End-to-End Learning for Structured Prediction Energy Networks
Authors: David Belanger, Bishan Yang, Andrew McCallum
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our SPEN training methods on two diverse tasks. We first consider depth image denoising on the 7-Scenes dataset (Newcombe et al., 2011), where we employ deep convolutional networks as priors over images. This provides a significant performance improvement, from 36.3 to 40.4 PSNR, over the recent work of (Wang et al., 2016)... |
| Researcher Affiliation | Academia | 1University of Massachusetts, Amherst 2Carnegie Mellon University. Correspondence to: David Belanger <belanger@cs.umass.edu>. |
| Pseudocode | No | The paper describes methods and equations but does not contain structured pseudocode or algorithm blocks labeled as 'Algorithm' or 'Pseudocode'. |
| Open Source Code | No | The paper mentions 'learning and prediction for all models is performed using the same gradient-based prediction and end-to-end learning code' but does not explicitly state that their source code is publicly available or provide a link. |
| Open Datasets | Yes | We evaluate SPENs on image denoising and semantic role labeling (SRL) tasks. ... We first consider depth image denoising on the 7-Scenes dataset (Newcombe et al., 2011)... After that, we apply SPENs to semantic role labeling (SRL) on the Co NLL-2005 dataset (Carreras & M arquez, 2005). |
| Dataset Splits | Yes | We consider the Co NLL 2005 shared task data (Carreras & M arquez, 2005), with standard data splits and official evaluation scripts. ... Table 2 contains results on the Co NLL 2005 WSJ dev and test sets and the Brown test set. |
| Hardware Specification | No | The paper does not provide specific hardware details (like exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., Python 3.8, PyTorch 1.9) needed to replicate the experiment. |
| Experiment Setup | Yes | We unroll 20 steps of gradient descent with momentum 0.75 and use the modification in Eq. (14). ... Our best system unrolls for 10 iterations, trains per-iteration learning rates, uses no momentum, and unrolls Eq. (8). |