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Found 13 papers, 2 papers with code
Column2Vec: Structural Understanding via Distributed Representations of Database Schemas
We present Column2Vec, a distributed representation of database columns based on column metadata.
A Comparative Study of Rule Extraction for Recurrent Neural Networks
Then we empirically evaluate different recurrent networks for their performance of DFA extraction on all Tomita grammars.
Learning to Adapt by Minimizing Discrepancy
We investigate the viability of a more neurocognitively-grounded approach in the context of unsupervised generative modeling of sequences.
An Empirical Evaluation of Rule Extraction from Recurrent Neural Networks
Rule extraction from black-box models is critical in domains that require model validation before implementation, as can be the case in credit scoring and medical diagnosis.
Learning a Hierarchical Latent-Variable Model of 3D Shapes
We propose the Variational Shape Learner (VSL), a generative model that learns the underlying structure of voxelized 3D shapes in an unsupervised fashion.
Ranked #6 on
3D Object Recognition
on ModelNet40
Learning Simpler Language Models with the Differential State Framework
The Differential State Framework (DSF) is a simple and high-performing design that unifies previously introduced gated neural models.
Learning Adversary-Resistant Deep Neural Networks
Despite the superior performance of DNNs in these applications, it has been recently shown that these models are susceptible to a particular type of attack that exploits a fundamental flaw in their design.
Piecewise Latent Variables for Neural Variational Text Processing
Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variables, such as variational autoencoders.
Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks
Deep neural networks have proven to be quite effective in a wide variety of machine learning tasks, ranging from improved speech recognition systems to advancing the development of autonomous vehicles.
Adversary Resistant Deep Neural Networks with an Application to Malware Detection
However, after a thorough analysis of the fundamental flaw in DNNs, we discover that the effectiveness of current defenses is limited and, more importantly, cannot provide theoretical guarantees as to their robustness against adversarial sampled-based attacks.
Using Neural Generative Models to Release Synthetic Twitter Corpora with Reduced Stylometric Identifiability of Users
We evaluate the three methods on measures of risk and utility.
Unifying Adversarial Training Algorithms with Flexible Deep Data Gradient Regularization
Many previous proposals for adversarial training of deep neural nets have included di- rectly modifying the gradient, training on a mix of original and adversarial examples, using contractive penalties, and approximately optimizing constrained adversarial ob- jective functions.
Online Semi-Supervised Learning with Deep Hybrid Boltzmann Machines and Denoising Autoencoders
Two novel deep hybrid architectures, the Deep Hybrid Boltzmann Machine and the Deep Hybrid Denoising Auto-encoder, are proposed for handling semi-supervised learning problems.
