Been working on a few projects where I need both historic weather data (~4-5 years in the past) and pollen count data. I haven't been able to find any data sets / APIs to get this data.
Has anyone else had this problem and found a solution? For context one project is looking at predicting public transit usage (using weather as the main factor) and the other is looking at marketing allocation using pollen data as a main factor.
[D] Are small transformers better than small LSTMs?[reddit]/r/MachineLearning
Transformers are currently beating the state of the art on different NLP tasks.
Some examples are:
Machine translation: Transformer Big + BT
Named entity recognition: BERT large
Natural language inference: RoBERTa
Something I noticed is that in all of the papers, the models are massive with maybe 20 layers and 100s of millions of parameters.
Of course, using larger models is a general trend in NLP but it begs the question if small transformers are any good. I recently had to train a sequence to sequence model from scratch and I was unable to get better results with a transformer than with LSTMs.
I am wondering if someone here has had similar experiences or knows of any papers on this topic.
I made a notebook with examples of cool Python features that either took me a long time to find out or were too intimidating for me to use. I especially focus on the features I find useful for machine learning. [github.com] @chipro
Excited to share our work: collaboration requires understanding! In Overcooked, self-play doesn't gel with humans: it expects them to play like itself. (1/4) Demo: [github.io] Blog: [berkeley.edu] Paper: [arxiv.org] Code: [github.com][twitter] @rohinmshahWe introduced a simple environment based on the game Overcooked that is particularly well-suited for studying coordination, and demonstrated quantitatively the poor performance of such agents when paired with a learned human model, and with actual humans
Looking for suggestions for biomedical datasets similar to the Wisconsin Breast cancer database[reddit]/r/datasets
I am looking for biomedical databases similar to the Wisconsin breast cancer database (available at https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original) ). This database has 9 features (each feature values being integers ranging from 1 to 10) and two classes – benign and malignant. Defining characteristic of this dataset is that the higher feature values generally indicate higher chance of abnormality (malignancy). I am looking for other biomedical datasets having features with this property (not necessarily integer valued, can also be real valued; preferably with low number of features also - less than 30 or so)
[D] What is the current state-of-art in unsupervised document/information retrieval for NLP tasks?[reddit]/r/MachineLearning
Hello everybody,
Are there any good unsupervised methods of retrieving top-k documents from corpus based on a rather short query?
I did a bit of googling but couldn't find anything that isn't tf-idf based.
Maybe it would be possible to somehow retrieve similarities between docs and query by utilising contextual embeddings (such as from BERT) and use some sort of scoring function to evaluate it.
Seems like there is a real revolution going on in protein analysis thanks to deep learning language models. Here's an example that just got published [nature.com]
[D] What's a hypothesis that you would really like to see tested, but never will get around to testing yourself, and hoping that someone else will get around to doing it?[reddit]/r/MachineLearning
My wishlist:
-I really want to see doc2vec but with contextualized vectors (Bert, Elmo, etc) instead of word2vec. I think it'll be a slam dunk. I don't think I'll ever get around to testing this. If anyone wants to do it, i'll be happy to give some guidance if it's needed.
-I would really like to see word2vec or glove tested with a context limited to other words within the same sentence as the target word. Or, perhaps extend the context to any word in the same paragraph. I was sort of planning on doing this, but lost some motivation with the rise of contextualized vectors. I think it would give some great insight though.
English has still a much higher cognitive overhead for me compared to my native language (I started learning at 15). Cognitive specialization in early years is real
