Comments (2)
snowkylin
commented on September 2, 2024
For content-based addressing, key vector is the "content" that is compared to each vector M_t(i) by a similarity measure. See section 3.3.1 of the NTM paper for details. It is one of the parameters of head and can be generated simply from the output of controller by some linear transformation. See line 29-47 and line 63 in ntm_cell.py for details.
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albertchristianto
commented on September 2, 2024
Okay, thank you very much for the response and the information @snowkylin . I will check it.
best regards,
Albert
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Related Issues (20)
- Can You please upload the link to the data set(MANN) ? HOT 1
- Can you please explain about the accuracy mechanism in the model ? HOT 1
- Converging of the network for different data sets? HOT 6
- copy_task.py stopped with NotFoundError HOT 3
- small correction about argument parsing in copy_task.py HOT 1
- No model_checkpoint_path error when copy_task.py --mode test HOT 1
- data folder for one_shot_learning.py ?
- num_parameters_per_head confused? HOT 3
- How to use model for Inference HOT 7
- Memory tends to be the same? And the confusion about MANN
- Using MANN in A3C (reinforcement learning model) HOT 3
- instance
- recurrence through the state dictionary HOT 2
- Training the Model HOT 1
- Initialization regarding the addressing and memory matrix . HOT 4
- can you explain about the plotting read vectors ? HOT 3
- How did you calculate the number of controller output parameters in MANN cell ? HOT 2
- confusion when calculating the least used weight vector in MANN HOT 6
- Why we need to set the least used memory location to zero in the memory matrix HOT 2
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