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52bc7a60-bbe5-4ce8-8a35-e2bed6a47d21 | 190412580 | 1904.12580 | null | http://arxiv.org/abs/1904.12580v1 | http://arxiv.org/pdf/1904.12580v1.pdf | Twitter Sentiment Analysis using Distributed Word and Sentence Representation | An important part of the information gathering and data analysis is to find
out what people think about, either a product or an entity. Twitter is an
opinion rich social networking site. The posts or tweets from this data can be
used for mining people's opinions. The recent surge of activity in this area
can be attributed to the computational treatment of data, which made opinion
extraction and sentiment analysis easier. This paper classifies tweets into
positive and negative sentiments, but instead of using traditional methods or
preprocessing text data here we use the distributed representations of words
and sentences to classify the tweets. We use Long Short Term Memory (LSTM)
Networks, Convolutional Neural Networks (CNNs) and Artificial Neural Networks.
The first two are used on Distributed Representation of words while the latter
is used on the distributed representation of sentences. This paper achieves
accuracies as high as 81%. It also suggests the best and optimal ways for
creating distributed representations of words for sentiment analysis, out of
the available methods. | ['Dr. N V Subba Reddy', 'Dwarampudi Mahidhar Reddy'] | 2019-04-01 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-1.89836010e-01 2.23051775e-02 -2.87200898e-01 -7.02671230e-01
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fbd9e1a6-cee9-4e21-9a3d-dcffb2848ae6 | meta-learning-for-relative-density-ratio | 2107.00801 | null | https://arxiv.org/abs/2107.00801v1 | https://arxiv.org/pdf/2107.00801v1.pdf | Meta-Learning for Relative Density-Ratio Estimation | The ratio of two probability densities, called a density-ratio, is a vital quantity in machine learning. In particular, a relative density-ratio, which is a bounded extension of the density-ratio, has received much attention due to its stability and has been used in various applications such as outlier detection and dataset comparison. Existing methods for (relative) density-ratio estimation (DRE) require many instances from both densities. However, sufficient instances are often unavailable in practice. In this paper, we propose a meta-learning method for relative DRE, which estimates the relative density-ratio from a few instances by using knowledge in related datasets. Specifically, given two datasets that consist of a few instances, our model extracts the datasets' information by using neural networks and uses it to obtain instance embeddings appropriate for the relative DRE. We model the relative density-ratio by a linear model on the embedded space, whose global optimum solution can be obtained as a closed-form solution. The closed-form solution enables fast and effective adaptation to a few instances, and its differentiability enables us to train our model such that the expected test error for relative DRE can be explicitly minimized after adapting to a few instances. We empirically demonstrate the effectiveness of the proposed method by using three problems: relative DRE, dataset comparison, and outlier detection. | ['Yasuhiro Fujiwara', 'Tomoharu Iwata', 'Atsutoshi Kumagai'] | 2021-07-02 | null | http://proceedings.neurips.cc/paper/2021/hash/ff49cc40a8890e6a60f40ff3026d2730-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/ff49cc40a8890e6a60f40ff3026d2730-Paper.pdf | neurips-2021-12 | ['density-ratio-estimation'] | ['methodology'] | [-1.00362308e-01 -1.22641288e-01 -2.59626418e-01 -4.26552951e-01
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4d95743c-7820-4a36-a1de-4097e84201f8 | ml-decoder-scalable-and-versatile | 2111.12933 | null | https://arxiv.org/abs/2111.12933v2 | https://arxiv.org/pdf/2111.12933v2.pdf | ML-Decoder: Scalable and Versatile Classification Head | In this paper, we introduce ML-Decoder, a new attention-based classification head. ML-Decoder predicts the existence of class labels via queries, and enables better utilization of spatial data compared to global average pooling. By redesigning the decoder architecture, and using a novel group-decoding scheme, ML-Decoder is highly efficient, and can scale well to thousands of classes. Compared to using a larger backbone, ML-Decoder consistently provides a better speed-accuracy trade-off. ML-Decoder is also versatile - it can be used as a drop-in replacement for various classification heads, and generalize to unseen classes when operated with word queries. Novel query augmentations further improve its generalization ability. Using ML-Decoder, we achieve state-of-the-art results on several classification tasks: on MS-COCO multi-label, we reach 91.4% mAP; on NUS-WIDE zero-shot, we reach 31.1% ZSL mAP; and on ImageNet single-label, we reach with vanilla ResNet50 backbone a new top score of 80.7%, without extra data or distillation. Public code is available at: https://github.com/Alibaba-MIIL/ML_Decoder | ['Asaf Noy', 'Emanuel Ben-Baruch', 'Avi Ben-Cohen', 'Gilad Sharir', 'Tal Ridnik'] | 2021-11-25 | null | null | null | null | ['multi-label-zero-shot-learning', 'fine-grained-image-classification'] | ['computer-vision', 'computer-vision'] | [-5.12030013e-02 9.70766768e-02 -3.63832235e-01 -4.06974345e-01
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31b171e3-f497-4ff7-8883-421bdd5237bf | mb-hgcn-a-hierarchical-graph-convolutional | 2306.10679 | null | https://arxiv.org/abs/2306.10679v1 | https://arxiv.org/pdf/2306.10679v1.pdf | MB-HGCN: A Hierarchical Graph Convolutional Network for Multi-behavior Recommendation | Collaborative filtering-based recommender systems that rely on a single type of behavior often encounter serious sparsity issues in real-world applications, leading to unsatisfactory performance. Multi-behavior Recommendation (MBR) is a method that seeks to learn user preferences, represented as vector embeddings, from auxiliary information. By leveraging these preferences for target behavior recommendations, MBR addresses the sparsity problem and improves the accuracy of recommendations. In this paper, we propose MB-HGCN, a novel multi-behavior recommendation model that uses a hierarchical graph convolutional network to learn user and item embeddings from coarse-grained on the global level to fine-grained on the behavior-specific level. Our model learns global embeddings from a unified homogeneous graph constructed by the interactions of all behaviors, which are then used as initialized embeddings for behavior-specific embedding learning in each behavior graph. We also emphasize the distinct of the user and item behaviorspecific embeddings and design two simple-yet-effective strategies to aggregate the behavior-specific embeddings for users and items, respectively. Finally, we adopt multi-task learning for optimization. Extensive experimental results on three real-world datasets demonstrate that our model significantly outperforms the baselines, achieving a relative improvement of 73.93% and 74.21% for HR@10 and NDCG@10, respectively, on the Tmall datasets. | ['Yuxin Peng', 'Fuming Sun', 'Jing Sun', 'Zhiyong Cheng', 'Mingshi Yan'] | 2023-06-19 | null | null | null | null | ['multi-task-learning', 'collaborative-filtering'] | ['methodology', 'miscellaneous'] | [-3.89085084e-01 -5.00582337e-01 -4.87044424e-01 -5.31485319e-01
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97606a02-f653-4d33-8b97-07b2299e7cb3 | fanet-a-feedback-attention-network-for | 2103.17235 | null | https://arxiv.org/abs/2103.17235v3 | https://arxiv.org/pdf/2103.17235v3.pdf | FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation | The increase of available large clinical and experimental datasets has contributed to a substantial amount of important contributions in the area of biomedical image analysis. Image segmentation, which is crucial for any quantitative analysis, has especially attracted attention. Recent hardware advancement has led to the success of deep learning approaches. However, although deep learning models are being trained on large datasets, existing methods do not use the information from different learning epochs effectively. In this work, we leverage the information of each training epoch to prune the prediction maps of the subsequent epochs. We propose a novel architecture called feedback attention network (FANet) that unifies the previous epoch mask with the feature map of the current training epoch. The previous epoch mask is then used to provide a hard attention to the learned feature maps at different convolutional layers. The network also allows to rectify the predictions in an iterative fashion during the test time. We show that our proposed \textit{feedback attention} model provides a substantial improvement on most segmentation metrics tested on seven publicly available biomedical imaging datasets demonstrating the effectiveness of FANet. The source code is available at \url{https://github.com/nikhilroxtomar/FANet}. | ['Sharib Ali', 'Pål Halvorsen', 'Jens Rittscher', 'Dag Johansen', 'Håvard D. Johansen', 'Michael A. Riegler', 'Debesh Jha', 'Nikhil Kumar Tomar'] | 2021-03-31 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 5.47034323e-01 9.92093235e-02 -6.51814416e-02 -6.03616893e-01
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4491b3df-12fe-4d1f-9f2d-02344b7ef5e5 | unfolding-of-crumpled-thin-sheets | 2102.09995 | null | https://arxiv.org/abs/2102.09995v1 | https://arxiv.org/pdf/2102.09995v1.pdf | Unfolding of Crumpled Thin Sheets | Crumpled thin sheets are complex fractal structures whose physical properties are influenced by a hierarchy of ridges. In this Letter, we report experiments that measure the stress-strain relation and show the coexistence of phases in the stretching of crumpled surfaces. The pull stress showed a change from a linear Hookean regime to a sublinear scaling with an exponent of $0.65 \pm 0.03$, which is identified with the Hurst exponent of the crumpled sheets. The stress fluctuations are studied, the statistical distribution of force peaks is analyzed and it is shown how the unpacking of crumpled sheets is guided by long distance interactions. | ['Marcelo A F Gomes', 'Francisco C B Leal'] | 2021-02-19 | null | null | null | null | ['stress-strain-relation'] | ['miscellaneous'] | [ 3.42192613e-02 -3.65611538e-02 -1.31804764e-01 4.75600362e-03
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ed8cae55-2197-44f3-8fa5-303d88f76bee | sepmark-deep-separable-watermarking-for | 2305.06321 | null | https://arxiv.org/abs/2305.06321v1 | https://arxiv.org/pdf/2305.06321v1.pdf | SepMark: Deep Separable Watermarking for Unified Source Tracing and Deepfake Detection | Malicious Deepfakes have led to a sharp conflict over distinguishing between genuine and forged faces. Although many countermeasures have been developed to detect Deepfakes ex-post, undoubtedly, passive forensics has not considered any preventive measures for the pristine face before foreseeable manipulations. To complete this forensics ecosystem, we thus put forward the proactive solution dubbed SepMark, which provides a unified framework for source tracing and Deepfake detection. SepMark originates from encoder-decoder-based deep watermarking but with two separable decoders. For the first time the deep separable watermarking, SepMark brings a new paradigm to the established study of deep watermarking, where a single encoder embeds one watermark elegantly, while two decoders can extract the watermark separately at different levels of robustness. The robust decoder termed Tracer that resists various distortions may have an overly high level of robustness, allowing the watermark to survive both before and after Deepfake. The semi-robust one termed Detector is selectively sensitive to malicious distortions, making the watermark disappear after Deepfake. Only SepMark comprising of Tracer and Detector can reliably trace the trusted source of the marked face and detect whether it has been altered since being marked; neither of the two alone can achieve this. Extensive experiments demonstrate the effectiveness of the proposed SepMark on typical Deepfakes, including face swapping, expression reenactment, and attribute editing. | ['Bo Ou', 'Xin Liao', 'Xiaoshuai Wu'] | 2023-05-10 | null | null | null | null | ['deepfake-detection', 'face-swapping'] | ['computer-vision', 'computer-vision'] | [ 4.42156881e-01 2.49437839e-01 -2.05513209e-01 1.86906248e-01
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89b6ba84-a133-4434-9de2-4483b5a28168 | online-parameter-estimation-and-change-point | 2302.04407 | null | https://arxiv.org/abs/2302.04407v2 | https://arxiv.org/pdf/2302.04407v2.pdf | Bayesian Non-parametric Hidden Markov Model for Agile Radar Pulse Sequences Streaming Analysis | Multi-function radars (MFRs) are sophisticated types of sensors with the capabilities of complex agile inter-pulse modulation implementation and dynamic work mode scheduling. The developments in MFRs pose great challenges to modern electronic reconnaissance systems or radar warning receivers for recognition and inference of MFR work modes. To address this issue, this paper proposes an online processing framework for parameter estimation and change point detection of MFR work modes. At first, this paper designed a fully-conjugate Bayesian non-parametric hidden Markov model with a designed prior distribution (agile BNP-HMM) to represent the MFR pulse agility characteristics. The proposed model allows fully-variational Bayesian inference. Then, the proposed framework is constructed by two main parts. The first part is the agile BNP-HMM model for automatically inferring the number of HMM hidden states and emission distribution of the corresponding hidden states. An estimation error lower bound on performance is derived and the proposed algorithm is shown to be close to the bound. The second part utilizes the streaming Bayesian updating to facilitate computation, and designed an online work mode change detection framework based upon a weighted sequential probability ratio test. We demonstrate that the proposed framework is consistently highly effective and robust to baseline methods on diverse simulated data-sets. | ['Shafei Wang', 'Mengtao Zhu', 'Yunjie Li', 'Jiadi Bao'] | 2023-02-09 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 5.91292143e-01 -4.15901780e-01 1.06368728e-01 -5.12416005e-01
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f9a2c7bd-1ddf-4ebf-90da-e83d2a1e52f6 | a-clustering-based-framework-for-classifying | 2106.11823 | null | https://arxiv.org/abs/2106.11823v1 | https://arxiv.org/pdf/2106.11823v1.pdf | A Clustering-based Framework for Classifying Data Streams | The non-stationary nature of data streams strongly challenges traditional machine learning techniques. Although some solutions have been proposed to extend traditional machine learning techniques for handling data streams, these approaches either require an initial label set or rely on specialized design parameters. The overlap among classes and the labeling of data streams constitute other major challenges for classifying data streams. In this paper, we proposed a clustering-based data stream classification framework to handle non-stationary data streams without utilizing an initial label set. A density-based stream clustering procedure is used to capture novel concepts with a dynamic threshold and an effective active label querying strategy is introduced to continuously learn the new concepts from the data streams. The sub-cluster structure of each cluster is explored to handle the overlap among classes. Experimental results and quantitative comparison studies reveal that the proposed method provides statistically better or comparable performance than the existing methods. | ['Edward Tunstel', 'Abenezer Girma', 'Mrinmoy Sarkar', 'Abdollah Homaifar', 'Xuyang Yan'] | 2021-06-22 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 2.15995237e-01 -4.39073086e-01 -5.24787724e-01 -6.64816320e-01
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13b04639-8556-4581-86ce-9fb84e81dcf6 | a-bandit-approach-to-online-pricing-for | 2302.06953 | null | https://arxiv.org/abs/2302.06953v1 | https://arxiv.org/pdf/2302.06953v1.pdf | A Bandit Approach to Online Pricing for Heterogeneous Edge Resource Allocation | Edge Computing (EC) offers a superior user experience by positioning cloud resources in close proximity to end users. The challenge of allocating edge resources efficiently while maximizing profit for the EC platform remains a sophisticated problem, especially with the added complexity of the online arrival of resource requests. To address this challenge, we propose to cast the problem as a multi-armed bandit problem and develop two novel online pricing mechanisms, the Kullback-Leibler Upper Confidence Bound (KL-UCB) algorithm and the Min-Max Optimal algorithm, for heterogeneous edge resource allocation. These mechanisms operate in real-time and do not require prior knowledge of demand distribution, which can be difficult to obtain in practice. The proposed posted pricing schemes allow users to select and pay for their preferred resources, with the platform dynamically adjusting resource prices based on observed historical data. Numerical results show the advantages of the proposed mechanisms compared to several benchmark schemes derived from traditional bandit algorithms, including the Epsilon-Greedy, basic UCB, and Thompson Sampling algorithms. | ['Vijay K. Bhargava', 'Duong Tung Nguyen', 'Lele Wang', 'Duong Thuy Anh Nguyen', 'Jiaming Cheng'] | 2023-02-14 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [-5.52294612e-01 -2.79609501e-01 -7.19505847e-01 6.48701563e-02
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bdd2096f-0252-4f0f-b0e8-4a788b1be8a9 | validation-of-a-deep-learning-mammography | 1911.00364 | null | https://arxiv.org/abs/1911.00364v1 | https://arxiv.org/pdf/1911.00364v1.pdf | Validation of a deep learning mammography model in a population with low screening rates | A key promise of AI applications in healthcare is in increasing access to quality medical care in under-served populations and emerging markets. However, deep learning models are often only trained on data from advantaged populations that have the infrastructure and resources required for large-scale data collection. In this paper, we aim to empirically investigate the potential impact of such biases on breast cancer detection in mammograms. We specifically explore how a deep learning algorithm trained on screening mammograms from the US and UK generalizes to mammograms collected at a hospital in China, where screening is not widely implemented. For the evaluation, we use a top-scoring model developed for the Digital Mammography DREAM Challenge. Despite the change in institution and population composition, we find that the model generalizes well, exhibiting similar performance to that achieved in the DREAM Challenge, even when controlling for tumor size. We also illustrate a simple but effective method for filtering predictions based on model variance, which can be particularly useful for deployment in new settings. While there are many components in developing a clinically effective system, these results represent a promising step towards increasing access to life-saving screening mammography in populations where screening rates are currently low. | ['Greg Sorensen', 'Bill Lotter', 'Yaping Wu', 'Hongna Tan', 'Meiyun Wang', 'Kevin Wu', 'Eric Wu'] | 2019-11-01 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 3.13154072e-01 3.95259112e-01 -4.61654305e-01 -6.22672319e-01
