I know that decision stumps are used as basic classifiers in boosting, but what other basic classifiers can be used? Can we use any classifier? SVMs are pretty accurate, and boosting, well, boosts the algorithm. So why not use SVMs as the basic binary classifier and boost the hell out of it with AdaBoost? What’s stopping us?

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PS: If this is a beginner question, tell me to delete it.

Hey, I am a Masters student and a TA for Machine Learning at my uni. What i noticed is that while teaching my students i would learn concepts much quicker. Hence why started a new twitter channel @Daily_ML_Tips so i can learn and teach more people directly. Twitter Link Here

Feel free to give me a follow and tweet at me if u have any specific ML questions. 🙂

have a great day

Back there with another question, have anyone considered disentangling/learning on style of temporal structure in videos ?

I have seen many examples of ‘artistic’/photorealistic style transfer usage on single images/frames, and also using a temporal constraint to transfer it coherently.. But i ain’t askin’ about spatial style transfer enforced by temporal structure, but rather exploiting the temporality of different videos (films would be very useful i can imagine) and transferring ’em..

I have two datasets that I ran a label encoder and onehotencoder on. A is the actual and B is the prediction.

I would like to be able to come up with the accuracy between the two. For example in B, the last row is different so I would like calculate an accuracy of 75%.

Is there a nice pythonian way of doing this?

A

B

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This is my preference for work enviroment right now. I find that they are hard to look up, since early stage companies aren’t focused on PR. So maybe there’s some indirect methods of looking up these companies.

Few days ago ,I see a article introduceed ShuffleNet V2 .

Based on my curiosity I trained ShuffleNet V2 on MNIST datase, and I got 99.14% accuracy .
( code here : https://github.com/allen108108/Model_on_MNIST/blob/master/MNIST%20-%20ShuffleNetV2.ipynb ).

The result looks good , but I got higher accuracy ( almost 99.5% ) when I trained a typically CNN model on the same dataset
( code here : https://github.com/allen108108/Model_on_MNIST/blob/master/MNIST%20-%20CNN.ipynb).

Is that normal situation ? Did I make some mistakes ?

PS : the ShuffleNet V2 model that I used : https://github.com/opconty/keras-shufflenetV2

Hi everyone,

I have a few questions on research practices that are generally followed but rarely mentioned in papers.

1. Let’s say I have a train-dev-test split. After finding the best hyperparameters on dev set, should I retrain the model on train+dev set before evaluating it on the test set? Some discussions say yes, others say it depends on you and how much data you have and some say no.
2. Let’s say I’m showcasing results on multiple datasets. Can one change the hyperparameters (learning rate, batch size, etc) from one dataset to another? More importantly, can I change, let’s say, number of units in a layer without adding more layers? Would this count as an architectural change?
3. If yes, how would answer to above question change if the same is done within the dataset itself containing multiple parts?
4. Are we allowed to change a publically available dataset? For example, removing outliers for a regression problem?

In neural network-based conditional density estimation (CDE), classic regularization approaches in the parameter space are mostly ineffective. To address this issue, we develop a model-agnostic noise regularization method for CDE that adds random perturbations to the data during training. We demonstrate that the proposed approach corresponds to a smoothness regularization, we prove its asymptotic consistency and show across 7 datasets and 3 CDE models that this works well. Result: makes neural network-based CDE the preferable method over previous non- and semi-parametric approaches, even when training data is scarce!

Paper: https://arxiv.org/abs/1907.08982
Code: https://github.com/freelunchtheorem/Conditional_Density_Estimation