7 citations · 10 across the 2 of their papers we have counts for
8 papers
When Are Solutions Connected in Deep Networks?
Quynh Nguyen, Pierre Brechet, Marco Mondelli
The question of how and why the phenomenon of mode connectivity occurs in training deep neural networks has gained remarkable attention in the research community. From a theoretica…
A Note on Connectivity of Sublevel Sets in Deep Learning
Quynh Nguyen
It is shown that for deep neural networks, a single wide layer of width ( being the number of training samples) suffices to prove the connectivity of sublevel sets of the…
On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths
Quynh Nguyen
We give a simple proof for the global convergence of gradient descent in training deep ReLU networks with the standard square loss, and show some of its improvements over the state…
Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology
Quynh Nguyen, Marco Mondelli
Recent works have shown that gradient descent can find a global minimum for over-parameterized neural networks where the widths of all the hidden layers scale polynomially with …
On Connected Sublevel Sets in Deep Learning
Quynh Nguyen
This paper shows that every sublevel set of the loss function of a class of deep over-parameterized neural nets with piecewise linear activation functions is connected and unbounde…
On the loss landscape of a class of deep neural networks with no bad local valleys
Quynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein
We identify a class of over-parameterized deep neural networks with standard activation functions and cross-entropy loss which provably have no bad local valley, in the sense that…