activity
20022024
most citedOn-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters

8 citations · 22 across the 8 of their papers we have counts for

collaborators

10 papers

cs.CL2024★ 1 cited

WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia

Yufang Hou, Alessandra Pascale, Javier Carnerero-Cano +5

Retrieval-augmented generation (RAG) has emerged as a promising solution to mitigate the limitations of large language models (LLMs), such as hallucinations and outdated informatio…

cs.AI2023

A recommender for the management of chronic pain in patients undergoing spinal cord stimulation

Tigran Tchrakian, Mykhaylo Zayats, Alessandra Pascale +7

Spinal cord stimulation (SCS) is a therapeutic approach used for the management of chronic pain. It involves the delivery of electrical impulses to the spinal cord via an implanted…

math.ST2018★ 8 cited

On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters

Mark Kozdoba, Jakub Marecek, Tigran Tchrakian +1

Kalman filter is a key tool for time-series forecasting and analysis. We show that the dependence of a prediction of Kalman filter on the past is decaying exponentially, whenever t…

cs.LG2018★ 2 cited

Robust Spectral Filtering and Anomaly Detection

Jakub Marecek, Tigran Tchrakian

We consider a setting, where the output of a linear dynamical system (LDS) is, with an unknown but fixed probability, replaced by noise. There, we present a robust method for the p…

math.OC2017★ 1 cited

Exponentially convergent data assimilation algorithm for Navier-Stokes equations

Jason Frank, Tigran Tchrakian, Sergiy Zhuk

The paper presents a new state estimation algorithm for a bilinear equation representing the Fourier- Galerkin (FG) approximation of the Navier-Stokes (NS) equations on a torus in…

math.OC2017★ 2 cited

Where computer vision can aid physics: dynamic cloud motion forecasting from satellite images

Sergiy Zhuk, Tigran Tchrakian, Albert Akhriev +2

This paper describes a new algorithm for solar energy forecasting from a sequence of Cloud Optical Depth (COD) images. The algorithm is based on the following simple observation: t…