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arXiv Machine Learning By Miha Kap\v{s}, Bla\v{z} Bertalani\v{c}, Carolina Fortuna

Backpropagation-free Deep Learning for Time-Series Classification

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The paper evaluates three backpropagation‑free learning rules—Direct Feedback Alignment, Direct Random Target Projection, and NoProp—on 112 univariate time‑series classification datasets using 1D convolutional networks. Each method achieves mean F1 scores between 0.76 and 0.79, slightly below state‑of‑the‑art backpropagation baselines. The study also analyzes how dataset properties, especially intra‑class temporal variability, affect performance gaps and examines computational trade‑offs such as FLOPs, training speed, and memory usage.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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