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RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network | 2505.22427v1 | el2015radar | \cite{el2015radar} | Radar and vision sensors calibration for outdoor 3D reconstruction | null | null | true | false | El Natour, Ghina and Aider, Omar Ait and Rouveure, Raphael and Berry, Fran{\c{c}}ois and Faure, Patrice | 2,015 | null | null | null | null | Radar and vision sensors calibration for outdoor 3D reconstruction | Radar and vision sensors calibration for outdoor 3D reconstruction | https://ieeexplore.ieee.org/document/7139473/ | In this paper we introduce a new geometric calibration algorithm, and a geometric method of 3D reconstruction using a panoramic microwave radar and a camera |
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network | 2505.22427v1 | li2023automatic | \cite{li2023automatic} | Automatic targetless LiDAR--camera calibration: a survey | null | null | true | false | Li, Xingchen and Xiao, Yuxuan and Wang, Beibei and Ren, Haojie and Zhang, Yanyong and Ji, Jianmin | 2,023 | null | null | null | Artificial Intelligence Review | Automatic targetless LiDAR--camera calibration: a survey | Automatic targetless LiDAR–camera calibration: a survey | https://link.springer.com/article/10.1007/s10462-022-10317-y | This paper reviews the existing calibration algorithms for automatic targetless calibration between LiDARs and cameras. Unmanned intelligent |
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network | 2505.22427v1 | pandey2012automatic | \cite{pandey2012automatic} | Automatic targetless extrinsic calibration of a 3d lidar and camera by maximizing mutual information | null | null | true | false | Pandey, Gaurav and McBride, James and Savarese, Silvio and Eustice, Ryan | 2,012 | null | null | null | null | Automatic targetless extrinsic calibration of a 3d lidar and camera by maximizing mutual information | (PDF) Automatic Targetless Extrinsic Calibration of a 3D Lidar and ... | https://www.researchgate.net/publication/267843813_Automatic_Targetless_Extrinsic_Calibration_of_a_3D_Lidar_and_Camera_by_Maximizing_Mutual_Information | This paper reports on an algorithm for automatic, targetless, extrinsic calibration of a lidar and optical camera system based upon the maximization of mutual |
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network | 2505.22427v1 | taylor2015motion | \cite{taylor2015motion} | Motion-based calibration of multimodal sensor arrays | null | null | true | false | Taylor, Zachary and Nieto, Juan | 2,015 | null | null | null | null | Motion-based calibration of multimodal sensor arrays | (PDF) Motion-Based Calibration of Multimodal Sensor Arrays | https://www.researchgate.net/publication/273576814_Motion-Based_Calibration_of_Multimodal_Sensor_Arrays | This paper formulates a new pipeline for automated extrinsic calibration of multi-sensor mobile platforms. The new method can operate on any combination of |
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network | 2505.22427v1 | levinson2013automatic | \cite{levinson2013automatic} | Automatic online calibration of cameras and lasers. | null | null | true | false | Levinson, Jesse and Thrun, Sebastian | 2,013 | null | null | null | null | Automatic online calibration of cameras and lasers. | Automatic Online Calibration of Cameras and Lasers | https://www.roboticsproceedings.org/rss09/p29.pdf | by J Levinson · Cited by 379 — In this paper, we introduce two new real-time techniques that enable camera-laser calibration online, automatically, and in arbitrary environments. The |
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network | 2505.22427v1 | yuan2021pixel | \cite{yuan2021pixel} | Pixel-level Extrinsic Self Calibration of High Resolution LiDAR and
Camera in Targetless Environments | http://arxiv.org/abs/2103.01627v2 | In this letter, we present a novel method for automatic extrinsic calibration
of high-resolution LiDARs and RGB cameras in targetless environments. Our
approach does not require checkerboards but can achieve pixel-level accuracy by
aligning natural edge features in the two sensors. On the theory level, we
analyze the c... | true | true | Yuan, Chongjian and Liu, Xiyuan and Hong, Xiaoping and Zhang, Fu | 2,021 | null | null | null | IEEE Robotics and Automation Letters | Pixel-level Extrinsic Self Calibration of High Resolution LiDAR and
Camera in Targetless Environments | Pixel-level Extrinsic Self Calibration of High Resolution LiDAR and ... | https://arxiv.org/abs/2103.01627 | In this letter, we present a novel method for automatic extrinsic calibration of high-resolution LiDARs and RGB cameras in targetless environments. |
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network | 2505.22427v1 | schneider2017regnet | \cite{schneider2017regnet} | RegNet: Multimodal Sensor Registration Using Deep Neural Networks | http://arxiv.org/abs/1707.03167v1 | In this paper, we present RegNet, the first deep convolutional neural network
(CNN) to infer a 6 degrees of freedom (DOF) extrinsic calibration between
multimodal sensors, exemplified using a scanning LiDAR and a monocular camera.
Compared to existing approaches, RegNet casts all three conventional
calibration steps (f... | true | true | Schneider, Nick and Piewak, Florian and Stiller, Christoph and Franke, Uwe | 2,017 | null | null | null | null | RegNet: Multimodal Sensor Registration Using Deep Neural Networks | RegNet: Multimodal Sensor Registration Using Deep Neural Networks | http://arxiv.org/pdf/1707.03167v1 | In this paper, we present RegNet, the first deep convolutional neural network
(CNN) to infer a 6 degrees of freedom (DOF) extrinsic calibration between
multimodal sensors, exemplified using a scanning LiDAR and a monocular camera.
Compared to existing approaches, RegNet casts all three conventional
calibration steps (f... |
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network | 2505.22427v1 | iyer2018calibnet | \cite{iyer2018calibnet} | CalibNet: Geometrically Supervised Extrinsic Calibration using 3D
Spatial Transformer Networks | http://arxiv.org/abs/1803.08181v2 | 3D LiDARs and 2D cameras are increasingly being used alongside each other in
sensor rigs for perception tasks. Before these sensors can be used to gather
meaningful data, however, their extrinsics (and intrinsics) need to be
accurately calibrated, as the performance of the sensor rig is extremely
sensitive to these cal... | true | true | Iyer, Ganesh and Ram, R Karnik and Murthy, J Krishna and Krishna, K Madhava | 2,018 | null | null | null | null | CalibNet: Geometrically Supervised Extrinsic Calibration using 3D
Spatial Transformer Networks | CalibNet: Geometrically Supervised Extrinsic Calibration ... | https://dl.acm.org/doi/10.1109/IROS.2018.8593693 | by G Iyer · 2018 · Cited by 247 — CalibNet: Geometrically Supervised Extrinsic Calibration using 3D Spatial Transformer Networks. Authors: Ganesh Iyer. Ganesh Iyer. Robotics Research Center |
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network | 2505.22427v1 | shi2020calibrcnn | \cite{shi2020calibrcnn} | Calibrcnn: Calibrating camera and lidar by recurrent convolutional neural network and geometric constraints | null | null | true | false | Shi, Jieying and Zhu, Ziheng and Zhang, Jianhua and Liu, Ruyu and Wang, Zhenhua and Chen, Shengyong and Liu, Honghai | 2,020 | null | null | null | null | Calibrcnn: Calibrating camera and lidar by recurrent convolutional neural network and geometric constraints | Calibrating Camera and LiDAR by recurrent convolutional neural ... | https://researchportal.port.ac.uk/en/publications/calibrcnn(a901bae3-8f6e-49d3-89e2-1c503f95db11).html | Missing: 04/08/2025 |
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network | 2505.22427v1 | sak2014long | \cite{sak2014long} | Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition | null | null | true | false | Sak, Ha{\c{s}}im and Senior, Andrew and Beaufays, Fran{\c{c}}oise | 2,014 | null | null | null | arXiv preprint arXiv:1402.1128 | Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition | long short-term memory based recurrent neural network ... - ar5iv | https://ar5iv.labs.arxiv.org/html/1402.1128 | In this paper, we show that LSTM based RNN architectures can obtain state of the art performance in a large vocabulary speech recognition system with thousands |
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network | 2505.22427v1 | lv2021lccnet | \cite{lv2021lccnet} | LCCNet: LiDAR and Camera Self-Calibration using Cost Volume Network | http://arxiv.org/abs/2012.13901v2 | In this paper, we propose a novel online self-calibration approach for Light
Detection and Ranging (LiDAR) and camera sensors. Compared to the previous
CNN-based methods that concatenate the feature maps of the RGB image and
decalibrated depth image, we exploit the cost volume inspired by the PWC-Net
for feature matchi... | true | true | Lv, Xudong and Wang, Boya and Dou, Ziwen and Ye, Dong and Wang, Shuo | 2,021 | null | null | null | null | LCCNet: LiDAR and Camera Self-Calibration using Cost Volume Network | LCCNet: LiDAR and Camera Self-Calibration using Cost ... | https://arxiv.org/abs/2012.13901 | by X Lv · 2020 · Cited by 175 — Abstract:In this paper, we propose a novel online self-calibration approach for Light Detection and Ranging (LiDAR) and camera sensors.See more |
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network | 2505.22427v1 | pervsic2021online | \cite{pervsic2021online} | Online multi-sensor calibration based on moving object tracking | null | null | true | false | Per{\v{s}}i{\'c}, Juraj and Petrovi{\'c}, Luka and Markovi{\'c}, Ivan and Petrovi{\'c}, Ivan | 2,021 | null | null | null | Advanced Robotics | Online multi-sensor calibration based on moving object tracking | Online multi-sensor calibration based on moving object tracking | https://www.researchgate.net/publication/345092954_Online_multi-sensor_calibration_based_on_moving_object_tracking | Peršić et al. [5] propose an online targetless multi-sensor calibration method based on the detection and tracking of moving objects. It employs the tracking- |
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network | 2505.22427v1 | scholler2019targetless | \cite{scholler2019targetless} | Targetless Rotational Auto-Calibration of Radar and Camera for
Intelligent Transportation Systems | http://arxiv.org/abs/1904.08743v2 | Most intelligent transportation systems use a combination of radar sensors
and cameras for robust vehicle perception. The calibration of these
heterogeneous sensor types in an automatic fashion during system operation is
challenging due to differing physical measurement principles and the high
sparsity of traffic radar... | true | true | Sch{\"o}ller, Christoph and Schnettler, Maximilian and Kr{\"a}mmer, Annkathrin and Hinz, Gereon and Bakovic, Maida and G{\"u}zet, M{\"u}ge and Knoll, Alois | 2,019 | null | null | null | null | Targetless Rotational Auto-Calibration of Radar and Camera for
Intelligent Transportation Systems | Targetless Rotational Auto-Calibration of Radar and Camera ... - arXiv | https://arxiv.org/abs/1904.08743 | Authors:Christoph Schöller, Maximilian Schnettler, Annkathrin Krämmer, Gereon Hinz, Maida Bakovic, Müge Güzet, Alois Knoll View a PDF of the paper titled Targetless Rotational Auto-Calibration of Radar and Camera for Intelligent Transportation Systems, by Christoph Sch\"oller and 6 other authors Comments:Accepted at th... |
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network | 2505.22427v1 | wise2021continuous | \cite{wise2021continuous} | A Continuous-Time Approach for 3D Radar-to-Camera Extrinsic Calibration | http://arxiv.org/abs/2103.07505v2 | Reliable operation in inclement weather is essential to the deployment of
safe autonomous vehicles (AVs). Robustness and reliability can be achieved by
fusing data from the standard AV sensor suite (i.e., lidars, cameras) with
weather robust sensors, such as millimetre-wavelength radar. Critically,
accurate sensor data... | true | true | Wise, Emmett and Per{\v{s}}i{\'c}, Juraj and Grebe, Christopher and Petrovi{\'c}, Ivan and Kelly, Jonathan | 2,021 | null | null | null | null | A Continuous-Time Approach for 3D Radar-to-Camera Extrinsic Calibration | A Continuous-Time Approach for 3D Radar-to-Camera ... | https://dl.acm.org/doi/10.1109/ICRA48506.2021.9561938 | by E Wise · 2021 · Cited by 42 — In this paper, we present a continuous-time 3D radar-to-camera extrinsic calibration algorithm that utilizes radar velocity measurements and, unlike the |
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network | 2505.22427v1 | wise2023spatiotemporal | \cite{wise2023spatiotemporal} | Spatiotemporal Calibration of 3D Millimetre-Wavelength Radar-Camera
Pairs | http://arxiv.org/abs/2211.01871v4 | Autonomous vehicles (AVs) fuse data from multiple sensors and sensing
modalities to impart a measure of robustness when operating in adverse
conditions. Radars and cameras are popular choices for use in sensor fusion;
although radar measurements are sparse in comparison to camera images, radar
scans penetrate fog, rain... | true | true | Wise, Emmett and Cheng, Qilong and Kelly, Jonathan | 2,023 | null | null | null | IEEE Transactions on Robotics | Spatiotemporal Calibration of 3D Millimetre-Wavelength Radar-Camera
