71 citations · 92 across the 8 of their papers we have counts for
5 papers · 1 filter
Utilization of Deep Reinforcement Learning for saccadic-based object visual search
Tomasz Kornuta, Kamil Rocki
The paper focuses on the problem of learning saccades enabling visual object search. The developed system combines reinforcement learning with a neural network for learning to pred…
Surprisal-Driven Zoneout
Kamil Rocki, Tomasz Kornuta, Tegan Maharaj
We propose a novel method of regularization for recurrent neural networks called suprisal-driven zoneout. In this method, states zoneout (maintain their previous value rather than…
Surprisal-Driven Feedback in Recurrent Networks
Kamil M Rocki
Recurrent neural nets are widely used for predicting temporal data. Their inherent deep feedforward structure allows learning complex sequential patterns. It is believed that top-d…
Recurrent Memory Array Structures
Kamil Rocki
The following report introduces ideas augmenting standard Long Short Term Memory (LSTM) architecture with multiple memory cells per hidden unit in order to improve its generalizati…
Towards Machine Intelligence
Kamil Rocki
There exists a theory of a single general-purpose learning algorithm which could explain the principles of its operation. This theory assumes that the brain has some initial rough…