439 citations · 867 across the 14 of their papers we have counts for
5 papers · 1 filter
The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation
Orevaoghene Ahia, Julia Kreutzer, Sara Hooker
A "bigger is better" explosion in the number of parameters in deep neural networks has made it increasingly challenging to make state-of-the-art networks accessible in compute-rest…
A Tale Of Two Long Tails
Daniel D'souza, Zach Nussbaum, Chirag Agarwal +1
As machine learning models are increasingly employed to assist human decision-makers, it becomes critical to communicate the uncertainty associated with these model predictions. Ho…
When does loss-based prioritization fail?
Niel Teng Hu, Xinyu Hu, Rosanne Liu +2
Not all examples are created equal, but standard deep neural network training protocols treat each training point uniformly. Each example is propagated forward and backward through…
Randomness In Neural Network Training: Characterizing The Impact of Tooling
Donglin Zhuang, Xingyao Zhang, Shuaiwen Leon Song +1
The quest for determinism in machine learning has disproportionately focused on characterizing the impact of noise introduced by algorithmic design choices. In this work, we addres…
Keep the Gradients Flowing: Using Gradient Flow to Study Sparse Network Optimization
Kale-ab Tessera, Sara Hooker, Benjamin Rosman
Training sparse networks to converge to the same performance as dense neural architectures has proven to be elusive. Recent work suggests that initialization is the key. However, w…