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for use in text compression. Our system improves upon previous neural network
based models by utilizing recent advances in syntactic parsing -- Google's
SyntaxNet -- to augment character-level recurrent neural networks. RNNs have
proven exceptional in modeling sequence data such as text, as their
architecture allows for modeling of long-term contextual information. | ['David Cox'] | 2016-08-08 | null | null | null | null | ['text-compression'] | ['natural-language-processing'] | [ 3.21956217e-01 2.58161366e-01 -5.85965216e-01 -5.68861246e-01
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5be8385e-ae70-47e2-8dee-6bc15a3071f8 | local-exceptionality-detection-in-time-series | 2108.11751 | null | https://arxiv.org/abs/2108.11751v1 | https://arxiv.org/pdf/2108.11751v1.pdf | Local Exceptionality Detection in Time Series Using Subgroup Discovery | In this paper, we present a novel approach for local exceptionality detection on time series data. This method provides the ability to discover interpretable patterns in the data, which can be used to understand and predict the progression of a time series. This being an exploratory approach, the results can be used to generate hypotheses about the relationships between the variables describing a specific process and its dynamics. We detail our approach in a concrete instantiation and exemplary implementation, specifically in the field of teamwork research. Using a real-world dataset of team interactions we include results from an example data analytics application of our proposed approach, showcase novel analysis options, and discuss possible implications of the results from the perspective of teamwork research. | ['Martin Atzmueller', 'Travis J. Wiltshire', 'Dan Hudson'] | 2021-08-05 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 1.85890853e-01 7.47618675e-02 2.78184023e-02 -2.24188060e-01
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04d1cac0-fb8a-4a7d-b6b4-5037f886d387 | geometrically-adaptive-dictionary-attack-on | 2111.04371 | null | https://arxiv.org/abs/2111.04371v1 | https://arxiv.org/pdf/2111.04371v1.pdf | Geometrically Adaptive Dictionary Attack on Face Recognition | CNN-based face recognition models have brought remarkable performance improvement, but they are vulnerable to adversarial perturbations. Recent studies have shown that adversaries can fool the models even if they can only access the models' hard-label output. However, since many queries are needed to find imperceptible adversarial noise, reducing the number of queries is crucial for these attacks. In this paper, we point out two limitations of existing decision-based black-box attacks. We observe that they waste queries for background noise optimization, and they do not take advantage of adversarial perturbations generated for other images. We exploit 3D face alignment to overcome these limitations and propose a general strategy for query-efficient black-box attacks on face recognition named Geometrically Adaptive Dictionary Attack (GADA). Our core idea is to create an adversarial perturbation in the UV texture map and project it onto the face in the image. It greatly improves query efficiency by limiting the perturbation search space to the facial area and effectively recycling previous perturbations. We apply the GADA strategy to two existing attack methods and show overwhelming performance improvement in the experiments on the LFW and CPLFW datasets. Furthermore, we also present a novel attack strategy that can circumvent query similarity-based stateful detection that identifies the process of query-based black-box attacks. | ['Changick Kim', 'Hyojun Go', 'Junyoung Byun'] | 2021-11-08 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [ 4.45367277e-01 -2.55932540e-01 1.88759461e-01 -1.28601402e-01
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d4d82bbc-13d9-41cd-afd0-eaa28c4313c1 | trojanpuzzle-covertly-poisoning-code | 2301.02344 | null | https://arxiv.org/abs/2301.02344v1 | https://arxiv.org/pdf/2301.02344v1.pdf | TrojanPuzzle: Covertly Poisoning Code-Suggestion Models | With tools like GitHub Copilot, automatic code suggestion is no longer a dream in software engineering. These tools, based on large language models, are typically trained on massive corpora of code mined from unvetted public sources. As a result, these models are susceptible to data poisoning attacks where an adversary manipulates the model's training or fine-tuning phases by injecting malicious data. Poisoning attacks could be designed to influence the model's suggestions at run time for chosen contexts, such as inducing the model into suggesting insecure code payloads. To achieve this, prior poisoning attacks explicitly inject the insecure code payload into the training data, making the poisoning data detectable by static analysis tools that can remove such malicious data from the training set. In this work, we demonstrate two novel data poisoning attacks, COVERT and TROJANPUZZLE, that can bypass static analysis by planting malicious poisoning data in out-of-context regions such as docstrings. Our most novel attack, TROJANPUZZLE, goes one step further in generating less suspicious poisoning data by never including certain (suspicious) parts of the payload in the poisoned data, while still inducing a model that suggests the entire payload when completing code (i.e., outside docstrings). This makes TROJANPUZZLE robust against signature-based dataset-cleansing methods that identify and filter out suspicious sequences from the training data. Our evaluation against two model sizes demonstrates that both COVERT and TROJANPUZZLE have significant implications for how practitioners should select code used to train or tune code-suggestion models. | ['Robert Sim', 'Ben Zorn', 'David Evans', 'Giovanni Vigna', 'Christopher Kruegel', 'Anant Kharkar', 'Xavier Fernandes', 'Andre Manoel', 'Wei Dai', 'Hojjat Aghakhani'] | 2023-01-06 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 1.20708898e-01 -1.18331112e-01 -4.99048322e-01 -2.34198086e-02
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ed6f43cf-452b-42ba-85b4-befc973cba5b | shared-task-on-feedback-comment-generation | null | null | https://aclanthology.org/2021.inlg-1.35 | https://aclanthology.org/2021.inlg-1.35.pdf | Shared Task on Feedback Comment Generation for Language Learners | In this paper, we propose a generation challenge called Feedback comment generation for language learners. It is a task where given a text and a span, a system generates, for the span, an explanatory note that helps the writer (language learner) improve their writing skills. The motivations for this challenge are: (i) practically, it will be beneficial for both language learners and teachers if a computer-assisted language learning system can provide feedback comments just as human teachers do; (ii) theoretically, feedback comment generation for language learners has a mixed aspect of other generation tasks together with its unique features and it will be interesting to explore what kind of generation technique is effective against what kind of writing rule. To this end, we have created a dataset and developed baseline systems to estimate baseline performance. With these preparations, we propose a generation challenge of feedback comment generation. | ['Olena Nahorna', 'Artem Chernodub', 'Masato Mita', 'Kazuaki Hanawa', 'Masato Hagiwara', 'Ryo Nagata'] | null | null | null | null | inlg-acl-2021-8 | ['comment-generation'] | ['natural-language-processing'] | [ 1.67348966e-01 6.37175918e-01 -1.94251508e-01 -8.15032274e-02
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a0f7d558-dbd8-4426-9b00-377600eaa56e | motchallenge-a-benchmark-for-single-camera | 2010.07548 | null | https://arxiv.org/abs/2010.07548v2 | https://arxiv.org/pdf/2010.07548v2.pdf | MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking | Standardized benchmarks have been crucial in pushing the performance of computer vision algorithms, especially since the advent of deep learning. Although leaderboards should not be over-claimed, they often provide the most objective measure of performance and are therefore important guides for research. We present MOTChallenge, a benchmark for single-camera Multiple Object Tracking (MOT) launched in late 2014, to collect existing and new data, and create a framework for the standardized evaluation of multiple object tracking methods. The benchmark is focused on multiple people tracking, since pedestrians are by far the most studied object in the tracking community, with applications ranging from robot navigation to self-driving cars. This paper collects the first three releases of the benchmark: (i) MOT15, along with numerous state-of-the-art results that were submitted in the last years, (ii) MOT16, which contains new challenging videos, and (iii) MOT17, that extends MOT16 sequences with more precise labels and evaluates tracking performance on three different object detectors. The second and third release not only offers a significant increase in the number of labeled boxes but also provide labels for multiple object classes beside pedestrians, as well as the level of visibility for every single object of interest. We finally provide a categorization of state-of-the-art trackers and a broad error analysis. This will help newcomers understand the related work and research trends in the MOT community, and hopefully shed some light on potential future research directions. | ['Laura Leal-Taixé', 'Stefan Roth', 'Ian Reid', 'Daniel Cremers', 'Konrad Schindler', 'Anton Milan', 'Aljoša Ošep', 'Patrick Dendorfer'] | 2020-10-15 | null | null | null | null | ['multiple-people-tracking'] | ['computer-vision'] | [-1.91067576e-01 -4.78866279e-01 -2.73110151e-01 -5.63086383e-02
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49520261-b9e4-4f19-a5f4-11037eaf1dd8 | causal-kl-evaluating-causal-discovery | 2111.06029 | null | https://arxiv.org/abs/2111.06029v1 | https://arxiv.org/pdf/2111.06029v1.pdf | Causal KL: Evaluating Causal Discovery | The two most commonly used criteria for assessing causal model discovery with artificial data are edit-distance and Kullback-Leibler divergence, measured from the true model to the learned model. Both of these metrics maximally reward the true model. However, we argue that they are both insufficiently discriminating in judging the relative merits of false models. Edit distance, for example, fails to distinguish between strong and weak probabilistic dependencies. KL divergence, on the other hand, rewards equally all statistically equivalent models, regardless of their different causal claims. We propose an augmented KL divergence, which we call Causal KL (CKL), which takes into account causal relationships which distinguish between observationally equivalent models. Results are presented for three variants of CKL, showing that Causal KL works well in practice. | ['Lloyd Allison', 'Kevin B. Korb', "Rodney T. O'Donnell"] | 2021-11-11 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 1.94073975e-01 1.97592989e-01 -3.91287684e-01 -5.61949253e-01
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fd31b67f-acc9-4cce-8b82-682a63ded1cc | biored-a-comprehensive-biomedical-relation | 2204.04263 | null | https://arxiv.org/abs/2204.04263v2 | https://arxiv.org/pdf/2204.04263v2.pdf | BioRED: A Rich Biomedical Relation Extraction Dataset | Automated relation extraction (RE) from biomedical literature is critical for many downstream text mining applications in both research and real-world settings. However, most existing benchmarking datasets for bio-medical RE only focus on relations of a single type (e.g., protein-protein interactions) at the sentence level, greatly limiting the development of RE systems in biomedicine. In this work, we first review commonly used named entity recognition (NER) and RE datasets. Then we present BioRED, a first-of-its-kind biomedical RE corpus with multiple entity types (e.g., gene/protein, disease, chemical) and relation pairs (e.g., gene-disease; chemical-chemical) at the document level, on a set of 600 PubMed abstracts. Further, we label each relation as describing either a novel finding or previously known background knowledge, enabling automated algorithms to differentiate between novel and background information. We assess the utility of BioRED by benchmarking several existing state-of-the-art methods, including BERT-based models, on the NER and RE tasks. Our results show that while existing approaches can reach high performance on the NER task (F-score of 89.3%), there is much room for improvement for the RE task, especially when extracting novel relations (F-score of 47.7%). Our experiments also demonstrate that such a rich dataset can successfully facilitate the development of more accurate, efficient, and robust RE systems for biomedicine. The BioRED dataset and annotation guideline are freely available at https://ftp.ncbi.nlm.nih.gov/pub/lu/BioRED/. | ['Zhiyong Lu', 'Cecilia N Arighi', 'Chih-Hsuan Wei', 'Po-Ting Lai', 'Ling Luo'] | 2022-04-08 | null | null | null | null | ['binary-relation-extraction'] | ['natural-language-processing'] | [ 1.71716303e-01 1.79215074e-01 -2.62967855e-01 -5.31259067e-02
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45f6776c-0218-4c96-9ded-11421a7e960f | computer-aided-tuberculosis-diagnosis-with | 2207.00251 | null | https://arxiv.org/abs/2207.00251v1 | https://arxiv.org/pdf/2207.00251v1.pdf | Computer-aided Tuberculosis Diagnosis with Attribute Reasoning Assistance | Although deep learning algorithms have been intensively developed for computer-aided tuberculosis diagnosis (CTD), they mainly depend on carefully annotated datasets, leading to much time and resource consumption. Weakly supervised learning (WSL), which leverages coarse-grained labels to accomplish fine-grained tasks, has the potential to solve this problem. In this paper, we first propose a new large-scale tuberculosis (TB) chest X-ray dataset, namely the tuberculosis chest X-ray attribute dataset (TBX-Att), and then establish an attribute-assisted weakly-supervised framework to classify and localize TB by leveraging the attribute information to overcome the insufficiency of supervision in WSL scenarios. Specifically, first, the TBX-Att dataset contains 2000 X-ray images with seven kinds of attributes for TB relational reasoning, which are annotated by experienced radiologists. It also includes the public TBX11K dataset with 11200 X-ray images to facilitate weakly supervised detection. Second, we exploit a multi-scale feature interaction model for TB area classification and detection with attribute relational reasoning. The proposed model is evaluated on the TBX-Att dataset and will serve as a solid baseline for future research. The code and data will be available at https://github.com/GangmingZhao/tb-attribute-weak-localization. | ['Yizhou Yu', 'Jinpeng Li', 'Dingwen Zhang', 'Chaowei Fang', 'Jiaheng Liu', 'Baolian Qi', 'Junjie Fang', 'Gangming Zhao', 'Chengwei Pan'] | 2022-07-01 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 0.33058122 0.0322638 -0.75918823 -0.5693452 -1.4069613 -0.10310203
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44cc0209-06aa-440b-95c4-6afb59bab043 | classification-of-categorical-time-series | 2102.02794 | null | https://arxiv.org/abs/2102.02794v1 | https://arxiv.org/pdf/2102.02794v1.pdf | Classification of Categorical Time Series Using the Spectral Envelope and Optimal Scalings | This article introduces a novel approach to the classification of categorical time series under the supervised learning paradigm. To construct meaningful features for categorical time series classification, we consider two relevant quantities: the spectral envelope and its corresponding set of optimal scalings. These quantities characterize oscillatory patterns in a categorical time series as the largest possible power at each frequency, or spectral envelope, obtained by assigning numerical values, or scalings, to categories that optimally emphasize oscillations at each frequency. Our procedure combines these two quantities to produce an interpretable and parsimonious feature-based classifier that can be used to accurately determine group membership for categorical time series. Classification consistency of the proposed method is investigated, and simulation studies are used to demonstrate accuracy in classifying categorical time series with various underlying group structures. Finally, we use the proposed method to explore key differences in oscillatory patterns of sleep stage time series for patients with different sleep disorders and accurately classify patients accordingly. | ['Tian Cai', 'Scott A. Bruce', 'Zeda Li'] | 2021-02-04 | null | null | null | null | ['image-classification-shift-consistency'] | ['computer-vision'] | [ 2.37782046e-01 -4.27792877e-01 -4.84619141e-01 -4.76252347e-01
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fa78b614-34fc-418d-95fd-9cdc40edb38b | pico-primitive-imitation-for-control | 2006.12551 | null | https://arxiv.org/abs/2006.12551v1 | https://arxiv.org/pdf/2006.12551v1.pdf | PICO: Primitive Imitation for COntrol | In this work, we explore a novel framework for control of complex systems called Primitive Imitation for Control PICO. The approach combines ideas from imitation learning, task decomposition, and novel task sequencing to generalize from demonstrations to new behaviors. Demonstrations are automatically decomposed into existing or missing sub-behaviors which allows the framework to identify novel behaviors while not duplicating existing behaviors. Generalization to new tasks is achieved through dynamic blending of behavior primitives. We evaluated the approach using demonstrations from two different robotic platforms. The experimental results show that PICO is able to detect the presence of a novel behavior primitive and build the missing control policy. | ['Edward W. Staley', 'Kapil D. Katyal', 'Bart L. Paulhamus', 'Chace Ashcraft', 'Katie M. Popek', 'Corban G. Rivera'] | 2020-06-22 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [-1.01113394e-02 1.35799110e-01 8.18133913e-03 1.18261404e-01
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dc2adbf5-fd97-406d-bee0-4d4d3c98b725 | chain-of-skills-a-configurable-model-for-open | 2305.03130 | null | https://arxiv.org/abs/2305.03130v2 | https://arxiv.org/pdf/2305.03130v2.pdf | Chain-of-Skills: A Configurable Model for Open-domain Question Answering | The retrieval model is an indispensable component for real-world knowledge-intensive tasks, e.g., open-domain question answering (ODQA). As separate retrieval skills are annotated for different datasets, recent work focuses on customized methods, limiting the model transferability and scalability. In this work, we propose a modular retriever where individual modules correspond to key skills that can be reused across datasets. Our approach supports flexible skill configurations based on the target domain to boost performance. To mitigate task interference, we design a novel modularization parameterization inspired by sparse Transformer. We demonstrate that our model can benefit from self-supervised pretraining on Wikipedia and fine-tuning using multiple ODQA datasets, both in a multi-task fashion. Our approach outperforms recent self-supervised retrievers in zero-shot evaluations and achieves state-of-the-art fine-tuned retrieval performance on NQ, HotpotQA and OTT-QA. | ['Jianfeng Gao', 'Eric Nyberg', 'Xiaodong Liu', 'Yu Zhang', 'Hao Cheng', 'Kaixin Ma'] | 2023-05-04 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [-1.31443173e-01 -5.86693101e-02 -1.13792801e-02 -2.12166533e-01
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8d211213-4ea6-429a-882a-9520b8c27610 | what-and-when-to-look-temporal-span-proposal | 2107.07154 | null | https://arxiv.org/abs/2107.07154v2 | https://arxiv.org/pdf/2107.07154v2.pdf | What and When to Look?: Temporal Span Proposal Network for Video Relation Detection | Identifying relations between objects is central to understanding the scene. While several works have been proposed for relation modeling in the image domain, there have been many constraints in the video domain due to challenging dynamics of spatio-temporal interactions (e.g., between which objects are there an interaction? when do relations start and end?). To date, two representative methods have been proposed to tackle Video Visual Relation Detection (VidVRD): segment-based and window-based. We first point out limitations of these methods and propose a novel approach named Temporal Span Proposal Network (TSPN). TSPN tells what to look: it sparsifies relation search space by scoring relationness of object pair, i.e., measuring how probable a relation exist. TSPN tells when to look: it simultaneously predicts start-end timestamps (i.e., temporal spans) and categories of the all possible relations by utilizing full video context. These two designs enable a win-win scenario: it accelerates training by 2X or more than existing methods and achieves competitive performance on two VidVRD benchmarks (ImageNet-VidVDR and VidOR). Moreover, comprehensive ablative experiments demonstrate the effectiveness of our approach. Codes are available at https://github.com/sangminwoo/Temporal-Span-Proposal-Network-VidVRD. | ['Kangil Kim', 'Junhyug Noh', 'Sangmin Woo'] | 2021-07-15 | null | null | null | null | ['video-visual-relation-detection'] | ['computer-vision'] | [ 2.41565574e-02 -2.17892990e-01 -5.88305593e-01 -1.93158492e-01
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44813055-8958-4a78-887a-27e3fa09dca1 | roi-tanh-polar-transformer-network-for-face | 2102.02717 | null | https://arxiv.org/abs/2102.02717v3 | https://arxiv.org/pdf/2102.02717v3.pdf | RoI Tanh-polar Transformer Network for Face Parsing in the Wild | Face parsing aims to predict pixel-wise labels for facial components of a target face in an image. Existing approaches usually crop the target face from the input image with respect to a bounding box calculated during pre-processing, and thus can only parse inner facial Regions of Interest~(RoIs). Peripheral regions like hair are ignored and nearby faces that are partially included in the bounding box can cause distractions. Moreover, these methods are only trained and evaluated on near-frontal portrait images and thus their performance for in-the-wild cases has been unexplored. To address these issues, this paper makes three contributions. First, we introduce iBugMask dataset for face parsing in the wild, which consists of 21,866 training images and 1,000 testing images. The training images are obtained by augmenting an existing dataset with large face poses. The testing images are manually annotated with $11$ facial regions and there are large variations in sizes, poses, expressions and background. Second, we propose RoI Tanh-polar transform that warps the whole image to a Tanh-polar representation with a fixed ratio between the face area and the context, guided by the target bounding box. The new representation contains all information in the original image, and allows for rotation equivariance in the convolutional neural networks~(CNNs). Third, we propose a hybrid residual representation learning block, coined HybridBlock, that contains convolutional layers in both the Tanh-polar space and the Tanh-Cartesian space, allowing for receptive fields of different shapes in CNNs. Through extensive experiments, we show that the proposed method improves the state-of-the-art for face parsing in the wild and does not require facial landmarks for alignment. | ['Maja Pantic', 'Yujiang Wang', 'Jie Shen', 'Yiming Lin'] | 2021-02-04 | null | null | null | null | ['face-parsing'] | ['computer-vision'] | [ 3.26824397e-01 5.17767012e-01 -7.75781870e-02 -9.24602687e-01
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58379462-e2e3-40a3-911e-0cc00d1ca145 | recycling-an-anechoic-pre-trained-speech | 2208.04626 | null | https://arxiv.org/abs/2208.04626v1 | https://arxiv.org/pdf/2208.04626v1.pdf | Recycling an anechoic pre-trained speech separation deep neural network for binaural dereverberation of a single source | Reverberation results in reduced intelligibility for both normal and hearing-impaired listeners. This paper presents a novel psychoacoustic approach of dereverberation of a single speech source by recycling a pre-trained binaural anechoic speech separation neural network. As training the deep neural network (DNN) is a lengthy and computationally expensive process, the advantage of using a pre-trained separation network for dereverberation is that the network does not need to be retrained, saving both time and computational resources. The interaural cues of a reverberant source are given to this pretrained neural network to discriminate between the direct path signal and the reverberant speech. The results show an average improvement of 1.3% in signal intelligibility, 0.83 dB in SRMR (signal to reverberation energy ratio) and 0.16 points in perceptual evaluation of speech quality (PESQ) over other state-of-the-art signal processing dereverberation algorithms and 14% in intelligibility and 0.35 points in quality over orthogonal matching pursuit with spectral subtraction (OSS), a machine learning based dereverberation algorithm. | ['Ata Ur-Rehman', 'Syed Waqar Shah', 'Muhammad Salman Khan', 'Sania Gul'] | 2022-08-09 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 2.71600902e-01 -2.93048322e-01 1.12884951e+00 -2.62147933e-02
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96f8c01f-1e7e-4df8-aa68-7618bad480a1 | backdoor-attacks-with-input-unique-triggers | 2303.14325 | null | https://arxiv.org/abs/2303.14325v1 | https://arxiv.org/pdf/2303.14325v1.pdf | Backdoor Attacks with Input-unique Triggers in NLP | Backdoor attack aims at inducing neural models to make incorrect predictions for poison data while keeping predictions on the clean dataset unchanged, which creates a considerable threat to current natural language processing (NLP) systems. Existing backdoor attacking systems face two severe issues:firstly, most backdoor triggers follow a uniform and usually input-independent pattern, e.g., insertion of specific trigger words, synonym replacement. This significantly hinders the stealthiness of the attacking model, leading the trained backdoor model being easily identified as malicious by model probes. Secondly, trigger-inserted poisoned sentences are usually disfluent, ungrammatical, or even change the semantic meaning from the original sentence, making them being easily filtered in the pre-processing stage. To resolve these two issues, in this paper, we propose an input-unique backdoor attack(NURA), where we generate backdoor triggers unique to inputs. IDBA generates context-related triggers by continuing writing the input with a language model like GPT2. The generated sentence is used as the backdoor trigger. This strategy not only creates input-unique backdoor triggers, but also preserves the semantics of the original input, simultaneously resolving the two issues above. Experimental results show that the IDBA attack is effective for attack and difficult to defend: it achieves high attack success rate across all the widely applied benchmarks, while is immune to existing defending methods. In addition, it is able to generate fluent, grammatical, and diverse backdoor inputs, which can hardly be recognized through human inspection. | ['Jun He', 'Muqiao Yang', 'Lingjuan Lyu', 'Tianwei Zhang', 'Jiwei Li', 'Xukun Zhou'] | 2023-03-25 | null | null | null | null | ['backdoor-attack'] | ['adversarial'] | [ 2.23267660e-01 -2.14229345e-01 -8.50515664e-02 -1.05991758e-01
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83540eb5-0f76-47ec-a6d4-37bd98b3db87 | a-bayesian-encourages-dropout | 1412.7003 | null | http://arxiv.org/abs/1412.7003v3 | http://arxiv.org/pdf/1412.7003v3.pdf | A Bayesian encourages dropout | Dropout is one of the key techniques to prevent the learning from
overfitting. It is explained that dropout works as a kind of modified L2
regularization. Here, we shed light on the dropout from Bayesian standpoint.
