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71c1e820-427b-4b7f-887a-d38ad810e33b | 3d-future-3d-furniture-shape-with-texture | 2009.09633 | null | https://arxiv.org/abs/2009.09633v1 | https://arxiv.org/pdf/2009.09633v1.pdf | 3D-FUTURE: 3D Furniture shape with TextURE | The 3D CAD shapes in current 3D benchmarks are mostly collected from online model repositories. Thus, they typically have insufficient geometric details and less informative textures, making them less attractive for comprehensive and subtle research in areas such as high-quality 3D mesh and texture recovery. This paper presents 3D Furniture shape with TextURE (3D-FUTURE): a richly-annotated and large-scale repository of 3D furniture shapes in the household scenario. At the time of this technical report, 3D-FUTURE contains 20,240 clean and realistic synthetic images of 5,000 different rooms. There are 9,992 unique detailed 3D instances of furniture with high-resolution textures. Experienced designers developed the room scenes, and the 3D CAD shapes in the scene are used for industrial production. Given the well-organized 3D-FUTURE, we provide baseline experiments on several widely studied tasks, such as joint 2D instance segmentation and 3D object pose estimation, image-based 3D shape retrieval, 3D object reconstruction from a single image, and texture recovery for 3D shapes, to facilitate related future researches on our database. | ['DaCheng Tao', 'Mingming Gong', 'Steve Maybank', 'Lin Gao', 'Huan Fu', 'Rongfei Jia', 'Binqiang Zhao'] | 2020-09-21 | null | null | null | null | ['3d-object-reconstruction', '3d-object-reconstruction-from-a-single-image'] | ['computer-vision', 'computer-vision'] | [ 1.50944851e-02 -4.24039304e-01 1.28063455e-01 -3.48794162e-01
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15346d9e-8c53-4f4f-ba67-b712a49ccc17 | sardino-ultra-fast-dynamic-ensemble-for | 2204.08189 | null | https://arxiv.org/abs/2204.08189v4 | https://arxiv.org/pdf/2204.08189v4.pdf | Sardino: Ultra-Fast Dynamic Ensemble for Secure Visual Sensing at Mobile Edge | Adversarial example attack endangers the mobile edge systems such as vehicles and drones that adopt deep neural networks for visual sensing. This paper presents {\em Sardino}, an active and dynamic defense approach that renews the inference ensemble at run time to develop security against the adaptive adversary who tries to exfiltrate the ensemble and construct the corresponding effective adversarial examples. By applying consistency check and data fusion on the ensemble's predictions, Sardino can detect and thwart adversarial inputs. Compared with the training-based ensemble renewal, we use HyperNet to achieve {\em one million times} acceleration and per-frame ensemble renewal that presents the highest level of difficulty to the prerequisite exfiltration attacks. We design a run-time planner that maximizes the ensemble size in favor of security while maintaining the processing frame rate. Beyond adversarial examples, Sardino can also address the issue of out-of-distribution inputs effectively. This paper presents extensive evaluation of Sardino's performance in counteracting adversarial examples and applies it to build a real-time car-borne traffic sign recognition system. Live on-road tests show the built system's effectiveness in maintaining frame rate and detecting out-of-distribution inputs due to the false positives of a preceding YOLO-based traffic sign detector. | ['Rui Tan', 'Wenjie Luo', 'Zhenyu Yan', 'Qun Song'] | 2022-04-18 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 2.90085912e-01 2.64297843e-01 1.57674044e-01 -4.33133803e-02
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878d0818-f423-489b-bee5-ea944bced71a | worth-of-knowledge-in-deep-learning | 2307.00712 | null | https://arxiv.org/abs/2307.00712v1 | https://arxiv.org/pdf/2307.00712v1.pdf | Worth of knowledge in deep learning | Knowledge constitutes the accumulated understanding and experience that humans use to gain insight into the world. In deep learning, prior knowledge is essential for mitigating shortcomings of data-driven models, such as data dependence, generalization ability, and compliance with constraints. To enable efficient evaluation of the worth of knowledge, we present a framework inspired by interpretable machine learning. Through quantitative experiments, we assess the influence of data volume and estimation range on the worth of knowledge. Our findings elucidate the complex relationship between data and knowledge, including dependence, synergistic, and substitution effects. Our model-agnostic framework can be applied to a variety of common network architectures, providing a comprehensive understanding of the role of prior knowledge in deep learning models. It can also be used to improve the performance of informed machine learning, as well as distinguish improper prior knowledge. | ['Dongxiao Zhang', 'Yuntian Chen', 'Hao Xu'] | 2023-07-03 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [-4.24569286e-02 2.95243412e-01 -5.58596134e-01 -6.16460741e-01
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1d359948-2aa0-4daa-8bb9-201a4e97b1b0 | towards-deep-learning-powered-ivf-a-large | 2203.00531 | null | https://arxiv.org/abs/2203.00531v2 | https://arxiv.org/pdf/2203.00531v2.pdf | Towards deep learning-powered IVF: A large public benchmark for morphokinetic parameter prediction | An important limitation to the development of Artificial Intelligence (AI)-based solutions for In Vitro Fertilization (IVF) is the absence of a public reference benchmark to train and evaluate deep learning (DL) models. In this work, we describe a fully annotated dataset of 704 videos of developing embryos, for a total of 337k images. We applied ResNet, LSTM, and ResNet-3D architectures to our dataset and demonstrate that they overperform algorithmic approaches to automatically annotate stage development phases. Altogether, we propose the first public benchmark that will allow the community to evaluate morphokinetic models. This is the first step towards deep learning-powered IVF. Of note, we propose highly detailed annotations with 16 different development phases, including early cell division phases, but also late cell divisions, phases after morulation, and very early phases, which have never been used before. We postulate that this original approach will help improve the overall performance of deep learning approaches on time-lapse videos of embryo development, ultimately benefiting infertile patients with improved clinical success rates (Code and data are available at https://gitlab.univ-nantes.fr/E144069X/bench_mk_pred.git). | ['Harold Mouchère', 'Thomas Fréour', 'Perrine Paul-Gilloteaux', 'Laurent David', 'Nicolas Normand', 'Magalie Feyeux', 'Tristan Gomez'] | 2022-03-01 | null | null | null | null | ['parameter-prediction'] | ['miscellaneous'] | [ 1.44015148e-01 5.39336145e-01 1.02756366e-01 -2.66876370e-01
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de9af1d6-7995-42c1-a8de-09c8fcb1b135 | improving-constituency-parsing-with-span | 2010.07543 | null | https://arxiv.org/abs/2010.07543v1 | https://arxiv.org/pdf/2010.07543v1.pdf | Improving Constituency Parsing with Span Attention | Constituency parsing is a fundamental and important task for natural language understanding, where a good representation of contextual information can help this task. N-grams, which is a conventional type of feature for contextual information, have been demonstrated to be useful in many tasks, and thus could also be beneficial for constituency parsing if they are appropriately modeled. In this paper, we propose span attention for neural chart-based constituency parsing to leverage n-gram information. Considering that current chart-based parsers with Transformer-based encoder represent spans by subtraction of the hidden states at the span boundaries, which may cause information loss especially for long spans, we incorporate n-grams into span representations by weighting them according to their contributions to the parsing process. Moreover, we propose categorical span attention to further enhance the model by weighting n-grams within different length categories, and thus benefit long-sentence parsing. Experimental results on three widely used benchmark datasets demonstrate the effectiveness of our approach in parsing Arabic, Chinese, and English, where state-of-the-art performance is obtained by our approach on all of them. | ['Tong Zhang', 'Fei Xia', 'Yan Song', 'Yuanhe Tian'] | 2020-10-15 | null | https://aclanthology.org/2020.findings-emnlp.153 | https://aclanthology.org/2020.findings-emnlp.153.pdf | findings-of-the-association-for-computational | ['constituency-parsing'] | ['natural-language-processing'] | [ 1.86074436e-01 1.14751354e-01 -4.41726983e-01 -5.53314149e-01
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53f2e6b3-e043-44d6-9d68-3d04c85edd32 | unsupervised-multimodal-neural-machine | 2005.03119 | null | https://arxiv.org/abs/2005.03119v1 | https://arxiv.org/pdf/2005.03119v1.pdf | Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting | Unsupervised machine translation (MT) has recently achieved impressive results with monolingual corpora only. However, it is still challenging to associate source-target sentences in the latent space. As people speak different languages biologically share similar visual systems, the potential of achieving better alignment through visual content is promising yet under-explored in unsupervised multimodal MT (MMT). In this paper, we investigate how to utilize visual content for disambiguation and promoting latent space alignment in unsupervised MMT. Our model employs multimodal back-translation and features pseudo visual pivoting in which we learn a shared multilingual visual-semantic embedding space and incorporate visually-pivoted captioning as additional weak supervision. The experimental results on the widely used Multi30K dataset show that the proposed model significantly improves over the state-of-the-art methods and generalizes well when the images are not available at the testing time. | ['Alexander Hauptmann', 'Po-Yao Huang', 'Xiaojun Chang', 'Junjie Hu'] | 2020-05-06 | unsupervised-multimodal-neural-machine-1 | https://aclanthology.org/2020.acl-main.731 | https://aclanthology.org/2020.acl-main.731.pdf | acl-2020-6 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 1.45902902e-01 2.52828151e-02 -5.11782110e-01 -2.44343281e-01
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f8335da8-1cb1-4a7d-8787-a962dd24bf98 | first-order-methods-with-markovian-noise-from | 2305.15938 | null | https://arxiv.org/abs/2305.15938v1 | https://arxiv.org/pdf/2305.15938v1.pdf | First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities | This paper delves into stochastic optimization problems that involve Markovian noise. We present a unified approach for the theoretical analysis of first-order gradient methods for stochastic optimization and variational inequalities. Our approach covers scenarios for both non-convex and strongly convex minimization problems. To achieve an optimal (linear) dependence on the mixing time of the underlying noise sequence, we use the randomized batching scheme, which is based on the multilevel Monte Carlo method. Moreover, our technique allows us to eliminate the limiting assumptions of previous research on Markov noise, such as the need for a bounded domain and uniformly bounded stochastic gradients. Our extension to variational inequalities under Markovian noise is original. Additionally, we provide lower bounds that match the oracle complexity of our method in the case of strongly convex optimization problems. | ['Eric Moulines', 'Alexey Naumov', 'Alexander Gasnikov', 'Marina Sheshukova', 'Sergey Samsonov', 'Aleksandr Beznosikov'] | 2023-05-25 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 1.53347701e-01 -1.96700543e-01 -2.75191031e-02 -5.48041612e-02
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3202cf74-bed5-497e-8774-5d72994378d9 | automatic-brain-structures-segmentation-using | 1811.04312 | null | http://arxiv.org/abs/1811.04312v1 | http://arxiv.org/pdf/1811.04312v1.pdf | Automatic Brain Structures Segmentation Using Deep Residual Dilated U-Net | Brain image segmentation is used for visualizing and quantifying anatomical
structures of the brain. We present an automated ap-proach using 2D deep
residual dilated networks which captures rich context information of different
tissues for the segmentation of eight brain structures. The proposed system was
evaluated in the MICCAI Brain Segmentation Challenge and ranked 9th out of 22
teams. We further compared the method with traditional U-Net using
leave-one-subject-out cross-validation setting on the public dataset.
Experimental results shows that the proposed method outperforms traditional
U-Net (i.e. 80.9% vs 78.3% in averaged Dice score, 4.35mm vs 11.59mm in
averaged robust Hausdorff distance) and is computationally efficient. | ['Bjoern Menze', 'Andrii Zhygallo', 'Hongwei Li'] | 2018-11-10 | null | null | null | null | ['brain-image-segmentation'] | ['medical'] | [-3.00680641e-02 1.04011469e-01 3.01334023e-01 -6.31112397e-01
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915c47ac-7b4f-44be-84c4-37a7962cd214 | efficient-and-low-overhead-website | 2302.13763 | null | https://arxiv.org/abs/2302.13763v1 | https://arxiv.org/pdf/2302.13763v1.pdf | Efficient and Low Overhead Website Fingerprinting Attacks and Defenses based on TCP/IP Traffic | Website fingerprinting attack is an extensively studied technique used in a web browser to analyze traffic patterns and thus infer confidential information about users. Several website fingerprinting attacks based on machine learning and deep learning tend to use the most typical features to achieve a satisfactory performance of attacking rate. However, these attacks suffer from several practical implementation factors, such as a skillfully pre-processing step or a clean dataset. To defend against such attacks, random packet defense (RPD) with a high cost of excessive network overhead is usually applied. In this work, we first propose a practical filter-assisted attack against RPD, which can filter out the injected noises using the statistical characteristics of TCP/IP traffic. Then, we propose a list-assisted defensive mechanism to defend the proposed attack method. To achieve a configurable trade-off between the defense and the network overhead, we further improve the list-based defense by a traffic splitting mechanism, which can combat the mentioned attacks as well as save a considerable amount of network overhead. In the experiments, we collect real-life traffic patterns using three mainstream browsers, i.e., Microsoft Edge, Google Chrome, and Mozilla Firefox, and extensive results conducted on the closed and open-world datasets show the effectiveness of the proposed algorithms in terms of defense accuracy and network efficiency. | ['Zhe Liu', 'Liming Fang', 'Chunpeng Ge', 'Yuwen Qian', 'Ming Ding', 'Chuan Ma', 'Guodong Huang'] | 2023-02-27 | null | null | null | null | ['website-fingerprinting-attacks'] | ['adversarial'] | [-5.45173734e-02 -8.04737210e-01 -2.21244469e-01 -2.83103138e-01
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2bc310fd-8422-451e-810c-aa392f453142 | conversational-analysis-using-utterance-level | 1805.06242 | null | http://arxiv.org/abs/1805.06242v2 | http://arxiv.org/pdf/1805.06242v2.pdf | Conversational Analysis using Utterance-level Attention-based Bidirectional Recurrent Neural Networks | Recent approaches for dialogue act recognition have shown that context from
preceding utterances is important to classify the subsequent one. It was shown
that the performance improves rapidly when the context is taken into account.
