14 citations · 44 across the 19 of their papers we have counts for
19 papers
Stimulus-Evoked Network Dynamics in Human Cortical Organoids: From a Graph-Computational Framework to Repeated-Stimulation Depression
Esmaeil S. Nadimi, Vinay C. Gogineni, Jan-Matthias Braun +3
Human cortical organoids provide an experimentally accessible model of early neural circuit formation, yet whether their activity reflects structured information processing rather…
Physics-informed Neural Operators for Predicting 3D Electromagnetic Fields Transformed by Metasurfaces
Orkun Furat, Vinay Chakravarthi Gogineni, Henrik Bindslev +1
Metasurfaces, typically realized as arrays of nanopillars, transform electromagnetic (EM) fields depending on their geometry and spatial arrangement. For solving the inverse proble…
Noise-Robust and Resource-Efficient ADMM-based Federated Learning
Ehsan Lari, Reza Arablouei, Vinay Chakravarthi Gogineni +1
Federated learning (FL) leverages client-server communications to train global models on decentralized data. However, communication noise or errors can impair model accuracy. To ad…
Efficient Knowledge Deletion from Trained Models through Layer-wise Partial Machine Unlearning
Vinay Chakravarthi Gogineni, Esmaeil S. Nadimi
Machine unlearning has garnered significant attention due to its ability to selectively erase knowledge obtained from specific training data samples in an already trained machine l…
Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing
Ehsan Lari, Reza Arablouei, Vinay Chakravarthi Gogineni +1
Federated learning (FL) allows training machine learning models on distributed data without compromising privacy. However, FL is vulnerable to model-poisoning attacks where malicio…
Smoothing ADMM for Sparse-Penalized Quantile Regression with Non-Convex Penalties
Reza Mirzaeifard, Naveen K. D. Venkategowda, Vinay Chakravarthi Gogineni +1
This paper investigates quantile regression in the presence of non-convex and non-smooth sparse penalties, such as the minimax concave penalty (MCP) and smoothly clipped absolute d…
Distributed Filtering Design with Enhanced Resilience to Coordinated Byzantine Attacks
Ashkan Moradi, Vinay Chakravarthi Gogineni, Naveen K. D. Venkategowda +1
This paper proposes a Byzantine-resilient consensus-based distributed filter (BR-CDF) wherein network agents employ partial sharing of state parameters. We characterize the perform…
Personalized Graph Federated Learning with Differential Privacy
Francois Gauthier, Vinay Chakravarthi Gogineni, Stefan Werner +2
This paper presents a personalized graph federated learning (PGFL) framework in which distributedly connected servers and their respective edge devices collaboratively learn device…
Asynchronous Online Federated Learning with Reduced Communication Requirements
Francois Gauthier, Vinay Chakravarthi Gogineni, Stefan Werner +2
Online federated learning (FL) enables geographically distributed devices to learn a global shared model from locally available streaming data. Most online FL literature considers…
Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regression
Francois Gauthier, Vinay Chakravarthi Gogineni, Stefan Werner +2
Many assumptions in the federated learning literature present a best-case scenario that can not be satisfied in most real-world applications. An asynchronous setting reflects the r…
Communication-Efficient Online Federated Learning Framework for Nonlinear Regression
Vinay Chakravarthi Gogineni, Stefan Werner, Yih-Fang Huang +1
Federated learning (FL) literature typically assumes that each client has a fixed amount of data, which is unrealistic in many practical applications. Some recent works introduced…
A Family of Constrained Adaptive filtering Algorithms Based on Logarithmic Cost
Vinay Chakravarthi Gogineni, Subrahmanyam Mula
This paper introduces a novel constraint adaptive filtering algorithm based on a relative logarithmic cost function which is termed as Constrained Least Mean Logarithmic Square (CL…
Proportionate Adaptive Filtering under Correntropy Criterion in Impulsive Noise Environments
Vinay Chakravarthi Gogineni, Subrahmanyam Mula
An improved proportionate adaptive filter based on the Maximum Correntropy Criterion (IP-MCC) is proposed for identifying the system with variable sparsity in an impulsive noise en…
Algorithm/Architecture Co-design of Proportionate-type LMS Adaptive Filters for Sparse System Identification
Subrahmanyam Mula, Vinay Chakravarthi Gogineni, Anindya Sundar Dhar
This paper investigates the problem of implementing proportionate-type LMS family of algorithms in hardware for sparse adaptive filtering applications especially the network echo c…
VLSI Friendly Framework for Scalable Video Coding based on Compressed Sensing
B. K. N. Srinivasarao, Vinay Chakravarthi Gogineni, Subrahmanyam Mula +1
This paper presents a new VLSI friendly framework for scalable video coding based on Compressed Sensing (CS). It achieves scalability through 3-Dimensional Discrete Wavelet Transfo…
Convergence Analysis of Proportionate-type Least Mean Square Algorithms
Vinay Chakravarthi Gogineni, Subrahmanyam Mula
In this paper, we present the convergence analysis of proportionate-type least mean square (Pt-LMS) algorithm that identifies the sparse system effectively and more suitable for re…
Distributed Multi-task APA over Adaptive Networks Based on Partial Diffusion
Vinay Chakravarthi Gogineni, Mrityunjoy Chakraborty
Distributed multi-task adaptive strategies are useful to estimate multiple parameter vectors simultaneously in a collaborative manner. The existed distributed multi-task strategies…
Adaptive Convex Combination of APA and ZA-APA algorithms for Sparse System Identification
Vinay Chakravarthi Gogineni
In general, one often encounters the systems that have sparse impulse response, with time varying system sparsity. Conventional adaptive filters which perform well for identificati…
Diffusion Adaptation Over Clustered Multitask Networks Based on the Affine Projection Algorithm
Vinay Chakravarthi Gogineni, Mrityunjoy Chakraborty
Distributed adaptive networks achieve better estimation performance by exploiting temporal and as well spatial diversity while consuming few resources. Recent works have studied th…