Summary of Empirical Tests Of Optimization Assumptions in Deep Learning, by Hoang Tran et al.
Empirical Tests of Optimization Assumptions in Deep Learningby Hoang Tran, Qinzi Zhang, Ashok CutkoskyFirst submitted…
Empirical Tests of Optimization Assumptions in Deep Learningby Hoang Tran, Qinzi Zhang, Ashok CutkoskyFirst submitted…
Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator…
BADM: Batch ADMM for Deep Learningby Ouya Wang, Shenglong Zhou, Geoffrey Ye LiFirst submitted to…
Scalable Nested Optimization for Deep Learningby Jonathan LorraineFirst submitted to arxiv on: 1 Jul 2024CategoriesMain:…
Physics-Inspired Deep Learning and Transferable Models for Bridge Scour Predictionby Negin Yousefpour, Bo WangFirst submitted…
Unveiling the Unseen: Exploring Whitebox Membership Inference through the Lens of Explainabilityby Chenxi Li, Abhinav…
Benchmarking Predictive Coding Networks – Made Simpleby Luca Pinchetti, Chang Qi, Oleh Lokshyn, Gaspard Olivers,…
Deep Learning Approach for Enhanced Transferability and Learning Capacity in Tool Wear Estimationby Zongshuo Li,…
Deep Learning Based Tool Wear Estimation Considering Cutting Conditionsby Zongshuo Li, Markus Meurer, Thomas BergsFirst…
How Does Overparameterization Affect Features?by Ahmet Cagri Duzgun, Samy Jelassi, Yuanzhi LiFirst submitted to arxiv…