D20

• Add P-threshold value to the json meta values. Items lower than this should be pale colors with black text

ML Seminar

• finish reading paper, put together some quick slides – Done! Had to turn one table into charts
• Good discussion. It makes me think that the way to have agents play games is to learn on text up to the latest point in the game as probe, and then add the question to the end of the probe, something like “<game text> + <character name> “. Have to try that with some novel text and talk to transformer. Here’s an example from the beginning of O Henry’s the Skylight Room. The Text begins: First Mrs. Parker would show you the double parlours. You would not dare to interrupt her description of their advantages and of the merits of the gentleman who had occupied them for eight years. Then you would manage to stammer forth the confession that you were neither a doctor nor a dentist. Mrs. Parker’s
  • O Henry: manner of receiving the admission was such that you could never afterward entertain the same feeling toward your parents, who had neglected to train you up in one of the professions that fitted Mrs. Parker’s parlours.
  • TTT 1: eyes would turn on you with the ferocity of her surgeon’s. “If I see a cripple with no teeth, I get him to be my apprentice, and he stays a month. I put him to work in a laundry. I give him a salary of seven hundred pounds a year, with a board and everything to live upon.”
  • TTT 2: memory is rather dim, and she was not accustomed to be questioned directly; and she was anxious to see that you could remember as little as possible. You felt sure that by telling a story from childhood that had so often caught your fancy, you would escape this forced duty. So you went in, decided to visit the fair each evening, and spent an hour or two putting it off.
  • TTT 3: vicarage would be a long, square room overlooking a grassy acre. Not less than four hundred acres were required to cover the floor of the park with his striking landscape. The grounds were monotonous, both in their beauty and in their repetition. To name a single flower would be so pedestrian an exercise that I am surprised that any of my country neighbours ever took that course. 
• So there is something there, but working out the right probe is still an issue.

GOES

• CNNs
  • Tried Mirrored strategy. It’s slower haha
  • Here’s a nice utility to set up the log files that cleans out the old ones and fixes the string for windows:

    def setup_tensorboard(dir_str: str) -> List:
       dir_str = dir_str.replace("/", "\\")
       try:
          shutil.rmtree(dir_str)
       except:
          print("no file {} at {}".format(dir_str, os.getcwd()))
    
       # use TensorBoard, princess Aurora!
       callbacks = [tf.keras.callbacks.TensorBoard(log_dir=dir_str)]
       return callbacks

  • Put it together with some plot utils in a tf_utils_1 library
  • Tips for Training Stable Generative Adversarial Networks
  • How to Develop a 1D Generative Adversarial Network From Scratch in Keras
• The frontier of simulation-based inference
  • Many domains of science have developed complex simulations to describe phenomena of interest. While these simulations provide high-fidelity models, they are poorly suited for inference and lead to challenging inverse problems. We review the rapidly developing field of simulation-based inference and identify the forces giving new momentum to the field. Finally, we describe how the frontier is expanding so that a broad audience can appreciate the profound change these developments may have on science.