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56ba82d5-424b-49b0-a060-317d6845f9c3 | 11-teraflops-per-second-photonic | 2011.07393 | null | https://arxiv.org/abs/2011.07393v1 | https://arxiv.org/pdf/2011.07393v1.pdf | 11 TeraFLOPs per second photonic convolutional accelerator for deep learning optical neural networks | Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network parametric complexity and enhance the predicting accuracy. They are of significant interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis. Optical neural networks offer the promise of dramatically accelerating computing speed to overcome the inherent bandwidth bottleneck of electronics. Here, we demonstrate a universal optical vector convolutional accelerator operating beyond 10 TeraFLOPS (floating point operations per second), generating convolutions of images of 250,000 pixels with 8 bit resolution for 10 kernels simultaneously, enough for facial image recognition. We then use the same hardware to sequentially form a deep optical CNN with ten output neurons, achieving successful recognition of full 10 digits with 900 pixel handwritten digit images with 88% accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as unmanned vehicle and real-time video recognition. | ['David J. Moss', 'Arnan Mitchell', 'Roberto Morandotti', 'Damien G. Hicks', 'Brent E. Little', 'Sai T. Chu', 'Thach G. Nguyen', 'Andreas Boes', 'Jiayang Wu', 'Bill Corcoran', 'Mengxi Tan', 'Xingyuan Xu'] | 2020-11-14 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 2.32297033e-01 -7.39165917e-02 -2.27861434e-01 -1.86973661e-01
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9cc4d30d-e41a-4005-ab54-1d87e09adf35 | residual-guide-feature-fusion-network-for | 1804.07493 | null | http://arxiv.org/abs/1804.07493v1 | http://arxiv.org/pdf/1804.07493v1.pdf | Residual-Guide Feature Fusion Network for Single Image Deraining | Single image rain streaks removal is extremely important since rainy images
adversely affect many computer vision systems. Deep learning based methods have
found great success in image deraining tasks. In this paper, we propose a novel
residual-guide feature fusion network, called ResGuideNet, for single image
deraining that progressively predicts highquality reconstruction. Specifically,
we propose a cascaded network and adopt residuals generated from shallower
blocks to guide deeper blocks. By using this strategy, we can obtain a coarse
to fine estimation of negative residual as the blocks go deeper. The outputs of
different blocks are merged into the final reconstruction. We adopt recursive
convolution to build each block and apply supervision to all intermediate
results, which enable our model to achieve promising performance on synthetic
and real-world data while using fewer parameters than previous required.
ResGuideNet is detachable to meet different rainy conditions. For images with
light rain streaks and limited computational resource at test time, we can
obtain a decent performance even with several building blocks. Experiments
validate that ResGuideNet can benefit other low- and high-level vision tasks. | ['Huafeng Wu', 'Zhiwen Fan', 'Yue Hunag', 'Xueyang Fu', 'Xinghao Ding'] | 2018-04-20 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 2.05765456e-01 -2.14236721e-01 4.03306484e-01 -5.31551838e-01
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f40dce37-e7f7-4de9-bda7-cee0aaff18f5 | learning-to-refine-object-contours-with-a-top | 1705.04456 | null | http://arxiv.org/abs/1705.04456v1 | http://arxiv.org/pdf/1705.04456v1.pdf | Learning to Refine Object Contours with a Top-Down Fully Convolutional Encoder-Decoder Network | We develop a novel deep contour detection algorithm with a top-down fully
convolutional encoder-decoder network. Our proposed method, named TD-CEDN,
solves two important issues in this low-level vision problem: (1) learning
multi-scale and multi-level features; and (2) applying an effective top-down
refined approach in the networks. TD-CEDN performs the pixel-wise prediction by
means of leveraging features at all layers of the net. Unlike skip connections
and previous encoder-decoder methods, we first learn a coarse feature map after
the encoder stage in a feedforward pass, and then refine this feature map in a
top-down strategy during the decoder stage utilizing features at successively
lower layers. Therefore, the deconvolutional process is conducted stepwise,
which is guided by Deeply-Supervision Net providing the integrated direct
supervision. The above proposed technologies lead to a more precise and clearer
prediction. Our proposed algorithm achieved the state-of-the-art on the BSDS500
dataset (ODS F-score of 0.788), the PASCAL VOC2012 dataset (ODS F-score of
0.588), and and the NYU Depth dataset (ODS F-score of 0.735). | ['Jian Yao', 'Yahui Liu', 'Xiaohu Lu', 'Li Li', 'Jing Han'] | 2017-05-12 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 2.61743188e-01 8.84498507e-02 1.04819514e-01 -3.29965353e-01
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8602f452-b244-4c4e-8436-139383fbcf2b | poly-gan-multi-conditioned-gan-for-fashion | 1909.02165 | null | https://arxiv.org/abs/1909.02165v1 | https://arxiv.org/pdf/1909.02165v1.pdf | Poly-GAN: Multi-Conditioned GAN for Fashion Synthesis | We present Poly-GAN, a novel conditional GAN architecture that is motivated by Fashion Synthesis, an application where garments are automatically placed on images of human models at an arbitrary pose. Poly-GAN allows conditioning on multiple inputs and is suitable for many tasks, including image alignment, image stitching, and inpainting. Existing methods have a similar pipeline where three different networks are used to first align garments with the human pose, then perform stitching of the aligned garment and finally refine the results. Poly-GAN is the first instance where a common architecture is used to perform all three tasks. Our novel architecture enforces the conditions at all layers of the encoder and utilizes skip connections from the coarse layers of the encoder to the respective layers of the decoder. Poly-GAN is able to perform a spatial transformation of the garment based on the RGB skeleton of the model at an arbitrary pose. Additionally, Poly-GAN can perform image stitching, regardless of the garment orientation, and inpainting on the garment mask when it contains irregular holes. Our system achieves state-of-the-art quantitative results on Structural Similarity Index metric and Inception Score metric using the DeepFashion dataset. | ['Nilesh Pandey', 'Andreas Savakis'] | 2019-09-05 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 6.45215988e-01 1.66729569e-01 4.79549244e-02 -1.33606300e-01
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a399f181-a620-4bbc-b113-231401a0ba24 | globaltrack-a-simple-and-strong-baseline-for | 1912.08531 | null | https://arxiv.org/abs/1912.08531v1 | https://arxiv.org/pdf/1912.08531v1.pdf | GlobalTrack: A Simple and Strong Baseline for Long-term Tracking | A key capability of a long-term tracker is to search for targets in very large areas (typically the entire image) to handle possible target absences or tracking failures. However, currently there is a lack of such a strong baseline for global instance search. In this work, we aim to bridge this gap. Specifically, we propose GlobalTrack, a pure global instance search based tracker that makes no assumption on the temporal consistency of the target's positions and scales. GlobalTrack is developed based on two-stage object detectors, and it is able to perform full-image and multi-scale search of arbitrary instances with only a single query as the guide. We further propose a cross-query loss to improve the robustness of our approach against distractors. With no online learning, no punishment on position or scale changes, no scale smoothing and no trajectory refinement, our pure global instance search based tracker achieves comparable, sometimes much better performance on four large-scale tracking benchmarks (i.e., 52.1% AUC on LaSOT, 63.8% success rate on TLP, 60.3% MaxGM on OxUvA and 75.4% normalized precision on TrackingNet), compared to state-of-the-art approaches that typically require complex post-processing. More importantly, our tracker runs without cumulative errors, i.e., any type of temporary tracking failures will not affect its performance on future frames, making it ideal for long-term tracking. We hope this work will be a strong baseline for long-term tracking and will stimulate future works in this area. Code is available at https://github.com/huanglianghua/GlobalTrack. | ['Xin Zhao', 'Lianghua Huang', 'Kaiqi Huang'] | 2019-12-18 | null | null | null | null | ['instance-search'] | ['computer-vision'] | [-4.95544642e-01 -4.64907646e-01 -4.42671984e-01 -2.32453048e-02
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492279cc-669f-48fd-a083-a4037d3a2d12 | discriminative-language-model-as-semantic | 2210.12763 | null | https://arxiv.org/abs/2210.12763v1 | https://arxiv.org/pdf/2210.12763v1.pdf | Discriminative Language Model as Semantic Consistency Scorer for Prompt-based Few-Shot Text Classification | This paper proposes a novel prompt-based finetuning method (called DLM-SCS) for few-shot text classification by utilizing the discriminative language model ELECTRA that is pretrained to distinguish whether a token is original or generated. The underlying idea is that the prompt instantiated with the true label should have higher semantic consistency score than other prompts with false labels. Since a prompt usually consists of several components (or parts), its semantic consistency can be decomposed accordingly. The semantic consistency of each component is then computed by making use of the pretrained ELECTRA model, without introducing extra parameters. Extensive experiments have shown that our model outperforms several state-of-the-art prompt-based few-shot methods. | ['Yahe Li', 'Zhipeng Xie'] | 2022-10-23 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 9.36196074e-02 -5.56815341e-02 -4.38813210e-01 -5.29918909e-01
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4df8fbde-732c-42d2-85b4-23c771e08e07 | phoenix-a-self-optimizing-chess-engine | 1603.09051 | null | http://arxiv.org/abs/1603.09051v4 | http://arxiv.org/pdf/1603.09051v4.pdf | Phoenix: A Self-Optimizing Chess Engine | Since the advent of computers, many tasks which required humans to spend a
lot of time and energy have been trivialized by the computers' ability to
perform repetitive tasks extremely quickly. Playing chess is one such task. It
was one of the first games which was `solved' using AI. With the advent of deep
learning, chess playing agents can surpass human ability with relative ease.
However algorithms using deep learning must learn millions of parameters. This
work looks at the game of chess through the lens of genetic algorithms. We
train a genetic player from scratch using only a handful of learnable
parameters. We use Multi-Niche Crowding to optimize positional Value Tables
(PVTs) which are used extensively in chess engines to evaluate the goodness of
a position. With a very simple setup and after only 1000 generations of
evolution, the player reaches the level of an International Master. | ['G. Srinivasaraghavan', 'Rahul Aralikatte'] | 2016-03-30 | null | null | null | null | ['game-of-chess'] | ['playing-games'] | [-1.26883358e-01 -8.81351233e-02 2.91714877e-01 7.97753111e-02
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be722937-1b40-4db1-bf48-d131355c4ef7 | parallel-data-helps-neural-entity-coreference | 2305.17709 | null | https://arxiv.org/abs/2305.17709v1 | https://arxiv.org/pdf/2305.17709v1.pdf | Parallel Data Helps Neural Entity Coreference Resolution | Coreference resolution is the task of finding expressions that refer to the same entity in a text. Coreference models are generally trained on monolingual annotated data but annotating coreference is expensive and challenging. Hardmeier et al.(2013) have shown that parallel data contains latent anaphoric knowledge, but it has not been explored in end-to-end neural models yet. In this paper, we propose a simple yet effective model to exploit coreference knowledge from parallel data. In addition to the conventional modules learning coreference from annotations, we introduce an unsupervised module to capture cross-lingual coreference knowledge. Our proposed cross-lingual model achieves consistent improvements, up to 1.74 percentage points, on the OntoNotes 5.0 English dataset using 9 different synthetic parallel datasets. These experimental results confirm that parallel data can provide additional coreference knowledge which is beneficial to coreference resolution tasks. | ['Christian Hardmeier', 'Gongbo Tang'] | 2023-05-28 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [-8.84145349e-02 5.71327090e-01 -7.78363585e-01 -4.99496132e-01
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6acf3617-a49a-4732-bac8-318d67eb8b33 | point-cloud-registration-based-on-graph | 2302.05844 | null | https://arxiv.org/abs/2302.05844v3 | https://arxiv.org/pdf/2302.05844v3.pdf | Graph Matching Optimization Network for Point Cloud Registration | Point Cloud Registration is a fundamental and challenging problem in 3D computer vision. Recent works often utilize the geometric structure information in point feature embedding or outlier rejection for registration while neglecting to consider explicitly isometry-preserving constraint ($e.g.,$ point pair linked edge's length preserving after transformation) in training. We claim that the explicit isometry-preserving constraint is also important for improving feature representation abilities in the feature training stage. To this end, we propose a \underline{G}raph \underline{M}atching \underline{O}ptimization based \underline{Net}work (GMONet for short), which utilizes the graph-matching optimizer to explicitly exert the isometry preserving constraints in the point feature training to improve the point feature representation. Specifically, we exploit a partial graph-matching optimizer to optimize the super point ($i.e.,$ down-sampled key points) features and a full graph-matching optimizer to optimize fine-level point features in the overlap region. Meanwhile, we leverage the inexact proximal point method and the mini-batch sampling technique to accelerate these two graph-matching optimizers. Given high discriminative point features in the evaluation stage, we utilize the RANSAC approach to estimate the transformation between the scanned pairs. The proposed method has been evaluated on the 3DMatch/3DLoMatch benchmarks and the KITTI benchmark. The experimental results show that our method performs competitively compared to state-of-the-art baselines. | ['Jin Xie', 'Haobo Jiang', 'Jian Yang', 'Lei Luo', 'Yaqing Ding', 'Guofeng Mei', 'Yaqi Shen', 'Qianliang Wu'] | 2023-02-12 | null | null | null | null | ['point-cloud-registration', 'graph-matching'] | ['computer-vision', 'graphs'] | [-2.21285626e-01 -1.06369406e-01 -8.24781284e-02 -3.60037327e-01
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364a96b3-d4ae-47fc-9d80-731cdd4e6a5f | maximum-margin-learning-of-t-spns-for-cell | 2303.09065 | null | https://arxiv.org/abs/2303.09065v3 | https://arxiv.org/pdf/2303.09065v3.pdf | Maximum margin learning of t-SPNs for cell classification with filtered input | An algorithm based on a deep probabilistic architecture referred to as a tree-structured sum-product network (t-SPN) is considered for cell classification. The t-SPN is constructed such that the unnormalized probability is represented as conditional probabilities of a subset of most similar cell classes. The constructed t-SPN architecture is learned by maximizing the margin, which is the difference in the conditional probability between the true and the most competitive false label. To enhance the generalization ability of the architecture, L2-regularization (REG) is considered along with the maximum margin (MM) criterion in the learning process. To highlight cell features, this paper investigates the effectiveness of two generic high-pass filters: ideal high-pass filtering and the Laplacian of Gaussian (LOG) filtering. On both HEp-2 and Feulgen benchmark datasets, the t-SPN architecture learned based on the max-margin criterion with regularization produced the highest accuracy rate compared to other state-of-the-art algorithms that include convolutional neural network (CNN) based algorithms. The ideal high-pass filter was more effective on the HEp-2 dataset, which is based on immunofluorescence staining, while the LOG was more effective on the Feulgen dataset, which is based on Feulgen staining. | ['Yongcheon Na', 'Chang D. Yoo', 'Haeyong Kang'] | 2023-03-16 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-3.75061557e-02 9.52601507e-02 4.85253371e-02 -1.65856287e-01
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ebab565d-5ee8-45dd-8bf6-5fe2a2416c6b | guts-generalized-uncertainty-aware-thompson | 2304.02075 | null | https://arxiv.org/abs/2304.02075v1 | https://arxiv.org/pdf/2304.02075v1.pdf | GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search | Robotic solutions for quick disaster response are essential to ensure minimal loss of life, especially when the search area is too dangerous or too vast for human rescuers. We model this problem as an asynchronous multi-agent active-search task where each robot aims to efficiently seek objects of interest (OOIs) in an unknown environment. This formulation addresses the requirement that search missions should focus on quick recovery of OOIs rather than full coverage of the search region. Previous approaches fail to accurately model sensing uncertainty, account for occlusions due to foliage or terrain, or consider the requirement for heterogeneous search teams and robustness to hardware and communication failures. We present the Generalized Uncertainty-aware Thompson Sampling (GUTS) algorithm, which addresses these issues and is suitable for deployment on heterogeneous multi-robot systems for active search in large unstructured environments. We show through simulation experiments that GUTS consistently outperforms existing methods such as parallelized Thompson Sampling and exhaustive search, recovering all OOIs in 80% of all runs. In contrast, existing approaches recover all OOIs in less than 40% of all runs. We conduct field tests using our multi-robot system in an unstructured environment with a search area of approximately 75,000 sq. m. Our system demonstrates robustness to various failure modes, achieving full recovery of OOIs (where feasible) in every field run, and significantly outperforming our baseline. | ['Jeff Schneider', 'Ramina Ghods', 'Tejus Gupta', 'Nikhil Angad Bakshi'] | 2023-04-04 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 2.19196025e-02 3.30809593e-01 3.56611684e-02 1.75673142e-01
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051f133c-2337-4da4-bd47-add357e4ab09 | synthesis-unit-and-question-set-definition | null | null | https://aclanthology.org/O13-1008 | https://aclanthology.org/O13-1008.pdf | 合成單元與問題集之定義於隱藏式馬可夫模型中文歌聲合成系統之建立 (Synthesis Unit and Question Set Definition for Mandarin HMM-based Singing Voice Synthesis) | null | ['Chung-Hsien Wu', 'Yi-chin Huang', 'Ju-Yun Cheng'] | 2013-10-01 | synthesis-unit-and-question-set-definition-1 | https://aclanthology.org/O13-1008 | https://aclanthology.org/O13-1008.pdf | roclingijclclp-2013-10 | ['singing-voice-synthesis'] | ['speech'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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0ae2790f-5ca6-40fc-869d-0eb802d9efde | hierarchical-classification-of-pulmonary | 2010.04049 | null | https://arxiv.org/abs/2010.04049v1 | https://arxiv.org/pdf/2010.04049v1.pdf | Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study | Diagnosis of pulmonary lesions from computed tomography (CT) is important but challenging for clinical decision making in lung cancer related diseases. Deep learning has achieved great success in computer aided diagnosis (CADx) area for lung cancer, whereas it suffers from label ambiguity due to the difficulty in the radiological diagnosis. Considering that invasive pathological analysis serves as the clinical golden standard of lung cancer diagnosis, in this study, we solve the label ambiguity issue via a large-scale radio-pathomics dataset containing 5,134 radiological CT images with pathologically confirmed labels, including cancers (e.g., invasive/non-invasive adenocarcinoma, squamous carcinoma) and non-cancer diseases (e.g., tuberculosis, hamartoma). This retrospective dataset, named Pulmonary-RadPath, enables development and validation of accurate deep learning systems to predict invasive pathological labels with a non-invasive procedure, i.e., radiological CT scans. A three-level hierarchical classification system for pulmonary lesions is developed, which covers most diseases in cancer-related diagnosis. We explore several techniques for hierarchical classification on this dataset, and propose a Leaky Dense Hierarchy approach with proven effectiveness in experiments. Our study significantly outperforms prior arts in terms of data scales (6x larger), disease comprehensiveness and hierarchies. The promising results suggest the potentials to facilitate precision medicine. | ['Chang Chen', 'Dong Xie', 'Yunlang She', 'Bingbing Ni', 'Kaiming Kuang', 'Mingze Gao', 'Jiancheng Yang'] | 2020-10-08 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [ 2.41251200e-01 1.50803208e-01 -7.80398488e-01 4.80892975e-03
