Search Results for author:
Found 9 papers, 6 papers with code
Unsupervised Discovery of 3D Hierarchical Structure with Generative Diffusion Features
Inspired by recent findings that generative diffusion models learn semantically meaningful representations, we use them to discover the intrinsic hierarchical structure in biomedical 3D images using unsupervised segmentation.
Smoothed Embeddings for Certified Few-Shot Learning
In this work, we extend randomized smoothing to few-shot learning models that map inputs to normalized embeddings.
CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks
In safety-critical machine learning applications, it is crucial to defend models against adversarial attacks -- small modifications of the input that change the predictions.
Robustness Threats of Differential Privacy
Differential privacy (DP) is a gold-standard concept of measuring and guaranteeing privacy in data analysis.
Adversarial Turing Patterns from Cellular Automata
Furthermore, we propose to use Turing patterns, generated by cellular automata, as universal perturbations, and experimentally show that they significantly degrade the performance of deep learning models.
Black-Box Face Recovery from Identity Features
In this work, we present a novel algorithm based on an it-erative sampling of random Gaussian blobs for black-box face recovery, given only an output feature vector of deep face recognition systems.
Follow the bisector: a simple method for multi-objective optimization
This descent direction is based on the normalized gradients of the individual losses.
Geometry-Inspired Top-k Adversarial Perturbations
The brittleness of deep image classifiers to small adversarial input perturbations has been extensively studied in the last several years.
Data Driven Chiller Plant Energy Optimization with Domain Knowledge
Refrigeration and chiller optimization is an important and well studied topic in mechanical engineering, mostly taking advantage of physical models, designed on top of over-simplified assumptions, over the equipments.
