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14d93748-9f8e-4bf9-88a6-129788c735e4 | online-adaptive-personalization-for-face-anti | 2207.12272 | null | https://arxiv.org/abs/2207.12272v1 | https://arxiv.org/pdf/2207.12272v1.pdf | Online Adaptive Personalization for Face Anti-spoofing | Face authentication systems require a robust anti-spoofing module as they can be deceived by fabricating spoof images of authorized users. Most recent face anti-spoofing methods rely on optimized architectures and training objectives to alleviate the distribution shift between train and test users. However, in real online scenarios, past data from a user contains valuable information that could be used to alleviate the distribution shift. We thus introduce OAP (Online Adaptive Personalization): a lightweight solution which can adapt the model online using unlabeled data. OAP can be applied on top of most anti-spoofing methods without the need to store original biometric images. Through experimental evaluation on the SiW dataset, we show that OAP improves recognition performance of existing methods on both single video setting and continual setting, where spoof videos are interleaved with live ones to simulate spoofing attacks. We also conduct ablation studies to confirm the design choices for our solution. | ['Fatih Porikli', 'Bence Major', 'Debasmit Das', 'Davide Belli'] | 2022-07-04 | null | null | null | null | ['face-anti-spoofing'] | ['computer-vision'] | [ 5.92774332e-01 -1.54495478e-01 -1.86761871e-01 -3.93799812e-01
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63a27cd4-cdc3-485c-9c33-b53b6a60ede5 | learning-spatial-context-with-graph-neural | 2104.02385 | null | https://arxiv.org/abs/2104.02385v1 | https://arxiv.org/pdf/2104.02385v1.pdf | Learning Spatial Context with Graph Neural Network for Multi-Person Pose Grouping | Bottom-up approaches for image-based multi-person pose estimation consist of two stages: (1) keypoint detection and (2) grouping of the detected keypoints to form person instances. Current grouping approaches rely on learned embedding from only visual features that completely ignore the spatial configuration of human poses. In this work, we formulate the grouping task as a graph partitioning problem, where we learn the affinity matrix with a Graph Neural Network (GNN). More specifically, we design a Geometry-aware Association GNN that utilizes spatial information of the keypoints and learns local affinity from the global context. The learned geometry-based affinity is further fused with appearance-based affinity to achieve robust keypoint association. Spectral clustering is used to partition the graph for the formation of the pose instances. Experimental results on two benchmark datasets show that our proposed method outperforms existing appearance-only grouping frameworks, which shows the effectiveness of utilizing spatial context for robust grouping. Source code is available at: https://github.com/jiahaoLjh/PoseGrouping. | ['Gim Hee Lee', 'Jiahao Lin'] | 2021-04-06 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [-3.23677182e-01 -9.77506787e-02 -7.50853568e-02 -3.44188452e-01
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d37ed4ca-fe73-4c8f-b421-2fb9c79e6e5a | deformable-part-descriptors-for-fine-grained | null | null | https://openaccess.thecvf.com/content_iccv_2013/papers/Zhang_Deformable_Part_Descriptors_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Zhang_Deformable_Part_Descriptors_2013_ICCV_paper.pdf | Deformable Part Descriptors for Fine-grained Recognition and Attribute Prediction | Recognizing objects in fine-grained domains can be
extremely challenging due to the subtle differences between subcategories. Discriminative markings are often
highly localized, leading traditional object recognition approaches to struggle with the large pose variation often
present in these domains. Pose-normalization seeks to align
training exemplars, either piecewise by part or globally
for the whole object, effectively factoring out differences
in pose and in viewing angle. Prior approaches relied
on computationally-expensive filter ensembles for part localization and required extensive supervision. This paper proposes two pose-normalized descriptors based on
computationally-efficient deformable part models. The
first leverages the semantics inherent in strongly-supervised
DPM parts. The second exploits weak semantic annotations to learn cross-component correspondences, computing pose-normalized descriptors from the latent parts of
a weakly-supervised DPM. These representations enable
pooling across pose and viewpoint, in turn facilitating tasks
such as fine-grained recognition and attribute prediction.
Experiments conducted on the Caltech-UCSD Birds 200
dataset and Berkeley Human Attribute dataset demonstrate
significant improvements over state-of-art algorithms. | ['Trevor Darrell', 'Forrest Iandola', 'Ryan Farrell', 'Ning Zhang'] | 2013-12-01 | null | null | null | iccv-2013-12 | ['fine-grained-image-classification'] | ['computer-vision'] | [ 2.69521505e-01 -2.44244099e-01 -3.55105877e-01 -7.72400022e-01
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7a291d83-25b7-4987-b8ce-4892b8cdb743 | advancing-topic-segmentation-and-outline | 2305.14790 | null | https://arxiv.org/abs/2305.14790v1 | https://arxiv.org/pdf/2305.14790v1.pdf | Advancing Topic Segmentation and Outline Generation in Chinese Texts: The Paragraph-level Topic Representation, Corpus, and Benchmark | Topic segmentation and outline generation strive to divide a document into coherent topic sections and generate corresponding subheadings. Such a process unveils the discourse topic structure of a document that benefits quickly grasping and understanding the overall context of the document from a higher level. However, research and applications in this field have been restrained due to the lack of proper paragraph-level topic representations and large-scale, high-quality corpora in Chinese compared to the success achieved in English. Addressing these issues, we introduce a hierarchical paragraph-level topic structure representation with title, subheading, and paragraph that comprehensively models the document discourse topic structure. In addition, we ensure a more holistic representation of topic distribution within the document by using sentences instead of keywords to represent sub-topics. Following this representation, we construct the largest Chinese Paragraph-level Topic Structure corpus (CPTS), four times larger than the previously largest one. We also employ a two-stage man-machine collaborative annotation method to ensure the high quality of the corpus both in form and semantics. Finally, we validate the computability of CPTS on two fundamental tasks (topic segmentation and outline generation) by several strong baselines, and its efficacy has been preliminarily confirmed on the downstream task: discourse parsing. The representation, corpus, and benchmark we established will provide a solid foundation for future studies. | ['Haizhou Li', 'Qiaoming Zhu', 'Peifeng Li', 'Xiaomin Chu', 'Weihao Liu', 'Feng Jiang'] | 2023-05-24 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [ 2.93082833e-01 7.54653096e-01 -3.75134379e-01 -3.29934478e-01
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3058714b-e574-4b11-be0c-1468fff2fff3 | compact-representation-of-uncertainty-in-1 | 2002.11661 | null | https://arxiv.org/abs/2002.11661v3 | https://arxiv.org/pdf/2002.11661v3.pdf | Data Structures & Algorithms for Exact Inference in Hierarchical Clustering | Hierarchical clustering is a fundamental task often used to discover meaningful structures in data, such as phylogenetic trees, taxonomies of concepts, subtypes of cancer, and cascades of particle decays in particle physics. Typically approximate algorithms are used for inference due to the combinatorial number of possible hierarchical clusterings. In contrast to existing methods, we present novel dynamic-programming algorithms for \emph{exact} inference in hierarchical clustering based on a novel trellis data structure, and we prove that we can exactly compute the partition function, maximum likelihood hierarchy, and marginal probabilities of sub-hierarchies and clusters. Our algorithms scale in time and space proportional to the powerset of $N$ elements which is super-exponentially more efficient than explicitly considering each of the (2N-3)!! possible hierarchies. Also, for larger datasets where our exact algorithms become infeasible, we introduce an approximate algorithm based on a sparse trellis that compares well to other benchmarks. Exact methods are relevant to data analyses in particle physics and for finding correlations among gene expression in cancer genomics, and we give examples in both areas, where our algorithms outperform greedy and beam search baselines. In addition, we consider Dasgupta's cost with synthetic data. | ['Andrew McCallum', 'Andrew Mcgregor', 'Kyle Cranmer', 'Ji-Ah Lee', 'Nicholas Monath', 'Patrick Flaherty', 'Craig S. Greenberg', 'Sebastian Macaluso'] | 2020-02-26 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 1.56991541e-01 -6.25757203e-02 2.61324309e-02 -3.54671001e-01
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1357ac88-8cec-4e39-b2eb-c986b2edcd1f | automatic-test-suite-generation-for-key | 2012.06511 | null | https://arxiv.org/abs/2012.06511v2 | https://arxiv.org/pdf/2012.06511v2.pdf | Automatic Test Suite Generation for Key-Points Detection DNNs using Many-Objective Search (Experience Paper) | Automatically detecting the positions of key-points (e.g., facial key-points or finger key-points) in an image is an essential problem in many applications, such as driver's gaze detection and drowsiness detection in automated driving systems. With the recent advances of Deep Neural Networks (DNNs), Key-Points detection DNNs (KP-DNNs) have been increasingly employed for that purpose. Nevertheless, KP-DNN testing and validation have remained a challenging problem because KP-DNNs predict many independent key-points at the same time -- where each individual key-point may be critical in the targeted application -- and images can vary a great deal according to many factors. In this paper, we present an approach to automatically generate test data for KP-DNNs using many-objective search. In our experiments, focused on facial key-points detection DNNs developed for an industrial automotive application, we show that our approach can generate test suites to severely mispredict, on average, more than 93% of all key-points. In comparison, random search-based test data generation can only severely mispredict 41% of them. Many of these mispredictions, however, are not avoidable and should not therefore be considered failures. We also empirically compare state-of-the-art, many-objective search algorithms and their variants, tailored for test suite generation. Furthermore, we investigate and demonstrate how to learn specific conditions, based on image characteristics (e.g., head posture and skin color), that lead to severe mispredictions. Such conditions serve as a basis for risk analysis or DNN retraining. | ['Jun Wang', 'Thomas Stifter', 'Lionel C. Briand', 'Donghwan Shin', 'Fitash Ul Haq'] | 2020-12-11 | null | null | null | null | ['dnn-testing'] | ['adversarial'] | [ 6.03427999e-02 1.45701319e-02 -1.32754326e-01 -4.63233888e-01
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76d5177e-88a5-4389-9266-ac31829ef0d0 | solving-occlusion-in-terrain-mapping-with | 2109.07150 | null | https://arxiv.org/abs/2109.07150v2 | https://arxiv.org/pdf/2109.07150v2.pdf | Reconstructing occluded Elevation Information in Terrain Maps with Self-supervised Learning | Accurate and complete terrain maps enhance the awareness of autonomous robots and enable safe and optimal path planning. Rocks and topography often create occlusions and lead to missing elevation information in the Digital Elevation Map (DEM). Currently, these occluded areas are either fully avoided during motion planning or the missing values in the elevation map are filled-in using traditional interpolation, diffusion or patch-matching techniques. These methods cannot leverage the high-level terrain characteristics and the geometric constraints of line of sight we humans use intuitively to predict occluded areas. We introduce a self-supervised learning approach capable of training on real-world data without a need for ground-truth information to reconstruct the occluded areas in the DEMs. We accomplish this by adding artificial occlusion to the incomplete elevation maps constructed on a real robot by performing ray casting. We first evaluate a supervised learning approach on synthetic data for which we have the full ground-truth available and subsequently move to several real-world datasets. These real-world datasets were recorded during exploration of both structured and unstructured terrain with a legged robot, and additionally in a planetary scenario on Lunar analogue terrain. We state a significant improvement compared to the baseline methods both on synthetic terrain and for the real-world datasets. Our neural network is able to run in real-time on both CPU and GPU with suitable sampling rates for autonomous ground robots. We motivate the applicability of reconstructing occlusion in elevation maps with preliminary motion planning experiments. | ['Marco Hutter', 'Martin Azkarate', 'Levin Gerdes', 'Takahiro Miki', 'Maximilian Stölzle'] | 2021-09-15 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 5.39570272e-01 5.93486071e-01 2.33714268e-01 -5.53880215e-01
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d92040e2-0d71-45ff-a7df-8ac48000c338 | bas-an-answer-selection-method-using-bert | 1911.01528 | null | https://arxiv.org/abs/1911.01528v4 | https://arxiv.org/pdf/1911.01528v4.pdf | BAS: An Answer Selection Method Using BERT Language Model | In recent years, Question Answering systems have become more popular and widely used by users. Despite the increasing popularity of these systems, the their performance is not even sufficient for textual data and requires further research. These systems consist of several parts that one of them is the Answer Selection component. This component detects the most relevant answer from a list of candidate answers. The methods presented in previous researches have attempted to provide an independent model to undertake the answer-selection task. An independent model cannot comprehend the syntactic and semantic features of questions and answers with a small training dataset. To fill this gap, language models can be employed in implementing the answer selection part. This action enables the model to have a better understanding of the language in order to understand questions and answers better than previous works. In this research, we will present the "BAS" (BERT Answer Selection) that uses the BERT language model to comprehend language. The empirical results of applying the model on the TrecQA Raw, TrecQA Clean, and WikiQA datasets demonstrate that using a robust language model such as BERT can enhance the performance. Using a more robust classifier also enhances the effect of the language model on the answer selection component. The results demonstrate that language comprehension is an essential requirement in natural language processing tasks such as answer-selection. | ['Mohammad Ali Nematbakhsh', 'Afsaneh Fatemi', 'Jamshid Mozafari'] | 2019-11-04 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [-1.44405603e-01 -1.38830231e-03 2.50210375e-01 -6.20346546e-01
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7068e7c0-2ab0-4ec1-939c-0a4a88471048 | explore-and-exploit-the-diverse-knowledge-in | 2306.02595 | null | https://arxiv.org/abs/2306.02595v1 | https://arxiv.org/pdf/2306.02595v1.pdf | Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization | The proliferation of pretrained models, as a result of advancements in pretraining techniques, has led to the emergence of a vast zoo of publicly available models. Effectively utilizing these resources to obtain models with robust out-of-distribution generalization capabilities for downstream tasks has become a crucial area of research. Previous research has primarily focused on identifying the most powerful models within the model zoo, neglecting to fully leverage the diverse inductive biases contained within. This paper argues that the knowledge contained in weaker models is valuable and presents a method for leveraging the diversity within the model zoo to improve out-of-distribution generalization capabilities. Specifically, we investigate the behaviors of various pretrained models across different domains of downstream tasks by characterizing the variations in their encoded representations in terms of two dimensions: diversity shift and correlation shift. This characterization enables us to propose a new algorithm for integrating diverse pretrained models, not limited to the strongest models, in order to achieve enhanced out-of-distribution generalization performance. Our proposed method demonstrates state-of-the-art empirical results on a variety of datasets, thus validating the benefits of utilizing diverse knowledge. | ['ZhiMing Ma', 'Zhenguo Li', 'Fengwei Zhou', 'Tianyang Hu', 'Yimeng Chen'] | 2023-06-05 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [ 1.66942894e-01 -3.15728694e-01 -3.89639884e-01 -5.83914518e-01
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683fe8d2-8f92-491d-99e4-75a51d8ed996 | carla-gear-a-dataset-generator-for-a | 2206.04365 | null | https://arxiv.org/abs/2206.04365v1 | https://arxiv.org/pdf/2206.04365v1.pdf | CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Models | Adversarial examples represent a serious threat for deep neural networks in several application domains and a huge amount of work has been produced to investigate them and mitigate their effects. Nevertheless, no much work has been devoted to the generation of datasets specifically designed to evaluate the adversarial robustness of neural models. This paper presents CARLA-GeAR, a tool for the automatic generation of photo-realistic synthetic datasets that can be used for a systematic evaluation of the adversarial robustness of neural models against physical adversarial patches, as well as for comparing the performance of different adversarial defense/detection methods. The tool is built on the CARLA simulator, using its Python API, and allows the generation of datasets for several vision tasks in the context of autonomous driving. The adversarial patches included in the generated datasets are attached to billboards or the back of a truck and are crafted by using state-of-the-art white-box attack strategies to maximize the prediction error of the model under test. Finally, the paper presents an experimental study to evaluate the performance of some defense methods against such attacks, showing how the datasets generated with CARLA-GeAR might be used in future work as a benchmark for adversarial defense in the real world. All the code and datasets used in this paper are available at http://carlagear.retis.santannapisa.it. | ['Giorgio Buttazzo', 'Alessandro Biondi', "Gianluca D'Amico", 'Giulio Rossolini', 'Federico Nesti'] | 2022-06-09 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 2.19555676e-01 3.00372601e-01 6.21066928e-01 -2.60594100e-01
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9d727c5c-79df-447e-9aa8-72d34859147f | fusion-of-an-ensemble-of-augmented-image | 1803.06554 | null | http://arxiv.org/abs/1803.06554v1 | http://arxiv.org/pdf/1803.06554v1.pdf | Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection | A significant challenge in object detection is accurate identification of an
object's position in image space, whereas one algorithm with one set of
parameters is usually not enough, and the fusion of multiple algorithms and/or
parameters can lead to more robust results. Herein, a new computational
intelligence fusion approach based on the dynamic analysis of agreement among
object detection outputs is proposed. Furthermore, we propose an online versus
just in training image augmentation strategy. Experiments comparing the results
both with and without fusion are presented. We demonstrate that the augmented
and fused combination results are the best, with respect to higher accuracy
rates and reduction of outlier influences. The approach is demonstrated in the
context of cone, pedestrian and box detection for Advanced Driver Assistance
Systems (ADAS) applications. | ['John E. Ball', 'Pan Wei', 'Derek T. Anderson'] | 2018-03-17 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [ 1.76015183e-01 -4.01871800e-01 2.68771827e-01 -4.10582304e-01
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834a0b01-acef-450a-a031-dd05f1371337 | long-term-visual-localization-with-mobile | 2304.07691 | null | https://arxiv.org/abs/2304.07691v1 | https://arxiv.org/pdf/2304.07691v1.pdf | Long-term Visual Localization with Mobile Sensors | Despite the remarkable advances in image matching and pose estimation, image-based localization of a camera in a temporally-varying outdoor environment is still a challenging problem due to huge appearance disparity between query and reference images caused by illumination, seasonal and structural changes. In this work, we propose to leverage additional sensors on a mobile phone, mainly GPS, compass, and gravity sensor, to solve this challenging problem. We show that these mobile sensors provide decent initial poses and effective constraints to reduce the searching space in image matching and final pose estimation. With the initial pose, we are also able to devise a direct 2D-3D matching network to efficiently establish 2D-3D correspondences instead of tedious 2D-2D matching in existing systems. As no public dataset exists for the studied problem, we collect a new dataset that provides a variety of mobile sensor data and significant scene appearance variations, and develop a system to acquire ground-truth poses for query images. We benchmark our method as well as several state-of-the-art baselines and demonstrate the effectiveness of the proposed approach. The code and dataset will be released publicly. | ['Xiaowei Zhou', 'Guofeng Zhang', 'Maojun Zhang', 'Haomin Liu', 'Zhen Peng', 'Zehong Shen', 'Long Wang', 'Yu Liu', 'Shen Yan'] | 2023-04-16 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yan_Long-Term_Visual_Localization_With_Mobile_Sensors_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yan_Long-Term_Visual_Localization_With_Mobile_Sensors_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-based-localization', 'visual-localization'] | ['computer-vision', 'computer-vision'] | [ 2.38515511e-01 -5.60871065e-01 -7.26623014e-02 -3.92133236e-01
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62092da1-cf35-435c-9bd7-74f6fd62f303 | minimax-analysis-for-inverse-risk-in | 2112.00213 | null | https://arxiv.org/abs/2112.00213v2 | https://arxiv.org/pdf/2112.00213v2.pdf | Minimax Analysis for Inverse Risk in Nonparametric Planer Invertible Regression | We study a minimax risk of estimating inverse functions on a plane, while keeping an estimator is also invertible. Learning invertibility from data and exploiting an invertible estimator are used in many domains, such as statistics, econometrics, and machine learning. Although the consistency and universality of invertible estimators have been well investigated, analysis on the efficiency of these methods is still under development. In this study, we study a minimax risk for estimating invertible bi-Lipschitz functions on a square in a $2$-dimensional plane. We first introduce an inverse $L^2$-risk to evaluate an estimator which preserves invertibility. Then, we derive lower and upper rates for a minimax inverse risk by exploiting a representation of invertible functions using level-sets. To obtain an upper bound, we develop an estimator asymptotically almost everywhere invertible, whose risk attains the derived minimax lower rate up to logarithmic factors. The derived minimax rate corresponds to that of the non-invertible bi-Lipschitz function, which rejects the expectation of whether invertibility improves the minimax rate, similar to other shape constraints. | ['Masaaki Imaizumi', 'Akifumi Okuno'] | 2021-12-01 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [ 3.86771262e-01 5.68063676e-01 -2.65706718e-01 -4.36383635e-01
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fc9f8875-bf8a-4690-9d13-a3317cb67993 | traffic-sign-classification-using-deep | 1511.02992 | null | http://arxiv.org/abs/1511.02992v2 | http://arxiv.org/pdf/1511.02992v2.pdf | Traffic Sign Classification Using Deep Inception Based Convolutional Networks | In this work, we propose a novel deep network for traffic sign classification
that achieves outstanding performance on GTSRB surpassing all previous methods.
