Hey, glad to see you like the concept! We’re actively working on improving the performance.
1. Everything is proprietary for now. After consideration we decided that this project is not well suited for open sourcing at this time.
2. Graphs are generated on the fly, but only for the first time. We keep the results in a cache so when another user asks for the same graph later, they’d get it instantly. Also, asking for graphs which are close in paper-space would also run faster.
3. We rely on external sources (like the Open Corpus by Semantic Scholar) for the citations database. Unfortunately, no database is perfect yet and sometimes citations are badly parsed.
4. First, we found this tool very fun for exploring paper-space in new domains. I sometimes just enter a keyword like “psychology” and start exploring. This gives me a nice overview of the type of titles and branches in new (for me) fields of science.
Second, I was surprised with how easy it was to recognize papers that are bridging multiple disciplines. Take a look at our example graph “deepfruits”, for example: there are two obvious clusters. One shows deep learning papers mostly about detection. The other shows papers that describe how these techniques were applied in agriculture.
5. We’ve experimented early on and arrived to a conclusion that more than ~50 papers on the screen is too much clutter, and it’s better to traverse paper-space by building more graphs. Avoiding specifics on purpose :)
One other feature I’d really like is the ability to save the papers (and then export) I find through this tool, which would probably require an account for persistence.
Are there plans for something like this in the works?
Hey, glad to see you like the concept! We’re actively working on improving the performance.
1. Everything is proprietary for now. After consideration we decided that this project is not well suited for open sourcing at this time.
2. Graphs are generated on the fly, but only for the first time. We keep the results in a cache so when another user asks for the same graph later, they’d get it instantly. Also, asking for graphs which are close in paper-space would also run faster.
3. We rely on external sources (like the Open Corpus by Semantic Scholar) for the citations database. Unfortunately, no database is perfect yet and sometimes citations are badly parsed.
4. First, we found this tool very fun for exploring paper-space in new domains. I sometimes just enter a keyword like “psychology” and start exploring. This gives me a nice overview of the type of titles and branches in new (for me) fields of science.
Second, I was surprised with how easy it was to recognize papers that are bridging multiple disciplines. Take a look at our example graph “deepfruits”, for example: there are two obvious clusters. One shows deep learning papers mostly about detection. The other shows papers that describe how these techniques were applied in agriculture.
5. We’ve experimented early on and arrived to a conclusion that more than ~50 papers on the screen is too much clutter, and it’s better to traverse paper-space by building more graphs. Avoiding specifics on purpose :)
Awesome, thanks for the answers!
One other feature I’d really like is the ability to save the papers (and then export) I find through this tool, which would probably require an account for persistence.
Are there plans for something like this in the works?
Yes—these are probably our most requested features and are high in our list of features to add.