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f4e1417e-c3f2-46b1-9c9f-09cd53590733 | towards-more-robust-interpretation-via-local | 2211.15900 | null | https://arxiv.org/abs/2211.15900v2 | https://arxiv.org/pdf/2211.15900v2.pdf | Towards More Robust Interpretation via Local Gradient Alignment | Neural network interpretation methods, particularly feature attribution methods, are known to be fragile with respect to adversarial input perturbations. To address this, several methods for enhancing the local smoothness of the gradient while training have been proposed for attaining \textit{robust} feature attributions. However, the lack of considering the normalization of the attributions, which is essential in their visualizations, has been an obstacle to understanding and improving the robustness of feature attribution methods. In this paper, we provide new insights by taking such normalization into account. First, we show that for every non-negative homogeneous neural network, a naive $\ell_2$-robust criterion for gradients is \textit{not} normalization invariant, which means that two functions with the same normalized gradient can have different values. Second, we formulate a normalization invariant cosine distance-based criterion and derive its upper bound, which gives insight for why simply minimizing the Hessian norm at the input, as has been done in previous work, is not sufficient for attaining robust feature attribution. Finally, we propose to combine both $\ell_2$ and cosine distance-based criteria as regularization terms to leverage the advantages of both in aligning the local gradient. As a result, we experimentally show that models trained with our method produce much more robust interpretations on CIFAR-10 and ImageNet-100 without significantly hurting the accuracy, compared to the recent baselines. To the best of our knowledge, this is the first work to verify the robustness of interpretation on a larger-scale dataset beyond CIFAR-10, thanks to the computational efficiency of our method. | ['Taesup Moon', 'Adrian Weller', 'Juyeon Heo', 'Seokhyeon Jeong', 'Sunghwan Joo'] | 2022-11-29 | null | null | null | null | ['network-interpretation'] | ['computer-vision'] | [ 3.68865699e-01 2.00638592e-01 -3.98801602e-02 -5.33618808e-01
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e432693a-58b0-4c08-ba54-7a5521f99e49 | text-is-all-you-need-learning-language | 2305.13731 | null | https://arxiv.org/abs/2305.13731v2 | https://arxiv.org/pdf/2305.13731v2.pdf | Text Is All You Need: Learning Language Representations for Sequential Recommendation | Sequential recommendation aims to model dynamic user behavior from historical interactions. Existing methods rely on either explicit item IDs or general textual features for sequence modeling to understand user preferences. While promising, these approaches still struggle to model cold-start items or transfer knowledge to new datasets. In this paper, we propose to model user preferences and item features as language representations that can be generalized to new items and datasets. To this end, we present a novel framework, named Recformer, which effectively learns language representations for sequential recommendation. Specifically, we propose to formulate an item as a "sentence" (word sequence) by flattening item key-value attributes described by text so that an item sequence for a user becomes a sequence of sentences. For recommendation, Recformer is trained to understand the "sentence" sequence and retrieve the next "sentence". To encode item sequences, we design a bi-directional Transformer similar to the model Longformer but with different embedding layers for sequential recommendation. For effective representation learning, we propose novel pretraining and finetuning methods which combine language understanding and recommendation tasks. Therefore, Recformer can effectively recommend the next item based on language representations. Extensive experiments conducted on six datasets demonstrate the effectiveness of Recformer for sequential recommendation, especially in low-resource and cold-start settings. | ['Julian McAuley', 'Jingbo Shang', 'Xin Shen', 'Jinmiao Fu', 'Jin Li', 'Ming Wang', 'Jiacheng Li'] | 2023-05-23 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 2.60089934e-01 -7.08791733e-01 -6.81662321e-01 -8.67893279e-01
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5d6b86d5-1a3c-426c-9747-d4dddf4ee7af | cognitively-inspired-agent-based-service | 1905.12630 | null | https://arxiv.org/abs/1905.12630v1 | https://arxiv.org/pdf/1905.12630v1.pdf | Cognitively-inspired Agent-based Service Composition for Mobile & Pervasive Computing | Automatic service composition in mobile and pervasive computing faces many challenges due to the complex and highly dynamic nature of the environment. Common approaches consider service composition as a decision problem whose solution is usually addressed from optimization perspectives which are not feasible in practice due to the intractability of the problem, limited computational resources of smart devices, service host's mobility, and time constraints to tailor composition plans. Thus, our main contribution is the development of a cognitively-inspired agent-based service composition model focused on bounded rationality rather than optimality, which allows the system to compensate for limited resources by selectively filtering out continuous streams of data. Our approach exhibits features such as distributedness, modularity, emergent global functionality, and robustness, which endow it with capabilities to perform decentralized service composition by orchestrating manifold service providers and conflicting goals from multiple users. The evaluation of our approach shows promising results when compared against state-of-the-art service composition models. | ['Oscar J. Romero'] | 2019-05-29 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [-1.89750031e-01 2.33780757e-01 -3.49988975e-02 6.25347951e-04
