Study classifies surface types for autonomous indoor robots using inertial data.
problem Classifying surface types for wheeled robots in indoor environments.
method Prepared a time series dataset of inertial measurements, used deep learning and ensemble machine learning models.
result Baseline model achieved over 68% accuracy on a nine-category surface type dataset.
Paper uses AI to predict option volatility surfaces with improved accuracy.
problem Difficult to predict dynamic evolution of option volatility smile surface.
method Combines deep learning (LSTM) with attention mechanism.
result Predicted volatility surfaces lead to higher returns and Sharpe ratios.
Deep learning models price options using volatility surfaces.
problem Pricing exotic options with high accuracy and efficiency.
method Variational autoencoder for volatility surface compression, multilayer perceptron for option pricing.
result Trained model achieves high accuracy across American and Asian options.
This paper introduces a novel approach to robust surface reconstruction from photometric stereo normal vector maps that is particularly well-suited for reconstructing surfaces from noisy gradients. Specifically, we propose an adaptive dictionary learning based approach that attempts to simultaneously integrate the grad…
Researchers classify and visualize 5-cube cubical surfaces.
problem Classifying and visualizing surfaces in a 5-dimensional cube.
method Exhaustive search, classification by genus and demigenus, 3D visualization, reinforcement learning for optimization.
result 2690 connected closed cubical surfaces in the 5-cube, visualized and optimized for 3D printing.
Optimally estimate distances on surfaces using reconstructed meshes.
problem Estimating intrinsic distances on smooth submanifolds.
method Reconstruction of the surface using a tangential Delaunay complex, and Isomap variant.
result Minimax optimality achieved for distance estimation.
Deep learning models reconstruct volatility surfaces from noisy data under no-arbitrage constraints.
problem Reconstructing implied volatility surfaces from sparse and noisy option quotes.
method Compared multiple neural architectures including Transformers, U-Nets, and variational autoencoders.
result Transformer and U-Net architectures achieve strong reconstruction accuracy, especially under sparse observation regimes.
Explains deep learning models and their geometric properties.
problem Understanding the geometric intuition behind deep learning models.
method Geometrical intuition and novel insights into loss surfaces of deep learning models.
result Deep neural networks carve out manifolds with multiplication neurons.
Machine learning classifies surface wave dispersion curves from ambient noise.
problem Classifying surface wave dispersion curves from ambient noise.
method Convolutional neural network (U-net) with transfer learning and supervised learning.
result Machine classification nearly identical to human-picked phases.
Deep learning improves defect classification in real-time surface inspection.
problem Real-time defect classification in manufacturing industry using limited datasets.
method Convolutional Neural Networks (CNNs) designed for speed and accuracy, neural data augmentation for class imbalance.
result 98.0% accuracy in binary defect classification with 22,000 labeled images.
We study properly embedded and immersed p(pseudohermitian)-minimal surfaces in the 3-dimensional Heisenberg group. From the recent work of Cheng, Hwang, Malchiodi, and Yang, we learn that such surfaces must be ruled surfaces. There are two types of such surfaces: band type and annulus type according to their topology. …
Random Matrix Theory explains loss surface Hessians in neural networks.
problem Understanding the loss surfaces of neural networks.
method Investigation of local spectral statistics of neural network Hessians.
result Excellent agreement with Gaussian Orthogonal Ensemble statistics.
Many modern data sets are sampled with error from complex high-dimensional surfaces. Methods such as tensor product splines or Gaussian processes are effective/well suited for characterizing a surface in two or three dimensions but may suffer from difficulties when representing higher dimensional surfaces. Motivated by…
Machine learning predicts minimal surfaces for knots, supporting a conjecture.
problem Predicting minimal surfaces for knots in hyperbolic space.
method Physics-Informed Neural Networks (PINNs) to solve minimal surface equation.
result Computational minimal surfaces align with Fine's Conjecture.
Deep learning ranks response surfaces for optimal stopping problems in finance.
problem Ranking response surfaces in stochastic control problems.
method Reformulate as image segmentation problem and apply deep learning algorithms.
result Deep learning provides an efficient method for solving optimal stopping problems.
Machine learning and deep learning infer surface/groundwater exchange from temperature data.
problem Inferring surface/groundwater exchange from temperature data with high temporal resolution.
method Application of machine learning and deep learning algorithms to infer surface/groundwater exchange flux from subsurface temperature observations.
result DL methods outperform ML methods in interpreting noisy temperature data, especially with a smoothing filter.
A hybrid model combines machine learning with a land surface model to improve soil moisture predictions.
problem Improving soil moisture predictions in climatological situations.
method Noah land-surface model integrated with Gaussian Processes, using autoregressive model for out-of-sample results.
result 3-fold reduction in RMSE using one-year leave-one-out cross-validation.
