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Peibo Duan

7 accepted papers

2026

Many Eyes, One Mind: Temporal Multi-Perspective and Progressive Distillation for Spiking Neural Networks

ICLR 2026poster

Spiking Neural Networks (SNNs), inspired by biological neurons, are attractive for their event-driven energy efficiency but still fall short of Artificial Neural Networks (ANNs) in accuracy. Knowledge distillation (KD) has emerged as a promising approach to narrow this gap by transferring ANN knowle…

Cited by 0SourcecodeScholar
2026

Not All Timesteps Matter Equally: Selective Alignment Knowledge Distillation for Spiking Neural Networks

IJCAI 2026

Spiking neural networks (SNNs), which are brain-inspired and spike-driven, achieve high energy efficiency. However, a performance gap between SNNs and artificial neural networks (ANNs) still remains. Knowledge distillation (KD) is commonly adopted to improve SNN performance, but existing methods typ

Cited by 0Scholar
2025

A Distillation-based Future-aware Graph Neural Network for Stock Trend Prediction

ICASSP 2025accepted

Stock trend prediction involves forecasting the future price movements by analyzing historical data and various market indicators. With the advancement of machine learning, graph neural networks (GNNs) have been extensively employed in stock prediction due to their powerful capability to capture spa…

Cited by 0SourceScholar
2025

HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding

COLING 2025main

Object categories are typically organized into a multi-granularity taxonomic hierarchy. When classifying categories at different hierarchy levels, traditional uni-modal approaches focus primarily on image features, revealing limitations in complex scenarios. Recent studies integrating Vision-Languag…

2025

ILIF: Temporal Inhibitory Leaky Integrate-and-Fire Neuron for Overactivation in Spiking Neural Networks

IJCAI 2025

The Spiking Neural Network (SNN) has drawn increasing attention for its energy-efficient, event-driven processing and biological plausibility. To train SNNs via backpropagation, surrogate gradients are used to approximate the non-differentiable spike function, but they only maintain nonzero derivati

2024

FasMe: Fast and Sample-efficient Meta Estimator for Precision Matrix Learning in Small Sample Settings

NeurIPS 2024poster

Precision matrix estimation is a ubiquitous task featuring numerous applications such as rare disease diagnosis and neural connectivity exploration. However, this task becomes challenging in small sample settings, where the number of samples is significantly less than the number of dimensions, leadi…

Cited by 0SourcePDFScholar
2023

NurViD: A Large Expert-Level Video Database for Nursing Procedure Activity Understanding

NeurIPS 2023poster

The application of deep learning to nursing procedure activity understanding has the potential to greatly enhance the quality and safety of nurse-patient interactions. By utilizing the technique, we can facilitate training and education, improve quality control, and enable operational compliance mon…