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📚TheoryIntermediate

Triplet Loss & Contrastive Loss

Triplet loss and contrastive loss are metric-learning objectives that teach a model to map similar items close together and dissimilar items far apart in an embedding space.

#triplet loss#contrastive loss#metric learning+12
📚TheoryAdvanced

Neural Collapse

Neural Collapse describes what happens at the end of training: the penultimate-layer features of each class concentrate tightly around a class mean.

#neural collapse
Advanced
Filtering by:
#cosine similarity
#simplex etf
#equiangular tight frame
+12
📚TheoryIntermediate

Self-Supervised Learning Theory

Self-supervised learning (SSL) teaches models to learn useful representations from unlabeled data by solving proxy tasks created directly from the data.

#self-supervised learning#contrastive learning#infonce+12
📚TheoryIntermediate

Contrastive Learning

Contrastive learning teaches models by pulling together similar examples (positives) and pushing apart dissimilar ones (negatives).

#contrastive learning#infonce#nt-xent+12
📚TheoryIntermediate

Embedding Spaces & Distributed Representations

Embedding spaces map discrete things like words or products to dense vectors so that similar items are close together.

#embeddings#dense vectors#cosine similarity+12
📚TheoryIntermediate

Key-Value Memory Systems

Key-Value memory systems store information as pairs where keys are used to look up values by similarity rather than exact match.

#key-value memory#attention#scaled dot-product+12
∑MathIntermediate

Inner Products & Norms

An inner product measures how much two vectors point in the same direction; in R^n it is the dot product.

#inner product#dot product#norm+12
📚TheoryIntermediate

Contrastive Learning Theory

Contrastive learning learns representations by pulling together positive pairs and pushing apart negatives using a softmax-based objective.

#contrastive learning#infonce#nt-xent+12