Euler and Chebyshev worked on map drawing and garment fitting.
problem Drawing geographical maps and fitting garments.
method Analyzing historical works of Euler and Chebyshev.
result Found connections between map drawing and garment fitting.
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169,341 papers · 148 categories
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48 results for “garment fitting”
A new model for simulating cloth manipulation in robots, accurate to within 1cm.
problem Accurately simulating cloth manipulation in robots, especially in moderate stress environments.
method A continuous, isometric strain model for textiles, treating them as inextensible surfaces with only isometric motions. Aerodynamic effects are incorporated through virtual uncoupling of mass.
result Simulations are accurate to within 1cm compared to real-world manipulation, even with coarse meshes.
The paper disentangles multiple input conditions in GANs for fashion design.
problem Controlling multiple input attributes in GANs for realistic image generation.
method Customized conditional GANs with consistency loss functions.
result The method can generate novel and realistic images of clothing articles.
Robot learns to dress people safely using deep learning.
problem Robots lack common sense about physical interactions with clothing.
method Deep recurrent model predicts garment forces, used with MPC.
result Robot can better assist in dressing without high forces.
Recommendation or Discrimination?: Quantifying Distribution Parity in Information Retrieval Systemsstat.ML
Test for fairness in IR systems based on protected variables.
problem Unfairness in IR systems due to correlation with protected variables.
method Statistical test for 'distribution parity' in top-K IR results.
result Ensures fairness in IR systems for all users.
Paper generates high-resolution fashion images based on body pose.
problem Limited variety of outfit images for shopping.
method Generative model trained on body pose and style transfer.
result Realistic high-resolution images of models in custom outfits.
Robotic clothing manipulation improved with fashion image analysis techniques.
problem Automated identification of clothing categories and landmarks for robotic tasks.
method Training data augmentation methods and rotation invariant convolutions.
result Our approach outperforms state-of-the-art models on unseen datasets.
Method retrieves similar fashion items from images and text, enabling style refinement.
problem Lack of intuitive, interactive refinement in search engines for fashion items.
method Joint visual-textual embedding training, Mini-Batch Match Retrieval, attribute extraction.
result Improved performance in multimodal style search, demonstrated through benchmark.
A new algorithm optimizes L1-norm error fitting problems efficiently.
problem Optimizing L1-norm error fitting models with data aggregation.
method Data aggregation-based algorithm with monotonic convergence to global optimum.
result The proposed algorithm optimally solves any L1-norm error fitting model.
A new metric uses nonparametric comparison for fitting parametric distributions.
problem Measuring goodness-of-fit for nonlinear models using maximum likelihood estimation.
method Survival Jensen-Shannon divergence () and its empirical counterpart () for nonparametric comparison.
result The can be used as a measure of goodness-of-fit in maximum likelihood estimation.
The paper studies efficient Hessian fitting methods for stochastic optimization.
problem Efficient Hessian fitting for stochastic optimization.
method Preconditioned Stochastic Gradient Descent (PSGD) method and Lie groups.
result Hessian fitting problem is strongly convex in certain Lie groups.
Paper analyzes Bezier simplex fitting risks and optimal sampling.
problem Analyzing risks and optimal sampling for Bezier simplex fitting.
method Two fitting methods: inductive skeleton and all-at-once.
result Optimal subsample ratio for inductive skeleton fitting reduces risk.
Derives formulas for swaption prices in HJM model and uses nonparametric fit to identify arbitrage opportunities.
problem Deriving swaption prices in the HJM model and identifying arbitrage opportunities.
method Derives closed form formulas for swaption prices in HJM model and uses nonparametric fit of deterministic forward volatility.
result Demonstrates that the derived formulas and nonparametric fit work well and can identify arbitrage opportunities.
CTEF fits ellipsoids to noisy data in any dimension.
problem Fitting ellipsoids to noisy data in arbitrary dimensions.
method Uses the Cayley transform to fit ellipsoids.
result CTEF outperforms other methods, especially when data are not uniformly distributed.
Unified model for irregular time series with flexible representations.
problem Missing values, irregularly collected samples, and multi-resolution signals in multivariate time series data.
method Multi-resolution Flexible Irregular Time series Network (Multi-FIT) using FIT networks and FIT-V.
result Improves predictive tasks, including forecasting patient survival.
Recognising perfect fitsmath.GT
Algorithm decides if pseudo-Anosov flows have perfect fits.
problem Determining if pseudo-Anosov flows have specific asymptotic properties.
method Algorithm based on box decompositions and universal cover analysis.
result Algorithmic decision on pseudo-Anosov flows' perfect fit status.
