2 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2021★ 2 cited
How many degrees of freedom do we need to train deep networks: a loss landscape perspective
Brett W. Larsen, Stanislav Fort, Nic Becker +1
A variety of recent works, spanning pruning, lottery tickets, and training within random subspaces, have shown that deep neural networks can be trained using far fewer degrees of f…
cs.NI2016★ 1 cited
A Non-stationary Service Curve Model for Estimation of Cellular Sleep Scheduling
Nico Becker, Markus Fidler
While steady-state solutions of backlog and delay have been derived for essential wireless systems, the analysis of transient phases still poses significant challenges. Considering…
cs.NI2015★ 1 cited
A Non-stationary Service Curve Model for Performance Analysis of Transient Phases
Nico Becker, Markus Fidler
Steady-state solutions for a variety of relevant queueing systems are known today, e.g., from queueing theory, effective bandwidths, and network calculus. The behavior during trans…