The ability of fast similarity search at large scale is of great importance to many Information Retrieval (IR) applications. A promising way to accelerate similarity search is semantic hashing which designs compact binary codes for a large number of documents so that semantically similar documents are mapped to similar codes (within a short Hamming distance). Since each bit in the binary code f...

This research investigated the potential contribution of self-efficacy and different forms of experience to the numbers of sucessful doctoral and masters degree supervisions of academics. Using a comprehensive purposive sampling procedure, academics at a large South African higher educational institution were sampled; 225 responses were received. Spearman Rho tests of association, Pearson parti...

Localizing natural language phrases in images is a challenging problem that requires joint understanding of both the textual and visual modalities. In the unsupervised setting, lack of supervisory signals exacerbate this difficulty. In this paper, we propose a novel framework for unsupervised visual grounding which uses concept learning as a proxy task to obtain self-supervision. The simple int...

The variety, abundance, and structured nature of hashtags make them an interesting data source for training vision models. For instance, hashtags have the potential to significantly reduce the problem of manual supervision and annotation when learning vision models for a large number of concepts. However, a key challenge when learning from hashtags is that they are inherently subjective because...

The problem of building disease progression models with longitudinal data has long been addressed parametric mixed-effect models. They provide interpretable at the cost modeling assumptions on profiles and their variability across subjects. Their deep learning counterparts, other hand, strive flexible data-driven modeling, additional interpretability - or, as far generative are involved, disent...

Self-supervised pre-training appears as an advantageous alternative to supervised pre-trained for transfer learning. By synthesizing annotations on pretext tasks, self-supervision allows models large amounts of pseudo-labels before fine-tuning them the target task. In this work, we assess diagnosing skin lesions, comparing three self-supervised pipelines a challenging baseline, five test datase...