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The original transformer architecture (Attention Is All You Need, 2017) which was developed for machine translation tasks utilized both an encoder and a decoder. … Over the years various encoder-only models have been developed based on the encoder module of the original transformer model with notable examples including BERT (Bidirectional Encoder Representations …

Obviously the decoder-only transformers like GPT or CTRL models are more naturally suited for generative autocorrection tasks using BPE like subword tokenization methods along with their vast training …

You’re right that encoder-decoder transformer aligns with the traditional autoencoder (AE) structure except AE’s encoder output is usually a compressed latent representation while transformer’s encoder … Also transformer decoders are optimized for token-by-token autoregressive generation, while your sliding windows require reprocessing overlapping inputs which can be computationally expensive. …

In many NLP tasks information is intrinsically structured hierarchically, for instance, early layers in a transformer model may specialize in short-range dependencies such as syntactic relationships between … Multiple transformer blocks allow the model to perform a hierarchical transformation of the input at different levels of contextualized abstraction. …

Your intuition is right. For each token in the sequence, GPT-2 linearly projects token’s embedding into three different Q/K/V spaces using three separate linear layers, each of which has weights and b …

Without positional embeddings, the self-attention mechanism even with causal mask is permutation-invariant, meaning it simply treats the causally attended input sequence portion as a bag of words with …

It depends on the context bound limitation of your transformer and how temporal relations in your time series should be captured for your task. … If your hardware and transformer can handle the memory and computational cost and if capturing long-range dependencies across the entire 5000 length time series is critical to your task such as detecting …

For autoregressive tasks like language modeling, decoder-only models can process long sequences in a straightforward way and avoid the encoder step entirely. The CE loss is calculated for each token p …

During inference, autoregressive models predicts text one token at a time sequentially. At each prediction step, attention mechanism takes query from only the current token and scores with all previou …

Since learning rate is a hyperparameter, using different learning rates or epochs for each model is not only allowed but generally encouraged as long as the hyperparameter tuning process such as grid …

Indeed several studies provide evidence that the design of tokenizers significantly impacts transformer model performance such as the original BERT paper that introduced WordPiece tokenization algorithm … Therefore without subword tokenization, an extremely powerful transformer cannot deal with out-of-vocabulary (OOV) word well such as 'rel8tivity'. …

According to this reference: feature extraction involves creating new features that still capture the essential information from the original data but in a more efficient way. Feature extraction te …

To further address you comment question, it's GAN's generator network's deep learning ability consisting of multiple layers of nonlinear transformations (e.g., convolutional layers, transposed convolu …

Indeed transformer with fixed-length positional encodings such as your SinePositionEncoding doesn't inherently generalize well to sequence lengths that differ from those used during training, because … To resolve this issue you may try alternative positional embeddings such as rotary positional embedding (RoPE) or relative position encodings used in Transformer-XL and T5. …