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d8becd92-ab79-43ac-afa2-a42b0cf93e91 | on-the-state-of-the-art-in-authorship | 2209.06869 | null | https://arxiv.org/abs/2209.06869v2 | https://arxiv.org/pdf/2209.06869v2.pdf | On the State of the Art in Authorship Attribution and Authorship Verification | Despite decades of research on authorship attribution (AA) and authorship verification (AV), inconsistent dataset splits/filtering and mismatched evaluation methods make it difficult to assess the state of the art. In this paper, we present a survey of the fields, resolve points of confusion, introduce Valla that standardizes and benchmarks AA/AV datasets and metrics, provide a large-scale empirical evaluation, and provide apples-to-apples comparisons between existing methods. We evaluate eight promising methods on fifteen datasets (including distribution-shifted challenge sets) and introduce a new large-scale dataset based on texts archived by Project Gutenberg. Surprisingly, we find that a traditional Ngram-based model performs best on 5 (of 7) AA tasks, achieving an average macro-accuracy of $76.50\%$ (compared to $66.71\%$ for a BERT-based model). However, on the two AA datasets with the greatest number of words per author, as well as on the AV datasets, BERT-based models perform best. While AV methods are easily applied to AA, they are seldom included as baselines in AA papers. We show that through the application of hard-negative mining, AV methods are competitive alternatives to AA methods. Valla and all experiment code can be found here: https://github.com/JacobTyo/Valla | ['Zachary C. Lipton', 'Bhuwan Dhingra', 'Jacob Tyo'] | 2022-09-14 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [ 1.15930205e-02 -2.90476322e-01 -5.00161350e-01 -2.92322159e-01
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a4683762-066c-494e-9e63-218ea685a06c | musical-information-extraction-from-the | 2204.03166 | null | https://arxiv.org/abs/2204.03166v1 | https://arxiv.org/pdf/2204.03166v1.pdf | Musical Information Extraction from the Singing Voice | Music information retrieval is currently an active research area that addresses the extraction of musically important information from audio signals, and the applications of such information. The extracted information can be used for search and retrieval of music in recommendation systems, or to aid musicological studies or even in music learning. Sophisticated signal processing techniques are applied to convert low-level acoustic signal properties to musical attributes which are further embedded in a rule-based or statistical classification framework to link with high-level descriptions such as melody, genre, mood and artist type. Vocal music comprises a large and interesting category of music where the lead instrument is the singing voice. The singing voice is more versatile than many musical instruments and therefore poses interesting challenges to information retrieval systems. In this paper, we provide a brief overview of research in vocal music processing followed by a description of related work at IIT Bombay leading to the development of an interface for melody detection of singing voice in polyphony. | ['Preeti Rao'] | 2022-04-07 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [ 4.52519715e-01 -5.32023787e-01 -8.55667591e-02 -2.59958766e-02
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d00c056f-22f3-4f44-b7c7-74d758bca655 | data-augmentation-for-learning-bilingual-word | 2006.00262 | null | https://arxiv.org/abs/2006.00262v3 | https://arxiv.org/pdf/2006.00262v3.pdf | Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings | Unsupervised cross-lingual word embedding (CLWE) methods learn a linear transformation matrix that maps two monolingual embedding spaces that are separately trained with monolingual corpora. This method relies on the assumption that the two embedding spaces are structurally similar, which does not necessarily hold true in general. In this paper, we argue that using a pseudo-parallel corpus generated by an unsupervised machine translation model facilitates the structural similarity of the two embedding spaces and improves the quality of CLWEs in the unsupervised mapping method. We show that our approach outperforms other alternative approaches given the same amount of data, and, through detailed analysis, we show that data augmentation with the pseudo data from unsupervised machine translation is especially effective for mapping-based CLWEs because (1) the pseudo data makes the source and target corpora (partially) parallel; (2) the pseudo data contains information on the original language that helps to learn similar embedding spaces between the source and target languages. | ['Ryokan Ri', 'Yoshimasa Tsuruoka', 'Sosuke Nishikawa'] | 2020-05-30 | null | https://aclanthology.org/2021.acl-srw.17 | https://aclanthology.org/2021.acl-srw.17.pdf | acl-2021-5 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [-1.01002164e-01 2.44462311e-01 -4.26633149e-01 -1.85015246e-01
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dbfb724b-ab28-4a99-a6ed-610e09d81647 | online-continuous-hyperparameter-optimization | 2302.09440 | null | https://arxiv.org/abs/2302.09440v2 | https://arxiv.org/pdf/2302.09440v2.pdf | Online Continuous Hyperparameter Optimization for Contextual Bandits | In stochastic contextual bandits, an agent sequentially makes actions from a time-dependent action set based on past experience to minimize the cumulative regret. Like many other machine learning algorithms, the performance of bandits heavily depends on their multiple hyperparameters, and theoretically derived parameter values may lead to unsatisfactory results in practice. Moreover, it is infeasible to use offline tuning methods like cross-validation to choose hyperparameters under the bandit environment, as the decisions should be made in real time. To address this challenge, we propose the first online continuous hyperparameter tuning framework for contextual bandits to learn the optimal parameter configuration within a search space on the fly. Specifically, we use a double-layer bandit framework named CDT (Continuous Dynamic Tuning) and formulate the hyperparameter optimization as a non-stationary continuum-armed bandit, where each arm represents a combination of hyperparameters, and the corresponding reward is the algorithmic result. For the top layer, we propose the Zooming TS algorithm that utilizes Thompson Sampling (TS) for exploration and a restart technique to get around the switching environment. The proposed CDT framework can be easily used to tune contextual bandit algorithms without any pre-specified candidate set for hyperparameters. We further show that it could achieve sublinear regret in theory and performs consistently better on both synthetic and real datasets in practice. | ['Thomas C. M. Lee', 'Cho-Jui Hsieh', 'Yue Kang'] | 2023-02-18 | null | null | null | null | ['thompson-sampling', 'multi-armed-bandits'] | ['methodology', 'miscellaneous'] | [ 1.01297587e-01 -2.32736930e-01 -8.05542767e-01 -5.46200834e-02
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9b8e5cc1-acb8-46b6-81bf-ea49776555fb | cross-modal-weighting-network-for-rgb-d | 2007.04901 | null | https://arxiv.org/abs/2007.04901v1 | https://arxiv.org/pdf/2007.04901v1.pdf | Cross-Modal Weighting Network for RGB-D Salient Object Detection | Depth maps contain geometric clues for assisting Salient Object Detection (SOD). In this paper, we propose a novel Cross-Modal Weighting (CMW) strategy to encourage comprehensive interactions between RGB and depth channels for RGB-D SOD. Specifically, three RGB-depth interaction modules, named CMW-L, CMW-M and CMW-H, are developed to deal with respectively low-, middle- and high-level cross-modal information fusion. These modules use Depth-to-RGB Weighing (DW) and RGB-to-RGB Weighting (RW) to allow rich cross-modal and cross-scale interactions among feature layers generated by different network blocks. To effectively train the proposed Cross-Modal Weighting Network (CMWNet), we design a composite loss function that summarizes the errors between intermediate predictions and ground truth over different scales. With all these novel components working together, CMWNet effectively fuses information from RGB and depth channels, and meanwhile explores object localization and details across scales. Thorough evaluations demonstrate CMWNet consistently outperforms 15 state-of-the-art RGB-D SOD methods on seven popular benchmarks. | ['Haibin Ling', 'Gongyang Li', 'Yang Wang', 'Linwei Ye', 'Zhi Liu'] | 2020-07-09 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2864_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123620647.pdf | eccv-2020-8 | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [-1.04143173e-01 1.34313971e-01 4.44468763e-03 -5.29452145e-01
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b6af5f07-dd61-4f0f-9641-9c92b1d897ea | ultrafast-non-destructive-measurement-of-the | 2012.12069 | null | https://arxiv.org/abs/2012.12069v1 | https://arxiv.org/pdf/2012.12069v1.pdf | Ultrafast non-destructive measurement of the quantum state of light using free electrons | Since the birth of quantum optics, the measurement of quantum states of nonclassical light has been of tremendous importance for advancement in the field. To date, conventional detectors such as photomultipliers, avalanche photodiodes, and superconducting nanowires, all rely at their core on linear excitation of bound electrons with light, posing fundamental restrictions on the detection. In contrast, the interaction of free electrons with light in the context of quantum optics is highly nonlinear and offers exciting possibilities. The first experiments that promoted this direction appeared over the past decade as part of photon-induced nearfield electron microscopy (PINEM), wherein free electrons are capable of high-order multi-photon absorption and emission. Here we propose using free electrons for quantum-optical detection of the complete quantum state of light. We show how the precise control of the electron before and after its interaction with quantum light enables to extract the photon statistics and implement full quantum state tomography using PINEM. This technique can reach sub-attosecond time resolutions, measure temporal coherence of any degree (e.g., g(1), g(2)), and simultaneously detect large numbers of photons with each electron. Importantly, the interaction of the electron with light is non-destructive, thereby leaving the photonic state (modified by the interaction) intact, which is conceptually different from conventional detectors. By using a pulse of multiple electrons, we envision how PINEM quantum detectors could achieve a single-shot measurement of the complete state of quantum light, even for non-reproducible emission events. Altogether, our work paves the way to novel kinds of photodetectors that utilize the ultrafast duration, high nonlinearity, and non-destructive nature of electron-light interactions. | ['Ido Kaminer', "Avi Pe'er", 'Eliahu Cohen', 'Raphael Dahan', 'Aviv Karnieli', 'Alexey Gorlach'] | 2020-12-22 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 4.83432055e-01 -3.17295820e-01 2.86214113e-01 2.66167875e-02
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12eb224a-0309-4d77-9924-b397b89bbfc7 | assessing-the-potential-of-classical-q | 1810.06078 | null | http://arxiv.org/abs/1810.06078v1 | http://arxiv.org/pdf/1810.06078v1.pdf | Assessing the Potential of Classical Q-learning in General Game Playing | After the recent groundbreaking results of AlphaGo and AlphaZero, we have
seen strong interests in deep reinforcement learning and artificial general
intelligence (AGI) in game playing. However, deep learning is
resource-intensive and the theory is not yet well developed. For small games,
simple classical table-based Q-learning might still be the algorithm of choice.
General Game Playing (GGP) provides a good testbed for reinforcement learning
to research AGI. Q-learning is one of the canonical reinforcement learning
methods, and has been used by (Banerjee $\&$ Stone, IJCAI 2007) in GGP. In this
paper we implement Q-learning in GGP for three small-board games (Tic-Tac-Toe,
Connect Four, Hex)\footnote{source code: https://github.com/wh1992v/ggp-rl}, to
allow comparison to Banerjee et al.. We find that Q-learning converges to a
high win rate in GGP. For the $\epsilon$-greedy strategy, we propose a first
enhancement, the dynamic $\epsilon$ algorithm. In addition, inspired by (Gelly
$\&$ Silver, ICML 2007) we combine online search (Monte Carlo Search) to
enhance offline learning, and propose QM-learning for GGP. Both enhancements
improve the performance of classical Q-learning. In this work, GGP allows us to
show, if augmented by appropriate enhancements, that classical table-based
Q-learning can perform well in small games. | ['Michael Emmerich', 'Hui Wang', 'Aske Plaat'] | 2018-10-14 | null | null | null | null | ['board-games'] | ['playing-games'] | [-5.47566414e-01 -7.89215639e-02 -2.30167821e-01 3.21733296e-01
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ae87ab2d-eece-4e04-919c-55bf0f7dc3e5 | image-outpainting-and-harmonization-using | 1912.10960 | null | https://arxiv.org/abs/1912.10960v2 | https://arxiv.org/pdf/1912.10960v2.pdf | Image Outpainting and Harmonization using Generative Adversarial Networks | Although the inherently ambiguous task of predicting what resides beyond all four edges of an image has rarely been explored before, we demonstrate that GANs hold powerful potential in producing reasonable extrapolations. Two outpainting methods are proposed that aim to instigate this line of research: the first approach uses a context encoder inspired by common inpainting architectures and paradigms, while the second approach adds an extra post-processing step using a single-image generative model. This way, the hallucinated details are integrated with the style of the original image, in an attempt to further boost the quality of the result and possibly allow for arbitrary output resolutions to be supported. | ['Basile Van Hoorick'] | 2019-12-23 | null | null | null | null | ['image-outpainting'] | ['computer-vision'] | [ 6.69885933e-01 4.62507069e-01 1.12457991e-01 -2.70456433e-01
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1a4233a6-10a1-44ab-9ead-34e127d96cfd | predictive-incrementality-by-experimentation | 2304.06828 | null | https://arxiv.org/abs/2304.06828v1 | https://arxiv.org/pdf/2304.06828v1.pdf | Predictive Incrementality by Experimentation (PIE) for Ad Measurement | We present a novel approach to causal measurement for advertising, namely to use exogenous variation in advertising exposure (RCTs) for a subset of ad campaigns to build a model that can predict the causal effect of ad campaigns that were run without RCTs. This approach -- Predictive Incrementality by Experimentation (PIE) -- frames the task of estimating the causal effect of an ad campaign as a prediction problem, with the unit of observation being an RCT itself. In contrast, traditional causal inference approaches with observational data seek to adjust covariate imbalance at the user level. A key insight is to use post-campaign features, such as last-click conversion counts, that do not require an RCT, as features in our predictive model. We find that our PIE model recovers RCT-derived incremental conversions per dollar (ICPD) much better than the program evaluation approaches analyzed in Gordon et al. (forthcoming). The prediction errors from the best PIE model are 48%, 42%, and 62% of the RCT-based average ICPD for upper-, mid-, and lower-funnel conversion outcomes, respectively. In contrast, across the same data, the average prediction error of stratified propensity score matching exceeds 491%, and that of double/debiased machine learning exceeds 2,904%. Using a decision-making framework inspired by industry, we show that PIE leads to different decisions compared to RCTs for only 6% of upper-funnel, 7% of mid-funnel, and 13% of lower-funnel outcomes. We conclude that PIE could enable advertising platforms to scale causal ad measurement by extrapolating from a limited number of RCTs to a large set of non-experimental ad campaigns. | ['Florian Zettelmeyer', 'Robert Moakler', 'Brett R. Gordon'] | 2023-04-13 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 4.31042969e-01 4.07581657e-01 -1.33828521e+00 -5.06363213e-01
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6d76232c-f45b-42c0-a3d1-2b9b7483a9ad | let-2d-diffusion-model-know-3d-consistency | 2303.07937 | null | https://arxiv.org/abs/2303.07937v3 | https://arxiv.org/pdf/2303.07937v3.pdf | Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation | Text-to-3D generation has shown rapid progress in recent days with the advent of score distillation, a methodology of using pretrained text-to-2D diffusion models to optimize neural radiance field (NeRF) in the zero-shot setting. However, the lack of 3D awareness in the 2D diffusion models destabilizes score distillation-based methods from reconstructing a plausible 3D scene. To address this issue, we propose 3DFuse, a novel framework that incorporates 3D awareness into pretrained 2D diffusion models, enhancing the robustness and 3D consistency of score distillation-based methods. We realize this by first constructing a coarse 3D structure of a given text prompt and then utilizing projected, view-specific depth map as a condition for the diffusion model. Additionally, we introduce a training strategy that enables the 2D diffusion model learns to handle the errors and sparsity within the coarse 3D structure for robust generation, as well as a method for ensuring semantic consistency throughout all viewpoints of the scene. Our framework surpasses the limitations of prior arts, and has significant implications for 3D consistent generation of 2D diffusion models. | ['Seungryong Kim', 'Jiyoung Lee', 'Jin-Hwa Kim', 'Junho Kim', 'Hyeonsu Kim', 'Jaehoon Ko', 'Min-Seop Kwak', 'Wooseok Jang', 'Junyoung Seo'] | 2023-03-14 | null | null | null | null | ['single-view-3d-reconstruction', 'text-to-3d'] | ['computer-vision', 'computer-vision'] | [ 1.06569618e-01 1.82954386e-01 5.32839745e-02 -2.60318637e-01
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3841a2a0-e638-4ce0-adf3-5bb1192aad66 | data-augmentation-for-low-resource-dialogue | null | null | https://openreview.net/forum?id=8KHmFphT9BA | https://openreview.net/pdf?id=8KHmFphT9BA | Data Augmentation for Low-Resource Dialogue Summarization | We present DADS, a novel Data Augmentation technique for low-resource Dialogue Summarization. Our method generates synthetic examples by replacing sections of text from both the input dialogue and summary while preserving the augmented summary to correspond to a viable summary for the augmented dialogue. We utilize pretrained language models that produce highly likely dialogue alternatives while still being free to generate diverse alternatives. We applied our data augmentation method to the SAMSum dataset in low resource scenarios, mimicking real world problems such as chat, thread, and meeting summarization where large scale supervised datasets with human-written summaries are scarce. Through both automatic and human evaluations, we show that DADS shows strong improvements for low resource scenarios while generating topically diverse summaries without introducing additional hallucinations to the summaries. | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['meeting-summarization'] | ['natural-language-processing'] | [ 5.10931671e-01 9.76717293e-01 -2.70963442e-02 -3.34236294e-01
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b5ef67a0-0e07-4d1d-8dd7-22c9f24e64c8 | meta-learning-for-mixed-linear-regression | 2002.08936 | null | https://arxiv.org/abs/2002.08936v1 | https://arxiv.org/pdf/2002.08936v1.pdf | Meta-learning for mixed linear regression | In modern supervised learning, there are a large number of tasks, but many of them are associated with only a small amount of labeled data. These include data from medical image processing and robotic interaction. Even though each individual task cannot be meaningfully trained in isolation, one seeks to meta-learn across the tasks from past experiences by exploiting some similarities. We study a fundamental question of interest: When can abundant tasks with small data compensate for lack of tasks with big data? We focus on a canonical scenario where each task is drawn from a mixture of $k$ linear regressions, and identify sufficient conditions for such a graceful exchange to hold; The total number of examples necessary with only small data tasks scales similarly as when big data tasks are available. To this end, we introduce a novel spectral approach and show that we can efficiently utilize small data tasks with the help of $\tilde\Omega(k^{3/2})$ medium data tasks each with $\tilde\Omega(k^{1/2})$ examples. | ['Zhao Song', 'Sewoong Oh', 'Raghav Somani', 'Weihao Kong', 'Sham Kakade'] | 2020-02-20 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/6124-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/6124-Paper.pdf | icml-2020-1 | ['small-data'] | ['computer-vision'] | [ 4.27447051e-01 2.85135329e-01 -2.02127337e-01 -2.69559294e-01
