Flexible models of non-flexible polyhedra explained.
problem Non-flexible Siamese dipyramids behave like flexible ones.
method Simple mathematical method to explain model flexibility.
result Physical models of Siamese dipyramids are flexible.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,291 papers · 148 categories
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48 results for “modeling flexibility”
Flexible priors improve deep generative models.
problem Training deep generative models with complex deterministic models.
method Induce flexible code distributions directly from data.
result More powerful generative models, better latent structure modeling, explicit generalization control.
Flexible model for complex relationships using Bayesian nonparametrics.
problem Complex relationships between variables not well captured by simple models.
method Hierarchical generation of nonlinear features, Bayesian inference, variable selection.
result Find interpretable models with a small set of important features.
Flexible deep learning models for dynamic accuracy and speed trade-offs.
problem Dynamic accuracy and speed trade-offs in real-world applications.
method Training deep neural networks with a new method allowing flexible numerical precision during inference.
result Achieved comparable accuracy to dedicated models trained at the same precision with dynamic precision settings.
Frengression models causal data flexibly and faithfully.
problem Challenges in robust benchmarking and evaluation of causal inference with real-world data.
method Introduces frengression, a deep generative model for joint distribution of covariates, treatments, and outcomes.
result Frengression provides accurate estimation and flexible simulation of multivariate, time-varying data.
Extends Hawkes process for flexible residual modeling in point processes.
problem Modeling high-frequency financial data with complex residual distributions.
method Introduces self and mutually exciting point process with discretely Markovian dynamics.
result Flexible residual distributions improve intensity modeling and high-frequency data estimation.
TM-VI uses flexible transformation models to approximate complex posteriors in Bayesian models.
problem Approximating complex posteriors in Bayesian models with limited flexibility.
method Transformation models for variational inference (TM-VI).
result TM-VI allows accurate approximation of complex posteriors in models with one parameter and works in a mean-field fashion for multi-parameter models.
Combines MCTM and NF for flexible multivariate density regression with interpretable marginals.
problem Difficult interpretation of flexible NF models and limitations of MCTM in flexibility.
method Hybrid approach combining MCTM for interpretable marginals and NF for complex joint distributions.
result Demonstrates versatility and improved performance compared to MCTM and other NF models.
New ADMM method for PARAFAC2 tensor decomposition with flexible regularization.
problem Challenges in applying regularisation to the evolving mode of PARAFAC2.
method Alternating Direction Method of Multipliers (AO-ADMM) for PARAFAC2 tensor fitting.
result The proposed ADMM-based approach accurately recovers underlying components from simulated data.
Flexible model captures commodity skews with maturity effects.
problem Capturing market skew in commodity futures with maturity effects.
method Non-parametric extension with leverage functions, calibrated using Monte Carlo simulation.
result Model accurately captures market smile and implied variance accumulation.
How fast can one overcome the paradox of the energy transition? A physico-economic model for the European power gridphysics.soc-ph
A model shows faster energy transition rewards flexible production more quickly.
problem Energy transition discourages investments in flexible production.
method Modeling future electricity prices from residual load, investigating revenues for various flexibility levels.
result Faster energy transition rewards flexible production more quickly.
Shows flexible sheaves as fibrant objects for Gromov's h-principle.
problem Applying the h-principle to partial differential relations.
method Interprets flexible sheaves as fibrant objects in a model structure.
result Flexible sheaves can be understood as fibrant objects.
A new flow-based method improves VAE flexibility.
problem Limited flexibility of VAE latent distributions.
method Householder flow for more flexible variational posterior.
result More flexible variational posterior leads to better results.
Flexible Hawkes model with Gaussian process self-effects for time-dependent data.
problem Modeling time-dependent point processes with history dependence and self-effects.
method Extended Hawkes process with Gaussian process self-effects for both excitatory and inhibitory types, using Bayesian inference and mean-field variational approximation.
result Efficient approximate Bayesian inference achieved via data augmentation and mean-field variational approach.
The authors characterize flexibility in power and energy markets considering time, spatiality, resource, and risk.
problem Evaluating and maximizing flexibility in power systems and markets.
method Characterization of flexibility dimensions (time, spatiality, resource, risk) and their interrelations with flexibility assets, products, and services.
result Flexibility should be evaluated based on multiple dimensions for efficient power systems and markets.
A new HP model balances interpretability and flexibility for EHR event sequences.
problem Balancing interpretability and flexibility in modeling diagnostic event sequences.
method Proposes a neural network-based HP with flexible impact kernel and transformer layers.
result Accurately recovers impact functions, competitive performance, and clinically meaningful interpretation.
