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1. Tensorized Feature Spaces for Feature Explosion

   https://doi.org/10.1109/ICPR48806.2021.9412320  

   Pasricha, Ravdeep S. ; Devineni, Pravallika ; Papalexakis, Evangelos E. ; Kannan, Ramakrishnan ( January 2021 , International Conference on Pattern Recognition (ICPR) 2020)
2. Optimum Feature Ordering for Dynamic Instance–Wise Joint Feature Selection and Classification

   https://doi.org/10.1109/ICASSP39728.2021.9414669  

   Liyanage, Yasitha Warahena ; Zois, Daphney-Stavroula ( June 2021 , 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))
3. Inverse Feature Learning: Feature Learning Based on Representation Learning of Error

   https://doi.org/10.1109/ACCESS.2020.3009902  

   Ghazanfari, Behzad ; Afghah, Fatemeh ; Hajiaghayi, Mohammadtaghi ( January 2020 , IEEE Access)
4. Many Faces of Feature Importance: Comparing Built-in and Post-hoc Feature Importance in Text Classification

   https://doi.org/10.18653/v1/D19-1046  

   Lai, Vivian ; Cai, Jon Z. ; Tan, Chenhao ( January 2019 , Empirical Methods in Natural Language Processing)

   Feature importance is commonly used to explain machine predictions. While feature importance can be derived from a machine learning model with a variety of methods, the consistency of feature importance via different methods remains understudied. In this work, we systematically compare feature importance from built-in mechanisms in a model such as attention values and post-hoc methods that approximate model behavior such as LIME. Using text classification as a testbed, we find that 1) no matter which method we use, important features from traditional models such as SVM and XGBoost are more similar with each other, than with deep learning models; 2) post-hoc methods tend to generate more similar important features for two models than built-in methods. We further demonstrate how such similarity varies across instances. Notably, important features do not always resemble each other better when two models agree on the predicted label than when they disagree.
5. Position-Aware Recalibration Module: Learning From Feature Semantics and Feature Position

   https://doi.org/10.24963/ijcai.2020/111  

   Ma, Xu ; Fu, Song ( July 2020 , Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence)

   We present a new method to improve the representational power of the features in Convolutional Neural Networks (CNNs). By studying traditional image processing methods and recent CNN architectures, we propose to use positional information in CNNs for effective exploration of feature dependencies. Rather than considering feature semantics alone, we incorporate spatial positions as an augmentation for feature semantics in our design. From this vantage, we present a Position-Aware Recalibration Module (PRM in short) which recalibrates features leveraging both feature semantics and position. Furthermore, inspired by multi-head attention, our module is capable of performing multiple recalibrations where results are concatenated as the output. As PRM is efficient and easy to implement, it can be seamlessly integrated into various base networks and applied to many position-aware visual tasks. Compared to original CNNs, our PRM introduces a negligible number of parameters and FLOPs, while yielding better performance. Experimental results on ImageNet and MS COCO benchmarks show that our approach surpasses related methods by a clear margin with less computational overhead. For example, we improve the ResNet50 by absolute 1.75% (77.65% vs. 75.90%) on ImageNet 2012 validation dataset, and 1.5%~1.9% mAP on MS COCO validation dataset with almost no computational overhead. Codes are mademore »publicly available.

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