Bayesian interpretation enables us to optimize the dropout rate, which is
beneficial for learning of weight parameters and prediction after learning. The
experiment result also encourages the optimization of the dropout. | ['Shin-ichi Maeda'] | 2014-12-22 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-3.25777531e-01 2.51626074e-01 -6.51764691e-01 -5.19559979e-01
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cd3628f2-9b9a-4d99-9f2c-04d403578b0e | bayesian-hyperparameter-optimization-for-deep | 2207.09902 | null | https://arxiv.org/abs/2207.09902v1 | https://arxiv.org/pdf/2207.09902v1.pdf | Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection | Traditional network intrusion detection approaches encounter feasibility and sustainability issues to combat modern, sophisticated, and unpredictable security attacks. Deep neural networks (DNN) have been successfully applied for intrusion detection problems. The optimal use of DNN-based classifiers requires careful tuning of the hyper-parameters. Manually tuning the hyperparameters is tedious, time-consuming, and computationally expensive. Hence, there is a need for an automatic technique to find optimal hyperparameters for the best use of DNN in intrusion detection. This paper proposes a novel Bayesian optimization-based framework for the automatic optimization of hyperparameters, ensuring the best DNN architecture. We evaluated the performance of the proposed framework on NSL-KDD, a benchmark dataset for network intrusion detection. The experimental results show the framework's effectiveness as the resultant DNN architecture demonstrates significantly higher intrusion detection performance than the random search optimization-based approach in terms of accuracy, precision, recall, and f1-score. | ['Alfredo Cuzzocrea', 'Muhaiminul I. Adnan', 'Mohammad A. Rahman', 'Md Abdullah Khan', 'Maria Valero', 'Md Jobair Hossain Faruk', 'Hisham Haddad', 'Hossain Shahriar', 'Mohammad Masum'] | 2022-07-07 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [-2.33639672e-01 -6.72933102e-01 -6.82826787e-02 -5.01004755e-01
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b62af475-a5ef-48e9-a256-997bba2b6646 | faster-and-better-grammar-based-text-to-sql | 2204.12186 | null | https://arxiv.org/abs/2204.12186v1 | https://arxiv.org/pdf/2204.12186v1.pdf | Faster and Better Grammar-based Text-to-SQL Parsing via Clause-level Parallel Decoding and Alignment Loss | Grammar-based parsers have achieved high performance in the cross-domain text-to-SQL parsing task, but suffer from low decoding efficiency due to the much larger number of actions for grammar selection than that of tokens in SQL queries. Meanwhile, how to better align SQL clauses and question segments has been a key challenge for parsing performance. Therefore, this paper proposes clause-level parallel decoding and alignment loss to enhance two high-performance grammar-based parsers, i.e., RATSQL and LGESQL. Experimental results of two parsers show that our method obtains consistent improvements both in accuracy and decoding speed. | ['Xinyan Xiao', 'Zhenghua Li', 'Lijie Wang', 'Kun Wu'] | 2022-04-26 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [-5.66361146e-03 -4.75750044e-02 -1.28935620e-01 -8.00078869e-01
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6618d9d6-323a-468e-bb68-d7a95c36fd90 | rec4ad-a-free-lunch-to-mitigate-sample | 2306.03527 | null | https://arxiv.org/abs/2306.03527v1 | https://arxiv.org/pdf/2306.03527v1.pdf | Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao | Click-Through Rate (CTR) prediction serves as a fundamental component in online advertising. A common practice is to train a CTR model on advertisement (ad) impressions with user feedback. Since ad impressions are purposely selected by the model itself, their distribution differs from the inference distribution and thus exhibits sample selection bias (SSB) that affects model performance. Existing studies on SSB mainly employ sample re-weighting techniques which suffer from high variance and poor model calibration. Another line of work relies on costly uniform data that is inadequate to train industrial models. Thus mitigating SSB in industrial models with a uniform-data-free framework is worth exploring. Fortunately, many platforms display mixed results of organic items (i.e., recommendations) and sponsored items (i.e., ads) to users, where impressions of ads and recommendations are selected by different systems but share the same user decision rationales. Based on the above characteristics, we propose to leverage recommendations samples as a free lunch to mitigate SSB for ads CTR model (Rec4Ad). After elaborating data augmentation, Rec4Ad learns disentangled representations with alignment and decorrelation modules for enhancement. When deployed in Taobao display advertising system, Rec4Ad achieves substantial gains in key business metrics, with a lift of up to +6.6\% CTR and +2.9\% RPM. | ['Bo Zheng', 'Jian Xu', 'Yuning Jiang', 'Siran Yang', 'Han Zhu', 'Shuguang Han', 'Jingyue Gao'] | 2023-06-06 | null | null | null | null | ['click-through-rate-prediction', 'selection-bias'] | ['miscellaneous', 'natural-language-processing'] | [ 9.46385562e-02 7.41545111e-02 -6.79149449e-01 -7.56228507e-01
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d134d447-084c-4718-9a4b-b2d19b4452aa | provably-sample-efficient-rl-with-side | 2205.14237 | null | https://arxiv.org/abs/2205.14237v1 | https://arxiv.org/pdf/2205.14237v1.pdf | Provably Sample-Efficient RL with Side Information about Latent Dynamics | We study reinforcement learning (RL) in settings where observations are high-dimensional, but where an RL agent has access to abstract knowledge about the structure of the state space, as is the case, for example, when a robot is tasked to go to a specific room in a building using observations from its own camera, while having access to the floor plan. We formalize this setting as transfer reinforcement learning from an abstract simulator, which we assume is deterministic (such as a simple model of moving around the floor plan), but which is only required to capture the target domain's latent-state dynamics approximately up to unknown (bounded) perturbations (to account for environment stochasticity). Crucially, we assume no prior knowledge about the structure of observations in the target domain except that they can be used to identify the latent states (but the decoding map is unknown). Under these assumptions, we present an algorithm, called TASID, that learns a robust policy in the target domain, with sample complexity that is polynomial in the horizon, and independent of the number of states, which is not possible without access to some prior knowledge. In synthetic experiments, we verify various properties of our algorithm and show that it empirically outperforms transfer RL algorithms that require access to "full simulators" (i.e., those that also simulate observations). | ['Robert E. Schapire', 'Miro Dudík', 'Dipendra Misra', 'Yao Liu'] | 2022-05-27 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [ 2.05907047e-01 6.10315382e-01 -1.39453024e-01 2.38567188e-01
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5769fa1f-ee71-473f-b8ca-0a84d9f1b58d | the-emotions-that-we-perceive-in-music-the | 1909.05882 | null | https://arxiv.org/abs/1909.05882v2 | https://arxiv.org/pdf/1909.05882v2.pdf | The emotions that we perceive in music: the influence of language and lyrics comprehension on agreement | In the present study, we address the relationship between the emotions perceived in pop and rock music (mainly in Euro-American styles with English lyrics) and the language spoken by the listener. Our goal is to understand the influence of lyrics comprehension on the perception of emotions and use this information to improve Music Emotion Recognition (MER) models. Two main research questions are addressed: 1. Are there differences and similarities between the emotions perceived in pop/rock music by listeners raised with different mother tongues? 2. Do personal characteristics have an influence on the perceived emotions for listeners of a given language? Personal characteristics include the listeners' general demographics, familiarity and preference for the fragments, and music sophistication. Our hypothesis is that inter-rater agreement (as defined by Krippendorff's alpha coefficient) from subjects is directly influenced by the comprehension of lyrics. | ['Estefanía Cano', 'Emilia Gómez', 'Perfecto Herrera', 'Juan Sebastián Gómez Cañón'] | 2019-09-12 | null | null | null | null | ['music-emotion-recognition'] | ['music'] | [-2.54668087e-01 -2.47647032e-01 -2.58674294e-01 -2.94602960e-01
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939c8707-73c6-450f-9ea1-60e4bfa7ee7b | site-assessment-and-layout-optimization-for | 2212.03516 | null | https://arxiv.org/abs/2212.03516v2 | https://arxiv.org/pdf/2212.03516v2.pdf | Site Assessment and Layout Optimization for Rooftop Solar Energy Generation in Worldview-3 Imagery | With the growth of residential rooftop PV adoption in recent decades, the problem of effective layout design has become increasingly important in recent years. Although a number of automated methods have been introduced, these tend to rely on simplifying assumptions and heuristics to improve computational tractability. We demonstrate a fully automated layout design pipeline that attempts to solve a more general formulation with greater geometric flexibility that accounts for shading losses. Our approach generates rooftop areas from satellite imagery and uses MINLP optimization to select panel positions, azimuth angles and tilt angles on an individual basis rather than imposing any predefined layouts. Our results demonstrate that shading plays a critical role in automated rooftop PV optimization and significantly changes the resulting layouts. Additionally, they suggest that, although several common heuristics are often effective, they may not be universally suitable due to complications resulting from geometric restrictions and shading losses. Finally, we evaluate a few specific heuristics from the literature and propose a potential new rule of thumb that may help improve rooftop solar energy potential when shading effects are considered. | ['Olivier L. de Weck', 'Abdulelah H. Habib', 'Abdulaziz Alharbi', 'Zeyad Awwad'] | 2022-12-07 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [ 1.31686971e-01 -9.98777151e-02 1.49555624e-01 -2.60776550e-01
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87053943-4417-49b4-8988-0ee2a841ba7d | personal-comfort-estimation-in-partial | 2112.00971 | null | https://arxiv.org/abs/2112.00971v4 | https://arxiv.org/pdf/2112.00971v4.pdf | Towards Personalization of User Preferences in Partially Observable Smart Home Environments | The technologies used in smart homes have recently improved to learn the user preferences from feedback in order to enhance the user convenience and quality of experience. Most smart homes learn a uniform model to represent the thermal preferences of users, which generally fails when the pool of occupants includes people with different sensitivities to temperature, for instance due to age and physiological factors. Thus, a smart home with a single optimal policy may fail to provide comfort when a new user with a different preference is integrated into the home. In this paper, we propose a Bayesian Reinforcement learning framework that can approximate the current occupant state in a partially observable smart home environment using its thermal preference, and then identify the occupant as a new user or someone is already known to the system. Our proposed framework can be used to identify users based on the temperature and humidity preferences of the occupant when performing different activities to enable personalization and improve comfort. We then compare the proposed framework with a baseline long short-term memory learner that learns the thermal preference of the user from the sequence of actions which it takes. We perform these experiments with up to 5 simulated human models each based on hierarchical reinforcement learning. The results show that our framework can approximate the belief state of the current user just by its temperature and humidity preferences across different activities with a high degree of accuracy. | ['Francois Rivest', 'Ali Etemad', 'Shashi Suman'] | 2021-12-02 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-2.35091701e-01 -1.00264266e-01 1.55796260e-02 -4.33347106e-01
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2c54bc42-0017-4301-9d61-59b5b1c60ab9 | revisiting-modulated-convolutions-for-visual | 2004.11883 | null | https://arxiv.org/abs/2004.11883v3 | https://arxiv.org/pdf/2004.11883v3.pdf | MoVie: Revisiting Modulated Convolutions for Visual Counting and Beyond | This paper focuses on visual counting, which aims to predict the number of occurrences given a natural image and a query (e.g. a question or a category). Unlike most prior works that use explicit, symbolic models which can be computationally expensive and limited in generalization, we propose a simple and effective alternative by revisiting modulated convolutions that fuse the query and the image locally. Following the design of residual bottleneck, we call our method MoVie, short for Modulated conVolutional bottlenecks. Notably, MoVie reasons implicitly and holistically and only needs a single forward-pass during inference. Nevertheless, MoVie showcases strong performance for counting: 1) advancing the state-of-the-art on counting-specific VQA tasks while being more efficient; 2) outperforming prior-art on difficult benchmarks like COCO for common object counting; 3) helped us secure the first place of 2020 VQA challenge when integrated as a module for 'number' related questions in generic VQA models. Finally, we show evidence that modulated convolutions such as MoVie can serve as a general mechanism for reasoning tasks beyond counting. | ['Xinlei Chen', 'Duy-Kien Nguyen', 'Vedanuj Goswami'] | 2020-04-24 | movie-revisiting-modulated-convolutions-for | https://openreview.net/forum?id=8e6BrwU6AjQ | https://openreview.net/pdf?id=8e6BrwU6AjQ | iclr-2021-1 | ['object-counting'] | ['computer-vision'] | [-4.97079268e-02 -1.72676116e-01 1.08453035e-01 -4.76470798e-01
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06f87327-dcda-45a1-a32b-35e759154257 | discrete-predictor-corrector-diffusion-models | null | null | https://openreview.net/forum?id=VM8batVBWvg | https://openreview.net/pdf?id=VM8batVBWvg | Discrete Predictor-Corrector Diffusion Models for Image Synthesis | We introduce Discrete Predictor-Corrector diffusion models (DPC), extending predictor-corrector samplers in Gaussian diffusion models to the discrete case. Predictor-corrector samplers are a class of samplers for diffusion models, which improve on ancestral samplers by correcting the sampling distribution of intermediate diffusion states using MCMC methods. In DPC, the Langevin corrector, which does not have a direct counterpart in discrete space, is replaced with a discrete MCMC transition defined by a learned corrector kernel. The corrector kernel is trained to make the correction steps achieve asymptotic convergence, in distribution, to the correct marginal of the intermediate diffusion states. Equipped with DPC, we revisit recent transformer-based non-autoregressive generative models through the lens of discrete diffusion, and find that DPC can alleviate the compounding decoding error due to the parallel sampling of visual tokens. Our experiments show that DPC improves upon existing discrete latent space models for class-conditional image generation on ImageNet, and outperforms continuous diffusion models and GANs, according to standard metrics and user preference studies. | ['Anonymous'] | 2022-09-29 | null | null | null | iclr-anonymous-submission-2022-9 | ['conditional-image-generation'] | ['computer-vision'] | [ 4.29439694e-01 3.38723421e-01 -2.75482684e-01 -2.40774542e-01
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133bdf85-f9e5-4b74-b66c-813aedaed3e0 | automatic-product-categorization-for-official | null | null | https://aclanthology.org/W19-3623 | https://aclanthology.org/W19-3623.pdf | Automatic Product Categorization for Official Statistics | The North American Product Classification System (NAPCS) is a comprehensive, hierarchical classification system for products (goods and services) that is consistent across the three North American countries. Beginning in 2017, the Economic Census will use NAPCS to produce economy-wide product tabulations. Respondents are asked to report data from a long, pre-specified list of potential products in a given industry, with some lists containing more than 50 potential products. Businesses have expressed the desire to alternatively supply Universal Product Codes (UPC) to the U. S. Census Bureau. Much work has been done around the categorization of products using product descriptions. No study has applied these efforts for the calculation of official statistics (statistics published by government agencies) using only the text of UPC product descriptions. The question we address in this paper is: Given UPC codes and their associated product descriptions, can we accurately predict NAPCS? We tested the feasibility of businesses submitting a spreadsheet with Universal Product Codes and their associated text descriptions. This novel strategy classified text with very high accuracy rates, all of our algorithms surpassed over 90 percent. | ['Andrea Roberson'] | 2019-08-01 | null | null | null | ws-2019-8 | ['product-categorization'] | ['miscellaneous'] | [-2.05686435e-01 -2.28811860e-01 -9.65888739e-01 -4.60785300e-01
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2a0f1074-b009-4105-98e6-919d1e251245 | putting-the-con-in-context-identifying-1 | 2207.02253 | null | https://arxiv.org/abs/2207.02253v1 | https://arxiv.org/pdf/2207.02253v1.pdf | Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia | While neural networks demonstrate a remarkable ability to model linguistic content, capturing contextual information related to a speaker's conversational role is an open area of research. In this work, we analyze the effect of speaker role on language use through the game of Mafia, in which participants are assigned either an honest or a deceptive role. In addition to building a framework to collect a dataset of Mafia game records, we demonstrate that there are differences in the language produced by players with different roles. We confirm that classification models are able to rank deceptive players as more suspicious than honest ones based only on their use of language. Furthermore, we show that training models on two auxiliary tasks outperforms a standard BERT-based text classification approach. We also present methods for using our trained models to identify features that distinguish between player roles, which could be used to assist players during the Mafia game. | ['John DeNero', 'Gaoyue Zhou', 'Samee Ibraheem'] | 2022-07-05 | null | https://aclanthology.org/2022.naacl-main.11 | https://aclanthology.org/2022.naacl-main.11.pdf | naacl-2022-7 | ['deception-detection', 'dialogue-understanding'] | ['miscellaneous', 'natural-language-processing'] | [-1.09566011e-01 1.66917130e-01 -1.09139038e-02 -5.41383326e-01
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7a8a0bbd-6950-4067-99ed-9eb60df7046b | hippo-recurrent-memory-with-optimal | 2008.07669 | null | https://arxiv.org/abs/2008.07669v2 | https://arxiv.org/pdf/2008.07669v2.pdf | HiPPO: Recurrent Memory with Optimal Polynomial Projections | A central problem in learning from sequential data is representing cumulative history in an incremental fashion as more data is processed. We introduce a general framework (HiPPO) for the online compression of continuous signals and discrete time series by projection onto polynomial bases. Given a measure that specifies the importance of each time step in the past, HiPPO produces an optimal solution to a natural online function approximation problem. As special cases, our framework yields a short derivation of the recent Legendre Memory Unit (LMU) from first principles, and generalizes the ubiquitous gating mechanism of recurrent neural networks such as GRUs. This formal framework yields a new memory update mechanism (HiPPO-LegS) that scales through time to remember all history, avoiding priors on the timescale. HiPPO-LegS enjoys the theoretical benefits of timescale robustness, fast updates, and bounded gradients. By incorporating the memory dynamics into recurrent neural networks, HiPPO RNNs can empirically capture complex temporal dependencies. On the benchmark permuted MNIST dataset, HiPPO-LegS sets a new state-of-the-art accuracy of 98.3%. Finally, on a novel trajectory classification task testing robustness to out-of-distribution timescales and missing data, HiPPO-LegS outperforms RNN and neural ODE baselines by 25-40% accuracy. | ['Albert Gu', 'Christopher Re', 'Stefano Ermon', 'Atri Rudra', 'Tri Dao'] | 2020-08-17 | null | http://proceedings.neurips.cc/paper/2020/hash/102f0bb6efb3a6128a3c750dd16729be-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/102f0bb6efb3a6128a3c750dd16729be-Paper.pdf | neurips-2020-12 | ['sequential-image-classification'] | ['computer-vision'] | [ 7.81859756e-02 -1.39255524e-01 -2.88649529e-01 -3.03119332e-01
