In a constant-time unsupervised learning, our neural model approximates optimal pattern clustering from training example images through a memory adaptation process, and builds a compression codebook in its syna
Neural Image Compression Most neural image codecs are based on hyperprior [4] where some bits are first used to provide basic contexts for entropy coding. Then, the auto-regressive prior [40] pro- poses using neighbour contexts to capture spatial correla- tion. Recently works [18, 26, 44,...
Model/Gradient Compression, Distributed Machine Learning, Anomaly Detection, Multimodal Remote Sensing Miao Xu The University of Queensland, Brisbane, Queensland, Australia Semi-supervised learning, Weakly-supervised learning, Multi-label learning, Matrix factorization ...
2018-ICLR-Towards Image Understanding from Deep Compression Without Decoding 2018-ICLR-Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training 2018-ICLR-Mixed Precision Training of Convolutional Neural Networks using Integer Operations 2018-ICLR-Mixed Precision Training 2018-ICLR...
For a high definition (HD) image size of 1920×1080 pixels distributed into an 8×8 array of PEs 210, each PE 210 will hold a 240×135 slice. At the other extreme, a deep convolutional layer may be only 14×14 having an xmax of just 1 or 2. When large sizes are too large to ...
The non-zero weights selected by these methods are randomly distributed and do not reduce the memory consumption due to the matrix operations widely adopted in nowadays deep learning architectures as shown in Figure 1(b.2). The implementation of such a non-structured sparse matrix in cuDNN [5...
Common interface for compression methods. GPU-accelerated layers for faster compressed model fine-tuning. Distributed training support. Git patch for prominent third-party repository (huggingface-transformers) demonstrating the process of integrating NNCF into custom training pipelines. ...
The system 565 may be included within a distributed network and/or cloud computing environment. The network interface 535 may include one or more receivers, transmitters, and/or transceivers that enable the system 565 to communicate with other computing devices via an electronic communication network,...
Inspired by the distributed data processing in the human brain, deep neural networks are designed to process the input data using interconnected layers of neurons (nodes), which can be trained using a set of training data to learn a specific task. Once trained, the network can be used to pe...
Nodes within Boltzmann Machines network are distributed into two categories, visible units and hidden units. The input is mapped onto the visible units. Information contained by the visible units is processed to approximate it in a “lower energy/equilibrium state” that is stored in hidden units....