NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling | Paper Citation Record

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This paper cites Dual Lottery Ticket Hypothesis.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Dual Lottery Ticket Hypothesis

Reference 1

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This paper cites Deep Rewiring: Training very sparse deep networks.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Deep Rewiring: Training very sparse deep networks

Reference 2

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Unresolved cited work

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This paper cites Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better

Reference 4

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling PIQA: Reasoning about Physical Commonsense in Natural Language

Reference 5

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Bagging predictors

Reference 6

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This paper cites Sparsity Made Easy – Introducing the Cerebras PyTorch Sparsity Library - Cerebras , 2024.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Sparsity Made Easy – Introducing the Cerebras PyTorch Sparsity Library - Cerebras , 2024

Reference 7

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This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 8

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This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 9

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Truly Sparse Neural Networks at Scale

Reference 10

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling ImageNet: A large-scale hierarchical image database

Reference 11

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Sparse Networks from Scratch: Faster Training without Losing Performance

Reference 12

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Dietterich

Reference 13

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This paper cites Rigging the Lottery: Making All Tickets Winners.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Rigging the Lottery: Making All Tickets Winners

Reference 14

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Deep Ensembles: A Loss Landscape Perspective

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 18

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This paper cites A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting

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This paper cites A Survey on Ensemble Learning for Data Stream Classification.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling A Survey on Ensemble Learning for Data Stream Classification

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This paper cites The State of Sparse Training in Deep Reinforcement Learning.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling The State of Sparse Training in Deep Reinforcement Learning

Reference 21

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning

Reference 22

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling On Calibration of Modern Neural Networks

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Learning both Weights and Connections for Efficient Neural Networks

Reference 24

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Neural Network Ensembles

Reference 25

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling The Elements of Statistical Learning

Reference 26

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Training independent subnetworks for robust prediction

Reference 27

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Deep Residual Learning for Image Recognition

Reference 28

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Reference 29

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Measuring Massive Multitask Language Understanding

Reference 30

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Distilling the Knowledge in a Neural Network

Reference 31

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Jacobs, Michael I

Reference 32

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Joint Training of Deep Ensembles Fails Due to Learner Collusion

Reference 33

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Mercer, Lalit R

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Adam: A Method for Stochastic Optimization

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Learning Multiple Layers of Features from Tiny Images

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Kuncheva and Christopher J

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Unresolved cited work

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Optimal Brain Damage

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Network Fission Ensembles for Low-Cost Self-Ensembles

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling SNIP: Single-shot Network Pruning based on Connection Sensitivity

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks

Reference 43

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Popular Ensemble Methods: An Empirical Study

Reference 49

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This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science

Reference 51

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment

Reference 52

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Obtaining Well Calibrated Probabilities Using Bayesian Binning

Reference 53

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This paper cites DeepSparse Inference Engine , 2021.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling DeepSparse Inference Engine , 2021

Reference 54

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training

Reference 55

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Sparser, Better, Deeper, Stronger: Improving Sparse Training with Exact Orthogonal Initialization

Reference 56

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling ResNet50 v1.5 for PyTorch , 2024

Reference 57

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This paper cites Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:44:29.008177Z

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This paper cites A Stochastic Approximation Method.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling A Stochastic Approximation Method

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:44:28.779551Z

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This paper cites WinoGrande: An Adversarial Winograd Schema Challenge at Scale.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling WinoGrande: An Adversarial Winograd Schema Challenge at Scale

Reference 60

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This paper cites Towards Memory-Efficient Training for Extremely Large Output Spaces -- Learning with 500k Labels on a Single Commodity GPU.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Towards Memory-Efficient Training for Extremely Large Output Spaces -- Learning with 500k Labels on a Single Commodity GPU

Reference 61

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Unresolved cited work

Reference 62

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This paper cites Dynamic Sparse Training for Deep Reinforcement Learning.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Dynamic Sparse Training for Deep Reinforcement Learning

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:44:25.537900Z

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Observation 082ecf70-70a8-4bef-9b68-6c286d644f3d · outbound

