Zobrazeno 1 - 10
of 3 790
pro vyhledávání: '"A. Vamshi"'
Autor:
Xiao, Weihua, Putrevu, Venkata Sai Charan, Hemadri, Raghu Vamshi, Garg, Siddharth, Karri, Ramesh
Prefix circuits are fundamental components in digital adders, widely used in digital systems due to their efficiency in calculating carry signals. Synthesizing prefix circuits with minimized area and delay is crucial for enhancing the performance of
Externí odkaz:
http://arxiv.org/abs/2412.02594
We present a self-supervised U-Net-based masked autoencoder and noise removal model designed to reconstruct original images. Once adequately trained, this model extracts high-level features, which are then combined with features from the EfficientNet
Externí odkaz:
http://arxiv.org/abs/2410.19899
Important applications of advancements in machine learning, are in the area of healthcare, more so for neurological disorder detection. A crucial step towards understanding the neurological status, is to estimate the brain age using structural MRI vo
Externí odkaz:
http://arxiv.org/abs/2407.06686
Autor:
Kodali, Prashant, Goel, Anmol, Asapu, Likhith, Bonagiri, Vamshi Krishna, Govil, Anirudh, Choudhury, Monojit, Shrivastava, Manish, Kumaraguru, Ponnurangam
Current computational approaches for analysing or generating code-mixed sentences do not explicitly model "naturalness" or "acceptability" of code-mixed sentences, but rely on training corpora to reflect distribution of acceptable code-mixed sentence
Externí odkaz:
http://arxiv.org/abs/2405.05572
This paper focuses on improving object detection performance by addressing the issue of image distortions, commonly encountered in uncontrolled acquisition environments. High-level computer vision tasks such as object detection, recognition, and segm
Externí odkaz:
http://arxiv.org/abs/2404.08293
The language diversity in India's education sector poses a significant challenge, hindering inclusivity. Despite the democratization of knowledge through online educational content, the dominance of English, as the internet's lingua franca, limits ac
Externí odkaz:
http://arxiv.org/abs/2403.04178
Autor:
Goel, Mansi, Agarwal, Ayush, Agrawal, Shubham, Kapuriya, Janak, Konam, Akhil Vamshi, Gupta, Rishabh, Rastogi, Shrey, Niharika, Bagler, Ganesh
Food touches our lives through various endeavors, including flavor, nourishment, health, and sustainability. Recipes are cultural capsules transmitted across generations via unstructured text. Automated protocols for recognizing named entities, the b
Externí odkaz:
http://arxiv.org/abs/2402.17447
Autor:
Govil, Priyanshul, Jain, Hemang, Bonagiri, Vamshi Krishna, Chadha, Aman, Kumaraguru, Ponnurangam, Gaur, Manas, Dey, Sanorita
Large Language Models (LLMs) often inherit biases from the web data they are trained on, which contains stereotypes and prejudices. Current methods for evaluating and mitigating these biases rely on bias-benchmark datasets. These benchmarks measure b
Externí odkaz:
http://arxiv.org/abs/2402.14889
Autor:
Bonagiri, Vamshi Krishna, Vennam, Sreeram, Govil, Priyanshul, Kumaraguru, Ponnurangam, Gaur, Manas
Despite recent advancements showcasing the impressive capabilities of Large Language Models (LLMs) in conversational systems, we show that even state-of-the-art LLMs are morally inconsistent in their generations, questioning their reliability (and tr
Externí odkaz:
http://arxiv.org/abs/2402.13709
A Large Language Model (LLM) is considered consistent if semantically equivalent prompts produce semantically equivalent responses. Despite recent advancements showcasing the impressive capabilities of LLMs in conversational systems, we show that eve
Externí odkaz:
http://arxiv.org/abs/2402.01719