RNN Symposium 2016: Alex Graves - Differentiable Neural Computer

RNN Symposium 2016: Nal Kalchbrenner - Generative Modelling as Sequence Learning

RNN Symposium 2016: Panel Discussion - The Future of Machines that Learn Algorithms

Christopher D Manning: A Neural Network Model That Can Reason (ICLR 2018 invited talk)

Eric J. Ma - An Attempt At Demystifying Bayesian Deep Learning

Token Turing Machines - CVPR 2023

RNN Symposium 2016: Jürgen Schmidhuber - Intro to RNNs and Other Machines that Learn Algorithms

Neural Turing Machines (Paper Explained)

RNN Symposium: Nando de Freitas - Learning to Learn, to Program, to Explore and to Seek Knowledge

Alex Graves, IJCNN 2017 Plenary Talk: Frontiers in Recurrent Neural Network Research Pt. 1

RNN Symposium 2016: Mike Mozer - Neural Hawkes Process Memories

Geoffrey Hinton: The Foundations of Deep Learning

Что такое квантовая теория

Symposium: Deep Learning - Alex Graves

RNN Symposium 2016: Jürgen Schmidhuber - Asymptotically fastest solver of all well-defined problems

Теренс Тао о том, как Григорий Перельман решил гипотезу Пуанкаре | Лекс Фридман

History of Bayesian Neural Networks (Keynote talk)

Differentiable Neural Computer (LIVE)

LSTM is dead. Long Live Transformers!

How to Learn an Algorithm - Jürgen Schmidhuber

Самая сложная модель из тех, что мы реально понимаем