We propose an utterance-level attention-based bidirectional recurrent neural
network (Utt-Att-BiRNN) model to analyze the importance of preceding utterances
to classify the current one. In our setup, the BiRNN is given the input set of
current and preceding utterances. Our model outperforms previous models that
use only preceding utterances as context on the used corpus. Another
contribution of the article is to discover the amount of information in each
utterance to classify the subsequent one and to show that context-based
learning not only improves the performance but also achieves higher confidence
in the classification. We use character- and word-level features to represent
the utterances. The results are presented for character and word feature
representations and as an ensemble model of both representations. We found that
when classifying short utterances, the closest preceding utterances contributes
to a higher degree. | ['Cornelius Weber', 'Sven Magg', 'Stefan Wermter', 'Chandrakant Bothe'] | 2018-05-16 | null | null | null | null | ['dialog-act-classification', 'dialogue-act-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.64649826e-01 3.21733713e-01 -9.65852141e-02 -7.53594637e-01
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75fb5c4b-099d-423c-9e0b-28cf9fbb5627 | precision-psychiatry-predicting | 2306.12462 | null | https://arxiv.org/abs/2306.12462v1 | https://arxiv.org/pdf/2306.12462v1.pdf | Precision psychiatry: predicting predictability | Precision psychiatry is an ermerging field that aims to provide individualized approaches to mental health care. Multivariate analysis and machine learning are used to create outcome prediction models based on clinical data such as demographics, symptom assessments, genetic information, and brain imaging. While much emphasis has been placed on technical innovation, the complex and varied nature of mental health presents significant challenges to the successful implementation of these models. From this perspective, I review ten challenges in the field of precision psychiatry, including the need for studies on real-world populations and realistic clinical outcome definitions, consideration of treatment-related factors such as placebo effects and non-adherence to prescriptions. Fairness, prospective validation in comparison to current practice and implementation studies of prediction models are other key issues that are currently understudied. A shift is proposed from retrospective studies based on linear and static concepts of disease towards prospective research that considers the importance of contextual factors and the dynamic and complex nature of mental health. | ['Edwin van Dellen'] | 2023-06-21 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [ 5.14508545e-01 1.09925777e-01 -9.45302546e-01 -5.88649035e-01
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b366f811-0f0a-4b24-bab1-c71d16e21779 | emotional-responses-in-artificial-agent-based | 1401.2121 | null | http://arxiv.org/abs/1401.2121v1 | http://arxiv.org/pdf/1401.2121v1.pdf | Emotional Responses in Artificial Agent-Based Systems: Reflexivity and Adaptation in Artificial Life | The current work addresses a virtual environment with self-replicating agents
whose decisions are based on a form of "somatic computation" (soma - body) in
which basic emotional responses, taken in parallelism to actual living
organisms, are introduced as a way to provide the agents with greater reflexive
abilities. The work provides a contribution to the field of Artificial
Intelligence (AI) and Artificial Life (ALife) in connection to a
neurobiology-based cognitive framework for artificial systems and virtual
environments' simulations. The performance of the agents capable of emotional
responses is compared with that of self-replicating automata, and the
implications of research on emotions and AI, in connection to both virtual
agents as well as robots, is addressed regarding possible future directions and
applications. | ['Carlos Pedro Gonçalves'] | 2014-01-09 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-1.55961454e-01 5.30314684e-01 2.39358664e-01 2.36928225e-01
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c82fc1ea-b79d-404c-bac8-3fb8345eb481 | density-ratio-estimation-based-bayesian | 2305.15612 | null | https://arxiv.org/abs/2305.15612v1 | https://arxiv.org/pdf/2305.15612v1.pdf | Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning | Bayesian optimization has attracted huge attention from diverse research areas in science and engineering, since it is capable of finding a global optimum of an expensive-to-evaluate black-box function efficiently. In general, a probabilistic regression model, e.g., Gaussian processes, random forests, and Bayesian neural networks, is widely used as a surrogate function to model an explicit distribution over function evaluations given an input to estimate and a training dataset. Beyond the probabilistic regression-based Bayesian optimization, density ratio estimation-based Bayesian optimization has been suggested in order to estimate a density ratio of the groups relatively close and relatively far to a global optimum. Developing this line of research further, a supervised classifier can be employed to estimate a class probability for the two groups instead of a density ratio. However, the supervised classifiers used in this strategy tend to be overconfident for a global solution candidate. To solve this overconfidence problem, we propose density ratio estimation-based Bayesian optimization with semi-supervised learning. Finally, we demonstrate the experimental results of our methods and several baseline methods in two distinct scenarios with unlabeled point sampling and a fixed-size pool. | ['Jungtaek Kim'] | 2023-05-24 | null | null | null | null | ['density-ratio-estimation', 'bayesian-optimization'] | ['methodology', 'methodology'] | [-2.14482933e-01 -1.70627967e-01 -4.27493125e-01 -7.04163730e-01
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80a5aeb3-cac1-4be3-a7b0-9249bcf539f4 | automated-speech-tools-for-helping | 2204.07272 | null | https://arxiv.org/abs/2204.07272v2 | https://arxiv.org/pdf/2204.07272v2.pdf | Automated speech tools for helping communities process restricted-access corpora for language revival efforts | Many archival recordings of speech from endangered languages remain unannotated and inaccessible to community members and language learning programs. One bottleneck is the time-intensive nature of annotation. An even narrower bottleneck occurs for recordings with access constraints, such as language that must be vetted or filtered by authorised community members before annotation can begin. We propose a privacy-preserving workflow to widen both bottlenecks for recordings where speech in the endangered language is intermixed with a more widely-used language such as English for meta-linguistic commentary and questions (e.g. What is the word for 'tree'?). We integrate voice activity detection (VAD), spoken language identification (SLI), and automatic speech recognition (ASR) to transcribe the metalinguistic content, which an authorised person can quickly scan to triage recordings that can be annotated by people with lower levels of access. We report work-in-progress processing 136 hours archival audio containing a mix of English and Muruwari. Our collaborative work with the Muruwari custodian of the archival materials show that this workflow reduces metalanguage transcription time by 20% even given only minimal amounts of annotated training data: 10 utterances per language for SLI and for ASR at most 39 minutes, and possibly as little as 39 seconds. | ['Tolúlopé Ògúnrèmí', 'Dan Jurafsky', 'Jane Simpson', 'Roy Barker', 'Michael Higgins', 'Ruben Thompson', 'Alison Mount', 'Martijn Bartelds', 'Nay San'] | 2022-04-15 | null | https://aclanthology.org/2022.computel-1.6 | https://aclanthology.org/2022.computel-1.6.pdf | computel-acl-2022-5 | ['activity-detection', 'spoken-language-identification'] | ['computer-vision', 'speech'] | [ 1.10413477e-01 2.61019856e-01 3.53688926e-01 -3.13724160e-01
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3c2bd2e3-1849-43df-ada4-3f21b127ca4a | g-matt-single-step-retrosynthesis-prediction | 2305.03153 | null | https://arxiv.org/abs/2305.03153v1 | https://arxiv.org/pdf/2305.03153v1.pdf | G-MATT: Single-step Retrosynthesis Prediction using Molecular Grammar Tree Transformer | In recent years, several reaction templates-based and template-free approaches have been reported for single-step retrosynthesis prediction. Even though many of these approaches perform well from traditional data-driven metrics standpoint, there is a disconnect between model architectures used and underlying chemistry principles governing retrosynthesis. Here, we propose a novel chemistry-aware retrosynthesis prediction framework that combines powerful data-driven models with chemistry knowledge. We report a tree-to-sequence transformer architecture based on hierarchical SMILES grammar trees as input containing underlying chemistry information that is otherwise ignored by models based on purely SMILES-based representations. The proposed framework, grammar-based molecular attention tree transformer (G-MATT), achieves significant performance improvements compared to baseline retrosynthesis models. G-MATT achieves a top-1 accuracy of 51% (top-10 accuracy of 79.1%), invalid rate of 1.5%, and bioactive similarity rate of 74.8%. Further analyses based on attention maps demonstrate G-MATT's ability to preserve chemistry knowledge without having to use extremely complex model architectures. | ['Venkat Venkatasubramanian', 'Vipul Mann', 'Kevin Zhang'] | 2023-05-04 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 4.62268472e-01 7.46428147e-02 -3.56885612e-01 -1.23333961e-01
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103fa6e9-6583-4264-a5d7-5688b5cd44bd | learnable-expansion-and-compression-network | 2104.02281 | null | https://arxiv.org/abs/2104.02281v1 | https://arxiv.org/pdf/2104.02281v1.pdf | Learnable Expansion-and-Compression Network for Few-shot Class-Incremental Learning | Few-shot class-incremental learning (FSCIL), which targets at continuously expanding model's representation capacity under few supervisions, is an important yet challenging problem. On the one hand, when fitting new tasks (novel classes), features trained on old tasks (old classes) could significantly drift, causing catastrophic forgetting. On the other hand, training the large amount of model parameters with few-shot novel-class examples leads to model over-fitting. In this paper, we propose a learnable expansion-and-compression network (LEC-Net), with the aim to simultaneously solve catastrophic forgetting and model over-fitting problems in a unified framework. By tentatively expanding network nodes, LEC-Net enlarges the representation capacity of features, alleviating feature drift of old network from the perspective of model regularization. By compressing the expanded network nodes, LEC-Net purses minimal increase of model parameters, alleviating over-fitting of the expanded network from a perspective of compact representation. Experiments on the CUB/CIFAR-100 datasets show that LEC-Net improves the baseline by 5~7% while outperforms the state-of-the-art by 5~6%. LEC-Net also demonstrates the potential to be a general incremental learning approach with dynamic model expansion capability. | ['Qixiang Ye', 'Rongrong Ji', 'Chang Liu', 'Mengying Fu', 'Binghao Liu', 'Mingbao Lin', 'Boyu Yang'] | 2021-04-06 | null | null | null | null | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 2.28835225e-01 2.63686091e-01 -5.32533834e-03 -1.69592157e-01
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7a811abe-2d9b-4797-816d-d2bfca9fdf7d | learning-audio-visual-dereverberation | 2106.07732 | null | https://arxiv.org/abs/2106.07732v2 | https://arxiv.org/pdf/2106.07732v2.pdf | Learning Audio-Visual Dereverberation | Reverberation not only degrades the quality of speech for human perception, but also severely impacts the accuracy of automatic speech recognition. Prior work attempts to remove reverberation based on the audio modality only. Our idea is to learn to dereverberate speech from audio-visual observations. The visual environment surrounding a human speaker reveals important cues about the room geometry, materials, and speaker location, all of which influence the precise reverberation effects. We introduce Visually-Informed Dereverberation of Audio (VIDA), an end-to-end approach that learns to remove reverberation based on both the observed monaural sound and visual scene. In support of this new task, we develop a large-scale dataset SoundSpaces-Speech that uses realistic acoustic renderings of speech in real-world 3D scans of homes offering a variety of room acoustics. Demonstrating our approach on both simulated and real imagery for speech enhancement, speech recognition, and speaker identification, we show it achieves state-of-the-art performance and substantially improves over audio-only methods. | ['Kristen Grauman', 'David Harwath', 'Wei Sun', 'Changan Chen'] | 2021-06-14 | learning-audio-visual-dereverberation-1 | https://openreview.net/forum?id=ExJ4lMbZcqa | https://openreview.net/pdf?id=ExJ4lMbZcqa | null | ['speaker-identification'] | ['speech'] | [ 3.40089887e-01 -2.98119605e-01 1.17281449e+00 -3.22991788e-01
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3091842e-9051-45fd-bc9b-c8b259543c4b | sleep-arousal-detection-from-polysomnography | 1810.08875 | null | http://arxiv.org/abs/1810.08875v1 | http://arxiv.org/pdf/1810.08875v1.pdf | Sleep Arousal Detection from Polysomnography using the Scattering Transform and Recurrent Neural Networks | Sleep disorders are implicated in a growing number of health problems. In
this paper, we present a signal-processing/machine learning approach to
detecting arousals in the multi-channel polysomnographic recordings of the
Physionet/CinC Challenge2018 dataset.
Methods: Our network architecture consists of two components. Inputs were
presented to a Scattering Transform (ST) representation layer which fed a
recurrent neural network for sequence learning using three layers of Long
Short-Term Memory (LSTM). The STs were calculated for each signal with
downsampling parameters chosen to give approximately 1 s time resolution,
resulting in an eighteen-fold data reduction. The LSTM layers then operated at
this downsampled rate.
Results: The proposed approach detected arousal regions on the 10% random
sample of the hidden test set with an AUROC of 88.0% and an AUPRC of 42.1%. | ['Masun Nabhan Homsi', 'Philip Warrick'] | 2018-10-21 | null | null | null | null | ['sleep-arousal-detection'] | ['medical'] | [ 6.72696054e-01 4.96262237e-02 2.34275073e-01 -4.56802547e-01
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b9a1a149-535d-478e-a398-f885c5f770ca | pre-training-language-models-for-comparative | 2305.14457 | null | https://arxiv.org/abs/2305.14457v1 | https://arxiv.org/pdf/2305.14457v1.pdf | Pre-training Language Models for Comparative Reasoning | In this paper, we propose a novel framework to pre-train language models for enhancing their abilities of comparative reasoning over texts. While recent research has developed models for NLP tasks that require comparative reasoning, they suffer from costly manual data labeling and limited generalizability to different tasks. Our approach involves a scalable method for collecting data for text-based entity comparison, which leverages both structured and unstructured data, and the design of three novel pre-training tasks. Evaluation on a range of downstream tasks including comparative question answering, question generation, and summarization shows that our pre-training framework significantly improves the comparative reasoning abilities of language models, especially under low-resource conditions. This work also releases the first integrated benchmark for comparative reasoning over texts. | ['Meng Jiang', 'Wenhao Yu', 'Zhihan Zhang', 'Mengxia Yu'] | 2023-05-23 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 1.05529226e-01 5.03130853e-01 -3.07793051e-01 -4.58156109e-01
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524ac8d8-0be3-44cb-9d29-4d265a87f135 | a-text-guided-protein-design-framework | 2302.04611 | null | https://arxiv.org/abs/2302.04611v1 | https://arxiv.org/pdf/2302.04611v1.pdf | A Text-guided Protein Design Framework | Current AI-assisted protein design mainly utilizes protein sequential and structural information. Meanwhile, there exists tremendous knowledge curated by humans in the text format describing proteins' high-level properties. Yet, whether the incorporation of such text data can help protein design tasks has not been explored. To bridge this gap, we propose ProteinDT, a multi-modal framework that leverages textual descriptions for protein design. ProteinDT consists of three subsequent steps: ProteinCLAP that aligns the representation of two modalities, a facilitator that generates the protein representation from the text modality, and a decoder that generates the protein sequences from the representation. To train ProteinDT, we construct a large dataset, SwissProtCLAP, with 441K text and protein pairs. We empirically verify the effectiveness of ProteinDT from three aspects: (1) consistently superior performance on four out of six protein property prediction benchmarks; (2) over 90% accuracy for text-guided protein generation; and (3) promising results for zero-shot text-guided protein editing. | ['Anima Anandkumar', 'Hongyu Guo', 'Jian Tang', 'Chaowei Xiao', 'Anthony Gitter', 'Weili Nie', 'Zhao Xu', 'Jiarui Lu', 'Yutao Zhu', 'Shengchao Liu'] | 2023-02-09 | null | null | null | null | ['protein-design'] | ['medical'] | [ 7.42392063e-01 1.71317697e-01 -1.83562100e-01 -4.65481192e-01
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d5230020-cc92-4d87-83b7-79c62b383922 | a-unified-model-for-video-understanding-and | 2211.10624 | null | https://arxiv.org/abs/2211.10624v2 | https://arxiv.org/pdf/2211.10624v2.pdf | A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset | Video understanding is an important task in short video business platforms and it has a wide application in video recommendation and classification. Most of the existing video understanding works only focus on the information that appeared within the video content, including the video frames, audio and text. However, introducing common sense knowledge from the external Knowledge Graph (KG) dataset is essential for video understanding when referring to the content which is less relevant to the video. Owing to the lack of video knowledge graph dataset, the work which integrates video understanding and KG is rare. In this paper, we propose a heterogeneous dataset that contains the multi-modal video entity and fruitful common sense relations. This dataset also provides multiple novel video inference tasks like the Video-Relation-Tag (VRT) and Video-Relation-Video (VRV) tasks. Furthermore, based on this dataset, we propose an end-to-end model that jointly optimizes the video understanding objective with knowledge graph embedding, which can not only better inject factual knowledge into video understanding but also generate effective multi-modal entity embedding for KG. Comprehensive experiments indicate that combining video understanding embedding with factual knowledge benefits the content-based video retrieval performance. Moreover, it also helps the model generate better knowledge graph embedding which outperforms traditional KGE-based methods on VRT and VRV tasks with at least 42.36% and 17.73% improvement in HITS@10. | ['Zhongyuan Wang', 'Ruiji Fu', 'Size Li', 'Fan Yang', 'Gaofeng Meng', 'Ximan Liu', 'Xiangyu Wu', 'Haojie Pan', 'Dong Shen', 'Jiaxin Deng'] | 2022-11-19 | null | null | null | null | ['video-understanding', 'knowledge-graph-embedding', 'common-sense-reasoning'] | ['computer-vision', 'graphs', 'reasoning'] | [-1.69734418e-01 -1.76444650e-01 -7.50133097e-01 -7.14922845e-02
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b631b57a-42be-4af6-9f9d-deacf12ddd5a | efficient-diffusion-policies-for-offline | 2305.20081 | null | https://arxiv.org/abs/2305.20081v1 | https://arxiv.org/pdf/2305.20081v1.pdf | Efficient Diffusion Policies for Offline Reinforcement Learning | Offline reinforcement learning (RL) aims to learn optimal policies from offline datasets, where the parameterization of policies is crucial but often overlooked. Recently, Diffsuion-QL significantly boosts the performance of offline RL by representing a policy with a diffusion model, whose success relies on a parametrized Markov Chain with hundreds of steps for sampling. However, Diffusion-QL suffers from two critical limitations. 1) It is computationally inefficient to forward and backward through the whole Markov chain during training. 2) It is incompatible with maximum likelihood-based RL algorithms (e.g., policy gradient methods) as the likelihood of diffusion models is intractable. Therefore, we propose efficient diffusion policy (EDP) to overcome these two challenges. EDP approximately constructs actions from corrupted ones at training to avoid running the sampling chain. We conduct extensive experiments on the D4RL benchmark. The results show that EDP can reduce the diffusion policy training time from 5 days to 5 hours on gym-locomotion tasks. Moreover, we show that EDP is compatible with various offline RL algorithms (TD3, CRR, and IQL) and achieves new state-of-the-art on D4RL by large margins over previous methods. Our code is available at https://github.com/sail-sg/edp. | ['Shuicheng Yan', 'Tianyu Pang', 'Chao Du', 'Xiao Ma', 'Bingyi Kang'] | 2023-05-31 | null | null | null | null | ['policy-gradient-methods', 'offline-rl', 'd4rl'] | ['methodology', 'playing-games', 'robots'] | [-3.87271106e-01 -7.63094425e-02 -7.60481656e-01 1.12349883e-01
