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bc1c7fe6-2fb0-4ec9-968e-fbd1fbfc2fea | near-optimal-glimpse-sequences-for-improved | 1906.05462 | null | https://arxiv.org/abs/1906.05462v2 | https://arxiv.org/pdf/1906.05462v2.pdf | Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training | Hard visual attention is a promising approach to reduce the computational burden of modern computer vision methodologies. Hard attention mechanisms are typically non-differentiable. They can be trained with reinforcement learning but the high-variance training this entails hinders more widespread application. We show how hard attention for image classification can be framed as a Bayesian optimal experimental design (BOED) problem. From this perspective, the optimal locations to attend to are those which provide the greatest expected reduction in the entropy of the classification distribution. We introduce methodology from the BOED literature to approximate this optimal behaviour, and use it to generate `near-optimal' sequences of attention locations. We then show how to use such sequences to partially supervise, and therefore speed up, the training of a hard attention mechanism. Although generating these sequences is computationally expensive, they can be reused by any other networks later trained on the same task. | ['Michael Teng', 'Frank Wood', 'William Harvey'] | 2019-06-13 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 4.00655508e-01 5.84042072e-01 -4.25663330e-02 -2.60008365e-01
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3864d243-3aff-4cad-9d36-c31f988390aa | evi-multilingual-spoken-dialogue-tasks-and-1 | 2204.13496 | null | https://arxiv.org/abs/2204.13496v1 | https://arxiv.org/pdf/2204.13496v1.pdf | EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification | Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services. Such systems should be able to enrol (E), verify (V), and identify (I) new and recurring users based on their personal information, e.g. postcode, name, and date of birth. In this work, we formalise the three authentication tasks and their evaluation protocols, and we present EVI, a challenging spoken multilingual dataset with 5,506 dialogues in English, Polish, and French. Our proposed models set the first competitive benchmarks, explore the challenges of multilingual natural language processing of spoken dialogue, and set directions for future research. | ['Paweł Budzianowski', 'Iñigo Casanueva', 'Michał Lis', 'Ivan Vulić', 'Georgios P. Spithourakis'] | 2022-04-28 | null | https://aclanthology.org/2022.findings-naacl.124 | https://aclanthology.org/2022.findings-naacl.124.pdf | findings-naacl-2022-7 | ['spoken-dialogue-systems', 'speaker-identification'] | ['speech', 'speech'] | [-2.93234617e-01 2.01162130e-01 1.64945535e-02 -5.43909252e-01
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bbd794d1-ba77-48a9-97b1-bc0767e95ac2 | synthesizing-light-field-video-from-monocular | 2207.10357 | null | https://arxiv.org/abs/2207.10357v1 | https://arxiv.org/pdf/2207.10357v1.pdf | Synthesizing Light Field Video from Monocular Video | The hardware challenges associated with light-field(LF) imaging has made it difficult for consumers to access its benefits like applications in post-capture focus and aperture control. Learning-based techniques which solve the ill-posed problem of LF reconstruction from sparse (1, 2 or 4) views have significantly reduced the requirement for complex hardware. LF video reconstruction from sparse views poses a special challenge as acquiring ground-truth for training these models is hard. Hence, we propose a self-supervised learning-based algorithm for LF video reconstruction from monocular videos. We use self-supervised geometric, photometric and temporal consistency constraints inspired from a recent self-supervised technique for LF video reconstruction from stereo video. Additionally, we propose three key techniques that are relevant to our monocular video input. We propose an explicit disocclusion handling technique that encourages the network to inpaint disoccluded regions in a LF frame, using information from adjacent input temporal frames. This is crucial for a self-supervised technique as a single input frame does not contain any information about the disoccluded regions. We also propose an adaptive low-rank representation that provides a significant boost in performance by tailoring the representation to each input scene. Finally, we also propose a novel refinement block that is able to exploit the available LF image data using supervised learning to further refine the reconstruction quality. Our qualitative and quantitative analysis demonstrates the significance of each of the proposed building blocks and also the superior results compared to previous state-of-the-art monocular LF reconstruction techniques. We further validate our algorithm by reconstructing LF videos from monocular videos acquired using a commercial GoPro camera. | ['Kaushik Mitra', 'Sarah', 'Prasan Shedligeri', 'Shrisudhan Govindarajan'] | 2022-07-21 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 4.67829704e-01 -1.66486517e-01 -2.61709571e-01 -3.89761001e-01
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2ed6a06a-81e2-4516-9062-79a570b6100c | rccnet-an-efficient-convolutional-neural | 1810.02797 | null | https://arxiv.org/abs/1810.02797v3 | https://arxiv.org/pdf/1810.02797v3.pdf | RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification | Efficient and precise classification of histological cell nuclei is of utmost importance due to its potential applications in the field of medical image analysis. It would facilitate the medical practitioners to better understand and explore various factors for cancer treatment. The classification of histological cell nuclei is a challenging task due to the cellular heterogeneity. This paper proposes an efficient Convolutional Neural Network (CNN) based architecture for classification of histological routine colon cancer nuclei named as RCCNet. The main objective of this network is to keep the CNN model as simple as possible. The proposed RCCNet model consists of only 1,512,868 learnable parameters which are significantly less compared to the popular CNN models such as AlexNet, CIFARVGG, GoogLeNet, and WRN. The experiments are conducted over publicly available routine colon cancer histological dataset "CRCHistoPhenotypes". The results of the proposed RCCNet model are compared with five state-of-the-art CNN models in terms of the accuracy, weighted average F1 score and training time. The proposed method has achieved a classification accuracy of 80.61% and 0.7887 weighted average F1 score. The proposed RCCNet is more efficient and generalized terms of the training time and data over-fitting, respectively. | ['S. H. Shabbeer Basha', 'Soumen Ghosh', 'Snehasis Mukherjee', 'Kancharagunta Kishan Babu', 'Viswanath Pulabaigari', 'Shiv Ram Dubey'] | 2018-09-30 | null | null | null | null | ['nuclei-classification'] | ['medical'] | [-2.32060254e-01 -1.04457535e-01 -1.21337280e-01 2.05104370e-02
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ac8b97b8-929e-4eb7-8e0b-d0820d48a9f8 | an-equivariant-generative-framework-for | 2304.12436 | null | https://arxiv.org/abs/2304.12436v1 | https://arxiv.org/pdf/2304.12436v1.pdf | An Equivariant Generative Framework for Molecular Graph-Structure Co-Design | Designing molecules with desirable physiochemical properties and functionalities is a long-standing challenge in chemistry, material science, and drug discovery. Recently, machine learning-based generative models have emerged as promising approaches for \emph{de novo} molecule design. However, further refinement of methodology is highly desired as most existing methods lack unified modeling of 2D topology and 3D geometry information and fail to effectively learn the structure-property relationship for molecule design. Here we present MolCode, a roto-translation equivariant generative framework for \underline{Mol}ecular graph-structure \underline{Co-de}sign. In MolCode, 3D geometric information empowers the molecular 2D graph generation, which in turn helps guide the prediction of molecular 3D structure. Extensive experimental results show that MolCode outperforms previous methods on a series of challenging tasks including \emph{de novo} molecule design, targeted molecule discovery, and structure-based drug design. Particularly, MolCode not only consistently generates valid (99.95$\%$ Validity) and diverse (98.75$\%$ Uniqueness) molecular graphs/structures with desirable properties, but also generate drug-like molecules with high affinity to target proteins (61.8$\%$ high-affinity ratio), which demonstrates MolCode's potential applications in material design and drug discovery. Our extensive investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design, and provide new insights into machine learning-based molecule representation and generation. | ['Enhong Chen', 'Chang-Yu Hsieh', 'Chee-Kong Lee', 'Qi Liu', 'Zaixi Zhang'] | 2023-04-12 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 1.70772806e-01 -2.11066306e-01 -5.68827629e-01 -4.72996831e-02
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8ac1cb63-59f1-4a0a-afca-2f108ae7fb80 | generalized-munchausen-reinforcement-learning | 2301.11476 | null | https://arxiv.org/abs/2301.11476v1 | https://arxiv.org/pdf/2301.11476v1.pdf | Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence | Many policy optimization approaches in reinforcement learning incorporate a Kullback-Leilbler (KL) divergence to the previous policy, to prevent the policy from changing too quickly. This idea was initially proposed in a seminal paper on Conservative Policy Iteration, with approximations given by algorithms like TRPO and Munchausen Value Iteration (MVI). We continue this line of work by investigating a generalized KL divergence -- called the Tsallis KL divergence -- which use the $q$-logarithm in the definition. The approach is a strict generalization, as $q = 1$ corresponds to the standard KL divergence; $q > 1$ provides a range of new options. We characterize the types of policies learned under the Tsallis KL, and motivate when $q >1$ could be beneficial. To obtain a practical algorithm that incorporates Tsallis KL regularization, we extend MVI, which is one of the simplest approaches to incorporate KL regularization. We show that this generalized MVI($q$) obtains significant improvements over the standard MVI($q = 1$) across 35 Atari games. | ['Martha White', 'Takamitsu Matsubara', 'Zheng Chen', 'Lingwei Zhu'] | 2023-01-27 | null | null | null | null | ['atari-games'] | ['playing-games'] | [-2.84597009e-01 7.19340285e-03 -6.23614788e-01 -2.42487177e-01
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ee4f039b-e29c-4684-aba6-b9bfa81a5fdd | optimality-preserving-reduction-of-chemical | 2301.08553 | null | https://arxiv.org/abs/2301.08553v1 | https://arxiv.org/pdf/2301.08553v1.pdf | Optimality-preserving Reduction of Chemical Reaction Networks | Across many disciplines, chemical reaction networks (CRNs) are an established population model defined as a system of coupled nonlinear ordinary differential equations. In many applications, for example, in systems biology and epidemiology, CRN parameters such as the kinetic reaction rates can be used as control inputs to steer the system toward a given target. Unfortunately, the resulting optimal control problem is nonlinear, therefore, computationally very challenging. We address this issue by introducing an optimality-preserving reduction algorithm for CRNs. The algorithm partitions the original state variables into a reduced set of macro-variables for which one can define a reduced optimal control problem from which one can exactly recover the solution of the original control problem. Notably, the reduction algorithm runs with polynomial time complexity in the size of the CRN. We use this result to reduce reachability and control problems of large-scale protein-interaction networks and vaccination models with hundreds of thousands of state variables. | ['Andrea Vandin', 'Max Tschaikowski', 'Mirco Tribastone', 'Daniele Toller', 'Kim G. Larsen'] | 2023-01-20 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 5.11107504e-01 1.99042723e-01 -8.80319774e-02 4.58450407e-01
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08530d38-6b6d-4ec5-8990-c9b9f017db55 | graph-decoupling-attention-markov-networks | 2104.13718 | null | https://arxiv.org/abs/2104.13718v2 | https://arxiv.org/pdf/2104.13718v2.pdf | Graph Decoupling Attention Markov Networks for Semi-supervised Graph Node Classification | Graph neural networks (GNN) have been ubiquitous in graph node classification tasks. Most of GNN methods update the node embedding iteratively by aggregating its neighbors' information. However, they often suffer from negative disturbance, due to edges connecting nodes with different labels. One approach to alleviate this negative disturbance is to use attention to learn the weights of aggregation, but current attention-based GNNs only consider feature similarity and also suffer from the lack of supervision. In this paper, we consider the label dependency of graph nodes and propose a decoupling attention mechanism to learn both hard and soft attention. The hard attention is learned on labels for a refined graph structure with fewer inter-class edges, so that the aggregation's negative disturbance can be reduced. The soft attention aims to learn the aggregation weights based on features over the refined graph structure to enhance information gains during message passing. Particularly, we formulate our model under the EM framework, and the learned attention is used to guide the label propagation in the M-step and the feature propagation in the E-step, respectively. Extensive experiments are performed on six well-known benchmark graph datasets to verify the effectiveness of the proposed method. | ['Junbin Gao', 'Junping Zhang', 'Jian Pu', 'Mingyuan Bai', 'Shouzhen Chen', 'Jie Chen'] | 2021-04-28 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 1.51899293e-01 2.97160506e-01 -2.64965624e-01 -4.67233777e-01
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c18ef8b8-391e-4e64-b6c3-7b012611ad54 | knowledge-distilled-graph-neural-networks-for | 2304.06038 | null | https://arxiv.org/abs/2304.06038v1 | https://arxiv.org/pdf/2304.06038v1.pdf | Knowledge-Distilled Graph Neural Networks for Personalized Epileptic Seizure Detection | Wearable devices for seizure monitoring detection could significantly improve the quality of life of epileptic patients. However, existing solutions that mostly rely on full electrode set of electroencephalogram (EEG) measurements could be inconvenient for every day use. In this paper, we propose a novel knowledge distillation approach to transfer the knowledge from a sophisticated seizure detector (called the teacher) trained on data from the full set of electrodes to learn new detectors (called the student). They are both providing lightweight implementations and significantly reducing the number of electrodes needed for recording the EEG. We consider the case where the teacher and the student seizure detectors are graph neural networks (GNN), since these architectures actively use the connectivity information. We consider two cases (a) when a single student is learnt for all the patients using preselected channels; and (b) when personalized students are learnt for every individual patient, with personalized channel selection using a Gumbelsoftmax approach. Our experiments on the publicly available Temple University Hospital EEG Seizure Data Corpus (TUSZ) show that both knowledge-distillation and personalization play significant roles in improving performance of seizure detection, particularly for patients with scarce EEG data. We observe that using as few as two channels, we are able to obtain competitive seizure detection performance. This, in turn, shows the potential of our approach in more realistic scenario of wearable devices for personalized monitoring of seizures, even with few recordings. | ['Pascal Frossard', 'Simona Petravic', 'Arun Venkitaraman', 'Qinyue Zheng'] | 2023-04-03 | null | null | null | null | ['seizure-detection', 'eeg', 'eeg'] | ['medical', 'methodology', 'time-series'] | [ 2.47575119e-01 2.74384081e-01 3.01221013e-01 -1.33051127e-01
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7bb22605-63e8-44be-be3d-c7aca5a41de8 | benchmarking-and-analyzing-3d-aware-image | 2306.12423 | null | https://arxiv.org/abs/2306.12423v1 | https://arxiv.org/pdf/2306.12423v1.pdf | Benchmarking and Analyzing 3D-aware Image Synthesis with a Modularized Codebase | Despite the rapid advance of 3D-aware image synthesis, existing studies usually adopt a mixture of techniques and tricks, leaving it unclear how each part contributes to the final performance in terms of generality. Following the most popular and effective paradigm in this field, which incorporates a neural radiance field (NeRF) into the generator of a generative adversarial network (GAN), we build a well-structured codebase, dubbed Carver, through modularizing the generation process. Such a design allows researchers to develop and replace each module independently, and hence offers an opportunity to fairly compare various approaches and recognize their contributions from the module perspective. The reproduction of a range of cutting-edge algorithms demonstrates the availability of our modularized codebase. We also perform a variety of in-depth analyses, such as the comparison across different types of point feature, the necessity of the tailing upsampler in the generator, the reliance on the camera pose prior, etc., which deepen our understanding of existing methods and point out some further directions of the research work. We release code and models at https://github.com/qiuyu96/Carver to facilitate the development and evaluation of this field. | ['Yujun Shen', 'Sida Peng', 'Yinghao Xu', 'Kecheng Zheng', 'Zifan Shi', 'Qiuyu Wang'] | 2023-06-21 | null | null | null | null | ['3d-aware-image-synthesis', 'benchmarking', 'benchmarking'] | ['computer-vision', 'miscellaneous', 'robots'] | [ 5.38871288e-02 -1.03286557e-01 -5.81287546e-03 -2.07700267e-01
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4cafd361-217c-4cd4-adcd-cd28eb31d606 | mnhn-tree-tools-a-toolbox-for-tree-inference | null | null | https://academic.oup.com/bioinformatics/article/37/21/3947/6294927 | https://hal.science/hal-03451406/document | MNHN-Tree-Tools: a toolbox for tree inference using multi-scale clustering of a set of sequences | Abstract
Summary
Genomic sequences are widely used to infer the evolutionary history of a given group of individuals. Many methods have been developed for sequence clustering and tree building. In the early days of genome sequencing, these were often limited to hundreds of sequences but due to the surge of high throughput sequencing, it is now common to have millions of sampled sequences at hand. We introduce MNHN-Tree-Tools, a high performance set of algorithms that builds multi-scale, nested clusters of sequences found in a FASTA file. MNHN-Tree-Tools does not rely on multiple sequence alignment and can thus be used on large datasets to infer a sequence tree. Herein, we outline two applications: a human alpha-satellite repeats classification and a tree of life derivation from 16S/18S rDNA sequences.
Availability and implementation
Open source with a Zlib License via the Git protocol: https://gitlab.in2p3.fr/mnhn-tools/mnhn-tree-tools.
Manual
A detailed users guide and tutorial: https://gitlab.in2p3.fr/mnhn-tools/mnhn-tree-tools-manual/-/raw/master/manual.pdf.
