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Found 6 papers, 3 papers with code
Scaffolding a Student to Instill Knowledge
We propose a novel knowledge distillation (KD) method to selectively instill teacher knowledge into a student model motivated by situations where the student's capacity is significantly smaller than that of the teachers.
FedHeN: Federated Learning in Heterogeneous Networks
We propose a novel training recipe for federated learning with heterogeneous networks where each device can have different architectures.
Federated Learning Based on Dynamic Regularization
We propose a novel federated learning method for distributively training neural network models, where the server orchestrates cooperation between a subset of randomly chosen devices in each round.
Faster Algorithms for Learning Convex Functions
For the task of convex Lipschitz regression, we establish that our proposed algorithm converges with iteration complexity of $ O(n\sqrt{d}/\epsilon)$ for a dataset $\bm X \in \mathbb R^{n\times d}$ and $\epsilon > 0$.
Budget Learning via Bracketing
We propose a new formulation for the BL problem via the concept of bracketings.
Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices
The second layer consumes the output of the first layer using a second RNN thus capturing long dependencies.
