RELARM uses relative PCA attributes and k-means clustering for object rating.
problem Rating objects based on complex parameter vectors.
method Relative PCA attributes, k-means clustering, rating vector projection.
result High approximation to existing rating models (S & P, Moody's, Fitch).
The paper introduces relative objects and proves a transversality theorem.
problem Lack of general notions for relative objects in differential topology.
method Introduces the notion of arrangements of manifolds and constructs jet bundles.
result Proves a relative version of the Transversality Theorem.
OGRePy simplifies tensor calculations in general relativity.
problem Complex tensor calculations in general relativity.
method Object-oriented Python package for symbolic tensor calculations.
result Reproduces functionality of Mathematica package OGRe with improvements.
Proposes a new objective function to learn robust deep features.
problem Learning robust deep representations of noisy or unavailable features.
method Maximizes mutual information of all subsets of features relative to supervising signal.
result Surrogate objective function encourages non-redundant and conditionally independent features.
The outlying property detection problem is the problem of discovering the properties distinguishing a given object, known in advance to be an outlier in a database, from the other database objects. In this paper, we analyze the problem within a context where numerical attributes are taken into account, which represents…
Proposes a multi-objective Q-network for dynamic weights in deep RL.
problem Balancing multiple conflicting objectives that change over time.
method Introduces a multi-objective Q-network conditioned on dynamic weights and Diverse Experience Replay.
result Our method outperforms adapted algorithms across various weight change scenarios and domains.
OGRe simplifies tensor calculations in general relativity.
problem Complex tensor calculations in general relativity.
method Object-oriented design for tensor calculus, automatic transformations, and optimized algorithms.
result Eliminates user errors and simplifies tensor calculations.
New perspective on Sinkhorn algorithm using stochastic mirror descent.
problem Optimal transport with unbounded domain and non-smooth objective.
method Stochastic mirror descent applied to relative smoothness.
result Sinkhorn algorithm as a special case of stochastic mirror descent.
Adapts GRPO for off-policy RL, improving reward.
problem Improving training stability and efficiency in RL.
method Adapts GRPO to off-policy setting, uses clipped surrogate objectives.
result Off-policy GRPO outperforms on-policy GRPO in empirical tests.
New algorithms optimize without knowing problem parameters.
problem Optimizing large-scale problems without knowing key parameters.
method Combining mirror descent with dual averaging techniques.
result Converges without prior knowledge of problem parameters.
The alignment of a set of objects by means of transformations plays an important role in computer vision. Whilst the case for only two objects can be solved globally, when multiple objects are considered usually iterative methods are used. In practice the iterative methods perform well if the relative transformations b…
A new local outlier detection method using KDE and RDOS.
problem Detecting local outliers in data.
method Local kernel density estimation (KDE) and Relative Density-based Outlier Score (RDOS).
result The method outperforms state-of-the-art methods in experiments.
MO-CBO optimizes multiple outcomes in causal systems with minimal data.
problem Optimizing multiple outcomes in causal systems with limited data.
method Decomposes MO-CBO into multi-objective optimization tasks and uses relative hypervolume improvement for sequential intervention balancing.
result MO-CBO outperforms traditional multi-objective Bayesian optimization in causal settings.
The concept of an objective spatial direction in special relativity is investigated and theories assuming light-speed isotropy while accepting the existence of a privileged spatial direction are classified. A natural generalization of the proper time principle is introduced which makes it possible to devise experimenta…
Paper tackles object detection in limited data scenarios.
problem Limited annotated data for object detection.
method Generative modeling with a novel unrolling mechanism to optimize both generation and detection.
result Improves object detection performance on NIH Chest X-ray dataset by 20%.
The paper extends trisection theory to 4-manifolds with multiple boundary components.
problem Trisection theory for 4-manifolds with multiple boundary components.
method Extending relative trisection theory to include multiple boundary components and providing conditions for gluing.
result Definition of a category Tri whose objects are 3-manifolds with open book decompositions and morphisms are relatively trisected cobordisms.
