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07/25/2023
A Novel Computationally Efficient Group Signature for Anonymous and Secure V2X Communications
The use of vehicle-to-everything (V2X) communication is expected to sign...
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05/24/2023
Jointly Optimizing Image Compression with Low-light Image Enhancement
Learning-based image compression methods have made great progress. Most ...
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03/10/2023
Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning
Contrastive loss has been increasingly used in learning representations ...
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09/12/2022
High-Fidelity Variable-Rate Image Compression via Invertible Activation Transformation
Learning-based methods have effectively promoted the community of image ...
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02/28/2022
Multi-modal Alignment using Representation Codebook
Aligning signals from different modalities is an important step in visio...
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02/21/2022
Vision-Language Pre-Training with Triple Contrastive Learning
Vision-language representation learning largely benefits from image-text...
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12/01/2021
Learning Oriented Remote Sensing Object Detection via Naive Geometric Computing
Detecting oriented objects along with estimating their rotation informat...
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02/05/2021
Securing emergent behaviour in swarm robotics
Swarm robotics is the study of how a large number of relatively simple r...
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12/15/2020
Wasserstein Contrastive Representation Distillation
The primary goal of knowledge distillation (KD) is to encapsulate the in...
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10/12/2020
Improving Text Generation with Student-Forcing Optimal Transport
Neural language models are often trained with maximum likelihood estimat...
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09/16/2020
Contextualized Perturbation for Textual Adversarial Attack
Adversarial examples expose the vulnerabilities of natural language proc...
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08/14/2020
Weakly supervised cross-domain alignment with optimal transport
Cross-domain alignment between image objects and text sequences is key t...
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06/26/2020
Graph Optimal Transport for Cross-Domain Alignment
Cross-domain alignment between two sets of entities (e.g., objects in an...
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11/20/2019
Graph-Driven Generative Models for Heterogeneous Multi-Task Learning
We propose a novel graph-driven generative model, that unifies multiple ...
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09/30/2019
Improving Textual Network Learning with Variational Homophilic Embeddings
The performance of many network learning applications crucially hinges o...
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06/24/2019
LMVP: Video Predictor with Leaked Motion Information
We propose a Leaked Motion Video Predictor (LMVP) to predict future fram...
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06/05/2019
Improving Textual Network Embedding with Global Attention via Optimal Transport
Constituting highly informative network embeddings is an important tool ...
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02/01/2019
Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models
Variational autoencoders (VAEs) have received much attention recently as...
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01/18/2019
Improving Sequence-to-Sequence Learning via Optimal Transport
Sequence-to-sequence models are commonly trained via maximum likelihood ...
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11/19/2018
Anonymous Single Sign-on with Proxy Re-Verification
An anonymous Single Sign-On (ASSO) scheme allows users to access multipl...
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11/02/2018
Sequence Generation with Guider Network
Sequence generation with reinforcement learning (RL) has received signif...
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09/17/2018
Adversarial Text Generation via Feature-Mover's Distance
Generative adversarial networks (GANs) have achieved significant success...
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05/29/2018
A Unified Particle-Optimization Framework for Scalable Bayesian Sampling
There has been recent interest in developing scalable Bayesian sampling ...
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04/19/2018
Anonymous Single-Sign-On for n designated services with traceability
Anonymous Single-Sign-On authentication schemes have been proposed to al...
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09/19/2017
Triangle Generative Adversarial Networks
A Triangle Generative Adversarial Network (Δ-GAN) is developed for semi-...
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09/06/2017
Symmetric Variational Autoencoder and Connections to Adversarial Learning
A new form of the variational autoencoder (VAE) is proposed, based on th...
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09/05/2017
ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
We investigate the non-identifiability issues associated with bidirectio...
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09/04/2017
Continuous-Time Flows for Deep Generative Models
Normalizing flows have been developed recently as a method for drawing s...
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06/11/2015