no code implementations • 16 Mar 2024 • Zhenxiao Fu, Min Yang, Cheng Chu, Yilun Xu, Gang Huang, Fan Chen
Variational quantum circuits (VQCs) have become a powerful tool for implementing Quantum Neural Networks (QNNs), addressing a wide range of complex problems.
no code implementations • 3 Dec 2023 • Kyle R. Myers, Wei Yang Tham, Jerry Thursby, Marie Thursby, Nina Cohodes, Karim Lakhani, Rachel Mural, Yilun Xu
We introduce a new survey of professors at roughly 150 of the most research-intensive institutions of higher education in the US.
1 code implementation • 19 Oct 2023 • Gabriele Corso, Yilun Xu, Valentin De Bortoli, Regina Barzilay, Tommi Jaakkola
In light of the widespread success of generative models, a significant amount of research has gone into speeding up their sampling time.
2 code implementations • NeurIPS 2023 • Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi Jaakkola
Restart not only outperforms the previous best SDE results, but also accelerates the sampling speed by 10-fold / 2-fold on CIFAR-10 / ImageNet $64 \times 64$.
no code implementations • 5 Apr 2023 • Ziming Liu, Di Luo, Yilun Xu, Tommi Jaakkola, Max Tegmark
We introduce a general family, Generative Models from Physical Processes (GenPhys), where we translate partial differential equations (PDEs) describing physical processes to generative models.
1 code implementation • 8 Feb 2023 • Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi Jaakkola
The new models reduce to PFGM when $D{=}1$ and to diffusion models when $D{\to}\infty$.
Ranked #1 on Image Generation on FFHQ 64x64 - 4x upscaling
1 code implementation • 1 Feb 2023 • Yilun Xu, Shangyuan Tong, Tommi Jaakkola
We show that the procedure indeed helps in the challenging intermediate regime by reducing (the trace of) the covariance of training targets.
Ranked #13 on Image Generation on CIFAR-10
1 code implementation • 22 Sep 2022 • Yilun Xu, Ziming Liu, Max Tegmark, Tommi Jaakkola
We interpret the data points as electrical charges on the $z=0$ hyperplane in a space augmented with an additional dimension $z$, generating a high-dimensional electric field (the gradient of the solution to Poisson equation).
Ranked #32 on Image Generation on CIFAR-10
1 code implementation • 6 Sep 2022 • Hanqun Cao, Cheng Tan, Zhangyang Gao, Yilun Xu, Guangyong Chen, Pheng-Ann Heng, Stan Z. Li
Deep generative models are a prominent approach for data generation, and have been used to produce high quality samples in various domains.
1 code implementation • ICLR 2022 • Yilun Xu, Hao He, Tianxiao Shen, Tommi Jaakkola
We propose to identify directions invariant to a given classifier so that these directions can be controlled in tasks such as style transfer.
no code implementations • 14 Nov 2021 • Yilun Xu, Ziyang Liu, Xingming Wu, Weihai Chen, Changyun Wen, Zhengguo Li
For the former challenge, a spatially varying convolution (SVC) is designed to process the Bayer images carried with varying exposures.
no code implementations • 14 Nov 2021 • Yilun Xu, Zhengguo Li, Weihai Chen, Changyun Wen
It is challenging to align the brightness distribution of the images with different exposures due to possible color distortion and loss of details in the brightest and darkest regions of input images.
2 code implementations • 19 Oct 2021 • Yilun Xu, Tommi Jaakkola
We further demonstrate the impact of optimizing such transfer risk on two controlled settings, each representing a different pattern of environment shift, as well as on two real-world datasets.
no code implementations • 5 Jun 2021 • Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron Courville
Can models with particular structure avoid being biased towards spurious correlation in out-of-distribution (OOD) generalization?
1 code implementation • ICLR 2021 • Yilun Xu, Yang song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon
Experimentally, we demonstrate in several image and audio generation tasks that sample quality degrades gracefully as we reduce the computational budget for sampling.
no code implementations • ECCV 2020 • Xinwei Sun, Yilun Xu, Peng Cao, Yuqing Kong, Lingjing Hu, Shanghang Zhang, Yizhou Wang
In this paper, we propose a novel information-theoretic approach, namely \textbf{T}otal \textbf{C}orrelation \textbf{G}ain \textbf{M}aximization (TCGM), for semi-supervised multi-modal learning, which is endowed with promising properties: (i) it can utilize effectively the information across different modalities of unlabeled data points to facilitate training classifiers of each modality (ii) it has theoretical guarantee to identify Bayesian classifiers, i. e., the ground truth posteriors of all modalities.
1 code implementation • ICLR 2020 • Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon
We propose a new framework for reasoning about information in complex systems.
no code implementations • NeurIPS 2019 • Yilun Xu, Peng Cao, Yuqing Kong, Yizhou Wang
To the best of our knowledge, L_DMI is the first loss function that is provably robust to instance-independent label noise, regardless of noise pattern, and it can be applied to any existing classification neural networks straightforwardly without any auxiliary information.
Ranked #36 on Image Classification on Clothing1M (using extra training data)
2 code implementations • 8 Sep 2019 • Yilun Xu, Peng Cao, Yuqing Kong, Yizhou Wang
\emph{To the best of our knowledge, $\mathcal{L}_{DMI}$ is the first loss function that is provably robust to instance-independent label noise, regardless of noise pattern, and it can be applied to any existing classification neural networks straightforwardly without any auxiliary information}.
Ranked #36 on Image Classification on Clothing1M
1 code implementation • ICLR 2019 • Peng Cao, Yilun Xu, Yuqing Kong, Yizhou Wang
Furthermore, we devise an accurate data-crowds forecaster that employs both the data and the crowdsourced labels to forecast the ground truth.