Vivek Sivaraman Narayanaswamy — accepted AI-conference papers

2025

Leveraging Registers in Vision Transformers for Robust Adaptation

Srikar Yellapragada, Kowshik Thopalli, Vivek Sivaraman Narayanaswamy, Wesam A. Sakla, Yang Liu, Yamen Mubarka +2

ICASSP 2025accepted

Vision Transformers (ViTs) have shown success across a variety of tasks due to their ability to capture global image representations. Recent studies have identified the existence of high-norm tokens in ViTs, which can interfere with unsupervised object discovery. To address this, the use of "registe…

Cited by 3 Source ↗Scholar ↗

2024

DECIDER: Leveraging Foundation Model Priors for Improved Model Failure Detection and Explanation

Rakshith Subramanyam*, Kowshik Thopalli*, Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan

ECCV 2024poster

"Reliably detecting when a deployed machine learning model is likely to fail on a given input is crucial for ensuring safe operation. In this work, we propose DECIDER (Debiasing Classifiers to Identify Errors Reliably), a novel approach that leverages priors from large language models (LLMs) and vis…

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2024

The Double-Edged Sword Of Ai Safety: Balancing Anomaly Detection and OOD Generalization Via Model Anchoring

Vivek Sivaraman Narayanaswamy, Rushil Anirudh, Jayaraman J. Thiagarajan

ICASSP 2024accepted

Safe deployment of AI systems requires models to accurately flag anomalous or semantically unrelated data, while also generalizing to unseen shifts in the data distribution. While both these problems have been extensively studied, there is a risk for undesirable trade-off when exclusively optimizing…

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2023

Signal Analysis-Synthesis Using the Quantum Fourier Transform

Aradhita Sharma, Glen S. Uehara, Vivek Sivaraman Narayanaswamy, Leslie Miller, Andreas Spanias

ICASSP 2023accepted

This paper presents the development of Quantum Fourier transform (QFT) education tools in the object-oriented Java-DSP (J-DSP) simulation environment. More specifically, QFT and Inverse QFT (IQFT) user-friendly J-DSP functions are developed to expose undergraduate students to quantum computing. Thes…

Cited by 14 Source ↗Scholar ↗

2022

Predicting the Generalization Gap in Deep Models using Anchoring

Vivek Sivaraman Narayanaswamy, Rushil Anirudh, Irene Kim, Yamen Mubarka, Andreas Spanias, Jayaraman J. Thiagarajan

ICASSP 2022accepted

We address the problem of predicting the generalization gap of deep neural networks under large, natural, and synthetic distribution shifts between source and target domains. This is crucial in understanding how models behave in uncontrollable ‘in-the-wild’ scenarios, but existing techniques fail wh…

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2021

Using Deep Image Priors to Generate Counterfactual Explanations

Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Andreas Spanias

ICASSP 2021accepted

Through the use of carefully tailored convolutional neural network architectures, a deep image prior (DIP) can be used to obtain pre-images from latent representation encodings. Though DIP inversion has been known to be superior to conventional regularized inversion strategies such as total variatio…

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2019

Designing an Effective Metric Learning Pipeline for Speaker Diarization

Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Huan Song, Andreas Spanias

ICASSP 2019accepted

State-of-the-art speaker diarization systems utilize knowledge from external data, in the form of a pre-trained distance metric, to effectively determine relative speaker identities to unseen data. However, much of recent focus has been on choosing the appropriate feature extractor, ranging from pre…

Cited by 15 Source ↗Scholar ↗

2019

Introducing Machine Learning in Undergraduate DSP Classes

Uday Shankar Shanthamallu, Sunil Rao, Abhinav Dixit, Vivek Sivaraman Narayanaswamy, Jie Fan, Andreas Spanias

ICASSP 2019accepted

Machine Learning (ML) and Artificial Intelligence (AI) algorithms are enabling several modern smart products and devices. Furthermore, several initiatives such as smart cities and autonomous vehicles utilize AI and ML computational engines. The current and emerging applications and the growing indus…

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