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Found 26 papers, 12 papers with code
Data Interpreter: An LLM Agent For Data Science
Large Language Model (LLM)-based agents have demonstrated remarkable effectiveness.
Audio-Visual Segmentation
To deal with the AVS problem, we propose a novel method that uses a temporal pixel-wise audio-visual interaction module to inject audio semantics as guidance for the visual segmentation process.
Audio-Visual Segmentation with Semantics
To deal with these problems, we propose a new baseline method that uses a temporal pixel-wise audio-visual interaction module to inject audio semantics as guidance for the visual segmentation process.
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs
Combinatorial Optimization (CO) has been a long-standing challenging research topic featured by its NP-hard nature.
C3KG: A Chinese Commonsense Conversation Knowledge Graph
Existing commonsense knowledge bases often organize tuples in an isolated manner, which is deficient for commonsense conversational models to plan the next steps.
C^3KG: A Chinese Commonsense Conversation Knowledge Graph
Existing commonsense knowledge bases often organize tuples in an isolated manner, which is deficient for commonsense conversational models to plan the next steps.
Writing Polishment with Simile: Task, Dataset and A Neural Approach
In this paper, we propose a new task of Writing Polishment with Simile (WPS) to investigate whether machines are able to polish texts with similes as we human do.
Learning Audio-Visual Source Localization via False Negative Aware Contrastive Learning
Based on this observation, we propose a new learning strategy named False Negative Aware Contrastive (FNAC) to mitigate the problem of misleading the training with such false negative samples.
Energy-Efficient Cell-Free Massive MIMO Through Sparse Large-Scale Fading Processing
In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-term signal processing coefficients are jointly optimized.
Reasoning in Dialog: Improving Response Generation by Context Reading Comprehension
Hence, in this paper, we propose to improve the response generation performance by examining the model's ability to answer a reading comprehension question, where the question is focused on the omitted information in the dialog.
Focus-Constrained Attention Mechanism for CVAE-based Response Generation
To model diverse responses for a given post, one promising way is to introduce a latent variable into Seq2Seq models.
Using Think-Aloud Data to Understand Relations between Self-Regulation Cycle Characteristics and Student Performance in Intelligent Tutoring Systems
We discuss system re-design opportunities to add SRL support during stages of processing and paths forward for using machine learning to speed research depending on the assessment of SRL based on transcription of think-aloud data.
EnsembleGAN: Adversarial Learning for Retrieval-Generation Ensemble Model on Short-Text Conversation
Aiming at generating responses that approximate the ground-truth and receive high ranking scores from the discriminator, the two generators learn to generate improved highly relevant responses and competitive unobserved candidates respectively, while the discriminative ranker is trained to identify true responses from adversarial ones, thus featuring the merits of both generator counterparts.
Structured Massive Access for Scalable Cell-Free Massive MIMO Systems
However, there are still many unsolved practical issues in cell-free massive MIMO systems, whereof scalable massive access implementation is one of the most vital.
Physical Layer Security Enhancement With Reconfigurable Intelligent Surface-Aided Networks
Reconfigurable intelligent surface (RIS)-aided wireless communications have drawn significant attention recently.
Information Theory Signal Processing Information Theory
Coverage Probability Analysis of IRS-Aided Communication Systems
The intelligent reflective surface (IRS) technology has received many interests in recent years, thanks to its potential uses in future wireless communications, in which one of the promising use cases is to widen coverage, especially in the line-of-sight-blocked scenarios.
Deep Learning-Based Power Control for Uplink Cell-Free Massive MIMO Systems
In this paper, a general framework for deep learning-based power control methods for max-min, max-product and max-sum-rate optimization in uplink cell-free massive multiple-input multiple-output (CF mMIMO) systems is proposed.
A Survey for Solving Mixed Integer Programming via Machine Learning
This paper surveys the trend of leveraging machine learning to solve mixed integer programming (MIP) problems.
Reconfigurable Intelligent Surface-Aided Joint Radar and Covert Communications: Fundamentals, Optimization, and Challenges
Thus, we first apply covert communication into JRC and propose a joint radar and covert communication (JRCC) system to achieve high spectrum utilization and secure data transmission simultaneously.
Reconfigurable Intelligent Surface (RIS)-aided Vehicular Networks: Their Protocols, Resource Allocation, and Performance
Reconfigurable intelligent surfaces (RISs) assist in paving the way for the evolution of conventional vehicular networks to autonomous driving.
Exploiting Deep Reinforcement Learning for Edge Caching in Cell-Free Massive MIMO Systems
Cell-free massive multiple-input-multiple-output is promising to meet the stringent quality-of-experience (QoE) requirements of railway wireless communications by coordinating many successional access points (APs) to serve the onboard users coherently.
Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion Module
Specifically, we propose a fusion module named Multi-scale Attention Fusion (MAF) module for our dual-stream framework to effectively integrate features of the two tasks.
Applications of Reconfigurable Intelligent Surface in Smart High Speed Train Communications
Reconfigurable intelligent surface (RIS) is one of the most promising technologies for 5G-Adv and 6G.
Learning towards Selective Data Augmentation for Dialogue Generation
As it is cumbersome and expensive to acquire a huge amount of data for training neural dialog models, data augmentation is proposed to effectively utilize existing training samples.
Generative AI-aided Joint Training-free Secure Semantic Communications via Multi-modal Prompts
The system jointly optimizes the diffusion step, jamming, and transmitting power with the aid of the generative diffusion models, enabling successful and secure transmission of the source messages.
Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision
Next-generation multiple input multiple output (MIMO) is expected to be intelligent and scalable.