Using the language and terminology of relative homological algebra, in particular that of derived functors, we introduce equivariant cohomology over a general Lie-Rinehart algebra and equivariant de Rham cohomology over a locally trivial Lie groupoid in terms of suitably defined monads (also known as triples) and the a…
Simple object representations improve model-free RL performance.
problem Current reinforcement learning agents lack object recognition.
method Used simple, feature-engineered object representations with the Rainbow model.
result Object representations significantly boost performance on Atari games.
Extends specific relative entropy to multidimensional continuous martingales.
problem Mutual singularity of martingale laws in continuous time.
method Extension of specific relative entropy from one to multiple dimensions, including closed-form expressions for simple examples.
result Establishes that the lower bound on specific relative entropy from Gantert carries over to higher dimensions and is tight.
An estimate on the number of distinct relative periodic orbits around a stable relative equilibrium in a Hamiltonian system with continuous symmetry is given. This result constitutes a generalization to the Hamiltonian symmetric framework of a classical result by Weinstein and Moser on the existence of periodic orbits …
In this paper, we have tried to apply the concepts of fuzzy sets to Lie groups and its relative concepts. First, we define a C1 fuzzy submanifold after reviewing C1−fuzzy manifold definition. In main section, we defined the Lie group and some its relative concepts such as fuzzy transformation group,…
Study equivariant vector fields near relative equilibria using isomorphic categories.
problem Lack of linearization and non-smooth orbit space at relative equilibria.
method Categorify equivariant vector fields, introduce isomorphic equivariant vector fields, apply to bifurcations.
result Equivariant bifurcations from relative equilibria are studied and conditions for genericity are established.
The algebra of transactions as fundamental measurements is constructed on the basis of the analysis of their properties and represents an expansion of the Boolean algebra. The notion of the generalized economic measurements of the economic quantity and quality of objects of transactions is introduced. It has been shown…
Improved object classification with voxel-based models.
problem Challenges in three-dimensional data representation.
method Voxel-based variational autoencoders, deep CNN for classification.
result 51.5% relative improvement in object classification.
Simplified approach to Galois theories in category theory.
problem Understanding Galois structures and epimorphisms in category theory.
method Introducing a simplified categorical framework for Galois theories.
result Simplified approach to several Galois theories.
Survey on a nonlinear d'Alembertian from general relativity.
problem Understanding a new nonlinear operator from relativity.
method Review of a new distributional d'Alembertian, comparison estimates, and exact representation formulas.
result Control of the timelike cut locus through optimal transport.
MPO optimizes policies using relative entropy, outperforming existing methods in reinforcement learning.
problem Improving sample efficiency and robustness in reinforcement learning.
method Maximum a posteriori Policy Optimisation (MPO) based on coordinate ascent on relative entropy.
result MPO outperforms existing methods in continuous control tasks.
A refined form of the `Folk Theorem' that a smooth action by a compact Lie group can be (canonically) resolved, by iterated blow up, to have unique isotropy type was established by the authors in the context of manifolds with corners; the canonical construction induces fibrations on the boundary faces of the resolution…
Two entropy measures quantify suboptimal portfolio performance.
problem Measuring suboptimality in investment portfolios.
method Relative entropy (KL divergence) calculations.
result Suboptimal portfolios appear better than Kelly portfolios under certain measures.
LaRP framework improves object classification using random projections.
problem Efficiently approximating nonlinear kernels in high-dimensional spaces.
method Separates linear kernels and nonlinearity using a layered random projection approach.
result Notable improvement in object classification performance.
The `Folk Theorem' that a smooth action by a compact Lie group can be (canonically) resolved, by iterated blow up, to have unique isotropy type is proved in the context of manifolds with corners. This procedure is shown to capture the simultaneous resolution of all isotropy types in a `resolution tower' which projects …
Generative model learns to compose images of objects from different distributions.
problem Capturing complex interactions between objects in scenes.
method Composition-by-Decomposition (CoDe) network.
result Model generates realistic composite images capturing interactions between input objects.