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d36ad80b-7e88-4161-ae7e-a6bf02af5931 | in-defense-of-sparsity-based-face-recognition | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Deng_In_Defense_of_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Deng_In_Defense_of_2013_CVPR_paper.pdf | In Defense of Sparsity Based Face Recognition | The success of sparse representation based classification (SRC) has largely boosted the research of sparsity based face recognition in recent years. A prevailing view is that the sparsity based face recognition performs well only when the training images have been carefully controlled and the number of samples per class is sufficiently large. This paper challenges the prevailing view by proposing a "prototype plus variation" representation model for sparsity based face recognition. Based on the new model, a Superposed SRC (SSRC), in which the dictionary is assembled by the class centroids and the sample-to-centroid differences, leads to a substantial improvement on SRC. The experiments results on AR, FERET and FRGC databases validate that, if the proposed prototype plus variation representation model is applied, sparse coding plays a crucial role in face recognition, and performs well even when the dictionary bases are collected under uncontrolled conditions and only a single sample per classes is available. | ['Weihong Deng', 'Jiani Hu', 'Jun Guo'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 4.32544053e-01 -3.92360747e-01 -3.45027328e-01 -5.24457514e-01
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ec1e8d35-4cf1-4ee6-bf41-fba13ead8ec4 | feature-embedding-by-template-matching-as-a | 2210.00992 | null | https://arxiv.org/abs/2210.00992v1 | https://arxiv.org/pdf/2210.00992v1.pdf | Feature Embedding by Template Matching as a ResNet Block | Convolution blocks serve as local feature extractors and are the key to success of the neural networks. To make local semantic feature embedding rather explicit, we reformulate convolution blocks as feature selection according to the best matching kernel. In this manner, we show that typical ResNet blocks indeed perform local feature embedding via template matching once batch normalization (BN) followed by a rectified linear unit (ReLU) is interpreted as arg-max optimizer. Following this perspective, we tailor a residual block that explicitly forces semantically meaningful local feature embedding through using label information. Specifically, we assign a feature vector to each local region according to the classes that the corresponding region matches. We evaluate our method on three popular benchmark datasets with several architectures for image classification and consistently show that our approach substantially improves the performance of the baseline architectures. | ['A. Aydin Alatan', 'Yeti Z. Gurbuz', 'Ada Gorgun'] | 2022-10-03 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 6.36018440e-02 -1.87894665e-02 -4.62672621e-01 -6.87956870e-01
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ef89a023-4e8e-484b-b490-73ee9fd961a2 | robust-sparse-mean-estimation-via-incremental | 2305.15276 | null | https://arxiv.org/abs/2305.15276v1 | https://arxiv.org/pdf/2305.15276v1.pdf | Robust Sparse Mean Estimation via Incremental Learning | In this paper, we study the problem of robust sparse mean estimation, where the goal is to estimate a $k$-sparse mean from a collection of partially corrupted samples drawn from a heavy-tailed distribution. Existing estimators face two critical challenges in this setting. First, they are limited by a conjectured computational-statistical tradeoff, implying that any computationally efficient algorithm needs $\tilde\Omega(k^2)$ samples, while its statistically-optimal counterpart only requires $\tilde O(k)$ samples. Second, the existing estimators fall short of practical use as they scale poorly with the ambient dimension. This paper presents a simple mean estimator that overcomes both challenges under moderate conditions: it runs in near-linear time and memory (both with respect to the ambient dimension) while requiring only $\tilde O(k)$ samples to recover the true mean. At the core of our method lies an incremental learning phenomenon: we introduce a simple nonconvex framework that can incrementally learn the top-$k$ nonzero elements of the mean while keeping the zero elements arbitrarily small. Unlike existing estimators, our method does not need any prior knowledge of the sparsity level $k$. We prove the optimality of our estimator by providing a matching information-theoretic lower bound. Finally, we conduct a series of simulations to corroborate our theoretical findings. Our code is available at https://github.com/huihui0902/Robust_mean_estimation. | ['Wei Hu', 'Salar Fattahi', 'Yinghui He', 'Rui Ray Chen', 'Jianhao Ma'] | 2023-05-24 | null | null | null | null | ['incremental-learning'] | ['methodology'] | [ 9.27924514e-02 6.84665143e-02 -2.05003873e-01 -1.34291276e-01
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a1df6ebf-d86f-4013-b153-401de157b8ff | transfer-and-active-learning-for-dissonance | 2305.02459 | null | https://arxiv.org/abs/2305.02459v2 | https://arxiv.org/pdf/2305.02459v2.pdf | Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge | While transformer-based systems have enabled greater accuracies with fewer training examples, data acquisition obstacles still persist for rare-class tasks -- when the class label is very infrequent (e.g. < 5% of samples). Active learning has in general been proposed to alleviate such challenges, but choice of selection strategy, the criteria by which rare-class examples are chosen, has not been systematically evaluated. Further, transformers enable iterative transfer-learning approaches. We propose and investigate transfer- and active learning solutions to the rare class problem of dissonance detection through utilizing models trained on closely related tasks and the evaluation of acquisition strategies, including a proposed probability-of-rare-class (PRC) approach. We perform these experiments for a specific rare class problem: collecting language samples of cognitive dissonance from social media. We find that PRC is a simple and effective strategy to guide annotations and ultimately improve model accuracy while transfer-learning in a specific order can improve the cold-start performance of the learner but does not benefit iterations of active learning. | ['H. Andrew Schwartz', 'Christian Luhmann', 'Jonah Luby', 'Xiaoran Liu', 'Syeda Mahwish', 'Swanie Juhng', 'Vasudha Varadarajan'] | 2023-05-03 | null | null | null | null | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 5.07104099e-01 2.41183117e-01 -3.36471945e-01 -5.54792702e-01
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82018f1b-2203-43c5-a15f-6bc8606f8b37 | estimating-risk-aware-flexibility-areas-for | 2301.00564 | null | https://arxiv.org/abs/2301.00564v1 | https://arxiv.org/pdf/2301.00564v1.pdf | Estimating Risk-Aware Flexibility Areas for EV Charging Pools via Stochastic AC-OPF | This paper introduces a stochastic AC-OPF (SOPF) for the flexibility management of electric vehicle (EV) charging pools in distribution networks under uncertainty. The SOPF considers discrete utility functions from charging pools as a compensation mechanism for eventual energy not served to their charging tasks. An application of the proposed SOPF is described where a distribution system operator (DSO) requires flexibility to each charging pool in a day-ahead time frame, minimizing the cost for flexibility while guaranteeing technical limits. Flexibility areas are defined for each charging pool and calculated as a function of a risk parameter involving the solution's uncertainty. Results show that all players can benefit from this approach, i.e., the DSO obtains a risk-aware solution, while charging pools/tasks perceive a reduction in the total energy payment due to flexibility services. | ['Johann L. Hurink', 'Gerwin Hoogsteen', 'Maria Vlasiou', 'Pedro P. Vergara', 'Nataly Banol Arias', 'Juan S. Giraldo'] | 2023-01-02 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-4.29720730e-01 5.44394612e-01 -2.04086870e-01 -4.29256529e-01
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5a5c83ea-690f-4a8b-8ce1-53cb014b3a1f | hsc-rocket-an-interactive-dialogue-assistant | null | null | https://openreview.net/forum?id=tiuszgEsW9 | https://openreview.net/pdf?id=tiuszgEsW9 | HSC-Rocket: An interactive dialogue assistant to make agents composing service better through human feedback | Facing the current dynamic service environment, fast and efficient service composition has attracted great attention in recent years. Users prefer to express their personal requirements based on natural language, and their real-time feedback could better reflect the effect of service composition to a great extent. Consequently, this paper designs an interactive dialogue assistant, HSC-Rocket, to better provide service composition by considering human feedback. Firstly, we propose a human-computer interaction dynamic service composition algorithm based on reinforcement learning. The design of the reward mechanism considers the quality of service (QoS) and real-time feedback, which can more accurately meet the demands of users. Then, the functional requirements are analyzed through word embedding, to realize the dynamic composition of abstract and concrete services. Furthermore, we utilize the sample enhancement method to alleviate the issue of fewer sample data in the initial stage of user interaction, which improves the robustness of our system. Accordingly, we have implemented the HSC-Rocket prototype, which allows users to obtain multi-domain dialogue requirements. Extensive experiments on the RapidAPI dataset have demonstrated the superiority and effectiveness of the HSC-Rocket. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['service-composition'] | ['miscellaneous'] | [-1.22809455e-01 -3.91531706e-01 2.55444981e-02 -7.87503779e-01
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d47d62dc-ea19-4b02-9489-6bc33429d669 | progrest-prototypical-graph-regression-soft | 2210.03745 | null | https://arxiv.org/abs/2210.03745v2 | https://arxiv.org/pdf/2210.03745v2.pdf | ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Prediction | In this work, we propose the novel Prototypical Graph Regression Self-explainable Trees (ProGReST) model, which combines prototype learning, soft decision trees, and Graph Neural Networks. In contrast to other works, our model can be used to address various challenging tasks, including compound property prediction. In ProGReST, the rationale is obtained along with prediction due to the model's built-in interpretability. Additionally, we introduce a new graph prototype projection to accelerate model training. Finally, we evaluate PRoGReST on a wide range of chemical datasets for molecular property prediction and perform in-depth analysis with chemical experts to evaluate obtained interpretations. Our method achieves competitive results against state-of-the-art methods. | ['Tomasz Danel', 'Daniel Dobrowolski', 'Dawid Rymarczyk'] | 2022-10-07 | null | null | null | null | ['graph-regression', 'molecular-property-prediction'] | ['graphs', 'miscellaneous'] | [ 7.70543873e-01 5.81025898e-01 -9.03876364e-01 -2.66450137e-01
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37b24651-d1c9-495c-b685-aece151a7f77 | laugh-betrays-you-learning-robust-speaker | 2210.16028 | null | https://arxiv.org/abs/2210.16028v2 | https://arxiv.org/pdf/2210.16028v2.pdf | Laugh Betrays You? Learning Robust Speaker Representation From Speech Containing Non-Verbal Fragments | The success of automatic speaker verification shows that discriminative speaker representations can be extracted from neutral speech. However, as a kind of non-verbal voice, laughter should also carry speaker information intuitively. Thus, this paper focuses on exploring speaker verification about utterances containing non-verbal laughter segments. We collect a set of clips with laughter components by conducting a laughter detection script on VoxCeleb and part of the CN-Celeb dataset. To further filter untrusted clips, probability scores are calculated by our binary laughter detection classifier, which is pre-trained by pure laughter and neutral speech. After that, based on the clips whose scores are over the threshold, we construct trials under two different evaluation scenarios: Laughter-Laughter (LL) and Speech-Laughter (SL). Then a novel method called Laughter-Splicing based Network (LSN) is proposed, which can significantly boost performance in both scenarios and maintain the performance on the neutral speech, such as the VoxCeleb1 test set. Specifically, our system achieves relative 20% and 22% improvement on Laughter-Laughter and Speech-Laughter trials, respectively. The meta-data and sample clips have been released at https://github.com/nevermoreLin/Laugh_LSN. | ['Ming Li', 'Zhenyi Zhu', 'Huahua Cui', 'Xiaoyi Qin', 'Yuke Lin'] | 2022-10-28 | null | null | null | null | ['speaker-verification'] | ['speech'] | [-9.13331062e-02 8.53837579e-02 -1.79569095e-01 -6.23163342e-01
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410d548d-b803-425f-8aa8-91d5ebf572b4 | evaluating-bayesian-model-visualisations | 2201.03604 | null | https://arxiv.org/abs/2201.03604v1 | https://arxiv.org/pdf/2201.03604v1.pdf | Evaluating Bayesian Model Visualisations | Probabilistic models inform an increasingly broad range of business and policy decisions ultimately made by people. Recent algorithmic, computational, and software framework development progress facilitate the proliferation of Bayesian probabilistic models, which characterise unobserved parameters by their joint distribution instead of point estimates. While they can empower decision makers to explore complex queries and to perform what-if-style conditioning in theory, suitable visualisations and interactive tools are needed to maximise users' comprehension and rational decision making under uncertainty. In this paper, propose a protocol for quantitative evaluation of Bayesian model visualisations and introduce a software framework implementing this protocol to support standardisation in evaluation practice and facilitate reproducibility. We illustrate the evaluation and analysis workflow on a user study that explores whether making Boxplots and Hypothetical Outcome Plots interactive can increase comprehension or rationality and conclude with design guidelines for researchers looking to conduct similar studies in the future. | ['John H. Williamson', 'Sebastian Stein'] | 2022-01-10 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 2.20899716e-01 5.09106576e-01 -1.90566123e-01 -6.69723690e-01
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238ff60e-6d20-46b7-b1bf-3b8cfc423176 | human-pose-estimation-with-parsing-induced | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Nie_Human_Pose_Estimation_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Nie_Human_Pose_Estimation_CVPR_2018_paper.pdf | Human Pose Estimation With Parsing Induced Learner | Human pose estimation still faces various difficulties in challenging scenarios. Human parsing, as a closely related task, can provide valuable cues for better pose estimation, which however has not been fully exploited. In this paper, we propose a novel Parsing Induced Learner to exploit parsing information to effectively assist pose estimation by learning to fast adapt the base pose estimation model. The proposed Parsing Induced Learner is composed of a parsing encoder and a pose model parameter adapter, which together learn to predict dynamic parameters of the pose model to extract complementary useful features for more accurate pose estimation. Comprehensive experiments on benchmarks LIP and extended PASCAL-Person-Part show that the proposed Parsing Induced Learner can improve performance of both single- and multi-person pose estimation to new state-of-the-art. Cross-dataset experiments also show that the proposed Parsing Induced Learner from LIP dataset can accelerate learning of a human pose estimation model on MPII benchmark in addition to achieving outperforming performance. | ['Shuicheng Yan', 'Yiming Zuo', 'Jiashi Feng', 'Xuecheng Nie'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['human-parsing'] | ['computer-vision'] | [ 1.49721101e-01 1.88686267e-01 -3.99413079e-01 -6.71729505e-01
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c3596677-4075-4c43-ad85-f4285a61462b | damix-density-aware-data-augmentation-for | 2109.12544 | null | https://arxiv.org/abs/2109.12544v2 | https://arxiv.org/pdf/2109.12544v2.pdf | DAMix: A Density-Aware Mixup Augmentation for Single Image Dehazing under Domain Shift | Deep learning-based methods have achieved considerable success on single image dehazing in recent years. However, these methods are often subject to performance degradation when domain shifts are confronted. Specifically, haze density gaps exist among the existing datasets, often resulting in poor performance when these methods are tested across datasets. To address this issue, we propose a density-aware mixup augmentation (DAMix). DAMix generates samples in an attempt to minimize the Wasserstein distance with the hazy images in the target domain. These DAMix-ed samples not only mitigate domain gaps but are also proven to comply with the atmospheric scattering model. Thus, DAMix achieves comprehensive improvements on domain adaptation. Furthermore, we show that DAMix is helpful with respect to data efficiency. Specifically, a network trained with half of the source dataset using DAMix can achieve even better adaptivity than that trained with the whole source dataset but without DAMix. | ['Tsung-Nan Lin', 'Chia-Ming Chang'] | 2021-09-26 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 1.77730456e-01 -1.49823561e-01 3.16762865e-01 -2.47329265e-01
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9d08a894-920a-46c2-94d2-6dae725d3657 | deep-learning-based-edm-subgenre | 2110.08862 | null | https://arxiv.org/abs/2110.08862v1 | https://arxiv.org/pdf/2110.08862v1.pdf | Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features | Along with the evolution of music technology, a large number of styles, or "subgenres," of Electronic Dance Music(EDM) have emerged in recent years. While the classification task of distinguishing between EDM and non-EDM has been often studied in the context of music genre classification, little work has been done on the more challenging EDM subgenre classification. The state-of-art model is based on extremely randomized trees and could be improved by deep learning methods. In this paper, we extend the state-of-art music auto-tagging model "short-chunkCNN+Resnet" to EDM subgenre classification, with the addition of two mid-level tempo-related feature representations, called the Fourier tempogram and autocorrelation tempogram. And, we explore two fusion strategies, early fusion and late fusion, to aggregate the two types of tempograms. We evaluate the proposed models using a large dataset consisting of 75,000 songs for 30 different EDM subgenres, and show that the adoption of deep learning models and tempo features indeed leads to higher classification accuracy. | ['Yi-Hsuan Yang', 'Bo-Yu Chen', 'Wei-Han Hsu'] | 2021-10-17 | null | null | null | null | ['genre-classification', 'music-auto-tagging'] | ['computer-vision', 'music'] | [ 4.34895568e-02 -3.84060085e-01 -1.42510772e-01 -5.58708534e-02
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e0b15770-5af8-47ca-865e-b34ddf085764 | activity-recognition-from-videos-with | 1505.00581 | null | http://arxiv.org/abs/1505.00581v1 | http://arxiv.org/pdf/1505.00581v1.pdf | Activity recognition from videos with parallel hypergraph matching on GPUs | In this paper, we propose a method for activity recognition from videos based
on sparse local features and hypergraph matching. We benefit from special
properties of the temporal domain in the data to derive a sequential and fast
graph matching algorithm for GPUs.
Traditionally, graphs and hypergraphs are frequently used to recognize
complex and often non-rigid patterns in computer vision, either through graph
matching or point-set matching with graphs. Most formulations resort to the
minimization of a difficult discrete energy function mixing geometric or
structural terms with data attached terms involving appearance features.
Traditional methods solve this minimization problem approximately, for instance
with spectral techniques.
