Language Models
Neural Probabilistic Language Model
A Neural Probablistic Language Model is an early language modelling architecture. It involves a feedforward architecture that takes in input vector representations (i.e. word embeddings) of the previous $n$ words, which are looked up in a table $C$.
The word embeddings are concatenated and fed into a hidden layer which then feeds into a softmax layer to estimate the probability of the word given the context.
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Language Modelling | 5 | 31.25% |
| Machine Translation | 2 | 12.50% |
| Translation | 2 | 12.50% |
| Text Generation | 1 | 6.25% |
| Graph Embedding | 1 | 6.25% |
| Knowledge Graph Embedding | 1 | 6.25% |
| Knowledge Graph Embeddings | 1 | 6.25% |
| Type prediction | 1 | 6.25% |
| Vocal Bursts Type Prediction | 1 | 6.25% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
