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.
New framework models echo chamber learning, proving tight bounds on algorithm performance.
Machine learning is used extensively in recommender systems deployed in products. The decisions made by these systems can influence user beliefs and preferences which in turn affect the feedback the learning system receives - thus creating a feedback loop. This phenomenon can give rise to the so-called "echo chambers" …
Echocardiography (echo) is a common means of evaluating cardiac conditions. Due to the label scarcity, semi-supervised paradigms in automated echo analysis are getting traction. One of the most sought-after problems in echo is the segmentation of cardiac structures (e.g. chambers). Accordingly, we propose an echocardio…
We present the Bayesian Echo Chamber, a new Bayesian generative model for social interaction data. By modeling the evolution of people's language usage over time, this model discovers latent influence relationships between them. Unlike previous work on inferring influence, which has primarily focused on simple temporal…
Model financial markets with social media influences using hierarchical networks.
Model clusters authors and topics in short texts like social media posts.
A new method suppresses echo more effectively with lower latency.
We introduce a basis of the Orlik-Solomon algebra labeled by chambers, so called chamber basis. We consider structure constants of the Orlik-Solomon algebra with respect to the chamber basis and prove that these structure constants recover D. Cohen's minimal complex from the Aomoto complex.
This paper optimizes performative risk by focusing on convex properties and developing efficient algorithms.
The paper proposes count echo state networks for forecasting graduate student enrollments.
Echo state networks with random weights can approximate any continuous system.
Transthoracic echo is one of the most common means of cardiac studies in the clinical routines. During the echo exam, the sonographer captures a set of standard cross sections (echo views) of the heart. Each 2D echo view cuts through the 3D cardiac geometry via a unique plane. Consequently, different views share some l…
Paper detects social media influencers affecting financial markets.
Neuro-inspired recurrent neural network algorithms, such as echo state networks, are computationally lightweight and thereby map well onto untethered devices. The baseline echo state network algorithms are shown to be efficient in solving small-scale spatio-temporal problems. However, they underperform for complex task…
Bayesian fusion improves radar target recognition for UAVs.
Study analyzes Echo State Network parameters for Rossler attractor dynamics.
Efficient echo state network with explicit memory performs well on benchmark tasks.
Proposes Textual Echo Cancellation to improve speech recognition.
Developed causal chambers for AI validation, providing real-world data.
Multi-echo magnetic resonance (MR) images are acquired by changing the echo times (for T2 weighted) or relaxation times (for T1 weighted) of scans. The resulting (multi-echo) images are usually used for quantitative MR imaging. Acquiring MR images is a slow process and acquiring multi scans of the same cross section fo…
The paper studies chambered invariants of real Cauchy-Riemann operators on Riemann surfaces.
The paper studies proper discontinuity of actions on Weyl chamber flow spaces.
Modular curves parametrize elliptic curves with a point of order . They can be identified with connected components of projectivized strata of meromorphic differentials. As strata of meromorphic differentials, they have a canonical walls-and-chambers structure defined by the …
Tuna-AI uses ML to predict tuna biomass from oceanography and echo-sounder data.
Compression is at the heart of effective representation learning. However, lossy compression is typically achieved through simple parametric models like Gaussian noise to preserve analytic tractability, and the limitations this imposes on learning are largely unexplored. Further, the Gaussian prior assumptions in model…
New approach speeds up optimization with repeated gradient steps on same batch.
We consider the problem of simultaneous reduction of acoustic echo, reverberation and noise. In real scenarios, these distortion sources may occur simultaneously and reducing them implies combining the corresponding distortion-specific filters. As these filters interact with each other, they must be jointly optimized. …
This research unifies concepts of fading memory in RNNs.
Ensemble of MFESNs improves macroeconomic forecasting.
Echo state networks are powerful recurrent neural networks. However, they are often unstable and shaky, making the process of finding an good ESN for a specific dataset quite hard. Obtaining a superb accuracy by using the Echo State Network is a challenging task. We create, develop and implement a family of predictably…
Unified treatment of RC in stochastic and deterministic settings.
This work introduces a tensor-based method to perform supervised classification on spatiotemporal data processed in an echo state network. Typically when performing supervised classification tasks on data processed in an echo state network, the entire collection of hidden layer node states from the training dataset is …
New compactification for character varieties with good topological properties.
In this paper we set up the family Seiberg-Witten theory. It can be applied to the counting of nodal pseudo-holomorphic curves in a symplectic 4-manifold (especially a Kahler surface). A new feature in this theory is that the chamber structure plays a more prominent role. We derive some wall crossing formulas measuring…
CESAR improves wind speed and power forecasting for high-resolution simulations.
Many neural networks use the tanh activation function, however when given a probability distribution as input, the problem of computing the output distribution in neural networks with tanh activation has not yet been addressed. One important example is the initialization of the echo state network in reservoir computing…
We investigate the geometry in a real Euclidean building X of type A2 of some simple configurations in the associated projective plane at infinity P, seen as ideal configurations in X, and relate it with the projective invariants (from the cross ratio on P). In particular we establish a geometric classification of gene…
New method adapts to user preferences dynamically, improving recommendation models.
New approach simulates reputation dynamics using information compression.
Discrete return (DR) Laser Detection and Ranging (Ladar) systems provide a series of echoes that reflect from objects in a scene. These can be first, last or multi-echo returns. In contrast, Full-Waveform (FW)-Ladar systems measure the intensity of light reflected from objects continuously over a period of time. In a c…
ESN models predict intraday stock returns efficiently.
We study the space $\nua{m}{d}$ of clouds in $\bbr^d$ (ordered sets of points modulo the action of the group of affine isometries). We show that $\nua{m}{d}$ is a smooth space, stratified over a certain hyperplane arrangement in $\bbr^m$. We give an algorithm to list all the chambers and other strata (this is indep…
The radius of the star-shaped set need not have been continuous.
Uncertainty of labels in clinical data resulting from intra-observer variability can have direct impact on the reliability of assessments made by deep neural networks. In this paper, we propose a method for modelling such uncertainty in the context of 2D echocardiography (echo), which is a routine procedure for detecti…
Optical ESNs enable flexible, efficient machine learning with reduced energy.
The generalized soap bubble problem seeks the least perimeter way to enclose and separate n given volumes in R^m. We study the possible configurations for perimeter minimizing bubble complexes enclosing more than two regions. We prove that perimeter minimizing planar bubble complexes with equal pressure regions and wit…
State-space systems generate probabilistic dependencies between inputs and outputs.
We investigate discrete groups of isometries of a complete connected Riemannian manifold which are generated by reflections, in particular those generated by disecting reflections. We show that these are Coxeter groups, and that the the orbit space is isometric to a Weyl chamber which is a Riemannian …