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5b85a0d7-85fb-476f-82e6-f8e00d2c0307 | buying-information-for-stochastic | 2306.03607 | null | https://arxiv.org/abs/2306.03607v1 | https://arxiv.org/pdf/2306.03607v1.pdf | Buying Information for Stochastic Optimization | Stochastic optimization is one of the central problems in Machine Learning and Theoretical Computer Science. In the standard model, the algorithm is given a fixed distribution known in advance. In practice though, one may acquire at a cost extra information to make better decisions. In this paper, we study how to buy information for stochastic optimization and formulate this question as an online learning problem. Assuming the learner has an oracle for the original optimization problem, we design a $2$-competitive deterministic algorithm and a $e/(e-1)$-competitive randomized algorithm for buying information. We show that this ratio is tight as the problem is equivalent to a robust generalization of the ski-rental problem, which we call super-martingale stopping. We also consider an adaptive setting where the learner can choose to buy information after taking some actions for the underlying optimization problem. We focus on the classic optimization problem, Min-Sum Set Cover, where the goal is to quickly find an action that covers a given request drawn from a known distribution. We provide an $8$-competitive algorithm running in polynomial time that chooses actions and decides when to buy information about the underlying request. | ['Christos Tzamos', 'Mingchen Ma'] | 2023-06-06 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 2.43494436e-01 1.39597267e-01 -6.18303359e-01 -6.04689181e-01
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f73d9093-798f-4d03-99d5-6a74931984e7 | qbitopt-fast-and-accurate-bitwidth | 2307.04535 | null | https://arxiv.org/abs/2307.04535v1 | https://arxiv.org/pdf/2307.04535v1.pdf | QBitOpt: Fast and Accurate Bitwidth Reallocation during Training | Quantizing neural networks is one of the most effective methods for achieving efficient inference on mobile and embedded devices. In particular, mixed precision quantized (MPQ) networks, whose layers can be quantized to different bitwidths, achieve better task performance for the same resource constraint compared to networks with homogeneous bitwidths. However, finding the optimal bitwidth allocation is a challenging problem as the search space grows exponentially with the number of layers in the network. In this paper, we propose QBitOpt, a novel algorithm for updating bitwidths during quantization-aware training (QAT). We formulate the bitwidth allocation problem as a constraint optimization problem. By combining fast-to-compute sensitivities with efficient solvers during QAT, QBitOpt can produce mixed-precision networks with high task performance guaranteed to satisfy strict resource constraints. This contrasts with existing mixed-precision methods that learn bitwidths using gradients and cannot provide such guarantees. We evaluate QBitOpt on ImageNet and confirm that we outperform existing fixed and mixed-precision methods under average bitwidth constraints commonly found in the literature. | ['Tijmen Blankevoort', 'Mart van Baalen', 'Markus Nagel', 'Marios Fournarakis', 'Jorn Peters'] | 2023-07-10 | null | null | null | null | ['quantization'] | ['methodology'] | [ 1.74390540e-01 -1.20591663e-01 -7.82852411e-01 -4.47776169e-01
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d4237f66-674e-45a9-8ccb-38a85c8b5d20 | oriented-object-detection-in-aerial-images-1 | 2109.10187 | null | https://arxiv.org/abs/2109.10187v5 | https://arxiv.org/pdf/2109.10187v5.pdf | Oriented Object Detection in Aerial Images Based on Area Ratio of Parallelogram | Oriented object detection is a challenging task in aerial images since the objects in aerial images are displayed in arbitrary directions and are frequently densely packed. The mainstream detectors describe rotating objects using a five-parament or eight-parament representations, which suffer from representation ambiguity for orientated object definition. In this paper, we propose a novel representation method based on area ratio of parallelogram, called ARP. Specifically, ARP regresses the minimum bounding rectangle of the oriented object and three area ratios. Three area ratios include the area ratio of a directed object to the smallest circumscribed rectangle and two parallelograms to the minimum circumscribed rectangle. It simplifies offset learning and eliminates the issue of angular periodicity or label point sequences for oriented objects. To further remedy the confusion issue of nearly horizontal objects, the area ratio between the object and its minimal circumscribed rectangle is employed to guide the selection of horizontal or oriented detection for each object. Moreover, the rotated efficient Intersection over Union (R-EIoU) loss with horizontal bounding box and three area ratios are designed to optimize the bounding box regression for rotating objects. Experimental results on remote sensing datasets, including HRSC2016, DOTA, and UCAS-AOD, show that our method achieves superior detection performance than many state-of-the-art approaches. | ['Xinyi Yu', 'Linlin Ou', 'Jiangping Lu', 'Mi Lin'] | 2021-09-21 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 2.40652397e-01 -4.51159060e-01 9.94211584e-02 -2.31916487e-01
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b8915a94-619f-479d-865e-ca3002b2dd8a | dynamic-learning-with-frequent-new-product | 1904.12445 | null | http://arxiv.org/abs/1904.12445v1 | http://arxiv.org/pdf/1904.12445v1.pdf | Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem | Motivated by the phenomenon that companies introduce new products to keep
abreast with customers' rapidly changing tastes, we consider a novel online
learning setting where a profit-maximizing seller needs to learn customers'
preferences through offering recommendations, which may contain existing
products and new products that are launched in the middle of a selling period.
We propose a sequential multinomial logit (SMNL) model to characterize
customers' behavior when product recommendations are presented in tiers. For
the offline version with known customers' preferences, we propose a
polynomial-time algorithm and characterize the properties of the optimal tiered
product recommendation. For the online problem, we propose a learning algorithm
and quantify its regret bound. Moreover, we extend the setting to incorporate a
constraint which ensures every new product is learned to a given accuracy. Our
results demonstrate the tier structure can be used to mitigate the risks
associated with learning new products. | ['Junyu Cao', 'Wei Sun'] | 2019-04-29 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-7.12128207e-02 2.08029017e-01 -9.06503975e-01 -5.95606863e-01
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229061f8-ba83-48de-aeef-11f408c6bd0c | l3das22-challenge-learning-3d-audio-sources | 2202.10372 | null | https://arxiv.org/abs/2202.10372v1 | https://arxiv.org/pdf/2202.10372v1.pdf | L3DAS22 Challenge: Learning 3D Audio Sources in a Real Office Environment | The L3DAS22 Challenge is aimed at encouraging the development of machine learning strategies for 3D speech enhancement and 3D sound localization and detection in office-like environments. This challenge improves and extends the tasks of the L3DAS21 edition. We generated a new dataset, which maintains the same general characteristics of L3DAS21 datasets, but with an extended number of data points and adding constrains that improve the baseline model's efficiency and overcome the major difficulties encountered by the participants of the previous challenge. We updated the baseline model of Task 1, using the architecture that ranked first in the previous challenge edition. We wrote a new supporting API, improving its clarity and ease-of-use. In the end, we present and discuss the results submitted by all participants. L3DAS22 Challenge website: www.l3das.com/icassp2022. | ['Danilo Comminiello', 'Aurelio Uncini', 'Bruno Masiero', 'Chen Zhang', 'Xiguang Zheng', 'Xinlei Ren', 'Marco Pennese', 'Christian Marinoni', 'Eric Guizzo'] | 2022-02-21 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [-3.33346128e-01 1.53912768e-01 4.07887906e-01 -1.18644543e-01
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2d2aab58-70c8-4ea6-9655-e89b099d1b10 | l-cad-language-based-colorization-with-any | 2305.15217 | null | https://arxiv.org/abs/2305.15217v2 | https://arxiv.org/pdf/2305.15217v2.pdf | L-CAD: Language-based Colorization with Any-level Descriptions | Language-based colorization produces plausible and visually pleasing colors under the guidance of user-friendly natural language descriptions. Previous methods implicitly assume that users provide comprehensive color descriptions for most of the objects in the image, which leads to suboptimal performance. In this paper, we propose a unified model to perform language-based colorization with any-level descriptions. We leverage the pretrained cross-modality generative model for its robust language understanding and rich color priors to handle the inherent ambiguity of any-level descriptions. We further design modules to align with input conditions to preserve local spatial structures and prevent the ghosting effect. With the proposed novel sampling strategy, our model achieves instance-aware colorization in diverse and complex scenarios. Extensive experimental results demonstrate our advantages of effectively handling any-level descriptions and outperforming both language-based and automatic colorization methods. The code and pretrained models are available at: https://github.com/changzheng123/L-CAD. | ['Boxin Shi', 'Si Li', 'Yu Li', 'Peixuan Zhang', 'Shuchen Weng', 'Zheng Chang'] | 2023-05-24 | null | null | null | null | ['colorization'] | ['computer-vision'] | [-1.60462320e-01 -2.88566709e-01 -8.84247571e-02 -4.00678009e-01
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f841bc56-340b-41f0-8481-9afdab93bac2 | multilexnorm-a-shared-task-on-multilingual | null | null | https://aclanthology.org/2021.wnut-1.55 | https://aclanthology.org/2021.wnut-1.55.pdf | MultiLexNorm: A Shared Task on Multilingual Lexical Normalization | Lexical normalization is the task of transforming an utterance into its standardized form. This task is beneficial for downstream analysis, as it provides a way to harmonize (often spontaneous) linguistic variation. Such variation is typical for social media on which information is shared in a multitude of ways, including diverse languages and code-switching. Since the seminal work of Han and Baldwin (2011) a decade ago, lexical normalization has attracted attention in English and multiple other languages. However, there exists a lack of a common benchmark for comparison of systems across languages with a homogeneous data and evaluation setup. The MultiLexNorm shared task sets out to fill this gap. We provide the largest publicly available multilingual lexical normalization benchmark including 13 language variants. We propose a homogenized evaluation setup with both intrinsic and extrinsic evaluation. As extrinsic evaluation, we use dependency parsing and part-of-speech tagging with adapted evaluation metrics (a-LAS, a-UAS, and a-POS) to account for alignment discrepancies. The shared task hosted at W-NUT 2021 attracted 9 participants and 18 submissions. The results show that neural normalization systems outperform the previous state-of-the-art system by a large margin. Downstream parsing and part-of-speech tagging performance is positively affected but to varying degrees, with improvements of up to 1.72 a-LAS, 0.85 a-UAS, and 1.54 a-POS for the winning system. | ['Wladimir Sidorenko', 'Tommaso Caselli', 'Timothy Baldwin', 'Talha Çolakoğlu', 'Rahmad Mahendra', 'Özlem Çetinoğlu', 'Nikola Ljubešić', 'Iñaki San Vicente Roncal', 'Benjamin Muller', 'Barbara Plank', 'Arkaitz Zubiaga', 'Alan Ramponi', 'Rob van der Goot'] | null | null | null | null | emnlp-wnut-2021-11 | ['lexical-normalization'] | ['natural-language-processing'] | [-2.43000388e-02 -1.40233338e-01 -3.77116054e-01 -6.45423412e-01
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37d1df7f-e4cb-4eba-b929-390c667f76ea | data-to-text-generation-with-macro-planning | 2102.02723 | null | https://arxiv.org/abs/2102.02723v1 | https://arxiv.org/pdf/2102.02723v1.pdf | Data-to-text Generation with Macro Planning | Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or variants thereof. These models generate text which is fluent (but often imprecise) and perform quite poorly at selecting appropriate content and ordering it coherently. To overcome some of these issues, we propose a neural model with a macro planning stage followed by a generation stage reminiscent of traditional methods which embrace separate modules for planning and surface realization. Macro plans represent high level organization of important content such as entities, events and their interactions; they are learnt from data and given as input to the generator. Extensive experiments on two data-to-text benchmarks (RotoWire and MLB) show that our approach outperforms competitive baselines in terms of automatic and human evaluation. | ['Mirella Lapata', 'Ratish Puduppully'] | 2021-02-04 | null | null | null | null | ['data-to-text-generation'] | ['natural-language-processing'] | [ 4.11067337e-01 9.52056289e-01 -9.87474471e-02 -4.62359309e-01
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36ca9cc5-fe0a-4545-978c-5dd3ff7ed1d8 | a-few-shot-attention-recurrent-residual-u-net | 2303.01582 | null | https://arxiv.org/abs/2303.01582v1 | https://arxiv.org/pdf/2303.01582v1.pdf | A Few-Shot Attention Recurrent Residual U-Net for Crack Segmentation | Recent studies indicate that deep learning plays a crucial role in the automated visual inspection of road infrastructures. However, current learning schemes are static, implying no dynamic adaptation to users' feedback. To address this drawback, we present a few-shot learning paradigm for the automated segmentation of road cracks, which is based on a U-Net architecture with recurrent residual and attention modules (R2AU-Net). The retraining strategy dynamically fine-tunes the weights of the U-Net as a few new rectified samples are being fed into the classifier. Extensive experiments show that the proposed few-shot R2AU-Net framework outperforms other state-of-the-art networks in terms of Dice and IoU metrics, on a new dataset, named CrackMap, which is made publicly available at https://github.com/ikatsamenis/CrackMap. | ['Athanasios Voulodimos', 'Nikolaos Doulamis', 'Anastasios Doulamis', 'Nikolaos Bakalos', 'Eftychios Protopapadakis', 'Iason Katsamenis'] | 2023-03-02 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [ 6.25673011e-02 4.26966399e-02 -1.31359801e-01 -3.16631228e-01
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78179022-11b5-477c-85ea-ea62ecdd048f | demo2vec-reasoning-object-affordances-from | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Fang_Demo2Vec_Reasoning_Object_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Fang_Demo2Vec_Reasoning_Object_CVPR_2018_paper.pdf | Demo2Vec: Reasoning Object Affordances From Online Videos | Watching expert demonstrations is an important way for humans and robots to reason about affordances of unseen objects. In this paper, we consider the problem of reasoning object affordances through the feature embedding of demonstration videos. We design the Demo2Vec model which learns to extract embedded vectors of demonstration videos and predicts the interaction region and the action label on a target image of the same object. We introduce the Online Product Review dataset for Affordance (OPRA) by collecting and labeling diverse YouTube product review videos. Our Demo2Vec model outperforms various recurrent neural network baselines on the collected dataset. | ['Daniel Yang', 'Te-Lin Wu', 'Silvio Savarese', 'Kuan Fang', 'Joseph J. Lim'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['video-to-image-affordance-grounding'] | ['computer-vision'] | [-1.91108480e-01 2.67814666e-01 -4.29731488e-01 -6.57716691e-01
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a09b84bf-9326-4ee5-86d4-15f2483ac454 | self-improving-safety-performance-of | 2210.16575 | null | https://arxiv.org/abs/2210.16575v3 | https://arxiv.org/pdf/2210.16575v3.pdf | Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms | In this work, we propose a self-improving artificial intelligence system to enhance the safety performance of reinforcement learning (RL)-based autonomous driving (AD) agents using black-box verification methods. RL algorithms have become popular in AD applications in recent years. However, the performance of existing RL algorithms heavily depends on the diversity of training scenarios. A lack of safety-critical scenarios during the training phase could result in poor generalization performance in real-world driving applications. We propose a novel framework in which the weaknesses of the training set are explored through black-box verification methods. After discovering AD failure scenarios, the RL agent's training is re-initiated via transfer learning to improve the performance of previously unsafe scenarios. Simulation results demonstrate that our approach efficiently discovers safety failures of action decisions in RL-based adaptive cruise control (ACC) applications and significantly reduces the number of vehicle collisions through iterative applications of our method. The source code is publicly available at https://github.com/data-and-decision-lab/self-improving-RL. | ['Nazim Kemal Ure', 'Halil Durmus', 'Resul Dagdanov'] | 2022-10-29 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [-9.61066186e-02 2.93174908e-02 -3.63869339e-01 -3.83777767e-01
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c4f3b9c5-45fb-4ee3-9691-6200ef35e90c | training-free-lexical-backdoor-attacks-on | 2302.04116 | null | https://arxiv.org/abs/2302.04116v1 | https://arxiv.org/pdf/2302.04116v1.pdf | Training-free Lexical Backdoor Attacks on Language Models | Large-scale language models have achieved tremendous success across various natural language processing (NLP) applications. Nevertheless, language models are vulnerable to backdoor attacks, which inject stealthy triggers into models for steering them to undesirable behaviors. Most existing backdoor attacks, such as data poisoning, require further (re)training or fine-tuning language models to learn the intended backdoor patterns. The additional training process however diminishes the stealthiness of the attacks, as training a language model usually requires long optimization time, a massive amount of data, and considerable modifications to the model parameters. In this work, we propose Training-Free Lexical Backdoor Attack (TFLexAttack) as the first training-free backdoor attack on language models. Our attack is achieved by injecting lexical triggers into the tokenizer of a language model via manipulating its embedding dictionary using carefully designed rules. These rules are explainable to human developers which inspires attacks from a wider range of hackers. The sparse manipulation of the dictionary also habilitates the stealthiness of our attack. We conduct extensive experiments on three dominant NLP tasks based on nine language models to demonstrate the effectiveness and universality of our attack. The code of this work is available at https://github.com/Jinxhy/TFLexAttack. | ['Chunyang Chen', 'Xingliang Yuan', 'Han Hu', 'Qiongkai Xu', 'Terry Yue Zhuo', 'Yujin Huang'] | 2023-02-08 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-2.67629445e-01 -1.84279129e-01 -5.62646210e-01 3.05072833e-02
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8e8107f5-f205-495b-a6e4-5ca5f2fb794c | adapting-crisp-dm-for-idea-mining-a-data | 2105.00574 | null | https://arxiv.org/abs/2105.00574v1 | https://arxiv.org/pdf/2105.00574v1.pdf | Adapting CRISP-DM for Idea Mining: A Data Mining Process for Generating Ideas Using a Textual Dataset | Data mining project managers can benefit from using standard data mining process models. The benefits of using standard process models for data mining, such as the de facto and the most popular, Cross-Industry-Standard-Process model for Data Mining (CRISP-DM) are reduced cost and time. Also, standard models facilitate knowledge transfer, reuse of best practices, and minimize knowledge requirements. On the other hand, to unlock the potential of ever-growing textual data such as publications, patents, social media data, and documents of various forms, digital innovation is increasingly needed. Furthermore, the introduction of cutting-edge machine learning tools and techniques enable the elicitation of ideas. The processing of unstructured textual data to generate new and useful ideas is referred to as idea mining. Existing literature about idea mining merely overlooks the utilization of standard data mining process models. Therefore, the purpose of this paper is to propose a reusable model to generate ideas, CRISP-DM, for Idea Mining (CRISP-IM). The design and development of the CRISP-IM are done following the design science approach. The CRISP-IM facilitates idea generation, through the use of Dynamic Topic Modeling (DTM), unsupervised machine learning, and subsequent statistical analysis on a dataset of scholarly articles. The adapted CRISP-IM can be used to guide the process of identifying trends using scholarly literature datasets or temporally organized patent or any other textual dataset of any domain to elicit ideas. The ex-post evaluation of the CRISP-IM is left for future study. | ['W. Y. Ayele'] | 2021-05-02 | null | null | null | null | ['dynamic-topic-modeling'] | ['natural-language-processing'] | [ 1.07962139e-01 2.37979725e-01 -5.26890993e-01 8.70831534e-02
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dc7fe704-093a-4953-83fe-a982efcf04a3 | drift-reduction-for-monocular-visual-odometry | 2207.00909 | null | https://arxiv.org/abs/2207.00909v1 | https://arxiv.org/pdf/2207.00909v1.pdf | Drift Reduction for Monocular Visual Odometry of Intelligent Vehicles using Feedforward Neural Networks | In this paper, an approach for reducing the drift in monocular visual odometry algorithms is proposed based on a feedforward neural network. A visual odometry algorithm computes the incremental motion of the vehicle between the successive camera frames, then integrates these increments to determine the pose of the vehicle. The proposed neural network reduces the errors in the pose estimation of the vehicle which results from the inaccuracies in features detection and matching, camera intrinsic parameters, and so on. These inaccuracies are propagated to the motion estimation of the vehicle causing larger amounts of estimation errors. The drift reducing neural network identifies such errors based on the motion of features in the successive camera frames leading to more accurate incremental motion estimates. The proposed drift reducing neural network is trained and validated using the KITTI dataset and the results show the efficacy of the proposed approach in reducing the errors in the incremental orientation estimation, thus reducing the overall error in the pose estimation. | ['Sherif Hammad', 'Mohamed I. Awad', 'Mostafa Osman', 'Hassan Wagih'] | 2022-07-02 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-2.25892141e-01 -3.31275538e-02 -9.19986963e-02 -2.24065274e-01
