Search Results for author: Kuan Wang

Found 13 papers, 4 papers with code

ToolQA: A Dataset for LLM Question Answering with External Tools

1 code implementation23 Jun 2023 Yuchen Zhuang, Yue Yu, Kuan Wang, Haotian Sun, Chao Zhang

To address this issue, we introduce a new dataset called ToolQA, which is designed to faithfully evaluate LLMs' ability to use external tools for question answering.

Question Answering

Generation-Augmented Query Expansion For Code Retrieval

no code implementations20 Dec 2022 Dong Li, Yelong Shen, Ruoming Jin, Yi Mao, Kuan Wang, Weizhu Chen

Pre-trained language models have achieved promising success in code retrieval tasks, where a natural language documentation query is given to find the most relevant existing code snippet.

Code Generation Retrieval

GNN is a Counter? Revisiting GNN for Question Answering

no code implementations ICLR 2022 Kuan Wang, Yuyu Zhang, Diyi Yang, Le Song, Tao Qin

To open the black box of GNN and investigate these problems, we dissect state-of-the-art GNN modules for QA and analyze their reasoning capability.

Knowledge Graphs Question Answering

TSM: Temporal Shift Module for Efficient and Scalable Video Understanding on Edge Device

2 code implementations27 Sep 2021 Ji Lin, Chuang Gan, Kuan Wang, Song Han

Secondly, TSM has high efficiency; it achieves a high frame rate of 74fps and 29fps for online video recognition on Jetson Nano and Galaxy Note8.

Video Recognition Video Understanding

How to Design Sample and Computationally Efficient VQA Models

no code implementations22 Mar 2021 Karan Samel, Zelin Zhao, Binghong Chen, Kuan Wang, Robin Luo, Le Song

In multi-modal reasoning tasks, such as visual question answering (VQA), there have been many modeling and training paradigms tested.

Question Answering Visual Question Answering

Differentiable End-to-End Program Executor for Sample and Computationally Efficient VQA

no code implementations1 Jan 2021 Karan Samel, Zelin Zhao, Kuan Wang, Robin Luo, Binghong Chen, Le Song

We present a differentiable end-to-end program executor (DePe), which addresses Visual Question Answering (VQA) in a sample and computationally efficient manner.

Question Answering Visual Question Answering

Hardware-Centric AutoML for Mixed-Precision Quantization

no code implementations11 Aug 2020 Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, Song Han

Compared with conventional methods, our framework is fully automated and can specialize the quantization policy for different neural network architectures and hardware architectures.

AutoML Quantization

APQ: Joint Search for Network Architecture, Pruning and Quantization Policy

1 code implementation CVPR 2020 Tianzhe Wang, Kuan Wang, Han Cai, Ji Lin, Zhijian Liu, Song Han

However, training this quantization-aware accuracy predictor requires collecting a large number of quantized <model, accuracy> pairs, which involves quantization-aware finetuning and thus is highly time-consuming.

Quantization

Design Automation for Efficient Deep Learning Computing

no code implementations24 Apr 2019 Song Han, Han Cai, Ligeng Zhu, Ji Lin, Kuan Wang, Zhijian Liu, Yujun Lin

Moreover, we shorten the design cycle by 200x than previous work, so that we can afford to design specialized neural network models for different hardware platforms.

Quantization

HAQ: Hardware-Aware Automated Quantization with Mixed Precision

11 code implementations CVPR 2019 Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, Song Han

Compared with conventional methods, our framework is fully automated and can specialize the quantization policy for different neural network architectures and hardware architectures.

Quantization

SnapQuant: A Probabilistic and Nested Parameterization for Binary Networks

no code implementations27 Sep 2018 Kuan Wang, Hao Zhao, Anbang Yao, Aojun Zhou, Dawei Sun, Yurong Chen

During the training phase, we generate binary weights on-the-fly since what we actually maintain is the policy network, and all the binary weights are used in a burn-after-reading style.

Explicit Loss-Error-Aware Quantization for Low-Bit Deep Neural Networks

no code implementations CVPR 2018 Aojun Zhou, Anbang Yao, Kuan Wang, Yurong Chen

Through explicitly regularizing the loss perturbation and the weight approximation error in an incremental way, we show that such a new optimization method is theoretically reasonable and practically effective.

Quantization

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