Zobrazeno 1 - 10
of 28 913
pro vyhledávání: '"Nigam, A"'
Autor:
Nigam, Shubham Kumar, Patnaik, Balaramamahanthi Deepak, Mishra, Shivam, Shallum, Noel, Ghosh, Kripabandhu, Bhattacharya, Arnab
The integration of artificial intelligence (AI) in legal judgment prediction (LJP) has the potential to transform the legal landscape, particularly in jurisdictions like India, where a significant backlog of cases burdens the legal system. This paper
Externí odkaz:
http://arxiv.org/abs/2412.08385
Autor:
Huo, Zepeng, Fries, Jason Alan, Lozano, Alejandro, Valanarasu, Jeya Maria Jose, Steinberg, Ethan, Blankemeier, Louis, Chaudhari, Akshay S., Langlotz, Curtis, Shah, Nigam H.
With the rise of medical foundation models and the growing availability of imaging data, scalable pretraining techniques offer a promising way to identify imaging biomarkers predictive of future disease risk. While current self-supervised methods for
Externí odkaz:
http://arxiv.org/abs/2411.09361
Autor:
Sharma, Geetanjali, Tandon, Abhishek, Jaswal, Gaurav, Nigam, Aditya, Ramachandra, Raghavendra
Iris recognition technology plays a critical role in biometric identification systems, but their performance can be affected by variations in iris pigmentation. In this work, we investigate the impact of iris pigmentation on the efficacy of biometric
Externí odkaz:
http://arxiv.org/abs/2411.08490
Autor:
Goel, Anoushkrit, Singh, Bipanjit, Joshi, Ankita, Jha, Ranjeet Ranjan, Ahuja, Chirag, Nigam, Aditya, Bhavsar, Arnav
White matter tract segmentation is crucial for studying brain structural connectivity and neurosurgical planning. However, segmentation remains challenging due to issues like class imbalance between major and minor tracts, structural similarity, subj
Externí odkaz:
http://arxiv.org/abs/2411.08187
Autor:
Joshi, Ankita, Sharma, Ashutosh, Goel, Anoushkrit, Jha, Ranjeet Ranjan, Ahuja, Chirag, Bhavsar, Arnav, Nigam, Aditya
Fiber tractography is a cornerstone of neuroimaging, enabling the detailed mapping of the brain's white matter pathways through diffusion MRI. This is crucial for understanding brain connectivity and function, making it a valuable tool in neurologica
Externí odkaz:
http://arxiv.org/abs/2411.05757
Autor:
Jin, Yilun, Li, Zheng, Zhang, Chenwei, Cao, Tianyu, Gao, Yifan, Jayarao, Pratik, Li, Mao, Liu, Xin, Sarkhel, Ritesh, Tang, Xianfeng, Wang, Haodong, Wang, Zhengyang, Xu, Wenju, Yang, Jingfeng, Yin, Qingyu, Li, Xian, Nigam, Priyanka, Xu, Yi, Chen, Kai, Yang, Qiang, Jiang, Meng, Yin, Bing
Online shopping is a complex multi-task, few-shot learning problem with a wide and evolving range of entities, relations, and tasks. However, existing models and benchmarks are commonly tailored to specific tasks, falling short of capturing the full
Externí odkaz:
http://arxiv.org/abs/2410.20745
This study investigates judgment prediction in a realistic scenario within the context of Indian judgments, utilizing a range of transformer-based models, including InLegalBERT, BERT, and XLNet, alongside LLMs such as Llama-2 and GPT-3.5 Turbo. In th
Externí odkaz:
http://arxiv.org/abs/2410.10542
Autor:
Katz-Samuels, Julian, Li, Zheng, Yun, Hyokun, Nigam, Priyanka, Xu, Yi, Petricek, Vaclav, Yin, Bing, Chilimbi, Trishul
The ability of large language models (LLMs) to execute complex instructions is essential for their real-world applications. However, several recent studies indicate that LLMs struggle with challenging instructions. In this paper, we propose Evolution
Externí odkaz:
http://arxiv.org/abs/2410.07513
Autor:
Steinberg, Ethan, Wornow, Michael, Bedi, Suhana, Fries, Jason Alan, McDermott, Matthew B. A., Shah, Nigam H.
The growing demand for machine learning in healthcare requires processing increasingly large electronic health record (EHR) datasets, but existing pipelines are not computationally efficient or scalable. In this paper, we introduce meds_reader, an op
Externí odkaz:
http://arxiv.org/abs/2409.09095
Autor:
Nigam, Dhruv
Deep neural network(DNN) based classifiers do extremely well in discriminating between observations, resulting in higher ROC AUC and accuracy metrics, but their outputs are often miscalibrated with respect to true event likelihoods. Post-hoc calibrat
Externí odkaz:
http://arxiv.org/abs/2409.02446