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c482c15b-f824-4381-9425-cf838f55ffbf | behavior-driven-synthesis-of-human-dynamics | 2103.04677 | null | https://arxiv.org/abs/2103.04677v2 | https://arxiv.org/pdf/2103.04677v2.pdf | Behavior-Driven Synthesis of Human Dynamics | Generating and representing human behavior are of major importance for various computer vision applications. Commonly, human video synthesis represents behavior as sequences of postures while directly predicting their likely progressions or merely changing the appearance of the depicted persons, thus not being able to exercise control over their actual behavior during the synthesis process. In contrast, controlled behavior synthesis and transfer across individuals requires a deep understanding of body dynamics and calls for a representation of behavior that is independent of appearance and also of specific postures. In this work, we present a model for human behavior synthesis which learns a dedicated representation of human dynamics independent of postures. Using this representation, we are able to change the behavior of a person depicted in an arbitrary posture, or to even directly transfer behavior observed in a given video sequence. To this end, we propose a conditional variational framework which explicitly disentangles posture from behavior. We demonstrate the effectiveness of our approach on this novel task, evaluating capturing, transferring, and sampling fine-grained, diverse behavior, both quantitatively and qualitatively. Project page is available at https://cutt.ly/5l7rXEp | ['Björn Ommer', 'Michael Dorkenwald', 'Timo Milbich', 'Andreas Blattmann'] | 2021-03-08 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Blattmann_Behavior-Driven_Synthesis_of_Human_Dynamics_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Blattmann_Behavior-Driven_Synthesis_of_Human_Dynamics_CVPR_2021_paper.pdf | cvpr-2021-1 | ['human-dynamics'] | ['computer-vision'] | [ 3.13578188e-01 -4.39459272e-02 -3.68810967e-02 -2.99959838e-01
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c43f69ce-b879-4b47-8558-88e388a246b8 | precise-stock-price-prediction-for-robust | 2201.05570 | null | https://arxiv.org/abs/2201.05570v1 | https://arxiv.org/pdf/2201.05570v1.pdf | Precise Stock Price Prediction for Robust Portfolio Design from Selected Sectors of the Indian Stock Market | Stock price prediction is a challenging task and a lot of propositions exist in the literature in this area. Portfolio construction is a process of choosing a group of stocks and investing in them optimally to maximize the return while minimizing the risk. Since the time when Markowitz proposed the Modern Portfolio Theory, several advancements have happened in the area of building efficient portfolios. An investor can get the best benefit out of the stock market if the investor invests in an efficient portfolio and could take the buy or sell decision in advance, by estimating the future asset value of the portfolio with a high level of precision. In this project, we have built an efficient portfolio and to predict the future asset value by means of individual stock price prediction of the stocks in the portfolio. As part of building an efficient portfolio we have studied multiple portfolio optimization methods beginning with the Modern Portfolio theory. We have built the minimum variance portfolio and optimal risk portfolio for all the five chosen sectors by using past daily stock prices over the past five years as the training data, and have also conducted back testing to check the performance of the portfolio. A comparative study of minimum variance portfolio and optimal risk portfolio with equal weight portfolio is done by backtesting. | ['Praveen Varukolu', 'Koushik Tulasi', 'Kaushik Muthukrishnan', 'Geetha Joseph', 'Ashwin Kumar R S', 'Jaydip Sen'] | 2022-01-14 | null | null | null | null | ['portfolio-optimization', 'stock-price-prediction'] | ['time-series', 'time-series'] | [-1.66629881e-01 3.56317535e-02 3.22194882e-02 -2.31180146e-01
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53535010-e660-467e-b4b6-7d996b31046e | model-driven-engineering-method-to-support | 2307.04495 | null | https://arxiv.org/abs/2307.04495v1 | https://arxiv.org/pdf/2307.04495v1.pdf | Model-Driven Engineering Method to Support the Formalization of Machine Learning using SysML | Methods: This work introduces a method supporting the collaborative definition of machine learning tasks by leveraging model-based engineering in the formalization of the systems modeling language SysML. The method supports the identification and integration of various data sources, the required definition of semantic connections between data attributes, and the definition of data processing steps within the machine learning support. Results: By consolidating the knowledge of domain and machine learning experts, a powerful tool to describe machine learning tasks by formalizing knowledge using the systems modeling language SysML is introduced. The method is evaluated based on two use cases, i.e., a smart weather system that allows to predict weather forecasts based on sensor data, and a waste prevention case for 3D printer filament that cancels the printing if the intended result cannot be achieved (image processing). Further, a user study is conducted to gather insights of potential users regarding perceived workload and usability of the elaborated method. Conclusion: Integrating machine learning-specific properties in systems engineering techniques allows non-data scientists to understand formalized knowledge and define specific aspects of a machine learning problem, document knowledge on the data, and to further support data scientists to use the formalized knowledge as input for an implementation using (semi-) automatic code generation. In this respect, this work contributes by consolidating knowledge from various domains and therefore, fosters the integration of machine learning in industry by involving several stakeholders. | ['Stefanie Rinderle-Ma', 'Juergen Mangler', 'Simon Raedler'] | 2023-07-10 | null | null | null | null | ['code-generation'] | ['computer-code'] | [ 1.60335541e-01 4.16360468e-01 7.39408582e-02 -5.09874463e-01
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5bf1ac8d-3e57-4a02-bdaf-d505591be2b8 | collaborative-policy-learning-for-dynamic | 2307.00541 | null | https://arxiv.org/abs/2307.00541v1 | https://arxiv.org/pdf/2307.00541v1.pdf | Collaborative Policy Learning for Dynamic Scheduling Tasks in Cloud-Edge-Terminal IoT Networks Using Federated Reinforcement Learning | In this paper, we examine cloud-edge-terminal IoT networks, where edges undertake a range of typical dynamic scheduling tasks. In these IoT networks, a central policy for each task can be constructed at a cloud server. The central policy can be then used by the edges conducting the task, thereby mitigating the need for them to learn their own policy from scratch. Furthermore, this central policy can be collaboratively learned at the cloud server by aggregating local experiences from the edges, thanks to the hierarchical architecture of the IoT networks. To this end, we propose a novel collaborative policy learning framework for dynamic scheduling tasks using federated reinforcement learning. For effective learning, our framework adaptively selects the tasks for collaborative learning in each round, taking into account the need for fairness among tasks. In addition, as a key enabler of the framework, we propose an edge-agnostic policy structure that enables the aggregation of local policies from different edges. We then provide the convergence analysis of the framework. Through simulations, we demonstrate that our proposed framework significantly outperforms the approaches without collaborative policy learning. Notably, it accelerates the learning speed of the policies and allows newly arrived edges to adapt to their tasks more easily. | ['Hyun-Suk Lee', 'Ji-Wan Kim', 'Da-Eun Lee', 'Do-Yup Kim'] | 2023-07-02 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [-2.74731010e-01 1.72844887e-01 -4.64559972e-01 -1.34835839e-01
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40640991-0bf9-43ea-bb6e-1dff07354209 | lensid-a-cnn-rnn-based-framework-towards-lens | 2107.00875 | null | https://arxiv.org/abs/2107.00875v1 | https://arxiv.org/pdf/2107.00875v1.pdf | LensID: A CNN-RNN-Based Framework Towards Lens Irregularity Detection in Cataract Surgery Videos | A critical complication after cataract surgery is the dislocation of the lens implant leading to vision deterioration and eye trauma. In order to reduce the risk of this complication, it is vital to discover the risk factors during the surgery. However, studying the relationship between lens dislocation and its suspicious risk factors using numerous videos is a time-extensive procedure. Hence, the surgeons demand an automatic approach to enable a larger-scale and, accordingly, more reliable study. In this paper, we propose a novel framework as the major step towards lens irregularity detection. In particular, we propose (I) an end-to-end recurrent neural network to recognize the lens-implantation phase and (II) a novel semantic segmentation network to segment the lens and pupil after the implantation phase. The phase recognition results reveal the effectiveness of the proposed surgical phase recognition approach. Moreover, the segmentation results confirm the proposed segmentation network's effectiveness compared to state-of-the-art rival approaches. | ['Klaus Schoeffmann', 'Yosuf El-Shabrawi', 'Stephanie Sarny', 'Doris Putzgruber-Adamitsch', 'Mario Taschwer', 'Negin Ghamsarian'] | 2021-07-02 | null | null | null | null | ['surgical-phase-recognition'] | ['computer-vision'] | [ 3.57161522e-01 4.46213819e-02 -1.85716040e-02 -9.84787848e-03
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be24fb17-d247-48ea-a1a8-edb900ce3b22 | fundamental-limits-of-two-layer-autoencoders | 2212.13468 | null | https://arxiv.org/abs/2212.13468v1 | https://arxiv.org/pdf/2212.13468v1.pdf | Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods | Autoencoders are a popular model in many branches of machine learning and lossy data compression. However, their fundamental limits, the performance of gradient methods and the features learnt during optimization remain poorly understood, even in the two-layer setting. In fact, earlier work has considered either linear autoencoders or specific training regimes (leading to vanishing or diverging compression rates). Our paper addresses this gap by focusing on non-linear two-layer autoencoders trained in the challenging proportional regime in which the input dimension scales linearly with the size of the representation. Our results characterize the minimizers of the population risk, and show that such minimizers are achieved by gradient methods; their structure is also unveiled, thus leading to a concise description of the features obtained via training. For the special case of a sign activation function, our analysis establishes the fundamental limits for the lossy compression of Gaussian sources via (shallow) autoencoders. Finally, while the results are proved for Gaussian data, numerical simulations on standard datasets display the universality of the theoretical predictions. | ['Marco Mondelli', 'Hamed Hassani', 'Kevin Kögler', 'Alexander Shevchenko'] | 2022-12-27 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 6.01231083e-02 4.94442135e-01 -1.26407474e-01 -2.56658043e-03
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395facfc-6b2d-4168-8867-b1d2f06ff216 | automatic-negation-and-speculation-detection | null | null | https://aclanthology.org/U17-1008 | https://aclanthology.org/U17-1008.pdf | Automatic Negation and Speculation Detection in Veterinary Clinical Text | null | ['Timothy Baldwin', 'Katherine Cheng', 'Karin Verspoor'] | 2017-12-01 | automatic-negation-and-speculation-detection-1 | https://aclanthology.org/U17-1008 | https://aclanthology.org/U17-1008.pdf | alta-2017-12 | ['speculation-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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83085097-8b4f-4449-91b7-0e3d81fcf4cd | masked-discrimination-for-self-supervised | 2203.11183 | null | https://arxiv.org/abs/2203.11183v2 | https://arxiv.org/pdf/2203.11183v2.pdf | Masked Discrimination for Self-Supervised Learning on Point Clouds | Masked autoencoding has achieved great success for self-supervised learning in the image and language domains. However, mask based pretraining has yet to show benefits for point cloud understanding, likely due to standard backbones like PointNet being unable to properly handle the training versus testing distribution mismatch introduced by masking during training. In this paper, we bridge this gap by proposing a discriminative mask pretraining Transformer framework, MaskPoint}, for point clouds. Our key idea is to represent the point cloud as discrete occupancy values (1 if part of the point cloud; 0 if not), and perform simple binary classification between masked object points and sampled noise points as the proxy task. In this way, our approach is robust to the point sampling variance in point clouds, and facilitates learning rich representations. We evaluate our pretrained models across several downstream tasks, including 3D shape classification, segmentation, and real-word object detection, and demonstrate state-of-the-art results while achieving a significant pretraining speedup (e.g., 4.1x on ScanNet) compared to the prior state-of-the-art Transformer baseline. Code is available at https://github.com/haotian-liu/MaskPoint. | ['Yong Jae Lee', 'Mu Cai', 'Haotian Liu'] | 2022-03-21 | null | null | null | null | ['3d-shape-retrieval', 'few-shot-3d-point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [ 2.66290605e-01 1.07834384e-01 -2.56781340e-01 -3.16553652e-01
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c3e20f00-06f2-438c-861e-f78be0841c9b | learn-from-structural-scope-improving-aspect | 2204.12784 | null | https://arxiv.org/abs/2204.12784v1 | https://arxiv.org/pdf/2204.12784v1.pdf | Learn from Structural Scope: Improving Aspect-Level Sentiment Analysis with Hybrid Graph Convolutional Networks | Aspect-level sentiment analysis aims to determine the sentiment polarity towards a specific target in a sentence. The main challenge of this task is to effectively model the relation between targets and sentiments so as to filter out noisy opinion words from irrelevant targets. Most recent efforts capture relations through target-sentiment pairs or opinion spans from a word-level or phrase-level perspective. Based on the observation that targets and sentiments essentially establish relations following the grammatical hierarchy of phrase-clause-sentence structure, it is hopeful to exploit comprehensive syntactic information for better guiding the learning process. Therefore, we introduce the concept of Scope, which outlines a structural text region related to a specific target. To jointly learn structural Scope and predict the sentiment polarity, we propose a hybrid graph convolutional network (HGCN) to synthesize information from constituency tree and dependency tree, exploring the potential of linking two syntax parsing methods to enrich the representation. Experimental results on four public datasets illustrate that our HGCN model outperforms current state-of-the-art baselines. | ['Jiawei Peng', 'Ming Cai', 'Jianwang Wu', 'Xiaoxuan Pang', 'Lvxiaowei Xu'] | 2022-04-27 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 6.00668132e-01 5.48987091e-01 -5.86387455e-01 -9.94097710e-01
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a6ffa53e-75ee-47f3-aff4-3d8f2284219f | spatio-temporal-fusion-based-convolutional | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhang_Spatio-Temporal_Fusion_Based_Convolutional_Sequence_Learning_for_Lip_Reading_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhang_Spatio-Temporal_Fusion_Based_Convolutional_Sequence_Learning_for_Lip_Reading_ICCV_2019_paper.pdf | Spatio-Temporal Fusion Based Convolutional Sequence Learning for Lip Reading | Current state-of-the-art approaches for lip reading are based on sequence-to-sequence architectures that are designed for natural machine translation and audio speech recognition. Hence, these methods do not fully exploit the characteristics of the lip dynamics, causing two main drawbacks. First, the short-range temporal dependencies, which are critical to the mapping from lip images to visemes, receives no extra attention. Second, local spatial information is discarded in the existing sequence models due to the use of global average pooling (GAP). To well solve these drawbacks, we propose a Temporal Focal block to sufficiently describe short-range dependencies and a Spatio-Temporal Fusion Module (STFM) to maintain the local spatial information and to reduce the feature dimensions as well. From the experiment results, it is demonstrated that our method achieves comparable performance with the state-of-the-art approach using much less training data and much lighter Convolutional Feature Extractor. The training time is reduced by 12 days due to the convolutional structure and the local self-attention mechanism.
| [' Shilin Wang', ' Feng Cheng', 'Xingxuan Zhang'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['lipreading'] | ['computer-vision'] | [ 1.09456733e-01 -1.84711769e-01 -3.78110409e-01 -3.26523334e-02
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91df46e9-6909-4a05-a296-7eb1f20f3bdc | online-neural-coreference-resolution-with | null | null | https://aclanthology.org/2022.crac-1.2 | https://aclanthology.org/2022.crac-1.2.pdf | Online Neural Coreference Resolution with Rollback | Humans process natural language online, whether reading a document or participating in multiparty dialogue. Recent advances in neural coreference resolution have focused on offline approaches that assume the full communication history as input. This is neither realistic nor sufficient if we wish to support dialogue understanding in real-time. We benchmark two existing, offline, models and highlight their shortcomings in the online setting. We then modify these models to perform online inference and introduce rollback: a short-term mechanism to correct mistakes. We demonstrate across five English datasets the effectiveness of this approach against an offline and a naive online model in terms of latency, final document-level coreference F1, and average running F1. | ['Benjamin Van Durme', 'Patrick Xia'] | null | null | null | null | coling-crac-2022-10 | ['dialogue-understanding', 'coreference-resolution'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.62920594e-01 6.23315156e-01 -2.47930408e-01 -7.72599280e-01
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fcda4c77-10d1-497b-99ed-6fdfb89f7e50 | counterfactual-vision-and-language-navigation-2 | null | null | http://proceedings.neurips.cc/paper/2020/hash/39016cfe079db1bfb359ca72fcba3fd8-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/39016cfe079db1bfb359ca72fcba3fd8-Paper.pdf | Counterfactual Vision-and-Language Navigation: Unravelling the Unseen | The task of vision-and-language navigation (VLN) requires an agent to follow text instructions to find its way through simulated household environments. A prominent challenge is to train an agent capable of generalising to new environments at test time, rather than one that simply memorises trajectories and visual details observed during training. We propose a new learning strategy that learns both from observations and generated counterfactual environments. We describe an effective algorithm to generate counterfactual observations on the fly for VLN, as linear combinations of existing environments. Simultaneously, we encourage the agent's actions to remain stable between original and counterfactual environments through our novel training objective-effectively removing the spurious features that otherwise bias the agent. Our experiments show that this technique provides significant improvements in generalisation on benchmarks for Room-to-Room navigation and Embodied Question Answering. | ['Anton Van Den Hengel', 'Qinfeng Shi', 'Damien Teney', 'Ehsan Abbasnejad', 'Amin Parvaneh'] | 2020-12-01 | null | null | null | neurips-2020-12 | ['embodied-question-answering'] | ['computer-vision'] | [ 4.76089239e-01 3.66982698e-01 4.39098299e-01 -4.80232328e-01
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97087fbb-dd3a-4782-ab17-b566bfd4a759 | automatic-and-manual-web-annotations-in-an | null | null | https://aclanthology.org/L18-1384 | https://aclanthology.org/L18-1384.pdf | Automatic and Manual Web Annotations in an Infrastructure to handle Fake News and other Online Media Phenomena | null | ['Julian Moreno-Schneider', 'Peter Bourgonje', 'Georg Rehm'] | 2018-05-01 | automatic-and-manual-web-annotations-in-an-1 | https://aclanthology.org/L18-1384 | https://aclanthology.org/L18-1384.pdf | lrec-2018-5 | ['rumour-detection', 'news-annotation'] | ['natural-language-processing', 'natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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6bdc77a0-094e-493b-8173-1143945c6675 | thematic-context-vector-association-based-on | 2304.01423 | null | https://arxiv.org/abs/2304.01423v1 | https://arxiv.org/pdf/2304.01423v1.pdf | Thematic context vector association based on event uncertainty for Twitter | Keyword extraction is a crucial process in text mining. The extraction of keywords with respective contextual events in Twitter data is a big challenge. The challenging issues are mainly because of the informality in the language used. The use of misspelled words, acronyms, and ambiguous terms causes informality. The extraction of keywords with informal language in current systems is pattern based or event based. In this paper, contextual keywords are extracted using thematic events with the help of data association. The thematic context for events is identified using the uncertainty principle in the proposed system. The thematic contexts are weighed with the help of vectors called thematic context vectors which signifies the event as certain or uncertain. The system is tested on the Twitter COVID-19 dataset and proves to be effective. The system extracts event-specific thematic context vectors from the test dataset and ranks them. The extracted thematic context vectors are used for the clustering of contextual thematic vectors which improves the silhouette coefficient by 0.5% than state of art methods namely TF and TF-IDF. The thematic context vector can be used in other applications like Cyberbullying, sarcasm detection, figurative language detection, etc. | ['Parag Kulkarni', 'Swapnil Mane', 'Vaibhav Khatavkar'] | 2023-04-04 | null | null | null | null | ['sarcasm-detection', 'keyword-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.39241582e-02 -2.14468911e-01 -1.30255610e-01 -9.50757861e-02
