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learning with Group Noise

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arXiv 2021年

作者： Wang, Qizhou Yao, Jiangchao Gong, Chen Liu, Tongliang Gong, Mingming Yang, Hongxia Han, Bo Department of Computer Science Hong Kong Baptist University Hong Kong Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of MoE School of Computer Science and Engineering Nanjing University of Science and Technology China Data Analytics and Intelligence Lab Alibaba Group China Department of Computing Hong Kong Polytechnic University Hong Kong Trustworthy Machine Learning Lab School of Computer Science University of Sydney Australia School of Mathematics and Statistics University of Melbourne Australia

machine learning in the context of noise is a challenging but practical setting to plenty of real-world applications. Most of the previous approaches in this area focus on the pairwise relation (casual or correlational relationship) with noise, such as learning with noisy labels. However, the group noise, which is parasitic on the coarse-grained accurate relation with the fine-grained uncertainty, is also universal and has not been well investigated. The challenge under this setting is how to discover true pairwise connections concealed by the group relation with its fine-grained noise. To overcome this issue, we propose a novel Max-Matching method for learning with group noise. Specifically, it utilizes a matching mechanism to evaluate the relation confidence of each object (cf. Figure 1) w.r.t. the target, meanwhile considering the Non-IID characteristics among objects in the group. Only the most confident object is considered to learn the model, so that the fine-grained noise is mostly dropped. The performance on a range of real-world datasets in the area of several learning paradigms demonstrates the effectiveness of Max-Matching. Copyright © 2021, The Authors. All rights reserved.

关键词： machine learning

Alchemical harmonic approximation based potential for iso-electronic diatomics: Foundational baseline for ∆-machine learning

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arXiv 2024年

作者： Krug, Simon León Khan, Danish von Lilienfeld, O. Anatole Machine Learning Group Technische Universität Berlin Berlin Charlottenburg 10587 Germany Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Department of Chemistry University of Toronto St. George campus TorontoONM5S 3H6 Canada Berlin Institute for the Foundations of Learning and Data Charlottenburg Berlin10587 Germany Acceleration Consortium University of Toronto 80 St George St TorontoONM5S 3H6 Canada Department of Materials Science and Engineering University of Toronto St. George campus TorontoONM5S 3E4 Canada Department of Physics University of Toronto St. George campus TorontoONM5S 1A7 Canada

We introduce the alchemical harmonic approximation (AHA) of the absolute electronic energy for charge-neutral iso-electronic diatomics at fixed interatomic distance d0. To account for variations in distance, we combine AHA with this Ansatz for the electronic binding potential, E(d) = (Eu − Es)(formula presented), where Eu, Ec, Es correspond to the energies of united atom, calibration at d0, and sum of infinitely separated atoms, respectively. Our model covers the two-dimensional electronic potential energy surface spanned by distances of 0.7 to 2.5 Åand differences in nuclear charge from which only one single point (with elements of nuclear charge Z1, Z2 and distance d0) is drawn to calibrate Ec. Using reference data from pbe0/cc-pVDZ, we present numerical evidence for the electronic ground-state of all neutral diatomics with 8, 10, 12, 14 electrons. We assess the validity of our model by comparison to legacy interatomic potentials (Harmonic oscillator, Lennard-Jones, and Morse) within the most relevant range of binding (0.7-2.5 Å), and find comparable accuracy if restricted to single diatomics, and significantly better predictive power when extrapolating to the entire iso-electronic series. We also investigated ∆-learning of the electronic absolute energy using our model as baseline. This baseline model results in a systematic improvement, effectively reducing training data needs for reaching chemical accuracy by up to an order of magnitude from ∼1000 to ∼100. By contrast, using AHA+Morse as a baseline hardly leads to any improvement, and sometimes even deteriorates the predictive power. Inferring the energy of unseen CO converges to a prediction error of ∼0.1 Ha in direct learning, and ∼0.04 Ha with our baseline. © 2024, CC BY.

