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f48edbbc-662d-42ee-81fb-8df5793f99f9 | maximum-likelihood-based-gridless-doa | 2210.03266 | null | https://arxiv.org/abs/2210.03266v1 | https://arxiv.org/pdf/2210.03266v1.pdf | Maximum Likelihood-based Gridless DoA Estimation Using Structured Covariance Matrix Recovery and SBL with Grid Refinement | We consider the parametric data model employed in applications such as line spectral estimation and direction-of-arrival estimation. We focus on the stochastic maximum likelihood estimation (MLE) framework and offer approaches to estimate the parameter of interest in a gridless manner, overcoming the model complexities of the past. This progress is enabled by the modern trend of reparameterization of the objective and exploiting the sparse Bayesian learning (SBL) approach. The latter is shown to be a correlation-aware method, and for the underlying problem it is identified as a grid-based technique for recovering a structured covariance matrix of the measurements. For the case when the structured matrix is expressible as a sampled Toeplitz matrix, such as when measurements are sampled in time or space at regular intervals, additional constraints and reparameterization of the SBL objective leads to the proposed structured matrix recovery technique based on MLE. The proposed optimization problem is non-convex, and we propose a majorization-minimization based iterative procedure to estimate the structured matrix; each iteration solves a semidefinite program. We recover the parameter of interest in a gridless manner by appealing to the Caratheodory-Fejer result on decomposition of PSD Toeplitz matrices. For the general case of irregularly spaced time or spatial samples, we propose an iterative SBL procedure that refines grid points to increase resolution near potential source locations, while maintaining a low per iteration complexity. We provide numerical results to evaluate and compare the performance of the proposed techniques with other gridless techniques, and the CRB. The proposed correlation-aware approach is more robust to environmental/system effects such as low number of snapshots, correlated sources, small separation between source locations and improves sources identifiability. | ['Bhaskar D. Rao', 'Rohan R. Pote'] | 2022-10-07 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 3.80860597e-01 -2.87652373e-01 7.39472434e-02 2.03071252e-01
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d435b5cf-30f7-4ce9-bbcb-bff9c6f35e07 | skin-lesion-segmentation-and-classification-3 | 2112.10307 | null | https://arxiv.org/abs/2112.10307v1 | https://arxiv.org/pdf/2112.10307v1.pdf | Skin lesion segmentation and classification using deep learning and handcrafted features | Accurate diagnostics of a skin lesion is a critical task in classification dermoscopic images. In this research, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single method features. This study involves a new technique where we inject the handcrafted features or feature transfer into the fully connected layer of Convolutional Neural Network (CNN) model during the training process. Based on our literature review until now, no study has examined or investigated the impact on classification performance by injecting the handcrafted features into the CNN model during the training process. In addition, we also investigated the impact of segmentation mask and its effect on the overall classification performance. Our model achieves an 92.3% balanced multiclass accuracy, which is 6.8% better than the typical single method classifier architecture for deep learning. | ['Hussin K. Ragb', 'Redha Ali'] | 2021-12-20 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 3.69118094e-01 2.35359699e-01 -4.26040560e-01 -4.40789640e-01
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f30ce8f2-35f4-44c2-990f-f462fc1cb1ce | learning-one-class-hyperspectral-classifier | 2210.15457 | null | https://arxiv.org/abs/2210.15457v1 | https://arxiv.org/pdf/2210.15457v1.pdf | Learning One-Class Hyperspectral Classifier from Positive and Unlabeled Data for Low Proportion Target | Hyperspectral imagery (HSI) one-class classification is aimed at identifying a single target class from the HSI by using only positive labels, which can significantly reduce the requirements for annotation. However, HSI one-class classification is far more challenging than HSI multi-class classification, due the lack of negative labels and the low target proportion, which are issues that have rarely been considered in the previous HSI classification studies. In this paper, a weakly supervised HSI one-class classifier, namely HOneCls is proposed to solve the problem of under-fitting of the positive class occurs in the HSI data with low target proportion, where a risk estimator -- the One-Class Risk Estimator -- is particularly introduced to make the full convolutional neural network (FCN) with the ability of one class classification. The experimental results obtained on challenging hyperspectral classification datasets, which includes 20 kinds of ground objects with very similar spectra, demonstrate the efficiency and feasibility of the proposed One-Class Risk Estimator. Compared with the state-of-the-art one-class classifiers, the F1-score is improved significantly in the HSI data with low target proportion. | ['Hong Shu', 'Xinyu Wang', 'Xin He', 'Yanfei Zhong', 'Hengwei Zhao'] | 2022-10-27 | null | null | null | null | ['one-class-classifier', 'one-class-classification'] | ['methodology', 'miscellaneous'] | [ 8.52266014e-01 6.12785220e-02 -3.86662874e-03 -4.59342092e-01
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8ebf145e-9a9e-401d-97b5-523e55ed559e | quantification-of-damage-using-indirect | 2301.09791 | null | https://arxiv.org/abs/2301.09791v1 | https://arxiv.org/pdf/2301.09791v1.pdf | Quantification of Damage Using Indirect Structural Health Monitoring | Structural health monitoring is important to make sure bridges do not fail. Since direct monitoring can be complicated and expensive, indirect methods have been a focus on research. Indirect monitoring can be much cheaper and easier to conduct, however there are challenges with getting accurate results. This work focuses on damage quantification by using accelerometers. Tests were conducted on a model bridge and car with four accelerometers attached to to the vehicle. Different weights were placed on the bridge to simulate different levels of damage, and 31 tests were run for 20 different damage levels. The acceleration data collected was normalized and a Fast-Fourier Transform (FFT) was performed on that data. Both the normalized acceleration data and the normalized FFT data were inputted into a Non-Linear Principal Component Analysis (separately) and three principal components were extracted for each data set. Support Vector Regression (SVR) and Gaussian Process Regression (GPR) were used as the supervised machine learning methods to develop models. Multiple models were created so that the best one could be selected, and the models were compared by looking at their Mean Squared Errors (MSE). This methodology should be applied in the field to measure how effective it can be in real world applications. | ['Achyuth Madabhushi'] | 2023-01-24 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-4.54652682e-02 -2.67114252e-01 8.97504166e-02 -9.19112489e-02
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9ee96350-c9d0-4f4a-8a7d-75eaa16a0666 | discriminative-co-saliency-and-background | 2305.00514 | null | https://arxiv.org/abs/2305.00514v2 | https://arxiv.org/pdf/2305.00514v2.pdf | Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection | Most previous co-salient object detection works mainly focus on extracting co-salient cues via mining the consistency relations across images while ignoring explicit exploration of background regions. In this paper, we propose a Discriminative co-saliency and background Mining Transformer framework (DMT) based on several economical multi-grained correlation modules to explicitly mine both co-saliency and background information and effectively model their discrimination. Specifically, we first propose a region-to-region correlation module for introducing inter-image relations to pixel-wise segmentation features while maintaining computational efficiency. Then, we use two types of pre-defined tokens to mine co-saliency and background information via our proposed contrast-induced pixel-to-token correlation and co-saliency token-to-token correlation modules. We also design a token-guided feature refinement module to enhance the discriminability of the segmentation features under the guidance of the learned tokens. We perform iterative mutual promotion for the segmentation feature extraction and token construction. Experimental results on three benchmark datasets demonstrate the effectiveness of our proposed method. The source code is available at: https://github.com/dragonlee258079/DMT. | ['Fahad Shahbaz Khan', 'Rao Muhammad Anwer', 'Hisham Cholakkal', 'Salman Khan', 'Nian Liu', 'Ni Zhang', 'Junwei Han', 'Long Li'] | 2023-04-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Discriminative_Co-Saliency_and_Background_Mining_Transformer_for_Co-Salient_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Discriminative_Co-Saliency_and_Background_Mining_Transformer_for_Co-Salient_Object_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['co-saliency-detection', 'salient-object-detection-1'] | ['computer-vision', 'computer-vision'] | [ 4.43807364e-01 -2.55931735e-01 -3.97656471e-01 -3.44780535e-01
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ef7b7ec5-a0b6-4140-b350-588be81970ef | vehicle-speed-estimation-using-computer | null | null | https://openreview.net/forum?id=Pl7uHR-Oe6l | https://openreview.net/pdf?id=Pl7uHR-Oe6l | Vehicle Speed Estimation Using Computer Vision And Evolutionary Camera Calibration | Currently, the standard for vehicle speed estimation is radar or lidar speed signs which can be costly to buy and maintain. However, most major cities already implement networks of traffic surveillance cameras that can be utilized for vehicle speed estimation using computer vision. This work implements such a system using homography estimation, YOLOv4 object detector, and an object tracker capable of vehicle speed estimation. The homography component uses world plane-image plane point correspondences, located by humans. Moreover, a new method is developed specifically for this use case, using the estimation of density evolutionary algorithm. It aims at correcting the points misalignment in between planes. In addition, a basic direct linear transformation (DLT) and a random sample consensus robust version of DLT are implemented for comparison. Finally, the results show that the proposed homography method reduces the projection error from world to image point by 97\%, when compared to the other two methods, and the complete workflow can successfully estimate speed distributions expected from vehicles on urban traffic and handle dynamic changes in vehicle speed. | ['Rigoberto Fonseca', 'Israel Pineda', 'Ezequiel López-Rubio', 'Esteban Palomo', 'Hector Mejia'] | 2021-10-16 | null | null | null | neurips-workshop-latinx-in-ai-2021-12 | ['vehicle-speed-estimation', 'homography-estimation'] | ['computer-vision', 'computer-vision'] | [-3.35821480e-01 -3.68565291e-01 -2.49150321e-01 -3.33136916e-01
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b3ac1cb4-6b48-4b06-bfea-2dbb4c79839f | multi-label-topic-classification-for-covid-19 | 2204.06758 | null | https://arxiv.org/abs/2204.06758v1 | https://arxiv.org/pdf/2204.06758v1.pdf | Multi-label topic classification for COVID-19 literature with Bioformer | We describe Bioformer team's participation in the multi-label topic classification task for COVID-19 literature (track 5 of BioCreative VII). Topic classification is performed using different BERT models (BioBERT, PubMedBERT, and Bioformer). We formulate the topic classification task as a sentence pair classification problem, where the title is the first sentence, and the abstract is the second sentence. Our results show that Bioformer outperforms BioBERT and PubMedBERT in this task. Compared to the baseline results, our best model increased micro, macro, and instance-based F1 score by 8.8%, 15.5%, 7.4%, respectively. Bioformer achieved the highest micro F1 and macro F1 scores in this challenge. In post-challenge experiments, we found that pretraining of Bioformer on COVID-19 articles further improves the performance. | ['Kai Wang', 'Li Fang'] | 2022-04-14 | null | null | null | null | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 6.50638267e-02 2.90730298e-01 -5.99293113e-01 -1.29975051e-01
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02acdbc4-4db6-4d92-a7d7-0d04a25c680f | switch-based-active-deep-dyna-q-efficient | 1811.07550 | null | http://arxiv.org/abs/1811.07550v1 | http://arxiv.org/pdf/1811.07550v1.pdf | Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning | Training task-completion dialogue agents with reinforcement learning usually
requires a large number of real user experiences. The Dyna-Q algorithm extends
Q-learning by integrating a world model, and thus can effectively boost
training efficiency using simulated experiences generated by the world model.
The effectiveness of Dyna-Q, however, depends on the quality of the world model
- or implicitly, the pre-specified ratio of real vs. simulated experiences used
for Q-learning. To this end, we extend the recently proposed Deep Dyna-Q (DDQ)
framework by integrating a switcher that automatically determines whether to
use a real or simulated experience for Q-learning. Furthermore, we explore the
use of active learning for improving sample efficiency, by encouraging the
world model to generate simulated experiences in the state-action space where
the agent has not (fully) explored. Our results show that by combining switcher
and active learning, the new framework named as Switch-based Active Deep Dyna-Q
(Switch-DDQ), leads to significant improvement over DDQ and Q-learning
baselines in both simulation and human evaluations. | ['Jingjing Liu', 'Jianfeng Gao', 'Yuexin Wu', 'Yiming Yang', 'Xiujun Li'] | 2018-11-19 | null | null | null | null | ['task-completion-dialogue-policy-learning'] | ['natural-language-processing'] | [-2.57461578e-01 1.91304579e-01 -1.17925204e-01 -1.05630659e-01
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284a28d6-eeee-4d5b-b68e-5faf9fa59481 | convergence-analysis-of-map-based-blur-kernel | 1611.07752 | null | http://arxiv.org/abs/1611.07752v2 | http://arxiv.org/pdf/1611.07752v2.pdf | Convergence Analysis of MAP based Blur Kernel Estimation | One popular approach for blind deconvolution is to formulate a maximum a
posteriori (MAP) problem with sparsity priors on the gradients of the latent
image, and then alternatingly estimate the blur kernel and the latent image.
While several successful MAP based methods have been proposed, there has been
much controversy and confusion about their convergence, because sparsity priors
have been shown to prefer blurry images to sharp natural images. In this paper,
we revisit this problem and provide an analysis on the convergence of MAP based
approaches. We first introduce a slight modification to a conventional joint
energy function for blind deconvolution. The reformulated energy function
yields the same alternating estimation process, but more clearly reveals how
blind deconvolution works. We then show the energy function can actually favor
the right solution instead of the no-blur solution under certain conditions,
which explains the success of previous MAP based approaches. The reformulated
energy function and our conditions for the convergence also provide a way to
compare the qualities of different blur kernels, and we demonstrate its
applicability to automatic blur kernel size selection, blur kernel estimation
using light streaks, and defocus estimation. | ['Seungyong Lee', 'Sunghyun Cho'] | 2016-11-23 | convergence-analysis-of-map-based-blur-kernel-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Cho_Convergence_Analysis_of_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Cho_Convergence_Analysis_of_ICCV_2017_paper.pdf | iccv-2017-10 | ['defocus-estimation'] | ['computer-vision'] | [ 1.63095921e-01 -3.26001972e-01 4.52180684e-01 -3.48728657e-01
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fed69c25-ba1e-4ee0-aa3b-2cb99fdeafcc | anchors-based-method-for-fingertips-position | 2005.01351 | null | https://arxiv.org/abs/2005.01351v2 | https://arxiv.org/pdf/2005.01351v2.pdf | Anchors Based Method for Fingertips Position Estimation from a Monocular RGB Image using Deep Neural Network | In Virtual, augmented, and mixed reality, the use of hand gestures is increasingly becoming popular to reduce the difference between the virtual and real world. The precise location of the fingertip is essential/crucial for a seamless experience. Much of the research work is based on using depth information for the estimation of the fingertips position. However, most of the work using RGB images for fingertips detection is limited to a single finger. The detection of multiple fingertips from a single RGB image is very challenging due to various factors. In this paper, we propose a deep neural network (DNN) based methodology to estimate the fingertips position. We christened this methodology as an Anchor based Fingertips Position Estimation (ABFPE), and it is a two-step process. The fingertips location is estimated using regression by computing the difference in the location of a fingertip from the nearest anchor point. The proposed framework performs the best with limited dependence on hand detection results. In our experiments on the SCUT-Ego-Gesture dataset, we achieved the fingertips detection error of 2.3552 pixels on a video frame with a resolution of $640 \times 480$ and about $92.98\%$ of test images have average pixel errors of five pixels. | ['Purnendu Mishra', 'Kishor Sarawadekar'] | 2020-05-04 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [ 1.78966597e-01 -4.74057496e-01 1.15468457e-01 -5.88490665e-02
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a841897b-7f71-45af-a595-84c2d953280e | text-is-no-more-enough-a-benchmark-for | 2112.11953 | null | https://arxiv.org/abs/2112.11953v3 | https://arxiv.org/pdf/2112.11953v3.pdf | Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding | Current researches on spoken language understanding (SLU) heavily are limited to a simple setting: the plain text-based SLU that takes the user utterance as input and generates its corresponding semantic frames (e.g., intent and slots). Unfortunately, such a simple setting may fail to work in complex real-world scenarios when an utterance is semantically ambiguous, which cannot be achieved by the text-based SLU models. In this paper, we first introduce a new and important task, Profile-based Spoken Language Understanding (ProSLU), which requires the model that not only relies on the plain text but also the supporting profile information to predict the correct intents and slots. To this end, we further introduce a large-scale human-annotated Chinese dataset with over 5K utterances and their corresponding supporting profile information (Knowledge Graph (KG), User Profile (UP), Context Awareness (CA)). In addition, we evaluate several state-of-the-art baseline models and explore a multi-level knowledge adapter to effectively incorporate profile information. Experimental results reveal that all existing text-based SLU models fail to work when the utterances are semantically ambiguous and our proposed framework can effectively fuse the supporting information for sentence-level intent detection and token-level slot filling. Finally, we summarize key challenges and provide new points for future directions, which hopes to facilitate the research. | ['Wanxiang Che', 'Linlin Li', 'Guoxing Wu', 'Kaiji Chen', 'Libo Qin', 'Xiao Xu'] | 2021-12-22 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [ 4.84656096e-01 2.62280434e-01 -2.21722081e-01 -7.70199716e-01
