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Linear Mode Connectivity in Differentiable Tree Ensembles

Linear Mode Connectivity in Differentiable Tree Ensembles

International Conference on Learning Representations (ICLR), 2024
17 February 2025
Ryuichi Kanoh
M. Sugiyama
ArXiv (abs)PDFHTMLGithub

Papers citing "Linear Mode Connectivity in Differentiable Tree Ensembles"

23 / 23 papers shown
Deep Learning is Not So Mysterious or Different
Deep Learning is Not So Mysterious or Different
Andrew Gordon Wilson
495
36
0
03 Mar 2025
Going Beyond Linear Mode Connectivity: The Layerwise Linear Feature
  Connectivity
Going Beyond Linear Mode Connectivity: The Layerwise Linear Feature ConnectivityNeural Information Processing Systems (NeurIPS), 2023
Zhanpeng Zhou
Yongyi Yang
Xiaojiang Yang
Junchi Yan
Wei Hu
373
50
0
17 Jul 2023
TIES-Merging: Resolving Interference When Merging Models
TIES-Merging: Resolving Interference When Merging ModelsNeural Information Processing Systems (NeurIPS), 2023
Prateek Yadav
Derek Tam
Leshem Choshen
Colin Raffel
Joey Tianyi Zhou
MoMe
473
640
0
02 Jun 2023
Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained
  Models
Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained ModelsNeural Information Processing Systems (NeurIPS), 2023
Guillermo Ortiz-Jiménez
Alessandro Favero
P. Frossard
MoMe
700
203
0
22 May 2023
Re-basin via implicit Sinkhorn differentiation
Re-basin via implicit Sinkhorn differentiationComputer Vision and Pattern Recognition (CVPR), 2022
F. Guerrero-Peña
H. R. Medeiros
Thomas Dubail
Masih Aminbeidokhti
Mohammadhadi Shateri
M. Pedersoli
MoMe
380
62
0
22 Dec 2022
Editing Models with Task Arithmetic
Editing Models with Task ArithmeticInternational Conference on Learning Representations (ICLR), 2022
Gabriel Ilharco
Marco Tulio Ribeiro
Mitchell Wortsman
Suchin Gururangan
Ludwig Schmidt
Hannaneh Hajishirzi
Ali Farhadi
KELMMoMeMU
1.5K
921
0
08 Dec 2022
REPAIR: REnormalizing Permuted Activations for Interpolation Repair
REPAIR: REnormalizing Permuted Activations for Interpolation RepairInternational Conference on Learning Representations (ICLR), 2022
Keller Jordan
Hanie Sedghi
O. Saukh
R. Entezari
Behnam Neyshabur
MoMe
530
124
0
15 Nov 2022
Git Re-Basin: Merging Models modulo Permutation Symmetries
Git Re-Basin: Merging Models modulo Permutation SymmetriesInternational Conference on Learning Representations (ICLR), 2022
Samuel K. Ainsworth
J. Hayase
S. Srinivasa
MoMe
1.1K
462
0
11 Sep 2022
Quark: Controllable Text Generation with Reinforced Unlearning
Quark: Controllable Text Generation with Reinforced UnlearningNeural Information Processing Systems (NeurIPS), 2022
Ximing Lu
Sean Welleck
Jack Hessel
Liwei Jiang
Lianhui Qin
Peter West
Prithviraj Ammanabrolu
Yejin Choi
MU
621
263
0
26 May 2022
Analyzing Tree Architectures in Ensembles via Neural Tangent Kernel
Analyzing Tree Architectures in Ensembles via Neural Tangent KernelInternational Conference on Learning Representations (ICLR), 2022
Ryuichi Kanoh
M. Sugiyama
346
3
0
25 May 2022
Model soups: averaging weights of multiple fine-tuned models improves
  accuracy without increasing inference time
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference timeInternational Conference on Machine Learning (ICML), 2022
Mitchell Wortsman
Gabriel Ilharco
S. Gadre
Rebecca Roelofs
