Dictionary Learning is an important problem in multiple areas, ranging from computational neuroscience, machine learning, to computer vision and image processing. The general goal is to find a good basis for given data. More formally, in the Dictionary Learning problem, also known as sparse coding, we are given samples of a random vector $y\in\mathbb{R}^n$, of the form $y=Ax$ where $A$ is some unknown matrix in $\mathbb{R}^{n×m}$, called dictionary, and $x$ is sampled from an unknown distribution over sparse vectors. The goal is to approximately recover the dictionary $A$.

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# CDLNet: Robust and Interpretable Denoising Through Deep Convolutional Dictionary Learning

5 Mar 2021nikopj/CDLNet

In addition, we leverage the model's interpretable construction to propose an augmentation of the network's thresholds that enables state-of-the-art blind denoising performance and near-perfect generalization on noise-levels unseen during training.

0
05 Mar 2021

# Learning low-rank latent mesoscale structures in networks

13 Feb 2021HanbaekLyu/ONMF_ONTF_NDL

It is common to use networks to encode the architecture of interactions between entities in complex systems in the physical, biological, social, and information sciences.

5
13 Feb 2021

# An End-To-End-Trainable Iterative Network Architecture for Accelerated Radial Multi-Coil 2D Cine MR Image Reconstruction

The network is based on a computationally light CNN-component and a subsequent conjugate gradient (CG) method which can be jointly trained end-to-end using an efficient training strategy.

1
01 Feb 2021

# Deep Semantic Dictionary Learning for Multi-label Image Classification

23 Dec 2020ZFT-CQU/DSDL

Compared with single-label image classification, multi-label image classification is more practical and challenging.

13
23 Dec 2020

# K-Deep Simplex: Deep Manifold Learning via Local Dictionaries

3 Dec 2020pbt17/manifold-learning-with-simplex-constraints

We propose K-Deep Simplex (KDS), a unified optimization framework for nonlinear dimensionality reduction that combines the strengths of manifold learning and sparse dictionary learning.

1
03 Dec 2020

# Interpreting U-Nets via Task-Driven Multiscale Dictionary Learning

25 Nov 2020liutianlin0121/ISTA-U-Net

U-Nets have been tremendously successful in many imaging inverse problems.

7
25 Nov 2020

# Extraction of Nystagmus Patterns from Eye-Tracker Data with Convolutional Sparse Coding

25 Nov 2020tommoral/detrending_csc_oculo

The analysis of the Nystagmus waveforms from eye-tracking records is crucial for the clinicial interpretation of this pathological movement.

0
25 Nov 2020

# A Neuro-Inspired Autoencoding Defense Against Adversarial Perturbations

21 Nov 2020canbakiskan/neuro-inspired-defense

Our nominal design is to train the decoder and classifier together in standard supervised fashion, but we also consider unsupervised decoder training based on a regression objective (as in a conventional autoencoder) with separate supervised training of the classifier.

0
21 Nov 2020

# Neuro-Symbolic Representations for Video Captioning: A Case for Leveraging Inductive Biases for Vision and Language

18 Nov 2020hassanhub/R3Transformer

In this paper, we propose a new model architecture for learning multi-modal neuro-symbolic representations for video captioning.

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18 Nov 2020

# Online tensor factorization and CP-dictionary learning for Markovian data

16 Sep 2020HanbaekLyu/OnlineCPDL

We prove that our algorithm converges almost surely to the set of stationary points of the objective function under the hypothesis that the sequence of data tensors is generated by some underlying Markov chain.

0
16 Sep 2020