Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Variational Generative Stochastic Networks with Collaborative Shaping
Authors: Philip Bachman, Doina Precup
ICML 2015 | Venue PDF | 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... |