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Found 13 papers, 5 papers with code
Reconstructing the Hemodynamic Response Function via a Bimodal Transformer
The relationship between blood flow and neuronal activity is widely recognized, with blood flow frequently serving as a surrogate for neuronal activity in fMRI studies.
Deep Quantum Error Correction
Quantum error correction codes (QECC) are a key component for realizing the potential of quantum computing.
Denoising Diffusion Error Correction Codes
Error correction code (ECC) is an integral part of the physical communication layer, ensuring reliable data transfer over noisy channels.
Error Correction Code Transformer
Error correction code is a major part of the communication physical layer, ensuring the reliable transfer of data over noisy channels.
Efficient Meta Subspace Optimization
Subspace optimization methods have the attractive property of reducing large-scale optimization problems to a sequence of low-dimensional subspace optimization problems.
Geometric Transformer for End-to-End Molecule Properties Prediction
Transformers have become methods of choice in many applications thanks to their ability to represent complex interactions between elements.
Primal-Dual Sequential Subspace Optimization for Saddle-point Problems
We introduce a new sequential subspace optimization method for large-scale saddle-point problems.
Low-bit Quantization of Neural Networks for Efficient Inference
Recent machine learning methods use increasingly large deep neural networks to achieve state of the art results in various tasks.
Deep Discriminative Latent Space for Clustering
In most of these works a deep auto-encoder is initially pre-trained to minimize a reconstruction loss, and then jointly optimized with clustering centroids in order to improve the clustering objective.
Deep Learning for Decoding of Linear Codes - A Syndrome-Based Approach
We present a novel framework for applying deep neural networks (DNN) to soft decoding of linear codes at arbitrary block lengths.
Sparse Approximation of 3D Meshes using the Spectral Geometry of the Hamiltonian Operator
Here, we incorporate the order of vertices into an operator that defines a novel spectral domain.
Hamiltonian operator for spectral shape analysis
Many shape analysis methods treat the geometry of an object as a metric space that can be captured by the Laplace-Beltrami operator.
Consistent Discretization and Minimization of the L1 Norm on Manifolds
The L1 norm has been tremendously popular in signal and image processing in the past two decades due to its sparsity-promoting properties.