In this work, instead of solving the problem approximatively, the exact
solution for the optimal assignment is calculated in parallel on GPUs. The
graphical structure is simplified and regularized, which allows to derive an
efficient recursive minimization algorithm. The algorithm distributes
subproblems over the calculation units of a GPU, which solves them in parallel,
allowing the system to run faster than real-time on medium-end GPUs. | ['Bülent Sankur', 'Christian Wolf', 'Eric Lombardi', 'Oya Celiktutan'] | 2015-05-04 | null | null | null | null | ['set-matching', 'hypergraph-matching'] | ['computer-vision', 'graphs'] | [ 2.74840057e-01 -2.01777101e-01 -4.20544744e-02 -9.69457477e-02
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6ff76458-32bc-4ec6-9946-24a350d66b04 | contour-detection-using-cost-sensitive | 1412.6857 | null | http://arxiv.org/abs/1412.6857v5 | http://arxiv.org/pdf/1412.6857v5.pdf | Contour Detection Using Cost-Sensitive Convolutional Neural Networks | We address the problem of contour detection via per-pixel classifications of
edge point. To facilitate the process, the proposed approach leverages with
DenseNet, an efficient implementation of multiscale convolutional neural
networks (CNNs), to extract an informative feature vector for each pixel and
uses an SVM classifier to accomplish contour detection. The main challenge lies
in adapting a pre-trained per-image CNN model for yielding per-pixel image
features. We propose to base on the DenseNet architecture to achieve pixelwise
fine-tuning and then consider a cost-sensitive strategy to further improve the
learning with a small dataset of edge and non-edge image patches. In the
experiment of contour detection, we look into the effectiveness of combining
per-pixel features from different CNN layers and obtain comparable performances
to the state-of-the-art on BSDS500. | ['Tyng-Luh Liu', 'Jyh-Jing Hwang'] | 2014-12-22 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 3.85134101e-01 -7.24531636e-02 -1.51698932e-01 -1.11076601e-01
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f2ab91f0-0c38-4272-aca6-ff6c9f84c4c0 | a-joint-3d-2d-based-method-for-free-space | 1711.02144 | null | http://arxiv.org/abs/1711.02144v3 | http://arxiv.org/pdf/1711.02144v3.pdf | A Joint 3D-2D based Method for Free Space Detection on Roads | In this paper, we address the problem of road segmentation and free space
detection in the context of autonomous driving. Traditional methods either use
3-dimensional (3D) cues such as point clouds obtained from LIDAR, RADAR or
stereo cameras or 2-dimensional (2D) cues such as lane markings, road
boundaries and object detection. Typical 3D point clouds do not have enough
resolution to detect fine differences in heights such as between road and
pavement. Image based 2D cues fail when encountering uneven road textures such
as due to shadows, potholes, lane markings or road restoration. We propose a
novel free road space detection technique combining both 2D and 3D cues. In
particular, we use CNN based road segmentation from 2D images and plane/box
fitting on sparse depth data obtained from SLAM as priors to formulate an
energy minimization using conditional random field (CRF), for road pixels
classification. While the CNN learns the road texture and is unaffected by
depth boundaries, the 3D information helps in overcoming texture based
classification failures. Finally, we use the obtained road segmentation with
the 3D depth data from monocular SLAM to detect the free space for the
navigation purposes. Our experiments on KITTI odometry dataset, Camvid dataset,
as well as videos captured by us, validate the superiority of the proposed
approach over the state of the art. | ['Suvam Patra', 'Subhashis Banerjee', 'Shashank Yadav', 'Pranjal Maheshwari', 'Chetan Arora'] | 2017-11-06 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 4.20363873e-01 5.84638258e-03 -7.47427642e-02 -4.59537894e-01
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015aab7f-571e-4999-a117-056b43d9c29e | text2tex-text-driven-texture-synthesis-via | 2303.11396 | null | https://arxiv.org/abs/2303.11396v1 | https://arxiv.org/pdf/2303.11396v1.pdf | Text2Tex: Text-driven Texture Synthesis via Diffusion Models | We present Text2Tex, a novel method for generating high-quality textures for 3D meshes from the given text prompts. Our method incorporates inpainting into a pre-trained depth-aware image diffusion model to progressively synthesize high resolution partial textures from multiple viewpoints. To avoid accumulating inconsistent and stretched artifacts across views, we dynamically segment the rendered view into a generation mask, which represents the generation status of each visible texel. This partitioned view representation guides the depth-aware inpainting model to generate and update partial textures for the corresponding regions. Furthermore, we propose an automatic view sequence generation scheme to determine the next best view for updating the partial texture. Extensive experiments demonstrate that our method significantly outperforms the existing text-driven approaches and GAN-based methods. | ['Matthias Nießner', 'Sergey Tulyakov', 'Hsin-Ying Lee', 'Yawar Siddiqui', 'Dave Zhenyu Chen'] | 2023-03-20 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 5.55454969e-01 8.35397616e-02 1.53640993e-02 -1.73418820e-01
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853a7ef9-5cd5-486a-84c4-328758cf0db6 | fer-former-multi-modal-transformer-for-facial | 2303.12997 | null | https://arxiv.org/abs/2303.12997v1 | https://arxiv.org/pdf/2303.12997v1.pdf | FER-former: Multi-modal Transformer for Facial Expression Recognition | The ever-increasing demands for intuitive interactions in Virtual Reality has triggered a boom in the realm of Facial Expression Recognition (FER). To address the limitations in existing approaches (e.g., narrow receptive fields and homogenous supervisory signals) and further cement the capacity of FER tools, a novel multifarious supervision-steering Transformer for FER in the wild is proposed in this paper. Referred as FER-former, our approach features multi-granularity embedding integration, hybrid self-attention scheme, and heterogeneous domain-steering supervision. In specific, to dig deep into the merits of the combination of features provided by prevailing CNNs and Transformers, a hybrid stem is designed to cascade two types of learning paradigms simultaneously. Wherein, a FER-specific transformer mechanism is devised to characterize conventional hard one-hot label-focusing and CLIP-based text-oriented tokens in parallel for final classification. To ease the issue of annotation ambiguity, a heterogeneous domains-steering supervision module is proposed to make image features also have text-space semantic correlations by supervising the similarity between image features and text features. On top of the collaboration of multifarious token heads, diverse global receptive fields with multi-modal semantic cues are captured, thereby delivering superb learning capability. Extensive experiments on popular benchmarks demonstrate the superiority of the proposed FER-former over the existing state-of-the-arts. | ['Li Liu', 'Yonggang Lu', 'Minglun Gong', 'Mingjie Wang', 'Yande Li'] | 2023-03-23 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 3.19874197e-01 2.00137436e-01 -2.61172920e-01 -6.08750463e-01
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e19de147-c77d-4485-9a56-6b9d8a10ecbe | light-vqa-a-multi-dimensional-quality | 2305.09512 | null | https://arxiv.org/abs/2305.09512v1 | https://arxiv.org/pdf/2305.09512v1.pdf | Light-VQA: A Multi-Dimensional Quality Assessment Model for Low-Light Video Enhancement | Recently, Users Generated Content (UGC) videos becomes ubiquitous in our daily lives. However, due to the limitations of photographic equipments and techniques, UGC videos often contain various degradations, in which one of the most visually unfavorable effects is the underexposure. Therefore, corresponding video enhancement algorithms such as Low-Light Video Enhancement (LLVE) have been proposed to deal with the specific degradation. However, different from video enhancement algorithms, almost all existing Video Quality Assessment (VQA) models are built generally rather than specifically, which measure the quality of a video from a comprehensive perspective. To the best of our knowledge, there is no VQA model specially designed for videos enhanced by LLVE algorithms. To this end, we first construct a Low-Light Video Enhancement Quality Assessment (LLVE-QA) dataset in which 254 original low-light videos are collected and then enhanced by leveraging 8 LLVE algorithms to obtain 2,060 videos in total. Moreover, we propose a quality assessment model specialized in LLVE, named Light-VQA. More concretely, since the brightness and noise have the most impact on low-light enhanced VQA, we handcraft corresponding features and integrate them with deep-learning-based semantic features as the overall spatial information. As for temporal information, in addition to deep-learning-based motion features, we also investigate the handcrafted brightness consistency among video frames, and the overall temporal information is their concatenation. Subsequently, spatial and temporal information is fused to obtain the quality-aware representation of a video. Extensive experimental results show that our Light-VQA achieves the best performance against the current State-Of-The-Art (SOTA) on LLVE-QA and public dataset. Dataset and Codes can be found at https://github.com/wenzhouyidu/Light-VQA. | ['Guangtao Zhai', 'Tao Tan', 'Xunchu Zhou', 'Yixuan Gao', 'Xiaohong Liu', 'Yunlong Dong'] | 2023-05-16 | null | null | null | null | ['video-quality-assessment', 'video-enhancement', 'video-quality-assessment'] | ['computer-vision', 'computer-vision', 'time-series'] | [-4.47493186e-03 -7.60181129e-01 6.41036630e-02 -3.04690033e-01
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789cc368-543c-4b8e-a211-cf7d6862ac74 | risk-averse-contextual-multi-armed-bandit | 2206.12463 | null | https://arxiv.org/abs/2206.12463v1 | https://arxiv.org/pdf/2206.12463v1.pdf | Risk-averse Contextual Multi-armed Bandit Problem with Linear Payoffs | In this paper we consider the contextual multi-armed bandit problem for linear payoffs under a risk-averse criterion. At each round, contexts are revealed for each arm, and the decision maker chooses one arm to pull and receives the corresponding reward. In particular, we consider mean-variance as the risk criterion, and the best arm is the one with the largest mean-variance reward. We apply the Thompson Sampling algorithm for the disjoint model, and provide a comprehensive regret analysis for a variant of the proposed algorithm. For $T$ rounds, $K$ actions, and $d$-dimensional feature vectors, we prove a regret bound of $O((1+\rho+\frac{1}{\rho}) d\ln T \ln \frac{K}{\delta}\sqrt{d K T^{1+2\epsilon} \ln \frac{K}{\delta} \frac{1}{\epsilon}})$ that holds with probability $1-\delta$ under the mean-variance criterion with risk tolerance $\rho$, for any $0<\epsilon<\frac{1}{2}$, $0<\delta<1$. The empirical performance of our proposed algorithms is demonstrated via a portfolio selection problem. | ['Enlu Zhou', 'Yuhao Wang', 'Yifan Lin'] | 2022-06-24 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 4.26863320e-02 2.83203185e-01 -4.01568830e-01 -2.46074200e-02
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1078771f-4202-4c25-879f-b58e1f08c181 | self-supervised-transformers-for-unsupervised | 2202.11539 | null | https://arxiv.org/abs/2202.11539v2 | https://arxiv.org/pdf/2202.11539v2.pdf | Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut | Transformers trained with self-supervised learning using self-distillation loss (DINO) have been shown to produce attention maps that highlight salient foreground objects. In this paper, we demonstrate a graph-based approach that uses the self-supervised transformer features to discover an object from an image. Visual tokens are viewed as nodes in a weighted graph with edges representing a connectivity score based on the similarity of tokens. Foreground objects can then be segmented using a normalized graph-cut to group self-similar regions. We solve the graph-cut problem using spectral clustering with generalized eigen-decomposition and show that the second smallest eigenvector provides a cutting solution since its absolute value indicates the likelihood that a token belongs to a foreground object. Despite its simplicity, this approach significantly boosts the performance of unsupervised object discovery: we improve over the recent state of the art LOST by a margin of 6.9%, 8.1%, and 8.1% respectively on the VOC07, VOC12, and COCO20K. The performance can be further improved by adding a second stage class-agnostic detector (CAD). Our proposed method can be easily extended to unsupervised saliency detection and weakly supervised object detection. For unsupervised saliency detection, we improve IoU for 4.9%, 5.2%, 12.9% on ECSSD, DUTS, DUT-OMRON respectively compared to previous state of the art. For weakly supervised object detection, we achieve competitive performance on CUB and ImageNet. | ['Dominique Vaufreydaz', 'James Crowley', 'Yuan Yuan', 'Shell Hu', 'Xi Shen', 'Yangtao Wang'] | 2022-02-23 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Self-Supervised_Transformers_for_Unsupervised_Object_Discovery_Using_Normalized_Cut_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Self-Supervised_Transformers_for_Unsupervised_Object_Discovery_Using_Normalized_Cut_CVPR_2022_paper.pdf | cvpr-2022-1 | ['single-object-discovery'] | ['computer-vision'] | [ 5.73392868e-01 5.05126834e-01 -1.44267038e-01 -2.07712993e-01
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0a7a91fe-013c-423d-b3de-62f8c62a2681 | deep-metric-multi-view-hashing-for-multimedia | 2304.06358 | null | https://arxiv.org/abs/2304.06358v1 | https://arxiv.org/pdf/2304.06358v1.pdf | Deep Metric Multi-View Hashing for Multimedia Retrieval | Learning the hash representation of multi-view heterogeneous data is an important task in multimedia retrieval. However, existing methods fail to effectively fuse the multi-view features and utilize the metric information provided by the dissimilar samples, leading to limited retrieval precision. Current methods utilize weighted sum or concatenation to fuse the multi-view features. We argue that these fusion methods cannot capture the interaction among different views. Furthermore, these methods ignored the information provided by the dissimilar samples. We propose a novel deep metric multi-view hashing (DMMVH) method to address the mentioned problems. Extensive empirical evidence is presented to show that gate-based fusion is better than typical methods. We introduce deep metric learning to the multi-view hashing problems, which can utilize metric information of dissimilar samples. On the MIR-Flickr25K, MS COCO, and NUS-WIDE, our method outperforms the current state-of-the-art methods by a large margin (up to 15.28 mean Average Precision (mAP) improvement). | ['Lingfang Zeng', 'Yongli Cheng', 'Yu Cui', 'Xiaohu Ruan', 'Zhangmin Huang', 'Jian Zhu'] | 2023-04-13 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [-4.36376274e-01 -1.04001951e+00 -4.07091081e-01 -3.06778580e-01
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ac48bed2-585a-4713-beca-9e8b6d5d6bdd | high-resolution-peak-demand-estimation-using | 2203.03342 | null | https://arxiv.org/abs/2203.03342v2 | https://arxiv.org/pdf/2203.03342v2.pdf | High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks | This paper covers predicting high-resolution electricity peak demand features given lower-resolution data. This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future high-resolution peak loads when the high-resolution data is no longer available. That question is particularly interesting for network operators considering replacing high-resolution monitoring predictive models due to economic considerations. We propose models to predict half-hourly minima and maxima of high-resolution (every minute) electricity load data while model inputs are of a lower resolution (30 minutes). We combine predictions of generalized additive models (GAM) and deep artificial neural networks (DNN), which are popular in load forecasting. We extensively analyze the prediction models, including the input parameters' importance, focusing on load, weather, and seasonal effects. The proposed method won a data competition organized by Western Power Distribution, a British distribution network operator. In addition, we provide a rigorous evaluation study that goes beyond the competition frame to analyze the models' robustness. The results show that the proposed methods are superior to the competition benchmark concerning the out-of-sample root mean squared error (RMSE). This holds regarding the competition month and the supplementary evaluation study, which covers an additional eleven months. Overall, our proposed model combination reduces the out-of-sample RMSE by 57.4\% compared to the benchmark. | ['Florian Ziel', 'Michał Narajewski', 'Jonathan Berrisch'] | 2022-03-07 | null | null | null | null | ['additive-models'] | ['methodology'] | [-1.59325063e-01 5.04820701e-03 -1.86995864e-01 -4.81691301e-01
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cbf0a7de-c9de-4884-be8d-afeac15b3897 | challenges-in-gaussian-processes-for-non | 2211.13018 | null | https://arxiv.org/abs/2211.13018v1 | https://arxiv.org/pdf/2211.13018v1.pdf | Challenges in Gaussian Processes for Non Intrusive Load Monitoring | Non-intrusive load monitoring (NILM) or energy disaggregation aims to break down total household energy consumption into constituent appliances. Prior work has shown that providing an energy breakdown can help people save up to 15\% of energy. In recent years, deep neural networks (deep NNs) have made remarkable progress in the domain of NILM. In this paper, we demonstrate the performance of Gaussian Processes (GPs) for NILM. We choose GPs due to three main reasons: i) GPs inherently model uncertainty; ii) equivalence between infinite NNs and GPs; iii) by appropriately designing the kernel we can incorporate domain expertise. We explore and present the challenges of applying our GP approaches to NILM. | ['Nipun Batra', 'Zeel B Patel', 'Gautam Vashishtha', 'Aadesh Desai'] | 2022-11-18 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-7.59544075e-02 3.31800908e-01 -6.25000298e-02 -4.26347136e-01
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9e0b24dc-38ee-4a1d-a0a0-63844dc0f681 | whoi-plankton-a-large-scale-fine-grained | 1510.00745 | null | http://arxiv.org/abs/1510.00745v1 | http://arxiv.org/pdf/1510.00745v1.pdf | WHOI-Plankton- A Large Scale Fine Grained Visual Recognition Benchmark Dataset for Plankton Classification | Planktonic organisms are of fundamental importance to marine ecosystems: they
form the basis of the food web, provide the link between the atmosphere and the
deep ocean, and influence global-scale biogeochemical cycles. Scientists are
increasingly using imaging-based technologies to study these creatures in their
natural habit. Images from such systems provide an unique opportunity to model
and understand plankton ecosystems, but the collected datasets can be enormous.
The Imaging FlowCytobot (IFCB) at Woods Hole Oceanographic Institution, for
example, is an \emph{in situ} system that has been continuously imaging
plankton since 2006. To date, it has generated more than 700 million samples.
Manual classification of such a vast image collection is impractical due to the
size of the data set. In addition, the annotation task is challenging due to
the large space of relevant classes, intra-class variability, and inter-class
similarity. Methods for automated classification exist, but the accuracy is
often below that of human experts. Here we introduce WHOI-Plankton: a large
scale, fine-grained visual recognition dataset for plankton classification,
which comprises over 3.4 million expert-labeled images across 70 classes. The
labeled image set is complied from over 8 years of near continuous data
collection with the IFCB at the Martha's Vineyard Coastal Observatory (MVCO).
