LLM training data mixture optimization breaks when training pools shift — every prior proxy experiment becomes stale.
NVIDIA diffusion language model Nemotron TwoTower achieves 2.42x LLM inference throughput without a full retraining run, ...
AI; he uses AI tools regularly and sees potential in many of those tools as useful plugins or cool new apps. But he is ...
Causal analysis is used to answer what-if questions. Unlike forecasting, which answers the question of what will happen next if everyone keeps behaving as they have in the past, causal inference ...
Abstract: Ensuring transparency and explainability in Artificial Intelligence (AI)/Machine Learning (ML) models is crucial for their effective deployment in wireless networks. This paper addresses the ...
What is this book about? Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that ...
Decades of research have established a significant link between physical activity and health, influencing agenda setting, policy making and community awareness.1–4 However, the field continues to ...
Large Language Models (LLMs) have come a long way in their ability to solve a wide range of problems. Yet, LLM decision-making still relies primarily on pattern recognition, which may limit its ...
Abstract: Deep neural networks (DNNs) often struggle with out-of-distribution data, limiting their reliability in real-world visual applications. To address this issue, domain generalization methods ...
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