Website and FAQ
http://treetools.haschka.net. | ['Julien Mozziconacci', 'Christophe Escudé', 'Loic Ponger', 'Thomas Haschka'] | 2021-06-08 | null | null | null | bioinformatics-2021-6 | ['multiple-sequence-alignment'] | ['medical'] | [ 3.03297997e-01 -5.50897717e-01 1.04704373e-01 -1.52460977e-01
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03ab8e3b-8bf9-4322-9bc8-30d4eebc23e1 | davd-net-deep-audio-aided-video-decompression | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_DAVD-Net_Deep_Audio-Aided_Video_Decompression_of_Talking_Heads_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_DAVD-Net_Deep_Audio-Aided_Video_Decompression_of_Talking_Heads_CVPR_2020_paper.pdf | DAVD-Net: Deep Audio-Aided Video Decompression of Talking Heads | Close-up talking heads are among the most common and salient object in video contents, such as face-to-face conversations in social media, teleconferences, news broadcasting, talk shows, etc. Due to the high sensitivity of human visual system to faces, compression distortions in talking heads videos are highly visible and annoying. To address this problem, we present a novel deep convolutional neural network (DCNN) method for very low bit rate video reconstruction of talking heads. The key innovation is a new DCNN architecture that can exploit the audio-video correlations to repair compression defects in the face region. We further improve reconstruction quality by embedding into our DCNN the encoder information of the video compression standards and introducing a constraining projection module in the network. Extensive experiments demonstrate that the proposed DCNN method outperforms the existing state-of-the-art methods on videos of talking heads.
| [' Chengjie Tu', ' Xianye Ben', ' Xinliang Zhai', ' Xiaolin Wu', 'Xi Zhang'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['video-reconstruction'] | ['computer-vision'] | [ 9.87712741e-02 3.36214036e-01 -3.76764610e-02 -4.33908910e-01
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fd9f31b9-cbf3-4d9e-9bc5-93ff4aaba3a6 | facial-action-unit-detection-using-attention | 1808.03457 | null | https://arxiv.org/abs/1808.03457v3 | https://arxiv.org/pdf/1808.03457v3.pdf | Facial Action Unit Detection Using Attention and Relation Learning | Attention mechanism has recently attracted increasing attentions in the field of facial action unit (AU) detection. By finding the region of interest of each AU with the attention mechanism, AU-related local features can be captured. Most of the existing attention based AU detection works use prior knowledge to predefine fixed attentions or refine the predefined attentions within a small range, which limits their capacity to model various AUs. In this paper, we propose an end-to-end deep learning based attention and relation learning framework for AU detection with only AU labels, which has not been explored before. In particular, multi-scale features shared by each AU are learned firstly, and then both channel-wise and spatial attentions are adaptively learned to select and extract AU-related local features. Moreover, pixel-level relations for AUs are further captured to refine spatial attentions so as to extract more relevant local features. Without changing the network architecture, our framework can be easily extended for AU intensity estimation. Extensive experiments show that our framework (i) soundly outperforms the state-of-the-art methods for both AU detection and AU intensity estimation on the challenging BP4D, DISFA, FERA 2015 and BP4D+ benchmarks, (ii) can adaptively capture the correlated regions of each AU, and (iii) also works well under severe occlusions and large poses. | ['Zhilei Liu', 'Yunsheng Wu', 'Jianfei Cai', 'Zhiwen Shao', 'Lizhuang Ma'] | 2018-08-10 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 3.14234644e-01 -2.87573840e-02 -2.57705748e-01 8.58431496e-03
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28c9a16b-d5a6-4479-bfbc-7cf115a1172d | image-classification-on-small-datasets-via | 2202.11616 | null | https://arxiv.org/abs/2202.11616v2 | https://arxiv.org/pdf/2202.11616v2.pdf | ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing | Deep convolutional neural networks require large amounts of labeled data samples. For many real-world applications, this is a major limitation which is commonly treated by augmentation methods. In this work, we address the problem of learning deep neural networks on small datasets. Our proposed architecture called ChimeraMix learns a data augmentation by generating compositions of instances. The generative model encodes images in pairs, combines the features guided by a mask, and creates new samples. For evaluation, all methods are trained from scratch without any additional data. Several experiments on benchmark datasets, e.g. ciFAIR-10, STL-10, and ciFAIR-100, demonstrate the superior performance of ChimeraMix compared to current state-of-the-art methods for classification on small datasets. | ['Bodo Rosenhahn', 'Frederik Schubert', 'Christoph Reinders'] | 2022-02-23 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 3.48170072e-01 2.62304582e-02 -2.27827579e-01 -5.68953097e-01
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36bed7c7-1a66-4717-99ae-35016ec03838 | quantifying-privacy-risks-of-masked-language | 2203.03929 | null | https://arxiv.org/abs/2203.03929v2 | https://arxiv.org/pdf/2203.03929v2.pdf | Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks | The wide adoption and application of Masked language models~(MLMs) on sensitive data (from legal to medical) necessitates a thorough quantitative investigation into their privacy vulnerabilities -- to what extent do MLMs leak information about their training data? Prior attempts at measuring leakage of MLMs via membership inference attacks have been inconclusive, implying the potential robustness of MLMs to privacy attacks. In this work, we posit that prior attempts were inconclusive because they based their attack solely on the MLM's model score. We devise a stronger membership inference attack based on likelihood ratio hypothesis testing that involves an additional reference MLM to more accurately quantify the privacy risks of memorization in MLMs. We show that masked language models are extremely susceptible to likelihood ratio membership inference attacks: Our empirical results, on models trained on medical notes, show that our attack improves the AUC of prior membership inference attacks from 0.66 to an alarmingly high 0.90 level, with a significant improvement in the low-error region: at 1% false positive rate, our attack is 51X more powerful than prior work. | ['Reza Shokri', 'Taylor Berg-Kirkpatrick', 'Archit Uniyal', 'Kartik Goyal', 'FatemehSadat Mireshghallah'] | 2022-03-08 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 4.65488255e-01 4.71047014e-01 -2.07106516e-01 -4.71252084e-01
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08ac142e-0df4-4704-8dd2-45cbe735c179 | unsupervised-end-to-end-learning-for | 1711.08608 | null | http://arxiv.org/abs/1711.08608v2 | http://arxiv.org/pdf/1711.08608v2.pdf | Unsupervised End-to-end Learning for Deformable Medical Image Registration | We propose a registration algorithm for 2D CT/MRI medical images with a new
unsupervised end-to-end strategy using convolutional neural networks. The
contributions of our algorithm are threefold: (1) We transplant traditional
image registration algorithms to an end-to-end convolutional neural network
framework, while maintaining the unsupervised nature of image registration
problems. The image-to-image integrated framework can simultaneously learn both
image features and transformation matrix for registration. (2) Training with
additional data without any label can further improve the registration
performance by approximately 10 %. (3) The registration speed is 100x faster
than traditional methods. The proposed network is easy to implement and can be
trained efficiently. Experiments demonstrate that our system achieves
state-of-the-art results on 2D brain registration and achieves comparable
results on 2D liver registration. It can be extended to register other organs
beyond liver and brain such as kidney, lung, and heart. | ['Eric I-Chao Chang', 'Xiaoqing Guo', 'Yan Xu', 'Wen Yan', 'Yubo Fan', 'Siyuan Shan'] | 2017-11-23 | null | null | null | null | ['deformable-medical-image-registration'] | ['medical'] | [-2.19951998e-02 2.76451558e-01 -2.05487505e-01 -7.03404725e-01
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a3dbe36c-6814-4ed2-8377-8f0531fb5880 | distilled-pruning-using-synthetic-data-to-win | 2307.03364 | null | https://arxiv.org/abs/2307.03364v1 | https://arxiv.org/pdf/2307.03364v1.pdf | Distilled Pruning: Using Synthetic Data to Win the Lottery | This work introduces a novel approach to pruning deep learning models by using distilled data. Unlike conventional strategies which primarily focus on architectural or algorithmic optimization, our method reconsiders the role of data in these scenarios. Distilled datasets capture essential patterns from larger datasets, and we demonstrate how to leverage this capability to enable a computationally efficient pruning process. Our approach can find sparse, trainable subnetworks (a.k.a. Lottery Tickets) up to 5x faster than Iterative Magnitude Pruning at comparable sparsity on CIFAR-10. The experimental results highlight the potential of using distilled data for resource-efficient neural network pruning, model compression, and neural architecture search. | ['Daniel Cummings', 'Luke McDermott'] | 2023-07-07 | null | null | null | null | ['network-pruning', 'model-compression', 'architecture-search'] | ['methodology', 'methodology', 'methodology'] | [ 2.24493355e-01 2.99250156e-01 -4.47208494e-01 -5.95216095e-01
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10b482ff-a653-491c-b673-ccaf0db15590 | 190508617 | 1905.08617 | null | https://arxiv.org/abs/1905.08617v2 | https://arxiv.org/pdf/1905.08617v2.pdf | Automatic Long-Term Deception Detection in Group Interaction Videos | Most work on automated deception detection (ADD) in video has two restrictions: (i) it focuses on a video of one person, and (ii) it focuses on a single act of deception in a one or two minute video. In this paper, we propose a new ADD framework which captures long term deception in a group setting. We study deception in the well-known Resistance game (like Mafia and Werewolf) which consists of 5-8 players of whom 2-3 are spies. Spies are deceptive throughout the game (typically 30-65 minutes) to keep their identity hidden. We develop an ensemble predictive model to identify spies in Resistance videos. We show that features from low-level and high-level video analysis are insufficient, but when combined with a new class of features that we call LiarRank, produce the best results. We achieve AUCs of over 0.70 in a fully automated setting. Our demo can be found at http://home.cs.dartmouth.edu/~mbolonkin/scan/demo/ | ['V. S. Subrahmanian', 'Bharat Singh', 'Chao Chen', 'Zhe Wu', 'Judee Burgoon', 'Norah Dunbar', 'Maksim Bolonkin', 'Chongyang Bai'] | 2019-05-15 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [-2.10223757e-02 -4.63161767e-01 -3.81748714e-02 -2.07162216e-01
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b97303e6-05dd-4d6c-b8b2-464667640f7a | a-modular-framework-for-centrality-and | 2111.11623 | null | https://arxiv.org/abs/2111.11623v1 | https://arxiv.org/pdf/2111.11623v1.pdf | A Modular Framework for Centrality and Clustering in Complex Networks | The structure of many complex networks includes edge directionality and weights on top of their topology. Network analysis that can seamlessly consider combination of these properties are desirable. In this paper, we study two important such network analysis techniques, namely, centrality and clustering. An information-flow based model is adopted for clustering, which itself builds upon an information theoretic measure for computing centrality. Our principal contributions include a generalized model of Markov entropic centrality with the flexibility to tune the importance of node degrees, edge weights and directions, with a closed-form asymptotic analysis. It leads to a novel two-stage graph clustering algorithm. The centrality analysis helps reason about the suitability of our approach to cluster a given graph, and determine `query' nodes, around which to explore local community structures, leading to an agglomerative clustering mechanism. The entropic centrality computations are amortized by our clustering algorithm, making it computationally efficient: compared to prior approaches using Markov entropic centrality for clustering, our experiments demonstrate multiple orders of magnitude of speed-up. Our clustering algorithm naturally inherits the flexibility to accommodate edge directionality, as well as different interpretations and interplay between edge weights and node degrees. Overall, this paper thus not only makes significant theoretical and conceptual contributions, but also translates the findings into artifacts of practical relevance, yielding new, effective and scalable centrality computations and graph clustering algorithms, whose efficacy has been validated through extensive benchmarking experiments. | ['Anwitaman Datta', 'Silivanxay Phetsouvanh', 'Frederique Oggier'] | 2021-11-23 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-6.61619455e-02 2.08786637e-01 -2.42256567e-01 7.92944431e-02
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03acd27d-1756-48e7-8b9a-7a4939b89bed | monte-carlo-graph-search-for-alphazero | 2012.11045 | null | https://arxiv.org/abs/2012.11045v1 | https://arxiv.org/pdf/2012.11045v1.pdf | Monte-Carlo Graph Search for AlphaZero | The AlphaZero algorithm has been successfully applied in a range of discrete domains, most notably board games. It utilizes a neural network, that learns a value and policy function to guide the exploration in a Monte-Carlo Tree Search. Although many search improvements have been proposed for Monte-Carlo Tree Search in the past, most of them refer to an older variant of the Upper Confidence bounds for Trees algorithm that does not use a policy for planning. We introduce a new, improved search algorithm for AlphaZero which generalizes the search tree to a directed acyclic graph. This enables information flow across different subtrees and greatly reduces memory consumption. Along with Monte-Carlo Graph Search, we propose a number of further extensions, such as the inclusion of Epsilon-greedy exploration, a revised terminal solver and the integration of domain knowledge as constraints. In our evaluations, we use the CrazyAra engine on chess and crazyhouse as examples to show that these changes bring significant improvements to AlphaZero. | ['Kristian Kersting', 'Patrick Korus', 'Johannes Czech'] | 2020-12-20 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 6.15563942e-03 4.02990282e-01 -4.77219522e-01 -1.92830727e-01
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ba13c093-e3e0-4b38-b673-a1b7893a7279 | consisttl-modeling-consistency-in-transfer | 2212.04262 | null | https://arxiv.org/abs/2212.04262v1 | https://arxiv.org/pdf/2212.04262v1.pdf | ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation | Transfer learning is a simple and powerful method that can be used to boost model performance of low-resource neural machine translation (NMT). Existing transfer learning methods for NMT are static, which simply transfer knowledge from a parent model to a child model once via parameter initialization. In this paper, we propose a novel transfer learning method for NMT, namely ConsistTL, which can continuously transfer knowledge from the parent model during the training of the child model. Specifically, for each training instance of the child model, ConsistTL constructs the semantically-equivalent instance for the parent model and encourages prediction consistency between the parent and child for this instance, which is equivalent to the child model learning each instance under the guidance of the parent model. Experimental results on five low-resource NMT tasks demonstrate that ConsistTL results in significant improvements over strong transfer learning baselines, with a gain up to 1.7 BLEU over the existing back-translation model on the widely-used WMT17 Turkish-English benchmark. Further analysis reveals that ConsistTL can improve the inference calibration of the child model. Code and scripts are freely available at https://github.com/NLP2CT/ConsistTL. | ['Min Zhang', 'Lidia S. Chao', 'Derek F. Wong', 'Xuebo Liu', 'Zhaocong Li'] | 2022-12-08 | null | null | null | null | ['nmt', 'low-resource-neural-machine-translation'] | ['computer-code', 'natural-language-processing'] | [ 2.69713610e-01 3.31305623e-01 -7.06802189e-01 -5.23778200e-01
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bd122165-a8c7-4a1d-99ee-5b08be0c2c23 | towards-visual-affordance-learning-a | 2203.14092 | null | https://arxiv.org/abs/2203.14092v2 | https://arxiv.org/pdf/2203.14092v2.pdf | Towards Visual Affordance Learning: A Benchmark for Affordance Segmentation and Recognition | The physical and textural attributes of objects have been widely studied for recognition, detection and segmentation tasks in computer vision.~A number of datasets, such as large scale ImageNet, have been proposed for feature learning using data hungry deep neural networks and for hand-crafted feature extraction. To intelligently interact with objects, robots and intelligent machines need the ability to infer beyond the traditional physical/textural attributes, and understand/learn visual cues, called visual affordances, for affordance recognition, detection and segmentation. To date there is no publicly available large dataset for visual affordance understanding and learning. In this paper, we introduce a large scale multi-view RGBD visual affordance learning dataset, a benchmark of 47210 RGBD images from 37 object categories, annotated with 15 visual affordance categories. To the best of our knowledge, this is the first ever and the largest multi-view RGBD visual affordance learning dataset. We benchmark the proposed dataset for affordance segmentation and recognition tasks using popular Vision Transformer and Convolutional Neural Networks. Several state-of-the-art deep learning networks are evaluated each for affordance recognition and segmentation tasks. Our experimental results showcase the challenging nature of the dataset and present definite prospects for new and robust affordance learning algorithms. The dataset is publicly available at https://sites.google.com/view/afaqshah/dataset. | ['Zeyad Khalifa', 'Syed Afaq Ali Shah'] | 2022-03-26 | null | null | null | null | ['affordance-recognition'] | ['computer-vision'] | [-5.12098661e-04 -3.03561032e-01 -3.85843664e-01 -5.03815472e-01
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225a6a0d-d2c9-4474-bdbb-6df5a57104fd | efficient-framework-for-learning-code | 2009.02731 | null | https://arxiv.org/abs/2009.02731v8 | https://arxiv.org/pdf/2009.02731v8.pdf | Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations | We propose Corder, a self-supervised contrastive learning framework for source code model. Corder is designed to alleviate the need of labeled data for code retrieval and code summarization tasks. The pre-trained model of Corder can be used in two ways: (1) it can produce vector representation of code which can be applied to code retrieval tasks that do not have labeled data; (2) it can be used in a fine-tuning process for tasks that might still require label data such as code summarization. The key innovation is that we train the source code model by asking it to recognize similar and dissimilar code snippets through a contrastive learning objective. To do so, we use a set of semantic-preserving transformation operators to generate code snippets that are syntactically diverse but semantically equivalent. Through extensive experiments, we have shown that the code models pretrained by Corder substantially outperform the other baselines for code-to-code retrieval, text-to-code retrieval, and code-to-text summarization tasks. | ['Yijun Yu', 'Lingxiao Jiang', 'Nghi D. Q. Bui'] | 2020-09-06 | null | null | null | null | ['code-summarization', 'method-name-prediction'] | ['computer-code', 'natural-language-processing'] | [ 3.21567386e-01 1.81034371e-01 -3.85962486e-01 -4.31816489e-01
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3a805482-5f8f-4dbb-974b-4a09bd27244c | character-preserving-coherent-story | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2639_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123620018.pdf | Character-Preserving Coherent Story Visualization | Story visualization aims at generating a sequence of images to narrate each sentence in a multi-sentence story. Different from video generation that focuses on maintaining the continuity of generated images (frames), story visualization emphasizes preserving the global consistency of characters and scenes across different story pictures, which is very challenging since story sentences only provide sparse signals for generating images. Therefore, we propose a new framework named Character-Preserving Coherent Story Visualization (CP-CSV) to tackle the challenges. CP-CSV effectively learns to visualize the story by three critical modules: story and context encoder (story and sentence representation learning), figure-ground segmentation (auxiliary task to provide information for preserving character and story consistency), and figure-ground aware generation (image sequence generation by incorporating figure-ground information). Moreover, we propose a metric named Fr\'{e}chet Story Distance (FSD) to evaluate the performance of story visualization. Extensive experiments demonstrate that CP-CSV maintains the details of character information and achieves high consistency among different frames, while FSD better measures the performance of story visualization. | ['Hong-Han Shuai', 'Huiao-Han Lu', 'Hung-Jen Chen', 'Zhi Rui Tam', 'Yun-Zhu Song'] | null | null | null | null | eccv-2020-8 | ['story-visualization'] | ['computer-vision'] | [ 3.56135041e-01 -2.62034703e-02 2.31947690e-01 -3.50123554e-01
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754095b9-d975-45e0-8b8d-3668973e0a50 | unified-perception-efficient-video-panoptic | 2303.01991 | null | https://arxiv.org/abs/2303.01991v2 | https://arxiv.org/pdf/2303.01991v2.pdf | Unified Perception: Efficient Depth-Aware Video Panoptic Segmentation with Minimal Annotation Costs | Depth-aware video panoptic segmentation is a promising approach to camera based scene understanding. However, the current state-of-the-art methods require costly video annotations and use a complex training pipeline compared to their image-based equivalents. In this paper, we present a new approach titled Unified Perception that achieves state-of-the-art performance without requiring video-based training. Our method employs a simple two-stage cascaded tracking algorithm that (re)uses object embeddings computed in an image-based network. Experimental results on the Cityscapes-DVPS dataset demonstrate that our method achieves an overall DVPQ of 57.1, surpassing state-of-the-art methods. Furthermore, we show that our tracking strategies are effective for long-term object association on KITTI-STEP, achieving an STQ of 59.1 which exceeded the performance of state-of-the-art methods that employ the same backbone network. Code is available at: https://tue-mps.github.io/unipercept | ['Gijs Dubbelman', 'Kurt Stolle'] | 2023-03-03 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 5.71449008e-03 -2.31353462e-01 -3.07184011e-01 -2.84762710e-01
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884d73a8-3fb1-4806-9a7f-f9e7f5d446d6 | ldedit-towards-generalized-text-guided-image | 2210.02249 | null | https://arxiv.org/abs/2210.02249v1 | https://arxiv.org/pdf/2210.02249v1.pdf | LDEdit: Towards Generalized Text Guided Image Manipulation via Latent Diffusion Models | Research in vision-language models has seen rapid developments off-late, enabling natural language-based interfaces for image generation and manipulation. Many existing text guided manipulation techniques are restricted to specific classes of images, and often require fine-tuning to transfer to a different style or domain. Nevertheless, generic image manipulation using a single model with flexible text inputs is highly desirable. Recent work addresses this task by guiding generative models trained on the generic image datasets using pretrained vision-language encoders. While promising, this approach requires expensive optimization for each input. In this work, we propose an optimization-free method for the task of generic image manipulation from text prompts. Our approach exploits recent Latent Diffusion Models (LDM) for text to image generation to achieve zero-shot text guided manipulation. We employ a deterministic forward diffusion in a lower dimensional latent space, and the desired manipulation is achieved by simply providing the target text to condition the reverse diffusion process. We refer to our approach as LDEdit. We demonstrate the applicability of our method on semantic image manipulation and artistic style transfer. Our method can accomplish image manipulation on diverse domains and enables editing multiple attributes in a straightforward fashion. Extensive experiments demonstrate the benefit of our approach over competing baselines. | ['Kanchana Vaishnavi Gandikota', 'Paramanand Chandramouli'] | 2022-10-05 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 7.79407561e-01 -1.28279060e-01 -1.41455680e-01 -4.41870570e-01
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df041d34-5760-43a3-841f-7e384839247d | schema-driven-information-extraction-from | 2305.14336 | null | https://arxiv.org/abs/2305.14336v1 | https://arxiv.org/pdf/2305.14336v1.pdf | Schema-Driven Information Extraction from Heterogeneous Tables | In this paper, we explore the question of whether language models (LLMs) can support cost-efficient information extraction from complex tables. We introduce schema-driven information extraction, a new task that uses LLMs to transform tabular data into structured records following a human-authored schema. To assess various LLM's capabilities on this task, we develop a benchmark composed of tables from three diverse domains: machine learning papers, chemistry tables, and webpages. Accompanying the benchmark, we present InstrucTE, a table extraction method based on instruction-tuned LLMs. This method necessitates only a human-constructed extraction schema, and incorporates an error-recovery strategy. Notably, InstrucTE demonstrates competitive performance without task-specific labels, achieving an F1 score ranging from 72.3 to 95.7. Moreover, we validate the feasibility of distilling more compact table extraction models to minimize extraction costs and reduce API reliance. This study paves the way for the future development of instruction-following models for cost-efficient table extraction. | ['Alan Ritter', 'Dayne Freitag', 'Gabriel Stanovsky', 'Junmo Kang', 'Fan Bai'] | 2023-05-23 | null | null | null | null | ['table-extraction', 'instruction-following'] | ['miscellaneous', 'natural-language-processing'] | [ 3.51650454e-02 3.64656210e-01 -6.81779623e-01 -3.25154185e-01
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24f16ff6-6df3-4b42-8fb3-1cf8fd1a27ba | end-to-end-person-search-sequentially-trained | 2201.09604 | null | https://arxiv.org/abs/2201.09604v1 | https://arxiv.org/pdf/2201.09604v1.pdf | End-to-end Person Search Sequentially Trained on Aggregated Dataset | In video surveillance applications, person search is a challenging task consisting in detecting people and extracting features from their silhouette for re-identification (re-ID) purpose. We propose a new end-to-end model that jointly computes detection and feature extraction steps through a single deep Convolutional Neural Network architecture. Sharing feature maps between the two tasks for jointly describing people commonalities and specificities allows faster runtime, which is valuable in real-world applications. In addition to reaching state-of-the-art accuracy, this multi-task model can be sequentially trained task-by-task, which results in a broader acceptance of input dataset types. Indeed, we show that aggregating more pedestrian detection datasets without costly identity annotations makes the shared feature maps more generic, and improves re-ID precision. Moreover, these boosted shared feature maps result in re-ID features more robust to a cross-dataset scenario. | ['Romaric Audigier', 'Jaonary Rabarisoa', 'Angelique Loesch'] | 2022-01-24 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-1.72225565e-01 -4.37538534e-01 -4.16303426e-02 -4.93994176e-01
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22675a2f-c526-471c-9cda-0e86167fe52b | optical-font-recognition-in-smartphone | 1810.08016 | null | http://arxiv.org/abs/1810.08016v1 | http://arxiv.org/pdf/1810.08016v1.pdf | Optical Font Recognition in Smartphone-Captured Images, and its Applicability for ID Forgery Detection | In this paper, we consider the problem of detecting counterfeit identity
documents in images captured with smartphones. As the number of documents
contain special fonts, we study the applicability of convolutional neural
networks (CNNs) for detection of the conformance of the fonts used with the
ones, corresponding to the government standards. Here, we use multi-task
learning to differentiate samples by both fonts and characters and compare the
resulting classifier with its analogue trained for binary font classification.
