Geoff Pleiss
Publications by Year
Research Areas
Gaussian Processes and Bayesian Inference, Machine Learning and Data Classification, Neural Networks and Applications, Advanced Multi-Objective Optimization Algorithms, Advanced Neural Network Applications
Most-Cited Works
- → On Calibration of Modern Neural Networks(2017)1,715 cited
- → Convolutional Networks with Dense Connectivity(2019)561 cited
- → GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration(2018)536 cited
- → On Fairness and Calibration(2017)288 cited
- → Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous\n Driving(2019)220 cited
- → Snapshot Ensembles: Train 1, get M for free(2017)118 cited
- → Memory-Efficient Implementation of DenseNets(2017)86 cited