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Found 6 papers, 0 papers with code
ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning
ScatterSample employs a sampling module termed DiverseUncertainty to collect instances with large uncertainty from different regions of the sample space for labeling.
Federated Multiple Label Hashing (FedMLH): Communication Efficient Federated Learning on Extreme Classification Tasks
Federated learning enables many local devices to train a deep learning model jointly without sharing the local data.
A Step-Wise Weighting Approach for Controllable Text Generation
Many existing approaches either require extensive training/fine-tuning of the LM for each single attribute under control or are slow to generate text.
Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier with Application to Real-Time Information Filtering on the Web
Recent work suggests improving the performance of Bloom filter by incorporating a machine learning model as a binary classifier.
Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier
Recent work suggests improving the performance of Bloom filter by incorporating a machine learning model as a binary classifier.
Channel Normalization in Convolutional Neural Network avoids Vanishing Gradients
This effect prevails in deep single-channel linear convolutional networks, and we show that without channel normalization, gradient descent takes at least exponentially many steps to come close to an optimum.