We train neural networks for authenticity estimation of the fonts used in
machine-readable zones and ID numbers of the Russian national passport and test
them on samples of individual characters acquired from 3238 images of the
Russian national passport. Our results show that the usage of multi-task
learning increases sensitivity and specificity of the classifier. Moreover, the
resulting CNNs demonstrate high generalization ability as they correctly
classify fonts which were not present in the training set. We conclude that the
proposed method is sufficient for authentication of the fonts and can be used
as a part of the forgery detection system for images acquired with a smartphone
camera. | ['Alexander V. Sheshkus', 'Ekaterina S. Gushchanskaia', 'Yulia S. Chernyshova', 'Mikhail A. Aliev'] | 2018-10-18 | null | null | null | null | ['font-recognition'] | ['computer-vision'] | [ 1.02645680e-01 -5.10188222e-01 2.46639028e-01 -1.50581971e-01
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831b50f1-5a92-403a-adfe-220e404995ae | exploring-generative-models-for-joint | 2208.07130 | null | https://arxiv.org/abs/2208.07130v1 | https://arxiv.org/pdf/2208.07130v1.pdf | Exploring Generative Models for Joint Attribute Value Extraction from Product Titles | Attribute values of the products are an essential component in any e-commerce platform. Attribute Value Extraction (AVE) deals with extracting the attributes of a product and their values from its title or description. In this paper, we propose to tackle the AVE task using generative frameworks. We present two types of generative paradigms, namely, word sequence-based and positional sequence-based, by formulating the AVE task as a generation problem. We conduct experiments on two datasets where the generative approaches achieve the new state-of-the-art results. This shows that we can use the proposed framework for AVE tasks without additional tagging or task-specific model design. | ['Pawan Goyal', 'Tapas Nayak', 'Kalyani Roy'] | 2022-08-15 | null | null | null | null | ['attribute-value-extraction'] | ['natural-language-processing'] | [ 4.54807699e-01 1.87581062e-01 -3.10310662e-01 -6.31135225e-01
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d8dbf4d8-e2bd-496f-bc62-cbdba9d8b906 | deception-detection-with-feature-augmentation | 2305.01011 | null | https://arxiv.org/abs/2305.01011v1 | https://arxiv.org/pdf/2305.01011v1.pdf | Deception Detection with Feature-Augmentation by soft Domain Transfer | In this era of information explosion, deceivers use different domains or mediums of information to exploit the users, such as News, Emails, and Tweets. Although numerous research has been done to detect deception in all these domains, information shortage in a new event necessitates these domains to associate with each other to battle deception. To form this association, we propose a feature augmentation method by harnessing the intermediate layer representation of neural models. Our approaches provide an improvement over the self-domain baseline models by up to 6.60%. We find Tweets to be the most helpful information provider for Fake News and Phishing Email detection, whereas News helps most in Tweet Rumor detection. Our analysis provides a useful insight for domain knowledge transfer which can help build a stronger deception detection system than the existing literature. | ['Omprakash Gnawali', 'Arjun Mukherjee', 'Sadat Shahriar'] | 2023-05-01 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [-2.78197646e-01 -3.02604102e-02 -6.88166559e-01 -3.41989160e-01
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90fabe5c-6406-46fd-9ed6-07e8bd40bdce | counterfactual-explanation-and-instance | 2301.08939 | null | https://arxiv.org/abs/2301.08939v1 | https://arxiv.org/pdf/2301.08939v1.pdf | Counterfactual Explanation and Instance-Generation using Cycle-Consistent Generative Adversarial Networks | The image-based diagnosis is now a vital aspect of modern automation assisted diagnosis. To enable models to produce pixel-level diagnosis, pixel-level ground-truth labels are essentially required. However, since it is often not straight forward to obtain the labels in many application domains such as in medical image, classification-based approaches have become the de facto standard to perform the diagnosis. Though they can identify class-salient regions, they may not be useful for diagnosis where capturing all of the evidences is important requirement. Alternatively, a counterfactual explanation (CX) aims at providing explanations using a casual reasoning process of form "If X has not happend, Y would not heppend". Existing CX approaches, however, use classifier to explain features that can change its predictions. Thus, they can only explain class-salient features, rather than entire object of interest. This hence motivates us to propose a novel CX strategy that is not reliant on image classification. This work is inspired from the recent developments in generative adversarial networks (GANs) based image-to-image domain translation, and leverages to translate an abnormal image to counterpart normal image (i.e. counterfactual instance CI) to find discrepancy maps between the two. Since it is generally not possible to obtain abnormal and normal image pairs, we leverage Cycle-Consistency principle (a.k.a CycleGAN) to perform the translation in unsupervised way. We formulate CX in terms of a discrepancy map that, when added from the abnormal image, will make it indistinguishable from the CI. We evaluate our method on three datasets including a synthetic, tuberculosis and BraTS dataset. All these experiments confirm the supremacy of propose method in generating accurate CX and CI. | ['Shakeeb Murtaza', 'Zeeshan Nisar', 'Tehseen Zia'] | 2023-01-21 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 9.18595433e-01 6.17708027e-01 -1.78604558e-01 -2.46044621e-01
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d4fad827-e2ff-4d53-b088-fa66f300c7ff | argumentative-link-prediction-using-residual | null | null | https://aclanthology.org/W18-5201 | https://aclanthology.org/W18-5201.pdf | Argumentative Link Prediction using Residual Networks and Multi-Objective Learning | We explore the use of residual networks for argumentation mining, with an emphasis on link prediction. The method we propose makes no assumptions on document or argument structure. We evaluate it on a challenging dataset consisting of user-generated comments collected from an online platform. Results show that our model outperforms an equivalent deep network and offers results comparable with state-of-the-art methods that rely on domain knowledge. | ['Andrea Galassi', 'Marco Lippi', 'Paolo Torroni'] | 2018-11-01 | null | null | null | ws-2018-11 | ['component-classification'] | ['natural-language-processing'] | [ 9.35344920e-02 1.00765073e+00 -7.45122433e-01 -2.45826855e-01
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a180f349-3725-4aa7-a98c-e3c3828f4726 | knowledge-based-reasoning-and-learning-under | 2306.00790 | null | https://arxiv.org/abs/2306.00790v1 | https://arxiv.org/pdf/2306.00790v1.pdf | Knowledge-based Reasoning and Learning under Partial Observability in Ad Hoc Teamwork | Ad hoc teamwork refers to the problem of enabling an agent to collaborate with teammates without prior coordination. Data-driven methods represent the state of the art in ad hoc teamwork. They use a large labeled dataset of prior observations to model the behavior of other agent types and to determine the ad hoc agent's behavior. These methods are computationally expensive, lack transparency, and make it difficult to adapt to previously unseen changes, e.g., in team composition. Our recent work introduced an architecture that determined an ad hoc agent's behavior based on non-monotonic logical reasoning with prior commonsense domain knowledge and predictive models of other agents' behavior that were learned from limited examples. In this paper, we substantially expand the architecture's capabilities to support: (a) online selection, adaptation, and learning of the models that predict the other agents' behavior; and (b) collaboration with teammates in the presence of partial observability and limited communication. We illustrate and experimentally evaluate the capabilities of our architecture in two simulated multiagent benchmark domains for ad hoc teamwork: Fort Attack and Half Field Offense. We show that the performance of our architecture is comparable or better than state of the art data-driven baselines in both simple and complex scenarios, particularly in the presence of limited training data, partial observability, and changes in team composition. | ['Mohan Sridharan', 'Hasra Dodampegama'] | 2023-06-01 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [-6.21458627e-02 2.93119073e-01 5.98808303e-02 -3.00020814e-01
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539cd59d-8f1b-484b-b710-d02335904eed | labelprompt-effective-prompt-based-learning | 2302.08068 | null | https://arxiv.org/abs/2302.08068v1 | https://arxiv.org/pdf/2302.08068v1.pdf | LabelPrompt: Effective Prompt-based Learning for Relation Classification | Recently, prompt-based learning has become a very popular solution in many Natural Language Processing (NLP) tasks by inserting a template into model input, which converts the task into a cloze-style one to smoothing out differences between the Pre-trained Language Model (PLM) and the current task. But in the case of relation classification, it is difficult to map the masked output to the relation labels because of its abundant semantic information, e.g. org:founded_by''. Therefore, a pre-trained model still needs enough labelled data to fit the relations. To mitigate this challenge, in this paper, we present a novel prompt-based learning method, namely LabelPrompt, for the relation classification task. It is an extraordinary intuitive approach by a motivation: ``GIVE MODEL CHOICES!''. First, we define some additional tokens to represent the relation labels, which regards these tokens as the verbalizer with semantic initialisation and constructs them with a prompt template method. Then we revisit the inconsistency of the predicted relation and the given entities, an entity-aware module with the thought of contrastive learning is designed to mitigate the problem. At last, we apply an attention query strategy to self-attention layers to resolve two types of tokens, prompt tokens and sequence tokens. The proposed strategy effectively improves the adaptation capability of prompt-based learning in the relation classification task when only a small labelled data is available. Extensive experimental results obtained on several bench-marking datasets demonstrate the superiority of the proposed LabelPrompt method, particularly in the few-shot scenario. | ['XiaoJun Wu', 'Tianyang Xu', 'ZhenHua Feng', 'Xiaoning Song', 'Wenjie Zhang'] | 2023-02-16 | null | null | null | null | ['relation-classification'] | ['natural-language-processing'] | [ 4.45647538e-01 4.66296822e-01 -2.86593139e-01 -5.23176253e-01
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f1468dab-b057-4771-9232-fce02e8b5352 | rethinking-deconvolution-for-2d-human-pose | 2111.04226 | null | https://arxiv.org/abs/2111.04226v1 | https://arxiv.org/pdf/2111.04226v1.pdf | Rethinking Deconvolution for 2D Human Pose Estimation Light yet Accurate Model for Real-time Edge Computing | In this study, we present a pragmatic lightweight pose estimation model. Our model can achieve real-time predictions using low-power embedded devices. This system was found to be very accurate and achieved a 94.5% accuracy of SOTA HRNet 256x192 using a computational cost of only 3.8% on COCO test dataset. Our model adopts an encoder-decoder architecture and is carefully downsized to improve its efficiency. We especially focused on optimizing the deconvolution layers and observed that the channel reduction of the deconvolution layers contributes significantly to reducing computational resource consumption without degrading the accuracy of this system. We also incorporated recent model agnostic techniques such as DarkPose and distillation training to maximize the efficiency of our model. Furthermore, we applied model quantization to exploit multi/mixed precision features. Our FP16'ed model (COCO AP 70.0) operates at ~60-fps on NVIDIA Jetson AGX Xavier and ~200 fps on NVIDIA Quadro RTX6000. | ['Eigo Mori', 'Masayuki Yamazaki'] | 2021-11-08 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-1.59008563e-01 9.61474553e-02 -2.32984945e-02 -4.52360898e-01
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d98a8a65-e2d0-4c66-aafd-93194656faac | fmtfusing-multi-task-convolutional-neural | 2003.00406 | null | https://arxiv.org/abs/2003.00406v1 | https://arxiv.org/pdf/2003.00406v1.pdf | FMT:Fusing Multi-task Convolutional Neural Network for Person Search | Person search is to detect all persons and identify the query persons from detected persons in the image without proposals and bounding boxes, which is different from person re-identification. In this paper, we propose a fusing multi-task convolutional neural network(FMT-CNN) to tackle the correlation and heterogeneity of detection and re-identification with a single convolutional neural network. We focus on how the interplay of person detection and person re-identification affects the overall performance. We employ person labels in region proposal network to produce features for person re-identification and person detection network, which can improve the accuracy of detection and re-identification simultaneously. We also use a multiple loss to train our re-identification network. Experiment results on CUHK-SYSU Person Search dataset show that the performance of our proposed method is superior to state-of-the-art approaches in both mAP and top-1. | ['Xiao Wang', 'Sulan Zhai', 'Jin Tang', 'Shunqiang Liu'] | 2020-03-01 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-2.17364877e-01 -4.99150157e-01 1.73341215e-01 -3.88138235e-01
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e8995b43-46f5-4366-b8c8-443d2797e1fc | applications-of-machine-learning-in-fintech | null | null | https://ieeexplore.ieee.org/document/9491903 | https://ieeexplore.ieee.org/document/9491903 | Applications of Machine Learning in Fintech Credit Card Fraud Detection | Fintech utilizes innovative technology to offer
improved monetary administrations and financial solutions.
According to data from the prediction of Autonomous Research
artificial intelligence (AI) technologies will allow financial
institutions to reduce their operational costs by 22% by 2030.
