Search Results for author:
Found 9 papers, 2 papers with code
Learning Cartesian Product Graphs with Laplacian Constraints
We establish statistical consistency for the penalized maximum likelihood estimation (MLE) of a Cartesian product Laplacian, and propose an efficient algorithm to solve the problem.
Graph Laplacian Learning with Exponential Family Noise
A common challenge in applying graph machine learning methods is that the underlying graph of a system is often unknown.
Exploring Compositional Visual Generation with Latent Classifier Guidance
We show that this paradigm based on latent classifier guidance is agnostic to pre-trained generative models, and present competitive results for both image generation and sequential manipulation of real and synthetic images.
Conditional Image-to-Video Generation with Latent Flow Diffusion Models
In this paper, we propose an approach for cI2V using novel latent flow diffusion models (LFDM) that synthesize an optical flow sequence in the latent space based on the given condition to warp the given image.
Learning Disentangled Behavior Embeddings
To understand the relationship between behavior and neural activity, experiments in neuroscience often include an animal performing a repeated behavior such as a motor task.
CropDefender: deep watermark which is more convenient to train and more robust against cropping
We found that the causes of vulnerability to cropping is not the loss of information on the edge, but the movement of watermark position.
Online Adversarial Purification based on Self-Supervision
To the best of our knowledge, our paper is the first that generalizes the idea of using self-supervised signals to perform online test-time purification.
Provable Robustness by Geometric Regularization of ReLU Networks
Recent work has demonstrated that neural networks are vulnerable to small, adversarial perturbations of their input.
Online Adversarial Purification based on Self-supervised Learning
Deep neural networks are known to be vulnerable to adversarial examples, where a perturbation in the input space leads to an amplified shift in the latent network representation.
