Related Issues (20)
- Table 4 experiments HOT 7
- Reproduce Kinship, FB15k-237 results HOT 5
- sharing the pre-trained embeddings learned by Dismult HOT 1
- Reproduce WN18RR results HOT 7
- Dataset definition HOT 1
- parameter settings used in paper
- train/dev/test split HOT 4
- Unable to load the saved_model HOT 4
- Encode the relation and entity ? HOT 4
- Question in code/model/trainer.py HOT 1
- num_rollouts HOT 2
- Testing problem HOT 7
- How do you extract the general rules from the trained policy(like the (i) example in table 8)?
- the accuracy can be better after fine tune the function `calc_cum_discounted_reward ` as follows HOT 2
- Pytorch implementation for MINERVA HOT 1
- Export the mode using `tf.saved_model` and deploy it to production environment HOT 1
- On comparison of MINERVA with other KBE baselines
- meaning of last-step in return_next_actions
- Is this code an official implementation to the method in paper "Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks"?
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