Pairs | Spatiotemporal Calibration of 3-D Millimetre-Wavelength Radar ... | http://ieeexplore.ieee.org/iel7/8860/10352149/10256219.pdf | During calibration, the approach in [6] filters radar-camera measurement pairs by return intensity; the intensity is maximal for reflectors that lie on the |
Q-VDiT: Towards Accurate Quantization and Distillation of
Video-Generation Diffusion Transformers | 2505.22167v1 | ho2020ddpm | \cite{ho2020ddpm} | Denoising Diffusion Probabilistic Models | http://arxiv.org/abs/2006.11239v2 | We present high quality image synthesis results using diffusion probabilistic
models, a class of latent variable models inspired by considerations from
nonequilibrium thermodynamics. Our best results are obtained by training on a
weighted variational bound designed according to a novel connection between
diffusion prob... | true | true | Ho, Jonathan and Jain, Ajay and Abbeel, Pieter | 2,020 | null | null | null | Advances in neural information processing systems | Denoising Diffusion Probabilistic Models | Denoising Diffusion Probabilistic Models | http://arxiv.org/pdf/2006.11239v2 | We present high quality image synthesis results using diffusion probabilistic
models, a class of latent variable models inspired by considerations from
nonequilibrium thermodynamics. Our best results are obtained by training on a
weighted variational bound designed according to a novel connection between
diffusion prob... |
Q-VDiT: Towards Accurate Quantization and Distillation of
Video-Generation Diffusion Transformers | 2505.22167v1 | rombach2022ldm | \cite{rombach2022ldm} | High-resolution image synthesis with latent diffusion models | null | null | true | false | Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj{\"o}rn | 2,022 | null | null | null | null | High-resolution image synthesis with latent diffusion models | [PDF] High-Resolution Image Synthesis With Latent Diffusion Models | https://openaccess.thecvf.com/content/CVPR2022/papers/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.pdf | High-Resolution Image Synthesis with Latent Diffusion Models Robin Rombach1 ∗ Andreas Blattmann1 ∗ Dominik Lorenz1 Patrick Esser Bj¨ orn Ommer1 1Ludwig Maximilian University of Munich & IWR, Heidelberg University, Germany Runway ML https://github.com/CompVis/latent-diffusion Abstract By decomposing the image formation ... |
Q-VDiT: Towards Accurate Quantization and Distillation of
Video-Generation Diffusion Transformers | 2505.22167v1 | li2024qdm | \cite{li2024qdm} | Q-dm: An efficient low-bit quantized diffusion model | null | null | true | false | Li, Yanjing and Xu, Sheng and Cao, Xianbin and Sun, Xiao and Zhang, Baochang | 2,024 | null | null | null | Advances in Neural Information Processing Systems | Q-dm: An efficient low-bit quantized diffusion model | Q-DM: An Efficient Low-bit Quantized Diffusion Model | https://proceedings.neurips.cc/paper_files/paper/2023/hash/f1ee1cca0721de55bb35cf28ab95e1b4-Abstract-Conference.html | We propose an efficient Q-DM to calculate low-bit DMs by considering both training and inference process in the same framework. |
Q-VDiT: Towards Accurate Quantization and Distillation of
Video-Generation Diffusion Transformers | 2505.22167v1 | zheng2024binarydm | \cite{zheng2024binarydm} | Binarydm: Towards accurate binarization of diffusion model | null | null | true | false | Zheng, Xingyu and Qin, Haotong and Ma, Xudong and Zhang, Mingyuan and Hao, Haojie and Wang, Jiakai and Zhao, Zixiang and Guo, Jinyang and Liu, Xianglong | 2,024 | null | null | null | arXiv preprint arXiv:2404.05662 | Binarydm: Towards accurate binarization of diffusion model | BinaryDM: Towards Accurate Binarization of Diffusion Model | https://arxiv.org/abs/2404.05662v1/ | In this paper, we propose BinaryDM, a novel accurate quantization-aware training approach to push the weights of diffusion models towards the limit of 1-bit. |
Q-VDiT: Towards Accurate Quantization and Distillation of
Video-Generation Diffusion Transformers | 2505.22167v1 | zheng2024bidm | \cite{zheng2024bidm} | BiDM: Pushing the Limit of Quantization for Diffusion Models | http://arxiv.org/abs/2412.05926v1 | Diffusion models (DMs) have been significantly developed and widely used in
various applications due to their excellent generative qualities. However, the
expensive computation and massive parameters of DMs hinder their practical use
in resource-constrained scenarios. As one of the effective compression
approaches, qua... | true | true | Zheng, Xingyu and Liu, Xianglong and Bian, Yichen and Ma, Xudong and Zhang, Yulun and Wang, Jiakai and Guo, Jinyang and Qin, Haotong | 2,024 | null | null | null | arXiv preprint arXiv:2412.05926 | BiDM: Pushing the Limit of Quantization for Diffusion Models | BiDM: Pushing the Limit of Quantization for Diffusion Models | http://arxiv.org/pdf/2412.05926v1 | Diffusion models (DMs) have been significantly developed and widely used in
various applications due to their excellent generative qualities. However, the
expensive computation and massive parameters of DMs hinder their practical use
in resource-constrained scenarios. As one of the effective compression
approaches, qua... |
Q-VDiT: Towards Accurate Quantization and Distillation of
Video-Generation Diffusion Transformers | 2505.22167v1 | lu2024terdit | \cite{lu2024terdit} | TerDiT: Ternary Diffusion Models with Transformers | http://arxiv.org/abs/2405.14854v2 | Recent developments in large-scale pre-trained text-to-image diffusion models
have significantly improved the generation of high-fidelity images,
particularly with the emergence of diffusion transformer models (DiTs). Among
diffusion models, diffusion transformers have demonstrated superior
image-generation capabilitie... | true | true | Lu, Xudong and Zhou, Aojun and Lin, Ziyi and Liu, Qi and Xu, Yuhui and Zhang, Renrui and Wen, Yafei and Ren, Shuai and Gao, Peng and Yan, Junchi and others | 2,024 | null | null | null | arXiv preprint arXiv:2405.14854 | TerDiT: Ternary Diffusion Models with Transformers | TerDiT: Ternary Diffusion Models with Transformers | http://arxiv.org/pdf/2405.14854v2 | Recent developments in large-scale pre-trained text-to-image diffusion models
have significantly improved the generation of high-fidelity images,
particularly with the emergence of diffusion transformer models (DiTs). Among
diffusion models, diffusion transformers have demonstrated superior
image-generation capabilitie... |
Q-VDiT: Towards Accurate Quantization and Distillation of
Video-Generation Diffusion Transformers | 2505.22167v1 | li2023qdiffusion | \cite{li2023qdiffusion} | Q-Diffusion: Quantizing Diffusion Models | http://arxiv.org/abs/2302.04304v3 | Diffusion models have achieved great success in image synthesis through
iterative noise estimation using deep neural networks. However, the slow
inference, high memory consumption, and computation intensity of the noise
estimation model hinder the efficient adoption of diffusion models. Although
post-training quantizat... | true | true | Li, Xiuyu and Liu, Yijiang and Lian, Long and Yang, Huanrui and Dong, Zhen and Kang, Daniel and Zhang, Shanghang and Keutzer, Kurt | 2,023 | null | null | null | null | Q-Diffusion: Quantizing Diffusion Models | Q-Diffusion: Quantizing Diffusion Models | http://arxiv.org/pdf/2302.04304v3 | Diffusion models have achieved great success in image synthesis through
iterative noise estimation using deep neural networks. However, the slow
inference, high memory consumption, and computation intensity of the noise
estimation model hinder the efficient adoption of diffusion models. Although
post-training quantizat... |
Q-VDiT: Towards Accurate Quantization and Distillation of
Video-Generation Diffusion Transformers | 2505.22167v1 | shang2023ptq4dm | \cite{shang2023ptq4dm} | Post-training Quantization on Diffusion Models | http://arxiv.org/abs/2211.15736v3 | Denoising diffusion (score-based) generative models have recently achieved
significant accomplishments in generating realistic and diverse data. These
approaches define a forward diffusion process for transforming data into noise
and a backward denoising process for sampling data from noise. Unfortunately,
the generati... | true | true | Shang, Yuzhang and Yuan, Zhihang and Xie, Bin and Wu, Bingzhe and Yan, Yan | 2,023 | null | null | null | null | Post-training Quantization on Diffusion Models | [2211.15736] Post-training Quantization on Diffusion Models - arXiv | https://arxiv.org/abs/2211.15736 | Our method can directly quantize full-precision DMs into 8-bit models while maintaining or even improving their performance in a training-free manner. |
Q-VDiT: Towards Accurate Quantization and Distillation of
Video-Generation Diffusion Transformers | 2505.22167v1 | he2024ptqd | \cite{he2024ptqd} | PTQD: Accurate Post-Training Quantization for Diffusion Models | http://arxiv.org/abs/2305.10657v4 | Diffusion models have recently dominated image synthesis tasks. However, the
iterative denoising process is expensive in computations at inference time,
making diffusion models less practical for low-latency and scalable real-world
applications. Post-training quantization (PTQ) of diffusion models can
significantly red... | true | true | He, Yefei and Liu, Luping and Liu, Jing and Wu, Weijia and Zhou, Hong and Zhuang, Bohan | 2,024 | null | null | null | Advances in Neural Information Processing Systems | PTQD: Accurate Post-Training Quantization for Diffusion Models | PTQD: Accurate Post-Training Quantization for Diffusion Models | https://arxiv.org/abs/2305.10657 | Post-training quantization (PTQ) of diffusion models can significantly reduce the model size and accelerate the sampling process without re-training. |
Q-VDiT: Towards Accurate Quantization and Distillation of
Video-Generation Diffusion Transformers | 2505.22167v1 | huang2024tfmq | \cite{huang2024tfmq} | Tfmq-dm: Temporal feature maintenance quantization for diffusion models | null | null | true | false | Huang, Yushi and Gong, Ruihao and Liu, Jing and Chen, Tianlong and Liu, Xianglong | 2,024 | null | null | null | null | Tfmq-dm: Temporal feature maintenance quantization for diffusion models | TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models | http://arxiv.org/pdf/2311.16503v3 | The Diffusion model, a prevalent framework for image generation, encounters
significant challenges in terms of broad applicability due to its extended
inference times and substantial memory requirements. Efficient Post-training
Quantization (PTQ) is pivotal for addressing these issues in traditional
models. Different f... |
Q-VDiT: Towards Accurate Quantization and Distillation of
Video-Generation Diffusion Transformers | 2505.22167v1 | wang2024quest | \cite{wang2024quest} | QuEST: Low-bit Diffusion Model Quantization via Efficient Selective
Finetuning | http://arxiv.org/abs/2402.03666v6 | The practical deployment of diffusion models is still hindered by the high
memory and computational overhead. Although quantization paves a way for model
compression and acceleration, existing methods face challenges in achieving
low-bit quantization efficiently. In this paper, we identify imbalanced
activation distrib... | true | true | Wang, Haoxuan and Shang, Yuzhang and Yuan, Zhihang and Wu, Junyi and Yan, Yan | 2,024 | null | null | null | arXiv preprint arXiv:2402.03666 | QuEST: Low-bit Diffusion Model Quantization via Efficient Selective
Finetuning | Low-bit Diffusion Model Quantization via Efficient Selective Finetuning | https://arxiv.org/abs/2402.03666 | In this paper, we identify imbalanced activation distributions as a primary source of quantization difficulty, and propose to adjust these distributions |
Q-VDiT: Towards Accurate Quantization and Distillation of
Video-Generation Diffusion Transformers | 2505.22167v1 | he2023efficientdm | \cite{he2023efficientdm} | EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit
Diffusion Models | http://arxiv.org/abs/2310.03270v4 | Diffusion models have demonstrated remarkable capabilities in image synthesis
and related generative tasks. Nevertheless, their practicality for real-world
applications is constrained by substantial computational costs and latency
issues. Quantization is a dominant way to compress and accelerate diffusion
models, where... | true | true | He, Yefei and Liu, Jing and Wu, Weijia and Zhou, Hong and Zhuang, Bohan | 2,023 | null | null | null | arXiv preprint arXiv:2310.03270 | EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit
Diffusion Models | Efficient Quantization-Aware Fine-Tuning of Low-Bit ... | https://openreview.net/forum?id=UmMa3UNDAz | by Y He · Cited by 59 — We introduce a data-free, quantization-aware and parameter-efficient fine-tuning framework for low-bit diffusion models, dubbed EfficientDM, to achieve QAT- |
Q-VDiT: Towards Accurate Quantization and Distillation of
Video-Generation Diffusion Transformers | 2505.22167v1 | zhao2025mixdq | \cite{zhao2025mixdq} | MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with
Metric-Decoupled Mixed Precision Quantization | http://arxiv.org/abs/2405.17873v2 | Diffusion models have achieved significant visual generation quality.