Raphael Gontijo-Lopes
...
Hongseok Namkoong
Ali Farhadi
Y. Carmon
Simon Kornblith
Ludwig Schmidt
MoMe
964
1,434
1
10 Mar 2022
The Role of Permutation Invariance in Linear Mode Connectivity of Neural
  Networks
The Role of Permutation Invariance in Linear Mode Connectivity of Neural NetworksInternational Conference on Learning Representations (ICLR), 2021
R. Entezari
Hanie Sedghi
O. Saukh
Behnam Neyshabur
MoMe
682
296
0
12 Oct 2021
A Neural Tangent Kernel Perspective of Infinite Tree Ensembles
A Neural Tangent Kernel Perspective of Infinite Tree EnsemblesInternational Conference on Learning Representations (ICLR), 2021
Ryuichi Kanoh
M. Sugiyama
228
7
0
10 Sep 2021
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep
  Learning
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep LearningInternational Conference on Learning Representations (ICLR), 2021
C. Chang
R. Caruana
Anna Goldenberg
AI4CE
393
114
0
03 Jun 2021
Linear Mode Connectivity in Multitask and Continual Learning
Linear Mode Connectivity in Multitask and Continual LearningInternational Conference on Learning Representations (ICLR), 2020
Seyed Iman Mirzadeh
Mehrdad Farajtabar
Dilan Görür
Razvan Pascanu
H. Ghasemzadeh
CLL
423
177
0
09 Oct 2020
Sharpness-Aware Minimization for Efficiently Improving Generalization
Sharpness-Aware Minimization for Efficiently Improving GeneralizationInternational Conference on Learning Representations (ICLR), 2020
Pierre Foret
Ariel Kleiner
H. Mobahi
Behnam Neyshabur
AAML
984
1,843
0
03 Oct 2020
GShard: Scaling Giant Models with Conditional Computation and Automatic
  Sharding
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
Dmitry Lepikhin
HyoukJoong Lee
Yuanzhong Xu
Dehao Chen
Orhan Firat
Yanping Huang
M. Krikun
Noam M. Shazeer
Zhiwen Chen
MoE
514
1,882
0
30 Jun 2020
The Tree Ensemble Layer: Differentiability meets Conditional Computation
The Tree Ensemble Layer: Differentiability meets Conditional ComputationInternational Conference on Machine Learning (ICML), 2020
Hussein Hazimeh
Natalia Ponomareva
P. Mol
Zhenyu Tan
Rahul Mazumder
UQCVAI4CE
682
93
0
18 Feb 2020
Linear Mode Connectivity and the Lottery Ticket Hypothesis
Linear Mode Connectivity and the Lottery Ticket HypothesisInternational Conference on Machine Learning (ICML), 2019
Jonathan Frankle
Gintare Karolina Dziugaite
Daniel M. Roy
Michael Carbin
MoMe
932
741
0
11 Dec 2019
Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data
Neural Oblivious Decision Ensembles for Deep Learning on Tabular DataInternational Conference on Learning Representations (ICLR), 2019
Sergei Popov
S. Morozov
Artem Babenko
LMTD
495
397
0
13 Sep 2019
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Arthur Jacot
Franck Gabriel
Clément Hongler
3.5K
3,892
0
20 Jun 2018
Don't Decay the Learning Rate, Increase the Batch Size
Don't Decay the Learning Rate, Increase the Batch Size
Samuel L. Smith
Pieter-Jan Kindermans
Chris Ying
Quoc V. Le
ODL
905
1,107
0
01 Nov 2017
Outrageously Large Neural Networks: The Sparsely-Gated
  Mixture-of-Experts Layer
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts LayerInternational Conference on Learning Representations (ICLR), 2017
Noam M. Shazeer
Azalia Mirhoseini
Krzysztof Maziarz
Andy Davis
Quoc V. Le
Geoffrey E. Hinton
J. Dean
MoE
770
4,375
0
23 Jan 2017
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