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62d5ed78-2a91-4fe1-851e-a102dd540029 | socialformer-social-network-inspired-long | 2202.10870 | null | https://arxiv.org/abs/2202.10870v1 | https://arxiv.org/pdf/2202.10870v1.pdf | Socialformer: Social Network Inspired Long Document Modeling for Document Ranking | Utilizing pre-trained language models has achieved great success for neural document ranking. Limited by the computational and memory requirements, long document modeling becomes a critical issue. Recent works propose to modify the full attention matrix in Transformer by designing sparse attention patterns. However, most of them only focus on local connections of terms within a fixed-size window. How to build suitable remote connections between terms to better model document representation remains underexplored. In this paper, we propose the model Socialformer, which introduces the characteristics of social networks into designing sparse attention patterns for long document modeling in document ranking. Specifically, we consider several attention patterns to construct a graph like social networks. Endowed with the characteristic of social networks, most pairs of nodes in such a graph can reach with a short path while ensuring the sparsity. To facilitate efficient calculation, we segment the graph into multiple subgraphs to simulate friend circles in social scenarios. Experimental results confirm the effectiveness of our model on long document modeling. | ['Zhengyi Ma', 'Huaying Yuan', 'Zhicheng Dou', 'Yujia Zhou'] | 2022-02-22 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [-1.68979943e-01 2.35227905e-02 -3.79732668e-01 -3.37188452e-01
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f5e23873-c146-4e89-9e9d-75d4d92c3c84 | unbiased-loss-functions-for-multilabel | 2109.11282 | null | https://arxiv.org/abs/2109.11282v1 | https://arxiv.org/pdf/2109.11282v1.pdf | Unbiased Loss Functions for Multilabel Classification with Missing Labels | This paper considers binary and multilabel classification problems in a setting where labels are missing independently and with a known rate. Missing labels are a ubiquitous phenomenon in extreme multi-label classification (XMC) tasks, such as matching Wikipedia articles to a small subset out of the hundreds of thousands of possible tags, where no human annotator can possibly check the validity of all the negative samples. For this reason, propensity-scored precision -- an unbiased estimate for precision-at-k under a known noise model -- has become one of the standard metrics in XMC. Few methods take this problem into account already during the training phase, and all are limited to loss functions that can be decomposed into a sum of contributions from each individual label. A typical approach to training is to reduce the multilabel problem into a series of binary or multiclass problems, and it has been shown that if the surrogate task should be consistent for optimizing recall, the resulting loss function is not decomposable over labels. Therefore, this paper derives the unique unbiased estimators for the different multilabel reductions, including the non-decomposable ones. These estimators suffer from increased variance and may lead to ill-posed optimization problems, which we address by switching to convex upper-bounds. The theoretical considerations are further supplemented by an experimental study showing that the switch to unbiased estimators significantly alters the bias-variance trade-off and may thus require stronger regularization, which in some cases can negate the benefits of unbiased estimation. | ['Rohit Babbar', 'Erik Schultheis'] | 2021-09-23 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 5.17175376e-01 2.31464431e-01 -4.55024660e-01 -6.16228044e-01
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9a202ac8-c55e-43b0-8a89-65eb4b25b60c | few-shot-protein-generation | 2204.01168 | null | https://arxiv.org/abs/2204.01168v1 | https://arxiv.org/pdf/2204.01168v1.pdf | Few Shot Protein Generation | We present the MSA-to-protein transformer, a generative model of protein sequences conditioned on protein families represented by multiple sequence alignments (MSAs). Unlike existing approaches to learning generative models of protein families, the MSA-to-protein transformer conditions sequence generation directly on a learned encoding of the multiple sequence alignment, circumventing the need for fitting dedicated family models. By training on a large set of well-curated multiple sequence alignments in Pfam, our MSA-to-protein transformer generalizes well to protein families not observed during training and outperforms conventional family modeling approaches, especially when MSAs are small. Our generative approach accurately models epistasis and indels and allows for exact inference and efficient sampling unlike other approaches. We demonstrate the protein sequence modeling capabilities of our MSA-to-protein transformer and compare it with alternative sequence modeling approaches in comprehensive benchmark experiments. | ['Tristan Bepler', 'Soumya Ram'] | 2022-04-03 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 7.14742661e-01 1.46673694e-01 -2.35232443e-01 -6.63088381e-01
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39cd7def-2973-44e1-92fe-a339ceb6078a | protodiv-prototype-guided-division-of | 2304.06652 | null | https://arxiv.org/abs/2304.06652v1 | https://arxiv.org/pdf/2304.06652v1.pdf | ProtoDiv: Prototype-guided Division of Consistent Pseudo-bags for Whole-slide Image Classification | Due to the limitations of inadequate Whole-Slide Image (WSI) samples with weak labels, pseudo-bag-based multiple instance learning (MIL) appears as a vibrant prospect in WSI classification. However, the pseudo-bag dividing scheme, often crucial for classification performance, is still an open topic worth exploring. Therefore, this paper proposes a novel scheme, ProtoDiv, using a bag prototype to guide the division of WSI pseudo-bags. Rather than designing complex network architecture, this scheme takes a plugin-and-play approach to safely augment WSI data for effective training while preserving sample consistency. Furthermore, we specially devise an attention-based prototype that could be optimized dynamically in training to adapt to a classification task. We apply our ProtoDiv scheme on seven baseline models, and then carry out a group of comparison experiments on two public WSI datasets. Experiments confirm our ProtoDiv could usually bring obvious performance improvements to WSI classification. | ['Luping Ji', 'Pei Liu', 'Rui Yang'] | 2023-04-13 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 2.55231500e-01 7.55929872e-02 -5.21878660e-01 -6.95284188e-01