However, their significant computational and memory costs pose challenge for
their application on resource-constrained mobile devices or even desktop GPUs.
Recent few-step diffusion models reduces the inference time by reducing the
denoising steps. H... | true | true | Zhao, Tianchen and Ning, Xuefei and Fang, Tongcheng and Liu, Enshu and Huang, Guyue and Lin, Zinan and Yan, Shengen and Dai, Guohao and Wang, Yu | 2,025 | null | null | null | null | MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with
Metric-Decoupled Mixed Precision Quantization | MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion ... | https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/02212.pdf | by T Zhao12 · Cited by 29 — MixDQ is a mixed-precision quantization method for few-step text-to-image models, compressing memory by 3.4x without performance loss. |
Q-VDiT: Towards Accurate Quantization and Distillation of
Video-Generation Diffusion Transformers | 2505.22167v1 | chen2024qdit | \cite{chen2024qdit} | Q-dit: Accurate post-training quantization for diffusion transformers | null | null | true | false | Chen, Lei and Meng, Yuan and Tang, Chen and Ma, Xinzhu and Jiang, Jingyan and Wang, Xin and Wang, Zhi and Zhu, Wenwu | 2,024 | null | null | null | arXiv preprint arXiv:2406.17343 | Q-dit: Accurate post-training quantization for diffusion transformers | [PDF] Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers | https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Q-DiT_Accurate_Post-Training_Quantization_for_Diffusion_Transformers_CVPR_2025_paper.pdf | Post-Training. Quantization (PTQ) emerges as a promising solution, en- abling model compression and accelerated inference for pretrained models, without the |
Q-VDiT: Towards Accurate Quantization and Distillation of
Video-Generation Diffusion Transformers | 2505.22167v1 | wu2024ptq4dit | \cite{wu2024ptq4dit} | PTQ4DiT: Post-training Quantization for Diffusion Transformers | http://arxiv.org/abs/2405.16005v3 | The recent introduction of Diffusion Transformers (DiTs) has demonstrated
exceptional capabilities in image generation by using a different backbone
architecture, departing from traditional U-Nets and embracing the scalable
nature of transformers. Despite their advanced capabilities, the wide
deployment of DiTs, partic... | true | true | Wu, Junyi and Wang, Haoxuan and Shang, Yuzhang and Shah, Mubarak and Yan, Yan | 2,024 | null | null | null | arXiv preprint arXiv:2405.16005 | PTQ4DiT: Post-training Quantization for Diffusion Transformers | PTQ4DiT: Post-training Quantization for Diffusion Transformers | https://openreview.net/forum?id=NLmAGkN6nn&referrer=%5Bthe%20profile%20of%20Haoxuan%20Wang%5D(%2Fprofile%3Fid%3D~Haoxuan_Wang1) | This paper presents PTQ4DiT, a quantization method designed for diffusion transformers. The method focuses on addressing quantization challenges |
Q-VDiT: Towards Accurate Quantization and Distillation of
Video-Generation Diffusion Transformers | 2505.22167v1 | li2024svdqunat | \cite{li2024svdqunat} | Svdqunat: Absorbing outliers by low-rank components for 4-bit diffusion models | null | null | true | false | Li, Muyang and Lin, Yujun and Zhang, Zhekai and Cai, Tianle and Li, Xiuyu and Guo, Junxian and Xie, Enze and Meng, Chenlin and Zhu, Jun-Yan and Han, Song | 2,024 | null | null | null | arXiv preprint arXiv:2411.05007 | Svdqunat: Absorbing outliers by low-rank components for 4-bit diffusion models | SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit ... | https://arxiv.org/html/2411.05007v1 | SVDQuant is a post-training quantization technique for 4-bit weights and activations that well maintains visual fidelity. |
Q-VDiT: Towards Accurate Quantization and Distillation of
Video-Generation Diffusion Transformers | 2505.22167v1 | zhao2024vidit | \cite{zhao2024vidit} | ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation | null | null | true | false | Zhao, Tianchen and Fang, Tongcheng and Liu, Enshu and Rui, Wan and Soedarmadji, Widyadewi and Li, Shiyao and Lin, Zinan and Dai, Guohao and Yan, Shengen and Yang, Huazhong and others | 2,024 | null | null | null | arXiv preprint arXiv:2406.02540 | ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation | ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation | http://arxiv.org/pdf/2406.02540v3 | Diffusion transformers have demonstrated remarkable performance in visual
generation tasks, such as generating realistic images or videos based on
textual instructions. However, larger model sizes and multi-frame processing
for video generation lead to increased computational and memory costs, posing
challenges for pra... |
ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation
with Lightweight Specialized LLM | 2505.22552v1 | fever | \cite{fever} | FEVER: a large-scale dataset for Fact Extraction and VERification | http://arxiv.org/abs/1803.05355v3 | In this paper we introduce a new publicly available dataset for verification
against textual sources, FEVER: Fact Extraction and VERification. It consists
of 185,445 claims generated by altering sentences extracted from Wikipedia and
subsequently verified without knowledge of the sentence they were derived from.
The cl... | true | true | James Thorne and
Andreas Vlachos and
Christos Christodoulopoulos and
Arpit Mittal | 2,018 | null | https://doi.org/10.18653/v1/n18-1074 | 10.18653/V1/N18-1074 | null | FEVER: a large-scale dataset for Fact Extraction and VERification | FEVER: a Large-scale Dataset for Fact Extraction and ... | https://aclanthology.org/N18-1074/ | by J Thorne · 2018 · Cited by 2060 — In this paper we introduce a new publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification. |
ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation
with Lightweight Specialized LLM | 2505.22552v1 | faviq | \cite{faviq} | {F}a{VIQ}: {FA}ct Verification from Information-seeking Questions | null | null | true | false | Park, Jungsoo and
Min, Sewon and
Kang, Jaewoo and
Zettlemoyer, Luke and
Hajishirzi, Hannaneh | 2,022 | null | https://aclanthology.org/2022.acl-long.354/ | 10.18653/v1/2022.acl-long.354 | null | {F}a{VIQ}: {FA}ct Verification from Information-seeking Questions | FAVIQ: FAct Verification from Information-seeking Questions | https://aclanthology.org/2022.acl-long.354.pdf | by J Park · 2022 · Cited by 39 — We construct a fact verification dataset from highly ambiguous information-seeking questions. Our claims have significantly less lexical bias |
ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation
with Lightweight Specialized LLM | 2505.22552v1 | vitamin-c | \cite{vitamin-c} | Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence | http://arxiv.org/abs/2103.08541v1 | Typical fact verification models use retrieved written evidence to verify
claims. Evidence sources, however, often change over time as more information
is gathered and revised. In order to adapt, models must be sensitive to subtle
differences in supporting evidence. We present VitaminC, a benchmark infused
with challen... | true | true | Schuster, Tal and
Fisch, Adam and
Barzilay, Regina | 2,021 | null | https://aclanthology.org/2021.naacl-main.52/ | 10.18653/v1/2021.naacl-main.52 | null | Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence | Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence | http://arxiv.org/pdf/2103.08541v1 | Typical fact verification models use retrieved written evidence to verify
claims. Evidence sources, however, often change over time as more information
is gathered and revised. In order to adapt, models must be sensitive to subtle
differences in supporting evidence. We present VitaminC, a benchmark infused
with challen... |
ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation
with Lightweight Specialized LLM | 2505.22552v1 | hover | \cite{hover} | HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification | http://arxiv.org/abs/2011.03088v2 | We introduce HoVer (HOppy VERification), a dataset for many-hop evidence
extraction and fact verification. It challenges models to extract facts from
several Wikipedia articles that are relevant to a claim and classify whether
the claim is Supported or Not-Supported by the facts. In HoVer, the claims
require evidence t... | true | true | Yichen Jiang and
Shikha Bordia and
Zheng Zhong and
Charles Dognin and
Maneesh Kumar Singh and
Mohit Bansal | 2,020 | null | https://doi.org/10.18653/v1/2020.findings-emnlp.309 | 10.18653/V1/2020.FINDINGS-EMNLP.309 | null | HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification | HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification | https://arxiv.org/abs/2011.03088 | We introduce HoVer (HOppy VERification), a dataset for many-hop evidence extraction and fact verification. It challenges models to extract facts from several |
ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation
with Lightweight Specialized LLM | 2505.22552v1 | graph-review | \cite{graph-review} | Graph Neural Networks: A Review of Methods and Applications | http://arxiv.org/abs/1812.08434v6 | Lots of learning tasks require dealing with graph data which contains rich
relation information among elements. Modeling physics systems, learning
molecular fingerprints, predicting protein interface, and classifying diseases
demand a model to learn from graph inputs. In other domains such as learning
from non-structur... | true | true | Jie Zhou and
Ganqu Cui and
Shengding Hu and
Zhengyan Zhang and
Cheng Yang and
Zhiyuan Liu and
Lifeng Wang and
Changcheng Li and
Maosong Sun | 2,020 | null | https://doi.org/10.1016/j.aiopen.2021.01.001 | 10.1016/J.AIOPEN.2021.01.001 | {AI} Open | Graph Neural Networks: A Review of Methods and Applications | Graph Neural Networks: A Review of Methods and Applications | http://arxiv.org/pdf/1812.08434v6 | Lots of learning tasks require dealing with graph data which contains rich
relation information among elements. Modeling physics systems, learning
molecular fingerprints, predicting protein interface, and classifying diseases
demand a model to learn from graph inputs. In other domains such as learning
from non-structur... |
ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation
with Lightweight Specialized LLM | 2505.22552v1 | tapas | \cite{tapas} | TAPAS: Weakly Supervised Table Parsing via Pre-training | http://arxiv.org/abs/2004.02349v2 | Answering natural language questions over tables is usually seen as a
semantic parsing task. To alleviate the collection cost of full logical forms,
one popular approach focuses on weak supervision consisting of denotations
instead of logical forms. However, training semantic parsers from weak
supervision poses difficu... | true | true | Herzig, Jonathan and
Nowak, Pawel Krzysztof and
M{\"u}ller, Thomas and
Piccinno, Francesco and
Eisenschlos, Julian | 2,020 | null | https://aclanthology.org/2020.acl-main.398/ | 10.18653/v1/2020.acl-main.398 | null | TAPAS: Weakly Supervised Table Parsing via Pre-training | TaPas: Weakly Supervised Table Parsing via Pre-training | https://aclanthology.org/2020.acl-main.398/ | by J Herzig · 2020 · Cited by 784 — TaPas trains from weak supervision, and predicts the denotation by selecting table cells and optionally applying a corresponding aggregation operator to such |
ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation
with Lightweight Specialized LLM | 2505.22552v1 | rat-sql | \cite{rat-sql} | {RAT-SQL}: Relation-Aware Schema Encoding and Linking for Text-to-{SQL} Parsers | null | null | true | false | Wang, Bailin and
