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Striving for Simplicity: The All Convolutional Net
v1v2v3 (latest)

Striving for Simplicity: The All Convolutional Net

International Conference on Learning Representations (ICLR), 2014
21 December 2014
Jost Tobias Springenberg
Alexey Dosovitskiy
Thomas Brox
Martin Riedmiller
    FAtt
ArXiv (abs)PDFHTML

Papers citing "Striving for Simplicity: The All Convolutional Net"

50 / 1,917 papers shown
Deep Divergence-Based Approach to Clustering
Deep Divergence-Based Approach to ClusteringNeural Networks (NN), 2019
Michael C. Kampffmeyer
Sigurd Løkse
F. Bianchi
L. Livi
Arnt-Børre Salberg
Robert Jenssen
211
72
0
13 Feb 2019
Sample Variance Decay in Randomly Initialized ReLU Networks
Sample Variance Decay in Randomly Initialized ReLU Networks
Kyle L. Luther
H. S. Seung
126
3
0
13 Feb 2019
Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency
  Maps
Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps
Beomsu Kim
Junghoon Seo
Seunghyun Jeon
Jamyoung Koo
J. Choe
Taegyun Jeon
FAtt
170
83
0
13 Feb 2019
RespNet: A deep learning model for extraction of respiration from
  photoplethysmogram
RespNet: A deep learning model for extraction of respiration from photoplethysmogramAnnual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019
Vignesh Ravichandran
Balamurali Murugesan
Vaishali Balakarthikeyan
Sharath M. Shankaranarayana
Keerthi Ram
S. Preejith
J. Joseph
M. Sivaprakasam
125
52
0
12 Feb 2019
Taking a HINT: Leveraging Explanations to Make Vision and Language
  Models More Grounded
Taking a HINT: Leveraging Explanations to Make Vision and Language Models More GroundedIEEE International Conference on Computer Vision (ICCV), 2019
Ramprasaath R. Selvaraju
Stefan Lee
Yilin Shen
Hongxia Jin
Shalini Ghosh
Larry Heck
Dhruv Batra
Devi Parikh
FAttVLM
288
282
0
11 Feb 2019
Learning From Noisy Labels By Regularized Estimation Of Annotator
  Confusion
Learning From Noisy Labels By Regularized Estimation Of Annotator ConfusionComputer Vision and Pattern Recognition (CVPR), 2019
Ryutaro Tanno
A. Saeedi
S. Sankaranarayanan
Daniel C. Alexander
N. Silberman
NoLa
407
268
0
10 Feb 2019
Improving Deep Image Clustering With Spatial Transformer Layers
Improving Deep Image Clustering With Spatial Transformer LayersInternational Conference on Artificial Neural Networks (ICANN), 2019
Thiago V. M. Souza
Cleber Zanchettin
170
5
0
09 Feb 2019
Software-Defined FPGA Accelerator Design for Mobile Deep Learning
  Applications
Software-Defined FPGA Accelerator Design for Mobile Deep Learning ApplicationsInternational Workshop on Applied Reconfigurable Computing (ARC), 2019
Panagiotis G. Mousouliotis
L. Petrou
224
13
0
08 Feb 2019
Crop Yield Prediction Using Deep Neural Networks
Crop Yield Prediction Using Deep Neural Networks
S. Khaki
Lizhi Wang
237
656
0
07 Feb 2019
CHIP: Channel-wise Disentangled Interpretation of Deep Convolutional
  Neural Networks
CHIP: Channel-wise Disentangled Interpretation of Deep Convolutional Neural Networks
Xinrui Cui
Dan Wang
F. I. Z. Jane Wang
FAttBDL
156
13
0
07 Feb 2019
Fooling Neural Network Interpretations via Adversarial Model
  Manipulation
Fooling Neural Network Interpretations via Adversarial Model Manipulation
Juyeon Heo
Sunghwan Joo
Taesup Moon
AAMLFAtt
422
225
0
06 Feb 2019
Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of
  Key Ideas and Publications, and Bibliography for Explainable AI
Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI
Shane T. Mueller
R. Hoffman
W. Clancey
Abigail Emrey
Gary Klein
XAI
259
308
0
05 Feb 2019
Learning Decision Trees Recurrently Through Communication
Learning Decision Trees Recurrently Through Communication
Stephan Alaniz
Diego Marcos
Bernt Schiele
Zeynep Akata
157
16
0
05 Feb 2019
DVOLVER: Efficient Pareto-Optimal Neural Network Architecture Search
DVOLVER: Efficient Pareto-Optimal Neural Network Architecture Search
