Extracting Global Dynamics of Loss Landscape in Deep Learning Models
- URL: http://arxiv.org/abs/2106.07683v1
- Date: Mon, 14 Jun 2021 18:07:05 GMT
- Title: Extracting Global Dynamics of Loss Landscape in Deep Learning Models
- Authors: Mohammed Eslami, Hamed Eramian, Marcio Gameiro, William Kalies,
Konstantin Mischaikow
- Abstract summary: We present a toolkit for the Dynamical Organization Of Deep Learning Loss Landscapes, or DOODL3.
DOODL3 formulates the training of neural networks as a dynamical system, analyzes the learning process, and presents an interpretable global view of trajectories in the loss landscape.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Deep learning models evolve through training to learn the manifold in which
the data exists to satisfy an objective. It is well known that evolution leads
to different final states which produce inconsistent predictions of the same
test data points. This calls for techniques to be able to empirically quantify
the difference in the trajectories and highlight problematic regions. While
much focus is placed on discovering what models learn, the question of how a
model learns is less studied beyond theoretical landscape characterizations and
local geometric approximations near optimal conditions. Here, we present a
toolkit for the Dynamical Organization Of Deep Learning Loss Landscapes, or
DOODL3. DOODL3 formulates the training of neural networks as a dynamical
system, analyzes the learning process, and presents an interpretable global
view of trajectories in the loss landscape. Our approach uses the coarseness of
topology to capture the granularity of geometry to mitigate against states of
instability or elongated training. Overall, our analysis presents an empirical
framework to extract the global dynamics of a model and to use that information
to guide the training of neural networks.
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