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e68643c1-1603-4e1b-8547-3e8e677cf620 | classical-shadows-with-noise | 2011.11580 | null | https://arxiv.org/abs/2011.11580v2 | https://arxiv.org/pdf/2011.11580v2.pdf | Classical Shadows With Noise | The classical shadows protocol, recently introduced by Huang, Kueng, and Preskill [Nat. Phys. 16, 1050 (2020)], is a quantum-classical protocol to estimate properties of an unknown quantum state. Unlike full quantum state tomography, the protocol can be implemented on near-term quantum hardware and requires few quantum measurements to make many predictions with a high success probability. In this paper, we study the effects of noise on the classical shadows protocol. In particular, we consider the scenario in which the quantum circuits involved in the protocol are subject to various known noise channels and derive an analytical upper bound for the sample complexity in terms of a shadow seminorm for both local and global noise. Additionally, by modifying the classical post-processing step of the noiseless protocol, we define a new estimator that remains unbiased in the presence of noise. As applications, we show that our results can be used to prove rigorous sample complexity upper bounds in the cases of depolarizing noise and amplitude damping. | ['Sabee Grewal', 'Dax Enshan Koh'] | 2020-11-23 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 6.19513690e-01 1.30724296e-01 3.35468501e-01 -1.29922837e-01
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bd97bd54-9bd9-4902-812e-65844617a0d8 | run-procrustes-run-on-the-convergence-of | 1806.00534 | null | https://arxiv.org/abs/1806.00534v5 | https://arxiv.org/pdf/1806.00534v5.pdf | Provably convergent acceleration in factored gradient descent with applications in matrix sensing | We present theoretical results on the convergence of \emph{non-convex} accelerated gradient descent in matrix factorization models with $\ell_2$-norm loss. The purpose of this work is to study the effects of acceleration in non-convex settings, where provable convergence with acceleration should not be considered a \emph{de facto} property. The technique is applied to matrix sensing problems, for the estimation of a rank $r$ optimal solution $X^\star \in \mathbb{R}^{n \times n}$. Our contributions can be summarized as follows. $i)$ We show that acceleration in factored gradient descent converges at a linear rate; this fact is novel for non-convex matrix factorization settings, under common assumptions. $ii)$ Our proof technique requires the acceleration parameter to be carefully selected, based on the properties of the problem, such as the condition number of $X^\star$ and the condition number of objective function. $iii)$ Currently, our proof leads to the same dependence on the condition number(s) in the contraction parameter, similar to recent results on non-accelerated algorithms. $iv)$ Acceleration is observed in practice, both in synthetic examples and in two real applications: neuronal multi-unit activities recovery from single electrode recordings, and quantum state tomography on quantum computing simulators. | ['Tayo Ajayi', 'Kristofer Bouchard', 'David Mildebrath', 'Georgios Kollias', 'Shashanka Ubaru', 'Anastasios Kyrillidis'] | 2018-06-01 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 3.28679979e-01 7.48266652e-02 1.14584565e-01 -1.10357910e-01
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2a37af5b-2cf0-42de-80d2-31701a7292a8 | estimating-the-pose-of-a-euro-pallet-with-an | 2210.06001 | null | https://arxiv.org/abs/2210.06001v1 | https://arxiv.org/pdf/2210.06001v1.pdf | Estimating the Pose of a Euro Pallet with an RGB Camera based on Synthetic Training Data | Estimating the pose of a pallet and other logistics objects is crucial for various use cases, such as automatized material handling or tracking. Innovations in computer vision, computing power, and machine learning open up new opportunities for device-free localization based on cameras and neural networks. Large image datasets with annotated poses are required for training the network. Manual annotation, especially of 6D poses, is an extremely labor-intensive process. Hence, newer approaches often leverage synthetic training data to automatize the process of generating annotated image datasets. In this work, the generation of synthetic training data for 6D pose estimation of pallets is presented. The data is then used to train the Deep Object Pose Estimation (DOPE) algorithm. The experimental validation of the algorithm proves that the 6D pose estimation of a standardized Euro pallet with a Red-Green-Blue (RGB) camera is feasible. The comparison of the results from three varying datasets under different lighting conditions shows the relevance of an appropriate dataset design to achieve an accurate and robust localization. The quantitative evaluation shows an average position error of less than 20 cm for the preferred dataset. The validated training dataset and a photorealistic model of a Euro pallet are publicly provided. | ['Jochen Kreutzfeldt', 'Johannes Hinckeldeyn', 'Asan Adamanov', 'Jakob Schyga', 'Markus Knitt'] | 2022-10-12 | null | null | null | null | ['6d-pose-estimation-1'] | ['computer-vision'] | [ 2.98088174e-02 -3.35735306e-02 2.93946952e-01 -4.54221338e-01
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52c19bdd-7299-4a16-92b8-b3928c776155 | cnn-rnn-a-unified-framework-for-multi-label | 1604.04573 | null | http://arxiv.org/abs/1604.04573v1 | http://arxiv.org/pdf/1604.04573v1.pdf | CNN-RNN: A Unified Framework for Multi-label Image Classification | While deep convolutional neural networks (CNNs) have shown a great success in
single-label image classification, it is important to note that real world
images generally contain multiple labels, which could correspond to different
objects, scenes, actions and attributes in an image. Traditional approaches to
multi-label image classification learn independent classifiers for each
category and employ ranking or thresholding on the classification results.
These techniques, although working well, fail to explicitly exploit the label
dependencies in an image. In this paper, we utilize recurrent neural networks
(RNNs) to address this problem. Combined with CNNs, the proposed CNN-RNN
framework learns a joint image-label embedding to characterize the semantic
label dependency as well as the image-label relevance, and it can be trained
end-to-end from scratch to integrate both information in a unified framework.
Experimental results on public benchmark datasets demonstrate that the proposed
architecture achieves better performance than the state-of-the-art multi-label
classification model | ['Wei Xu', 'Chang Huang', 'Zhiheng Huang', 'Yi Yang', 'Junhua Mao', 'Jiang Wang'] | 2016-04-15 | cnn-rnn-a-unified-framework-for-multi-label-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Wang_CNN-RNN_A_Unified_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Wang_CNN-RNN_A_Unified_CVPR_2016_paper.pdf | cvpr-2016-6 | ['multi-label-image-classification'] | ['computer-vision'] | [ 5.62499166e-01 -3.89730871e-01 -4.73236203e-01 -8.58892918e-01
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16f43e34-0f0b-452e-9df8-6872112064ab | embedding-neurophysiological-signals | null | null | https://doi.org/10.1109/MetroXRAINE54828.2022.9967496 | https://hal.inria.fr/hal-03878615v1/file/MetroXRAINE_2022_GuePapTan.pdf | Embedding neurophysiological signals | Neurophysiological time-series recordings of brain activity like the electroencephalogram (EEG) or local field potentials can be decoded by machine learning models in order to either control an application, e.g., for communication or rehabilitation after stroke, or to passively monitor the ongoing brain state of the subject, e.g., in a demanding work environment. A typical decoding challenge faced by a brain-computer interface (BCI) is the small dataset size compared to other domains of machine learning like computer vision or natural language processing. The possibilities to tackle classification or regression problems in BCI are to either train a regular model on the available small training data sets or through transfer learning, which utilizes data from other sessions, subjects, or even datasets to train a model. Transfer learning is non-trivial because of the non-stationary of EEG signals between subjects but also within subjects. This variability calls for explicit calibration phases at the start of every session, before BCI applications can be used online. In this study, we present arguments to BCI researchers to encourage the use of embeddings for EEG decoding. In particular, we introduce a simple domain adaptation technique involving both deep learning (when learning the embeddings from the source data) and classical machine learning (for fast calibration on the target data). This technique allows us to learn embeddings across subjects, which deliver a generalized data representation. These can then be fed into subject-specific classifiers in order to minimize their need for calibration data. We conducted offline experiments on the 14 subjects of the High Gamma EEG-BCI Dataset [1]. Embedding functions were obtained by training EEGNet [2] using a leave-one-subject-out (LOSO) protocol, and the embedding vectors were classified by the logistic regression algorithm. Our pipeline was compared to two baseline approaches: EEGNet without subject-specific calibration and the standard FBCSP pipeline in a within-subject training. We observed that the representations learned by the embedding functions were indeed non-stationary across subjects, justifying the need for an additional subject-specific calibration. We also observed that the subject-specific calibration indeed improved the score. Finally, our data suggest, that building upon embeddings requires fewer individual calibration data than the FBCSP baseline to reach satisfactory scores. | ['Michael Tangermann', 'Théodore Papadopoulo', 'Pierre Guetschel'] | 2022-10-25 | null | null | null | ieee-international-conference-on-metrology | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 2.95590490e-01 3.82511392e-02 3.47624213e-01 -5.05717278e-01
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1dd48dfd-4dfd-4c42-9bd8-e5e2d31fe29e | visual-commonsense-graphs-reasoning-about-the | 2004.10796 | null | https://arxiv.org/abs/2004.10796v3 | https://arxiv.org/pdf/2004.10796v3.pdf | VisualCOMET: Reasoning about the Dynamic Context of a Still Image | Even from a single frame of a still image, people can reason about the dynamic story of the image before, after, and beyond the frame. For example, given an image of a man struggling to stay afloat in water, we can reason that the man fell into the water sometime in the past, the intent of that man at the moment is to stay alive, and he will need help in the near future or else he will get washed away. We propose VisualComet, the novel framework of visual commonsense reasoning tasks to predict events that might have happened before, events that might happen next, and the intents of the people at present. To support research toward visual commonsense reasoning, we introduce the first large-scale repository of Visual Commonsense Graphs that consists of over 1.4 million textual descriptions of visual commonsense inferences carefully annotated over a diverse set of 60,000 images, each paired with short video summaries of before and after. In addition, we provide person-grounding (i.e., co-reference links) between people appearing in the image and people mentioned in the textual commonsense descriptions, allowing for tighter integration between images and text. We establish strong baseline performances on this task and demonstrate that integration between visual and textual commonsense reasoning is the key and wins over non-integrative alternatives. | ['Roozbeh Mottaghi', 'Yejin Choi', 'Jae Sung Park', 'Chandra Bhagavatula', 'Ali Farhadi'] | 2020-04-22 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3684_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500494.pdf | eccv-2020-8 | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 3.34405184e-01 2.61870772e-01 -3.33393253e-02 -3.23592365e-01
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ca80df60-cc83-4dbb-a348-c667f83aa72f | image-stitching-and-rectification-for-hand | 2008.09229 | null | https://arxiv.org/abs/2008.09229v1 | https://arxiv.org/pdf/2008.09229v1.pdf | Image Stitching and Rectification for Hand-Held Cameras | In this paper, we derive a new differential homography that can account for the scanline-varying camera poses in Rolling Shutter (RS) cameras, and demonstrate its application to carry out RS-aware image stitching and rectification at one stroke. Despite the high complexity of RS geometry, we focus in this paper on a special yet common input -- two consecutive frames from a video stream, wherein the inter-frame motion is restricted from being arbitrarily large. This allows us to adopt simpler differential motion model, leading to a straightforward and practical minimal solver. To deal with non-planar scene and camera parallax in stitching, we further propose an RS-aware spatially-varying homography field in the principle of As-Projective-As-Possible (APAP). We show superior performance over state-of-the-art methods both in RS image stitching and rectification, especially for images captured by hand-held shaking cameras. | ['Quoc-Huy Tran', 'Bingbing Zhuang'] | 2020-08-20 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/184_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520239.pdf | eccv-2020-8 | ['image-stitching'] | ['computer-vision'] | [ 6.39512539e-01 -1.39184624e-01 9.85724777e-02 1.02731936e-01
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95225abf-f6e4-4416-b7ae-b951dd5a83c5 | adversarial-domain-adaptation-with-self | 2107.04470 | null | https://arxiv.org/abs/2107.04470v4 | https://arxiv.org/pdf/2107.04470v4.pdf | ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training | Sleep staging is of great importance in the diagnosis and treatment of sleep disorders. Recently, numerous data-driven deep learning models have been proposed for automatic sleep staging. They mainly train the model on a large public labeled sleep dataset and test it on a smaller one with subjects of interest. However, they usually assume that the train and test data are drawn from the same distribution, which may not hold in real-world scenarios. Unsupervised domain adaption (UDA) has been recently developed to handle this domain shift problem. However, previous UDA methods applied for sleep staging have two main limitations. First, they rely on a totally shared model for the domain alignment, which may lose the domain-specific information during feature extraction. Second, they only align the source and target distributions globally without considering the class information in the target domain, which hinders the classification performance of the model while testing. In this work, we propose a novel adversarial learning framework called ADAST to tackle the domain shift problem in the unlabeled target domain. First, we develop an unshared attention mechanism to preserve the domain-specific features in both domains. Second, we design an iterative self-training strategy to improve the classification performance on the target domain via target domain pseudo labels. We also propose dual distinct classifiers to increase the robustness and quality of the pseudo labels. The experimental results on six cross-domain scenarios validate the efficacy of our proposed framework and its advantage over state-of-the-art UDA methods. The source code is available at https://github.com/emadeldeen24/ADAST. | ['Cuntai Guan', 'XiaoLi Li', 'Chee-Keong Kwoh', 'Min Wu', 'Zhenghua Chen', 'Mohamed Ragab', 'Emadeldeen Eldele'] | 2021-07-09 | null | null | null | null | ['sleep-stage-detection', 'sleep-staging', 'automatic-sleep-stage-classification', 'eeg-based-sleep-staging'] | ['medical', 'medical', 'medical', 'time-series'] | [ 7.60055631e-02 -2.05044836e-01 -3.09716761e-01 -5.86973548e-01
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6abf3595-974a-4cee-99bc-3ffafc256074 | learning-set-functions-under-the-optimal | 2203.01693 | null | https://arxiv.org/abs/2203.01693v4 | https://arxiv.org/pdf/2203.01693v4.pdf | Learning Neural Set Functions Under the Optimal Subset Oracle | Learning neural set functions becomes increasingly more important in many applications like product recommendation and compound selection in AI-aided drug discovery. The majority of existing works study methodologies of set function learning under the function value oracle, which, however, requires expensive supervision signals. This renders it impractical for applications with only weak supervisions under the Optimal Subset (OS) oracle, the study of which is surprisingly overlooked. In this work, we present a principled yet practical maximum likelihood learning framework, termed as EquiVSet, that simultaneously meets the following desiderata of learning set functions under the OS oracle: i) permutation invariance of the set mass function being modeled; ii) permission of varying ground set; iii) minimum prior; and iv) scalability. The main components of our framework involve: an energy-based treatment of the set mass function, DeepSet-style architectures to handle permutation invariance, mean-field variational inference, and its amortized variants. Thanks to the elegant combination of these advanced architectures, empirical studies on three real-world applications (including Amazon product recommendation, set anomaly detection, and compound selection for virtual screening) demonstrate that EquiVSet outperforms the baselines by a large margin. | ['Yatao Bian', 'Peilin Zhao', 'Yingzhen Li', 'Qinliang Su', 'Tingyang Xu', 'Zijing Ou'] | 2022-03-03 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 6.17073238e-01 -1.43008351e-01 -5.10948122e-01 -3.55079681e-01
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299597c1-98ac-4aa5-8196-b98fcba36763 | feedback-graph-attention-convolutional | 2006.13863 | null | https://arxiv.org/abs/2006.13863v2 | https://arxiv.org/pdf/2006.13863v2.pdf | Feedback Graph Attention Convolutional Network for Medical Image Enhancement | Artifacts, blur and noise are the common distortions degrading MRI images during the acquisition process, and deep neural networks have been demonstrated to help in improving image quality. To well exploit global structural information and texture details, we propose a novel biomedical image enhancement network, named Feedback Graph Attention Convolutional Network (FB-GACN). As a key innovation, we consider the global structure of an image by building a graph network from image sub-regions that we consider to be node features, linking them non-locally according to their similarity. The proposed model consists of three main parts: 1) The parallel graph similarity branch and content branch, where the graph similarity branch aims at exploiting the similarity and symmetry across different image sub-regions in low-resolution feature space and provides additional priors for the content branch to enhance texture details. 2) A feedback mechanism with a recurrent structure to refine low-level representations with high-level information and generate powerful high-level texture details by handling the feedback connections. 3) A reconstruction to remove the artifacts and recover super-resolution images by using the estimated sub-region correlation priors obtained from the graph similarity branch. We evaluate our method on two image enhancement tasks: i) cross-protocol super resolution of diffusion MRI; ii) artifact removal of FLAIR MR images. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art methods. | ['Amirhossein Bayat', 'Yu Zhao', 'Yanyang Yan', 'Bjoern Menze', 'Xiaobin Hu', 'Wenqi Ren', 'Hongwei Li'] | 2020-06-24 | null | null | null | null | ['medical-image-enhancement', 'graph-similarity'] | ['computer-vision', 'graphs'] | [ 5.46470225e-01 1.54818058e-01 1.42415181e-01 -2.72708446e-01
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1cac1dda-1a19-4a50-b20c-a3bafd34fb14 | its-all-relative-monocular-3d-human-pose | 1805.06880 | null | http://arxiv.org/abs/1805.06880v2 | http://arxiv.org/pdf/1805.06880v2.pdf | It's all Relative: Monocular 3D Human Pose Estimation from Weakly Supervised Data | We address the problem of 3D human pose estimation from 2D input images using
only weakly supervised training data. Despite showing considerable success for
2D pose estimation, the application of supervised machine learning to 3D pose
estimation in real world images is currently hampered by the lack of varied
training images with corresponding 3D poses. Most existing 3D pose estimation
algorithms train on data that has either been collected in carefully controlled
studio settings or has been generated synthetically. Instead, we take a
different approach, and propose a 3D human pose estimation algorithm that only
requires relative estimates of depth at training time. Such training signal,
although noisy, can be easily collected from crowd annotators, and is of
sufficient quality for enabling successful training and evaluation of 3D pose
algorithms. Our results are competitive with fully supervised regression based
approaches on the Human3.6M dataset, despite using significantly weaker
training data. Our proposed algorithm opens the door to using existing
widespread 2D datasets for 3D pose estimation by allowing fine-tuning with
noisy relative constraints, resulting in more accurate 3D poses. | ['Pietro Perona', 'Robert Eng', 'Oisin Mac Aodha', 'Matteo Ruggero Ronchi'] | 2018-05-17 | null | null | null | null | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [ 2.02643782e-01 2.78507441e-01 -2.02800825e-01 -5.11296511e-01
