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Workshop: I Can’t Believe It’s Not Better: Understanding Deep Learning Through Empirical Falsification

Lempel-Ziv Networks

Rebecca Saul · Mohammad Mahmudul Alam · John Hurwitz · Edward Raff · Tim Oates · James Holt

Abstract: Sequence processing has long been a central area of machine learning research. Recurrent neural nets have been successful in processing sequences for a number of tasks; however, they are known to be both ineffective and computationally expensive when applied to very long sequences. Compression-based methods have demonstrated more robustness when processing such sequences --- in particular, an approach pairing the Lempel-Ziv Jaccard Distance (LZJD) with the k-Nearest Neighbor algorithm has shown promise on long sequence problems (up to $T=200,000,000$ steps) involving malware classification. Unfortunately, use of LZJD is limited to discrete domains. To extend the benefits of LZJD to a continuous domain, we investigate the effectiveness of a deep-learning analog of the algorithm, the Lempel-Ziv Network. While we achieve successful proof-of-concept, we are unable to meaningfully improve on the performance of a standard LSTM across a variety of datasets and sequence processing tasks. In addition to presenting this negative result, our work highlights the problem of sub-par baseline tuning in newer research areas.

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