19 citations · 36 across the 18 of their papers we have counts for
20 papers
Beyond Imitation: A Framework and Benchmark for LLM-Assisted Peer Review
Rachel S. Y. Teo, Yutaro Yamada, Shashank Kotyan +2
The rapid growth of scientific publishing has strained peer review, particularly in machine learning, raising concerns about declining review quality and increasing reviewer worklo…
Prior-Conditioned Gaussian Discriminants for Generalizable AI-generated Image Detection
Shashank Kotyan, Makoto Shing, Yuki Imajuku +2
Diffusion-based generators have made synthetic images ubiquitous, but detectors often fail under simultaneous shifts in generator, prompt/style, and source-domain. We study AI-gene…
Sakana Fugu Technical Report
Yujin Tang, Edoardo Cetin, Jinglue Xu +11
The capabilities of frontier Large Language Models (LLMs) continue to advance, with different providers increasingly specializing in distinct domains. This raises a natural next ob…
Linking Robustness and Generalization: A k* Distribution Analysis of Concept Clustering in Latent Space for Vision Models
Shashank Kotyan, Pin-Yu Chen, Danilo Vasconcellos Vargas
Most evaluations of vision models use indirect methods to assess latent space quality. These methods often involve adding extra layers to project the latent space into a new one. T…
Breaking Free: How to Hack Safety Guardrails in Black-Box Diffusion Models!
Shashank Kotyan, Po-Yuan Mao, Pin-Yu Chen +1
Deep neural networks can be exploited using natural adversarial samples, which do not impact human perception. Current approaches often rely on deep neural networks' white-box natu…
k* Distribution: Evaluating the Latent Space of Deep Neural Networks using Local Neighborhood Analysis
Shashank Kotyan, Tatsuya Ueda, Danilo Vasconcellos Vargas
Most examinations of neural networks' learned latent spaces typically employ dimensionality reduction techniques such as t-SNE or UMAP. These methods distort the local neighborhood…
Synthetic Shifts to Initial Seed Vector Exposes the Brittle Nature of Latent-Based Diffusion Models
Mao Po-Yuan, Shashank Kotyan, Tham Yik Foong +1
Recent advances in Conditional Diffusion Models have led to substantial capabilities in various domains. However, understanding the impact of variations in the initial seed vector…
The Challenges of Image Generation Models in Generating Multi-Component Images
Tham Yik Foong, Shashank Kotyan, Po Yuan Mao +1
Recent advances in text-to-image generators have led to substantial capabilities in image generation. However, the complexity of prompts acts as a bottleneck in the quality of imag…
Towards Improving Robustness Against Common Corruptions using Mixture of Class Specific Experts
Shashank Kotyan, Danilo Vasconcellos Vargas
Neural networks have demonstrated significant accuracy across various domains, yet their vulnerability to subtle input alterations remains a persistent challenge. Conventional meth…
Towards Improving Robustness Against Common Corruptions in Object Detectors Using Adversarial Contrastive Learning
Shashank Kotyan, Danilo Vasconcellos Vargas
Neural networks have revolutionized various domains, exhibiting remarkable accuracy in tasks like natural language processing and computer vision. However, their vulnerability to s…
Improving Robustness for Vision Transformer with a Simple Dynamic Scanning Augmentation
Shashank Kotyan, Danilo Vasconcellos Vargas
Vision Transformer (ViT) has demonstrated promising performance in computer vision tasks, comparable to state-of-the-art neural networks. Yet, this new type of deep neural network…
A reading survey on adversarial machine learning: Adversarial attacks and their understanding
Shashank Kotyan
Deep Learning has empowered us to train neural networks for complex data with high performance. However, with the growing research, several vulnerabilities in neural networks have…
Deep neural network loses attention to adversarial images
Shashank Kotyan, Danilo Vasconcellos Vargas
Adversarial algorithms have shown to be effective against neural networks for a variety of tasks. Some adversarial algorithms perturb all the pixels in the image minimally for the…
Evolving Robust Neural Architectures to Defend from Adversarial Attacks
Shashank Kotyan, Danilo Vasconcellos Vargas
Neural networks are prone to misclassify slightly modified input images. Recently, many defences have been proposed, but none have improved the robustness of neural networks consis…
Representation Quality Of Neural Networks Links To Adversarial Attacks and Defences
Shashank Kotyan, Danilo Vasconcellos Vargas, Moe Matsuki
Neural networks have been shown vulnerable to a variety of adversarial algorithms. A crucial step to understanding the rationale for this lack of robustness is to assess the potent…
Adversarial Robustness Assessment: Why both and Attacks Are Necessary
Shashank Kotyan, Danilo Vasconcellos Vargas
There exists a vast number of adversarial attacks and defences for machine learning algorithms of various types which makes assessing the robustness of algorithms a daunting task.…
Self Training Autonomous Driving Agent
Shashank Kotyan, Danilo Vasconcellos Vargas, Venkanna U
Intrinsically, driving is a Markov Decision Process which suits well the reinforcement learning paradigm. In this paper, we propose a novel agent which learns to drive a vehicle wi…
Analysis and Comparison of Different Fuzzy Inference Systems used in Decision Making for Secondary Users in Cognitive Radio Network
Ashish Upadhyay, Shashank Kotyan, Shrivishal Tripathi +1
Spectrum scarcity is one of the major challenges that the modern communication engineers are going through because of inefficient utilization of allocated frequency spectrum. The s…
HAUAR: Home Automation Using Action Recognition
Shashank Kotyan, Nishant Kumar, Pankaj Kumar Sahu +1
Today, many of the home automation systems deployed are mostly controlled by humans. This control by humans restricts the automation of home appliances to an extent. Also, most of…
Drishtikon: An advanced navigational aid system for visually impaired people
Shashank Kotyan, Nishant Kumar, Pankaj Kumar Sahu +1
Today, many of the aid systems deployed for visually impaired people are mostly made for a single purpose. Be it navigation, object detection, or distance perceiving. Also, most of…