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https://papers.nips.cc/paper/2021/hash/325eaeac5bef34937cfdc1bd73034d17-Abstract.html Conditioning Sparse Variational Gaussian Processes for Online Decision-making gaussian processes conditioning sparse variational online https://docs.variational.io/ What Is Variational | Variational Docs what is variational docs https://www.mdpi.com/1424-8220/22/5/1779 Gearbox Fault Diagnosis Based on Improved Variational Mode Extraction Gearboxes are widely used in drive systems of rotating machinery. The health status of gearboxes considerably influences the normal and reliable operation of... fault diagnosis based on gearbox improved variational https://arxiv.org/abs/2510.02067 [2510.02067] Adaptive Kernel Selection for Stein Variational Gradient Descent Abstract page for arXiv paper 2510.02067: Adaptive Kernel Selection for Stein Variational Gradient Descent 2510 02067 adaptive kernel selection https://deepai.org/publication/latent-tree-variational-autoencoder-for-joint-representation-learning-and-multidimensional-clustering Latent Tree Variational Autoencoder for Joint Representation Learning and Multidimensional... Mar 14, 2018 - 03/14/18 - Recently, deep learning based clustering methods are shown superior to traditional ones by jointly conducting representation learn... variational autoencoder joint representation latent tree learning https://openreview.net/forum?id=LCbHsdtvOR Expected Variational Inequalities | OpenReview *Variational inequalities (VIs)* encompass many fundamental problems in diverse areas ranging from engineering to economics and machine learning. However,... variational inequalities expected openreview https://www.kth.se/eecs/kalender/black-box-variational-inference-1.1405442 Black-Box Variational Inference | KTH Mixture Models, Efficient Learning, and Applications black box variational inference kth https://arxiv.org/abs/2405.08441v3 [2405.08441v3] Unveiling quantum phase transitions from traps in variational quantum algorithms Abstract page for arXiv paper 2405.08441v3: Unveiling quantum phase transitions from traps in variational quantum algorithms quantum phase transitions 2405 unveiling https://openreview.net/forum?id=rk49Mg-CW Stochastic Variational Video Prediction | OpenReview Stochastic variational video prediction in real-world settings. stochastic variational video prediction openreview https://openreview.net/forum?id=SA8xDYrUYB Purrception: Variational Flow Matching for Vector-Quantized Image Generation | OpenReview We introduce Purrception, a variational flow matching approach for vector-quantized image generation that provides explicit categorical supervision while... flow matching image generation variational vector quantized https://openreview.net/forum?id=YAv9enSDW-a On the Value of Infinite Gradients in Variational Autoencoder Models | OpenReview We demonstrate that infinite gradients, although perhaps at times difficult to address in practical, can serve a useful role in pruning the latent space of... on the variational autoencoder value infinite https://openreview.net/forum?id=KqhMpsWiz2 Variational Transdimensional Inference | OpenReview The expressiveness of flow-based models combined with stochastic variational inference (SVI) has expanded the application of optimization-based Bayesian... variational transdimensional inference openreview https://openreview.net/forum?id=4XtUj6Uzt3&referrer=%5Bthe%20profile%20of%20Junbo%20Li%5D(%2Fprofile%3Fid%3D~Junbo_Li3) Training Bayesian Neural Networks with Sparse Subspace Variational Inference | OpenReview Bayesian neural networks (BNNs) offer uncertainty quantification but come with the downside of substantially increased training and inference costs. Sparse... neural networks variational inference training bayesian sparse https://arxiv.org/abs/1901.05534 [1901.05534] Lagging Inference Networks and Posterior Collapse in Variational Autoencoders Abstract page for arXiv paper 1901.05534: Lagging Inference Networks and Posterior Collapse in Variational Autoencoders 1901 05534 lagging inference networks https://openreview.net/forum?id=L33DSu3zvq Tighter sparse variational Gaussian processes | OpenReview Sparse variational Gaussian process (GP) approximations based on inducing points have become the de facto standard for scaling GPs to large datasets, owing