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Prune Responsibly
Irrespective of the specific definition of fairness in a machine learnin...
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Bespoke vs. Prêt-à-Porter Lottery Tickets: Exploiting Mask Similarity for Trainable Sub-Network Finding
The observation of sparse trainable sub-networks within over-parametrize...
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dagger: A Python Framework for Reproducible Machine Learning Experiment Orchestration
Many research directions in machine learning, particularly in deep learn...
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Streamlining Tensor and Network Pruning in PyTorch
In order to contrast the explosion in size of state-of-the-art machine l...
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On Iterative Neural Network Pruning, Reinitialization, and the Similarity of Masks
We examine how recently documented, fundamental phenomena in deep learni...
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One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
The success of lottery ticket initializations (Frankle and Carbin, 2019)...
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The Scientific Method in the Science of Machine Learning
In the quest to align deep learning with the sciences to address calls f...
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Machine Learning Solutions for High Energy Physics: Applications to Electromagnetic Shower Generation, Flavor Tagging, and the Search for di-Higgs Production
This thesis demonstrate the efficacy of designing and developing machine...
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Machine Learning in High Energy Physics Community White Paper
Machine learning is an important research area in particle physics, begi...
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Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC
There has been considerable recent activity applying deep convolutional ...
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CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
Simulation is a key component of physics analysis in particle physics an...
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Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
We provide a bridge between generative modeling in the Machine Learning ...
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