Posts about open-science
- Using singularity on slurm
- Modelling wind ripples
- Implementing a retinotopic transform using `grid_sample` from pyTorch
- Elections présidentielles 2022: estimation du transfert de voix
- COSYNE reviewer feedback
- Experimenting with transfer learning for visual categorization
- Extracting music from the screenshots of a Spotify playlist
- Fitting COVID data
- Benchmarking CNNs
- nesting jupyter runs
- Colors of the sky
- Caustic (optics)
- Creating an hexagonal grid
- Fitting a psychometric curve using pyTorch
- Converting a bunch of files in a few lines
- Mainen & Sejnowski, 1995
- Extending datasets in pyTorch
- Generating an unique seed for a given filename
- Predictive coding of variable motion
- accessing the data from a pupil recording
- MEUL with a non-parametric homeostasis
- Designing a A0 poster using matplotlib
- The fastest 2D convolution in the world
- Le jeu de l'urne
- testing COMPs-fastPcum_scripted
- testing COMPs-fastPcum
- testing COMPs-Pcum
- Extending Olshausens classical SparseNet
- Reproducing Olshausen's classical SparseNet (part 3)
- Reproducing Olshausens classical SparseNet (part 2)
- Reproducing Olshausen's classical SparseNet (part 1)
- Some basics around probabilities
- Bogacz (2017) A tutorial on free-energy
- Resizing a bunch of files using the command-line interface
- Using generators in Python
- Finding extremal values in a nd-array
- Saving and displaying movies and dynamic figures
- A pi-pie named Monte Carlo
- Une tarte au pi nommée Monte Carlo
- Predictive coding of motion in an aperture
- Static Motion Clouds
- compiling notebooks into a report
- Compiling and using pyNN + NEST + python3
- Compiling and using pyNN + NEST + python3
- Reproducing Olshausen's classical SparseNet (part 3)
- Reproducing Olshausen's classical SparseNet (part 4)
- Extending Olshausens classical SparseNet
- Reproducing Olshausens classical SparseNet (part 2)
- Reproducing Olshausen's classical SparseNet (part 1)