Would you please explain more

In what format do you want to add the heartbeat values? Are they boolean? A voice channel? Are they postprocessed?

it is the calculated feature on the ECG and EDA, also the average ecg and eda are there as well in the dataset: HR is heart rate, HRV is heart rate variablity, and the others are related to eda

also it is the description in the dataset about them:
"- features_ECG/*.arff
arff files containing features computed from the filtered ECG signal with a sliding centred window which size depends on the modality (arousal -> ws=4s, valence -> ws=10s; optimised on the dev partition).  
Features are thus provided separately for each of those two dimensions.  
The first feature vector is assigned to the center of the window, and duplicated for the previous frames - from 1 to ws/(2*sampling_period)-1, with the sampling period being equal to 40ms.  
The last feature vector is also duplicated for the last frames.
19 features x 7501 frames per file"

this is the Raw data structure :

if I want to feed this data to the model what approach do you suggest is better?
1)scaling all the data for instance in range of [-1,1]
2) or instead, using a Batchnormalization layer as a input layer to model without any data scaling?

You can try both, I think it won't give much difference. In ML there are tools to predict feature importance, you might try those on a dataset first before spending too much time on the details of individual features preprocessing.

> tools to predict feature importance
https://github.com/marcotcr/lime (https://github.com/thomasp85/lime) as an example. It can be used for DL models, too (see original paper https://arxiv.org/abs/1602.04938)

thank you so much Andrey

Добрый вечер!
Ищу человека, который бы отвечал за deep learning сторону проекта
Сейчас в команде я - отвечаю почти за все вопросы и немного за разработку и андроид-разработчик
Если кому-то будет интересно, пишите в лс

Hi again everybody
My new question is:
Let say we have a sychology related experiment to collect data and each person are asked for giving rating feedback.
As usuall in almost all sychology domains people have quiet different rates based on their understanding, mood, knowledge,...
If we want to use these feedbacks as the label for any classification or regression of some dataset we will face a wide veriety of user's rate for every specific rating question and it makes problem that we wont have a reliable ground truth just by simply using the average of rate through all rater.
We need a weighted avege that gives higher weights to more correlated and simillar rates.
So could you please guide me how to define such a inter rater reliability coefficent ??
Or do you have other suggestion?

Specially when the feedbacks are not categorical and they are a real number between range [a,b]

There exist Cohen Kappa statistic but it's only for categorical data and i dont know any others

Hello guys.
I'm fairly new to reinforcement learning. I have implemented DQN in the past and now I'm working on A3C for a custom environment. And I noticed that in DQN I used an epsilon greedy policy, so I used something like this to force exploration:

if eps <= random.random():

return random.randint(0, num_actions-1)

else:

return np.argmax(model.predict(state))

But in A3C I am using this instead:

policy = model.predict(state)

return np.random.choice(num_actions, p=policy)

As far as I know, this is used to make model conservative about its actions, so we are trying to encourage the model to give a much higher probability (close to 1) for good actions and reduce unpredictability .

In A3C we use a critic model to predict value, which is basically a n-step return (expected reward for future n steps) right?

But the question is why do we use different approaches? Can I use epsilon greedy policy in A3C or vise versa? Which one is better and when? Or is there certain type of environment which requires to use one of them? And what if my environment is impossible to predict (I mean the future reward), but it is possible to develop a strategy that can beat the game. Let's say, it is a game where you start from a random point and never know what obstacle will come out, but you know for sure that you have to avoid them. Do I have to predict the value then?

Hi every body
As you know it's common to reuse pretrained DNN Model for most of the Image processing project in the preliminary layeres, Im looking for such a transfer learning strategy for ecg or eeg signal processing to speed up the process. Do you know any existing model to refer me there?
or do have any comment or idea for such a decision? do recommend it?
Thanks.

ребята, подскажите насчет speech recognition: я там вижу разные виды audio processing: mfcc, filter banks, including delta+ delta-delta. Получается очень разный размер инпута: от (timesteps, 13) with mfcc, до (timesteps, 39) или даже (timesteps, 161) for linear spectrograms. Это все для LibriSpeech на DeepSpeech моделях.

привет. я сейчас  этим занимаюсь

какой код используешь?

в данный момент я свой пишу, пока даже без нейронок, я споткнулся от них, и понял что надо классически методы рассмотреть.