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Cosmology with Galaxy Photometry Alone

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arXiv 2023年

作者： Hahn, ChangHoon Villaescusa-Navarro, Francisco Melchior, Peter Teyssier, Romain Department of Astrophysical Sciences Princeton University Peyton Hall PrincetonNJ08544 United States Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Center for Statistics & Machine Learning Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University Fine Hall Washington Road PrincetonNJ08544-1000 United States

We present the first cosmological constraints using only the observed photometry of galaxies. Villaescusa-Navarro et al. (2022b) recently demonstrated that the internal physical properties of a single simulated galaxy contain a significant amount of cosmological information. These physical properties, however, cannot be directly measured from observations. In this work, we present how we can go beyond theoretical demonstrations to infer cosmological constraints from actual galaxy observables (e.g. optical photometry) using neural density estimation and the CAMELS suite of hydrodynamical simulations. We find that the cosmological information in the photometry of a single galaxy is limited. However, we combine the constraining power of photometry from many galaxies using hierarchical population inference and place significant cosmological constraints. With the observed photometry of ∼20,000 NASA-Sloan Atlas galaxies, we constrain (formula presented) and (formula presented). © 2023, CC BY.

关键词： Galaxies

The optical and infrared are connected

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arXiv 2025年

作者： Jespersen, Christian Kragh Melchior, Peter Spergel, David N. Goulding, Andy D. Hahn, ChangHoon Iyer, Kartheik G. Department of Astrophysical Sciences Princeton University PrincetonNJ08544 United States Center for Statistics and Machine Learning Princeton University PrincetonNJ08544 United States Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Columbia University Columbia Astrophysics Lab 550 W 120th St New YorkNY10010 United States

Galaxies are often modelled as composites of separable components with distinct spectral signatures, implying that different wavelength ranges are only weakly correlated. They are not. We present a data-driven model which exploits subtle correlations between physical processes to accurately predict infrared (IR) WISE photometry from a neural summary of optical SDSS spectra. The model achieves accuracies of χ2N ≈ 1 for all photometric bands in WISE, as well as good colors. We are also able to tightly constrain typically IR-derived properties, e.g. the bolometric luminosities of AGN and dust parameters such as qPAH. We find that current SED-fitting methods are incapable of making comparable predictions, and that model misspecification often leads to correlated biases in star-formation rates and AGN luminosities. To help improve SED models, we determine what features of the optical spectrum are responsible for our improved predictions, and identify several lines (CaII, SrII, FeI, [OII] and Hα), which point to the complex chronology of star formation and chemical enrichment being incorrectly modelled. © 2025, CC BY.

关键词： Stars

Atomic Filter: a Weak Form of Shift Operator for Graph Signals

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arXiv 2022年

作者： Yang, Lihua Zhang, Qing Zhang, Qian Huang, Chao School of Mathematics Sun Yat-sen University Guangzhou510275 China Guangdong Province Key Laboratory of Computational Science China College of Mathematics and Statistics Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

The shift operation plays a crucial role in the classical signal processing. It is the generator of all the filters and the basic operation for time-frequency analysis, such as windowed Fourier transform and wavelet transform. With the rapid development of internet technology and big data science, a large amount of data are expressed as signals defined on graphs. In order to establish the theory of filtering, windowed Fourier transform and wavelet transform in the setting of graph signals, we need to extend the shift operation of classical signals to graph signals. It is a fundamental problem since the vertex set of a graph is usually not a vector space and the addition operation cannot be defined on the vertex set of the graph. In this paper, based on our understanding on the core role of shift operation in classical signal processing we propose the concept of atomic filters, which can be viewed as a weak form of the shift operator for graph signals. Then, we study the conditions such that an atomic filter is norm-preserving, periodic, or real-preserving. The property of real-preserving holds naturally in the classical signal processing, but no the research has been reported on this topic in the graph signal setting. With these conditions we propose the concept of normal atomic filters for graph signals, which degenerates into the classical shift operator under mild conditions if the graph is circulant. Typical examples of graphs that have or have not normal atomic filters are given. Finally, as an application, atomic filters are utilized to construct time-frequency atoms which constitute a frame of the graph signal space. Copyright © 2022, The Authors. All rights reserved.

关键词： Wavelet transforms

The Role of Global Labels in Few-Shot Classification and How to Infer Them

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arXiv 2021年

作者： Wang, Ruohan Pontil, Massimiliano Ciliberto, Carlo Center for AI Department of Computer Science University College London London United Kingdom Institute of Infocomm Research A*STAR Singapore Computational Statistics and Machine Learning Istituto Italiano di Tecnologia Genova Italy

Few-shot learning is a central problem in meta-learning, where learners must quickly adapt to new tasks given limited training data. Recently, feature pre-training has become a ubiquitous component in state-of-the-art meta-learning methods and is shown to provide significant performance improvement. However, there is limited theoretical understanding of the connection between pre-training and meta-learning. Further, pre-training requires global labels shared across tasks, which may be unavailable in practice. In this paper, we show why exploiting pre-training is theoretically advantageous for meta-learning, and in particular the critical role of global labels. This motivates us to propose Meta Label learning (MeLa), a novel meta-learning framework that automatically infers global labels to obtains robust few-shot models. Empirically, we demonstrate that MeLa is competitive with existing methods and provide extensive ablation experiments to highlight its key properties. © 2021, CC BY.

关键词： learning systems

A Novel ECG Signal Classification Algorithm Based on Common and Specific Components Separation 2nd

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A Novel ECG Signal Classification Algorithm Based on Common ...