Throughout this paper, we study how AI and machine learning
algorithms can lead to credit card fraud detection. After making
the theoretical approach to the subject, we develop two different
methods Autoencoder (semi-supervised learning) and Logistic
Regression (supervised learning) for fraud detection with a high
level of accuracy. The results obtained with both methods are
promising as we were able to predict fraud transactions with 94%
certainty. | ['J.', 'Saniie', 'F.', 'Lacruz'] | 2021-07-26 | null | null | null | ieee-international-conference-on-electro | ['fraud-detection'] | ['miscellaneous'] | [-7.26853371e-01 1.85919300e-01 -6.43524081e-02 -4.74191397e-01
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051ae130-0564-4b23-9f44-0dac9fc5d19f | adaptiveweighted-attention-network-with | 2005.09305 | null | https://arxiv.org/abs/2005.09305v1 | https://arxiv.org/pdf/2005.09305v1.pdf | AdaptiveWeighted Attention Network with Camera Spectral Sensitivity Prior for Spectral Reconstruction from RGB Images | Recent promising effort for spectral reconstruction (SR) focuses on learning a complicated mapping through using a deeper and wider convolutional neural networks (CNNs). Nevertheless, most CNN-based SR algorithms neglect to explore the camera spectral sensitivity (CSS) prior and interdependencies among intermediate features, thus limiting the representation ability of the network and performance of SR. To conquer these issues, we propose a novel adaptive weighted attention network (AWAN) for SR, whose backbone is stacked with multiple dual residual attention blocks (DRAB) decorating with long and short skip connections to form the dual residual learning. Concretely, we investigate an adaptive weighted channel attention (AWCA) module to reallocate channel-wise feature responses via integrating correlations between channels. Furthermore, a patch-level second-order non-local (PSNL) module is developed to capture long-range spatial contextual information by second-order non-local operations for more powerful feature representations. Based on the fact that the recovered RGB images can be projected by the reconstructed hyperspectral image (HSI) and the given CSS function, we incorporate the discrepancies of the RGB images and HSIs as a finer constraint for more accurate reconstruction. Experimental results demonstrate the effectiveness of our proposed AWAN network in terms of quantitative comparison and perceptual quality over other state-of-the-art SR methods. In the NTIRE 2020 Spectral Reconstruction Challenge, our entries obtain the 1st ranking on the Clean track and the 3rd place on the Real World track. Codes are available at https://github.com/Deep-imagelab/AWAN. | ['Fei Liu', 'Yunsong Li', 'Jiaojiao Li', 'Chaoxiong Wu', 'Rui Song'] | 2020-05-19 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 5.94715416e-01 -3.72868508e-01 3.08703668e-02 -3.56277436e-01
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932a1e27-ba57-448f-a9c8-5935bfdb00f1 | vietnamese-end-to-end-speech-recognition | null | null | https://github.com/vietai/ASR | https://github.com/vietai/ASR | Vietnamese end-to-end speech recognition using wav2vec 2.0 | Our models are pre-trained on 13k hours of Vietnamese youtube audio (un-label data) and fine-tuned on 250 hours labeled of VLSP ASR dataset on 16kHz sampled speech audio. We use wav2vec2 architecture for the pre-trained model. For fine-tuning phase, wav2vec2 is fine-tuned using Connectionist Temporal Classification (CTC), which is an algorithm that is used to train neural networks for sequence-to-sequence problems and mainly in Automatic Speech Recognition and handwriting recognition. On the Vivos dataset, we achieved a WER score of 6.15 | ['Thai Binh Nguyen'] | 2021-09-02 | null | null | null | https-github-com-vietai-asr-2021-9 | ['handwriting-recognition'] | ['computer-vision'] | [ 1.73363686e-01 -7.09991455e-02 -1.30163521e-01 -5.30971646e-01
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e5ef0347-0a42-40bd-b5ed-6a177eb9727c | effective-few-shot-named-entity-linking-by | 2207.05280 | null | https://arxiv.org/abs/2207.05280v2 | https://arxiv.org/pdf/2207.05280v2.pdf | Effective Few-Shot Named Entity Linking by Meta-Learning | Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base, which is significant and fundamental for various downstream applications, e.g., knowledge base completion, question answering, and information extraction. While great efforts have been devoted to this task, most of these studies follow the assumption that large-scale labeled data is available. However, when the labeled data is insufficient for specific domains due to labor-intensive annotation work, the performance of existing algorithms will suffer an intolerable decline. In this paper, we endeavor to solve the problem of few-shot entity linking, which only requires a minimal amount of in-domain labeled data and is more practical in real situations. Specifically, we firstly propose a novel weak supervision strategy to generate non-trivial synthetic entity-mention pairs based on mention rewriting. Since the quality of the synthetic data has a critical impact on effective model training, we further design a meta-learning mechanism to assign different weights to each synthetic entity-mention pair automatically. Through this way, we can profoundly exploit rich and precious semantic information to derive a well-trained entity linking model under the few-shot setting. The experiments on real-world datasets show that the proposed method can extensively improve the state-of-the-art few-shot entity linking model and achieve impressive performance when only a small amount of labeled data is available. Moreover, we also demonstrate the outstanding ability of the model's transferability. | ['Jianyong Wang', 'Zhiyuan Liu', 'Wei zhang', 'Haitao Yuan', 'Ning Liu', 'Zhengyan Zhang', 'Zhenyu Li', 'Xiuxing Li'] | 2022-07-12 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [ 5.21799065e-02 2.40931362e-01 -4.85963792e-01 -3.08204979e-01
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f319b986-2729-4533-800c-cb17c339cf5f | care-mi-chinese-benchmark-for-misinformation | 2307.01458 | null | https://arxiv.org/abs/2307.01458v1 | https://arxiv.org/pdf/2307.01458v1.pdf | CARE-MI: Chinese Benchmark for Misinformation Evaluation in Maternity and Infant Care | The recent advances in NLP, have led to a new trend of applying LLMs to real-world scenarios. While the latest LLMs are astonishingly fluent when interacting with humans, they suffer from the misinformation problem by unintentionally generating factually false statements. This can lead to harmful consequences, especially when produced within sensitive contexts, such as healthcare. Yet few previous works have focused on evaluating misinformation in the long-form generation of LLMs, especially for knowledge-intensive topics. Moreover, although LLMs have been shown to perform well in different languages, misinformation evaluation has been mostly conducted in English. To this end, we present a benchmark, CARE-MI, for evaluating LLM misinformation in: 1) a sensitive topic, specifically the maternity and infant care domain; and 2) a language other than English, namely Chinese. Most importantly, we provide an innovative paradigm for building long-form generation evaluation benchmarks that can be transferred to other knowledge-intensive domains and low-resourced languages. Our proposed benchmark fills the gap between the extensive usage of LLMs and the lack of datasets for assessing the misinformation generated by these models. It contains 1,612 expert-checked questions, accompanied with human-selected references. Using our benchmark, we conduct extensive experiments and found that current Chinese LLMs are far from perfect in the topic of maternity and infant care. In an effort to minimize the reliance on human resources for performance evaluation, we offer a judgment model for automatically assessing the long-form output of LLMs using the benchmark questions. Moreover, we compare potential solutions for long-form generation evaluation and provide insights for building more robust and efficient automated metric. | ['Noa Garcia', 'Bowen Wang', 'Lu Wei', 'Mingbai Bai', 'Wangyue Li', 'Liangzhi Li', 'Tong Xiang'] | 2023-07-04 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [ 2.98589706e-01 6.51741087e-01 -1.77704450e-02 -4.36842114e-01
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c868aecd-b820-473e-8803-9d903ba81d1e | personalized-embedding-based-e-commerce | 2102.06156 | null | https://arxiv.org/abs/2102.06156v1 | https://arxiv.org/pdf/2102.06156v1.pdf | Personalized Embedding-based e-Commerce Recommendations at eBay | Recommender systems are an essential component of e-commerce marketplaces, helping consumers navigate massive amounts of inventory and find what they need or love. In this paper, we present an approach for generating personalized item recommendations in an e-commerce marketplace by learning to embed items and users in the same vector space. In order to alleviate the considerable cold-start problem present in large marketplaces, item and user embeddings are computed using content features and multi-modal onsite user activity respectively. Data ablation is incorporated into the offline model training process to improve the robustness of the production system. In offline evaluation using a dataset collected from eBay traffic, our approach was able to improve the Recall@k metric over the Recently-Viewed-Item (RVI) method. This approach to generating personalized recommendations has been launched to serve production traffic, and the corresponding scalable engineering architecture is also presented. Initial A/B test results show that compared to the current personalized recommendation module in production, the proposed method increases the surface rate by $\sim$6\% to generate recommendations for 90\% of listing page impressions. | ['Sriganesh Madhvanath', 'Yuri M. Brovman', 'Tian Wang'] | 2021-02-11 | null | null | null | null | ['data-ablation'] | ['computer-vision'] | [-2.07600638e-01 1.19386479e-01 -1.96015149e-01 -5.28210878e-01
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26212bc1-6718-474d-b474-242c785a6563 | fixed-point-quantization-aware-training-for | 2303.02284 | null | https://arxiv.org/abs/2303.02284v1 | https://arxiv.org/pdf/2303.02284v1.pdf | Fixed-point quantization aware training for on-device keyword-spotting | Fixed-point (FXP) inference has proven suitable for embedded devices with limited computational resources, and yet model training is continually performed in floating-point (FLP). FXP training has not been fully explored and the non-trivial conversion from FLP to FXP presents unavoidable performance drop. We propose a novel method to train and obtain FXP convolutional keyword-spotting (KWS) models. We combine our methodology with two quantization-aware-training (QAT) techniques - squashed weight distribution and absolute cosine regularization for model parameters, and propose techniques for extending QAT over transient variables, otherwise neglected by previous paradigms. Experimental results on the Google Speech Commands v2 dataset show that we can reduce model precision up to 4-bit with no loss in accuracy. Furthermore, on an in-house KWS dataset, we show that our 8-bit FXP-QAT models have a 4-6% improvement in relative false discovery rate at fixed false reject rate compared to full precision FLP models. During inference we argue that FXP-QAT eliminates q-format normalization and enables the use of low-bit accumulators while maximizing SIMD throughput to reduce user perceived latency. We demonstrate that we can reduce execution time by 68% without compromising KWS model's predictive performance or requiring model architectural changes. Our work provides novel findings that aid future research in this area and enable accurate and efficient models. | ['Yuzong Liu', 'Sree Hari Krishnan Parthasarathi', 'Santosh Kumar Cheekatmalla', 'Robbie Armitano', 'Francesco Caliva', 'Alex Escott', 'Om Oza', 'Sashank Macha'] | 2023-03-04 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 3.18138003e-01 -2.74410695e-01 -5.14091074e-01 -5.79701483e-01
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c8020946-8754-4af4-a9bd-a75373e6d690 | on-evaluation-of-document-classification | 2306.12550 | null | https://arxiv.org/abs/2306.12550v1 | https://arxiv.org/pdf/2306.12550v1.pdf | On Evaluation of Document Classification using RVL-CDIP | The RVL-CDIP benchmark is widely used for measuring performance on the task of document classification. Despite its widespread use, we reveal several undesirable characteristics of the RVL-CDIP benchmark. These include (1) substantial amounts of label noise, which we estimate to be 8.1% (ranging between 1.6% to 16.9% per document category); (2) presence of many ambiguous or multi-label documents; (3) a large overlap between test and train splits, which can inflate model performance metrics; and (4) presence of sensitive personally-identifiable information like US Social Security numbers (SSNs). We argue that there is a risk in using RVL-CDIP for benchmarking document classifiers, as its limited scope, presence of errors (state-of-the-art models now achieve accuracy error rates that are within our estimated label error rate), and lack of diversity make it less than ideal for benchmarking. We further advocate for the creation of a new document classification benchmark, and provide recommendations for what characteristics such a resource should include. | ['Kevin Leach', 'Gordon Lim', 'Stefan Larson'] | 2023-06-21 | null | null | null | null | ['classification-1', 'benchmarking', 'document-classification', 'benchmarking'] | ['methodology', 'miscellaneous', 'natural-language-processing', 'robots'] | [ 2.55030662e-01 -2.08812267e-01 -3.73379111e-01 -4.89867270e-01
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b8898cff-a0cf-4865-8fbc-4218f9a22ecc | adaptive-modeling-against-adversarial-attacks | 2112.12431 | null | https://arxiv.org/abs/2112.12431v1 | https://arxiv.org/pdf/2112.12431v1.pdf | Adaptive Modeling Against Adversarial Attacks | Adversarial training, the process of training a deep learning model with adversarial data, is one of the most successful adversarial defense methods for deep learning models. We have found that the robustness to white-box attack of an adversarially trained model can be further improved if we fine tune this model in inference stage to adapt to the adversarial input, with the extra information in it. We introduce an algorithm that "post trains" the model at inference stage between the original output class and a "neighbor" class, with existing training data. The accuracy of pre-trained Fast-FGSM CIFAR10 classifier base model against white-box projected gradient attack (PGD) can be significantly improved from 46.8% to 64.5% with our algorithm. | ['Teck Khim Ng', 'Zhiwen Yan'] | 2021-12-23 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 2.68952429e-01 3.36804211e-01 2.51249015e-01 -4.07737672e-01
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0c674e74-a1a0-45a1-8dfb-c859aaf2349d | fast-and-private-submodular-and-k-submodular | 2006.15744 | null | https://arxiv.org/abs/2006.15744v1 | https://arxiv.org/pdf/2006.15744v1.pdf | Fast and Private Submodular and $k$-Submodular Functions Maximization with Matroid Constraints | The problem of maximizing nonnegative monotone submodular functions under a certain constraint has been intensively studied in the last decade, and a wide range of efficient approximation algorithms have been developed for this problem. Many machine learning problems, including data summarization and influence maximization, can be naturally modeled as the problem of maximizing monotone submodular functions. However, when such applications involve sensitive data about individuals, their privacy concerns should be addressed. In this paper, we study the problem of maximizing monotone submodular functions subject to matroid constraints in the framework of differential privacy. We provide $(1-\frac{1}{\mathrm{e}})$-approximation algorithm which improves upon the previous results in terms of approximation guarantee. This is done with an almost cubic number of function evaluations in our algorithm. Moreover, we study $k$-submodularity, a natural generalization of submodularity. We give the first $\frac{1}{2}$-approximation algorithm that preserves differential privacy for maximizing monotone $k$-submodular functions subject to matroid constraints. The approximation ratio is asymptotically tight and is obtained with an almost linear number of function evaluations. | ['Yuichi Yoshida', 'Akbar Rafiey'] | 2020-06-28 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/3576-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/3576-Paper.pdf | icml-2020-1 | ['data-summarization'] | ['miscellaneous'] | [ 2.78650016e-01 5.45779765e-01 -3.20328534e-01 -6.51114881e-01
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9aef55d6-f64f-410f-b305-588d8535edfa | effective-early-stopping-of-point-cloud | 2209.15308 | null | https://arxiv.org/abs/2209.15308v1 | https://arxiv.org/pdf/2209.15308v1.pdf | Effective Early Stopping of Point Cloud Neural Networks | Early stopping techniques can be utilized to decrease the time cost, however currently the ultimate goal of early stopping techniques is closely related to the accuracy upgrade or the ability of the neural network to generalize better on unseen data without being large or complex in structure and not directly with its efficiency. Time efficiency is a critical factor in neural networks, especially when dealing with the segmentation of 3D point cloud data, not only because a neural network itself is computationally expensive, but also because point clouds are large and noisy data, making learning processes even more costly. In this paper, we propose a new early stopping technique based on fundamental mathematics aiming to upgrade the trade-off between the learning efficiency and accuracy of neural networks dealing with 3D point clouds. Our results show that by employing our early stopping technique in four distinct and highly utilized neural networks in segmenting 3D point clouds, the training time efficiency of the models is greatly improved, with efficiency gain values reaching up to 94\%, while the models achieving in just a few epochs approximately similar segmentation accuracy metric values like the ones that are obtained in the training of the neural networks in 200 epochs. Also, our proposal outperforms four conventional early stopping approaches in segmentation accuracy, implying a promising innovative early stopping technique in point cloud segmentation. | ['Anna Puig', 'Maria Salamó', 'Thanasis Zoumpekas'] | 2022-09-30 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 8.31216499e-02 1.02469698e-01 9.95447338e-02 -3.16129476e-01
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9908b552-f504-40ff-8e8d-a8a82a538506 | agent-based-simulators-for-covid-19 | 2209.02887 | null | https://arxiv.org/abs/2209.02887v2 | https://arxiv.org/pdf/2209.02887v2.pdf | Agent based simulators for epidemic modelling: Simulating larger models using smaller ones | Agent-based simulators (ABS) are a popular epidemiological modelling tool to study the impact of various non-pharmaceutical interventions in managing an epidemic in a city (or a region). They provide the flexibility to accurately model a heterogeneous population with time and location varying, person-specific interactions as well as detailed governmental mobility restrictions. Typically, for accuracy, each person is modelled separately. This however may make computational time prohibitive when the city population and the simulated time is large. In this paper, we dig deeper into the underlying probabilistic structure of a generic, locally detailed ABS for epidemiology to arrive at modifications that allow smaller models (models with less number of agents) to give accurate statistics for larger ones, thus substantially speeding up the simulation. We observe that simply considering a smaller aggregate model and scaling up the output leads to inaccuracies. We exploit the observation that in the initial disease spread phase, the starting infections create a family tree of infected individuals more-or-less independent of the other trees and are modelled well as a multi-type super-critical branching process. Further, although this branching process grows exponentially, the relative proportions amongst the population types stabilise quickly. Once enough people have been infected, the future evolution of the epidemic is closely approximated by its mean field limit with a random starting state. We build upon these insights to develop a shifted, scaled and restart-based algorithm that accurately evaluates the ABS's performance using a much smaller model while carefully reducing the bias that may otherwise arise. We apply our algorithm for Covid-19 epidemic in a city and theoretically support the proposed algorithm through an asymptotic analysis where the population size increases to infinity. | ['Shubhada Agrawal', 'Sandeep Juneja', 'Daksh Mittal'] | 2022-09-07 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 1.82601679e-02 1.40868038e-01 7.16087520e-02 3.96266669e-01
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ed669143-5c35-4379-bb0a-fb019abf27d5 | self-supervised-relation-alignment-for-scene | 2302.01403 | null | https://arxiv.org/abs/2302.01403v1 | https://arxiv.org/pdf/2302.01403v1.pdf | Self-Supervised Relation Alignment for Scene Graph Generation | The goal of scene graph generation is to predict a graph from an input image, where nodes correspond to identified and localized objects and edges to their corresponding interaction predicates. Existing methods are trained in a fully supervised manner and focus on message passing mechanisms, loss functions, and/or bias mitigation. In this work we introduce a simple-yet-effective self-supervised relational alignment regularization designed to improve the scene graph generation performance. The proposed alignment is general and can be combined with any existing scene graph generation framework, where it is trained alongside the original model's objective. The alignment is achieved through distillation, where an auxiliary relation prediction branch, that mirrors and shares parameters with the supervised counterpart, is designed. In the auxiliary branch, relational input features are partially masked prior to message passing and predicate prediction. The predictions for masked relations are then aligned with the supervised counterparts after the message passing. We illustrate the effectiveness of this self-supervised relational alignment in conjunction with two scene graph generation architectures, SGTR and Neural Motifs, and show that in both cases we achieve significantly improved performance. | ['Leonid Sigal', 'Renjie Liao', 'Bicheng Xu'] | 2023-02-02 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 9.57400262e-01 8.43632221e-01 -1.70675009e-01 -5.64864039e-01
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7baf59b3-7597-471e-aea6-12ce49be6fd1 | veram-view-enhanced-recurrent-attention-model | 1808.06698 | null | http://arxiv.org/abs/1808.06698v1 | http://arxiv.org/pdf/1808.06698v1.pdf | VERAM: View-Enhanced Recurrent Attention Model for 3D Shape Classification | Multi-view deep neural network is perhaps the most successful approach in 3D
shape classification. However, the fusion of multi-view features based on max
or average pooling lacks a view selection mechanism, limiting its application
in, e.g., multi-view active object recognition by a robot. This paper presents
VERAM, a recurrent attention model capable of actively selecting a sequence of
views for highly accurate 3D shape classification. VERAM addresses an important
issue commonly found in existing attention-based models, i.e., the unbalanced
training of the subnetworks corresponding to next view estimation and shape
classification. The classification subnetwork is easily overfitted while the
view estimation one is usually poorly trained, leading to a suboptimal
classification performance. This is surmounted by three essential
view-enhancement strategies: 1) enhancing the information flow of gradient
backpropagation for the view estimation subnetwork, 2) devising a highly
informative reward function for the reinforcement training of view estimation
and 3) formulating a novel loss function that explicitly circumvents view
duplication. Taking grayscale image as input and AlexNet as CNN architecture,
VERAM with 9 views achieves instance-level and class-level accuracy of 95:5%
and 95:3% on ModelNet10, 93:7% and 92:1% on ModelNet40, both are the
state-of-the-art performance under the same number of views. | ['Zhixin Sun', 'Lintao Zheng', 'Yan Zhang', 'Songle Chen', 'Kai Xu'] | 2018-08-20 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [-5.98839819e-02 1.95603251e-01 -6.95435703e-02 -3.90538037e-01
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3a655929-b0ef-4642-91c1-91e2ea762bb4 | incorporating-features-learned-by-an-enhanced | 1806.03256 | null | http://arxiv.org/abs/1806.03256v1 | http://arxiv.org/pdf/1806.03256v1.pdf | Incorporating Features Learned by an Enhanced Deep Knowledge Tracing Model for STEM/Non-STEM Job Prediction | The 2017 ASSISTments Data Mining competition aims to use data from a
longitudinal study for predicting a brand-new outcome of students which had
never been studied before by the educational data mining research community.