关键词： Harmonic analysis

Cross-population coupling of neural activity based on Gaussian process current source densities

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arXiv 2021年

作者： Klein, Natalie Siegle, Joshua H. Teichert, Tobias Kass, Robert E. Department of Statistics and Data Science Carnegie Mellon University PittsburghPA United States Machine Learning Department Carnegie Mellon University PittsburghPA United States MindScope Program Allen Institute SeattleWA United States Department of Psychiatry University of Pittsburgh PittsburghPA United States Department of Bioengineering University of Pittsburgh PittsburghPA United States Neuroscience Institute Carnegie Mellon University PittsburghPA United States

Because local field potentials (LFPs) arise from multiple sources in different spatial locations, they do not easily reveal coordinated activity across neural populations on a trial-to-trial basis. As we show here, however, once disparate source signals are decoupled, their trial-to-trial fluctuations become more accessible, and cross-population correlations become more apparent. To decouple sources we introduce a general framework for estimation of current source densities (CSDs). In this framework, the set of LFPs result from noise being added to the transform of the CSD by a biophysical forward model, while the CSD is considered to be the sum of a zero-mean, stationary, spatiotemporal Gaussian process, having fast and slow components, and a mean function, which is the sum of multiple time-varying functions distributed across space, each varying across trials. We derived biophysical forward models relevant to the data we analyzed. In simulation studies this approach improved identification of source signals compared to existing CSD estimation methods. Using data recorded from primate auditory cortex, we analyzed trial-to-trial fluctuations in both steady-state and task-evoked signals. We found cortical layer-specific phase coupling between two probes and showed that the same analysis applied directly to LFPs did not recover these patterns. We also found task-evoked CSDs to be correlated across probes, at specific cortical depths. Using data from Neuropixels probes in mouse visual areas, we again found evidence for depth-specific phase coupling of primary visual cortex and lateromedial area based on the CSDs. © 2021, CC BY.

关键词： Probes

Occam's razor for AI: Coarse-graining Hammett Inspired Product Ansatz in Chemical Space

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arXiv 2023年

作者： Bragato, Marco Von Rudorff, Guido Falk Von Lilienfeld, O. Anatole University of Vienna Faculty of Physics Kolingasse 1416 WienAT1090 Austria University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Heinrich-Plett-Straße 40 Kassel34132 Germany Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Germany Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

data-hungry machine learning methods have become a new standard to efficiently navigate chemical compound space for molecular and materials design and discovery. Due to the severe scarcity and cost of high-quality experimental or synthetic simulated training data, however, data-acquisition costs can be considerable. Relying on reasonably accurate approximate baseline labels with low computational complexity represents one of the most effective strategies to curb data-needs, e.g. through Δ-, transfer-, or multi-fidelity learning. We introduce baseline labels in the form of a generic coarse-graining Hammett Inspired Product (HIP) Ansatz which generalizes the empirical Hammett equation towards arbitrary systems and properties. Numerical evidence for the applicability of HIP includes solvation free energies of molecules, formation energies of quaternary elpasolite crystals, carbon adsorption energies on heterogeneous catalytic surfaces, HOMO-LUMO gaps of metallorganic complexes, and activation energies for SN2 reactions. After calibration on the same training sets, HIP yields an effective baseline for improved Δ-quantum machine learning models with superior data-efficiency when compared to previously introduced specialised domain-specific models. © 2023, CC0.

关键词： data acquisition

Deep learning Meets Teleconnections: Improving S2S Predictions for European Winter Weather

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arXiv 2025年

作者： Bommer, Philine L. Kretschmer, Marlene Spuler, Fiona R. Bykov, Kirill Höhne, Marina M.-C. Understandable Machine Intelligence Lab TU Berlin Berlin Germany Department of Data Science ATB Potsdam Germany Leipzig Institute for Meteorology Leipzig University Leipzig Germany Department of Meteorology University of Reading Reading United Kingdom The Alan Turing Institute London United Kingdom Institute of Computer Science University of Potsdam Potsdam Germany BIFOLD Berlin Institute for the Foundations of Learning and Data Berlin Germany

Predictions on subseasonal-to-seasonal (S2S) timescales—ranging from two weeks to two months—are crucial for early warning systems but remain challenging owing to chaos in the climate system. Teleconnections, such as the stratospheric polar vortex (SPV) and Madden-Julian Oscillation (MJO), offer windows of enhanced predictability, however, their complex interactions remain underutilized in operational forecasting. Here, we developed and evaluated deep learning architectures to predict North Atlantic-European (NAE) weather regimes, systematically assessing the role of remote drivers in improving S2S forecast skill of deep learning models. We implemented (1) a Long Short-term Memory (LSTM) network predicting the NAE regimes of the next six weeks based on previous regimes, (2) an Index-LSTM incorporating SPV and MJO indices, and (3) a ViT-LSTM using a Vision Transformer to directly encode stratospheric wind and tropical outgoing longwave radiation fields. These models are compared with operational hindcasts as well as other AI models. Our results show that leveraging teleconnection information enhances skill at longer lead times. Notably, the ViT-LSTM outperforms ECMWF’s subseasonal hindcasts beyond week 4 by improving Scandinavian Blocking (SB) and Atlantic Ridge (AR) predictions. Analysis of high-confidence predictions reveals that NAO−, SB, and AR opportunity forecasts can be associated with SPV variability and MJO phase patterns aligning with established pathways, also indicating new patterns. Overall, our work demonstrates that encoding physically meaningful climate fields can enhance S2S prediction skill, advancing AI-driven subseasonal forecast. Moreover, the experiments highlight the potential of deep learning methods as investigative tools, providing new insights into atmospheric dynamics and predictability. © 2025, CC BY.

