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446e8bcf-95fc-48e2-923f-2a887bc05c3d | learning-to-aggregate-and-personalize-3d-face | 2106.07852 | null | https://arxiv.org/abs/2106.07852v1 | https://arxiv.org/pdf/2106.07852v1.pdf | Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo Collection | Non-parametric face modeling aims to reconstruct 3D face only from images without shape assumptions. While plausible facial details are predicted, the models tend to over-depend on local color appearance and suffer from ambiguous noise. To address such problem, this paper presents a novel Learning to Aggregate and Personalize (LAP) framework for unsupervised robust 3D face modeling. Instead of using controlled environment, the proposed method implicitly disentangles ID-consistent and scene-specific face from unconstrained photo set. Specifically, to learn ID-consistent face, LAP adaptively aggregates intrinsic face factors of an identity based on a novel curriculum learning approach with relaxed consistency loss. To adapt the face for a personalized scene, we propose a novel attribute-refining network to modify ID-consistent face with target attribute and details. Based on the proposed method, we make unsupervised 3D face modeling benefit from meaningful image facial structure and possibly higher resolutions. Extensive experiments on benchmarks show LAP recovers superior or competitive face shape and texture, compared with state-of-the-art (SOTA) methods with or without prior and supervision. | ['Feiyue Huang', 'Jilin Li', 'Chengjie Wang', 'Jian Yang', 'Yan Yan', 'Ying Tai', 'Renwang Chen', 'Yanhao Ge', 'Zhenyu Zhang'] | 2021-06-15 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhang_Learning_To_Aggregate_and_Personalize_3D_Face_From_In-the-Wild_Photo_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhang_Learning_To_Aggregate_and_Personalize_3D_Face_From_In-the-Wild_Photo_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-face-modeling'] | ['computer-vision'] | [ 2.67170101e-01 3.14561188e-01 -1.48187904e-02 -8.40005815e-01
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9192013f-e268-4dba-91b1-cd705d567a26 | open-domain-targeted-sentiment-analysis-via | 1906.03820 | null | https://arxiv.org/abs/1906.03820v1 | https://arxiv.org/pdf/1906.03820v1.pdf | Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification | Open-domain targeted sentiment analysis aims to detect opinion targets along with their sentiment polarities from a sentence. Prior work typically formulates this task as a sequence tagging problem. However, such formulation suffers from problems such as huge search space and sentiment inconsistency. To address these problems, we propose a span-based extract-then-classify framework, where multiple opinion targets are directly extracted from the sentence under the supervision of target span boundaries, and corresponding polarities are then classified using their span representations. We further investigate three approaches under this framework, namely the pipeline, joint, and collapsed models. Experiments on three benchmark datasets show that our approach consistently outperforms the sequence tagging baseline. Moreover, we find that the pipeline model achieves the best performance compared with the other two models. | ['Minghao Hu', 'Yiwei Lv', 'Zhen Huang', 'Yuxing Peng', 'Dongsheng Li'] | 2019-06-10 | open-domain-targeted-sentiment-analysis-via-1 | https://aclanthology.org/P19-1051 | https://aclanthology.org/P19-1051.pdf | acl-2019-7 | ['aspect-term-extraction-and-sentiment'] | ['natural-language-processing'] | [ 5.26133180e-01 -1.35965168e-01 -5.46790302e-01 -6.88245773e-01
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69e3e715-5efd-4032-9738-73e4c986da93 | language-acquisition-do-children-and-language | 2306.03586 | null | https://arxiv.org/abs/2306.03586v1 | https://arxiv.org/pdf/2306.03586v1.pdf | Language acquisition: do children and language models follow similar learning stages? | During language acquisition, children follow a typical sequence of learning stages, whereby they first learn to categorize phonemes before they develop their lexicon and eventually master increasingly complex syntactic structures. However, the computational principles that lead to this learning trajectory remain largely unknown. To investigate this, we here compare the learning trajectories of deep language models to those of children. Specifically, we test whether, during its training, GPT-2 exhibits stages of language acquisition comparable to those observed in children aged between 18 months and 6 years. For this, we train 48 GPT-2 models from scratch and evaluate their syntactic and semantic abilities at each training step, using 96 probes curated from the BLiMP, Zorro and BIG-Bench benchmarks. We then compare these evaluations with the behavior of 54 children during language production. Our analyses reveal three main findings. First, similarly to children, the language models tend to learn linguistic skills in a systematic order. Second, this learning scheme is parallel: the language tasks that are learned last improve from the very first training steps. Third, some - but not all - learning stages are shared between children and these language models. Overall, these results shed new light on the principles of language acquisition, and highlight important divergences in how humans and modern algorithms learn to process natural language. | ['Jean-Rémi King', 'Yair Lakretz', 'Linnea Evanson'] | 2023-06-06 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 5.87567762e-02 2.72721499e-01 -2.14209780e-02 -4.75040495e-01
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80549c49-2450-4b43-9817-10070ac45663 | dadagp-a-dataset-of-tokenized-guitarpro-songs | 2107.14653 | null | https://arxiv.org/abs/2107.14653v1 | https://arxiv.org/pdf/2107.14653v1.pdf | DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models | Originating in the Renaissance and burgeoning in the digital era, tablatures are a commonly used music notation system which provides explicit representations of instrument fingerings rather than pitches. GuitarPro has established itself as a widely used tablature format and software enabling musicians to edit and share songs for musical practice, learning, and composition. In this work, we present DadaGP, a new symbolic music dataset comprising 26,181 song scores in the GuitarPro format covering 739 musical genres, along with an accompanying tokenized format well-suited for generative sequence models such as the Transformer. The tokenized format is inspired by event-based MIDI encodings, often used in symbolic music generation models. The dataset is released with an encoder/decoder which converts GuitarPro files to tokens and back. We present results of a use case in which DadaGP is used to train a Transformer-based model to generate new songs in GuitarPro format. We discuss other relevant use cases for the dataset (guitar-bass transcription, music style transfer and artist/genre classification) as well as ethical implications. DadaGP opens up the possibility to train GuitarPro score generators, fine-tune models on custom data, create new styles of music, AI-powered songwriting apps, and human-AI improvisation. | ['Yi-Hsuan Yang', 'Mathieu Barthet', 'Zack Zukowski', 'CJ Carr', 'Adarsh Kumar', 'Pedro Sarmento'] | 2021-07-30 | null | null | null | null | ['music-generation', 'genre-classification', 'music-generation'] | ['audio', 'computer-vision', 'music'] | [ 3.50648791e-01 -1.09562710e-01 2.19303906e-01 -7.02853426e-02
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c0be35b0-d28c-4f05-aa9f-becc4daf52c2 | sample-level-multi-view-graph-clustering | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tan_Sample-Level_Multi-View_Graph_Clustering_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tan_Sample-Level_Multi-View_Graph_Clustering_CVPR_2023_paper.pdf | Sample-Level Multi-View Graph Clustering | Multi-view clustering have hitherto been studied due to their effectiveness in dealing with heterogeneous data. Despite the empirical success made by recent works, there still exists several severe challenges. Particularly, previous multi-view clustering algorithms seldom consider the topological structure in data, which is essential for clustering data on manifold. Moreover, existing methods cannot fully consistency the consistency of local structures between different views as they explore the clustering structure in a view-wise manner. In this paper, we propose to exploit the implied data manifold by learning the topological structure of data. Besides, considering that the consistency of multiple views is manifested in the generally similar local structure while the inconsistent structures are minority, we further explore the intersections of multiple views in the sample level such that the cross-view consistency can be better maintained. We model the above concerns in a unified framework and design an efficient algorithm to solve the corresponding optimization problem. Experimental results on various multi-view datasets certificate the effectiveness of the proposed method and verify its superiority over other SOTA approaches. | ['Jiancheng Lv', 'Wentao Feng', 'Shudong Huang', 'Yixi Liu', 'Yuze Tan'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['graph-clustering'] | ['graphs'] | [-3.94810885e-01 -3.33701909e-01 -2.89339304e-01 -3.35964918e-01
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e977b3a1-47a5-4242-8343-5fa49eb1d60d | rotogbml-towards-out-of-distribution | 2303.06679 | null | https://arxiv.org/abs/2303.06679v1 | https://arxiv.org/pdf/2303.06679v1.pdf | RotoGBML: Towards Out-of-Distribution Generalization for Gradient-Based Meta-Learning | Gradient-based meta-learning (GBML) algorithms are able to fast adapt to new tasks by transferring the learned meta-knowledge, while assuming that all tasks come from the same distribution (in-distribution, ID). However, in the real world, they often suffer from an out-of-distribution (OOD) generalization problem, where tasks come from different distributions. OOD exacerbates inconsistencies in magnitudes and directions of task gradients, which brings challenges for GBML to optimize the meta-knowledge by minimizing the sum of task gradients in each minibatch. To address this problem, we propose RotoGBML, a novel approach to homogenize OOD task gradients. RotoGBML uses reweighted vectors to dynamically balance diverse magnitudes to a common scale and uses rotation matrixes to rotate conflicting directions close to each other. To reduce overhead, we homogenize gradients with the features rather than the network parameters. On this basis, to avoid the intervention of non-causal features (e.g., backgrounds), we also propose an invariant self-information (ISI) module to extract invariant causal features (e.g., the outlines of objects). Finally, task gradients are homogenized based on these invariant causal features. Experiments show that RotoGBML outperforms other state-of-the-art methods on various few-shot image classification benchmarks. | ['Wenbin Li', 'Donglin Wang', 'Zhitao Wang', 'Zifeng Zhuang', 'Min Zhang'] | 2023-03-12 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [-8.35638419e-02 -4.52012509e-01 -2.34906465e-01 -3.41840237e-01
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3ebe6939-87b8-4daa-9cc4-e1d0c2ae666b | towards-reading-hidden-emotions-a-comparative | 1511.00423 | null | http://arxiv.org/abs/1511.00423v2 | http://arxiv.org/pdf/1511.00423v2.pdf | Towards Reading Hidden Emotions: A comparative Study of Spontaneous Micro-expression Spotting and Recognition Methods | Micro-expressions (MEs) are rapid, involuntary facial expressions which
reveal emotions that people do not intend to show. Studying MEs is valuable as
recognizing them has many important applications, particularly in forensic
science and psychotherapy. However, analyzing spontaneous MEs is very
challenging due to their short duration and low intensity. Automatic ME
analysis includes two tasks: ME spotting and ME recognition. For ME spotting,
previous studies have focused on posed rather than spontaneous videos. For ME
recognition, the performance of previous studies is low. To address these
challenges, we make the following contributions: (i)We propose the first method
for spotting spontaneous MEs in long videos (by exploiting feature difference
contrast). This method is training free and works on arbitrary unseen videos.
(ii)We present an advanced ME recognition framework, which outperforms previous
work by a large margin on two challenging spontaneous ME databases (SMIC and
CASMEII). (iii)We propose the first automatic ME analysis system (MESR), which
can spot and recognize MEs from spontaneous video data. Finally, we show our
method outperforms humans in the ME recognition task by a large margin, and
achieves comparable performance to humans at the very challenging task of
spotting and then recognizing spontaneous MEs. | ['Matti Pietikäinen', 'Xiaopeng Hong', 'Xiaobai Li', 'Antti Moilanen', 'Xiaohua Huang', 'Tomas Pfister', 'Guoying Zhao'] | 2015-11-02 | null | null | null | null | ['micro-expression-spotting'] | ['computer-vision'] | [ 2.30289862e-01 -2.87589610e-01 -1.59659341e-01 -3.90699834e-01
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c322636e-f9ee-4c0a-8968-ce60864cf2e5 | email-spam-detection-using-hierarchical | 2204.07390 | null | https://arxiv.org/abs/2204.07390v2 | https://arxiv.org/pdf/2204.07390v2.pdf | Email Spam Detection Using Hierarchical Attention Hybrid Deep Learning Method | Email is one of the most widely used ways to communicate, with millions of people and businesses relying on it to communicate and share knowledge and information on a daily basis. Nevertheless, the rise in email users has occurred a dramatic increase in spam emails in recent years. Processing and managing emails properly for individuals and companies are getting increasingly difficult. This article proposes a novel technique for email spam detection that is based on a combination of convolutional neural networks, gated recurrent units, and attention mechanisms. During system training, the network is selectively focused on necessary parts of the email text. The usage of convolution layers to extract more meaningful, abstract, and generalizable features by hierarchical representation is the major contribution of this study. Additionally, this contribution incorporates cross-dataset evaluation, which enables the generation of more independent performance results from the model's training dataset. According to cross-dataset evaluation results, the proposed technique advances the results of the present attention-based techniques by utilizing temporal convolutions, which give us more flexible receptive field sizes are utilized. The suggested technique's findings are compared to those of state-of-the-art models and show that our approach outperforms them. | ['Seyhmus Yilmaz', 'Sultan Zavrak'] | 2022-04-15 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 1.41100332e-01 -3.98414850e-01 9.40401256e-02 -3.83574665e-01
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d6eef7d6-7281-4818-a017-63f6666cbdd3 | face-generation-from-textual-features-using | 2301.09123 | null | https://arxiv.org/abs/2301.09123v1 | https://arxiv.org/pdf/2301.09123v1.pdf | Face Generation from Textual Features using Conditionally Trained Inputs to Generative Adversarial Networks | Generative Networks have proved to be extremely effective in image restoration and reconstruction in the past few years. Generating faces from textual descriptions is one such application where the power of generative algorithms can be used. The task of generating faces can be useful for a number of applications such as finding missing persons, identifying criminals, etc. This paper discusses a novel approach to generating human faces given a textual description regarding the facial features. We use the power of state of the art natural language processing models to convert face descriptions into learnable latent vectors which are then fed to a generative adversarial network which generates faces corresponding to those features. While this paper focuses on high level descriptions of faces only, the same approach can be tailored to generate any image based on fine grained textual features. | ['Mihir Tale', 'Aniket Ghorpade', 'Tejas Pradhan', 'Sandeep Shinde'] | 2023-01-22 | null | null | null | null | ['face-generation'] | ['computer-vision'] | [ 4.89540398e-01 3.62149328e-01 2.11426795e-01 -6.16027713e-01
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0409fd13-5e27-4dfb-a65d-44330632c1ce | uln-towards-underspecified-vision-and | 2210.10020 | null | https://arxiv.org/abs/2210.10020v1 | https://arxiv.org/pdf/2210.10020v1.pdf | ULN: Towards Underspecified Vision-and-Language Navigation | Vision-and-Language Navigation (VLN) is a task to guide an embodied agent moving to a target position using language instructions. Despite the significant performance improvement, the wide use of fine-grained instructions fails to characterize more practical linguistic variations in reality. To fill in this gap, we introduce a new setting, namely Underspecified vision-and-Language Navigation (ULN), and associated evaluation datasets. ULN evaluates agents using multi-level underspecified instructions instead of purely fine-grained or coarse-grained, which is a more realistic and general setting. As a primary step toward ULN, we propose a VLN framework that consists of a classification module, a navigation agent, and an Exploitation-to-Exploration (E2E) module. Specifically, we propose to learn Granularity Specific Sub-networks (GSS) for the agent to ground multi-level instructions with minimal additional parameters. Then, our E2E module estimates grounding uncertainty and conducts multi-step lookahead exploration to improve the success rate further. Experimental results show that existing VLN models are still brittle to multi-level language underspecification. Our framework is more robust and outperforms the baselines on ULN by ~10% relative success rate across all levels. | ['William Yang Wang', 'Yujie Lu', 'Tsu-Jui Fu', 'Weixi Feng'] | 2022-10-18 | null | null | null | null | ['vision-and-language-navigation'] | ['robots'] | [-1.39652580e-01 -3.10631320e-02 -3.04183006e-01 -3.14280987e-01
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6265eb40-f7a6-4d33-9a99-72a166c8471a | neural-syntactic-generative-models-with-exact | null | null | https://aclanthology.org/N18-1086 | https://aclanthology.org/N18-1086.pdf | Neural Syntactic Generative Models with Exact Marginalization | We present neural syntactic generative models with exact marginalization that support both dependency parsing and language modeling. Exact marginalization is made tractable through dynamic programming over shift-reduce parsing and minimal RNN-based feature sets. Our algorithms complement previous approaches by supporting batched training and enabling online computation of next word probabilities. For supervised dependency parsing, our model achieves a state-of-the-art result among generative approaches. We also report empirical results on unsupervised syntactic models and their role in language modeling. We find that our model formulation of latent dependencies with exact marginalization do not lead to better intrinsic language modeling performance than vanilla RNNs, and that parsing accuracy is not correlated with language modeling perplexity in stack-based models. | ['Jan Buys', 'Phil Blunsom'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-1.69030670e-02 7.00236022e-01 -4.23072547e-01 -8.90833437e-01
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e3e86539-6e4c-478e-89e1-dcc7eeeff6f0 | infoverse-a-universal-framework-for-dataset | 2305.19344 | null | https://arxiv.org/abs/2305.19344v2 | https://arxiv.org/pdf/2305.19344v2.pdf | infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information | The success of NLP systems often relies on the availability of large, high-quality datasets. However, not all samples in these datasets are equally valuable for learning, as some may be redundant or noisy. Several methods for characterizing datasets based on model-driven meta-information (e.g., model's confidence) have been developed, but the relationship and complementary effects of these methods have received less attention. In this paper, we introduce infoVerse, a universal framework for dataset characterization, which provides a new feature space that effectively captures multidimensional characteristics of datasets by incorporating various model-driven meta-information. infoVerse reveals distinctive regions of the dataset that are not apparent in the original semantic space, hence guiding users (or models) in identifying which samples to focus on for exploration, assessment, or annotation. Additionally, we propose a novel sampling method on infoVerse to select a set of data points that maximizes informativeness. In three real-world applications (data pruning, active learning, and data annotation), the samples chosen on infoVerse space consistently outperform strong baselines in all applications. Our code and demo are publicly available. | ['Dongyeop Kang', 'Jinwoo Shin', 'Karin de Langis', 'Yekyung Kim', 'Jaehyung Kim'] | 2023-05-30 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 1.53983697e-01 9.57677960e-02 -7.35673964e-01 -6.78824246e-01
