2010: 116-120 - [i2]Soheil Feizi-Khankandi, Muriel Médard:
On Network Functional Compression. CoRR abs/1011.5496 (2010) - [i1]Soheil Feizi-Khankandi, Muriel Médard, Michelle Effros:
Compressive Sensing Over Networks. CoRR abs/1012.0955 (2010)
2000 – 2009
- 2009
- [j2]Sina Zahedpour, Soheil Feizi, Arash Amini, Mahmoud Ferdosizadeh Naeiny, Farrokh Marvasti:
Impulsive Noise Cancellation Based on Soft Decision and Recursion. IEEE Trans. Instrum. Meas. 58(8): 2780-2790 (2009) - [j1]Mohammad Ali Akhaee, Mohammad Javad Saberian, Soheil Feizi, Farokh Marvasti:
Robust Audio Data Hiding Using Correlated Quantization With Histogram-Based Detector. IEEE Trans. Multim. 11(5): 834-842 (2009) - [c4]Soheil Feizi-Khankandi, Muriel Médard:
Multi-Functional Compression with Side Information. GLOBECOM 2009: 1-5 - 2008
- [c3]Soheil Feizi, Sina Zahedpour, Mahdi Soltanolkotabi, Arash Amini, Farokh Marvasti:
Salt and pepper noise removal for image signals. ICT 2008: 1-5 - [c2]Sina Zahedpour, Soheil Feizi, Arash Amini, Mahmoud Ferdosizadeh Naeiny, Farokh Marvasti:
Impulsive noise cancellation using CFAR and iterative techniques. ICT 2008: 1-5