reproducing retweet results

Hi, we have been trying to reproduce the paper's result on THP and NHP models. The predictions we get are quite far from the ground truth labels, specifically for the next event time:

• labels:
  array([[191.000000, 42.000000, 609.000000, ..., 718.000000, 11668.000000,
  8999.000000],
  [1837.000000, 8.000000, 1060.000000, ..., 1087.000000,
  6687.000000, 2488.000000],
  [1239.000000, 5326.000000, 1958.000000, ..., 59971.000000,
  32630.000000, 38632.000000],
  ...,
  [1.000000, 14.000000, 63.000000, ..., 457.000000, 252.000000,
  1397.000000],
  [313.000000, 4136.000000, 4994.000000, ..., 141400.000000,
  25386.000000, 65401.000000],
  [519.000000, 590.000000, 405.000000, ..., 62900.000000,
  28759.000000, 67504.000000]])
  we get;

• NHP predicted:
  array([[191.000000, 0.842482, 0.987651, ..., 0.785294, 0.987130,
  0.969165],
  [1837.000000, 0.116420, 0.131135, ..., 0.044110, 0.146753,
  0.044110],
  [1239.000000, 0.171356, 0.993292, ..., 0.129321, 0.171356,
  0.171356],
  ...,
  [1.000000, 0.024143, 0.032704, ..., 0.502413, 0.028608, 0.028608],
  [313.000000, 0.956142, 0.142735, ..., 0.933639, 0.956142,
  0.142735],
  [519.000000, 0.655705, 0.117873, ..., 0.132342, 0.116001,
  0.116318]])

• THP predicted:
  array([[191.000000, 5.000000, 5.000000, ..., 5.000000, 5.000000,
  5.000000],
  [1837.000000, 5.000000, 5.000000, ..., 5.000000, 5.000000,
  5.000000],
  [1239.000000, 5.000000, 5.000000, ..., 5.000000, 5.000000,
  5.000000],
  ...,
  [1.000000, 5.000000, 5.000000, ..., 5.000000, 5.000000, 5.000000],
  [313.000000, 5.000000, 5.000000, ..., 5.000000, 5.000000,
  5.000000],
  [519.000000, 5.000000, 5.000000, ..., 5.000000, 5.000000,
  5.000000]])

We are using the example config, changing shuffle to true and increasing model hidden size and changing lr were not particularly helpful, increasing max_dtime lead to model predicting max_dtime at every time stamp for THP. We are wondering if this is a config issue and if there is a specific config that you use for the retweet with these models that you could share? we would like to verify retweet results on all the provided models.

Also wondering how you tackle predicting long horizon events for datasets with highly variable sequence lengths such as retweet and how extra padding at prediction time will affect the predicted results?

Start with the example config and the retweet runs for the THP and NHP models, comparing the reported labels with each model's next-event-time predictions. Investigate the effects of max_dtime, variable sequence lengths, and padding at prediction time. Done requires a reproducible configuration or a confirmed explanation for the divergent outputs.