Our deep network consists of spatial transformer layers and a modified version
of inception module specifically designed for capturing local and global
features together. This features adoption allows our network to classify
precisely intraclass samples even under deformations. Use of spatial
transformer layer makes this network more robust to deformations such as
translation, rotation, scaling of input images. Unlike existing approaches that
are developed with hand-crafted features, multiple deep networks with huge
parameters and data augmentations, our method addresses the concern of
exploding parameters and augmentations. We have achieved the state-of-the-art
performance of 99.81\% on GTSRB dataset. | ['Mrinal Haloi'] | 2015-11-10 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [-1.93672478e-01 -3.69766384e-01 -2.72053033e-01 -6.57308400e-01
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bbf87cd3-aca6-4b5e-b7d3-5c549d4ca784 | tight-sample-complexity-of-large-margin | null | null | http://papers.nips.cc/paper/4032-tight-sample-complexity-of-large-margin-learning | http://papers.nips.cc/paper/4032-tight-sample-complexity-of-large-margin-learning.pdf | Tight Sample Complexity of Large-Margin Learning | We obtain a tight distribution-specific characterization of the sample complexity of large-margin classification with L2 regularization: We introduce the gamma-adapted-dimension, which is a simple function of the spectrum of a distribution's covariance matrix, and show distribution-specific upper and lower bounds on the sample complexity, both governed by the gamma-adapted-dimension of the source distribution. We conclude that this new quantity tightly characterizes the true sample complexity of large-margin classification. The bounds hold for a rich family of sub-Gaussian distributions. | ['Sivan Sabato', 'Nathan Srebro', 'Naftali Tishby'] | 2010-12-01 | null | null | null | neurips-2010-12 | ['l2-regularization'] | ['methodology'] | [-2.13351011e-01 2.35454977e-01 -5.76306283e-01 -3.32082272e-01
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0338e5d6-d50d-44ee-afbe-8d6e72602ce3 | m3ae-multimodal-representation-learning-for | 2303.05302 | null | https://arxiv.org/abs/2303.05302v1 | https://arxiv.org/pdf/2303.05302v1.pdf | M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities | Multimodal magnetic resonance imaging (MRI) provides complementary information for sub-region analysis of brain tumors. Plenty of methods have been proposed for automatic brain tumor segmentation using four common MRI modalities and achieved remarkable performance. In practice, however, it is common to have one or more modalities missing due to image corruption, artifacts, acquisition protocols, allergy to contrast agents, or simply cost. In this work, we propose a novel two-stage framework for brain tumor segmentation with missing modalities. In the first stage, a multimodal masked autoencoder (M3AE) is proposed, where both random modalities (i.e., modality dropout) and random patches of the remaining modalities are masked for a reconstruction task, for self-supervised learning of robust multimodal representations against missing modalities. To this end, we name our framework M3AE. Meanwhile, we employ model inversion to optimize a representative full-modal image at marginal extra cost, which will be used to substitute for the missing modalities and boost performance during inference. Then in the second stage, a memory-efficient self distillation is proposed to distill knowledge between heterogenous missing-modal situations while fine-tuning the model for supervised segmentation. Our M3AE belongs to the 'catch-all' genre where a single model can be applied to all possible subsets of modalities, thus is economic for both training and deployment. Extensive experiments on BraTS 2018 and 2020 datasets demonstrate its superior performance to existing state-of-the-art methods with missing modalities, as well as the efficacy of its components. Our code is available at: https://github.com/ccarliu/m3ae. | ['Yefeng Zheng', 'Liansheng Wang', 'Jinghan Sun', 'Donghuan Lu', 'Dong Wei', 'Hong Liu'] | 2023-03-09 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 4.73221421e-01 1.69043481e-01 -2.87492573e-01 -2.41057634e-01
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168d777f-2661-4f5e-98b4-d86864d883de | development-and-whole-body-validation-of | 2305.13918 | null | https://arxiv.org/abs/2305.13918v1 | https://arxiv.org/pdf/2305.13918v1.pdf | Development and Whole-Body Validation of Personalizable Female and Male Pedestrian SAFER Human Body Models | Vulnerable road users are overrepresented in the worldwide number of road-traffic injury victims. Developing biofidelic male and female pedestrian HBMs representing a range of anthropometries is imperative to follow through with the efforts to increase road safety and propose intervention strategies. In this study, a 50th percentile male and female pedestrian of the SAFER HBM was developed via a newly developed image registration-based mesh morphing framework for subject personalization. The HBM and its accompanied personalization framework were evaluated by means of a set of cadaver experiments, where subjects were struck laterally by a generic sedan buck. In the simulated whole-body pedestrian collisions, the personalized HBMs demonstrate a good capability of reproducing the trajectories and head kinematics observed in lateral impacts. The presented pedestrian HBMs and personalization framework provide robust means to thoroughly and accurately reconstruct and evaluate pedestrian-to-vehicle collisions. | ['Xiaogai Li', 'Svein Kleiven', 'Bengt Pipkorn', 'Qiantailang Yuan', 'Natalia Lindgren'] | 2023-05-11 | null | null | null | null | ['image-registration'] | ['computer-vision'] | [-2.28680268e-01 2.49245703e-01 3.48867178e-02 -3.23613316e-01
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bd13b042-bc4e-42cd-8201-46eb29ffa7a5 | detecting-pulse-from-head-motions-in-video | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Balakrishnan_Detecting_Pulse_from_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Balakrishnan_Detecting_Pulse_from_2013_CVPR_paper.pdf | Detecting Pulse from Head Motions in Video | We extract heart rate and beat lengths from videos by measuring subtle head motion caused by the Newtonian reaction to the influx of blood at each beat. Our method tracks features on the head and performs principal component analysis (PCA) to decompose their trajectories into a set of component motions. It then chooses the component that best corresponds to heartbeats based on its temporal frequency spectrum. Finally, we analyze the motion projected to this component and identify peaks of the trajectories, which correspond to heartbeats. When evaluated on 18 subjects, our approach reported heart rates nearly identical to an electrocardiogram device. Additionally we were able to capture clinically relevant information about heart rate variability. | ['John Guttag', 'Guha Balakrishnan', 'Fredo Durand'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['heart-rate-variability'] | ['medical'] | [-8.95190239e-02 -2.17494741e-02 -1.76597461e-02 1.29163787e-01
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fcc83f29-e303-40e4-a14f-c3b13ecb8c3f | kalmannet-a-learnable-kalman-filter-for | 2301.12363 | null | https://arxiv.org/abs/2301.12363v2 | https://arxiv.org/pdf/2301.12363v2.pdf | NeuralKalman: A Learnable Kalman Filter for Acoustic Echo Cancellation | The Kalman filter is widely used for addressing acoustic echo cancellation (AEC) problems due to their robustness to double-talk and fast convergence. However, the inability to model nonlinearity and the need to tune control parameters cast limitations on such adaptive filtering algorithms. In this paper, we integrate the frequency domain Kalman filter (FDKF) and deep neural networks (DNNs) into a hybrid method, called NeuralKalman, to leverage the advantages of deep learning and adaptive filtering algorithms. Specifically, we employ a DNN to estimate nonlinearly distorted far-end signals, a transition factor, and the nonlinear transition function in the state equation of the FDKF algorithm. Experimental results show that the proposed NeuralKalman improves the performance of FDKF significantly and outperforms strong baseline methods. | ['DeLiang Wang', 'Dong Yu', 'Hao Zhang', 'Meng Yu', 'Yixuan Zhang'] | 2023-01-29 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [-2.18250737e-01 -5.46922445e-01 3.82127583e-01 -3.51386726e-01
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fbedcf72-6c44-49cb-a4da-831e77432d9d | sketchanimar-sketch-based-3d-animal-fine | 2304.05731 | null | https://arxiv.org/abs/2304.05731v1 | https://arxiv.org/pdf/2304.05731v1.pdf | SketchANIMAR: Sketch-based 3D Animal Fine-Grained Retrieval | The retrieval of 3D objects has gained significant importance in recent years due to its broad range of applications in computer vision, computer graphics, virtual reality, and augmented reality. However, the retrieval of 3D objects presents significant challenges due to the intricate nature of 3D models, which can vary in shape, size, and texture, and have numerous polygons and vertices. To this end, we introduce a novel SHREC challenge track that focuses on retrieving relevant 3D animal models from a dataset using sketch queries and expedites accessing 3D models through available sketches. Furthermore, a new dataset named ANIMAR was constructed in this study, comprising a collection of 711 unique 3D animal models and 140 corresponding sketch queries. Our contest requires participants to retrieve 3D models based on complex and detailed sketches. We receive satisfactory results from eight teams and 204 runs. Although further improvement is necessary, the proposed task has the potential to incentivize additional research in the domain of 3D object retrieval, potentially yielding benefits for a wide range of applications. We also provide insights into potential areas of future research, such as improving techniques for feature extraction and matching, and creating more diverse datasets to evaluate retrieval performance. | ['Minh-Triet Tran', 'Akihiro Sugimoto', 'Hai-Dang Nguyen', 'Minh-Hoa Doan', 'Hoai-Danh Vo', 'Minh-Quang Nguyen', 'Nhu-Vinh Hoang', 'Kim-Phat Tran', 'Tuan-Anh Yang', 'Thien-Phuc Tran', 'Xuan-Hieu Nguyen', 'Khanh-Duy Ho', 'Tuong-Nghiem Diep', 'Truong Hoai Phong', 'Tuong-Vy Truong-Thuy', 'Ngoc-Linh Nguyen-Ha', 'Thanh-Danh Le', 'Tuan-Luc Huynh', 'Trong-Hieu Nguyen-Mau', 'Quang-Binh Nguyen', 'Trong-Vu Hoang', 'Huu-Phuc Pham', 'Nhat-Quynh Le-Pham', 'Vinh-Tiep Nguyen', 'Thang-Long Nguyen-Ho', 'Nhat Hoang-Xuan', 'Mai-Khiem Tran', 'Khanh-Duy Le', 'Trong-Le Do', 'Viet-Tham Huynh', 'Trong-Thuan Nguyen', 'Minh-Quan Le', 'Tam V. Nguyen', 'Trung-Nghia Le'] | 2023-04-12 | null | null | null | null | ['3d-object-retrieval'] | ['computer-vision'] | [-9.80079323e-02 -6.69358850e-01 -3.37445037e-03 -2.64150590e-01
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77f9e22a-5b45-4148-89f3-a590d0c7c4c6 | pressure-predictions-of-turbine-blades-with | 1806.06940 | null | http://arxiv.org/abs/1806.06940v1 | http://arxiv.org/pdf/1806.06940v1.pdf | Pressure Predictions of Turbine Blades with Deep Learning | Deep learning has been used in many areas, such as feature detections in
images and the game of go. This paper presents a study that attempts to use the
deep learning method to predict turbomachinery performance. Three different
deep neural networks are built and trained to predict the pressure
distributions of turbine airfoils. The performance of a library of turbine
airfoils were firstly predicted using methods based on Euler equations, which
were then used to train and validate the deep learning neural networks. The
results show that network with four layers of convolutional neural network and
two layers of fully connected neural network provides the best predictions. For
the best neural network architecture, the pressure prediction on more than 99%
locations are better than 3% and 90% locations are better than 1%. | ["Cheng'an Bai", 'Chao Zhou'] | 2018-06-12 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-8.87375951e-01 -7.15295747e-02 2.40759328e-01 -8.25226754e-02
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cc28da78-a6f6-4c73-8d1f-342f95f5ac45 | safe-and-sample-efficient-reinforcement | 2303.14265 | null | https://arxiv.org/abs/2303.14265v1 | https://arxiv.org/pdf/2303.14265v1.pdf | Safe and Sample-efficient Reinforcement Learning for Clustered Dynamic Environments | This study proposes a safe and sample-efficient reinforcement learning (RL) framework to address two major challenges in developing applicable RL algorithms: satisfying safety constraints and efficiently learning with limited samples. To guarantee safety in real-world complex environments, we use the safe set algorithm (SSA) to monitor and modify the nominal controls, and evaluate SSA+RL in a clustered dynamic environment which is challenging to be solved by existing RL algorithms. However, the SSA+RL framework is usually not sample-efficient especially in reward-sparse environments, which has not been addressed in previous safe RL works. To improve the learning efficiency, we propose three techniques: (1) avoiding behaving overly conservative by adapting the SSA; (2) encouraging safe exploration using random network distillation with safety constraints; (3) improving policy convergence by treating SSA as expert demonstrations and directly learn from that. The experimental results show that our framework can achieve better safety performance compare to other safe RL methods during training and solve the task with substantially fewer episodes. Project website: https://hychen-naza.github.io/projects/Safe_RL/. | ['Changliu Liu', 'Hongyi Chen'] | 2023-03-24 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-3.65049988e-02 1.59791127e-01 -5.44024110e-01 1.77263632e-01
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05b8bb58-66ef-45d2-8fed-69446b07b6f7 | real-face-foundation-representation-learning | 2303.08439 | null | https://arxiv.org/abs/2303.08439v1 | https://arxiv.org/pdf/2303.08439v1.pdf | Real Face Foundation Representation Learning for Generalized Deepfake Detection | The emergence of deepfake technologies has become a matter of social concern as they pose threats to individual privacy and public security. It is now of great significance to develop reliable deepfake detectors. However, with numerous face manipulation algorithms present, it is almost impossible to collect sufficient representative fake faces, and it is hard for existing detectors to generalize to all types of manipulation. Therefore, we turn to learn the distribution of real faces, and indirectly identify fake images that deviate from the real face distribution. In this study, we propose Real Face Foundation Representation Learning (RFFR), which aims to learn a general representation from large-scale real face datasets and detect potential artifacts outside the distribution of RFFR. Specifically, we train a model on real face datasets by masked image modeling (MIM), which results in a discrepancy between input faces and the reconstructed ones when applying the model on fake samples. This discrepancy reveals the low-level artifacts not contained in RFFR, making it easier to build a deepfake detector sensitive to all kinds of potential artifacts outside the distribution of RFFR. Extensive experiments demonstrate that our method brings about better generalization performance, as it significantly outperforms the state-of-the-art methods in cross-manipulation evaluations, and has the potential to further improve by introducing extra real faces for training RFFR. | ['Shiguang Shan', 'Jie Zhang', 'Liang Shi'] | 2023-03-15 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [-7.42077306e-02 3.71997431e-02 -2.19069123e-02 -4.55869317e-01
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42350be9-3119-4f4b-a9b0-a32350a62e7c | intention-aware-feature-propagation-network | 2203.05145 | null | https://arxiv.org/abs/2203.05145v2 | https://arxiv.org/pdf/2203.05145v2.pdf | Intention-aware Feature Propagation Network for Interactive Segmentation | We aim to tackle the problem of point-based interactive segmentation, in which two key challenges are to infer user's intention correctly and to propagate the user-provided annotations to unlabeled regions efficiently. To address those challenges, we propose a novel intention-aware feature propagation strategy that performs explicit user intention estimation and learns an efficient click-augmented feature representation for high-resolution foreground segmentation. Specifically, we develop a coarse-to-fine sparse propagation network for each interactive segmentation step, which consists of a coarse-level network for more effective tracking of user's interest, and a fine-level network for zooming to the target object and performing fine-level segmentation. Moreover, we design a new sparse graph network module for both levels to enable efficient long-range propagation of click information. Extensive experiments show that our method surpasses the previous state-of-the-art methods on all popular benchmarks, demonstrating its efficacy. | ['Xuming He', 'Yongfei Liu', 'Chuanyang Hu', 'Chuyu Zhang'] | 2022-03-10 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 3.22751999e-01 -4.99657057e-02 -4.19605523e-01 -4.10017520e-01
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5a1c96f5-72c6-447e-aedc-81c136e629fb | msstn-multi-scale-spatial-temporal-network | null | null | https://ieeexplore.ieee.org/document/9005574 | https://ieeexplore.ieee.org/document/9005574 | MSSTN: Multi-Scale Spatial Temporal Network for Air Pollution Prediction | Air pollution has become an important factor constraining city development and threatening public health in recent years. Air pollution prediction has been considered as the key part for the early warning of pollution event. Considering the multi-scale nature of geo-sensory data such as air pollution signal, in this paper we adopt a multi-level graph data structure for better utilization of multi-scale spatio-temporal information. We further present a novel deep convolutional neural network model, named Multi-Scale Spatial Temporal Network (MSSTN), for the learning task on this data structure. The MSSTN is specially designed to better discover multi-scale spatial temporal patterns and their high-level interactions, by explicitly using multi-scale neural network structure in both spatial and temporal component. We conduct extensive experiments and ablation studies on Urban Air Pollution Datasets in North China, where the MSSTN can make hourly PM2.5 concentration predictions jointly for a number of cities. And our results shows an outstanding prediction accuracy as well as high computational efficiency compared to existing works. | ['Zhiyuan Wu ; Yue Wang ; Lin Zhang'] | 2019-09-12 | null | null | null | 2019-ieee-international-conference-on-big-3 | ['air-pollution-prediction'] | ['miscellaneous'] | [-1.29742920e-01 -6.53891087e-01 -3.92226223e-03 -9.91941690e-02
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fde9d741-c379-470e-9dcc-7c032325160d | a-comparison-of-decision-analysis-and-expert | 1304.2362 | null | http://arxiv.org/abs/1304.2362v1 | http://arxiv.org/pdf/1304.2362v1.pdf | A Comparison of Decision Analysis and Expert Rules for Sequential Diagnosis | There has long been debate about the relative merits of decision theoretic
methods and heuristic rule-based approaches for reasoning under uncertainty. We
report an experimental comparison of the performance of the two approaches to
troubleshooting, specifically to test selection for fault diagnosis. We use as
experimental testbed the problem of diagnosing motorcycle engines. The first
approach employs heuristic test selection rules obtained from expert mechanics.
