Mnemosyne from Google – Learning to train transformers with transformers
Training complex machine learning (ML) architectures requires a compute and time consuming process of selecting the right optimizer and tuning its hyper-parameters. A new paradigm of learning optimizers from data has emerged as a better alternative to hand-designed ML optimizers. We propose Mnemosyne optimizer, that uses Performers: implicit low-rank attention Transformers. It can learn to train entire neural network architectures including other Transformers without any task-specific optimizer tuning. We show that Mnemosyne: (a) generalizes better than popular LSTM optimizer, (b) in particular can successfully train Vision Transformers (ViTs) while meta–trained on standard MLPs and (c) can initialize optimizers for faster convergence in Robotics applications. We believe that these results open the possibility of using Transformers to build foundational optimization models that can address the challenges of regular Transformer training. We complement our results with an extensive theoretical analysis of the compact associative memory used by Mnemosyne.
Large pretrained language models have shown surprising In-Context Learning (ICL) ability. With a few demonstration input-label pairs, they can predict the label for an unseen input without additional parameter updates. Despite the great success in performance, the working mechanism of ICL still remains an open problem. In order to better understand how ICL works, this […]
It is well established that increasing scale in deep transformer networks leads to improved quality and performance. This increase in scale often comes with an increase in compute cost and inference latency. Consequently, research into methods which help realize the benefits of increased scale without leading to an increase in the compute cost becomes important. […]
Masked Language Modeling (MLM) has been one of the most prominent approaches for pretraining bidirectional text encoders due to its simplicity and effectiveness. One notable concern about MLM is that the special [MASK] symbol causes a discrepancy between pretraining data and downstream data as it is present only in pretraining but not in fine-tuning. In this work, […]
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