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1ed4596f-d0a2-4ccf-a582-754d307609ba | temperate-fish-detection-and-classification-a | 2005.07518 | null | https://arxiv.org/abs/2005.07518v1 | https://arxiv.org/pdf/2005.07518v1.pdf | Temperate Fish Detection and Classification: a Deep Learning based Approach | A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that can automatically detect and recognize species captured on film. Classifying fish species from videos and images in natural environments can be challenging because of noise and variation in illumination and the surrounding habitat. In this paper, we propose a two-step deep learning approach for the detection and classification of temperate fishes without pre-filtering. The first step is to detect each single fish in an image, independent of species and sex. For this purpose, we employ the You Only Look Once (YOLO) object detection technique. In the second step, we adopt a Convolutional Neural Network (CNN) with the Squeeze-and-Excitation (SE) architecture for classifying each fish in the image without pre-filtering. We apply transfer learning to overcome the limited training samples of temperate fishes and to improve the accuracy of the classification. This is done by training the object detection model with ImageNet and the fish classifier via a public dataset (Fish4Knowledge), whereupon both the object detection and classifier are updated with temperate fishes of interest. The weights obtained from pre-training are applied to post-training as a priori. Our solution achieves the state-of-the-art accuracy of 99.27\% on the pre-training. The percentage values for accuracy on the post-training are good; 83.68\% and 87.74\% with and without image augmentation, respectively, indicating that the solution is viable with a more extensive dataset. | ['Tonje Knutsen Sørdalen', 'Arne Wiklund', 'Kristian Muri Knausgård', 'Morten Goodwin', 'Alf Ring Kleiven', 'Kim Halvorsen', 'Lei Jiao'] | 2020-05-14 | null | null | null | null | ['fish-detection'] | ['computer-vision'] | [ 1.10733375e-01 -2.81939447e-01 6.41250849e-01 -4.52213705e-01
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9e6104a9-dce9-4a8a-85ee-6fd73f76f3cc | symbolic-brittleness-in-sequence-models-on | 2109.13986 | null | https://arxiv.org/abs/2109.13986v2 | https://arxiv.org/pdf/2109.13986v2.pdf | Symbolic Brittleness in Sequence Models: on Systematic Generalization in Symbolic Mathematics | Neural sequence models trained with maximum likelihood estimation have led to breakthroughs in many tasks, where success is defined by the gap between training and test performance. However, their ability to achieve stronger forms of generalization remains unclear. We consider the problem of symbolic mathematical integration, as it requires generalizing systematically beyond the test set. We develop a methodology for evaluating generalization that takes advantage of the problem domain's structure and access to a verifier. Despite promising in-distribution performance of sequence-to-sequence models in this domain, we demonstrate challenges in achieving robustness, compositionality, and out-of-distribution generalization, through both carefully constructed manual test suites and a genetic algorithm that automatically finds large collections of failures in a controllable manner. Our investigation highlights the difficulty of generalizing well with the predominant modeling and learning approach, and the importance of evaluating beyond the test set, across different aspects of generalization. | ['Yejin Choi', 'Jize Cao', 'Peter West', 'Sean Welleck'] | 2021-09-28 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 6.60193741e-01 -2.68262535e-01 -1.73186332e-01 -3.11700255e-01
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b6ae23ec-46a0-4874-b74a-f5ff1b523efe | using-deep-learning-method-for-classification | 1703.02182 | null | http://arxiv.org/abs/1703.02182v2 | http://arxiv.org/pdf/1703.02182v2.pdf | Using Deep Learning Method for Classification: A Proposed Algorithm for the ISIC 2017 Skin Lesion Classification Challenge | Skin cancer, the most common human malignancy, is primarily diagnosed
visually by physicians [1]. Classification with an automated method like CNN
[2, 3] shows potential for challenging tasks [1]. By now, the deep
convolutional neural networks are on par with human dermatologist [1]. This
abstract is dedicated on developing a Deep Learning method for ISIC [5] 2017
Skin Lesion Detection Competition hosted at [6] to classify the dermatology
pictures, which is aimed at improving the diagnostic accuracy rate and general
level of the human health. The challenge falls into three sub-challenges,
including Lesion Segmentation, Lesion Dermoscopic Feature Extraction and Lesion
Classification. This project only participates in the Lesion Classification
part. This algorithm is comprised of three steps: (1) original images
preprocessing, (2) modelling the processed images using CNN [2, 3] in Caffe [4]
framework, (3) predicting the test images and calculating the scores that
represent the likelihood of corresponding classification. The models are built
on the source images are using the Caffe [4] framework. The scores in
prediction step are obtained by two different models from the source images. | ['Wenhao Zhang', 'Liangcai Gao', 'Runtao Liu'] | 2017-03-07 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 4.61440563e-01 1.96310699e-01 -2.66915172e-01 -2.79864162e-01
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6059ab7c-2b77-4db7-9028-826399f8f46d | learned-multiphysics-inversion-with | 2304.05592 | null | https://arxiv.org/abs/2304.05592v1 | https://arxiv.org/pdf/2304.05592v1.pdf | Learned multiphysics inversion with differentiable programming and machine learning | We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM) open-source software framework for computational geophysics and, more generally, inverse problems involving the wave-equation (e.g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations. By integrating multiple layers of abstraction, our software is designed to be both readable and scalable. This allows researchers to easily formulate their problems in an abstract fashion while exploiting the latest developments in high-performance computing. We illustrate and demonstrate our design principles and their benefits by means of building a scalable prototype for permeability inversion from time-lapse crosswell seismic data, which aside from coupling of wave physics and multiphase flow, involves machine learning. | ['Felix J. Herrmann', 'Gerard J. Gorman', 'Olav Møyner', 'Philipp A. Witte', 'Gabrio Rizzuti', 'Ali Siahkoohi', 'Thomas J. Grady II', 'Rafael Orozco', 'Ziyi Yin', 'Mathias Louboutin'] | 2023-04-12 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [ 2.36756831e-01 -2.59579383e-02 4.64102060e-01 -1.64188564e-01
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f0f944f8-b225-4724-9d20-1bfa931e5b76 | lit-zero-shot-transfer-with-locked-image-text | 2111.07991 | null | https://arxiv.org/abs/2111.07991v3 | https://arxiv.org/pdf/2111.07991v3.pdf | LiT: Zero-Shot Transfer with Locked-image text Tuning | This paper presents contrastive-tuning, a simple method employing contrastive training to align image and text models while still taking advantage of their pre-training. In our empirical study we find that locked pre-trained image models with unlocked text models work best. We call this instance of contrastive-tuning "Locked-image Tuning" (LiT), which just teaches a text model to read out good representations from a pre-trained image model for new tasks. A LiT model gains the capability of zero-shot transfer to new vision tasks, such as image classification or retrieval. The proposed LiT is widely applicable; it works reliably with multiple pre-training methods (supervised and unsupervised) and across diverse architectures (ResNet, Vision Transformers and MLP-Mixer) using three different image-text datasets. With the transformer-based pre-trained ViT-g/14 model, the LiT model achieves 85.2% zero-shot transfer accuracy on the ImageNet test set, and 82.5% on the challenging out-of-distribution ObjectNet test set. | ['Lucas Beyer', 'Alexander Kolesnikov', 'Daniel Keysers', 'Andreas Steiner', 'Basil Mustafa', 'Xiao Wang', 'Xiaohua Zhai'] | 2021-11-15 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhai_LiT_Zero-Shot_Transfer_With_Locked-Image_Text_Tuning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhai_LiT_Zero-Shot_Transfer_With_Locked-Image_Text_Tuning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['zero-shot-transfer-image-classification'] | ['computer-vision'] | [ 5.02516329e-01 1.88949868e-01 -1.62706554e-01 -5.62518895e-01
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59f32fa5-20b6-4513-84be-6b5efd60a61d | style-based-point-generator-with-adversarial | 2103.02535 | null | https://arxiv.org/abs/2103.02535v3 | https://arxiv.org/pdf/2103.02535v3.pdf | Style-based Point Generator with Adversarial Rendering for Point Cloud Completion | In this paper, we proposed a novel Style-based Point Generator with Adversarial Rendering (SpareNet) for point cloud completion. Firstly, we present the channel-attentive EdgeConv to fully exploit the local structures as well as the global shape in point features. Secondly, we observe that the concatenation manner used by vanilla foldings limits its potential of generating a complex and faithful shape. Enlightened by the success of StyleGAN, we regard the shape feature as style code that modulates the normalization layers during the folding, which considerably enhances its capability. Thirdly, we realize that existing point supervisions, e.g., Chamfer Distance or Earth Mover's Distance, cannot faithfully reflect the perceptual quality of the reconstructed points. To address this, we propose to project the completed points to depth maps with a differentiable renderer and apply adversarial training to advocate the perceptual realism under different viewpoints. Comprehensive experiments on ShapeNet and KITTI prove the effectiveness of our method, which achieves state-of-the-art quantitative performance while offering superior visual quality. | ['Fang Wen', 'Dong Chen', 'Hao Yang', 'Bo Zhang', 'Chuxin Wang', 'Chulin Xie'] | 2021-03-03 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Xie_Style-Based_Point_Generator_With_Adversarial_Rendering_for_Point_Cloud_Completion_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Xie_Style-Based_Point_Generator_With_Adversarial_Rendering_for_Point_Cloud_Completion_CVPR_2021_paper.pdf | cvpr-2021-1 | ['point-cloud-completion'] | ['computer-vision'] | [ 1.84909448e-01 2.13487566e-01 1.83658630e-01 -2.31330171e-01
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e73b603a-6296-4f95-a49e-1d485f30b5ce | wgansing | null | null | https://arxiv.org/pdf/1903.10729.pdf | https://arxiv.org/pdf/1903.10729.pdf | WGANSing | We present a deep neural network based singing
voice synthesizer, inspired by the Deep Convolutions Generative
Adversarial Networks (DCGAN) architecture and optimized using the Wasserstein-GAN algorithm. We use vocoder parameters
for acoustic modelling, to separate the influence of pitch and
timbre. This facilitates the modelling of the large variability
of pitch in the singing voice. Our network takes a block of
consecutive frame-wise linguistic and fundamental frequency
features, along with global singer identity as input and outputs
vocoder features, corresponding to the block of features. This
block-wise approach, along with the training methodology allows
us to model temporal dependencies within the features of the
input block. For inference, sequential blocks are concatenated
using an overlap-add procedure. We show that the performance
of our model is competitive with regards to the state-of-the-art
and the original sample using objective metrics and a subjective
listening test. We also present examples of the synthesis on a
supplementary website and the source code via GitHub. | ['Merlijn Blaauw', 'Pritish Chandna'] | 2019-03-01 | null | null | null | interspeech-2019-3 | ['acoustic-modelling'] | ['speech'] | [-8.93023517e-03 1.37581035e-01 3.59728396e-01 -4.02750224e-02
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cc32fb5c-1a39-46d8-9c59-34970700c7e0 | 3d-convolutional-networks-for-action | 2204.08460 | null | https://arxiv.org/abs/2204.08460v1 | https://arxiv.org/pdf/2204.08460v1.pdf | 3D Convolutional Networks for Action Recognition: Application to Sport Gesture Recognition | 3D convolutional networks is a good means to perform tasks such as video segmentation into coherent spatio-temporal chunks and classification of them with regard to a target taxonomy. In the chapter we are interested in the classification of continuous video takes with repeatable actions, such as strokes of table tennis. Filmed in a free marker less ecological environment, these videos represent a challenge from both segmentation and classification point of view. The 3D convnets are an efficient tool for solving these problems with window-based approaches. | ['J Morlier', 'A Zemmari', 'R Péteri', 'J Benois-Pineau', 'Pierre-Etienne Martin'] | 2022-04-13 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 3.32083851e-02 -4.42393333e-01 -4.11578357e-01 -9.63453948e-02
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4dfa597c-95fa-4f1d-9793-cd96caaebf95 | counterfactual-cycle-consistent-learning-for | 2203.16586 | null | https://arxiv.org/abs/2203.16586v1 | https://arxiv.org/pdf/2203.16586v1.pdf | Counterfactual Cycle-Consistent Learning for Instruction Following and Generation in Vision-Language Navigation | Since the rise of vision-language navigation (VLN), great progress has been made in instruction following -- building a follower to navigate environments under the guidance of instructions. However, far less attention has been paid to the inverse task: instruction generation -- learning a speaker~to generate grounded descriptions for navigation routes. Existing VLN methods train a speaker independently and often treat it as a data augmentation tool to strengthen the follower while ignoring rich cross-task relations. Here we describe an approach that learns the two tasks simultaneously and exploits their intrinsic correlations to boost the training of each: the follower judges whether the speaker-created instruction explains the original navigation route correctly, and vice versa. Without the need of aligned instruction-path pairs, such cycle-consistent learning scheme is complementary to task-specific training targets defined on labeled data, and can also be applied over unlabeled paths (sampled without paired instructions). Another agent, called~creator is added to generate counterfactual environments. It greatly changes current scenes yet leaves novel items -- which are vital for the execution of original instructions -- unchanged. Thus more informative training scenes are synthesized and the three agents compose a powerful VLN learning system. Extensive experiments on a standard benchmark show that our approach improves the performance of various follower models and produces accurate navigation instructions. | ['Wenguan Wang', 'Luc van Gool', 'Jianbing Shen', 'Wei Liang', 'Hanqing Wang'] | 2022-03-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Counterfactual_Cycle-Consistent_Learning_for_Instruction_Following_and_Generation_in_Vision-Language_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Counterfactual_Cycle-Consistent_Learning_for_Instruction_Following_and_Generation_in_Vision-Language_CVPR_2022_paper.pdf | cvpr-2022-1 | ['vision-language-navigation'] | ['computer-vision'] | [ 5.43812633e-01 4.04510438e-01 -1.61542282e-01 -6.61549032e-01
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176cbc53-264c-4607-8043-c6894dc21591 | a-comparison-of-approaches-for-imbalanced | 2205.01600 | null | https://arxiv.org/abs/2205.01600v1 | https://arxiv.org/pdf/2205.01600v1.pdf | A Comparison of Approaches for Imbalanced Classification Problems in the Context of Retrieving Relevant Documents for an Analysis | One of the first steps in many text-based social science studies is to retrieve documents that are relevant for the analysis from large corpora of otherwise irrelevant documents. The conventional approach in social science to address this retrieval task is to apply a set of keywords and to consider those documents to be relevant that contain at least one of the keywords. But the application of incomplete keyword lists risks drawing biased inferences. More complex and costly methods such as query expansion techniques, topic model-based classification rules, and active as well as passive supervised learning could have the potential to more accurately separate relevant from irrelevant documents and thereby reduce the potential size of bias. Yet, whether applying these more expensive approaches increases retrieval performance compared to keyword lists at all, and if so, by how much, is unclear as a comparison of these approaches is lacking. This study closes this gap by comparing these methods across three retrieval tasks associated with a data set of German tweets (Linder, 2017), the Social Bias Inference Corpus (SBIC) (Sap et al., 2020), and the Reuters-21578 corpus (Lewis, 1997). Results show that query expansion techniques and topic model-based classification rules in most studied settings tend to decrease rather than increase retrieval performance. Active supervised learning, however, if applied on a not too small set of labeled training instances (e.g. 1,000 documents), reaches a substantially higher retrieval performance than keyword lists. | ['Sandra Wankmüller'] | 2022-05-03 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 3.10152858e-01 2.21305370e-01 -6.32837951e-01 -8.49038176e-03
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bce590d9-d66e-4583-b531-b2b0db0d580a | isometricmt-neural-machine-translation-for | 2112.08682 | null | https://arxiv.org/abs/2112.08682v3 | https://arxiv.org/pdf/2112.08682v3.pdf | Isometric MT: Neural Machine Translation for Automatic Dubbing | Automatic dubbing (AD) is among the machine translation (MT) use cases where translations should match a given length to allow for synchronicity between source and target speech. For neural MT, generating translations of length close to the source length (e.g. within +-10% in character count), while preserving quality is a challenging task. Controlling MT output length comes at a cost to translation quality, which is usually mitigated with a two step approach of generating N-best hypotheses and then re-ranking based on length and quality. This work introduces a self-learning approach that allows a transformer model to directly learn to generate outputs that closely match the source length, in short Isometric MT. In particular, our approach does not require to generate multiple hypotheses nor any auxiliary ranking function. We report results on four language pairs (English - French, Italian, German, Spanish) with a publicly available benchmark. Automatic and manual evaluations show that our method for Isometric MT outperforms more complex approaches proposed in the literature. | ['Marcello Federico', 'Prashant Mathur', 'Yogesh Virkar', 'Surafel M. Lakew'] | 2021-12-16 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 5.74247599e-01 2.70878851e-01 -1.48500353e-01 -2.80061007e-01
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d6e143c9-1a48-4eed-b756-720e0b803994 | asymptotic-decoupling-of-population-growth | 2209.14683 | null | https://arxiv.org/abs/2209.14683v1 | https://arxiv.org/pdf/2209.14683v1.pdf | Asymptotic decoupling of population growth rate and cell size distribution | The rate at which individual bacterial cells grow depends on the concentrations of cellular components such as ribosomes and proteins. These concentrations continuously fluctuate over time and are inherited from mother to daughter cells leading to correlations between the growth rates of cells across generations. Division sizes of cells are also stochastic and correlated across generations due to a phenomenon known as cell size regulation. Fluctuations and correlations from both growth and division processes affect the population dynamics of an exponentially growing culture. Here, we provide analytic solutions for the population dynamics of cells with continuously fluctuating growth rates coupled with a generic model of cell-size regulation. We show that in balanced growth, the effects of growth and division processes decouple; the population growth rate only depends on the single-cell growth rate process, and the population cell size distribution only depends on the model of division and cell size regulation. The population growth rate is always higher than the average single-cell growth rate, and the difference increases with growth rate variability and its correlation time. This difference also sets the timescale for the population to reach its steady state. We provide analytical solutions for oscillations in population growth rate and traveling waves in size distribution during this approach to the steady state. | ['Farshid Jafarpour', 'Yaïr Hein'] | 2022-09-29 | null | null | null | null | ['culture'] | ['speech'] | [-6.53873086e-02 -2.05122486e-01 -8.58108774e-02 8.30340028e-01
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d40df8c2-3839-48b7-9bec-e50ad9c5d617 | bess-balanced-entity-sampling-and-sharing-for | 2211.12281 | null | https://arxiv.org/abs/2211.12281v1 | https://arxiv.org/pdf/2211.12281v1.pdf | BESS: Balanced Entity Sampling and Sharing for Large-Scale Knowledge Graph Completion | We present the award-winning submission to the WikiKG90Mv2 track of OGB-LSC@NeurIPS 2022. The task is link-prediction on the large-scale knowledge graph WikiKG90Mv2, consisting of 90M+ nodes and 600M+ edges. Our solution uses a diverse ensemble of $85$ Knowledge Graph Embedding models combining five different scoring functions (TransE, TransH, RotatE, DistMult, ComplEx) and two different loss functions (log-sigmoid, sampled softmax cross-entropy). Each individual model is trained in parallel on a Graphcore Bow Pod$_{16}$ using BESS (Balanced Entity Sampling and Sharing), a new distribution framework for KGE training and inference based on balanced collective communications between workers. Our final model achieves a validation MRR of 0.2922 and a test-challenge MRR of 0.2562, winning the first place in the competition. The code is publicly available at: https://github.com/graphcore/distributed-kge-poplar/tree/2022-ogb-submission. | ['Carlo Luschi', 'Blazej Banaszewski', 'Andrew Fitzgibbon', 'Thorin Farnsworth', 'Zhenying Liu', 'Jerome Maloberti', 'Douglas Orr', 'Harry Mellor', 'Daniel Justus', 'Alberto Cattaneo'] | 2022-11-22 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-5.64076960e-01 8.68033171e-01 -3.19088519e-01 -3.07790458e-01
