Variational Generative Stochastic Networks with Collaborative Shaping
Authors: Philip Bachman, Doina Precup
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present empirical results on the MNIST and TFD datasets which show that our approach offers state-of-the-art performance, both quantitatively and from a qualitative point of view. |
| Researcher Affiliation | Academia | Mc Gill University, School of Computer Science |
| Pseudocode | Yes | Algorithm 1 Walkback for a General GSN |
| Open Source Code | Yes | Code implementing the models described in this paper is available online at: github.com/Philip-Bachman/ICML-2015. |
| Open Datasets | Yes | We present tests examining the behavior of our models on the MNIST and TFD datasets. |
| Dataset Splits | Yes | We performed model updates using gradients computed from mini-batches of 100 distinct examples from the training set... and ...150 examples selected at random from a validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper states 'We implemented our models in Python using the THEANO library (Bergstra et al., 2010)' but does not specify version numbers for Python or THEANO. |
| Experiment Setup | Yes | We represented qφ and p using neural networks with two hidden layers of 1500 rectified-linear units and we set the latent space Z to R64... We used a Maxout network (Goodfellow et al., 2013) with two hidden layers of 1200 units in 300 groups of 4 for the guide function f . For Lg in Eq. 7, we used a halfrectified elastic-net (Zou & Hastie, 2005), with the linear and quadratic parts weighted equally. We unrolled our chains for 6 steps... We performed model updates using gradients computed from mini-batches of 100 distinct examples from the training set... We trained using plain SGD. We pre-trained p and qφ as a variational autoencoder (VAE) for 100k updates... |