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8180fc7f-4553-421d-8177-f41731b34e8a | learning-to-encode-position-for-transformer | 2003.09229 | null | https://arxiv.org/abs/2003.09229v1 | https://arxiv.org/pdf/2003.09229v1.pdf | Learning to Encode Position for Transformer with Continuous Dynamical Model | We introduce a new way of learning to encode position information for non-recurrent models, such as Transformer models. Unlike RNN and LSTM, which contain inductive bias by loading the input tokens sequentially, non-recurrent models are less sensitive to position. The main reason is that position information among input units is not inherently encoded, i.e., the models are permutation equivalent; this problem justifies why all of the existing models are accompanied by a sinusoidal encoding/embedding layer at the input. However, this solution has clear limitations: the sinusoidal encoding is not flexible enough as it is manually designed and does not contain any learnable parameters, whereas the position embedding restricts the maximum length of input sequences. It is thus desirable to design a new position layer that contains learnable parameters to adjust to different datasets and different architectures. At the same time, we would also like the encodings to extrapolate in accordance with the variable length of inputs. In our proposed solution, we borrow from the recent Neural ODE approach, which may be viewed as a versatile continuous version of a ResNet. This model is capable of modeling many kinds of dynamical systems. We model the evolution of encoded results along position index by such a dynamical system, thereby overcoming the above limitations of existing methods. We evaluate our new position layers on a variety of neural machine translation and language understanding tasks, the experimental results show consistent improvements over the baselines. | ['Cho-Jui Hsieh', 'Hsiang-Fu Yu', 'Xuanqing Liu', 'Inderjit Dhillon'] | 2020-03-13 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/955-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/955-Paper.pdf | icml-2020-1 | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 2.37715468e-01 1.36810467e-01 -2.58632869e-01 -1.83358014e-01
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078b4438-8107-4ce7-84b5-4e450fd442aa | inferring-point-clouds-from-single-monocular | 1812.01402 | null | https://arxiv.org/abs/1812.01402v3 | https://arxiv.org/pdf/1812.01402v3.pdf | Inferring Point Clouds from Single Monocular Images by Depth Intermediation | In this paper, we propose a pipeline to generate 3D point cloud of an object from a single-view RGB image. Most previous work predict the 3D point coordinates from single RGB images directly. We decompose this problem into depth estimation from single images and point cloud completion from partial point clouds. Our method sequentially predicts the depth maps from images and then infers the complete 3D object point clouds based on the predicted partial point clouds. We explicitly impose the camera model geometrical constraint in our pipeline and enforce the alignment of the generated point clouds and estimated depth maps. Experimental results for the single image 3D object reconstruction task show that the proposed method outperforms existing state-of-the-art methods. Both the qualitative and quantitative results demonstrate the generality and suitability of our method. | ['Theo Gevers', 'Sezer Karaoglu', 'Wei Zeng'] | 2018-12-04 | null | null | null | null | ['point-cloud-completion', '3d-object-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 7.58018643e-02 1.90095469e-01 1.94961041e-01 -5.04151344e-01
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3223631d-d967-457a-bea2-754f47e3481e | analyzing-and-reducing-the-performance-gap-in | 2305.11449 | null | https://arxiv.org/abs/2305.11449v1 | https://arxiv.org/pdf/2305.11449v1.pdf | Analyzing and Reducing the Performance Gap in Cross-Lingual Transfer with Fine-tuning Slow and Fast | Existing research has shown that a multilingual pre-trained language model fine-tuned with one (source) language also performs well on downstream tasks for non-source languages, even though no fine-tuning is done on these languages. However, there is a clear gap between the performance of the source language and that of the non-source languages. This paper analyzes the fine-tuning process, discovers when the performance gap changes and identifies which network weights affect the overall performance most. Additionally, the paper seeks to answer to what extent the gap can be reduced by reducing forgetting. Based on the analysis results, a method named Fine-tuning slow and fast with four training policies is proposed to address these issues. Experimental results show the proposed method outperforms baselines by a clear margin. | ['Duan Nan', 'Bing Liu', 'Dongyan Zhao', 'Yaobo Liang', 'Yiduo Guo'] | 2023-05-19 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [-4.36397016e-01 1.06610045e-01 -5.60713053e-01 -4.77090180e-01
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28633c11-c8ed-4d1b-921c-0e43a7d2d7f2 | whole-slide-images-based-cancer-survival | 2009.11169 | null | https://arxiv.org/abs/2009.11169v1 | https://arxiv.org/pdf/2009.11169v1.pdf | Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks | Traditional image-based survival prediction models rely on discriminative patch labeling which make those methods not scalable to extend to large datasets. Recent studies have shown Multiple Instance Learning (MIL) framework is useful for histopathological images when no annotations are available in classification task. Different to the current image-based survival models that limit to key patches or clusters derived from Whole Slide Images (WSIs), we propose Deep Attention Multiple Instance Survival Learning (DeepAttnMISL) by introducing both siamese MI-FCN and attention-based MIL pooling to efficiently learn imaging features from the WSI and then aggregate WSI-level information to patient-level. Attention-based aggregation is more flexible and adaptive than aggregation techniques in recent survival models. We evaluated our methods on two large cancer whole slide images datasets and our results suggest that the proposed approach is more effective and suitable for large datasets and has better interpretability in locating important patterns and features that contribute to accurate cancer survival predictions. The proposed framework can also be used to assess individual patient's risk and thus assisting in delivering personalized medicine. Codes are available at https://github.com/uta-smile/DeepAttnMISL_MEDIA. | ['Xinliang Zhu', 'Nicholas Hawkins', 'Junzhou Huang', 'Jitendra Jonnagaddala', 'Jiawen Yao'] | 2020-09-23 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 1.49038970e-01 1.19395718e-01 -4.20104921e-01 -4.95971113e-01
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892c9220-9118-4dba-9930-c1039a5603f9 | on-learning-word-embeddings-from | null | null | https://aclanthology.org/W19-0508 | https://aclanthology.org/W19-0508.pdf | On Learning Word Embeddings From Linguistically Augmented Text Corpora | Word embedding is a technique in Natural Language Processing (NLP) to map words into vector space representations. Since it has boosted the performance of many NLP downstream tasks, the task of learning word embeddings has been addressing significantly. Nevertheless, most of the underlying word embedding methods such as word2vec and GloVe fail to produce high-quality embeddings if the text corpus is small and sparse. This paper proposes a method to generate effective word embeddings from limited data. Through experiments, we show that our proposed model outperforms existing works for the classical word similarity task and for a domain-specific application. | ['Amila Silva', 'Chathurika Amarathunga'] | 2019-05-01 | null | null | null | ws-2019-5 | ['learning-word-embeddings'] | ['methodology'] | [-1.94166079e-01 -7.20274597e-02 -4.17453855e-01 -2.02093959e-01
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3e83ef54-902f-4180-8a6c-bb192d83e6da | birdsnap-large-scale-fine-grained-visual | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Berg_Birdsnap_Large-scale_Fine-grained_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Berg_Birdsnap_Large-scale_Fine-grained_2014_CVPR_paper.pdf | Birdsnap: Large-scale Fine-grained Visual Categorization of Birds | We address the problem of large-scale fine-grained visual categorization, describing new methods we have used to produce an online field guide to 500 North American bird species. We focus on the challenges raised when such a system is asked to distinguish between highly similar species of birds. First, we introduce "one-vs-most classifiers." By eliminating highly similar species during training, these classifiers achieve more accurate and intuitive results than common one-vs-all classifiers. Second, we show how to estimate spatio-temporal class priors from observations that are sampled at irregular and biased locations. We show how these priors can be used to significantly improve performance. We then show state-of-the-art recognition performance on a new, large dataset that we make publicly available. These recognition methods are integrated into the online field guide, which is also publicly available. | ['Thomas Berg', 'Seung Woo Lee', 'David W. Jacobs', 'Peter N. Belhumeur', 'Jiongxin Liu', 'Michelle L. Alexander'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 2.38205064e-02 -7.74212837e-01 -2.01537535e-01 -6.33745432e-01
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b51b1008-b1bb-4251-840e-9e6042e11ec2 | self-supervised-spatio-temporal-2 | 2003.02692 | null | https://arxiv.org/abs/2003.02692v2 | https://arxiv.org/pdf/2003.02692v2.pdf | Self-Supervised Visual Learning by Variable Playback Speeds Prediction of a Video | We propose a self-supervised visual learning method by predicting the variable playback speeds of a video. Without semantic labels, we learn the spatio-temporal visual representation of the video by leveraging the variations in the visual appearance according to different playback speeds under the assumption of temporal coherence. To learn the spatio-temporal visual variations in the entire video, we have not only predicted a single playback speed but also generated clips of various playback speeds and directions with randomized starting points. Hence the visual representation can be successfully learned from the meta information (playback speeds and directions) of the video. We also propose a new layer dependable temporal group normalization method that can be applied to 3D convolutional networks to improve the representation learning performance where we divide the temporal features into several groups and normalize each one using the different corresponding parameters. We validate the effectiveness of our method by fine-tuning it to the action recognition and video retrieval tasks on UCF-101 and HMDB-51. | ['Tae-hoon Kim', 'Hyung Jin Chang', 'Wonjun Hwang', 'Hyeon Cho'] | 2020-03-05 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [-2.06502825e-01 -7.37450898e-01 -5.42065740e-01 -6.50261641e-01
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0c24190f-d476-4c1c-9380-3541d908eb1c | counterfactual-explanations-for-arbitrary | 2106.15212 | null | https://arxiv.org/abs/2106.15212v1 | https://arxiv.org/pdf/2106.15212v1.pdf | Counterfactual Explanations for Arbitrary Regression Models | We present a new method for counterfactual explanations (CFEs) based on Bayesian optimisation that applies to both classification and regression models. Our method is a globally convergent search algorithm with support for arbitrary regression models and constraints like feature sparsity and actionable recourse, and furthermore can answer multiple counterfactual questions in parallel while learning from previous queries. We formulate CFE search for regression models in a rigorous mathematical framework using differentiable potentials, which resolves robustness issues in threshold-based objectives. We prove that in this framework, (a) verifying the existence of counterfactuals is NP-complete; and (b) that finding instances using such potentials is CLS-complete. We describe a unified algorithm for CFEs using a specialised acquisition function that composes both expected improvement and an exponential-polynomial (EP) family with desirable properties. Our evaluation on real-world benchmark domains demonstrate high sample-efficiency and precision. | ['Daniele Magazzeni', 'Jiahao Chen', 'Jon Shepard', 'Jason Long', 'Danial Dervovic', 'Thomas Spooner'] | 2021-06-29 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 4.57945347e-01 4.76705194e-01 -6.31811678e-01 -3.66338998e-01
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0afe8f44-c460-486d-a0a8-b83d6ef723bf | stance-prediction-and-analysis-of-twitter | 2306.14203 | null | https://arxiv.org/abs/2306.14203v2 | https://arxiv.org/pdf/2306.14203v2.pdf | Stance Prediction and Analysis of Twitter data : A case study of Ghana 2020 Presidential Elections | On December 7, 2020, Ghanaians participated in the polls to determine their president for the next four years. To gain insights from this presidential election, we conducted stance analysis (which is not always equivalent to sentiment analysis) to understand how Twitter, a popular social media platform, reflected the opinions of its users regarding the two main presidential candidates. We collected a total of 99,356 tweets using the Twitter API (Tweepy) and manually annotated 3,090 tweets into three classes: Against, Neutral, and Support. We then performed preprocessing on the tweets. The resulting dataset was evaluated using two lexicon-based approaches, VADER and TextBlob, as well as five supervised machine learning-based approaches: Support Vector Machine (SVM), Logistic Regression (LR), Multinomial Na\"ive Bayes (MNB), Stochastic Gradient Descent (SGD), and Random Forest (RF), based on metrics such as accuracy, precision, recall, and F1-score. The best performance was achieved by Logistic Regression with an accuracy of 71.13%. We utilized Logistic Regression to classify all the extracted tweets and subsequently conducted an analysis and discussion of the results. For access to our data and code, please visit: https://github.com/ShesterG/Stance-Detection-Ghana-2020-Elections.git | ['Rose-Mary Owusuaa Mensah Gyening', 'Shester Gueuwou'] | 2023-06-25 | null | null | null | null | ['sentiment-analysis', 'stance-detection'] | ['natural-language-processing', 'natural-language-processing'] | [-2.77596802e-01 8.68923441e-02 -7.00309217e-01 -4.78755295e-01
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961d7577-2c42-441f-9769-cf170381ba42 | learning-neural-implicit-functions-as-object | null | null | https://openreview.net/forum?id=I-nQMZfQz7F | https://openreview.net/pdf?id=I-nQMZfQz7F | Learning Neural Implicit Functions as Object Representations for Robotic Manipulation | Robotic manipulation planning is the problem of finding a sequence of robot configurations that involves interactions with objects in the scene, e.g., grasp, placement, tool-use, etc. To achieve such interactions, traditional approaches require hand-designed features and object representations, and it still remains an open question how to describe such interactions with arbitrary objects in a flexible and efficient way. Inspired by neural implicit representations in 3D modeling, e.g. NeRF, we propose a method to represent objects as neural implicit functions upon which we can define and jointly train interaction features. The proposed pixel-aligned representation is directly inferred from camera images with known camera geometry, naturally acting as a perception component in the whole manipulation pipeline, while at the same time enabling sequential robot manipulation planning. | ['Marc Toussaint', 'Danny Driess', 'Jung-Su Ha'] | 2021-09-29 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 2.69828349e-01 2.99568415e-01 1.27576545e-01 -3.98225993e-01
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83d171b9-67f9-491b-b61a-7fad08fdcde4 | an-overview-of-challenges-in-egocentric-text | 2306.04345 | null | https://arxiv.org/abs/2306.04345v1 | https://arxiv.org/pdf/2306.04345v1.pdf | An Overview of Challenges in Egocentric Text-Video Retrieval | Text-video retrieval contains various challenges, including biases coming from diverse sources. We highlight some of them supported by illustrations to open a discussion. Besides, we address one of the biases, frame length bias, with a simple method which brings a very incremental but promising increase. We conclude with future directions. | ['Joo Hwee Lim', 'Hanwang Zhang', 'Hongyuan Zhu', 'Burak Satar'] | 2023-06-07 | null | null | null | null | ['video-retrieval'] | ['computer-vision'] | [ 2.56426632e-01 -3.60574037e-01 -5.82534432e-01 -1.11567356e-01
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e5534e0f-9868-42ea-8675-b1c4e4d2b0d3 | torsion-graph-neural-networks | 2306.13541 | null | https://arxiv.org/abs/2306.13541v1 | https://arxiv.org/pdf/2306.13541v1.pdf | Torsion Graph Neural Networks | Geometric deep learning (GDL) models have demonstrated a great potential for the analysis of non-Euclidian data. They are developed to incorporate the geometric and topological information of non-Euclidian data into the end-to-end deep learning architectures. Motivated by the recent success of discrete Ricci curvature in graph neural network (GNNs), we propose TorGNN, an analytic Torsion enhanced Graph Neural Network model. The essential idea is to characterize graph local structures with an analytic torsion based weight formula. Mathematically, analytic torsion is a topological invariant that can distinguish spaces which are homotopy equivalent but not homeomorphic. In our TorGNN, for each edge, a corresponding local simplicial complex is identified, then the analytic torsion (for this local simplicial complex) is calculated, and further used as a weight (for this edge) in message-passing process. Our TorGNN model is validated on link prediction tasks from sixteen different types of networks and node classification tasks from three types of networks. It has been found that our TorGNN can achieve superior performance on both tasks, and outperform various state-of-the-art models. This demonstrates that analytic torsion is a highly efficient topological invariant in the characterization of graph structures and can significantly boost the performance of GNNs. | ['Kelin Xia', 'Jiawei Luo', 'Xiang Liu', 'Cong Shen'] | 2023-06-23 | null | null | null | null | ['node-classification', 'link-prediction'] | ['graphs', 'graphs'] | [-3.97298127e-01 2.36849517e-01 -9.21363384e-02 -1.28045872e-01
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51197adb-88b2-4597-a466-a4219a44cefd | cs60075-team2-at-semeval-2021-task-1-lexical-1 | null | null | https://aclanthology.org/2021.semeval-1.87 | https://aclanthology.org/2021.semeval-1.87.pdf | cs60075\_team2 at SemEval-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-trained on various text corpora | The main contribution of this paper is to fine-tune transformer-based language models pre-trained on several text corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the corpora from which the CompLex Dataset was extracted, and others being from other specific domains such as Finance, Law, etc. We perform ablation studies on selecting the transformer models and how their individual complexity scores are aggregated to get the resulting complexity scores. Our method achieves a best Pearson Correlation of 0.784 in sub-task 1 (single word) and 0.836 in sub-task 2 (multiple word expressions). | ['Sai Mahesh Pokala', 'Tanurima Halder', 'Sayantan Adak', 'Abhilash Nandy'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-1.92187905e-01 1.92082658e-01 -1.27657697e-01 -1.75266638e-01
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9d5a219c-aab5-4abe-8ab2-b32cc9096aaa | multi-label-zero-shot-learning-with-transfer | 1808.02474 | null | http://arxiv.org/abs/1808.02474v1 | http://arxiv.org/pdf/1808.02474v1.pdf | Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection | Zero-shot learning transfers knowledge from seen classes to novel unseen
classes to reduce human labor of labelling data for building new classifiers.
