Scattering Decomposition for Massive Signal Classification: From Theory to Fast Algorithm and Implementation with Validation on International Bioacoustic Benchmark. - Randall Balestriero; Hervé Glotin (ICDM Workshops, 2015)

Best basis selection using sparsity driven multi-family wavelet transform. - Romain Cosentino; Randall Balestriero; Behnaam Aazhang (GlobalSIP, 2016)

Fast Chirplet Transform feeding CNN, application to orca and bird bioacoustics. - Hervé Glotin; Julien Ricard; Randall Balestriero (CoRR, 2016)

Fast Chirplet Transform Injects Priors in Deep Learning of Animal Calls and Speech. - Hervé Glotin; Julien Ricard; Randall Balestriero (ICLR (Workshop), 2017)

Neural Decision Trees. - Randall Balestriero (CoRR, 2017)

Multiscale Residual Mixture of PCA: Dynamic Dictionaries for Optimal Basis Learning. - Randall Balestriero (CoRR, 2017)

Linear Time Complexity Deep Fourier Scattering Network and Extension to Nonlinear Invariants. - Randall Balestriero; Hervé Glotin (CoRR, 2017)

Adaptive Partitioning Spline Neural Networks: Template Matching, Memorization, Inhibitor Connections, Inversion, Semi-Sup, Topology Search. - Randall Balestriero; Richard G. Baraniuk (CoRR, 2017)

Semi-Supervised Learning via New Deep Network Inversion. - Randall Balestriero; Vincent Roger; Hervé Glotin; Richard G. Baraniuk (CoRR, 2017)

Overcomplete Frame Thresholding for Acoustic Scene Analysis. - Romain Cosentino; Randall Balestriero; Richard G. Baraniuk; Ankit B. Patel (CoRR, 2017)

A Spline Theory of Deep Networks. - Randall Balestriero; Richard G. Baraniuk (ICML, 2018)

Spline Filters For End-to-End Deep Learning. - Randall Balestriero; Romain Cosentino; Hervé Glotin; Richard G. Baraniuk (ICML, 2018)

Semi-Supervised Learning Enabled by Multiscale Deep Neural Network Inversion. - Randall Balestriero; Hervé Glotin; Richard G. Baraniuk (CoRR, 2018)

A Spline Theory of Deep Networks (Extended Version). - Randall Balestriero; Richard G. Baraniuk (CoRR, 2018)

From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference. - Randall Balestriero; Richard G. Baraniuk (ICLR (Poster), 2019)

A Max-Affine Spline Perspective of Recurrent Neural Networks. - Zichao Wang 001; Randall Balestriero; Richard G. Baraniuk (ICLR (Poster), 2019)

The Geometry of Deep Networks: Power Diagram Subdivision. - Randall Balestriero; Romain Cosentino; Behnaam Aazhang; Richard G. Baraniuk (NeurIPS, 2019)

A Hessian Based Complexity Measure for Deep Networks. - Hamid Javadi; Randall Balestriero; Richard G. Baraniuk (CoRR, 2019)

Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks. - Randall Balestriero; Sébastien Paris; Richard G. Baraniuk (NeurIPS, 2020)

Max-Affine Spline Insights into Deep Generative Networks. - Randall Balestriero; Sébastien Paris; Richard G. Baraniuk (CoRR, 2020)

SymJAX: symbolic CPU/GPU/TPU programming. - Randall Balestriero (CoRR, 2020)

Interpretable Super-Resolution via a Learned Time-Series Representation. - Randall Balestriero; Hervé Glotin; Richard G. Baraniuk (CoRR, 2020)

Analytical Probability Distributions and EM-Learning for Deep Generative Networks. - Randall Balestriero; Sébastien Paris; Richard G. Baraniuk (CoRR, 2020)

Ensembles of Generative Adversarial Networks for Disconnected Data. - Lorenzo Luzi; Randall Balestriero; Richard G. Baraniuk (CoRR, 2020)

Provable Finite Data Generalization with Group Autoencoder. - Romain Cosentino; Randall Balestriero; Richard G. Baraniuk; Behnaam Aazhang (CoRR, 2020)

Scalable Neural Tangent Kernel of Recurrent Architectures. - Sina Alemohammad; Randall Balestriero; Zichao Wang 001; Richard G. Baraniuk (CoRR, 2020)

