28 citations · 98 across the 15 of their papers we have counts for
16 papers
Distill Gold from Massive Ores: Bi-level Data Pruning towards Efficient Dataset Distillation
Yue Xu, Yong-Lu Li, Kaitong Cui +4
Data-efficient learning has garnered significant attention, especially given the current trend of large multi-modal models. Recently, dataset distillation has become an effective a…
NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attacks
Wenkai Tan, Justus Renkhoff, Alvaro Velasquez +7
Deep Learning (DL) and Deep Neural Networks (DNNs) are widely used in various domains. However, adversarial attacks can easily mislead a neural network and lead to wrong decisions.…
Object-centric Learning with Cyclic Walks between Parts and Whole
Ziyu Wang, Mike Zheng Shou, Mengmi Zhang
Learning object-centric representations from complex natural environments enables both humans and machines with reasoning abilities from low-level perceptual features. To capture c…
Modeling Perceptual Loudness of Piano Tone: Theory and Applications
Yang Qu, Yutian Qin, Lecheng Chao +3
The relationship between perceptual loudness and physical attributes of sound is an important subject in both computer music and psychoacoustics. Early studies of "equal-loudness c…
Spectral Representation Learning for Conditional Moment Models
Ziyu Wang, Yucen Luo, Yueru Li +2
Many problems in causal inference and economics can be formulated in the framework of conditional moment models, which characterize the target function through a collection of cond…
Fast Instrument Learning with Faster Rates
Ziyu Wang, Yuhao Zhou, Jun Zhu
We investigate nonlinear instrumental variable (IV) regression given high-dimensional instruments. We propose a simple algorithm which combines kernelized IV methods and an arbitra…
Kubric: A scalable dataset generator
Klaus Greff, Francois Belletti, Lucas Beyer +32
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and trainin…
DynaMixer: A Vision MLP Architecture with Dynamic Mixing
Ziyu Wang, Wenhao Jiang, Yiming Zhu +3
Recently, MLP-like vision models have achieved promising performances on mainstream visual recognition tasks. In contrast with vision transformers and CNNs, the success of MLP-like…
Audio-to-symbolic Arrangement via Cross-modal Music Representation Learning
Ziyu Wang, Dejing Xu, Gus Xia +1
Could we automatically derive the score of a piano accompaniment based on the audio of a pop song? This is the audio-to-symbolic arrangement problem we tackle in this paper. A good…
Tractable and Near-Optimal Adversarial Algorithms for Robust Estimation in Contaminated Gaussian Models
Ziyue Wang, Zhiqiang Tan
Consider the problem of simultaneous estimation of location and variance matrix under Huber's contaminated Gaussian model. First, we study minimum -divergence estimation at the…
Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings
Qipeng Guo, Zhijing Jin, Ziyu Wang +5
Cycle-consistent training is widely used for jointly learning a forward and inverse mapping between two domains of interest without the cumbersome requirement of collecting matched…
Further Analysis of Outlier Detection with Deep Generative Models
Ziyu Wang, Bin Dai, David Wipf +1
The recent, counter-intuitive discovery that deep generative models (DGMs) can frequently assign a higher likelihood to outliers has implications for both outlier detection applica…
A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models
Ziyu Wang, Shuyu Cheng, Yueru Li +2
Score matching provides an effective approach to learning flexible unnormalized models, but its scalability is limited by the need to evaluate a second-order derivative. In this pa…
The Usual Suspects? Reassessing Blame for VAE Posterior Collapse
Bin Dai, Ziyu Wang, David Wipf
In narrow asymptotic settings Gaussian VAE models of continuous data have been shown to possess global optima aligned with ground-truth distributions. Even so, it is well known tha…
Function Space Particle Optimization for Bayesian Neural Networks
Ziyu Wang, Tongzheng Ren, Jun Zhu +1
While Bayesian neural networks (BNNs) have drawn increasing attention, their posterior inference remains challenging, due to the high-dimensional and over-parameterized nature. To…
Deep Fried Convnets
Zichao Yang, Marcin Moczulski, Misha Denil +4
The fully connected layers of a deep convolutional neural network typically contain over 90% of the network parameters, and consume the majority of the memory required to store the…