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06ca8956-26bb-4039-8005-77a556c91e27 | learning-joint-spatial-temporal | 2007.10247 | null | https://arxiv.org/abs/2007.10247v1 | https://arxiv.org/pdf/2007.10247v1.pdf | Learning Joint Spatial-Temporal Transformations for Video Inpainting | High-quality video inpainting that completes missing regions in video frames is a promising yet challenging task. State-of-the-art approaches adopt attention models to complete a frame by searching missing contents from reference frames, and further complete whole videos frame by frame. However, these approaches can suffer from inconsistent attention results along spatial and temporal dimensions, which often leads to blurriness and temporal artifacts in videos. In this paper, we propose to learn a joint Spatial-Temporal Transformer Network (STTN) for video inpainting. Specifically, we simultaneously fill missing regions in all input frames by self-attention, and propose to optimize STTN by a spatial-temporal adversarial loss. To show the superiority of the proposed model, we conduct both quantitative and qualitative evaluations by using standard stationary masks and more realistic moving object masks. Demo videos are available at https://github.com/researchmm/STTN. | ['Jianlong Fu', 'Yanhong Zeng', 'Hongyang Chao'] | 2020-07-20 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2590_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123610511.pdf | eccv-2020-8 | ['seeing-beyond-the-visible', 'video-inpainting'] | ['computer-vision', 'computer-vision'] | [ 1.24032997e-01 -1.70409098e-01 -1.54413909e-01 -2.05760762e-01
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ff12998a-38f8-4f13-9cf1-3dfcaae828fd | cross-modal-vertical-federated-learning-for | 2306.02673 | null | https://arxiv.org/abs/2306.02673v1 | https://arxiv.org/pdf/2306.02673v1.pdf | Cross-Modal Vertical Federated Learning for MRI Reconstruction | Federated learning enables multiple hospitals to cooperatively learn a shared model without privacy disclosure. Existing methods often take a common assumption that the data from different hospitals have the same modalities. However, such a setting is difficult to fully satisfy in practical applications, since the imaging guidelines may be different between hospitals, which makes the number of individuals with the same set of modalities limited. To this end, we formulate this practical-yet-challenging cross-modal vertical federated learning task, in which shape data from multiple hospitals have different modalities with a small amount of multi-modality data collected from the same individuals. To tackle such a situation, we develop a novel framework, namely Federated Consistent Regularization constrained Feature Disentanglement (Fed-CRFD), for boosting MRI reconstruction by effectively exploring the overlapping samples (individuals with multi-modalities) and solving the domain shift problem caused by different modalities. Particularly, our Fed-CRFD involves an intra-client feature disentangle scheme to decouple data into modality-invariant and modality-specific features, where the modality-invariant features are leveraged to mitigate the domain shift problem. In addition, a cross-client latent representation consistency constraint is proposed specifically for the overlapping samples to further align the modality-invariant features extracted from different modalities. Hence, our method can fully exploit the multi-source data from hospitals while alleviating the domain shift problem. Extensive experiments on two typical MRI datasets demonstrate that our network clearly outperforms state-of-the-art MRI reconstruction methods. The source code will be publicly released upon the publication of this work. | ['Yefeng Zheng', 'Yong Xu', 'Yuexiang Li', 'Lei Zhu', 'Nanjun He', 'Yawen Huang', 'Hong Wang', 'Yunlu Yan'] | 2023-06-05 | null | null | null | null | ['mri-reconstruction', 'disentanglement'] | ['computer-vision', 'methodology'] | [ 6.99911416e-02 -2.96199452e-02 -5.20510972e-01 -7.00752616e-01
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bac5a5a9-e358-4c57-8d07-d54f083927b7 | stegoappdb-a-steganography-apps-forensics | 1904.09360 | null | http://arxiv.org/abs/1904.09360v1 | http://arxiv.org/pdf/1904.09360v1.pdf | StegoAppDB: a Steganography Apps Forensics Image Database | In this paper, we present a new reference dataset simulating digital evidence
for image steganography. Steganography detection is a digital image forensic
topic that is relatively unknown in practical forensics, although stego app use
in the wild is on the rise. This paper introduces the first database consisting
of mobile phone photographs and stego images produced from mobile stego apps,
including a rich set of side information, offering simulated digital evidence.
StegoAppDB, a steganography apps forensics image database, contains over
810,000 innocent and stego images using a minimum of 10 different phone models
from 24 distinct devices, with detailed provenanced data comprising a wide
range of ISO and exposure settings, EXIF data, message information, embedding
rates, etc. We develop a camera app, Cameraw, specifically for data
acquisition, with multiple images per scene, saving simultaneously in both DNG
and high-quality JPEG formats. Stego images are created from these original
images using selected mobile stego apps through a careful process of reverse
engineering. StegoAppDB contains cover-stego image pairs including for apps
that resize the stego dimensions. We retainthe original devices and continue to
enlarge the database, and encourage the image forensics community to use
StegoAppDB. While designed for steganography, we discuss uses of this publicly
available database to other digital image forensic topics. | [] | 2019-04-19 | null | null | null | null | ['image-steganography', 'image-forensics'] | ['computer-vision', 'computer-vision'] | [ 7.58680820e-01 5.84874349e-03 1.41709358e-01 3.73935908e-01
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07125704-35a4-4368-942d-ce18ea9c3ea1 | lip-movements-information-disentanglement-for | 2202.06198 | null | https://arxiv.org/abs/2202.06198v2 | https://arxiv.org/pdf/2202.06198v2.pdf | Data standardization for robust lip sync | Lip sync is a fundamental audio-visual task. However, existing lip sync methods fall short of being robust to the incredible diversity of videos taken in the wild, and the majority of the diversity is caused by compound distracting factors that could degrade existing lip sync methods. To address these issues, this paper proposes a data standardization pipeline that can produce standardized expressive images while preserving lip motion information from the input and reducing the effects of compound distracting factors. Based on recent advances in 3D face reconstruction, we first create a model that can consistently disentangle expressions, with lip motion information embedded. Then, to reduce the effects of compound distracting factors on synthesized images, we synthesize images with only expressions from the input, intentionally setting all other attributes at predefined values independent of the input. Using synthesized images, existing lip sync methods improve their data efficiency and robustness, and they achieve competitive performance for the active speaker detection task. | ['Chun Wang'] | 2022-02-13 | null | null | null | null | ['3d-face-reconstruction', 'face-model', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.08047077e-01 -1.92609012e-01 -3.67966741e-01 -2.14207754e-01
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0406f692-023b-4acc-aaf3-2ecba2b8c291 | stock-trading-optimization-through-model-1 | 2301.09297 | null | https://arxiv.org/abs/2301.09297v3 | https://arxiv.org/pdf/2301.09297v3.pdf | Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization | With the fast development of quantitative portfolio optimization in financial engineering, lots of AI-based algorithmic trading strategies have demonstrated promising results, among which reinforcement learning begins to manifest competitive advantages. However, the environment from real financial markets is complex and hard to be fully simulated, considering the observation of abrupt transitions, unpredictable hidden causal factors, heavy tail properties and so on. Thus, in this paper, first, we adopt a heavy-tailed preserving normalizing flows to simulate high-dimensional joint probability of the complex trading environment and develop a model-based reinforcement learning framework to better understand the intrinsic mechanisms of quantitative online trading. Second, we experiment with various stocks from three different financial markets (Dow, NASDAQ and S&P) and show that among these three financial markets, Dow gets the best performance based on various evaluation metrics under our back-testing system. Especially, our proposed method is able to mitigate the impact of unpredictable financial market crises during the COVID-19 pandemic period, resulting in a lower maximum drawdown. Third, we also explore the explanation of our RL algorithm. (1), we utilize the pattern causality method to study the interactive relation among different stocks in the environment. (2), We analyze the dynamic loss and actor loss to ensure the convergence of our strategies. (3), by visualizing high dimensional state transition data comparisons from real and virtual buffers with t-SNE, we uncover some effective patterns of better portfolio optimization strategies. (4), we also utilize eigenvalue analysis to study the convergence properties of the environmen's model. | ['Nan Du', 'Peng Zhang', 'Jin Guo', 'Pengbo Li', 'Ting Gao', 'Huifang Huang'] | 2023-01-23 | null | null | null | null | ['algorithmic-trading', 'portfolio-optimization'] | ['time-series', 'time-series'] | [-5.36651134e-01 -5.17862201e-01 7.49278143e-02 3.06613505e-01
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9c1fc679-090f-448f-80c7-3b288e849f1a | graph-based-compensated-wavelet-lifting-for-3 | 2301.04839 | null | https://arxiv.org/abs/2301.04839v1 | https://arxiv.org/pdf/2301.04839v1.pdf | Graph-based compensated wavelet lifting for 3-D+t medical CT data | An efficient scalable data representation is an important task especially in the medical area, e.g. for volumes from Computed Tomography (CT) or Magnetic Resonance Tomography (MRT), when a downscaled version of the original signal is needed. Image and video coders based on wavelet transforms provide an adequate way to naturally achieve scalability. This paper presents a new approach for improving the visual quality of the lowpass band by using a novel graph-based method for motion compensation, which is an important step considering data compression. We compare different kinds of neighborhoods for graph construction and demonstrate that a higher amount of referenced nodes increases the quality of the lowpass band while the mean energy of the highpass band decreases. We show that for cardiac CT data the proposed method outperforms a traditional mesh-based approach of motion compensation by approximately 11 dB in terms of PSNR of the lowpass band. Also the mean energy of the highpass band decreases by around 30%. | ['André Kaup', 'Daniela Lanz'] | 2023-01-12 | null | null | null | null | ['motion-compensation', 'data-compression'] | ['computer-vision', 'time-series'] | [ 4.76067275e-01 4.99392301e-02 1.01463340e-01 3.09503209e-02
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98162fa0-f9d7-4d72-b32e-953b2055c5a6 | structural-relational-reasoning-of-point | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Duan_Structural_Relational_Reasoning_of_Point_Clouds_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Duan_Structural_Relational_Reasoning_of_Point_Clouds_CVPR_2019_paper.pdf | Structural Relational Reasoning of Point Clouds | The symmetry for the corners of a box, the continuity for the surfaces of a monitor, the linkage between the torso and other body parts --- it suggests that 3D objects may have common and underlying inner relations between local structures, and it is a fundamental ability for intelligent species to reason for them. In this paper, we propose an effective plug-and-play module called the structural relation network (SRN) to reason about the structural dependencies of local regions in 3D point clouds. Existing network architectures on point sets such as PointNet++ capture local structures individually, without considering their inner interactions. Instead, our SRN simultaneously exploits local information by modeling their geometrical and locational relations, which play critical roles for our humans to understand 3D objects. The proposed SRN module is simple, interpretable, and does not require any additional supervision signals, which can be easily equipped with the existing networks. Experimental results on benchmark datasets indicate promising boosts on the tasks of 3D point cloud classification and segmentation by capturing structural relations with the SRN module.