Sparse Multi-Family Deep Scattering Network. - Romain Cosentino; Randall Balestriero (CoRR, 2020)

Interpretable Image Clustering via Diffeomorphism-Aware K-Means. - Romain Cosentino; Randall Balestriero; Yanis Bahroun; Anirvan M. Sengupta; Richard G. Baraniuk; Behnaam Aazhang (CoRR, 2020)

Universal Frame Thresholding. - Romain Cosentino; Randall Balestriero; Richard G. Baraniuk; Behnaam Aazhang (IEEE Signal Process. Lett., 2020)

Wearing A Mask: Compressed Representations of Variable-Length Sequences Using Recurrent Neural Tangent Kernels. - Sina Alemohammad; Hossein Babaei; Randall Balestriero; Matt Y. Cheung; Ahmed Imtiaz Humayun; Daniel LeJeune; Naiming Liu; Lorenzo Luzi; Jasper Tan; Zichao Wang 001; Richard G. Baraniuk (ICASSP, 2021)

The Recurrent Neural Tangent Kernel. - Sina Alemohammad; Zichao Wang 001; Randall Balestriero; Richard G. Baraniuk (ICLR, 2021)

Interpretable and Learnable Super-Resolution Time-Frequency Representation. - Randall Balestriero; Hervé Glotin; Richard G. Baraniuk (MSML, 2021)

Deep Autoencoders: From Understanding to Generalization Guarantees. - Romain Cosentino; Randall Balestriero; Richard G. Baraniuk; Behnaam Aazhang (MSML, 2021)

Max-Affine Spline Insights Into Deep Network Pruning. - Randall Balestriero; Haoran You; Zhihan Lu; Yutong Kou; Yingyan Lin; Richard G. Baraniuk (CoRR, 2021)

Fast Jacobian-Vector Product for Deep Networks. - Randall Balestriero; Richard G. Baraniuk (CoRR, 2021)

NeuroView: Explainable Deep Network Decision Making. - C. J. Barberan; Randall Balestriero; Richard G. Baraniuk (CoRR, 2021)

Learning in High Dimension Always Amounts to Extrapolation. - Randall Balestriero; Jerome Pesenti; Yann LeCun (CoRR, 2021)

Mad Max: Affine Spline Insights Into Deep Learning. - Randall Balestriero; Richard G. Baraniuk (Proc. IEEE, 2021)

Spatial Transformer K-Means. - Romain Cosentino; Randall Balestriero; Yanis Bahroun; Anirvan M. Sengupta; Richard G. Baraniuk; Behnaam Aazhang (IEEECONF, 2022)

Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values. - Ahmed Imtiaz Humayun; Randall Balestriero; Richard G. Baraniuk (CVPR, 2022)

NeuroView-RNN: It's About Time. - C. J. Barberan; Sina Alemmohammad; Naiming Liu; Randall Balestriero; Richard G. Baraniuk (FAccT, 2022)

DeepHull: Fast Convex Hull Approximation in High Dimensions. - Randall Balestriero; Zichao Wang 001; Richard G. Baraniuk (ICASSP, 2022)

No More Than 6ft Apart: Robust K-Means via Radius Upper Bounds. - Ahmed Imtiaz Humayun; Randall Balestriero; Anastasios Kyrillidis; Richard G. Baraniuk (ICASSP, 2022)

MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining. - Ahmed Imtiaz Humayun; Randall Balestriero; Richard G. Baraniuk (ICLR, 2022)

The Effects of Regularization and Data Augmentation are Class Dependent. - Randall Balestriero; Léon Bottou; Yann LeCun (NeurIPS, 2022)

Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods. - Randall Balestriero; Yann LeCun (NeurIPS, 2022)

A Data-Augmentation Is Worth A Thousand Samples: Analytical Moments And Sampling-Free Training. - Randall Balestriero; Ishan Misra; Yann LeCun (NeurIPS, 2022)

projUNN: efficient method for training deep networks with unitary matrices. - Bobak Toussi Kiani; Randall Balestriero; Yann LeCun; Seth Lloyd (NeurIPS, 2022)

A Data-Augmentation Is Worth A Thousand Samples: Exact Quantification From Analytical Augmented Sample Moments. - Randall Balestriero; Ishan Misra; Yann LeCun (CoRR, 2022)