Shin, Richard and
Liu, Xiaodong and
Polozov, Oleksandr and
Richardson, Matthew | 2,020 | null | https://aclanthology.org/2020.acl-main.677/ | 10.18653/v1/2020.acl-main.677 | null | {RAT-SQL}: Relation-Aware Schema Encoding and Linking for Text-to-{SQL} Parsers | RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to ... | https://arxiv.org/abs/1911.04942 | View a PDF of the paper titled RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers, by Bailin Wang and 4 other authors. |
ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation
with Lightweight Specialized LLM | 2505.22552v1 | programfc | \cite{programfc} | Fact-Checking Complex Claims with Program-Guided Reasoning | http://arxiv.org/abs/2305.12744v1 | Fact-checking real-world claims often requires collecting multiple pieces of
evidence and applying complex multi-step reasoning. In this paper, we present
Program-Guided Fact-Checking (ProgramFC), a novel fact-checking model that
decomposes complex claims into simpler sub-tasks that can be solved using a
shared library... | true | true | Liangming Pan and
Xiaobao Wu and
Xinyuan Lu and
Anh Tuan Luu and
William Yang Wang and
Min{-}Yen Kan and
Preslav Nakov | 2,023 | null | https://doi.org/10.18653/v1/2023.acl-long.386 | 10.18653/V1/2023.ACL-LONG.386 | null | Fact-Checking Complex Claims with Program-Guided Reasoning | Fact-Checking Complex Claims with Program-Guided ... | https://aclanthology.org/2023.acl-long.386/ | by L Pan · 2023 · Cited by 158 — A novel fact-checking model that decomposes complex claims into simpler sub-tasks that can be solved using a shared library of specialized functions.See more |
ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation
with Lightweight Specialized LLM | 2505.22552v1 | folk | \cite{folk} | Explainable Claim Verification via Knowledge-Grounded Reasoning with
Large Language Models | http://arxiv.org/abs/2310.05253v2 | Claim verification plays a crucial role in combating misinformation. While
existing works on claim verification have shown promising results, a crucial
piece of the puzzle that remains unsolved is to understand how to verify claims
without relying on human-annotated data, which is expensive to create at a
large scale. ... | true | true | Haoran Wang and
Kai Shu | 2,023 | null | https://doi.org/10.18653/v1/2023.findings-emnlp.416 | 10.18653/V1/2023.FINDINGS-EMNLP.416 | null | Explainable Claim Verification via Knowledge-Grounded Reasoning with
Large Language Models | [PDF] Explainable Claim Verification via Knowledge-Grounded Reasoning ... | https://aclanthology.org/2023.findings-emnlp.416.pdf | FOLK uses LLMs to translate claims into First-Order Logic, then uses knowledge-grounded reasoning to verify claims and generate explanations. |
ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation
with Lightweight Specialized LLM | 2505.22552v1 | factkg | \cite{factkg} | FactKG: Fact Verification via Reasoning on Knowledge Graphs | http://arxiv.org/abs/2305.06590v2 | In real world applications, knowledge graphs (KG) are widely used in various
domains (e.g. medical applications and dialogue agents). However, for fact
verification, KGs have not been adequately utilized as a knowledge source. KGs
can be a valuable knowledge source in fact verification due to their
reliability and broa... | true | true | Jiho Kim and
Sungjin Park and
Yeonsu Kwon and
Yohan Jo and
James Thorne and
Edward Choi | 2,023 | null | https://doi.org/10.18653/v1/2023.acl-long.895 | 10.18653/V1/2023.ACL-LONG.895 | null | FactKG: Fact Verification via Reasoning on Knowledge Graphs | FactKG: Fact Verification via Reasoning on Knowledge Graphs | http://arxiv.org/pdf/2305.06590v2 | In real world applications, knowledge graphs (KG) are widely used in various
domains (e.g. medical applications and dialogue agents). However, for fact
verification, KGs have not been adequately utilized as a knowledge source. KGs
can be a valuable knowledge source in fact verification due to their
reliability and broa... |
ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation
with Lightweight Specialized LLM | 2505.22552v1 | kg_gpt | \cite{kg_gpt} | {KG-GPT:} {A} General Framework for Reasoning on Knowledge Graphs
Using Large Language Models | null | null | true | false | Jiho Kim and
Yeonsu Kwon and
Yohan Jo and
Edward Choi | 2,023 | null | https://doi.org/10.18653/v1/2023.findings-emnlp.631 | 10.18653/V1/2023.FINDINGS-EMNLP.631 | null | {KG-GPT:} {A} General Framework for Reasoning on Knowledge Graphs
Using Large Language Models | KG-GPT: A General Framework for Reasoning on Knowledge ... | https://www.researchgate.net/publication/376404206_KG-GPT_A_General_Framework_for_Reasoning_on_Knowledge_Graphs_Using_Large_Language_Models | Recently, Large Language Models (LLMs) have shown remarkable proficiency, prompting growing interest in AQA among researchers.GraphLLM: A General Framework for Multi-hop Question Answering over Knowledge Graphs Using Large Language Models . |
ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation
with Lightweight Specialized LLM | 2505.22552v1 | struct-gpt | \cite{struct-gpt} | {S}truct{GPT}: A General Framework for Large Language Model to Reason over Structured Data | null | null | true | false | Jiang, Jinhao and
Zhou, Kun and
Dong, Zican and
Ye, Keming and
Zhao, Xin and
Wen, Ji-Rong | 2,023 | null | https://aclanthology.org/2023.emnlp-main.574/ | 10.18653/v1/2023.emnlp-main.574 | null | {S}truct{GPT}: A General Framework for Large Language Model to Reason over Structured Data | StructGPT: A General Framework for Large Language Model ... - arXiv | https://arxiv.org/abs/2305.09645 | View a PDF of the paper titled StructGPT: A General Framework for Large Language Model to Reason over Structured Data, by Jinhao Jiang and 4 other authors > Abstract:In this paper, we study how to improve the zero-shot reasoning ability of large language models~(LLMs) over structured data in a unified way. View a PDF o... |
ClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation
with Lightweight Specialized LLM | 2505.22552v1 | reasoningongraph | \cite{reasoningongraph} | Reasoning on Graphs: Faithful and Interpretable Large Language Model
Reasoning | http://arxiv.org/abs/2310.01061v2 | Large language models (LLMs) have demonstrated impressive reasoning abilities
in complex tasks. However, they lack up-to-date knowledge and experience
hallucinations during reasoning, which can lead to incorrect reasoning
processes and diminish their performance and trustworthiness. Knowledge graphs
(KGs), which captur... | true | true | Linhao Luo and
Yuan{-}Fang Li and
Gholamreza Haffari and
Shirui Pan | 2,024 | null | https://openreview.net/forum?id=ZGNWW7xZ6Q | null | null | Reasoning on Graphs: Faithful and Interpretable Large Language Model
Reasoning | Faithful and Interpretable Large Language Model Reasoning | https://arxiv.org/abs/2310.01061 | **arXiv:2310.01061** (cs) View a PDF of the paper titled Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning, by Linhao Luo and 3 other authors (or arXiv:2310.01061v2 [cs.CL] for this version) View a PDF of the paper titled Reasoning on Graphs: Faithful and Interpretable Large Language Model... |
VDDP: Verifiable Distributed Differential Privacy under the
Client-Server-Verifier Setup | 2504.21752v1 | DBLP:conf/eurosys/NarayanFPH15 | \cite{DBLP:conf/eurosys/NarayanFPH15} | Verifiable Differential Privacy | http://arxiv.org/abs/2208.09011v2 | Differential Privacy (DP) is often presented as a strong privacy-enhancing
technology with broad applicability and advocated as a de-facto standard for
releasing aggregate statistics on sensitive data. However, in many embodiments,
DP introduces a new attack surface: a malicious entity entrusted with releasing
statisti... | true | true | Arjun Narayan and
Ariel Feldman and
Antonis Papadimitriou and
Andreas Haeberlen | 2,015 | null | https://doi.org/10.1145/2741948.2741978 | 10.1145/2741948.2741978 | null | Verifiable Differential Privacy | Verifiable Differential Privacy | http://arxiv.org/pdf/2208.09011v2 | Differential Privacy (DP) is often presented as a strong privacy-enhancing
technology with broad applicability and advocated as a de-facto standard for
releasing aggregate statistics on sensitive data. However, in many embodiments,
DP introduces a new attack surface: a malicious entity entrusted with releasing
statisti... |
VDDP: Verifiable Distributed Differential Privacy under the
Client-Server-Verifier Setup | 2504.21752v1 | dprio | \cite{dprio} | DPrio: Efficient Differential Privacy with High Utility for Prio | null | null | true | false | Dana Keeler and
Chelsea Komlo and
Emily Lepert and
Shannon Veitch and
Xi He | 2,023 | null | https://doi.org/10.56553/popets-2023-0086 | 10.56553/POPETS-2023-0086 | Proc. Priv. Enhancing Technol. | DPrio: Efficient Differential Privacy with High Utility for Prio | DPrio: Efficient Differential Privacy with High Utility for Prio | https://petsymposium.org/popets/2023/popets-2023-0086.php | We present a lightweight method that we call DPrio to augment Prio and related systems with differential privacy assurances while ensuring higher data utility. |
VDDP: Verifiable Distributed Differential Privacy under the
Client-Server-Verifier Setup | 2504.21752v1 | KCY21 | \cite{KCY21} | Preventing Manipulation Attack in Local Differential Privacy using
Verifiable Randomization Mechanism | http://arxiv.org/abs/2104.06569v2 | Several randomization mechanisms for local differential privacy (LDP) (e.g.,
randomized response) are well-studied to improve the utility. However, recent
studies show that LDP is generally vulnerable to malicious data providers in
nature. Because a data collector has to estimate background data distribution
only from ... | true | true | Fumiyuki Kato and
Yang Cao and
Masatoshi Yoshikawa | 2,021 | null | https://doi.org/10.1007/978-3-030-81242-3\_3 | 10.1007/978-3-030-81242-3\_3 | null | Preventing Manipulation Attack in Local Differential Privacy using
Verifiable Randomization Mechanism | Preventing Manipulation Attack in Local Differential Privacy ... | https://inria.hal.science/hal-03677038v1 | In this paper, we propose secure and efficient verifiable LDP protocols to prevent manipulation attacks. Specifically, we leverage Cryptographic Randomized |
VDDP: Verifiable Distributed Differential Privacy under the
Client-Server-Verifier Setup | 2504.21752v1 | DBLP:conf/iclr/ShamsabadiTCBHP24 | \cite{DBLP:conf/iclr/ShamsabadiTCBHP24} | Confidential-DPproof: Confidential Proof of Differentially Private
Training | null | null | true | false | Ali Shahin Shamsabadi and
Gefei Tan and
Tudor Cebere and
Aur{\'{e}}lien Bellet and
Hamed Haddadi and
Nicolas Papernot and
Xiao Wang and
Adrian Weller | 2,024 | null | https://openreview.net/forum?id=PQY2v6VtGe | null | null | Confidential-DPproof: Confidential Proof of Differentially Private
Training | [PDF] Confidential-DPproof - OpenReview | https://openreview.net/pdf?id=PQY2v6VtGe | We introduce Confidential-. DPproof, a framework for Confidential Proof of Differentially Private Training, which enhances training with a certificate of the (ε |
VDDP: Verifiable Distributed Differential Privacy under the
Client-Server-Verifier Setup | 2504.21752v1 | BC23 | \cite{BC23} | Interactive Proofs For Differentially Private Counting | null | null | true | false | Ari Biswas and
