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08/31/2023
On the Equivalence between Implicit and Explicit Neural Networks: A High-dimensional Viewpoint
Implicit neural networks have demonstrated remarkable success in various...
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06/14/2023
Analysis and Approximate Inference of Large and Dense Random Kronecker Graphs
Random graph models are playing an increasingly important role in scienc...
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03/24/2022
Semantic Image Manipulation with Background-guided Internal Learning
Image manipulation has attracted a lot of interest due to its wide range...
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10/19/2021
Fine-Grained Control of Artistic Styles in Image Generation
Recent advances in generative models and adversarial training have enabl...
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10/05/2021
Random matrices in service of ML footprint: ternary random features with no performance loss
In this article, we investigate the spectral behavior of random features...
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03/02/2021
Hessian Eigenspectra of More Realistic Nonlinear Models
Given an optimization problem, the Hessian matrix and its eigenspectrum ...
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11/21/2020
Sparse sketches with small inversion bias
For a tall n× d matrix A and a random m× n sketching matrix S, the sketc...
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10/06/2020
Kernel regression in high dimension: Refined analysis beyond double descent
In this paper, we provide a precise characterize of generalization prope...
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10/03/2020
Sparse Quantized Spectral Clustering
Given a large data matrix, sparsifying, quantizing, and/or performing ot...
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06/18/2020
Precise expressions for random projections: Low-rank approximation and randomized Newton
It is often desirable to reduce the dimensionality of a large dataset by...
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06/09/2020
A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent
This article characterizes the exact asymptotics of random Fourier featu...
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12/21/2019
Towards Efficient Training for Neural Network Quantization
Quantization reduces computation costs of neural networks but suffers fr...
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12/20/2019
AdaBits: Neural Network Quantization with Adaptive Bit-Widths
Deep neural networks with adaptive configurations have gained increasing...
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09/15/2019
Inner-product Kernels are Asymptotically Equivalent to Binary Discrete Kernels
This article investigates the eigenspectrum of the inner product-type ke...
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06/06/2019
Complete Dictionary Learning via ℓ^4-Norm Maximization over the Orthogonal Group
This paper considers the fundamental problem of learning a complete (ort...
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05/31/2019
High Dimensional Classification via Empirical Risk Minimization: Improvements and Optimality
In this article, we investigate a family of classification algorithms de...
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04/01/2019
Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses
This paper focuses on learning transferable adversarial examples specifi...
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11/08/2018
A Geometric Approach of Gradient Descent Algorithms in Neural Networks
In this article we present a geometric framework to analyze convergence ...
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05/30/2018
The Dynamics of Learning: A Random Matrix Approach
Understanding the learning dynamics of neural networks is one of the key...
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05/30/2018
On the Spectrum of Random Features Maps of High Dimensional Data
Random feature maps are ubiquitous in modern statistical machine learnin...
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01/11/2017
A Large Dimensional Analysis of Least Squares Support Vector Machines
In this article, a large dimensional performance analysis of kernel leas...
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09/07/2016
Random matrices meet machine learning: a large dimensional analysis of LS-SVM
This article proposes a performance analysis of kernel least squares sup...
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06/16/2015
