Search Results for author: Jie Luo

Found 44 papers, 13 papers with code

Exploring Robot Morphology Spaces through Breadth-First Search and Random Query

no code implementations25 Sep 2023 Jie Luo

Using two different query mechanisms, Breadth-First Search (BFS) and Random Query, within the context of evolving robot morphologies using CPPNs and robot controllers using tensors, and testing them in two evolutionary frameworks, Lamarckian and Darwinian systems, this study investigates their influence on evolutionary outcomes and performance.

A comparison of controller architectures and learning mechanisms for arbitrary robot morphologies

no code implementations25 Sep 2023 Jie Luo, Jakub Tomczak, Karine Miras, Agoston E. Eiben

The main question this paper addresses is: What combination of a robot controller and a learning method should be used, if the morphology of the learning robot is not known in advance?

Reinforcement Learning (RL)

Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Better

no code implementations22 Sep 2023 Jie Luo, Karine Miras, Jakub Tomczak, Agoston E. Eiben

We research this issue through simulations with an evolutionary robot framework where morphologies (bodies) and controllers (brains) of robots are evolvable and robots also can improve their controllers through learning during their lifetime.

Modulate Your Spectrum in Self-Supervised Learning

1 code implementation26 May 2023 Xi Weng, Yunhao Ni, Tengwei Song, Jie Luo, Rao Muhammad Anwer, Salman Khan, Fahad Shahbaz Khan, Lei Huang

We show that whitening transformation is a special instance of ST by definition, and there exist other instances that can avoid collapse by our empirical investigation.

Self-Supervised Learning

SlicerTMS: Interactive Real-time Visualization of Transcranial Magnetic Stimulation using Augmented Reality and Deep Learning

1 code implementation10 May 2023 Loraine Franke, Tae Young Park, Jie Luo, Yogesh Rathi, Steve Pieper, Lipeng Ning, Daniel Haehn

Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation approach that effectively treats various brain disorders.

BiFSMNv2: Pushing Binary Neural Networks for Keyword Spotting to Real-Network Performance

1 code implementation13 Nov 2022 Haotong Qin, Xudong Ma, Yifu Ding, Xiaoyang Li, Yang Zhang, Zejun Ma, Jiakai Wang, Jie Luo, Xianglong Liu

We highlight that benefiting from the compact architecture and optimized hardware kernel, BiFSMNv2 can achieve an impressive 25. 1x speedup and 20. 2x storage-saving on edge hardware.

Binarization Keyword Spotting

Environment induced emergence of collective behaviour in evolving swarms with limited sensing

1 code implementation22 Mar 2022 Fuda van Diggelen, Jie Luo, Tugay Alperen Karagüzel, Nicolas Cambier, Eliseo Ferrante, A. E. Eiben

Designing controllers for robot swarms is challenging, because human developers have typically no good understanding of the link between the details of a controller that governs individual robots and the swarm behavior that is an indirect result of the interactions between swarm members and the environment.

Delving into the Estimation Shift of Batch Normalization in a Network

1 code implementation CVPR 2022 Lei Huang, Yi Zhou, Tian Wang, Jie Luo, Xianglong Liu

We define the estimation shift magnitude of BN to quantitatively measure the difference between its estimated population statistics and expected ones.

BiFSMN: Binary Neural Network for Keyword Spotting

1 code implementation14 Feb 2022 Haotong Qin, Xudong Ma, Yifu Ding, Xiaoyang Li, Yang Zhang, Yao Tian, Zejun Ma, Jie Luo, Xianglong Liu

Then, to allow the instant and adaptive accuracy-efficiency trade-offs at runtime, we also propose a Thinnable Binarization Architecture to further liberate the acceleration potential of the binarized network from the topology perspective.

Binarization Keyword Spotting

GPU optimization of the 3D Scale-invariant Feature Transform Algorithm and a Novel BRIEF-inspired 3D Fast Descriptor

1 code implementation19 Dec 2021 Jean-Baptiste Carluer, Laurent Chauvin, Jie Luo, William M. Wells III, Ines Machado, Rola Harmouche, Matthew Toews

This work details a highly efficient implementation of the 3D scale-invariant feature transform (SIFT) algorithm, for the purpose of machine learning from large sets of volumetric medical image data.

Keypoint Detection

The Effects of Learning in Morphologically Evolving Robot Systems

no code implementations18 Nov 2021 Jie Luo, Aart Stuurman, Jakub M. Tomczak, Jacintha Ellers, Agoston E. Eiben

Simultaneously evolving morphologies (bodies) and controllers (brains) of robots can cause a mismatch between the inherited body and brain in the offspring.

All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation

1 code implementation28 Sep 2021 Zhe Xu, Yixin Wang, Donghuan Lu, Lequan Yu, Jiangpeng Yan, Jie Luo, Kai Ma, Yefeng Zheng, Raymond Kai-yu Tong

Observing this, we ask an unexplored but interesting question: can we exploit the unlabeled data via explicit real label supervision for semi-supervised training?

