<p>It is a general problem in neural network, when you go deeper, you may have gradient explosion/vanishing problems. You may try to use gradient-clipping for you particular network.</p>

CC BY-SA 4.0
2022-07-30T16:18:19.427Z
Do you have any specific references that highlight this problem?
Haffi112
CC BY-SA 4.0
2022-07-30T17:35:03.513Z
Check it please: https://www.youtube.com/watch?v=qhXZsFVxGKo
ai-py
CC BY-SA 4.0
2022-07-31T15:02:23.207Z
But are you aware of an academic paper on the topic?
Haffi112
CC BY-SA 4.0
2023-08-25T08:33:46.920Z
Isn't LSTM specifically designed to avoid exploding/vanishing gradients?
NikoNyrh
CC BY-SA 4.0
2022-07-30T15:48:58.843Z
2022-07-30T15:48:58.843Z
ai-py
<p>I'm training an LSTM model for classification on accelerometer data, and I get better results when I downsample the signal to 25 Hz than when I use a 50 Hz signal.</p>
<p>I use the same time frame of 1.5 seconds. So with a 25 Hz signal, I have 37 data points; with a 50 Hz signal, I have 75 data points as input to my model.</p>
<p>I think that LSTM models have a harder time dealing with long sequences, which might explain this difference. However, I am not sure about that claim. Are you aware of any publications that go into depth about the limitations of LSTMs for long sequence tasks?</p>

<long-short-term-memory><sequence-modeling>
Limitations of LSTMs
Haffi112
# Limitations of LSTMs

(Asked by: username_0 on 2022-07-30)

I'm training an LSTM model for classification on accelerometer data, and I get better results when I downsample the signal to 25 Hz than when I use a 50 Hz signal.

I use the same time frame of 1.5 seconds. So with a 25 Hz signal, I have 37 data points; with a 50 Hz signal, I have 75 data points as input to my model.

I think that LSTM models have a harder time dealing with long sequences, which might explain this difference. However, I am not sure about that claim. Are you aware of any publications that go into depth about the limitations of LSTMs for long sequence tasks?

**Tags:** long-short-term-memory, sequence-modeling


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## 1 answers

### Answer by username_1 on 2022-07-30. Score: 0

It is a general problem in neural network, when you go deeper, you may have gradient explosion/vanishing problems. You may try to use gradient-clipping for you particular network.

> **Comments:**
> **username_0**: Do you have any specific references that highlight this problem?  
> **username_1**: Check it please: https://www.youtube.com/watch?v=qhXZsFVxGKo  
> **username_0**: But are you aware of an academic paper on the topic?  
> **username_2**: Isn't LSTM specifically designed to avoid exploding/vanishing gradients?  

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