Four Deep Learning Papers to Read in January 2022

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Four Deep Learning Papers to Read in January 2022

From Bootstrapped Meta-Learning to Time Series Forecasting with Deep Learning, the Relationship between Extrapolation & Generalization and Exploring Diverse Optima with Ridge Rider

Welcome to the January edition of the ‚Machine-Learning-Collage‘ series, where I provide an overview of the different Deep Learning research streams. So what is a ML collage? Simply put, I draft one-slide visual summaries of one of my favourite recent papers. Every single week. At the end of the month all of the resulting visual collages are collected in a summary blog post. Thereby, I hope to give you a visual and intuitive deep dive into some of the coolest trends. So without further ado: Here are my four favourite papers that I recently read and why I believe them to be important for the future of Deep Learning.

‘Bootstrapped Meta-Learning’

Authors: Flennerhag et al. (2021) | 📝 Paper

One Paragraph Summary: Meta-Learning algorithms aim to automatically discover inductive biases, which allow for fast adaptation across many tasks. Classic examples include MAML or RLˆ2. Commonly these system are trained on a bi-level optimisation problem, where in a fast inner loop one considers only a single task instantiation. In a second and slower outer loop the weights of the system are then updated by batching across many of such individual tasks. The system is thereby forced to discover and exploit the underlying structure of the task distribution. Most of the times the outer update has to propagate gradients through the inner loop update procedure. This can lead to two problems: How should one choose the length of the inner loop? Short horizons allow for easier optimization, while being potentially short-sighted. Furthermore, the meta objective can behave erratic, suffering from vanishing and exploding gradients. So how might we overcome this myopia & optimisation difficulty? Bootstrapped meta-learning proposes to construct a so-called bootstrap target by running the inner loop a little longer. We can then use the resulting network as a teacher for a shorter horizon student. Similar to DQNs, the bootstrap target is detached from the computation graph and simply acts as a fixed quantity in the loss computation. Thereby, we essentially pull the meta-agent forward. The metric used to compare the expert and the student can furthermore control the curvature of the meta objective. In a set of toy RL experiments, the authors show that bootstrapping can allow for fast exploration adaptation despite a short horizon and that it outperforms plain meta-gradients with a longer horizon. Together with the STACX meta-gradient agent, bootstrapped meta-gradients provide a new ATARI SOTA and can also be applied to multi-task few-shot learning. All in all, this work opens many new perspectives on how to positively manipulate the meta-learning problem formulation.


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