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afb97fba-c275-4999-aec3-f0fcb6ed761b | using-document-summarization-techniques-for | null | null | https://aclanthology.org/N13-1086 | https://aclanthology.org/N13-1086.pdf | Using Document Summarization Techniques for Speech Data Subset Selection | null | ['Kai Wei', 'Jeff Bilmes', 'Katrin Kirchhoff', 'Yuzong Liu'] | 2013-06-01 | null | null | null | naacl-2013-6 | ['extractive-document-summarization'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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c051f8c9-ad20-4909-ae6f-e924bec3be2e | learning-monocular-visual-odometry-with-dense | 1803.02286 | null | http://arxiv.org/abs/1803.02286v2 | http://arxiv.org/pdf/1803.02286v2.pdf | Learning monocular visual odometry with dense 3D mapping from dense 3D flow | This paper introduces a fully deep learning approach to monocular SLAM, which
can perform simultaneous localization using a neural network for learning
visual odometry (L-VO) and dense 3D mapping. Dense 2D flow and a depth image
are generated from monocular images by sub-networks, which are then used by a
3D flow associated layer in the L-VO network to generate dense 3D flow. Given
this 3D flow, the dual-stream L-VO network can then predict the 6DOF relative
pose and furthermore reconstruct the vehicle trajectory. In order to learn the
correlation between motion directions, the Bivariate Gaussian modelling is
employed in the loss function. The L-VO network achieves an overall performance
of 2.68% for average translational error and 0.0143 deg/m for average
rotational error on the KITTI odometry benchmark. Moreover, the learned depth
is fully leveraged to generate a dense 3D map. As a result, an entire visual
SLAM system, that is, learning monocular odometry combined with dense 3D
mapping, is achieved. | ['Pulak Purkait', 'Tom Duckett', 'Cheng Zhao', 'Rustam Stolkin', 'Li Sun'] | 2018-03-06 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-5.82311749e-01 2.04508491e-02 -3.89000267e-01 -4.10816938e-01
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c71a41a6-71a9-4a87-aacf-ec25266ff17e | custom-pretrainings-and-adapted-3d-convnext | 2206.15073 | null | https://arxiv.org/abs/2206.15073v2 | https://arxiv.org/pdf/2206.15073v2.pdf | COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom Pretrainings | Since COVID strongly affects the respiratory system, lung CT-scans can be used for the analysis of a patients health. We introduce a neural network for the prediction of the severity of lung damage and the detection of a COVID-infection using three-dimensional CT-data. Therefore, we adapt the recent ConvNeXt model to process three-dimensional data. Furthermore, we design and analyze different pretraining methods specifically designed to improve the models ability to handle three-dimensional CT-data. We rank 2nd in the 1st COVID19 Severity Detection Challenge and 3rd in the 2nd COVID19 Detection Challenge. | ['Rainer Lienhart', 'Katja Ludwig', 'Robin Schön', 'Julian Lorenz', 'Daniel Kienzle'] | 2022-06-30 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [-1.47573769e-01 -4.39230293e-01 4.85334210e-02 -2.45440185e-01
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f31259b2-29a2-474a-90df-a0c67f4fdcc2 | the-value-of-ai-guidance-in-human-examination | 2208.10544 | null | https://arxiv.org/abs/2208.10544v1 | https://arxiv.org/pdf/2208.10544v1.pdf | The Value of AI Guidance in Human Examination of Synthetically-Generated Faces | Face image synthesis has progressed beyond the point at which humans can effectively distinguish authentic faces from synthetically generated ones. Recently developed synthetic face image detectors boast "better-than-human" discriminative ability, especially those guided by human perceptual intelligence during the model's training process. In this paper, we investigate whether these human-guided synthetic face detectors can assist non-expert human operators in the task of synthetic image detection when compared to models trained without human-guidance. We conducted a large-scale experiment with more than 1,560 subjects classifying whether an image shows an authentic or synthetically-generated face, and annotate regions that supported their decisions. In total, 56,015 annotations across 3,780 unique face images were collected. All subjects first examined samples without any AI support, followed by samples given (a) the AI's decision ("synthetic" or "authentic"), (b) class activation maps illustrating where the model deems salient for its decision, or (c) both the AI's decision and AI's saliency map. Synthetic faces were generated with six modern Generative Adversarial Networks. Interesting observations from this experiment include: (1) models trained with human-guidance offer better support to human examination of face images when compared to models trained traditionally using cross-entropy loss, (2) binary decisions presented to humans offers better support than saliency maps, (3) understanding the AI's accuracy helps humans to increase trust in a given model and thus increase their overall accuracy. This work demonstrates that although humans supported by machines achieve better-than-random accuracy of synthetic face detection, the ways of supplying humans with AI support and of building trust are key factors determining high effectiveness of the human-AI tandem. | ['Adam Czajka', 'Kevin Bowyer', 'Patrick Tinsley', 'Aidan Boyd'] | 2022-08-22 | null | null | null | null | ['face-detection', 'synthetic-image-detection'] | ['computer-vision', 'computer-vision'] | [ 4.66167569e-01 6.27131343e-01 1.32932439e-01 -6.62392616e-01
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8c817d5a-d7ea-4d70-9d73-f0fe807f4417 | multi-stage-neural-networks-with-single-sided | 1703.00311 | null | http://arxiv.org/abs/1703.00311v3 | http://arxiv.org/pdf/1703.00311v3.pdf | Multi-stage Neural Networks with Single-sided Classifiers for False Positive Reduction and its Evaluation using Lung X-ray CT Images | Lung nodule classification is a class imbalanced problem because nodules are
found with much lower frequency than non-nodules. In the class imbalanced
problem, conventional classifiers tend to be overwhelmed by the majority class
and ignore the minority class. We therefore propose cascaded convolutional
neural networks to cope with the class imbalanced problem. In the proposed
approach, multi-stage convolutional neural networks that perform as
single-sided classifiers filter out obvious non-nodules. Successively, a
convolutional neural network trained with a balanced data set calculates nodule
probabilities. The proposed method achieved the sensitivity of 92.4\% and 94.5%
at 4 and 8 false positives per scan in Free Receiver Operating Characteristics
(FROC) curve analysis, respectively. | ['Taro Sekiyama', 'Masaharu Sakamoto', 'Kun Zhao', 'Hiroki Nakano'] | 2017-03-01 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [ 2.49985471e-01 4.56583530e-01 -6.62559032e-01 -3.67225915e-01
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784d98a2-3b2b-42d9-8dd7-e0d782625e6e | constrained-online-two-stage-stochastic | 2302.00997 | null | https://arxiv.org/abs/2302.00997v2 | https://arxiv.org/pdf/2302.00997v2.pdf | Constrained Online Two-stage Stochastic Optimization: Near Optimal Algorithms via Adversarial Learning | We consider an online two-stage stochastic optimization with long-term constraints over a finite horizon of $T$ periods. At each period, we take the first-stage action, observe a model parameter realization and then take the second-stage action from a feasible set that depends both on the first-stage decision and the model parameter. We aim to minimize the cumulative objective value while guaranteeing that the long-term average second-stage decision belongs to a set. We develop online algorithms for the online two-stage problem from adversarial learning algorithms. Also, the regret bound of our algorithm cam be reduced to the regret bound of embedded adversarial learning algorithms. Based on our framework, we obtain new results under various settings. When the model parameter at each period is drawn from identical distributions, we derive state-of-art $O(\sqrt{T})$ regret that improves previous bounds under special cases. Our algorithm is also robust to adversarial corruptions of model parameter realizations. When the model parameters are drawn from unknown non-stationary distributions and we are given prior estimates of the distributions, we develop a new algorithm from our framework with a regret $O(W_T+\sqrt{T})$, where $W_T$ measures the total inaccuracy of the prior estimates. | ['Jiashuo Jiang'] | 2023-02-02 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 1.48625091e-01 3.19719225e-01 -3.19718093e-01 -1.64960966e-01
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e3989450-2368-421c-90fc-fa15dbf8667a | learning-to-rank-visual-stories-from-human | null | null | https://openreview.net/forum?id=b8lMsO5YtpR | https://openreview.net/pdf?id=b8lMsO5YtpR | Learning to Rank Visual Stories From Human Ranking Data | Visual storytelling (VIST) is a typical vision and language task that has seen extensive development in the natural language generation research domain. However, it remains unclear whether conventional automatic evaluation metrics for text generation are applicable on VIST.
In this paper, we present the VHED (VIST Human Evaluation Data) dataset, which first re-purposes human evaluation results for automatic evaluation; hence we develop Vrank (VIST Ranker), a novel reference-free VIST metric for story evaluation. We first show that the results from commonly adopted automatic metrics for text generation have little correlation with those obtained from human evaluation, which motivates us to directly utilize human evaluation results to learn the automatic evaluation model. In the experiments, we evaluate the generated texts to predict story ranks using our model as well as other reference-based and reference-free metrics. Results show that Vrank prediction is significantly more aligned to human evaluation than other metrics with almost 30\% higher accuracy when ranking story pairs. Moreover, we demonstrate that only Vrank shows human-like behavior in its strong ability to find better stories when the quality gap between two stories is high. Finally, we show the superiority of Vrank by its generalizability to pure textual stories, and conclude that this reuse of human evaluation results puts Vrank in a strong position for continued future advances. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['visual-storytelling'] | ['natural-language-processing'] | [ 1.76391512e-01 2.30899110e-01 -1.10398903e-01 -1.45583436e-01
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a3f63fbe-cbcd-4bd9-96ae-eb8e5a7f5058 | morphological-analysis-and-disambiguation-for | null | null | https://aclanthology.org/N13-1044 | https://aclanthology.org/N13-1044.pdf | Morphological Analysis and Disambiguation for Dialectal Arabic | null | ['Esk', 'Ryan Roth', 'Owen Rambow', 'Nadi Tomeh', 'Ramy er', 'Nizar Habash'] | 2013-06-01 | null | null | null | naacl-2013-6 | ['morphological-tagging'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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6a01be34-4399-49f4-8b1a-30650dba3ff1 | hybridfusion-lidar-and-vision-cross-source | 2304.04508 | null | https://arxiv.org/abs/2304.04508v1 | https://arxiv.org/pdf/2304.04508v1.pdf | HybridFusion: LiDAR and Vision Cross-Source Point Cloud Fusion | Recently, cross-source point cloud registration from different sensors has become a significant research focus. However, traditional methods confront challenges due to the varying density and structure of cross-source point clouds. In order to solve these problems, we propose a cross-source point cloud fusion algorithm called HybridFusion. It can register cross-source dense point clouds from different viewing angle in outdoor large scenes. The entire registration process is a coarse-to-fine procedure. First, the point cloud is divided into small patches, and a matching patch set is selected based on global descriptors and spatial distribution, which constitutes the coarse matching process. To achieve fine matching, 2D registration is performed by extracting 2D boundary points from patches, followed by 3D adjustment. Finally, the results of multiple patch pose estimates are clustered and fused to determine the final pose. The proposed approach is evaluated comprehensively through qualitative and quantitative experiments. In order to compare the robustness of cross-source point cloud registration, the proposed method and generalized iterative closest point method are compared. Furthermore, a metric for describing the degree of point cloud filling is proposed. The experimental results demonstrate that our approach achieves state-of-the-art performance in cross-source point cloud registration. | ['Ke Li', 'Xuefeng Cao', 'Kun Li', 'Yifei Dong', 'Lin Chen', 'Shuhui Bu', 'Yu Wang'] | 2023-04-10 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-3.96023579e-02 -6.24072611e-01 1.09678231e-01 -1.49087682e-01
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63aa22aa-3744-4aa7-b97f-f96bc9565da8 | unsupervised-episode-generation-for-graph | 2306.15217 | null | https://arxiv.org/abs/2306.15217v1 | https://arxiv.org/pdf/2306.15217v1.pdf | Unsupervised Episode Generation for Graph Meta-learning | In this paper, we investigate Unsupervised Episode Generation methods to solve Few-Shot Node-Classification (FSNC) problem via Meta-learning without labels. Dominant meta-learning methodologies for FSNC were developed under the existence of abundant labeled nodes for training, which however may not be possible to obtain in the real-world. Although few studies have been proposed to tackle the label-scarcity problem, they still rely on a limited amount of labeled data, which hinders the full utilization of the information of all nodes in a graph. Despite the effectiveness of Self-Supervised Learning (SSL) approaches on FSNC without labels, they mainly learn generic node embeddings without consideration on the downstream task to be solved, which may limit its performance. In this work, we propose unsupervised episode generation methods to benefit from their generalization ability for FSNC tasks while resolving label-scarcity problem. We first propose a method that utilizes graph augmentation to generate training episodes called g-UMTRA, which however has several drawbacks, i.e., 1) increased training time due to the computation of augmented features and 2) low applicability to existing baselines. Hence, we propose Neighbors as Queries (NaQ), which generates episodes from structural neighbors found by graph diffusion. Our proposed methods are model-agnostic, that is, they can be plugged into any existing graph meta-learning models, while not sacrificing much of their performance or sometimes even improving them. We provide theoretical insights to support why our unsupervised episode generation methodologies work, and extensive experimental results demonstrate the potential of our unsupervised episode generation methods for graph meta-learning towards FSNC problems. | ['Chanyoung Park', 'Sungwon Kim', 'Sangwoo Seo', 'Jihyeong Jung'] | 2023-06-27 | null | null | null | null | ['self-supervised-learning', 'node-classification', 'meta-learning'] | ['computer-vision', 'graphs', 'methodology'] | [ 4.50832367e-01 4.14909899e-01 -6.80863142e-01 -4.75463942e-02
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8b499b3c-f744-4298-94f4-60548973cdc6 | meshtalk-3d-face-animation-from-speech-using | 2104.08223 | null | https://arxiv.org/abs/2104.08223v2 | https://arxiv.org/pdf/2104.08223v2.pdf | MeshTalk: 3D Face Animation from Speech using Cross-Modality Disentanglement | This paper presents a generic method for generating full facial 3D animation from speech. Existing approaches to audio-driven facial animation exhibit uncanny or static upper face animation, fail to produce accurate and plausible co-articulation or rely on person-specific models that limit their scalability. To improve upon existing models, we propose a generic audio-driven facial animation approach that achieves highly realistic motion synthesis results for the entire face. At the core of our approach is a categorical latent space for facial animation that disentangles audio-correlated and audio-uncorrelated information based on a novel cross-modality loss. Our approach ensures highly accurate lip motion, while also synthesizing plausible animation of the parts of the face that are uncorrelated to the audio signal, such as eye blinks and eye brow motion. We demonstrate that our approach outperforms several baselines and obtains state-of-the-art quality both qualitatively and quantitatively. A perceptual user study demonstrates that our approach is deemed more realistic than the current state-of-the-art in over 75% of cases. We recommend watching the supplemental video before reading the paper: https://github.com/facebookresearch/meshtalk | ['Yaser Sheikh', 'Fernando de la Torre', 'Yandong Wen', 'Michael Zollhoefer', 'Alexander Richard'] | 2021-04-16 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Richard_MeshTalk_3D_Face_Animation_From_Speech_Using_Cross-Modality_Disentanglement_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Richard_MeshTalk_3D_Face_Animation_From_Speech_Using_Cross-Modality_Disentanglement_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-face-animation'] | ['computer-vision'] | [ 2.38498840e-02 4.09912497e-01 -1.17433503e-01 -1.58408448e-01
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0c37f821-ccc9-47d0-a4da-b2760298675a | the-case-for-hierarchical-deep-learning | 2304.11763 | null | https://arxiv.org/abs/2304.11763v1 | https://arxiv.org/pdf/2304.11763v1.pdf | The Case for Hierarchical Deep Learning Inference at the Network Edge | Resource-constrained Edge Devices (EDs), e.g., IoT sensors and microcontroller units, are expected to make intelligent decisions using Deep Learning (DL) inference at the edge of the network. Toward this end, there is a significant research effort in developing tinyML models - Deep Learning (DL) models with reduced computation and memory storage requirements - that can be embedded on these devices. However, tinyML models have lower inference accuracy. On a different front, DNN partitioning and inference offloading techniques were studied for distributed DL inference between EDs and Edge Servers (ESs). In this paper, we explore Hierarchical Inference (HI), a novel approach proposed by Vishnu et al. 2023, arXiv:2304.00891v1 , for performing distributed DL inference at the edge. Under HI, for each data sample, an ED first uses a local algorithm (e.g., a tinyML model) for inference. Depending on the application, if the inference provided by the local algorithm is incorrect or further assistance is required from large DL models on edge or cloud, only then the ED offloads the data sample. At the outset, HI seems infeasible as the ED, in general, cannot know if the local inference is sufficient or not. Nevertheless, we present the feasibility of implementing HI for machine fault detection and image classification applications. We demonstrate its benefits using quantitative analysis and argue that using HI will result in low latency, bandwidth savings, and energy savings in edge AI systems. | ['Jaya Prakash Champati', 'James Gross', 'Vishnu Narayanan Moothedath', 'Adarsh Prasad Behera', 'Andrea Fresa', 'Ghina Al-Atat'] | 2023-04-23 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-3.62789124e-01 2.54493058e-01 -3.34126145e-01 -1.21097803e-01
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0295b0c8-0fa1-4794-872d-ca0b64c4ef0f | evaluation-of-peppermint-leaf-flavonoids-as | 2102.12651 | null | https://arxiv.org/abs/2102.12651v2 | https://arxiv.org/pdf/2102.12651v2.pdf | Evaluation of Peppermint Leaf Flavonoids as SARS-CoV-2 Spike Receptor-Binding Domain Attachment Inhibitors to the Human ACE2 Receptor: A Molecular Docking Study | Virtual screening is a computational technique widely used for identifying small molecules which are most likely to bind to a protein target. Here, we performed a molecular docking study to propose potential candidates to prevent the RBD/ACE2 attachment. These candidates are sixteen different flavonoids present in the peppermint leaf. Results showed that Luteolin 7-O-neohesperidoside is the peppermint flavonoid with a higher binding affinity regarding the RBD/ACE2 complex (about -9.18 Kcal/mol). On the other hand, Sakuranetin presented the lowest affinity (about -6.38 Kcal/mol). Binding affinities of the other peppermint flavonoids ranged from -6.44 Kcal/mol up to -9.05 Kcal/mol. The binding site surface analysis showed pocket-like regions on the RBD/ACE2 complex that yield several interactions (mostly hydrogen bonds) between the flavonoid and the amino acid residues of the proteins. This study can open channels for the understanding of the roles of flavonoids against COVID-19 infection. | ['L. A. Ribeiro Júnior', 'W. F. Giozza', 'G. D. Amvame Nze', 'R. T. de Sousa Junior', 'M. L. Pereira Júnior'] | 2021-02-25 | null | null | null | null | ['molecular-docking'] | ['medical'] | [-1.09500319e-01 9.66334864e-02 -3.24359745e-01 2.88675539e-02
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70f7c16a-7520-42f6-9708-1c2aca513936 | hypernetworks | 1609.09106 | null | http://arxiv.org/abs/1609.09106v4 | http://arxiv.org/pdf/1609.09106v4.pdf | HyperNetworks | This work explores hypernetworks: an approach of using a one network, also
known as a hypernetwork, to generate the weights for another network.
Hypernetworks provide an abstraction that is similar to what is found in
nature: the relationship between a genotype - the hypernetwork - and a
phenotype - the main network. Though they are also reminiscent of HyperNEAT in
evolution, our hypernetworks are trained end-to-end with backpropagation and
thus are usually faster. The focus of this work is to make hypernetworks useful
for deep convolutional networks and long recurrent networks, where
hypernetworks can be viewed as relaxed form of weight-sharing across layers.
Our main result is that hypernetworks can generate non-shared weights for LSTM
and achieve near state-of-the-art results on a variety of sequence modelling
tasks including character-level language modelling, handwriting generation and
neural machine translation, challenging the weight-sharing paradigm for
recurrent networks. Our results also show that hypernetworks applied to
convolutional networks still achieve respectable results for image recognition
tasks compared to state-of-the-art baseline models while requiring fewer
learnable parameters. | ['David Ha', 'Andrew Dai', 'Quoc V. Le'] | 2016-09-27 | null | null | null | null | ['handwriting-generation'] | ['computer-vision'] | [ 6.96280599e-01 6.97200358e-01 -1.34189025e-01 -1.85949445e-01
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108d40d7-7195-4e0d-b021-a6fd99c08c6b | image-retargetability | 1802.04392 | null | https://arxiv.org/abs/1802.04392v2 | https://arxiv.org/pdf/1802.04392v2.pdf | Image Retargetability | Real-world applications could benefit from the ability to automatically retarget an image to different aspect ratios and resolutions, while preserving its visually and semantically important content. However, not all images can be equally well processed that way. In this work, we introduce the notion of image retargetability to describe how well a particular image can be handled by content-aware image retargeting. We propose to learn a deep convolutional neural network to rank photo retargetability in which the relative ranking of photo retargetability is directly modeled in the loss function. Our model incorporates joint learning of meaningful photographic attributes and image content information which can help regularize the complicated retargetability rating problem. To train and analyze this model, we have collected a database which contains retargetability scores and meaningful image attributes assigned by six expert raters. Experiments demonstrate that our unified model can generate retargetability rankings that are highly consistent with human labels. To further validate our model, we show applications of image retargetability in retargeting method selection, retargeting method assessment and photo collage generation. | ['Wei-Ming Dong', 'Tong-Yee Lee', 'Fan Tang', 'Yiping Meng', 'Fuzhang Wu', 'Xinrui Li', 'Chongyang Ma'] | 2018-02-12 | null | null | null | null | ['image-retargeting'] | ['computer-vision'] | [ 4.42005068e-01 1.09603845e-01 -1.47169024e-01 -5.36145091e-01
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8422db8d-8b10-4ccd-8785-f8440379710e | tuning-computer-vision-models-with-task | 2302.08242 | null | https://arxiv.org/abs/2302.08242v1 | https://arxiv.org/pdf/2302.08242v1.pdf | Tuning computer vision models with task rewards | Misalignment between model predictions and intended usage can be detrimental for the deployment of computer vision models. The issue is exacerbated when the task involves complex structured outputs, as it becomes harder to design procedures which address this misalignment. In natural language processing, this is often addressed using reinforcement learning techniques that align models with a task reward. We adopt this approach and show its surprising effectiveness across multiple computer vision tasks, such as object detection, panoptic segmentation, colorization and image captioning. We believe this approach has the potential to be widely useful for better aligning models with a diverse range of computer vision tasks. | ['Xiaohua Zhai', 'Lucas Beyer', 'Yuge Shi', 'Alexander Kolesnikov', 'André Susano Pinto'] | 2023-02-16 | null | null | null | null | ['panoptic-segmentation', 'colorization'] | ['computer-vision', 'computer-vision'] | [ 5.90419531e-01 9.93557274e-02 -1.27768606e-01 -6.32535577e-01
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650fdbc2-9f56-4430-8dd0-88432c14d070 | community-detection-attack-against | 2306.08929 | null | https://arxiv.org/abs/2306.08929v1 | https://arxiv.org/pdf/2306.08929v1.pdf | Community Detection Attack against Collaborative Learning-based Recommender Systems | Collaborative-learning based recommender systems emerged following the success of collaborative learning techniques such as Federated Learning (FL) and Gossip Learning (GL). In these systems, users participate in the training of a recommender system while keeping their history of consumed items on their devices. While these solutions seemed appealing for preserving the privacy of the participants at a first glance, recent studies have shown that collaborative learning can be vulnerable to a variety of privacy attacks. In this paper we propose a novel privacy attack called Community Detection Attack (CDA), which allows an adversary to discover the members of a community based on a set of items of her choice (e.g., discovering users interested in LGBT content). Through experiments on three real recommendation datasets and by using two state-of-the-art recommendation models, we assess the sensitivity of an FL-based recommender system as well as two flavors of Gossip Learning-based recommender systems to CDA. Results show that on all models and all datasets, the FL setting is more vulnerable to CDA than Gossip settings. We further evaluated two off-the-shelf mitigation strategies, namely differential privacy (DP) and a share less policy, which consists in sharing a subset of model parameters. Results show a better privacy-utility trade-off for the share less policy compared to DP especially in the Gossip setting. | ['Anthony Simonet-Boulogne', 'Mohamed Maouche', 'Sonia Ben Mokhtar', 'Yacine Belal'] | 2023-06-15 | null | null | null | null | ['community-detection'] | ['graphs'] | [-2.39417568e-01 1.56227559e-01 1.04973681e-01 -2.69853085e-01
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3d799326-8339-4185-9ff7-b887b78737d6 | bounce-a-reliable-bayesian-optimization | 2307.00618 | null | https://arxiv.org/abs/2307.00618v1 | https://arxiv.org/pdf/2307.00618v1.pdf | Bounce: a Reliable Bayesian Optimization Algorithm for Combinatorial and Mixed Spaces | Impactful applications such as materials discovery, hardware design, neural architecture search, or portfolio optimization require optimizing high-dimensional black-box functions with mixed and combinatorial input spaces. While Bayesian optimization has recently made significant progress in solving such problems, an in-depth analysis reveals that the current state-of-the-art methods are not reliable. Their performances degrade substantially when the unknown optima of the function do not have a certain structure. To fill the need for a reliable algorithm for combinatorial and mixed spaces, this paper proposes Bounce that relies on a novel map of various variable types into nested embeddings of increasing dimensionality. Comprehensive experiments show that Bounce reliably achieves and often even improves upon state-of-the-art performance on a variety of high-dimensional problems. | ['Matthias Poloczek', 'Luigi Nardi', 'Leonard Papenmeier'] | 2023-07-02 | null | null | null | null | ['architecture-search', 'bayesian-optimization', 'portfolio-optimization'] | ['methodology', 'methodology', 'time-series'] | [-1.62627742e-01 -2.65843987e-01 -4.52395409e-01 -2.95948356e-01
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515bfb2a-d765-4b5f-91eb-58ed5e66fc6a | binary-patterns-encoded-convolutional-neural | 1706.01171 | null | http://arxiv.org/abs/1706.01171v2 | http://arxiv.org/pdf/1706.01171v2.pdf | Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification | Designing discriminative powerful texture features robust to realistic
imaging conditions is a challenging computer vision problem with many
applications, including material recognition and analysis of satellite or
aerial imagery. In the past, most texture description approaches were based on
dense orderless statistical distribution of local features. However, most
recent approaches to texture recognition and remote sensing scene
classification are based on Convolutional Neural Networks (CNNs). The d facto
practice when learning these CNN models is to use RGB patches as input with
training performed on large amounts of labeled data (ImageNet). In this paper,
we show that Binary Patterns encoded CNN models, codenamed TEX-Nets, trained
using mapped coded images with explicit texture information provide
complementary information to the standard RGB deep models. Additionally, two
deep architectures, namely early and late fusion, are investigated to combine
the texture and color information. To the best of our knowledge, we are the
first to investigate Binary Patterns encoded CNNs and different deep network
fusion architectures for texture recognition and remote sensing scene
classification. We perform comprehensive experiments on four texture
recognition datasets and four remote sensing scene classification benchmarks:
UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with
7 categories and the recently introduced large scale aerial image dataset (AID)
with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary
information to standard RGB deep model of the same network architecture. Our
late fusion TEX-Net architecture always improves the overall performance
compared to the standard RGB network on both recognition problems. Our final
combination outperforms the state-of-the-art without employing fine-tuning or
ensemble of RGB network architectures. | ['Jorma Laaksonen', 'Joost Van de Weijer', 'Fahad Shahbaz Khan', 'Rao Muhammad Anwer', 'Matthieu Molinier'] | 2017-06-05 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [ 6.01488292e-01 -5.32222748e-01 7.01335743e-02 -7.21449554e-01
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60bed26e-b2e3-42e4-9035-f13f8b0bd8be | unifying-consciousness-and-time-to-enhance | 2301.08742 | null | https://arxiv.org/abs/2301.08742v1 | https://arxiv.org/pdf/2301.08742v1.pdf | Unifying Consciousness and Time to Enhance Artificial Intelligence | Consciousness is a sequential process of awareness which can focus on one piece of information at a time. This process of awareness experiences causation which underpins the notion of time while it interplays with matter and energy, forming reality. The study of Consciousness, time and reality is complex and evolving fast in many fields, including metaphysics and fundamental physics. Reality composes patterns in human Consciousness in response to the regularities in nature. These regularities could be physical (e.g., astronomical, environmental), biological, chemical, mental, social, etc. The patterns that emerged in Consciousness were correlated to the environment, life and social behaviours followed by constructed frameworks, systems and structures. The complex constructs evolved as cultures, customs, norms and values, which created a diverse society. In the evolution of responsible AI, it is important to be attuned to the evolved cultural, ethical and moral values through Consciousness. This requires the advocated design of self-learning AI aware of time perception and human ethics. | ['Mahendra Samarawickrama'] | 2023-01-10 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 8.42663124e-02 -1.66887626e-01 2.82207310e-01 -3.34266722e-02
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54372206-21ec-4853-bb3b-b3e57cf1ce76 | attention-based-feature-decomposition | 2111.14340 | null | https://arxiv.org/abs/2111.14340v1 | https://arxiv.org/pdf/2111.14340v1.pdf | Attention-based Feature Decomposition-Reconstruction Network for Scene Text Detection | Recently, scene text detection has been a challenging task. Texts with arbitrary shape or large aspect ratio are usually hard to detect. Previous segmentation-based methods can describe curve text more accurately but suffer from over segmentation and text adhesion. In this paper, we propose attention-based feature decomposition-reconstruction network for scene text detection, which utilizes contextual information and low-level feature to enhance the performance of segmentation-based text detector. In the phase of feature fusion, we introduce cross level attention module to enrich contextual information of text by adding attention mechanism on fused multi-scaled feature. In the phase of probability map generation, a feature decomposition-reconstruction module is proposed to alleviate the over segmentation problem of large aspect ratio text, which decomposes text feature according to their frequency characteristic and then reconstructs it by adding low-level feature. Experiments have been conducted on two public benchmark datasets and results show that our proposed method achieves state-of-the-art performance. | ['Lijiang Chen', 'Shuchang Lyu', 'YuFei Wang', 'Qi Zhao'] | 2021-11-29 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 4.18902725e-01 -5.63764393e-01 5.30594178e-02 -1.75390065e-01
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5ba528ae-590d-4303-8768-0864c3607222 | sodeep-a-sorting-deep-net-to-learn-ranking | 1904.04272 | null | http://arxiv.org/abs/1904.04272v1 | http://arxiv.org/pdf/1904.04272v1.pdf | SoDeep: a Sorting Deep net to learn ranking loss surrogates | Several tasks in machine learning are evaluated using non-differentiable
metrics such as mean average precision or Spearman correlation. However, their
non-differentiability prevents from using them as objective functions in a
learning framework. Surrogate and relaxation methods exist but tend to be
specific to a given metric.
In the present work, we introduce a new method to learn approximations of
such non-differentiable objective functions. Our approach is based on a deep
architecture that approximates the sorting of arbitrary sets of scores. It is
trained virtually for free using synthetic data. This sorting deep (SoDeep) net
can then be combined in a plug-and-play manner with existing deep
architectures. We demonstrate the interest of our approach in three different
tasks that require ranking: Cross-modal text-image retrieval, multi-label image
classification and visual memorability ranking. Our approach yields very
competitive results on these three tasks, which validates the merit and the
flexibility of SoDeep as a proxy for sorting operation in ranking-based losses. | ['Patrick Pérez', 'Matthieu Cord', 'Martin Engilberge', 'Louis Chevallier'] | 2019-04-08 | sodeep-a-sorting-deep-net-to-learn-ranking-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Engilberge_SoDeep_A_Sorting_Deep_Net_to_Learn_Ranking_Loss_Surrogates_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Engilberge_SoDeep_A_Sorting_Deep_Net_to_Learn_Ranking_Loss_Surrogates_CVPR_2019_paper.pdf | cvpr-2019-6 | ['multi-label-image-classification'] | ['computer-vision'] | [ 6.21063635e-02 -6.25472590e-02 -7.42698163e-02 -8.33024383e-01
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bfb80f1c-9ab0-4986-952d-6d24dedb418c | depth-estimation-and-image-restoration-by | 2302.10730 | null | https://arxiv.org/abs/2302.10730v1 | https://arxiv.org/pdf/2302.10730v1.pdf | Depth Estimation and Image Restoration by Deep Learning from Defocused Images | Monocular depth estimation and image deblurring are two fundamental tasks in computer vision, given their crucial role in understanding 3D scenes. Performing any of them by relying on a single image is an ill-posed problem. The recent advances in the field of deep convolutional neural networks (DNNs) have revolutionized many tasks in computer vision, including depth estimation and image deblurring. When it comes to using defocused images, the depth estimation and the recovery of the All-in-Focus (Aif) image become related problems due to defocus physics. In spite of this, most of the existing models treat them separately. There are, however, recent models that solve these problems simultaneously by concatenating two networks in a sequence to first estimate the depth or defocus map and then reconstruct the focused image based on it. We propose a DNN that solves the depth estimation and image deblurring in parallel. Our Two-headed Depth Estimation and Deblurring Network (2HDED:NET) extends a conventional Depth from Defocus (DFD) network with a deblurring branch that shares the same encoder as the depth branch. The proposed method has been successfully tested on two benchmarks, one for indoor and the other for outdoor scenes: NYU-v2 and Make3D. Extensive experiments with 2HDED:NET on these benchmarks have demonstrated superior or close performances to those of the state-of-the-art models for depth estimation and image deblurring. | ['Daniela Coltuc', 'Víctor M. Brea', 'Manuel Mucientes', 'Lorenzo Vaquero', 'Saqib Nazir'] | 2023-02-21 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 5.29411197e-01 -3.51713151e-01 1.76718295e-01 -3.53340358e-01
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6b6bf285-e7a8-420b-bb76-6796cac59cef | set-transformer-a-framework-for-attention | 1810.00825 | null | https://arxiv.org/abs/1810.00825v3 | https://arxiv.org/pdf/1810.00825v3.pdf | Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks | Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances. Since solutions to such problems do not depend on the order of elements of the set, models used to address them should be permutation invariant. We present an attention-based neural network module, the Set Transformer, specifically designed to model interactions among elements in the input set. The model consists of an encoder and a decoder, both of which rely on attention mechanisms. In an effort to reduce computational complexity, we introduce an attention scheme inspired by inducing point methods from sparse Gaussian process literature. It reduces the computation time of self-attention from quadratic to linear in the number of elements in the set. We show that our model is theoretically attractive and we evaluate it on a range of tasks, demonstrating the state-of-the-art performance compared to recent methods for set-structured data. | ['Yee Whye Teh', 'Jungtaek Kim', 'Seungjin Choi', 'Juho Lee', 'Adam R. Kosiorek', 'Yoonho Lee'] | 2018-10-01 | set-transformer | https://openreview.net/forum?id=Hkgnii09Ym | https://openreview.net/pdf?id=Hkgnii09Ym | null | ['3d-shape-recognition'] | ['computer-vision'] | [ 5.5756587e-01 1.3671972e-01 -4.0636059e-02 -2.6778072e-01
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b0d8eaa6-141f-4d9d-905d-42304a8a3893 | are-there-intelligent-turing-machines | 1503.03787 | null | http://arxiv.org/abs/1503.03787v1 | http://arxiv.org/pdf/1503.03787v1.pdf | Are there intelligent Turing machines? | This paper introduces a new computing model based on the cooperation among
Turing machines called orchestrated machines. Like universal Turing machines,
orchestrated machines are also designed to simulate Turing machines but they
can also modify the original operation of the included Turing machines to
create a new layer of some kind of collective behavior. Using this new model we
can define some interested notions related to cooperation ability of Turing
machines such as the intelligence quotient or the emotional intelligence
quotient for Turing machines. | ['Norbert Bátfai'] | 2015-03-12 | null | null | null | null | ['emotional-intelligence'] | ['natural-language-processing'] | [-3.47269028e-01 7.69731104e-01 4.05646503e-01 -3.39234382e-01
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1a1a0450-4ac1-4755-bd90-4b6dc5e521c9 | benchmarking-probabilistic-deep-learning | 2302.01427 | null | https://arxiv.org/abs/2302.01427v1 | https://arxiv.org/pdf/2302.01427v1.pdf | Benchmarking Probabilistic Deep Learning Methods for License Plate Recognition | Learning-based algorithms for automated license plate recognition implicitly assume that the training and test data are well aligned. However, this may not be the case under extreme environmental conditions, or in forensic applications where the system cannot be trained for a specific acquisition device. Predictions on such out-of-distribution images have an increased chance of failing. But this failure case is oftentimes hard to recognize for a human operator or an automated system. Hence, in this work we propose to model the prediction uncertainty for license plate recognition explicitly. Such an uncertainty measure allows to detect false predictions, indicating an analyst when not to trust the result of the automated license plate recognition. In this paper, we compare three methods for uncertainty quantification on two architectures. The experiments on synthetic noisy or blurred low-resolution images show that the predictive uncertainty reliably finds wrong predictions. We also show that a multi-task combination of classification and super-resolution improves the recognition performance by 109\% and the detection of wrong predictions by 29 %. | ['Christian Riess', 'Anatol Maier', 'Benedikt Lorch', 'Franziska Schirrmacher'] | 2023-02-02 | null | null | null | null | ['license-plate-recognition', 'probabilistic-deep-learning'] | ['computer-vision', 'computer-vision'] | [ 2.67975450e-01 -1.93548024e-01 3.20938021e-01 -4.59558427e-01
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aaf464f9-a4a9-4083-984d-8d18e5c390fe | cross-lingual-and-cross-domain-discourse-1 | 1704.04100 | null | http://arxiv.org/abs/1704.04100v2 | http://arxiv.org/pdf/1704.04100v2.pdf | Cross-lingual and cross-domain discourse segmentation of entire documents | Discourse segmentation is a crucial step in building end-to-end discourse
parsers. However, discourse segmenters only exist for a few languages and
domains. Typically they only detect intra-sentential segment boundaries,
assuming gold standard sentence and token segmentation, and relying on
high-quality syntactic parses and rich heuristics that are not generally
available across languages and domains. In this paper, we propose statistical
discourse segmenters for five languages and three domains that do not rely on
gold pre-annotations. We also consider the problem of learning discourse
segmenters when no labeled data is available for a language. Our fully
supervised system obtains 89.5% F1 for English newswire, with slight drops in
performance on other domains, and we report supervised and unsupervised
(cross-lingual) results for five languages in total. | ['Anders Søgaard', 'Ophélie Lacroix', 'Chloé Braud'] | 2017-04-13 | null | null | null | null | ['discourse-segmentation'] | ['natural-language-processing'] | [ 2.14459822e-01 7.27021515e-01 -5.94824731e-01 -4.07072991e-01
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45afb061-7733-4b57-a27a-8a0397fc010a | editable-indoor-lighting-estimation | 2211.03928 | null | https://arxiv.org/abs/2211.03928v2 | https://arxiv.org/pdf/2211.03928v2.pdf | Editable Indoor Lighting Estimation | We present a method for estimating lighting from a single perspective image of an indoor scene. Previous methods for predicting indoor illumination usually focus on either simple, parametric lighting that lack realism, or on richer representations that are difficult or even impossible to understand or modify after prediction. We propose a pipeline that estimates a parametric light that is easy to edit and allows renderings with strong shadows, alongside with a non-parametric texture with high-frequency information necessary for realistic rendering of specular objects. Once estimated, the predictions obtained with our model are interpretable and can easily be modified by an artist/user with a few mouse clicks. Quantitative and qualitative results show that our approach makes indoor lighting estimation easier to handle by a casual user, while still producing competitive results. | ['Jean-François Lalonde', 'Mathieu Garon', 'Henrique Weber'] | 2022-11-08 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 4.95051742e-01 -8.68292451e-02 7.20964015e-01 -6.85534358e-01
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4c62c85e-4f4a-43c7-ac3b-a85cfc1ab12f | programming-by-example-and-text-to-code | 2211.11554 | null | https://arxiv.org/abs/2211.11554v3 | https://arxiv.org/pdf/2211.11554v3.pdf | Programming by Example and Text-to-Code Translation for Conversational Code Generation | Dialogue systems is an increasingly popular task of natural language processing. However, the dialogue paths tend to be deterministic, restricted to the system rails, regardless of the given request or input text. Recent advances in program synthesis have led to systems which can synthesize programs from very general search spaces, e.g. Programming by Example, and to systems with very accessible interfaces for writing programs, e.g. text-to-code translation, but have not achieved both of these qualities in the same system. We propose Modular Programs for Text-guided Hierarchical Synthesis (MPaTHS), a method for integrating Programming by Example and text-to-code systems which offers an accessible natural language interface for synthesizing general programs. We present a program representation that allows our method to be applied to the problem of task-oriented dialogue. Finally, we demo MPaTHS using our program representation. | ['Marc Franco-Salvador', 'Yauhen Klimovich', 'William Gerard', 'Eli Whitehouse'] | 2022-11-21 | null | null | null | null | ['code-translation', 'program-synthesis'] | ['computer-code', 'computer-code'] | [ 2.00407580e-01 6.06282115e-01 -1.09355398e-01 -5.24433374e-01
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4caac0f2-f860-4985-9317-a7bdf8b10132 | skin-lesion-segmentation-using-segnet-with | null | null | https://raw.githubusercontent.com/hashbanger/Skin_Lesion_Segmentation/master/abstract.txt | https://drive.google.com/file/d/1pgAXmKgY2NerSMzvaS9M8PKnP0cTrbQM/view?usp=sharing | Skin Lesion Segmentation using SegNet with Binary Cross-Entropy | In this paper a simple and computationally efficient approach as per the complexity has been presented for Automatic Skin Lesion Segmentation using a Deep Learning architecture called SegNet including some additional specifications for the improvisation of the results. The secondary objective is to keep the pre/post -processing of the images minimal. The presented model is trained on limited images from the PH2 dataset which includes dermoscopic images, manually segmented. It also contains their masks, the clinical diagnosis and the identification of several dermoscopic structures, performed by professional dermatologists. The aim is to achieve a performance threshold Jaccard Index (IOU) 92% after evaluation. | ['Prashant Brahmbhatt', 'Siddhi Nath Rajan'] | 2019-11-15 | null | null | null | international-conference-on-artificial | ['skin-lesion-segmentation', 'skin-cancer-segmentation'] | ['medical', 'medical'] | [ 5.04354239e-01 5.18909931e-01 3.61650348e-01 -3.75782251e-01
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4403d1b0-eba3-4e44-ab5a-059836097cad | gram-regularization-for-multi-view-3d-shape | 2011.07733 | null | https://arxiv.org/abs/2011.07733v1 | https://arxiv.org/pdf/2011.07733v1.pdf | Gram Regularization for Multi-view 3D Shape Retrieval | How to obtain the desirable representation of a 3D shape is a key challenge in 3D shape retrieval task. Most existing 3D shape retrieval methods focus on capturing shape representation with different neural network architectures, while the learning ability of each layer in the network is neglected. A common and tough issue that limits the capacity of the network is overfitting. To tackle this, L2 regularization is applied widely in existing deep learning frameworks. However,the effect on the generalization ability with L2 regularization is limited as it only controls large value in parameters. To make up the gap, in this paper, we propose a novel regularization term called Gram regularization which reinforces the learning ability of the network by encouraging the weight kernels to extract different information on the corresponding feature map. By forcing the variance between weight kernels to be large, the regularizer can help to extract discriminative features. The proposed Gram regularization is data independent and can converge stably and quickly without bells and whistles. Moreover, it can be easily plugged into existing off-the-shelf architectures. Extensive experimental results on the popular 3D object retrieval benchmark ModelNet demonstrate the effectiveness of our method. | ['Zhaoqun Li'] | 2020-11-16 | null | null | null | null | ['multi-view-3d-shape-retrieval', '3d-object-retrieval', 'l2-regularization'] | ['computer-vision', 'computer-vision', 'methodology'] | [-3.65221471e-01 -3.12920064e-01 -2.40883827e-01 -3.22014391e-01
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aa04442b-0b29-48d1-9d32-606576768eb7 | learning-structured-representations-of-visual | 2207.04200 | null | https://arxiv.org/abs/2207.04200v1 | https://arxiv.org/pdf/2207.04200v1.pdf | Learning Structured Representations of Visual Scenes | As the intermediate-level representations bridging the two levels, structured representations of visual scenes, such as visual relationships between pairwise objects, have been shown to not only benefit compositional models in learning to reason along with the structures but provide higher interpretability for model decisions. Nevertheless, these representations receive much less attention than traditional recognition tasks, leaving numerous open challenges unsolved. In the thesis, we study how machines can describe the content of the individual image or video with visual relationships as the structured representations. Specifically, we explore how structured representations of visual scenes can be effectively constructed and learned in both the static-image and video settings, with improvements resulting from external knowledge incorporation, bias-reducing mechanism, and enhanced representation models. At the end of this thesis, we also discuss some open challenges and limitations to shed light on future directions of structured representation learning for visual scenes. | ['Meng-Jiun Chiou'] | 2022-07-09 | null | null | null | null | ['visual-relationship-detection', 'scene-graph-generation', 'unbiased-scene-graph-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.27102673e-01 2.72493511e-01 -4.80596155e-01 -9.78911936e-01
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72f70ee0-49bc-41a8-8c30-f3bb831d9f36 | salient-skin-lesion-segmentation-via-dilated | 2205.10272 | null | https://arxiv.org/abs/2205.10272v2 | https://arxiv.org/pdf/2205.10272v2.pdf | Salient Skin Lesion Segmentation via Dilated Scale-Wise Feature Fusion Network | Skin lesion detection in dermoscopic images is essential in the accurate and early diagnosis of skin cancer by a computerized apparatus. Current skin lesion segmentation approaches show poor performance in challenging circumstances such as indistinct lesion boundaries, low contrast between the lesion and the surrounding area, or heterogeneous background that causes over/under segmentation of the skin lesion. To accurately recognize the lesion from the neighboring regions, we propose a dilated scale-wise feature fusion network based on convolution factorization. Our network is designed to simultaneously extract features at different scales which are systematically fused for better detection. The proposed model has satisfactory accuracy and efficiency. Various experiments for lesion segmentation are performed along with comparisons with the state-of-the-art models. Our proposed model consistently showcases state-of-the-art results. | ['Huiyu Zhou', 'Eric Granger', 'Masoumeh Zareapoor', 'Pourya Shamsolmoali'] | 2022-05-20 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 5.47525704e-01 -3.46755773e-01 -3.94922793e-01 3.18477824e-02
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0816a0ea-f6a1-4d1e-8de0-9e2d30579b68 | does-syntax-help-discourse-segmentation-not | null | null | https://aclanthology.org/D17-1258 | https://aclanthology.org/D17-1258.pdf | Does syntax help discourse segmentation? Not so much | Discourse segmentation is the first step in building discourse parsers. Most work on discourse segmentation does not scale to real-world discourse parsing across languages, for two reasons: (i) models rely on constituent trees, and (ii) experiments have relied on gold standard identification of sentence and token boundaries. We therefore investigate to what extent constituents can be replaced with universal dependencies, or left out completely, as well as how state-of-the-art segmenters fare in the absence of sentence boundaries. Our results show that dependency information is less useful than expected, but we provide a fully scalable, robust model that only relies on part-of-speech information, and show that it performs well across languages in the absence of any gold-standard annotation. | ['Anders S{\\o}gaard', "Oph{\\'e}lie Lacroix", "Chlo{\\'e} Braud"] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['discourse-segmentation'] | ['natural-language-processing'] | [ 2.78884500e-01 5.15440404e-01 -2.52713352e-01 -2.87063241e-01
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03dea23c-f90a-4e52-bb2c-f8d05282e79d | a-comparison-of-modeling-units-in-sequence-to | 1805.06239 | null | http://arxiv.org/abs/1805.06239v2 | http://arxiv.org/pdf/1805.06239v2.pdf | A Comparison of Modeling Units in Sequence-to-Sequence Speech Recognition with the Transformer on Mandarin Chinese | The choice of modeling units is critical to automatic speech recognition
(ASR) tasks. Conventional ASR systems typically choose context-dependent states
(CD-states) or context-dependent phonemes (CD-phonemes) as their modeling
units. However, it has been challenged by sequence-to-sequence attention-based
models, which integrate an acoustic, pronunciation and language model into a
single neural network. On English ASR tasks, previous attempts have already
shown that the modeling unit of graphemes can outperform that of phonemes by
sequence-to-sequence attention-based model.
In this paper, we are concerned with modeling units on Mandarin Chinese ASR
tasks using sequence-to-sequence attention-based models with the Transformer.
Five modeling units are explored including context-independent phonemes
(CI-phonemes), syllables, words, sub-words and characters. Experiments on HKUST
datasets demonstrate that the lexicon free modeling units can outperform
lexicon related modeling units in terms of character error rate (CER). Among
five modeling units, character based model performs best and establishes a new
state-of-the-art CER of $26.64\%$ on HKUST datasets without a hand-designed
lexicon and an extra language model integration, which corresponds to a $4.8\%$
relative improvement over the existing best CER of $28.0\%$ by the joint
CTC-attention based encoder-decoder network. | ['Bo Xu', 'Shuang Xu', 'Shiyu Zhou', 'Linhao Dong'] | 2018-05-16 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 4.25042301e-01 -1.58676431e-01 -6.56114705e-03 -2.22859532e-01
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ee77c56f-e13f-405a-87ff-4a318bfdba73 | vision-infused-deep-audio-inpainting-1 | 1910.10997 | null | https://arxiv.org/abs/1910.10997v1 | https://arxiv.org/pdf/1910.10997v1.pdf | Vision-Infused Deep Audio Inpainting | Multi-modality perception is essential to develop interactive intelligence. In this work, we consider a new task of visual information-infused audio inpainting, \ie synthesizing missing audio segments that correspond to their accompanying videos. We identify two key aspects for a successful inpainter: (1) It is desirable to operate on spectrograms instead of raw audios. Recent advances in deep semantic image inpainting could be leveraged to go beyond the limitations of traditional audio inpainting. (2) To synthesize visually indicated audio, a visual-audio joint feature space needs to be learned with synchronization of audio and video. To facilitate a large-scale study, we collect a new multi-modality instrument-playing dataset called MUSIC-Extra-Solo (MUSICES) by enriching MUSIC dataset. Extensive experiments demonstrate that our framework is capable of inpainting realistic and varying audio segments with or without visual contexts. More importantly, our synthesized audio segments are coherent with their video counterparts, showing the effectiveness of our proposed Vision-Infused Audio Inpainter (VIAI). Code, models, dataset and video results are available at https://hangz-nju-cuhk.github.io/projects/AudioInpainting | ['Ping Luo', 'Xudong Xu', 'Hang Zhou', 'Ziwei Liu', 'Xiaogang Wang'] | 2019-10-24 | vision-infused-deep-audio-inpainting | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhou_Vision-Infused_Deep_Audio_Inpainting_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhou_Vision-Infused_Deep_Audio_Inpainting_ICCV_2019_paper.pdf | iccv-2019-10 | ['audio-inpainting'] | ['audio'] | [ 4.05490726e-01 -3.04137290e-01 3.11551690e-02 -4.69776578e-02
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b1c290d5-fb8c-4228-a7f1-2e4421419170 | skipdecode-autoregressive-skip-decoding-with | 2307.02628 | null | https://arxiv.org/abs/2307.02628v1 | https://arxiv.org/pdf/2307.02628v1.pdf | SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference | Autoregressive large language models (LLMs) have made remarkable progress in various natural language generation tasks. However, they incur high computation cost and latency resulting from the autoregressive token-by-token generation. To address this issue, several approaches have been proposed to reduce computational cost using early-exit strategies. These strategies enable faster text generation using reduced computation without applying the full computation graph to each token. While existing token-level early exit methods show promising results for online inference, they cannot be readily applied for batch inferencing and Key-Value caching. This is because they have to wait until the last token in a batch exits before they can stop computing. This severely limits the practical application of such techniques. In this paper, we propose a simple and effective token-level early exit method, SkipDecode, designed to work seamlessly with batch inferencing and KV caching. It overcomes prior constraints by setting up a singular exit point for every token in a batch at each sequence position. It also guarantees a monotonic decrease in exit points, thereby eliminating the need to recompute KV Caches for preceding tokens. Rather than terminating computation prematurely as in prior works, our approach bypasses lower to middle layers, devoting most of the computational resources to upper layers, allowing later tokens to benefit from the compute expenditure by earlier tokens. Our experimental results show that SkipDecode can obtain 2x to 5x inference speedups with negligible regression across a variety of tasks. This is achieved using OPT models of 1.3 billion and 6.7 billion parameters, all the while being directly compatible with batching and KV caching optimization techniques. | ['Subhabrata Mukherjee', 'Ahmed Awadallah', 'Bin Yu', 'Sahaj Agarwal', 'Allie Del Giorno', 'Luciano del Corro'] | 2023-07-05 | null | null | null | null | ['text-generation'] | ['natural-language-processing'] | [ 4.01445851e-02 -8.06934834e-02 -2.23859459e-01 -2.35376999e-01
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ad8578af-6dd4-461b-81ce-fdcc4c131026 | adaptive-sharpness-aware-pruning-for-robust | 2306.14306 | null | https://arxiv.org/abs/2306.14306v1 | https://arxiv.org/pdf/2306.14306v1.pdf | Adaptive Sharpness-Aware Pruning for Robust Sparse Networks | Robustness and compactness are two essential components of deep learning models that are deployed in the real world. The seemingly conflicting aims of (i) generalization across domains as in robustness, and (ii) specificity to one domain as in compression, are why the overall design goal of achieving robust compact models, despite being highly important, is still a challenging open problem. We introduce Adaptive Sharpness-Aware Pruning, or AdaSAP, a method that yields robust sparse networks. The central tenet of our approach is to optimize the loss landscape so that the model is primed for pruning via adaptive weight perturbation, and is also consistently regularized toward flatter regions for improved robustness. This unifies both goals through the lens of network sharpness. AdaSAP achieves strong performance in a comprehensive set of experiments. For classification on ImageNet and object detection on Pascal VOC datasets, AdaSAP improves the robust accuracy of pruned models by +6% on ImageNet C, +4% on ImageNet V2, and +4% on corrupted VOC datasets, over a wide range of compression ratios, saliency criteria, and network architectures, outperforming recent pruning art by large margins. | ['Jose Alvarez', 'Pavlo Molchanov', 'Maying Shen', 'Hongxu Yin', 'Anna Bair'] | 2023-06-25 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 3.48408699e-01 8.06844831e-02 -2.20542312e-01 -2.85899282e-01
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79514af2-1a3d-4a8c-8e80-64720f40cb97 | cooperative-lane-changing-in-mixed-traffic | 2303.16948 | null | https://arxiv.org/abs/2303.16948v1 | https://arxiv.org/pdf/2303.16948v1.pdf | Cooperative Lane Changing in Mixed Traffic can be Robust to Human Driver Behavior | We derive time and energy-optimal control policies for a Connected Autonomous Vehicle (CAV) to complete lane change maneuvers in mixed traffic. The interaction between CAVs and Human-Driven Vehicles (HDVs) requires designing the best possible response of a CAV to actions by its neighboring HDVs. This interaction is formulated using a bilevel optimization setting with an appropriate behavioral model for an HDV's. Then, an iterated best response (IBR) method is used to determine a Nash equilibrium. However, we also show that when a common and simple-to-detect condition applies, the optimal lane-changing policy is in fact independent of HDV behavior with a CAV changing lanes by cooperating with another CAV in the target lane and always merging ahead of it. Thus, the dependence on the interaction between CAVs and HDVs may be eliminated in such cases. Simulation results are included to show the effectiveness of our controllers in terms of cost, safety guarantees, and disruption to the traffic flow when uncontrollable HDVs are present. | ['Christos G. Cassandras', 'Andres S. Chavez Armijos', 'Anni Li'] | 2023-03-29 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-2.23516330e-01 5.87104261e-01 -3.04872096e-01 1.57211408e-01
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316e4a3c-ad9f-4429-b7f6-9fb5373957eb | gps-reviving-the-art-of-message-passing-for | 2302.02947 | null | https://arxiv.org/abs/2302.02947v2 | https://arxiv.org/pdf/2302.02947v2.pdf | GPS++: Reviving the Art of Message Passing for Molecular Property Prediction | We present GPS++, a hybrid Message Passing Neural Network / Graph Transformer model for molecular property prediction. Our model integrates a well-tuned local message passing component and biased global attention with other key ideas from prior literature to achieve state-of-the-art results on large-scale molecular dataset PCQM4Mv2. Through a thorough ablation study we highlight the impact of individual components and find that nearly all of the model's performance can be maintained without any use of global self-attention, showing that message passing is still a competitive approach for 3D molecular property prediction despite the recent dominance of graph transformers. We also find that our approach is significantly more accurate than prior art when 3D positional information is not available. | ['Dominique Beaini', 'Ladislav Rampášek', 'Shenyang Huang', 'Andrew Fitzgibbon', 'Deniz Beker', 'Hatem Helal', 'Adam Sanders', 'Sam Maddrell-Mander', 'Zhiyi Li', 'Kerstin Klaser', 'Josef Dean', 'Dominic Masters'] | 2023-02-06 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 3.89879972e-01 2.23783515e-02 -5.90374589e-01 1.04727084e-02
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4ba1f742-6391-4863-9a8c-e80c313f51c3 | intelligence-of-astronomical-optical | 2306.16834 | null | https://arxiv.org/abs/2306.16834v1 | https://arxiv.org/pdf/2306.16834v1.pdf | Intelligence of Astronomical Optical Telescope: Present Status and Future Perspectives | Artificial intelligence technology has been widely used in astronomy, and new artificial intelligence technologies and application scenarios are constantly emerging. There have been a large number of papers reviewing the application of artificial intelligence technology in astronomy. However, relevant articles seldom mention telescope intelligence separately, and it is difficult to understand the current development status and research hotspots of telescope intelligence from these papers. This paper combines the development history of artificial intelligence technology and the difficulties of critical technologies of telescopes, comprehensively introduces the development and research hotspots of telescope intelligence, then conducts statistical analysis on various research directions of telescope intelligence and defines the research directions' merits. All kinds of research directions are evaluated, and the research trend of each telescope's intelligence is pointed out. Finally, according to the advantages of artificial intelligence technology and the development trend of telescopes, future research hotspots of telescope intelligence are given. | ['Xiangqun Cui', 'Huaiqing Wang', 'Yong Zhang', 'Yonghui Hou', 'Xiushan Pang', 'Jingyi Cai', 'Kang Huang', 'Tianzhu Hu'] | 2023-06-29 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [-4.92348671e-01 -4.18616951e-01 2.71278005e-02 -1.29712494e-02
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03d7e78f-994a-4ac0-90bc-8618ad84f369 | sundown-model-driven-per-panel-solar-anomaly | 2005.12181 | null | https://arxiv.org/abs/2005.12181v1 | https://arxiv.org/pdf/2005.12181v1.pdf | SunDown: Model-driven Per-Panel Solar Anomaly Detection for Residential Arrays | There has been significant growth in both utility-scale and residential-scale solar installations in recent years, driven by rapid technology improvements and falling prices. Unlike utility-scale solar farms that are professionally managed and maintained, smaller residential-scale installations often lack sensing and instrumentation for performance monitoring and fault detection. As a result, faults may go undetected for long periods of time, resulting in generation and revenue losses for the homeowner. In this paper, we present SunDown, a sensorless approach designed to detect per-panel faults in residential solar arrays. SunDown does not require any new sensors for its fault detection and instead uses a model-driven approach that leverages correlations between the power produced by adjacent panels to detect deviations from expected behavior. SunDown can handle concurrent faults in multiple panels and perform anomaly classification to determine probable causes. Using two years of solar generation data from a real home and a manually generated dataset of multiple solar faults, we show that our approach has a MAPE of 2.98\% when predicting per-panel output. Our results also show that SunDown is able to detect and classify faults, including from snow cover, leaves and debris, and electrical failures with 99.13% accuracy, and can detect multiple concurrent faults with 97.2% accuracy. | ['Prashant Shenoy', 'Menghong Feng', 'Noman Bashir', 'David Irwin', 'Beka Kosanovic'] | 2020-05-25 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [ 2.62380362e-01 -1.12600856e-01 2.69373238e-01 -5.16325086e-02
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54c82644-393a-4c71-8871-204883a3ed8d | mitigating-embedding-and-class-assignment | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4802_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123690749.pdf | Mitigating Embedding and Class Assignment Mismatch in Unsupervised Image Classification | Unsupervised image classification is a challenging computer vision task. Deep learning-based algorithms have achieved superb results, where the latest approach adopts unified losses from embedding and class assignment processes. Since these processes inherently have different goals, jointly optimizing them may lead to a suboptimal solution. To address this limitation, we propose a novel two-stage algorithm in which an embedding module for pretraining precedes a refining module that concurrently performs embedding and class assignment. Our model outperforms SOTA when tested with multiple datasets, by substantially high accuracy of 81.0% for the CIFAR-10 dataset (i.e., increased by 19.3 percent points), 35.3% accuracy for CIFAR-100-20 (9.6 pp) and 66.5% accuracy for STL-10 (6.9 pp) in unsupervised tasks. | ['Sungwon Park', 'Sungwon Han', 'Sungkyu Park', 'Sundong Kim', 'Meeyoung Cha'] | null | null | null | null | eccv-2020-8 | ['image-clustering', 'unsupervised-image-classification'] | ['computer-vision', 'computer-vision'] | [-5.48486710e-02 1.44843468e-02 -2.61738598e-01 -4.10307527e-01
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cc2d4753-65d2-4563-bdbb-09d1c1f1f19b | characterization-multimodal-connectivity-of | 2107.09953 | null | https://arxiv.org/abs/2107.09953v1 | https://arxiv.org/pdf/2107.09953v1.pdf | Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis | Using multimodal neuroimaging data to characterize brain network is currently an advanced technique for Alzheimer's disease(AD) Analysis. Over recent years the neuroimaging community has made tremendous progress in the study of resting-state functional magnetic resonance imaging (rs-fMRI) derived from blood-oxygen-level-dependent (BOLD) signals and Diffusion Tensor Imaging (DTI) derived from white matter fiber tractography. However, Due to the heterogeneity and complexity between BOLD signals and fiber tractography, Most existing multimodal data fusion algorithms can not sufficiently take advantage of the complementary information between rs-fMRI and DTI. To overcome this problem, a novel Hypergraph Generative Adversarial Networks(HGGAN) is proposed in this paper, which utilizes Interactive Hyperedge Neurons module (IHEN) and Optimal Hypergraph Homomorphism algorithm(OHGH) to generate multimodal connectivity of Brain Network from rs-fMRI combination with DTI. To evaluate the performance of this model, We use publicly available data from the ADNI database to demonstrate that the proposed model not only can identify discriminative brain regions of AD but also can effectively improve classification performance. | ['Shuqiang Wang', 'Zhiguang Feng', 'Yong liu', 'Yanyan Shen', 'Baiying Lei', 'Junren Pan'] | 2021-07-21 | null | null | null | null | ['white-matter-fiber-tractography'] | ['medical'] | [ 2.56679475e-01 -1.56689420e-01 2.20110029e-01 -6.03489101e-01
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1a8564fc-a6d5-4c12-b3d7-bad0c0827c23 | analyzing-impact-of-socio-economic-factors-on | 2303.00517 | null | https://arxiv.org/abs/2303.00517v1 | https://arxiv.org/pdf/2303.00517v1.pdf | Analyzing Impact of Socio-Economic Factors on COVID-19 Mortality Prediction Using SHAP Value | This paper applies multiple machine learning (ML) algorithms to a dataset of de-identified COVID-19 patients provided by the COVID-19 Research Database. The dataset consists of 20,878 COVID-positive patients, among which 9,177 patients died in the year 2020. This paper aims to understand and interpret the association of socio-economic characteristics of patients with their mortality instead of maximizing prediction accuracy. According to our analysis, a patients households annual and disposable income, age, education, and employment status significantly impacts a machine learning models prediction. We also observe several individual patient data, which gives us insight into how the feature values impact the prediction for that data point. This paper analyzes the global and local interpretation of machine learning models on socio-economic data of COVID patients. | ['Ying Ding', 'Justin F Rousseau', 'Jooyeong Kang', 'Redoan Rahman'] | 2023-02-27 | null | null | null | null | ['mortality-prediction'] | ['medical'] | [-2.37605184e-01 -1.78854018e-01 -5.52850604e-01 -4.13797915e-01
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4e55e1dd-4539-484b-bacc-425fb2e59abd | understanding-grounded-language-learning-1 | null | null | https://openreview.net/forum?id=ByZmGjkA- | https://openreview.net/pdf?id=ByZmGjkA- | Understanding Grounded Language Learning Agents | Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and even execute symbolic instructions as first-person actors in partially-observable worlds. To achieve this so-called grounded language learning, models must overcome certain well-studied learning challenges that are also fundamental to infants learning their first words. While it is notable that models with no meaningful prior knowledge overcome these learning obstacles, AI researchers and practitioners currently lack a clear understanding of exactly how they do so. Here we address this question as a way of achieving a clearer general understanding of grounded language learning, both to inform future research and to improve confidence in model predictions. For maximum control and generality, we focus on a simple neural network-based language learning agent trained via policy-gradient methods to interpret synthetic linguistic instructions in a simulated 3D world. We apply experimental paradigms from developmental psychology to this agent, exploring the conditions under which established human biases and learning effects emerge. We further propose a novel way to visualise and analyse semantic representation in grounded language learning agents that yields a plausible computational account of the observed effects. | ['Karl Moritz Hermann', 'Felix Hill', 'Stephen Clark', 'Phil Blunsom'] | 2018-01-01 | null | null | null | iclr-2018-1 | ['grounded-language-learning'] | ['natural-language-processing'] | [ 4.51897919e-01 6.97317183e-01 1.66710634e-02 -5.40524125e-01
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739ab4f3-62d1-4c04-a979-89841b2f224e | real-time-salient-object-detection-with-a | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Tu_Real-Time_Salient_Object_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Tu_Real-Time_Salient_Object_CVPR_2016_paper.pdf | Real-Time Salient Object Detection With a Minimum Spanning Tree | In this paper, we present a real-time salient object detection system based on the minimum spanning tree. Due to the fact that background regions are typically connected to the image boundaries, salient objects can be extracted by computing the distances to the boundaries. However, measuring the image boundary connectivity efficiently is a challenging problem. Existing methods either rely on superpixel representation to reduce the processing units or approximate the distance transform. Instead, we propose an exact and iteration free solution on a minimum spanning tree. The minimum spanning tree representation of an image inherently reveals the object geometry information in a scene. Meanwhile, it largely reduces the search space of shortest paths, resulting an efficient and high quality distance transform algorithm. We further introduce a boundary dissimilarity measure to compliment the shortage of distance transform for salient object detection. Extensive evaluations show that the proposed algorithm achieves the leading performance compared to the state-of-the-art methods in terms of efficiency and accuracy. | ['Shao-Yi Chien', 'Wei-Chih Tu', 'Shengfeng He', 'Qingxiong Yang'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['video-salient-object-detection'] | ['computer-vision'] | [ 3.85738701e-01 -2.67809451e-01 -1.27555549e-01 -2.00671777e-01
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38205aa6-f24c-4d1a-bcce-be35a499f3d6 | rendezvous-attention-mechanisms-for-the | 2109.03223 | null | https://arxiv.org/abs/2109.03223v2 | https://arxiv.org/pdf/2109.03223v2.pdf | Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos | Out of all existing frameworks for surgical workflow analysis in endoscopic videos, action triplet recognition stands out as the only one aiming to provide truly fine-grained and comprehensive information on surgical activities. This information, presented as <instrument, verb, target> combinations, is highly challenging to be accurately identified. Triplet components can be difficult to recognize individually; in this task, it requires not only performing recognition simultaneously for all three triplet components, but also correctly establishing the data association between them. To achieve this task, we introduce our new model, the Rendezvous (RDV), which recognizes triplets directly from surgical videos by leveraging attention at two different levels. We first introduce a new form of spatial attention to capture individual action triplet components in a scene; called Class Activation Guided Attention Mechanism (CAGAM). This technique focuses on the recognition of verbs and targets using activations resulting from instruments. To solve the association problem, our RDV model adds a new form of semantic attention inspired by Transformer networks; called Multi-Head of Mixed Attention (MHMA). This technique uses several cross and self attentions to effectively capture relationships between instruments, verbs, and targets. We also introduce CholecT50 - a dataset of 50 endoscopic videos in which every frame has been annotated with labels from 100 triplet classes. Our proposed RDV model significantly improves the triplet prediction mean AP by over 9% compared to the state-of-the-art methods on this dataset. | ['Nicolas Padoy', 'Jacques Marescaux', 'Didier Mutter', 'Pietro Mascagni', 'Barbara Seeliger', 'Cristians Gonzalez', 'Tong Yu', 'Chinedu Innocent Nwoye'] | 2021-09-07 | null | null | null | null | ['action-triplet-recognition'] | ['computer-vision'] | [ 3.00289631e-01 -4.02585901e-02 -3.06105345e-01 4.09131078e-03
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2db146b0-ca56-4707-aa55-39343aa9f1dd | tetratsdf-3d-human-reconstruction-from-a | 2004.10534 | null | https://arxiv.org/abs/2004.10534v1 | https://arxiv.org/pdf/2004.10534v1.pdf | TetraTSDF: 3D human reconstruction from a single image with a tetrahedral outer shell | Recovering the 3D shape of a person from its 2D appearance is ill-posed due to ambiguities. Nevertheless, with the help of convolutional neural networks (CNN) and prior knowledge on the 3D human body, it is possible to overcome such ambiguities to recover detailed 3D shapes of human bodies from single images. Current solutions, however, fail to reconstruct all the details of a person wearing loose clothes. This is because of either (a) huge memory requirement that cannot be maintained even on modern GPUs or (b) the compact 3D representation that cannot encode all the details. In this paper, we propose the tetrahedral outer shell volumetric truncated signed distance function (TetraTSDF) model for the human body, and its corresponding part connection network (PCN) for 3D human body shape regression. Our proposed model is compact, dense, accurate, and yet well suited for CNN-based regression task. Our proposed PCN allows us to learn the distribution of the TSDF in the tetrahedral volume from a single image in an end-to-end manner. Results show that our proposed method allows to reconstruct detailed shapes of humans wearing loose clothes from single RGB images. | ['Rin-ichiro Taniguchi', 'Zehra Hayirci', 'Hideaki Uchiyama', 'Diego Thomas', 'Akihiro Sugimoto', 'Hayato Onizuka'] | 2020-04-22 | tetratsdf-3d-human-reconstruction-from-a-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Onizuka_TetraTSDF_3D_Human_Reconstruction_From_a_Single_Image_With_a_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Onizuka_TetraTSDF_3D_Human_Reconstruction_From_a_Single_Image_With_a_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-human-reconstruction'] | ['computer-vision'] | [-5.26146889e-02 -3.13412920e-02 4.87723172e-01 -3.43102485e-01
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5de5139c-ccaf-45fd-9132-edcb8a7b7fc8 | natural-scene-text-editing-based-on-ai | 2111.15475 | null | https://arxiv.org/abs/2111.15475v1 | https://arxiv.org/pdf/2111.15475v1.pdf | Natural Scene Text Editing Based on AI | In a recorded situation, textual information is crucial for scene interpretation and decision making. The ability to edit text directly on images has a number of advantages, including error correction, text restoration, and image reusability. This research shows how to change image text at the letter and digits level. I devised a two-part letters-digits network (LDN) to encode and decode digital images, as well as learn and transfer the font style of the source characters to the target characters. This method allows you to update the uppercase letters, lowercase letters and digits in the picture. | ['Yujie Zhang'] | 2021-11-26 | null | null | null | null | ['scene-text-editing'] | ['computer-vision'] | [ 8.32480431e-01 -4.94755238e-01 1.91781640e-01 -4.76458490e-01
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9a4f49d4-ea10-4c70-831c-bb6ef92f20ba | a-survey-of-numerical-algorithms-that-can | 2303.03576 | null | https://arxiv.org/abs/2303.03576v1 | https://arxiv.org/pdf/2303.03576v1.pdf | A Survey of Numerical Algorithms that can Solve the Lasso Problems | In statistics, the least absolute shrinkage and selection operator (Lasso) is a regression method that performs both variable selection and regularization. There is a lot of literature available, discussing the statistical properties of the regression coefficients estimated by the Lasso method. However, there lacks a comprehensive review discussing the algorithms to solve the optimization problem in Lasso. In this review, we summarize five representative algorithms to optimize the objective function in Lasso, including the iterative shrinkage threshold algorithm (ISTA), fast iterative shrinkage-thresholding algorithms (FISTA), coordinate gradient descent algorithm (CGDA), smooth L1 algorithm (SLA), and path following algorithm (PFA). Additionally, we also compare their convergence rate, as well as their potential strengths and weakness. | ['Xiaoming Huo', 'Yujie Zhao'] | 2023-03-07 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 1.07618429e-01 -3.34683716e-01 -5.29143572e-01 -5.99874735e-01
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ece5acc1-50d1-412f-8d5c-8732f333d6f6 | robust-autoregressive-hidden-semi-markov | 2010.08641 | null | https://arxiv.org/abs/2010.08641v2 | https://arxiv.org/pdf/2010.08641v2.pdf | Deep Neural Dynamic Bayesian Networks applied to EEG sleep spindles modeling | We propose a generative model for single-channel EEG that incorporates the constraints experts actively enforce during visual scoring. The framework takes the form of a dynamic Bayesian network with depth in both the latent variables and the observation likelihoods-while the hidden variables control the durations, state transitions, and robustness, the observation architectures parameterize Normal-Gamma distributions. The resulting model allows for time series segmentation into local, reoccurring dynamical regimes by exploiting probabilistic models and deep learning. Unlike typical detectors, our model takes the raw data (up to resampling) without pre-processing (e.g., filtering, windowing, thresholding) or post-processing (e.g., event merging). This not only makes the model appealing to real-time applications, but it also yields interpretable hyperparameters that are analogous to known clinical criteria. We derive algorithms for exact, tractable inference as a special case of Generalized Expectation Maximization via dynamic programming and backpropagation. We validate the model on three public datasets and provide support that more complex models are able to surpass state-of-the-art detectors while being transparent, auditable, and generalizable. | ['Laura L. Colgin', 'Carlos A. Loza'] | 2020-10-16 | null | null | null | null | ['sleep-spindles-detection'] | ['time-series'] | [ 2.01400757e-01 1.29020140e-01 -1.05176911e-01 -4.54831541e-01
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2f385a93-dcf7-4dfc-8f34-05c00a8c60d5 | umfa-a-photorealistic-style-transfer-method | 2108.06113 | null | https://arxiv.org/abs/2108.06113v1 | https://arxiv.org/pdf/2108.06113v1.pdf | UMFA: A photorealistic style transfer method based on U-Net and multi-layer feature aggregation | In this paper, we propose a photorealistic style transfer network to emphasize the natural effect of photorealistic image stylization. In general, distortion of the image content and lacking of details are two typical issues in the style transfer field. To this end, we design a novel framework employing the U-Net structure to maintain the rich spatial clues, with a multi-layer feature aggregation (MFA) method to simultaneously provide the details obtained by the shallow layers in the stylization processing. In particular, an encoder based on the dense block and a decoder form a symmetrical structure of U-Net are jointly staked to realize an effective feature extraction and image reconstruction. Besides, a transfer module based on MFA and "adaptive instance normalization" (AdaIN) is inserted in the skip connection positions to achieve the stylization. Accordingly, the stylized image possesses the texture of a real photo and preserves rich content details without introducing any mask or post-processing steps. The experimental results on public datasets demonstrate that our method achieves a more faithful structural similarity with a lower style loss, reflecting the effectiveness and merit of our approach. | ['T. Y. Xu', 'J. Kittler', 'H. Li', 'X. J. Wu', 'D. Y. Rao'] | 2021-08-13 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 1.72881693e-01 -1.78978980e-01 2.15799436e-01 -3.61550808e-01
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a7ea1037-b031-4286-9712-129f62d213d1 | density-aware-chamfer-distance-as-a | 2111.12702 | null | https://arxiv.org/abs/2111.12702v1 | https://arxiv.org/pdf/2111.12702v1.pdf | Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion | Chamfer Distance (CD) and Earth Mover's Distance (EMD) are two broadly adopted metrics for measuring the similarity between two point sets. However, CD is usually insensitive to mismatched local density, and EMD is usually dominated by global distribution while overlooks the fidelity of detailed structures. Besides, their unbounded value range induces a heavy influence from the outliers. These defects prevent them from providing a consistent evaluation. To tackle these problems, we propose a new similarity measure named Density-aware Chamfer Distance (DCD). It is derived from CD and benefits from several desirable properties: 1) it can detect disparity of density distributions and is thus a more intensive measure of similarity compared to CD; 2) it is stricter with detailed structures and significantly more computationally efficient than EMD; 3) the bounded value range encourages a more stable and reasonable evaluation over the whole test set. We adopt DCD to evaluate the point cloud completion task, where experimental results show that DCD pays attention to both the overall structure and local geometric details and provides a more reliable evaluation even when CD and EMD contradict each other. We can also use DCD as the training loss, which outperforms the same model trained with CD loss on all three metrics. In addition, we propose a novel point discriminator module that estimates the priority for another guided down-sampling step, and it achieves noticeable improvements under DCD together with competitive results for both CD and EMD. We hope our work could pave the way for a more comprehensive and practical point cloud similarity evaluation. Our code will be available at: https://github.com/wutong16/Density_aware_Chamfer_Distance . | ['Dahua Lin', 'Ziwei Liu', 'Tai Wang', 'Junzhe Zhang', 'Liang Pan', 'Tong Wu'] | 2021-11-24 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-4.07936066e-01 -4.30534035e-01 -2.02165380e-01 -3.43872815e-01
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898c9199-951a-4972-9312-e1e1ceaafc00 | contrastive-representation-learning-for | 2109.01484 | null | https://arxiv.org/abs/2109.01484v1 | https://arxiv.org/pdf/2109.01484v1.pdf | Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation | Exemplar-Guided Paraphrase Generation (EGPG) aims to generate a target sentence which conforms to the style of the given exemplar while encapsulating the content information of the source sentence. In this paper, we propose a new method with the goal of learning a better representation of the style andthe content. This method is mainly motivated by the recent success of contrastive learning which has demonstrated its power in unsupervised feature extraction tasks. The idea is to design two contrastive losses with respect to the content and the style by considering two problem characteristics during training. One characteristic is that the target sentence shares the same content with the source sentence, and the second characteristic is that the target sentence shares the same style with the exemplar. These two contrastive losses are incorporated into the general encoder-decoder paradigm. Experiments on two datasets, namely QQP-Pos and ParaNMT, demonstrate the effectiveness of our proposed constrastive losses. | ['Piji Li', 'Wai Lam', 'Haoran Yang'] | 2021-09-03 | null | https://aclanthology.org/2021.findings-emnlp.409 | https://aclanthology.org/2021.findings-emnlp.409.pdf | findings-emnlp-2021-11 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 2.79264838e-01 3.31321508e-01 -3.31674665e-02 -4.35255826e-01
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b960f334-940d-4885-9acb-0c2e6f0d2a05 | automated-identification-of-disaster-news-for | 2301.09896 | null | https://arxiv.org/abs/2301.09896v1 | https://arxiv.org/pdf/2301.09896v1.pdf | Automated Identification of Disaster News For Crisis Management Using Machine Learning | A lot of news sources picked up on Typhoon Rai (also known locally as Typhoon Odette), along with fake news outlets. The study honed in on the issue, to create a model that can identify between legitimate and illegitimate news articles. With this in mind, we chose the following machine learning algorithms in our development: Logistic Regression, Random Forest and Multinomial Naive Bayes. Bag of Words, TF-IDF and Lemmatization were implemented in the Model. Gathering 160 datasets from legitimate and illegitimate sources, the machine learning was trained and tested. By combining all the machine learning techniques, the Combined BOW model was able to reach an accuracy of 91.07%, precision of 88.33%, recall of 94.64%, and F1 score of 91.38% and Combined TF-IDF model was able to reach an accuracy of 91.18%, precision of 86.89%, recall of 94.64%, and F1 score of 90.60%. | ['Angie M. Ceniza-Canillo', 'Ai Matsushita', 'Lord Christian Carl H. Regacho'] | 2023-01-24 | null | null | null | null | ['lemmatization'] | ['natural-language-processing'] | [-4.84465927e-01 1.81486249e-01 -5.95621765e-01 9.66880023e-02
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098dbd47-3b3f-4d4e-9159-42b4f368cfc0 | plwordnet-in-word-sense-disambiguation-task | null | null | https://aclanthology.org/2016.gwc-1.41 | https://aclanthology.org/2016.gwc-1.41.pdf | plWordNet in Word Sense Disambiguation task | The paper explores the application of plWordNet, a very large wordnet of Polish, in weakly supervised Word Sense Disambiguation (WSD). Because plWordNet provides only partial descriptions by glosses and usage examples, and does not include sense-disambiguated glosses, PageRank-based WSD methods perform slightly worse than for English. However, we show that the use of weights for the relation types and the order in which lexical units have been added for sense re-ranking can significantly improve WSD precision. The evaluation was done on two Polish corpora (KPWr and Składnica) including manual WSD. We discuss the fundamental difference in the construction of both corpora and very different test results. | ['Marlena Orlińska', 'Paweł Kędzia', 'Maciej Piasecki'] | null | null | null | null | gwc-2016-1 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-5.51990345e-02 1.76722720e-01 -3.85861486e-01 -7.08013475e-02
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a0708bc8-b54e-4426-9f4e-570f3e35a880 | maskplace-fast-chip-placement-via-reinforced | 2211.13382 | null | https://arxiv.org/abs/2211.13382v1 | https://arxiv.org/pdf/2211.13382v1.pdf | MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning | Placement is an essential task in modern chip design, aiming at placing millions of circuit modules on a 2D chip canvas. Unlike the human-centric solution, which requires months of intense effort by hardware engineers to produce a layout to minimize delay and energy consumption, deep reinforcement learning has become an emerging autonomous tool. However, the learning-centric method is still in its early stage, impeded by a massive design space of size ten to the order of a few thousand. This work presents MaskPlace to automatically generate a valid chip layout design within a few hours, whose performance can be superior or comparable to recent advanced approaches. It has several appealing benefits that prior arts do not have. Firstly, MaskPlace recasts placement as a problem of learning pixel-level visual representation to comprehensively describe millions of modules on a chip, enabling placement in a high-resolution canvas and a large action space. It outperforms recent methods that represent a chip as a hypergraph. Secondly, it enables training the policy network by an intuitive reward function with dense reward, rather than a complicated reward function with sparse reward from previous methods. Thirdly, extensive experiments on many public benchmarks show that MaskPlace outperforms existing RL approaches in all key performance metrics, including wirelength, congestion, and density. For example, it achieves 60%-90% wirelength reduction and guarantees zero overlaps. We believe MaskPlace can improve AI-assisted chip layout design. The deliverables are released at https://laiyao1.github.io/maskplace. | ['Ping Luo', 'Yao Mu', 'Yao Lai'] | 2022-11-24 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [-2.05738410e-01 2.56727159e-01 -5.11231005e-01 2.26922911e-02
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7b608840-69c3-472c-b6c3-4dbae174ff2d | sketch-a-net-that-beats-humans | 1501.07873 | null | http://arxiv.org/abs/1501.07873v3 | http://arxiv.org/pdf/1501.07873v3.pdf | Sketch-a-Net that Beats Humans | We propose a multi-scale multi-channel deep neural network framework that,
for the first time, yields sketch recognition performance surpassing that of
humans. Our superior performance is a result of explicitly embedding the unique
characteristics of sketches in our model: (i) a network architecture designed
for sketch rather than natural photo statistics, (ii) a multi-channel
generalisation that encodes sequential ordering in the sketching process, and
(iii) a multi-scale network ensemble with joint Bayesian fusion that accounts
for the different levels of abstraction exhibited in free-hand sketches. We
show that state-of-the-art deep networks specifically engineered for photos of
natural objects fail to perform well on sketch recognition, regardless whether
they are trained using photo or sketch. Our network on the other hand not only
delivers the best performance on the largest human sketch dataset to date, but
also is small in size making efficient training possible using just CPUs. | ['Yi-Zhe Song', 'Timothy Hospedales', 'Qian Yu', 'Yongxin Yang', 'Tao Xiang'] | 2015-01-30 | null | null | null | null | ['sketch-recognition'] | ['computer-vision'] | [ 1.41767010e-01 -2.50240386e-01 -3.88637558e-02 -3.14400315e-01
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40416335-85ce-4865-ae00-919a25e2cdc1 | bandit-based-model-selection-for-deformable | 1703.10254 | null | http://arxiv.org/abs/1703.10254v1 | http://arxiv.org/pdf/1703.10254v1.pdf | Bandit-Based Model Selection for Deformable Object Manipulation | We present a novel approach to deformable object manipulation that does not
rely on highly-accurate modeling. The key contribution of this paper is to
formulate the task as a Multi-Armed Bandit problem, with each arm representing
a model of the deformable object. To "pull" an arm and evaluate its utility, we
use the arm's model to generate a velocity command for the gripper(s) holding
the object and execute it. As the task proceeds and the object deforms, the
utility of each model can change. Our framework estimates these changes and
balances exploration of the model set with exploitation of high-utility models.
We also propose an approach based on Kalman Filtering for Non-stationary
Multi-armed Normal Bandits (KF-MANB) to leverage the coupling between models to
learn more from each arm pull. We demonstrate that our method outperforms
previous methods on synthetic trials, and performs competitively on several
manipulation tasks in simulation. | ['Dmitry Berenson', 'Dale McConachie'] | 2017-03-29 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [ 1.64141804e-01 1.49645343e-01 -8.11105132e-01 2.55779088e-01
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754f66a2-2fc4-4ce1-84a7-ce07ef6ca5bb | xcit-cross-covariance-image-transformers | 2106.09681 | null | https://arxiv.org/abs/2106.09681v2 | https://arxiv.org/pdf/2106.09681v2.pdf | XCiT: Cross-Covariance Image Transformers | Following their success in natural language processing, transformers have recently shown much promise for computer vision. The self-attention operation underlying transformers yields global interactions between all tokens ,i.e. words or image patches, and enables flexible modelling of image data beyond the local interactions of convolutions. This flexibility, however, comes with a quadratic complexity in time and memory, hindering application to long sequences and high-resolution images. We propose a "transposed" version of self-attention that operates across feature channels rather than tokens, where the interactions are based on the cross-covariance matrix between keys and queries. The resulting cross-covariance attention (XCA) has linear complexity in the number of tokens, and allows efficient processing of high-resolution images. Our cross-covariance image transformer (XCiT) is built upon XCA. It combines the accuracy of conventional transformers with the scalability of convolutional architectures. We validate the effectiveness and generality of XCiT by reporting excellent results on multiple vision benchmarks, including image classification and self-supervised feature learning on ImageNet-1k, object detection and instance segmentation on COCO, and semantic segmentation on ADE20k. | ['Hervé Jegou', 'Jakob Verbeek', 'Gabriel Synnaeve', 'Natalia Neverova', 'Ivan Laptev', 'Armand Joulin', 'Matthijs Douze', 'Piotr Bojanowski', 'Mathilde Caron', 'Hugo Touvron', 'Alaaeldin El-Nouby'] | 2021-06-17 | null | http://proceedings.neurips.cc/paper/2021/hash/a655fbe4b8d7439994aa37ddad80de56-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/a655fbe4b8d7439994aa37ddad80de56-Paper.pdf | neurips-2021-12 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 3.19921404e-01 -1.97411671e-01 2.10151941e-01 -4.67523456e-01
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96fad8bd-005e-4c20-a4fa-4134bdfb8519 | mild-modeling-the-instance-learning-dynamics | 2306.11560 | null | https://arxiv.org/abs/2306.11560v1 | https://arxiv.org/pdf/2306.11560v1.pdf | MILD: Modeling the Instance Learning Dynamics for Learning with Noisy Labels | Despite deep learning has achieved great success, it often relies on a large amount of training data with accurate labels, which are expensive and time-consuming to collect. A prominent direction to reduce the cost is to learn with noisy labels, which are ubiquitous in the real-world applications. A critical challenge for such a learning task is to reduce the effect of network memorization on the falsely-labeled data. In this work, we propose an iterative selection approach based on the Weibull mixture model, which identifies clean data by considering the overall learning dynamics of each data instance. In contrast to the previous small-loss heuristics, we leverage the observation that deep network is easy to memorize and hard to forget clean data. In particular, we measure the difficulty of memorization and forgetting for each instance via the transition times between being misclassified and being memorized in training, and integrate them into a novel metric for selection. Based on the proposed metric, we retain a subset of identified clean data and repeat the selection procedure to iteratively refine the clean subset, which is finally used for model training. To validate our method, we perform extensive experiments on synthetic noisy datasets and real-world web data, and our strategy outperforms existing noisy-label learning methods. | ['Xuming He', 'Zhitong Gao', 'Shipeng Yan', 'Chuanyang Hu'] | 2023-06-20 | null | null | null | null | ['learning-with-noisy-labels', 'memorization', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [ 1.76253840e-01 -2.30735675e-01 6.08188361e-02 -4.49444026e-01
