Gradient Estimation Methods of Approximate Multipliers for High-Accuracy Retraining of Deep Learning Models
Fuente:
arXiv
Guardado en:
| Autores principales: | Meng, Chang, Burleson, Wayne, De Micheli, Giovanni |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
TRAM: Training Approximate Multiplier Structures for Low-Power AI Accelerators
por: Meng, Chang, et al.
Publicado: (2026)
por: Meng, Chang, et al.
Publicado: (2026)
Pruning Foundation Models for High Accuracy without Retraining
por: Zhao, Pu, et al.
Publicado: (2024)
por: Zhao, Pu, et al.
Publicado: (2024)
Retraining with Predicted Hard Labels Provably Increases Model Accuracy
por: Das, Rudrajit, et al.
Publicado: (2024)
por: Das, Rudrajit, et al.
Publicado: (2024)
Leveraging Highly Approximated Multipliers in DNN Inference
por: Zervakis, Georgios, et al.
Publicado: (2024)
por: Zervakis, Georgios, et al.
Publicado: (2024)
Approximate Multiplier Induced Error Propagation in Deep Neural Networks
por: Alahakoon, A. M. H. H., et al.
Publicado: (2025)
por: Alahakoon, A. M. H. H., et al.
Publicado: (2025)
Deep Reinforcement Learning for Online Optimal Execution Strategies
por: Micheli, Alessandro, et al.
Publicado: (2024)
por: Micheli, Alessandro, et al.
Publicado: (2024)
Sustainable Machine Learning Retraining: Optimizing Energy Efficiency Without Compromising Accuracy
por: Poenaru-Olaru, Lorena, et al.
Publicado: (2025)
por: Poenaru-Olaru, Lorena, et al.
Publicado: (2025)
A New Stochastic Approximation Method for Gradient-based Simulated Parameter Estimation
por: Li, Zehao, et al.
Publicado: (2025)
por: Li, Zehao, et al.
Publicado: (2025)
Watermarking and Anomaly Detection in Machine Learning Models for LORA RF Fingerprinting
por: Mahajan, Aarushi, et al.
Publicado: (2025)
por: Mahajan, Aarushi, et al.
Publicado: (2025)
Gaussian Approximation and Multiplier Bootstrap for Federated Linear Stochastic Approximation
por: Levin, Ilya, et al.
Publicado: (2026)
por: Levin, Ilya, et al.
Publicado: (2026)
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent
por: Sheshukova, Marina, et al.
Publicado: (2025)
por: Sheshukova, Marina, et al.
Publicado: (2025)
Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift
por: Xie, Renchunzi, et al.
Publicado: (2024)
por: Xie, Renchunzi, et al.
Publicado: (2024)
Estimating the Effects of Sample Training Orders for Large Language Models without Retraining
por: Yang, Hao, et al.
Publicado: (2025)
por: Yang, Hao, et al.
Publicado: (2025)
Reconstruct the Pruned Model without Any Retraining
por: Wang, Pingjie, et al.
Publicado: (2024)
por: Wang, Pingjie, et al.
Publicado: (2024)
ApproxDARTS: Differentiable Neural Architecture Search with Approximate Multipliers
por: Pinos, Michal, et al.
Publicado: (2024)
por: Pinos, Michal, et al.
Publicado: (2024)
Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing
por: Javanmard, Adel, et al.
Publicado: (2025)
por: Javanmard, Adel, et al.
Publicado: (2025)
Multilinear Tensor Low-Rank Approximation for Policy-Gradient Methods in Reinforcement Learning
por: Rozada, Sergio, et al.
Publicado: (2025)
por: Rozada, Sergio, et al.
Publicado: (2025)
PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining
por: Kazmi, Mishaal, et al.
Publicado: (2024)
por: Kazmi, Mishaal, et al.
Publicado: (2024)
Entropy-regularized Gradient Estimators for Approximate Bayesian Inference
por: Kaur, Jasmeet
Publicado: (2025)
por: Kaur, Jasmeet
Publicado: (2025)
DOMAC: Differentiable Optimization for High-Speed Multipliers and Multiply-Accumulators
por: Xue, Chenhao, et al.
Publicado: (2025)
por: Xue, Chenhao, et al.
Publicado: (2025)
On the Stability of Iterative Retraining of Generative Models on their own Data
por: Bertrand, Quentin, et al.
Publicado: (2023)
por: Bertrand, Quentin, et al.
Publicado: (2023)
Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods
por: Kim, Bo-Kyeong, et al.
