**DenseNAS** is a [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) method that utilises a densely connected search space. The search space is represented as a dense super network, which is built upon designed routing blocks. In the super network, routing blocks are densely connected and we search for the best path between them to derive the final architecture. A chained cost estimation algorithm is used to approximate the model cost during the search.

DenseNAS

Densely Connected Search Space for More Flexible Neural Architecture Search

CP N3

Canonical Tensor Decomposition for Knowledge Base Completion

**Contrastive Language-Image Pre-training** (**CLIP**), consisting of a simplified version of ConVIRT trained from scratch, is an efficient method of image representation learning from natural language supervision. , CLIP jointly trains an image encoder and a text encoder to predict the correct pairings of a batch of (image, text) training examples. At test time the learned text encoder synthesizes a zero-shot linear classifier by embedding the names or descriptions of the target dataset’s classes. 

For pre-training, CLIP is trained to predict which of the $N X N$ possible (image, text) pairings across a batch actually occurred. CLIP learns a multi-modal embedding space by jointly training an image encoder and text encoder to maximize the cosine similarity of the image and text embeddings of the $N$ real pairs in the batch while minimizing the cosine similarity of the embeddings of the $N^2 - N$ incorrect pairings. A symmetric cross entropy loss is optimized over these similarity scores. 

Image credit: [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/pdf/2103.00020.pdf)

Source	Canonical Tensor Decomposition for Knowledge Base Completion
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com