A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Adversarially Learned Inference
[article]
2017
arXiv
pre-print
Preserved Fulltext
Other Versions
We introduce the adversarially learned inference (ALI) model, which jointly learns a generation network and an inference network using an adversarial process. ...
We illustrate the ability of the model to learn mutually coherent inference and generation networks through the inspections of model samples and reconstructions and confirm the usefulness of the learned ...
CONCLUSION We introduced the adversarially learned inference (ALI) model, which jointly learns a generation network and an inference network using an adversarial process. ...
arXiv:1606.00704v3
fatcat:kvwvkt427zhzdcbhdxximgx5si
Decomposed Adversarial Learned Inference
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We propose a novel approach, Decomposed Adversarial Learned Inference (DALI), which explicitly matches prior and conditional distributions in both data and code spaces, and puts a direct constraint on ...
Effective inference for a generative adversarial model remains an important and challenging problem. ...
To overcome the aforementioned issues, in this paper, we propose a novel approach, decomposed adversarial learned inference (DALI), that integrates efficient inference to GAN and overcomes the limitations ...
arXiv:2004.10267v1
fatcat:ly55llcy5bfizkllduhxulf5pe
Generalized Adversarially Learned Inference
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2020 pre-print arXiv:2006.08089v2 fatcat:ramqlb3omzfi3d5dog5ycchxfiOther Versions
Recent approaches, such as ALI and BiGAN frameworks, develop methods of inference of latent variables in GANs by adversarially training an image generator along with an encoder to match two joint distributions ...
Allowing effective inference of latent vectors while training GANs can greatly increase their applicability in various downstream tasks. ...
In the rest of the paper, we refer to our proposed framework as Generalized Adversarially Learned Inference (GALI). ...
arXiv:2006.08089v3
fatcat:2ji66xyarzd4peuzetlesk2tei
Hierarchical Adversarially Learned Inference
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Both the generative and inference model are trained using the adversarial learning paradigm. ...
There, we show that the features learned by our model in an unsupervised way outperform the best handcrafted features. ...
Recent work, known as either ALI or BiGAN , has shown that the adversarial learning paradigm can be extended to incorporate the learning of an inference network. ...
arXiv:1802.01071v1
fatcat:infu7db6frgwdbyysjggfnlpoi
Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
FBGAN is pre-trained on the clean dataset in an unsupervised manner, adversarially learning a bidirectional mapping between the high-dimensional data space and the low-dimensional semantic space; also ...
After the bidirectional mapping, the adversarial data can be reconstructed to denoised data, which could be fed into any pre-trained classifier. ...
Hence, it is natural to extract those semantic features and doing the inference solely based on semantic information. ...
arXiv:1805.07862v2
fatcat:rhre6ae4uze2faj4aspmpf2mtu
Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Lastly, we demonstrate that the state-of-the-art cycle-consistent adversarial learning (CYCLEGAN) models can be derived as a special case within our proposed VI framework, thus establishing its connection ...
We formalize the problem of learning interdomain correspondences in the absence of paired data as Bayesian inference in a latent variable model (LVM), where one seeks the underlying hidden representations ...
arXiv:1806.01771v3 [stat.ML] 24 Aug 2018 Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference First contribution ( § 2). ...
arXiv:1806.01771v3
fatcat:dxqs2zoerjfvtikn7ucedlwchu
Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Instead, we propose an inference-agnostic adversarial training framework which produces an infinitely-large ensemble of graphical models (AGMs). ...
AGMs perform comparably with EGMs on inference tasks that the latter were specifically optimized for. ...
[2018] learn tractable graphical models using exact inference through adversarial objectives. Chongxuan et al. ...
arXiv:2007.05033v3
fatcat:rnakpa5qajhudmjgzhanvkjhnm
Adversarial Learned Molecular Graph Inference and Generation
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
In this work, we propose ALMGIG, a likelihood-free adversarial learning framework for inference and de novo molecule generation that avoids explicitly computing a reconstruction loss. ...
Our approach extends generative adversarial networks by including an adversarial cycle-consistency loss to implicitly enforce the reconstruction property. ...
