PreNeT: Leveraging Computational Features to Predict Deep Neural Network Training Time
Fuente:
arXiv
Guardado en:
| Autores principales: | Pourali, Alireza, Boukani, Arian, Khazaei, Hamzeh |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
A Learning-Based Caching Mechanism for Edge Content Delivery
por: Torabi, Hoda, et al.
Publicado: (2024)
por: Torabi, Hoda, et al.
Publicado: (2024)
Physics-informed Convolutional Neural Network for Microgrid Economic Dispatch
por: Ge, Xiaoyu, et al.
Publicado: (2024)
por: Ge, Xiaoyu, et al.
Publicado: (2024)
CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering
por: Khazaei, Ensieh, et al.
Publicado: (2024)
por: Khazaei, Ensieh, et al.
Publicado: (2024)
Data-Aware Training Quality Monitoring and Certification for Reliable Deep Learning
por: Yeganegi, Farhang, et al.
Publicado: (2024)
por: Yeganegi, Farhang, et al.
Publicado: (2024)
Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits
por: Qi, Jun, et al.
Publicado: (2024)
por: Qi, Jun, et al.
Publicado: (2024)
On the Neural Feature Ansatz for Deep Neural Networks
por: Tansley, Edward, et al.
Publicado: (2025)
por: Tansley, Edward, et al.
Publicado: (2025)
Contextual Bandit Optimization with Pre-Trained Neural Networks
por: Terekhov, Mikhail
Publicado: (2025)
por: Terekhov, Mikhail
Publicado: (2025)
Trust, but Verify: Peeling Low-Bit Transformer Networks for Training Monitoring
por: Eamaz, Arian, et al.
Publicado: (2026)
por: Eamaz, Arian, et al.
Publicado: (2026)
Explaining Deep Neural Networks by Leveraging Intrinsic Methods
por: La Rosa, Biagio
Publicado: (2024)
por: La Rosa, Biagio
Publicado: (2024)
BSFA: Leveraging the Subspace Dichotomy to Accelerate Neural Network Training
por: Zhou, Wenjie, et al.
Publicado: (2025)
por: Zhou, Wenjie, et al.
Publicado: (2025)
Physics-Inspired Binary Neural Networks: Interpretable Compression with Theoretical Guarantees
por: Eamaz, Arian, et al.
Publicado: (2025)
por: Eamaz, Arian, et al.
Publicado: (2025)
Improving Generalization of Deep Neural Networks by Leveraging Margin Distribution
por: Lyu, Shen-Huan, et al.
Publicado: (2018)
por: Lyu, Shen-Huan, et al.
Publicado: (2018)
Leveraging Intermediate Neural Collapse with Simplex ETFs for Efficient Deep Neural Networks
por: Liu, Emily
Publicado: (2024)
por: Liu, Emily
Publicado: (2024)
Error Diffusion: Post Training Quantization with Block-Scaled Number Formats for Neural Networks
por: Khodamoradi, Alireza, et al.
Publicado: (2024)
por: Khodamoradi, Alireza, et al.
Publicado: (2024)
Understanding and Minimising Outlier Features in Neural Network Training
por: He, Bobby, et al.
Publicado: (2024)
por: He, Bobby, et al.
Publicado: (2024)
Neural Network Training with Approximate Logarithmic Computations
por: Sanyal, Arnab, et al.
Publicado: (2019)
por: Sanyal, Arnab, et al.
Publicado: (2019)
Multiscale Dubuc: A New Similarity Measure for Time Series
por: Khazaei, Mahsa, et al.
Publicado: (2024)
por: Khazaei, Mahsa, et al.
Publicado: (2024)
On the Hardness of Training Deep Neural Networks Discretely
por: Doron-Arad, Ilan
Publicado: (2024)
por: Doron-Arad, Ilan
Publicado: (2024)
Deep Fusion: Efficient Network Training via Pre-trained Initializations
por: Mazzawi, Hanna, et al.
Publicado: (2023)
por: Mazzawi, Hanna, et al.
Publicado: (2023)
PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks
por: Ge, Qingqing, et al.
Publicado: (2023)
por: Ge, Qingqing, et al.
Publicado: (2023)
Designing Latent Safety Filters using Pre-Trained Vision Models
por: Tabbara, Ihab, et al.
Publicado: (2025)
por: Tabbara, Ihab, et al.
Publicado: (2025)
Concurrent Training and Layer Pruning of Deep Neural Networks
por: Guenter, Valentin Frank Ingmar, et al.
