Clayton Sanford
Clayton Sanford
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Learning single-index models with shallow neural networks
A Bietti, J Bruna, C Sanford, MJ Song
Advances in Neural Information Processing Systems 35, 9768-9783, 2022
Representational strengths and limitations of transformers
C Sanford, DJ Hsu, M Telgarsky
Advances in Neural Information Processing Systems 36, 2024
On the Approximation Power of Two-Layer Networks of Random ReLUs
D Hsu, C Sanford, RA Servedio, EV Vlatakis-Gkaragkounis
Conference on Learning Theory, 2021, 2021
Support vector machines and linear regression coincide with very high-dimensional features
N Ardeshir, C Sanford, DJ Hsu
Advances in Neural Information Processing Systems 34, 4907-4918, 2021
Spliceman2: a computational web server that predicts defects in pre-mRNA splicing
KJ Cygan, CH Sanford, WG Fairbrother
Bioinformatics 33 (18), 2943-2945, 2017
Improving the Reliability of ML‐Corrected Climate Models With Novelty Detection
C Sanford, A Kwa, O Watt‐Meyer, SK Clark, N Brenowitz, J McGibbon, ...
Journal of Advances in Modeling Earth Systems 15 (11), e2023MS003809, 2023
Near-Optimal Statistical Query Lower Bounds for Agnostically Learning Intersections of Halfspaces with Gaussian Marginals
DJ Hsu, CH Sanford, R Servedio, EV Vlatakis-Gkaragkounis
Conference on Learning Theory, 283-312, 2022
Enabling equation-free modeling via diffusion maps
T Chin, J Ruth, C Sanford, R Santorella, P Carter, B Sandstede
Journal of Dynamics and Differential Equations, 1-20, 2022
Transformers, parallel computation, and logarithmic depth
C Sanford, D Hsu, M Telgarsky
arXiv preprint arXiv:2402.09268, 2024
On Scrambling Phenomena for Randomly Initialized Recurrent Networks
V Chatziafratis, I Panageas, C Sanford, S Stavroulakis
Advances in Neural Information Processing Systems 35, 18501-18513, 2022
Expressivity of Neural Networks via Chaotic Itineraries beyond Sharkovsky’s Theorem
CH Sanford, V Chatziafratis
International Conference on Artificial Intelligence and Statistics, 9505-9549, 2022
Intrinsic dimensionality and generalization properties of the R-norm inductive bias
N Ardeshir, DJ Hsu, CH Sanford
The Thirty Sixth Annual Conference on Learning Theory, 3264-3303, 2023
Understanding Transformer Reasoning Capabilities via Graph Algorithms
C Sanford, B Fatemi, E Hall, A Tsitsulin, M Kazemi, J Halcrow, B Perozzi, ...
arXiv preprint arXiv:2405.18512, 2024
Representational Capabilities of Feed-forward and Sequential Neural Architectures
CH Sanford
Columbia University, 2024
Applying Rademacher-Like Bounds to Combinatorial Samples and Function Selection
C Sanford
Projected Water Needs and Intervention Strategies in India.
J Gross, C Sanford, G Kocks
UMAP Journal 37 (2), 2016
The Effect of Model Capacity on the Emergence of In-Context Learning
B Ottlik, N Ri, D Hsu, C Sanford
ICLR 2024 Workshop on Mathematical and Empirical Understanding of Foundation …, 0
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