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Generating music in the waveform domain

This is a write-up of a presentation on generating music in the waveform domain, which was part of a tutorial that I co-presented at ISMIR 2019 earlier this month. ....

Wavenet Sample , Jordi Pons , Jongpil Lee , Jaan Altosaar , Eric Jang , Generative Adversarial Networks , Subscale Pixel Networks , Music Translation Network , Evidence Lower , Parallel Wavenet , Sparse Transformer , Universal Music Translation , Relentless Mutation , Differentiable Digital Signal Processing , Subscale Pixel , Law Audio , Deep Learning , Generative Models ,

Five years of GPT progress

Five years of GPT progress
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Rabe Staats , Sparse Transformer , Reinforcement Learning , Human Feedback ,

The Transformer Family Version 2.0

Many new Transformer architecture improvements have been proposed since my last post on “The Transformer Family” about three years ago. Here I did a big refactoring and enrichment of that 2020 post — restructure the hierarchy of sections and improve many sections with more recent papers. Version 2.0 is a superset of the old version, about twice the length.
Notations Symbol Meaning $d$ The model size / hidden state dimension / positional encoding size. ....

Mostafa Dehghani , Olah Carter , Emilio Parisotto , Sainbayar Sukhbaatar , Alex Graves , Longformer Beltagy , Niki Parmar , Ashish Vaswani , Nikita Kitaev , Zihang Dai , Linformer Wang , Rahimi Recht , Aidann Gomez , Adaptive Computation Time For Recurrent Neural Networks , A Survey , Recurrent Neural Networks , Rotary Position Embedding , Memorizing Transformer , Aware Transformer , Linear Biases , Universal Transformer , Adaptive Attention , Adaptive Computation Time , Depth Adaptive Transformer , Confident Adaptive Language Model , Efficient Transformers ,

Large Transformer Model Inference Optimization

Large transformer models are mainstream nowadays, creating SoTA results for a variety of tasks. They are powerful but very expensive to train and use. The extremely high inference cost, in both time and memory, is a big bottleneck for adopting a powerful transformer for solving real-world tasks at scale.
Why is it hard to run inference for large transformer models? Besides the increasing size of SoTA models, there are two main factors contributing to the inference challenge (Pope et al. ....

Noam Shazeer , Zhou Ma , Zhu Gupta , Elsen Hooker , Zeroquant Yao , Xiao Lin , Xiao Lin Smoothquant , Frantar Alistarh , Smoothquant Xiao Lin , Frankle Carbin , Neural Network Compression , Trainable Neural Networks , Sinkhorn Sorting Network , A Survey , Neural Networks , Training Quantization , Aware Training , Optimal Brain Quantization , Layer By Knowledge Distillation , Lottery Ticket Hypothesis , Gradual Magnitude Pruning , Ticket Hypothesis , Straight Through Estimator , Scaling Transformer , Vision Moe , Vision Transformer ,

The End of Programming

The end of classical Computer Science is coming, and most of us are dinosaurs waiting for the meteor to hit. I came of age in the 1980s, programming personal computers like the Commodore VIC-20 and… ....

Computer Science , Sparse Transformer ,