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Training Artificial Neural Network to Classify Correct and Erroneous Interpretations of Visual Stimuli before Behavioral Response 6

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Training Artificial Neural Network to Classify Correct and E...

6th Scientific School "Dynamics of Complex Networks and their Applications", DCNA 2022

作者： Kuc, Alexander Batmanova, Alisa Maksimenko, Vladimir Immanuel Kant Baltic Federal University Baltic Center for Artificial Intelligence and Neurotechnology Kaliningrad Russia Financial University The Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia Innopolis University Neuroscience and Cognitive Technology Lab Republic of Tatarstan Russia

ISBN: (数字)9781665461740

ISBN: (纸本)9781665461740

We have trained an artificial neural network (ANN) to predict correct and erroneous interpretations of complex visual stimuli, Necker cubes with different levels of ambiguity. For the selected configuration of ANN, the classification accuracy was 88%. These results are prospective for developing the new-generation assistive technologies. © 2022 IEEE.

关键词： Neural networks

On the Impact of Explanations on Understanding of Algorithmic Decision-Making

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arXiv 2023年

作者： Schmude, Timothée Koesten, Laura Möller, Torsten Tschiatschek, Sebastian University of Vienna Faculty of Computer Science Research Network Data Science UniVie Doctoral School Computer Science DoCS Austria University of Vienna Faculty of Computer Science Research Group Visualization and Data Analysis Austria University of Vienna Faculty of Computer Science Research Network Data Science Research Group Visualization and Data Analysis Austria University of Vienna Faculty of Computer Science Research Network Data Science Research Group Data Mining and Machine Learning Austria

Ethical principles for algorithms are gaining importance as more and more stakeholders are affected by "high-risk" algorithmic decision-making (ADM) systems. Understanding how these systems work enables stakeholders to make informed decisions and to assess the systems’ adherence to ethical values. Explanations are a promising way to create understanding, but current explainable artificial intelligence (XAI) research does not always consider existent theories on how understanding is formed and evaluated. In this work, we aim to contribute to a better understanding of understanding by conducting a qualitative task-based study with 30 participants, including users and affected stakeholders. We use three explanation modalities (textual, dialogue, and interactive) to explain a "high-risk" ADM system to participants and analyse their responses both inductively and deductively, using the "six facets of understanding" framework by Wiggins & McTighe [63]. Our findings indicate that the "six facets" framework is a promising approach to analyse participants’ thought processes in understanding, providing categories for both rational and emotional understanding. We further introduce the "dialogue" modality as a valid explanation approach to increase participant engagement and interaction with the "explainer", allowing for more insight into their understanding in the process. Our analysis further suggests that individuality in understanding affects participants’ perceptions of algorithmic fairness, demonstrating the interdependence between understanding and ADM assessment that previous studies have outlined. We posit that drawing from theories on learning and understanding like the "six facets" and leveraging explanation modalities can guide XAI research to better suit explanations to learning processes of individuals and consequently enable their assessment of ethical values of ADM systems. © 2023, CC BY.

关键词： Decision making

Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Making

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arXiv 2023年

We argue that explanations for "algorithmic decision-making" (ADM) systems can profit by adopting practices that are already used in the learning sciences. We shortly introduce the importance of explaining ADM systems, give a brief overview of approaches drawing from other disciplines to improve explanations, and present the results of our qualitative task-based study incorporating the "six facets of understanding" [24] framework. We close with questions guiding the discussion of how future studies can leverage an interdisciplinary approach. © 2023, CC BY.

