scholarly journals Genetic Programming for Evolving a Front of Interpretable Models for Data Visualization

2020 ◽  
Author(s):  
Andrew Lensen ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Data Visualization ◽  
State Of The Art ◽  
Visual Quality ◽  
Model Complexity ◽  
High Quality ◽  
Depth Analysis ◽  
Multiobjective Approach ◽  
Interpretable Models ◽  
Insight Into

Data visualization is a key tool in data mining for understanding big datasets. Many visualization methods have been proposed, including the well-regarded state-of-the-art method t-distributed stochastic neighbor embedding. However, the most powerful visualization methods have a significant limitation: the manner in which they create their visualization from the original features of the dataset is completely opaque. Many domains require an understanding of the data in terms of the original features; there is hence a need for powerful visualization methods which use understandable models. In this article, we propose a genetic programming (GP) approach called GP-tSNE for evolving interpretable mappings from the dataset to high-quality visualizations. A multiobjective approach is designed that produces a variety of visualizations in a single run which gives different tradeoffs between visual quality and model complexity. Testing against baseline methods on a variety of datasets shows the clear potential of GP-tSNE to allow deeper insight into data than that provided by existing visualization methods. We further highlight the benefits of a multiobjective approach through an in-depth analysis of a candidate front, which shows how multiple models can be analyzed jointly to give increased insight into the dataset.

scholarly journals Genetic Programming for Evolving a Front of Interpretable Models for Data Visualization

2020 ◽  
Author(s):  
Andrew Lensen ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Data Visualization ◽  
State Of The Art ◽  
Visual Quality ◽  
Model Complexity ◽  
High Quality ◽  
Depth Analysis ◽  
Multiobjective Approach ◽  
Interpretable Models ◽  
Insight Into

Data visualization is a key tool in data mining for understanding big datasets. Many visualization methods have been proposed, including the well-regarded state-of-the-art method t-distributed stochastic neighbor embedding. However, the most powerful visualization methods have a significant limitation: the manner in which they create their visualization from the original features of the dataset is completely opaque. Many domains require an understanding of the data in terms of the original features; there is hence a need for powerful visualization methods which use understandable models. In this article, we propose a genetic programming (GP) approach called GP-tSNE for evolving interpretable mappings from the dataset to high-quality visualizations. A multiobjective approach is designed that produces a variety of visualizations in a single run which gives different tradeoffs between visual quality and model complexity. Testing against baseline methods on a variety of datasets shows the clear potential of GP-tSNE to allow deeper insight into data than that provided by existing visualization methods. We further highlight the benefits of a multiobjective approach through an in-depth analysis of a candidate front, which shows how multiple models can be analyzed jointly to give increased insight into the dataset.

scholarly journals Genetic Programming for Evolving a Front of Interpretable Models for Data Visualization

2020 ◽  
Author(s):  
Andrew Lensen ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Data Visualization ◽  
State Of The Art ◽  
Visual Quality ◽  
Model Complexity ◽  
High Quality ◽  
Depth Analysis ◽  
Multiobjective Approach ◽  
Interpretable Models ◽  
Insight Into

Data visualization is a key tool in data mining for understanding big datasets. Many visualization methods have been proposed, including the well-regarded state-of-the-art method t-distributed stochastic neighbor embedding. However, the most powerful visualization methods have a significant limitation: the manner in which they create their visualization from the original features of the dataset is completely opaque. Many domains require an understanding of the data in terms of the original features; there is hence a need for powerful visualization methods which use understandable models. In this article, we propose a genetic programming (GP) approach called GP-tSNE for evolving interpretable mappings from the dataset to high-quality visualizations. A multiobjective approach is designed that produces a variety of visualizations in a single run which gives different tradeoffs between visual quality and model complexity. Testing against baseline methods on a variety of datasets shows the clear potential of GP-tSNE to allow deeper insight into data than that provided by existing visualization methods. We further highlight the benefits of a multiobjective approach through an in-depth analysis of a candidate front, which shows how multiple models can be analyzed jointly to give increased insight into the dataset.

scholarly journals Genetic Programming for Evolving a Front of Interpretable Models for Data Visualization

