:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Pourali, Alireza, Boukani, Arian, Khazaei, Hamzeh
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2412.15519
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Learning-Based Caching Mechanism for Edge Content Delivery
by: Torabi, Hoda, et al.
Published: (2024)

Physics-informed Convolutional Neural Network for Microgrid Economic Dispatch
by: Ge, Xiaoyu, et al.
Published: (2024)

CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering
by: Khazaei, Ensieh, et al.
Published: (2024)

Data-Aware Training Quality Monitoring and Certification for Reliable Deep Learning
by: Yeganegi, Farhang, et al.
Published: (2024)

Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits
by: Qi, Jun, et al.
Published: (2024)

Trust, but Verify: Peeling Low-Bit Transformer Networks for Training Monitoring
by: Eamaz, Arian, et al.
Published: (2026)

Designing Latent Safety Filters using Pre-Trained Vision Models
by: Tabbara, Ihab, et al.
Published: (2025)

On the Neural Feature Ansatz for Deep Neural Networks
by: Tansley, Edward, et al.
Published: (2025)

Physics-Inspired Binary Neural Networks: Interpretable Compression with Theoretical Guarantees
by: Eamaz, Arian, et al.
Published: (2025)

Multiscale Dubuc: A New Similarity Measure for Time Series
by: Khazaei, Mahsa, et al.
Published: (2024)

Contextual Bandit Optimization with Pre-Trained Neural Networks
by: Terekhov, Mikhail
Published: (2025)

Explaining Deep Neural Networks by Leveraging Intrinsic Methods
by: La Rosa, Biagio
Published: (2024)

BSFA: Leveraging the Subspace Dichotomy to Accelerate Neural Network Training
by: Zhou, Wenjie, et al.
Published: (2025)

Error Diffusion: Post Training Quantization with Block-Scaled Number Formats for Neural Networks
by: Khodamoradi, Alireza, et al.
Published: (2024)

Beyond Point Matching: Evaluating Multiscale Dubuc Distance for Time Series Similarity
by: Ahmadzadeh, Azim, et al.
Published: (2025)

Improving Generalization of Deep Neural Networks by Leveraging Margin Distribution
by: Lyu, Shen-Huan, et al.
Published: (2018)

Leveraging Intermediate Neural Collapse with Simplex ETFs for Efficient Deep Neural Networks
by: Liu, Emily
Published: (2024)

Understanding and Minimising Outlier Features in Neural Network Training
by: He, Bobby, et al.
Published: (2024)

Neural Network Training with Approximate Logarithmic Computations
by: Sanyal, Arnab, et al.
Published: (2019)

Deep Fusion: Efficient Network Training via Pre-trained Initializations
by: Mazzawi, Hanna, et al.
Published: (2023)

On the Hardness of Training Deep Neural Networks Discretely
by: Doron-Arad, Ilan
Published: (2024)

PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks
by: Ge, Qingqing, et al.
Published: (2023)

Utilizing Graph Neural Networks for Effective Link Prediction in Microservice Architectures
by: Khodabandeh, Ghazal, et al.
Published: (2025)

Informed Forecasting: Leveraging Auxiliary Knowledge to Boost LLM Performance on Time Series Forecasting
by: Ghasemloo, Mohammadmahdi, et al.
Published: (2025)

Leveraging Deep Neural Networks for Aspect-Based Sentiment Classification
by: Li, Chen, et al.
Published: (2025)

Concurrent Training and Layer Pruning of Deep Neural Networks
by: Guenter, Valentin Frank Ingmar, et al.
Published: (2024)

Training Deep Morphological Neural Networks as Universal Approximators
by: Fotopoulos, Konstantinos, et al.
Published: (2025)

Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings
by: von Kleist, Henrik, et al.
Published: (2023)

TreeLUT: An Efficient Alternative to Deep Neural Networks for Inference Acceleration Using Gradient Boosted Decision Trees
by: Khataei, Alireza, et al.
Published: (2025)

Leveraging Exogenous Signals for Hydrology Time Series Forecasting
by: He, Junyang, et al.
Published: (2025)

Deep Neural Networks Tend To Extrapolate Predictably
by: Kang, Katie, et al.
Published: (2023)

Towards the Training of Deeper Predictive Coding Neural Networks
by: Qi, Chang, et al.
Published: (2025)

Enhancing Deep Neural Network Training Efficiency and Performance through Linear Prediction
by: Ying, Hejie, et al.
Published: (2023)

Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition
by: Carrasquilla, Juan, et al.
Published: (2023)

Iterative Training of Physics-Informed Neural Networks with Fourier-enhanced Features
by: Wu, Yulun, et al.
Published: (2025)

DeepRTE: Pre-trained Attention-based Neural Network for Radiative Transfer
by: Zhu, Yekun, et al.
Published: (2025)

On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks
by: Zhu, Jiong, et al.
Published: (2024)

Rethinking Post-Training Quantization: Introducing a Statistical Pre-Calibration Approach
by: Ghaffari, Alireza, et al.
Published: (2025)

Bulk-Switching Memristor-based Compute-In-Memory Module for Deep Neural Network Training
by: Wu, Yuting, et al.
Published: (2023)

AGMARL-DKS: An Adaptive Graph-Enhanced Multi-Agent Reinforcement Learning for Dynamic Kubernetes Scheduling
by: Hamzeh, Hamed
Published: (2026)