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Characterizing catastrophic forgetting via the Neural Tangent Kernel
Published:
Neural networks have achieved near optimal performance for supervised learning tasks.However when facing a sequence of tasks where data distribution is changing over time, they tend to forget what has been learned in the past leading to Catastrophic Forgetting (CF). This is one critical problem Continual Learning (CL) aims to solve. Although, there has been plenty of empirical works trying to study that pathology, very few tackled it from a theoretical side. In this work, we provide a theoretical analysis of CF under the Neural Tangent Kernel (NTK) regime where neural networks behave linearly.
Deep Reinforcement and InfoMax Learning
Published:
Link to poster: link
Introduction to Comet.ml
Published:
Comet is a neat alternative to the classical Tensorboard experiment tracker, which supports multiple functions such as logging of experiment metrics, plots, gradients, model weights, as well as an online dashboard similar to that of Tensorboard. There are other similar products such as Weights&Biases, but Comet has the best support on the Compute Canada clusters.
Normalizing flows in Pyro (PyTorch)
Published:
NFs (or more generally, invertible neural networks) have been used in:
- Generative models with $1\times1$ invertible convolutions Link to paper
- Reinforcement learning, to improve upon the (not always optimal) Gaussian policy Link to paper
- Simulating attraction-repulsion forces in actor-critic Link to paper
publications
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.