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39febac2-044c-4be5-a253-74325285acab | self-supervised-tumor-segmentation-through | 2109.03230 | null | https://arxiv.org/abs/2109.03230v2 | https://arxiv.org/pdf/2109.03230v2.pdf | Self-supervised Tumor Segmentation through Layer Decomposition | In this paper, we target self-supervised representation learning for zero-shot tumor segmentation. We make the following contributions: First, we advocate a zero-shot setting, where models from pre-training should be directly applicable for the downstream task, without using any manual annotations. Second, we take inspiration from "layer-decomposition", and innovate on the training regime with simulated tumor data. Third, we conduct extensive ablation studies to analyse the critical components in data simulation, and validate the necessity of different proxy tasks. We demonstrate that, with sufficient texture randomization in simulation, model trained on synthetic data can effortlessly generalise to segment real tumor data. Forth, our approach achieves superior results for zero-shot tumor segmentation on different downstream datasets, BraTS2018 for brain tumor segmentation and LiTS2017 for liver tumor segmentation. While evaluating the model transferability for tumor segmentation under a low-annotation regime, the proposed approach also outperforms all existing self-supervised approaches, opening up the usage of self-supervised learning in practical scenarios. | ['Qi Tian', 'Xin Chen', 'Yanfeng Wang', 'Ya zhang', 'Chaoqin Huang', 'Weidi Xie', 'Xiaoman Zhang'] | 2021-09-07 | null | null | null | null | ['zero-shot-segmentation'] | ['computer-vision'] | [ 5.73000073e-01 7.61551857e-01 -3.29791605e-01 -4.52456586e-02
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670a1bb8-7d8a-4483-b665-522cf0716d71 | biophysical-model-for-signal-embedded-droplet | 2305.06438 | null | https://arxiv.org/abs/2305.06438v1 | https://arxiv.org/pdf/2305.06438v1.pdf | Biophysical Model for Signal-Embedded Droplet Soaking into 2D Cell Culture | Using agar plates hosting a 2D cell population stimulated with signaling molecules is crucial for experiments such as gene regulation and drug discovery in a wide range of biological studies. In this paper, a biophysical model is proposed that incorporates droplet soaking, diffusion of molecules within agar, cell growth over an agar surface, and absorption of signaling molecules by cells. The proposed model describes the channel response and provides valuable insights for designing experiments more efficiently and accurately. The molecule release rate due to droplet soaking into agar, which is characterized and modeled as the source term for the diffusion model, is derived. Furthermore, cell growth is considered over the surface, which dictates the dynamics of signaling molecule reactions and leads to a variable boundary condition. As a case study, genetically-modified $E.\,coli$ bacteria are spread over the surface of agar and Isopropyl-beta-D thiogalactopyranoside (IPTG) is considered as a signaling molecule. IPTG droplets are dropped onto the bacteria-covered agar surface. The parameters for the IPTG molecule release rate as a diffusion source into the agar are estimated from this experiment. Then, a particle-based simulator is used to obtain the spatio-temporal profile of the signaling molecules received by the surface bacteria. The results indicate that the number of molecules reacting with or absorbed by bacteria at different locations on the surface could be widely different, which highlights the importance of taking this variation into account for biological inferences. | ['Adam Noel', 'Christophe Corre', 'Hamidreza Arjmandi', 'Ibrahim Isik'] | 2023-05-10 | null | null | null | null | ['drug-discovery', 'culture'] | ['medical', 'speech'] | [ 3.51693362e-01 -3.86230528e-01 1.61550894e-01 4.61751491e-01
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b127ee02-efe2-4669-87e1-f8f806c98193 | cstr-a-classification-perspective-on-scene | 2102.10884 | null | https://arxiv.org/abs/2102.10884v3 | https://arxiv.org/pdf/2102.10884v3.pdf | Revisiting Classification Perspective on Scene Text Recognition | The prevalent perspectives of scene text recognition are from sequence to sequence (seq2seq) and segmentation. Nevertheless, the former is composed of many components which makes implementation and deployment complicated, while the latter requires character level annotations that is expensive. In this paper, we revisit classification perspective that models scene text recognition as an image classification problem. Classification perspective has a simple pipeline and only needs word level annotations. We revive classification perspective by devising a scene text recognition model named as CSTR, which performs as well as methods from other perspectives. The CSTR model consists of CPNet (classification perspective network) and SPPN (separated conv with global average pooling prediction network). CSTR is as simple as image classification model like ResNet \cite{he2016deep} which makes it easy to implement and deploy. We demonstrate the effectiveness of the classification perspective on scene text recognition with extensive experiments. Futhermore, CSTR achieves nearly state-of-the-art performance on six public benchmarks including regular text, irregular text. The code will be available at https://github.com/Media-Smart/vedastr. | ['Yichao Xiong', 'Jun Sun', 'Hongxiang Cai'] | 2021-02-22 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 5.78220308e-01 -4.82702792e-01 -1.29910916e-01 -4.46915269e-01
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3d1613a6-8207-4a83-95d6-e11027c0c41c | learning-policies-from-self-play-with-policy | 1905.05809 | null | https://arxiv.org/abs/1905.05809v1 | https://arxiv.org/pdf/1905.05809v1.pdf | Learning Policies from Self-Play with Policy Gradients and MCTS Value Estimates | In recent years, state-of-the-art game-playing agents often involve policies that are trained in self-playing processes where Monte Carlo tree search (MCTS) algorithms and trained policies iteratively improve each other. The strongest results have been obtained when policies are trained to mimic the search behaviour of MCTS by minimising a cross-entropy loss. Because MCTS, by design, includes an element of exploration, policies trained in this manner are also likely to exhibit a similar extent of exploration. In this paper, we are interested in learning policies for a project with future goals including the extraction of interpretable strategies, rather than state-of-the-art game-playing performance. For these goals, we argue that such an extent of exploration is undesirable, and we propose a novel objective function for training policies that are not exploratory. We derive a policy gradient expression for maximising this objective function, which can be estimated using MCTS value estimates, rather than MCTS visit counts. We empirically evaluate various properties of resulting policies, in a variety of board games. | ['Éric Piette', 'Cameron Browne', 'Dennis J. N. J. Soemers', 'Matthew Stephenson'] | 2019-05-14 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 1.61122456e-01 3.24273646e-01 -2.94991076e-01 -1.62004694e-01
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de8af402-373e-40b8-9a53-4c12ce6ec323 | omnisafe-an-infrastructure-for-accelerating | 2305.09304 | null | https://arxiv.org/abs/2305.09304v1 | https://arxiv.org/pdf/2305.09304v1.pdf | OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research | AI systems empowered by reinforcement learning (RL) algorithms harbor the immense potential to catalyze societal advancement, yet their deployment is often impeded by significant safety concerns. Particularly in safety-critical applications, researchers have raised concerns about unintended harms or unsafe behaviors of unaligned RL agents. The philosophy of safe reinforcement learning (SafeRL) is to align RL agents with harmless intentions and safe behavioral patterns. In SafeRL, agents learn to develop optimal policies by receiving feedback from the environment, while also fulfilling the requirement of minimizing the risk of unintended harm or unsafe behavior. However, due to the intricate nature of SafeRL algorithm implementation, combining methodologies across various domains presents a formidable challenge. This had led to an absence of a cohesive and efficacious learning framework within the contemporary SafeRL research milieu. In this work, we introduce a foundational framework designed to expedite SafeRL research endeavors. Our comprehensive framework encompasses an array of algorithms spanning different RL domains and places heavy emphasis on safety elements. Our efforts are to make the SafeRL-related research process more streamlined and efficient, therefore facilitating further research in AI safety. Our project is released at: https://github.com/PKU-Alignment/omnisafe. | ['Yaodong Yang', 'Mickel Liu', 'Yiran Geng', 'Weidong Huang', 'Ruiyang Sun', 'Xuehai Pan', 'Juntao Dai', 'Borong Zhang', 'Jiayi Zhou', 'Jiaming Ji'] | 2023-05-16 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [ 7.85631314e-02 2.41339207e-01 -6.26783490e-01 -9.64999124e-02
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9e9aa6ad-3b20-47ff-a76e-34aea35fa30a | erfnet-efficient-residual-factorized-convnet | null | null | https://ieeexplore.ieee.org/abstract/document/8063438 | http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf | ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation | Semantic segmentation is a challenging task that addresses most of the perception needs of Intelligent Vehicles (IV) in an unified way. Deep Neural Networks excel at this task, as they can be trained end-to-end to accurately classify multiple object categories in an image at pixel level. However, a good trade-off between high quality and computational resources is yet not present in state-of-the-art semantic segmentation approaches, limiting their application in real vehicles. In this paper, we propose a deep architecture that is able to run in real-time while providing accurate semantic segmentation. The core of our architecture is a novel layer that uses residual connections and factorized convolutions in order to remain efficient while retaining remarkable accuracy. Our approach is able to run at over 83 FPS in a single Titan X, and 7 FPS in a Jetson TX1 (embedded GPU). A comprehensive set of experiments on the publicly available Cityscapes dataset demonstrates that our system achieves an accuracy that is similar to the state of the art, while being orders of magnitude faster to compute than other architectures that achieve top precision. The resulting trade-off makes our model an ideal approach for scene understanding in IV applications. The code is publicly available at: https://github.com/Eromera/erfnet | ['L. M. Bergasa and R. Arroyo', 'E. Romera', 'J. M. Alvarez'] | 2017-10-09 | null | null | null | transactions-on-intelligent-transportation | ['thermal-image-segmentation'] | ['computer-vision'] | [-1.19110420e-02 4.50352468e-02 4.36835177e-02 -5.65039575e-01
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f83a353e-4b2c-4d7d-9f50-72cf0bae3f52 | leafai-query-generator-for-clinical-cohort | 2304.06203 | null | https://arxiv.org/abs/2304.06203v1 | https://arxiv.org/pdf/2304.06203v1.pdf | LeafAI: query generator for clinical cohort discovery rivaling a human programmer | Objective: Identifying study-eligible patients within clinical databases is a critical step in clinical research. However, accurate query design typically requires extensive technical and biomedical expertise. We sought to create a system capable of generating data model-agnostic queries while also providing novel logical reasoning capabilities for complex clinical trial eligibility criteria. Materials and Methods: The task of query creation from eligibility criteria requires solving several text-processing problems, including named entity recognition and relation extraction, sequence-to-sequence transformation, normalization, and reasoning. We incorporated hybrid deep learning and rule-based modules for these, as well as a knowledge base of the Unified Medical Language System (UMLS) and linked ontologies. To enable data-model agnostic query creation, we introduce a novel method for tagging database schema elements using UMLS concepts. To evaluate our system, called LeafAI, we compared the capability of LeafAI to a human database programmer to identify patients who had been enrolled in 8 clinical trials conducted at our institution. We measured performance by the number of actual enrolled patients matched by generated queries. Results: LeafAI matched a mean 43% of enrolled patients with 27,225 eligible across 8 clinical trials, compared to 27% matched and 14,587 eligible in queries by a human database programmer. The human programmer spent 26 total hours crafting queries compared to several minutes by LeafAI. Conclusions: Our work contributes a state-of-the-art data model-agnostic query generation system capable of conditional reasoning using a knowledge base. We demonstrate that LeafAI can rival a human programmer in finding patients eligible for clinical trials. | ['Meliha Yetisgen', 'Ozlem Uzuner', 'Robert Harrington', 'H. Nina Kim', 'Kristine Lan', 'Weipeng Zhou', 'Bin Han', 'Nicholas J Dobbins'] | 2023-04-13 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 1.04524232e-01 3.54449838e-01 -5.90207875e-01 -5.55552185e-01
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52cd00c7-0c41-4002-a1d7-a4f1ea0d093c | semantic-feature-integration-network-for-fine | 2302.10275 | null | https://arxiv.org/abs/2302.10275v1 | https://arxiv.org/pdf/2302.10275v1.pdf | Semantic Feature Integration network for Fine-grained Visual Classification | Fine-Grained Visual Classification (FGVC) is known as a challenging task due to subtle differences among subordinate categories. Many current FGVC approaches focus on identifying and locating discriminative regions by using the attention mechanism, but neglect the presence of unnecessary features that hinder the understanding of object structure. These unnecessary features, including 1) ambiguous parts resulting from the visual similarity in object appearances and 2) noninformative parts (e.g., background noise), can have a significant adverse impact on classification results. In this paper, we propose the Semantic Feature Integration network (SFI-Net) to address the above difficulties. By eliminating unnecessary features and reconstructing the semantic relations among discriminative features, our SFI-Net has achieved satisfying performance. The network consists of two modules: 1) the multi-level feature filter (MFF) module is proposed to remove unnecessary features with different receptive field, and then concatenate the preserved features on pixel level for subsequent disposal; 2) the semantic information reconstitution (SIR) module is presented to further establish semantic relations among discriminative features obtained from the MFF module. These two modules are carefully designed to be light-weighted and can be trained end-to-end in a weakly-supervised way. Extensive experiments on four challenging fine-grained benchmarks demonstrate that our proposed SFI-Net achieves the state-of-the-arts performance. Especially, the classification accuracy of our model on CUB-200-2011 and Stanford Dogs reaches 92.64% and 93.03%, respectively. | ['Haichi Luo', 'Yueyang Li', 'Hui Wang'] | 2023-02-13 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 8.04219395e-02 -3.23402643e-01 -1.81492001e-01 -6.27436101e-01
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a80ebaa9-34ca-47df-a8ff-7f3543802c0c | a-reproducible-and-realistic-evaluation-of | 2210.01210 | null | https://arxiv.org/abs/2210.01210v1 | https://arxiv.org/pdf/2210.01210v1.pdf | A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods | Unsupervised Domain Adaptation (UDA) aims at classifying unlabeled target images leveraging source labeled ones. In this work, we consider the Partial Domain Adaptation (PDA) variant, where we have extra source classes not present in the target domain. Most successful algorithms use model selection strategies that rely on target labels to find the best hyper-parameters and/or models along training. However, these strategies violate the main assumption in PDA: only unlabeled target domain samples are available. Moreover, there are also inconsistencies in the experimental settings - architecture, hyper-parameter tuning, number of runs - yielding unfair comparisons. The main goal of this work is to provide a realistic evaluation of PDA methods with the different model selection strategies under a consistent evaluation protocol. We evaluate 7 representative PDA algorithms on 2 different real-world datasets using 7 different model selection strategies. Our two main findings are: (i) without target labels for model selection, the accuracy of the methods decreases up to 30 percentage points; (ii) only one method and model selection pair performs well on both datasets. Experiments were performed with our PyTorch framework, BenchmarkPDA, which we open source. | ['Adam Oberman', 'Ioannis Mitliagkas', 'Kilian Fatras', 'Tiago Salvador'] | 2022-10-03 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 3.90439332e-01 -1.37470454e-01 -6.45549119e-01 -4.18832392e-01
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d033c7a3-2a82-4280-b98e-a3132c9b5515 | contour-knowledge-transfer-for-salient-object | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Xin_Li_Contour_Knowledge_Transfer_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Xin_Li_Contour_Knowledge_Transfer_ECCV_2018_paper.pdf | Contour Knowledge Transfer for Salient Object Detection | In recent years, deep Convolutional Neural Networks (CNNs) have broken all records in salient object detection. However, training such a deep model requires a large amount of manual annotations. Our goal is to overcome this limitation by automatically converting an existing deep contour detection model into a salient object detection model without using any manual salient object masks. For this purpose, we have created a deep network architecture, namely Contour-to-Saliency Network (C2S-Net), by grafting a new branch onto a well-trained contour detection network. Therefore, our C2S-Net has two branches for performing two different tasks: 1) predicting contours with the original contour branch, and 2) estimating per-pixel saliency score of each image with the newly-added saliency branch. To bridge the gap between these two tasks, we further propose a contour-to-saliency transferring method to automatically generate salient object masks which can be used to train the saliency branch from outputs of the contour branch. Finally, we introduce a novel alternating training pipeline to gradually update the network parameters. In this scheme, the contour branch generates saliency masks for training the saliency branch, while the saliency branch, in turn, feeds back saliency knowledge in the form of saliency-aware contour labels, for fine-tuning the contour branch. The proposed method achieves state-of-the-art performance on five well-known benchmarks, outperforming existing fully supervised methods while also maintaining high efficiency. | ['Hong Cheng', 'Dinggang Shen', 'Wei Liu', 'Fan Yang', 'Xin Li'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['contour-detection'] | ['computer-vision'] | [ 5.99603295e-01 3.68536204e-01 -1.67655453e-01 -3.39869976e-01
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6d178cd1-55b9-4883-b324-a64e7db39e51 | gastric-cancer-detection-from-x-ray-images | 2108.08158 | null | https://arxiv.org/abs/2108.08158v2 | https://arxiv.org/pdf/2108.08158v2.pdf | Practical X-ray Gastric Cancer Screening Using Refined Stochastic Data Augmentation and Hard Boundary Box Training | In gastric cancer screening, X-rays can be performed by radiographers, allowing them to see far more patients than endoscopy, which can only be performed by physicians. However, due to subsequent diagnostic difficulties, the sensitivity of gastric X-ray is only 85.5%, and little research has been done on automated diagnostic aids that directly target gastric cancer. This paper proposes a practical gastric cancer screening system for X-ray images taken under realistic clinical imaging conditions. Our system not only provides a diagnostic result for each image, but also provides an explanation for the result by displaying candidate cancer areas with bounding boxes. Training object detection models to do this was very expensive in terms of assigning supervised labels, and had the disadvantage of not being able to use negative (i.e., non-cancer) data for training. Our proposal consists of two novel techniques: (1) refined stochastic gastric image augmentation (R-sGAIA) and (2) hard boundary box training (HBBT). The R-sGAIA probabilistically highlights the gastric folds in the X-ray image based on medical knowledge, thus increasing the detection efficiency of gastric cancer. The HBBT is a new, efficient, and versatile training method that can reduce the number of false positive detections by actively using negative samples. The results showed that the proposed R-sGAIA and HBBT significantly improved the F1 score by 5.9% compared to the baseline EfficientDet-D7 + RandAugment (F1: 57.8%, recall: 90.2%, precision: 42.5%). This score is higher than the physician's cancer detection rate, indicating that at least 2 out of 5 areas detected are cancerous, confirming the utility of gastric cancer screening. | ['Hitoshi Iyatomi', 'Jun Hashimoto', 'Kazuhito Nabeshima', 'Takakiyo Nomura', 'Hideaki Okamoto'] | 2021-08-18 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 2.19602153e-01 6.06042027e-01 -2.07239375e-01 1.18004225e-01