to... gaussian processes tighter sparse variational openreview https://openreview.net/forum?id=e4XidX6AHd Gacs-Korner Common Information Variational Autoencoder | OpenReview We propose a notion of common information that allows one to quantify and separate the information that is shared between two random variables from the... variational autoencoder gacs korner common information https://www.ub.edu/ubtv/index.php/en/node/123189 Variational Discretizations of Gauge Field Theories Using Group-Equivariant Interpolation Spaces |... gauge field theories variational discretizations https://arxiv.org/abs/cond-mat/0306386 [cond-mat/0306386] An Extended Variational Principle for the SK Spin-Glass Model Abstract page for arXiv paper cond-mat/0306386: An Extended Variational Principle for the SK Spin-Glass Model https://openreview.net/forum?id=Z8QlQ207V6 Markovian Gaussian Process Variational Autoencoders | OpenReview Sequential VAEs have been successfully considered for many high-dimensional time series modelling problems, with many variant models relying on discrete-time... gaussian process variational autoencoders markovian openreview https://openreview.net/forum?id=Z28nPtAVxx Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable... We consider the problem of solving stochastic monotone variational inequalities with a separable structure using a stochastic first-order oracle. Building on... variational inequalities optimal based algorithms stochastic https://openreview.net/forum?id=r2NuhIUoceq&referrer=%5Bthe%20profile%20of%20%C3%87a%C4%9Fatay%20Y%C4%B1ld%C4%B1z%5D(%2Fprofile%3Fid%3D~%C3%87a%C4%9Fatay_Y%C4%B1ld%C4%B1z1) Variational multiple shooting for Bayesian ODEs with Gaussian processes | OpenReview We introduce a method for efficiently performing Bayesian inference on unknown ODEs using Gaussian processes gaussian processes variational multiple shooting bayesian https://arxiv.org/abs/1207.4159 [1207.4159] Convergence and asymptotic normality of variational Bayesian approximations for... Abstract page for arXiv paper 1207.4159: Convergence and asymptotic normality of variational Bayesian approximations for exponential family models with missing... asymptotic normality 1207 4159 convergence https://www.ub.edu/ubtv/index.php/en/node/123188 Dynamic Formulation of Optimal Transportation and Variational Relaxation of Euler Equations | UBtv euler equations dynamic formulation optimal transportation https://openreview.net/forum?id=EUcTJf5Knm&referrer=%5Bthe%20profile%20of%20Pan%20Kessel%5D(%2Fprofile%3Fid%3D~Pan_Kessel1) Physics-Informed Bayesian Optimization of Variational Quantum Circuits | OpenReview In this paper, we propose a novel and powerful method to harness Bayesian optimization for Variational Quantum Eigensolvers (VQEs) -- a hybrid... bayesian optimization physics informed variational quantum https://deepai.org/publication/conservative-policy-construction-using-variational-autoencoders-for-logged-data-with-missing-values Conservative Policy Construction Using Variational Autoencoders for Logged Data with Missing Values... Sep 8, 2021 - 09/08/21 - In high-stakes applications of data-driven decision making like healthcare, it is of paramount importance to learn a policy that m... variational autoencoders https://openreview.net/forum?id=0eRDQQK2TW A Finite-Particle Convergence Rate for Stein Variational Gradient Descent | OpenReview We provide the first finite-particle convergence rate for Stein variational gradient descent (SVGD), a popular algorithm for approximating a probability... convergence rate gradient descent finite particle https://arxiv.org/abs/2409.11063 [2409.11063] Variational approach to nonholonomic and inequality-constrained mechanics Abstract page for arXiv paper 2409.11063: Variational approach to nonholonomic and inequality-constrained mechanics 2409 11063 variational approach nonholonomic https://openreview.net/forum?id=IhnkcyN876&referrer=%5Bthe%20profile%20of%20Alexander%20Tschantz%5D(%2Fprofile%3Fid%3D~Alexander_Tschantz1) Gradient-free variational learning with conditional mixture networks | OpenReview Balancing computational efficiency with robust predictive performance is crucial in supervised learning, especially for critical applications. Standard deep... gradient free variational learning conditional mixture