Adversarially Learned Inference We first describe Adversarially Learned Inference with Conditional Entropy (ALICE) [20] , which is at the core of ALMGIG and has not been used for de novo chemical design ...
arXiv:1905.10310v2
fatcat:lpephwlkl5cqnklxck44wxygya
Adversary-resilient Distributed and Decentralized Statistical Inference and Machine Learning
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
While the last few decades have witnessed a huge body of work devoted to inference and learning in distributed and decentralized setups, much of this work assumes a non-adversarial setting in which individual ...
As a result, we now have a plethora of algorithmic approaches that guarantee robustness of distributed and/or decentralized inference and learning under different adversarial threat models. ...
As a result, we now have an abundance of algorithmic approaches that guarantee robustness of distributed and/or decentralized inference and learning under different adversarial threat models; see, e.g. ...
arXiv:1908.08649v2
fatcat:de356dvwinfv5g5njo64qmzpvi
Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
To overcome these challenges, we propose a neural network framework called Adversarial Balancing-based representation learning for Causal Effect Inference (ABCEI), based on the recent advances in representation ...
To ensure the identification of CATE, ABCEI uses adversarial learning to balance the distributions of covariates in treatment and control groups in the latent representation space, without any assumption ...
We propose a novel model: Adversarial Balancing-based representation learning for Causal Effect Inference (ABCEI) with observational data. ...
arXiv:1904.13335v3
fatcat:pvsjysmofvfenk3ntzpfuke2a4
HASI: Hardware-Accelerated Stochastic Inference, A Defense Against Adversarial Machine Learning Attacks
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
HASI uses the output distribution characteristics of noisy inference compared to a non-noisy reference to detect adversarial inputs. ...
We show that by carefully injecting noise into the model at inference time, we can differentiate adversarial inputs from benign ones. ...
Adversarial Attacks Adversarial attacks were first introduced by Szegedy et al. in [32] , which showed that despite the high accuracy of machine learning models, small perturbations to inputs can reliably ...
arXiv:2106.05825v3
fatcat:xr2nxttx5nf23f7uh7chhfnk5u
Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2021 pre-print arXiv:2105.00822v1 fatcat:w7see43oyze2nm52y3cgfo7pma
Recent advances in reinforcement learning have inspired increasing interest in learning user modeling adaptively through dynamic interactions, e.g., in reinforcement learning based recommender systems. ...
We propose a generative inverse reinforcement learning for user behavioral preference modelling, to address the above issues. ...
Learning from demonstrations not only avoids the need for inferring a reward function but also reduces computational complexity. ...
arXiv:2105.00822v2
fatcat:itlyo4txfjbodp3wyb7oqehg3a
AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
We find the minimum noise via adapting existing evasion attacks in adversarial machine learning. ...
Users in various web and mobile applications are vulnerable to attribute inference attacks, in which an attacker leverages a machine learning classifier to infer a target user's private attributes (e.g ...
In adversarial machine learning, this is known as evasion attack. ...
arXiv:1805.04810v2
fatcat:ijtcty5pgja6rdep5awx25m2um
Learning and inference on generative adversarial quantum circuits
2019
Physical Review A
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2018 pre-print arXiv:1808.03425v1 fatcat:64mxawmrsza75nhkjtaentnlki
We numerically simulate the learning and inference of generative adversarial quantum circuit using the prototypical Bars-and-Stripes dataset. ...
Generative adversarial quantum circuits is a fresh approach to machine learning which may enjoy the practically useful quantum advantage on near-term quantum devices. ...
ACKNOWLEDGMENT Learning and inference of the generative adversarial quantum circuits are implemented using Yao.jl ...
doi:10.1103/physreva.99.052306
fatcat:7ctrtz4xcjf5npstoxreye7wua
Defending against Machine Learning based Inference Attacks via Adversarial Examples: Opportunities and Challenges
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Our key observation is that attackers rely on ML classifiers in inference attacks. The adversarial machine learning community has demonstrated that ML classifiers have various vulnerabilities. ...
As machine learning (ML) becomes more and more powerful and easily accessible, attackers increasingly leverage ML to perform automated large-scale inference attacks in various domains. ...
Defending against Inference Attacks via Adversarial Examples ...
arXiv:1909.08526v2
fatcat:6vcswmo5hzbw3fgveamzjcubre
« Previous
Showing results 1 — 15 out of 69,536 results