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745178be-8b51-4098-9aaf-9e84526e1370 | grids-interactive-layout-design-with-integer | 2001.02921 | null | https://arxiv.org/abs/2001.02921v1 | https://arxiv.org/pdf/2001.02921v1.pdf | GRIDS: Interactive Layout Design with Integer Programming | Grid layouts are used by designers to spatially organise user interfaces when sketching and wireframing. However, their design is largely time consuming manual work. This is challenging due to combinatorial explosion and complex objectives, such as alignment, balance, and expectations regarding positions. This paper proposes a novel optimisation approach for the generation of diverse grid-based layouts. Our mixed integer linear programming (MILP) model offers a rigorous yet efficient method for grid generation that ensures packing, alignment, grouping, and preferential positioning of elements. Further, we present techniques for interactive diversification, enhancement, and completion of grid layouts (Figure 1). These capabilities are demonstrated using GRIDS1, a wireframing tool that provides designers with real-time layout suggestions. We report findings from a ratings study (N = 13) and a design study (N = 16), lending evidence for the benefit of computational grid generation during early stages of design. | ['Antti Oulasvirta', 'Taru Saarelainen', 'Niraj Dayama', 'Kashyap Todi'] | 2020-01-09 | grids-interactive-layout-design-with-integer-1 | https://dl.acm.org/doi/abs/10.1145/3313831.3376553 | https://dl.acm.org/doi/pdf/10.1145/3313831.3376553 | null | ['layout-design'] | ['computer-vision'] | [ 2.33322665e-01 1.84799924e-01 -2.80736804e-01 -1.93857849e-01
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c943e46d-8063-4066-bde2-a22cb02cea18 | endomapper-dataset-of-complete-calibrated | 2204.14240 | null | https://arxiv.org/abs/2204.14240v1 | https://arxiv.org/pdf/2204.14240v1.pdf | EndoMapper dataset of complete calibrated endoscopy procedures | Computer-assisted systems are becoming broadly used in medicine. In endoscopy, most research focuses on automatic detection of polyps or other pathologies, but localization and navigation of the endoscope is completely performed manually by physicians. To broaden this research and bring spatial Artificial Intelligence to endoscopies, data from complete procedures are needed. This data will be used to build a 3D mapping and localization systems that can perform special task like, for example, detect blind zones during exploration, provide automatic polyp measurements, guide doctors to a polyp found in a previous exploration and retrieve previous images of the same area aligning them for easy comparison. These systems will provide an improvement in the quality and precision of the procedures while lowering the burden on the physicians. This paper introduces the Endomapper dataset, the first collection of complete endoscopy sequences acquired during regular medical practice, including slow and careful screening explorations, making secondary use of medical data. Its original purpose is to facilitate the development and evaluation of VSLAM (Visual Simultaneous Localization and Mapping) methods in real endoscopy data. The first release of the dataset is composed of 59 sequences with more than 15 hours of video. It is also the first endoscopic dataset that includes both the computed geometric and photometric endoscope calibration with the original calibration videos. Meta-data and annotations associated to the dataset varies from anatomical landmark and description of the procedure labeling, tools segmentation masks, COLMAP 3D reconstructions, simulated sequences with groundtruth and meta-data related to special cases, such as sequences from the same patient. This information will improve the research in endoscopic VSLAM, as well as other research lines, and create new research lines. | ['José M. M. Montiel', 'Angel Lanas', 'Ana Cristina Murillo', 'Juan D. Tardós', 'Javier Civera', 'Cristina Oriol', 'Julia López', 'Richard Elvira', 'Juan J. Gómez-Rodríguez', 'Victor M. Batlle', 'David Recasens', 'Javier Morlana', 'Oscar León Barbed', 'Clara Tomasini', 'Luis Riazuelo', 'Ángel Ferrandez', 'Carlos Sostres', 'Pablo Azagra'] | 2022-04-29 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [-3.23022634e-01 6.65098876e-02 -1.03222691e-01 -1.34297013e-01
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b98a65bf-3584-4e83-8b5b-6b00e96606d8 | embodied-concept-learner-self-supervised | 2304.03767 | null | https://arxiv.org/abs/2304.03767v1 | https://arxiv.org/pdf/2304.03767v1.pdf | Embodied Concept Learner: Self-supervised Learning of Concepts and Mapping through Instruction Following | Humans, even at a very early age, can learn visual concepts and understand geometry and layout through active interaction with the environment, and generalize their compositions to complete tasks described by natural languages in novel scenes. To mimic such capability, we propose Embodied Concept Learner (ECL) in an interactive 3D environment. Specifically, a robot agent can ground visual concepts, build semantic maps and plan actions to complete tasks by learning purely from human demonstrations and language instructions, without access to ground-truth semantic and depth supervisions from simulations. ECL consists of: (i) an instruction parser that translates the natural languages into executable programs; (ii) an embodied concept learner that grounds visual concepts based on language descriptions; (iii) a map constructor that estimates depth and constructs semantic maps by leveraging the learned concepts; and (iv) a program executor with deterministic policies to execute each program. ECL has several appealing benefits thanks to its modularized design. Firstly, it enables the robotic agent to learn semantics and depth unsupervisedly acting like babies, e.g., ground concepts through active interaction and perceive depth by disparities when moving forward. Secondly, ECL is fully transparent and step-by-step interpretable in long-term planning. Thirdly, ECL could be beneficial for the embodied instruction following (EIF), outperforming previous works on the ALFRED benchmark when the semantic label is not provided. Also, the learned concept can be reused for other downstream tasks, such as reasoning of object states. Project page: http://ecl.csail.mit.edu/ | ['Chuang Gan', 'Joshua B. Tenenbaum', 'Ping Luo', 'David Daniel Cox', 'Zhenfang Chen', 'Yan Xu', 'Mingyu Ding'] | 2023-04-07 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [-9.75799561e-02 8.50149751e-01 2.57226452e-02 -4.70382541e-01
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ea05b1b3-8a83-45b1-ae54-c00a76e9f66d | zero-shot-and-few-shot-learning-for-lung | 2205.15290 | null | https://arxiv.org/abs/2205.15290v2 | https://arxiv.org/pdf/2205.15290v2.pdf | Zero-Shot and Few-Shot Learning for Lung Cancer Multi-Label Classification using Vision Transformer | Lung cancer is the leading cause of cancer-related death worldwide. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the most common histologic subtypes of non-small-cell lung cancer (NSCLC). Histology is an essential tool for lung cancer diagnosis. Pathologists make classifications according to the dominant subtypes. Although morphology remains the standard for diagnosis, significant tool needs to be developed to elucidate the diagnosis. In our study, we utilize the pre-trained Vision Transformer (ViT) model to classify multiple label lung cancer on histologic slices (from dataset LC25000), in both Zero-Shot and Few-Shot settings. Then we compare the performance of Zero-Shot and Few-Shot ViT on accuracy, precision, recall, sensitivity and specificity. Our study show that the pre-trained ViT model has a good performance in Zero-Shot setting, a competitive accuracy ($99.87\%$) in Few-Shot setting ({epoch = 1}) and an optimal result ($100.00\%$ on both validation set and test set) in Few-Shot seeting ({epoch = 5}). | ['Yingfang Fan', 'Fu-Ming Guo'] | 2022-05-30 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [-3.36759202e-02 -2.44876876e-01 -5.33698380e-01 2.10295200e-01
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33b4ec82-9a78-4440-9181-581cf7a0fb1f | image-based-detection-of-surface-defects-in | 2208.02313 | null | https://arxiv.org/abs/2208.02313v2 | https://arxiv.org/pdf/2208.02313v2.pdf | Image-based Detection of Surface Defects in Concrete during Construction | Defects increase the cost and duration of construction projects as they require significant inspection and documentation efforts. Automating defect detection could significantly reduce these efforts. This work focuses on detecting honeycombs, a substantial defect in concrete structures that may affect structural integrity. We compared honeycomb images scraped from the web with images obtained from real construction inspections. We found that web images do not capture the complete variance found in real-case scenarios and that there is still a lack of data in this domain. Our dataset is therefore freely available for further research. A Mask R-CNN and EfficientNet-B0 were trained for honeycomb detection. The Mask R-CNN model allows detecting honeycombs based on instance segmentation, whereas the EfficientNet-B0 model allows a patch-based classification. Our experiments demonstrate that both approaches are suitable for solving and automating honeycomb detection. In the future, this solution can be incorporated into defect documentation systems. | ['Olaf Hellwich', 'Monika Kwiatkowski', 'Dominik Kuhnke'] | 2022-08-03 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 3.18073303e-01 3.86870146e-01 5.49327731e-01 -6.96647028e-03
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9fec4cb1-3873-4337-b02a-309f359953b0 | question-answering-and-question-generation | 2211.13794 | null | https://arxiv.org/abs/2211.13794v1 | https://arxiv.org/pdf/2211.13794v1.pdf | Question Answering and Question Generation for Finnish | Recent advances in the field of language modeling have improved the state-of-the-art in question answering (QA) and question generation (QG). However, the development of modern neural models, their benchmarks, and datasets for training them has mainly focused on English. Finnish, like many other languages, faces a shortage of large QA/QG model training resources, which has prevented experimenting with state-of-the-art QA/QG fine-tuning methods. We present the first neural QA and QG models that work with Finnish. To train the models, we automatically translate the SQuAD dataset and then use normalization methods to reduce the amount of problematic data created during the translation. Using the synthetic data, together with the Finnish partition of the TyDi-QA dataset, we fine-tune several transformer-based models to both QA and QG and evaluate their performance. To the best of our knowledge, the resulting dataset is the first large-scale QA/QG resource for Finnish. This paper also sets the initial benchmarks for Finnish-language QA and QG. | ['Roman Yangarber', 'Ilmari Kylliäinen'] | 2022-11-24 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 1.1543890e-01 2.0125560e-01 3.1118444e-01 -3.8322964e-01
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b916b8cf-50f9-47ef-aedc-f4c2e72372f0 | detecting-out-of-context-multimodal | 2304.07633 | null | https://arxiv.org/abs/2304.07633v1 | https://arxiv.org/pdf/2304.07633v1.pdf | Detecting Out-of-Context Multimodal Misinformation with interpretable neural-symbolic model | Recent years have witnessed the sustained evolution of misinformation that aims at manipulating public opinions. Unlike traditional rumors or fake news editors who mainly rely on generated and/or counterfeited images, text and videos, current misinformation creators now more tend to use out-of-context multimedia contents (e.g. mismatched images and captions) to deceive the public and fake news detection systems. This new type of misinformation increases the difficulty of not only detection but also clarification, because every individual modality is close enough to true information. To address this challenge, in this paper we explore how to achieve interpretable cross-modal de-contextualization detection that simultaneously identifies the mismatched pairs and the cross-modal contradictions, which is helpful for fact-check websites to document clarifications. The proposed model first symbolically disassembles the text-modality information to a set of fact queries based on the Abstract Meaning Representation of the caption and then forwards the query-image pairs into a pre-trained large vision-language model select the ``evidences" that are helpful for us to detect misinformation. Extensive experiments indicate that the proposed methodology can provide us with much more interpretable predictions while maintaining the accuracy same as the state-of-the-art model on this task. | ['Yan Liu', 'Zijun Cui', 'Defu Cao', 'Loc Trinh', 'Yizhou Zhang'] | 2023-04-15 | null | null | null | null | ['misinformation', 'fake-news-detection'] | ['miscellaneous', 'natural-language-processing'] | [ 2.68173099e-01 3.65581393e-01 -3.21771115e-01 -2.00310424e-01
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d45496af-34c9-4feb-a478-c2d080b2690c | an-fnet-based-auto-encoder-for-long-sequence | 2211.08295 | null | https://arxiv.org/abs/2211.08295v2 | https://arxiv.org/pdf/2211.08295v2.pdf | An FNet based Auto Encoder for Long Sequence News Story Generation | In this paper, we design an auto encoder based off of Google's FNet Architecture in order to generate text from a subset of news stories contained in Google's C4 dataset. We discuss previous attempts and methods to generate text from autoencoders and non LLM Models. FNET poses multiple advantages to BERT based encoders in the realm of efficiency which train 80% faster on GPUs and 70% faster on TPUs. We then compare outputs of how this autencoder perfroms on different epochs. Finally, we analyze what outputs the encoder produces with different seed text. | ['Rakeshkumar Mahto', 'Paul K. Mandal'] | 2022-11-15 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [-1.26232475e-01 6.17288113e-01 1.14374757e-01 -3.04819673e-01
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a3e25c6c-09ac-4fc8-b22d-4839f778b6ea | modeling-arterial-pulse-waves-in-healthy | null | null | https://doi.org/10.1152/ajpheart.00218.2019 | https://journals.physiology.org/doi/full/10.1152/ajpheart.00218.2019 | Modeling arterial pulse waves in healthy aging: a database for in silico evaluation of hemodynamics and pulse wave indexes | The arterial pulse wave (PW) is a rich source of information on cardiovascular (CV) health. It is widely measured by both consumer and clinical devices. However, the physical determinants of the PW are not yet fully understood, and the development of PW analysis algorithms is limited by a lack of PW data sets containing reference CV measurements. Our aim was to create a database of PWs simulated by a computer to span a range of CV conditions, representative of a sample of healthy adults. The typical CV properties of 25–75 yr olds were identified through a literature review. These were used as inputs to a computational model to simulate PWs for subjects of each age decade. Pressure, flow velocity, luminal area, and photoplethysmographic PWs were simulated at common measurement sites, and PW indexes were extracted. The database, containing PWs from 4,374 virtual subjects, was verified by comparing the simulated PWs and derived indexes with corresponding in vivo data. Good agreement was observed, with well-reproduced age-related changes in hemodynamic parameters and PW morphology. The utility of the database was demonstrated through case studies providing novel hemodynamic insights, in silico assessment of PW algorithms, and pilot data to inform the design of clinical PW algorithm assessments. In conclusion, the publicly available PW database is a valuable resource for understanding CV determinants of PWs and for the development and preclinical assessment of PW analysis algorithms. It is particularly useful because the exact CV properties that generated each PW are known. | ['and Jordi Alastruey', 'Phil Chowienczyk', 'Ye Li', 'Samuel Vennin', 'Jorge Mariscal Harana', 'Peter H. Charlton'] | 2019-10-24 | null | null | null | null | ['photoplethysmography-ppg', 'pulse-wave-simulation'] | ['medical', 'medical'] | [-4.34112772e-02 -2.34474257e-01 -3.34736630e-02 -2.38146737e-01
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4ec7e962-0883-4401-b014-bb5927b7eaca | dreem-open-datasets-multi-scored-sleep | 1911.03221 | null | https://arxiv.org/abs/1911.03221v4 | https://arxiv.org/pdf/1911.03221v4.pdf | Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging | Sleep stage classification constitutes an important element of sleep disorder diagnosis. It relies on the visual inspection of polysomnography records by trained sleep technologists. Automated approaches have been designed to alleviate this resource-intensive task. However, such approaches are usually compared to a single human scorer annotation despite an inter-rater agreement of about 85 % only. The present study introduces two publicly-available datasets, DOD-H including 25 healthy volunteers and DOD-O including 55 patients suffering from obstructive sleep apnea (OSA). Both datasets have been scored by 5 sleep technologists from different sleep centers. We developed a framework to compare automated approaches to a consensus of multiple human scorers. Using this framework, we benchmarked and compared the main literature approaches. We also developed and benchmarked a new deep learning method, SimpleSleepNet, inspired by current state-of-the-art. We demonstrated that many methods can reach human-level performance on both datasets. SimpleSleepNet achieved an F1 of 89.9 % vs 86.8 % on average for human scorers on DOD-H, and an F1 of 88.3 % vs 84.8 % on DOD-O. Our study highlights that using state-of-the-art automated sleep staging outperforms human scorers performance for healthy volunteers and patients suffering from OSA. Consideration could be made to use automated approaches in the clinical setting. | ['Emmanuel H. During', 'Fabien Sauvet', 'Antoine Guillot', 'Valentin Thorey'] | 2019-10-31 | null | null | null | null | ['sleep-stage-detection', 'multimodal-sleep-stage-detection', 'sleep-staging', 'automatic-sleep-stage-classification'] | ['medical', 'medical', 'medical', 'medical'] | [-1.42234743e-01 1.46716684e-01 -1.87377930e-01 -5.96131980e-01
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fae243fc-cc62-464b-b0d2-69cf3ae93cec | hitmi-t-at-semeval-2021-task-5-integrating | null | null | https://aclanthology.org/2021.semeval-1.117 | https://aclanthology.org/2021.semeval-1.117.pdf | HITMI\&T at SemEval-2021 Task 5: Integrating Transformer and CRF for Toxic Spans Detection | This paper introduces our system at SemEval-2021 Task 5: Toxic Spans Detection. The task aims to accurately locate toxic spans within a text. Using BIO tagging scheme, we model the task as a token-level sequence labeling task. Our system uses a single model built on the model of multi-layer bidirectional transformer encoder. And we introduce conditional random field (CRF) to make the model learn the constraints between tags. We use ERNIE as pre-trained model, which is more suitable for the task accroding to our experiments. In addition, we use adversarial training with the fast gradient method (FGM) to improve the robustness of the system. Our system obtains 69.85{\%} F1 score, ranking 3rd for the official evaluation. | ['Tiejun Zhao', 'Tianshu Liu', 'Chenyi Wang'] | 2021-08-01 | null | null | null | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [ 1.58104792e-01 4.03401516e-02 -2.85938084e-01 -2.03268871e-01
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4109f72b-af5a-479d-b359-c1f783ce5a8b | a-hybrid-feature-selection-and-construction | 2306.09491 | null | https://arxiv.org/abs/2306.09491v1 | https://arxiv.org/pdf/2306.09491v1.pdf | A Hybrid Feature Selection and Construction Method for Detection of Wind Turbine Generator Heating Faults | Preprocessing of information is an essential step for the effective design of machine learning applications. Feature construction and selection are powerful techniques used for this aim. In this paper, a feature selection and construction approach is presented for the detection of wind turbine generator heating faults. Data were collected from Supervisory Control and Data Acquisition (SCADA) system of a wind turbine. The original features directly collected from the data collection system consist of wind characteristics, operational data, temperature measurements and status information. In addition to these original features, new features were created in the feature construction step to obtain information that can be more powerful indications of the faults. After the construction of new features, a hybrid feature selection technique was implemented to find out the most relevant features in the overall set to increase the classification accuracy and decrease the computational burden. Feature selection step consists of filter and wrapper-based parts. Filter based feature selection was applied to exclude the features which are non-discriminative and wrapper-based method was used to determine the final features considering the redundancies and mutual relations amongst them. Artificial Neural Networks were used both in the detection phase and as the induction algorithm of the wrapper-based feature selection part. The results show that, the proposed approach contributes to the fault detection system to be more reliable especially in terms of reducing the number of false fault alarms. | ['Burak Barutcu', 'Ayse Gokcen Kavaz'] | 2023-06-15 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 1.27743930e-01 -3.60678852e-01 4.99211460e-01 -1.88368037e-01