We discuss relevant metrics for evaluation of classification performance and
provide results for a traditional method based on hand-engineered features and
two methods based on convolutional neural networks. | ['Heidi M. Sosik', 'Eric C. Orenstein', 'Emily E. Peacock', 'Oscar Beijbom'] | 2015-10-02 | null | null | null | null | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 2.98980437e-02 -5.86643696e-01 5.31679511e-01 -7.45565891e-02
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e1fa35ff-320d-49e6-a2c4-e75274c8d1ef | generalized-few-shot-semantic-segmentation-1 | 2112.10982 | null | https://arxiv.org/abs/2112.10982v3 | https://arxiv.org/pdf/2112.10982v3.pdf | Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning | Generalized few-shot semantic segmentation was introduced to move beyond only evaluating few-shot segmentation models on novel classes to include testing their ability to remember base classes. While the current state-of-the-art approach is based on meta-learning, it performs poorly and saturates in learning after observing only a few shots. We propose the first fine-tuning solution, and demonstrate that it addresses the saturation problem while achieving state-of-the-art results on two datasets, PASCAL-5i and COCO-20i. We also show that it outperforms existing methods, whether fine-tuning multiple final layers or only the final layer. Finally, we present a triplet loss regularization that shows how to redistribute the balance of performance between novel and base categories so that there is a smaller gap between them. | ['Danna Gurari', 'Scott Cohen', 'Brian Price', 'Yinan Zhao', 'Josh Myers-Dean'] | 2021-12-21 | null | null | null | null | ['generalized-few-shot-semantic-segmentation'] | ['computer-vision'] | [ 3.38421613e-01 2.80322552e-01 -3.09414595e-01 -4.24005091e-01
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a5500343-a9df-4f54-af96-cdded167cb54 | adversarial-training-a-simple-and-efficient | null | null | https://openreview.net/forum?id=EO4VJGAllb | https://openreview.net/pdf?id=EO4VJGAllb | Adversarial Training: A simple and efficient technique to Improving NLP Robustness | NLP models are shown to be prone to adversarial attacks which undermines their robustness, i.e. a small perturbation to the input text can fool an NLP model to incorrectly classify text. In this study, we present a new Adversarial Text Generation technique that, given an input text, generates adversarial texts through quickly and efficiently. For example, in order to attack a model for sentiment classification, we can use the product categories as the attribute which should not change the sentiment of the reviews. We conducted experiments on real-world NLP datasets to demonstrate that our technique can generate more meaningful and diverse adversarial texts, compared to many existing adversarial text generation approaches. We further use our generated adversarial examples to improve models through adversarial training, and we demonstrate that our generated attacks are more robust against model re training and different model architectures. | ['marwan omar'] | 2021-09-29 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 6.67568803e-01 5.80837727e-01 2.23837897e-01 -3.56566280e-01
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10e6ae0d-870b-40e6-be3c-ae299d142810 | neural-sentence-embedding-models-for-semantic | 2110.15708 | null | https://arxiv.org/abs/2110.15708v1 | https://arxiv.org/pdf/2110.15708v1.pdf | Neural sentence embedding models for semantic similarity estimation in the biomedical domain | BACKGROUND: In this study, we investigated the efficacy of current state-of-the-art neural sentence embedding models for semantic similarity estimation of sentences from biomedical literature. We trained different neural embedding models on 1.7 million articles from the PubMed Open Access dataset, and evaluated them based on a biomedical benchmark set containing 100 sentence pairs annotated by human experts and a smaller contradiction subset derived from the original benchmark set. RESULTS: With a Pearson correlation of 0.819, our best unsupervised model based on the Paragraph Vector Distributed Memory algorithm outperforms previous state-of-the-art results achieved on the BIOSSES biomedical benchmark set. Moreover, our proposed supervised model that combines different string-based similarity metrics with a neural embedding model surpasses previous ontology-dependent supervised state-of-the-art approaches in terms of Pearson's r (r=0.871) on the biomedical benchmark set. In contrast to the promising results for the original benchmark, we found our best models' performance on the smaller contradiction subset to be poor. CONCLUSIONS: In this study we highlighted the value of neural network-based models for semantic similarity estimation in the biomedical domain by showing that they can keep up with and even surpass previous state-of-the-art approaches for semantic similarity estimation that depend on the availability of laboriously curated ontologies when evaluated on a biomedical benchmark set. Capturing contradictions and negations in biomedical sentences, however, emerged as an essential area for further work. | ['Matthias Samwald', 'Asan Agibetov', 'Hong Xu', 'Kathrin Blagec'] | 2021-10-01 | null | null | null | null | ['sentence-embeddings-for-biomedical-texts', 'sentence-embeddings-for-biomedical-texts'] | ['methodology', 'natural-language-processing'] | [ 2.91090161e-01 3.50970894e-01 -2.42096167e-02 -4.07665581e-01
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f6a8a6e1-dcb0-428d-a9fe-a6d56862ac1a | genetic-algorithm-optimized-long-short-term | null | null | https://www.mdpi.com/2071-1050/10/10/3765 | https://www.mdpi.com/2071-1050/10/10/3765/pdf?version=1539866453 | Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction | With recent advances in computing technology, massive amounts of data and information are being constantly accumulated. Especially in the field of finance, we have great opportunities to create useful insights by analyzing that information, because the financial market produces a tremendous amount of real-time data, including transaction records. Accordingly, this study intends to develop a novel stock market prediction model using the available financial data. We adopt deep learning technique because of its excellent learning ability from the massive dataset. In this study, we propose a hybrid approach integrating long short-term memory (LSTM) network and genetic algorithm (GA). Heretofore, trial and error based on heuristics is commonly used to estimate the time window size and architectural factors of LSTM network. This research investigates the temporal property of stock market data by suggesting a systematic method to determine the time window size and topology for the LSTM network using GA. To evaluate the proposed hybrid approach, we have chosen daily Korea Stock Price Index (KOSPI) data. The experimental result demonstrates that the hybrid model of LSTM network and GA outperforms the benchmark model. | ['Kyung-shik Shin', 'Hyejung Chung'] | 2018-10-18 | null | null | null | sustainability-2018-10 | ['stock-market-prediction'] | ['time-series'] | [-4.67764586e-01 -7.03064859e-01 -4.06682901e-02 3.71656679e-02
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e144f2f2-147d-409d-936a-802e3e3b2de3 | word-representation-models-for | 1606.04217 | null | http://arxiv.org/abs/1606.04217v1 | http://arxiv.org/pdf/1606.04217v1.pdf | Word Representation Models for Morphologically Rich Languages in Neural Machine Translation | Dealing with the complex word forms in morphologically rich languages is an
open problem in language processing, and is particularly important in
translation. In contrast to most modern neural systems of translation, which
discard the identity for rare words, in this paper we propose several
architectures for learning word representations from character and morpheme
level word decompositions. We incorporate these representations in a novel
machine translation model which jointly learns word alignments and translations
via a hard attention mechanism. Evaluating on translating from several
morphologically rich languages into English, we show consistent improvements
over strong baseline methods, of between 1 and 1.5 BLEU points. | ['Gholamreza Haffari', 'Ekaterina Vylomova', 'Trevor Cohn', 'Xuanli He'] | 2016-06-14 | word-representation-models-for-1 | https://aclanthology.org/W17-4115 | https://aclanthology.org/W17-4115.pdf | ws-2017-9 | ['hard-attention'] | ['methodology'] | [ 3.01264286e-01 -1.20696239e-02 -5.38998187e-01 -3.30778688e-01
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7f5dfed1-dc46-4e4b-bc2e-fc27db3143ba | dc-net-divide-and-conquer-for-salient-object | 2305.14955 | null | https://arxiv.org/abs/2305.14955v2 | https://arxiv.org/pdf/2305.14955v2.pdf | DC-Net: Divide-and-Conquer for Salient Object Detection | In this paper, we introduce Divide-and-Conquer into the salient object detection (SOD) task to enable the model to learn prior knowledge that is for predicting the saliency map. We design a novel network, Divide-and-Conquer Network (DC-Net) which uses two encoders to solve different subtasks that are conducive to predicting the final saliency map, here is to predict the edge maps with width 4 and location maps of salient objects and then aggregate the feature maps with different semantic information into the decoder to predict the final saliency map. The decoder of DC-Net consists of our newly designed two-level Residual nested-ASPP (ResASPP$^{2}$) modules, which have the ability to capture a large number of different scale features with a small number of convolution operations and have the advantages of maintaining high resolution all the time and being able to obtain a large and compact effective receptive field (ERF). Based on the advantage of Divide-and-Conquer's parallel computing, we use Parallel Acceleration to speed up DC-Net, allowing it to achieve competitive performance on six LR-SOD and five HR-SOD datasets under high efficiency (60 FPS and 55 FPS). Codes and results are available: https://github.com/PiggyJerry/DC-Net. | ['Abdulmotaleb Elsaddik', 'Xuebin Qin', 'JiaYi Zhu'] | 2023-05-24 | null | null | null | null | ['salient-object-detection-1'] | ['computer-vision'] | [ 1.52444914e-01 1.96193531e-02 1.18816696e-01 -4.59351510e-01
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c3a7fc21-6e44-4903-ae3b-f8250636323f | vqn-variable-quantization-noise-for-neural | null | null | https://openreview.net/forum?id=VbtRUvpzht | https://openreview.net/pdf?id=VbtRUvpzht | VQN: Variable Quantization Noise for Neural Network Compression | Quantization refers to a set of methods that compress a neural network by representing its parameters with fewer bits. However, applying quantization to a neural network after training often leads to severe performance regressions. Quantization Aware Training (QAT) addresses this problem by applying simulated training-time quantization for the model to learn robustness to inference-time quantization. One key drawback of this approach is that quantization functions induce biased gradient flow through the network during backpropagation, thus preventing the network from best-fitting to the learning task. Fan et al. addressed this issue by proposing Quant-Noise, in which simulated quantization is applied to a fixed proportion, called the quantization noise rate, of parameters during training. Our study, Variable Quantization Noise (VQN), builds upon their technique by exploring a variable quantization noise rate instead of a fixed one. We craft three candidate functions to vary noise rate during training and evaluate the variants with 3 datasets and 3 quantization schemes for each dataset. First, we report negative results on our hand-crafted candidate functions. Second, we observe somewhat positive results on a method, originally intended as an ablation study, of randomly varying the noise rate during training. This method outperforms Quant-Noise on two out of three quantization schemes for all three tested datasets. Moreover, on two of the datasets, this method at 4x compression matches or exceeds performance of even the uncompressed model. Future work should determine whether these unexpected results hold for more datasets and quantization schemes, as well as investigating other schemes for varying the noise rate during training. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 5.79145610e-01 -7.88452923e-02 -1.94701388e-01 -2.93952525e-01
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dbabf9a3-69aa-409c-9060-69f610d45846 | label-only-membership-inference-attacks | 2007.14321 | null | https://arxiv.org/abs/2007.14321v3 | https://arxiv.org/pdf/2007.14321v3.pdf | Label-Only Membership Inference Attacks | Membership inference attacks are one of the simplest forms of privacy leakage for machine learning models: given a data point and model, determine whether the point was used to train the model. Existing membership inference attacks exploit models' abnormal confidence when queried on their training data. These attacks do not apply if the adversary only gets access to models' predicted labels, without a confidence measure. In this paper, we introduce label-only membership inference attacks. Instead of relying on confidence scores, our attacks evaluate the robustness of a model's predicted labels under perturbations to obtain a fine-grained membership signal. These perturbations include common data augmentations or adversarial examples. We empirically show that our label-only membership inference attacks perform on par with prior attacks that required access to model confidences. We further demonstrate that label-only attacks break multiple defenses against membership inference attacks that (implicitly or explicitly) rely on a phenomenon we call confidence masking. These defenses modify a model's confidence scores in order to thwart attacks, but leave the model's predicted labels unchanged. Our label-only attacks demonstrate that confidence-masking is not a viable defense strategy against membership inference. Finally, we investigate worst-case label-only attacks, that infer membership for a small number of outlier data points. We show that label-only attacks also match confidence-based attacks in this setting. We find that training models with differential privacy and (strong) L2 regularization are the only known defense strategies that successfully prevents all attacks. This remains true even when the differential privacy budget is too high to offer meaningful provable guarantees. | ['Christopher A. Choquette-Choo', 'Nicolas Papernot', 'Florian Tramer', 'Nicholas Carlini'] | 2020-07-28 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 3.87475312e-01 2.68251568e-01 -2.98559129e-01 -4.48127359e-01
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b0c5eaeb-cb03-4718-a9fd-39fb2eddad15 | benchmarking-data-driven-surrogate-simulators | null | null | https://openreview.net/forum?id=-or413Lh_aF | https://openreview.net/pdf?id=-or413Lh_aF | Benchmarking Data-driven Surrogate Simulators for Artificial Electromagnetic Materials | Artificial electromagnetic materials (AEMs), including metamaterials, derive their electromagnetic properties from geometry rather than chemistry. With the appropriate geometric design, AEMs have achieved exotic properties not realizable with conventional materials (e.g., cloaking or negative refractive index). However, understanding the relationship between the AEM structure and its properties is often poorly understood. While computational electromagnetic simulation (CEMS) may help design new AEMs, its use is limited due to its long computational time. Recently, it has been shown that deep learning can be an alternative solution to infer the relationship between an AEM geometry and its properties using a (relatively) small pool of CEMS data. However, the limited publicly released datasets and models and no widely-used benchmark for comparison have made using deep learning approaches even more difficult. Furthermore, configuring CEMS for a specific problem requires substantial expertise and time, making reproducibility challenging. Here, we develop a collection of three classes of AEM problems: metamaterials, nanophotonics, and color filter designs. We also publicly release software, allowing other researchers to conduct additional simulations for each system easily. Finally, we conduct experiments on our benchmark datasets with three recent neural network architectures: the multilayer perceptron (MLP), MLP-mixer, and transformer. We identify the methods and models that generalize best over the three problems to establish the best practice and baseline results upon which future research can build. | ['Jordan Malof', 'Willie Padilla', 'Vahid Tarokh', 'Mohammadreza Soltani', 'Omar Khatib', 'Simiao Ren*', 'Juncheng Dong*', 'Yang Deng*'] | 2021-11-06 | null | null | null | neurips-2021-11 | ['neural-network-simulation'] | ['computer-code'] | [ 3.74053448e-01 -1.67775586e-01 5.55060685e-01 -2.44785830e-01
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e25761b6-72dc-4913-92ad-db217ef8c40f | permutation-aware-action-segmentation-via | 2305.19478 | null | https://arxiv.org/abs/2305.19478v2 | https://arxiv.org/pdf/2305.19478v2.pdf | Permutation-Aware Action Segmentation via Unsupervised Frame-to-Segment Alignment | This paper presents an unsupervised transformer-based framework for temporal activity segmentation which leverages not only frame-level cues but also segment-level cues. This is in contrast with previous methods which often rely on frame-level information only. Our approach begins with a frame-level prediction module which estimates framewise action classes via a transformer encoder. The frame-level prediction module is trained in an unsupervised manner via temporal optimal transport. To exploit segment-level information, we utilize a segment-level prediction module and a frame-to-segment alignment module. The former includes a transformer decoder for estimating video transcripts, while the latter matches frame-level features with segment-level features, yielding permutation-aware segmentation results. Moreover, inspired by temporal optimal transport, we introduce simple-yet-effective pseudo labels for unsupervised training of the above modules. Our experiments on four public datasets, i.e., 50 Salads, YouTube Instructions, Breakfast, and Desktop Assembly show that our approach achieves comparable or better performance than previous methods in unsupervised activity segmentation. | ['M. Zeeshan Zia', 'Andrey Konin', 'Anas Zafar', 'Muhammad Naufil', 'Muhammad Ahmed', 'Ahmed Mehmood', 'Quoc-Huy Tran'] | 2023-05-31 | null | null | null | null | ['action-segmentation'] | ['computer-vision'] | [ 6.54085159e-01 7.58433864e-02 -6.22436821e-01 -5.44731021e-01
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684a2df1-a2f7-4ac1-ba54-7fa4e79fdcf1 | vitol-vision-transformer-for-weakly | 2204.06772 | null | https://arxiv.org/abs/2204.06772v1 | https://arxiv.org/pdf/2204.06772v1.pdf | ViTOL: Vision Transformer for Weakly Supervised Object Localization | Weakly supervised object localization (WSOL) aims at predicting object locations in an image using only image-level category labels. Common challenges that image classification models encounter when localizing objects are, (a) they tend to look at the most discriminative features in an image that confines the localization map to a very small region, (b) the localization maps are class agnostic, and the models highlight objects of multiple classes in the same image and, (c) the localization performance is affected by background noise. To alleviate the above challenges we introduce the following simple changes through our proposed method ViTOL. We leverage the vision-based transformer for self-attention and introduce a patch-based attention dropout layer (p-ADL) to increase the coverage of the localization map and a gradient attention rollout mechanism to generate class-dependent attention maps. We conduct extensive quantitative, qualitative and ablation experiments on the ImageNet-1K and CUB datasets. We achieve state-of-the-art MaxBoxAcc-V2 localization scores of 70.47% and 73.17% on the two datasets respectively. Code is available on https://github.com/Saurav-31/ViTOL | ['Rahul Tallamraju', 'Abhay Rawat', 'Sourav Lakhotia', 'Saurav Gupta'] | 2022-04-14 | null | null | null | null | ['weakly-supervised-object-localization'] | ['computer-vision'] | [-1.19586520e-01 -1.93632971e-02 -2.90151715e-01 -4.18316394e-01
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597a88e7-7095-44d1-bd7b-985e217f173f | sparsity-based-feature-selection-for | 2201.02008 | null | https://arxiv.org/abs/2201.02008v1 | https://arxiv.org/pdf/2201.02008v1.pdf | Sparsity-based Feature Selection for Anomalous Subgroup Discovery | Anomalous pattern detection aims to identify instances where deviation from normalcy is evident, and is widely applicable across domains. Multiple anomalous detection techniques have been proposed in the state of the art. However, there is a common lack of a principled and scalable feature selection method for efficient discovery. Existing feature selection techniques are often conducted by optimizing the performance of prediction outcomes rather than its systemic deviations from the expected. In this paper, we proposed a sparsity-based automated feature selection (SAFS) framework, which encodes systemic outcome deviations via the sparsity of feature-driven odds ratios. SAFS is a model-agnostic approach with usability across different discovery techniques. SAFS achieves more than $3\times$ reduction in computation time while maintaining detection performance when validated on publicly available critical care dataset. SAFS also results in a superior performance when compared against multiple baselines for feature selection. | ['Skyler Speakman', 'Aisha Walcott-Bryant', 'Vibha Anand', "Isaiah Onando Mulang'", 'Charles Wachira', 'Catherine Wanjiru', 'William Ogallo', 'Girmaw Abebe Tadesse'] | 2022-01-06 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 3.98091316e-01 -3.02977711e-01 -1.96108088e-01 -5.26845992e-01
