Ian

Ian Goodfellow

Ian
#3,848
Most Influential Person Now

American computer scientist

Ian Goodfellow's Academic­Influence.com Rankings

Ian Goodfellow
Computer Science
#184
World Rank
#191
Historical Rank
#111
USA Rank
Machine Learning
#17
World Rank
#17
Historical Rank
#7
USA Rank
Artificial Intelligence
#41
World Rank
#44
Historical Rank
#21
USA Rank
Algorithms
#59
World Rank
#59
Historical Rank
#20
USA Rank
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Why Is Ian Goodfellow Influential?

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According to Wikipedia, Ian J. Goodfellow is an American computer scientist, engineer, and executive, most noted for his work on artificial neural networks and deep learning. He was previously employed as a research scientist at Google Brain and director of machine learning at Apple and has made several important contributions to the field of deep learning including the invention of the generative adversarial network . Goodfellow co-wrote, as the first author, the textbook Deep Learning and wrote the chapter on deep learning in the authoritative textbook of the field of artificial intelligence, Artificial Intelligence: A Modern Approach .

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Ian Goodfellow's Published Works

Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
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Published Works

  • Generative Adversarial Nets (2014) (36807)
  • Explaining and Harnessing Adversarial Examples (2014) (12890)
  • Intriguing properties of neural networks (2013) (10943)
  • TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (2016) (9876)
  • Improved Techniques for Training GANs (2016) (6705)
  • Adversarial examples in the physical world (2016) (4166)
  • Deep Learning with Differential Privacy (2016) (3519)
  • Practical Black-Box Attacks against Machine Learning (2016) (2786)
  • Self-Attention Generative Adversarial Networks (2018) (2626)
  • Adversarial Machine Learning at Scale (2016) (2337)
  • Theano: A Python framework for fast computation of mathematical expressions (2016) (2219)
  • Maxout Networks (2013) (1961)
  • MixMatch: A Holistic Approach to Semi-Supervised Learning (2019) (1810)
  • Adversarial Autoencoders (2015) (1581)
  • Theano: new features and speed improvements (2012) (1404)
  • Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples (2016) (1374)
  • NIPS 2016 Tutorial: Generative Adversarial Networks (2016) (1319)
  • Sanity Checks for Saliency Maps (2018) (1231)
  • Challenges in representation learning: A report on three machine learning contests (2013) (1050)
  • An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks (2013) (922)
  • Unsupervised Learning for Physical Interaction through Video Prediction (2016) (920)
  • Generative adversarial networks (2020) (881)
  • Adversarial Training Methods for Semi-Supervised Text Classification (2016) (760)
  • Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (2016) (746)
  • Realistic Evaluation of Deep Semi-Supervised Learning Algorithms (2018) (720)
  • Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks (2013) (632)
  • Adversarial Attacks on Neural Network Policies (2017) (598)
  • On Evaluating Adversarial Robustness (2019) (583)
  • Net2Net: Accelerating Learning via Knowledge Transfer (2015) (548)
  • Improving the Robustness of Deep Neural Networks via Stability Training (2016) (538)
  • Thermometer Encoding: One Hot Way To Resist Adversarial Examples (2018) (514)
  • Adversarial Logit Pairing (2018) (485)
  • Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples (2016) (453)
  • The Space of Transferable Adversarial Examples (2017) (445)
  • Measuring Invariances in Deep Networks (2009) (442)
  • MaskGAN: Better Text Generation via Filling in the ______ (2018) (421)
  • Qualitatively characterizing neural network optimization problems (2014) (420)
  • Technical Report on the CleverHans v2.1.0 Adversarial Examples Library (2016) (370)
  • Pylearn2: a machine learning research library (2013) (306)
  • Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition (2019) (282)
  • Making machine learning robust against adversarial inputs (2018) (261)
  • Theano: Deep Learning on GPUs with Python (2012) (250)
  • Cleverhans V0.1: an Adversarial Machine Learning Library (2016) (241)
  • TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing (2018) (235)
  • Adversarial Attacks and Defences Competition (2018) (234)
  • Unsupervised and Transfer Learning Challenge: a Deep Learning Approach (2011) (227)
  • Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer (2018) (210)
  • Adversarial Spheres (2018) (203)
  • Adversarial Examples that Fool both Computer Vision and Time-Limited Humans (2018) (196)
