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Feature relevance bounds for ordinal regression 27

Feature relevance bounds for ordinal regression

引用

27th European Symposium on Artificial Neural Networks, Computational Intelligence and machine learning, ESANN 2019

作者： Pfannschmidt, Lukas Jakob, Jonathan Biehl, Michael Tino, Peter Hammer, Barbara Machine Learning Group Bielefeld University Germany Intelligent Systems Group University of Groningen Netherlands Computer Science University of Birmingham United Kingdom

ISBN: (纸本)9782875870650

The increasing occurrence of ordinal data, mainly sociodemographic, led to a renewed research interest in ordinal regression, i.e. the prediction of ordered classes. Besides model accuracy, the interpretation of these models itself is of high relevance, and existing approaches therefore enforce e.g. model sparsity. For high dimensional or highly correlated data, however, this might be misleading due to strong variable dependencies. In this contribution, we aim for an identification of feature relevance bounds which – besides identifying all relevant features – explicitly differentiates between strongly and weakly relevant features.1 © 2019 ESANN (***). All rights reserved.

关键词： machine learning

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Classification of Vulnerable Road Users based on Range-Doppler Maps of 77 GHz MIMO Radar using Different machine learning Approaches 22

Classification of Vulnerable Road Users based on Range-Doppl...

引用

6th International Conference on Graphics and Signal Processing, ICGSP 2022

作者： Bayram, Fatih S. Pütz, Florian Weiß, Julian Radtke, Roman Jesser, Alexander Stache, Nicolaj C. Institute for Intelligent Cyber-Physical Systems Heilbronn University of Applied Sciences Germany Center for Machine Learning Heilbronn University of Applied Sciences Germany

ISBN: (纸本)9781450396370

This paper involves the development of an intelligent delineator for road traffic detecting potential conflict situations between motor vehicles and vulnerable road users at an early stage. By emitting warning signals, collisions between the road users concerned can then be prevented. The prototype used here includes, among other sensors, a high-resolution FMCW radar capable of detecting, and imaging objects. The goal of this work is to develop a machine learning (ML) model for object classification of vulnerable road users in radar frames. A 77 GHz chirp-sequence radar is used to record Range-Doppler maps from object classes of car, bicyclist, pedestrian and empty street at different locations. Objective of this is to cover different levels of background noise in the data caused by the different environments due to trees or bushes. For the data acquisition, simple traffic scenarios have been simulated at Heilbronn University. In selecting a suitable ML algorithm for the classifier, the main challenge was that modern machine learning methods are data-based models which require a lot of data and are generally lacking in explainability, such as neural networks. However, the great advantage is that the correlations in the data are learned automatically. With knowledge-based methods, on the other hand, the big advantage is that they are explainable and require much less data, but assume an extensive domain knowledge. Hybrid learning, also called Informed ML, represents a combination of the methods previously mentioned and their advantages. In this paper, one approach from each of these methods is selected as well as trained, and its results are compared to each other. The respective approaches investigated are a deep neural network (DNN), a Support Vector machine (SVM), and a hybrid model of a SVM and a specific neural network for feature extraction called Autoencoder (AE). In this comparison the SVM performs with prediction accuracies around 80%. The hybrid model perfo

关键词： Support vector machines

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Flow Matching for Scalable Simulation-Based Inference 37

Flow Matching for Scalable Simulation-Based Inference

引用

37th Conference on Neural Information Processing systems, NeurIPS 2023

作者： Wildberger, Jonas Buchholz, Simon Macke, Jakob H. Dax, Maximilian Green, Stephen R. Schölkopf, Bernhard Max Planck Institute for Intelligent Systems Tübingen Germany Max Planck Institute for Intelligent Systems & Machine Learning in Science University of Tübingen Tübingen Germany University of Nottingham Nottingham United Kingdom

ISBN: (纸本)9781713899921

Neural posterior estimation methods based on discrete normalizing flows have become established tools for simulation-based inference (SBI), but scaling them to high-dimensional problems can be challenging. Building on recent advances in generative modeling, we here present flow matching posterior estimation (FMPE), a technique for SBI using continuous normalizing flows. Like diffusion models, and in contrast to discrete flows, flow matching allows for unconstrained architectures, providing enhanced flexibility for complex data modalities. Flow matching, therefore, enables exact density evaluation, fast training, and seamless scalability to large architectures-making it ideal for SBI. We show that FMPE achieves competitive performance on an established SBI benchmark, and then demonstrate its improved scalability on a challenging scientific problem: for gravitational-wave inference, FMPE outperforms methods based on comparable discrete flows, reducing training time by 30% with substantially improved accuracy. Our work underscores the potential of FMPE to enhance performance in challenging inference scenarios, thereby paving the way for more advanced applications to scientific problems. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Scalability

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Wasserstein auto-encoders: Latent dimensionality and random encoders 6

Wasserstein auto-encoders: Latent dimensionality and random ...