In this paper we define and study flexible links and flexible isotopy in projective space. Flexible links are meant to capture the topological properties of real algebraic links. We classify all flexible links up to flexible isotopy using Ekholms interpretation of Viros encomplexed writhe.
VTA offers flexible DL specialization for evolving workloads.
problem Inflexible specialized DL hardware accelerators.
method Parametrizable architecture, two-level ISA, JIT compiler.
result Flexible deep learning specialization on edge-class FPGAs.
Flexible priors improve VAE-based CF models for better user preference modeling.
problem Simplistic priors in VAEs limit user preference modeling and deeper representation learning.
method Incorporated flexible priors and gating mechanisms into VAEs for collaborative filtering.
result Flexible priors and gating mechanisms significantly improve recommendation performance.
TriTPP models enable faster and more flexible event data modeling.
problem Inflexibility and slow sampling in traditional TPP models.
method Triangular Maps and Normalizing Flows for parallel sampling and likelihood computation.
result TriTPP models achieve orders of magnitude faster sampling while maintaining flexibility.
NoLimits.jl: Flexible and Composable Nonlinear Mixed-Effects Modeling in Julia
problem Flexible and composable nonlinear mixed-effects modeling
method Macro-based modeling language and unified interface
result Substantially expand the range of nonlinear mixed-effects models
Proposes a variational autoencoder for long-term customer revenue forecasting.
problem Predicting long-term customer revenue from sparse and irregular transaction data.
method Variational Autoencoder (VAE) with flexible latent representation.
result Improves upon latest benchmarks in multiple real-world datasets.
Flexible spatial models improve predictive performance over nonstationary alternatives.
problem Improving predictive performance in nonstationary spatial modeling.
method Introduces a modular parametric covariance function that extends nonstationary spatial models.
result The proposed covariance function outperforms nonparametric methods in predictive performance.
Neural model synthesizes music with flexible timbre controls.
problem Creating audio samples with varied timbres from musical scores.
method Recurrent neural network conditioned on learned instrument embedding followed by WaveNet vocoder.
result Learned embedding space captures diverse timbres and enables interpolation for morphing.
Labels distilled from images improve model training efficiency and flexibility.
problem Creating synthetic labels for a small set of real images to train models effectively.
method Introduce a more robust and flexible meta-learning algorithm for distillation and an effective first-order strategy based on convex optimization layers.
result Label distillation leads to improved results and greater flexibility in neural architectures.
A new STAR framework models integer-valued data with flexible distributions.
problem Modeling integer-valued data with flexibility and accuracy.
method Simultaneously Transforming and Rounding (STAR) a continuous-valued process.
result STAR framework designs a new BART model for integer-valued data with impressive predictive accuracy.
GPDFlow models extreme threshold exceedance with flexible dependence using normalizing flows.
problem Challenges in modeling multivariate threshold exceedance probabilities due to infinite parametrizations.
method GPDFlow uses normalizing flows to flexibly represent dependence without explicit parametric assumptions.
result GPDFlow significantly improves modeling accuracy and flexibility compared to traditional parametric methods.
Proposes SQUAD for better predictive uncertainty in deep latent models.
problem Intractable inference in deep latent variable models lead to overconfident predictions.
method Introduces Stochastic Quantized Activation Distributions (SQUAD) for flexible yet tractable latent variable distributions.
result The model provides competitive quality predictive uncertainty and learns non-linearities.
The study finds that real-time prices can lead to higher costs due to consumer flexibility.
problem The integration of consumer flexibility into electricity markets leads to higher costs.
method Agent-based model and multi-agent simulation to study market integration.
result Real-time prices can lead to higher costs due to consumer flexibility and market structure.
From Partial Exchangeability to Predictive Probability: A Bayesian Perspective on Classificationstat.ME
Bayesian model improves classification performance with flexible uncertainty modeling.
problem Improving classification performance with flexible uncertainty modeling.
method Combines Gaussian process and Dirichlet process priors for latent function and link function, respectively.
result Outperforms standard logistic regression on simulated data.
GNet uses Gaussian processes for scalable, flexible neural networks.
problem Large-scale predictive modeling with high computational and storage costs.
method GNet employs Gaussian processes with nonparametric activation functions and a fast algorithm for training and predictions.
result GNet achieves competitive performance across various test problems, including nonlinear function prediction and real-world data regression.
GNet uses Gaussian processes for scalable, flexible neural networks.
problem Large-scale predictive modeling with high computational and storage costs.
method GNet employs Gaussian processes with nonparametric activation functions and a fast algorithm for efficient training and predictions.
result GNet achieves competitive performance across various test problems, including nonlinear function prediction and real-world data regression.
