Sparsity-aware roofline models are required for accurate SpMM performance prediction because matrix structure alters arithmetic intensity and a single unified model fails across patterns like block, banded, scale-free, and random.
Beyond Human-level Accuracy: Computational Challenges in Deep Learning , Url =
4 Pith papers cite this work. Polarity classification is still indexing.
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Language models show good calibration when asked to estimate the probability that their own answers are correct, with performance improving as models get larger.
Ranked preference modeling outperforms imitation learning for language model alignment and scales more favorably with model size.
Effective data transferred from pre-training to fine-tuning is described by a power law in model parameter count and fine-tuning dataset size, acting like a multiplier on the fine-tuning data.
citing papers explorer
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Sparsity-Aware Roofline Models for Sparse Matrix-Matrix Multiplication
Sparsity-aware roofline models are required for accurate SpMM performance prediction because matrix structure alters arithmetic intensity and a single unified model fails across patterns like block, banded, scale-free, and random.
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Language Models (Mostly) Know What They Know
Language models show good calibration when asked to estimate the probability that their own answers are correct, with performance improving as models get larger.
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A General Language Assistant as a Laboratory for Alignment
Ranked preference modeling outperforms imitation learning for language model alignment and scales more favorably with model size.
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Scaling Laws for Transfer
Effective data transferred from pre-training to fine-tuning is described by a power law in model parameter count and fine-tuning dataset size, acting like a multiplier on the fine-tuning data.