Much effort on zero-shot learning however has focused on the standard
multi-class setting, the more challenging multi-label zero-shot problem has
received limited attention. In this paper we propose a transfer-aware embedding
projection approach to tackle multi-label zero-shot learning. The approach
projects the label embedding vectors into a low-dimensional space to induce
better inter-label relationships and explicitly facilitate information transfer
from seen labels to unseen labels, while simultaneously learning a max-margin
multi-label classifier with the projected label embeddings. Auxiliary
information can be conveniently incorporated to guide the label embedding
projection to further improve label relation structures for zero-shot knowledge
transfer. We conduct experiments for zero-shot multi-label image
classification. The results demonstrate the efficacy of the proposed approach. | ['Yuhong Guo', 'Meng Ye'] | 2018-08-07 | null | null | null | null | ['multi-label-zero-shot-learning', 'multi-label-image-classification'] | ['computer-vision', 'computer-vision'] | [ 6.12112343e-01 2.65853852e-01 -5.60533643e-01 -6.86563849e-01
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d97e5877-685d-4b5f-818d-eba8cfa46a58 | salsa-attacking-lattice-cryptography-with | 2207.04785 | null | https://arxiv.org/abs/2207.04785v2 | https://arxiv.org/pdf/2207.04785v2.pdf | SALSA: Attacking Lattice Cryptography with Transformers | Currently deployed public-key cryptosystems will be vulnerable to attacks by full-scale quantum computers. Consequently, "quantum resistant" cryptosystems are in high demand, and lattice-based cryptosystems, based on a hard problem known as Learning With Errors (LWE), have emerged as strong contenders for standardization. In this work, we train transformers to perform modular arithmetic and combine half-trained models with statistical cryptanalysis techniques to propose SALSA: a machine learning attack on LWE-based cryptographic schemes. SALSA can fully recover secrets for small-to-mid size LWE instances with sparse binary secrets, and may scale to attack real-world LWE-based cryptosystems. | ['Kristin Lauter', 'François Charton', 'Mingjie Chen', 'Emily Wenger'] | 2022-07-11 | null | null | null | null | ['cryptanalysis'] | ['miscellaneous'] | [ 2.12896496e-01 -1.14313349e-01 -1.16814360e-01 -7.08311722e-02
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21ef82e2-25bf-4b3f-b8f8-f0ebbffa6b45 | can-current-explainability-help-provide | 2210.15882 | null | https://arxiv.org/abs/2210.15882v1 | https://arxiv.org/pdf/2210.15882v1.pdf | Can Current Explainability Help Provide References in Clinical Notes to Support Humans Annotate Medical Codes? | The medical codes prediction problem from clinical notes has received substantial interest in the NLP community, and several recent studies have shown the state-of-the-art (SOTA) code prediction results of full-fledged deep learning-based methods. However, most previous SOTA works based on deep learning are still in early stages in terms of providing textual references and explanations of the predicted codes, despite the fact that this level of explainability of the prediction outcomes is critical to gaining trust from professional medical coders. This raises the important question of how well current explainability methods apply to advanced neural network models such as transformers to predict correct codes and present references in clinical notes that support code prediction. First, we present an explainable Read, Attend, and Code (xRAC) framework and assess two approaches, attention score-based xRAC-ATTN and model-agnostic knowledge-distillation-based xRAC-KD, through simplified but thorough human-grounded evaluations with SOTA transformer-based model, RAC. We find that the supporting evidence text highlighted by xRAC-ATTN is of higher quality than xRAC-KD whereas xRAC-KD has potential advantages in production deployment scenarios. More importantly, we show for the first time that, given the current state of explainability methodologies, using the SOTA medical codes prediction system still requires the expertise and competencies of professional coders, even though its prediction accuracy is superior to that of human coders. This, we believe, is a very meaningful step toward developing explainable and accurate machine learning systems for fully autonomous medical code prediction from clinical notes. | ['Varun Ganapathi', 'Philip S. Yu', 'Zhongfen Deng', 'Byung-Hak Kim'] | 2022-10-28 | null | null | null | null | ['medical-code-prediction'] | ['medical'] | [-5.42146042e-02 7.48922944e-01 -2.25099504e-01 -5.21249652e-01
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68523289-ead3-4c0c-8345-63aeef0a1a87 | robust-anomaly-map-assisted-multiple-defect | 2212.09352 | null | https://arxiv.org/abs/2212.09352v1 | https://arxiv.org/pdf/2212.09352v1.pdf | Robust Anomaly Map Assisted Multiple Defect Detection with Supervised Classification Techniques | Industry 4.0 aims to optimize the manufacturing environment by leveraging new technological advances, such as new sensing capabilities and artificial intelligence. The DRAEM technique has shown state-of-the-art performance for unsupervised classification. The ability to create anomaly maps highlighting areas where defects probably lie can be leveraged to provide cues to supervised classification models and enhance their performance. Our research shows that the best performance is achieved when training a defect detection model by providing an image and the corresponding anomaly map as input. Furthermore, such a setting provides consistent performance when framing the defect detection as a binary or multiclass classification problem and is not affected by class balancing policies. We performed the experiments on three datasets with real-world data provided by Philips Consumer Lifestyle BV. | ['Dunja Mladenić', 'Blaž Fortuna', 'Erik Koehorst', 'Spyros Theodoropoulos', 'Patrik Zajec', 'Jože M. Rožanec'] | 2022-12-19 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 3.16963434e-01 7.57014528e-02 -1.02450170e-01 -5.29593289e-01
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755bd3ab-6aec-4c79-9bf2-833dd57f9339 | parallel-residual-bi-fusion-feature-pyramid | 2012.01724 | null | https://arxiv.org/abs/2012.01724v5 | https://arxiv.org/pdf/2012.01724v5.pdf | Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object Detection | This paper proposes the Parallel Residual Bi-Fusion Feature Pyramid Network (PRB-FPN) for fast and accurate single-shot object detection. Feature Pyramid (FP) is widely used in recent visual detection, however the top-down pathway of FP cannot preserve accurate localization due to pooling shifting. The advantage of FP is weakened as deeper backbones with more layers are used. In addition, it cannot keep up accurate detection of both small and large objects at the same time. To address these issues, we propose a new parallel FP structure with bi-directional (top-down and bottom-up) fusion and associated improvements to retain high-quality features for accurate localization. We provide the following design improvements: (1) A parallel bifusion FP structure with a bottom-up fusion module (BFM) to detect both small and large objects at once with high accuracy. (2) A concatenation and re-organization (CORE) module provides a bottom-up pathway for feature fusion, which leads to the bi-directional fusion FP that can recover lost information from lower-layer feature maps. (3) The CORE feature is further purified to retain richer contextual information. Such CORE purification in both top-down and bottom-up pathways can be finished in only a few iterations. (4) The adding of a residual design to CORE leads to a new Re-CORE module that enables easy training and integration with a wide range of deeper or lighter backbones. The proposed network achieves state-of-the-art performance on the UAVDT17 and MS COCO datasets. Code is available at https://github.com/pingyang1117/PRBNet_PyTorch. | ['Yong-Sheng Chen', 'Jun-Wei Hsieh', 'Ming-Ching Chang', 'Ping-Yang Chen'] | 2020-12-03 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-1.37377530e-01 -4.35766071e-01 7.69797862e-02 -1.42093316e-01
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9a1ac1b7-0a01-4249-9ac0-258918dacd4e | sociocultural-knowledge-is-needed-for | 2304.01890 | null | https://arxiv.org/abs/2304.01890v4 | https://arxiv.org/pdf/2304.01890v4.pdf | Sociocultural knowledge is needed for selection of shots in hate speech detection tasks | We introduce HATELEXICON, a lexicon of slurs and targets of hate speech for the countries of Brazil, Germany, India and Kenya, to aid training and interpretability of models. We demonstrate how our lexicon can be used to interpret model predictions, showing that models developed to classify extreme speech rely heavily on target words when making predictions. Further, we propose a method to aid shot selection for training in low-resource settings via HATELEXICON. In few-shot learning, the selection of shots is of paramount importance to model performance. In our work, we simulate a few-shot setting for German and Hindi, using HASOC data for training and the Multilingual HateCheck (MHC) as a benchmark. We show that selecting shots based on our lexicon leads to models performing better on MHC than models trained on shots sampled randomly. Thus, when given only a few training examples, using our lexicon to select shots containing more sociocultural information leads to better few-shot performance. | ['Hinrich Schütze', 'Abdullatif Köksal', 'Antonis Maronikolakis'] | 2023-04-04 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [ 4.30579595e-02 1.41349435e-01 -1.76547110e-01 -1.90251797e-01
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2226f56a-a73e-4d60-b00a-22e454534de1 | zqm-at-semeval-2019-task9-a-single-layer-cnn | null | null | https://aclanthology.org/S19-2226 | https://aclanthology.org/S19-2226.pdf | ZQM at SemEval-2019 Task9: A Single Layer CNN Based on Pre-trained Model for Suggestion Mining | This paper describes our system that competed at SemEval 2019 Task 9 - SubTask A: {''}Sug- gestion Mining from Online Reviews and Forums{''}. Our system fuses the convolutional neural network and the latest BERT model to conduct suggestion mining. In our system, the input of convolutional neural network is the embedding vectors which are drawn from the pre-trained BERT model. And to enhance the effectiveness of the whole system, the pre-trained BERT model is fine-tuned by provided datasets before the procedure of embedding vectors extraction. Empirical results show the effectiveness of our model which obtained 9th position out of 34 teams with F1 score equals to 0.715. | ['Zhengxin Zhang', 'Linmao Wang', 'Qimin Zhou', 'Hao Wu'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [-3.59104782e-01 2.55439818e-01 -2.33210012e-01 -4.64924991e-01
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7d62d234-803a-4e43-8dba-5032034cf7db | moe-fusion-instance-embedded-mixture-of | 2302.01392 | null | https://arxiv.org/abs/2302.01392v2 | https://arxiv.org/pdf/2302.01392v2.pdf | Multi-modal Gated Mixture of Local-to-Global Experts for Dynamic Image Fusion | Infrared and visible image fusion aims to integrate comprehensive information from multiple sources to achieve superior performances on various practical tasks, such as detection, over that of a single modality. However, most existing methods directly combined the texture details and object contrast of different modalities, ignoring the dynamic changes in reality, which diminishes the visible texture in good lighting conditions and the infrared contrast in low lighting conditions. To fill this gap, we propose a dynamic image fusion framework with a multi-modal gated mixture of local-to-global experts, termed MoE-Fusion, to dynamically extract effective and comprehensive information from the respective modalities. Our model consists of a Mixture of Local Experts (MoLE) and a Mixture of Global Experts (MoGE) guided by a multi-modal gate. The MoLE performs specialized learning of multi-modal local features, prompting the fused images to retain the local information in a sample-adaptive manner, while the MoGE focuses on the global information that complements the fused image with overall texture detail and contrast. Extensive experiments show that our MoE-Fusion outperforms state-of-the-art methods in preserving multi-modal image texture and contrast through the local-to-global dynamic learning paradigm, and also achieves superior performance on detection tasks. Our code will be available: https://github.com/SunYM2020/MoE-Fusion. | ['QinGhua Hu', 'Pengfei Zhu', 'Bing Cao', 'Yiming Sun'] | 2023-02-02 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 1.78780407e-01 -5.53818345e-01 -7.01640174e-02 -1.66189611e-01
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f6caf367-9af5-4305-8cb7-1369b235b985 | discourse-aware-prompt-design-for-text-1 | null | null | https://openreview.net/forum?id=cTgq8D-LFj0 | https://openreview.net/pdf?id=cTgq8D-LFj0 | Discourse-Aware Prompt Design for Text Generation | Current efficient fine-tuning methods (e.g., adapters, prefix-tuning, etc.) have optimized conditional text generation via training a small set of extra parameters of the neural language model, while freezing the rest for efficiency. While showing strong performance on some generation tasks, they don't generalize across all generation tasks. In this work, we show that prompt based conditional text generation can be improved with simple and efficient methods that simulate modeling the discourse structure of human written text.We introduce two key design choices: First, we show that a higher-level discourse structure of human written text can be modelled with hierarchical blocking on prefix parameters. It enables spanning different parts of the input and output text and yields more coherent output generations. Second, we propose sparse prefix tuning by introducing attention sparsity on the prefix parameters at different layers of the network and learn sparse transformations on the softmax-function, respectively. We find that sparse attention enables the prefix-tuning to better control of the input contents (salient facts) yielding more efficient tuning of the prefix-parameters. Our experiments show that structured design of prefix parameters can yield more coherent, faithful and relevant generations than baseline prefix-tuning on all generation tasks and perform at par with fine-tuning while being more efficient. | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['conditional-text-generation'] | ['natural-language-processing'] | [ 3.69531751e-01 8.83159280e-01 -3.38207573e-01 -3.21150482e-01
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1f4e71c0-3be5-4402-8124-041d8af13bff | tweet-normalization-with-syllables | null | null | https://aclanthology.org/P15-1089 | https://aclanthology.org/P15-1089.pdf | Tweet Normalization with Syllables | null | ['Chin-Hui Lee', 'Yunqing Xia', 'Ke Xu'] | 2015-07-01 | tweet-normalization-with-syllables-1 | https://aclanthology.org/P15-1089 | https://aclanthology.org/P15-1089.pdf | ijcnlp-2015-7 | ['lexical-normalization'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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d7e77a9c-c9d6-4077-abdd-e88b88035052 | causal-discovery-using-bayesian-model | 2306.02931 | null | https://arxiv.org/abs/2306.02931v1 | https://arxiv.org/pdf/2306.02931v1.pdf | Causal Discovery using Bayesian Model Selection | With only observational data on two variables, and without other assumptions, it is not possible to infer which one causes the other. Much of the causal literature has focused on guaranteeing identifiability of causal direction in statistical models for datasets where strong assumptions hold, such as additive noise or restrictions on parameter count. These methods are then subsequently tested on realistic datasets, most of which violate their assumptions. Building on previous attempts, we show how to use causal assumptions within the Bayesian framework. This allows us to specify models with realistic assumptions, while also encoding independent causal mechanisms, leading to an asymmetry between the causal directions. Identifying causal direction then becomes a Bayesian model selection problem. We analyse why Bayesian model selection works for known identifiable cases and flexible model classes, while also providing correctness guarantees about its behaviour. To demonstrate our approach, we construct a Bayesian non-parametric model that can flexibly model the joint. We then outperform previous methods on a wide range of benchmark datasets with varying data generating assumptions showing the usefulness of our method. | ['Mark van der Wilk', 'Anish Dhir'] | 2023-06-05 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 6.10898077e-01 3.04013550e-01 -4.58804816e-01 -4.04058635e-01
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9240d97d-210b-464a-983d-e2e659903bfa | curriculum-graph-co-teaching-for-multi-target | 2104.00808 | null | https://arxiv.org/abs/2104.00808v1 | https://arxiv.org/pdf/2104.00808v1.pdf | Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation | In this paper we address multi-target domain adaptation (MTDA), where given one labeled source dataset and multiple unlabeled target datasets that differ in data distributions, the task is to learn a robust predictor for all the target domains. We identify two key aspects that can help to alleviate multiple domain-shifts in the MTDA: feature aggregation and curriculum learning. To this end, we propose Curriculum Graph Co-Teaching (CGCT) that uses a dual classifier head, with one of them being a graph convolutional network (GCN) which aggregates features from similar samples across the domains. To prevent the classifiers from over-fitting on its own noisy pseudo-labels we develop a co-teaching strategy with the dual classifier head that is assisted by curriculum learning to obtain more reliable pseudo-labels. Furthermore, when the domain labels are available, we propose Domain-aware Curriculum Learning (DCL), a sequential adaptation strategy that first adapts on the easier target domains, followed by the harder ones. We experimentally demonstrate the effectiveness of our proposed frameworks on several benchmarks and advance the state-of-the-art in the MTDA by large margins (e.g. +5.6% on the DomainNet). | ['Elisa Ricci', 'Nicu Sebe', 'Zhun Zhong', 'Evgeny Krivosheev', 'Subhankar Roy'] | 2021-04-01 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Roy_Curriculum_Graph_Co-Teaching_for_Multi-Target_Domain_Adaptation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Roy_Curriculum_Graph_Co-Teaching_for_Multi-Target_Domain_Adaptation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['blended-target-domain-adaptation', 'multi-target-domain-adaptation'] | ['computer-vision', 'computer-vision'] | [ 0.34355512 0.17667098 -0.1389821 -0.5522069 -0.8443007 -0.7337771
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6b9c1d0f-1535-4084-966e-8a84a547094c | sg-gan-fine-stereoscopic-aware-generation-for | 2305.12646 | null | https://arxiv.org/abs/2305.12646v1 | https://arxiv.org/pdf/2305.12646v1.pdf | SG-GAN: Fine Stereoscopic-Aware Generation for 3D Brain Point Cloud Up-sampling from a Single Image | In minimally-invasive brain surgeries with indirect and narrow operating environments, 3D brain reconstruction is crucial. However, as requirements of accuracy for some new minimally-invasive surgeries (such as brain-computer interface surgery) are higher and higher, the outputs of conventional 3D reconstruction, such as point cloud (PC), are facing the challenges that sample points are too sparse and the precision is insufficient. On the other hand, there is a scarcity of high-density point cloud datasets, which makes it challenging to train models for direct reconstruction of high-density brain point clouds. In this work, a novel model named stereoscopic-aware graph generative adversarial network (SG-GAN) with two stages is proposed to generate fine high-density PC conditioned on a single image. The Stage-I GAN sketches the primitive shape and basic structure of the organ based on the given image, yielding Stage-I point clouds. The Stage-II GAN takes the results from Stage-I and generates high-density point clouds with detailed features. The Stage-II GAN is capable of correcting defects and restoring the detailed features of the region of interest (ROI) through the up-sampling process. Furthermore, a parameter-free-attention-based free-transforming module is developed to learn the efficient features of input, while upholding a promising performance. Comparing with the existing methods, the SG-GAN model shows superior performance in terms of visual quality, objective measurements, and performance in classification, as demonstrated by comprehensive results measured by several evaluation metrics including PC-to-PC error and Chamfer distance. | ['Shuqiang Wang', 'Baiying Lei', 'Bowen Hu'] | 2023-05-22 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [ 1.32817179e-01 4.60523516e-01 2.47365281e-01 -9.60928798e-02
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c3f0207a-c015-47d0-864d-a321da5301fb | discourse-connectors-for-latent-subjectivity | null | null | https://aclanthology.org/N13-1100 | https://aclanthology.org/N13-1100.pdf | Discourse Connectors for Latent Subjectivity in Sentiment Analysis | null | ['Rakshit Trivedi', 'Jacob Eisenstein'] | 2013-06-01 | null | null | null | naacl-2013-6 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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68279a47-de01-4c76-b391-e35590d7ae8a | semantic-aware-chinese-zero-pronoun | null | null | https://aclanthology.org/2020.ccl-1.77 | https://aclanthology.org/2020.ccl-1.77.pdf | Semantic-aware Chinese Zero Pronoun Resolution with Pre-trained Semantic Dependency Parser | Deep learning-based Chinese zero pronoun resolution model has achieved better performance than traditional machine learning-based model. However, the existing work related to Chinese zero pronoun resolution has not yet well integrated linguistic information into the deep learningbased Chinese zero pronoun resolution model.This paper adopts the idea based on the pre-trained model, and integrates the semantic representations in the pre-trained Chinese semantic dependency graph parser into the Chinese zero pronoun resolution model. The experimental results on OntoNotes-5.0 dataset show that our proposed Chinese zero pronoun resolution model with pretrained Chinese semantic dependency parser improves the F-score by 0.4% compared with our baseline model, and obtains better results than other deep learning-based Chinese zero pronoun resolution models. In addition, we integrate the BERT representations into our model so that the performance of our model was improved by 0.7% compared with our baseline model. | ['Yanqiu Shao', 'Zizhuo Shen', 'Lanqiu Zhang'] | null | null | null | null | ccl-2020-10 | ['chinese-zero-pronoun-resolution'] | ['natural-language-processing'] | [-3.76237661e-01 3.79053205e-01 -2.62311190e-01 -2.05064908e-01
