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Michael Kolomenkin

3 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3

Across the 3 of 3 papers where every author was matched, so the position is known.

fields
  • cs.LG3

identity via Semantic Scholar / OpenAlex

most citedGeneralized Quantile Loss for Deep Neural Networks

9 citations · 12 across the 3 of their papers we have counts for

collaborators

3 papers

cs.LG2021★ 2 cited

DL-DDA -- Deep Learning based Dynamic Difficulty Adjustment with UX and Gameplay constraints

Dvir Ben Or, Michael Kolomenkin, Gil Shabat

Dynamic difficulty adjustment (DDA) is a process of automatically changing a game difficulty for the optimization of user experience. It is a vital part of almost any modern game…

cs.LG2020★ 9 cited

Generalized Quantile Loss for Deep Neural Networks

Dvir Ben Or, Michael Kolomenkin, Gil Shabat

This note presents a simple way to add a count (or quantile) constraint to a regression neural net, such that given n samples in the training set it guarantees that the predictio…

cs.LG2020★ 1 cited

Majority Voting and the Condorcet's Jury Theorem

Hanan Shteingart, Eran Marom, Igor Itkin +4

There is a striking relationship between a three hundred years old Political Science theorem named "Condorcet's jury theorem" (1785), which states that majorities are more likely t…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.