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Found 13 papers, 3 papers with code
SAVE: Spectral-Shift-Aware Adaptation of Image Diffusion Models for Text-driven Video Editing
Text-to-Image (T2I) diffusion models have achieved remarkable success in synthesizing high-quality images conditioned on text prompts.
Neural Routing in Meta Learning
Inspired by the widely recognized finding in neuroscience that distinct parts of the brain are highly specialized for different types of tasks, we aim to improve the model performance of the current meta learning algorithms by selectively using only parts of the model conditioned on the input tasks.
Two-way Spectrum Pursuit for CUR Decomposition and Its Application in Joint Column/Row Subset Selection
The problem of simultaneous column and row subset selection is addressed in this paper.
Asymptotic Optimality of Self-Representative Low-Rank Approximation and Its Applications
The approach is based on the concept of {\em self-rank}, defined as the minimum number of samples needed to reconstruct all samples with an accuracy proportional to the rank-$K$ approximation.
Select to Better Learn: Fast and Accurate Deep Learning Using Data Selection From Nonlinear Manifolds
Finding a small subset of data whose linear combination spans other data points, also called column subset selection problem (CSSP), is an important open problem in computer science with many applications in computer vision and deep learning.
AI-enabled Blockchain: An Outlier-aware Consensus Protocol for Blockchain-based IoT Networks
To that end, we propose AI-enabled blockchain (AIBC) with a 2-step consensus protocol that uses an outlier detection algorithm for consensus in an IoT network implemented on hyperledger fabric platform.
Large-Scale Spectrum Occupancy Learning via Tensor Decomposition and LSTM Networks
A new paradigm for large-scale spectrum occupancy learning based on long short-term memory (LSTM) recurrent neural networks is proposed.
Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer Vision
In our algorithm, at each iteration, the maximum information from the structure of the data is captured by one selected sample, and the captured information is neglected in the next iterations by projection on the null-space of previously selected samples.
Joint Dictionary Learning for Example-based Image Super-resolution
Using the sparse representation coefficients of these LR patches over the LR dictionary, the high-resolution (HR) dictionary is trained by minimizing the reconstruction error of HR sample patches.
A Study on Clustering for Clustering Based Image De-Noising
In the present paper, we suggest a method based on global clustering of image constructing blocks.
Union of Low-Rank Subspaces Detector
The problem of signal detection using a flexible and general model is considered.
Sparse Auto-Regressive: Robust Estimation of AR Parameters
In this paper I present a new approach for regression of time series using their own samples.
Optimization of Clustering for Clustering-based Image Denoising
In this paper, the problem of de-noising of an image contaminated with additive white Gaussian noise (AWGN) is studied.
