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Wenrui Zhang

5 accepted papers

2026

MolSight: Optical Chemical Structure Recognition with SMILES Pretraining, Multi-Granularity Learning and Reinforcement Learning

AAAI 2026technical

Optical Chemical Structure Recognition (OCSR) plays a pivotal role in modern chemical informatics, enabling the automated conversion of chemical structure images from scientific literature, patents, and educational materials into machine-readable molecular representations. This capability is essenti

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2021

Backpropagated Neighborhood Aggregation for Accurate Training of Spiking Neural Networks

ICML 2021spotlight

While Backpropagation (BP) has been applied to spiking neural networks (SNNs) achieving encouraging results, a key challenge involved is to backpropagate a differentiable continuous-valued loss over layers of spiking neurons exhibiting discontinuous all-or-none firing activities. Existing methods de…

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2020

Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks

NeurIPS 2020spotlight

Spiking neural networks (SNNs) are well suited for spatio-temporal learning and implementations on energy-efficient event-driven neuromorphic processors. However, existing SNN error backpropagation (BP) methods lack proper handling of spiking discontinuities and suffer from low performance compared…

2019

Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks

NeurIPS 2019poster

Spiking neural networks (SNNs) well support spatiotemporal learning and energy-efficient event-driven hardware neuromorphic processors. As an important class of SNNs, recurrent spiking neural networks (RSNNs) possess great computational power. However, the practical application of RSNNs is severely…

2018

Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks

NeurIPS 2018poster

Spiking neural networks (SNNs) are positioned to enable spatio-temporal information processing and ultra-low power event-driven neuromorphic hardware. However, SNNs are yet to reach the same performances of conventional deep artificial neural networks (ANNs), a long-standing challenge due to complex…