Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author | user:1234 user:me (yours) |
| Score | score:3 (3+) score:0 (none) |
| Answers | answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections | title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status | closed:yes duplicate:no migrated:no wiki:no |
| Types | is:question is:answer |
| Exclude | -[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with neural-network
Search options not deleted user 62846
Artificial neural networks (ANN), are composed of 'neurons' - programming constructs that mimic the properties of biological neurons. A set of weighted connections between the neurons allows information to propagate through the network to solve artificial intelligence problems without the network designer having had a model of a real system.
0 votes
Accepted
Is there room to improve the model? if the train data accuracy is 99.8% but test data accura...
Sqlova reach 99.8% in training and 90.0% in testing. This work improve Sqlova further in testing.
- 721
0 votes
2 answers
59 views
Is there room to improve the model? if the train data accuracy is 99.8% but test data accura...
I understand this is a wide question. But there can be some suggestions. I can try some methods which I do not know. I think the model is already prefect on train data. But the test accuracy is a …
- 721
1 vote
Accepted
What is the model architectural difference between transductive GCN and inductive GraphSAGE?
The main novelty of GraphSAGE is a neighborhood sampling step (but this is independent of whether these models are used inductively or transductively). You can think of GraphSAGE as GCN with subsample …
- 721
1 vote
2 answers
2k views
What is the model architectural difference between transductive GCN and inductive GraphSAGE?
Difference of the model design. It seems the difference is that GraphSAGE sample the data. But what is the difference in model architecture.
- 721
6 votes
Accepted
What is difference between transductive and inductive in GNN?
In inductive learning, during training you are unaware of the nodes used for testing. For the specific inductive dataset here (PPI), the test graphs are disjoint and entirely unseen by the GNN during …
- 721
7 votes
1 answer
4k views
What is difference between transductive and inductive in GNN?
It seems in GNN(graph neural network), in transductive situation, we input the whole graph and we mask the label of valid data and predict the label for the valid data. But is seems in inductive sit …
- 721
0 votes
Accepted
What are the simplest methods for the label noise problem?
If the labeling people is sufficient, the best solution is to re-label the noisy data.
- 721
0 votes
1 answer
64 views
What are the simplest methods for the label noise problem?
If I have enough low quality data from unsupervised methods or rule-based methods. I read from https://github.com/subeeshvasu/Awesome-Learning-with-Label-Noise ,but these methods are a little complex …
- 721
3 votes
3 answers
3k views
What's the advantage of multi-gpu training in real?
The decreasing speed of training loss is almost the same between one gpu and multi-gpu. After averaging the gradients, the only benefit from multi-gpu is that the model seems to see more data in the …
- 721
0 votes
Accepted
What's the advantage of multi-gpu training in real?
More gpu means more data in a batch. And the gradients of a batch data is averaged for back-propagation. If the learning rate of a batch is fixed, then the learning rate of a data is smaller. If the l …
- 721
-1 votes
1 answer
439 views
What is the difference between TextGAN and LM for text generation?
I'm new to LeakGAN or SeqGAN or TextGAN. I know GAN is to generate text and let discriminator un-judge-able to real text and gen-text. LM(language model) is the task of predicting the next word and c …
- 721
1 vote
1 answer
68 views
The principle of LM deep model
Language model(LM) is the task of predicting the next word. Does the deep model need the encoder? From the ptb code of tensor2tensor, I find the deep model do not contains the encoder. Or both with- …
- 721
21 votes
1 answer
12k views
Can BERT do the next-word-predict task?
As BERT is bidirectional (uses bi-directional transformer), is it possible to use it for the next-word-predict task? If yes, what needs to be tweaked?
- 721