Search Results for author:
Found 7 papers, 4 papers with code
Multi Time Scale World Models
Intelligent agents use internal world models to reason and make predictions about different courses of their actions at many scales.
Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios
Recurrent State-space models (RSSMs) are highly expressive models for learning patterns in time series data and system identification.
End-to-End Learning of Hybrid Inverse Dynamics Models for Precise and Compliant Impedance Control
These models need to precisely capture the robot dynamics, which consist of well-understood components, e. g., rigid body dynamics, and effects that remain challenging to capture, e. g., stick-slip friction and mechanical flexibilities.
Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning
We adopt a recent probabilistic recurrent neural network architecture, called Re-current Kalman Networks (RKNs), to model learning by conditioning its transition dynamics on the control actions.
Adversarial Fooling Beyond "Flipping the Label"
Therefore, the metric to quantify the vulnerability of the models should capture the severity of the flipping as well.
Zero-Shot Knowledge Distillation in Deep Networks
Without even using any meta-data, we synthesize the Data Impressions from the complex Teacher model and utilize these as surrogates for the original training data samples to transfer its learning to Student via knowledge distillation.
Learning Sparse Adversarial Dictionaries For Multi-Class Audio Classification
Audio events are quite often overlapping in nature, and more prone to noise than visual signals.