We compare it with the optimal decision analytic algorithm for test selection
which employs estimated component failure probabilities and test costs. The
decision analytic algorithm was found to reduce the expected cost (i.e. time)
to arrive at a diagnosis by an average of 14% relative to the expert rules.
Sensitivity analysis shows the results are quite robust to inaccuracy in the
probability and cost estimates. This difference suggests some interesting
implications for knowledge acquisition. | ['Max Henrion', 'Jayant Kalagnanam'] | 2013-03-27 | null | null | null | null | ['sequential-diagnosis'] | ['medical'] | [-3.47616039e-02 3.91365379e-01 -1.00022078e-01 -3.30809981e-01
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c8b66165-936b-4c0b-b028-17bc4e181584 | 190412658 | 1904.12658 | null | http://arxiv.org/abs/1904.12658v2 | http://arxiv.org/pdf/1904.12658v2.pdf | MSDC-Net: Multi-Scale Dense and Contextual Networks for Automated Disparity Map for Stereo Matching | Disparity prediction from stereo images is essential to computer vision
applications including autonomous driving, 3D model reconstruction, and object
detection. To predict accurate disparity map, we propose a novel deep learning
architecture for detectingthe disparity map from a rectified pair of stereo
images, called MSDC-Net. Our MSDC-Net contains two modules: multi-scale fusion
2D convolution and multi-scale residual 3D convolution modules. The multi-scale
fusion 2D convolution module exploits the potential multi-scale features, which
extracts and fuses the different scale features by Dense-Net. The multi-scale
residual 3D convolution module learns the different scale geometry context from
the cost volume which aggregated by the multi-scale fusion 2D convolution
module. Experimental results on Scene Flow and KITTI datasets demonstrate that
our MSDC-Net significantly outperforms other approaches in the non-occluded
region. | ['Zhibo Rao', 'Renjie He', 'Bo Li', 'Mingyi He', 'Zhidong Zhu', 'Yuchao Dai'] | 2019-04-25 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [-4.92133163e-02 -2.45026395e-01 3.80001664e-02 -6.08355224e-01
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0fed5793-d831-455c-b400-10f58df13983 | tricubenet-2d-kernel-based-object | 2104.11435 | null | https://arxiv.org/abs/2104.11435v2 | https://arxiv.org/pdf/2104.11435v2.pdf | TricubeNet: 2D Kernel-Based Object Representation for Weakly-Occluded Oriented Object Detection | We present a novel approach for oriented object detection, named TricubeNet, which localizes oriented objects using visual cues ($i.e.,$ heatmap) instead of oriented box offsets regression. We represent each object as a 2D Tricube kernel and extract bounding boxes using simple image-processing algorithms. Our approach is able to (1) obtain well-arranged boxes from visual cues, (2) solve the angle discontinuity problem, and (3) can save computational complexity due to our anchor-free modeling. To further boost the performance, we propose some effective techniques for size-invariant loss, reducing false detections, extracting rotation-invariant features, and heatmap refinement. To demonstrate the effectiveness of our TricubeNet, we experiment on various tasks for weakly-occluded oriented object detection: detection in an aerial image, densely packed object image, and text image. The extensive experimental results show that our TricubeNet is quite effective for oriented object detection. Code is available at https://github.com/qjadud1994/TricubeNet. | ['Junmo Kim', 'Doyeon Kim', 'Sihaeng Lee', 'Janghyeon Lee', 'Beomyoung Kim'] | 2021-04-23 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-3.79312970e-02 -2.36451164e-01 -9.97983366e-02 8.00323039e-02
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443c3d95-3582-4467-9a21-b9bb0bf2e092 | policy-gradient-search-online-planning-and | 1904.03646 | null | http://arxiv.org/abs/1904.03646v1 | http://arxiv.org/pdf/1904.03646v1.pdf | Policy Gradient Search: Online Planning and Expert Iteration without Search Trees | Monte Carlo Tree Search (MCTS) algorithms perform simulation-based search to
improve policies online. During search, the simulation policy is adapted to
explore the most promising lines of play. MCTS has been used by
state-of-the-art programs for many problems, however a disadvantage to MCTS is
that it estimates the values of states with Monte Carlo averages, stored in a
search tree; this does not scale to games with very high branching factors. We
propose an alternative simulation-based search method, Policy Gradient Search
(PGS), which adapts a neural network simulation policy online via policy
gradient updates, avoiding the need for a search tree. In Hex, PGS achieves
comparable performance to MCTS, and an agent trained using Expert Iteration
with PGS was able defeat MoHex 2.0, the strongest open-source Hex agent, in 9x9
Hex. | ['Tim Salimans', 'John Schulman', 'Thomas Anthony', 'Robert Nishihara', 'Philipp Moritz'] | 2019-04-07 | null | null | null | null | ['neural-network-simulation'] | ['computer-code'] | [-5.06816208e-01 -1.78025827e-01 -6.07947171e-01 4.05822881e-02
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712cb9e0-c992-4324-970e-6e7ace816e48 | what-s-the-meaning-of-superhuman-performance | 2305.08414 | null | https://arxiv.org/abs/2305.08414v1 | https://arxiv.org/pdf/2305.08414v1.pdf | What's the Meaning of Superhuman Performance in Today's NLU? | In the last five years, there has been a significant focus in Natural Language Processing (NLP) on developing larger Pretrained Language Models (PLMs) and introducing benchmarks such as SuperGLUE and SQuAD to measure their abilities in language understanding, reasoning, and reading comprehension. These PLMs have achieved impressive results on these benchmarks, even surpassing human performance in some cases. This has led to claims of superhuman capabilities and the provocative idea that certain tasks have been solved. In this position paper, we take a critical look at these claims and ask whether PLMs truly have superhuman abilities and what the current benchmarks are really evaluating. We show that these benchmarks have serious limitations affecting the comparison between humans and PLMs and provide recommendations for fairer and more transparent benchmarks. | ['Roberto Navigli', 'Ekaterina Shutova', 'Rico Sennrich', 'Steven Schockaert', 'Simon Krek', 'Alexander Koller', 'Eduard H. Hovy', 'Daniel Hershcovich', 'Jan Hajic', 'Thierry Declerck', 'Johan Bos', 'Simone Tedeschi'] | 2023-05-15 | null | null | null | null | ['reading-comprehension'] | ['natural-language-processing'] | [-8.42747912e-02 6.77473366e-01 -1.47219989e-02 -4.01382983e-01
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20c89590-71ee-47cf-929d-f5564e53bea7 | multi-task-attentive-residual-networks-for | 2102.12227 | null | https://arxiv.org/abs/2102.12227v3 | https://arxiv.org/pdf/2102.12227v3.pdf | Multi-Task Attentive Residual Networks for Argument Mining | We explore the use of residual networks and neural attention for multiple argument mining tasks. We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble, without any assumption on document or argument structure. We present an extensive experimental evaluation on five different corpora of user-generated comments, scientific publications, and persuasive essays. Our results show that our approach is a strong competitor against state-of-the-art architectures with a higher computational footprint or corpus-specific design, representing an interesting compromise between generality, performance accuracy and reduced model size. | ['Paolo Torroni', 'Marco Lippi', 'Andrea Galassi'] | 2021-02-24 | null | null | null | null | ['component-classification'] | ['natural-language-processing'] | [ 1.66561007e-01 5.58297932e-01 -3.64023954e-01 -2.49210924e-01
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73952034-068c-462d-a1e3-2b7fa78c4b84 | independent-policy-gradient-for-large-scale | 2202.04129 | null | https://arxiv.org/abs/2202.04129v3 | https://arxiv.org/pdf/2202.04129v3.pdf | Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence | We examine global non-asymptotic convergence properties of policy gradient methods for multi-agent reinforcement learning (RL) problems in Markov potential games (MPG). To learn a Nash equilibrium of an MPG in which the size of state space and/or the number of players can be very large, we propose new independent policy gradient algorithms that are run by all players in tandem. When there is no uncertainty in the gradient evaluation, we show that our algorithm finds an $\epsilon$-Nash equilibrium with $O(1/\epsilon^2)$ iteration complexity which does not explicitly depend on the state space size. When the exact gradient is not available, we establish $O(1/\epsilon^5)$ sample complexity bound in a potentially infinitely large state space for a sample-based algorithm that utilizes function approximation. Moreover, we identify a class of independent policy gradient algorithms that enjoys convergence for both zero-sum Markov games and Markov cooperative games with the players that are oblivious to the types of games being played. Finally, we provide computational experiments to corroborate the merits and the effectiveness of our theoretical developments. | ['Mihailo R. Jovanović', 'Kaiqing Zhang', 'Chen-Yu Wei', 'Dongsheng Ding'] | 2022-02-08 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-3.88744026e-01 1.46974102e-01 -2.70224214e-01 2.74377853e-01
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1060f9e3-7d4a-428f-8754-a5d285b42596 | olagpt-empowering-llms-with-human-like | 2305.16334 | null | https://arxiv.org/abs/2305.16334v1 | https://arxiv.org/pdf/2305.16334v1.pdf | OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilities | In most current research, large language models (LLMs) are able to perform reasoning tasks by generating chains of thought through the guidance of specific prompts. However, there still exists a significant discrepancy between their capability in solving complex reasoning problems and that of humans. At present, most approaches focus on chains of thought (COT) and tool use, without considering the adoption and application of human cognitive frameworks. It is well-known that when confronting complex reasoning challenges, humans typically employ various cognitive abilities, and necessitate interaction with all aspects of tools, knowledge, and the external environment information to accomplish intricate tasks. This paper introduces a novel intelligent framework, referred to as OlaGPT. OlaGPT carefully studied a cognitive architecture framework, and propose to simulate certain aspects of human cognition. The framework involves approximating different cognitive modules, including attention, memory, reasoning, learning, and corresponding scheduling and decision-making mechanisms. Inspired by the active learning mechanism of human beings, it proposes a learning unit to record previous mistakes and expert opinions, and dynamically refer to them to strengthen their ability to solve similar problems. The paper also outlines common effective reasoning frameworks for human problem-solving and designs Chain-of-Thought (COT) templates accordingly. A comprehensive decision-making mechanism is also proposed to maximize model accuracy. The efficacy of OlaGPT has been stringently evaluated on multiple reasoning datasets, and the experimental outcomes reveal that OlaGPT surpasses state-of-the-art benchmarks, demonstrating its superior performance. Our implementation of OlaGPT is available on GitHub: \url{https://github.com/oladata-team/OlaGPT}. | ['Zang Li', 'Bo Hu', 'Chengxiang Zhuo', 'Lei Chen', 'Beibei Kong', 'Chenglin Li', 'WenTao Wei', 'Mingxiong Lin', 'Tao Xie', 'Yuanzhen Xie'] | 2023-05-23 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [-1.75986841e-01 2.69265532e-01 8.36367682e-02 -2.27325007e-01
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b956c707-9330-4b8d-bb54-373f1f32d3f4 | marginal-contrastive-correspondence-for | 2204.00442 | null | https://arxiv.org/abs/2204.00442v1 | https://arxiv.org/pdf/2204.00442v1.pdf | Marginal Contrastive Correspondence for Guided Image Generation | Exemplar-based image translation establishes dense correspondences between a conditional input and an exemplar (from two different domains) for leveraging detailed exemplar styles to achieve realistic image translation. Existing work builds the cross-domain correspondences implicitly by minimizing feature-wise distances across the two domains. Without explicit exploitation of domain-invariant features, this approach may not reduce the domain gap effectively which often leads to sub-optimal correspondences and image translation. We design a Marginal Contrastive Learning Network (MCL-Net) that explores contrastive learning to learn domain-invariant features for realistic exemplar-based image translation. Specifically, we design an innovative marginal contrastive loss that guides to establish dense correspondences explicitly. Nevertheless, building correspondence with domain-invariant semantics alone may impair the texture patterns and lead to degraded texture generation. We thus design a Self-Correlation Map (SCM) that incorporates scene structures as auxiliary information which improves the built correspondences substantially. Quantitative and qualitative experiments on multifarious image translation tasks show that the proposed method outperforms the state-of-the-art consistently. | ['Changgong Zhang', 'Shijian Lu', 'Jiahui Zhang', 'Rongliang Wu', 'Yingchen Yu', 'Fangneng Zhan'] | 2022-04-01 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhan_Marginal_Contrastive_Correspondence_for_Guided_Image_Generation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhan_Marginal_Contrastive_Correspondence_for_Guided_Image_Generation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['texture-synthesis'] | ['computer-vision'] | [ 3.81436080e-01 -1.11330986e-01 -1.48925036e-01 -3.54774833e-01
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82df2a9c-c917-4ec4-8f85-a511263862d2 | unsupervised-pre-training-for-temporal-action | 2203.13609 | null | https://arxiv.org/abs/2203.13609v1 | https://arxiv.org/pdf/2203.13609v1.pdf | Unsupervised Pre-training for Temporal Action Localization Tasks | Unsupervised video representation learning has made remarkable achievements in recent years. However, most existing methods are designed and optimized for video classification. These pre-trained models can be sub-optimal for temporal localization tasks due to the inherent discrepancy between video-level classification and clip-level localization. To bridge this gap, we make the first attempt to propose a self-supervised pretext task, coined as Pseudo Action Localization (PAL) to Unsupervisedly Pre-train feature encoders for Temporal Action Localization tasks (UP-TAL). Specifically, we first randomly select temporal regions, each of which contains multiple clips, from one video as pseudo actions and then paste them onto different temporal positions of the other two videos. The pretext task is to align the features of pasted pseudo action regions from two synthetic videos and maximize the agreement between them. Compared to the existing unsupervised video representation learning approaches, our PAL adapts better to downstream TAL tasks by introducing a temporal equivariant contrastive learning paradigm in a temporally dense and scale-aware manner. Extensive experiments show that PAL can utilize large-scale unlabeled video data to significantly boost the performance of existing TAL methods. Our codes and models will be made publicly available at https://github.com/zhang-can/UP-TAL. | ['Yuexian Zou', 'Jue Wang', 'Meng Cao', 'Junwu Weng', 'Tianyu Yang', 'Can Zhang'] | 2022-03-25 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhang_Unsupervised_Pre-Training_for_Temporal_Action_Localization_Tasks_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhang_Unsupervised_Pre-Training_for_Temporal_Action_Localization_Tasks_CVPR_2022_paper.pdf | cvpr-2022-1 | ['action-localization', 'unsupervised-pre-training'] | ['computer-vision', 'methodology'] | [ 3.61669034e-01 -3.16289037e-01 -6.01799846e-01 -2.99313873e-01
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03b5360f-9620-4dc5-87e9-0a4bfb35aa64 | pamir-parametric-model-conditioned-implicit | 2007.03858 | null | https://arxiv.org/abs/2007.03858v2 | https://arxiv.org/pdf/2007.03858v2.pdf | PaMIR: Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction | Modeling 3D humans accurately and robustly from a single image is very challenging, and the key for such an ill-posed problem is the 3D representation of the human models. To overcome the limitations of regular 3D representations, we propose Parametric Model-Conditioned Implicit Representation (PaMIR), which combines the parametric body model with the free-form deep implicit function. In our PaMIR-based reconstruction framework, a novel deep neural network is proposed to regularize the free-form deep implicit function using the semantic features of the parametric model, which improves the generalization ability under the scenarios of challenging poses and various clothing topologies. Moreover, a novel depth-ambiguity-aware training loss is further integrated to resolve depth ambiguities and enable successful surface detail reconstruction with imperfect body reference. Finally, we propose a body reference optimization method to improve the parametric model estimation accuracy and to enhance the consistency between the parametric model and the implicit function. With the PaMIR representation, our framework can be easily extended to multi-image input scenarios without the need of multi-camera calibration and pose synchronization. Experimental results demonstrate that our method achieves state-of-the-art performance for image-based 3D human reconstruction in the cases of challenging poses and clothing types. | ['Zerong Zheng', 'Yebin Liu', 'Tao Yu', 'Qionghai Dai'] | 2020-07-08 | null | null | null | null | ['3d-human-reconstruction'] | ['computer-vision'] | [ 7.71193802e-02 -5.38809448e-02 -8.43464304e-03 -3.67855340e-01
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d77defaf-6210-43ca-9a43-dd5a77a5d9fd | a-bert-based-universal-model-for-both-within | null | null | https://aclanthology.org/W19-1908 | https://aclanthology.org/W19-1908.pdf | A BERT-based Universal Model for Both Within- and Cross-sentence Clinical Temporal Relation Extraction | Classic methods for clinical temporal relation extraction focus on relational candidates within a sentence. On the other hand, break-through Bidirectional Encoder Representations from Transformers (BERT) are trained on large quantities of arbitrary spans of contiguous text instead of sentences. In this study, we aim to build a sentence-agnostic framework for the task of CONTAINS temporal relation extraction. We establish a new state-of-the-art result for the task, 0.684F for in-domain (0.055-point improvement) and 0.565F for cross-domain (0.018-point improvement), by fine-tuning BERT and pre-training domain-specific BERT models on sentence-agnostic temporal relation instances with WordPiece-compatible encodings, and augmenting the labeled data with automatically generated {``}silver{''} instances. | ['Guergana Savova', 'Dmitriy Dligach', 'Chen Lin', 'Timothy Miller', 'Steven Bethard'] | 2019-06-01 | null | null | null | ws-2019-6 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 3.56538355e-01 6.36538625e-01 -6.26941442e-01 -5.27611613e-01
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166efdc2-86c7-4e69-80b0-5933a093a4d8 | multi-dimensional-nuanced-and-subjective | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Bryant_Multi-Dimensional_Nuanced_and_Subjective_-_Measuring_the_Perception_of_Facial_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Bryant_Multi-Dimensional_Nuanced_and_Subjective_-_Measuring_the_Perception_of_Facial_CVPR_2022_paper.pdf | Multi-Dimensional, Nuanced and Subjective - Measuring the Perception of Facial Expressions | Humans can perceive multiple expressions, each one with varying intensity, in the picture of a face. We propose a methodology for collecting and modeling multidimensional modulated expression annotations from human annotators. Our data reveals that the perception of some expressions can be quite different across observers; thus, our model is designed to represent ambiguity alongside intensity. An empirical exploration of how many dimensions are necessary to capture the perception of facial expression suggests six principal expression dimensions are sufficient. Using our method, we collected multidimensional modulated expression annotations for 1,000 images culled from the popular ExpW in-the-wild dataset. As a proof of principle of our improved measurement technique, we used these annotations to benchmark four public domain algorithms for automated facial expression prediction. | ['Pietro Perona', 'Wei Xia', 'Nashlie Sephus', 'Siqi Deng', "De'Aira Bryant"] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['facial-expression-recognition'] | ['computer-vision'] | [ 2.26291344e-01 1.99708909e-01 -1.82310924e-01 -9.81970489e-01
