What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
His primary areas of investigation include Artificial intelligence, Machine learning, Artificial neural network, Inference and Reinforcement learning.
His work carried out in the field of Artificial intelligence brings together such families of science as Computer engineering and Computer vision.
His research in the fields of Unsupervised learning and Statistical model overlaps with other disciplines such as Reliability and Crowds.
Yuandong Tian combines subjects such as Algorithm, Quantization and Differentiable function with his study of Artificial neural network.
His Inference research includes elements of Hierarchical database model, Representation and k-nearest neighbors algorithm.
His studies deal with areas such as Computer architecture and Real-time strategy as well as Reinforcement learning.
His most cited work include:
- FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search (390 citations)
- Single Image 3D Interpreter Network (244 citations)
- Simple Baseline for Visual Question Answering (211 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Reinforcement learning, Artificial neural network and Algorithm.
His Artificial intelligence study focuses on Object detection in particular.
His Machine learning research incorporates themes from Image and Inference.
His Reinforcement learning study which covers Mathematical optimization that intersects with Uncertainty quantification.
His research integrates issues of Feature and Computer Go in his study of Artificial neural network.
His research in Algorithm intersects with topics in Gradient descent, Differentiable function and Maxima and minima.
He most often published in these fields:
- Artificial intelligence (54.63%)
- Machine learning (21.30%)
- Reinforcement learning (19.44%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (54.63%)
- Machine learning (21.30%)
- Artificial neural network (17.59%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Machine learning, Artificial neural network, Reinforcement learning and Algorithm.
His Heuristics research extends to Artificial intelligence, which is thematically connected.
His work in the fields of Machine learning, such as Leverage and Evolutionary algorithm, overlaps with other areas such as Sample, Curiosity and Boundary.
His Artificial neural network research incorporates elements of Feature and Joint.
His Reinforcement learning research is multidisciplinary, incorporating elements of Bayesian optimization and Curse of dimensionality.
His studies in Algorithm integrate themes in fields like Function and Differentiable function.
Between 2019 and 2021, his most popular works were:
- FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions (47 citations)
- Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP (31 citations)
- FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function (21 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Yuandong Tian focuses on Algorithm, Artificial neural network, Artificial intelligence, Architecture and Feature.
In his work, Feature and Convolution is strongly intertwined with Differentiable function, which is a subfield of Algorithm.
Artificial neural network is the subject of his research, which falls under Machine learning.
His Hyperparameter study, which is part of a larger body of work in Machine learning, is frequently linked to Recipe, bridging the gap between disciplines.
Many of his studies involve connections with topics such as Natural language processing and Artificial intelligence.
His Feature research integrates issues from Evolutionary algorithm, Recommender system, Feature vector and Click-through rate.
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