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8a49dcb2-9b35-4c14-b504-1a2fb6e9c924 | semi-weakly-supervised-object-kinematic | 2303.17774 | null | https://arxiv.org/abs/2303.17774v2 | https://arxiv.org/pdf/2303.17774v2.pdf | Semi-Weakly Supervised Object Kinematic Motion Prediction | Given a 3D object, kinematic motion prediction aims to identify the mobile parts as well as the corresponding motion parameters. Due to the large variations in both topological structure and geometric details of 3D objects, this remains a challenging task and the lack of large scale labeled data also constrain the performance of deep learning based approaches. In this paper, we tackle the task of object kinematic motion prediction problem in a semi-weakly supervised manner. Our key observations are two-fold. First, although 3D dataset with fully annotated motion labels is limited, there are existing datasets and methods for object part semantic segmentation at large scale. Second, semantic part segmentation and mobile part segmentation is not always consistent but it is possible to detect the mobile parts from the underlying 3D structure. Towards this end, we propose a graph neural network to learn the map between hierarchical part-level segmentation and mobile parts parameters, which are further refined based on geometric alignment. This network can be first trained on PartNet-Mobility dataset with fully labeled mobility information and then applied on PartNet dataset with fine-grained and hierarchical part-level segmentation. The network predictions yield a large scale of 3D objects with pseudo labeled mobility information and can further be used for weakly-supervised learning with pre-existing segmentation. Our experiments show there are significant performance boosts with the augmented data for previous method designed for kinematic motion prediction on 3D partial scans. | ['Ruizhen Hu', 'Hui Huang', 'Li Yi', 'Yulan Guo', 'Chongyang Ma', 'Haibin Huang', 'Qian Sun', 'Gengxin Liu'] | 2023-03-31 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_Semi-Weakly_Supervised_Object_Kinematic_Motion_Prediction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_Semi-Weakly_Supervised_Object_Kinematic_Motion_Prediction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['motion-prediction'] | ['computer-vision'] | [ 1.07691042e-01 5.23267925e-01 -7.01277077e-01 -3.79420400e-01
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044b38d2-58e0-434a-bc1b-6540d296aacd | a-quantum-kernel-learning-approach-to | 2211.01263 | null | https://arxiv.org/abs/2211.01263v1 | https://arxiv.org/pdf/2211.01263v1.pdf | A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition | We propose a quantum kernel learning (QKL) framework to address the inherent data sparsity issues often encountered in training large-scare acoustic models in low-resource scenarios. We project acoustic features based on classical-to-quantum feature encoding. Different from existing quantum convolution techniques, we utilize QKL with features in the quantum space to design kernel-based classifiers. Experimental results on challenging spoken command recognition tasks for a few low-resource languages, such as Arabic, Georgian, Chuvash, and Lithuanian, show that the proposed QKL-based hybrid approach attains good improvements over existing classical and quantum solutions. | ['Chin-Hui Lee', 'Sabato Marco Siniscalchi', 'Tara N. Sainath', 'Nanxin Chen', 'Yu Zhang', 'Bo Li', 'Chao-Han Huck Yang'] | 2022-11-02 | null | null | null | null | ['spoken-command-recognition'] | ['speech'] | [ 7.77564868e-02 -2.88080424e-01 1.34750381e-01 -6.77454650e-01
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26acb018-04f6-468d-a7c7-bb1ea5a9ef2e | deep-human-parsing-with-active-template | 1503.02391 | null | http://arxiv.org/abs/1503.02391v1 | http://arxiv.org/pdf/1503.02391v1.pdf | Deep Human Parsing with Active Template Regression | In this work, the human parsing task, namely decomposing a human image into
semantic fashion/body regions, is formulated as an Active Template Regression
(ATR) problem, where the normalized mask of each fashion/body item is expressed
as the linear combination of the learned mask templates, and then morphed to a
more precise mask with the active shape parameters, including position, scale
and visibility of each semantic region. The mask template coefficients and the
active shape parameters together can generate the human parsing results, and
are thus called the structure outputs for human parsing. The deep Convolutional
Neural Network (CNN) is utilized to build the end-to-end relation between the
input human image and the structure outputs for human parsing. More
specifically, the structure outputs are predicted by two separate networks. The
first CNN network is with max-pooling, and designed to predict the template
coefficients for each label mask, while the second CNN network is without
max-pooling to preserve sensitivity to label mask position and accurately
predict the active shape parameters. For a new image, the structure outputs of
the two networks are fused to generate the probability of each label for each
pixel, and super-pixel smoothing is finally used to refine the human parsing
result. Comprehensive evaluations on a large dataset well demonstrate the
significant superiority of the ATR framework over other state-of-the-arts for
human parsing. In particular, the F1-score reaches $64.38\%$ by our ATR
framework, significantly higher than $44.76\%$ based on the state-of-the-art
algorithm. | ['Luoqi Liu', 'Liang Lin', 'Xiaohui Shen', 'Xiaodan Liang', 'Jianchao Yang', 'Si Liu', 'Jian Dong', 'Shuicheng Yan'] | 2015-03-09 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 5.36106288e-01 5.19986808e-01 -3.94689851e-02 -6.41368389e-01
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f10cd973-7f12-4edf-ad16-e74a301a582e | detecting-heart-disease-from-multi-view | 2306.00003 | null | https://arxiv.org/abs/2306.00003v1 | https://arxiv.org/pdf/2306.00003v1.pdf | Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning | Aortic stenosis (AS) is a degenerative valve condition that causes substantial morbidity and mortality. This condition is under-diagnosed and under-treated. In clinical practice, AS is diagnosed with expert review of transthoracic echocardiography, which produces dozens of ultrasound images of the heart. Only some of these views show the aortic valve. To automate screening for AS, deep networks must learn to mimic a human expert's ability to identify views of the aortic valve then aggregate across these relevant images to produce a study-level diagnosis. We find previous approaches to AS detection yield insufficient accuracy due to relying on inflexible averages across images. We further find that off-the-shelf attention-based multiple instance learning (MIL) performs poorly. We contribute a new end-to-end MIL approach with two key methodological innovations. First, a supervised attention technique guides the learned attention mechanism to favor relevant views. Second, a novel self-supervised pretraining strategy applies contrastive learning on the representation of the whole study instead of individual images as commonly done in prior literature. Experiments on an open-access dataset and an external validation set show that our approach yields higher accuracy while reducing model size. | ['Michael C. Hughes', 'Benjamin S. Wessler', 'Zhe Huang'] | 2023-05-25 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 2.14301452e-01 3.73107284e-01 -2.72382349e-01 -2.80622482e-01
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512bebe6-4afc-4d71-a689-ba707dd11c29 | an-online-semantic-enhanced-dirichlet-model | null | null | https://aclanthology.org/2020.acl-main.70 | https://aclanthology.org/2020.acl-main.70.pdf | An Online Semantic-enhanced Dirichlet Model for Short Text Stream Clustering | Clustering short text streams is a challenging task due to its unique properties: infinite length, sparse data representation and cluster evolution. Existing approaches often exploit short text streams in a batch way. However, determine the optimal batch size is usually a difficult task since we have no priori knowledge when the topics evolve. In addition, traditional independent word representation in graphical model tends to cause {``}term ambiguity{''} problem in short text clustering. Therefore, in this paper, we propose an Online Semantic-enhanced Dirichlet Model for short sext stream clustering, called OSDM, which integrates the word-occurance semantic information (i.e., context) into a new graphical model and clusters each arriving short text automatically in an online way. Extensive results have demonstrated that OSDM has better performance compared to many state-of-the-art algorithms on both synthetic and real-world data sets. | ['Salah Uddin', 'Jay Kumar', 'Wazir Ali', 'Junming Shao'] | 2020-07-01 | null | null | null | acl-2020-6 | ['text-clustering', 'short-text-clustering'] | ['natural-language-processing', 'natural-language-processing'] | [-4.03844416e-02 -6.70958698e-01 4.35348786e-02 -4.78692621e-01
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823b7d39-3fe5-47b8-a6f5-74882050e2d0 | advsmo-black-box-adversarial-attack-by | 2206.10988 | null | https://arxiv.org/abs/2206.10988v1 | https://arxiv.org/pdf/2206.10988v1.pdf | AdvSmo: Black-box Adversarial Attack by Smoothing Linear Structure of Texture | Black-box attacks usually face two problems: poor transferability and the inability to evade the adversarial defense. To overcome these shortcomings, we create an original approach to generate adversarial examples by smoothing the linear structure of the texture in the benign image, called AdvSmo. We construct the adversarial examples without relying on any internal information to the target model and design the imperceptible-high attack success rate constraint to guide the Gabor filter to select appropriate angles and scales to smooth the linear texture from the input images to generate adversarial examples. Benefiting from the above design concept, AdvSmo will generate adversarial examples with strong transferability and solid evasiveness. Finally, compared to the four advanced black-box adversarial attack methods, for the eight target models, the results show that AdvSmo improves the average attack success rate by 9% on the CIFAR-10 and 16% on the Tiny-ImageNet dataset compared to the best of these attack methods. | ['Zi Kang', 'Shuliang Jiang', 'Rui Zhang', 'Hui Xia'] | 2022-06-22 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 3.44604403e-01 3.70621502e-01 2.63892502e-01 5.23244701e-02
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eaf67d20-52ff-449b-a921-301eb0468bcb | speaker-identification-from-emotional-and | 2210.12701 | null | https://arxiv.org/abs/2210.12701v1 | https://arxiv.org/pdf/2210.12701v1.pdf | Speaker Identification from emotional and noisy speech data using learned voice segregation and Speech VGG | Speech signals are subjected to more acoustic interference and emotional factors than other signals. Noisy emotion-riddled speech data is a challenge for real-time speech processing applications. It is essential to find an effective way to segregate the dominant signal from other external influences. An ideal system should have the capacity to accurately recognize required auditory events from a complex scene taken in an unfavorable situation. This paper proposes a novel approach to speaker identification in unfavorable conditions such as emotion and interference using a pre-trained Deep Neural Network mask and speech VGG. The proposed model obtained superior performance over the recent literature in English and Arabic emotional speech data and reported an average speaker identification rate of 85.2\%, 87.0\%, and 86.6\% using the Ryerson audio-visual dataset (RAVDESS), speech under simulated and actual stress (SUSAS) dataset and Emirati-accented Speech dataset (ESD) respectively. | ['Naoufel Werghi', 'Ernesto Damiani', 'Youssef Iraqi', 'Ismail Shahin', 'Shibani Hamsa'] | 2022-10-23 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 3.00334506e-02 -2.71553636e-01 7.52125382e-01 -3.74467790e-01
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20ce49fe-698c-4707-ad3f-f29fefe78413 | controlled-random-search-improves-hyper | 1809.01712 | null | http://arxiv.org/abs/1809.01712v3 | http://arxiv.org/pdf/1809.01712v3.pdf | Coverage-Based Designs Improve Sample Mining and Hyper-Parameter Optimization | Sampling one or more effective solutions from large search spaces is a
recurring idea in machine learning, and sequential optimization has become a
popular solution. Typical examples include data summarization, sample mining
for predictive modeling and hyper-parameter optimization. Existing solutions
attempt to adaptively trade-off between global exploration and local
exploitation, wherein the initial exploratory sample is critical to their
success. While discrepancy-based samples have become the de facto approach for
exploration, results from computer graphics suggest that coverage-based
designs, e.g. Poisson disk sampling, can be a superior alternative. In order to
successfully adopt coverage-based sample designs to ML applications, which were
originally developed for 2-d image analysis, we propose fundamental advances by
constructing a parameterized family of designs with provably improved coverage
characteristics, and by developing algorithms for effective sample synthesis.
Using experiments in sample mining and hyper-parameter optimization for
supervised learning, we show that our approach consistently outperforms
existing exploratory sampling methods in both blind exploration, and sequential
search with Bayesian optimization. | ['Peer-Timo Bremer', 'Bhavya Kailkhura', 'Jayaraman J. Thiagarajan', 'Gowtham Muniraju', 'Andreas Spanias', 'Cihan Tepedelenlioglu'] | 2018-09-05 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 4.02360886e-01 1.45937055e-01 -9.28043902e-01 -3.21796030e-01
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dd31775f-1db3-4070-9739-e633cf94f365 | probing-neural-dialog-models-for | 2006.08331 | null | https://arxiv.org/abs/2006.08331v1 | https://arxiv.org/pdf/2006.08331v1.pdf | Probing Neural Dialog Models for Conversational Understanding | The predominant approach to open-domain dialog generation relies on end-to-end training of neural models on chat datasets. However, this approach provides little insight as to what these models learn (or do not learn) about engaging in dialog. In this study, we analyze the internal representations learned by neural open-domain dialog systems and evaluate the quality of these representations for learning basic conversational skills. Our results suggest that standard open-domain dialog systems struggle with answering questions, inferring contradiction, and determining the topic of conversation, among other tasks. We also find that the dyadic, turn-taking nature of dialog is not fully leveraged by these models. By exploring these limitations, we highlight the need for additional research into architectures and training methods that can better capture high-level information about dialog. | ['Yonatan Belinkov', 'Abdelrhman Saleh', 'Stuart Shieber', 'Tovly Deutsch', 'Stephen Casper'] | 2020-06-07 | probing-neural-dialog-models-for-1 | https://aclanthology.org/2020.nlp4convai-1.15 | https://aclanthology.org/2020.nlp4convai-1.15.pdf | ws-2020-7 | ['open-domain-dialog'] | ['natural-language-processing'] | [-1.03571445e-01 8.16939712e-01 -1.53258350e-02 -6.51610732e-01
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37a69e80-498d-4952-8f9c-991321511339 | bactrian-x-a-multilingual-replicable | 2305.15011 | null | https://arxiv.org/abs/2305.15011v1 | https://arxiv.org/pdf/2305.15011v1.pdf | Bactrian-X : A Multilingual Replicable Instruction-Following Model with Low-Rank Adaptation | Instruction tuning has shown great promise in the field of natural language processing. However, the research on multilingual instruction tuning has been limited due to the scarcity of high-quality instruction-response datasets. To address this gap, we present Bactrian-X, a comprehensive multilingual parallel dataset of 3.4 million instruction-response pairs across 52 languages. Leveraging this dataset, we train a set of adapters using low-rank adaptation (LoRA), which are lightweight components seamlessly integrated with foundational models. These adapters have a significantly smaller parameter count than the base model, making them easily replaceable and usable as plug-ins for different languages or language groups. Through extensive experiments on 52 languages, we demonstrate the superior performance of our models in various multilingual evaluation settings. Our proposed models outperform both the vanilla models and the existing instruction-tuned models. The code and models are publicly available at https://github.com/mbzuai-nlp/bactrian-x. | ['Timothy Baldwin', 'Alham Fikri Aji', 'Minghao Wu', 'Fajri Koto', 'Haonan Li'] | 2023-05-24 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [-5.21510780e-01 -7.43611872e-01 -7.77126551e-01 -6.71501756e-01
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3b9a4eb6-5ef7-4e32-afc3-bef589c336e3 | cooperative-thresholded-lasso-for-sparse | 2305.19161 | null | https://arxiv.org/abs/2305.19161v1 | https://arxiv.org/pdf/2305.19161v1.pdf | Cooperative Thresholded Lasso for Sparse Linear Bandit | We present a novel approach to address the multi-agent sparse contextual linear bandit problem, in which the feature vectors have a high dimension $d$ whereas the reward function depends on only a limited set of features - precisely $s_0 \ll d$. Furthermore, the learning follows under information-sharing constraints. The proposed method employs Lasso regression for dimension reduction, allowing each agent to independently estimate an approximate set of main dimensions and share that information with others depending on the network's structure. The information is then aggregated through a specific process and shared with all agents. Each agent then resolves the problem with ridge regression focusing solely on the extracted dimensions. We represent algorithms for both a star-shaped network and a peer-to-peer network. The approaches effectively reduce communication costs while ensuring minimal cumulative regret per agent. Theoretically, we show that our proposed methods have a regret bound of order $\mathcal{O}(s_0 \log d + s_0 \sqrt{T})$ with high probability, where $T$ is the time horizon. To our best knowledge, it is the first algorithm that tackles row-wise distributed data in sparse linear bandits, achieving comparable performance compared to the state-of-the-art single and multi-agent methods. Besides, it is widely applicable to high-dimensional multi-agent problems where efficient feature extraction is critical for minimizing regret. To validate the effectiveness of our approach, we present experimental results on both synthetic and real-world datasets. | ['Setareh Maghsudi', 'Xiaotong Cheng', 'Haniyeh Barghi'] | 2023-05-30 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-7.51186088e-02 1.84167385e-01 -7.05351651e-01 -1.51775435e-01
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2f395d21-0cd8-4897-8a9e-e3006c29ca90 | leapfrog-diffusion-model-for-stochastic | 2303.10895 | null | https://arxiv.org/abs/2303.10895v1 | https://arxiv.org/pdf/2303.10895v1.pdf | Leapfrog Diffusion Model for Stochastic Trajectory Prediction | To model the indeterminacy of human behaviors, stochastic trajectory prediction requires a sophisticated multi-modal distribution of future trajectories. Emerging diffusion models have revealed their tremendous representation capacities in numerous generation tasks, showing potential for stochastic trajectory prediction. However, expensive time consumption prevents diffusion models from real-time prediction, since a large number of denoising steps are required to assure sufficient representation ability. To resolve the dilemma, we present LEapfrog Diffusion model (LED), a novel diffusion-based trajectory prediction model, which provides real-time, precise, and diverse predictions. The core of the proposed LED is to leverage a trainable leapfrog initializer to directly learn an expressive multi-modal distribution of future trajectories, which skips a large number of denoising steps, significantly accelerating inference speed. Moreover, the leapfrog initializer is trained to appropriately allocate correlated samples to provide a diversity of predicted future trajectories, significantly improving prediction performances. Extensive experiments on four real-world datasets, including NBA/NFL/SDD/ETH-UCY, show that LED consistently improves performance and achieves 23.7%/21.9% ADE/FDE improvement on NFL. The proposed LED also speeds up the inference 19.3/30.8/24.3/25.1 times compared to the standard diffusion model on NBA/NFL/SDD/ETH-UCY, satisfying real-time inference needs. Code is available at https://github.com/MediaBrain-SJTU/LED. | ['Yanfeng Wang', 'Siheng Chen', 'Qi Zhu', 'Chenxin Xu', 'Weibo Mao'] | 2023-03-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Mao_Leapfrog_Diffusion_Model_for_Stochastic_Trajectory_Prediction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Mao_Leapfrog_Diffusion_Model_for_Stochastic_Trajectory_Prediction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['trajectory-prediction'] | ['computer-vision'] | [-2.77794600e-01 -4.13641781e-01 -4.39924061e-01 -3.63036484e-01
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58c023d2-f8ac-4cc0-b01b-b85744bd55f6 | ospc-online-sequential-photometric | 2305.17673 | null | https://arxiv.org/abs/2305.17673v2 | https://arxiv.org/pdf/2305.17673v2.pdf | OSPC: Online Sequential Photometric Calibration | Photometric calibration is essential to many computer vision applications. One of its key benefits is enhancing the performance of Visual SLAM, especially when it depends on a direct method for tracking, such as the standard KLT algorithm. Another advantage could be in retrieving the sensor irradiance values from measured intensities, as a pre-processing step for some vision algorithms, such as shape-from-shading. Current photometric calibration systems rely on a joint optimization problem and encounter an ambiguity in the estimates, which can only be resolved using ground truth information. We propose a novel method that solves for photometric parameters using a sequential estimation approach. Our proposed method achieves high accuracy in estimating all parameters; furthermore, the formulations are linear and convex, which makes the solution fast and suitable for online applications. Experiments on a Visual Odometry system validate the proposed method and demonstrate its advantages. | ['Daniel Asmar', 'Douaa Khalil', 'Jawad Haidar'] | 2023-05-28 | null | null | null | null | ['visual-odometry'] | ['robots'] | [ 1.39693186e-01 -5.26554167e-01 8.53945613e-02 -3.55559230e-01
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