This document contains a description of physics entirely based on a geometric presentation: all of the theory is described giving only a pseudo-riemannian manifold (M, g) of dimension n > 5 for which the g tensor is, in studied domains, almost everywhere of signature (-, -, +, ..., +). No object is added to this space-…
Develops a new method to improve performance in multi-objective learning problems.
problem Gradient bias in multi-objective learning leading to degraded performance.
method Stochastic Multi-objective gradient Correction (MoCo) method that guarantees convergence without increasing batch size.
result Demonstrates effectiveness of MoCo method in simulations on multi-task learning.
Generative model generates images with multiple object classes.
problem Generating images with multiple object classes.
method Conditional Deep Convolutional GAN architecture, stabilized against collapse.
result System generates diverse samples with inter-object relationships.
The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-point detection algorithm based on non-parametric divergence estimation between time-series samples from two retrospective segments. Our method uses the rela…
Rigidity theorem shows massless hyperboloidal data embeds into Minkowski space.
problem Characterizing massless initial data sets in General Relativity.
method Precise decay estimates for spinors on harmonic level sets.
result Asymptotically hyperboloidal IDS with zero mass embed isometrically into Minkowski space.
We define an order relation among oriented PD4-complexes. We show that with respect to this relation, two PD4-complexes over the same complex are homotopy equivalent if and only if there is an isometry between the second homology groups. We also consider minimal objects of this relation.
This paper optimizes trading strategies to minimize risk and maximize profit while accounting for market uncertainty.
problem Optimizing trading strategies to minimize risk and maximize profit while accounting for market uncertainty.
method Relative entropy-regularized robust optimal control problem, modeled as a stochastic differential game.
result Analytical expressions for optimal strategy and trajectory are derived under specific assumptions.
Paper proposes energy objective for training normalizing flows without determinants.
problem Challenges in training normalizing flows due to Jacobian determinants.
method Introduces energy objective based on proper scoring rules, determinant-free.
result Energy objective supports novel model families and competitive performance.
KeypointNet learns 3D keypoints for object pose estimation without ground-truth.
problem Learning 3D keypoints for object pose estimation without manual annotations.
method End-to-end geometric reasoning framework to discover keypoints.
result End-to-end framework outperforms fully supervised baseline.
Study finds RVIs unreliable for SP selection in clustering.
problem Reliability of RVIs for selecting Similarity Paradigms (SPs) in clustering.
method Extensive experiments with 7 RVIs on synthetic and real-world datasets.
result RVIs are unreliable for SP selection.
We propose an extension of the concept of Expected Improvement criterion commonly used in Kriging based optimization. We extend it for more complex Kriging models, e.g. models using derivatives. The target field of application are CFD problems, where objective function are extremely expensive to evaluate, but the theor…
Develops objective metrics to evaluate NFL offensive linemen performance.
problem Objective evaluation of NFL offensive linemen performance is lacking.
method Uses statistical analysis of performance metrics to objectively evaluate offensive linemen.
result Identifies overvalued and undervalued offensive linemen.
Spin networks are at the core of quantum gravity. Our aim is to plug the mathematical community at large into the procedures turn to create a finite quantum theory of general relativity. For this, because of the different cultural backgraund, we would like to change the tack: to relate discrete (combinatorial) objects …
The paper proposes a method to learn 3D object pose manifolds using GANs and elasticae.
problem Learning image manifolds of 3D objects with limited data.
method Geom-SGAN and elasticae for geometry-preserving image interpolation.
result The method outperforms state-of-the-art GANs and VAEs in learning rotation paths.
A novel UNet detector detects sheep in UAV imagery.
problem Detecting small objects (sheep) in UAV imagery.
method Developed a novel dataset, used various object detectors, and evaluated their performance.
result UNet detector with weighted Hausdorff distance is best for sheep detection.
Machine learning classifies object code for target architecture and endianess.
problem Classifying un-labeled compiled code for analysis.
method Simple byte-value histograms and heuristic features from operands.
result High accuracy in classifying target architecture and endianess.