Guillaume Michel
M. Alaoui
A. Lebois
Amal Feriani
Mehdi Felhi
3DV
106
10
0
05 Feb 2019
Using Pre-Training Can Improve Model Robustness and Uncertainty
Using Pre-Training Can Improve Model Robustness and Uncertainty
Dan Hendrycks
Kimin Lee
Mantas Mazeika
NoLa
560
797
0
28 Jan 2019
On the (In)fidelity and Sensitivity for Explanations
On the (In)fidelity and Sensitivity for Explanations
Chih-Kuan Yeh
Cheng-Yu Hsieh
A. Suggala
David I. Inouye
Pradeep Ravikumar
FAtt
385
531
0
27 Jan 2019
ISeeU: Visually interpretable deep learning for mortality prediction
  inside the ICU
ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU
William Caicedo-Torres
Jairo Gutiérrez
127
87
0
24 Jan 2019
SISC: End-to-end Interpretable Discovery Radiomics-Driven Lung Cancer
  Prediction via Stacked Interpretable Sequencing Cells
SISC: End-to-end Interpretable Discovery Radiomics-Driven Lung Cancer Prediction via Stacked Interpretable Sequencing Cells
Vignesh Sankar
Devinder Kumar
David A Clausi
Graham W. Taylor
Alexander Wong
119
25
0
15 Jan 2019
Interpretable machine learning: definitions, methods, and applications
Interpretable machine learning: definitions, methods, and applications
W. James Murdoch
Chandan Singh
Karl Kumbier
R. Abbasi-Asl
Bin Yu
XAIHAI
404
1,642
0
14 Jan 2019
RRAM based neuromorphic algorithms
RRAM based neuromorphic algorithms
Roshan Gopalakrishnan
84
2
0
12 Jan 2019
Deep Learning for Ranking Response Surfaces with Applications to Optimal
  Stopping Problems
Deep Learning for Ranking Response Surfaces with Applications to Optimal Stopping Problems
Ruimeng Hu
OOD
225
15
0
11 Jan 2019
DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video
  Action Recognition
DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition
Zheng Shou
Xudong Lin
Yannis Kalantidis
Laura Sevilla-Lara
Marcus Rohrbach
Shih-Fu Chang
Zhicheng Yan
VGen
283
129
0
11 Jan 2019
Low-Cost Transfer Learning of Face Tasks
Low-Cost Transfer Learning of Face Tasks
T. John
Isha Dua
V. Balasubramanian
C. V. Jawahar
CLIPCVBMVLM
127
0
0
09 Jan 2019
Spatial-Winograd Pruning Enabling Sparse Winograd Convolution
Spatial-Winograd Pruning Enabling Sparse Winograd Convolution
Jiecao Yu
Jongsoo Park
Maxim Naumov
79
7
0
08 Jan 2019
Enhancing Sound Texture in CNN-Based Acoustic Scene Classification
Enhancing Sound Texture in CNN-Based Acoustic Scene Classification
Yuzhong Wu
Tan Lee
155
40
0
06 Jan 2019
Channel Locality Block: A Variant of Squeeze-and-Excitation
Channel Locality Block: A Variant of Squeeze-and-Excitation
Huayu Li
78
8
0
06 Jan 2019
Stealing Neural Networks via Timing Side Channels
Stealing Neural Networks via Timing Side Channels
Vasisht Duddu
D. Samanta
D. V. Rao
V. Balas
AAMLMLAUFedML
314
148
0
31 Dec 2018
Learn to Interpret Atari Agents
Learn to Interpret Atari Agents
Zhao Yang
S. Bai
Li Zhang
Juil Sock
231
30
0
29 Dec 2018
Attention Branch Network: Learning of Attention Mechanism for Visual
  Explanation
Attention Branch Network: Learning of Attention Mechanism for Visual Explanation
Hiroshi Fukui
Tsubasa Hirakawa
Takayoshi Yamashita
H. Fujiyoshi
XAIFAtt
397
449
0
25 Dec 2018
Neural Persistence: A Complexity Measure for Deep Neural Networks Using
  Algebraic Topology
Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology
Bastian Rieck
Matteo Togninalli
Christian Bock
Michael Moor
Max Horn
Thomas Gumbsch
Karsten Borgwardt
337
122
0
23 Dec 2018
Expanding the Reach of Federated Learning by Reducing Client Resource
  Requirements
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
S. Caldas
Jakub Konecný
H. B. McMahan
Ameet Talwalkar
330
499
0
18 Dec 2018
Interactive Naming for Explaining Deep Neural Networks: A Formative
  Study
Interactive Naming for Explaining Deep Neural Networks: A Formative Study
M. Hamidi-Haines
Chen Ma
Alan Fern
Fuxin Li
Prasad Tadepalli
FAttHAI
171
11
0
18 Dec 2018
A Survey of Safety and Trustworthiness of Deep Neural Networks:
