Search Results for author:
Found 3 papers, 1 papers with code
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations
Recent progress in self-supervised learning has demonstrated promising results in multiple visual tasks.
Ranked #83 on
Image Classification
on ObjectNet
(using extra training data)
Can a Fruit Fly Learn Word Embeddings?
In this work we study a mathematical formalization of this network motif and apply it to learning the correlational structure between words and their context in a corpus of unstructured text, a common natural language processing (NLP) task.
Bio-Inspired Hashing for Unsupervised Similarity Search
Building on inspiration from FlyHash and the ubiquity of sparse expansive representations in neurobiology, our work proposes a novel hashing algorithm BioHash that produces sparse high dimensional hash codes in a data-driven manner.