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f405f3f2-3163-4aa7-8880-3f13268be368 | 190505700 | 1905.05700 | null | https://arxiv.org/abs/1905.05700v1 | https://arxiv.org/pdf/1905.05700v1.pdf | Learning meters of Arabic and English poems with Recurrent Neural Networks: a step forward for language understanding and synthesis | Recognizing a piece of writing as a poem or prose is usually easy for the majority of people; however, only specialists can determine which meter a poem belongs to. In this paper, we build Recurrent Neural Network (RNN) models that can classify poems according to their meters from plain text. The input text is encoded at the character level and directly fed to the models without feature handcrafting. This is a step forward for machine understanding and synthesis of languages in general, and Arabic language in particular. Among the 16 poem meters of Arabic and the 4 meters of English the networks were able to correctly classify poem with an overall accuracy of 96.38\% and 82.31\% respectively. The poem datasets used to conduct this research were massive, over 1.5 million of verses, and were crawled from different nontechnical sources, almost Arabic and English literature sites, and in different heterogeneous and unstructured formats. These datasets are now made publicly available in clean, structured, and documented format for other future research. To the best of the authors' knowledge, this research is the first to address classifying poem meters in a machine learning approach, in general, and in RNN featureless based approach, in particular. In addition, the dataset is the first publicly available dataset ready for the purpose of future computational research. | ['Omar M. Ibrahime', 'Taha M. Madbouly', 'Waleed A. Yousef', 'Moustafa A. Mahmoud'] | 2019-05-07 | null | null | null | null | ['poem-meters-classification'] | ['natural-language-processing'] | [ 1.48627564e-01 -1.97505634e-02 -1.93517238e-01 4.77435924e-02
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fdda595e-2d1c-4bc2-b952-4408f6d2017b | data-driven-stochastic-motion-evaluation-and | 2302.05041 | null | https://arxiv.org/abs/2302.05041v1 | https://arxiv.org/pdf/2302.05041v1.pdf | Data-Driven Stochastic Motion Evaluation and Optimization with Image by Spatially-Aligned Temporal Encoding | This paper proposes a probabilistic motion prediction method for long motions. The motion is predicted so that it accomplishes a task from the initial state observed in the given image. While our method evaluates the task achievability by the Energy-Based Model (EBM), previous EBMs are not designed for evaluating the consistency between different domains (i.e., image and motion in our method). Our method seamlessly integrates the image and motion data into the image feature domain by spatially-aligned temporal encoding so that features are extracted along the motion trajectory projected onto the image. Furthermore, this paper also proposes a data-driven motion optimization method, Deep Motion Optimizer (DMO), that works with EBM for motion prediction. Different from previous gradient-based optimizers, our self-supervised DMO alleviates the difficulty of hyper-parameter tuning to avoid local minima. The effectiveness of the proposed method is demonstrated with a variety of experiments with similar SOTA methods. | ['Norimichi Ukita', 'Takeru Oba'] | 2023-02-10 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [ 1.37824640e-01 -3.92650850e-02 -6.43836915e-01 -1.25026211e-01
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0fa00393-6468-4c08-bf57-89d054da438c | speck-a-smart-event-based-vision-sensor-with | 2304.06793 | null | https://arxiv.org/abs/2304.06793v1 | https://arxiv.org/pdf/2304.06793v1.pdf | Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline | Edge computing solutions that enable the extraction of high level information from a variety of sensors is in increasingly high demand. This is due to the increasing number of smart devices that require sensory processing for their application on the edge. To tackle this problem, we present a smart vision sensor System on Chip (Soc), featuring an event-based camera and a low power asynchronous spiking Convolutional Neuronal Network (sCNN) computing architecture embedded on a single chip. By combining both sensor and processing on a single die, we can lower unit production costs significantly. Moreover, the simple end-to-end nature of the SoC facilitates small stand-alone applications as well as functioning as an edge node in a larger systems. The event-driven nature of the vision sensor delivers high-speed signals in a sparse data stream. This is reflected in the processing pipeline, focuses on optimising highly sparse computation and minimising latency for 9 sCNN layers to $3.36\mu s$. Overall, this results in an extremely low-latency visual processing pipeline deployed on a small form factor with a low energy budget and sensor cost. We present the asynchronous architecture, the individual blocks, the sCNN processing principle and benchmark against other sCNN capable processors. | ['Ning Qiao', 'Tugba Demirci', 'Sadique Sheik', 'Qian Liu', 'Yudi Ren', 'Roberto Cattaneo', 'Merkourios Katsimpris', 'Carsten Nielsen', 'Michele De Marchi', 'Yannan Xing', 'Ole Richter'] | 2023-04-13 | null | null | null | null | ['event-based-vision', 'edge-computing'] | ['computer-vision', 'time-series'] | [ 5.59865475e-01 5.25172763e-02 6.11068964e-01 -1.56614497e-01
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51401a43-f8ea-46d9-91d8-180dcb8309e2 | local-search-for-integer-linear-programming | 2305.00188 | null | https://arxiv.org/abs/2305.00188v3 | https://arxiv.org/pdf/2305.00188v3.pdf | New Characterizations and Efficient Local Search for General Integer Linear Programming | Integer linear programming (ILP) models a wide range of practical combinatorial optimization problems and has significant impacts in industry and management sectors. This work proposes new characterizations of ILP with the concept of boundary solutions. Motivated by the new characterizations, we develop an efficient local search solver, which is the first local search solver for general ILP validated on a large heterogeneous problem dataset. We propose a new local search framework that switches between three modes, namely Search, Improve, and Restore modes. We design tailored operators adapted to different modes, thus improving the quality of the current solution according to different situations. For the Search and Restore modes, we propose an operator named tight move, which adaptively modifies variables' values, trying to make some constraint tight. For the Improve mode, an efficient operator lift move is proposed to improve the quality of the objective function while maintaining feasibility. Putting these together, we develop a local search solver for integer linear programming called Local-ILP. Experiments conducted on the MIPLIB dataset show the effectiveness of our solver in solving large-scale hard integer linear programming problems within a reasonably short time. Local-ILP is competitive and complementary to the state-of-the-art commercial solver Gurobi and significantly outperforms the state-of-the-art non-commercial solver SCIP. Moreover, our solver establishes new records for 6 MIPLIB open instances. The theoretical analysis of our algorithm is also presented, which shows our algorithm could avoid visiting unnecessary regions and also maintain good connectivity of targeted solutions. | ['JinKun Lin', 'Mengchuan Zou', 'Shaowei Cai', 'Peng Lin'] | 2023-04-29 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 8.25237408e-02 1.17558941e-01 -8.81209195e-01 -5.05974889e-02
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c9a5613d-115b-4885-972a-c1ea69af9224 | singapore-soundscape-site-selection-survey-s5 | 2206.03112 | null | https://arxiv.org/abs/2206.03112v1 | https://arxiv.org/pdf/2206.03112v1.pdf | Singapore Soundscape Site Selection Survey (S5): Identification of Characteristic Soundscapes of Singapore via Weighted k-means Clustering | The ecological validity of soundscape studies usually rests on a choice of soundscapes that are representative of the perceptual space under investigation. For example, a soundscape pleasantness study might investigate locations with soundscapes ranging from "pleasant" to "annoying". The choice of soundscapes is typically researcher-led, but a participant-led process can reduce selection bias and improve result reliability. Hence, we propose a robust participant-led method to pinpoint characteristic soundscapes possessing arbitrary perceptual attributes. We validate our method by identifying Singaporean soundscapes spanning the perceptual quadrants generated from the "Pleasantness" and "Eventfulness" axes of the ISO 12913-2 circumplex model of soundscape perception, as perceived by local experts. From memory and experience, 67 participants first selected locations corresponding to each perceptual quadrant in each major planning region of Singapore. We then performed weighted k-means clustering on the selected locations, with weights for each location derived from previous frequencies and durations spent in each location by each participant. Weights hence acted as proxies for participant confidence. In total, 62 locations were thereby identified as suitable locations with characteristic soundscapes for further research utilizing the ISO 12913-2 perceptual quadrants. Audio-visual recordings and acoustic characterization of the soundscapes will be made in a future study. | ['Woon-Seng Gan', 'Zhen-Ting Ong', 'Karn N. Watcharasupat', 'Joo Young Hong', 'Bhan Lam', 'Kenneth Ooi'] | 2022-06-07 | null | null | null | null | ['unsupervised-spatial-clustering'] | ['time-series'] | [-2.09284127e-01 -7.45698869e-01 4.01710749e-01 -6.18011132e-02
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4bb8891c-c110-49f4-b14e-39cdbd544d9c | which-contrast-does-matter-towards-a-deep | 1905.04105 | null | https://arxiv.org/abs/1905.04105v1 | https://arxiv.org/pdf/1905.04105v1.pdf | Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GAN | Thanks to the recent success of generative adversarial network (GAN) for image synthesis, there are many exciting GAN approaches that successfully synthesize MR image contrast from other images with different contrasts. These approaches are potentially important for image imputation problems, where complete set of data is often difficult to obtain and image synthesis is one of the key solutions for handling the missing data problem. Unfortunately, the lack of the scalability of the existing GAN-based image translation approaches poses a fundamental challenge to understand the nature of the MR contrast imputation problem: which contrast does matter? Here, we present a systematic approach using Collaborative Generative Adversarial Networks (CollaGAN), which enable the learning of the joint image manifold of multiple MR contrasts to investigate which contrasts are essential. Our experimental results showed that the exogenous contrast from contrast agents is not replaceable, but other endogenous contrast such as T1, T2, etc can be synthesized from other contrast. These findings may give important guidance to the acquisition protocol design for MR in real clinical environment. | ['Won-Jin Moon', 'Dongwook Lee', 'Jong Chul Ye'] | 2019-05-10 | null | null | null | null | ['image-imputation'] | ['computer-vision'] | [ 5.99697053e-01 1.81320667e-01 1.46169037e-01 -2.56654412e-01
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d2472a6e-dfe3-4293-b334-84abda3b6039 | distributional-reinforcement-learning-with-4 | 2106.03228 | null | https://arxiv.org/abs/2106.03228v3 | https://arxiv.org/pdf/2106.03228v3.pdf | Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks | The distributional reinforcement learning (RL) approach advocates for representing the complete probability distribution of the random return instead of only modelling its expectation. A distributional RL algorithm may be characterised by two main components, namely the representation of the distribution together with its parameterisation and the probability metric defining the loss. The present research work considers the unconstrained monotonic neural network (UMNN) architecture, a universal approximator of continuous monotonic functions which is particularly well suited for modelling different representations of a distribution. This property enables the efficient decoupling of the effect of the function approximator class from that of the probability metric. The research paper firstly introduces a methodology for learning different representations of the random return distribution (PDF, CDF and QF). Secondly, a novel distributional RL algorithm named unconstrained monotonic deep Q-network (UMDQN) is presented. To the authors' knowledge, it is the first distributional RL method supporting the learning of three, valid and continuous representations of the random return distribution. Lastly, in light of this new algorithm, an empirical comparison is performed between three probability quasi-metrics, namely the Kullback-Leibler divergence, Cramer distance, and Wasserstein distance. The results highlight the main strengths and weaknesses associated with each probability metric together with an important limitation of the Wasserstein distance. | ['Damien Ernst', 'Gilles Louppe', 'Adrien Bolland', 'Antoine Wehenkel', 'Thibaut Théate'] | 2021-06-06 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.08101392e-01 1.63645327e-01 -9.67196897e-02 -3.68988752e-01
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8969c80f-80ec-4b4a-9e27-b6a76087dd03 | stereo-matching-with-color-and-monochrome | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Jeon_Stereo_Matching_With_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Jeon_Stereo_Matching_With_CVPR_2016_paper.pdf | Stereo Matching With Color and Monochrome Cameras in Low-Light Conditions | Consumer devices with stereo cameras have become popular because of their low-cost depth sensing capability. However, those systems usually suffer from low imaging quality and inaccurate depth acquisition under low-light conditions. To address the problem, we present a new stereo matching method with a color and monochrome camera pair. We focus on the fundamental trade-off that monochrome cameras have much better light-efficiency than color-filtered cameras. Our key ideas involve compensating for the radiometric difference between two cross-spectral images and taking full advantage of complementary data. Consequently, our method produces both an accurate depth map and high-quality images, which are applicable for various depth-aware image processing. Our method is evaluated using various datasets and the performance of our depth estimation consistently outperforms state-of-the-art methods. | ['Joon-Young Lee', 'Hae-Gon Jeon', 'In So Kweon', 'Hyowon Ha', 'Sunghoon Im'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['stereo-matching'] | ['computer-vision'] | [ 5.62539279e-01 -5.56861162e-01 -1.34794684e-02 -3.81071597e-01
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6bae08fb-13ed-4fd1-91a0-e7c68bc73ec6 | mutual-mean-teaching-pseudo-label-refinery-1 | 2001.01526 | null | https://arxiv.org/abs/2001.01526v2 | https://arxiv.org/pdf/2001.01526v2.pdf | Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification | Person re-identification (re-ID) aims at identifying the same persons' images across different cameras. However, domain diversities between different datasets pose an evident challenge for adapting the re-ID model trained on one dataset to another one. State-of-the-art unsupervised domain adaptation methods for person re-ID transferred the learned knowledge from the source domain by optimizing with pseudo labels created by clustering algorithms on the target domain. Although they achieved state-of-the-art performances, the inevitable label noise caused by the clustering procedure was ignored. Such noisy pseudo labels substantially hinders the model's capability on further improving feature representations on the target domain. In order to mitigate the effects of noisy pseudo labels, we propose to softly refine the pseudo labels in the target domain by proposing an unsupervised framework, Mutual Mean-Teaching (MMT), to learn better features from the target domain via off-line refined hard pseudo labels and on-line refined soft pseudo labels in an alternative training manner. In addition, the common practice is to adopt both the classification loss and the triplet loss jointly for achieving optimal performances in person re-ID models. However, conventional triplet loss cannot work with softly refined labels. To solve this problem, a novel soft softmax-triplet loss is proposed to support learning with soft pseudo triplet labels for achieving the optimal domain adaptation performance. The proposed MMT framework achieves considerable improvements of 14.4%, 18.2%, 13.1% and 16.4% mAP on Market-to-Duke, Duke-to-Market, Market-to-MSMT and Duke-to-MSMT unsupervised domain adaptation tasks. Code is available at https://github.com/yxgeee/MMT. | ['Dapeng Chen', 'Yixiao Ge', 'Hongsheng Li'] | 2020-01-06 | null | https://openreview.net/forum?id=rJlnOhVYPS | https://openreview.net/pdf?id=rJlnOhVYPS | iclr-2020-1 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 4.84830067e-02 -1.42767489e-01 -1.09029435e-01 -6.79868817e-01
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70ae81dc-07c3-4d63-bc5d-e4dc5efd8985 | lepard-learning-partial-point-cloud-matching | 2111.12591 | null | https://arxiv.org/abs/2111.12591v2 | https://arxiv.org/pdf/2111.12591v2.pdf | Lepard: Learning partial point cloud matching in rigid and deformable scenes | We present Lepard, a Learning based approach for partial point cloud matching in rigid and deformable scenes. The key characteristics are the following techniques that exploit 3D positional knowledge for point cloud matching: 1) An architecture that disentangles point cloud representation into feature space and 3D position space. 2) A position encoding method that explicitly reveals 3D relative distance information through the dot product of vectors. 3) A repositioning technique that modifies the crosspoint-cloud relative positions. Ablation studies demonstrate the effectiveness of the above techniques. In rigid cases, Lepard combined with RANSAC and ICP demonstrates state-of-the-art registration recall of 93.9% / 71.3% on the 3DMatch / 3DLoMatch. In deformable cases, Lepard achieves +27.1% / +34.8% higher non-rigid feature matching recall than the prior art on our newly constructed 4DMatch / 4DLoMatch benchmark. | ['Tatsuya Harada', 'Yang Li'] | 2021-11-24 | lepard-learning-partial-point-cloud-matching-1 | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Lepard_Learning_Partial_Point_Cloud_Matching_in_Rigid_and_Deformable_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Lepard_Learning_Partial_Point_Cloud_Matching_in_Rigid_and_Deformable_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-feature-matching', '3d-point-cloud-matching', 'partial-point-cloud-matching'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.43979111e-01 -3.08420956e-01 -1.84070528e-01 -2.72769421e-01
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56f36a20-d25b-4fd2-9b7e-a3ac03c61344 | batch-monte-carlo-tree-search | 2104.04278 | null | https://arxiv.org/abs/2104.04278v1 | https://arxiv.org/pdf/2104.04278v1.pdf | Batch Monte Carlo Tree Search | Making inferences with a deep neural network on a batch of states is much faster with a GPU than making inferences on one state after another. We build on this property to propose Monte Carlo Tree Search algorithms using batched inferences. Instead of using either a search tree or a transposition table we propose to use both in the same algorithm. The transposition table contains the results of the inferences while the search tree contains the statistics of Monte Carlo Tree Search. We also propose to analyze multiple heuristics that improve the search: the $\mu$ FPU, the Virtual Mean, the Last Iteration and the Second Move heuristics. They are evaluated for the game of Go using a MobileNet neural network. | ['Tristan Cazenave'] | 2021-04-09 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-1.67498097e-01 2.24510223e-01 1.06016316e-01 -2.97520280e-01
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db778b84-f23d-414a-afd9-d06a8e8a84bf | dwrseg-dilation-wise-residual-network-for | 2212.01173 | null | https://arxiv.org/abs/2212.01173v1 | https://arxiv.org/pdf/2212.01173v1.pdf | DWRSeg: Dilation-wise Residual Network for Real-time Semantic Segmentation | Real-time semantic segmentation has played an important role in intelligent vehicle scenarios. Recently, numerous networks have incorporated information from multi-size receptive fields to facilitate feature extraction in real-time semantic segmentation tasks. However, these methods preferentially adopt massive receptive fields to elicit more contextual information, which may result in inefficient feature extraction. We believe that the elaborated receptive fields are crucial, considering the demand for efficient feature extraction in real-time tasks. Therefore, we propose an effective and efficient architecture termed Dilation-wise Residual segmentation (DWRSeg), which possesses different sets of receptive field sizes within different stages. The architecture involves (i) a Dilation-wise Residual (DWR) module for extracting features based on different scales of receptive fields in the high level of the network; (ii) a Simple Inverted Residual (SIR) module that uses an inverted bottleneck structure to extract features from the low stage; and (iii) a simple fully convolutional network (FCN)-like decoder for aggregating multiscale feature maps to generate the prediction. Extensive experiments on the Cityscapes and CamVid datasets demonstrate the effectiveness of our method by achieving a state-of-the-art trade-off between accuracy and inference speed, in addition to being lighter weight. Without using pretraining or resorting to any training trick, we achieve 72.7% mIoU on the Cityscapes test set at a speed of 319.5 FPS on one NVIDIA GeForce GTX 1080 Ti card, which is significantly faster than existing methods. The code and trained models are publicly available. | ['Xiangyang Xu', 'Yaping Dai', 'Zhongjian Dai', 'Shouchun Xu', 'Xu Liu', 'Haoran Wei'] | 2022-12-02 | null | null | null | null | ['real-time-semantic-segmentation'] | ['computer-vision'] | [ 1.95411175e-01 7.69788772e-02 6.08520880e-02 -4.94584471e-01
