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Found 7 papers, 2 papers with code
A Recurrent Convolutional Neural Network Approach for Sensorless Force Estimation in Robotic Surgery
The results suggest that the force estimation quality is better when both, the tool data and video sequences, are processed by the neural network model.
Robustifying Models Against Adversarial Attacks by Langevin Dynamics
Adversarial attacks on deep learning models have compromised their performance considerably.
Entropy-Constrained Training of Deep Neural Networks
We propose a general framework for neural network compression that is motivated by the Minimum Description Length (MDL) principle.
DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression
We present DeepCABAC, a novel context-adaptive binary arithmetic coder for compressing deep neural networks.
DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks
The field of video compression has developed some of the most sophisticated and efficient compression algorithms known in the literature, enabling very high compressibility for little loss of information.
Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T)
To address this problem, we propose Entropy-Constrained Trained Ternarization (EC2T), a general framework to create sparse and ternary neural networks which are efficient in terms of storage (e. g., at most two binary-masks and two full-precision values are required to save a weight matrix) and computation (e. g., MAC operations are reduced to a few accumulations plus two multiplications).
Communication-Efficient Federated Distillation
Communication constraints are one of the major challenges preventing the wide-spread adoption of Federated Learning systems.
