Online/temporal learning
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 20/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- julia
- Domain
- machine-learning
Research direction
No specific file, test, or entry point is named. Start by reviewing MXNet.jl's existing examples and model-fitting support for batch versus one-point-at-a-time updates, then compare the requested LSTM, HTM, spiking RNN, and reservoir approaches. Done would require a decided scope and documented support or a concrete contribution plan.
Written by the indexing model from the issue text.
Description
Is it possible to update a model fit one data point at a time? More importantly, in what capacity is real-time time series analysis supported? Most/all of the examples are focused on static datasets with batch fitting, but I'm not sure if that's the only way to do it with MXNet.
Some topics that I'm interested in:
- LSTM-g
- HTM (Heirarchical Temporal Memory)
- Spiking RNNs
- Reservoir Computing (Liquid State Machines)
with applications in:
- Video sequence analysis
- Auditory processing
- Semantic analysis
- Data stream sequencing, non-linear econometrics
To what extent are any of these possible through MXNet? (Even if it's not possible today, would the framework support them naturally?) I would consider contributing some algorithms here, or creating standalone packages that could be used by MXNet or my experimental package: OnlineAI.jl. If the MXNet framework does not support these topics naturally, I'll probably just continue to develop my OnlineAI framework independently.
- Dominant language
- No language data
- Stars
- 370
- Forks
- 63
- PR merge metrics
- No merged PRs in 30d
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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