3 citations · 3 across the 9 of their papers we have counts for
6 papers · 1 filter
Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery
Takanobu Furuhashi, Hidekata Hontani, Qibin Zhao +1
The convex Latent Optimal Partition (LOP)-l2/l1 approach enables block-sparse signal recovery with unknown partitions but relies on manual hyperparameter tuning. Additionally, nume…
Nonconvex Latent Optimally Partitioned Block-Sparse Recovery via Log-Sum and Minimax Concave Penalties
Takanobu Furuhashi, Hiroki Kuroda, Masahiro Yukawa +3
We propose two nonconvex regularization methods, LogLOP-l2/l1 and AdaLOP-l2/l1, for recovering block-sparse signals with unknown block partitions. These methods address the underes…
WEEP: A Differentiable Nonconvex Sparse Regularizer via Weakly-Convex Envelope
Takanobu Furuhashi, Hidekata Hontani, Qibin Zhao +1
Sparse regularization is fundamental in signal processing and feature extraction but often relies on non-differentiable penalties, conflicting with gradient-based optimizers. We pr…
Very Basics of Tensors with Graphical Notations: Unfolding, Calculations, and Decompositions
Tatsuya Yokota
Tensor network diagram (graphical notation) is a useful tool that graphically represents multiplications between multiple tensors using nodes and edges. Using the graphical notatio…
Broadcast Product: Redefining Shape-aligned Element-wise Multiplication and Beyond
Yusuke Matsui, Tatsuya Yokota
Broadcast operations are widely used in scientific computing libraries, yet their mathematical formulation is often implicit and inconsistently represented in machine learning lite…
Adaptive Block Sparse Regularization under Arbitrary Linear Transform
Takanobu Furuhashi, Hidekata Hontani, Tatsuya Yokota
We propose a convex and fast signal reconstruction method for block sparsity under arbitrary linear transform with unknown block structure. The proposed method is a generalization…