3 citations · 3 across the 4 of their papers we have counts for
6 papers
A Novel Framework for Learning Stochastic Representations for Sequence Generation and Recognition
Jungsik Hwang, Ahmadreza Ahmadi
The ability to generate and recognize sequential data is fundamental for autonomous systems operating in dynamic environments. Inspired by the key principles of the brain-predictiv…
Semi-supervised Gated Recurrent Neural Networks for Robotic Terrain Classification
Ahmadreza Ahmadi, Tønnes Nygaard, Navinda Kottege +2
Legged robots are popular candidates for missions in challenging terrains due to the wide variety of locomotion strategies they can employ. Terrain classification is a key enabling…
Towards hybrid primary intersubjectivity: a neural robotics library for human science
Hendry F. Chame, Ahmadreza Ahmadi, Jun Tani
Human-robot interaction is becoming an interesting area of research in cognitive science, notably, for the study of social cognition. Interaction theorists consider primary intersu…
A Novel Predictive-Coding-Inspired Variational RNN Model for Online Prediction and Recognition
Ahmadreza Ahmadi, Jun Tani
This study introduces PV-RNN, a novel variational RNN inspired by the predictive-coding ideas. The model learns to extract the probabilistic structures hidden in fluctuating tempor…
Predictive Coding-based Deep Dynamic Neural Network for Visuomotor Learning
Jungsik Hwang, Jinhyung Kim, Ahmadreza Ahmadi +2
This study presents a dynamic neural network model based on the predictive coding framework for perceiving and predicting the dynamic visuo-proprioceptive patterns. In our previous…
Bridging the Gap between Probabilistic and Deterministic Models: A Simulation Study on a Variational Bayes Predictive Coding Recurrent Neural Network Model
Ahmadreza Ahmadi, Jun Tani
The current paper proposes a novel variational Bayes predictive coding RNN model, which can learn to generate fluctuated temporal patterns from exemplars. The model learns to maxim…