13.4k citations · 61k across the 117 of their papers we have counts for
127 papers
Improving the Predictive Performance of Bootstrap Aggregating by Dirichlet Resampling
Quoc Viet Le, Joonha Park
We revisit Breiman's observation that reducing inter-tree correlation without weakening individual trees can improve random forests. Building on this principle, we introduce two va…
Large Language Models as Optimizers
Chengrun Yang, Xuezhi Wang, Yifeng Lu +4
Optimization is ubiquitous. While derivative-based algorithms have been powerful tools for various problems, the absence of gradient imposes challenges on many real-world applicati…
FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search
Jordan Dotzel, Gang Wu, Andrew Li +9
Quantization has become a mainstream compression technique for reducing model size, computational requirements, and energy consumption for modern deep neural networks (DNNs). With…
Noise2Music: Text-conditioned Music Generation with Diffusion Models
Qingqing Huang, Daniel S. Park, Tao Wang +12
We introduce Noise2Music, where a series of diffusion models is trained to generate high-quality 30-second music clips from text prompts. Two types of diffusion models, a generator…
The Flan Collection: Designing Data and Methods for Effective Instruction Tuning
Shayne Longpre, Le Hou, Tu Vu +8
We study the design decisions of publicly available instruction tuning methods, and break down the development of Flan 2022 (Chung et al., 2022). Through careful ablation studies o…
Unified Functional Hashing in Automatic Machine Learning
Ryan Gillard, Stephen Jonany, Yingjie Miao +7
The field of Automatic Machine Learning (AutoML) has recently attained impressive results, including the discovery of state-of-the-art machine learning solutions, such as neural im…