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b930cce7-99da-44b7-b4cd-777dc03473bc | improved-speech-enhancement-with-the-wave-u-1 | null | null | https://openreview.net/forum?id=B1zKGg3soX | https://openreview.net/pdf?id=B1zKGg3soX | Improved Speech Enhancement with the Wave-U-Net | We study the use of the Wave-U-Net architecture for speech enhancement, a model introduced by Stoller et al for the separation of music vocals and accompaniment. This end-to-end learning method for audio source separation operates directly in the time domain, permitting the integrated modelling of phase information and being able to take large temporal contexts into account. Our experiments show that the proposed method improves several metrics, namely PESQ, CSIG, CBAK, COVL and SSNR, over the state-of-the-art with respect to the speech enhancement task on the Voice Bank corpus (VCTK) dataset. We find that a reduced number of hidden layers is sufficient for speech enhancement in comparison to the original system designed for singing voice separation in music. We see this initial result as an encouraging signal to further explore speech enhancement in the time-domain, both as an end in itself and as a pre-processing step to speech recognition systems. | ['Anonymous'] | 2018-10-22 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 1.93941951e-01 -4.53771353e-02 2.88858205e-01 -1.03421688e-01
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ae3f4e82-5550-4781-af1f-77617d518562 | stylelight-hdr-panorama-generation-for | 2207.14811 | null | https://arxiv.org/abs/2207.14811v1 | https://arxiv.org/pdf/2207.14811v1.pdf | StyleLight: HDR Panorama Generation for Lighting Estimation and Editing | We present a new lighting estimation and editing framework to generate high-dynamic-range (HDR) indoor panorama lighting from a single limited field-of-view (LFOV) image captured by low-dynamic-range (LDR) cameras. Existing lighting estimation methods either directly regress lighting representation parameters or decompose this problem into LFOV-to-panorama and LDR-to-HDR lighting generation sub-tasks. However, due to the partial observation, the high-dynamic-range lighting, and the intrinsic ambiguity of a scene, lighting estimation remains a challenging task. To tackle this problem, we propose a coupled dual-StyleGAN panorama synthesis network (StyleLight) that integrates LDR and HDR panorama synthesis into a unified framework. The LDR and HDR panorama synthesis share a similar generator but have separate discriminators. During inference, given an LDR LFOV image, we propose a focal-masked GAN inversion method to find its latent code by the LDR panorama synthesis branch and then synthesize the HDR panorama by the HDR panorama synthesis branch. StyleLight takes LFOV-to-panorama and LDR-to-HDR lighting generation into a unified framework and thus greatly improves lighting estimation. Extensive experiments demonstrate that our framework achieves superior performance over state-of-the-art methods on indoor lighting estimation. Notably, StyleLight also enables intuitive lighting editing on indoor HDR panoramas, which is suitable for real-world applications. Code is available at https://style-light.github.io. | ['Ziwei Liu', 'Chen Change Loy', 'Yinuo Yang', 'Guangcong Wang'] | 2022-07-29 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 5.51641822e-01 -4.41964954e-01 -7.79219270e-02 -4.40485746e-01
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1c3c2254-3f58-438c-b20b-d4e50d38183c | enhancing-automated-essay-scoring-performance | null | null | https://aclanthology.org/2020.findings-emnlp.141 | https://aclanthology.org/2020.findings-emnlp.141.pdf | Enhancing Automated Essay Scoring Performance via Fine-tuning Pre-trained Language Models with Combination of Regression and Ranking | Automated Essay Scoring (AES) is a critical text regression task that automatically assigns scores to essays based on their writing quality. Recently, the performance of sentence prediction tasks has been largely improved by using Pre-trained Language Models via fusing representations from different layers, constructing an auxiliary sentence, using multi-task learning, etc. However, to solve the AES task, previous works utilize shallow neural networks to learn essay representations and constrain calculated scores with regression loss or ranking loss, respectively. Since shallow neural networks trained on limited samples show poor performance to capture deep semantic of texts. And without an accurate scoring function, ranking loss and regression loss measures two different aspects of the calculated scores. To improve AES{'}s performance, we find a new way to fine-tune pre-trained language models with multiple losses of the same task. In this paper, we propose to utilize a pre-trained language model to learn text representations first. With scores calculated from the representations, mean square error loss and the batch-wise ListNet loss with dynamic weights constrain the scores simultaneously. We utilize Quadratic Weighted Kappa to evaluate our model on the Automated Student Assessment Prize dataset. Our model outperforms not only state-of-the-art neural models near 3 percent but also the latest statistic model. Especially on the two narrative prompts, our model performs much better than all other state-of-the-art models. | ['Xiaodong He', 'Youzheng Wu', 'Zhiyuan Wen', 'Jiannong Cao', 'Ruosong Yang'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['automated-essay-scoring'] | ['natural-language-processing'] | [-1.15555540e-01 -1.76111192e-01 -2.87327051e-01 -7.54381359e-01
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ee6d54bb-c9c6-4467-968b-f117b860d963 | a-pervasive-framework-for-human-detection-and | 2303.11170 | null | https://arxiv.org/abs/2303.11170v1 | https://arxiv.org/pdf/2303.11170v1.pdf | A Pervasive Framework for Human Detection and Tracking | The advent of the Edge Computing (EC) leads to a huge ecosystem where numerous nodes can interact with data collection devices located close to end users. Human detection and tracking can be realized at edge nodes that perform the surveillance of an area under consideration through the assistance of a set of sensors (e.g., cameras). Our target is to incorporate the discussed functionalities to embedded devices present at the edge keeping their size limited while increasing their processing capabilities. In this paper, we propose two models for human detection accompanied by algorithms for tracing the corresponding trajectories. We provide the description of the proposed models and extend them to meet the challenges of the problem. Our evaluation aims at identifying models' accuracy while presenting their requirements to have them executed in embedded devices. | ['Kostas Kolomvatsos', 'Bratsos Dimitrios', 'Fesatidis Georgios'] | 2023-02-17 | null | null | null | null | ['human-detection'] | ['computer-vision'] | [-1.32324761e-02 2.00016230e-01 1.44423321e-01 1.70898616e-01
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2af0f492-386c-441e-8121-29d6900dc5d1 | semantic-parsing-for-conversational-question | 2301.12217 | null | https://arxiv.org/abs/2301.12217v1 | https://arxiv.org/pdf/2301.12217v1.pdf | Semantic Parsing for Conversational Question Answering over Knowledge Graphs | In this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a conversation with a user and ground them to formal queries over definitions in a general purpose knowledge graph (KG) with very large vocabularies (covering thousands of concept names and relations, and millions of entities). To this end, we develop a dataset where user questions are annotated with Sparql parses and system answers correspond to execution results thereof. We present two different semantic parsing approaches and highlight the challenges of the task: dealing with large vocabularies, modelling conversation context, predicting queries with multiple entities, and generalising to new questions at test time. We hope our dataset will serve as useful testbed for the development of conversational semantic parsers. Our dataset and models are released at https://github.com/EdinburghNLP/SPICE. | ['Mirella Lapata', 'Emilio Monti', 'Parag Jain', 'Laura Perez-Beltrachini'] | 2023-01-28 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [-2.26710320e-01 7.60595858e-01 9.32437852e-02 -6.58771574e-01
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73bf7f41-574f-45fd-9f6d-dd6012488d04 | does-manipulating-tokenization-aid-cross | 2304.10158 | null | https://arxiv.org/abs/2304.10158v1 | https://arxiv.org/pdf/2304.10158v1.pdf | Does Manipulating Tokenization Aid Cross-Lingual Transfer? A Study on POS Tagging for Non-Standardized Languages | One of the challenges with finetuning pretrained language models (PLMs) is that their tokenizer is optimized for the language(s) it was pretrained on, but brittle when it comes to previously unseen variations in the data. This can for instance be observed when finetuning PLMs on one language and evaluating them on data in a closely related language variety with no standardized orthography. Despite the high linguistic similarity, tokenization no longer corresponds to meaningful representations of the target data, leading to low performance in, e.g., part-of-speech tagging. In this work, we finetune PLMs on seven languages from three different families and analyze their zero-shot performance on closely related, non-standardized varieties. We consider different measures for the divergence in the tokenization of the source and target data, and the way they can be adjusted by manipulating the tokenization during the finetuning step. Overall, we find that the similarity between the percentage of words that get split into subwords in the source and target data (the split word ratio difference) is the strongest predictor for model performance on target data. | ['Barbara Plank', 'Hinrich Schütze', 'Verena Blaschke'] | 2023-04-20 | null | null | null | null | ['part-of-speech-tagging', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [-7.49683529e-02 -1.33100152e-01 -3.53101403e-01 -5.83038747e-01
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ca3cf8b0-17a4-4bef-b800-395849cbd84a | proceedings-ninetheenth-conference-on | 2307.04005 | null | https://arxiv.org/abs/2307.04005v1 | https://arxiv.org/pdf/2307.04005v1.pdf | Proceedings Ninetheenth conference on Theoretical Aspects of Rationality and Knowledge | The TARK conference (Theoretical Aspects of Rationality and Knowledge) is a conference that aims to bring together researchers from a wide variety of fields, including computer science, artificial intelligence, game theory, decision theory, philosophy, logic, linguistics, and cognitive science. Its goal is to further our understanding of interdisciplinary issues involving reasoning about rationality and knowledge. Previous conferences have been held biennially around the world since 1986, on the initiative of Joe Halpern (Cornell University). Topics of interest include, but are not limited to, semantic models for knowledge, belief, awareness and uncertainty, bounded rationality and resource-bounded reasoning, commonsense epistemic reasoning, epistemic logic, epistemic game theory, knowledge and action, applications of reasoning about knowledge and other mental states, belief revision, computational social choice, algorithmic game theory, and foundations of multi-agent systems. Information about TARK, including conference proceedings, is available at the website http://www.tark.org/ These proceedings contain the papers that have been accepted for presentation at the Nineteenth Conference on Theoretical Aspects of Rationality and Knowledge (TARK 2023), held between June 28 and June 30, 2023, at the University of Oxford, United Kingdom. The conference website can be found at https://sites.google.com/view/tark-2023 | ['Rineke Verbrugge'] | 2023-07-08 | null | null | null | null | ['epistemic-reasoning', 'philosophy'] | ['miscellaneous', 'miscellaneous'] | [-3.24645370e-01 6.95700943e-01 -2.63843596e-01 8.71559978e-02
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1fdb3f45-31df-4d75-bcf6-eceec85caa9b | a-survey-on-hyperdimensional-computing-aka | 2111.06077 | null | https://arxiv.org/abs/2111.06077v1 | https://arxiv.org/pdf/2111.06077v1.pdf | A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations | This two-part comprehensive survey is devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properties of their key operations to incorporate the advantages of structured symbolic representations and vector distributed representations. Notable models in the HDC/VSA family are Tensor Product Representations, Holographic Reduced Representations, Multiply-Add-Permute, Binary Spatter Codes, and Sparse Binary Distributed Representations but there are other models too. HDC/VSA is a highly interdisciplinary area with connections to computer science, electrical engineering, artificial intelligence, mathematics, and cognitive science. This fact makes it challenging to create a thorough overview of the area. However, due to a surge of new researchers joining the area in recent years, the necessity for a comprehensive survey of the area has become extremely important. Therefore, amongst other aspects of the area, this Part I surveys important aspects such as: known computational models of HDC/VSA and transformations of various input data types to high-dimensional distributed representations. Part II of this survey is devoted to applications, cognitive computing and architectures, as well as directions for future work. The survey is written to be useful for both newcomers and practitioners. | ['Abbas Rahimi', 'Evgeny Osipov', 'Dmitri A. Rachkovskij', 'Denis Kleyko'] | 2021-11-11 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [ 2.22675413e-01 -6.85965121e-02 -1.40252084e-01 -2.12837130e-01
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d26222a6-9868-44bc-8a62-e828e5446e40 | detrs-with-collaborative-hybrid-assignments | 2211.12860 | null | https://arxiv.org/abs/2211.12860v4 | https://arxiv.org/pdf/2211.12860v4.pdf | DETRs with Collaborative Hybrid Assignments Training | In this paper, we provide the observation that too few queries assigned as positive samples in DETR with one-to-one set matching leads to sparse supervisions on the encoder's output which considerably hurt the discriminative feature learning of the encoder and vice visa for attention learning in the decoder. To alleviate this, we present a novel collaborative hybrid assignments training scheme, namely Co-DETR, to learn more efficient and effective DETR-based detectors from versatile label assignment manners. This new training scheme can easily enhance the encoder's learning ability in end-to-end detectors by training the multiple parallel auxiliary heads supervised by one-to-many label assignments such as ATSS and Faster RCNN. In addition, we conduct extra customized positive queries by extracting the positive coordinates from these auxiliary heads to improve the training efficiency of positive samples in the decoder. In inference, these auxiliary heads are discarded and thus our method introduces no additional parameters and computational cost to the original detector while requiring no hand-crafted non-maximum suppression (NMS). We conduct extensive experiments to evaluate the effectiveness of the proposed approach on DETR variants, including DAB-DETR, Deformable-DETR, and DINO-Deformable-DETR. Specifically, we improve the basic Deformable-DETR by 5.8% AP in 12-epoch training and 3.2% AP in 36-epoch training. The state-of-the-art DINO-Deformable-DETR with Swin-L can still be improved from 58.5% to 59.5% AP on COCO val. Surprisingly, incorporated with ViT-L backbone, we achieve 65.6% AP on COCO test-dev, outperforming previous methods with much fewer model sizes. Codes will be available at https://github.com/Sense-X/Co-DETR. | ['Yu Liu', 'Guanglu Song', 'Zhuofan Zong'] | 2022-11-22 | null | null | null | null | ['set-matching'] | ['computer-vision'] | [ 5.46488054e-02 2.37519126e-02 -3.00283879e-01 -4.46141511e-01
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85ccad78-cc13-4e17-bb03-8f020a9b42d1 | the-representation-jensen-shannon-divergence | 2305.16446 | null | https://arxiv.org/abs/2305.16446v1 | https://arxiv.org/pdf/2305.16446v1.pdf | The Representation Jensen-Shannon Divergence | Statistical divergences quantify the difference between probability distributions finding multiple uses in machine-learning. However, a fundamental challenge is to estimate divergence from empirical samples since the underlying distributions of the data are usually unknown. In this work, we propose the representation Jensen-Shannon Divergence, a novel divergence based on covariance operators in reproducing kernel Hilbert spaces (RKHS). Our approach embeds the data distributions in an RKHS and exploits the spectrum of the covariance operators of the representations. We provide an estimator from empirical covariance matrices by explicitly mapping the data to an RKHS using Fourier features. This estimator is flexible, scalable, differentiable, and suitable for minibatch-based optimization problems. Additionally, we provide an estimator based on kernel matrices without having an explicit mapping to the RKHS. We show that this quantity is a lower bound on the Jensen-Shannon divergence, and we propose a variational approach to estimate it. We applied our divergence to two-sample testing outperforming related state-of-the-art techniques in several datasets. We used the representation Jensen-Shannon divergence as a cost function to train generative adversarial networks which intrinsically avoids mode collapse and encourages diversity. | ['Luis G. Sanchez-Giraldo', 'Jhoan K. Hoyos-Osorio'] | 2023-05-25 | null | null | null | null | ['hypothesis-testing', 'hypothesis-testing'] | ['methodology', 'miscellaneous'] | [ 5.18783815e-02 2.09076419e-01 2.06471160e-01 -2.97172546e-01
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77661e1d-e9b0-4614-8c5e-48933d85e133 | towards-building-self-aware-object-detectors-1 | 2307.00934 | null | https://arxiv.org/abs/2307.00934v1 | https://arxiv.org/pdf/2307.00934v1.pdf | Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration | The current approach for testing the robustness of object detectors suffers from serious deficiencies such as improper methods of performing out-of-distribution detection and using calibration metrics which do not consider both localisation and classification quality. In this work, we address these issues, and introduce the Self-Aware Object Detection (SAOD) task, a unified testing framework which respects and adheres to the challenges that object detectors face in safety-critical environments such as autonomous driving. Specifically, the SAOD task requires an object detector to be: robust to domain shift; obtain reliable uncertainty estimates for the entire scene; and provide calibrated confidence scores for the detections. We extensively use our framework, which introduces novel metrics and large scale test datasets, to test numerous object detectors in two different use-cases, allowing us to highlight critical insights into their robustness performance. Finally, we introduce a simple baseline for the SAOD task, enabling researchers to benchmark future proposed methods and move towards robust object detectors which are fit for purpose. Code is available at https://github.com/fiveai/saod | ['Puneet K. Dokania', 'Tom Joy', 'Kemal Oksuz'] | 2023-07-03 | towards-building-self-aware-object-detectors | http://openaccess.thecvf.com//content/CVPR2023/html/Oksuz_Towards_Building_Self-Aware_Object_Detectors_via_Reliable_Uncertainty_Quantification_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Oksuz_Towards_Building_Self-Aware_Object_Detectors_via_Reliable_Uncertainty_Quantification_and_CVPR_2023_paper.pdf | cvpr-2023-1 | ['out-of-distribution-detection'] | ['computer-vision'] | [-7.66428513e-03 -2.01003641e-01 -2.43189968e-02 -4.81326014e-01
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84f4580d-3a76-47d9-98c3-fba76db73b74 | dasps-a-database-for-anxious-states-based-on | 1901.02942 | null | https://arxiv.org/abs/1901.02942v2 | https://arxiv.org/pdf/1901.02942v2.pdf | DASPS: A Database for Anxious States based on a Psychological Stimulation | Anxiety affects human capabilities and behavior as much as it affects productivity and quality of life. It can be considered as the main cause of depression and suicide. Anxious states are easily detectable by humans due to their acquired cognition, humans interpret the interlocutor's tone of speech, gesture, facial expressions and recognize their mental state. There is a need for non-invasive reliable techniques that performs the complex task of anxiety detection. In this paper, we present DASPS database containing recorded Electroencephalogram (EEG) signals of 23 participants during anxiety elicitation by means of face-to-face psychological stimuli. EEG signals were captured with Emotiv Epoc headset as it's a wireless wearable low-cost equipment. In our study, we investigate the impact of different parameters, notably: trial duration, feature type, feature combination and anxiety levels number. Our findings showed that anxiety is well elicited in 1 second. For instance, stacked sparse autoencoder with different type of features achieves 83.50% and 74.60% for 2 and 4 anxiety levels detection, respectively. The presented results prove the benefits of the use of a low-cost EEG headset instead of medical non-wireless devices and create a starting point for new researches in the field of anxiety detection. | ['Adel M. ALIMI', 'Rahma Fourati', 'Asma Baghdadi', 'Yassine Aribi', 'Najla Halouani', 'Patrick Siarry'] | 2019-01-09 | null | null | null | null | ['anxiety-detection'] | ['medical'] | [-6.34672567e-02 -1.32911995e-01 6.97364092e-01 -4.96287137e-01
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ac9d4c98-a68c-4a9e-88b9-1107061ff195 | learning-long-term-style-preserving-blind | 2103.07278 | null | https://arxiv.org/abs/2103.07278v1 | https://arxiv.org/pdf/2103.07278v1.pdf | Learning Long-Term Style-Preserving Blind Video Temporal Consistency | When trying to independently apply image-trained algorithms to successive frames in videos, noxious flickering tends to appear. State-of-the-art post-processing techniques that aim at fostering temporal consistency, generate other temporal artifacts and visually alter the style of videos. We propose a postprocessing model, agnostic to the transformation applied to videos (e.g. style transfer, image manipulation using GANs, etc.), in the form of a recurrent neural network. Our model is trained using a Ping Pong procedure and its corresponding loss, recently introduced for GAN video generation, as well as a novel style preserving perceptual loss. The former improves long-term temporal consistency learning, while the latter fosters style preservation. We evaluate our model on the DAVIS and videvo.net datasets and show that our approach offers state-of-the-art results concerning flicker removal, and better keeps the overall style of the videos than previous approaches. | ['Matthieu Perrot', 'Robin Kips', 'Julien Despois', 'Hugo Thimonier'] | 2021-03-12 | null | null | null | null | ['video-temporal-consistency'] | ['computer-vision'] | [ 6.05793357e-01 -1.42091542e-01 1.83311135e-01 -2.36949362e-02
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2ba7ed58-a046-4262-bd63-98cc4a663eae | zero-resource-multilingual-model-transfer | 1810.03552 | null | https://arxiv.org/abs/1810.03552v3 | https://arxiv.org/pdf/1810.03552v3.pdf | Multi-Source Cross-Lingual Model Transfer: Learning What to Share | Modern NLP applications have enjoyed a great boost utilizing neural networks models. Such deep neural models, however, are not applicable to most human languages due to the lack of annotated training data for various NLP tasks. Cross-lingual transfer learning (CLTL) is a viable method for building NLP models for a low-resource target language by leveraging labeled data from other (source) languages. In this work, we focus on the multilingual transfer setting where training data in multiple source languages is leveraged to further boost target language performance. Unlike most existing methods that rely only on language-invariant features for CLTL, our approach coherently utilizes both language-invariant and language-specific features at instance level. Our model leverages adversarial networks to learn language-invariant features, and mixture-of-experts models to dynamically exploit the similarity between the target language and each individual source language. This enables our model to learn effectively what to share between various languages in the multilingual setup. Moreover, when coupled with unsupervised multilingual embeddings, our model can operate in a zero-resource setting where neither target language training data nor cross-lingual resources are available. Our model achieves significant performance gains over prior art, as shown in an extensive set of experiments over multiple text classification and sequence tagging tasks including a large-scale industry dataset. | ['Xilun Chen', 'Ahmed Hassan Awadallah', 'Wei Wang', 'Hany Hassan', 'Claire Cardie'] | 2018-10-08 | zero-resource-multilingual-model-transfer-1 | https://aclanthology.org/P19-1299 | https://aclanthology.org/P19-1299.pdf | acl-2019-7 | ['cross-lingual-ner'] | ['natural-language-processing'] | [-6.29581511e-02 -2.54325688e-01 -6.35588527e-01 -2.42994517e-01
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203b5680-5c11-42ed-9938-35d193d2bd1e | one-model-to-rule-them-all-ranking-slovene | 2306.11518 | null | https://arxiv.org/abs/2306.11518v1 | https://arxiv.org/pdf/2306.11518v1.pdf | One model to rule them all: ranking Slovene summarizers | Text summarization is an essential task in natural language processing, and researchers have developed various approaches over the years, ranging from rule-based systems to neural networks. However, there is no single model or approach that performs well on every type of text. We propose a system that recommends the most suitable summarization model for a given text. The proposed system employs a fully connected neural network that analyzes the input content and predicts which summarizer should score the best in terms of ROUGE score for a given input. The meta-model selects among four different summarization models, developed for the Slovene language, using different properties of the input, in particular its Doc2Vec document representation. The four Slovene summarization models deal with different challenges associated with text summarization in a less-resourced language. We evaluate the proposed SloMetaSum model performance automatically and parts of it manually. The results show that the system successfully automates the step of manually selecting the best model. | ['Marko Robnik-Šikonja', 'Aleš Žagar'] | 2023-06-20 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [ 1.78999290e-01 2.08870679e-01 -2.06860915e-01 -2.70418048e-01
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6bddaed7-eb07-4439-8097-2d01f82df47f | origin-destination-network-generation-via | 2306.03390 | null | https://arxiv.org/abs/2306.03390v1 | https://arxiv.org/pdf/2306.03390v1.pdf | Origin-Destination Network Generation via Gravity-Guided GAN | Origin-destination (OD) flow, which contains valuable population mobility information including direction and volume, is critical in many urban applications, such as urban planning, transportation management, etc. However, OD data is not always easy to access due to high costs or privacy concerns. Therefore, we must consider generating OD through mathematical models. Existing works utilize physics laws or machine learning (ML) models to build the association between urban structures and OD flows while these two kinds of methods suffer from the limitation of over-simplicity and poor generalization ability, respectively. In this paper, we propose to adopt physics-informed ML paradigm, which couple the physics scientific knowledge and data-driven ML methods, to construct a model named Origin-Destination Generation Networks (ODGN) for better population mobility modeling by leveraging the complementary strengths of combining physics and ML methods. Specifically, we first build a Multi-view Graph Attention Networks (MGAT) to capture the urban features of every region and then use a gravity-guided predictor to obtain OD flow between every two regions. Furthermore, we use a conditional GAN training strategy and design a sequence-based discriminator to consider the overall topological features of OD as a network. Extensive experiments on real-world datasets have been done to demonstrate the superiority of our proposed method compared with baselines. | ['Yong Li', 'Huandong Wang', 'Can Rong'] | 2023-06-06 | null | null | null | null | ['graph-attention'] | ['graphs'] | [-2.53451049e-01 -1.51979504e-02 -1.90130591e-01 -2.97527254e-01
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0fbf930f-2142-41d3-8b95-1c62d908e673 | asl-citizen-a-community-sourced-dataset-for | 2304.05934 | null | https://arxiv.org/abs/2304.05934v2 | https://arxiv.org/pdf/2304.05934v2.pdf | ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign Language Recognition | Sign languages are used as a primary language by approximately 70 million D/deaf people world-wide. However, most communication technologies operate in spoken and written languages, creating inequities in access. To help tackle this problem, we release ASL Citizen, the first crowdsourced Isolated Sign Language Recognition (ISLR) dataset, collected with consent and containing 83,399 videos for 2,731 distinct signs filmed by 52 signers in a variety of environments. We propose that this dataset be used for sign language dictionary retrieval for American Sign Language (ASL), where a user demonstrates a sign to their webcam to retrieve matching signs from a dictionary. We show that training supervised machine learning classifiers with our dataset advances the state-of-the-art on metrics relevant for dictionary retrieval, achieving 63% accuracy and a recall-at-10 of 91%, evaluated entirely on videos of users who are not present in the training or validation sets. An accessible PDF of this article is available at the following link: https://aashakadesai.github.io/research/ASLCitizen_arxiv_updated.pdf | ['Danielle Bragg', 'Naomi Caselli', 'Alex X. Lu', 'Hal Daumé III', 'Richard E. Ladner', 'Kriston Pumphrey', 'Chinmay Singh', 'Vanessa Milan', 'Fyodor O. Minakov', 'Lauren Berger', 'Aashaka Desai'] | 2023-04-12 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [-1.12242140e-01 -3.42618793e-01 -5.96357822e-01 -8.78895819e-02
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66d4ba05-6d51-41f0-bb3c-b74057824af4 | incnsa-detecting-communities-incrementally | null | null | https://www.worldscientific.com/doi/abs/10.1142/S0129183120500941 | https://www.worldscientific.com/doi/abs/10.1142/S0129183120500941 | IncNSA: Detecting communities incrementally from time-evolving networks based on node similarity | Many real-world systems can be abstracted as networks. As those systems always change dynamically in nature, the corresponding networks also evolve over time in general, and detecting communities from such time-evolving networks has become a critical task. In this paper, we propose an incremental detection method, which can stably detect high-quality community structures from time-evolving networks. When the network evolves from the previous snapshot to the current one, the proposed method only considers the community affiliations of partial nodes efficiently, which are either newborn nodes or some active nodes from the previous snapshot. Thus, the first phase of our method is determining active nodes that should be reassigned due to the change of their community affiliations in the evolution. Then, we construct subgraphs for these nodes to obtain the preliminary communities in the second phase. Finally, the final result can be obtained through optimizing the primary communities in the third phase. To test its performance, extensive experiments are conducted on both some synthetic networks and some real-world dynamic networks, the results show that our method can detect satisfactory community structure from each of snapshot graphs efficiently and steadily, and outperforms the competitors significantly. | ['Xiaoyun Chen', 'Wenbo Zhang', 'Mingwei Leng', 'Haijuan Yang', 'Jianjun Cheng', 'Xing Su'] | 2020-07-16 | null | null | null | international-journal-of-modern-physics-c-1 | ['dynamic-community-detection'] | ['graphs'] | [-5.41597307e-02 -2.35861540e-01 9.37550142e-02 1.54354185e-01
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97040835-ad28-46d0-ab7e-e87533319fd5 | large-scale-transfer-learning-for-natural | null | null | https://aclanthology.org/P19-1608 | https://aclanthology.org/P19-1608.pdf | Large-Scale Transfer Learning for Natural Language Generation | Large-scale pretrained language models define state of the art in natural language processing, achieving outstanding performance on a variety of tasks. We study how these architectures can be applied and adapted for natural language generation, comparing a number of architectural and training schemes. We focus in particular on open-domain dialog as a typical high entropy generation task, presenting and comparing different architectures for adapting pretrained models with state of the art results. | ['er', 'Sergey Golovanov', 'Alex Tselousov', 'Rauf Kurbanov', 'Thomas Wolf', 'Sergey Nikolenko', 'Kyryl Truskovskyi'] | 2019-07-01 | null | null | null | acl-2019-7 | ['open-domain-dialog'] | ['natural-language-processing'] | [ 2.38772377e-01 8.70758414e-01 -4.36814176e-03 -5.03934205e-01
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4d41fcc0-f22c-4517-8f1d-7bfd33525ee7 | efficient-flow-guided-multi-frame-de-fencing | 2301.10759 | null | https://arxiv.org/abs/2301.10759v1 | https://arxiv.org/pdf/2301.10759v1.pdf | Efficient Flow-Guided Multi-frame De-fencing | Taking photographs ''in-the-wild'' is often hindered by fence obstructions that stand between the camera user and the scene of interest, and which are hard or impossible to avoid. De-fencing is the algorithmic process of automatically removing such obstructions from images, revealing the invisible parts of the scene. While this problem can be formulated as a combination of fence segmentation and image inpainting, this often leads to implausible hallucinations of the occluded regions. Existing multi-frame approaches rely on propagating information to a selected keyframe from its temporal neighbors, but they are often inefficient and struggle with alignment of severely obstructed images. In this work we draw inspiration from the video completion literature and develop a simplified framework for multi-frame de-fencing that computes high quality flow maps directly from obstructed frames and uses them to accurately align frames. Our primary focus is efficiency and practicality in a real-world setting: the input to our algorithm is a short image burst (5 frames) - a data modality commonly available in modern smartphones - and the output is a single reconstructed keyframe, with the fence removed. Our approach leverages simple yet effective CNN modules, trained on carefully generated synthetic data, and outperforms more complicated alternatives real bursts, both quantitatively and qualitatively, while running real-time. | ['Alex Levinshtein', 'Allan Jepson', 'Fengjia Zhang', 'Stavros Tsogkas'] | 2023-01-25 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 5.54311097e-01 -2.96856780e-02 6.93985671e-02 1.74438685e-01
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14ccd3cf-81bd-46bb-83d7-881dfb0c7d91 | corruptencoder-data-poisoning-based-backdoor | 2211.08229 | null | https://arxiv.org/abs/2211.08229v3 | https://arxiv.org/pdf/2211.08229v3.pdf | CorruptEncoder: Data Poisoning based Backdoor Attacks to Contrastive Learning | Contrastive learning (CL) pre-trains general-purpose encoders using an unlabeled pre-training dataset, which consists of images or image-text pairs. CL is vulnerable to data poisoning based backdoor attacks (DPBAs), in which an attacker injects poisoned inputs into the pre-training dataset so the encoder is backdoored. However, existing DPBAs achieve limited effectiveness. In this work, we propose new DPBAs called CorruptEncoder to CL. CorruptEncoder uses a theory-guided method to create optimal poisoned inputs to maximize attack effectiveness. Our experiments show that CorruptEncoder substantially outperforms existing DPBAs. In particular, CorruptEncoder is the first DPBA that achieves more than 90% attack success rates with only a few (3) reference images and a small poisoning ratio (0.5%). Moreover, we also propose a defense, called localized cropping, to defend against DPBAs. Our results show that our defense can reduce the effectiveness of DPBAs, though it slightly sacrifices the utility of the encoder. | ['Neil Zhenqiang Gong', 'Jinyuan Jia', 'Hongbin Liu', 'Jinghuai Zhang'] | 2022-11-15 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 6.92072362e-02 -3.36977979e-03 -3.02587837e-01 2.82702208e-01
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6c77f9a8-d99f-4670-9021-16dd889e4c6a | protein-secondary-structure-prediction-using-2 | 1512.00843 | null | http://arxiv.org/abs/1512.00843v3 | http://arxiv.org/pdf/1512.00843v3.pdf | Protein secondary structure prediction using deep convolutional neural fields | Protein secondary structure (SS) prediction is important for studying protein
structure and function. When only the sequence (profile) information is used as
input feature, currently the best predictors can obtain ~80% Q3 accuracy, which
has not been improved in the past decade. Here we present DeepCNF (Deep
Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep
Learning extension of Conditional Neural Fields (CNF), which is an integration
of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can
model not only complex sequence-structure relationship by a deep hierarchical
architecture, but also interdependency between adjacent SS labels, so it is
much more powerful than CNF. Experimental results show that DeepCNF can obtain
~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the
CASP and CAMEO test proteins, greatly outperforming currently popular
predictors. As a general framework, DeepCNF can be used to predict other
protein structure properties such as contact number, disorder regions, and
solvent accessibility. | ['Sheng Wang', 'Jianzhu Ma', 'Jinbo Xu', 'Jian Peng'] | 2015-12-02 | null | null | null | null | ['protein-secondary-structure-prediction'] | ['medical'] | [ 1.66504949e-01 -1.89875618e-01 -3.27385128e-01 -7.49071717e-01
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270d0087-52fe-46d5-9501-1ee907880b3d | optimally-coordinated-energy-management | 2307.00277 | null | https://arxiv.org/abs/2307.00277v1 | https://arxiv.org/pdf/2307.00277v1.pdf | Optimally Coordinated Energy Management Framework for Profit Maximization Considering Dispatchable and Non-Dispatchable Energy Resources | Contemporary distribution network can be seen with diverse dispatchable and non-dispatchable energy resources. The coordinated scheduling of these dispatchable resources with non-dispatchable resources can provide several techno-economic and social benefits. Since, battery energy storage systems (BESSs) and microturbine (MT) units are capital intensive, a thorough investigation of their coordinated scheduling on pure economic basis will be an interesting and challenging task while considering dynamic electricity price and uncertainty handling of non-dispatchable resources and load demand. This paper proposes a new methodology for optimal coordinated scheduling of BESSs and MT units considering existing renewable energy resources and dynamic electricity price to maximize daily profit function of the utility by employing a recently explored modified African buffalo optimization (MABO) algorithm. The key attributes of the proposed methodology are comprised of mean price-based adaptive scheduling embedded within a decision mechanism system (DMS) to maximize arbitrage benefits. DMS keeps a track of system states as a-priori thus guides the artificial intelligence based solution technique for sequential optimization. This may also reduce the computational burden of complex real-life engineering optimization problems. Further, a novel concept of fictitious charges is proposed to restrict the counterproductive operational management of BESSs. The application results investigated and compared on a benchmark 33-bus test distribution system highlights the importance of the proposed methodology. | ['Jin Yang', 'Nand K. Meena', 'Anil Swarnkar', 'K. R. Niazi', 'Nikhil Gupta', 'Rayees Ahmad Thokar'] | 2023-07-01 | null | null | null | null | ['management', 'energy-management'] | ['miscellaneous', 'time-series'] | [-4.04036969e-01 -2.70060301e-01 4.57753614e-02 -1.52707040e-01
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c71fc03c-ae77-4f19-9ffc-8fcece7f6326 | translation-between-molecules-and-natural | 2204.11817 | null | https://arxiv.org/abs/2204.11817v3 | https://arxiv.org/pdf/2204.11817v3.pdf | Translation between Molecules and Natural Language | We present $\textbf{MolT5}$ $-$ a self-supervised learning framework for pretraining models on a vast amount of unlabeled natural language text and molecule strings. $\textbf{MolT5}$ allows for new, useful, and challenging analogs of traditional vision-language tasks, such as molecule captioning and text-based de novo molecule generation (altogether: translation between molecules and language), which we explore for the first time. Since $\textbf{MolT5}$ pretrains models on single-modal data, it helps overcome the chemistry domain shortcoming of data scarcity. Furthermore, we consider several metrics, including a new cross-modal embedding-based metric, to evaluate the tasks of molecule captioning and text-based molecule generation. Our results show that $\textbf{MolT5}$-based models are able to generate outputs, both molecules and captions, which in many cases are high quality. | ['Heng Ji', 'Kyunghyun Cho', 'Garrett Honke', 'Kevin Ros', 'Tuan Lai', 'Carl Edwards'] | 2022-04-25 | null | null | null | null | ['molecule-captioning', 'text-based-de-novo-molecule-generation'] | ['medical', 'medical'] | [ 6.33697808e-01 3.19565147e-01 -2.68783659e-01 -4.11080807e-01
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1a2bdece-46c3-457f-b6c4-76ade02dda36 | hybrid-data-augmentation-and-deep-attention | 2109.09026 | null | https://arxiv.org/abs/2109.09026v1 | https://arxiv.org/pdf/2109.09026v1.pdf | Hybrid Data Augmentation and Deep Attention-based Dilated Convolutional-Recurrent Neural Networks for Speech Emotion Recognition | Speech emotion recognition (SER) has been one of the significant tasks in Human-Computer Interaction (HCI) applications. However, it is hard to choose the optimal features and deal with imbalance labeled data. In this article, we investigate hybrid data augmentation (HDA) methods to generate and balance data based on traditional and generative adversarial networks (GAN) methods. To evaluate the effectiveness of HDA methods, a deep learning framework namely (ADCRNN) is designed by integrating deep dilated convolutional-recurrent neural networks with an attention mechanism. Besides, we choose 3D log Mel-spectrogram (MelSpec) features as the inputs for the deep learning framework. Furthermore, we reconfigure a loss function by combining a softmax loss and a center loss to classify the emotions. For validating our proposed methods, we use the EmoDB dataset that consists of several emotions with imbalanced samples. Experimental results prove that the proposed methods achieve better accuracy than the state-of-the-art methods on the EmoDB with 87.12% and 88.47% for the traditional and GAN-based methods, respectively. | ['Sy Dzung Nguyen', 'Duc Ngoc Minh Dang', 'Nhat Truong Pham'] | 2021-09-18 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-5.22672758e-02 -1.85721386e-02 4.90613967e-01 -4.18473870e-01
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b1975f6a-b729-43c8-affb-1334ba0d76d1 | towards-better-instruction-following-language | 2304.07854 | null | https://arxiv.org/abs/2304.07854v1 | https://arxiv.org/pdf/2304.07854v1.pdf | Towards Better Instruction Following Language Models for Chinese: Investigating the Impact of Training Data and Evaluation | Recently, significant public efforts have been directed towards developing low-cost models with capabilities akin to ChatGPT, thereby fostering the growth of open-source conversational models. However, there remains a scarcity of comprehensive and in-depth evaluations of these models' performance. In this study, we examine the influence of training data factors, including quantity, quality, and linguistic distribution, on model performance. Our analysis is grounded in several publicly accessible, high-quality instruction datasets, as well as our own Chinese multi-turn conversations. We assess various models using a evaluation set of 1,000 samples, encompassing nine real-world scenarios. Our goal is to supplement manual evaluations with quantitative analyses, offering valuable insights for the continued advancement of open-source chat models. Furthermore, to enhance the performance and training and inference efficiency of models in the Chinese domain, we extend the vocabulary of LLaMA - the model with the closest open-source performance to proprietary language models like GPT-3 - and conduct secondary pre-training on 3.4B Chinese words. We make our model, data, as well as code publicly available. | ['Xiangang Li', 'Baochang Ma', 'Qiang Niu', 'Yiping Peng', 'Yong Deng', 'Yan Gong', 'Yunjie Ji'] | 2023-04-16 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [-3.00748408e-01 7.68842874e-03 -2.06665620e-01 -5.13343573e-01
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5ae26d36-3b5b-44bb-bbc1-10e1a2634a60 | reservoir-of-diverse-adaptive-learners-and | 1709.02457 | null | http://arxiv.org/abs/1709.02457v1 | http://arxiv.org/pdf/1709.02457v1.pdf | Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams | The last decade has seen a surge of interest in adaptive learning algorithms
for data stream classification, with applications ranging from predicting ozone
level peaks, learning stock market indicators, to detecting computer security
violations. In addition, a number of methods have been developed to detect
concept drifts in these streams. Consider a scenario where we have a number of
classifiers with diverse learning styles and different drift detectors.
Intuitively, the current 'best' (classifier, detector) pair is application
dependent and may change as a result of the stream evolution. Our research
builds on this observation. We introduce the $\mbox{Tornado}$ framework that
implements a reservoir of diverse classifiers, together with a variety of drift
detection algorithms. In our framework, all (classifier, detector) pairs
proceed, in parallel, to construct models against the evolving data streams. At
any point in time, we select the pair which currently yields the best
performance. We further incorporate two novel stacking-based drift detection
methods, namely the $\mbox{FHDDMS}$ and $\mbox{FHDDMS}_{add}$ approaches. The
experimental evaluation confirms that the current 'best' (classifier, detector)
pair is not only heavily dependent on the characteristics of the stream, but
also that this selection evolves as the stream flows. Further, our
$\mbox{FHDDMS}$ variants detect concept drifts accurately in a timely fashion
while outperforming the state-of-the-art. | ['Herna Viktor', 'Eric Paquet', 'Ali Pesaranghader'] | 2017-09-07 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [-1.30891457e-01 -8.29114854e-01 -7.34406114e-02 -2.92940736e-01
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90e36c2b-e26d-4031-9fe9-a497e5762f46 | challenges-and-strategies-in-cross-cultural | 2203.10020 | null | https://arxiv.org/abs/2203.10020v1 | https://arxiv.org/pdf/2203.10020v1.pdf | Challenges and Strategies in Cross-Cultural NLP | Various efforts in the Natural Language Processing (NLP) community have been made to accommodate linguistic diversity and serve speakers of many different languages. However, it is important to acknowledge that speakers and the content they produce and require, vary not just by language, but also by culture. Although language and culture are tightly linked, there are important differences. Analogous to cross-lingual and multilingual NLP, cross-cultural and multicultural NLP considers these differences in order to better serve users of NLP systems. We propose a principled framework to frame these efforts, and survey existing and potential strategies. | ['Anders Søgaard', 'Phillip Rust', 'Katerina Margatina', 'Constanza Fierro', 'Ruixiang Cui', 'Ilias Chalkidis', 'Laura Cabello Piqueras', 'Emanuele Bugliarello', 'Stephanie Brandl', 'Mostafa Abdou', 'Miryam de Lhoneux', 'Heather Lent', 'Stella Frank', 'Daniel Hershcovich'] | 2022-03-18 | null | https://aclanthology.org/2022.acl-long.482 | https://aclanthology.org/2022.acl-long.482.pdf | acl-2022-5 | ['multilingual-nlp'] | ['natural-language-processing'] | [-4.09188837e-01 -2.63539385e-02 -5.49683332e-01 -4.30668890e-01
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01462367-211d-4ce2-b362-edcaef430062 | mastering-the-dungeon-grounded-language | 1711.07950 | null | http://arxiv.org/abs/1711.07950v3 | http://arxiv.org/pdf/1711.07950v3.pdf | Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent | Contrary to most natural language processing research, which makes use of
static datasets, humans learn language interactively, grounded in an
environment. In this work we propose an interactive learning procedure called
Mechanical Turker Descent (MTD) and use it to train agents to execute natural
language commands grounded in a fantasy text adventure game. In MTD, Turkers
compete to train better agents in the short term, and collaborate by sharing
their agents' skills in the long term. This results in a gamified, engaging
experience for the Turkers and a better quality teaching signal for the agents
compared to static datasets, as the Turkers naturally adapt the training data
to the agent's abilities. | ['Douwe Kiela', 'Jack Urbanek', 'Saizheng Zhang', 'Jason Weston', 'Arthur Szlam', 'Alexander H. Miller', 'Zhilin Yang', 'Will Feng'] | 2017-11-21 | mastering-the-dungeon-grounded-language-1 | https://openreview.net/forum?id=SJ-C6JbRW | https://openreview.net/pdf?id=SJ-C6JbRW | iclr-2018-1 | ['grounded-language-learning'] | ['natural-language-processing'] | [-7.43891418e-01 3.16684395e-01 3.26205999e-01 -2.55042702e-01
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b7fe8ffe-e838-443e-b288-13ee7f83a714 | lightweight-multi-person-total-motion-capture | 2108.10378 | null | https://arxiv.org/abs/2108.10378v1 | https://arxiv.org/pdf/2108.10378v1.pdf | Lightweight Multi-person Total Motion Capture Using Sparse Multi-view Cameras | Multi-person total motion capture is extremely challenging when it comes to handle severe occlusions, different reconstruction granularities from body to face and hands, drastically changing observation scales and fast body movements. To overcome these challenges above, we contribute a lightweight total motion capture system for multi-person interactive scenarios using only sparse multi-view cameras. By contributing a novel hand and face bootstrapping algorithm, our method is capable of efficient localization and accurate association of the hands and faces even on severe occluded occasions. We leverage both pose regression and keypoints detection methods and further propose a unified two-stage parametric fitting method for achieving pixel-aligned accuracy. Moreover, for extremely self-occluded poses and close interactions, a novel feedback mechanism is proposed to propagate the pixel-aligned reconstructions into the next frame for more accurate association. Overall, we propose the first light-weight total capture system and achieves fast, robust and accurate multi-person total motion capture performance. The results and experiments show that our method achieves more accurate results than existing methods under sparse-view setups. | ['Yebin Liu', 'Tao Yu', 'Mengcheng Li', 'Liang An', 'Zhe Li', 'Yuxiang Zhang'] | 2021-08-23 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhang_Lightweight_Multi-Person_Total_Motion_Capture_Using_Sparse_Multi-View_Cameras_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhang_Lightweight_Multi-Person_Total_Motion_Capture_Using_Sparse_Multi-View_Cameras_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-multi-person-pose-estimation'] | ['computer-vision'] | [-7.83088282e-02 -5.14033079e-01 -1.53450593e-01 -1.38197497e-01
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58d9f0ac-f005-473f-b7bb-033014099554 | contextual-similarity-is-more-valuable-than | 2207.09217 | null | https://arxiv.org/abs/2207.09217v3 | https://arxiv.org/pdf/2207.09217v3.pdf | Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking | Chinese Spell Checking (CSC) task aims to detect and correct Chinese spelling errors. Recently, related researches focus on introducing character similarity from confusion set to enhance the CSC models, ignoring the context of characters that contain richer information. To make better use of contextual information, we propose a simple yet effective Curriculum Learning (CL) framework for the CSC task. With the help of our model-agnostic CL framework, existing CSC models will be trained from easy to difficult as humans learn Chinese characters and achieve further performance improvements. Extensive experiments and detailed analyses on widely used SIGHAN datasets show that our method outperforms previous state-of-the-art methods. More instructively, our study empirically suggests that contextual similarity is more valuable than character similarity for the CSC task. | ['Hai-Tao Zheng', 'Yunbo Cao', 'Yangning Li', 'Shirong Ma', 'Qingyu Zhou', 'Yinghui Li', 'Ding Zhang'] | 2022-07-17 | null | null | null | null | ['chinese-spell-checking'] | ['natural-language-processing'] | [ 3.76197606e-01 -6.39405370e-01 -1.68513119e-01 -2.35765815e-01
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86938b92-8bb5-4fae-96d3-c617b0af1f01 | prompt-based-editing-for-text-style-transfer | 2301.11997 | null | https://arxiv.org/abs/2301.11997v1 | https://arxiv.org/pdf/2301.11997v1.pdf | Prompt-Based Editing for Text Style Transfer | Prompting approaches have been recently explored in text style transfer, where a textual prompt is used to query a pretrained language model to generate style-transferred texts word by word in an autoregressive manner. However, such a generation process is less controllable and early prediction errors may affect future word predictions. In this paper, we present a prompt-based editing approach for text style transfer. Specifically, we prompt a pretrained language model for style classification and use the classification probability to compute a style score. Then, we perform discrete search with word-level editing to maximize a comprehensive scoring function for the style-transfer task. In this way, we transform a prompt-based generation problem into a classification one, which is a training-free process and more controllable than the autoregressive generation of sentences. In our experiments, we performed both automatic and human evaluation on three style-transfer benchmark datasets, and show that our approach largely outperforms the state-of-the-art systems that have 20 times more parameters. Additional empirical analyses further demonstrate the effectiveness of our approach. | ['Mauajama Firdaus', 'Lili Mou', 'Yu Tong Han', 'Guoqing Luo'] | 2023-01-27 | null | null | null | null | ['text-style-transfoer'] | ['natural-language-processing'] | [ 7.99115419e-01 1.81442887e-01 3.58773628e-03 -7.28466034e-01
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2e4b9a3f-c146-43e7-94d8-2effb639a00e | quantifying-the-effect-of-x-ray-scattering | 2305.12822 | null | https://arxiv.org/abs/2305.12822v1 | https://arxiv.org/pdf/2305.12822v1.pdf | Quantifying the effect of X-ray scattering for data generation in real-time defect detection | X-ray imaging is widely used for non-destructive detection of defects in industrial products on a conveyor belt. Real-time detection requires highly accurate, robust, and fast algorithms to analyze X-ray images. Deep convolutional neural networks (DCNNs) satisfy these requirements if a large amount of labeled data is available. To overcome the challenge of collecting these data, different methods of X-ray image generation can be considered. Depending on the desired level of similarity to real data, various physical effects either should be simulated or can be ignored. X-ray scattering is known to be computationally expensive to simulate, and this effect can heavily influence the accuracy of a generated X-ray image. We propose a methodology for quantitative evaluation of the effect of scattering on defect detection. This methodology compares the accuracy of DCNNs trained on different versions of the same data that include and exclude the scattering signal. We use the Probability of Detection (POD) curves to find the size of the smallest defect that can be detected with a DCNN and evaluate how this size is affected by the choice of training data. We apply the proposed methodology to a model problem of defect detection in cylinders. Our results show that the exclusion of the scattering signal from the training data has the largest effect on the smallest detectable defects. Furthermore, we demonstrate that accurate inspection is more reliant on high-quality training data for images with a high quantity of scattering. We discuss how the presented methodology can be used for other tasks and objects. | ['K. Joost Batenburg', 'Tristan van Leeuwen', 'Robert van Liere', 'Vladyslav Andriiashen'] | 2023-05-22 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 3.71730417e-01 -2.56234646e-01 4.73052055e-01 -4.41886663e-01
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4a1a2c81-92c3-4170-a7c8-50a04d478704 | a-modern-perspective-on-query-likelihood-with | 2106.13618 | null | https://arxiv.org/abs/2106.13618v1 | https://arxiv.org/pdf/2106.13618v1.pdf | A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models | Existing neural ranking models follow the text matching paradigm, where document-to-query relevance is estimated through predicting the matching score. Drawing from the rich literature of classical generative retrieval models, we introduce and formalize the paradigm of deep generative retrieval models defined via the cumulative probabilities of generating query terms. This paradigm offers a grounded probabilistic view on relevance estimation while still enabling the use of modern neural architectures. In contrast to the matching paradigm, the probabilistic nature of generative rankers readily offers a fine-grained measure of uncertainty. We adopt several current neural generative models in our framework and introduce a novel generative ranker (T-PGN), which combines the encoding capacity of Transformers with the Pointer Generator Network model. We conduct an extensive set of evaluation experiments on passage retrieval, leveraging the MS MARCO Passage Re-ranking and TREC Deep Learning 2019 Passage Re-ranking collections. Our results show the significantly higher performance of the T-PGN model when compared with other generative models. Lastly, we demonstrate that exploiting the uncertainty information of deep generative rankers opens new perspectives to query/collection understanding, and significantly improves the cut-off prediction task. | ['Markus Schedl', 'Carsten Eickhoff', 'Klaus Antonius Grasserbauer', 'Daniel Cohen', 'Navid Rekabsaz', 'Oleg Lesota'] | 2021-06-25 | null | null | null | null | ['passage-re-ranking'] | ['natural-language-processing'] | [-4.86287177e-02 -1.43795386e-01 2.13925950e-02 -3.70252818e-01
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f1183137-f885-4b80-a7ae-11502c686586 | ehanet-an-effective-hierarchical-aggregation | null | null | https://www.mdpi.com/2076-3417/10/9/3135 | https://www.researchgate.net/publication/341129398_EHANet_An_Effective_Hierarchical_Aggregation_Network_for_Face_Parsing | EHANet: An Effective Hierarchical Aggregation Network for Face Parsing | In recent years, benefiting from deep convolutional neural networks (DCNNs), face parsing has developed rapidly. However, it still has the following problems: (1) Existing state-of-the-art frameworks usually do not satisfy real-time while pursuing performance; (2) similar appearances cause incorrect pixel label assignments, especially in the boundary; (3) to promote multi-scale prediction, deep features and shallow features are used for fusion without considering the semantic gap between them. To overcome these drawbacks, we propose an effective and efficient hierarchical aggregation network called EHANet for fast and accurate face parsing. More specifically, we first propose a stage contextual attention mechanism (SCAM), which uses higher-level contextual information to re-encode the channel according to its importance. Secondly, a semantic gap compensation block (SGCB) is presented to ensure the effective aggregation of hierarchical information. Thirdly, the advantages of weighted boundary-aware loss effectively make up for the ambiguity of boundary semantics. Without any bells and whistles, combined with a lightweight backbone, we achieve outstanding results on both CelebAMask-HQ (78.19% mIoU) and Helen datasets (90.7% F1-score). Furthermore, our model can achieve 55 FPS on a single GTX 1080Ti card with 640 × 640 input and further reach over 300 FPS with a resolution of 256 × 256, which is suitable for real-world applications. | ['Xinglong Feng', 'Dingyu Xue', 'Ling Luo'] | 2020-04-07 | null | null | null | applied-sciences-2020-4 | ['face-parsing'] | ['computer-vision'] | [ 2.65567482e-01 -5.85196577e-02 -1.83450311e-01 -6.50838256e-01
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9e040888-31ba-4aba-bc9f-b7cc57992c86 | a-practical-test-for-a-planted-community-in | 2101.05928 | null | https://arxiv.org/abs/2101.05928v1 | https://arxiv.org/pdf/2101.05928v1.pdf | A practical test for a planted community in heterogeneous networks | One of the fundamental task in graph data mining is to find a planted community(dense subgraph), which has wide application in biology, finance, spam detection and so on. For a real network data, the existence of a dense subgraph is generally unknown. Statistical tests have been devised to testing the existence of dense subgraph in a homogeneous random graph. However, many networks present extreme heterogeneity, that is, the degrees of nodes or vertexes don't concentrate on a typical value. The existing tests designed for homogeneous random graph are not straightforwardly applicable to the heterogeneous case. Recently, scan test was proposed for detecting a dense subgraph in heterogeneous(inhomogeneous) graph(\cite{BCHV19}). However, the computational complexity of the scan test is generally not polynomial in the graph size, which makes the test impractical for large or moderate networks. In this paper, we propose a polynomial-time test that has the standard normal distribution as the null limiting distribution. The power of the test is theoretically investigated and we evaluate the performance of the test by simulation and real data example. | ['Qian Wen', 'Mingao Yuan'] | 2021-01-15 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 1.10773720e-01 1.72874838e-01 -1.80083007e-01 -6.76410496e-02
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6b85842e-bb0e-4298-aaec-201fe4558fb9 | discovering-pdes-from-multiple-experiments | 2109.11939 | null | https://arxiv.org/abs/2109.11939v2 | https://arxiv.org/pdf/2109.11939v2.pdf | Discovering PDEs from Multiple Experiments | Automated model discovery of partial differential equations (PDEs) usually considers a single experiment or dataset to infer the underlying governing equations. In practice, experiments have inherent natural variability in parameters, initial and boundary conditions that cannot be simply averaged out. We introduce a randomised adaptive group Lasso sparsity estimator to promote grouped sparsity and implement it in a deep learning based PDE discovery framework. It allows to create a learning bias that implies the a priori assumption that all experiments can be explained by the same underlying PDE terms with potentially different coefficients. Our experimental results show more generalizable PDEs can be found from multiple highly noisy datasets, by this grouped sparsity promotion rather than simply performing independent model discoveries. | ['Remy Kusters', 'Gert-Jan Both', 'Georges Tod'] | 2021-09-24 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 3.97294424e-02 -1.17478356e-01 -6.68877140e-02 -1.31632671e-01
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5c2fc1b0-f5b4-4d42-bd44-c4375ecf1a5e | boosting-active-learning-via-improving-test | 2112.05683 | null | https://arxiv.org/abs/2112.05683v2 | https://arxiv.org/pdf/2112.05683v2.pdf | Boosting Active Learning via Improving Test Performance | Central to active learning (AL) is what data should be selected for annotation. Existing works attempt to select highly uncertain or informative data for annotation. Nevertheless, it remains unclear how selected data impacts the test performance of the task model used in AL. In this work, we explore such an impact by theoretically proving that selecting unlabeled data of higher gradient norm leads to a lower upper-bound of test loss, resulting in better test performance. However, due to the lack of label information, directly computing gradient norm for unlabeled data is infeasible. To address this challenge, we propose two schemes, namely expected-gradnorm and entropy-gradnorm. The former computes the gradient norm by constructing an expected empirical loss while the latter constructs an unsupervised loss with entropy. Furthermore, we integrate the two schemes in a universal AL framework. We evaluate our method on classical image classification and semantic segmentation tasks. To demonstrate its competency in domain applications and its robustness to noise, we also validate our method on a cellular imaging analysis task, namely cryo-Electron Tomography subtomogram classification. Results demonstrate that our method achieves superior performance against the state of the art. Our source code is available at https://github.com/xulabs/aitom/blob/master/doc/projects/al_gradnorm.md. | ['Min Xu', 'Cheng-Zhong Xu', 'Siyu Huang', 'Xiangrui Zeng', 'Guosheng Hu', 'Pengkun Yang', 'Xingjian Li', 'Tianyang Wang'] | 2021-12-10 | null | null | null | null | ['electron-tomography'] | ['medical'] | [ 3.40077609e-01 1.70323446e-01 -2.11440697e-01 -5.35138011e-01
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8627d7e3-8f46-493c-9e33-220be1d225d0 | chain-of-thought-prompting-elicits-knowledge | 2307.01640 | null | https://arxiv.org/abs/2307.01640v1 | https://arxiv.org/pdf/2307.01640v1.pdf | Chain of Thought Prompting Elicits Knowledge Augmentation | The knowledge-augmented deep learning paradigm refers to a paradigm in which domain knowledge is identified and integrated into deep models. Conventional methods typically employ task-specific approaches to gather external knowledge from various sources. In contrast, large language models are extensively pre-trained and can serve as a comprehensive source of external knowledge. In this paper, we propose CoT-KA, a Chain-of-Thought-based method that augments knowledge for deep learning. CoT-KA avoids the need for additional knowledge retrieval or knowledge reasoning models, as required in conventional augmentation methods. Our results demonstrate that CoT-KA outperforms both pure CoT-based methods and the non-augmented method across the majority of eleven publicly available benchmarks for various reasoning tasks. | ['Xinmei Huang', 'Jing Zhang', 'Dingjun Wu'] | 2023-07-04 | null | null | null | null | ['retrieval'] | ['methodology'] | [-4.49242383e-01 2.86624759e-01 -4.85329568e-01 -3.16498160e-01
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9755a6b4-8492-4d99-b82c-a697777bf1cf | full-resolution-encoder-decoder-networks-with | 2106.00566 | null | https://arxiv.org/abs/2106.00566v1 | https://arxiv.org/pdf/2106.00566v1.pdf | Full-Resolution Encoder-Decoder Networks with Multi-Scale Feature Fusion for Human Pose Estimation | To achieve more accurate 2D human pose estimation, we extend the successful encoder-decoder network, simple baseline network (SBN), in three ways. To reduce the quantization errors caused by the large output stride size, two more decoder modules are appended to the end of the simple baseline network to get full output resolution. Then, the global context blocks (GCBs) are added to the encoder and decoder modules to enhance them with global context features. Furthermore, we propose a novel spatial-attention-based multi-scale feature collection and distribution module (SA-MFCD) to fuse and distribute multi-scale features to boost the pose estimation. Experimental results on the MS COCO dataset indicate that our network can remarkably improve the accuracy of human pose estimation over SBN, our network using ResNet34 as the backbone network can even achieve the same accuracy as SBN with ResNet152, and our networks can achieve superior results with big backbone networks. | ['Hong Wu', 'Mingjian Chen', 'Jie Ou'] | 2021-06-01 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-2.10273549e-01 -1.82931330e-02 1.98717602e-02 -3.18953067e-01
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457510fb-071f-4e26-934a-cede304a13c8 | one-button-machine-for-automating-feature | 1706.00327 | null | http://arxiv.org/abs/1706.00327v1 | http://arxiv.org/pdf/1706.00327v1.pdf | One button machine for automating feature engineering in relational databases | Feature engineering is one of the most important and time consuming tasks in
predictive analytics projects. It involves understanding domain knowledge and
data exploration to discover relevant hand-crafted features from raw data. In
this paper, we introduce a system called One Button Machine, or OneBM for
short, which automates feature discovery in relational databases. OneBM
automatically performs a key activity of data scientists, namely, joining of
database tables and applying advanced data transformations to extract useful
features from data. We validated OneBM in Kaggle competitions in which OneBM
achieved performance as good as top 16% to 24% data scientists in three Kaggle
competitions. More importantly, OneBM outperformed the state-of-the-art system
in a Kaggle competition in terms of prediction accuracy and ranking on Kaggle
leaderboard. The results show that OneBM can be useful for both data scientists
and non-experts. It helps data scientists reduce data exploration time allowing
them to try and error many ideas in short time. On the other hand, it enables
non-experts, who are not familiar with data science, to quickly extract value
from their data with a little effort, time and cost. | ['Johann-Michael Thiebaut', 'Hoang Thanh Lam', 'Bei Chen', 'Oznur Alkan', 'Mathieu Sinn', 'Tiep Mai'] | 2017-06-01 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [-5.11031866e-01 8.91882107e-02 -1.64010987e-01 -5.22012711e-01
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7ef0900c-003b-42b4-9a6f-ac8379b3ab5e | towards-an-end-to-end-framework-for-flow | 2204.02663 | null | https://arxiv.org/abs/2204.02663v2 | https://arxiv.org/pdf/2204.02663v2.pdf | Towards An End-to-End Framework for Flow-Guided Video Inpainting | Optical flow, which captures motion information across frames, is exploited in recent video inpainting methods through propagating pixels along its trajectories. However, the hand-crafted flow-based processes in these methods are applied separately to form the whole inpainting pipeline. Thus, these methods are less efficient and rely heavily on the intermediate results from earlier stages. In this paper, we propose an End-to-End framework for Flow-Guided Video Inpainting (E$^2$FGVI) through elaborately designed three trainable modules, namely, flow completion, feature propagation, and content hallucination modules. The three modules correspond with the three stages of previous flow-based methods but can be jointly optimized, leading to a more efficient and effective inpainting process. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods both qualitatively and quantitatively and shows promising efficiency. The code is available at https://github.com/MCG-NKU/E2FGVI. | ['Ming-Ming Cheng', 'Chun-Le Guo', 'Jianhua Qin', 'Cheng-Ze Lu', 'Zhen Li'] | 2022-04-06 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Towards_an_End-to-End_Framework_for_Flow-Guided_Video_Inpainting_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Towards_an_End-to-End_Framework_for_Flow-Guided_Video_Inpainting_CVPR_2022_paper.pdf | cvpr-2022-1 | ['seeing-beyond-the-visible', 'video-inpainting'] | ['computer-vision', 'computer-vision'] | [-6.41450956e-02 -2.73034036e-01 -1.98541716e-01 -1.28311012e-02
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0d1f6e4c-ba0c-478f-bf9d-76b6eb649741 | can-string-kernels-pass-the-test-of-time-in | 1707.08349 | null | http://arxiv.org/abs/1707.08349v2 | http://arxiv.org/pdf/1707.08349v2.pdf | Can string kernels pass the test of time in Native Language Identification? | We describe a machine learning approach for the 2017 shared task on Native
Language Identification (NLI). The proposed approach combines several kernels
using multiple kernel learning. While most of our kernels are based on
character p-grams (also known as n-grams) extracted from essays or speech
transcripts, we also use a kernel based on i-vectors, a low-dimensional
representation of audio recordings, provided by the shared task organizers. For
the learning stage, we choose Kernel Discriminant Analysis (KDA) over Kernel
Ridge Regression (KRR), because the former classifier obtains better results
than the latter one on the development set. In our previous work, we have used
a similar machine learning approach to achieve state-of-the-art NLI results.
The goal of this paper is to demonstrate that our shallow and simple approach
based on string kernels (with minor improvements) can pass the test of time and
reach state-of-the-art performance in the 2017 NLI shared task, despite the
recent advances in natural language processing. We participated in all three
tracks, in which the competitors were allowed to use only the essays (essay
track), only the speech transcripts (speech track), or both (fusion track).
Using only the data provided by the organizers for training our models, we have
reached a macro F1 score of 86.95% in the closed essay track, a macro F1 score
of 87.55% in the closed speech track, and a macro F1 score of 93.19% in the
closed fusion track. With these scores, our team (UnibucKernel) ranked in the
first group of teams in all three tracks, while attaining the best scores in
the speech and the fusion tracks. | ['Marius Popescu', 'Radu Tudor Ionescu'] | 2017-07-26 | can-string-kernels-pass-the-test-of-time-in-1 | https://aclanthology.org/W17-5024 | https://aclanthology.org/W17-5024.pdf | ws-2017-9 | ['native-language-identification'] | ['natural-language-processing'] | [-1.07907094e-01 -1.44334823e-01 -1.23277307e-01 -1.65437058e-01
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be5de5a0-d132-4946-8902-1f99720dd7c3 | evoquer-enhancing-temporal-grounding-with | 2109.04600 | null | https://arxiv.org/abs/2109.04600v1 | https://arxiv.org/pdf/2109.04600v1.pdf | EVOQUER: Enhancing Temporal Grounding with Video-Pivoted BackQuery Generation | Temporal grounding aims to predict a time interval of a video clip corresponding to a natural language query input. In this work, we present EVOQUER, a temporal grounding framework incorporating an existing text-to-video grounding model and a video-assisted query generation network. Given a query and an untrimmed video, the temporal grounding model predicts the target interval, and the predicted video clip is fed into a video translation task by generating a simplified version of the input query. EVOQUER forms closed-loop learning by incorporating loss functions from both temporal grounding and query generation serving as feedback. Our experiments on two widely used datasets, Charades-STA and ActivityNet, show that EVOQUER achieves promising improvements by 1.05 and 1.31 at R@0.7. We also discuss how the query generation task could facilitate error analysis by explaining temporal grounding model behavior. | ['Rui Zhang', 'Huayan Wang', 'Xin Chen', 'Jason Wang', 'Lulu Liu', 'Yanjun Gao'] | 2021-09-10 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [ 2.80740291e-01 2.22158074e-01 -6.30421519e-01 -4.94470119e-01
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d17af62c-8b0c-476b-93d9-377108a6d752 | joined-prior-guided-multi-task-learning-for | 2203.00461 | null | https://arxiv.org/abs/2203.00461v1 | https://arxiv.org/pdf/2203.00461v1.pdf | JOINED : Prior Guided Multi-task Learning for Joint Optic Disc/Cup Segmentation and Fovea Detection | Fundus photography has been routinely used to document the presence and severity of various retinal degenerative diseases such as age-related macula degeneration, glaucoma, and diabetic retinopathy, for which the fovea, optic disc (OD), and optic cup (OC) are important anatomical landmarks. Identification of those anatomical landmarks is of great clinical importance. However, the presence of lesions, drusen, and other abnormalities during retinal degeneration severely complicates automatic landmark detection and segmentation. Most existing works treat the identification of each landmark as a single task and typically do not make use of any clinical prior information. In this paper, we present a novel method, named JOINED, for prior guided multi-task learning for joint OD/OC segmentation and fovea detection. An auxiliary branch for distance prediction, in addition to a segmentation branch and a detection branch, is constructed to effectively utilize the distance information from each image pixel to landmarks of interest. Our proposed JOINED pipeline consists of a coarse stage and a fine stage. At the coarse stage, we obtain the OD/OC coarse segmentation and the heatmap localization of fovea through a joint segmentation and detection module. Afterwards, we crop the regions of interest for subsequent fine processing and use predictions obtained at the coarse stage as additional information for better performance and faster convergence. Experimental results reveal that our proposed JOINED outperforms existing state-of-the-art approaches on the publicly-available GAMMA, PALM, and REFUGE datasets of fundus images. Furthermore, JOINED ranked the 5th on the OD/OC segmentation and fovea detection tasks in the GAMMA challenge hosted by the MICCAI2021 workshop OMIA8. | ['Xiaoying Tang', 'Zhiyuan Cai', 'Li Lin', 'Huaqing He'] | 2022-03-01 | null | null | null | null | ['fovea-detection'] | ['medical'] | [ 7.92416260e-02 -1.93030074e-01 -4.43967655e-02 -1.48407906e-01
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092dc9e1-198f-462b-80e2-c13dc906780e | a0c-alpha-zero-in-continuous-action-space | 1805.09613 | null | http://arxiv.org/abs/1805.09613v1 | http://arxiv.org/pdf/1805.09613v1.pdf | A0C: Alpha Zero in Continuous Action Space | A core novelty of Alpha Zero is the interleaving of tree search and deep
learning, which has proven very successful in board games like Chess, Shogi and
Go. These games have a discrete action space. However, many real-world
reinforcement learning domains have continuous action spaces, for example in
robotic control, navigation and self-driving cars. This paper presents the
necessary theoretical extensions of Alpha Zero to deal with continuous action
space. We also provide some preliminary experiments on the Pendulum swing-up
task, empirically showing the feasibility of our approach. Thereby, this work
provides a first step towards the application of iterated search and learning
in domains with a continuous action space. | ['Joost Broekens', 'Thomas M. Moerland', 'Aske Plaat', 'Catholijn M. Jonker'] | 2018-05-24 | null | null | null | null | ['board-games'] | ['playing-games'] | [-9.11617950e-02 1.22013658e-01 -4.86441880e-01 7.44039640e-02
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14d3e119-5367-4046-b25b-66fb11b0444c | unsupervised-domain-adaptation-through-inter | 2004.10016 | null | https://arxiv.org/abs/2004.10016v1 | https://arxiv.org/pdf/2004.10016v1.pdf | Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition | Unsupervised Domain Adaptation (DA) exploits the supervision of a label-rich source dataset to make predictions on an unlabeled target dataset by aligning the two data distributions. In robotics, DA is used to take advantage of automatically generated synthetic data, that come with "free" annotation, to make effective predictions on real data. However, existing DA methods are not designed to cope with the multi-modal nature of RGB-D data, which are widely used in robotic vision. We propose a novel RGB-D DA method that reduces the synthetic-to-real domain shift by exploiting the inter-modal relation between the RGB and depth image. Our method consists of training a convolutional neural network to solve, in addition to the main recognition task, the pretext task of predicting the relative rotation between the RGB and depth image. To evaluate our method and encourage further research in this area, we define two benchmark datasets for object categorization and instance recognition. With extensive experiments, we show the benefits of leveraging the inter-modal relations for RGB-D DA. | ['Barbara Caputo', 'Luca Robbiano', 'Mohammad Reza Loghmani', 'Mirco Planamente', 'Markus Vincze', 'Kiru Park'] | 2020-04-21 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 5.22294521e-01 2.42048204e-01 -1.66221976e-01 -8.69234204e-01
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8d3b28f3-0fae-4afd-abc5-78ef854bc973 | exascale-deep-learning-for-scientific-inverse | 1909.11150 | null | https://arxiv.org/abs/1909.11150v1 | https://arxiv.org/pdf/1909.11150v1.pdf | Exascale Deep Learning for Scientific Inverse Problems | We introduce novel communication strategies in synchronous distributed Deep Learning consisting of decentralized gradient reduction orchestration and computational graph-aware grouping of gradient tensors. These new techniques produce an optimal overlap between computation and communication and result in near-linear scaling (0.93) of distributed training up to 27,600 NVIDIA V100 GPUs on the Summit Supercomputer. We demonstrate our gradient reduction techniques in the context of training a Fully Convolutional Neural Network to approximate the solution of a longstanding scientific inverse problem in materials imaging. The efficient distributed training on a dataset size of 0.5 PB, produces a model capable of an atomically-accurate reconstruction of materials, and in the process reaching a peak performance of 2.15(4) EFLOPS$_{16}$. | ['Junqi Yin', 'Albina Borisevich', 'Nouamane Laanait', 'Joshua Romero', 'Vitalii Starchenko', 'Sean Treichler', 'M. Todd Young', 'Michael Matheson', 'Alex Sergeev'] | 2019-09-24 | null | null | null | null | ['materials-imaging'] | ['computer-vision'] | [-4.51605052e-01 -1.06325641e-01 4.01864588e-01 -3.40065539e-01
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bae8efb9-fa17-4980-bbe1-88a537e19687 | unsupervised-discovery-of-object-radiance | 2107.07905 | null | https://arxiv.org/abs/2107.07905v2 | https://arxiv.org/pdf/2107.07905v2.pdf | Unsupervised Discovery of Object Radiance Fields | We study the problem of inferring an object-centric scene representation from a single image, aiming to derive a representation that explains the image formation process, captures the scene's 3D nature, and is learned without supervision. Most existing methods on scene decomposition lack one or more of these characteristics, due to the fundamental challenge in integrating the complex 3D-to-2D image formation process into powerful inference schemes like deep networks. In this paper, we propose unsupervised discovery of Object Radiance Fields (uORF), integrating recent progresses in neural 3D scene representations and rendering with deep inference networks for unsupervised 3D scene decomposition. Trained on multi-view RGB images without annotations, uORF learns to decompose complex scenes with diverse, textured background from a single image. We show that uORF enables novel tasks, such as scene segmentation and editing in 3D, and it performs well on these tasks and on novel view synthesis on three datasets. | ['Jiajun Wu', 'Leonidas J. Guibas', 'Hong-Xing Yu'] | 2021-07-16 | unsupervised-discovery-of-object-radiance-1 | https://openreview.net/forum?id=rwE8SshAlxw | https://openreview.net/pdf?id=rwE8SshAlxw | iclr-2022-4 | ['scene-segmentation'] | ['computer-vision'] | [ 7.63118267e-01 -3.30606732e-03 8.88444558e-02 -5.08740187e-01
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070a7747-4d33-4188-8027-31b990bd3c19 | label-definitions-improve-semantic-role | null | null | https://aclanthology.org/2022.naacl-main.411 | https://aclanthology.org/2022.naacl-main.411.pdf | Label Definitions Improve Semantic Role Labeling | Argument classification is at the core of Semantic Role Labeling. Given a sentence and the predicate, a semantic role label is assigned to each argument of the predicate. While semantic roles come with meaningful definitions, existing work has treated them as symbolic. Learning symbolic labels usually requires ample training data, which is frequently unavailable due to the cost of annotation. We instead propose to retrieve and leverage the definitions of these labels from the annotation guidelines. For example, the verb predicate “work” has arguments defined as “worker”, “job”, “employer”, etc. Our model achieves state-of-the-art performance on the CoNLL09 dataset injected with label definitions given the predicate senses. The performance improvement is even more pronounced in low-resource settings when training data is scarce. | ['Yunyao Li', 'Ishan Jindal', 'Li Zhang'] | null | null | null | null | naacl-2022-7 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 5.90554118e-01 4.94510293e-01 -7.64572799e-01 -7.23887146e-01
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32453d41-5322-4190-a5f3-b53a309ab985 | one-class-autoencoder-approach-for-optimal | 2104.04546 | null | https://arxiv.org/abs/2104.04546v3 | https://arxiv.org/pdf/2104.04546v3.pdf | One-class Autoencoder Approach for Optimal Electrode Set-up Identification in Wearable EEG Event Monitoring | A limiting factor towards the wide routine use of wearables devices for continuous healthcare monitoring is their cumbersome and obtrusive nature. This is particularly true for electroencephalography (EEG) recordings, which require the placement of multiple electrodes in contact with the scalp. In this work, we propose to identify the optimal wearable EEG electrode set-up, in terms of minimal number of electrodes, comfortable location and performance, for EEG-based event detection and monitoring. By relying on the demonstrated power of autoencoder (AE) networks to learn latent representations from high-dimensional data, our proposed strategy trains an AE architecture in a one-class classification setup with different electrode set-ups as input data. The resulting models are assessed using the F-score and the best set-up is chosen according to the established optimal criteria. Using alpha wave detection as use case, we demonstrate that the proposed method allows to detect an alpha state from an optimal set-up consisting of electrodes in the forehead and behind the ear, with an average F-score of 0.78. Our results suggest that a learning-based approach can be used to enable the design and implementation of optimized wearable devices for real-life healthcare monitoring. | ['Maria A. Zuluaga', 'François Bremond', 'Esma Ismailova', 'Paolo Volpe', 'Guy Abi Hanna', 'Laura M. Ferrari'] | 2021-04-09 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 1.74536824e-01 -2.93065677e-03 4.53513175e-01 -2.39075407e-01
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32f1cb72-026a-48d4-9de0-0121a25bbdd2 | on-gaze-deployment-to-audio-visual-cues-of | null | null | https://ieeexplore.ieee.org/document/9184838 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9184838 | On gaze deployment to audio-visual cues of social interactions | Attention supports our urge to forage on social cues. Under certain circumstances, we spend the majority of time scrutinising people, markedly their eyes and faces, and spotting persons that are talking. To account for such behaviour, this paper develops a computational model for the deployment of gaze within a multimodal landscape, namely a conversational scene. Gaze dynamics is derived in a principled way by reformulating attention deployment as a stochastic foraging problem. Model simulation experiments on a publicly available dataset of eye-tracked subjects are presented. Results show that the simulated scan paths exhibit similar trends of eye movements of human observers watching and listening to conversational clips in a free-viewing condition. | ['Raffaella Lanzarotti', 'Alessandro D’Amelio', 'Vittorio Cuculo', 'Giuseppe Boccignone', 'Giuliano Grossi'] | 2020-09-02 | null | null | null | ieee-access-2020-9 | ['scanpath-prediction'] | ['computer-vision'] | [ 2.06863135e-01 3.51272583e-01 2.66325265e-01 -4.13492531e-01
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0ef49c56-c5b4-4ee8-860f-323ff67ece15 | different-contexts-lead-to-different-word | null | null | https://aclanthology.org/C16-1073 | https://aclanthology.org/C16-1073.pdf | Different Contexts Lead to Different Word Embeddings | Recent work for learning word representations has applied successfully to many NLP applications, such as sentiment analysis and question answering. However, most of these models assume a single vector per word type without considering polysemy and homonymy. In this paper, we present an extension to the CBOW model which not only improves the quality of embeddings but also makes embeddings suitable for polysemy. It differs from most of the related work in that it learns one semantic center embedding and one context bias instead of training multiple embeddings per word type. Different context leads to different bias which is defined as the weighted average embeddings of local context. Experimental results on similarity task and analogy task show that the word representations learned by the proposed method outperform the competitive baselines. | ['Nan Zheng', 'Jiajun Zhang', 'Wenpeng Hu'] | 2016-12-01 | different-contexts-lead-to-different-word-1 | https://aclanthology.org/C16-1073 | https://aclanthology.org/C16-1073.pdf | coling-2016-12 | ['learning-word-embeddings'] | ['methodology'] | [-4.44970690e-02 -2.06907481e-01 -3.75601143e-01 -3.40193570e-01
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86e3c363-d54b-428b-934f-9664a76d09bd | unh-at-semeval-2019-task-12-toponym | null | null | https://aclanthology.org/S19-2230 | https://aclanthology.org/S19-2230.pdf | UNH at SemEval-2019 Task 12: Toponym Resolution in Scientific Papers | The SemEval-2019 Task 12 is toponym resolution in scientific papers. We focus on Subtask 1: Toponym Detection which is the identification of spans of text for place names mentioned in a document. We propose two methods: 1) sliding window convolutional neural network using ELMo embeddings (cnn-elmo), and 2) sliding window multi-Layer perceptron using ELMo embeddings (mlp-elmo). We also submit Bi-lateral LSTM with Conditional Random Fields (bi-LSTM) as a strong baseline given its state-of-art performance in Named Entity Recognition (NER) task. Our best performing model is cnn-elmo with a F1 of 0.844 which was below bi-LSTM F1 of 0.862 when evaluated on overlap macro detection. Eight teams participated in this subtask with a total of 21 submissions. | ['Laura Dietz', 'Matthew Magnusson'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['toponym-resolution'] | ['natural-language-processing'] | [-1.83376670e-01 5.55709377e-02 -5.02005629e-02 5.73177636e-02
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ed8419bb-538c-40dd-8b23-d15402f7054f | bi-directional-weakly-supervised-knowledge | 2210.03664 | null | https://arxiv.org/abs/2210.03664v2 | https://arxiv.org/pdf/2210.03664v2.pdf | Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification | Computer-aided pathology diagnosis based on the classification of Whole Slide Image (WSI) plays an important role in clinical practice, and it is often formulated as a weakly-supervised Multiple Instance Learning (MIL) problem. Existing methods solve this problem from either a bag classification or an instance classification perspective. In this paper, we propose an end-to-end weakly supervised knowledge distillation framework (WENO) for WSI classification, which integrates a bag classifier and an instance classifier in a knowledge distillation framework to mutually improve the performance of both classifiers. Specifically, an attention-based bag classifier is used as the teacher network, which is trained with weak bag labels, and an instance classifier is used as the student network, which is trained using the normalized attention scores obtained from the teacher network as soft pseudo labels for the instances in positive bags. An instance feature extractor is shared between the teacher and the student to further enhance the knowledge exchange between them. In addition, we propose a hard positive instance mining strategy based on the output of the student network to force the teacher network to keep mining hard positive instances. WENO is a plug-and-play framework that can be easily applied to any existing attention-based bag classification methods. Extensive experiments on five datasets demonstrate the efficiency of WENO. Code is available at https://github.com/miccaiif/WENO. | ['Zhijian Song', 'Manning Wang', 'Xiaoyuan Luo', 'Linhao Qu'] | 2022-10-07 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 4.20062393e-01 3.50164890e-01 -4.71891552e-01 -5.09760797e-01
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184881c5-5d97-48b0-9778-b84182068c70 | accelerating-guided-diffusion-sampling-with | 2301.11558 | null | https://arxiv.org/abs/2301.11558v1 | https://arxiv.org/pdf/2301.11558v1.pdf | Accelerating Guided Diffusion Sampling with Splitting Numerical Methods | Guided diffusion is a technique for conditioning the output of a diffusion model at sampling time without retraining the network for each specific task. One drawback of diffusion models, however, is their slow sampling process. Recent techniques can accelerate unguided sampling by applying high-order numerical methods to the sampling process when viewed as differential equations. On the contrary, we discover that the same techniques do not work for guided sampling, and little has been explored about its acceleration. This paper explores the culprit of this problem and provides a solution based on operator splitting methods, motivated by our key finding that classical high-order numerical methods are unsuitable for the conditional function. Our proposed method can re-utilize the high-order methods for guided sampling and can generate images with the same quality as a 250-step DDIM baseline using 32-58% less sampling time on ImageNet256. We also demonstrate usage on a wide variety of conditional generation tasks, such as text-to-image generation, colorization, inpainting, and super-resolution. | ['Supasorn Suwajanakorn', 'Suttisak Wizadwongsa'] | 2023-01-27 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 4.65350807e-01 2.05672652e-01 1.40892014e-01 -7.37377927e-02
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cce6a309-50d6-489a-ac5e-b32ec67f55cb | robust-iris-segmentation-based-on-fully | 1809.00769 | null | http://arxiv.org/abs/1809.00769v1 | http://arxiv.org/pdf/1809.00769v1.pdf | Robust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks | The iris can be considered as one of the most important biometric traits due
to its high degree of uniqueness. Iris-based biometrics applications depend
mainly on the iris segmentation whose suitability is not robust for different
environments such as near-infrared (NIR) and visible (VIS) ones. In this paper,
two approaches for robust iris segmentation based on Fully Convolutional
Networks (FCNs) and Generative Adversarial Networks (GANs) are described.
Similar to a common convolutional network, but without the fully connected
layers (i.e., the classification layers), an FCN employs at its end a
combination of pooling layers from different convolutional layers. Based on the
game theory, a GAN is designed as two networks competing with each other to
generate the best segmentation. The proposed segmentation networks achieved
promising results in all evaluated datasets (i.e., BioSec, CasiaI3, CasiaT4,
IITD-1) of NIR images and (NICE.I, CrEye-Iris and MICHE-I) of VIS images in
both non-cooperative and cooperative domains, outperforming the baselines
techniques which are the best ones found so far in the literature, i.e., a new
state of the art for these datasets. Furthermore, we manually labeled 2,431
images from CasiaT4, CrEye-Iris and MICHE-I datasets, making the masks
available for research purposes. | ['Alceu S. Britto Jr.', 'Lucas F. Oliveira', 'Rayson Laroca', 'Diego R. Lucio', 'David Menotti', 'Evair Severo', 'Cides S. Bezerra'] | 2018-09-04 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 2.29146495e-01 1.60747319e-01 2.32561585e-02 -9.76002812e-02
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af58f3c6-3535-4c97-a178-e7f01d5c3cd8 | language-model-prior-for-low-resource-neural | 2004.14928 | null | https://arxiv.org/abs/2004.14928v3 | https://arxiv.org/pdf/2004.14928v3.pdf | Language Model Prior for Low-Resource Neural Machine Translation | The scarcity of large parallel corpora is an important obstacle for neural machine translation. A common solution is to exploit the knowledge of language models (LM) trained on abundant monolingual data. In this work, we propose a novel approach to incorporate a LM as prior in a neural translation model (TM). Specifically, we add a regularization term, which pushes the output distributions of the TM to be probable under the LM prior, while avoiding wrong predictions when the TM "disagrees" with the LM. This objective relates to knowledge distillation, where the LM can be viewed as teaching the TM about the target language. The proposed approach does not compromise decoding speed, because the LM is used only at training time, unlike previous work that requires it during inference. We present an analysis of the effects that different methods have on the distributions of the TM. Results on two low-resource machine translation datasets show clear improvements even with limited monolingual data. | ['Alexandra Birch', 'Christos Baziotis', 'Barry Haddow'] | 2020-04-30 | null | https://aclanthology.org/2020.emnlp-main.615 | https://aclanthology.org/2020.emnlp-main.615.pdf | emnlp-2020-11 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 2.01307610e-01 3.32164377e-01 -3.07511985e-01 -3.48908335e-01
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960dc7b0-5eb4-4b8a-bde7-1b88f0629d0c | bayesian-optimization-enhanced-deep | 2212.13396 | null | https://arxiv.org/abs/2212.13396v1 | https://arxiv.org/pdf/2212.13396v1.pdf | Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks | In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories and network formation to expedite data transmissions via multi-hop relaying. The trajectory planning aims to collect all GUs' data, while the UAVs' network formation optimizes the multi-hop UAV network topology to minimize the energy consumption and transmission delay. The joint network formation and trajectory optimization is solved by a two-step iterative approach. Firstly, we devise the adaptive network formation scheme by using a heuristic algorithm to balance the UAVs' energy consumption and data queue size. Then, with the fixed network formation, the UAVs' trajectories are further optimized by using multi-agent deep reinforcement learning without knowing the GUs' traffic demands and spatial distribution. To improve the learning efficiency, we further employ Bayesian optimization to estimate the UAVs' flying decisions based on historical trajectory points. This helps avoid inefficient action explorations and improves the convergence rate in the model training. The simulation results reveal close spatial-temporal couplings between the UAVs' trajectory planning and network formation. Compared with several baselines, our solution can better exploit the UAVs' cooperation in data offloading, thus improving energy efficiency and delay performance. | ['Dusit Niyato', 'Dinh Thai Hoang', 'Wenjie Zhang', 'Bo Gu', 'Meng Wang', 'Shimin Gong'] | 2022-12-27 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-4.26646471e-01 2.32237294e-01 -4.90564466e-01 3.59753489e-01
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4c8eac26-4b34-41de-95bd-c41bc895c62d | object-category-aware-reinforcement-learning | 2210.07802 | null | https://arxiv.org/abs/2210.07802v1 | https://arxiv.org/pdf/2210.07802v1.pdf | Object-Category Aware Reinforcement Learning | Object-oriented reinforcement learning (OORL) is a promising way to improve the sample efficiency and generalization ability over standard RL. Recent works that try to solve OORL tasks without additional feature engineering mainly focus on learning the object representations and then solving tasks via reasoning based on these object representations. However, none of these works tries to explicitly model the inherent similarity between different object instances of the same category. Objects of the same category should share similar functionalities; therefore, the category is the most critical property of an object. Following this insight, we propose a novel framework named Object-Category Aware Reinforcement Learning (OCARL), which utilizes the category information of objects to facilitate both perception and reasoning. OCARL consists of three parts: (1) Category-Aware Unsupervised Object Discovery (UOD), which discovers the objects as well as their corresponding categories; (2) Object-Category Aware Perception, which encodes the category information and is also robust to the incompleteness of (1) at the same time; (3) Object-Centric Modular Reasoning, which adopts multiple independent and object-category-specific networks when reasoning based on objects. Our experiments show that OCARL can improve both the sample efficiency and generalization in the OORL domain. | ['Yunji Chen', 'Qi Guo', 'Xishan Zhang', 'Zidong Du', 'Xing Hu', 'Jiaming Guo', 'Shaohui Peng', 'Rui Zhang', 'Qi Yi'] | 2022-10-13 | null | null | null | null | ['object-discovery'] | ['computer-vision'] | [-2.88879257e-02 2.65317768e-01 -1.68826893e-01 -4.21128750e-01
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66760079-a26c-4c88-852b-bc912094467b | is-synthetic-voice-detection-research-going | null | null | https://openaccess.thecvf.com/content/CVPR2022W/WMF/html/Borzi_Is_Synthetic_Voice_Detection_Research_Going_Into_the_Right_Direction_CVPRW_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022W/WMF/papers/Borzi_Is_Synthetic_Voice_Detection_Research_Going_Into_the_Right_Direction_CVPRW_2022_paper.pdf | Is Synthetic Voice Detection Research Going Into the Right Direction? | Machine Learning, and in general Artificial Intelligence approaches, brought a great advance in each and every field of Computer Science increasing accuracy levels of predictors in any known problem. Indeed, this evolution enabled the construction of effective frameworks and solutions able to be used in investigative and forensics scenarios for detection of fakes and, in general, manipulations in multimedia contents. On the other hand, can we trust these systems? Is research activity going in the right direction? Are we just taking the low-hanging fruit without taking into account many real-case-in-the-wild situations? The purpose of this paper is to raise an alert to the research community in the specific context of synthetic voice detection, where data available for training is not big enough to give sufficient trust in the techniques available in the literature. To this aim, an exploratory investigation of the most common voice spoofing dataset was carried out and it was surprisingly easy to build simple classifiers without any Deep Learning techniques. Simple considerations on bitrate were sufficient to achieve an effective detection performance. | ['Dario Allegra', 'Filippo Stanco', 'Oliver Giudice', 'Stefano Borzì'] | 2022-06-19 | null | null | null | ieee-cvf-conference-on-computer-vision-and-5 | ['fake-voice-detection'] | ['audio'] | [ 2.72324253e-02 8.76686424e-02 -1.39406640e-02 -2.07601599e-02
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fa025c7e-2511-4ec6-8f2b-2e682b7755ef | the-shaped-transformer-attention-models-in | 2306.17759 | null | https://arxiv.org/abs/2306.17759v1 | https://arxiv.org/pdf/2306.17759v1.pdf | The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit | In deep learning theory, the covariance matrix of the representations serves as a proxy to examine the network's trainability. Motivated by the success of Transformers, we study the covariance matrix of a modified Softmax-based attention model with skip connections in the proportional limit of infinite-depth-and-width. We show that at initialization the limiting distribution can be described by a stochastic differential equation (SDE) indexed by the depth-to-width ratio. To achieve a well-defined stochastic limit, the Transformer's attention mechanism is modified by centering the Softmax output at identity, and scaling the Softmax logits by a width-dependent temperature parameter. We examine the stability of the network through the corresponding SDE, showing how the scale of both the drift and diffusion can be elegantly controlled with the aid of residual connections. The existence of a stable SDE implies that the covariance structure is well-behaved, even for very large depth and width, thus preventing the notorious issues of rank degeneracy in deep attention models. Finally, we show, through simulations, that the SDE provides a surprisingly good description of the corresponding finite-size model. We coin the name shaped Transformer for these architectural modifications. | ['Daniel M. Roy', 'Chris Maddison', 'Thomas Hofmann', 'Bobby He', 'Mufan Bill Li', 'Chuning Li', 'Lorenzo Noci'] | 2023-06-30 | null | null | null | null | ['deep-attention', 'learning-theory', 'deep-attention'] | ['computer-vision', 'miscellaneous', 'natural-language-processing'] | [-1.80233177e-02 4.27358210e-01 -8.00510347e-02 -1.49663329e-01
