Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Mukherjee, Koyel"'
Auto-regressive Large Language Models (LLMs) demonstrate remarkable performance across domanins such as vision and language processing. However, due to sequential processing through a stack of transformer layers, autoregressive decoding faces signifi
Externí odkaz:
http://arxiv.org/abs/2410.12513
Autor:
Guo, Anxin, Li, Jingwei, Sukprasert, Pattara, Khuller, Samir, Deshpande, Amol, Mukherjee, Koyel
In this work, we study the cost efficient data versioning problem, where the goal is to optimize the storage and reconstruction (retrieval) costs of data versions, given a graph of datasets as nodes and edges capturing edit/delta information. One cen
Externí odkaz:
http://arxiv.org/abs/2402.11741
Autor:
Shekhar, Shivanshu, Dubey, Tanishq, Mukherjee, Koyel, Saxena, Apoorv, Tyagi, Atharv, Kotla, Nishanth
Generative AI and LLMs in particular are heavily used nowadays for various document processing tasks such as question answering and summarization. However, different LLMs come with different capabilities for different tasks as well as with different
Externí odkaz:
http://arxiv.org/abs/2402.01742
Autor:
Shah, Raunak, Mukherjee, Koyel, Tyagi, Atharv, Karnam, Sai Keerthana, Joshi, Dhruv, Bhosale, Shivam, Mitra, Subrata
Publikováno v:
Proc. ACM Manag. Data 1, 4, Article 268 (December 2023), 25 pages
Enterprise data lakes often suffer from substantial amounts of duplicate and redundant data, with data volumes ranging from terabytes to petabytes. This leads to both increased storage costs and unnecessarily high maintenance costs for these datasets
Externí odkaz:
http://arxiv.org/abs/2312.13427
Autor:
Agarwal, Shubham, Mitra, Subrata, Chakraborty, Sarthak, Karanam, Srikrishna, Mukherjee, Koyel, Saini, Shiv
Text-to-image generation using diffusion models has seen explosive popularity owing to their ability in producing high quality images adhering to text prompts. However, production-grade diffusion model serving is a resource intensive task that not on
Externí odkaz:
http://arxiv.org/abs/2312.04429
Autor:
Mukherjee, Koyel, Shah, Raunak, Saini, Shiv Kumar, Singh, Karanpreet, Khushi, Kesarwani, Harsh, Barnwal, Kavya, Chauhan, Ayush
We study the problem of optimizing data storage and access costs on the cloud while ensuring that the desired performance or latency is unaffected. We first propose an optimizer that optimizes the data placement tier (on the cloud) and the choice of
Externí odkaz:
http://arxiv.org/abs/2305.14818
Training neural networks on image datasets generally require extensive experimentation to find the optimal learning rate regime. Especially, for the cases of adversarial training or for training a newly synthesized model, one would not know the best
Externí odkaz:
http://arxiv.org/abs/1910.11605
Despite the phenomenal success of deep learning in recent years, there remains a gap in understanding the fundamental mechanics of neural nets. More research is focussed on handcrafting complex and larger networks, and the design decisions are often
Externí odkaz:
http://arxiv.org/abs/1904.10689
Autor:
Biswas, Arpita, Gopalakrishnan, Ragavendran, Tulabandhula, Theja, Metrewar, Asmita, Mukherjee, Koyel, Thangaraj, Raja Subramaniam
This paper provides efficient solutions to maximize profit for commercial ridesharing services, under a pricing model with detour-based discounts for passengers. We propose greedy heuristics for real-time ride matching that offer different trade-offs
Externí odkaz:
http://arxiv.org/abs/1706.02682
We study the problem of learning to partition users into groups, where one must learn the compatibilities between the users to achieve optimal groupings. We define four natural objectives that optimize for average and worst case compatibilities and p
Externí odkaz:
http://arxiv.org/abs/1703.07807