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3d26a028-95ab-4ef1-8cd3-153215ec58fc | malm-mixing-augmented-language-modeling-for | 2210.00320 | null | https://arxiv.org/abs/2210.00320v1 | https://arxiv.org/pdf/2210.00320v1.pdf | MALM: Mixing Augmented Language Modeling for Zero-Shot Machine Translation | Large pre-trained language models have brought remarkable progress in NLP. Pre-training and Fine-tuning have given state-of-art performance across tasks in text processing. Data Augmentation techniques have also helped build state-of-art models on low or zero resource tasks. Many works in the past have attempted at learning a single massively-multilingual machine translation model for zero-shot translation. Although those translation models are producing correct translations, the main challenge is those models are producing the wrong languages for zero-shot translation. This work and its results indicate that prompt conditioned large models do not suffer from off-target language errors i.e. errors arising due to translation to wrong languages. We empirically demonstrate the effectiveness of self-supervised pre-training and data augmentation for zero-shot multi-lingual machine translation. | ['Kshitij Gupta'] | 2022-10-01 | null | null | null | null | ['zero-shot-machine-translation'] | ['natural-language-processing'] | [ 2.86908090e-01 -4.10530940e-02 -6.69329345e-01 -3.23637873e-01
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fb8706c1-5dcb-4c96-afb6-cdfc2e6a9bda | identity-guided-human-semantic-parsing-for | 2007.13467 | null | https://arxiv.org/abs/2007.13467v1 | https://arxiv.org/pdf/2007.13467v1.pdf | Identity-Guided Human Semantic Parsing for Person Re-Identification | Existing alignment-based methods have to employ the pretrained human parsing models to achieve the pixel-level alignment, and cannot identify the personal belongings (e.g., backpacks and reticule) which are crucial to person re-ID. In this paper, we propose the identity-guided human semantic parsing approach (ISP) to locate both the human body parts and personal belongings at pixel-level for aligned person re-ID only with person identity labels. We design the cascaded clustering on feature maps to generate the pseudo-labels of human parts. Specifically, for the pixels of all images of a person, we first group them to foreground or background and then group the foreground pixels to human parts. The cluster assignments are subsequently used as pseudo-labels of human parts to supervise the part estimation and ISP iteratively learns the feature maps and groups them. Finally, local features of both human body parts and personal belongings are obtained according to the selflearned part estimation, and only features of visible parts are utilized for the retrieval. Extensive experiments on three widely used datasets validate the superiority of ISP over lots of state-of-the-art methods. Our code is available at https://github.com/CASIA-IVA-Lab/ISP-reID. | ['Zhiwei Liu', 'Jinqiao Wang', 'Ming Tang', 'Haiyun Guo', 'Kuan Zhu'] | 2020-07-27 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/415_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480358.pdf | eccv-2020-8 | ['human-parsing'] | ['computer-vision'] | [ 1.85034107e-02 8.77360776e-02 -8.02533701e-02 -5.06298482e-01
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8eacd0b6-71c1-4fd6-a076-c2ff41e52442 | 3d-object-detection-and-viewpoint-estimation | null | null | http://papers.nips.cc/paper/4562-3d-object-detection-and-viewpoint-estimation-with-a-deformable-3d-cuboid-model | http://papers.nips.cc/paper/4562-3d-object-detection-and-viewpoint-estimation-with-a-deformable-3d-cuboid-model.pdf | 3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model | This paper addresses the problem of category-level 3D object detection. Given a monocular image, our aim is to localize the objects in 3D by enclosing them with tight oriented 3D bounding boxes. We propose a novel approach that extends the well-acclaimed deformable part-based model[Felz.] to reason in 3D. Our model represents an object class as a deformable 3D cuboid composed of faces and parts, which are both allowed to deform with respect to their anchors on the 3D box. We model the appearance of each face in fronto-parallel coordinates, thus effectively factoring out the appearance variation induced by viewpoint. Our model reasons about face visibility patters called aspects. We train the cuboid model jointly and discriminatively and share weights across all aspects to attain efficiency. Inference then entails sliding and rotating the box in 3D and scoring object hypotheses. While for inference we discretize the search space, the variables are continuous in our model. We demonstrate the effectiveness of our approach in indoor and outdoor scenarios, and show that our approach outperforms the state-of-the-art in both 2D[Felz09] and 3D object detection[Hedau12]. | ['Raquel Urtasun', 'Sanja Fidler', 'Sven Dickinson'] | 2012-12-01 | null | null | null | neurips-2012-12 | ['viewpoint-estimation'] | ['computer-vision'] | [-1.56556964e-01 2.00888649e-01 7.63072073e-02 -3.82946849e-01
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b9e17ef4-31ce-4bb8-81a2-90cf7ece36db | hdr-cgan-single-ldr-to-hdr-image-translation | 2110.01660 | null | https://arxiv.org/abs/2110.01660v2 | https://arxiv.org/pdf/2110.01660v2.pdf | HDR-cGAN: Single LDR to HDR Image Translation using Conditional GAN | The prime goal of digital imaging techniques is to reproduce the realistic appearance of a scene. Low Dynamic Range (LDR) cameras are incapable of representing the wide dynamic range of the real-world scene. The captured images turn out to be either too dark (underexposed) or too bright (overexposed). Specifically, saturation in overexposed regions makes the task of reconstructing a High Dynamic Range (HDR) image from single LDR image challenging. In this paper, we propose a deep learning based approach to recover details in the saturated areas while reconstructing the HDR image. We formulate this problem as an image-to-image (I2I) translation task. To this end, we present a novel conditional GAN (cGAN) based framework trained in an end-to-end fashion over the HDR-REAL and HDR-SYNTH datasets. Our framework uses an overexposed mask obtained from a pre-trained segmentation model to facilitate the hallucination task of adding details in the saturated regions. We demonstrate the effectiveness of the proposed method by performing an extensive quantitative and qualitative comparison with several state-of-the-art single-image HDR reconstruction techniques. | ['Shanmuganathan Raman', 'Rohil Pal', 'Prarabdh Raipurkar'] | 2021-10-04 | null | null | null | null | ['hdr-reconstruction'] | ['computer-vision'] | [ 8.16799521e-01 1.59796193e-01 3.71337324e-01 -3.12187046e-01
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6a1c1d85-831e-43ec-ad72-1ad3e7dd2c1d | hierarchical-cyber-attack-detection-in-large | 2209.13874 | null | https://arxiv.org/abs/2209.13874v1 | https://arxiv.org/pdf/2209.13874v1.pdf | Hierarchical Cyber-Attack Detection in Large-Scale Interconnected Systems | In this paper we present a hierarchical scheme to detect cyber-attacks in a hierarchical control architecture for large-scale interconnected systems (LSS). We consider the LSS as a network of physically coupled subsystems, equipped with a two-layer controller: on the local level, decentralized controllers guarantee overall stability and reference tracking; on the supervisory level, a centralized coordinator sets references for the local regulators. We present a scheme to detect attacks that occur at the local level, with malicious agents capable of affecting the local control. The detection scheme is computed at the supervisory level, requiring only limited exchange of data and model knowledge. We offer detailed theoretical analysis of the proposed scheme, highlighting its detection properties in terms of robustness, detectability and stealthiness conditions. | ['Riccardo M. G. Ferrari', 'Alexander J. Gallo', 'Twan Keijzer'] | 2022-09-28 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [-1.29501536e-01 5.70102453e-01 5.70281185e-02 7.34125197e-01
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e1d3cfcc-2371-44bb-bd2a-6acf207a1405 | self-supervised-representations-for-singing | 2303.12197 | null | https://arxiv.org/abs/2303.12197v1 | https://arxiv.org/pdf/2303.12197v1.pdf | Self-Supervised Representations for Singing Voice Conversion | A singing voice conversion model converts a song in the voice of an arbitrary source singer to the voice of a target singer. Recently, methods that leverage self-supervised audio representations such as HuBERT and Wav2Vec 2.0 have helped further the state-of-the-art. Though these methods produce more natural and melodic singing outputs, they often rely on confusion and disentanglement losses to render the self-supervised representations speaker and pitch-invariant. In this paper, we circumvent disentanglement training and propose a new model that leverages ASR fine-tuned self-supervised representations as inputs to a HiFi-GAN neural vocoder for singing voice conversion. We experiment with different f0 encoding schemes and show that an f0 harmonic generation module that uses a parallel bank of transposed convolutions (PBTC) alongside ASR fine-tuned Wav2Vec 2.0 features results in the best singing voice conversion quality. Additionally, the model is capable of making a spoken voice sing. We also show that a simple f0 shifting scheme during inference helps retain singer identity and bolsters the performance of our singing voice conversion model. Our results are backed up by extensive MOS studies that compare different ablations and baselines. | ['Qing He', 'Vimal Manohar', 'David Kant', 'Leda Sari', 'JiLong Wu', 'Tejas Jayashankar'] | 2023-03-21 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [ 1.10980205e-01 7.24460557e-02 -7.90282264e-02 -1.33053660e-01
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361517a6-0d73-490c-ad2a-35d7f21d4a14 | learning-mid-level-filters-for-person-re | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Zhao_Learning_Mid-level_Filters_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Zhao_Learning_Mid-level_Filters_2014_CVPR_paper.pdf | Learning Mid-level Filters for Person Re-identification | In this paper, we propose a novel approach of learning mid-level filters from automatically discovered patch clusters for person re-identification. It is well motivated by our study on what are good filters for person re-identification. Our mid-level filters are discriminatively learned for identifying specific visual patterns and distinguishing persons, and have good cross-view invariance. First, local patches are qualitatively measured and classified with their discriminative power. Discriminative and representative patches are collected for filter learning. Second, patch clusters with coherent appearance are obtained by pruning hierarchical clustering trees, and a simple but effective cross-view training strategy is proposed to learn filters that are view-invariant and discriminative. Third, filter responses are integrated with patch matching scores in RankSVM training. The effectiveness of our approach is validated on the VIPeR dataset and the CUHK01 dataset. The learned mid-level features are complementary to existing handcrafted low-level features, and improve the best Rank-1 matching rate on the VIPeR dataset by 14%. | ['Rui Zhao', 'Wanli Ouyang', 'Xiaogang Wang'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['patch-matching'] | ['computer-vision'] | [-1.53786913e-01 -4.66269374e-01 -2.50287384e-01 -4.24310654e-01
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16546b58-2bda-4aa3-8615-7a31f44c0137 | explainability-in-practice-estimating | 2211.06277 | null | https://arxiv.org/abs/2211.06277v2 | https://arxiv.org/pdf/2211.06277v2.pdf | Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal | Explainable artificial intelligence (XAI) provides explanations for not interpretable machine learning (ML) models. While many technical approaches exist, there is a lack of validation of these techniques on real-world datasets. In this work, we present a use-case of XAI: an ML model which is trained to estimate electrification rates based on mobile phone data in Senegal. The data originate from the Data for Development challenge by Orange in 2014/15. We apply two model-agnostic, local explanation techniques and find that while the model can be verified, it is biased with respect to the population density. We conclude our paper by pointing to the two main challenges we encountered during our work: data processing and model design that might be restricted by currently available XAI methods, and the importance of domain knowledge to interpret explanations. | ['Zbigniew Smoreda', 'Stefania Rubrichi', 'Hadrien Salat', 'Laura State'] | 2022-11-11 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 2.85810858e-01 1.14714468e+00 -7.34949052e-01 -5.91377556e-01
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0986de72-63d9-4f7d-a848-a1eb829aa22b | ultrahigh-dimensional-instrument-detection | 2007.15769 | null | https://arxiv.org/abs/2007.15769v2 | https://arxiv.org/pdf/2007.15769v2.pdf | Instrument variable detection with graph learning : an application to high dimensional GIS-census data for house pricing | Endogeneity bias and instrument variable validation have always been important topics in statistics and econometrics. In the era of big data, such issues typically combine with dimensionality issues and, hence, require even more attention. In this paper, we merge two well-known tools from machine learning and biostatistics---variable selection algorithms and probablistic graphs---to estimate house prices and the corresponding causal structure using 2010 data on Sydney. The estimation uses a 200-gigabyte ultrahigh dimensional database consisting of local school data, GIS information, census data, house characteristics and other socio-economic records. Using "big data", we show that it is possible to perform a data-driven instrument selection efficiently and purge out the invalid instruments. Our approach improves the sparsity of variable selection, stability and robustness in the presence of high dimensionality, complicated causal structures and the consequent multicollinearity, and recovers a sparse and intuitive causal structure. The approach also reveals an efficiency and effectiveness in endogeneity detection, instrument validation, weak instrument pruning and the selection of valid instruments. From the perspective of machine learning, the estimation results both align with and confirms the facts of Sydney house market, the classical economic theories and the previous findings of simultaneous equations modeling. Moreover, the estimation results are consistent with and supported by classical econometric tools such as two-stage least square regression and different instrument tests. All the code may be found at \url{https://github.com/isaac2math/solar_graph_learning}. | ['Ning Xu', 'Timothy C. G. Fisher', 'Jian Hong'] | 2020-07-30 | null | null | null | null | ['variable-detection'] | ['natural-language-processing'] | [-4.97268379e-01 -8.52754042e-02 -6.88155591e-01 -4.72522937e-02
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ed284117-84ca-44a7-91bf-50bca3af5903 | valley-video-assistant-with-large-language | 2306.07207 | null | https://arxiv.org/abs/2306.07207v1 | https://arxiv.org/pdf/2306.07207v1.pdf | Valley: Video Assistant with Large Language model Enhanced abilitY | Recently, several multi-modal models have been developed for joint image and language understanding, which have demonstrated impressive chat abilities by utilizing advanced large language models (LLMs). The process of developing such models is straightforward yet effective. It involves pre-training an adaptation module to align the semantics of the vision encoder and language model, followed by fine-tuning on the instruction-following data. However, despite the success of this pipeline in image and language understanding, its effectiveness in joint video and language understanding has not been widely explored. In this paper, we aim to develop a novel multi-modal foundation model capable of perceiving video, image, and language within a general framework. To achieve this goal, we introduce Valley: Video Assistant with Large Language model Enhanced ability. Specifically, our proposed Valley model is designed with a simple projection module that bridges video, image, and language modalities, and is further unified with a multi-lingual LLM. We also collect multi-source vision-text pairs and adopt a spatio-temporal pooling strategy to obtain a unified vision encoding of video and image input for pre-training. Furthermore, we generate multi-task instruction-following video data, including multi-shot captions, long video descriptions, action recognition, causal relationship inference, etc. To obtain the instruction-following data, we design diverse rounds of task-oriented conversations between humans and videos, facilitated by ChatGPT. Qualitative examples demonstrate that our proposed model has the potential to function as a highly effective multilingual video assistant that can make complex video understanding scenarios easy. Code, data, and models will be available at https://github.com/RupertLuo/Valley. | ['Zhongyu Wei', 'Tao Wang', 'Pengcheng Lu', 'Minghui Qiu', 'Junwei DOng', 'Min Yang', 'Ziwang Zhao', 'Ruipu Luo'] | 2023-06-12 | null | null | null | null | ['action-recognition-in-videos', 'video-understanding', 'instruction-following'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [-5.62213771e-02 -2.01452151e-02 -1.61418170e-01 -4.91713136e-01
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1b2749c7-999b-45dd-af7f-bbc8242fe920 | deep-model-compression-also-helps-models | 2306.07061 | null | https://arxiv.org/abs/2306.07061v1 | https://arxiv.org/pdf/2306.07061v1.pdf | Deep Model Compression Also Helps Models Capture Ambiguity | Natural language understanding (NLU) tasks face a non-trivial amount of ambiguous samples where veracity of their labels is debatable among annotators. NLU models should thus account for such ambiguity, but they approximate the human opinion distributions quite poorly and tend to produce over-confident predictions. To address this problem, we must consider how to exactly capture the degree of relationship between each sample and its candidate classes. In this work, we propose a novel method with deep model compression and show how such relationship can be accounted for. We see that more reasonably represented relationships can be discovered in the lower layers and that validation accuracies are converging at these layers, which naturally leads to layer pruning. We also see that distilling the relationship knowledge from a lower layer helps models produce better distribution. Experimental results demonstrate that our method makes substantial improvement on quantifying ambiguity without gold distribution labels. As positive side-effects, our method is found to reduce the model size significantly and improve latency, both attractive aspects of NLU products. | ['Jong C. Park', 'Hancheol Park'] | 2023-06-12 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 8.06390271e-02 6.25586450e-01 -5.30914068e-01 -7.96276987e-01
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67da790b-61c2-4b63-becc-8b8a2d6ab564 | olia-an-open-source-digital-lock-in-amplifier | 2211.08889 | null | https://arxiv.org/abs/2211.08889v2 | https://arxiv.org/pdf/2211.08889v2.pdf | OLIA: an open-source digital lock-in amplifier | The Open Lock-In Amplifier (OLIA) is a microcontroller-based digital lock-in amplifier built from a small number of inexpensive and easily sourced electronic components. Despite its small credit card-sized form-factor and low build-cost of around US$35, OLIA is a capable instrument that offers many features associated with far costlier commercial devices. Key features include dual-phase lock-in detection at multiple harmonic frequencies up to 50 kHz, internal and external reference modes, adjustable levels of input gain, a choice between low-pass filtering and synchronous filtering, noise estimation, and a comprehensive programming interface for remote software control. OLIA comes with an optional optical breakout board that allows noise-tolerant optical detection down to the 40 pW level. OLIA and its breakout board are released here as open hardware, with technical diagrams, full parts-lists, and source-code for the firmware provided as Supporting Information. | ['John C. de Mello', 'Andrew J. Harvie'] | 2022-11-16 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 9.66998488e-02 -4.82436746e-01 -1.11409537e-01 2.10675448e-02
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33d46319-29ad-4a81-90a6-89d146603097 | stereo-based-multi-motion-visual-odometry-for | 1910.06607 | null | https://arxiv.org/abs/1910.06607v1 | https://arxiv.org/pdf/1910.06607v1.pdf | Stereo-based Multi-motion Visual Odometry for Mobile Robots | With the development of computer vision, visual odometry is adopted by more and more mobile robots. However, we found that not only its own pose, but the poses of other moving objects are also crucial for the decision of the robot. In addition, the visual odometry will be greatly disturbed when a significant moving object appears. In this letter, a stereo-based multi-motion visual odometry method is proposed to acquire the poses of the robot and other moving objects. In order to obtain the poses simultaneously, a continuous motion segmentation module and a coordinate conversion module are applied to the traditional visual odometry pipeline. As a result, poses of all moving objects can be acquired and transformed into the ground coordinate system. The experimental results show that the proposed multi-motion visual odometry can effectively eliminate the influence of moving objects on the visual odometry, as well as achieve 10 cm in position and 3{\deg} in orientation RMSE (Root Mean Square Error) of each moving object. | ['Bin Luo', 'Yun Zhang', 'Qing Zhao'] | 2019-10-15 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [-3.29648107e-01 -2.04433113e-01 2.47965142e-01 -6.66882694e-02
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41a96a95-0ba0-4b59-b55b-1d4bed0e401a | retrack-a-flexible-and-efficient-framework | null | null | https://aclanthology.org/2021.acl-demo.39 | https://aclanthology.org/2021.acl-demo.39.pdf | ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering | We present Retriever-Transducer-Checker (ReTraCk), a neural semantic parsing framework for large scale knowledge base question answering (KBQA). ReTraCk is designed as a modular framework to maintain high flexibility. It includes a retriever to retrieve relevant KB items efficiently, a transducer to generate logical form with syntax correctness guarantees and a checker to improve transduction procedure. ReTraCk is ranked at top1 overall performance on the GrailQA leaderboard and obtains highly competitive performance on the typical WebQuestionsSP benchmark. Our system can interact with users timely, demonstrating the efficiency of the proposed framework. | ['Feng Jiang', 'Jian-Guang Lou', 'Chin-Yew Lin', 'Zhiwei Yu', 'Qian Liu', 'Shuang Chen'] | 2021-08-01 | null | null | null | acl-2021-5 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [ 5.55496365e-02 3.06885630e-01 -1.17110506e-01 -3.74216765e-01
