Search Results for author:
Found 15 papers, 8 papers with code
Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning
One major challenge for autonomous attitude takeover control for on-orbit servicing of spacecraft is that an accurate dynamic motion model of the combined vehicles is highly nonlinear, complex and often costly to identify online, which makes traditional model-based control impractical for this task.
Watermarking LLMs with Weight Quantization
Abuse of large language models reveals high risks as large language models are being deployed at an astonishing speed.
SeqXGPT: Sentence-Level AI-Generated Text Detection
Therefore, it is important to build strong AI-generated text (AIGT) detectors.
PerturbScore: Connecting Discrete and Continuous Perturbations in NLP
Through experimental results, we find that we can build a connection between discrete and continuous perturbations and use the proposed PerturbScore to learn such correlation, surpassing previous methods used in discrete perturbation measuring.
The Uncertainty-based Retrieval Framework for Ancient Chinese CWS and POS
Automatic analysis for modern Chinese has greatly improved the accuracy of text mining in related fields, but the study of ancient Chinese is still relatively rare.
RVAE-EM: Generative speech dereverberation based on recurrent variational auto-encoder and convolutive transfer function
In this work, we propose a generative dereverberation method.
SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities
Multi-modal large language models are regarded as a crucial step towards Artificial General Intelligence (AGI) and have garnered significant interest with the emergence of ChatGPT.
Origin Tracing and Detecting of LLMs
The extraordinary performance of large language models (LLMs) heightens the importance of detecting whether the context is generated by an AI system.
Learning For Predictive Control: A Dual Gaussian Process Approach
Gaussian process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data.
EventHPE: Event-based 3D Human Pose and Shape Estimation
Event camera is an emerging imaging sensor for capturing dynamics of moving objects as events, which motivates our work in estimating 3D human pose and shape from the event signals.
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
3D Shape Segmentation via Shape Fully Convolutional Networks
3D shapes are represented as graph structures in the SFCN architecture, based on novel graph convolution and pooling operations, which are similar to convolution and pooling operations used on images.
Stochastic Collapsed Variational Inference for Hidden Markov Models
In this paper, we propose a stochastic collapsed variational inference algorithm for hidden Markov models, in a sequential data setting.
Stochastic Collapsed Variational Inference for Sequential Data
Stochastic variational inference for collapsed models has recently been successfully applied to large scale topic modelling.
