Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox

"Topic": models, code, and papers

Personalized sentence generation using generative adversarial networks with author-specific word usage

Apr 20, 2019
Chenhan Yuan, Yi-Chin Huang

The author-specific word usage is a vital feature to let readers perceive the writing style of the author. In this work, a personalized sentence generation method based on generative adversarial networks (GANs) is proposed to cope with this issue. The frequently used function word and content word are incorporated not only as the input features but also as the sentence structure constraint for the GAN training. For the sentence generation with the related topics decided by the user, the Named Entity Recognition (NER) information of the input words is also used in the network training. We compared the proposed method with the GAN-based sentence generation methods, and the experimental results showed that the generated sentences using our method are more similar to the original sentences of the same author based on the objective evaluation such as BLEU and SimHash score.

* slightly changed version of the paper accepted to the CICling 2019 conference 

  Access Paper or Ask Questions

Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks

Mar 09, 2019
Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann

Adversarial attacks and the development of (deep) neural networks robust against them are currently two widely researched topics. The robustness of Learning Vector Quantization (LVQ) models against adversarial attacks has however not yet been studied to the same extent. We therefore present an extensive evaluation of three LVQ models: Generalized LVQ, Generalized Matrix LVQ and Generalized Tangent LVQ. The evaluation suggests that both Generalized LVQ and Generalized Tangent LVQ have a high base robustness, on par with the current state-of-the-art in robust neural network methods. In contrast to this, Generalized Matrix LVQ shows a high susceptibility to adversarial attacks, scoring consistently behind all other models. Additionally, our numerical evaluation indicates that increasing the number of prototypes per class improves the robustness of the models.

* to be published in 13th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization 

  Access Paper or Ask Questions

Queue-based Resampling for Online Class Imbalance Learning

Sep 27, 2018
Kleanthis Malialis, Christos Panayiotou, Marios M. Polycarpou

Online class imbalance learning constitutes a new problem and an emerging research topic that focusses on the challenges of online learning under class imbalance and concept drift. Class imbalance deals with data streams that have very skewed distributions while concept drift deals with changes in the class imbalance status. Little work exists that addresses these challenges and in this paper we introduce queue-based resampling, a novel algorithm that successfully addresses the co-existence of class imbalance and concept drift. The central idea of the proposed resampling algorithm is to selectively include in the training set a subset of the examples that appeared in the past. Results on two popular benchmark datasets demonstrate the effectiveness of queue-based resampling over state-of-the-art methods in terms of learning speed and quality.


  Access Paper or Ask Questions

Coordinating and Integrating Faceted Classification with Rich Semantic Modeling

Sep 25, 2018
Robert B. Allen, Jaihyun Park

Faceted classifications define dimensions for the types of entities included. In effect, the facets provide an "ontological commitment". We compare a faceted thesaurus, the Art and Architecture Thesaurus (AAT), with ontologies derived from the Basic Formal Ontology (BFO2), which is an upper (or formal) ontology widely used to describe entities in biomedicine. We consider how the AAT and BFO2-based ontologies could be coordinated and integrated into a Human Activity and Infrastructure Foundry (HAIF). To extend the AAT to enable this coordination and integration, we describe how a wider range of relationships among its terms could be introduced. Using these extensions, we explore richer modeling of topics from AAT that deal with Technology. Finally, we consider how ontology-based frames and semantic role frames can be integrated to make rich semantic statements about changes in the world.


  Access Paper or Ask Questions

Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty

Jun 10, 2018
Hao Henry Zhou, Yunyang Xiong, Vikas Singh

We provide simple schemes to build Bayesian Neural Networks (BNNs), block by block, inspired by a recent idea of computation skeletons. We show how by adjusting the types of blocks that are used within the computation skeleton, we can identify interesting relationships with Deep Gaussian Processes (DGPs), deep kernel learning (DKL), random features type approximation and other topics. We give strategies to approximate the posterior via doubly stochastic variational inference for such models which yield uncertainty estimates. We give a detailed theoretical analysis and point out extensions that may be of independent interest. As a special case, we instantiate our procedure to define a Bayesian {\em additive} Neural network -- a promising strategy to identify statistical interactions and has direct benefits for obtaining interpretable models.