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6ccbd02f-ba53-4654-bfbf-4c19c7724018 | self-attentive-constituency-parsing-for-ucca | 2110.00621 | null | https://arxiv.org/abs/2110.00621v1 | https://arxiv.org/pdf/2110.00621v1.pdf | Self-Attentive Constituency Parsing for UCCA-based Semantic Parsing | Semantic parsing provides a way to extract the semantic structure of a text that could be understood by machines. It is utilized in various NLP applications that require text comprehension such as summarization and question answering. Graph-based representation is one of the semantic representation approaches to express the semantic structure of a text. Such representations generate expressive and adequate graph-based target structures. In this paper, we focus primarily on UCCA graph-based semantic representation. The paper not only presents the existing approaches proposed for UCCA representation, but also proposes a novel self-attentive neural parsing model for the UCCA representation. We present the results for both single-lingual and cross-lingual tasks using zero-shot and few-shot learning for low-resource languages. | ['Burcu Can', 'Necva Bölücü'] | 2021-10-01 | null | null | null | null | ['constituency-parsing'] | ['natural-language-processing'] | [ 4.36119974e-01 6.72601521e-01 -1.70508996e-01 -5.34171700e-01
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4afd7392-f019-4af2-9415-e4a8b2415efc | from-cad-models-to-soft-point-cloud-labels-an | 2302.03114 | null | https://arxiv.org/abs/2302.03114v2 | https://arxiv.org/pdf/2302.03114v2.pdf | From CAD models to soft point cloud labels: An automatic annotation pipeline for cheaply supervised 3D semantic segmentation | We propose a fully automatic annotation scheme which takes a raw 3D point cloud with a set of fitted CAD models as input, and outputs convincing point-wise labels which can be used as cheap training data for point cloud segmentation. Compared to manual annotations, we show that our automatic labels are accurate while drastically reducing the annotation time, and eliminating the need for manual intervention or dataset-specific parameters. Our labeling pipeline outputs semantic classes and soft point-wise object scores which can either be binarized into standard one-hot-encoded labels, thresholded into weak labels with ambiguous points left unlabeled, or used directly as soft labels during training. We evaluate the label quality and segmentation performance of PointNet++ on a dataset of real industrial point clouds and Scan2CAD, a public dataset of indoor scenes. Our results indicate that reducing supervision in areas which are more difficult to label automatically is beneficial, compared to the conventional approach of naively assigning a hard "best guess" label to every point. | ['Andreas Møgelmose', 'Simon Buus Jensen', 'Galadrielle Humblot-Renaux'] | 2023-02-06 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 3.39300543e-01 3.75867605e-01 -5.89286275e-02 -9.87127066e-01
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29de9ffe-a59a-43aa-baa0-9231c0b49c6d | multilingual-seq2seq-training-with-similarity | null | null | https://aclanthology.org/W18-3023 | https://aclanthology.org/W18-3023.pdf | Multilingual Seq2seq Training with Similarity Loss for Cross-Lingual Document Classification | In this paper we continue experiments where neural machine translation training is used to produce joint cross-lingual fixed-dimensional sentence embeddings. In this framework we introduce a simple method of adding a loss to the learning objective which penalizes distance between representations of bilingually aligned sentences. We evaluate cross-lingual transfer using two approaches, cross-lingual similarity search on an aligned corpus (Europarl) and cross-lingual document classification on a recently published benchmark Reuters corpus, and we find the similarity loss significantly improves performance on both. Furthermore, we notice that while our Reuters results are very competitive, our English results are not as competitive, showing room for improvement in the current cross-lingual state-of-the-art. Our results are based on a set of 6 European languages. | ['Haoran Li', 'Katherine Yu', 'Barlas Oguz'] | 2018-07-01 | null | null | null | ws-2018-7 | ['cross-lingual-document-classification'] | ['natural-language-processing'] | [-9.50243548e-02 -1.28033414e-01 -6.43827021e-01 -6.01921618e-01
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8cc2950c-148b-4713-8026-d3b06a986d6a | on-cmos-high-throughput-multi-modal | 2208.00248 | null | https://arxiv.org/abs/2208.00248v1 | https://arxiv.org/pdf/2208.00248v1.pdf | On-CMOS High-Throughput Multi-Modal Amperometric DNA Analysis with Distributed Thermal Regulation | Accurate temperature regulation is critical for amperometric DNA analysis to achieve high fidelity, reliability, and throughput. In this work, a 9x6 cell array of mixed-signal CMOS distributed temperature regulators for on-CMOS multi-modal amperometric DNA analysis is presented. Three DNA analysis methods are supported, including constant potential amperometry (CPA), cyclic voltammetry (CV), and impedance spectroscopy (IS). In-cell heating and temperature sensing elements are implemented in standard CMOS technology without post-processing. Using proportional-integral-derivative (PID) control, the local temperature can be regulated to within +/-0.5C of any desired value between 20C and 90C. The two computationally intensive operations in the PID algorithm, multiplication, and subtraction, are performed by an in-cell dual-slope multiplying ADC in the mixed-signal domain, resulting in a small area and low power consumption. Over 95% of the circuit blocks are synergistically shared among the four operating modes, including CPA, CV, IS, and the proposed temperature regulation mode. A 3mmx3mm CMOS prototype fabricated in a 0.13um CMOS technology has been fully experimentally characterized. Each channel occupies an area of 0.06mm2 and consumes 42uW from a 1.2V supply. The proposed distributed temperature regulation design and the mixed-signal PID implementation can be applied to a wide range of sensory and other applications. | ['Roman Genov', 'Xilin Liu', 'Hamed M. Jafari'] | 2022-07-30 | null | null | null | null | ['dna-analysis'] | ['medical'] | [ 8.25610936e-01 -4.30617332e-01 -6.00601994e-02 -7.13827834e-02
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dfa979da-0a1e-4ffa-9912-440fc2c893f4 | generating-post-hoc-explanations-for-skip | 2304.12036 | null | https://arxiv.org/abs/2304.12036v3 | https://arxiv.org/pdf/2304.12036v3.pdf | Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness | Node representation learning in a network is an important machine learning technique for encoding relational information in a continuous vector space while preserving the inherent properties and structures of the network. Recently, unsupervised node embedding methods such as DeepWalk, LINE, struc2vec, PTE, UserItem2vec, and RWJBG have emerged from the Skip-gram model and perform better performance in several downstream tasks such as node classification and link prediction than the existing relational models. However, providing post-hoc explanations of Skip-gram-based embeddings remains a challenging problem because of the lack of explanation methods and theoretical studies applicable for embeddings. In this paper, we first show that global explanations to the Skip-gram-based embeddings can be found by computing bridgeness under a spectral cluster-aware local perturbation. Moreover, a novel gradient-based explanation method, which we call GRAPH-wGD, is proposed that allows the top-q global explanations about learned graph embedding vectors more efficiently. Experiments show that the ranking of nodes by scores using GRAPH-wGD is highly correlated with true bridgeness scores. We also observe that the top-q node-level explanations selected by GRAPH-wGD have higher importance scores and produce more changes in class label prediction when perturbed, compared with the nodes selected by recent alternatives, using five real-world graphs. | ['Jennifer Neville', 'Hogun Park'] | 2023-04-24 | null | null | null | null | ['graph-embedding'] | ['graphs'] | [-2.58921713e-01 6.94976687e-01 -7.57606447e-01 -2.17417806e-01
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c8d5e79f-5459-42bb-88fc-9d8391acfa13 | novelty-detection-via-contrastive-learning | 2106.09958 | null | https://arxiv.org/abs/2106.09958v1 | https://arxiv.org/pdf/2106.09958v1.pdf | Novelty Detection via Contrastive Learning with Negative Data Augmentation | Novelty detection is the process of determining whether a query example differs from the learned training distribution. Previous methods attempt to learn the representation of the normal samples via generative adversarial networks (GANs). However, they will suffer from instability training, mode dropping, and low discriminative ability. Recently, various pretext tasks (e.g. rotation prediction and clustering) have been proposed for self-supervised learning in novelty detection. However, the learned latent features are still low discriminative. We overcome such problems by introducing a novel decoder-encoder framework. Firstly, a generative network (a.k.a. decoder) learns the representation by mapping the initialized latent vector to an image. In particular, this vector is initialized by considering the entire distribution of training data to avoid the problem of mode-dropping. Secondly, a contrastive network (a.k.a. encoder) aims to ``learn to compare'' through mutual information estimation, which directly helps the generative network to obtain a more discriminative representation by using a negative data augmentation strategy. Extensive experiments show that our model has significant superiority over cutting-edge novelty detectors and achieves new state-of-the-art results on some novelty detection benchmarks, e.g. CIFAR10 and DCASE. Moreover, our model is more stable for training in a non-adversarial manner, compared to other adversarial based novelty detection methods. | ['Lizhuang Ma', 'Yi Zhang', 'Xin Tan', 'Jian Zhou', 'Ruizhi Qiao', 'Shaohui Lin', 'Yuan Xie', 'Chengwei Chen'] | 2021-06-18 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 0.07821293 -0.27747837 -0.11663184 -0.16989367 -0.8057688 -0.44143468
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0f00ac83-4a0e-4938-94c8-f431b3154dd8 | tailmix-overcoming-the-label-sparsity-for | null | null | https://openreview.net/forum?id=jDK19MUBT4_ | https://openreview.net/pdf?id=jDK19MUBT4_ | TailMix: Overcoming the Label Sparsity for Extreme Multi-label Classification | Extreme multi-label classification (XMC) aims at finding the most relevant labels from a huge label set at the industrial scale. The XMC problem inherently poses two challenges: data scalability and label sparsity. This work introduces a new augmentation method, namely TailMix, to address the label sparsity issue, i.e., the long-tail labels in XMC have few positive instances. TailMix utilizes the context vector generated from the label attention layer in a label-wise manner instead of using the existing Mixup methods in a sample-wise manner. In this process, TailMix selectively chooses two context vectors and augments the most plausible positive instances to improve the accuracy for long-tail labels. Despite the simplicity of TailMix, extensive experimental results show that TailMix consistently improves the baseline models without TailMix and other Mixup-based methods on three benchmark datasets. Notably, TailMix is effective for improving the performance for long-tail labels on PSP@k and PSN@k, which are the common metrics that reflect the propensity of labels. | ['Jongwuk Lee', 'Chan Lim', 'Sangwoo Han'] | 2021-09-29 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 3.47802758e-01 -1.51248857e-01 -6.58388257e-01 -5.08871436e-01
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6bde97f2-a28c-4cf9-9ce8-5ae15fd82230 | matrix-tri-factorization-over-the-tropical | 2305.06624 | null | https://arxiv.org/abs/2305.06624v1 | https://arxiv.org/pdf/2305.06624v1.pdf | Matrix tri-factorization over the tropical semiring | Tropical semiring has proven successful in several research areas, including optimal control, bioinformatics, discrete event systems, or solving a decision problem. In previous studies, a matrix two-factorization algorithm based on the tropical semiring has been applied to investigate bipartite and tripartite networks. Tri-factorization algorithms based on standard linear algebra are used for solving tasks such as data fusion, co-clustering, matrix completion, community detection, and more. However, there is currently no tropical matrix tri-factorization approach, which would allow for the analysis of multipartite networks with a high number of parts. To address this, we propose the triFastSTMF algorithm, which performs tri-factorization over the tropical semiring. We apply it to analyze a four-partition network structure and recover the edge lengths of the network. We show that triFastSTMF performs similarly to Fast-NMTF in terms of approximation and prediction performance when fitted on the whole network. When trained on a specific subnetwork and used to predict the whole network, triFastSTMF outperforms Fast-NMTF by several orders of magnitude smaller error. The robustness of triFastSTMF is due to tropical operations, which are less prone to predict large values compared to standard operations. | ['Tomaž Curk', 'Polona Oblak', 'Amra Omanović'] | 2023-05-11 | null | null | null | null | ['community-detection', 'matrix-completion'] | ['graphs', 'methodology'] | [ 1.37021363e-01 -1.78684458e-01 -3.94273102e-02 8.68679732e-02
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59ad95c8-aedd-4860-9c2e-2dbf34c04e7d | a-novel-approach-for-detecting-normal-covid | null | null | https://www.sciencedirect.com/science/article/pii/S2772528622000310 | https://www.sciencedirect.com/sdfe/reader/pii/S2772528622000310/pdf | A Novel Approach for detecting Normal, COVID-19 and Pneumonia patient using only binary classifications from chest CT-Scans | The novel Coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spread all over the world, causing a dramatic shift in circumstances that resulted in a massive pandemic, affecting the world's well-being and stability. It is an RNA virus that can infect both humans as well as animals. Diagnosis of the virus as soon as possible could contain and avoid a serious COVID-19 outbreak. Current pharmaceutical techniques and diagnostic methods tests such as Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Serology tests are time-consuming, expensive, and require a well-equipped laboratory for analysis, making them restrictive and inaccessible to everyone. Deep Learning has grown in popularity in recent years, and it now plays a crucial role in Image Classification, which also involves Medical Imaging. Using chest CT scans, this study explores the problem statement automation of differentiating COVID-19 contaminated individuals from healthy individuals. Convolutional Neural Networks (CNNs) can be trained to detect patterns in computed tomography scans (CT scans). Hence, different CNN models were used in the current study to identify variations in chest CT scans, with accuracies ranging from 91% to 98%. The Multi class Classification method is used to build these architectures. This study also proposes a new approach for classifying CT images that uses two binary classifications combined to work together, achieving 98.38% accuracy. All of these architectures' performances were compared using different classification metrics. | ['Ankit KumarSanjeev Sharma', 'Maganti Bhargav Hemanth', 'Peddaputha Akash', 'Sanskar Hasija'] | 2022-03-28 | null | null | null | neuroscience-informatics-2022-3 | ['covid-19-detection'] | ['medical'] | [ 1.96553856e-01 -6.55768037e-01 -3.66002247e-02 -8.62447172e-02
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010d0bd5-dbe2-4565-a12f-adf04a2a1b0c | two-decades-of-bengali-handwritten-digit | 2206.02234 | null | https://arxiv.org/abs/2206.02234v3 | https://arxiv.org/pdf/2206.02234v3.pdf | Two Decades of Bengali Handwritten Digit Recognition: A Survey | Handwritten Digit Recognition (HDR) is one of the most challenging tasks in the domain of Optical Character Recognition (OCR). Irrespective of language, there are some inherent challenges of HDR, which mostly arise due to the variations in writing styles across individuals, writing medium and environment, inability to maintain the same strokes while writing any digit repeatedly, etc. In addition to that, the structural complexities of the digits of a particular language may lead to ambiguous scenarios of HDR. Over the years, researchers have developed numerous offline and online HDR pipelines, where different image processing techniques are combined with traditional Machine Learning (ML)-based and/or Deep Learning (DL)-based architectures. Although evidence of extensive review studies on HDR exists in the literature for languages, such as English, Arabic, Indian, Farsi, Chinese, etc., few surveys on Bengali HDR (BHDR) can be found, which lack a comprehensive analysis of the challenges, the underlying recognition process, and possible future directions. In this paper, the characteristics and inherent ambiguities of Bengali handwritten digits along with a comprehensive insight of two decades of state-of-the-art datasets and approaches towards offline BHDR have been analyzed. Furthermore, several real-life application-specific studies, which involve BHDR, have also been discussed in detail. This paper will also serve as a compendium for researchers interested in the science behind offline BHDR, instigating the exploration of newer avenues of relevant research that may further lead to better offline recognition of Bengali handwritten digits in different application areas. | ['Md. Hasanul Kabir', 'Mohammad Ridwan Kabir', 'Md. Hamjajul Ashmafee', 'Tasnim Ahmed', 'Sabbir Ahmed', 'Md. Bakhtiar Hasan', 'A. B. M. Ashikur Rahman'] | 2022-06-05 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 1.85781822e-01 -7.25672126e-01 8.28628317e-02 -3.17515373e-01
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ccd9c291-fbf0-4652-b668-f2bc277c3010 | namf-a-non-local-adaptive-mean-filter-for | 1910.07787 | null | https://arxiv.org/abs/1910.07787v2 | https://arxiv.org/pdf/1910.07787v2.pdf | NAMF: A Non-local Adaptive Mean Filter for Salt-and-Pepper Noise Removal | In this paper, a novel algorithm called a non-local adaptive mean filter (NAMF) for removing salt-and-pepper (SAP) noise from corrupted images is presented. We employ an efficient window detector with adaptive size to detect the noise, the noisy pixel will be replaced by the combination of its neighboring pixels, and finally we use a SAP noise based non-local mean filter to reconstruct the intensity values of noisy pixels. Extensive experimental results demonstrate that NAMF can obtain better performance in terms of quality for restoring images at all levels of SAP noise. | ['Houwang Zhang', 'Yuan Zhu', 'Hanying Zheng'] | 2019-10-17 | null | null | null | null | ['salt-and-pepper-noise-removal'] | ['computer-vision'] | [ 4.03768450e-01 -9.50843275e-01 3.57009679e-01 -8.38730484e-02
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41a7a6a7-2bb3-42ed-8ef9-ca8353ed06b3 | cascade-attention-network-for-person-search | 1809.08440 | null | https://arxiv.org/abs/1809.08440v3 | https://arxiv.org/pdf/1809.08440v3.pdf | Pose-Guided Multi-Granularity Attention Network for Text-Based Person Search | Text-based person search aims to retrieve the corresponding person images in an image database by virtue of a describing sentence about the person, which poses great potential for various applications such as video surveillance. Extracting visual contents corresponding to the human description is the key to this cross-modal matching problem. Moreover, correlated images and descriptions involve different granularities of semantic relevance, which is usually ignored in previous methods. To exploit the multilevel corresponding visual contents, we propose a pose-guided multi-granularity attention network (PMA). Firstly, we propose a coarse alignment network (CA) to select the related image regions to the global description by a similarity-based attention. To further capture the phrase-related visual body part, a fine-grained alignment network (FA) is proposed, which employs pose information to learn latent semantic alignment between visual body part and textual noun phrase. To verify the effectiveness of our model, we perform extensive experiments on the CUHK Person Description Dataset (CUHK-PEDES) which is currently the only available dataset for text-based person search. Experimental results show that our approach outperforms the state-of-the-art methods by 15 \% in terms of the top-1 metric. | ['Jun-Bo Wang', 'Liang Wang', 'Chenyang Si', 'Ya Jing', 'Tieniu Tan', 'Wei Wang'] | 2018-09-22 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-8.37688893e-02 -3.93181056e-01 -3.77223581e-01 -3.80076468e-01
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48ea7a67-21d8-4633-9dba-0ed7269b6fdb | learning-to-remember-more-with-less | 1901.01347 | null | http://arxiv.org/abs/1901.01347v2 | http://arxiv.org/pdf/1901.01347v2.pdf | Learning to Remember More with Less Memorization | Memory-augmented neural networks consisting of a neural controller and an
external memory have shown potentials in long-term sequential learning. Current
RAM-like memory models maintain memory accessing every timesteps, thus they do
not effectively leverage the short-term memory held in the controller. We
hypothesize that this scheme of writing is suboptimal in memory utilization and
introduces redundant computation. To validate our hypothesis, we derive a
theoretical bound on the amount of information stored in a RAM-like system and
formulate an optimization problem that maximizes the bound. The proposed
solution dubbed Uniform Writing is proved to be optimal under the assumption of
equal timestep contributions. To relax this assumption, we introduce
modifications to the original solution, resulting in a solution termed Cached
Uniform Writing. This method aims to balance between maximizing memorization
and forgetting via overwriting mechanisms. Through an extensive set of
experiments, we empirically demonstrate the advantages of our solutions over
other recurrent architectures, claiming the state-of-the-arts in various
sequential modeling tasks. | ['Truyen Tran', 'Svetha Venkatesh', 'Hung Le'] | 2019-01-05 | learning-to-remember-more-with-less-1 | https://openreview.net/forum?id=r1xlvi0qYm | https://openreview.net/pdf?id=r1xlvi0qYm | iclr-2019-5 | ['sequential-image-classification'] | ['computer-vision'] | [ 2.80664057e-01 3.34798992e-01 -5.11728287e-01 -4.36226949e-02
