The Iris.ai research team works with a combination of in-house research and external libraries.
Here are our own publications.
- [Date] Nov 2021
[Title] Domain-adaptation of spherical embeddings
[Summary] The recent spherical embedding model (JoSE) proposed in arXiv:1911.01196 jointly learns word and document embeddings during training on the multi-dimensional unit sphere, which performs well for document classification and word correlation tasks. But, we show a non-convergence caused by global rotations during its training prevents it from domain adaptation.
- [Date] May 2018
[Title] Scithon™ – An evaluation framework for assessing research productivity tools
[Summary] We develop the novel framework, Scithon™, for performing evaluation tasks on the science discovery tools. The framework, organized around live events, involves a systematic and cross-disciplinary comparison that focuses on productivity gains and takes into account user engagement.
- [Date] 15 December 2017
[Title] Word importance-based similarity of documents metric (WISDM): Fast and scalable document similarity metric for analysis of scientific documents
[Summary] Word importance-based similarity of documents metric (WISDM) is a fast and scalable novel method for document similarity/distance computation for analysis of scientific documents. It is based on recent advancements in the area of word embeddings and it achieves significant performance speed-up in comparison to state-of-the-art metrics with a very marginal drop in precision.