What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
His scientific interests lie mostly in Robustness, Artificial neural network, Artificial intelligence, MNIST database and Algorithm.
His Robustness research is multidisciplinary, incorporating perspectives in Contextual image classification and Pareto principle.
Artificial intelligence connects with themes related to Machine learning in his study.
His study in the fields of Deep learning, Autoencoder and Dimensionality reduction under the domain of Machine learning overlaps with other disciplines such as Bilinear interpolation and Zeroth order.
The MNIST database study combines topics in areas such as Optimization problem, Adversarial machine learning and Image.
His study focuses on the intersection of Algorithm and fields such as Stochastic gradient descent with connections in the field of Ensemble forecasting.
His most cited work include:
- ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models (658 citations)
- Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent (365 citations)
- EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples. (236 citations)
What are the main themes of his work throughout his whole career to date?
Huan Zhang spends much of his time researching Robustness, Artificial intelligence, Artificial neural network, Adversarial system and Algorithm.
His studies in Robustness integrate themes in fields like Contextual image classification and MNIST database.
His study connects Machine learning and Artificial intelligence.
His Artificial neural network research is multidisciplinary, incorporating elements of Computer engineering and Transformer.
The concepts of his Adversarial system study are interwoven with issues in Regularization and Reinforcement learning.
His work on Computation is typically connected to Stationary point as part of general Algorithm study, connecting several disciplines of science.
He most often published in these fields:
- Robustness (64.77%)
- Artificial intelligence (40.91%)
- Artificial neural network (38.64%)
What were the highlights of his more recent work (between 2019-2021)?
- Robustness (64.77%)
- Artificial neural network (38.64%)
- Artificial intelligence (40.91%)
In recent papers he was focusing on the following fields of study:
Huan Zhang mainly investigates Robustness, Artificial neural network, Artificial intelligence, Adversarial system and Code.
Huan Zhang has included themes like Algorithm, MNIST database and Subspace topology in his Robustness study.
His research brings together the fields of Bounding overwatch and MNIST database.
His study in Artificial neural network is interdisciplinary in nature, drawing from both Computational complexity theory, Linear programming, Computer engineering and Transformer.
Huan Zhang usually deals with Artificial intelligence and limits it to topics linked to Machine learning and Contextual image classification.
The study incorporates disciplines such as Regularization, Data mining and Reinforcement learning in addition to Adversarial system.
Between 2019 and 2021, his most popular works were:
- Towards Stable and Efficient Training of Verifiably Robust Neural Networks (47 citations)
- Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples (22 citations)
- MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius (20 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Huan Zhang focuses on Robustness, Artificial intelligence, Artificial neural network, MNIST database and Scalability.
Artificial intelligence is closely attributed to Machine learning in his study.
His studies deal with areas such as Algorithm and Transformer as well as Artificial neural network.
His study in the field of Computation is also linked to topics like Differentiable function, Perturbation theory and Perturbation.
His MNIST database study frequently links to other fields, such as Bounding overwatch.
His Adversarial system research integrates issues from Classifier, Contextual image classification, Gradient method, Automatic summarization and Machine translation.
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.
