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pro vyhledávání: '"Rosenbluth A."'
Living systems continually respond to signals from the surrounding environment. Survival requires that their responses adapt quickly and robustly to the changes in the environment. One particularly challenging example is olfactory navigation in turbu
Externí odkaz:
http://arxiv.org/abs/2405.20135
Publikováno v:
The Twelfth International Conference on Learning Representations (2024)
Graph Transformers (GTs) such as SAN and GPS are graph processing models that combine Message-Passing GNNs (MPGNNs) with global Self-Attention. They were shown to be universal function approximators, with two reservations: 1. The initial node feature
Externí odkaz:
http://arxiv.org/abs/2405.11951
Autor:
Grohe, Martin, Rosenbluth, Eran
Graph neural networks (GNN) are deep learning architectures for graphs. Essentially, a GNN is a distributed message passing algorithm, which is controlled by parameters learned from data. It operates on the vertices of a graph: in each iteration, ver
Externí odkaz:
http://arxiv.org/abs/2403.06817
The recent Long-Range Graph Benchmark (LRGB, Dwivedi et al. 2022) introduced a set of graph learning tasks strongly dependent on long-range interaction between vertices. Empirical evidence suggests that on these tasks Graph Transformers significantly
Externí odkaz:
http://arxiv.org/abs/2309.00367
Autor:
Heather I. Greenwood, Cristian K. Maldonado Rodas, Rita I. Freimanis, Alexa C. Glencer, Phoebe N. Miller, Rita A. Mukhtar, Case Brabham, Christina Yau, Jennifer M. Rosenbluth, Gillian L. Hirst, Michael J. Campbell, Alexander Borowsky, Nola Hylton, Laura J. Esserman, Amrita Basu
Publikováno v:
npj Breast Cancer, Vol 10, Iss 1, Pp 1-9 (2024)
Abstract New approaches are needed to determine which ductal carcinoma in situ (DCIS) is at high risk for progression to invasive ductal carcinoma (IDC). We retrospectively studied DCIS patients who declined surgery (2002–2019), and received endocr
Externí odkaz:
https://doaj.org/article/1470ee587e164affa1e41c9b96a0b37b
The expressivity of Graph Neural Networks (GNNs) is dependent on the aggregation functions they employ. Theoretical works have pointed towards Sum aggregation GNNs subsuming every other GNNs, while certain practical works have observed a clear advant
Externí odkaz:
http://arxiv.org/abs/2302.11603
Autor:
Rosenbluth, Eran
Various real-world problems consist of partitioning a set of locations into disjoint subsets, each subset spread in a way that it covers the whole set with a certain radius. Given a finite set S, a metric d, and a radius r, define a subset (of S) S'
Externí odkaz:
http://arxiv.org/abs/2302.03451
Publikováno v:
Mayo Clinic Proceedings: Digital Health, Vol 2, Iss 1, Pp 34-37 (2024)
Externí odkaz:
https://doaj.org/article/dd70b696f8c84780a2dba871688fcfef
How are the advantage relations between a set of agents playing a game organized and how do they reflect the structure of the game? In this paper, we illustrate "Principal Trade-off Analysis" (PTA), a decomposition method that embeds games into a low
Externí odkaz:
http://arxiv.org/abs/2206.07520
Autor:
Ide, Jaime S., Mićović, Daria, Guarino, Michael J., Alcedo, Kevin, Rosenbluth, David, Pope, Adrian P.
Reusing previously trained models is critical in deep reinforcement learning to speed up training of new agents. However, it is unclear how to acquire new skills when objectives and constraints are in conflict with previously learned skills. Moreover
Externí odkaz:
http://arxiv.org/abs/2202.02918