173 citations · 383 across the 11 of their papers we have counts for
12 papers
CSPMNet: Pareto-Efficient Automatic Modulation Classification With Learnable Complex Subband Phase Motion
Ruixiang Zhang, Zinan Zhou, Yezhuo Zhang +2
Automatic modulation classification (AMC) is an essential technique for noncooperative spectrum monitoring and intelligent wireless receivers. However, practical AMC models must id…
SEI-SHIELD: Robust Specific Emitter Identification Under Label Noise Via Self-Supervised Filtering and Iterative Rescue
Ruixiang Zhang, Zinan Zhou, Yezhuo Zhang +2
Specific Emitter Identification (SEI) provides physical-layer device authentication for wireless communications and Internet of Things (IoT) systems. While deep learning (DL) has s…
Discrete Neural Flow Samplers with Locally Equivariant Transformer
Zijing Ou, Ruixiang Zhang, Yingzhen Li
Sampling from unnormalised discrete distributions is a fundamental problem across various domains. While Markov chain Monte Carlo offers a principled approach, it often suffers fro…
Robust and Controllable Object-Centric Learning through Energy-based Models
Ruixiang Zhang, Tong Che, Boris Ivanovic +4
Humans are remarkably good at understanding and reasoning about complex visual scenes. The capability to decompose low-level observations into discrete objects allows us to build a…
Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning
Ting Chen, Ruixiang Zhang, Geoffrey Hinton
We present Bit Diffusion: a simple and generic approach for generating discrete data with continuous state and continuous time diffusion models. The main idea behind our approach i…
Improving unsupervised anomaly localization by applying multi-scale memories to autoencoders
Yifei Yang, Shibing Xiang, Ruixiang Zhang
Autoencoder and its variants have been widely applicated in anomaly detection.The previous work memory-augmented deep autoencoder proposed memorizing normality to detect anomaly, h…
Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective
Ruixiang Zhang, Masanori Koyama, Katsuhiko Ishiguro
Learning controllable and generalizable representation of multivariate data with desired structural properties remains a fundamental problem in machine learning. In this paper, we…
Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling
Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein +4
We show that the sum of the implicit generator log-density of a GAN with the logit score of the discriminator defines an energy function which yields the true data densi…
Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models
Tong Che, Xiaofeng Liu, Site Li +4
AI Safety is a major concern in many deep learning applications such as autonomous driving. Given a trained deep learning model, an important natural problem is how to reliably ver…
Perceptual Generative Autoencoders
Zijun Zhang, Ruixiang Zhang, Zongpeng Li +2
Modern generative models are usually designed to match target distributions directly in the data space, where the intrinsic dimension of data can be much lower than the ambient dim…
Understanding Hidden Memories of Recurrent Neural Networks
Yao Ming, Shaozu Cao, Ruixiang Zhang +4
Recurrent neural networks (RNNs) have been successfully applied to various natural language processing (NLP) tasks and achieved better results than conventional methods. However, t…
Maximum-Likelihood Augmented Discrete Generative Adversarial Networks
Tong Che, Yanran Li, Ruixiang Zhang +4
Despite the successes in capturing continuous distributions, the application of generative adversarial networks (GANs) to discrete settings, like natural language tasks, is rather…