Search Results for author:
Found 13 papers, 9 papers with code
Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion
As a result, we may be able to craft more potent poisons by carefully choosing the base samples.
Universal Guidance for Diffusion Models
Typical diffusion models are trained to accept a particular form of conditioning, most commonly text, and cannot be conditioned on other modalities without retraining.
Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries
As industrial applications are increasingly automated by machine learning models, enforcing personal data ownership and intellectual property rights requires tracing training data back to their rightful owners.
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
We observe that the generative behavior of diffusion models is not strongly dependent on the choice of image degradation, and in fact an entire family of generative models can be constructed by varying this choice.
Certified Neural Network Watermarks with Randomized Smoothing
Watermarking is a commonly used strategy to protect creators' rights to digital images, videos and audio.
Transfer Learning with Deep Tabular Models
In this work, we demonstrate that upstream data gives tabular neural networks a decisive advantage over widely used GBDT models.
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective
We also use decision boundary methods to visualize double descent phenomena.
End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking
Algorithmic extrapolation can be achieved through recurrent systems, which can be iterated many times to solve difficult reasoning problems.
Thinking Deeper With Recurrent Networks: Logical Extrapolation Without Overthinking
Classical machine learning systems perform best when they are trained and tested on the same distribution, and they lack a mechanism to increase model power after training is complete.
Datasets for Studying Generalization from Easy to Hard Examples
We describe new datasets for studying generalization from easy to hard examples.
MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data
Class-imbalanced data, in which some classes contain far more samples than others, is ubiquitous in real-world applications.
Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release
Large organizations such as social media companies continually release data, for example user images.
PAG-Net: Progressive Attention Guided Depth Super-resolution Network
In this paper, we propose a novel method for the challenging problem of guided depth map super-resolution, called PAGNet.
