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Found 8 papers, 5 papers with code
Sparse Subspace Clustering with Entropy-Norm
Finally, we provide the experimental analysis to compare the efficiency and effectiveness of sparse subspace clustering and spectral clustering on ten benchmark data sets.
Graph External Attention Enhanced Transformer
The Transformer architecture has recently gained considerable attention in the field of graph representation learning, as it naturally overcomes several limitations of Graph Neural Networks (GNNs) with customized attention mechanisms or positional and structural encodings.
PipeOptim: Ensuring Effective 1F1B Schedule with Optimizer-Dependent Weight Prediction
The key insight of our proposal is that we employ a weight prediction strategy in the forward pass to ensure that each mini-batch uses consistent and staleness-free weights to compute the forward pass.
Towards Privacy-Aware Causal Structure Learning in Federated Setting
In this paper, we study a privacy-aware causal structure learning problem in the federated setting and propose a novel Federated PC (FedPC) algorithm with two new strategies for preserving data privacy without centralizing data.
Sparse Regularized Correlation Filter for UAV Object Tracking with adaptive Contextual Learning and Keyfilter Selection
Another approaches can alleviate the temporal degeneration of learned filters by introducing the temporal regularizer, which depends on the assumption that the filers between consecutive frames should be coherent.
How Powerful are Shallow Neural Networks with Bandlimited Random Weights?
Despite the well-known fact that a neural network is a universal approximator, in this study, we mathematically show that when hidden parameters are distributed in a bounded domain, the network may not achieve zero approximation error.
Logic could be learned from images
This task is to learn and reason the logic relation from images, without presetting any reasoning patterns.
An Ensemble Classification Algorithm Based on Information Entropy for Data Streams
Information entropy is an important and effective method for measuring uncertainty.
