The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research.
Domain generalization aims to learn a generalization model that can perform well on unseen test domains by only training on limited source domains.
A strategy of subgraph sampling is designed in LARA to improve the scalability of the training process.
Anomaly detection, where data instances are discovered containing feature patterns different from the majority, plays a fundamental role in various applications.
Motivated by these sufficient conditions, we propose robust fairness regularization (RFR) by considering the worst case within the model weight perturbation ball for each sensitive attribute group.
In this work, we propose a COntrastive Real-Time eXplanation (CoRTX) framework to learn the explanation-oriented representation and relieve the intensive dependence of explainer training on explanation labels.
Finally, we summarize the challenges of deploying XAI acceleration methods to real-world scenarios, overcoming the trade-off between faithfulness and efficiency, and the selection of different acceleration methods.
Activation compressed training provides a solution towards reducing the memory cost of training deep neural networks~(DNNs).
Existing work on fairness modeling commonly assumes that sensitive attributes for all instances are fully available, which may not be true in many real-world applications due to the high cost of acquiring sensitive information.
Even though Shapley value provides an effective explanation for a DNN model prediction, the computation relies on the enumeration of all possible input feature coalitions, which leads to the exponentially growing complexity.
3 code implementations • 14 Feb 2022 • Guanchu Wang, Zaid Pervaiz Bhat, Zhimeng Jiang, Yi-Wei Chen, Daochen Zha, Alfredo Costilla Reyes, Afshin Niktash, Gorkem Ulkar, Erman Okman, Xuanting Cai, Xia Hu
DNNs have been an effective tool for data processing and analysis.
This process not only requires a lot of instance-level annotations for sensitive attributes, it also does not guarantee that all fairness sensitive information has been removed from the encoder.
We present TODS, an automated Time Series Outlier Detection System for research and industrial applications.