105 citations · 112 across the 9 of their papers we have counts for
9 papers
RLCascadeRouter: Quality-Estimator-Free Cascade Routing via Reinforcement Learning
Shihong Huang, Shengjie Wang, Hong Ma +1
The growing ecosystem of large language models (LLMs) offers huge potential to optimize performance-cost trade-offs. However, their heterogeneous capabilities and inference costs m…
Vehicle-as-Prompt: A Unified Deep Reinforcement Learning Framework for Heterogeneous Fleet Vehicle Routing Problem
Shihong Huang, Shengjie Wang, Lei Gao +4
Unlike traditional homogeneous routing problems, the Heterogeneous Fleet Vehicle Routing Problem (HFVRP) involves heterogeneous fixed costs, variable travel costs, and capacity con…
Submodular Context Partitioning and Compression for In-Context Learning
Shaoyi Zheng, Canyu Zhang, Tianyi Zhou +1
In-context learning (ICL) enables efficient few-shot learning in large language models (LLMs) without training, but suffers from the quadratic input complexity of transformers, lim…
One4Many-StablePacker: An Efficient Deep Reinforcement Learning Framework for the 3D Bin Packing Problem
Lei Gao, Shihong Huang, Shengjie Wang +5
The three-dimensional bin packing problem (3D-BPP) is widely applied in logistics and warehousing. Existing learning-based approaches often neglect practical stability-related cons…
Do Deep Convolutional Nets Really Need to be Deep and Convolutional?
Gregor Urban, Krzysztof J. Geras, Samira Ebrahimi Kahou +6
Yes, they do. This paper provides the first empirical demonstration that deep convolutional models really need to be both deep and convolutional, even when trained with methods suc…
Blending LSTMs into CNNs
Krzysztof J. Geras, Abdel-rahman Mohamed, Rich Caruana +6
We consider whether deep convolutional networks (CNNs) can represent decision functions with similar accuracy as recurrent networks such as LSTMs. First, we show that a deep CNN wi…
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications to Parallel Machine Learning and Multi-Label Image Segmentation
Kai Wei, Rishabh Iyer, Shengjie Wang +2
We study two mixed robust/average-case submodular partitioning problems that we collectively call Submodular Partitioning. These problems generalize both purely robust instances of…
Faster graphical model identification of tandem mass spectra using peptide word lattices
Shengjie Wang, John T. Halloran, Jeff A. Bilmes +1
Liquid chromatography coupled with tandem mass spectrometry, also known as shotgun proteomics, is a widely-used high-throughput technology for identifying proteins in complex biolo…
Multiple Location Profiling for Users and Relationships from Social Network and Content
Rui Li, Shengjie Wang, Kevin Chen-Chuan Chang
Users' locations are important for many applications such as personalized search and localized content delivery. In this paper, we study the problem of profiling Twitter users' loc…