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1 contributors · 49 revisions · 385 pts
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48 page 1 fix figure worked-example review translation - Learning by Comparison · Triplet Loss: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Learning by Comparison · SimCLR: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Learning by Comparison · References: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Learning by Comparison · N-pair Loss: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Learning by Comparison · Noise-Contrastive Estimation: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Learning by Comparison · MoCo: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Learning by Comparison · Lifted Structured Loss: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Learning by Comparison · Introduction: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Learning by Comparison · InfoNCE Loss: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Mixture of Experts · A Decision With No Label: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Mixture of Experts · References: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Mixture of Experts · The Layer That Reads Everything: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Mixture of Experts · Compute You Can Skip: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Mixture of Experts · Committee or Competition: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Mixture of Experts · Fixed Buffers, Dropped Tokens: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Metrics · What a Metric Actually Is: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Metrics · TPR and FPR: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Metrics · References: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Metrics · Precision: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Metrics · Partial AUROC: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Metrics · F1: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Metrics · Balanced Accuracy: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Metrics · ROC and AUROC: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Metrics · PR Curves and AUPRC: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Metrics · Accuracy: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Embeddings · Learning by Prediction: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Embeddings · The Vocabulary Problem: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Embeddings · References: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Embeddings · Reading a Modern Embedding Model: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Embeddings · The One-Hot Baseline: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Embeddings · Where One Vector per Word Runs Out: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Embeddings · Word Has to Become a Vector: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Embeddings · The Shape of the Space: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Embeddings · The Company a Word Keeps: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Embeddings · Contextual Embeddings: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Attention · Measuring Similarity: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings fix, 1 pts #3
- Attention · References: Populate the shelf: self-supervised learning, mixture of experts, metrics, embeddings page, 8 pts #3
- Attention · The Transformer Block: content/attention chapters wip page, 8 pts #2
- Attention · From Scores to Weights: content/attention chapters wip page, 8 pts #2
- Attention · Measuring Similarity: content/attention chapters wip page, 8 pts #2
- Attention · Self-Attention: content/attention chapters wip page, 8 pts #2
- Attention · Scaled Dot-Product Attention: content/attention chapters wip page, 8 pts #2
- Attention · Positional Information: content/attention chapters wip page, 8 pts #2
- Attention · Multi-Head Attention: content/attention chapters wip page, 8 pts #2
- Attention · Masking and Causality: content/attention chapters wip page, 8 pts #2
- Attention · Why Attention?: content/attention chapters wip page, 8 pts #2
- Attention · The Whole Model: content/attention chapters wip page, 8 pts #2
- Attention · The Dictionary Analogy: content/attention chapters wip page, 8 pts #2
- Attention · Why Attention?: Add the "Why Attention?" chapter to the Attention tutorial page, 8 pts #1