Brain Tumor Segmentation Image Segmentation +3

Gait-learning with morphologically evolving robots generated by L-system

1 code implementation17 Jul 2021 Jie Luo, Daan Zeeuwe, Agoston E. Eiben

The second approach is evolution plus learning which means the brain of a child is inherited as well, but additionally is developed by a learning algorithm - RevDEknn.

Double-Uncertainty Guided Spatial and Temporal Consistency Regularization Weighting for Learning-based Abdominal Registration

no code implementations6 Jul 2021 Zhe Xu, Jie Luo, Donghuan Lu, Jiangpeng Yan, Sarah Frisken, Jayender Jagadeesan, William Wells III, Xiu Li, Yefeng Zheng, Raymond Tong

Such convention has two limitations: (i) Besides the laborious grid search for the optimal fixed weight, the regularization strength of a specific image pair should be associated with the content of the images, thus the "one value fits all" training scheme is not ideal; (ii) Only spatially regularizing the transformation may neglect some informative clues related to the ill-posedness.

Image Registration

Trust It or Not: Confidence-Guided Automatic Radiology Report Generation

no code implementations21 Jun 2021 Yixin Wang, Zihao Lin, Zhe Xu, Haoyu Dong, Jiang Tian, Jie Luo, Zhongchao shi, Yang Zhang, Jianping Fan, Zhiqiang He

Experimental results have demonstrated that the proposed method for model uncertainty characterization and estimation can produce more reliable confidence scores for radiology report generation, and the modified loss function, which takes into account the uncertainties, leads to better model performance on two public radiology report datasets.

Decision Making Image Captioning +1

Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation

1 code implementation3 Jun 2021 Zhe Xu, Donghuan Lu, Yixin Wang, Jie Luo, Jayender Jagadeesan, Kai Ma, Yefeng Zheng, Xiu Li

Manually segmenting the hepatic vessels from Computer Tomography (CT) is far more expertise-demanding and laborious than other structures due to the low-contrast and complex morphology of vessels, resulting in the extreme lack of high-quality labeled data.

Deep-Learning-Enabled Inverse Engineering of Multi-Wavelength Invisibility-to-Superscattering Switching with Phase-Change Materials

no code implementations25 Dec 2020 Jie Luo, Xun Li, Xinyuan Zhang, Jiajie Guo, Wei Liu, Yun Lai, Yaohui Zhan, Min Huang

Inverse design of nanoparticles for desired scattering spectra and dynamic switching between the two opposite scattering anomalies, i. e. superscattering and invisibility, is important in realizing cloaking, sensing and functional devices.


Unsupervised Multimodal Image Registration with Adaptative Gradient Guidance

no code implementations12 Nov 2020 Zhe Xu, Jiangpeng Yan, Jie Luo, Xiu Li, Jayender Jagadeesan

Multimodal image registration (MIR) is a fundamental procedure in many image-guided therapies.

Image Registration

Unimodal Cyclic Regularization for Training Multimodal Image Registration Networks

no code implementations12 Nov 2020 Zhe Xu, Jiangpeng Yan, Jie Luo, William Wells, Xiu Li, Jayender Jagadeesan

The loss function of an unsupervised multimodal image registration framework has two terms, i. e., a metric for similarity measure and regularization.

Image Registration

F3RNet: Full-Resolution Residual Registration Network for Deformable Image Registration

no code implementations15 Sep 2020 Zhe Xu, Jie Luo, Jiangpeng Yan, Xiu Li, Jagadeesan Jayender

In this paper, we propose a novel unsupervised registration network, namely the Full-Resolution Residual Registration Network (F3RNet), for deformable registration of severely deformed organs.

Image Registration

Adversarial Uni- and Multi-modal Stream Networks for Multimodal Image Registration

no code implementations6 Jul 2020 Zhe Xu, Jie Luo, Jiangpeng Yan, Ritvik Pulya, Xiu Li, William Wells III, Jayender Jagadeesan

Deformable image registration between Computed Tomography (CT) images and Magnetic Resonance (MR) imaging is essential for many image-guided therapies.

Computed Tomography (CT) Image Registration +2

Do Public Datasets Assure Unbiased Comparisons for Registration Evaluation?

no code implementations20 Mar 2020 Jie Luo, Guangshen Ma, Sarah Frisken, Parikshit Juvekar, Nazim Haouchine, Zhe Xu, Yiming Xiao, Alexandra Golby, Patrick Codd, Masashi Sugiyama, William Wells III

In this study, we use the variogram to screen the manually annotated landmarks in two datasets used to benchmark registration in image-guided neurosurgeries.

Image Registration

An Investigation of Feature-based Nonrigid Image Registration using Gaussian Process

no code implementations12 Jan 2020 Siming Bayer, Ute Spiske, Jie Luo, Tobias Geimer, William M. Wells III, Martin Ostermeier, Rebecca Fahrig, Arya Nabavi, Christoph Bert, Ilker Eyupoglo, Andreas Maier

For a wide range of clinical applications, such as adaptive treatment planning or intraoperative image update, feature-based deformable registration (FDR) approaches are widely employed because of their simplicity and low computational complexity.

