1 code implementation • 5 Jul 2023 • David Stutz, Ali Taylan Cemgil, Abhijit Guha Roy, Tatiana Matejovicova, Melih Barsbey, Patricia Strachan, Mike Schaekermann, Jan Freyberg, Rajeev Rikhye, Beverly Freeman, Javier Perez Matos, Umesh Telang, Dale R. Webster, YuAn Liu, Greg S. Corrado, Yossi Matias, Pushmeet Kohli, Yun Liu, Arnaud Doucet, Alan Karthikesalingam
In contrast, we propose a framework where aggregation is done using a statistical model.
1 code implementation • 13 Jun 2023 • Yijun Wan, Melih Barsbey, Abdellatif Zaidi, Umut Simsekli
Neural network compression has been an increasingly important subject, not only due to its practical relevance, but also due to its theoretical implications, as there is an explicit connection between compressibility and generalization error.
no code implementations • 2 Jun 2022 • Anant Raj, Melih Barsbey, Mert Gürbüzbalaban, Lingjiong Zhu, Umut Şimşekli
Recent studies have shown that heavy tails can emerge in stochastic optimization and that the heaviness of the tails have links to the generalization error.
3 code implementations • 4 Jul 2021 • Rıza Özçelik, Alperen Bağ, Berk Atıl, Melih Barsbey, Arzucan Özgür, Elif Özkırımlı
Here, we present DebiasedDTA, a novel drug-target affinity (DTA) prediction model training framework that addresses dataset biases to improve the generalizability of affinity prediction models.
1 code implementation • NeurIPS 2021 • Melih Barsbey, Milad Sefidgaran, Murat A. Erdogdu, Gaël Richard, Umut Şimşekli
Neural network compression techniques have become increasingly popular as they can drastically reduce the storage and computation requirements for very large networks.
no code implementations • pproximateinference AABI Symposium 2019 • Mehmet Burak Kurutmaz, Melih Barsbey, Ali Taylan Cemgil, Sinan Yildirim, Umut Şimşekli
We believe that the Bayesian approach to causal discovery both allows the rich methodology of Bayesian inference to be used in various difficult aspects of this problem and provides a unifying framework to causal discovery research.
1 code implementation • 11 Mar 2019 • Ali Taylan Cemgil, Mehmet Burak Kurutmaz, Sinan Yildirim, Melih Barsbey, Umut Simsekli
We introduce a dynamic generative model, Bayesian allocation model (BAM), which establishes explicit connections between nonnegative tensor factorization (NTF), graphical models of discrete probability distributions and their Bayesian extensions, and the topic models such as the latent Dirichlet allocation.