# BLoad: Enhancing Neural Network Training with Efficient Sequential Data Handling

arXiv: https://arxiv.org/abs/2310.10879 · HTML: https://mlsanity.com/paper/2310.10879/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 5 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Builds on (most cited first, 4 of 4)

- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [MOTR: End-to-End Multiple-Object Tracking with Transformer](https://mlsanity.com/paper/2105.03247/) (2021)
- [Spatial-Temporal Transformer for Dynamic Scene Graph Generation](https://mlsanity.com/paper/2107.12309/) (2021)
- [DDS: Decoupled Dynamic Scene-Graph Generation Network](https://mlsanity.com/paper/2301.07666/) (2023)

## Abstract (arXiv)

The increasing complexity of modern deep neural network models and the expanding sizes of datasets necessitate the development of optimized and scalable training methods. In this white paper, we addressed the challenge of efficiently training neural network models using sequences of varying sizes. To address this challenge, we propose a novel training scheme that enables efficient distributed data-parallel training on sequences of different sizes with minimal overhead. By using this scheme we were able to reduce the padding amount by more than 100x while not deleting a single frame, resulting in an overall increased performance on both training time and Recall in our experiments.