Graham Cormode | 2,023 | null | https://doi.org/10.1145/3576915.3616681 | 10.1145/3576915.3616681 | null | Interactive Proofs For Differentially Private Counting | Interactive Proofs For Differentially Private Counting | https://dl.acm.org/doi/10.1145/3576915.3616681 | We introduce the idea of Interactive Proofs For Differential Privacy, which requires the publishing entity to output a zero knowledge proof. |
VDDP: Verifiable Distributed Differential Privacy under the
Client-Server-Verifier Setup | 2504.21752v1 | DBLP:conf/pkc/AmbainisJL04 | \cite{DBLP:conf/pkc/AmbainisJL04} | Cryptographic Randomized Response Techniques | http://arxiv.org/abs/cs/0302025v2 | We develop cryptographically secure techniques to guarantee unconditional
privacy for respondents to polls. Our constructions are efficient and
practical, and are shown not to allow cheating respondents to affect the
``tally'' by more than their own vote -- which will be given the exact same
weight as that of other res... | true | true | Andris Ambainis and
Markus Jakobsson and
Helger Lipmaa | 2,004 | null | https://doi.org/10.1007/978-3-540-24632-9\_31 | 10.1007/978-3-540-24632-9\_31 | null | Cryptographic Randomized Response Techniques | Cryptographic Randomized Response Techniques | http://arxiv.org/pdf/cs/0302025v2 | We develop cryptographically secure techniques to guarantee unconditional
privacy for respondents to polls. Our constructions are efficient and
practical, and are shown not to allow cheating respondents to affect the
``tally'' by more than their own vote -- which will be given the exact same
weight as that of other res... |
VDDP: Verifiable Distributed Differential Privacy under the
Client-Server-Verifier Setup | 2504.21752v1 | DBLP:conf/sp/BonehBCGI21 | \cite{DBLP:conf/sp/BonehBCGI21} | Lightweight Techniques for Private Heavy Hitters | http://arxiv.org/abs/2012.14884v5 | This paper presents Poplar, a new system for solving the private
heavy-hitters problem. In this problem, there are many clients and a small set
of data-collection servers. Each client holds a private bitstring. The servers
want to recover the set of all popular strings, without learning anything else
about any client's... | true | true | Dan Boneh and
Elette Boyle and
Henry Corrigan{-}Gibbs and
Niv Gilboa and
Yuval Ishai | 2,021 | null | https://doi.org/10.1109/SP40001.2021.00048 | 10.1109/SP40001.2021.00048 | null | Lightweight Techniques for Private Heavy Hitters | Lightweight Techniques for Private Heavy Hitters | http://arxiv.org/pdf/2012.14884v5 | This paper presents Poplar, a new system for solving the private
heavy-hitters problem. In this problem, there are many clients and a small set
of data-collection servers. Each client holds a private bitstring. The servers
want to recover the set of all popular strings, without learning anything else
about any client's... |
VDDP: Verifiable Distributed Differential Privacy under the
Client-Server-Verifier Setup | 2504.21752v1 | DBLP:conf/sigmod/ChowdhuryW0MJ20 | \cite{DBLP:conf/sigmod/ChowdhuryW0MJ20} | Crypt$ε$: Crypto-Assisted Differential Privacy on Untrusted
Servers | http://arxiv.org/abs/1902.07756v5 | Differential privacy (DP) has steadily become the de-facto standard for
achieving privacy in data analysis, which is typically implemented either in
the "central" or "local" model. The local model has been more popular for
commercial deployments as it does not require a trusted data collector. This
increased privacy, h... | true | true | Amrita Roy Chowdhury and
Chenghong Wang and
Xi He and
Ashwin Machanavajjhala and
Somesh Jha | 2,020 | null | https://doi.org/10.1145/3318464.3380596 | 10.1145/3318464.3380596 | null | Crypt$ε$: Crypto-Assisted Differential Privacy on Untrusted
Servers | Crypt$ε$: Crypto-Assisted Differential Privacy on Untrusted Servers | https://arxiv.org/abs/1902.07756 | Crypt\epsilon allows data analysts to author logical DP programs that are automatically translated to secure protocols that work on encrypted data. |
VDDP: Verifiable Distributed Differential Privacy under the
Client-Server-Verifier Setup | 2504.21752v1 | DBLP:conf/ccs/BellBGL020 | \cite{DBLP:conf/ccs/BellBGL020} | Secure Single-Server Aggregation with (Poly)Logarithmic Overhead | null | null | true | false | James Henry Bell and
Kallista A. Bonawitz and
Adri{\`{a}} Gasc{\'{o}}n and
Tancr{\`{e}}de Lepoint and
Mariana Raykova | 2,020 | null | https://doi.org/10.1145/3372297.3417885 | 10.1145/3372297.3417885 | null | Secure Single-Server Aggregation with (Poly)Logarithmic Overhead | Secure Single-Server Aggregation with (Poly)Logarithmic Overhead | https://eprint.iacr.org/2020/704 | We present the first constructions for secure aggregation that achieve polylogarithmic communication and computation per client. |
VDDP: Verifiable Distributed Differential Privacy under the
Client-Server-Verifier Setup | 2504.21752v1 | DBLP:conf/eurocrypt/DworkKMMN06 | \cite{DBLP:conf/eurocrypt/DworkKMMN06} | Our Data, Ourselves: Privacy Via Distributed Noise Generation | null | null | true | false | Cynthia Dwork and
Krishnaram Kenthapadi and
Frank McSherry and
Ilya Mironov and
Moni Naor | 2,006 | null | https://doi.org/10.1007/11761679\_29 | 10.1007/11761679\_29 | null | Our Data, Ourselves: Privacy Via Distributed Noise Generation | [PDF] Our Data, Ourselves: Privacy via Distributed Noise Generation - IACR | https://iacr.org/archive/eurocrypt2006/40040493/40040493.pdf | Abstract. In this work we provide efficient distributed protocols for generating shares of random noise, secure against malicious participants. The purpose |
VDDP: Verifiable Distributed Differential Privacy under the
Client-Server-Verifier Setup | 2504.21752v1 | DBLP:conf/ccs/ChampionSU19 | \cite{DBLP:conf/ccs/ChampionSU19} | Securely Sampling Biased Coins with Applications to Differential Privacy | null | null | true | false | Jeffrey Champion and
Abhi Shelat and
Jonathan R. Ullman | 2,019 | null | https://doi.org/10.1145/3319535.3354256 | 10.1145/3319535.3354256 | null | Securely Sampling Biased Coins with Applications to Differential Privacy | Securely Sampling Biased Coins with Applications to ... | https://www.cs.utexas.edu/~jchamps/Slides/SecurelySampling.pdf | by J Champion · Cited by 37 — Securely Sampling Biased Coins with. Applications to Differential Privacy. Jeffrey Champion, abhi shelat, Jonathan Ullman. Northeastern University. Page 2 |
VDDP: Verifiable Distributed Differential Privacy under the
Client-Server-Verifier Setup | 2504.21752v1 | DBLP:conf/uss/BohlerK20 | \cite{DBLP:conf/uss/BohlerK20} | Secure Multi-party Computation of Differentially Private Median | null | null | true | false | Jonas B{\"{o}}hler and
Florian Kerschbaum | 2,020 | null | https://www.usenix.org/conference/usenixsecurity20/presentation/boehler | null | null | Secure Multi-party Computation of Differentially Private Median | [PDF] Secure Multi-party Computation of Differentially Private Median | https://www.usenix.org/system/files/sec20-bohler.pdf | In the following, we introduce preliminaries for differential privacy and secure multi-party computation. We consider a set of input parties P = |
VDDP: Verifiable Distributed Differential Privacy under the
Client-Server-Verifier Setup | 2504.21752v1 | DBLP:conf/ccs/BohlerK21 | \cite{DBLP:conf/ccs/BohlerK21} | Secure Multi-party Computation of Differentially Private Heavy Hitters | null | null | true | false | Jonas B{\"{o}}hler and
Florian Kerschbaum | 2,021 | null | https://doi.org/10.1145/3460120.3484557 | 10.1145/3460120.3484557 | null | Secure Multi-party Computation of Differentially Private Heavy Hitters | Secure Multi-party Computation of Differentially Private Heavy ... | https://dl.acm.org/doi/10.1145/3460120.3484557 | * Zhang Y Ye Q Hu H(2025)Federated Heavy Hitter Analytics with Local Differential Privacy Proceedings of the ACM on Management of Data 10.1145/3709739**3**:1(1-27)Online publication date: 11-Feb-2025https://dl.acm.org/doi/10.1145/3709739 * Fu Y Wang T Luo B Liao X Xu J Kirda E Lie D(2024)Benchmarking Secure Sampli... |
VDDP: Verifiable Distributed Differential Privacy under the
Client-Server-Verifier Setup | 2504.21752v1 | DBLP:journals/corr/abs-2109-10074 | \cite{DBLP:journals/corr/abs-2109-10074} | {STAR:} Distributed Secret Sharing for Private Threshold Aggregation
Reporting | null | null | true | false | Alex Davidson and
Peter Snyder and
E. B. Quirk and
Joseph Genereux and
Benjamin Livshits | 2,021 | null | https://arxiv.org/abs/2109.10074 | null | CoRR | {STAR:} Distributed Secret Sharing for Private Threshold Aggregation
Reporting | draft-dss-star-02 - STAR: Distributed Secret Sharing for ... | https://datatracker.ietf.org/doc/draft-dss-star/ | In this document we describe STAR, an efficient and secure threshold aggregation protocol for collecting measurements from clients by an untrusted aggregation |
VDDP: Verifiable Distributed Differential Privacy under the
Client-Server-Verifier Setup | 2504.21752v1 | DBLP:conf/ccs/WeiYFCW23 | \cite{DBLP:conf/ccs/WeiYFCW23} | Securely Sampling Discrete Gaussian Noise for Multi-Party Differential
Privacy | null | null | true | false | Chengkun Wei and
Ruijing Yu and
Yuan Fan and
Wenzhi Chen and
Tianhao Wang | 2,023 | null | https://doi.org/10.1145/3576915.3616641 | 10.1145/3576915.3616641 | null | Securely Sampling Discrete Gaussian Noise for Multi-Party Differential
Privacy | Securely Sampling Discrete Gaussian Noise for Multi-Party ... | https://dl.acm.org/doi/10.1145/3576915.3616641 | Our work presents the first MPC solution for sampling discrete Gaussian, a common type of noise used for constructing DP mechanisms, which plays nicely with |
VDDP: Verifiable Distributed Differential Privacy under the
Client-Server-Verifier Setup | 2504.21752v1 | DBLP:conf/ccs/FuW24 | \cite{DBLP:conf/ccs/FuW24} | Benchmarking Secure Sampling Protocols for Differential Privacy | http://arxiv.org/abs/2409.10667v2 | Differential privacy (DP) is widely employed to provide privacy protection
for individuals by limiting information leakage from the aggregated data. Two
well-known models of DP are the central model and the local model. The former
requires a trustworthy server for data aggregation, while the latter requires
individuals... | true | true | Yucheng Fu and
Tianhao Wang | 2,024 | null | https://doi.org/10.1145/3658644.3690257 | 10.1145/3658644.3690257 | null | Benchmarking Secure Sampling Protocols for Differential Privacy | Benchmarking Secure Sampling Protocols for Differential Privacy | http://arxiv.org/pdf/2409.10667v2 | Differential privacy (DP) is widely employed to provide privacy protection
for individuals by limiting information leakage from the aggregated data. Two
well-known models of DP are the central model and the local model. The former
requires a trustworthy server for data aggregation, while the latter requires
individuals... |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | TabelDiscovery | \cite{TabelDiscovery} | Table Discovery in Data Lakes: State-of-the-art and Future Directions | null | null | true | false | Grace Fan and
Jin Wang and
Yuliang Li and
Ren{\'{e}}e J. Miller | 2,023 | null | null | null | null | Table Discovery in Data Lakes: State-of-the-art and Future Directions | Table Discovery in Data Lakes: State-of-the-art and Future Directions | https://dl.acm.org/doi/pdf/10.1145/3555041.3589409 | We will cover table understanding tasks such as domain discov- ery, table annotation, and table representation learning which help data lake |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | DataLake_Survey | \cite{DataLake_Survey} | Data Lakes: A Survey of Functions and Systems | http://arxiv.org/abs/2106.09592v2 | Data lakes are becoming increasingly prevalent for big data management and
