Growing arbitrary DAG networks: method and strategies

Styliani Douka (INRIA Paris)

Feb 05. 2025, 14:00 — 14:30

Deep learning has shown impressive results obtained at the cost of training huge neural networks. However, the larger the architecture, the higher the computational, financial, and environmental costs during training and inference. We aim at reducing both training and inference durations. We focus on Neural Architecture Growth, which can increase the size of a small model when needed, directly during training using information from the backpropagation. We expand existing work and freely grow neural networks in the form of any Directed Acyclic Graph by reducing expressivity bottlenecks in the architecture. We explore strategies to reduce excessive computations and steer network growth toward more parameter-efficient architectures.

Further Information
Venue:
ESI Boltzmann Lecture Hall
Recordings:
Recording
Associated Event:
Infinite-dimensional Geometry: Theory and Applications (Thematic Programme)
Organizer(s):
Tomasz Goliński (U of Białystok)
Gabriel Larotonda (U of Buenos Aires)
Alice Barbara Tumpach (WPI, Vienna)
Cornelia Vizman (WU of Timisoara)