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f2c92006-10ed-490c-a6d2-39ebb0c30040 | the-multital-nlp-tool-infrastructure | null | null | https://aclanthology.org/W16-4021 | https://aclanthology.org/W16-4021.pdf | The MultiTal NLP tool infrastructure | This paper gives an overview of the MultiTal project, which aims to create a research infrastructure that ensures long-term distribution of NLP tools descriptions. The goal is to make NLP tools more accessible and usable to end-users of different disciplines. The infrastructure is built on a meta-data scheme modelling and standardising multilingual NLP tools documentation. The model is conceptualised using an OWL ontology. The formal representation of the ontology allows us to automatically generate organised and structured documentation in different languages for each represented tool. | ['Satenik Mkhitaryan', 'Driss Sadoun', 'Damien Nouvel', 'Mathieu Valette'] | 2016-12-01 | null | null | null | ws-2016-12 | ['multilingual-nlp'] | ['natural-language-processing'] | [-2.52872229e-01 6.75600290e-01 -4.28229064e-01 -3.99513990e-01
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61284fa7-d69a-4876-b49f-b2762bcb57af | analysis-and-classification-of-heart-diseases | null | null | https://doi.org/10.1186/s40537-019-0244-x | https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-019-0244-x | Analysis and classification of heart diseases using heartbeat features and machine learning algorithms | This study proposed an ECG (Electrocardiogram) classification approach using machine learning based on several ECG features. An electrocardiogram (ECG) is a signal that measures the electric activity of the heart. The proposed approach is implemented using ML-libs and Scala language on Apache Spark framework; MLlib is Apache Spark’s scalable machine learning library. The key challenge in ECG classification is to handle the irregularities in the ECG signals which is very important to detect the patient status. Therefore, we have proposed an efficient approach to classify ECG signals with high accuracy Each heartbeat is a combination of action impulse waveforms produced by different specialized cardiac heart tissues. Heartbeats classification faces some difficulties because these waveforms differ from person to another, they are described by some features. These features are the inputs of machine learning algorithm. In general, using Spark–Scala tools simplifies the usage of many algorithms such as machine-learning (ML) algorithms. On other hand, Spark–Scala is preferred to be used more than other tools when size of processing data is too large. In our case, we have used a dataset with 205,146 records to evaluate the performance of our approach. Machine learning libraries in Spark–Scala provide easy ways to implement many classification algorithms (Decision Tree, Random Forests, Gradient-Boosted Trees (GDB), etc.). The proposed method is evaluated and validated on baseline MIT-BIH Arrhythmia and MIT-BIH Supraventricular Arrhythmia database. The results show that our approach achieved an overall accuracy of 96.75% using GDB Tree algorithm and 97.98% using random Forest for binary classification. For multi class classification, it achieved to 98.03% accuracy using Random Forest, Gradient Boosting tree supports only binary classification. | ['Fajr Ibrahem Alarsan', 'Mamoon Younes'] | 2019-08-31 | null | null | null | journal-of-big-data-2019-2019-8 | ['ecg-classification', 'heartbeat-classification', 'electrocardiography-ecg'] | ['medical', 'medical', 'methodology'] | [-2.11466774e-01 -4.79699701e-01 3.09242994e-01 -4.02686656e-01
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23cd23c1-e4a5-4cd0-9f14-fbee81d24e56 | from-cities-to-series-complex-networks-and | 2206.01176 | null | https://arxiv.org/abs/2206.01176v1 | https://arxiv.org/pdf/2206.01176v1.pdf | From Cities to Series: Complex Networks and Deep Learning for Improved Spatial and Temporal Analytics* | Graphs have often been used to answer questions about the interaction between real-world entities by taking advantage of their capacity to represent complex topologies. Complex networks are known to be graphs that capture such non-trivial topologies; they are able to represent human phenomena such as epidemic processes, the dynamics of populations, and the urbanization of cities. The investigation of complex networks has been extrapolated to many fields of science, with particular emphasis on computing techniques, including artificial intelligence. In such a case, the analysis of the interaction between entities of interest is transposed to the internal learning of algorithms, a paradigm whose investigation is able to expand the state of the art in Computer Science. By exploring this paradigm, this thesis puts together complex networks and machine learning techniques to improve the understanding of the human phenomena observed in pandemics, pendular migration, and street networks. Accordingly, we contribute with: (i) a new neural network architecture capable of modeling dynamic processes observed in spatial and temporal data with applications in epidemics propagation, weather forecasting, and patient monitoring in intensive care units; (ii) a machine-learning methodology for analyzing and predicting links in the scope of human mobility between all the cities of Brazil; and, (iii) techniques for identifying inconsistencies in the urban planning of cities while tracking the most influential vertices, with applications over Brazilian and worldwide cities. We obtained results sustained by sound evidence of advances to the state of the art in artificial intelligence, rigorous formalisms, and ample experimentation. Our findings rely upon real-world applications in a range of domains, demonstrating the applicability of our methodologies. | ['Jose F. Rodrigues-Jr', 'Gabriel Spadon'] | 2022-06-01 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [ 8.50984827e-03 3.53267223e-01 -1.16636448e-01 2.66431451e-01
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dd6ca596-4984-4b6c-9bcf-2fe823f4f51b | iiit-at-semeval-2016-task-11-complex-word | null | null | https://aclanthology.org/S16-1158 | https://aclanthology.org/S16-1158.pdf | IIIT at SemEval-2016 Task 11: Complex Word Identification using Nearest Centroid Classification | null | ['Radhika Mamidi', 'Ashish Palakurthi'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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66fdbc20-a872-440a-831a-16fb1a9e9b42 | vpe-variational-policy-embedding-for-transfer | 1809.03548 | null | http://arxiv.org/abs/1809.03548v2 | http://arxiv.org/pdf/1809.03548v2.pdf | VPE: Variational Policy Embedding for Transfer Reinforcement Learning | Reinforcement Learning methods are capable of solving complex problems, but
resulting policies might perform poorly in environments that are even slightly
different. In robotics especially, training and deployment conditions often
vary and data collection is expensive, making retraining undesirable.
Simulation training allows for feasible training times, but on the other hand
suffers from a reality-gap when applied in real-world settings. This raises the
need of efficient adaptation of policies acting in new environments. We
consider this as a problem of transferring knowledge within a family of similar
Markov decision processes.
For this purpose we assume that Q-functions are generated by some
low-dimensional latent variable. Given such a Q-function, we can find a master
policy that can adapt given different values of this latent variable. Our
method learns both the generative mapping and an approximate posterior of the
latent variables, enabling identification of policies for new tasks by
searching only in the latent space, rather than the space of all policies. The
low-dimensional space, and master policy found by our method enables policies
to quickly adapt to new environments. We demonstrate the method on both a
pendulum swing-up task in simulation, and for simulation-to-real transfer on a
pushing task. | ['Johannes A. Stork', 'Isac Arnekvist', 'Danica Kragic'] | 2018-09-10 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [ 1.00441992e-01 1.74515613e-03 -1.60749868e-01 -3.46986651e-02
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92e13da0-0768-4e80-8505-02e3e96370f9 | evaluating-text-segmentation-using-boundary | null | null | https://aclanthology.org/P13-1167 | https://aclanthology.org/P13-1167.pdf | Evaluating Text Segmentation using Boundary Edit Distance | null | ['Chris Fournier'] | 2013-08-01 | null | null | null | acl-2013-8 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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49f348fe-43ee-415b-ad2b-988f8acdd37e | white-box-testing-of-nlp-models-with-mask | 2205.05050 | null | https://arxiv.org/abs/2205.05050v1 | https://arxiv.org/pdf/2205.05050v1.pdf | White-box Testing of NLP models with Mask Neuron Coverage | Recent literature has seen growing interest in using black-box strategies like CheckList for testing the behavior of NLP models. Research on white-box testing has developed a number of methods for evaluating how thoroughly the internal behavior of deep models is tested, but they are not applicable to NLP models. We propose a set of white-box testing methods that are customized for transformer-based NLP models. These include Mask Neuron Coverage (MNCOVER) that measures how thoroughly the attention layers in models are exercised during testing. We show that MNCOVER can refine testing suites generated by CheckList by substantially reduce them in size, for more than 60\% on average, while retaining failing tests -- thereby concentrating the fault detection power of the test suite. Further we show how MNCOVER can be used to guide CheckList input generation, evaluate alternative NLP testing methods, and drive data augmentation to improve accuracy. | ['Yanjun Qi', 'Matthew B. Dwyer', 'Yangfeng Ji', 'Arshdeep Sekhon'] | 2022-05-10 | null | https://aclanthology.org/2022.findings-naacl.116 | https://aclanthology.org/2022.findings-naacl.116.pdf | findings-naacl-2022-7 | ['fault-detection'] | ['miscellaneous'] | [ 3.37874919e-01 4.95927155e-01 -2.60338902e-01 -4.22121435e-01
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6e7ded37-288b-481c-b19b-d1811f9413da | sampling-free-inference-for-ab-initio | 2205.14962 | null | https://arxiv.org/abs/2205.14962v3 | https://arxiv.org/pdf/2205.14962v3.pdf | Sampling-free Inference for Ab-Initio Potential Energy Surface Networks | Recently, it has been shown that neural networks not only approximate the ground-state wave functions of a single molecular system well but can also generalize to multiple geometries. While such generalization significantly speeds up training, each energy evaluation still requires Monte Carlo integration which limits the evaluation to a few geometries. In this work, we address the inference shortcomings by proposing the Potential learning from ab-initio Networks (PlaNet) framework, in which we simultaneously train a surrogate model in addition to the neural wave function. At inference time, the surrogate avoids expensive Monte-Carlo integration by directly estimating the energy, accelerating the process from hours to milliseconds. In this way, we can accurately model high-resolution multi-dimensional energy surfaces for larger systems that previously were unobtainable via neural wave functions. Finally, we explore an additional inductive bias by introducing physically-motivated restricted neural wave function models. We implement such a function with several additional improvements in the new PESNet++ model. In our experimental evaluation, PlaNet accelerates inference by 7 orders of magnitude for larger molecules like ethanol while preserving accuracy. Compared to previous energy surface networks, PESNet++ reduces energy errors by up to 74%. | ['Stephan Günnemann', 'Nicholas Gao'] | 2022-05-30 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [ 1.55865744e-01 6.54970035e-02 -2.17608824e-01 -4.82072711e-01
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0b07afcc-7636-43bc-9344-457fdb2f1078 | diverse-and-consistent-multi-view-networks | null | null | https://openreview.net/forum?id=J9_7t9m8xRj | https://openreview.net/pdf?id=J9_7t9m8xRj | Diverse and Consistent Multi-view Networks for Semi-supervised Regression | Label collection is costly in many applications, which poses the need for label-efficient learning. In this work, we present Diverse and Consistent Multi-view Networks (DiCoM) — a novel semi-supervised regression technique based on a multi-view learning framework. DiCoM combines diversity with consistency — two seemingly opposing yet complementary principles of multi-view learning - based on underlying probabilistic graphical assumptions. Given multiple deep views of the same input, DiCoM encourages a negative correlation among the views' predictions on labeled data, while simultaneously enforces their agreement on unlabeled data. DiCoM can utilize either multi-network or multi-branch architectures to make a trade-off between computational cost and modeling performance. Under realistic evaluation setups, DiCoM outperforms competing methods on tabular and image data. Our ablation studies confirm the importance of having both consistency and diversity. | ['Chuan-Sheng Foo', 'Kangkang Lu', 'Balagopal Unnikrishnan', 'Xun Xu', 'Arun Raja', 'Le Zhang', 'Cuong Manh Nguyen'] | 2021-09-29 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-1.47912160e-01 1.56826258e-01 -7.59264112e-01 -8.88273537e-01
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cb74fa46-62f5-4554-9344-bf9800dbbf3c | relation-aware-compositional-zero-shot | 2108.04603 | null | https://arxiv.org/abs/2108.04603v1 | https://arxiv.org/pdf/2108.04603v1.pdf | Relation-aware Compositional Zero-shot Learning for Attribute-Object Pair Recognition | This paper proposes a novel model for recognizing images with composite attribute-object concepts, notably for composite concepts that are unseen during model training. We aim to explore the three key properties required by the task --- relation-aware, consistent, and decoupled --- to learn rich and robust features for primitive concepts that compose attribute-object pairs. To this end, we propose the Blocked Message Passing Network (BMP-Net). The model consists of two modules. The concept module generates semantically meaningful features for primitive concepts, whereas the visual module extracts visual features for attributes and objects from input images. A message passing mechanism is used in the concept module to capture the relations between primitive concepts. Furthermore, to prevent the model from being biased towards seen composite concepts and reduce the entanglement between attributes and objects, we propose a blocking mechanism that equalizes the information available to the model for both seen and unseen concepts. Extensive experiments and ablation studies on two benchmarks show the efficacy of the proposed model. | ['Mohan Kankanhalli', 'Yongkang Wong', 'Guangzhi Wang', 'Ziwei Xu'] | 2021-08-10 | null | null | null | null | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 3.98781687e-01 3.36450130e-01 -4.42086440e-03 -6.46169603e-01
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85fcaf3b-0541-4a18-8c8e-cc0689e86b41 | embedding-decomposition-for-artifacts-removal | 2112.00989 | null | https://arxiv.org/abs/2112.00989v2 | https://arxiv.org/pdf/2112.00989v2.pdf | Embedding Decomposition for Artifacts Removal in EEG Signals | Electroencephalogram (EEG) recordings are often contaminated with artifacts. Various methods have been developed to eliminate or weaken the influence of artifacts. However, most of them rely on prior experience for analysis. Here, we propose an deep learning framework to separate neural signal and artifacts in the embedding space and reconstruct the denoised signal, which is called DeepSeparator. DeepSeparator employs an encoder to extract and amplify the features in the raw EEG, a module called decomposer to extract the trend, detect and suppress artifact and a decoder to reconstruct the denoised signal. Besides, DeepSeparator can extract the artifact, which largely increases the model interpretability. The proposed method is tested with a semi-synthetic EEG dataset and a real task-related EEG dataset, suggesting that DeepSeparator outperforms the conventional models in both EOG and EMG artifact removal. DeepSeparator can be extended to multi-channel EEG and data of any length. It may motivate future developments and application of deep learning-based EEG denoising. The code for DeepSeparator is available at https://github.com/ncclabsustech/DeepSeparator. | ['Quanying Liu', 'Chen Wei', 'Kexin Lou', 'Chenyi Li', 'Junjie Yu'] | 2021-12-02 | null | null | null | null | ['eeg-denoising'] | ['methodology'] | [ 2.14842800e-02 -3.18363100e-01 6.13327086e-01 -4.00854737e-01
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528230c5-ba39-43df-ab7d-1765cfcf6d6b | 2d-image-relighting-with-image-to-image | 2006.07816 | null | https://arxiv.org/abs/2006.07816v2 | https://arxiv.org/pdf/2006.07816v2.pdf | 2D Image Relighting with Image-to-Image Translation | With the advent of Generative Adversarial Networks (GANs), a finer level of control in manipulating various features of an image has become possible. One example of such fine manipulation is changing the position of the light source in a scene. This is fundamentally an ill-posed problem, since it requires understanding the scene geometry to generate proper lighting effects. This problem is not a trivial one and can become even more complicated if we want to change the direction of the light source from any direction to a specific one. Here we provide our attempt to solve this problem using GANs. Specifically, pix2pix [arXiv:1611.07004] trained with the dataset VIDIT [arXiv:2005.05460] which contains images of the same scene with different types of light temperature and 8 different light source positions (N, NE, E, SE, S, SW, W, NW). The results are 8 neural networks trained to be able to change the direction of the light source from any direction to one of the 8 previously mentioned. Additionally, we provide, as a tool, a simple CNN trained to identify the direction of the light source in an image. | ['Paul Gafton', 'Erick Maraz'] | 2020-06-14 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 3.64278376e-01 -2.00605690e-01 5.31171679e-01 -1.98847011e-01
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3fef50d8-974e-46e8-940f-b4d9d34c5a98 | neuro-symbolic-zero-shot-code-cloning-with | 2304.13350 | null | https://arxiv.org/abs/2304.13350v1 | https://arxiv.org/pdf/2304.13350v1.pdf | Neuro-symbolic Zero-Shot Code Cloning with Cross-Language Intermediate Representation | In this paper, we define a neuro-symbolic approach to address the task of finding semantically similar clones for the codes of the legacy programming language COBOL, without training data. We define a meta-model that is instantiated to have an Intermediate Representation (IR) in the form of Abstract Syntax Trees (ASTs) common across codes in C and COBOL. We linearize the IRs using Structure Based Traversal (SBT) to create sequential inputs. We further fine-tune UnixCoder, the best-performing model for zero-shot cross-programming language code search, for the Code Cloning task with the SBT IRs of C code-pairs, available in the CodeNet dataset. This allows us to learn latent representations for the IRs of the C codes, which are transferable to the IRs of the COBOL codes. With this fine-tuned UnixCoder, we get a performance improvement of 12.85 MAP@2 over the pre-trained UniXCoder model, in a zero-shot setting, on the COBOL test split synthesized from the CodeNet dataset. This demonstrates the efficacy of our meta-model based approach to facilitate cross-programming language transfer. | ['Ravindra Naik', 'Lovekesh Vig', 'Raveendra Kumar Medicherla', 'Manasi Patwardhan', 'Shrishti Pradhan', 'Krishnam Hasija'] | 2023-04-26 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [ 6.03643712e-03 -8.02698359e-02 -5.08799076e-01 -2.65714705e-01
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7133c50e-f502-45d0-8353-6a270160e40e | shape-and-spatially-varying-reflectance | 1512.05278 | null | http://arxiv.org/abs/1512.05278v3 | http://arxiv.org/pdf/1512.05278v3.pdf | Shape and Spatially-Varying Reflectance Estimation From Virtual Exemplars | This paper addresses the problem of estimating the shape of objects that
exhibit spatially-varying reflectance. We assume that multiple images of the
object are obtained under a fixed view-point and varying illumination, i.e.,
the setting of photometric stereo. At the core of our techniques is the
assumption that the BRDF at each pixel lies in the non-negative span of a known
BRDF dictionary.This assumption enables a per-pixel surface normal and BRDF
estimation framework that is computationally tractable and requires no
initialization in spite of the underlying problem being non-convex. Our
estimation framework first solves for the surface normal at each pixel using a
variant of example-based photometric stereo. We design an efficient multi-scale
search strategy for estimating the surface normal and subsequently, refine this
estimate using a gradient descent procedure. Given the surface normal estimate,
we solve for the spatially-varying BRDF by constraining the BRDF at each pixel
to be in the span of the BRDF dictionary, here, we use additional priors to
further regularize the solution. A hallmark of our approach is that it does not
require iterative optimization techniques nor the need for careful
initialization, both of which are endemic to most state-of-the-art techniques.
