Zobrazeno 1 - 10
of 656 452
pro vyhledávání: '"and Shah, A."'
Autor:
Morris, Jonathan Wellington, Shah, Vishrut, Besanceney, Alex, Shah, Daksh, Gilpin, Leilani H.
Sim-to real gap in Reinforcement Learning is when a model trained in a simulator does not translate to the real world. This is a problem for Autonomous Vehicles (AVs) as vehicle dynamics can vary from simulation to reality, and also from vehicle to v
Externí odkaz:
http://arxiv.org/abs/2409.10532
We introduce a novel framework for human-AI collaboration in prediction and decision tasks. Our approach leverages human judgment to distinguish inputs which are algorithmically indistinguishable, or "look the same" to any feasible predictive algorit
Externí odkaz:
http://arxiv.org/abs/2410.08783
Autor:
Hill, Ryan M., Rivero, Gonzalo Reina, Tyler, Ashley J., Schofield, Holly, Doyle, Cody, Osborne, James, Bobela, David, Rier, Lukas, Gibson, Joseph, Tanner, Zoe, Boto, Elena, Bowtell, Richard, Brookes, Matthew J., Shah, Vishal, Holmes, Niall
Optically pumped magnetometers (OPMs) are compact and lightweight sensors that can measure magnetic fields generated by current flow in neuronal assemblies in the brain. Such sensors enable construction of magnetoencephalography (MEG) instrumentation
Externí odkaz:
http://arxiv.org/abs/2410.08718
Autor:
Shah, Paul, Davis, Tamara M., Vincenzi, Maria, Armstrong, Patrick, Brout, Dillon, Camilleri, Ryan, Galbany, Lluis, Garcia-Bellido, Juan, Gill, Mandeep S. S., Lahav, Ofer, Lee, Jason, Lidman, Chris, Moeller, Anais, Sako, Masao, Sanchez, Bruno O., Sullivan, Mark, Whiteway, Lorne, Wiseman, Phillip, Allam, S., Aguena, M., Bocquet, S., Brooks, D., Burke, D. L., Rosell, A. Carnero, da Costa, L. N., Pereira, M. E. S., Desai, S., Dodelson, S., Doel, P., Ferrero, I., Flaugher, B., Frieman, J., Gaztanaga, E., Gruen, D., Gruendl, R. A., Gutierrez, G., Herner, K., Hinton, S. R., Hollowood, D. L., Honscheid, K., James, D. J., Kuehn, K., Lee, S., Marshall, J. L., Mena-Fernandez, J., Miquel, R., Myles, J., Palmese, A., Pieres, A., Malagon, A. A. Plazas, Roodman, A., Samuroff, S., Sanchez, E., Sevilla-Noarbe, I., Smith, M., Suchyta, E., Swanson, M. E. C., Tarle, G., To, C., Vikram, V.
Gravitational lensing magnification of Type Ia supernovae (SNe Ia) allows information to be obtained about the distribution of matter on small scales. In this paper, we derive limits on the fraction $\alpha$ of the total matter density in compact obj
Externí odkaz:
http://arxiv.org/abs/2410.07956
Autor:
Meo, Cristian, Lica, Mircea, Ikram, Zarif, Nakano, Akihiro, Shah, Vedant, Didolkar, Aniket Rajiv, Liu, Dianbo, Goyal, Anirudh, Dauwels, Justin
Deep Reinforcement Learning (RL) has become the leading approach for creating artificial agents in complex environments. Model-based approaches, which are RL methods with world models that predict environment dynamics, are among the most promising di
Externí odkaz:
http://arxiv.org/abs/2410.07836
Autor:
Ahmed, Fatimaelzahraa Ali, Yousef, Mahmoud, Ahmed, Mariam Ali, Ali, Hasan Omar, Mahboob, Anns, Ali, Hazrat, Shah, Zubair, Aboumarzouk, Omar, Ansari, Abdulla Al, Balakrishnan, Shidin
Applying deep learning (DL) for annotating surgical instruments in robot-assisted minimally invasive surgeries (MIS) represents a significant advancement in surgical technology. This systematic review examines 48 studies that and advanced DL methods
Externí odkaz:
http://arxiv.org/abs/2410.07269
This work presents the measurements of the cosmic muon charge ratio as a function of full azimuthal angle and momentum within the range of 0.8 to 3.0 GeV/c, using the mini-ICAL detector. The detector, comprising 10 layers of RPCs, has collected cosmi
Externí odkaz:
http://arxiv.org/abs/2410.05719
To operate at a building scale, service robots must perform very long-horizon mobile manipulation tasks by navigating to different rooms, accessing different floors, and interacting with a wide and unseen range of everyday objects. We refer to these
Externí odkaz:
http://arxiv.org/abs/2410.06237
Graph neural networks (GNNs) have demonstrated remarkable success in graph representation learning, and various sampling approaches have been proposed to scale GNNs to applications with large-scale graphs. A class of promising GNN training algorithms
Externí odkaz:
http://arxiv.org/abs/2410.05416
Diffusion models have found phenomenal success as expressive priors for solving inverse problems, but their extension beyond natural images to more structured scientific domains remains limited. Motivated by applications in materials science, we aim
Externí odkaz:
http://arxiv.org/abs/2410.05143