NeuroView-RNN: It's About Time. - C. J. Barberan; Sina Alemohammad; Naiming Liu; Randall Balestriero; Richard G. Baraniuk (CoRR, 2022)

Singular Value Perturbation and Deep Network Optimization. - Rudolf H. Riedi; Randall Balestriero; Richard G. Baraniuk (CoRR, 2022)

Guillotine Regularization: Improving Deep Networks Generalization by Removing their Head. - Florian Bordes; Randall Balestriero; Quentin Garrido; Adrien Bardes; Pascal Vincent (CoRR, 2022)

What Do We Maximize in Self-Supervised Learning? - Ravid Shwartz-Ziv; Randall Balestriero; Yann LeCun (CoRR, 2022)

Batch Normalization Explained. - Randall Balestriero; Richard G. Baraniuk (CoRR, 2022)

Joint Embedding Self-Supervised Learning in the Kernel Regime. - Bobak Toussi Kiani; Randall Balestriero; Yubei Chen; Seth Lloyd; Yann LeCun (CoRR, 2022)

Variance Covariance Regularization Enforces Pairwise Independence in Self-Supervised Representations. - Grégoire Mialon; Randall Balestriero; Yann LeCun (CoRR, 2022)

Recurrent Scattering Network Detects Metastable Behavior in Polyphonic Seismo-Volcanic Signals for Volcano Eruption Forecasting. - Ángel Bueno Rodríguez; Randall Balestriero; Silvio De Angelis; M. Carmen Benítez; Luciano Zuccarello; Richard G. Baraniuk; Jesús M. Ibáñez; Maarten V. de Hoop (IEEE Trans. Geosci. Remote. Sens., 2022)

High Fidelity Visualization of What Your Self-Supervised Representation Knows About. - Florian Bordes; Randall Balestriero; Pascal Vincent (Trans. Mach. Learn. Res., 2022)

Max-Affine Spline Insights Into Deep Network Pruning. - Haoran You; Randall Balestriero; Zhihan Lu; Yutong Kou; Huihong Shi; Shunyao Zhang; Shang Wu 003; Yingyan Lin; Richard G. Baraniuk (Trans. Mach. Learn. Res., 2022)

SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries. - Ahmed Imtiaz Humayun; Randall Balestriero; Guha Balakrishnan; Richard G. Baraniuk (CVPR, 2023)

Fast and Exact Enumeration of Deep Networks Partitions Regions. - Randall Balestriero; Yann LeCun (ICASSP, 2023)

Police: Provably Optimal Linear Constraint Enforcement For Deep Neural Networks. - Randall Balestriero; Yann LeCun (ICASSP, 2023)

On Minimal Variations for Unsupervised Representation Learning. - Vivien Cabannes; Alberto Bietti; Randall Balestriero (ICASSP, 2023)

Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need. - Vivien Cabannes; Léon Bottou; Yann LeCun; Randall Balestriero (ICCV, 2023)

The hidden uniform cluster prior in self-supervised learning. - Mido Assran; Randall Balestriero; Quentin Duval; Florian Bordes; Ishan Misra; Piotr Bojanowski; Pascal Vincent; Michael G. Rabbat; Nicolas Ballas (ICLR, 2023)

ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations. - Badr Youbi Idrissi; Diane Bouchacourt; Randall Balestriero; Ivan Evtimov; Caner Hazirbas; Nicolas Ballas; Pascal Vincent; Michal Drozdzal; David Lopez-Paz; Mark Ibrahim (ICLR, 2023)

The SSL Interplay: Augmentations, Inductive Bias, and Generalization. - Vivien Cabannes; Bobak Toussi Kiani; Randall Balestriero; Yann LeCun; Alberto Bietti (ICML, 2023)

RankMe: Assessing the Downstream Performance of Pretrained Self-Supervised Representations by Their Rank. - Quentin Garrido; Randall Balestriero; Laurent Najman; Yann LeCun (ICML, 2023)

Understanding the detrimental class-level effects of data augmentation. - Polina Kirichenko; Mark Ibrahim; Randall Balestriero; Diane Bouchacourt; Shanmukha Ramakrishna Vedantam; Hamed Firooz; Andrew Gordon Wilson (NeurIPS, 2023)

An Information Theory Perspective on Variance-Invariance-Covariance Regularization. - Ravid Shwartz-Ziv; Randall Balestriero; Kenji Kawaguchi; Tim G. J. Rudner; Yann LeCun (NeurIPS, 2023)

