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Learned Compressive Representations for Single-Photon 3D Imaging

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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

Learned Compressive Representations for Single-Photon 3D Imaging

Graph Chatbot

Chattez avec Graph Search

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Aggregating Spatial and Photometric Context for Photometric Stereo

Generalization of Scaled Deep ResNets in the Mean-Field Regime

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

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

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

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Aggregating Spatial and Photometric Context for Photometric Stereo

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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