Publicado: (2024)
por: Kim, Bo-Kyeong, et al.
Publicado: (2024)
Time to Retrain? Detecting Concept Drifts in Machine Learning Systems
por: Pham, Tri Minh Triet, et al.
Publicado: (2024)
por: Pham, Tri Minh Triet, et al.
Publicado: (2024)
Inverting the Leverage Score Gradient: An Efficient Approximate Newton Method
por: Li, Chenyang, et al.
Publicado: (2024)
por: Li, Chenyang, et al.
Publicado: (2024)
Principled Approximation Methods for Efficient and Scalable Deep Learning
por: Savarese, Pedro
Publicado: (2025)
por: Savarese, Pedro
Publicado: (2025)
Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Retraining
por: Hose, Henrik, et al.
Publicado: (2024)
por: Hose, Henrik, et al.
Publicado: (2024)
On the Impossibility of Retrain Equivalence in Machine Unlearning
por: Yu, Jiatong, et al.
Publicado: (2025)
por: Yu, Jiatong, et al.
Publicado: (2025)
On the Unreasonable Effectiveness of Last-layer Retraining
por: Hill, John C., et al.
Publicado: (2025)
por: Hill, John C., et al.
Publicado: (2025)
The Limitations of Model Retraining in the Face of Performativity
por: Kabra, Anmol, et al.
Publicado: (2024)
por: Kabra, Anmol, et al.
Publicado: (2024)
Retraining-Free Merging of Sparse MoE via Hierarchical Clustering
por: Chen, I-Chun, et al.
Publicado: (2024)
por: Chen, I-Chun, et al.
Publicado: (2024)
Matrix Low-Rank Approximation For Policy Gradient Methods
por: Rozada, Sergio, et al.
Publicado: (2024)
por: Rozada, Sergio, et al.
Publicado: (2024)
Exploring DNN Robustness Against Adversarial Attacks Using Approximate Multipliers
por: Askarizadeh, Mohammad Javad, et al.
Publicado: (2024)
por: Askarizadeh, Mohammad Javad, et al.
Publicado: (2024)
Online Resource Allocation for Edge Intelligence with Colocated Model Retraining and Inference
por: Cai, Huaiguang, et al.
Publicado: (2024)
por: Cai, Huaiguang, et al.
Publicado: (2024)
NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification
por: Monod, Mélodie, et al.
Publicado: (2025)
por: Monod, Mélodie, et al.
Publicado: (2025)
Water Quality Estimation Through Machine Learning Multivariate Analysis
por: Cardia, Marco, et al.
Publicado: (2025)
por: Cardia, Marco, et al.
Publicado: (2025)
Bit-Accurate Modeling of GPU Matrix Multiply-Accumulate Units: Demystifying Numerical Discrepancy and Accuracy
por: Xie, Peichen, et al.
Publicado: (2025)
por: Xie, Peichen, et al.
Publicado: (2025)
Validation, Robustness, and Accuracy of Perturbation-Based Sensitivity Analysis Methods for Time-Series Deep Learning Models
por: Wang, Zhengguang
Publicado: (2024)
por: Wang, Zhengguang
Publicado: (2024)
Rethinking Explanation Evaluation under the Retraining Scheme
por: Cai, Yi, et al.
Publicado: (2025)
por: Cai, Yi, et al.
Publicado: (2025)
Rank-1 Approximation of Inverse Fisher for Natural Policy Gradients in Deep Reinforcement Learning
por: Huo, Yingxiao, et al.
Publicado: (2026)
por: Huo, Yingxiao, et al.
Publicado: (2026)
Gradient-based Explanations for Deep Learning Survival Models
por: Langbein, Sophie Hanna, et al.
Publicado: (2025)
por: Langbein, Sophie Hanna, et al.
Publicado: (2025)
Ejemplares similares
-
TRAM: Training Approximate Multiplier Structures for Low-Power AI Accelerators
por: Meng, Chang, et al.
Publicado: (2026) -
Pruning Foundation Models for High Accuracy without Retraining
por: Zhao, Pu, et al.
Publicado: (2024) -
Retraining with Predicted Hard Labels Provably Increases Model Accuracy
por: Das, Rudrajit, et al.
Publicado: (2024) -
Leveraging Highly Approximated Multipliers in DNN Inference
por: Zervakis, Georgios, et al.
Publicado: (2024) -
Approximate Multiplier Induced Error Propagation in Deep Neural Networks
por: Alahakoon, A. M. H. H., et al.
Publicado: (2025)