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40de27cb-7b05-445c-ab5b-05bad0408a4c | betray-oneself-a-novel-audio-deepfake | 2305.16353 | null | https://arxiv.org/abs/2305.16353v1 | https://arxiv.org/pdf/2305.16353v1.pdf | Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo Conversion | Audio Deepfake Detection (ADD) aims to detect the fake audio generated by text-to-speech (TTS), voice conversion (VC) and replay, etc., which is an emerging topic. Traditionally we take the mono signal as input and focus on robust feature extraction and effective classifier design. However, the dual-channel stereo information in the audio signal also includes important cues for deepfake, which has not been studied in the prior work. In this paper, we propose a novel ADD model, termed as M2S-ADD, that attempts to discover audio authenticity cues during the mono-to-stereo conversion process. We first projects the mono to a stereo signal using a pretrained stereo synthesizer, then employs a dual-branch neural architecture to process the left and right channel signals, respectively. In this way, we effectively reveal the artifacts in the fake audio, thus improve the ADD performance. The experiments on the ASVspoof2019 database show that M2S-ADD outperforms all baselines that input mono. We release the source code at \url{https://github.com/AI-S2-Lab/M2S-ADD}. | ['Haizhou Li', 'Guanglai Gao', 'Jinhua Zhang', 'Rui Liu'] | 2023-05-25 | null | null | null | null | ['voice-conversion', 'deepfake-detection', 'face-swapping', 'voice-conversion'] | ['audio', 'computer-vision', 'computer-vision', 'speech'] | [ 6.61075264e-02 -3.44711840e-01 1.58438787e-01 1.30155727e-01
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a03f9ab2-d4c8-4604-b4a6-cba89b7d20e9 | utility-decomposition-with-deep-corrections | 1802.01772 | null | http://arxiv.org/abs/1802.01772v2 | http://arxiv.org/pdf/1802.01772v2.pdf | Decomposition Methods with Deep Corrections for Reinforcement Learning | Decomposition methods have been proposed to approximate solutions to large
sequential decision making problems. In contexts where an agent interacts with
multiple entities, utility decomposition can be used to separate the global
objective into local tasks considering each individual entity independently. An
arbitrator is then responsible for combining the individual utilities and
selecting an action in real time to solve the global problem. Although these
techniques can perform well empirically, they rely on strong assumptions of
independence between the local tasks and sacrifice the optimality of the global
solution. This paper proposes an approach that improves upon such approximate
solutions by learning a correction term represented by a neural network. We
demonstrate this approach on a fisheries management problem where multiple
boats must coordinate to maximize their catch over time as well as on a
pedestrian avoidance problem for autonomous driving. In each problem,
decomposition methods can scale to multiple boats or pedestrians by using
strategies involving one entity. We verify empirically that the proposed
correction method significantly improves the decomposition method and
outperforms a policy trained on the full scale problem without utility
decomposition. | ['Kyle Julian', 'Maxime Bouton', 'Kikuo Fujimura', 'Mykel J. Kochenderfer', 'Alireza Nakhaei'] | 2018-02-06 | null | null | null | null | ['problem-decomposition'] | ['miscellaneous'] | [ 1.17386088e-01 4.04026121e-01 -6.83207214e-02 -3.09188932e-01
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a46d470c-62f3-4b3e-9b2d-04fd1a552e12 | deep-learning-for-finger-vein-recognition-a | 2207.02148 | null | https://arxiv.org/abs/2207.02148v1 | https://arxiv.org/pdf/2207.02148v1.pdf | Deep Learning for Finger Vein Recognition: A Brief Survey of Recent Trend | Finger vein image recognition technology plays an important role in biometric recognition and has been successfully applied in many fields. Because veins are buried beneath the skin tissue, finger vein image recognition has an unparalleled advantage, which is not easily disturbed by external factors. This review summarizes 46 papers about deep learning for finger vein image recognition from 2017 to 2021. These papers are summarized according to the tasks of deep neural networks. Besides, we present the challenges and potential development directions of finger vein image recognition. | ['Jinghua Zhang', 'Chen Li', 'Wanxia Deng', 'Yimin Yin', 'Renye Zhang'] | 2022-07-05 | null | null | null | null | ['finger-vein-recognition'] | ['computer-vision'] | [ 1.46313056e-01 -3.99319053e-01 -4.48373884e-01 -2.93364525e-01
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86ef59a4-69ad-4a97-89a3-833cf090f2b1 | tensor-networks-for-unsupervised-machine | 2106.12974 | null | https://arxiv.org/abs/2106.12974v2 | https://arxiv.org/pdf/2106.12974v2.pdf | Tensor networks for unsupervised machine learning | Modeling the joint distribution of high-dimensional data is a central task in unsupervised machine learning. In recent years, many interests have been attracted to developing learning models based on tensor networks, which have the advantages of a principle understanding of the expressive power using entanglement properties, and as a bridge connecting classical computation and quantum computation. Despite the great potential, however, existing tensor network models for unsupervised machine learning only work as a proof of principle, as their performance is much worse than the standard models such as restricted Boltzmann machines and neural networks. In this Letter, we present autoregressive matrix product states (AMPS), a tensor network model combining matrix product states from quantum many-body physics and autoregressive modeling from machine learning. Our model enjoys the exact calculation of normalized probability and unbiased sampling. We demonstrate the performance of our model using two applications, generative modeling on synthetic and real-world data, and reinforcement learning in statistical physics. Using extensive numerical experiments, we show that the proposed model significantly outperforms the existing tensor network models and the restricted Boltzmann machines, and is competitive with state-of-the-art neural network models. | ['Pan Zhang', 'Jiang Zhang', 'Sujie Li', 'Jing Liu'] | 2021-06-24 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-1.98331177e-02 -4.78520170e-02 -1.01357043e-01 -3.11223030e-01
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a6847c73-59d9-47e5-826c-72cf2880d517 | artgan-artwork-synthesis-with-conditional | 1702.03410 | null | http://arxiv.org/abs/1702.03410v2 | http://arxiv.org/pdf/1702.03410v2.pdf | ArtGAN: Artwork Synthesis with Conditional Categorical GANs | This paper proposes an extension to the Generative Adversarial Networks
(GANs), namely as ARTGAN to synthetically generate more challenging and complex
images such as artwork that have abstract characteristics. This is in contrast
to most of the current solutions that focused on generating natural images such
as room interiors, birds, flowers and faces. The key innovation of our work is
to allow back-propagation of the loss function w.r.t. the labels (randomly
assigned to each generated images) to the generator from the discriminator.
With the feedback from the label information, the generator is able to learn
faster and achieve better generated image quality. Empirically, we show that
the proposed ARTGAN is capable to create realistic artwork, as well as generate
compelling real world images that globally look natural with clear shape on
CIFAR-10. | ['Kiyoshi Tanaka', 'Chee Seng Chan', 'Wei Ren Tan', 'Hernan Aguirre'] | 2017-02-11 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 5.10072529e-01 4.43135113e-01 4.80676472e-01 -1.89768046e-01
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1a56943d-ac42-41ea-8b85-41e2135565cc | are-deep-sequence-classifiers-good-at-non | 2210.13082 | null | https://arxiv.org/abs/2210.13082v2 | https://arxiv.org/pdf/2210.13082v2.pdf | Are Deep Sequence Classifiers Good at Non-Trivial Generalization? | Recent advances in deep learning models for sequence classification have greatly improved their classification accuracy, specially when large training sets are available. However, several works have suggested that under some settings the predictions made by these models are poorly calibrated. In this work we study binary sequence classification problems and we look at model calibration from a different perspective by asking the question: Are deep learning models capable of learning the underlying target class distribution? We focus on sparse sequence classification, that is problems in which the target class is rare and compare three deep learning sequence classification models. We develop an evaluation that measures how well a classifier is learning the target class distribution. In addition, our evaluation disentangles good performance achieved by mere compression of the training sequences versus performance achieved by proper model generalization. Our results suggest that in this binary setting the deep-learning models are indeed able to learn the underlying class distribution in a non-trivial manner, i.e. by proper generalization beyond data compression. | ['Xavier Carreras', 'Ariadna Quattoni', 'Francesco Cazzaro'] | 2022-10-24 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 7.19831645e-01 6.29342943e-02 -4.19147879e-01 -5.73262572e-01
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1b74e7a8-0a0c-4361-86ab-b7bad2af1e5a | autoclip-adaptive-gradient-clipping-for | 2007.14469 | null | https://arxiv.org/abs/2007.14469v1 | https://arxiv.org/pdf/2007.14469v1.pdf | AutoClip: Adaptive Gradient Clipping for Source Separation Networks | Clipping the gradient is a known approach to improving gradient descent, but requires hand selection of a clipping threshold hyperparameter. We present AutoClip, a simple method for automatically and adaptively choosing a gradient clipping threshold, based on the history of gradient norms observed during training. Experimental results show that applying AutoClip results in improved generalization performance for audio source separation networks. Observation of the training dynamics of a separation network trained with and without AutoClip show that AutoClip guides optimization into smoother parts of the loss landscape. AutoClip is very simple to implement and can be integrated readily into a variety of applications across multiple domains. | ['Bryan Pardo', 'Prem Seetharaman', 'Jonathan Le Roux', 'Gordon Wichern'] | 2020-07-25 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [-2.30745837e-01 -4.22384650e-01 -2.32848391e-01 -5.41608334e-01
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30af183d-0194-43ca-ba53-f4c058facdb9 | mmg-ego4d-multimodal-generalization-in | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Gong_MMG-Ego4D_Multimodal_Generalization_in_Egocentric_Action_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gong_MMG-Ego4D_Multimodal_Generalization_in_Egocentric_Action_Recognition_CVPR_2023_paper.pdf | MMG-Ego4D: Multimodal Generalization in Egocentric Action Recognition | In this paper, we study a novel problem in egocentric action recognition, which we term as "Multimodal Generalization" (MMG). MMG aims to study how systems can generalize when data from certain modalities is limited or even completely missing. We thoroughly investigate MMG in the context of standard supervised action recognition and the more challenging few-shot setting for learning new action categories. MMG consists of two novel scenarios, designed to support security, and efficiency considerations in real-world applications: (1) missing modality generalization where some modalities that were present during the train time are missing during the inference time, and (2) cross-modal zero-shot generalization, where the modalities present during the inference time and the training time are disjoint. To enable this investigation, we construct a new dataset MMG-Ego4D containing data points with video, audio, and inertial motion sensor (IMU) modalities. Our dataset is derived from Ego4D dataset, but processed and thoroughly re-annotated by human experts to facilitate research in the MMG problem. We evaluate a diverse array of models on MMG-Ego4D and propose new methods with improved generalization ability. In particular, we introduce a new fusion module with modality dropout training, contrastive-based alignment training, and a novel cross-modal prototypical loss for better few-shot performance. We hope this study will serve as a benchmark and guide future research in multimodal generalization problems. The benchmark and code are available at https://github.com/facebookresearch/MMG_Ego4D | ['Rakesh Ranjan', 'Zhangyang Wang', 'Yilei Li', 'Jean-Charles Bazin', 'Naina Dhingra', 'Sreyas Mohan', 'Xinyu Gong'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['action-recognition-in-videos'] | ['computer-vision'] | [ 3.36978644e-01 -1.53647915e-01 -4.68929410e-01 -2.84682393e-01
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e282dff2-159d-4f24-8247-887763dfb1d0 | token-manipulation-generative-adversarial | 2005.02794 | null | https://arxiv.org/abs/2005.02794v2 | https://arxiv.org/pdf/2005.02794v2.pdf | Token Manipulation Generative Adversarial Network for Text Generation | MaskGAN opens the query for the conditional language model by filling in the blanks between the given tokens. In this paper, we focus on addressing the limitations caused by having to specify blanks to be filled. We decompose conditional text generation problem into two tasks, make-a-blank and fill-in-the-blank, and extend the former to handle more complex manipulations on the given tokens. We cast these tasks as a hierarchical multi agent RL problem and introduce a conditional adversarial learning that allows the agents to reach a goal, producing realistic texts, in cooperative setting. We show that the proposed model not only addresses the limitations but also provides good results without compromising the performance in terms of quality and diversity. | ['DaeJin Jo'] | 2020-05-06 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [ 3.33036095e-01 3.82504821e-01 1.92131903e-02 -7.31553324e-03
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838a435f-d038-4732-a31b-b9ec9156d2ba | diverse-parallel-data-synthesis-for-cross | 2210.16613 | null | https://arxiv.org/abs/2210.16613v1 | https://arxiv.org/pdf/2210.16613v1.pdf | Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers | Text-to-SQL parsers typically struggle with databases unseen during the train time. Adapting parsers to new databases is a challenging problem due to the lack of natural language queries in the new schemas. We present ReFill, a framework for synthesizing high-quality and textually diverse parallel datasets for adapting a Text-to-SQL parser to a target schema. ReFill learns to retrieve-and-edit text queries from the existing schemas and transfers them to the target schema. We show that retrieving diverse existing text, masking their schema-specific tokens, and refilling with tokens relevant to the target schema, leads to significantly more diverse text queries than achievable by standard SQL-to-Text generation methods. Through experiments spanning multiple databases, we demonstrate that fine-tuning parsers on datasets synthesized using ReFill consistently outperforms the prior data-augmentation methods. | ['Sunita Sarawagi', 'Ashutosh Sathe', 'Abhijeet Awasthi'] | 2022-10-29 | null | null | null | null | ['sql-to-text', 'text-to-sql'] | ['computer-code', 'computer-code'] | [ 5.78189015e-01 3.52342337e-01 -1.56338945e-01 -9.97755826e-01
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090cbbf0-8d7a-4ad0-9940-5ed908599dbd | towards-reliable-misinformation-mitigation | 2305.14928 | null | https://arxiv.org/abs/2305.14928v1 | https://arxiv.org/pdf/2305.14928v1.pdf | Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4 | Misinformation poses a critical societal challenge, and current approaches have yet to produce an effective solution. We propose focusing on generalization, soft classification, and leveraging recent large language models to create more practical tools in contexts where perfect predictions remain unattainable. We begin by demonstrating that GPT-4 and other language models can outperform existing methods in the literature. Next, we explore their generalization, revealing that GPT-4 and RoBERTa-large exhibit critical differences in failure modes, which offer potential for significant performance improvements. Finally, we show that these models can be employed in soft classification frameworks to better quantify uncertainty. We find that models with inferior hard classification results can achieve superior soft classification performance. Overall, this research lays groundwork for future tools that can drive real-world progress on misinformation. | ['Reihaneh Rabbany', 'Joel Christoph', 'Caleb Gupta', 'Meilina Reksoprodjo', 'Kellin Pelrine'] | 2023-05-24 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [ 2.27313593e-01 3.11917901e-01 -8.26846898e-01 -4.77883309e-01
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eda287e4-e173-42a9-acb7-0848de917559 | align-and-attend-network-for-globally-and | 1905.13066 | null | https://arxiv.org/abs/1905.13066v1 | https://arxiv.org/pdf/1905.13066v1.pdf | Align-and-Attend Network for Globally and Locally Coherent Video Inpainting | We propose a novel feed-forward network for video inpainting. We use a set of sampled video frames as the reference to take visible contents to fill the hole of a target frame. Our video inpainting network consists of two stages. The first stage is an alignment module that uses computed homographies between the reference frames and the target frame. The visible patches are then aggregated based on the frame similarity to fill in the target holes roughly. The second stage is a non-local attention module that matches the generated patches with known reference patches (in space and time) to refine the previous global alignment stage. Both stages consist of large spatial-temporal window size for the reference and thus enable modeling long-range correlations between distant information and the hole regions. Therefore, even challenging scenes with large or slowly moving holes can be handled, which have been hardly modeled by existing flow-based approach. Our network is also designed with a recurrent propagation stream to encourage temporal consistency in video results. Experiments on video object removal demonstrate that our method inpaints the holes with globally and locally coherent contents. | ['Joon-Young Lee', 'Dahun Kim', 'Sanghyun Woo', 'In So Kweon', 'KwanYong Park'] | 2019-05-30 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [ 1.57487690e-01 -9.70974341e-02 -6.86906800e-02 -4.44198074e-03
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54490bae-fdab-4652-b9e1-2a2d71ceffcd | hybridpoint-point-cloud-registration-based-on | 2303.16526 | null | https://arxiv.org/abs/2303.16526v2 | https://arxiv.org/pdf/2303.16526v2.pdf | HybridPoint: Point Cloud Registration Based on Hybrid Point Sampling and Matching | Patch-to-point matching has become a robust way of point cloud registration. However, previous patch-matching methods employ superpoints with poor localization precision as nodes, which may lead to ambiguous patch partitions. In this paper, we propose a HybridPoint-based network to find more robust and accurate correspondences. Firstly, we propose to use salient points with prominent local features as nodes to increase patch repeatability, and introduce some uniformly distributed points to complete the point cloud, thus constituting hybrid points. Hybrid points not only have better localization precision but also give a complete picture of the whole point cloud. Furthermore, based on the characteristic of hybrid points, we propose a dual-classes patch matching module, which leverages the matching results of salient points and filters the matching noise of non-salient points. Experiments show that our model achieves state-of-the-art performance on 3DMatch, 3DLoMatch, and KITTI odometry, especially with 93.0% Registration Recall on the 3DMatch dataset. Our code and models are available at https://github.com/liyih/HybridPoint. | ['Shaoyi Du', 'Feng Wen', 'Aixue Ye', 'Runzhao Yao', 'Canhui Tang', 'Yiheng Li'] | 2023-03-29 | null | null | null | null | ['point-cloud-registration', 'patch-matching'] | ['computer-vision', 'computer-vision'] | [-4.67350096e-01 -2.06740797e-01 -3.64603996e-01 -7.40624964e-02
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1dc3e4ea-632a-4a93-9136-b49b5ec1240a | real-time-target-sound-extraction | 2211.02250 | null | https://arxiv.org/abs/2211.02250v3 | https://arxiv.org/pdf/2211.02250v3.pdf | Real-Time Target Sound Extraction | We present the first neural network model to achieve real-time and streaming target sound extraction. To accomplish this, we propose Waveformer, an encoder-decoder architecture with a stack of dilated causal convolution layers as the encoder, and a transformer decoder layer as the decoder. This hybrid architecture uses dilated causal convolutions for processing large receptive fields in a computationally efficient manner while also leveraging the generalization performance of transformer-based architectures. Our evaluations show as much as 2.2-3.3 dB improvement in SI-SNRi compared to the prior models for this task while having a 1.2-4x smaller model size and a 1.5-2x lower runtime. We provide code, dataset, and audio samples: https://waveformer.cs.washington.edu/. | ['Shyamnath Gollakota', 'Takuya Yoshioka', 'Tuochao Chen', 'Malek Itani', 'Justin Chan', 'Bandhav Veluri'] | 2022-11-04 | null | null | null | null | ['streaming-target-sound-extraction', 'target-sound-extraction'] | ['audio', 'audio'] | [ 2.38626570e-01 5.82231805e-02 2.48977333e-01 -3.08486193e-01
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3c2a49c6-1772-4c2b-a1c3-3a2b8a5ea1f3 | cross-modal-place-recognition-in-image | 2307.01047 | null | https://arxiv.org/abs/2307.01047v1 | https://arxiv.org/pdf/2307.01047v1.pdf | Cross-modal Place Recognition in Image Databases using Event-based Sensors | Visual place recognition is an important problem towards global localization in many robotics tasks. One of the biggest challenges is that it may suffer from illumination or appearance changes in surrounding environments. Event cameras are interesting alternatives to frame-based sensors as their high dynamic range enables robust perception in difficult illumination conditions. However, current event-based place recognition methods only rely on event information, which restricts downstream applications of VPR. In this paper, we present the first cross-modal visual place recognition framework that is capable of retrieving regular images from a database given an event query. Our method demonstrates promising results with respect to the state-of-the-art frame-based and event-based methods on the Brisbane-Event-VPR dataset under different scenarios. We also verify the effectiveness of the combination of retrieval and classification, which can boost performance by a large margin. | ['Laurent Kneip', 'Huiliang Shang', 'Yifu Wang', 'Jiaxin Wei', 'Xiang Ji'] | 2023-07-03 | null | null | null | null | ['visual-place-recognition', 'retrieval'] | ['computer-vision', 'methodology'] | [ 1.02696285e-01 -6.96944833e-01 -6.32683560e-02 -2.80200750e-01
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9bc818a1-fb26-4f56-a827-07598ce68ab5 | chinesefoodnet-a-large-scale-image-dataset | 1705.02743 | null | http://arxiv.org/abs/1705.02743v3 | http://arxiv.org/pdf/1705.02743v3.pdf | ChineseFoodNet: A large-scale Image Dataset for Chinese Food Recognition | In this paper, we introduce a new and challenging large-scale food image
dataset called "ChineseFoodNet", which aims to automatically recognizing
pictured Chinese dishes. Most of the existing food image datasets collected
food images either from recipe pictures or selfie. In our dataset, images of
each food category of our dataset consists of not only web recipe and menu
pictures but photos taken from real dishes, recipe and menu as well.
