Judah Goldfeder

End-to-end learned subspace projections shatter prior compression limits, achieving 70% weight reduction in Llama-2-7B with near-dense performance and drastically shrinking KV cache memory.

XTC boosts creative generation diversity by up to 38% while slashing repetition by 71%, all without sacrificing fluency.

DRY cuts verbatim looping by nearly half without sacrificing performance, revolutionizing how we approach text generation in LLMs.

The latest AI City Challenge reveals that integrating foundation models with synthetic data design can significantly boost performance in complex urban scenarios.

S-JEPA sets a new standard in speech representation learning by achieving top performance with fewer parameters and without the cumbersome offline re-clustering process.