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1bd2b8cc-65cd-491b-928a-1ac34bdcb783 | a-hybrid-attention-mechanism-for-weakly | 2101.00545 | null | https://arxiv.org/abs/2101.00545v3 | https://arxiv.org/pdf/2101.00545v3.pdf | A Hybrid Attention Mechanism for Weakly-Supervised Temporal Action Localization | Weakly supervised temporal action localization is a challenging vision task due to the absence of ground-truth temporal locations of actions in the training videos. With only video-level supervision during training, most existing methods rely on a Multiple Instance Learning (MIL) framework to predict the start and end frame of each action category in a video. However, the existing MIL-based approach has a major limitation of only capturing the most discriminative frames of an action, ignoring the full extent of an activity. Moreover, these methods cannot model background activity effectively, which plays an important role in localizing foreground activities. In this paper, we present a novel framework named HAM-Net with a hybrid attention mechanism which includes temporal soft, semi-soft and hard attentions to address these issues. Our temporal soft attention module, guided by an auxiliary background class in the classification module, models the background activity by introducing an "action-ness" score for each video snippet. Moreover, our temporal semi-soft and hard attention modules, calculating two attention scores for each video snippet, help to focus on the less discriminative frames of an action to capture the full action boundary. Our proposed approach outperforms recent state-of-the-art methods by at least 2.2% mAP at IoU threshold 0.5 on the THUMOS14 dataset, and by at least 1.3% mAP at IoU threshold 0.75 on the ActivityNet1.2 dataset. Code can be found at: https://github.com/asrafulashiq/hamnet. | ['Richard Radke', 'Chengjiang Long', 'Ashraful Islam'] | 2021-01-03 | null | null | null | null | ['weakly-supervised-temporal-action', 'hard-attention'] | ['computer-vision', 'methodology'] | [ 3.33920270e-01 -1.22821569e-01 -5.11350930e-01 -1.16224997e-01
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07e42fcb-74f4-4145-b899-47f1222957e0 | dcu-uva-multimodal-mt-system-report | null | null | https://aclanthology.org/W16-2359 | https://aclanthology.org/W16-2359.pdf | DCU-UvA Multimodal MT System Report | null | ['Iacer Calixto', 'Stella Frank', 'Desmond Elliott'] | 2016-08-01 | null | null | null | ws-2016-8 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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0ed1cc61-f246-4d2f-868d-ae554738625b | decouvrir-de-nouvelles-classes-dans-des | 2211.16352 | null | https://arxiv.org/abs/2211.16352v1 | https://arxiv.org/pdf/2211.16352v1.pdf | Découvrir de nouvelles classes dans des données tabulaires | In Novel Class Discovery (NCD), the goal is to find new classes in an unlabeled set given a labeled set of known but different classes. While NCD has recently gained attention from the community, no framework has yet been proposed for heterogeneous tabular data, despite being a very common representation of data. In this paper, we propose TabularNCD, a new method for discovering novel classes in tabular data. We show a way to extract knowledge from already known classes to guide the discovery process of novel classes in the context of tabular data which contains heterogeneous variables. A part of this process is done by a new method for defining pseudo labels, and we follow recent findings in Multi-Task Learning to optimize a joint objective function. Our method demonstrates that NCD is not only applicable to images but also to heterogeneous tabular data. | ['Vincent Lemaire', 'Alexandre Reiffers-Masson', 'Sandrine Vaton', 'Stéphane Gosselin', 'Joachim Flocon-Cholet', 'Colin Troisemaine'] | 2022-11-28 | null | null | null | null | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 4.56287444e-01 1.31176353e-01 -5.14726400e-01 -6.17421091e-01
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0f68b1a1-1fab-402c-a346-b5ba341f6735 | hyperparameter-selection-for-the-discrete | 2109.13651 | null | https://arxiv.org/abs/2109.13651v2 | https://arxiv.org/pdf/2109.13651v2.pdf | Hyperparameter selection for Discrete Mumford-Shah | This work focuses on a parameter-free joint piecewise smooth image denoising and contour detection. Formulated as the minimization of a discrete Mumford-Shah functional and estimated via a theoretically grounded alternating minimization scheme, the bottleneck of such a variational approach lies in the need to fine-tune their hyperparameters, while not having access to ground truth data. To that aim, a Stein-like strategy providing optimal hyperparameters is designed, based on the minimization of an unbiased estimate of the quadratic risk. Efficient and automated minimization of the estimate of the risk crucially relies on an unbiased estimate of the gradient of the risk with respect to hyperparameters. Its practical implementation is performed using a forward differentiation of the alternating scheme minimizing the Mumford-Shah functional, requiring exact differentiation of the proximity operators involved. Intensive numerical experiments are performed on synthetic images with different geometry and noise levels, assessing the accuracy and the robustness of the proposed procedure. The resulting parameter-free piecewise-smooth estimation and contour detection procedure, not requiring prior image processing expertise nor annotated data, can then be applied to real-world images. | ['Patrice Abry', 'Nelly Pustelnik', 'Barbara Pascal', 'Charles-Gérard Lucas'] | 2021-09-28 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 3.02678168e-01 2.62446880e-01 4.55178320e-01 -2.13492364e-01
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b9a7f648-811d-4ba0-a95c-c9c3db20c70d | exact-bayesian-inference-on-discrete-models | 2305.17058 | null | https://arxiv.org/abs/2305.17058v1 | https://arxiv.org/pdf/2305.17058v1.pdf | Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach | We present an exact Bayesian inference method for discrete statistical models, which can find exact solutions to many discrete inference problems, even with infinite support and continuous priors. To express such models, we introduce a probabilistic programming language that supports discrete and continuous sampling, discrete observations, affine functions, (stochastic) branching, and conditioning on events. Our key tool is probability generating functions: they provide a compact closed-form representation of distributions that are definable by programs, thus enabling the exact computation of posterior probabilities, expectation, variance, and higher moments. Our inference method is provably correct, fully automated and uses automatic differentiation (specifically, Taylor polynomials), but does not require computer algebra. Our experiments show that its performance on a range of real-world examples is competitive with approximate Monte Carlo methods, while avoiding approximation errors. | ['Luke Ong', 'Andrzej S. Murawski', 'Fabian Zaiser'] | 2023-05-26 | null | null | null | null | ['bayesian-inference', 'probabilistic-programming'] | ['methodology', 'methodology'] | [-4.87508513e-02 -2.82510668e-02 -3.61524910e-01 -4.11979407e-01
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9aacd44c-9040-4c35-a74a-219781b9c366 | binbert-binary-code-understanding-with-a-fine | 2208.06692 | null | https://arxiv.org/abs/2208.06692v1 | https://arxiv.org/pdf/2208.06692v1.pdf | BinBert: Binary Code Understanding with a Fine-tunable and Execution-aware Transformer | A recent trend in binary code analysis promotes the use of neural solutions based on instruction embedding models. An instruction embedding model is a neural network that transforms sequences of assembly instructions into embedding vectors. If the embedding network is trained such that the translation from code to vectors partially preserves the semantic, the network effectively represents an assembly code model. In this paper we present BinBert, a novel assembly code model. BinBert is built on a transformer pre-trained on a huge dataset of both assembly instruction sequences and symbolic execution information. BinBert can be applied to assembly instructions sequences and it is fine-tunable, i.e. it can be re-trained as part of a neural architecture on task-specific data. Through fine-tuning, BinBert learns how to apply the general knowledge acquired with pre-training to the specific task. We evaluated BinBert on a multi-task benchmark that we specifically designed to test the understanding of assembly code. The benchmark is composed of several tasks, some taken from the literature, and a few novel tasks that we designed, with a mix of intrinsic and downstream tasks. Our results show that BinBert outperforms state-of-the-art models for binary instruction embedding, raising the bar for binary code understanding. | ['Leonardo Querzoni', 'Giuseppe A. Di Luna', 'Marco Mormando', 'Fiorella Artuso'] | 2022-08-13 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [ 2.13204607e-01 1.61729872e-01 -4.84103471e-01 -4.69920814e-01
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da354bf3-5b3b-4cd2-a82a-52f438c3ba43 | a-nonconvex-projection-method-for-robust-pca | 1805.07962 | null | https://arxiv.org/abs/1805.07962v2 | https://arxiv.org/pdf/1805.07962v2.pdf | A Nonconvex Projection Method for Robust PCA | Robust principal component analysis (RPCA) is a well-studied problem with the goal of decomposing a matrix into the sum of low-rank and sparse components. In this paper, we propose a nonconvex feasibility reformulation of RPCA problem and apply an alternating projection method to solve it. To the best of our knowledge, we are the first to propose a method that solves RPCA problem without considering any objective function, convex relaxation, or surrogate convex constraints. We demonstrate through extensive numerical experiments on a variety of applications, including shadow removal, background estimation, face detection, and galaxy evolution, that our approach matches and often significantly outperforms current state-of-the-art in various ways. | ['Peter Richtárik', 'Filip Hanzely', 'Aritra Dutta'] | 2018-05-21 | null | null | null | null | ['shadow-removal'] | ['computer-vision'] | [ 3.64586025e-01 -3.54639024e-01 2.13893563e-01 7.45126829e-02
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c41653d0-5153-4ba2-a95d-e95f4c71f972 | lesionseg-semantic-segmentation-of-skin | 1703.03372 | null | http://arxiv.org/abs/1703.03372v3 | http://arxiv.org/pdf/1703.03372v3.pdf | LesionSeg: Semantic segmentation of skin lesions using Deep Convolutional Neural Network | We present a method for skin lesion segmentation for the ISIC 2017 Skin
Lesion Segmentation Challenge. Our approach is based on a Fully Convolutional
Network architecture which is trained end to end, from scratch, on a limited
dataset. Our semantic segmentation architecture utilizes several recent
innovations in particularly in the combined use of (i) use of atrous
convolutions to increase the effective field of view of the network's receptive
field without increasing the number of parameters, (ii) the use of
network-in-network $1\times1$ convolution layers to add capacity to the network
and (iii) state-of-art super-resolution upsampling of predictions using
subpixel CNN layers. We reported a mean IOU score of 0.642 on the validation
set provided by the organisers. | ['Dhanesh Ramachandram', 'Terrance DeVries'] | 2017-03-09 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 7.76913702e-01 7.13639557e-01 6.30814396e-03 -3.76829147e-01
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f70f7402-aa1c-4bb2-b316-1808db75786f | lotr-face-landmark-localization-using | 2109.10057 | null | https://arxiv.org/abs/2109.10057v3 | https://arxiv.org/pdf/2109.10057v3.pdf | LOTR: Face Landmark Localization Using Localization Transformer | This paper presents a novel Transformer-based facial landmark localization network named Localization Transformer (LOTR). The proposed framework is a direct coordinate regression approach leveraging a Transformer network to better utilize the spatial information in the feature map. An LOTR model consists of three main modules: 1) a visual backbone that converts an input image into a feature map, 2) a Transformer module that improves the feature representation from the visual backbone, and 3) a landmark prediction head that directly predicts the landmark coordinates from the Transformer's representation. Given cropped-and-aligned face images, the proposed LOTR can be trained end-to-end without requiring any post-processing steps. This paper also introduces the smooth-Wing loss function, which addresses the gradient discontinuity of the Wing loss, leading to better convergence than standard loss functions such as L1, L2, and Wing loss. Experimental results on the JD landmark dataset provided by the First Grand Challenge of 106-Point Facial Landmark Localization indicate the superiority of LOTR over the existing methods on the leaderboard and two recent heatmap-based approaches. On the WFLW dataset, the proposed LOTR framework demonstrates promising results compared with several state-of-the-art methods. Additionally, we report the improvement in state-of-the-art face recognition performance when using our proposed LOTRs for face alignment. | ['Benjaphan Sommana', 'Nakarin Sritrakool', 'Samuel W. F. Earp', 'Aubin Samacoits', 'Ankush Ganguly', 'Pavit Noinongyao', 'Sanjana Jain', 'Ukrit Watchareeruetai'] | 2021-09-21 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [-1.58106804e-01 -6.07123896e-02 -1.87345892e-01 -7.38788068e-01
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4899ab7f-e8a2-4247-903a-02ac8c83c1af | the-brain-tumor-segmentation-brats-challenge-1 | 2305.08992 | null | https://arxiv.org/abs/2305.08992v1 | https://arxiv.org/pdf/2305.08992v1.pdf | The Brain Tumor Segmentation (BraTS) Challenge 2023: Local Synthesis of Healthy Brain Tissue via Inpainting | A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with a scan that is already pathological. This poses problems, as many algorithms are designed to analyze healthy brains and provide no guarantees for images featuring lesions. Examples include but are not limited to algorithms for brain anatomy parcellation, tissue segmentation, and brain extraction. To solve this dilemma, we introduce the BraTS 2023 inpainting challenge. Here, the participants' task is to explore inpainting techniques to synthesize healthy brain scans from lesioned ones. The following manuscript contains the task formulation, dataset, and submission procedure. Later it will be updated to summarize the findings of the challenge. The challenge is organized as part of the BraTS 2023 challenge hosted at the MICCAI 2023 conference in Vancouver, Canada. | ['Bjoern Menze', 'Marie Piraud', 'Ivan Ezhov', 'Benedikt Wiestler', 'Daniel Rueckert', 'Spyridon Bakas', 'Koen van Leemput', 'Juan Eugenio Iglesias', 'Mariam Aboian', 'Udunna Anazodo', 'Jake Albrecht', 'Andras Jakab', 'Priscila Crivellaro', 'Errol Colak', 'Javier Villanueva-Meyer', 'Soonmee Cha', 'Christopher Hess', 'John Mongan', 'Suyash Mohan', 'Abhishek Mahajan', 'Marc-André Weber', 'Arash Nazeri', 'Mikhail Milchenko', 'Daniel Marcus', 'Pamela Lamontagne', 'Aikaterini Kotrotsou', 'Rivka R Colen', 'Jan Kirschke', 'Roland Wiest', 'Hassan M Fathallah-Shaykh', 'Michel Bilello', 'Justin Kirby', 'John Freymann', 'Christos Davatzikos', 'Zeke Meier', 'Elaine Johanson', 'Gian-Marco Conte', 'Ariana Familiar', 'Zhifan Jiang', 'Xinyang Liu', 'Chunhao Wang', 'Zachary Reitman', 'Walter Wiggins', 'Farouk Dako', 'Russell Takeshi Shinohara', 'Verena Chung', 'Timothy Bergquist', 'James Eddy', 'Keyvan Farahani', 'Ahmed W Moawad', 'Dominic LaBella', 'Anahita Fathi Kazerooni', 'Anastasia Janas', 'Syed Muhammad Anwar', 'Maruf Adewole', 'Christina Bukas', 'Diana Waldmannstetter', 'Izabela Horvath', 'Oezguen Turgut', 'Florian Hoelzl', 'Ujjwal Baid', 'Hongwei Bran Li', 'Ezequiel de da Rosa', 'Eva Oswald', 'Robert Graf', 'Felix Steinbauer', 'Felix Meissen', 'Florian Kofler'] | 2023-05-15 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation', 'anatomy'] | ['computer-vision', 'medical', 'miscellaneous'] | [ 5.56948066e-01 3.01755518e-01 1.43690392e-01 -5.03656447e-01
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823f17c8-2a84-4fb5-a917-dafd5695c004 | paris-personalized-activity-recommendation | 2110.13745 | null | https://arxiv.org/abs/2110.13745v1 | https://arxiv.org/pdf/2110.13745v1.pdf | PARIS: Personalized Activity Recommendation for Improving Sleep Quality | The quality of sleep has a deep impact on people's physical and mental health. People with insufficient sleep are more likely to report physical and mental distress, activity limitation, anxiety, and pain. Moreover, in the past few years, there has been an explosion of applications and devices for activity monitoring and health tracking. Signals collected from these wearable devices can be used to study and improve sleep quality. In this paper, we utilize the relationship between physical activity and sleep quality to find ways of assisting people improve their sleep using machine learning techniques. People usually have several behavior modes that their bio-functions can be divided into. Performing time series clustering on activity data, we find cluster centers that would correlate to the most evident behavior modes for a specific subject. Activity recipes are then generated for good sleep quality for each behavior mode within each cluster. These activity recipes are supplied to an activity recommendation engine for suggesting a mix of relaxed to intense activities to subjects during their daily routines. The recommendations are further personalized based on the subjects' lifestyle constraints, i.e. their age, gender, body mass index (BMI), resting heart rate, etc, with the objective of the recommendation being the improvement of that night's quality of sleep. This would in turn serve a longer-term health objective, like lowering heart rate, improving the overall quality of sleep, etc. | ['Jaideep Srivastava', 'Louis Kazaglis', 'Abhiraj Mohan', 'Saksham Goel', 'Meghna Singh'] | 2021-10-26 | null | null | null | null | ['sleep-quality-prediction', 'time-series-clustering'] | ['medical', 'time-series'] | [-2.37295732e-01 -4.54215296e-02 -8.24841440e-01 -3.32038581e-01
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a2a45996-7ef8-49c5-bc0d-37c7a447dd9c | explainable-interpretable-trustworthy-ai-for | 2301.06676 | null | https://arxiv.org/abs/2301.06676v1 | https://arxiv.org/pdf/2301.06676v1.pdf | Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life | Machine learning (ML) and Artificial Intelligence (AI) are increasingly used in energy and engineering systems, but these models must be fair, unbiased, and explainable. It is critical to have confidence in AI's trustworthiness. ML techniques have been useful in predicting important parameters and improving model performance. However, for these AI techniques to be useful for making decisions, they need to be audited, accounted for, and easy to understand. Therefore, the use of Explainable AI (XAI) and interpretable machine learning (IML) is crucial for the accurate prediction of prognostics, such as remaining useful life (RUL) in a digital twin system to make it intelligent while ensuring that the AI model is transparent in its decision-making processes and that the predictions it generates can be understood and trusted by users. By using AI that is explainable, interpretable, and trustworthy, intelligent digital twin systems can make more accurate predictions of RUL, leading to better maintenance and repair planning and, ultimately, improved system performance. The objective of this paper is to understand the idea of XAI and IML and justify the important role of ML/AI in the Digital Twin framework and components, which requires XAI to understand the prediction better. This paper explains the importance of XAI and IML in both local and global aspects to ensure the use of trustworthy ML/AI applications for RUL prediction. This paper used the RUL prediction for the XAI and IML studies and leveraged the integrated python toolbox for interpretable machine learning (PiML). | ['Syed B. Alam', 'Souvik Chakraborty', 'Md Nazmus Sakib', 'Bader Almutairi', 'Kazuma Kobayashi'] | 2023-01-17 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [-1.73261017e-01 4.56752360e-01 -2.34452650e-01 -3.54182035e-01
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7ad416ce-4dc8-48fe-b82e-b7beef7ab5ac | unsupervised-domain-adaptation-by | 1409.7495 | null | http://arxiv.org/abs/1409.7495v2 | http://arxiv.org/pdf/1409.7495v2.pdf | Unsupervised Domain Adaptation by Backpropagation | Top-performing deep architectures are trained on massive amounts of labeled
data. In the absence of labeled data for a certain task, domain adaptation
often provides an attractive option given that labeled data of similar nature
but from a different domain (e.g. synthetic images) are available. Here, we
propose a new approach to domain adaptation in deep architectures that can be
trained on large amount of labeled data from the source domain and large amount
of unlabeled data from the target domain (no labeled target-domain data is
necessary).
As the training progresses, the approach promotes the emergence of "deep"
features that are (i) discriminative for the main learning task on the source
domain and (ii) invariant with respect to the shift between the domains. We
show that this adaptation behaviour can be achieved in almost any feed-forward
model by augmenting it with few standard layers and a simple new gradient
reversal layer. The resulting augmented architecture can be trained using
standard backpropagation.
