Search Results for author:
Found 12 papers, 4 papers with code
DUE: Dynamic Uncertainty-Aware Explanation Supervision via 3D Imputation
Explanation supervision aims to enhance deep learning models by integrating additional signals to guide the generation of model explanations, showcasing notable improvements in both the predictability and explainability of the model.
3DPFIX: Improving Remote Novices' 3D Printing Troubleshooting through Human-AI Collaboration
While troubleshooting plays an essential part of 3D printing, the process remains challenging for many remote novices even with the help of well-developed online sources, such as online troubleshooting archives and online community help.
A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks
Federated learning (FL) is a distributed machine learning (ML) paradigm, allowing multiple clients to collaboratively train shared machine learning (ML) models without exposing clients' data privacy.
Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations
To mitigate the gap, we designed DeepFuse, the first interactive design that realizes the direct feedback loop between a user and CNNs in diagnosing and revising CNN's vulnerability using local explanations.
MAGI: Multi-Annotated Explanation-Guided Learning
This technique aims to improve the predictability of the model by incorporating human understanding of the prediction process into the training phase.
Saliency-Augmented Memory Completion for Continual Learning
Specifically, we innovatively propose to store the part of the image most important to the tasks in episodic memory by saliency map extraction and memory encoding.
Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning
As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing DNNs become more complex and diverse, ranging from improving a conventional model accuracy metric to infusing advanced human virtues such as fairness, accountability, transparency (FaccT), and unbiasedness.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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RES: A Robust Framework for Guiding Visual Explanation
Despite the fast progress of explanation techniques in modern Deep Neural Networks (DNNs) where the main focus is handling "how to generate the explanations", advanced research questions that examine the quality of the explanation itself (e. g., "whether the explanations are accurate") and improve the explanation quality (e. g., "how to adjust the model to generate more accurate explanations when explanations are inaccurate") are still relatively under-explored.
Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignment
We propose a novel framework of Interactive Attention Alignment (IAA) that aims at realizing human-steerable Deep Neural Networks (DNNs).
Schematic Memory Persistence and Transience for Efficient and Robust Continual Learning
However, since forgetting is inevitable given bounded memory and unbounded task loads, 'how to reasonably forget' is a problem continual learning must address in order to reduce the performance gap between AIs and humans, in terms of 1) memory efficiency, 2) generalizability, and 3) robustness when dealing with noisy data.
BEAN: Interpretable Representation Learning with Biologically-Enhanced Artificial Neuronal Assembly Regularization
Deep neural networks (DNNs) are known for extracting useful information from large amounts of data.
DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums
In this paper, we first formulate the transition of user activities as a dynamic graph with multi-attributed nodes, then formalize the health stage inference task as a dynamic graph-to-sequence learning problem, and hence propose a novel dynamic graph-to-sequence neural networks architecture (DynGraph2Seq) to address all the challenges.
