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Found 10 papers, 1 papers with code
CrossMPT: Cross-attention Message-Passing Transformer for Error Correcting Codes
The mask matrices in these cross-attention blocks are determined by the code's parity-check matrix that delineates the relationship between magnitude and syndrome vectors.
Attention-aware Semantic Communications for Collaborative Inference
In our framework, the lightweight ViT model on the edge device acts as a semantic encoder, efficiently identifying and selecting the crucial image information required for the classification task.
Optimizing Layerwise Polynomial Approximation for Efficient Private Inference on Fully Homomorphic Encryption: A Dynamic Programming Approach
Unlike the previous works approximating activation functions uniformly and conservatively, this paper presents a \emph{layerwise} degree optimization of activation functions to aggressively reduce the inference time while maintaining classification accuracy by taking into account the characteristics of each layer.
Boosting Learning for LDPC Codes to Improve the Error-Floor Performance
The proposed NMS decoder, optimized solely through novel training methods without additional modules, can be integrated into existing LDPC decoders without incurring extra hardware costs.
How to Mask in Error Correction Code Transformer: Systematic and Double Masking
In communication and storage systems, error correction codes (ECCs) are pivotal in ensuring data reliability.
Communication-Efficient and Drift-Robust Federated Learning via Elastic Net
Federated learning (FL) is a distributed method to train a global model over a set of local clients while keeping data localized.
High-Precision Bootstrapping for Approximate Homomorphic Encryption by Error Variance Minimization
The Cheon-Kim-Kim-Song (CKKS) scheme (Asiacrypt’17) is one of the most promising homomorphic encryption (HE) schemes as it enables privacy-preserving computing over real (or complex) numbers.
Boosting Classifiers with Noisy Inference
Suppose that the base classifiers' outputs are noisy or communicated over noisy channels; these noisy outputs will degrade the final classification accuracy.
Analytical Guarantees on Numerical Precision of Deep Neural Networks
We focus on numerical precision – a key parameter defining the complexity of neural networks.
Understanding the Energy and Precision Requirements for Online Learning
Lower bounds on the data precision are derived in terms of the the desired classification accuracy and precision of the hyperparameters used in the classifier.
