We propose a complexity measure of a neural network mapping function bas...
We introduce a deep recursive octree network for the compression of 3D v...
We show that reinforcement learning agents that learn by surprise (surpr...
Pseudo-rehearsal allows neural networks to learn a sequence of tasks wit...
Exploration in environments with continuous control and sparse rewards
r...
We introduce switched linear projections for expressing the activity of ...
Any generic deep machine learning algorithm is essentially a function fi...
We present a conditional probabilistic framework for collaborative
repre...
We present an end-to-end CNN architecture for fine-grained visual recogn...
Neural networks can achieve extraordinary results on a wide variety of t...
We show that the number of unique function mappings in a neural network
...
In this paper we introduce new bounds on the approximation of functions ...
In general, neural networks are not currently capable of learning tasks ...
This letter introduces the LOOP binary descriptor (local optimal oriente...
Binary codes can be used to speed up nearest neighbor search tasks in la...