Zobrazeno 1 - 10
of 36 968
pro vyhledávání: '"Sutter, A."'
Autor:
Agostini, Andrea, Chopard, Daphné, Meng, Yang, Fortin, Norbert, Shahbaba, Babak, Mandt, Stephan, Sutter, Thomas M., Vogt, Julia E.
Multimodal data integration and label scarcity pose significant challenges for machine learning in medical settings. To address these issues, we conduct an in-depth evaluation of the newly proposed Multimodal Variational Mixture-of-Experts (MMVM) VAE
Externí odkaz:
http://arxiv.org/abs/2411.10356
Autor:
Chen, Boqi, Zhu, Yuanzhi, Ao, Yunke, Caprara, Sebastiano, Sutter, Reto, Rätsch, Gunnar, Konukoglu, Ender, Susmelj, Anna
Single-source domain generalization (SDG) aims to learn a model from a single source domain that can generalize well on unseen target domains. This is an important task in computer vision, particularly relevant to medical imaging where domain shifts
Externí odkaz:
http://arxiv.org/abs/2411.05223
Autor:
Petersen, Felix, Borgelt, Christian, Sutter, Tobias, Kuehne, Hilde, Deussen, Oliver, Ermon, Stefano
When training neural networks with custom objectives, such as ranking losses and shortest-path losses, a common problem is that they are, per se, non-differentiable. A popular approach is to continuously relax the objectives to provide gradients, ena
Externí odkaz:
http://arxiv.org/abs/2410.19055
Autor:
Chown, Ryan, Leroy, Adam K., Sandstrom, Karin, Chastenet, Jeremy, Sutter, Jessica, Koch, Eric W., Koziol, Hannah B., Neumann, Lukas, Sun, Jiayi, Williams, Thomas G., Baron, Dalya, Anand, Gagandeep S., Barnes, Ashley T., Bazzi, Zein, Belfiore, Francesco, Bolatto, Alberto, Boquien, Mederic, Cao, Yixian, Chevance, Melanie, Colombo, Dario, Dale, Daniel A., Egorov, Oleg V., Eibensteiner, Cosima, Emsellem, Eric, Hassani, Hamid, Henshaw, Jonathan D., He, Hao, Kim, Jaeyeon, Kreckel, Kathryn, Meidt, Sharon E., Murphy, Eric J., Oakes, Elias K., Ostriker, Eve C., Pan, Hsi-An, Pathak, Debosmita, Rosolowsky, Erik, Sarbadhicary, Sumit K., Schinnerer, Eva, Teng, Yu-Hsuan
Combining Atacama Large Millimeter/sub-millimeter Array CO(2-1) mapping and JWST near- and mid-infrared imaging, we characterize the relationship between CO(2-1) and polycyclic aromatic hydrocarbon (PAH) emission at ~100 pc resolution in 66 nearby st
Externí odkaz:
http://arxiv.org/abs/2410.05397
Autor:
Chastenet, Jérémy, Sandstrom, Karin M., Leroy, Adam K., Bot, Caroline, Chiang, I-Da, Chown, Ryan, Gordon, Karl D., Koch, Eric W., Roussel, Hélène, Sutter, Jessica, Williams, Thomas G.
We present resolved $3.6-250~\mu$m dust spectral energy distribution (SED) fitting for $\sim 800$ nearby galaxies. We measure the distribution of radiation field intensities heating the dust, the dust mass surface density ($\Sigma_{\rm d}$), and the
Externí odkaz:
http://arxiv.org/abs/2410.03835
Photosynthetic organisms rely on sophisticated photoprotective mechanisms to prevent oxidative damage under high or fluctuating solar illumination. Cyanobacteria, which have evolved a modular, water-soluble light harvesting complex - the phycobilisom
Externí odkaz:
http://arxiv.org/abs/2410.03899
Autor:
Baron, Dalya, Sandstrom, Karin, Sutter, Jessica, Hassani, Hamid, Groves, Brent, Leroy, Adam, Schinnerer, Eva, Boquien, Médéric, Brazzini, Matilde, Chastenet, Jérémy, Dale, Daniel, Egorov, Oleg, Glover, Simon, Klessen, Ralf, Pathak, Debosmita, Rosolowsky, Erik, Bigiel, Frank, Chevance, Mélanie, Grasha, Kathryn, Hughes, Annie, Méndez-Delgado, J. Eduardo, Pety, Jérôme, Williams, Thomas, Hannon, Stephen, Sarbadhicary, Sumit
The structure and chemistry of the dusty interstellar medium (ISM) are shaped by complex processes that depend on the local radiation field, gas composition, and dust grain properties. Of particular importance are Polycyclic Aromatic Hydrocarbons (PA
Externí odkaz:
http://arxiv.org/abs/2410.02864
Understanding how models process and interpret time series data remains a significant challenge in deep learning to enable applicability in safety-critical areas such as healthcare. In this paper, we introduce Sequence Dreaming, a technique that adap
Externí odkaz:
http://arxiv.org/abs/2408.10628
Entanglement distillation, the process of converting weakly entangled states into maximally entangled ones using Local Operations and Classical Communication (LOCC), is pivotal for robust entanglement-assisted quantum information processing in error-
Externí odkaz:
http://arxiv.org/abs/2408.02383
Bound entanglement is a special form of quantum entanglement that cannot be used for distillation, i.e., the local transformation of copies of arbitrarily entangled states into a smaller number of approximately maximally entangled states. Implying an
Externí odkaz:
http://arxiv.org/abs/2406.13491