Discuss bidirectional RNNs
Answer:-
Bidirectional Recurrent Neural Networks (BiRNNs)
- Causal Structure in RNNs:
- Traditional RNNs process sequences in a causal manner, considering only past and present information to predict the current output.
- Need for Future Context:
- In applications like speech recognition, handwriting recognition, and bioinformatics, predictions often depend on both past and future inputs.
- Example: In speech recognition, the current phoneme may rely on future phonemes for accurate interpretation.
- BiRNN Architecture:
- A BiRNN combines two RNNs:
- Forward RNN: Processes the sequence from left to right.
- Backward RNN: Processes the sequence from right to left.
- The output at each time step is a combination of the forward and backward hidden states.
- A BiRNN combines two RNNs:
- Output Calculation:
- At time t, the output o(t) is derived from the forward and backward RNNs:
- h(t) (forward hidden state)
- g(t) (backward hidden state)
- Final output: o(t)=g(h(t),g(t))
- At time t, the output o(t) is derived from the forward and backward RNNs:
- Advantages of BiRNNs:
- Capture Future Context: BiRNNs utilize both past and future sequence information, leading to better predictions.
- Improved Performance: Particularly beneficial in tasks like speech and handwriting recognition, where future context is essential for disambiguation.
- Dynamic Representation: The model is sensitive to the inputs around time tt, without needing a fixed-size window.
- Applications of BiRNNs:
- Speech Recognition: Interprets phonemes considering both past and future phonemes.
- Handwriting Recognition: Recognizes characters by using both past and future strokes.
- Bioinformatics: Helps in tasks like gene sequence prediction where future context is important.
- Extension to 2D Inputs:
- BiRNNs can be extended to 2-dimensional input, such as images, where RNNs are applied in four directions (up, down, left, right) to capture spatial dependencies.
- Comparison with Convolutional Networks:
- BiRNNs are typically more computationally expensive than convolutional networks but allow for long-range lateral interactions across features in the same feature map, which can be beneficial for certain tasks.