100 citations · 308 across the 28 of their papers we have counts for
30 papers
Conditional Image-to-Video Generation with Latent Flow Diffusion Models
Haomiao Ni, Changhao Shi, Kai Li +2
Conditional image-to-video (cI2V) generation aims to synthesize a new plausible video starting from an image (e.g., a person's face) and a condition (e.g., an action class label li…
Attribute-Centric Compositional Text-to-Image Generation
Yuren Cong, Martin Renqiang Min, Li Erran Li +2
Despite the recent impressive breakthroughs in text-to-image generation, generative models have difficulty in capturing the data distribution of underrepresented attribute composit…
Disentangled Wasserstein Autoencoder for T-Cell Receptor Engineering
Tianxiao Li, Hongyu Guo, Filippo Grazioli +2
In protein biophysics, the separation between the functionally important residues (forming the active site or binding surface) and those that create the overall structure (the fold…
StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis
Zhiheng Li, Martin Renqiang Min, Kai Li +1
Although progress has been made for text-to-image synthesis, previous methods fall short of generalizing to unseen or underrepresented attribute compositions in the input text. Lac…
AE-StyleGAN: Improved Training of Style-Based Auto-Encoders
Ligong Han, Sri Harsha Musunuri, Martin Renqiang Min +3
StyleGANs have shown impressive results on data generation and manipulation in recent years, thanks to its disentangled style latent space. A lot of efforts have been made in inver…
Dual Projection Generative Adversarial Networks for Conditional Image Generation
Ligong Han, Martin Renqiang Min, Anastasis Stathopoulos +4
Conditional Generative Adversarial Networks (cGANs) extend the standard unconditional GAN framework to learning joint data-label distributions from samples, and have been establish…
Disentangled Recurrent Wasserstein Autoencoder
Jun Han, Martin Renqiang Min, Ligong Han +2
Learning disentangled representations leads to interpretable models and facilitates data generation with style transfer, which has been extensively studied on static data such as i…
Ranking-based Convolutional Neural Network Models for Peptide-MHC Binding Prediction
Ziqi Chen, Martin Renqiang Min, Xia Ning
T-cell receptors can recognize foreign peptides bound to major histocompatibility complex (MHC) class-I proteins, and thus trigger the adaptive immune response. Therefore, identify…
A Deep Generative Model for Molecule Optimization via One Fragment Modification
Ziqi Chen, Martin Renqiang Min, Srinivasan Parthasarathy +1
Molecule optimization is a critical step in drug development to improve desired properties of drug candidates through chemical modification. We developed a novel deep generative mo…
Improving Disentangled Text Representation Learning with Information-Theoretic Guidance
Pengyu Cheng, Martin Renqiang Min, Dinghan Shen +4
Learning disentangled representations of natural language is essential for many NLP tasks, e.g., conditional text generation, style transfer, personalized dialogue systems, etc. Si…
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation
Yizhe Zhu, Martin Renqiang Min, Asim Kadav +1
We propose a sequential variational autoencoder to learn disentangled representations of sequential data (e.g., videos and audios) under self-supervision. Specifically, we exploit…
CNN-based Dual-Chain Models for Knowledge Graph Learning
Bo Peng, Renqiang Min, Xia Ning
Knowledge graph learning plays a critical role in integrating domain specific knowledge bases when deploying machine learning and data mining models in practice. Existing methods o…
Rethinking Zero-Shot Learning: A Conditional Visual Classification Perspective
Kai Li, Martin Renqiang Min, Yun Fu
Zero-shot learning (ZSL) aims to recognize instances of unseen classes solely based on the semantic descriptions of the classes. Existing algorithms usually formulate it as a seman…
A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction
Xiaoyuan Liang, Guiling Wang, Martin Renqiang Min +2
In spite of its importance, passenger demand prediction is a highly challenging problem, because the demand is simultaneously influenced by the complex interactions among many spat…
Disentangled Deep Autoencoding Regularization for Robust Image Classification
Zhenyu Duan, Martin Renqiang Min, Li Erran Li +3
In spite of achieving revolutionary successes in machine learning, deep convolutional neural networks have been recently found to be vulnerable to adversarial attacks and difficult…
Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding
Yao Li, Martin Renqiang Min, Wenchao Yu +3
Recent studies have demonstrated the vulnerability of deep convolutional neural networks against adversarial examples. Inspired by the observation that the intrinsic dimension of i…
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations
Ting Chen, Martin Renqiang Min, Yizhou Sun
Conventional embedding methods directly associate each symbol with a continuous embedding vector, which is equivalent to applying a linear transformation based on a "one-hot" encod…
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
Dinghan Shen, Guoyin Wang, Wenlin Wang +6
Many deep learning architectures have been proposed to model the compositionality in text sequences, requiring a substantial number of parameters and expensive computations. Howeve…
Convolutional Neural Knowledge Graph Learning
Feipeng Zhao, Martin Renqiang Min, Chen Shen +1
Previous models for learning entity and relationship embeddings of knowledge graphs such as TransE, TransH, and TransR aim to explore new links based on learned representations. Ho…
Parametric t-Distributed Stochastic Exemplar-centered Embedding
Martin Renqiang Min, Hongyu Guo, Dinghan Shen
Parametric embedding methods such as parametric t-SNE (pt-SNE) have been widely adopted for data visualization and out-of-sample data embedding without further computationally expe…
Video Generation From Text
Yitong Li, Martin Renqiang Min, Dinghan Shen +2
Generating videos from text has proven to be a significant challenge for existing generative models. We tackle this problem by training a conditional generative model to extract bo…
Learning Context-Sensitive Convolutional Filters for Text Processing
Dinghan Shen, Martin Renqiang Min, Yitong Li +1
Convolutional neural networks (CNNs) have recently emerged as a popular building block for natural language processing (NLP). Despite their success, most existing CNN models employ…
Exemplar-Centered Supervised Shallow Parametric Data Embedding
Martin Renqiang Min, Hongyu Guo, Dongjin Song
Metric learning methods for dimensionality reduction in combination with k-Nearest Neighbors (kNN) have been extensively deployed in many classification, data embedding, and inform…
A Context-aware Attention Network for Interactive Question Answering
Huayu Li, Martin Renqiang Min, Yong Ge +1
Neural network based sequence-to-sequence models in an encoder-decoder framework have been successfully applied to solve Question Answering (QA) problems, predicting answers from s…