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Found 16 papers, 9 papers with code
Large Language Models As Faithful Explainers
In this work, we introduce a generative explanation framework, xLLM, to improve the faithfulness of the explanations provided in natural language formats for LLMs.
LETA: Learning Transferable Attribution for Generic Vision Explainer
To address this problem, we develop a pre-trained, DNN-based, generic explainer on large-scale image datasets, and leverage its transferability to explain various vision models for downstream tasks.
DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research
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.
DISPEL: Domain Generalization via Domain-Specific Liberating
Domain generalization aims to learn a generalization model that can perform well on unseen test domains by only training on limited source domains.
Efficient GNN Explanation via Learning Removal-based Attribution
A strategy of subgraph sampling is designed in LARA to improve the scalability of the training process.
Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model
While the model parameters do contribute to memory usage, the primary memory bottleneck during training arises from storing feature maps, also known as activations, as they are crucial for gradient calculation.
Interactive System-wise Anomaly Detection
Anomaly detection, where data instances are discovered containing feature patterns different from the majority, plays a fundamental role in various applications.
Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach
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.
CoRTX: Contrastive Framework for Real-time Explanation
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.
Efficient XAI Techniques: A Taxonomic Survey
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.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
DIVISION: Memory Efficient Training via Dual Activation Precision
Activation compressed training provides a solution towards reducing the memory cost of training deep neural networks~(DNNs).
Mitigating Algorithmic Bias with Limited Annotations
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.
Accelerating Shapley Explanation via Contributive Cooperator Selection
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.
BED: A Real-Time Object Detection System for Edge Devices
DNNs have been an effective tool for data processing and analysis.
Fairness via Representation Neutralization
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.
TODS: An Automated Time Series Outlier Detection System
We present TODS, an automated Time Series Outlier Detection System for research and industrial applications.