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9c8c1d1c-dbe1-48e9-b165-95f1af21484e | an-end-to-end-ocr-text-re-organization | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4879_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700086.pdf | An End-to-End OCR Text Re-organization Sequence Learning for Rich-text Detail Image Comprehension | Nowadays rich description on detail images help users know more about the commodities. With the help of OCR technology, the description text can be detected and recognized as auxiliary information to remove the comprehending barriers among the visual impaired users. However, for lack of proper logical structure among these OCR text blocks, it is challenging to comprehend the detail images accurately. To tackle the above problems, we propose a novel end-to-end OCR text reorganizing model. Specifically, we create a Graph Neural Network with an attention map to encode the text blocks with visual layout features, with which an attention based sequence decoder inspired by the Pointer Network and a Sinkhorn global optimization will reorder the OCR text into a proper sequence. Experimental results illustrate that our model outperforms the other baselines, and the real experiment of the blind users' experience shows that our model improves their comprehension. | ['Zhi Yu', 'Jiajun Bu', 'Feiyu Gao', 'Yongpan Wang', 'Qi Zheng', 'Liangcheng Li'] | null | null | null | null | eccv-2020-8 | ['image-comprehension'] | ['computer-vision'] | [ 4.13458526e-01 5.84041029e-02 7.54998922e-02 -2.00467303e-01
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c5c70669-bafe-455f-95e2-3d4199e003ab | evaluation-and-analysis-of-different | 2202.08261 | null | https://arxiv.org/abs/2202.08261v2 | https://arxiv.org/pdf/2202.08261v2.pdf | Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentation | Availability of large, diverse, and multi-national datasets is crucial for the development of effective and clinically applicable AI systems in the medical imaging domain. However, forming a global model by bringing these datasets together at a central location, comes along with various data privacy and ownership problems. To alleviate these problems, several recent studies focus on the federated learning paradigm, a distributed learning approach for decentralized data. Federated learning leverages all the available data without any need for sharing collaborators' data with each other or collecting them on a central server. Studies show that federated learning can provide competitive performance with conventional central training, while having a good generalization capability. In this work, we have investigated several federated learning approaches on the brain tumor segmentation problem. We explore different strategies for faster convergence and better performance which can also work on strong Non-IID cases. | ['Alptekin Temizel', 'Altan Kocyigit', 'Gorkem Polat', 'Ece Isik-Polat'] | 2022-02-16 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [-2.88898289e-01 9.65425149e-02 -5.32927513e-01 -6.35165691e-01
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2a7ef47c-54e3-4ea0-b5ab-b9fc273167ad | mutual-balancing-in-state-object-components | 2211.10647 | null | https://arxiv.org/abs/2211.10647v1 | https://arxiv.org/pdf/2211.10647v1.pdf | Mutual Balancing in State-Object Components for Compositional Zero-Shot Learning | Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions from seen states and objects. The disparity between the manually labeled semantic information and its actual visual features causes a significant imbalance of visual deviation in the distribution of various object classes and state classes, which is ignored by existing methods. To ameliorate these issues, we consider the CZSL task as an unbalanced multi-label classification task and propose a novel method called MUtual balancing in STate-object components (MUST) for CZSL, which provides a balancing inductive bias for the model. In particular, we split the classification of the composition classes into two consecutive processes to analyze the entanglement of the two components to get additional knowledge in advance, which reflects the degree of visual deviation between the two components. We use the knowledge gained to modify the model's training process in order to generate more distinct class borders for classes with significant visual deviations. Extensive experiments demonstrate that our approach significantly outperforms the state-of-the-art on MIT-States, UT-Zappos, and C-GQA when combined with the basic CZSL frameworks, and it can improve various CZSL frameworks. Our codes are available on https://anonymous.4open.science/r/MUST_CGE/. | ['Ling Shao', 'Haofeng Zhang', 'Yuming Shen', 'Shidong Wang', 'Dubing Chen', 'Chenyi Jiang'] | 2022-11-19 | null | null | null | null | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 3.68098646e-01 -8.56506750e-02 -3.66250813e-01 -2.03791901e-01
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84742806-46d4-4521-aa40-aa03cce433a4 | data-driven-color-augmentation-techniques-for | 1703.03702 | null | http://arxiv.org/abs/1703.03702v1 | http://arxiv.org/pdf/1703.03702v1.pdf | Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis | Dermoscopic skin images are often obtained with different imaging devices,
under varying acquisition conditions. In this work, instead of attempting to
perform intensity and color normalization, we propose to leverage computational
color constancy techniques to build an artificial data augmentation technique
suitable for this kind of images. Specifically, we apply the \emph{shades of
gray} color constancy technique to color-normalize the entire training set of
images, while retaining the estimated illuminants. We then draw one sample from
the distribution of training set illuminants and apply it on the normalized
image. We employ this technique for training two deep convolutional neural
networks for the tasks of skin lesion segmentation and skin lesion
classification, in the context of the ISIC 2017 challenge and without using any
external dermatologic image set. Our results on the validation set are
promising, and will be supplemented with extended results on the hidden test
set when available. | ['Aurélio Campilho', 'A. M. Mendonça', 'Guilherme Aresta', 'Teresa Araújo', 'Aitor Alvarez-Gila', 'Maria Ines Meyer', 'Adrian Galdran', 'Pedro Costa', 'Estibaliz Garrote', 'Cristina L. Saratxaga'] | 2017-03-10 | null | null | null | null | ['color-constancy', 'skin-lesion-segmentation', 'skin-lesion-classification'] | ['computer-vision', 'medical', 'medical'] | [ 1.04584205e+00 -1.02174357e-01 -1.79219037e-01 -4.03037101e-01
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5f0a2c37-1cdb-4019-a44f-d70ecdc604a0 | towards-generalizable-and-robust-text-to-sql | 2210.12674 | null | https://arxiv.org/abs/2210.12674v1 | https://arxiv.org/pdf/2210.12674v1.pdf | Towards Generalizable and Robust Text-to-SQL Parsing | Text-to-SQL parsing tackles the problem of mapping natural language questions to executable SQL queries. In practice, text-to-SQL parsers often encounter various challenging scenarios, requiring them to be generalizable and robust. While most existing work addresses a particular generalization or robustness challenge, we aim to study it in a more comprehensive manner. In specific, we believe that text-to-SQL parsers should be (1) generalizable at three levels of generalization, namely i.i.d., zero-shot, and compositional, and (2) robust against input perturbations. To enhance these capabilities of the parser, we propose a novel TKK framework consisting of Task decomposition, Knowledge acquisition, and Knowledge composition to learn text-to-SQL parsing in stages. By dividing the learning process into multiple stages, our framework improves the parser's ability to acquire general SQL knowledge instead of capturing spurious patterns, making it more generalizable and robust. Experimental results under various generalization and robustness settings show that our framework is effective in all scenarios and achieves state-of-the-art performance on the Spider, SParC, and CoSQL datasets. Code can be found at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/tkk. | ['Yongbin Li', 'Luo Si', 'Fei Huang', 'Binhua Li', 'Wai Lam', 'Wenxuan Zhang', 'Bowen Li', 'Chang Gao'] | 2022-10-23 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 4.89244983e-02 -7.01355413e-02 -1.99789599e-01 -7.61752427e-01
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444a157a-c864-406e-960d-d01af10f50fd | littleyolo-spp-a-delicate-real-time-vehicle | 2011.05940 | null | https://arxiv.org/abs/2011.05940v1 | https://arxiv.org/pdf/2011.05940v1.pdf | LittleYOLO-SPP: A Delicate Real-Time Vehicle Detection Algorithm | Vehicle detection in real-time is a challenging and important task. The existing real-time vehicle detection lacks accuracy and speed. Real-time systems must detect and locate vehicles during criminal activities like theft of vehicle and road traffic violations with high accuracy. Detection of vehicles in complex scenes with occlusion is also extremely difficult. In this study, a lightweight model of deep neural network LittleYOLO-SPP based on the YOLOv3-tiny network is proposed to detect vehicles effectively in real-time. The YOLOv3-tiny object detection network is improved by modifying its feature extraction network to increase the speed and accuracy of vehicle detection. The proposed network incorporated Spatial pyramid pooling into the network, which consists of different scales of pooling layers for concatenation of features to enhance network learning capability. The Mean square error (MSE) and Generalized IoU (GIoU) loss function for bounding box regression is used to increase the performance of the network. The network training includes vehicle-based classes from PASCAL VOC 2007,2012 and MS COCO 2014 datasets such as car, bus, and truck. LittleYOLO-SPP network detects the vehicle in real-time with high accuracy regardless of video frame and weather conditions. The improved network achieves a higher mAP of 77.44% on PASCAL VOC and 52.95% mAP on MS COCO datasets. | ['Esther Rani P', 'Sri Jamiya S'] | 2020-11-11 | null | null | null | null | ['fast-vehicle-detection'] | ['computer-vision'] | [-2.91344523e-01 -5.26373863e-01 3.85325029e-02 -4.02849555e-01
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2406f2f1-5dbf-44cb-9a56-6ba26388c4d2 | 2d-gans-meet-unsupervised-single-view-3d | 2207.10183 | null | https://arxiv.org/abs/2207.10183v1 | https://arxiv.org/pdf/2207.10183v1.pdf | 2D GANs Meet Unsupervised Single-view 3D Reconstruction | Recent research has shown that controllable image generation based on pre-trained GANs can benefit a wide range of computer vision tasks. However, less attention has been devoted to 3D vision tasks. In light of this, we propose a novel image-conditioned neural implicit field, which can leverage 2D supervisions from GAN-generated multi-view images and perform the single-view reconstruction of generic objects. Firstly, a novel offline StyleGAN-based generator is presented to generate plausible pseudo images with full control over the viewpoint. Then, we propose to utilize a neural implicit function, along with a differentiable renderer to learn 3D geometry from pseudo images with object masks and rough pose initializations. To further detect the unreliable supervisions, we introduce a novel uncertainty module to predict uncertainty maps, which remedy the negative effect of uncertain regions in pseudo images, leading to a better reconstruction performance. The effectiveness of our approach is demonstrated through superior single-view 3D reconstruction results of generic objects. | ['Xiaoming Liu', 'Feng Liu'] | 2022-07-20 | null | null | null | null | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 3.46709818e-01 5.67886531e-01 1.52517855e-01 -4.14771408e-01
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442b2efc-7d69-4ce0-989d-b4df492625c3 | on-second-thought-let-s-not-think-step-by | 2212.08061 | null | https://arxiv.org/abs/2212.08061v2 | https://arxiv.org/pdf/2212.08061v2.pdf | On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning | Generating a Chain of Thought (CoT) has been shown to consistently improve large language model (LLM) performance on a wide range of NLP tasks. However, prior work has mainly focused on logical reasoning tasks (e.g. arithmetic, commonsense QA); it remains unclear whether improvements hold for more diverse types of reasoning, especially in socially situated contexts. Concretely, we perform a controlled evaluation of zero-shot CoT across two socially sensitive domains: harmful questions and stereotype benchmarks. We find that zero-shot CoT reasoning in sensitive domains significantly increases a model's likelihood to produce harmful or undesirable output, with trends holding across different prompt formats and model variants. Furthermore, we show that harmful CoTs increase with model size, but decrease with improved instruction following. Our work suggests that zero-shot CoT should be used with caution on socially important tasks, especially when marginalized groups or sensitive topics are involved. | ['Diyi Yang', 'Michael Bernstein', 'William Held', 'Hongxin Zhang', 'Omar Shaikh'] | 2022-12-15 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 9.04778093e-02 6.15603387e-01 -5.44313975e-02 -5.04082680e-01
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96a4376b-640a-4360-8906-73d077719a88 | on-the-limitations-of-continual-learning-for | 2208.06568 | null | https://arxiv.org/abs/2208.06568v1 | https://arxiv.org/pdf/2208.06568v1.pdf | On the Limitations of Continual Learning for Malware Classification | Malicious software (malware) classification offers a unique challenge for continual learning (CL) regimes due to the volume of new samples received on a daily basis and the evolution of malware to exploit new vulnerabilities. On a typical day, antivirus vendors receive hundreds of thousands of unique pieces of software, both malicious and benign, and over the course of the lifetime of a malware classifier, more than a billion samples can easily accumulate. Given the scale of the problem, sequential training using continual learning techniques could provide substantial benefits in reducing training and storage overhead. To date, however, there has been no exploration of CL applied to malware classification tasks. In this paper, we study 11 CL techniques applied to three malware tasks covering common incremental learning scenarios, including task, class, and domain incremental learning (IL). Specifically, using two realistic, large-scale malware datasets, we evaluate the performance of the CL methods on both binary malware classification (Domain-IL) and multi-class malware family classification (Task-IL and Class-IL) tasks. To our surprise, continual learning methods significantly underperformed naive Joint replay of the training data in nearly all settings -- in some cases reducing accuracy by more than 70 percentage points. A simple approach of selectively replaying 20% of the stored data achieves better performance, with 50% of the training time compared to Joint replay. Finally, we discuss potential reasons for the unexpectedly poor performance of the CL techniques, with the hope that it spurs further research on developing techniques that are more effective in the malware classification domain. | ['Matthew Wright', 'Scott E. Coull', 'Mohammad Saidur Rahman'] | 2022-08-13 | null | null | null | null | ['classification'] | ['methodology'] | [ 2.49323770e-01 -5.46432257e-01 -5.86708307e-01 -1.44180208e-01
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a208fb4e-d5bd-4f1e-ba1f-3af6d65e82b2 | a-sequence-to-sequence-approach-for-document | 2204.01098 | null | https://arxiv.org/abs/2204.01098v2 | https://arxiv.org/pdf/2204.01098v2.pdf | A sequence-to-sequence approach for document-level relation extraction | Motivated by the fact that many relations cross the sentence boundary, there has been increasing interest in document-level relation extraction (DocRE). DocRE requires integrating information within and across sentences, capturing complex interactions between mentions of entities. Most existing methods are pipeline-based, requiring entities as input. However, jointly learning to extract entities and relations can improve performance and be more efficient due to shared parameters and training steps. In this paper, we develop a sequence-to-sequence approach, seq2rel, that can learn the subtasks of DocRE (entity extraction, coreference resolution and relation extraction) end-to-end, replacing a pipeline of task-specific components. Using a simple strategy we call entity hinting, we compare our approach to existing pipeline-based methods on several popular biomedical datasets, in some cases exceeding their performance. We also report the first end-to-end results on these datasets for future comparison. Finally, we demonstrate that, under our model, an end-to-end approach outperforms a pipeline-based approach. Our code, data and trained models are available at {\url{https://github.com/johngiorgi/seq2rel}}. An online demo is available at {\url{https://share.streamlit.io/johngiorgi/seq2rel/main/demo.py}}. | ['Bo wang', 'Gary D. Bader', 'John Giorgi'] | 2022-04-03 | null | https://aclanthology.org/2022.bionlp-1.2 | https://aclanthology.org/2022.bionlp-1.2.pdf | bionlp-acl-2022-5 | ['document-level-relation-extraction', 'joint-entity-and-relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.13712573e-02 4.57619429e-01 -8.99426043e-02 -4.63373482e-01
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62b4ead1-d873-4efd-ba36-4838a309e348 | signet-scalable-embeddings-for-signed | 1702.06819 | null | http://arxiv.org/abs/1702.06819v5 | http://arxiv.org/pdf/1702.06819v5.pdf | Distributed Representations of Signed Networks | Recent successes in word embedding and document embedding have motivated
researchers to explore similar representations for networks and to use such
representations for tasks such as edge prediction, node label prediction, and
community detection. Such network embedding methods are largely focused on
finding distributed representations for unsigned networks and are unable to
discover embeddings that respect polarities inherent in edges. We propose
SIGNet, a fast scalable embedding method suitable for signed networks. Our
proposed objective function aims to carefully model the social structure
implicit in signed networks by reinforcing the principles of social balance
theory. Our method builds upon the traditional word2vec family of embedding
approaches and adds a new targeted node sampling strategy to maintain
structural balance in higher-order neighborhoods. We demonstrate the
superiority of SIGNet over state-of-the-art methods proposed for both signed
and unsigned networks on several real world datasets from different domains. In
particular, SIGNet offers an approach to generate a richer vocabulary of
features of signed networks to support representation and reasoning. | ['Naren Ramakrishnan', 'B. Aditya Prakash', 'Mohammad Raihanul Islam'] | 2017-02-22 | null | null | null | null | ['document-embedding'] | ['methodology'] | [-5.02396747e-03 6.39312267e-01 -7.03092754e-01 -3.91977429e-01
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3f13e8aa-ab0a-478c-9102-442c67f4889d | oneee-a-one-stage-framework-for-fast | 2209.02693 | null | https://arxiv.org/abs/2209.02693v1 | https://arxiv.org/pdf/2209.02693v1.pdf | OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction | Event extraction (EE) is an essential task of information extraction, which aims to extract structured event information from unstructured text. Most prior work focuses on extracting flat events while neglecting overlapped or nested ones. A few models for overlapped and nested EE includes several successive stages to extract event triggers and arguments,which suffer from error propagation. Therefore, we design a simple yet effective tagging scheme and model to formulate EE as word-word relation recognition, called OneEE. The relations between trigger or argument words are simultaneously recognized in one stage with parallel grid tagging, thus yielding a very fast event extraction speed. The model is equipped with an adaptive event fusion module to generate event-aware representations and a distance-aware predictor to integrate relative distance information for word-word relation recognition, which are empirically demonstrated to be effective mechanisms. Experiments on 3 overlapped and nested EE benchmarks, namely FewFC, Genia11, and Genia13, show that OneEE achieves the state-of-the-art (SOTA) results. Moreover, the inference speed of OneEE is faster than those of baselines in the same condition, and can be further substantially improved since it supports parallel inference. | ['Donghong Ji', 'Liang Zhao', 'Bobo Li', 'Shengqiong Wu', 'Hao Fei', 'Fei Li', 'Fangfang Su', 'Jingye Li', 'Hu Cao'] | 2022-09-06 | null | https://aclanthology.org/2022.coling-1.170 | https://aclanthology.org/2022.coling-1.170.pdf | coling-2022-10 | ['event-extraction'] | ['natural-language-processing'] | [ 1.20801158e-01 1.87916327e-02 -2.76161462e-01 -2.74146289e-01
