But they were all of them deceived, for another leche was made...
Pavel
Indeed..
here is "generate the first page of a great Nature paper that will get lots of citations. It must be very novel."
Added a JAX translation of the excellent Proteina-Complexa (from nvidia, @kdidi.bsky.social , @karstenkreis.bsky.social ) to mosaic. You can do beam search with any mosaic loss (e.g. protenix + mpnn) and JAX with generate efficient GPU/TPU code.
LinkedIn as an employer looking for candidates is like ipTM: great for filtering candidates discovered elsewhere, but absolute dogshit for discovery. Quite a few people find ways to fluff up mostly-empty CVs and post a firehose of AI-generated posts. Anyway, role still open
The training details for VHHBERT are light, but seems like there is no clustering at all. Anyway, the finding suggests that these models all represent CDRH3 residues as a linear combination of residue type features and position features, which is incredibly underpowered
This should replace CAPTCHAs on biorxiv
Does anyone know what paper this figure is from? The citation I have for it turned out to be wrong
Takeda Pharma is hiring scientists at both the straight-out-of-PhD and senior levels in the AIML group, particularly those with experience training, modifying, and applying foundation models. Please reach out if youre interested and want to make the jump to industry, link below👇
Despite the huge amount of training data, ESM-C is still unable to distinguish mean-pooled CDRH3 representations of real antibodies from those of antibodies w/ scrambled CDRs. Only VHHBERT seems to do this, and only for natural sequences