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17bceb9a-ca6f-4d56-87d5-b5bfcced785a | towards-fast-adaptation-of-pretrained | 2206.02082 | null | https://arxiv.org/abs/2206.02082v4 | https://arxiv.org/pdf/2206.02082v4.pdf | Towards Fast Adaptation of Pretrained Contrastive Models for Multi-channel Video-Language Retrieval | Multi-channel video-language retrieval require models to understand information from different channels (e.g. video$+$question, video$+$speech) to correctly link a video with a textual response or query. Fortunately, contrastive multimodal models are shown to be highly effective at aligning entities in images/videos and text, e.g., CLIP; text contrastive models are extensively studied recently for their strong ability of producing discriminative sentence embeddings, e.g., SimCSE. However, there is not a clear way to quickly adapt these two lines to multi-channel video-language retrieval with limited data and resources. In this paper, we identify a principled model design space with two axes: how to represent videos and how to fuse video and text information. Based on categorization of recent methods, we investigate the options of representing videos using continuous feature vectors or discrete text tokens; for the fusion method, we explore the use of a multimodal transformer or a pretrained contrastive text model. We extensively evaluate the four combinations on five video-language datasets. We surprisingly find that discrete text tokens coupled with a pretrained contrastive text model yields the best performance, which can even outperform state-of-the-art on the iVQA and How2QA datasets without additional training on millions of video-text data. Further analysis shows that this is because representing videos as text tokens captures the key visual information and text tokens are naturally aligned with text models that are strong retrievers after the contrastive pretraining process. All the empirical analysis establishes a solid foundation for future research on affordable and upgradable multimodal intelligence. | ['Shih-Fu Chang', 'Heng Ji', 'Mike Zheng Shou', 'Manling Li', 'Shiyuan Huang', 'Simran Tiwari', 'Xudong Lin'] | 2022-06-05 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lin_Towards_Fast_Adaptation_of_Pretrained_Contrastive_Models_for_Multi-Channel_Video-Language_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lin_Towards_Fast_Adaptation_of_Pretrained_Contrastive_Models_for_Multi-Channel_Video-Language_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-question-answering'] | ['computer-vision'] | [ 1.81262344e-01 -5.70052743e-01 -3.51216465e-01 -3.17132264e-01
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1f230910-946b-497e-b5e0-36a99207f3dd | nr-dfernet-noise-robust-network-for-dynamic | 2206.04975 | null | https://arxiv.org/abs/2206.04975v1 | https://arxiv.org/pdf/2206.04975v1.pdf | NR-DFERNet: Noise-Robust Network for Dynamic Facial Expression Recognition | Dynamic facial expression recognition (DFER) in the wild is an extremely challenging task, due to a large number of noisy frames in the video sequences. Previous works focus on extracting more discriminative features, but ignore distinguishing the key frames from the noisy frames. To tackle this problem, we propose a noise-robust dynamic facial expression recognition network (NR-DFERNet), which can effectively reduce the interference of noisy frames on the DFER task. Specifically, at the spatial stage, we devise a dynamic-static fusion module (DSF) that introduces dynamic features to static features for learning more discriminative spatial features. To suppress the impact of target irrelevant frames, we introduce a novel dynamic class token (DCT) for the transformer at the temporal stage. Moreover, we design a snippet-based filter (SF) at the decision stage to reduce the effect of too many neutral frames on non-neutral sequence classification. Extensive experimental results demonstrate that our NR-DFERNet outperforms the state-of-the-art methods on both the DFEW and AFEW benchmarks. | ['Feng Zhao', 'Zhaoqing Zhu', 'Mingzhe Sui', 'Hanting Li'] | 2022-06-10 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 2.67597318e-01 -6.72601104e-01 3.97044197e-02 -6.22245908e-01
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23dbec8c-c186-4809-a945-d0d48aa6eb65 | decker-double-check-with-heterogeneous | 2305.05921 | null | https://arxiv.org/abs/2305.05921v2 | https://arxiv.org/pdf/2305.05921v2.pdf | Decker: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification | Commonsense fact verification, as a challenging branch of commonsense question-answering (QA), aims to verify through facts whether a given commonsense claim is correct or not. Answering commonsense questions necessitates a combination of knowledge from various levels. However, existing studies primarily rest on grasping either unstructured evidence or potential reasoning paths from structured knowledge bases, yet failing to exploit the benefits of heterogeneous knowledge simultaneously. In light of this, we propose Decker, a commonsense fact verification model that is capable of bridging heterogeneous knowledge by uncovering latent relationships between structured and unstructured knowledge. Experimental results on two commonsense fact verification benchmark datasets, CSQA2.0 and CREAK demonstrate the effectiveness of our Decker and further analysis verifies its capability to seize more precious information through reasoning. | ['Hai Zhao', 'Zhuosheng Zhang', 'Anni Zou'] | 2023-05-10 | null | null | null | null | ['fact-verification'] | ['natural-language-processing'] | [ 2.79253185e-01 5.86050093e-01 -4.19504613e-01 -3.52027491e-02
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73b2d325-80c6-4c95-8fa6-e890b4bba42d | recup-fl-reconciling-utility-and-privacy-in | 2304.05135 | null | https://arxiv.org/abs/2304.05135v1 | https://arxiv.org/pdf/2304.05135v1.pdf | RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense | Federated learning (FL) provides a variety of privacy advantages by allowing clients to collaboratively train a model without sharing their private data. However, recent studies have shown that private information can still be leaked through shared gradients. To further minimize the risk of privacy leakage, existing defenses usually require clients to locally modify their gradients (e.g., differential privacy) prior to sharing with the server. While these approaches are effective in certain cases, they regard the entire data as a single entity to protect, which usually comes at a large cost in model utility. In this paper, we seek to reconcile utility and privacy in FL by proposing a user-configurable privacy defense, RecUP-FL, that can better focus on the user-specified sensitive attributes while obtaining significant improvements in utility over traditional defenses. Moreover, we observe that existing inference attacks often rely on a machine learning model to extract the private information (e.g., attributes). We thus formulate such a privacy defense as an adversarial learning problem, where RecUP-FL generates slight perturbations that can be added to the gradients before sharing to fool adversary models. To improve the transferability to un-queryable black-box adversary models, inspired by the idea of meta-learning, RecUP-FL forms a model zoo containing a set of substitute models and iteratively alternates between simulations of the white-box and the black-box adversarial attack scenarios to generate perturbations. Extensive experiments on four datasets under various adversarial settings (both attribute inference attack and data reconstruction attack) show that RecUP-FL can meet user-specified privacy constraints over the sensitive attributes while significantly improving the model utility compared with state-of-the-art privacy defenses. | ['Jian Liu', 'Jiaxin Zhang', 'Luyang Liu', 'Zhuohang Li', 'Syed Irfan Ali Meerza', 'Yue Cui'] | 2023-04-11 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 1.87576637e-01 -2.01130323e-02 -2.29622215e-01 -6.52469575e-01
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213afeea-7395-402c-b11d-08d9e6b23497 | emulating-reader-behaviors-for-fake-news | 2306.15231 | null | https://arxiv.org/abs/2306.15231v1 | https://arxiv.org/pdf/2306.15231v1.pdf | Emulating Reader Behaviors for Fake News Detection | The wide dissemination of fake news has affected our lives in many aspects, making fake news detection important and attracting increasing attention. Existing approaches make substantial contributions in this field by modeling news from a single-modal or multi-modal perspective. However, these modal-based methods can result in sub-optimal outcomes as they ignore reader behaviors in news consumption and authenticity verification. For instance, they haven't taken into consideration the component-by-component reading process: from the headline, images, comments, to the body, which is essential for modeling news with more granularity. To this end, we propose an approach of Emulating the behaviors of readers (Ember) for fake news detection on social media, incorporating readers' reading and verificating process to model news from the component perspective thoroughly. Specifically, we first construct intra-component feature extractors to emulate the behaviors of semantic analyzing on each component. Then, we design a module that comprises inter-component feature extractors and a sequence-based aggregator. This module mimics the process of verifying the correlation between components and the overall reading and verification sequence. Thus, Ember can handle the news with various components by emulating corresponding sequences. We conduct extensive experiments on nine real-world datasets, and the results demonstrate the superiority of Ember. | ['Jia Wang', 'Yinqiu Huang', 'Zehua Zhao', 'Kai Shu', 'Min Gao', 'Junwei Yin'] | 2023-06-27 | null | null | null | null | ['fake-news-detection'] | ['natural-language-processing'] | [-1.41553823e-02 -2.53787726e-01 -3.00354868e-01 -2.23050028e-01
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146d326e-bc23-4f90-8fbd-1e9e8cc2545a | traffic-data-imputation-using-deep | 2002.04406 | null | https://arxiv.org/abs/2002.04406v1 | https://arxiv.org/pdf/2002.04406v1.pdf | Traffic Data Imputation using Deep Convolutional Neural Networks | We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information. Using a convolutional encoder-decoder based architecture, we show that a well trained neural network can learn spatio-temporal traffic speed dynamics from time-space diagrams. We demonstrate this for a homogeneous road section using simulated vehicle trajectories and then validate it using real-world data from NGSIM. Our results show that with probe vehicle penetration levels as low as 5\%, the proposed estimation method can provide a sound reconstruction of macroscopic traffic speeds and reproduce realistic shockwave patterns, implying applicability in a variety of traffic conditions. We further discuss the model's reconstruction mechanisms and confirm its ability to differentiate various traffic behaviors such as congested and free-flow traffic states, transition dynamics, and shockwave propagation. | ['Monica Menendez', 'Saif Eddin Jabari', 'Ouafa Benkraouda', 'Hwasoo Yeo', 'Bilal Thonnam Thodi'] | 2020-01-21 | null | null | null | null | ['traffic-data-imputation'] | ['time-series'] | [-3.36192906e-01 -3.09110343e-01 -3.58099550e-01 -1.89446479e-01
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e3c7647d-ec3f-44bf-9848-742c042b6832 | on-the-robustness-of-arabic-speech-dialect | 2306.03789 | null | https://arxiv.org/abs/2306.03789v1 | https://arxiv.org/pdf/2306.03789v1.pdf | On the Robustness of Arabic Speech Dialect Identification | Arabic dialect identification (ADI) tools are an important part of the large-scale data collection pipelines necessary for training speech recognition models. As these pipelines require application of ADI tools to potentially out-of-domain data, we aim to investigate how vulnerable the tools may be to this domain shift. With self-supervised learning (SSL) models as a starting point, we evaluate transfer learning and direct classification from SSL features. We undertake our evaluation under rich conditions, with a goal to develop ADI systems from pretrained models and ultimately evaluate performance on newly collected data. In order to understand what factors contribute to model decisions, we carry out a careful human study of a subset of our data. Our analysis confirms that domain shift is a major challenge for ADI models. We also find that while self-training does alleviate this challenges, it may be insufficient for realistic conditions. | ['Muhammad Abdul-Mageed', 'AbdelRahim Elmadany', 'Peter Sullivan'] | 2023-06-01 | null | null | null | null | ['dialect-identification'] | ['natural-language-processing'] | [ 2.20091537e-01 -1.00900143e-01 1.65895373e-01 -7.44338512e-01
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444e1efe-3483-43bc-ab4b-f6642f2185de | probing-toxic-content-in-large-pre-trained | null | null | https://aclanthology.org/2021.acl-long.329/ | https://aclanthology.org/2021.acl-long.329.pdf | Probing Toxic Content in Large Pre-Trained Language Models | Large pre-trained language models (PTLMs) have been shown to carry biases towards different social groups which leads to the reproduction of stereotypical and toxic content by major NLP systems. We propose a method based on logistic regression classifiers to probe English, French, and Arabic PTLMs and quantify the potentially harmful content that they convey with respect to a set of templates. The templates are prompted by a name of a social group followed by a cause-effect relation. We use PTLMs to predict masked tokens at the end of a sentence in order to examine how likely they enable toxicity towards specific communities. We shed the light on how such negative content can be triggered within unrelated and benign contexts based on evidence from a large-scale study, then we explain how to take advantage of our methodology to assess and mitigate the toxicity transmitted by PTLMs. | ['Dit-yan Yeung', 'Yangqiu Song', 'Tianqing Fang', 'Xinran Zhao', 'Nedjma Ousidhoum'] | 2021-08-01 | null | https://aclanthology.org/2021.acl-long.329 | https://aclanthology.org/2021.acl-long.329.pdf | acl-2021-5 | ['probing-language-models'] | ['natural-language-processing'] | [ 6.08757257e-01 1.53869167e-01 3.81290652e-02 -2.49676913e-01
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6b7ffc90-0a37-47ee-95c6-a6730db9b791 | super-resolution-with-deep-convolutional | 1511.05666 | null | http://arxiv.org/abs/1511.05666v4 | http://arxiv.org/pdf/1511.05666v4.pdf | Super-Resolution with Deep Convolutional Sufficient Statistics | Inverse problems in image and audio, and super-resolution in particular, can
be seen as high-dimensional structured prediction problems, where the goal is
to characterize the conditional distribution of a high-resolution output given
its low-resolution corrupted observation. When the scaling ratio is small,
point estimates achieve impressive performance, but soon they suffer from the
regression-to-the-mean problem, result of their inability to capture the
multi-modality of this conditional distribution. Modeling high-dimensional
image and audio distributions is a hard task, requiring both the ability to
model complex geometrical structures and textured regions. In this paper, we
propose to use as conditional model a Gibbs distribution, where its sufficient
statistics are given by deep convolutional neural networks. The features
computed by the network are stable to local deformation, and have reduced
variance when the input is a stationary texture. These properties imply that
the resulting sufficient statistics minimize the uncertainty of the target
signals given the degraded observations, while being highly informative. The
filters of the CNN are initialized by multiscale complex wavelets, and then we
propose an algorithm to fine-tune them by estimating the gradient of the
conditional log-likelihood, which bears some similarities with Generative
Adversarial Networks. We evaluate experimentally the proposed approach in the
image super-resolution task, but the approach is general and could be used in
other challenging ill-posed problems such as audio bandwidth extension. | ['Yann Lecun', 'Joan Bruna', 'Pablo Sprechmann'] | 2015-11-18 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [ 3.93136770e-01 2.54554093e-01 1.90418720e-01 -6.00932427e-02
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4fdf9b5a-bf0f-4c09-9338-8169dc8d55ec | drug-repurposing-for-sars-cov-2-a-molecular | 2201.00287 | null | https://arxiv.org/abs/2201.00287v3 | https://arxiv.org/pdf/2201.00287v3.pdf | Drug repurposing for SARS-COV-2: A high-throughput molecular docking, molecular dynamics, machine learning, & ab-initio study | A molecule of dimension 125nm has caused around 479 Million human infections (80M for the USA) & 6.1 Million human deaths (977,000 for the USA) worldwide and slashed the global economy by US$ 8.5 Trillion over two years. The only other events in recent history that caused comparative human life loss through direct usage (either by (wo)man or nature, respectively) of structure-property relations of 'nano-structures' (either (wo)man-made or nature, respectively) were nuclear bomb attacks of Japanese cities by the USA during World War II and 1918 Flu Pandemic. This molecule is SARS-CoV-2, which causes a disease known as COVID-19. The high liability cost of the pandemic had incentivized various private, government, and academic entities to work towards finding a cure for these & emerging diseases. As result, multiple vaccine candidates are discovered to avoid the infection in first place. But so far, there has been no success in finding fully effective therapeutics candidates. In this paper, we attempted to provide multiple therapy candidates based upon a sophisticated multi-scale in-silico framework. We have used the following robust framework to screen the ligands; Step-I: high throughput docking, Step-II: molecular dynamics, Step-III: density functional theory analysis. In total, we have analyzed 2.2 Million unique protein binding site/ligand combinations. The proteins were selected based on recent experimental studies. Step-I had filtered that number down to 10 ligands/protein based on molecular docking binding energy, further screening down to 2 ligands/protein based on drug-likeness analysis. Additionally, these two ligands/proteins were investigated in Step-II with a molecular dynamic based RMSD analysis. It finally suggested three ligands (ZINC1176619532, ZINC517580540, ZINC952855827) attacking different binding sites of the protein(7BV2), which were further analyzed in Step III. | ['Dibakar Datta', 'Jatin Kashyap'] | 2022-01-02 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 1.00692518e-01 -1.61623627e-01 9.30093154e-02 1.54175535e-01
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ec2abd0c-11af-4e36-91fa-771e4cff424c | speckle2void-deep-self-supervised-sar | 2007.02075 | null | https://arxiv.org/abs/2007.02075v1 | https://arxiv.org/pdf/2007.02075v1.pdf | Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks | Information extraction from synthetic aperture radar (SAR) images is heavily impaired by speckle noise, hence despeckling is a crucial preliminary step in scene analysis algorithms. The recent success of deep learning envisions a new generation of despeckling techniques that could outperform classical model-based methods. However, current deep learning approaches to despeckling require supervision for training, whereas clean SAR images are impossible to obtain. In the literature, this issue is tackled by resorting to either synthetically speckled optical images, which exhibit different properties with respect to true SAR images, or multi-temporal SAR images, which are difficult to acquire or fuse accurately. In this paper, inspired by recent works on blind-spot denoising networks, we propose a self-supervised Bayesian despeckling method. The proposed method is trained employing only noisy SAR images and can therefore learn features of real SAR images rather than synthetic data. Experiments show that the performance of the proposed approach is very close to the supervised training approach on synthetic data and superior on real data in both quantitative and visual assessments. | ['Andrea Bordone Molini', 'Giulia Fracastoro', 'Enrico Magli', 'Diego Valsesia'] | 2020-07-04 | null | null | null | null | ['sar-image-despeckling'] | ['computer-vision'] | [ 7.47049510e-01 -3.02401632e-01 5.79414487e-01 -4.91927683e-01
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cffe35a7-2be6-4743-bbda-d123dff9020a | sql-rank-a-listwise-approach-to-collaborative | 1803.00114 | null | http://arxiv.org/abs/1803.00114v3 | http://arxiv.org/pdf/1803.00114v3.pdf | SQL-Rank: A Listwise Approach to Collaborative Ranking | In this paper, we propose a listwise approach for constructing user-specific
rankings in recommendation systems in a collaborative fashion. We contrast the
listwise approach to previous pointwise and pairwise approaches, which are
based on treating either each rating or each pairwise comparison as an
independent instance respectively. By extending the work of (Cao et al. 2007),
we cast listwise collaborative ranking as maximum likelihood under a
permutation model which applies probability mass to permutations based on a low
rank latent score matrix. We present a novel algorithm called SQL-Rank, which
can accommodate ties and missing data and can run in linear time. We develop a
theoretical framework for analyzing listwise ranking methods based on a novel
representation theory for the permutation model. Applying this framework to
collaborative ranking, we derive asymptotic statistical rates as the number of
users and items grow together. We conclude by demonstrating that our SQL-Rank
method often outperforms current state-of-the-art algorithms for implicit
feedback such as Weighted-MF and BPR and achieve favorable results when
compared to explicit feedback algorithms such as matrix factorization and
