Zobrazeno 1 - 10
of 244
pro vyhledávání: '"DESAI, ADITYA"'
Autor:
Nandanwar, Siddharth, Desai, Aditya, Esfidani, S. Maryam Vaghefi, McMillan, Tristan, Janzen, Eli, Edgar, James H., Folland, Thomas G.
Van-der-Waals materials have been shown to support numerous exotic polaritonic phenomena originating from their layered structures and associated vibrational and electronic properties. This includes emergent polaritonic phenomena, including hyperboli
Externí odkaz:
http://arxiv.org/abs/2408.12015
Gene sequence search is a fundamental operation in computational genomics. Due to the petabyte scale of genome archives, most gene search systems now use hashing-based data structures such as Bloom Filters (BF). The state-of-the-art systems such as C
Externí odkaz:
http://arxiv.org/abs/2406.14901
Recommendation systems (RS) for items (e.g., movies, books) and ads are widely used to tailor content to users on various internet platforms. Traditionally, recommendation models are trained on a central server. However, due to rising concerns for da
Externí odkaz:
http://arxiv.org/abs/2311.01722
Autor:
Desai, Aditya, Shrivastava, Anshumali
When considering a model architecture, there are several ways to reduce its memory footprint. Historically, popular approaches included selecting smaller architectures and creating sparse networks through pruning. More recently, randomized parameter-
Externí odkaz:
http://arxiv.org/abs/2310.11611
Autor:
Liu, Zichang, Desai, Aditya, Liao, Fangshuo, Wang, Weitao, Xie, Victor, Xu, Zhaozhuo, Kyrillidis, Anastasios, Shrivastava, Anshumali
Large language models(LLMs) have sparked a new wave of exciting AI applications. Hosting these models at scale requires significant memory resources. One crucial memory bottleneck for the deployment stems from the context window. It is commonly recog
Externí odkaz:
http://arxiv.org/abs/2305.17118
Autor:
Desai, Aditya, Shrivastava, Anshumali
Embedding tables dominate industrial-scale recommendation model sizes, using up to terabytes of memory. A popular and the largest publicly available machine learning MLPerf benchmark on recommendation data is a Deep Learning Recommendation Model (DLR
Externí odkaz:
http://arxiv.org/abs/2207.10731
Advancements in deep learning are often associated with increasing model sizes. The model size dramatically affects the deployment cost and latency of deep models. For instance, models like BERT cannot be deployed on edge devices and mobiles due to t
Externí odkaz:
http://arxiv.org/abs/2207.10702
Deep learning for recommendation data is one of the most pervasive and challenging AI workload in recent times. State-of-the-art recommendation models are one of the largest models matching the likes of GPT-3 and Switch Transformer. Challenges in dee
Externí odkaz:
http://arxiv.org/abs/2108.02191
Autor:
Xu, Zhaozhuo, Desai, Aditya, Gupta, Menal, Chandran, Anu, Vial-Aussavy, Antoine, Shrivastava, Anshumali
Traditional seismic processing workflows (SPW) are expensive, requiring over a year of human and computational effort. Deep learning (DL) based data-driven seismic workflows (DSPW) hold the potential to reduce these timelines to a few minutes. Raw se
Externí odkaz:
http://arxiv.org/abs/2102.13631
We introduce Density sketches (DS): a succinct online summary of the data distribution. DS can accurately estimate point wise probability density. Interestingly, DS also provides a capability to sample unseen novel data from the underlying data distr
Externí odkaz:
http://arxiv.org/abs/2102.12301