  • Motivating the Rules of the Game for Adversarial Example Research (2018) (192)
  • Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step (2017) (191)
  • Multi-Prediction Deep Boltzmann Machines (2013) (134)
  • Mining (2011) (123)
  • On distinguishability criteria for estimating generative models (2014) (119)
  • Discriminator Rejection Sampling (2018) (104)
  • Is Generator Conditioning Causally Related to GAN Performance? (2018) (99)
  • Virtual Adversarial Training for Semi-Supervised Text Classification (2016) (97)
  • An empirical analysis of dropout in piecewise linear networks (2013) (97)
  • Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values (2018) (96)
  • Large-Scale Feature Learning With Spike-and-Slab Sparse Coding (2012) (96)
  • Adversarial Reprogramming of Neural Networks (2018) (83)
  • Unrestricted Adversarial Examples (2018) (83)
  • Adversarial Examples that Fool both Human and Computer Vision (2018) (77)
  • Realistic Evaluation of Semi-Supervised Learning Algorithms (2018) (77)
  • Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming (2020) (69)
  • Spike-and-Slab Sparse Coding for Unsupervised Feature Discovery (2012) (61)
  • Efficient Per-Example Gradient Computations (2015) (53)
  • On the Challenges of Physical Implementations of RBMs (2013) (50)
  • On the Protection of Private Information in Machine Learning Systems: Two Recent Approches (2017) (46)
  • Scaling Up Spike-and-Slab Models for Unsupervised Feature Learning (2013) (38)
  • Defense Against the Dark Arts: An overview of adversarial example security research and future research directions (2018) (36)
  • Skill Rating for Generative Models (2018) (33)
  • Generative Adversarial Networks ( GANs ) (2016) (32)
  • A Domain Agnostic Measure for Monitoring and Evaluating GANs (2018) (29)
  • Joint Training of Deep Boltzmann Machines (2012) (27)
  • Joint Training Deep Boltzmann Machines for Classification (2013) (24)
  • Gradient Masking Causes CLEVER to Overestimate Adversarial Perturbation Size (2018) (24)
  • GENERATIVE ADVERSARIAL NETWORKS FOR IMAGE STEGANOGRAPHY (2016) (22)
  • A Research Agenda: Dynamic Models to Defend Against Correlated Attacks (2019) (21)
  • The Relationship Between High-Dimensional Geometry and Adversarial Examples (2018) (19)
  • Help me help you: Interfaces for personal robots (2010) (17)
  • Creating High Resolution Images with a Latent Adversarial Generator (2020) (12)
  • DLVM: A MODERN COMPILER FRAMEWORK FOR NEURAL NETWORK DSLS (2017) (8)
  • Piecewise Linear Multilayer Perceptrons and Dropout (2013) (6)
  • On Large-Cohort Training for Federated Learning (2021) (5)
  • Adversarial Forces of Physical Models (2020) (5)
  • A ug 2 01 7 On the Protection of Private Information in Machine Learning Systems : Two Recent Approaches ( Invited Paper ) (2018) (4)
  • Deep learning of representations and its application to computer vision (2015) (3)
  • Clustering Methods for Improving Language Models CS 224 N Natural Language Processing Final Project June (2007) (2)
  • New CleverHans Feature: Better Adversarial Robustness Evaluations with Attack Bundling (2018) (2)
  • Improving Interpolation in Autoencoders (2019) (1)
  • Introduction to NIPS 2017 Competition Track (2018) (1)
  • Adversarial examples influence human visual perception (2019) (1)
  • On Evaluating Adversarial Robustness 2 Principles of Rigorous Evaluations 2 (2019) (1)
  • Learning Classical Planning Transition Functions by Deep Neural Networks (2020) (0)
  • Transforming the output of GANs by fine-tuning them with features from different datasets (2019) (0)
  • THERMOMETER ENCODING: ONE HOT WAY TO RESIST (2018) (0)
  • Anatomically Constrained ResNets Exhibit Opponent Receptive Fields; So What? (2020) (0)
  • M L ] 2 0 A ug 2 01 3 Pylearn 2 : a machine learning research library (2014) (0)
  • NEW CLEVERHANS FEATURE: BETTER ADVER- (2018) (0)
  • Supplementary Material for Imperceptible , Robust , and Targeted Adversarial Examples for Automatic Speech Recognition (2019) (0)
  • Porting a Deep Convolutional Generative Adversarial Network on imx8MMini with eIQ (2020) (0)
  • FEND AGAINST CORRELATED ATTACKS (2019) (0)
  • Gradient Penalties for Generative Adversarial Networks (2018) (0)
  • Joint Training of Partially-Directed Deep Boltzmann Machines (2012) (0)
  • Successive Randomization of Inception v 3 Weights Gradient Gradient-SG Gradient - (2018) (0)
  • NEURAL NETWORK BASED SURROGATE MODELS FOR THE PREDICTION OF EFFECTIVE MECHANICAL PROPERTIES AND THE INVERSE DESIGN OF MICROSTRUCTURES (2018) (0)
  • Detecting Handwritten Text from Forms using Deep Learning (2020) (0)
  • A DVERSARIAL S PHERES (2018) (0)
  • ON NEURAL NETWORK POLICIES (2017) (0)
  • On the Integrity of Deep Learning Oracles (2016) (0)

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