Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Dorigatti, Emilio"'
Autor:
Kook, Lucas, Kolb, Chris, Schiele, Philipp, Dold, Daniel, Arpogaus, Marcel, Fritz, Cornelius, Baumann, Philipp F., Kopper, Philipp, Pielok, Tobias, Dorigatti, Emilio, Rügamer, David
Neural network representations of simple models, such as linear regression, are being studied increasingly to better understand the underlying principles of deep learning algorithms. However, neural representations of distributional regression models
Externí odkaz:
http://arxiv.org/abs/2405.05429
Semi-supervised learning by self-training heavily relies on pseudo-label selection (PLS). The selection often depends on the initial model fit on labeled data. Early overfitting might thus be propagated to the final model by selecting instances with
Externí odkaz:
http://arxiv.org/abs/2302.08883
Autor:
Ziegler, Ingo, Ma, Bolei, Nie, Ercong, Bischl, Bernd, Rügamer, David, Schubert, Benjamin, Dorigatti, Emilio
Epitope vaccines are a promising direction to enable precision treatment for cancer, autoimmune diseases, and allergies. Effectively designing such vaccines requires accurate prediction of proteasomal cleavage in order to ensure that the epitopes in
Externí odkaz:
http://arxiv.org/abs/2210.12991
Autor:
Zheng, Shunjie-Fabian, Nam, JaeEun, Dorigatti, Emilio, Bischl, Bernd, Azizi, Shekoofeh, Rezaei, Mina
Contrastive learning is among the most successful methods for visual representation learning, and its performance can be further improved by jointly performing clustering on the learned representations. However, existing methods for joint clustering
Externí odkaz:
http://arxiv.org/abs/2209.06941
Accurate in silico modeling of the antigen processing pathway is crucial to enable personalized epitope vaccine design for cancer. An important step of such pathway is the degradation of the vaccine into smaller peptides by the proteasome, some of wh
Externí odkaz:
http://arxiv.org/abs/2209.07527
Learning from positive and unlabeled (PU) data is a setting where the learner only has access to positive and unlabeled samples while having no information on negative examples. Such PU setting is of great importance in various tasks such as medical
Externí odkaz:
http://arxiv.org/abs/2209.02459
Autor:
Drost, Felix, Dorigatti, Emilio, Straub, Adrian, Hilgendorf, Philipp, Wagner, Karolin I., Heyer, Kersten, López Montes, Marta, Bischl, Bernd, Busch, Dirk H., Schober, Kilian, Schubert, Benjamin
Publikováno v:
In Cell Genomics 11 September 2024 4(9)
Positive-unlabeled learning (PUL) aims at learning a binary classifier from only positive and unlabeled training data. Even though real-world applications often involve imbalanced datasets where the majority of examples belong to one class, most cont
Externí odkaz:
http://arxiv.org/abs/2201.13192
One of the most promising approaches for unsupervised learning is combining deep representation learning and deep clustering. Some recent works propose to simultaneously learn representation using deep neural networks and perform clustering by defini
Externí odkaz:
http://arxiv.org/abs/2109.05232
During 2020, the infection rate of COVID-19 has been investigated by many scholars from different research fields. In this context, reliable and interpretable forecasts of disease incidents are a vital tool for policymakers to manage healthcare resou
Externí odkaz:
http://arxiv.org/abs/2101.00661