https://www.muni.cz/vyzkum/publikace/1369263 The calculus of variations on jet bundles as a universal approach for a variational formulation of... https://arxiv.org/abs/2007.03898 [2007.03898] NVAE: A Deep Hierarchical Variational Autoencoder Abstract page for arXiv paper 2007.03898: NVAE: A Deep Hierarchical Variational Autoencoder a deep 2007 03898 hierarchical variational https://arxiv.org/abs/2302.05479 [2302.05479] A variational encoder-decoder approach to precise spectroscopic age estimation for... Abstract page for arXiv paper 2302.05479: A variational encoder-decoder approach to precise spectroscopic age estimation for large Galactic surveys https://deepai.org/publication/pits-variational-pitch-inference-without-fundamental-frequency-for-end-to-end-pitch-controllable-tts PITS: Variational Pitch Inference without Fundamental Frequency for End-to-End Pitch-controllable... Feb 24, 2023 - 02/24/23 - Previous pitch-controllable text-to-speech (TTS) models rely on directly modeling fundamental frequency, leading to low variance i... fundamental frequency pits variational pitch inference https://openreview.net/forum?id=SJgsCjCqt7 Variational Autoencoders with Jointly Optimized Latent Dependency Structure | OpenReview We propose a method for learning latent dependency structure in variational autoencoders. variational autoencoders dependency structure jointly optimized latent https://www.kth.se/sv/math/kalender/advances-in-sequential-monte-carlo-based-statistical-learning-online-algorithmic-and-variational-inference-1.1369978?date=2024-12-13&orgdate=2024-08-08&length=1&orglength=146 Advances in Sequential Monte Carlo-based Statistical Learning: Online Algorithmic and Variational... sequential monte carlo https://openreview.net/forum?id=AkPwb9dvAlP How to Combine Variational Bayesian Networks in Federated Learning | OpenReview Federated Learning enables multiple data centers to train a central model collaboratively without exposing any confidential data. Even though deterministic... how to bayesian networks federated learning combine variational https://arxiv.org/abs/1007.2517v1 [1007.2517v1] Variational cluster approach to ferromagnetism in infinite dimensions and in... Abstract page for arXiv paper 1007.2517v1: Variational cluster approach to ferromagnetism in infinite dimensions and in one-dimensional chains 1007 variational cluster approach https://deepai.org/publication/creativity-generating-diverse-questions-using-variational-autoencoders Creativity: Generating Diverse Questions using Variational Autoencoders | DeepAI Apr 11, 2017 - 04/11/17 - Generating diverse questions for given images is an important task for computational education, entertainment and AI assistants. D... variational autoencoders creativity generating diverse questions https://openreview.net/forum?id=kjtvCSkSsy Exponential Family Variational Flow Matching for Tabular Data Generation | OpenReview While denoising diffusion and flow matching have driven major advances in generative modeling, their application to tabular data remains limited, despite its... exponential family flow matching tabular data variational generation https://openreview.net/forum?id=LVYEjD25tZ Supervising Variational Autoencoder Latent Representations with Language | OpenReview Supervising latent representations of data is of great interest for modern multi-modal generative machine learning. In this work, we propose two new methods to... variational autoencoder supervising latent representations language https://jmlr.org/papers/v21/19-1015.html Convergence of Sparse Variational Inference in Gaussian Processes Regression variational inference gaussian processes convergence sparse regression https://www.ub.edu/ubtv/video/dynamic-formulation Dynamic Formulation of Optimal Transportation and Variational Relaxation of Euler Equations | UBtv euler equations dynamic formulation optimal transportation https://arxiv.org/abs/2102.08352 [2102.08352] Stochastic Variance Reduction for Variational Inequality Methods Abstract page for arXiv paper 2102.08352: Stochastic Variance Reduction for Variational Inequality Methods stochastic variance reduction variational inequality 2102 08352 methods https://arxiv.org/abs/1911.08411 [1911.08411] Mixed-curvature Variational Autoencoders Abstract page for arXiv paper 1911.08411: Mixed-curvature Variational Autoencoders 