7 citations · 10 across the 5 of their papers we have counts for
6 papers
Xe-Forge: Multi-Stage LLM-Powered Kernel Optimization for Intel GPU
Marcin Spoczynski, Daniel Fleischer, Moshe Berchansky +5
Porting deep learning algorithms to new hardware accelerators requires developers to repeatedly apply the same low-level optimizations -- quantization, memory access coalescing, ti…
QuaLA-MiniLM: a Quantized Length Adaptive MiniLM
Shira Guskin, Moshe Wasserblat, Chang Wang +1
Limited computational budgets often prevent transformers from being used in production and from having their high accuracy utilized. A knowledge distillation approach addresses the…
Dynamic-TinyBERT: Boost TinyBERT's Inference Efficiency by Dynamic Sequence Length
Shira Guskin, Moshe Wasserblat, Ke Ding +1
Limited computational budgets often prevent transformers from being used in production and from having their high accuracy utilized. TinyBERT addresses the computational efficiency…
Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models
Peter Izsak, Shira Guskin, Moshe Wasserblat
Training models on low-resource named entity recognition tasks has been shown to be a challenge, especially in industrial applications where deploying updated models is a continuou…
Term Set Expansion based NLP Architect by Intel AI Lab
Jonathan Mamou, Oren Pereg, Moshe Wasserblat +5
We present SetExpander, a corpus-based system for expanding a seed set of terms into amore complete set of terms that belong to the same semantic class. SetExpander implements an i…
Term Set Expansion based on Multi-Context Term Embeddings: an End-to-end Workflow
Jonathan Mamou, Oren Pereg, Moshe Wasserblat +7
We present SetExpander, a corpus-based system for expanding a seed set of terms into a more complete set of terms that belong to the same semantic class. SetExpander implements an…