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Active Learning Polynomial Threshold Functions
We initiate the study of active learning polynomial threshold functions (PTFs).
What is Learned in Knowledge Graph Embeddings?
Their relative success is often credited in the literature to their ability to learn logical rules between the relations.
Learning Multimodal Affinities for Textual Editing in Images
Nowadays, as cameras are rapidly adopted in our daily routine, images of documents are becoming both abundant and prevalent.
Adversarial Laws of Large Numbers and Optimal Regret in Online Classification
Laws of large numbers guarantee that given a large enough sample from some population, the measure of any fixed sub-population is well-estimated by its frequency in the sample.
READ: Recursive Autoencoders for Document Layout Generation
Creating large varieties of plausible document layouts can be a tedious task, requiring numerous constraints to be satisfied, including local ones relating different semantic elements and global constraints on the general appearance and spacing.
Deleting and Testing Forbidden Patterns in Multi-Dimensional Arrays
For any $\epsilon \in [0, 1]$ and any large enough pattern $P$ over any alphabet, other than a very small set of exceptional patterns, we design a tolerant tester that distinguishes between the case that the distance is at least $\epsilon$ and the case that it is at most $a_d \epsilon$, with query complexity and running time $c_d \epsilon^{-1}$, where $a_d < 1$ and $c_d$ depend only on $d$.