1911 08411 mixed curvature variational https://www.mdpi.com/2072-4292/13/16/3316 Self-Attention-Based Conditional Variational Auto-Encoder Generative Adversarial Networks for... Hyperspectral classification is an important technique for remote sensing image analysis. For the current classification methods, limited training data affect... self attention auto encoder adversarial networks based conditional https://arxiv.org/abs/2604.14318 [2604.14318] The free energy of the interacting Bose gas: a variational description with loops and... Abstract page for arXiv paper 2604.14318: The free energy of the interacting Bose gas: a variational description with loops and interlacements https://edubirdie.com/docs/massachusetts-institute-of-technology/16-920j-numerical-methods-for-partial/93180-finite-element-methods-for-elliptic-problems-variational-formulation-the-poisson-problem Finite Element Methods for Elliptic Problems; Variational Formulation: The Poisson Problem |... Understanding Finite Element Methods for Elliptic Problems; Variational Formulation: The Poisson Problem better is easy with our detailed Lecture Note and... finite element methods variational formulation elliptic https://github.com/gher-uliege/DIVAnd.jl GitHub - gher-uliege/DIVAnd.jl: DIVAnd performs an n-dimensional variational analysis of... DIVAnd performs an n-dimensional variational analysis of arbitrarily located observations - gher-uliege/DIVAnd.jl https://www.mapleprimes.com/questions/239291-Issues-With-Variational-Derivative-And-Commutator Issues with Variational Derivative and Commutator - MaplePrimes variational derivative issues commutator https://openreview.net/forum?id=YoQFRXYkLXC&referrer=%5Bthe%20profile%20of%20Voot%20Tangkaratt%5D(%2Fprofile%3Fid%3D~Voot_Tangkaratt1) Variational Imitation Learning with Diverse-quality Demonstrations | OpenReview Learning from demonstrations can be challenging when the quality of demonstrations is diverse, and even more so when the quality is unknown and there is no... imitation learning variational diverse quality demonstrations https://arxiv.org/abs/2506.12903 [2506.12903] Variational Learning Finds Flatter Solutions at the Edge of Stability Abstract page for arXiv paper 2506.12903: Variational Learning Finds Flatter Solutions at the Edge of Stability at the edge https://arxiv.org/abs/2106.07832 [2106.07832] Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient... Abstract page for arXiv paper 2106.07832: Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent 2106 07832 learning equivariant energy https://openreview.net/forum?id=cXBv07GKvk Variational Learning is Effective for Large Deep Networks | OpenReview We give extensive empirical evidence against the common belief that variational learning is ineffective for large neural networks. We show that an optimizer... variational learning effective large deep https://arxiv.org/abs/1907.13623 [1907.13623] Minimizing State Preparations in Variational Quantum Eigensolver by Partitioning into... Abstract page for arXiv paper 1907.13623: Minimizing State Preparations in Variational Quantum Eigensolver by Partitioning into Commuting Families https://openreview.net/forum?id=pxStyaf2oJ5 Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation | OpenReview Previous studies have shown that leveraging "domain index" can significantly boost domain adaptation performance (Wang et al., 2020; Xu et al., 2022). However,... variational bayes domain indexing adaptation openreview https://www.stir.ac.uk/research/hub/publication/1725114 Conference Paper (published) | The Variational InfoMax AutoEncoder | University of Stirling conference paper university of published variational infomax https://deepai.org/publication/variational-inference-with-gaussian-score-matching Variational Inference with Gaussian Score Matching | DeepAI Jul 15, 2023 - 07/15/23 - Variational inference (VI) is a method to approximate the computationally intractable posterior distributions that arise in Bayesi... variational inference gaussian score matching deepai https://openreview.net/forum?id=ghIBaprxsV Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration |... Semi-implicit variational inference (SIVI) has been introduced to expand the analytical variational families by defining expressive semi-implicit distributions... variational inference