Search papers, labs, and topics across Lattice.
11 papers from Meta AI (FAIR) on Architecture Design (Transformers, SSMs, MoE)
Controllable 3D generation takes a leap forward with 3D-ReGen, a framework that leverages an initial 3D shape for tasks like enhancement and editing, outperforming existing methods.
Serverless functions can get a 37% density boost and significantly reduced overhead by offloading I/O to a shared backend, without sacrificing ecosystem compatibility.
Training 3D avatar diffusion models on millions of in-the-wild videos is now possible, thanks to a clever 3D tokenization and visibility-aware training strategy that overcomes partial observability.
Forget scaling laws: a large VLM strategically paired with a smaller model's reasoning tokens can rival the performance of a much larger, monolithic model.
Stop avatars from looking like they're having a seizure: this method uses autoregressive prediction of appearance latents to create temporally stable and high-fidelity 3D Gaussian avatars.
Forget imbalanced LoRA usage: ReMix leverages reinforcement learning to route effectively among LoRAs, boosting performance in parameter-efficient fine-tuning.
Forget quadratic complexity: ULTRA-HSTU achieves 21x faster inference and 4-8% better engagement in large-scale recommendation by co-designing input sequences, sparse attention, and model topology.
Achieve zero-collision embedding tables in production recommenders without sacrificing training speed, unlocking better personalization via fresher and higher-quality item embeddings.
Ditch ANN search altogether: MFLI learns a hierarchical index alongside item embeddings, boosting recall by up to 11.8% and cold-content delivery by 57.29% in large-scale recommender systems.
Ditch the pre-trained models: PAST directly learns speech tokens from phonetic data, outperforming existing methods in representation and reconstruction.
Edit the bassline, drums, or other instruments of any song with this new open-source multi-stem music generation model.
