Multi-Resolution Weak Supervision for Sequential Data
Authors: Paroma Varma, Frederic Sala, Shiori Sagawa, Jason Fries, Daniel Fu, Saelig Khattar, Ashwini Ramamoorthy, Ke Xiao, Kayvon Fatahalian, James Priest, Christopher Ré
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally validate our framework on five real-world sequential classification tasks over modalities like medical video, gait sensor data, and industry-scale video data. |
| Researcher Affiliation | Academia | {fredsala, paroma, jfries, danfu, sagawas, saelig, ashwinir, kayvonf, jpriest, chrismre}@stanford.edu, kexiao@cs.umass.edu |
| Pseudocode | Yes | Algorithm 1: Accuracy Parameter Estimation |
| Open Source Code | No | The paper does not explicitly state that its source code is open or provide a link to a repository for its methodology. |
| Open Datasets | Yes | The Bicuspid Aortic Valve (BAV) [6] task is to classify a congenital heart defect over MRI videos from a population-scale dataset [23]... Basketball operatesoverasubsetof Activity Net[3]. |
| Dataset Splits | Yes | All datasets include a small hand-labeled development set (< 10% of the unlabeled data) used to tune supervision sources and end model hyperparameters. |
| Hardware Specification | No | The paper mentions 'modern frameworks like Py Torch' but does not specify any hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not provide a specific version number or other software dependencies with version numbers. |
| Experiment Setup | Yes | Results are reported on test set as the mean S.D. of F1 scores across 5 random weight initializations. |