What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Eric P. Xing mostly deals with Artificial intelligence, Machine learning, Inference, Theoretical computer science and Pattern recognition.
His biological study spans a wide range of topics, including Data mining and Natural language processing.
His Machine learning study incorporates themes from Contextual image classification, Data parallelism, Regression and Set.
His Inference study combines topics in areas such as Topic model, Latent Dirichlet allocation, Latent variable, Support vector machine and Probabilistic logic.
The Theoretical computer science study combines topics in areas such as Scalability, Dependency grammar, Parsing, Graphical model and Network topology.
His Pattern recognition research includes elements of Feature, Set, Tree, Image and Lasso.
His most cited work include:
- Distance Metric Learning with Application to Clustering with Side-Information (2389 citations)
- Mixed Membership Stochastic Blockmodels (1587 citations)
- Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification (823 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Machine learning, Inference, Pattern recognition and Theoretical computer science.
His Artificial intelligence research focuses on Natural language processing and how it relates to Semantics.
His Machine learning research incorporates themes from Domain, Generalization and Bayesian probability.
His work carried out in the field of Inference brings together such families of science as Algorithm, Data mining, Cluster analysis and Markov chain Monte Carlo.
Eric P. Xing is interested in Segmentation, which is a branch of Pattern recognition.
Topic model is a subfield of Information retrieval that Eric P. Xing explores.
He most often published in these fields:
- Artificial intelligence (53.53%)
- Machine learning (31.22%)
- Inference (16.83%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (53.53%)
- Machine learning (31.22%)
- Deep learning (5.90%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Machine learning, Deep learning, Robustness and Artificial neural network.
His research in Artificial intelligence is mostly focused on Convolutional neural network.
His research in Machine learning intersects with topics in Sample, Black box and Benchmark.
His studies deal with areas such as Segmentation and Distributed computing as well as Deep learning.
The various areas that Eric P. Xing examines in his Robustness study include Algorithm and Regularization.
His Artificial neural network research includes themes of Python, Scalability and Hyperparameter.
Between 2018 and 2021, his most popular works were:
- Theoretically Principled Trade-off between Robustness and Accuracy (348 citations)
- Theoretically Principled Trade-off between Robustness and Accuracy (204 citations)
- Rethinking Knowledge Graph Propagation for Zero-Shot Learning (112 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of study are Artificial intelligence, Machine learning, Convolutional neural network, Robustness and Differentiable function.
His research on Artificial intelligence frequently connects to adjacent areas such as Domain.
He combines subjects such as Black box, Benchmark, Set and Bayesian inference with his study of Machine learning.
His Convolutional neural network study combines topics from a wide range of disciplines, such as Contextual image classification, Salient and Generalization.
Eric P. Xing interconnects Graphical model, Probability distribution and Algorithm in the investigation of issues within Robustness.
His Pattern recognition research integrates issues from Pixel, Feature and Representation.
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