Overall, the approach can be implemented with little effort using any of the
deep-learning packages. The method performs very well in a series of image
classification experiments, achieving adaptation effect in the presence of big
domain shifts and outperforming previous state-of-the-art on Office datasets. | ['Yaroslav Ganin', 'Victor Lempitsky'] | 2014-09-26 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 2.06794038e-01 1.37361854e-01 -1.11798398e-01 -7.03841329e-01
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3cd956a8-426e-4df4-b041-289bba350b72 | lidar-guided-stereo-matching-with-a-spatial | 2202.09953 | null | https://arxiv.org/abs/2202.09953v2 | https://arxiv.org/pdf/2202.09953v2.pdf | LiDAR-guided Stereo Matching with a Spatial Consistency Constraint | The complementary fusion of light detection and ranging (LiDAR) data and image data is a promising but challenging task for generating high-precision and high-density point clouds. This study proposes an innovative LiDAR-guided stereo matching approach called LiDAR-guided stereo matching (LGSM), which considers the spatial consistency represented by continuous disparity or depth changes in the homogeneous region of an image. The LGSM first detects the homogeneous pixels of each LiDAR projection point based on their color or intensity similarity. Next, we propose a riverbed enhancement function to optimize the cost volume of the LiDAR projection points and their homogeneous pixels to improve the matching robustness. Our formulation expands the constraint scopes of sparse LiDAR projection points with the guidance of image information to optimize the cost volume of pixels as much as possible. We applied LGSM to semi-global matching and AD-Census on both simulated and real datasets. When the percentage of LiDAR points in the simulated datasets was 0.16%, the matching accuracy of our method achieved a subpixel level, while that of the original stereo matching algorithm was 3.4 pixels. The experimental results show that LGSM is suitable for indoor, street, aerial, and satellite image datasets and provides good transferability across semi-global matching and AD-Census. Furthermore, the qualitative and quantitative evaluations demonstrate that LGSM is superior to two state-of-the-art optimizing cost volume methods, especially in reducing mismatches in difficult matching areas and refining the boundaries of objects. | ['Yongxiang Yao', 'Yi Wan', 'Xu Huang', 'Xinyi Liu', 'Siyuan Zou', 'Yongjun Zhang'] | 2022-02-21 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 3.37757409e-01 -4.91453588e-01 2.59062834e-02 -3.77795935e-01
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0d5ba94b-773d-402f-8290-e54f7288e6a6 | combining-gcn-and-transformer-for-chinese | 2105.09085 | null | https://arxiv.org/abs/2105.09085v3 | https://arxiv.org/pdf/2105.09085v3.pdf | Combining GCN and Transformer for Chinese Grammatical Error Detection | This paper describes our system at NLPTEA-2020 Task: Chinese Grammatical Error Diagnosis (CGED). The goal of CGED is to diagnose four types of grammatical errors: word selection (S), redundant words (R), missing words (M), and disordered words (W). The automatic CGED system contains two parts including error detection and error correction and our system is designed to solve the error detection problem. Our system is built on three models: 1) a BERT-based model leveraging syntactic information; 2) a BERT-based model leveraging contextual embeddings; 3) a lexicon-based graph neural network leveraging lexical information. We also design an ensemble mechanism to improve the single model's performance. Finally, our system achieves the highest F1 scores at detection level and identification level among all teams participating in the CGED 2020 task. | ['Jinhong Zhang'] | 2021-05-19 | null | null | null | null | ['grammatical-error-detection'] | ['natural-language-processing'] | [-1.79004461e-01 3.13798875e-01 4.55112278e-01 -3.07464123e-01
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3934bbc7-8983-4559-a27b-ed05bcfc7d54 | cross-market-product-recommendation | 2109.05929 | null | https://arxiv.org/abs/2109.05929v1 | https://arxiv.org/pdf/2109.05929v1.pdf | Cross-Market Product Recommendation | We study the problem of recommending relevant products to users in relatively resource-scarce markets by leveraging data from similar, richer in resource auxiliary markets. We hypothesize that data from one market can be used to improve performance in another. Only a few studies have been conducted in this area, partly due to the lack of publicly available experimental data. To this end, we collect and release XMarket, a large dataset covering 18 local markets on 16 different product categories, featuring 52.5 million user-item interactions. We introduce and formalize the problem of cross-market product recommendation, i.e., market adaptation. We explore different market-adaptation techniques inspired by state-of-the-art domain-adaptation and meta-learning approaches and propose a novel neural approach for market adaptation, named FOREC. Our model follows a three-step procedure -- pre-training, forking, and fine-tuning -- in order to fully utilize the data from an auxiliary market as well as the target market. We conduct extensive experiments studying the impact of market adaptation on different pairs of markets. Our proposed approach demonstrates robust effectiveness, consistently improving the performance on target markets compared to competitive baselines selected for our analysis. In particular, FOREC improves on average 24% and up to 50% in terms of nDCG@10, compared to the NMF baseline. Our analysis and experiments suggest specific future directions in this research area. We release our data and code for academic purposes. | ['James Allan', 'Evangelos Kanoulas', 'Ali Vardasbi', 'Mohammad Aliannejadi', 'Hamed Bonab'] | 2021-09-13 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 1.02626368e-01 -4.41017747e-01 -7.98009157e-01 -4.18023348e-01
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9de503bf-fe3f-4dbc-8c5e-797ce0455326 | discovering-outstanding-subgroup-lists-for | 2006.09186 | null | https://arxiv.org/abs/2006.09186v1 | https://arxiv.org/pdf/2006.09186v1.pdf | Discovering outstanding subgroup lists for numeric targets using MDL | The task of subgroup discovery (SD) is to find interpretable descriptions of subsets of a dataset that stand out with respect to a target attribute. To address the problem of mining large numbers of redundant subgroups, subgroup set discovery (SSD) has been proposed. State-of-the-art SSD methods have their limitations though, as they typically heavily rely on heuristics and/or user-chosen hyperparameters. We propose a dispersion-aware problem formulation for subgroup set discovery that is based on the minimum description length (MDL) principle and subgroup lists. We argue that the best subgroup list is the one that best summarizes the data given the overall distribution of the target. We restrict our focus to a single numeric target variable and show that our formalization coincides with an existing quality measure when finding a single subgroup, but that-in addition-it allows to trade off subgroup quality with the complexity of the subgroup. We next propose SSD++, a heuristic algorithm for which we empirically demonstrate that it returns outstanding subgroup lists: non-redundant sets of compact subgroups that stand out by having strongly deviating means and small spread. | ['Thomas Bäck', 'Peter Grünwald', 'Hugo M. Proença', 'Matthijs van Leeuwen'] | 2020-06-16 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 4.99248356e-01 4.33586776e-01 -5.71922958e-01 -5.59220493e-01
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39ad5e69-231d-4727-8796-763d1cd55a10 | relation-aware-graph-attention-network-for | 1903.12314 | null | https://arxiv.org/abs/1903.12314v3 | https://arxiv.org/pdf/1903.12314v3.pdf | Relation-Aware Graph Attention Network for Visual Question Answering | In order to answer semantically-complicated questions about an image, a Visual Question Answering (VQA) model needs to fully understand the visual scene in the image, especially the interactive dynamics between different objects. We propose a Relation-aware Graph Attention Network (ReGAT), which encodes each image into a graph and models multi-type inter-object relations via a graph attention mechanism, to learn question-adaptive relation representations. Two types of visual object relations are explored: (i) Explicit Relations that represent geometric positions and semantic interactions between objects; and (ii) Implicit Relations that capture the hidden dynamics between image regions. Experiments demonstrate that ReGAT outperforms prior state-of-the-art approaches on both VQA 2.0 and VQA-CP v2 datasets. We further show that ReGAT is compatible to existing VQA architectures, and can be used as a generic relation encoder to boost the model performance for VQA. | ['Yu Cheng', 'Linjie Li', 'Jingjing Liu', 'Zhe Gan'] | 2019-03-29 | relation-aware-graph-attention-network-for-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Li_Relation-Aware_Graph_Attention_Network_for_Visual_Question_Answering_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Li_Relation-Aware_Graph_Attention_Network_for_Visual_Question_Answering_ICCV_2019_paper.pdf | iccv-2019-10 | ['implicit-relations'] | ['natural-language-processing'] | [-1.51600823e-01 3.74000102e-01 -5.08861654e-02 -5.61298668e-01
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d54a6514-6b94-4b8c-90e0-b8676211091a | deep-learning-for-channel-coding-via-neural | 1903.02865 | null | http://arxiv.org/abs/1903.02865v1 | http://arxiv.org/pdf/1903.02865v1.pdf | Deep Learning for Channel Coding via Neural Mutual Information Estimation | End-to-end deep learning for communication systems, i.e., systems whose
encoder and decoder are learned, has attracted significant interest recently,
due to its performance which comes close to well-developed classical
encoder-decoder designs. However, one of the drawbacks of current learning
approaches is that a differentiable channel model is needed for the training of
the underlying neural networks. In real-world scenarios, such a channel model
is hardly available and often the channel density is not even known at all.
Some works, therefore, focus on a generative approach, i.e., generating the
channel from samples, or rely on reinforcement learning to circumvent this
problem. We present a novel approach which utilizes a recently proposed neural
estimator of mutual information. We use this estimator to optimize the encoder
for a maximized mutual information, only relying on channel samples. Moreover,
we show that our approach achieves the same performance as state-of-the-art
end-to-end learning with perfect channel model knowledge. | ['Gerhard Wunder', 'Rick Fritschek', 'Rafael F. Schaefer'] | 2019-03-07 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 2.47583747e-01 3.23357224e-01 -1.22600839e-01 -2.23064885e-01
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1bcb18a3-fc0d-4b36-842c-1464b22fd2ee | bio-plausible-unsupervised-delay-learning-for | 2011.09380 | null | https://arxiv.org/abs/2011.09380v1 | https://arxiv.org/pdf/2011.09380v1.pdf | Bio-plausible Unsupervised Delay Learning for Extracting Temporal Features in Spiking Neural Networks | The plasticity of the conduction delay between neurons plays a fundamental role in learning. However, the exact underlying mechanisms in the brain for this modulation is still an open problem. Understanding the precise adjustment of synaptic delays could help us in developing effective brain-inspired computational models in providing aligned insights with the experimental evidence. In this paper, we propose an unsupervised biologically plausible learning rule for adjusting the synaptic delays in spiking neural networks. Then, we provided some mathematical proofs to show that our learning rule gives a neuron the ability to learn repeating spatio-temporal patterns. Furthermore, the experimental results of applying an STDP-based spiking neural network equipped with our proposed delay learning rule on Random Dot Kinematogram indicate the efficacy of the proposed delay learning rule in extracting temporal features. | ['Mohammad Ganjtabesh', 'Alireza Nadafian'] | 2020-11-18 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [ 2.91995376e-01 -5.14956534e-01 6.17211591e-03 -1.34352446e-01
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e18153aa-0ab6-4eea-9d66-33ef29a8172b | disto-evaluating-textual-distractors-for | 2304.04881 | null | https://arxiv.org/abs/2304.04881v1 | https://arxiv.org/pdf/2304.04881v1.pdf | DISTO: Evaluating Textual Distractors for Multi-Choice Questions using Negative Sampling based Approach | Multiple choice questions (MCQs) are an efficient and common way to assess reading comprehension (RC). Every MCQ needs a set of distractor answers that are incorrect, but plausible enough to test student knowledge. Distractor generation (DG) models have been proposed, and their performance is typically evaluated using machine translation (MT) metrics. However, MT metrics often misjudge the suitability of generated distractors. We propose DISTO: the first learned evaluation metric for generated distractors. We validate DISTO by showing its scores correlate highly with human ratings of distractor quality. At the same time, DISTO ranks the performance of state-of-the-art DG models very differently from MT-based metrics, showing that MT metrics should not be used for distractor evaluation. | ['Alona Fyshe', 'Bilal Ghanem'] | 2023-04-10 | null | null | null | null | ['distractor-generation', 'reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [-2.93068498e-01 3.67097855e-01 -3.32267731e-02 -2.02296317e-01
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50f7b7a1-6799-4e33-ab7a-377a2732c7ba | test-time-adaptation-for-nighttime-color | 2307.04470 | null | https://arxiv.org/abs/2307.04470v1 | https://arxiv.org/pdf/2307.04470v1.pdf | Test-Time Adaptation for Nighttime Color-Thermal Semantic Segmentation | The ability to scene understanding in adverse visual conditions, e.g., nighttime, has sparked active research for RGB-Thermal (RGB-T) semantic segmentation. However, it is essentially hampered by two critical problems: 1) the day-night gap of RGB images is larger than that of thermal images, and 2) the class-wise performance of RGB images at night is not consistently higher or lower than that of thermal images. we propose the first test-time adaptation (TTA) framework, dubbed Night-TTA, to address the problems for nighttime RGBT semantic segmentation without access to the source (daytime) data during adaptation. Our method enjoys three key technical parts. Firstly, as one modality (e.g., RGB) suffers from a larger domain gap than that of the other (e.g., thermal), Imaging Heterogeneity Refinement (IHR) employs an interaction branch on the basis of RGB and thermal branches to prevent cross-modal discrepancy and performance degradation. Then, Class Aware Refinement (CAR) is introduced to obtain reliable ensemble logits based on pixel-level distribution aggregation of the three branches. In addition, we also design a specific learning scheme for our TTA framework, which enables the ensemble logits and three student logits to collaboratively learn to improve the quality of predictions during the testing phase of our Night TTA. Extensive experiments show that our method achieves state-of-the-art (SoTA) performance with a 13.07% boost in mIoU. | ['Lin Wang', 'Athanasios Vasilakos', 'Jinjing Zhu', 'Guoyang Zhao', 'Weiming Zhang', 'Yexin Liu'] | 2023-07-10 | null | null | null | null | ['semantic-segmentation', 'scene-understanding'] | ['computer-vision', 'computer-vision'] | [ 1.69053003e-01 -3.12005401e-01 2.13551641e-01 -5.31275392e-01
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4e97f217-ae83-46d8-a290-01ec001463b2 | quantitative-trading-using-deep-q-learning | 2304.06037 | null | https://arxiv.org/abs/2304.06037v1 | https://arxiv.org/pdf/2304.06037v1.pdf | Quantitative Trading using Deep Q Learning | Reinforcement learning (RL) is a branch of machine learning that has been used in a variety of applications such as robotics, game playing, and autonomous systems. In recent years, there has been growing interest in applying RL to quantitative trading, where the goal is to make profitable trades in financial markets. This paper explores the use of RL in quantitative trading and presents a case study of a RL-based trading algorithm. The results show that RL can be a powerful tool for quantitative trading, and that it has the potential to outperform traditional trading algorithms. The use of reinforcement learning in quantitative trading represents a promising area of research that can potentially lead to the development of more sophisticated and effective trading systems. Future work could explore the use of alternative reinforcement learning algorithms, incorporate additional data sources, and test the system on different asset classes. Overall, our research demonstrates the potential of using reinforcement learning in quantitative trading and highlights the importance of continued research and development in this area. By developing more sophisticated and effective trading systems, we can potentially improve the efficiency of financial markets and generate greater returns for investors. | ['Soumyadip Sarkar'] | 2023-04-03 | null | null | null | null | ['q-learning'] | ['methodology'] | [-4.02761966e-01 -4.64660488e-02 -2.64698505e-01 -1.70448720e-01
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cb7295a0-f3f4-4f41-9d60-8e8b2268edda | exploring-long-short-range-temporal | 2208.03754 | null | https://arxiv.org/abs/2208.03754v2 | https://arxiv.org/pdf/2208.03754v2.pdf | Exploring Long & Short Range Temporal Information for Learned Video Compression | Learned video compression methods have gained a variety of interest in the video coding community since they have matched or even exceeded the rate-distortion (RD) performance of traditional video codecs. However, many current learning-based methods are dedicated to utilizing short-range temporal information, thus limiting their performance. In this paper, we focus on exploiting the unique characteristics of video content and further exploring temporal information to enhance compression performance. Specifically, for long-range temporal information exploitation, we propose temporal prior that can update continuously within the group of pictures (GOP) during inference. In that case temporal prior contains valuable temporal information of all decoded images within the current GOP. As for short-range temporal information, we propose a progressive guided motion compensation to achieve robust and effective compensation. In detail, we design a hierarchical structure to achieve multi-scale compensation. More importantly, we use optical flow guidance to generate pixel offsets between feature maps at each scale, and the compensation results at each scale will be used to guide the following scale's compensation. Sufficient experimental results demonstrate that our method can obtain better RD performance than state-of-the-art video compression approaches. The code is publicly available on: https://github.com/Huairui/LSTVC. | ['Zhenzhong Chen', 'Huairui Wang'] | 2022-08-07 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 3.23887259e-01 -2.88946867e-01 -4.93448585e-01 -2.10747182e-01
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92124e46-1fd0-450c-9d29-4ed0cff32a92 | self-supervised-transformer-architecture-for | 2302.02025 | null | https://arxiv.org/abs/2302.02025v1 | https://arxiv.org/pdf/2302.02025v1.pdf | Self-Supervised Transformer Architecture for Change Detection in Radio Access Networks | Radio Access Networks (RANs) for telecommunications represent large agglomerations of interconnected hardware consisting of hundreds of thousands of transmitting devices (cells). Such networks undergo frequent and often heterogeneous changes caused by network operators, who are seeking to tune their system parameters for optimal performance. The effects of such changes are challenging to predict and will become even more so with the adoption of 5G/6G networks. Therefore, RAN monitoring is vital for network operators. We propose a self-supervised learning framework that leverages self-attention and self-distillation for this task. It works by detecting changes in Performance Measurement data, a collection of time-varying metrics which reflect a set of diverse measurements of the network performance at the cell level. Experimental results show that our approach outperforms the state of the art by 4% on a real-world based dataset consisting of about hundred thousands timeseries. It also has the merits of being scalable and generalizable. This allows it to provide deep insight into the specifics of mode of operation changes while relying minimally on expert knowledge. | ['Gregory Dudek', 'Xue Liu', 'Di wu', 'Wei-Di Chang', 'Dmitriy Rivkin', 'Igor Kozlov'] | 2023-02-03 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [-1.66629195e-01 -3.19253534e-01 -3.15436780e-01 -2.82283366e-01
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b91ce872-57b0-4acc-9014-183e2acc0b2b | transferrable-operative-difficulty-assessment | 1906.04934 | null | https://arxiv.org/abs/1906.04934v2 | https://arxiv.org/pdf/1906.04934v2.pdf | Transferrable Operative Difficulty Assessment in Robot-assisted Teleoperation: A Domain Adaptation Approach | Providing an accurate and efficient assessment of operative difficulty is important for designing robot-assisted teleoperation interfaces that are easy and natural for human operators to use. In this paper, we aim to develop a data-driven approach to numerically characterize the operative difficulty demand of complex teleoperation. In effort to provide an entirely task-independent assessment, we consider using only data collected from the human user including: (1) physiological response, and (2) movement kinematics. By leveraging an unsupervised domain adaptation technique, our approach learns the user information that defines task difficulty in a well-known source, namely, a Fitt's target reaching task, and generalizes that knowledge to a more complex human motor control scenario, namely, the teleoperation of a robotic system. Our approach consists of two main parts: (1) The first part accounts for the inherent variances of user physiological and kinematic response between these cross-domain motor control scenarios that are vastly different. (2) A stacked two-layer learner is designed to improve the overall modeling performance, yielding a 96.6% accuracy in predicting the known difficulty of a Fitts' reaching task when using movement kinematic features. We then validate the effectiveness of our model by investigating teleoperated robotic needle steering as a case study. Compared with a standard NASA TLX user survey, our results indicate significant differences in the difficulty demand for various choices of needle steering control algorithms, p<0.05, as well as the difficulty of steering the needle to different targets, p<0.05. The results highlight the potential of our approach to be used as a design tool to create more intuitive and natural teleoperation interfaces in robot-assisted systems. | ['Ziheng Wang', 'Jie Zhang', 'Cong Feng', 'Ann Majewicz Fey'] | 2019-06-12 | null | null | null | null | ['steering-control'] | ['computer-vision'] | [ 2.09614888e-01 1.03048600e-01 -2.78243244e-01 -1.04757652e-01
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55883a53-fa15-49b6-9eff-0cf90d322b4e | early-life-imprints-the-hierarchy-of-t-cell | 2007.11113 | null | https://arxiv.org/abs/2007.11113v1 | https://arxiv.org/pdf/2007.11113v1.pdf | Early life imprints the hierarchy of T cell clone sizes | The adaptive immune system responds to pathogens by selecting clones of cells with specific receptors. While clonal selection in response to particular antigens has been studied in detail, it is unknown how a lifetime of exposures to many antigens collectively shape the immune repertoire. Here, through mathematical modeling and statistical analyses of T cell receptor sequencing data we demonstrate that clonal expansions during a perinatal time window leave a long-lasting imprint on the human T cell repertoire. We demonstrate how the empirical scaling law relating the rank of the largest clones to their size can emerge from clonal growth during repertoire formation. We statistically identify early founded clones and find that they are indeed highly enriched among the largest clones. This enrichment persists even after decades of human aging, in a way that is quantitatively predicted by a model of fluctuating clonal selection. Our work presents a quantitative theory of human T cell dynamics compatible with the statistical laws of repertoire organization and provides a mechanism for how early clonal dynamics imprint the hierarchy of T cell clone sizes with implications for pathogen defense and autoimmunity. | ['Andreas Mayer', 'Jonathan Desponds', 'Maximilian Nguyen', 'Mario U. Gaimann'] | 2020-07-21 | null | null | null | null | ['human-aging'] | ['miscellaneous'] | [ 4.99469995e-01 -3.98951203e-01 -2.86791831e-01 -3.07965249e-01
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86cede29-25c5-4bab-ab74-f4d23f6a55f4 | reinforcement-learning-guided-multi-objective | 2303.01042 | null | https://arxiv.org/abs/2303.01042v1 | https://arxiv.org/pdf/2303.01042v1.pdf | Reinforcement Learning Guided Multi-Objective Exam Paper Generation | To reduce the repetitive and complex work of instructors, exam paper generation (EPG) technique has become a salient topic in the intelligent education field, which targets at generating high-quality exam paper automatically according to instructor-specified assessment criteria. The current advances utilize the ability of heuristic algorithms to optimize several well-known objective constraints, such as difficulty degree, number of questions, etc., for producing optimal solutions. However, in real scenarios, considering other equally relevant objectives (e.g., distribution of exam scores, skill coverage) is extremely important. Besides, how to develop an automatic multi-objective solution that finds an optimal subset of questions from a huge search space of large-sized question datasets and thus composes a high-quality exam paper is urgent but non-trivial. To this end, we skillfully design a reinforcement learning guided Multi-Objective Exam Paper Generation framework, termed MOEPG, to simultaneously optimize three exam domain-specific objectives including difficulty degree, distribution of exam scores, and skill coverage. Specifically, to accurately measure the skill proficiency of the examinee group, we first employ deep knowledge tracing to model the interaction information between examinees and response logs. We then design the flexible Exam Q-Network, a function approximator, which automatically selects the appropriate question to update the exam paper composition process. Later, MOEPG divides the decision space into multiple subspaces to better guide the updated direction of the exam paper. Through extensive experiments on two real-world datasets, we demonstrate that MOEPG is feasible in addressing the multiple dilemmas of exam paper generation scenario. | ['Kun Liang', 'Yimeng Ren', 'Xiankun Zhang', 'Hao Peng', 'Lihong Wang', 'Xuexiong Luo', 'Yuhu Shang'] | 2023-03-02 | null | null | null | null | ['knowledge-tracing', 'paper-generation'] | ['miscellaneous', 'natural-language-processing'] | [-8.10985640e-02 -9.09971371e-02 -2.30400801e-01 -2.42069870e-01
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e44ef50b-1615-482a-8c8c-614f177313ec | transfer-learning-of-fmri-dynamics | 1911.06813 | null | https://arxiv.org/abs/1911.06813v1 | https://arxiv.org/pdf/1911.06813v1.pdf | Transfer Learning of fMRI Dynamics | As a mental disorder progresses, it may affect brain structure, but brain function expressed in brain dynamics is affected much earlier. Capturing the moment when brain dynamics express the disorder is crucial for early diagnosis. The traditional approach to this problem via training classifiers either proceeds from handcrafted features or requires large datasets to combat the $m>>n$ problem when a high dimensional fMRI volume only has a single label that carries learning signal. Large datasets may not be available for a study of each disorder, or rare disorder types or sub-populations may not warrant for them. In this paper, we demonstrate a self-supervised pre-training method that enables us to pre-train directly on fMRI dynamics of healthy control subjects and transfer the learning to much smaller datasets of schizophrenia. Not only we enable classification of disorder directly based on fMRI dynamics in small data but also significantly speed up the learning when possible. This is encouraging evidence of informative transfer learning across datasets and diagnostic categories. | ['Zening Fu', 'Sergey Plis', 'Md Mahfuzur Rahman', 'Alex Fedorov', 'Usman Mahmood'] | 2019-11-16 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 1.71388105e-01 -3.56176123e-02 -9.33271721e-02 -6.16762757e-01
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4190a5fa-97c6-4645-9afe-204d1552f164 | a-comparative-analysis-of-expected-and | 1901.11084 | null | http://arxiv.org/abs/1901.11084v2 | http://arxiv.org/pdf/1901.11084v2.pdf | A Comparative Analysis of Expected and Distributional Reinforcement Learning | Since their introduction a year ago, distributional approaches to
reinforcement learning (distributional RL) have produced strong results
relative to the standard approach which models expected values (expected RL).
