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SMF_NeurIPS_2023-6
Our framework applies to a wide range of SMF-type problems for multi-class classification with auxiliary features.
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- North America > United States > Wisconsin > Dane County > Madison (0.14)
- North America > United States > California > Riverside County > Riverside (0.14)
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- Health & Medicine > Therapeutic Area > Oncology (1.00)
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- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Regression (0.70)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (0.68)
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AT Lemmas
Let k (,) be the ν = 2 .5 Matérn kernel.
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- North America > United States > Massachusetts > Middlesex County > Cambridge (0.04)
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- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (0.95)
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- Information Technology > Artificial Intelligence > Robots (0.93)
On the Overlooked Structure of Stochastic Gradients Zeke Xie
Stochastic gradients closely relate to both optimization and generalization of deep neural networks (DNNs).
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- Europe > United Kingdom > England > Oxfordshire > Oxford (0.04)
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- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Gradient Descent (0.74)
Implicit variance regularization in non-contrastive SSL
In this work, we provide a comparative analysis of the learning dynamics for the Euclidean and cosine-based asymmetric losses in the eigenspace of the closed-form predictor DirectPred.
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- Europe > Switzerland > Basel-City > Basel (0.04)
- Asia > Middle East > Israel (0.04)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)