• FFT1
• Fourier transform1
• Galaxy Zoo3
• Kaggle4
• Lasagne1
• MIR2
• National Data Science Bowl1
• Python1
• Spotify1
• Theano9
• VAE1
• arxiv1
• audio1
• autoencoder1
• autoencoders1
• autoregression2
• code1
• cold start problem1
• collaborative filtering1
• competition4
• consistency models1
• convnets9
• convolution4
• convolutional neural networks9
• deep learning22
• denoising1
• diffusion9
• diffusion model1
• distillation1
• fast Fourier transform1
• generative models12
• geometry1
• guidance2
• internship1
• language1
• latent space1
• likelihood2
• music2
• music information retrieval2
• music recommendation1
• natural images1
• neural networks1
• noise schedule1
• optimization1
• paper1
• parameterisation1
• plankton1
• probability2
• raw audio1
• score function5
• slides1
• spectral analysis1
• spectrum1
• talk1
• typicality2
• vectors1
• waveform1

FFT

• Even faster convolutions in Theano using FFTs

Fourier transform

• Diffusion is spectral autoregression

Galaxy Zoo

• Paper about my Galaxy Challenge solution
• Galaxy Zoo Challenge: code published
• My solution for the Galaxy Zoo challenge

Kaggle

• Paper about my Galaxy Challenge solution
• Classifying plankton with deep neural networks
• Galaxy Zoo Challenge: code published
• My solution for the Galaxy Zoo challenge

Lasagne

• New Lasagne feature: arbitrary expressions as layer parameters

MIR

• Recommending music on Spotify with deep learning
• Slides for my talk at the Deep Learning London meetup

National Data Science Bowl

• Classifying plankton with deep neural networks

Python

• Galaxy Zoo Challenge: code published

Spotify

• Recommending music on Spotify with deep learning

Theano

• New Lasagne feature: arbitrary expressions as layer parameters
• Classifying plankton with deep neural networks
• The fastest convolutions in Theano with meta-optimization
• Recommending music on Spotify with deep learning
• Slides for my talk at the Deep Learning London meetup
• Even faster convolutions in Theano using FFTs
• Galaxy Zoo Challenge: code published
• My solution for the Galaxy Zoo challenge
• 3x faster convolutions in Theano

VAE

• Generative modelling in latent space

arxiv

• Paper about my Galaxy Challenge solution

audio

• Generating music in the waveform domain

autoencoder

• Generative modelling in latent space

autoencoders

• Diffusion models are autoencoders

autoregression

• Generative modelling in latent space
• Diffusion is spectral autoregression

code

• Galaxy Zoo Challenge: code published

cold start problem

• Recommending music on Spotify with deep learning

collaborative filtering

• Recommending music on Spotify with deep learning

competition

• Paper about my Galaxy Challenge solution
• Classifying plankton with deep neural networks
• Galaxy Zoo Challenge: code published
• My solution for the Galaxy Zoo challenge

consistency models

• The paradox of diffusion distillation

convnets

• Paper about my Galaxy Challenge solution
• Classifying plankton with deep neural networks
• The fastest convolutions in Theano with meta-optimization
• Recommending music on Spotify with deep learning
• Slides for my talk at the Deep Learning London meetup
• Even faster convolutions in Theano using FFTs
• Galaxy Zoo Challenge: code published
• My solution for the Galaxy Zoo challenge
• 3x faster convolutions in Theano

convolution

• The fastest convolutions in Theano with meta-optimization
• Recommending music on Spotify with deep learning
• Slides for my talk at the Deep Learning London meetup
• Even faster convolutions in Theano using FFTs

convolutional neural networks

• Paper about my Galaxy Challenge solution
• Classifying plankton with deep neural networks
• The fastest convolutions in Theano with meta-optimization
• Recommending music on Spotify with deep learning
• Slides for my talk at the Deep Learning London meetup
• Even faster convolutions in Theano using FFTs
• Galaxy Zoo Challenge: code published
• My solution for the Galaxy Zoo challenge
• 3x faster convolutions in Theano

deep learning

• Generative modelling in latent space
• Diffusion is spectral autoregression
• Noise schedules considered harmful
• The paradox of diffusion distillation
• The geometry of diffusion guidance
• Perspectives on diffusion
• Diffusion language models
• Guidance: a cheat code for diffusion models
• Diffusion models are autoencoders
• Musings on typicality
• Addendum: quantifying our flawed intuitions
• Generating music in the waveform domain
• New Lasagne feature: arbitrary expressions as layer parameters
• Paper about my Galaxy Challenge solution
• Classifying plankton with deep neural networks
• The fastest convolutions in Theano with meta-optimization
• Recommending music on Spotify with deep learning
• Slides for my talk at the Deep Learning London meetup
• Even faster convolutions in Theano using FFTs
• Galaxy Zoo Challenge: code published
• My solution for the Galaxy Zoo challenge
• 3x faster convolutions in Theano

denoising

• Diffusion models are autoencoders

diffusion

• Generative modelling in latent space
• Diffusion is spectral autoregression
• Noise schedules considered harmful
• The paradox of diffusion distillation
• The geometry of diffusion guidance
• Perspectives on diffusion
• Diffusion language models
• Guidance: a cheat code for diffusion models
• Diffusion models are autoencoders

diffusion model

• Noise schedules considered harmful

distillation

• The paradox of diffusion distillation

fast Fourier transform

• Even faster convolutions in Theano using FFTs

generative models

• Generative modelling in latent space
• Diffusion is spectral autoregression
• Noise schedules considered harmful
• The paradox of diffusion distillation
• The geometry of diffusion guidance
• Perspectives on diffusion
• Diffusion language models
• Guidance: a cheat code for diffusion models
• Diffusion models are autoencoders
• Musings on typicality
• Addendum: quantifying our flawed intuitions
• Generating music in the waveform domain

geometry

• The geometry of diffusion guidance

guidance

• The geometry of diffusion guidance
• Guidance: a cheat code for diffusion models

internship

• Recommending music on Spotify with deep learning

language

• Diffusion language models

latent space

• Generative modelling in latent space

likelihood

• Musings on typicality
• Addendum: quantifying our flawed intuitions

music

• Generating music in the waveform domain
• Recommending music on Spotify with deep learning

music information retrieval

• Recommending music on Spotify with deep learning
• Slides for my talk at the Deep Learning London meetup

music recommendation

• Recommending music on Spotify with deep learning

natural images

• Diffusion is spectral autoregression

neural networks

• New Lasagne feature: arbitrary expressions as layer parameters

noise schedule

• Noise schedules considered harmful

optimization

• The fastest convolutions in Theano with meta-optimization

paper

• Paper about my Galaxy Challenge solution

parameterisation

• Noise schedules considered harmful

plankton

• Classifying plankton with deep neural networks

probability

• Musings on typicality
• Addendum: quantifying our flawed intuitions

raw audio

• Generating music in the waveform domain

score function

• The geometry of diffusion guidance
• Perspectives on diffusion
• Diffusion language models
• Guidance: a cheat code for diffusion models
• Diffusion models are autoencoders

slides

• Slides for my talk at the Deep Learning London meetup

spectral analysis

• Diffusion is spectral autoregression

spectrum

• Diffusion is spectral autoregression

talk

• Slides for my talk at the Deep Learning London meetup

typicality

• Musings on typicality
• Addendum: quantifying our flawed intuitions

vectors

• The geometry of diffusion guidance

waveform

• Generating music in the waveform domain