Zobrazeno 1 - 10
of 3 076
pro vyhledávání: '"Ghodrati, A."'
Autor:
Schlappa, Justine, Ghiringhelli, Giacomo, Van Kuiken, Benjamin E., Teichmann, Martin, Miedema, Piter S., Delitz, Jan Torben, Gerasimova, Natalia, Molodtsov, Serguei, Adriano, Luigi, Baranasic, Bernard, Broers, Carsten, Carley, Robert, Gessler, Patrick, Ghodrati, Nahid, Hickin, David, Hoang, Le Phuong, Izquierdo, Manuel, Mercadier, Laurent, Mercurio, Giuseppe, Parchenko, Sergii, Stupar, Marijan, Yin, Zhong, Martinelli, Leonardo, Merzoni, Giacomo, Peng, Ying Ying, Reuss, Torben, Lalithambika, Sreeju Sreekantan Nair, Techert, Simone, Laarmann, Tim, Huotari, Simo, Schroeter, Christian, Langer, Burkhard, Giessel, Tatjana, Buechner, Robby, Buchheim, Jana, da Cruz, Vinicius Vaz, Eckert, Sebastian, Gwalt, Grzegorz, Liu, Chun-Yu, Siewert, Frank, Sohrt, Christian, Weniger, Christian, Pietzsch, Annette, Neppl, Stefan, Senf, Friedmar, Scherz, Andreas, Föhlisch, Alexander
Resonant Inelastic X-ray Scattering (RIXS) is an ideal X-ray spectroscopy method to push the combination of energy and time resolutions to the Fourier transform ultimate limit, because it is unaffected by the core-hole lifetime energy broadening. And
Externí odkaz:
http://arxiv.org/abs/2403.08461
Autor:
Habibian, Amirhossein, Ghodrati, Amir, Fathima, Noor, Sautiere, Guillaume, Garrepalli, Risheek, Porikli, Fatih, Petersen, Jens
This work aims to improve the efficiency of text-to-image diffusion models. While diffusion models use computationally expensive UNet-based denoising operations in every generation step, we identify that not all operations are equally relevant for th
Externí odkaz:
http://arxiv.org/abs/2312.08128
Autor:
Kinzer, Sean, Ghodrati, Soroush, Mahapatra, Rohan, Ahn, Byung Hoon, Mascarenhas, Edwin, Li, Xiaolong, Matai, Janarbek, Zhang, Liang, Esmaeilzadeh, Hadi
Deep learning accelerators address the computational demands of Deep Neural Networks (DNNs), departing from the traditional Von Neumann execution model. They leverage specialized hardware to align with the application domain's structure. Compilers fo
Externí odkaz:
http://arxiv.org/abs/2310.17912
Autor:
Esmaeilzadeh, Hadi, Ghodrati, Soroush, Kahng, Andrew B., Kim, Joon Kyung, Kinzer, Sean, Kundu, Sayak, Mahapatra, Rohan, Manasi, Susmita Dey, Sapatnekar, Sachin, Wang, Zhiang, Zeng, Ziqing
Parameterizable machine learning (ML) accelerators are the product of recent breakthroughs in ML. To fully enable their design space exploration (DSE), we propose a physical-design-driven, learning-based prediction framework for hardware-accelerated
Externí odkaz:
http://arxiv.org/abs/2308.12120
Autor:
Ghodrati, Mahdis
In the background of several holographic confining backgrounds, we present the connections between the behaviors of string scattering amplitudes and mutual information. We lay down the analogies between the logarithmic branch cut behavior of the stri
Externí odkaz:
http://arxiv.org/abs/2307.13454
Autor:
Parsapoor, Mahboobeh, Ghodrati, Hamed, Dentamaro, Vincenzo, Madan, Christopher R., Lazarou, Ioulietta, Nikolopoulos, Spiros, Kompatsiaris, Ioannis
Current progress in the artificial intelligence domain has led to the development of various types of AI-powered dementia assessments, which can be employed to identify patients at the early stage of dementia. It can revolutionize the dementia care s
Externí odkaz:
http://arxiv.org/abs/2307.01210
Autor:
Esmaeilzadeh, Hadi, Ghodrati, Soroush, Kahng, Andrew B., Kinzer, Sean, Manasi, Susmita Dey, Sapatnekar, Sachin S., Wang, Zhiang
Today's performance analysis frameworks for deep learning accelerators suffer from two significant limitations. First, although modern convolutional neural network (CNNs) consist of many types of layers other than convolution, especially during train
Externí odkaz:
http://arxiv.org/abs/2306.16767
Autor:
Ghodrati, Laya, Panaretos, Victor M.
We present an optimal transport framework for performing regression when both the covariate and the response are probability distributions on a compact Euclidean subset $\Omega\subset\mathbb{R}^d$, where $d>1$. Extending beyond compactly supported di
Externí odkaz:
http://arxiv.org/abs/2305.17503
Autor:
Ghodrati, Laya, Panaretos, Victor M.
We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of $\mathbb{R}$. An order-$1$ autoregressive model in this context is to be understood as a Markov chain, where on
Externí odkaz:
http://arxiv.org/abs/2303.09469
Autor:
Mahapatra, Rohan, Ghodrati, Soroush, Ahn, Byung Hoon, Kinzer, Sean, Wang, Shu-ting, Xu, Hanyang, Karthikeyan, Lavanya, Sharma, Hardik, Yazdanbakhsh, Amir, Alian, Mohammad, Esmaeilzadeh, Hadi
While (1) serverless computing is emerging as a popular form of cloud execution, datacenters are going through major changes: (2) storage dissaggregation in the system infrastructure level and (3) integration of domain-specific accelerators in the ha
Externí odkaz:
http://arxiv.org/abs/2303.03483