Hello fellow researchers!
Do you read a lot of Scientific Papers?
Have you ever wondered what are the overarching themes in the papers that you've read and how all the papers are semantically connected to one another?
Look no further!
Leverage the power of NLP Topic Modeling, Semantic Similarity, and Network analysis to study the themes and semantic relations within a corpus of research papers. Just `pip install stripnet`
✅ Generate the STriP Network on your own collection of research papers with just three lines of code!
✅ Interactive plots to quickly identify research themes and most important papers
✅ This is only the initial release, with lots of work planned.
🚀 Github: https://github.com/stephenleo/stripnet
👉 If you get the chance to play around, please share your feedback. Please leave a ⭐ to let me know that STriP Net has been helpful to you so that I can dedicate more of my time working on it.
A repository containing comprehensive Neural Networks based PyTorch implementations for the semantic text similarity task, including architectures such as Siamese-LSTM, Siamese-LSTM-Attention, Siamese-Transformer, and Siamese-BERT.
https://github.com/shahrukhx01/siamese-nn-semantic-text-similarity
The semantic web is using {RDF, OWL, ...} which have their own way of describing semantics. But, how do artificial NNs do it? Unlike our semantic web, NNs are simple nodes and edges with weights assigned, there is no pre-defined predicates, etc. The same goes for our own brains, somehow they create semantics, but how?
I'd ove to know if this is an active topic of research or if there are any reasonable theories!
I made a simple tool that lets you search a video *semantically* with AI. 🎞️🔍
✨ Live web app: http://whichframe.com ✨
Example: Which video frame has a person with sunglasses and earphones?
The querying is powered by OpenAI’s CLIP neural network for performing "zero-shot" image classification and the interface was built with Streamlit.
Try searching with text, image, or text + image and please share your discoveries!
👇 More examples
https://twitter.com/chuanenlin/status/1383411082853683208
I am studying the semi-supervised learning papers recently, most of which focuses on improving image classification tasks. Take the famous SimCLR v2 as an example, it augments one input image in 2 ways and force the corresponding embeddings to be close to each other, but for negative pairs (different images), it forces the embeddings to be farther from each other.
However, it is not very straight forward for semantic segmentation. What are the meaningful augmentations for such application?
I feel like these 2 can over cross can someone help distinguish them for me?
I keep hearing all of these but I keep getting confused. I want to create a project that involves taking data from a distance sensor and a camera to identify objects on the road. How would I go about this? What software or what "type" of ML do I use?
I made a simple tool that lets you search a video *semantically* with AI. 🎞️🔍
✨ Live web app: http://whichframe.com ✨
Example: Which video frame has a person with sunglasses and earphones?
The querying is powered by OpenAI’s CLIP neural network for performing "zero-shot" image classification and the interface was built with Streamlit.
Try searching with text, image, or text + image and please share your discoveries!
👇 More examples https://twitter.com/chuanenlin/status/1383411082853683208
I made a simple tool that lets you search a video *semantically* with AI. 🎞️🔍
✨ Live web app: http://whichframe.com ✨
Example: Which video frame has a person with sunglasses and earphones?
The querying is powered by OpenAI’s CLIP neural network for performing "zero-shot" image classification and the interface was built with Streamlit.
Try searching with text, image, or text + image and please share your discoveries!
👇 More examples https://twitter.com/chuanenlin/status/1383411082853683208