Unsupervised Learning on a DIET: Datum IndEx as Target Free of Self-Supervision, Reconstruction, Projector Head. - Randall Balestriero (CoRR, 2023)

FAIR-Ensemble: When Fairness Naturally Emerges From Deep Ensembling. - Wei-Yin Ko; Daniel D'souza; Karina Nguyen; Randall Balestriero; Sara Hooker (CoRR, 2023)

An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization. - Ravid Shwartz-Ziv; Randall Balestriero; Kenji Kawaguchi; Tim G. J. Rudner; Yann LeCun (CoRR, 2023)

Towards Democratizing Joint-Embedding Self-Supervised Learning. - Florian Bordes; Randall Balestriero; Pascal Vincent (CoRR, 2023)

A surprisingly simple technique to control the pretraining bias for better transfer: Expand or Narrow your representation. - Florian Bordes; Samuel Lavoie; Randall Balestriero; Nicolas Ballas; Pascal Vincent (CoRR, 2023)

A Cookbook of Self-Supervised Learning. - 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; et al. (CoRR, 2023)

Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders. - Ali Siahkoohi; Rudy Morel; Randall Balestriero; Erwan Allys; Grégory Sainton; Taichi Kawamura; Maarten V. de Hoop (CoRR, 2023)

Training Dynamics of Deep Network Linear Regions. - Ahmed Imtiaz Humayun; Randall Balestriero; Richard G. Baraniuk (CoRR, 2023)

Characterizing Large Language Model Geometry Solves Toxicity Detection and Generation. - Randall Balestriero; Romain Cosentino; Sarath Shekkizhar (CoRR, 2023)

Guillotine Regularization: Why removing layers is needed to improve generalization in Self-Supervised Learning. - Florian Bordes; Randall Balestriero; Quentin Garrido; Adrien Bardes; Pascal Vincent (Trans. Mach. Learn. Res., 2023)

Characterizing Large Language Model Geometry Helps Solve Toxicity Detection and Generation. - Randall Balestriero; Romain Cosentino; Sarath Shekkizhar (ICML, 2024)

How Learning by Reconstruction Produces Uninformative Features For Perception. - Randall Balestriero; Yann LeCun (ICML, 2024)

Deep Networks Always Grok and Here is Why. - Ahmed Imtiaz Humayun; Randall Balestriero; Richard G. Baraniuk (ICML, 2024)

UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling. - Haider Al-Tahan; Quentin Garrido; Randall Balestriero; Diane Bouchacourt; Caner Hazirbas; Mark Ibrahim (NeurIPS, 2024)

GPS-SSL: Guided Positive Sampling to Inject Prior Into Self-Supervised Learning. - Aarash Feizi; Randall Balestriero; Adriana Romero-Soriano; Reihaneh Rabbany (CoRR, 2024)

Learning by Reconstruction Produces Uninformative Features For Perception. - Randall Balestriero; Yann LeCun (CoRR, 2024)

ScaLES: Scalable Latent Exploration Score for Pre-Trained Generative Networks. - Omer Ronen; Ahmed Imtiaz Humayun; Randall Balestriero; Richard G. Baraniuk; Bin Yu 001 (CoRR, 2024)

Occam's Razor for Self Supervised Learning: What is Sufficient to Learn Good Representations? - Mark Ibrahim; David A. Klindt; Randall Balestriero (CoRR, 2024)

On the Geometry of Deep Learning. - Randall Balestriero; Ahmed Imtiaz Humayun; Richard G. Baraniuk (CoRR, 2024)

Self-Supervised Anomaly Detection in the Wild: Favor Joint Embeddings Methods. - Daniel Otero; Rafael Mateus; Randall Balestriero (CoRR, 2024)

ALLoRA: Adaptive Learning Rate Mitigates LoRA Fatal Flaws. - Hai Huang; Randall Balestriero (CoRR, 2024)

The Fair Language Model Paradox. - Andrea Pinto; Tomer Galanti; Randall Balestriero (CoRR, 2024)

Self-supervised Video Instance Segmentation Can Boost Geographic Entity Alignment in Historical Maps. - Xue Xia; Randall Balestriero; Tao Zhang; Lorenz Hurni (CoRR, 2024)

