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pro vyhledávání: '"P, Vineeth"'
Despite the importance of developing generative AI models that can effectively resist scams, current literature lacks a structured framework for evaluating their vulnerability to such threats. In this work, we address this gap by constructing a bench
Externí odkaz:
http://arxiv.org/abs/2410.13893
Autor:
Yu, Xiao, Peng, Baolin, Vajipey, Vineeth, Cheng, Hao, Galley, Michel, Gao, Jianfeng, Yu, Zhou
Autonomous agents have demonstrated significant potential in automating complex multistep decision-making tasks. However, even state-of-the-art vision-language models (VLMs), such as GPT-4o, still fall short of human-level performance, particularly i
Externí odkaz:
http://arxiv.org/abs/2410.02052
Autor:
Choi, Sunjin, Jain, Diksha, Kim, Seok, Krishna, Vineeth, Lee, Eunwoo, Minwalla, Shiraz, Patel, Chintan
Charged Black holes in $AdS_5 \times S^5$ suffer from superradiant instabilities over a range of energies. Hairy black hole solutions (constructed within gauged supergravity) have previously been proposed as endpoints to this instability. We demonstr
Externí odkaz:
http://arxiv.org/abs/2409.18178
Detecting and measuring confounding effects from data is a key challenge in causal inference. Existing methods frequently assume causal sufficiency, disregarding the presence of unobserved confounding variables. Causal sufficiency is both unrealistic
Externí odkaz:
http://arxiv.org/abs/2409.17840
Autor:
Mekala, Anmol, Dorna, Vineeth, Dubey, Shreya, Lalwani, Abhishek, Koleczek, David, Rungta, Mukund, Hasan, Sadid, Lobo, Elita
Machine unlearning aims to efficiently eliminate the influence of specific training data, known as the forget set, from the model. However, existing unlearning methods for Large Language Models (LLMs) face a critical challenge: they rely solely on ne
Externí odkaz:
http://arxiv.org/abs/2409.13474
Autor:
Chavan, Aneesh, Agrawal, Vaibhav, Bhat, Vineeth, Chittawar, Sarthak, Srivastava, Siddharth, Arora, Chetan, Krishna, K Madhava
Re-identification (ReID) is a critical challenge in computer vision, predominantly studied in the context of pedestrians and vehicles. However, robust object-instance ReID, which has significant implications for tasks such as autonomous exploration,
Externí odkaz:
http://arxiv.org/abs/2409.12002
Autor:
Ramachandran, Rahul, Kulkarni, Tejal, Sharma, Charchit, Vijaykeerthy, Deepak, Balasubramanian, Vineeth N
Evaluating models and datasets in computer vision remains a challenging task, with most leaderboards relying solely on accuracy. While accuracy is a popular metric for model evaluation, it provides only a coarse assessment by considering a single mod
Externí odkaz:
http://arxiv.org/abs/2409.04041
In this letter, we study a Networked Control System (NCS) with multiplexed communication and Bernoulli packet drops. Multiplexed communication refers to the constraint that transmission of a control signal and an observation signal cannot occur simul
Externí odkaz:
http://arxiv.org/abs/2409.00949
Autor:
Chudasama, Vishal, Sarkar, Hiran, Wasnik, Pankaj, Balasubramanian, Vineeth N, Kalla, Jayateja
Object detection is a critical field in computer vision focusing on accurately identifying and locating specific objects in images or videos. Traditional methods for object detection rely on large labeled training datasets for each object category, w
Externí odkaz:
http://arxiv.org/abs/2408.14249
Autor:
Veliche, Irina-Elena, Huang, Zhuangqun, Kochaniyan, Vineeth Ayyat, Peng, Fuchun, Kalinli, Ozlem, Seltzer, Michael L.
The current public datasets for speech recognition (ASR) tend not to focus specifically on the fairness aspect, such as performance across different demographic groups. This paper introduces a novel dataset, Fair-Speech, a publicly released corpus to
Externí odkaz:
http://arxiv.org/abs/2408.12734