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Luis Felipe Barba Flores

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Related publications (4)

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Implicit Gradient Alignment in Distributed and Federated Learning

Martin Jaggi, Yatin Dandi, Luis Felipe Barba Flores

A major obstacle to achieving global convergence in distributed and federated learning is the misalignment of gradients across clients, or mini-batches due to heterogeneity and stochasticity of the distributed data. In this work, we show that data heteroge ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2022

High-speed identification of suspended carbon nanotubes using Raman spectroscopy and deep learning

Martin Jaggi, Luis Felipe Barba Flores

The identification of nanomaterials with the properties required for energy-efficient electronic systems is usually a tedious human task. A workflow to rapidly localize and characterize nanomaterials at the various stages of their integration into large-sc ...

SPRINGERNATURE2022

Enhancing spatial resolution in MÖNCH for electron microscopy via deep learning

Benjamin Bejar Haro, Luis Felipe Barba Flores, Carl Pierre Jacques Remlinger

However, their use in imaging applications has been limited by their relatively large pixel size (≥ 55 μm) and high-energy (>80 keV) electrons scattering over multiple pixels in the sensor layer. To realize the full potential of fast, radiation-hard HPDs a ...

2024

Dynamic Model Pruning with Feedback

Martin Jaggi, Sebastian Urban Stich, Tao Lin, Luis Felipe Barba Flores

Deep neural networks often have millions of parameters. This can hinder their deployment to low-end devices, not only due to high memory requirements but also because of increased latency at inference. We propose a novel model compression method that gener ...

2020