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Found 23 papers, 13 papers with code
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.
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.
SpeechAlign: Aligning Speech Generation to Human Preferences
However, the integration of human feedback to align speech outputs to human preferences is often neglected.
SpeechAgents: Human-Communication Simulation with Multi-Modal Multi-Agent Systems
In this paper, we propose SpeechAgents, a multi-modal LLM based multi-agent system designed for simulating human communication.
SeqXGPT: Sentence-Level AI-Generated Text Detection
Therefore, it is important to build strong AI-generated text (AIGT) detectors.
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.
RVAE-EM: Generative speech dereverberation based on recurrent variational auto-encoder and convolutive transfer function
In this work, we propose a generative dereverberation method.
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.
Watermarking LLMs with Weight Quantization
Abuse of large language models reveals high risks as large language models are being deployed at an astonishing speed.
InferAligner: Inference-Time Alignment for Harmlessness through Cross-Model Guidance
With the rapid development of large language models (LLMs), they are not only used as general-purpose AI assistants but are also customized through further fine-tuning to meet the requirements of different applications.
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation Learning
These methods regularize the representation space by pulling similar sentence representations closer and pushing away the dissimilar ones and have been proven effective in various NLP tasks, e. g., semantic textual similarity (STS) tasks.
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 Open-World Lottery Ticket Hypothesis for OOD Intent Classification
Most existing methods of Out-of-Domain (OOD) intent classification rely on extensive auxiliary OOD corpora or specific training paradigms.
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 Sequential Data
Stochastic variational inference for collapsed models has recently been successfully applied to large scale topic modelling.
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.
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.
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.
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.
NewsBench: Systematic Evaluation of LLMs for Writing Proficiency and Safety Adherence in Chinese Journalistic Editorial Applications
This study presents NewsBench, a novel benchmark framework developed to evaluate the capability of Large Language Models (LLMs) in Chinese Journalistic Writing Proficiency (JWP) and their Safety Adherence (SA), addressing the gap between journalistic ethics and the risks associated with AI utilization.
Medical Image Synthesis via Fine-Grained Image-Text Alignment and Anatomy-Pathology Prompting
To address these challenges, we propose a novel medical image synthesis model that leverages fine-grained image-text alignment and anatomy-pathology prompts to generate highly detailed and accurate synthetic medical images.
Direct Shooting Method for Numerical Optimal Control: A Modified Transcription Approach
Direct shooting is an efficient method to solve numerical optimal control.