Publicado: (2024)
por: Guenter, Valentin Frank Ingmar, et al.
Publicado: (2024)
Training Deep Morphological Neural Networks as Universal Approximators
por: Fotopoulos, Konstantinos, et al.
Publicado: (2025)
por: Fotopoulos, Konstantinos, et al.
Publicado: (2025)
Utilizing Graph Neural Networks for Effective Link Prediction in Microservice Architectures
por: Khodabandeh, Ghazal, et al.
Publicado: (2025)
por: Khodabandeh, Ghazal, et al.
Publicado: (2025)
Deep Neural Networks Tend To Extrapolate Predictably
por: Kang, Katie, et al.
Publicado: (2023)
por: Kang, Katie, et al.
Publicado: (2023)
Leveraging Deep Neural Networks for Aspect-Based Sentiment Classification
por: Li, Chen, et al.
Publicado: (2025)
por: Li, Chen, et al.
Publicado: (2025)
Informed Forecasting: Leveraging Auxiliary Knowledge to Boost LLM Performance on Time Series Forecasting
por: Ghasemloo, Mohammadmahdi, et al.
Publicado: (2025)
por: Ghasemloo, Mohammadmahdi, et al.
Publicado: (2025)
Towards the Training of Deeper Predictive Coding Neural Networks
por: Qi, Chang, et al.
Publicado: (2025)
por: Qi, Chang, et al.
Publicado: (2025)
Iterative Training of Physics-Informed Neural Networks with Fourier-enhanced Features
por: Wu, Yulun, et al.
Publicado: (2025)
por: Wu, Yulun, et al.
Publicado: (2025)
Beyond Point Matching: Evaluating Multiscale Dubuc Distance for Time Series Similarity
por: Ahmadzadeh, Azim, et al.
Publicado: (2025)
por: Ahmadzadeh, Azim, et al.
Publicado: (2025)
DeepRTE: Pre-trained Attention-based Neural Network for Radiative Transfer
por: Zhu, Yekun, et al.
Publicado: (2025)
por: Zhu, Yekun, et al.
Publicado: (2025)
On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks
por: Zhu, Jiong, et al.
Publicado: (2024)
por: Zhu, Jiong, et al.
Publicado: (2024)
Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings
por: von Kleist, Henrik, et al.
Publicado: (2023)
por: von Kleist, Henrik, et al.
Publicado: (2023)
Exact Gauss-Newton Optimization for Training Deep Neural Networks
por: Korbit, Mikalai, et al.
Publicado: (2024)
por: Korbit, Mikalai, et al.
Publicado: (2024)
Leveraging Exogenous Signals for Hydrology Time Series Forecasting
por: He, Junyang, et al.
Publicado: (2025)
por: He, Junyang, et al.
Publicado: (2025)
HG-Adapter: Improving Pre-Trained Heterogeneous Graph Neural Networks with Dual Adapters
por: Mo, Yujie, et al.
Publicado: (2024)
por: Mo, Yujie, et al.
Publicado: (2024)
Understanding the Benefits of SimCLR Pre-Training in Two-Layer Convolutional Neural Networks
por: Zhang, Han, et al.
Publicado: (2024)
por: Zhang, Han, et al.
Publicado: (2024)
TreeLUT: An Efficient Alternative to Deep Neural Networks for Inference Acceleration Using Gradient Boosted Decision Trees
por: Khataei, Alireza, et al.
Publicado: (2025)
por: Khataei, Alireza, et al.
Publicado: (2025)
Scaling Law for Large-Scale Pre-Training Using Chaotic Time Series and Predictability in Financial Time Series
por: Takemoto, Yuki
Publicado: (2025)
por: Takemoto, Yuki
Publicado: (2025)
Enhancing Deep Neural Network Training Efficiency and Performance through Linear Prediction
por: Ying, Hejie, et al.
Publicado: (2023)
por: Ying, Hejie, et al.
Publicado: (2023)
Ejemplares similares
-
A Learning-Based Caching Mechanism for Edge Content Delivery
por: Torabi, Hoda, et al.
Publicado: (2024) -
Physics-informed Convolutional Neural Network for Microgrid Economic Dispatch
por: Ge, Xiaoyu, et al.
Publicado: (2024) -
CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering
por: Khazaei, Ensieh, et al.
Publicado: (2024) -
Data-Aware Training Quality Monitoring and Certification for Reliable Deep Learning
por: Yeganegi, Farhang, et al.
Publicado: (2024) -
Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits
por: Qi, Jun, et al.
Publicado: (2024)