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ORCIDRandall Balestriero
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2020 – today
- 2022
[j3]Ángel Bueno Rodríguez
, Randall Balestriero, Silvio De Angelis, M. Carmen Benítez, Luciano Zuccarello, Richard G. Baraniuk, Jesús M. Ibáñez, Maarten V. de Hoop:
Recurrent Scattering Network Detects Metastable Behavior in Polyphonic Seismo-Volcanic Signals for Volcano Eruption Forecasting. IEEE Trans. Geosci. Remote. Sens. 60: 1-23 (2022)- [c18]Ahmed Imtiaz Humayun, Randall Balestriero, Richard G. Baraniuk:
Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values. CVPR 2022: 10631-10640 - [c17]C. J. Barberan, Sina Alemmohammad, Naiming Liu, Randall Balestriero, Richard G. Baraniuk:
NeuroView-RNN: It's About Time. FAccT 2022: 1683-1697 - [c16]Randall Balestriero, Zichao Wang, Richard G. Baraniuk:
DeepHull: Fast Convex Hull Approximation in High Dimensions. ICASSP 2022: 3888-3892 - [c15]Ahmed Imtiaz Humayun, Randall Balestriero, Anastasios Kyrillidis, Richard G. Baraniuk:
No More Than 6ft Apart: Robust K-Means via Radius Upper Bounds. ICASSP 2022: 4433-4437 - [c14]Ahmed Imtiaz Humayun, Randall Balestriero, Richard G. Baraniuk:
MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining. ICLR 2022 - [i49]Romain Cosentino, Randall Balestriero, Yanis Bahroun, Anirvan M. Sengupta, Richard G. Baraniuk, Behnaam Aazhang:
Spatial Transformer K-Means. CoRR abs/2202.07829 (2022) - [i48]Randall Balestriero, Ishan Misra, Yann LeCun:
A Data-Augmentation Is Worth A Thousand Samples: Exact Quantification From Analytical Augmented Sample Moments. CoRR abs/2202.08325 (2022) - [i47]C. J. Barberan, Sina Alemohammad, Naiming Liu, Randall Balestriero, Richard G. Baraniuk:
NeuroView-RNN: It's About Time. CoRR abs/2202.11811 (2022) - [i46]Ahmed Imtiaz Humayun, Randall Balestriero, Richard G. Baraniuk:
Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values. CoRR abs/2203.01993 (2022) - [i45]Ahmed Imtiaz Humayun, Randall Balestriero, Anastasios Kyrillidis, Richard G. Baraniuk:
No More Than 6ft Apart: Robust K-Means via Radius Upper Bounds. CoRR abs/2203.02502 (2022) - [i44]Rudolf H. Riedi, Randall Balestriero, Richard G. Baraniuk:
Singular Value Perturbation and Deep Network Optimization. CoRR abs/2203.03099 (2022) - [i43]Bobak Toussi Kiani, Randall Balestriero, Yann LeCun, Seth Lloyd:
projUNN: efficient method for training deep networks with unitary matrices. CoRR abs/2203.05483 (2022) - [i42]Vishwanath Saragadam, Randall Balestriero, Ashok Veeraraghavan, Richard G. Baraniuk:
DeepTensor: Low-Rank Tensor Decomposition with Deep Network Priors. CoRR abs/2204.03145 (2022) - [i41]Randall Balestriero, Léon Bottou, Yann LeCun:
The Effects of Regularization and Data Augmentation are Class Dependent. CoRR abs/2204.03632 (2022) - [i40]Randall Balestriero, Yann LeCun:
Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods. CoRR abs/2205.11508 (2022) - [i39]Florian Bordes, Randall Balestriero, Quentin Garrido, Adrien Bardes, Pascal Vincent:
Guillotine Regularization: Improving Deep Networks Generalization by Removing their Head. CoRR abs/2206.13378 (2022) - [i38]Ravid Shwartz-Ziv, Randall Balestriero, Yann LeCun:
What Do We Maximize in Self-Supervised Learning? CoRR abs/2207.10081 (2022) - [i37]Randall Balestriero, Richard G. Baraniuk:
Batch Normalization Explained. CoRR abs/2209.14778 (2022) - [i36]Bobak Toussi Kiani, Randall Balestriero, Yubei Chen, Seth Lloyd, Yann LeCun:
Joint Embedding Self-Supervised Learning in the Kernel Regime. CoRR abs/2209.14884 (2022) - [i35]Grégoire Mialon, Randall Balestriero, Yann LeCun:
Variance Covariance Regularization Enforces Pairwise Independence in Self-Supervised Representations. CoRR abs/2209.14905 (2022) - [i34]Quentin Garrido, Randall Balestriero, Laurent Najman, Yann LeCun:
RankMe: Assessing the downstream performance of pretrained self-supervised representations by their rank. CoRR abs/2210.02885 (2022) - [i33]Mahmoud Assran, Randall Balestriero, Quentin Duval, Florian Bordes, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael G. Rabbat, Nicolas Ballas:
