116 citations · 649 across the 37 of their papers we have counts for
32 papers · 1 filter
Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time
Zichang Liu, Jue Wang, Tri Dao +8
Large language models (LLMs) with hundreds of billions of parameters have sparked a new wave of exciting AI applications. However, they are computationally expensive at inference t…
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks
Qiang Huang, Jiawei Jiang, Xi Susie Rao +10
To handle graphs in which features or connectivities are evolving over time, a series of temporal graph neural networks (TGNNs) have been proposed. Despite the success of these TGN…
Improving Retrieval-Augmented Large Language Models via Data Importance Learning
Xiaozhong Lyu, Stefan Grafberger, Samantha Biegel +4
Retrieval augmentation enables large language models to take advantage of external knowledge, for example on tasks like question answering and data imputation. However, the perform…
OpenBox: A Python Toolkit for Generalized Black-box Optimization
Huaijun Jiang, Yu Shen, Yang Li +5
Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, experimental design, and database knob tuning. However, users still face chall…
Modelling graph dynamics in fraud detection with "Attention"
Susie Xi Rao, Clémence Lanfranchi, Shuai Zhang +7
At online retail platforms, detecting fraudulent accounts and transactions is crucial to improve customer experience, minimize loss, and avoid unauthorized transactions. Despite th…
Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale
Yang Li, Yu Shen, Huaijun Jiang +5
The ever-growing demand and complexity of machine learning are putting pressure on hyper-parameter tuning systems: while the evaluation cost of models continues to increase, the sc…