arXiv subjects

Liqun Chen

Publications and source records attributed to Liqun Chen.

52 records · Page 3Linked to original sources

arXiv2019

LMVP: Video Predictor with Leaked Motion Information

We propose a Leaked Motion Video Predictor (LMVP) to predict future frames by capturing the spatial and temporal dependencies from given inputs. The motion is modeled by a newly proposed component, motion guider, which plays the role of both learner and teacher. Specifically, it {\em learns} the temporal features from real data and {\em guides} the generator to predict future frames. The spatial consistency in video is modeled by an adaptive filtering network. To further ensure the spatio-temporal consistency of the prediction, a discriminator is also adopted to distinguish the real and generated frames. Further, the discriminator leaks information to the motion guider and the generator to help the learning of motion. The proposed LMVP can effectively learn the static and temporal features in videos without the need for human labeling. Experiments on synthetic and real data demonstrate that LMVP can yield state-of-the-art results.

cs.CV↗

arXiv2019

Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models

Variational autoencoders (VAEs) have received much attention recently as an end-to-end architecture for text generation with latent variables. In this paper, we investigate several multi-level structures to learn a VAE model to generate long, and coherent text. In particular, we use a hierarchy of stochastic layers between the encoder and decoder networks to generate more informative latent codes. We also investigate a multi-level decoder structure to learn a coherent long-term structure by generating intermediate sentence representations as high-level plan vectors. Empirical results demonstrate that a multi-level VAE model produces more coherent and less repetitive long text compared to the standard VAE models and can further mitigate the posterior-collapse issue.

cs.CL↗

arXiv2019

Improving Textual Network Embedding with Global Attention via Optimal Transport

Constituting highly informative network embeddings is an important tool for network analysis. It encodes network topology, along with other useful side information, into low-dimensional node-based feature representations that can be exploited by statistical modeling. This work focuses on learning context-aware network embeddings augmented with text data. We reformulate the network-embedding problem, and present two novel strategies to improve over traditional attention mechanisms: ($i$) a content-aware sparse attention module based on optimal transport, and ($ii$) a high-level attention parsing module. Our approach yields naturally sparse and self-normalized relational inference. It can capture long-term interactions between sequences, thus addressing the challenges faced by existing textual network embedding schemes. Extensive experiments are conducted to demonstrate our model can consistently outperform alternative state-of-the-art methods.

cs.CL↗

arXiv2019

Anonymous Single Sign-on with Proxy Re-Verification

An anonymous Single Sign-On (ASSO) scheme allows users to access multiple services anonymously using one credential. We propose a new ASSO scheme, where users can access services anonymously through the use of anonymous credentials and unlinkably through the provision of designated verifiers. Notably, verifiers cannot link service requests of a user even if they collude. The novelty is that when a designated verifier is unavailable, a central authority can authorise new verifiers to authenticate the user on behalf of the original verifier. Furthermore, if required, a central verifier is authorised to deanonymise users and trace their service requests. We formalise the scheme along with a security proof and provide an empirical evaluation of its performance. This scheme can be applied to smart ticketing where minimising the collection of personal information of users is increasingly important to transport organisations due to privacy regulations such as General Data Protection Regulations (GDPR).

cs.CR↗

arXiv2019

Improving Sequence-to-Sequence Learning via Optimal Transport

Sequence-to-sequence models are commonly trained via maximum likelihood estimation (MLE). However, standard MLE training considers a word-level objective, predicting the next word given the previous ground-truth partial sentence. This procedure focuses on modeling local syntactic patterns, and may fail to capture long-range semantic structure. We present a novel solution to alleviate these issues. Our approach imposes global sequence-level guidance via new supervision based on optimal transport, enabling the overall characterization and preservation of semantic features. We further show that this method can be understood as a Wasserstein gradient flow trying to match our model to the ground truth sequence distribution. Extensive experiments are conducted to validate the utility of the proposed approach, showing consistent improvements over a wide variety of NLP tasks, including machine translation, abstractive text summarization, and image captioning.

cs.CL↗

arXiv2018

Sequence Generation with Guider Network

Sequence generation with reinforcement learning (RL) has received significant attention recently. However, a challenge with such methods is the sparse-reward problem in the RL training process, in which a scalar guiding signal is often only available after an entire sequence has been generated. This type of sparse reward tends to ignore the global structural information of a sequence, causing generation of sequences that are semantically inconsistent. In this paper, we present a model-based RL approach to overcome this issue. Specifically, we propose a novel guider network to model the sequence-generation environment, which can assist next-word prediction and provide intermediate rewards for generator optimization. Extensive experiments show that the proposed method leads to improved performance for both unconditional and conditional sequence-generation tasks.