  Verification, Testing, Adversarial Attack and Defence, and Interpretability
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability
Xiaowei Huang
Daniel Kroening
Wenjie Ruan
Marta Kwiatkowska
Youcheng Sun
Emese Thamo
Min Wu
Xinping Yi
AAML
496
52
0
18 Dec 2018
Context-encoding Variational Autoencoder for Unsupervised Anomaly
  Detection
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection
David Zimmerer
Simon A. A. Kohl
Jens Petersen
Hyunjin Park
Klaus H. Maier-Hein
DRL
243
139
0
14 Dec 2018
Feedback alignment in deep convolutional networks
Feedback alignment in deep convolutional networks
Theodore H. Moskovitz
Ashok Litwin-Kumar
L. F. Abbott
170
66
0
12 Dec 2018
Diagnostic Visualization for Deep Neural Networks Using Stochastic
  Gradient Langevin Dynamics
Diagnostic Visualization for Deep Neural Networks Using Stochastic Gradient Langevin Dynamics
Biye Jiang
David M. Chan
Tianhao Zhang
John F. Canny
FAtt
99
0
0
11 Dec 2018
Grounded Human-Object Interaction Hotspots from Video
Grounded Human-Object Interaction Hotspots from Video
Tushar Nagarajan
Christoph Feichtenhofer
Kristen Grauman
299
185
0
11 Dec 2018
Video Colorization using CNNs and Keyframes extraction: An application
  in saving bandwidth
Video Colorization using CNNs and Keyframes extraction: An application in saving bandwidth
Ankur Singh
Anurag Chanani
H. Karnick
127
2
0
07 Dec 2018
Guided Zoom: Questioning Network Evidence for Fine-grained
  Classification
Guided Zoom: Questioning Network Evidence for Fine-grained Classification
Sarah Adel Bargal
Andrea Zunino
Vitali Petsiuk
Jianming Zhang
Kate Saenko
Vittorio Murino
Stan Sclaroff
180
16
0
06 Dec 2018
Understanding Individual Decisions of CNNs via Contrastive
  Backpropagation
Understanding Individual Decisions of CNNs via Contrastive Backpropagation
Jindong Gu
Yinchong Yang
Volker Tresp
FAtt
243
103
0
05 Dec 2018
Interpretable Deep Learning under Fire
Interpretable Deep Learning under Fire
Xinyang Zhang
Ningfei Wang
Hua Shen
S. Ji
Xiapu Luo
Ting Wang
AAMLAI4CE
249
186
0
03 Dec 2018
Analyzing Federated Learning through an Adversarial Lens
Analyzing Federated Learning through an Adversarial Lens
A. Bhagoji
Supriyo Chakraborty
Prateek Mittal
S. Calo
FedML
712
1,225
0
29 Nov 2018
A Multiclass Multiple Instance Learning Method with Exact Likelihood
A Multiclass Multiple Instance Learning Method with Exact Likelihood
Xi-Lin Li
WSOD
298
2
0
29 Nov 2018
Bootstrapping Deep Neural Networks from Approximate Image Processing
  Pipelines
Bootstrapping Deep Neural Networks from Approximate Image Processing Pipelines
Kilho Son
Jesse Hostetler
S. Chai
113
0
0
29 Nov 2018
Deep learning for pedestrians: backpropagation in CNNs
Deep learning for pedestrians: backpropagation in CNNs
L. Boué
3DVPINN
147
7
0
29 Nov 2018
PointCloud Saliency Maps
PointCloud Saliency Maps
Tianhang Zheng
Changyou Chen
Junsong Yuan
Bo Li
K. Ren
3DPC
248
256
0
28 Nov 2018
Synaptic Plasticity Dynamics for Deep Continuous Local Learning
  (DECOLLE)
Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE)
Jacques Kaiser
Hesham Mostafa
Emre Neftci
202
23
0
27 Nov 2018
GP-CNAS: Convolutional Neural Network Architecture Search with Genetic
  Programming
GP-CNAS: Convolutional Neural Network Architecture Search with Genetic Programming
Yiheng Zhu
Yichen Yao
Zili Wu
Yujie Chen
Guozheng Li
Haoyuan Hu
Yinghui Xu
107
7
0
26 Nov 2018
An overview of deep learning in medical imaging focusing on MRI
An overview of deep learning in medical imaging focusing on MRIZeitschrift für Medizinische Physik (Z Med Phys), 2018
A. Lundervold
A. Lundervold
OOD
355
1,815
0
25 Nov 2018
Spectral Multigraph Networks for Discovering and Fusing Relationships in
  Molecules
Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules
Boris Knyazev
Xiao Lin
Mohamed R. Amer
Graham W. Taylor
GNN
142
31
0
23 Nov 2018
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