Publications de Thijs Vogels | EPFL Graph Search

MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks

Martin Jaggi, Mary-Anne Hartley, Vinitra Swamy, Jibril Albachir Frej, Thierry Bossy, Thijs Vogels, Malika Satayeva

Predicting multiple real-world tasks in a single model often requires a particularly diverse feature space. Multimodal (MM) models aim to extract the synergistic predictive potential of multiple data types to create a shared feature space with aligned sema ...

2023

Beyond Spectral Gap: The Role of the Topology in Decentralized Learning

Martin Jaggi, Thijs Vogels, Hadrien Hendrikx

In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. In the decentralized setting, in which workers commu ...

Brookline2023

Deep Compositional Denoising for High-quality Monte Carlo Rendering

Thijs Vogels, Marios Papas, Xianyao Zhang

We propose a deep-learning method for automatically decomposing noisy Monte Carlo renderings into components that kernelpredicting denoisers can denoise more effectively. In our model, a neural decomposition module learns to predict noisy components and co ...

WILEY2021

Practical Low-Rank Communication Compression in Decentralized Deep Learning

Martin Jaggi, Sai Praneeth Reddy Karimireddy, Thijs Vogels

Lossy gradient compression has become a practical tool to overcome the communication bottleneck in centrally coordinated distributed training of machine learning models. However, algorithms for decentralized training with compressed communication over arbi ...

2020

Optimizer Benchmarking Needs to Account for Hyperparameter Tuning

François Fleuret, Martin Jaggi, Thijs Vogels, Jan Frederik Jonas Florian Mai, Prabhu Teja Sivaprasad

The performance of optimizers, particularly in deep learning, depends considerably on their chosen hyperparameter configuration. The efficacy of optimizers is often studied under near-optimal problem-specific hyperparameters, and finding these settings may ...

2020

PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization

Martin Jaggi, Thijs Vogels

We study lossy gradient compression methods to alleviate the communication bottleneck in data-parallel distributed optimization. Despite the significant attention received, current compression schemes either do not scale well, or fail to achieve the target ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

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Thijs Vogels

Communication-efficient distributed training of machine learning models

MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks

Beyond Spectral Gap: The Role of the Topology in Decentralized Learning

Deep Compositional Denoising for High-quality Monte Carlo Rendering

Practical Low-Rank Communication Compression in Decentralized Deep Learning

Optimizer Benchmarking Needs to Account for Hyperparameter Tuning

PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization

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Chattez avec Graph Search

MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks

Practical Low-Rank Communication Compression in Decentralized Deep Learning

Deep Compositional Denoising for High-quality Monte Carlo Rendering

Optimizer Benchmarking Needs to Account for Hyperparameter Tuning

PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization