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1539734e-4aec-400a-a0f0-111740e8324d | uniter-learning-universal-image-text-1 | 1909.11740 | null | https://arxiv.org/abs/1909.11740v3 | https://arxiv.org/pdf/1909.11740v3.pdf | UNITER: UNiversal Image-TExt Representation Learning | Joint image-text embedding is the bedrock for most Vision-and-Language (V+L) tasks, where multimodality inputs are simultaneously processed for joint visual and textual understanding. In this paper, we introduce UNITER, a UNiversal Image-TExt Representation, learned through large-scale pre-training over four image-text datasets (COCO, Visual Genome, Conceptual Captions, and SBU Captions), which can power heterogeneous downstream V+L tasks with joint multimodal embeddings. We design four pre-training tasks: Masked Language Modeling (MLM), Masked Region Modeling (MRM, with three variants), Image-Text Matching (ITM), and Word-Region Alignment (WRA). Different from previous work that applies joint random masking to both modalities, we use conditional masking on pre-training tasks (i.e., masked language/region modeling is conditioned on full observation of image/text). In addition to ITM for global image-text alignment, we also propose WRA via the use of Optimal Transport (OT) to explicitly encourage fine-grained alignment between words and image regions during pre-training. Comprehensive analysis shows that both conditional masking and OT-based WRA contribute to better pre-training. We also conduct a thorough ablation study to find an optimal combination of pre-training tasks. Extensive experiments show that UNITER achieves new state of the art across six V+L tasks (over nine datasets), including Visual Question Answering, Image-Text Retrieval, Referring Expression Comprehension, Visual Commonsense Reasoning, Visual Entailment, and NLVR$^2$. Code is available at https://github.com/ChenRocks/UNITER. | ['Yu Cheng', 'Yen-Chun Chen', 'Ahmed El Kholy', 'Linjie Li', 'Licheng Yu', 'Jingjing Liu', 'Zhe Gan', 'Faisal Ahmed'] | 2019-09-25 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/7093_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123750103.pdf | eccv-2020-8 | ['zero-shot-cross-modal-retrieval', 'visual-commonsense-reasoning', 'visual-entailment'] | ['miscellaneous', 'reasoning', 'reasoning'] | [ 4.50695723e-01 2.11754605e-01 -3.54839534e-01 -5.21988213e-01
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b5d3da2a-a82c-4068-be42-b46b7445e64a | dsac-differentiable-ransac-for-camera | 1611.05705 | null | http://arxiv.org/abs/1611.05705v4 | http://arxiv.org/pdf/1611.05705v4.pdf | DSAC - Differentiable RANSAC for Camera Localization | RANSAC is an important algorithm in robust optimization and a central
building block for many computer vision applications. In recent years,
traditionally hand-crafted pipelines have been replaced by deep learning
pipelines, which can be trained in an end-to-end fashion. However, RANSAC has
so far not been used as part of such deep learning pipelines, because its
hypothesis selection procedure is non-differentiable. In this work, we present
two different ways to overcome this limitation. The most promising approach is
inspired by reinforcement learning, namely to replace the deterministic
hypothesis selection by a probabilistic selection for which we can derive the
expected loss w.r.t. to all learnable parameters. We call this approach DSAC,
the differentiable counterpart of RANSAC. We apply DSAC to the problem of
camera localization, where deep learning has so far failed to improve on
traditional approaches. We demonstrate that by directly minimizing the expected
loss of the output camera poses, robustly estimated by RANSAC, we achieve an
increase in accuracy. In the future, any deep learning pipeline can use DSAC as
a robust optimization component. | ['Frank Michel', 'Jamie Shotton', 'Eric Brachmann', 'Carsten Rother', 'Stefan Gumhold', 'Sebastian Nowozin', 'Alexander Krull'] | 2016-11-17 | dsac-differentiable-ransac-for-camera-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Brachmann_DSAC_-_Differentiable_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Brachmann_DSAC_-_Differentiable_CVPR_2017_paper.pdf | cvpr-2017-7 | ['camera-localization'] | ['computer-vision'] | [-1.17501587e-01 -2.23781943e-01 1.62334248e-01 -4.47497547e-01
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2c137b6d-eee3-4d31-b536-00c440ffa8df | on-learning-latent-models-with-multi-instance | 2306.13796 | null | https://arxiv.org/abs/2306.13796v1 | https://arxiv.org/pdf/2306.13796v1.pdf | On Learning Latent Models with Multi-Instance Weak Supervision | We consider a weakly supervised learning scenario where the supervision signal is generated by a transition function $\sigma$ of labels associated with multiple input instances. We formulate this problem as \emph{multi-instance Partial Label Learning (multi-instance PLL)}, which is an extension to the standard PLL problem. Our problem is met in different fields, including latent structural learning and neuro-symbolic integration. Despite the existence of many learning techniques, limited theoretical analysis has been dedicated to this problem. In this paper, we provide the first theoretical study of multi-instance PLL with possibly an unknown transition $\sigma$. Our main contributions are as follows. Firstly, we propose a necessary and sufficient condition for the learnability of the problem. This condition non-trivially generalizes and relaxes the existing small ambiguity degree in the PLL literature, since we allow the transition to be deterministic. Secondly, we derive Rademacher-style error bounds based on a top-$k$ surrogate loss that is widely used in the neuro-symbolic literature. Furthermore, we conclude with empirical experiments for learning under unknown transitions. The empirical results align with our theoretical findings; however, they also expose the issue of scalability in the weak supervision literature. | ['Dan Roth', 'Efi Tsamoura', 'Kaifu Wang'] | 2023-06-23 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 7.14256108e-01 5.64841688e-01 -5.71854889e-01 -3.49804252e-01
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15311f0d-78c0-4cbc-b289-2943f3884f27 | active-uncertainty-reduction-for-safe-and | 2302.00171 | null | https://arxiv.org/abs/2302.00171v1 | https://arxiv.org/pdf/2302.00171v1.pdf | Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach | The ability to accurately predict the opponent's behavior is central to the safety and efficiency of robotic systems in interactive settings, such as human-robot interaction and multi-robot teaming tasks. Unfortunately, robots often lack access to key information on which these predictions may hinge, such as opponent's goals, attention, and willingness to cooperate. Dual control theory addresses this challenge by treating unknown parameters of a predictive model as hidden states and inferring their values at runtime using information gathered during system operation. While able to optimally and automatically trade off exploration and exploitation, dual control is computationally intractable for general interactive motion planning. In this paper, we present a novel algorithmic approach to enable active uncertainty reduction for interactive motion planning based on the implicit dual control paradigm. Our approach relies on sampling-based approximation of stochastic dynamic programming, leading to a model predictive control problem. The resulting policy is shown to preserve the dual control effect for a broad class of predictive models with both continuous and categorical uncertainty. To ensure the safe operation of the interacting agents, we leverage a supervisory control scheme, oftentimes referred to as ``shielding'', which overrides the ego agent's dual control policy with a safety fallback strategy when a safety-critical event is imminent. We then augment the dual control framework with an improved variant of the recently proposed shielding-aware robust planning scheme, which proactively balances the nominal planning performance with the risk of high-cost emergency maneuvers triggered by low-probability opponent's behaviors. We demonstrate the efficacy of our approach with both simulated driving examples and hardware experiments using 1/10 scale autonomous vehicles. | ['Jaime F. Fisac', 'Sangjae Bae', 'David Isele', 'Haimin Hu'] | 2023-02-01 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 3.70563149e-01 7.96489179e-01 -4.35651988e-01 -8.05870816e-02
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0659acfa-2d7e-4ac9-995f-3f71ac98f869 | singaug-data-augmentation-for-singing-voice | 2203.17001 | null | https://arxiv.org/abs/2203.17001v2 | https://arxiv.org/pdf/2203.17001v2.pdf | SingAug: Data Augmentation for Singing Voice Synthesis with Cycle-consistent Training Strategy | Deep learning based singing voice synthesis (SVS) systems have been demonstrated to flexibly generate singing with better qualities, compared to conventional statistical parametric based methods. However, neural systems are generally data-hungry and have difficulty to reach reasonable singing quality with limited public available training data. In this work, we explore different data augmentation methods to boost the training of SVS systems, including several strategies customized to SVS based on pitch augmentation and mix-up augmentation. To further stabilize the training, we introduce the cycle-consistent training strategy. Extensive experiments on two public singing databases demonstrate that our proposed augmentation methods and the stabilizing training strategy can significantly improve the performance on both objective and subjective evaluations. | ['Qin Jin', 'Shinji Watanabe', 'Tao Qian', 'Jiatong Shi', 'Shuai Guo'] | 2022-03-31 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-0.05403638 -0.35265535 -0.25203028 -0.1389206 -0.9976493 -0.4579129
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b8645337-39d0-49e3-9d1f-243fc3665379 | knowledge-enriched-transformer-for-emotion | 1909.10681 | null | https://arxiv.org/abs/1909.10681v2 | https://arxiv.org/pdf/1909.10681v2.pdf | Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations | Messages in human conversations inherently convey emotions. The task of detecting emotions in textual conversations leads to a wide range of applications such as opinion mining in social networks. However, enabling machines to analyze emotions in conversations is challenging, partly because humans often rely on the context and commonsense knowledge to express emotions. In this paper, we address these challenges by proposing a Knowledge-Enriched Transformer (KET), where contextual utterances are interpreted using hierarchical self-attention and external commonsense knowledge is dynamically leveraged using a context-aware affective graph attention mechanism. Experiments on multiple textual conversation datasets demonstrate that both context and commonsense knowledge are consistently beneficial to the emotion detection performance. In addition, the experimental results show that our KET model outperforms the state-of-the-art models on most of the tested datasets in F1 score. | ['Chunyan Miao', 'Di Wang', 'Peixiang Zhong'] | 2019-09-24 | knowledge-enriched-transformer-for-emotion-1 | https://aclanthology.org/D19-1016 | https://aclanthology.org/D19-1016.pdf | ijcnlp-2019-11 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 3.33286196e-01 2.74252027e-01 -5.70725277e-02 -6.35539830e-01
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b41e3a2e-2d47-4f40-a9f7-707c63a7ad96 | dagad-data-augmentation-for-graph-anomaly | 2210.09766 | null | https://arxiv.org/abs/2210.09766v1 | https://arxiv.org/pdf/2210.09766v1.pdf | DAGAD: Data Augmentation for Graph Anomaly Detection | Graph anomaly detection in this paper aims to distinguish abnormal nodes that behave differently from the benign ones accounting for the majority of graph-structured instances. Receiving increasing attention from both academia and industry, yet existing research on this task still suffers from two critical issues when learning informative anomalous behavior from graph data. For one thing, anomalies are usually hard to capture because of their subtle abnormal behavior and the shortage of background knowledge about them, which causes severe anomalous sample scarcity. Meanwhile, the overwhelming majority of objects in real-world graphs are normal, bringing the class imbalance problem as well. To bridge the gaps, this paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs, equipped with three specially designed modules: 1) an information fusion module employing graph neural network encoders to learn representations, 2) a graph data augmentation module that fertilizes the training set with generated samples, and 3) an imbalance-tailored learning module to discriminate the distributions of the minority (anomalous) and majority (normal) classes. A series of experiments on three datasets prove that DAGAD outperforms ten state-of-the-art baseline detectors concerning various mostly-used metrics, together with an extensive ablation study validating the strength of our proposed modules. | ['Charu C. Aggarwal', 'Quan Z. Sheng', 'Hao Peng', 'Chuan Zhou', 'Amin Beheshti', 'Shan Xue', 'Jian Yang', 'Jia Wu', 'Xiaoxiao Ma', 'Fanzhen Liu'] | 2022-10-18 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [ 4.37244296e-01 5.73095202e-01 -2.40668610e-01 -3.03918988e-01
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102ba60d-bbfb-4c30-86de-9fa75625fcbb | sia-ftp-a-spoken-instruction-aware-flight | 2305.01661 | null | https://arxiv.org/abs/2305.01661v1 | https://arxiv.org/pdf/2305.01661v1.pdf | SIA-FTP: A Spoken Instruction Aware Flight Trajectory Prediction Framework | Ground-air negotiation via speech communication is a vital prerequisite for ensuring safety and efficiency in air traffic control (ATC) operations. However, with the increase in traffic flow, incorrect instructions caused by human factors bring a great threat to ATC safety. Existing flight trajectory prediction (FTP) approaches primarily rely on the flight status of historical trajectory, leading to significant delays in the prediction of real-time maneuvering instruction, which is not conducive to conflict detection. A major reason is that spoken instructions and flight trajectories are presented in different modalities in the current air traffic control (ATC) system, bringing great challenges to considering the maneuvering instruction in the FTP tasks. In this paper, a spoken instruction-aware FTP framework, called SIA-FTP, is innovatively proposed to support high-maneuvering FTP tasks by incorporating instant spoken instruction. To address the modality gap and minimize the data requirements, a 3-stage learning paradigm is proposed to implement the SIA-FTP framework in a progressive manner, including trajectory-based FTP pretraining, intent-oriented instruction embedding learning, and multi-modal finetuning. Specifically, the FTP model and the instruction embedding with maneuvering semantics are pre-trained using volumes of well-resourced trajectory and text data in the 1st and 2nd stages. In succession, a multi-modal fusion strategy is proposed to incorporate the pre-trained instruction embedding into the FTP model and integrate the two pre-trained networks into a joint model. Finally, the joint model is finetuned using the limited trajectory-instruction data to enhance the FTP performance within maneuvering instruction scenarios. The experimental results demonstrated that the proposed framework presents an impressive performance improvement in high-maneuvering scenarios. | ['Yi Lin', 'Jianwei Zhang', 'Dongyue Guo'] | 2023-05-02 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [ 1.98112205e-01 -9.92777124e-02 -2.18320295e-01 -3.93377602e-01
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842d5267-2091-4cdb-b467-d30b492c1cd4 | open-ended-reinforcement-learning-with-neural | 2202.08266 | null | https://arxiv.org/abs/2202.08266v2 | https://arxiv.org/pdf/2202.08266v2.pdf | Open-Ended Reinforcement Learning with Neural Reward Functions | Inspired by the great success of unsupervised learning in Computer Vision and Natural Language Processing, the Reinforcement Learning community has recently started to focus more on unsupervised discovery of skills. Most current approaches, like DIAYN or DADS, optimize some form of mutual information objective. We propose a different approach that uses reward functions encoded by neural networks. These are trained iteratively to reward more complex behavior. In high-dimensional robotic environments our approach learns a wide range of interesting skills including front-flips for Half-Cheetah and one-legged running for Humanoid. In the pixel-based Montezuma's Revenge environment our method also works with minimal changes and it learns complex skills that involve interacting with items and visiting diverse locations. The implementation of our approach can be found in this link: https://github.com/amujika/Open-Ended-Reinforcement-Learning-with-Neural-Reward-Functions. | ['Asier Mujika', 'Robert Meier'] | 2022-02-16 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-1.84796214e-01 2.13084042e-01 6.48702681e-02 -5.29112875e-01
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dd360ea4-de23-4e08-9af8-b1d710c6af43 | efficient-implementation-of-a-multi-layer | 2305.19468 | null | https://arxiv.org/abs/2305.19468v1 | https://arxiv.org/pdf/2305.19468v1.pdf | Efficient Implementation of a Multi-Layer Gradient-Free Online-Trainable Spiking Neural Network on FPGA | This paper presents an efficient hardware implementation of the recently proposed Optimized Deep Event-driven Spiking Neural Network Architecture (ODESA). ODESA is the first network to have end-to-end multi-layer online local supervised training without using gradients and has the combined adaptation of weights and thresholds in an efficient hierarchical structure. This research shows that the network architecture and the online training of weights and thresholds can be implemented efficiently on a large scale in hardware. The implementation consists of a multi-layer Spiking Neural Network (SNN) and individual training modules for each layer that enable online self-learning without using back-propagation. By using simple local adaptive selection thresholds, a Winner-Takes-All (WTA) constraint on each layer, and a modified weight update rule that is more amenable to hardware, the trainer module allocates neuronal resources optimally at each layer without having to pass high-precision error measurements across layers. All elements in the system, including the training module, interact using event-based binary spikes. The hardware-optimized implementation is shown to preserve the performance of the original algorithm across multiple spatial-temporal classification problems with significantly reduced hardware requirements. | ['Saeed Afshar', 'Andrew Wabnitz', 'André van Schaik', 'Yeshwanth Bethi', 'Ali Mehrabi'] | 2023-05-31 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 2.46704757e-01 -3.22903007e-01 4.36308503e-01 -5.71266413e-01
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e2ea0b9c-1d03-49ff-99df-7841a8fb22fd | detrs-beat-yolos-on-real-time-object | 2304.08069 | null | https://arxiv.org/abs/2304.08069v2 | https://arxiv.org/pdf/2304.08069v2.pdf | DETRs Beat YOLOs on Real-time Object Detection | Recently, end-to-end transformer-based detectors~(DETRs) have achieved remarkable performance. However, the issue of the high computational cost of DETRs has not been effectively addressed, limiting their practical application and preventing them from fully exploiting the benefits of no post-processing, such as non-maximum suppression (NMS). In this paper, we first analyze the influence of NMS in modern real-time object detectors on inference speed, and establish an end-to-end speed benchmark. To avoid the inference delay caused by NMS, we propose a Real-Time DEtection TRansformer (RT-DETR), the first real-time end-to-end object detector to our best knowledge. Specifically, we design an efficient hybrid encoder to efficiently process multi-scale features by decoupling the intra-scale interaction and cross-scale fusion, and propose IoU-aware query selection to improve the initialization of object queries. In addition, our proposed detector supports flexibly adjustment of the inference speed by using different decoder layers without the need for retraining, which facilitates the practical application of real-time object detectors. Our RT-DETR-L achieves 53.0% AP on COCO val2017 and 114 FPS on T4 GPU, while RT-DETR-X achieves 54.8% AP and 74 FPS, outperforming all YOLO detectors of the same scale in both speed and accuracy. Furthermore, our RT-DETR-R50 achieves 53.1% AP and 108 FPS, outperforming DINO-Deformable-DETR-R50 by 2.2% AP in accuracy and by about 21 times in FPS. ource code and pre-trained models are available at https://github.com/lyuwenyu/RT-DETR. | ['Yi Liu', 'Qingqing Dang', 'Yuning Du', 'Cheng Cui', 'Jinman Wei', 'Guanzhong Wang', 'Yian Zhao', 'Shangliang Xu', 'Wenyu Lv'] | 2023-04-17 | null | null | null | null | ['2d-object-detection', 'real-time-object-detection'] | ['computer-vision', 'computer-vision'] | [-1.59947112e-01 -4.29477662e-01 -6.48126900e-02 -2.28004456e-01
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c7e37baa-0db7-4028-a48c-68d4fc476322 | reinforcement-learning-with-simple-sequence | 2305.17109 | null | https://arxiv.org/abs/2305.17109v1 | https://arxiv.org/pdf/2305.17109v1.pdf | Reinforcement Learning with Simple Sequence Priors | Everything else being equal, simpler models should be preferred over more complex ones. In reinforcement learning (RL), simplicity is typically quantified on an action-by-action basis -- but this timescale ignores temporal regularities, like repetitions, often present in sequential strategies. We therefore propose an RL algorithm that learns to solve tasks with sequences of actions that are compressible. We explore two possible sources of simple action sequences: Sequences that can be learned by autoregressive models, and sequences that are compressible with off-the-shelf data compression algorithms. Distilling these preferences into sequence priors, we derive a novel information-theoretic objective that incentivizes agents to learn policies that maximize rewards while conforming to these priors. We show that the resulting RL algorithm leads to faster learning, and attains higher returns than state-of-the-art model-free approaches in a series of continuous control tasks from the DeepMind Control Suite. These priors also produce a powerful information-regularized agent that is robust to noisy observations and can perform open-loop control. | ['Eric Schulz', 'Marcel Binz', 'Peter Dayan', 'Noémi Éltető', 'Tankred Saanum'] | 2023-05-26 | null | null | null | null | ['continuous-control', 'data-compression'] | ['playing-games', 'time-series'] | [ 2.80150741e-01 2.51790255e-01 -6.51045918e-01 -2.02658951e-01
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d6e4b7af-206c-4def-992a-e489cf43ff6f | rank-one-network-an-effective-framework-for | 2011.12610 | null | https://arxiv.org/abs/2011.12610v1 | https://arxiv.org/pdf/2011.12610v1.pdf | Rank-One Network: An Effective Framework for Image Restoration | The principal rank-one (RO) components of an image represent the self-similarity of the image, which is an important property for image restoration. However, the RO components of a corrupted image could be decimated by the procedure of image denoising. We suggest that the RO property should be utilized and the decimation should be avoided in image restoration. To achieve this, we propose a new framework comprised of two modules, i.e., the RO decomposition and RO reconstruction. The RO decomposition is developed to decompose a corrupted image into the RO components and residual. This is achieved by successively applying RO projections to the image or its residuals to extract the RO components. The RO projections, based on neural networks, extract the closest RO component of an image. The RO reconstruction is aimed to reconstruct the important information, respectively from the RO components and residual, as well as to restore the image from this reconstructed information. Experimental results on four tasks, i.e., noise-free image super-resolution (SR), realistic image SR, gray-scale image denoising, and color image denoising, show that the method is effective and efficient for image restoration, and it delivers superior performance for realistic image SR and color image denoising. | ['Xiahai Zhuang', 'Shangqi Gao'] | 2020-11-25 | null | null | null | null | ['color-image-denoising'] | ['computer-vision'] | [ 7.33594120e-01 -4.81902689e-01 4.90286827e-01 -5.92417782e-03
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3525e417-721d-4964-a901-8132bbfb8e9e | clipmasterprints-fooling-contrastive-language | 2307.03798 | null | https://arxiv.org/abs/2307.03798v1 | https://arxiv.org/pdf/2307.03798v1.pdf | CLIPMasterPrints: Fooling Contrastive Language-Image Pre-training Using Latent Variable Evolution | Models leveraging both visual and textual data such as Contrastive Language-Image Pre-training (CLIP), are increasingly gaining importance. In this work, we show that despite their versatility, such models are vulnerable to what we refer to as fooling master images. Fooling master images are capable of maximizing the confidence score of a CLIP model for a significant number of widely varying prompts, while being unrecognizable for humans. We demonstrate how fooling master images can be mined by searching the latent space of generative models by means of an evolution strategy or stochastic gradient descent. We investigate the properties of the mined fooling master images, and find that images trained on a small number of image captions potentially generalize to a much larger number of semantically related captions. Further, we evaluate two possible mitigation strategies and find that vulnerability to fooling master examples is closely related to a modality gap in contrastive pre-trained multi-modal networks. From the perspective of vulnerability to off-manifold attacks, we therefore argue for the mitigation of modality gaps in CLIP and related multi-modal approaches. Source code and mined CLIPMasterPrints are available at https://github.com/matfrei/CLIPMasterPrints. | ['Sebastian Risi', 'Anders Sundnes Løvlie', 'Peter Kun', 'Matthias Freiberger'] | 2023-07-07 | null | null | null | null | ['image-captioning'] | ['computer-vision'] | [ 4.73032504e-01 1.06758378e-01 -1.04912175e-02 -2.08758876e-01
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38a60d84-3a70-4d4f-8e8e-f5ab10a55b10 | the-halliday-centre-tagger-an-online-platform | null | null | https://aclanthology.org/L14-1593 | https://aclanthology.org/L14-1593.pdf | The Halliday Centre Tagger: An Online Platform for Semi-automatic Text Annotation and Analysis | This paper reports the latest development of The Halliday Centre Tagger (the Tagger), an online platform provided with semi-automatic features to facilitate text annotation and analysis. The Tagger is featured for its web-based architecture with all functionalities and file storage space provided online, and a theory-neutral design where users can define their own labels for annotating various kinds of linguistic information. The Tagger is currently optimized for text annotation of Systemic Functional Grammar (SFG), providing by default a pre-defined set of SFG grammatical features, and the function of automatic identification of process types for English verbs. Apart from annotation, the Tagger also offers the features of visualization and summarization to aid text analysis. The visualization feature combines and illustrates multi-dimensional layers of annotation in a unified way of presentation, while the summarization feature categorizes annotated entries according to different SFG systems, i.e., transitivity, theme, logical-semantic relations, etc. Such features help users identify grammatical patterns in an annotated text. | ['Billy T. M. Wong', 'Hengbin Yan', 'Jonathan J. Webster', 'Ian C. Chow'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['text-annotation'] | ['natural-language-processing'] | [-2.88450837e-01 5.30714869e-01 -5.39962314e-02 -3.49737376e-01
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bbadc7ef-f774-4ff1-bd63-a8f1628cc4cf | securing-optimized-code-against-power-side | 2207.02614 | null | https://arxiv.org/abs/2207.02614v2 | https://arxiv.org/pdf/2207.02614v2.pdf | Securing Optimized Code Against Power Side Channels | Side-channel attacks impose a serious threat to cryptographic algorithms, including widely employed ones, such as AES and RSA. These attacks take advantage of the algorithm implementation in hardware or software to extract secret information via side channels. Software masking is a mitigation approach against power side-channel attacks aiming at hiding the secret-revealing dependencies from the power footprint of a vulnerable implementation. However, this type of software mitigation often depends on general-purpose compilers, which do not preserve non-functional properties. Moreover, microarchitectural features, such as the memory bus and register reuse, may also leak secret information. These abstractions are not visible at the high-level implementation of the program. Instead, they are decided at compile time. To remedy these problems, security engineers often sacrifice code efficiency by turning off compiler optimization and/or performing local, post-compilation transformations. This paper proposes Secure by Construction Code Generation (SecCG), a constraint-based compiler approach that generates optimized yet secure against power side channels code. SecCG controls the quality of the mitigated program by efficiently searching the best possible low-level implementation according to a processor cost model. In our experiments with twelve masked cryptographic functions up to 100 lines of code on Mips32 and ARM Thumb, SecCG speeds up the generated code from 75% to 8 times compared to non-optimized secure code with an overhead of up to 7% compared to non-secure optimized code at the expense of a high compilation cost. In summary, this paper proposes a formal model to generate power side channel free low-level code. | ['Panagiotis Papadimitratos', 'Elena Troubitsyna', 'Roberto Castañeda Lozano', 'Rodothea Myrsini Tsoupidi'] | 2022-07-06 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [ 3.56663525e-01 -4.31333780e-02 -4.87861633e-01 1.04238968e-02
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aa586f97-239e-4f2c-bf70-0362564427b3 | a-real-time-junk-food-recognition-system | 2203.11836 | null | https://arxiv.org/abs/2203.11836v1 | https://arxiv.org/pdf/2203.11836v1.pdf | A Real-time Junk Food Recognition System based on Machine Learning | $ $As a result of bad eating habits, humanity may be destroyed. People are constantly on the lookout for tasty foods, with junk foods being the most common source. As a consequence, our eating patterns are shifting, and we're gravitating toward junk food more than ever, which is bad for our health and increases our risk of acquiring health problems. Machine learning principles are applied in every aspect of our lives, and one of them is object recognition via image processing. However, because foods vary in nature, this procedure is crucial, and traditional methods like ANN, SVM, KNN, PLS etc., will result in a low accuracy rate. All of these issues were defeated by the Deep Neural Network. In this work, we created a fresh dataset of 10,000 data points from 20 junk food classifications to try to recognize junk foods. All of the data in the data set was gathered using the Google search engine, which is thought to be one-of-a-kind in every way. The goal was achieved using Convolution Neural Network (CNN) technology, which is well-known for image processing. We achieved a 98.05\% accuracy rate throughout the research, which was satisfactory. In addition, we conducted a test based on a real-life event, and the outcome was extraordinary. Our goal is to advance this research to the next level, so that it may be applied to a future study. Our ultimate goal is to create a system that would encourage people to avoid eating junk food and to be health-conscious. \keywords{ Machine Learning \and junk food \and object detection \and YOLOv3 \and custom food dataset.} | ['Md. Kishor Morol', 'Niloy Kumar', 'Nila Maitra Chaity', 'Sabikunnahar Talukder Pyaasa', 'Takitazwar Parthib', 'Sirajum Munira Shifat'] | 2022-03-22 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [-2.22838536e-01 -4.85672027e-01 -3.66302520e-01 -4.05229717e-01
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9b2e2a5e-a880-4108-8511-adee5406a93b | a-hierarchical-architecture-for-optimal-unit | 2306.16119 | null | https://arxiv.org/abs/2306.16119v1 | https://arxiv.org/pdf/2306.16119v1.pdf | A Hierarchical Architecture for Optimal Unit Commitment and Control of an Ensemble of Steam Generators | A hierarchical architecture for the optimal management of an ensemble of steam generators is presented. The subsystems are coordinated by a multilayer scheme for jointly sustaining a common load. The high level optimizes the load allocation and the generator schedule, considering activation dynamics by a hybrid model. At the medium level, a robust tube-based model predictive control (MPC) tracks a time-varying demand using a centralized--but aggregate--model, whose order does not scale with the number of subsystems. A nonlinear optimization, at medium level, addresses MPC infeasibility due to abrupt changes of ensemble configuration. Low-level decentralized controllers stabilize the generators. This control scheme enables the dynamical modification of the ensemble configuration and plug and play operations. Simulations demonstrate the approach potentialities. | ['Andrea Ballarino', 'Marcello Farina', 'Stefano Spinelli'] | 2023-06-28 | null | null | null | null | ['management'] | ['miscellaneous'] | [-4.07002896e-01 3.56228113e-01 1.39194995e-01 4.84312117e-01
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96ff4e25-4cbf-4b0b-89a9-b4d93ccf736c | ad-hoc-table-retrieval-using-semantic | 1802.06159 | null | https://arxiv.org/abs/1802.06159v3 | https://arxiv.org/pdf/1802.06159v3.pdf | Ad Hoc Table Retrieval using Semantic Similarity | We introduce and address the problem of ad hoc table retrieval: answering a keyword query with a ranked list of tables. This task is not only interesting on its own account, but is also being used as a core component in many other table-based information access scenarios, such as table completion or table mining. The main novel contribution of this work is a method for performing semantic matching between queries and tables. Specifically, we (i) represent queries and tables in multiple semantic spaces (both discrete sparse and continuous dense vector representations) and (ii) introduce various similarity measures for matching those semantic representations. We consider all possible combinations of semantic representations and similarity measures and use these as features in a supervised learning model. Using a purpose-built test collection based on Wikipedia tables, we demonstrate significant and substantial improvements over a state-of-the-art baseline. | ['Krisztian Balog', 'Shuo Zhang'] | 2018-02-16 | null | null | null | null | ['table-retrieval'] | ['natural-language-processing'] | [ 2.59206146e-01 5.59291393e-02 -4.86397803e-01 -4.67806667e-01
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f601665a-3d9f-472b-99bf-fea7f18c0c55 | scrnet-a-retinex-structure-based-low-light | 2305.08053 | null | https://arxiv.org/abs/2305.08053v1 | https://arxiv.org/pdf/2305.08053v1.pdf | SCRNet: a Retinex Structure-based Low-light Enhancement Model Guided by Spatial Consistency | Images captured under low-light conditions are often plagued by several challenges, including diminished contrast, increased noise, loss of fine details, and unnatural color reproduction. These factors can significantly hinder the performance of computer vision tasks such as object detection and image segmentation. As a result, improving the quality of low-light images is of paramount importance for practical applications in the computer vision domain.To effectively address these challenges, we present a novel low-light image enhancement model, termed Spatial Consistency Retinex Network (SCRNet), which leverages the Retinex-based structure and is guided by the principle of spatial consistency.Specifically, our proposed model incorporates three levels of consistency: channel level, semantic level, and texture level, inspired by the principle of spatial consistency.These levels of consistency enable our model to adaptively enhance image features, ensuring more accurate and visually pleasing results.Extensive experimental evaluations on various low-light image datasets demonstrate that our proposed SCRNet outshines existing state-of-the-art methods, highlighting the potential of SCRNet as an effective solution for enhancing low-light images. | ['Shenghui Zhong', 'Yiqing Shen', 'Miao Zhang'] | 2023-05-14 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 5.44287384e-01 -5.15770495e-01 -1.06228486e-01 -2.22183973e-01
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5343fad1-7695-4478-b335-2290d9736e5a | masked-language-model-based-textual | 2304.08767 | null | https://arxiv.org/abs/2304.08767v2 | https://arxiv.org/pdf/2304.08767v2.pdf | Masked Language Model Based Textual Adversarial Example Detection | Adversarial attacks are a serious threat to the reliable deployment of machine learning models in safety-critical applications. They can misguide current models to predict incorrectly by slightly modifying the inputs. Recently, substantial work has shown that adversarial examples tend to deviate from the underlying data manifold of normal examples, whereas pre-trained masked language models can fit the manifold of normal NLP data. To explore how to use the masked language model in adversarial detection, we propose a novel textual adversarial example detection method, namely Masked Language Model-based Detection (MLMD), which can produce clearly distinguishable signals between normal examples and adversarial examples by exploring the changes in manifolds induced by the masked language model. MLMD features a plug and play usage (i.e., no need to retrain the victim model) for adversarial defense and it is agnostic to classification tasks, victim model's architectures, and to-be-defended attack methods. We evaluate MLMD on various benchmark textual datasets, widely studied machine learning models, and state-of-the-art (SOTA) adversarial attacks (in total $3*4*4 = 48$ settings). Experimental results show that MLMD can achieve strong performance, with detection accuracy up to 0.984, 0.967, and 0.901 on AG-NEWS, IMDB, and SST-2 datasets, respectively. Additionally, MLMD is superior, or at least comparable to, the SOTA detection defenses in detection accuracy and F1 score. Among many defenses based on the off-manifold assumption of adversarial examples, this work offers a new angle for capturing the manifold change. The code for this work is openly accessible at \url{https://github.com/mlmddetection/MLMDdetection}. | ['Leo Yu Zhang', 'Shengshan Hu', 'Yanjun Zhang', 'Xufei Zheng', 'Qi Zhong', 'Zhaoxi Zhang', 'Xiaomei Zhang'] | 2023-04-18 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 8.06899071e-02 -3.21238972e-02 -1.29255489e-01 3.24037448e-02
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b7a24046-566b-4d63-a43d-0e91c3816c8e | latent-space-laplacian-pyramids-for | 1912.06466 | null | https://arxiv.org/abs/1912.06466v1 | https://arxiv.org/pdf/1912.06466v1.pdf | Latent-Space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds | Constructing high-quality generative models for 3D shapes is a fundamental task in computer vision with diverse applications in geometry processing, engineering, and design. Despite the recent progress in deep generative modelling, synthesis of finely detailed 3D surfaces, such as high-resolution point clouds, from scratch has not been achieved with existing approaches. In this work, we propose to employ the latent-space Laplacian pyramid representation within a hierarchical generative model for 3D point clouds. We combine the recently proposed latent-space GAN and Laplacian GAN architectures to form a multi-scale model capable of generating 3D point clouds at increasing levels of detail. Our evaluation demonstrates that our model outperforms the existing generative models for 3D point clouds. | ['Vage Egiazarian', 'Savva Ignatyev', 'Youyi Zheng', 'Oleg Voynov', 'Andrey Kravchenko', 'Alexey Artemov', 'Luiz Velho', 'Evgeny Burnaev'] | 2019-12-13 | null | null | null | null | ['generating-3d-point-clouds'] | ['computer-vision'] | [ 9.78346467e-02 7.54866973e-02 4.45617527e-01 -1.46671236e-01
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81493768-e023-4a97-9eb8-0db0193f207c | leveraging-world-knowledge-in-implicit-hate | 2212.14100 | null | https://arxiv.org/abs/2212.14100v1 | https://arxiv.org/pdf/2212.14100v1.pdf | Leveraging World Knowledge in Implicit Hate Speech Detection | While much attention has been paid to identifying explicit hate speech, implicit hateful expressions that are disguised in coded or indirect language are pervasive and remain a major challenge for existing hate speech detection systems. This paper presents the first attempt to apply Entity Linking (EL) techniques to both explicit and implicit hate speech detection, where we show that such real world knowledge about entity mentions in a text does help models better detect hate speech, and the benefit of adding it into the model is more pronounced when explicit entity triggers (e.g., rally, KKK) are present. We also discuss cases where real world knowledge does not add value to hate speech detection, which provides more insights into understanding and modeling the subtleties of hate speech. | ['Jessica Lin'] | 2022-12-28 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-1.33131325e-01 2.28043109e-01 -3.72197092e-01 5.59253506e-02
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9c722f4d-450d-4652-a37d-a1b90d692e80 | learning-to-execute-or-ask-clarification | null | null | https://openreview.net/forum?id=6c8_5EyZYxH | https://openreview.net/pdf?id=6c8_5EyZYxH | Learning to execute or ask clarification questions | Collaborative tasks are ubiquitous activities where a form of communication is required in order to reach a joint goal. Collaborative building is one of such tasks. To this end, we wish to develop an intelligent builder agent in a simulated building environment (Minecraft) that can build whatever users wish to build by just talking to the agent. However, in order to achieve this goal, such agents need to be able to take the initiative by asking clarification questions when further information is needed. Existing work on Minecraft Corpus Dataset only learned to execute instructions neglecting the importance of asking for clarifications. In this paper, we extend the Minecraft Corpus Dataset by annotating all builder utterances into eight types, including clarification questions, and propose a new builder agent model capable of determining when to ask or execute instructions. Experimental results show that our model achieves state-of-the-art performance on the collaborative building task with a substantial improvement. We also provide baselines for the new tasks, learning to ask and the joint tasks, which consists in solving both collaborating building and learning to ask tasks jointly. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['learning-to-execute'] | ['computer-code'] | [ 8.72929096e-02 3.82134050e-01 4.41696197e-01 -9.11677063e-01
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3168b632-a7dd-471c-bcf0-57e90e518b4a | optimism-and-adaptivity-in-policy | 2306.10587 | null | https://arxiv.org/abs/2306.10587v1 | https://arxiv.org/pdf/2306.10587v1.pdf | Optimism and Adaptivity in Policy Optimization | We work towards a unifying paradigm for accelerating policy optimization methods in reinforcement learning (RL) through \emph{optimism} \& \emph{adaptivity}. Leveraging the deep connection between policy iteration and policy gradient methods, we recast seemingly unrelated policy optimization algorithms as the repeated application of two interleaving steps (i) an \emph{optimistic policy improvement operator} maps a prior policy $\pi_t$ to a hypothesis $\pi_{t+1}$ using a \emph{gradient ascent prediction}, followed by (ii) a \emph{hindsight adaptation} of the optimistic prediction based on a partial evaluation of the performance of $\pi_{t+1}$. We use this shared lens to jointly express other well-known algorithms, including soft and optimistic policy iteration, natural actor-critic methods, model-based policy improvement based on forward search, and meta-learning algorithms. By doing so, we shed light on collective theoretical properties related to acceleration via optimism \& adaptivity. Building on these insights, we design an \emph{adaptive \& optimistic policy gradient} algorithm via meta-gradient learning, and empirically highlight several design choices pertaining to optimism, in an illustrative task. | ['Sebastian Flennerhag', 'Doina Precup', 'Arthur Guez', 'Tom Zahavy', 'Veronica Chelu'] | 2023-06-18 | null | null | null | null | ['meta-learning', 'policy-gradient-methods'] | ['methodology', 'methodology'] | [ 1.05404615e-01 4.19539034e-01 -6.48109674e-01 -9.08995047e-02
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2a342b98-df1d-4c37-820f-587abd4bb1fa | convergent-bregman-plug-and-play-image | 2306.03466 | null | https://arxiv.org/abs/2306.03466v1 | https://arxiv.org/pdf/2306.03466v1.pdf | Convergent Bregman Plug-and-Play Image Restoration for Poisson Inverse Problems | Plug-and-Play (PnP) methods are efficient iterative algorithms for solving ill-posed image inverse problems. PnP methods are obtained by using deep Gaussian denoisers instead of the proximal operator or the gradient-descent step within proximal algorithms. Current PnP schemes rely on data-fidelity terms that have either Lipschitz gradients or closed-form proximal operators, which is not applicable to Poisson inverse problems. Based on the observation that the Gaussian noise is not the adequate noise model in this setting, we propose to generalize PnP using theBregman Proximal Gradient (BPG) method. BPG replaces the Euclidean distance with a Bregman divergence that can better capture the smoothness properties of the problem. We introduce the Bregman Score Denoiser specifically parametrized and trained for the new Bregman geometry and prove that it corresponds to the proximal operator of a nonconvex potential. We propose two PnP algorithms based on the Bregman Score Denoiser for solving Poisson inverse problems. Extending the convergence results of BPG in the nonconvex settings, we show that the proposed methods converge, targeting stationary points of an explicit global functional. Experimental evaluations conducted on various Poisson inverse problems validate the convergence results and showcase effective restoration performance. | ['Nicolas Papadakis', 'Arthur Leclaire', 'Ulugbek Kamilov', 'Samuel Hurault'] | 2023-06-06 | null | null | null | null | ['image-restoration'] | ['computer-vision'] | [ 2.43851095e-02 5.28079756e-02 2.12654039e-01 -4.26175371e-02
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ea10240a-36d6-4ac8-84af-1cb749939128 | gaitsada-self-aligned-domain-adaptation-for | 2301.13384 | null | https://arxiv.org/abs/2301.13384v3 | https://arxiv.org/pdf/2301.13384v3.pdf | GaitSADA: Self-Aligned Domain Adaptation for mmWave Gait Recognition | mmWave radar-based gait recognition is a novel user identification method that captures human gait biometrics from mmWave radar return signals. This technology offers privacy protection and is resilient to weather and lighting conditions. However, its generalization performance is yet unknown and limits its practical deployment. To address this problem, in this paper, a non-synthetic dataset is collected and analyzed to reveal the presence of spatial and temporal domain shifts in mmWave gait biometric data, which significantly impacts identification accuracy. To mitigate this issue, a novel self-aligned domain adaptation method called GaitSADA is proposed. GaitSADA improves system generalization performance by using a two-stage semi-supervised model training approach. The first stage employs semi-supervised contrastive learning to learn a compact gait representation from both source and target domain data, aligning source-target domain distributions implicitly. The second stage uses semi-supervised consistency training with centroid alignment to further close source-target domain gap by pseudo-labelling the target-domain samples, clustering together the samples belonging to the same class but from different domains, and pushing the class centroid close to the weight vector of each class. Experiments show that GaitSADA outperforms representative domain adaptation methods with an improvement ranging from 15.41\% to 26.32\% on average accuracy in low data regimes. Code and dataset will be available at https://exitudio.github.io/GaitSADA | ['Zhi Sun', 'Chen Chen', 'Qucheng Peng', 'Minwoo Lee', 'Pu Wang', 'Kalvik Jakkala', 'Ayman Ali', 'Ekkasit Pinyoanuntapong'] | 2023-01-31 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [ 2.14542776e-01 -3.63225818e-01 -2.39547387e-01 -3.32218379e-01
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aed4949e-e6a3-4785-9742-b0a5ca3b17d3 | deep-self-taught-learning-for-remote-sensing | 1710.07096 | null | http://arxiv.org/abs/1710.07096v2 | http://arxiv.org/pdf/1710.07096v2.pdf | Deep Self-taught Learning for Remote Sensing Image Classification | This paper addresses the land cover classification task for remote sensing
images by deep self-taught learning. Our self-taught learning approach learns
suitable feature representations of the input data using sparse representation
and undercomplete dictionary learning. We propose a deep learning framework
which extracts representations in multiple layers and use the output of the
deepest layer as input to a classification algorithm. We evaluate our approach
using a multispectral Landsat 5 TM image of a study area in the North of Novo
Progresso (South America) and the Zurich Summer Data Set provided by the
University of Zurich. Experiments indicate that features learned by a deep
self-taught learning framework can be used for classification and improve the
results compared to classification results using the original feature
representation. | ['Susanne Wenzel', 'Anika Bettge', 'Ribana Roscher'] | 2017-10-19 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 3.12721044e-01 -2.15453119e-03 -3.02207053e-01 -7.52763391e-01
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9d6ab21c-a736-498d-a614-edbfa1671cb5 | shadow-detection-a-survey-and-comparative | 1304.1233 | null | http://arxiv.org/abs/1304.1233v1 | http://arxiv.org/pdf/1304.1233v1.pdf | Shadow Detection: A Survey and Comparative Evaluation of Recent Methods | This paper presents a survey and a comparative evaluation of recent
techniques for moving cast shadow detection. We identify shadow removal as a
critical step for improving object detection and tracking. The survey covers
methods published during the last decade, and places them in a feature-based
taxonomy comprised of four categories: chromacity, physical, geometry and
textures. A selection of prominent methods across the categories is compared in
terms of quantitative performance measures (shadow detection and discrimination
rates, colour desaturation) as well as qualitative observations. Furthermore,
we propose the use of tracking performance as an unbiased approach for
determining the practical usefulness of shadow detection methods. The
evaluation indicates that all shadow detection approaches make different
contributions and all have individual strength and weaknesses. Out of the
selected methods, the geometry-based technique has strict assumptions and is
not generalisable to various environments, but it is a straightforward choice
when the objects of interest are easy to model and their shadows have different
orientation. The chromacity based method is the fastest to implement and run,
but it is sensitive to noise and less effective in low saturated scenes. The
physical method improves upon the accuracy of the chromacity method by adapting
to local shadow models, but fails when the spectral properties of the objects
are similar to that of the background. The small-region texture based method is
especially robust for pixels whose neighbourhood is textured, but may take
longer to implement and is the most computationally expensive. The large-region
texture based method produces the most accurate results, but has a significant
computational load due to its multiple processing steps. | ['Brian C. Lovell', 'Andres Sanin', 'Conrad Sanderson'] | 2013-04-04 | null | null | null | null | ['shadow-removal', 'shadow-detection'] | ['computer-vision', 'computer-vision'] | [ 5.00756323e-01 -6.13942802e-01 1.24193043e-01 -3.28574218e-02
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721e2069-735b-4ae8-93a7-88a30f4422a2 | epillid-dataset-a-low-shot-fine-grained | 2005.14288 | null | https://arxiv.org/abs/2005.14288v2 | https://arxiv.org/pdf/2005.14288v2.pdf | ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill Identification | Identifying prescription medications is a frequent task for patients and medical professionals; however, this is an error-prone task as many pills have similar appearances (e.g. white round pills), which increases the risk of medication errors. In this paper, we introduce ePillID, the largest public benchmark on pill image recognition, composed of 13k images representing 9804 appearance classes (two sides for 4902 pill types). For most of the appearance classes, there exists only one reference image, making it a challenging low-shot recognition setting. We present our experimental setup and evaluation results of various baseline models on the benchmark. The best baseline using a multi-head metric-learning approach with bilinear features performed remarkably well; however, our error analysis suggests that they still fail to distinguish particularly confusing classes. The code and data are available at https://github.com/usuyama/ePillID-benchmark. | ['Naoto Usuyama', 'Natalia Larios Delgado', 'Jessica Lundin', 'Amanda K. Hall'] | 2020-05-28 | null | null | null | null | ['fine-grained-image-recognition', 'fine-grained-visual-categorization'] | ['computer-vision', 'computer-vision'] | [ 2.25269020e-01 -5.48958898e-01 -6.44670606e-01 -3.87186825e-01
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c5a0a855-fb98-4646-aa77-04f2161449fb | controllable-unsupervised-text-attribute | 1905.12926 | null | https://arxiv.org/abs/1905.12926v2 | https://arxiv.org/pdf/1905.12926v2.pdf | Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation | Unsupervised text attribute transfer automatically transforms a text to alter a specific attribute (e.g. sentiment) without using any parallel data, while simultaneously preserving its attribute-independent content. The dominant approaches are trying to model the content-independent attribute separately, e.g., learning different attributes' representations or using multiple attribute-specific decoders. However, it may lead to inflexibility from the perspective of controlling the degree of transfer or transferring over multiple aspects at the same time. To address the above problems, we propose a more flexible unsupervised text attribute transfer framework which replaces the process of modeling attribute with minimal editing of latent representations based on an attribute classifier. Specifically, we first propose a Transformer-based autoencoder to learn an entangled latent representation for a discrete text, then we transform the attribute transfer task to an optimization problem and propose the Fast-Gradient-Iterative-Modification algorithm to edit the latent representation until conforming to the target attribute. Extensive experimental results demonstrate that our model achieves very competitive performance on three public data sets. Furthermore, we also show that our model can not only control the degree of transfer freely but also allow to transfer over multiple aspects at the same time. | ['Ke Wang', 'Xiaojun Wan', 'Hang Hua'] | 2019-05-30 | controllable-unsupervised-text-attribute-1 | http://papers.nips.cc/paper/9284-controllable-unsupervised-text-attribute-transfer-via-editing-entangled-latent-representation | http://papers.nips.cc/paper/9284-controllable-unsupervised-text-attribute-transfer-via-editing-entangled-latent-representation.pdf | neurips-2019-12 | ['text-attribute-transfer'] | ['natural-language-processing'] | [ 6.63512886e-01 3.70180726e-01 -7.94302151e-02 -8.28216314e-01
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05bb6c89-a7bb-48af-89c2-39520a2f65a5 | egocentric-activity-prediction-via-event | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Yang_Shen_Egocentric_Activity_Prediction_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Yang_Shen_Egocentric_Activity_Prediction_ECCV_2018_paper.pdf | Egocentric Activity Prediction via Event Modulated Attention | Predicting future activities from an egocentric viewpoint is of particular interest in assisted living. However, state-of-the-art egocentric activity understanding techniques are mostly NOT capable of predictive tasks, as their synchronous processing architecture performs poorly in either modeling event dependency or pruning temporal redundant features. This work explicitly addresses these issues by proposing an asynchronous gaze-event driven attentive activity prediction network. This network is built on a gaze-event extraction module inspired by the fact that gaze moving in/out a certain object most probably indicates the occurrence/ending of a certain activity. The extracted gaze events are input to: 1) an asynchronous module which reasons about the temporal dependency between events and 2) a synchronous module which softly attends to informative temporal durations for more compact and discriminative feature extraction. Both modules are seamlessly integrated for collaborative prediction. Extensive experimental results on egocentric activity prediction as well as recognition well demonstrate the effectiveness of the proposed method. | ['Bingbing Ni', 'Yang Shen', 'Zefan Li', 'Ning Zhuang'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 4.13808674e-01 2.72101402e-01 -2.50962704e-01 -5.68529904e-01
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3dd1df53-7069-4cd4-89c0-f4dfe485d387 | conditional-graph-information-bottleneck-for | 2305.01520 | null | https://arxiv.org/abs/2305.01520v2 | https://arxiv.org/pdf/2305.01520v2.pdf | Conditional Graph Information Bottleneck for Molecular Relational Learning | Molecular relational learning, whose goal is to learn the interaction behavior between molecular pairs, got a surge of interest in molecular sciences due to its wide range of applications. Recently, graph neural networks have recently shown great success in molecular relational learning by modeling a molecule as a graph structure, and considering atom-level interactions between two molecules. Despite their success, existing molecular relational learning methods tend to overlook the nature of chemistry, i.e., a chemical compound is composed of multiple substructures such as functional groups that cause distinctive chemical reactions. In this work, we propose a novel relational learning framework, called CGIB, that predicts the interaction behavior between a pair of graphs by detecting core subgraphs therein. The main idea is, given a pair of graphs, to find a subgraph from a graph that contains the minimal sufficient information regarding the task at hand conditioned on the paired graph based on the principle of conditional graph information bottleneck. We argue that our proposed method mimics the nature of chemical reactions, i.e., the core substructure of a molecule varies depending on which other molecule it interacts with. Extensive experiments on various tasks with real-world datasets demonstrate the superiority of CGIB over state-of-the-art baselines. Our code is available at https://github.com/Namkyeong/CGIB. | ['Chanyoung Park', 'Junseok Lee', 'Sungwon Kim', 'Gyoung S. Na', 'Dongmin Hyun', 'Namkyeong Lee'] | 2023-04-29 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 3.39059353e-01 1.35838598e-01 -5.30853391e-01 -1.84282556e-01
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79d66c26-c39c-4161-9dad-66eb8a378643 | ace-cooperative-multi-agent-q-learning-with | 2211.16068 | null | https://arxiv.org/abs/2211.16068v2 | https://arxiv.org/pdf/2211.16068v2.pdf | ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency | Multi-agent reinforcement learning (MARL) suffers from the non-stationarity problem, which is the ever-changing targets at every iteration when multiple agents update their policies at the same time. Starting from first principle, in this paper, we manage to solve the non-stationarity problem by proposing bidirectional action-dependent Q-learning (ACE). Central to the development of ACE is the sequential decision-making process wherein only one agent is allowed to take action at one time. Within this process, each agent maximizes its value function given the actions taken by the preceding agents at the inference stage. In the learning phase, each agent minimizes the TD error that is dependent on how the subsequent agents have reacted to their chosen action. Given the design of bidirectional dependency, ACE effectively turns a multiagent MDP into a single-agent MDP. We implement the ACE framework by identifying the proper network representation to formulate the action dependency, so that the sequential decision process is computed implicitly in one forward pass. To validate ACE, we compare it with strong baselines on two MARL benchmarks. Empirical experiments demonstrate that ACE outperforms the state-of-the-art algorithms on Google Research Football and StarCraft Multi-Agent Challenge by a large margin. In particular, on SMAC tasks, ACE achieves 100% success rate on almost all the hard and super-hard maps. We further study extensive research problems regarding ACE, including extension, generalization, and practicability. Code is made available to facilitate further research. | ['Wanli Ouyang', 'Yu Liu', 'Yaodong Yang', 'Yazhe Niu', 'Yuhong Wei', 'Yinmin Zhang', 'Jie Liu', 'Chuming Li'] | 2022-11-29 | null | null | null | null | ['smac-1', 'starcraft', 'smac'] | ['playing-games', 'playing-games', 'playing-games'] | [-2.64952779e-02 5.76988719e-02 -5.42679310e-01 -1.72542393e-01
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0a523909-0cc0-4c42-a970-514d42cd1af7 | learning-to-control-latent-representations | 1911.08542 | null | https://arxiv.org/abs/1911.08542v1 | https://arxiv.org/pdf/1911.08542v1.pdf | Learning to Control Latent Representations for Few-Shot Learning of Named Entities | Humans excel in continuously learning with small data without forgetting how to solve old problems. However, neural networks require large datasets to compute latent representations across different tasks while minimizing a loss function. For example, a natural language understanding (NLU) system will often deal with emerging entities during its deployment as interactions with users in realistic scenarios will generate new and infrequent names, events, and locations. Here, we address this scenario by introducing an RL trainable controller that disentangles the representation learning of a neural encoder from its memory management role. Our proposed solution is straightforward and simple: we train a controller to execute an optimal sequence of reading and writing operations on an external memory with the goal of leveraging diverse activations from the past and provide accurate predictions. Our approach is named Learning to Control (LTC) and allows few-shot learning with two degrees of memory plasticity. We experimentally show that our system obtains accurate results for few-shot learning of entity recognition in the Stanford Task-Oriented Dialogue dataset. | ['Omar U. Florez', 'Erik Mueller'] | 2019-11-19 | null | https://openreview.net/forum?id=rJleFREKDr | https://openreview.net/pdf?id=rJleFREKDr | null | ['small-data'] | ['computer-vision'] | [ 1.68768927e-01 5.77923238e-01 -1.94882691e-01 -4.68632758e-01
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ef2d6111-7f76-44e3-974c-0f45c9bfa344 | a-comparative-assessment-of-deep-learning | 2302.12168 | null | https://arxiv.org/abs/2302.12168v1 | https://arxiv.org/pdf/2302.12168v1.pdf | A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers | Short-term load forecasting (STLF) is vital for the daily operation of power grids. However, the non-linearity, non-stationarity, and randomness characterizing electricity demand time series renders STLF a challenging task. To that end, different forecasting methods have been proposed in the literature for day-ahead load forecasting, including a variety of deep learning models that are currently considered to achieve state-of-the-art performance. In order to compare the accuracy of such models, we focus on national net aggregated STLF and examine well-established autoregressive neural networks of indicative architectures, namely multi-layer perceptrons, N-BEATS, long short-term memory neural networks, and temporal convolutional networks, for the case of Portugal. To investigate the factors that affect the performance of each model and identify the most appropriate per case, we also conduct a post-hoc analysis, correlating forecast errors with key calendar and weather features. Our results indicate that N-BEATS consistently outperforms the rest of the examined deep learning models. Additionally, we find that external factors can significantly impact accuracy, affecting both the actual and relative performance of the models. | ['Dimitris Askounis', 'Spiros Mouzakitis', 'Evangelos Karakolis', 'Theodosios Pountridis', 'Evangelos Spiliotis', 'Ioannis-Konstantinos Seisopoulos', 'Sotiris Pelekis'] | 2023-02-23 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-5.57627499e-01 -5.11892915e-01 7.28567839e-02 -3.52971494e-01
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5690d088-3737-4722-8282-cded402e5b02 | no-reference-quality-assessment-of-contrast | 1904.08879 | null | http://arxiv.org/abs/1904.08879v1 | http://arxiv.org/pdf/1904.08879v1.pdf | No-Reference Quality Assessment of Contrast-Distorted Images using Contrast Enhancement | No-reference image quality assessment (NR-IQA) aims to measure the image
quality without reference image. However, contrast distortion has been
overlooked in the current research of NR-IQA. In this paper, we propose a very
simple but effective metric for predicting quality of contrast-altered images
based on the fact that a high-contrast image is often more similar to its
contrast enhanced image. Specifically, we first generate an enhanced image
through histogram equalization. We then calculate the similarity of the
original image and the enhanced one by using structural-similarity index (SSIM)
as the first feature. Further, we calculate the histogram based entropy and
cross entropy between the original image and the enhanced one respectively, to
gain a sum of 4 features. Finally, we learn a regression module to fuse the
aforementioned 5 features for inferring the quality score. Experiments on four
publicly available databases validate the superiority and efficiency of the
proposed technique. | ['Xin Fu', 'Jie Li', 'Jia Yan'] | 2019-04-18 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 5.07833600e-01 -5.53259850e-01 1.10464640e-01 -3.96702290e-01
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909cb669-b363-42e8-9a10-8a168f535351 | dynamic-review-based-recommenders | 2110.14747 | null | https://arxiv.org/abs/2110.14747v2 | https://arxiv.org/pdf/2110.14747v2.pdf | Dynamic Review-based Recommenders | Just as user preferences change with time, item reviews also reflect those same preference changes. In a nutshell, if one is to sequentially incorporate review content knowledge into recommender systems, one is naturally led to dynamical models of text. In the present work we leverage the known power of reviews to enhance rating predictions in a way that (i) respects the causality of review generation and (ii) includes, in a bidirectional fashion, the ability of ratings to inform language review models and vice-versa, language representations that help predict ratings end-to-end. Moreover, our representations are time-interval aware and thus yield a continuous-time representation of the dynamics. We provide experiments on real-world datasets and show that our methodology is able to outperform several state-of-the-art models. Source code for all models can be found at [1]. | ['Cesar Ojeda', 'Christian Bauckhage', 'Ramses J. Sanchez', 'Kostadin Cvejoski'] | 2021-10-27 | null | null | null | null | ['review-generation'] | ['natural-language-processing'] | [-1.32012144e-01 1.33578870e-02 -6.10599697e-01 -3.45292032e-01
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aeb9bbe5-5418-477e-b5d7-20efed97bc6f | optimizing-sampling-patterns-for-compressed | 2306.03284 | null | https://arxiv.org/abs/2306.03284v1 | https://arxiv.org/pdf/2306.03284v1.pdf | Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models | Diffusion-based generative models have been used as powerful priors for magnetic resonance imaging (MRI) reconstruction. We present a learning method to optimize sub-sampling patterns for compressed sensing multi-coil MRI that leverages pre-trained diffusion generative models. Crucially, during training we use a single-step reconstruction based on the posterior mean estimate given by the diffusion model and the MRI measurement process. Experiments across varying anatomies, acceleration factors, and pattern types show that sampling operators learned with our method lead to competitive, and in the case of 2D patterns, improved reconstructions compared to baseline patterns. Our method requires as few as five training images to learn effective sampling patterns. | ['Alexandros G. Dimakis', 'Jonathan I. Tamir', 'Ajil Jalal', 'Brett Levac', 'Sriram Ravula'] | 2023-06-05 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 6.15375280e-01 2.98190445e-01 -1.32543713e-01 -3.16861510e-01
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4abaa12a-53cf-4130-a8e3-dce990a7340d | deep-recurrent-semi-supervised-eeg | 2107.13505 | null | https://arxiv.org/abs/2107.13505v1 | https://arxiv.org/pdf/2107.13505v1.pdf | Deep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recognition | EEG-based emotion recognition often requires sufficient labeled training samples to build an effective computational model. Labeling EEG data, on the other hand, is often expensive and time-consuming. To tackle this problem and reduce the need for output labels in the context of EEG-based emotion recognition, we propose a semi-supervised pipeline to jointly exploit both unlabeled and labeled data for learning EEG representations. Our semi-supervised framework consists of both unsupervised and supervised components. The unsupervised part maximizes the consistency between original and reconstructed input data using an autoencoder, while simultaneously the supervised part minimizes the cross-entropy between the input and output labels. We evaluate our framework using both a stacked autoencoder and an attention-based recurrent autoencoder. We test our framework on the large-scale SEED EEG dataset and compare our results with several other popular semi-supervised methods. Our semi-supervised framework with a deep attention-based recurrent autoencoder consistently outperforms the benchmark methods, even when small sub-sets (3\%, 5\% and 10\%) of the output labels are available during training, achieving a new state-of-the-art semi-supervised performance. | ['Ali Etemad', 'Guangyi Zhang'] | 2021-07-28 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 1.58230796e-01 1.60283238e-01 2.94573367e-01 -7.45895922e-01
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6156dea4-8150-4dae-bf4c-5caba102984a | detection-of-adversarial-examples-in-text | null | null | https://aclanthology.org/2022.findings-acl.289 | https://aclanthology.org/2022.findings-acl.289.pdf | Detection of Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density Estimation | Word-level adversarial attacks have shown success in NLP models, drastically decreasing the performance of transformer-based models in recent years. As a countermeasure, adversarial defense has been explored, but relatively few efforts have been made to detect adversarial examples. However, detecting adversarial examples may be crucial for automated tasks (e.g. review sentiment analysis) that wish to amass information about a certain population and additionally be a step towards a robust defense system. To this end, we release a dataset for four popular attack methods on four datasets and four models to encourage further research in this field. Along with it, we propose a competitive baseline based on density estimation that has the highest auc on 29 out of 30 dataset-attack-model combinations. The source code is released (https://github.com/bangawayoo/adversarial-examples-in-text-classification). | ['Nojun Kwak', 'Jiho Jang', 'Jangho Kim', 'KiYoon Yoo'] | null | null | null | null | findings-acl-2022-5 | ['adversarial-defense'] | ['adversarial'] | [ 4.79116142e-02 -1.14420965e-01 -2.63484687e-01 -3.04074347e-01
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f440a88a-7a23-4954-ab7a-4c01da670abd | replug-retrieval-augmented-black-box-language | 2301.12652 | null | https://arxiv.org/abs/2301.12652v4 | https://arxiv.org/pdf/2301.12652v4.pdf | REPLUG: Retrieval-Augmented Black-Box Language Models | We introduce REPLUG, a retrieval-augmented language modeling framework that treats the language model (LM) as a black box and augments it with a tuneable retrieval model. Unlike prior retrieval-augmented LMs that train language models with special cross attention mechanisms to encode the retrieved text, REPLUG simply prepends retrieved documents to the input for the frozen black-box LM. This simple design can be easily applied to any existing retrieval and language models. Furthermore, we show that the LM can be used to supervise the retrieval model, which can then find documents that help the LM make better predictions. Our experiments demonstrate that REPLUG with the tuned retriever significantly improves the performance of GPT-3 (175B) on language modeling by 6.3%, as well as the performance of Codex on five-shot MMLU by 5.1%. | ['Wen-tau Yih', 'Luke Zettlemoyer', 'Mike Lewis', 'Rich James', 'Minjoon Seo', 'Michihiro Yasunaga', 'Sewon Min', 'Weijia Shi'] | 2023-01-30 | null | null | null | null | ['multi-task-language-understanding'] | ['methodology'] | [-3.74064654e-01 -8.91244859e-02 -5.52714825e-01 -1.12181671e-01
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6acea42d-fe63-49ae-9a9b-36914ac38054 | revisiting-hate-speech-benchmarks-from-data | 2306.01105 | null | https://arxiv.org/abs/2306.01105v2 | https://arxiv.org/pdf/2306.01105v2.pdf | Revisiting Hate Speech Benchmarks: From Data Curation to System Deployment | Social media is awash with hateful content, much of which is often veiled with linguistic and topical diversity. The benchmark datasets used for hate speech detection do not account for such divagation as they are predominantly compiled using hate lexicons. However, capturing hate signals becomes challenging in neutrally-seeded malicious content. Thus, designing models and datasets that mimic the real-world variability of hate warrants further investigation. To this end, we present GOTHate, a large-scale code-mixed crowdsourced dataset of around 51k posts for hate speech detection from Twitter. GOTHate is neutrally seeded, encompassing different languages and topics. We conduct detailed comparisons of GOTHate with the existing hate speech datasets, highlighting its novelty. We benchmark it with 10 recent baselines. Our extensive empirical and benchmarking experiments suggest that GOTHate is hard to classify in a text-only setup. Thus, we investigate how adding endogenous signals enhances the hate speech detection task. We augment GOTHate with the user's timeline information and ego network, bringing the overall data source closer to the real-world setup for understanding hateful content. Our proposed solution HEN-mBERT is a modular, multilingual, mixture-of-experts model that enriches the linguistic subspace with latent endogenous signals from history, topology, and exemplars. HEN-mBERT transcends the best baseline by 2.5% and 5% in overall macro-F1 and hate class F1, respectively. Inspired by our experiments, in partnership with Wipro AI, we are developing a semi-automated pipeline to detect hateful content as a part of their mission to tackle online harm. | ['Tanmoy Chakraborty', 'Vikram Goyal', 'Sarah Masud', 'Atharva Kulkarni'] | 2023-06-01 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-4.26853985e-01 -7.60932341e-02 -1.99341252e-01 1.47349030e-01
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64237826-e7e0-42dc-86ef-447084590ada | robust-neural-routing-through-space | 2012.04746 | null | https://arxiv.org/abs/2012.04746v2 | https://arxiv.org/pdf/2012.04746v2.pdf | Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments | Localizing the camera in a known indoor environment is a key building block for scene mapping, robot navigation, AR, etc. Recent advances estimate the camera pose via optimization over the 2D/3D-3D correspondences established between the coordinates in 2D/3D camera space and 3D world space. Such a mapping is estimated with either a convolution neural network or a decision tree using only the static input image sequence, which makes these approaches vulnerable to dynamic indoor environments that are quite common yet challenging in the real world. To address the aforementioned issues, in this paper, we propose a novel outlier-aware neural tree which bridges the two worlds, deep learning and decision tree approaches. It builds on three important blocks: (a) a hierarchical space partition over the indoor scene to construct the decision tree; (b) a neural routing function, implemented as a deep classification network, employed for better 3D scene understanding; and (c) an outlier rejection module used to filter out dynamic points during the hierarchical routing process. Our proposed algorithm is evaluated on the RIO-10 benchmark developed for camera relocalization in dynamic indoor environments. It achieves robust neural routing through space partitions and outperforms the state-of-the-art approaches by around 30% on camera pose accuracy, while running comparably fast for evaluation. | ['Leonidas Guibas', 'Baoquan Chen', 'Thomas Funkhouser', 'Li Yi', 'Ji Shi', 'He Wang', 'Qingnan Fan', 'Siyan Dong'] | 2020-12-08 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Dong_Robust_Neural_Routing_Through_Space_Partitions_for_Camera_Relocalization_in_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Dong_Robust_Neural_Routing_Through_Space_Partitions_for_Camera_Relocalization_in_CVPR_2021_paper.pdf | cvpr-2021-1 | ['camera-relocalization'] | ['computer-vision'] | [-5.01437187e-02 -3.43452752e-01 1.73026741e-01 -3.66115838e-01
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b0e540ba-3618-470c-a5c8-0453c6eeb390 | volume-droid-a-real-time-implementation-of | 2306.06850 | null | https://arxiv.org/abs/2306.06850v1 | https://arxiv.org/pdf/2306.06850v1.pdf | Volume-DROID: A Real-Time Implementation of Volumetric Mapping with DROID-SLAM | This paper presents Volume-DROID, a novel approach for Simultaneous Localization and Mapping (SLAM) that integrates Volumetric Mapping and Differentiable Recurrent Optimization-Inspired Design (DROID). Volume-DROID takes camera images (monocular or stereo) or frames from a video as input and combines DROID-SLAM, point cloud registration, an off-the-shelf semantic segmentation network, and Convolutional Bayesian Kernel Inference (ConvBKI) to generate a 3D semantic map of the environment and provide accurate localization for the robot. The key innovation of our method is the real-time fusion of DROID-SLAM and Convolutional Bayesian Kernel Inference (ConvBKI), achieved through the introduction of point cloud generation from RGB-Depth frames and optimized camera poses. This integration, engineered to enable efficient and timely processing, minimizes lag and ensures effective performance of the system. Our approach facilitates functional real-time online semantic mapping with just camera images or stereo video input. Our paper offers an open-source Python implementation of the algorithm, available at https://github.com/peterstratton/Volume-DROID. | ['Emaad Gerami', 'Nibarkavi Amutha', 'Ashwin Saxena', 'Sandilya Sai Garimella', 'Peter Stratton'] | 2023-06-12 | null | null | null | null | ['simultaneous-localization-and-mapping', 'point-cloud-registration', 'point-cloud-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.14649484e-01 -3.36500481e-02 1.54019430e-01 -4.11041379e-01
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b8085460-20b3-4569-b5ef-ad5b9f1c864c | grow-and-clip-informative-yet-concise | 2201.05088 | null | https://arxiv.org/abs/2201.05088v2 | https://arxiv.org/pdf/2201.05088v2.pdf | Grow-and-Clip: Informative-yet-Concise Evidence Distillation for Answer Explanation | Interpreting the predictions of existing Question Answering (QA) models is critical to many real-world intelligent applications, such as QA systems for healthcare, education, and finance. However, existing QA models lack interpretability and provide no feedback or explanation for end-users to help them understand why a specific prediction is the answer to a question. In this research, we argue that the evidences of an answer is critical to enhancing the interpretability of QA models. Unlike previous research that simply extracts several sentence(s) in the context as evidence, we are the first to explicitly define the concept of evidence as the supporting facts in a context which are informative, concise, and readable. Besides, we provide effective strategies to quantitatively measure the informativeness, conciseness and readability of evidence. Furthermore, we propose Grow-and-Clip Evidence Distillation (GCED) algorithm to extract evidences from the contexts by trade-off informativeness, conciseness, and readability. We conduct extensive experiments on the SQuAD and TriviaQA datasets with several baseline models to evaluate the effect of GCED on interpreting answers to questions. Human evaluation are also carried out to check the quality of distilled evidences. Experimental results show that automatic distilled evidences have human-like informativeness, conciseness and readability, which can enhance the interpretability of the answers to questions. | ['Bang Liu', 'Yanghua Xiao', 'Yuyan Chen'] | 2022-01-13 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [ 3.19025181e-02 3.94116253e-01 -8.15453902e-02 -8.13412249e-01
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042bb71e-243b-442e-8818-67b090c98514 | mmvc-learned-multi-mode-video-compression | 2304.02273 | null | https://arxiv.org/abs/2304.02273v1 | https://arxiv.org/pdf/2304.02273v1.pdf | MMVC: Learned Multi-Mode Video Compression with Block-based Prediction Mode Selection and Density-Adaptive Entropy Coding | Learning-based video compression has been extensively studied over the past years, but it still has limitations in adapting to various motion patterns and entropy models. In this paper, we propose multi-mode video compression (MMVC), a block wise mode ensemble deep video compression framework that selects the optimal mode for feature domain prediction adapting to different motion patterns. Proposed multi-modes include ConvLSTM-based feature domain prediction, optical flow conditioned feature domain prediction, and feature propagation to address a wide range of cases from static scenes without apparent motions to dynamic scenes with a moving camera. We partition the feature space into blocks for temporal prediction in spatial block-based representations. For entropy coding, we consider both dense and sparse post-quantization residual blocks, and apply optional run-length coding to sparse residuals to improve the compression rate. In this sense, our method uses a dual-mode entropy coding scheme guided by a binary density map, which offers significant rate reduction surpassing the extra cost of transmitting the binary selection map. We validate our scheme with some of the most popular benchmarking datasets. Compared with state-of-the-art video compression schemes and standard codecs, our method yields better or competitive results measured with PSNR and MS-SSIM. | ['Hun-Seok Kim', 'Shiyu Liu', 'Rakesh Chowdary Machineni', 'Yu Chen', 'Bowen Liu'] | 2023-04-05 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_MMVC_Learned_Multi-Mode_Video_Compression_With_Block-Based_Prediction_Mode_Selection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_MMVC_Learned_Multi-Mode_Video_Compression_With_Block-Based_Prediction_Mode_Selection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['ms-ssim'] | ['computer-vision'] | [ 4.74000007e-01 -4.76478428e-01 -4.21068400e-01 -2.36044303e-01
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0ecbd528-bf6a-4051-9ac6-d3d73d1bd48d | far-field-speaker-recognition-benchmark | null | null | https://aclanthology.org/2022.lrec-1.209 | https://aclanthology.org/2022.lrec-1.209.pdf | Far-Field Speaker Recognition Benchmark Derived From The DiPCo Corpus | In this paper, we present a far-field speaker verification benchmark derived from the publicly-available DiPCo corpus. This corpus comprise three different tasks that involve enrollment and test conditions with single- and/or multi-channels recordings. The main goal of this corpus is to foster research in far-field and multi-channel text-independent speaker verification. Also, it can be used for other speaker recognition tasks such as dereverberation, denoising and speech enhancement. In addition, we release a Kaldi and SpeechBrain system to facilitate further research. And we validate the evaluation design with a single-microphone state-of-the-art speaker recognition system (i.e. ResNet-101). The results show that the proposed tasks are very challenging. And we hope these resources will inspire the speech community to develop new methods and systems for this challenging domain. | ['Mohammad Mohammadamini', 'Mickael Rouvier'] | null | null | null | null | lrec-2022-6 | ['text-independent-speaker-verification'] | ['speech'] | [ 4.52299975e-02 -4.65482324e-01 2.00324148e-01 -7.67861485e-01
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b7fcc354-a453-4955-a5c4-cb577cca82bc | deformable-convolutions-and-lstm-based | 2306.00834 | null | https://arxiv.org/abs/2306.00834v1 | https://arxiv.org/pdf/2306.00834v1.pdf | Deformable Convolutions and LSTM-based Flexible Event Frame Fusion Network for Motion Deblurring | Event cameras differ from conventional RGB cameras in that they produce asynchronous data sequences. While RGB cameras capture every frame at a fixed rate, event cameras only capture changes in the scene, resulting in sparse and asynchronous data output. Despite the fact that event data carries useful information that can be utilized in motion deblurring of RGB cameras, integrating event and image information remains a challenge. Recent state-of-the-art CNN-based deblurring solutions produce multiple 2-D event frames based on the accumulation of event data over a time period. In most of these techniques, however, the number of event frames is fixed and predefined, which reduces temporal resolution drastically, particularly for scenarios when fast-moving objects are present or when longer exposure times are required. It is also important to note that recent modern cameras (e.g., cameras in mobile phones) dynamically set the exposure time of the image, which presents an additional problem for networks developed for a fixed number of event frames. A Long Short-Term Memory (LSTM)-based event feature extraction module has been developed for addressing these challenges, which enables us to use a dynamically varying number of event frames. Using these modules, we constructed a state-of-the-art deblurring network, Deformable Convolutions and LSTM-based Flexible Event Frame Fusion Network (DLEFNet). It is particularly useful for scenarios in which exposure times vary depending on factors such as lighting conditions or the presence of fast-moving objects in the scene. It has been demonstrated through evaluation results that the proposed method can outperform the existing state-of-the-art networks for deblurring task in synthetic and real-world data sets. | ['Mehmet Yamac', 'Dan Yang'] | 2023-06-01 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 2.47376814e-01 -7.45425999e-01 1.48549289e-01 -1.70690101e-02
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1d55f5c2-0c92-485c-aa88-5df068bfc0cc | active-learning-applied-to-patient-adaptive | null | null | http://papers.nips.cc/paper/4091-active-learning-applied-to-patient-adaptive-heartbeat-classification | http://papers.nips.cc/paper/4091-active-learning-applied-to-patient-adaptive-heartbeat-classification.pdf | Active Learning Applied to Patient-Adaptive Heartbeat Classification | While clinicians can accurately identify different types of heartbeats in electrocardiograms (ECGs) from different patients, researchers have had limited success in applying supervised machine learning to the same task. The problem is made challenging by the variety of tasks, inter- and intra-patient differences, an often severe class imbalance, and the high cost of getting cardiologists to label data for individual patients. We address these difficulties using active learning to perform patient-adaptive and task-adaptive heartbeat classification. When tested on a benchmark database of cardiologist annotated ECG recordings, our method had considerably better performance than other recently proposed methods on the two primary classification tasks recommended by the Association for the Advancement of Medical Instrumentation. Additionally, our method required over 90% less patient-specific training data than the methods to which we compared it. | ['Jenna Wiens', 'John V. Guttag'] | 2010-12-01 | null | null | null | neurips-2010-12 | ['heartbeat-classification'] | ['medical'] | [ 5.42582631e-01 1.02362163e-01 -2.75477499e-01 -5.62220931e-01
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f1606381-5381-4693-97a4-d4cbb8d44aad | lcaunet-a-skin-lesion-segmentation-network | 2305.00837 | null | https://arxiv.org/abs/2305.00837v1 | https://arxiv.org/pdf/2305.00837v1.pdf | LCAUnet: A skin lesion segmentation network with enhanced edge and body fusion | Accurate segmentation of skin lesions in dermatoscopic images is crucial for the early diagnosis of skin cancer and improving the survival rate of patients. However, it is still a challenging task due to the irregularity of lesion areas, the fuzziness of boundaries, and other complex interference factors. In this paper, a novel LCAUnet is proposed to improve the ability of complementary representation with fusion of edge and body features, which are often paid little attentions in traditional methods. First, two separate branches are set for edge and body segmentation with CNNs and Transformer based architecture respectively. Then, LCAF module is utilized to fuse feature maps of edge and body of the same level by local cross-attention operation in encoder stage. Furthermore, PGMF module is embedded for feature integration with prior guided multi-scale adaption. Comprehensive experiments on public available dataset ISIC 2017, ISIC 2018, and PH2 demonstrate that LCAUnet outperforms most state-of-the-art methods. The ablation studies also verify the effectiveness of the proposed fusion techniques. | ['Yuhai Zhao', 'Lisheng Xu', 'Gao Wang', 'Keming Mao', 'Qisen Ma'] | 2023-05-01 | null | null | null | null | ['skin-lesion-segmentation', 'lesion-segmentation'] | ['medical', 'medical'] | [ 5.30552268e-01 -1.09190978e-01 -2.74131745e-01 -2.93171406e-01
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bfd2dde2-ee5e-454d-ae57-ab33c43fd84d | generative-ai-empowered-simulation-for | 2302.08418 | null | https://arxiv.org/abs/2302.08418v1 | https://arxiv.org/pdf/2302.08418v1.pdf | Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses | In the vehicular mixed reality (MR) Metaverse, the distance between physical and virtual entities can be overcome by fusing the physical and virtual environments with multi-dimensional communications in autonomous driving systems. Assisted by digital twin (DT) technologies, connected autonomous vehicles (AVs), roadside units (RSU), and virtual simulators can maintain the vehicular MR Metaverse via digital simulations for sharing data and making driving decisions collaboratively. However, large-scale traffic and driving simulation via realistic data collection and fusion from the physical world for online prediction and offline training in autonomous driving systems are difficult and costly. In this paper, we propose an autonomous driving architecture, where generative AI is leveraged to synthesize unlimited conditioned traffic and driving data in simulations for improving driving safety and traffic efficiency. First, we propose a multi-task DT offloading model for the reliable execution of heterogeneous DT tasks with different requirements at RSUs. Then, based on the preferences of AV's DTs and collected realistic data, virtual simulators can synthesize unlimited conditioned driving and traffic datasets to further improve robustness. Finally, we propose a multi-task enhanced auction-based mechanism to provide fine-grained incentives for RSUs in providing resources for autonomous driving. The property analysis and experimental results demonstrate that the proposed mechanism and architecture are strategy-proof and effective, respectively. | ['Zhu Han', 'Shiwen Mao', 'Zehui Xiong', 'Jiawen Kang', 'Hongliang Zhang', 'Junlong Chen', 'Dusit Niyato', 'Minrui Xu'] | 2023-02-16 | null | null | null | null | ['mixed-reality'] | ['computer-vision'] | [-6.50542974e-01 2.48755500e-01 -3.88190806e-01 -3.40528518e-01
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ebd5f190-87cf-4f4a-94c0-9cbe54030c49 | resus-warm-up-cold-users-via-meta-learning | 2210.16080 | null | https://arxiv.org/abs/2210.16080v1 | https://arxiv.org/pdf/2210.16080v1.pdf | RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction | Click-Through Rate (CTR) prediction on cold users is a challenging task in recommender systems. Recent researches have resorted to meta-learning to tackle the cold-user challenge, which either perform few-shot user representation learning or adopt optimization-based meta-learning. However, existing methods suffer from information loss or inefficient optimization process, and they fail to explicitly model global user preference knowledge which is crucial to complement the sparse and insufficient preference information of cold users. In this paper, we propose a novel and efficient approach named RESUS, which decouples the learning of global preference knowledge contributed by collective users from the learning of residual preferences for individual users. Specifically, we employ a shared predictor to infer basis user preferences, which acquires global preference knowledge from the interactions of different users. Meanwhile, we develop two efficient algorithms based on the nearest neighbor and ridge regression predictors, which infer residual user preferences via learning quickly from a few user-specific interactions. Extensive experiments on three public datasets demonstrate that our RESUS approach is efficient and effective in improving CTR prediction accuracy on cold users, compared with various state-of-the-art methods. | ['Kangyi Lin', 'Wenwen Zhou', 'Zibin Zhang', 'Weiyu Cheng', 'Lifan Zhao', 'Yanyan Shen'] | 2022-10-28 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-1.98979214e-01 -8.90779734e-01 -5.16367972e-01 -4.94821042e-01
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80e74965-3391-4379-a740-303d0d76ebe6 | mind-the-gap-cross-lingual-information | 2112.13510 | null | https://arxiv.org/abs/2112.13510v1 | https://arxiv.org/pdf/2112.13510v1.pdf | Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement | Cross-Lingual Information Retrieval (CLIR) aims to rank the documents written in a language different from the user's query. The intrinsic gap between different languages is an essential challenge for CLIR. In this paper, we introduce the multilingual knowledge graph (KG) to the CLIR task due to the sufficient information of entities in multiple languages. It is regarded as a "silver bullet" to simultaneously perform explicit alignment between queries and documents and also broaden the representations of queries. And we propose a model named CLIR with hierarchical knowledge enhancement (HIKE) for our task. The proposed model encodes the textual information in queries, documents and the KG with multilingual BERT, and incorporates the KG information in the query-document matching process with a hierarchical information fusion mechanism. Particularly, HIKE first integrates the entities and their neighborhood in KG into query representations with a knowledge-level fusion, then combines the knowledge from both source and target languages to further mitigate the linguistic gap with a language-level fusion. Finally, experimental results demonstrate that HIKE achieves substantial improvements over state-of-the-art competitors. | ['Qing He', 'Yi Wei', 'Fuzhen Zhuang', 'Dehong Gao', 'Xiang Ao', 'Zhao Zhang', 'Fuwei Zhang'] | 2021-12-27 | null | null | null | null | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-5.21464407e-01 -3.16484213e-01 -4.95221257e-01 -2.59297222e-01
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9e05ef7f-05ea-4d87-875f-9bdcf6e815be | gator-graph-aware-transformer-with-motion | 2303.05652 | null | https://arxiv.org/abs/2303.05652v1 | https://arxiv.org/pdf/2303.05652v1.pdf | GATOR: Graph-Aware Transformer with Motion-Disentangled Regression for Human Mesh Recovery from a 2D Pose | 3D human mesh recovery from a 2D pose plays an important role in various applications. However, it is hard for existing methods to simultaneously capture the multiple relations during the evolution from skeleton to mesh, including joint-joint, joint-vertex and vertex-vertex relations, which often leads to implausible results. To address this issue, we propose a novel solution, called GATOR, that contains an encoder of Graph-Aware Transformer (GAT) and a decoder with Motion-Disentangled Regression (MDR) to explore these multiple relations. Specifically, GAT combines a GCN and a graph-aware self-attention in parallel to capture physical and hidden joint-joint relations. Furthermore, MDR models joint-vertex and vertex-vertex interactions to explore joint and vertex relations. Based on the clustering characteristics of vertex offset fields, MDR regresses the vertices by composing the predicted base motions. Extensive experiments show that GATOR achieves state-of-the-art performance on two challenging benchmarks. | ['Runwei Ding', 'Ti Wang', 'Wenhao Li', 'Xia Li', 'Hong Liu', 'Yingxuan You'] | 2023-03-10 | null | null | null | null | ['human-mesh-recovery'] | ['computer-vision'] | [ 1.44146970e-02 1.77664548e-01 -2.14222297e-01 -2.43060678e-01
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ba5ca3cb-d9d0-475b-bb32-36b00b33180d | how-trial-to-trial-learning-shapes-mappings | 2207.00430 | null | https://arxiv.org/abs/2207.00430v2 | https://arxiv.org/pdf/2207.00430v2.pdf | How trial-to-trial learning shapes mappings in the mental lexicon: Modelling Lexical Decision with Linear Discriminative Learning | Trial-to-trial effects have been found in a number of studies, indicating that processing a stimulus influences responses in subsequent trials. A special case are priming effects which have been modelled successfully with error-driven learning (Marsolek, 2008), implying that participants are continuously learning during experiments. This study investigates whether trial-to-trial learning can be detected in an unprimed lexical decision experiment. We used the Discriminative Lexicon Model (DLM; Baayen et al., 2019), a model of the mental lexicon with meaning representations from distributional semantics, which models error-driven incremental learning with the Widrow-Hoff rule. We used data from the British Lexicon Project (BLP; Keuleers et al., 2012) and simulated the lexical decision experiment with the DLM on a trial-by-trial basis for each subject individually. Then, reaction times were predicted with Generalised Additive Models (GAMs), using measures derived from the DLM simulations as predictors. We extracted measures from two simulations per subject (one with learning updates between trials and one without), and used them as input to two GAMs. Learning-based models showed better model fit than the non-learning ones for the majority of subjects. Our measures also provide insights into lexical processing and individual differences. This demonstrates the potential of the DLM to model behavioural data and leads to the conclusion that trial-to-trial learning can indeed be detected in unprimed lexical decision. Our results support the possibility that our lexical knowledge is subject to continuous changes. | ['R. Harald Baayen', 'Yu-Ying Chuang', 'Maria Heitmeier'] | 2022-07-01 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 1.04177922e-01 -1.02207437e-01 -8.71018171e-02 -3.69329989e-01
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a4dffe1d-b147-413b-8d2c-bcdf056dc176 | multi-time-horizon-solar-forecasting-using | 1807.05459 | null | http://arxiv.org/abs/1807.05459v1 | http://arxiv.org/pdf/1807.05459v1.pdf | Multi-time-horizon Solar Forecasting Using Recurrent Neural Network | The non-stationarity characteristic of the solar power renders traditional
point forecasting methods to be less useful due to large prediction errors.
This results in increased uncertainties in the grid operation, thereby
negatively affecting the reliability and increased cost of operation. This
research paper proposes a unified architecture for multi-time-horizon
predictions for short and long-term solar forecasting using Recurrent Neural
Networks (RNN). The paper describes an end-to-end pipeline to implement the
architecture along with the methods to test and validate the performance of the
prediction model. The results demonstrate that the proposed method based on the
unified architecture is effective for multi-horizon solar forecasting and
achieves a lower root-mean-squared prediction error compared to the previous
best-performing methods which use one model for each time-horizon. The proposed
method enables multi-horizon forecasts with real-time inputs, which have a high
potential for practical applications in the evolving smart grid. | ['Praveen Palanisamy', 'Sakshi Mishra'] | 2018-07-14 | null | null | null | null | ['3d-anomaly-detection-and-segmentation'] | ['methodology'] | [-2.20428675e-01 -4.52637702e-01 1.48665756e-01 -3.94777894e-01
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8995476e-5e55-4ea1-8836-157a00c53317 | outlier-detection-by-consistent-data | 1712.04129 | null | http://arxiv.org/abs/1712.04129v2 | http://arxiv.org/pdf/1712.04129v2.pdf | Outlier Detection by Consistent Data Selection Method | Often the challenge associated with tasks like fraud and spam detection[1] is
the lack of all likely patterns needed to train suitable supervised learning
models. In order to overcome this limitation, such tasks are attempted as
outlier or anomaly detection tasks. We also hypothesize that out- liers have
behavioral patterns that change over time. Limited data and continuously
changing patterns makes learning significantly difficult. In this work we are
proposing an approach that detects outliers in large data sets by relying on
data points that are consistent. The primary contribution of this work is that
it will quickly help retrieve samples for both consistent and non-outlier data
sets and is also mindful of new outlier patterns. No prior knowledge of each
set is required to extract the samples. The method consists of two phases, in
the first phase, consistent data points (non- outliers) are retrieved by an
ensemble method of unsupervised clustering techniques and in the second phase a
one class classifier trained on the consistent data point set is ap- plied on
the remaining sample set to identify the outliers. The approach is tested on
three publicly available data sets and the performance scores are competitive. | ['Smruthi Mukund', 'Utkarsh Porwal'] | 2017-12-12 | null | null | null | null | ['one-class-classifier', 'spam-detection'] | ['methodology', 'natural-language-processing'] | [-7.57032260e-02 -2.47747764e-01 4.14953493e-02 -4.95632082e-01
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7b3341f9-76bb-416e-b3ca-0d665c8d3277 | autism-spectrum-disorder-classification-based | 2208.08902 | null | https://arxiv.org/abs/2208.08902v2 | https://arxiv.org/pdf/2208.08902v2.pdf | Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning? | Research in machine learning for autism spectrum disorder (ASD) classification bears the promise to improve clinical diagnoses. However, recent studies in clinical imaging have shown the limited generalization of biomarkers across and beyond benchmark datasets. Despite increasing model complexity and sample size in neuroimaging, the classification performance of ASD remains far away from clinical application. This raises the question of how we can overcome these barriers to develop early biomarkers for ASD. One approach might be to rethink how we operationalize the theoretical basis of this disease in machine learning models. Here we introduced unsupervised graph representations that explicitly map the neural mechanisms of a core aspect of ASD, deficits in dyadic social interaction, as assessed by dual brain recordings, termed hyperscanning, and evaluated their predictive performance. The proposed method differs from existing approaches in that it is more suitable to capture social interaction deficits on a neural level and is applicable to young children and infants. First results from functional near-infrared spectroscopy data indicate potential predictive capacities of a task-agnostic, interpretable graph representation. This first effort to leverage interaction-related deficits on neural level to classify ASD may stimulate new approaches and methods to enhance existing models to achieve developmental ASD biomarkers in the future. | ['Vanessa Reindl', 'Martin Schulte-Rüther', 'Jana Kruppa', 'Kerstin Konrad', 'Christian Gerloff'] | 2022-08-17 | null | null | null | null | ['classification'] | ['methodology'] | [ 5.87937355e-01 5.28024495e-01 -3.63867655e-02 -4.08336580e-01
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b2bbe440-e1a3-4df5-aeaa-627395139c2a | improving-twitter-sentiment-analysis-with | null | null | https://aclanthology.org/P14-2071 | https://aclanthology.org/P14-2071.pdf | Improving Twitter Sentiment Analysis with Topic-Based Mixture Modeling and Semi-Supervised Training | null | ['Liang Zhou', 'Bing Xiang'] | 2014-06-01 | null | null | null | acl-2014-6 | ['twitter-sentiment-analysis', 'stock-prediction'] | ['natural-language-processing', 'time-series'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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dcc44aef-602e-4fff-bae6-f0e0d3a54b9b | boosting-offline-reinforcement-learning-with-1 | 2306.03362 | null | https://arxiv.org/abs/2306.03362v1 | https://arxiv.org/pdf/2306.03362v1.pdf | Boosting Offline Reinforcement Learning with Action Preference Query | Training practical agents usually involve offline and online reinforcement learning (RL) to balance the policy's performance and interaction costs. In particular, online fine-tuning has become a commonly used method to correct the erroneous estimates of out-of-distribution data learned in the offline training phase. However, even limited online interactions can be inaccessible or catastrophic for high-stake scenarios like healthcare and autonomous driving. In this work, we introduce an interaction-free training scheme dubbed Offline-with-Action-Preferences (OAP). The main insight is that, compared to online fine-tuning, querying the preferences between pre-collected and learned actions can be equally or even more helpful to the erroneous estimate problem. By adaptively encouraging or suppressing policy constraint according to action preferences, OAP could distinguish overestimation from beneficial policy improvement and thus attains a more accurate evaluation of unseen data. Theoretically, we prove a lower bound of the behavior policy's performance improvement brought by OAP. Moreover, comprehensive experiments on the D4RL benchmark and state-of-the-art algorithms demonstrate that OAP yields higher (29% on average) scores, especially on challenging AntMaze tasks (98% higher). | ['Gao Huang', 'Shiji Song', 'Matthieu Gaetan Lin', 'Shenzhi Wang', 'Qisen Yang'] | 2023-06-06 | null | null | null | null | ['d4rl'] | ['robots'] | [-1.55218497e-01 2.40911305e-01 -4.19587731e-01 -2.70372897e-01
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fa59c25e-7c13-43cb-a4b8-f4465358c9b3 | fact-enhanced-synthetic-news-generation | 2012.04778 | null | https://arxiv.org/abs/2012.04778v2 | https://arxiv.org/pdf/2012.04778v2.pdf | Fact-Enhanced Synthetic News Generation | The advanced text generation methods have witnessed great success in text summarization, language translation, and synthetic news generation. However, these techniques can be abused to generate disinformation and fake news. To better understand the potential threats of synthetic news, we develop a new generation method FactGen to generate high-quality news content. The existing text generation methods either afford limited supplementary information or lose consistency between the input and output which makes the synthetic news less trustworthy. To address these issues, FactGen retrieves external facts to enrich the output and reconstructs the input claim from the generated content to improve the consistency among the input and the output. Experiment results on real-world datasets show that the generated news contents of FactGen are consistent and contain rich facts. We also discuss the possible defending method to identify these synthetic news pieces if FactGen is used to generate synthetic news. | ['Huan Liu', 'Kaize Ding', 'Yichuan Li', 'Kai Shu'] | 2020-12-08 | null | null | null | null | ['news-generation'] | ['natural-language-processing'] | [ 2.27637425e-01 8.32051694e-01 -6.03799880e-01 2.33448431e-01
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6cb0e755-c362-4942-988f-5a22433497fe | perturbation-based-frequency-domain-linear | 2105.03973 | null | https://arxiv.org/abs/2105.03973v1 | https://arxiv.org/pdf/2105.03973v1.pdf | Perturbation-based Frequency Domain Linear and Nonlinear Noise Estimation | In this paper, a new method for the separation of noise categories based on Four-Wave Mixing is presented. The theoretical analysis is grounded in the Gaussian Noise model and verified by split step simulations. The noise categories react differently to the introduced perturbations, by performing a set of perturbations the behaviour of the different categories can be separated by means of a least-square fitting. Given ASE is independent of the induced perturbations, it is possible to separate noise contributions. The analysis includes constant and variable power perturbations. The estimation of the noise categories is discussed from two points of view: NSR evolution post-DSP processing, and over the power spectral density in a notched region. The NSR estimation can only be performed at reception, whereas the power spectral density approach can be performed along the optical link if a high resolution Optical Spectrum Analyzer is available. Additionally, we perform a simple experimental verification considering of two WaveLogic 3 transceivers for the NSR, successfully estimating the noise contributions. | ['S. J. Savory', 'D. J. Ives', 'F. J. Vaquero-Caballero'] | 2021-05-09 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 1.96835220e-01 -2.98201859e-01 5.70849180e-01 -8.56315047e-02
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87bcf354-df35-4542-ae4c-bfd175808b2f | evolving-three-dimension-3d-abstract-art | 2304.12932 | null | https://arxiv.org/abs/2304.12932v1 | https://arxiv.org/pdf/2304.12932v1.pdf | Evolving Three Dimension (3D) Abstract Art: Fitting Concepts by Language | Computational creativity has contributed heavily to abstract art in modern era, allowing artists to create high quality, abstract two dimension (2D) arts with a high level of controllability and expressibility. However, even with computational approaches that have promising result in making concrete 3D art, computationally addressing abstract 3D art with high-quality and controllability remains an open question. To fill this gap, we propose to explore computational creativity in making abstract 3D art by bridging evolution strategies (ES) and 3D rendering through customizable parameterization of scenes. We demonstrate that our approach is capable of placing semi-transparent triangles in 3D scenes that, when viewed from specified angles, render into films that look like artists' specification expressed in natural language. This provides a new way for the artist to easily express creativity ideas for abstract 3D art. The supplementary material, which contains code, animation for all figures, and more examples, is here: https://es3dart.github.io/ | ['Yingtao Tian'] | 2023-04-24 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 3.69942367e-01 1.59282625e-01 3.01715583e-01 1.78683281e-01
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7130a8f5-b8a0-410d-9c50-e4e7c917320a | contrasinver-voxel-wise-contrastive-semi | 2302.06441 | null | https://arxiv.org/abs/2302.06441v2 | https://arxiv.org/pdf/2302.06441v2.pdf | ContrasInver: Voxel-wise Contrastive Semi-supervised Learning for Seismic Inversion | Recent studies have shown that learning theories have been very successful in hydrocarbon exploration. Inversion of seismic into various attributes through the relationship of 1D well-logs and 3D seismic is an essential step in reservoir description, among which, acoustic impedance is one of the most critical attributes, and although current deep learningbased impedance inversion obtains promising results, it relies on a large number of logs (1D labels, typically more than 30 well-logs are required per inversion), which is unacceptable in many practical explorations. In this work, we define acoustic impedance inversion as a regression task for learning sparse 1D labels from 3D volume data and propose a voxel-wise semisupervised contrastive learning framework, ContrasInver, for regression tasks under sparse labels. ConstraInver consists of several key components, including a novel pre-training method for 3D seismic data inversion, a contrastive semi-supervised strategy for diffusing well-log information to the global, and a continuous-value vectorized characterization method for a contrastive learning-based regression task, and also designed the distance TopK sampling method for improving the training efficiency. We performed a complete ablation study on SEAM Phase I synthetic data to verify the effectiveness of each component and compared our approach with the current mainstream methods on this data, and our approach demonstrated very significant advantages. In this data we achieved an SSIM of 0.92 and an MSE of 0.079 with only four well-logs. ConstraInver is the first purely data-driven approach to invert two classic field data, F3 Netherlands (only four well-logs) and Delft (only three well-logs) and achieves very reasonable and reliable results. | ['Zhifeng Xu', 'Hongjie Duan', 'Kewen Li', 'Timing Li', 'YiMin Dou'] | 2023-02-13 | null | null | null | null | ['seismic-inversion'] | ['miscellaneous'] | [-1.16044335e-01 -3.74872051e-02 1.38129652e-01 -2.73894995e-01
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b8609b55-bf8e-4b96-a925-eec800fd6f8a | learning-canonical-embeddings-for | 2209.02152 | null | https://arxiv.org/abs/2209.02152v2 | https://arxiv.org/pdf/2209.02152v2.pdf | Learning Canonical Embeddings for Unsupervised Shape Correspondence with Locally Linear Transformations | We present a new approach to unsupervised shape correspondence learning between pairs of point clouds. We make the first attempt to adapt the classical locally linear embedding algorithm (LLE) -- originally designed for nonlinear dimensionality reduction -- for shape correspondence. The key idea is to find dense correspondences between shapes by first obtaining high-dimensional neighborhood-preserving embeddings of low-dimensional point clouds and subsequently aligning the source and target embeddings using locally linear transformations. We demonstrate that learning the embedding using a new LLE-inspired point cloud reconstruction objective results in accurate shape correspondences. More specifically, the approach comprises an end-to-end learnable framework of extracting high-dimensional neighborhood-preserving embeddings, estimating locally linear transformations in the embedding space, and reconstructing shapes via divergence measure-based alignment of probabilistic density functions built over reconstructed and target shapes. Our approach enforces embeddings of shapes in correspondence to lie in the same universal/canonical embedding space, which eventually helps regularize the learning process and leads to a simple nearest neighbors approach between shape embeddings for finding reliable correspondences. Comprehensive experiments show that the new method makes noticeable improvements over state-of-the-art approaches on standard shape correspondence benchmark datasets covering both human and nonhuman shapes. | ['Anand Rangarajan', 'Sanjay Ranka', 'Patrick Emami', 'Pan He'] | 2022-09-05 | null | null | null | null | ['point-cloud-reconstruction'] | ['computer-vision'] | [-3.50305170e-01 -1.28077762e-02 -9.23399720e-03 -4.73959923e-01
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233d10e3-3a0a-4c77-8cd8-65f1b43166ea | example-based-motion-synthesis-via-generative | 2306.00378 | null | https://arxiv.org/abs/2306.00378v1 | https://arxiv.org/pdf/2306.00378v1.pdf | Example-based Motion Synthesis via Generative Motion Matching | We present GenMM, a generative model that "mines" as many diverse motions as possible from a single or few example sequences. In stark contrast to existing data-driven methods, which typically require long offline training time, are prone to visual artifacts, and tend to fail on large and complex skeletons, GenMM inherits the training-free nature and the superior quality of the well-known Motion Matching method. GenMM can synthesize a high-quality motion within a fraction of a second, even with highly complex and large skeletal structures. At the heart of our generative framework lies the generative motion matching module, which utilizes the bidirectional visual similarity as a generative cost function to motion matching, and operates in a multi-stage framework to progressively refine a random guess using exemplar motion matches. In addition to diverse motion generation, we show the versatility of our generative framework by extending it to a number of scenarios that are not possible with motion matching alone, including motion completion, key frame-guided generation, infinite looping, and motion reassembly. Code and data for this paper are at https://wyysf-98.github.io/GenMM/ | ['Baoquan Chen', 'Olga Sorkine-Hornung', 'Peizhuo Li', 'Xuelin Chen', 'Weiyu Li'] | 2023-06-01 | null | null | null | null | ['motion-synthesis'] | ['computer-vision'] | [ 2.58314192e-01 1.07240304e-01 -1.29607886e-01 1.11838482e-01
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00b1e039-456b-4bc1-aca1-6dab6accb1f1 | towards-source-free-domain-adaptive-semantic | 2306.01598 | null | https://arxiv.org/abs/2306.01598v2 | https://arxiv.org/pdf/2306.01598v2.pdf | Towards Source-free Domain Adaptive Semantic Segmentation via Importance-aware and Prototype-contrast Learning | Domain adaptive semantic segmentation enables robust pixel-wise understanding in real-world driving scenes. Source-free domain adaptation, as a more practical technique, addresses the concerns of data privacy and storage limitations in typical unsupervised domain adaptation methods. It utilizes a well-trained source model and unlabeled target data to achieve adaptation in the target domain. However, in the absence of source data and target labels, current solutions cannot sufficiently reduce the impact of domain shift and fully leverage the information from the target data. In this paper, we propose an end-to-end source-free domain adaptation semantic segmentation method via Importance-Aware and Prototype-Contrast (IAPC) learning. The proposed IAPC framework effectively extracts domain-invariant knowledge from the well-trained source model and learns domain-specific knowledge from the unlabeled target domain. Specifically, considering the problem of domain shift in the prediction of the target domain by the source model, we put forward an importance-aware mechanism for the biased target prediction probability distribution to extract domain-invariant knowledge from the source model. We further introduce a prototype-contrast strategy, which includes a prototype-symmetric cross-entropy loss and a prototype-enhanced cross-entropy loss, to learn target intra-domain knowledge without relying on labels. A comprehensive variety of experiments on two domain adaptive semantic segmentation benchmarks demonstrates that the proposed end-to-end IAPC solution outperforms existing state-of-the-art methods. Code will be made publicly available at https://github.com/yihong-97/Source-free_IAPC. | ['Yaonan Wang', 'Kailun Yang', 'Zheng Xiao', 'Xiao Lu', 'HUI ZHANG', 'Yihong Cao'] | 2023-06-02 | null | null | null | null | ['source-free-domain-adaptation', 'unsupervised-domain-adaptation'] | ['computer-vision', 'methodology'] | [ 4.76157993e-01 1.29240468e-01 -5.90562284e-01 -6.94039106e-01
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edc2b1b9-6d99-4b00-bb39-28242129a0e3 | iit-bhu-system-description-for-lsdsem17 | null | null | https://aclanthology.org/W17-0912 | https://aclanthology.org/W17-0912.pdf | IIT (BHU): System Description for LSDSem'17 Shared Task | This paper describes an ensemble system submitted as part of the LSDSem Shared Task 2017 - the Story Cloze Test. The main conclusion from our results is that an approach based on semantic similarity alone may not be enough for this task. We test various approaches and compare them with two ensemble systems. One is based on voting and the other on logistic regression based classifier. Our final system is able to outperform the previous state of the art for the Story Cloze test. Another very interesting observation is the performance of sentiment based approach which works almost as well on its own as our final ensemble system. | ['Anil Kumar Singh', 'Pranav Goel'] | 2017-04-01 | null | null | null | ws-2017-4 | ['cloze-test'] | ['natural-language-processing'] | [-1.82909250e-01 -6.44191983e-04 2.73768157e-01 -5.75893462e-01
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3f4019b8-9ae9-4123-ae8a-73f9fe6ca8ab | two-stage-robust-and-sparse-distributed | 2208.08230 | null | https://arxiv.org/abs/2208.08230v1 | https://arxiv.org/pdf/2208.08230v1.pdf | Two-Stage Robust and Sparse Distributed Statistical Inference for Large-Scale Data | In this paper, we address the problem of conducting statistical inference in settings involving large-scale data that may be high-dimensional and contaminated by outliers. The high volume and dimensionality of the data require distributed processing and storage solutions. We propose a two-stage distributed and robust statistical inference procedures coping with high-dimensional models by promoting sparsity. In the first stage, known as model selection, relevant predictors are locally selected by applying robust Lasso estimators to the distinct subsets of data. The variable selections from each computation node are then fused by a voting scheme to find the sparse basis for the complete data set. It identifies the relevant variables in a robust manner. In the second stage, the developed statistically robust and computationally efficient bootstrap methods are employed. The actual inference constructs confidence intervals, finds parameter estimates and quantifies standard deviation. Similar to stage 1, the results of local inference are communicated to the fusion center and combined there. By using analytical methods, we establish the favorable statistical properties of the robust and computationally efficient bootstrap methods including consistency for a fixed number of predictors, and robustness. The proposed two-stage robust and distributed inference procedures demonstrate reliable performance and robustness in variable selection, finding confidence intervals and bootstrap approximations of standard deviations even when data is high-dimensional and contaminated by outliers. | ['Visa Koivunen', 'Emadaldin Mozafari-Majd'] | 2022-08-17 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 1.62978470e-01 -3.82276148e-01 -3.44986081e-01 -3.71310651e-01
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35ec9d14-5d3a-4a07-b48f-57f238c4a520 | the-2019-bbn-cross-lingual-information | null | null | https://aclanthology.org/2020.clssts-1.8 | https://aclanthology.org/2020.clssts-1.8.pdf | The 2019 BBN Cross-lingual Information Retrieval System | In this paper, we describe a cross-lingual information retrieval (CLIR) system that, given a query in English, and a set of audio and text documents in a foreign language, can return a scored list of relevant documents, and present findings in a summary form in English. Foreign audio documents are first transcribed by a state-of-the-art pretrained multilingual speech recognition model that is finetuned to the target language. For text documents, we use multiple multilingual neural machine translation (MT) models to achieve good translation results, especially for low/medium resource languages. The processed documents and queries are then scored using a probabilistic CLIR model that makes use of the probability of translation from GIZA translation tables and scores from a Neural Network Lexical Translation Model (NNLTM). Additionally, advanced score normalization, combination, and thresholding schemes are employed to maximize the Average Query Weighted Value (AQWV) scores. The CLIR output, together with multiple translation renderings, are selected and translated into English snippets via a summarization model. Our turnkey system is language agnostic and can be quickly trained for a new low-resource language in few days. | ['Manaj Srivastava', 'Richard Schwartz', 'Le Zhang', 'Lee Tarlin', 'Damianos Karakos', 'Sanjay Krishna Gouda', 'Lingjun Zhao', 'David Akodes', 'Numra Bathool', 'John Makhoul', 'Zhuolin Jiang', 'William Hartmann'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [ 3.42743427e-01 -2.33227223e-01 -6.11720562e-01 -2.76766390e-01
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8c86fbf0-8e69-45e2-9fb2-ba5de5a03f42 | fitting-mixed-logit-random-regret | 2301.01091 | null | https://arxiv.org/abs/2301.01091v1 | https://arxiv.org/pdf/2301.01091v1.pdf | Fitting mixed logit random regret minimization models using maximum simulated likelihood | This article describes the mixrandregret command, which extends the randregret command introduced in Guti\'errez-Vargas et al. (2021, The Stata Journal 21: 626-658) incorporating random coefficients for Random Regret Minimization models. The newly developed command mixrandregret allows the inclusion of random coefficients in the regret function of the classical RRM model introduced in Chorus (2010, European Journal of Transport and Infrastructure Research 10: 181-196). The command allows the user to specify a combination of fixed and random coefficients. In addition, the user can specify normal and log-normal distributions for the random coefficients using the commands' options. The models are fitted using simulated maximum likelihood using numerical integration to approximate the choice probabilities. | ['Martina Vandebroek', 'Álvaro A. Gutiérrez-Vargas', 'Ziyue Zhu'] | 2023-01-03 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-3.97015184e-01 4.98710945e-02 -4.85107899e-01 -6.07156456e-01
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0819565f-a395-44a5-8313-77cf6043137b | styleflow-disentangle-latent-representations | 2212.09670 | null | https://arxiv.org/abs/2212.09670v1 | https://arxiv.org/pdf/2212.09670v1.pdf | StyleFlow: Disentangle Latent Representations via Normalizing Flow for Unsupervised Text Style Transfer | Text style transfer aims to alter the style of a sentence while preserving its content. Due to the lack of parallel corpora, most recent work focuses on unsupervised methods and often uses cycle construction to train models. Since cycle construction helps to improve the style transfer ability of the model by rebuilding transferred sentences back to original-style sentences, it brings about a content loss in unsupervised text style transfer tasks. In this paper, we propose a novel disentanglement-based style transfer model StyleFlow to enhance content preservation. Instead of the typical encoder-decoder scheme, StyleFlow can not only conduct the forward process to obtain the output, but also infer to the input through the output. We design an attention-aware coupling layers to disentangle the content representations and the style representations of a sentence. Besides, we propose a data augmentation method based on Normalizing Flow to improve the robustness of the model. Experiment results demonstrate that our model preserves content effectively and achieves the state-of-the-art performance on the most metrics. | ['Xiaoguang Mao', 'Ruifeng Luo', 'Zhiliang Tian', 'Kangchen Zhu'] | 2022-12-19 | null | null | null | null | ['text-style-transfoer'] | ['natural-language-processing'] | [ 4.19539183e-01 -1.69216972e-02 9.02639255e-02 -6.68015659e-01
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f505ac82-b5f9-493f-a2d1-78d748ed981f | inference-optimized-ai-and-high-performance | 2201.11133 | null | https://arxiv.org/abs/2201.11133v2 | https://arxiv.org/pdf/2201.11133v2.pdf | Inference-optimized AI and high performance computing for gravitational wave detection at scale | We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V100 GPUs, within 2 hours. Once fully trained, we optimized these models for accelerated inference using NVIDIA TensorRT. We deployed our inference-optimized AI ensemble in the ThetaGPU supercomputer at Argonne Leadership Computer Facility to conduct distributed inference. Using the entire ThetaGPU supercomputer, consisting of 20 nodes each of which has 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, our NVIDIA TensorRT-optimized AI ensemble processed an entire month of advanced LIGO data (including Hanford and Livingston data streams) within 50 seconds. Our inference-optimized AI ensemble retains the same sensitivity of traditional AI models, namely, it identifies all known binary black hole mergers previously identified in this advanced LIGO dataset and reports no misclassifications, while also providing a 3X inference speedup compared to traditional artificial intelligence models. We used time slides to quantify the performance of our AI ensemble to process up to 5 years worth of advanced LIGO data. In this synthetically enhanced dataset, our AI ensemble reports an average of one misclassification for every month of searched advanced LIGO data. We also present the receiver operating characteristic curve of our AI ensemble using this 5 year long advanced LIGO dataset. This approach provides the required tools to conduct accelerated, AI-driven gravitational wave detection at scale. | ['Huihuo Zheng', 'E. A. Huerta', 'Minyang Tian', 'Asad Khan', 'Pranshu Chaturvedi'] | 2022-01-26 | null | null | null | null | ['gravitational-wave-detection'] | ['miscellaneous'] | [-8.14264178e-01 -3.76721658e-02 2.54978925e-01 1.32979630e-02
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665b796a-36fa-4e64-b027-011f3122705c | medical-dialogue-generation-via-dual-flow | 2305.18109 | null | https://arxiv.org/abs/2305.18109v1 | https://arxiv.org/pdf/2305.18109v1.pdf | Medical Dialogue Generation via Dual Flow Modeling | Medical dialogue systems (MDS) aim to provide patients with medical services, such as diagnosis and prescription. Since most patients cannot precisely describe their symptoms, dialogue understanding is challenging for MDS. Previous studies mainly addressed this by extracting the mentioned medical entities as critical dialogue history information. In this work, we argue that it is also essential to capture the transitions of the medical entities and the doctor's dialogue acts in each turn, as they help the understanding of how the dialogue flows and enhance the prediction of the entities and dialogue acts to be adopted in the following turn. Correspondingly, we propose a Dual Flow enhanced Medical (DFMed) dialogue generation framework. It extracts the medical entities and dialogue acts used in the dialogue history and models their transitions with an entity-centric graph flow and a sequential act flow, respectively. We employ two sequential models to encode them and devise an interweaving component to enhance their interactions. Experiments on two datasets demonstrate that our method exceeds baselines in both automatic and manual evaluations. | ['Wenjie Li', 'Jian Wang', 'Yi Cheng', 'Wenjun Hou', 'Kaishuai Xu'] | 2023-05-29 | null | null | null | null | ['dialogue-generation', 'dialogue-understanding', 'dialogue-generation'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 2.12920919e-01 9.87674952e-01 -3.06740493e-01 -6.55260623e-01
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50c6aae7-a993-42f9-b6d3-89280c3b030e | evaluation-of-deep-learning-models-for-1 | null | null | https://ieeexplore.ieee.org/document/9275976 | https://ieeexplore.ieee.org/document/9275976 | Evaluation of Deep Learning Models for Kannada Handwritten Digit Recognition | Handwritten digit recognition is a basic and important problem in computer vision. While it has been studied intensively, the newer datasets keep bring new challenges to existed solutions. A new Kannada-MNIST dataset is mainly digital images of the Kannada language, which contains 70,000 28×28 gray-scale sample images and 10 classes. It also contains a Dig-MNIST dataset, which is an out-of domain test sample dataset and a more challenging test dataset for recognition. In this paper, we evaluate the state-of-art deep learning models for Kannada-MNIST and improve their performance with data augmentation and transfer learning techniques. We manage to achieve a best accuracy of 90.27% for Dig-MNIST in the literature with Inception Model. | ['Qisheng Hu'] | 2020-12-09 | null | null | null | 01-02-august-2020-12 | ['handwritten-digit-recognition'] | ['computer-vision'] | [-1.78041101e-01 -5.48972428e-01 -1.02137879e-01 -4.36325014e-01
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8463abaa-9b93-4259-af4b-b984637c1f3e | deep-set-to-set-matching-and-learning | 1910.09972 | null | https://arxiv.org/abs/1910.09972v2 | https://arxiv.org/pdf/1910.09972v2.pdf | Exchangeable deep neural networks for set-to-set matching and learning | Matching two different sets of items, called heterogeneous set-to-set matching problem, has recently received attention as a promising problem. The difficulties are to extract features to match a correct pair of different sets and also preserve two types of exchangeability required for set-to-set matching: the pair of sets, as well as the items in each set, should be exchangeable. In this study, we propose a novel deep learning architecture to address the abovementioned difficulties and also an efficient training framework for set-to-set matching. We evaluate the methods through experiments based on two industrial applications: fashion set recommendation and group re-identification. In these experiments, we show that the proposed method provides significant improvements and results compared with the state-of-the-art methods, thereby validating our architecture for the heterogeneous set matching problem. | ['Hirotaka Hachiya', 'Yuki Saito', 'Kenji Fukumizu', 'Takuma Nakamura'] | 2019-10-22 | exchangeable-deep-neural-networks-for-set-to | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2844_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123620613.pdf | eccv-2020-8 | ['set-matching'] | ['computer-vision'] | [ 1.45610690e-01 -5.46932042e-01 -2.67190278e-01 -7.23877966e-01
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30fa5010-7609-4ebf-a695-b3200b66d7bf | does-bert-understand-idioms-a-probing-based | null | null | https://aclanthology.org/2021.ranlp-main.156 | https://aclanthology.org/2021.ranlp-main.156.pdf | Does BERT Understand Idioms? A Probing-Based Empirical Study of BERT Encodings of Idioms | Understanding idioms is important in NLP. In this paper, we study to what extent pre-trained BERT model can encode the meaning of a potentially idiomatic expression (PIE) in a certain context. We make use of a few existing datasets and perform two probing tasks: PIE usage classification and idiom paraphrase identification. Our experiment results suggest that BERT indeed can separate the literal and idiomatic usages of a PIE with high accuracy. It is also able to encode the idiomatic meaning of a PIE to some extent. | ['Jing Jiang', 'Minghuan Tan'] | null | null | https://aclanthology.org/2021.ranlp-1.156 | https://aclanthology.org/2021.ranlp-1.156.pdf | ranlp-2021-9 | ['paraphrase-identification'] | ['natural-language-processing'] | [ 2.18444854e-01 -5.59937954e-02 -9.20069396e-01 -6.43710375e-01
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a4c55280-7a72-4ac4-b26c-ae8a717cf9bc | reinforcement-learning-based-control-of-4 | 2306.03951 | null | https://arxiv.org/abs/2306.03951v2 | https://arxiv.org/pdf/2306.03951v2.pdf | Reinforcement Learning-Based Control of CrazyFlie 2.X Quadrotor | The objective of the project is to explore synergies between classical control algorithms such as PID and contemporary reinforcement learning algorithms to come up with a pragmatic control mechanism to control the CrazyFlie 2.X quadrotor. The primary objective would be performing PID tuning using reinforcement learning strategies. The secondary objective is to leverage the learnings from the first task to implement control for navigation by integrating with the lighthouse positioning system. Two approaches are considered for navigation, a discrete navigation problem using Deep Q-Learning with finite predefined motion primitives, and deep reinforcement learning for a continuous navigation approach. Simulations for RL training will be performed on gym-pybullet-drones, an open-source gym-based environment for reinforcement learning, and the RL implementations are provided by stable-baselines3 | ['Valentín López Jiménez', 'Arshad Javeed'] | 2023-06-06 | null | null | null | null | ['q-learning'] | ['methodology'] | [-4.93005991e-01 3.90565425e-01 7.00645149e-02 1.76946849e-01
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9759daf2-46ef-45ab-9413-c85af39c2533 | winner-weakly-supervised-hierarchical | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_WINNER_Weakly-Supervised_hIerarchical_decompositioN_and_aligNment_for_Spatio-tEmporal_Video_gRounding_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_WINNER_Weakly-Supervised_hIerarchical_decompositioN_and_aligNment_for_Spatio-tEmporal_Video_gRounding_CVPR_2023_paper.pdf | WINNER: Weakly-Supervised hIerarchical decompositioN and aligNment for Spatio-tEmporal Video gRounding | Spatio-temporal video grounding aims to localize the aligned visual tube corresponding to a language query. Existing techniques achieve such alignment by exploiting dense boundary and bounding box annotations, which can be prohibitively expensive. To bridge the gap, we investigate the weakly-supervised setting, where models learn from easily accessible video-language data without annotations. We identify that intra-sample spurious correlations among video-language components can be alleviated if the model captures the decomposed structures of video and language data. In this light, we propose a novel framework, namely WINNER, for hierarchical video-text understanding. WINNER first builds the language decomposition tree in a bottom-up manner, upon which the structural attention mechanism and top-down feature backtracking jointly build a multi-modal decomposition tree, permitting a hierarchical understanding of unstructured videos. The multi-modal decomposition tree serves as the basis for multi-hierarchy language-tube matching. A hierarchical contrastive learning objective is proposed to learn the multi-hierarchy correspondence and distinguishment with intra-sample and inter-sample video-text decomposition structures, achieving video-language decomposition structure alignment. Extensive experiments demonstrate the rationality of our design and its effectiveness beyond state-of-the-art weakly supervised methods, even some supervised methods. | ['Fei Wu', 'Wei Ji', 'Shengyu Zhang', 'Zhou Zhao', 'Jiaxu Miao', 'Wenqiao Zhang', 'Han Wang', 'Mengze Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-grounding', 'spatio-temporal-video-grounding'] | ['computer-vision', 'computer-vision'] | [ 1.2105909e-01 7.2719276e-02 -6.4015412e-01 -2.0845827e-01
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5f347457-9f3d-4e6a-8892-8a049f1d9b78 | geometry-and-uncertainty-aware-3d-point-cloud | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Geometry_and_Uncertainty-Aware_3D_Point_Cloud_Class-Incremental_Semantic_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Geometry_and_Uncertainty-Aware_3D_Point_Cloud_Class-Incremental_Semantic_Segmentation_CVPR_2023_paper.pdf | Geometry and Uncertainty-Aware 3D Point Cloud Class-Incremental Semantic Segmentation | Despite the significant recent progress made on 3D point cloud semantic segmentation, the current methods require training data for all classes at once, and are not suitable for real-life scenarios where new categories are being continuously discovered. Substantial memory storage and expensive re-training is required to update the model to sequentially arriving data for new concepts. In this paper, to continually learn new categories using previous knowledge, we introduce class-incremental semantic segmentation of 3D point cloud. Unlike 2D images, 3D point clouds are disordered and unstructured, making it difficult to store and transfer knowledge especially when the previous data is not available. We further face the challenge of semantic shift, where previous/future classes are indiscriminately collapsed and treated as the background in the current step, causing a dramatic performance drop on past classes. We exploit the structure of point cloud and propose two strategies to address these challenges. First, we design a geometry-aware distillation module that transfers point-wise feature associations in terms of their geometric characteristics. To counter forgetting caused by the semantic shift, we further develop an uncertainty-aware pseudo-labelling scheme that eliminates noise in uncertain pseudo-labels by label propagation within a local neighborhood. Our extensive experiments on S3DIS and ScanNet in a class-incremental setting show impressive results comparable to the joint training strategy (upper bound). Code is available at: https://github.com/leolyj/3DPC-CISS | ['Yinjie Lei', 'Chao Ren', 'Zhao Jin', 'Munawar Hayat', 'Yuwei Yang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['class-incremental-semantic-segmentation'] | ['computer-vision'] | [ 1.55922771e-01 1.50491789e-01 -1.13155171e-01 -5.52519500e-01
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814293b7-5b09-4fab-8185-bb04ecdeab63 | robust-tumor-detection-from-coarse | 2303.16533 | null | https://arxiv.org/abs/2303.16533v1 | https://arxiv.org/pdf/2303.16533v1.pdf | Robust Tumor Detection from Coarse Annotations via Multi-Magnification Ensembles | Cancer detection and classification from gigapixel whole slide images of stained tissue specimens has recently experienced enormous progress in computational histopathology. The limitation of available pixel-wise annotated scans shifted the focus from tumor localization to global slide-level classification on the basis of (weakly-supervised) multiple-instance learning despite the clinical importance of local cancer detection. However, the worse performance of these techniques in comparison to fully supervised methods has limited their usage until now for diagnostic interventions in domains of life-threatening diseases such as cancer. In this work, we put the focus back on tumor localization in form of a patch-level classification task and take up the setting of so-called coarse annotations, which provide greater training supervision while remaining feasible from a clinical standpoint. To this end, we present a novel ensemble method that not only significantly improves the detection accuracy of metastasis on the open CAMELYON16 data set of sentinel lymph nodes of breast cancer patients, but also considerably increases its robustness against noise while training on coarse annotations. Our experiments show that better results can be achieved with our technique making it clinically feasible to use for cancer diagnosis and opening a new avenue for translational and clinical research. | ['Maria Kalweit', 'Joschka Boedecker', 'Marc Metzger', 'Philipp Poxleitner', 'Ignacio Mastroleo', 'Gabriel Kalweit', 'Mehdi Naouar'] | 2023-03-29 | null | null | null | null | ['whole-slide-images', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 6.60936236e-01 1.39841884e-01 -5.91475248e-01 -1.61973909e-01
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763da000-31ed-41c9-8f2f-744606545df9 | clustering-us-counties-to-find-patterns | 2303.11936 | null | https://arxiv.org/abs/2303.11936v1 | https://arxiv.org/pdf/2303.11936v1.pdf | Clustering US Counties to Find Patterns Related to the COVID-19 Pandemic | When COVID-19 first started spreading and quarantine was implemented, the Society for Industrial and Applied Mathematics (SIAM) Student Chapter at the University of Minnesota-Twin Cities began a collaboration with Ecolab to use our skills as data scientists and mathematicians to extract useful insights from relevant data relating to the pandemic. This collaboration consisted of multiple groups working on different projects. In this write-up we focus on using clustering techniques to help us find groups of similar counties in the US and use that to help us understand the pandemic. Our team for this project consisted of University of Minnesota students Cora Brown, Sarah Milstein, Tianyi Sun, and Cooper Zhao, with help from Ecolab Data Scientist Jimmy Broomfield and University of Minnesota student Skye Ke. In the sections below we describe all of the work done for this project. In Section 2, we list the data we gathered, as well as the feature engineering we performed. In Section 3, we describe the metrics we used for evaluating our models. In Section 4, we explain the methods we used for interpreting the results of our various clustering approaches. In Section 5, we describe the different clustering methods we implemented. In Section 6, we present the results of our clustering techniques and provide relevant interpretation. Finally, in Section 7, we provide some concluding remarks comparing the different clustering methods. | ['Cooper Zhao', 'Tianyi Sun', 'Sarah Milstein', 'Cora Brown'] | 2023-03-19 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-5.55788934e-01 -3.83482367e-01 -8.44797119e-03 -1.83672875e-01
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bb9faac8-829d-4c22-a8bc-45fb840af3d9 | nonlinear-feature-aggregation-two-algorithms | 2306.11143 | null | https://arxiv.org/abs/2306.11143v1 | https://arxiv.org/pdf/2306.11143v1.pdf | Nonlinear Feature Aggregation: Two Algorithms driven by Theory | Many real-world machine learning applications are characterized by a huge number of features, leading to computational and memory issues, as well as the risk of overfitting. Ideally, only relevant and non-redundant features should be considered to preserve the complete information of the original data and limit the dimensionality. Dimensionality reduction and feature selection are common preprocessing techniques addressing the challenge of efficiently dealing with high-dimensional data. Dimensionality reduction methods control the number of features in the dataset while preserving its structure and minimizing information loss. Feature selection aims to identify the most relevant features for a task, discarding the less informative ones. Previous works have proposed approaches that aggregate features depending on their correlation without discarding any of them and preserving their interpretability through aggregation with the mean. A limitation of methods based on correlation is the assumption of linearity in the relationship between features and target. In this paper, we relax such an assumption in two ways. First, we propose a bias-variance analysis for general models with additive Gaussian noise, leading to a dimensionality reduction algorithm (NonLinCFA) which aggregates non-linear transformations of features with a generic aggregation function. Then, we extend the approach assuming that a generalized linear model regulates the relationship between features and target. A deviance analysis leads to a second dimensionality reduction algorithm (GenLinCFA), applicable to a larger class of regression problems and classification settings. Finally, we test the algorithms on synthetic and real-world datasets, performing regression and classification tasks, showing competitive performances. | ['Marcello Restelli', 'Alberto Maria Metelli', 'Paolo Bonetti'] | 2023-06-19 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 3.90102535e-01 -1.09484047e-01 5.30972481e-02 -3.00888151e-01
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93a869a1-78a2-46b7-8794-d46609379240 | shape-completion-with-points-in-the-shadow | 2209.08345 | null | https://arxiv.org/abs/2209.08345v3 | https://arxiv.org/pdf/2209.08345v3.pdf | Shape Completion with Points in the Shadow | Single-view point cloud completion aims to recover the full geometry of an object based on only limited observation, which is extremely hard due to the data sparsity and occlusion. The core challenge is to generate plausible geometries to fill the unobserved part of the object based on a partial scan, which is under-constrained and suffers from a huge solution space. Inspired by the classic shadow volume technique in computer graphics, we propose a new method to reduce the solution space effectively. Our method considers the camera a light source that casts rays toward the object. Such light rays build a reasonably constrained but sufficiently expressive basis for completion. The completion process is then formulated as a point displacement optimization problem. Points are initialized at the partial scan and then moved to their goal locations with two types of movements for each point: directional movements along the light rays and constrained local movement for shape refinement. We design neural networks to predict the ideal point movements to get the completion results. We demonstrate that our method is accurate, robust, and generalizable through exhaustive evaluation and comparison. Moreover, it outperforms state-of-the-art methods qualitatively and quantitatively on MVP datasets. | ['Ruizhen Hu', 'He Wang', 'Xi Zhao', 'BoWen Zhang'] | 2022-09-17 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [ 2.34424636e-01 7.09752813e-02 2.93184906e-01 -2.10819349e-01
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e8eb096c-edbb-4361-9484-971bacd25069 | supervised-anomaly-detection-via-conditional | 2104.11952 | null | https://arxiv.org/abs/2104.11952v1 | https://arxiv.org/pdf/2104.11952v1.pdf | Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active Learning | Anomaly detection has wide applications in machine intelligence but is still a difficult unsolved problem. Major challenges include the rarity of labeled anomalies and it is a class highly imbalanced problem. Traditional unsupervised anomaly detectors are suboptimal while supervised models can easily make biased predictions towards normal data. In this paper, we present a new supervised anomaly detector through introducing the novel Ensemble Active Learning Generative Adversarial Network (EAL-GAN). EAL-GAN is a conditional GAN having a unique one generator vs. multiple discriminators architecture where anomaly detection is implemented by an auxiliary classifier of the discriminator. In addition to using the conditional GAN to generate class balanced supplementary training data, an innovative ensemble learning loss function ensuring each discriminator makes up for the deficiencies of the others is designed to overcome the class imbalanced problem, and an active learning algorithm is introduced to significantly reduce the cost of labeling real-world data. We present extensive experimental results to demonstrate that the new anomaly detector consistently outperforms a variety of SOTA methods by significant margins. The codes are available on Github. | ['Guoping Qiu', 'Li Kang', 'Jiang Duan', 'Zhi Chen'] | 2021-04-24 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 4.18196559e-01 2.91603148e-01 -1.22945376e-01 -4.11433846e-01
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0693e47b-445d-4b4d-ace3-0e19215a0e97 | contrastive-learning-of-natural-language-and | null | null | https://openreview.net/forum?id=eiAkrltBTh4 | https://openreview.net/pdf?id=eiAkrltBTh4 | Contrastive Learning of Natural Language and Code Representations for Semantic Code Search | Retrieving semantically relevant code functions given a natural language (NL) or programming language (PL) query is a task of great practical value towards building productivity enhancing tools for software developers. Recent approaches to solve this task involve leveraging transformer based masked language models that are pre-trained on NL and PL and fine-tuned for code search using a contrastive learning objective. However, these approaches suffer from uninformative in-batch negative samples.
We propose DyHardCode: a contrastive learning framework that leverages hard negative examples, which are mined globally from the entire training corpus to improve the quality of code and natural language representations. We experiment with different hard negative mining strategies, and provide explanations to the effectiveness of our method from the perspectives of optimization and adversarial learning. We show that DyHardCode leads to improvements in multiple code search tasks. Our approach achieves an average (across 6 programming languages) mean reciprocal ranking (MRR) score of $0.750$ as opposed to the previous state of the art result of $0.713$ MRR on the CodeSearchNet benchmark. | ['Anonymous'] | 2021-06-16 | null | null | null | acl-arr-jun-2021-6 | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [ 2.02882141e-01 7.78543353e-02 -3.97535175e-01 -3.35498065e-01
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69b32453-f69a-4e4c-82e7-66de9cb2e3af | gender-prediction-from-tweets-improving | 1908.09919 | null | https://arxiv.org/abs/1908.09919v2 | https://arxiv.org/pdf/1908.09919v2.pdf | Gender Prediction from Tweets: Improving Neural Representations with Hand-Crafted Features | Author profiling is the characterization of an author through some key attributes such as gender, age, and language. In this paper, a RNN model with Attention (RNNwA) is proposed to predict the gender of a twitter user using their tweets. Both word level and tweet level attentions are utilized to learn 'where to look'. This model (https://github.com/Darg-Iztech/gender-prediction-from-tweets) is improved by concatenating LSA-reduced n-gram features with the learned neural representation of a user. Both models are tested on three languages: English, Spanish, Arabic. The improved version of the proposed model (RNNwA + n-gram) achieves state-of-the-art performance on English and has competitive results on Spanish and Arabic. | ['Ozan Polatbilek', 'Selma Tekir', 'Erhan Sezerer'] | 2019-08-22 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [-5.07688642e-01 -6.99427724e-02 -4.36856598e-01 -5.74977279e-01
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83502243-21f6-4b1b-8f31-da8cbefd9f28 | when-transformer-meets-robotic-grasping | 2202.11911 | null | https://arxiv.org/abs/2202.11911v3 | https://arxiv.org/pdf/2202.11911v3.pdf | When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detection | In this paper, we present a transformer-based architecture, namely TF-Grasp, for robotic grasp detection. The developed TF-Grasp framework has two elaborate designs making it well suitable for visual grasping tasks. The first key design is that we adopt the local window attention to capture local contextual information and detailed features of graspable objects. Then, we apply the cross window attention to model the long-term dependencies between distant pixels. Object knowledge, environmental configuration, and relationships between different visual entities are aggregated for subsequent grasp detection. The second key design is that we build a hierarchical encoder-decoder architecture with skip-connections, delivering shallow features from encoder to decoder to enable a multi-scale feature fusion. Due to the powerful attention mechanism, the TF-Grasp can simultaneously obtain the local information (i.e., the contours of objects), and model long-term connections such as the relationships between distinct visual concepts in clutter. Extensive computational experiments demonstrate that the TF-Grasp achieves superior results versus state-of-art grasping convolutional models and attain a higher accuracy of 97.99% and 94.6% on Cornell and Jacquard grasping datasets, respectively. Real-world experiments using a 7DoF Franka Emika Panda robot also demonstrate its capability of grasping unseen objects in a variety of scenarios. The code and pre-trained models will be available at https://github.com/WangShaoSUN/grasp-transformer | ['Zhen Kan', 'Zhangli Zhou', 'Shaochen Wang'] | 2022-02-24 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [-1.2138499e-01 -4.1422090e-01 7.2731704e-02 -3.6944649e-01
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eb7698f9-b226-4404-89ce-cd5c22914d67 | neural-light-field-estimation-for-street | 2208.09480 | null | https://arxiv.org/abs/2208.09480v1 | https://arxiv.org/pdf/2208.09480v1.pdf | Neural Light Field Estimation for Street Scenes with Differentiable Virtual Object Insertion | We consider the challenging problem of outdoor lighting estimation for the goal of photorealistic virtual object insertion into photographs. Existing works on outdoor lighting estimation typically simplify the scene lighting into an environment map which cannot capture the spatially-varying lighting effects in outdoor scenes. In this work, we propose a neural approach that estimates the 5D HDR light field from a single image, and a differentiable object insertion formulation that enables end-to-end training with image-based losses that encourage realism. Specifically, we design a hybrid lighting representation tailored to outdoor scenes, which contains an HDR sky dome that handles the extreme intensity of the sun, and a volumetric lighting representation that models the spatially-varying appearance of the surrounding scene. With the estimated lighting, our shadow-aware object insertion is fully differentiable, which enables adversarial training over the composited image to provide additional supervisory signal to the lighting prediction. We experimentally demonstrate that our hybrid lighting representation is more performant than existing outdoor lighting estimation methods. We further show the benefits of our AR object insertion in an autonomous driving application, where we obtain performance gains for a 3D object detector when trained on our augmented data. | ['Sanja Fidler', 'Jan Kautz', 'David Acuna', 'Wenzheng Chen', 'Zian Wang'] | 2022-08-19 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 2.76792675e-01 9.71255153e-02 3.95670921e-01 -6.37195408e-01
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f4e511a8-fc18-49c6-b5a7-f37ca0e8ba4d | coarse-to-fine-semantic-segmentation-from | 1812.10885 | null | http://arxiv.org/abs/1812.10885v1 | http://arxiv.org/pdf/1812.10885v1.pdf | Coarse-to-fine Semantic Segmentation from Image-level Labels | Deep neural network-based semantic segmentation generally requires
large-scale cost extensive annotations for training to obtain better
performance. To avoid pixel-wise segmentation annotations which are needed for
most methods, recently some researchers attempted to use object-level labels
(e.g. bounding boxes) or image-level labels (e.g. image categories). In this
paper, we propose a novel recursive coarse-to-fine semantic segmentation
framework based on only image-level category labels. For each image, an initial
coarse mask is first generated by a convolutional neural network-based
unsupervised foreground segmentation model and then is enhanced by a graph
model. The enhanced coarse mask is fed to a fully convolutional neural network
to be recursively refined. Unlike existing image-level label-based semantic
segmentation methods which require to label all categories for images contain
multiple types of objects, our framework only needs one label for each image
and can handle images contains multi-category objects. With only trained on
ImageNet, our framework achieves comparable performance on PASCAL VOC dataset
as other image-level label-based state-of-the-arts of semantic segmentation.
Furthermore, our framework can be easily extended to foreground object
segmentation task and achieves comparable performance with the state-of-the-art
supervised methods on the Internet Object dataset. | ['Yu-cheng Chen', 'YingLi Tian', 'Longlong Jing'] | 2018-12-28 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 6.81702256e-01 3.20118040e-01 -2.15421483e-01 -6.06670499e-01
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09ddd167-9082-4f86-9e7c-ec18d618ffa3 | control-flow-in-active-inference-systems | 2303.01514 | null | https://arxiv.org/abs/2303.01514v1 | https://arxiv.org/pdf/2303.01514v1.pdf | Control flow in active inference systems | Living systems face both environmental complexity and limited access to free-energy resources. Survival under these conditions requires a control system that can activate, or deploy, available perception and action resources in a context specific way. We show here that when systems are described as executing active inference driven by the free-energy principle (and hence can be considered Bayesian prediction-error minimizers), their control flow systems can always be represented as tensor networks (TNs). We show how TNs as control systems can be implmented within the general framework of quantum topological neural networks, and discuss the implications of these results for modeling biological systems at multiple scales. | ['Antonino Marciano', 'Michael Levin', 'Hananel Hazan', 'James F. Glazebrook', 'Karl Friston', 'Filippo Fabrocini', 'Chris Fields'] | 2023-02-25 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 4.45549726e-01 4.79889035e-01 -6.48978651e-02 -1.12316705e-01
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1adc13be-c287-4e23-8552-75b642974829 | progressive-class-semantic-matching-for-semi-1 | 2205.10189 | null | https://arxiv.org/abs/2205.10189v1 | https://arxiv.org/pdf/2205.10189v1.pdf | Progressive Class Semantic Matching for Semi-supervised Text Classification | Semi-supervised learning is a promising way to reduce the annotation cost for text-classification. Combining with pre-trained language models (PLMs), e.g., BERT, recent semi-supervised learning methods achieved impressive performance. In this work, we further investigate the marriage between semi-supervised learning and a pre-trained language model. Unlike existing approaches that utilize PLMs only for model parameter initialization, we explore the inherent topic matching capability inside PLMs for building a more powerful semi-supervised learning approach. Specifically, we propose a joint semi-supervised learning process that can progressively build a standard $K$-way classifier and a matching network for the input text and the Class Semantic Representation (CSR). The CSR will be initialized from the given labeled sentences and progressively updated through the training process. By means of extensive experiments, we show that our method can not only bring remarkable improvement to baselines, but also overall be more stable, and achieves state-of-the-art performance in semi-supervised text classification. | ['Ehsan Abbasnejad', 'Lingqiao Liu', 'Hai-Ming Xu'] | 2022-05-20 | null | https://aclanthology.org/2022.naacl-main.219 | https://aclanthology.org/2022.naacl-main.219.pdf | naacl-2022-7 | ['classification', 'semi-supervised-text-classification-1'] | ['methodology', 'natural-language-processing'] | [ 2.98654288e-01 3.80927682e-01 -6.51776373e-01 -1.02027690e+00
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2ca065e0-3c54-4198-a7cc-cfd99cc3bfb4 | temporal-analysis-of-reddit-networks-via-role | 1908.05192 | null | https://arxiv.org/abs/1908.05192v1 | https://arxiv.org/pdf/1908.05192v1.pdf | Temporal Analysis of Reddit Networks via Role Embeddings | Inspired by diachronic word analysis from the field of natural language processing, we propose an approach for uncovering temporal insights regarding user roles from social networks using graph embedding methods. Specifically, we apply the role embedding algorithm, struc2vec, to a collection of social networks exhibiting either "loyal" or "vagrant" characteristics derived from the popular online social news aggregation website Reddit. For each subreddit, we extract nine months of data and create network role embeddings on consecutive time windows. We are then able to compare and contrast how user roles change over time by aligning the resulting temporal embeddings spaces. In particular, we analyse temporal role embeddings from an individual and a community-level perspective for both loyal and vagrant communities present on Reddit. | ['Siobhan Grayson', 'Derek Greene'] | 2019-08-14 | null | null | null | null | ['role-embedding'] | ['graphs'] | [-1.74765244e-01 1.14149913e-01 -2.77083039e-01 -6.67943135e-02
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2ca34838-3c09-4018-a016-35ad237c3959 | joint-segmentation-and-discontinuity | 2211.13828 | null | https://arxiv.org/abs/2211.13828v1 | https://arxiv.org/pdf/2211.13828v1.pdf | Joint segmentation and discontinuity-preserving deformable registration: Application to cardiac cine-MR images | Medical image registration is a challenging task involving the estimation of spatial transformations to establish anatomical correspondence between pairs or groups of images. Recently, deep learning-based image registration methods have been widely explored, and demonstrated to enable fast and accurate image registration in a variety of applications. However, most deep learning-based registration methods assume that the deformation fields are smooth and continuous everywhere in the image domain, which is not always true, especially when registering images whose fields of view contain discontinuities at tissue/organ boundaries. In such scenarios, enforcing smooth, globally continuous deformation fields leads to incorrect/implausible registration results. We propose a novel discontinuity-preserving image registration method to tackle this challenge, which ensures globally discontinuous and locally smooth deformation fields, leading to more accurate and realistic registration results. The proposed method leverages the complementary nature of image segmentation and registration and enables joint segmentation and pair-wise registration of images. A co-attention block is proposed in the segmentation component of the network to learn the structural correlations in the input images, while a discontinuity-preserving registration strategy is employed in the registration component of the network to ensure plausibility in the estimated deformation fields at tissue/organ interfaces. We evaluate our method on the task of intra-subject spatio-temporal image registration using large-scale cinematic cardiac magnetic resonance image sequences, and demonstrate that our method achieves significant improvements over the state-of-the-art for medical image registration, and produces high-quality segmentation masks for the regions of interest. | ['Alejandro F Frangi', 'Nishant Ravikumar', 'Yan Xia', 'Xiang Chen'] | 2022-11-24 | null | null | null | null | ['medical-image-registration'] | ['medical'] | [ 3.91392231e-01 -4.11718376e-02 -8.32944214e-02 -5.28806090e-01
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1174923f-772f-4cdf-8753-81867a95b100 | open-vocabulary-one-stage-detection-with | 2203.10593 | null | https://arxiv.org/abs/2203.10593v1 | https://arxiv.org/pdf/2203.10593v1.pdf | Open-Vocabulary One-Stage Detection with Hierarchical Visual-Language Knowledge Distillation | Open-vocabulary object detection aims to detect novel object categories beyond the training set. The advanced open-vocabulary two-stage detectors employ instance-level visual-to-visual knowledge distillation to align the visual space of the detector with the semantic space of the Pre-trained Visual-Language Model (PVLM). However, in the more efficient one-stage detector, the absence of class-agnostic object proposals hinders the knowledge distillation on unseen objects, leading to severe performance degradation. In this paper, we propose a hierarchical visual-language knowledge distillation method, i.e., HierKD, for open-vocabulary one-stage detection. Specifically, a global-level knowledge distillation is explored to transfer the knowledge of unseen categories from the PVLM to the detector. Moreover, we combine the proposed global-level knowledge distillation and the common instance-level knowledge distillation to learn the knowledge of seen and unseen categories simultaneously. Extensive experiments on MS-COCO show that our method significantly surpasses the previous best one-stage detector with 11.9\% and 6.7\% $AP_{50}$ gains under the zero-shot detection and generalized zero-shot detection settings, and reduces the $AP_{50}$ performance gap from 14\% to 7.3\% compared to the best two-stage detector. | ['Weiming Hu', 'Congxuan Zhang', 'Shaoru Wang', 'Yuxin Chen', 'Liang Li', 'Jin Gao', 'Guan Luo', 'Zongyang Ma'] | 2022-03-20 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ma_Open-Vocabulary_One-Stage_Detection_With_Hierarchical_Visual-Language_Knowledge_Distillation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ma_Open-Vocabulary_One-Stage_Detection_With_Hierarchical_Visual-Language_Knowledge_Distillation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['open-vocabulary-object-detection'] | ['computer-vision'] | [ 5.88370040e-02 1.17924348e-01 -6.01053238e-02 -1.53050572e-03
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43edcc5f-c2fd-4120-a969-dc16e887ed9b | how-to-augment-a-small-learning-set-for | 1801.04076 | null | http://arxiv.org/abs/1801.04076v2 | http://arxiv.org/pdf/1801.04076v2.pdf | How to augment a small learning set for improving the performances of a CNN-based steganalyzer? | Deep learning and convolutional neural networks (CNN) have been intensively
used in many image processing topics during last years. As far as steganalysis
is concerned, the use of CNN allows reaching the state-of-the-art results. The
performances of such networks often rely on the size of their learning
database. An obvious preliminary assumption could be considering that "the
bigger a database is, the better the results are". However, it appears that
cautions have to be taken when increasing the database size if one desire to
improve the classification accuracy i.e. enhance the steganalysis efficiency.
To our knowledge, no study has been performed on the enrichment impact of a
learning database on the steganalysis performance. What kind of images can be
added to the initial learning set? What are the sensitive criteria: the camera
models used for acquiring the images, the treatments applied to the images, the
cameras proportions in the database, etc? This article continues the work
carried out in a previous paper, and explores the ways to improve the
performances of CNN. It aims at studying the effects of "base augmentation" on
the performance of steganalysis using a CNN. We present the results of this
study using various experimental protocols and various databases to define the
good practices in base augmentation for steganalysis. | ['Frédéric Comby', 'Marc Chaumont', 'Mehdi Yedroudj'] | 2018-01-12 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 2.76751995e-01 4.52037714e-02 2.56363600e-01 -5.01771681e-02
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db38a810-da67-4943-bcdf-ef261630a3b1 | ranking-social-media-news-feeds-a-comparative | null | null | https://link.springer.com/chapter/10.1007/978-3-030-96311-8_19 | http://dspace.univ-eloued.dz/bitstream/123456789/10831/1/ranking%20social%20media%20news%20feeds%20a%20comparative%20study%20of%20personalized%20and%20non%20personalized.pdf | Ranking Social Media News Feeds: A Comparative Study of Personalized and Non-personalized Prediction Models | Home Artificial Intelligence and Its Applications Conference paper
Ranking Social Media News Feeds: A Comparative Study of Personalized and Non-personalized Prediction Models
Sami Belkacem, Kamel Boukhalfa & Omar Boussaid
Conference paper
First Online: 12 March 2022
416 Accesses
Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 413)
Abstract
Ranking news feed updates by relevance has been proposed to help social media users catch up with the content they may find interesting. For this matter, a single non-personalized model has been used to predict the relevance for all users. However, as user interests and preferences are different, we believe that using a personalized model for each user is crucial to refine the ranking. In this work, to predict the relevance of news feed updates and improve user experience, we use the random forest algorithm to train and introduce a personalized prediction model for each user. Then, we compare personalized and non-personalized models according to six criteria: (1) the overall prediction performance; (2) the amount of data in the training set; (3) the cold-start problem; (4) the incorporation of user preferences over time; (5) the model fine-tuning; and (6) the personalization of feature importance for users. Experimental results on Twitter show that a single non-personalized model for all users is easy to manage and fine-tune, is less likely to overfit, and it addresses the problem of cold-start and inactive users. On the other hand, the personalized models we introduce allow personalized feature importance, take into consideration the preferences of each user, and allow to track changes in user preferences over time. Furthermore, personalized models give a higher prediction accuracy than non-personalized models. | ['Omar Boussaid', 'Kamel Boukhalfa', 'Sami Belkacem'] | 2022-03-12 | null | null | null | international-conference-on-artificial-5 | ['social-media-popularity-prediction', 'social-media-popularity-prediction'] | ['miscellaneous', 'time-series'] | [-0.27327722 0.02101434 -0.40591705 -0.49153116 -0.2782803 -0.33777633
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b28ab7fc-c221-4d65-943e-cb64d27e5eca | data-leakage-and-evaluation-issues-in-micro | 2211.11425 | null | https://arxiv.org/abs/2211.11425v2 | https://arxiv.org/pdf/2211.11425v2.pdf | Data Leakage and Evaluation Issues in Micro-Expression Analysis | Micro-expressions have drawn increasing interest lately due to various potential applications. The task is, however, difficult as it incorporates many challenges from the fields of computer vision, machine learning and emotional sciences. Due to the spontaneous and subtle characteristics of micro-expressions, the available training and testing data are limited, which make evaluation complex. We show that data leakage and fragmented evaluation protocols are issues among the micro-expression literature. We find that fixing data leaks can drastically reduce model performance, in some cases even making the models perform similarly to a random classifier. To this end, we go through common pitfalls, propose a new standardized evaluation protocol using facial action units with over 2000 micro-expression samples, and provide an open source library that implements the evaluation protocols in a standardized manner. Code is publicly available in \url{https://github.com/tvaranka/meb}. | ['Guoying Zhao', 'Wei Peng', 'Yante Li', 'Tuomas Varanka'] | 2022-11-21 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 9.24385935e-02 -1.21397629e-01 -3.55547071e-01 -7.37850964e-01
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88e75766-6952-4231-b948-b883824e4b83 | span-labeling-approach-for-vietnamese-and | 2110.00156 | null | https://arxiv.org/abs/2110.00156v1 | https://arxiv.org/pdf/2110.00156v1.pdf | Span Labeling Approach for Vietnamese and Chinese Word Segmentation | In this paper, we propose a span labeling approach to model n-gram information for Vietnamese word segmentation, namely SPAN SEG. We compare the span labeling approach with the conditional random field by using encoders with the same architecture. Since Vietnamese and Chinese have similar linguistic phenomena, we evaluated the proposed method on the Vietnamese treebank benchmark dataset and five Chinese benchmark datasets. Through our experimental results, the proposed approach SpanSeg achieves higher performance than the sequence tagging approach with the state-of-the-art F-score of 98.31% on the Vietnamese treebank benchmark, when they both apply the contextual pre-trained language model XLM-RoBERTa and the predicted word boundary information. Besides, we do fine-tuning experiments for the span labeling approach on BERT and ZEN pre-trained language model for Chinese with fewer parameters, faster inference time, and competitive or higher F-scores than the previous state-of-the-art approach, word segmentation with word-hood memory networks, on five Chinese benchmarks. | ['Ngan Luu-Thuy Nguyen', 'Dang Van Thin', 'Linh-Bao Vo', 'Duc-Vu Nguyen'] | 2021-10-01 | null | null | null | null | ['vietnamese-word-segmentation', 'chinese-word-segmentation'] | ['natural-language-processing', 'natural-language-processing'] | [-8.16107914e-02 6.00405484e-02 -5.53986788e-01 -4.79972363e-01
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-3.19401383e-01 -1.94096804e-01 2.79461682e-01 6.82251006e-02] | [10.0691556930542, 10.033583641052246] |
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