关键词： Decision making

BLESSEMFLOOD21: ADVANCING FLOOD analysis WITH A HIGH-RESOLUTION GEOREFERENCED dataSET FOR HUMANITARIAN AID SUPPORT

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arXiv 2024年

作者： Polushko, Vladyslav Jenal, Alexander Bongartz, Jens Weber, Immanuel Hatic, Damjan Rösch, Ronald März, Thomas Rauhut, Markus Weinmann, Andreas Image Processing Department Fraunhofer ITWM Kaiserslautern Germany Working Group Algorithms for Computer Vision Imaging and Data Analysis Hochschule Darmstadt Darmstadt Germany Center for Machine Learning and Sensor Technology Hochschule Koblenz Remagen Germany

Floods are an increasingly common global threat, causing emergencies and severe damage to infrastructure. During crises, organisations such as the World Food Programme use remotely sensed imagery, typically obtained through drones, for rapid situational analysis to plan life-saving actions. Computer Vision tools are needed to support task force experts on-site in the evaluation of the imagery to improve their efficiency and to allocate resources strategically. We introduce the BlessemFlood21 dataset to stimulate research on efficient flood detection tools. The imagery was acquired during the 2021 Erftstadt-Blessem flooding event and consists of high-resolution and georeferenced RGB-NIR images. In the resulting RGB dataset, the images are supplemented with detailed water masks, obtained via a semi-supervised human-in-the-loop technique, where in particular the NIR information is leveraged to classify pixels as either water or non-water. We evaluate our dataset by training and testing established Deep learning models for semantic segmentation. With BlessemFlood21 we provide labeled high-resolution RGB data and a baseline for further development of algorithmic solutions tailored to flood detection in RGB imagery. © 2024, CC BY-NC-ND.

关键词： Floods

An improved finite-time analysis of temporal difference learning with deep neural networks 24

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An improved finite-time analysis of temporal difference lear...

Proceedings of the 41st International Conference on machine learning

作者： Zhifa Ke Zaiwen Wen Junyu Zhang Center for Data Science Peking University China Beijing International Center for Mathematical Research Center for Machine Learning Research and Changsha Institute for Computing and Digital Economy Beijing China Department of Industrial Systems Engineering and Management National University of Singapore Singapore

Temporal difference (TD) learning algorithms with neural network function parameterization have well-established empirical success in many practical large-scale reinforcement learning tasks. However, theoretical understanding of these algorithms remains challenging due to the nonlinearity of the action-value approximation. In this paper, we develop an improved nonasymptotic analysis of the neural TD method with a general L-layer neural network. New proof techniques are developed and an improved new Õ(ε-1) sample complexity is derived. To our best knowledge, this is the first finite-time analysis of neural TD that achieves an Õ(ε-1) complexity under the Markovian sampling, as opposed to the best known Õ(ε-2) complexity in the existing literature.

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An Improved Finite-time analysis of Temporal Difference learning with Deep Neural Networks

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arXiv 2024年

作者： Ke, Zhifa Wen, Zaiwen Zhang, Junyu Center for Data Science Peking University China Beijing International Center for Mathematical Research Center for Machine Learning Research Changsha Institute for Computing and Digital Economy Beijing China Department of Industrial Systems Engineering and Management National University of Singapore Singapore

Temporal difference (TD) learning algorithms with neural network function parameterization have well-established empirical success in many practical large-scale reinforcement learning tasks. However, theoretical understanding of these algorithms remains challenging due to the nonlinearity of the action-value approximation. In this paper, we develop an improved non-asymptotic analysis of the neural TD method with a general L-layer neural network. New proof techniques are developed and an improved new Õ(ϵ−1) sample complexity is derived. To our best knowledge, this is the first finite-time analysis of neural TD that achieves an Õ(ϵ−1) complexity under the Markovian sampling, as opposed to the best known Õ(ϵ−2) complexity in the existing literature. Copyright © 2024, The Authors. All rights reserved.

