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Diffusion models (DMs) have been adopted across diverse fields with its remarkable abilities in capturing intricate data distributions. In this paper, we propose a Fast Diffusion Model (FDM) to significantly speed up DMs from a stochastic…

Computer Vision and Pattern Recognition · Computer Science 2023-10-05 Zike Wu , Pan Zhou , Kenji Kawaguchi , Hanwang Zhang

Channel state information (CSI) feedback is critical for frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems. Most conventional algorithms are based on compressive sensing (CS) and are highly dependent on the…

Signal Processing · Electrical Eng. & Systems 2020-04-17 Hongyuan Ye , Feifei Gao , Jing Qian , Hao Wang , Geoffrey Ye Li

We consider in this work an inverse acoustic scattering problem when only phaseless data is available. The inverse problem is highly nonlinear and ill-posed due to the lack of the phase information. Solving inverse scattering problems with…

Numerical Analysis · Mathematics 2025-03-26 Jianfeng Ning , Fuqun Han , Jun Zou

Discrete diffusion models are a powerful class of generative models with strong performance across many domains. For efficiency, however, discrete diffusion typically parameterizes the generative (reverse) process with factorized…

Machine Learning · Statistics 2026-05-19 Grigory Bartosh , Teodora Pandeva , Sushrut Karmalkar , Javier Zazo

We study the scattering of photons propagating in a semi-infinite waveguide terminated by a mirror and interacting with a quantum emitter. This paradigm constitutes an example of coherent quantum feedback, where light emitted towards the…

Quantum Physics · Physics 2017-09-15 Pierre-Olivier Guimond , Mikhail Pletyukhov , Hannes Pichler , Peter Zoller

Deep Convolutional Neural Networks (CNNs), such as Dense Convolutional Networks (DenseNet), have achieved great success for image representation by discovering deep hierarchical information. However, most existing networks simply stacks the…

Computer Vision and Pattern Recognition · Computer Science 2021-02-10 Zhao Zhang , Zemin Tang , Yang Wang , Zheng Zhang , Choujun Zhan , Zhengjun Zha , Meng Wang

Feed-forward neural networks consist of a sequence of layers, in which each layer performs some processing on the information from the previous layer. A downside to this approach is that each layer (or module, as multiple modules can…

Machine Learning · Computer Science 2020-10-19 Alex Lamb , Anirudh Goyal , Agnieszka Słowik , Michael Mozer , Philippe Beaudoin , Yoshua Bengio

In Wi-Fi systems, channel state information (CSI) plays a crucial role in enabling access points to execute beamforming operations. However, the feedback overhead associated with CSI significantly hampers the throughput improvements. Recent…

Signal Processing · Electrical Eng. & Systems 2024-07-09 Fan Qi , Jiajia Guo , Yiming Cui , Xiangyi Li , Chao-Kai Wen , Shi Jin

Accurate dose distribution prediction is crucial in the radiotherapy planning. Although previous methods based on convolutional neural network have shown promising performance, they have the problem of over-smoothing, leading to prediction…

Image and Video Processing · Electrical Eng. & Systems 2024-10-31 Xin Liao , Zhenghao Feng , Jianghong Xiao , Xingchen Peng , Yan Wang

We study a deep learning (DL) based limited feedback methods for multi-antenna systems. Deep neural networks (DNNs) are introduced to replace an end-to-end limited feedback procedure including pilot-aided channel training process, channel…

Information Theory · Computer Science 2019-12-20 Jeonghyeon Jang , Hoon Lee , Sangwon Hwang , Haibao Ren , Inkyu Lee

Despite the proliferation of generative models, achieving fast sampling during inference without compromising sample diversity and quality remains challenging. Existing models such as Denoising Diffusion Probabilistic Models (DDPM) deliver…

Machine Learning · Computer Science 2023-10-12 Yanwu Xu , Mingming Gong , Shaoan Xie , Wei Wei , Matthias Grundmann , Kayhan Batmanghelich , Tingbo Hou

Calculating perturbation response properties of materials from first principles provides a vital link between theory and experiment, but is bottlenecked by the high computational cost. Here a general framework is proposed to perform density…

Computational Physics · Physics 2024-03-01 He Li , Zechen Tang , Jingheng Fu , Wen-Han Dong , Nianlong Zou , Xiaoxun Gong , Wenhui Duan , Yong Xu

The WS time delay matrix relates a lossless and reciprocal system's scattering matrix to its frequency derivative, and enables the synthesis of modes that experience well-defined group delays when interacting with the system. The elements…

Computational Physics · Physics 2021-07-28 Utkarsh R. Patel , Eric Michielssen

Denoising Diffusion Probabilistic Models (DDPM) have recently gained significant attention. DDPMs compose a Markovian process that begins in the data domain and gradually adds noise until reaching pure white noise. DDPMs generate…

Computer Vision and Pattern Recognition · Computer Science 2024-07-09 Shady Abu-Hussein , Raja Giryes

Foundation Models (FMs) have revolutionized machine learning with their adaptability and high performance across tasks; yet, their integration into Federated Learning (FL) is challenging due to substantial communication overhead from their…

Machine Learning · Computer Science 2023-11-30 Vasileios Tsouvalas , Yuki Asano , Aaqib Saeed

We introduce the Fast Free Memory method (FFM), a new fast method for the numerical evaluation of convolution products. Inheriting from the Fast Multipole Method, the FFM is a descent-only and kernel-independent algorithm. We give the…

Numerical Analysis · Mathematics 2019-09-13 Matthieu Aussal , Marc Bakry

Generative models have shown robust performance on speech enhancement and restoration tasks, but most prior approaches operate offline with high latency, making them unsuitable for streaming applications. In this work, we investigate the…

Audio and Speech Processing · Electrical Eng. & Systems 2025-10-21 Tsun-An Hsieh , Sebastian Braun

Diffusion models have demonstrated remarkable potential in generating high-quality images. However, their tendency to replicate training data raises serious privacy concerns, particularly when the training datasets contain sensitive or…

Computer Vision and Pattern Recognition · Computer Science 2025-05-29 Jingqi Xu , Chenghao Li , Yuke Zhang , Peter A. Beerel

Intracellular biochemical networks fluctuate dynamically due to various internal and external sources of fluctuation. Dissecting the fluctuation into biologically relevant components is important for understanding how a cell controls and…

Molecular Networks · Quantitative Biology 2016-02-17 Tetsuya J. Kobayashi , Ryo Yokota , Kazuyuki Aihara

In order to integrate uncertainty estimates into deep time-series modelling, Kalman Filters (KFs) (Kalman et al., 1960) have been integrated with deep learning models, however, such approaches typically rely on approximate inference…

Machine Learning · Computer Science 2019-05-20 Philipp Becker , Harit Pandya , Gregor Gebhardt , Cheng Zhao , James Taylor , Gerhard Neumann