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4834e4e5-ff87-45a5-b243-7df85295901b | retrieval-augmented-multilingual-keyphrase | 2205.10471 | null | https://arxiv.org/abs/2205.10471v2 | https://arxiv.org/pdf/2205.10471v2.pdf | Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training | Keyphrase generation is the task of automatically predicting keyphrases given a piece of long text. Despite its recent flourishing, keyphrase generation on non-English languages haven't been vastly investigated. In this paper, we call attention to a new setting named multilingual keyphrase generation and we contribute two new datasets, EcommerceMKP and AcademicMKP, covering six languages. Technically, we propose a retrieval-augmented method for multilingual keyphrase generation to mitigate the data shortage problem in non-English languages. The retrieval-augmented model leverages keyphrase annotations in English datasets to facilitate generating keyphrases in low-resource languages. Given a non-English passage, a cross-lingual dense passage retrieval module finds relevant English passages. Then the associated English keyphrases serve as external knowledge for keyphrase generation in the current language. Moreover, we develop a retriever-generator iterative training algorithm to mine pseudo parallel passage pairs to strengthen the cross-lingual passage retriever. Comprehensive experiments and ablations show that the proposed approach outperforms all baselines. | ['Michael R. Lyu', 'Irwin King', 'Bing Yin', 'Tong Zhao', 'Rui Meng', 'Zheng Li', 'Qingyu Yin', 'Yifan Gao'] | 2022-05-21 | null | https://aclanthology.org/2022.findings-naacl.92 | https://aclanthology.org/2022.findings-naacl.92.pdf | findings-naacl-2022-7 | ['keyphrase-generation', 'passage-retrieval'] | ['natural-language-processing', 'natural-language-processing'] | [-2.22326130e-01 -2.09978789e-01 -3.99406016e-01 3.97545010e-01
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91144e94-75f1-4211-88dc-6fe08be36fb5 | the-role-of-output-vocabulary-in-t2t-lms-for | 2305.15108 | null | https://arxiv.org/abs/2305.15108v1 | https://arxiv.org/pdf/2305.15108v1.pdf | The Role of Output Vocabulary in T2T LMs for SPARQL Semantic Parsing | In this work, we analyse the role of output vocabulary for text-to-text (T2T) models on the task of SPARQL semantic parsing. We perform experiments within the the context of knowledge graph question answering (KGQA), where the task is to convert questions in natural language to the SPARQL query language. We observe that the query vocabulary is distinct from human vocabulary. Language Models (LMs) are pre-dominantly trained for human language tasks, and hence, if the query vocabulary is replaced with a vocabulary more attuned to the LM tokenizer, the performance of models may improve. We carry out carefully selected vocabulary substitutions on the queries and find absolute gains in the range of 17% on the GrailQA dataset. | ['Chris Biemann', 'Ricardo Usbeck', 'Pranav Ajit Nair', 'Debayan Banerjee'] | 2023-05-24 | null | null | null | null | ['graph-question-answering', 'semantic-parsing'] | ['graphs', 'natural-language-processing'] | [ 2.30087250e-01 9.55339015e-01 -2.22493634e-01 -4.14313525e-01
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6161d62f-3c50-4058-942b-e8d0e2e33421 | scalable-primal-dual-actor-critic-method-for | 2305.17568 | null | https://arxiv.org/abs/2305.17568v1 | https://arxiv.org/pdf/2305.17568v1.pdf | Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities | We investigate safe multi-agent reinforcement learning, where agents seek to collectively maximize an aggregate sum of local objectives while satisfying their own safety constraints. The objective and constraints are described by {\it general utilities}, i.e., nonlinear functions of the long-term state-action occupancy measure, which encompass broader decision-making goals such as risk, exploration, or imitations. The exponential growth of the state-action space size with the number of agents presents challenges for global observability, further exacerbated by the global coupling arising from agents' safety constraints. To tackle this issue, we propose a primal-dual method utilizing shadow reward and $\kappa$-hop neighbor truncation under a form of correlation decay property, where $\kappa$ is the communication radius. In the exact setting, our algorithm converges to a first-order stationary point (FOSP) at the rate of $\mathcal{O}\left(T^{-2/3}\right)$. In the sample-based setting, we demonstrate that, with high probability, our algorithm requires $\widetilde{\mathcal{O}}\left(\epsilon^{-3.5}\right)$ samples to achieve an $\epsilon$-FOSP with an approximation error of $\mathcal{O}(\phi_0^{2\kappa})$, where $\phi_0\in (0,1)$. Finally, we demonstrate the effectiveness of our model through extensive numerical experiments. | ['Javad Lavaei', 'Alec Koppel', 'Yuhao Ding', 'Yunkai Zhang', 'Donghao Ying'] | 2023-05-27 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-1.09283157e-01 3.62629324e-01 -2.22414359e-01 3.36432517e-01
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28d5a515-9afb-4733-90b2-d36f29cba9f7 | fine-grained-population-mapping-from-coarse | 2211.04039 | null | https://arxiv.org/abs/2211.04039v1 | https://arxiv.org/pdf/2211.04039v1.pdf | Fine-grained Population Mapping from Coarse Census Counts and Open Geodata | Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations. Unfortunately, in many countries only aggregate census counts over large spatial units are collected, moreover, these are not always up-to-date. We present POMELO, a deep learning model that employs coarse census counts and open geodata to estimate fine-grained population maps with 100m ground sampling distance. Moreover, the model can also estimate population numbers when no census counts at all are available, by generalizing across countries. In a series of experiments for several countries in sub-Saharan Africa, the maps produced with POMELOare in good agreement with the most detailed available reference counts: disaggregation of coarse census counts reaches R2 values of 85-89%; unconstrained prediction in the absence of any counts reaches 48-69%. | ['Devis Tuia', 'Konrad Schindler', 'Muhammad Imran', 'Ferda Ofli', 'Thao Ton-That Whelan', 'Benjamin Kellenberger', 'Rodrigo C. Daudt', 'John E. Vargas-Muñoz', 'Nando Metzger'] | 2022-11-08 | null | null | null | null | ['population-mapping'] | ['computer-vision'] | [-4.95028019e-01 -5.61974128e-04 -2.12159321e-01 -8.84324163e-02
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2a1c987e-3f75-4096-b9d7-bf5abbf5d99e | objects-in-semantic-topology | 2110.02687 | null | https://arxiv.org/abs/2110.02687v2 | https://arxiv.org/pdf/2110.02687v2.pdf | Objects in Semantic Topology | A more realistic object detection paradigm, Open-World Object Detection, has arisen increasing research interests in the community recently. A qualified open-world object detector can not only identify objects of known categories, but also discover unknown objects, and incrementally learn to categorize them when their annotations progressively arrive. Previous works rely on independent modules to recognize unknown categories and perform incremental learning, respectively. In this paper, we provide a unified perspective: Semantic Topology. During the life-long learning of an open-world object detector, all object instances from the same category are assigned to their corresponding pre-defined node in the semantic topology, including the `unknown' category. This constraint builds up discriminative feature representations and consistent relationships among objects, thus enabling the detector to distinguish unknown objects out of the known categories, as well as making learned features of known objects undistorted when learning new categories incrementally. Extensive experiments demonstrate that semantic topology, either randomly-generated or derived from a well-trained language model, could outperform the current state-of-the-art open-world object detectors by a large margin, e.g., the absolute open-set error is reduced from 7832 to 2546, exhibiting the inherent superiority of semantic topology on open-world object detection. | ['Min Xu', 'Ping Luo', 'Changhu Wang', 'Zehuan Yuan', 'Ruiheng Zhang', 'Xiaobo Xia', 'Yi Jiang', 'Peize Sun', 'Shuo Yang'] | 2021-10-06 | objects-in-semantic-topology-1 | https://openreview.net/forum?id=d5SCUJ5t1k | https://openreview.net/pdf?id=d5SCUJ5t1k | iclr-2022-4 | ['open-world-object-detection'] | ['computer-vision'] | [-2.03961641e-01 1.66484892e-01 -2.31021062e-01 -4.48476285e-01
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ee337aa2-e16d-4c92-8ae4-0d258b04ec40 | quiz-style-question-generation-for-news | 2102.09094 | null | https://arxiv.org/abs/2102.09094v1 | https://arxiv.org/pdf/2102.09094v1.pdf | Quiz-Style Question Generation for News Stories | A large majority of American adults get at least some of their news from the Internet. Even though many online news products have the goal of informing their users about the news, they lack scalable and reliable tools for measuring how well they are achieving this goal, and therefore have to resort to noisy proxy metrics (e.g., click-through rates or reading time) to track their performance. As a first step towards measuring news informedness at a scale, we study the problem of quiz-style multiple-choice question generation, which may be used to survey users about their knowledge of recent news. In particular, we formulate the problem as two sequence-to-sequence tasks: question-answer generation (QAG) and distractor, or incorrect answer, generation (DG). We introduce NewsQuizQA, the first dataset intended for quiz-style question-answer generation, containing 20K human written question-answer pairs from 5K news article summaries. Using this dataset, we propose a series of novel techniques for applying large pre-trained Transformer encoder-decoder models, namely PEGASUS and T5, to the tasks of question-answer generation and distractor generation. We show that our models outperform strong baselines using both automated metrics and human raters. We provide a case study of running weekly quizzes on real-world users via the Google Surveys platform over the course of two months. We found that users generally found the automatically generated questions to be educational and enjoyable. Finally, to serve the research community, we are releasing the NewsQuizQA dataset. | ['Cong Yu', 'Vinh Q. Tran', 'Adam D. Lelkes'] | 2021-02-18 | null | null | null | null | ['distractor-generation', 'question-answer-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.05683243e-02 2.73672223e-01 -8.95409830e-05 -5.16576529e-01
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0b9ae832-b552-486c-9a66-baca56e0fa16 | crowdsourced-collective-entity-resolution | 2002.09361 | null | https://arxiv.org/abs/2002.09361v1 | https://arxiv.org/pdf/2002.09361v1.pdf | Crowdsourced Collective Entity Resolution with Relational Match Propagation | Knowledge bases (KBs) store rich yet heterogeneous entities and facts. Entity resolution (ER) aims to identify entities in KBs which refer to the same real-world object. Recent studies have shown significant benefits of involving humans in the loop of ER. They often resolve entities with pairwise similarity measures over attribute values and resort to the crowds to label uncertain ones. However, existing methods still suffer from high labor costs and insufficient labeling to some extent. In this paper, we propose a novel approach called crowdsourced collective ER, which leverages the relationships between entities to infer matches jointly rather than independently. Specifically, it iteratively asks human workers to label picked entity pairs and propagates the labeling information to their neighbors in distance. During this process, we address the problems of candidate entity pruning, probabilistic propagation, optimal question selection and error-tolerant truth inference. Our experiments on real-world datasets demonstrate that, compared with state-of-the-art methods, our approach achieves superior accuracy with much less labeling. | ['Jiacheng Huang', 'Zhifeng Bao', 'Wei Hu', 'Yuzhong Qu'] | 2020-02-21 | null | null | null | null | ['question-selection'] | ['natural-language-processing'] | [-3.13358128e-01 4.69590753e-01 -3.67465287e-01 -3.23307723e-01
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e9754689-1d55-4d02-9047-3fcc4bc933e0 | improved-nl2sql-based-on-multi-layer-expert | 2306.17727 | null | https://arxiv.org/abs/2306.17727v1 | https://arxiv.org/pdf/2306.17727v1.pdf | Improved NL2SQL based on Multi-layer Expert Network | The Natural Language to SQL (NL2SQL) technique is used to convert natural language queries into executable SQL statements. Typically, slot-filling is employed as a classification method for multi-task cases to achieve this goal. However, slot-filling can result in inaccurate SQL statement generation due to negative migration issues arising from different classification tasks. To overcome this limitation, this study introduces a new approach called Multi-Layer Expert Generate SQL (MLEG-SQL), which utilizes a dedicated multi-task hierarchical network. The lower layer of the network extracts semantic features of natural language statements, while the upper layer builds a specialized expert system for handling specific classification tasks. This hierarchical approach mitigates performance degradation resulting from different task conflicts. The proposed method was evaluated on the WiKSQL dataset and was found to be effective in generating accurate SQL statements. | ['Xu Zhang', 'Chenduo Hao'] | 2023-06-30 | null | null | null | null | ['classification-1', 'slot-filling'] | ['methodology', 'natural-language-processing'] | [ 2.58356392e-01 3.82047355e-01 1.99936442e-02 -6.82784915e-01
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038eb8c4-6c86-43fb-b166-9034c4c8d4ce | 190600679 | 1906.00679 | null | https://arxiv.org/abs/1906.00679v1 | https://arxiv.org/pdf/1906.00679v1.pdf | The Adversarial Machine Learning Conundrum: Can The Insecurity of ML Become The Achilles' Heel of Cognitive Networks? | The holy grail of networking is to create \textit{cognitive networks} that organize, manage, and drive themselves. Such a vision now seems attainable thanks in large part to the progress in the field of machine learning (ML), which has now already disrupted a number of industries and revolutionized practically all fields of research. But are the ML models foolproof and robust to security attacks to be in charge of managing the network? Unfortunately, many modern ML models are easily misled by simple and easily-crafted adversarial perturbations, which does not bode well for the future of ML-based cognitive networks unless ML vulnerabilities for the cognitive networking environment are identified, addressed, and fixed. The purpose of this article is to highlight the problem of insecure ML and to sensitize the readers to the danger of adversarial ML by showing how an easily-crafted adversarial ML example can compromise the operations of the cognitive self-driving network. In this paper, we demonstrate adversarial attacks on two simple yet representative cognitive networking applications (namely, intrusion detection and network traffic classification). We also provide some guidelines to design secure ML models for cognitive networks that are robust to adversarial attacks on the ML pipeline of cognitive networks. | ['Ala Al-Fuqaha', 'Mounir Hamdi', 'Junaid Qadir', 'Muhammad Usama'] | 2019-06-03 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 3.45149308e-01 4.21561480e-01 2.06258863e-01 -1.52571782e-01
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af46327f-032e-4624-a1eb-0398ca7e95dc | bidirectional-representations-for-low | 2211.14320 | null | https://arxiv.org/abs/2211.14320v1 | https://arxiv.org/pdf/2211.14320v1.pdf | Bidirectional Representations for Low Resource Spoken Language Understanding | Most spoken language understanding systems use a pipeline approach composed of an automatic speech recognition interface and a natural language understanding module. This approach forces hard decisions when converting continuous inputs into discrete language symbols. Instead, we propose a representation model to encode speech in rich bidirectional encodings that can be used for downstream tasks such as intent prediction. The approach uses a masked language modelling objective to learn the representations, and thus benefits from both the left and right contexts. We show that the performance of the resulting encodings before fine-tuning is better than comparable models on multiple datasets, and that fine-tuning the top layers of the representation model improves the current state of the art on the Fluent Speech Command dataset, also in a low-data regime, when a limited amount of labelled data is used for training. Furthermore, we propose class attention as a spoken language understanding module, efficient both in terms of speed and number of parameters. Class attention can be used to visually explain the predictions of our model, which goes a long way in understanding how the model makes predictions. We perform experiments in English and in Dutch. | ['Hugo Van hamme', 'Marie-Francine Moens', 'Quentin Meeus'] | 2022-11-24 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 4.01047528e-01 6.51484668e-01 -5.45325801e-02 -8.45830619e-01
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82a3b52d-8ac8-42df-90bb-aec25bca2fe4 | magicvideo-efficient-video-generation-with | 2211.11018 | null | https://arxiv.org/abs/2211.11018v2 | https://arxiv.org/pdf/2211.11018v2.pdf | MagicVideo: Efficient Video Generation With Latent Diffusion Models | We present an efficient text-to-video generation framework based on latent diffusion models, termed MagicVideo. MagicVideo can generate smooth video clips that are concordant with the given text descriptions. Due to a novel and efficient 3D U-Net design and modeling video distributions in a low-dimensional space, MagicVideo can synthesize video clips with 256x256 spatial resolution on a single GPU card, which takes around 64x fewer computations than the Video Diffusion Models (VDM) in terms of FLOPs. In specific, unlike existing works that directly train video models in the RGB space, we use a pre-trained VAE to map video clips into a low-dimensional latent space and learn the distribution of videos' latent codes via a diffusion model. Besides, we introduce two new designs to adapt the U-Net denoiser trained on image tasks to video data: a frame-wise lightweight adaptor for the image-to-video distribution adjustment and a directed temporal attention module to capture temporal dependencies across frames. Thus, we can exploit the informative weights of convolution operators from a text-to-image model for accelerating video training. To ameliorate the pixel dithering in the generated videos, we also propose a novel VideoVAE auto-encoder for better RGB reconstruction. We conduct extensive experiments and demonstrate that MagicVideo can generate high-quality video clips with either realistic or imaginary content. Refer to \url{https://magicvideo.github.io/#} for more examples. | ['Jiashi Feng', 'Yizhe Zhu', 'Weiwei Lv', 'Hanshu Yan', 'Weimin WANG', 'Daquan Zhou'] | 2022-11-20 | null | null | null | null | ['video-generation', 'text-to-video-generation'] | ['computer-vision', 'natural-language-processing'] | [-6.58430681e-02 -1.22876570e-01 -1.42607391e-01 -5.06033190e-02
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428076f0-97a8-4259-90e4-74b819fa43ce | protsi-prototypical-siamese-network-with-data | 2211.09855 | null | https://arxiv.org/abs/2211.09855v1 | https://arxiv.org/pdf/2211.09855v1.pdf | ProtSi: Prototypical Siamese Network with Data Augmentation for Few-Shot Subjective Answer Evaluation | Subjective answer evaluation is a time-consuming and tedious task, and the quality of the evaluation is heavily influenced by a variety of subjective personal characteristics. Instead, machine evaluation can effectively assist educators in saving time while also ensuring that evaluations are fair and realistic. However, most existing methods using regular machine learning and natural language processing techniques are generally hampered by a lack of annotated answers and poor model interpretability, making them unsuitable for real-world use. To solve these challenges, we propose ProtSi Network, a unique semi-supervised architecture that for the first time uses few-shot learning to subjective answer evaluation. To evaluate students' answers by similarity prototypes, ProtSi Network simulates the natural process of evaluator scoring answers by combining Siamese Network which consists of BERT and encoder layers with Prototypical Network. We employed an unsupervised diverse paraphrasing model ProtAugment, in order to prevent overfitting for effective few-shot text classification. By integrating contrastive learning, the discriminative text issue can be mitigated. Experiments on the Kaggle Short Scoring Dataset demonstrate that the ProtSi Network outperforms the most recent baseline models in terms of accuracy and quadratic weighted kappa. | ['Gaurav Gupta', 'Jingxi Qiu', 'Yining Lu'] | 2022-11-17 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 3.04720942e-02 -2.23951176e-01 -3.12104374e-01 -6.56601250e-01
