Recent research has explored how Language Models (LMs) can be used for feature representation and prediction in tabular machine learning tasks. This involves employing text serialization and supervised fine-tuning (SFT) techniques. Despite the simplicity of these techniques, significant gaps remain in our understanding of the applicability and reliability of LMs in this context. Our study assesses how emerging LM technologies compare with traditional paradigms in tabular machine learning and evaluates the feasibility of adopting similar approaches with these advanced technologies. At the data level, we investigate various methods of data representation and curation of serialized tabular data, exploring their impact on prediction performance. At the classification level, we examine whether text serialization combined with LMs enhances performance on tabular datasets (e.g. class imbalance, distribution shift, biases, and high dimensionality), and assess whether this method represents a state-of-the-art (SOTA) approach for addressing tabular machine learning challenges. Our findings reveal current pre-trained models should not replace conventional approaches. 

pdf/text-serialization-and-their-relationship-with-the-1im2sxdrxm.pdf

Text Serialization and Their Relationship with the Conventional
  Paradigms of Tabular Machine Learning

In the rapid development of artificial intelligence, solving complex AI tasks is a crucial technology in intelligent mobile networks. Despite the good performance of specialized AI models in intelligent mobile networks, they are unable to handle complicated AI tasks. To address this challenge, we propose Systematic Artificial Intelligence (SAI), which is a framework designed to solve AI tasks by leveraging Large Language Models (LLMs) and JSON-format intent-based input to connect self-designed model library and database. Specifically, we first design a multi-input component, which simultaneously integrates Large Language Models (LLMs) and JSON-format intent-based inputs to fulfill the diverse intent requirements of different users. In addition, we introduce a model library module based on model cards which employ model cards to pairwise match between different modules for model composition. Model cards contain the corresponding model's name and the required performance metrics. Then when receiving user network requirements, we execute each subtask for multiple selected model combinations and provide output based on the execution results and LLM feedback. By leveraging the language capabilities of LLMs and the abundant AI models in the model library, SAI can complete numerous complex AI tasks in the communication network, achieving impressive results in network optimization, resource allocation, and other challenging tasks.

pdf/sai-solving-ai-tasks-with-systematic-artificial-intelligence-1fgss60gv7.pdf

SAI: Solving AI Tasks with Systematic Artificial Intelligence in Communication Network

Cellular traffic prediction is a prerequisite for the intelligent 5G network. However, accurate cellular traffic prediction is challenging. Because there are more complicated spatial and temporal correlations compared with road traffic prediction. And there are correlations between historical and future time steps in the long-term prediction. In this paper, we propose an effective framework called spatial-temporal adaptive convolutional network based on external factors (STACN-EF) for cellular traffic prediction. The framework mainly includes five deep learning components: input spatial-temporal features division module, adaptive spatial graph convolution network (ASGCN), gate recurrent unit (GRU), history-future time conversion network (HFTCN), and gated fusion. The input spatial-temporal features division module boosts the prediction's accuracy by learning the relationship between the central region and the hotspots region. Moreover, ASGCN and GRU are employed to extract inherent spatial-temporal correlations adaptively according to specific prediction tasks without any pre-defined information. In addition, HFTCN is designed to capture the relationship between prediction horizons and historical information and lead optimization direction, so as to improve the accuracy of long-term and short-term prediction concurrently. The experimental results in a real-world cellular traffic dataset show that STACN-EF outperforms state-of-the-art baselines and achieves optimal accuracy in all prediction horizons for the first time.

Spatial-Temporal Adaptive Convolutional Network with External Factors for Cellular Traffic Prediction

The prediction of cellular services can help operators adjust the network status in advance to meet the QoS requirements of users. Evaluating the upper boundary of prediction is the key to designing prediction algorithms. The traffic of cells in cellular networks has strong temporal periodicity and is related to the traffic features of adjacent cells. Therefore, three types of entropy are proposed to measure its predictability: (1) Temporal entropy, (2) Spatial entropy, and (3) Spatiotemporal entropy. The Fano equation is used to calculate the predictability boundary, which provides a prediction boundary for the base station traffic prediction of cells in cellular networks, and the effectiveness of the proposed entropy has been verified through simulation experiments. 

Predictability of Cellular Network Traffic Based on Conditional Entropy

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Lei Yao

Author Tools

Chat about Author

Papers

SAI: Solving AI Tasks with Systematic Artificial Intelligence in Communication Network

Spatial-Temporal Adaptive Convolutional Network with External Factors for Cellular Traffic Prediction

Predictability of Cellular Network Traffic Based on Conditional Entropy