Beyond [cls]: Exploring the true potential of Masked Image Modeling representations. - Marcin Przewiezlikowski; Randall Balestriero; Wojciech Jasinski; Marek Smieja; Bartosz Zielinski 001 (CoRR, 2024)

DeepTensor: Low-Rank Tensor Decomposition With Deep Network Priors. - Vishwanath Saragadam; Randall Balestriero; Ashok Veeraraghavan; Richard G. Baraniuk (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data. - Daniel Cai; Randall Balestriero (ICLR, 2025)

Cross-Entropy Is All You Need To Invert the Data Generating Process. - Patrik Reizinger; Alice Bizeul; Attila Juhos; Julia E. Vogt; Randall Balestriero; Wieland Brendel; David A. Klindt (ICLR, 2025)

X-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs. - Vlad Sobal; Mark Ibrahim; Randall Balestriero; Vivien Cabannes; Diane Bouchacourt; Pietro Astolfi; Kyunghyun Cho; Yann LeCun (ICLR, 2025)

Beyond and Free from Diffusion: Invertible Guided Consistency Training. - Chia-Hong Hsu; Shiu-Hong Kao; Randall Balestriero (CoRR, 2025)

Curvature Tuning: Provable Training-free Model Steering From a Single Parameter. - Leyang Hu; Randall Balestriero (CoRR, 2025)

Eidetic Learning: an Efficient and Provable Solution to Catastrophic Forgetting. - Nicholas Dronen; Randall Balestriero (CoRR, 2025)

Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models. - Vlad Sobal; Wancong Zhang; Kynghyun Cho; Randall Balestriero; Tim G. J. Rudner; Yann LeCun (CoRR, 2025)

An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research. - Patrik Reizinger; Randall Balestriero; David A. Klindt; Wieland Brendel (CoRR, 2025)

SplInterp: Improving our Understanding and Training of Sparse Autoencoders. - Jeremy Budd; Javier Ideami; Benjamin Macdowall Rynne; Keith Duggar; Randall Balestriero (CoRR, 2025)

Ditch the Denoiser: Emergence of Noise Robustness in Self-Supervised Learning from Data Curriculum. - Wenquan Lu; Jiaqi Zhang; Hugues Van Assel; Randall Balestriero (CoRR, 2025)

Joint Embedding vs Reconstruction: Provable Benefits of Latent Space Prediction for Self Supervised Learning. - Hugues Van Assel; Mark Ibrahim; Tommaso Biancalani; Aviv Regev; Randall Balestriero (CoRR, 2025)

Next Token Perception Score: Analytical Assessment of your LLM Perception Skills. - Yu-Ang Cheng; Leyang Hu; Hai Huang; Randall Balestriero (CoRR, 2025)

LoRA Users Beware: A Few Spurious Tokens Can Manipulate Your Finetuned Model. - Pradyut Sekhsaria; Marcel Mateos Salles; Hai Huang; Randall Balestriero (CoRR, 2025)

GrokAlign: Geometric Characterisation and Acceleration of Grokking. - Thomas Walker; Ahmed Imtiaz Humayun; Randall Balestriero; Richard G. Baraniuk (CoRR, 2025)

General Methods Make Great Domain-specific Foundation Models: A Case-study on Fetal Ultrasound. - Jakob Ambsdorf; Asbjørn Munk; Sebastian Nørgaard Llambias; Anders Nymark Christensen; Kamil Wojciech Mikolaj; Randall Balestriero; Martin Grønnebæk Tolsgaard; Aasa Feragen; Mads Nielsen (CoRR, 2025)

FastDINOv2: Frequency Based Curriculum Learning Improves Robustness and Training Speed. - Jiaqi Zhang; Juntuo Wang; Zhixin Sun; John Zou; Randall Balestriero (CoRR, 2025)

Task Priors: Enhancing Model Evaluation by Considering the Entire Space of Downstream Tasks. - Niket Patel; Randall Balestriero (CoRR, 2025)

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations. - Camilo Tamayo-Rousseau; Yunjia Zhao; Yiqun Zhang; Randall Balestriero (CoRR, 2025)

From Linearity to Non-Linearity: How Masked Autoencoders Capture Spatial Correlations. - Anthony Bisulco; Rahul Ramesh; Randall Balestriero; Pratik Chaudhari (CoRR, 2025)