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.
The paper defines matrices related to cluster transformations and proves certain quivers have no maximal sequences.
Study pinching sequences to understand degeneration of anti-de Sitter structures.
A faster method for optimizing DNA and protein sequences using machine learning.
The paper establishes conditions for optimal sampling configurations on complex manifolds.
Expectation Maximization (EM) is among the most popular algorithms for estimating parameters of statistical models. However, EM, which is an iterative algorithm based on the maximum likelihood principle, is generally only guaranteed to find stationary points of the likelihood objective, and these points may be far from…
Improves neural program synthesis by addressing aliasing and syntax issues.
Proposes a curriculum learning algorithm to maximize cumulative return in reinforcement learning.
Paper clusters event sequences using a reinforcement learning approach with policy mixture model.
The goal of temporal alignment is to establish time correspondence between two sequences, which has many applications in a variety of areas such as speech processing, bioinformatics, computer vision, and computer graphics. In this paper, we propose a novel temporal alignment method called least-squares dynamic time war…
Expands Hidden Markov Model to include Markov chain observations.
We recently discovered a relationship between the volume density spectrum and the determinant density spectrum for infinite sequences of hyperbolic knots. Here, we extend this study to new quantum density spectra associated to quantum invariants, such as Jones polynomials, Kashaev invariants and knot homology. We also …
This paper studies stability of the exponential utility maximization when there are small variations on agent's utility function. Two settings are considered. First, in a general semimartingale model where random endowments are present, a sequence of utilities defined on R converges to the exponential utility. Under a …
Enhances sequence memory capacity in neural networks.
In the large financial market, which is described by a model with countably many traded assets, we formulate the problem of the expected utility maximization. Assuming that the preferences of an economic agent are modeled with a stochastic utility and that the consumption occurs according to a stochastic clock, we obta…
HARMLESS meta-learning method models short event sequences with relational information.
A generic geodesic on a finite area, hyperbolic 2-orbifold exhibits an infinite sequence of penetrations into a neighborhood of a cone singularity, so that the sequence of depths of maximal penetration has a limiting distribution. The distribution function is the same for all such surfaces and is described by a fairly …
Stability of the utility maximization problem with random endowment and indifference prices is studied for a sequence of financial markets in an incomplete Brownian setting. Our novelty lies in the nonequivalence of markets, in which the volatility of asset prices (as well as the drift) varies. Degeneracies arise from …
Study calculates stable norm of slit tori using Farey sequence.
Paper uses PPO and PPO-dynamic for sequence generation tasks, improving stability and performance.
In this paper, the second of a series of two, we continue the study of higher index theory for expanders. We prove that if a sequence of graphs has girth tending to infinity, then the maximal coarse Baum-Connes assembly map is an isomorphism for the associated metric space . As discussed in the first paper in this s…
Deep models generate and optimize DNA sequences for protein binding.
Optimizes profit in targeted marketing across multiple markets with varying marketing expenditures.
In this paper we prove that if we consider the standard real metric on simplicial rooted trees then the category Tower-Set of inverse sequences can be described by means of the bounded coarse geometry of the naturally associated trees. Using this we give a geometrical characterization of Mittag-Leffler property in inve…
BestChanID identifies the channel with maximal capacity using training sequences.
Deep Convolutional Neural Networks (DCNN) has shown excellent performance in a variety of machine learning tasks. This manuscript presents Deep Convolutional Neural Fields (DeepCNF), a combination of DCNN with Conditional Random Field (CRF), for sequence labeling with highly imbalanced label distribution. The widely-us…
QATS efficiently decodes HMMs with polylogarithmic complexity.
LES optimizes designs by sampling descent sequences, achieving strong sample efficiency.
We prove that the Yang-Mills -functional satisfies the Palais-Smale condition. This guarantees the existence of critical points, which are called Yang-Mills -connections. It was shown by Hong, Tian and Yin in [10] (to appear in Comm. Math. Helv.) that as , a sequence of Yang-Mills -connections converge…
New method combines personal and reference genomes for better machine learning in DNA sequencing.
In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixed-length vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of t…
We discuss the geometry of some arithmetic orbifolds locally isometric to a product of real hyperbolic spaces of dimension two and three, and prove that certain sequences of non-uniform orbifolds are convergent to this space in a geometric ("Benjamini--Schramm") sense for hyperbolic three--space and a product of hyperb…
The paper proves the existence of boundary minimal hypersurfaces in compact manifolds with boundary.
A new regularizer boosts long-range dependency in sequence data.
New algorithm for quickly deciding on tech innovations to maximize ROI.
The decorated hypercube found in the construction of Khovanov homology for links is an example of a Boolean lattice equipped with a presheaf of modules. One can place this in a wider setting as an example of a coloured poset, that is to say a poset with a unique maximal element equipped with a presheaf of modules. In t…
Empirical Bayes method improves Gaussian sequence model inference.
In order to alleviate data sparsity and overfitting problems in maximum likelihood estimation (MLE) for sequence prediction tasks, we propose the Generative Bridging Network (GBN), in which a novel bridge module is introduced to assist the training of the sequence prediction model (the generator network). Unlike MLE di…
In this paper, we prove that a normal subgroup N of an n-dimensional crystallographic group G determines a geometric fibered orbifold structure on the flat orbifold E^n/G, and conversely every geometric fibered orbifold structure on E^n/G is determined by a normal subgroup N of G, which is maximal in its commensurabili…
Study eigenvalues and shapes, proving sharp inequalities for Steklov eigenvalues.
The study extracts market direction from transaction data.
Binary sequence correlation estimation fails but trinary data succeeds.
Classifies homogeneous CR hypersurfaces in low dimensions with maximal symmetry.
Differentiable submodular maximization combines learning and optimization.
Algorithm optimizes biological sequences using bootstrapped training with a score-conditioned generator.
A new model uses normalizing flows for discrete sequences, improving generation speed.
Given a sequence of curves on a surface, we provide conditions which ensure that (1) the sequence is an infinite quasi-geodesic in the curve complex, (2) the limit in the Gromov boundary is represented by a nonuniquely ergodic ending lamination, and (3) the sequence divides into a finite set of subsequences, each of wh…
MASA discovers motifs in noisy time series data.
Modeling disease progression using irregular time intervals in EHRs.