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78a08103-c6fd-4d02-909a-d7b0c38cf957 | jpg-jointly-learn-to-align-automated-disease | null | null | https://aclanthology.org/2022.coling-1.523 | https://aclanthology.org/2022.coling-1.523.pdf | JPG - Jointly Learn to Align: Automated Disease Prediction and Radiology Report Generation | Automated radiology report generation aims to generate paragraphs that describe fine-grained visual differences among cases, especially those between the normal and the diseased. Existing methods seldom consider the cross-modal alignment between textual and visual features and tend to ignore disease tags as an auxiliary for report generation. To bridge the gap between textual and visual information, in this study, we propose a “Jointly learning framework for automated disease Prediction and radiology report Generation (JPG)” to improve the quality of reports through the interaction between the main task (report generation) and two auxiliary tasks (feature alignment and disease prediction). The feature alignment and disease prediction help the model learn text-correlated visual features and record diseases as keywords so that it can output high-quality reports. Besides, the improved reports in turn provide additional harder samples for feature alignment and disease prediction to learn more precise visual and textual representations and improve prediction accuracy. All components are jointly trained in a manner that helps improve them iteratively and progressively. Experimental results demonstrate the effectiveness of JPG on the most commonly used IU X-RAY dataset, showing its superior performance over multiple state-of-the-art image captioning and medical report generation methods with regard to BLEU, METEOR, and ROUGE metrics. | ['Kenji Suzuki', 'Manabu Okumura', 'Dongyuan Li', 'Jingyi You'] | null | null | null | null | coling-2022-10 | ['medical-report-generation', 'disease-prediction'] | ['medical', 'medical'] | [ 3.44416916e-01 2.21720859e-01 -2.94529796e-01 -2.65852422e-01
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be9c0636-64c5-48aa-ab2c-07cd91a7efec | adversarial-samples-for-deep-monocular-6d | 2203.00302 | null | https://arxiv.org/abs/2203.00302v2 | https://arxiv.org/pdf/2203.00302v2.pdf | Adversarial samples for deep monocular 6D object pose estimation | Estimating 6D object pose from an RGB image is important for many real-world applications such as autonomous driving and robotic grasping. Recent deep learning models have achieved significant progress on this task but their robustness received little research attention. In this work, for the first time, we study adversarial samples that can fool deep learning models with imperceptible perturbations to input image. In particular, we propose a Unified 6D pose estimation Attack, namely U6DA, which can successfully attack several state-of-the-art (SOTA) deep learning models for 6D pose estimation. The key idea of our U6DA is to fool the models to predict wrong results for object instance localization and shape that are essential for correct 6D pose estimation. Specifically, we explore a transfer-based black-box attack to 6D pose estimation. We design the U6DA loss to guide the generation of adversarial examples, the loss aims to shift the segmentation attention map away from its original position. We show that the generated adversarial samples are not only effective for direct 6D pose estimation models, but also are able to attack two-stage models regardless of their robust RANSAC modules. Extensive experiments were conducted to demonstrate the effectiveness, transferability, and anti-defense capability of our U6DA on large-scale public benchmarks. We also introduce a new U6DA-Linemod dataset for robustness study of the 6D pose estimation task. Our codes and dataset will be available at \url{https://github.com/cuge1995/U6DA}. | ['Jihong Zhu', 'Hao Wang', 'Shuang Liang', 'Weiming Li', 'Jinlai Zhang'] | 2022-03-01 | null | null | null | null | ['6d-pose-estimation', 'robotic-grasping'] | ['computer-vision', 'robots'] | [-2.31990702e-02 1.42505877e-02 2.00410932e-01 -3.17523003e-01
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9e53acca-a586-4103-b330-f9b6bc3802d7 | diagnostic-spatio-temporal-transformer-with | 2305.17149 | null | https://arxiv.org/abs/2305.17149v1 | https://arxiv.org/pdf/2305.17149v1.pdf | Diagnostic Spatio-temporal Transformer with Faithful Encoding | This paper addresses the task of anomaly diagnosis when the underlying data generation process has a complex spatio-temporal (ST) dependency. The key technical challenge is to extract actionable insights from the dependency tensor characterizing high-order interactions among temporal and spatial indices. We formalize the problem as supervised dependency discovery, where the ST dependency is learned as a side product of multivariate time-series classification. We show that temporal positional encoding used in existing ST transformer works has a serious limitation in capturing higher frequencies (short time scales). We propose a new positional encoding with a theoretical guarantee, based on discrete Fourier transform. We also propose a new ST dependency discovery framework, which can provide readily consumable diagnostic information in both spatial and temporal directions. Finally, we demonstrate the utility of the proposed model, DFStrans (Diagnostic Fourier-based Spatio-temporal Transformer), in a real industrial application of building elevator control. | ['Xabier De Carlos', 'Ekhi Zugasti', 'Pin-Yu Chen', 'Tsuyoshi Idé', 'Jokin Labaien'] | 2023-05-26 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 2.58490860e-01 -4.14361060e-01 4.38424870e-02 -1.11211948e-01
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65253701-b384-472c-b5ee-9b41769421e6 | expressing-high-level-scientific-claims-with | 2109.12907 | null | https://arxiv.org/abs/2109.12907v3 | https://arxiv.org/pdf/2109.12907v3.pdf | Expressing High-Level Scientific Claims with Formal Semantics | The use of semantic technologies is gaining significant traction in science communication with a wide array of applications in disciplines including the Life Sciences, Computer Science, and the Social Sciences. Languages like RDF, OWL, and other formalisms based on formal logic are applied to make scientific knowledge accessible not only to human readers but also to automated systems. These approaches have mostly focused on the structure of scientific publications themselves, on the used scientific methods and equipment, or on the structure of the used datasets. The core claims or hypotheses of scientific work have only been covered in a shallow manner, such as by linking mentioned entities to established identifiers. In this research, we therefore want to find out whether we can use existing semantic formalisms to fully express the content of high-level scientific claims using formal semantics in a systematic way. Analyzing the main claims from a sample of scientific articles from all disciplines, we find that their semantics are more complex than what a straight-forward application of formalisms like RDF or OWL account for, but we managed to elicit a clear semantic pattern which we call the 'super-pattern'. We show here how the instantiation of the five slots of this super-pattern leads to a strictly defined statement in higher-order logic. We successfully applied this super-pattern to an enlarged sample of scientific claims. We show that knowledge representation experts, when instructed to independently instantiate the super-pattern with given scientific claims, show a high degree of consistency and convergence given the complexity of the task and the subject. These results therefore open the door for expressing high-level scientific findings in a manner they can be automatically interpreted, which on the longer run can allow us to do automated consistency checking, and much more. | ['Jacco van Ossenbruggen', 'Davide Ceolin', 'Tobias Kuhn', 'Cristina-Iulia Bucur'] | 2021-09-27 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 1.13764383e-01 6.44635916e-01 -2.11418360e-01 -3.50918323e-01
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fccb1324-6a8c-4dd6-bb96-334473eea562 | planning-irregular-object-packing-via | 2211.09382 | null | https://arxiv.org/abs/2211.09382v1 | https://arxiv.org/pdf/2211.09382v1.pdf | Planning Irregular Object Packing via Hierarchical Reinforcement Learning | Object packing by autonomous robots is an im-portant challenge in warehouses and logistics industry. Most conventional data-driven packing planning approaches focus on regular cuboid packing, which are usually heuristic and limit the practical use in realistic applications with everyday objects. In this paper, we propose a deep hierarchical reinforcement learning approach to simultaneously plan packing sequence and placement for irregular object packing. Specifically, the top manager network infers packing sequence from six principal view heightmaps of all objects, and then the bottom worker network receives heightmaps of the next object to predict the placement position and orientation. The two networks are trained hierarchically in a self-supervised Q-Learning framework, where the rewards are provided by the packing results based on the top height , object volume and placement stability in the box. The framework repeats sequence and placement planning iteratively until all objects have been packed into the box or no space is remained for unpacked items. We compare our approach with existing robotic packing methods for irregular objects in a physics simulator. Experiments show that our approach can pack more objects with less time cost than the state-of-the-art packing methods of irregular objects. We also implement our packing plan with a robotic manipulator to show the generalization ability in the real world. | ['Jiwen Lu', 'Jie zhou', 'Ziwei Wang', 'Sichao Huang'] | 2022-11-17 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-4.22203213e-01 4.76537526e-01 -1.89495087e-01 -2.70440936e-01
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5f806eba-2ddf-4d97-9895-340b5a7afcff | mulco-recognizing-chinese-nested-named | 2211.10854 | null | https://arxiv.org/abs/2211.10854v1 | https://arxiv.org/pdf/2211.10854v1.pdf | Mulco: Recognizing Chinese Nested Named Entities Through Multiple Scopes | Nested Named Entity Recognition (NNER) has been a long-term challenge to researchers as an important sub-area of Named Entity Recognition. NNER is where one entity may be part of a longer entity, and this may happen on multiple levels, as the term nested suggests. These nested structures make traditional sequence labeling methods unable to properly recognize all entities. While recent researches focus on designing better recognition methods for NNER in a variety of languages, the Chinese NNER (CNNER) still lacks attention, where a free-for-access, CNNER-specialized benchmark is absent. In this paper, we aim to solve CNNER problems by providing a Chinese dataset and a learning-based model to tackle the issue. To facilitate the research on this task, we release ChiNesE, a CNNER dataset with 20,000 sentences sampled from online passages of multiple domains, containing 117,284 entities failing in 10 categories, where 43.8 percent of those entities are nested. Based on ChiNesE, we propose Mulco, a novel method that can recognize named entities in nested structures through multiple scopes. Each scope use a designed scope-based sequence labeling method, which predicts an anchor and the length of a named entity to recognize it. Experiment results show that Mulco has outperformed several baseline methods with the different recognizing schemes on ChiNesE. We also conduct extensive experiments on ACE2005 Chinese corpus, where Mulco has achieved the best performance compared with the baseline methods. | ['Yu Xu', 'Di Niu', 'Jerry Chen', 'Weidong Guo', 'Jinwen Luo', 'Jiuding Yang'] | 2022-11-20 | null | null | null | null | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-1.78372547e-01 -2.46537685e-01 -1.85753569e-01 -3.54490161e-01
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41e2264f-f518-4456-9086-2157698020b6 | a-near-optimal-algorithm-for-bilevel | 2302.08766 | null | https://arxiv.org/abs/2302.08766v2 | https://arxiv.org/pdf/2302.08766v2.pdf | A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization | Bilevel optimization problems, which are problems where two optimization problems are nested, have more and more applications in machine learning. In many practical cases, the upper and the lower objectives correspond to empirical risk minimization problems and therefore have a sum structure. In this context, we propose a bilevel extension of the celebrated SARAH algorithm. We demonstrate that the algorithm requires $\mathcal{O}((n+m)^{\frac12}\varepsilon^{-1})$ gradient computations to achieve $\varepsilon$-stationarity with $n+m$ the total number of samples, which improves over all previous bilevel algorithms. Moreover, we provide a lower bound on the number of oracle calls required to get an approximate stationary point of the objective function of the bilevel problem. This lower bound is attained by our algorithm, which is therefore optimal in terms of sample complexity. | ['Pierre Ablin', 'Samuel Vaiter', 'Thomas Moreau', 'Mathieu Dagréou'] | 2023-02-17 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-1.25653908e-01 1.29476279e-01 -1.12037174e-01 -2.27641955e-01
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8590188d-6ba7-4cd8-b49f-ae3d14a15f30 | learning-self-game-play-agents-for | 1903.03674 | null | https://arxiv.org/abs/1903.03674v2 | https://arxiv.org/pdf/1903.03674v2.pdf | Learning Self-Game-Play Agents for Combinatorial Optimization Problems | Recent progress in reinforcement learning (RL) using self-game-play has shown remarkable performance on several board games (e.g., Chess and Go) as well as video games (e.g., Atari games and Dota2). It is plausible to consider that RL, starting from zero knowledge, might be able to gradually approximate a winning strategy after a certain amount of training. In this paper, we explore neural Monte-Carlo-Tree-Search (neural MCTS), an RL algorithm which has been applied successfully by DeepMind to play Go and Chess at a super-human level. We try to leverage the computational power of neural MCTS to solve a class of combinatorial optimization problems. Following the idea of Hintikka's Game-Theoretical Semantics, we propose the Zermelo Gamification (ZG) to transform specific combinatorial optimization problems into Zermelo games whose winning strategies correspond to the solutions of the original optimization problem. The ZG also provides a specially designed neural MCTS. We use a combinatorial planning problem for which the ground-truth policy is efficiently computable to demonstrate that ZG is promising. | ['Karl Lieberherr', 'Ruiyang Xu'] | 2019-03-08 | null | null | null | null | ['board-games'] | ['playing-games'] | [-4.82586920e-02 5.46514392e-01 -1.99750662e-01 4.38691080e-02
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9bf461ca-d555-41c4-a126-7a1d84db1f77 | biomedical-interpretable-entity | 2106.09502 | null | https://arxiv.org/abs/2106.09502v1 | https://arxiv.org/pdf/2106.09502v1.pdf | Biomedical Interpretable Entity Representations | Pre-trained language models induce dense entity representations that offer strong performance on entity-centric NLP tasks, but such representations are not immediately interpretable. This can be a barrier to model uptake in important domains such as biomedicine. There has been recent work on general interpretable representation learning (Onoe and Durrett, 2020), but these domain-agnostic representations do not readily transfer to the important domain of biomedicine. In this paper, we create a new entity type system and training set from a large corpus of biomedical texts by mapping entities to concepts in a medical ontology, and from these to Wikipedia pages whose categories are our types. From this mapping we derive Biomedical Interpretable Entity Representations(BIERs), in which dimensions correspond to fine-grained entity types, and values are predicted probabilities that a given entity is of the corresponding type. We propose a novel method that exploits BIER's final sparse and intermediate dense representations to facilitate model and entity type debugging. We show that BIERs achieve strong performance in biomedical tasks including named entity disambiguation and entity label classification, and we provide error analysis to highlight the utility of their interpretability, particularly in low-supervision settings. Finally, we provide our induced 68K biomedical type system, the corresponding 37 million triples of derived data used to train BIER models and our best performing model. | ['Kush R. Varshney', 'Byron C. Wallace', 'Joydeep Ghosh', 'Ioana Baldini', 'Yasumasa Onoe', 'Diego Garcia-Olano'] | 2021-06-17 | null | https://aclanthology.org/2021.findings-acl.311 | https://aclanthology.org/2021.findings-acl.311.pdf | findings-acl-2021-8 | ['entity-disambiguation'] | ['natural-language-processing'] | [ 3.22523415e-01 9.62674797e-01 -4.04188901e-01 -4.96787846e-01
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9a52988f-0f5e-4fd9-b91f-8c9657ee3d65 | coupled-learning-for-facial-deblur | 1904.08671 | null | http://arxiv.org/abs/1904.08671v1 | http://arxiv.org/pdf/1904.08671v1.pdf | Coupled Learning for Facial Deblur | Blur in facial images significantly impedes the efficiency of recognition
approaches. However, most existing blind deconvolution methods cannot generate
satisfactory results due to their dependence on strong edges, which are
sufficient in natural images but not in facial images. In this paper, we
represent point spread functions (PSFs) by the linear combination of a set of
pre-defined orthogonal PSFs, and similarly, an estimated intrinsic (EI) sharp
face image is represented by the linear combination of a set of pre-defined
orthogonal face images. In doing so, PSF and EI estimation is simplified to
discovering two sets of linear combination coefficients, which are
simultaneously found by our proposed coupled learning algorithm. To make our
method robust to different types of blurry face images, we generate several
candidate PSFs and EIs for a test image, and then, a non-blind deconvolution
method is adopted to generate more EIs by those candidate PSFs. Finally, we
deploy a blind image quality assessment metric to automatically select the
optimal EI. Thorough experiments on the facial recognition technology database,
extended Yale face database B, CMU pose, illumination, and expression (PIE)
database, and face recognition grand challenge database version 2.0 demonstrate
that the proposed approach effectively restores intrinsic sharp face images
and, consequently, improves the performance of face recognition. | ['DaCheng Tao', 'Dayong Tian'] | 2019-04-18 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [ 1.27289966e-01 -6.08927429e-01 3.82725775e-01 -3.62657636e-01
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ebadb889-16f7-4054-9e0e-c8ffa9880e23 | contrastive-attention-mechanism-for | 1910.13114 | null | https://arxiv.org/abs/1910.13114v2 | https://arxiv.org/pdf/1910.13114v2.pdf | Contrastive Attention Mechanism for Abstractive Sentence Summarization | We propose a contrastive attention mechanism to extend the sequence-to-sequence framework for abstractive sentence summarization task, which aims to generate a brief summary of a given source sentence. The proposed contrastive attention mechanism accommodates two categories of attention: one is the conventional attention that attends to relevant parts of the source sentence, the other is the opponent attention that attends to irrelevant or less relevant parts of the source sentence. Both attentions are trained in an opposite way so that the contribution from the conventional attention is encouraged and the contribution from the opponent attention is discouraged through a novel softmax and softmin functionality. Experiments on benchmark datasets show that, the proposed contrastive attention mechanism is more focused on the relevant parts for the summary than the conventional attention mechanism, and greatly advances the state-of-the-art performance on the abstractive sentence summarization task. We release the code at https://github.com/travel-go/Abstractive-Text-Summarization | ['Yue Zhang', 'Mingming Yin', 'Weihua Luo', 'Xiangyu Duan', 'Min Zhang', 'Hoongfei Yu'] | 2019-10-29 | contrastive-attention-mechanism-for-1 | https://aclanthology.org/D19-1301 | https://aclanthology.org/D19-1301.pdf | ijcnlp-2019-11 | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 5.38344383e-01 5.44730246e-01 7.72298723e-02 -3.05430591e-01
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f9fc0e79-ab60-4767-be90-99040ddbeb27 | stag-a-stable-fiducial-marker-system | 1707.06292 | null | https://arxiv.org/abs/1707.06292v2 | https://arxiv.org/pdf/1707.06292v2.pdf | STag: A Stable Fiducial Marker System | Fiducial markers provide better-defined features than the ones naturally available in the scene. For this reason, they are widely utilized in computer vision applications where reliable pose estimation is required. Factors such as imaging noise and subtle changes in illumination induce jitter on the estimated pose. Jitter impairs robustness in vision and robotics applications, and deteriorates the sense of presence and immersion in AR/VR applications. In this paper, we propose STag, a fiducial marker system that provides stable pose estimation. STag is designed to be robust against jitter factors, thus sustains pose stability better than the existing solutions. This is achieved by utilizing geometric features that can be localized more repeatably. The outer square border of the marker is used for detection and homography estimation. This is followed by a novel homography refinement step using the inner circular border. After refinement, the pose can be estimated stably and robustly across viewing conditions. These features are demonstrated with a comprehensive set of experiments, including comparisons with the state of the art fiducial marker systems. | ['Cuneyt Akinlar', 'Burak Benligiray', 'Cihan Topal'] | 2017-07-19 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [-1.39094010e-01 -3.18785727e-01 -1.93896014e-02 1.05409756e-01