This paper cites RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch

Reference 64

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:44:25.215575Z

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Observation a06df54a-89cd-4602-842e-7f92ec4b5a76 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling LLaMA: Open and Efficient Foundation Language Models

Reference 65

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This paper cites Varrette, H.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Varrette, H

Reference 66

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This paper cites Attention Is All You Need.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Attention Is All You Need

Reference 67

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This paper cites Picking Winning Tickets Before Training by Preserving Gradient Flow.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 68

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Observation 7b458f2c-16c2-49af-baee-fd168ae858dc · outbound

This paper cites Learning Robust Global Representations by Penalizing Local Predictive Power.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Learning Robust Global Representations by Penalizing Local Predictive Power

Reference 69

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Observation 254e1cb9-0974-45c5-9563-18fcb67add63 · outbound

This paper cites BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning

Reference 70

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Observation 7a0f6a7e-ca27-44cb-bf6b-4861c2f88367 · outbound

This paper cites Nerva: a Truly Sparse Implementation of Neural Networks.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Nerva: a Truly Sparse Implementation of Neural Networks

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:44:24.632843Z

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Observation 80af6930-0542-4929-9201-1fa2278a87c8 · outbound

This paper cites Prune and Tune Ensembles: Low-Cost Ensemble Learning With Sparse Independent Subnetworks.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Prune and Tune Ensembles: Low-Cost Ensemble Learning With Sparse Independent Subnetworks

Reference 72

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:44:24.414672Z

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Observation a24298ac-d6d7-44a8-8833-586e4296502e · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T14:44:22.165258Z

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Observation 7ee3a99d-23ef-40d2-b382-84c99634a2f6 · outbound

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NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Unresolved cited work

Reference 74

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:44:28.200572Z

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Observation a93aa535-4cb6-49d9-a8d2-eb4c852f792c · outbound

This paper cites Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness

Reference 75

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:44:24.210999Z

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Observation 00c1b99f-a601-44c6-ae5a-b96b42b7662f · outbound

This paper cites Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates

Reference 76

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:44:23.992254Z

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Observation 83154707-d389-4e84-a03c-0e9a35a6b810 · outbound

This paper cites Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity

Reference 77

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Observation 965697d7-2da6-4398-9ee5-3364f3fcd26c · outbound

This paper cites Drawing Early-Bird Tickets: Towards More Efficient Training of Deep Networks.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Drawing Early-Bird Tickets: Towards More Efficient Training of Deep Networks

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T14:44:22.610368Z

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Observation 66b6f0d6-7a53-4bbd-aff4-509c9269ea76 · outbound

This paper cites MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge

Reference 79

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:44:23.793230Z

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Observation ceba48ee-4491-4cff-9677-d2c3e4ec64ca · outbound

This paper cites Wide Residual Networks.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Wide Residual Networks

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-07T14:44:22.864692Z

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Observation 3be92767-aab7-481d-9441-5efc1227d076 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-07T14:44:23.014536Z

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Observation 7b2f1288-742e-4992-bcec-2ad267819673 · outbound

This paper cites Brain-inspired sparse training enables Transformers and LLMs to perform as fully connected.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Brain-inspired sparse training enables Transformers and LLMs to perform as fully connected

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-07T14:44:23.115529Z

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Observation 4f0d66af-7995-4241-a8f3-cbd11731efc1 · outbound

This paper cites GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T14:44:23.169462Z

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Observation fcc68dc2-e072-4c77-b691-d4632248eb78 · outbound

This paper cites Robust Lottery Tickets for Pre-trained Language Models.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Robust Lottery Tickets for Pre-trained Language Models

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-07T14:44:23.206606Z

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Observation 64500586-55ab-44f4-9a0c-51c43c7515b2 · outbound

This paper cites Ensemble Methods: Foundations and Algorithms.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Ensemble Methods: Foundations and Algorithms

Reference 85

Resolution: verified exact; doi, observed 2026-08-07T14:44:23.274690Z

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Pith citing papers