Summary of The Sampling Complexity Of Learning Invertible Residual Neural Networks, by Yuanyuan Li et al.
The sampling complexity of learning invertible residual neural networksby Yuanyuan Li, Philipp Grohs, Philipp PetersenFirst…
Summary of Theoretically Informed Selection Of Latent Activation in Autoencoder Based Recommender Systems, by Aviad Susman
Theoretically informed selection of latent activation in autoencoder based recommender systemsby Aviad SusmanFirst submitted to…
Summary of Generalization and Risk Bounds For Recurrent Neural Networks, by Xuewei Cheng and Ke Huang and Shujie Ma
Generalization and Risk Bounds for Recurrent Neural Networksby Xuewei Cheng, Ke Huang, Shujie MaFirst submitted…
Summary of A Convex Relaxation Approach to Generalization Analysis For Parallel Positively Homogeneous Networks, by Uday Kiran Reddy Tadipatri et al.
A Convex Relaxation Approach to Generalization Analysis for Parallel Positively Homogeneous Networksby Uday Kiran Reddy…
Summary of Sparsing Law: Towards Large Language Models with Greater Activation Sparsity, by Yuqi Luo et al.
Sparsing Law: Towards Large Language Models with Greater Activation Sparsityby Yuqi Luo, Chenyang Song, Xu…
Summary of Theoretical Characterisation Of the Gauss-newton Conditioning in Neural Networks, by Jim Zhao et al.
Theoretical characterisation of the Gauss-Newton conditioning in Neural Networksby Jim Zhao, Sidak Pal Singh, Aurelien…
Summary of Nonparametric Estimation Of Hawkes Processes with Rkhss, by Anna Bonnet and Maxime Sangnier
Nonparametric estimation of Hawkes processes with RKHSsby Anna Bonnet, Maxime SangnierFirst submitted to arxiv on:…
Summary of Convex Formulations For Training Two-layer Relu Neural Networks, by Karthik Prakhya et al.
Convex Formulations for Training Two-Layer ReLU Neural Networksby Karthik Prakhya, Tolga Birdal, Alp YurtseverFirst submitted…
Summary of Hamiltonian Monte Carlo on Relu Neural Networks Is Inefficient, by Vu C. Dinh et al.
Hamiltonian Monte Carlo on ReLU Neural Networks is Inefficientby Vu C. Dinh, Lam Si Tung…
Summary of Minimax Optimality Of Deep Neural Networks on Dependent Data Via Pac-bayes Bounds, by Pierre Alquier and William Kengne
Minimax optimality of deep neural networks on dependent data via PAC-Bayes boundsby Pierre Alquier, William…