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ad7bb817-213e-4c83-b338-b1fb05b50ef6 | generating-data-for-symbolic-language-with | 2305.13917 | null | https://arxiv.org/abs/2305.13917v1 | https://arxiv.org/pdf/2305.13917v1.pdf | Generating Data for Symbolic Language with Large Language Models | While large language models (LLMs) bring not only performance but also complexity, recent work has started to turn LLMs into data generators rather than task inferencers, where another affordable task model is trained for efficient deployment and inference. However, such an approach has primarily been applied to natural language tasks and has not yet been explored for symbolic language tasks with complex structured outputs (e.g., semantic parsing and code generation). In this paper, we propose SymGen which utilizes LLMs for generating various annotation-expensive symbolic language data. SymGen consists of an informative prompt to steer generation and an agreement-based verifier to improve data correctness. We conduct extensive experiments on six symbolic language tasks across various settings. Compared with the LLMs, we demonstrate the 1\%-sized task model can achieve comparable or better performance, largely cutting inference and deployment costs. We also show that generated data with only a few human demonstrations can be as effective as over 10 times the amount of human-annotated data when training the task model, saving a considerable amount of annotation effort. SymGen sheds new light on data generation for complex tasks, and we release the code at \href{https://github.com/HKUNLP/SymGen}{https://github.com/HKUNLP/SymGen}. | ['Tao Yu', 'Lingpeng Kong', 'Chengzu Li', 'Jiacheng Ye'] | 2023-05-23 | null | null | null | null | ['code-generation', 'semantic-parsing'] | ['computer-code', 'natural-language-processing'] | [ 1.89693466e-01 7.20615387e-01 -1.69459641e-01 -6.00019157e-01
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13715e1f-b22f-4acb-83d5-9d021079f475 | pmt-iqa-progressive-multi-task-learning-for | 2301.01182 | null | https://arxiv.org/abs/2301.01182v1 | https://arxiv.org/pdf/2301.01182v1.pdf | PMT-IQA: Progressive Multi-task Learning for Blind Image Quality Assessment | Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance. | ['Pei Yang', 'Jingyi Zhang', 'Letu Qingge', 'Ning Guo', 'Qingyi Pan'] | 2023-01-03 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [-5.77705679e-03 -9.41352487e-01 1.46963105e-01 -2.64535218e-01
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71b6f313-8174-4318-9c5e-7c9150314905 | triple-classification-for-scholarly-knowledge | 2111.11845 | null | https://arxiv.org/abs/2111.11845v1 | https://arxiv.org/pdf/2111.11845v1.pdf | Triple Classification for Scholarly Knowledge Graph Completion | Scholarly Knowledge Graphs (KGs) provide a rich source of structured information representing knowledge encoded in scientific publications. With the sheer volume of published scientific literature comprising a plethora of inhomogeneous entities and relations to describe scientific concepts, these KGs are inherently incomplete. We present exBERT, a method for leveraging pre-trained transformer language models to perform scholarly knowledge graph completion. We model triples of a knowledge graph as text and perform triple classification (i.e., belongs to KG or not). The evaluation shows that exBERT outperforms other baselines on three scholarly KG completion datasets in the tasks of triple classification, link prediction, and relation prediction. Furthermore, we present two scholarly datasets as resources for the research community, collected from public KGs and online resources. | ['Sören Auer', 'Markus Stocker', 'Kuldeep Singh', 'Mohamad Yaser Jaradeh'] | 2021-11-23 | null | null | null | null | ['triple-classification'] | ['graphs'] | [-4.14437473e-01 6.15994096e-01 -1.01192057e+00 1.59818791e-02
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c7aaa5a0-1feb-4336-b3b4-51f6ec4e35e0 | vision-aided-environment-semantics-extraction | 2301.08973 | null | https://arxiv.org/abs/2301.08973v2 | https://arxiv.org/pdf/2301.08973v2.pdf | Vision Aided Environment Semantics Extraction and Its Application in mmWave Beam Selection | In this letter, we propose a novel mmWave beam selection method based on the environment semantics extracted from user-side camera images. Specifically, we first define the environment semantics as the spatial distribution of the scatterers that affect the wireless propagation channels and utilize the keypoint detection technique to extract them from the input images. Then, we design a deep neural network with the environment semantics as the input that can output the optimal beam pairs at the mobile station (MS) and the base station (BS). Compared with the existing beam selection approaches that directly use images as the input, the proposed semantic-based method can explicitly obtain the environmental features that account for the propagation of wireless signals, thus reducing the storage and computational burden. Simulation results show that the proposed method can precisely estimate the location of the scatterers and outperform the existing works based on computer vision or light detection and ranging (LIDAR). | ['Guangyi Liu', 'Chengkang Pan', 'Feifei Gao', 'Weihua Xu', 'Feiyang Wen'] | 2023-01-21 | null | null | null | null | ['keypoint-detection'] | ['computer-vision'] | [ 1.21391360e-02 -5.72930336e-01 1.96769968e-01 -5.90641260e-01
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9ee0fd31-5b49-482b-98e5-e51a7ff1af4a | efficient-passage-retrieval-with-hashing-for | 2106.00882 | null | https://arxiv.org/abs/2106.00882v1 | https://arxiv.org/pdf/2106.00882v1.pdf | Efficient Passage Retrieval with Hashing for Open-domain Question Answering | Most state-of-the-art open-domain question answering systems use a neural retrieval model to encode passages into continuous vectors and extract them from a knowledge source. However, such retrieval models often require large memory to run because of the massive size of their passage index. In this paper, we introduce Binary Passage Retriever (BPR), a memory-efficient neural retrieval model that integrates a learning-to-hash technique into the state-of-the-art Dense Passage Retriever (DPR) to represent the passage index using compact binary codes rather than continuous vectors. BPR is trained with a multi-task objective over two tasks: efficient candidate generation based on binary codes and accurate reranking based on continuous vectors. Compared with DPR, BPR substantially reduces the memory cost from 65GB to 2GB without a loss of accuracy on two standard open-domain question answering benchmarks: Natural Questions and TriviaQA. Our code and trained models are available at https://github.com/studio-ousia/bpr. | ['Hannaneh Hajishirzi', 'Akari Asai', 'Ikuya Yamada'] | 2021-06-02 | null | https://aclanthology.org/2021.acl-short.123 | https://aclanthology.org/2021.acl-short.123.pdf | acl-2021-5 | ['triviaqa'] | ['miscellaneous'] | [-2.01054484e-01 -3.49751115e-01 -2.77400881e-01 -1.01318493e-01
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f96bee77-46f8-4585-be43-cd17527f4253 | graphpb-graphical-representations-of-prosody | 2012.02626 | null | https://arxiv.org/abs/2012.02626v1 | https://arxiv.org/pdf/2012.02626v1.pdf | GraphPB: Graphical Representations of Prosody Boundary in Speech Synthesis | This paper introduces a graphical representation approach of prosody boundary (GraphPB) in the task of Chinese speech synthesis, intending to parse the semantic and syntactic relationship of input sequences in a graphical domain for improving the prosody performance. The nodes of the graph embedding are formed by prosodic words, and the edges are formed by the other prosodic boundaries, namely prosodic phrase boundary (PPH) and intonation phrase boundary (IPH). Different Graph Neural Networks (GNN) like Gated Graph Neural Network (GGNN) and Graph Long Short-term Memory (G-LSTM) are utilised as graph encoders to exploit the graphical prosody boundary information. Graph-to-sequence model is proposed and formed by a graph encoder and an attentional decoder. Two techniques are proposed to embed sequential information into the graph-to-sequence text-to-speech model. The experimental results show that this proposed approach can encode the phonetic and prosody rhythm of an utterance. The mean opinion score (MOS) of these GNN models shows comparative results with the state-of-the-art sequence-to-sequence models with better performance in the aspect of prosody. This provides an alternative approach for prosody modelling in end-to-end speech synthesis. | ['Jing Xiao', 'Lingwei Kong', 'Zhen Zeng', 'Huayi Peng', 'Ning Cheng', 'Jianzong Wang', 'Aolan Sun'] | 2020-12-03 | null | null | null | null | ['graph-to-sequence'] | ['natural-language-processing'] | [ 2.11797431e-01 6.40032887e-01 -3.36271256e-01 -1.40017092e-01
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20650e11-fd84-4554-8bc2-cc6272e12032 | ibm-research-at-the-conll-2018-shared-task-on | null | null | https://aclanthology.org/K18-2009 | https://aclanthology.org/K18-2009.pdf | IBM Research at the CoNLL 2018 Shared Task on Multilingual Parsing | This paper presents the IBM Research AI submission to the CoNLL 2018 Shared Task on Parsing Universal Dependencies. Our system implements a new joint transition-based parser, based on the Stack-LSTM framework and the Arc-Standard algorithm, that handles tokenization, part-of-speech tagging, morphological tagging and dependency parsing in one single model. By leveraging a combination of character-based modeling of words and recursive composition of partially built linguistic structures we qualified 13th overall and 7th in low resource. We also present a new sentence segmentation neural architecture based on Stack-LSTMs that was the 4th best overall. | ['Miguel Ballesteros', 'Vittorio Castelli', 'Young-suk Lee', 'Hui Wan', 'Tahira Naseem'] | 2018-10-01 | null | null | null | conll-2018-10 | ['morphological-tagging'] | ['natural-language-processing'] | [ 1.40296444e-01 3.69290024e-01 -2.01375544e-01 -5.90236962e-01
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89ffc02a-9690-4fb0-91e8-9e49f5371e7b | pysindy-a-comprehensive-python-package-for | 2111.08481 | null | https://arxiv.org/abs/2111.08481v2 | https://arxiv.org/pdf/2111.08481v2.pdf | PySINDy: A comprehensive Python package for robust sparse system identification | Automated data-driven modeling, the process of directly discovering the governing equations of a system from data, is increasingly being used across the scientific community. PySINDy is a Python package that provides tools for applying the sparse identification of nonlinear dynamics (SINDy) approach to data-driven model discovery. In this major update to PySINDy, we implement several advanced features that enable the discovery of more general differential equations from noisy and limited data. The library of candidate terms is extended for the identification of actuated systems, partial differential equations (PDEs), and implicit differential equations. Robust formulations, including the integral form of SINDy and ensembling techniques, are also implemented to improve performance for real-world data. Finally, we provide a range of new optimization algorithms, including several sparse regression techniques and algorithms to enforce and promote inequality constraints and stability. Together, these updates enable entirely new SINDy model discovery capabilities that have not been reported in the literature, such as constrained PDE identification and ensembling with different sparse regression optimizers. | ['Steven L. Brunton', 'J. Nathan Kutz', 'Zachary G. Nicolaou', 'Charles B. Delahunt', 'Jared L. Callaham', 'Andy J. Goldschmidt', 'Jean-Christophe Loiseau', 'Kathleen Champion', 'Kadierdan Kaheman', 'Urban Fasel', 'Brian M. de Silva', 'Alan A. Kaptanoglu'] | 2021-11-12 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [-1.31707296e-01 -3.74574661e-01 7.30606467e-02 -1.19247347e-01
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8b20cb19-1e09-421e-849f-6d22bb113369 | visual-textual-attentive-semantic-consistency | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhou_Visual-Textual_Attentive_Semantic_Consistency_for_Medical_Report_Generation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhou_Visual-Textual_Attentive_Semantic_Consistency_for_Medical_Report_Generation_ICCV_2021_paper.pdf | Visual-Textual Attentive Semantic Consistency for Medical Report Generation | Diagnosing diseases from medical radiographs and writing reports requires professional knowledge and is time-consuming. To address this, automatic medical report generation approaches have recently gained interest. However, identifying diseases as well as correctly predicting their corresponding sizes, locations and other medical description patterns, which is essential for generating high-quality reports, is challenging. Although previous methods focused on producing readable reports, how to accurately detect and describe findings that match with the query X-Ray has not been successfully addressed. In this paper, we propose a multi-modality semantic attention model to integrate visual features, predicted key finding embeddings, as well as clinical features, and progressively decode reports with visual-textual semantic consistency. First, multi-modality features are extracted and attended with the hidden states from the sentence decoder, to encode enriched context vectors for better decoding a report. These modalities include regional visual features of scans, semantic word embeddings of the top-K findings predicted with high probabilities, and clinical features of indications. Second, the progressive report decoder consists of a sentence decoder and a word decoder, where we propose image-sentence matching and description accuracy losses to constrain the visual-textual semantic consistency. Extensive experiments on the public MIMIC-CXR and IU X-Ray datasets show that our model achieves consistent improvements over the state-of-the-art methods. | ['Ling Shao', 'Huazhu Fu', 'Tao Zhou', 'Lei Huang', 'Yi Zhou'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['medical-report-generation'] | ['medical'] | [ 4.91329491e-01 1.14700615e-01 -2.79829383e-01 -6.38361335e-01
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b3df0f4a-5cba-45a2-b628-8b4a98bba386 | tattoo-image-search-at-scale-joint-detection | 1811.00218 | null | http://arxiv.org/abs/1811.00218v1 | http://arxiv.org/pdf/1811.00218v1.pdf | Tattoo Image Search at Scale: Joint Detection and Compact Representation Learning | The explosive growth of digital images in video surveillance and social media
has led to the significant need for efficient search of persons of interest in
law enforcement and forensic applications. Despite tremendous progress in
primary biometric traits (e.g., face and fingerprint) based person
identification, a single biometric trait alone cannot meet the desired
recognition accuracy in forensic scenarios. Tattoos, as one of the important
soft biometric traits, have been found to be valuable for assisting in person
identification. However, tattoo search in a large collection of unconstrained
images remains a difficult problem, and existing tattoo search methods mainly
focus on matching cropped tattoos, which is different from real application
scenarios. To close the gap, we propose an efficient tattoo search approach
that is able to learn tattoo detection and compact representation jointly in a
single convolutional neural network (CNN) via multi-task learning. While the
features in the backbone network are shared by both tattoo detection and
compact representation learning, individual latent layers of each sub-network
optimize the shared features toward the detection and feature learning tasks,
respectively. We resolve the small batch size issue inside the joint tattoo
detection and compact representation learning network via random image stitch
and preceding feature buffering. We evaluate the proposed tattoo search system
using multiple public-domain tattoo benchmarks, and a gallery set with about
300K distracter tattoo images compiled from these datasets and images from the
Internet. In addition, we also introduce a tattoo sketch dataset containing 300
tattoos for sketch-based tattoo search. Experimental results show that the
proposed approach has superior performance in tattoo detection and tattoo
search at scale compared to several state-of-the-art tattoo retrieval
algorithms. | ['Xilin Chen', 'Anil K. Jain', 'Shiguang Shan', 'Jie Li', 'Hu Han'] | 2018-11-01 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 1.05996929e-01 -9.01191950e-01 -8.36296305e-02 -3.17044288e-01
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1adadcc1-3140-4eb2-ba88-28c8ba7832e7 | pppne-personalized-proximity-preserved | null | null | http://fange.pro/files/2021PPPNE.pdf | http://fange.pro/files/2021PPPNE.pdf | PPPNE: Personalized proximity preserved network embedding | After being proved extremely useful in many applications, the network embedding has played a critical role in the network analysis. Most of recent works usually model the network by minimizing the joint probability that the target node co-occurs with its neighboring nodes. These methods may fail to capture the personalized informativeness of each vertex. In this work, we propose a method named Personalized Proximity Preserved Network Embedding (PPPNE) to adaptively capture the personalization of vertices based on the personalized ranking loss. Our theoretical analysis shows that PPPNE generalizes prior work based on the matrix factorization or the neural network with a single layer, and we argue that preserving personalized proximity is the key to learning more informative representations. Moreover, to better capture the network structure in multiple scales, we exploit the distance ordering of each vertex. Our method can be efficiently optimized with a vertex-anchored sampling strategy. The results of extensive experiments on five real-world networks demonstrate that our approach outperforms state-of-the-art network embedding methods with a considerable improvement on several common tasks including link prediction and vertex classification. Additionally, PPPNE is efficient and can be easily accelerated by parallel computing, which enables PPPNE to work on large-scale networks. | ['Wei Zeng', 'Changjie Fan', 'Kai Wang', 'Jianrong Tao', 'Biao Geng', 'Ge Fan'] | 2022-02-01 | null | null | null | neurocomputing-2022-2 | ['network-embedding'] | ['methodology'] | [-1.01079218e-01 1.79904044e-01 -7.87872195e-01 -2.15167522e-01
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efd5d862-619a-49fd-b297-9b26f869ee1b | large-neural-networks-learning-from-scratch | 2205.08836 | null | https://arxiv.org/abs/2205.08836v2 | https://arxiv.org/pdf/2205.08836v2.pdf | Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization | Recent findings have shown that highly over-parameterized Neural Networks generalize without pretraining or explicit regularization. It is achieved with zero training error, i.e., complete over-fitting by memorizing the training data. This is surprising, since it is completely against traditional machine learning wisdom. In our empirical study we fortify these findings in the domain of fine-grained image classification. We show that very large Convolutional Neural Networks with millions of weights do learn with only a handful of training samples and without image augmentation, explicit regularization or pretraining. We train the architectures ResNet018, ResNet101 and VGG19 on subsets of the difficult benchmark datasets Caltech101, CUB_200_2011, FGVCAircraft, Flowers102 and StanfordCars with 100 classes and more, perform a comprehensive comparative study and draw implications for the practical application of CNNs. Finally, we show that a randomly initialized VGG19 with 140 million weights learns to distinguish airplanes and motorbikes with up to 95% accuracy using only 20 training samples per class. | ['Thomas Martinetz', 'Christoph Linse'] | 2022-05-18 | null | null | null | null | ['image-augmentation', 'fine-grained-image-classification'] | ['computer-vision', 'computer-vision'] | [ 1.54816806e-01 3.97907972e-01 -1.13667235e-01 -5.03081262e-01
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02d0b6f7-9deb-4385-87ae-d4708c9aa6e1 | prompting-language-models-for-linguistic | 2211.07830 | null | https://arxiv.org/abs/2211.07830v2 | https://arxiv.org/pdf/2211.07830v2.pdf | Prompting Language Models for Linguistic Structure | Although pretrained language models (PLMs) can be prompted to perform a wide range of language tasks, it remains an open question how much this ability comes from generalizable linguistic understanding versus surface-level lexical patterns. To test this, we present a structured prompting approach for linguistic structured prediction tasks, allowing us to perform zero- and few-shot sequence tagging with autoregressive PLMs. We evaluate this approach on part-of-speech tagging, named entity recognition, and sentence chunking, demonstrating strong few-shot performance in all cases. We also find that while PLMs contain significant prior knowledge of task labels due to task leakage into the pretraining corpus, structured prompting can also retrieve linguistic structure with arbitrary labels. These findings indicate that the in-context learning ability and linguistic knowledge of PLMs generalizes beyond memorization of their training data. | ['Luke Zettlemoyer', 'Hila Gonen', 'Terra Blevins'] | 2022-11-15 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 3.86098087e-01 5.34566998e-01 -4.00687903e-01 -6.47944868e-01
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3713f331-22ba-47b5-99a2-b14f618dc65a | machine-learning-enabled-experimental-design | 2306.02015 | null | https://arxiv.org/abs/2306.02015v1 | https://arxiv.org/pdf/2306.02015v1.pdf | Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics | Advanced experimental measurements are crucial for driving theoretical developments and unveiling novel phenomena in condensed matter and material physics, which often suffer from the scarcity of facility resources and increasing complexities. To address the limitations, we introduce a methodology that combines machine learning with Bayesian optimal experimental design (BOED), exemplified with x-ray photon fluctuation spectroscopy (XPFS) measurements for spin fluctuations. Our method employs a neural network model for large-scale spin dynamics simulations for precise distribution and utility calculations in BOED. The capability of automatic differentiation from the neural network model is further leveraged for more robust and accurate parameter estimation. Our numerical benchmarks demonstrate the superior performance of our method in guiding XPFS experiments, predicting model parameters, and yielding more informative measurements within limited experimental time. Although focusing on XPFS and spin fluctuations, our method can be adapted to other experiments, facilitating more efficient data collection and accelerating scientific discoveries. | ['Joshua J. Turner', 'Chun Hong Yoon', 'Sugata Chowdhury', 'Alana Okullo', 'Sathya R. Chitturi', 'Alexander N. Petsch', 'Cheng Peng', 'Zhantao Chen'] | 2023-06-03 | null | null | null | null | ['experimental-design'] | ['methodology'] | [ 1.61020622e-01 -5.66973269e-01 -4.85779554e-01 -4.00410861e-01
