Copertina

Head-Tail-Aware KL Divergence in Knowledge Distillation for Spiking Neural Networks

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhang, Tianqing, Zhu, Zixin, Yu, Kairong, Wang, Hongwei
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

DA-LIF: Dual Adaptive Leaky Integrate-and-Fire Model for Deep Spiking Neural Networks
di: Zhang, Tianqing, et al.
Pubblicazione: (2025)

TS-SNN: Temporal Shift Module for Spiking Neural Networks
di: Yu, Kairong, et al.
Pubblicazione: (2025)

Temporal Separation with Entropy Regularization for Knowledge Distillation in Spiking Neural Networks
di: Yu, Kairong, et al.
Pubblicazione: (2025)

FSTA-SNN:Frequency-based Spatial-Temporal Attention Module for Spiking Neural Networks
di: Yu, Kairong, et al.
Pubblicazione: (2024)

STARS: Spike Tail-Aware Relational Synthesis for ANN-to-SNN Data-Free Knowledge Distillation
di: Ye, Shuhan, et al.
Pubblicazione: (2026)

Cross Knowledge Distillation between Artificial and Spiking Neural Networks
di: Ye, Shuhan, et al.
Pubblicazione: (2025)

STAA-SNN: Spatial-Temporal Attention Aggregator for Spiking Neural Networks
di: Zhang, Tianqing, et al.
Pubblicazione: (2025)

Not All Timesteps Matter Equally: Selective Alignment Knowledge Distillation for Spiking Neural Networks
di: Sun, Kai, et al.
Pubblicazione: (2026)

Efficient Speech Command Recognition Leveraging Spiking Neural Network and Curriculum Learning-based Knowledge Distillation
di: Wang, Jiaqi, et al.
Pubblicazione: (2024)

Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation
di: Zhou, Yuhang, et al.
Pubblicazione: (2024)

From Spikes to Heavy Tails: Unveiling the Spectral Evolution of Neural Networks
di: Kothapalli, Vignesh, et al.
Pubblicazione: (2024)

Geometry-Aware Spiking Graph Neural Network
di: Zhang, Bowen, et al.
Pubblicazione: (2025)

Optimal Stability of KL Divergence under Gaussian Perturbations
di: Pan, Jialu, et al.
Pubblicazione: (2026)

Online Adversarial Knowledge Distillation for Graph Neural Networks
di: Wang, Can, et al.
Pubblicazione: (2021)

Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence
di: Zhu, Lingwei, et al.
Pubblicazione: (2023)

Graph Unlearning: Efficient Node Removal in Graph Neural Networks
di: Guan, Faqian, et al.
Pubblicazione: (2025)

Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models
di: Wu, Taiqiang, et al.
Pubblicazione: (2024)

Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher
di: Ji, Guangda, et al.
Pubblicazione: (2020)

Efficient ANN-Guided Distillation: Aligning Rate-based Features of Spiking Neural Networks through Hybrid Block-wise Replacement
di: Yang, Shu, et al.
Pubblicazione: (2025)

Beyond KL Divergence: Policy Optimization with Flexible Bregman Divergences for LLM Reasoning
di: Yuan, Rui, et al.
Pubblicazione: (2026)

A Functional Perspective on Knowledge Distillation in Neural Networks
di: Mason-Williams, Israel, et al.
Pubblicazione: (2025)

Hyperdimensional Decoding of Spiking Neural Networks
di: Kinavuidi, Cedrick, et al.
Pubblicazione: (2025)

On Flow Matching KL Divergence
di: Su, Maojiang, et al.
Pubblicazione: (2025)

Enhanced Self-Distillation Framework for Efficient Spiking Neural Network Training
di: Zhao, Xiaochen, et al.
Pubblicazione: (2025)

Breaking Global Self-Attention Bottlenecks in Transformer-based Spiking Neural Networks with Local Structure-Aware Self-Attention
di: Li, Lingdong, et al.
Pubblicazione: (2026)

Learning with Spike Synchrony in Spiking Neural Networks
di: Tian, Yuchen, et al.
Pubblicazione: (2025)

Fully Spiking Neural Network for Legged Robots
di: Jiang, Xiaoyang, et al.
Pubblicazione: (2023)

BadSNN: Backdoor Attacks on Spiking Neural Networks via Adversarial Spiking Neuron
di: Miah, Abdullah Arafat, et al.
Pubblicazione: (2026)

Sparsity-Aware Hardware-Software Co-Design of Spiking Neural Networks: An Overview
di: Aliyev, Ilkin, et al.
Pubblicazione: (2024)

Preference-Based Self-Distillation: Beyond KL Matching via Reward Regularization
di: Yu, Xin, et al.
Pubblicazione: (2026)

Binary Spiking Neural Networks as Causal Models
di: Kar, Aditya, et al.
Pubblicazione: (2026)

BiSpikCLM: A Spiking Language Model integrating Softmax-Free Spiking Attention and Spike-Aware Alignment Distillation
di: Guo, Sihang, et al.
Pubblicazione: (2026)

Large Language Models Merging for Enhancing the Link Stealing Attack on Graph Neural Networks
di: Guan, Faqian, et al.
Pubblicazione: (2024)

Spikingformer: A Key Foundation Model for Spiking Neural Networks
di: Zhou, Chenlin, et al.
Pubblicazione: (2023)

Neural Tangent Knowledge Distillation for Optical Convolutional Networks
di: Xiang, Jinlin, et al.
Pubblicazione: (2025)

Towards Reliable Evaluation of Adversarial Robustness for Spiking Neural Networks
di: Wang, Jihang, et al.
Pubblicazione: (2025)

Diversity-Aware Reverse Kullback-Leibler Divergence for Large Language Model Distillation
di: Luong, Hoang-Chau, et al.
Pubblicazione: (2026)

KL for a KL: On-Policy Distillation with Control Variate Baseline
di: Oh, Minjae, et al.
Pubblicazione: (2026)

Energy-Aware Spike Budgeting for Continual Learning in Spiking Neural Networks for Neuromorphic Vision
di: Meem, Anika Tabassum, et al.
Pubblicazione: (2026)

A Novel Spike Transformer Network for Depth Estimation from Event Cameras via Cross-modality Knowledge Distillation
di: Zhang, Xin, et al.
Pubblicazione: (2024)