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Ruohui Wang

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ICML Conference 2024 Conference Paper

Differentiable Model Scaling using Differentiable Topk

  • Kai Liu
  • Ruohui Wang
  • Jianfei Gao 0003
  • Kai Chen 0026

Over the past few years, as large language models have ushered in an era of intelligence emergence, there has been an intensified focus on scaling networks. Although Neural Architecture Search (NAS) methods have been proposed to automate this process, they suffer from low search efficiency. This study introduces Differentiable Model Scaling (DMS), increasing the efficiency for searching optimal width and depth in networks. DMS can model both width and depth in a direct and fully differentiable way, making it easy to optimize. We have evaluated our DMS across diverse tasks, ranging from vision tasks to NLP tasks and various network architectures, including CNNs and Transformers. Results consistently indicate that our DMS can find improved structures and outperforms state-of-the-art NAS methods. Specifically, for image classification on ImageNet, our DMS improves the top-1 accuracy of EfficientNet-B0 and Deit-Tiny by 1. 4% and 0. 6%, respectively, and outperforms the state-of-the-art zero-shot NAS method, ZiCo, by 1. 3% while requiring only 0. 4 GPU days for searching. For object detection on COCO, DMS improves the mAP of Yolo-v8-n by 2. 0%. For language modeling, our pruned Llama-7B outperforms the prior method with lower perplexity and higher zero-shot classification accuracy. Our code is available at https: //github. com/LKJacky/Differentiable-Model-Scaling.

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IJCAI Conference 2017 Conference Paper

Scalable Estimation of Dirichlet Process Mixture Models on Distributed Data

  • Ruohui Wang
  • Dahua Lin

We consider the estimation of Dirichlet Process Mixture Models (DPMMs) in distributed environments, where data are distributed across multiple computing nodes. A key advantage of Bayesian nonparametric models such as DPMMs is that they allow new components to be introduced on the fly as needed. This, however, posts an important challenge to distributed estimation -- how to handle new components efficiently and consistently. To tackle this problem, we propose a new estimation method, which allows new components to be created locally in individual computing nodes. Components corresponding to the same cluster will be identified and merged via a probabilistic consolidation scheme. In this way, we can maintain the consistency of estimation with very low communication cost. Experiments on large real-world data sets show that the proposed method can achieve high scalability in distributed and asynchronous environments without compromising the mixing performance.

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