Self-Supervised Learning (SSL) has proven to be effective in various domains, including speech processing. However, SSL is computationally and memory expensive. This is in part due the quadratic complexity of multi-head self-attention (MHSA). Alternatives for MHSA have been proposed and used in the speech domain, but have yet to be investigated properly in an SSL setting. In this work, we study the effects of replacing MHSA with recent state-of-the-art alternatives that have linear complexity, namely, HyperMixing, Fastformer, SummaryMixing, and Mamba. We evaluate these methods by looking at the speed, the amount of VRAM consumed, and the performance on the SSL MP3S benchmark. Results show that these linear alternatives maintain competitive performance compared to MHSA while, on average, decreasing VRAM consumption by around 20% to 60% and increasing speed from 7% to 65% for input sequences ranging from 20 to 80 seconds.

Reproduced under a CC BY 4.0 license. Here are the most tweeted papers that were uploaded onto arXiv during August 2021. Results are powered by Arxiv Sanity Preserver. How to avoid machine learning pitfalls: a guide for academic researchers Michael A. Lones Submitted to arXiv on: 5 August 2021 Abstract: This document gives a concise outline of some of the common mistakes that occur when using machine learning techniques, and what can be done to avoid them. It is intended primarily as a guide for research students, and focuses on issues that are of particular concern within academic research, such as the need to do rigorous comparisons and reach valid conclusions.

Transformers have become the dominant model class in the last few years for large data, but their quadratic complexity in terms of sequence length has plagued them until now. Fastformer claims to be the fastest and most performant linear attention variant, able to consume long contexts at once. This is achieved by a combination of additive attention and elementwise products. OUTLINE: 0:00 - Intro & Outline 2:15 - Fastformer description 5:20 - Baseline: Classic Attention 10:00 - Fastformer architecture 12:50 - Additive Attention 18:05 - Query-Key element-wise multiplication 21:35 - Redundant modules in Fastformer 25:00 - Problems with the architecture 27:30 - Is this even attention? Abstract: Transformer is a powerful model for text understanding.