Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tensor Programs V: Tuning Large Neural Networks via Zero-Shot
  Hyperparameter Transfer   Hyperparameter (HP) tuning in deep learning is an expensive process,
prohibitively so for neural networks (NNs) with billions of parameters. We show
that, in the recently discovered Maximal Update Parametrization (muP), many
optimal HPs remain stable even as model size changes. This leads to a new HP
tuning paradigm we call muTransfer: parametrize the target model in muP, tune
the HP indirectly on a smaller model, and zero-shot transfer them to the
full-sized model, i.e., without directly tuning the latter at all. We verify
muTransfer on Transformer and ResNet. For example, 1) by transferring
pretraining HPs from a model of 13M parameters, we outperform published numbers
of BERT-large (350M parameters), with a total tuning cost equivalent to
pretraining BERT-large once; 2) by transferring from 40M parameters, we
outperform published numbers of the 6.7B GPT-3 model, with tuning cost only 7%
of total pretraining cost. A Pytorch implementation of our technique can be
found at github.com/microsoft/mup and installable via `pip install mup`.

25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th