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Overcoming catastrophic forgetting in neural networks
It is shown that it is possible to overcome the limitation of connectionist models and train networks that can maintain expertise on tasks that they have not experienced for a long time and selectively slowing down learning on the weights important for previous tasks. Expand
Dimensionality Reduction by Learning an Invariant Mapping
This work presents a method - called Dimensionality Reduction by Learning an Invariant Mapping (DrLIM) - for learning a globally coherent nonlinear function that maps the data evenly to the output manifold. Expand
Learning a similarity metric discriminatively, with application to face verification
The idea is to learn a function that maps input patterns into a target space such that the L/sub 1/ norm in the target space approximates the "semantic" distance in the input space. Expand
Progressive Neural Networks
This work evaluates this progressive networks architecture extensively on a wide variety of reinforcement learning tasks, and demonstrates that transfer occurs at both low-level sensory and high-level control layers of the learned policy. Expand
Meta-Learning with Latent Embedding Optimization
This work shows that latent embedding optimization can achieve state-of-the-art performance on the competitive miniImageNet and tieredImageNet few-shot classification tasks, and indicates LEO is able to capture uncertainty in the data, and can perform adaptation more effectively by optimizing in latent space. Expand
A Tutorial on Energy-Based Learning
Energy-Based Models (EBMs) capture dependencies between variables by associating a scalar energy to each configuration of the variab les. Inference consists in clamping the value of observedExpand
Progress & Compress: A scalable framework for continual learning
The progress & compress approach is demonstrated on sequential classification of handwritten alphabets as well as two reinforcement learning domains: Atari games and 3D maze navigation. Expand
Policy Distillation
A novel method called policy distillation is presented that can be used to extract the policy of a reinforcement learning agent and train a new network that performs at the expert level while being dramatically smaller and more efficient. Expand
Learning to Navigate in Complex Environments
This work considers jointly learning the goal-driven reinforcement learning problem with auxiliary depth prediction and loop closure classification tasks and shows that data efficiency and task performance can be dramatically improved by relying on additional auxiliary tasks leveraging multimodal sensory inputs. Expand
Distral: Robust multitask reinforcement learning
This work proposes a new approach for joint training of multiple tasks, which it refers to as Distral (Distill & transfer learning), and shows that the proposed learning process is more robust and more stable---attributes that are critical in deep reinforcement learning. Expand