# Divergence of RNN

## #Variants on recurrent nets

• Architectures
• How to train recurrent networks of different architectures
• Synchrony
• The target output is time-synchronous with the input
• The target output is order-synchronous, but not time synchronous

### #One to one • No recurrence in model

• Exactly as many outputs as inputs
• One to one correspondence between desired output and actual output
• Common assumption $$\nabla_{Y(t)} \operatorname{Div}\left(Y_{\text {target}}(1 \ldots T), Y(1 \ldots T)\right)=w_{t} \nabla_{Y(t)} \operatorname{Div}\left(Y_{\text {target}}(t), Y(t)\right)$$

• $w_t$ is typically set to 1.0

### #Many to many • The divergence computed is between the sequence of outputs by the network and the desired sequence of outputs
• This is not just the sum of the divergences at individual times

#### #Language modelling: Representing words

• Represent words as one-hot vectors

• Sparse problem
• Makes no assumptions about the relative importance of words
• The Projected word vectors

• Replace every one-hot vector $W_i$ by $PW_i$
• $P$ is an $M\times N$ matrix
• How to learn projections • Soft bag of words
• Predict word based on words in immediate context
• Without considering specific position
• Skip-grams
• Predict adjacent words based on current word ◎ Generating Language

### #Many to one

• Example
• Question answering
• Input : Sequence of words
• Output: Answer at the end of the question
• Speech recognition
• Input : Sequence of feature vectors (e.g. Mel spectra)
• Output: Phoneme ID at the end of the sequence • Outputs are actually produced for every input

• We only read it at the end of the sequence
• How to train

• Define the divergence everywhere
• $D I V\left(Y_{\text {target}}, Y\right)=\sum_{t} w_{t} \operatorname{Xent}(Y(t), \text { Phoneme})$
• Typical weighting scheme for speech
• All are equally important
• Problem like question answering
• Answer only expected after the question ends

### #Sequence-to-sequence • How do we know when to output symbols
• In fact, the network produces outputs at every time
• Which of these are the real outputs
• Outputs that represent the definitive occurrence of a symbol • Option 1: Simply select the most probable symbol at each time
• Merge adjacent repeated symbols, and place the actual emission of the symbol in the final instant
• Cannot distinguish between an extended symbol and repetitions of the symbol
• Resulting sequence may be meaningless
• Option 2: Impose external constraints on what sequences are allowed
• Only allow sequences corresponding to dictionary words
• Sub-symbol units
• How to train when no timing information provided • Only the sequence of output symbols is provided for the training data
• But no indication of which one occurs where
• How do we compute the divergence?
• And how do we compute its gradient
Load Comments?