Gary S. W. Goh

selected publications

1. ICPR

   Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution

   Goh, Gary S. W., Lapuschkin, Sebastian, Weber, Leander, Samek, Wojciech, and Binder, Alexander

   In 2020 25th International Conference on Pattern Recognition (ICPR)

   Abstract arXiv HTML PDF Code Poster Slides Talk Bib

   

   Integrated Gradients as an attribution method for deep neural network models offers simple implementability. However, it suffers from noisiness of explanations which affects the ease of interpretability. The SmoothGrad technique is proposed to solve the noisiness issue and smoothen the attribution maps of any gradient-based attribution method. In this paper, we present SmoothTaylor as a novel theoretical concept bridging Integrated Gradients and SmoothGrad, from the Taylor’s theorem perspective. We apply the methods to the image classification problem, using the ILSVRC2012 ImageNet object recognition dataset, and a couple of pretrained image models to generate attribution maps. These attribution maps are empirically evaluated using quantitative measures for sensitivity and noise level. We further propose adaptive noising to optimize for the noise scale hyperparameter value. From our experiments, we find that the SmoothTaylor approach together with adaptive noising is able to generate better quality saliency maps with lesser noise and higher sensitivity to the relevant points in the input space as compared to Integrated Gradients.

   @inproceedings{GohLWSB21Understanding,
     author = {Goh, Gary S. W. and Lapuschkin, Sebastian and Weber, Leander and Samek, Wojciech and Binder, Alexander},
     title = {Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution},
     booktitle = {2020 25th International Conference on Pattern Recognition (ICPR)},
     pages = {4949--4956},
     publisher = {IEEE},
     year = {2021},
     address = {Virtual Event / Milan, Italy},
     doi = {10.1109/ICPR48806.2021.9413242},
     google_scholar_id = {j3f4tGmQtD8C},
     arxiv = {2004.10484}
   }

2. ICDM

   Twitter-Informed Crowd Flow Prediction

   Goh, Gary S. W., Koh, Jing Yu, and Zhang, Yue

   In 2018 IEEE International Conference on Data Mining Workshops (ICDM)

   Abstract HTML PDF Code Poster Slides Bib

   Award: Research Showcase Award (Data Science) at FIRST Industry Workshop 2018

   

   We explore the usage of real-time tweets to explain for non-recurring large-scale spatio-temporal crowd movement. The aim is to evaluate the usefulness of tweets to improve the performance of city-wide crowd flow prediction. We conduct experiments in the context of Singapore city to investigate our proposition by extending upon an existing crowd flow prediction model. Implemented using a deep-neural-network-based approach, an end-to-end predictive model is configured to take in tweets as additional inputs to forecast the future flow of crowds in an urban environment. We extract various features from tweets, such as tweet counts, tweet tenses and sentiments as additional signals to the predictive model. From the experimental results, we show that some models are able to improve the prediction accuracy, and share our insights on how tweets are related to crowd flows.

   @inproceedings{GohKZ18Twitter,
     author = {Goh, Gary S. W. and Koh, Jing Yu and Zhang, Yue},
     title = {Twitter-Informed Crowd Flow Prediction},
     booktitle = {2018 IEEE International Conference on Data Mining Workshops (ICDM)},
     pages = {624--631},
     publisher = {IEEE},
     year = {2018},
     address = {Singapore},
     doi = {10.1109/ICDMW.2018.00097},
     google_scholar_id = {4JMBOYKVnBMC}
   }