引用

6th International Conference on learning Representations, ICLR 2018

作者： Rubenstein, Paul Schölkopf, Bernhard Tolstikhin, Ilya Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany Machine Learning Group Engineering Department University of Cambridge United Kingdom

We study the role of latent space dimensionality in Wasserstein auto-encoders (WAEs). Through experimentation on synthetic and real datasets, we argue that random encoders should be preferred over deterministic encoders. © 6th International Conference on learning Representations, ICLR 2018 - Workshop Track Proceedings. All rights reserved.

关键词： Signal encoding

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Software traceability with topic modeling 10

Software traceability with topic modeling

引用

32nd ACM/IEEE International Conference on Software Engineering, ICSE 2010

作者： Asuncion, Hazeline U. Asuncion, Arthur U. Taylor, Richard N. Institute for Software Research University of California Irvine CA United States Center for Machine Learning and Intelligent Systems University of California Irvine CA United States

ISBN: (纸本)9781605587196

Software traceability is a fundamentally important task in software engineering. The need for automated traceability increases as projects become more complex and as the number of artifacts increases. We propose an automated technique that combines traceability with a machine learning technique known as topic modeling. Our approach automatically records traceability links during the software development process and learns a probabilistic topic model over artifacts. The learned model allows for the semantic categorization of artifacts and the topical visualization of the software system. To test our approach, we have implemented several tools: an artifact search tool combining keyword-based search and topic modeling, a recording tool that performs prospective traceability, and a visualization tool that allows one to navigate the software architecture and view semantic topics associated with relevant artifacts and architectural components. We apply our approach to several data sets and discuss how topic modeling enhances software traceability, and vice versa. © 2010 ACM.

关键词： Software architecture

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They are not equally reliable: Semantic event search using differentiated concept classifiers

They are not equally reliable: Semantic event search using d...

引用

2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016

作者： Chang, Xiaojun Yu, Yao-Liang Yang, Yi Xing, Eric P. Centre for Quantum Computation and Intelligent Systems University of Technology Sydney Australia Machine Learning Department Carnegie Mellon University United States

ISBN: (纸本)9781467388511

Complex event detection on unconstrained Internet videos has seen much progress in recent years. However, state-of-the-art performance degrades dramatically when the number of positive training exemplars falls short. Since label acquisition is costly, laborious, and time-consuming, there is a real need to consider the much more challenging semantic event search problem, where no example video is given. In this paper, we present a state-of-the-art event search system without any example videos. Relying on the key observation that events (e.g. dog show) are usually compositions of multiple mid-level concepts (e.g. "dog," "theater," and "dog jumping"), we first train a skip-gram model to measure the relevance of each concept with the event of interest. The relevant concept classifiers then cast votes on the test videos but their reliability, due to lack of labeled training videos, has been largely unaddressed. We propose to combine the concept classifiers based on a principled estimate of their accuracy on the unlabeled test videos. A novel warping technique is proposed to improve the performance and an efficient highly-scalable algorithm is provided to quickly solve the resulting optimization. We conduct extensive experiments on the latest TRECVID MEDTest 2014, MEDTest 2013 and CCV datasets, and achieve state-of-the-art performances.

关键词： Semantic Web

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Informed machine learning 1

Informed Machine Learning

引用

丛书名： Cognitive Technologies

1000年

作者： Daniel Schulz Christian Bauckhage

ISBN: (数字)9783031830976

ISBN: (纸本)9783031830969;9783031830990

This open access book presents the concept of Informed machine learning and demonstrates its practical use with a compelling collection of applications of this paradigm in industrial and business use cases. These range from health care over manufacturing and material science to more advanced combinations with deep learning, say, in the form of physical informed neural networks. The book is intended for those interested in modern informed machine learning for a wide range of practical applications where the aspect of small data sets is a challenge. machine learning with small amounts of data? After the recent success of Artificial Intelligence based on training with massive amounts of data, this idea may sound exotic. However, it addresses crucial needs of practitioners in industry. While many industrial applications stand to benefit from the use of AI, the amounts of data needed by current learning paradigms are often hard to come by in industrial settings. As an alternative, learning methods and models are called for which integrate other sources of knowledge in order to compensate for the lack of data. This is where the principle of “Informed machine learning” comes into play. Informed machine learning combines purely data driven learning and knowledge-based techniques to learn from both data and knowledge. This has several advantages. It reduces the need for data, it often results in smaller, less complex and more robust models, and even makes machine learning applicable in settings where data is scarce. The kind of knowledge to be incorporated into learning processes can take many different forms, for example, differential equations, analytical models, simulation results, logical rules, knowledge graphs, or human feedback which makes the approach overall very powerful and widely applicable.

