Artificial intelligence (AI) is rapidly becoming one of the key technolo...
We propose the predictive forward-forward (PFF) algorithm for conducting...
In this work, we develop convolutional neural generative coding (Conv-NG...
Unlike most neural language models, humans learn language in a rich,
mul...
In this article, we propose a backpropagation-free approach to robotic
c...
While current deep learning algorithms have been successful for a wide
v...
Recent advances in deep learning have led to superhuman performance acro...
Recent advances in deep learning have resulted in image compression
algo...
Given a single RGB panorama, the goal of 3D layout reconstruction is to
...
Automated mathematical reasoning is a challenging problem that requires ...
Given a collection of strings belonging to a context free grammar (CFG) ...
Training deep neural networks on large-scale datasets requires significa...
For lossy image compression, we develop a neural-based system which lear...
In order to learn complex grammars, recurrent neural networks (RNNs) req...
In lifelong learning systems, especially those based on artificial neura...
Temporal models based on recurrent neural networks have proven to be qui...
Temporal models based on recurrent neural networks have proven to be qui...
We propose novel neural temporal models for short-term motion prediction...
The theory of situated cognition postulates that language is inseparable...
Finding biologically plausible alternatives to back-propagation of error...
For lossy image compression systems, we develop an algorithm called iter...
The use of back-propagation and its variants to train deep networks is o...