by Rylan Schaeffer
I couldn’t find a Stanford LaTeX Poster template and no one else knew of one either, so I made a poster template and shared it on GitHub. It looks quite nice, if I do say so myself :)
I sent Noah results from our CLIP-GPT2 experiment, trying to test whether GPT-2 can learn to generate a natural-language utterance describing the COCO class of each image.
On a concept-by-concept basis, losses are also falling. I’m keen to test how well the model generalizes to (a) held-out concepts and (b) logical combinations of concepts (e.g. human and dog).
At Dan Yamins’s lab meeting, Eli Wang presented Physion. To me, the two takeaways were that:
This raises the questions of (1) whether the brain tries to represent something like physics engine particles or something similar but a bit more conceptually abstract, and (2) how well neural networks can learn to predict particles from images. I thought that the answer to (2) would be high performance because it’s a supervised learning problem on which we can generate an infinite amount of data, but Dan said networks almost always fail to generalize.
I met with my MIT UROP, who has decided she’d prefer to pursue a slightly different project. I’m slowly learning how to adapt to peoples’ interests.
My Metacognitive Actor Critic gridworld results are puzzling. An Actor-Critic with a \(Q\) baseline and an Actor-Critic with a \(Q-V\) baseline both perform pretty poorly compared to PG or Actor-Critic with a \(V\) baseline. I’m not sufficiently familiar with RL to know if this should be expected, but I thought that \(Q-V\) should be the optimal baseline?tags: idea-machine - stanford - goodman