I’m using a MLP to recognise gestures for my VR game.
The gesture gets preprocessed into a 2D grid, every grid cell becomes an input.
However I would like to add the direction into the recognition. My first thought was for each cell also input a 0 value for never entered and 0.5 – 1 for first – last cell.
Another idea was to add the start – end vector as two values. (x,y)
Any tips?

model: https://www.dropbox.com/s/y8petm9dd74uv9g/allface2.pkl

interpolation: https://twitter.com/roadrunning01/status/1156368137669005314?s=20

I am working on a project which involves video action recognition, and am using the fantastic I3D approach.

However, I am now interested in localising the region of video which contains the action being classified (i.e. with a bounding box). For example, if my I3D network classifies a segment of video containing the action of a human “Eating an Apple”, I now want to draw a bounding box over the person eating the apple in each frame of video. Note that I am not interested in drawing a region around “Apple” or “Human”, but instead the region in which the action itself is being performed.

I am familiar with similar approaches for image classification (e.g. YOLO), but am having trouble finding work in the video domain for actions. Can anyone point me to some good papers which cover this if they exist?

Hello I want to create a DQN for a 2 player strategic board game. I read that experience replay is recommended to break correlation. I want to know how I create the experience replay table?

Can I build it using random agent?(Playing random moves). After I have built the table I want to proceed like that: I want to randomly take one state out of experience replay table, take an action according to epsilon greedy policy. Put in the next state into a second neural network and backpropagate the difference Q values(From second neural network and first) in the first neuronal network. After that I randomly choose another state and repeat the step from above. Is this correct? Thank you

Do neural networks learn what we think they learn? Benjamin Heinzerling reviews research that suggests that they often instead fall prey to the so-called Clever Hans effect and discusses its implications for NLP.

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https://thegradient.pub/nlps-clever-hans-moment-has-arrived/

Billed as the single chip with the greatest computing density, Ascend 910 delivers performance of up to 256 teraFLOPS under FP16 and 512 teraOPS under IN8 with declared max power consumption of 310W. In comparison, the GPU Tesla V100 delivers up to 125 teraFLOPS with a max power consumption of 300W, while Google’s TPU 2.0 with four ASICs can reach 180 teraFLOPS.

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Link: https://medium.com/syncedreview/huaweis-first-commercial-ai-chip-doubles-the-training-performance-of-nvidia-s-flagship-gpu-86e4d0078f6f

On GitHub

JAXnet is a deep learning library based on Google’s JAX. JAXnet’s functional API provides unique benefits over TensorFlow2, Keras and PyTorch, while maintaining user-friendliness, modularity and scalability:

• More robustness through immutable weights, no global compute graph.
• GPU-compiled numpy code for networks, training loops, pre- and postprocessing.
• Regularization and reparametrization of any module or whole networks in one line.
• No global random state, flexible random key control.

Your feedback is welcome!

I used transfer learning to train Stylegan using Gwern‘s This Waifu Does not exist anime model.

the dataset was originally around 3k images augmented to 13k.

Here are some interpolation results.

Interactive/controlled image generation results. (Thanks to u/halcy for GUI🙂