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A Fast, Hessian-Free Pruning Framework for Efficient Compression of Large Language Models
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s42514-022-00095-yopenalex-snapshot
MT-3000: a heterogeneous multi-zone processor for HPC
inferredcites →https://doi.org/10.1016/j.frl.2007.06.001openalex-snapshot
Optimality of the RiskMetrics VaR model
inferredcites →https://doi.org/10.1080/00036846.2014.982853openalex-snapshot
What should the value of lambda be in the exponentially weighted moving average volatility model?
inferredcites →https://doi.org/10.1145/3079758openalex-snapshot
Throughput-Optimized FPGA Accelerator for Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.1198/004017001750386279openalex-snapshot
The Performance of Exponentially Weighted Moving Average Charts With Estimated Parameters
inferredcites →https://doi.org/10.1609/aaai.v34i05.6239openalex-snapshot
PIQA: Reasoning about Physical Commonsense in Natural Language
inferredcites →https://doi.org/10.52202/079017-3008openalex-snapshot
Adam with model exponential moving average is effective for nonconvex optimization
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