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 Pruning deep neural networks for lottery tickets (LTs)
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Preference 1Preference 2Preference 3TopicMain paperSecondary paper
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Shreyash1) Iterative Magnitude PruningOn the Predictability of Pruning Across ScalesComparing Rewinding and Fine-tuning in Neural Network Pruning
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2) Learning rateComparing Rewinding and Fine-tuning in Neural Network PruningLottery Ticket Preserves Weight Correlation: Is It Desirable or Not?
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Yamini3) Parameter initialization A Signal Propagation Perspective for Pruning Neural Networks at Initialization Robust Pruning at Initialization
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Amrita, Josephine Taniha, AdarshNirav, Pramila4) Universal LTsOne ticket to win them all: generalizing lottery ticket initializations across datasets and optimizersUniversality of Winning Tickets: A Renormalization Group PerspectiveOn The Existence of Universal Lottery Tickets
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Shreyash, PramilaAmrita, Josephine TanihaSiddhartha5) Sanity checking Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot
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Yamini6) Random pruningAre wider nets better given the same number of parameters? The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training
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7) Dynamic Sparse trainingSparse Training via Boosting Pruning Plasticity with NeuroregenerationDo We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training
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RahulYamini, Arthur Sanin, Adarsh8) Gradient flowGradient Flow in Sparse Neural Networks and How Lottery Tickets WinUnmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask?
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SiddharthaAdarsh9) Neuron sparsityCoarsening the Granularity: Towards Structurally Sparse Lottery Ticketshen to Prune? A Policy towards Early Structural Pruning
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NiravJosephine Taniha, Pramila10) Pruning for strong lottery ticketsWhat's Hidden in a Randomly Weighted Neural Network?Deconstructing Lottery Tickets: Zeros, Signs, and the Supermaskk
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Arthur SaninRahul11) Theory - Existence of LTsOptimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is SufficientProving the Lottery Ticket Hypothesis: Pruning is All You Need
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SeverinRahul12) BenchmarkingPlant 'n' Seek: Can You Rind the Winning Ticket?Pruning Neural Networks at Initialization: Why Are We Missing the Mark?
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Severin13) QuantizationMulti-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted NetworkA Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness
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Severin14) Pruning with regularizationWinning the Lottery with Continuous SparsificationEffective Sparsification of Neural Networks with Global Sparsity Constraint
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Siwen ChenArthur SaninNirav15) Core setsLottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable NetworksEfficient Lottery Ticket Finding: Less Data is More
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SiddharthaShreyash16) Adversarial robustnessCan You Win Everything with A Lottery Ticket?HYDRA: Pruning Adversarially Robust Neural Networks
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Siwen ChenGNNsA Unified Lottery Ticket Hypothesis for Graph Neural Networks
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Siwen ChenProb. approachA Probabilistic Approach to Neural Network Pruning
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Sample complexityWhy Lottery Ticket Wins? A Theoretical Perspectiveof Sample Complexity on Pruned Neural Networks
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