Improved agnostic learning time via Gaussian surface area analysis.
problem Learning polynomial threshold functions under Gaussian marginals.
method Improvement of polynomial degree required for approximation.
result Near optimal bounds on agnostic learning complexity.
We explore xor function using copula representations and error surface projections.
problem The exclusive or (xor) function and its approximation problems.
method Probabilistic logic, associative copula functions, and comparison of error surfaces with different activation functions.
result Copula representations extend xor from Boolean to real values.
Enhanced hedging for S&P 500 options using volatility surface data.
problem Optimizing hedging strategies for S&P 500 options with transaction costs.
method Deep policy gradient reinforcement learning with volatility surface feedback.
result Outperforms conventional hedging methods in simulations and backtesting.
Deep learning framework predicts surface texture parameters and their uncertainties.
problem Predicting surface texture parameters and their uncertainties from multi-instrument datasets.
method Reproducible deep learning framework using multi-instrument dataset, quantile and heteroscedastic heads for uncertainty modeling, and post-hoc conformal calibration.
result High fidelity predictions (R2: Ra 0.9824, Rz 0.9847, RONt 0.9918) and well-modelled uncertainty targets (Ra_uncert 0.9899, Rz_uncert 0.9955).
Fast ML framework for derivative valuation from volatility surfaces.
problem Derivative valuation from complex volatility surfaces.
method Parameterized SVI model, synthetic market scenarios, Gaussian Process Regressor.
result Very accurate and fast (3-4 orders of magnitude) derivative valuations.
Study proposes GRU-D networks for missing value handling in road surface friction prediction.
problem Missing values in road surface friction data affect prediction accuracy.
method Gated Recurrent Unit (GRU) network with decay mechanism.
result GRU-D networks outperform baseline models in road surface friction prediction.
Improves MRI-based brain surface reconstruction with minimal deformation energy loss.
problem Ensuring optimal deformation energy and consistency in learning-based cortical surface reconstruction.
method Design and implementation of a Minimal Energy Deformation (MED) loss in the V2C-Flow model.
result Significant improvements in training consistency and reproducibility without sacrificing reconstruction accuracy and topological correctness.
This study investigates porosity and topological properties of TPMS using machine learning.
problem Understanding the relationships between porosity and topological properties of TPMS.
method Application of machine learning techniques to analyze porosity and shape factor of TPMS.
result Conjectures suggesting polynomial relationships between porosity and shape factor of TPMS.
The paper learns compact implicit surface maps from streaming data using an ensemble of sparse Gaussian processes.
problem Creating compact and accurate implicit surface maps from streaming range data.
method An ensemble of sparse Gaussian process experts, incrementally adjusted, trades-off between model complexity and prediction error.
result The approach learns compact and accurate implicit surface models comparable to or better than exact GP regression with subsampled data.
Framework predicts implied volatility surface without arbitrage.
problem Predicting implied volatility surface without static arbitrage.
method Two-step framework: feature selection and deep neural network (DNN) construction.
result DNN model for surface construction removes static arbitrage and reduces prediction error.
Study uses sentiment analysis to predict implied volatility surface, improving prediction accuracy.
problem Improving prediction accuracy of implied volatility surface.
method Constructed daily high-frequency sentiment data, used VAR method, deep learning (BERT, LSTM), FFT, EMD for sentiment decomposition.
result High-frequency sentiment correlates with ATM options' implied volatility, low-frequency with DOTM options.
A neural flow method minimizes Willmore energy for 2-surfaces in 3D space.
problem Minimizing Willmore energy for closed oriented 2-surfaces in 3D space.
method Introducing neural Willmore flow to model and minimize the Willmore energy using neural architectures.
result The neural flow reproduces expected round sphere and Clifford torus for genus 0 and 1 surfaces, respectively, and finds minimal Willmore surfaces for genus 2.
Geomstats introduces shape module for analyzing shapes of objects.
problem Analyzing shapes of objects represented as landmarks, curves, and surfaces.
method Implementing shape spaces, group actions, fiber bundles, quotient spaces, and Riemannian metrics.
result Users can compare, average, and interpolate shapes inside shape spaces.
Federated edge learning improves with CSIT-free model aggregation using RIS.
problem Lack of CSIT in federated edge learning systems.
method Use RIS to align channel coefficients for model aggregation without CSIT, optimize RIS and receiver jointly.
result Achieves similar learning accuracy as CSIT-based methods without CSIT.
DeepBark improves tree bark re-identification accuracy.
problem Challenging illuminations make tree bark hard to re-identify.
method Used a large dataset of 2,400 bark images to train DeepBark and SqueezeBark.
result DeepBark achieves 87.2% mAP in retrieving relevant bark images.