data analytics. In contrast to traditional 'schema-on-write' approaches such as
data warehouses, data lakes are repositories storing raw data in its original
formats and providing a common access interface. Despite the strong interest
raised fro... | true | true | Rihan Hai and
Christos Koutras and
Christoph Quix and
Matthias Jarke | 2,023 | null | null | null | {IEEE} Trans. Knowl. Data Eng. | Data Lakes: A Survey of Functions and Systems | Data Lakes: A Survey of Functions and Systems | http://arxiv.org/pdf/2106.09592v2 | Data lakes are becoming increasingly prevalent for big data management and
data analytics. In contrast to traditional 'schema-on-write' approaches such as
data warehouses, data lakes are repositories storing raw data in its original
formats and providing a common access interface. Despite the strong interest
raised fro... |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | AdelfioS13 | \cite{AdelfioS13} | Schema Extraction for Tabular Data on the Web | null | null | true | false | Marco D. Adelfio and
Hanan Samet | 2,013 | null | null | null | Proc. {VLDB} Endow. | Schema Extraction for Tabular Data on the Web | [PDF] Schema Extraction for Tabular Data on the Web ∗ - VLDB Endowment | http://www.vldb.org/pvldb/vol6/p421-adelfio.pdf | The schemas of these data ta- bles are determined using a classification technique based on conditional random fields in combination with a novel fea- ture |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | GoogleSearch | \cite{GoogleSearch} | Google Dataset Search: Building a search engine for datasets in an
open Web ecosystem | null | null | true | false | Dan Brickley and
Matthew Burgess and
Natasha F. Noy | 2,019 | null | null | null | null | Google Dataset Search: Building a search engine for datasets in an
open Web ecosystem | Building a search engine for datasets in an open Web ecosystem | https://research.google/pubs/google-dataset-search-building-a-search-engine-for-datasets-in-an-open-web-ecosystem/ | In this paper, we discuss Google Dataset Search, a dataset-discovery tool that provides search capabilities over potentially all datasets published on the Web. |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | JOSIE | \cite{JOSIE} | {JOSIE:} Overlap Set Similarity Search for Finding Joinable Tables in Data Lakes | null | null | true | false | Erkang Zhu and
Dong Deng and
Fatemeh Nargesian and
Ren{\'{e}}e J. Miller | 2,019 | null | null | null | null | {JOSIE:} Overlap Set Similarity Search for Finding Joinable Tables in Data Lakes | JOSIE: Overlap Set Similarity Search for Finding Joinable Tables in ... | https://dl.acm.org/doi/10.1145/3299869.3300065 | - JOSIE: Overlap Set Similarity Search for Finding Joinable Tables in Data Lakes # JOSIE: Overlap Set Similarity Search for Finding Joinable Tables in Data Lakes JOSIE: Overlap Set Similarity Search for Finding Joinable Tables in Data Lakes We show that JOSIE completely out performs the state-of-the-art overlap set sim... |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | Deepjoin | \cite{Deepjoin} | DeepJoin: Joinable Table Discovery with Pre-trained Language Models | http://arxiv.org/abs/2212.07588v2 | Due to the usefulness in data enrichment for data analysis tasks, joinable
table discovery has become an important operation in data lake management.
Existing approaches target equi-joins, the most common way of combining tables
for creating a unified view, or semantic joins, which tolerate misspellings and
different f... | true | true | Yuyang Dong and
Chuan Xiao and
Takuma Nozawa and
Masafumi Enomoto and
Masafumi Oyamada | 2,023 | null | null | null | Proc. {VLDB} Endow. | DeepJoin: Joinable Table Discovery with Pre-trained Language Models | [PDF] DeepJoin: Joinable Table Discovery with Pre-trained Language ... | https://www.vldb.org/pvldb/vol16/p2458-dong.pdf | DeepJoin is a deep learning model using a pre-trained language model for joinable table discovery, handling both equi- and semantic joins. |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | Snoopy | \cite{Snoopy} | Snoopy: Effective and Efficient Semantic Join Discovery via Proxy
Columns | http://arxiv.org/abs/2502.16813v1 | Semantic join discovery, which aims to find columns in a table repository
with high semantic joinabilities to a query column, is crucial for dataset
discovery. Existing methods can be divided into two categories: cell-level
methods and column-level methods. However, neither of them ensures both
effectiveness and effici... | true | true | Guo, Yuxiang and Mao, Yuren and Hu, Zhonghao and Chen, Lu and Gao, Yunjun | 2,025 | null | null | null | arXiv preprint arXiv:2502.16813 | Snoopy: Effective and Efficient Semantic Join Discovery via Proxy
Columns | Effective and Efficient Semantic Join Discovery via Proxy Columns | https://arxiv.org/abs/2502.16813 | A novel column-level semantic join discovery framework, Snoopy, is presented, leveraging proxy-column-based embeddings to bridge effectiveness and efficiency. |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | starmine | \cite{starmine} | Semantics-aware Dataset Discovery from Data Lakes with Contextualized
Column-based Representation Learning | http://arxiv.org/abs/2210.01922v2 | Dataset discovery from data lakes is essential in many real application
scenarios. In this paper, we propose Starmie, an end-to-end framework for
dataset discovery from data lakes (with table union search as the main use
case). Our proposed framework features a contrastive learning method to train
column encoders from ... | true | true | Grace Fan and
Jin Wang and
Yuliang Li and
Dan Zhang and
Ren{\'{e}}e J. Miller | 2,023 | null | null | null | Proc. {VLDB} Endow. | Semantics-aware Dataset Discovery from Data Lakes with Contextualized
Column-based Representation Learning | Semantics-aware Dataset Discovery from Data Lakes with ... | https://www.researchgate.net/publication/364194737_Semantics-aware_Dataset_Discovery_from_Data_Lakes_with_Contextualized_Column-based_Representation_Learning | Our proposed framework features a contrastive learning method to train column encoders from pre-trained language models in a fully unsupervised |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | santos | \cite{santos} | SANTOS: Relationship-based Semantic Table Union Search | http://arxiv.org/abs/2209.13589v1 | Existing techniques for unionable table search define unionability using
metadata (tables must have the same or similar schemas) or column-based metrics
(for example, the values in a table should be drawn from the same domain). In
this work, we introduce the use of semantic relationships between pairs of
columns in a t... | true | true | Aamod Khatiwada and
Grace Fan and
Roee Shraga and
Zixuan Chen and
Wolfgang Gatterbauer and
Ren{\'{e}}e J. Miller and
Mirek Riedewald | 2,023 | null | null | null | Proc. {ACM} Manag. Data | SANTOS: Relationship-based Semantic Table Union Search | SANTOS: Relationship-based Semantic Table Union Search | https://dl.acm.org/doi/10.1145/3588689 | Our new unionability search algorithm, called SANTOS, outperforms a state-of-the-art union search that uses a wide variety of column-based semantics. |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | TUS | \cite{TUS} | Table Union Search on Open Data | null | null | true | false | Fatemeh Nargesian and
Erkang Zhu and
Ken Q. Pu and
Ren{\'{e}}e J. Miller | 2,018 | null | null | null | Proc. {VLDB} Endow. | Table Union Search on Open Data | [PDF] Table Union Search on Open Data | https://www.semanticscholar.org/paper/Table-Union-Search-on-Open-Data-Nargesian-Zhu/5cadff7988d29c1596689d5b864f87f371783a50 | This work defines the table union search problem and presents a probabilistic solution for finding tables that are unionable with a query table within |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | Solo | \cite{Solo} | Solo: Data Discovery Using Natural Language Questions Via A
Self-Supervised Approach | http://arxiv.org/abs/2301.03560v2 | Most deployed data discovery systems, such as Google Datasets, and open data
portals only support keyword search. Keyword search is geared towards general
audiences but limits the types of queries the systems can answer. We propose a
new system that lets users write natural language questions directly. A major
barrier ... | true | true | Qiming Wang and
Raul Castro Fernandez | 2,023 | null | null | null | Proc. {ACM} Manag. Data | Solo: Data Discovery Using Natural Language Questions Via A
Self-Supervised Approach | [PDF] Solo: Data Discovery Using Natural Language Questions Via A Self ... | https://arxiv.org/pdf/2301.03560 | Solo is a system that allows users to write natural language questions for data discovery, using a self-supervised approach to train the system. |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | OpenDTR | \cite{OpenDTR} | Open Domain Question Answering over Tables via Dense Retrieval | http://arxiv.org/abs/2103.12011v2 | Recent advances in open-domain QA have led to strong models based on dense
retrieval, but only focused on retrieving textual passages. In this work, we
tackle open-domain QA over tables for the first time, and show that retrieval
can be improved by a retriever designed to handle tabular context. We present
an effective... | true | true | Jonathan Herzig and
Thomas M{\"{u}}ller and
Syrine Krichene and
Julian Martin Eisenschlos | 2,021 | null | null | null | null | Open Domain Question Answering over Tables via Dense Retrieval | Open Domain Question Answering over Tables via Dense Retrieval | http://arxiv.org/pdf/2103.12011v2 | Recent advances in open-domain QA have led to strong models based on dense
retrieval, but only focused on retrieving textual passages. In this work, we
tackle open-domain QA over tables for the first time, and show that retrieval
can be improved by a retriever designed to handle tabular context. We present
an effective... |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | OpenWiki | \cite{OpenWiki} | Open-WikiTable : Dataset for Open Domain Question Answering with Complex
Reasoning over Table | null | null | true | false | Sunjun Kweon and
Yeonsu Kwon and
Seonhee Cho and
Yohan Jo and
Edward Choi | 2,023 | null | null | null | null | Open-WikiTable : Dataset for Open Domain Question Answering with Complex
Reasoning over Table | Open-WikiTable :Dataset for Open Domain Question Answering with ... | https://github.com/sean0042/Open_WikiTable | The first ODQA dataset that requires complex reasoning over tables. Open-WikiTable is built upon WikiSQL and WikiTableQuestions to be applicable in the open- |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | TAPAS | \cite{TAPAS} | TAPAS: Weakly Supervised Table Parsing via Pre-training | http://arxiv.org/abs/2004.02349v2 | Answering natural language questions over tables is usually seen as a
semantic parsing task. To alleviate the collection cost of full logical forms,
one popular approach focuses on weak supervision consisting of denotations
instead of logical forms. However, training semantic parsers from weak
supervision poses difficu... | true | true | Jonathan Herzig and
Pawel Krzysztof Nowak and