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1043a9b4-d433-4cb1-a460-0f7c3d451b0d | mdcspell-a-multi-task-detector-corrector | null | null | https://aclanthology.org/2022.findings-acl.98 | https://aclanthology.org/2022.findings-acl.98.pdf | MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction | Chinese Spelling Correction (CSC) is a task to detect and correct misspelled characters in Chinese texts. CSC is challenging since many Chinese characters are visually or phonologically similar but with quite different semantic meanings. Many recent works use BERT-based language models to directly correct each character of the input sentence. However, these methods can be sub-optimal since they correct every character of the sentence only by the context which is easily negatively affected by the misspelled characters. Some other works propose to use an error detector to guide the correction by masking the detected errors. Nevertheless, these methods dampen the visual or phonological features from the misspelled characters which could be critical for correction. In this work, we propose a novel general detector-corrector multi-task framework where the corrector uses BERT to capture the visual and phonological features from each character in the raw sentence and uses a late fusion strategy to fuse the hidden states of the corrector with that of the detector to minimize the negative impact from the misspelled characters. Comprehensive experiments on benchmarks demonstrate that our proposed method can significantly outperform the state-of-the-art methods in the CSC task. | ['Feng Mao', 'Boyu Zhang', 'Ziqiang Ying', 'Chenxi Zhu'] | null | null | null | null | findings-acl-2022-5 | ['spelling-correction'] | ['natural-language-processing'] | [ 4.81718600e-01 -4.77494717e-01 3.24297339e-01 -1.22866392e-01
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5cc985dd-d11f-436d-a041-3c155c2142ac | graph-neural-networks-with-learnable-1 | 2110.07875 | null | https://arxiv.org/abs/2110.07875v2 | https://arxiv.org/pdf/2110.07875v2.pdf | Graph Neural Networks with Learnable Structural and Positional Representations | Graph neural networks (GNNs) have become the standard learning architectures for graphs. GNNs have been applied to numerous domains ranging from quantum chemistry, recommender systems to knowledge graphs and natural language processing. A major issue with arbitrary graphs is the absence of canonical positional information of nodes, which decreases the representation power of GNNs to distinguish e.g. isomorphic nodes and other graph symmetries. An approach to tackle this issue is to introduce Positional Encoding (PE) of nodes, and inject it into the input layer, like in Transformers. Possible graph PE are Laplacian eigenvectors. In this work, we propose to decouple structural and positional representations to make easy for the network to learn these two essential properties. We introduce a novel generic architecture which we call LSPE (Learnable Structural and Positional Encodings). We investigate several sparse and fully-connected (Transformer-like) GNNs, and observe a performance increase for molecular datasets, from 1.79% up to 64.14% when considering learnable PE for both GNN classes. | ['Xavier Bresson', 'Yoshua Bengio', 'Thomas Laurent', 'Anh Tuan Luu', 'Vijay Prakash Dwivedi'] | 2021-10-15 | graph-neural-networks-with-learnable | https://openreview.net/forum?id=wTTjnvGphYj | https://openreview.net/pdf?id=wTTjnvGphYj | iclr-2022-4 | ['graph-regression'] | ['graphs'] | [ 4.32465196e-01 3.83688867e-01 -2.52551824e-01 -4.89028431e-02
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77b0ba0c-712c-4685-a833-d275bc6f0135 | end-to-end-deep-residual-learning-with | 1909.12923 | null | https://arxiv.org/abs/1909.12923v1 | https://arxiv.org/pdf/1909.12923v1.pdf | End-to-End Deep Residual Learning with Dilated Convolutions for Myocardial Infarction Detection and Localization | In this report, I investigate the use of end-to-end deep residual learning with dilated convolutions for myocardial infarction (MI) detection and localization from electrocardiogram (ECG) signals. Although deep residual learning has already been applied to MI detection and localization, I propose a more accurate system that distinguishes among a higher number (i.e., six) of MI locations. Inspired by speech waveform processing with neural networks, I found a more robust front-end than directly arranging the multi-lead ECG signal into an input matrix consisting of the use of a single one-dimensional convolutional layer per ECG lead to extract a pseudo-time-frequency representation and create a compact and discriminative input feature volume. As a result, I end up with a system achieving an MI detection and localization accuracy of 99.99% on the well-known Physikalisch-Technische Bundesanstalt (PTB) database. | ['Iván López-Espejo'] | 2019-09-15 | null | null | null | null | ['myocardial-infarction-detection'] | ['medical'] | [ 3.63501400e-01 -2.42960677e-01 2.39556462e-01 -4.69516784e-01
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6ce35740-fea6-4232-8a3b-fe4bdb7755e8 | better-smatch-better-parser-amr-evaluation-is | 2210.06461 | null | https://arxiv.org/abs/2210.06461v1 | https://arxiv.org/pdf/2210.06461v1.pdf | Better Smatch = Better Parser? AMR evaluation is not so simple anymore | Recently, astonishing advances have been observed in AMR parsing, as measured by the structural Smatch metric. In fact, today's systems achieve performance levels that seem to surpass estimates of human inter annotator agreement (IAA). Therefore, it is unclear how well Smatch (still) relates to human estimates of parse quality, as in this situation potentially fine-grained errors of similar weight may impact the AMR's meaning to different degrees. We conduct an analysis of two popular and strong AMR parsers that -- according to Smatch -- reach quality levels on par with human IAA, and assess how human quality ratings relate to Smatch and other AMR metrics. Our main findings are: i) While high Smatch scores indicate otherwise, we find that AMR parsing is far from being solved: we frequently find structurally small, but semantically unacceptable errors that substantially distort sentence meaning. ii) Considering high-performance parsers, better Smatch scores may not necessarily indicate consistently better parsing quality. To obtain a meaningful and comprehensive assessment of quality differences of parse(r)s, we recommend augmenting evaluations with macro statistics, use of additional metrics, and more human analysis. | ['Anette Frank', 'Juri Opitz'] | 2022-10-12 | null | null | null | null | ['amr-parsing'] | ['natural-language-processing'] | [ 1.92804039e-01 4.93048072e-01 3.64846140e-01 -6.92654312e-01
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5b8ad32e-609a-4bc5-9fb9-7e393a5b65d7 | disentangled-variational-autoencoder-for | 2305.14071 | null | https://arxiv.org/abs/2305.14071v1 | https://arxiv.org/pdf/2305.14071v1.pdf | Disentangled Variational Autoencoder for Emotion Recognition in Conversations | In Emotion Recognition in Conversations (ERC), the emotions of target utterances are closely dependent on their context. Therefore, existing works train the model to generate the response of the target utterance, which aims to recognise emotions leveraging contextual information. However, adjacent response generation ignores long-range dependencies and provides limited affective information in many cases. In addition, most ERC models learn a unified distributed representation for each utterance, which lacks interpretability and robustness. To address these issues, we propose a VAD-disentangled Variational AutoEncoder (VAD-VAE), which first introduces a target utterance reconstruction task based on Variational Autoencoder, then disentangles three affect representations Valence-Arousal-Dominance (VAD) from the latent space. We also enhance the disentangled representations by introducing VAD supervision signals from a sentiment lexicon and minimising the mutual information between VAD distributions. Experiments show that VAD-VAE outperforms the state-of-the-art model on two datasets. Further analysis proves the effectiveness of each proposed module and the quality of disentangled VAD representations. The code is available at https://github.com/SteveKGYang/VAD-VAE. | ['Sophia Ananiadou', 'Tianlin Zhang', 'Kailai Yang'] | 2023-05-23 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [-1.64682209e-01 1.15138784e-01 -1.69846192e-02 -7.11083114e-01
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0a62bd39-02d1-42bf-ac25-2e7b1ac32c15 | deep-multi-view-learning-using-neuron-wise | 1904.11151 | null | http://arxiv.org/abs/1904.11151v1 | http://arxiv.org/pdf/1904.11151v1.pdf | Deep Multi-View Learning using Neuron-Wise Correlation-Maximizing Regularizers | Many machine learning problems concern with discovering or associating common
patterns in data of multiple views or modalities. Multi-view learning is of the
methods to achieve such goals. Recent methods propose deep multi-view networks
via adaptation of generic Deep Neural Networks (DNNs), which concatenate
features of individual views at intermediate network layers (i.e., fusion
layers). In this work, we study the problem of multi-view learning in such
end-to-end networks. We take a regularization approach via multi-view learning
criteria, and propose a novel, effective, and efficient neuron-wise
correlation-maximizing regularizer. We implement our proposed regularizers
collectively as a correlation-regularized network layer (CorrReg). CorrReg can
be applied to either fully-connected or convolutional fusion layers, simply by
replacing them with their CorrReg counterparts. By partitioning neurons of a
hidden layer in generic DNNs into multiple subsets, we also consider a
multi-view feature learning perspective of generic DNNs. Such a perspective
enables us to study deep multi-view learning in the context of regularized
network training, for which we present control experiments of benchmark image
classification to show the efficacy of our proposed CorrReg. To investigate how
CorrReg is useful for practical multi-view learning problems, we conduct
experiments of RGB-D object/scene recognition and multi-view based 3D object
recognition, using networks with fusion layers that concatenate intermediate
features of individual modalities or views for subsequent classification.
Applying CorrReg to fusion layers of these networks consistently improves
classification performance. In particular, we achieve the new state of the art
on the benchmark RGB-D object and RGB-D scene datasets. We make the
implementation of CorrReg publicly available. | ['DaCheng Tao', 'Kui Jia', 'Mingkui Tan', 'Jiehong Lin'] | 2019-04-25 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-1.63689512e-03 -3.17207724e-01 -3.92062925e-02 -5.82647681e-01
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85df3761-0e45-4955-96c7-d04f71a57a9f | integratedpifu-integrated-pixel-aligned | 2211.07955 | null | https://arxiv.org/abs/2211.07955v1 | https://arxiv.org/pdf/2211.07955v1.pdf | IntegratedPIFu: Integrated Pixel Aligned Implicit Function for Single-view Human Reconstruction | We propose IntegratedPIFu, a new pixel aligned implicit model that builds on the foundation set by PIFuHD. IntegratedPIFu shows how depth and human parsing information can be predicted and capitalised upon in a pixel-aligned implicit model. In addition, IntegratedPIFu introduces depth oriented sampling, a novel training scheme that improve any pixel aligned implicit model ability to reconstruct important human features without noisy artefacts. Lastly, IntegratedPIFu presents a new architecture that, despite using less model parameters than PIFuHD, is able to improves the structural correctness of reconstructed meshes. Our results show that IntegratedPIFu significantly outperforms existing state of the arts methods on single view human reconstruction. Our code has been made available online. | ['Weisi Lin', 'Haiyu Zhao', 'Guosheng Lin', 'Kennard Yanting Chan'] | 2022-11-15 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 3.14878494e-01 8.50162506e-01 -1.44956172e-01 -1.77750424e-01
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f4c1a29f-5a29-4b4b-bf2e-1ed886273406 | sa-net-a-deep-spectral-analysis-network-for | 2009.07026 | null | https://arxiv.org/abs/2009.07026v1 | https://arxiv.org/pdf/2009.07026v1.pdf | SA-Net: A deep spectral analysis network for image clustering | Although supervised deep representation learning has attracted enormous attentions across areas of pattern recognition and computer vision, little progress has been made towards unsupervised deep representation learning for image clustering. In this paper, we propose a deep spectral analysis network for unsupervised representation learning and image clustering. While spectral analysis is established with solid theoretical foundations and has been widely applied to unsupervised data mining, its essential weakness lies in the fact that it is difficult to construct a proper affinity matrix and determine the involving Laplacian matrix for a given dataset. In this paper, we propose a SA-Net to overcome these weaknesses and achieve improved image clustering by extending the spectral analysis procedure into a deep learning framework with multiple layers. The SA-Net has the capability to learn deep representations and reveal deep correlations among data samples. Compared with the existing spectral analysis, the SA-Net achieves two advantages: (i) Given the fact that one spectral analysis procedure can only deal with one subset of the given dataset, our proposed SA-Net elegantly integrates multiple parallel and consecutive spectral analysis procedures together to enable interactive learning across different units towards a coordinated clustering model; (ii) Our SA-Net can identify the local similarities among different images at patch level and hence achieves a higher level of robustness against occlusions. Extensive experiments on a number of popular datasets support that our proposed SA-Net outperforms 11 benchmarks across a number of image clustering applications. | ['Jinghua Wang', 'Jianmin Jiang'] | 2020-09-11 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 2.06966177e-01 -3.42513829e-01 -2.57196911e-02 -2.54677206e-01
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843df6b0-f10d-4279-8d75-852b0c877479 | task-splitting-for-dnn-based-acoustic-echo | 2205.06931 | null | https://arxiv.org/abs/2205.06931v2 | https://arxiv.org/pdf/2205.06931v2.pdf | Task splitting for DNN-based acoustic echo and noise removal | Neural networks have led to tremendous performance gains for single-task speech enhancement, such as noise suppression and acoustic echo cancellation (AEC). In this work, we evaluate whether it is more useful to use a single joint or separate modules to tackle these problems. We describe different possible implementations and give insights into their performance and efficiency. We show that using a separate echo cancellation module and a module for noise and residual echo removal results in less near-end speech distortion and better performance during double-talk at same complexity. | ['Maria Luis Valero', 'Sebastian Braun'] | 2022-05-13 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 2.58641183e-01 -3.59891690e-02 6.66675270e-01 -3.86071801e-01
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391bec05-9f62-46cb-964c-a09238e590f8 | sync-draw-automatic-video-generation-using | 1611.10314 | null | http://arxiv.org/abs/1611.10314v4 | http://arxiv.org/pdf/1611.10314v4.pdf | Sync-DRAW: Automatic Video Generation using Deep Recurrent Attentive Architectures | This paper introduces a novel approach for generating videos called
Synchronized Deep Recurrent Attentive Writer (Sync-DRAW). Sync-DRAW can also
perform text-to-video generation which, to the best of our knowledge, makes it
the first approach of its kind. It combines a Variational Autoencoder~(VAE)
with a Recurrent Attention Mechanism in a novel manner to create a temporally
dependent sequence of frames that are gradually formed over time. The recurrent
attention mechanism in Sync-DRAW attends to each individual frame of the video
in sychronization, while the VAE learns a latent distribution for the entire
video at the global level. Our experiments with Bouncing MNIST, KTH and UCF-101
suggest that Sync-DRAW is efficient in learning the spatial and temporal
information of the videos and generates frames with high structural integrity,
and can generate videos from simple captions on these datasets. (Accepted as
oral paper in ACM-Multimedia 2017) | ['Gaurav Mittal', 'Tanya Marwah', 'Vineeth N. Balasubramanian'] | 2016-11-30 | null | null | null | null | ['text-to-video-generation'] | ['natural-language-processing'] | [-6.02958258e-03 2.20143870e-01 -2.29379237e-02 1.23409308e-01
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905b5973-4574-4ba3-8d77-f69226f3378c | ir-gan-image-manipulation-with-linguistic | 2204.00792 | null | https://arxiv.org/abs/2204.00792v1 | https://arxiv.org/pdf/2204.00792v1.pdf | IR-GAN: Image Manipulation with Linguistic Instruction by Increment Reasoning | Conditional image generation is an active research topic including text2image and image translation. Recently image manipulation with linguistic instruction brings new challenges of multimodal conditional generation. However, traditional conditional image generation models mainly focus on generating high-quality and visually realistic images, and lack resolving the partial consistency between image and instruction. To address this issue, we propose an Increment Reasoning Generative Adversarial Network (IR-GAN), which aims to reason the consistency between visual increment in images and semantic increment in instructions. First, we introduce the word-level and instruction-level instruction encoders to learn user's intention from history-correlated instructions as semantic increment. Second, we embed the representation of semantic increment into that of source image for generating target image, where source image plays the role of referring auxiliary. Finally, we propose a reasoning discriminator to measure the consistency between visual increment and semantic increment, which purifies user's intention and guarantees the good logic of generated target image. Extensive experiments and visualization conducted on two datasets show the effectiveness of IR-GAN. | ['Qingming Huang', 'Shuhui Wang', 'Qianqian Xu', 'Shaofei Cai', 'Liang Li', 'Jincan Deng', 'Zhenhuan Liu'] | 2022-04-02 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 6.76548958e-01 3.19699377e-01 -1.81420892e-01 -3.04597706e-01
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fd59de6f-f3ea-45c7-a7ec-ee7e13b51cb5 | the-power-of-character-n-grams-in-native | null | null | https://aclanthology.org/W17-5043 | https://aclanthology.org/W17-5043.pdf | The Power of Character N-grams in Native Language Identification | In this paper, we explore the performance of a linear SVM trained on language independent character features for the NLI Shared Task 2017. Our basic system (GRONINGEN) achieves the best performance (87.56 F1-score) on the evaluation set using only 1-9 character n-grams as features. We compare this against several ensemble and meta-classifiers in order to examine how the linear system fares when combined with other, especially non-linear classifiers. Special emphasis is placed on the topic bias that exists by virtue of the assessment essay prompt distribution. | ['Gertjan Van Noord', 'Artur Kulmizev', 'Martijn Wieling', 'Johannes Bjerva', 'Malvina Nissim', 'Bo Blankers', 'Barbara Plank'] | 2017-09-01 | null | null | null | ws-2017-9 | ['native-language-identification'] | ['natural-language-processing'] | [-6.39842302e-02 1.05593331e-01 -5.69455445e-01 -5.21685898e-01
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e3644184-64ae-4467-950e-25fc6c25047a | model-based-gym-environments-for-limit-order | 2209.07823 | null | https://arxiv.org/abs/2209.07823v1 | https://arxiv.org/pdf/2209.07823v1.pdf | Model-based gym environments for limit order book trading | Within the mathematical finance literature there is a rich catalogue of mathematical models for studying algorithmic trading problems -- such as market-making and optimal execution -- in limit order books. This paper introduces \mbtgym, a Python module that provides a suite of gym environments for training reinforcement learning (RL) agents to solve such model-based trading problems. The module is set up in an extensible way to allow the combination of different aspects of different models. It supports highly efficient implementations of vectorized environments to allow faster training of RL agents. In this paper, we motivate the challenge of using RL to solve such model-based limit order book problems in mathematical finance, we explain the design of our gym environment, and then demonstrate its use in solving standard and non-standard problems from the literature. Finally, we lay out a roadmap for further development of our module, which we provide as an open source repository on GitHub so that it can serve as a focal point for RL research in model-based algorithmic trading. | ['Martin Herdegen', 'Rahul Savani', 'Leandro Sanchez-Betancourt', 'Joseph Jerome'] | 2022-09-16 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-8.18612933e-01 -2.03850791e-01 -3.46040964e-01 -2.36754343e-01
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4c4ee918-4686-4e47-8a9a-578b50c81046 | predictive-modelling-of-training-loads-and | 1706.04336 | null | http://arxiv.org/abs/1706.04336v1 | http://arxiv.org/pdf/1706.04336v1.pdf | Predictive modelling of training loads and injury in Australian football | To investigate whether training load monitoring data could be used to predict
injuries in elite Australian football players, data were collected from elite
athletes over 3 seasons at an Australian football club. Loads were quantified
using GPS devices, accelerometers and player perceived exertion ratings.
