Zobrazeno 1 - 10
of 16 207
pro vyhledávání: '"P., Rajendran"'
Millicharged particles are generic in theories of dark sectors. A cosmic or local abundance of them may be produced by the early universe, stellar environments, or the decay or annihilation of dark matter/dark energy. Furthermore, if such particles a
Externí odkaz:
http://arxiv.org/abs/2412.03643
Autor:
Li, Chen, Rajendran, Bipin.
We present Noise Adaptor, a novel method for constructing competitive low-latency spiking neural networks (SNNs) by converting noise-injected, low-bit artificial neural networks (ANNs). This approach builds on existing ANN-to-SNN conversion technique
Externí odkaz:
http://arxiv.org/abs/2411.17431
Analog in-memory computing (AIMC) has emerged as a promising solution to overcome the von Neumann bottleneck, accelerating neural network computations and improving computational efficiency. While AIMC has demonstrated success with architectures such
Externí odkaz:
http://arxiv.org/abs/2411.17367
Autor:
Gohil, Vasudev, DeLorenzo, Matthew, Nallam, Veera Vishwa Achuta Sai Venkat, See, Joey, Rajendran, Jeyavijayan
The rapid advancement of large language models (LLMs) has enabled the ability to effectively analyze and generate code nearly instantaneously, resulting in their widespread adoption in software development. Following this advancement, researchers and
Externí odkaz:
http://arxiv.org/abs/2411.16111
Autor:
Melnychuk, Oleksandr, Giaccone, Bianca, Bornman, Nicholas, Cervantes, Raphael, Grassellino, Anna, Harnik, Roni, Kaplan, David E., Nahal, Geev, Pilipenko, Roman, Posen, Sam, Rajendran, Surjeet, Sushkov, Alexander O.
There are strong arguments that quantum mechanics may be nonlinear in its dynamics. A discovery of nonlinearity would hint at a novel understanding of the interplay between gravity and quantum field theory, for example. As such, experiments searching
Externí odkaz:
http://arxiv.org/abs/2411.09611
Bayesian Neural Networks (BNNs) provide superior estimates of uncertainty by generating an ensemble of predictive distributions. However, inference via ensembling is resource-intensive, requiring additional entropy sources to generate stochasticity w
Externí odkaz:
http://arxiv.org/abs/2411.07902
Bayesian Neural Networks (BNNs) provide principled estimates of model and data uncertainty by encoding parameters as distributions. This makes them key enablers for reliable AI that can be deployed on safety critical edge systems. These systems can b
Externí odkaz:
http://arxiv.org/abs/2411.07842
Inspired by biological processes, neuromorphic computing utilizes spiking neural networks (SNNs) to perform inference tasks, offering significant efficiency gains for workloads involving sequential data. Recent advances in hardware and software have
Externí odkaz:
http://arxiv.org/abs/2411.04728
Autor:
Qiu, Liang, Chi, Wenhao, Xing, Xiaohan, Rajendran, Praveenbalaji, Li, Mingjie, Jiang, Yuming, Pastor-Serrano, Oscar, Yang, Sen, Wang, Xiyue, Ji, Yuanfeng, Wen, Qiang
Precision therapy for liver cancer necessitates accurately delineating liver sub-regions to protect healthy tissue while targeting tumors, which is essential for reducing recurrence and improving survival rates. However, the segmentation of hepatic s
Externí odkaz:
http://arxiv.org/abs/2411.02815
Sequence models have demonstrated the ability to perform tasks like channel equalization and symbol detection by automatically adapting to current channel conditions. This is done without requiring any explicit optimization and by leveraging not only
Externí odkaz:
http://arxiv.org/abs/2410.23882