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Hybrid Generative Models for Two-Dimensional Datasets [article]

Hoda Shajari, Jaemoon Lee, Sanjay Ranka, Anand Rangarajan
2021 arXiv   pre-print
The proposed approach is general and can be applied to any dataset, representation basis, or generative model.  ...  We provide a comprehensive performance comparison of various combinations of generative models and representation basis spaces.  ...  deep generative models and representation bases.  ... 
arXiv:2106.00203v2 fatcat:ifvldtdg6ffh3abcs7vkd6tgye

Self-Supervised Learning of a Biologically-Inspired Visual Texture Model [article]

Nikhil Parthasarathy, Eero P. Simoncelli
2020 arXiv   pre-print
Moreover, we show that the learned model exhibits stronger representational similarity to texture responses of neural populations recorded in primate V2 than pre-trained deep CNNs.  ...  When evaluated on texture classification, the trained model achieves substantially greater data-efficiency than a variety of deep hierarchical model architectures.  ...  Acknowledgments This work has been supported by the Howard Hughes Medical Institute (EPS and NP) and the NIH Training Grant in Visual Neuroscience (T32 EY007136-27)  ... 
arXiv:2006.16976v1 fatcat:572ohsdwwrac5hhzdufsp3k2g4

Neural Mixture Models with Expectation-Maximization for End-to-end Deep Clustering [article]

Dumindu Tissera, Kasun Vithanage, Rukshan Wijesinghe, Alex Xavier, Sanath Jayasena, Subha Fernando, Ranga Rodrigo
2021 arXiv   pre-print
Mixture model-based methods model clusters with pre-defined statistical distributions and allocate data to those clusters based on the cluster likelihoods.  ...  In this paper, we realize mixture model-based clustering with a neural network where the final layer neurons, with the aid of an additional transformation, approximate cluster distribution outputs.  ...  of mixture modeling.  ... 
arXiv:2107.02453v1 fatcat:d3q6ih6xo5fezogn4tswi3i4qq

Hierarchical Few-Shot Generative Models [article]

Giorgio Giannone, Ole Winther
2022 arXiv   pre-print
deep generative models.  ...  We explore iterative data sampling, likelihood-based model comparison, and adaptation-free out of distribution generalization.  ...  would like to thank Anders Christensen, Marco Ciccone, Andrea Dittadi, Pierluca D'Oro, Søren Hauberg, Valentin Liévin, Didrik Nielsen, Mathias Schreiner, Timon Willi, Max Wilson for insightful comments and  ... 
arXiv:2110.12279v2 fatcat:acgndbfvlvhadmk4r4be5ntma4

Modeling micro-heterogeneity in mixtures: The role of many body correlations

Anthony Baptista, Aurélien Perera
2019 Journal of Chemical Physics  
aqueous mixtures.  ...  A two-component interaction model is introduced herein, which allows to describe macroscopic miscibility with various modes of tunable micro-segregation, ranging from phase separation to micro-segregation  ...  This way, the BT representation allows to separate the homogeneous and heterogeneous components from the S ab (k) structure factors.  ... 
doi:10.1063/1.5066598 fatcat:tnywrsoqwrebrbsxxdrziu7hvq

Representation of the Weddell Sea deep water masses in the ocean component of the NCAR-CCSM model

Rodrigo Kerr, Ilana Wainer, Mauricio M. Mata
2009 Antarctic Science  
outputs of the NCAR-CCSM model, and probably in other ocean and climate models.  ...  Results point out the need for better representation (and inclusion) of ice-related processes in order to improve the oceanic characteristics and variability of dense Southern Ocean water masses in the  ...  Special thanks go to NCAR/OCE for providing the model outputs.  ... 
doi:10.1017/s0954102009001825 fatcat:vfxd3hmjhnelnirbwqzzrwucue

Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited [article]

Wesley J. Maddox, Gregory Benton, Andrew Gordon Wilson
2020 arXiv   pre-print
understanding of the interplay between parameters and functions in deep models.  ...  We relate effective dimensionality to posterior contraction in Bayesian deep learning, model selection, width-depth tradeoffs, double descent, and functional diversity in loss surfaces, leading to a richer  ...  Function-Space Homogeneity Theorem (Function-Space Homogeneity in Linear Models).  ... 
arXiv:2003.02139v2 fatcat:ib5kxydz5vg6lgbv3ib5aox2ne

Statistical Latent Space Approach for Mixed Data Modelling and Applications [article]

Tu Dinh Nguyen, Truyen Tran, Dinh Phung, Svetha Venkatesh
2017 arXiv   pre-print
homogeneous representation.  ...  Our proposed methods are applied in various applications including latent patient profile modelling in medical data analysis and representation learning for image retrieval.  ...  Separate modelling Similarly to mapping into higher-level representation, in this approach, the models capture data types separately, retrieve latent representations with projections and then fuse them  ... 
arXiv:1708.05594v1 fatcat:5wgecztyvnhu3lua6hawt5mday

Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopy [article]

Claire Donnat, Axel Levy, Frederic Poitevin, Nina Miolane
2022 arXiv   pre-print
In light of the proliferation of such methods and given the interdisciplinary nature of the task, we propose here a critical review of recent advances in the field of deep generative modeling for high  ...  The review begins with an introduction to the mathematical and computational challenges of deep generative models for cryo-EM volume reconstruction, along with an overview of the baseline methodology shared  ...  The approximate posterior of V provides an approximate measure of uncertainty on the homogeneous reconstruction.  ... 
arXiv:2201.02867v2 fatcat:nizvnr5zgzcxlc2mvob3yvbvhu

A model for the freezing of binary colloidal hard spheres

P Bartlett
1990 Journal of Physics: Condensed Matter  
Results are reported for mixtures of diameter ratio y = 0.85 and 0.65. It is shown that the fluid phase is stable to a higher density in the binary mixture than the monodisperse system.  ...  Phase boundaries are calculated for the freezing of a binary mixture of colloidal hard spheres which are assumed to be immiscible in a single solid phase.  ...  Acknowledgments I am particularly indebted to P N Pusey and R H Ottewill for a number of very useful discussions and comments on various aspects of this work.  ... 
doi:10.1088/0953-8984/2/22/018 fatcat:zi2j2sgihfdrdojx45obobw3fi

Multiscale Hybrid Linear Models for Lossy Image Representation

Wei Hong, John Wright, Kun Huang, Yi Ma
2006 IEEE Transactions on Image Processing  
In this paper, we introduce a simple and efficient representation for natural images.  ...  Despite a small overhead of the model, our careful and extensive experimental results show that this new model gives more compact representations for a wide variety of natural images under a wide range  ...  Heterogeneous data can be better-represented using a mixture of parametric models, one for each homogeneous subset. Bases for each model are adaptive to the particular homogeneous subset.  ... 
doi:10.1109/tip.2006.882016 pmid:17153941 fatcat:lyxe7osq2zg6hh7hnwld35n4dm

Statistical mechanics of inhomogeneous model colloid—polymer mixtures

JOSEPH M. BRADER, ROBERT EVANS, MATTHIAS SCHMIDT
2003 Molecular Physics  
, that treats colloid and polymer on an equal footing and which accounts for the fluid-fluid phase separation occurring for larger values of q.  ...  Using the DFT we investigate the properties of the 'free' interface between colloidrich (liquid) and colloid-poor (gas) fluid phases and adsorption phenomena at the interface between the AO mixture and  ...  deep depletion potentials).  ... 
doi:10.1080/0026897032000174263 fatcat:3f7yrw3yprfybjh4b5itg3tndy

A Deep Bag-of-Features Model for Music Auto-Tagging [article]

Juhan Nam, Jorge Herrera, Kyogu Lee
2016 arXiv   pre-print
Feature learning and deep learning have drawn great attention in recent years as a way of transforming input data into more effective representations using learning algorithms.  ...  Through the experiment, we rigorously examine training choices and tuning parameters, and show that the model achieves high performance on Magnatagatune, a popularly used dataset in music auto-tagging.  ...  ACKNOWLEDGMENT This work was supported by Korea Advanced Institute of Science and Technology (Project No. G04140049).  ... 
arXiv:1508.04999v3 fatcat:dc2zlksbgnhbxmodrtgpvtd7te

Text as Environment: A Deep Reinforcement Learning Text Readability Assessment Model [article]

Hamid Mohammadi, Seyed Hossein Khasteh
2019 arXiv   pre-print
Deep reinforcement learning models are demonstrated to be helpful in further improvement of state-of-the-art text readability assessment models.  ...  Sophisticated features and models are being used to evaluate the comprehensibility of texts accurately.  ...  In order to overcome these problems, the current advances in deep learning, reinforcement learning, and their mixture, deep reinforcement learning became advantageous.  ... 
arXiv:1912.05957v2 fatcat:zg5mzwwld5hn3mbxigudopmble

Energy-based Models for Video Anomaly Detection [article]

Hung Vu, Dinh Phung, Tu Dinh Nguyen, Anthony Trevors, Svetha Venkatesh
2017 arXiv   pre-print
Moreover, by leverage on the power of generative networks, i.e. energy-based models, we are also able to learn the feature representation automatically rather than replying on hand-crafted features that  ...  By learning generative model that capture the normality distribution in data, we can isolate abnormal data points that result in low normality scores (high abnormality scores).  ...  Many application domains, including video analysis and scene understanding, can benefit from the results of our research. or deep Gaussian mixture model (Deep GMM) (Feng et al., 2017) .  ... 
arXiv:1708.05211v1 fatcat:fkb2m2vdx5fs5grz3mbbmlyctu
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