cs.CL↗

arXiv2018

Continuous-Time Flows for Efficient Inference and Density Estimation

Two fundamental problems in unsupervised learning are efficient inference for latent-variable models and robust density estimation based on large amounts of unlabeled data. Algorithms for the two tasks, such as normalizing flows and generative adversarial networks (GANs), are often developed independently. In this paper, we propose the concept of {\em continuous-time flows} (CTFs), a family of diffusion-based methods that are able to asymptotically approach a target distribution. Distinct from normalizing flows and GANs, CTFs can be adopted to achieve the above two goals in one framework, with theoretical guarantees. Our framework includes distilling knowledge from a CTF for efficient inference, and learning an explicit energy-based distribution with CTFs for density estimation. Both tasks rely on a new technique for distribution matching within amortized learning. Experiments on various tasks demonstrate promising performance of the proposed CTF framework, compared to related techniques.

stat.ML↗

arXiv2018

A Unified Particle-Optimization Framework for Scalable Bayesian Sampling

There has been recent interest in developing scalable Bayesian sampling methods such as stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD) for big-data analysis. A standard SG-MCMC algorithm simulates samples from a discrete-time Markov chain to approximate a target distribution, thus samples could be highly correlated, an undesired property for SG-MCMC. In contrary, SVGD directly optimizes a set of particles to approximate a target distribution, and thus is able to obtain good approximations with relatively much fewer samples. In this paper, we propose a principle particle-optimization framework based on Wasserstein gradient flows to unify SG-MCMC and SVGD, and to allow new algorithms to be developed. Our framework interprets SG-MCMC as particle optimization on the space of probability measures, revealing a strong connection between SG-MCMC and SVGD. The key component of our framework is several particle-approximate techniques to efficiently solve the original partial differential equations on the space of probability measures. Extensive experiments on both synthetic data and deep neural networks demonstrate the effectiveness and efficiency of our framework for scalable Bayesian sampling.

stat.ML↗

arXiv2018

Privacy-Preserving Electronic Ticket Scheme with Attribute-based Credentials

Electronic tickets (e-tickets) are electronic versions of paper tickets, which enable users to access intended services and improve services' efficiency. However, privacy may be a concern of e-ticket users. In this paper, a privacy-preserving electronic ticket scheme with attribute-based credentials is proposed to protect users' privacy and facilitate ticketing based on a user's attributes. Our proposed scheme makes the following contributions: (1) users can buy different tickets from ticket sellers without releasing their exact attributes; (2) two tickets of the same user cannot be linked; (3) a ticket cannot be transferred to another user; (4) a ticket cannot be double spent; (5) the security of the proposed scheme is formally proven and reduced to well known (q-strong Diffie-Hellman) complexity assumption; (6) the scheme has been implemented and its performance empirically evaluated. To the best of our knowledge, our privacy-preserving attribute-based e-ticket scheme is the first one providing these five features. Application areas of our scheme include event or transport tickets where users must convince ticket sellers that their attributes (e.g. age, profession, location) satisfy the ticket price policies to buy discounted tickets. More generally, our scheme can be used in any system where access to services is only dependent on a user's attributes (or entitlements) but not their identities.

cs.CR↗

arXiv2018

Anonymous Single-Sign-On for n designated services with traceability

Anonymous Single-Sign-On authentication schemes have been proposed to allow users to access a service protected by a verifier without revealing their identity which has become more important due to the introduction of strong privacy regulations. In this paper we describe a new approach whereby anonymous authentication to different verifiers is achieved via authorisation tags and pseudonyms. The particular innovation of our scheme is authentication can only occur between a user and its designated verifier for a service, and the verification cannot be performed by any other verifier. The benefit of this authentication approach is that it prevents information leakage of a user's service access information, even if the verifiers for these services collude which each other. Our scheme also supports a trusted third party who is authorised to de-anonymise the user and reveal her whole services access information if required. Furthermore, our scheme is lightweight because it does not rely on attribute or policy-based signature schemes to enable access to multiple services. The scheme's security model is given together with a security proof, an implementation and a performance evaluation.