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29e660df-436b-4a82-bbd6-4a95217f4638 | ads-me-anomaly-detection-system-for-micro | 1903.04354 | null | http://arxiv.org/abs/1903.04354v1 | http://arxiv.org/pdf/1903.04354v1.pdf | ADS-ME: Anomaly Detection System for Micro-expression Spotting | Micro-expressions (MEs) are infrequent and uncontrollable facial events that
can highlight emotional deception and appear in a high-stakes environment. This
paper propose an algorithm for spatiotemporal MEs spotting. Since MEs are
unusual events, we treat them as abnormal patterns that diverge from expected
Normal Facial Behaviour (NFBs) patterns. NFBs correspond to facial muscle
activations, eye blink/gaze events and mouth opening/closing movements that are
all facial deformation but not MEs. We propose a probabilistic model to
estimate the probability density function that models the spatiotemporal
distributions of NFBs patterns. To rank the outputs, we compute the negative
log-likelihood and we developed an adaptive thresholding technique to identify
MEs from NFBs. While working only with NFBs data, the main challenge is to
capture intrinsic spatiotemoral features, hence we design a recurrent
convolutional autoencoder for feature representation. Finally, we show that our
system is superior to previous works for MEs spotting. | ['Alice Caplier', 'Dawood Al Chanti'] | 2019-03-11 | null | null | null | null | ['micro-expression-spotting'] | ['computer-vision'] | [ 3.70586440e-02 8.06751177e-02 -2.19555832e-02 -4.66124833e-01
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4355948f-48ea-4e7e-adf1-7820bedaa315 | implicit-compressibility-of-overparametrized | 2306.08125 | null | https://arxiv.org/abs/2306.08125v1 | https://arxiv.org/pdf/2306.08125v1.pdf | Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGD | Neural network compression has been an increasingly important subject, due to its practical implications in terms of reducing the computational requirements and its theoretical implications, as there is an explicit connection between compressibility and the generalization error. Recent studies have shown that the choice of the hyperparameters of stochastic gradient descent (SGD) can have an effect on the compressibility of the learned parameter vector. Even though these results have shed some light on the role of the training dynamics over compressibility, they relied on unverifiable assumptions and the resulting theory does not provide a practical guideline due to its implicitness. In this study, we propose a simple modification for SGD, such that the outputs of the algorithm will be provably compressible without making any nontrivial assumptions. We consider a one-hidden-layer neural network trained with SGD and we inject additive heavy-tailed noise to the iterates at each iteration. We then show that, for any compression rate, there exists a level of overparametrization (i.e., the number of hidden units), such that the output of the algorithm will be compressible with high probability. To achieve this result, we make two main technical contributions: (i) we build on a recent study on stochastic analysis and prove a 'propagation of chaos' result with improved rates for a class of heavy-tailed stochastic differential equations, and (ii) we derive strong-error estimates for their Euler discretization. We finally illustrate our approach on experiments, where the results suggest that the proposed approach achieves compressibility with a slight compromise from the training and test error. | ['Umut Simsekli', 'Abdellatif Zaidi', 'Yijun Wan'] | 2023-06-13 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 7.71503597e-02 1.43921182e-01 6.62889779e-02 -8.51762518e-02
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285b14fd-29c5-4687-9071-be038ecc97c7 | t-star-truthful-style-transfer-using-amr | 2212.01667 | null | https://arxiv.org/abs/2212.01667v1 | https://arxiv.org/pdf/2212.01667v1.pdf | T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation | Unavailability of parallel corpora for training text style transfer (TST) models is a very challenging yet common scenario. Also, TST models implicitly need to preserve the content while transforming a source sentence into the target style. To tackle these problems, an intermediate representation is often constructed that is devoid of style while still preserving the meaning of the source sentence. In this work, we study the usefulness of Abstract Meaning Representation (AMR) graph as the intermediate style agnostic representation. We posit that semantic notations like AMR are a natural choice for an intermediate representation. Hence, we propose T-STAR: a model comprising of two components, text-to-AMR encoder and a AMR-to-text decoder. We propose several modeling improvements to enhance the style agnosticity of the generated AMR. To the best of our knowledge, T-STAR is the first work that uses AMR as an intermediate representation for TST. With thorough experimental evaluation we show T-STAR significantly outperforms state of the art techniques by achieving on an average 15.2% higher content preservation with negligible loss (3% approx.) in style accuracy. Through detailed human evaluation with 90,000 ratings, we also show that T-STAR has up to 50% lesser hallucinations compared to state of the art TST models. | ['Aravindan Raghuveer', 'Preksha Nema', 'Anubhav Jangra'] | 2022-12-03 | null | null | null | null | ['text-style-transfoer'] | ['natural-language-processing'] | [ 3.97292972e-01 2.79284120e-01 1.71879325e-02 -4.72863883e-01
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34ddf460-61ef-4e91-99b2-26148f093f9c | b-bacn-bayesian-boundary-aware-convolutional | 2302.06827 | null | https://arxiv.org/abs/2302.06827v3 | https://arxiv.org/pdf/2302.06827v3.pdf | B-BACN: Bayesian Boundary-Aware Convolutional Network for Crack Characterization | Accurately detecting crack boundaries is crucial for reliability assessment and risk management of structures and materials, such as structural health monitoring, diagnostics, prognostics, and maintenance scheduling. Uncertainty quantification of crack detection is challenging due to various stochastic factors, such as measurement noises, signal processing, and model simplifications. A machine learning-based approach is proposed to quantify both epistemic and aleatoric uncertainties concurrently. We introduce a Bayesian Boundary-Aware Convolutional Network (B-BACN) that emphasizes uncertainty-aware boundary refinement to generate precise and reliable crack boundary detections. The proposed method employs a multi-task learning approach, where we use Monte Carlo Dropout to learn the epistemic uncertainty and a Gaussian sampling function to predict each sample's aleatoric uncertainty. Moreover, we include a boundary refinement loss to B-BACN to enhance the determination of defect boundaries. The proposed method is demonstrated with benchmark experimental results and compared with several existing methods. The experimental results illustrate the effectiveness of our proposed approach in uncertainty-aware crack boundary detection, minimizing misclassification rate, and improving model calibration capabilities. | ['Yongming Liu', 'Yutian Pang', 'Rahul Rathnakumar'] | 2023-02-14 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 1.52906418e-01 -1.27839059e-01 1.68242052e-01 -4.10599113e-01
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d060db4f-c9a9-4bbe-baae-9a30a1a6ad41 | joint-multilingual-supervision-for-cross | 1809.07657 | null | http://arxiv.org/abs/1809.07657v1 | http://arxiv.org/pdf/1809.07657v1.pdf | Joint Multilingual Supervision for Cross-lingual Entity Linking | Cross-lingual Entity Linking (XEL) aims to ground entity mentions written in
any language to an English Knowledge Base (KB), such as Wikipedia. XEL for most
languages is challenging, owing to limited availability of resources as
supervision. We address this challenge by developing the first XEL approach
that combines supervision from multiple languages jointly. This enables our
approach to: (a) augment the limited supervision in the target language with
additional supervision from a high-resource language (like English), and (b)
train a single entity linking model for multiple languages, improving upon
individually trained models for each language. Extensive evaluation on three
benchmark datasets across 8 languages shows that our approach significantly
improves over the current state-of-the-art. We also provide analyses in two
limited resource settings: (a) zero-shot setting, when no supervision in the
target language is available, and in (b) low-resource setting, when some
supervision in the target language is available. Our analysis provides insights
into the limitations of zero-shot XEL approaches in realistic scenarios, and
shows the value of joint supervision in low-resource settings. | ['Shyam Upadhyay', 'Dan Roth', 'Nitish Gupta'] | 2018-09-20 | joint-multilingual-supervision-for-cross-1 | https://aclanthology.org/D18-1270 | https://aclanthology.org/D18-1270.pdf | emnlp-2018-10 | ['cross-lingual-entity-linking'] | ['natural-language-processing'] | [-2.79052466e-01 3.31903726e-01 -7.01007783e-01 -8.07136521e-02
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d5158adf-47b0-4587-b3fb-cf97f0e9820c | autonoml-towards-an-integrated-framework-for | 2012.12600 | null | https://arxiv.org/abs/2012.12600v2 | https://arxiv.org/pdf/2012.12600v2.pdf | AutonoML: Towards an Integrated Framework for Autonomous Machine Learning | Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence, stimulated by advances in optimisation techniques and their impact on selecting ML models/algorithms. Central to this drive is the appeal of engineering a computational system that both discovers and deploys high-performance solutions to arbitrary ML problems with minimal human interaction. Beyond this, an even loftier goal is the pursuit of autonomy, which describes the capability of the system to independently adjust an ML solution over a lifetime of changing contexts. However, these ambitions are unlikely to be achieved in a robust manner without the broader synthesis of various mechanisms and theoretical frameworks, which, at the present time, remain scattered across numerous research threads. Accordingly, this review seeks to motivate a more expansive perspective on what constitutes an automated/autonomous ML system, alongside consideration of how best to consolidate those elements. In doing so, we survey developments in the following research areas: hyperparameter optimisation, multi-component models, neural architecture search, automated feature engineering, meta-learning, multi-level ensembling, dynamic adaptation, multi-objective evaluation, resource constraints, flexible user involvement, and the principles of generalisation. We also develop a conceptual framework throughout the review, augmented by each topic, to illustrate one possible way of fusing high-level mechanisms into an autonomous ML system. Ultimately, we conclude that the notion of architectural integration deserves more discussion, without which the field of automated ML risks stifling both its technical advantages and general uptake. | ['Bogdan Gabrys', 'Katarzyna Musial', 'David Jacob Kedziora'] | 2020-12-23 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [ 5.38617730e-01 1.49034381e-01 -1.32663339e-01 -2.84780502e-01
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940359ac-814d-4f85-8a87-edbdd0782b45 | improving-autoencoder-based-outlier-detection | 2304.00709 | null | https://arxiv.org/abs/2304.00709v1 | https://arxiv.org/pdf/2304.00709v1.pdf | Improving Autoencoder-based Outlier Detection with Adjustable Probabilistic Reconstruction Error and Mean-shift Outlier Scoring | Autoencoders were widely used in many machine learning tasks thanks to their strong learning ability which has drawn great interest among researchers in the field of outlier detection. However, conventional autoencoder-based methods lacked considerations in two aspects. This limited their performance in outlier detection. First, the mean squared error used in conventional autoencoders ignored the judgment uncertainty of the autoencoder, which limited their representation ability. Second, autoencoders suffered from the abnormal reconstruction problem: some outliers can be unexpectedly reconstructed well, making them difficult to identify from the inliers. To mitigate the aforementioned issues, two novel methods were proposed in this paper. First, a novel loss function named Probabilistic Reconstruction Error (PRE) was constructed to factor in both reconstruction bias and judgment uncertainty. To further control the trade-off of these two factors, two weights were introduced in PRE producing Adjustable Probabilistic Reconstruction Error (APRE), which benefited the outlier detection in different applications. Second, a conceptually new outlier scoring method based on mean-shift (MSS) was proposed to reduce the false inliers caused by the autoencoder. Experiments on 32 real-world outlier detection datasets proved the effectiveness of the proposed methods. The combination of the proposed methods achieved 41% of the relative performance improvement compared to the best baseline. The MSS improved the performance of multiple autoencoder-based outlier detectors by an average of 20%. The proposed two methods have the potential to advance autoencoder's development in outlier detection. The code is available on www.OutlierNet.com for reproducibility. | ['Susanto Rahardja', 'Sylwan Rahardja', 'Junqi Chen', 'Jiawei Yang', 'Xu Tan'] | 2023-04-03 | null | null | null | null | ['outlier-detection'] | ['methodology'] | [-4.77564484e-01 -1.44569039e-01 3.63828123e-01 -1.76077351e-01
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3a5bed1e-f06e-4292-bc54-7164e9d9f6ee | anomalous-motion-detection-on-highway-using | 2006.08143 | null | https://arxiv.org/abs/2006.08143v1 | https://arxiv.org/pdf/2006.08143v1.pdf | Anomalous Motion Detection on Highway Using Deep Learning | Research in visual anomaly detection draws much interest due to its applications in surveillance. Common datasets for evaluation are constructed using a stationary camera overlooking a region of interest. Previous research has shown promising results in detecting spatial as well as temporal anomalies in these settings. The advent of self-driving cars provides an opportunity to apply visual anomaly detection in a more dynamic application yet no dataset exists in this type of environment. This paper presents a new anomaly detection dataset - the Highway Traffic Anomaly (HTA) dataset - for the problem of detecting anomalous traffic patterns from dash cam videos of vehicles on highways. We evaluate state-of-the-art deep learning anomaly detection models and propose novel variations to these methods. Our results show that state-of-the-art models built for settings with a stationary camera do not translate well to a more dynamic environment. The proposed variations to these SoTA methods show promising results on the new HTA dataset. | ['Harpreet Singh', 'Emily M. Hand', 'Kostas Alexis'] | 2020-06-15 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 1.14976447e-02 -4.39043254e-01 1.57582268e-01 -4.07660455e-01
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dd4cb117-1cd1-49b1-bd96-b7894124acb1 | synclay-interactive-synthesis-of-histology | 2212.13780 | null | https://arxiv.org/abs/2212.13780v1 | https://arxiv.org/pdf/2212.13780v1.pdf | SynCLay: Interactive Synthesis of Histology Images from Bespoke Cellular Layouts | Automated synthesis of histology images has several potential applications in computational pathology. However, no existing method can generate realistic tissue images with a bespoke cellular layout or user-defined histology parameters. In this work, we propose a novel framework called SynCLay (Synthesis from Cellular Layouts) that can construct realistic and high-quality histology images from user-defined cellular layouts along with annotated cellular boundaries. Tissue image generation based on bespoke cellular layouts through the proposed framework allows users to generate different histological patterns from arbitrary topological arrangement of different types of cells. SynCLay generated synthetic images can be helpful in studying the role of different types of cells present in the tumor microenvironmet. Additionally, they can assist in balancing the distribution of cellular counts in tissue images for designing accurate cellular composition predictors by minimizing the effects of data imbalance. We train SynCLay in an adversarial manner and integrate a nuclear segmentation and classification model in its training to refine nuclear structures and generate nuclear masks in conjunction with synthetic images. During inference, we combine the model with another parametric model for generating colon images and associated cellular counts as annotations given the grade of differentiation and cell densities of different cells. We assess the generated images quantitatively and report on feedback from trained pathologists who assigned realism scores to a set of images generated by the framework. The average realism score across all pathologists for synthetic images was as high as that for the real images. We also show that augmenting limited real data with the synthetic data generated by our framework can significantly boost prediction performance of the cellular composition prediction task. | ['Nasir Rajpoot', 'Fayyaz Minhas', 'Muhammad Dawood', 'Srijay Deshpande'] | 2022-12-28 | null | null | null | null | ['nuclear-segmentation'] | ['medical'] | [ 3.18488330e-01 2.55415797e-01 3.14933300e-01 -1.04375727e-01
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cce69782-fdd2-4b78-97f8-c6e7d6e4ff34 | error-corrected-margin-based-deep-cross-modal | 2004.03378 | null | https://arxiv.org/abs/2004.03378v1 | https://arxiv.org/pdf/2004.03378v1.pdf | Error-Corrected Margin-Based Deep Cross-Modal Hashing for Facial Image Retrieval | Cross-modal hashing facilitates mapping of heterogeneous multimedia data into a common Hamming space, which can beutilized for fast and flexible retrieval across different modalities. In this paper, we propose a novel cross-modal hashingarchitecture-deep neural decoder cross-modal hashing (DNDCMH), which uses a binary vector specifying the presence of certainfacial attributes as an input query to retrieve relevant face images from a database. The DNDCMH network consists of two separatecomponents: an attribute-based deep cross-modal hashing (ADCMH) module, which uses a margin (m)-based loss function toefficiently learn compact binary codes to preserve similarity between modalities in the Hamming space, and a neural error correctingdecoder (NECD), which is an error correcting decoder implemented with a neural network. The goal of NECD network in DNDCMH isto error correct the hash codes generated by ADCMH to improve the retrieval efficiency. The NECD network is trained such that it hasan error correcting capability greater than or equal to the margin (m) of the margin-based loss function. This results in NECD cancorrect the corrupted hash codes generated by ADCMH up to the Hamming distance of m. We have evaluated and comparedDNDCMH with state-of-the-art cross-modal hashing methods on standard datasets to demonstrate the superiority of our method. | ['Fariborz Taherkhani', 'Nasser M. Nasrabadi', 'Matthew C. Valenti', 'Veeru Talreja'] | 2020-04-03 | null | null | null | null | ['face-image-retrieval'] | ['computer-vision'] | [-1.25998586e-01 -7.30175823e-02 -3.33986908e-01 -3.18523288e-01
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f4546d00-20ab-4032-a4e1-3ff4afb94324 | planning-for-manipulation-among-movable | 2303.13385 | null | https://arxiv.org/abs/2303.13385v1 | https://arxiv.org/pdf/2303.13385v1.pdf | Planning for Manipulation among Movable Objects: Deciding Which Objects Go Where, in What Order, and How | We are interested in pick-and-place style robot manipulation tasks in cluttered and confined 3D workspaces among movable objects that may be rearranged by the robot and may slide, tilt, lean or topple. A recently proposed algorithm, M4M, determines which objects need to be moved and where by solving a Multi-Agent Pathfinding MAPF abstraction of this problem. It then utilises a nonprehensile push planner to compute actions for how the robot might realise these rearrangements and a rigid body physics simulator to check whether the actions satisfy physics constraints encoded in the problem. However, M4M greedily commits to valid pushes found during planning, and does not reason about orderings over pushes if multiple objects need to be rearranged. Furthermore, M4M does not reason about other possible MAPF solutions that lead to different rearrangements and pushes. In this paper, we extend M4M and present Enhanced-M4M (E-M4M) -- a systematic graph search-based solver that searches over orderings of pushes for movable objects that need to be rearranged and different possible rearrangements of the scene. We introduce several algorithmic optimisations to circumvent the increased computational complexity, discuss the space of problems solvable by E-M4M and show that experimentally, both on the real robot and in simulation, it significantly outperforms the original M4M algorithm, as well as other state-of-the-art alternatives when dealing with complex scenes. | ['Maxim Likhachev', 'Dhruv Saxena'] | 2023-03-23 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 2.58956254e-01 4.12990600e-01 1.05141602e-01 8.95389635e-03
