Pytorch Model
PyTorch is a widely used open-source machine learning framework primarily focused on facilitating the development and deployment of deep learning models. Current research emphasizes improving efficiency and accessibility through optimized implementations of various algorithms (e.g., symbolic regression, bundle adjustment, spiking neural networks) and the creation of specialized toolkits for tasks like species distribution modeling, adversarial machine learning, and multi-objective optimization. This framework's impact stems from its ease of use, extensibility, and support for GPU acceleration, enabling researchers and practitioners across diverse scientific disciplines to leverage deep learning for complex problems.
Papers
Comparative Analysis of CPU and GPU Profiling for Deep Learning Models
Dipesh Gyawali
nanoT5: A PyTorch Framework for Pre-training and Fine-tuning T5-style Models with Limited Resources
Piotr Nawrot
TensorBank: Tensor Lakehouse for Foundation Model Training
Romeo Kienzler, Leonardo Pondian Tizzei, Benedikt Blumenstiel, Zoltan Arnold Nagy, S. Karthik Mukkavilli, Johannes Schmude, Marcus Freitag, Michael Behrendt, Daniel Salles Civitarese, Naomi Simumba, Daiki Kimura, Hendrik Hamann
Hyperparameter Tuning Cookbook: A guide for scikit-learn, PyTorch, river, and spotPython
Thomas Bartz-Beielstein
Evaluating and Enhancing Robustness of Deep Recommendation Systems Against Hardware Errors
Dongning Ma, Xun Jiao, Fred Lin, Mengshi Zhang, Alban Desmaison, Thomas Sellinger, Daniel Moore, Sriram Sankar