Summary of Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks, by Hristo Papazov et al.
Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networksby Hristo Papazov, Scott Pesme,…
Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networksby Hristo Papazov, Scott Pesme,…
Evidence, Definitions and Algorithms regarding the Existence of Cohesive-Convergence Groups in Neural Network Optimizationby Thien…
On the Markov Property of Neural Algorithmic Reasoning: Analyses and Methodsby Montgomery Bohde, Meng Liu,…
A spatiotemporal style transfer algorithm for dynamic visual stimulus generationby Antonino Greco, Markus SiegelFirst submitted…
Restricted Bayesian Neural Networkby Sourav Ganguly, Saprativa BhattacharjeeFirst submitted to arxiv on: 6 Mar 2024CategoriesMain:…
Lightweight Cross-Modal Representation Learningby Bilal Faye, Hanane Azzag, Mustapha Lebbah, Djamel BouchaffraFirst submitted to arxiv…
T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformersby Mariano V. Ntrougkas, Nikolaos…
SWAP-NAS: Sample-Wise Activation Patterns for Ultra-fast NASby Yameng Peng, Andy Song, Haytham M. Fayek, Vic…
A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neural Networkby Ruichen Ma, Guanchao Qiao, Yian Liu, Liwei Meng,…
Online model error correction with neural networks: application to the Integrated Forecasting Systemby Alban Farchi,…