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2f627de3-4ee4-47bb-a0ff-59a419e2c244 | non-flat-aba-is-an-instance-of-bipolar | 2305.12453 | null | https://arxiv.org/abs/2305.12453v1 | https://arxiv.org/pdf/2305.12453v1.pdf | Non-flat ABA is an Instance of Bipolar Argumentation | Assumption-based Argumentation (ABA) is a well-known structured argumentation formalism, whereby arguments and attacks between them are drawn from rules, defeasible assumptions and their contraries. A common restriction imposed on ABA frameworks (ABAFs) is that they are flat, i.e., each of the defeasible assumptions can only be assumed, but not derived. While it is known that flat ABAFs can be translated into abstract argumentation frameworks (AFs) as proposed by Dung, no translation exists from general, possibly non-flat ABAFs into any kind of abstract argumentation formalism. In this paper, we close this gap and show that bipolar AFs (BAFs) can instantiate general ABAFs. To this end we develop suitable, novel BAF semantics which borrow from the notion of deductive support. We investigate basic properties of our BAFs, including computational complexity, and prove the desired relation to ABAFs under several semantics. Finally, in order to support computation and explainability, we propose the notion of dispute trees for our BAF semantics. | ['Francesca Toni', 'Nico Potyka', 'Markus Ulbricht'] | 2023-05-21 | null | null | null | null | ['abstract-argumentation', 'abstract-argumentation'] | ['natural-language-processing', 'reasoning'] | [ 2.32349426e-01 9.74098563e-01 -2.07553044e-01 -5.42201519e-01
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e21545dd-06df-472b-892e-c80b6a72dc1a | the-study-of-highway-for-lifelong-multi-agent | 2304.04217 | null | https://arxiv.org/abs/2304.04217v1 | https://arxiv.org/pdf/2304.04217v1.pdf | The Study of Highway for Lifelong Multi-Agent Path Finding | In modern fulfillment warehouses, agents traverse the map to complete endless tasks that arrive on the fly, which is formulated as a lifelong Multi-Agent Path Finding (lifelong MAPF) problem. The goal of tackling this challenging problem is to find the path for each agent in a finite runtime while maximizing the throughput. However, existing methods encounter exponential growth of runtime and undesirable phenomena of deadlocks and rerouting as the map size or agent density grows. To address these challenges in lifelong MAPF, we explore the idea of highways mainly studied for one-shot MAPF (i.e., finding paths at once beforehand), which reduces the complexity of the problem by encouraging agents to move in the same direction. We utilize two methods to incorporate the highway idea into the lifelong MAPF framework and discuss the properties that minimize the existing problems of deadlocks and rerouting. The experimental results demonstrate that the runtime is considerably reduced and the decay of throughput is gradually insignificant as the map size enlarges under the settings of the highway. Furthermore, when the density of agents increases, the phenomena of deadlocks and rerouting are significantly reduced by leveraging the highway. | ['Min Sun', 'Ming-Feng Li'] | 2023-04-09 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-3.45179051e-01 1.38163149e-01 -8.75368491e-02 -1.19703777e-01
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eaf6df04-11b2-4865-ad72-0145baf646df | multi-task-pre-finetuning-for-zero-shot-cross | null | null | https://aclanthology.org/2021.icon-main.57 | https://aclanthology.org/2021.icon-main.57.pdf | Multi-task pre-finetuning for zero-shot cross lingual transfer | Building machine learning models for low resource languages is extremely challenging due to the lack of available training data (either un-annotated or annotated). To support such scenarios, zero-shot cross lingual transfer is used where the machine learning model is trained on a resource rich language and is directly tested on the resource poor language. In this paper, we present a technique which improves the performance of zero-shot cross lingual transfer. Our method performs multi-task pre-finetuning on a resource rich language using a multilingual pre-trained model. The pre-finetuned model is then tested in a zero-shot manner on the resource poor languages. We test the performance of our method on 8 languages and for two tasks, namely, Intent Classification (IC) & Named Entity Recognition (NER) using the MultiAtis++ dataset. The results showed that our method improves IC performance in 7 out of 8 languages and NER performance in 4 languages. Our method also leads to faster convergence during finetuning. The usage of pre-finetuning demonstrates a data efficient way for supporting new languages and geographies across the world. | ['Anurag Dwarakanath', 'Pritam Varma', 'Moukthika Yerramilli'] | null | null | null | null | icon-2021-12 | ['intent-classification'] | ['natural-language-processing'] | [-2.05161050e-01 -1.97188705e-01 -3.54831368e-01 -3.69813740e-01
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071f4901-5af4-4422-89ec-b220ad8501a6 | gaitgl-learning-discriminative-global-local | 2208.01380 | null | https://arxiv.org/abs/2208.01380v1 | https://arxiv.org/pdf/2208.01380v1.pdf | GaitGL: Learning Discriminative Global-Local Feature Representations for Gait Recognition | Existing gait recognition methods either directly establish Global Feature Representation (GFR) from original gait sequences or generate Local Feature Representation (LFR) from several local parts. However, GFR tends to neglect local details of human postures as the receptive fields become larger in the deeper network layers. Although LFR allows the network to focus on the detailed posture information of each local region, it neglects the relations among different local parts and thus only exploits limited local information of several specific regions. To solve these issues, we propose a global-local based gait recognition network, named GaitGL, to generate more discriminative feature representations. To be specific, a novel Global and Local Convolutional Layer (GLCL) is developed to take full advantage of both global visual information and local region details in each layer. GLCL is a dual-branch structure that consists of a GFR extractor and a mask-based LFR extractor. GFR extractor aims to extract contextual information, e.g., the relationship among various body parts, and the mask-based LFR extractor is presented to exploit the detailed posture changes of local regions. In addition, we introduce a novel mask-based strategy to improve the local feature extraction capability. Specifically, we design pairs of complementary masks to randomly occlude feature maps, and then train our mask-based LFR extractor on various occluded feature maps. In this manner, the LFR extractor will learn to fully exploit local information. Extensive experiments demonstrate that GaitGL achieves better performance than state-of-the-art gait recognition methods. The average rank-1 accuracy on CASIA-B, OU-MVLP, GREW and Gait3D is 93.6%, 98.7%, 68.0% and 63.8%, respectively, significantly outperforming the competing methods. The proposed method has won the first prize in two competitions: HID 2020 and HID 2021. | ['Xin Yu', 'Lincheng Li', 'Ming Wang', 'Shunli Zhang', 'Beibei Lin'] | 2022-08-02 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [-1.73241466e-01 -5.05868673e-01 -2.10545823e-01 -3.12187344e-01
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ab683b01-dbf6-465c-8a4d-e94325596ab5 | multiplicative-fourier-level-of-detail | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Dou_Multiplicative_Fourier_Level_of_Detail_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Dou_Multiplicative_Fourier_Level_of_Detail_CVPR_2023_paper.pdf | Multiplicative Fourier Level of Detail | We develop a simple yet surprisingly effective implicit representing scheme called Multiplicative Fourier Level of Detail (MFLOD) motivated by the recent success of multiplicative filter network. Built on multi-resolution feature grid/volume (e.g., the sparse voxel octree), each level's feature is first modulated by a sinusoidal function and then element-wisely multiplied by a linear transformation of previous layer's representation in a layer-to-layer recursive manner, yielding the scale-aggregated encodings for a subsequent simple linear forward to get final output. In contrast to previous hybrid representations relying on interleaved multilevel fusion and nonlinear activation-based decoding, MFLOD could be elegantly characterized as a linear combination of sine basis functions with varying amplitude, frequency, and phase upon the learned multilevel features, thus offering great feasibility in Fourier analysis. Comprehensive experimental results on implicit neural representation learning tasks including image fitting, 3D shape representation, and neural radiance fields well demonstrate the superior quality and generalizability achieved by the proposed MFLOD scheme. | ['Bingbing Ni', 'Qiaoqiao Jin', 'Zhong Zheng', 'Yishun Dou'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-shape-representation'] | ['computer-vision'] | [ 4.87625748e-01 -1.82877481e-02 1.21457025e-01 -5.24935365e-01
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e250c23b-11d9-496e-bf4e-052173a4da9c | value-based-ctde-methods-in-symmetric-two | 2211.11886 | null | https://arxiv.org/abs/2211.11886v2 | https://arxiv.org/pdf/2211.11886v2.pdf | Value-based CTDE Methods in Symmetric Two-team Markov Game: from Cooperation to Team Competition | In this paper, we identify the best learning scenario to train a team of agents to compete against multiple possible strategies of opposing teams. We evaluate cooperative value-based methods in a mixed cooperative-competitive environment. We restrict ourselves to the case of a symmetric, partially observable, two-team Markov game. We selected three training methods based on the centralised training and decentralised execution (CTDE) paradigm: QMIX, MAVEN and QVMix. For each method, we considered three learning scenarios differentiated by the variety of team policies encountered during training. For our experiments, we modified the StarCraft Multi-Agent Challenge environment to create competitive environments where both teams could learn and compete simultaneously. Our results suggest that training against multiple evolving strategies achieves the best results when, for scoring their performances, teams are faced with several strategies. | ['Damien Ernst', 'Jonathan Pisane', 'Pascal Leroy'] | 2022-11-21 | null | null | null | null | ['starcraft'] | ['playing-games'] | [-8.86379480e-02 1.35418847e-01 1.42716557e-01 2.35718280e-01
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3b0bd44a-4d53-490b-a398-04ae9baccf9a | soft-activation-mapping-of-lung-nodules-in | 1810.12494 | null | https://arxiv.org/abs/1810.12494v2 | https://arxiv.org/pdf/1810.12494v2.pdf | Shape and Margin-Aware Lung Nodule Classification in Low-dose CT Images via Soft Activation Mapping | A number of studies on lung nodule classification lack clinical/biological interpretations of the features extracted by convolutional neural network (CNN). The methods like class activation mapping (CAM) and gradient-based CAM (Grad-CAM) are tailored for interpreting localization and classification tasks while they ignored fine-grained features. Therefore, CAM and Grad-CAM cannot provide optimal interpretation for lung nodule categorization task in low-dose CT images, in that fine-grained pathological clues like discrete and irregular shape and margins of nodules are capable of enhancing sensitivity and specificity of nodule classification with regards to CNN. In this paper, we first develop a soft activation mapping (SAM) to enable fine-grained lung nodule shape \& margin (LNSM) feature analysis with a CNN so that it can access rich discrete features. Secondly, by combining high-level convolutional features with SAM, we further propose a high-level feature enhancement scheme (HESAM) to localize LNSM features. Experiments on the LIDC-IDRI dataset indicate that 1) SAM captures more fine-grained and discrete attention regions than existing methods, 2) HESAM localizes more accurately on LNSM features and obtains the state-of-the-art predictive performance, reducing the false positive rate, and 3) we design and conduct a visually matching experiment which incorporates radiologists study to increase the confidence level of applying our method to clinical diagnosis. | ['Hongming Shan', 'Mannudeep Kalra', 'Junping Zhang', 'Yukun Tian', 'Yiming Lei', 'Ge Wang'] | 2018-10-30 | null | null | null | null | ['mapping-of-lung-nodules-in-low-dose-ct-images', 'lung-nodule-classification'] | ['medical', 'medical'] | [ 1.37769058e-01 2.94394284e-01 -4.82623339e-01 -2.28796616e-01
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774ae18b-5f9b-4325-88a3-be00b548cbb4 | adaptive-graph-convolutional-network | 2205.04885 | null | https://arxiv.org/abs/2205.04885v1 | https://arxiv.org/pdf/2205.04885v1.pdf | Adaptive Graph Convolutional Network Framework for Multidimensional Time Series Prediction | In the real world, long sequence time-series forecasting (LSTF) is needed in many cases, such as power consumption prediction and air quality prediction.Multi-dimensional long time series model has more strict requirements on the model, which not only needs to effectively capture the accurate long-term dependence between input and output, but also needs to capture the relationship between data of different dimensions.Recent research shows that the Informer model based on Transformer has achieved excellent performance in long time series prediction.However, this model still has some deficiencies in multidimensional prediction,it cannot capture the relationship between different dimensions well. We improved Informer to address its shortcomings in multidimensional forecasting. First,we introduce an adaptive graph neural network to capture hidden dimension dependencies in mostly time series prediction. Secondly,we integrate adaptive graph convolutional networks into various spatio-temporal series prediction models to solve the defect that they cannot capture the relationship between different dimensions. Thirdly,After experimental testing with multiple data sets, the accuracy of our framework improved by about 10\% after being introduced into the model. | ['Ning Wang'] | 2022-05-08 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-4.80959177e-01 -5.17340899e-01 3.28629054e-02 -1.72235966e-01
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7dd25caa-2df8-4426-8dad-3a4cc385c550 | uniform-in-time-wasserstein-stability-bounds | 2305.12056 | null | https://arxiv.org/abs/2305.12056v1 | https://arxiv.org/pdf/2305.12056v1.pdf | Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent | Algorithmic stability is an important notion that has proven powerful for deriving generalization bounds for practical algorithms. The last decade has witnessed an increasing number of stability bounds for different algorithms applied on different classes of loss functions. While these bounds have illuminated various properties of optimization algorithms, the analysis of each case typically required a different proof technique with significantly different mathematical tools. In this study, we make a novel connection between learning theory and applied probability and introduce a unified guideline for proving Wasserstein stability bounds for stochastic optimization algorithms. We illustrate our approach on stochastic gradient descent (SGD) and we obtain time-uniform stability bounds (i.e., the bound does not increase with the number of iterations) for strongly convex losses and non-convex losses with additive noise, where we recover similar results to the prior art or extend them to more general cases by using a single proof technique. Our approach is flexible and can be generalizable to other popular optimizers, as it mainly requires developing Lyapunov functions, which are often readily available in the literature. It also illustrates that ergodicity is an important component for obtaining time-uniform bounds -- which might not be achieved for convex or non-convex losses unless additional noise is injected to the iterates. Finally, we slightly stretch our analysis technique and prove time-uniform bounds for SGD under convex and non-convex losses (without additional additive noise), which, to our knowledge, is novel. | ['Umut Simsekli', 'Anant Raj', 'Mert Gurbuzbalaban', 'Lingjiong Zhu'] | 2023-05-20 | null | null | null | null | ['stochastic-optimization', 'generalization-bounds'] | ['methodology', 'methodology'] | [-9.35909450e-02 -1.15029849e-01 -1.34128138e-01 -1.10038973e-01
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06e3cd23-1b8d-46fc-acde-41b75af39a61 | a-system-for-multilingual-dependency-parsing | null | null | https://aclanthology.org/K17-3006 | https://aclanthology.org/K17-3006.pdf | A System for Multilingual Dependency Parsing based on Bidirectional LSTM Feature Representations | In this paper, we present our multilingual dependency parser developed for the CoNLL 2017 UD Shared Task dealing with {``}Multilingual Parsing from Raw Text to Universal Dependencies{''}. Our parser extends the monolingual BIST-parser as a multi-source multilingual trainable parser. Thanks to multilingual word embeddings and one hot encodings for languages, our system can use both monolingual and multi-source training. We trained 69 monolingual language models and 13 multilingual models for the shared task. Our multilingual approach making use of different resources yield better results than the monolingual approach for 11 languages. Our system ranked 5 th and achieved 70.93 overall LAS score over the 81 test corpora (macro-averaged LAS F1 score). | ['KyungTae Lim', 'Thierry Poibeau'] | 2017-08-01 | null | null | null | conll-2017-8 | ['multilingual-word-embeddings'] | ['methodology'] | [-5.87682247e-01 4.22258116e-02 -4.96457547e-01 -4.63911384e-01
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dc182b00-ce5d-438c-8b8f-0c71db91cd69 | one-shot-model-for-mixed-precision | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Koryakovskiy_One-Shot_Model_for_Mixed-Precision_Quantization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Koryakovskiy_One-Shot_Model_for_Mixed-Precision_Quantization_CVPR_2023_paper.pdf | One-Shot Model for Mixed-Precision Quantization | Neural network quantization is a popular approach for model compression. Modern hardware supports quantization in mixed-precision mode, which allows for greater compression rates but adds the challenging task of searching for the optimal bit width. The majority of existing searchers find a single mixed-precision architecture. To select an architecture that is suitable in terms of performance and resource consumption, one has to restart searching multiple times. We focus on a specific class of methods that find tensor bit width using gradient-based optimization. First, we theoretically derive several methods that were empirically proposed earlier. Second, we present a novel One-Shot method that finds a diverse set of Pareto-front architectures in O(1) time. For large models, the proposed method is 5 times more efficient than existing methods. We verify the method on two classification and super-resolution models and show above 0.93 correlation score between the predicted and actual model performance. The Pareto-front architecture selection is straightforward and takes only 20 to 40 supernet evaluations, which is the new state-of-the-art result to the best of our knowledge. | ['Gleb Odinokikh', 'Temur Isaev', 'Valentin Buchnev', 'Alexandra Yakovleva', 'Ivan Koryakovskiy'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['model-compression'] | ['methodology'] | [ 1.97822496e-01 -4.97720271e-01 -5.60493410e-01 -1.66634366e-01
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40c5070b-7737-4127-ac69-56da2b8b6957 | deep-reinforcement-learning-based-dynamic-3 | null | null | https://ieeexplore.ieee.org/document/9214878 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9214878 | Deep Reinforcement Learning-Based Dynamic Resource Management for Mobile Edge Computing in Industrial Internet of Things | Nowadays, driven by the rapid development ofsmart mobile equipments and 5G network technologies, theapplication scenarios of Internet of Things (IoT) technologyare becoming increasingly widespread. The integration ofIoT and industrial manufacturing systems forms the industrial IoT (IIoT). Because of the limitation of resources,such as the computation unit and battery capacity in theIIoT equipments (IIEs), computation-intensive tasks need tobe executed in the mobile edge computing (MEC) server.However, the dynamics and continuity of task generationlead to a severe challenge to the management of limitedresources in IIoT. In this article, we investigate the dynamicresource management problem of joint power control andcomputing resource allocation for MEC in IIoT. In order to minimize the long-term average delay of the tasks, theoriginal problem is transformed into a Markov decision process (MDP). Considering the dynamics and continuity oftask generation, we propose a deep reinforcement learningbased dynamic resource management (DDRM) algorithm to
solve the formulated MDP problem. Our DDRM algorithmexploits the deep deterministic policy gradient and can deal
with the high-dimensional continuity of the action and state spaces. Extensive simulation results demonstrate that the
DDRM can reduce the long-term average delay of the taskseffectively. | ['Lian Zhao', 'Xin Chen', 'Yuan Wu', 'Yongchao Zhang', 'Zhiyong Liu', 'Ying Chen'] | 2020-10-06 | null | null | null | ieee-2020-10 | ['edge-computing'] | ['time-series'] | [-3.34661007e-01 -2.32143015e-01 -4.01676357e-01 3.56262118e-01
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68d9aca2-c711-44e3-8670-ecb9c74c7cb9 | pixel-wise-deep-learning-for-contour | 1504.01989 | null | http://arxiv.org/abs/1504.01989v1 | http://arxiv.org/pdf/1504.01989v1.pdf | Pixel-wise Deep Learning for Contour Detection | We address the problem of contour detection via per-pixel classifications of
edge point. To facilitate the process, the proposed approach leverages with
DenseNet, an efficient implementation of multiscale convolutional neural
networks (CNNs), to extract an informative feature vector for each pixel and
uses an SVM classifier to accomplish contour detection. In the experiment of