| [' Qi Tian', ' Jie Zhou', ' Jiwen Lu', ' Yu Zheng', 'Yueqi Duan'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['3d-part-segmentation'] | ['computer-vision'] | [-4.50356036e-01 3.48173201e-01 -2.80473560e-01 -6.71996057e-01
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f54a47f6-03a7-4ea1-8121-1d665fcbd1d6 | multimodal-optimal-transport-based-co | 2306.08330 | null | https://arxiv.org/abs/2306.08330v1 | https://arxiv.org/pdf/2306.08330v1.pdf | Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Prediction | Survival prediction is a complicated ordinal regression task that aims to predict the ranking risk of death, which generally benefits from the integration of histology and genomic data. Despite the progress in joint learning from pathology and genomics, existing methods still suffer from challenging issues: 1) Due to the large size of pathological images, it is difficult to effectively represent the gigapixel whole slide images (WSIs). 2) Interactions within tumor microenvironment (TME) in histology are essential for survival analysis. Although current approaches attempt to model these interactions via co-attention between histology and genomic data, they focus on only dense local similarity across modalities, which fails to capture global consistency between potential structures, i.e. TME-related interactions of histology and co-expression of genomic data. To address these challenges, we propose a Multimodal Optimal Transport-based Co-Attention Transformer framework with global structure consistency, in which optimal transport (OT) is applied to match patches of a WSI and genes embeddings for selecting informative patches to represent the gigapixel WSI. More importantly, OT-based co-attention provides a global awareness to effectively capture structural interactions within TME for survival prediction. To overcome high computational complexity of OT, we propose a robust and efficient implementation over micro-batch of WSI patches by approximating the original OT with unbalanced mini-batch OT. Extensive experiments show the superiority of our method on five benchmark datasets compared to the state-of-the-art methods. The code is released. | ['Hao Chen', 'Yingxue Xu'] | 2023-06-14 | null | null | null | null | ['whole-slide-images', 'survival-analysis'] | ['computer-vision', 'miscellaneous'] | [ 6.50018752e-02 -1.75363660e-01 -1.23995349e-01 -3.55126530e-01
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20e2d7da-2b10-4893-bdb6-88b93f4cc907 | an-end-to-end-progressive-multi-task-learning | null | null | https://aclanthology.org/2021.acl-long.485 | https://aclanthology.org/2021.acl-long.485.pdf | An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization | Medical named entity recognition (NER) and normalization (NEN) are fundamental for constructing knowledge graphs and building QA systems. Existing implementations for medical NER and NEN are suffered from the error propagation between the two tasks. The mispredicted mentions from NER will directly influence the results of NEN. Therefore, the NER module is the bottleneck of the whole system. Besides, the learnable features for both tasks are beneficial to improving the model performance. To avoid the disadvantages of existing models and exploit the generalized representation across the two tasks, we design an end-to-end progressive multi-task learning model for jointly modeling medical NER and NEN in an effective way. There are three level tasks with progressive difficulty in the framework. The progressive tasks can reduce the error propagation with the incremental task settings which implies the lower level tasks gain the supervised signals other than errors from the higher level tasks to improve their performances. Besides, the context features are exploited to enrich the semantic information of entity mentions extracted by NER. The performance of NEN profits from the enhanced entity mention features. The standard entities from knowledge bases are introduced into the NER module for extracting corresponding entity mentions correctly. The empirical results on two publicly available medical literature datasets demonstrate the superiority of our method over nine typical methods. | ['Xiaojie Yuan', 'Ying Zhang', 'Xiangrui Cai', 'Baohang Zhou'] | 2021-08-01 | null | null | null | acl-2021-5 | ['medical-named-entity-recognition'] | ['natural-language-processing'] | [-2.58257682e-03 3.27000707e-01 -2.61115134e-01 -3.21166575e-01
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8795a8ea-36a1-4975-ab7b-3a1f1108dae6 | deep-learning-for-anomaly-detection-in-log | 2207.03820 | null | https://arxiv.org/abs/2207.03820v2 | https://arxiv.org/pdf/2207.03820v2.pdf | Deep Learning for Anomaly Detection in Log Data: A Survey | Automatic log file analysis enables early detection of relevant incidents such as system failures. In particular, self-learning anomaly detection techniques capture patterns in log data and subsequently report unexpected log event occurrences to system operators without the need to provide or manually model anomalous scenarios in advance. Recently, an increasing number of approaches leveraging deep learning neural networks for this purpose have been presented. These approaches have demonstrated superior detection performance in comparison to conventional machine learning techniques and simultaneously resolve issues with unstable data formats. However, there exist many different architectures for deep learning and it is non-trivial to encode raw and unstructured log data to be analyzed by neural networks. We therefore carry out a systematic literature review that provides an overview of deployed models, data pre-processing mechanisms, anomaly detection techniques, and evaluations. The survey does not quantitatively compare existing approaches but instead aims to help readers understand relevant aspects of different model architectures and emphasizes open issues for future work. | ['Markus Wurzenberger', 'Florian Skopik', 'Sebastian Onder', 'Max Landauer'] | 2022-07-08 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [-5.26220575e-02 -1.95855215e-01 1.30013600e-01 -3.60529393e-01
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078cdc8a-d3ce-4f56-b461-ebb0ae7f765e | splatnet-sparse-lattice-networks-for-point | 1802.08275 | null | http://arxiv.org/abs/1802.08275v4 | http://arxiv.org/pdf/1802.08275v4.pdf | SPLATNet: Sparse Lattice Networks for Point Cloud Processing | We present a network architecture for processing point clouds that directly
operates on a collection of points represented as a sparse set of samples in a
high-dimensional lattice. Naively applying convolutions on this lattice scales
poorly, both in terms of memory and computational cost, as the size of the
lattice increases. Instead, our network uses sparse bilateral convolutional