关键词： Deep learning

Nuclear Neural Networks: Emulating Late Burning Stages in Core Collapse Supernova Progenitors

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arXiv 2025年

作者： Grichener, Aldana Renzo, Mathieu Kerzendorf, Wolfgang E. Farmer, Rob de Mink, Selma E. Bellinger, Earl Patrick Chan, Chi-Kwan Chen, Nutan Farag, Ebraheem Justham, Stephen Steward Steward Observatory Department of Astronomy University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Max Planck Institute for Astrophysics Karl-Schwarzschild-Str. 1 Garching85748 Germany Department of Physics Technion Haifa3200003 Israel Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Ludwig-Maximilians-Universitat Munchen Geschwister-Scholl-Platz 1 Munchen80539 Germany Department of Astronomy Yale University New HavenCT06511 United States Steward Observatory Department of Astronomy University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Avenue TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 North Santa Rita TucsonAZ85721 United States Machine Learning Research Lab Volkswagen AG Munich38440 Germany

One of the main challenges in modeling massive stars to the onset of core collapse is the computational bottleneck of nucleosynthesis during advanced burning stages. The number of isotopes formed requires solving a large set of fully-coupled stiff ordinary differential equations (ODEs), making the simulations computationally intensive and prone to numerical instability. To overcome this barrier, we design a nuclear neural network (NNN) framework with multiple hidden layers to emulate nucleosynthesis calculations and conduct a proof-of-concept to evaluate its performance. The NNN takes the temperature, density and composition of a burning region as input and predicts the resulting isotopic abundances along with the energy generation and loss rates. We generate training sets for initial conditions corresponding to oxygen core depletion and beyond using large nuclear reaction networks, and compare the predictions of the NNNs to results from a commonly used small net. We find that the NNNs improve the accuracy of the electron fraction by 280 − 660 % and the nuclear energy generation by 250 − 750 %, consistently outperforming the small network across all timesteps. They also achieve significantly better predictions of neutrino losses on relatively short timescales, with improvements ranging from 100 − 106 %. While further work is needed to enhance their accuracy and applicability to different stellar conditions, integrating NNN trained models into stellar evolution codes is promising for facilitating large-scale generation of core-collapse supernova (CCSN) progenitors with higher physical fidelity. © 2025, CC BY.

关键词： Nucleosynthesis

Species-specific responses of canopy greenness to the extreme droughts of 2018 and 2022 for four abundant tree species in Germany

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science of the Total Environment 2025年 958卷 177938页

作者： Wang, Yixuan Rammig, Anja Blickensdörfer, Lukas Wang, Yuanyuan Zhu, Xiao Xiang Buras, Allan Professorship for Land Surface-Atmosphere Interactions Technical University of Munich Hans-Carl-v.-Carlowitz-Platz 2 Freising85354 Germany Thünen Institute of Farm Economics Bundesallee 63 Braunschweig38116 Germany Thünen Institute of Forest Ecosystems Alfred-Moeller-Straße 1 Eberswalde16225 Germany Earth Observation Lab Geography Department Humboldt University of Berlin Unter den Linden 6 Berlin10099 Germany Chair of Data Science in Earth Observation Technical University of Munich Arcisstraße 21 Munich80333 Germany Munich Center for Machine Learning Arcisstraße 21 Munich80333 Germany