contour detection, we look into the effectiveness of combining per-pixel
features from different CNN layers and verify their performance on BSDS500. | ['Tyng-Luh Liu', 'Jyh-Jing Hwang'] | 2015-04-08 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 8.54322836e-02 -1.98013499e-01 -3.07679445e-01 -1.77406240e-02
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ecae67f2-88e3-4b28-b992-14a5c6d0a1f3 | real-time-distributed-model-predictive | 2208.12531 | null | https://arxiv.org/abs/2208.12531v1 | https://arxiv.org/pdf/2208.12531v1.pdf | Real-Time Distributed Model Predictive Control with Limited Communication Data Rates | The application of distributed model predictive controllers (DMPC) for multi-agent systems (MASs) necessitates communication between agents, yet the consequence of communication data rates is typically overlooked. This work focuses on developing stability-guaranteed control methods for MASs with limited data rates. Initially, a distributed optimization algorithm with dynamic quantization is considered for solving the DMPC problem. Due to the limited data rate, the optimization process suffers from inexact iterations caused by quantization noise and premature termination, leading to sub-optimal solutions. In response, we propose a novel real-time DMPC framework with a quantization refinement scheme that updates the quantization parameters on-line so that both the quantization noise and the optimization sub-optimality decrease asymptotically. To facilitate the stability analysis, we treat the sub-optimally controlled MAS, the quantization refinement scheme, and the optimization process as three interconnected subsystems. The cyclic-small-gain theorem is used to derive sufficient conditions on the data rate for guaranteeing the asymptotic stability of the system. Finally, the proposed algorithm and theoretical findings are demonstrated on a multi-AUV formation control example. | ['Ye Pu', 'Chris Manzie', 'Ye Wang', 'Yujia Yang'] | 2022-08-26 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [ 2.29534358e-02 3.69533181e-01 -1.73860416e-01 5.26182532e-01
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9d4b8779-98fa-4f85-8258-92117545a567 | centralized-adversarial-learning-for-robust | 2204.10779 | null | https://arxiv.org/abs/2204.10779v6 | https://arxiv.org/pdf/2204.10779v6.pdf | CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval | Deep hashing has been extensively utilized in massive image retrieval because of its efficiency and effectiveness. However, deep hashing models are vulnerable to adversarial examples, making it essential to develop adversarial defense methods for image retrieval. Existing solutions achieved limited defense performance because of using weak adversarial samples for training and lacking discriminative optimization objectives to learn robust features. In this paper, we present a min-max based Center-guided Adversarial Training, namely CgAT, to improve the robustness of deep hashing networks through worst adversarial examples. Specifically, we first formulate the center code as a semantically-discriminative representative of the input image content, which preserves the semantic similarity with positive samples and dissimilarity with negative examples. We prove that a mathematical formula can calculate the center code immediately. After obtaining the center codes in each optimization iteration of the deep hashing network, they are adopted to guide the adversarial training process. On the one hand, CgAT generates the worst adversarial examples as augmented data by maximizing the Hamming distance between the hash codes of the adversarial examples and the center codes. On the other hand, CgAT learns to mitigate the effects of adversarial samples by minimizing the Hamming distance to the center codes. Extensive experiments on the benchmark datasets demonstrate the effectiveness of our adversarial training algorithm in defending against adversarial attacks for deep hashing-based retrieval. Compared with the current state-of-the-art defense method, we significantly improve the defense performance by an average of 18.61\%, 12.35\%, and 11.56\% on FLICKR-25K, NUS-WIDE, and MS-COCO, respectively. The code is available at https://github.com/xunguangwang/CgAT. | ['Yiqun Lin', 'Xiaomeng Li', 'Xunguang Wang'] | 2022-04-18 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [-2.79253155e-01 -4.42225784e-01 1.89857800e-02 -1.30791634e-01
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1a35042e-a327-4504-a51e-cdab324a2340 | rethinking-of-radar-s-role-a-camera-radar | 2105.05207 | null | https://arxiv.org/abs/2105.05207v1 | https://arxiv.org/pdf/2105.05207v1.pdf | Rethinking of Radar's Role: A Camera-Radar Dataset and Systematic Annotator via Coordinate Alignment | Radar has long been a common sensor on autonomous vehicles for obstacle ranging and speed estimation. However, as a robust sensor to all-weather conditions, radar's capability has not been well-exploited, compared with camera or LiDAR. Instead of just serving as a supplementary sensor, radar's rich information hidden in the radio frequencies can potentially provide useful clues to achieve more complicated tasks, like object classification and detection. In this paper, we propose a new dataset, named CRUW, with a systematic annotator and performance evaluation system to address the radar object detection (ROD) task, which aims to classify and localize the objects in 3D purely from radar's radio frequency (RF) images. To the best of our knowledge, CRUW is the first public large-scale dataset with a systematic annotation and evaluation system, which involves camera RGB images and radar RF images, collected in various driving scenarios. | ['Jenq-Neng Hwang', 'Hui Liu', 'Hung-Min Hsu', 'Gaoang Wang', 'Yizhou Wang'] | 2021-05-11 | null | null | null | null | ['radar-object-detection'] | ['robots'] | [ 1.73814848e-01 -2.68665642e-01 -2.43065044e-01 -7.29310513e-01
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296713ab-199b-4975-8b77-ee178820b512 | unite-a-unified-benchmark-for-text-to-sql | 2305.16265 | null | https://arxiv.org/abs/2305.16265v2 | https://arxiv.org/pdf/2305.16265v2.pdf | UNITE: A Unified Benchmark for Text-to-SQL Evaluation | A practical text-to-SQL system should generalize well on a wide variety of natural language questions, unseen database schemas, and novel SQL query structures. To comprehensively evaluate text-to-SQL systems, we introduce a \textbf{UNI}fied benchmark for \textbf{T}ext-to-SQL \textbf{E}valuation (UNITE). It is composed of publicly available text-to-SQL datasets, containing natural language questions from more than 12 domains, SQL queries from more than 3.9K patterns, and 29K databases. Compared to the widely used Spider benchmark \cite{yu-etal-2018-spider}, we introduce $\sim$120K additional examples and a threefold increase in SQL patterns, such as comparative and boolean questions. We conduct a systematic study of six state-of-the-art (SOTA) text-to-SQL parsers on our new benchmark and show that: 1) Codex performs surprisingly well on out-of-domain datasets; 2) specially designed decoding methods (e.g. constrained beam search) can improve performance for both in-domain and out-of-domain settings; 3) explicitly modeling the relationship between questions and schemas further improves the Seq2Seq models. More importantly, our benchmark presents key challenges towards compositional generalization and robustness issues -- which these SOTA models cannot address well. \footnote{Our code and data processing script will be available at \url{https://github.com/XXXX.}} | ['Bing Xiang', 'Patrick Ng', 'Vittorio Castelli', 'Stephen Ash', 'Jiarong Jiang', 'Jun Wang', 'Mingwen Dong', 'Lin Pan', 'Yiqun Hu', 'Joseph Lilien', 'Chung-Wei Hang', 'Sheng Zhang', 'Jiang Guo', 'Alexander Li', 'Henghui Zhu', 'Anuj Chauhan', 'Zhiguo Wang', 'Wuwei Lan'] | 2023-05-25 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [-3.92346876e-03 -6.97871149e-02 -1.14574902e-01 -1.00678611e+00
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f7b5d088-0db0-4ef9-a0d0-b3a2779c4e7f | g2t-a-simple-but-versatile-framework-for | 2304.06653 | null | https://arxiv.org/abs/2304.06653v3 | https://arxiv.org/pdf/2304.06653v3.pdf | Graph2topic: an opensource topic modeling framework based on sentence embedding and community detection | It has been reported that clustering-based topic models, which cluster high-quality sentence embeddings with an appropriate word selection method, can generate better topics than generative probabilistic topic models. However, these approaches suffer from the inability to select appropriate parameters and incomplete models that overlook the quantitative relation between words with topics and topics with text. To solve these issues, we propose graph to topic (G2T), a simple but effective framework for topic modelling. The framework is composed of four modules. First, document representation is acquired using pretrained language models. Second, a semantic graph is constructed according to the similarity between document representations. Third, communities in document semantic graphs are identified, and the relationship between topics and documents is quantified accordingly. Fourth, the word--topic distribution is computed based on a variant of TFIDF. Automatic evaluation suggests that G2T achieved state-of-the-art performance on both English and Chinese documents with different lengths. | ['Qiang Yan', 'Jiapeng Liu', 'Leihang Zhang'] | 2023-04-13 | null | null | null | null | ['community-detection', 'sentence-embeddings', 'sentence-embeddings', 'topic-models'] | ['graphs', 'methodology', 'natural-language-processing', 'natural-language-processing'] | [-1.97819263e-01 1.58579081e-01 -4.27288622e-01 -2.57739604e-01
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d1a24e99-26e0-472f-a020-ff10519e661d | the-contextual-lasso-sparse-linear-models-via | 2302.00878 | null | https://arxiv.org/abs/2302.00878v2 | https://arxiv.org/pdf/2302.00878v2.pdf | The contextual lasso: Sparse linear models via deep neural networks | Sparse linear models are a gold standard tool for interpretable machine learning, a field of emerging importance as predictive models permeate decision-making in many domains. Unfortunately, sparse linear models are far less flexible as functions of their input features than black-box models like deep neural networks. With this capability gap in mind, we study a not-uncommon situation where the input features dichotomize into two groups: explanatory features, which are candidates for inclusion as variables in an interpretable model, and contextual features, which select from the candidate variables and determine their effects. This dichotomy leads us to the contextual lasso, a new statistical estimator that fits a sparse linear model to the explanatory features such that the sparsity pattern and coefficients vary as a function of the contextual features. The fitting process learns this function nonparametrically via a deep neural network. To attain sparse coefficients, we train the network with a novel lasso regularizer in the form of a projection layer that maps the network's output onto the space of $\ell_1$-constrained linear models. An extensive suite of experiments on real and synthetic data suggests that the learned models, which remain highly transparent, can be sparser than the regular lasso without sacrificing the predictive power of a standard deep neural network. | ['Robert Kohn', 'Amir Dezfouli', 'Ryan Thompson'] | 2023-02-02 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 2.51290888e-01 3.08446944e-01 -5.96735120e-01 -7.33464599e-01
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3197684f-5a2c-46ba-bdca-7ebf62aaefe1 | automatic-image-stylization-using-deep-fully | 1811.10872 | null | http://arxiv.org/abs/1811.10872v1 | http://arxiv.org/pdf/1811.10872v1.pdf | Automatic Image Stylization Using Deep Fully Convolutional Networks | Color and tone stylization strives to enhance unique themes with artistic
color and tone adjustments. It has a broad range of applications from
professional image postprocessing to photo sharing over social networks.
Mainstream photo enhancement softwares provide users with predefined styles,
which are often hand-crafted through a trial-and-error process. Such photo
adjustment tools lack a semantic understanding of image contents and the
resulting global color transform limits the range of artistic styles it can
represent. On the other hand, stylistic enhancement needs to apply distinct
adjustments to various semantic regions. Such an ability enables a broader
range of visual styles. In this paper, we propose a novel deep learning
architecture for automatic image stylization, which learns local enhancement
styles from image pairs. Our deep learning architecture is an end-to-end deep
fully convolutional network performing semantics-aware feature extraction as
well as automatic image adjustment prediction. Image stylization can be
efficiently accomplished with a single forward pass through our deep network.
Experiments on existing datasets for image stylization demonstrate the
effectiveness of our deep learning architecture. | ['Feida Zhu', 'Yizhou Yu'] | 2018-11-27 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 5.51628113e-01 -1.07207417e-01 -1.35231256e-01 -6.39352202e-01
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ab351ee1-a3d5-4693-95ff-26bba0cb84b7 | a-novel-multi-task-learning-method-for | 2201.05782 | null | https://arxiv.org/abs/2201.05782v1 | https://arxiv.org/pdf/2201.05782v1.pdf | A Novel Multi-Task Learning Method for Symbolic Music Emotion Recognition | Symbolic Music Emotion Recognition(SMER) is to predict music emotion from symbolic data, such as MIDI and MusicXML. Previous work mainly focused on learning better representation via (mask) language model pre-training but ignored the intrinsic structure of the music, which is extremely important to the emotional expression of music. In this paper, we present a simple multi-task framework for SMER, which incorporates the emotion recognition task with other emotion-related auxiliary tasks derived from the intrinsic structure of the music. The results show that our multi-task framework can be adapted to different models. Moreover, the labels of auxiliary tasks are easy to be obtained, which means our multi-task methods do not require manually annotated labels other than emotion. Conducting on two publicly available datasets (EMOPIA and VGMIDI), the experiments show that our methods perform better in SMER task. Specifically, accuracy has been increased by 4.17 absolute point to 67.58 in EMOPIA dataset, and 1.97 absolute point to 55.85 in VGMIDI dataset. Ablation studies also show the effectiveness of multi-task methods designed in this paper. | ['Tong Zhang', 'C. L. Philip Chen', 'Jibao Qiu'] | 2022-01-15 | null | null | null | null | ['music-emotion-recognition'] | ['music'] | [ 7.65275117e-03 -3.72429281e-01 4.96604592e-02 -3.32932174e-01
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643d4885-4e4b-437a-af6e-9dc817a16115 | s-3-hqa-a-three-stage-approach-for-multi-hop | 2305.11725 | null | https://arxiv.org/abs/2305.11725v1 | https://arxiv.org/pdf/2305.11725v1.pdf | S$^3$HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering | Answering multi-hop questions over hybrid factual knowledge from the given text and table (TextTableQA) is a challenging task. Existing models mainly adopt a retriever-reader framework, which have several deficiencies, such as noisy labeling in training retriever, insufficient utilization of heterogeneous information over text and table, and deficient ability for different reasoning operations. In this paper, we propose a three-stage TextTableQA framework S3HQA, which comprises of retriever, selector, and reasoner. We use a retriever with refinement training to solve the noisy labeling problem. Then, a hybrid selector considers the linked relationships between heterogeneous data to select the most relevant factual knowledge. For the final stage, instead of adapting a reading comprehension module like in previous methods, we employ a generation-based reasoner to obtain answers. This includes two approaches: a row-wise generator and an LLM prompting generator~(first time used in this task). The experimental results demonstrate that our method achieves competitive results in the few-shot setting. When trained on the full dataset, our approach outperforms all baseline methods, ranking first on the HybridQA leaderboard. | ['Kang Liu', 'Jun Zhao', 'Yiming Huang', 'Shizhu He', 'Yifan Wei', 'Xiang Li', 'Fangyu Lei'] | 2023-05-19 | null | null | null | null | ['reading-comprehension'] | ['natural-language-processing'] | [ 2.16815189e-01 5.03113866e-01 -1.23925865e-01 -3.41891348e-01
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5fe631a6-1b07-4479-9136-5b93981ddbb2 | cavl-learning-contrastive-and-adaptive | 2304.04399 | null | https://arxiv.org/abs/2304.04399v1 | https://arxiv.org/pdf/2304.04399v1.pdf | CAVL: Learning Contrastive and Adaptive Representations of Vision and Language | Visual and linguistic pre-training aims to learn vision and language representations together, which can be transferred to visual-linguistic downstream tasks. However, there exists semantic confusion between language and vision during the pre-training stage. Moreover, current pre-trained models tend to take lots of computation resources for fine-tuning when transferred to downstream tasks. In this work, we present a simple but effective approach for learning Contrastive and Adaptive representations of Vision and Language, namely CAVL. Specifically, we introduce a pair-wise contrastive loss to learn alignments between the whole sentence and each image in the same batch during the pre-training process. At the fine-tuning stage, we introduce two lightweight adaptation networks to reduce model parameters and increase training speed for saving computation resources. We evaluate our CAVL on six main downstream tasks, including Visual Question Answering (VQA), Visual Commonsense Reasoning (VCR), Natural Language for Visual Reasoning (NLVR), Region-to-Phrase Grounding (RPG), Text-to-Image Retrieval (TIR), and Zero-shot Text-to-Image Retrieval (ZS-TIR). Compared to baselines, we achieve superior performance and reduce the fine-tuning time by a large margin (in particular, 76.17%). Extensive experiments and ablation studies demonstrate the efficiency of contrastive pre-training and adaptive fine-tuning proposed in our CAVL. | ['Ihor Markevych', 'Jingfei Xia', 'Shentong Mo'] | 2023-04-10 | null | null | null | null | ['visual-reasoning', 'phrase-grounding', 'visual-commonsense-reasoning', 'visual-reasoning'] | ['computer-vision', 'natural-language-processing', 'reasoning', 'reasoning'] | [ 1.97053865e-01 -4.43621203e-02 8.50123614e-02 -5.52458942e-01
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604970d8-dc81-40d9-95e2-2e84fd0451cc | antisemitic-messages-a-guide-to-high-quality | 2304.14599 | null | https://arxiv.org/abs/2304.14599v1 | https://arxiv.org/pdf/2304.14599v1.pdf | Antisemitic Messages? A Guide to High-Quality Annotation and a Labeled Dataset of Tweets | One of the major challenges in automatic hate speech detection is the lack of datasets that cover a wide range of biased and unbiased messages and that are consistently labeled. We propose a labeling procedure that addresses some of the common weaknesses of labeled datasets. We focus on antisemitic speech on Twitter and create a labeled dataset of 6,941 tweets that cover a wide range of topics common in conversations about Jews, Israel, and antisemitism between January 2019 and December 2021 by drawing from representative samples with relevant keywords. Our annotation process aims to strictly apply a commonly used definition of antisemitism by forcing annotators to specify which part of the definition applies, and by giving them the option to personally disagree with the definition on a case-by-case basis. Labeling tweets that call out antisemitism, report antisemitism, or are otherwise related to antisemitism (such as the Holocaust) but are not actually antisemitic can help reduce false positives in automated detection. The dataset includes 1,250 tweets (18%) that are antisemitic according to the International Holocaust Remembrance Alliance (IHRA) definition of antisemitism. It is important to note, however, that the dataset is not comprehensive. Many topics are still not covered, and it only includes tweets collected from Twitter between January 2019 and December 2021. Additionally, the dataset only includes tweets that were written in English. Despite these limitations, we hope that this is a meaningful contribution to improving the automated detection of antisemitic speech. | ['Katharina Soemer', 'Daniel Miehling', 'Sameer Karali', 'Gunther Jikeli'] | 2023-04-28 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [ 2.92233169e-01 1.89408898e-01 -4.28200185e-01 -4.04729217e-01
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d23f0fd8-d412-4703-a74e-f5df90f3aab5 | combining-multiple-clusterings-via-crowd | 1405.1297 | null | http://arxiv.org/abs/1405.1297v2 | http://arxiv.org/pdf/1405.1297v2.pdf | Combining Multiple Clusterings via Crowd Agreement Estimation and Multi-Granularity Link Analysis | The clustering ensemble technique aims to combine multiple clusterings into a
probably better and more robust clustering and has been receiving an increasing
attention in recent years. There are mainly two aspects of limitations in the
existing clustering ensemble approaches. Firstly, many approaches lack the
ability to weight the base clusterings without access to the original data and
can be affected significantly by the low-quality, or even ill clusterings.
