Zobrazeno 1 - 10
of 13 106
pro vyhledávání: '"Subramanyam A"'
We propose a novel generalized framework for grant-free random-access (GFRA) in cell-free massive multiple input multiple-output systems where multiple geographically separated access points (APs) or base stations (BSs) aim to detect sporadically act
Externí odkaz:
http://arxiv.org/abs/2411.09328
The scenario approach is widely used in robust control system design and chance-constrained optimization, maintaining convexity without requiring assumptions about the probability distribution of uncertain parameters. However, the approach can demand
Externí odkaz:
http://arxiv.org/abs/2411.07361
Autor:
Lefebvre, Henri, Subramanyam, Anirudh
We provide a correction to the sufficient conditions under which closed-form expressions for the optimal Lagrange multiplier are provided in arXiv:2112.13138 [math.OC]. We first present a simple counterexample where the original conditions are insuff
Externí odkaz:
http://arxiv.org/abs/2411.04307
We revisit knowledge-aware text-based visual question answering, also known as Text-KVQA, in the light of modern advancements in large multimodal models (LMMs), and make the following contributions: (i) We propose VisTEL - a principled approach to pe
Externí odkaz:
http://arxiv.org/abs/2410.19144
Autor:
Sahoo, Subramanyam, Dutta, Kamlesh
The relentless pursuit of technological advancements has ushered in a new era where artificial intelligence (AI) is not only a powerful tool but also a critical economic driver. At the forefront of this transformation is Generative AI, which is catal
Externí odkaz:
http://arxiv.org/abs/2410.15212
Autor:
Chen, Jianfa, Shen, Emily, Bavalatti, Trupti, Lin, Xiaowen, Wang, Yongkai, Hu, Shuming, Subramanyam, Harihar, Vepuri, Ksheeraj Sai, Jiang, Ming, Qi, Ji, Chen, Li, Jiang, Nan, Jain, Ankit
Robust content moderation classifiers are essential for the safety of Generative AI systems. Content moderation, or safety classification, is notoriously ambiguous: differences between safe and unsafe inputs are often extremely subtle, making it diff
Externí odkaz:
http://arxiv.org/abs/2410.14881
Autor:
He, Zecheng, Sun, Bo, Juefei-Xu, Felix, Ma, Haoyu, Ramchandani, Ankit, Cheung, Vincent, Shah, Siddharth, Kalia, Anmol, Subramanyam, Harihar, Zareian, Alireza, Chen, Li, Jain, Ankit, Zhang, Ning, Zhang, Peizhao, Sumbaly, Roshan, Vajda, Peter, Sinha, Animesh
Diffusion models have demonstrated remarkable efficacy across various image-to-image tasks. In this research, we introduce Imagine yourself, a state-of-the-art model designed for personalized image generation. Unlike conventional tuning-based persona
Externí odkaz:
http://arxiv.org/abs/2409.13346
Autor:
Morgado, F. F., Stephenson, L. T., Bhatt, S., Freysoldt, C., Neumeier, S., Katnagallu, S., Subramanyam, A. P. A., Pietka, I., Hammerschmidt, T., Vurpillot, F., Gault, B.
Stacking faults (SF) are important structural defects that play an essential role in the deformation of engineering alloys. However, direct observation of stacking faults at the atomic scale can be challenging. Here, we use the analytical field ion m
Externí odkaz:
http://arxiv.org/abs/2408.03167
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
Externí odkaz:
http://arxiv.org/abs/2408.00331
Autor:
Krishna, Madhava, Subramanyam, A V
SimMIM is a widely used method for pretraining vision transformers using masked image modeling. However, despite its success in fine-tuning performance, it has been shown to perform sub-optimally when used for linear probing. We propose an efficient
Externí odkaz:
http://arxiv.org/abs/2407.13873