The Hidden Uniform Cluster Prior in Self-Supervised Learning. CoRR abs/2210.07277 (2022) - [i32]Randall Balestriero, Yann LeCun:
POLICE: Provably Optimal Linear Constraint Enforcement for Deep Neural Networks. CoRR abs/2211.01340 (2022) - [i31]Badr Youbi Idrissi, Diane Bouchacourt, Randall Balestriero, Ivan Evtimov, Caner Hazirbas, Nicolas Ballas, Pascal Vincent, Michal Drozdzal, David Lopez-Paz, Mark Ibrahim:
ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations. CoRR abs/2211.01866 (2022) - [i30]Vivien Cabannes, Alberto Bietti, Randall Balestriero:
On minimal variations for unsupervised representation learning. CoRR abs/2211.03782 (2022) - 2021
- [j2]Randall Balestriero, Richard G. Baraniuk:
Mad Max: Affine Spline Insights Into Deep Learning. Proc. IEEE 109(5): 704-727 (2021) - [c13]Sina Alemohammad, Hossein Babaei, Randall Balestriero, Matt Y. Cheung, Ahmed Imtiaz Humayun, Daniel LeJeune, Naiming Liu, Lorenzo Luzi, Jasper Tan, Zichao Wang, Richard G. Baraniuk:
Wearing A Mask: Compressed Representations of Variable-Length Sequences Using Recurrent Neural Tangent Kernels. ICASSP 2021: 2950-2954 - [c12]Sina Alemohammad, Zichao Wang, Randall Balestriero, Richard G. Baraniuk:
The Recurrent Neural Tangent Kernel. ICLR 2021 - [c11]Randall Balestriero, Hervé Glotin, Richard G. Baraniuk:
Interpretable and Learnable Super-Resolution Time-Frequency Representation. MSML 2021: 118-152 - [c10]Romain Cosentino, Randall Balestriero, Richard G. Baraniuk, Behnaam Aazhang:
Deep Autoencoders: From Understanding to Generalization Guarantees. MSML 2021: 197-222 - [i29]Randall Balestriero, Haoran You, Zhihan Lu, Yutong Kou, Yingyan Lin, Richard G. Baraniuk:
Max-Affine Spline Insights Into Deep Network Pruning. CoRR abs/2101.02338 (2021) - [i28]Randall Balestriero, Richard G. Baraniuk:
Fast Jacobian-Vector Product for Deep Networks. CoRR abs/2104.00219 (2021) - [i27]C. J. Barberan, Randall Balestriero, Richard G. Baraniuk:
NeuroView: Explainable Deep Network Decision Making. CoRR abs/2110.07778 (2021) - [i26]Ahmed Imtiaz Humayun, Randall Balestriero, Richard G. Baraniuk:
MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining. CoRR abs/2110.08009 (2021) - [i25]Randall Balestriero, Jerome Pesenti, Yann LeCun:
Learning in High Dimension Always Amounts to Extrapolation. CoRR abs/2110.09485 (2021) - [i24]Florian Bordes, Randall Balestriero, Pascal Vincent:
High Fidelity Visualization of What Your Self-Supervised Representation Knows About. CoRR abs/2112.09164 (2021) - 2020
- [j1]Romain Cosentino, Randall Balestriero, Richard G. Baraniuk, Behnaam Aazhang:
Universal Frame Thresholding. IEEE Signal Process. Lett. 27: 1115-1119 (2020) - [c9]Randall Balestriero, Sebastien Paris, Richard G. Baraniuk:
Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks. NeurIPS 2020 - [i23]Randall Balestriero, Sebastien Paris, Richard G. Baraniuk:
Max-Affine Spline Insights into Deep Generative Networks. CoRR abs/2002.11912 (2020) - [i22]Randall Balestriero:
SymJAX: symbolic CPU/GPU/TPU programming. CoRR abs/2005.10635 (2020) - [i21]Randall Balestriero, Hervé Glotin, Richard G. Baraniuk:
Interpretable Super-Resolution via a Learned Time-Series Representation. CoRR abs/2006.07713 (2020) - [i20]Randall Balestriero, Sebastien Paris, Richard G. Baraniuk:
Analytical Probability Distributions and EM-Learning for Deep Generative Networks. CoRR abs/2006.10023 (2020) - [i19]Sina Alemohammad, Zichao Wang, Randall Balestriero, Richard G. Baraniuk:
The Recurrent Neural Tangent Kernel. CoRR abs/2006.10246 (2020) - [i18]Lorenzo Luzi, Randall Balestriero, Richard G. Baraniuk:
Ensembles of Generative Adversarial Networks for Disconnected Data. CoRR abs/2006.14600 (2020) - [i17]Romain Cosentino, Randall Balestriero, Richard G. Baraniuk, Behnaam Aazhang:
