An implicit function learning approach for parametric modal regression
Authors: Yangchen Pan, Ehsan Imani, Amir-massoud Farahmand, Martha White
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate on several synthetic problems that our method (i) can learn multi-valued functions and produce the conditional modes, (ii) scales well to high-dimensional inputs, and (iii) can even be more effective for certain uni-modal problems, particularly for high-frequency functions. We demonstrate that our method is competitive in a real-world modal regression problem and two regular regression datasets. |
| Researcher Affiliation | Academia | Yangchen Pan, Ehsan Imani Univ. of Alberta & Amii {pan6,imani}@ualberta.ca; Amir-massoud Farahmand Vector Institute & Univ. of Toronto farahmand@vectorinstitute.ai; Martha White Univ. of Alberta whitem@ualberta.ca |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for its methodology. |
| Open Datasets | Yes | We construct a modal regression dataset from the Medical Cost Personal Dataset (Lantz, 2013) using the following steps. |
| Dataset Splits | No | The paper states "All of our results are averaged over 5 runs and for each run, the data is randomly split into training and testing sets." but does not explicitly mention or quantify a validation set split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware specifications (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "Tensor Flow" as a software reference but does not specify any software dependencies with version numbers that are directly used for its experiments. |
| Experiment Setup | Yes | For both our algorithm (Implicit) and MDN, we use a 16 16 tanh units NN and train by stochastic gradient descent with mini-batch size 128. We optimize both algorithms by sweeping the learning rate from {0.01, 0.001, 0.0001}. |