[Discussion]

The data set is time series with 1 min frequency for the last four years. There are 40 features associated with the asset. Then there is a target which has a 0 when there is no fault and 1 when there is fault.

having this data, currently I have approached it in this way.

1. I have set the column 0 as index and set the type as date-time.
2. If the 39 features had any empty values in-between, I interpolated linearly for now to get the values assigned to them

Now when I browse online, I only see people picking one column and doing time series analysis to identify anomaly, in my case, I want to use the features in time sequence and then identify if the asset will fail or not in the future anytime/ any date.

Can someone help me understand what approach I need to take for this kind of a problem and also provide some sample for me to learn from.

I’ve typically only performed transfer learning via fine-tuning with neural networks (eg. image classifiers from pre-trained MobileNet, etc.), but does the same idea hold for a model like logistic regression or CRF? I’d argue yes because your essentially just training a new model with non-randomized initial weights (a prior). But am I missing something?

I’m currently looking into cross-domain transfer learning for non-neural NER models, and I wanted to fine-tune the weights of a pre-trained CRF with some newly annotated user-generated data.

A group of researchers who were working at NVIDIA has introduced Kaolin, a new PyTorch library with an aim to accelerate 3D deep learning research. Kaolin is home for future 3D DL research and you are welcome to make contributions.

Read more: https://medium.com/ai%C2%B3-theory-practice-business/pytorch-library-for-accelerating-3d-deep-learning-research-6b83df2073bf

Hello guys,

I recently developed a CNN to analyse and cut images (image text documents). I saved the entire network locally as “.h5” extension and whenever I have to use it I call it. I also use “pytesseract” (OCR Library) to extract data from the cutouts. I have created a data frame using pandas where I append the results from tesseract to maintain the logs. Currently, I am using Jupyter notebook.

I want to upload it all on the server to automate this process so I can daily check the DataFrame without the hassle of running all the notebooks. I currently have a subscription for DigitalOcean’s server.

Any leads or help on how to do this will be appreciated.

So I’ve been pretty interested in this paper A Closer Look at Memorization in Deep Networks and particularly the first experiment they did where they showed that certain data points are consistently fit in the first epoch of training whereas other data points consistently take longer epochs to fit.

But I haven’t seen any discussions anywhere about why that would be the case? Like what is it about these data points that allows them to be easily fit in the first epoch? How can we formalize this notion of “simpleness”?

My first thought is that the “simple” data are just the ones which have a gradient direction that is close to the averaged gradient direction for a given minibatch?

Anyone aware of any work specifically expanding on these questions?

Unfortunately I don’t have anyone in my lab to discuss these things with so I just resort to the next best place lol.

I’ve been experimenting with word embeddings lately from different frameworks like BERT and ELMo. I’ve tried applying these to a sequence classification problem (generated sequence embeddings by taking mean of token embeddings in the second to last hidden layer of the BERT model) and running logistic regression and random forest models using these embeddings.

However, it doesn’t seem like this works that well for small datasets (in my case, 500 data points for a 3-label classification problem). Am I correct in saying that classification using these embeddings only works well given tens of thousands of data points? All the sequence classification problems I’ve seen using these embeddings seem to support this since they have way more data (e.g. Google’s IMDB movie review sentiment example). Or are there ways you can get robust classification models with less data to work with? I was thinking of trying fine-tuning or PCA to reduce the dimensionality of the sequence embeddings and ultimately build a better classification model.

Doesn’t related directly with ML research, but still interesting to see whether it applies as much as other scientific fields:

Article from Nature Communications: Early coauthorship with top scientists predicts success in academic careers

Abstract

We examined the long-term impact of coauthorship with established, highly-cited scientists on the careers of junior researchers in four scientific disciplines. Here, using matched pair analysis, we find that junior researchers who coauthor work with top scientists enjoy a persistent competitive advantage throughout the rest of their careers, compared to peers with similar early career profiles but without top coauthors. Such early coauthorship predicts a higher probability of repeatedly coauthoring work with top-cited scientists, and, ultimately, a higher probability of becoming one. Junior researchers affiliated with less prestigious institutions show the most benefits from coauthorship with a top scientist. As a consequence, we argue that such institutions may hold vast amounts of untapped potential, which may be realised by improving access to top scientists.

https://www.nature.com/articles/s41467-019-13130-4