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02/08/2023
SLaM: Student-Label Mixing for Semi-Supervised Knowledge Distillation
Semi-supervised knowledge distillation is a powerful training paradigm f...
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01/31/2023
The Power of External Memory in Increasing Predictive Model Capacity
One way of introducing sparsity into deep networks is by attaching an ex...
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01/30/2023
Alternating Updates for Efficient Transformers
It is well established that increasing scale in deep transformer network...
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10/13/2022
Weighted Distillation with Unlabeled Examples
Distillation with unlabeled examples is a popular and powerful method fo...
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10/03/2022
Robust Active Distillation
Distilling knowledge from a large teacher model to a lightweight one is ...
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08/08/2022
A Theoretical View on Sparsely Activated Networks
Deep and wide neural networks successfully fit very complex functions to...
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02/08/2022
Bandit Sampling for Multiplex Networks
Graph neural networks have gained prominence due to their excellent perf...
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06/06/2021
Graph Belief Propagation Networks
With the wide-spread availability of complex relational data, semi-super...
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04/06/2021
Low-Regret Active learning
We develop an online learning algorithm for identifying unlabeled data p...
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03/04/2021
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy
Neural network pruning is a popular technique used to reduce the inferen...
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02/15/2020
On Coresets for Support Vector Machines
We present an efficient coreset construction algorithm for large-scale S...
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11/18/2019
Provable Filter Pruning for Efficient Neural Networks
We present a provable, sampling-based approach for generating compact Co...
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10/11/2019
SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural Networks
We introduce a pruning algorithm that provably sparsifies the parameters...
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04/15/2018