Image Registration valid

Distant Supervised Relation Extraction with Separate Head-Tail CNN

no code implementations WS 2019 Rui Xing, Jie Luo

Distant supervised relation extraction is an efficient and effective strategy to find relations between entities in texts.

Relation Extraction

I Know You'll Be Back: Interpretable New User Clustering and Churn Prediction on a Mobile Social Application

no code implementations29 Sep 2019 Carl Yang, Xiaolin Shi, Jie Luo, Jiawei Han

Then we design a novel deep learning pipeline based on LSTM and attention to accurately predict user churn with very limited initial behavior data, by leveraging the correlations among users' multi-dimensional activities and the underlying user types.


Are Registration Uncertainty and Error Monotonically Associated

no code implementations21 Aug 2019 Jie Luo, Sarah Frisken, Duo Wang, Alexandra Golby, Masashi Sugiyama, William M. Wells III

Probabilistic image registration (PIR) methods provide measures of registration uncertainty, which could be a surrogate for assessing the registration error.

Image Registration

Temporal Registration in Application to In-utero MRI Time Series

no code implementations6 Mar 2019 Ruizhi Liao, Esra A. Turk, Miaomiao Zhang, Jie Luo, Elfar Adalsteinsson, P. Ellen Grant, Polina Golland

To achieve accurate and robust alignment, we make a Markov assumption on the nature of motion and take advantage of the temporal smoothness in the image data.

Time Series Time Series Alignment

Deep Information Theoretic Registration

no code implementations31 Dec 2018 Alireza Sedghi, Jie Luo, Alireza Mehrtash, Steve Pieper, Clare M. Tempany, Tina Kapur, Parvin Mousavi, William M. Wells III

This paper establishes an information theoretic framework for deep metric based image registration techniques.

Image Registration

Synthesizing dynamic MRI using long-term recurrent convolutional networks

1 code implementation24 Jul 2018 Frank Preiswerk, Cheng-Chieh Cheng, Jie Luo, Bruno Madore

A method is proposed for converting raw ultrasound signals of respiratory organ motion into high frame rate dynamic MRI using a long-term recurrent convolutional neural network.

On the Applicability of Registration Uncertainty

no code implementations14 Mar 2018 Jie Luo, Alireza Sedghi, Karteek Popuri, Dana Cobzas, Miaomiao Zhang, Frank Preiswerk, Matthew Toews, Alexandra Golby, Masashi Sugiyama, William M. Wells III, Sarah Frisken

For probabilistic image registration (PIR), the predominant way to quantify the registration uncertainty is using summary statistics of the distribution of transformation parameters.

Image Registration

mvn2vec: Preservation and Collaboration in Multi-View Network Embedding

1 code implementation19 Jan 2018 Yu Shi, Fangqiu Han, Xinwei He, Xinran He, Carl Yang, Jie Luo, Jiawei Han

With experiments on a series of synthetic datasets, a large-scale internal Snapchat dataset, and two public datasets, we confirm the validity and importance of preservation and collaboration as two objectives for multi-view network embedding.

Network Embedding

Misdirected Registration Uncertainty

no code implementations26 Apr 2017 Jie Luo, Karteek Popuri, Dana Cobzas, Hongyi Ding, William M. Wells III, Masashi Sugiyama

Since the transformation is such an essential component of registration, most existing researches conventionally quantify the registration uncertainty, which is the confidence in the estimated spatial correspondences, by the transformation uncertainty.

Image Registration Medical Image Registration

Reinterpreting the Transformation Posterior in Probabilistic Image Registration

no code implementations7 Apr 2016 Jie Luo, Karteek Popuri, Dana Cobzas, Hongyi Ding, Masashi Sugiyama

Meanwhile, summary statistics of the posterior are employed to evaluate the registration uncertainty, that is the trustworthiness of the registered image.

Image Registration

Learning from Candidate Labeling Sets

no code implementations NeurIPS 2010 Jie Luo, Francesco Orabona

In this paper, we propose a semi-supervised framework to model this kind of problems.

Who’s Doing What: Joint Modeling of Names and Verbs for Simultaneous Face and Pose Annotation

no code implementations NeurIPS 2009 Jie Luo, Barbara Caputo, Vittorio Ferrari

Given a corpus of news items consisting of images accompanied by text captions, we want to find out ``whos doing what, i. e. associate names and action verbs in the captions to the face and body pose of the persons in the images.

Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree

no code implementations NeurIPS 2008 Daphna Weinshall, Hynek Hermansky, Alon Zweig, Jie Luo, Holly Jimison, Frank Ohl, Misha Pavel

We define a formal framework for the representation and processing of incongruent events: starting from the notion of label hierarchy, we show how partial order on labels can be deduced from such hierarchies.

Novelty Detection Object Recognition +2

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