  Access Paper or Ask Questions

Twitter User Geolocation using Deep Multiview Learning

May 11, 2018
Tien Huu Do, Duc Minh Nguyen, Evaggelia Tsiligianni, Bruno Cornelis, Nikos Deligiannis

Predicting the geographical location of users on social networks like Twitter is an active research topic with plenty of methods proposed so far. Most of the existing work follows either a content-based or a network-based approach. The former is based on user-generated content while the latter exploits the structure of the network of users. In this paper, we propose a more generic approach, which incorporates not only both content-based and network-based features, but also other available information into a unified model. Our approach, named Multi-Entry Neural Network (MENET), leverages the latest advances in deep learning and multiview learning. A realization of MENET with textual, network and metadata features results in an effective method for Twitter user geolocation, achieving the state of the art on two well-known datasets.

* Presented at IEEE International Conference on Acoustics, Speech and Signal Processing, 2018 

  Access Paper or Ask Questions

Game-theoretic Network Centrality: A Review

Dec 31, 2017
Mateusz K. Tarkowski, Tomasz P. Michalak, Talal Rahwan, Michael Wooldridge

Game-theoretic centrality is a flexible and sophisticated approach to identify the most important nodes in a network. It builds upon the methods from cooperative game theory and network theory. The key idea is to treat nodes as players in a cooperative game, where the value of each coalition is determined by certain graph-theoretic properties. Using solution concepts from cooperative game theory, it is then possible to measure how responsible each node is for the worth of the network. The literature on the topic is already quite large, and is scattered among game-theoretic and computer science venues. We review the main game-theoretic network centrality measures from both bodies of literature and organize them into two categories: those that are more focused on the connectivity of nodes, and those that are more focused on the synergies achieved by nodes in groups. We present and explain each centrality, with a focus on algorithms and complexity.


  Access Paper or Ask Questions

Production Ready Chatbots: Generate if not Retrieve

Nov 27, 2017
Aniruddha Tammewar, Monik Pamecha, Chirag Jain, Apurva Nagvenkar, Krupal Modi

In this paper, we present a hybrid model that combines a neural conversational model and a rule-based graph dialogue system that assists users in scheduling reminders through a chat conversation. The graph based system has high precision and provides a grammatically accurate response but has a low recall. The neural conversation model can cater to a variety of requests, as it generates the responses word by word as opposed to using canned responses. The hybrid system shows significant improvements over the existing baseline system of rule based approach and caters to complex queries with a domain-restricted neural model. Restricting the conversation topic and combination of graph based retrieval system with a neural generative model makes the final system robust enough for a real world application.

* DEEPDIAL-18, AAAI-2018 

  Access Paper or Ask Questions

Study of Set-Membership Kernel Adaptive Algorithms and Applications

Aug 27, 2017
R. C. de Lamare, André Flores

Adaptive algorithms based on kernel structures have been a topic of significant research over the past few years. The main advantage is that they form a family of universal approximators, offering an elegant solution to problems with nonlinearities. Nevertheless these methods deal with kernel expansions, creating a growing structure also known as dictionary, whose size depends on the number of new inputs. In this paper we derive the set-membership kernel-based normalized least-mean square (SM-NKLMS) algorithm, which is capable of limiting the size of the dictionary created in stationary environments. We also derive as an extension the set-membership kernelized affine projection (SM-KAP) algorithm. Finally several experiments are presented to compare the proposed SM-NKLMS and SM-KAP algorithms to the existing methods.

* 4 figures, 6 pages 

  Access Paper or Ask Questions

<<
249
250
251
252
253
254
255
256
257
258
259
260
261
>>