Difference between revisions of "Proposals"
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* '''Invariants and compositions of physical activities''' | * '''Invariants and compositions of physical activities''' | ||
We have to create a model to describe simple hand movements and compositions of movements. The composition of physical models must fit the composition of machine learning models. | We have to create a model to describe simple hand movements and compositions of movements. The composition of physical models must fit the composition of machine learning models. | ||
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| + | * '''Generating WANN: weighted agnostic neural networks''' | ||
| + | We have to generate agnostic nets and ensembles of agnostic nets to solve one of the well-known reinforcement learning problems: controlling cars on the road, steps, pendulum. | ||
| + | |||
| + | * '''GAN creates WANN: generative adversarial network generates agnostic networks''' | ||
| + | We have to develop variational autoencoder to generate simple agnostic networks, which are evaluated by some discriminative model. | ||
Revision as of 18:27, 17 August 2020
List of the proposed projects
- CASF: Comparison analysis scoring functions for docking problem
Pharmacological research is concentrated to constructing a molecule, a ligand, which docks a given protein. This research is expensive since it goes in-vitro. We have to propose a deep neural net to forecast the probability of docking.
- 3D-image reconstruction for the lens-free microscopy
One has to reconstruct an image of a small biological object (of hundreds micrometers, like a eucaryotic cell). Its video comes from a device of a brand-new technology: the free lens microscopy. This project combines modelling of interference images and its Fourier transform and GANs, the generative and discriminative deep learning models.
- Atom-resolution synchrotron image reconstruction
For a given synchrotron image one has to reconstruct an object of nano-meter size. There given a reciprocal lattice image. We have to train a neural net to reconstruct the real and the complex part of a sample object.
- Long-live health monitoring with wearable devices
For daily accelerometer and gyroscope time series we have to reconstruct typical hand movements and represent these movements as clusters in the phase space.
- Time series segmentation in low-dimensional space
One has to mark-up zero-phase segments of various hand movements using electronic watches. Neural nets on spherical harmonics seems to be a good tool.
- Invariants and compositions of physical activities
We have to create a model to describe simple hand movements and compositions of movements. The composition of physical models must fit the composition of machine learning models.
- Generating WANN: weighted agnostic neural networks
We have to generate agnostic nets and ensembles of agnostic nets to solve one of the well-known reinforcement learning problems: controlling cars on the road, steps, pendulum.
- GAN creates WANN: generative adversarial network generates agnostic networks
We have to develop variational autoencoder to generate simple agnostic networks, which are evaluated by some discriminative model.