Three-phase training to address data sparsity in Neural Machine Translation
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationTranslationSimilar Papers 제목 키워드 기반
Mixed Sparsity Training: Achieving 4$\times$ FLOP Reduction for Transformer Pretraining
Large language models (LLMs) have made significant strides in complex tasks, yet their widespread adoption is impeded by substantial computational demands. With hundreds of billion parameters, transformer-based LLMs nece…
GPUScalable and Sparsity-Aware Privacy-Preserving K-means Clustering with Application to Fraud Detection
K-means is one of the most widely used clustering models in practice. Due to the problem of data isolation and the requirement for high model performance, how to jointly build practical and secure K-means for multiple pa…
ClusteringFraud DetectionPrivacy PreservingSparsity assisted solution to the twin image problem in phase retrieval
The iterative phase retrieval problem for complex-valued objects from Fourier transform magnitude data is known to suffer from the twin image problem. In particular, when the object support is centro-symmetric, the itera…
ObjectRetrievalLifelong Matrix Completion with Sparsity-Number
Matrix completion problem has been previously studied under various adaptive and passive settings. Previously, researchers have proposed passive, two-phase and single-phase algorithms using coherence parameter, and multi…
Matrix CompletionScout Before You Attend: Sketch-and-Walk Sparse Attention for Efficient LLM Inference
Self-attention dominates the computational and memory cost of long-context LLM inference across both prefill and decode phases. To address this challenge, we introduce Sketch&Walk Attention, a training-free sparse attent…