Search Results for author:
Found 7 papers, 1 papers with code
Optimal Block-Level Draft Verification for Accelerating Speculative Decoding
To the best of our knowledge, our work is the first to establish improvement over speculative decoding through a better draft verification algorithm.
Efficient Language Model Architectures for Differentially Private Federated Learning
Cross-device federated learning (FL) is a technique that trains a model on data distributed across typically millions of edge devices without data leaving the devices.
SpecTr: Fast Speculative Decoding via Optimal Transport
We show that the optimal draft selection algorithm (transport plan) can be computed via linear programming, whose best-known runtime is exponential in $k$.
Scaling Language Model Size in Cross-Device Federated Learning
Most studies in cross-device federated learning focus on small models, due to the server-client communication and on-device computation bottlenecks.
Correlated quantization for distributed mean estimation and optimization
We show that applying the proposed protocol as sub-routine in distributed optimization algorithms leads to better convergence rates.
Transformer-based Models of Text Normalization for Speech Applications
Text normalization, or the process of transforming text into a consistent, canonical form, is crucial for speech applications such as text-to-speech synthesis (TTS).
FedJAX: Federated learning simulation with JAX
Federated learning is a machine learning technique that enables training across decentralized data.
