jthickstun

Stanford UniversityPalo Alto, CA

Pinned Repositories

alignment-eval
Dataset and evaluation pipeline for music-to-score alignment
Language:Jupyter Notebook11 2 00
anticipation
Anticipatory Autoregressive Models
Language:Python157 5 1429
basis-separation
Implementation of the BASIS algorithm for source separation with deep generative priors
Language:Python37 7 06
ismir2019coupled
Experiments for Coupled Recurrent Networks for Polyphonic Music Composition
Language:Jupyter Notebook3 3 02
lean
Some experiments with the Lean proof assistant
Language:Lean6 2 11
mini-musicnet
The mini-MusicNet dataset
Language:Jupyter Notebook9 2 01
pytorch_musicnet
PyTorch DataSet and Jupyter demos for MusicNet
Language:Jupyter Notebook69 4 822
thickstun2017learning
Experiments for Learning Features of Music From Scratch, ICLR 2017
Language:Jupyter Notebook14 5 00
thickstun2018invariances
Experiments for Invariances and Data Augmentation for Supervised Music Transcription
Language:Jupyter Notebook314
watermark
Code for watermarking language models
Language:Python74 4 89

jthickstun's Repositories

jthickstun/anticipation
Anticipatory Autoregressive Models
Language:Python157 5 1429
jthickstun/watermark
Code for watermarking language models
Language:Python74 4 89
jthickstun/pytorch_musicnet
PyTorch DataSet and Jupyter demos for MusicNet
Language:Jupyter Notebook69 4 822
jthickstun/basis-separation
Implementation of the BASIS algorithm for source separation with deep generative priors
Language:Python37 7 06
jthickstun/thickstun2018invariances
Experiments for Invariances and Data Augmentation for Supervised Music Transcription
Language:Jupyter Notebook314
jthickstun/alignment-eval
Dataset and evaluation pipeline for music-to-score alignment
Language:Jupyter Notebook11 2 00
jthickstun/mini-musicnet
The mini-MusicNet dataset
Language:Jupyter Notebook9 2 01
jthickstun/lean
Some experiments with the Lean proof assistant
Language:Lean6 2 11
jthickstun/ismir2019coupled
Experiments for Coupled Recurrent Networks for Polyphonic Music Composition
Language:Jupyter Notebook3 3 02
jthickstun/gm-hw1
Homework 1 for Generative Models
Language:Jupyter Notebook2 2 12
jthickstun/jthickstun.github.io
A beautiful, simple, clean, and responsive Jekyll theme for academics
Language:JavaScript2 1 0
jthickstun/gm-hw2
Homework 2 for Generative Models
Language:Jupyter Notebook1 2 04
jthickstun/gm-hw3
Homework 3 for Generative Models
Language:Python12
jthickstun/bach-wtc
Digital edition of J.S. Bach's Well-tempered Clavier, Books I & II in the Humdrum file format.
Language:Makefile
jthickstun/beethoven-piano-sonatas
Digital edition of L. van Beethoven's piano sonatas in the Humdrum file format.
Language:Makefile2 0
jthickstun/beethoven-string-quartets
Digital edition of L. van Beethoven's string quartests in the Humdrum file format.
Language:Makefile
jthickstun/Bus
Music of Antoine Busnoys as Humdrum digital scores.
jthickstun/chopin-mazurkas
Digital edition of Frédéric Chopin's mazurkas in the Humdrum file format.
Language:Makefile
jthickstun/datasets-1
🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
Language:Python
jthickstun/glow
Code for reproducing results in "Glow: Generative Flow with Invertible 1x1 Convolutions"
Language:Python1 01
jthickstun/humdrum-haydn-quartets
Digital edition of Joseph Haydn's string quartets in the Humdrum file format.
Language:Makefile
jthickstun/humdrum-mozart-quartets
Digital edition of W.A. Mozart's string quartets in the Humdrum file format.
Language:Makefile
jthickstun/hummel-preludes
Digital edition of Johann Nepomuk Hummel's preludes in the Humdrum file format.
Language:Makefile2 0
jthickstun/joplin-rags
Music of Scott Joplin in the Humdrum file format.
Language:Makefile
jthickstun/Jos
Music of Josquin des Prez (both secure and non-secure attributions) as Humdrum digital scores.
2 0
jthickstun/levanter
Legibile, Scalable, Reproducible Foundation Models with Named Tensors and Jax
Language:Python
jthickstun/longformer
Longformer: The Long-Document Transformer
jthickstun/mistral
Mistral: A strong, northwesterly wind: Framework for transparent and accessible large-scale language model training, built with Hugging Face 🤗 Transformers.
Language:Python
jthickstun/scarlatti-keyboard-sonatas
Digital edition of Domenico Scarlatti's keyboard sonatas in the Humdrum file format
Language:Makefile
jthickstun/transformers-1
🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.