We showcase the performance of our technique on a wide range of simulated and
real scenes where we outperform competing methods. | ['Aswin C. Sankaranarayanan', 'Zhuo Hui'] | 2015-12-16 | null | null | null | null | ['brdf-estimation'] | ['computer-vision'] | [ 5.93820512e-01 -8.87791812e-02 3.97866994e-01 -2.85735011e-01
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5141a9fe-3195-4b86-925f-2a668bd297b3 | unsupervised-partial-sentence-matching-for | null | null | https://aclanthology.org/2022.sdp-1.11 | https://aclanthology.org/2022.sdp-1.11.pdf | Unsupervised Partial Sentence Matching for Cited Text Identification | Given a citation in the body of a research paper, cited text identification aims to find the sentences in the cited paper that are most relevant to the citing sentence. The task is fundamentally one of sentence matching, where affinity is often assessed by a cosine similarity between sentence embeddings. However, (a) sentences may not be well-represented by a single embedding because they contain multiple distinct semantic aspects, and (b) good matches may not require a strong match in all aspects. To overcome these limitations, we propose a simple and efficient unsupervised method for cited text identification that adapts an asymmetric similarity measure to allow partial matches of multiple aspects in both sentences. On the CL-SciSumm dataset we find that our method outperforms a baseline symmetric approach, and, surprisingly, also outperforms all supervised and unsupervised systems submitted to past editions of CL-SciSumm Shared Task 1a. | ['Andrew McCallum', 'Purujit Goyal', 'Haw-Shiuan Chang', 'Kathryn Ricci'] | null | null | null | null | sdp-coling-2022-10 | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 2.06841305e-01 5.59054315e-03 -3.90723765e-01 -4.54526767e-02
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dfe8cef9-dc91-4190-8ffc-258529722815 | learning-knowledge-rich-sequential-model-for | 2304.02715 | null | https://arxiv.org/abs/2304.02715v1 | https://arxiv.org/pdf/2304.02715v1.pdf | Learning Knowledge-Rich Sequential Model for Planar Homography Estimation in Aerial Video | This paper presents an unsupervised approach that leverages raw aerial videos to learn to estimate planar homographic transformation between consecutive video frames. Previous learning-based estimators work on pairs of images to estimate their planar homographic transformations but suffer from severe over-fitting issues, especially when applying over aerial videos. To address this concern, we develop a sequential estimator that directly processes a sequence of video frames and estimates their pairwise planar homographic transformations in batches. We also incorporate a set of spatial-temporal knowledge to regularize the learning of such a sequence-to-sequence model. We collect a set of challenging aerial videos and compare the proposed method to the alternative algorithms. Empirical studies suggest that our sequential model achieves significant improvement over alternative image-based methods and the knowledge-rich regularization further boosts our system performance. Our codes and dataset could be found at https://github.com/Paul-LiPu/DeepVideoHomography | ['Xiaobai Liu', 'Pu Li'] | 2023-04-05 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 2.71358073e-01 -2.26156101e-01 -1.74400687e-01 -4.35804188e-01
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d46773b9-bd1c-4abe-b1b9-4dc418693d14 | fairer-fairness-as-decision-rationale | 2306.15299 | null | https://arxiv.org/abs/2306.15299v1 | https://arxiv.org/pdf/2306.15299v1.pdf | FAIRER: Fairness as Decision Rationale Alignment | Deep neural networks (DNNs) have made significant progress, but often suffer from fairness issues, as deep models typically show distinct accuracy differences among certain subgroups (e.g., males and females). Existing research addresses this critical issue by employing fairness-aware loss functions to constrain the last-layer outputs and directly regularize DNNs. Although the fairness of DNNs is improved, it is unclear how the trained network makes a fair prediction, which limits future fairness improvements. In this paper, we investigate fairness from the perspective of decision rationale and define the parameter parity score to characterize the fair decision process of networks by analyzing neuron influence in various subgroups. Extensive empirical studies show that the unfair issue could arise from the unaligned decision rationales of subgroups. Existing fairness regularization terms fail to achieve decision rationale alignment because they only constrain last-layer outputs while ignoring intermediate neuron alignment. To address the issue, we formulate the fairness as a new task, i.e., decision rationale alignment that requires DNNs' neurons to have consistent responses on subgroups at both intermediate processes and the final prediction. To make this idea practical during optimization, we relax the naive objective function and propose gradient-guided parity alignment, which encourages gradient-weighted consistency of neurons across subgroups. Extensive experiments on a variety of datasets show that our method can significantly enhance fairness while sustaining a high level of accuracy and outperforming other approaches by a wide margin. | ['Yang Liu', 'Zhiming Li', 'Mengnan Du', 'Aishan Liu', 'Qing Guo', 'Tianlin Li'] | 2023-06-27 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [-5.41114584e-02 1.38308465e-01 -5.07634819e-01 -1.04986072e+00
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cd32dd78-56aa-4be7-a936-c25c75943620 | an-empirical-approach-for-modeling-fuzzy | 1703.10429 | null | http://arxiv.org/abs/1703.10429v1 | http://arxiv.org/pdf/1703.10429v1.pdf | An Empirical Approach for Modeling Fuzzy Geographical Descriptors | We present a novel heuristic approach that defines fuzzy geographical
descriptors using data gathered from a survey with human subjects. The
participants were asked to provide graphical interpretations of the descriptors
`north' and `south' for the Galician region (Spain). Based on these
interpretations, our approach builds fuzzy descriptors that are able to compute
membership degrees for geographical locations. We evaluated our approach in
terms of efficiency and precision. The fuzzy descriptors are meant to be used
as the cornerstones of a geographical referring expression generation algorithm
that is able to linguistically characterize geographical locations and regions.
This work is also part of a general research effort that intends to establish a
methodology which reunites the empirical studies traditionally practiced in
data-to-text and the use of fuzzy sets to model imprecision and vagueness in
words and expressions for text generation purposes. | ['Kees Van Deemter', 'Alejandro Ramos-Soto', 'Jose M. Alonso', 'Ehud Reiter', 'Albert Gatt'] | 2017-03-30 | null | null | null | null | ['referring-expression-generation'] | ['computer-vision'] | [-1.08413264e-01 3.22590411e-01 -6.14146106e-02 -7.44780123e-01
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761abf5e-dae1-40b6-a9f2-2b97f44b844f | masked-spatial-spectral-autoencoders-are | 2207.07803 | null | https://arxiv.org/abs/2207.07803v1 | https://arxiv.org/pdf/2207.07803v1.pdf | Masked Spatial-Spectral Autoencoders Are Excellent Hyperspectral Defenders | Deep learning methodology contributes a lot to the development of hyperspectral image (HSI) analysis community. However, it also makes HSI analysis systems vulnerable to adversarial attacks. To this end, we propose a masked spatial-spectral autoencoder (MSSA) in this paper under self-supervised learning theory, for enhancing the robustness of HSI analysis systems. First, a masked sequence attention learning module is conducted to promote the inherent robustness of HSI analysis systems along spectral channel. Then, we develop a graph convolutional network with learnable graph structure to establish global pixel-wise combinations.In this way, the attack effect would be dispersed by all the related pixels among each combination, and a better defense performance is achievable in spatial aspect.Finally, to improve the defense transferability and address the problem of limited labelled samples, MSSA employs spectra reconstruction as a pretext task and fits the datasets in a self-supervised manner.Comprehensive experiments over three benchmarks verify the effectiveness of MSSA in comparison with the state-of-the-art hyperspectral classification methods and representative adversarial defense strategies. | ['Ping Zhong', 'Yu Zhang', 'Wei Xue', 'YongQian Li', 'Chen Chen', 'Kangcheng Bin', 'Xingyue Liu', 'Zhiqiang Gong', 'Jiahao Qi'] | 2022-07-16 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 6.67608559e-01 -1.60882935e-01 2.71135509e-01 -6.14827052e-02
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dab44789-3f64-454e-ad2a-88ffe498f303 | exploring-stylegan-latent-space-for-face | null | null | https://hal.archives-ouvertes.fr/INRIA/hal-03778322v1 | https://hal.archives-ouvertes.fr/hal-03778322/document | Exploring StyleGAN Latent Space for Face Alignment with Limited Training Data | With deep learning models growing in size over the years, sometimes exceeding a billion parameters now, the need for large, annotated training datasets grows too. To alleviate this problem, the interest in self-supervised learning is also increasing. In this domain, with the rise of Generative Adversarial Networks (GANs) and particularly StyleGAN, the quality of image generation is significantly improving. In this paper, we propose to use StyleGAN to perform face alignment with limited training data instead of image generation. Our proposed framework Face Alignment using StyleGAN Embeddings (FASE) projects real images into StyleGAN latent space and then predicts facial landmarks from the latent vectors. Our method achieves state-of-the-art on multiple face alignment datasets in the few-shot setting. | ['Bertrand Coüasnon', 'Yann Ricquebourg', 'Christian Raymond', 'Philippe-Henri Gosselin', 'Martin Dornier'] | 2022-09-16 | null | null | null | hal-2022-9 | ['face-alignment'] | ['computer-vision'] | [ 2.73200452e-01 4.10744309e-01 3.83010954e-02 -4.89530176e-01
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99e21f1c-89fd-4f63-824f-c4b1efdd2b0f | modelling-neuronal-behaviour-with-time-series-1 | null | null | https://openreview.net/forum?id=k-sNDIPY-1T | https://openreview.net/pdf?id=k-sNDIPY-1T | Modelling neuronal behaviour with time series regression: Recurrent Neural Networks on synthetic C. elegans data | Given the inner complexity of the human nervous system, insight into the dynamics of brain activity can be gained from understanding smaller and simpler organisms, such as the nematode C. elegans. The behavioural and structural biology of these organisms is well-known, making them prime candidates for benchmarking modelling and simulation techniques. In these complex neuronal collections, classical white-box modelling techniques based on intrinsic structural or behavioural information are either unable to capture the profound nonlinearities of the neuronal response to different stimuli or generate extremely complex models, which are computationally intractable. In this paper we investigate whether it is possible to generate lower complexity black-box models that can capture the system dynamics with low error using only measured or simulated input-output information. We show how the nervous system of C. elegans can be modelled and simulated with data-driven models using different neural network architectures. Specifically, we target the use of state of the art recurrent neural networks architectures such as LSTMs and GRUs and compare these architectures in terms of their properties and their RMSE, as well as the complexity of the resulting models.
