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.
Study evaluates deep learning methods for dermatology, finding they perform poorly under non-ideal conditions.
Review of diffusion models for SBI in non-ideal data scenarios.
TxSim models DNN training on resistive crossbars, improving accuracy and feasibility.
Paper connects fair machine learning to political philosophy, highlighting flaws in ideal approaches.
Proposes a Gaussian process model for constrained dynamics learning.
Chances of a gambler are always lower than chances of a casino in the case of an ideal, mathematically perfect roulette, if the capital of the gambler is limited and the minimum and maximum allowed bets are limited by the casino. However, a realistic roulette is not ideal: the probabilities of realisation of different …
In this work we present a technique to use natural language to help reinforcement learning generalize to unseen environments. This technique uses neural machine translation, specifically the use of encoder-decoder networks, to learn associations between natural language behavior descriptions and state-action informatio…
Two non-local asymptotic invariants of magnetic fields for the ideal magnetohydrodynamics are introduced. The velocity of variation of the invariants for a non-ideal magnetohydrodynamics with a small magnetic dissipation is estimated. By means of the invariants the spectra of electromagnetic fields are investigated. A …
Photometric stereo is a method for estimating the normal vectors of an object from images of the object under varying lighting conditions. Motivated by several recent works that extend photometric stereo to more general objects and lighting conditions, we study a new robust approach to photometric stereo that utilizes …
Geometry of hypersurfaces defined by the relation which generalizes classical formula for free energy in terms of microstates is studied. Induced metric, Riemann curvature tensor, Gauss-Kronecker curvature and associated entropy are calculated. Special class of ideal statistical hypersurfaces is analyzed in details. No…
Paper proposes a new model and methods for robustly de-interleaving HMP mixtures.
Noisy PN learning is the problem of binary classification when training examples may be mislabeled (flipped) uniformly with noise rate rho1 for positive examples and rho0 for negative examples. We propose Rank Pruning (RP) to solve noisy PN learning and the open problem of estimating the noise rates, i.e. the fraction …
Geometric approach to thermodynamics of chemical reaction networks.
Proposes ESCFR to estimate treatment effects from biased data.
We propose a new algorithm for training neural networks with binary activations and multi-level weights, which enables efficient processing-in-memory circuits with embedded nonvolatile memories (eNVM). Binary activations obviate costly DACs and ADCs. Multi-level weights leverage multi-level eNVM cells. Compared to exis…
We obtain the best known quantitative estimates for the -Poincaré and log-Sobolev inequalities on domains in various sub-Riemannian manifolds, including ideal Carnot groups and in particular ideal generalized H-type Carnot groups and the Heisenberg groups, corank Carnot groups, the Grushin plane, and various H…
Classical convolutional neural networks (cCNNs) are very good at categorizing objects in images. But, unlike human vision which is relatively robust to noise in images, the performance of cCNNs declines quickly as image quality worsens. Here we propose to use recurrent connections within the convolutional layers to mak…
Photometric stereo is a method that seeks to reconstruct the normal vectors of an object from a set of images of the object illuminated under different light sources. While effective in some situations, classical photometric stereo relies on a diffuse surface model that cannot handle objects with complex reflectance pa…
New method predicts activity coefficients for binary mixtures without using physical descriptors.
This work presents a novel approach for robust PCA with total variation regularization for foreground-background separation and denoising on noisy, moving camera video. Our proposed algorithm registers the raw (possibly corrupted) frames of a video and then jointly processes the registered frames to produce a decomposi…
Study finds knots with ideal length need not have smallest volume.
Nearest Neighbors Algorithm is a Lazy Learning Algorithm, in which the algorithm tries to approximate the predictions with the help of similar existing vectors in the training dataset. The predictions made by the K-Nearest Neighbors algorithm is based on averaging the target values of the spatial neighbors. The selecti…
PET-TURTLE improves clustering accuracy for imbalanced data.
We first, introduce a deep learning based framework named as DeepIrisNet2 for visible spectrum and NIR Iris representation. The framework can work without classical iris normalization step or very accurate iris segmentation; allowing to work under non-ideal situation. The framework contains spatial transformer layers t…
Robust Q-learning algorithm resists corrupted rewards.
Study laws of cosines and sines for hyperbolic shapes with ideal vertices.
Un-trained neural networks outperform trained methods in MRI reconstruction.
Situational awareness in vehicular networks could be substantially improved utilizing reliable trajectory prediction methods. More precise situational awareness, in turn, results in notably better performance of critical safety applications, such as Forward Collision Warning (FCW), as well as comfort applications like …
Hardware architectures composed of resistive cross-point device arrays can provide significant power and speed benefits for deep neural network training workloads using stochastic gradient descent (SGD) and backpropagation (BP) algorithm. The training accuracy on this imminent analog hardware however strongly depends o…
New method uses single quantum state for machine learning tasks, improving accuracy.
A new complexity measure MDL-COMP for overparameterized models improves generalization performance.
Generates samples conditioned on labels using optimal transport.
The paper classifies Finsler surfaces satisfying the T-condition or σT-condition.
The paper develops a new approach to conditional risk measures using modular convex analysis.
Paper constructs solutions to Bogomolny equations with specific boundary and asymptotic conditions.
We extend probabilistic programming to handle conditioning on marginal distributions.
New tests for conditional copulas based on decision trees.
Paper finds necessary condition for logarithmic Minkowski problem in higher dimensions.
This paper introduces a neural operator for probabilistic conditioning.
CSI method learns conditional distributions by estimating flow equations.
New conditional risk measures called conditional generalized quantiles defined and characterized.
A new method for learning conditional distributions using ODEs and neural networks.
Sharp statistical theory for conditional diffusion models.
An analysis is made of reality conditions within the context of noncommutative geometry. We show that if a covariant derivative satisfies a given left Leibniz rule then a right Leibniz rule is equivalent to the reality condition. We show also that the matrix which determines the reality condition must satisfy the Yang-…
New conditions prevent gaps in optimal control problems.
We identify conditional parity as a general notion of non-discrimination in machine learning. In fact, several recently proposed notions of non-discrimination, including a few counterfactual notions, are instances of conditional parity. We show that conditional parity is amenable to statistical analysis by studying ran…
We consider families of strongly consistent multivariate conditional risk measures. We show that under strong consistency these families admit a decomposition into a conditional aggregation function and a univariate conditional risk measure as introduced Hoffmann et al. (2016). Further, in analogy to the univariate cas…
Proposes a new method for interpreting feature importance and effects in dependent feature models.