ChineseFoodNet contains over 180,000 food photos of 208 categories, with each
category covering a large variations in presentations of same Chinese food. We
present our efforts to build this large-scale image dataset, including food
category selection, data collection, and data clean and label, in particular
how to use machine learning methods to reduce manual labeling work that is an
expensive process. We share a detailed benchmark of several state-of-the-art
deep convolutional neural networks (CNNs) on ChineseFoodNet. We further propose
a novel two-step data fusion approach referred as "TastyNet", which combines
prediction results from different CNNs with voting method. Our proposed
approach achieves top-1 accuracies of 81.43% on the validation set and 81.55%
on the test set, respectively. The latest dataset is public available for
research and can be achieved at https://sites.google.com/view/chinesefoodnet. | ['Hua Zhou', 'Liang Diao', 'Dongyan Wang', 'Yu Zhu', 'Xin Chen'] | 2017-05-08 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [ 1.68859839e-01 -4.54192162e-01 -2.03859862e-02 -5.36874771e-01
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4100c9f1-fa70-49ae-9199-631d709e1819 | deep-learning-for-landslide-recognition-in | null | null | https://ieeexplore.ieee.org/document/9159123 | https://ieeexplore.ieee.org/document/9159123 | Deep Learning for Landslide Recognition in Satellite Architecture | Using the optical camera in remote sensing is limited in various environmental conditions. This paper presents a system of combining deep learning and image transform algorithms to detect landslide location in satellite images. In the deep learning part, a convolution neural network is used to classify satellite images contain landslides. From landslide images classified, in order to accurately identify landslides under different lighting conditions, this paper proposes a transformation algorithm Hue - Bi-dimensional empirical mode decomposition (H-BEMD) to determine the landslide region and size. After the location of landslide is detected, we discover the size change of the landslide based on different time points. In this study, we record an accuracy of up to 96% in the classification process, and the accuracy of landslide location almost absolute. | ['Kyo Tan', 'Clarissa Loh', 'Kai-Yew Lum', 'Pei-Jun Lee', 'Trong-An Bui'] | 2020-08-05 | null | null | null | null | ['landslide-segmentation'] | ['computer-vision'] | [-1.28420874e-01 -8.54186296e-01 1.41092971e-01 -2.30646059e-01
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a1800635-1b00-45f3-a808-2b04c0e22b02 | alignment-augmented-consistent-translation | null | null | https://aclanthology.org/2022.acl-long.179 | https://aclanthology.org/2022.acl-long.179.pdf | Alignment-Augmented Consistent Translation for Multilingual Open Information Extraction | Progress with supervised Open Information Extraction (OpenIE) has been primarily limited to English due to the scarcity of training data in other languages. In this paper, we explore techniques to automatically convert English text for training OpenIE systems in other languages. We introduce the Alignment-Augmented Constrained Translation (AACTrans) model to translate English sentences and their corresponding extractions consistently with each other — with no changes to vocabulary or semantic meaning which may result from independent translations. Using the data generated with AACTrans, we train a novel two-stage generative OpenIE model, which we call Gen2OIE, that outputs for each sentence: 1) relations in the first stage and 2) all extractions containing the relation in the second stage. Gen2OIE increases relation coverage using a training data transformation technique that is generalizable to multiple languages, in contrast to existing models that use an English-specific training loss. Evaluations on 5 languages — Spanish, Portuguese, Chinese, Hindi and Telugu — show that the Gen2OIE with AACTrans data outperforms prior systems by a margin of 6-25% in F1. | ['Mausam .', 'Soumen Chakrabarti', 'Shubham Mittal', 'Muqeeth Mohammed', 'Keshav Kolluru'] | null | null | null | null | acl-2022-5 | ['open-information-extraction'] | ['natural-language-processing'] | [ 3.77389431e-01 6.89965427e-01 -3.33621621e-01 -3.56240630e-01
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0ef75da8-cd12-4dd5-8cb4-0a98150d7a16 | a-hypergraph-partitioned-vertex-programming | 1308.6823 | null | http://arxiv.org/abs/1308.6823v1 | http://arxiv.org/pdf/1308.6823v1.pdf | A Hypergraph-Partitioned Vertex Programming Approach for Large-scale Consensus Optimization | In modern data science problems, techniques for extracting value from big
data require performing large-scale optimization over heterogenous, irregularly
structured data. Much of this data is best represented as multi-relational
graphs, making vertex programming abstractions such as those of Pregel and
GraphLab ideal fits for modern large-scale data analysis. In this paper, we
describe a vertex-programming implementation of a popular consensus
optimization technique known as the alternating direction of multipliers
(ADMM). ADMM consensus optimization allows elegant solution of complex
objectives such as inference in rich probabilistic models. We also introduce a
novel hypergraph partitioning technique that improves over state-of-the-art
partitioning techniques for vertex programming and significantly reduces the
communication cost by reducing the number of replicated nodes up to an order of
magnitude. We implemented our algorithm in GraphLab and measure scaling
performance on a variety of realistic bipartite graph distributions and a large
synthetic voter-opinion analysis application. In our experiments, we are able
to achieve a 50% improvement in runtime over the current state-of-the-art
GraphLab partitioning scheme. | ['Lise Getoor', 'Hui Miao', 'Bert Huang', 'Xiangyang Liu'] | 2013-08-30 | null | null | null | null | ['hypergraph-partitioning'] | ['graphs'] | [-3.98179144e-01 2.27665916e-01 -6.93739727e-02 -3.07771683e-01
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7983afd3-12b5-445c-9eef-6a55fcd4dc42 | modeling-composite-labels-for-neural | 1810.08815 | null | http://arxiv.org/abs/1810.08815v1 | http://arxiv.org/pdf/1810.08815v1.pdf | Modeling Composite Labels for Neural Morphological Tagging | Neural morphological tagging has been regarded as an extension to POS tagging
task, treating each morphological tag as a monolithic label and ignoring its
internal structure. We propose to view morphological tags as composite labels
and explicitly model their internal structure in a neural sequence tagger. For
this, we explore three different neural architectures and compare their
performance with both CRF and simple neural multiclass baselines. We evaluate
our models on 49 languages and show that the neural architecture that models
the morphological labels as sequences of morphological category values performs
significantly better than both baselines establishing state-of-the-art results
in morphological tagging for most languages. | ['Kairit Sirts', 'Alexander Tkachenko'] | 2018-10-20 | modeling-composite-labels-for-neural-1 | https://aclanthology.org/K18-1036 | https://aclanthology.org/K18-1036.pdf | conll-2018-10 | ['morphological-tagging'] | ['natural-language-processing'] | [ 1.34921446e-01 3.48559290e-01 -3.07873994e-01 -6.67092144e-01
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8e44d24b-6bc2-44a8-adf4-0f617b7ce28e | how-do-deepfakes-move-motion-magnification | 2212.14033 | null | https://arxiv.org/abs/2212.14033v1 | https://arxiv.org/pdf/2212.14033v1.pdf | How Do Deepfakes Move? Motion Magnification for Deepfake Source Detection | With the proliferation of deep generative models, deepfakes are improving in quality and quantity everyday. However, there are subtle authenticity signals in pristine videos, not replicated by SOTA GANs. We contrast the movement in deepfakes and authentic videos by motion magnification towards building a generalized deepfake source detector. The sub-muscular motion in faces has different interpretations per different generative models which is reflected in their generative residue. Our approach exploits the difference between real motion and the amplified GAN fingerprints, by combining deep and traditional motion magnification, to detect whether a video is fake and its source generator if so. Evaluating our approach on two multi-source datasets, we obtain 97.17% and 94.03% for video source detection. We compare against the prior deepfake source detector and other complex architectures. We also analyze the importance of magnification amount, phase extraction window, backbone network architecture, sample counts, and sample lengths. Finally, we report our results for different skin tones to assess the bias. | ['Ilke Demir', 'Umur Aybars Ciftci'] | 2022-12-28 | null | null | null | null | ['face-swapping', 'motion-magnification'] | ['computer-vision', 'computer-vision'] | [ 5.68871081e-01 2.34410688e-01 -1.03508040e-01 9.25946012e-02
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c68e3f8a-b59b-4887-a09c-bb82ad3ea446 | face-to-bmi-using-computer-vision-to-infer | 1703.03156 | null | http://arxiv.org/abs/1703.03156v1 | http://arxiv.org/pdf/1703.03156v1.pdf | Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media | A person's weight status can have profound implications on their life,
ranging from mental health, to longevity, to financial income. At the societal
level, "fat shaming" and other forms of "sizeism" are a growing concern, while
increasing obesity rates are linked to ever raising healthcare costs. For these
reasons, researchers from a variety of backgrounds are interested in studying
obesity from all angles. To obtain data, traditionally, a person would have to
accurately self-report their body-mass index (BMI) or would have to see a
doctor to have it measured. In this paper, we show how computer vision can be
used to infer a person's BMI from social media images. We hope that our tool,
which we release, helps to advance the study of social aspects related to body
weight. | ['Antonio Torralba', 'Ferda Ofli', 'Javier Marin', 'Mustafa Camurcu', 'Ingmar Weber', 'Yusuf Aytar', 'Enes Kocabey'] | 2017-03-09 | null | null | null | null | ['body-mass-index-bmi-prediction'] | ['computer-vision'] | [ 1.42510831e-01 3.98052037e-01 -6.53879404e-01 -4.96148795e-01
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0d80e414-80bc-4b12-8cb1-4c4674193940 | otter-a-multi-modal-model-with-in-context | 2305.03726 | null | https://arxiv.org/abs/2305.03726v1 | https://arxiv.org/pdf/2305.03726v1.pdf | Otter: A Multi-Modal Model with In-Context Instruction Tuning | Large language models (LLMs) have demonstrated significant universal capabilities as few/zero-shot learners in various tasks due to their pre-training on vast amounts of text data, as exemplified by GPT-3, which boosted to InstrctGPT and ChatGPT, effectively following natural language instructions to accomplish real-world tasks. In this paper, we propose to introduce instruction tuning into multi-modal models, motivated by the Flamingo model's upstream interleaved format pretraining dataset. We adopt a similar approach to construct our MultI-Modal In-Context Instruction Tuning (MIMIC-IT) dataset. We then introduce Otter, a multi-modal model based on OpenFlamingo (open-sourced version of DeepMind's Flamingo), trained on MIMIC-IT and showcasing improved instruction-following ability and in-context learning. We also optimize OpenFlamingo's implementation for researchers, democratizing the required training resources from 1$\times$ A100 GPU to 4$\times$ RTX-3090 GPUs, and integrate both OpenFlamingo and Otter into Huggingface Transformers for more researchers to incorporate the models into their customized training and inference pipelines. | ['Ziwei Liu', 'Jingkang Yang', 'Jinghao Wang', 'Liangyu Chen', 'Yuanhan Zhang', 'Bo Li'] | 2023-05-05 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [-2.66517907e-01 -1.80308372e-01 -5.86547613e-01 -5.43661118e-01
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a3d7f3da-1695-449f-bfc5-b663915afa65 | communication-efficient-federated-2 | 2302.04969 | null | https://arxiv.org/abs/2302.04969v3 | https://arxiv.org/pdf/2302.04969v3.pdf | Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation | Federated bilevel optimization has attracted increasing attention due to emerging machine learning and communication applications. The biggest challenge lies in computing the gradient of the upper-level objective function (i.e., hypergradient) in the federated setting due to the nonlinear and distributed construction of a series of global Hessian matrices. In this paper, we propose a novel communication-efficient federated hypergradient estimator via aggregated iterative differentiation (AggITD). AggITD is simple to implement and significantly reduces the communication cost by conducting the federated hypergradient estimation and the lower-level optimization simultaneously. We show that the proposed AggITD-based algorithm achieves the same sample complexity as existing approximate implicit differentiation (AID)-based approaches with much fewer communication rounds in the presence of data heterogeneity. Our results also shed light on the great advantage of ITD over AID in the federated/distributed hypergradient estimation. This differs from the comparison in the non-distributed bilevel optimization, where ITD is less efficient than AID. Our extensive experiments demonstrate the great effectiveness and communication efficiency of the proposed method. | ['Kaiyi Ji', 'Peiyao Xiao'] | 2023-02-09 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-7.61790454e-01 -2.43788600e-01 4.64295223e-02 -1.66070133e-01
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969d8608-d7db-4510-9bf3-ad7b050b2dfc | exploiting-discourse-relations-between | null | null | https://aclanthology.org/W12-4702 | https://aclanthology.org/W12-4702.pdf | Exploiting Discourse Relations between Sentences for Text Clustering | null | ['Nik Adilah Hanin Binti Zahri', 'Suguru Matsuyoshi', 'Fumiyo Fukumoto'] | 2012-12-01 | exploiting-discourse-relations-between-1 | https://aclanthology.org/W12-4702 | https://aclanthology.org/W12-4702.pdf | ws-2012-12 | ['text-clustering'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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363eefab-bcc9-40e1-b3f6-b78d49264453 | a-fast-palette-reordering-technique-based-on | null | null | https://ieeexplore.ieee.org/document/8451221/authors | https://ieeexplore.ieee.org/document/8451221/authors | A Fast Palette Reordering Technique Based on GPU-Optimized Genetic Algorithms | Color re-indexing is one of main approaches for improving the loss-less compression of color indexed images. Zero-order entropy reduction of indexes matrix is the key to obtain high compression ratio. However, obtaining the optimal re-indexed palette is a challenging problem that cannot be solved by brute-force approaches. In this paper we propose a novel re-indexing approach where the Travelling Salesman Problem is solved through Ant Colony Optimization. Our method is proved to achieve high quality results by outperforming state-of-art ones in term of compression gain. Additionally, we exploit clustering and GPU computing to make our solution extremely fast. | ['Sebastiano Battiato', 'Giorgio Grasso', 'Filippo Stanco', 'Dario Allegra', 'Oliver Giudice'] | 2018-09-08 | null | null | null | ieee-international-conference-on-image-11 | ['clustering'] | ['methodology'] | [ 4.03658509e-01 -6.56658173e-01 -1.56482518e-01 1.66155532e-01
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ac8f9d44-3fce-4cbe-93d2-860483f6c15f | classification-uncertainty-of-deep-neural | 1805.08440 | null | http://arxiv.org/abs/1805.08440v2 | http://arxiv.org/pdf/1805.08440v2.pdf | Classification Uncertainty of Deep Neural Networks Based on Gradient Information | We study the quantification of uncertainty of Convolutional Neural Networks
(CNNs) based on gradient metrics. Unlike the classical softmax entropy, such
metrics gather information from all layers of the CNN. We show for the EMNIST
digits data set that for several such metrics we achieve the same meta
classification accuracy -- i.e. the task of classifying predictions as correct
or incorrect without knowing the actual label -- as for entropy thresholding.
