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Forward Thinking: Building and Training Neural Networks One Layer at a
  Time

Forward Thinking: Building and Training Neural Networks One Layer at a Time

8 June 2017
Chris Hettinger
Tanner Christensen
Ben Ehlert
J. Humpherys
Tyler J. Jarvis
Sean Wade
    AI4CE
ArXiv (abs)PDFHTML

Papers citing "Forward Thinking: Building and Training Neural Networks One Layer at a Time"

23 / 23 papers shown
Information flow in multilayer perceptrons: an in-depth analysis
Information flow in multilayer perceptrons: an in-depth analysis
Giuliano Armano
111
0
0
11 Oct 2025
Early-Exit Graph Neural Networks
Early-Exit Graph Neural Networks
Andrea Giuseppe Di Francesco
Maria Sofia Bucarelli
F. M. Nardini
R. Perego
Nicola Tonellotto
Fabrizio Silvestri
441
0
0
23 May 2025
Scalable Model Merging with Progressive Layer-wise Distillation
Scalable Model Merging with Progressive Layer-wise Distillation
Jing Xu
Jiazheng Li
J.N. Zhang
MoMeFedML
715
9
0
18 Feb 2025
FedProphet: Memory-Efficient Federated Adversarial Training via Robust and Consistent Cascade Learning
FedProphet: Memory-Efficient Federated Adversarial Training via Robust and Consistent Cascade Learning
Minxue Tang
Yitu Wang
Jingyang Zhang
Louis DiValentin
Aolin Ding
Amin Hass
Yiran Chen
Hai "Helen" Li
FedMLAAML
265
0
0
12 Sep 2024
Training Neural Networks Using Reproducing Kernel Space Interpolation
  and Model Reduction
Training Neural Networks Using Reproducing Kernel Space Interpolation and Model Reduction
Eric A. Werneburg
159
0
0
31 Aug 2023
Aggregating Capacity in FL through Successive Layer Training for
  Computationally-Constrained Devices
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained DevicesNeural Information Processing Systems (NeurIPS), 2023
Kilian Pfeiffer
R. Khalili
J. Henkel
FedML
356
10
0
26 May 2023
FADE: Enabling Federated Adversarial Training on Heterogeneous
  Resource-Constrained Edge Devices
FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices
Minxue Tang
Jianyi Zhang
Mingyuan Ma
Louis DiValentin
Aolin Ding
Amin Hassanzadeh
Xue Yang
Yiran Chen
FedML
166
0
0
08 Sep 2022
OccamNets: Mitigating Dataset Bias by Favoring Simpler Hypotheses
OccamNets: Mitigating Dataset Bias by Favoring Simpler HypothesesEuropean Conference on Computer Vision (ECCV), 2022
Robik Shrestha
Kushal Kafle
Christopher Kanan
CML
453
17
0
05 Apr 2022
How much pre-training is enough to discover a good subnetwork?
How much pre-training is enough to discover a good subnetwork?
Cameron R. Wolfe
Fangshuo Liao
Qihan Wang
Junhyung Lyle Kim
Anastasios Kyrillidis
318
4
0
31 Jul 2021
Train your classifier first: Cascade Neural Networks Training from upper
  layers to lower layers
Train your classifier first: Cascade Neural Networks Training from upper layers to lower layersIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2021
Shucong Zhang
Cong-Thanh Do
R. Doddipatla
Erfan Loweimi
P. Bell
Steve Renals
301
2
0
09 Feb 2021
An Analytic Layer-wise Deep Learning Framework with Applications to
  Robotics
An Analytic Layer-wise Deep Learning Framework with Applications to Robotics
Huu-Thiet Nguyen
C. Cheah
Kar-Ann Toh
178
23
0
07 Feb 2021
Growing Deep Forests Efficiently with Soft Routing and Learned
  Connectivity
Growing Deep Forests Efficiently with Soft Routing and Learned Connectivity
Jianghao Shen
Sicheng Wang
Zinan Lin
204
0
0
29 Dec 2020
Layerwise learning for quantum neural networks
Layerwise learning for quantum neural networks
Andrea Skolik
Jarrod R. McClean
Masoud Mohseni
Patrick van der Smagt
Martin Leib
233
331
0
26 Jun 2020
On sparse connectivity, adversarial robustness, and a novel model of the
  artificial neuron
On sparse connectivity, adversarial robustness, and a novel model of the artificial neuron
Sergey Bochkanov
AAML
228
1
0
16 Jun 2020
Why should we add early exits to neural networks?
Why should we add early exits to neural networks?Cognitive Computation (Cogn Comput), 2020
Simone Scardapane
M. Scarpiniti
E. Baccarelli
A. Uncini
428
139
0
27 Apr 2020
Automated Architecture Design for Deep Neural Networks
Automated Architecture Design for Deep Neural Networks
Steven Abreu
3DVAI4CE
150
22
0
22 Aug 2019
SSFN -- Self Size-estimating Feed-forward Network with Low Complexity,
  Limited Need for Human Intervention, and Consistent Behaviour across Trials
SSFN -- Self Size-estimating Feed-forward Network with Low Complexity, Limited Need for Human Intervention, and Consistent Behaviour across Trials
Saikat Chatterjee
Alireza M. Javid
M. Sadeghi
Shumpei Kikuta
Dong Liu
P. Mitra
Mikael Skoglund
240
6
0
17 May 2019
An Adaptive Weighted Deep Forest Classifier
An Adaptive Weighted Deep Forest Classifier
Lev V. Utkin
A. Konstantinov
V. Chukanov
M. V. Kots
A. Meldo
108
2
0
04 Jan 2019
Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern
  Classification
Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern ClassificationIEEE Transactions on Cybernetics (IEEE Trans. Cybern.), 2018
C. Low
Jaewoo Park
Andrew Beng Jin Teoh
289
55
0
17 Nov 2018
Self Configuration in Machine Learning
Self Configuration in Machine Learning
Eugene Wong
ODL
87
1
0
17 Sep 2018
Learning Representations for Neural Network-Based Classification Using
  the Information Bottleneck Principle
Learning Representations for Neural Network-Based Classification Using the Information Bottleneck PrincipleIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018
Rana Ali Amjad
Bernhard C. Geiger
562
210
0
27 Feb 2018
Progressive Learning for Systematic Design of Large Neural Networks
Progressive Learning for Systematic Design of Large Neural Networks
Saikat Chatterjee
Alireza M. Javid
M. Sadeghi
P. Mitra
Mikael Skoglund
AI4CEODL
110
27
0
23 Oct 2017
Forward Thinking: Building Deep Random Forests
Forward Thinking: Building Deep Random Forests
Kevin Miller
Chris Hettinger
J. Humpherys
Tyler J. Jarvis
David Kartchner
AI4CE
142
43
0
20 May 2017
1
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