Search Results for author:
Found 34 papers, 20 papers with code
TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts
Our approach is flexible, could be used for both text2motion and motion2text tasks.
Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection
The laborious and time-consuming manual annotation has become a real bottleneck in various practical scenarios.
Origins of Low-dimensional Adversarial Perturbations
Finally, we show that if a decision-region is compact, then it admits a universal adversarial perturbation with $L_2$ norm which is $\sqrt{d}$ times smaller than the typical $L_2$ norm of a data point.
Privacy-Aware Compression for Federated Data Analysis
Federated data analytics is a framework for distributed data analysis where a server compiles noisy responses from a group of distributed low-bandwidth user devices to estimate aggregate statistics.
Does Label Differential Privacy Prevent Label Inference Attacks?
Label differential privacy (LDP) is a popular framework for training private ML models on datasets with public features and sensitive private labels.
Bounding Training Data Reconstruction in Private (Deep) Learning
Differential privacy is widely accepted as the de facto method for preventing data leakage in ML, and conventional wisdom suggests that it offers strong protection against privacy attacks.
Submix: Practical Private Prediction for Large-Scale Language Models
Recent data-extraction attacks have exposed that language models can memorize some training samples verbatim.
Generating Diverse and Natural 3D Human Motions From Text
Automated generation of 3D human motions from text is a challenging problem.
ReAct: Out-of-distribution Detection With Rectified Activations
Out-of-distribution (OOD) detection has received much attention lately due to its practical importance in enhancing the safe deployment of neural networks.
On the Importance of Difficulty Calibration in Membership Inference Attacks
The vulnerability of machine learning models to membership inference attacks has received much attention in recent years.
Action2video: Generating Videos of Human 3D Actions
Action2motion stochastically generates plausible 3D pose sequences of a prescribed action category, which are processed and rendered by motion2video to form 2D videos.
BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining
Neural network robustness has become a central topic in machine learning in recent years.
EventHPE: Event-based 3D Human Pose and Shape Estimation
Event camera is an emerging imaging sensor for capturing dynamics of moving objects as events, which motivates our work in estimating 3D human pose and shape from the event signals.
Human Pose and Shape Estimation from Single Polarization Images
This paper focuses on a new problem of estimating human pose and shape from single polarization images.
Online Adaptation to Label Distribution Shift
Machine learning models often encounter distribution shifts when deployed in the real world.
Byzantine-Robust and Privacy-Preserving Framework for FedML
This learning setting presents, among others, two unique challenges: how to protect privacy of the clients' data during training, and how to ensure integrity of the trained model.
Gradient-based Adversarial Attacks against Text Transformers
We propose the first general-purpose gradient-based attack against transformer models.
Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems
Machine-learning systems such as self-driving cars or virtual assistants are composed of a large number of machine-learning models that recognize image content, transcribe speech, analyze natural language, infer preferences, rank options, etc.
Measuring Data Leakage in Machine-Learning Models with Fisher Information
This information leaks either through the model itself or through predictions made by the model.
Making Paper Reviewing Robust to Bid Manipulation Attacks
We develop a novel approach for paper bidding and assignment that is much more robust against such attacks.
Action2Motion: Conditioned Generation of 3D Human Motions
Action recognition is a relatively established task, where givenan input sequence of human motion, the goal is to predict its ac-tion category.
Polarization Human Shape and Pose Dataset
Polarization images are known to be able to capture polarized reflected lights that preserve rich geometric cues of an object, which has motivated its recent applications in reconstructing detailed surface normal of the objects of interest.
On Hiding Neural Networks Inside Neural Networks
Modern neural networks often contain significantly more parameters than the size of their training data.
Secure multiparty computations in floating-point arithmetic
Secure multiparty computations enable the distribution of so-called shares of sensitive data to multiple parties such that the multiple parties can effectively process the data while being unable to glean much information about the data (at least not without collusion among all parties to put back together all the shares).
TrojanNet: Exposing the Danger of Trojan Horse Attack on Neural Networks
The complexity of large-scale neural networks can lead to poor understanding of their internal details.
Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces
In extreme classification settings, embedding-based neural network models are currently not competitive with sparse linear and tree-based methods in terms of accuracy.
Certified Data Removal from Machine Learning Models
Good data stewardship requires removal of data at the request of the data's owner.
A New Defense Against Adversarial Images: Turning a Weakness into a Strength
Natural images are virtually surrounded by low-density misclassified regions that can be efficiently discovered by gradient-guided search --- enabling the generation of adversarial images.
Simple Black-box Adversarial Attacks
We propose an intriguingly simple method for the construction of adversarial images in the black-box setting.
Low Frequency Adversarial Perturbation
In this paper we propose to restrict the search for adversarial images to a low frequency domain.
An empirical study on evaluation metrics of generative adversarial networks
Evaluating generative adversarial networks (GANs) is inherently challenging.
Countering Adversarial Images using Input Transformations
This paper investigates strategies that defend against adversarial-example attacks on image-classification systems by transforming the inputs before feeding them to the system.
On Calibration of Modern Neural Networks
Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications.
Supervised Word Mover's Distance
Accurately measuring the similarity between text documents lies at the core of many real world applications of machine learning.
