48 citations · 87 across the 13 of their papers we have counts for
14 papers
The Tokens Strike Back: Safety-Aware Denoising with Directional Embedding-based Revision
Amman Yusuf, Jiwoo Shin, Ruizhi Deng +3
The Safety-Aware Denoiser (SAD) is a recent, training-free, safety-guidance framework for text diffusion models. During inference, SAD steers text samples toward safe regions of th…
ReDiTT: Retrieval Augmented Conditional Diffusion Transformers for Asynchronous Time Series
Saiyue Lyu, Zhitian Zhang, Ruizhi Deng +1
We present a diffusion based model for asynchronous time series prediction, where the goal is to predict the next inter event time and event type. To address the inherent uncertain…
ADiff4TPP: Asynchronous Diffusion Models for Temporal Point Processes
Amartya Mukherjee, Ruizhi Deng, He Zhao +3
This work introduces a novel approach to modeling temporal point processes using diffusion models with an asynchronous noise schedule. At each step of the diffusion process, the no…
Pretext Training Algorithms for Event Sequence Data
Yimu Wang, He Zhao, Ruizhi Deng +2
Pretext training followed by task-specific fine-tuning has been a successful approach in vision and language domains. This paper proposes a self-supervised pretext training framewo…
PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models
Jiacheng Chen, Ruizhi Deng, Yasutaka Furukawa
This paper presents PolyDiffuse, a novel structured reconstruction algorithm that transforms visual sensor data into polygonal shapes with Diffusion Models (DM), an emerging machin…
Continuous-time Particle Filtering for Latent Stochastic Differential Equations
Ruizhi Deng, Greg Mori, Andreas M. Lehrmann
Particle filtering is a standard Monte-Carlo approach for a wide range of sequential inference tasks. The key component of a particle filter is a set of particles with importance w…
Continuous Latent Process Flows
Ruizhi Deng, Marcus A. Brubaker, Greg Mori +1
Partial observations of continuous time-series dynamics at arbitrary time stamps exist in many disciplines. Fitting this type of data using statistical models with continuous dynam…
Adaptive Appearance Rendering
Mengyao Zhai, Ruizhi Deng, Jiacheng Chen +3
We propose an approach to generate images of people given a desired appearance and pose. Disentangled representations of pose and appearance are necessary to handle the compound va…
Variational Hyper RNN for Sequence Modeling
Ruizhi Deng, Yanshuai Cao, Bo Chang +3
In this work, we propose a novel probabilistic sequence model that excels at capturing high variability in time series data, both across sequences and within an individual sequence…
Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
Ruizhi Deng, Bo Chang, Marcus A. Brubaker +2
Normalizing flows transform a simple base distribution into a complex target distribution and have proved to be powerful models for data generation and density estimation. In this…
Point Process Flows
Nazanin Mehrasa, Ruizhi Deng, Mohamed Osama Ahmed +5
Event sequences can be modeled by temporal point processes (TPPs) to capture their asynchronous and probabilistic nature. We propose an intensity-free framework that directly model…
Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation
Chaowei Xiao, Ruizhi Deng, Bo Li +3
Deep Neural Networks (DNNs) have been widely applied in various recognition tasks. However, recently DNNs have been shown to be vulnerable against adversarial examples, which can m…
Sparsely Aggregated Convolutional Networks
Ligeng Zhu, Ruizhi Deng, Michael Maire +3
We explore a key architectural aspect of deep convolutional neural networks: the pattern of internal skip connections used to aggregate outputs of earlier layers for consumption by…
Learning to Forecast Videos of Human Activity with Multi-granularity Models and Adaptive Rendering
Mengyao Zhai, Jiacheng Chen, Ruizhi Deng +3
We propose an approach for forecasting video of complex human activity involving multiple people. Direct pixel-level prediction is too simple to handle the appearance variability i…