AlphaFold is a deep learning based algorithm for accurate protein structure prediction. AlphaFold incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.

Description from: [Highly accurate protein structure prediction with AlphaFold](https://paperswithcode.com/paper/highly-accurate-protein-structure-prediction)

Image credit: [DeepMind](https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology)

Decoder architecture inspired on the [UNet++](https://paperswithcode.com/method/unet) structure and the [EfficientNet](https://paperswithcode.com/method/efficientnet) building blocks. Keeping the UNet++ structure, the EfficientUNet++ achieves higher performance and significantly lower computational complexity through two simple modifications:

* Replaces the 3x3 convolutions of the UNet++ with residual bottleneck blocks with depthwise convolutions
* Applies channel and spatial attention to the bottleneck feature maps using [concurrent spatial and channel squeeze & excitation (scSE)](https://paperswithcode.com/method/scse) blocks

EfficientUNet++

Encoder-Decoder Architectures for Clinically Relevant Coronary Artery Segmentation

AlphaFold

Highly accurate protein structure prediction with AlphaFold

**Jukebox** is a model that generates music with singing in the raw audio domain. It tackles the long context of raw audio using a multi-scale [VQ-VAE](https://paperswithcode.com/method/vq-vae) to compress it to discrete codes, and modeling those using [autoregressive Transformers](https://paperswithcode.com/methods/category/autoregressive-transformers). It can condition on artist and genre to steer the musical and vocal style, and on unaligned lyrics to make the singing more controllable.

Three separate VQ-VAE models are trained with different temporal resolutions. At each level, the input audio is segmented and encoded into latent vectors $\mathbf{h}\_{t}$, which are then quantized to the closest codebook vectors $\mathbf{e}\_{z\_{t}}$. The code $z\_{t}$ is a discrete representation of the audio that we later train our prior on. The decoder takes the sequence of codebook vectors and reconstructs the audio. The top level learns the highest degree of abstraction, since it is encoding longer audio per token while keeping the codebook size the same. Audio can be reconstructed using the codes at any one of the abstraction levels, where the least abstract bottom-level codes result in the highest-quality audio.

Source	Highly accurate protein structure prediction with AlphaFold
Year	2000
Data Source	Papers with Code archive (CC BY-SA)