We show that GRU models with a hidden layer size of 4 units are able to accurately reproduce the system's response to very different stimuli. | ['L. Miguel Silveira', 'Arlindo L. Oliveira', 'Ruxandra Barbulescu', 'Gonçalo Leote Cardoso Mestre'] | 2021-09-29 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 3.83986056e-01 -1.77528039e-01 5.86408734e-01 7.15965114e-04
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448c9777-5307-4a34-8b36-6fd2aca846c3 | nio-lightweight-neural-operator-based | 2211.10791 | null | https://arxiv.org/abs/2211.10791v2 | https://arxiv.org/pdf/2211.10791v2.pdf | AdaFNIO: Adaptive Fourier Neural Interpolation Operator for video frame interpolation | We present, AdaFNIO - Adaptive Fourier Neural Interpolation Operator, a neural operator-based architecture to perform video frame interpolation. Current deep learning based methods rely on local convolutions for feature learning and suffer from not being scale-invariant, thus requiring training data to be augmented through random flipping and re-scaling. On the other hand, AdaFNIO, learns the features in the frames, independent of input resolution, through token mixing and global convolution in the Fourier space or the spectral domain by using Fast Fourier Transform (FFT). We show that AdaFNIO can produce visually smooth and accurate results. To evaluate the visual quality of our interpolated frames, we calculate the structural similarity index (SSIM) and Peak Signal to Noise Ratio (PSNR) between the generated frame and the ground truth frame. We provide the quantitative performance of our model on Vimeo-90K dataset, DAVIS, UCF101 and DISFA+ dataset. | ['Aniket Bera', 'Rashmi Bhaskara', 'Md Ashiqur Rahman', 'Hrishikesh Viswanath'] | 2022-11-19 | null | null | null | null | ['video-frame-interpolation'] | ['computer-vision'] | [ 1.80098981e-01 -4.79445964e-01 1.08742341e-01 -4.68711615e-01
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53972540-5fc9-46a5-8784-c393a2e00dcf | 3d-point-capsule-networks | 1812.10775 | null | https://arxiv.org/abs/1812.10775v2 | https://arxiv.org/pdf/1812.10775v2.pdf | 3D Point Capsule Networks | In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data. 3D capsule networks arise as a direct consequence of our novel unified 3D auto-encoder formulation. Their dynamic routing scheme and the peculiar 2D latent space deployed by our approach bring in improvements for several common point cloud-related tasks, such as object classification, object reconstruction and part segmentation as substantiated by our extensive evaluations. Moreover, it enables new applications such as part interpolation and replacement. | ['Federico Tombari', 'Tolga Birdal', 'Haowen Deng', 'Yongheng Zhao'] | 2018-12-27 | 3d-point-capsule-networks-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhao_3D_Point_Capsule_Networks_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_3D_Point_Capsule_Networks_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-feature-matching', '3d-object-classification', '3d-object-reconstruction', '3d-shape-generation', '3d-point-cloud-matching', '3d-shape-representation', '3d-geometry-perception', '3d-part-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-1.98684886e-01 3.79625261e-01 -1.87422812e-01 -9.34320390e-02
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ebbb5e45-6275-41a4-b193-b80d44209c2b | dh-fbk-at-semeval-2022-task-4-leveraging | null | null | https://aclanthology.org/2022.semeval-1.42 | https://aclanthology.org/2022.semeval-1.42.pdf | DH-FBK at SemEval-2022 Task 4: Leveraging Annotators’ Disagreement and Multiple Data Views for Patronizing Language Detection | The subtle and typically unconscious use of patronizing and condescending language (PCL) in large-audience media outlets undesirably feeds stereotypes and strengthens power-knowledge relationships, perpetuating discrimination towards vulnerable communities. Due to its subjective and subtle nature, PCL detection is an open and challenging problem, both for computational methods and human annotators. In this paper we describe the systems submitted by the DH-FBK team to SemEval-2022 Task 4, aiming at detecting PCL towards vulnerable communities in English media texts. Motivated by the subjectivity of human interpretation, we propose to leverage annotators’ uncertainty and disagreement to better capture the shades of PCL in a multi-task, multi-view learning framework. Our approach achieves competitive results, largely outperforming baselines and ranking on the top-left side of the leaderboard on both PCL identification and classification. Noticeably, our approach does not rely on any external data or model ensemble, making it a viable and attractive solution for real-world use. | ['Elisa Leonardelli', 'Alan Ramponi'] | null | null | null | null | semeval-naacl-2022-7 | ['multi-view-learning', 'semeval-2022-task-4-1-binary-pcl-detection', 'semeval-2022-task-4-1-binary-pcl-detection', 'semeval-2022-task-4-2-multi-label-pcl', 'semeval-2022-task-4-1-binary-pcl-detection'] | ['computer-vision', 'miscellaneous', 'music', 'natural-language-processing', 'natural-language-processing'] | [ 2.61547510e-02 1.67224392e-01 -4.25672024e-01 -3.56378779e-02
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e2b62981-e49c-4c7d-bf5c-f67401ced05d | the-tembusu-treebank-an-english-learner | null | null | https://aclanthology.org/2022.lrec-1.515 | https://aclanthology.org/2022.lrec-1.515.pdf | The Tembusu Treebank: An English Learner Treebank | This paper reports on the creation and development of the Tembusu Learner Treebank — an open treebank created from the NTU Corpus of Learner English, unique for incorporating mal-rules in the annotation of ungrammatical sentences. It describes the motivation and development of the treebank, as well as its exploitation to build a new parse-ranking model for the English Resource Grammar, designed to help improve the parse selection of ungrammatical sentences and diagnose these sentences through mal-rules. The corpus contains 25,000 sentences, of which 4,900 are treebanked. The paper concludes with an evaluation experiment that shows the usefulness of this new treebank in the tasks of grammatical error detection and diagnosis. | ['Roger V. P. Winder', 'Francis Bond', 'Luís Morgado da Costa'] | null | null | null | null | lrec-2022-6 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-1.73860744e-01 8.34192038e-01 2.97758728e-01 -6.60578907e-01
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4eeb4bfa-a3b2-47c1-b173-d902da5769fc | instant-neural-radiance-fields-stylization | 2303.16884 | null | https://arxiv.org/abs/2303.16884v1 | https://arxiv.org/pdf/2303.16884v1.pdf | Instant Neural Radiance Fields Stylization | We present Instant Neural Radiance Fields Stylization, a novel approach for multi-view image stylization for the 3D scene. Our approach models a neural radiance field based on neural graphics primitives, which use a hash table-based position encoder for position embedding. We split the position encoder into two parts, the content and style sub-branches, and train the network for normal novel view image synthesis with the content and style targets. In the inference stage, we execute AdaIN to the output features of the position encoder, with content and style voxel grid features as reference. With the adjusted features, the stylization of novel view images could be obtained. Our method extends the style target from style images to image sets of scenes and does not require additional network training for stylization. Given a set of images of 3D scenes and a style target(a style image or another set of 3D scenes), our method can generate stylized novel views with a consistent appearance at various view angles in less than 10 minutes on modern GPU hardware. Extensive experimental results demonstrate the validity and superiority of our method. | ['Ye Pan', 'Shaoxu Li'] | 2023-03-29 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 3.84738654e-01 -9.84052289e-03 2.92095214e-01 -4.56513703e-01
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22c93f37-3271-445a-badd-198804ed3092 | enhancing-dialogue-generation-via-dynamic | 2306.16195 | null | https://arxiv.org/abs/2306.16195v1 | https://arxiv.org/pdf/2306.16195v1.pdf | Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation | Incorporating external graph knowledge into neural chatbot models has been proven effective for enhancing dialogue generation. However, in conventional graph neural networks (GNNs), message passing on a graph is independent from text, resulting in the graph representation hidden space differing from that of the text. This training regime of existing models therefore leads to a semantic gap between graph knowledge and text. In this study, we propose a novel framework for knowledge graph enhanced dialogue generation. We dynamically construct a multi-hop knowledge graph with pseudo nodes to involve the language model in feature aggregation within the graph at all steps. To avoid the semantic biases caused by learning on vanilla subgraphs, the proposed framework applies hierarchical graph attention to aggregate graph features on pseudo nodes and then attains a global feature. Therefore, the framework can better utilise the heterogeneous features from both the post and external graph knowledge. Extensive experiments demonstrate that our framework outperforms state-of-the-art (SOTA) baselines on dialogue generation. Further analysis also shows that our representation learning framework can fill the semantic gap by coagulating representations of both text and graph knowledge. Moreover, the language model also learns how to better select knowledge triples for a more informative response via exploiting subgraph patterns within our feature aggregation process. Our code and resources are available at https://github.com/tangg555/SaBART. | ['Frank Guerin', 'Chenghua Lin', 'Tyler Loakman', 'Hongbo Zhang', 'Chen Tang'] | 2023-06-28 | null | null | null | null | ['graph-attention', 'chatbot', 'dialogue-generation', 'chatbot', 'dialogue-generation'] | ['graphs', 'methodology', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 1.64603516e-01 1.08084261e+00 -2.35877246e-01 -8.56399685e-02
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d7baaf8a-0d14-46ad-a4b5-9e5567c26f2a | q-yolop-quantization-aware-you-only-look-once | 2307.04537 | null | https://arxiv.org/abs/2307.04537v1 | https://arxiv.org/pdf/2307.04537v1.pdf | Q-YOLOP: Quantization-aware You Only Look Once for Panoptic Driving Perception | In this work, we present an efficient and quantization-aware panoptic driving perception model (Q- YOLOP) for object detection, drivable area segmentation, and lane line segmentation, in the context of autonomous driving. Our model employs the Efficient Layer Aggregation Network (ELAN) as its backbone and task-specific heads for each task. We employ a four-stage training process that includes pretraining on the BDD100K dataset, finetuning on both the BDD100K and iVS datasets, and quantization-aware training (QAT) on BDD100K. During the training process, we use powerful data augmentation techniques, such as random perspective and mosaic, and train the model on a combination of the BDD100K and iVS datasets. Both strategies enhance the model's generalization capabilities. The proposed model achieves state-of-the-art performance with an mAP@0.5 of 0.622 for object detection and an mIoU of 0.612 for segmentation, while maintaining low computational and memory requirements. | ['Kai-Chiang Wu', 'Kuan-Cheng Lin', 'Yu-Chen Lu', 'Sheng-Feng Yu', 'Pei-Shuo Wang', 'Wei-Cheng Lin', 'Chi-Chih Chang'] | 2023-07-10 | null | null | null | null | ['object-detection', 'autonomous-driving', 'data-augmentation', 'quantization'] | ['computer-vision', 'computer-vision', 'methodology', 'methodology'] | [ 3.63639463e-03 6.20216914e-02 -1.95926785e-01 -6.85714602e-01
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6.82369173e-01 -7.61942565e-01 -7.92620033e-02 -1.71192825e-01] | [8.082719802856445, -1.4309128522872925] |
f577c175-a7dc-43f1-9576-adfbb0b8e5d2 | enhancing-unsupervised-generative-dependency | null | null | https://aclanthology.org/P19-1526 | https://aclanthology.org/P19-1526.pdf | Enhancing Unsupervised Generative Dependency Parser with Contextual Information | Most of the unsupervised dependency parsers are based on probabilistic generative models that learn the joint distribution of the given sentence and its parse. Probabilistic generative models usually explicit decompose the desired dependency tree into factorized grammar rules, which lack the global features of the entire sentence. In this paper, we propose a novel probabilistic model called discriminative neural dependency model with valence (D-NDMV) that generates a sentence and its parse from a continuous latent representation, which encodes global contextual information of the generated sentence. We propose two approaches to model the latent representation: the first deterministically summarizes the representation from the sentence and the second probabilistically models the representation conditioned on the sentence. Our approach can be regarded as a new type of autoencoder model to unsupervised dependency parsing that combines the benefits of both generative and discriminative techniques. In particular, our approach breaks the context-free independence assumption in previous generative approaches and therefore becomes more expressive. Our extensive experimental results on seventeen datasets from various sources show that our approach achieves competitive accuracy compared with both generative and discriminative state-of-the-art unsupervised dependency parsers. | ['Kewei Tu', 'Yong Jiang', 'Wenjuan Han'] | 2019-07-01 | null | null | null | acl-2019-7 | ['constituency-grammar-induction', 'dependency-grammar-induction', 'unsupervised-dependency-parsing'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.68163121e-01 4.06445235e-01 -6.16722219e-02 -8.54372621e-01
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c89d8a3f-ff9d-4047-95ab-eff4922806fc | generating-a-temporally-coherent-visual-story | null | null | https://openreview.net/forum?id=L99I9HrEtEm | https://openreview.net/pdf?id=L99I9HrEtEm | Generating a Temporally Coherent Visual Story by Multimodal Recurrent Transformers | Story visualization is a challenging text-to-image generation task for the difficulty of rendering visual details from abstract text descriptions. Besides the difficulty of image generation, the generator also needs to conform to the narrative of a multi-sentence story input. While prior arts in this domain have focused on improving semantic relevance between generated images and input text, controlling the generated images to be temporally consistent still remains a challenge. Moreover, existing generators are trained on single text-image pairs and fail to consider the variations of natural language captions that can describe a given image, causing poor model generalization. To address such problems, we leverage a cyclic training methodology involving pseudo-text descriptions as an intermediate step that decouples the image’s visual appearance from the variations of natural language descriptions. Additionally, to generate a semantically coherent image sequence, we consider an explicit memory controller which can augment the temporal coherence of images in the multi-modal autoregressive transformer. To sum up all components, we call it Cyclic Story visualization by MultimodAl Recurrent Transformers or C-SMART for short. Our method generates high-resolution, high-quality images, outperforming prior works by a significant margin across multiple evaluation metrics on the Pororo-SV dataset. | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['story-visualization'] | ['computer-vision'] | [ 5.38222790e-01 1.97204337e-01 2.20475987e-01 -2.38933519e-01
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41298e64-d503-42d5-a255-9a00eea163f5 | nu-wave-2-a-general-neural-audio-upsampling | 2206.08545 | null | https://arxiv.org/abs/2206.08545v2 | https://arxiv.org/pdf/2206.08545v2.pdf | NU-Wave 2: A General Neural Audio Upsampling Model for Various Sampling Rates | Conventionally, audio super-resolution models fixed the initial and the target sampling rates, which necessitate the model to be trained for each pair of sampling rates. We introduce NU-Wave 2, a diffusion model for neural audio upsampling that enables the generation of 48 kHz audio signals from inputs of various sampling rates with a single model. Based on the architecture of NU-Wave, NU-Wave 2 uses short-time Fourier convolution (STFC) to generate harmonics to resolve the main failure modes of NU-Wave, and incorporates bandwidth spectral feature transform (BSFT) to condition the bandwidths of inputs in the frequency domain. We experimentally demonstrate that NU-Wave 2 produces high-resolution audio regardless of the sampling rate of input while requiring fewer parameters than other models. The official code and the audio samples are available at https://mindslab-ai.github.io/nuwave2. | ['Junhyeok Lee', 'Seungu Han'] | 2022-06-17 | null | null | null | null | ['audio-super-resolution', 'audio-super-resolution'] | ['audio', 'music'] | [ 8.81135464e-02 -1.15981296e-01 -1.23172037e-01 2.76732929e-02
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80e42d96-e345-484e-ad9c-52ee3853e262 | t-rain-robust-generalization-under-weather | 2305.08302 | null | https://arxiv.org/abs/2305.08302v1 | https://arxiv.org/pdf/2305.08302v1.pdf | t-RAIN: Robust generalization under weather-aliasing label shift attacks | In the classical supervised learning settings, classifiers are fit with the assumption of balanced label distributions and produce remarkable results on the same. In the real world, however, these assumptions often bend and in turn adversely impact model performance. Identifying bad learners in skewed target distributions is even more challenging. Thus achieving model robustness under these "label shift" settings is an important task in autonomous perception. In this paper, we analyze the impact of label shift on the task of multi-weather classification for autonomous vehicles. We use this information as a prior to better assess pedestrian detection in adverse weather. We model the classification performance as an indicator of robustness under 4 label shift scenarios and study the behavior of multiple classes of models. We propose t-RAIN a similarity mapping technique for synthetic data augmentation using large scale generative models and evaluate the performance on DAWN dataset. This mapping boosts model test accuracy by 2.1, 4.4, 1.9, 2.7 % in no-shift, fog, snow, dust shifts respectively. We present state-of-the-art pedestrian detection results on real and synthetic weather domains with best performing 82.69 AP (snow) and 62.31 AP (fog) respectively. | ['Sanjana Prabhu', 'Aboli Marathe'] | 2023-05-15 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [ 1.66874930e-01 -2.92075306e-01 5.84046543e-01 -6.26338243e-01
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d940720f-6d7e-4356-8bf5-0b418384e23c | github-typo-corpus-a-large-scale-multilingual | 1911.12893 | null | https://arxiv.org/abs/1911.12893v1 | https://arxiv.org/pdf/1911.12893v1.pdf | GitHub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors | The lack of large-scale datasets has been a major hindrance to the development of NLP tasks such as spelling correction and grammatical error correction (GEC). As a complementary new resource for these tasks, we present the GitHub Typo Corpus, a large-scale, multilingual dataset of misspellings and grammatical errors along with their corrections harvested from GitHub, a large and popular platform for hosting and sharing git repositories. The dataset, which we have made publicly available, contains more than 350k edits and 65M characters in more than 15 languages, making it the largest dataset of misspellings to date. We also describe our process for filtering true typo edits based on learned classifiers on a small annotated subset, and demonstrate that typo edits can be identified with F1 ~ 0.9 using a very simple classifier with only three features. The detailed analyses of the dataset show that existing spelling correctors merely achieve an F-measure of approx. 0.5, suggesting that the dataset serves as a new, rich source of spelling errors that complement existing datasets. | ['Masato Mita', 'Masato Hagiwara'] | 2019-11-28 | github-typo-corpus-a-large-scale-multilingual-1 | https://aclanthology.org/2020.lrec-1.835 | https://aclanthology.org/2020.lrec-1.835.pdf | lrec-2020-5 | ['spelling-correction'] | ['natural-language-processing'] | [ 1.05748005e-01 3.39317024e-02 2.06139241e-03 -3.37422729e-01
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6e674cb8-3be2-46ad-a0cc-9c79d5a71c92 | ernie-enhanced-language-representation-with | 1905.07129 | null | https://arxiv.org/abs/1905.07129v3 | https://arxiv.org/pdf/1905.07129v3.pdf | ERNIE: Enhanced Language Representation with Informative Entities | Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks. However, the existing pre-trained language models rarely consider incorporating knowledge graphs (KGs), which can provide rich structured knowledge facts for better language understanding. We argue that informative entities in KGs can enhance language representation with external knowledge. In this paper, we utilize both large-scale textual corpora and KGs to train an enhanced language representation model (ERNIE), which can take full advantage of lexical, syntactic, and knowledge information simultaneously. The experimental results have demonstrated that ERNIE achieves significant improvements on various knowledge-driven tasks, and meanwhile is comparable with the state-of-the-art model BERT on other common NLP tasks. The source code of this paper can be obtained from https://github.com/thunlp/ERNIE. | ['Qun Liu', 'Zhengyan Zhang', 'Maosong Sun', 'Zhiyuan Liu', 'Xu Han', 'Xin Jiang'] | 2019-05-17 | ernie-enhanced-language-representation-with-1 | https://aclanthology.org/P19-1139 | https://aclanthology.org/P19-1139.pdf | acl-2019-7 | ['linguistic-acceptability'] | ['natural-language-processing'] | [-4.78977799e-01 1.75077558e-01 -8.07577908e-01 -2.15516970e-01