collaborative ranking. | ['Cho-Jui Hsieh', 'Liwei Wu', 'James Sharpnack'] | 2018-02-28 | sql-rank-a-listwise-approach-to-collaborative-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2098 | http://proceedings.mlr.press/v80/wu18c/wu18c.pdf | icml-2018-7 | ['collaborative-ranking'] | ['graphs'] | [ 1.61245987e-01 -1.64287284e-01 -4.29676533e-01 -5.59531033e-01
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40968b32-417f-482e-8a8a-0886f35b0c3d | robust-time-series-denoising-with-learnable | 2206.06126 | null | https://arxiv.org/abs/2206.06126v4 | https://arxiv.org/pdf/2206.06126v4.pdf | Robust Time Series Denoising with Learnable Wavelet Packet Transform | Signal denoising is a key preprocessing step for many applications, as the performance of a learning task is closely related to the quality of the input data. In this paper, we apply a signal processing based deep neural network architecture, a learnable extension of the wavelet packet transform. As main advantages, this model has few parameters, an intuitive initialization and strong learning capabilities. Moreover, we show that it is possible to easily modify the parameters of the model after the training step to tailor to different noise intensities. Two case studies are conducted to compare this model with the state of the art and commonly used denoising procedures. The first experiment uses standard signals to study denoising properties of the algorithms. The second experiment is a real application with the objective to remove audio background noises. We show that the learnable wavelet packet transform has the learning capabilities of deep learning methods while maintaining the robustness of standard signal processing approaches. More specifically, we demonstrate that our approach maintains excellent denoising performances on signal classes separate from those used during the training step. Moreover, the learnable wavelet packet transform was found to be robust when different noise intensities, noise varieties and artifacts are considered. | ['Olga Fink', 'Gaetan Frusque'] | 2022-06-13 | null | null | null | null | ['time-series-denoising'] | ['time-series'] | [ 2.63786316e-01 -3.28782141e-01 3.69555742e-01 -3.75816703e-01
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f95acf5c-faf6-4138-8737-5c23dd167dfa | survey-on-3d-face-reconstruction-from | 2011.05740 | null | https://arxiv.org/abs/2011.05740v2 | https://arxiv.org/pdf/2011.05740v2.pdf | Survey on 3D face reconstruction from uncalibrated images | Recently, a lot of attention has been focused on the incorporation of 3D data into face analysis and its applications. Despite providing a more accurate representation of the face, 3D facial images are more complex to acquire than 2D pictures. As a consequence, great effort has been invested in developing systems that reconstruct 3D faces from an uncalibrated 2D image. However, the 3D-from-2D face reconstruction problem is ill-posed, thus prior knowledge is needed to restrict the solutions space. In this work, we review 3D face reconstruction methods proposed in the last decade, focusing on those that only use 2D pictures captured under uncontrolled conditions. We present a classification of the proposed methods based on the technique used to add prior knowledge, considering three main strategies, namely, statistical model fitting, photometry, and deep learning, and reviewing each of them separately. In addition, given the relevance of statistical 3D facial models as prior knowledge, we explain the construction procedure and provide a list of the most popular publicly available 3D facial models. After the exhaustive study of 3D-from-2D face reconstruction approaches, we observe that the deep learning strategy is rapidly growing since the last few years, becoming the standard choice in replacement of the widespread statistical model fitting. Unlike the other two strategies, photometry-based methods have decreased in number due to the need for strong underlying assumptions that limit the quality of their reconstructions compared to statistical model fitting and deep learning methods. The review also identifies current challenges and suggests avenues for future research. | ['Federico M. Sukno', 'Gemma Piella', 'Araceli Morales'] | 2020-11-11 | null | null | null | null | ['face-reconstruction'] | ['computer-vision'] | [ 3.81405987e-02 8.95055160e-02 -7.07962885e-02 -4.54569817e-01
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7c72e053-fb65-442a-8edc-fb3697938669 | how-to-efficiently-adapt-large-segmentation | 2306.13731 | null | https://arxiv.org/abs/2306.13731v1 | https://arxiv.org/pdf/2306.13731v1.pdf | How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images | The emerging scale segmentation model, Segment Anything (SAM), exhibits impressive capabilities in zero-shot segmentation for natural images. However, when applied to medical images, SAM suffers from noticeable performance drop. To make SAM a real ``foundation model" for the computer vision community, it is critical to find an efficient way to customize SAM for medical image dataset. In this work, we propose to freeze SAM encoder and finetune a lightweight task-specific prediction head, as most of weights in SAM are contributed by the encoder. In addition, SAM is a promptable model, while prompt is not necessarily available in all application cases, and precise prompts for multiple class segmentation are also time-consuming. Therefore, we explore three types of prompt-free prediction heads in this work, include ViT, CNN, and linear layers. For ViT head, we remove the prompt tokens in the mask decoder of SAM, which is named AutoSAM. AutoSAM can also generate masks for different classes with one single inference after modification. To evaluate the label-efficiency of our finetuning method, we compare the results of these three prediction heads on a public medical image segmentation dataset with limited labeled data. Experiments demonstrate that finetuning SAM significantly improves its performance on medical image dataset, even with just one labeled volume. Moreover, AutoSAM and CNN prediction head also has better segmentation accuracy than training from scratch and self-supervised learning approaches when there is a shortage of annotations. | ['Yiyu Shi', 'Xiaowei Xu', 'Xinrong Hu'] | 2023-06-23 | null | null | null | null | ['zero-shot-segmentation', 'self-supervised-learning', 'medical-image-segmentation'] | ['computer-vision', 'computer-vision', 'medical'] | [ 3.93454432e-01 3.33453417e-01 -3.94557983e-01 -4.63391870e-01
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f0c50828-5195-4a7e-8f00-0bfa342d8803 | animatediff-animate-your-personalized-text-to | 2307.04725 | null | https://arxiv.org/abs/2307.04725v1 | https://arxiv.org/pdf/2307.04725v1.pdf | AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning | With the advance of text-to-image models (e.g., Stable Diffusion) and corresponding personalization techniques such as DreamBooth and LoRA, everyone can manifest their imagination into high-quality images at an affordable cost. Subsequently, there is a great demand for image animation techniques to further combine generated static images with motion dynamics. In this report, we propose a practical framework to animate most of the existing personalized text-to-image models once and for all, saving efforts in model-specific tuning. At the core of the proposed framework is to insert a newly initialized motion modeling module into the frozen text-to-image model and train it on video clips to distill reasonable motion priors. Once trained, by simply injecting this motion modeling module, all personalized versions derived from the same base T2I readily become text-driven models that produce diverse and personalized animated images. We conduct our evaluation on several public representative personalized text-to-image models across anime pictures and realistic photographs, and demonstrate that our proposed framework helps these models generate temporally smooth animation clips while preserving the domain and diversity of their outputs. Code and pre-trained weights will be publicly available at https://animatediff.github.io/ . | ['Bo Dai', 'Dahua Lin', 'Yu Qiao', 'Yaohui Wang', 'Anyi Rao', 'Ceyuan Yang', 'Yuwei Guo'] | 2023-07-10 | null | null | null | null | ['image-animation'] | ['computer-vision'] | [ 4.09625694e-02 -9.75490063e-02 -1.46940663e-01 -3.09286058e-01
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f64bee1d-805e-4dbf-ae04-928e476c4ebe | distilling-efficient-language-specific-models | 2306.01709 | null | https://arxiv.org/abs/2306.01709v1 | https://arxiv.org/pdf/2306.01709v1.pdf | Distilling Efficient Language-Specific Models for Cross-Lingual Transfer | Massively multilingual Transformers (MMTs), such as mBERT and XLM-R, are widely used for cross-lingual transfer learning. While these are pretrained to represent hundreds of languages, end users of NLP systems are often interested only in individual languages. For such purposes, the MMTs' language coverage makes them unnecessarily expensive to deploy in terms of model size, inference time, energy, and hardware cost. We thus propose to extract compressed, language-specific models from MMTs which retain the capacity of the original MMTs for cross-lingual transfer. This is achieved by distilling the MMT bilingually, i.e., using data from only the source and target language of interest. Specifically, we use a two-phase distillation approach, termed BiStil: (i) the first phase distils a general bilingual model from the MMT, while (ii) the second, task-specific phase sparsely fine-tunes the bilingual "student" model using a task-tuned variant of the original MMT as its "teacher". We evaluate this distillation technique in zero-shot cross-lingual transfer across a number of standard cross-lingual benchmarks. The key results indicate that the distilled models exhibit minimal degradation in target language performance relative to the base MMT despite being significantly smaller and faster. Furthermore, we find that they outperform multilingually distilled models such as DistilmBERT and MiniLMv2 while having a very modest training budget in comparison, even on a per-language basis. We also show that bilingual models distilled from MMTs greatly outperform bilingual models trained from scratch. Our code and models are available at https://github.com/AlanAnsell/bistil. | ['Ivan Vulić', 'Anna Korhonen', 'Edoardo Maria Ponti', 'Alan Ansell'] | 2023-06-02 | null | null | null | null | ['zero-shot-cross-lingual-transfer', 'cross-lingual-transfer', 'xlm-r'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-5.62344640e-02 -1.39040962e-01 -5.88618219e-01 -3.22466612e-01
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de34bfa6-77d8-4b98-a5d2-e8d499693062 | abd-net-attention-based-decomposition-network | 2108.04221 | null | https://arxiv.org/abs/2108.04221v1 | https://arxiv.org/pdf/2108.04221v1.pdf | ABD-Net: Attention Based Decomposition Network for 3D Point Cloud Decomposition | In this paper, we propose Attention Based Decomposition Network (ABD-Net), for point cloud decomposition into basic geometric shapes namely, plane, sphere, cone and cylinder. We show improved performance of 3D object classification using attention features based on primitive shapes in point clouds. Point clouds, being the simple and compact representation of 3D objects have gained increasing popularity. They demand robust methods for feature extraction due to unorderness in point sets. In ABD-Net the proposed Local Proximity Encapsulator captures the local geometric variations along with spatial encoding around each point from the input point sets. The encapsulated local features are further passed to proposed Attention Feature Encoder to learn basic shapes in point cloud. Attention Feature Encoder models geometric relationship between the neighborhoods of all the points resulting in capturing global point cloud information. We demonstrate the results of our proposed ABD-Net on ANSI mechanical component and ModelNet40 datasets. We also demonstrate the effectiveness of ABD-Net over the acquired attention features by improving the performance of 3D object classification on ModelNet40 benchmark dataset and compare them with state-of-the-art techniques. | ['Uma Mudenagudi', 'Ramesh Ashok Tabib', 'Akshaykumar Gunari', 'Shashidhar V Kudari', 'Siddharth Katageri'] | 2021-07-09 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [-6.29521012e-01 -2.04586044e-01 1.97191387e-01 -2.37192035e-01
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75bf0183-e52c-43d9-937a-711d34e1cc59 | talla-at-semeval-2017-task-3-identifying | null | null | https://aclanthology.org/S17-2062 | https://aclanthology.org/S17-2062.pdf | Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection | This paper describes our approach to the SemEval-2017 shared task of determining question-question similarity in a community question-answering setting (Task 3B). We extracted both syntactic and semantic similarity features between candidate questions, performed pairwise-preference learning to optimize for ranking order, and then trained a random forest classifier to predict whether the candidate questions are paraphrases of each other. This approach achieved a MAP of 45.7{\%} out of max achievable 67.0{\%} on the test set. | ['Daniel Shank', 'Byron Galbraith', 'Bhanu Pratap'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['question-similarity'] | ['natural-language-processing'] | [ 2.06032261e-01 3.40532690e-01 1.37638405e-01 -8.52542460e-01
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86f47216-3c9c-4209-922b-1b6cfc5c4f62 | unsupervised-feature-learning-based-on-deep | 1607.03681 | null | http://arxiv.org/abs/1607.03681v2 | http://arxiv.org/pdf/1607.03681v2.pdf | Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging | Environmental audio tagging aims to predict only the presence or absence of
certain acoustic events in the interested acoustic scene. In this paper we make
contributions to audio tagging in two parts, respectively, acoustic modeling
and feature learning. We propose to use a shrinking deep neural network (DNN)
framework incorporating unsupervised feature learning to handle the multi-label
classification task. For the acoustic modeling, a large set of contextual
frames of the chunk are fed into the DNN to perform a multi-label
classification for the expected tags, considering that only chunk (or
utterance) level rather than frame-level labels are available. Dropout and
background noise aware training are also adopted to improve the generalization
capability of the DNNs. For the unsupervised feature learning, we propose to
use a symmetric or asymmetric deep de-noising auto-encoder (sDAE or aDAE) to
generate new data-driven features from the Mel-Filter Banks (MFBs) features.
The new features, which are smoothed against background noise and more compact
with contextual information, can further improve the performance of the DNN
baseline. Compared with the standard Gaussian Mixture Model (GMM) baseline of
the DCASE 2016 audio tagging challenge, our proposed method obtains a
significant equal error rate (EER) reduction from 0.21 to 0.13 on the
development set. The proposed aDAE system can get a relative 6.7% EER reduction
compared with the strong DNN baseline on the development set. Finally, the
results also show that our approach obtains the state-of-the-art performance
with 0.15 EER on the evaluation set of the DCASE 2016 audio tagging task while
EER of the first prize of this challenge is 0.17. | ['Philip J. B. Jackson', 'Wenwu Wang', 'Qiang Huang', 'Peter Foster', 'Yong Xu', 'Siddharth Sigtia', 'Mark D. Plumbley'] | 2016-07-13 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 3.22422236e-01 -1.44126695e-02 3.59844714e-01 -5.73793530e-01
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9b9634d3-06e3-4b66-8abb-4a3141ab3215 | canonical-capsules-unsupervised-capsules-in | 2012.04718 | null | https://arxiv.org/abs/2012.04718v2 | https://arxiv.org/pdf/2012.04718v2.pdf | Canonical Capsules: Self-Supervised Capsules in Canonical Pose | We propose a self-supervised capsule architecture for 3D point clouds. We compute capsule decompositions of objects through permutation-equivariant attention, and self-supervise the process by training with pairs of randomly rotated objects. Our key idea is to aggregate the attention masks into semantic keypoints, and use these to supervise a decomposition that satisfies the capsule invariance/equivariance properties. This not only enables the training of a semantically consistent decomposition, but also allows us to learn a canonicalization operation that enables object-centric reasoning. To train our neural network we require neither classification labels nor manually-aligned training datasets. Yet, by learning an object-centric representation in a self-supervised manner, our method outperforms the state-of-the-art on 3D point cloud reconstruction, canonicalization, and unsupervised classification. | ['Kwang Moo Yi', 'Geoffrey Hinton', 'Soroosh Yazdani', 'Sara Sabour', 'Boyang Deng', 'Andrea Tagliasacchi', 'Weiwei Sun'] | 2020-12-08 | canonical-capsules-self-supervised-capsules | http://proceedings.neurips.cc/paper/2021/hash/d1ee59e20ad01cedc15f5118a7626099-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/d1ee59e20ad01cedc15f5118a7626099-Paper.pdf | neurips-2021-12 | ['3d-point-cloud-reconstruction', 'point-cloud-reconstruction'] | ['computer-vision', 'computer-vision'] | [-1.19809039e-01 1.84192464e-01 -1.24471687e-01 -3.61236751e-01
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0cc5985c-86bc-427c-82de-8d46fd4dcc45 | fully-convolutional-networks-for-semantic-1 | 1411.4038 | null | http://arxiv.org/abs/1411.4038v2 | http://arxiv.org/pdf/1411.4038v2.pdf | Fully Convolutional Networks for Semantic Segmentation | Convolutional networks are powerful visual models that yield hierarchies of
features. We show that convolutional networks by themselves, trained
end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic
segmentation. Our key insight is to build "fully convolutional" networks that
take input of arbitrary size and produce correspondingly-sized output with
efficient inference and learning. We define and detail the space of fully
convolutional networks, explain their application to spatially dense prediction
tasks, and draw connections to prior models. We adapt contemporary
classification networks (AlexNet, the VGG net, and GoogLeNet) into fully
convolutional networks and transfer their learned representations by
fine-tuning to the segmentation task. We then define a novel architecture that
combines semantic information from a deep, coarse layer with appearance
information from a shallow, fine layer to produce accurate and detailed
segmentations. Our fully convolutional network achieves state-of-the-art
segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012),
NYUDv2, and SIFT Flow, while inference takes one third of a second for a
typical image. | ['Jonathan Long', 'Evan Shelhamer', 'Trevor Darrell'] | 2014-11-14 | fully-convolutional-networks-for-semantic-2 | http://openaccess.thecvf.com/content_cvpr_2015/html/Long_Fully_Convolutional_Networks_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf | cvpr-2015-6 | ['thermal-image-segmentation', 'multi-tissue-nucleus-segmentation'] | ['computer-vision', 'medical'] | [ 2.93513089e-01 2.16641054e-01 -2.55202115e-01 -5.26256561e-01
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59d08dac-299a-4c37-85b4-6d32bfc3f907 | uninl-aligning-representation-learning-with | 2210.10722 | null | https://arxiv.org/abs/2210.10722v1 | https://arxiv.org/pdf/2210.10722v1.pdf | UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning | Detecting out-of-domain (OOD) intents from user queries is essential for avoiding wrong operations in task-oriented dialogue systems. The key challenge is how to distinguish in-domain (IND) and OOD intents. Previous methods ignore the alignment between representation learning and scoring function, limiting the OOD detection performance. In this paper, we propose a unified neighborhood learning framework (UniNL) to detect OOD intents. Specifically, we design a K-nearest neighbor contrastive learning (KNCL) objective for representation learning and introduce a KNN-based scoring function for OOD detection. We aim to align representation learning with scoring function. Experiments and analysis on two benchmark datasets show the effectiveness of our method. | ['Weiran Xu', 'Wei Wu', 'Jingang Wang', 'Yanan Wu', 'Keqing He', 'Pei Wang', 'Yutao Mou'] | 2022-10-19 | null | null | null | null | ['task-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 2.62613234e-04 -2.09089983e-02 -4.51473087e-01 -7.22515702e-01
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5e3d0511-bafe-4d07-a824-f4727a3eaf85 | multi-order-networks-for-action-unit | 2202.00446 | null | https://arxiv.org/abs/2202.00446v2 | https://arxiv.org/pdf/2202.00446v2.pdf | Multi-Order Networks for Action Unit Detection | Action Units (AU) are muscular activations used to describe facial expressions. Therefore accurate AU recognition unlocks unbiaised face representation which can improve face-based affective computing applications. From a learning standpoint AU detection is a multi-task problem with strong inter-task dependencies. To solve such problem, most approaches either rely on weight sharing, or add explicit dependency modelling by decomposing the joint task distribution using Bayes chain rule. If the latter strategy yields comprehensive inter-task relationships modelling, it requires imposing an arbitrary order into an unordered task set. Crucially, this ordering choice has been identified as a source of performance variations. In this paper, we present Multi-Order Network (MONET), a multi-task method with joint task order optimization. MONET uses a differentiable order selection to jointly learn task-wise modules with their optimal chaining order. Furthermore, we introduce warmup and order dropout to enhance order selection by encouraging order exploration. Experimentally, we first demonstrate MONET capacity to retrieve the optimal order in a toy environment. Second, we validate MONET architecture by showing that MONET outperforms existing multi-task baselines on multiple attribute detection problems chosen for their wide range of dependency settings. More importantly, we demonstrate that MONET significantly extends state-of-the-art performance in AU detection. | ['Kevin Bailly', 'Arnaud Dapogny', 'Gauthier Tallec'] | 2022-02-01 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 5.45538843e-01 -6.57444298e-02 -2.99274355e-01 -4.58286196e-01