2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： Huang, Jianfeng Huang, Chao Yang, Lihua Zhang, Qian School of Financial Mathematics and Statistics Guangdong University of Finance Guangzhou China College of Mathematics and Statistics Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen China Guangdong Province Key Laboratory of Computational Science School of Mathematics Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783030598297

Electrocardiography (ECG) signal classification is a challenging task since the characteristics of ECG signals vary significantly for different patients. In this paper, we propose a new method for ECG signal classification based on the separation of common and specific components of a signal. The common components are obtained via Canonical Correlation Analysis (CCA). After removing the common components from the signal, we map the specific components to a lower dimensional feature space for classification. We first establish a basic model in the binary classification setting and then extend it to a more general version. Numerical experiments results on the MIT-BIH Arrhythmia Database are presented and discussed. © 2020, Springer Nature Switzerland AG.

关键词： Electrocardiography

Gravitational-Wave Parameter Estimation in non-Gaussian noise using Score-Based Likelihood Characterization

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arXiv 2024年

作者： Legin, Ronan Isi, Maximiliano Wong, Kaze W.K. Hezaveh, Yashar Perreault-Levasseur, Laurence Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning MontréalQC Canada Department of Physics Université de Montréal MontréalQC Canada Mila - Quebec Artificial Intelligence Institute MontréalQC Canada Center for Computational Astrophysics Flatiron Institute New YorkNY United States Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD United States Trottier Space Institute MontréalQC Canada Perimeter Institute for Theoretical Physics WaterlooON Canada

Gravitational-wave (GW) parameter estimation typically assumes that instrumental noise is Gaussian and stationary. Obvious departures from this idealization are typically handled on a case-by-case basis, e.g., through bespoke procedures to "clean" non-Gaussian noise transients (glitches), as was famously the case for the GW170817 neutron-star binary. Although effective, manipulating the data in this way can introduce biases in the inference of key astrophysical properties, like binary precession, and compound in unpredictable ways when combining multiple observations;alternative procedures free of the same biases, like joint inference of noise and signal properties, have so far proved too computationally expensive to execute at scale. Here we take a different approach: rather than explicitly modeling individual non-Gaussianities to then apply the traditional GW likelihood, we seek to learn the true distribution of instrumental noise without presuming Gaussianity and stationarity in the first place. Assuming only noise additivity, we employ score-based diffusion models to learn an empirical noise distribution directly from detector data and then combine it with a deterministic waveform model to provide an unbiased estimate of the likelihood function. We validate the method by performing inference on a subset of GW parameters from 400 mock observations, containing real LIGO noise from either the Livingston or Hanford detectors. We show that the proposed method can recover the true parameters even in the presence of loud glitches, and that the inference is unbiased over a population of signals without applying any cleaning to the data. This work provides a promising avenue for extracting unbiased source properties in future GW observations over the coming decade. © 2024, CC BY.

关键词： Background noise

Position: Bayesian Deep learning is Needed in the Age of Large-Scale AI 41

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Position: Bayesian Deep Learning is Needed in the Age of Lar...

41st International Conference on machine learning, ICML 2024

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)

关键词： Contrastive learning

Rationalising data collection for supporting decision making in building energy systems using Value of Information analysis

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arXiv 2024年

作者： Langtry, Max Zhuang, Chaoqun Ward, Rebecca Makasis, Nikolas Kreitmair, Monika J. Conti, Zack Xuereb Di Francesco, Domenic Choudhary, Ruchi Energy Efficient Cities Initiative Department of Engineering University of Cambridge Trumpington Street CambridgeCB2 1PZ United Kingdom Data-Centric Engineering The Alan Turing Institute British Library LondonNW1 2DB United Kingdom School of Sustainability Civil & Environmental Engineering University of Surrey GuilfordGU2 7XH United Kingdom Computational Statistics & Machine Learning Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom

The use of data collection to support decision making through the reduction of uncertainty is ubiquitous in the management, operation, and design of building energy systems. However, no existing studies in the building energy systems literature have quantified the economic benefits of data collection strategies to determine whether they are worth their cost. This work demonstrates that Value of Information analysis (VoI), a Bayesian Decision Analysis framework, provides a suitable methodology for quantifying the benefits of data collection. Three example decision problems in building energy systems are studied: air-source heat pump maintenance scheduling, ventilation scheduling for indoor air quality, and ground-source heat pump system design. Smart meters, occupancy monitoring systems, and ground thermal tests are shown to be economically beneficial for supporting these decisions respectively. It is proposed that further study of VoI in building energy systems would allow expenditure on data collection to be economised and prioritised, avoiding wastage. © 2024, CC BY.

关键词： Indoor air pollution

On the Iteration Complexity of Hypergradient Computation

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arXiv 2020年

作者： Grazzi, Riccardo Franceschi, Luca Pontil, Massimiliano Salzo, Saverio Computational Statistics and Machine Learning Istituto Italiano di Tecnologia Genoa Italy Department of Computer Science University College London London United Kingdom

We study a general class of bilevel problems, consisting in the minimization of an upper-level objective which depends on the solution to a parametric fixed-point equation. Important instances arising in machine learning include hyperparameter optimization, meta-learning, and certain graph and recurrent neural networks. Typically the gradient of the upper-level objective (hypergradient) is hard or even impossible to compute exactly, which has raised the interest in approximation methods. We investigate some popular approaches to compute the hypergradient, based on reverse mode iterative differentiation and approximate implicit differentiation. Under the hypothesis that the fixed point equation is defined by a contraction mapping, we present a unified analysis which allows for the first time to quantitatively compare these methods, providing explicit bounds for their iteration complexity. This analysis suggests a hierarchy in terms of computational efficiency among the above methods, with approximate implicit differentiation based on conjugate gradient performing best. We present an extensive experimental comparison among the methods which confirm the theoretical findings. Copyright © 2020, The Authors. All rights reserved.

关键词： computational efficiency