Search Results for author:
Found 19 papers, 10 papers with code
Dazzle: Using Optimized Generative Adversarial Networks to Address Security Data Class Imbalance Issue
Conclusion: Based on this study, we would suggest the use of optimized GANs as an alternative method for security vulnerability data class imbalanced issues.
Mind Your Bits and Errors: Prioritizing the Bits that Matter in Variational Autoencoders
In this work, we consider the task of image generative modeling with variational autoencoders and posit that the nature of high-dimensional image data distributions poses an intrinsic challenge.
Temporal Predictive Coding For Model-Based Planning In Latent Space
High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments.
Anytime Sampling for Autoregressive Models via Ordered Autoencoding
Experimentally, we demonstrate in several image and audio generation tasks that sample quality degrades gracefully as we reduce the computational budget for sampling.
Non-Markovian Predictive Coding For Planning In Latent Space
High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments.
Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack
Conclusion: When employing ensemble defense against adversarial evasion attacks, we suggest creating an ensemble with unexpected models that are distant from the attacker's expected model (i. e., target model) through methods such as hyperparameter optimization.
Predictive Coding for Locally-Linear Control
High-dimensional observations and unknown dynamics are major challenges when applying optimal control to many real-world decision making tasks.
How to Better Distinguish Security Bug Reports (using Dual Hyperparameter Optimization
For example, in a study of security bug reports from the Chromium dataset, the median recalls of FARSEC and Swift were 15. 7% and 77. 4%, respectively.
Software Engineering
Fair Generative Modeling via Weak Supervision
Real-world datasets are often biased with respect to key demographic factors such as race and gender.
Weakly Supervised Disentanglement with Guarantees
Learning disentangled representations that correspond to factors of variation in real-world data is critical to interpretable and human-controllable machine learning.
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control
A promising approach is to embed the high-dimensional observations into a lower-dimensional latent representation space, estimate the latent dynamics model, then utilize this model for control in the latent space.
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
Given datasets from multiple domains, a key challenge is to efficiently exploit these data sources for modeling a target domain.
Training Variational Autoencoders with Buffered Stochastic Variational Inference
The recognition network in deep latent variable models such as variational autoencoders (VAEs) relies on amortized inference for efficient posterior approximation that can scale up to large datasets.
Amortized Inference Regularization
In this paper, we leverage the fact that VAEs rely on amortized inference and propose techniques for amortized inference regularization (AIR) that control the smoothness of the inference model.
Constructing Unrestricted Adversarial Examples with Generative Models
Then, conditioned on a desired class, we search over the AC-GAN latent space to find images that are likely under the generative model and are misclassified by a target classifier.
A DIRT-T Approach to Unsupervised Domain Adaptation
Domain adaptation refers to the problem of leveraging labeled data in a source domain to learn an accurate model in a target domain where labels are scarce or unavailable.
Robust Locally-Linear Controllable Embedding
We also propose a principled variational approximation of the embedding posterior that takes the future observation into account, and thus, makes the variational approximation more robust against the noise.
Bottleneck Conditional Density Estimation
We introduce a new framework for training deep generative models for high-dimensional conditional density estimation.
Automated Attribution and Intertextual Analysis
In this work, we employ quantitative methods from the realm of statistics and machine learning to develop novel methodologies for author attribution and textual analysis.
