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.
We extend the Fourier cosine method to discrete probability distributions, achieving faster convergence rates.
New method for European option pricing faster and more robust.
The COS method proposed in Fang and Oosterlee (2008), although highly efficient, may lack robustness for a number of cases. In this paper, we present a Stable pricing of call options based on Fourier cosine series expansion. The Stability of the pricing methods is demonstrated by error analysis, as well as by a series …
Improved barrier option pricing in Heston model using COS-BEM method.
iCOS method estimates risk-neutral densities and option prices without model assumptions.
Paper introduces a new method for efficient portfolio risk quantification.
A new NUFFT method speeds up option pricing for various strikes.
The COS method for European options pricing is improved with a new bound for the number of terms.
Unified method for calculating financial option prices from characteristic functions.
Nonnegative matrix factorization (NMF) is a popular method for audio spectral unmixing. While NMF is traditionally applied to off-the-shelf time-frequency representations based on the short-time Fourier or Cosine transforms, the ability to learn transforms from raw data attracts increasing attention. However, this adds…
A Lissajous knot is one that can be parameterized by a single cosine function in each coordinate. Lissajous knots are highly symmetric, and for this reason, not all knots are Lissajous. We prove several theorems which allow us to place bounds on the number of Lissajous knot types with given frequencies and to efficient…
Random Fourier features improve tabular deep learning convergence.
A new SINC method for fast and accurate option pricing.
A new method integrates Fourier basis expansion and mapping for improved time series forecasting.
Coherent Multiplex analyzes real-time wavelet coherence among multiple signals.
In this paper, we propose several dictionary learning algorithms for sparse representations that also impose specific structures on the learned dictionaries such that they are numerically efficient to use: reduced number of addition/multiplications and even avoiding multiplications altogether. We base our work on facto…
We give a new algorithm for approximating the Discrete Fourier transform of an approximately sparse signal that has been corrupted by worst-case noise, namely a bounded number of coordinates of the signal have been corrupted arbitrarily. Our techniques generalize to a wide range of linear transformations that are…
New COS method formula improves option pricing accuracy.
Study shows neural networks learn low frequencies first, proposing solutions.
Novel IMEX scheme solves financial PDEs with mixed derivatives.
Modified cosine distance improves similarity performance in data with variance and correlation.
Cosine similarity can force points to grow in magnitude, causing convergence issues.
This study proposes a trainable adaptive window switching (AWS) method and apply it to a deep-neural-network (DNN) for speech enhancement in the modified discrete cosine transform domain. Time-frequency (T-F) mask processing in the short-time Fourier transform (STFT)-domain is a typical speech enhancement method. To re…
Feedback alignment methods need to be evaluated for accuracy and gradient cosine similarity.
Extends option pricing framework without risk-free asset using Levy jumps.
Improved MoE performance through perturbing cosine router.
Traditionally, multi-layer neural networks use dot product between the output vector of previous layer and the incoming weight vector as the input to activation function. The result of dot product is unbounded, thus increases the risk of large variance. Large variance of neuron makes the model sensitive to the change o…
Researchers establish bounds and continuity of decomposed Möbius energies using cosine formula.
Cosine schedule is optimal for discrete diffusion models.
Person recognition aims at recognizing the same identity across time and space with complicated scenes and similar appearance. In this paper, we propose a novel method to address this task by training a network to obtain robust and representative features. The intuition is that we directly compare and optimize the cosi…
Derives hyperbolic laws of cosines and sines with fermionic corrections.
CWGD measures gradient diversity weighted by curvature, improving SGD convergence.
We study the rigidity of polyhedral surfaces using variational principle. The action functionals are derived from the cosine laws. The main focus of this paper is on the cosine law for a non-triangular region bounded by three possibly disjoint geodesics. Several of these cosine laws were first discovered and used by Fe…
The note evaluates different methods for option pricing using Shannon Wavelets.
Two things seem to be indisputable in the contemporary deep learning discourse: 1. The categorical cross-entropy loss after softmax activation is the method of choice for classification. 2. Training a CNN classifier from scratch on small datasets does not work well. In contrast to this, we show that the cosine loss fun…
Beta-SOD detects and corrects noisy object re-identification using cosine similarity and Beta mixtures.
The main purpose of incremental learning is to learn new knowledge while not forgetting the knowledge which have been learned before. At present, the main challenge in this area is the catastrophe forgetting, namely the network will lose their performance in the old tasks after training for new tasks. In this paper, we…
This paper tackles noise in raw datasets to improve representation learning efficiency.
T-PSDA improves speaker recognition accuracy on toroidal submanifolds.
We do further investigation in a certain cosine function defined for smooth Minkowski spaces. We prove that such function is symmetric if and only if the referred space is Euclidean, and also that it can be given in terms of the Gateaux derivative of the norm. As an application we use it to study the ratio between the …
The spherical Radon transform on the unit sphere can be regarded as a member of the analytic family of suitably normalized generalized cosine transforms. We derive new formulas for these transforms and apply them to study classes of intersections bodies in convex geometry.
Fast linear transforms are ubiquitous in machine learning, including the discrete Fourier transform, discrete cosine transform, and other structured transformations such as convolutions. All of these transforms can be represented by dense matrix-vector multiplication, yet each has a specialized and highly efficient (su…
In this short article, we extend the cosine formula for the Möbius energy to generalized O'Hara energies. The newly derived formula gives us a condition for which the right circle minimizes the energy under the length-constraint. Furthermore, it shows us how far the energy is from the Möbius invariant property.
The paper evaluates integrals for fBm with various Hurst indices.
SPEQ improves quantized neural networks by stochastic precision sharing and cosine similarity loss.
The study compares Euclidean and cosine distances in medical drug prescription prediction.
A large body of research into semantic textual similarity has focused on constructing state-of-the-art embeddings using sophisticated modelling, careful choice of learning signals and many clever tricks. By contrast, little attention has been devoted to similarity measures between these embeddings, with cosine similari…
Our work presents extensive empirical evidence that layer rotation, i.e. the evolution across training of the cosine distance between each layer's weight vector and its initialization, constitutes an impressively consistent indicator of generalization performance. In particular, larger cosine distances between final an…