Search papers, labs, and topics across Lattice.
8 papers published across 1 lab.
Exploit sparse derivative structure to slash AD passes for Jacobians and Hessians, offering drop-in replacements for JAX users.
Token-level LLM routing, previously hindered by system inefficiencies, now achieves up to 64x higher throughput with a novel request-centric, model-centric serving architecture.
Training higher-order Topological Neural Networks on massive graphs like Reddit is now possible, thanks to a novel clustering-based mini-batching strategy that slashes GPU memory requirements by over 80%.
Heterogeneous experts can be dynamically coordinated via MoE gating to form composite model services, outperforming monolithic models and standard MoE in cost-aware marketplaces.
Gradient clipping, often seen as a heuristic, unlocks optimal convergence and linear agent speed-up for decentralized SGD under heavy-tailed noise, outperforming normalization in this regime.
Token-level LLM routing, previously hindered by system inefficiencies, now achieves up to 64x higher throughput with a novel request-centric, model-centric serving architecture.
Exploit sparse derivative structure to slash AD passes for Jacobians and Hessians, offering drop-in replacements for JAX users.
Training higher-order Topological Neural Networks on massive graphs like Reddit is now possible, thanks to a novel clustering-based mini-batching strategy that slashes GPU memory requirements by over 80%.
Heterogeneous experts can be dynamically coordinated via MoE gating to form composite model services, outperforming monolithic models and standard MoE in cost-aware marketplaces.
Gradient clipping, often seen as a heuristic, unlocks optimal convergence and linear agent speed-up for decentralized SGD under heavy-tailed noise, outperforming normalization in this regime.
Geometric weighting by update norm offers a subtle but measurable improvement in personalized federated learning, outperforming uniform aggregation and FedPer-R1 on CIFAR-10's native client accuracy.
Average-reward RL for general multichain MDPs is now possible without model knowledge or reduction to discounted problems.
LLMs develop inherent fault tolerance as they scale, learning to compute within error-correcting codes that remain efficient even for massive models.
