20 citations · 74 across the 14 of their papers we have counts for
15 papers
Leading-Silence Augmentation and Multi-Stage Synthetic Supervision for the Second MLC-SLM Challenge
Kexin Shi, Renhe Sun, Yuge Huang +4
The second Multilingual Conversational Speech Language Model (MLC-SLM) Challenge evaluates two tasks over complete, unsegmented multilingual conversations: speaker diarization and…
SNN: Sub-bit Spiking Neural Networks
Wenjie Wei, Malu Zhang, Jieyuan Zhang +8
Spiking Neural Networks (SNNs) offer an energy-efficient paradigm for machine intelligence, but their continued scaling poses challenges for resource-limited deployment. Despite re…
What Is Next for LLMs? Next-Generation AI Computing Hardware Using Photonic Chips
Renjie Li, Wenjie Wei, Qi Xin +7
Large language models (LLMs) are rapidly pushing the limits of contemporary computing hardware. For example, training GPT-3 has been estimated to consume around 1300 MWh of electri…
Global-Local Convolution with Spiking Neural Networks for Energy-efficient Keyword Spotting
Shuai Wang, Dehao Zhang, Kexin Shi +4
Thanks to Deep Neural Networks (DNNs), the accuracy of Keyword Spotting (KWS) has made substantial progress. However, as KWS systems are usually implemented on edge devices, energy…
Q-SNNs: Quantized Spiking Neural Networks
Wenjie Wei, Yu Liang, Ammar Belatreche +6
Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to represent information and process them in an asynchronous event-driven manner, offering an energy-efficient…
Event-Driven Learning for Spiking Neural Networks
Wenjie Wei, Malu Zhang, Jilin Zhang +7
Brain-inspired spiking neural networks (SNNs) have gained prominence in the field of neuromorphic computing owing to their low energy consumption during feedforward inference on ne…
CoAVT: A Cognition-Inspired Unified Audio-Visual-Text Pre-Training Model for Multimodal Processing
Xianghu Yue, Xiaohai Tian, Lu Lu +3
There has been a long-standing quest for a unified audio-visual-text model to enable various multimodal understanding tasks, which mimics the listening, seeing and reading process…
A Comprehensive Analysis of the Effectiveness of Large Language Models as Automatic Dialogue Evaluators
Chen Zhang, Luis Fernando D'Haro, Yiming Chen +2
Automatic evaluation is an integral aspect of dialogue system research. The traditional reference-based NLG metrics are generally found to be unsuitable for dialogue assessment. Co…
LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization
Qianhui Liu, Jiaqi Yan, Malu Zhang +2
Spiking Neural Networks (SNNs) mimic the information-processing mechanisms of the human brain and are highly energy-efficient, making them well-suited for low-power edge devices. H…
LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding
Qu Yang, Malu Zhang, Jibin Wu +2
The biological neurons use precise spike times, in addition to the spike firing rate, to communicate with each other. The time-to-first-spike (TTFS) coding is inspired by such biol…
Delayed Memory Unit: Modelling Temporal Dependency Through Delay Gate
Pengfei Sun, Jibin Wu, Malu Zhang +2
Recurrent Neural Networks (RNNs) are widely recognized for their proficiency in modeling temporal dependencies, making them highly prevalent in sequential data processing applicati…
Tensor Decomposition Based Attention Module for Spiking Neural Networks
Haoyu Deng, Ruijie Zhu, Xuerui Qiu +3
The attention mechanism has been proven to be an effective way to improve spiking neural network (SNN). However, based on the fact that the current SNN input data flow is split int…
Seeing What You Said: Talking Face Generation Guided by a Lip Reading Expert
Jiadong Wang, Xinyuan Qian, Malu Zhang +2
Talking face generation, also known as speech-to-lip generation, reconstructs facial motions concerning lips given coherent speech input. The previous studies revealed the importan…
Training Spiking Neural Networks with Local Tandem Learning
Qu Yang, Jibin Wu, Malu Zhang +3
Spiking neural networks (SNNs) are shown to be more biologically plausible and energy efficient over their predecessors. However, there is a lack of an efficient and generalized tr…
Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks
Jibin Wu, Chenglin Xu, Daquan Zhou +2
Spiking neural networks (SNNs) have shown clear advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency, due to their event-…