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Found 6 papers, 4 papers with code
Section-Aware Commonsense Knowledge-Grounded Dialogue Generation with Pre-trained Language Model
However, a dialogue is always aligned to a lot of retrieved fact candidates; as a result, the linearized text is always lengthy and then significantly increases the burden of using PLMs.
IR2Net: Information Restriction and Information Recovery for Accurate Binary Neural Networks
In contrast, considering the limited learning ability and information loss caused by the limited representational capability of BNNs, we propose IR$^2$Net to stimulate the potential of BNNs and improve the network accuracy by restricting the input information and recovering the feature information, including: 1) information restriction: for a BNN, by evaluating the learning ability on the input information, discarding some of the information it cannot focus on, and limiting the amount of input information to match its learning ability; 2) information recovery: due to the information loss in forward propagation, the output feature information of the network is not enough to support accurate classification.
AIP: Adversarial Iterative Pruning Based on Knowledge Transfer for Convolutional Neural Networks
We propose a novel adversarial iterative pruning method (AIP) for CNNs based on knowledge transfer.
Self-Distribution Binary Neural Networks
In this work, we study the binary neural networks (BNNs) of which both the weights and activations are binary (i. e., 1-bit representation).
ACP: Automatic Channel Pruning via Clustering and Swarm Intelligence Optimization for CNN
While the accuracy loss after pruning based on the structure sensitivity is relatively slight, the process is time-consuming and the algorithm complexity is notable.
UCP: Uniform Channel Pruning for Deep Convolutional Neural Networks Compression and Acceleration
For ResNet with bottlenecks, we use the pruning method with traditional CNN to trim the 3x3 convolutional layer in the middle of the blocks.
