Approach to Finding a Robust Deep Learning Model | Paper Citation Record

Observation 3c5f47c9-d456-4e9f-ae30-dc9cb91b8122 · outbound

This paper cites Approximation by superpositions of a sigmoidal function.

Approach to Finding a Robust Deep Learning Model Approximation by superpositions of a sigmoidal function

Reference 1

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Observation 61b38e9c-abe6-4c97-a098-aefa16f72bb7 · outbound

This paper cites Approximating Continuous Functions by ReLU Nets of Minimal Width.

Approach to Finding a Robust Deep Learning Model Approximating Continuous Functions by ReLU Nets of Minimal Width

Reference 2

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This paper cites Speeding up the hyperparameter optimization of deep convolutional neural networks.

Approach to Finding a Robust Deep Learning Model Speeding up the hyperparameter optimization of deep convolutional neural networks

Reference 3

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This paper cites A comprehensive survey of neural architecture search: Challenges and solutions.

Approach to Finding a Robust Deep Learning Model A comprehensive survey of neural architecture search: Challenges and solutions

Reference 4

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This paper cites Neural architecture search benchmarks: Insights and survey.

Approach to Finding a Robust Deep Learning Model Neural architecture search benchmarks: Insights and survey

Reference 5

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Observation f429807e-5974-48c6-8702-3fc33ca53151 · outbound

This paper cites Nas-bench-101: Towards reproducible neural architecture search.

Approach to Finding a Robust Deep Learning Model Nas-bench-101: Towards reproducible neural architecture search

Reference 6

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This paper cites NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search.

Approach to Finding a Robust Deep Learning Model NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search

Reference 7

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This paper cites Nas-bench-nlp: neural architecture search benchmark for natural language processing.

Approach to Finding a Robust Deep Learning Model Nas-bench-nlp: neural architecture search benchmark for natural language processing

Reference 8

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Observation 19d59a7c-f645-41f8-89e4-028af26695a2 · outbound

This paper cites Automl: A survey of the state-of-the-art.

Approach to Finding a Robust Deep Learning Model Automl: A survey of the state-of-the-art

Reference 9

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Observation cf5f9534-60bb-43ce-a9f3-2ba23303f40b · outbound

This paper cites Toward the end-to-end optimization of particle physics instruments with differentiable programming.

Approach to Finding a Robust Deep Learning Model Toward the end-to-end optimization of particle physics instruments with differentiable programming

Reference 10

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Observation 69d5fa78-3e6c-4495-89e2-d93dac5260f4 · outbound

This paper cites Huber and E.M.

Approach to Finding a Robust Deep Learning Model Huber and E.M

Reference 11

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Observation bce6da62-d8ee-4a41-9200-12efdfbe9bd1 · outbound

This paper cites Training set size requirements for the classification of a specific class.

Approach to Finding a Robust Deep Learning Model Training set size requirements for the classification of a specific class

Reference 12

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Observation 6263156b-e6af-41e1-984d-20bdd3849265 · outbound

This paper cites Riesz networks: Scale-invariant neural networks in a single forward pass.

Approach to Finding a Robust Deep Learning Model Riesz networks: Scale-invariant neural networks in a single forward pass

Reference 13

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Observation 5b2234c8-c40e-4eb9-babc-9d8e50a6a729 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Approach to Finding a Robust Deep Learning Model An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 14

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This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

Approach to Finding a Robust Deep Learning Model Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 15

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This paper cites Imagenet: A large-scale hierarchical image database.

Approach to Finding a Robust Deep Learning Model Imagenet: A large-scale hierarchical image database

Reference 16

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Observation b8813d06-49d1-4bbd-9ce1-5bd34c9305dd · outbound

This paper cites Robust training and initialization of deep neural networks: An adaptive basis viewpoint.

Approach to Finding a Robust Deep Learning Model Robust training and initialization of deep neural networks: An adaptive basis viewpoint

Reference 17

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Observation 4626ceeb-c6a8-4859-a373-4f97bc7e2357 · outbound

This paper cites Deep residual learning for image recognition.

Approach to Finding a Robust Deep Learning Model Deep residual learning for image recognition

Reference 18

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Observation 17697ea9-6aab-4e56-93eb-753677686386 · outbound

This paper cites Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization).

Approach to Finding a Robust Deep Learning Model Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)

Reference 19

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Observation be9a9ef2-2038-4cbc-9e16-ce952066c6e7 · outbound

This paper cites Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle.

Approach to Finding a Robust Deep Learning Model Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle

Reference 20

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This paper cites The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and smale’s 18th problem.

Approach to Finding a Robust Deep Learning Model The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and smale’s 18th problem

Reference 21

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This paper cites Stable architectures for deep neural networks.

Approach to Finding a Robust Deep Learning Model Stable architectures for deep neural networks

Reference 22

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This paper cites The many faces of robustness: A critical analysis of out-of-distribution generalization.

Approach to Finding a Robust Deep Learning Model The many faces of robustness: A critical analysis of out-of-distribution generalization

Reference 23

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This paper cites Inductive biases for deep learning of higher-level cognition.

Approach to Finding a Robust Deep Learning Model Inductive biases for deep learning of higher-level cognition

Reference 24

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This paper cites Wilds: A benchmark of in-the-wild distribution shifts.

Approach to Finding a Robust Deep Learning Model Wilds: A benchmark of in-the-wild distribution shifts

Reference 25

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This paper cites Im- proving robustness against common corruptions by covariate shift adaptation.