Thomas M{\"{u}}ller and
Francesco Piccinno and
Julian Martin Eisenschlos | 2,020 | null | null | null | null | TAPAS: Weakly Supervised Table Parsing via Pre-training | TaPas: Weakly Supervised Table Parsing via Pre-training | https://aclanthology.org/2020.acl-main.398/ | by J Herzig · 2020 · Cited by 784 — TaPas trains from weak supervision, and predicts the denotation by selecting table cells and optionally applying a corresponding aggregation operator to such |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | GTR | \cite{GTR} | Retrieving Complex Tables with Multi-Granular Graph Representation
Learning | http://arxiv.org/abs/2105.01736v1 | The task of natural language table retrieval (NLTR) seeks to retrieve
semantically relevant tables based on natural language queries. Existing
learning systems for this task often treat tables as plain text based on the
assumption that tables are structured as dataframes. However, tables can have
complex layouts which ... | true | true | Fei Wang and
Kexuan Sun and
Muhao Chen and
Jay Pujara and
Pedro A. Szekely | 2,021 | null | null | null | null | Retrieving Complex Tables with Multi-Granular Graph Representation
Learning | [PDF] Retrieving Complex Tables with Multi-Granular Graph ... - arXiv | https://arxiv.org/pdf/2105.01736 | GTR leverages state-of-the-art graph representation learning techniques to capture both content and layout structures of complex tables. |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | AdHoc_TR | \cite{AdHoc_TR} | Ad Hoc Table Retrieval using Semantic Similarity | http://arxiv.org/abs/1802.06159v3 | We introduce and address the problem of ad hoc table retrieval: answering a
keyword query with a ranked list of tables. This task is not only interesting
on its own account, but is also being used as a core component in many other
table-based information access scenarios, such as table completion or table
mining. The m... | true | true | Shuo Zhang and
Krisztian Balog | 2,018 | null | null | null | null | Ad Hoc Table Retrieval using Semantic Similarity | Ad Hoc Table Retrieval using Semantic Similarity | http://arxiv.org/pdf/1802.06159v3 | We introduce and address the problem of ad hoc table retrieval: answering a
keyword query with a ranked list of tables. This task is not only interesting
on its own account, but is also being used as a core component in many other
table-based information access scenarios, such as table completion or table
mining. The m... |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | TableSearch | \cite{TableSearch} | Table Search Using a Deep Contextualized Language Model | http://arxiv.org/abs/2005.09207v2 | Pretrained contextualized language models such as BERT have achieved
impressive results on various natural language processing benchmarks.
Benefiting from multiple pretraining tasks and large scale training corpora,
pretrained models can capture complex syntactic word relations. In this paper,
we use the deep contextua... | true | true | Zhiyu Chen and
Mohamed Trabelsi and
Jeff Heflin and
Yinan Xu and
Brian D. Davison | 2,020 | null | null | null | null | Table Search Using a Deep Contextualized Language Model | Table Search Using a Deep Contextualized Language Model | http://arxiv.org/pdf/2005.09207v2 | Pretrained contextualized language models such as BERT have achieved
impressive results on various natural language processing benchmarks.
Benefiting from multiple pretraining tasks and large scale training corpora,
pretrained models can capture complex syntactic word relations. In this paper,
we use the deep contextua... |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | DSI | \cite{DSI} | Transformer Memory as a Differentiable Search Index | http://arxiv.org/abs/2202.06991v3 | In this paper, we demonstrate that information retrieval can be accomplished
with a single Transformer, in which all information about the corpus is encoded
in the parameters of the model. To this end, we introduce the Differentiable
Search Index (DSI), a new paradigm that learns a text-to-text model that maps
string q... | true | true | Tay, Yi and Tran, Vinh Q and Dehghani, Mostafa and Ni, Jianmo and Bahri, Dara and Mehta, Harsh and Qin, Zhen and Hui, Kai and Zhao, Zhe and Gupta, Jai and others | 2,022 | null | null | null | null | Transformer Memory as a Differentiable Search Index | Transformer Memory as a Differentiable Search Index | http://arxiv.org/pdf/2202.06991v3 | In this paper, we demonstrate that information retrieval can be accomplished
with a single Transformer, in which all information about the corpus is encoded
in the parameters of the model. To this end, we introduce the Differentiable
Search Index (DSI), a new paradigm that learns a text-to-text model that maps
string q... |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | NCI | \cite{NCI} | A Neural Corpus Indexer for Document Retrieval | http://arxiv.org/abs/2206.02743v3 | Current state-of-the-art document retrieval solutions mainly follow an
index-retrieve paradigm, where the index is hard to be directly optimized for
the final retrieval target. In this paper, we aim to show that an end-to-end
deep neural network unifying training and indexing stages can significantly
improve the recall... | true | true | Wang, Yujing and Hou, Yingyan and Wang, Haonan and Miao, Ziming and Wu, Shibin and Sun, Hao and Chen, Qi and Xia, Yuqing and Chi, Chengmin and Zhao, Guoshuai and others | 2,022 | null | null | null | null | A Neural Corpus Indexer for Document Retrieval | A Neural Corpus Indexer for Document Retrieval | http://arxiv.org/pdf/2206.02743v3 | Current state-of-the-art document retrieval solutions mainly follow an
index-retrieve paradigm, where the index is hard to be directly optimized for
the final retrieval target. In this paper, we aim to show that an end-to-end
deep neural network unifying training and indexing stages can significantly
improve the recall... |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | DSI-QG | \cite{DSI-QG} | Bridging the Gap Between Indexing and Retrieval for Differentiable
Search Index with Query Generation | http://arxiv.org/abs/2206.10128v3 | The Differentiable Search Index (DSI) is an emerging paradigm for information
retrieval. Unlike traditional retrieval architectures where index and retrieval
are two different and separate components, DSI uses a single transformer model
to perform both indexing and retrieval.
In this paper, we identify and tackle an ... | true | true | Shengyao Zhuang and
Houxing Ren and
Linjun Shou and
Jian Pei and
Ming Gong and
Guido Zuccon and
Daxin Jiang | 2,022 | null | null | null | CoRR | Bridging the Gap Between Indexing and Retrieval for Differentiable
Search Index with Query Generation | Bridging the Gap Between Indexing and Retrieval for Differentiable ... | https://arxiv.org/abs/2206.10128 | Missing: 04/08/2025 |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | CorpusLM | \cite{CorpusLM} | CorpusLM: Towards a Unified Language Model on Corpus for
Knowledge-Intensive Tasks | http://arxiv.org/abs/2402.01176v2 | Large language models (LLMs) have gained significant attention in various
fields but prone to hallucination, especially in knowledge-intensive (KI)
tasks. To address this, retrieval-augmented generation (RAG) has emerged as a
popular solution to enhance factual accuracy. However, traditional retrieval
modules often rel... | true | true | Xiaoxi Li and
Zhicheng Dou and
Yujia Zhou and
Fangchao Liu | 2,024 | null | null | null | null | CorpusLM: Towards a Unified Language Model on Corpus for
Knowledge-Intensive Tasks | CorpusLM: Towards a Unified Language Model on Corpus ... | https://dl.acm.org/doi/10.1145/3626772.3657778 | In this paper, we propose CorpusLM, a unified language model that leverages external corpus to tackle various knowledge-intensive tasks. |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | Tiger | \cite{Tiger} | Recommender Systems with Generative Retrieval | http://arxiv.org/abs/2305.05065v3 | Modern recommender systems perform large-scale retrieval by first embedding
queries and item candidates in the same unified space, followed by approximate
nearest neighbor search to select top candidates given a query embedding. In
this paper, we propose a novel generative retrieval approach, where the
retrieval model ... | true | true | Rajput, Shashank and Mehta, Nikhil and Singh, Anima and Keshavan, Raghunandan and Vu, Trung and Heidt, Lukasz and Hong, Lichan and Tay, Yi and Tran, Vinh Q and Samost, Jonah and others | 2,023 | null | null | null | null | Recommender Systems with Generative Retrieval | Recommender Systems with Generative Retrieval | http://arxiv.org/pdf/2305.05065v3 | Modern recommender systems perform large-scale retrieval by first embedding
queries and item candidates in the same unified space, followed by approximate
nearest neighbor search to select top candidates given a query embedding. In
this paper, we propose a novel generative retrieval approach, where the
retrieval model ... |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | DSI++ | \cite{DSI++} | {DSI++:} Updating Transformer Memory with New Documents | null | null | true | false | Sanket Vaibhav Mehta and
Jai Gupta and
Yi Tay and
Mostafa Dehghani and
Vinh Q. Tran and
Jinfeng Rao and
Marc Najork and
Emma Strubell and
Donald Metzler | 2,023 | null | null | null | null | {DSI++:} Updating Transformer Memory with New Documents | DSI++: Updating Transformer Memory with New Documents | https://aclanthology.org/2023.emnlp-main.510/ | DSI++: Updating Transformer Memory with New Documents - ACL Anthology Anthology ID:2023.emnlp-main.510 Volume:Proceedings of the 2023 Conference on Empirical Methods in Natural Language ProcessingMonth:December Year:2023 Address:Singapore Editors:Houda Bouamor, Juan Pino, Kalika BaliVenue:EMNLPSIG:Publisher:Association... |
Birdie: Natural Language-Driven Table Discovery Using Differentiable
Search Index | 2504.21282v1 | CLEVER | \cite{CLEVER} | Continual Learning for Generative Retrieval over Dynamic Corpora | http://arxiv.org/abs/2308.14968v1 | Generative retrieval (GR) directly predicts the identifiers of relevant
documents (i.e., docids) based on a parametric model. It has achieved solid
performance on many ad-hoc retrieval tasks. So far, these tasks have assumed a
static document collection. In many practical scenarios, however, document
collections are dy... | true | true | Jiangui Chen and
Ruqing Zhang and
Jiafeng Guo and
Maarten de Rijke and
Wei Chen and
Yixing Fan and
Xueqi Cheng | 2,023 | null | null | null | null | Continual Learning for Generative Retrieval over Dynamic Corpora | Continual Learning for Generative Retrieval over Dynamic Corpora | http://arxiv.org/pdf/2308.14968v1 | Generative retrieval (GR) directly predicts the identifiers of relevant
documents (i.e., docids) based on a parametric model. It has achieved solid
performance on many ad-hoc retrieval tasks. So far, these tasks have assumed a
static document collection. In many practical scenarios, however, document
