Tag: causal machine learning
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Zero-Shot Concept Bottleneck Models for AI Interpretability
Discover zero-shot concept bottleneck models that enable interpretable AI without training, using dynamic concept retrieval and large-scale concept banks.
Efficient Causal Graph Discovery with Large Language Models
Discover causal graphs efficiently using LLMs and BFS, reducing queries and improving accuracy with observational data integration.
Limitations of LLMs in Contextual Math Reasoning
Explore why large language models still struggle with contextual mathematical reasoning despite advances in abstract problem-solving.
Gradient-Boosted Attention: Enhancing Single Layer Performance
Discover gradient-boosted attention, a novel method improving single-layer attention by correcting errors via a second pass for better NLP model accuracy.
Reflective Context Learning: Optimizing Context Space Efficiently
Discover Reflective Context Learning, a unified framework enhancing optimization in context space for robust, efficient, and adaptable learning agents.