layers as building blocks. These layers maintain efficiency by using indexing
structures to apply convolutions only on occupied parts of the lattice, and
allow flexible specifications of the lattice structure enabling hierarchical
and spatially-aware feature learning, as well as joint 2D-3D reasoning. Both
point-based and image-based representations can be easily incorporated in a
network with such layers and the resulting model can be trained in an
end-to-end manner. We present results on 3D segmentation tasks where our
approach outperforms existing state-of-the-art techniques. | ['Jan Kautz', 'Ming-Hsuan Yang', 'Evangelos Kalogerakis', 'Subhransu Maji', 'Deqing Sun', 'Varun Jampani', 'Hang Su'] | 2018-02-22 | splatnet-sparse-lattice-networks-for-point-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Su_SPLATNet_Sparse_Lattice_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Su_SPLATNet_Sparse_Lattice_CVPR_2018_paper.pdf | cvpr-2018-6 | ['3d-part-segmentation'] | ['computer-vision'] | [-7.61562288e-02 1.78635553e-01 1.64814726e-01 -4.94744211e-01
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a87372ff-22b9-4fa4-a65f-38fee36c3012 | blank-collapse-compressing-ctc-emission-for | 2210.17017 | null | https://arxiv.org/abs/2210.17017v2 | https://arxiv.org/pdf/2210.17017v2.pdf | Blank Collapse: Compressing CTC emission for the faster decoding | Connectionist Temporal Classification (CTC) model is a very efficient method for modeling sequences, especially for speech data. In order to use CTC model as an Automatic Speech Recognition (ASR) task, the beam search decoding with an external language model like n-gram LM is necessary to obtain reasonable results. In this paper we analyze the blank label in CTC beam search deeply and propose a very simple method to reduce the amount of calculation resulting in faster beam search decoding speed. With this method, we can get up to 78% faster decoding speed than ordinary beam search decoding with a very small loss of accuracy in LibriSpeech datasets. We prove this method is effective not only practically by experiments but also theoretically by mathematical reasoning. We also observe that this reduction is more obvious if the accuracy of the model is higher. | ['Soonshin Seo', 'Seunghyun Seo', 'Ohhyeok Kwon', 'Minkyu Jung'] | 2022-10-31 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 2.17377797e-01 -2.46855944e-01 -2.44152904e-01 -2.84635156e-01
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fd4e7c47-c3b0-46e1-a554-168e181f82ab | alma-alternating-minimization-algorithm-for | 2102.10226 | null | https://arxiv.org/abs/2102.10226v4 | https://arxiv.org/pdf/2102.10226v4.pdf | ALMA: Alternating Minimization Algorithm for Clustering Mixture Multilayer Network | The paper considers a Mixture Multilayer Stochastic Block Model (MMLSBM), where layers can be partitioned into groups of similar networks, and networks in each group are equipped with a distinct Stochastic Block Model. The goal is to partition the multilayer network into clusters of similar layers, and to identify communities in those layers. Jing et al. (2020) introduced the MMLSBM and developed a clustering methodology, TWIST, based on regularized tensor decomposition. The present paper proposes a different technique, an alternating minimization algorithm (ALMA), that aims at simultaneous recovery of the layer partition, together with estimation of the matrices of connection probabilities of the distinct layers. Compared to TWIST, ALMA achieves higher accuracy both theoretically and numerically. | ['Teng Zhang', 'Feng Yu', 'Marianna Pensky', 'Xing Fan'] | 2021-02-20 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [-2.74539441e-01 -4.19921242e-02 -1.89997822e-01 1.09089337e-01
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cb5e238d-99b4-41eb-abca-898179545d23 | ddcolor-towards-photo-realistic-and-semantic | 2212.11613 | null | https://arxiv.org/abs/2212.11613v3 | https://arxiv.org/pdf/2212.11613v3.pdf | DDColor: Towards Photo-Realistic and Semantic-Aware Image Colorization via Dual Decoders | Automatic image colorization is a challenging problem. Due to the high illness and multi-modal uncertainty, directly training a deep neural network usually leads to incorrect semantic colors and low color richness. Recent transformer-based methods can deliver better results, but they often rely on manually designed priors, which are hard to implement and suffer from poor generalization ability. Moreover, they tend to introduce serious color bleeding effects since color attention is performed on single-scale features, thus fail to exploit sufficient semantic information. To address these issues, we propose DDColor, a new end-to-end method with dual decoders for image colorization. Our approach includes a multi-scale image decoder and a transformer-based color decoder. The former restores the spatial resolution of the image, while the latter establishes the correlation between color and semantic representations via cross-attention. Rather than using additional priors, our two decoders work together to leverage multi-scale image features to guide optimization of adaptive color queries, significantly alleviating color bleeding effects. In addition, a simple yet effective colorfulness loss is introduced to further enhance the color richness of generated results. Our extensive experiments demonstrate that DDColor achieves significantly superior performance to existing state-of-the-art works both quantitatively and qualitatively. Codes will be made publicly available at https://github.com/piddnad/DDColor. | ['Xuansong Xie', 'Lingzhi Li', 'Peiran Ren', 'Wenqi Ouyang', 'Tao Yang', 'Xiaoyang Kang'] | 2022-12-22 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 2.94854920e-02 -2.41408437e-01 4.28234451e-02 -2.64396846e-01
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3511d42c-4b17-4fb6-a14f-44531248d119 | feature-aligned-n-beats-with-sinkhorn | 2305.15196 | null | https://arxiv.org/abs/2305.15196v1 | https://arxiv.org/pdf/2305.15196v1.pdf | Feature-aligned N-BEATS with Sinkhorn divergence | In this study, we propose Feature-aligned N-BEATS as a domain generalization model for univariate time series forecasting problems. The proposed model is an extension of the doubly residual stacking architecture of N-BEATS (Oreshkin et al. [34]) into a representation learning framework. The model is a new structure that involves marginal feature probability measures (i.e., pushforward measures of multiple source domains) induced by the intricate composition of residual operators of N-BEATS in each stack and aligns them stack-wise via an entropic regularized Wasserstein distance referred to as the Sinkhorn divergence (Genevay et al. [14]). The loss function consists of a typical forecasting loss for multiple source domains and an alignment loss calculated with the Sinkhorn divergence, which allows the model to learn invariant features stack-wise across multiple source data sequences while retaining N-BEATS's interpretable design. We conduct a comprehensive experimental evaluation of the proposed approach and the results demonstrate the model's forecasting and generalization capabilities in comparison with methods based on the original N-BEATS. | ['Kyunghyun Park', 'Joonhun Lee', 'Myungjoo Kang', 'Myeongho Jeon'] | 2023-05-24 | null | null | null | null | ['univariate-time-series-forecasting'] | ['time-series'] | [ 3.67809981e-01 -1.54701009e-01 -4.00807150e-02 -7.32780993e-01
