2018 - Hellman Fellow
His primary scientific interests are in Artificial intelligence, Machine learning, Generative grammar, Adversarial system and MNIST database. His research investigates the connection between Artificial intelligence and topics such as Pattern recognition that intersect with problems in Iterative refinement. His Machine learning research includes elements of Variation, Probabilistic logic, Training set and Key.
Stefano Ermon has researched Key in several fields, including Decoding methods, Latent variable and Flexibility. Generative grammar combines with fields such as SIGNAL and Imitation learning in his work. Stefano Ermon usually deals with MNIST database and limits it to topics linked to Generative model and Matrix norm, Generative adversarial network, Sampling, Manifold and Gaussian noise.
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Algorithm, Inference and Generative grammar. His Artificial intelligence study frequently links to adjacent areas such as Pattern recognition. His Machine learning research focuses on Key and how it relates to Data science.
His Algorithm study combines topics from a wide range of disciplines, such as Sampling, Graphical model, Upper and lower bounds and Matching. The Inference study combines topics in areas such as Theoretical computer science, Latent variable, Estimator, Belief propagation and Mathematical optimization. His Generative model study in the realm of Generative grammar interacts with subjects such as SIGNAL.
His primary areas of investigation include Artificial intelligence, Machine learning, Algorithm, Sampling and Probabilistic logic. His work in Artificial intelligence addresses subjects such as Computer vision, which are connected to disciplines such as Information extraction. As part of one scientific family, he deals mainly with the area of Machine learning, narrowing it down to issues related to the Contextual image classification, and often Anomaly detection.
His studies deal with areas such as Matching, Upper and lower bounds, Ode and Joint probability distribution as well as Algorithm. His Sampling research is multidisciplinary, incorporating elements of Data mining and Autoregressive model. Stefano Ermon combines subjects such as Sample, Compensation, Inference and Operations research with his study of Probabilistic logic.
His main research concerns Artificial intelligence, Algorithm, Machine learning, Computation and Sampling. His studies in Artificial intelligence integrate themes in fields like Satellite imagery and Computer vision. The various areas that Stefano Ermon examines in his Algorithm study include Artificial neural network, Probability distribution, Probabilistic logic and Ode.
His Machine learning research incorporates themes from Test data generation, Generative grammar and Key. His study on Computation also encompasses disciplines like
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Generative Adversarial Imitation Learning
Jonathan Ho;Stefano Ermon.
neural information processing systems (2016)
Combining satellite imagery and machine learning to predict poverty
Neal Jean;Marshall Burke;Marshall Burke;Michael Xie;W. Matthew Davis.
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples
Yang Song;Taesup Kim;Sebastian Nowozin;Stefano Ermon.
international conference on learning representations (2017)
Coupling between oxygen redox and cation migration explains unusual electrochemistry in lithium-rich layered oxides
William E. Gent;Kipil Lim;Yufeng Liang;Qinghao Li.
Nature Communications (2017)
InfoVAE: Information Maximizing Variational Autoencoders
Shengjia Zhao;Jiaming Song;Stefano Ermon.
arXiv: Learning (2017)
A DIRT-T Approach to Unsupervised Domain Adaptation
Rui Shu;Hung H. Bui;Hirokazu Narui;Stefano Ermon.
international conference on learning representations (2018)
Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data
Jiaxuan You;Xiaocheng Li;Melvin Low;David B. Lobell.
national conference on artificial intelligence (2017)
Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning.
Chi-Sing Ho;Neal Jean;Catherine A. Hogan;Lena Blackmon.
Nature Communications (2019)
Closed-loop optimization of fast-charging protocols for batteries with machine learning.
Peter M. Attia;Aditya Grover;Norman Jin;Kristen A. Severson.
A Survey on Behavior Recognition Using WiFi Channel State Information
Siamak Yousefi;Hirokazu Narui;Sankalp Dayal;Stefano Ermon.
IEEE Communications Magazine (2017)
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