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bca43abc-9d1e-475b-b8d3-f032665a562f | semi-blind-source-separation-using | 2207.01556 | null | https://arxiv.org/abs/2207.01556v1 | https://arxiv.org/pdf/2207.01556v1.pdf | Semi-blind source separation using convolutive transfer function for nonlinear acoustic echo cancellation | The recently proposed semi-blind source separation (SBSS) method for nonlinear acoustic echo cancellation (NAEC) outperforms adaptive NAEC in attenuating the nonlinear acoustic echo. However, the multiplicative transfer function (MTF) approximation makes it unsuitable for real-time applications especially in highly reverberant environments, and the natural gradient makes it hard to balance well between fast convergence speed and stability. In this paper, we propose two more effective SBSS methods based on auxiliary-function-based independent vector analysis (AuxIVA) and independent low-rank matrix analysis (ILRMA). The convolutive transfer function (CTF) approximation is used instead of MTF so that a long impulse response can be modeled with a short latency. The optimization schemes used in AuxIVA and ILRMA are carefully regularized according to the constrained demixing matrix of NAEC. Experimental results validate significantly better echo cancellation performance of the proposed methods. | ['Jing Lu', 'Changbao Zhu', 'Yuxiang Hu', 'Kai Chen', 'Lele Liao', 'Guoliang Cheng'] | 2022-07-04 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 1.85860768e-01 -7.73774743e-01 7.95030653e-01 -1.88485421e-02
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d50aa94a-8cf1-460f-b9dc-f258ba1aab03 | human-body-pose-estimation-for-gait | 2305.13765 | null | https://arxiv.org/abs/2305.13765v1 | https://arxiv.org/pdf/2305.13765v1.pdf | Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models | Person identification is a problem that has received substantial attention, particularly in security domains. Gait recognition is one of the most convenient approaches enabling person identification at a distance without the need of high-quality images. There are several review studies addressing person identification such as the utilization of facial images, silhouette images, and wearable sensor. Despite skeleton-based person identification gaining popularity while overcoming the challenges of traditional approaches, existing survey studies lack the comprehensive review of skeleton-based approaches to gait identification. We present a detailed review of the human pose estimation and gait analysis that make the skeleton-based approaches possible. The study covers various types of related datasets, tools, methodologies, and evaluation metrics with associated challenges, limitations, and application domains. Detailed comparisons are presented for each of these aspects with recommendations for potential research and alternatives. A common trend throughout this paper is the positive impact that deep learning techniques are beginning to have on topics such as human pose estimation and gait identification. The survey outcomes might be useful for the related research community and other stakeholders in terms of performance analysis of existing methodologies, potential research gaps, application domains, and possible contributions in the future. | ['Abir Hussain', 'Dhiya Al-Jumeily', 'Wasiq Khan', 'Luke K. Topham'] | 2023-05-23 | null | null | null | null | ['gait-recognition', 'person-identification', 'gait-identification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.38225228e-01 -6.19222939e-01 -3.17522496e-01 -2.94775158e-01
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5ade1573-3bd9-46de-bba0-aeadd2fe90bb | sampling-frequency-independent-audio-source | 2105.04079 | null | https://arxiv.org/abs/2105.04079v1 | https://arxiv.org/pdf/2105.04079v1.pdf | Sampling-Frequency-Independent Audio Source Separation Using Convolution Layer Based on Impulse Invariant Method | Audio source separation is often used as preprocessing of various applications, and one of its ultimate goals is to construct a single versatile model capable of dealing with the varieties of audio signals. Since sampling frequency, one of the audio signal varieties, is usually application specific, the preceding audio source separation model should be able to deal with audio signals of all sampling frequencies specified in the target applications. However, conventional models based on deep neural networks (DNNs) are trained only at the sampling frequency specified by the training data, and there are no guarantees that they work with unseen sampling frequencies. In this paper, we propose a convolution layer capable of handling arbitrary sampling frequencies by a single DNN. Through music source separation experiments, we show that the introduction of the proposed layer enables a conventional audio source separation model to consistently work with even unseen sampling frequencies. | ['Hiroshi Saruwatari', 'Yuma Koizumi', 'Kohei Yatabe', 'Tomohiko Nakamura', 'Koichi Saito'] | 2021-05-10 | null | null | null | null | ['audio-source-separation', 'music-source-separation'] | ['audio', 'music'] | [ 3.99728656e-01 -4.55546796e-01 -9.64217037e-02 -1.84196867e-02
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af709b41-4e9c-4fd4-a54e-8074ca3ee593 | towards-efficient-coarse-to-fine-networks-for | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/7035_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123750035.pdf | Towards Efficient Coarse-to-Fine Networks for Action and Gesture Recognition | State-of-the-art approaches to video-based action and gesture recognition often employ two key concepts: First, they employ multistream processing; second, they use an ensemble of convolutional networks. We improve and extend both aspects. First, we systematically yield enhanced receptive fields for complementary feature extraction via coarse-to-fine decomposition of input imagery along the spatial and temporal dimensions, and adaptively focus on training important feature pathways using a reparameterized fully connected layer. Second, we develop a `use when needed' scheme with a `coarse-exit' strategy that allows selective use of expensive high-resolution processing in a data-dependent fashion to retain accuracy while reducing computation cost. Our C2F learning approach builds ensemble networks that outperform most competing methods in terms of both reduced computation cost and improved accuracy on the Something-Something V1, V2, and Jester datasets, while also remaining competitive on the Kinetics-400 dataset. Uniquely, our C2F ensemble networks can operate at varying computation budget constraints. | ['Peng Dai', 'Juwei Lu', 'Wei Li', 'Niamul Quader'] | null | null | null | null | eccv-2020-8 | ['3d-human-action-recognition'] | ['computer-vision'] | [ 3.62366974e-01 -5.49388766e-01 -2.82512128e-01 -2.98237175e-01
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12b2ad67-1681-470f-b9be-24e7b23c0c29 | diffusion-models-in-nlp-a-survey-1 | 2305.14671 | null | https://arxiv.org/abs/2305.14671v2 | https://arxiv.org/pdf/2305.14671v2.pdf | A Survey of Diffusion Models in Natural Language Processing | This survey paper provides a comprehensive review of the use of diffusion models in natural language processing (NLP). Diffusion models are a class of mathematical models that aim to capture the diffusion of information or signals across a network or manifold. In NLP, diffusion models have been used in a variety of applications, such as natural language generation, sentiment analysis, topic modeling, and machine translation. This paper discusses the different formulations of diffusion models used in NLP, their strengths and limitations, and their applications. We also perform a thorough comparison between diffusion models and alternative generative models, specifically highlighting the autoregressive (AR) models, while also examining how diverse architectures incorporate the Transformer in conjunction with diffusion models. Compared to AR models, diffusion models have significant advantages for parallel generation, text interpolation, token-level controls such as syntactic structures and semantic contents, and robustness. Exploring further permutations of integrating Transformers into diffusion models would be a valuable pursuit. Also, the development of multimodal diffusion models and large-scale diffusion language models with notable capabilities for few-shot learning would be important directions for the future advance of diffusion models in NLP. | ['Dongyeop Kang', 'Zae Myung Kim', 'Hao Zou'] | 2023-05-24 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-2.99601611e-02 1.29051894e-01 -4.62856531e-01 -1.23848192e-01
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21ae0dfe-03ad-4baa-ac8b-8a61585713c4 | comparison-of-probabilistic-deep-learning | 2303.12707 | null | https://arxiv.org/abs/2303.12707v1 | https://arxiv.org/pdf/2303.12707v1.pdf | Comparison of Probabilistic Deep Learning Methods for Autism Detection | Autism Spectrum Disorder (ASD) is one neuro developmental disorder that is now widespread in the world. ASD persists throughout the life of an individual, impacting the way they behave and communicate, resulting to notable deficits consisting of social life retardation, repeated behavioural traits and a restriction in their interests. Early detection of the disorder helps in the onset treatment and helps one to lead a normal life. There are clinical approaches used in detection of autism, relying on behavioural data and in worst cases, neuroimaging. Quantitative methods involving machine learning have been studied and developed to overcome issues with clinical approaches. These quantitative methods rely on machine learning, with some complex methods based on deep learning developed to accelerate detection and diagnosis of ASD. These literature is aimed at exploring most state-of-the-art probabilistic methods in use today, characterizing them with the type of dataset they're most applied on, their accuracy according to their novel research and how well they are suited in ASD classification. The findings will purposely serve as a benchmark in selection of the model to use when performing ASD detection. | ['Kinyua Gikunda', 'Golda Moni', 'Kenneth Chesoli', 'Godfrin Ismail'] | 2023-03-09 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [-1.42767103e-02 4.81777899e-02 1.34727150e-01 -2.45578825e-01
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e240963c-2c3f-40bf-a387-2abc7d642e1c | few-shot-learning-with-global-class | 1908.05257 | null | https://arxiv.org/abs/1908.05257v1 | https://arxiv.org/pdf/1908.05257v1.pdf | Few-Shot Learning with Global Class Representations | In this paper, we propose to tackle the challenging few-shot learning (FSL) problem by learning global class representations using both base and novel class training samples. In each training episode, an episodic class mean computed from a support set is registered with the global representation via a registration module. This produces a registered global class representation for computing the classification loss using a query set. Though following a similar episodic training pipeline as existing meta learning based approaches, our method differs significantly in that novel class training samples are involved in the training from the beginning. To compensate for the lack of novel class training samples, an effective sample synthesis strategy is developed to avoid overfitting. Importantly, by joint base-novel class training, our approach can be easily extended to a more practical yet challenging FSL setting, i.e., generalized FSL, where the label space of test data is extended to both base and novel classes. Extensive experiments show that our approach is effective for both of the two FSL settings. | ['Li-Wei Wang', 'Tao Xiang', 'Aoxue Li', 'Weiran Huang', 'Tiange Luo'] | 2019-08-14 | few-shot-learning-with-global-class-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Li_Few-Shot_Learning_With_Global_Class_Representations_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Li_Few-Shot_Learning_With_Global_Class_Representations_ICCV_2019_paper.pdf | iccv-2019-10 | ['generalized-few-shot-classification'] | ['computer-vision'] | [ 6.11295879e-01 1.82572097e-01 -4.79463518e-01 -3.99266243e-01
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92a832e5-bb61-4bdc-a39d-e9889fff5df6 | stratified-sampling-for-extreme-multi-label | 2103.03494 | null | https://arxiv.org/abs/2103.03494v1 | https://arxiv.org/pdf/2103.03494v1.pdf | Stratified Sampling for Extreme Multi-Label Data | Extreme multi-label classification (XML) is becoming increasingly relevant in the era of big data. Yet, there is no method for effectively generating stratified partitions of XML datasets. Instead, researchers typically rely on provided test-train splits that, 1) aren't always representative of the entire dataset, and 2) are missing many of the labels. This can lead to poor generalization ability and unreliable performance estimates, as has been established in the binary and multi-class settings. As such, this paper presents a new and simple algorithm that can efficiently generate stratified partitions of XML datasets with millions of unique labels. We also examine the label distributions of prevailing benchmark splits, and investigate the issues that arise from using unrepresentative subsets of data for model development. The results highlight the difficulty of stratifying XML data, and demonstrate the importance of using stratified partitions for training and evaluation. | ['Lan Du', 'Maximillian Merrillees'] | 2021-03-05 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 4.77056563e-01 -8.15867037e-02 -2.28216618e-01 -7.49783337e-01
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5b530e9a-0105-4b79-a729-1ad93e6308e4 | multi-mask-aggregators-for-graph-neural | null | null | https://openreview.net/forum?id=hZ3b8CskgC | https://openreview.net/pdf?id=hZ3b8CskgC | Multi-Mask Aggregators for Graph Neural Networks | One of the most critical operations in graph neural networks (GNNs) is the aggregation operation, which aims to extract information from neighbors of the target node. Several convolution methods have been proposed such as standard graph convolution (GCN), graph attention (GAT), and message passing (MPNN). In this study, we propose an aggregation method called Multi-Mask Aggregators (MMA), where the model learns a weighted mask for each aggregator before collecting neighboring messages. MMA draws similarities with the GAT and MPNN but has some theoretical and practical advantages. Intuitively, our framework is not limited by the number of heads from GAT and has more discriminative than an MPNN. The performance of MMA was compared with the well-known baseline methods in both node classification and graph regression tasks on widely-used benchmarking datasets, and it has shown improved performance. Dataset and codes are available at https://github.com/asarigun/mma. | ['Ahmet Sureyya Rifaioglu', 'Ahmet Sarıgün'] | 2022-11-24 | null | null | null | learning-on-graphs-conference-2022-11 | ['graph-regression'] | ['graphs'] | [ 6.58654645e-02 4.12195444e-01 -1.82716995e-01 -3.94498944e-01
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27b3cda7-47d8-45ee-9e62-9ba6be8c0978 | iot-device-identification-based-on-network-1 | 2303.12800 | null | https://arxiv.org/abs/2303.12800v1 | https://arxiv.org/pdf/2303.12800v1.pdf | IoT Device Identification Based on Network Communication Analysis Using Deep Learning | Attack vectors for adversaries have increased in organizations because of the growing use of less secure IoT devices. The risk of attacks on an organization's network has also increased due to the bring your own device (BYOD) policy which permits employees to bring IoT devices onto the premises and attach them to the organization's network. To tackle this threat and protect their networks, organizations generally implement security policies in which only white listed IoT devices are allowed on the organization's network. To monitor compliance with such policies, it has become essential to distinguish IoT devices permitted within an organization's network from non white listed (unknown) IoT devices. In this research, deep learning is applied to network communication for the automated identification of IoT devices permitted on the network. In contrast to existing methods, the proposed approach does not require complex feature engineering of the network communication, because the 'communication behavior' of IoT devices is represented as small images which are generated from the device's network communication payload. The proposed approach is applicable for any IoT device, regardless of the protocol used for communication. As our approach relies on the network communication payload, it is also applicable for the IoT devices behind a network address translation (NAT) enabled router. In this study, we trained various classifiers on a publicly accessible dataset to identify IoT devices in different scenarios, including the identification of known and unknown IoT devices, achieving over 99% overall average detection accuracy. | ['Yuval Elovici', 'Jaidip Kotak'] | 2023-03-02 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 4.05768335e-01 9.92627349e-03 -2.70246953e-01 1.78584069e-01
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06f71786-3c47-4ce5-8b9c-325bd6f3666a | removing-human-bottlenecks-in-bird | 2305.02097 | null | https://arxiv.org/abs/2305.02097v1 | https://arxiv.org/pdf/2305.02097v1.pdf | Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning | Birds are important indicators for monitoring both biodiversity and habitat health; they also play a crucial role in ecosystem management. Decline in bird populations can result in reduced eco-system services, including seed dispersal, pollination and pest control. Accurate and long-term monitoring of birds to identify species of concern while measuring the success of conservation interventions is essential for ecologists. However, monitoring is time consuming, costly and often difficult to manage over long durations and at meaningfully large spatial scales. Technology such as camera traps, acoustic monitors and drones provide methods for non-invasive monitoring. There are two main problems with using camera traps for monitoring: a) cameras generate many images, making it difficult to process and analyse the data in a timely manner; and b) the high proportion of false positives hinders the processing and analysis for reporting. In this paper, we outline an approach for overcoming these issues by utilising deep learning for real-time classi-fication of bird species and automated removal of false positives in camera trap data. Images are classified in real-time using a Faster-RCNN architecture. Images are transmitted over 3/4G cam-eras and processed using Graphical Processing Units (GPUs) to provide conservationists with key detection metrics therefore removing the requirement for manual observations. Our models achieved an average sensitivity of 88.79%, a specificity of 98.16% and accuracy of 96.71%. This demonstrates the effectiveness of using deep learning for automatic bird monitoring. | ['Amira Nuseibeh', 'Jens Mudde', 'Naomi Matthews', 'Chris Sutherland', 'Philip Stephens', 'Naomi Davies Walsh', 'Steven N Longmore', 'Serge Wich', 'Paul Fergus', 'Carl Chalmers'] | 2023-05-03 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 2.37569049e-01 -7.68340647e-01 7.48259872e-02 -5.59860468e-02
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d4d4fa1f-faf0-42fa-be59-43b725ac81e6 | multi-branch-with-attention-network-for-hand | 2108.02234 | null | https://arxiv.org/abs/2108.02234v5 | https://arxiv.org/pdf/2108.02234v5.pdf | Multi-Branch with Attention Network for Hand-Based Person Recognition | In this paper, we propose a novel hand-based person recognition method for the purpose of criminal investigations since the hand image is often the only available information in cases of serious crime such as sexual abuse. Our proposed method, Multi-Branch with Attention Network (MBA-Net), incorporates both channel and spatial attention modules in branches in addition to a global (without attention) branch to capture global structural information for discriminative feature learning. The attention modules focus on the relevant features of the hand image while suppressing the irrelevant backgrounds. In order to overcome the weakness of the attention mechanisms, equivariant to pixel shuffling, we integrate relative positional encodings into the spatial attention module to capture the spatial positions of pixels. Extensive evaluations on two large multi-ethnic and publicly available hand datasets demonstrate that our proposed method achieves state-of-the-art performance, surpassing the existing hand-based identification methods. | ['Sue Black', 'Plamen Angelov', 'Hossein Rahmani', 'Bryan Williams', 'Nathanael L. Baisa'] | 2021-08-04 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 1.47637725e-01 -5.48532188e-01 -1.62361190e-01 -3.65707487e-01