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c2ba281b-d649-4133-bf19-8b60f7d55c30 | exploring-the-limits-of-natural-language | 2112.07434 | null | https://arxiv.org/abs/2112.07434v1 | https://arxiv.org/pdf/2112.07434v1.pdf | Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent Detection | One of the core components of goal-oriented dialog systems is the task of Intent Detection. Few-shot Learning upon Intent Detection is challenging due to the scarcity of available annotated utterances. Although recent works making use of metric-based and optimization-based methods have been proposed, the task is still challenging in large label spaces and much smaller number of shots. Generalized Few-shot learning is more difficult due to the presence of both novel and seen classes during the testing phase. In this work, we propose a simple and effective method based on Natural Language Inference that not only tackles the problem of few shot intent detection, but also proves useful in zero-shot and generalized few shot learning problems. Our extensive experiments on a number of Natural Language Understanding (NLU) and Spoken Language Understanding (SLU) datasets show the effectiveness of our approach. In addition, we highlight the settings in which our NLI based method outperforms the baselines by huge margins. | ['Jithendra Veppa', 'Ayush Kumar', 'Vijit Malik'] | 2021-12-14 | null | null | null | null | ['generalized-few-shot-learning', 'goal-oriented-dialog'] | ['methodology', 'natural-language-processing'] | [ 1.70236066e-01 3.71606126e-02 -9.17859972e-02 -5.27217090e-01
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e2c500ac-e751-4c51-ae04-1266400410a9 | comparing-exploratory-graphical-analyses-and | 2210.13230 | null | https://arxiv.org/abs/2210.13230v3 | https://arxiv.org/pdf/2210.13230v3.pdf | An Experimental Study of Dimension Reduction Methods on Machine Learning Algorithms with Applications to Psychometrics | Developing interpretable machine learning models has become an increasingly important issue. One way in which data scientists have been able to develop interpretable models has been to use dimension reduction techniques. In this paper, we examine several dimension reduction techniques including two recent approaches developed in the network psychometrics literature called exploratory graph analysis (EGA) and unique variable analysis (UVA). We compared EGA and UVA with two other dimension reduction techniques common in the machine learning literature (principal component analysis and independent component analysis) as well as no reduction to the variables real data. We show that EGA and UVA perform as well as the other reduction techniques or no reduction. Consistent with previous literature, we show that dimension reduction can decrease, increase, or provide the same accuracy as no reduction of variables. Our tentative results find that dimension reduction tends to lead to better performance when used for classification tasks. | ['Alexander P. Christensen', 'Sean H. Merritt'] | 2022-10-19 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 6.47524446e-02 3.92961800e-01 -8.26018527e-02 -2.03825489e-01
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9cb4d11c-e138-40ef-ae00-ce87958af878 | over-the-air-federated-learning-in-satellite | 2306.02996 | null | https://arxiv.org/abs/2306.02996v1 | https://arxiv.org/pdf/2306.02996v1.pdf | Over-the-Air Federated Learning in Satellite systems | Federated learning in satellites offers several advantages. Firstly, it ensures data privacy and security, as sensitive data remains on the satellites and is not transmitted to a central location. This is particularly important when dealing with sensitive or classified information. Secondly, federated learning allows satellites to collectively learn from a diverse set of data sources, benefiting from the distributed knowledge across the satellite network. Lastly, the use of federated learning reduces the communication bandwidth requirements between satellites and the central server, as only model updates are exchanged instead of raw data. By leveraging federated learning, satellites can collaborate and continuously improve their machine learning models while preserving data privacy and minimizing communication overhead. This enables the development of more intelligent and efficient satellite systems for various applications, such as Earth observation, weather forecasting, and space exploration. | ['Mitra Hassani', 'Raphael Pinard', 'Edward Akito Carlos'] | 2023-06-05 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-3.67049515e-01 2.32800022e-02 -2.58429021e-01 -4.75224555e-01
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1f51e5d8-3608-453a-bf43-044a65ac1e17 | mitigating-the-accuracy-robustness-trade-off | 2306.16170 | null | https://arxiv.org/abs/2306.16170v2 | https://arxiv.org/pdf/2306.16170v2.pdf | Mitigating the Accuracy-Robustness Trade-off via Multi-Teacher Adversarial Distillation | Adversarial training is a practical approach for improving the robustness of deep neural networks against adversarial attacks. Although bringing reliable robustness, the performance toward clean examples is negatively affected after adversarial training, which means a trade-off exists between accuracy and robustness. Recently, some studies have tried to use knowledge distillation methods in adversarial training, achieving competitive performance in improving the robustness but the accuracy for clean samples is still limited. In this paper, to mitigate the accuracy-robustness trade-off, we introduce the Multi-Teacher Adversarial Robustness Distillation (MTARD) to guide the model's adversarial training process by applying a strong clean teacher and a strong robust teacher to handle the clean examples and adversarial examples, respectively. During the optimization process, to ensure that different teachers show similar knowledge scales, we design the Entropy-Based Balance algorithm to adjust the teacher's temperature and keep the teachers' information entropy consistent. Besides, to ensure that the student has a relatively consistent learning speed from multiple teachers, we propose the Normalization Loss Balance algorithm to adjust the learning weights of different types of knowledge. A series of experiments conducted on public datasets demonstrate that MTARD outperforms the state-of-the-art adversarial training and distillation methods against various adversarial attacks. | ['Xingxing Wei', 'Xizhe Wang', 'Shiji Zhao'] | 2023-06-28 | null | null | null | null | ['adversarial-robustness'] | ['adversarial'] | [-2.22019896e-01 -2.04445445e-03 2.39015426e-02 -2.37504706e-01
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640bdde3-ff7b-48bb-9682-73f4c08276f7 | multimodal-image-text-matching-improves | 2303.17579 | null | https://arxiv.org/abs/2303.17579v2 | https://arxiv.org/pdf/2303.17579v2.pdf | Multimodal Image-Text Matching Improves Retrieval-based Chest X-Ray Report Generation | Automated generation of clinically accurate radiology reports can improve patient care. Previous report generation methods that rely on image captioning models often generate incoherent and incorrect text due to their lack of relevant domain knowledge, while retrieval-based attempts frequently retrieve reports that are irrelevant to the input image. In this work, we propose Contrastive X-Ray REport Match (X-REM), a novel retrieval-based radiology report generation module that uses an image-text matching score to measure the similarity of a chest X-ray image and radiology report for report retrieval. We observe that computing the image-text matching score with a language-image model can effectively capture the fine-grained interaction between image and text that is often lost when using cosine similarity. X-REM outperforms multiple prior radiology report generation modules in terms of both natural language and clinical metrics. Human evaluation of the generated reports suggests that X-REM increased the number of zero-error reports and decreased the average error severity compared to the baseline retrieval approach. Our code is available at: https://github.com/rajpurkarlab/X-REM | ['Pranav Rajpurkar', 'Subathra Adithan', 'Michael Pohlen', 'David Osayande', 'Juan Calle', 'Fardad Behzadi', 'Sina Hartung', 'Andrew Li', 'Katherine Tian', 'Jaehwan Jeong'] | 2023-03-29 | null | null | null | null | ['text-matching'] | ['natural-language-processing'] | [ 4.35423940e-01 2.45298207e-01 5.64199723e-02 -3.76557350e-01
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bfdf10ad-95ba-46f8-956d-3961368475c2 | a-semi-supervised-geometric-driven | 2207.05514 | null | https://arxiv.org/abs/2207.05514v2 | https://arxiv.org/pdf/2207.05514v2.pdf | A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vessels | Automatic Identification System (AIS) messages are useful for tracking vessel activity across oceans worldwide using radio links and satellite transceivers. Such data plays a significant role in tracking vessel activity and mapping mobility patterns such as those found in fishing. Accordingly, this paper proposes a geometric-driven semi-supervised approach for fishing activity detection from AIS data. Through the proposed methodology we show how to explore the information included in the messages to extract features describing the geometry of the vessel route. To this end, we leverage the unsupervised nature of cluster analysis to label the trajectory geometry highlighting the changes in the vessel's moving pattern which tends to indicate fishing activity. The labels obtained by the proposed unsupervised approach are used to detect fishing activities, which we approach as a time-series classification task. In this context, we propose a solution using recurrent neural networks on AIS data streams with roughly 87% of the overall $F$-score on the whole trajectories of 50 different unseen fishing vessels. Such results are accompanied by a broad benchmark study assessing the performance of different Recurrent Neural Network (RNN) architectures. In conclusion, this work contributes by proposing a thorough process that includes data preparation, labeling, data modeling, and model validation. Therefore, we present a novel solution for mobility pattern detection that relies upon unfolding the trajectory in time and observing their inherent geometry. | ['Stan Matwin', 'Amilcar Soares', 'Gabriel Spadon', 'Martha Dais Ferreira'] | 2022-07-12 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 1.96233299e-02 -2.60063678e-01 3.95834707e-02 -3.97149503e-01
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9904040e-f1b1-46b8-a9aa-6c7e4fe356dd | graphcast-learning-skillful-medium-range | 2212.12794 | null | https://arxiv.org/abs/2212.12794v1 | https://arxiv.org/pdf/2212.12794v1.pdf | GraphCast: Learning skillful medium-range global weather forecasting | We introduce a machine-learning (ML)-based weather simulator--called "GraphCast"--which outperforms the most accurate deterministic operational medium-range weather forecasting system in the world, as well as all previous ML baselines. GraphCast is an autoregressive model, based on graph neural networks and a novel high-resolution multi-scale mesh representation, which we trained on historical weather data from the European Centre for Medium-Range Weather Forecasts (ECMWF)'s ERA5 reanalysis archive. It can make 10-day forecasts, at 6-hour time intervals, of five surface variables and six atmospheric variables, each at 37 vertical pressure levels, on a 0.25-degree latitude-longitude grid, which corresponds to roughly 25 x 25 kilometer resolution at the equator. Our results show GraphCast is more accurate than ECMWF's deterministic operational forecasting system, HRES, on 90.0% of the 2760 variable and lead time combinations we evaluated. GraphCast also outperforms the most accurate previous ML-based weather forecasting model on 99.2% of the 252 targets it reported. GraphCast can generate a 10-day forecast (35 gigabytes of data) in under 60 seconds on Cloud TPU v4 hardware. Unlike traditional forecasting methods, ML-based forecasting scales well with data: by training on bigger, higher quality, and more recent data, the skill of the forecasts can improve. Together these results represent a key step forward in complementing and improving weather modeling with ML, open new opportunities for fast, accurate forecasting, and help realize the promise of ML-based simulation in the physical sciences. | ['Peter Battaglia', 'Shakir Mohamed', 'Oriol Vinyals', 'Jacklynn Stott', 'George Holland', 'Stephan Hoyer', 'Alexander Merose', 'Weihua Hu', 'Zach Eaton-Rosen', 'Ferran Alet', 'Timo Ewalds', 'Suman Ravuri', 'Alexander Pritzel', 'Meire Fortunato', 'Peter Wirnsberger', 'Matthew Willson', 'Alvaro Sanchez-Gonzalez', 'Remi Lam'] | 2022-12-24 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-6.17573678e-01 -1.40440211e-01 -1.17587065e-02 -2.69695520e-01
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2d13a438-20a6-4f0a-97b0-8d5dce2824f0 | using-auxiliary-tasks-in-multimodal-fusion-of | 2302.13661 | null | https://arxiv.org/abs/2302.13661v1 | https://arxiv.org/pdf/2302.13661v1.pdf | Using Auxiliary Tasks In Multimodal Fusion Of Wav2vec 2.0 And BERT For Multimodal Emotion Recognition | The lack of data and the difficulty of multimodal fusion have always been challenges for multimodal emotion recognition (MER). In this paper, we propose to use pretrained models as upstream network, wav2vec 2.0 for audio modality and BERT for text modality, and finetune them in downstream task of MER to cope with the lack of data. For the difficulty of multimodal fusion, we use a K-layer multi-head attention mechanism as a downstream fusion module. Starting from the MER task itself, we design two auxiliary tasks to alleviate the insufficient fusion between modalities and guide the network to capture and align emotion-related features. Compared to the previous state-of-the-art models, we achieve a better performance by 78.42% Weighted Accuracy (WA) and 79.71% Unweighted Accuracy (UA) on the IEMOCAP dataset. | ['Jiqing Han', 'Yancheng He', 'Dekai Sun'] | 2023-02-27 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-8.14637020e-02 -1.52198300e-01 1.40299141e-01 -5.00596583e-01
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2ce0df34-9942-462b-b6e9-83667ecf733e | few-shot-viewpoint-estimation | 1905.04957 | null | https://arxiv.org/abs/1905.04957v2 | https://arxiv.org/pdf/1905.04957v2.pdf | Few-Shot Viewpoint Estimation | Viewpoint estimation for known categories of objects has been improved significantly thanks to deep networks and large datasets, but generalization to unknown categories is still very challenging. With an aim towards improving performance on unknown categories, we introduce the problem of category-level few-shot viewpoint estimation. We design a novel framework to successfully train viewpoint networks for new categories with few examples (10 or less). We formulate the problem as one of learning to estimate category-specific 3D canonical shapes, their associated depth estimates, and semantic 2D keypoints. We apply meta-learning to learn weights for our network that are amenable to category-specific few-shot fine-tuning. Furthermore, we design a flexible meta-Siamese network that maximizes information sharing during meta-learning. Through extensive experimentation on the ObjectNet3D and Pascal3D+ benchmark datasets, we demonstrate that our framework, which we call MetaView, significantly outperforms fine-tuning the state-of-the-art models with few examples, and that the specific architectural innovations of our method are crucial to achieving good performance. | ['Ming-Hsuan Yang', 'Hung-Yu Tseng', 'Jonathan Tremblay', 'Sifei Liu', 'Jan Kautz', 'Stan Birchfield', 'Shalini De Mello'] | 2019-05-13 | null | null | null | null | ['viewpoint-estimation'] | ['computer-vision'] | [ 4.08210419e-02 3.96893546e-02 -1.72137380e-01 -5.88975668e-01
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060695cb-2dc1-4d38-a126-51d42f0a3882 | policy-contrastive-imitation-learning | 2307.02829 | null | https://arxiv.org/abs/2307.02829v1 | https://arxiv.org/pdf/2307.02829v1.pdf | Policy Contrastive Imitation Learning | Adversarial imitation learning (AIL) is a popular method that has recently achieved much success. However, the performance of AIL is still unsatisfactory on the more challenging tasks. We find that one of the major reasons is due to the low quality of AIL discriminator representation. Since the AIL discriminator is trained via binary classification that does not necessarily discriminate the policy from the expert in a meaningful way, the resulting reward might not be meaningful either. We propose a new method called Policy Contrastive Imitation Learning (PCIL) to resolve this issue. PCIL learns a contrastive representation space by anchoring on different policies and generates a smooth cosine-similarity-based reward. Our proposed representation learning objective can be viewed as a stronger version of the AIL objective and provide a more meaningful comparison between the agent and the policy. From a theoretical perspective, we show the validity of our method using the apprenticeship learning framework. Furthermore, our empirical evaluation on the DeepMind Control suite demonstrates that PCIL can achieve state-of-the-art performance. Finally, qualitative results suggest that PCIL builds a smoother and more meaningful representation space for imitation learning. | ['Yang Gao', 'Yingdong Hu', 'ZhaoHeng Yin', 'Jialei Huang'] | 2023-07-06 | null | null | null | null | ['representation-learning', 'imitation-learning'] | ['methodology', 'methodology'] | [-1.12679899e-02 7.26440270e-03 -7.45072007e-01 -3.91980255e-04
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9b767f8e-e3b1-4bf5-9406-a7f7809ba2d1 | weakly-supervised-information-extraction-from | 2306.06823 | null | https://arxiv.org/abs/2306.06823v1 | https://arxiv.org/pdf/2306.06823v1.pdf | Weakly supervised information extraction from inscrutable handwritten document images | State-of-the-art information extraction methods are limited by OCR errors. They work well for printed text in form-like documents, but unstructured, handwritten documents still remain a challenge. Adapting existing models to domain-specific training data is quite expensive, because of two factors, 1) limited availability of the domain-specific documents (such as handwritten prescriptions, lab notes, etc.), and 2) annotations become even more challenging as one needs domain-specific knowledge to decode inscrutable handwritten document images. In this work, we focus on the complex problem of extracting medicine names from handwritten prescriptions using only weakly labeled data. The data consists of images along with the list of medicine names in it, but not their location in the image. We solve the problem by first identifying the regions of interest, i.e., medicine lines from just weak labels and then injecting a domain-specific medicine language model learned using only synthetically generated data. Compared to off-the-shelf state-of-the-art methods, our approach performs >2.5x better in medicine names extraction from prescriptions. | ['Gaurav Aggarwal', 'Pradeep Kumar', 'Narayan Hegde', 'Akankshya Mishra', 'Gagan Madan', 'Sujoy Paul'] | 2023-06-12 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 3.89821470e-01 6.59323782e-02 -4.28372592e-01 -2.57456452e-01
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fc29facd-7e9f-4565-a09c-d8136b76f073 | thoops-a-multi-aspect-analytical-framework | 1712.01199 | null | http://arxiv.org/abs/1712.01199v5 | http://arxiv.org/pdf/1712.01199v5.pdf | tHoops: A Multi-Aspect Analytical Framework Spatio-Temporal Basketball Data | During the past few years advancements in sports information systems and
technology has allowed us to collect a number of detailed spatio-temporal data
capturing various aspects of basketball. For example, shot charts, that is,
maps capturing locations of (made or missed) shots, and spatio-temporal
trajectories for all the players on the court can capture information about the
offensive and defensive tendencies and schemes of a team. Characterization of
these processes is important for player and team comparisons, pre-game
scouting, game preparation etc. Playing tendencies among teams have
traditionally been compared in a heuristic manner. Recently automated ways for
similar comparisons have appeared in the sports analytics literature. However,
these approaches are almost exclusively focused on the spatial distribution of
the underlying actions (usually shots taken), ignoring a multitude of other
parameters that can affect the action studied. In this work, we propose a
framework based on tensor decomposition for obtaining a set of prototype
spatio-temporal patterns based on the core spatiotemporal information and
contextual meta-data. The core of our framework is a 3D tensor X, whose
dimensions represent the entity under consideration (team, player, possession
etc.), the location on the court and time. We make use of the PARAFAC
decomposition and we decompose the tensor into several interpretable patterns,
that can be thought of as prototype patterns of the process examined (e.g.,
shot selection, offensive schemes etc.). We also introduce an approach for
choosing the number of components to be considered. Using the tensor
components, we can then express every entity as a weighted combination of these
components. The framework introduced in this paper can have further
applications in the work-flow of the basketball operations of a franchise,
which we also briefly discuss. | ['Konstantinos Pelechrinis', 'Evangelos Papalexakis'] | 2017-12-04 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [-1.10313490e-01 -6.94865108e-01 -5.51236644e-02 3.44010405e-02
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77c70655-dfac-4ed3-9896-7f820592f2d5 | statistical-dialogue-management-using | null | null | https://aclanthology.org/I13-1127 | https://aclanthology.org/I13-1127.pdf | Statistical Dialogue Management using Intention Dependency Graph | null | ['Koichiro Yoshino', 'John R. Hershey', 'Shinji Watanabe', 'Jonathan Le Roux'] | 2013-10-01 | statistical-dialogue-management-using-1 | https://aclanthology.org/I13-1127 | https://aclanthology.org/I13-1127.pdf | ijcnlp-2013-10 | ['dialogue-management'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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554bd489-6a7f-42b6-8e6e-be856b1152fa | a-new-cross-domain-strategy-based-xai-models | 2302.02122 | null | https://arxiv.org/abs/2302.02122v1 | https://arxiv.org/pdf/2302.02122v1.pdf | A New cross-domain strategy based XAI models for fake news detection | In this study, we presented a four-level cross-domain strategy for fake news detection on pre-trained models. Cross-domain text classification is a task of a model adopting a target domain by using the knowledge of the source domain. Explainability is crucial in understanding the behaviour of these complex models. A fine-tune BERT model is used to. perform cross-domain classification with several experiments using datasets from different domains. Explanatory models like Anchor, ELI5, LIME and SHAP are used to design a novel explainable approach to cross-domain levels. The experimental analysis has given an ideal pair of XAI models on different levels of cross-domain. | ['Deepak Kanneganti'] | 2023-02-04 | null | null | null | null | ['cross-domain-text-classification'] | ['natural-language-processing'] | [-1.87410221e-01 4.05074090e-01 -6.92632556e-01 -4.42085654e-01
