Search Results for author: David Liu

Found 13 papers, 1 papers with code

MMViT: Multiscale Multiview Vision Transformers

no code implementations28 Apr 2023 Yuchen Liu, Natasha Ong, Kaiyan Peng, Bo Xiong, Qifan Wang, Rui Hou, Madian Khabsa, Kaiyue Yang, David Liu, Donald S. Williamson, Hanchao Yu

Our model encodes different views of the input signal and builds several channel-resolution feature stages to process the multiple views of the input at different resolutions in parallel.

Image Classification

Deep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer

no code implementations21 Apr 2023 Yuzhen Ding, Hongying Feng, Yunze Yang, Jason Holmes, Zhengliang Liu, David Liu, William W. Wong, Nathan Y. Yu, Terence T. Sio, Steven E. Schild, Baoxin Li, Wei Liu

Conclusion: A patient-specific vision-transformer-based network was developed and shown to be accurate and efficient to reconstruct 3D CT images from kV images.

Anatomy Image Registration

Core-Periphery Principle Guided Redesign of Self-Attention in Transformers

no code implementations27 Mar 2023 Xiaowei Yu, Lu Zhang, Haixing Dai, Yanjun Lyu, Lin Zhao, Zihao Wu, David Liu, Tianming Liu, Dajiang Zhu

Designing more efficient, reliable, and explainable neural network architectures is critical to studies that are based on artificial intelligence (AI) techniques.

Coupling Artificial Neurons in BERT and Biological Neurons in the Human Brain

no code implementations27 Mar 2023 Xu Liu, Mengyue Zhou, Gaosheng Shi, Yu Du, Lin Zhao, Zihao Wu, David Liu, Tianming Liu, Xintao Hu

Linking computational natural language processing (NLP) models and neural responses to language in the human brain on the one hand facilitates the effort towards disentangling the neural representations underpinning language perception, on the other hand provides neurolinguistics evidence to evaluate and improve NLP models.

Eye-gaze-guided Vision Transformer for Rectifying Shortcut Learning

no code implementations25 May 2022 Chong Ma, Lin Zhao, Yuzhong Chen, Lu Zhang, Zhenxiang Xiao, Haixing Dai, David Liu, Zihao Wu, Zhengliang Liu, Sheng Wang, Jiaxing Gao, Changhe Li, Xi Jiang, Tuo Zhang, Qian Wang, Dinggang Shen, Dajiang Zhu, Tianming Liu

To address this problem, we propose to infuse human experts' intelligence and domain knowledge into the training of deep neural networks.

A universal probabilistic spike count model reveals ongoing modulation of neural variability

no code implementations NeurIPS 2021 David Liu, Mate Lengyel

We find that variability in these cells defies a simple parametric relationship with mean spike count as assumed in standard models, its modulation by external covariates can be comparably strong to that of the mean firing rate, and slow low-dimensional latent factors explain away neural correlations.

Gaussian Processes Variational Inference

RAWLSNET: Altering Bayesian Networks to Encode Rawlsian Fair Equality of Opportunity

no code implementations16 Mar 2021 David Liu, Zohair Shafi, William Fleisher, Tina Eliassi-Rad, Scott Alfeld

We present RAWLSNET, a system for altering Bayesian Network (BN) models to satisfy the Rawlsian principle of fair equality of opportunity (FEO).

Feature-Align Network with Knowledge Distillation for Efficient Denoising

no code implementations2 Mar 2021 Lucas D. Young, Fitsum A. Reda, Rakesh Ranjan, Jon Morton, Jun Hu, Yazhu Ling, Xiaoyu Xiang, David Liu, Vikas Chandra

(2) A novel Feature Matching Loss that allows knowledge distillation from large denoising networks in the form of a perceptual content loss.

Efficient Neural Network Image Denoising +2

Human Curation and Convnets: Powering Item-to-Item Recommendations on Pinterest

no code implementations12 Nov 2015 Dmitry Kislyuk, Yuchen Liu, David Liu, Eric Tzeng, Yushi Jing

This paper presents Pinterest Related Pins, an item-to-item recommendation system that combines collaborative filtering with content-based ranking.

Collaborative Filtering

Visual Search at Pinterest

no code implementations28 May 2015 Yushi Jing, David Liu, Dmitry Kislyuk, Andrew Zhai, Jiajing Xu, Jeff Donahue, Sarah Tavel

We demonstrate that, with the availability of distributed computation platforms such as Amazon Web Services and open-source tools, it is possible for a small engineering team to build, launch and maintain a cost-effective, large-scale visual search system with widely available tools.

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