Search papers, labs, and topics across Lattice.
9 papers published across 1 lab.
This study proposes an adaptive EFL writing feedback generation framework that combines a dual-stream generative network, low-rank adaptation, and proximal policy optimization to solve the challenges of coarse intervention granularity and homogeneous feedback in EFL writing.
Inspired by the efficient communication principles of federated learning (FL), this work introduces two hybrid algorithms - FL+FSDP and FL+HSDP - interleaving sharded DP with FedAvg-style aggregations, demonstrating superior computational efficiency and improved model quality.
Xronos is proposed, a collaborative fine-tuning framework for heterogeneous CPU edge devices that uses TP as its execution backbone and combines lightweight profiling with heterogeneity-aware tensor partitioning to reduce the straggler bottleneck.
This study proposes an adaptive EFL writing feedback generation framework that combines a dual-stream generative network, low-rank adaptation, and proximal policy optimization to solve the challenges of coarse intervention granularity and homogeneous feedback in EFL writing.
Inspired by the efficient communication principles of federated learning (FL), this work introduces two hybrid algorithms - FL+FSDP and FL+HSDP - interleaving sharded DP with FedAvg-style aggregations, demonstrating superior computational efficiency and improved model quality.
Xronos is proposed, a collaborative fine-tuning framework for heterogeneous CPU edge devices that uses TP as its execution backbone and combines lightweight profiling with heterogeneity-aware tensor partitioning to reduce the straggler bottleneck.
SIF is introduced, a metric built on the notion of partial-nodes that is independent of historical workload knowledge that matches both theory and production and COMPASS-ABS is proposed, which employs the COMPact-ASSured (COMPASS) algorithm to confine the cluster state within a tight Anchor-Based Space (ABS).
GeoMesh is presented, a synchronous geo-distributed training framework for heterogeneous GPUs that balances per-worker workloads by assigning each GPU a suitable batch size and number of inner steps, so faster GPUs do more useful work instead of waiting.
AccelPact, a parallel runtime enabling zero-I/O in-memory fault recovery for sharded distributed training, and operates directly on native communicator instances, providing an efficient foundation for resilient deep learning.
