Summary of Benign Overfitting in Single-head Attention, by Roey Magen et al.
Benign Overfitting in Single-Head Attentionby Roey Magen, Shuning Shang, Zhiwei Xu, Spencer Frei, Wei Hu,…
Summary of Toward Generalizable Learning Of All (linear) First-order Methods Via Memory Augmented Transformers, by Sanchayan Dutta (uc Davis) et al.
Toward generalizable learning of all (linear) first-order methods via memory augmented Transformersby Sanchayan Dutta, Suvrit…
Summary of Utilizing Lyapunov Exponents in Designing Deep Neural Networks, by Tirthankar Mittra
Utilizing Lyapunov Exponents in designing deep neural networksby Tirthankar MittraFirst submitted to arxiv on: 8…
Summary of Extended Convexity and Smoothness and Their Applications in Deep Learning, by Binchuan Qi et al.
Extended convexity and smoothness and their applications in deep learningby Binchuan Qi, Wei Gong, Li…
Summary of Score-based Variational Inference For Inverse Problems, by Zhipeng Xue et al.
Score-Based Variational Inference for Inverse Problemsby Zhipeng Xue, Penghao Cai, Xiaojun Yuan, Xiqi GaoFirst submitted…
Summary of On the Impacts Of the Random Initialization in the Neural Tangent Kernel Theory, by Guhan Chen et al.
On the Impacts of the Random Initialization in the Neural Tangent Kernel Theoryby Guhan Chen,…
Summary of On the Optimization and Generalization Of Two-layer Transformers with Sign Gradient Descent, by Bingrui Li et al.
On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descentby Bingrui Li, Wei…
Summary of Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse, by Arthur Jacot et al.
Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapseby Arthur Jacot, Peter Súkeník,…
Summary of Sgd with Memory: Fundamental Properties and Stochastic Acceleration, by Dmitry Yarotsky et al.
SGD with memory: fundamental properties and stochastic accelerationby Dmitry Yarotsky, Maksim VelikanovFirst submitted to arxiv…
Summary of Visualising Feature Learning in Deep Neural Networks by Diagonalizing the Forward Feature Map, By Yoonsoo Nam et al.
Visualising Feature Learning in Deep Neural Networks by Diagonalizing the Forward Feature Mapby Yoonsoo Nam,…