diffusion model hierarchical semi implicit https://www.atlantis-press.com/proceedings/icca-16/25847660 Generalized Nonlinear Vector Variational-like Inequalitie with set-valued mappings | Atlantis Press In this paper, we introduce and study a class of generalized nonlinear vector variational-like inequalities. By utilizing maximal element theorem, we prove the... https://openreview.net/forum?id=wClmeg9u7G Distributed Methods with Compressed Communication for Solving Variational Inequalities, with... Variational inequalities in general and saddle point problems in particular are increasingly relevant in machine learning applications, including adversarial... distributed methods compressed communication solving https://arxiv.org/abs/2506.14914 [2506.14914] Recursive Variational Autoencoders for 3D Blood Vessel Generative Modeling Abstract page for arXiv paper 2506.14914: Recursive Variational Autoencoders for 3D Blood Vessel Generative Modeling variational autoencoders blood vessel 2506 recursive https://github.com/insujeon/NVDPs GitHub - insujeon/NVDPs: An official (PyTorch) Implementation of "Neural Variational Dropout... An official (PyTorch) Implementation of "Neural Variational Dropout Processes, ICLR 2022" - insujeon/NVDPs github official https://deepai.org/publication/improved-variational-autoencoders-for-text-modeling-using-dilated-convolutions Improved Variational Autoencoders for Text Modeling using Dilated Convolutions | DeepAI Feb 27, 2017 - 02/27/17 - Recent work on generative modeling of text has found that variational auto-encoders (VAE) incorporating LSTM decoders perform wors... variational autoencoders improved text modeling using https://openreview.net/forum?id=8rt7bIDlY2&referrer=%5Bthe%20profile%20of%20Alain%20Ryser%5D(%2Fprofile%3Fid%3D~Alain_Ryser1) Tree Variational Autoencoders | OpenReview We propose a new generative hierarchical clustering model that learns a flexible tree-based posterior distribution over latent variables. The proposed Tree... variational autoencoders tree openreview https://openreview.net/forum?id=jGYxcXSg8C QuantumDARTS: Differentiable Quantum Architecture Search for Variational Quantum Algorithms |... With the arrival of the Noisy Intermediate-Scale Quantum (NISQ) era and the fast development of machine learning, variational quantum algorithms (VQA)... architecture search differentiable quantum variational algorithms https://openreview.net/forum?id=55BcghgicI Differentially private partitioned variational inference | OpenReview Learning a privacy-preserving model from sensitive data which are distributed across multiple devices is an increasingly important problem. The problem is... variational inference private partitioned openreview https://arxiv.org/abs/2206.01095v1 [2206.01095v1] Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise Abstract page for arXiv paper 2206.01095v1: Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise stochastic methods https://openreview.net/forum?id=9ISlKio3Bt Variational Autoencoder with Differentiable Physics Engine for Human Gait Analysis and Synthesis |... We use a generative model integrated with a differentiable physics engine for modeling human gait. human gait analysis variational autoencoder physics engine https://github.com/pollytur/mnist-nd GitHub - pollytur/mnist-nd: Implementation of Gaussian Mixture Variational Autoencoder (GMVAE) for... Implementation of Gaussian Mixture Variational Autoencoder (GMVAE) for Unsupervised Clustering - pollytur/mnist-nd https://arxiv.org/abs/2412.02202 [2412.02202] 3D representation in 512-Byte:Variational tokenizer is the key for autoregressive 3D... Abstract page for arXiv paper 2412.02202: 3D representation in 512-Byte:Variational tokenizer is the key for autoregressive 3D generation https://openreview.net/forum?id=1vrpdV9U3i¬eId=knLYhLgSNq Variational Search Distributions | OpenReview We develop VSD, a method for conditioning a generative model of discrete, combinatorial designs on a rare desired class by efficiently evaluating a black-box... variational search distributions openreview https://github.com/sony/sqvae GitHub - sony/sqvae: Pytorch implementation of stochastically quantized variational autoencoder... Pytorch implementation of stochastically quantized variational autoencoder (SQ-VAE) - sony/sqvae github sony pytorch implementation