PhD Seminar • Artificial Intelligence • Few-Shot Non-Parametric Learning with Deep Latent Variable Model

Please note: This PhD seminar will take place online.

Zhiying Jiang, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Jimmy Lin

Most real-world problems that machine learning algorithms are expected to solve face the situation with (1) unknown data distribution; (2) little domain-specific knowledge; and (3) datasets with limited annotation. We propose Non-Parametric learning by Compression with Latent Variables (NPC-LV), a learning framework for any dataset with abundant unlabeled data but very few labeled ones. By only training a generative model in an unsupervised way, the framework utilizes the data distribution to build a compressor.

Using a compressor-based distance metric derived from Kolmogorov complexity, together with few labeled data, NPC-LV classifies without further training. We show that NPC-LV outperforms supervised methods on all three datasets on image classification in the low data regime and even outperforms semi-supervised learning methods on CIFAR-10. We demonstrate how and when negative evidence lowerbound (nELBO) can be used as an approximate compressed length for classification. By revealing the correlation between compression rate and classification accuracy, we illustrate that under NPC-LV how the improvement of generative models can enhance downstream classification accuracy.

Paper link: https://openreview.net/pdf?id=24fiAU_9vT

To join this PhD seminar on Zoom, please go to https://uwaterloo.zoom.us/j/96169405889.