2020 ◽  
Author(s):  
Andrew Lensen ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Data Visualization ◽  
State Of The Art ◽  
Visual Quality ◽  
Model Complexity ◽  
High Quality ◽  
Depth Analysis ◽  
Multiobjective Approach ◽  
Interpretable Models ◽  
Insight Into

Data visualization is a key tool in data mining for understanding big datasets. Many visualization methods have been proposed, including the well-regarded state-of-the-art method t-distributed stochastic neighbor embedding. However, the most powerful visualization methods have a significant limitation: the manner in which they create their visualization from the original features of the dataset is completely opaque. Many domains require an understanding of the data in terms of the original features; there is hence a need for powerful visualization methods which use understandable models. In this article, we propose a genetic programming (GP) approach called GP-tSNE for evolving interpretable mappings from the dataset to high-quality visualizations. A multiobjective approach is designed that produces a variety of visualizations in a single run which gives different tradeoffs between visual quality and model complexity. Testing against baseline methods on a variety of datasets shows the clear potential of GP-tSNE to allow deeper insight into data than that provided by existing visualization methods. We further highlight the benefits of a multiobjective approach through an in-depth analysis of a candidate front, which shows how multiple models can be analyzed jointly to give increased insight into the dataset.

scholarly journals Frame and Feature-Context Video Super-Resolution

2019 ◽  
Vol 33 ◽  
pp. 5597-5604
Author(s):  
Bo Yan ◽  
Chuming Lin ◽  
Weimin Tan
Keyword(s):  
Information Flow ◽  
State Of The Art ◽  
Super Resolution ◽  
Sliding Window ◽  
Visual Quality ◽  
Local Network ◽  
Context Information ◽  
High Quality ◽  
Current State ◽  
Inter Frame

For video super-resolution, current state-of-the-art approaches either process multiple low-resolution (LR) frames to produce each output high-resolution (HR) frame separately in a sliding window fashion or recurrently exploit the previously estimated HR frames to super-resolve the following frame. The main weaknesses of these approaches are: 1) separately generating each output frame may obtain high-quality HR estimates while resulting in unsatisfactory flickering artifacts, and 2) combining previously generated HR frames can produce temporally consistent results in the case of short information flow, but it will cause significant jitter and jagged artifacts because the previous super-resolving errors are constantly accumulated to the subsequent frames.In this paper, we propose a fully end-to-end trainable frame and feature-context video super-resolution (FFCVSR) network that consists of two key sub-networks: local network and context network, where the first one explicitly utilizes a sequence of consecutive LR frames to generate local feature and local SR frame, and the other combines the outputs of local network and the previously estimated HR frames and features to super-resolve the subsequent frame. Our approach takes full advantage of the inter-frame information from multiple LR frames and the context information from previously predicted HR frames, producing temporally consistent highquality results while maintaining real-time speed by directly reusing previous features and frames. Extensive evaluations and comparisons demonstrate that our approach produces state-of-the-art results on a standard benchmark dataset, with advantages in terms of accuracy, efficiency, and visual quality over the existing approaches.

scholarly journals Visions of Automation: A Comparative Discussion of Two Approaches

Societies ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 63
Author(s):  
Philipp Frey
Keyword(s):  
Public Discourse ◽  
State Of The Art ◽  
Critical Social Theory ◽  
General Introduction ◽  
Technological Unemployment ◽  
Depth Analysis ◽  
The Future ◽  
Nuanced Understanding ◽  
The One ◽  
Insight Into

In recent years, fears of technological unemployment have (re-)emerged strongly in public discourse. In response, policymakers and researchers have tried to gain a more nuanced understanding of the future of work in an age of automation. In these debates, it has become common practice to signal expertise on automation by referencing a plethora of studies, rather than limiting oneself to the careful discussion of a small number of selected papers whose epistemic limitations one might actually be able to grasp comprehensively. This paper addresses this shortcoming. I will first give a very general introduction to the state of the art of research on potentials for automation, using the German case as an example. I will then provide an in-depth analysis of two studies of the field that exemplify two competing approaches to the question of automatability: studies that limit themselves to discussing technological potentials for automation on the one hand, and macroeconomic scenario methods that claim to provide more concrete assessments of the connection between job losses (or job creation) and technological innovation in the future on the other. Finally, I will provide insight into the epistemic limitations and the specific vices and virtues of these two approaches from the perspective of critical social theory, thereby contributing to a more enlightened and reflexive debate on the future of automation.