stochastically https://openreview.net/forum?id=b-88mXTMg4J Dual Parameterization of Sparse Variational Gaussian Processes | OpenReview Leveraging dual-parameterization for efficient inference and learning of hyperparameters in sparse variational GP models gaussian processes dual parameterization sparse variational https://deepai.org/publication/variational-imbalanced-regression Variational Imbalanced Regression | DeepAI Jun 11, 2023 - 06/11/23 - Existing regression models tend to fall short in both accuracy and uncertainty estimation when the label distribution is imbalance... variational imbalanced regression deepai https://openreview.net/forum?id=ry-TW-WAb Variational Network Quantization | OpenReview We quantize and prune neural network weights using variational Bayesian inference with a multi-modal, sparsity inducing prior. variational network quantization openreview https://openreview.net/forum?id=r1l4eQW0Z Kernel Implicit Variational Inference | OpenReview Recent progress in variational inference has paid much attention to the flexibility of variational posteriors. One promising direction is to use implicit... variational inference kernel implicit openreview https://openreview.net/forum?id=c4p3ng0SCt Uncovering the latent dynamics of whole-brain fMRI tasks with a sequential variational autoencoder... The neural dynamics underlying brain activity are critical to understanding cognitive processes and mental disorders. However, current voxel-based whole-brain... https://openreview.net/forum?id=JCpQcyjI7W High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of... During the recent years the interest of optimization and machine learning communities in high-probability convergence of stochastic optimization methods has... stochastic optimization https://arxiv.org/abs/2110.07375v1 [2110.07375v1] Multiple Style Transfer via Variational AutoEncoder Abstract page for arXiv paper 2110.07375v1: Multiple Style Transfer via Variational AutoEncoder style transfer 2110 multiple via variational https://www.unsw.edu.au/science/our-schools/maths/engage-with-us/seminars/2012/mean-field-variational-bayes-approaches-selection-linear-models Mean field variational Bayes approaches to the selection of linear models | School of Mathematics... to the selection https://openreview.net/forum?id=FZoZ7a31GCW Ancestral protein sequence reconstruction using a tree-structured Ornstein-Uhlenbeck variational... We introduce a deep generative model for representation learning of biological sequences that, unlike existing models, explicitly represents the evolutionary... protein sequence a tree ornstein uhlenbeck ancestral reconstruction https://arxiv.org/abs/1709.09567v2 [1709.09567v2] Modified equations for variational integrators applied to Lagrangians linear in... Abstract page for arXiv paper 1709.09567v2: Modified equations for variational integrators applied to Lagrangians linear in velocities https://easychair.org/smart-slide/slide/6rf3 Adiabatic Training for Variational Quantum Algorithms adiabatic training variational quantum algorithms https://openreview.net/forum?id=SygQvs0cFQ Variational Smoothing in Recurrent Neural Network Language Models | OpenReview We present a new theoretical perspective of data noising in recurrent neural network language models (Xie et al., 2017). We show that each variant of data... recurrent neural network language models variational smoothing openreview https://deepai.org/publication/multi-level-monte-carlo-variational-inference Multi-level Monte Carlo Variational Inference | DeepAI Feb 1, 2019 - 02/01/19 - In many statistics and machine learning frameworks, stochastic optimization with high variance gradients has become an important p... multi level monte carlo variational inference deepai https://arxiv.org/abs/2009.13472 [2009.13472] Targeted VAE: Variational and Targeted Learning for Causal Inference Abstract page for arXiv paper 2009.13472: Targeted VAE: Variational and Targeted Learning for Causal Inference learning for 2009 targeted vae variational https://openreview.net/forum?id=GjWDguPZRmr Improving Variational Autoencoders with Density Gap-based Regularization | OpenReview We propose a novel Density Gap-based regularization for VAEs to both solve posterior collapse and avoid hole problem. variational autoencoders improving density gap based