2 citations · 2 across the 1 of their papers we have counts for
4 papers
Nothing makes sense in deep learning, except in the light of evolution
Artem Kaznatcheev, Konrad Paul Kording
Deep Learning (DL) is a surprisingly successful branch of machine learning. The success of DL is usually explained by focusing analysis on a particular recent algorithm and its tra…
Weighted automata are compact and actively learnable
Artem Kaznatcheev, Prakash Panangaden
We show that weighted automata over the field of two elements can be exponentially more compact than non-deterministic finite state automata. To show this, we combine ideas from au…
Steepest ascent can be exponential in bounded treewidth problems
David A. Cohen, Martin C. Cooper, Artem Kaznatcheev +1
We investigate the complexity of local search based on steepest ascent. We show that even when all variables have domains of size two and the underlying constraint graph of variabl…
Representing fitness landscapes by valued constraints to understand the complexity of local search
Artem Kaznatcheev, David A. Cohen, Peter G. Jeavons
Local search is widely used to solve combinatorial optimisation problems and to model biological evolution, but the performance of local search algorithms on different kinds of fit…