t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern Data

Abstract--Modern datasets and models are notoriously difficult to explore and analyze due to their inherent high dimensionality and massive numbers of samples. Existing visualization methods which employ dimensionality reduction to two or three dimensions are often inefficient and/or ineffective for these datasets. This paper introduces t-SNE-CUDA, a GPU-accelerated implementation of t-distributed Symmetric Neighbour Embedding (t-SNE) for visualizing datasets and models. These speedups enable, for the first time, visualization of the neural network activations on the entire ImageNet dataset - a feat that was previously computationally intractable. We also demonstrate visualization performance in the NLP domain by visualizing the GloV e embedding vectors. From these visualizations, we can draw interesting conclusions about using the L2 metric in these embedding spaces. The recent emergence of large-scale, high-dimensional datasets has been a major factor contributing to advances in the areas of Machine Learning and Artificial Intelligence. While researchers have developed numerous methods for visualizing medium-sized data-sets, such visualizations are often inefficient or ineffective for high-dimensional or large-scale data.