Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Azarafrooz, Ari"'
Autor:
Azarafrooz, Ari, Faal, Farshid
Recent research has shown the potential of Nash Learning via Human Feedback for large language model alignment by incorporating the notion of a preference model in a minimax game setup. We take this idea further by casting the alignment as a mirror d
Externí odkaz:
http://arxiv.org/abs/2406.15890
Autor:
Azarafrooz, Ari
A Differentiable Neural Computer (DNC) is a neural network with an external memory which allows for iterative content modification via read, write and delete operations. We show that information theoretic properties of the memory contents play an imp
Externí odkaz:
http://arxiv.org/abs/2211.02987
Autor:
Azarafrooz, Ari
Game-theoretic models of learning are a powerful set of models that optimize multi-objective architectures. Among these models are zero-sum architectures that have inspired adversarial learning frameworks. An important shortcoming of these zeros-sum
Externí odkaz:
http://arxiv.org/abs/2002.06476
Autor:
Azarafrooz, Ari, Brock, John
We describe a novel extension of soft actor-critics for hierarchical Deep Q-Networks (HDQN) architectures using mutual information metric. The proposed extension provides a suitable framework for encouraging explorations in such hierarchical networks
Externí odkaz:
http://arxiv.org/abs/1906.07122
Autor:
Azarafrooz, Ari, Brock, John
Measuring the similarity of two files is an important task in malware analysis, with fuzzy hash functions being a popular approach. Traditional fuzzy hash functions are data agnostic: they do not learn from a particular dataset how to determine simil
Externí odkaz:
http://arxiv.org/abs/1812.07071