This tutorial explains fitting mixture distributions to data.
problem Fitting mixture distributions to data.
method Step-by-step tutorial covering two and multiple distributions, including numerical simulations.
result Detailed explanation of fitting mixture distributions, including examples and applications.
We study a resource utilization scenario characterized by intrinsic fitness. To describe the growth and organization of different cities, we consider a model for resource utilization where many restaurants compete, as in a game, to attract customers using an iterative learning process. Results for the case of restauran…
Smoothed fitness landscape improves protein optimization.
problem Infeasibility of combinatorially large protein sequence space.
method Formulate protein fitness as a graph signal, smooth using Tikunov regularization, and optimize with Gibbs sampling.
result 2.5 fold fitness improvement over training set.
JAXFit speeds up curve fitting on GPUs.
problem Nonlinear least squares curve fitting problems.
method Trust region method on GPU with automatic differentiation.
result Significantly faster than CPU and other GPU libraries.
Modeling resource accumulation in a population game to explain wealth distribution.
problem Explaining the distribution of wealth in a population game.
method Modeling resource accumulation as a population game with Hawk-Dove interactions, analyzing fitness/wealth distribution and evolution over time.
result Long-run average fitness/wealth is non-monotonic with resource value, explaining the 'curse of riches'.
Kernel Multigrid accelerates Back-fitting for additive Gaussian Processes.
problem Slow convergence of Back-fitting in training additive Gaussian Processes.
method Kernel Packets (KP) and Sparse Gaussian Process Regression (GPR) to enhance Back-fitting.
result Kernel Multigrid reduces the required iterations to .
A framework for eliciting utility functions from investor preferences.
problem Hard elicitation of specific utility functions in portfolio selection.
method Preference-fitting method using probability-wealth pairs and PHARA approximation.
result Fitted utility function converges to the optimal one as more data is used.
New method tests model fit for complex distributions.
problem Measuring differences between distributions with complex, high-dimensional data.
method Combines Stein's identity with kernel methods for probability distributions.
result Developed powerful goodness-of-fit tests applicable to complex distributions.
Machine learning speeds up the construction of virus assembly fitness landscapes.
problem Constructing realistic evolutionary fitness landscapes for viruses is computationally expensive.
method Developed a neural network to model virus assembly efficiency from a whole genome/phenotype space.
result Machine learning significantly reduces the computational time for constructing fitness landscapes.
Fitted Q iteration improves algorithmic trading by addressing dimensionality issues and data scarcity.
problem Dimensionality issues and data scarcity in algorithmic trading.
method Fitted Q iteration combined with model fitting and data simulation.
result The method performs well in both simulated and real-world environments.
This paper illustrates a procedure for fitting financial data with -stable distributions. After using all the available methods to evaluate the distribution parameters, one can qualitatively select the best estimate and run some goodness-of-fit tests on this estimate, in order to quantitatively assess its quality. I…
Perfectly Fitting CDO Prices Across Tranches: A Theoretical Framework with Efficient Algorithmsq-fin.RM
Paper proposes a perfect-fit model for CDO tranches.
problem Achieving a perfect fit to market prices across all CDO tranches.
method Introduces compatibility levels, derives conditions, constructs copula models.
result Demonstrates efficient verification and construction of perfect-fit models.
Study shows how varying levels of supervision and orthonormality constraints affect generalization errors in subspace fitting.
problem Effects of varying levels of supervision and orthonormality constraints on generalization errors in subspace fitting.
method Flexible family of problems connecting unsupervised and supervised subspace fitting tasks, explored over a supervision-orthonormality plane.
result Generalization errors of subspace fitting problems follow double descent trends as they become more supervised and less orthonormally constrained.
BO method improves model fitting for complex parameter landscapes.
problem Optimizing complex, noisy parameter landscapes for computational models.
method Bayesian adaptive direct search (BADS) algorithm.
result BADS consistently finds comparable or better solutions than other methods.
Study presents MMC model for better fitting multiple choice data.
problem Improving accuracy of latent trait estimates in IRT models.
method Fit autoencoders to MMC model, demonstrating better fit than nominal response model.
result MMC model outperforms traditional IRT models in fit.
Paper uses DRL to improve volatility fitting in equity derivatives.
problem Improving volatility fitting in equity derivatives markets.
method Apply Deep Reinforcement Learning (DRL) to solve the fitting problem.
result DRL algorithms achieve at least as good as standard fitting methods.
