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2020 – today
- 2024
[j7]Vishwanath Saragadam
, Randall Balestriero, Ashok Veeraraghavan, Richard G. Baraniuk:
DeepTensor: Low-Rank Tensor Decomposition With Deep Network Priors. IEEE Trans. Pattern Anal. Mach. Intell. 46(12): 10337-10348 (2024)- [c37]Randall Balestriero, Romain Cosentino, Sarath Shekkizhar:
Characterizing Large Language Model Geometry Helps Solve Toxicity Detection and Generation. ICML 2024 - [c36]Randall Balestriero, Yann LeCun:
How Learning by Reconstruction Produces Uninformative Features For Perception. ICML 2024 - [c35]Ahmed Imtiaz Humayun, Randall Balestriero, Richard G. Baraniuk:
Deep Networks Always Grok and Here is Why. ICML 2024 - [i77]Polina Kirichenko, Mark Ibrahim, Randall Balestriero, Diane Bouchacourt, Ramakrishna Vedantam, Hamed Firooz, Andrew Gordon Wilson:
Understanding the Detrimental Class-level Effects of Data Augmentation. CoRR abs/2401.01764 (2024) - [i76]Aarash Feizi, Randall Balestriero, Adriana Romero-Soriano, Reihaneh Rabbany:
GPS-SSL: Guided Positive Sampling to Inject Prior Into Self-Supervised Learning. CoRR abs/2401.01990 (2024) - [i75]Randall Balestriero, Yann LeCun:
Fast and Exact Enumeration of Deep Networks Partitions Regions. CoRR abs/2401.11188 (2024) - [i74]Randall Balestriero, Yann LeCun:
Learning by Reconstruction Produces Uninformative Features For Perception. CoRR abs/2402.11337 (2024) - [i73]Ahmed Imtiaz Humayun, Randall Balestriero, Richard G. Baraniuk:
Deep Networks Always Grok and Here is Why. CoRR abs/2402.15555 (2024) - [i72]Omer Ronen, Ahmed Imtiaz Humayun, Randall Balestriero, Richard G. Baraniuk, Bin Yu:
ScaLES: Scalable Latent Exploration Score for Pre-Trained Generative Networks. CoRR abs/2406.09657 (2024) - [i71]Mark Ibrahim, David A. Klindt, Randall Balestriero:
Occam's Razor for Self Supervised Learning: What is Sufficient to Learn Good Representations? CoRR abs/2406.10743 (2024) - [i70]Vlad Sobal, Mark Ibrahim, Randall Balestriero, Vivien Cabannes, Diane Bouchacourt, Pietro Astolfi, Kyunghyun Cho, Yann LeCun:
𝕏-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs. CoRR abs/2407.18134 (2024) - [i69]Randall Balestriero, Ahmed Imtiaz Humayun, Richard G. Baraniuk:
On the Geometry of Deep Learning. CoRR abs/2408.04809 (2024) - [i68]Haider Al-Tahan, Quentin Garrido, Randall Balestriero, Diane Bouchacourt, Caner Hazirbas, Mark Ibrahim:
UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling. CoRR abs/2408.04810 (2024) - [i67]Daniel Otero, Rafael Mateus, Randall Balestriero:
Self-Supervised Anomaly Detection in the Wild: Favor Joint Embeddings Methods. CoRR abs/2410.04289 (2024) - [i66]Hai Huang, Randall Balestriero:
ALLoRA: Adaptive Learning Rate Mitigates LoRA Fatal Flaws. CoRR abs/2410.09692 (2024) - [i65]Andrea Pinto, Tomer Galanti, Randall Balestriero:
The Fair Language Model Paradox. CoRR abs/2410.11985 (2024) - [i64]Patrik Reizinger, Alice Bizeul, Attila Juhos, Julia E. Vogt, Randall Balestriero, Wieland Brendel, David A. Klindt:
Cross-Entropy Is All You Need To Invert the Data Generating Process. CoRR abs/2410.21869 (2024) - [i63]Xue Xia, Randall Balestriero, Tao Zhang, Lorenz Hurni:
Self-supervised Video Instance Segmentation Can Boost Geographic Entity Alignment in Historical Maps. CoRR abs/2411.17425 (2024) - [i62]Marcin Przewiezlikowski, Randall Balestriero, Wojciech Jasinski, Marek Smieja, Bartosz Zielinski:
Beyond [cls]: Exploring the true potential of Masked Image Modeling representations. CoRR abs/2412.03215 (2024) - 2023
- [j6]Florian Bordes, Randall Balestriero, Quentin Garrido, Adrien Bardes, Pascal Vincent:
Guillotine Regularization: Why removing layers is needed to improve generalization in Self-Supervised Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c34]Ahmed Imtiaz Humayun, Randall Balestriero, Guha Balakrishnan, Richard G. Baraniuk:
SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries. CVPR 2023: 3789-3798 - [c33]Randall Balestriero, Yann LeCun:
Fast and Exact Enumeration of Deep Networks Partitions Regions. ICASSP 2023: 1-5 - [c32]Randall Balestriero, Yann LeCun:
Police: Provably Optimal Linear Constraint Enforcement For Deep Neural Networks. ICASSP 2023: 1-5 - [c31]Vivien Cabannes, Alberto Bietti, Randall Balestriero:
On Minimal Variations for Unsupervised Representation Learning. ICASSP 2023: 1-5 - [c30]Vivien Cabannes, Léon Bottou, Yann LeCun, Randall Balestriero:
Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need. ICCV 2023: 16228-16237 - [c29]Mido 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. ICLR 2023 - [c28]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. ICLR 2023 - [c27]Vivien Cabannes, Bobak Toussi Kiani, Randall Balestriero, Yann LeCun, Alberto Bietti:
The SSL Interplay: Augmentations, Inductive Bias, and Generalization. ICML 2023: 3252-3298 - [c26]Quentin Garrido, Randall Balestriero, Laurent Najman, Yann LeCun:
RankMe: Assessing the Downstream Performance of Pretrained Self-Supervised Representations by Their Rank. ICML 2023: 10929-10974 - [c25]Polina Kirichenko, Mark Ibrahim, Randall Balestriero, Diane Bouchacourt, Shanmukha Ramakrishna Vedantam, Hamed Firooz, Andrew Gordon Wilson:
Understanding the detrimental class-level effects of data augmentation. NeurIPS 2023 - [c24]Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann LeCun:
An Information Theory Perspective on Variance-Invariance-Covariance Regularization. NeurIPS 2023 - [i61]Vivien Cabannes, Bobak Toussi Kiani, Randall Balestriero, Yann LeCun, Alberto Bietti:
The SSL Interplay: Augmentations, Inductive Bias, and Generalization. CoRR abs/2302.02774 (2023) - [i60]Randall Balestriero:
Unsupervised Learning on a DIET: Datum IndEx as Target Free of Self-Supervision, Reconstruction, Projector Head. CoRR abs/2302.10260 (2023) - [i59]Ahmed Imtiaz Humayun, Randall Balestriero, Guha Balakrishnan, Richard G. Baraniuk:
SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries. CoRR abs/2302.12828 (2023) - [i58]Wei-Yin Ko, Daniel D'souza, Karina Nguyen, Randall Balestriero, Sara Hooker:
FAIR-Ensemble: When Fairness Naturally Emerges From Deep Ensembling. CoRR abs/2303.00586 (2023) - [i57]Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann LeCun:
An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization. CoRR abs/2303.00633 (2023) - [i56]Florian Bordes, Randall Balestriero, Pascal Vincent:
Towards Democratizing Joint-Embedding Self-Supervised Learning. CoRR abs/2303.01986 (2023) - [i55]Vivien Cabannes, Léon Bottou, Yann LeCun, Randall Balestriero:
Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need. CoRR abs/2303.15256 (2023) - [i54]Florian Bordes, Samuel Lavoie, Randall Balestriero, Nicolas Ballas, Pascal Vincent:
