Video Pixel Networks
Authors: Nal Kalchbrenner, Aäron Oord, Karen Simonyan, Ivo Danihelka, Oriol Vinyals, Alex Graves, Koray Kavukcuoglu
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate VPNs on two benchmarks. The first is the Moving MNIST dataset... The second benchmark is the Robotic Pushing dataset... |
| Researcher Affiliation | Industry | 1Google Deep Mind, London, UK. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is open-source or publicly available. |
| Open Datasets | Yes | The first is the Moving MNIST dataset (Srivastava et al., 2015a)... The second benchmark is the Robotic Pushing dataset (Finn et al., 2016) |
| Dataset Splits | Yes | The Robotic Pushing dataset consists of sequences of 20 frames... The data consists of a training set of 50000 sequences, a validation set, and two test sets of 1500 sequences each |
| Hardware Specification | No | The paper does not specify any particular hardware used for running the experiments (e.g., GPU models, CPU types, or cloud compute instances). |
| Software Dependencies | No | The paper mentions 'RMSProp for the optimization' but does not specify software versions for any libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | We train the models for 300000 steps with 20-frame sequences predicting the last 10 frames of each sequence. Each step corresponds to a batch of 64 sequences. We use RMSProp for the optimization with an initial learning rate of 3 x 10−4 and multiply the learning rate by 0.3 when learning flatlines. |