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Found 6 papers, 2 papers with code
Make Inference Faster: Efficient GPU Memory Management for Butterfly Sparse Matrix Multiplication
We show that these memory operations can be optimized by introducing a new CUDA kernel that minimizes the transfers between the different levels of GPU memory, achieving a median speed-up factor of x1. 4 while also reducing energy consumption (median of x0. 85).
Sparsity in neural networks can improve their privacy
This article measures how sparsity can make neural networks more robust to membership inference attacks.
Can sparsity improve the privacy of neural networks?
Sparse neural networks are mainly motivated by ressource efficiency since they use fewer parameters than their dense counterparts but still reach comparable accuracies.
Self-supervised learning with rotation-invariant kernels
We introduce a regularization loss based on kernel mean embeddings with rotation-invariant kernels on the hypersphere (also known as dot-product kernels) for self-supervised learning of image representations.
Identifiability in Two-Layer Sparse Matrix Factorization
In particular, in the case of fixed-support sparse matrix factorization, we give a general sufficient condition for identifiability based on rank-one matrix completability, and we derive from it a completion algorithm that can verify if this sufficient condition is satisfied, and recover the entries in the two sparse factors if this is the case.
Efficient Identification of Butterfly Sparse Matrix Factorizations
Our main contribution is to prove that any $N \times N$ matrix having the so-called butterfly structure admits an essentially unique factorization into $J$ butterfly factors (where $N = 2^{J}$), and that the factors can be recovered by a hierarchical factorization method, which consists in recursively factorizing the considered matrix into two factors.
