Deep Probability Estimation
Authors: Sheng Liu, Aakash Kaku, Weicheng Zhu, Matan Leibovich, Sreyas Mohan, Boyang Yu, Haoxiang Huang, Laure Zanna, Narges Razavian, Jonathan Niles-Weed, Carlos Fernandez-Granda
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate existing methods on the synthetic data as well as on three real-world probability estimation tasks, all of which involve inherent uncertainty: precipitation forecasting from radar images, predicting cancer patient survival from histopathology images, and predicting car crashes from dashcam videos. |
| Researcher Affiliation | Academia | 1Center for Data Science, New York University, New York, USA 2Courant Institute of Mathematical Sciences, New York University, New York, USA 3Department of Population Health & Department of Radiology, NYU School of Medicine, New York, USA. |
| Pseudocode | Yes | Algorithm 1 Pseudocode for Ca PE |
| Open Source Code | Yes | Code available at https://jackzhu727.github.io/deep-probability-estimation/. |
| Open Datasets | Yes | To benchmark probability-estimation methods, we build a synthetic dataset based on UTKFace (Zhang et al., 2017b), containing face images and associated ages. We use the German Weather service dataset4, which contains quality-controlled rainfall-depth composites from 17 operational Doppler radars. Following (Kim et al., 2019), we use 0.3 seconds of real dashcam videos from You Tube Crash dataset as input |
| Dataset Splits | Yes | The synthetic data is split into training, validation, and test sets with 16641, 4738, and 2329 samples, respectively. |
| Hardware Specification | No | The paper mentions training 'deep neural networks' but does not provide specific details on the hardware used, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not specify the versions of any software dependencies or libraries used for the experiments (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | The paper describes the 'ResNet-18 backbone architecture' and mentions 'stochastic gradient descent' but does not provide specific hyperparameter values like learning rate, batch size, or number of epochs in the main text. |