Spotlight AI Theories
2024
A Neural Network Approach for Efficiently Answering Most Probable Explanation Queries in Probabilistic Models
·11719 words·56 mins·
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AI Theory
Optimization
🏢 University of Texas at Dallas
A novel neural network efficiently answers arbitrary Most Probable Explanation (MPE) queries in large probabilistic models, eliminating the need for slow inference algorithms.
A generalized neural tangent kernel for surrogate gradient learning
·1667 words·8 mins·
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AI Theory
Generalization
🏢 University of Bern
Researchers introduce a generalized neural tangent kernel for analyzing surrogate gradient learning in neural networks with non-differentiable activation functions, providing a strong theoretical foun…
4+3 Phases of Compute-Optimal Neural Scaling Laws
·3282 words·16 mins·
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AI Theory
Optimization
🏢 McGill University
Researchers discovered four distinct compute-optimal phases for training neural networks, offering new predictions for resource-efficient large model training.