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9fbf2505-0443-4b4e-b5b7-741975c70ee7 | automatic-correction-of-human-translations | 2206.08593 | null | https://arxiv.org/abs/2206.08593v1 | https://arxiv.org/pdf/2206.08593v1.pdf | Automatic Correction of Human Translations | We introduce translation error correction (TEC), the task of automatically correcting human-generated translations. Imperfections in machine translations (MT) have long motivated systems for improving translations post-hoc with automatic post-editing. In contrast, little attention has been devoted to the problem of automatically correcting human translations, despite the intuition that humans make distinct errors that machines would be well-suited to assist with, from typos to inconsistencies in translation conventions. To investigate this, we build and release the Aced corpus with three TEC datasets. We show that human errors in TEC exhibit a more diverse range of errors and far fewer translation fluency errors than the MT errors in automatic post-editing datasets, suggesting the need for dedicated TEC models that are specialized to correct human errors. We show that pre-training instead on synthetic errors based on human errors improves TEC F-score by as much as 5.1 points. We conducted a human-in-the-loop user study with nine professional translation editors and found that the assistance of our TEC system led them to produce significantly higher quality revised translations. | ['John DeNero', 'Joern Wuebker', 'Aditya Shastry', 'Geza Kovacs', 'Jessy Lin'] | 2022-06-17 | null | https://aclanthology.org/2022.naacl-main.36 | https://aclanthology.org/2022.naacl-main.36.pdf | naacl-2022-7 | ['automatic-post-editing', 'automatic-post-editing'] | ['computer-vision', 'natural-language-processing'] | [ 5.50807536e-01 3.30902547e-01 -3.78895253e-02 -5.85325420e-01
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cd1f66c9-a371-486e-b76f-924a09a1ec6f | causal-identification-under-markov | 1812.06209 | null | http://arxiv.org/abs/1812.06209v1 | http://arxiv.org/pdf/1812.06209v1.pdf | Causal Identification under Markov Equivalence | Assessing the magnitude of cause-and-effect relations is one of the central
challenges found throughout the empirical sciences. The problem of
identification of causal effects is concerned with determining whether a causal
effect can be computed from a combination of observational data and substantive
knowledge about the domain under investigation, which is formally expressed in
the form of a causal graph. In many practical settings, however, the knowledge
available for the researcher is not strong enough so as to specify a unique
causal graph. Another line of investigation attempts to use observational data
to learn a qualitative description of the domain called a Markov equivalence
class, which is the collection of causal graphs that share the same set of
observed features. In this paper, we marry both approaches and study the
problem of causal identification from an equivalence class, represented by a
partial ancestral graph (PAG). We start by deriving a set of graphical
properties of PAGs that are carried over to its induced subgraphs. We then
develop an algorithm to compute the effect of an arbitrary set of variables on
an arbitrary outcome set. We show that the algorithm is strictly more powerful
than the current state of the art found in the literature. | ['Amin Jaber', 'Jiji Zhang', 'Elias Bareinboim'] | 2018-12-15 | null | null | null | null | ['causal-identification'] | ['reasoning'] | [ 5.93671978e-01 1.98183358e-01 -6.58875585e-01 -3.43393713e-01
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26a69ee7-3a93-4d8f-96ca-6fde186492cd | first-go-then-post-explore-the-benefits-of | 2212.03251 | null | https://arxiv.org/abs/2212.03251v2 | https://arxiv.org/pdf/2212.03251v2.pdf | First Go, then Post-Explore: the Benefits of Post-Exploration in Intrinsic Motivation | Go-Explore achieved breakthrough performance on challenging reinforcement learning (RL) tasks with sparse rewards. The key insight of Go-Explore was that successful exploration requires an agent to first return to an interesting state ('Go'), and only then explore into unknown terrain ('Explore'). We refer to such exploration after a goal is reached as 'post-exploration'. In this paper, we present a clear ablation study of post-exploration in a general intrinsically motivated goal exploration process (IMGEP) framework, that the Go-Explore paper did not show. We study the isolated potential of post-exploration, by turning it on and off within the same algorithm under both tabular and deep RL settings on both discrete navigation and continuous control tasks. Experiments on a range of MiniGrid and Mujoco environments show that post-exploration indeed helps IMGEP agents reach more diverse states and boosts their performance. In short, our work suggests that RL researchers should consider to use post-exploration in IMGEP when possible since it is effective, method-agnostic and easy to implement. | ['Aske Plaat', 'Mike Preuss', 'Thomas M. Moerland', 'Zhao Yang'] | 2022-12-06 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-7.25600868e-02 2.56572962e-01 -1.27142072e-01 -2.89327390e-02
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54fd0cd7-d29a-48b4-9bd2-8ef4f97e4a90 | compound-figure-separation-of-biomedical-1 | 2208.14357 | null | https://arxiv.org/abs/2208.14357v1 | https://arxiv.org/pdf/2208.14357v1.pdf | Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised Learning | With the rapid development of self-supervised learning (e.g., contrastive learning), the importance of having large-scale images (even without annotations) for training a more generalizable AI model has been widely recognized in medical image analysis. However, collecting large-scale task-specific unannotated data at scale can be challenging for individual labs. Existing online resources, such as digital books, publications, and search engines, provide a new resource for obtaining large-scale images. However, published images in healthcare (e.g., radiology and pathology) consist of a considerable amount of compound figures with subplots. In order to extract and separate compound figures into usable individual images for downstream learning, we propose a simple compound figure separation (SimCFS) framework without using the traditionally required detection bounding box annotations, with a new loss function and a hard case simulation. Our technical contribution is four-fold: (1) we introduce a simulation-based training framework that minimizes the need for resource extensive bounding box annotations; (2) we propose a new side loss that is optimized for compound figure separation; (3) we propose an intra-class image augmentation method to simulate hard cases; and (4) to the best of our knowledge, this is the first study that evaluates the efficacy of leveraging self-supervised learning with compound image separation. From the results, the proposed SimCFS achieved state-of-the-art performance on the ImageCLEF 2016 Compound Figure Separation Database. The pretrained self-supervised learning model using large-scale mined figures improved the accuracy of downstream image classification tasks with a contrastive learning algorithm. The source code of SimCFS is made publicly available at https://github.com/hrlblab/ImageSeperation. | ['Yuankai Huo', 'Catie Chang', 'Haichun Yang', 'Bennett A. Landman', 'Agnes B. Fogo', 'Mengyang Zhao', 'Shunxing Bao', 'Zuhayr Asad', 'Aadarsh Jha', 'Jiachen Xu', 'Yuanhan Tian', 'Ruining Deng', 'Quan Liu', 'Jun Long', 'Chang Qu', 'Tianyuan Yao'] | 2022-08-30 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 4.05507922e-01 2.49283776e-01 -2.37301871e-01 -3.40614498e-01
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68e0b27a-f256-4020-a8b4-5b05556a4768 | delta-keyword-transformer-bringing | 2204.03479 | null | https://arxiv.org/abs/2204.03479v1 | https://arxiv.org/pdf/2204.03479v1.pdf | Delta Keyword Transformer: Bringing Transformers to the Edge through Dynamically Pruned Multi-Head Self-Attention | Multi-head self-attention forms the core of Transformer networks. However, their quadratically growing complexity with respect to the input sequence length impedes their deployment on resource-constrained edge devices. We address this challenge by proposing a dynamic pruning method, which exploits the temporal stability of data across tokens to reduce inference cost. The threshold-based method only retains significant differences between the subsequent tokens, effectively reducing the number of multiply-accumulates, as well as the internal tensor data sizes. The approach is evaluated on the Google Speech Commands Dataset for keyword spotting, and the performance is compared against the baseline Keyword Transformer. Our experiments show that we can reduce ~80% of operations while maintaining the original 98.4% accuracy. Moreover, a reduction of ~87-94% operations can be achieved when only degrading the accuracy by 1-4%, speeding up the multi-head self-attention inference by a factor of ~7.5-16. | ['Marian Verhelst', 'Zuzana Jelčicová'] | 2022-03-20 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 1.85526416e-01 -3.96701694e-03 -3.34684938e-01 -4.99181859e-02
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33e6ea0b-8d11-4dd6-8d7c-c34fa117ef73 | large-language-models-are-frame-level | 2305.14330 | null | https://arxiv.org/abs/2305.14330v2 | https://arxiv.org/pdf/2305.14330v2.pdf | Large Language Models are Frame-level Directors for Zero-shot Text-to-Video Generation | In the paradigm of AI-generated content (AIGC), there has been increasing attention in extending pre-trained text-to-image (T2I) models to text-to-video (T2V) generation. Despite their effectiveness, these frameworks face challenges in maintaining consistent narratives and handling rapid shifts in scene composition or object placement from a single user prompt. This paper introduces a new framework, dubbed DirecT2V, which leverages instruction-tuned large language models (LLMs) to generate frame-by-frame descriptions from a single abstract user prompt. DirecT2V utilizes LLM directors to divide user inputs into separate prompts for each frame, enabling the inclusion of time-varying content and facilitating consistent video generation. To maintain temporal consistency and prevent object collapse, we propose a novel value mapping method and dual-softmax filtering. Extensive experimental results validate the effectiveness of the DirecT2V framework in producing visually coherent and consistent videos from abstract user prompts, addressing the challenges of zero-shot video generation. | ['Seungryong Kim', 'Heeseong Shin', 'Sunghwan Hong', 'Junyoung Seo', 'Susung Hong'] | 2023-05-23 | null | null | null | null | ['video-generation', 'zero-shot-text-to-video-generation', 'text-to-video-generation'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 4.31982756e-01 7.41353408e-02 -1.45449415e-01 -3.35398197e-01
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f8e7147a-242f-45e9-920e-34da3b477d4d | online-data-selection-for-federated-learning | 2209.00195 | null | https://arxiv.org/abs/2209.00195v4 | https://arxiv.org/pdf/2209.00195v4.pdf | To Store or Not? Online Data Selection for Federated Learning with Limited Storage | Machine learning models have been deployed in mobile networks to deal with massive data from different layers to enable automated network management and intelligence on devices. To overcome high communication cost and severe privacy concerns of centralized machine learning, federated learning (FL) has been proposed to achieve distributed machine learning among networked devices. While the computation and communication limitation has been widely studied, the impact of on-device storage on the performance of FL is still not explored. Without an effective data selection policy to filter the massive streaming data on devices, classical FL can suffer from much longer model training time ($4\times$) and significant inference accuracy reduction ($7\%$), observed in our experiments. In this work, we take the first step to consider the online data selection for FL with limited on-device storage. We first define a new data valuation metric for data evaluation and selection in FL with theoretical guarantees for speeding up model convergence and enhancing final model accuracy, simultaneously. We further design {\ttfamily ODE}, a framework of \textbf{O}nline \textbf{D}ata s\textbf{E}lection for FL, to coordinate networked devices to store valuable data samples. Experimental results on one industrial dataset and three public datasets show the remarkable advantages of {\ttfamily ODE} over the state-of-the-art approaches. Particularly, on the industrial dataset, {\ttfamily ODE} achieves as high as $2.5\times$ speedup of training time and $6\%$ increase in inference accuracy, and is robust to various factors in practical environments. | ['Fan Wu', 'Guihai Chen', 'Yunfeng Shao', 'Bingshuai Li', 'Zhenzhe Zheng', 'Chen Gong'] | 2022-09-01 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 8.24073926e-02 3.33450884e-02 -6.19670570e-01 -6.09187782e-01
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b3654006-0c9e-433d-92eb-73695f61a1f8 | ecnu-at-semeval-2016-task-3-exploring | null | null | https://aclanthology.org/S16-1135 | https://aclanthology.org/S16-1135.pdf | ECNU at SemEval-2016 Task 3: Exploring Traditional Method and Deep Learning Method for Question Retrieval and Answer Ranking in Community Question Answering | null | ['Man Lan', 'Guoshun Wu'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['question-similarity'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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7132c428-048c-4283-9d4b-67ece33ab750 | an-algebraic-framework-for-stock-flow | 2211.01290 | null | https://arxiv.org/abs/2211.01290v3 | https://arxiv.org/pdf/2211.01290v3.pdf | A Categorical Framework for Modeling with Stock and Flow Diagrams | Stock and flow diagrams are already an important tool in epidemiology, but category theory lets us go further and treat these diagrams as mathematical entities in their own right. In this chapter we use communicable disease models created with our software, StockFlow.jl, to explain the benefits of the categorical approach. We first explain the category of stock-flow diagrams and note the clear separation between the syntax of these diagrams and their semantics, demonstrating three examples of semantics already implemented in the software: ODEs, causal loop diagrams, and system structure diagrams. We then turn to two methods for building large stock-flow diagrams from smaller ones in a modular fashion: composition and stratification. Finally, we introduce the open-source ModelCollab software for diagram-based collaborative modeling. The graphical user interface of this web-based software lets modelers take advantage of the ideas discussed here without any knowledge of their categorical foundations. | ['Eric Redekopp', 'Nathaniel D. Osgood', 'Xiaoyan Li', 'John C. Baez', 'Sophie Libkind'] | 2022-11-01 | null | null | null | null | ['epidemiology'] | ['medical'] | [-2.93105811e-01 5.55958807e-01 -1.84920177e-01 -3.07425112e-01
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7b4146de-72e6-4d50-a1a7-70f258bdabe9 | multi-objective-design-of-multilayer | 2101.10858 | null | https://arxiv.org/abs/2101.10858v1 | https://arxiv.org/pdf/2101.10858v1.pdf | Multi-objective design of multilayer microwave dielectric filters using artificial bee colony algorithm | Artificial bee colony algorithm (ABC) developed by inspiring the foraging phenomena of the natural honey bees is a simple and powerful metaheuristic optimization algorithm. The performance of single objective ABC performance has been well demonstrated by implemented to different design optimization problems from elec-trical and mechanical to civil engineering. Its efficacy is anticipated in the multi-objective design optimization of multilayer microwave dielectric filters (MMDFs) which is a challenging multi-objective problem falls into the context of electromagnetic (EM) design in electrical engineering. The MMDF is composed of superimposed multiple dielectric layers in order to pass or stop the EM wave at the desired frequency bands. An accurate dual-objective EM model of MMDF is constituted to be the total reflection coefficients for oblique incident wave angular range with transverse electric (TE) or transverse magnetic (TM) polarizations. The objective functions depend on the total reflection (TR) at the pass and stop band re-gions. Three types of the MDFs that are low pass (LP), high pass (HP) and band pass (BP) MMDFs are optimally designed across the constituted dual-objective EM model with a frequency dependent material database through using the multi-objective ABC (MO-ABC) strategy. The Pareto optimal solutions are refined within the possible solutions for synchronously minimizing the objective func-tions. The global optimal MMDF designs are selected from the Pare-to optimal solutions by providing the trade-off therein between the objective functions. The performance study regarding the frequen-cy characteristics of the designed MMDFs are well presented via numerical and graphical results. It is hence demonstrated that MO-ABC is even versatile and robust in the multi-objective design of MMDFs. | ['Abdurrahim Toktas'] | 2021-01-22 | null | null | null | null | ['metaheuristic-optimization', 'electrical-engineering'] | ['methodology', 'miscellaneous'] | [-1.09210461e-01 -2.79922128e-01 4.49746072e-01 -7.83807263e-02
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32873f79-e175-4b24-827c-e93705d4ea8c | uper-boosting-multi-document-summarization | null | null | https://aclanthology.org/2022.coling-1.550 | https://aclanthology.org/2022.coling-1.550.pdf | UPER: Boosting Multi-Document Summarization with an Unsupervised Prompt-based Extractor | Multi-Document Summarization (MDS) commonly employs the 2-stage extract-then-abstract paradigm, which first extracts a relatively short meta-document, then feeds it into the deep neural networks to generate an abstract. Previous work usually takes the ROUGE score as the label for training a scoring model to evaluate source documents. However, the trained scoring model is prone to under-fitting for low-resource settings, as it relies on the training data. To extract documents effectively, we construct prompting templates that invoke the underlying knowledge in Pre-trained Language Model (PLM) to calculate the document and keyword’s perplexity, which can assess the document’s semantic salience. Our unsupervised approach can be applied as a plug-in to boost other metrics for evaluating a document’s salience, thus improving the subsequent abstract generation. We get positive results on 2 MDS datasets, 2 data settings, and 2 abstractive backbone models, showing our method’s effectiveness. Our code is available at https://github.com/THU-KEG/UPER | ['Jian-Yun Nie', 'Lei Hou', 'Juanzi Li', 'Fangwei Zhu', 'Jifan Yu', 'Shangqing Tu'] | null | null | null | null | coling-2022-10 | ['document-summarization'] | ['natural-language-processing'] | [ 2.66229689e-01 2.63556570e-01 -3.39819759e-01 -3.32584977e-01
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be028a5e-ae7f-4d2d-abc8-b332aee7c658 | the-power-of-subsampling-in-submodular | 2104.02772 | null | https://arxiv.org/abs/2104.02772v1 | https://arxiv.org/pdf/2104.02772v1.pdf | The Power of Subsampling in Submodular Maximization | We propose subsampling as a unified algorithmic technique for submodular maximization in centralized and online settings. The idea is simple: independently sample elements from the ground set, and use simple combinatorial techniques (such as greedy or local search) on these sampled elements. We show that this approach leads to optimal/state-of-the-art results despite being much simpler than existing methods. In the usual offline setting, we present SampleGreedy, which obtains a $(p + 2 + o(1))$-approximation for maximizing a submodular function subject to a $p$-extendible system using $O(n + nk/p)$ evaluation and feasibility queries, where $k$ is the size of the largest feasible set. The approximation ratio improves to $p+1$ and $p$ for monotone submodular and linear objectives, respectively. In the streaming setting, we present SampleStreaming, which obtains a $(4p +2 - o(1))$-approximation for maximizing a submodular function subject to a $p$-matchoid using $O(k)$ memory and $O(km/p)$ evaluation and feasibility queries per element, where $m$ is the number of matroids defining the $p$-matchoid. The approximation ratio improves to $4p$ for monotone submodular objectives. We empirically demonstrate the effectiveness of our algorithms on video summarization, location summarization, and movie recommendation tasks. | ['Amin Karbasi', 'Moran Feldman', 'Ehsan Kazemi', 'Christopher Harshaw'] | 2021-04-06 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [ 1.27397720e-02 2.97788709e-01 -7.10586131e-01 4.54723947e-02