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ee4c9045-0e2d-495d-a39d-852f1e037349 | data-driven-response-regime-exploration-and | 2304.05822 | null | https://arxiv.org/abs/2304.05822v1 | https://arxiv.org/pdf/2304.05822v1.pdf | Data-Driven Response Regime Exploration and Identification for Dynamical Systems | Data-Driven Response Regime Exploration and Identification (DR$^2$EI) is a novel and fully data-driven method for identifying and classifying response regimes of a dynamical system without requiring human intervention. This approach is a valuable tool for exploring and discovering response regimes in complex dynamical systems, especially when the governing equations and the number of response regimes are unknown, and the system is expensive to sample. Additionally, the method is useful for order reduction, as it can be used to identify the most dominant response regimes of a given dynamical system. DR$^2$EI utilizes unsupervised learning algorithms to transform the system's response into an embedding space that facilitates regime classification. An active sequential sampling approach based on Gaussian Process Regression (GPR) is used to efficiently sample the parameter space, quantify uncertainty, and provide optimal trade-offs between exploration and exploitation. The performance of the DR$^2$EI method was evaluated by analyzing three established dynamical systems: the mathematical pendulum, the Lorenz system, and the Duffing oscillator. The method was shown to effectively identify a variety of response regimes with both similar and distinct topological features and frequency content, demonstrating its versatility in capturing a wide range of behaviors. While it may not be possible to guarantee that all possible regimes will be identified, the method provides an automated and efficient means for exploring the parameter space of a dynamical system and identifying its underlying "sufficiently dominant" response regimes without prior knowledge of the system's equations or behavior. | ['Maor Farid'] | 2023-04-07 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 3.00331503e-01 -4.47725832e-01 -2.33881339e-01 2.53780752e-01
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3a492eaa-954e-4246-9bbe-616495a51dbf | sgeitl-scene-graph-enhanced-image-text | 2112.08587 | null | https://arxiv.org/abs/2112.08587v1 | https://arxiv.org/pdf/2112.08587v1.pdf | SGEITL: Scene Graph Enhanced Image-Text Learning for Visual Commonsense Reasoning | Answering complex questions about images is an ambitious goal for machine intelligence, which requires a joint understanding of images, text, and commonsense knowledge, as well as a strong reasoning ability. Recently, multimodal Transformers have made great progress in the task of Visual Commonsense Reasoning (VCR), by jointly understanding visual objects and text tokens through layers of cross-modality attention. However, these approaches do not utilize the rich structure of the scene and the interactions between objects which are essential in answering complex commonsense questions. We propose a Scene Graph Enhanced Image-Text Learning (SGEITL) framework to incorporate visual scene graphs in commonsense reasoning. To exploit the scene graph structure, at the model structure level, we propose a multihop graph transformer for regularizing attention interaction among hops. As for pre-training, a scene-graph-aware pre-training method is proposed to leverage structure knowledge extracted in the visual scene graph. Moreover, we introduce a method to train and generate domain-relevant visual scene graphs using textual annotations in a weakly-supervised manner. Extensive experiments on VCR and other tasks show a significant performance boost compared with the state-of-the-art methods and prove the efficacy of each proposed component. | ['Shih-Fu Chang', 'Kai-Wei Chang', 'Yiqing Liang', 'Suji Park', 'Alireza Zareian', 'Liunian Harold Li', 'Haoxuan You', 'Zhecan Wang'] | 2021-12-16 | null | null | null | null | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 4.12763864e-01 1.15771718e-01 1.04388865e-02 -4.48109150e-01
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a8529c81-9bd1-42da-8a2a-751d49b07799 | object-detection-in-aerial-images-a-large | 2102.12219 | null | https://arxiv.org/abs/2102.12219v2 | https://arxiv.org/pdf/2102.12219v2.pdf | Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges | In the past decade, object detection has achieved significant progress in natural images but not in aerial images, due to the massive variations in the scale and orientation of objects caused by the bird's-eye view of aerial images. More importantly, the lack of large-scale benchmarks has become a major obstacle to the development of object detection in aerial images (ODAI). In this paper,we present a large-scale Dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images. Based on this large-scale and well-annotated dataset, we build baselines covering 10 state-of-the-art algorithms with over 70 configurations, where the speed and accuracy performances of each model have been evaluated. Furthermore, we provide a code library for ODAI and build a website for evaluating different algorithms. Previous challenges run on DOTA have attracted more than 1300 teams worldwide. We believe that the expanded large-scale DOTA dataset, the extensive baselines, the code library and the challenges can facilitate the designs of robust algorithms and reproducible research on the problem of object detection in aerial images. | ['Liangpei Zhang', 'Marcello Pelillo', 'Mihai Datcu', 'Jiebo Luo', 'Serge Belongie', 'Micheal Ying Yang', 'Wen Yang', 'Xiang Bai', 'Gui-Song Xia', 'Nan Xue', 'Jian Ding'] | 2021-02-24 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 5.67833148e-03 -6.53560281e-01 2.40928486e-01 -2.64674783e-01
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12743006-8c1a-4251-9a9d-889431c06b1d | network-representation-learning-a-macro-and | 2111.10772 | null | https://arxiv.org/abs/2111.10772v1 | https://arxiv.org/pdf/2111.10772v1.pdf | Network representation learning: A macro and micro view | Graph is a universe data structure that is widely used to organize data in real-world. Various real-word networks like the transportation network, social and academic network can be represented by graphs. Recent years have witnessed the quick development on representing vertices in the network into a low-dimensional vector space, referred to as network representation learning. Representation learning can facilitate the design of new algorithms on the graph data. In this survey, we conduct a comprehensive review of current literature on network representation learning. Existing algorithms can be categorized into three groups: shallow embedding models, heterogeneous network embedding models, graph neural network based models. We review state-of-the-art algorithms for each category and discuss the essential differences between these algorithms. One advantage of the survey is that we systematically study the underlying theoretical foundations underlying the different categories of algorithms, which offers deep insights for better understanding the development of the network representation learning field. | ['Jie Tang', 'Xueyi Liu'] | 2021-11-21 | null | null | null | null | ['network-embedding'] | ['methodology'] | [ 1.11621387e-01 4.61848706e-01 -8.53126526e-01 -1.05912164e-01
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a7e22c9c-e71b-499c-b076-246b1d383fae | ontology-based-sms-controller-for-smart | 1509.01379 | null | http://arxiv.org/abs/1509.01379v1 | http://arxiv.org/pdf/1509.01379v1.pdf | Ontology Based SMS Controller for Smart Phones | Text analysis includes lexical analysis of the text and has been widely
studied and used in diverse applications. In the last decade, researchers have
proposed many efficient solutions to analyze / classify large text dataset,
however, analysis / classification of short text is still a challenge because
1) the data is very sparse 2) It contains noise words and 3) It is difficult to
understand the syntactical structure of the text. Short Messaging Service (SMS)
is a text messaging service for mobile/smart phone and this service is
frequently used by all mobile users. Because of the popularity of SMS service,
marketing companies nowadays are also using this service for direct marketing
also known as SMS marketing.In this paper, we have proposed Ontology based SMS
Controller which analyze the text message and classify it using ontology
aslegitimate or spam. The proposed system has been tested on different
scenarios and experimental results shows that the proposed solution is
effective both in terms of efficiency and time. | ['Mohammed A. Balubaid', 'Umar Manzoor'] | 2015-09-04 | null | null | null | null | ['lexical-analysis'] | ['natural-language-processing'] | [ 4.01906252e-01 -1.12925850e-01 -5.19737042e-02 -3.05359900e-01
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5427b273-d322-4e06-bbd7-676af182d701 | sql-palm-improved-large-language | 2306.00739 | null | https://arxiv.org/abs/2306.00739v3 | https://arxiv.org/pdf/2306.00739v3.pdf | SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL | One impressive emergent capability of large language models (LLMs) is generation of code, including Structured Query Language (SQL) for databases. For the task of converting natural language text to SQL queries, Text-to-SQL, adaptation of LLMs is of paramount importance, both in in-context learning and fine-tuning settings, depending on the amount of adaptation data used. In this paper, we propose an LLM-based Text-to-SQL model SQL-PaLM, leveraging on PaLM-2, that pushes the state-of-the-art in both settings. Few-shot SQL-PaLM is based on an execution-based self-consistency prompting approach designed for Text-to-SQL, and achieves 77.3% in test-suite accuracy on Spider, which to our best knowledge is the first to outperform previous state-of-the-art with fine-tuning by a significant margin, 4%. Furthermore, we demonstrate that the fine-tuned SQL-PALM outperforms it further by another 1%. Towards applying SQL-PaLM to real-world scenarios we further evaluate its robustness on other challenging variants of Spider and demonstrate the superior generalization capability of SQL-PaLM. In addition, via extensive case studies, we demonstrate the impressive intelligent capabilities and various success enablers of LLM-based Text-to-SQL. | ['Sercan O. Arik', 'Tomas Pfister', 'Pengcheng Yin', 'Rajarishi Sinha', 'Hanjun Dai', 'Hootan Nakhost', 'Ruoxi Sun'] | 2023-05-26 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [-5.78044169e-02 -6.41784072e-02 -3.96469861e-01 -5.41164517e-01
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44d61e14-11fa-47e0-82c3-f1193281676c | paradigm-shift-in-sustainability-disclosure | 2306.15518 | null | https://arxiv.org/abs/2306.15518v1 | https://arxiv.org/pdf/2306.15518v1.pdf | Paradigm Shift in Sustainability Disclosure Analysis: Empowering Stakeholders with CHATREPORT, a Language Model-Based Tool | This paper introduces a novel approach to enhance Large Language Models (LLMs) with expert knowledge to automate the analysis of corporate sustainability reports by benchmarking them against the Task Force for Climate-Related Financial Disclosures (TCFD) recommendations. Corporate sustainability reports are crucial in assessing organizations' environmental and social risks and impacts. However, analyzing these reports' vast amounts of information makes human analysis often too costly. As a result, only a few entities worldwide have the resources to analyze these reports, which could lead to a lack of transparency. While AI-powered tools can automatically analyze the data, they are prone to inaccuracies as they lack domain-specific expertise. This paper introduces a novel approach to enhance LLMs with expert knowledge to automate the analysis of corporate sustainability reports. We christen our tool CHATREPORT, and apply it in a first use case to assess corporate climate risk disclosures following the TCFD recommendations. CHATREPORT results from collaborating with experts in climate science, finance, economic policy, and computer science, demonstrating how domain experts can be involved in developing AI tools. We make our prompt templates, generated data, and scores available to the public to encourage transparency. | ['Markus Leippold', 'Tingyu Yu', 'Tobias Wekhof', 'Nicolas Webersinke', 'Qian Wang', 'Saeid Ashraf Vaghefi', 'Dominik Stammbach', 'Tobias Schimanski', 'Glen Gostlow', 'Mathias Kraus', 'Chiara Colesanti-Senni', 'Julia Bingler', 'Jingwei Ni'] | 2023-06-27 | null | null | null | null | ['benchmarking', 'benchmarking'] | ['miscellaneous', 'robots'] | [-1.36754319e-01 4.70436901e-01 -8.06132033e-02 -1.49734393e-01
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f67e29fd-aa33-41c9-8703-fddf6a87af91 | cosmic-commonsense-knowledge-for-emotion | 2010.02795 | null | https://arxiv.org/abs/2010.02795v1 | https://arxiv.org/pdf/2010.02795v1.pdf | COSMIC: COmmonSense knowledge for eMotion Identification in Conversations | In this paper, we address the task of utterance level emotion recognition in conversations using commonsense knowledge. We propose COSMIC, a new framework that incorporates different elements of commonsense such as mental states, events, and causal relations, and build upon them to learn interactions between interlocutors participating in a conversation. Current state-of-the-art methods often encounter difficulties in context propagation, emotion shift detection, and differentiating between related emotion classes. By learning distinct commonsense representations, COSMIC addresses these challenges and achieves new state-of-the-art results for emotion recognition on four different benchmark conversational datasets. Our code is available at https://github.com/declare-lab/conv-emotion. | ['Soujanya Poria', 'Rada Mihalcea', 'Alexander Gelbukh', 'Navonil Majumder', 'Deepanway Ghosal'] | 2020-10-06 | null | https://aclanthology.org/2020.findings-emnlp.224 | https://aclanthology.org/2020.findings-emnlp.224.pdf | findings-of-the-association-for-computational | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 2.69109279e-01 -2.12003458e-02 -1.74950287e-02 -7.59808064e-01
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2bd88cfa-ede2-4555-9736-80a44ce826de | portfolio-transformer-for-attention-based | 2206.03246 | null | https://arxiv.org/abs/2206.03246v1 | https://arxiv.org/pdf/2206.03246v1.pdf | Portfolio Transformer for Attention-Based Asset Allocation | Traditional approaches to financial asset allocation start with returns forecasting followed by an optimization stage that decides the optimal asset weights. Any errors made during the forecasting step reduce the accuracy of the asset weightings, and hence the profitability of the overall portfolio. The Portfolio Transformer (PT) network, introduced here, circumvents the need to predict asset returns and instead directly optimizes the Sharpe ratio, a risk-adjusted performance metric widely used in practice. The PT is a novel end-to-end portfolio optimization framework, inspired by the numerous successes of attention mechanisms in natural language processing. With its full encoder-decoder architecture, specialized time encoding layers, and gating components, the PT has a high capacity to learn long-term dependencies among portfolio assets and hence can adapt more quickly to changing market conditions such as the COVID-19 pandemic. To demonstrate its robustness, the PT is compared against other algorithms, including the current LSTM-based state of the art, on three different datasets, with results showing that it offers the best risk-adjusted performance. | ['Denise Gorse', 'Damian Kisiel'] | 2022-06-07 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-7.50706196e-02 -7.56710172e-02 -1.49024770e-01 -3.67960066e-01
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ad068bb8-7a5e-44f6-a1e0-bb16a168bb28 | deep-learning-for-covid-19-diagnosis-based | 2104.07279 | null | https://arxiv.org/abs/2104.07279v4 | https://arxiv.org/pdf/2104.07279v4.pdf | COVID-19 detection using deep convolutional neural networks and binary-differential-algorithm-based feature selection on X-ray images | The new Coronavirus is spreading rapidly, and it has taken the lives of many people so far. The virus has destructive effects on the human lung, and early detection is very important. Deep Convolution neural networks are such powerful tools in classifying images. Therefore, in this paper, a hybrid approach based on a deep network is presented. Feature vectors were extracted by applying a deep convolution neural network on the images, and useful features were selected by the binary differential meta-heuristic algorithm. These optimized features were given to the SVM classifier. A database consisting of three categories of images such as COVID-19, pneumonia, and healthy included in 1092 X-ray samples was considered. The proposed method achieved an accuracy of 99.43%, a sensitivity of 99.16%, and a specificity of 99.57%. Our results demonstrate that the suggested approach is better than recent studies on COVID-19 detection with X-ray images. | ['Jafar Tanha', 'Mohammad-Reza Feizi-Derakhshi', 'Mohammad Saber Iraji'] | 2021-04-15 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 1.34829536e-01 -6.53177917e-01 -2.00534478e-01 -4.49882984e-01
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d46ad764-f8c0-4ef5-889e-4fca8ed31876 | the-inception-team-at-nsurl-2019-task-8 | 2004.11964 | null | https://arxiv.org/abs/2004.11964v1 | https://arxiv.org/pdf/2004.11964v1.pdf | The Inception Team at NSURL-2019 Task 8: Semantic Question Similarity in Arabic | This paper describes our method for the task of Semantic Question Similarity in Arabic in the workshop on NLP Solutions for Under-Resourced Languages (NSURL). The aim is to build a model that is able to detect similar semantic questions in the Arabic language for the provided dataset. Different methods of determining questions similarity are explored in this work. The proposed models achieved high F1-scores, which range from (88% to 96%). Our official best result is produced from the ensemble model of using a pre-trained multilingual BERT model with different random seeds with 95.924% F1-Score, which ranks the first among nine participants teams. | ['Aisha Al-Sadi', 'Hana Al-Theiabat'] | 2020-04-24 | null | https://aclanthology.org/2019.nsurl-1.17 | https://aclanthology.org/2019.nsurl-1.17.pdf | nsurl-2019-9 | ['question-similarity'] | ['natural-language-processing'] | [-3.48199964e-01 3.59742045e-01 6.07648253e-01 -4.56687361e-01
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7d5f5206-159e-4d25-a04a-bb387c730175 | mars-a-motif-based-autoregressive-model-for | 2209.13178 | null | https://arxiv.org/abs/2209.13178v1 | https://arxiv.org/pdf/2209.13178v1.pdf | MARS: A Motif-based Autoregressive Model for Retrosynthesis Prediction | Retrosynthesis is a major task for drug discovery. It is formulated as a graph-generating problem by many existing approaches. Specifically, these methods firstly identify the reaction center, and break target molecule accordingly to generate synthons. Reactants are generated by either adding atoms sequentially to synthon graphs or directly adding proper leaving groups. However, both two strategies suffer since adding atoms results in a long prediction sequence which increases generation difficulty, while adding leaving groups can only consider the ones in the training set which results in poor generalization. In this paper, we propose a novel end-to-end graph generation model for retrosynthesis prediction, which sequentially identifies the reaction center, generates the synthons, and adds motifs to the synthons to generate reactants. Since chemically meaningful motifs are bigger than atoms and smaller than leaving groups, our method enjoys lower prediction complexity than adding atoms and better generalization than adding leaving groups. Experiments on a benchmark dataset show that the proposed model significantly outperforms previous state-of-the-art algorithms. | ['Peilin Zhao', 'Le Ou-Yang', 'Junzhou Huang', 'Chan Lu', 'Yang Yu', 'Chaochao Yan', 'Jiahan Liu'] | 2022-09-27 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 6.54047191e-01 2.23731697e-01 -4.10039425e-01 1.77021697e-01
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04df4efc-a9f4-4298-b8d7-50181ed728f6 | sentiment-analysis-for-modern-standard-arabic | 1505.03105 | null | http://arxiv.org/abs/1505.03105v1 | http://arxiv.org/pdf/1505.03105v1.pdf | Sentiment Analysis For Modern Standard Arabic And Colloquial | The rise of social media such as blogs and social networks has fueled
interest in sentiment analysis. With the proliferation of reviews, ratings,
recommendations and other forms of online expression, online opinion has turned
into a kind of virtual currency for businesses looking to market their
products, identify new opportunities and manage their reputations, therefore
many are now looking to the field of sentiment analysis. In this paper, we
present a feature-based sentence level approach for Arabic sentiment analysis.