Absolute and relative training load metrics were calculated for each player
each day (rolling average, exponentially weighted moving average, acute:chronic
workload ratio, monotony and strain). Injury prediction models (regularised
logistic regression, generalised estimating equations, random forests and
support vector machines) were built for non-contact, non-contact time-loss and
hamstring specific injuries using the first two seasons of data. Injury
predictions were generated for the third season and evaluated using the area
under the receiver operator characteristic (AUC). Predictive performance was
only marginally better than chance for models of non-contact and non-contact
time-loss injuries (AUC$<$0.65). The best performing model was a multivariate
logistic regression for hamstring injuries (best AUC=0.76). Learning curves
suggested logistic regression was underfitting the load-injury relationship and
that using a more complex model or increasing the amount of model building data
may lead to future improvements. Injury prediction models built using training
load data from a single club showed poor ability to predict injuries when
tested on previously unseen data, suggesting they are limited as a daily
decision tool for practitioners. Focusing the modelling approach on specific
injury types and increasing the amount of training data may lead to the
development of improved predictive models for injury prevention. | ['Kok-Leong Ong', 'Rod Whiteley', 'Meg E. Morris', 'Kay M. Crossley', 'Justin Crow', 'David L. Carey'] | 2017-06-14 | null | null | null | null | ['injury-prediction'] | ['playing-games'] | [ 7.55648464e-02 -2.03025132e-01 -5.04063189e-01 7.14448839e-02
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1bc3333f-fb5f-41d7-9f41-4bbbd5a321e6 | chatgpt-is-not-all-you-need-a-state-of-the | 2301.04655 | null | https://arxiv.org/abs/2301.04655v1 | https://arxiv.org/pdf/2301.04655v1.pdf | ChatGPT is not all you need. A State of the Art Review of large Generative AI models | During the last two years there has been a plethora of large generative models such as ChatGPT or Stable Diffusion that have been published. Concretely, these models are able to perform tasks such as being a general question and answering system or automatically creating artistic images that are revolutionizing several sectors. Consequently, the implications that these generative models have in the industry and society are enormous, as several job positions may be transformed. For example, Generative AI is capable of transforming effectively and creatively texts to images, like the DALLE-2 model; text to 3D images, like the Dreamfusion model; images to text, like the Flamingo model; texts to video, like the Phenaki model; texts to audio, like the AudioLM model; texts to other texts, like ChatGPT; texts to code, like the Codex model; texts to scientific texts, like the Galactica model or even create algorithms like AlphaTensor. This work consists on an attempt to describe in a concise way the main models are sectors that are affected by generative AI and to provide a taxonomy of the main generative models published recently. | ['Eduardo C. Garrido-Merchan', 'Roberto Gozalo-Brizuela'] | 2023-01-11 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 1.03407405e-01 4.24576074e-01 4.12322015e-01 -1.71890676e-01
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dd1c3fc2-306f-4063-b63b-8a2474e21fba | exploring-chain-of-thought-style-prompting | 2305.14215 | null | https://arxiv.org/abs/2305.14215v1 | https://arxiv.org/pdf/2305.14215v1.pdf | Exploring Chain-of-Thought Style Prompting for Text-to-SQL | Conventional supervised approaches for text-to-SQL parsing often require large amounts of annotated data, which is costly to obtain in practice. Recently, in-context learning with large language models (LLMs) has caught increasing attention due to its superior few-shot performance in a wide range of tasks. However, most attempts to use in-context learning for text-to-SQL parsing still lag behind supervised methods. We hypothesize that the under-performance is because text-to-SQL parsing requires complex, multi-step reasoning. In this paper, we systematically study how to enhance the reasoning ability of LLMs for text-to-SQL parsing through chain-of-thought (CoT) style promptings including CoT prompting and Least-to-Most prompting. Our experiments demonstrate that iterative prompting as in Least-to-Most prompting may be unnecessary for text-to-SQL parsing and directly applying existing CoT style prompting methods leads to error propagation issues. By improving multi-step reasoning while avoiding much detailed information in the reasoning steps which may lead to error propagation, our new method outperforms existing ones by 2.4 point absolute gains on the Spider development set. | ['Huan Sun', 'Xiang Deng', 'Tianshu Zhang', 'Ziru Chen', 'Chang-You Tai'] | 2023-05-23 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 1.77471995e-01 3.33780795e-01 -2.11576238e-01 -8.18351030e-01
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aca7dc2b-c69a-4570-ab55-d488a3538a7c | u-sleep-resilient-to-aasm-guidelines | 2209.11173 | null | https://arxiv.org/abs/2209.11173v3 | https://arxiv.org/pdf/2209.11173v3.pdf | U-Sleep's resilience to AASM guidelines | AASM guidelines are the result of decades of efforts aiming at standardizing sleep scoring procedure, with the final goal of sharing a worldwide common methodology. The guidelines cover several aspects from the technical/digital specifications,e.g., recommended EEG derivations, to detailed sleep scoring rules accordingly to age. Automated sleep scoring systems have always largely exploited the standards as fundamental guidelines. In this context, deep learning has demonstrated better performance compared to classical machine learning. Our present work shows that a deep learning based sleep scoring algorithm may not need to fully exploit the clinical knowledge or to strictly adhere to the AASM guidelines. Specifically, we demonstrate that U-Sleep, a state-of-the-art sleep scoring algorithm, can be strong enough to solve the scoring task even using clinically non-recommended or non-conventional derivations, and with no need to exploit information about the chronological age of the subjects. We finally strengthen a well-known finding that using data from multiple data centers always results in a better performing model compared with training on a single cohort. Indeed, we show that this latter statement is still valid even by increasing the size and the heterogeneity of the single data cohort. In all our experiments we used 28528 polysomnography studies from 13 different clinical studies. | ['Jan D. Warncke', 'Francesca D. Faraci', 'Paolo Favaro', 'Athina Tzovara', 'Claudio L. A. Bassetti', 'Markus H. Schmidt', 'Marco Pesce', 'Julia van der Meer', 'Giuliana Monachino', 'Luigi Fiorillo'] | 2022-09-19 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [-1.84211344e-01 -1.83758080e-01 -2.16603458e-01 -3.97354007e-01
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80215ffe-c16c-4f2d-a7a4-a59f88a1c6d2 | a-simple-but-powerful-graph-encoder-for-1 | 2112.07791 | null | https://arxiv.org/abs/2112.07791v2 | https://arxiv.org/pdf/2112.07791v2.pdf | A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion | Knowledge graphs contain rich knowledge about various entities and the relational information among them, while temporal knowledge graphs (TKGs) describe and model the interactions of the entities over time. In this context, automatic temporal knowledge graph completion (TKGC) has gained great interest. Recent TKGC methods integrate advanced deep learning techniques, e.g., Transformers, and achieve superior model performance. However, this also introduces a large number of excessive parameters, which brings a heavier burden for parameter optimization. In this paper, we propose a simple but powerful graph encoder for TKGC, called TARGCN. TARGCN is parameter-efficient, and it extensively explores every entity's temporal context for learning contextualized representations. We find that instead of adopting various kinds of complex modules, it is more beneficial to efficiently capture the temporal contexts of entities. We experiment TARGCN on three benchmark datasets. Our model can achieve a more than 46% relative improvement on the GDELT dataset compared with state-of-the-art TKGC models. Meanwhile, it outperforms the strongest baseline on the ICEWS05-15 dataset with around 18% fewer parameters. | ['Volker Tresp', 'Bailan He', 'Yunpu Ma', 'Zifeng Ding'] | 2021-12-14 | null | null | null | null | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-2.67769843e-01 1.23095445e-01 -5.64225912e-01 -1.35083675e-01
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446893de-f13a-4f9c-9c44-96386ee6325d | multimodal-argument-mining-a-case-study-in | null | null | https://aclanthology.org/2022.argmining-1.15 | https://aclanthology.org/2022.argmining-1.15.pdf | Multimodal Argument Mining: A Case Study in Political Debates | We propose a study on multimodal argument mining in the domain of political debates. We collate and extend existing corpora and provide an initial empirical study on multimodal architectures, with a special emphasis on input encoding methods. Our results provide interesting indications about future directions in this important domain. | ['Paolo Torroni', 'Andrea Galassi', 'Federico Ruggeri', 'Eleonora Mancini'] | null | null | null | null | argmining-acl-2022-10 | ['argument-mining'] | ['natural-language-processing'] | [ 3.52261215e-01 8.37014318e-01 -5.42129159e-01 -6.98524833e-01
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6f56fa60-0361-4c69-886e-8c35c6af9a28 | finer-grained-correlations-location-priors | 2211.16290 | null | https://arxiv.org/abs/2211.16290v2 | https://arxiv.org/pdf/2211.16290v2.pdf | LocPoseNet: Robust Location Prior for Unseen Object Pose Estimation | Object location priors have been shown to be critical for the standard 6D object pose estimation setting, where the training and testing objects are the same. Specifically, they can be used to initialize the 3D object translation and facilitate 3D object rotation estimation. Unfortunately, the object detectors that are used for this purpose do not generalize to unseen objects, i.e., objects from new categories at test time. Therefore, existing 6D pose estimation methods for previously-unseen objects either assume the ground-truth object location to be known, or yield inaccurate results when it is unavailable. In this paper, we address this problem by developing a method, LocPoseNet, able to robustly learn location prior for unseen objects. Our method builds upon a template matching strategy, where we propose to distribute the reference kernels and convolve them with a query to efficiently compute multi-scale correlations. We then introduce a novel translation estimator, which decouples scale-aware and scale-robust features to predict different object location parameters. Our method outperforms existing works by a large margin on LINEMOD and GenMOP. We further construct a challenging synthetic dataset, which allows us to highlight the better robustness of our method to various noise sources. | ['Mathieu Salzmann', 'Yinlin Hu', 'Chen Zhao'] | 2022-11-29 | null | null | null | null | ['template-matching', '6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.55106202e-01 -3.41860861e-01 -1.68274909e-01 -3.10572386e-01
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f431e499-f75d-49d1-bbd0-e8e25a2cd0ab | unsupervised-part-segmentation-through | 2105.12405 | null | https://arxiv.org/abs/2105.12405v1 | https://arxiv.org/pdf/2105.12405v1.pdf | Unsupervised Part Segmentation through Disentangling Appearance and Shape | We study the problem of unsupervised discovery and segmentation of object parts, which, as an intermediate local representation, are capable of finding intrinsic object structure and providing more explainable recognition results. Recent unsupervised methods have greatly relaxed the dependency on annotated data which are costly to obtain, but still rely on additional information such as object segmentation mask or saliency map. To remove such a dependency and further improve the part segmentation performance, we develop a novel approach by disentangling the appearance and shape representations of object parts followed with reconstruction losses without using additional object mask information. To avoid degenerated solutions, a bottleneck block is designed to squeeze and expand the appearance representation, leading to a more effective disentanglement between geometry and appearance. Combined with a self-supervised part classification loss and an improved geometry concentration constraint, we can segment more consistent parts with semantic meanings. Comprehensive experiments on a wide variety of objects such as face, bird, and PASCAL VOC objects demonstrate the effectiveness of the proposed method. | ['Jun Zhu', 'Hang Su', 'Xiao Yang', 'Lei Zhang', 'Shilong Liu'] | 2021-05-26 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Liu_Unsupervised_Part_Segmentation_Through_Disentangling_Appearance_and_Shape_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Liu_Unsupervised_Part_Segmentation_Through_Disentangling_Appearance_and_Shape_CVPR_2021_paper.pdf | cvpr-2021-1 | ['unsupervised-facial-landmark-detection'] | ['computer-vision'] | [ 3.78888190e-01 3.19578618e-01 -1.76040605e-01 -5.52167833e-01
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4b6471b7-dafa-4532-a35c-6cd0d465b730 | progressive-refinement-network-for-occluded | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4211_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123680035.pdf | Progressive Refinement Network for Occluded Pedestrian Detection | We present Progressive Refinement Network (PRNet), a novel single-stage detector that tackles occluded pedestrian detection. Motivated by human's progressive process on annotating occluded pedestrians, PRNet achieves sequential refinement by three phases: Finding high-confident anchors of visible parts, calibrating such anchors to a full-body template derived from occlusion statistics, and then adjusting the calibrated anchors to final full-body regions. Unlike conventional methods that exploit predefined anchors, the confidence-aware calibration offers adaptive anchor initialization for detection with occlusions, and helps reduce the gap between visible-part and full-body detection. In addition, we introduce an occlusion loss to up-weigh hard examples, and a Receptive Field Backfeed (RFB) module to diversify receptive fields in early layers that commonly fire only on visible parts or small-size full-body regions. Experiments were performed within and across CityPersons, ETH, and Caltech datasets. Results show that PRNet can match the speed of existing single-stage detectors, consistently outperforms alternatives in terms of overall miss rate, and offers significantly better cross-dataset generalization. Code is available. | ['Xiaolin Song Kaili Zhao Wen-Sheng Chu Honggang Zhang Jun Guo'] | null | null | null | null | eccv-2020-8 | ['body-detection'] | ['computer-vision'] | [-5.45125455e-02 1.62090749e-01 -1.27457559e-01 -5.22599220e-01
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79e0ad45-46fd-4cda-9fa2-8a9e3bae9b45 | unsupervised-visual-defect-detection-with | 2211.16092 | null | https://arxiv.org/abs/2211.16092v1 | https://arxiv.org/pdf/2211.16092v1.pdf | Unsupervised Visual Defect Detection with Score-Based Generative Model | Anomaly Detection (AD), as a critical problem, has been widely discussed. In this paper, we specialize in one specific problem, Visual Defect Detection (VDD), in many industrial applications. And in practice, defect image samples are very rare and difficult to collect. Thus, we focus on the unsupervised visual defect detection and localization tasks and propose a novel framework based on the recent score-based generative models, which synthesize the real image by iterative denoising through stochastic differential equations (SDEs). Our work is inspired by the fact that with noise injected into the original image, the defects may be changed into normal cases in the denoising process (i.e., reconstruction). First, based on the assumption that the anomalous data lie in the low probability density region of the normal data distribution, we explain a common phenomenon that occurs when reconstruction-based approaches are applied to VDD: normal pixels also change during the reconstruction process. Second, due to the differences in normal pixels between the reconstructed and original images, a time-dependent gradient value (i.e., score) of normal data distribution is utilized as a metric, rather than reconstruction loss, to gauge the defects. Third, a novel $T$ scales approach is developed to dramatically reduce the required number of iterations, accelerating the inference process. These practices allow our model to generalize VDD in an unsupervised manner while maintaining reasonably good performance. We evaluate our method on several datasets to demonstrate its effectiveness. | ['Siyu Xia', 'Ming Shao', 'Fuzhen Cai', 'Haoyang Li', 'Yapeng Teng'] | 2022-11-29 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 3.53354067e-01 -3.35097432e-01 2.29096085e-01 -2.87682004e-02
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a9e964fe-305f-4801-b0d8-4c708cf0a53c | three-dimensional-lip-motion-network-for-text | 2010.06363 | null | https://arxiv.org/abs/2010.06363v1 | https://arxiv.org/pdf/2010.06363v1.pdf | Three-Dimensional Lip Motion Network for Text-Independent Speaker Recognition | Lip motion reflects behavior characteristics of speakers, and thus can be used as a new kind of biometrics in speaker recognition. In the literature, lots of works used two-dimensional (2D) lip images to recognize speaker in a textdependent context. However, 2D lip easily suffers from various face orientations. To this end, in this work, we present a novel end-to-end 3D lip motion Network (3LMNet) by utilizing the sentence-level 3D lip motion (S3DLM) to recognize speakers in both the text-independent and text-dependent contexts. A new regional feedback module (RFM) is proposed to obtain attentions in different lip regions. Besides, prior knowledge of lip motion is investigated to complement RFM, where landmark-level and frame-level features are merged to form a better feature representation. Moreover, we present two methods, i.e., coordinate transformation and face posture correction to pre-process the LSD-AV dataset, which contains 68 speakers and 146 sentences per speaker. The evaluation results on this dataset demonstrate that our proposed 3LMNet is superior to the baseline models, i.e., LSTM, VGG-16 and ResNet-34, and outperforms the state-of-the-art using 2D lip image as well as the 3D face. The code of this work is released at https://github.com/wutong18/Three-Dimensional-Lip- Motion-Network-for-Text-Independent-Speaker-Recognition. | ['Li Liu', 'Ju Zhang', 'Qiang Fang', 'Mei Yu', 'Shanyu Wang', 'Tong Wu', 'Jianrong Wang'] | 2020-10-13 | null | null | null | null | ['text-independent-speaker-recognition'] | ['speech'] | [-3.77622932e-01 -5.47927976e-01 -5.63555241e-01 -4.43868309e-01
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40fa055c-30d0-4821-bb8f-f71db0c118b9 | reasoning-for-complex-data-through-ensemble | 2202.03126 | null | https://arxiv.org/abs/2202.03126v4 | https://arxiv.org/pdf/2202.03126v4.pdf | Leveraging Ensembles and Self-Supervised Learning for Fully-Unsupervised Person Re-Identification and Text Authorship Attribution | Learning from fully-unlabeled data is challenging in Multimedia Forensics problems, such as Person Re-Identification and Text Authorship Attribution. Recent self-supervised learning methods have shown to be effective when dealing with fully-unlabeled data in cases where the underlying classes have significant semantic differences, as intra-class distances are substantially lower than inter-class distances. However, this is not the case for forensic applications in which classes have similar semantics and the training and test sets have disjoint identities. General self-supervised learning methods might fail to learn discriminative features in this scenario, thus requiring more robust strategies. We propose a strategy to tackle Person Re-Identification and Text Authorship Attribution by enabling learning from unlabeled data even when samples from different classes are not prominently diverse. We propose a novel ensemble-based clustering strategy whereby clusters derived from different configurations are combined to generate a better grouping for the data samples in a fully-unsupervised way. This strategy allows clusters with different densities and higher variability to emerge, reducing intra-class discrepancies without requiring the burden of finding an optimal configuration per dataset. We also consider different Convolutional Neural Networks for feature extraction and subsequent distance computations between samples. We refine these distances by incorporating context and grouping them to capture complementary information. Our method is robust across both tasks, with different data modalities, and outperforms state-of-the-art methods with a fully-unsupervised solution without any labeling or human intervention. | ['Antônio Theophilo', 'Anderson Rocha', 'Fernanda Andaló', 'Gabriel Bertocco'] | 2022-02-07 | null | null | null | null | ['unsupervised-person-re-identification', 'authorship-verification'] | ['computer-vision', 'natural-language-processing'] | [ 2.78141111e-01 -1.20398305e-01 1.09817460e-03 -4.52795744e-01
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4d38cb2b-b36c-4c26-b4d7-6182f5b16bd3 | improving-inference-performance-of-machine | 2301.05099 | null | https://arxiv.org/abs/2301.05099v2 | https://arxiv.org/pdf/2301.05099v2.pdf | Improving Inference Performance of Machine Learning with the Divide-and-Conquer Principle | Many popular machine learning models scale poorly when deployed on CPUs. In this paper we explore the reasons why and propose a simple, yet effective approach based on the well-known Divide-and-Conquer Principle to tackle this problem of great practical importance. Given an inference job, instead of using all available computing resources (i.e., CPU cores) for running it, the idea is to break the job into independent parts that can be executed in parallel, each with the number of cores according to its expected computational cost. We implement this idea in the popular OnnxRuntime framework and evaluate its effectiveness with several use cases, including the well-known models for optical character recognition (PaddleOCR) and natural language processing (BERT). | ['Alex Kogan'] | 2023-01-12 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [-1.15581967e-01 -1.28548115e-01 -2.90260781e-02 -2.77374685e-01
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75a66e91-ebff-4d0d-abbd-7412435c5731 | a-multi-task-deep-learning-model-for-the | 1812.00422 | null | http://arxiv.org/abs/1812.00422v1 | http://arxiv.org/pdf/1812.00422v1.pdf | A multi-task deep learning model for the classification of Age-related Macular Degeneration | Age-related Macular Degeneration (AMD) is a leading cause of blindness.
