1 code implementation • 16 Mar 2023 • Junchen Yang, Ofir Lindenbaum, Yuval Kluger, Ariel Jaffe
Multi-modal high throughput biological data presents a great scientific opportunity and a significant computational challenge.
1 code implementation • 10 Nov 2022 • Ram Dyuthi Sristi, Gal Mishne, Ariel Jaffe
Selecting subsets of features that differentiate between two conditions is a key task in a broad range of scientific domains.
no code implementations • 7 Feb 2022 • Erez Peterfreund, Ioannis G. Kevrekidis, Ariel Jaffe
Inferring the location of a mobile device in an indoor setting is an open problem of utmost significance.
1 code implementation • 26 Feb 2021 • Yariv Aizenbud, Ariel Jaffe, Meng Wang, Amber Hu, Noah Amsel, Boaz Nadler, Joseph T. Chang, Yuval Kluger
For large trees, a common approach, termed divide-and-conquer, is to recover the tree structure in two steps.
3 code implementations • 28 Feb 2020 • Ariel Jaffe, Noah Amsel, Yariv Aizenbud, Boaz Nadler, Joseph T. Chang, Yuval Kluger
A common assumption in multiple scientific applications is that the distribution of observed data can be modeled by a latent tree graphical model.
no code implementations • 27 Feb 2020 • Ariel Jaffe, Yuval Kluger, Ofir Lindenbaum, Jonathan Patsenker, Erez Peterfreund, Stefan Steinerberger
word2vec due to Mikolov \textit{et al.} (2013) is a word embedding method that is widely used in natural language processing.
1 code implementation • ICML 2018 • Ariel Jaffe, Roi Weiss, Shai Carmi, Yuval Kluger, Boaz Nadler
Latent variable models with hidden binary units appear in various applications.
no code implementations • 7 Nov 2016 • Amit Moscovich, Ariel Jaffe, Boaz Nadler
We consider semi-supervised regression when the predictor variables are drawn from an unknown manifold.
1 code implementation • 6 Feb 2016 • Uri Shaham, Xiuyuan Cheng, Omer Dror, Ariel Jaffe, Boaz Nadler, Joseph Chang, Yuval Kluger
We show how deep learning methods can be applied in the context of crowdsourcing and unsupervised ensemble learning.
no code implementations • 20 Oct 2015 • Ariel Jaffe, Ethan Fetaya, Boaz Nadler, Tingting Jiang, Yuval Kluger
In unsupervised ensemble learning, one obtains predictions from multiple sources or classifiers, yet without knowing the reliability and expertise of each source, and with no labeled data to assess it.
no code implementations • 29 Jul 2014 • Ariel Jaffe, Boaz Nadler, Yuval Kluger
In various situations one is given only the predictions of multiple classifiers over a large unlabeled test data.