The Living Thing / Notebooks
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pytorch
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[ #torched ]
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2018-05-04
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2019-11-28
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Audio source separation
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2019-11-04
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2019-11-26
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Probabilistic neural nets
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[ This is Machinelearnese for βBayesianβ ]
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2017-01-11
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2019-11-23
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Controllerism
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[ Also gestural interfaces, and other fancy words for making thing happen by waving your arms about on stage ]
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2014-11-17
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2019-11-22
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Tunings
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2015-10-29
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2019-11-18
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Auditory features
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[ descriptors, maps, representations for audio ]
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2019-11-13
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2019-11-13
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Machine listening
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[ Statistical models for audio ]
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2014-10-10
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2019-11-12
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Javascript machine learning
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2017-01-13
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2019-11-09
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ISMIR 2019
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[ Music Nerds in Delft ]
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2019-11-04
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2019-11-09
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Analysis/resynthesis of audio
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2016-01-15
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2019-11-04
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Hereβs how I would do art with machine learning if I had to
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2016-06-06
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2019-11-04
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Survey modelling
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[ Adjusting for the Lizardman constant ]
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2019-08-29
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2019-10-30
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Inference on graphical models
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[ Given what I know about what I know, what do I know? ]
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2017-09-20
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2019-10-28
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Learning in adaptive systems
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[ On staring into scopophilic abysses ]
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2019-10-19
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2019-10-19
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Deep learning as a dynamical system
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2018-08-13
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2019-10-15
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Bio computing
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2016-05-29
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2019-10-14
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Why does deep learning work?
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[ despite the fact we are totally just making this shit up ]
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2017-05-30
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2019-10-09
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Probabilistic graphical models over continuous index sets
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2014-08-05
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2019-09-25
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Differentiable learning of automata
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2016-10-14
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2019-09-11
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Hierarchical models
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[ DAGs, multilevel models, random coefficient models, mixed effect models⦠]
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2015-06-07
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2019-08-19
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Causal inference
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[ Confounding! This scientist performed miracle graph surgery during an intervention and you wonβt believe what happened next ]
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2016-10-26
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2019-08-07
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Neural nets
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[ designing the fanciest usable differentiable loss surface ]
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2016-10-14
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2019-05-27
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Natural language processing
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[ Dave, although you took very thorough precautions in the pod against my hearing you, I could see your lips move. ]
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2018-01-11
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2019-04-22
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Model interpretation, fairness and trust
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2018-11-29
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2019-02-15
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Overparameterization
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[ a.k.a. improper learning ]
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2018-04-04
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2018-12-11
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Machine vision
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2015-01-03
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2018-11-14
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Gesture recognition
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2014-10-17
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2018-11-12
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Compressing artificial neural networks
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2016-10-12
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2018-10-23
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Learnable indexes and hashes
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2018-01-12
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2018-01-12
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Moral calculus
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2014-08-04
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2017-11-21
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Convolutional neural networks
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2017-11-10
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2017-11-10
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Recurrent neural networks
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2016-06-16
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2017-10-25
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Model interpretation
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2016-09-01
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2017-10-13
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Distributed sensing and swarm sensing
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2014-10-13
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2017-10-12
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Learning graphical models from data
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[ what is independent of what ]
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2017-09-20
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2017-09-27
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(Probabilistic) graphical models
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2014-08-05
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2017-09-11
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Brains
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[ Neural networks made of real neurons, in functioning brains ]
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2014-11-03
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2017-08-15
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Quantum-probabilistic graphical models
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2017-08-07
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2017-08-07
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Generalisation in neural networks
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[ Generalisation for street fighters ]
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2017-02-12
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2017-06-20
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Marketing psychology
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2017-04-27
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2017-05-29
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Machine learning for physics
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[ Turbulent mixing at the boundary between two disciplines with differing inertia ]
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2017-05-15
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2017-05-25
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Human behaviour control, methods for
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2017-04-04
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2017-04-04
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Entity embeddings
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2017-04-01
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2017-04-01
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Musical metrics and manifolds
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2014-09-26
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2017-03-27
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Random neural networks
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2017-02-17
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2017-02-19
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Neural network activation functions
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2017-01-12
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2017-01-12
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Greatest hits
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2016-11-19
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2016-12-12
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UNSW Stats reading group 2016 - Causal DAGs
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[ An introduction to conditional independence DAGs and their use for causal data. ]
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2016-10-17
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2016-10-21
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Inference from disorder
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2016-10-19
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2016-10-19
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Generalised linear models
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2016-03-24
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2016-08-31
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Probably Approximately Correct
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2014-11-24
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2016-05-29
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Pattern machine
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2011-06-27
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2015-11-24
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Grammatical inference
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2011-05-30
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2015-02-19
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