Although the Age-Related Eye Disease Study group previously developed a 9-step
AMD severity scale for manual classification of AMD severity from color fundus
images, manual grading of images is time-consuming and expensive. Built on our
previous work DeepSeeNet, we developed a novel deep learning model for
automated classification of images into the 9-step scale. Instead of predicting
the 9-step score directly, our approach simulates the reading center grading
process. It first detects four AMD characteristics (drusen area, geographic
atrophy, increased pigment, and depigmentation), then combines these to derive
the overall 9-step score. Importantly, we applied multi-task learning
techniques, which allowed us to train classification of the four
characteristics in parallel, share representation, and prevent overfitting.
Evaluation on two image datasets showed that the accuracy of the model exceeded
the current state-of-the-art model by > 10%. | ['Elvira Agron', 'Tiarnan Keenan', 'Wai T. Wong', 'Shazia Dharssi', 'Qingyu Chen', 'Emily Y. Chew', 'Yifan Peng', 'Zhiyong Lu'] | 2018-12-02 | null | null | null | null | ['classification-of-age-related-macular'] | ['medical'] | [ 7.00173751e-02 -9.50463265e-02 7.66669512e-02 -3.59220356e-01
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3a8bc2c0-1317-4cb6-af3a-f6410cb788b9 | mcts-geb-monte-carlo-tree-search-is-a-good-e | 2303.04651 | null | https://arxiv.org/abs/2303.04651v3 | https://arxiv.org/pdf/2303.04651v3.pdf | MCTS-GEB: Monte Carlo Tree Search is a Good E-graph Builder | Rewrite systems [6, 10, 12] have been widely employing equality saturation [9], which is an optimisation methodology that uses a saturated e-graph to represent all possible sequences of rewrite simultaneously, and then extracts the optimal one. As such, optimal results can be achieved by avoiding the phase-ordering problem. However, we observe that when the e-graph is not saturated, it cannot represent all possible rewrite opportunities and therefore the phase-ordering problem is re-introduced during the construction phase of the e-graph. To address this problem, we propose MCTS-GEB, a domain-general rewrite system that applies reinforcement learning (RL) to e-graph construction. At its core, MCTS-GEB uses a Monte Carlo Tree Search (MCTS) [3] to efficiently plan for the optimal e-graph construction, and therefore it can effectively eliminate the phase-ordering problem at the construction phase and achieve better performance within a reasonable time. Evaluation in two different domains shows MCTS-GEB can outperform the state-of-the-art rewrite systems by up to 49x, while the optimisation can generally take less than an hour, indicating MCTS-GEB is a promising building block for the future generation of rewrite systems. | ['Eiko Yoneki', 'Zak Singh', 'Guoliang He'] | 2023-03-08 | null | null | null | null | ['graph-construction'] | ['graphs'] | [ 2.55390927e-02 4.60197896e-01 -4.83300745e-01 1.51107579e-01
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943e1638-4f51-4ef6-91bd-c6f16f7cf3d2 | meds-net-self-distilled-multi-encoders | 2211.00003 | null | https://arxiv.org/abs/2211.00003v2 | https://arxiv.org/pdf/2211.00003v2.pdf | MEDS-Net: Self-Distilled Multi-Encoders Network with Bi-Direction Maximum Intensity projections for Lung Nodule Detection | In this study, we propose a lung nodule detection scheme which fully incorporates the clinic workflow of radiologists. Particularly, we exploit Bi-Directional Maximum intensity projection (MIP) images of various thicknesses (i.e., 3, 5 and 10mm) along with a 3D patch of CT scan, consisting of 10 adjacent slices to feed into self-distillation-based Multi-Encoders Network (MEDS-Net). The proposed architecture first condenses 3D patch input to three channels by using a dense block which consists of dense units which effectively examine the nodule presence from 2D axial slices. This condensed information, along with the forward and backward MIP images, is fed to three different encoders to learn the most meaningful representation, which is forwarded into the decoded block at various levels. At the decoder block, we employ a self-distillation mechanism by connecting the distillation block, which contains five lung nodule detectors. It helps to expedite the convergence and improves the learning ability of the proposed architecture. Finally, the proposed scheme reduces the false positives by complementing the main detector with auxiliary detectors. The proposed scheme has been rigorously evaluated on 888 scans of LUNA16 dataset and obtained a CPM score of 93.6\%. The results demonstrate that incorporating of bi-direction MIP images enables MEDS-Net to effectively distinguish nodules from surroundings which help to achieve the sensitivity of 91.5% and 92.8% with false positives rate of 0.25 and 0.5 per scan, respectively. | ['Yeong Gil Shin', 'Byung il Lee', 'Sung Hyun Kim', 'Byoung Dai Lee', 'Shi Sub Byon', 'Siddique Latif', 'Abdullah Shahid', 'Azka Rehman', 'Muhammad Usman'] | 2022-10-30 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 3.02445740e-01 4.29715365e-01 -1.85857370e-01 -5.91590255e-02
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b168be21-0a5a-45db-baac-29dec49b8398 | diversification-quotient-measuring | 2206.13679 | null | https://arxiv.org/abs/2206.13679v4 | https://arxiv.org/pdf/2206.13679v4.pdf | Diversification quotients: Quantifying diversification via risk measures | We establish the first axiomatic theory for diversification indices using six intuitive axioms -- non-negativity, location invariance, scale invariance, rationality, normalization, and continuity -- together with risk measures. The unique class of indices satisfying these axioms, called the diversification quotients (DQs), are defined based on a parametric family of risk measures. DQ has many attractive properties, and it can address several theoretical and practical limitations of existing indices. In particular, for the popular risk measures Value-at-Risk and Expected Shortfall, DQ admits simple formulas, it is efficient to optimize in portfolio selection, and it can properly capture tail heaviness and common shocks which are neglected by traditional diversification indices. When illustrated with financial data, DQ is intuitive to interpret, and its performance is competitive against other diversification indices. | ['Ruodu Wang', 'Liyuan Lin', 'Xia Han'] | 2022-06-28 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-5.68217516e-01 -2.49286890e-01 -4.54372764e-01 -2.49107450e-01
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abcafc4c-84b9-4b4d-9620-7bc75f6de9cb | a-problem-reduction-approach-for-visual | 1809.09828 | null | http://arxiv.org/abs/1809.09828v1 | http://arxiv.org/pdf/1809.09828v1.pdf | A Problem Reduction Approach for Visual Relationships Detection | Identifying different objects (man and cup) is an important problem on its
own, but identifying the relationship between them (holding) is critical for
many real world use cases. This paper describes an approach to reduce a visual
relationship detection problem to object detection problems. The method was
applied to Google AI Open Images V4 Visual Relationship Track Challenge, which
was held in conjunction with 2018 European Conference on Computer Vision (ECCV
2018) and it finished as a prize winner. The challenge was to build an
algorithm that detects pairs of objects in particular relations: things like
"woman playing guitar," "beer on table," or "dog inside car.". | ['Toshiyuki Fukuzawa'] | 2018-09-26 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 1.06021119e-02 1.30215352e-02 -4.47837859e-02 -3.29479247e-01
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b33bfa55-4d8a-422a-ac88-318292a66555 | ddx7-differentiable-fm-synthesis-of-musical | 2208.06169 | null | https://arxiv.org/abs/2208.06169v1 | https://arxiv.org/pdf/2208.06169v1.pdf | DDX7: Differentiable FM Synthesis of Musical Instrument Sounds | FM Synthesis is a well-known algorithm used to generate complex timbre from a compact set of design primitives. Typically featuring a MIDI interface, it is usually impractical to control it from an audio source. On the other hand, Differentiable Digital Signal Processing (DDSP) has enabled nuanced audio rendering by Deep Neural Networks (DNNs) that learn to control differentiable synthesis layers from arbitrary sound inputs. The training process involves a corpus of audio for supervision, and spectral reconstruction loss functions. Such functions, while being great to match spectral amplitudes, present a lack of pitch direction which can hinder the joint optimization of the parameters of FM synthesizers. In this paper, we take steps towards enabling continuous control of a well-established FM synthesis architecture from an audio input. Firstly, we discuss a set of design constraints that ease spectral optimization of a differentiable FM synthesizer via a standard reconstruction loss. Next, we present Differentiable DX7 (DDX7), a lightweight architecture for neural FM resynthesis of musical instrument sounds in terms of a compact set of parameters. We train the model on instrument samples extracted from the URMP dataset, and quantitatively demonstrate its comparable audio quality against selected benchmarks. | ['Mark Sandler', 'Andrew McPherson', 'Franco Caspe'] | 2022-08-12 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 6.01608098e-01 1.02488779e-01 1.65754661e-01 -9.28694978e-02
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31f20be1-a93e-42a4-9992-8aa0b443db36 | umutextstats-a-linguistic-feature-extraction | null | null | https://aclanthology.org/2022.lrec-1.649 | https://aclanthology.org/2022.lrec-1.649.pdf | UMUTextStats: A linguistic feature extraction tool for Spanish | Feature Engineering consists in the application of domain knowledge to select and transform relevant features to build efficient machine learning models. In the Natural Language Processing field, the state of the art concerning automatic document classification tasks relies on word and sentence embeddings built upon deep learning models based on transformers that have outperformed the competition in several tasks. However, the models built from these embeddings are usually difficult to interpret. On the contrary, linguistic features are easy to understand, they result in simpler models, and they usually achieve encouraging results. Moreover, both linguistic features and embeddings can be combined with different strategies which result in more reliable machine-learning models. The de facto tool for extracting linguistic features in Spanish is LIWC. However, this software does not consider specific linguistic phenomena of Spanish such as grammatical gender and lacks certain verb tenses. In order to solve these drawbacks, we have developed UMUTextStats, a linguistic extraction tool designed from scratch for Spanish. Furthermore, this tool has been validated to conduct different experiments in areas such as infodemiology, hate-speech detection, author profiling, authorship verification, humour or irony detection, among others. The results indicate that the combination of linguistic features and embeddings based on transformers are beneficial in automatic document classification. | ['Rafael Valencia-García', 'Ángela Almela', 'Pedro José Vivancos-Vicente', 'José Antonio García-Díaz'] | null | null | null | null | lrec-2022-6 | ['sentence-embeddings', 'sentence-embeddings', 'document-classification', 'authorship-verification'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-4.31576997e-01 -7.33657256e-02 -5.83955869e-02 -1.33835733e-01
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c273a3d3-a4df-4dbe-aa00-e4ae40b108b3 | deeper-task-specificity-improves-joint-entity | 2002.06424 | null | https://arxiv.org/abs/2002.06424v1 | https://arxiv.org/pdf/2002.06424v1.pdf | Deeper Task-Specificity Improves Joint Entity and Relation Extraction | Multi-task learning (MTL) is an effective method for learning related tasks, but designing MTL models necessitates deciding which and how many parameters should be task-specific, as opposed to shared between tasks. We investigate this issue for the problem of jointly learning named entity recognition (NER) and relation extraction (RE) and propose a novel neural architecture that allows for deeper task-specificity than does prior work. In particular, we introduce additional task-specific bidirectional RNN layers for both the NER and RE tasks and tune the number of shared and task-specific layers separately for different datasets. We achieve state-of-the-art (SOTA) results for both tasks on the ADE dataset; on the CoNLL04 dataset, we achieve SOTA results on the NER task and competitive results on the RE task while using an order of magnitude fewer trainable parameters than the current SOTA architecture. An ablation study confirms the importance of the additional task-specific layers for achieving these results. Our work suggests that previous solutions to joint NER and RE undervalue task-specificity and demonstrates the importance of correctly balancing the number of shared and task-specific parameters for MTL approaches in general. | ['Phil Crone'] | 2020-02-15 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 1.39233936e-02 1.29089534e-01 -6.48305267e-02 -5.27416050e-01
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b66bb078-69e3-40d9-9dde-1fb9e45973e3 | physical-energy-cost-serves-as-the-invisible | 2306.02328 | null | https://arxiv.org/abs/2306.02328v1 | https://arxiv.org/pdf/2306.02328v1.pdf | Physical energy cost serves as the ''invisible hand'' governing economic valuation: Direct evidence from biogeochemical data and the U.S. metal market | Energy supply is mandatory for the production of economic value. Nevertheless, tradition dictates that an enigmatic 'invisible hand' governs economic valuation. Physical scientists have long proposed alternative but testable energy cost theories of economic valuation, and have shown the gross correlation between energy consumption and economic output at the national level through input-output energy analysis. However, due to the difficulty of precise energy analysis and highly complicated real markets, no decisive evidence directly linking energy costs to the selling prices of individual commodities has yet been found. Over the past century, the US metal market has accumulated a huge body of price data, which for the first time ever provides us the opportunity to quantitatively examine the direct energy-value correlation. Here, by analyzing the market price data of 65 purified chemical elements (mainly metals) relative to the total energy consumption for refining them from naturally occurring geochemical conditions, we found a clear correlation between the energy cost and their market prices. The underlying physics we proposed has compatibility with conventional economic concepts such as the ratio between supply and demand or scarcity's role in economic valuation. It demonstrates how energy cost serves as the 'invisible hand' governing economic valuation. Thorough understanding of this energy connection between the human economic and the Earth's biogeochemical metabolism is essential for improving the overall energy efficiency and furthermore the sustainability of the human society. | ['Zhicen Liu'] | 2023-06-04 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-1.45179421e-01 -1.45184919e-01 -5.83460450e-01 2.20858991e-01
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0a0e1249-ad4c-4f9c-9b89-4810cf987ee0 | high-resolution-talking-face-generation-via | 1812.06589 | null | https://arxiv.org/abs/1812.06589v2 | https://arxiv.org/pdf/1812.06589v2.pdf | Arbitrary Talking Face Generation via Attentional Audio-Visual Coherence Learning | Talking face generation aims to synthesize a face video with precise lip synchronization as well as a smooth transition of facial motion over the entire video via the given speech clip and facial image. Most existing methods mainly focus on either disentangling the information in a single image or learning temporal information between frames. However, cross-modality coherence between audio and video information has not been well addressed during synthesis. In this paper, we propose a novel arbitrary talking face generation framework by discovering the audio-visual coherence via the proposed Asymmetric Mutual Information Estimator (AMIE). In addition, we propose a Dynamic Attention (DA) block by selectively focusing the lip area of the input image during the training stage, to further enhance lip synchronization. Experimental results on benchmark LRW dataset and GRID dataset transcend the state-of-the-art methods on prevalent metrics with robust high-resolution synthesizing on gender and pose variations. | ['Ran He', 'Aihua Zheng', 'Huaibo Huang', 'Yi Li', 'Hao Zhu'] | 2018-12-17 | null | null | null | null | ['talking-face-generation'] | ['computer-vision'] | [ 3.11879724e-01 -4.28783596e-02 -2.73249120e-01 -4.18704748e-01
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ce7e0a26-547e-4896-83fa-e418fbbd94d9 | adaptive-probabilistic-forecasting-of | 2301.10090 | null | https://arxiv.org/abs/2301.10090v2 | https://arxiv.org/pdf/2301.10090v2.pdf | Adaptive Probabilistic Forecasting of Electricity (Net-)Load | Electricity load forecasting is a necessary capability for power system operators and electricity market participants. The proliferation of local generation, demand response, and electrification of heat and transport are changing the fundamental drivers of electricity load and increasing the complexity of load modelling and forecasting. We address this challenge in two ways. First, our setting is adaptive; our models take into account the most recent observations available, yielding a forecasting strategy able to automatically respond to changes in the underlying process. Second, we consider probabilistic rather than point forecasting; indeed, uncertainty quantification is required to operate electricity systems efficiently and reliably. Our methodology relies on the Kalman filter, previously used successfully for adaptive point load forecasting. The probabilistic forecasts are obtained by quantile regressions on the residuals of the point forecasting model. We achieve adaptive quantile regressions using the online gradient descent; we avoid the choice of the gradient step size considering multiple learning rates and aggregation of experts. We apply the method to two data sets: the regional net-load in Great Britain and the demand of seven large cities in the United States. Adaptive procedures improve forecast performance substantially in both use cases for both point and probabilistic forecasting. | ['Olivier Wintenberger', 'Yannig Goude', 'Matteo Fasiolo', 'Jethro Browell', 'Joseph de Vilmarest'] | 2023-01-24 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-2.31207833e-01 -5.67050017e-02 -1.74325295e-02 -3.50602776e-01
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59455a38-d516-40d2-b49a-09f058bec5f5 | a-study-on-extracting-named-entities-from | 2212.03749 | null | https://arxiv.org/abs/2212.03749v1 | https://arxiv.org/pdf/2212.03749v1.pdf | A Study on Extracting Named Entities from Fine-tuned vs. Differentially Private Fine-tuned BERT Models | Privacy preserving deep learning is an emerging field in machine learning that aims to mitigate the privacy risks in the use of deep neural networks. One such risk is training data extraction from language models that have been trained on datasets , which contain personal and privacy sensitive information. In our study, we investigate the extent of named entity memorization in fine-tuned BERT models. We use single-label text classification as representative downstream task and employ three different fine-tuning setups in our experiments, including one with Differentially Privacy (DP). We create a large number of text samples from the fine-tuned BERT models utilizing a custom sequential sampling strategy with two prompting strategies. We search in these samples for named entities and check if they are also present in the fine-tuning datasets. We experiment with two benchmark datasets in the domains of emails and blogs. We show that the application of DP has a huge effect on the text generation capabilities of BERT. Furthermore, we show that a fine-tuned BERT does not generate more named entities entities specific to the fine-tuning dataset than a BERT model that is pre-trained only. This suggests that BERT is unlikely to emit personal or privacy sensitive named entities. Overall, our results are important to understand to what extent BERT-based services are prone to training data extraction attacks. | ['Ansgar Scherp', 'Aygul Garifullina', 'Nicolas Lell', 'Andor Diera'] | 2022-12-07 | null | null | null | null | ['privacy-preserving-deep-learning', 'memorization', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 1.29332960e-01 3.22236717e-01 3.05020697e-02 -5.89532495e-01
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153b1264-468d-4072-b3ec-7a5b28b05644 | autodime-automatic-design-of-interesting | 2203.02481 | null | https://arxiv.org/abs/2203.02481v1 | https://arxiv.org/pdf/2203.02481v1.pdf | AutoDIME: Automatic Design of Interesting Multi-Agent Environments | Designing a distribution of environments in which RL agents can learn interesting and useful skills is a challenging and poorly understood task, for multi-agent environments the difficulties are only exacerbated. One approach is to train a second RL agent, called a teacher, who samples environments that are conducive for the learning of student agents. However, most previous proposals for teacher rewards do not generalize straightforwardly to the multi-agent setting. We examine a set of intrinsic teacher rewards derived from prediction problems that can be applied in multi-agent settings and evaluate them in Mujoco tasks such as multi-agent Hide and Seek as well as a diagnostic single-agent maze task. Of the intrinsic rewards considered we found value disagreement to be most consistent across tasks, leading to faster and more reliable emergence of advanced skills in Hide and Seek and the maze task. Another candidate intrinsic reward considered, value prediction error, also worked well in Hide and Seek but was susceptible to noisy-TV style distractions in stochastic environments. Policy disagreement performed well in the maze task but did not speed up learning in Hide and Seek. Our results suggest that intrinsic teacher rewards, and in particular value disagreement, are a promising approach for automating both single and multi-agent environment design. | ['Harri Edwards', 'Ingmar Kanitscheider'] | 2022-03-04 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [-1.84743315e-01 2.72455007e-01 3.78201120e-02 -1.40063614e-01
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2ee5c142-e03e-47af-a636-146cbd7a313b | cross-corpus-data-augmentation-for-acoustic | null | null | https://aclanthology.org/W19-5933 | https://aclanthology.org/W19-5933.pdf | Cross-Corpus Data Augmentation for Acoustic Addressee Detection | Acoustic addressee detection (AD) is a modern paralinguistic and dialogue challenge that especially arises in voice assistants. In the present study, we distinguish addressees in two settings (a conversation between several people and a spoken dialogue system, and a conversation between several adults and a child) and introduce the first competitive baseline (unweighted average recall equals 0.891) for the Voice Assistant Conversation Corpus that models the first setting. We jointly solve both classification problems, using three models: a linear support vector machine dealing with acoustic functionals and two neural networks utilising raw waveforms alongside with acoustic low-level descriptors. We investigate how different corpora influence each other, applying the mixup approach to data augmentation. We also study the influence of various acoustic context lengths on AD. Two-second speech fragments turn out to be sufficient for reliable AD. Mixup is shown to be beneficial for merging acoustic data (extracted features but not raw waveforms) from different domains that allows us to reach a higher classification performance on human-machine AD and also for training a multipurpose neural network that is capable of solving both human-machine and adult-child AD problems. | ['Ingo Siegert', 'Wolfgang Minker', 'Oleg Akhtiamov', 'Alexey Karpov'] | 2019-09-01 | null | null | null | ws-2019-9 | ['cross-corpus'] | ['computer-vision'] | [ 4.98585105e-01 4.03959155e-01 4.02101785e-01 -4.65025544e-01
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f830d852-2663-41fd-8b8e-85ceaafd0ee4 | code-synonyms-do-matter-multiple-synonyms | null | null | https://openreview.net/forum?id=kXo7lEh7OaX | https://openreview.net/pdf?id=kXo7lEh7OaX | Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Coding | Automatic ICD coding is defined as assigning disease codes to electronic medical records (EMRs).