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a220b4f8-7e9a-4809-86d6-1a8189434306 | msa-transformer | null | null | https://www.biorxiv.org/content/10.1101/2021.02.12.430858v1 | https://www.biorxiv.org/content/10.1101/2021.02.12.430858v1.full.pdf | MSA Transformer | Unsupervised protein language models trained across millions of diverse sequences learn structure and function of proteins. Protein language models studied to date have been trained to perform inference from individual sequences. The longstanding approach in computational biology has been to make inferences from a family of evolutionarily related sequences by fitting a model to each family independently. In this work we combine the two paradigms. We introduce a protein language model which takes as input a set of sequences in the form of a multiple sequence alignment. The model interleaves row and column attention across the input sequences and is trained with a variant of the masked language modeling objective across many protein families. The performance of the model surpasses current state-of-the-art unsupervised structure learning methods by a wide margin, with far greater parameter efficiency than prior state-of-the-art protein language models. | ['Alexander Rives', 'Tom Sercu', 'Pieter Abbeel', 'John F. Canny', 'Joshua Meier', 'Robert Verkuil', 'Jason Liu', 'Roshan Rao'] | 2021-02-13 | null | null | null | null | ['protein-language-model', 'multiple-sequence-alignment'] | ['medical', 'medical'] | [ 6.54933751e-01 2.01237127e-01 -2.03749865e-01 -5.73280156e-01
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97aec89b-8b30-4920-b664-2344376cd035 | grab-a-dataset-of-whole-body-human-grasping | 2008.11200 | null | https://arxiv.org/abs/2008.11200v1 | https://arxiv.org/pdf/2008.11200v1.pdf | GRAB: A Dataset of Whole-Body Human Grasping of Objects | Training computers to understand, model, and synthesize human grasping requires a rich dataset containing complex 3D object shapes, detailed contact information, hand pose and shape, and the 3D body motion over time. While "grasping" is commonly thought of as a single hand stably lifting an object, we capture the motion of the entire body and adopt the generalized notion of "whole-body grasps". Thus, we collect a new dataset, called GRAB (GRasping Actions with Bodies), of whole-body grasps, containing full 3D shape and pose sequences of 10 subjects interacting with 51 everyday objects of varying shape and size. Given MoCap markers, we fit the full 3D body shape and pose, including the articulated face and hands, as well as the 3D object pose. This gives detailed 3D meshes over time, from which we compute contact between the body and object. This is a unique dataset, that goes well beyond existing ones for modeling and understanding how humans grasp and manipulate objects, how their full body is involved, and how interaction varies with the task. We illustrate the practical value of GRAB with an example application; we train GrabNet, a conditional generative network, to predict 3D hand grasps for unseen 3D object shapes. The dataset and code are available for research purposes at https://grab.is.tue.mpg.de. | ['Michael J. Black', 'Dimitrios Tzionas', 'Omid Taheri', 'Nima Ghorbani'] | 2020-08-25 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2534_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490562.pdf | eccv-2020-8 | ['human-object-interaction-motion-tracking', 'grasp-generation', 'human-grasp-contact-prediction'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [-2.41433144e-01 -2.34203964e-01 4.93693538e-02 -1.60050690e-01
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c8a287cc-0ad9-41d0-9f8e-5d92295b8eb8 | topological-map-construction-and-scene | null | null | https://link.springer.com/article/10.1007%2Fs10514-017-9638-9#Sec2 | https://link.springer.com/article/10.1007%2Fs10514-017-9638-9#Sec2 | Topological map construction and scene recognition for vehicle localization | This paper presents a vehicle localization method
to assist vehicle navigation based on topological map construction
and scene recognition. A topological map is constructed
using omni-directional image sequences, and the
node information of the topological map is used for place
recognition and derivation of vehicle location. In topological
map construction and scene change detection, we utilize the
Extended-HCT method for semantic description and feature
extraction. Content-based and feature-based image retrieval
approaches are adopted for place recognition and vehicle
localization on the real scene image dataset. The proposed
technique is able to construct a real-time image retrieval system
for navigation assistance and validate the correctness of
the route. Experiments are carried out in both the indoor and
outdoor environments using real world images. | ['Huei-Yung Lin'] | 2018-01-01 | null | null | null | conference-2018-1 | ['scene-change-detection'] | ['computer-vision'] | [-4.08135653e-02 -7.66968012e-01 -1.58032216e-02 -7.39712298e-01
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2a37cdcd-8032-4f3a-80b3-570c80911701 | body-gesture-recognition-to-control-a-social | 2206.07538 | null | https://arxiv.org/abs/2206.07538v1 | https://arxiv.org/pdf/2206.07538v1.pdf | Body Gesture Recognition to Control a Social Robot | In this work, we propose a gesture based language to allow humans to interact with robots using their body in a natural way. We have created a new gesture detection model using neural networks and a custom dataset of humans performing a set of body gestures to train our network. Furthermore, we compare body gesture communication with other communication channels to acknowledge the importance of adding this knowledge to robots. The presented approach is extensively validated in diverse simulations and real-life experiments with non-trained volunteers. This attains remarkable results and shows that it is a valuable framework for social robotics applications, such as human robot collaboration or human-robot interaction. | ['Anaís Garrell', 'Alberto Sanfeliu', 'Ramón Romero', 'Joan Jaume Oliver', 'Javier Laplaza'] | 2022-06-15 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 5.02486229e-02 5.24022758e-01 3.91199179e-02 -4.55061138e-01
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57df312f-9cd3-44a4-86a1-6bdf2ac3ca2d | image-free-multi-character-recognition | 2112.10587 | null | https://arxiv.org/abs/2112.10587v1 | https://arxiv.org/pdf/2112.10587v1.pdf | Image-free multi-character recognition | The recently developed image-free sensing technique maintains the advantages of both the light hardware and software, which has been applied in simple target classification and motion tracking. In practical applications, however, there usually exist multiple targets in the field of view, where existing trials fail to produce multi-semantic information. In this letter, we report a novel image-free sensing technique to tackle the multi-target recognition challenge for the first time. Different from the convolutional layer stack of image-free single-pixel networks, the reported CRNN network utilities the bidirectional LSTM architecture to predict the distribution of multiple characters simultaneously. The framework enables to capture the long-range dependencies, providing a high recognition accuracy of multiple characters. We demonstrated the technique's effectiveness in license plate detection, which achieved 87.60% recognition accuracy at a 5% sampling rate with a higher than 100 FPS refresh rate. | ['Liheng Bian', 'Chunli Zhu', 'Huayi Wang'] | 2021-12-20 | null | null | null | null | ['license-plate-detection'] | ['computer-vision'] | [ 6.54821873e-01 -7.90982187e-01 6.75250217e-02 -9.02044326e-02
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3b17c440-262d-4366-a890-d48095f1f57b | serving-graph-neural-networks-with | 2307.01684 | null | https://arxiv.org/abs/2307.01684v1 | https://arxiv.org/pdf/2307.01684v1.pdf | Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services | Graph Neural Networks (GNNs) have gained growing interest in miscellaneous applications owing to their outstanding ability in extracting latent representation on graph structures. To render GNN-based service for IoT-driven smart applications, traditional model serving paradigms usually resort to the cloud by fully uploading geo-distributed input data to remote datacenters. However, our empirical measurements reveal the significant communication overhead of such cloud-based serving and highlight the profound potential in applying the emerging fog computing. To maximize the architectural benefits brought by fog computing, in this paper, we present Fograph, a novel distributed real-time GNN inference framework that leverages diverse and dynamic resources of multiple fog nodes in proximity to IoT data sources. By introducing heterogeneity-aware execution planning and GNN-specific compression techniques, Fograph tailors its design to well accommodate the unique characteristics of GNN serving in fog environments. Prototype-based evaluation and case study demonstrate that Fograph significantly outperforms the state-of-the-art cloud serving and fog deployment by up to 5.39x execution speedup and 6.84x throughput improvement. | ['Zhi Zhou', 'Xiaoxi Zhang', 'Ke Luo', 'Peng Huang', 'Xu Chen', 'Liekang Zeng'] | 2023-07-04 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-5.02451718e-01 8.77550468e-02 -7.48830512e-02 -2.19700068e-01
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1bde6372-ed09-43ba-b650-44617e6e4307 | multi-source-contrastive-learning-from | 2302.07077 | null | https://arxiv.org/abs/2302.07077v2 | https://arxiv.org/pdf/2302.07077v2.pdf | Multi-Source Contrastive Learning from Musical Audio | Contrastive learning constitutes an emerging branch of self-supervised learning that leverages large amounts of unlabeled data, by learning a latent space, where pairs of different views of the same sample are associated. In this paper, we propose musical source association as a pair generation strategy in the context of contrastive music representation learning. To this end, we modify COLA, a widely used contrastive learning audio framework, to learn to associate a song excerpt with a stochastically selected and automatically extracted vocal or instrumental source. We further introduce a novel modification to the contrastive loss to incorporate information about the existence or absence of specific sources. Our experimental evaluation in three different downstream tasks (music auto-tagging, instrument classification and music genre classification) using the publicly available Magna-Tag-A-Tune (MTAT) as a source dataset yields competitive results to existing literature methods, as well as faster network convergence. The results also show that this pre-training method can be steered towards specific features, according to the selected musical source, while also being dependent on the quality of the separated sources. | ['Petros Maragos', 'Athanasia Zlatintsi', 'Christos Garoufis'] | 2023-02-14 | null | null | null | null | ['genre-classification', 'music-auto-tagging'] | ['computer-vision', 'music'] | [ 6.09864533e-01 -2.54880637e-02 -3.14259261e-01 -3.40596914e-01
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9923ce39-9c39-460b-9751-ff0a51de44e8 | ensemble-classifier-approach-in-breast-cancer | 1704.03801 | null | http://arxiv.org/abs/1704.03801v1 | http://arxiv.org/pdf/1704.03801v1.pdf | Ensemble classifier approach in breast cancer detection and malignancy grading- A review | The diagnosed cases of Breast cancer is increasing annually and unfortunately
getting converted into a high mortality rate. Cancer, at the early stages, is
hard to detect because the malicious cells show similar properties (density) as
shown by the non-malicious cells. The mortality ratio could have been minimized
if the breast cancer could have been detected in its early stages. But the
current systems have not been able to achieve a fully automatic system which is
not just capable of detecting the breast cancer but also can detect the stage
of it. Estimation of malignancy grading is important in diagnosing the degree
of growth of malicious cells as well as in selecting a proper therapy for the
patient. Therefore, a complete and efficient clinical decision support system
is proposed which is capable of achieving breast cancer malignancy grading
scheme very efficiently. The system is based on Image processing and machine
learning domains. Classification Imbalance problem, a machine learning problem,
occurs when instances of one class is much higher than the instances of the
other class resulting in an inefficient classification of samples and hence a
bad decision support system. Therefore EUSBoost, ensemble based classifier is
proposed which is efficient and is able to outperform other classifiers as it
takes the benefits of both-boosting algorithm with Random Undersampling
techniques. Also comparison of EUSBoost with other techniques is shown in the
paper. | ['Deepti Ameta'] | 2017-04-11 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [-4.73095179e-02 5.78576811e-02 -1.49165422e-01 -1.09437458e-01
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63158a18-6f47-42f6-b075-9da482d5afd1 | raild-towards-leveraging-relation-features | 2211.11407 | null | https://arxiv.org/abs/2211.11407v1 | https://arxiv.org/pdf/2211.11407v1.pdf | RAILD: Towards Leveraging Relation Features for Inductive Link Prediction In Knowledge Graphs | Due to the open world assumption, Knowledge Graphs (KGs) are never complete. In order to address this issue, various Link Prediction (LP) methods are proposed so far. Some of these methods are inductive LP models which are capable of learning representations for entities not seen during training. However, to the best of our knowledge, none of the existing inductive LP models focus on learning representations for unseen relations. In this work, a novel Relation Aware Inductive Link preDiction (RAILD) is proposed for KG completion which learns representations for both unseen entities and unseen relations. In addition to leveraging textual literals associated with both entities and relations by employing language models, RAILD also introduces a novel graph-based approach to generate features for relations. Experiments are conducted with different existing and newly created challenging benchmark datasets and the results indicate that RAILD leads to performance improvement over the state-of-the-art models. Moreover, since there are no existing inductive LP models which learn representations for unseen relations, we have created our own baselines and the results obtained with RAILD also outperform these baselines. | ['Mehwish Alam', 'Harald Sack', 'Genet Asefa Gesese'] | 2022-11-21 | null | null | null | null | ['inductive-link-prediction'] | ['graphs'] | [-1.08431697e-01 8.96829307e-01 -5.02403557e-01 -2.22002670e-01
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43cfda6b-6e2a-48d6-bc3f-4b2996107518 | non-neural-models-matter-a-re-evaluation-of | 2203.08274 | null | https://arxiv.org/abs/2203.08274v1 | https://arxiv.org/pdf/2203.08274v1.pdf | Non-neural Models Matter: A Re-evaluation of Neural Referring Expression Generation Systems | In recent years, neural models have often outperformed rule-based and classic Machine Learning approaches in NLG. These classic approaches are now often disregarded, for example when new neural models are evaluated. We argue that they should not be overlooked, since, for some tasks, well-designed non-neural approaches achieve better performance than neural ones. In this paper, the task of generating referring expressions in linguistic context is used as an example. We examined two very different English datasets (WEBNLG and WSJ), and evaluated each algorithm using both automatic and human evaluations. Overall, the results of these evaluations suggest that rule-based systems with simple rule sets achieve on-par or better performance on both datasets compared to state-of-the-art neural REG systems. In the case of the more realistic dataset, WSJ, a machine learning-based system with well-designed linguistic features performed best. We hope that our work can encourage researchers to consider non-neural models in future. | ['Kees Van Deemter', 'Guanyi Chen', 'Fahime Same'] | 2022-03-15 | null | https://aclanthology.org/2022.acl-long.380 | https://aclanthology.org/2022.acl-long.380.pdf | acl-2022-5 | ['referring-expression-generation'] | ['computer-vision'] | [ 1.42635763e-01 4.89343584e-01 -2.15306312e-01 -6.77034736e-01
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0ad590ae-8b87-4321-b357-23cf66c87366 | perception-oriented-stereo-image-super | 2207.06617 | null | https://arxiv.org/abs/2207.06617v1 | https://arxiv.org/pdf/2207.06617v1.pdf | Perception-Oriented Stereo Image Super-Resolution | Recent studies of deep learning based stereo image super-resolution (StereoSR) have promoted the development of StereoSR. However, existing StereoSR models mainly concentrate on improving quantitative evaluation metrics and neglect the visual quality of super-resolved stereo images. To improve the perceptual performance, this paper proposes the first perception-oriented stereo image super-resolution approach by exploiting the feedback, provided by the evaluation on the perceptual quality of StereoSR results. To provide accurate guidance for the StereoSR model, we develop the first special stereo image super-resolution quality assessment (StereoSRQA) model, and further construct a StereoSRQA database. Extensive experiments demonstrate that our StereoSR approach significantly improves the perceptual quality and enhances the reliability of stereo images for disparity estimation. | ['Xuhao Jiang', 'Weimin Tan', 'Bo Yan', 'Chenxi Ma'] | 2022-07-14 | null | null | null | null | ['disparity-estimation', 'stereo-image-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 2.41200939e-01 -1.82565793e-01 4.66735363e-02 -4.73720551e-01
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525a2765-da7c-40a6-9501-9cfbba660200 | early-covid-19-diagnosis-from-lung-ultrasound | null | null | https://ieeexplore.ieee.org/document/9756430 | https://ieeexplore.ieee.org/document/9756430 | Early COVID-19 Diagnosis from Lung Ultrasound Images Combining RIULBP-TP and 3D-DenseNet | The pandemic of COVID-19 has affected the world with the high deaths rate. Early diagnosis of this disease is the bottleneck to the patient's health recovery. Its symptoms appear through the wide range of experiments especially accompany with the severe lung lesions. These lesions could be spotted on the lung ultrasound data. Being non-intrusive, low cost, portable, and accurate enough are among the main pros of ultrasound imaging. However, this imaging modality most often contain variety of noises. In order to overcome this challenge, we propose a novel approach combining Rotation Invariant Uniform LBP on 3 Planes (RIULBP-TP) and 3D-DenseNet. These methods are proved to be robust against various noises. Accordingly, our method reaches outstanding results comparing to related most state-of-the-art methods. | ['Seyed Omid Shahdi', 'Mahmood Mohassel Feghhi', 'Vida Esmaeili'] | 2022-04-19 | null | null | null | 9th-iranian-joint-congress-on-fuzzy-and | ['covid-19-detection'] | ['medical'] | [-5.58007285e-02 -7.69298196e-01 2.72770096e-02 3.30481291e-01
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ea281b64-0744-4e90-b547-c4df09567ebc | coarse-to-fine-cascaded-networks-with-smooth | 2203.13052 | null | https://arxiv.org/abs/2203.13052v4 | https://arxiv.org/pdf/2203.13052v4.pdf | Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression Recognition | Facial expression recognition plays an important role in human-computer interaction. In this paper, we propose the Coarse-to-Fine Cascaded network with Smooth Predicting (CFC-SP) to improve the performance of facial expression recognition. CFC-SP contains two core components, namely Coarse-to-Fine Cascaded networks (CFC) and Smooth Predicting (SP). For CFC, it first groups several similar emotions to form a rough category, and then employs a network to conduct a coarse but accurate classification. Later, an additional network for these grouped emotions is further used to obtain fine-grained predictions. For SP, it improves the recognition capability of the model by capturing both universal and unique expression features. To be specific, the universal features denote the general characteristic of facial emotions within a period and the unique features denote the specific characteristic at this moment. Experiments on Aff-Wild2 show the effectiveness of the proposed CFSP. We achieved 3rd place in the Expression Classification Challenge of the 3rd Competition on Affective Behavior Analysis in-the-wild. The code will be released at https://github.com/BR-IDL/PaddleViT. | ['Guodong Guo', 'Zhongsong Ma', 'Yu Zhu', 'Zichang Tan', 'Fanglei Xue'] | 2022-03-24 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [-6.50520846e-02 -2.89556295e-01 -2.04706132e-01 -8.63409698e-01
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7705d5e1-9c1f-4e2b-9ecc-f44095496701 | singlish-message-paraphrasing-a-joint-task-of | null | null | https://aclanthology.org/2022.coling-1.345 | https://aclanthology.org/2022.coling-1.345.pdf | Singlish Message Paraphrasing: A Joint Task of Creole Translation and Text Normalization | Within the natural language processing community, English is by far the most resource-rich language. There is emerging interest in conducting translation via computational approaches to conform its dialects or creole languages back to standard English. This computational approach paves the way to leverage generic English language backbones, which are beneficial for various downstream tasks. However, in practical online communication scenarios, the use of language varieties is often accompanied by noisy user-generated content, making this translation task more challenging. In this work, we introduce a joint paraphrasing task of creole translation and text normalization of Singlish messages, which can shed light on how to process other language varieties and dialects. We formulate the task in three different linguistic dimensions: lexical level normalization, syntactic level editing, and semantic level rewriting. We build an annotated dataset of Singlish-to-Standard English messages, and report performance on a perturbation-resilient sequence-to-sequence model. Experimental results show that the model produces reasonable generation results, and can improve the performance of downstream tasks like stance detection. | ['Nancy F. Chen', 'Ai Ti Aw', 'Shikang Ni', 'Zhengyuan Liu'] | null | null | null | null | coling-2022-10 | ['stance-detection'] | ['natural-language-processing'] | [ 5.88305652e-01 -7.52484575e-02 -4.02835608e-01 -4.42031085e-01
