4 citations · 8 across the 6 of their papers we have counts for
6 papers
iScript: A Domain-Adapted Large Language Model and Benchmark for Physical Design Tcl Script Generation
Ning Xu, Zhaoyang Zhang, Senlin Shu +10
Modern EDA flows rely heavily on Tcl scripting, yet general LLMs perform poorly in this domain due to extreme data scarcity, domain-specific semantics, and the high reliability req…
An Unbiased Risk Estimator for Partial Label Learning with Augmented Classes
Jiayu Hu, Senlin Shu, Beibei Li +2
Partial Label Learning (PLL) is a typical weakly supervised learning task, which assumes each training instance is annotated with a set of candidate labels containing the ground-tr…
A Generalized Unbiased Risk Estimator for Learning with Augmented Classes
Senlin Shu, Shuo He, Haobo Wang +3
In contrast to the standard learning paradigm where all classes can be observed in training data, learning with augmented classes (LAC) tackles the problem where augmented classes…
Multi-Class Classification from Single-Class Data with Confidences
Yuzhou Cao, Lei Feng, Senlin Shu +4
Can we learn a multi-class classifier from only data of a single class? We show that without any assumptions on the loss functions, models, and optimizers, we can successfully lear…
Pointwise Binary Classification with Pairwise Confidence Comparisons
Lei Feng, Senlin Shu, Nan Lu +5
To alleviate the data requirement for training effective binary classifiers in binary classification, many weakly supervised learning settings have been proposed. Among them, some…
Incorporating Multiple Cluster Centers for Multi-Label Learning
Senlin Shu, Fengmao Lv, Yan Yan +3
Multi-label learning deals with the problem that each instance is associated with multiple labels simultaneously. Most of the existing approaches aim to improve the performance of…