Existing methods apply label attention with code representations to match related text snippets for coding.
Unlike these works that model the label with the code hierarchy or description, we argue that the code synonyms can provide more comprehensive knowledge based on the observation that the code expressions in EMRs vary from their descriptions in ICD.
By aligning codes to concepts in UMLS, we collect synonyms of every code in ICD. Then, we propose a multiple synonyms matching network to leverage synonyms for better code representation learning, and finally help the code classification.
Experiments on two settings of the MIMIC-III dataset show that our proposed method outperforms previous state-of-the-art methods.
| ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['code-classification'] | ['computer-code'] | [ 1.69970691e-01 2.07587749e-01 -9.70798016e-01 -5.31729817e-01
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171fe629-029e-4267-9207-f907f981ebd6 | fc4-fully-convolutional-color-constancy-with | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Hu_FC4_Fully_Convolutional_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Hu_FC4_Fully_Convolutional_CVPR_2017_paper.pdf | FC4: Fully Convolutional Color Constancy With Confidence-Weighted Pooling | Improvements in color constancy have arisen from the use of convolutional neural networks (CNNs). However, the patch-based CNNs that exist for this problem are faced with the issue of estimation ambiguity, where a patch may contain insufficient information to establish a unique or even a limited possible range of illumination colors. Image patches with estimation ambiguity not only appear with great frequency in photographs, but also significantly degrade the quality of network training and inference. To overcome this problem, we present a fully convolutional network architecture in which patches throughout an image can carry different confidence weights according to the value they provide for color constancy estimation. These confidence weights are learned and applied within a novel pooling layer where the local estimates are merged into a global solution. With this formulation, the network is able to determine "what to learn" and "how to pool" automatically from color constancy datasets without additional supervision. The proposed network also allows for end-to-end training, and achieves higher efficiency and accuracy. On standard benchmarks, our network outperforms the previous state-of-the-art while achieving 120x greater efficiency.
| ['Stephen Lin', 'Baoyuan Wang', 'Yuanming Hu'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['color-constancy'] | ['computer-vision'] | [ 2.38625765e-01 -3.05213183e-01 2.87474953e-02 -5.76536596e-01
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6550ad54-6974-49e7-a5af-00a6c8f2fd67 | justifying-and-improving-meta-agent-conflict | 1410.6519 | null | http://arxiv.org/abs/1410.6519v1 | http://arxiv.org/pdf/1410.6519v1.pdf | Justifying and Improving Meta-Agent Conflict-Based Search | The Meta-Agent Conflict-Based Search~(MA-CBS) is a recently proposed
algorithm for the multi-agent path finding problem. The algorithm is an
extension of Conflict-Based Search~(CBS), which automatically merges
conflicting agents into meta-agents if the number of conflicts exceeds a
certain threshold. However, the decision to merge agents is made according to
an empirically chosen fixed threshold on the number of conflicts. The best
threshold depends both on the domain and on the number of agents, and the
nature of the dependence is not clearly understood.
We suggest a justification for the use of a fixed threshold on the number of
conflicts based on the analysis of a model problem. Following the suggested
justification, we introduce new decision policies for the MA-CBS algorithm,
which considerably improve the algorithm's performance. The improved variants
of the algorithm are evaluated on several sets of problems, chosen to underline
different aspects of the algorithms. | ['David Tolpin'] | 2014-10-23 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 1.57230169e-01 5.90433665e-02 -2.85032421e-01 -1.47077352e-01
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397d629e-722d-433f-9968-752c379beec0 | interactive-shadow-removal-and-ground-truth | null | null | https://www.osapublishing.org/abstract.cfm?uri=josaa-33-9-1798 | https://arxiv.org/pdf/1608.00762 | Interactive Shadow Removal and Ground Truth for Difficult Shadow Scenes | A user-centric method for fast, interactive, robust and high-quality shadow removal is presented. Our algorithm can perform detection and removal in a range of difficult cases: such as highly textured and colored shadows. To perform detection an on-the-fly learning approach is adopted guided by two rough user inputs for the pixels of the shadow and the lit area. After detection, shadow removal is performed by registering the penumbra to a normalized frame which allows us efficient estimation of non-uniform shadow illumination changes, resulting in accurate and robust removal. Another major contribution of this work is the first validated and multi-scene category ground truth for shadow removal algorithms. This data set containing 186 images eliminates inconsistencies between shadow and shadow-free images and provides a range of different shadow types such as soft, textured, colored and broken shadow. Using this data, the most thorough comparison of state-of-the-art shadow removal methods to date is performed, showing our proposed new algorithm to outperform the state-of-the-art across several measures and shadow category. To complement our dataset, an online shadow removal benchmark website is also presented to encourage future open comparisons in this challenging field of research. | ['Han Gong; Darren Cosker'] | 2016-09-01 | null | null | null | josa-a-2016-9 | ['shadow-removal'] | ['computer-vision'] | [ 8.59075189e-01 -2.26960167e-01 4.33357120e-01 -3.92069280e-01
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f8f9397b-f0c7-4a00-9da8-5d1ddaf8150f | multi-view-neural-surface-reconstruction-with | 2211.11971 | null | https://arxiv.org/abs/2211.11971v1 | https://arxiv.org/pdf/2211.11971v1.pdf | Multi-View Neural Surface Reconstruction with Structured Light | Three-dimensional (3D) object reconstruction based on differentiable rendering (DR) is an active research topic in computer vision. DR-based methods minimize the difference between the rendered and target images by optimizing both the shape and appearance and realizing a high visual reproductivity. However, most approaches perform poorly for textureless objects because of the geometrical ambiguity, which means that multiple shapes can have the same rendered result in such objects. To overcome this problem, we introduce active sensing with structured light (SL) into multi-view 3D object reconstruction based on DR to learn the unknown geometry and appearance of arbitrary scenes and camera poses. More specifically, our framework leverages the correspondences between pixels in different views calculated by structured light as an additional constraint in the DR-based optimization of implicit surface, color representations, and camera poses. Because camera poses can be optimized simultaneously, our method realizes high reconstruction accuracy in the textureless region and reduces efforts for camera pose calibration, which is required for conventional SL-based methods. Experiment results on both synthetic and real data demonstrate that our system outperforms conventional DR- and SL-based methods in a high-quality surface reconstruction, particularly for challenging objects with textureless or shiny surfaces. | ['Hiroharu Kato', 'Eiichi Matsumoto', 'Taisuke Hashimoto', 'Chunyu Li'] | 2022-11-22 | null | null | null | null | ['3d-object-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 6.36716664e-01 -1.24526024e-01 2.58865923e-01 -2.51548320e-01
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49161d03-8fed-4b12-a94b-827ed78dada8 | nibbling-at-the-hard-core-of-word-sense | null | null | https://aclanthology.org/2022.acl-long.324 | https://aclanthology.org/2022.acl-long.324.pdf | Nibbling at the Hard Core of Word Sense Disambiguation | With state-of-the-art systems having finally attained estimated human performance, Word Sense Disambiguation (WSD) has now joined the array of Natural Language Processing tasks that have seemingly been solved, thanks to the vast amounts of knowledge encoded into Transformer-based pre-trained language models. And yet, if we look below the surface of raw figures, it is easy to realize that current approaches still make trivial mistakes that a human would never make. In this work, we provide evidence showing why the F1 score metric should not simply be taken at face value and present an exhaustive analysis of the errors that seven of the most representative state-of-the-art systems for English all-words WSD make on traditional evaluation benchmarks.In addition, we produce and release a collection of test sets featuring (a) an amended version of the standard evaluation benchmark that fixes its lexical and semantic inaccuracies, (b) 42D, a challenge set devised to assess the resilience of systems with respect to least frequent word senses and senses not seen at training time, and (c) hardEN, a challenge set made up solely of instances which none of the investigated state-of-the-art systems can solve. We make all of the test sets and model predictions available to the research community at https://github.com/SapienzaNLP/wsd-hard-benchmark. | ['Roberto Navigli', 'Michele Bevilacqua', 'Simone Conia', 'Marco Maru'] | null | null | null | null | acl-2022-5 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 2.67151505e-01 2.25915864e-01 1.54232398e-01 -2.78333157e-01
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09b05c26-b49f-4eaa-a630-2340d58fc0e2 | detection-and-segmentation-of-lesion-areas-in | null | null | https://www.medrxiv.org/content/10.1101/2020.10.23.20218461v1 | https://www.medrxiv.org/content/10.1101/2020.10.23.20218461v1.full.pdf | Detection and Segmentation of Lesion Areas in Chest CT Scans For The Prediction of COVID-19 | In this paper we compare the models for the detection and segmentation of Ground Glass Opacity and Consolidation in chest CT scans. These lesion areas are often associated both with common pneumonia and COVID-19. We train a Mask R-CNN model to segment these areas with high accuracy using three approaches: merging masks for these lesions into one, deleting the mask for Consolidation, and using both masks separately. The best model achieves the mean average precision of 44.68% using MS COCO criterion for instance segmentation across all accuracy thresholds. The classification model, COVID-CT-Mask-Net, which learns to predict the presence of COVID-19 vs common pneumonia vs control, achieves the 93.88% COVID-19 sensitivity, 95.64% overall accuracy, 95.06% common pneumonia sensitivity and 96.91% true negative rate on the COVIDx-CT test split (21192 CT scans) using a small fraction of the training data. We also analyze the effect of Non-Maximum Suppression of overlapping object predictions, both on the segmentation and classification accuracy. The full source code, models and pretrained weights are available on https://github.com/AlexTS1980/COVID-CT-Mask-Net. | ['Aram Ter-Sarkisov'] | 2020-10-26 | null | null | null | null | ['covid-19-image-segmentation'] | ['computer-vision'] | [ 2.27865234e-01 5.16336337e-02 -1.31842971e-01 -2.72092372e-01
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e9b8f57f-d7a7-4f2c-aadc-d1b2076bfb54 | data-augmentation-for-conflict-and-duplicate | 2305.09608 | null | https://arxiv.org/abs/2305.09608v1 | https://arxiv.org/pdf/2305.09608v1.pdf | Data Augmentation for Conflict and Duplicate Detection in Software Engineering Sentence Pairs | This paper explores the use of text data augmentation techniques to enhance conflict and duplicate detection in software engineering tasks through sentence pair classification. The study adapts generic augmentation techniques such as shuffling, back translation, and paraphrasing and proposes new data augmentation techniques such as Noun-Verb Substitution, target-lemma replacement and Actor-Action Substitution for software requirement texts. A comprehensive empirical analysis is conducted on six software text datasets to identify conflicts and duplicates among sentence pairs. The results demonstrate that data augmentation techniques have a significant impact on the performance of all software pair text datasets. On the other hand, in cases where the datasets are relatively balanced, the use of augmentation techniques may result in a negative effect on the classification performance. | ['Ayşe Başar', 'Mucahit Cevik', 'Garima Malik'] | 2023-05-16 | null | null | null | null | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 5.80504298e-01 -1.01445643e-02 -7.76860416e-02 -5.97991526e-01
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dfc70f8a-5343-477c-8333-d83b4d8f064b | integrating-pre-trained-model-into-rule-based | 2102.08553 | null | https://arxiv.org/abs/2102.08553v1 | https://arxiv.org/pdf/2102.08553v1.pdf | Integrating Pre-trained Model into Rule-based Dialogue Management | Rule-based dialogue management is still the most popular solution for industrial task-oriented dialogue systems for their interpretablility. However, it is hard for developers to maintain the dialogue logic when the scenarios get more and more complex. On the other hand, data-driven dialogue systems, usually with end-to-end structures, are popular in academic research and easier to deal with complex conversations, but such methods require plenty of training data and the behaviors are less interpretable. In this paper, we propose a method to leverages the strength of both rule-based and data-driven dialogue managers (DM). We firstly introduce the DM of Carina Dialog System (CDS, an advanced industrial dialogue system built by Microsoft). Then we propose the "model-trigger" design to make the DM trainable thus scalable to scenario changes. Furthermore, we integrate pre-trained models and empower the DM with few-shot capability. The experimental results demonstrate the effectiveness and strong few-shot capability of our method. | ['Daxin Jiang', 'Yang Yang', 'Yongzhi Li', 'Ruiling Deng', 'Jun Tian', 'Fangxin Ouyang', 'Yuchen Dong', 'Yiming Liu', 'Deyi Xiong', 'Qiang Gan', 'Meng Yang', 'Jun Quan'] | 2021-02-17 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [-1.98720574e-01 5.17510831e-01 4.24655601e-02 -6.22692943e-01
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961b072f-97d3-492a-8fc9-7eea32e337f0 | co-driven-recognition-of-semantic-consistency | 2302.10570 | null | https://arxiv.org/abs/2302.10570v1 | https://arxiv.org/pdf/2302.10570v1.pdf | Co-Driven Recognition of Semantic Consistency via the Fusion of Transformer and HowNet Sememes Knowledge | Semantic consistency recognition aims to detect and judge whether the semantics of two text sentences are consistent with each other. However, the existing methods usually encounter the challenges of synonyms, polysemy and difficulty to understand long text. To solve the above problems, this paper proposes a co-driven semantic consistency recognition method based on the fusion of Transformer and HowNet sememes knowledge. Multi-level encoding of internal sentence structures via data-driven is carried out firstly by Transformer, sememes knowledge base HowNet is introduced for knowledge-driven to model the semantic knowledge association among sentence pairs. Then, interactive attention calculation is carried out utilizing soft-attention and fusion the knowledge with sememes matrix. Finally, bidirectional long short-term memory network (BiLSTM) is exploited to encode the conceptual semantic information and infer the semantic consistency. Experiments are conducted on two financial text matching datasets (BQ, AFQMC) and a cross-lingual adversarial dataset (PAWSX) for paraphrase identification. Compared with lightweight models including DSSM, MwAN, DRCN, and pre-training models such as ERNIE etc., the proposed model can not only improve the accuracy of semantic consistency recognition effectively (by 2.19%, 5.57% and 6.51% compared with the DSSM, MWAN and DRCN models on the BQ dataset), but also reduce the number of model parameters (to about 16M). In addition, driven by the HowNet sememes knowledge, the proposed method is promising to adapt to scenarios with long text. | ['Ruixian He', 'Jinxuan Zhu', 'Kang Luo', 'Xinfang Zhang', 'Yan Huang', 'Fan Chen'] | 2023-02-21 | null | null | null | null | ['text-matching', 'paraphrase-identification'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.47616370e-02 -1.79451048e-01 1.38331994e-01 -4.87521350e-01
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6cb016fd-3f82-48c7-b8c7-107975f9fbe6 | h2tne-temporal-heterogeneous-information | 2304.06970 | null | https://arxiv.org/abs/2304.06970v2 | https://arxiv.org/pdf/2304.06970v2.pdf | H2TNE: Temporal Heterogeneous Information Network Embedding in Hyperbolic Spaces | Temporal heterogeneous information network (temporal HIN) embedding, aiming to represent various types of nodes of different timestamps into low dimensional spaces while preserving structural and semantic information, is of vital importance in diverse real-life tasks. Researchers have made great efforts on temporal HIN embedding in Euclidean spaces and got some considerable achievements. However, there is always a fundamental conflict that many real-world networks show hierarchical property and power-law distribution, and are not isometric of Euclidean spaces. Recently, representation learning in hyperbolic spaces has been proved to be valid for data with hierarchical and power-law structure. Inspired by this character, we propose a hyperbolic heterogeneous temporal network embedding (H2TNE) model for temporal HINs. Specifically, we leverage a temporally and heterogeneously double-constrained random walk strategy to capture the structural and semantic information, and then calculate the embedding by exploiting hyperbolic distance in proximity measurement. Experimental results show that our method has superior performance on temporal link prediction and node classification compared with SOTA models. | ['Xiaojie Yuan', 'Lin Zhang', 'Changli Nie', 'Haiwei Zhang', 'JiaWen Guo', 'Qijie Bai'] | 2023-04-14 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-3.38874251e-01 -2.62207165e-02 -3.29716265e-01 -1.59761578e-01
