Inductive biases for deep learning of higher-level cognition
A fascinating hypothesis is that human and animal intelligence could be explained by a few
principles (rather than an encyclopaedic list of heuristics). If that hypothesis was correct, we …
principles (rather than an encyclopaedic list of heuristics). If that hypothesis was correct, we …
Deep reinforcement learning: An overview
Y Li - arXiv preprint arXiv:1701.07274, 2017 - arxiv.org
We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …
We discuss six core elements, six important mechanisms, and twelve applications. We start …
Deep image deblurring: A survey
Image deblurring is a classic problem in low-level computer vision with the aim to recover a
sharp image from a blurred input image. Advances in deep learning have led to significant …
sharp image from a blurred input image. Advances in deep learning have led to significant …
Dynamic neural networks: A survey
Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …
models which have fixed computational graphs and parameters at the inference stage …
Object-centric learning with slot attention
F Locatello, D Weissenborn… - Advances in neural …, 2020 - proceedings.neurips.cc
Learning object-centric representations of complex scenes is a promising step towards
enabling efficient abstract reasoning from low-level perceptual features. Yet, most deep …
enabling efficient abstract reasoning from low-level perceptual features. Yet, most deep …
[PDF][PDF] The computational limits of deep learning
Deep learning's recent history has been one of achievement: from triumphing over humans
in the game of Go to world-leading performance in image classification, voice recognition …
in the game of Go to world-leading performance in image classification, voice recognition …
Conditional object-centric learning from video
Object-centric representations are a promising path toward more systematic generalization
by providing flexible abstractions upon which compositional world models can be built …
by providing flexible abstractions upon which compositional world models can be built …
[引用][C] An introduction to variational autoencoders
An Introduction to Variational Autoencoders Page 1 An Introduction to Variational Autoencoders
Page 2 Other titles in Foundations and Trends R in Machine Learning Computational Optimal …
Page 2 Other titles in Foundations and Trends R in Machine Learning Computational Optimal …
On the binding problem in artificial neural networks
Contemporary neural networks still fall short of human-level generalization, which extends
far beyond our direct experiences. In this paper, we argue that the underlying cause for this …
far beyond our direct experiences. In this paper, we argue that the underlying cause for this …
Simple unsupervised object-centric learning for complex and naturalistic videos
Unsupervised object-centric learning aims to represent the modular, compositional, and
causal structure of a scene as a set of object representations and thereby promises to …
causal structure of a scene as a set of object representations and thereby promises to …