cs.CR↗

arXiv2017

Triangle Generative Adversarial Networks

A Triangle Generative Adversarial Network ($Δ$-GAN) is developed for semi-supervised cross-domain joint distribution matching, where the training data consists of samples from each domain, and supervision of domain correspondence is provided by only a few paired samples. $Δ$-GAN consists of four neural networks, two generators and two discriminators. The generators are designed to learn the two-way conditional distributions between the two domains, while the discriminators implicitly define a ternary discriminative function, which is trained to distinguish real data pairs and two kinds of fake data pairs. The generators and discriminators are trained together using adversarial learning. Under mild assumptions, in theory the joint distributions characterized by the two generators concentrate to the data distribution. In experiments, three different kinds of domain pairs are considered, image-label, image-image and image-attribute pairs. Experiments on semi-supervised image classification, image-to-image translation and attribute-based image generation demonstrate the superiority of the proposed approach.

cs.LG↗

arXiv2017

Adversarial Symmetric Variational Autoencoder

A new form of variational autoencoder (VAE) is developed, in which the joint distribution of data and codes is considered in two (symmetric) forms: ($i$) from observed data fed through the encoder to yield codes, and ($ii$) from latent codes drawn from a simple prior and propagated through the decoder to manifest data. Lower bounds are learned for marginal log-likelihood fits observed data and latent codes. When learning with the variational bound, one seeks to minimize the symmetric Kullback-Leibler divergence of joint density functions from ($i$) and ($ii$), while simultaneously seeking to maximize the two marginal log-likelihoods. To facilitate learning, a new form of adversarial training is developed. An extensive set of experiments is performed, in which we demonstrate state-of-the-art data reconstruction and generation on several image benchmark datasets.

cs.LG↗

arXiv2017

ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching

We investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching. Within a framework of conditional entropy, we propose both adversarial and non-adversarial approaches to learn desirable matched joint distributions for unsupervised and supervised tasks. We unify a broad family of adversarial models as joint distribution matching problems. Our approach stabilizes learning of unsupervised bidirectional adversarial learning methods. Further, we introduce an extension for semi-supervised learning tasks. Theoretical results are validated in synthetic data and real-world applications.

stat.ML↗

arXiv2017

Symmetric Variational Autoencoder and Connections to Adversarial Learning

A new form of the variational autoencoder (VAE) is proposed, based on the symmetric Kullback-Leibler divergence. It is demonstrated that learning of the resulting symmetric VAE (sVAE) has close connections to previously developed adversarial-learning methods. This relationship helps unify the previously distinct techniques of VAE and adversarially learning, and provides insights that allow us to ameliorate shortcomings with some previously developed adversarial methods. In addition to an analysis that motivates and explains the sVAE, an extensive set of experiments validate the utility of the approach.

stat.ML↗

arXiv2017

Formal Analysis of V2X Revocation Protocols

Research on vehicular networking (V2X) security has produced a range of security mechanisms and protocols tailored for this domain, addressing both security and privacy. Typically, the security analysis of these proposals has largely been informal. However, formal analysis can be used to expose flaws and ultimately provide a higher level of assurance in the protocols. This paper focusses on the formal analysis of a particular element of security mechanisms for V2X found in many proposals: the revocation of malicious or misbehaving vehicles from the V2X system by invalidating their credentials. This revocation needs to be performed in an unlinkable way for vehicle privacy even in the context of vehicles regularly changing their pseudonyms. The REWIRE scheme by Forster et al. and its subschemes BASIC and RTOKEN aim to solve this challenge by means of cryptographic solutions and trusted hardware. Formal analysis using the TAMARIN prover identifies two flaws with some of the functional correctness and authentication properties in these schemes. We then propose Obscure Token (OTOKEN), an extension of REWIRE to enable revocation in a privacy preserving manner. Our approach addresses the functional and authentication properties by introducing an additional key-pair, which offers a stronger and verifiable guarantee of successful revocation of vehicles without resolving the long-term identity. Moreover OTOKEN is the first V2X revocation protocol to be co-designed with a formal model.

cs.CR↗

arXiv2016

Private reputation retrieval in public - a privacy-aware announcement scheme for VANETs

An announcement scheme is a system that facilitates vehicles to broadcast road-related information in vehicular ad hoc networks (VANETs) in order to improve road safety and efficiency. Here we propose a new cryptographic primitive for public updating of reputation score based on the Boneh-Boyen-Shacham short group signature scheme. This allows private reputation score retrieval without a secure channel. Using this we devise a privacy-aware announcement scheme using reputation systems which is reliable, auditable and robust.

cs.CR↗