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 Sparse Coding with Learned Thresholding
Authors: Kion Fallah, Christopher J Rozell
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We first evaluate and analyze our method by training a linear generator, showing that it has superior performance, statistical efficiency, and gradient estimation compared to other sparse distributions. We then compare to a standard variational autoencoder using a DNN generator on the Fashion MNIST and Celeb A datasets. |
| Researcher Affiliation | Academia | Kion Fallah 1 Christopher J. Rozell 1 1ML@GT, Georgia Institute of Technology, Atlanta, Georgia. Correspondence to: Kion Fallah <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Training with Thresholded Samples |
| Open Source Code | Yes | 1Code available at: https://github.com/kfallah/variational-sparse-coding. |
| Open Datasets | Yes | We train on 80,000 16x16 training patches... We showcase the performance of our method compared to other inference strategies by training and analyzing a linear generator on whitened image patches (Olshausen & Field, 1996) and a DNN generator on the Fashion MNIST (Xiao et al., 2017) and Celeb A (Liu et al., 2015) datasets. |
| Dataset Splits | Yes | We train on 80,000 16x16 training patches... We train for 300 epochs using a batch size of 100. For Celeb A, we use 150,000 training samples and 19,000 validation samples. |
| Hardware Specification | Yes | We train for 300 epochs using a batch size of 512 across two Nvidia RTX 3080s. |
| Software Dependencies | Yes | Additionally, we use the automatic mixed precision (AMP) and Distributed Data Parallel implementations included in Py Torch 1.10 (Paszke et al., 2019). |
| Experiment Setup | Yes | We train for 300 epochs using a batch size of 100. Our initial learning rate for the dictionary is 5E 01 and we apply an exponential decay by a factor of 0.99 each epoch. Our inference network is trained with an initial learning rate of 1E 02, using an SGD+Nesterov optimizer with a Cycle Scheduler. We use an initial learning rate of 3E 04 using the Adam optimizer with β = (0.5, 0.999), weight decay equal to 1E 05, and a sample budget of J = 10. |