Preprint2026
Neural Scaling Laws and Science Without Theory
Unknown
This paper critically examines the relationship between neural scaling laws and the role of theory in machine learning science.
0Jan 1, 2026Scaling Laws
AI Research
AI & ML Research Papers
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39 papersPreprint2024
Neural scaling laws in robotics
Unknown
A study quantifying neural scaling laws for Robot Foundation Models to guide data and model scaling in robotics.
0May 1, 2024Scaling Laws
arXiv
Preprint2025
Neural scaling laws from large-N field theory: solvable model beyond the ridgeless limit
Unknown
The paper applies large-N field theory to derive neural scaling laws and proposes solvable models that go beyond the ridgeless limit.
0Jan 1, 2025Scaling Laws
Preprint2026
Implicit bias produces neural scaling laws in learning curves, from perceptrons to deep networks
Unknown
The paper argues that implicit bias in learning dynamics controls neural scaling laws during training, linking growth to scaling behavior from perceptrons to deep networks.
0Jan 1, 2026Scaling Laws
Preprint2026
A universal compression theory for lottery ticket hypothesis and neural scaling laws
Unknown
Proposes a universal compression theory linking the lottery ticket hypothesis and neural scaling laws, showing how compression can significantly improve scaling laws.
0Jan 1, 2026Scaling Laws
Preprint2026
How Text Quality Interventions Reshape Neural Scaling Laws for LLMs: Empirical Study
Unknown
An empirical study showing how text quality interventions reshape neural scaling laws for LLMs, affecting performance projection and resource planning.
0Jan 1, 2026Scaling Laws
Preprint2025
A solvable attention for neural scaling laws
Unknown
The paper derives a solvable attention mechanism that enables precise characterization of neural scaling laws for linear self-attention.
0Jan 1, 2025Attention MechanismsScaling Laws
Preprint2024
Understanding scaling laws with statistical and approximation theory for transformer neural networks on intrinsically low-dimensional data
Unknown
A statistical and approximation theory framework predicting neural scaling laws for transformers on intrinsically low-dimensional data, quantifying data and model scaling exponents.
0Jan 1, 2024TransformersScaling Laws
Preprint2026
Scaling laws and spectra of shallow neural networks in the feature learning regime
Unknown
Investigates neural scaling laws for the ERM minimizer in the teacher-student setting beyond the random features regime, focusing on feature learning.
0Jan 1, 2026Scaling LawsNeural Networks
Preprint2025
Large reasoning models learn better alignment from flawed thinking
Unknown
RECAP post-training teaches large reasoning models to override flawed reasoning trajectories, improving alignment.
0Oct 1, 2025ReasoningAlignment
arXiv
PreprintMaastricht Journal of European and Comparative Law2020
Artificial intelligence and human rights: Between law and ethics
Giovanni Sartor
This paper examines the relationship between AI ethics and law, arguing that human rights and social values can serve as a common normative framework to coordinate their demands.
11Dec 1, 2020
PreprintRobotica2014
Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey
Michael Hoy, Alexey S. Matveev, Andrey V. Savkin
This survey reviews collision-free navigation techniques for unmanned vehicles in unknown cluttered environments, focusing on reactive local-sensory-data methods and recent model predictive and sliding mode control laws.
471Mar 4, 2014Attention Mechanisms