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0af69b7d-b1ec-4088-ac23-a02ca16e437c | active-learning-based-non-intrusive-model | 2204.08523 | null | https://arxiv.org/abs/2204.08523v1 | https://arxiv.org/pdf/2204.08523v1.pdf | Active-learning-based non-intrusive Model Order Reduction | The Model Order Reduction (MOR) technique can provide compact numerical models for fast simulation. Different from the intrusive MOR methods, the non-intrusive MOR does not require access to the Full Order Models (FOMs), especially system matrices. Since the non-intrusive MOR methods strongly rely on the snapshots of the FOMs, constructing good snapshot sets becomes crucial. In this work, we propose a new active learning approach with two novelties. A novel idea with our approach is the use of single-time step snapshots from the system states taken from an estimation of the reduced-state space. These states are selected using a greedy strategy supported by an error estimator based Gaussian Process Regression (GPR). Additionally, we introduce a use case-independent validation strategy based on Probably Approximately Correct (PAC) learning. In this work, we use Artificial Neural Networks (ANNs) to identify the Reduced Order Model (ROM), however the method could be similarly applied to other ROM identification methods. The performance of the whole workflow is tested by a 2-D thermal conduction and a 3-D vacuum furnace model. With little required user interaction and a training strategy independent to a specific use case, the proposed method offers a huge potential for industrial usage to create so-called executable Digital Twins (DTs). | ['Juan Manuel Lorenzi', 'Hans Joachim Bungartz', 'Dirk Hartmann', 'Qinyu Zhuang'] | 2022-04-08 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 2.09432140e-01 6.59902319e-02 2.17644811e-01 3.28381777e-01
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33e17664-6bb3-413b-930d-248ff11e5703 | pseudo-bag-mixup-augmentation-for-multiple | 2306.16180 | null | https://arxiv.org/abs/2306.16180v2 | https://arxiv.org/pdf/2306.16180v2.pdf | Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification | Given the special situation of modeling gigapixel images, multiple instance learning (MIL) has become one of the most important frameworks for Whole Slide Image (WSI) classification. In current practice, most MIL networks often face two unavoidable problems in training: i) insufficient WSI data, and ii) the sample memorization inclination inherent in neural networks. These problems may hinder MIL models from adequate and efficient training, suppressing the continuous performance promotion of classification models on WSIs. Inspired by the basic idea of Mixup, this paper proposes a new Pseudo-bag Mixup (PseMix) data augmentation scheme to improve the training of MIL models. This scheme generalizes the Mixup strategy for general images to special WSIs via pseudo-bags so as to be applied in MIL-based WSI classification. Cooperated by pseudo-bags, our PseMix fulfills the critical size alignment and semantic alignment in Mixup strategy. Moreover, it is designed as an efficient and decoupled method, neither involving time-consuming operations nor relying on MIL model predictions. Comparative experiments and ablation studies are specially designed to evaluate the effectiveness and advantages of our PseMix. Experimental results show that PseMix could often assist state-of-the-art MIL networks to refresh the classification performance on WSIs. Besides, it could also boost the generalization ability of MIL models, and promote their robustness to patch occlusion and noisy labels. Our source code is available at https://github.com/liupei101/PseMix. | ['Feng Ye', 'Xinyu Zhang', 'Luping Ji', 'Pei Liu'] | 2023-06-28 | null | null | null | null | ['classification-1', 'multiple-instance-learning', 'memorization'] | ['methodology', 'methodology', 'natural-language-processing'] | [ 2.95025259e-01 -3.59169059e-02 -5.90992510e-01 -1.72005609e-01
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c9a61058-1309-4af9-a3cb-5a4f69b4b845 | learning-feature-engineering-for | null | null | https://dl.acm.org/citation.cfm?id=3172240 | https://www.ijcai.org/proceedings/2017/0352.pdf | Learning Feature Engineering for Classification | Feature engineering is the task of improving predictive modelling performance on a dataset by transforming its feature space. Existing approaches to automate this process rely on either transformed feature space exploration through evaluation-guided search, or explicit expansion of datasets with all transformed features followed by feature selection. Such approaches incur high computational costs in runtime and/or memory. We present a novel technique, called Learning Feature Engineering (LFE), for automating feature engineering in classification tasks. LFE is based on learning the effectiveness of applying a transformation (e.g., arithmetic or aggregate operators) on numerical features, from past feature engineering experiences. Given a new dataset, LFE recommends a set of useful transformations to be applied on features without relying on model evaluation or explicit feature expansion and selection. Using a collection of datasets, we train a set of neural networks, which aim at predicting the transformation that impacts classification performance positively. Our empirical results show that LFE outperforms other feature engineering approaches for an overwhelming majority (89%) of the datasets from various sources while incurring a substantially lower computational cost. | ['Udayan Khurana', 'Horst Samulowitz', 'Deepak Turaga', 'Fatemeh Nargesian', 'Elias B. Khalil'] | 2017-01-01 | null | null | null | ijcai-2017-2017-1 | ['automated-feature-engineering'] | ['methodology'] | [ 4.40094262e-01 -1.42135277e-01 -1.72355533e-01 -7.40671992e-01
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c9aad2e7-d2b3-4cc9-a2fc-82547c87696a | bullying10k-a-neuromorphic-dataset-towards | 2306.11546 | null | https://arxiv.org/abs/2306.11546v1 | https://arxiv.org/pdf/2306.11546v1.pdf | Bullying10K: A Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition | The prevalence of violence in daily life poses significant threats to individuals' physical and mental well-being. Using surveillance cameras in public spaces has proven effective in proactively deterring and preventing such incidents. However, concerns regarding privacy invasion have emerged due to their widespread deployment. To address the problem, we leverage Dynamic Vision Sensors (DVS) cameras to detect violent incidents and preserve privacy since it captures pixel brightness variations instead of static imagery. We introduce the Bullying10K dataset, encompassing various actions, complex movements, and occlusions from real-life scenarios. It provides three benchmarks for evaluating different tasks: action recognition, temporal action localization, and pose estimation. With 10,000 event segments, totaling 12 billion events and 255 GB of data, Bullying10K contributes significantly by balancing violence detection and personal privacy persevering. And it also poses a challenge to the neuromorphic dataset. It will serve as a valuable resource for training and developing privacy-protecting video systems. The Bullying10K opens new possibilities for innovative approaches in these domains. | ['Yi Zeng', 'Guobin Shen', 'Dongcheng Zhao', 'Yang Li', 'Yiting Dong'] | 2023-06-20 | null | null | null | null | ['pose-estimation', 'action-recognition-in-videos', 'action-localization', 'action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 4.54224437e-01 -1.45417973e-01 -1.42549276e-01 -4.43360239e-01
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12dc49c5-ef4b-4e02-a863-dd3ed17cfade | clutter-detection-and-removal-in-3d-scenes | 2304.03763 | null | https://arxiv.org/abs/2304.03763v1 | https://arxiv.org/pdf/2304.03763v1.pdf | Clutter Detection and Removal in 3D Scenes with View-Consistent Inpainting | Removing clutter from scenes is essential in many applications, ranging from privacy-concerned content filtering to data augmentation. In this work, we present an automatic system that removes clutter from 3D scenes and inpaints with coherent geometry and texture. We propose techniques for its two key components: 3D segmentation from shared properties and 3D inpainting, both of which are important porblems. The definition of 3D scene clutter (frequently-moving objects) is not well captured by commonly-studied object categories in computer vision. To tackle the lack of well-defined clutter annotations, we group noisy fine-grained labels, leverage virtual rendering, and impose an instance-level area-sensitive loss. Once clutter is removed, we inpaint geometry and texture in the resulting holes by merging inpainted RGB-D images. This requires novel voting and pruning strategies that guarantee multi-view consistency across individually inpainted images for mesh reconstruction. Experiments on ScanNet and Matterport dataset show that our method outperforms baselines for clutter segmentation and 3D inpainting, both visually and quantitatively. | ['Szymon Rusinkiewicz', 'Thomas Funkhouser', 'Fangyin Wei'] | 2023-04-07 | null | null | null | null | ['3d-inpainting'] | ['computer-vision'] | [ 5.21994531e-01 -8.16795230e-02 2.25876644e-01 -3.53170931e-01
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35e91125-037e-4e73-b594-62e544c91bf1 | non-contact-transmittance | 1503.06775 | null | http://arxiv.org/abs/1503.06775v1 | http://arxiv.org/pdf/1503.06775v1.pdf | Non-contact transmittance photoplethysmographic imaging (PPGI) for long-distance cardiovascular monitoring | Photoplethysmography (PPG) devices are widely used for monitoring
cardiovascular function. However, these devices require skin contact, which
restrict their use to at-rest short-term monitoring using single-point
measurements. Photoplethysmographic imaging (PPGI) has been recently proposed
as a non-contact monitoring alternative by measuring blood pulse signals across
a spatial region of interest. Existing systems operate in reflectance mode, of
which many are limited to short-distance monitoring and are prone to temporal
changes in ambient illumination. This paper is the first study to investigate
the feasibility of long-distance non-contact cardiovascular monitoring at the
supermeter level using transmittance PPGI. For this purpose, a novel PPGI
system was designed at the hardware and software level using ambient correction
via temporally coded illumination (TCI) and signal processing for PPGI signal
extraction. Experimental results show that the processing steps yield a
substantially more pulsatile PPGI signal than the raw acquired signal,
resulting in statistically significant increases in correlation to ground-truth
PPG in both short- ($p \in [<0.0001, 0.040]$) and long-distance ($p \in
[<0.0001, 0.056]$) monitoring. The results support the hypothesis that
long-distance heart rate monitoring is feasible using transmittance PPGI,
allowing for new possibilities of monitoring cardiovascular function in a
non-contact manner. | ['Kaylen J. Pfisterer', 'Farnoud Kazemzadeh', 'Alexander Wong', 'Robert Amelard', 'Christian Scharfenberger', 'Bill S. Lin', 'David A. Clausi'] | 2015-03-23 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 3.70002866e-01 -1.36079237e-01 3.60366404e-01 -2.65082270e-01
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5805ea8c-bffc-4aac-a9d2-dee52a88a637 | global-object-proposals-for-improving-multi | null | null | https://ieeexplore.ieee.org/document/9533883 | https://ieeexplore.ieee.org/document/9533883 | Global Object Proposals for Improving Multi-Sentence Video Descriptions | There has been significant progress in image captioning in recent years. The generation of video descriptions is still in its early stages; this is due to the complex nature of videos in comparison to images. Generating paragraph descriptions of a video is even more challenging. Amongst the main issues are temporal object dependencies and complex object-object relationships. Recently, many works are proposed on the generation of multi-sentence video descriptions. The majority of the proposed works are based on a two-step approach: 1) event proposals and 2) caption generation. While these approaches produce good results, they miss out on globally available information. Here we propose the use of global object proposals while generating the video captions. Experimental results on ActivityNet dataset illustrate that the use of global object proposals can produce more informative and correct captions. We also propose three scores to evaluate the object detection capacity of the generator. A qualitative comparison of captions generated by the proposed method and the state-of-the-art techniques proves the efficacy of the proposed method. | ['Pushpak Bhattacharyya', 'Sriparna Saha', 'Chandresh S. Kanani'] | 2021-07-18 | null | null | null | international-joint-conference-on-neural-3 | ['dense-video-captioning'] | ['computer-vision'] | [ 3.70098323e-01 1.81586713e-01 -7.06801517e-03 -4.16705638e-01
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557f61d0-0758-4d19-8f55-bb62f2399338 | event-driven-vision-and-control-for-uavs-on-a | 2108.03694 | null | https://arxiv.org/abs/2108.03694v2 | https://arxiv.org/pdf/2108.03694v2.pdf | Event-driven Vision and Control for UAVs on a Neuromorphic Chip | Event-based vision sensors achieve up to three orders of magnitude better speed vs. power consumption trade off in high-speed control of UAVs compared to conventional image sensors. Event-based cameras produce a sparse stream of events that can be processed more efficiently and with a lower latency than images, enabling ultra-fast vision-driven control. Here, we explore how an event-based vision algorithm can be implemented as a spiking neuronal network on a neuromorphic chip and used in a drone controller. We show how seamless integration of event-based perception on chip leads to even faster control rates and lower latency. In addition, we demonstrate how online adaptation of the SNN controller can be realised using on-chip learning. Our spiking neuronal network on chip is the first example of a neuromorphic vision-based controller solving a high-speed UAV control task. The excellent scalability of processing in neuromorphic hardware opens the possibility to solve more challenging visual tasks in the future and integrate visual perception in fast control loops. | ['Yulia Sandamirskaya', 'Davide Scaramuzza', 'Celine Nauer', 'Alpha Renner', 'Antonio Vitale'] | 2021-08-08 | null | null | null | null | ['event-based-vision', 'drone-controller'] | ['computer-vision', 'robots'] | [ 3.55286032e-01 -3.34520400e-01 5.78551769e-01 -9.19412822e-02
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793628c1-38a9-45ca-9244-7708fbae5c0f | a-learning-based-adaptive-compliance-method | 2303.15262 | null | https://arxiv.org/abs/2303.15262v1 | https://arxiv.org/pdf/2303.15262v1.pdf | A Learning-based Adaptive Compliance Method for Symmetric Bi-manual Manipulation | Symmetric bi-manual manipulation is essential for various on-orbit operations due to its potent load capacity. As a result, there exists an emerging research interest in the problem of achieving high operation accuracy while enhancing adaptability and compliance. However, previous works relied on an inefficient algorithm framework that separates motion planning from compliant control. Additionally, the compliant controller lacks robustness due to manually adjusted parameters. This paper proposes a novel Learning-based Adaptive Compliance algorithm (LAC) that improves the efficiency and robustness of symmetric bi-manual manipulation. Specifically, first, the algorithm framework combines desired trajectory generation with impedance-parameter adjustment to improve efficiency and robustness. Second, we introduce a centralized Actor-Critic framework with LSTM networks, enhancing the synchronization of bi-manual manipulation. LSTM networks pre-process the force states obtained by the agents, further ameliorating the performance of compliance operations. When evaluated in the dual-arm cooperative handling and peg-in-hole assembly experiments, our method outperforms baseline algorithms in terms of optimality and robustness. | ['Tao Zhang', 'Wenke Ma', 'Xiang Zheng', 'Shengjie Wang', 'Yuxue Cao'] | 2023-03-27 | null | null | null | null | ['motion-planning'] | ['robots'] | [-3.03754389e-01 1.75351366e-01 -3.20601404e-01 7.36408234e-02
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b54ec177-7c38-4e7c-ae5c-fdb875e4ef07 | neural-waveshaping-synthesis | 2107.05050 | null | https://arxiv.org/abs/2107.05050v2 | https://arxiv.org/pdf/2107.05050v2.pdf | Neural Waveshaping Synthesis | We present the Neural Waveshaping Unit (NEWT): a novel, lightweight, fully causal approach to neural audio synthesis which operates directly in the waveform domain, with an accompanying optimisation (FastNEWT) for efficient CPU inference. The NEWT uses time-distributed multilayer perceptrons with periodic activations to implicitly learn nonlinear transfer functions that encode the characteristics of a target timbre. Once trained, a NEWT can produce complex timbral evolutions by simple affine transformations of its input and output signals. We paired the NEWT with a differentiable noise synthesiser and reverb and found it capable of generating realistic musical instrument performances with only 260k total model parameters, conditioned on F0 and loudness features. We compared our method to state-of-the-art benchmarks with a multi-stimulus listening test and the Fr\'echet Audio Distance and found it performed competitively across the tested timbral domains. Our method significantly outperformed the benchmarks in terms of generation speed, and achieved real-time performance on a consumer CPU, both with and without FastNEWT, suggesting it is a viable basis for future creative sound design tools. | ['György Fazekas', 'Charalampos Saitis', 'Ben Hayes'] | 2021-07-11 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 4.90938395e-01 -2.35561773e-01 5.43938398e-01 -1.04172945e-01
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6d7c8ab0-5652-42cf-97e9-6b9824f53a9e | sharing-models-or-coresets-a-study-based-on | 2007.02977 | null | https://arxiv.org/abs/2007.02977v1 | https://arxiv.org/pdf/2007.02977v1.pdf | Sharing Models or Coresets: A Study based on Membership Inference Attack | Distributed machine learning generally aims at training a global model based on distributed data without collecting all the data to a centralized location, where two different approaches have been proposed: collecting and aggregating local models (federated learning) and collecting and training over representative data summaries (coreset). While each approach preserves data privacy to some extent thanks to not sharing the raw data, the exact extent of protection is unclear under sophisticated attacks that try to infer the raw data from the shared information. We present the first comparison between the two approaches in terms of target model accuracy, communication cost, and data privacy, where the last is measured by the accuracy of a state-of-the-art attack strategy called the membership inference attack. Our experiments quantify the accuracy-privacy-cost tradeoff of each approach, and reveal a nontrivial comparison that can be used to guide the design of model training processes. | ['Shiqiang Wang', 'Kevin S. Chan', 'Ting He', 'Hanlin Lu', 'Changchang Liu'] | 2020-07-06 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [-2.72738524e-02 3.53323609e-01 -5.35142660e-01 -4.77709234e-01
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f49f7bd6-3d35-4a42-826c-bf8882679ef6 | cd-ctfm-a-lightweight-cnn-transformer-network | 2306.07186 | null | https://arxiv.org/abs/2306.07186v1 | https://arxiv.org/pdf/2306.07186v1.pdf | CD-CTFM: A Lightweight CNN-Transformer Network for Remote Sensing Cloud Detection Fusing Multiscale Features | Clouds in remote sensing images inevitably affect information extraction, which hinder the following analysis of satellite images. Hence, cloud detection is a necessary preprocessing procedure. However, the existing methods have numerous calculations and parameters. In this letter, a lightweight CNN-Transformer network, CD-CTFM, is proposed to solve the problem. CD-CTFM is based on encoder-decoder architecture and incorporates the attention mechanism. In the decoder part, we utilize a lightweight network combing CNN and Transformer as backbone, which is conducive to extract local and global features simultaneously. Moreover, a lightweight feature pyramid module is designed to fuse multiscale features with contextual information. In the decoder part, we integrate a lightweight channel-spatial attention module into each skip connection between encoder and decoder, extracting low-level features while suppressing irrelevant information without introducing many parameters. Finally, the proposed model is evaluated on two cloud datasets, 38-Cloud and MODIS. The results demonstrate that CD-CTFM achieves comparable accuracy as the state-of-art methods. At the same time, CD-CTFM outperforms state-of-art methods in terms of efficiency. | ['Li Zhang', 'Xubing Yang', 'Wenxuan Ge'] | 2023-06-12 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [-4.83100042e-02 -7.17555761e-01 3.17412883e-01 -3.07280511e-01
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a85fdee3-d94a-4be0-9604-334a737d8819 | lasr-learning-articulated-shape | 2105.02976 | null | https://arxiv.org/abs/2105.02976v1 | https://arxiv.org/pdf/2105.02976v1.pdf | LASR: Learning Articulated Shape Reconstruction from a Monocular Video | Remarkable progress has been made in 3D reconstruction of rigid structures from a video or a collection of images. However, it is still challenging to reconstruct nonrigid structures from RGB inputs, due to its under-constrained nature. While template-based approaches, such as parametric shape models, have achieved great success in modeling the "closed world" of known object categories, they cannot well handle the "open-world" of novel object categories or outlier shapes. In this work, we introduce a template-free approach to learn 3D shapes from a single video. It adopts an analysis-by-synthesis strategy that forward-renders object silhouette, optical flow, and pixel values to compare with video observations, which generates gradients to adjust the camera, shape and motion parameters. Without using a category-specific shape template, our method faithfully reconstructs nonrigid 3D structures from videos of human, animals, and objects of unknown classes. Code will be available at lasr-google.github.io . | ['Ce Liu', 'William T. Freeman', 'Deva Ramanan', 'Huiwen Chang', 'Forrester Cole', 'Daniel Vlasic', 'Varun Jampani', 'Deqing Sun', 'Gengshan Yang'] | 2021-05-06 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yang_LASR_Learning_Articulated_Shape_Reconstruction_From_a_Monocular_Video_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yang_LASR_Learning_Articulated_Shape_Reconstruction_From_a_Monocular_Video_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-shape-reconstruction-from-videos'] | ['computer-vision'] | [ 1.07454076e-01 -9.37862471e-02 1.62881404e-01 -1.95516571e-01