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4d782e5c-268e-4721-8130-9d0c7f0c5aa5 | human-activity-recognition-from-wi-fi-csi | 2212.13161 | null | https://arxiv.org/abs/2212.13161v1 | https://arxiv.org/pdf/2212.13161v1.pdf | Human Activity Recognition from Wi-Fi CSI Data Using Principal Component-Based Wavelet CNN | Human Activity Recognition (HAR) is an emerging technology with several applications in surveillance, security, and healthcare sectors. Noninvasive HAR systems based on Wi-Fi Channel State Information (CSI) signals can be developed leveraging the quick growth of ubiquitous Wi-Fi technologies, and the correlation between CSI dynamics and body motions. In this paper, we propose Principal Component-based Wavelet Convolutional Neural Network (or PCWCNN) -- a novel approach that offers robustness and efficiency for practical real-time applications. Our proposed method incorporates two efficient preprocessing algorithms -- the Principal Component Analysis (PCA) and the Discrete Wavelet Transform (DWT). We employ an adaptive activity segmentation algorithm that is accurate and computationally light. Additionally, we used the Wavelet CNN for classification, which is a deep convolutional network analogous to the well-studied ResNet and DenseNet networks. We empirically show that our proposed PCWCNN model performs very well on a real dataset, outperforming existing approaches. | ['Hafiz Imtiaz', 'Tahsina Farah Sanam', 'Ishtiaque Ahmed Showmik'] | 2022-12-26 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 4.03706431e-01 -6.98485225e-02 -2.03147888e-01 1.05519608e-01
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dbc94ac0-6cdf-439c-a3bf-d81a5d06579a | 190600318 | 1906.00318 | null | https://arxiv.org/abs/1906.00318v1 | https://arxiv.org/pdf/1906.00318v1.pdf | Question Answering as an Automatic Evaluation Metric for News Article Summarization | Recent work in the field of automatic summarization and headline generation focuses on maximizing ROUGE scores for various news datasets. We present an alternative, extrinsic, evaluation metric for this task, Answering Performance for Evaluation of Summaries. APES utilizes recent progress in the field of reading-comprehension to quantify the ability of a summary to answer a set of manually created questions regarding central entities in the source article. We first analyze the strength of this metric by comparing it to known manual evaluation metrics. We then present an end-to-end neural abstractive model that maximizes APES, while increasing ROUGE scores to competitive results. | ['Tal Baumel', 'Matan Eyal', 'Michael Elhadad'] | 2019-06-02 | question-answering-as-an-automatic-evaluation | https://aclanthology.org/N19-1395 | https://aclanthology.org/N19-1395.pdf | naacl-2019-6 | ['headline-generation'] | ['natural-language-processing'] | [ 3.41584086e-01 7.09689021e-01 -1.58789620e-01 -3.77010018e-01
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46072dcb-1800-4848-8f3d-20cbcdae1a83 | benchmarking-machine-learning-models-on-eicu | 1910.00964 | null | https://arxiv.org/abs/1910.00964v3 | https://arxiv.org/pdf/1910.00964v3.pdf | Benchmarking machine learning models on multi-centre eICU critical care dataset | Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as computer vision and natural language processing) have established various competitions and public benchmarks. Recent availability of large clinical datasets has enabled the possibility of establishing public benchmarks. Taking advantage of this opportunity, we propose a public benchmark suite to address four areas of critical care, namely mortality prediction, estimation of length of stay, patient phenotyping and risk of decompensation. We define each task and compare the performance of both clinical models as well as baseline and deep learning models using eICU critical care dataset of around 73,000 patients. This is the first public benchmark on a multi-centre critical care dataset, comparing the performance of clinical gold standard with our predictive model. We also investigate the impact of numerical variables as well as handling of categorical variables on each of the defined tasks. The source code, detailing our methods and experiments is publicly available such that anyone can replicate our results and build upon our work. | ['Vevake Balaraman', 'Seyedmostafa Sheikhalishahi', 'Venet Osmani'] | 2019-10-02 | null | null | null | null | ['patient-phenotyping'] | ['medical'] | [ 1.64828122e-01 -4.49847095e-02 -1.23984836e-01 -3.60059112e-01
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7c7b0f04-5573-45a3-84b1-5fe5cf0d22de | x-2-vlm-all-in-one-pre-trained-model-for | 2211.12402 | null | https://arxiv.org/abs/2211.12402v1 | https://arxiv.org/pdf/2211.12402v1.pdf | X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks | Vision language pre-training aims to learn alignments between vision and language from a large amount of data. We proposed multi-grained vision language pre-training, a unified approach which can learn vision language alignments in multiple granularity. This paper advances the proposed method by unifying image and video encoding in one model and scaling up the model with large-scale data. We present X$^2$-VLM, a pre-trained VLM with a modular architecture for both image-text tasks and video-text tasks. Experiment results show that X$^2$-VLM performs the best on base and large scale for both image-text and video-text tasks, making a good trade-off between performance and model scale. Moreover, we show that the modular design of X$^2$-VLM results in high transferability for X$^2$-VLM to be utilized in any language or domain. For example, by simply replacing the text encoder with XLM-R, X$^2$-VLM outperforms state-of-the-art multilingual multi-modal pre-trained models without any multilingual pre-training. The code and pre-trained models will be available at github.com/zengyan-97/X2-VLM. | ['Wangchunshu Zhou', 'Jipeng Zhang', 'Jiawei Wang', 'Hang Li', 'Xinsong Zhang', 'Yan Zeng'] | 2022-11-22 | null | null | null | null | ['video-question-answering', 'visual-reasoning', 'xlm-r', 'visual-reasoning'] | ['computer-vision', 'computer-vision', 'natural-language-processing', 'reasoning'] | [-7.69673884e-02 -4.62280735e-02 -2.01683551e-01 -3.87827426e-01