scholarly journals An Adaptive Hierarchical Compositional Model for Phrase Embedding

2018 ◽  
Author(s):  
Bing Li ◽  
Xiaochun Yang ◽  
Bin Wang ◽  
Wei Wang ◽  
Wei Cui ◽  
...  
Keyword(s):  
Vector Space ◽  
Experimental Evaluation ◽  
State Of The Art ◽  
Model Complexity ◽  
Semantic Structure ◽  
Time Cost ◽  
Compositional Model ◽  
Novel Method ◽  
Adaptively Adjusting ◽  
Insight Into

Phrase embedding aims at representing phrases in a vector space and it is important for the performance of many NLP tasks. Existing models only regard a phrase as either full-compositional or non-compositional, while ignoring the hybrid-compositionality that widely exists, especially in long phrases. This drawback prevents them from having a deeper insight into the semantic structure for long phrases and as a consequence, weakens the accuracy of the embeddings. In this paper, we present a novel method for jointly learning compositionality and phrase embedding by adaptively weighting different compositions using an implicit hierarchical structure. Our model has the ability of adaptively adjusting among different compositions without entailing too much model complexity and time cost. To the best of our knowledge, our work is the first effort that considers hybrid-compositionality in phrase embedding. The experimental evaluation demonstrates that our model outperforms state-of-the-art methods in both similarity tasks and analogy tasks.

scholarly journals High-Quality On-Patient Medical Data Visualization in a Markerless Augmented Reality Environment

2014 ◽  
Vol 5 (3) ◽  
pp. 1
Author(s):  
Márcio Cerqueira de Farias Macedo ◽  
Antônio Lopes Apolinário Júnior ◽  
Antonio Carlos dos Santos Souza ◽  
Gilson Antônio Giraldi
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Data Visualization ◽  
Volume Rendering ◽  
Reference Model ◽  
Region Of Interest ◽  
Image Data ◽  
Visual Quality ◽  
Medical Data ◽  
High Quality

To provide on-patient medical data visualization, a medical augmented reality environment must support volume rendering, accurate tracking, real-time performance and high visual quality in the final rendering. Another interesting feature is markerless registration, to solve the intrusiveness introduced by the use of fiducial markers for tracking. In this paper we address the problem of on-patient medical data visualization in a real-time high-quality markerless augmented reality environment. The medical data consists of a volume reconstructed from 3D computed tomography image data. Markerless registration is done by generating a 3D reference model of the region of interest in the patient and tracking it from the depth stream of an RGB-D sensor. From the estimated camera pose, the volumetric medical data and the reference model are combined allowing a visualization of the patient as well as part of his anatomy. To improve the visual perception of the scene, focus+context visualization is used in the augmented reality scene to dynamically define which parts of the medical volume will be visualized in the context of the patient’s image. Moreover, context-preserving volume rendering is employed to dynamically control which parts of the volume will be rendered. The results obtained show that the markerless environment runs in real-time and the techniques applied greatly improve the visual quality of the final rendering.

scholarly journals Leveraging TSP Solver Complementarity through Machine Learning

2018 ◽  
Vol 26 (4) ◽  
pp. 597-620 ◽  
Author(s):  
Pascal Kerschke ◽  
Lars Kotthoff ◽  
Jakob Bossek ◽  
Holger H. Hoos ◽  
Heike Trautmann
Keyword(s):  
Performance Improvement ◽  
State Of The Art ◽  
Large Set ◽  
Depth Analysis ◽  
Benchmark Instances ◽  
Hard Problems ◽  
Improved Performance ◽  
First Time ◽  
Travelling Salesperson Problem ◽  
Insight Into

The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard problems. Over the years, many different solution approaches and solvers have been developed. For the first time, we directly compare five state-of-the-art inexact solvers—namely, LKH, EAX, restart variants of those, and MAOS—on a large set of well-known benchmark instances and demonstrate complementary performance, in that different instances may be solved most effectively by different algorithms. We leverage this complementarity to build an algorithm selector, which selects the best TSP solver on a per-instance basis and thus achieves significantly improved performance compared to the single best solver, representing an advance in the state of the art in solving the Euclidean TSP. Our in-depth analysis of the selectors provides insight into what drives this performance improvement.

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