We apply meta classification to unknown concepts (out-of-distribution samples)
-- EMNIST/Omniglot letters, CIFAR10 and noise -- and demonstrate that meta
classification rates for unknown concepts can be increased when using entropy
together with several gradient based metrics as input quantities for a meta
classifier. Meta classifiers only trained on the uncertainty metrics of known
concepts, i.e. EMNIST digits, usually do not perform equally well for all
unknown concepts. If we however allow the meta classifier to be trained on
uncertainty metrics for some out-of-distribution samples, meta classification
for concepts remote from EMNIST digits (then termed known unknowns) can be
improved considerably. | ['Philipp Oberdiek', 'Hanno Gottschalk', 'Matthias Rottmann'] | 2018-05-22 | null | null | null | null | ['known-unknowns'] | ['miscellaneous'] | [-8.86691138e-02 4.34277356e-01 -8.76350626e-02 -7.95221746e-01
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c911f38e-52e5-4cad-b877-c98a1d161372 | evading-classifiers-in-discrete-domains-with | 1810.10939 | null | https://arxiv.org/abs/1810.10939v3 | https://arxiv.org/pdf/1810.10939v3.pdf | Evading classifiers in discrete domains with provable optimality guarantees | Machine-learning models for security-critical applications such as bot, malware, or spam detection, operate in constrained discrete domains. These applications would benefit from having provable guarantees against adversarial examples. The existing literature on provable adversarial robustness of models, however, exclusively focuses on robustness to gradient-based attacks in domains such as images. These attacks model the adversarial cost, e.g., amount of distortion applied to an image, as a $p$-norm. We argue that this approach is not well-suited to model adversarial costs in constrained domains where not all examples are feasible. We introduce a graphical framework that (1) generalizes existing attacks in discrete domains, (2) can accommodate complex cost functions beyond $p$-norms, including financial cost incurred when attacking a classifier, and (3) efficiently produces valid adversarial examples with guarantees of minimal adversarial cost. These guarantees directly translate into a notion of adversarial robustness that takes into account domain constraints and the adversary's capabilities. We show how our framework can be used to evaluate security by crafting adversarial examples that evade a Twitter-bot detection classifier with provably minimal number of changes; and to build privacy defenses by crafting adversarial examples that evade a privacy-invasive website-fingerprinting classifier. | ['Carmela Troncoso', 'Nikita Samarin', 'Jamie Hayes', 'Bogdan Kulynych'] | 2018-10-25 | null | null | null | null | ['twitter-bot-detection', 'spam-detection'] | ['miscellaneous', 'natural-language-processing'] | [ 3.57412785e-01 6.68554008e-02 -1.43410519e-01 -3.07013184e-01
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e5ccaef9-4308-4b04-b552-80b7e6c4d6f1 | can-semi-supervised-learning-reduce-the | 2111.04357 | null | https://arxiv.org/abs/2111.04357v4 | https://arxiv.org/pdf/2111.04357v4.pdf | Can semi-supervised learning reduce the amount of manual labelling required for effective radio galaxy morphology classification? | In this work, we examine the robustness of state-of-the-art semi-supervised learning (SSL) algorithms when applied to morphological classification in modern radio astronomy. We test whether SSL can achieve performance comparable to the current supervised state of the art when using many fewer labelled data points and if these results generalise to using truly unlabelled data. We find that although SSL provides additional regularisation, its performance degrades rapidly when using very few labels, and that using truly unlabelled data leads to a significant drop in performance. | ['Anna M. M. Scaife', 'Inigo V. Slijepcevic'] | 2021-11-08 | null | null | null | null | ['morphology-classification'] | ['computer-vision'] | [ 2.54282713e-01 2.31709078e-01 -3.24270159e-01 -5.00848413e-01
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b349df66-1f0b-4318-ba1c-1163eb395505 | differentiable-genetic-programming-for-high | 2304.08915 | null | https://arxiv.org/abs/2304.08915v1 | https://arxiv.org/pdf/2304.08915v1.pdf | Differentiable Genetic Programming for High-dimensional Symbolic Regression | Symbolic regression (SR) is the process of discovering hidden relationships from data with mathematical expressions, which is considered an effective way to reach interpretable machine learning (ML). Genetic programming (GP) has been the dominator in solving SR problems. However, as the scale of SR problems increases, GP often poorly demonstrates and cannot effectively address the real-world high-dimensional problems. This limitation is mainly caused by the stochastic evolutionary nature of traditional GP in constructing the trees. In this paper, we propose a differentiable approach named DGP to construct GP trees towards high-dimensional SR for the first time. Specifically, a new data structure called differentiable symbolic tree is proposed to relax the discrete structure to be continuous, thus a gradient-based optimizer can be presented for the efficient optimization. In addition, a sampling method is proposed to eliminate the discrepancy caused by the above relaxation for valid symbolic expressions. Furthermore, a diversification mechanism is introduced to promote the optimizer escaping from local optima for globally better solutions. With these designs, the proposed DGP method can efficiently search for the GP trees with higher performance, thus being capable of dealing with high-dimensional SR. To demonstrate the effectiveness of DGP, we conducted various experiments against the state of the arts based on both GP and deep neural networks. The experiment results reveal that DGP can outperform these chosen peer competitors on high-dimensional regression benchmarks with dimensions varying from tens to thousands. In addition, on the synthetic SR problems, the proposed DGP method can also achieve the best recovery rate even with different noisy levels. It is believed this work can facilitate SR being a powerful alternative to interpretable ML for a broader range of real-world problems. | ['Jiancheng Lv', 'Mengjie Zhang', 'Yanan sun', 'Yuwei Ou', 'Andrew Lensen', 'Xiaotian Song', 'Peng Zeng'] | 2023-04-18 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 1.53165489e-01 1.12114303e-01 -2.87898570e-01 -2.13740170e-01
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822de508-a96a-4f92-9de0-3c4ae028ffae | meta-compositional-referring-expression | 2304.04415 | null | https://arxiv.org/abs/2304.04415v3 | https://arxiv.org/pdf/2304.04415v3.pdf | Meta Compositional Referring Expression Segmentation | Referring expression segmentation aims to segment an object described by a language expression from an image. Despite the recent progress on this task, existing models tackling this task may not be able to fully capture semantics and visual representations of individual concepts, which limits their generalization capability, especially when handling novel compositions of learned concepts. In this work, through the lens of meta learning, we propose a Meta Compositional Referring Expression Segmentation (MCRES) framework to enhance model compositional generalization performance. Specifically, to handle various levels of novel compositions, our framework first uses training data to construct a virtual training set and multiple virtual testing sets, where data samples in each virtual testing set contain a level of novel compositions w.r.t. the virtual training set. Then, following a novel meta optimization scheme to optimize the model to obtain good testing performance on the virtual testing sets after training on the virtual training set, our framework can effectively drive the model to better capture semantics and visual representations of individual concepts, and thus obtain robust generalization performance even when handling novel compositions. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our framework. | ['Jun Liu', 'Ying Sun', 'Zehuan Yuan', 'Xindi Shang', 'Mark He Huang', 'Li Xu'] | 2023-04-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_Meta_Compositional_Referring_Expression_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_Meta_Compositional_Referring_Expression_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['referring-expression', 'referring-expression-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.61556995e-01 3.49229411e-03 -4.43768591e-01 -4.54581290e-01
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8598b1e1-21d2-47d6-a25f-d0c0c8d51962 | awesome-gpu-memory-constrained-long-document | 2305.14806 | null | https://arxiv.org/abs/2305.14806v1 | https://arxiv.org/pdf/2305.14806v1.pdf | AWESOME: GPU Memory-constrained Long Document Summarization using Memory Mechanism and Global Salient Content | Long document summarization systems are critical for domains with lengthy and jargonladen text, yet they present significant challenges to researchers and developers with limited computing resources. Existing solutions mainly focus on efficient attentions or divide-and-conquer strategies. The former reduces theoretical time complexity, but is still memory-heavy. The latter methods sacrifice global context, leading to uninformative and incoherent summaries. This work aims to leverage the memory-efficient nature of divide-and-conquer methods while preserving global context. Concretely, our framework AWESOME uses two novel mechanisms: (1) External memory mechanisms track previously encoded document segments and their corresponding summaries, to enhance global document understanding and summary coherence. (2) Global salient content is further identified beforehand to augment each document segment to support its summarization. Extensive experiments on diverse genres of text, including government reports, transcripts, scientific papers, and novels, show that AWESOME produces summaries with improved informativeness, faithfulness, and coherence than competitive baselines on longer documents, while having a similar or smaller GPU memory footprint. | ['Lu Wang', 'Shuyang Cao'] | 2023-05-24 | null | null | null | null | ['document-summarization'] | ['natural-language-processing'] | [ 3.04746389e-01 1.42262341e-03 -5.62802911e-01 -1.17136717e-01
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764a015c-998c-41c6-8fdd-4cfc137634f6 | efficient-hyperparameter-optimization-of-deep | 1607.08316 | null | http://arxiv.org/abs/1607.08316v2 | http://arxiv.org/pdf/1607.08316v2.pdf | Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates | Automatically searching for optimal hyperparameter configurations is of
crucial importance for applying deep learning algorithms in practice. Recently,
Bayesian optimization has been proposed for optimizing hyperparameters of
various machine learning algorithms. Those methods adopt probabilistic
surrogate models like Gaussian processes to approximate and minimize the
validation error function of hyperparameter values. However, probabilistic
surrogates require accurate estimates of sufficient statistics (e.g.,
covariance) of the error distribution and thus need many function evaluations
with a sizeable number of hyperparameters. This makes them inefficient for
optimizing hyperparameters of deep learning algorithms, which are highly
expensive to evaluate. In this work, we propose a new deterministic and
efficient hyperparameter optimization method that employs radial basis
functions as error surrogates. The proposed mixed integer algorithm, called
HORD, searches the surrogate for the most promising hyperparameter values
through dynamic coordinate search and requires many fewer function evaluations.
HORD does well in low dimensions but it is exceptionally better in higher
dimensions. Extensive evaluations on MNIST and CIFAR-10 for four deep neural
networks demonstrate HORD significantly outperforms the well-established
Bayesian optimization methods such as GP, SMAC, and TPE. For instance, on
average, HORD is more than 6 times faster than GP-EI in obtaining the best
configuration of 19 hyperparameters. | ['Jiashi Feng', 'Ilija Ilievski', 'Taimoor Akhtar', 'Christine Annette Shoemaker'] | 2016-07-28 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-6.78277969e-01 -1.09142907e-01 -1.76607873e-02 -3.60314369e-01
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e7b8364f-a748-429c-abe2-1db488f024b6 | efficient-personalized-learning-for-wearable | 2208.01095 | null | https://arxiv.org/abs/2208.01095v1 | https://arxiv.org/pdf/2208.01095v1.pdf | Efficient Personalized Learning for Wearable Health Applications using HyperDimensional Computing | Health monitoring applications increasingly rely on machine learning techniques to learn end-user physiological and behavioral patterns in everyday settings. Considering the significant role of wearable devices in monitoring human body parameters, on-device learning can be utilized to build personalized models for behavioral and physiological patterns, and provide data privacy for users at the same time. However, resource constraints on most of these wearable devices prevent the ability to perform online learning on them. To address this issue, it is required to rethink the machine learning models from the algorithmic perspective to be suitable to run on wearable devices. Hyperdimensional computing (HDC) offers a well-suited on-device learning solution for resource-constrained devices and provides support for privacy-preserving personalization. Our HDC-based method offers flexibility, high efficiency, resilience, and performance while enabling on-device personalization and privacy protection. We evaluate the efficacy of our approach using three case studies and show that our system improves the energy efficiency of training by up to $45.8\times$ compared with the state-of-the-art Deep Neural Network (DNN) algorithms while offering a comparable accuracy. | ['Amir M. Rahmani', 'Nikil Dutt', 'Mohsen Imani', 'Emad Kasaeyan Naeini', 'Hamidreza Alikhani', 'Yang Ni', 'Sina Shahhosseini'] | 2022-08-01 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [ 1.75039440e-01 3.42721641e-02 -4.19360518e-01 -5.61537504e-01
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74103b94-00f8-4ed6-81d2-dcab2a7ad8b7 | fuzzy-clustering-of-ordinal-time-series-based | 2304.12249 | null | https://arxiv.org/abs/2304.12249v1 | https://arxiv.org/pdf/2304.12249v1.pdf | Fuzzy clustering of ordinal time series based on two novel distances with economic applications | Time series clustering is a central machine learning task with applications in many fields. While the majority of the methods focus on real-valued time series, very few works consider series with discrete response. In this paper, the problem of clustering ordinal time series is addressed. To this aim, two novel distances between ordinal time series are introduced and used to construct fuzzy clustering procedures. Both metrics are functions of the estimated cumulative probabilities, thus automatically taking advantage of the ordering inherent to the series' range. The resulting clustering algorithms are computationally efficient and able to group series generated from similar stochastic processes, reaching accurate results even though the series come from a wide variety of models. Since the dynamic of the series may vary over the time, we adopt a fuzzy approach, thus enabling the procedures to locate each series into several clusters with different membership degrees. An extensive simulation study shows that the proposed methods outperform several alternative procedures. Weighted versions of the clustering algorithms are also presented and their advantages with respect to the original methods are discussed. Two specific applications involving economic time series illustrate the usefulness of the proposed approaches. | ['José Antonio Vilar', 'Christian Weiss', 'Ángel López Oriona'] | 2023-04-24 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 1.68815911e-01 -5.00710070e-01 1.00123711e-01 -3.05348426e-01
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501b1672-8e6f-413e-a289-eefb42b2ea2f | team-ufal-at-cmcl-2022-shared-task-figuring | 2204.04998 | null | https://arxiv.org/abs/2204.04998v1 | https://arxiv.org/pdf/2204.04998v1.pdf | Team ÚFAL at CMCL 2022 Shared Task: Figuring out the correct recipe for predicting Eye-Tracking features using Pretrained Language Models | Eye-Tracking data is a very useful source of information to study cognition and especially language comprehension in humans. In this paper, we describe our systems for the CMCL 2022 shared task on predicting eye-tracking information. We describe our experiments with pretrained models like BERT and XLM and the different ways in which we used those representations to predict four eye-tracking features. Along with analysing the effect of using two different kinds of pretrained multilingual language models and different ways of pooling the tokenlevel representations, we also explore how contextual information affects the performance of the systems. Finally, we also explore if factors like augmenting linguistic information affect the predictions. Our submissions achieved an average MAE of 5.72 and ranked 5th in the shared task. The average MAE showed further reduction to 5.25 in post task evaluation. | ['Ondrej Bojar', 'Rishu Kumar', 'Sunit Bhattacharya'] | 2022-04-11 | null | https://aclanthology.org/2022.cmcl-1.15 | https://aclanthology.org/2022.cmcl-1.15.pdf | cmcl-acl-2022-5 | ['pretrained-multilingual-language-models'] | ['natural-language-processing'] | [-3.99623692e-01 2.12710351e-01 2.86330462e-01 -5.34214079e-01
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c0720978-3e93-408a-b7be-93b8dc76bcc6 | centralized-control-for-multi-agent-rl-in-a | 2304.13004 | null | https://arxiv.org/abs/2304.13004v1 | https://arxiv.org/pdf/2304.13004v1.pdf | Centralized control for multi-agent RL in a complex Real-Time-Strategy game | Multi-agent Reinforcement learning (MARL) studies the behaviour of multiple learning agents that coexist in a shared environment. MARL is more challenging than single-agent RL because it involves more complex learning dynamics: the observations and rewards of each agent are functions of all other agents. In the context of MARL, Real-Time Strategy (RTS) games represent very challenging environments where multiple players interact simultaneously and control many units of different natures all at once. In fact, RTS games are so challenging for the current RL methods, that just being able to tackle them with RL is interesting. This project provides the end-to-end experience of applying RL in the Lux AI v2 Kaggle competition, where competitors design agents to control variable-sized fleets of units and tackle a multi-variable optimization, resource gathering, and allocation problem in a 1v1 scenario against other competitors. We use a centralized approach for training the RL agents, and report multiple design decisions along the process. We provide the source code of the project: https://github.com/roger-creus/centralized-control-lux. | ['Roger Creus Castanyer'] | 2023-04-25 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-3.58648241e-01 1.35464063e-02 3.90596711e-03 1.81531638e-01
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41dc954f-d61a-4b37-a5d0-63a1b5c746c2 | ofdm-based-massive-connectivity-for-leo | 2210.17355 | null | https://arxiv.org/abs/2210.17355v1 | https://arxiv.org/pdf/2210.17355v1.pdf | OFDM-Based Massive Connectivity for LEO Satellite Internet of Things | Low earth orbit (LEO) satellite has been considered as a potential supplement for the terrestrial Internet of Things (IoT). In this paper, we consider grant-free non-orthogonal random access (GF-NORA) in orthogonal frequency division multiplexing (OFDM) system to increase access capacity and reduce access latency for LEO satellite-IoT. We focus on the joint device activity detection (DAD) and channel estimation (CE) problem at the satellite access point. The delay and the Doppler effect of the LEO satellite channel are assumed to be partially compensated. We propose an OFDM-symbol repetition technique to better distinguish the residual Doppler frequency shifts, and present a grid-based parametric probability model to characterize channel sparsity in the delay-Doppler-user domain, as well as to characterize the relationship between the channel states and the device activity. Based on that, we develop a robust Bayesian message passing algorithm named modified variance state propagation (MVSP) for joint DAD and CE. Moreover, to tackle the mismatch between the real channel and its on-grid representation, an expectation-maximization (EM) framework is proposed to learn the grid parameters. Simulation results demonstrate that our proposed algorithms significantly outperform the existing approaches in both activity detection probability and channel estimation accuracy. | ['Xiaojun Yuan', 'Shaojie Ni', 'Sixian Li', 'Mingchen Zhang', 'Mingyang Yue', 'Yong Zuo'] | 2022-10-31 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 1.93968974e-02 -1.97011143e-01 -5.93506396e-01 3.26592863e-01
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57808874-3153-4414-93bf-0eefd045b6ea | a-review-of-intelligent-music-generation | 2211.09124 | null | https://arxiv.org/abs/2211.09124v2 | https://arxiv.org/pdf/2211.09124v2.pdf | A Review of Intelligent Music Generation Systems | Intelligent music generation, one of the most popular subfields of computer creativity, can lower the creative threshold for non-specialists and increase the efficiency of music creation. In the last five years, the quality of algorithm-based automatic music generation has increased significantly, motivated by the use of modern generative algorithms to learn the patterns implicit within a piece of music based on rule constraints or a musical corpus, thus generating music samples in various styles. Some of the available literature reviews lack a systematic benchmark of generative models and are traditional and conservative in their perspective, resulting in a vision of the future development of the field that is not deeply integrated with the current rapid scientific progress. In this paper, we conduct a comprehensive survey and analysis of recent intelligent music generation techniques,provide a critical discussion, explicitly identify their respective characteristics, and present them in a general table. We first introduce how music as a stream of information is encoded and the relevant datasets, then compare different types of generation algorithms, summarize their strengths and weaknesses, and discuss existing methods for evaluation. Finally, the development of artificial intelligence in composition is studied, especially by comparing the different characteristics of music generation techniques in the East and West and analyzing the development prospects in this field. | ['Qidi Wu', 'Lei Wang', 'Yi Qin', 'Maoqing Zhang', 'Junwei Pang', 'Song Li', 'Hanwei Liu', 'Ziyi Zhao'] | 2022-11-16 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 4.51923072e-01 -9.40890983e-02 -2.04644933e-01 2.55658418e-01
