Related questions:
– Briefly describe the architecture of a Recurrent Neural Network (RNN)
– What is Long-Short Term Memory (LSTM)?
– What are transformers? Discuss the major breakthroughs in transformer models
Source: Colah’s blog, and Attention paper. Compiled by AIML.com Research
RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit) and Transformers are all types of neural networks designed to handle sequential data. However, they differ in their architecture and capabilities. Here’s a breakdown of the key differences between RNN, LSTM, GRU and Transformers:
| Description | Recurrent Neural Network (RNN) | Long Short Term Memory (LSTM) | Gated Recurrent Unit (GRU) | Transformers |
|---|---|---|---|---|
| Overview | RNNs are foundational sequence models that process sequences iteratively, using the output from the previous step as an input to the current step. | LSTMs are an enhancement over standard RNNs, designed to better capture long-term dependencies in sequences. | GRUs are a variation of LSTMs with a simplified gating mechanism. | Transformers move away from recurrence and focus on self-attention mechanisms to process data in parallel |
| Key characteristics | - Recurrent connections allow for the retention of "memory" from previous time steps. | - Uses gates (input, forget, and output) to regulate the flow of information. - Has a cell state in addition to the hidden state to carry information across long sequences. | - Contains two gates: reset gate and update gate. - Merges the cell state and hidden state. | - Uses Self-attention mechanisms to weigh the importance of different parts of the input data. - Consists of multiple encoder and decoder blocks. - Processes data in parallel rather than sequentially. |
| Advantages | - Simple structure. - Suitable for tasks with short sequences. | - Can capture and remember long-term dependencies in data. - Mitigates the vanishing gradient problem of RNNs. | - Fewer parameters than LSTM, often leading to faster training times. - Simplified structure while retaining the ability to capture long-term dependencies. | - Can capture long-range dependencies without relying on recurrence - Highly parallelizable, leading to faster training on suitable hardware. |
| Disadvantages | - Suffers from the vanishing and exploding gradient problem, making it hard to learn long-term dependencies - Limited memory span | - More computationally intensive than RNNs - Complexity can lead to longer training times. | - Might not capture long-term dependencies as effectively as LSTM in some tasks. | - Requires a large amount of data and computing power for training. - Can be memory-intensive due to the attention mechanism, especially for long sequences. |
| Use Cases | Due to its limitations, plain RNNs are less common in modern applications. Used in simple language modeling, time series prediction | Machine translation, speech recognition, sentiment analysis, and other tasks that require understanding of longer context. | Text generation, sentiment analysis, and other sequence tasks where model efficiency is a priority. | - State-of-the-art performance in various NLP tasks, including machine translation, text summarization. - Forms the backbone for models like BERT and GPT. |
| Model variants | Vanilla RNN, Bidirectional RNN, Deep (Stacked) RNN | Vanilla LSTM, Bidirectional LSTM, Peephole LSTM, Deep (Stacked) LSTM | GRU | Original Transformer (Seq-to-Seq), Encoder only (Eg: BERT), Decoder only (Eg: GPT), Text to Text (Eg: T5) |
Source: AIML.com Research
Comparing results of different models (from Scientific journals)
Included below are brief excerpts from scientific journals that provides a comparative analysis of different models. They offer an intuitive perspective on how model performance varies across various tasks.
- Transformer model for traffic flow forecasting with a comparative analysis to RNNs (LSTM and GRU) [Time Series problem]
Source: Paper by Reza et.al, Universidade do Porto, Portugal
- RoBERTa-LSTM: A Hybrid Model for Sentiment Analysis With Transformer and Recurrent Neural Network [Text Classification problem]
Source: Paper by Tan et. al., Multimedia University, Melaka, Malaysia
- Improving Language Understanding by Generative Pre-Training [Diverse: textual entailment, question answering, semantic similarity assessment, and document classification]
(CoLA, SST2, and others are a collection of datasets under the GLUE benchmark for evaluating Natural Language Systems)
Source: GPT paper by Radford et. al., Open AI
Conclusion
As shown above, while RNNs, LSTMs, and GRUs all operate on the principle of recurrence and sequential processing of data, Transformers introduce a new paradigm focusing on attention mechanisms to understand the context in data. Each model has its strengths and ideal applications, and you may choose the model depending upon the specific task, data, and available resources.
Video Explanation
- This video is part of the ‘Introduction to Deep Learning’ course at MIT. In this lecture, Professor Ava Amini delves into the concepts of Sequence modeling, and covers the full gamut of sequence models including RNN, LSTM and Transformers. This presentation offers valuable insights into the conceptual understanding, advantages, limitations and use cases of each model. (Runtime: 1 hr 2 mins)
- The second video is part of the ‘NLP with Deep Learning’ course offered by Stanford University. In this lecture, Dr. John Lewitt delivers a great explanation of the transition from Recurrent Models to Transformers, and a clear comparative analysis of the distinctions between the two. (Runtime: 1 hr 16 mins)