Enhanced VAE with DT improves flexibility in latent variable modeling.
problem Limitations of VAE's diagonal covariance matrix in matching true posterior distribution.
method Proposes dyadic transformation (DT) to model multivariate normal distributions.
result DT enhances posterior flexibility and achieves competitive results.
Flexible log file parsing using HMM adapts to evolving content.
problem Dynamic log file processing with evolving content.
method Modeling frequent patterns into HMM for flexible log file parsing.
result High accuracy (over 99%) in parsing different system log files.
New graph operations for stochastic processes improve model flexibility.
problem Limited flexibility in graph convolutional networks for stochastic processes.
method Parameterization of graph-dependent linear operations using functional calculus.
result Improved learning flexibility and richer behaviors in graph models.
Develops deep probabilistic graphical modeling for better flexibility and interpretability.
problem Lack of flexibility in probabilistic graphical models and interpretability in deep learning.
method Combines deep learning and probabilistic graphical modeling to create flexible models with interpretable latent structures.
result Solves problems in probabilistic topic models and introduces new learning algorithms.
New inequality for odd-degree flexible curves using surface doubling.
problem Bounding the number of non-empty ovals of odd-degree flexible curves.
method Defining an Arnold surface for odd-degree flexible curves and using it to derive a Viro--Zvonilov-type inequality.
result Upper bound on the number of non-empty ovals of odd-degree flexible curves.
PFNs4BO uses neural processes for flexible Bayesian Optimization.
problem Efficient surrogate modeling for Bayesian Optimization.
method In-context learning of PFNs to approximate posterior predictive distribution.
result PFNs outperform traditional GP, BNN in BO tasks.
New framework identifies strongly identifiable models from flexible generators.
problem Indeterminacies in generative models that prevent unique latent codes.
method Theoretical framework for analyzing latent variable models, excluding certain indeterminacies.
result Strong identifiability possible even with flexible nonlinear generators.
Edward is a flexible probabilistic programming language that integrates with TensorFlow.
problem Probabilistic programming challenges in flexibility and efficiency.
method Compositional representations of random variables and inference, flexible inference methods, integration with TensorFlow.
result Edward achieves significant speedups in probabilistic modeling and inference.
Unsupervised detection and open-set classification of fast-ramped flexibility activation eventseess.SY
Automates detection of fast-ramped flexibility events for DSOs.
problem Monitoring and supervising flexibility activations in power systems.
method Unsupervised detection and open-set classification.
result Automatically identifies critical flexibility activations for early intervention.
Flexible Surfaces in and math.GT
Flexible surfaces found in complex projective and product spaces.
problem Finding flexible surfaces in complex projective and product spaces.
method Constructing flexible surfaces within prescribed homology classes.
result Flexible surfaces exist in both and .
New method learns flexible posterior distributions in generative models.
problem Learning accurate posterior distributions in hierarchical latent-variable models.
method Distributed distributional code Helmholtz machine with an extended wake-sleep algorithm.
result Outperforms state-of-the-art methods on various datasets.
Hi-fi priors enhance BNNs by learning flexible activations.
problem Challenging to impose function-space priors on BNNs.
method Optimization techniques to learn flexible activations.
result BNNs with flexible activations can achieve desired priors.
Flexible VAEs using FIFs improve model likelihood on image datasets.
problem Limitations of diagonal Gaussian posteriors in VAEs.
method Regularized Free-form Injective Flow (FIF) for flexible posterior.
result Full covariance VAEs outperform diagonal Gaussian posteriors.
New PAC-Bayesian framework for flexible meta-learning.
problem Limitations in transferring knowledge between tasks in meta-learning.
method PAC-Bayesian theory applied to learning the learning algorithm.
result Flexibility in meta-learning mechanisms and improved prediction quality.
Flexible Deep Neural Networks for Partially Linear Survival Data: Estimation and Survival Inferencestat.ML
Flexible DNN for survival data, avoiding proportional hazards assumption.
problem Survival analysis with complex interactions and non-proportional hazards.
method Partially linear DNN model with a flexible nonparametric component.
result FLEXI-Haz achieves optimal convergence rates and asymptotic efficiency.
Flexible Cox model for time-dependent covariates with complex sparsity patterns.
problem Lack of flexibility in enforcing specific sparsity patterns in time-dependent Cox models.
method Proposes a flexible framework for variable selection in time-dependent Cox models, accommodating complex selection rules.
result Achieves accurate estimation with low false alarm rates for complex covariate structures.