New approach detects small defects on car surfaces with high accuracy.
problem Automated detection of small defects on specular car surfaces.
method Spline smoothing for feature extraction and k-nearest neighbour classifier.
result Near zero misclassification error rate achieved with standard learning classifiers.
Study compares machine learning algorithms for predicting SST in the Great Barrier Reef.
problem Predicting sea surface temperature in the Great Barrier Reef region.
method Ridge regression, LASSO, Random Forest, and Extreme Gradient Boosting (XGBoost) algorithms were evaluated.
result XGBoost significantly outperforms other algorithms in terms of predictive accuracy and Kullback-Leibler Divergence.
Study on how neural network weights evolve and form structures.
problem Understanding the structure and evolution of neural network weights during training.
method Applied topological data analysis to monitor and analyze the weights of neural networks.
result Weights evolve to form trees or smooth surfaces, revealing important factors of variation.
EuLearn creates diverse 3D topological datasets for machine learning.
problem Training machine learning systems to discern topological features.
method Developed novel sampling and neural network architectures for graph and manifold data.
result Incorporating topological information improves deep learning performance on EuLearn datasets.
We use barcodes to analyze neural networks' loss surfaces, revealing important properties.
problem Understanding the topology of neural networks' loss surfaces.
method Topological data analysis using Morse complexes and barcodes.
result Barcodes of local minima are located in a small part of the loss function's range and decrease with network depth and width.
New change surfaces for multidimensional changes and counterfactuals.
problem Limited expressiveness of standard changepoint models in multidimensional settings.
method Model-agnostic formalization of change surfaces, using Gaussian Process Change Surfaces (GPCS).
result Discovery of complex, heterogeneous changes in measles incidence and lead testing kit requests.
Proposes a new SVM model for binary classification with theoretical and practical advantages.
problem Binary classification in supervised learning.
method Quadratic surface support vector machine with L1 norm regularization.
result The model can detect true sparsity patterns and is efficient for both synthetic and real data.
New model combines physics and machine learning for ocean dynamics.
problem Discovering hidden laws governing ocean dynamics.
method Develops Deep Neural Numerical Models (DNNMs) to learn hidden variables of physical laws.
result Illustrates DNNMs applied to Sea Surface Height dynamics, connecting to QG model.
Study optimizes CANN for actuarial tasks using RSM.
problem Optimizing hyperparameters for neural networks in actuarial science.
method Factorial design and response surface methodology (RSM).
result Reduced hyperparameter optimization from 288 to 188, achieving near-optimal performance.
A new method simulates implied volatility surfaces for multiple assets.
problem Generating consistent market scenarios for multiple asset implied volatilities.
method Combining functional data analysis and neural SDEs with a penalty for model misspecification.
result Simulated market scenarios are consistent with historical features and lie within the sub-manifold of essentially free static arbitrage.
Measures neural network decision boundary volume to predict model performance.
problem Understanding the geometry of deep learning models for better performance.
method Local surface volumes to measure decision boundary, applying Weyl's tube formula.
result Smaller surface volume correlates with higher classification accuracy.
Develops a deep learning method for enforcing no-arbitrage in local volatility surfaces.
problem No-arbitrage conditions not enforced in deep learning approaches for local volatility.
method Jointly interpolates European vanilla option prices, enforcing no-arbitrage through modified loss functions or network architectures.
result Demonstrates the effectiveness of enforcing no-arbitrage in local volatility surfaces using deep learning.
Lectures on surface evolution through singularities.
problem Analyzing the mean curvature flow of surfaces and their singularities.
method Analysis of neck and conical singularities, using monotonicity formulas, epsilon-regularity, weak solutions, and blowup techniques.
result Unique evolution through neck singularities, nonuniqueness through conical singularities.
Learning to optimize - the idea that we can learn from data algorithms that optimize a numerical criterion - has recently been at the heart of a growing number of research efforts. One of the most challenging issues within this approach is to learn a policy that is able to optimize over classes of functions that are fa…
The paper uses thermodynamics to improve machine learning representation quality.
problem Improving the quality of learned representations for transfer learning.
method Formal connection with thermodynamics, iso-classification process, traversing the equilibrium surface.
result Demonstrates how to transfer representations while keeping classification loss constant.
A method to control neural level sets for improved generalization and robustness.
problem Improving the properties of neural networks, particularly their decision boundaries and robustness.
method Sampling neural level sets and relating them to network parameters through a sample network.
result High fidelity surface reconstruction from raw 3D point clouds and comparable robust accuracy to state-of-the-art methods.