Secondly, they generally focus on the instance level or cluster level in the
ensemble system and fail to integrate multi-granularity cues into a unified
model. To address these two limitations, this paper proposes to solve the
clustering ensemble problem via crowd agreement estimation and
multi-granularity link analysis. We present the normalized crowd agreement
index (NCAI) to evaluate the quality of base clusterings in an unsupervised
manner and thus weight the base clusterings in accordance with their clustering
validity. To explore the relationship between clusters, the source aware
connected triple (SACT) similarity is introduced with regard to their common
neighbors and the source reliability. Based on NCAI and multi-granularity
information collected among base clusterings, clusters, and data instances, we
further propose two novel consensus functions, termed weighted evidence
accumulation clustering (WEAC) and graph partitioning with multi-granularity
link analysis (GP-MGLA) respectively. The experiments are conducted on eight
real-world datasets. The experimental results demonstrate the effectiveness and
robustness of the proposed methods. | ['Chang-Dong Wang', 'Jian-Huang Lai', 'Dong Huang'] | 2014-05-06 | null | null | null | null | ['clustering-ensemble'] | ['graphs'] | [-2.89460748e-01 -3.50355595e-01 3.94351780e-02 -1.77021399e-01
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f90b0ae2-a49b-4ad2-bd49-0e0158a7120c | joint-event-extraction-via-structural | 2306.03469 | null | https://arxiv.org/abs/2306.03469v1 | https://arxiv.org/pdf/2306.03469v1.pdf | Joint Event Extraction via Structural Semantic Matching | Event Extraction (EE) is one of the essential tasks in information extraction, which aims to detect event mentions from text and find the corresponding argument roles. The EE task can be abstracted as a process of matching the semantic definitions and argument structures of event types with the target text. This paper encodes the semantic features of event types and makes structural matching with target text. Specifically, Semantic Type Embedding (STE) and Dynamic Structure Encoder (DSE) modules are proposed. Also, the Joint Structural Semantic Matching (JSSM) model is built to jointly perform event detection and argument extraction tasks through a bidirectional attention layer. The experimental results on the ACE2005 dataset indicate that our model achieves a significant performance improvement | ['Weiping Li', 'Jingkun Wang', 'Tianhao Gao', 'Haochen Li'] | 2023-06-06 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 3.55000705e-01 5.85077286e-01 -4.30400014e-01 -6.32131219e-01
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27903ae9-3cfa-4f26-ad25-f0746950ab00 | adafit-rethinking-learning-based-normal | 2108.05836 | null | https://arxiv.org/abs/2108.05836v1 | https://arxiv.org/pdf/2108.05836v1.pdf | AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds | This paper presents a neural network for robust normal estimation on point clouds, named AdaFit, that can deal with point clouds with noise and density variations. Existing works use a network to learn point-wise weights for weighted least squares surface fitting to estimate the normals, which has difficulty in finding accurate normals in complex regions or containing noisy points. By analyzing the step of weighted least squares surface fitting, we find that it is hard to determine the polynomial order of the fitting surface and the fitting surface is sensitive to outliers. To address these problems, we propose a simple yet effective solution that adds an additional offset prediction to improve the quality of normal estimation. Furthermore, in order to take advantage of points from different neighborhood sizes, a novel Cascaded Scale Aggregation layer is proposed to help the network predict more accurate point-wise offsets and weights. Extensive experiments demonstrate that AdaFit achieves state-of-the-art performance on both the synthetic PCPNet dataset and the real-word SceneNN dataset. | ['Bisheng Yang', 'Wenping Wang', 'YuAn Wang', 'Tengping Jiang', 'Zhen Dong', 'YuAn Liu', 'Runsong Zhu'] | 2021-08-12 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhu_AdaFit_Rethinking_Learning-Based_Normal_Estimation_on_Point_Clouds_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhu_AdaFit_Rethinking_Learning-Based_Normal_Estimation_on_Point_Clouds_ICCV_2021_paper.pdf | iccv-2021-1 | ['surface-normals-estimation'] | ['computer-vision'] | [-1.07149228e-01 -4.06795204e-01 1.15829870e-01 -6.33751214e-01
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e6458860-33c0-46f0-bd35-c0ac79b4b7d2 | fast-context-annotated-classification-of | 1806.02374 | null | http://arxiv.org/abs/1806.02374v1 | http://arxiv.org/pdf/1806.02374v1.pdf | Fast Context-Annotated Classification of Different Types of Web Service Descriptions | In the recent rapid growth of web services, IoT, and cloud computing, many
web services and APIs appeared on the web. With the failure of global UDDI
registries, different service repositories started to appear, trying to list
and categorize various types of web services for client applications' discover
and use. In order to increase the effectiveness and speed up the task of
finding compatible Web Services in the brokerage when performing service
composition or suggesting Web Services to the requests, high-level
functionality of the service needs to be determined. Due to the lack of
structured support for specifying such functionality, classification of
services into a set of abstract categories is necessary. We employ a wide range
of Machine Learning and Signal Processing algorithms and techniques in order to
find the highest precision achievable in the scope of this article for the fast
classification of three type of service descriptions: WSDL, REST, and WADL. In
addition, we complement our approach by showing the importance and effect of
contextual information on the classification of the service descriptions and
show that it improves the accuracy in 5 different categories of services. | ['Arash Khodadadi', 'Serguei A. Mokhov', 'Joey Paquet'] | 2018-05-31 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [-2.72971243e-02 -3.70077342e-01 2.37205904e-02 -5.54268241e-01
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c1c3d21f-8181-49fe-9c1b-dec964fb2f40 | neural-prototype-trees-for-interpretable-fine | 2012.02046 | null | https://arxiv.org/abs/2012.02046v2 | https://arxiv.org/pdf/2012.02046v2.pdf | Neural Prototype Trees for Interpretable Fine-grained Image Recognition | Prototype-based methods use interpretable representations to address the black-box nature of deep learning models, in contrast to post-hoc explanation methods that only approximate such models. We propose the Neural Prototype Tree (ProtoTree), an intrinsically interpretable deep learning method for fine-grained image recognition. ProtoTree combines prototype learning with decision trees, and thus results in a globally interpretable model by design. Additionally, ProtoTree can locally explain a single prediction by outlining a decision path through the tree. Each node in our binary tree contains a trainable prototypical part. The presence or absence of this learned prototype in an image determines the routing through a node. Decision making is therefore similar to human reasoning: Does the bird have a red throat? And an elongated beak? Then it's a hummingbird! We tune the accuracy-interpretability trade-off using ensemble methods, pruning and binarizing. We apply pruning without sacrificing accuracy, resulting in a small tree with only 8 learned prototypes along a path to classify a bird from 200 species. An ensemble of 5 ProtoTrees achieves competitive accuracy on the CUB-200- 2011 and Stanford Cars data sets. Code is available at https://github.com/M-Nauta/ProtoTree | ['Christin Seifert', 'Ron van Bree', 'Meike Nauta'] | 2020-12-03 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Nauta_Neural_Prototype_Trees_for_Interpretable_Fine-Grained_Image_Recognition_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Nauta_Neural_Prototype_Trees_for_Interpretable_Fine-Grained_Image_Recognition_CVPR_2021_paper.pdf | cvpr-2021-1 | ['fine-grained-image-recognition'] | ['computer-vision'] | [-1.80096738e-03 6.64234698e-01 -4.00827080e-01 -8.20669711e-01
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0b02b4dd-3f6c-47c2-a773-139c018188a9 | forecasting-solar-irradiance-without-direct | 2303.06010 | null | https://arxiv.org/abs/2303.06010v2 | https://arxiv.org/pdf/2303.06010v2.pdf | Local-Global Methods for Generalised Solar Irradiance Forecasting | As the use of solar power increases, having accurate and timely forecasts will be essential for smooth grid operators. There are many proposed methods for forecasting solar irradiance / solar power production. However, many of these methods formulate the problem as a time-series, relying on near real-time access to observations at the location of interest to generate forecasts. This requires both access to a real-time stream of data and enough historical observations for these methods to be deployed. In this paper, we propose the use of Global methods to train our models in a generalised way, enabling them to generate forecasts for unseen locations. We apply this approach to both classical ML and state of the art methods. Using data from 20 locations distributed throughout the UK and widely available weather data, we show that it is possible to build systems that do not require access to this data. We utilise and compare both satellite and ground observations (e.g. temperature, pressure) of weather data. Leveraging weather observations and measurements from other locations we show it is possible to create models capable of accurately forecasting solar irradiance at new locations. This could facilitate use planning and optimisation for both newly deployed solar farms and domestic installations from the moment they come online. Additionally, we show that training a single global model for multiple locations can produce a more robust model with more consistent and accurate results across locations. | ['Isaac Triguero', 'Dario Landa-Silva', 'Timothy Cargan'] | 2023-03-10 | null | null | null | null | ['solar-irradiance-forecasting'] | ['time-series'] | [-1.76678710e-02 -2.91750163e-01 2.43437067e-01 -4.17286396e-01
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31ad1002-7eb5-44fc-968f-314fa99f7071 | flow-based-svdd-for-anomaly-detection | 2108.04907 | null | https://arxiv.org/abs/2108.04907v1 | https://arxiv.org/pdf/2108.04907v1.pdf | Flow-based SVDD for anomaly detection | We propose FlowSVDD -- a flow-based one-class classifier for anomaly/outliers detection that realizes a well-known SVDD principle using deep learning tools. Contrary to other approaches to deep SVDD, the proposed model is instantiated using flow-based models, which naturally prevents from collapsing of bounding hypersphere into a single point. Experiments show that FlowSVDD achieves comparable results to the current state-of-the-art methods and significantly outperforms related deep SVDD methods on benchmark datasets. | ['Jacek Tabor', 'Przemysław Spurek', 'Łukasz Struski', 'Łukasz Maziarka', 'Marek Śmieja', 'Marcin Sendera'] | 2021-08-10 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [-8.87538671e-01 -2.93443114e-01 5.86476959e-02 -2.86923852e-02
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12bbfaed-fafb-484b-b98d-79a1bad4be86 | speechformer-reducing-information-loss-in | 2109.04574 | null | https://arxiv.org/abs/2109.04574v1 | https://arxiv.org/pdf/2109.04574v1.pdf | Speechformer: Reducing Information Loss in Direct Speech Translation | Transformer-based models have gained increasing popularity achieving state-of-the-art performance in many research fields including speech translation. However, Transformer's quadratic complexity with respect to the input sequence length prevents its adoption as is with audio signals, which are typically represented by long sequences. Current solutions resort to an initial sub-optimal compression based on a fixed sampling of raw audio features. Therefore, potentially useful linguistic information is not accessible to higher-level layers in the architecture. To solve this issue, we propose Speechformer, an architecture that, thanks to reduced memory usage in the attention layers, avoids the initial lossy compression and aggregates information only at a higher level according to more informed linguistic criteria. Experiments on three language pairs (en->de/es/nl) show the efficacy of our solution, with gains of up to 0.8 BLEU on the standard MuST-C corpus and of up to 4.0 BLEU in a low resource scenario. | ['Marco Turchi', 'Matteo Negri', 'Marco Gaido', 'Sara Papi'] | 2021-09-09 | null | https://aclanthology.org/2021.emnlp-main.127 | https://aclanthology.org/2021.emnlp-main.127.pdf | emnlp-2021-11 | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 4.65883940e-01 2.87735760e-01 -9.29959789e-02 -2.88629889e-01
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3ed7458d-608c-4106-ab73-dd65f2064a92 | unsupervised-visual-representation-learning-3 | 2105.11527 | null | https://arxiv.org/abs/2105.11527v3 | https://arxiv.org/pdf/2105.11527v3.pdf | Unsupervised Visual Representation Learning by Online Constrained K-Means | Cluster discrimination is an effective pretext task for unsupervised representation learning, which often consists of two phases: clustering and discrimination. Clustering is to assign each instance a pseudo label that will be used to learn representations in discrimination. The main challenge resides in clustering since prevalent clustering methods (e.g., k-means) have to run in a batch mode. Besides, there can be a trivial solution consisting of a dominating cluster. To address these challenges, we first investigate the objective of clustering-based representation learning. Based on this, we propose a novel clustering-based pretext task with online \textbf{Co}nstrained \textbf{K}-m\textbf{e}ans (\textbf{CoKe}). Compared with the balanced clustering that each cluster has exactly the same size, we only constrain the minimal size of each cluster to flexibly capture the inherent data structure. More importantly, our online assignment method has a theoretical guarantee to approach the global optimum. By decoupling clustering and discrimination, CoKe can achieve competitive performance when optimizing with only a single view from each instance. Extensive experiments on ImageNet and other benchmark data sets verify both the efficacy and efficiency of our proposal. Code is available at \url{https://github.com/idstcv/CoKe}. | ['Rong Jin', 'Hao Li', 'Juhua Hu', 'Yuanhong Xu', 'Qi Qian'] | 2021-05-24 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Qian_Unsupervised_Visual_Representation_Learning_by_Online_Constrained_K-Means_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Qian_Unsupervised_Visual_Representation_Learning_by_Online_Constrained_K-Means_CVPR_2022_paper.pdf | cvpr-2022-1 | ['self-supervised-image-classification', 'online-clustering'] | ['computer-vision', 'computer-vision'] | [ 1.91017538e-01 1.02663592e-01 -3.87140751e-01 -4.95846599e-01
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b9c163d3-5415-44b8-a33a-5740edae5c74 | adaptive-pyramid-context-network-for-semantic | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/He_Adaptive_Pyramid_Context_Network_for_Semantic_Segmentation_CVPR_2019_paper.pdf | Adaptive Pyramid Context Network for Semantic Segmentation | Recent studies witnessed that context features can significantly improve the performance of deep semantic segmentation networks. Current context based segmentation methods differ with each other in how to construct context features and perform differently in practice. This paper firstly introduces three desirable properties of context features in segmentation task. Specially, we find that Global-guided Local Affinity (GLA) can play a vital role in constructing effective context features, while this property has been largely ignored in previous works. Based on this analysis, this paper proposes Adaptive Pyramid Context Network (APCNet) for semantic segmentation. APCNet adaptively constructs multi-scale contextual representations with multiple well-designed Adaptive Context Modules (ACMs). Specifically, each ACM leverages a global image representation as a guidance to estimate the local affinity coefficients for each sub-region, and then calculates a context vector with these affinities. We empirically evaluate our APCNet on three semantic segmentation and scene parsing datasets, including PASCAL VOC 2012, Pascal-Context, and ADE20K dataset. Experimental results show that APCNet achieves state-of-the-art performance on all three benchmarks, and obtains a new record 84.2% on PASCAL VOC 2012 test set without MS COCO pre-trained and any post-processing.
