projects, ensemble_nets, jax
ensemble nets; using jax vmap to batch over not just the inputs of a model but also sets of multiple models parameters.
an objax tutorial on using metric learning for image similarity.
random embedding networks can be used to generate weakly labelled data for metric learning and they see a large benefit from being run in ensembles. can we represent these ensembles as a single forward pass in jax? why yes! yes we can!
bnn : counting bees with a rasp pi
drivebot : learning to do laps with reinforcement learning and neural nets
wikipedia philosophy : do all first links on wikipedia lead to philosophy?
cartpole++ : deep RL hacking with a complex 3d cart pole environment
malmomo : deep RL hacking on minecraft with malmo
some papers from my time at google research / brain...
- Natural Questions: a Benchmark for Question Answering Research
- Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
- WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia
my honours thesis
the co-evolution of cooperative behaviour (1997) evolving neural nets with genetic algorithms for communication problems.
- latent semantic analysis via the singular value decomposition (for dummies)
- semi supervised naive bayes
- statistical synonyms
- round the world tweets
- decomposing social graphs on twitter
- do it yourself statistically improbable phrases
- should i burn it?
- the median of a trillion numbers
- deduping with resemblance metrics
- simple supervised learning / should i read it?
- audioscrobbler experiments
- chaoscope experiment