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56d15853-19bd-4475-b5cf-6a863d627878 | matrix-completion-from-general-deterministic | 2306.02283 | null | https://arxiv.org/abs/2306.02283v1 | https://arxiv.org/pdf/2306.02283v1.pdf | Matrix Completion from General Deterministic Sampling Patterns | Most of the existing works on provable guarantees for low-rank matrix completion algorithms rely on some unrealistic assumptions such that matrix entries are sampled randomly or the sampling pattern has a specific structure. In this work, we establish theoretical guarantee for the exact and approximate low-rank matrix completion problems which can be applied to any deterministic sampling schemes. For this, we introduce a graph having observed entries as its edge set, and investigate its graph properties involving the performance of the standard constrained nuclear norm minimization algorithm. We theoretically and experimentally show that the algorithm can be successful as the observation graph is well-connected and has similar node degrees. Our result can be viewed as an extension of the works by Bhojanapalli and Jain [2014] and Burnwal and Vidyasagar [2020], in which the node degrees of the observation graph were assumed to be the same. In particular, our theory significantly improves their results when the underlying matrix is symmetric. | ['Jean Honorio', 'Qifan Song', 'Rahul Mazumder', 'Hanbyul Lee'] | 2023-06-04 | null | null | null | null | ['low-rank-matrix-completion', 'matrix-completion'] | ['methodology', 'methodology'] | [ 2.98645705e-01 4.39291388e-01 -3.48452777e-01 -3.06367618e-03
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7d641d48-09a0-4e44-93c7-55f24ab216d3 | c-vton-context-driven-image-based-virtual-try | 2212.04437 | null | https://arxiv.org/abs/2212.04437v1 | https://arxiv.org/pdf/2212.04437v1.pdf | C-VTON: Context-Driven Image-Based Virtual Try-On Network | Image-based virtual try-on techniques have shown great promise for enhancing the user-experience and improving customer satisfaction on fashion-oriented e-commerce platforms. However, existing techniques are currently still limited in the quality of the try-on results they are able to produce from input images of diverse characteristics. In this work, we propose a Context-Driven Virtual Try-On Network (C-VTON) that addresses these limitations and convincingly transfers selected clothing items to the target subjects even under challenging pose configurations and in the presence of self-occlusions. At the core of the C-VTON pipeline are: (i) a geometric matching procedure that efficiently aligns the target clothing with the pose of the person in the input images, and (ii) a powerful image generator that utilizes various types of contextual information when synthesizing the final try-on result. C-VTON is evaluated in rigorous experiments on the VITON and MPV datasets and in comparison to state-of-the-art techniques from the literature. Experimental results show that the proposed approach is able to produce photo-realistic and visually convincing results and significantly improves on the existing state-of-the-art. | ['Vitomir Štruc', 'Peter Peer', 'Ajda Lampe', 'Benjamin Fele'] | 2022-12-08 | null | null | null | null | ['geometric-matching', 'virtual-try-on'] | ['computer-vision', 'computer-vision'] | [ 1.29439503e-01 -2.26549625e-01 2.96041906e-01 -5.26570022e-01
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babff6cb-0088-4a32-8052-ac95d98c8d1e | bop-challenge-2020-on-6d-object-localization | 2009.07378 | null | https://arxiv.org/abs/2009.07378v2 | https://arxiv.org/pdf/2009.07378v2.pdf | BOP Challenge 2020 on 6D Object Localization | This paper presents the evaluation methodology, datasets, and results of the BOP Challenge 2020, the third in a series of public competitions organized with the goal to capture the status quo in the field of 6D object pose estimation from an RGB-D image. In 2020, to reduce the domain gap between synthetic training and real test RGB images, the participants were provided 350K photorealistic training images generated by BlenderProc4BOP, a new open-source and light-weight physically-based renderer (PBR) and procedural data generator. Methods based on deep neural networks have finally caught up with methods based on point pair features, which were dominating previous editions of the challenge. Although the top-performing methods rely on RGB-D image channels, strong results were achieved when only RGB channels were used at both training and test time - out of the 26 evaluated methods, the third method was trained on RGB channels of PBR and real images, while the fifth on RGB channels of PBR images only. Strong data augmentation was identified as a key component of the top-performing CosyPose method, and the photorealism of PBR images was demonstrated effective despite the augmentation. The online evaluation system stays open and is available on the project website: bop.felk.cvut.cz. | ['Martin Sundermeyer', 'Jiri Matas', 'Yann Labbe', 'Tomas Hodan', 'Frank Michel', 'Bertram Drost', 'Eric Brachmann', 'Carsten Rother'] | 2020-09-15 | null | null | null | null | ['6d-pose-estimation-using-rgbd'] | ['computer-vision'] | [-1.18169941e-01 3.19602579e-01 4.00072068e-01 -1.16763048e-01
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3b20acb2-efee-469c-8453-a033a699c742 | introspective-action-advising-for | 2306.12314 | null | https://arxiv.org/abs/2306.12314v1 | https://arxiv.org/pdf/2306.12314v1.pdf | Introspective Action Advising for Interpretable Transfer Learning | Transfer learning can be applied in deep reinforcement learning to accelerate the training of a policy in a target task by transferring knowledge from a policy learned in a related source task. This is commonly achieved by copying pretrained weights from the source policy to the target policy prior to training, under the constraint that they use the same model architecture. However, not only does this require a robust representation learned over a wide distribution of states -- often failing to transfer between specialist models trained over single tasks -- but it is largely uninterpretable and provides little indication of what knowledge is transferred. In this work, we propose an alternative approach to transfer learning between tasks based on action advising, in which a teacher trained in a source task actively guides a student's exploration in a target task. Through introspection, the teacher is capable of identifying when advice is beneficial to the student and should be given, and when it is not. Our approach allows knowledge transfer between policies agnostic of the underlying representations, and we empirically show that this leads to improved convergence rates in Gridworld and Atari environments while providing insight into what knowledge is transferred. | ['Katia Sycara', 'Simon Stepputtis', 'Fiona Xie', 'Yue Guo', 'Joseph Campbell'] | 2023-06-21 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [ 4.23403621e-01 3.14447463e-01 -3.85785162e-01 -2.84282297e-01
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6f9291ab-5c2a-4078-8e08-726a4fff2bf0 | periodic-graph-transformers-for-crystal | 2209.11807 | null | https://arxiv.org/abs/2209.11807v1 | https://arxiv.org/pdf/2209.11807v1.pdf | Periodic Graph Transformers for Crystal Material Property Prediction | We consider representation learning on periodic graphs encoding crystal materials. Different from regular graphs, periodic graphs consist of a minimum unit cell repeating itself on a regular lattice in 3D space. How to effectively encode these periodic structures poses unique challenges not present in regular graph representation learning. In addition to being E(3) invariant, periodic graph representations need to be periodic invariant. That is, the learned representations should be invariant to shifts of cell boundaries as they are artificially imposed. Furthermore, the periodic repeating patterns need to be captured explicitly as lattices of different sizes and orientations may correspond to different materials. In this work, we propose a transformer architecture, known as Matformer, for periodic graph representation learning. Our Matformer is designed to be invariant to periodicity and can capture repeating patterns explicitly. In particular, Matformer encodes periodic patterns by efficient use of geometric distances between the same atoms in neighboring cells. Experimental results on multiple common benchmark datasets show that our Matformer outperforms baseline methods consistently. In addition, our results demonstrate the importance of periodic invariance and explicit repeating pattern encoding for crystal representation learning. | ['Shuiwang Ji', 'Yuchao Lin', 'Yi Liu', 'Keqiang Yan'] | 2022-09-23 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [ 3.89021546e-01 1.97544873e-01 -2.85149783e-01 -1.04158729e-01
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69fe4b7c-127e-4edc-9dc0-a3613395f338 | trajectory-prediction-with-observations-of | 2306.05478 | null | https://arxiv.org/abs/2306.05478v1 | https://arxiv.org/pdf/2306.05478v1.pdf | Trajectory Prediction with Observations of Variable-Length for Motion Planning in Highway Merging scenarios | Accurate trajectory prediction of nearby vehicles is crucial for the safe motion planning of automated vehicles in dynamic driving scenarios such as highway merging. Existing methods cannot initiate prediction for a vehicle unless observed for a fixed duration of two or more seconds. This prevents a fast reaction by the ego vehicle to vehicles that enter its perception range, thus creating safety concerns. Therefore, this paper proposes a novel transformer-based trajectory prediction approach, specifically trained to handle any observation length larger than one frame. We perform a comprehensive evaluation of the proposed method using two large-scale highway trajectory datasets, namely the highD and exiD. In addition, we study the impact of the proposed prediction approach on motion planning and control tasks using extensive merging scenarios from the exiD dataset. To the best of our knowledge, this marks the first instance where such a large-scale highway merging dataset has been employed for this purpose. The results demonstrate that the prediction model achieves state-of-the-art performance on highD dataset and maintains lower prediction error w.r.t. the constant velocity across all observation lengths in exiD. Moreover, it significantly enhances safety, comfort, and efficiency in dense traffic scenarios, as compared to the constant velocity model. | ['Mehrdad Dianati', 'Graham Lee', 'Konstantinos Koufos', 'Mreza Alipour Sormoli', 'Sajjad Mozaffari'] | 2023-06-08 | null | null | null | null | ['trajectory-prediction', 'motion-planning'] | ['computer-vision', 'robots'] | [-3.02825391e-01 1.40968338e-01 -5.50217509e-01 -3.61862242e-01
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a8d4863f-9fc6-4573-a986-407fa659f4a7 | propbank-comes-of-age-larger-smarter-and-more | null | null | https://aclanthology.org/2022.starsem-1.24 | https://aclanthology.org/2022.starsem-1.24.pdf | PropBank Comes of Age—Larger, Smarter, and more Diverse | This paper describes the evolution of the PropBank approach to semantic role labeling over the last two decades. During this time the PropBank frame files have been expanded to include non-verbal predicates such as adjectives, prepositions and multi-word expressions. The number of domains, genres and languages that have been PropBanked has also expanded greatly, creating an opportunity for much more challenging and robust testing of the generalization capabilities of PropBank semantic role labeling systems. We also describe the substantial effort that has gone into ensuring the consistency and reliability of the various annotated datasets and resources, to better support the training and evaluation of such systems | ['Martha Palmer', 'Kristin Wright-Bettner', 'James Gung', 'Tim O’Gorman', 'Kathryn Conger', 'Skatje Myers', 'Julia Bonn', 'Sameer Pradhan'] | null | null | null | null | sem-naacl-2022-7 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 3.19465250e-02 6.04116797e-01 -6.01970077e-01 -7.59452343e-01
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d1140c31-02e0-434c-8b5e-a529dbacb746 | uncrtaints-uncertainty-quantification-for | 2304.05464 | null | https://arxiv.org/abs/2304.05464v1 | https://arxiv.org/pdf/2304.05464v1.pdf | UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series | Clouds and haze often occlude optical satellite images, hindering continuous, dense monitoring of the Earth's surface. Although modern deep learning methods can implicitly learn to ignore such occlusions, explicit cloud removal as pre-processing enables manual interpretation and allows training models when only few annotations are available. Cloud removal is challenging due to the wide range of occlusion scenarios -- from scenes partially visible through haze, to completely opaque cloud coverage. Furthermore, integrating reconstructed images in downstream applications would greatly benefit from trustworthy quality assessment. In this paper, we introduce UnCRtainTS, a method for multi-temporal cloud removal combining a novel attention-based architecture, and a formulation for multivariate uncertainty prediction. These two components combined set a new state-of-the-art performance in terms of image reconstruction on two public cloud removal datasets. Additionally, we show how the well-calibrated predicted uncertainties enable a precise control of the reconstruction quality. | ['Xiao Xiang Zhu', 'Jan Dirk Wegner', 'Michael Schmitt', 'Vivien Sainte Fare Garnot', 'Patrick Ebel'] | 2023-04-11 | null | null | null | null | ['cloud-removal', 'image-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 1.69780403e-02 -3.07819515e-01 1.26548231e-01 -4.88384396e-01
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f0994b1e-6b8f-44af-a99a-9bafc3df990a | pre-training-on-dynamic-graph-neural-networks | 2102.12380 | null | https://arxiv.org/abs/2102.12380v2 | https://arxiv.org/pdf/2102.12380v2.pdf | Pre-Training on Dynamic Graph Neural Networks | The pre-training on the graph neural network model can learn the general features of large-scale networks or networks of the same type by self-supervised methods, which allows the model to work even when node labels are missing. However, the existing pre-training methods do not take network evolution into consideration. This paper proposes a pre-training method on dynamic graph neural networks (PT-DGNN), which uses dynamic attributed graph generation tasks to simultaneously learn the structure, semantics, and evolution features of the graph. The method includes two steps: 1) dynamic sub-graph sampling, and 2) pre-training with dynamic attributed graph generation task. Comparative experiments on three realistic dynamic network datasets show that the proposed method achieves the best results on the link prediction fine-tuning task. | ['Yuxuan Dai', 'Yunyun Wang', 'Linpu Jiang', 'Jiajun Zhang', 'Ke-Jia Chen'] | 2021-02-24 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 1.69644848e-01 4.80603218e-01 -5.62904775e-01 -4.04444963e-01
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5651ba39-5fab-407a-a07f-e40dcf543702 | kernel-cf-collaborative-filtering-done-right | 2303.04561 | null | https://arxiv.org/abs/2303.04561v1 | https://arxiv.org/pdf/2303.04561v1.pdf | Kernel-CF: Collaborative filtering done right with social network analysis and kernel smoothing | Collaborative filtering is the simplest but oldest machine learning algorithm in the field of recommender systems. In spite of its long history, it remains a discussion topic in research venues. Usually people use users/items whose similarity scores with the target customer greater than 0 to compute the algorithms. However, this might not be the optimal solution after careful scrutiny. In this paper, we transform the recommender system input data into a 2-D social network, and apply kernel smoothing to compute preferences for unknown values in the user item rating matrix. We unifies the theoretical framework of recommender system and non-parametric statistics and provides an algorithmic procedure with optimal parameter selection method to achieve the goal. | ['Hao Wang'] | 2023-03-08 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-3.05856407e-01 -3.56848836e-01 -2.19524279e-01 -5.23738563e-01
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e922b8ce-f175-4b0d-a9ca-90517aedceca | learning-embeddings-for-image-clustering-an | 2007.03123 | null | https://arxiv.org/abs/2007.03123v1 | https://arxiv.org/pdf/2007.03123v1.pdf | Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches | In this work, we evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings. Specifically, we train a convolutional neural network to learn discriminative features by optimizing two popular versions of the Triplet Loss in order to study their clustering properties under the assumption of noisy labels. Additionally, we propose a new, simple Triplet Loss formulation, which shows desirable properties with respect to formal clustering objectives and outperforms the existing methods. We evaluate all three Triplet loss formulations for K-means and correlation clustering on the CIFAR-10 image classification dataset. | ['Franz-Josef Pfreundt', 'Margret Keuper', 'Janis Keuper', 'Kalun Ho'] | 2020-07-06 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [-7.13160038e-02 -3.60461652e-01 -7.35800192e-02 -7.88830876e-01
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58faeb94-f5ec-4d6c-89a6-791ca9528596 | spell-correction-for-azerbaijani-language | 2102.03218 | null | https://arxiv.org/abs/2102.03218v1 | https://arxiv.org/pdf/2102.03218v1.pdf | Spell Correction for Azerbaijani Language using Deep Neural Networks | Spell correction is used to detect and correct orthographic mistakes in texts. Most of the time, traditional dictionary lookup with string similarity methods is suitable for the languages that have a less complex structure such as the English language. However, the Azerbaijani language has a more complex structure and due to its morphological structure, the derivation of words is plenty that several words are derived from adding suffices, affixes to the words. Therefore, in this paper sequence to sequence model with an attention mechanism is used to develop spelling correction for Azerbaijani. Total 12000 wrong and correct sentence pairs used for training, and the model is tested on 1000 real-world misspelled words and F1-score results are 75% for distance 0, 90% for distance 1, and 96% for distance 2. | ['Saber Malekzadeh', 'Ahmad Ahmadzade'] | 2021-02-05 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 5.43653518e-02 -6.45981967e-01 1.00798249e-01 -1.45544499e-01
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ae570a66-1d31-4acc-9d73-50fcaec2f495 | cross-lktcn-modern-convolution-utilizing | 2306.02326 | null | https://arxiv.org/abs/2306.02326v1 | https://arxiv.org/pdf/2306.02326v1.pdf | Cross-LKTCN: Modern Convolution Utilizing Cross-Variable Dependency for Multivariate Time Series Forecasting Dependency for Multivariate Time Series Forecasting | The past few years have witnessed the rapid development in multivariate time series forecasting. The key to accurate forecasting results is capturing the long-term dependency between each time step (cross-time dependency) and modeling the complex dependency between each variable (cross-variable dependency) in multivariate time series. However, recent methods mainly focus on the cross-time dependency but seldom consider the cross-variable dependency. To fill this gap, we find that convolution, a traditional technique but recently losing steam in time series forecasting, meets the needs of respectively capturing the cross-time and cross-variable dependency. Based on this finding, we propose a modern pure convolution structure, namely Cross-LKTCN, to better utilize both cross-time and cross-variable dependency for time series forecasting. Specifically in each Cross-LKTCN block, a depth-wise large kernel convolution with large receptive field is proposed to capture cross-time dependency, and then two successive point-wise group convolution feed forward networks are proposed to capture cross-variable dependency. Experimental results on real-world benchmarks show that Cross-LKTCN achieves state-of-the-art forecasting performance and improves the forecasting accuracy significantly compared with existing convolutional-based models and cross-variable methods. | ['Xue Wang', 'Donghao Luo'] | 2023-06-04 | null | null | null | null | ['multivariate-time-series-forecasting'] | ['time-series'] | [-3.60082239e-01 -1.04177392e+00 -1.50022998e-01 -6.50914013e-01
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0700bd60-52a1-4bc7-ada2-fc768cad8198 | concrete-problems-in-ai-safety | 1606.06565 | null | http://arxiv.org/abs/1606.06565v2 | http://arxiv.org/pdf/1606.06565v2.pdf | Concrete Problems in AI Safety | Rapid progress in machine learning and artificial intelligence (AI) has
brought increasing attention to the potential impacts of AI technologies on
society. In this paper we discuss one such potential impact: the problem of
accidents in machine learning systems, defined as unintended and harmful
behavior that may emerge from poor design of real-world AI systems. We present
a list of five practical research problems related to accident risk,
categorized according to whether the problem originates from having the wrong
objective function ("avoiding side effects" and "avoiding reward hacking"), an
objective function that is too expensive to evaluate frequently ("scalable
supervision"), or undesirable behavior during the learning process ("safe
exploration" and "distributional shift"). We review previous work in these
areas as well as suggesting research directions with a focus on relevance to
cutting-edge AI systems. Finally, we consider the high-level question of how to
think most productively about the safety of forward-looking applications of AI. | ['Dan Mané', 'Paul Christiano', 'John Schulman', 'Jacob Steinhardt', 'Dario Amodei', 'Chris Olah'] | 2016-06-21 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 3.44256401e-01 7.62404621e-01 -1.84717730e-01 -5.16049564e-01
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