关键词： Asymptotic analysis

HERALD: A NATURAL LANGUAGE ANNOTATED LEAN 4 dataSET

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arXiv 2024年

作者： Gao, Guoxiong Wang, Yutong Jiang, Jiedong Gao, Qi Qin, Zihan Xu, Tianyi Dong, Bin Peking University China National University of Singapore Singapore Center for Data Science Peking University China Beijing International Center for Mathematical Research The New Cornerstone Science Laboratory Peking University China Center for Machine Learning Research Peking University China

Verifiable formal languages like Lean have profoundly impacted mathematical reasoning, particularly through the use of large language models (LLMs) for automated reasoning. A significant challenge in training LLMs for these formal languages is the lack of parallel datasets that align natural language with formal language proofs. To address this challenge, this paper introduces a novel framework for translating the Mathlib4 corpus (a unified library of mathematics in formal language Lean 4) into natural language. Building upon this, we employ a dual augmentation strategy that combines tactic-based and informal-based approaches, leveraging the Lean-jixia system, a Lean 4 analyzer. We present the results of this pipeline on Mathlib4 as Herald (Hierarchy and Retrieval-based Translated Lean dataset). We also propose the Herald Translator, which is fine-tuned on Herald. Herald translator achieves a 93.2% accuracy (Pass@128) on formalizing statements in the miniF2F-test and a 22.5% accuracy on our internal graduate-level textbook dataset, outperforming InternLM2-Math-Plus-7B (74.0% and 7.5%) and TheoremLlama (50.1% and 4.0%). Furthermore, we propose a section-level translation framework for real-world applications. As a direct application of Herald translator, we have successfully translated a template section in the Stack project, marking a notable progress in the automatic formalization of graduate-level mathematical literature. Our model, along with the datasets, are open-sourced to the public.1 © 2024, CC BY.

关键词： Lean production

RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement

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RecycleNet: Latent Feature Recycling Leads to Iterative Deci...

IEEE Workshop on Applications of Computer Vision (WACV)

作者： Gregor Koehler Tassilo Wald Constantin Ulrich David Zimmerer Paul F. Jaeger Jörg K. H. Franke Simon Kohl Fabian Isensee Klaus H. Maier-Hein Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Information and Data Science School for Health Karlsruhe/Heidelberg Germany Helmholtz Imaging DKFZ National Center for Tumor Diseases (NCT) NCT Heidelberg a Partnership Between DKFZ University Medical Center Heidelberg Interactive Machine Learning Group DKFZ Machine Learning Lab University of Freiburg Freiburg Germany Latent Labs (***) London UK Applied Computer Vision Lab DKFZ Pattern Analysis and Learning Group Heidelberg University Hospital Heidelberg Germany

Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we can not only form a decision on the spot, but also ponder, revisiting an initial guess from different angles, distilling relevant information, arriving at a better decision. Here, we propose RecycleNet, a latent feature recycling method, instilling the pondering capability for neural networks to refine initial decisions over a number of recycling steps, where outputs are fed back into earlier network layers in an iterative fashion. This approach makes minimal assumptions about the neural network architecture and thus can be implemented in a wide variety of contexts. Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision. We evaluate this across a variety of segmentation benchmarks and show consistent improvements even compared with top-performing segmentation methods. This allows trading increased computation time for improved performance, which can be beneficial, especially for safety-critical applications.

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Can a bifurcation diagram contain loops?

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Can a bifurcation diagram contain loops?

2021 International Conference "Nonlinearity, Information and Robotics", NIR 2021

作者： Palshin, Gleb P. Ryabov, Pavel E. Sokolov, Sergei V. Financial University Department of Data Analysis and Machine Learning Moscow Russia Moscow Institute of Physics and Technology National Research University Department of Theoretical Mechanics Moscow Region Dolgoprudny Russia

ISBN: (纸本)9781665424066

The bifurcation diagram plays a major role in the study of the phase topology of completely Liouville-integrable Hamiltonian systems. In the works of A.T. Fomenko and A.V. Bolsinov, the problem of the permissible form of bifurcation diagrams is formulated. In particular, can a bifurcation diagram contain loops? The answer is positive. In this paper, the critical set of the integral mapping and the bifurcation diagram, which contains or almost contains a loop, is given by the example of the vortex dynamics problem. © 2021 IEEE.

关键词： Bifurcation (mathematics)