| [' Yu Qiao', ' Yali Wang', ' Lei Zhou', ' Zhongying Deng', 'Junjun He'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['thermal-image-segmentation'] | ['computer-vision'] | [ 4.93286490e-01 -2.92873472e-01 -2.14899927e-01 -8.03508699e-01
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1913ec24-1d6d-43d7-a32f-fe101298c7a2 | principled-hyperedge-prediction-with | 2106.04292 | null | https://arxiv.org/abs/2106.04292v4 | https://arxiv.org/pdf/2106.04292v4.pdf | Principled Hyperedge Prediction with Structural Spectral Features and Neural Networks | Hypergraph offers a framework to depict the multilateral relationships in real-world complex data. Predicting higher-order relationships, i.e hyperedge, becomes a fundamental problem for the full understanding of complicated interactions. The development of graph neural network (GNN) has greatly advanced the analysis of ordinary graphs with pair-wise relations. However, these methods could not be easily extended to the case of hypergraph. In this paper, we generalize the challenges of GNN in representing higher-order data in principle, which are edge- and node-level ambiguities. To overcome the challenges, we present SNALS that utilizes bipartite graph neural network with structural features to collectively tackle the two ambiguity issues. SNALS captures the joint interactions of a hyperedge by its local environment, which is retrieved by collecting the spectrum information of their connections. As a result, SNALS achieves nearly 30% performance increase compared with most recent GNN-based models. In addition, we applied SNALS to predict genetic higher-order interactions on 3D genome organization data. SNALS showed consistently high prediction accuracy across different chromosomes, and generated novel findings on 4-way gene interaction, which is further validated by existing literature. | ['Chi Zhang', 'Pan Li', 'Sha Cao', 'Wei Hao', 'Muhan Zhang', 'Changlin Wan'] | 2021-06-08 | null | null | null | null | ['hyperedge-prediction'] | ['graphs'] | [ 2.37737939e-01 2.59321123e-01 -1.31630942e-01 -1.25853464e-01
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fc54bf97-b1ba-4cb3-9b3d-127077745e0e | federated-learning-based-hierarchical-3d | 2303.00450 | null | https://arxiv.org/abs/2303.00450v1 | https://arxiv.org/pdf/2303.00450v1.pdf | Federated Learning based Hierarchical 3D Indoor Localization | The proliferation of connected devices in indoor environments opens the floor to a myriad of indoor applications with positioning services as key enablers. However, as privacy issues and resource constraints arise, it becomes more challenging to design accurate positioning systems as required by most applications. To overcome the latter challenges, we present in this paper, a federated learning (FL) framework for hierarchical 3D indoor localization using a deep neural network. Indeed, we firstly shed light on the prominence of exploiting the hierarchy between floors and buildings in a multi-building and multi-floor indoor environment. Then, we propose an FL framework to train the designed hierarchical model. The performance evaluation shows that by adopting a hierarchical learning scheme, we can improve the localization accuracy by up to 24.06% compared to the non-hierarchical approach. We also obtain a building and floor prediction accuracy of 99.90% and 94.87% respectively. With the proposed FL framework, we can achieve a near-performance characteristic as of the central training with an increase of only 7.69% in the localization error. Moreover, the conducted scalability study reveals that the FL system accuracy is improved when more devices join the training. | ['El Mehdi Amhoud', 'Wafa Njima', 'Yaya Etiabi'] | 2023-03-01 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-7.75846094e-02 7.07212463e-02 4.82135937e-02 -4.26745206e-01
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9e65f6f7-2b78-4296-b72c-49787a3fb704 | temporal-alignment-prediction-for-supervised | null | null | https://openreview.net/forum?id=p3DKPQ7uaAi | https://openreview.net/pdf?id=p3DKPQ7uaAi | Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification | Explainable distances for sequence data depend on temporal alignment to tackle sequences with different lengths and local variances. Most sequence alignment methods infer the optimal alignment by solving an optimization problem under pre-defined feasible alignment constraints, which not only is time-consuming, but also makes end-to-end sequence learning intractable. In this paper, we propose a learnable sequence distance called Temporal Alignment Prediction (TAP). TAP employs a lightweight convolutional neural network to directly predict the optimal alignment between two sequences, so that only straightforward calculations are required and no optimization is involved in inference. TAP can be applied in different distance-based machine learning tasks. For supervised sequence representation learning, we show that TAP trained with various metric learning losses achieves completive performances with much faster inference speed. For few-shot action classification, we apply TAP as the distance measure in the metric learning-based episode-training paradigm. This simple strategy achieves comparable results with state-of-the-art few-shot action recognition methods. | ['Ji-Rong Wen', 'Bing Su'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['few-shot-action-recognition'] | ['computer-vision'] | [ 6.51183367e-01 -1.52227476e-01 -4.09979343e-01 -4.49906856e-01
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acf75cb4-9476-468e-a1cd-5e5c4cc88ad7 | ideal-gas-behavior-of-rotamerically-defined | 1408.5803 | null | http://arxiv.org/abs/1408.5803v2 | http://arxiv.org/pdf/1408.5803v2.pdf | Ideal gas behavior of rotamerically defined conformers in native globular proteins | Protein conformational transitions, which are essential for function, may be
driven either by entropy or enthalpy when molecular systems comprising solute
and solvent molecules are the focus. Revealing thermodynamic origin of a given
molecular process is an important but difficult task, and general principles
governing protein conformational distributions remain elusive. Here we
demonstrate that when protein molecules are taken as thermodynamic systems and
solvents being treated as the environment, conformational entropy is an
excellent proxy for free energy and is sufficient to explain protein
conformational distributions. Specifically, by defining each unique combination
of side chain torsional state as a conformer, the population distribution (or
free energy) on an arbitrarily given order parameter is approximately a linear
function of conformational entropy. Additionally, span of various microscopic
potential energy terms is observed to be highly correlated with both
conformational entropy and free energy. Presently widely utilized free energy
proxies, including minimum potential energy, average potential energy terms by
themselves or in combination with vibrational entropy\cite, are found to
correlate with free energy rather poorly. Therefore, our findings provide a
fundamentally new theoretical base for development of significantly more
reliable and efficient next generation computational tools, where the number of
available conformers,rather than poential energy of microscopic configurations,
is the central focus. We anticipate that many related research fields,
including structure based drug design and discovery, protein design, docking
and prediction of general intermolecular interactions involving proteins, are
expected to benefit greatly. | [] | 2015-04-30 | null | null | null | null | ['protein-design'] | ['medical'] | [ 2.86850750e-01 -3.48424196e-01 -4.71319348e-01 -4.06871617e-01
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e9112455-c405-421e-8253-f80ca3a59948 | defect-detection-using-weakly-supervised | 2303.15092 | null | https://arxiv.org/abs/2303.15092v1 | https://arxiv.org/pdf/2303.15092v1.pdf | Defect detection using weakly supervised learning | In many real-world scenarios, obtaining large amounts of labeled data can be a daunting task. Weakly supervised learning techniques have gained significant attention in recent years as an alternative to traditional supervised learning, as they enable training models using only a limited amount of labeled data. In this paper, the performance of a weakly supervised classifier to its fully supervised counterpart is compared on the task of defect detection. Experiments are conducted on a dataset of images containing defects, and evaluate the two classifiers based on their accuracy, precision, and recall. Our results show that the weakly supervised classifier achieves comparable performance to the supervised classifier, while requiring significantly less labeled data. | ['Antonios Gasteratos', 'Spyridon Mouroutsos', 'Athanasios Psomoulis', 'Vasiliki Balaska', 'George Pavlidis', 'Vasileios Sevetlidis'] | 2023-03-27 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 2.58108258e-01 3.20813924e-01 -7.02891052e-01 -6.64758623e-01
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d08f5353-92bb-4441-8c34-8bba08abdcaa | towards-training-bilingual-and-code-switched | 2306.08753 | null | https://arxiv.org/abs/2306.08753v1 | https://arxiv.org/pdf/2306.08753v1.pdf | Towards training Bilingual and Code-Switched Speech Recognition models from Monolingual data sources | Multilingual Automatic Speech Recognition (ASR) models are capable of transcribing audios across multiple languages, eliminating the need for separate models. In addition, they can perform Language Identification (LID) and handle code-switched speech. However, training these models requires special code-switch and multilingual speech corpora which are sparsely available. In this paper, we evaluate different approaches towards training of bilingual as well as code-switched ASR models using purely monolingual data sources. We introduce the concept of aggregate tokenizers that differs from the current prevalent technique of generating LIDs at the boundaries of monolingual samples and produces LID for each emitted token instead. We compare bilingual and monolingual model performance, showcase the efficacy of aggregate tokenizers, present a synthetic code-switched ASR data generation technique and demonstrate the effectiveness of the proposed code-switched ASR models for the tasks of speech recognition and spoken language identification. | ['Boris Ginsburg', 'Dima Rekesh', 'Kunal Dhawan'] | 2023-06-14 | null | null | null | null | ['language-identification', 'language-identification', 'automatic-speech-recognition', 'spoken-language-identification'] | ['audio', 'natural-language-processing', 'speech', 'speech'] | [ 6.01631589e-02 -1.30992085e-01 4.01186310e-02 -3.07028294e-01
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3b323152-50cd-4520-afc8-bd799a8538b4 | handling-group-fairness-in-federated-learning | 2307.04417 | null | https://arxiv.org/abs/2307.04417v1 | https://arxiv.org/pdf/2307.04417v1.pdf | Handling Group Fairness in Federated Learning Using Augmented Lagrangian Approach | Federated learning (FL) has garnered considerable attention due to its privacy-preserving feature. Nonetheless, the lack of freedom in managing user data can lead to group fairness issues, where models might be biased towards sensitive factors such as race or gender, even if they are trained using a legally compliant process. To redress this concern, this paper proposes a novel FL algorithm designed explicitly to address group fairness issues. We show empirically on CelebA and ImSitu datasets that the proposed method can improve fairness both quantitatively and qualitatively with minimal loss in accuracy in the presence of statistical heterogeneity and with different numbers of clients. Besides improving fairness, the proposed FL algorithm is compatible with local differential privacy (LDP), has negligible communication costs, and results in minimal overhead when migrating existing FL systems from the common FL protocol such as FederatedAveraging (FedAvg). We also provide the theoretical convergence rate guarantee for the proposed algorithm and the required noise level of the Gaussian mechanism to achieve desired LDP. This innovative approach holds significant potential to enhance the fairness and effectiveness of FL systems, particularly in sensitive applications such as healthcare or criminal justice. | ['Shenghui Song', 'Gerry Windiarto Mohamad Dunda'] | 2023-07-10 | null | null | null | null | ['fairness', 'federated-learning', 'fairness'] | ['computer-vision', 'methodology', 'miscellaneous'] | [-1.12781018e-01 2.25588843e-01 -3.39374036e-01 -6.60479546e-01
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1b62bf76-073f-4b6e-ba75-66154fc47782 | robust-motion-in-betweening | 2102.04942 | null | https://arxiv.org/abs/2102.04942v1 | https://arxiv.org/pdf/2102.04942v1.pdf | Robust Motion In-betweening | In this work we present a novel, robust transition generation technique that can serve as a new tool for 3D animators, based on adversarial recurrent neural networks. The system synthesizes high-quality motions that use temporally-sparse keyframes as animation constraints. This is reminiscent of the job of in-betweening in traditional animation pipelines, in which an animator draws motion frames between provided keyframes. We first show that a state-of-the-art motion prediction model cannot be easily converted into a robust transition generator when only adding conditioning information about future keyframes. To solve this problem, we then propose two novel additive embedding modifiers that are applied at each timestep to latent representations encoded inside the network's architecture. One modifier is a time-to-arrival embedding that allows variations of the transition length with a single model. The other is a scheduled target noise vector that allows the system to be robust to target distortions and to sample different transitions given fixed keyframes. To qualitatively evaluate our method, we present a custom MotionBuilder plugin that uses our trained model to perform in-betweening in production scenarios. To quantitatively evaluate performance on transitions and generalizations to longer time horizons, we present well-defined in-betweening benchmarks on a subset of the widely used Human3.6M dataset and on LaFAN1, a novel high quality motion capture dataset that is more appropriate for transition generation. We are releasing this new dataset along with this work, with accompanying code for reproducing our baseline results. | ['Christopher Pal', 'Derek Nowrouzezahrai', 'Mike Yurick', 'Félix G. Harvey'] | 2021-02-09 | null | null | null | null | ['human-pose-forecasting'] | ['computer-vision'] | [ 2.08772108e-01 1.73570603e-01 -1.19394414e-01 6.00836650e-02
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26521f46-8a07-42fd-b27a-8b17ea31a3b5 | vizseq-a-visual-analysis-toolkit-for-text | 1909.05424 | null | https://arxiv.org/abs/1909.05424v1 | https://arxiv.org/pdf/1909.05424v1.pdf | VizSeq: A Visual Analysis Toolkit for Text Generation Tasks | Automatic evaluation of text generation tasks (e.g. machine translation, text summarization, image captioning and video description) usually relies heavily on task-specific metrics, such as BLEU and ROUGE. They, however, are abstract numbers and are not perfectly aligned with human assessment. This suggests inspecting detailed examples as a complement to identify system error patterns. In this paper, we present VizSeq, a visual analysis toolkit for instance-level and corpus-level system evaluation on a wide variety of text generation tasks. It supports multimodal sources and multiple text references, providing visualization in Jupyter notebook or a web app interface. It can be used locally or deployed onto public servers for centralized data hosting and benchmarking. It covers most common n-gram based metrics accelerated with multiprocessing, and also provides latest embedding-based metrics such as BERTScore. | ['Jiatao Gu', 'Anirudh Jain', 'Danlu Chen', 'Changhan Wang'] | 2019-09-12 | vizseq-a-visual-analysis-toolkit-for-text-1 | https://aclanthology.org/D19-3043 | https://aclanthology.org/D19-3043.pdf | ijcnlp-2019-11 | ['video-description'] | ['computer-vision'] | [ 3.26742440e-01 -9.79911722e-03 -5.82054257e-02 -3.80672552e-02
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36f5982d-e4d4-4a2d-b479-6545918e7927 | decoding-and-interpreting-cortical-signals | null | null | https://iopscience.iop.org/article/10.1088/1741-2552/abe20e | https://iopscience.iop.org/article/10.1088/1741-2552/abe20e | Decoding and interpreting cortical signals with a compact convolutional neural network | Objective. Brain–computer interfaces (BCIs) decode information from neural activity and send it to external devices. The use of Deep Learning approaches for decoding allows for automatic feature engineering within the specific decoding task. Physiologically plausible interpretation of the network parameters ensures the robustness of the learned decision rules and opens the exciting opportunity for automatic knowledge discovery. Approach. We describe a compact convolutional network-based architecture for adaptive decoding of electrocorticographic (ECoG) data into finger kinematics. We also propose a novel theoretically justified approach to interpreting the spatial and temporal weights in the architectures that combine adaptation in both space and time. The obtained spatial and frequency patterns characterizing the neuronal populations pivotal to the specific decoding task can then be interpreted by fitting appropriate spatial and dynamical models. Main results. We first tested our solution using realistic Monte-Carlo simulations. Then, when applied to the ECoG data from Berlin BCI competition IV dataset, our architecture performed comparably to the competition winners without requiring explicit feature engineering. Using the proposed approach to the network weights interpretation we could unravel the spatial and the spectral patterns of the neuronal processes underlying the successful decoding of finger kinematics from an ECoG dataset. Finally we have also applied the entire pipeline to the analysis of a 32-channel EEG motor-imagery dataset and observed physiologically plausible patterns specific to the task. Significance. We described a compact and interpretable CNN architecture derived from the basic principles and encompassing the knowledge in the field of neural electrophysiology. For the first time in the context of such multibranch architectures with factorized spatial and temporal processing we presented theoretically justified weights interpretation rules. We verified our recipes using simulations and real data and demonstrated that the proposed solution offers a good decoder and a tool for investigating motor control neural mechanisms. | ['Alexei Ossadtchi', 'Mikhail Lebedev', 'Mikhail Sinkin', 'Artur Petrosyan'] | 2021-03-02 | null | null | null | journal-of-neural-engineering-2021-3 | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 3.37469757e-01 -8.71554092e-02 4.85883027e-01 -2.99737174e-02
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1b815f2e-5e8f-47cc-a6c6-a5e959021209 | causal-interventions-based-few-shot-named | 2305.01914 | null | https://arxiv.org/abs/2305.01914v1 | https://arxiv.org/pdf/2305.01914v1.pdf | Causal Interventions-based Few-Shot Named Entity Recognition | Few-shot named entity recognition (NER) systems aims at recognizing new classes of entities based on a few labeled samples. A significant challenge in the few-shot regime is prone to overfitting than the tasks with abundant samples. The heavy overfitting in few-shot learning is mainly led by spurious correlation caused by the few samples selection bias. To alleviate the problem of the spurious correlation in the few-shot NER, in this paper, we propose a causal intervention-based few-shot NER method. Based on the prototypical network, the method intervenes in the context and prototype via backdoor adjustment during training. In particular, intervening in the context of the one-shot scenario is very difficult, so we intervene in the prototype via incremental learning, which can also avoid catastrophic forgetting. Our experiments on different benchmarks show that our approach achieves new state-of-the-art results (achieving up to 29% absolute improvement and 12% on average for all tasks). | ['Chunping Ouyang', 'Yongbin Liu', 'Zhen Yang'] | 2023-05-03 | null | null | null | null | ['incremental-learning', 'few-shot-ner', 'named-entity-recognition-ner', 'selection-bias'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.23032773e-01 4.63659078e-01 -2.67841548e-01 -1.01975463e-01
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7d5ecb65-0047-4e97-a9cd-a31c8729bfc1 | seq3-differentiable-sequence-to-sequence-to | 1904.03651 | null | https://arxiv.org/abs/1904.03651v2 | https://arxiv.org/pdf/1904.03651v2.pdf | SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression | Neural sequence-to-sequence models are currently the dominant approach in several natural language processing tasks, but require large parallel corpora. We present a sequence-to-sequence-to-sequence autoencoder (SEQ^3), consisting of two chained encoder-decoder pairs, with words used as a sequence of discrete latent variables. We apply the proposed model to unsupervised abstractive sentence compression, where the first and last sequences are the input and reconstructed sentences, respectively, while the middle sequence is the compressed sentence. Constraining the length of the latent word sequences forces the model to distill important information from the input. A pretrained language model, acting as a prior over the latent sequences, encourages the compressed sentences to be human-readable. Continuous relaxations enable us to sample from categorical distributions, allowing gradient-based optimization, unlike alternatives that rely on reinforcement learning. The proposed model does not require parallel text-summary pairs, achieving promising results in unsupervised sentence compression on benchmark datasets. | ['Ioannis Konstas', 'Christos Baziotis', 'Ion Androutsopoulos', 'Alexandros Potamianos'] | 2019-04-07 | null | null | null | null | ['sentence-compression', 'unsupervised-abstractive-sentence-compression'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.06054091e-01 4.34965491e-01 -2.83215970e-01 -4.44623649e-01
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27674571-0e48-46cd-974b-19869ba2cf57 | ensemble-knowledge-distillation-of-self | 2302.12757 | null | https://arxiv.org/abs/2302.12757v1 | https://arxiv.org/pdf/2302.12757v1.pdf | Ensemble knowledge distillation of self-supervised speech models | Distilled self-supervised models have shown competitive performance and efficiency in recent years. However, there is a lack of experience in jointly distilling multiple self-supervised speech models. In our work, we performed Ensemble Knowledge Distillation (EKD) on various self-supervised speech models such as HuBERT, RobustHuBERT, and WavLM. We tried two different aggregation techniques, layerwise-average and layerwise-concatenation, to the representations of different teacher models and found that the former was more effective. On top of that, we proposed a multiple prediction head method for student models to predict different layer outputs of multiple teacher models simultaneously. The experimental results show that our method improves the performance of the distilled models on four downstream speech processing tasks, Phoneme Recognition, Speaker Identification, Emotion Recognition, and Automatic Speech Recognition in the hidden-set track of the SUPERB benchmark. | ['Hung-Yi Lee', 'Kai-Wei Chang', 'Wei-Cheng Tseng', 'Po-Chieh Yen', 'Tsu-Yuan Hsu', 'Yu-Kuan Fu', 'Tzu-hsun Feng', 'Kuan-Po Huang'] | 2023-02-24 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 1.08941033e-01 3.23687941e-01 -3.31304632e-02 -8.04708004e-01
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-2.29017109e-01 -5.90356708e-01 -6.21732533e-01 1.72310695e-01
-1.47862107e-01 1.44757080e+00 7.65859708e-02 -6.04535580e-01
2.72516489e-01 9.92784202e-01 2.92602122e-01 -6.47108197e-01
-1.06228352e+00 -1.20771818e-01 5.12388170e-01 -2.00838760e-01
-4.55718786e-01 -2.62023151e-01 -1.19011521e+00 7.66730681e-02
-5.76828241e-01 5.35481215e-01 8.46302330e-01 1.05846512e+00
4.14817542e-01 6.89490020e-01 7.47462094e-01 -5.22507489e-01
-1.01373947e+00 -1.38061261e+00 -4.73153740e-01 3.56489085e-02
3.52198154e-01 -4.50687617e-01 -7.58601308e-01 1.64036661e-01] | [14.508342742919922, 6.462913990020752] |
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