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[2602.13348] Exploring the Performance of ML/DL Architectures on the MNIST-1D Dataset
Summary
This article evaluates the performance of advanced machine learning architectures on the MNIST-1D dataset, demonstrating their effectiveness in capturing sequential patterns and improving model performance.
Why It Matters
The MNIST-1D dataset serves as a valuable benchmark for assessing machine learning models, particularly in resource-limited environments. This research highlights the significance of architectural innovations in enhancing model capabilities, which is crucial for advancing the field of machine learning.
Key Takeaways
- MNIST-1D provides a more complex environment for evaluating ML architectures.
- Advanced models like TCN and DCNN outperform simpler models, achieving near-human performance.
- The study emphasizes the importance of inductive biases in small datasets.
- ResNet shows significant improvements, validating its effectiveness in sequential data tasks.
- Findings support the use of MNIST-1D as a benchmark for future ML research.
Computer Science > Machine Learning arXiv:2602.13348 (cs) [Submitted on 12 Feb 2026] Title:Exploring the Performance of ML/DL Architectures on the MNIST-1D Dataset Authors:Michael Beebe, GodsGift Uzor, Manasa Chepuri, Divya Sree Vemula, Angel Ayala View a PDF of the paper titled Exploring the Performance of ML/DL Architectures on the MNIST-1D Dataset, by Michael Beebe and 4 other authors View PDF HTML (experimental) Abstract:Small datasets like MNIST have historically been instrumental in advancing machine learning research by providing a controlled environment for rapid experimentation and model evaluation. However, their simplicity often limits their utility for distinguishing between advanced neural network architectures. To address these challenges, Greydanus et al. introduced the MNIST-1D dataset, a one-dimensional adaptation of MNIST designed to explore inductive biases in sequential data. This dataset maintains the advantages of small-scale datasets while introducing variability and complexity that make it ideal for studying advanced architectures. In this paper, we extend the exploration of MNIST-1D by evaluating the performance of Residual Networks (ResNet), Temporal Convolutional Networks (TCN), and Dilated Convolutional Neural Networks (DCNN). These models, known for their ability to capture sequential patterns and hierarchical features, were implemented and benchmarked alongside previously tested architectures such as logistic regression, MLPs, CNNs, and GRUs. ...
