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Outline
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Introduction
Recurrent neural networks (RNNs) have been widely used for sequence modeling and machine translation tasks
RNNs have sequential computation which limits parallelization
Attention mechanisms have been used with RNNs but still rely on sequential computation
Proposed Model: Transformer
Proposes Transformer model using self-attention instead of recurrence
Dispenses with recurrence and convolutions entirely for encoder-decoder attention
Model Architecture
Encoder and Decoder Stacks
Stack of identical layers with two sub-layers: self-attention and fully connected feed-forward network
Residual connections and layer normalization around each sub-layer
Attention
Computes dot products of query with keys, divides by sqrt(dk) and applies softmax
Allows model to jointly attend to different representation subspaces
Projects queries, keys and values with different learned projections
Performs attention in parallel and concatenates/projects results
Training and Results
Training Data and Batching
Trained on WMT 2014 English-German and English-French datasets
Batches by approximate sequence length
Results
Achieves new state-of-the-art on WMT 2014 English-German and English-French tasks
Outperforms previous best models including ensembles
Projects queries, keys and values with different learned projections
Trains significantly faster than RNN/CNN architectures
Model Variations
Varies number of layers, heads, dimensions, dropout
Replace sinusoidal positional encodings with learned embeddings
Analysis
Attention Visualizations
Visualizations show attention following long-distance dependencies
Heads specialized for tasks like anaphora resolution
Critical Analysis
Pros
The report provides a comprehensive and detailed analysis of the global humanoid robot market, covering key drivers, technological advancements, cost trends, and potential demand.
The analysts have taken a balanced and objective approach, acknowledging both the opportunities and challenges facing the industry.
Cons
The report provides a comprehensive and detailed analysis of the global humanoid robot market, covering key drivers, technological advancements, cost trends, and potential demand.
The analysts have taken a balanced and objective approach, acknowledging both the opportunities and challenges facing the industry.
Distinctive Sentences
"The AI progress surprised us the most: We view hardware technology is mostly ready while progress in end-to-end AI (completely different from rule-based control) could potentially enable much faster humanoid robot iterations as seen from the improvement of manipulation and interaction capabilities of various products in 2023 (for e.g., Tesla Optimus Gen 2)."
"The AI progress surprised us the most: We view hardware technology is mostly ready while progress in end-to-end AI (completely different from rule-based control) could potentially enable much faster humanoid robot iterations as seen from the improvement of manipulation and interaction capabilities of various products in 2023 (for e.g., Tesla Optimus Gen 2)."
"The AI progress surprised us the most: We view hardware technology is mostly ready while progress in end-to-end AI (completely different from rule-based control) could potentially enable much faster humanoid robot iterations as seen from the improvement of manipulation and interaction capabilities of various products in 2023 (for e.g., Tesla Optimus Gen 2)."

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