Our approach is using Arabic idioms/saying phrases lexicon as a key importance
for improving the detection of the sentiment polarity in Arabic sentences as
well as a number of novels and rich set of linguistically motivated features
contextual Intensifiers, contextual Shifter and negation handling), syntactic
features for conflicting phrases which enhance the sentiment classification
accuracy. Furthermore, we introduce an automatic expandable wide coverage
polarity lexicon of Arabic sentiment words. The lexicon is built with
gold-standard sentiment words as a seed which is manually collected and
annotated and it expands and detects the sentiment orientation automatically of
new sentiment words using synset aggregation technique and free online Arabic
lexicons and thesauruses. Our data focus on modern standard Arabic (MSA) and
Egyptian dialectal Arabic tweets and microblogs (hotel reservation, product
reviews, etc.). The experimental results using our resources and techniques
with SVM classifier indicate high performance levels, with accuracies of over
95%. | ['Hossam S. Ibrahim', 'Sherif M. Abdou', 'Mervat Gheith'] | 2015-05-12 | null | null | null | null | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-1.32326081e-01 -1.25737920e-01 -7.52566010e-02 -5.78458726e-01
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7a06c8fb-dc7a-4cfe-b0e4-4a9dedb4d2bb | finding-covid-19-from-chest-x-rays-using-deep | 2004.02060 | null | https://arxiv.org/abs/2004.02060v4 | https://arxiv.org/pdf/2004.02060v4.pdf | Finding Covid-19 from Chest X-rays using Deep Learning on a Small Dataset | Testing for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly. This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease. We have obtained 122 chest X-rays of COVID-19 and over 4,000 chest X-rays of viral and bacterial pneumonia. A pretrained deep convolutional neural network has been tuned on 102 COVID-19 cases and 102 other pneumonia cases in a 10-fold cross validation. The results were all 102 COVID-19 cases were correctly classified and there were 8 false positives resulting in an AUC of 0.997. On a test set of 20 unseen COVID-19 cases all were correctly classified and more than 95% of 4171 other pneumonia examples were correctly classified. This study has flaws, most critically a lack of information about where in the disease process the COVID-19 cases were and the small data set size. More COVID-19 case images will enable a better answer to the question of how useful chest X-rays can be for diagnosing COVID-19 (so please send them). | ['Dmitry B. Goldgof', 'Gregory M. Goldgof', 'Lawrence O. Hall', 'Rahul Paul'] | 2020-04-05 | null | null | null | null | ['small-data'] | ['computer-vision'] | [-2.38227993e-02 -2.35281453e-01 4.90497015e-02 -3.84436786e-01
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ee48666e-3a84-4e9a-b5af-e9a64318b5f2 | real-time-system-of-hand-detection-and | 1502.07243 | null | http://arxiv.org/abs/1502.07243v1 | http://arxiv.org/pdf/1502.07243v1.pdf | Real-Time System of Hand Detection And Gesture Recognition In Cyber Presence Interactive System For E-Learning | The development of technologies of multimedia, linked to that of Internet and
democratization of high outflow, has made henceforth E-learning possible for
learners being in virtual classes and geographically distributed. The quality
and quantity of asynchronous and synchronous communications are the key
elements for E-learning success. It is important to have a propitious
supervision to reduce the feeling of isolation in E-learning. This feeling of
isolation is among the main causes of loss and high rates of stalling in
E-learning. The researches to be conducted in this domain aim to bring
solutions of convergence coming from real time image for the capture and
recognition of hand gestures. These gestures will be analyzed by the system and
transformed as indicator of participation. This latter is displayed in the
table of performance of the tutor as a curve according to the time. In case of
isolation of learner, the indicator of participation will become red and the
tutor will be informed of learners with difficulties to participate during
learning session. | ['Bousaaid Mourad', 'Ayaou Tarik', 'Estraillier Pascal', 'Afdel Karim'] | 2014-12-08 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [-2.78936833e-01 1.78293109e-01 8.87563266e-03 -1.99805856e-01
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b9adf4ac-e801-4719-9009-9c69853e49d6 | lfps-net-a-lightweight-fast-pulse-simulation | 2206.12558 | null | https://arxiv.org/abs/2206.12558v3 | https://arxiv.org/pdf/2206.12558v3.pdf | FastBVP-Net: a lightweight pulse extraction network for measuring heart rhythm via facial videos | Remote photoplethysmography (rPPG) is an attractive camera-based health monitoring method that can measure the heart rhythm from facial videos. Many well-established deep-learning models have been reported to measure heart rate (HR) and heart rate variability (HRV). However, most of these models usually require a 30-second facial video and enormous computational resources to obtain accurate and robust results, which significantly limits their applications in real-world scenarios. Hence, we propose a lightweight pulse extraction network, FastBVP-Net, to quickly measure heart rhythm via facial videos. The proposed FastBVP-Net uses a multi-frequency mode signal fusion (MMSF) mechanism to characterize the different modes of the raw signals in a decompose module and reconstruct the blood volume pulse (BVP) signal under a complex noise environment in a compose module. Meanwhile, an oversampling training scheme is used to solve the over-fitting problem caused by the limitations of the datasets. Then, the HR and HRV can be estimated based on the extracted BVP signals. Comprehensive experiments are conducted on the benchmark datasets to validate the proposed FastBVP-Net. For intra-dataset and cross-dataset testing, the proposed approach achieves better performance for HR and HRV estimation from 30-second facial videos with fewer computational burdens than the current well-established methods. Moreover, the proposed approach also achieves competitive results from 15-second facial videos. Therefore, the proposed FastBVP-Net has the potential to be applied in many real-world scenarios with shorter videos. | ['Yun Zhang', 'Xiujuan Zheng', 'YuHeng Chen', 'Jialiang Zhuang'] | 2022-06-25 | null | null | null | null | ['heart-rate-variability', 'heart-rate-estimation'] | ['medical', 'medical'] | [-7.92143494e-02 -4.48549807e-01 -7.13963201e-03 -4.29389387e-01
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abebb3c1-1003-48f2-aa5c-c845139a67c4 | opengait-revisiting-gait-recognition-towards | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Fan_OpenGait_Revisiting_Gait_Recognition_Towards_Better_Practicality_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Fan_OpenGait_Revisiting_Gait_Recognition_Towards_Better_Practicality_CVPR_2023_paper.pdf | OpenGait: Revisiting Gait Recognition Towards Better Practicality | Gait recognition is one of the most critical long-distance identification technologies and increasingly gains popularity in both research and industry communities. Despite the significant progress made in indoor datasets, much evidence shows that gait recognition techniques perform poorly in the wild. More importantly, we also find that some conclusions drawn from indoor datasets cannot be generalized to real applications. Therefore, the primary goal of this paper is to present a comprehensive benchmark study for better practicality rather than only a particular model for better performance. To this end, we first develop a flexible and efficient gait recognition codebase named OpenGait. Based on OpenGait, we deeply revisit the recent development of gait recognition by re-conducting the ablative experiments. Encouragingly,we detect some unperfect parts of certain prior woks, as well as new insights. Inspired by these discoveries, we develop a structurally simple, empirically powerful, and practically robust baseline model, GaitBase. Experimentally, we comprehensively compare GaitBase with many current gait recognition methods on multiple public datasets, and the results reflect that GaitBase achieves significantly strong performance in most cases regardless of indoor or outdoor situations. Code is available at https://github.com/ShiqiYu/OpenGait. | ['Shiqi Yu', 'Yongzhen Huang', 'Saihui Hou', 'Chuanfu Shen', 'Junhao Liang', 'Chao Fan'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['gait-recognition'] | ['computer-vision'] | [-1.17321700e-01 -6.81662202e-01 -3.48819315e-01 -1.89679921e-01
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bf71f16b-caed-4fd3-a49b-0993e0ccd970 | negative-deceptive-opinion-spam | null | null | https://aclanthology.org/N13-1053 | https://aclanthology.org/N13-1053.pdf | Negative Deceptive Opinion Spam | null | ['Myle Ott', 'Jeffrey T. Hancock', 'Claire Cardie'] | 2013-06-01 | null | null | null | naacl-2013-6 | ['deception-detection'] | ['miscellaneous'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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ae744f25-c47f-41d3-a2d7-86c73c7f8b2a | residual-networks-as-flows-of-velocity-fields | 2106.11911 | null | https://arxiv.org/abs/2106.11911v1 | https://arxiv.org/pdf/2106.11911v1.pdf | Residual Networks as Flows of Velocity Fields for Diffeomorphic Time Series Alignment | Non-linear (large) time warping is a challenging source of nuisance in time-series analysis. In this paper, we propose a novel diffeomorphic temporal transformer network for both pairwise and joint time-series alignment. Our ResNet-TW (Deep Residual Network for Time Warping) tackles the alignment problem by compositing a flow of incremental diffeomorphic mappings. Governed by the flow equation, our Residual Network (ResNet) builds smooth, fluid and regular flows of velocity fields and consequently generates smooth and invertible transformations (i.e. diffeomorphic warping functions). Inspired by the elegant Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, the final transformation is built by the flow of time-dependent vector fields which are none other than the building blocks of our Residual Network. The latter is naturally viewed as an Eulerian discretization schema of the flow equation (an ODE). Once trained, our ResNet-TW aligns unseen data by a single inexpensive forward pass. As we show in experiments on both univariate (84 datasets from UCR archive) and multivariate time-series (MSR Action-3D, Florence-3D and MSR Daily Activity), ResNet-TW achieves competitive performance in joint alignment and classification. | ['Yi Fang', 'Fan Zhu', 'Xichan Lin', 'Boulbaba Ben Amor', 'Hao Huang'] | 2021-06-22 | null | null | null | null | ['time-series-alignment'] | ['time-series'] | [-1.27248049e-01 -1.30876765e-01 1.57384008e-01 -5.72992302e-02
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cec82c35-d411-4f65-9850-11e1c8b7c419 | contextformer-a-transformer-with-spatio | 2203.02452 | null | https://arxiv.org/abs/2203.02452v2 | https://arxiv.org/pdf/2203.02452v2.pdf | Contextformer: A Transformer with Spatio-Channel Attention for Context Modeling in Learned Image Compression | Entropy modeling is a key component for high-performance image compression algorithms. Recent developments in autoregressive context modeling helped learning-based methods to surpass their classical counterparts. However, the performance of those models can be further improved due to the underexploited spatio-channel dependencies in latent space, and the suboptimal implementation of context adaptivity. Inspired by the adaptive characteristics of the transformers, we propose a transformer-based context model, named Contextformer, which generalizes the de facto standard attention mechanism to spatio-channel attention. We replace the context model of a modern compression framework with the Contextformer and test it on the widely used Kodak, CLIC2020, and Tecnick image datasets. Our experimental results show that the proposed model provides up to 11% rate savings compared to the standard Versatile Video Coding (VVC) Test Model (VTM) 16.2, and outperforms various learning-based models in terms of PSNR and MS-SSIM. | ['Eckehard Steinbach', 'Elena Alshina', 'Georgii Gaikov', 'Atanas Boev', 'Han Gao', 'A. Burakhan Koyuncu'] | 2022-03-04 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 4.21282798e-01 -5.25138021e-01 -1.40253499e-01 -2.37783819e-01
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c26f65e2-18ad-4296-9eb3-5e2192085f20 | aim-2020-scene-relighting-and-illumination | 2009.12798 | null | https://arxiv.org/abs/2009.12798v1 | https://arxiv.org/pdf/2009.12798v1.pdf | AIM 2020: Scene Relighting and Illumination Estimation Challenge | We review the AIM 2020 challenge on virtual image relighting and illumination estimation. This paper presents the novel VIDIT dataset used in the challenge and the different proposed solutions and final evaluation results over the 3 challenge tracks. The first track considered one-to-one relighting; the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation (i.e., light source position). The goal of the second track was to estimate illumination settings, namely the color temperature and orientation, from a given image. Lastly, the third track dealt with any-to-any relighting, thus a generalization of the first track. The target color temperature and orientation, rather than being pre-determined, are instead given by a guide image. Participants were allowed to make use of their track 1 and 2 solutions for track 3. The tracks had 94, 52, and 56 registered participants, respectively, leading to 20 confirmed submissions in the final competition stage. | ['Jiji C. V', 'Hrishikesh P. S', 'A. N. Rajagopalan', 'Maitreya Suin', 'Akashdeep Jassal', 'Nisarg A. Shah', 'Chu-Tak Li', 'Zhi-Song Liu', 'Li-Wen Wang', 'Sabine Süsstrunk', 'Tongtong Zhao', 'Sourya Dipta Das', 'Sabari Nathan', 'Ruofan Zhou', 'R. Suganya', 'Radu Timofte', 'M. Parisa Beham', 'Melvin Kuriakose', 'Chenghua Li', 'Zhongyun Hu', 'Mahmoud Afifi', 'Jian Cheng', 'Yaning Li', 'Liping Dong', 'Kuldeep Purohit', 'Kele Xu', 'Hengxing Cai', 'Cong Leng', 'Zhuolong Jiang', 'Yu Zhu', 'Yuzhong Liu', 'Xin Huang', 'Shanshan Zhao', 'Qing Wang', 'Michael S. Brown', 'Majed El Helou', 'Densen Puthussery'] | 2020-09-27 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 1.92201450e-01 -3.07808101e-01 4.23214883e-01 -5.12620270e-01
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de656131-5c40-471d-87f5-57ac7a04b7a7 | qub-at-semeval-2017-task-6-cascaded | null | null | https://aclanthology.org/S17-2063 | https://aclanthology.org/S17-2063.pdf | QUB at SemEval-2017 Task 6: Cascaded Imbalanced Classification for Humor Analysis in Twitter | This paper presents our submission to SemEval-2017 Task 6: {\#}HashtagWars: Learning a Sense of Humor. There are two subtasks: A. Pairwise Comparison, and B. Semi-Ranking. Our assumption is that the distribution of humorous and non-humorous texts in real life language is naturally imbalanced. Using Na{\"\i}ve Bayes Multinomial with standard text-representation features, we approached Subtask B as a sequence of imbalanced classification problems, and optimized our system per the macro-average recall. Subtask A was then solved via the Semi-Ranking results. On the final test, our system was ranked 10th for Subtask A, and 3rd for Subtask B. | ['Gregory Toner', 'Xiwu Han'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['humor-detection'] | ['natural-language-processing'] | [-3.00216734e-01 1.11985974e-01 -4.96243089e-02 -2.93864995e-01
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058af64a-3ce5-411c-8579-257c1d7140ae | audio-visual-speech-recognition-is-worth-32 | 2109.09536 | null | https://arxiv.org/abs/2109.09536v1 | https://arxiv.org/pdf/2109.09536v1.pdf | Audio-Visual Speech Recognition is Worth 32$\times$32$\times$8 Voxels | Audio-visual automatic speech recognition (AV-ASR) introduces the video modality into the speech recognition process, often by relying on information conveyed by the motion of the speaker's mouth. The use of the video signal requires extracting visual features, which are then combined with the acoustic features to build an AV-ASR system [1]. This is traditionally done with some form of 3D convolutional network (e.g. VGG) as widely used in the computer vision community. Recently, image transformers [2] have been introduced to extract visual features useful for image classification tasks. In this work, we propose to replace the 3D convolutional visual front-end with a video transformer front-end. We train our systems on a large-scale dataset composed of YouTube videos and evaluate performance on the publicly available LRS3-TED set, as well as on a large set of YouTube videos. On a lip-reading task, the transformer-based front-end shows superior performance compared to a strong convolutional baseline. On an AV-ASR task, the transformer front-end performs as well as (or better than) the convolutional baseline. Fine-tuning our model on the LRS3-TED training set matches previous state of the art. Thus, we experimentally show the viability of the convolution-free model for AV-ASR. | ['Olivier Siohan', 'Otavio Braga', 'Dmitriy Serdyuk'] | 2021-09-20 | null | null | null | null | ['audio-visual-speech-recognition'] | ['speech'] | [ 5.28331175e-02 -1.75757006e-01 -1.34828433e-01 -2.23915577e-01
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a8e4066b-b77f-47ef-91aa-9c81f17b0aec | a-unified-view-for-unsupervised | 2101.02083 | null | https://arxiv.org/abs/2101.02083v2 | https://arxiv.org/pdf/2101.02083v2.pdf | Representation learning for maximization of MI, nonlinear ICA and nonlinear subspaces with robust density ratio estimation | Contrastive learning is a recent promising approach in unsupervised representation learning where a feature representation of data is learned by solving a pseudo classification problem from unlabelled data. However, it is not straightforward to understand what representation contrastive learning yields. In addition, contrastive learning is often based on the maximum likelihood estimation, which tends to be vulnerable to the contamination by outliers. To promote the understanding to contrastive learning, this paper first theoretically shows a connection to maximization of mutual information (MI). Our result indicates that density ratio estimation is necessary and sufficient for maximization of MI under some conditions. Thus, contrastive learning related to density ratio estimation as done in popular objective functions can be interpreted as maximizing MI. Next, with the density ratio, we establish new recovery conditions for the latent source components in nonlinear independent component analysis (ICA). In contrast with existing work, the established conditions include a novel insight for the dimensionality of data, which is clearly supported by numerical experiments. Furthermore, inspired by nonlinear ICA, we propose a novel framework to estimate a nonlinear subspace for lower-dimensional latent source components, and some theoretical conditions for the subspace estimation are established with the density ratio. Then, we propose a practical method through outlier-robust density ratio estimation, which can be seen as performing maximization of MI, nonlinear ICA or nonlinear subspace estimation. Moreover, a sample-efficient nonlinear ICA method is also proposed. We theoretically investigate outlier-robustness of the proposed methods. Finally, the usefulness of the proposed methods is numerically demonstrated in nonlinear ICA and through application to linear classification. | ['Takashi Takenouchi', 'Hiroaki Sasaki'] | 2021-01-06 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 1.29718602e-01 -2.19551608e-01 -2.68863857e-01 -1.36098653e-01
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1.80900797e-01 -7.78704941e-01 -1.22340167e+00 1.06861629e-01] | [7.820302963256836, 4.2173895835876465] |
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