48 citations · 144 across the 28 of their papers we have counts for
29 papers
Semantic Communication for Efficient Point Cloud Transmission
Shangzhuo Xie, Qianqian Yang, Yuyi Sun +3
As three-dimensional acquisition technologies like LiDAR cameras advance, the need for efficient transmission of 3D point clouds is becoming increasingly important. In this paper,…
Semantic-aware Transmission for Robust Point Cloud Classification
Tianxiao Han, Kaiyi Chi, Qianqian Yang +1
As three-dimensional (3D) data acquisition devices become increasingly prevalent, the demand for 3D point cloud transmission is growing. In this study, we introduce a semantic-awar…
Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting
Deqian Kong, Bo Pang, Tian Han +1
Generation of molecules with desired chemical and biological properties such as high drug-likeness, high binding affinity to target proteins, is critical for drug discovery. In thi…
Learning Joint Latent Space EBM Prior Model for Multi-layer Generator
Jiali Cui, Ying Nian Wu, Tian Han
This paper studies the fundamental problem of learning multi-layer generator models. The multi-layer generator model builds multiple layers of latent variables as a prior model on…
Generative Model Based Highly Efficient Semantic Communication Approach for Image Transmission
Tianxiao Han, Jiancheng Tang, Qianqian Yang +3
Deep learning (DL) based semantic communication methods have been explored to transmit images efficiently in recent years. In this paper, we propose a generative model based semant…
Learning Sparse Latent Representations for Generator Model
Hanao Li, Tian Han
Sparsity is a desirable attribute. It can lead to more efficient and more effective representations compared to the dense model. Meanwhile, learning sparse latent representations h…
Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model
Zhisheng Xiao, Tian Han
This paper studies the fundamental problem of learning energy-based model (EBM) in the latent space of the generator model. Learning such prior model typically requires running cos…
A Modified Union Bound on Symbol Error Probability for Fading Channels
Tian Han, Rajitha Senanayake, Peter Smith +1
In this paper, we propose a new upper bound on the error probability performance of maximum-likelihood (ML) detection. The proposed approach provides a much tighter upper bound whe…
Semantic-preserved Communication System for Highly Efficient Speech Transmission
Tianxiao Han, Qianqian Yang, Zhiguo Shi +2
Deep learning (DL) based semantic communication methods have been explored for the efficient transmission of images, text, and speech in recent years. In contrast to traditional wi…
Semantic-aware Speech to Text Transmission with Redundancy Removal
Tianxiao Han, Qianqian Yang, Zhiguo Shi +2
Deep learning (DL) based semantic communication methods have been explored for the efficient transmission of images, text, and speech in recent years. In contrast to traditional wi…
Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs
Chang Lu, Tian Han, Yue Ning
With the wide application of electronic health records (EHR) in healthcare facilities, health event prediction with deep learning has gained more and more attention. A common featu…
Learning from the Tangram to Solve Mini Visual Tasks
Yizhou Zhao, Liang Qiu, Pan Lu +3
Current pre-training methods in computer vision focus on natural images in the daily-life context. However, abstract diagrams such as icons and symbols are common and important in…
GenMotion: Data-driven Motion Generators for Real-time Animation Synthesis
Yizhou Zhao, Wensi Ai, Liang Qiu +4
With the recent success of deep learning algorithms, many researchers have focused on generative models for human motion animation. However, the research community lacks a platform…
Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI
Quanshi Zhang, Tian Han, Lixin Fan +5
This is the Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI. Deep neural networks (DNNs) have undoubtedly brought grea…
STAR: Sparse Transformer-based Action Recognition
Feng Shi, Chonghan Lee, Liang Qiu +6
The cognitive system for human action and behavior has evolved into a deep learning regime, and especially the advent of Graph Convolution Networks has transformed the field in rec…
Combined Radar and Communications with Phase-Modulated Frequency Permutations
Tian Han, Rajitha Senanayake, Peter Smith +3
This paper focuses on the combined radar and communications problem and conducts a thorough analytical investigation on the effect of phase and frequency change on the communicatio…
Generative Text Modeling through Short Run Inference
Bo Pang, Erik Nijkamp, Tian Han +1
Latent variable models for text, when trained successfully, accurately model the data distribution and capture global semantic and syntactic features of sentences. The prominent ap…
Frequency Permutations for Joint Radar and Communications
Rajitha Senanayake, Peter Smith, Tian Han +3
This paper presents a new joint radar and communication technique based on the classical stepped frequency radar waveform. The randomization in the waveform, which is achieved by u…
Semi-supervised Learning by Latent Space Energy-Based Model of Symbol-Vector Coupling
Bo Pang, Erik Nijkamp, Jiali Cui +2
This paper proposes a latent space energy-based prior model for semi-supervised learning. The model stands on a generator network that maps a latent vector to the observed example.…
Learning Latent Space Energy-Based Prior Model for Molecule Generation
Bo Pang, Tian Han, Ying Nian Wu
Deep generative models have recently been applied to molecule design. If the molecules are encoded in linear SMILES strings, modeling becomes convenient. However, models relying on…
Joint Training of Variational Auto-Encoder and Latent Energy-Based Model
Tian Han, Erik Nijkamp, Linqi Zhou +3
This paper proposes a joint training method to learn both the variational auto-encoder (VAE) and the latent energy-based model (EBM). The joint training of VAE and latent EBM are b…
Learning Latent Space Energy-Based Prior Model
Bo Pang, Tian Han, Erik Nijkamp +2
We propose to learn energy-based model (EBM) in the latent space of a generator model, so that the EBM serves as a prior model that stands on the top-down network of the generator…
Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference
Erik Nijkamp, Bo Pang, Tian Han +3
This paper studies the fundamental problem of learning deep generative models that consist of multiple layers of latent variables organized in top-down architectures. Such models h…
On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models
Erik Nijkamp, Mitch Hill, Tian Han +2
This study investigates the effects of Markov chain Monte Carlo (MCMC) sampling in unsupervised Maximum Likelihood (ML) learning. Our attention is restricted to the family of unnor…