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09/28/2022
Disentangling Transfer in Continual Reinforcement Learning
The ability of continual learning systems to transfer knowledge from pre...
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06/28/2022
Hebbian Continual Representation Learning
Continual Learning aims to bring machine learning into a more realistic ...
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06/16/2022
Continual Learning with Guarantees via Weight Interval Constraints
We introduce a new training paradigm that enforces interval constraints ...
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10/07/2021
On the relationship between disentanglement and multi-task learning
One of the main arguments behind studying disentangled representations i...
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09/28/2021
SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies
In this paper we present the first safe system for full control of self-...
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09/27/2021
Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients
In this work we are the first to present an offline policy gradient meth...
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09/18/2021
PluGeN: Multi-Label Conditional Generation From Pre-Trained Models
Modern generative models achieve excellent quality in a variety of tasks...
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06/09/2021
Zero Time Waste: Recycling Predictions in Early Exit Neural Networks
The problem of reducing processing time of large deep learning models is...
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05/23/2021
Continual World: A Robotic Benchmark For Continual Reinforcement Learning
Continual learning (CL) – the ability to continuously learn, building on...
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04/17/2020
Finding the Optimal Network Depth in Classification Tasks
We develop a fast end-to-end method for training lightweight neural netw...
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10/07/2019
Biologically-Inspired Spatial Neural Networks
We introduce bio-inspired artificial neural networks consisting of neuro...
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06/21/2019
SeGMA: Semi-Supervised Gaussian Mixture Auto-Encoder
We propose a semi-supervised generative model, SeGMA, which learns a joi...
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02/27/2019