https://openreview.net/forum?id=QLo5lGkiyg Variational Stochastic Gradient Descent for Deep Neural Networks | OpenReview Optimizing deep neural networks (DNNs) is one of the main tasks in successful deep learning. Current state-of-the-art optimizers are adaptive gradient-based... stochastic gradient descent deep neural networks variational openreview https://www.osti.gov/pages/biblio/1993525-quench-dynamics-schwinger-model-via-variational-quantum-algorithms Quench dynamics of the Schwinger model via variational quantum algorithms (Journal Article) |... The U.S. Department of Energy's Office of Scientific and Technical Information of the schwinger model https://openreview.net/forum?id=ShJWT0n7kX Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling | OpenReview Rare event sampling in dynamical systems is a fundamental problem arising in the natural sciences, which poses significant computational challenges due to an... https://openreview.net/forum?id=pyEsZ1jQXw Deep Variational Semi-Supervised Novelty Detection | OpenReview We proposed two VAE modifications that account for negative data examples, and used them for semi-supervised anomaly detection. novelty detection deep variational semi supervised https://arxiv.org/abs/1209.1530 [1209.1530] Hahn's Symmetric Quantum Variational Calculus Abstract page for arXiv paper 1209.1530: Hahn's Symmetric Quantum Variational Calculus hahn s 1209 1530 symmetric quantum https://deepai.org/publication/bigeminal-priors-variational-auto-encoder Bigeminal Priors Variational auto-encoder | DeepAI Oct 5, 2020 - 10/05/20 - Variational auto-encoders (VAEs) are an influential and generally-used class of likelihood-based generative models in unsupervised... auto encoder bigeminal priors variational deepai https://deepai.org/publication/mixed-variational-inference Mixed Variational Inference | DeepAI Jan 15, 2019 - 01/15/19 - The Laplace approximation has been one of the workhorses of Bayesian inference. It often delivers good approximations in practice ... variational inference mixed deepai https://www.preprints.org/manuscript/202510.1031 Variational Quantum Regression for Binding Affinity Prediction: A Hybrid Quantum-Classical... Predicting drug-target binding affinity with limited training data remains a central challenge in computational drug discovery. We introduce a hybrid... binding affinity variational quantum regression prediction https://openreview.net/forum?id=uHUWHmmBPz&referrer=%5Bthe%20profile%20of%20Iason%20Oikonomidis%5D(%2Fprofile%3Fid%3D~Iason_Oikonomidis2) RV-VAE: Integrating Random Variable Algebra into Variational Autoencoders | OpenReview Among deep generative models, variational autoencoders (VAEs) are a central approach in generating new samples from a learned, latent space while effectively... random variable variational autoencoders rv vae integrating https://arxiv.org/abs/1911.06443 [1911.06443] Gated Variational AutoEncoders: Incorporating Weak Supervision to Encourage... Abstract page for arXiv paper 1911.06443: Gated Variational AutoEncoders: Incorporating Weak Supervision to Encourage Disentanglement variational autoencoders weak supervision 1911 06443 gated https://openreview.net/forum?id=7fFO4cMBx_9 Variational Neural Cellular Automata | OpenReview In nature, the process of cellular growth and differentiation has lead to an amazing diversity of organisms --- algae, starfish, giant sequoia, tardigrades,... cellular automata variational neural openreview https://arxiv.org/abs/1712.01951 [1712.01951] A New Phase-Field Approach to Variational Implicit Solvation of Charged Molecules with... Abstract page for arXiv paper 1712.01951: A New Phase-Field Approach to Variational Implicit Solvation of Charged Molecules with the Coulomb-Field Approximation https://www.sissa.it/calendar/joint-ictpsissa-colloquium-some-variational-problems-involving-functions-bounded-hessian Joint ICTP-SISSA Colloquium - On some variational problems involving functions with bounded Hessian... Jan 5, 2024 - Wednesday April 12, 16:00 Budinich Lecture Hall - ICTP Campus Abstract: In this talk Prof. Ambrosio will illustrate new questions, at the interface between... https://openreview.net/forum?id=tvxL1eqPl9Y Nested Variational Inference | OpenReview Variational Inference Framework based on Nested Importance Sampling variational inference nested openreview