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Martin Jaggi

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Related publications (76)

Page 4 of 4

On the Relationship between Self-Attention and Convolutional Layers

Martin Jaggi, Andreas Loukas, Jean-Baptiste Francis Marie Juliette Cordonnier

Recent trends of incorporating attention mechanisms in vision have led re- searchers to reconsider the supremacy of convolutional layers as a primary build- ing block. Beyond helping CNNs to handle long-range dependencies, Ramachandran et al. (2019) showed ...

2020

Linearly Convergent Frank-Wolfe with Backtracking Line-Search

Martin Jaggi

Structured constraints in Machine Learning have recently brought the Frank-Wolfe (FW) family of algorithms back into the spotlight. While the classical FW algorithm has poor local convergence properties, Away-steps FW and Pairwise FW have emerged as improv ...

ADDISON-WESLEY PUBL CO2020

Stochastic Variance Reduced Gradient Optimization of Generative Adversarial Networks

François Fleuret, Martin Jaggi, Sebastian Urban Stich, Tatjana Chavdarova

2018

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

Don't Use Large Mini-Batches, Use Local SGD

Martin Jaggi, Sebastian Urban Stich, Tao Lin

Mini-batch stochastic gradient methods (SGD) are state of the art for distributed training of deep neural networks. Drastic increases in the mini-batch sizes have lead to key efficiency and scalability gains in recent years. However, progress faces a major ...

2019

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

Lightweight Cross-Lingual Sentence Representation Learning

Martin Jaggi, Prakhar Gupta

Large-scale models for learning fixed-dimensional cross-lingual sentence representations like LASER (Artetxe and Schwenk, 2019b) lead to significant improvement in performance on downstream tasks. However, further increases and modifications based on such ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

DeepBreath-automated detection of respiratory pathology from lung auscultation in 572 pediatric outpatients across 5 countries

Martin Jaggi, Mary-Anne Hartley, Tatjana Chavdarova, Jonathan Dönz

The interpretation of lung auscultation is highly subjective and relies on non-specific nomenclature. Computer-aided analysis has the potential to better standardize and automate evaluation. We used 35.9 hours of auscultation audio from 572 pediatric outpa ...

NATURE PORTFOLIO2023

Self-Supervised Neural Topic Modeling

Martin Jaggi

Topic models are useful tools for analyzing and interpreting the main underlying themes of large corpora of text. Most topic models rely on word co-occurrence for computing a topic, i.e., a weighted set of words that together represent a high-level semanti ...

Assoc Computational Linguistics-Acl2021

Correlating Twitter Language with Community-Level Health Outcomes

Martin Jaggi

We study how language on social media is linked to diseases such as atherosclerotic heart disease (AHD), diabetes and various types of cancer. Our proposed model leverages state-of-the-art sentence embeddings, followed by a regression model and clustering, ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2019

Exact Optimization of Conformal Predictors via Incremental and Decremental Learning

Martin Jaggi

Conformal Predictors (CP) are wrappers around ML models, providing error guarantees under weak assumptions on the data distribution. They are suitable for a wide range of problems, from classification and regression to anomaly detection. Unfortunately, the ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Evaluating the search phase of neural architecture search

Mathieu Salzmann, Martin Jaggi, Claudiu-Cristian Musat, Kaicheng Yu

Neural Architecture Search (NAS) aims to facilitate the design of deep networks fornew tasks. Existing techniques rely on two stages: searching over the architecture space and validating the best architecture. NAS algorithms are currently compared solely b ...

2020

CoBo: Collaborative Learning via Bilevel Optimization

Martin Jaggi, Lie He

Collaborative learning is an important tool to train multiple clients more effectively by enabling communication among clients. Identifying helpful clients, however, presents challenging and often introduces significant overhead. In this paper, we model cl ...

2024

A Distributed Second-Order Algorithm You Can Trust

Martin Jaggi, Aurélien Lucchi

Due to the rapid growth of data and computational resources, distributed optimization has become an active research area in recent years. While first-order methods seem to dominate the field, second-order methods are nevertheless attractive as they potenti ...

2018

Unsupervised learning of sentence embeddings using compositional n-gram features

Martin Jaggi, Prakhar Gupta, Matteo Pagliardini

2018

Learning from History for Byzantine Robust Optimization

Martin Jaggi, Sai Praneeth Reddy Karimireddy, Lie He

Byzantine robustness has received significant attention recently given its importance for distributed and federated learning. In spite of this, we identify severe flaws in existing algorithms even when the data across the participants is identically distri ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021