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VisEval: A Benchmark for Data Visualization in the Era of Large Language Models

Autor: Chen, Nan, Zhang, Yuge, Xu, Jiahang, Ren, Kan, Yang, Yuqing

Translating natural language to visualization (NL2VIS) has shown great promise for visual data analysis, but it remains a challenging task that requires multiple low-level implementations, such as natural language processing and visualization design.

Externí odkaz: http://arxiv.org/abs/2407.00981

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Benchmarking Data Science Agents

Autor: Zhang, Yuge, Jiang, Qiyang, Han, Xingyu, Chen, Nan, Yang, Yuqing, Ren, Kan

In the era of data-driven decision-making, the complexity of data analysis necessitates advanced expertise and tools of data science, presenting significant challenges even for specialists. Large Language Models (LLMs) have emerged as promising aids

Externí odkaz: http://arxiv.org/abs/2402.17168

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MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks

Autor: Zhang, Lei, Zhang, Yuge, Ren, Kan, Li, Dongsheng, Yang, Yuqing

The field of machine learning (ML) has gained widespread adoption, leading to significant demand for adapting ML to specific scenarios, which is yet expensive and non-trivial. The predominant approaches towards the automation of solving ML tasks (e.g

Externí odkaz: http://arxiv.org/abs/2304.14979

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AutoTaskFormer: Searching Vision Transformers for Multi-task Learning

Autor: Liu, Yang, Yan, Shen, Zhang, Yuge, Ren, Kan, Zhang, Quanlu, Ren, Zebin, Cai, Deng, Zhang, Mi

Vision Transformers have shown great performance in single tasks such as classification and segmentation. However, real-world problems are not isolated, which calls for vision transformers that can perform multiple tasks concurrently. Existing multi-

Externí odkaz: http://arxiv.org/abs/2304.08756

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Reinforcement Learning with Automated Auxiliary Loss Search

Autor: He, Tairan, Zhang, Yuge, Ren, Kan, Liu, Minghuan, Wang, Che, Zhang, Weinan, Yang, Yuqing, Li, Dongsheng

A good state representation is crucial to solving complicated reinforcement learning (RL) challenges. Many recent works focus on designing auxiliary losses for learning informative representations. Unfortunately, these handcrafted objectives rely hea

Externí odkaz: http://arxiv.org/abs/2210.06041

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Privacy-preserving Online AutoML for Domain-Specific Face Detection

Autor: Yan, Chenqian, Zhang, Yuge, Zhang, Quanlu, Yang, Yaming, Jiang, Xinyang, Yang, Yuqing, Wang, Baoyuan

Despite the impressive progress of general face detection, the tuning of hyper-parameters and architectures is still critical for the performance of a domain-specific face detector. Though existing AutoML works can speedup such process, they either r

Externí odkaz: http://arxiv.org/abs/2203.08399

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