Cover Image

HPM-KD: Hierarchical Progressive Multi-Teacher Framework for Knowledge Distillation and Efficient Model Compression

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Haase, Gustavo Coelho, da Silva, Paulo Henrique Dourado
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Applications
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

DeepBridge: A Unified and Production-Ready Framework for Multi-Dimensional Machine Learning Validation
by: Haase, Gustavo Coelho, et al.
Published: (2025)

Knowledge Distillation with Adapted Weight
by: Wu, Sirong, et al.
Published: (2025)

Efficient Model Compression for Bayesian Neural Networks
by: Saha, Diptarka, et al.
Published: (2024)

Joint Progression Modeling (JPM): A Probabilistic Framework for Mixed-Pathology Progression
by: Hao, Hongtao, et al.
Published: (2025)

CoDistill-GRPO: A Co-Distillation Recipe for Efficient Group Relative Policy Optimization
by: Kwon, Soo Min, et al.
Published: (2026)

Deep Autocorrelation Modeling for Time-Series Forecasting: Progress and Prospects
by: Wang, Hao, et al.
Published: (2026)

A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation
by: Song, Xinran, et al.
Published: (2025)

Hierarchical Semi-Markov Models with Duration-Aware Dynamics for Activity Sequences
by: Dube, Rohit, et al.
Published: (2025)

Modeling Parkinson's Disease Progression Using Longitudinal Voice Biomarkers: A Comparative Study of Statistical and Neural Mixed-Effects Models
by: Tong, Ran, et al.
Published: (2025)

Longitudinal Risk Prediction in Mammography with Privileged History Distillation
by: Karimian, Banafsheh, et al.
Published: (2026)

Learning Bayesian Networks with Heterogeneous Agronomic Data Sets via Mixed-Effect Models and Hierarchical Clustering
by: Valleggi, Lorenzo, et al.
Published: (2023)

What is in a Price? Estimating Willingness-to-Pay with Bayesian Hierarchical Models
by: Pillai, Srijesh, et al.
Published: (2025)

Hierarchical Probabilistic Conformal Prediction for Distributed Energy Resources Adoption
by: Zhou, Wenbin, et al.
Published: (2024)

Hierarchical Count Echo State Network Models with Application to Graduate Student Enrollments
by: Wang, Qi, et al.
Published: (2025)

Profit over Proxies: A Scalable Bayesian Decision Framework for Optimizing Multi-Variant Online Experiments
by: Pillai, Srijesh, et al.
Published: (2025)

CLIP-Embed-KD: Computationally Efficient Knowledge Distillation Using Embeddings as Teachers
by: Nair, Lakshmi
Published: (2024)

Multi-task Modeling for Engineering Applications with Sparse Data
by: Comlek, Yigitcan, et al.
Published: (2026)

A Latent Variable Framework for Scaling Laws in Large Language Models
by: Cai, Peiyao, et al.
Published: (2025)

SFedKD: Sequential Federated Learning with Discrepancy-Aware Multi-Teacher Knowledge Distillation
by: Xu, Haotian, et al.
Published: (2025)

Factor Analysis with Correlated Topic Model for Multi-Modal Data
by: Łazęcka, Małgorzata, et al.
Published: (2025)

Digital Twin-Centered Hybrid Data-Driven Multi-Stage Deep Learning Framework for Enhanced Nuclear Reactor Power Prediction
by: Daniell, James, et al.
Published: (2022)

Multi-output Extreme Spatial Model for Complex Aircraft Production Systems
by: Lee, Cheolhei, et al.
Published: (2026)

Model-Agnostic Interpretation Framework in Machine Learning: A Comparative Study in NBA Sports
by: Liu, Shun
Published: (2024)

Bayes-xG: Player and Position Correction on Expected Goals (xG) using Bayesian Hierarchical Approach
by: Scholtes, Alexander, et al.
Published: (2023)

Federated Item Response Models: A Gradient-driven Privacy-preserving Framework for Distributed Psychometric Estimation
by: Zhou, Biying, et al.
Published: (2025)

Hierarchical Probabilistic Principal Component Analysis of Longitudinal Data
by: Zhang, Xinyu, et al.
Published: (2026)

Dynamic Classification of Latent Disease Progression with Auxiliary Surrogate Labels
by: Cai, Zexi, et al.
Published: (2024)

A review of feature selection strategies utilizing graph data structures and knowledge graphs
by: Shao, Sisi, et al.
Published: (2024)

Changepoint Detection As Model Selection: A General Framework
by: Grantham, Michael, et al.
Published: (2026)

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

Efficient Generative Modeling via Penalized Optimal Transport Network
by: Lu, Wenhui Sophia, et al.
Published: (2024)

Enhancing Phenotype Discovery in Electronic Health Records through Prior Knowledge-Guided Unsupervised Learning
by: Mayer, Melanie, et al.
Published: (2025)

Forecasting Medium-Horizon Alzheimer's Disease Progression: Residual Gap-Aware Transformers for 24-Month CDR-SB Change from ADNI Clinical and Biomarker Histories
by: Tong, Ran, et al.
Published: (2026)

Bayesian algorithmic perfumery: A Hierarchical Relevance Vector Machine for the Estimation of Personalized Fragrance Preferences based on Three Sensory Layers and Jungian Personality Archetypes
by: Martinez, Rolando Gonzales
Published: (2024)

Efficient Causal Discovery for Autoregressive Time Series
by: Fesanghary, Mohammad, et al.
Published: (2025)

Flexible and Efficient Drift Detection without Labels
by: Tan, Nelvin, et al.
Published: (2025)

VOPy: A Framework for Black-box Vector Optimization
by: Yıldırım, Yaşar Cahit, et al.
Published: (2024)

Efficient Causal Structure Learning via Modular Subgraph Integration
by: Sun, Haixiang, et al.
Published: (2026)

A Two-armed Bandit Framework for A/B Testing
by: Wang, Jinjuan, et al.
Published: (2025)

A Framework for Scalable Ambient Air Pollution Concentration Estimation
by: Berrisford, Liam J, et al.
Published: (2024)