What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Jeff Schneider mainly investigates Artificial intelligence, Machine learning, Mathematical optimization, Bayesian probability and Algorithm.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Data mining, Categorical variable and Pattern recognition.
His study in the field of Thompson sampling also crosses realms of Task analysis.
Jeff Schneider has included themes like Function, Probability distribution, Covariant transformation and Reinforcement learning in his Mathematical optimization study.
His Bayesian probability research is multidisciplinary, incorporating elements of Additive model, Structure, Key and Regret.
His Algorithm study combines topics in areas such as Point cloud, Equivariant map and Permutation.
His most cited work include:
- Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization (440 citations)
- Autonomous helicopter control using reinforcement learning policy search methods (244 citations)
- Efficiently learning the accuracy of labeling sources for selective sampling (183 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Machine learning, Algorithm, Mathematical optimization and Data mining.
As a part of the same scientific family, Jeff Schneider mostly works in the field of Artificial intelligence, focusing on State and, on occasion, Motion prediction.
The various areas that Jeff Schneider examines in his Machine learning study include Motion and Trajectory.
His Algorithm research integrates issues from Estimator, Kernel and Cluster analysis.
His Mathematical optimization research includes themes of Function, Regret, Gaussian process and Reinforcement learning.
His Anomaly detection study, which is part of a larger body of work in Data mining, is frequently linked to Detector, bridging the gap between disciplines.
He most often published in these fields:
- Artificial intelligence (46.27%)
- Machine learning (29.41%)
- Algorithm (16.86%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (46.27%)
- Machine learning (29.41%)
- Trajectory (4.31%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Machine learning, Trajectory, Reinforcement learning and Bayesian optimization.
Particularly relevant to Deep learning is his body of work in Artificial intelligence.
In his study, Hyperparameter and Probabilistic logic is inextricably linked to Bayesian inference, which falls within the broad field of Machine learning.
In his research on the topic of Reinforcement learning, Supervised learning, Visualization and Optimal control is strongly related with Human–computer interaction.
His biological study spans a wide range of topics, including Search algorithm, Gaussian process and Compressed sensing.
Jeff Schneider interconnects Graph, Kernel, Optimization problem, Mathematical optimization and Sample in the investigation of issues within Gaussian process.
Between 2018 and 2021, his most popular works were:
- Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks (154 citations)
- Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving (28 citations)
- Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets (28 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Trajectory, Motion and Gaussian process.
His Artificial intelligence study typically links adjacent topics like State.
His study in the fields of Hyperparameter under the domain of Machine learning overlaps with other disciplines such as Component.
His Hyperparameter research is multidisciplinary, incorporating perspectives in Artificial neural network and Bayesian probability.
He has researched Trajectory in several fields, including Robotics, Hidden Markov model and Object based.
His work deals with themes such as Bayesian optimization, Mathematical optimization, Black box function, Value and Robot, which intersect with Gaussian process.
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