Search Results for author:
Found 13 papers, 5 papers with code
A Deep Learning Method for Simultaneous Denoising and Missing Wedge Reconstruction in Cryogenic Electron Tomography
At the same time, DeepDeWedge is simpler than this two-step approach, as it does denoising and missing wedge reconstruction simultaneously rather than sequentially.
Quantum Policy Iteration via Amplitude Estimation and Grover Search -- Towards Quantum Advantage for Reinforcement Learning
We present a full implementation and simulation of a novel quantum reinforcement learning method.
FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4bit-Compact Multilayer Perceptrons
With the growing demand for deploying deep learning models to the "edge", it is paramount to develop techniques that allow to execute state-of-the-art models within very tight and limited resource constraints.
Dithered backprop: A sparse and quantized backpropagation algorithm for more efficient deep neural network training
In this work we propose a method for reducing the computational cost of backprop, which we named dithered backprop.
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).
Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning
The success of convolutional neural networks (CNNs) in various applications is accompanied by a significant increase in computation and parameter storage costs.
Explainable Artificial Intelligence (XAI)
Model Compression
+2
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.
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.
Robust and Communication-Efficient Federated Learning from Non-IID Data
Federated Learning allows multiple parties to jointly train a deep learning model on their combined data, without any of the participants having to reveal their local data to a centralized server.
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.
Compact and Computationally Efficient Representations of Deep Neural Networks
However, most of these common matrix storage formats make strong statistical assumptions about the distribution of the elements in the matrix, and can therefore not efficiently represent the entire set of matrices that exhibit low entropy statistics (thus, the entire set of compressed neural network weight matrices).
Compact and Computationally Efficient Representation of Deep Neural Networks
These new matrix formats have the novel property that their memory and algorithmic complexity are implicitly bounded by the entropy of the matrix, consequently implying that they are guaranteed to become more efficient as the entropy of the matrix is being reduced.
Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication
A major issue in distributed training is the limited communication bandwidth between contributing nodes or prohibitive communication cost in general.