Specifically, it facilitates research in developing predictive models that
predict whether the first job of a student out of college belongs to a STEM
(the acronym for science, technology, engineering, and mathematics) field. This
is based on the student's learning history on the ASSISTments blended learning
platform in the form of extensive clickstream data gathered during the middle
school years. To tackle this challenge, we first estimate the expected
knowledge state of students with respect to different mathematical skills using
a deep knowledge tracing (DKT) model and an enhanced DKT (DKT+) model. We then
combine the features corresponding to the DKT/DKT+ expected knowledge state
with other features extracted directly from the student profile in the dataset
to train several machine learning models for the STEM/non-STEM job prediction.
Our experiments show that models trained with the combined features generally
perform better than the models trained with the student profile alone. Detailed
analysis of the student's knowledge state reveals that, when compared with
non-STEM students, STEM students generally show a higher mastery level and a
higher learning gain in mathematics. | ['Dit-yan Yeung', 'Chun-kit Yeung', 'Zizheng Lin', 'Kai Yang'] | 2018-06-06 | null | null | null | null | ['job-prediction'] | ['natural-language-processing'] | [-2.32349426e-01 5.72050549e-03 -7.31709659e-01 -3.14480543e-01
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5c22ca9a-8607-40ac-a89b-4373077739c7 | a-tutorial-on-thompson-sampling | 1707.02038 | null | https://arxiv.org/abs/1707.02038v3 | https://arxiv.org/pdf/1707.02038v3.pdf | A Tutorial on Thompson Sampling | Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immediate performance and investing to accumulate new information that may improve future performance. The algorithm addresses a broad range of problems in a computationally efficient manner and is therefore enjoying wide use. This tutorial covers the algorithm and its application, illustrating concepts through a range of examples, including Bernoulli bandit problems, shortest path problems, product recommendation, assortment, active learning with neural networks, and reinforcement learning in Markov decision processes. Most of these problems involve complex information structures, where information revealed by taking an action informs beliefs about other actions. We will also discuss when and why Thompson sampling is or is not effective and relations to alternative algorithms. | ['Daniel Russo', 'Benjamin Van Roy', 'Zheng Wen', 'Ian Osband', 'Abbas Kazerouni'] | 2017-07-07 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 5.05077541e-01 3.80603462e-01 -1.16539788e+00 -4.34290886e-01
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dde180e4-c268-44ec-9a45-fe5240f3fe9f | symmetric-nonnegative-matrix-factorization | null | null | https://epubs.siam.org/doi/abs/10.1137/1.9781611972825.10?mobileUi=0 | https://www.cc.gatech.edu/~hpark/papers/DaDingParkSDM12.pdf | Symmetric Nonnegative Matrix Factorization for Graph Clustering | Nonnegative matrix factorization (NMF) provides a lower rank approximation of a nonnegative matrix, and has been successfully used as a clustering method. In this paper, we offer some conceptual understanding for the capabilities and shortcomings of NMF as a clustering method. Then, we propose Symmetric NMF (SymNMF) as a general framework for graph clustering, which inherits the advantages of NMF by enforcing nonnegativity on the clustering assignment matrix. Unlike NMF, however, SymNMF is based on a similarity measure between data points, and factorizes a symmetric matrix containing pairwise similarity values (not necessarily nonnegative). We compare SymNMF with the widely-used spectral clustering methods, and give an intuitive explanation of why SymNMF captures the cluster structure embedded in the graph representation more naturally. In addition, we develop a Newton-like algorithm that exploits second-order information efficiently, so as to show the feasibility of SymNMF as a practical framework for graph clustering. Our experiments on artificial graph data, text data, and image data demonstrate the substantially enhanced clustering quality of SymNMF over spectral clustering and NMF. Therefore, SymNMF is able to achieve better clustering results on both linear and nonlinear manifolds, and serves as a potential basis for many extensions and applications. | ['Da Kuang', 'Chris Ding', 'Haesun Park'] | 2012-02-27 | null | null | null | sdm-2012-2 | ['spectral-graph-clustering'] | ['graphs'] | [-9.13493186e-02 -1.86601117e-01 -2.02230990e-01 -1.94979347e-02
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d425ded3-9f7d-47d2-b3f0-c10a06996091 | tree-decomposed-graph-neural-network | 2108.11022 | null | https://arxiv.org/abs/2108.11022v1 | https://arxiv.org/pdf/2108.11022v1.pdf | Tree Decomposed Graph Neural Network | Graph Neural Networks (GNNs) have achieved significant success in learning better representations by performing feature propagation and transformation iteratively to leverage neighborhood information. Nevertheless, iterative propagation restricts the information of higher-layer neighborhoods to be transported through and fused with the lower-layer neighborhoods', which unavoidably results in feature smoothing between neighborhoods in different layers and can thus compromise the performance, especially on heterophily networks. Furthermore, most deep GNNs only recognize the importance of higher-layer neighborhoods while yet to fully explore the importance of multi-hop dependency within the context of different layer neighborhoods in learning better representations. In this work, we first theoretically analyze the feature smoothing between neighborhoods in different layers and empirically demonstrate the variance of the homophily level across neighborhoods at different layers. Motivated by these analyses, we further propose a tree decomposition method to disentangle neighborhoods in different layers to alleviate feature smoothing among these layers. Moreover, we characterize the multi-hop dependency via graph diffusion within our tree decomposition formulation to construct Tree Decomposed Graph Neural Network (TDGNN), which can flexibly incorporate information from large receptive fields and aggregate this information utilizing the multi-hop dependency. Comprehensive experiments demonstrate the superior performance of TDGNN on both homophily and heterophily networks under a variety of node classification settings. Extensive parameter analysis highlights the ability of TDGNN to prevent over-smoothing and incorporate features from shallow layers with deeper multi-hop dependencies, which provides new insights towards deeper graph neural networks. Code of TDGNN: http://github.com/YuWVandy/TDGNN | ['Tyler Derr', 'Yu Wang'] | 2021-08-25 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [-5.81429414e-02 3.97462770e-02 -2.46055856e-01 -2.21945196e-01
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17560032-dcfc-4f63-b2bd-10ea7d9cdc5e | benchmark-performance-of-machine-and-deep | 2003.01345 | null | https://arxiv.org/abs/2003.01345v1 | https://arxiv.org/pdf/2003.01345v1.pdf | Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification | In order to provide benchmark performance for Urdu text document classification, the contribution of this paper is manifold. First, it pro-vides a publicly available benchmark dataset manually tagged against 6 classes. Second, it investigates the performance impact of traditional machine learning based Urdu text document classification methodologies by embedding 10 filter-based feature selection algorithms which have been widely used for other languages. Third, for the very first time, it as-sesses the performance of various deep learning based methodologies for Urdu text document classification. In this regard, for experimentation, we adapt 10 deep learning classification methodologies which have pro-duced best performance figures for English text classification. Fourth, it also investigates the performance impact of transfer learning by utiliz-ing Bidirectional Encoder Representations from Transformers approach for Urdu language. Fifth, it evaluates the integrity of a hybrid approach which combines traditional machine learning based feature engineering and deep learning based automated feature engineering. Experimental results show that feature selection approach named as Normalised Dif-ference Measure along with Support Vector Machine outshines state-of-the-art performance on two closed source benchmark datasets CLE Urdu Digest 1000k, and CLE Urdu Digest 1Million with a significant margin of 32%, and 13% respectively. Across all three datasets, Normalised Differ-ence Measure outperforms other filter based feature selection algorithms as it significantly uplifts the performance of all adopted machine learning, deep learning, and hybrid approaches. The source code and presented dataset are available at Github repository. | ['Muhammad Usman Ghani', 'Muhammad Ali Ibrahim', 'Waqar Mahmood', 'Sheraz Ahmad', 'Muhammad Nabeel Asim', 'Andreas Dengel'] | 2020-03-03 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [-2.20737115e-01 -2.91516840e-01 -2.19404012e-01 -2.60882884e-01
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043d75aa-48c8-4943-b3a4-49a34fab70b6 | pishgu-universal-path-prediction-architecture | 2210.08057 | null | https://arxiv.org/abs/2210.08057v3 | https://arxiv.org/pdf/2210.08057v3.pdf | Pishgu: Universal Path Prediction Network Architecture for Real-time Cyber-physical Edge Systems | Path prediction is an essential task for many real-world Cyber-Physical Systems (CPS) applications, from autonomous driving and traffic monitoring/management to pedestrian/worker safety. These real-world CPS applications need a robust, lightweight path prediction that can provide a universal network architecture for multiple subjects (e.g., pedestrians and vehicles) from different perspectives. However, most existing algorithms are tailor-made for a unique subject with a specific camera perspective and scenario. This article presents Pishgu, a universal lightweight network architecture, as a robust and holistic solution for path prediction. Pishgu's architecture can adapt to multiple path prediction domains with different subjects (vehicles, pedestrians), perspectives (bird's-eye, high-angle), and scenes (sidewalk, highway). Our proposed architecture captures the inter-dependencies within the subjects in each frame by taking advantage of Graph Isomorphism Networks and the attention module. We separately train and evaluate the efficacy of our architecture on three different CPS domains across multiple perspectives (vehicle bird's-eye view, pedestrian bird's-eye view, and human high-angle view). Pishgu outperforms state-of-the-art solutions in the vehicle bird's-eye view domain by 42% and 61% and pedestrian high-angle view domain by 23% and 22% in terms of ADE and FDE, respectively. Additionally, we analyze the domain-specific details for various datasets to understand their effect on path prediction and model interpretation. Finally, we report the latency and throughput for all three domains on multiple embedded platforms showcasing the robustness and adaptability of Pishgu for real-world integration into CPS applications. | ['Hamed Tabkhi', 'Christopher Neff', 'Armin Danesh Pazho', 'Vinit Katariya', 'Ghazal Alinezhad Noghre'] | 2022-10-14 | null | null | null | null | ['trajectory-forecasting'] | ['computer-vision'] | [-3.35249841e-01 -7.73958340e-02 -2.65720248e-01 -2.95197487e-01
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d4582b76-4b98-4cce-ae42-a23a1fb09d79 | clusterfug-clustering-fully-connected-graphs | 2301.12159 | null | https://arxiv.org/abs/2301.12159v2 | https://arxiv.org/pdf/2301.12159v2.pdf | ClusterFuG: Clustering Fully connected Graphs by Multicut | We propose a graph clustering formulation based on multicut (a.k.a. weighted correlation clustering) on the complete graph. Our formulation does not need specification of the graph topology as in the original sparse formulation of multicut, making our approach simpler and potentially better performing. In contrast to unweighted correlation clustering we allow for a more expressive weighted cost structure. In dense multicut, the clustering objective is given in a factorized form as inner products of node feature vectors. This allows for an efficient formulation and inference in contrast to multicut/weighted correlation clustering, which has at least quadratic representation and computation complexity when working on the complete graph. We show how to rewrite classical greedy algorithms for multicut in our dense setting and how to modify them for greater efficiency and solution quality. In particular, our algorithms scale to graphs with tens of thousands of nodes. Empirical evidence on instance segmentation on Cityscapes and clustering of ImageNet datasets shows the merits of our approach. | ['Paul Swoboda', 'Ahmed Abbas'] | 2023-01-28 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 1.29375653e-02 3.62685680e-01 -1.18265308e-01 -1.80014610e-01
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609a93fb-df99-4e29-a30e-3d1ba7310ea2 | accurate-protein-structure-prediction-by | 1911.05531 | null | https://arxiv.org/abs/1911.05531v1 | https://arxiv.org/pdf/1911.05531v1.pdf | Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations | Proteins are the major building blocks of life, and actuators of almost all chemical and biophysical events in living organisms. Their native structures in turn enable their biological functions which have a fundamental role in drug design. This motivates predicting the structure of a protein from its sequence of amino acids, a fundamental problem in computational biology. In this work, we demonstrate state-of-the-art protein structure prediction (PSP) results using embeddings and deep learning models for prediction of backbone atom distance matrices and torsion angles. We recover 3D coordinates of backbone atoms and reconstruct full atom protein by optimization. We create a new gold standard dataset of proteins which is comprehensive and easy to use. Our dataset consists of amino acid sequences, Q8 secondary structures, position specific scoring matrices, multiple sequence alignment co-evolutionary features, backbone atom distance matrices, torsion angles, and 3D coordinates. We evaluate the quality of our structure prediction by RMSD on the latest Critical Assessment of Techniques for Protein Structure Prediction (CASP) test data and demonstrate competitive results with the winning teams and AlphaFold in CASP13 and supersede the results of the winning teams in CASP12. We make our data, models, and code publicly available. | ["Itsik Pe'er", 'Weilong Fu', 'Linyong Nan', 'Yuan Gao', 'Mohammed AlQuraishi', 'Jinhao Lei', 'Fan Wu', 'Yueqi Wang', 'Weiyi Lu', 'Sashank Karri', 'Iddo Drori', 'Dimitri Leggas', 'Darshan Thaker', 'Chen Keasar', 'Anand Kannan', 'Daniel Jeong', 'Arjun Srivatsa', 'Antonio Moretti'] | 2019-11-09 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 1.69647902e-01 -3.19464691e-02 -3.00369322e-01 -3.41628075e-01
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be1f2b95-7539-4c5d-951d-cc04b602246b | temporalmaxer-maximize-temporal-context-with | 2303.09055 | null | https://arxiv.org/abs/2303.09055v1 | https://arxiv.org/pdf/2303.09055v1.pdf | TemporalMaxer: Maximize Temporal Context with only Max Pooling for Temporal Action Localization | Temporal Action Localization (TAL) is a challenging task in video understanding that aims to identify and localize actions within a video sequence. Recent studies have emphasized the importance of applying long-term temporal context modeling (TCM) blocks to the extracted video clip features such as employing complex self-attention mechanisms. In this paper, we present the simplest method ever to address this task and argue that the extracted video clip features are already informative to achieve outstanding performance without sophisticated architectures. To this end, we introduce TemporalMaxer, which minimizes long-term temporal context modeling while maximizing information from the extracted video clip features with a basic, parameter-free, and local region operating max-pooling block. Picking out only the most critical information for adjacent and local clip embeddings, this block results in a more efficient TAL model. We demonstrate that TemporalMaxer outperforms other state-of-the-art methods that utilize long-term TCM such as self-attention on various TAL datasets while requiring significantly fewer parameters and computational resources. The code for our approach is publicly available at https://github.com/TuanTNG/TemporalMaxer | ['Kwanghoon Sohn', 'Kwonyoung Kim', 'Tuan N. Tang'] | 2023-03-16 | null | null | null | null | ['video-understanding', 'action-localization'] | ['computer-vision', 'computer-vision'] | [ 1.68950126e-01 -2.55932063e-01 -3.82218540e-01 -2.02277333e-01
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d2848e18-76f9-4a19-b4df-8d329a1f63a2 | probabilistically-robust-optimization-of-irs | 2201.13151 | null | https://arxiv.org/abs/2201.13151v1 | https://arxiv.org/pdf/2201.13151v1.pdf | Probabilistically Robust Optimization of IRS-aided SWIPT Under Coordinated Spectrum Underlay | This study considers the Joint Transmit/Reflect Beamforming and Power Splitting (JTRBPS) optimization problem in a spectrum underlay setting, such that the transmit sum-energy of the intelligent reflecting surface (IRS)-aided secondary transmitter (ST) is minimized subject to the quality-of-service requirements of the PS-simultaneous wireless information and power transfer (SWIPT) secondary receivers and the interference constraints of the primary receivers (PR). The interference at the PRs caused by the reception of IRS-reflected signals sent by the primary transmitter is taken into account. A coordinated channel state information (CSI) acquisition protocol is proposed. Next, assuming availability at the ST of perfect CSI for all direct and IRS-cascaded transmitter--receiver channels, two penalty-based iterative algorithms are developed: an alternating minimization algorithm that involves semi-definite relaxation in JTBPS design and successive convex approximation in RB optimization, and a block coordinate descent algorithm that employs the Riemannian conjugate gradient algorithm in RB updates. Finally, an outage-constrained robust design under imperfect CSI is devised. Numerical simulations highlight the performance gains of the proposed strategies over benchmarks, corroborate the benefits of using an IRS, and provide valuable insights. | ['Ioannis Krikidis', 'Konstantinos Ntougias'] | 2022-01-31 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 7.01817691e-01 6.04465365e-01 -1.65523574e-01 2.12014616e-02
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eb937ba6-f7dc-44c4-aa52-ff1f64776ce7 | statistical-component-separation-for-targeted | 2306.15012 | null | https://arxiv.org/abs/2306.15012v1 | https://arxiv.org/pdf/2306.15012v1.pdf | Statistical Component Separation for Targeted Signal Recovery in Noisy Mixtures | Separating signals from an additive mixture may be an unnecessarily hard problem when one is only interested in specific properties of a given signal. In this work, we tackle simpler "statistical component separation" problems that focus on recovering a predefined set of statistical descriptors of a target signal from a noisy mixture. Assuming access to samples of the noise process, we investigate a method devised to match the statistics of the solution candidate corrupted by noise samples with those of the observed mixture. We first analyze the behavior of this method using simple examples with analytically tractable calculations. Then, we apply it in an image denoising context employing 1) wavelet-based descriptors, 2) ConvNet-based descriptors on astrophysics and ImageNet data. In the case of 1), we show that our method better recovers the descriptors of the target data than a standard denoising method in most situations. Additionally, despite not constructed for this purpose, it performs surprisingly well in terms of peak signal-to-noise ratio on full signal reconstruction. In comparison, representation 2) appears less suitable for image denoising. Finally, we extend this method by introducing a diffusive stepwise algorithm which gives a new perspective to the initial method and leads to promising results for image denoising under specific circumstances. | ['Michael Eickenberg', 'Bruno Régaldo-Saint Blancard'] | 2023-06-26 | null | null | null | null | ['image-denoising'] | ['computer-vision'] | [ 4.26694363e-01 -2.77409494e-01 5.46069264e-01 -9.34648588e-02
