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The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
v1v2v3v4v5 (latest)

The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

9 March 2018
Jonathan Frankle
Michael Carbin
ArXiv (abs)PDFHTML

Papers citing "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks"

50 / 2,185 papers shown
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Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates
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Aline Villavicencio
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Lean Unet: A Compact Model for Image Segmentation
Lean Unet: A Compact Model for Image Segmentation
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Ida Åkerholm
Marcus Nordström
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Understanding and Harnessing Sparsity in Unified Multimodal Models
Understanding and Harnessing Sparsity in Unified Multimodal Models
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Chaorui Deng
Ang Li
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Parameter Reduction Improves Vision Transformers: A Comparative Study of Sharing and Width Reduction
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30 Nov 2025
Forgetting by Pruning: Data Deletion in Join Cardinality Estimation
Forgetting by Pruning: Data Deletion in Join Cardinality Estimation
Chaowei He
Yuanjun Liu
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Xizhao Luo
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An Liu
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ModHiFi: Identifying High Fidelity predictive components for Model Modification
ModHiFi: Identifying High Fidelity predictive components for Model Modification
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Chaitanya Murti
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Exploiting the Experts: Unauthorized Compression in MoE-LLMs
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Layer-wise Weight Selection for Power-Efficient Neural Network Acceleration
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E$^3$-Pruner: Towards Efficient, Economical, and Effective Layer Pruning for Large Language Models
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Dynamic Black-box Backdoor Attacks on IoT Sensory Data
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Stephen Lee
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156
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Weight-sparse transformers have interpretable circuits
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Achyuta Rajaram
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Which Sparse Autoencoder Features Are Real? Model-X Knockoffs for False Discovery Rate Control
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Hardware-Aware YOLO Compression for Low-Power Edge AI on STM32U5 for Weeds Detection in Digital Agriculture
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CAMP-HiVe: Cyclic Pair Merging based Efficient DNN Pruning with Hessian-Vector Approximation for Resource-Constrained Systems
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The Strong Lottery Ticket Hypothesis for Multi-Head Attention Mechanisms
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