87 citations · 202 across the 51 of their papers we have counts for
12 papers · 1 filter
Harnessing Task Overload for Scalable Jailbreak Attacks on Large Language Models
Yiting Dong, Guobin Shen, Dongcheng Zhao +2
Large Language Models (LLMs) remain vulnerable to jailbreak attacks that bypass their safety mechanisms. Existing attack methods are fixed or specifically tailored for certain mode…
Jailbreak Antidote: Runtime Safety-Utility Balance via Sparse Representation Adjustment in Large Language Models
Guobin Shen, Dongcheng Zhao, Yiting Dong +2
As large language models (LLMs) become integral to various applications, ensuring both their safety and utility is paramount. Jailbreak attacks, which manipulate LLMs into generati…
Revealing Untapped DSP Optimization Potentials for FPGA-Based Systolic Matrix Engines
Jindong Li, Tenglong Li, Guobin Shen +3
Systolic architectures are widely embraced by neural network accelerators for their superior performance in highly parallelized computation. The DSP48E2s serve as dedicated arithme…
StressPrompt: Does Stress Impact Large Language Models and Human Performance Similarly?
Guobin Shen, Dongcheng Zhao, Aorigele Bao +3
Human beings often experience stress, which can significantly influence their performance. This study explores whether Large Language Models (LLMs) exhibit stress responses similar…
Brain-Inspired Stepwise Patch Merging for Vision Transformers
Yonghao Yu, Dongcheng Zhao, Guobin Shen +2
The hierarchical architecture has become a mainstream design paradigm for Vision Transformers (ViTs), with Patch Merging serving as the pivotal component that transforms a columnar…
CACE-Net: Co-guidance Attention and Contrastive Enhancement for Effective Audio-Visual Event Localization
Xiang He, Xiangxi Liu, Yang Li +5
The audio-visual event localization task requires identifying concurrent visual and auditory events from unconstrained videos within a network model, locating them, and classifying…
FireFly-S: Exploiting Dual-Side Sparsity for Spiking Neural Networks Acceleration with Reconfigurable Spatial Architecture
Tenglong Li, Jindong Li, Guobin Shen +3
Spiking Neural Networks (SNNs), with brain-inspired structure using discrete spikes instead of continuous activations, are gaining attention for their efficient processing on neuro…
Time Cell Inspired Temporal Codebook in Spiking Neural Networks for Enhanced Image Generation
Linghao Feng, Dongcheng Zhao, Sicheng Shen +3
This paper presents a novel approach leveraging Spiking Neural Networks (SNNs) to construct a Variational Quantized Autoencoder (VQ-VAE) with a temporal codebook inspired by hippoc…
EventZoom: A Progressive Approach to Event-Based Data Augmentation for Enhanced Neuromorphic Vision
Yiting Dong, Xiang He, Guobin Shen +3
Dynamic Vision Sensors (DVS) capture event data with high temporal resolution and low power consumption, presenting a more efficient solution for visual processing in dynamic and r…
Neuro-Vision to Language: Enhancing Brain Recording-based Visual Reconstruction and Language Interaction
Guobin Shen, Dongcheng Zhao, Xiang He +5
Decoding non-invasive brain recordings is pivotal for advancing our understanding of human cognition but faces challenges due to individual differences and complex neural signal re…
Brain-inspired and Self-based Artificial Intelligence
Yi Zeng, Feifei Zhao, Yuxuan Zhao +17
The question "Can machines think?" and the Turing Test to assess whether machines could achieve human-level intelligence is one of the roots of AI. With the philosophical argument…
TIM: An Efficient Temporal Interaction Module for Spiking Transformer
Sicheng Shen, Dongcheng Zhao, Guobin Shen +1
Spiking Neural Networks (SNNs), as the third generation of neural networks, have gained prominence for their biological plausibility and computational efficiency, especially in pro…