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Diagonal matrix

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Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Diagonal matrix

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SPEEDING UP KRYLOV SUBSPACE METHODS FOR COMPUTING f(A)b VIA RANDOMIZATION

Accelerator-driven Data Arrangement to Minimize Transformers Run-time on Multi-core Architectures

The Power of Two Matrices in Spectral Algorithms for Community Recovery

Randomized low-rank approximation and its applications

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Preserving the positivity of the deformation gradient determinant in intergrid interpolation by combining RBFs and SVD: Application to cardiac electromechanics

T-5 configurations and hyperbolic systems

A 16-bit Floating-Point Near-SRAM Architecture for Low-power Sparse Matrix-Vector Multiplication

Learned Compressive Representations for Single-Photon 3D Imaging

Post-Moore's Law Fusion: High-Bandwidth Memory, Accelerators, and Native Half-Precision Processing for CPU-Local Analytics

The Power of Two Matrices in Spectral Algorithms for Community Recovery

Accelerator-driven Data Arrangement to Minimize Transformers Run-time on Multi-core Architectures

T-5 configurations and hyperbolic systems

Randomized low-rank approximation and its applications

A 16-bit Floating-Point Near-SRAM Architecture for Low-power Sparse Matrix-Vector Multiplication

Post-Moore's Law Fusion: High-Bandwidth Memory, Accelerators, and Native Half-Precision Processing for CPU-Local Analytics

Learned Compressive Representations for Single-Photon 3D Imaging

Deep Learning Theory Through the Lens of Diagonal Linear Networks

SPEEDING UP KRYLOV SUBSPACE METHODS FOR COMPUTING f(A)b VIA RANDOMIZATION

Preserving the positivity of the deformation gradient determinant in intergrid interpolation by combining RBFs and SVD: Application to cardiac electromechanics