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Bringing the Discussion of Minima Sharpness to the Audio Domain: a
  Filter-Normalised Evaluation for Acoustic Scene Classification
v1v2 (latest)

Bringing the Discussion of Minima Sharpness to the Audio Domain: a Filter-Normalised Evaluation for Acoustic Scene Classification

28 September 2023
M. Milling
Andreas Triantafyllopoulos
Iosif Tsangko
Simon Rampp
F. Schlüter
ArXiv (abs)PDFHTML

Papers citing "Bringing the Discussion of Minima Sharpness to the Audio Domain: a Filter-Normalised Evaluation for Acoustic Scene Classification"

16 / 16 papers shown
Title
Visualizing high-dimensional loss landscapes with Hessian directions
Visualizing high-dimensional loss landscapes with Hessian directions
Lucas Böttcher
Gregory R. Wheeler
68
14
0
28 Aug 2022
Fairness and underspecification in acoustic scene classification: The
  case for disaggregated evaluations
Fairness and underspecification in acoustic scene classification: The case for disaggregated evaluations
Andreas Triantafyllopoulos
M. Milling
Konstantinos Drossos
Björn W. Schuller
58
7
0
04 Oct 2021
An Encoder-Decoder Based Audio Captioning System With Transfer and
  Reinforcement Learning
An Encoder-Decoder Based Audio Captioning System With Transfer and Reinforcement Learning
Xinhao Mei
Qiushi Huang
Xubo Liu
Gengyun Chen
Jingqian Wu
...
Tom Ko
H. Tang
Xingkun Shao
Mark D. Plumbley
Wenwu Wang
75
54
0
05 Aug 2021
Relating Adversarially Robust Generalization to Flat Minima
Relating Adversarially Robust Generalization to Flat Minima
David Stutz
Matthias Hein
Bernt Schiele
OOD
87
67
0
09 Apr 2021
Why flatness does and does not correlate with generalization for deep
  neural networks
Why flatness does and does not correlate with generalization for deep neural networks
Shuo Zhang
Isaac Reid
Guillermo Valle Pérez
A. Louis
50
8
0
10 Mar 2021
SWAD: Domain Generalization by Seeking Flat Minima
SWAD: Domain Generalization by Seeking Flat Minima
Junbum Cha
Sanghyuk Chun
Kyungjae Lee
Han-Cheol Cho
Seunghyun Park
Yunsung Lee
Sungrae Park
MoMe
301
458
0
17 Feb 2021
Towards Theoretically Understanding Why SGD Generalizes Better Than ADAM
  in Deep Learning
Towards Theoretically Understanding Why SGD Generalizes Better Than ADAM in Deep Learning
Pan Zhou
Jiashi Feng
Chao Ma
Caiming Xiong
Guosheng Lin
E. Weinan
84
234
0
12 Oct 2020
Pruning artificial neural networks: a way to find well-generalizing,
  high-entropy sharp minima
Pruning artificial neural networks: a way to find well-generalizing, high-entropy sharp minima
Enzo Tartaglione
Andrea Bragagnolo
Marco Grangetto
63
12
0
30 Apr 2020
PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern
  Recognition
PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
Qiuqiang Kong
Yin Cao
Turab Iqbal
Yuxuan Wang
Wenwu Wang
Mark D. Plumbley
VLMSSL
194
1,084
0
21 Dec 2019
PyHessian: Neural Networks Through the Lens of the Hessian
PyHessian: Neural Networks Through the Lens of the Hessian
Z. Yao
A. Gholami
Kurt Keutzer
Michael W. Mahoney
ODL
63
303
0
16 Dec 2019
Asymmetric Valleys: Beyond Sharp and Flat Local Minima
Asymmetric Valleys: Beyond Sharp and Flat Local Minima
Haowei He
Gao Huang
Yang Yuan
ODLMLT
69
150
0
02 Feb 2019
Visualizing the Loss Landscape of Neural Nets
Visualizing the Loss Landscape of Neural Nets
Hao Li
Zheng Xu
Gavin Taylor
Christoph Studer
Tom Goldstein
258
1,898
0
28 Dec 2017
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys
Pratik Chaudhari
A. Choromańska
Stefano Soatto
Yann LeCun
Carlo Baldassi
C. Borgs
J. Chayes
Levent Sagun
R. Zecchina
ODL
96
774
0
06 Nov 2016
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp
  Minima
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
N. Keskar
Dheevatsa Mudigere
J. Nocedal
M. Smelyanskiy
P. T. P. Tang
ODL
429
2,945
0
15 Sep 2016
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
James Martens
Roger C. Grosse
ODL
104
1,023
0
19 Mar 2015
Qualitatively characterizing neural network optimization problems
Qualitatively characterizing neural network optimization problems
Ian Goodfellow
Oriol Vinyals
Andrew M. Saxe
ODL
112
523
0
19 Dec 2014
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