Analysis of different Mathematical Models for Different Case Studies Using Statistical Fittingmath.DG
Study evaluates different mathematical models for three case studies using statistical fitting.
problem Estimating outcomes in population dynamics, temperature variations, and market equilibrium.
method Applied various statistical equations (e.g., fractional exponential, sinusoidal) to three case studies.
result Optimal models differ by case study (fractional exponential for population dynamics, sinusoidal for temperature and market equilibrium).
In quantitative finance, we often fit a parametric semimartingale model to asset prices. To ensure our model is correct, we must then perform goodness-of-fit tests. In this paper, we give a new goodness-of-fit test for volatility-like processes, which is easily applied to a variety of semimartingale models. In each cas…
Framework uses neural networks to learn fitness functions for machine programming.
problem Automatic software generation and crafting effective fitness functions.
method Genetic algorithms augmented with neural networks and a search heuristic.
result Framework discovers more correct programs with fewer candidate generations.
New method extends fitted Q-evaluation for distributional off-policy reinforcement learning.
problem Estimating return distribution in reinforcement learning using offline data.
method Developed a set of guiding principles and new FDE methods with theoretical justification.
result FDE methods outperform existing approaches in simulations and real-world games.
A new method to improve image restoration by re-fitting standard methods.
problem Systematic errors in popular restoration algorithms for image processing.
method Developing a re-fitting approach that preserves covariant information and Jacobian of original estimators.
result Improved image restoration results on numerical simulations.
Deep learning predicts fit for fashion e-commerce.
problem Predicting correct fit for customer satisfaction and cost reduction.
method Deep learning content-collaborative approach using customer and article embeddings.
result Significant improvement over state-of-the-art methods.
Finitely many pseudo-Anosov flows without perfect fits in a 3-manifold.
problem Finite number of pseudo-Anosov flows without perfect fits in a 3-manifold.
method Analysis of veering triangulations and pseudo-Anosov flows.
result Finiteness of pseudo-Anosov flows without perfect fits.
Fitting models for non-Poisson point processes is complicated by the lack of tractable models for much of the data. By using large samples of independent and identically distributed realizations and statistical learning, it is possible to identify absence of fit through finding a classification rule that can efficientl…
A new kernel Stein test assesses fit for variable-length sequential data.
problem Evaluating goodness of fit for varying-dimensional data like text documents of different lengths.
method Extends kernel Stein discrepancy (KSD) to variable-dimension settings by identifying appropriate Stein operators and proposing a novel KSD goodness-of-fit test.
result The proposed test performs well on discrete sequential data benchmarks.
New findings show a balance between data fit and complexity in kernel hyperparameters.
problem Overcorrelation due to reparametrization of kernel hyperparameters.
method Reparametrization of kernel hyperparameters and analysis of marginal likelihood.
result Data fit term influences all other kernel hyperparameters, not just the complexity penalty.
FIT is a fast nonparametric test for conditional independence.
problem Testing conditional independence for high-dimensional data.
method Based on the conditional independence principle, FIT assesses whether additional variables improve predictions.
result FIT is significantly faster and more accurate than existing methods for large datasets.
A test assesses how well data fits a target density function.
problem Measuring how well data fits a target density function without assuming a specific form.
method Stein's method using Reproducing Kernel Hilbert Space functions to construct a divergence measure, estimated via V-statistic.
result The proposed test accurately assesses goodness of fit for various data types and contexts.
New tests measure model goodness of fit with interpretable features.
problem Measuring relative goodness of fit between two models.
method Nonparametric, computationally efficient tests producing informative features.
result Test power matches state-of-the-art but is one order faster.
Hawkes processes show weak causality; likelihoods are nearly equal for forward and backward event times.
problem Testing the causality of Hawkes processes with time reversal.
method Maximum likelihood estimation and goodness-of-fit tests.
result Parameter estimation of Hawkes processes is weakly dependent on the direction of time, and significant fits may favor backward time.
New robustness test for kernel goodness-of-fit tests.
problem Lack of robustness in existing kernel goodness-of-fit tests.
method Proposes a new robust kernel goodness-of-fit test using kernel Stein discrepancy (KSD) balls.
result First robust kernel goodness-of-fit test addressing both qualitative and quantitative robustness.
Neural networks fit fewer samples than their parameters suggest in practice.
problem Understanding the practical limitations of neural network flexibility.
method Examination of neural network optimization, parameter efficiency, and loss surfaces.
result Neural networks can only fit training sets with significantly fewer samples than their parameters suggest.