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73367228-1491-42ce-b3ff-04fdff88a472 | adapt-at-semeval-2018-task-9-skip-gram-word | null | null | https://aclanthology.org/S18-1151 | https://aclanthology.org/S18-1151.pdf | ADAPT at SemEval-2018 Task 9: Skip-Gram Word Embeddings for Unsupervised Hypernym Discovery in Specialised Corpora | This paper describes a simple but competitive unsupervised system for hypernym discovery. The system uses skip-gram word embeddings with negative sampling, trained on specialised corpora. Candidate hypernyms for an input word are predicted based based on cosine similarity scores. Two sets of word embedding models were trained separately on two specialised corpora: a medical corpus and a music industry corpus. Our system scored highest in the medical domain among the competing unsupervised systems but performed poorly on the music industry domain. Our system does not depend on any external data other than raw specialised corpora. | ['Filip Klubi{\\v{c}}ka', 'Alfredo Maldonado'] | 2018-06-01 | null | null | null | semeval-2018-6 | ['hypernym-discovery'] | ['natural-language-processing'] | [ 2.11095572e-01 5.80727935e-01 -2.83687115e-01 -1.42360985e-01
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42a71a7c-c40d-44e6-9601-0a142e0de576 | phonemic-transcription-of-low-resource-tonal | null | null | https://aclanthology.org/U17-1006 | https://aclanthology.org/U17-1006.pdf | Phonemic Transcription of Low-Resource Tonal Languages | null | ['Alexis Michaud', 'Trevor Cohn', 'Oliver Adams', 'Graham Neubig'] | 2017-12-01 | phonemic-transcription-of-low-resource-tonal-1 | https://aclanthology.org/U17-1006 | https://aclanthology.org/U17-1006.pdf | alta-2017-12 | ['acoustic-modelling'] | ['speech'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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228bb6d2-fc37-486c-b7df-83369a694b8a | why-an-android-app-is-classified-as-malware | 2004.11516 | null | https://arxiv.org/abs/2004.11516v2 | https://arxiv.org/pdf/2004.11516v2.pdf | Why an Android App is Classified as Malware? Towards Malware Classification Interpretation | Machine learning (ML) based approach is considered as one of the most promising techniques for Android malware detection and has achieved high accuracy by leveraging commonly-used features. In practice, most of the ML classifications only provide a binary label to mobile users and app security analysts. However, stakeholders are more interested in the reason why apps are classified as malicious in both academia and industry. This belongs to the research area of interpretable ML but in a specific research domain (i.e., mobile malware detection). Although several interpretable ML methods have been exhibited to explain the final classification results in many cutting-edge Artificial Intelligent (AI) based research fields, till now, there is no study interpreting why an app is classified as malware or unveiling the domain-specific challenges. In this paper, to fill this gap, we propose a novel and interpretable ML-based approach (named XMal) to classify malware with high accuracy and explain the classification result meanwhile. (1) The first classification phase of XMal hinges multi-layer perceptron (MLP) and attention mechanism, and also pinpoints the key features most related to the classification result. (2) The second interpreting phase aims at automatically producing neural language descriptions to interpret the core malicious behaviors within apps. We evaluate the behavior description results by comparing with the existing interpretable ML-based methods (i.e., Drebin and LIME) to demonstrate the effectiveness of XMal. We find that XMal is able to reveal the malicious behaviors more accurately. Additionally, our experiments show that XMal can also interpret the reason why some samples are misclassified by ML classifiers. Our study peeks into the interpretable ML through the research of Android malware detection and analysis. | ['Weiping Wen', 'Michael R. Lyu', 'Bozhi Wu', 'Yang Liu', 'Sen Chen', 'Cuiyun Gao', 'Lingling Fan'] | 2020-04-24 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 3.29740077e-01 -6.36977926e-02 -7.93209493e-01 -3.10020715e-01
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54e22e53-6582-4f4e-9811-d499331492a0 | anomaly-detection-with-inexact-labels | 1909.04807 | null | https://arxiv.org/abs/1909.04807v1 | https://arxiv.org/pdf/1909.04807v1.pdf | Anomaly Detection with Inexact Labels | We propose a supervised anomaly detection method for data with inexact anomaly labels, where each label, which is assigned to a set of instances, indicates that at least one instance in the set is anomalous. Although many anomaly detection methods have been proposed, they cannot handle inexact anomaly labels. To measure the performance with inexact anomaly labels, we define the inexact AUC, which is our extension of the area under the ROC curve (AUC) for inexact labels. The proposed method trains an anomaly score function so that the smooth approximation of the inexact AUC increases while anomaly scores for non-anomalous instances become low. We model the anomaly score function by a neural network-based unsupervised anomaly detection method, e.g., autoencoders. The proposed method performs well even when only a small number of inexact labels are available by incorporating an unsupervised anomaly detection mechanism with inexact AUC maximization. Using various datasets, we experimentally demonstrate that our proposed method improves the anomaly detection performance with inexact anomaly labels, and outperforms existing unsupervised and supervised anomaly detection and multiple instance learning methods. | ['Shotaro Tora', 'Machiko Toyoda', 'Tomoharu Iwata', 'Naonori Ueda'] | 2019-09-11 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 1.16320916e-01 -5.31084538e-02 1.54582366e-01 -7.90124416e-01
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9065801b-d5ae-4a8b-a628-cf9d4888c780 | improving-convergence-for-nonconvex-composite | 2009.10629 | null | https://arxiv.org/abs/2009.10629v4 | https://arxiv.org/pdf/2009.10629v4.pdf | Accelerated Gradient Methods for Sparse Statistical Learning with Nonconvex Penalties | Nesterov's accelerated gradient (AG) is a popular technique to optimize objective functions comprising two components: a convex loss and a penalty function. While AG methods perform well for convex penalties, such as the LASSO, convergence issues may arise when it is applied to nonconvex penalties, such as SCAD. A recent proposal generalizes Nesterov's AG method to the nonconvex setting. The proposed algorithm requires specification of several hyperparameters for its practical application. Aside from some general conditions, there is no explicit rule for selecting the hyperparameters, and how different selection can affect convergence of the algorithm. In this article, we propose a hyperparameter setting based on the complexity upper bound to accelerate convergence, and consider the application of this nonconvex AG algorithm to high-dimensional linear and logistic sparse learning problems. We further establish the rate of convergence and present a simple and useful bound to characterize our proposed optimal damping sequence. Simulation studies show that convergence can be made, on average, considerably faster than that of the conventional proximal gradient algorithm. Our experiments also show that the proposed method generally outperforms the current state-of-the-art methods in terms of signal recovery. | ['Masoud Asgharian', 'Sahir Bhatnagar', 'Kai Yang'] | 2020-09-22 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [-7.91488513e-02 -2.50706196e-01 -2.41147488e-01 -3.83787125e-01
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a3cd2945-b104-4006-9abf-f6cbdcf7c69a | qos-aware-big-service-composition-using | null | null | https://onlinelibrary.wiley.com/doi/10.1002/cpe.6362 | https://onlinelibrary.wiley.com/share/author/NKUW9XJGQQ4WEXWI9XZI?target=10.1002/cpe.6362 | QoS-aware Big Service Composition using Distributed Co-Evolutionary Algorithm | Big services are collections of interrelated web services across virtual and physical domains, processing Big Data. Existing service selection and composition algorithms fail to achieve the global optimum solution in a reasonable time. In this paper, we design an efficient quality of service‐aware big service composition methodology using a distributed co‐evolutionary algorithm. In our proposed model, we develop a distributed NSGA‐III for finding the optimal Pareto front and a distributed multi‐objective Jaya algorithm for enhancing the diversity of solutions. The distributed co‐evolutionary algorithm finds the near‐optimal solution in a fast and scalable way. | ['Ugo Fiore', 'G R Gangadharan', 'Chandrashekar Jatoth', 'Avik Dutta'] | 2021-09-14 | null | null | null | concurrency-and-computation-practice-and | ['service-composition'] | ['miscellaneous'] | [-4.75924343e-01 -7.18726516e-01 2.27345139e-01 -4.63129580e-01
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ba1f3b0f-7e09-4a8a-ac11-6280bd659173 | optimal-counterfactual-explanations-in-tree | 2106.06631 | null | https://arxiv.org/abs/2106.06631v2 | https://arxiv.org/pdf/2106.06631v2.pdf | Optimal Counterfactual Explanations in Tree Ensembles | Counterfactual explanations are usually generated through heuristics that are sensitive to the search's initial conditions. The absence of guarantees of performance and robustness hinders trustworthiness. In this paper, we take a disciplined approach towards counterfactual explanations for tree ensembles. We advocate for a model-based search aiming at "optimal" explanations and propose efficient mixed-integer programming approaches. We show that isolation forests can be modeled within our framework to focus the search on plausible explanations with a low outlier score. We provide comprehensive coverage of additional constraints that model important objectives, heterogeneous data types, structural constraints on the feature space, along with resource and actionability restrictions. Our experimental analyses demonstrate that the proposed search approach requires a computational effort that is orders of magnitude smaller than previous mathematical programming algorithms. It scales up to large data sets and tree ensembles, where it provides, within seconds, systematic explanations grounded on well-defined models solved to optimality. | ['Thibaut Vidal', 'Axel Parmentier'] | 2021-06-11 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 5.30041933e-01 6.81932092e-01 -6.42332733e-01 -2.39403099e-01
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c5453116-7cbb-4f08-a2db-9f33b92ee381 | exploring-the-effectiveness-of-dataset | 2306.11763 | null | https://arxiv.org/abs/2306.11763v1 | https://arxiv.org/pdf/2306.11763v1.pdf | Exploring the Effectiveness of Dataset Synthesis: An application of Apple Detection in Orchards | Deep object detection models have achieved notable successes in recent years, but one major obstacle remains: the requirement for a large amount of training data. Obtaining such data is a tedious process and is mainly time consuming, leading to the exploration of new research avenues like synthetic data generation techniques. In this study, we explore the usability of Stable Diffusion 2.1-base for generating synthetic datasets of apple trees for object detection and compare it to a baseline model trained on real-world data. After creating a dataset of realistic apple trees with prompt engineering and utilizing a previously trained Stable Diffusion model, the custom dataset was annotated and evaluated by training a YOLOv5m object detection model to predict apples in a real-world apple detection dataset. YOLOv5m was chosen for its rapid inference time and minimal hardware demands. Results demonstrate that the model trained on generated data is slightly underperforming compared to a baseline model trained on real-world images when evaluated on a set of real-world images. However, these findings remain highly promising, as the average precision difference is only 0.09 and 0.06, respectively. Qualitative results indicate that the model can accurately predict the location of apples, except in cases of heavy shading. These findings illustrate the potential of synthetic data generation techniques as a viable alternative to the collection of extensive training data for object detection models. | ['Klaas Dijkstra', 'Maya Aghaei', 'Alexander van Meekeren'] | 2023-06-20 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation', 'prompt-engineering'] | ['medical', 'miscellaneous', 'natural-language-processing'] | [ 4.53269541e-01 9.72824171e-02 2.09157571e-01 -1.06628530e-01
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967c60e6-ac3d-4de2-9418-d19bb11b6350 | online-clustering-of-bandits | 1401.8257 | null | http://arxiv.org/abs/1401.8257v3 | http://arxiv.org/pdf/1401.8257v3.pdf | Online Clustering of Bandits | We introduce a novel algorithmic approach to content recommendation based on
adaptive clustering of exploration-exploitation ("bandit") strategies. We
provide a sharp regret analysis of this algorithm in a standard stochastic
noise setting, demonstrate its scalability properties, and prove its
effectiveness on a number of artificial and real-world datasets. Our
experiments show a significant increase in prediction performance over
state-of-the-art methods for bandit problems. | ['Giovanni Zappella', 'Shuai Li', 'Claudio Gentile'] | 2014-01-31 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [ 8.71071294e-02 -1.16417661e-01 -9.77679074e-01 -2.31718972e-01
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329fdded-6dae-44f9-8a74-7288f65c327c | scaling-spherical-cnns | 2306.05420 | null | https://arxiv.org/abs/2306.05420v1 | https://arxiv.org/pdf/2306.05420v1.pdf | Scaling Spherical CNNs | Spherical CNNs generalize CNNs to functions on the sphere, by using spherical convolutions as the main linear operation. The most accurate and efficient way to compute spherical convolutions is in the spectral domain (via the convolution theorem), which is still costlier than the usual planar convolutions. For this reason, applications of spherical CNNs have so far been limited to small problems that can be approached with low model capacity. In this work, we show how spherical CNNs can be scaled for much larger problems. To achieve this, we make critical improvements including novel variants of common model components, an implementation of core operations to exploit hardware accelerator characteristics, and application-specific input representations that exploit the properties of our model. Experiments show our larger spherical CNNs reach state-of-the-art on several targets of the QM9 molecular benchmark, which was previously dominated by equivariant graph neural networks, and achieve competitive performance on multiple weather forecasting tasks. Our code is available at https://github.com/google-research/spherical-cnn. | ['Ameesh Makadia', 'Jean-Jacques Slotine', 'Carlos Esteves'] | 2023-06-08 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-1.62104532e-01 3.32843453e-01 9.63201001e-02 -1.83538631e-01
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3c2dc417-04a5-4977-9ec5-07f312b3a0b0 | the-first-international-ancient-chinese-word | null | null | https://aclanthology.org/2022.lt4hala-1.19 | https://aclanthology.org/2022.lt4hala-1.19.pdf | The First International Ancient Chinese Word Segmentation and POS Tagging Bakeoff: Overview of the EvaHan 2022 Evaluation Campaign | This paper presents the results of the First Ancient Chinese Word Segmentation and POS Tagging Bakeoff (EvaHan), which was held at the Second Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) 2022, in the context of the 13th Edition of the Language Resources and Evaluation Conference (LREC 2022). We give the motivation for having an international shared contest, as well as the data and tracks. The contest is consisted of two modalities, closed and open. In the closed modality, the participants are only allowed to use the training data, obtained the highest F1 score of 96.03% and 92.05% in word segmentation and POS tagging. In the open modality, the participants can use whatever resource they have, with the highest F1 score of 96.34% and 92.56% in word segmentation and POS tagging. The scores on the blind test dataset decrease around 3 points, which shows that the out-of-vocabulary words still are the bottleneck for lexical analyzers. | ['Dongbo Wang', 'Weiguang Qu', 'Chao Xu', 'Minxuan Feng', 'Jingya Lu', 'Yiguo Yuan', 'Bin Li'] | null | null | null | null | lt4hala-lrec-2022-6 | ['chinese-word-segmentation'] | ['natural-language-processing'] | [-2.59469569e-01 1.50327891e-01 -4.51999425e-04 -7.85252079e-03
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6326ed40-7b4d-4f1c-a8d9-8ad389b6955c | scene-parsing-via-dense-recurrent-neural | 1811.04778 | null | http://arxiv.org/abs/1811.04778v1 | http://arxiv.org/pdf/1811.04778v1.pdf | Scene Parsing via Dense Recurrent Neural Networks with Attentional Selection | Recurrent neural networks (RNNs) have shown the ability to improve scene
parsing through capturing long-range dependencies among image units. In this
paper, we propose dense RNNs for scene labeling by exploring various long-range
semantic dependencies among image units. Different from existing RNN based
approaches, our dense RNNs are able to capture richer contextual dependencies
for each image unit by enabling immediate connections between each pair of
image units, which significantly enhances their discriminative power. Besides,
to select relevant dependencies and meanwhile to restrain irrelevant ones for
each unit from dense connections, we introduce an attention model into dense
RNNs. The attention model allows automatically assigning more importance to
helpful dependencies while less weight to unconcerned dependencies. Integrating
with convolutional neural networks (CNNs), we develop an end-to-end scene
labeling system. Extensive experiments on three large-scale benchmarks
demonstrate that the proposed approach can improve the baselines by large
margins and outperform other state-of-the-art algorithms. | ['Heng Fan', 'Haibin Ling', 'Longin Jan Latecki', 'Peng Chu'] | 2018-11-09 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 4.85543400e-01 3.44505578e-01 -2.84934223e-01 -7.08617210e-01
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2ad551aa-bcf5-480f-9cd0-be20bce0541f | inference-in-sparse-graphs-with-pairwise | 1703.02728 | null | http://arxiv.org/abs/1703.02728v3 | http://arxiv.org/pdf/1703.02728v3.pdf | Inference in Sparse Graphs with Pairwise Measurements and Side Information | We consider the statistical problem of recovering a hidden "ground truth"
binary labeling for the vertices of a graph up to low Hamming error from noisy
edge and vertex measurements. We present new algorithms and a sharp
finite-sample analysis for this problem on trees and sparse graphs with poor
expansion properties such as hypergrids and ring lattices. Our method
generalizes and improves over that of Globerson et al. (2015), who introduced
the problem for two-dimensional grid lattices.
For trees we provide a simple, efficient, algorithm that infers the ground
truth with optimal Hamming error has optimal sample complexity and implies
recovery results for all connected graphs. Here, the presence of side
information is critical to obtain a non-trivial recovery rate. We then show how
to adapt this algorithm to tree decompositions of edge-subgraphs of certain
graph families such as lattices, resulting in optimal recovery error rates that
can be obtained efficiently
The thrust of our analysis is to 1) use the tree decomposition along with
edge measurements to produce a small class of viable vertex labelings and 2)
apply an analysis influenced by statistical learning theory to show that we can
infer the ground truth from this class using vertex measurements. We show the
power of our method in several examples including hypergrids, ring lattices,
and the Newman-Watts model for small world graphs. For two-dimensional grids,
our results improve over Globerson et al. (2015) by obtaining optimal recovery
in the constant-height regime. | ['Dylan J. Foster', 'Daniel Reichman', 'Karthik Sridharan'] | 2017-03-08 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [ 5.39114833e-01 7.25669086e-01 -1.96458116e-01 2.07292184e-01
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614a4119-6c28-460b-9a73-55f3d5680a48 | understanding-metrics-for-paraphrasing | 2205.13119 | null | https://arxiv.org/abs/2205.13119v1 | https://arxiv.org/pdf/2205.13119v1.pdf | Understanding Metrics for Paraphrasing | Paraphrase generation is a difficult problem. This is not only because of the limitations in text generation capabilities but also due that to the lack of a proper definition of what qualifies as a paraphrase and corresponding metrics to measure how good it is. Metrics for evaluation of paraphrasing quality is an on going research problem. Most of the existing metrics in use having been borrowed from other tasks do not capture the complete essence of a good paraphrase, and often fail at borderline-cases. In this work, we propose a novel metric $ROUGE_P$ to measure the quality of paraphrases along the dimensions of adequacy, novelty and fluency. We also provide empirical evidence to show that the current natural language generation metrics are insufficient to measure these desired properties of a good paraphrase. We look at paraphrase model fine-tuning and generation from the lens of metrics to gain a deeper understanding of what it takes to generate and evaluate a good paraphrase. | ['Tarun Joshi', 'Rahul Singh', 'Omkar Patil'] | 2022-05-26 | null | null | null | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 1.59071311e-01 -2.40373593e-02 -8.63350406e-02 -2.72001714e-01
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-9.61441994e-02 -2.75726765e-01 -3.52265000e-01 -3.06114376e-01] | [11.442992210388184, 9.145482063293457] |
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