Permutative redundancy and uncertainty of the objective in deep learning

no code implementations • 11 Nov 2024 • Vacslav Glukhov

Implications of uncertain objective functions and permutative symmetry of traditional deep learning architectures are discussed.

Deep Learning

Reductive MDPs: A Perspective Beyond Temporal Horizons

no code implementations • 15 May 2022 • Thomas Spooner, Rui Silva, Joshua Lockhart, Jason Long, Vacslav Glukhov

Solving general Markov decision processes (MDPs) is a computationally hard problem.

Reward is not enough: can we liberate AI from the reinforcement learning paradigm?

no code implementations • 3 Feb 2022 • Vacslav Glukhov

I present arguments against the hypothesis put forward by Silver, Singh, Precup, and Sutton ( https://www. sciencedirect. com/science/article/pii/S0004370221000862 ) : reward maximization is not enough to explain many activities associated with natural and artificial intelligence including knowledge, learning, perception, social intelligence, evolution, language, generalisation and imitation.

reinforcement-learning Reinforcement Learning (RL)