# Projection: A Mixed-Initiative Research Process

arXiv: https://arxiv.org/abs/2201.03107 · HTML: https://mlsanity.com/paper/2201.03107/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 9 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations](https://mlsanity.com/paper/1806.05662/) (2018)
- [Exploratory Search with Sentence Embeddings](https://mlsanity.com/paper/2007.11198/) (2020)

## Abstract (arXiv)

Communication of dense information between humans and machines is relatively low bandwidth. Many modern search and recommender systems operate as machine learning black boxes, giving little insight as to how they represent information or why they take certain actions. We present Projection, a mixed-initiative interface that aims to increase the bandwidth of communication between humans and machines throughout the research process. The interface supports adding context to searches and visualizing information in multiple dimensions with techniques such as hierarchical clustering and spatial projections. Potential customers have shown interest in the application integrating their research outlining and search processes, enabling them to structure their searches in hierarchies, and helping them visualize related spaces of knowledge.