A surprisingly simple technique to control the pretraining bias for better transfer: Expand or Narrow your representation. CoRR abs/2304.05369 (2023) - [i53]Randall Balestriero, Mark Ibrahim, Vlad Sobal, Ari Morcos, Shashank Shekhar, Tom Goldstein, Florian Bordes, Adrien Bardes, Grégoire Mialon, Yuandong Tian, Avi Schwarzschild, Andrew Gordon Wilson, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Pirsiavash, Yann LeCun, Micah Goldblum:
A Cookbook of Self-Supervised Learning. CoRR abs/2304.12210 (2023) - [i52]Ali Siahkoohi, Rudy Morel, Randall Balestriero, Erwan Allys, Grégory Sainton, Taichi Kawamura, Maarten V. de Hoop:
Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders. CoRR abs/2305.16189 (2023) - [i51]Ahmed Imtiaz Humayun, Randall Balestriero, Richard G. Baraniuk:
Training Dynamics of Deep Network Linear Regions. CoRR abs/2310.12977 (2023) - [i50]Randall Balestriero, Romain Cosentino, Sarath Shekkizhar:
Characterizing Large Language Model Geometry Solves Toxicity Detection and Generation. CoRR abs/2312.01648 (2023) - 2022
- [j5]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) - [j4]Florian Bordes, Randall Balestriero, Pascal Vincent:
High Fidelity Visualization of What Your Self-Supervised Representation Knows About. Trans. Mach. Learn. Res. 2022 (2022) - [j3]Haoran You, Randall Balestriero, Zhihan Lu, Yutong Kou, Huihong Shi, Shunyao Zhang, Shang Wu, Yingyan Lin, Richard G. Baraniuk:
Max-Affine Spline Insights Into Deep Network Pruning. Trans. Mach. Learn. Res. 2022 (2022) - [c23]Romain Cosentino, Randall Balestriero, Yanis Bahroun, Anirvan M. Sengupta, Richard G. Baraniuk, Behnaam Aazhang:
Spatial Transformer K-Means. IEEECONF 2022: 1444-1448 - [c22]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 - [c21]C. J. Barberan, Sina Alemmohammad, Naiming Liu, Randall Balestriero, Richard G. Baraniuk:
NeuroView-RNN: It's About Time. FAccT 2022: 1683-1697 - [c20]Randall Balestriero, Zichao Wang, Richard G. Baraniuk:
DeepHull: Fast Convex Hull Approximation in High Dimensions. ICASSP 2022: 3888-3892 - [c19]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 - [c18]Ahmed Imtiaz Humayun, Randall Balestriero, Richard G. Baraniuk:
MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining. ICLR 2022 - [c17]Randall Balestriero, Léon Bottou, Yann LeCun:
The Effects of Regularization and Data Augmentation are Class Dependent. NeurIPS 2022 - [c16]Randall Balestriero, Yann LeCun:
Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods. NeurIPS 2022 - [c15]Randall Balestriero, Ishan Misra, Yann LeCun:
A Data-Augmentation Is Worth A Thousand Samples: Analytical Moments And Sampling-Free Training. NeurIPS 2022 - [c14]Bobak Toussi Kiani, Randall Balestriero, Yann LeCun, Seth Lloyd:
projUNN: efficient method for training deep networks with unitary matrices. NeurIPS 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, Sébastien Paris, Richard G. Baraniuk:
Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks. NeurIPS 2020 - [i23]Randall Balestriero, Sébastien 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, Sébastien 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
[j7] [c35] [i73] [i72] [i69] [c34] [i59] [i51] [j5] [j3] [c23] [c22] [c21] [c20] [c19] [c18] [i49] [i47] [i46] [i45] [i44] [i42] [i37] [j2] [c13] [c12] [c11] [c10] [i29] [i28] [i27] [i26] [j1] [c9] [i23] [i21] [i20] [i19] [i18] [i17] [i16] [i15] [i13] [c8] [c7] [c6] [i12] [i11] [c5] [c4] [i10] [i9] [i8] [i4] [i3] [i2]
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last updated on 2025-01-14 22:18 CET by the dblp team
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