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3e8aebdc-07c3-4a8c-b51c-273ccd3125d5 | sea-a-scalable-entity-alignment-system | 2304.07065 | null | https://arxiv.org/abs/2304.07065v1 | https://arxiv.org/pdf/2304.07065v1.pdf | SEA: A Scalable Entity Alignment System | Entity alignment (EA) aims to find equivalent entities in different knowledge graphs (KGs). State-of-the-art EA approaches generally use Graph Neural Networks (GNNs) to encode entities. However, most of them train the models and evaluate the results in a fullbatch fashion, which prohibits EA from being scalable on largescale datasets. To enhance the usability of GNN-based EA models in real-world applications, we present SEA, a scalable entity alignment system that enables to (i) train large-scale GNNs for EA, (ii) speed up the normalization and the evaluation process, and (iii) report clear results for users to estimate different models and parameter settings. SEA can be run on a computer with merely one graphic card. Moreover, SEA encompasses six state-of-the-art EA models and provides access for users to quickly establish and evaluate their own models. Thus, SEA allows users to perform EA without being involved in tedious implementations, such as negative sampling and GPU-accelerated evaluation. With SEA, users can gain a clear view of the model performance. In the demonstration, we show that SEA is user-friendly and is of high scalability even on computers with limited computational resources. | ['Ziheng Wei', 'Yunjun Gao', 'Lu Chen', 'Tianyi Li', 'Junyang Wu'] | 2023-04-14 | null | null | null | null | ['entity-alignment', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing'] | [-2.45028317e-01 -1.11379102e-01 -1.26035705e-01 -2.14232877e-01
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f0e51e0a-38b2-4d84-92a0-bb26e0e98a1b | kornia-an-open-source-differentiable-computer | 1910.02190 | null | https://arxiv.org/abs/1910.02190v2 | https://arxiv.org/pdf/1910.02190v2.pdf | Kornia: an Open Source Differentiable Computer Vision Library for PyTorch | This work presents Kornia -- an open source computer vision library which consists of a set of differentiable routines and modules to solve generic computer vision problems. The package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions. Inspired by OpenCV, Kornia is composed of a set of modules containing operators that can be inserted inside neural networks to train models to perform image transformations, camera calibration, epipolar geometry, and low level image processing techniques, such as filtering and edge detection that operate directly on high dimensional tensor representations. Examples of classical vision problems implemented using our framework are provided including a benchmark comparing to existing vision libraries. | ['Daniel Ponsa', 'Edgar Riba', 'Dmytro Mishkin', 'Ethan Rublee', 'Gary Bradski'] | 2019-10-05 | null | null | null | null | ['image-smoothing', 'image-stitching', 'image-morphing', 'image-matching', 'image-cropping'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-3.15771341e-01 -1.66534573e-01 5.95337033e-01 -3.17980289e-01
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71642c5b-1832-4dc0-b747-7582892ce4b5 | a-14uj-decision-keyword-spotting-accelerator | 2205.04665 | null | https://arxiv.org/abs/2205.04665v1 | https://arxiv.org/pdf/2205.04665v1.pdf | A 14uJ/Decision Keyword Spotting Accelerator with In-SRAM-Computing and On Chip Learning for Customization | Keyword spotting has gained popularity as a natural way to interact with consumer devices in recent years. However, because of its always-on nature and the variety of speech, it necessitates a low-power design as well as user customization. This paper describes a low-power, energy-efficient keyword spotting accelerator with SRAM based in-memory computing (IMC) and on-chip learning for user customization. However, IMC is constrained by macro size, limited precision, and non-ideal effects. To address the issues mentioned above, this paper proposes bias compensation and fine-tuning using an IMC-aware model design. Furthermore, because learning with low-precision edge devices results in zero error and gradient values due to quantization, this paper proposes error scaling and small gradient accumulation to achieve the same accuracy as ideal model training. The simulation results show that with user customization, we can recover the accuracy loss from 51.08\% to 89.76\% with compensation and fine-tuning and further improve to 96.71\% with customization. The chip implementation can successfully run the model with only 14$uJ$ per decision. When compared to the state-of-the-art works, the presented design has higher energy efficiency with additional on-chip model customization capabilities for higher accuracy. | ['Shyh Jye Jou', 'Tian-Sheuan Chang', 'Yu-Hsiang Chiang'] | 2022-05-10 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 1.35375774e-02 -4.06870514e-01 -5.35496712e-01 -2.64593095e-01
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f25ab1ac-dfce-4153-9d96-589e8641afdb | uncovering-probabilistic-implications-in | 1906.07389 | null | https://arxiv.org/abs/1906.07389v1 | https://arxiv.org/pdf/1906.07389v1.pdf | Uncovering Probabilistic Implications in Typological Knowledge Bases | The study of linguistic typology is rooted in the implications we find between linguistic features, such as the fact that languages with object-verb word ordering tend to have post-positions. Uncovering such implications typically amounts to time-consuming manual processing by trained and experienced linguists, which potentially leaves key linguistic universals unexplored. In this paper, we present a computational model which successfully identifies known universals, including Greenberg universals, but also uncovers new ones, worthy of further linguistic investigation. Our approach outperforms baselines previously used for this problem, as well as a strong baseline from knowledge base population. | ['Isabelle Augenstein', 'Johannes Bjerva', 'Yova Kementchedjhieva', 'Ryan Cotterell'] | 2019-06-18 | uncovering-probabilistic-implications-in-1 | https://aclanthology.org/P19-1382 | https://aclanthology.org/P19-1382.pdf | acl-2019-7 | ['knowledge-base-population'] | ['natural-language-processing'] | [-4.68110479e-02 6.93356916e-02 -6.55030787e-01 -1.69119164e-01
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067afe7d-399b-40ca-b023-b98195d65ab1 | variance-reduction-for-policy-gradient | 2206.06827 | null | https://arxiv.org/abs/2206.06827v2 | https://arxiv.org/pdf/2206.06827v2.pdf | Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization | Policy-gradient methods in Reinforcement Learning(RL) are very universal and widely applied in practice but their performance suffers from the high variance of the gradient estimate. Several procedures were proposed to reduce it including actor-critic(AC) and advantage actor-critic(A2C) methods. Recently the approaches have got new perspective due to the introduction of Deep RL: both new control variates(CV) and new sub-sampling procedures became available in the setting of complex models like neural networks. The vital part of CV-based methods is the goal functional for the training of the CV, the most popular one is the least-squares criterion of A2C. Despite its practical success, the criterion is not the only one possible. In this paper we for the first time investigate the performance of the one called Empirical Variance(EV). We observe in the experiments that not only EV-criterion performs not worse than A2C but sometimes can be considerably better. Apart from that, we also prove some theoretical guarantees of the actual variance reduction under very general assumptions and show that A2C least-squares goal functional is an upper bound for EV goal. Our experiments indicate that in terms of variance reduction EV-based methods are much better than A2C and allow stronger variance reduction. | ['Denis Belomestny', 'Alexander Golubev', 'Maxim Kaledin'] | 2022-06-14 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-1.88845143e-01 3.64536852e-01 -2.17056006e-01 3.75396758e-02
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ae01c1cf-bef1-4c9c-935c-7404d3987dec | a-simple-and-effective-baseline-for | 2306.14708 | null | https://arxiv.org/abs/2306.14708v2 | https://arxiv.org/pdf/2306.14708v2.pdf | A Simple and Effective Baseline for Attentional Generative Adversarial Networks | Synthesising a text-to-image model of high-quality images by guiding the generative model through the Text description is an innovative and challenging task. In recent years, AttnGAN based on the Attention mechanism to guide GAN training has been proposed, SD-GAN, which adopts a self-distillation technique to improve the performance of the generator and the quality of image generation, and Stack-GAN++, which gradually improves the details and quality of the image by stacking multiple generators and discriminators. However, this series of improvements to GAN all have redundancy to a certain extent, which affects the generation performance and complexity to a certain extent. We use the popular simple and effective idea (1) to remove redundancy structure and improve the backbone network of AttnGAN. (2) to integrate and reconstruct multiple losses of DAMSM. Our improvements have significantly improved the model size and training efficiency while ensuring that the model's performance is unchanged and finally proposed our SEAttnGAN. Code is avalilable at https://github.com/jmyissb/SEAttnGAN. | ['Xi Yang', 'Xiaobo Jin', 'Haochen Xue', 'Qinkai Yu', 'Chong Zhang', 'Mingyu Jin'] | 2023-06-26 | null | null | null | null | ['image-generation'] | ['computer-vision'] | [ 2.46508524e-01 4.08419192e-01 2.07949668e-01 -1.54618129e-01
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34e91057-e4d7-4638-96bb-25f7779b0e7a | zero-shot-text-to-image-generation | 2102.12092 | null | https://arxiv.org/abs/2102.12092v2 | https://arxiv.org/pdf/2102.12092v2.pdf | Zero-Shot Text-to-Image Generation | Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data. With sufficient data and scale, our approach is competitive with previous domain-specific models when evaluated in a zero-shot fashion. | ['Ilya Sutskever', 'Mark Chen', 'Alec Radford', 'Chelsea Voss', 'Scott Gray', 'Gabriel Goh', 'Mikhail Pavlov', 'Aditya Ramesh'] | 2021-02-24 | null | null | null | null | ['zero-shot-text-to-image-generation'] | ['natural-language-processing'] | [ 5.10667682e-01 5.20125866e-01 -2.34405234e-01 -7.14775324e-01
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f1ff04f9-9a7b-4bd9-874c-23e5827b6e1a | discriminative-models-can-still-outperform | null | null | https://openreview.net/forum?id=uZaiKvl7C5p | https://openreview.net/pdf?id=uZaiKvl7C5p | Discriminative Models Can Still Outperform Generative Models in Aspect Based Sentiment Analysis | Aspect-based Sentiment Analysis (ABSA) helps to explain customers' opinions towards products and services. In the past, ABSA models were discriminative, but more recently generative models have been used to generate aspects and polarities directly from text. In contrast, discriminative models commonly first select aspects from the text, and then classify the aspect's polarity. Previous results showed that generative models outperform discriminative models on several English ABSA datasets. Here, we evaluate and contrast two state-of-the-art discriminative and generative models in several settings: cross-lingual, cross-domain, and cross-lingual and domain, to understand generalizability in settings other than English mono-lingual in-domain. Our more thorough evaluation shows that, contrary to previous studies, discriminative models can still outperform generative models in almost all settings. | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-1.78237036e-01 6.75371066e-02 -3.78101587e-01 -9.06745255e-01
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a2363b9a-486e-452b-a471-bc6154ebb907 | 70-years-of-machine-learning-in-geoscience-in | 2006.13311 | null | https://arxiv.org/abs/2006.13311v3 | https://arxiv.org/pdf/2006.13311v3.pdf | 70 years of machine learning in geoscience in review | This review gives an overview of the development of machine learning in geoscience. A thorough analysis of the co-developments of machine learning applications throughout the last 70 years relates the recent enthusiasm for machine learning to developments in geoscience. I explore the shift of kriging towards a mainstream machine learning method and the historic application of neural networks in geoscience, following the general trend of machine learning enthusiasm through the decades. Furthermore, this chapter explores the shift from mathematical fundamentals and knowledge in software development towards skills in model validation, applied statistics, and integrated subject matter expertise. The review is interspersed with code examples to complement the theoretical foundations and illustrate model validation and machine learning explainability for science. The scope of this review includes various shallow machine learning methods, e.g. Decision Trees, Random Forests, Support-Vector Machines, and Gaussian Processes, as well as, deep neural networks, including feed-forward neural networks, convolutional neural networks, recurrent neural networks and generative adversarial networks. Regarding geoscience, the review has a bias towards geophysics but aims to strike a balance with geochemistry, geostatistics, and geology, however excludes remote sensing, as this would exceed the scope. In general, I aim to provide context for the recent enthusiasm surrounding deep learning with respect to research, hardware, and software developments that enable successful application of shallow and deep machine learning in all disciplines of Earth science. | ['Jesper Sören Dramsch'] | 2020-06-16 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [ 4.21113847e-03 1.52996451e-01 7.89627805e-02 -6.32795691e-02
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4dcbf9b8-31f4-4fd4-b2bd-92d88c157e67 | subject-specific-lesion-generation-and-pseudo | 2208.02135 | null | https://arxiv.org/abs/2208.02135v1 | https://arxiv.org/pdf/2208.02135v1.pdf | Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images | Understanding the intensity characteristics of brain lesions is key for defining image-based biomarkers in neurological studies and for predicting disease burden and outcome. In this work, we present a novel foreground-based generative method for modelling the local lesion characteristics that can both generate synthetic lesions on healthy images and synthesize subject-specific pseudo-healthy images from pathological images. Furthermore, the proposed method can be used as a data augmentation module to generate synthetic images for training brain image segmentation networks. Experiments on multiple sclerosis (MS) brain images acquired on magnetic resonance imaging (MRI) demonstrate that the proposed method can generate highly realistic pseudo-healthy and pseudo-pathological brain images. Data augmentation using the synthetic images improves the brain image segmentation performance compared to traditional data augmentation methods as well as a recent lesion-aware data augmentation technique, CarveMix. The code will be released at https://github.com/dogabasaran/lesion-synthesis. | ['Wenjia Bai', 'Paul M. Matthews', 'Mengyun Qiao', 'Berke Doga Basaran'] | 2022-08-03 | null | null | null | null | ['brain-image-segmentation'] | ['medical'] | [ 8.40543568e-01 1.53347269e-01 -3.75262834e-02 -4.95537847e-01
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a8389267-eeae-4d37-b107-8ecfaeb9f590 | crad-clustering-with-robust-autocuts-and | 1904.04020 | null | http://arxiv.org/abs/1904.04020v1 | http://arxiv.org/pdf/1904.04020v1.pdf | CRAD: Clustering with Robust Autocuts and Depth | We develop a new density-based clustering algorithm named CRAD which is based
on a new neighbor searching function with a robust data depth as the
dissimilarity measure. Our experiments prove that the new CRAD is highly
competitive at detecting clusters with varying densities, compared with the
existing algorithms such as DBSCAN, OPTICS and DBCA. Furthermore, a new
effective parameter selection procedure is developed to select the optimal
underlying parameter in the real-world clustering, when the ground truth is
unknown. Lastly, we suggest a new clustering framework that extends CRAD from
spatial data clustering to time series clustering without a-priori knowledge of
the true number of clusters. The performance of CRAD is evaluated through
extensive experimental studies. | ['Yulia R. Gel', 'Xin Huang'] | 2019-04-08 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-5.73232174e-01 -8.29551697e-01 4.14325185e-02 -2.16700062e-01
-7.23937035e-01 -5.21200001e-01 4.23971236e-01 -9.14606545e-03
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16a0e863-9ab6-49cf-a44e-adb153731412 | user-response-and-sentiment-prediction-for | 2111.08808 | null | https://arxiv.org/abs/2111.08808v2 | https://arxiv.org/pdf/2111.08808v2.pdf | User Response and Sentiment Prediction for Automatic Dialogue Evaluation | Automatic evaluation is beneficial for open-domain dialog system development. However, standard word-overlap metrics (BLEU, ROUGE) do not correlate well with human judgements of open-domain dialog systems. In this work we propose to use the sentiment of the next user utterance for turn or dialog level evaluation. Specifically we propose three methods: one that predicts the next sentiment directly, and two others that predict the next user utterance using an utterance or a feedback generator model and then classify its sentiment. Experiments show our model outperforming existing automatic evaluation metrics on both written and spoken open-domain dialogue datasets. | ['Dilek Hakkani-Tur', 'Yang Liu', 'Alexandros Papangelis', 'Behnam Hedayatnia', 'Sarik Ghazarian'] | 2021-11-16 | null | null | null | null | ['dialogue-evaluation', 'open-domain-dialog'] | ['natural-language-processing', 'natural-language-processing'] | [-1.18345894e-01 6.58326328e-01 9.63281989e-02 -8.84324610e-01
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d7c29acb-7a61-4f2f-8001-eb2553348f49 | the-impact-of-twitter-sentiments-on-stock | 2302.07244 | null | https://arxiv.org/abs/2302.07244v1 | https://arxiv.org/pdf/2302.07244v1.pdf | The Impact of Twitter Sentiments on Stock Market Trends | The Web is a vast virtual space where people can share their opinions, impacting all aspects of life and having implications for marketing and communication. The most up-to-date and comprehensive information can be found on social media because of how widespread and straightforward it is to post a message. Proportionately, they are regarded as a valuable resource for making precise market predictions. In particular, Twitter has developed into a potent tool for understanding user sentiment. This article examines how well tweets can influence stock symbol trends. We analyze the volume, sentiment, and mentions of the top five stock symbols in the S&P 500 index on Twitter over three months. Long Short-Term Memory, Bernoulli Na\"ive Bayes, and Random Forest were the three algorithms implemented in this process. Our study revealed a significant correlation between stock prices and Twitter sentiment. | ['Adel Karshenas', 'Niloufar Saeedi', 'Ali Seraj', 'Melvin Mokhtari'] | 2023-02-14 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-7.76395202e-01 -4.02617246e-01 -9.86220956e-01 -7.87417442e-02
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7472b945-38ef-4f0a-be31-5fd4ed557be3 | improving-knowledge-aware-recommendation-with | 2208.10061 | null | https://arxiv.org/abs/2208.10061v1 | https://arxiv.org/pdf/2208.10061v1.pdf | Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning | Incorporating Knowledge Graphs (KG) into recommeder system has attracted considerable attention. Recently, the technical trend of Knowledge-aware Recommendation (KGR) is to develop end-to-end models based on graph neural networks (GNNs). However, the extremely sparse user-item interactions significantly degrade the performance of the GNN-based models, as: 1) the sparse interaction, means inadequate supervision signals and limits the supervised GNN-based models; 2) the combination of sparse interactions (CF part) and redundant KG facts (KG part) results in an unbalanced information utilization. Besides, the GNN paradigm aggregates local neighbors for node representation learning, while ignoring the non-local KG facts and making the knowledge extraction insufficient. Inspired by the recent success of contrastive learning in mining supervised signals from data itself, in this paper, we focus on exploring contrastive learning in KGR and propose a novel multi-level interactive contrastive learning mechanism. Different from traditional contrastive learning methods which contrast nodes of two generated graph views, interactive contrastive mechanism conducts layer-wise self-supervised learning by contrasting layers of different parts within graphs, which is also an "interaction" action. Specifically, we first construct local and non-local graphs for user/item in KG, exploring more KG facts for KGR. Then an intra-graph level interactive contrastive learning is performed within each graph, which contrasts layers of the CF and KG parts, for more consistent information leveraging. Besides, an inter-graph level interactive contrastive learning is performed between the local and non-local graphs, for sufficiently and coherently extracting non-local KG signals. Extensive experiments conducted on three benchmark datasets show the superior performance of our proposed method over the state-of-the-arts. | ['Dangyang Chen', 'Rui Fang', 'Feida Zhu', 'Xian-Ling Mao', 'Ziyang Wang', 'Wei Wei', 'Ding Zou'] | 2022-08-22 | null | null | null | null | ['knowledge-aware-recommendation'] | ['miscellaneous'] | [ 1.77720760e-03 3.15462232e-01 -6.39740407e-01 -1.59371451e-01
