4 citations · 10 across the 6 of their papers we have counts for
6 papers
GUIDE: Guided Initialization and Distillation of Embeddings
Khoa Trinh, Gaurav Menghani, Erik Vee
Algorithmic efficiency techniques such as distillation (\cite{hinton2015distillation}) are useful in improving model quality without increasing serving costs, provided a larger tea…
Linear Projections of Teacher Embeddings for Few-Class Distillation
Noel Loo, Fotis Iliopoulos, Wei Hu +1
Knowledge Distillation (KD) has emerged as a promising approach for transferring knowledge from a larger, more complex teacher model to a smaller student model. Traditionally, KD i…
LayerNAS: Neural Architecture Search in Polynomial Complexity
Yicheng Fan, Dana Alon, Jingyue Shen +7
Neural Architecture Search (NAS) has become a popular method for discovering effective model architectures, especially for target hardware. As such, NAS methods that find optimal a…
SLaM: Student-Label Mixing for Distillation with Unlabeled Examples
Vasilis Kontonis, Fotis Iliopoulos, Khoa Trinh +3
Knowledge distillation with unlabeled examples is a powerful training paradigm for generating compact and lightweight student models in applications where the amount of labeled dat…
Weighted Distillation with Unlabeled Examples
Fotis Iliopoulos, Vasilis Kontonis, Cenk Baykal +3
Distillation with unlabeled examples is a popular and powerful method for training deep neural networks in settings where the amount of labeled data is limited: A large ''teacher''…
Robust Active Distillation
Cenk Baykal, Khoa Trinh, Fotis Iliopoulos +2
Distilling knowledge from a large teacher model to a lightweight one is a widely successful approach for generating compact, powerful models in the semi-supervised learning setting…