Interactive Graph Construction for Graph-Based Semi-Supervised Learning

Chen, Changjian; Wang, Zhaowei; Wu, Jing; Wang, Xiting; Guo, Lan-Zhe; Li, Yufeng; Liu, Shixia

doi:10.1109/tvcg.2021.3084694

Cited by 21 publications

(4 citation statements)

References 48 publications

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“…Users can label instances recommended by an active learning algorithm or select informative instances to label with the help of visualization, which are used to further improve the underlying model. Such an integration is also substantiated by other work [25], [26], [27], [28], [29], [30], [31].…”

Section: Visualization For Annotation Quality Improvementsupporting

confidence: 76%

Towards Better Caption Supervision for Object Detection

Chen¹,

Wu²,

Wang³

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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Section: Visualization For Annotation Quality Improvementsupporting

confidence: 76%

Towards Better Caption Supervision for Object Detection

Chen¹,

Wu²,

Wang³

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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“…Recently, clustering algorithms have become popular in visualizing dense networks to reduce screen space constraints and reveal patterns on large networks. For example, Cakmak et al [24] applied a hierarchical density-based spatial clustering algorithm [25] to reduce VC in visualizing long sequences of dynamic graphs. The clustering algorithm grouped and arranged similar features in pixel-based visualizations to reveal the temporal patterns.…”

Section: Related Workmentioning

confidence: 99%

Visual Analytics in Explaining Neural Networks with Neuron Clustering

Alicioglu,

Sun

2024

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Deep learning (DL) models have achieved state-of-the-art performance in many domains. The interpretation of their working mechanisms and decision-making process is essential because of their complex structure and black-box nature, especially for sensitive domains such as healthcare. Visual analytics (VA) combined with DL methods have been widely used to discover data insights, but they often encounter visual clutter (VC) issues. This study presents a compact neural network (NN) view design to reduce the visual clutter in explaining the DL model components for domain experts and end users. We utilized clustering algorithms to group hidden neurons based on their activation similarities. This design supports the overall and detailed view of the neuron clusters. We used a tabular healthcare dataset as a case study. The design for clustered results reduced visual clutter among neuron representations by 54% and connections by 88.7% and helped to observe similar neuron activations learned during the training process.

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“…MMI uses a variety of human sensory channels and action channels (such as speech, handwriting, posture, sight, expression, touch, smell, taste, and other inputs) to interact with the computer environment in a parallel and imprecise manner. It frees people from the shackles of traditional interaction methods and enables people to enter a period of natural and harmonious human-computer interaction [135,136].…”

Section: Human-computer Interactionmentioning

confidence: 99%

Intelligent Computing: The Latest Advances, Challenges, and Future

Zhu

et al. 2023

Intell Comput

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Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence, and internet of things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human–computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: Intelligent computing is not only intelligence oriented but also intelligence driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing. Intelligent computing is still in its infancy, and an abundance of innovations in the theories, systems, and applications of intelligent computing is expected to occur soon. We present the first comprehensive survey of literature on intelligent computing, covering its theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives. We believe that this survey is highly timely and will provide a comprehensive reference and cast valuable insights into intelligent computing for academic and industrial researchers and practitioners.

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Interactive Graph Construction for Graph-Based Semi-Supervised Learning

Abstract: Filtering 1 Nodes Predicted Class u n la b el ed ai rp la n e b ir d ca r ca t d ee r d o g h o rs e m o n ke y sh ip tr u ck

Cited by 21 publications

References 48 publications

Towards Better Caption Supervision for Object Detection

Towards Better Caption Supervision for Object Detection

Visual Analytics in Explaining Neural Networks with Neuron Clustering

Intelligent Computing: The Latest Advances, Challenges, and Future

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