Martin Jaggi

Name: dblp XML data dump
Creator: Schloss Dagstuhl - Leibniz Center for Informatics
Published: 1993
License: https://creativecommons.org/publicdomain/zero/1.0/
Keywords: dblp, XML, computer science, scholarly publications, metadata

◀ ▶ joint publications with François Fleuret

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Publications

2024
[i132]
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  - journals/corr/abs-2402-02622
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Matteo Pagliardini, Amirkeivan Mohtashami, François Fleuret, Martin Jaggi:
DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging. CoRR abs/2402.02622 (2024)
2023
[c91]
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  - conf/iclr/PagliardiniJFK23
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Matteo Pagliardini, Martin Jaggi, François Fleuret, Sai Praneeth Karimireddy:
Agree to Disagree: Diversity through Disagreement for Better Transferability. ICLR 2023
[c85]
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Matteo Pagliardini, Daniele Paliotta, Martin Jaggi, François Fleuret:
Fast Attention Over Long Sequences With Dynamic Sparse Flash Attention. NeurIPS 2023
[i113]
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  - journals/corr/abs-2306-01160
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Matteo Pagliardini, Daniele Paliotta, Martin Jaggi, François Fleuret:
Faster Causal Attention Over Large Sequences Through Sparse Flash Attention. CoRR abs/2306.01160 (2023)
2022
[i101]
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  - journals/corr/abs-2202-04414
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Matteo Pagliardini, Martin Jaggi, François Fleuret, Sai Praneeth Karimireddy:
Agree to Disagree: Diversity through Disagreement for Better Transferability. CoRR abs/2202.04414 (2022)
2021
[c68]
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  - conf/iclr/ChavdarovaPSFJ21
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Tatjana Chavdarova, Matteo Pagliardini, Sebastian U. Stich, François Fleuret, Martin Jaggi:
Taming GANs with Lookahead-Minmax. ICLR 2021
2020
[c48]
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  - conf/icml/SivaprasadMVJF20
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Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels, Martin Jaggi, François Fleuret:
Optimizer Benchmarking Needs to Account for Hyperparameter Tuning. ICML 2020: 9036-9045
[i62]
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  - journals/corr/abs-2006-14567
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Tatjana Chavdarova, Matteo Pagliardini, Martin Jaggi, François Fleuret:
Taming GANs with Lookahead. CoRR abs/2006.14567 (2020)
2019
[i40]
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  - journals/corr/abs-1910-11758
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Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels, Martin Jaggi, François Fleuret:
On the Tunability of Optimizers in Deep Learning. CoRR abs/1910.11758 (2019)

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