What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
His primary scientific interests are in Artificial intelligence, Stochastic gradient descent, Information retrieval, Machine learning and Inference.
His study connects Pattern recognition and Artificial intelligence.
His work carried out in the field of Stochastic gradient descent brings together such families of science as Algorithm, Server and Rate of convergence.
His research in Information retrieval intersects with topics in Relation and Data curation.
The various areas that Ce Zhang examines in his Machine learning study include Scalability, Data mining, Web page, Knowledge base and Data science.
His studies in Inference integrate themes in fields like Data integration, Documentation, Complex data type and Taxonomy.
His most cited work include:
- Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. (389 citations)
- Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent (365 citations)
- Incremental knowledge base construction using DeepDive (158 citations)
What are the main themes of his work throughout his whole career to date?
Ce Zhang mainly focuses on Artificial intelligence, Machine learning, Speedup, Theoretical computer science and Stochastic gradient descent.
His Artificial intelligence study integrates concerns from other disciplines, such as Pattern recognition and Natural language processing.
His work in Machine learning addresses issues such as Scalability, which are connected to fields such as Knowledge base.
His Knowledge base study incorporates themes from Web page and Information retrieval.
He has researched Speedup in several fields, including Field-programmable gate array, Computer engineering and Data structure.
His Stochastic gradient descent research integrates issues from Algorithm, Server and Rate of convergence.
He most often published in these fields:
- Artificial intelligence (47.30%)
- Machine learning (26.58%)
- Speedup (9.46%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (47.30%)
- Machine learning (26.58%)
- Scalability (6.76%)
In recent papers he was focusing on the following fields of study:
Ce Zhang mainly investigates Artificial intelligence, Machine learning, Scalability, Robustness and Key.
He interconnects Natural language processing and Pattern recognition in the investigation of issues within Artificial intelligence.
His Machine learning study combines topics in areas such as Range, Pipeline and Baseline.
His Scalability research includes themes of Lossy compression, Feature, Convergence and Data management.
The concepts of his Robustness study are interwoven with issues in Smoothing, Ensemble forecasting and Theoretical computer science.
Ce Zhang has included themes like Collaborative filtering and Representation in his Key study.
Between 2019 and 2021, his most popular works were:
- RAB: Provable Robustness Against Backdoor Attacks (18 citations)
- TextNAS: A Neural Architecture Search Space Tailored for Text Representation (8 citations)
- Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His primary areas of investigation include Artificial intelligence, Machine learning, k-nearest neighbors algorithm, Robustness and Classifier.
His Artificial intelligence research is multidisciplinary, incorporating elements of Network architecture, Shapley value and Pattern recognition.
His work blends Machine learning and Architecture studies together.
As a part of the same scientific study, Ce Zhang usually deals with the k-nearest neighbors algorithm, concentrating on Time complexity and frequently concerns with Classifier, Missing data and Theoretical computer science.
His Robustness research includes elements of Smoothing, Ensemble forecasting and Convolutional neural network.
His research integrates issues of Perspective, Benchmark and Word error rate in his study of Classifier.
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