collections are dy... |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | ErroDetection | \cite{ErroDetection} | Exploiting Active Learning in Novel Refractive Error Detection with Smartphones | null | null | true | false | Fu, Eugene Yujun and Yang, Zhongqi and Leong, Hong Va and Ngai, Grace and Do, Chi-wai and Chan, Lily | 2,020 | null | null | null | null | Exploiting Active Learning in Novel Refractive Error Detection with Smartphones | Exploiting active learning in novel refractive error detection with ... | https://repository.eduhk.hk/en/publications/exploiting-active-learning-in-novel-refractive-error-detection-wi | Dive into the research topics of 'Exploiting active learning in novel refractive error detection with smartphones'. Together they form a unique fingerprint. |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | ImageCaption | \cite{ImageCaption} | Structural Semantic Adversarial Active Learning for Image Captioning | null | null | true | false | Zhang, Beichen and Li, Liang and Su, Li and Wang, Shuhui and Deng, Jincan and Zha, Zheng-Jun and Huang, Qingming | 2,020 | null | null | null | null | Structural Semantic Adversarial Active Learning for Image Captioning | Structural Semantic Adversarial Active Learning for Image Captioning | https://dl.acm.org/doi/abs/10.1145/3394171.3413885 | We propose a structural semantic adversarial active learning (SSAAL) model that leverages both visual and textual information for deriving the most |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | PersonIdentification | \cite{PersonIdentification} | Cluster and Scatter: A Multi-Grained Active Semi-Supervised Learning Framework for Scalable Person Re-Identification | null | null | true | false | Hu, Bingyu and Zha, Zheng-Jun and Liu, Jiawei and Zhu, Xierong and Xie, Hongtao | 2,021 | null | null | null | null | Cluster and Scatter: A Multi-Grained Active Semi-Supervised Learning Framework for Scalable Person Re-Identification | arXiv:2204.10008v1 [cs.CV] 21 Apr 2022 | https://arxiv.org/pdf/2204.10008 | by D Jin · 2022 · Cited by 4 — Cluster and scatter: A multi-grained active semi-supervised learning framework for scalable person re-identification. In ACMMM, pages. 2605 |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | lewis1994heterogeneous | \cite{lewis1994heterogeneous} | Heterogeneous uncertainty sampling for supervised learning | null | null | true | false | Lewis, David D and Catlett, Jason | 1,994 | null | null | null | null | Heterogeneous uncertainty sampling for supervised learning | Heterogeneous Uncertainty Sampling for Supervised ... | https://www.sciencedirect.com/science/article/pii/B978155860335650026X | by DD Lewis · 1994 · Cited by 1814 — Uncertainty sampling methods iteratively request class labels for training instances whose classes are uncertain despite the previous labeled instances. |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | lewis1994sequential | \cite{lewis1994sequential} | A Sequential Algorithm for Training Text Classifiers | http://arxiv.org/abs/cmp-lg/9407020v2 | The ability to cheaply train text classifiers is critical to their use in
information retrieval, content analysis, natural language processing, and other
tasks involving data which is partly or fully textual. An algorithm for
sequential sampling during machine learning of statistical classifiers was
developed and teste... | true | true | Lewis, David D and Gale, William A | 1,994 | null | null | null | null | A Sequential Algorithm for Training Text Classifiers | A Sequential Algorithm for Training Text Classifiers | http://arxiv.org/pdf/cmp-lg/9407020v2 | The ability to cheaply train text classifiers is critical to their use in
information retrieval, content analysis, natural language processing, and other
tasks involving data which is partly or fully textual. An algorithm for
sequential sampling during machine learning of statistical classifiers was
developed and teste... |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | joshi2009multi | \cite{joshi2009multi} | Active Learning for Multi-class Image Classification | http://arxiv.org/abs/2505.06825v1 | A principle bottleneck in image classification is the large number of
training examples needed to train a classifier. Using active learning, we can
reduce the number of training examples to teach a CNN classifier by
strategically selecting examples. Assigning values to image examples using
different uncertainty metrics... | true | true | Joshi, Ajay J and Porikli, Fatih and Papanikolopoulos, Nikolaos | 2,009 | null | null | null | null | Active Learning for Multi-class Image Classification | Multi-Class Active Learning for Image Classification | https://porikli.com/mysite/pdfs/porikli%202009%20-%20Multi-Class%20Active%20Learning%20for%20Image%20Classification.pdf | by AJ Joshi · Cited by 989 — In this paper, we have proposed a simple active learning method for multi-class image classification. The proposed method achieves significant reduction in |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | luo2013latent | \cite{luo2013latent} | Latent structured active learning | null | null | true | false | Luo, Wenjie and Schwing, Alex and Urtasun, Raquel | 2,013 | null | null | null | NeurIPS | Latent structured active learning | [PDF] Latent Structured Active Learning - Alexander Schwing | https://www.alexander-schwing.de/papers/LuoEtAl_NIPS2013.pdf | In this paper we present active learning algorithms in the context of structured prediction problems. To reduce the amount of labeling necessary to learn |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | settles2012active | \cite{settles2012active} | Active learning: Synthesis lectures on artificial intelligence and machine learning | null | null | true | false | Settles, Burr | 2,012 | null | null | null | Morgan {\&} Claypool Publishers | Active learning: Synthesis lectures on artificial intelligence and machine learning | Active Learning - Book | https://link.springer.com/book/10.1007/978-3-031-01560-1 | by B Settles · Cited by 3007 — Part of the book series: Synthesis Lectures on Artificial Intelligence and Machine Learning (SLAIML) ... The key idea behind active learning is that a machine |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | blundell2015weight | \cite{blundell2015weight} | Weight Uncertainty in Neural Networks | http://arxiv.org/abs/1505.05424v2 | We introduce a new, efficient, principled and backpropagation-compatible
algorithm for learning a probability distribution on the weights of a neural
network, called Bayes by Backprop. It regularises the weights by minimising a
compression cost, known as the variational free energy or the expected lower
bound on the ma... | true | true | Blundell, Charles and Cornebise, Julien and Kavukcuoglu, Koray and Wierstra, Daan | 2,015 | null | null | null | null | Weight Uncertainty in Neural Networks | Weight Uncertainty in Neural Networks | http://arxiv.org/pdf/1505.05424v2 | We introduce a new, efficient, principled and backpropagation-compatible
algorithm for learning a probability distribution on the weights of a neural
network, called Bayes by Backprop. It regularises the weights by minimising a
compression cost, known as the variational free energy or the expected lower
bound on the ma... |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | gal2016dropout | \cite{gal2016dropout} | Dropout as a Bayesian Approximation: Representing Model Uncertainty in
Deep Learning | http://arxiv.org/abs/1506.02142v6 | Deep learning tools have gained tremendous attention in applied machine
learning. However such tools for regression and classification do not capture
model uncertainty. In comparison, Bayesian models offer a mathematically
grounded framework to reason about model uncertainty, but usually come with a
prohibitive computa... | true | true | Yarin Gal and Zoubin Ghahramani | 2,016 | null | null | null | null | Dropout as a Bayesian Approximation: Representing Model Uncertainty in
Deep Learning | Representing Model Uncertainty in Deep Learning - arXiv | https://arxiv.org/abs/1506.02142 | In this paper we develop a new theoretical framework casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | huang2021semi | \cite{huang2021semi} | Semi-Supervised Active Learning with Temporal Output Discrepancy | null | null | true | false | Huang, Siyu and Wang, Tianyang and Xiong, Haoyi and Huan, Jun and Dou, Dejing | 2,021 | null | null | null | null | Semi-Supervised Active Learning with Temporal Output Discrepancy | Supplementary Material: Semi-Supervised Active Learning ... | https://openaccess.thecvf.com/content/ICCV2021/supplemental/Huang_Semi-Supervised_Active_Learning_ICCV_2021_supplemental.pdf | Semi-Supervised Active Learning with Temporal Output Discrepancy. Siyu Huang1. Tianyang Wang2. Haoyi Xiong1. Jun Huan3. Dejing Dou1. 1Baidu Research. 2Austin |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | guo2010active | \cite{guo2010active} | Active instance sampling via matrix partition. | null | null | true | false | Guo, Yuhong | 2,010 | null | null | null | null | Active instance sampling via matrix partition. | Active instance sampling via matrix partition - Volume 1 | https://dl.acm.org/doi/10.5555/2997189.2997279 | by Y Guo · 2010 · Cited by 183 — By employing a Gaussian process framework, this mutual information based instance selection problem can be formulated as a matrix partition problem. Although |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | yang2015multi | \cite{yang2015multi} | Multi-class active learning by uncertainty sampling with diversity maximization | null | null | true | false | Yang, Yi and Ma, Zhigang and Nie, Feiping and Chang, Xiaojun and Hauptmann, Alexander G | 2,015 | null | null | null | Int. J. Comput. Vis. | Multi-class active learning by uncertainty sampling with diversity maximization | Multi-class active learning by uncertainty sampling with diversity ... | https://research.monash.edu/en/publications/multi-class-active-learning-by-uncertainty-sampling-with-diversit | As a multi-class active learning algorithm, our algorithm is able to exploit uncertainty across multiple classes. An efficient algorithm is used to optimize the |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | nguyen2004active | \cite{nguyen2004active} | Active learning using pre-clustering | null | null | true | false | Nguyen, Hieu T and Smeulders, Arnold | 2,004 | null | null | null | null | Active learning using pre-clustering | Active learning using pre-clustering | Proceedings of the ... | https://dl.acm.org/doi/10.1145/1015330.1015349 | The main contribution of the paper is a formal framework that incorporates clustering into active learning. The algorithm first constructs a classifier on the |
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated
Active Learning | 2504.17448v1 | sener2018active | \cite{sener2018active} | Active Learning for Convolutional Neural Networks: A Core-Set Approach | http://arxiv.org/abs/1708.00489v4 | Convolutional neural networks (CNNs) have been successfully applied to many
recognition and learning tasks using a universal recipe; training a deep model
on a very large dataset of supervised examples. However, this approach is
rather restrictive in practice since collecting a large set of labeled images
is very expen... | true | true | Sener, Ozan and Savarese, Silvio | 2,018 | null | null | null | null | Active Learning for Convolutional Neural Networks: A Core-Set Approach | Active Learning for Convolutional Neural Networks: A Core ... | https://arxiv.org/abs/1708.00489 | by O Sener · 2017 · Cited by 2576 — We define the problem of active learning as core-set selection, ie. choosing set of points such that a model learned over the selected subset is competitive |
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