Germany experienced extreme drought periods in 2018 and 2022, which significantly affected forests. These drought periods were natural experiments, providing valuable insights into how different tree species respond to drought. The quantification of species-specific drought responses may help to identify the most climate-change-resilient tree species, thereby informing effective forest regeneration strategies. In this study, we used remotely sensed peak-season canopy greenness as a proxy for tree vitality to estimate the drought response of four widely abundant tree species in Germany (oak, beech, spruce, and pine). We focused on two questions: (1) How were the four tree species affected by these two droughts? (2) Which environmental parameters primarily determined canopy greenness? To address these questions, we combined a recently published tree species classification map with remotely sensed canopy greenness and environmental variables related to plant available water capacity (PAWC) and atmospheric vapor pressure deficit (VPD). Our results indicate that the more isohydric species featured a greater decline in canopy greenness under these droughts compared to the more anisohydric species despite similar soil moisture conditions. Based on spatial lag models, we found that the influence of PAWC on canopy greenness increases with increasing isohydricity while the influence of VPD decreases. Our statistical analysis indicates that oak was the only species with significantly higher canopy greenness in 2022 compared to 2018. Yet, all species are likely to be susceptible to accumulated drought effects, such as insufficient recovery time and increased vulnerability to biotic pathogens, in the coming years. Our study provides critical insights into the diverse responses of different tree species to changing environments over a large environmental gradient in Central Europe and sheds light on the complex interactions between soil moisture, climate variables, and canopy gre

关键词： Forest ecology

Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States

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arXiv 2022年

作者： Ray, Evan L. Brooks, Logan C. Bien, Jacob Biggerstaff, Matthew Bosse, Nikos I. Bracher, Johannes Cramer, Estee Y. Funk, Sebastian Gerding, Aaron Johansson, Michael A. Rumack, Aaron Wang, Yijin Zorn, Martha Tibshirani, Ryan J. Reich, Nicholas G. School of Public Health and Health Sciences University of Massachusetts Amherst United States Machine Learning Department Carnegie Mellon University United States Department of Data Sciences and Operations University of Southern California United States COVID-19 Response U.S. Centers for Disease Control and Prevention United States London School of Hygiene & Tropical Medicine United Kingdom Statistical Methods and Econometrics Karlsruhe Institute of Technology Germany Computational Statistics Group Heidelberg Institute for Theoretical Studies Germany

The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term burden of COVID-19 in the United States from many contributing teams. We study methods for building an ensemble that combines forecasts from these teams. These experiments have informed the ensemble methods used by the Hub. To be most useful to policy makers, ensemble forecasts must have stable performance in the presence of two key characteristics of the component forecasts: (1) occasional misalignment with the reported data, and (2) instability in the relative performance of component forecasters over time. Our results indicate that in the presence of these challenges, an untrained and robust approach to ensembling using an equally weighted median of all component forecasts is a good choice to support public health decision makers. In settings where some contributing forecasters have a stable record of good performance, trained ensembles that give those forecasters higher weight can also be helpful. © 2022, CC BY.

关键词： COVID-19

Evolutionary Monte Carlo of QM properties in chemical space: Electrolyte design

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arXiv 2023年

作者： Karandashev, Konstantin Weinreich, Jan Heinen, Stefan Arrieta, Daniel Jose Arismendi von Rudorff, Guido Falk Hermansson, Kersti von Lilienfeld, O. Anatole University of Vienna Faculty of Physics Kolingasse 14-16 WienAT-1090 Austria Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Department of Chemistry-Angström Laboratory Uppsala University Box 538 UppsalaSE-75121 Sweden University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Heinrich-Plett-Strase 40 Kassel34132 Germany Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

Optimizing a target function over the space of organic molecules is an important problem appearing in many fields of applied science, but also a very difficult one due to the vast number of possible molecular systems. We propose an Evolutionary Monte Carlo algorithm for solving such problems which is capable of straightforwardly tuning both exploration and exploitation characteristics of an optimization procedure while retaining favourable properties of genetic algorithms. The method, dubbed MOSAiCS (Metropolis Optimization by Sampling Adaptively in Chemical Space), is tested on problems related to optimizing components of battery electrolytes, namely minimizing solvation energy in water or maximizing dipole moment while enforcing a lower bound on the HOMO-LUMO gap;optimization was done over sets of molecular graphs inspired by QM9 and Electrolyte Genome Project (EGP) datasets. MOSAiCS reliably generated molecular candidates with good target quantity values, which were in most cases better than the ones found in QM9 or EGP. While the optimization results presented in this work sometimes required up to 106 QM calculations and were thus only feasible thanks to computationally efficient ab initio approximations of properties of interest, we discuss possible strategies for accelerating MOSAiCS using machine learning approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Electrolytes