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28ebc0ee-7743-43ab-b61c-4badf8b2f0ef | cascaded-fast-and-slow-models-for-efficient-1 | 2110.07811 | null | https://arxiv.org/abs/2110.07811v1 | https://arxiv.org/pdf/2110.07811v1.pdf | Cascaded Fast and Slow Models for Efficient Semantic Code Search | The goal of natural language semantic code search is to retrieve a semantically relevant code snippet from a fixed set of candidates using a natural language query. Existing approaches are neither effective nor efficient enough towards a practical semantic code search system. In this paper, we propose an efficient and accurate semantic code search framework with cascaded fast and slow models, in which a fast transformer encoder model is learned to optimize a scalable index for fast retrieval followed by learning a slow classification-based re-ranking model to improve the performance of the top K results from the fast retrieval. To further reduce the high memory cost of deploying two separate models in practice, we propose to jointly train the fast and slow model based on a single transformer encoder with shared parameters. The proposed cascaded approach is not only efficient and scalable, but also achieves state-of-the-art results with an average mean reciprocal ranking (MRR) score of 0.7795 (across 6 programming languages) as opposed to the previous state-of-the-art result of 0.713 MRR on the CodeSearchNet benchmark. | ['Steven C. H. Hoi', 'Shafiq Joty', 'Junnan Li', 'Akhilesh Deepak Gotmare'] | 2021-10-15 | cascaded-fast-and-slow-models-for-efficient | https://openreview.net/forum?id=Ysu4E5DhQIw | https://openreview.net/pdf?id=Ysu4E5DhQIw | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-1.38826519e-01 -3.95651966e-01 -2.93225169e-01 -3.59912515e-01
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7015ec11-d1a8-46a8-9bc5-3c39fb4a988b | stacked-conditional-generative-adversarial | 1712.02478 | null | http://arxiv.org/abs/1712.02478v1 | http://arxiv.org/pdf/1712.02478v1.pdf | Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal | Understanding shadows from a single image spontaneously derives into two
types of task in previous studies, containing shadow detection and shadow
removal. In this paper, we present a multi-task perspective, which is not
embraced by any existing work, to jointly learn both detection and removal in
an end-to-end fashion that aims at enjoying the mutually improved benefits from
each other. Our framework is based on a novel STacked Conditional Generative
Adversarial Network (ST-CGAN), which is composed of two stacked CGANs, each
with a generator and a discriminator. Specifically, a shadow image is fed into
the first generator which produces a shadow detection mask. That shadow image,
concatenated with its predicted mask, goes through the second generator in
order to recover its shadow-free image consequently. In addition, the two
corresponding discriminators are very likely to model higher level
relationships and global scene characteristics for the detected shadow region
and reconstruction via removing shadows, respectively. More importantly, for
multi-task learning, our design of stacked paradigm provides a novel view which
is notably different from the commonly used one as the multi-branch version. To
fully evaluate the performance of our proposed framework, we construct the
first large-scale benchmark with 1870 image triplets (shadow image, shadow mask
image, and shadow-free image) under 135 scenes. Extensive experimental results
consistently show the advantages of ST-CGAN over several representative
state-of-the-art methods on two large-scale publicly available datasets and our
newly released one. | ['Jifeng Wang', 'Jian Yang', 'Le Hui', 'Xiang Li'] | 2017-12-07 | stacked-conditional-generative-adversarial-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Stacked_Conditional_Generative_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Stacked_Conditional_Generative_CVPR_2018_paper.pdf | cvpr-2018-6 | ['shadow-removal', 'shadow-detection'] | ['computer-vision', 'computer-vision'] | [ 8.51503730e-01 1.05684116e-01 5.14120519e-01 -3.17346901e-01
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bec1222d-841e-4290-ab87-8ed12dbd7ce5 | deep-learning-for-event-based-vision-a | 2302.08890 | null | https://arxiv.org/abs/2302.08890v1 | https://arxiv.org/pdf/2302.08890v1.pdf | Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks | Event cameras are bio-inspired sensors that capture the per-pixel intensity changes asynchronously and produce event streams encoding the time, pixel position, and polarity (sign) of the intensity changes. Event cameras possess a myriad of advantages over canonical frame-based cameras, such as high temporal resolution, high dynamic range, low latency, etc. Being capable of capturing information in challenging visual conditions, event cameras have the potential to overcome the limitations of frame-based cameras in the computer vision and robotics community. In very recent years, deep learning (DL) has been brought to this emerging field and inspired active research endeavors in mining its potential. However, the technical advances still remain unknown, thus making it urgent and necessary to conduct a systematic overview. To this end, we conduct the first yet comprehensive and in-depth survey, with a focus on the latest developments of DL techniques for event-based vision. We first scrutinize the typical event representations with quality enhancement methods as they play a pivotal role as inputs to the DL models. We then provide a comprehensive taxonomy for existing DL-based methods by structurally grouping them into two major categories: 1) image reconstruction and restoration; 2) event-based scene understanding 3D vision. Importantly, we conduct benchmark experiments for the existing methods in some representative research directions (eg, object recognition and optical flow estimation) to identify some critical insights and problems. Finally, we make important discussions regarding the challenges and provide new perspectives for motivating future research studies. | ['Lin Wang', 'DaCheng Tao', 'Weiming Zhang', 'Tianbo Pan', 'Tongyan Hua', 'Yunfan Lu', 'Yexin Liu', 'Xu Zheng'] | 2023-02-17 | null | null | null | null | ['object-recognition', 'event-based-vision'] | ['computer-vision', 'computer-vision'] | [ 5.19355595e-01 -6.57665908e-01 -1.74697042e-01 -1.36468247e-01
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79030e4f-82c3-430e-8295-3e19d2c7722b | deep-pneumonia-attention-based-contrastive | 2207.11393 | null | https://arxiv.org/abs/2207.11393v1 | https://arxiv.org/pdf/2207.11393v1.pdf | Deep Pneumonia: Attention-Based Contrastive Learning for Class-Imbalanced Pneumonia Lesion Recognition in Chest X-rays | Computer-aided X-ray pneumonia lesion recognition is important for accurate diagnosis of pneumonia. With the emergence of deep learning, the identification accuracy of pneumonia has been greatly improved, but there are still some challenges due to the fuzzy appearance of chest X-rays. In this paper, we propose a deep learning framework named Attention-Based Contrastive Learning for Class-Imbalanced X-Ray Pneumonia Lesion Recognition (denoted as Deep Pneumonia). We adopt self-supervised contrastive learning strategy to pre-train the model without using extra pneumonia data for fully mining the limited available dataset. In order to leverage the location information of the lesion area that the doctor has painstakingly marked, we propose mask-guided hard attention strategy and feature learning with contrastive regulation strategy which are applied on the attention map and the extracted features respectively to guide the model to focus more attention on the lesion area where contains more discriminative features for improving the recognition performance. In addition, we adopt Class-Balanced Loss instead of traditional Cross-Entropy as the loss function of classification to tackle the problem of serious class imbalance between different classes of pneumonia in the dataset. The experimental results show that our proposed framework can be used as a reliable computer-aided pneumonia diagnosis system to assist doctors to better diagnose pneumonia cases accurately. | ['YongJie Li', 'Xianshi Zhang', 'Haohan Bai', 'Xinxu Wei'] | 2022-07-23 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 3.16570282e-01 -3.29049587e-01 -2.00399771e-01 -3.14470738e-01
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78d0aa7d-1ec9-4a95-9560-a78e0f95354f | breaking-immutable-information-coupled | 2211.14782 | null | https://arxiv.org/abs/2211.14782v1 | https://arxiv.org/pdf/2211.14782v1.pdf | Breaking Immutable: Information-Coupled Prototype Elaboration for Few-Shot Object Detection | Few-shot object detection, expecting detectors to detect novel classes with a few instances, has made conspicuous progress. However, the prototypes extracted by existing meta-learning based methods still suffer from insufficient representative information and lack awareness of query images, which cannot be adaptively tailored to different query images. Firstly, only the support images are involved for extracting prototypes, resulting in scarce perceptual information of query images. Secondly, all pixels of all support images are treated equally when aggregating features into prototype vectors, thus the salient objects are overwhelmed by the cluttered background. In this paper, we propose an Information-Coupled Prototype Elaboration (ICPE) method to generate specific and representative prototypes for each query image. Concretely, a conditional information coupling module is introduced to couple information from the query branch to the support branch, strengthening the query-perceptual information in support features. Besides, we design a prototype dynamic aggregation module that dynamically adjusts intra-image and inter-image aggregation weights to highlight the salient information useful for detecting query images. Experimental results on both Pascal VOC and MS COCO demonstrate that our method achieves state-of-the-art performance in almost all settings. | ['Kun fu', 'Xian Sun', 'Peijin Wang', 'Junxi Li', 'Yongqiang Mao', 'Wenhui Diao', 'Xiaonan Lu'] | 2022-11-27 | null | null | null | null | ['few-shot-object-detection'] | ['computer-vision'] | [ 2.46031567e-01 -2.49494016e-01 -2.90435195e-01 -4.47300881e-01
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8279f6b3-4375-4ca1-86d8-68b0e3b2fa3b | unsupervised-person-re-identification-with-1 | 2108.06938 | null | https://arxiv.org/abs/2108.06938v2 | https://arxiv.org/pdf/2108.06938v2.pdf | Unsupervised Person Re-identification with Stochastic Training Strategy | Unsupervised person re-identification (re-ID) has attracted increasing research interests because of its scalability and possibility for real-world applications. State-of-the-art unsupervised re-ID methods usually follow a clustering-based strategy, which generates pseudo labels by clustering and maintains a memory to store instance features and represent the centroid of the clusters for contrastive learning. This approach suffers two problems. First, the centroid generated by unsupervised learning may not be a perfect prototype. Forcing images to get closer to the centroid emphasizes the result of clustering, which could accumulate clustering errors during iterations. Second, previous methods utilize features obtained at different training iterations to represent one centroid, which is not consistent with the current training sample, since the features are not directly comparable. To this end, we propose an unsupervised re-ID approach with a stochastic learning strategy. Specifically, we adopt a stochastic updated memory, where a random instance from a cluster is used to update the cluster-level memory for contrastive learning. In this way, the relationship between randomly selected pair of images are learned to avoid the training bias caused by unreliable pseudo labels. The stochastic memory is also always up-to-date for classifying to keep the consistency. Besides, to relieve the issue of camera variance, a unified distance matrix is proposed during clustering, where the distance bias from different camera domain is reduced and the variances of identities is emphasized. | ['Bo Du', 'Yutian Lin', 'Tianyang Liu'] | 2021-08-16 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-1.63854942e-01 -4.22991872e-01 -4.68133353e-02 -5.37976742e-01
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010df8d9-eeb0-463c-a843-9b5f8e297027 | question-answering-over-knowledge-base-using-1 | 2010.08883 | null | https://arxiv.org/abs/2010.08883v1 | https://arxiv.org/pdf/2010.08883v1.pdf | Question Answering over Knowledge Base using Language Model Embeddings | Knowledge Base, represents facts about the world, often in some form of subsumption ontology, rather than implicitly, embedded in procedural code, the way a conventional computer program does. While there is a rapid growth in knowledge bases, it poses a challenge of retrieving information from them. Knowledge Base Question Answering is one of the promising approaches for extracting substantial knowledge from Knowledge Bases. Unlike web search, Question Answering over a knowledge base gives accurate and concise results, provided that natural language questions can be understood and mapped precisely to an answer in the knowledge base. However, some of the existing embedding-based methods for knowledge base question answering systems ignore the subtle correlation between the question and the Knowledge Base (e.g., entity types, relation paths, and context) and suffer from the Out Of Vocabulary problem. In this paper, we focused on using a pre-trained language model for the Knowledge Base Question Answering task. Firstly, we used Bert base uncased for the initial experiments. We further fine-tuned these embeddings with a two-way attention mechanism from the knowledge base to the asked question and from the asked question to the knowledge base answer aspects. Our method is based on a simple Convolutional Neural Network architecture with a Multi-Head Attention mechanism to represent the asked question dynamically in multiple aspects. Our experimental results show the effectiveness and the superiority of the Bert pre-trained language model embeddings for question answering systems on knowledge bases over other well-known embedding methods. | ['Rekabdar Banafsheh', 'Sai Sharath Japa'] | 2020-10-17 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-2.93354005e-01 3.04201096e-01 -2.53304154e-01 -2.36872986e-01
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b29ffb77-60c3-4614-8df0-bfa9c007903d | model-assisted-probabilistic-safe-adaptive | 2307.00828 | null | https://arxiv.org/abs/2307.00828v1 | https://arxiv.org/pdf/2307.00828v1.pdf | Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning | Breaking safety constraints in control systems can lead to potential risks, resulting in unexpected costs or catastrophic damage. Nevertheless, uncertainty is ubiquitous, even among similar tasks. In this paper, we develop a novel adaptive safe control framework that integrates meta learning, Bayesian models, and control barrier function (CBF) method. Specifically, with the help of CBF method, we learn the inherent and external uncertainties by a unified adaptive Bayesian linear regression (ABLR) model, which consists of a forward neural network (NN) and a Bayesian output layer. Meta learning techniques are leveraged to pre-train the NN weights and priors of the ABLR model using data collected from historical similar tasks. For a new control task, we refine the meta-learned models using a few samples, and introduce pessimistic confidence bounds into CBF constraints to ensure safe control. Moreover, we provide theoretical criteria to guarantee probabilistic safety during the control processes. To validate our approach, we conduct comparative experiments in various obstacle avoidance scenarios. The results demonstrate that our algorithm significantly improves the Bayesian model-based CBF method, and is capable for efficient safe exploration even with multiple uncertain constraints. | ['Shiping Wen', 'TingWen Huang', 'Yuting Cao', 'Yin Yang', 'Ke Li', 'Shengbo Wang'] | 2023-07-03 | null | null | null | null | ['meta-learning', 'safe-exploration'] | ['methodology', 'robots'] | [ 2.55184203e-01 6.28698096e-02 -4.93281990e-01 -2.84235835e-01
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2da7fe65-9a39-4c84-bc52-42633bbcded8 | cross-modal-common-representation-learning | 2202.07901 | null | https://arxiv.org/abs/2202.07901v2 | https://arxiv.org/pdf/2202.07901v2.pdf | Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition | Cross-modal representation learning learns a shared embedding between two or more modalities to improve performance in a given task compared to using only one of the modalities. Cross-modal representation learning from different data types -- such as images and time-series data (e.g., audio or text data) -- requires a deep metric learning loss that minimizes the distance between the modality embeddings. In this paper, we propose to use the triplet loss, which uses positive and negative identities to create sample pairs with different labels, for cross-modal representation learning between image and time-series modalities (CMR-IS). By adapting the triplet loss for cross-modal representation learning, higher accuracy in the main (time-series classification) task can be achieved by exploiting additional information of the auxiliary (image classification) task. Our experiments on synthetic data and handwriting recognition data from sensor-enhanced pens show improved classification accuracy, faster convergence, and better generalizability. | ['Christopher Mutschler', 'Bernd Bischl', 'Lucas Heublein', 'David Rügamer', 'Felix Ott'] | 2022-02-16 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 5.39339185e-01 -1.41502708e-01 5.94364805e-03 -5.63547373e-01
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9bb2f8fe-c7ea-43a1-aa0a-ba6c9dd7a03d | mabsplit-faster-forest-training-using-multi | 2212.07473 | null | https://arxiv.org/abs/2212.07473v1 | https://arxiv.org/pdf/2212.07473v1.pdf | MABSplit: Faster Forest Training Using Multi-Armed Bandits | Random forests are some of the most widely used machine learning models today, especially in domains that necessitate interpretability. We present an algorithm that accelerates the training of random forests and other popular tree-based learning methods. At the core of our algorithm is a novel node-splitting subroutine, dubbed MABSplit, used to efficiently find split points when constructing decision trees. Our algorithm borrows techniques from the multi-armed bandit literature to judiciously determine how to allocate samples and computational power across candidate split points. We provide theoretical guarantees that MABSplit improves the sample complexity of each node split from linear to logarithmic in the number of data points. In some settings, MABSplit leads to 100x faster training (an 99% reduction in training time) without any decrease in generalization performance. We demonstrate similar speedups when MABSplit is used across a variety of forest-based variants, such as Extremely Random Forests and Random Patches. We also show our algorithm can be used in both classification and regression tasks. Finally, we show that MABSplit outperforms existing methods in generalization performance and feature importance calculations under a fixed computational budget. All of our experimental results are reproducible via a one-line script at https://github.com/ThrunGroup/FastForest. | ['Martin Jinye Zhang', 'Ilan Shomorony', 'Chris Piech', 'Sebastian Thrun', 'Je-Yong Lee', 'Ryan Kang', 'Mo Tiwari'] | 2022-12-14 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 2.99178720e-01 -2.29141071e-01 -8.33355963e-01 -4.49330688e-01
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c7c6ebf0-5885-4152-a015-46695d13a7ce | uppsala-university-at-semeval-2022-task-1-can | null | null | https://aclanthology.org/2022.semeval-1.10 | https://aclanthology.org/2022.semeval-1.10.pdf | Uppsala University at SemEval-2022 Task 1: Can Foreign Entries Enhance an English Reverse Dictionary? | We present the Uppsala University system for SemEval-2022 Task 1: Comparing Dictionaries and Word Embeddings (CODWOE). We explore the performance of multilingual reverse dictionaries as well as the possibility of utilizing annotated data in other languages to improve the quality of a reverse dictionary in the target language. We mainly focus on character-based embeddings.In our main experiment, we train multilingual models by combining the training data from multiple languages. In an additional experiment, using resources beyond the shared task, we use the training data in Russian and French to improve the English reverse dictionary using unsupervised embeddings alignment and machine translation. The results show that multilingual models occasionally but not consistently can outperform the monolingual baselines. In addition, we demonstrate an improvement of an English reverse dictionary using translated entries from the Russian training data set. | ['Sara Stymne', 'Rafal Cerniavski'] | null | null | null | null | semeval-naacl-2022-7 | ['reverse-dictionary'] | ['natural-language-processing'] | [-3.81076425e-01 -2.41396904e-01 -4.32306975e-01 -1.76784977e-01
