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.
T-PSDA improves speaker recognition accuracy on toroidal submanifolds.
A new method for speaker recognition on hyperspheres improves on PLDA's limitations.
Sensors which use electromagnetic induction (EMI) to excite a response in conducting bodies have long been investigated for subsurface explosive hazard detection. In particular, EMI sensors have been used to discriminate between different types of objects, and to detect objects with low metal content. One successful, p…
The NL score optimizes speaker recognition tasks.
CLS measures dataset similarity through decision rule performance.
Modified cosine distance improves similarity performance in data with variance and correlation.
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.
Derives hyperbolic laws of cosines and sines with fermionic corrections.
Efficiently applies NTK to large-scale datasets using random features.
The paper models and predicts co-occurrence counts using Gamma regression.
Cosine similarity can force points to grow in magnitude, causing convergence issues.
The study analyzes neural network predictions of knot invariants and finds that braid representations work best.
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…
New method for European option pricing faster and more robust.
Feedback alignment methods need to be evaluated for accuracy and gradient cosine similarity.
This paper tackles noise in raw datasets to improve representation learning efficiency.
Generative models on spheres improve discrete sequence sampling.
CWGD measures gradient diversity weighted by curvature, improving SGD convergence.
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.
We extend the Fourier cosine method to discrete probability distributions, achieving faster convergence rates.
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…
Task-specific scores are often used to optimize for and evaluate the performance of conditional text generation systems. However, such scores are non-differentiable and cannot be used in the standard supervised learning paradigm. Hence, policy gradient methods are used since the gradient can be computed without requiri…
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 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…
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…
Improved text classification performance through conformal transformations of kernels.
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 …
Deep learning is still not a very common tool in speaker verification field. We study deep convolutional neural network performance in the text-prompted speaker verification task. The prompted passphrase is segmented into word states - i.e. digits -to test each digit utterance separately. We train a single high-level f…
One of the ubiquitous representation of long DNA sequence is dividing it into shorter k-mer components. Unfortunately, the straightforward vector encoding of k-mer as a one-hot vector is vulnerable to the curse of dimensionality. Worse yet, the distance between any pair of one-hot vectors is equidistant. This is partic…
Improved barrier option pricing in Heston model using COS-BEM method.
Paper introduces a new method for efficient portfolio risk quantification.
The note evaluates different methods for option pricing using Shannon Wavelets.
Wolpert's cosine formula on Teichmüller space gives the Weil-Petersson Poisson bracket for geodesic length functions of closed curves as the sum of the cosines of the angle of intersection of the associated geodesics. This was recently generalized to Hitchin representations by Labourie. I…
Attribution methods aim to explain a neural network's prediction by highlighting the most relevant image areas. A popular approach is to backpropagate (BP) a custom relevance score using modified rules, rather than the gradient. We analyze an extensive set of modified BP methods: Deep Taylor Decomposition, Layer-wise R…
Beta-SOD detects and corrects noisy object re-identification using cosine similarity and Beta mixtures.
The paper analyzes and validates two step size schedules for SGD: exponential and cosine, proving their adaptivity and performance.
A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and selec…
Sharp 2-Wasserstein bounds for DDPMs derived from Föllmer process.
iCOS method estimates risk-neutral densities and option prices without model assumptions.
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…
Proposes a new neural head for asymmetric representation learning.
The paper examines how well node similarities are preserved by random projections in graph embeddings.
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…