30 citations · 87 across the 15 of their papers we have counts for
16 papers
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…
The Power of External Memory in Increasing Predictive Model Capacity
Cenk Baykal, Dylan J Cutler, Nishanth Dikkala +3
One way of introducing sparsity into deep networks is by attaching an external table of parameters that is sparsely looked up at different layers of the network. By storing the bul…
Alternating Updates for Efficient Transformers
Cenk Baykal, Dylan Cutler, Nishanth Dikkala +3
It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibit…
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…
A Theoretical View on Sparsely Activated Networks
Cenk Baykal, Nishanth Dikkala, Rina Panigrahy +2
Deep and wide neural networks successfully fit very complex functions today, but dense models are starting to be prohibitively expensive for inference. To mitigate this, one promis…
Bandit Sampling for Multiplex Networks
Cenk Baykal, Vamsi K. Potluru, Sameena Shah +1
Graph neural networks have gained prominence due to their excellent performance in many classification and prediction tasks. In particular, they are used for node classification an…
Graph Belief Propagation Networks
Junteng Jia, Cenk Baykal, Vamsi K. Potluru +1
With the wide-spread availability of complex relational data, semi-supervised node classification in graphs has become a central machine learning problem. Graph neural networks are…
Low-Regret Active learning
Cenk Baykal, Lucas Liebenwein, Dan Feldman +1
We develop an online learning algorithm for identifying unlabeled data points that are most informative for training (i.e., active learning). By formulating the active learning pro…
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy
Lucas Liebenwein, Cenk Baykal, Brandon Carter +2
Neural network pruning is a popular technique used to reduce the inference costs of modern, potentially overparameterized, networks. Starting from a pre-trained network, the proces…
On Coresets for Support Vector Machines
Murad Tukan, Cenk Baykal, Dan Feldman +1
We present an efficient coreset construction algorithm for large-scale Support Vector Machine (SVM) training in Big Data and streaming applications. A coreset is a small, represent…
Provable Filter Pruning for Efficient Neural Networks
Lucas Liebenwein, Cenk Baykal, Harry Lang +2
We present a provable, sampling-based approach for generating compact Convolutional Neural Networks (CNNs) by identifying and removing redundant filters from an over-parameterized…
SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural Networks
Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski +2
We introduce a pruning algorithm that provably sparsifies the parameters of a trained model in a way that approximately preserves the model's predictive accuracy. Our algorithm use…
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds
Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski +2
We present an efficient coresets-based neural network compression algorithm that sparsifies the parameters of a trained fully-connected neural network in a manner that provably app…
Training Support Vector Machines using Coresets
Cenk Baykal, Lucas Liebenwein, Wilko Schwarting
We present a novel coreset construction algorithm for solving classification tasks using Support Vector Machines (SVMs) in a computationally efficient manner. A coreset is a weight…
Detection of AQM on Paths using Machine Learning Methods
Cenk Baykal, Wilko Schwarting, Alex Wallar
In this paper, we address the problem of determining whether a bottleneck router on a given network path is using an AQM or a drop-tail scheme. We assume that we are given a source…