no code implementations • 7 Apr 2024 • Yiqun Duan, Qiang Zhang, Renjing Xu
The utilization of Large Language Models (LLMs) within the realm of reinforcement learning, particularly as planners, has garnered a significant degree of attention in recent scholarly literature.
no code implementations • 18 Mar 2024 • Jiaxu Wang, Qiang Zhang, Jingkai Sun, Jiahang Cao, Yecheng Shao, Renjing Xu
The quality of the environment representation directly influences the achievement of the learning task.
no code implementations • 17 Mar 2024 • Yuetong Fang, Ziqing Wang, Lingfeng Zhang, Jiahang Cao, Honglei Chen, Renjing Xu
Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep learning by mimicking the event-driven processing of the brain.
1 code implementation • 14 Mar 2024 • Mingyuan Sun, Donghao Zhang, ZongYuan Ge, Jiaxu Wang, Jia Li, Zheng Fang, Renjing Xu
Based on this, we propose EventRPG, which leverages relevance propagation on the spiking neural network for more efficient augmentation.
2 code implementations • 4 Mar 2024 • Yiqian Yang, Yiqun Duan, Qiang Zhang, Renjing Xu, Hui Xiong
In this paper, we explore the brain-to-text translation of MEG signals in a speech-decoding formation.
no code implementations • 29 Feb 2024 • Hao Cheng, Erjia Xiao, Jindong Gu, Le Yang, Jinhao Duan, Jize Zhang, Jiahang Cao, Kaidi Xu, Renjing Xu
Large Vision-Language Models (LVLMs) rely on vision encoders and Large Language Models (LLMs) to exhibit remarkable capabilities on various multi-modal tasks in the joint space of vision and language.
no code implementations • 25 Jan 2024 • Jiaxu Wang, Ziyi Zhang, Renjing Xu
Experiments show that our model can deliver better geometries, view consistencies, and rendering quality than all counterparts and benchmarks on three datasets in both generalization and finetuning settings, preliminarily proving the potential of the new paradigm for generalizable NeRF.
1 code implementation • 24 Nov 2023 • Ziqing Wang, Yuetong Fang, Jiahang Cao, Renjing Xu
Spiking Neural Networks (SNNs) have emerged as a promising energy-efficient alternative to traditional Artificial Neural Networks (ANNs).
no code implementations • 23 Nov 2023 • Fei Kong, Jinhao Duan, Lichao Sun, Hao Cheng, Renjing Xu, HengTao Shen, Xiaofeng Zhu, Xiaoshuang Shi, Kaidi Xu
Though diffusion models excel in image generation, their step-by-step denoising leads to slow generation speeds.
no code implementations • 18 Nov 2023 • Hao Cheng, Jiahang Cao, Erjia Xiao, Mengshu Sun, Le Yang, Jize Zhang, Xue Lin, Bhavya Kailkhura, Kaidi Xu, Renjing Xu
It posits that within dense neural networks, there exist winning tickets or subnetworks that are sparser but do not compromise performance.
no code implementations • 8 Oct 2023 • Xiaoyang Jiang, Qiang Zhang, Jingkai Sun, Jiahang Cao, Jingtong Ma, Renjing Xu
In recent years, legged robots based on deep reinforcement learning have made remarkable progress.
no code implementations • 23 Sep 2023 • Hao Cheng, Jiahang Cao, Erjia Xiao, Mengshu Sun, Renjing Xu
Deploying energy-efficient deep learning algorithms on computational-limited devices, such as robots, is still a pressing issue for real-world applications.
no code implementations • 23 Sep 2023 • Hao Cheng, Jinhao Duan, Hui Li, Lyutianyang Zhang, Jiahang Cao, Ping Wang, Jize Zhang, Kaidi Xu, Renjing Xu
Recently, there has been a surge of interest and attention in Transformer-based structures, such as Vision Transformer (ViT) and Vision Multilayer Perceptron (VMLP).
no code implementations • 20 Sep 2023 • Yifu Zhang, Zuozhu Liu, Yang Feng, Renjing Xu
Accurate representation of tooth position is extremely important in treatment.
no code implementations • 17 Sep 2023 • Jiahang Cao, Xu Zheng, Yuanhuiyi Lyu, Jiaxu Wang, Renjing Xu, Lin Wang
The ability to detect objects in all lighting (i. e., normal-, over-, and under-exposed) conditions is crucial for real-world applications, such as self-driving. Traditional RGB-based detectors often fail under such varying lighting conditions. Therefore, recent works utilize novel event cameras to supplement or guide the RGB modality; however, these methods typically adopt asymmetric network structures that rely predominantly on the RGB modality, resulting in limited robustness for all-day detection.
no code implementations • 19 Aug 2023 • Hongwei Ren, Yue Zhou, Haotian Fu, Yulong Huang, Renjing Xu, Bojun Cheng
In the experiment, TTPOINT emerged as the SOTA method on three datasets while also attaining SOTA among point cloud methods on all five datasets.
1 code implementation • 3 Jul 2023 • Jinhao Duan, Hao Cheng, Shiqi Wang, Alex Zavalny, Chenan Wang, Renjing Xu, Bhavya Kailkhura, Kaidi Xu
While Large Language Models (LLMs) have demonstrated remarkable potential in natural language generation and instruction following, a persistent challenge lies in their susceptibility to "hallucinations", which erodes trust in their outputs.
1 code implementation • 29 Jun 2023 • Jiahang Cao, Ziqing Wang, Hanzhong Guo, Hao Cheng, Qiang Zhang, Renjing Xu
In our paper, we put forward Spiking Denoising Diffusion Probabilistic Models (SDDPM), a new class of SNN-based generative models that achieve high sample quality.
1 code implementation • ICCV 2023 • Ziqing Wang, Yuetong Fang, Jiahang Cao, Qiang Zhang, Zhongrui Wang, Renjing Xu
The combination of Spiking Neural Networks (SNNs) and Transformers has attracted significant attention due to their potential for high energy efficiency and high-performance nature.