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cf07aeb5-ac81-431f-895c-c5947be43c91 | robust-importance-sampling-for-error | 2109.02150 | null | https://arxiv.org/abs/2109.02150v1 | https://arxiv.org/pdf/2109.02150v1.pdf | Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning | Classification has been a major task for building intelligent systems as it enables decision-making under uncertainty. Classifier design aims at building models from training data for representing feature-label distributions--either explicitly or implicitly. In many scientific or clinical settings, training data are typically limited, which makes designing accurate classifiers and evaluating their classification error extremely challenging. While transfer learning (TL) can alleviate this issue by incorporating data from relevant source domains to improve learning in a different target domain, it has received little attention for performance assessment, notably in error estimation. In this paper, we fill this gap by investigating knowledge transferability in the context of classification error estimation within a Bayesian paradigm. We introduce a novel class of Bayesian minimum mean-square error (MMSE) estimators for optimal Bayesian transfer learning (OBTL), which enables rigorous evaluation of classification error under uncertainty in a small-sample setting. Using Monte Carlo importance sampling, we employ the proposed estimator to evaluate the classification accuracy of a broad family of classifiers that span diverse learning capabilities. Experimental results based on both synthetic data as well as real-world RNA sequencing (RNA-seq) data show that our proposed OBTL error estimation scheme clearly outperforms standard error estimators, especially in a small-sample setting, by tapping into the data from other relevant domains. | ['Byung-Jun Yoon', 'Edward R. Dougherty', 'Francis J. Alexander', 'Xiaoning Qian', 'Omar Maddouri'] | 2021-09-05 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 7.77811646e-01 8.63115937e-02 -4.57796782e-01 -8.10506642e-01
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8ed69b9d-c89c-480b-929c-19a90cac413a | music-artist-classification-with-wavenet | 2004.04371 | null | https://arxiv.org/abs/2004.04371v3 | https://arxiv.org/pdf/2004.04371v3.pdf | MDCNN-SID: Multi-scale Dilated Convolution Network for Singer Identification | Most singer identification methods are processed in the frequency domain, which potentially leads to information loss during the spectral transformation. In this paper, instead of the frequency domain, we propose an end-to-end architecture that addresses this problem in the waveform domain. An encoder based on Multi-scale Dilated Convolution Neural Networks (MDCNN) was introduced to generate wave embedding from the raw audio signal. Specifically, dilated convolution layers are used in the proposed method to enlarge the receptive field, aiming to extract song-level features. Furthermore, skip connection in the backbone network integrates the multi-resolution acoustic features learned by the stack of convolution layers. Then, the obtained wave embedding is passed into the following networks for singer identification. In experiments, the proposed method achieves comparable performance on the benchmark dataset of Artist20, which significantly improves related works. | ['Jing Xiao', 'Ning Cheng', 'Jianzong Wang', 'xulong Zhang'] | 2020-04-09 | null | null | null | null | ['artist-classification', 'singer-identification'] | ['computer-vision', 'music'] | [ 3.37396055e-01 -3.11647683e-01 4.33897167e-01 -2.34409764e-01
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336094ab-d57a-4784-8efe-3d607a7c3b7b | hitsz-hlt-at-semeval-2021-task-5-ensemble | null | null | https://aclanthology.org/2021.semeval-1.63 | https://aclanthology.org/2021.semeval-1.63.pdf | HITSZ-HLT at SemEval-2021 Task 5: Ensemble Sequence Labeling and Span Boundary Detection for Toxic Span Detection | This paper presents the winning system that participated in SemEval-2021 Task 5: Toxic Spans Detection. This task aims to locate those spans that attribute to the text{'}s toxicity within a text, which is crucial for semi-automated moderation in online discussions. We formalize this task as the Sequence Labeling (SL) problem and the Span Boundary Detection (SBD) problem separately and employ three state-of-the-art models. Next, we integrate predictions of these models to produce a more credible and complement result. Our system achieves a char-level score of 70.83{\%}, ranking 1/91. In addition, we also explore the lexicon-based method, which is strongly interpretable and flexible in practice. | ['Ruifeng Xu', 'Yixue Dang', 'Qihui Lin', 'Xiang Li', 'Jingyi Sun', 'Yice Zhang', 'Zijie Lin', 'Qinglin Zhu'] | 2021-08-01 | null | null | null | semeval-2021 | ['boundary-detection', 'toxic-spans-detection'] | ['computer-vision', 'natural-language-processing'] | [ 2.44742811e-01 3.46229881e-01 -1.89062566e-01 4.19892371e-02
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55a86eac-d191-47c4-a81d-6c608aaa2a7d | hierarchical-kickstarting-for-skill-transfer | 2207.11584 | null | https://arxiv.org/abs/2207.11584v2 | https://arxiv.org/pdf/2207.11584v2.pdf | Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning | Practising and honing skills forms a fundamental component of how humans learn, yet artificial agents are rarely specifically trained to perform them. Instead, they are usually trained end-to-end, with the hope being that useful skills will be implicitly learned in order to maximise discounted return of some extrinsic reward function. In this paper, we investigate how skills can be incorporated into the training of reinforcement learning (RL) agents in complex environments with large state-action spaces and sparse rewards. To this end, we created SkillHack, a benchmark of tasks and associated skills based on the game of NetHack. We evaluate a number of baselines on this benchmark, as well as our own novel skill-based method Hierarchical Kickstarting (HKS), which is shown to outperform all other evaluated methods. Our experiments show that learning with a prior knowledge of useful skills can significantly improve the performance of agents on complex problems. We ultimately argue that utilising predefined skills provides a useful inductive bias for RL problems, especially those with large state-action spaces and sparse rewards. | ['Tim Rocktäschel', 'Edward Grefenstette', 'Jack Parker-Holder', 'Mikayel Samvelyan', 'Michael Matthews'] | 2022-07-23 | null | null | null | null | ['nethack'] | ['playing-games'] | [ 2.92268574e-01 2.99539655e-01 -4.99549992e-02 -2.06271246e-01
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eb52c411-cc28-4895-9e16-135a5f9a9185 | flexible-k-nearest-neighbors-classifier | 2304.10151 | null | https://arxiv.org/abs/2304.10151v1 | https://arxiv.org/pdf/2304.10151v1.pdf | Flexible K Nearest Neighbors Classifier: Derivation and Application for Ion-mobility Spectrometry-based Indoor Localization | The K Nearest Neighbors (KNN) classifier is widely used in many fields such as fingerprint-based localization or medicine. It determines the class membership of unlabelled sample based on the class memberships of the K labelled samples, the so-called nearest neighbors, that are closest to the unlabelled sample. The choice of K has been the topic of various studies and proposed KNN-variants. Yet no variant has been proven to outperform all other variants. In this paper a new KNN-variant is proposed which ensures that the K nearest neighbors are indeed close to the unlabelled sample and finds K along the way. The proposed algorithm is tested and compared to the standard KNN in theoretical scenarios and for indoor localization based on ion-mobility spectrometry fingerprints. It achieves a higher classification accuracy than the KNN in the tests, while requiring having the same computational demand. | ['Philipp Müller'] | 2023-04-20 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 3.87385875e-01 -4.15415376e-01 -5.03560960e-01 -4.78960276e-01
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a4f208ce-731e-4440-92c0-7db4c4e30290 | rotation-invariant-deep-cbir | 2006.13046 | null | https://arxiv.org/abs/2006.13046v1 | https://arxiv.org/pdf/2006.13046v1.pdf | Rotation Invariant Deep CBIR | Introduction of Convolutional Neural Networks has improved results on almost every image-based problem and Content-Based Image Retrieval is not an exception. But the CNN features, being rotation invariant, creates problems to build a rotation-invariant CBIR system. Though rotation-invariant features can be hand-engineered, the retrieval accuracy is very low because by hand engineering only low-level features can be created, unlike deep learning models that create high-level features along with low-level features. This paper shows a novel method to build a rotational invariant CBIR system by introducing a deep learning orientation angle detection model along with the CBIR feature extraction model. This paper also highlights that this rotation invariant deep CBIR can retrieve images from a large dataset in real-time. | ['Subhadip Maji', 'Smarajit Bose'] | 2020-06-21 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [-3.67687911e-01 -7.54498065e-01 -1.19070649e-01 -3.23313594e-01
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7e19c716-50be-49d5-94fd-85ab5240ed80 | gradient-free-structured-pruning-with | 2303.04185 | null | https://arxiv.org/abs/2303.04185v1 | https://arxiv.org/pdf/2303.04185v1.pdf | Gradient-Free Structured Pruning with Unlabeled Data | Large Language Models (LLMs) have achieved great success in solving difficult tasks across many domains, but such success comes with a high computation cost, and inference latency. As developers and third parties customize these models, the need to provide efficient inference has increased. Many efforts have attempted to reduce inference cost through model compression techniques such as pruning and distillation. However, these techniques either require labeled data, or are time-consuming as they require the compressed model to be retrained to regain accuracy. In this paper, we propose a gradient-free structured pruning framework that uses only unlabeled data. An evaluation on the GLUE and SQuAD benchmarks using BERT$_{BASE}$ and DistilBERT illustrates the effectiveness of the proposed approach. By only using the weights of the pre-trained model and unlabeled data, in a matter of a few minutes on a single GPU, up to 40% of the original FLOP count can be reduced with less than a 4% accuracy loss across all tasks considered. | ['Dale Schuurmans', 'Hanjun Dai', 'Azade Nova'] | 2023-03-07 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 1.06507115e-01 -6.30442202e-02 -1.55749902e-01 -6.18554592e-01
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370b61ad-5bce-4901-ac64-326b66077cb6 | towards-trustworthy-energy-disaggregation-a | 2207.02009 | null | https://arxiv.org/abs/2207.02009v1 | https://arxiv.org/pdf/2207.02009v1.pdf | Towards trustworthy Energy Disaggregation: A review of challenges, methods and perspectives for Non-Intrusive Load Monitoring | Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components. Over the years, signal processing and machine learning algorithms have been combined to achieve this. A lot of publications and extensive research works are performed on energy disaggregation or NILM for the state-of-the-art methods to reach on the desirable performance. The initial interest of the scientific community to formulate and describe mathematically the NILM problem using machine learning tools has now shifted into a more practical NILM. Nowadays, we are in the mature NILM period where there is an attempt for NILM to be applied in real-life application scenarios. Thus, complexity of the algorithms, transferability, reliability, practicality and in general trustworthiness are the main issues of interest. This review narrows the gap between the early immature NILM era and the mature one. In particular, the paper provides a comprehensive literature review of the NILM methods for residential appliances only. The paper analyzes, summarizes and presents the outcomes of a large number of recently published scholarly articles. Also, the paper discusses the highlights of these methods and introduces the research dilemmas that should be taken into consideration by researchers to apply NILM methods. Finally, we show the need for transferring the traditional disaggregation models into a practical and trustworthy framework. | ['Anastasios Doulamis', 'Nikolaos Doulamis', 'Athanasios Voulodimos', 'Eftychios Protopapadakis', 'Maria Kaselimi'] | 2022-07-05 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 6.42924160e-02 -2.29423180e-01 -3.08040231e-01 -2.99810797e-01
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b96e06de-bcdb-486a-977f-a8bbb6c80188 | permutation-models-for-collaborative-ranking | 1407.6128 | null | http://arxiv.org/abs/1407.6128v1 | http://arxiv.org/pdf/1407.6128v1.pdf | Permutation Models for Collaborative Ranking | We study the problem of collaborative filtering where ranking information is
available. Focusing on the core of the collaborative ranking process, the user
and their community, we propose new models for representation of the underlying
permutations and prediction of ranks. The first approach is based on the
assumption that the user makes successive choice of items in a stage-wise
manner. In particular, we extend the Plackett-Luce model in two ways -
introducing parameter factoring to account for user-specific contribution, and
modelling the latent community in a generative setting. The second approach
relies on log-linear parameterisation, which relaxes the discrete-choice
assumption, but makes learning and inference much more involved. We propose
MCMC-based learning and inference methods and derive linear-time prediction
algorithms. | ['Truyen Tran', 'Svetha Venkatesh'] | 2014-07-23 | null | null | null | null | ['collaborative-ranking'] | ['graphs'] | [ 1.68425873e-01 1.30849704e-01 -1.46385148e-01 -3.27556103e-01
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9a2b4672-8eb6-4178-af15-a040b39ec5f7 | rigidity-aware-detection-for-6d-object-pose | 2303.12396 | null | https://arxiv.org/abs/2303.12396v1 | https://arxiv.org/pdf/2303.12396v1.pdf | Rigidity-Aware Detection for 6D Object Pose Estimation | Most recent 6D object pose estimation methods first use object detection to obtain 2D bounding boxes before actually regressing the pose. However, the general object detection methods they use are ill-suited to handle cluttered scenes, thus producing poor initialization to the subsequent pose network. To address this, we propose a rigidity-aware detection method exploiting the fact that, in 6D pose estimation, the target objects are rigid. This lets us introduce an approach to sampling positive object regions from the entire visible object area during training, instead of naively drawing samples from the bounding box center where the object might be occluded. As such, every visible object part can contribute to the final bounding box prediction, yielding better detection robustness. Key to the success of our approach is a visibility map, which we propose to build using a minimum barrier distance between every pixel in the bounding box and the box boundary. Our results on seven challenging 6D pose estimation datasets evidence that our method outperforms general detection frameworks by a large margin. Furthermore, combined with a pose regression network, we obtain state-of-the-art pose estimation results on the challenging BOP benchmark. | ['Yinlin Hu', 'Mathieu Salzmann', 'Jiaojiao Li', 'Rui Song', 'Yang Hai'] | 2023-03-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hai_Rigidity-Aware_Detection_for_6D_Object_Pose_Estimation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hai_Rigidity-Aware_Detection_for_6D_Object_Pose_Estimation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.05867335e-01 2.02539757e-01 -2.04012781e-01 -1.37640148e-01
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d12818e8-a70c-4880-b5d1-4c6d78923572 | lymph-node-gross-tumor-volume-detection-in | 2008.13013 | null | https://arxiv.org/abs/2008.13013v1 | https://arxiv.org/pdf/2008.13013v1.pdf | Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network | Determining the spread of GTV$_{LN}$ is essential in defining the respective resection or irradiating regions for the downstream workflows of surgical resection and radiotherapy for many cancers. Different from the more common enlarged lymph node (LN), GTV$_{LN}$ also includes smaller ones if associated with high positron emission tomography signals and/or any metastasis signs in CT. This is a daunting task. In this work, we propose a unified LN appearance and inter-LN relationship learning framework to detect the true GTV$_{LN}$. This is motivated by the prior clinical knowledge that LNs form a connected lymphatic system, and the spread of cancer cells among LNs often follows certain pathways. Specifically, we first utilize a 3D convolutional neural network with ROI-pooling to extract the GTV$_{LN}$'s instance-wise appearance features. Next, we introduce a graph neural network to further model the inter-LN relationships where the global LN-tumor spatial priors are included in the learning process. This leads to an end-to-end trainable network to detect by classifying GTV$_{LN}$. We operate our model on a set of GTV$_{LN}$ candidates generated by a preliminary 1st-stage method, which has a sensitivity of $>85\%$ at the cost of high false positive (FP) ($>15$ FPs per patient). We validate our approach on a radiotherapy dataset with 142 paired PET/RTCT scans containing the chest and upper abdominal body parts. The proposed method significantly improves over the state-of-the-art (SOTA) LN classification method by $5.5\%$ and $13.1\%$ in F1 score and the averaged sensitivity value at $2, 3, 4, 6$ FPs per patient, respectively. | ['Tsung-Ying Ho', 'Chun-Hung Chao', 'Ke Yan', 'Xianghua Ye', 'Min Sun', 'Le Lu', 'Jinzheng Cai', 'Jing Xiao', 'Zhuotun Zhu', 'Dazhou Guo', 'Dakai Jin', 'Alan Yuille', 'Adam P. Harrison'] | 2020-08-29 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 1.83105543e-01 4.30870980e-01 -4.80869651e-01 -7.48878196e-02
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ad8d45c0-e4bd-48dc-8728-90a472747973 | advmil-adversarial-multiple-instance-learning | 2212.06515 | null | https://arxiv.org/abs/2212.06515v2 | https://arxiv.org/pdf/2212.06515v2.pdf | AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide Images | The survival analysis on histological whole-slide images (WSIs) is one of the most important means to estimate patient prognosis. Although many weakly-supervised deep learning models have been developed for gigapixel WSIs, their potential is generally restricted by classical survival analysis rules and fully-supervised learning requirements. As a result, these models provide patients only with a completely-certain point estimation of time-to-event, and they could only learn from the labeled WSI data currently at a small scale. To tackle these problems, we propose a novel adversarial multiple instance learning (AdvMIL) framework. This framework is based on adversarial time-to-event modeling, and integrates the multiple instance learning (MIL) that is much necessary for WSI representation learning. It is a plug-and-play one, so that most existing MIL-based end-to-end methods can be easily upgraded by applying this framework, gaining the improved abilities of survival distribution estimation and semi-supervised learning. Our extensive experiments show that AdvMIL not only could often bring performance improvement to mainstream WSI survival analysis methods at a relatively low computational cost, but also enables these methods to effectively utilize unlabeled data via semi-supervised learning. Moreover, it is observed that AdvMIL could help improving the robustness of models against patch occlusion and two representative image noises. The proposed AdvMIL framework could promote the research of survival analysis in computational pathology with its novel adversarial MIL paradigm. | ['Bo Fu', 'Feng Ye', 'Luping Ji', 'Pei Liu'] | 2022-12-13 | null | null | null | null | ['multiple-instance-learning', 'survival-analysis'] | ['methodology', 'miscellaneous'] | [ 1.03627786e-01 6.98874444e-02 -4.52451169e-01 -3.46531242e-01