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084b3ed3-53fd-46ec-8d68-2f3bd80d4cd7 | visfusion-visibility-aware-online-3d-scene | 2304.10687 | null | https://arxiv.org/abs/2304.10687v1 | https://arxiv.org/pdf/2304.10687v1.pdf | VisFusion: Visibility-aware Online 3D Scene Reconstruction from Videos | We propose VisFusion, a visibility-aware online 3D scene reconstruction approach from posed monocular videos. In particular, we aim to reconstruct the scene from volumetric features. Unlike previous reconstruction methods which aggregate features for each voxel from input views without considering its visibility, we aim to improve the feature fusion by explicitly inferring its visibility from a similarity matrix, computed from its projected features in each image pair. Following previous works, our model is a coarse-to-fine pipeline including a volume sparsification process. Different from their works which sparsify voxels globally with a fixed occupancy threshold, we perform the sparsification on a local feature volume along each visual ray to preserve at least one voxel per ray for more fine details. The sparse local volume is then fused with a global one for online reconstruction. We further propose to predict TSDF in a coarse-to-fine manner by learning its residuals across scales leading to better TSDF predictions. Experimental results on benchmarks show that our method can achieve superior performance with more scene details. Code is available at: https://github.com/huiyu-gao/VisFusion | ['Miaomiao Liu', 'Wei Mao', 'Huiyu Gao'] | 2023-04-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Gao_VisFusion_Visibility-Aware_Online_3D_Scene_Reconstruction_From_Videos_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gao_VisFusion_Visibility-Aware_Online_3D_Scene_Reconstruction_From_Videos_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 9.09793004e-02 -1.38562769e-02 -3.76837701e-02 -3.87745380e-01
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879454be-2ef5-4bca-ae59-fe213caf2402 | openmedia-open-source-medical-image-analysis | 2208.05616 | null | https://arxiv.org/abs/2208.05616v2 | https://arxiv.org/pdf/2208.05616v2.pdf | OpenMedIA: Open-Source Medical Image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms | In this paper, we present OpenMedIA, an open-source toolbox library containing a rich set of deep learning methods for medical image analysis under heterogeneous Artificial Intelligence (AI) computing platforms. Various medical image analysis methods, including 2D/3D medical image classification, segmentation, localisation, and detection, have been included in the toolbox with PyTorch and/or MindSpore implementations under heterogeneous NVIDIA and Huawei Ascend computing systems. To our best knowledge, OpenMedIA is the first open-source algorithm library providing compared PyTorch and MindSpore implementations and results on several benchmark datasets. The source codes and models are available at https://git.openi.org.cn/OpenMedIA. | ['Tong Zhang', 'Jie Chen', 'Ge Li', 'Wei Gao', 'Yue Yu', 'Jiancong Chen', 'Yang Yang', 'Xiansong Huang', 'Jia-Xin Zhuang'] | 2022-08-11 | null | null | null | null | ['medical-image-detection'] | ['computer-vision'] | [-4.82193053e-01 -2.63618648e-01 -3.30945961e-02 1.06012128e-01
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8f00a5e9-d469-47b9-a4ed-eb1442ef5579 | itreepack-protein-complex-side-chain-packing | 1504.05467 | null | http://arxiv.org/abs/1504.05467v1 | http://arxiv.org/pdf/1504.05467v1.pdf | iTreePack: Protein Complex Side-Chain Packing by Dual Decomposition | Protein side-chain packing is a critical component in obtaining the 3D
coordinates of a structure and drug discovery. Single-domain protein side-chain
packing has been thoroughly studied. A major challenge in generalizing these
methods to protein complexes is that they, unlike monomers, often have very
large treewidth, and thus algorithms such as TreePack cannot be directly
applied. To address this issue, SCWRL4 treats the complex effectively as a
monomer, heuristically excluding weak interactions to decrease treewidth; as a
result, SCWRL4 generates poor packings on protein interfaces. To date, few
side-chain packing methods exist that are specifically designed for protein
complexes. In this paper, we introduce a method, iTreePack, which solves the
side-chain packing problem for complexes by using a novel combination of dual
decomposition and tree decomposition. In particular, iTreePack overcomes the
problem of large treewidth by decomposing a protein complex into smaller
subgraphs and novelly reformulating the complex side-chain packing problem as a
dual relaxation problem; this allows us to solve the side-chain packing of each
small subgraph separately using tree-decomposition. A projected subgradient
algorithm is applied to enforcing the consistency among the side-chain packings
of all the small subgraphs. Computational results demonstrate that our
iTreePack program outperforms SCWRL4 on protein complexes. In particular,
iTreePack places side-chain atoms much more accurately on very large complexes,
which constitute a significant portion of protein-protein interactions.
Moreover, the advantage of iTreePack over SCWRL4 increases with respect to the
treewidth of a complex. Even for monomeric proteins, iTreePack is much more
efficient than SCWRL and slightly more accurate. | [] | 2015-04-21 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [ 3.71543206e-02 2.43794575e-01 -3.71647179e-01 7.54714459e-02
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63a3a53d-27e7-4ae6-a511-6457152367bc | spectral-data-augmentation-techniques-to | 2004.11989 | null | https://arxiv.org/abs/2004.11989v1 | https://arxiv.org/pdf/2004.11989v1.pdf | Spectral Data Augmentation Techniques to quantify Lung Pathology from CT-images | Data augmentation is of paramount importance in biomedical image processing tasks, characterized by inadequate amounts of labelled data, to best use all of the data that is present. In-use techniques range from intensity transformations and elastic deformations, to linearly combining existing data points to make new ones. In this work, we propose the use of spectral techniques for data augmentation, using the discrete cosine and wavelet transforms. We empirically evaluate our approaches on a CT texture analysis task to detect abnormal lung-tissue in patients with cystic fibrosis. Empirical experiments show that the proposed spectral methods perform favourably as compared to the existing methods. When used in combination with existing methods, our proposed approach can increase the relative minor class segmentation performance by 44.1% over a simple replication baseline. | ['Florian Dubost', 'Subhradeep Kayal', 'Marleen de Bruijne', 'Harm A. W. M. Tiddens'] | 2020-04-24 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 9.48046267e-01 2.15442091e-01 -1.06737591e-01 -7.91539550e-02
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c1489a49-c502-4a85-ac86-88125a7673d1 | a-neural-attention-model-for-urban-air | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/11871 | https://ojs.aaai.org/index.php/AAAI/article/view/11871/11730 | A Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations | Urban air pollution has attracted much attention these years for its adverse impacts on human health. While monitoring stations have been established to collect pollutant statistics, the number of stations is very limited due to the high cost. Thus, inferring fine-grained urban air quality information is becoming an essential issue for both government and people. In this paper, we propose a generic neural approach, named ADAIN, for urban air quality inference. We leverage both the information from monitoring stations and urban data that are closely related to air quality, including POIs, road networks and meteorology. ADAIN combines feedforward and recurrent neural networks for modeling static and sequential features as well as capturing deep feature interactions effectively. A novel attempt of ADAIN is an attention-based pooling layer that automatically learns the weights of features from different monitoring stations, to boost the performance. We conduct experiments on a real-world air quality dataset and our approach achieves the highest performance compared with various state-of-the-art solutions. | ['Linpeng Huang', 'Yanmin Zhu', 'Yanyan Shen', 'Weiyu Cheng'] | 2018-04-26 | null | null | null | aaai-2018-4 | ['air-quality-inference'] | ['miscellaneous'] | [-1.15270965e-01 -6.87738299e-01 -5.89658841e-02 -4.89967883e-01
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d6dadf97-a228-4188-9b95-04e3a5ad31c7 | demon-depth-and-motion-network-for-learning | 1612.02401 | null | http://arxiv.org/abs/1612.02401v2 | http://arxiv.org/pdf/1612.02401v2.pdf | DeMoN: Depth and Motion Network for Learning Monocular Stereo | In this paper we formulate structure from motion as a learning problem. We
train a convolutional network end-to-end to compute depth and camera motion
from successive, unconstrained image pairs. The architecture is composed of
multiple stacked encoder-decoder networks, the core part being an iterative
network that is able to improve its own predictions. The network estimates not
only depth and motion, but additionally surface normals, optical flow between
the images and confidence of the matching. A crucial component of the approach
is a training loss based on spatial relative differences. Compared to
traditional two-frame structure from motion methods, results are more accurate
and more robust. In contrast to the popular depth-from-single-image networks,
DeMoN learns the concept of matching and, thus, better generalizes to
structures not seen during training. | ['Thomas Brox', 'Huizhong Zhou', 'Benjamin Ummenhofer', 'Nikolaus Mayer', 'Alexey Dosovitskiy', 'Eddy Ilg', 'Jonas Uhrig'] | 2016-12-07 | demon-depth-and-motion-network-for-learning-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Ummenhofer_DeMoN_Depth_and_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Ummenhofer_DeMoN_Depth_and_CVPR_2017_paper.pdf | cvpr-2017-7 | ['depth-and-camera-motion'] | ['computer-vision'] | [ 2.71852225e-01 1.16442703e-01 3.38626131e-02 -4.14015919e-01
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dfd149de-299d-454a-af25-1087a7176a17 | behavioral-causal-inference | 2305.18916 | null | https://arxiv.org/abs/2305.18916v1 | https://arxiv.org/pdf/2305.18916v1.pdf | Behavioral Causal Inference | When inferring the causal effect of one variable on another from correlational data, a common practice by professional researchers as well as lay decision makers is to control for some set of exogenous confounding variables. Choosing an inappropriate set of control variables can lead to erroneous causal inferences. This paper presents a model of lay decision makers who use long-run observational data to learn the causal effect of their actions on a payoff-relevant outcome. Different types of decision makers use different sets of control variables. I obtain upper bounds on the equilibrium welfare loss due to wrong causal inferences, for various families of data-generating processes. The bounds depend on the structure of the type space. When types are "ordered" in a certain sense, the equilibrium condition greatly reduces the cost of wrong causal inference due to poor controls. | ['Ran Spiegler'] | 2023-05-30 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 2.65832186e-01 2.75720090e-01 -8.85406315e-01 -3.39252114e-01
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bbafbe2b-a722-4cf6-970d-f77992007255 | efficiently-explaining-csps-with-1 | 2303.11712 | null | https://arxiv.org/abs/2303.11712v1 | https://arxiv.org/pdf/2303.11712v1.pdf | Efficiently Explaining CSPs with Unsatisfiable Subset Optimization (extended algorithms and examples) | We build on a recently proposed method for stepwise explaining solutions of Constraint Satisfaction Problems (CSP) in a human-understandable way. An explanation here is a sequence of simple inference steps where simplicity is quantified using a cost function. The algorithms for explanation generation rely on extracting Minimal Unsatisfiable Subsets (MUS) of a derived unsatisfiable formula, exploiting a one-to-one correspondence between so-called non-redundant explanations and MUSs. However, MUS extraction algorithms do not provide any guarantee of subset minimality or optimality with respect to a given cost function. Therefore, we build on these formal foundations and tackle the main points of improvement, namely how to generate explanations efficiently that are provably optimal (with respect to the given cost metric). For that, we developed (1) a hitting set-based algorithm for finding the optimal constrained unsatisfiable subsets; (2) a method for re-using relevant information over multiple algorithm calls; and (3) methods exploiting domain-specific information to speed up the explanation sequence generation. We experimentally validated our algorithms on a large number of CSP problems. We found that our algorithms outperform the MUS approach in terms of explanation quality and computational time (on average up to 56 % faster than a standard MUS approach). | ['Tias Guns', 'Bart Bogaerts', 'Emilio Gamba'] | 2023-03-21 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [ 7.01607943e-01 8.27399790e-01 -4.03141305e-02 -4.06336755e-01
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0e0c22fa-dcdf-425f-a10a-db1ff0985522 | clawcranenet-leveraging-object-level-relation | 2103.10702 | null | https://arxiv.org/abs/2103.10702v3 | https://arxiv.org/pdf/2103.10702v3.pdf | ClawCraneNet: Leveraging Object-level Relation for Text-based Video Segmentation | Text-based video segmentation is a challenging task that segments out the natural language referred objects in videos. It essentially requires semantic comprehension and fine-grained video understanding. Existing methods introduce language representation into segmentation models in a bottom-up manner, which merely conducts vision-language interaction within local receptive fields of ConvNets. We argue that such interaction is not fulfilled since the model can barely construct region-level relationships given partial observations, which is contrary to the description logic of natural language/referring expressions. In fact, people usually describe a target object using relations with other objects, which may not be easily understood without seeing the whole video. To address the issue, we introduce a novel top-down approach by imitating how we human segment an object with the language guidance. We first figure out all candidate objects in videos and then choose the refereed one by parsing relations among those high-level objects. Three kinds of object-level relations are investigated for precise relationship understanding, i.e., positional relation, text-guided semantic relation, and temporal relation. Extensive experiments on A2D Sentences and J-HMDB Sentences show our method outperforms state-of-the-art methods by a large margin. Qualitative results also show our results are more explainable. | ['Yi Yang', 'Yawei Luo', 'Yu Wu', 'Chen Liang'] | 2021-03-19 | null | null | null | null | ['referring-expression-segmentation'] | ['computer-vision'] | [ 1.17528670e-01 1.94152176e-01 -3.57821882e-01 -6.98979557e-01
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bebaade0-ba10-46db-b31a-f43a0dea150c | reconfigurable-distributed-fpga-cluster | 2305.18332 | null | https://arxiv.org/abs/2305.18332v1 | https://arxiv.org/pdf/2305.18332v1.pdf | Reconfigurable Distributed FPGA Cluster Design for Deep Learning Accelerators | We propose a distributed system based on lowpower embedded FPGAs designed for edge computing applications focused on exploring distributing scheduling optimizations for Deep Learning (DL) workloads to obtain the best performance regarding latency and power efficiency. Our cluster was modular throughout the experiment, and we have implementations that consist of up to 12 Zynq-7020 chip-based boards as well as 5 UltraScale+ MPSoC FPGA boards connected through an ethernet switch, and the cluster will evaluate configurable Deep Learning Accelerator (DLA) Versatile Tensor Accelerator (VTA). This adaptable distributed architecture is distinguished by its capacity to evaluate and manage neural network workloads in numerous configurations which enables users to conduct multiple experiments tailored to their specific application needs. The proposed system can simultaneously execute diverse Neural Network (NN) models, arrange the computation graph in a pipeline structure, and manually allocate greater resources to the most computationally intensive layers of the NN graph. | ['Jafar Saniie', 'Alejandro Perez-Vicente', 'Tianyang Fang', 'Hans Johnson'] | 2023-05-24 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-6.59757257e-01 -2.08373263e-01 4.46778424e-02 -4.55798566e-01
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c5f927e2-4c60-4f1a-b259-4e069240569d | pcr-cg-point-cloud-registration-via-deep | 2302.14418 | null | https://arxiv.org/abs/2302.14418v1 | https://arxiv.org/pdf/2302.14418v1.pdf | PCR-CG: Point Cloud Registration via Deep Color and Geometry | In this paper, we introduce PCR-CG: a novel 3D point cloud registration module explicitly embedding the color signals into the geometry representation. Different from previous methods that only use geometry representation, our module is specifically designed to effectively correlate color into geometry for the point cloud registration task. Our key contribution is a 2D-3D cross-modality learning algorithm that embeds the deep features learned from color signals to the geometry representation. With our designed 2D-3D projection module, the pixel features in a square region centered at correspondences perceived from images are effectively correlated with point clouds. In this way, the overlapped regions can be inferred not only from point cloud but also from the texture appearances. Adding color is non-trivial. We compare against a variety of baselines designed for adding color to 3D, such as exhaustively adding per-pixel features or RGB values in an implicit manner. We leverage Predator [25] as the baseline method and incorporate our proposed module onto it. To validate the effectiveness of 2D features, we ablate different 2D pre-trained networks and show a positive correlation between the pre-trained weights and the task performance. Our experimental results indicate a significant improvement of 6.5% registration recall over the baseline method on the 3DLoMatch benchmark. We additionally evaluate our approach on SOTA methods and observe consistent improvements, such as an improvement of 2.4% registration recall over GeoTransformer as well as 3.5% over CoFiNet. Our study reveals a significant advantages of correlating explicit deep color features to the point cloud in the registration task. | ['Ji Hou', 'Wenhui Zhou', 'Xiaolin Huang', 'Junle Yu', 'Yu Zhang'] | 2023-02-28 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [ 4.21090014e-02 1.18832965e-03 1.37180403e-01 -4.19114560e-01
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a4e5478a-0e46-407b-afa5-cda1c49aead7 | sports-video-fine-grained-action-detection | 2112.11384 | null | https://arxiv.org/abs/2112.11384v1 | https://arxiv.org/pdf/2112.11384v1.pdf | Sports Video: Fine-Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2021 | Sports video analysis is a prevalent research topic due to the variety of application areas, ranging from multimedia intelligent devices with user-tailored digests up to analysis of athletes' performance. The Sports Video task is part of the MediaEval 2021 benchmark. This task tackles fine-grained action detection and classification from videos. The focus is on recordings of table tennis games. Running since 2019, the task has offered a classification challenge from untrimmed video recorded in natural conditions with known temporal boundaries for each stroke. This year, the dataset is extended and offers, in addition, a detection challenge from untrimmed videos without annotations. This work aims at creating tools for sports coaches and players in order to analyze sports performance. Movement analysis and player profiling may be built upon such technology to enrich the training experience of athletes and improve their performance. | ['Julien Morlier', 'Laurent Mascarilla', 'Renaud Péteri', 'Jenny Benois-Pineau', 'Boris Mansencal', 'Jordan Calandre', 'Pierre-Etienne Martin'] | 2021-12-16 | null | null | null | null | ['fine-grained-action-detection'] | ['computer-vision'] | [ 4.77389663e-01 -2.55075246e-01 -4.42869067e-01 -6.12851083e-02
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e5b72ab8-fe9e-491f-9425-b7366608d0af | cnn-based-dense-underwater-3d-scene | 1811.09675 | null | http://arxiv.org/abs/1811.09675v1 | http://arxiv.org/pdf/1811.09675v1.pdf | CNN based dense underwater 3D scene reconstruction by transfer learning using bubble database | Dense 3D shape acquisition of swimming human or live fish is an important
research topic for sports, biological science and so on. For this purpose,
active stereo sensor is usually used in the air, however it cannot be applied
to the underwater environment because of refraction, strong light attenuation
and severe interference of bubbles. Passive stereo is a simple solution for
capturing dynamic scenes at underwater environment, however the shape with
textureless surfaces or irregular reflections cannot be recovered. Recently,
the stereo camera pair with a pattern projector for adding artificial textures
on the objects is proposed. However, to use the system for underwater
environment, several problems should be compensated, i.e., disturbance by
fluctuation and bubbles. Simple solution is to use convolutional neural network
for stereo to cancel the effects of bubbles and/or water fluctuation. Since it
is not easy to train CNN with small size of database with large variation, we
develop a special bubble generation device to efficiently create real bubble
database of multiple size and density. In addition, we propose a transfer
learning technique for multi-scale CNN to effectively remove bubbles and
projected-patterns on the object. Further, we develop a real system and
actually captured live swimming human, which has not been done before.