关键词： machine learning Artificial Intelligence Natural Language Processing (NLP) Data Structures and Information Theory Knowledge based systems Multiagent systems

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Complex event detection using semantic saliency and nearly-isotonic SVM 32

Complex event detection using semantic saliency and nearly-i...

引用

32nd International Conference on machine learning, ICML 2015

作者： Chang, Xiaojun Yang, Yi Xing, Eric P. Yu, Yao-Liang Centre for Quantum Computation and Intelligent Systems University of Technology Sydney SydneyNSW Australia Machine Learning Department Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9781510810587

We aim to detect complex events in long Internet videos that may last for hours. A major challenge in this setting is that only a few shots in a long video are relevant to the event of interest while others are irrelevant or even misleading. Instead of indifferently pooling the shots, we first define a novel notion of semantic saliency that assesses the relevance of each shot with the event of interest. We then prioritize the shots according to their saliency scores since shots that are semantically more salient are expected to contribute more to the final event detector. Next, we propose a new isotonic regularizer that is able to exploit the semantic ordering information. The resulting nearly-isotonic SVM classifier exhibits higher discriminative power. Computationally, we develop an efficient implementation using the proximal gradient algorithm, and we prove new, closed-form proximal steps. We conduct extensive experiments on three real-world video datasets and confirm the effectiveness of the proposed approach. Copyright © 2015 by the author(s).

关键词： Semantics

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Energy Efficiency Optimization in Massive MIMO systems with Low-Resolution ADCs 2

Energy Efficiency Optimization in Massive MIMO Systems with ...

引用

2nd International Conference on Multidisciplinary Engineering and Applied Science, ICMEAS 2023

作者： Musa, Abdulwaheed Adebayo, Abdullateef Ola Peace Balogun, Oluwasijibomi Kwara State University Department of Electrical and Computer Engineering Malete Nigeria Kwara State University Centre for Artificial Intelligence and Machine Learning Systems Malete Nigeria University of Johannesburg Institute for Intelligent Systems South Africa

ISBN: (纸本)9798350358834

Massive multiple-input multiple-output (MIMO) is a promising 5G technology, but the high energy consumption from numerous radio components is a key challenge. Prior work has explored higher than 4-bit ADCs, but very low 1-2 bit remain uninvestigated. This paper provides an in-depth examination of the resolution versus power savings and signal quality degradation trade-offs with 1-3-bit analog-to-digital converters (ADCs) in Massive MIMO systems. Mathematical modeling and simulations quantify the exponential reduction in power consumption along with losses in key performance metrics like signal-to-interference-plus-noise ratio (SINR), throughput, and spectral efficiency with lower ADC resolutions stemming from increased quantization noise. Advanced linear detection techniques are shown to effectively recover the signal quality degradation. The analysis provides valuable insights on jointly optimizing ADC configuration and signal processing to maximize overall system efficiency. Energy savings of over 50% are demonstrated with 1-bit ADCs along with the ability to maintain acceptable performance metrics using linear detectors. The practical implementation feasibility is also discussed. This work enables ultra-low-resolution ADC deployments in energy-efficient Massive MIMO systems. © 2023 IEEE.

关键词： Energy efficiency

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Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping

Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Marc Tuscher Julian Hörz Danny Driess Marc Toussaint sereact Machine Learning and Robotics Lab University of Stuttgart Max-Planck Institute for Intelligent Systems Stuttgart Learning and Intelligent Systems TU Berlin

Robotic manipulation of unknown objects is an important field of research. Practical applications occur in many real-world settings where robots need to interact with an unknown environment. We tackle the problem of reactive grasping by proposing a method for unknown object tracking, grasp point sampling and dynamic trajectory planning. Our object tracking method combines Siamese Networks with an Iterative Closest Point approach for pointcloud registration into a method for 6-DoF unknown object tracking. The method does not require further training and is robust to noise and occlusion. We propose a robotic manipulation system, which is able to grasp a wide variety of formerly unseen objects and is robust against object perturbations and inferior grasping points.

关键词： Training Target tracking Automation Trajectory planning Perturbation methods Conferences Grasping

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