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0f6d5226-dde9-42ec-a56a-203fd0437272 | leno-adversarial-robust-salient-object | 2210.15392 | null | https://arxiv.org/abs/2210.15392v2 | https://arxiv.org/pdf/2210.15392v2.pdf | LeNo: Adversarial Robust Salient Object Detection Networks with Learnable Noise | Pixel-wise prediction with deep neural network has become an effective paradigm for salient object detection (SOD) and achieved remarkable performance. However, very few SOD models are robust against adversarial attacks which are visually imperceptible for human visual attention. The previous work robust saliency (ROSA) shuffles the pre-segmented superpixels and then refines the coarse saliency map by the densely connected conditional random field (CRF). Different from ROSA that relies on various pre- and post-processings, this paper proposes a light-weight Learnable Noise (LeNo) to defend adversarial attacks for SOD models. LeNo preserves accuracy of SOD models on both adversarial and clean images, as well as inference speed. In general, LeNo consists of a simple shallow noise and noise estimation that embedded in the encoder and decoder of arbitrary SOD networks respectively. Inspired by the center prior of human visual attention mechanism, we initialize the shallow noise with a cross-shaped gaussian distribution for better defense against adversarial attacks. Instead of adding additional network components for post-processing, the proposed noise estimation modifies only one channel of the decoder. With the deeply-supervised noise-decoupled training on state-of-the-art RGB and RGB-D SOD networks, LeNo outperforms previous works not only on adversarial images but also on clean images, which contributes stronger robustness for SOD. Our code is available at https://github.com/ssecv/LeNo. | ['Lin Wan', 'He Wang', 'He Tang'] | 2022-10-27 | null | null | null | null | ['superpixels', 'noise-estimation'] | ['computer-vision', 'medical'] | [ 1.76647782e-01 5.33353761e-02 2.18860582e-01 -8.60318840e-02
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8b7000f2-5598-49a7-8052-8b2d4aa71aa0 | fully-convolutional-networks-for-panoptic-1 | 2108.07682 | null | https://arxiv.org/abs/2108.07682v3 | https://arxiv.org/pdf/2108.07682v3.pdf | Fully Convolutional Networks for Panoptic Segmentation with Point-based Supervision | In this paper, we present a conceptually simple, strong, and efficient framework for fully- and weakly-supervised panoptic segmentation, called Panoptic FCN. Our approach aims to represent and predict foreground things and background stuff in a unified fully convolutional pipeline, which can be optimized with point-based fully or weak supervision. In particular, Panoptic FCN encodes each object instance or stuff category with the proposed kernel generator and produces the prediction by convolving the high-resolution feature directly. With this approach, instance-aware and semantically consistent properties for things and stuff can be respectively satisfied in a simple generate-kernel-then-segment workflow. Without extra boxes for localization or instance separation, the proposed approach outperforms the previous box-based and -free models with high efficiency. Furthermore, we propose a new form of point-based annotation for weakly-supervised panoptic segmentation. It only needs several random points for both things and stuff, which dramatically reduces the annotation cost of human. The proposed Panoptic FCN is also proved to have much superior performance in this weakly-supervised setting, which achieves 82% of the fully-supervised performance with only 20 randomly annotated points per instance. Extensive experiments demonstrate the effectiveness and efficiency of Panoptic FCN on COCO, VOC 2012, Cityscapes, and Mapillary Vistas datasets. And it sets up a new leading benchmark for both fully- and weakly-supervised panoptic segmentation. Our code and models are made publicly available at https://github.com/dvlab-research/PanopticFCN. | ['Jiaya Jia', 'Jian Sun', 'Zeming Li', 'LiWei Wang', 'Lu Qi', 'Yukang Chen', 'Xiaojuan Qi', 'Hengshuang Zhao', 'Yanwei Li'] | 2021-08-17 | null | null | null | null | ['weakly-supervised-panoptic-segmentation'] | ['computer-vision'] | [-2.02775806e-01 -1.41924292e-01 -2.87283570e-01 -5.50258040e-01
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80cbe5e5-a347-4acd-bb28-e4849bf8283a | kuairec-a-fully-observed-dataset-for | 2202.10842 | null | https://arxiv.org/abs/2202.10842v3 | https://arxiv.org/pdf/2202.10842v3.pdf | KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems | The progress of recommender systems is hampered mainly by evaluation as it requires real-time interactions between humans and systems, which is too laborious and expensive. This issue is usually approached by utilizing the interaction history to conduct offline evaluation. However, existing datasets of user-item interactions are partially observed, leaving it unclear how and to what extent the missing interactions will influence the evaluation. To answer this question, we collect a fully-observed dataset from Kuaishou's online environment, where almost all 1,411 users have been exposed to all 3,327 items. To the best of our knowledge, this is the first real-world fully-observed data with millions of user-item interactions. With this unique dataset, we conduct a preliminary analysis of how the two factors - data density and exposure bias - affect the evaluation results of multi-round conversational recommendation. Our main discoveries are that the performance ranking of different methods varies with the two factors, and this effect can only be alleviated in certain cases by estimating missing interactions for user simulation. This demonstrates the necessity of the fully-observed dataset. We release the dataset and the pipeline implementation for evaluation at https://kuairec.com | ['Biao Li', 'Jiawei Chen', 'Tat-Seng Chua', 'Jiaxin Mao', 'Xiangnan He', 'Peng Jiang', 'Wenqiang Lei', 'Shijun Li', 'Chongming Gao'] | 2022-02-22 | null | null | null | null | ['user-simulation'] | ['natural-language-processing'] | [-2.52960891e-01 -2.42895886e-01 -1.26746178e-01 -4.64160353e-01
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e89ea8d9-e23c-478e-bb72-4730173ba2c4 | dialog-system-using-real-time-crowdsourcing | null | null | https://aclanthology.org/W12-1631 | https://aclanthology.org/W12-1631.pdf | Dialog System Using Real-Time Crowdsourcing and Twitter Large-Scale Corpus | null | ['Yasuo Kuniyoshi', 'Fumihiro Bessho', 'Tatsuya Harada'] | 2012-07-01 | null | null | null | ws-2012-7 | ['stock-market-prediction'] | ['time-series'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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cf9562e9-b0bd-4852-9679-305e4ca6c644 | discretize-optimize-vs-optimize-discretize | 2005.13420 | null | https://arxiv.org/abs/2005.13420v2 | https://arxiv.org/pdf/2005.13420v2.pdf | Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows | We compare the discretize-optimize (Disc-Opt) and optimize-discretize (Opt-Disc) approaches for time-series regression and continuous normalizing flows (CNFs) using neural ODEs. Neural ODEs are ordinary differential equations (ODEs) with neural network components. Training a neural ODE is an optimal control problem where the weights are the controls and the hidden features are the states. Every training iteration involves solving an ODE forward and another backward in time, which can require large amounts of computation, time, and memory. Comparing the Opt-Disc and Disc-Opt approaches in image classification tasks, Gholami et al. (2019) suggest that Disc-Opt is preferable due to the guaranteed accuracy of gradients. In this paper, we extend the comparison to neural ODEs for time-series regression and CNFs. Unlike in classification, meaningful models in these tasks must also satisfy additional requirements beyond accurate final-time output, e.g., the invertibility of the CNF. Through our numerical experiments, we demonstrate that with careful numerical treatment, Disc-Opt methods can achieve similar performance as Opt-Disc at inference with drastically reduced training costs. Disc-Opt reduced costs in six out of seven separate problems with training time reduction ranging from 39% to 97%, and in one case, Disc-Opt reduced training from nine days to less than one day. | ['Lars Ruthotto', 'Derek Onken'] | 2020-05-27 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-6.16743676e-02 -1.27343625e-01 2.75380425e-02 -4.47070338e-02
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254c31c4-3616-403e-a56e-9aa82e0210cb | characterizing-and-predicting-repeat-food | 1909.07683 | null | https://arxiv.org/abs/1909.07683v1 | https://arxiv.org/pdf/1909.07683v1.pdf | Characterizing and Predicting Repeat Food Consumption Behavior for Just-in-Time Interventions | Human beings are creatures of habit. In their daily life, people tend to repeatedly consume similar types of food items over several days and occasionally switch to consuming different types of items when the consumptions become overly monotonous. However, the novel and repeat consumption behaviors have not been studied in food recommendation research. More importantly, the ability to predict daily eating habits of individuals is crucial to improve the effectiveness of food recommender systems in facilitating healthy lifestyle change. In this study, we analyze the patterns of repeat food consumptions using large-scale consumption data from a popular online fitness community called MyFitnessPal (MFP), conduct an offline evaluation of various state-of-the-art algorithms in predicting the next-day food consumption, and analyze their performance across different demographic groups and contexts. The experiment results show that algorithms incorporating the exploration-and-exploitation and temporal dynamics are more effective in the next-day recommendation task than most state-of-the-art algorithms. | ['Shou-De Lin', 'Tzu-Ling Cheng', 'Ee-Peng Lim', 'Helena Lee', 'Yue Liu', 'Palakorn Achananuparp'] | 2019-09-17 | characterizing-and-predicting-repeat-food-1 | https://arxiv.org/abs/1909.07683 | https://arxiv.org/pdf/1909.07683.pdf | null | ['food-recommendation'] | ['miscellaneous'] | [-2.90714443e-01 -3.91557485e-01 -8.13448370e-01 -4.41002041e-01
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f9ad3c8d-b069-46f6-8e5f-af585059cfe4 | joint-stereo-video-deblurring-scene-flow | 1910.02442 | null | https://arxiv.org/abs/1910.02442v1 | https://arxiv.org/pdf/1910.02442v1.pdf | Joint Stereo Video Deblurring, Scene Flow Estimation and Moving Object Segmentation | Stereo videos for the dynamic scenes often show unpleasant blurred effects due to the camera motion and the multiple moving objects with large depth variations. Given consecutive blurred stereo video frames, we aim to recover the latent clean images, estimate the 3D scene flow and segment the multiple moving objects. These three tasks have been previously addressed separately, which fail to exploit the internal connections among these tasks and cannot achieve optimality. In this paper, we propose to jointly solve these three tasks in a unified framework by exploiting their intrinsic connections. To this end, we represent the dynamic scenes with the piece-wise planar model, which exploits the local structure of the scene and expresses various dynamic scenes. Under our model, these three tasks are naturally connected and expressed as the parameter estimation of 3D scene structure and camera motion (structure and motion for the dynamic scenes). By exploiting the blur model constraint, the moving objects and the 3D scene structure, we reach an energy minimization formulation for joint deblurring, scene flow and segmentation. We evaluate our approach extensively on both synthetic datasets and publicly available real datasets with fast-moving objects, camera motion, uncontrolled lighting conditions and shadows. Experimental results demonstrate that our method can achieve significant improvement in stereo video deblurring, scene flow estimation and moving object segmentation, over state-of-the-art methods. | ['Quan Pan', 'Miaomiao Liu', 'Fatih Porikli', 'Liyuan Pan', 'Yuchao Dai'] | 2019-10-06 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [ 2.60902315e-01 -5.10425687e-01 1.21084303e-01 2.98089385e-02
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ab7cb2b8-db9e-4c91-8d84-0910ff082a6e | c2slr-consistency-enhanced-continuous-sign | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zuo_C2SLR_Consistency-Enhanced_Continuous_Sign_Language_Recognition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zuo_C2SLR_Consistency-Enhanced_Continuous_Sign_Language_Recognition_CVPR_2022_paper.pdf | C2SLR: Consistency-Enhanced Continuous Sign Language Recognition | The backbone of most deep-learning-based continuous sign language recognition (CSLR) models consists of a visual module, a sequential module, and an alignment module. However, such CSLR backbones are hard to be trained sufficiently with a single connectionist temporal classification loss. In this work, we propose two auxiliary constraints to enhance the CSLR backbones from the perspective of consistency. The first constraint aims to enhance the visual module, which easily suffers from the insufficient training problem. Specifically, since sign languages convey information mainly with signers' faces and hands, we insert a keypoint-guided spatial attention module into the visual module to enforce it to focus on informative regions, i.e., spatial attention consistency. Nevertheless, only enhancing the visual module may not fully exploit the power of the backbone. Motivated by that both the output features of the visual and sequential modules represent the same sentence, we further impose a sentence embedding consistency constraint between them to enhance the representation power of both the features. Experimental results over three representative backbones validate the effectiveness of the two constraints. More remarkably, with a transformer-based backbone, our model achieves state-of-the-art or competitive performance on three benchmarks, PHOENIX-2014, PHOENIX-2014-T, and CSL. | ['Brian Mak', 'Ronglai Zuo'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['sign-language-recognition'] | ['computer-vision'] | [-3.95451905e-03 -2.64410004e-02 -2.48473659e-01 -4.47493017e-01
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d2147fb4-abe1-41a2-a8a4-8fb763930303 | bridging-cross-lingual-gaps-during-leveraging | 2204.07834 | null | https://arxiv.org/abs/2204.07834v2 | https://arxiv.org/pdf/2204.07834v2.pdf | Bridging Cross-Lingual Gaps During Leveraging the Multilingual Sequence-to-Sequence Pretraining for Text Generation and Understanding | For multilingual sequence-to-sequence pretrained language models (multilingual Seq2Seq PLMs), e.g. mBART, the self-supervised pretraining task is trained on a wide range of monolingual languages, e.g. 25 languages from CommonCrawl, while the downstream cross-lingual tasks generally progress on a bilingual language subset, e.g. English-German, making there exists the data discrepancy, namely domain discrepancy, and cross-lingual learning objective discrepancy, namely task discrepancy, between the pretraining and finetuning stages. To bridge the above cross-lingual domain and task gaps, we extend the vanilla pretrain-finetune pipeline with extra code-switching restore task. Specifically, the first stage employs the self-supervised code-switching restore task as a pretext task, allowing the multilingual Seq2Seq PLMs to acquire some in-domain alignment information. And for the second stage, we fine-tune the model on downstream data normally. Experiments on both NLG evaluation (12 bilingual translation tasks, 30 zero-shot translation tasks, and 2 cross-lingual summarization tasks) and NLU evaluation (7 cross-lingual natural language inference tasks) show our model outperforms the strong baseline mBART with standard finetuning strategy, consistently. Analyses indicate our approach could narrow the Euclidean distance of cross-lingual sentence representations, and improve the model generalization with trivial computational cost. We release the code at: https://github.com/zanchangtong/CSR4mBART. | ['DaCheng Tao', 'Weifeng Liu', 'Yu Cao', 'Li Shen', 'Liang Ding', 'Changtong Zan'] | 2022-04-16 | null | null | null | null | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [ 2.16622204e-01 -1.59338355e-01 -5.63093662e-01 -4.53566551e-01
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ff4c77dd-166a-46d0-a2ab-7ed0fcb0e3a5 | efficient-gradient-approximation-method-for | 2302.01970 | null | https://arxiv.org/abs/2302.01970v1 | https://arxiv.org/pdf/2302.01970v1.pdf | Efficient Gradient Approximation Method for Constrained Bilevel Optimization | Bilevel optimization has been developed for many machine learning tasks with large-scale and high-dimensional data. This paper considers a constrained bilevel optimization problem, where the lower-level optimization problem is convex with equality and inequality constraints and the upper-level optimization problem is non-convex. The overall objective function is non-convex and non-differentiable. To solve the problem, we develop a gradient-based approach, called gradient approximation method, which determines the descent direction by computing several representative gradients of the objective function inside a neighborhood of the current estimate. We show that the algorithm asymptotically converges to the set of Clarke stationary points, and demonstrate the efficacy of the algorithm by the experiments on hyperparameter optimization and meta-learning. | ['Minghui Zhu', 'Siyuan Xu'] | 2023-02-03 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-5.45317054e-01 -9.32649598e-02 -4.20112342e-01 -2.15748444e-01
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d4fdfe76-746c-4b85-9a81-59ed80116c77 | molecular-insights-from-conformational | null | null | https://www.cell.com/biophysj/fulltext/S0006-3495(19)34401-7 | https://www.cell.com/biophysj/pdfExtended/S0006-3495(19)34401-7 | Molecular Insights from Conformational Ensembles via Machine Learning | Biomolecular simulations are intrinsically high dimensional and generate noisy data sets of ever-increasing size. Extracting important features from the data is crucial for understanding the biophysical properties of molecular processes, but remains a big challenge. Machine learning (ML) provides powerful dimensionality reduction tools. However, such methods are often criticized as resembling black boxes with limited human-interpretable insight. We use methods from supervised and unsupervised ML to efficiently create interpretable maps of important features from molecular simulations. We benchmark the performance of several methods, including neural networks, random forests, and principal component analysis, using a toy model with properties reminiscent of macromolecular behavior. We then analyze three diverse biological processes: conformational changes within the soluble protein calmodulin, ligand binding to a G protein-coupled receptor, and activation of an ion channel voltage-sensor domain, unraveling features critical for signal transduction, ligand binding, and voltage sensing. This work demonstrates the usefulness of ML in understanding biomolecular states and demystifying complex simulations. | ['Fleetwood O', 'Kasimova MA', 'Delemotte L', 'Westerlund AM'] | 2020-02-04 | null | null | null | biophys-journal-2020-2 | ['physical-simulations'] | ['miscellaneous'] | [ 5.63060820e-01 -4.05794442e-01 -1.90933600e-01 -3.10901970e-01
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e04c2838-ba2f-4699-b062-8971652ff27f | deep-learning-for-conversational-ai | null | null | https://aclanthology.org/N18-6006 | https://aclanthology.org/N18-6006.pdf | Deep Learning for Conversational AI | Spoken Dialogue Systems (SDS) have great commercial potential as they promise to revolutionise the way in which humans interact with machines. The advent of deep learning led to substantial developments in this area of NLP research, and the goal of this tutorial is to familiarise the research community with the recent advances in what some call the most difficult problem in NLP. From a research perspective, the design of spoken dialogue systems provides a number of significant challenges, as these systems depend on: a) solving several difficult NLP and decision-making tasks; and b) combining these into a functional dialogue system pipeline. A key long-term goal of dialogue system research is to enable open-domain systems that can converse about arbitrary topics and assist humans with completing a wide range of tasks. Furthermore, such systems need to autonomously learn on-line to improve their performance and recover from errors using both signals from their environment and from implicit and explicit user feedback. While the design of such systems has traditionally been modular, domain and language-specific, advances in deep learning have alleviated many of the design problems. The main purpose of this tutorial is to encourage dialogue research in the NLP community by providing the research background, a survey of available resources, and giving key insights to application of state-of-the-art SDS methodology into industry-scale conversational AI systems. We plan to introduce researchers to the pipeline framework for modelling goal-oriented dialogue systems, which includes three key components: 1) Language Understanding; 2) Dialogue Management; and 3) Language Generation. The differences between goal-oriented dialogue systems and chat-bot style conversational agents will be explained in order to show the motivation behind the design of both, with the main focus on the pipeline SDS framework. For each key component, we will define the research problem, provide a brief literature review and introduce the current state-of-the-art approaches. Complementary resources (e.g. available datasets and toolkits) will also be discussed. Finally, future work, outstanding challenges, and current industry practices will be presented. All of the presented material will be made available online for future reference. | ["Ivan Vuli{\\'c}", 'I{\\~n}igo Casanueva', "Nikola Mrk{\\v{s}}i{\\'c}", 'Pei-Hao Su'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 9.41050947e-02 6.65488482e-01 -1.24427071e-02 -5.00805855e-01
