-
Samet Oymak;
Christos Thrampoulidis (2021). "Provable
Benefits of
Overparameterization in
Model Compression: From
Double Descent to
Pruning Neural Networks"...
- such as
linear regression. In particular, it has been
shown that
overparameterization is
essential for
benign over****ing in this setting. In
other words...
- (underparameterized) and m ≥ n {\displaystyle m\geq n} (
overparameterized). In the
overparameterized case,
stochastic gradient descent converges to arg ...
- to the
distribution of x {\displaystyle x} . Note that this is an
overparameterization in the
sense that any one of α {\displaystyle \alpha } , β {\displaystyle...
-
stronger version of the hypothesis,
which is that a
sufficiently overparameterized untuned network will
typically contain a
subnetwork that is already...
-
Conference on
Learning Theory. Ribeiro, A. H.; Schön, T. B. (2023). "
Overparameterized Linear Regression under Adversarial Attacks". IEEE
Transactions on...
- Yuanzhi; Song, Zhao (2018). "A
convergence theory for deep
learning via
overparameterization". arXiv:1811.03962 [cs.LG]. Du,
Simon S; Zhai, Xiyu; Poczos, Barnabas;...
- zero bias, and the
parameters in the
first layer frozen. In the
overparameterized case, when 2 D ≥ N {\displaystyle 2D\geq N} , the
network linearly...
-
methods rely on the weight-sharing idea. In this approach, a
single overparameterized supernetwork (also
known as the one-shot model) is defined. A supernetwork...
- the data, but ML
estimation conducted using models that are
clearly overparameterized can lead to
drastically wrong conclusions. The NCM
model certainly...