However, aside from convergence guarantees, there have been few theoretical
results investigating the reasons behind the improvements distributional RL
provides. In this paper we begin the investigation into this fundamental
question by analyzing the differences in the tabular, linear approximation, and
non-linear approximation settings. We prove that in many realizations of the
tabular and linear approximation settings, distributional RL behaves exactly
the same as expected RL. In cases where the two methods behave differently,
distributional RL can in fact hurt performance when it does not induce
identical behaviour. We then continue with an empirical analysis comparing
distributional and expected RL methods in control settings with non-linear
approximators to tease apart where the improvements from distributional RL
methods are coming from. | ['Pablo Samuel Castro', 'Clare Lyle', 'Marc G. Bellemare'] | 2019-01-30 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-1.49209395e-01 2.52749056e-01 -4.87899214e-01 -1.07547432e-01
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901de0bd-624f-401a-bbce-6acec2c203bc | explaining-deep-convolutional-neural-networks | 1607.02444 | null | http://arxiv.org/abs/1607.02444v1 | http://arxiv.org/pdf/1607.02444v1.pdf | Explaining Deep Convolutional Neural Networks on Music Classification | Deep convolutional neural networks (CNNs) have been actively adopted in the
field of music information retrieval, e.g. genre classification, mood
detection, and chord recognition. However, the process of learning and
prediction is little understood, particularly when it is applied to
spectrograms. We introduce auralisation of a CNN to understand its underlying
mechanism, which is based on a deconvolution procedure introduced in [2].
Auralisation of a CNN is converting the learned convolutional features that are
obtained from deconvolution into audio signals. In the experiments and
discussions, we explain trained features of a 5-layer CNN based on the
deconvolved spectrograms and auralised signals. The pairwise correlations per
layers with varying different musical attributes are also investigated to
understand the evolution of the learnt features. It is shown that in the deep
layers, the features are learnt to capture textures, the patterns of continuous
distributions, rather than shapes of lines. | ['Keunwoo Choi', 'Mark Sandler', 'George Fazekas'] | 2016-07-08 | null | null | null | null | ['chord-recognition', 'genre-classification', 'music-classification'] | ['audio', 'computer-vision', 'music'] | [ 1.46857277e-01 -3.76231581e-01 3.43384832e-01 -2.54986018e-01
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7082fe52-32aa-42ec-9a87-c1bbde18b8fd | mret-multi-resolution-transformer-for-video | 2303.07489 | null | https://arxiv.org/abs/2303.07489v2 | https://arxiv.org/pdf/2303.07489v2.pdf | MRET: Multi-resolution Transformer for Video Quality Assessment | No-reference video quality assessment (NR-VQA) for user generated content (UGC) is crucial for understanding and improving visual experience. Unlike video recognition tasks, VQA tasks are sensitive to changes in input resolution. Since large amounts of UGC videos nowadays are 720p or above, the fixed and relatively small input used in conventional NR-VQA methods results in missing high-frequency details for many videos. In this paper, we propose a novel Transformer-based NR-VQA framework that preserves the high-resolution quality information. With the multi-resolution input representation and a novel multi-resolution patch sampling mechanism, our method enables a comprehensive view of both the global video composition and local high-resolution details. The proposed approach can effectively aggregate quality information across different granularities in spatial and temporal dimensions, making the model robust to input resolution variations. Our method achieves state-of-the-art performance on large-scale UGC VQA datasets LSVQ and LSVQ-1080p, and on KoNViD-1k and LIVE-VQC without fine-tuning. | ['Feng Yang', 'Peyman Milanfar', 'Yilin Wang', 'Tianhao Zhang', 'Junjie Ke'] | 2023-03-13 | null | null | null | null | ['video-recognition'] | ['computer-vision'] | [-1.66676324e-02 -8.56597245e-01 -2.18989328e-01 -2.60380924e-01
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e5f64202-ac3b-44cf-a4c1-c6a924cb37a7 | integrated-steganography-and-steganalysis | null | null | https://openreview.net/forum?id=r1Vx_oA5YQ | https://openreview.net/pdf?id=r1Vx_oA5YQ | Integrated Steganography and Steganalysis with Generative Adversarial Networks | Recently, generative adversarial network is the hotspot in research areas and industrial application areas. It's application on data generation in computer vision is most common usage. This paper extends its application to data hiding and security area. In this paper, we propose the novel framework to integrate steganography and steganalysis processes. The proposed framework applies generative adversarial networks as the core structure. The discriminative model simulate the steganalysis process, which can help us understand the sensitivity of cover images to semantic changes. The steganography generative model is to generate stego image which is aligned with the original cover image, and attempts to confuse steganalysis discriminative model. The introduction of cycle discriminative model and inconsistent loss can help to enhance the quality and security of generated stego image in the iterative training process. Training dataset is mixed with intact images as well as intentional attacked images. The mix training process can further improve the robustness and security of new framework. Through the qualitative, quantitative experiments and analysis, this novel framework shows compelling performance and advantages over the current state-of-the-art methods in steganography and steganalysis benchmarks. | ['Chong Yu'] | null | null | null | null | iclr-2019-5 | ['steganalysis'] | ['computer-vision'] | [ 1.00775003e+00 6.57730401e-02 2.77190715e-01 2.41284803e-01
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a9df4197-ebbc-4bfa-b306-bcc2de078ece | mmtm-multi-tasking-multi-decoder-transformer | 2206.01268 | null | https://arxiv.org/abs/2206.01268v1 | https://arxiv.org/pdf/2206.01268v1.pdf | MMTM: Multi-Tasking Multi-Decoder Transformer for Math Word Problems | Recently, quite a few novel neural architectures were derived to solve math word problems by predicting expression trees. These architectures varied from seq2seq models, including encoders leveraging graph relationships combined with tree decoders. These models achieve good performance on various MWPs datasets but perform poorly when applied to an adversarial challenge dataset, SVAMP. We present a novel model MMTM that leverages multi-tasking and multi-decoder during pre-training. It creates variant tasks by deriving labels using pre-order, in-order and post-order traversal of expression trees, and uses task-specific decoders in a multi-tasking framework. We leverage transformer architectures with lower dimensionality and initialize weights from RoBERTa model. MMTM model achieves better mathematical reasoning ability and generalisability, which we demonstrate by outperforming the best state of the art baseline models from Seq2Seq, GTS, and Graph2Tree with a relative improvement of 19.4% on an adversarial challenge dataset SVAMP. | ['Darshan Patel', 'Prashant Kikani', 'Amit Sheth', 'Keyur Faldu'] | 2022-06-02 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 2.81079084e-01 3.76401782e-01 -3.92873213e-02 -5.20571649e-01
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720d3944-171d-48a4-b5d5-d76cac868772 | lexical-normalization-for-code-switched-data | 2006.01175 | null | https://arxiv.org/abs/2006.01175v2 | https://arxiv.org/pdf/2006.01175v2.pdf | Lexical Normalization for Code-switched Data and its Effect on POS-tagging | Lexical normalization, the translation of non-canonical data to standard language, has shown to improve the performance of manynatural language processing tasks on social media. Yet, using multiple languages in one utterance, also called code-switching (CS), is frequently overlooked by these normalization systems, despite its common use in social media. In this paper, we propose three normalization models specifically designed to handle code-switched data which we evaluate for two language pairs: Indonesian-English (Id-En) and Turkish-German (Tr-De). For the latter, we introduce novel normalization layers and their corresponding language ID and POS tags for the dataset, and evaluate the downstream effect of normalization on POS tagging. Results show that our CS-tailored normalization models outperform Id-En state of the art and Tr-De monolingual models, and lead to 5.4% relative performance increase for POS tagging as compared to unnormalized input. | ['Özlem Çetinoğlu', 'Rob van der Goot'] | 2020-06-01 | null | null | null | null | ['lexical-normalization'] | ['natural-language-processing'] | [ 2.18741782e-02 -8.32803994e-02 -1.73277214e-01 -4.32068288e-01
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297b3da0-96e2-4fee-95f9-bc231ceeebe5 | dygcn-dynamic-graph-embedding-with-graph | 2104.02962 | null | https://arxiv.org/abs/2104.02962v1 | https://arxiv.org/pdf/2104.02962v1.pdf | DyGCN: Dynamic Graph Embedding with Graph Convolutional Network | Graph embedding, aiming to learn low-dimensional representations (aka. embeddings) of nodes, has received significant attention recently. Recent years have witnessed a surge of efforts made on static graphs, among which Graph Convolutional Network (GCN) has emerged as an effective class of models. However, these methods mainly focus on the static graph embedding. In this work, we propose an efficient dynamic graph embedding approach, Dynamic Graph Convolutional Network (DyGCN), which is an extension of GCN-based methods. We naturally generalizes the embedding propagation scheme of GCN to dynamic setting in an efficient manner, which is to propagate the change along the graph to update node embeddings. The most affected nodes are first updated, and then their changes are propagated to the further nodes and leads to their update. Extensive experiments conducted on various dynamic graphs demonstrate that our model can update the node embeddings in a time-saving and performance-preserving way. | ['Mengmeng Ai', 'Liang Wang', 'Qiang Liu', 'XiaoYu Zhang', 'Shu Wu', 'Zekun Li', 'Zeyu Cui'] | 2021-04-07 | null | null | null | null | ['dynamic-graph-embedding'] | ['graphs'] | [-2.54404753e-01 8.58910307e-02 -2.05927238e-01 2.66559422e-02
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1bc8992f-78c6-46f3-b058-5d22e383361d | need-for-design-patterns-interoperability | 2208.12480 | null | https://arxiv.org/abs/2208.12480v1 | https://arxiv.org/pdf/2208.12480v1.pdf | Need for Design Patterns: Interoperability Issues and Modelling Challenges for Observational Data | Interoperability issues concerning observational data have gained attention in recent times. Automated data integration is important when it comes to the scientific analysis of observational data from different sources. However, it is hampered by various data interoperability issues. We focus exclusively on semantic interoperability issues for observational characteristics. We propose a use-case-driven approach to identify general classes of interoperability issues. In this paper, this is exemplarily done for the use-case of citizen science fireball observations. We derive key concepts for the identified interoperability issues that are generalizable to observational data in other fields of science. These key concepts contain several modeling challenges, and we broadly describe each modeling challenges associated with its interoperability issue. We believe, that addressing these challenges with a set of ontology design patterns will be an effective means for unified semantic modeling, paving the way for a unified approach for resolving interoperability issues in observational data. We demonstrate this with one design pattern, highlighting the importance and need for ontology design patterns for observational data, and leave the remaining patterns to future work. Our paper thus describes interoperability issues along with modeling challenges as a starting point for developing a set of extensible and reusable design patterns. | ['Friederike Klan', 'Frank Löffler', 'Trupti Padiya'] | 2022-08-26 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-1.34819970e-01 1.01048827e-01 -1.20961085e-01 -3.47789884e-01
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9a8804d9-a48f-4f95-8c9d-8ce35c7cf2a1 | robust-calibrate-proxy-loss-for-deep-metric | 2304.09162 | null | https://arxiv.org/abs/2304.09162v1 | https://arxiv.org/pdf/2304.09162v1.pdf | Robust Calibrate Proxy Loss for Deep Metric Learning | The mainstream researche in deep metric learning can be divided into two genres: proxy-based and pair-based methods. Proxy-based methods have attracted extensive attention due to the lower training complexity and fast network convergence. However, these methods have limitations as the poxy optimization is done by network, which makes it challenging for the proxy to accurately represent the feature distrubtion of the real class of data. In this paper, we propose a Calibrate Proxy (CP) structure, which uses the real sample information to improve the similarity calculation in proxy-based loss and introduces a calibration loss to constraint the proxy optimization towards the center of the class features. At the same time, we set a small number of proxies for each class to alleviate the impact of intra-class differences on retrieval performance. The effectiveness of our method is evaluated by extensive experiments on three public datasets and multiple synthetic label-noise datasets. The results show that our approach can effectively improve the performance of commonly used proxy-based losses on both regular and noisy datasets. | ['Zhixiang Liu', 'Yanling Du', 'Wei Song', 'Jian Wang', 'Xinyue Li'] | 2023-04-06 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [-2.35452279e-01 -5.56553602e-01 -2.21127763e-01 -8.37615848e-01
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0b93610b-7a68-4f29-aa11-22a09a805283 | doppler-exploitation-in-bistatic-mmwave-radio | 2208.10204 | null | https://arxiv.org/abs/2208.10204v1 | https://arxiv.org/pdf/2208.10204v1.pdf | Doppler Exploitation in Bistatic mmWave Radio SLAM | Networks in 5G and beyond utilize millimeter wave (mmWave) radio signals, large bandwidths, and large antenna arrays, which bring opportunities in jointly localizing the user equipment and mapping the propagation environment, termed as simultaneous localization and mapping (SLAM). Existing approaches mainly rely on delays and angles, and ignore the Doppler, although it contains geometric information. In this paper, we study the benefits of exploiting Doppler in SLAM through deriving the posterior Cram\'er-Rao bounds (PCRBs) and formulating the extended Kalman-Poisson multi-Bernoulli sequential filtering solution with Doppler as one of the involved measurements. Both theoretical PCRB analysis and simulation results demonstrate the efficacy of utilizing Doppler. | ['Henk Wymeersch', 'Lennart Svensson', 'Mikko Valkama', 'Jukka Talvitie', 'Fan Jiang', 'Hui Chen', 'Ossi Kaltiokallio', 'Yu Ge'] | 2022-08-22 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-3.70629102e-01 -2.37896442e-01 -2.36257359e-01 -3.38155180e-02
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6bb92295-7b29-46c9-93ef-3085ce877eb8 | lightweight-event-based-optical-flow | 2211.13726 | null | https://arxiv.org/abs/2211.13726v3 | https://arxiv.org/pdf/2211.13726v3.pdf | Rethinking Event-based Optical Flow: Iterative Deblurring as an Alternative to Correlation Volumes | Inspired by frame-based methods, state-of-the-art event-based optical flow networks rely on the explicit construction of correlation volumes, which are expensive to compute and store, at the same time prohibiting them from estimating high-resolution flow. We observe that the spatiotemporally continuous traces of events provide a natural search direction for seeking pixel correspondences, obviating the need to rely on gradients of explicit correlation volumes as such search directions. We introduce IDNet (Iterative Deblurring Network), a lightweight yet high-performing event-based optical flow network directly estimating flow from event traces without using correlation volumes. We further propose two iterative update schemes: "ID" which iterates over the same batch of events, and "TID" which iterates over time with streaming events in an online fashion. Benchmark results show the "ID" scheme outperforms previous state-of-the-art by 9% of EPE with 52% fewer parameters and 77% savings in memory footprint, while the "TID" scheme is even more efficient promising 80% of compute savings and 18 times less latency at the cost of only 6% of performance drop. | ['Guido C. H. E. de Croon', 'Federico Paredes-Vallés', 'YiLun Wu'] | 2022-11-24 | null | null | null | null | ['event-based-optical-flow'] | ['computer-vision'] | [-1.94296122e-01 -3.34241331e-01 -2.85879020e-02 2.61740666e-02
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9763c445-a31e-40ff-9234-9a00dcc032dc | learning-to-summarize-and-answer-questions | 2306.09922 | null | https://arxiv.org/abs/2306.09922v1 | https://arxiv.org/pdf/2306.09922v1.pdf | Learning to Summarize and Answer Questions about a Virtual Robot's Past Actions | When robots perform long action sequences, users will want to easily and reliably find out what they have done. We therefore demonstrate the task of learning to summarize and answer questions about a robot agent's past actions using natural language alone. A single system with a large language model at its core is trained to both summarize and answer questions about action sequences given ego-centric video frames of a virtual robot and a question prompt. To enable training of question answering, we develop a method to automatically generate English-language questions and answers about objects, actions, and the temporal order in which actions occurred during episodes of robot action in the virtual environment. Training one model to both summarize and answer questions enables zero-shot transfer of representations of objects learned through question answering to improved action summarization. % involving objects not seen in training to summarize. | ['Daniel Bauer', 'Iretiayo Akinola', 'Chad DeChant'] | 2023-06-16 | null | null | null | null | ['question-answering'] | ['natural-language-processing'] | [ 3.63134861e-01 6.22614861e-01 1.10037342e-01 -4.49101657e-01
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3.50880057e-01 2.81230956e-01 4.72319245e-01 1.35344958e+00
3.64720911e-01 6.06477559e-01 3.88712674e-01 -1.01464105e+00
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7.47801542e-01 -1.81607693e-01 -9.02627259e-02 4.88466561e-01] | [4.498137474060059, 0.7680226564407349] |
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