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4b4b85e8-3154-473d-8f3d-5859faa753f8 | explainable-predictive-process-monitoring | 2008.01807 | null | https://arxiv.org/abs/2008.01807v2 | https://arxiv.org/pdf/2008.01807v2.pdf | Explainable Predictive Process Monitoring | Predictive Business Process Monitoring is becoming an essential aid for organizations, providing online operational support of their processes. This paper tackles the fundamental problem of equipping predictive business process monitoring with explanation capabilities, so that not only the what but also the why is reported when predicting generic KPIs like remaining time, or activity execution. We use the game theory of Shapley Values to obtain robust explanations of the predictions. The approach has been implemented and tested on real-life benchmarks, showing for the first time how explanations can be given in the field of predictive business process monitoring. | ['Nicolò Navarin', 'Bernat Coma-Puig', 'Josep Carmona', 'Riccardo Galanti', 'Massimiliano de Leoni'] | 2020-08-04 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 2.89279193e-01 7.36868382e-01 -5.88543154e-02 -1.81858882e-01
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faee39eb-e6c3-48d0-a5ed-cc406933e840 | unified-multi-intent-order-and-slot | null | null | https://aclanthology.org/2020.icon-workshop.2 | https://aclanthology.org/2020.icon-workshop.2.pdf | Unified Multi Intent Order and Slot Prediction using Selective Learning Propagation | Natural Language Understanding (NLU) involves two important task namely Intent Determination(ID) and Slot Filling (SF). With recent advancements in Intent Determination and Slot Filling tasks, explorations on handling of multiple intent information in a single utterance is increasing to make the NLU more conversation-based rather than command execution-based. Many have proven this task with huge multi-intent training data. In addition, lots of research have addressed multi intent problem only. The problem of multi intent also poses the challenge of addressing the order of execution of intents found. Hence, we are proposing a unified architecture to address multi-intent detection, associated slotsdetection and order of execution of found intents using low proportion multi-intent corpusin the training data. This architecture consists of Multi Word Importance relation propagator using Multi-Head GRU and Importance learner propagator module using self-attention. This architecture has beaten state-of-the-art by 2.58% on the MultiIntentData dataset. | ['Divya Verma Gogoi', 'Kritika Yadav', 'Priyank Chhipa', 'Bharatram Natarajan'] | null | null | null | null | icon-2020-12 | ['slot-filling'] | ['natural-language-processing'] | [ 3.94197941e-01 4.23671752e-01 -5.03351510e-01 -4.48576987e-01
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3280ca67-0220-4d54-a05d-94a04aa4ad17 | loopy-a-research-friendly-mix-framework-for | 2305.01051 | null | https://arxiv.org/abs/2305.01051v1 | https://arxiv.org/pdf/2305.01051v1.pdf | LooPy: A Research-Friendly Mix Framework for Music Information Retrieval on Electronic Dance Music | Music information retrieval (MIR) has gone through an explosive development with the advancement of deep learning in recent years. However, music genres like electronic dance music (EDM) has always been relatively less investigated compared to others. Considering its wide range of applications, we present a Python package for automated EDM audio generation as an infrastructure for MIR for EDM songs, to mitigate the difficulty of acquiring labelled data. It is a convenient tool that could be easily concatenated to the end of many symbolic music generation pipelines. Inside this package, we provide a framework to build professional-level templates that could render a well-produced track from specified melody and chords, or produce massive tracks given only a specific key by our probabilistic symbolic melody generator. Experiments show that our mixes could achieve the same quality of the original reference songs produced by world-famous artists, with respect to both subjective and objective criteria. Our code is accessible in this repository: https://github.com/Gariscat/loopy and the official site of the project is also online https://loopy4edm.com . | ['Xinyu Li'] | 2023-05-01 | null | null | null | null | ['audio-generation', 'music-generation', 'music-generation', 'music-information-retrieval'] | ['audio', 'audio', 'music', 'music'] | [ 2.18589325e-02 -1.75588623e-01 8.08369592e-02 7.18598142e-02
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ada25be3-7780-4aab-960e-673008c1c7a4 | multi-compartment-neuron-and-population | 2301.07275 | null | https://arxiv.org/abs/2301.07275v1 | https://arxiv.org/pdf/2301.07275v1.pdf | Multi-compartment Neuron and Population Encoding improved Spiking Neural Network for Deep Distributional Reinforcement Learning | Inspired by the information processing with binary spikes in the brain, the spiking neural networks (SNNs) exhibit significant low energy consumption and are more suitable for incorporating multi-scale biological characteristics. Spiking Neurons, as the basic information processing unit of SNNs, are often simplified in most SNNs which only consider LIF point neuron and do not take into account the multi-compartmental structural properties of biological neurons. This limits the computational and learning capabilities of SNNs. In this paper, we proposed a brain-inspired SNN-based deep distributional reinforcement learning algorithm with combination of bio-inspired multi-compartment neuron (MCN) model and population coding method. The proposed multi-compartment neuron built the structure and function of apical dendritic, basal dendritic, and somatic computing compartments to achieve the computational power close to that of biological neurons. Besides, we present an implicit fractional embedding method based on spiking neuron population encoding. We tested our model on Atari games, and the experiment results show that the performance of our model surpasses the vanilla ANN-based FQF model and ANN-SNN conversion method based Spiking-FQF models. The ablation experiments show that the proposed multi-compartment neural model and quantile fraction implicit population spike representation play an important role in realizing SNN-based deep distributional reinforcement learning. | ['Zhuoya Zhao', 'Feifei Zhao', 'Yi Zeng', 'Yinqian Sun'] | 2023-01-18 | null | null | null | null | ['distributional-reinforcement-learning', 'atari-games'] | ['methodology', 'playing-games'] | [ 1.71765629e-02 -3.83690506e-01 3.01859289e-01 2.22102165e-01
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413df58e-3d3c-4f67-b059-f17c0e01a51e | non-local-color-image-denoising-with | 1611.06757 | null | http://arxiv.org/abs/1611.06757v2 | http://arxiv.org/pdf/1611.06757v2.pdf | Non-Local Color Image Denoising with Convolutional Neural Networks | We propose a novel deep network architecture for grayscale and color image
denoising that is based on a non-local image model. Our motivation for the
overall design of the proposed network stems from variational methods that
exploit the inherent non-local self-similarity property of natural images. We
build on this concept and introduce deep networks that perform non-local
processing and at the same time they significantly benefit from discriminative
learning. Experiments on the Berkeley segmentation dataset, comparing several
state-of-the-art methods, show that the proposed non-local models achieve the
best reported denoising performance both for grayscale and color images for all
the tested noise levels. It is also worth noting that this increase in
performance comes at no extra cost on the capacity of the network compared to
existing alternative deep network architectures. In addition, we highlight a
direct link of the proposed non-local models to convolutional neural networks.
This connection is of significant importance since it allows our models to take
full advantage of the latest advances on GPU computing in deep learning and
makes them amenable to efficient implementations through their inherent
parallelism. | ['Stamatios Lefkimmiatis'] | 2016-11-21 | non-local-color-image-denoising-with-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Lefkimmiatis_Non-Local_Color_Image_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Lefkimmiatis_Non-Local_Color_Image_CVPR_2017_paper.pdf | cvpr-2017-7 | ['color-image-denoising'] | ['computer-vision'] | [-5.93549525e-03 -2.38123655e-01 3.37635487e-01 -4.61188734e-01
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0a14f754-0065-4f48-91de-8a3e9a7a0693 | rethinking-generalization-in-few-shot-1 | 2206.07267 | null | https://arxiv.org/abs/2206.07267v3 | https://arxiv.org/pdf/2206.07267v3.pdf | Rethinking Generalization in Few-Shot Classification | Single image-level annotations only correctly describe an often small subset of an image's content, particularly when complex real-world scenes are depicted. While this might be acceptable in many classification scenarios, it poses a significant challenge for applications where the set of classes differs significantly between training and test time. In this paper, we take a closer look at the implications in the context of $\textit{few-shot learning}$. Splitting the input samples into patches and encoding these via the help of Vision Transformers allows us to establish semantic correspondences between local regions across images and independent of their respective class. The most informative patch embeddings for the task at hand are then determined as a function of the support set via online optimization at inference time, additionally providing visual interpretability of `$\textit{what matters most}$' in the image. We build on recent advances in unsupervised training of networks via masked image modelling to overcome the lack of fine-grained labels and learn the more general statistical structure of the data while avoiding negative image-level annotation influence, $\textit{aka}$ supervision collapse. Experimental results show the competitiveness of our approach, achieving new state-of-the-art results on four popular few-shot classification benchmarks for $5$-shot and $1$-shot scenarios. | ['Tom Drummond', 'Mehrtash Harandi', 'Rongkai Ma', 'Markus Hiller'] | 2022-06-15 | rethinking-generalization-in-few-shot | https://arxiv.org/abs/2206.07267 | https://arxiv.org/pdf/2206.07267.pdf | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 6.17375672e-01 5.57605550e-02 -3.10178488e-01 -5.92363119e-01
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954ee451-7e92-4adf-b23b-ecbb36c683b2 | changing-the-representation-examining-1 | 2210.06312 | null | https://arxiv.org/abs/2210.06312v1 | https://arxiv.org/pdf/2210.06312v1.pdf | Changing the Representation: Examining Language Representation for Neural Sign Language Production | Neural Sign Language Production (SLP) aims to automatically translate from spoken language sentences to sign language videos. Historically the SLP task has been broken into two steps; Firstly, translating from a spoken language sentence to a gloss sequence and secondly, producing a sign language video given a sequence of glosses. In this paper we apply Natural Language Processing techniques to the first step of the SLP pipeline. We use language models such as BERT and Word2Vec to create better sentence level embeddings, and apply several tokenization techniques, demonstrating how these improve performance on the low resource translation task of Text to Gloss. We introduce Text to HamNoSys (T2H) translation, and show the advantages of using a phonetic representation for sign language translation rather than a sign level gloss representation. Furthermore, we use HamNoSys to extract the hand shape of a sign and use this as additional supervision during training, further increasing the performance on T2H. Assembling best practise, we achieve a BLEU-4 score of 26.99 on the MineDGS dataset and 25.09 on PHOENIX14T, two new state-of-the-art baselines. | ['Richard Bowden', 'Ben Saunders', 'Harry Walsh'] | 2022-09-16 | changing-the-representation-examining | https://aclanthology.org/2022.sltat-1.18 | https://aclanthology.org/2022.sltat-1.18.pdf | sltat-lrec-2022-6 | ['sign-language-translation', 'sign-language-production'] | ['computer-vision', 'natural-language-processing'] | [ 5.35529792e-01 1.00689232e-02 -5.67349531e-02 -4.88178760e-01
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bc372d7c-2060-488a-9f4f-68be74de6fa3 | an-interpretable-determinantal-choice-model | 2302.11477 | null | https://arxiv.org/abs/2302.11477v1 | https://arxiv.org/pdf/2302.11477v1.pdf | An Interpretable Determinantal Choice Model for Subset Selection | Understanding how subsets of items are chosen from offered sets is critical to assortment planning, wireless network planning, and many other applications. There are two seemingly unrelated subset choice models that capture dependencies between items: intuitive and interpretable random utility models; and tractable determinantal point processes (DPPs). This paper connects the two. First, all DPPs are shown to be random utility models. Next, a determinantal choice model that enjoys the best of both worlds is specified; the model is shown to subsume logistic regression when dependence is minimal, and MNL when dependence is maximally negative. This makes the model interpretable, while retaining the tractability of DPPs. A simulation study verifies that the model can learn a continuum of negative dependencies from data, and an applied study using original experimental data produces novel insights on wireless interference in LoRa networks. | ['Alex Coy', 'David B. Shmoys', 'Sander Aarts'] | 2023-02-22 | null | null | null | null | ['point-processes'] | ['methodology'] | [ 1.31984904e-01 9.28756222e-02 -6.08980000e-01 -5.01427233e-01
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5b0e7c4a-568f-4445-9ef7-0a6316dbd0e5 | perturbation-inactivation-based-adversarial | 2207.06035 | null | https://arxiv.org/abs/2207.06035v1 | https://arxiv.org/pdf/2207.06035v1.pdf | Perturbation Inactivation Based Adversarial Defense for Face Recognition | Deep learning-based face recognition models are vulnerable to adversarial attacks. To curb these attacks, most defense methods aim to improve the robustness of recognition models against adversarial perturbations. However, the generalization capacities of these methods are quite limited. In practice, they are still vulnerable to unseen adversarial attacks. Deep learning models are fairly robust to general perturbations, such as Gaussian noises. A straightforward approach is to inactivate the adversarial perturbations so that they can be easily handled as general perturbations. In this paper, a plug-and-play adversarial defense method, named perturbation inactivation (PIN), is proposed to inactivate adversarial perturbations for adversarial defense. We discover that the perturbations in different subspaces have different influences on the recognition model. There should be a subspace, called the immune space, in which the perturbations have fewer adverse impacts on the recognition model than in other subspaces. Hence, our method estimates the immune space and inactivates the adversarial perturbations by restricting them to this subspace. The proposed method can be generalized to unseen adversarial perturbations since it does not rely on a specific kind of adversarial attack method. This approach not only outperforms several state-of-the-art adversarial defense methods but also demonstrates a superior generalization capacity through exhaustive experiments. Moreover, the proposed method can be successfully applied to four commercial APIs without additional training, indicating that it can be easily generalized to existing face recognition systems. The source code is available at https://github.com/RenMin1991/Perturbation-Inactivate | ['Zhenan Sun', 'Yunlong Wang', 'Yuhao Zhu', 'Min Ren'] | 2022-07-13 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 1.57175928e-01 -1.57365814e-01 -8.99541937e-03 -1.23514697e-01
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d83da0af-8ad6-4b9c-a20b-2a89e17ac4f4 | rethink-darts-search-space-and-renovate-a-new | 2306.06852 | null | https://arxiv.org/abs/2306.06852v1 | https://arxiv.org/pdf/2306.06852v1.pdf | Rethink DARTS Search Space and Renovate a New Benchmark | DARTS search space (DSS) has become a canonical benchmark for NAS whereas some emerging works pointed out the issue of narrow accuracy range and claimed it would hurt the method ranking. We observe some recent studies already suffer from this issue that overshadows the meaning of scores. In this work, we first propose and orchestrate a suite of improvements to frame a larger and harder DSS, termed LHD, while retaining high efficiency in search. We step forward to renovate a LHD-based new benchmark, taking care of both discernibility and accessibility. Specifically, we re-implement twelve baselines and evaluate them across twelve conditions by combining two underexpolored influential factors: transductive robustness and discretization policy, to reasonably construct a benchmark upon multi-condition evaluation. Considering that the tabular benchmarks are always insufficient to adequately evaluate the methods of neural architecture search (NAS), our work can serve as a crucial basis for the future progress of NAS. https://github.com/chaoji90/LHD | ['Zhiming Ding', 'Jiuling Zhang'] | 2023-06-12 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 1.69016093e-01 -2.86035240e-01 -3.60797524e-01 -2.41164133e-01
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165cac9b-6e9a-4d81-bbc8-95291560830e | influence-based-mini-batching-for-graph | 2212.09083 | null | https://arxiv.org/abs/2212.09083v1 | https://arxiv.org/pdf/2212.09083v1.pdf | Influence-Based Mini-Batching for Graph Neural Networks | Using graph neural networks for large graphs is challenging since there is no clear way of constructing mini-batches. To solve this, previous methods have relied on sampling or graph clustering. While these approaches often lead to good training convergence, they introduce significant overhead due to expensive random data accesses and perform poorly during inference. In this work we instead focus on model behavior during inference. We theoretically model batch construction via maximizing the influence score of nodes on the outputs. This formulation leads to optimal approximation of the output when we do not have knowledge of the trained model. We call the resulting method influence-based mini-batching (IBMB). IBMB accelerates inference by up to 130x compared to previous methods that reach similar accuracy. Remarkably, with adaptive optimization and the right training schedule IBMB can also substantially accelerate training, thanks to precomputed batches and consecutive memory accesses. This results in up to 18x faster training per epoch and up to 17x faster convergence per runtime compared to previous methods. | ['Stephan Günnemann', 'Chendi Qian', 'Johannes Gasteiger'] | 2022-12-18 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 6.80489168e-02 4.01959091e-01 -2.71827251e-01 -4.18945223e-01
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2ed0946d-ef2b-4888-bf93-d4140eac579a | dilated-scale-aware-attention-convnet-for | 2012.08149 | null | https://arxiv.org/abs/2012.08149v1 | https://arxiv.org/pdf/2012.08149v1.pdf | Dilated-Scale-Aware Attention ConvNet For Multi-Class Object Counting | Object counting aims to estimate the number of objects in images. The leading counting approaches focus on the single category counting task and achieve impressive performance. Note that there are multiple categories of objects in real scenes. Multi-class object counting expands the scope of application of object counting task. The multi-target detection task can achieve multi-class object counting in some scenarios. However, it requires the dataset annotated with bounding boxes. Compared with the point annotations in mainstream object counting issues, the coordinate box-level annotations are more difficult to obtain. In this paper, we propose a simple yet efficient counting network based on point-level annotations. Specifically, we first change the traditional output channel from one to the number of categories to achieve multiclass counting. Since all categories of objects use the same feature extractor in our proposed framework, their features will interfere mutually in the shared feature space. We further design a multi-mask structure to suppress harmful interaction among objects. Extensive experiments on the challenging benchmarks illustrate that the proposed method achieves state-of-the-art counting performance. | ['Zhanyu Ma', 'Yixiao Zheng', 'Dingkang Liang', 'Wei Xu'] | 2020-12-15 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 1.56781733e-01 -5.24064302e-01 -3.13006602e-02 -4.90033507e-01
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b78ada50-69da-4c8b-a2ef-a07137db1d78 | similarity-weighted-construction-of | 2305.08495 | null | https://arxiv.org/abs/2305.08495v1 | https://arxiv.org/pdf/2305.08495v1.pdf | Similarity-weighted Construction of Contextualized Commonsense Knowledge Graphs for Knowledge-intense Argumentation Tasks | Arguments often do not make explicit how a conclusion follows from its premises. To compensate for this lack, we enrich arguments with structured background knowledge to support knowledge-intense argumentation tasks. We present a new unsupervised method for constructing Contextualized Commonsense Knowledge Graphs (CCKGs) that selects contextually relevant knowledge from large knowledge graphs (KGs) efficiently and at high quality. Our work goes beyond context-insensitive knowledge extraction heuristics by computing semantic similarity between KG triplets and textual arguments. Using these triplet similarities as weights, we extract contextualized knowledge paths that connect a conclusion to its premise, while maximizing similarity to the argument. We combine multiple paths into a CCKG that we optionally prune to reduce noise and raise precision. Intrinsic evaluation of the quality of our graphs shows that our method is effective for (re)constructing human explanation graphs. Manual evaluations in a large-scale knowledge selection setup confirm high recall and precision of implicit CSK in the CCKGs. Finally, we demonstrate the effectiveness of CCKGs in a knowledge-insensitive argument quality rating task, outperforming strong baselines and rivaling a GPT-3 based system. | ['Anette Frank', 'Philipp Cimiano', 'Philipp Heinisch', 'Juri Opitz', 'Moritz Plenz'] | 2023-05-15 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.29696596e-01 1.02102673e+00 -5.72450340e-01 -2.08793208e-01
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6c070691-9295-4037-bbcd-1a4affa7f465 | cmid-a-unified-self-supervised-learning | 2304.09670 | null | https://arxiv.org/abs/2304.09670v1 | https://arxiv.org/pdf/2304.09670v1.pdf | CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image Understanding | Self-supervised learning (SSL) has gained widespread attention in the remote sensing (RS) and earth observation (EO) communities owing to its ability to learn task-agnostic representations without human-annotated labels. Nevertheless, most existing RS SSL methods are limited to learning either global semantic separable or local spatial perceptible representations. We argue that this learning strategy is suboptimal in the realm of RS, since the required representations for different RS downstream tasks are often varied and complex. In this study, we proposed a unified SSL framework that is better suited for RS images representation learning. The proposed SSL framework, Contrastive Mask Image Distillation (CMID), is capable of learning representations with both global semantic separability and local spatial perceptibility by combining contrastive learning (CL) with masked image modeling (MIM) in a self-distillation way. Furthermore, our CMID learning framework is architecture-agnostic, which is compatible with both convolutional neural networks (CNN) and vision transformers (ViT), allowing CMID to be easily adapted to a variety of deep learning (DL) applications for RS understanding. Comprehensive experiments have been carried out on four downstream tasks (i.e. scene classification, semantic segmentation, object-detection, and change detection) and the results show that models pre-trained using CMID achieve better performance than other state-of-the-art SSL methods on multiple downstream tasks. The code and pre-trained models will be made available at https://github.com/NJU-LHRS/official-CMID to facilitate SSL research and speed up the development of RS images DL applications. | ['Feng Gu', 'Zhenshi Li', 'Pengfeng Xiao', 'Xueliang Zhang', 'Dilxat Muhtar'] | 2023-04-19 | null | null | null | null | ['change-detection', 'scene-classification'] | ['computer-vision', 'computer-vision'] | [ 4.32004333e-01 -3.97460312e-02 -1.09551266e-01 -5.33811629e-01
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7896b94f-719a-44cd-9bbb-d57632f00457 | a-dataset-of-dynamic-reverberant-sound-scenes | 2106.06999 | null | https://arxiv.org/abs/2106.06999v2 | https://arxiv.org/pdf/2106.06999v2.pdf | A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection | This report presents the dataset and baseline of Task 3 of the DCASE2021 Challenge on Sound Event Localization and Detection (SELD). The dataset is based on emulation of real recordings of static or moving sound events under real conditions of reverberation and ambient noise, using spatial room impulse responses captured in a variety of rooms and delivered in two spatial formats. The acoustical synthesis remains the same as in the previous iteration of the challenge, however the new dataset brings more challenging conditions of polyphony and overlapping instances of the same class. The most important difference of the new dataset is the introduction of directional interferers, meaning sound events that are localized in space but do not belong to the target classes to be detected and are not annotated. Since such interfering events are expected in every real-world scenario of SELD, the new dataset aims to promote systems that deal with this condition effectively. A modified SELDnet baseline employing the recent ACCDOA representation of SELD problems accompanies the dataset and it is shown to outperform the previous one. The new dataset is shown to be significantly more challenging for both baselines according to all considered metrics. To investigate the individual and combined effects of ambient noise, interferers, and reverberation, we study the performance of the baseline on different versions of the dataset excluding or including combinations of these factors. The results indicate that by far the most detrimental effects are caused by directional interferers. | ['Tuomas Virtanen', 'Prerak Srivastava', 'Antoine Deleforge', 'Daniel Krause', 'Sharath Adavanne', 'Archontis Politis'] | 2021-06-13 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [ 2.78284967e-01 -2.80320019e-01 8.49936187e-01 8.58122855e-02
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a05e2dba-beaf-4901-9274-2de85fccf218 | dual-space-compressed-sensing | 2207.07627 | null | https://arxiv.org/abs/2207.07627v1 | https://arxiv.org/pdf/2207.07627v1.pdf | Dual-space Compressed Sensing | Compressed sensing (CS) is a powerful method routinely employed to accelerate image acquisition. It is particularly suited to situations when the image under consideration is sparse but can be sampled in a basis where it is non-sparse. Here we propose an alternate CS regime in situations where the image can be sampled in two incoherent spaces simultaneously, with a special focus on image sampling in Fourier reciprocal spaces (e.g. real-space and k-space). Information is fed-forward from one space to the other, allowing new opportunities to efficiently solve the optimization problem at the heart of CS image reconstruction. We show that considerable gains in imaging acceleration are then possible over conventional CS. The technique provides enhanced robustness to noise, and is well suited to edge-detection problems. We envision applications for imaging collections of nanodiamond (ND) particles targeting specific regions in a volume of interest, exploiting the ability of lattice defects (NV centers) to allow ND particles to be imaged in reciprocal spaces simultaneously via optical fluorescence and 13C magnetic resonance imaging (MRI) respectively. Broadly this work suggests the potential to interface CS principles with hybrid sampling strategies to yield speedup in signal acquisition in many practical settings. | ['Ashok Ajoy', 'Xudong Lv'] | 2022-07-15 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [ 1.09501290e+00 -2.22312152e-01 2.11726815e-01 -3.83442752e-02
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de9e5749-b391-4025-9ec8-5ebb32518ccc | monoplflownet-permutohedral-lattice-flownet | 2111.12325 | null | https://arxiv.org/abs/2111.12325v1 | https://arxiv.org/pdf/2111.12325v1.pdf | MonoPLFlowNet: Permutohedral Lattice FlowNet for Real-Scale 3D Scene FlowEstimation with Monocular Images | Real-scale scene flow estimation has become increasingly important for 3D computer vision. Some works successfully estimate real-scale 3D scene flow with LiDAR. However, these ubiquitous and expensive sensors are still unlikely to be equipped widely for real application. Other works use monocular images to estimate scene flow, but their scene flow estimations are normalized with scale ambiguity, where additional depth or point cloud ground truth are required to recover the real scale. Even though they perform well in 2D, these works do not provide accurate and reliable 3D estimates. We present a deep learning architecture on permutohedral lattice - MonoPLFlowNet. Different from all previous works, our MonoPLFlowNet is the first work where only two consecutive monocular images are used as input, while both depth and 3D scene flow are estimated in real scale. Our real-scale scene flow estimation outperforms all state-of-the-art monocular-image based works recovered to real scale by ground truth, and is comparable to LiDAR approaches. As a by-product, our real-scale depth estimation also outperforms other state-of-the-art works. | ['Truong Nguyen', 'Runfa Li'] | 2021-11-24 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-8.12339261e-02 -4.03507859e-01 -2.26989448e-01 -1.95886970e-01
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cf5355b3-cd0e-4b18-abce-74378ecfb5f1 | a-hierarchy-of-graph-neural-networks-based-on-1 | 1911.05256 | null | https://arxiv.org/abs/1911.05256v1 | https://arxiv.org/pdf/1911.05256v1.pdf | A Hierarchy of Graph Neural Networks Based on Learnable Local Features | Graph neural networks (GNNs) are a powerful tool to learn representations on graphs by iteratively aggregating features from node neighbourhoods. Many variant models have been proposed, but there is limited understanding on both how to compare different architectures and how to construct GNNs systematically. Here, we propose a hierarchy of GNNs based on their aggregation regions. We derive theoretical results about the discriminative power and feature representation capabilities of each class. Then, we show how this framework can be utilized to systematically construct arbitrarily powerful GNNs. As an example, we construct a simple architecture that exceeds the expressiveness of the Weisfeiler-Lehman graph isomorphism test. We empirically validate our theory on both synthetic and real-world benchmarks, and demonstrate our example's theoretical power translates to strong results on node classification, graph classification, and graph regression tasks. | ['Alexander M. Rush', 'Michael Lingzhi Li', 'Meng Dong', 'Jiawei Zhou'] | 2019-11-13 | null | https://openreview.net/forum?id=ryeEr0EFvS | https://openreview.net/pdf?id=ryeEr0EFvS | null | ['graph-regression'] | ['graphs'] | [ 2.03602314e-01 5.09365201e-01 -2.74759173e-01 -3.67034405e-01
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cb5e0290-bb0a-4305-b1d8-6af9c6600ad1 | extraction-of-pharmacokinetic-evidence-of | 1412.0744 | null | http://arxiv.org/abs/1412.0744v2 | http://arxiv.org/pdf/1412.0744v2.pdf | Extraction of Pharmacokinetic Evidence of Drug-drug Interactions from the Literature | Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a
subject of intense scientific interest. Biomedical literature mining can aid
DDI research by extracting evidence for large numbers of potential interactions
from published literature and clinical databases. Though DDI is investigated in
domains ranging in scale from intracellular biochemistry to human populations,
literature mining has not been used to extract specific types of experimental
evidence, which are reported differently for distinct experimental goals. We
focus on pharmacokinetic evidence for DDI, essential for identifying causal
mechanisms of putative interactions and as input for further pharmacological
and pharmaco-epidemiology investigations. We used manually curated corpora of
PubMed abstracts and annotated sentences to evaluate the efficacy of literature
mining on two tasks: first, identifying PubMed abstracts containing
pharmacokinetic evidence of DDIs; second, extracting sentences containing such
evidence from abstracts. We implemented a text mining pipeline and evaluated it
using several linear classifiers and a variety of feature transforms. The most
important textual features in the abstract and sentence classification tasks
were analyzed. We also investigated the performance benefits of using features
derived from PubMed metadata fields, various publicly available named entity
recognizers, and pharmacokinetic dictionaries. Several classifiers performed
very well in distinguishing relevant and irrelevant abstracts (reaching
F1~=0.93, MCC~=0.74, iAUC~=0.99) and sentences (F1~=0.76, MCC~=0.65,
iAUC~=0.83). We found that word bigram features were important for achieving
optimal classifier performance and that features derived from Medical Subject
Headings (MeSH) terms significantly improved abstract classification. ... | ['Heng-Yi Wu', 'Anália Lourenço', 'Artemy Kolchinsky', 'Lang Li', 'Luis M. Rocha'] | 2014-12-02 | null | null | null | null | ['literature-mining'] | ['natural-language-processing'] | [ 2.76335537e-01 -1.02102362e-01 -5.45386791e-01 -2.47251451e-01
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76aa8e0b-2669-4f1b-8067-28c75734742f | traffic-signs-in-the-wild-highlights-from-the | 1810.06169 | null | http://arxiv.org/abs/1810.06169v2 | http://arxiv.org/pdf/1810.06169v2.pdf | Traffic Signs in the Wild: Highlights from the IEEE Video and Image Processing Cup 2017 Student Competition [SP Competitions] | Robust and reliable traffic sign detection is necessary to bring autonomous
vehicles onto our roads. State-of-the-art algorithms successfully perform
traffic sign detection over existing databases that mostly lack severe
challenging conditions. VIP Cup 2017 competition focused on detecting such
traffic signs under challenging conditions. To facilitate such task and
competition, we introduced a video dataset denoted as CURE-TSD that includes a
variety of challenging conditions. The goal of this challenge was to implement
traffic sign detection algorithms that can robustly perform under such
challenging conditions. In this article, we share an overview of the VIP Cup
2017 experience including competition setup, teams, technical approaches,
participation statistics, and competition experience through finalist teams
members' and organizers' eyes. | ['Ghassan AlRegib', 'Dogancan Temel'] | 2018-10-15 | null | null | null | null | ['traffic-sign-detection'] | ['computer-vision'] | [-3.37460518e-01 -7.55033374e-01 -4.75190103e-01 -4.52093005e-01
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5e447185-1460-48f6-bcbe-fd06dc552e4e | gated-recurrent-unit-for-video-denoising | 2210.09135 | null | https://arxiv.org/abs/2210.09135v1 | https://arxiv.org/pdf/2210.09135v1.pdf | Gated Recurrent Unit for Video Denoising | Current video denoising methods perform temporal fusion by designing convolutional neural networks (CNN) or combine spatial denoising with temporal fusion into basic recurrent neural networks (RNNs). However, there have not yet been works which adapt gated recurrent unit (GRU) mechanisms for video denoising. In this letter, we propose a new video denoising model based on GRU, namely GRU-VD. First, the reset gate is employed to mark the content related to the current frame in the previous frame output. Then the hidden activation works as an initial spatial-temporal denoising with the help from the marked relevant content. Finally, the update gate recursively fuses the initial denoised result with previous frame output to further increase accuracy. To handle various light conditions adaptively, the noise standard deviation of the current frame is also fed to these three modules. A weighted loss is adopted to regulate initial denoising and final fusion at the same time. The experimental results show that the GRU-VD network not only can achieve better quality than state of the arts objectively and subjectively, but also can obtain satisfied subjective quality on real video. | ['Jongseong Choi', 'Seungwon Choi', 'Kai Guo'] | 2022-10-17 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 6.93589002e-02 -5.22542715e-01 3.23574990e-01 -2.57669061e-01
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f732bd16-4d93-4d2b-830b-15c821db2321 | an-exploration-of-hierarchical-attention | 2210.05529 | null | https://arxiv.org/abs/2210.05529v1 | https://arxiv.org/pdf/2210.05529v1.pdf | An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification | Non-hierarchical sparse attention Transformer-based models, such as Longformer and Big Bird, are popular approaches to working with long documents. There are clear benefits to these approaches compared to the original Transformer in terms of efficiency, but Hierarchical Attention Transformer (HAT) models are a vastly understudied alternative. We develop and release fully pre-trained HAT models that use segment-wise followed by cross-segment encoders and compare them with Longformer models and partially pre-trained HATs. In several long document downstream classification tasks, our best HAT model outperforms equally-sized Longformer models while using 10-20% less GPU memory and processing documents 40-45% faster. In a series of ablation studies, we find that HATs perform best with cross-segment contextualization throughout the model than alternative configurations that implement either early or late cross-segment contextualization. Our code is on GitHub: https://github.com/coastalcph/hierarchical-transformers. | ['Desmond Elliott', 'Prodromos Malakasiotis', 'Manos Fergadiotis', 'Xiang Dai', 'Ilias Chalkidis'] | 2022-10-11 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [-1.48557305e-01 2.55962461e-02 4.06666324e-02 -2.87664682e-01
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31f6118d-bf71-4976-8117-ae811a97f4bd | deep-network-for-simultaneous-decomposition | 1801.05458 | null | http://arxiv.org/abs/1801.05458v2 | http://arxiv.org/pdf/1801.05458v2.pdf | Deep Network for Simultaneous Decomposition and Classification in UWB-SAR Imagery | Classifying buried and obscured targets of interest from other natural and
manmade clutter objects in the scene is an important problem for the U.S. Army.