Experiments are conducted to show the effectiveness of our method compared with
the state of the art techniques. | ['Hiroshi Kawasaki', 'Ryo Furukawa', 'Kazuto Ichimaru'] | 2018-11-21 | null | null | null | null | ['3d-scene-reconstruction', 'underwater-3d-scene-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 1.61601976e-01 -3.00550967e-01 1.14911878e+00 -4.75835316e-02
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e5742c91-06f0-4704-a301-557dd66bb740 | self-supervised-geometry-aware-encoder-for | 2212.07409 | null | https://arxiv.org/abs/2212.07409v2 | https://arxiv.org/pdf/2212.07409v2.pdf | Self-Supervised Geometry-Aware Encoder for Style-Based 3D GAN Inversion | StyleGAN has achieved great progress in 2D face reconstruction and semantic editing via image inversion and latent editing. While studies over extending 2D StyleGAN to 3D faces have emerged, a corresponding generic 3D GAN inversion framework is still missing, limiting the applications of 3D face reconstruction and semantic editing. In this paper, we study the challenging problem of 3D GAN inversion where a latent code is predicted given a single face image to faithfully recover its 3D shapes and detailed textures. The problem is ill-posed: innumerable compositions of shape and texture could be rendered to the current image. Furthermore, with the limited capacity of a global latent code, 2D inversion methods cannot preserve faithful shape and texture at the same time when applied to 3D models. To solve this problem, we devise an effective self-training scheme to constrain the learning of inversion. The learning is done efficiently without any real-world 2D-3D training pairs but proxy samples generated from a 3D GAN. In addition, apart from a global latent code that captures the coarse shape and texture information, we augment the generation network with a local branch, where pixel-aligned features are added to faithfully reconstruct face details. We further consider a new pipeline to perform 3D view-consistent editing. Extensive experiments show that our method outperforms state-of-the-art inversion methods in both shape and texture reconstruction quality. Code and data will be released. | ['Bo Dai', 'Chen Change Loy', 'Shuai Yang', 'Xuyi Meng', 'Yushi Lan'] | 2022-12-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lan_Self-Supervised_Geometry-Aware_Encoder_for_Style-Based_3D_GAN_Inversion_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lan_Self-Supervised_Geometry-Aware_Encoder_for_Style-Based_3D_GAN_Inversion_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 5.90154767e-01 4.73505527e-01 -7.87133235e-04 -3.29550922e-01
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5f65a801-ab67-4571-8186-e5975e459d42 | nmt5-is-parallel-data-still-relevant-for-pre-1 | null | null | https://aclanthology.org/2021.acl-short.87 | https://aclanthology.org/2021.acl-short.87.pdf | nmT5 - Is parallel data still relevant for pre-training massively multilingual language models? | Recently, mT5 - a massively multilingual version of T5 - leveraged a unified text-to-text format to attain state-of-the-art results on a wide variety of multilingual NLP tasks. In this paper, we investigate the impact of incorporating parallel data into mT5 pre-training. We find that multi-tasking language modeling with objectives such as machine translation during pre-training is a straightforward way to improve performance on downstream multilingual and cross-lingual tasks. However, the gains start to diminish as the model capacity increases, suggesting that parallel data might not be as essential for larger models. At the same time, even at larger model sizes, we find that pre-training with parallel data still provides benefits in the limited labelled data regime | ['Melvin Johnson', 'Noah Constant', 'Linting Xue', 'Rami Al-Rfou', 'Aditya Siddhant', 'Mihir Kale'] | 2021-08-01 | null | null | null | acl-2021-5 | ['multilingual-nlp'] | ['natural-language-processing'] | [-2.25636229e-01 2.68909354e-02 -6.02141440e-01 -4.20714468e-01
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ea0b2931-69e5-495d-86fb-7a48e2a70a9b | ernet-efficient-and-reliable-human-object | null | null | https://ieeexplore.ieee.org/abstract/document/10026602 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10026602 | ERNet: Efficient and Reliable Human-Object Interaction Detection | Human-Object Interaction (HOI) detection recognizes how persons interact with objects, which is advantageous in autonomous systems such as self-driving vehicles and collaborative robots. However, current HOI detectors are often plagued by model inefficiency and unreliability when making a prediction, which consequently limits its potential for real-world scenarios. In this paper, we address these challenges by proposing ERNet, an end-to-end trainable convolutional-transformer network for HOI detection. The proposed model employs an efficient multi-scale deformable attention to effectively capture vital HOI features. We also put forward a novel detection attention module to adaptively generate semantically rich instance and interaction tokens. These tokens undergo pre-emptive detections to produce initial region and vector proposals that also serve as queries which enhances the feature refinement process in the transformer decoders. Several impactful enhancements are also applied to improve the HOI representation learning. Additionally, we utilize a predictive uncertainty estimation framework in the instance and interaction classification heads to quantify the uncertainty behind each prediction. By doing so, we can accurately and reliably predict HOIs even under challenging scenarios. Experiment results on the HICO-Det, V-COCO, and HOI-A datasets demonstrate that the proposed model achieves state-of-the-art performance in detection accuracy and training efficiency. Codes are publicly available at https://github.com/Monash-CyPhi-AI-Research-Lab/ernet. | ['Massimo Tistarelli', 'John See', 'KokSheik Wong', 'Joanne Mun-Yee Lim', 'Vishnu Monn Baskaran', 'JunYi Lim'] | 2023-01-26 | null | null | null | ieee-transactions-on-image-processing-2023-1 | ['human-object-interaction-detection'] | ['computer-vision'] | [-4.11027819e-02 6.92941919e-02 4.31412496e-02 -4.09554422e-01
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a534d4f0-7fd2-4dee-84f6-ab134a290dad | multi-view-3d-object-reconstruction-and | 2306.11739 | null | https://arxiv.org/abs/2306.11739v1 | https://arxiv.org/pdf/2306.11739v1.pdf | Multi-view 3D Object Reconstruction and Uncertainty Modelling with Neural Shape Prior | 3D object reconstruction is important for semantic scene understanding. It is challenging to reconstruct detailed 3D shapes from monocular images directly due to a lack of depth information, occlusion and noise. Most current methods generate deterministic object models without any awareness of the uncertainty of the reconstruction. We tackle this problem by leveraging a neural object representation which learns an object shape distribution from large dataset of 3d object models and maps it into a latent space. We propose a method to model uncertainty as part of the representation and define an uncertainty-aware encoder which generates latent codes with uncertainty directly from individual input images. Further, we propose a method to propagate the uncertainty in the latent code to SDF values and generate a 3d object mesh with local uncertainty for each mesh component. Finally, we propose an incremental fusion method under a Bayesian framework to fuse the latent codes from multi-view observations. We evaluate the system in both synthetic and real datasets to demonstrate the effectiveness of uncertainty-based fusion to improve 3D object reconstruction accuracy. | ['Steven L. Waslander', 'Ziwei Liao'] | 2023-06-17 | null | null | null | null | ['3d-object-reconstruction', 'object-reconstruction', 'scene-understanding'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.06502482e-01 2.16134295e-01 -3.10711693e-02 -8.09037447e-01
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efaae00d-1b06-4423-9dae-6d2f76f466db | can-fairness-be-automated-guidelines-and | 2303.08485 | null | https://arxiv.org/abs/2303.08485v1 | https://arxiv.org/pdf/2303.08485v1.pdf | Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML | The field of automated machine learning (AutoML) introduces techniques that automate parts of the development of machine learning (ML) systems, accelerating the process and reducing barriers for novices. However, decisions derived from ML models can reproduce, amplify, or even introduce unfairness in our societies, causing harm to (groups of) individuals. In response, researchers have started to propose AutoML systems that jointly optimize fairness and predictive performance to mitigate fairness-related harm. However, fairness is a complex and inherently interdisciplinary subject, and solely posing it as an optimization problem can have adverse side effects. With this work, we aim to raise awareness among developers of AutoML systems about such limitations of fairness-aware AutoML, while also calling attention to the potential of AutoML as a tool for fairness research. We present a comprehensive overview of different ways in which fairness-related harm can arise and the ensuing implications for the design of fairness-aware AutoML. We conclude that while fairness cannot be automated, fairness-aware AutoML can play an important role in the toolbox of an ML practitioner. We highlight several open technical challenges for future work in this direction. Additionally, we advocate for the creation of more user-centered assistive systems designed to tackle challenges encountered in fairness work. | ['Frank Hutter', 'Bernd Bischl', 'Mykola Pechenizkiy', 'Joaquin Vanschoren', 'Noor Awad', 'Edward Bergman', 'Katharina Eggensperger', 'Matthias Feurer', 'Florian Pfisterer', 'Hilde Weerts'] | 2023-03-15 | null | null | null | null | ['automl'] | ['methodology'] | [-1.11203566e-01 4.97006446e-01 -3.08638662e-01 -7.83898413e-01
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4088838d-c549-4e32-bf7c-a8853a105cce | universal-low-rank-matrix-recovery-from-pauli | null | null | http://papers.nips.cc/paper/4222-universal-low-rank-matrix-recovery-from-pauli-measurements | http://papers.nips.cc/paper/4222-universal-low-rank-matrix-recovery-from-pauli-measurements.pdf | Universal low-rank matrix recovery from Pauli measurements | We study the problem of reconstructing an unknown matrix M of rank r and dimension d using O(rd polylog d) Pauli measurements. This has applications in quantum state tomography, and is a non-commutative analogue of a well-known problem in compressed sensing: recovering a sparse vector from a few of its Fourier coefficients. We show that almost all sets of O(rd log^6 d) Pauli measurements satisfy the rank-r restricted isometry property (RIP). This implies that M can be recovered from a fixed ("universal") set of Pauli measurements, using nuclear-norm minimization (e.g., the matrix Lasso), with nearly-optimal bounds on the error. A similar result holds for any class of measurements that use an orthonormal operator basis whose elements have small operator norm. Our proof uses Dudley's inequality for Gaussian processes, together with bounds on covering numbers obtained via entropy duality. | ['Yi-Kai Liu'] | 2011-12-01 | null | null | null | neurips-2011-12 | ['quantum-state-tomography'] | ['medical'] | [ 6.57383740e-01 3.75308871e-01 -2.32617453e-01 -1.99552819e-01
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8cc7800e-fb03-4fd7-be7e-8af91773557e | it-s-done-direct-one-shot-learning-without | 2204.13361 | null | https://arxiv.org/abs/2204.13361v3 | https://arxiv.org/pdf/2204.13361v3.pdf | It's DONE: Direct ONE-shot learning with quantile weight imprinting | Learning a new concept from one example is a superior function of the human brain and it is drawing attention in the field of machine learning as a one-shot learning task. In this paper, we propose one of the simplest methods for this task with a nonparametric weight imprinting, named Direct ONE-shot learning (DONE). DONE adds new classes to a pretrained deep neural network (DNN) classifier with neither training optimization nor pretrained-DNN modification. DONE is inspired by Hebbian theory and directly uses the neural activity input of the final dense layer obtained from data that belongs to the new additional class as the synaptic weight with a newly-provided-output neuron for the new class, transforming all statistical properties of the neural activity into those of synaptic weight by quantile normalization. DONE requires just one inference for learning a new concept and its procedure is simple, deterministic, not requiring parameter tuning and hyperparameters. DONE overcomes a severe problem of existing weight imprinting methods that DNN-dependently interfere with the classification of original-class images. The performance of DONE depends entirely on the pretrained DNN model used as a backbone model, and we confirmed that DONE with current well-trained backbone models perform at a decent accuracy. | ['Izumi Ohzawa', 'Hideki Kashioka', 'Tomohiro Mashita', 'Shigeto Seno', 'Keigo Nishida', 'Kazufumi Hosoda'] | 2022-04-28 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 4.82004821e-01 2.66152173e-01 -1.50771543e-01 -3.95462722e-01
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65fd24e9-3c1f-42fd-941b-6e82ddbaf4d4 | towards-arabic-multimodal-dataset-for | 2306.06322 | null | https://arxiv.org/abs/2306.06322v1 | https://arxiv.org/pdf/2306.06322v1.pdf | Towards Arabic Multimodal Dataset for Sentiment Analysis | Multimodal Sentiment Analysis (MSA) has recently become a centric research direction for many real-world applications. This proliferation is due to the fact that opinions are central to almost all human activities and are key influencers of our behaviors. In addition, the recent deployment of Deep Learning-based (DL) models has proven their high efficiency for a wide range of Western languages. In contrast, Arabic DL-based multimodal sentiment analysis (MSA) is still in its infantile stage due, mainly, to the lack of standard datasets. In this paper, our investigation is twofold. First, we design a pipeline that helps building our Arabic Multimodal dataset leveraging both state-of-the-art transformers and feature extraction tools within word alignment techniques. Thereafter, we validate our dataset using state-of-the-art transformer-based model dealing with multimodality. Despite the small size of the outcome dataset, experiments show that Arabic multimodality is very promising | ['Hadda Cherroun', 'Attia Nehar', 'Slimane Bellaouar', 'Abdelhamid Haouhat'] | 2023-06-10 | null | null | null | null | ['multimodal-sentiment-analysis', 'sentiment-analysis', 'word-alignment', 'multimodal-sentiment-analysis'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.31409094e-01 -3.91357273e-01 -2.80925352e-02 -4.31481302e-01
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17af1773-b82b-4f00-b7d4-1786665f79a5 | adapting-verbnet-to-french-using-existing | null | null | https://aclanthology.org/L14-1204 | https://aclanthology.org/L14-1204.pdf | Adapting VerbNet to French using existing resources | VerbNet is an English lexical resource for verbs that has proven useful for English NLP due to its high coverage and coherent classification. Such a resource doesnt exist for other languages, despite some (mostly automatic and unsupervised) attempts. We show how to semi-automatically adapt VerbNet using existing resources designed for di{\"\i}{\neg}erent purposes. This study focuses on French and uses two French resources: a semantic lexicon (Les Verbes Fran{\c{c}}ais) and a syntactic lexicon (Lexique-Grammaire). | ['Ga{\\"e}l de Chalendar', 'Quentin Pradet', 'Laurence Danlos'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['stock-prediction'] | ['time-series'] | [-6.14436679e-02 1.69636980e-01 -4.36680168e-01 -4.52486008e-01
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d5982b16-44ee-40a2-bde7-05334d56075f | affact-alignment-free-facial-attribute | 1611.06158 | null | http://arxiv.org/abs/1611.06158v2 | http://arxiv.org/pdf/1611.06158v2.pdf | AFFACT - Alignment-Free Facial Attribute Classification Technique | Facial attributes are soft-biometrics that allow limiting the search space,
e.g., by rejecting identities with non-matching facial characteristics such as
nose sizes or eyebrow shapes. In this paper, we investigate how the latest
versions of deep convolutional neural networks, ResNets, perform on the facial
attribute classification task. We test two loss functions: the sigmoid
cross-entropy loss and the Euclidean loss, and find that for classification
performance there is little difference between these two. Using an ensemble of
three ResNets, we obtain the new state-of-the-art facial attribute
classification error of 8.00% on the aligned images of the CelebA dataset. More
significantly, we introduce the Alignment-Free Facial Attribute Classification
Technique (AFFACT), a data augmentation technique that allows a network to
classify facial attributes without requiring alignment beyond detected face
bounding boxes. To our best knowledge, we are the first to report similar
accuracy when using only the detected bounding boxes -- rather than requiring
alignment based on automatically detected facial landmarks -- and who can
improve classification accuracy with rotating and scaling test images. We show
that this approach outperforms the CelebA baseline on unaligned images with a
relative improvement of 36.8%. | ['Manuel Günther', 'Andras Rozsa', 'Terrance E. Boult'] | 2016-11-18 | null | null | null | null | ['facial-attribute-classification'] | ['computer-vision'] | [ 2.48638690e-01 3.09918910e-01 -4.14343551e-02 -9.90482330e-01
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6.24301314e-01 -6.87920332e-01 -4.07215685e-01 -1.41615301e-01] | [13.274222373962402, 0.9564650654792786] |
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