Zobrazeno 1 - 10
of 86 652
pro vyhledávání: '"A. , Iyer"'
Autor:
Tripodi, Roberta, Martis, Nicholas, Markov, Vladan, Bradač, Maruša, Di Mascia, Fabio, Cammelli, Vieri, D'Eugenio, Francesco, Willott, Chris, Curti, Mirko, Bhatt, Maulik, Gallerani, Simona, Rihtaršič, Gregor, Singh, Jasbir, Gaspar, Gaia, Harshan, Anishya, Judež, Jon, Merida, Rosa M., Desprez, Guillaume, Sawicki, Marcin, Goovaerts, Ilias, Muzzin, Adam, Noirot, Gaël, Sarrouh, Ghassan T. E., Abraham, Roberto, Asada, Yoshihisa, Brammer, Gabriel, Carpenter, Vicente Estrada, Felicioni, Giordano, Fujimoto, Seiji, Iyer, Kartheik, Mowla, Lamiya, Strait, Victoria
The James Webb Space Telescope (JWST) has recently discovered a new population of objects at high redshift referred to as `Little Red Dots' (LRDs). Their nature currently remains elusive, despite their surprisingly high inferred number densities. Thi
Externí odkaz:
http://arxiv.org/abs/2412.04983
Autor:
The Multimodal Universe Collaboration, Audenaert, Jeroen, Bowles, Micah, Boyd, Benjamin M., Chemaly, David, Cherinka, Brian, Ciucă, Ioana, Cranmer, Miles, Do, Aaron, Grayling, Matthew, Hayes, Erin E., Hehir, Tom, Ho, Shirley, Huertas-Company, Marc, Iyer, Kartheik G., Jablonska, Maja, Lanusse, Francois, Leung, Henry W., Mandel, Kaisey, Martínez-Galarza, Juan Rafael, Melchior, Peter, Meyer, Lucas, Parker, Liam H., Qu, Helen, Shen, Jeff, Smith, Michael J., Stone, Connor, Walmsley, Mike, Wu, John F.
We present the MULTIMODAL UNIVERSE, a large-scale multimodal dataset of scientific astronomical data, compiled specifically to facilitate machine learning research. Overall, the MULTIMODAL UNIVERSE contains hundreds of millions of astronomical observ
Externí odkaz:
http://arxiv.org/abs/2412.02527
Autor:
Havrilla, Alex, Dai, Andrew, O'Mahony, Laura, Oostermeijer, Koen, Zisler, Vera, Albalak, Alon, Milo, Fabrizio, Raparthy, Sharath Chandra, Gandhi, Kanishk, Abbasi, Baber, Phung, Duy, Iyer, Maia, Mahan, Dakota, Blagden, Chase, Gureja, Srishti, Hamdy, Mohammed, Li, Wen-Ding, Paolini, Giovanni, Ammanamanchi, Pawan Sasanka, Meyerson, Elliot
Synthetic data generation with Large Language Models is a promising paradigm for augmenting natural data over a nearly infinite range of tasks. Given this variety, direct comparisons among synthetic data generation algorithms are scarce, making it di
Externí odkaz:
http://arxiv.org/abs/2412.02980
This article describes the development of a novel U-Net-enhanced Wavelet Neural Operator (U-WNO),which combines wavelet decomposition, operator learning, and an encoder-decoder mechanism. This approach harnesses the superiority of the wavelets in tim
Externí odkaz:
http://arxiv.org/abs/2411.16890
Parkinsons disease, the fastest growing neurodegenerative disorder globally, has seen a 50 percent increase in cases within just two years. As speech, memory, and motor symptoms worsen over time, early diagnosis is crucial for preserving patients qua
Externí odkaz:
http://arxiv.org/abs/2411.18640
Autor:
Iyer, Srikrishna
We present our submission to the BabyLM challenge, aiming to push the boundaries of data-efficient language model pretraining. Our method builds upon deep mutual learning, introducing a student model search for diverse initialization. We address the
Externí odkaz:
http://arxiv.org/abs/2411.16487
Autor:
Lovell, Christopher C., Starkenburg, Tjitske, Ho, Matthew, Anglés-Alcázar, Daniel, Davé, Romeel, Gabrielpillai, Austen, Iyer, Kartheik, Matthews, Alice E., Roper, William J., Somerville, Rachel, Sommovigo, Laura, Villaescusa-Navarro, Francisco
We perform the first direct cosmological and astrophysical parameter inference from the combination of galaxy luminosity functions and colours using a simulation based inference approach. Using the Synthesizer code we simulate the dust attenuated ult
Externí odkaz:
http://arxiv.org/abs/2411.13960
Autor:
Chen, Deming, Youssef, Alaa, Pendse, Ruchi, Schleife, André, Clark, Bryan K., Hamann, Hendrik, He, Jingrui, Laino, Teodoro, Varshney, Lav, Wang, Yuxiong, Sil, Avirup, Jabbarvand, Reyhaneh, Xu, Tianyin, Kindratenko, Volodymyr, Costa, Carlos, Adve, Sarita, Mendis, Charith, Zhang, Minjia, Núñez-Corrales, Santiago, Ganti, Raghu, Srivatsa, Mudhakar, Kim, Nam Sung, Torrellas, Josep, Huang, Jian, Seelam, Seetharami, Nahrstedt, Klara, Abdelzaher, Tarek, Eilam, Tamar, Zhao, Huimin, Manica, Matteo, Iyer, Ravishankar, Hirzel, Martin, Adve, Vikram, Marinov, Darko, Franke, Hubertus, Tong, Hanghang, Ainsworth, Elizabeth, Zhao, Han, Vasisht, Deepak, Do, Minh, Oliveira, Fabio, Pacifici, Giovanni, Puri, Ruchir, Nagpurkar, Priya
This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads through innovative, full-stack co
Externí odkaz:
http://arxiv.org/abs/2411.13239
Autor:
Iyer, Vishnu, Liang, Daniel
We study the problem of tolerant testing of stabilizer states. In particular, we give the first such algorithm that accepts mixed state inputs. Formally, given a mixed state $\rho$ that either has fidelity at least $\varepsilon_1$ with some stabilize
Externí odkaz:
http://arxiv.org/abs/2411.08765
Mixture-of-Experts (MoE) architectures have recently gained popularity in enabling efficient scaling of large language models. However, we uncover a fundamental tension: while MoEs are designed for selective expert activation, production serving requ
Externí odkaz:
http://arxiv.org/abs/2411.08982