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5819425c-e5ab-4691-90f7-3dcdb9f95e69 | that-is-a-suspicious-reaction-interpreting-1 | 2204.04636 | null | https://arxiv.org/abs/2204.04636v2 | https://arxiv.org/pdf/2204.04636v2.pdf | "That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks | Adversarial attacks are a major challenge faced by current machine learning research. These purposely crafted inputs fool even the most advanced models, precluding their deployment in safety-critical applications. Extensive research in computer vision has been carried to develop reliable defense strategies. However, the same issue remains less explored in natural language processing. Our work presents a model-agnostic detector of adversarial text examples. The approach identifies patterns in the logits of the target classifier when perturbing the input text. The proposed detector improves the current state-of-the-art performance in recognizing adversarial inputs and exhibits strong generalization capabilities across different NLP models, datasets, and word-level attacks. | ['Javier Rando', 'Georg Groh', 'Shreyash Agarwal', 'Edoardo Mosca'] | 2022-04-10 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 4.51701254e-01 -1.45176351e-01 -2.14413881e-01 -1.61840826e-01
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c6cccb58-005d-4b8e-b381-d40b916b6d70 | nasgem-neural-architecture-search-via-graph | 2007.04452 | null | https://arxiv.org/abs/2007.04452v2 | https://arxiv.org/pdf/2007.04452v2.pdf | NASGEM: Neural Architecture Search via Graph Embedding Method | Neural Architecture Search (NAS) automates and prospers the design of neural networks. Estimator-based NAS has been proposed recently to model the relationship between architectures and their performance to enable scalable and flexible search. However, existing estimator-based methods encode the architecture into a latent space without considering graph similarity. Ignoring graph similarity in node-based search space may induce a large inconsistency between similar graphs and their distance in the continuous encoding space, leading to inaccurate encoding representation and/or reduced representation capacity that can yield sub-optimal search results. To preserve graph correlation information in encoding, we propose NASGEM which stands for Neural Architecture Search via Graph Embedding Method. NASGEM is driven by a novel graph embedding method equipped with similarity measures to capture the graph topology information. By precisely estimating the graph distance and using an auxiliary Weisfeiler-Lehman kernel to guide the encoding, NASGEM can utilize additional structural information to get more accurate graph representation to improve the search efficiency. GEMNet, a set of networks discovered by NASGEM, consistently outperforms networks crafted by existing search methods in classification tasks, i.e., with 0.4%-3.6% higher accuracy while having 11%- 21% fewer Multiply-Accumulates. We further transfer GEMNet for COCO object detection. In both one-stage and twostage detectors, our GEMNet surpasses its manually-crafted and automatically-searched counterparts. | ['Shi-Yu Li', 'Feng Liang', 'Hsin-Pai Cheng', 'Yiran Chen', 'Vikas Chandra', 'Yixing Zhang', 'Meng Li', 'Tunhou Zhang', 'Hai Li', 'Feng Yan'] | 2020-07-08 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [-1.28354847e-01 9.14191529e-02 -3.38675767e-01 -1.44855082e-01
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8e246e1d-aae5-4a59-b2c8-bb188bad950a | detection-of-network-and-sensor-cyber-attacks | 2104.03798 | null | https://arxiv.org/abs/2104.03798v1 | https://arxiv.org/pdf/2104.03798v1.pdf | Detection of Network and Sensor Cyber-Attacks in Platoons of Cooperative Autonomous Vehicles: a Sliding-Mode Observer Approach | Platoons of autonomous vehicles are being investigated as a way to increase road capacity and fuel efficiency. Cooperative Adaptive Cruise Control (CACC) is an approach to achieve such platoons, in which vehicles collaborate using wireless communication. While this collaboration improves performance, it also makes the vehicles vulnerable to cyber-attacks. In this paper the performance of a sliding mode observer (SMO) based approach to cyber-attack detection is analysed, considering simultaneous attacks on the communication and local sensors. To this end, the considered cyber-attacks are divided into three classes for which relevant theoretical properties are proven. Furthermore, the harm that attacks within each of these classes can do to the system while avoiding detection is analysed based on simulation examples. | ['Riccardo M. G. Ferrari', 'Twan Keijzer'] | 2021-04-08 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [ 1.23555712e-01 7.26183116e-01 -2.25496650e-01 1.19267434e-01
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cd04f981-260f-49ce-8936-160f17afcb90 | self-supervised-learning-with-swin | 2105.04553 | null | https://arxiv.org/abs/2105.04553v2 | https://arxiv.org/pdf/2105.04553v2.pdf | Self-Supervised Learning with Swin Transformers | We are witnessing a modeling shift from CNN to Transformers in computer vision. In this work, we present a self-supervised learning approach called MoBY, with Vision Transformers as its backbone architecture. The approach basically has no new inventions, which is combined from MoCo v2 and BYOL and tuned to achieve reasonably high accuracy on ImageNet-1K linear evaluation: 72.8% and 75.0% top-1 accuracy using DeiT-S and Swin-T, respectively, by 300-epoch training. The performance is slightly better than recent works of MoCo v3 and DINO which adopt DeiT as the backbone, but with much lighter tricks. More importantly, the general-purpose Swin Transformer backbone enables us to also evaluate the learnt representations on downstream tasks such as object detection and semantic segmentation, in contrast to a few recent approaches built on ViT/DeiT which only report linear evaluation results on ImageNet-1K due to ViT/DeiT not tamed for these dense prediction tasks. We hope our results can facilitate more comprehensive evaluation of self-supervised learning methods designed for Transformer architectures. Our code and models are available at https://github.com/SwinTransformer/Transformer-SSL, which will be continually enriched. | ['Han Hu', 'Yue Cao', 'Qi Dai', 'Zheng Zhang', 'Zhuliang Yao', 'Yutong Lin', 'Zhenda Xie'] | 2021-05-10 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 6.96339458e-02 2.20617607e-01 -3.33108276e-01 -4.09074277e-01
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d6de7847-0516-4636-954e-cb11f65734de | ultra-fine-entity-typing-with-prior-knowledge | 2305.12802 | null | https://arxiv.org/abs/2305.12802v1 | https://arxiv.org/pdf/2305.12802v1.pdf | Ultra-Fine Entity Typing with Prior Knowledge about Labels: A Simple Clustering Based Strategy | Ultra-fine entity typing (UFET) is the task of inferring the semantic types, from a large set of fine-grained candidates, that apply to a given entity mention. This task is especially challenging because we only have a small number of training examples for many of the types, even with distant supervision strategies. State-of-the-art models, therefore, have to rely on prior knowledge about the type labels in some way. In this paper, we show that the performance of existing methods can be improved using a simple technique: we use pre-trained label embeddings to cluster the labels into semantic domains and then treat these domains as additional types. We show that this strategy consistently leads to improved results, as long as high-quality label embeddings are used. We furthermore use the label clusters as part of a simple post-processing technique, which results in further performance gains. Both strategies treat the UFET model as a black box and can thus straightforwardly be used to improve a wide range of existing models. | ['Steven Schockaert', 'Zied Bouraoui', 'Na Li'] | 2023-05-22 | null | null | null | null | ['entity-typing'] | ['natural-language-processing'] | [ 7.98773170e-02 2.40155205e-01 -4.20193732e-01 -5.29479325e-01
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be4770a8-6bd7-4c9d-8f48-682670c2a949 | in-context-learning-through-the-bayesian | 2306.04891 | null | https://arxiv.org/abs/2306.04891v1 | https://arxiv.org/pdf/2306.04891v1.pdf | In-Context Learning through the Bayesian Prism | In-context learning is one of the surprising and useful features of large language models. How it works is an active area of research. Recently, stylized meta-learning-like setups have been devised that train these models on a sequence of input-output pairs $(x, f(x))$ from a function class using the language modeling loss and observe generalization to unseen functions from the same class. One of the main discoveries in this line of research has been that for several problems such as linear regression, trained transformers learn algorithms for learning functions in context. However, the inductive biases of these models resulting in this behavior are not clearly understood. A model with unlimited training data and compute is a Bayesian predictor: it learns the pretraining distribution. It has been shown that high-capacity transformers mimic the Bayesian predictor for linear regression. In this paper, we show empirical evidence of transformers exhibiting the behavior of this ideal learner across different linear and non-linear function classes. We also extend the previous setups to work in the multitask setting and verify that transformers can do in-context learning in this setup as well and the Bayesian perspective sheds light on this setting also. Finally, via the example of learning Fourier series, we study the inductive bias for in-context learning. We find that in-context learning may or may not have simplicity bias depending on the pretraining data distribution. | ['Navin Goyal', 'Madhur Panwar', 'Kabir Ahuja'] | 2023-06-08 | null | null | null | null | ['meta-learning'] | ['methodology'] | [ 2.23609626e-01 8.13457891e-02 -1.87881365e-01 -6.11022711e-01
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87ac4826-7127-4b62-8e50-3e4df9471f52 | morphological-analysis-without-expert | null | null | https://aclanthology.org/E17-2034 | https://aclanthology.org/E17-2034.pdf | Morphological Analysis without Expert Annotation | The task of morphological analysis is to produce a complete list of lemma+tag analyses for a given word-form. We propose a discriminative string transduction approach which exploits plain inflection tables and raw text corpora, thus obviating the need for expert annotation. Experiments on four languages demonstrate that our system has much higher coverage than a hand-engineered FST analyzer, and is more accurate than a state-of-the-art morphological tagger. | ['Garrett Nicolai', 'Grzegorz Kondrak'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['morphological-tagging'] | ['natural-language-processing'] | [ 5.09439409e-01 -5.86844459e-02 -1.34785905e-01 -1.13112599e-01
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107006b8-cada-488f-bbf1-200ef34ffe64 | nonlinear-distributional-gradient-temporal | 1805.07732 | null | http://arxiv.org/abs/1805.07732v3 | http://arxiv.org/pdf/1805.07732v3.pdf | Nonlinear Distributional Gradient Temporal-Difference Learning | We devise a distributional variant of gradient temporal-difference (TD)
learning. Distributional reinforcement learning has been demonstrated to
outperform the regular one in the recent study
\citep{bellemare2017distributional}. In the policy evaluation setting, we
design two new algorithms called distributional GTD2 and distributional TDC
using the Cram{\'e}r distance on the distributional version of the Bellman
error objective function, which inherits advantages of both the nonlinear
gradient TD algorithms and the distributional RL approach. In the control
setting, we propose the distributional Greedy-GQ using the similar derivation.
We prove the asymptotic almost-sure convergence of distributional GTD2 and TDC
to a local optimal solution for general smooth function approximators, which
includes neural networks that have been widely used in recent study to solve
the real-life RL problems. In each step, the computational complexities of
above three algorithms are linear w.r.t.\ the number of the parameters of the
function approximator, thus can be implemented efficiently for neural networks. | ['Chao Qu', 'Shie Mannor', 'Huan Xu'] | 2018-05-20 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-3.13215196e-01 3.72279212e-02 -3.13704282e-01 -2.45548651e-01
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cf355972-2816-44c9-a095-392619373979 | matching-objects-across-the-textured-smooth | 1306.3297 | null | http://arxiv.org/abs/1306.3297v1 | http://arxiv.org/pdf/1306.3297v1.pdf | Matching objects across the textured-smooth continuum | The problem of 3D object recognition is of immense practical importance, with
the last decade witnessing a number of breakthroughs in the state of the art.
Most of the previous work has focused on the matching of textured objects using
local appearance descriptors extracted around salient image points. The
recently proposed bag of boundaries method was the first to address directly
the problem of matching smooth objects using boundary features. However, no
previous work has attempted to achieve a holistic treatment of the problem by
jointly using textural and shape features which is what we describe herein. Due
to the complementarity of the two modalities, we fuse the corresponding
matching scores and learn their relative weighting in a data specific manner by
optimizing discriminative performance on synthetically distorted data. For the
textural description of an object we adopt a representation in the form of a
histogram of SIFT based visual words. Similarly the apparent shape of an object
is represented by a histogram of discretized features capturing local shape. On
a large public database of a diverse set of objects, the proposed method is
shown to outperform significantly both purely textural and purely shape based
approaches for matching across viewpoint variation. | ['Ognjen Arandjelovic'] | 2013-06-14 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [ 4.23402280e-01 -2.49396786e-01 -5.06953076e-02 -6.02340281e-01
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e4ac69d1-6721-4869-9df8-c18b81887ed6 | real-time-semantic-segmentation-via-multiply | 1911.07217 | null | https://arxiv.org/abs/1911.07217v1 | https://arxiv.org/pdf/1911.07217v1.pdf | Real-Time Semantic Segmentation via Multiply Spatial Fusion Network | Real-time semantic segmentation plays a significant role in industry applications, such as autonomous driving, robotics and so on. It is a challenging task as both efficiency and performance need to be considered simultaneously. To address such a complex task, this paper proposes an efficient CNN called Multiply Spatial Fusion Network (MSFNet) to achieve fast and accurate perception. The proposed MSFNet uses Class Boundary Supervision to process the relevant boundary information based on our proposed Multi-features Fusion Module which can obtain spatial information and enlarge receptive field. Therefore, the final upsampling of the feature maps of 1/8 original image size can achieve impressive results while maintaining a high speed. Experiments on Cityscapes and Camvid datasets show an obvious advantage of the proposed approach compared with the existing approaches. Specifically, it achieves 77.1% Mean IOU on the Cityscapes test dataset with the speed of 41 FPS for a 1024*2048 input, and 75.4% Mean IOU with the speed of 91 FPS on the Camvid test dataset. | ['Feng Lu', 'Zhiqiang Zhang', 'Haiyang Si', 'Gang Yu', 'Feifan Lv'] | 2019-11-17 | null | null | null | null | ['2048'] | ['playing-games'] | [ 1.39627293e-01 -3.90312374e-01 1.28031686e-01 -4.23171520e-01
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941c1b00-937a-4bbb-bab3-ee49799f6caf | intent-discovery-for-enterprise-virtual | null | null | https://aclanthology.org/2022.naacl-industry.23 | https://aclanthology.org/2022.naacl-industry.23.pdf | Intent Discovery for Enterprise Virtual Assistants: Applications of Utterance Embedding and Clustering to Intent Mining | A key challenge in the creation and refinement of virtual assistants is the ability to mine unlabeled utterance data to discover common intents. We develop an approach to this problem that combines large-scale pre-training and multi-task learning to derive a semantic embedding that can be leveraged to identify clusters of utterances that correspond to unhandled intents. An utterance encoder is first trained with a language modeling objective and subsequently adapted to predict intent labels from a large collection of cross-domain enterprise virtual assistant data using a multi-task cosine softmax loss. Experimental evaluation shows significant advantages for this multi-step pre-training approach, with large gains in downstream clustering accuracy on new applications compared to standard sentence embedding approaches. The approach has been incorporated into an interactive discovery tool that enables visualization and exploration of intents by system analysts and builders. | ['Daniel Pressel', 'S. Eman Mahmoodi', 'Michael Johnston', 'Badrinath Jayakumar', 'Minhua Chen'] | null | null | null | null | naacl-acl-2022-7 | ['intent-discovery'] | ['natural-language-processing'] | [ 3.05882394e-01 6.58526421e-01 7.72631019e-02 -9.77707982e-01
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f27b59f5-bc14-40a5-b725-0693243a49d5 | mirror-matching-document-matching-approach-in | 2112.14318 | null | https://arxiv.org/abs/2112.14318v1 | https://arxiv.org/pdf/2112.14318v1.pdf | Mirror Matching: Document Matching Approach in Seed-driven Document Ranking for Medical Systematic Reviews | When medical researchers conduct a systematic review (SR), screening studies is the most time-consuming process: researchers read several thousands of medical literature and manually label them relevant or irrelevant. Screening prioritization (ie., document ranking) is an approach for assisting researchers by providing document rankings where relevant documents are ranked higher than irrelevant ones. Seed-driven document ranking (SDR) uses a known relevant document (ie., seed) as a query and generates such rankings. Previous work on SDR seeks ways to identify different term weights in a query document and utilizes them in a retrieval model to compute ranking scores. Alternatively, we formulate the SDR task as finding similar documents to a query document and produce rankings based on similarity scores. We propose a document matching measure named Mirror Matching, which calculates matching scores between medical abstract texts by incorporating common writing patterns, such as background, method, result, and conclusion in order. We conduct experiments on CLEF 2019 eHealth Task 2 TAR dataset, and the empirical results show this simple approach achieves the higher performance than traditional and neural retrieval models on Average Precision and Precision-focused metrics. | ['Aixin Sun', 'Grace E. Lee'] | 2021-12-28 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [ 6.68541193e-01 -3.52419764e-01 -6.39885783e-01 -3.68880630e-01
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9890fb02-bd5e-4d30-a081-51d0de3f25a2 | multi-head-temporal-attention-augmented | 2201.05459 | null | https://arxiv.org/abs/2201.05459v1 | https://arxiv.org/pdf/2201.05459v1.pdf | Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction | Financial time-series forecasting is one of the most challenging domains in the field of time-series analysis. This is mostly due to the highly non-stationary and noisy nature of financial time-series data. With progressive efforts of the community to design specialized neural networks incorporating prior domain knowledge, many financial analysis and forecasting problems have been successfully tackled. The temporal attention mechanism is a neural layer design that recently gained popularity due to its ability to focus on important temporal events. In this paper, we propose a neural layer based on the ideas of temporal attention and multi-head attention to extend the capability of the underlying neural network in focusing simultaneously on multiple temporal instances. The effectiveness of our approach is validated using large-scale limit-order book market data to forecast the direction of mid-price movements. Our experiments show that the use of multi-head temporal attention modules leads to enhanced prediction performances compared to baseline models. | ['Alexandros Iosifidis', 'Juho Kanniainen', 'Martin Magris', 'Dat Thanh Tran', 'Mostafa Shabani'] | 2022-01-14 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-2.72110641e-01 -3.17286372e-01 -9.13681984e-02 -4.61787611e-01
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1ae6c1c9-f021-43ae-9c4f-79a5de5190a0 | context-dependent-diffusion-network-for | 1809.06213 | null | http://arxiv.org/abs/1809.06213v1 | http://arxiv.org/pdf/1809.06213v1.pdf | Context-Dependent Diffusion Network for Visual Relationship Detection | Visual relationship detection can bridge the gap between computer vision and
natural language for scene understanding of images. Different from pure object
recognition tasks, the relation triplets of subject-predicate-object lie on an
extreme diversity space, such as \textit{person-behind-person} and
\textit{car-behind-building}, while suffering from the problem of combinatorial
explosion. In this paper, we propose a context-dependent diffusion network
(CDDN) framework to deal with visual relationship detection. To capture the
interactions of different object instances, two types of graphs, word semantic
graph and visual scene graph, are constructed to encode global context
interdependency. The semantic graph is built through language priors to model
semantic correlations across objects, whilst the visual scene graph defines the
connections of scene objects so as to utilize the surrounding scene
information. For the graph-structured data, we design a diffusion network to
adaptively aggregate information from contexts, which can effectively learn
latent representations of visual relationships and well cater to visual
relationship detection in view of its isomorphic invariance to graphs.