Provable Finite Data Generalization with Group Autoencoder. CoRR abs/2009.09525 (2020) - [i16]Sina Alemohammad, Hossein Babaei, Randall Balestriero, Matt Y. Cheung, Ahmed Imtiaz Humayun, Daniel LeJeune, Naiming Liu, Lorenzo Luzi, Jasper Tan, Zichao Wang, Richard G. Baraniuk:
Wearing a MASK: Compressed Representations of Variable-Length Sequences Using Recurrent Neural Tangent Kernels. CoRR abs/2010.13975 (2020) - [i15]Sina Alemohammad, Randall Balestriero, Zichao Wang, Richard G. Baraniuk:
Scalable Neural Tangent Kernel of Recurrent Architectures. CoRR abs/2012.04859 (2020) - [i14]Romain Cosentino, Randall Balestriero:
Sparse Multi-Family Deep Scattering Network. CoRR abs/2012.07662 (2020) - [i13]Romain Cosentino, Randall Balestriero, Yanis Bahroun, Anirvan M. Sengupta, Richard G. Baraniuk, Behnaam Aazhang:
Interpretable Image Clustering via Diffeomorphism-Aware K-Means. CoRR abs/2012.09743 (2020)
2010 – 2019
- 2019
- [c8]Randall Balestriero, Richard G. Baraniuk:
From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference. ICLR (Poster) 2019 - [c7]Zichao Wang, Randall Balestriero, Richard G. Baraniuk:
A Max-Affine Spline Perspective of Recurrent Neural Networks. ICLR (Poster) 2019 - [c6]Randall Balestriero, Romain Cosentino, Behnaam Aazhang, Richard G. Baraniuk:
The Geometry of Deep Networks: Power Diagram Subdivision. NeurIPS 2019: 15806-15815 - [i12]Randall Balestriero, Romain Cosentino, Behnaam Aazhang, Richard G. Baraniuk:
The Geometry of Deep Networks: Power Diagram Subdivision. CoRR abs/1905.08443 (2019) - [i11]Hamid Javadi, Randall Balestriero, Richard G. Baraniuk:
A Hessian Based Complexity Measure for Deep Networks. CoRR abs/1905.11639 (2019) - 2018
- [c5]Randall Balestriero, Romain Cosentino, Hervé Glotin, Richard G. Baraniuk:
Spline Filters For End-to-End Deep Learning. ICML 2018: 373-382 - [c4]Randall Balestriero, Richard G. Baraniuk:
A Spline Theory of Deep Networks. ICML 2018: 383-392 - [i10]Randall Balestriero, Hervé Glotin, Richard G. Baraniuk:
Semi-Supervised Learning Enabled by Multiscale Deep Neural Network Inversion. CoRR abs/1802.10172 (2018) - [i9]Randall Balestriero, Richard G. Baraniuk:
A Spline Theory of Deep Networks (Extended Version). CoRR abs/1805.06576 (2018) - [i8]Randall Balestriero, Richard G. Baraniuk:
From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference. CoRR abs/1810.09274 (2018) - 2017
- [c3]Hervé Glotin, Julien Ricard, Randall Balestriero:
Fast Chirplet Transform Injects Priors in Deep Learning of Animal Calls and Speech. ICLR (Workshop) 2017 - [i7]Randall Balestriero:
Neural Decision Trees. CoRR abs/1702.07360 (2017) - [i6]Randall Balestriero:
Multiscale Residual Mixture of PCA: Dynamic Dictionaries for Optimal Basis Learning. CoRR abs/1707.05840 (2017) - [i5]Randall Balestriero, Hervé Glotin:
Linear Time Complexity Deep Fourier Scattering Network and Extension to Nonlinear Invariants. CoRR abs/1707.05841 (2017) - [i4]Randall Balestriero, Richard G. Baraniuk:
Adaptive Partitioning Spline Neural Networks: Template Matching, Memorization, Inhibitor Connections, Inversion, Semi-Sup, Topology Search. CoRR abs/1710.09302 (2017) - [i3]Randall Balestriero, Vincent Roger, Hervé Glotin, Richard G. Baraniuk:
Semi-Supervised Learning via New Deep Network Inversion. CoRR abs/1711.04313 (2017) - [i2]Romain Cosentino, Randall Balestriero, Richard G. Baraniuk, Ankit B. Patel:
Overcomplete Frame Thresholding for Acoustic Scene Analysis. CoRR abs/1712.09117 (2017) - 2016
- [c2]Romain Cosentino, Randall Balestriero, Behnaam Aazhang:
Best basis selection using sparsity driven multi-family wavelet transform. GlobalSIP 2016: 252-256 - [i1]Hervé Glotin, Julien Ricard, Randall Balestriero:
Fast Chirplet Transform feeding CNN, application to orca and bird bioacoustics. CoRR abs/1611.08749 (2016) - 2015
- [c1]Randall Balestriero, Hervé Glotin:
Scattering Decomposition for Massive Signal Classification: From Theory to Fast Algorithm and Implementation with Validation on International Bioacoustic Benchmark. ICDM Workshops 2015: 753-761
Coauthor Index
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