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59f462ce-ab42-41cf-a654-e585a11b6668 | finding-regions-of-counterfactual | 2301.11113 | null | https://arxiv.org/abs/2301.11113v2 | https://arxiv.org/pdf/2301.11113v2.pdf | Finding Regions of Counterfactual Explanations via Robust Optimization | Counterfactual explanations play an important role in detecting bias and improving the explainability of data-driven classification models. A counterfactual explanation (CE) is a minimal perturbed data point for which the decision of the model changes. Most of the existing methods can only provide one CE, which may not be achievable for the user. In this work we derive an iterative method to calculate robust CEs, i.e. CEs that remain valid even after the features are slightly perturbed. To this end, our method provides a whole region of CEs allowing the user to choose a suitable recourse to obtain a desired outcome. We use algorithmic ideas from robust optimization and prove convergence results for the most common machine learning methods including logistic regression, decision trees, random forests, and neural networks. Our experiments show that our method can efficiently generate globally optimal robust CEs for a variety of common data sets and classification models. | ['Dick den Hertog', 'Ş. Ilker Birbil', 'Rob Goedhart', 'Tabea E. Röber', 'Jannis Kurtz', 'Donato Maragno'] | 2023-01-26 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 3.36625427e-01 2.95087188e-01 -7.45922327e-01 -4.03265834e-01
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0a3230f9-5695-4678-8abb-5a82085de59c | a-cross-modal-image-fusion-theory-guided-by | 1912.08577 | null | https://arxiv.org/abs/1912.08577v4 | https://arxiv.org/pdf/1912.08577v4.pdf | A Cross-Modal Image Fusion Method Guided by Human Visual Characteristics | The characteristics of feature selection, nonlinear combination and multi-task auxiliary learning mechanism of the human visual perception system play an important role in real-world scenarios, but the research of image fusion theory based on the characteristics of human visual perception is less. Inspired by the characteristics of human visual perception, we propose a robust multi-task auxiliary learning optimization image fusion theory. Firstly, we combine channel attention model with nonlinear convolutional neural network to select features and fuse nonlinear features. Then, we analyze the impact of the existing image fusion loss on the image fusion quality, and establish the multi-loss function model of unsupervised learning network. Secondly, aiming at the multi-task auxiliary learning mechanism of human visual perception system, we study the influence of multi-task auxiliary learning mechanism on image fusion task on the basis of single task multi-loss network model. By simulating the three characteristics of human visual perception system, the fused image is more consistent with the mechanism of human brain image fusion. Finally, in order to verify the superiority of our algorithm, we carried out experiments on the combined vision system image data set, and extended our algorithm to the infrared and visible image and the multi-focus image public data set for experimental verification. The experimental results demonstrate the superiority of our fusion theory over state-of-arts in generality and robustness. | ['Jiaqi Yang', 'Yanning Zhang', 'Xinbo Zhao', 'Aiqing Fang'] | 2019-12-18 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 1.41083255e-01 -6.93158329e-01 2.68474728e-01 -2.04262912e-01
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37e92fde-849f-4ff9-88f1-3f2ef5c86143 | multi-view-pointnet-for-3d-scene | 1909.13603 | null | https://arxiv.org/abs/1909.13603v1 | https://arxiv.org/pdf/1909.13603v1.pdf | Multi-view PointNet for 3D Scene Understanding | Fusion of 2D images and 3D point clouds is important because information from dense images can enhance sparse point clouds. However, fusion is challenging because 2D and 3D data live in different spaces. In this work, we propose MVPNet (Multi-View PointNet), where we aggregate 2D multi-view image features into 3D point clouds, and then use a point based network to fuse the features in 3D canonical space to predict 3D semantic labels. To this end, we introduce view selection along with a 2D-3D feature aggregation module. Extensive experiments show the benefit of leveraging features from dense images and reveal superior robustness to varying point cloud density compared to 3D-only methods. On the ScanNetV2 benchmark, our MVPNet significantly outperforms prior point cloud based approaches on the task of 3D Semantic Segmentation. It is much faster to train than the large networks of the sparse voxel approach. We provide solid ablation studies to ease the future design of 2D-3D fusion methods and their extension to other tasks, as we showcase for 3D instance segmentation. | ['Jiayuan Gu', 'Hao Su', 'Maximilian Jaritz'] | 2019-09-30 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 8.45143422e-02 -2.01172009e-03 -2.39990726e-02 -4.84006584e-01
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b4121718-2187-4c33-a57b-956c83ffca5f | lad-rcnn-a-powerful-tool-for-livestock-face | 2210.17146 | null | https://arxiv.org/abs/2210.17146v2 | https://arxiv.org/pdf/2210.17146v2.pdf | LAD-RCNN:A Powerful Tool for Livestock Face Detection and Normalization | With the demand for standardized large-scale livestock farming and the development of artificial intelligence technology, a lot of research in area of animal face recognition were carried on pigs, cattle, sheep and other livestock. Face recognition consists of three sub-task: face detection, face normalizing and face identification. Most of animal face recognition study focuses on face detection and face identification. Animals are often uncooperative when taking photos, so the collected animal face images are often in arbitrary directions. The use of non-standard images may significantly reduce the performance of face recognition system. However, there is no study on normalizing of the animal face image with arbitrary directions. In this study, we developed a light-weight angle detection and region-based convolutional network (LAD-RCNN) containing a new rotation angle coding method that can detect the rotation angle and the location of animal face in one-stage. LAD-RCNN has a frame rate of 72.74 FPS (including all steps) on a single GeForce RTX 2080 Ti GPU. LAD-RCNN has been evaluated on multiple dataset including goat dataset and gaot infrared image. Evaluation result show that the AP of face detection was more than 95% and the deviation between the detected rotation angle and the ground-truth rotation angle were less than 0.036 (i.e. 6.48{\deg}) on all the test dataset. This shows that LAD-RCNN has excellent performance on livestock face and its direction detection, and therefore it is very suitable for livestock face detection and Normalizing. Code is available at https://github.com/SheepBreedingLab-HZAU/LAD-RCNN/ | ['Xunping Jiang', 'Shiping Yang', 'Han Yang', 'Xu Wang', 'Junrui Liu', 'Guiqiong Liu', 'Ling Sun'] | 2022-10-31 | null | null | null | null | ['face-detection', 'face-identification'] | ['computer-vision', 'computer-vision'] | [-1.14741497e-01 -4.26819205e-01 1.16295464e-01 -7.15975583e-01
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115b9ae8-7f2d-4efe-87aa-a91bde915a4f | low-light-enhancement-method-based-on | 2208.09330 | null | https://arxiv.org/abs/2208.09330v2 | https://arxiv.org/pdf/2208.09330v2.pdf | Low-light Enhancement Method Based on Attention Map Net | Low-light image enhancement is a crucial preprocessing task for some complex vision tasks. Target detection, image segmentation, and image recognition outcomes are all directly impacted by the impact of image enhancement. However, the majority of the currently used image enhancement techniques do not produce satisfactory outcomes, and these enhanced networks have relatively weak robustness. We suggest an improved network called BrightenNet that uses U-Net as its primary structure and incorporates a number of different attention mechanisms as a solution to this issue. In a specific application, we employ the network as the generator and LSGAN as the training framework to achieve better enhancement results. We demonstrate the validity of the proposed network BrightenNet in the experiments that follow in this paper. The results it produced can both preserve image details and conform to human vision standards. | ['Xinwei Xu', 'Taiji Lan', 'Xucheng Xue', 'Mengfei Wu'] | 2022-08-19 | null | null | null | null | ['low-light-image-enhancement'] | ['computer-vision'] | [ 5.38995445e-01 -1.80384740e-01 9.79219452e-02 -2.62849957e-01
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b8b3f1c8-c8d0-4e03-9e4c-d66c546e5838 | improving-application-performance-with-biased | 2107.07642 | null | https://arxiv.org/abs/2107.07642v1 | https://arxiv.org/pdf/2107.07642v1.pdf | Improving application performance with biased distributions of quantum states | We consider the properties of a specific distribution of mixed quantum states of arbitrary dimension that can be biased towards a specific mean purity. In particular, we analyze mixtures of Haar-random pure states with Dirichlet-distributed coefficients. We analytically derive the concentration parameters required to match the mean purity of the Bures and Hilbert--Schmidt distributions in any dimension. Numerical simulations suggest that this value recovers the Hilbert--Schmidt distribution exactly, offering an alternative and intuitive physical interpretation for ensembles of Hilbert--Schmidt-distributed random quantum states. We then demonstrate how substituting these Dirichlet-weighted Haar mixtures in place of the Bures and Hilbert--Schmidt distributions results in measurable performance advantages in machine-learning-based quantum state tomography systems and Bayesian quantum state reconstruction. Finally, we experimentally characterize the distribution of quantum states generated by both a cloud-accessed IBM quantum computer and an in-house source of polarization-entangled photons. In each case, our method can more closely match the underlying distribution than either Bures or Hilbert--Schmidt distributed states for various experimental conditions. | ['Brian T. Kirby', 'Ryan T. Glasser', 'Thomas A. Searles', 'Daniel E. Jones', 'Joseph M. Lukens', 'Sanjaya Lohani'] | 2021-07-15 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 4.10702899e-02 -9.44004487e-03 6.94709793e-02 -2.74847150e-01
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d81d84cf-a896-4c0c-828d-6887c1975d64 | alephbert-a-hebrew-large-pre-trained-language | 2104.04052 | null | https://arxiv.org/abs/2104.04052v1 | https://arxiv.org/pdf/2104.04052v1.pdf | AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With | Large Pre-trained Language Models (PLMs) have become ubiquitous in the development of language understanding technology and lie at the heart of many artificial intelligence advances. While advances reported for English using PLMs are unprecedented, reported advances using PLMs in Hebrew are few and far between. The problem is twofold. First, Hebrew resources available for training NLP models are not at the same order of magnitude as their English counterparts. Second, there are no accepted tasks and benchmarks to evaluate the progress of Hebrew PLMs on. In this work we aim to remedy both aspects. First, we present AlephBERT, a large pre-trained language model for Modern Hebrew, which is trained on larger vocabulary and a larger dataset than any Hebrew PLM before. Second, using AlephBERT we present new state-of-the-art results on multiple Hebrew tasks and benchmarks, including: Segmentation, Part-of-Speech Tagging, full Morphological Tagging, Named-Entity Recognition and Sentiment Analysis. We make our AlephBERT model publicly available, providing a single point of entry for the development of Hebrew NLP applications. | ['Reut Tsarfaty', 'Refael Shaked Greenfeld', 'Idan Brusilovsky', 'Dan Bareket', 'Elron Bandel', 'Amit Seker'] | 2021-04-08 | null | null | null | null | ['morphological-tagging'] | ['natural-language-processing'] | [ 1.96576044e-02 1.45099312e-01 -4.14006889e-01 -5.86977839e-01
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3f40da53-3334-4cd3-b172-a46f4d93c521 | the-role-of-general-intelligence-in | 2104.13468 | null | https://arxiv.org/abs/2104.13468v1 | https://arxiv.org/pdf/2104.13468v1.pdf | The Role of General Intelligence in Mathematical Reasoning | Objects are a centerpiece of the mathematical realm and our interaction with and reasoning about it, just as they are of the physical one (if not more). And humans' mathematical reasoning must ultimately be grounded in our general intelligence. Yet in contemporary cognitive science and A.I., the physical and mathematical domains are customarily explored separately, which allows for baking in assumptions for what objects are for the system - and missing potential connections. In this paper, I put the issue into its philosophical and cognitive context. I then describe an abstract theoretical framework for learning object representations, that makes room for mathematical objects on par with non-mathematical ones. Finally, I describe a case study that builds on that view to show how our general ability for integrating different aspects of objects effects our conception of the natural numbers. | ['Aviv Keren'] | 2021-04-27 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 1.89987585e-01 4.97083277e-01 1.34708375e-01 -1.57439366e-01
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9dd02695-7e54-4131-8603-69110a391239 | negation-in-cognitive-reasoning | 2012.12641 | null | https://arxiv.org/abs/2012.12641v3 | https://arxiv.org/pdf/2012.12641v3.pdf | Negation in Cognitive Reasoning | Negation is both an operation in formal logic and in natural language by which a proposition is replaced by one stating the opposite, as by the addition of "not" or another negation cue. Treating negation in an adequate way is required for cognitive reasoning, which aims at modeling the human ability to draw meaningful conclusions despite incomplete and inconsistent knowledge. One task of cognitive reasoning is answering questions given by sentences in natural language. There are tools based on discourse representation theory to convert sentences automatically into a formal logic representation, and additional knowledge can be added using the predicate names in the formula and knowledge databases. However, the knowledge in logic databases in practice always is incomplete. Hence, forward reasoning of automated reasoning systems alone does not suffice to derive answers to questions because, instead of complete proofs, often only partial positive knowledge can be derived, while negative knowledge is used only during the reasoning process. In consequence, we aim at eliminating syntactic negation, strictly speaking, the negated event or property. In this paper, we describe an effective procedure to determine the negated event or property in order to replace it by its inverse. This lays the basis of cognitive reasoning, employing both logic and machine learning for general question answering. We evaluate our procedure by several benchmarks and demonstrate its practical usefulness in our cognitive reasoning system. | ['Frieder Stolzenburg', 'Sophie Siebert', 'Claudia Schon'] | 2020-12-23 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 3.51886421e-01 8.41375351e-01 -8.74042064e-02 -4.29847300e-01
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da44c7c3-9a46-48f9-9dcc-084ad1c47869 | hyperformer-learning-expressive-sparse | 2305.17386 | null | https://arxiv.org/abs/2305.17386v1 | https://arxiv.org/pdf/2305.17386v1.pdf | HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer | Learning expressive representations for high-dimensional yet sparse features has been a longstanding problem in information retrieval. Though recent deep learning methods can partially solve the problem, they often fail to handle the numerous sparse features, particularly those tail feature values with infrequent occurrences in the training data. Worse still, existing methods cannot explicitly leverage the correlations among different instances to help further improve the representation learning on sparse features since such relational prior knowledge is not provided. To address these challenges, in this paper, we tackle the problem of representation learning on feature-sparse data from a graph learning perspective. Specifically, we propose to model the sparse features of different instances using hypergraphs where each node represents a data instance and each hyperedge denotes a distinct feature value. By passing messages on the constructed hypergraphs based on our Hypergraph Transformer (HyperFormer), the learned feature representations capture not only the correlations among different instances but also the correlations among features. Our experiments demonstrate that the proposed approach can effectively improve feature representation learning on sparse features. | ['Derek Zhiyuan Cheng', 'Huan Liu', 'Ed H. Chi', 'Lichan Hong', 'Ruoxi Wang', 'Ting Chen', 'Bryan Perrozi', 'Albert Jiongqian Liang', 'Kaize Ding'] | 2023-05-27 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [ 1.70602664e-01 1.63794279e-01 -6.20515585e-01 -4.79667217e-01
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412b578b-69f4-4899-be2b-d3566cd85315 | detector-free-weakly-supervised-grounding-by | 2104.09829 | null | https://arxiv.org/abs/2104.09829v1 | https://arxiv.org/pdf/2104.09829v1.pdf | Detector-Free Weakly Supervised Grounding by Separation | Nowadays, there is an abundance of data involving images and surrounding free-form text weakly corresponding to those images. Weakly Supervised phrase-Grounding (WSG) deals with the task of using this data to learn to localize (or to ground) arbitrary text phrases in images without any additional annotations. However, most recent SotA methods for WSG assume the existence of a pre-trained object detector, relying on it to produce the ROIs for localization. In this work, we focus on the task of Detector-Free WSG (DF-WSG) to solve WSG without relying on a pre-trained detector. We directly learn everything from the images and associated free-form text pairs, thus potentially gaining an advantage on the categories unsupported by the detector. The key idea behind our proposed Grounding by Separation (GbS) method is synthesizing `text to image-regions' associations by random alpha-blending of arbitrary image pairs and using the corresponding texts of the pair as conditions to recover the alpha map from the blended image via a segmentation network. At test time, this allows using the query phrase as a condition for a non-blended query image, thus interpreting the test image as a composition of a region corresponding to the phrase and the complement region. Using this approach we demonstrate a significant accuracy improvement, of up to $8.5\%$ over previous DF-WSG SotA, for a range of benchmarks including Flickr30K, Visual Genome, and ReferIt, as well as a significant complementary improvement (above $7\%$) over the detector-based approaches for WSG. | ['Leonid Karlinsky', 'Rogerio Feris', 'Raja Giryes', 'Shimon Ullman', 'Kate Saenko', 'Alex Bronstein', 'Chun-Fu Chen', 'Rameswar Panda', 'Prasanna Sattigeri', 'Hila Barak Levi', 'Hilde Kuehne', 'Eli Schwartz', 'Guy Lev', 'Joseph Shtok', 'Amit Alfassy', 'Sivan Doveh', 'Assaf Arbelle'] | 2021-04-20 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Arbelle_Detector-Free_Weakly_Supervised_Grounding_by_Separation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Arbelle_Detector-Free_Weakly_Supervised_Grounding_by_Separation_ICCV_2021_paper.pdf | iccv-2021-1 | ['phrase-grounding'] | ['natural-language-processing'] | [ 4.46344167e-01 3.36850464e-01 -7.94502795e-02 -7.44500458e-02
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2.50584155e-01 -7.32785761e-01 1.29751861e-01 1.15378983e-01
2.03756079e-01 -1.76935166e-01 2.79036969e-01 -7.18825579e-01
-3.50350618e-01 7.30259359e-01 -8.24085027e-02 -4.31592494e-01
8.51168275e-01 -5.61242029e-02 -7.55095035e-02 3.76610667e-01
1.08363807e+00 3.00229281e-01 -1.21796966e+00 -3.85817021e-01
2.77676165e-01 -2.91861296e-01 1.68810621e-01 -8.45004022e-01
-9.98358250e-01 8.23334455e-01 5.77848792e-01 1.82556108e-01
1.06212533e+00 6.34570718e-01 5.31649947e-01 2.02820867e-01
3.31246853e-01 -1.02976274e+00 4.34912503e-01 2.29044124e-01
6.82101071e-01 -1.26309562e+00 -1.31815553e-01 -6.65770650e-01
-4.99882102e-01 8.60021889e-01 5.00009358e-01 -2.20420122e-01
5.30106008e-01 1.68949723e-01 1.07440829e-01 -2.69489437e-01
-2.50851423e-01 -6.88767135e-01 4.54727948e-01 3.91366184e-01
-4.39961590e-02 6.41191602e-02 5.42051792e-02 4.82687354e-01
-4.79632616e-02 -2.63972759e-01 2.20625654e-01 7.22215831e-01
-5.13939321e-01 -9.52584147e-01 -5.42499065e-01 5.21751881e-01
-3.74510318e-01 -3.09059411e-01 -6.58670008e-01 1.03851223e+00
5.50455868e-01 7.35406458e-01 1.66906551e-01 -3.73267293e-01
2.09849134e-01 1.92585215e-01 4.71135944e-01 -9.81399655e-01
-5.75984716e-01 6.80853486e-01 -2.12055221e-02 -3.87807786e-01
-5.60710907e-01 -6.36011302e-01 -1.40715492e+00 2.26001918e-01
-6.36927247e-01 4.46297564e-02 5.91568708e-01 1.17329443e+00
8.36054012e-02 2.76458740e-01 4.45439249e-01 -8.37121129e-01
-1.09040953e-01 -8.25242221e-01 -6.72454536e-01 5.91485918e-01
2.49274075e-01 -6.60751402e-01 -6.03420615e-01 3.00389767e-01] | [10.08019733428955, 1.057013750076294] |
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