28 citations · 34 across the 10 of their papers we have counts for
10 papers
When AI Designs AI: Innovation or Imitation?
Yikang Yang, Zhengxin Yang, Luzhou Peng +4
Recent advances in LLM agents have made them increasingly capable of designing methods for complex AI tasks. This raises two central questions about agent-designed methods relative…
GraDE: A Graph Diffusion Estimator for Frequent Subgraph Discovery in Neural Architectures
Yikang Yang, Zhengxin Yang, Minghao Luo +5
Finding frequently occurring subgraph patterns or network motifs in neural architectures is crucial for optimizing efficiency, accelerating design, and uncovering structural insigh…
TimeMosaic: Temporal Heterogeneity Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding
Kuiye Ding, Fanda Fan, Chunyi Hou +4
Multivariate time series forecasting is essential in domains such as finance, transportation, climate, and energy. However, existing patch-based methods typically adopt fixed-lengt…
Quantifying the Dynamics of Harm Caused by Retracted Research
Yunyou Huang, Jiahui Zhao, Dandan Cui +10
Despite enormous efforts devoted to understand the characteristics and impacts of retracted papers, little is known about the mechanisms underlying the dynamics of their harm and t…
Younger: The First Dataset for Artificial Intelligence-Generated Neural Network Architecture
Zhengxin Yang, Wanling Gao, Luzhou Peng +3
Designing and optimizing neural network architectures typically requires extensive expertise, starting with handcrafted designs and then manual or automated refinement. This depend…
Evaluatology: The Science and Engineering of Evaluation
Jianfeng Zhan, Lei Wang, Wanling Gao +10
Evaluation is a crucial aspect of human existence and plays a vital role in various fields. However, it is often approached in an empirical and ad-hoc manner, lacking consensus on…
Quality at the Tail of Machine Learning Inference
Zhengxin Yang, Wanling Gao, Chunjie Luo +4
Machine learning inference should be subject to stringent inference time constraints while ensuring high inference quality, especially in safety-critical (e.g., autonomous driving)…
Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation
Yang Feng, Shuhao Gu, Dengji Guo +2
Although teacher forcing has become the main training paradigm for neural machine translation, it usually makes predictions only conditioned on past information, and hence lacks gl…
Modeling Fluency and Faithfulness for Diverse Neural Machine Translation
Yang Feng, Wanying Xie, Shuhao Gu +4
Neural machine translation models usually adopt the teacher forcing strategy for training which requires the predicted sequence matches ground truth word by word and forces the pro…
Enhancing Context Modeling with a Query-Guided Capsule Network for Document-level Translation
Zhengxin Yang, Jinchao Zhang, Fandong Meng +3
Context modeling is essential to generate coherent and consistent translation for Document-level Neural Machine Translations. The widely used method for document-level translation…