Approach to Finding a Robust Deep Learning Model Im- proving robustness against common corruptions by covariate shift adaptation

Reference 26

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Observation 2dabc317-48a6-459d-ac00-4ff8b655dbe8 · outbound

This paper cites Recent Advances in Adversarial Training for Adversarial Robustness.

Approach to Finding a Robust Deep Learning Model Recent Advances in Adversarial Training for Adversarial Robustness

Reference 27

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This paper cites Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey.

Approach to Finding a Robust Deep Learning Model Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey

Reference 28

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This paper cites Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance.

Approach to Finding a Robust Deep Learning Model Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance

Reference 29

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Observation b408234b-cef3-4c2c-af14-fc80e9acf7cd · outbound

This paper cites Geant4—a simulation toolkit.

Approach to Finding a Robust Deep Learning Model Geant4—a simulation toolkit

Reference 30

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This paper cites Design and construction of electromagnetic calorimeter for lhcb experiment.

Approach to Finding a Robust Deep Learning Model Design and construction of electromagnetic calorimeter for lhcb experiment

Reference 31

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This paper cites The lhcb detector at the lhc.

Approach to Finding a Robust Deep Learning Model The lhcb detector at the lhc

Reference 32

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This paper cites Ml-assisted versatile approach to calorimeter r&d.

Approach to Finding a Robust Deep Learning Model Ml-assisted versatile approach to calorimeter r&d

Reference 33

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This paper cites Root—an object oriented data analysis framework.

Approach to Finding a Robust Deep Learning Model Root—an object oriented data analysis framework

Reference 34

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Approach to Finding a Robust Deep Learning Model Unresolved cited work

Reference 35

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This paper cites Pytorch: An imperative style, high-performance deep learning library.

Approach to Finding a Robust Deep Learning Model Pytorch: An imperative style, high-performance deep learning library

Reference 36

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This paper cites A simple method of shower localization and identification in laterally segmented calorimeters.

Approach to Finding a Robust Deep Learning Model A simple method of shower localization and identification in laterally segmented calorimeters

Reference 37

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This paper cites S-shape correction using a neural network.

Approach to Finding a Robust Deep Learning Model S-shape correction using a neural network

Reference 38

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This paper cites Position resolution of an atlas electromagnetic calorimeter module.

Approach to Finding a Robust Deep Learning Model Position resolution of an atlas electromagnetic calorimeter module

Reference 39

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This paper cites Calorimetry for particle physics.Reviews of Modern Physics, 75(4):1243, 2003.

Approach to Finding a Robust Deep Learning Model Calorimetry for particle physics.Reviews of Modern Physics, 75(4):1243, 2003

Reference 40

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This paper cites The effect of activation functions on accuracy, convergence speed, and misclassification confidence in cnn text classification: a comprehensive exploration.

Approach to Finding a Robust Deep Learning Model The effect of activation functions on accuracy, convergence speed, and misclassification confidence in cnn text classification: a comprehensive exploration

Reference 41

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This paper cites On the impact of the activation function on deep neural networks training.

Approach to Finding a Robust Deep Learning Model On the impact of the activation function on deep neural networks training

Reference 42

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This paper cites Searching for Activation Functions.

Approach to Finding a Robust Deep Learning Model Searching for Activation Functions

Reference 43

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This paper cites Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs).

Approach to Finding a Robust Deep Learning Model Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

Reference 44

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This paper cites Gaussian Error Linear Units (GELUs).

Approach to Finding a Robust Deep Learning Model Gaussian Error Linear Units (GELUs)

Reference 45

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This paper cites Adam: A Method for Stochastic Optimization.

Approach to Finding a Robust Deep Learning Model Adam: A Method for Stochastic Optimization

Reference 46

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This paper cites Backpropagation and stochastic gradient descent method.

Approach to Finding a Robust Deep Learning Model Backpropagation and stochastic gradient descent method

Reference 47

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This paper cites Train faster, generalize better: Stability of stochastic gradient descent.

Approach to Finding a Robust Deep Learning Model Train faster, generalize better: Stability of stochastic gradient descent

Reference 48

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This paper cites Decoupled Weight Decay Regularization.

Approach to Finding a Robust Deep Learning Model Decoupled Weight Decay Regularization

Reference 49

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This paper cites Adaptive subgradient methods for online learning and stochastic optimization.

Approach to Finding a Robust Deep Learning Model Adaptive subgradient methods for online learning and stochastic optimization

Reference 50

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This paper cites Neural networks for machine learning lecture notes, 2012.

Approach to Finding a Robust Deep Learning Model Neural networks for machine learning lecture notes, 2012

Reference 51

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This paper cites ADADELTA: An Adaptive Learning Rate Method.

Approach to Finding a Robust Deep Learning Model ADADELTA: An Adaptive Learning Rate Method

Reference 52

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This paper cites A method of solving a convex programming problem with convergence rate o (1/k** 2).

Approach to Finding a Robust Deep Learning Model A method of solving a convex programming problem with convergence rate o (1/k** 2)

Reference 53

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This paper cites Incorporating nesterov momentum into adam.

Approach to Finding a Robust Deep Learning Model Incorporating nesterov momentum into adam

Reference 54

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This paper cites Hpc resources of the higher school of economics.

Approach to Finding a Robust Deep Learning Model Hpc resources of the higher school of economics

Reference 55

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This paper cites torchinfo, March 2020.

Approach to Finding a Robust Deep Learning Model torchinfo, March 2020

Reference 56

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Observation c73b709e-873a-4eb6-a1e1-9b952fcd5494 · outbound

This paper cites Optuna: A next-generation hyperparameter optimization framework.

Approach to Finding a Robust Deep Learning Model Optuna: A next-generation hyperparameter optimization framework

Reference 57

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