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Found 15 papers, 10 papers with code
Cheating Suffix: Targeted Attack to Text-To-Image Diffusion Models with Multi-Modal Priors
The MMP-Attack shows a notable advantage over existing works with superior universality and transferability, which can effectively attack commercial text-to-image (T2I) models such as DALL-E 3.
Generating Adversarial Examples with Better Transferability via Masking Unimportant Parameters of Surrogate Model
In this paper, we propose to improve the transferability of adversarial examples in the transfer-based attack via masking unimportant parameters (MUP).
Boosting the Adversarial Transferability of Surrogate Models with Dark Knowledge
This paper proposes a method for training a surrogate model with dark knowledge to boost the transferability of the adversarial examples generated by the surrogate model.
CNN-Cap: Effective Convolutional Neural Network Based Capacitance Models for Full-Chip Parasitic Extraction
Accurate capacitance extraction is becoming more important for designing integrated circuits under advanced process technology.
AugSplicing: Synchronized Behavior Detection in Streaming Tensors
Therefore, we propose a fast streaming algorithm, AugSplicing, which can detect the top dense blocks by incrementally splicing the previous detection with the incoming ones in new tuples, avoiding re-runs over all the history data at every tracking time step.
Efficient Model-Based Collaborative Filtering with Fast Adaptive PCA
The proposed model-based CF approach is able to efficiently process the MovieLens data with 20M ratings and exhibits more than 10X speedup over the regularized matrix factorization based approach [2] and the fast singular value thresholding approach [3] with comparable or better accuracy.
RobFR: Benchmarking Adversarial Robustness on Face Recognition
Based on large-scale evaluations, the commercial FR API services fail to exhibit acceptable performance on robustness evaluation, and we also draw several important conclusions for understanding the adversarial robustness of FR models and providing insights for the design of robust FR models.
DP-Net: Dynamic Programming Guided Deep Neural Network Compression
In this work, we propose an effective scheme (called DP-Net) for compressing the deep neural networks (DNNs).
Kernelized Support Tensor Train Machines
In this paper, we propose a tensor train (TT)-based kernel technique for the first time, and apply it to the conventional support vector machine (SVM) for image classification.
Fast Randomized PCA for Sparse Data
The algorithm has similar accuracy to the basic randomized SVD (rPCA) algorithm (Halko et al., 2011), but is largely optimized for sparse data.
Faster Matrix Completion Using Randomized SVD
Then, with the rSVD-BKI algorithm and a new subspace recycling technique, we accelerate the singular value thresholding (SVT) method in [1] to realize faster matrix completion.
A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks
To address this issue, various approximation techniques have been investigated, which seek for a light weighted network with little performance degradation in exchange of smaller model size or faster inference.
Fast and Accurate Tensor Completion with Total Variation Regularized Tensor Trains
We propose a new tensor completion method based on tensor trains.
Single-Pass PCA of Large High-Dimensional Data
Principal component analysis (PCA) is a fundamental dimension reduction tool in statistics and machine learning.
A Fast Implementation of Singular Value Thresholding Algorithm using Recycling Rank Revealing Randomized Singular Value Decomposition
In this paper, we present a fast implementation of the Singular Value Thresholding (SVT) algorithm for matrix completion.
Numerical Analysis
