A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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An empirical study showing how text quality interventions reshape neural scaling laws for LLMs, affecting performance projection and resource planning.
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This paper introduces a method for causal representation learning from interventional data by using the geometric signature of do-interventions to guide an autoencoder.
Hongnan Ma, Yiwei Shi, Mengyue Yang, et al.
Introduces TimePNS, a necessity-aware framework for time-series explanation that uses counterfactual interventions to identify subsequences essential for model predictions.