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Source: paper_references, paper_reference_links, observed 2026-08-11T14:11:27.166202Z
Paper Citation Record · LEDGER
As of 15 August 2026, this Paper Citation Record lists 100 of 168 outbound references and 2 inbound Pith citation observations for arXiv:2412.12441.
A citation records a reference. It does not transfer a finding from one paper to another.
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Source: paper_references, paper_reference_links, observed 2026-08-11T14:11:27.166202Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T18:10:53.203470Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T15:11:03.194661Z
100 of 168 outbound references displayed
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Numerical Pruning for Efficient Autoregressive Models , " * write output.state after.block = add.period write newline
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Numerical Pruning for Efficient Autoregressive Models GPT-4 Technical Report
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Numerical Pruning for Efficient Autoregressive Models A.; and Wainwright, M
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Numerical Pruning for Efficient Autoregressive Models Fluctuation-based Adaptive Structured Pruning for Large Language Models
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Numerical Pruning for Efficient Autoregressive Models S.; Hu, W.; Li, Z.; Salakhutdinov, R
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Numerical Pruning for Efficient Autoregressive Models L.; Bousquet, O.; and Mendelson, S
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Numerical Pruning for Efficient Autoregressive Models Federated Empirical Risk Minimization via Second-Order Method
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Numerical Pruning for Efficient Autoregressive Models Training (Overparametrized) Neural Networks in Near-Linear Time
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Numerical Pruning for Efficient Autoregressive Models Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems
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Numerical Pruning for Efficient Autoregressive Models B.; Lee, Y
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Numerical Pruning for Efficient Autoregressive Models A direct formulation for sparse PCA using semidefinite programming
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Numerical Pruning for Efficient Autoregressive Models Attention Scheme Inspired Softmax Regression
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Reference 97
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Observation 827c5401-b6c8-4dea-8623-d3494d0ea2dc · outbound
Numerical Pruning for Efficient Autoregressive Models Beyond Linear Approximations: A Novel Pruning Approach for Attention Matrix
Reference 98
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Observation d47fb572-975d-4a5f-bf92-711ff7fed5ce · outbound
Numerical Pruning for Efficient Autoregressive Models Looped ReLU MLPs May Be All You Need as Practical Programmable Computers
Reference 99
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Observation b47a329f-5930-4b9f-815a-e636e020f842 · outbound
Numerical Pruning for Efficient Autoregressive Models Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time
Reference 100
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Observation 788d3fa3-001e-4dba-b718-7b1c1ed03a10 · inbound
High-Order Matching for One-Step Shortcut Diffusion Models Numerical Pruning for Efficient Autoregressive Models
Reference 56
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Observation 2ab26d5d-2cb1-4703-8da2-06c387b504c4 · inbound
Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse Numerical Pruning for Efficient Autoregressive Models
Reference 57
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