Summary of Analyzing Neural Scaling Laws in Two-layer Networks with Power-law Data Spectra, by Roman Worschech and Bernd Rosenow
Analyzing Neural Scaling Laws in Two-Layer Networks with Power-Law Data Spectraby Roman Worschech, Bernd RosenowFirst…
Analyzing Neural Scaling Laws in Two-Layer Networks with Power-Law Data Spectraby Roman Worschech, Bernd RosenowFirst…
Low-Dimension-to-High-Dimension Generalization And Its Implications for Length Generalizationby Yang Chen, Yitao Liang, Zhouchen LinFirst submitted…
Towards Sharper Risk Bounds for Minimax Problemsby Bowei Zhu, Shaojie Li, Yong LiuFirst submitted to…
Deeper Insights into Deep Graph Convolutional Networks: Stability and Generalizationby Guangrui Yang, Ming Li, Han…
Distributionally robust self-supervised learning for tabular databy Shantanu Ghosh, Tiankang Xie, Mikhail KuznetsovFirst submitted to…
Score Neural Operator: A Generative Model for Learning and Generalizing Across Multiple Probability Distributionsby Xinyu…
Metalic: Meta-Learning In-Context with Protein Language Modelsby Jacob Beck, Shikha Surana, Manus McAuliffe, Oliver Bent,…
JurEE not Judges: safeguarding llm interactions with small, specialised Encoder Ensemblesby Dom NasrabadiFirst submitted to…
Generalization from Starvation: Hints of Universality in LLM Knowledge Graph Learningby David D. Baek, Yuxiao…
Impact of Missing Values in Machine Learning: A Comprehensive Analysisby Abu Fuad Ahmad, Md Shohel…