1.3k citations · 2.2k across the 33 of their papers we have counts for
23 papers · 1 filter
Dataset Difficulty and the Role of Inductive Bias
Devin Kwok, Nikhil Anand, Jonathan Frankle +2
Motivated by the goals of dataset pruning and defect identification, a growing body of methods have been developed to score individual examples within a dataset. These methods, whi…
Knowledge Distillation for Efficient Sequences of Training Runs
Xingyu Liu, Alex Leonardi, Lu Yu +3
In many practical scenarios -- like hyperparameter search or continual retraining with new data -- related training runs are performed many times in sequence. Current practice is t…
The Effect of Data Dimensionality on Neural Network Prunability
Zachary Ankner, Alex Renda, Gintare Karolina Dziugaite +2
Practitioners prune neural networks for efficiency gains and generalization improvements, but few scrutinize the factors determining the prunability of a neural network the maximum…
Reduce, Reuse, Recycle: Improving Training Efficiency with Distillation
Cody Blakeney, Jessica Zosa Forde, Jonathan Frankle +2
Methods for improving the efficiency of deep network training (i.e. the resources required to achieve a given level of model quality) are of immediate benefit to deep learning prac…
Pruning's Effect on Generalization Through the Lens of Training and Regularization
Tian Jin, Michael Carbin, Daniel M. Roy +2
Practitioners frequently observe that pruning improves model generalization. A long-standing hypothesis based on bias-variance trade-off attributes this generalization improvement…
Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask?
Mansheej Paul, Feng Chen, Brett W. Larsen +3
Modern deep learning involves training costly, highly overparameterized networks, thus motivating the search for sparser networks that can still be trained to the same accuracy as…
Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks
Mansheej Paul, Brett W. Larsen, Surya Ganguli +2
A striking observation about iterative magnitude pruning (IMP; Frankle et al. 2020) is that $\unicode{x2014}$ after just a few hundred steps of dense training $\unicode{x2014}$ the…
Fast Benchmarking of Accuracy vs. Training Time with Cyclic Learning Rates
Jacob Portes, Davis Blalock, Cory Stephenson +1
Benchmarking the tradeoff between neural network accuracy and training time is computationally expensive. Here we show how a multiplicative cyclic learning rate schedule can be use…
Trade-offs of Local SGD at Scale: An Empirical Study
Jose Javier Gonzalez Ortiz, Jonathan Frankle, Mike Rabbat +2
As datasets and models become increasingly large, distributed training has become a necessary component to allow deep neural networks to train in reasonable amounts of time. Howeve…
What can linear interpolation of neural network loss landscapes tell us?
Tiffany Vlaar, Jonathan Frankle
Studying neural network loss landscapes provides insights into the nature of the underlying optimization problems. Unfortunately, loss landscapes are notoriously difficult to visua…
Revisiting "Qualitatively Characterizing Neural Network Optimization Problems"
Jonathan Frankle
We revisit and extend the experiments of Goodfellow et al. (2014), who showed that - for then state-of-the-art networks - "the objective function has a simple, approximately convex…
The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models
Tianlong Chen, Jonathan Frankle, Shiyu Chang +4
The computer vision world has been re-gaining enthusiasm in various pre-trained models, including both classical ImageNet supervised pre-training and recently emerged self-supervis…
Pruning Neural Networks at Initialization: Why are We Missing the Mark?
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy +1
Recent work has explored the possibility of pruning neural networks at initialization. We assess proposals for doing so: SNIP (Lee et al., 2019), GraSP (Wang et al., 2020), SynFlow…
The Lottery Ticket Hypothesis for Pre-trained BERT Networks
Tianlong Chen, Jonathan Frankle, Shiyu Chang +4
In natural language processing (NLP), enormous pre-trained models like BERT have become the standard starting point for training on a range of downstream tasks, and similar trends…
On the Predictability of Pruning Across Scales
Jonathan S. Rosenfeld, Jonathan Frankle, Michael Carbin +1
We show that the error of iteratively magnitude-pruned networks empirically follows a scaling law with interpretable coefficients that depend on the architecture and task. We funct…
What is the State of Neural Network Pruning?
Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle +1
Neural network pruning---the task of reducing the size of a network by removing parameters---has been the subject of a great deal of work in recent years. We provide a meta-analysi…
Comparing Rewinding and Fine-tuning in Neural Network Pruning
Alex Renda, Jonathan Frankle, Michael Carbin
Many neural network pruning algorithms proceed in three steps: train the network to completion, remove unwanted structure to compress the network, and retrain the remaining structu…
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs
Jonathan Frankle, David J. Schwab, Ari S. Morcos
A wide variety of deep learning techniques from style transfer to multitask learning rely on training affine transformations of features. Most prominent among these is the popular…
The Early Phase of Neural Network Training
Jonathan Frankle, David J. Schwab, Ari S. Morcos
Recent studies have shown that many important aspects of neural network learning take place within the very earliest iterations or epochs of training. For example, sparse, trainabl…
Linear Mode Connectivity and the Lottery Ticket Hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy +1
We study whether a neural network optimizes to the same, linearly connected minimum under different samples of SGD noise (e.g., random data order and augmentation). We find that st…
Dissecting Pruned Neural Networks
Jonathan Frankle, David Bau
Pruning is a standard technique for removing unnecessary structure from a neural network to reduce its storage footprint, computational demands, or energy consumption. Pruning can…
Stabilizing the Lottery Ticket Hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy +1
Pruning is a well-established technique for removing unnecessary structure from neural networks after training to improve the performance of inference. Several recent results have…
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Jonathan Frankle, Michael Carbin
Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of infere…