Experiments on two widely-used datasets demonstrate that our proposed method is
more effective and achieves the state-of-the-art performance. | ['Jian Yang', 'Zhen Cui', 'Wenming Zheng', 'Chunyan Xu'] | 2018-09-11 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 2.01070368e-01 -8.77943709e-02 -1.05149142e-01 -3.81649613e-01
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898435f0-b7ab-4aa2-af65-87aaa77be401 | mipa-mutual-information-based-paraphrase | null | null | https://aclanthology.org/I17-1009 | https://aclanthology.org/I17-1009.pdf | MIPA: Mutual Information Based Paraphrase Acquisition via Bilingual Pivoting | We present a pointwise mutual information (PMI)-based approach to formalize paraphrasability and propose a variant of PMI, called MIPA, for the paraphrase acquisition. Our paraphrase acquisition method first acquires lexical paraphrase pairs by bilingual pivoting and then reranks them by PMI and distributional similarity. The complementary nature of information from bilingual corpora and from monolingual corpora makes the proposed method robust. Experimental results show that the proposed method substantially outperforms bilingual pivoting and distributional similarity themselves in terms of metrics such as MRR, MAP, coverage, and Spearman{'}s correlation. | ['Daichi Mochihashi', 'Tomoyuki Kajiwara', 'Mamoru Komachi'] | 2017-11-01 | mipa-mutual-information-based-paraphrase-1 | https://aclanthology.org/I17-1009 | https://aclanthology.org/I17-1009.pdf | ijcnlp-2017-11 | ['learning-word-embeddings'] | ['methodology'] | [-1.07651256e-01 -3.60261917e-01 -8.35759580e-01 -4.52271849e-01
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c3532d12-e7a7-4969-b432-64420feb281a | single-channel-speech-dereverberation-using | 2204.08765 | null | https://arxiv.org/abs/2204.08765v2 | https://arxiv.org/pdf/2204.08765v2.pdf | Speech Dereverberation with A Reverberation Time Shortening Target | This work proposes a new learning target based on reverberation time shortening (RTS) for speech dereverberation. The learning target for dereverberation is usually set as the direct-path speech or optionally with some early reflections. This type of target suddenly truncates the reverberation, and thus it may not be suitable for network training. The proposed RTS target suppresses reverberation and meanwhile maintains the exponential decaying property of reverberation, which will ease the network training, and thus reduce signal distortion caused by the prediction error. Moreover, this work experimentally study to adapt our previously proposed FullSubNet speech denoising network to speech dereverberation. Experiments show that RTS is a more suitable learning target than direct-path speech and early reflections, in terms of better suppressing reverberation and signal distortion. FullSubNet is able to achieve outstanding dereverberation performance. | ['Xiaofei Li', 'Wenye Zhu', 'Rui Zhou'] | 2022-04-19 | null | null | null | null | ['speech-denoising', 'speech-dereverberation'] | ['speech', 'speech'] | [-1.35048598e-01 -2.07055047e-01 3.74876171e-01 -1.34209841e-01
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7e30f432-932d-4e4e-8cd1-d1eb5bd095b6 | characterbert-and-self-teaching-for-improving | 2204.00716 | null | https://arxiv.org/abs/2204.00716v2 | https://arxiv.org/pdf/2204.00716v2.pdf | CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos | Current dense retrievers are not robust to out-of-domain and outlier queries, i.e. their effectiveness on these queries is much poorer than what one would expect. In this paper, we consider a specific instance of such queries: queries that contain typos. We show that a small character level perturbation in queries (as caused by typos) highly impacts the effectiveness of dense retrievers. We then demonstrate that the root cause of this resides in the input tokenization strategy employed by BERT. In BERT, tokenization is performed using the BERT's WordPiece tokenizer and we show that a token with a typo will significantly change the token distributions obtained after tokenization. This distribution change translates to changes in the input embeddings passed to the BERT-based query encoder of dense retrievers. We then turn our attention to devising dense retriever methods that are robust to such queries with typos, while still being as performant as previous methods on queries without typos. For this, we use CharacterBERT as the backbone encoder and an efficient yet effective training method, called Self-Teaching (ST), that distills knowledge from queries without typos into the queries with typos. Experimental results show that CharacterBERT in combination with ST achieves significantly higher effectiveness on queries with typos compared to previous methods. Along with these results and the open-sourced implementation of the methods, we also provide a new passage retrieval dataset consisting of real-world queries with typos and associated relevance assessments on the MS MARCO corpus, thus supporting the research community in the investigation of effective and robust dense retrievers. Code, experimental results and dataset are made available at https://github.com/ielab/CharacterBERT-DR. | ['Guido Zuccon', 'Shengyao Zhuang'] | 2022-04-01 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-2.05107093e-01 -3.07488889e-01 -1.22122161e-01 -1.20871522e-01
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6d844367-ae00-4f54-ba4b-807dbbd30563 | si-lstm-speaker-hybrid-long-short-term-memory | 2305.03506 | null | https://arxiv.org/abs/2305.03506v3 | https://arxiv.org/pdf/2305.03506v3.pdf | SI-LSTM: Speaker Hybrid Long-short Term Memory and Cross Modal Attention for Emotion Recognition in Conversation | Emotion Recognition in Conversation~(ERC) across modalities is of vital importance for a variety of applications, including intelligent healthcare, artificial intelligence for conversation, and opinion mining over chat history. The crux of ERC is to model both cross-modality and cross-time interactions throughout the conversation. Previous methods have made progress in learning the time series information of conversation while lacking the ability to trace down the different emotional states of each speaker in a conversation. In this paper, we propose a recurrent structure called Speaker Information Enhanced Long-Short Term Memory (SI-LSTM) for the ERC task, where the emotional states of the distinct speaker can be tracked in a sequential way to enhance the learning of the emotion in conversation. Further, to improve the learning of multimodal features in ERC, we utilize a cross-modal attention component to fuse the features between different modalities and model the interaction of the important information from different modalities. Experimental results on two benchmark datasets demonstrate the superiority of the proposed SI-LSTM against the state-of-the-art baseline methods in the ERC task on multimodal data. | ['Ruifeng Xu', 'You Zou', 'Xingwei Liang'] | 2023-05-04 | null | null | null | null | ['emotion-recognition-in-conversation', 'opinion-mining'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.16059100e-03 -3.66584599e-01 1.26920924e-01 -5.91786087e-01
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d349740e-3ac4-4c87-855e-6086e906a614 | answering-ambiguous-questions-via-iterative | 2307.03897 | null | https://arxiv.org/abs/2307.03897v1 | https://arxiv.org/pdf/2307.03897v1.pdf | Answering Ambiguous Questions via Iterative Prompting | In open-domain question answering, due to the ambiguity of questions, multiple plausible answers may exist. To provide feasible answers to an ambiguous question, one approach is to directly predict all valid answers, but this can struggle with balancing relevance and diversity. An alternative is to gather candidate answers and aggregate them, but this method can be computationally costly and may neglect dependencies among answers. In this paper, we present AmbigPrompt to address the imperfections of existing approaches to answering ambiguous questions. Specifically, we integrate an answering model with a prompting model in an iterative manner. The prompting model adaptively tracks the reading process and progressively triggers the answering model to compose distinct and relevant answers. Additionally, we develop a task-specific post-pretraining approach for both the answering model and the prompting model, which greatly improves the performance of our framework. Empirical studies on two commonly-used open benchmarks show that AmbigPrompt achieves state-of-the-art or competitive results while using less memory and having a lower inference latency than competing approaches. Additionally, AmbigPrompt also performs well in low-resource settings. The code are available at: https://github.com/sunnweiwei/AmbigPrompt. | ['Zhaochun Ren', 'Maarten de Rijke', 'Zhumin Chen', 'Pengjie Ren', 'Hongshen Chen', 'Hengyi Cai', 'Weiwei Sun'] | 2023-07-08 | null | null | null | null | ['question-answering', 'open-domain-question-answering'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.72309905e-01 1.76195994e-01 -9.14882198e-02 -4.86666650e-01
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29f7cbc8-b03e-4484-bb38-b127fcb80589 | biot-cross-data-biosignal-learning-in-the | 2305.10351 | null | https://arxiv.org/abs/2305.10351v1 | https://arxiv.org/pdf/2305.10351v1.pdf | BIOT: Cross-data Biosignal Learning in the Wild | Biological signals, such as electroencephalograms (EEG), play a crucial role in numerous clinical applications, exhibiting diverse data formats and quality profiles. Current deep learning models for biosignals are typically specialized for specific datasets and clinical settings, limiting their broader applicability. Motivated by the success of large language models in text processing, we explore the development of foundational models that are trained from multiple data sources and can be fine-tuned on different downstream biosignal tasks. To overcome the unique challenges associated with biosignals of various formats, such as mismatched channels, variable sample lengths, and prevalent missing values, we propose a Biosignal Transformer (\method). The proposed \method model can enable cross-data learning with mismatched channels, variable lengths, and missing values by tokenizing diverse biosignals into unified "biosignal sentences". Specifically, we tokenize each channel into fixed-length segments containing local signal features, flattening them to form consistent "sentences". Channel embeddings and {\em relative} position embeddings are added to preserve spatio-temporal features. The \method model is versatile and applicable to various biosignal learning settings across different datasets, including joint pre-training for larger models. Comprehensive evaluations on EEG, electrocardiogram (ECG), and human activity sensory signals demonstrate that \method outperforms robust baselines in common settings and facilitates learning across multiple datasets with different formats. Use CHB-MIT seizure detection task as an example, our vanilla \method model shows 3\% improvement over baselines in balanced accuracy, and the pre-trained \method models (optimized from other data sources) can further bring up to 4\% improvements. | ['Jimeng Sun', 'M. Brandon Westover', 'Chaoqi Yang'] | 2023-05-10 | null | null | null | null | ['seizure-detection'] | ['medical'] | [ 3.65440547e-01 -4.57057863e-01 -1.86615512e-01 -4.88586783e-01
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aaaa8bc7-3071-4988-9566-3c05d817f564 | the-state-of-human-centered-nlp-technology | 2301.03056 | null | https://arxiv.org/abs/2301.03056v1 | https://arxiv.org/pdf/2301.03056v1.pdf | The State of Human-centered NLP Technology for Fact-checking | Misinformation threatens modern society by promoting distrust in science, changing narratives in public health, heightening social polarization, and disrupting democratic elections and financial markets, among a myriad of other societal harms. To address this, a growing cadre of professional fact-checkers and journalists provide high-quality investigations into purported facts. However, these largely manual efforts have struggled to match the enormous scale of the problem. In response, a growing body of Natural Language Processing (NLP) technologies have been proposed for more scalable fact-checking. Despite tremendous growth in such research, however, practical adoption of NLP technologies for fact-checking still remains in its infancy today. In this work, we review the capabilities and limitations of the current NLP technologies for fact-checking. Our particular focus is to further chart the design space for how these technologies can be harnessed and refined in order to better meet the needs of human fact-checkers. To do so, we review key aspects of NLP-based fact-checking: task formulation, dataset construction, modeling, and human-centered strategies, such as explainable models and human-in-the-loop approaches. Next, we review the efficacy of applying NLP-based fact-checking tools to assist human fact-checkers. We recommend that future research include collaboration with fact-checker stakeholders early on in NLP research, as well as incorporation of human-centered design practices in model development, in order to further guide technology development for human use and practical adoption. Finally, we advocate for more research on benchmark development supporting extrinsic evaluation of human-centered fact-checking technologies. | ['Matthew Lease', 'Venelin Kovatchev', 'Houjiang Liu', 'Anubrata Das'] | 2023-01-08 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 3.88884060e-02 6.55209005e-01 -5.73488176e-01 -4.96057272e-01
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cb83c8f2-21cd-47fa-bfe8-0e62a6b01fe2 | lung-nodule-classification-using-deep-local | 1904.10126 | null | http://arxiv.org/abs/1904.10126v1 | http://arxiv.org/pdf/1904.10126v1.pdf | Lung Nodule Classification using Deep Local-Global Networks | Purpose: Lung nodules have very diverse shapes and sizes, which makes
classifying them as benign/malignant a challenging problem. In this paper, we
propose a novel method to predict the malignancy of nodules that have the
capability to analyze the shape and size of a nodule using a global feature
extractor, as well as the density and structure of the nodule using a local
feature extractor. Methods: We propose to use Residual Blocks with a 3x3 kernel
size for local feature extraction, and Non-Local Blocks to extract the global
features. The Non-Local Block has the ability to extract global features
without using a huge number of parameters. The key idea behind the Non-Local
Block is to apply matrix multiplications between features on the same feature
maps. Results: We trained and validated the proposed method on the LIDC-IDRI
dataset which contains 1,018 computed tomography (CT) scans. We followed a
rigorous procedure for experimental setup namely, 10-fold cross-validation and
ignored the nodules that had been annotated by less than 3 radiologists. The
proposed method achieved state-of-the-art results with AUC=95.62%, while
significantly outperforming other baseline methods. Conclusions: Our proposed
Deep Local-Global network has the capability to accurately extract both local
and global features. Our new method outperforms state-of-the-art architecture
including Densenet and Resnet with transfer learning. | ['Kwan-Hoong Ng', 'Mundher Al-Shabi', 'Maxine Tan', 'Boon Leong Lan', 'Wai Yee Chan'] | 2019-04-23 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [-3.69555503e-02 2.64562905e-01 -9.95221511e-02 -2.07026973e-01
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8ac1642e-1bd4-4f34-840c-d5bb3593ae0e | squeezesegv2-improved-model-structure-and | 1809.08495 | null | http://arxiv.org/abs/1809.08495v1 | http://arxiv.org/pdf/1809.08495v1.pdf | SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud | Earlier work demonstrates the promise of deep-learning-based approaches for
point cloud segmentation; however, these approaches need to be improved to be
practically useful. To this end, we introduce a new model SqueezeSegV2 that is
more robust to dropout noise in LiDAR point clouds. With improved model
structure, training loss, batch normalization and additional input channel,
SqueezeSegV2 achieves significant accuracy improvement when trained on real
data. Training models for point cloud segmentation requires large amounts of
labeled point-cloud data, which is expensive to obtain. To sidestep the cost of
collection and annotation, simulators such as GTA-V can be used to create
unlimited amounts of labeled, synthetic data. However, due to domain shift,
models trained on synthetic data often do not generalize well to the real
world. We address this problem with a domain-adaptation training pipeline
consisting of three major components: 1) learned intensity rendering, 2)
geodesic correlation alignment, and 3) progressive domain calibration. When
trained on real data, our new model exhibits segmentation accuracy improvements
of 6.0-8.6% over the original SqueezeSeg. When training our new model on
synthetic data using the proposed domain adaptation pipeline, we nearly double
test accuracy on real-world data, from 29.0% to 57.4%. Our source code and
synthetic dataset will be open-sourced. | ['Kurt Keutzer', 'Xuanyu Zhou', 'Xiangyu Yue', 'Bichen Wu', 'Sicheng Zhao'] | 2018-09-22 | null | null | null | null | ['robust-3d-semantic-segmentation'] | ['computer-vision'] | [ 2.73483008e-01 9.23242047e-03 3.45290340e-02 -5.68861961e-01
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21b7579e-a59f-487e-a54b-5cb8aaee4d1e | not-only-look-but-also-listen-learning | 2007.04687 | null | https://arxiv.org/abs/2007.04687v2 | https://arxiv.org/pdf/2007.04687v2.pdf | Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision | Violence detection has been studied in computer vision for years. However, previous work are either superficial, e.g., classification of short-clips, and the single scenario, or undersupplied, e.g., the single modality, and hand-crafted features based multimodality. To address this problem, in this work we first release a large-scale and multi-scene dataset named XD-Violence with a total duration of 217 hours, containing 4754 untrimmed videos with audio signals and weak labels. Then we propose a neural network containing three parallel branches to capture different relations among video snippets and integrate features, where holistic branch captures long-range dependencies using similarity prior, localized branch captures local positional relation using proximity prior, and score branch dynamically captures the closeness of predicted score. Besides, our method also includes an approximator to meet the needs of online detection. Our method outperforms other state-of-the-art methods on our released dataset and other existing benchmark. Moreover, extensive experimental results also show the positive effect of multimodal (audio-visual) input and modeling relationships. The code and dataset will be released in https://roc-ng.github.io/XD-Violence/. | ['Zhaoyang Wu', 'Zhiwei Yang', 'Yujia Shi', 'Peng Wu', 'Fangtao Shao', 'Jing Liu', 'Yujia Sun'] | 2020-07-09 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/7476_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123750324.pdf | eccv-2020-8 | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [-7.88745657e-03 -6.20665908e-01 -1.95051298e-01 -2.82251567e-01
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d2f29f42-db94-420a-9444-82ac0255f9fb | unsupervised-text-summarization-via-mixed | 1908.08566 | null | https://arxiv.org/abs/1908.08566v1 | https://arxiv.org/pdf/1908.08566v1.pdf | Unsupervised Text Summarization via Mixed Model Back-Translation | Back-translation based approaches have recently lead to significant progress in unsupervised sequence-to-sequence tasks such as machine translation or style transfer. In this work, we extend the paradigm to the problem of learning a sentence summarization system from unaligned data. We present several initial models which rely on the asymmetrical nature of the task to perform the first back-translation step, and demonstrate the value of combining the data created by these diverse initialization methods. Our system outperforms the current state-of-the-art for unsupervised sentence summarization from fully unaligned data by over 2 ROUGE, and matches the performance of recent semi-supervised approaches. | ['Yacine Jernite'] | 2019-08-22 | null | null | null | null | ['abstractive-sentence-summarization', 'unsupervised-sentence-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 9.32111084e-01 4.36723262e-01 -2.73521185e-01 -5.90616822e-01
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81bcf840-1b34-4489-a8c2-8aa16c06e590 | contribution-of-data-categories-to | 1803.07850 | null | http://arxiv.org/abs/1803.07850v2 | http://arxiv.org/pdf/1803.07850v2.pdf | Contribution of Data Categories to Readmission Prediction Accuracy | Identification of patients at high risk for readmission could help reduce
morbidity and mortality as well as healthcare costs. Most of the existing
studies on readmission prediction did not compare the contribution of data
categories. In this study we analyzed relative contribution of 90,101 variables
across 398,884 admission records corresponding to 163,468 patients, including
patient demographics, historical hospitalization information, discharge
disposition, diagnoses, procedures, medications and laboratory test results. We
established an interpretable readmission prediction model based on Logistic
Regression in scikit-learn, and added the available variables to the model one
by one in order to analyze the influences of individual data categories on
readmission prediction accuracy. Diagnosis related groups (c-statistic
increment of 0.0933) and discharge disposition (c-statistic increment of
0.0269) were the strongest contributors to model accuracy. Additionally, we
also identified the top ten contributing variables in every data category. | [] | 2018-03-22 | null | null | null | null | ['readmission-prediction'] | ['medical'] | [-2.35911772e-01 -2.20691696e-01 -6.07470214e-01 -2.82405138e-01
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a07228e4-20b9-4c48-9c06-97d9ebc2e4c0 | named-entity-recognition-multi-task-learning | 2205.09651 | null | https://arxiv.org/abs/2205.09651v2 | https://arxiv.org/pdf/2205.09651v2.pdf | Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT | This paper presents Wojood, a corpus for Arabic nested Named Entity Recognition (NER). Nested entities occur when one entity mention is embedded inside another entity mention. Wojood consists of about 550K Modern Standard Arabic (MSA) and dialect tokens that are manually annotated with 21 entity types including person, organization, location, event and date. More importantly, the corpus is annotated with nested entities instead of the more common flat annotations. The data contains about 75K entities and 22.5% of which are nested. The inter-annotator evaluation of the corpus demonstrated a strong agreement with Cohen's Kappa of 0.979 and an F1-score of 0.976. To validate our data, we used the corpus to train a nested NER model based on multi-task learning and AraBERT (Arabic BERT). The model achieved an overall micro F1-score of 0.884. Our corpus, the annotation guidelines, the source code and the pre-trained model are publicly available. | ['Sana Ghanem', 'Mohammed Khalilia', 'Mustafa Jarrar'] | 2022-05-19 | null | https://aclanthology.org/2022.lrec-1.387 | https://aclanthology.org/2022.lrec-1.387.pdf | lrec-2022-6 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-6.91162705e-01 4.26025838e-01 6.81843758e-02 -2.87284493e-01
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74256ed8-dfbc-4cc8-a3f9-2efa80465718 | an-optimal-and-scalable-matrix-mechanism-for | 2305.08175 | null | https://arxiv.org/abs/2305.08175v1 | https://arxiv.org/pdf/2305.08175v1.pdf | An Optimal and Scalable Matrix Mechanism for Noisy Marginals under Convex Loss Functions | Noisy marginals are a common form of confidentiality-protecting data release and are useful for many downstream tasks such as contingency table analysis, construction of Bayesian networks, and even synthetic data generation. Privacy mechanisms that provide unbiased noisy answers to linear queries (such as marginals) are known as matrix mechanisms. We propose ResidualPlanner, a matrix mechanism for marginals with Gaussian noise that is both optimal and scalable. ResidualPlanner can optimize for many loss functions that can be written as a convex function of marginal variances (prior work was restricted to just one predefined objective function). ResidualPlanner can optimize the accuracy of marginals in large scale settings in seconds, even when the previous state of the art (HDMM) runs out of memory. It even runs on datasets with 100 attributes in a couple of minutes. Furthermore ResidualPlanner can efficiently compute variance/covariance values for each marginal (prior methods quickly run out of memory, even for relatively small datasets). | ['Daniel Kifer', 'Danfeng Zhang', 'Guanlin He', 'Yingtai Xiao'] | 2023-05-14 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 7.82362819e-02 3.57401669e-01 -6.26983866e-02 -5.51388383e-01
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2.27609411e-01 4.80584754e-03 -4.56664175e-01 -3.22212726e-01] | [6.004108428955078, 6.83538818359375] |
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