Targets of interest are often represented by signals captured using
low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar
(SAR) technology. This technology has been used in various applications,
including ground penetration and sensing-through-the-wall. However, the
technology still faces a significant issues regarding low-resolution SAR
imagery in this particular frequency band, low radar cross sections (RCS),
small objects compared to radar signal wavelengths, and heavy interference. The
classification problem has been firstly, and partially, addressed by sparse
representation-based classification (SRC) method which can extract noise from
signals and exploit the cross-channel information. Despite providing potential
results, SRC-related methods have drawbacks in representing nonlinear relations
and dealing with larger training sets. In this paper, we propose a Simultaneous
Decomposition and Classification Network (SDCN) to alleviate noise inferences
and enhance classification accuracy. The network contains two jointly trained
sub-networks: the decomposition sub-network handles denoising, while the
classification sub-network discriminates targets from confusers. Experimental
results show significant improvements over a network without decomposition and
SRC-related methods. | ['Vishal Monga', 'Tiantong Guo', 'Tiep Vu', 'Lam Nguyen'] | 2018-01-16 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 7.50349998e-01 -2.80124843e-01 2.24131793e-01 -2.34573811e-01
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e318dac9-86b4-48ed-bdf5-81c90185f4ae | lean-light-and-efficient-audio-classification | 2305.12712 | null | https://arxiv.org/abs/2305.12712v1 | https://arxiv.org/pdf/2305.12712v1.pdf | LEAN: Light and Efficient Audio Classification Network | Over the past few years, audio classification task on large-scale dataset such as AudioSet has been an important research area. Several deeper Convolution-based Neural networks have shown compelling performance notably Vggish, YAMNet, and Pretrained Audio Neural Network (PANN). These models are available as pretrained architecture for transfer learning as well as specific audio task adoption. In this paper, we propose a lightweight on-device deep learning-based model for audio classification, LEAN. LEAN consists of a raw waveform-based temporal feature extractor called as Wave Encoder and logmel-based Pretrained YAMNet. We show that using a combination of trainable wave encoder, Pretrained YAMNet along with cross attention-based temporal realignment, results in competitive performance on downstream audio classification tasks with lesser memory footprints and hence making it suitable for resource constraints devices such as mobile, edge devices, etc . Our proposed system achieves on-device mean average precision(mAP) of .445 with a memory footprint of a mere 4.5MB on the FSD50K dataset which is an improvement of 22% over baseline on-device mAP on same dataset. | ['Sumit Kumar', 'Punuru Sri Lakshmi', 'CR Karthik', 'Shwetank Choudhary'] | 2023-05-22 | null | null | null | null | ['audio-classification'] | ['audio'] | [ 2.25863665e-01 -1.59878597e-01 -1.36829689e-01 -3.00099492e-01
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e11a532f-1452-4745-bd62-b19720ac4b69 | anvita-machine-translation-system-for-wat | null | null | https://aclanthology.org/2021.wat-1.30 | https://aclanthology.org/2021.wat-1.30.pdf | ANVITA Machine Translation System for WAT 2021 MultiIndicMT Shared Task | This paper describes ANVITA-1.0 MT system, architected for submission to WAT2021 MultiIndicMT shared task by mcairt team, where the team participated in 20 translation directions: English→Indic and Indic→English; Indic set comprised of 10 Indian languages. ANVITA-1.0 MT system comprised of two multi-lingual NMT models one for the English→Indic directions and other for the Indic→English directions with shared encoder-decoder, catering 10 language pairs and twenty translation directions. The base models were built based on Transformer architecture and trained over MultiIndicMT WAT 2021 corpora and further employed back translation and transliteration for selective data augmentation, and model ensemble for better generalization. Additionally, MultiIndicMT WAT 2021 corpora was distilled using a series of filtering operations before putting up for training. ANVITA-1.0 achieved highest AM-FM score for English→Bengali, 2nd for English→Tamil and 3rd for English→Hindi, Bengali→English directions on official test set. In general, performance achieved by ANVITA for the Indic→English directions are relatively better than that of English→Indic directions for all the 10 language pairs when evaluated using BLEU and RIBES, although the same trend is not observed consistently when AM-FM based evaluation was carried out. As compared to BLEU, RIBES and AM-FM based scoring placed ANVITA relatively better among all the task participants. | ['Prasanna Kumar K R', 'Chitra Viswanathan', 'Biswajit Paul', 'Sivabhavani J', 'Pavanpankaj Vegi'] | null | null | null | null | acl-wat-2021-8 | ['transliteration'] | ['natural-language-processing'] | [-8.33837613e-02 1.60243124e-01 -3.06943029e-01 -3.15930605e-01
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1876d6ea-f833-4620-b1d0-802c1bfa5471 | a-method-of-accounting-bigrams-in-topic | null | null | https://aclanthology.org/W15-0901 | https://aclanthology.org/W15-0901.pdf | A Method of Accounting Bigrams in Topic Models | null | ['Natalia Loukachevitch', 'Michael Nokel'] | 2015-06-01 | null | null | null | ws-2015-6 | ['text-clustering'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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7ff15aa1-fc87-42c3-9363-ba0d70a8739d | spatial-temporal-graph-convolution-with-graph | 2211.06161 | null | https://arxiv.org/abs/2211.06161v1 | https://arxiv.org/pdf/2211.06161v1.pdf | Spatial Temporal Graph Convolution with Graph Structure Self-learning for Early MCI Detection | Graph neural networks (GNNs) have been successfully applied to early mild cognitive impairment (EMCI) detection, with the usage of elaborately designed features constructed from blood oxygen level-dependent (BOLD) time series. However, few works explored the feasibility of using BOLD signals directly as features. Meanwhile, existing GNN-based methods primarily rely on hand-crafted explicit brain topology as the adjacency matrix, which is not optimal and ignores the implicit topological organization of the brain. In this paper, we propose a spatial temporal graph convolutional network with a novel graph structure self-learning mechanism for EMCI detection. The proposed spatial temporal graph convolution block directly exploits BOLD time series as input features, which provides an interesting view for rsfMRI-based preclinical AD diagnosis. Moreover, our model can adaptively learn the optimal topological structure and refine edge weights with the graph structure self-learning mechanism. Results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database show that our method outperforms state-of-the-art approaches. Biomarkers consistent with previous studies can be extracted from the model, proving the reliable interpretability of our method. | ['Bo Liu', 'Bin Guo', 'Fugen Zhou', 'Yunpeng Zhao'] | 2022-11-11 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 1.11671975e-02 6.28096163e-02 6.35799542e-02 -5.04408121e-01
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57dc83bd-a662-4782-9c22-fa17a8f81740 | dynamic-label-graph-matching-for-unsupervised | 1709.09297 | null | http://arxiv.org/abs/1709.09297v1 | http://arxiv.org/pdf/1709.09297v1.pdf | Dynamic Label Graph Matching for Unsupervised Video Re-Identification | Label estimation is an important component in an unsupervised person
re-identification (re-ID) system. This paper focuses on cross-camera label
estimation, which can be subsequently used in feature learning to learn robust
re-ID models. Specifically, we propose to construct a graph for samples in each
camera, and then graph matching scheme is introduced for cross-camera labeling
association. While labels directly output from existing graph matching methods
may be noisy and inaccurate due to significant cross-camera variations, this
paper proposes a dynamic graph matching (DGM) method. DGM iteratively updates
the image graph and the label estimation process by learning a better feature
space with intermediate estimated labels. DGM is advantageous in two aspects:
1) the accuracy of estimated labels is improved significantly with the
iterations; 2) DGM is robust to noisy initial training data. Extensive
experiments conducted on three benchmarks including the large-scale MARS
dataset show that DGM yields competitive performance to fully supervised
baselines, and outperforms competing unsupervised learning methods. | ['Andy J. Ma', 'Jiawei Li', 'Mang Ye', 'P C Yuen', 'Liang Zheng'] | 2017-09-27 | dynamic-label-graph-matching-for-unsupervised-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Ye_Dynamic_Label_Graph_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Ye_Dynamic_Label_Graph_ICCV_2017_paper.pdf | iccv-2017-10 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 2.72770584e-01 -1.26028121e-01 -2.54578054e-01 -5.37602663e-01
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807f4d3d-1d8f-44e2-9616-652e57b8d137 | learning-to-decompose-hypothetical-question | 2210.16865 | null | https://arxiv.org/abs/2210.16865v1 | https://arxiv.org/pdf/2210.16865v1.pdf | Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts | Explicit decomposition modeling, which involves breaking down complex tasks into more straightforward and often more interpretable sub-tasks, has long been a central theme in developing robust and interpretable NLU systems. However, despite the many datasets and resources built as part of this effort, the majority have small-scale annotations and limited scope, which is insufficient to solve general decomposition tasks. In this paper, we look at large-scale intermediate pre-training of decomposition-based transformers using distant supervision from comparable texts, particularly large-scale parallel news. We show that with such intermediate pre-training, developing robust decomposition-based models for a diverse range of tasks becomes more feasible. For example, on semantic parsing, our model, DecompT5, improves 20% to 30% on two datasets, Overnight and TORQUE, over the baseline language model. We further use DecompT5 to build a novel decomposition-based QA system named DecompEntail, improving over state-of-the-art models, including GPT-3, on both HotpotQA and StrategyQA by 8% and 4%, respectively. | ['Dan Roth', 'Xiaodong Yu', 'Kyle Richardson', 'Ben Zhou'] | 2022-10-30 | null | null | null | null | ['semantic-parsing', 'strategyqa'] | ['natural-language-processing', 'reasoning'] | [-6.92413480e-04 5.72144151e-01 -2.84548908e-01 -5.70751429e-01
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3f10ebc7-ea4c-415e-b671-1681e9ec4cdf | unsupervised-uncertainty-estimation-using | 1901.01550 | null | http://arxiv.org/abs/1901.01550v1 | http://arxiv.org/pdf/1901.01550v1.pdf | Unsupervised uncertainty estimation using spatiotemporal cues in video saliency detection | In this paper, we address the problem of quantifying reliability of
computational saliency for videos, which can be used to improve saliency-based
video processing and enable more reliable performance and risk assessment of
such processing. Our approach is twofold. First, we explore spatial
correlations in both saliency map and eye-fixation map. Then, we learn
spatiotemporal correlations that define a reliable saliency map. We first study
spatiotemporal eye-fixation data from a public dataset and investigate a common
feature in human visual attention, which dictates correlation in saliency
between a pixel and its direct neighbors. Based on the study, we then develop
an algorithm that estimates a pixel-wise uncertainty map that reflects our
confidence in the associated computational saliency map by relating a pixel's
saliency to the saliency of its neighbors. To estimate such uncertainties, we
measure the divergence of a pixel, in a saliency map, from its local
neighborhood. Additionally, we propose a systematic procedure to evaluate the
estimation performance by explicitly computing uncertainty ground truth as a
function of a given saliency map and eye fixations of human subjects. In our
experiments, we explore multiple definitions of locality and neighborhoods in
spatiotemporal video signals. In addition, we examine the relationship between
the parameters of our proposed algorithm and the content of the videos. The
proposed algorithm is unsupervised, making it more suitable for generalization
to most natural videos. Also, it is computationally efficient and flexible for
customization to specific video content. Experiments using three publicly
available video datasets show that the proposed algorithm outperforms
state-of-the-art uncertainty estimation methods with improvement in accuracy up
to 63% and offers efficiency and flexibility that make it more useful in
practical situations. | ['Tariq Alshawi', 'Ghassan AlRegib', 'Zhiling Long'] | 2019-01-06 | null | null | null | null | ['video-saliency-detection'] | ['computer-vision'] | [ 2.60717750e-01 -2.78737068e-01 -1.50139332e-01 -3.88163060e-01
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16ff8a53-0119-4e1f-90e8-f6ef042abc0c | a-survey-on-graph-neural-networks-for | 2007.12374 | null | https://arxiv.org/abs/2007.12374v1 | https://arxiv.org/pdf/2007.12374v1.pdf | A Survey on Graph Neural Networks for Knowledge Graph Completion | Knowledge Graphs are increasingly becoming popular for a variety of downstream tasks like Question Answering and Information Retrieval. However, the Knowledge Graphs are often incomplete, thus leading to poor performance. As a result, there has been a lot of interest in the task of Knowledge Base Completion. More recently, Graph Neural Networks have been used to capture structural information inherently stored in these Knowledge Graphs and have been shown to achieve SOTA performance across a variety of datasets. In this survey, we understand the various strengths and weaknesses of the proposed methodology and try to find new exciting research problems in this area that require further investigation. | ['Siddhant Arora'] | 2020-07-24 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [ 2.34254837e-01 3.12775195e-01 -6.83704913e-01 -3.02029997e-01
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6df29830-282a-4e7e-a02c-eec49c124512 | squibs-constrained-arc-eager-dependency | null | null | https://aclanthology.org/J14-2001 | https://aclanthology.org/J14-2001.pdf | Squibs: Constrained Arc-Eager Dependency Parsing | null | ['Ryan Mcdonald', 'Joakim Nivre', 'Yoav Goldberg'] | 2014-06-01 | null | null | null | cl-2014-6 | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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1dbb828f-c11a-470d-a6e2-11be0325be65 | learning-meta-representations-of-one-shot | 2205.10621 | null | https://arxiv.org/abs/2205.10621v2 | https://arxiv.org/pdf/2205.10621v2.pdf | Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction | Few-shot relational learning for static knowledge graphs (KGs) has drawn greater interest in recent years, while few-shot learning for temporal knowledge graphs (TKGs) has hardly been studied. Compared to KGs, TKGs contain rich temporal information, thus requiring temporal reasoning techniques for modeling. This poses a greater challenge in learning few-shot relations in the temporal context. In this paper, we follow the previous work that focuses on few-shot relational learning on static KGs and extend two fundamental TKG reasoning tasks, i.e., interpolated and extrapolated link prediction, to the one-shot setting. We propose four new large-scale benchmark datasets and develop a TKG reasoning model for learning one-shot relations in TKGs. Experimental results show that our model can achieve superior performance on all datasets in both TKG link prediction tasks. | ['Volker Tresp', 'Zhen Han', 'Yunpu Ma', 'Bailan He', 'Zifeng Ding'] | 2022-05-21 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [-3.13632041e-01 3.53835642e-01 -1.00965142e+00 -1.69679791e-01
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cb83b00a-45ef-4a44-901d-0480a63ec089 | predictive-engagement-an-efficient-metric-for | 1911.01456 | null | https://arxiv.org/abs/1911.01456v2 | https://arxiv.org/pdf/1911.01456v2.pdf | Predictive Engagement: An Efficient Metric For Automatic Evaluation of Open-Domain Dialogue Systems | User engagement is a critical metric for evaluating the quality of open-domain dialogue systems. Prior work has focused on conversation-level engagement by using heuristically constructed features such as the number of turns and the total time of the conversation. In this paper, we investigate the possibility and efficacy of estimating utterance-level engagement and define a novel metric, {\em predictive engagement}, for automatic evaluation of open-domain dialogue systems. Our experiments demonstrate that (1) human annotators have high agreement on assessing utterance-level engagement scores; (2) conversation-level engagement scores can be predicted from properly aggregated utterance-level engagement scores. Furthermore, we show that the utterance-level engagement scores can be learned from data. These scores can improve automatic evaluation metrics for open-domain dialogue systems, as shown by correlation with human judgements. This suggests that predictive engagement can be used as a real-time feedback for training better dialogue models. | ['Nanyun Peng', 'Aram Galstyan', 'Ralph Weischedel', 'Sarik Ghazarian'] | 2019-11-04 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 3.49873677e-02 7.33224571e-01 -8.07082728e-02 -6.55875742e-01
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c10d6b4f-84b1-4c81-ba3b-ab473b524cc1 | document-level-relation-extraction-with-1 | 2012.11384 | null | https://arxiv.org/abs/2012.11384v1 | https://arxiv.org/pdf/2012.11384v1.pdf | Document-Level Relation Extraction with Reconstruction | In document-level relation extraction (DocRE), graph structure is generally used to encode relation information in the input document to classify the relation category between each entity pair, and has greatly advanced the DocRE task over the past several years. However, the learned graph representation universally models relation information between all entity pairs regardless of whether there are relationships between these entity pairs. Thus, those entity pairs without relationships disperse the attention of the encoder-classifier DocRE for ones with relationships, which may further hind the improvement of DocRE. To alleviate this issue, we propose a novel encoder-classifier-reconstructor model for DocRE. The reconstructor manages to reconstruct the ground-truth path dependencies from the graph representation, to ensure that the proposed DocRE model pays more attention to encode entity pairs with relationships in the training. Furthermore, the reconstructor is regarded as a relationship indicator to assist relation classification in the inference, which can further improve the performance of DocRE model. Experimental results on a large-scale DocRE dataset show that the proposed model can significantly improve the accuracy of relation extraction on a strong heterogeneous graph-based baseline. | ['Tiejun Zhao', 'Kehai Chen', 'Wang Xu'] | 2020-12-21 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-6.49569929e-02 4.86145884e-01 -6.41620159e-01 -1.79426551e-01
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82c38871-f55e-4349-b971-6f039303dd97 | 3dshape2vecset-a-3d-shape-representation-for | 2301.11445 | null | https://arxiv.org/abs/2301.11445v3 | https://arxiv.org/pdf/2301.11445v3.pdf | 3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models | We introduce 3DShape2VecSet, a novel shape representation for neural fields designed for generative diffusion models. Our shape representation can encode 3D shapes given as surface models or point clouds, and represents them as neural fields. The concept of neural fields has previously been combined with a global latent vector, a regular grid of latent vectors, or an irregular grid of latent vectors. Our new representation encodes neural fields on top of a set of vectors. We draw from multiple concepts, such as the radial basis function representation and the cross attention and self-attention function, to design a learnable representation that is especially suitable for processing with transformers. Our results show improved performance in 3D shape encoding and 3D shape generative modeling tasks. We demonstrate a wide variety of generative applications: unconditioned generation, category-conditioned generation, text-conditioned generation, point-cloud completion, and image-conditioned generation. | ['Peter Wonka', 'Matthias Niessner', 'Jiapeng Tang', 'Biao Zhang'] | 2023-01-26 | null | null | null | null | ['point-cloud-completion', '3d-shape-representation'] | ['computer-vision', 'computer-vision'] | [ 7.96745345e-02 2.89683133e-01 2.89955318e-01 -2.63252705e-01
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80e1c959-5970-4213-bac0-8d294e5b9cd0 | ibir-bug-report-driven-fault-injection | 2012.06506 | null | https://arxiv.org/abs/2012.06506v1 | https://arxiv.org/pdf/2012.06506v1.pdf | IBIR: Bug Report driven Fault Injection | Much research on software engineering and software testing relies on experimental studies based on fault injection. Fault injection, however, is not often relevant to emulate real-world software faults since it "blindly" injects large numbers of faults. It remains indeed challenging to inject few but realistic faults that target a particular functionality in a program. In this work, we introduce IBIR, a fault injection tool that addresses this challenge by exploring change patterns associated to user-reported faults. To inject realistic faults, we create mutants by retargeting a bug report driven automated program repair system, i.e., reversing its code transformation templates. IBIR is further appealing in practice since it requires deep knowledge of neither of the code nor the tests, but just of the program's relevant bug reports. Thus, our approach focuses the fault injection on the feature targeted by the bug report. We assess IBIR by considering the Defects4J dataset. Experimental results show that our approach outperforms the fault injection performed by traditional mutation testing in terms of semantic similarity with the original bug, when applied at either system or class levels of granularity, and provides better, statistically significant, estimations of test effectiveness (fault detection). Additionally, when injecting 100 faults, IBIR injects faults that couple with the real ones in 36% of the cases, while mutants from mutation testing inject less than 1%. Overall, IBIR targets real functionality and injects realistic and diverse faults. | ['Yves Le Traon', 'Jacques Klein', 'Tegawendé F. Bissyandé', 'Maxime Cordy', 'Mike Papadakis', 'Anil Koyuncu', 'Ahmed Khanfir'] | 2020-12-11 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 1.41068295e-01 1.48070082e-01 -4.36346605e-02 2.36345127e-01
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b2b7413c-12fd-44f3-9c9c-ebbb1e705418 | real-to-sim-deep-learning-with-auto-tuning-to | 2209.03210 | null | https://arxiv.org/abs/2209.03210v3 | https://arxiv.org/pdf/2209.03210v3.pdf | Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter | Achieving highly accurate dynamic or simulator models that are close to the real robot can facilitate model-based controls (e.g., model predictive control or linear-quadradic regulators), model-based trajectory planning (e.g., trajectory optimization), and decrease the amount of learning time necessary for reinforcement learning methods. Thus, the objective of this work is to learn the residual errors between a dynamic and/or simulator model and the real robot. This is achieved using a neural network, where the parameters of a neural network are updated through an Unscented Kalman Filter (UKF) formulation. Using this method, we model these residual errors with only small amounts of data -- a necessity as we improve the simulator/dynamic model by learning directly from real-world operation. We demonstrate our method on robotic hardware (e.g., manipulator arm, and a wheeled robot), and show that with the learned residual errors, we can further close the reality gap between dynamic models, simulations, and actual hardware. | ['Dennis Hong', 'Marcel Menner', 'Feng Xu', 'Yusuke Tanaka', 'Alexander Schperberg'] | 2022-09-07 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [ 4.05710116e-02 3.57473731e-01 -4.37944084e-01 7.52459764e-02
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6.00164115e-01 -5.54388702e-01 2.43115928e-02 -4.50074017e-01
5.30433238e-01 -1.15682030e+00 4.17083055e-01 1.05938053e+00
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1.30437598e-01 -5.92877805e-01 2.02332601e-01 -6.35944009e-01
2.09803507e-01 5.52448988e-01 2.80568190e-02 -1.01626635e-01
1.04977243e-01 3.70313495e-01 1.55773133e-01 -2.76423812e-01
1.24531376e+00 -1.96690857e-01 2.85906941e-01 6.27478719e-01
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1.30862966e-01 -2.46340677e-01 2.20185965e-01 -3.77559036e-01
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6.23957992e-01 -6.84334993e-01 4.86054629e-01 1.15202904e+00
8.92746568e-01 -1.47997153e+00 3.24779749e-02 9.57354248e-01
8.36623490e-01 -6.51178062e-01 8.57791957e-03 -4.11819726e-01
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2.05070749e-01 3.18351775e-01 1.08830857e+00 1.33421397e+00
-7.60405183e-01 -9.23525453e-01 -5.62346220e-01 1.47716105e-01
3.59520584e-01 5.88839293e-01 4.92712945e-01 1.28420901e+00
-4.31789994e-01 8.14027786e-01 5.51805906e-02 -4.37602550e-01
8.37768614e-01 -8.89919847e-02 5.43744504e-01 -5.05016446e-01
8.19860622e-02 -3.61645184e-02 1.93434149e-01 -8.90428543e-01
8.72922018e-02 -4.79583055e-01 -1.40317273e+00 -1.43390909e-01
-7.91248977e-01 5.21620661e-02 1.28679264e+00 8.12054873e-01
3.53061229e-01 6.79886818e-01 7.71885037e-01 -1.35715318e+00
-1.40332365e+00 -7.58388937e-01 -6.45459831e-01 -4.16602552e-01
4.13765460e-01 -1.09159088e+00 -2.59225696e-01 -1.08539509e-02] | [4.904374599456787, 1.9377496242523193] |
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