Post
1197
David + Imagenet = high% val.
AbstractPhil/gated-david
https://github.com/AbstractEyes/lattice_vocabulary/blob/master/src/geovocab2/train/model/core/david.py
David's code has been released. I am currently setting up a trainer and will release the process on how to condition David to behave. This isn't the easiest process, but it's necessary to run David on a curriculum rather than simply feeding the model with cross-entropy and hoping for the best.
David's internals involve a clock mechanism that allows direct control of David's freeze/unfreeze mechanisms at runtime - allowing for many opinions to be generated simultaneously.
David is multiple models in one, not just one - and yet David is single-shot oriented. The prototype to the route of thought that led me to find the Cantor's Stairs positional encodings solution and the prototype to ViT-Zana, ViT-Beatrix, ViT-Beatrix-Dual-Block, and today the direct porting of David's complex architecture and the process to train David has begun.
David is... a gate of sorts. David trains with freeze/unfreeze mechanisms, so the internals of David's structures are aware during training time which part is more important than the other parts based on the quality of generation.
David can handle imagenet features with minimal hassle of many variations, and the primary trainer will include direct links to the prepared imagenet features, and a simple generation system that allows you to generate your own features from a few common AIs - one of which will be vit-beatrix-dualstream trained on imagenet.
As of posting vit-beatrix and vit-beatrix-dualstream require some face-lifting and a refined version 2 to incorporate the more accurate batched cantor stairs equations. Additionally they require removal of some fail-point causers; like flow-geometric introducing bias towards seemingly unnecessary trajectory routes. This points more to a gradient drift, so I'll keep that one on the hot plate until it's ready.
AbstractPhil/gated-david
https://github.com/AbstractEyes/lattice_vocabulary/blob/master/src/geovocab2/train/model/core/david.py
David's code has been released. I am currently setting up a trainer and will release the process on how to condition David to behave. This isn't the easiest process, but it's necessary to run David on a curriculum rather than simply feeding the model with cross-entropy and hoping for the best.
David's internals involve a clock mechanism that allows direct control of David's freeze/unfreeze mechanisms at runtime - allowing for many opinions to be generated simultaneously.
David is multiple models in one, not just one - and yet David is single-shot oriented. The prototype to the route of thought that led me to find the Cantor's Stairs positional encodings solution and the prototype to ViT-Zana, ViT-Beatrix, ViT-Beatrix-Dual-Block, and today the direct porting of David's complex architecture and the process to train David has begun.
David is... a gate of sorts. David trains with freeze/unfreeze mechanisms, so the internals of David's structures are aware during training time which part is more important than the other parts based on the quality of generation.
David can handle imagenet features with minimal hassle of many variations, and the primary trainer will include direct links to the prepared imagenet features, and a simple generation system that allows you to generate your own features from a few common AIs - one of which will be vit-beatrix-dualstream trained on imagenet.
As of posting vit-beatrix and vit-beatrix-dualstream require some face-lifting and a refined version 2 to incorporate the more accurate batched cantor stairs equations. Additionally they require removal of some fail-point causers; like flow-geometric introducing bias towards seemingly unnecessary trajectory routes. This points more to a gradient drift, so I'll keep that one on the hot plate until it's ready.