# Attempted Blind Constrained Descent Experiments

arXiv: https://arxiv.org/abs/2102.09643 · HTML: https://mlsanity.com/paper/2102.09643/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 6 references that are in the map.
- First posted on arXiv 18 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [Revisiting Small Batch Training for Deep Neural Networks](https://mlsanity.com/paper/1804.07612/) (2018)
- [FreezeOut: Accelerate Training by Progressively Freezing Layers](https://mlsanity.com/paper/1706.04983/) (2017)
- [Blind Descent: A Prequel to Gradient Descent](https://mlsanity.com/paper/2006.11505/) (2020)

## Abstract (arXiv)

Blind Descent uses constrained but, guided approach to learn the weights. The probability density function is non-zero in the infinite space of the dimension (case in point: Gaussians and normal probability distribution functions). In Blind Descent paper, some of the implicit ideas involving layer by layer training and filter by filter training (with different batch sizes) were proposed as probable greedy solutions. The results of similar experiments are discussed. Octave (and proposed PyTorch variants) source code of the experiments of this paper can be found at https://github.com/PrasadNR/Attempted-Blind-Constrained-Descent-Experiments-ABCDE- . This is compared against the ABCDE derivatives of the original PyTorch source code of https://github.com/akshat57/Blind-Descent .