In continual learning, the neural network learns from a stream of data, acquiring new knowledge incrementally. It’s not possible to assume an i.i.d. stationary dataset available in one batch. Catastrophic forgetting of previous knowledge is a well known challenge. A wide variety of approaches fall broadly into 3 categories :… Read More »Continual Few-Shot Learning with Hippocampal Replay
Research Engineer at Project AGI
We recently presented 2 papers at the International Joint Conference on Neural Networks, IJCNN. The first one is about one-shot learning for the long term (with an artificial hippocampal algorithm), blog here. In this blog article, we are excited to share the other paper – Learning distant cause and effect using only… Read More »Video Prediction using Recurrent Sparse Memory
Adaptive optimization methods, such as Adam and Adagrad, maintain some statistics over time about the variables and gradients (e.g. moments) which affect the learning rate. These statistics won’t be very accurate when working with sparse tensors, where most of its elements are zero or near zero. We investigated the effects… Read More »Optimization using Adam on Sparse Tensors
It’s such a joy to be able to test an idea, go straight to the idea without wrestling with the tools. We recently developed an experimental setup which, so far, looks like it will do just that. I’m excited about it and hope it can help you too, so here it is. We’ll go through the why we created another framework, and how each module in the experiment setup works.
Eager Execution is an imperative, object oriented and more Pythonic way of using TensorFlow. It is a flexible machine learning platform for research and experimentation where operations are immediately evaluated and return concrete values, instead of constructing a computational graph that is executed later.
There are plenty of established machine learning frameworks out there, and new frameworks are popping up frequently to address specific niches. We were interested in examining if one of these frameworks fits in our workflow. I surveyed the most popular frameworks, and aim to provide a helpful comparative analysis.
SVHN is relatively new and popular dataset, a natural next step to MNIST and complement to other popular computer vision datasets. This is an overview of the common preprocessing techniques used and the best performance benchmarks, as well as a look at the state-of-the-art neural network architectures used.
This article assesses the research paper, ‘A Distributional Perspective on Reinforcement Learning’ by the authors, Marc G. Bellemare, Will Dabney and Remi Munos, published in the proceedings of the 34th International Conference on Machine Learning (ICML) in 2017. Bellemare et al.’s paper will be assessed on several criteria. Firstly, content… Read More »Literature Review: ‘A Distributional Perspective on Reinforcement Learning’