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1b23e7ec-d054-4106-a3de-5b418d14e5a3 | detection-of-epilepsy-seizure-using-different | 2302.12012 | null | https://arxiv.org/abs/2302.12012v1 | https://arxiv.org/pdf/2302.12012v1.pdf | Detection of Epilepsy Seizure using Different Dimensionality Reduction Techniques and Machine Learning on Transform Domain | An Electroencephalogram (EEG) is a non-invasive exam that records the electrical activity of the brain. This exam is used to help diagnose conditions such as different brain problems. EEG signals are taken for the purpose of epilepsy detection and with Discrete Wavelet Transform (DWT) and machine learning classifier, they perform epilepsy detection. In Epilepsy seizure detection, mainly machine learning classifiers and statistical features are used. The hidden information in the EEG signal is useful for detecting diseases affecting the brain. Sometimes it is very difficult to identify the minimum changes in the EEG in time and frequency domains purpose. The DWT can give a good decomposition of the signals in different frequency bands and feature extraction. We use the tri-dimensionality reduction algorithm.; Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Linear Discriminant Analysis (LDA). Finally, features are selected by using a fusion rule and at the last step three different classifiers Support Vector Machine (SVM), Naive Bayes (NB) and K-Nearest-Neighbor (KNN) has been used for the classification. The proposed framework is tested on the Bonn dataset and the simulation results provide the maximum accuracy for the combination of LDA and NB for 10-fold cross validation technique. It shows the maximum average sensitivity, specificity, accuracy, Precision and Recall of 100%, 100%, 100%, 100% and 100%. The results prove the effectiveness of this model. | ['Suparna Biswas', 'Nanda Dulal Jana', 'Rabel Guharoy'] | 2023-02-17 | null | null | null | null | ['seizure-detection'] | ['medical'] | [-1.65887773e-01 -6.09168410e-01 2.60955065e-01 -2.31360689e-01
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d1dc32e9-7041-4f7e-93be-710a72735970 | a-high-efficiency-framework-for-constructing | 1905.04830 | null | https://arxiv.org/abs/1905.04830v1 | https://arxiv.org/pdf/1905.04830v1.pdf | A High-Efficiency Framework for Constructing Large-Scale Face Parsing Benchmark | Face parsing, which is to assign a semantic label to each pixel in face images, has recently attracted increasing interest due to its huge application potentials. Although many face related fields (e.g., face recognition and face detection) have been well studied for many years, the existing datasets for face parsing are still severely limited in terms of the scale and quality, e.g., the widely used Helen dataset only contains 2,330 images. This is mainly because pixel-level annotation is a high cost and time-consuming work, especially for the facial parts without clear boundaries. The lack of accurate annotated datasets becomes a major obstacle in the progress of face parsing task. It is a feasible way to utilize dense facial landmarks to guide the parsing annotation. However, annotating dense landmarks on human face encounters the same issues as the parsing annotation. To overcome the above problems, in this paper, we develop a high-efficiency framework for face parsing annotation, which considerably simplifies and speeds up the parsing annotation by two consecutive modules. Benefit from the proposed framework, we construct a new Dense Landmark Guided Face Parsing (LaPa) benchmark. It consists of 22,000 face images with large variations in expression, pose, occlusion, etc. Each image is provided with accurate annotation of a 11-category pixel-level label map along with coordinates of 106-point landmarks. To the best of our knowledge, it is currently the largest public dataset for face parsing. To make full use of our LaPa dataset with abundant face shape and boundary priors, we propose a simple yet effective Boundary-Sensitive Parsing Network (BSPNet). Our network is taken as a baseline model on the proposed LaPa dataset, and meanwhile, it achieves the state-of-the-art performance on the Helen dataset without resorting to extra face alignment. | ['Hailin Shi', 'Yue Si', 'Yinglu Liu', 'Xiaobo Wang', 'Tao Mei', 'Hao Shen'] | 2019-05-13 | null | null | null | null | ['face-parsing'] | ['computer-vision'] | [ 2.21413642e-01 9.84180793e-02 -1.16318397e-01 -8.40056598e-01
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64db2180-8301-4f69-b90f-f50cf0925c50 | demonstration-of-the-emotewizard-of-oz | null | null | https://aclanthology.org/W13-4058 | https://aclanthology.org/W13-4058.pdf | Demonstration of the EmoteWizard of Oz Interface for Empathic Robotic Tutors | null | ['Ruth Aylett', 'Srinivasan Janarthanam', 'Ginevra Castellano', 'Helen Hastie', 'Amol Deshmukh', 'Shweta Bhargava', 'Lee Corrigan'] | 2013-08-01 | null | null | null | ws-2013-8 | ['gesture-generation'] | ['robots'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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43cf9eb9-7395-4b9f-a581-fdd6042b65b9 | energy-bounded-learning-for-robust-models-of | 2112.11226 | null | https://arxiv.org/abs/2112.11226v2 | https://arxiv.org/pdf/2112.11226v2.pdf | Energy-bounded Learning for Robust Models of Code | In programming, learning code representations has a variety of applications, including code classification, code search, comment generation, bug prediction, and so on. Various representations of code in terms of tokens, syntax trees, dependency graphs, code navigation paths, or a combination of their variants have been proposed, however, existing vanilla learning techniques have a major limitation in robustness, i.e., it is easy for the models to make incorrect predictions when the inputs are altered in a subtle way. To enhance the robustness, existing approaches focus on recognizing adversarial samples rather than on the valid samples that fall outside a given distribution, which we refer to as out-of-distribution (OOD) samples. Recognizing such OOD samples is the novel problem investigated in this paper. To this end, we propose to first augment the in=distribution datasets with out-of-distribution samples such that, when trained together, they will enhance the model's robustness. We propose the use of an energy-bounded learning objective function to assign a higher score to in-distribution samples and a lower score to out-of-distribution samples in order to incorporate such out-of-distribution samples into the training process of source code models. In terms of OOD detection and adversarial samples detection, our evaluation results demonstrate a greater robustness for existing source code models to become more accurate at recognizing OOD data while being more resistant to adversarial attacks at the same time. Furthermore, the proposed energy-bounded score outperforms all existing OOD detection scores by a large margin, including the softmax confidence score, the Mahalanobis score, and ODIN. | ['Yijun Yu', 'Nghi D. Q. Bui'] | 2021-12-20 | null | null | null | null | ['code-classification', 'code-search', 'code-search', 'comment-generation'] | ['computer-code', 'computer-code', 'computer-vision', 'natural-language-processing'] | [ 1.00557052e-03 1.38718009e-01 -4.68154132e-01 -2.71832705e-01
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39e57f5b-4c9b-4386-a540-f5501e3b48e8 | learnable-differencing-center-for-nighttime | 2306.14538 | null | https://arxiv.org/abs/2306.14538v2 | https://arxiv.org/pdf/2306.14538v2.pdf | Learnable Differencing Center for Nighttime Depth Perception | Depth completion is the task of recovering dense depth maps from sparse ones, usually with the help of color images. Existing image-guided methods perform well on daytime depth perception self-driving benchmarks, but struggle in nighttime scenarios with poor visibility and complex illumination. To address these challenges, we propose a simple yet effective framework called LDCNet. Our key idea is to use Recurrent Inter-Convolution Differencing (RICD) and Illumination-Affinitive Intra-Convolution Differencing (IAICD) to enhance the nighttime color images and reduce the negative effects of the varying illumination, respectively. RICD explicitly estimates global illumination by differencing two convolutions with different kernels, treating the small-kernel-convolution feature as the center of the large-kernel-convolution feature in a new perspective. IAICD softly alleviates local relative light intensity by differencing a single convolution, where the center is dynamically aggregated based on neighboring pixels and the estimated illumination map in RICD. On both nighttime depth completion and depth estimation tasks, extensive experiments demonstrate the effectiveness of our LDCNet, reaching the state of the art. | ['Jian Yang', 'Jun Li', 'Shuo Chen', 'Zhenyu Zhang', 'Xiang Li', 'Kun Wang', 'Yupeng Zheng', 'Zhiqiang Yan'] | 2023-06-26 | null | null | null | null | ['depth-estimation', 'depth-completion'] | ['computer-vision', 'computer-vision'] | [ 2.19187587e-01 -2.13866815e-01 3.53824347e-01 -5.64626753e-01
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001503fc-3bde-4528-affd-bf1c7f19409b | tet-gan-text-effects-transfer-via-stylization | 1812.06384 | null | http://arxiv.org/abs/1812.06384v2 | http://arxiv.org/pdf/1812.06384v2.pdf | TET-GAN: Text Effects Transfer via Stylization and Destylization | Text effects transfer technology automatically makes the text dramatically
more impressive. However, previous style transfer methods either study the
model for general style, which cannot handle the highly-structured text effects
along the glyph, or require manual design of subtle matching criteria for text
effects. In this paper, we focus on the use of the powerful representation
abilities of deep neural features for text effects transfer. For this purpose,
we propose a novel Texture Effects Transfer GAN (TET-GAN), which consists of a
stylization subnetwork and a destylization subnetwork. The key idea is to train
our network to accomplish both the objective of style transfer and style
removal, so that it can learn to disentangle and recombine the content and
style features of text effects images. To support the training of our network,
we propose a new text effects dataset with as much as 64 professionally
designed styles on 837 characters. We show that the disentangled feature
representations enable us to transfer or remove all these styles on arbitrary
glyphs using one network. Furthermore, the flexible network design empowers
TET-GAN to efficiently extend to a new text style via one-shot learning where
only one example is required. We demonstrate the superiority of the proposed
method in generating high-quality stylized text over the state-of-the-art
methods. | ['Zongming Guo', 'Jiaying Liu', 'Shuai Yang', 'Wenjing Wang'] | 2018-12-16 | null | null | null | null | ['text-effects-transfer'] | ['natural-language-processing'] | [ 5.44625878e-01 -1.07358202e-01 4.86361086e-02 -2.92666286e-01
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2e4a11a6-0029-449f-a534-4eeb37fe6301 | towards-applying-powerful-large-ai-models-in | 2305.03433 | null | https://arxiv.org/abs/2305.03433v2 | https://arxiv.org/pdf/2305.03433v2.pdf | Towards Applying Powerful Large AI Models in Classroom Teaching: Opportunities, Challenges and Prospects | This perspective paper proposes a series of interactive scenarios that utilize Artificial Intelligence (AI) to enhance classroom teaching, such as dialogue auto-completion, knowledge and style transfer, and assessment of AI-generated content. By leveraging recent developments in Large Language Models (LLMs), we explore the potential of AI to augment and enrich teacher-student dialogues and improve the quality of teaching. Our goal is to produce innovative and meaningful conversations between teachers and students, create standards for evaluation, and improve the efficacy of AI-for-Education initiatives. In Section 3, we discuss the challenges of utilizing existing LLMs to effectively complete the educated tasks and present a unified framework for addressing diverse education dataset, processing lengthy conversations, and condensing information to better accomplish more downstream tasks. In Section 4, we summarize the pivoting tasks including Teacher-Student Dialogue Auto-Completion, Expert Teaching Knowledge and Style Transfer, and Assessment of AI-Generated Content (AIGC), providing a clear path for future research. In Section 5, we also explore the use of external and adjustable LLMs to improve the generated content through human-in-the-loop supervision and reinforcement learning. Ultimately, this paper seeks to highlight the potential for AI to aid the field of education and promote its further exploration. | ['Song Yu', 'Chenyou Fan', 'Tianqi Pang', 'Kehui Tan'] | 2023-05-05 | null | null | null | null | ['style-transfer'] | ['computer-vision'] | [ 2.88733274e-01 7.91488230e-01 -7.99050853e-02 -5.20738006e-01
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261c3a49-eb70-419d-aae5-e8c9c4bb1f83 | variable-selection-with-copula-entropy | 1910.12389 | null | https://arxiv.org/abs/1910.12389v2 | https://arxiv.org/pdf/1910.12389v2.pdf | Variable Selection with Copula Entropy | Variable selection is of significant importance for classification and regression tasks in machine learning and statistical applications where both predictability and explainability are needed. In this paper, a Copula Entropy (CE) based method for variable selection which use CE based ranks to select variables is proposed. The method is both model-free and tuning-free. Comparison experiments between the proposed method and traditional variable selection methods, such as Distance Correlation, Hilbert-Schmidt Independence Criterion, Stepwise Selection, regularized generalized linear models and Adaptive LASSO, were conducted on the UCI heart disease data. Experimental results show that CE based method can select the `right' variables out more effectively and derive better interpretable results than traditional methods do without sacrificing accuracy performance. It is believed that CE based variable selection can help to build more explainable models. | ['Jian Ma'] | 2019-10-28 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [-2.49050371e-02 -3.27263474e-02 -4.82666612e-01 -5.68021834e-01
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99f3ee5f-c428-408a-89ee-722e58114226 | weakly-supervised-segmentation-using | 2206.05148 | null | https://arxiv.org/abs/2206.05148v1 | https://arxiv.org/pdf/2206.05148v1.pdf | Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification | Deep learning models have shown their potential for several applications. However, most of the models are opaque and difficult to trust due to their complex reasoning - commonly known as the black-box problem. Some fields, such as medicine, require a high degree of transparency to accept and adopt such technologies. Consequently, creating explainable/interpretable models or applying post-hoc methods on classifiers to build trust in deep learning models are required. Moreover, deep learning methods can be used for segmentation tasks, which typically require hard-to-obtain, time-consuming manually-annotated segmentation labels for training. This paper introduces three inherently-explainable classifiers to tackle both of these problems as one. The localisation heatmaps provided by the networks -- representing the models' focus areas and being used in classification decision-making -- can be directly interpreted, without requiring any post-hoc methods to derive information for model explanation. The models are trained by using the input image and only the classification labels as ground-truth in a supervised fashion - without using any information about the location of the region of interest (i.e. the segmentation labels), making the segmentation training of the models weakly-supervised through classification labels. The final segmentation is obtained by thresholding these heatmaps. The models were employed for the task of multi-class brain tumour classification using two different datasets, resulting in the best F1-score of 0.93 for the supervised classification task while securing a median Dice score of 0.67$\pm$0.08 for the weakly-supervised segmentation task. Furthermore, the obtained accuracy on a subset of tumour-only images outperformed the state-of-the-art glioma tumour grading binary classifiers with the best model achieving 98.7\% accuracy. | ['Oliver Speck', 'Andreas Nürnberger', 'Florian Dubost', 'Hadya Yassin', 'Soumick Chatterjee'] | 2022-06-10 | null | null | null | null | ['tumour-classification'] | ['medical'] | [ 3.49963605e-01 1.18884397e+00 -2.16093987e-01 -7.98077226e-01
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0b03ddaf-675b-4aeb-9e31-9b7929bc8e95 | scene-aware-prompt-for-multi-modal-dialogue | 2207.01823 | null | https://arxiv.org/abs/2207.01823v1 | https://arxiv.org/pdf/2207.01823v1.pdf | Scene-Aware Prompt for Multi-modal Dialogue Understanding and Generation | This paper introduces the schemes of Team LingJing's experiments in NLPCC-2022-Shared-Task-4 Multi-modal Dialogue Understanding and Generation (MDUG). The MDUG task can be divided into two phases: multi-modal context understanding and response generation. To fully leverage the visual information for both scene understanding and dialogue generation, we propose the scene-aware prompt for the MDUG task. Specifically, we utilize the multi-tasking strategy for jointly modelling the scene- and session- multi-modal understanding. The visual captions are adopted to aware the scene information, while the fixed-type templated prompt based on the scene- and session-aware labels are used to further improve the dialogue generation performance. Extensive experimental results show that the proposed method has achieved state-of-the-art (SOTA) performance compared with other competitive methods, where we rank the 1-st in all three subtasks in this MDUG competition. | ['Shutao Li', 'Bin Sun', 'Ziyu Ma', 'Yixuan Weng', 'Bin Li'] | 2022-07-05 | null | null | null | null | ['dialogue-understanding'] | ['natural-language-processing'] | [ 5.59307411e-02 3.25888276e-01 1.48888916e-01 -4.98512894e-01
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