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Found 23 papers, 5 papers with code
Qwen2.5-VL Technical Report
We introduce Qwen2. 5-VL, the latest flagship model of Qwen vision-language series, which demonstrates significant advancements in both foundational capabilities and innovative functionalities.
Ranked #2 on
Visual Question Answering (VQA)
on VLM2-Bench
Research on the Application of Spark Streaming Real-Time Data Analysis System and large language model Intelligent Agents
This study explores the integration of Agent AI with LangGraph to enhance real-time data analysis systems in big data environments.
Agent AI with LangGraph: A Modular Framework for Enhancing Machine Translation Using Large Language Models
This paper explores the transformative role of Agent AI and LangGraph in advancing the automation and effectiveness of machine translation (MT).
Intelligent Spark Agents: A Modular LangGraph Framework for Scalable, Visualized, and Enhanced Big Data Machine Learning Workflows
This paper emphasizes the integration of Spark with intelligent agents and graph-based workflows to redefine the development and execution of machine learning tasks in big data environments, paving the way for scalable and user-friendly AI solutions.
Exploration of LLM Multi-Agent Application Implementation Based on LangGraph+CrewAI
With the rapid development of large model technology, the application of agent technology in various fields is becoming increasingly widespread, profoundly changing people's work and lifestyles.
Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models
Large Language Models (LLMs) still face challenges when dealing with complex reasoning tasks, often resulting in hallucinations, which limit the practical application of LLMs.
ChannelGPT: A Large Model to Generate Digital Twin Channel for 6G Environment Intelligence
In practice, we also establish a ChannelGPT prototype to generate high-fidelity channel data for varied scenarios to validate the accuracy and generalization ability based on environment intelligence.
Wireless Environment Information Sensing, Feature, Semantic, and Knowledge: Four Steps Towards 6G AI-Enabled Air Interface
The air interface technology plays a crucial role in optimizing the communication quality for users.
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
We present the Qwen2-VL Series, an advanced upgrade of the previous Qwen-VL models that redefines the conventional predetermined-resolution approach in visual processing.
Ranked #3 on
Video Question Answering
on TVBench
Natural Language Visual Grounding
Temporal Relation Extraction
+2
Implicit Sentiment Analysis Based on Chain of Thought Prompting
Specifically, on the restaurant dataset, the F1 score reaches 75. 27, accompanied by an ISA score of 66. 29.
Multi-tool Integration Application for Math Reasoning Using Large Language Model
The experimental results showed that, based on Math Tool, Code Tool, and CoT Tool, in Task 4 task, our method achieved an accuracy of 89. 09, compared with the GPT3+FewShot baseline, Few Shot+ERNIE-4. 0+self consistency improved by 49. 09%, and compared with fine-tuning the Fine tuning baseline, Few Shot+ERNIE-4. 0+self consistency improved by 52. 29%
AutoTest: Evolutionary Code Solution Selection with Test Cases
Then, by executing the code solutions and evaluating their performance on the test cases, a consensus set is formed.
Qwen2 Technical Report
This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models.
Ranked #2 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
Suppressing seizure via optimal electrical stimulation to the hub of epileptic brain network
The electrical stimulation on the hub of the epileptic brain network shows remarkable performance as the direct stimulation of SOZ in suppressing seizure dynamics.
Electromagnetic Wave Property Inspired Radio Environment Knowledge Construction and AI-based Verification for 6G Digital Twin Channel
Since radio propagation is affected by the environment, constructing the relationship between the environment and radio wave propagation is the key to improving the accuracy of DTC, and the construction method based on artificial intelligence (AI) is the most concentrated.
Digital Twin Channel for 6G: Concepts, Architectures and Potential Applications
Digital twin channel (DTC) is the real-time mapping of a wireless channel from the physical world to the digital world, which is expected to provide significant performance enhancements for the sixth-generation (6G) air-interface design.
Timed-Elastic-Band Based Variable Splitting for Autonomous Trajectory Planning
Existing trajectory planning methods are struggling to handle the issue of autonomous track swinging during navigation, resulting in significant errors when reaching the destination.
Towards 6G Digital Twin Channel Using Radio Environment Knowledge Pool
The digital twin channel (DTC) is crucial for 6G wireless autonomous networks as it replicates the wireless channel fading states in 6G air interface transmissions.
SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification
Compared with the existing literature, our WNFG of EEG signals achieves up to 10 times of redundant edge reduction, and our approach achieves up to 97 times of model pruning without loss of classification accuracy.
Bidirectional RNN-based Few-shot Training for Detecting Multi-stage Attack
"Feint Attack" is simulated by the real attack inserted in the normal causal attack chain, and the addition of the real attack destroys the causal relationship of the original attack chain.
Visual-Texual Emotion Analysis with Deep Coupled Video and Danmu Neural Networks
To enhance the emotion discriminativeness of words in textual feature extraction, we propose Emotional Word Embedding (EWE) to learn text representations by jointly considering their semantics and emotions.
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance.
Ranked #2 on
Click-Through Rate Prediction
on Book-Crossing
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space.
Ranked #1 on
Node Classification
on Wikipedia
