A Transformer-based learning-to-rank model for selected configuration interaction achieves chemical accuracy with substantially fewer determinants than prior classification or regression baselines across tested molecules.
Advances in Neural Information Processing Systems , volume=
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Time-LLM reprograms frozen LLMs for time series forecasting via text prototypes and Prompt-as-Prefix, outperforming specialized models in standard, few-shot, and zero-shot settings.
Common ID estimators fail to track the true intrinsic dimension of neural representations and are instead driven by other factors.
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Learning to Rank for Selected Configuration Interaction
A Transformer-based learning-to-rank model for selected configuration interaction achieves chemical accuracy with substantially fewer determinants than prior classification or regression baselines across tested molecules.
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Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
Time-LLM reprograms frozen LLMs for time series forecasting via text prototypes and Prompt-as-Prefix, outperforming specialized models in standard, few-shot, and zero-shot settings.
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Rethinking Intrinsic Dimension Estimation in Neural Representations
Common ID estimators fail to track the true intrinsic dimension of neural representations and are instead driven by other factors.