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546e07c2-7f51-4fed-9d10-eca0b15eb291 | parameter-efficient-fine-tuning-of-llama-for | 2307.03042 | null | https://arxiv.org/abs/2307.03042v1 | https://arxiv.org/pdf/2307.03042v1.pdf | Parameter-Efficient Fine-Tuning of LLaMA for the Clinical Domain | Adapting pretrained language models to novel domains, such as clinical applications, traditionally involves retraining their entire set of parameters. However, this approach is increasingly proven to be impractical owing to the substantial computational requirements associated with training such large language models. To address this issue, Parameter-Efficient Fine-Tuning (PEFT) techniques offer a viable solution by selectively fine-tuning a small subset of additional parameters, significantly reducing the computational requirements for domain adaptation. In this study, we propose Clinical LLaMA-LoRA, a PEFT adapter layer built upon the open-sourced LLaMA model. Clinical LLaMA-LoRA is trained using clinical notes obtained from the MIMIC-IV database, thereby creating a specialised adapter designed for the clinical domain. Additionally, we propose a two-step PEFT framework which fuses Clinical LLaMA-LoRA with Downstream LLaMA-LoRA, another PEFT adapter specialised for downstream tasks. We evaluate this framework on multiple clinical outcome prediction datasets, comparing it to clinically trained language models. Our proposed framework achieves a state-of-the-art AUROC score averaged across all clinical downstream tasks. We observe substantial improvements of 6-9% AUROC score in the large-scale multilabel classification tasks, such as diagnoses and procedures classification. | ['Beatrice Alex', 'Pasquale Minervini', 'Luke Daines', 'Aryo Gema'] | 2023-07-06 | null | null | null | null | ['domain-adaptation'] | ['methodology'] | [ 2.77757555e-01 2.31918871e-01 -3.96834552e-01 -5.46928644e-01
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0c59d953-f96b-4406-9dfc-c0a42b57f367 | visual-saliency-transformer | 2104.12099 | null | https://arxiv.org/abs/2104.12099v2 | https://arxiv.org/pdf/2104.12099v2.pdf | Visual Saliency Transformer | Existing state-of-the-art saliency detection methods heavily rely on CNN-based architectures. Alternatively, we rethink this task from a convolution-free sequence-to-sequence perspective and predict saliency by modeling long-range dependencies, which can not be achieved by convolution. Specifically, we develop a novel unified model based on a pure transformer, namely, Visual Saliency Transformer (VST), for both RGB and RGB-D salient object detection (SOD). It takes image patches as inputs and leverages the transformer to propagate global contexts among image patches. Unlike conventional architectures used in Vision Transformer (ViT), we leverage multi-level token fusion and propose a new token upsampling method under the transformer framework to get high-resolution detection results. We also develop a token-based multi-task decoder to simultaneously perform saliency and boundary detection by introducing task-related tokens and a novel patch-task-attention mechanism. Experimental results show that our model outperforms existing methods on both RGB and RGB-D SOD benchmark datasets. Most importantly, our whole framework not only provides a new perspective for the SOD field but also shows a new paradigm for transformer-based dense prediction models. Code is available at https://github.com/nnizhang/VST. | ['Junwei Han', 'Ling Shao', 'Kaiyuan Wan', 'Ni Zhang', 'Nian Liu'] | 2021-04-25 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Liu_Visual_Saliency_Transformer_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_Visual_Saliency_Transformer_ICCV_2021_paper.pdf | iccv-2021-1 | ['rgb-d-salient-object-detection', 'thermal-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.03772950e-01 -1.04317293e-01 -1.80059031e-01 -4.20855284e-01
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755e886a-54dd-4946-ac7b-ad207bbc22ba | masked-metaphor-modeling-to-transfer-literal | 2210.04756 | null | https://arxiv.org/abs/2210.04756v2 | https://arxiv.org/pdf/2210.04756v2.pdf | Metaphorical Paraphrase Generation: Feeding Metaphorical Language Models with Literal Texts | This study presents a new approach to metaphorical paraphrase generation by masking literal tokens of literal sentences and unmasking them with metaphorical language models. Unlike similar studies, the proposed algorithm does not only focus on verbs but also on nouns and adjectives. Despite the fact that the transfer rate for the former is the highest (56%), the transfer of the latter is feasible (24% and 31%). Human evaluation showed that our system-generated metaphors are considered more creative and metaphorical than human-generated ones while when using our transferred metaphors for data augmentation improves the state of the art in metaphorical sentence classification by 3% in F1. | ['John Pavlopoulos', 'Giorgio Ottolina'] | 2022-10-10 | null | null | null | null | ['paraphrase-generation', 'sentence-classification', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing', 'natural-language-processing'] | [ 2.60812521e-01 4.92444903e-01 -1.42622992e-01 -9.06962231e-02
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5698e2be-349e-4e94-a21b-957cd8d3f2aa | towards-expressive-speaking-style-modelling | 2203.12201 | null | https://arxiv.org/abs/2203.12201v2 | https://arxiv.org/pdf/2203.12201v2.pdf | Towards Expressive Speaking Style Modelling with Hierarchical Context Information for Mandarin Speech Synthesis | Previous works on expressive speech synthesis mainly focus on current sentence. The context in adjacent sentences is neglected, resulting in inflexible speaking style for the same text, which lacks speech variations. In this paper, we propose a hierarchical framework to model speaking style from context. A hierarchical context encoder is proposed to explore a wider range of contextual information considering structural relationship in context, including inter-phrase and inter-sentence relations. Moreover, to encourage this encoder to learn style representation better, we introduce a novel training strategy with knowledge distillation, which provides the target for encoder training. Both objective and subjective evaluations on a Mandarin lecture dataset demonstrate that the proposed method can significantly improve the naturalness and expressiveness of the synthesized speech. | ['Helen Meng', 'Shiyin Kang', 'Zhiyong Wu', 'Liyang Chen', 'Yixuan Zhou', 'Shun Lei'] | 2022-03-23 | null | null | null | null | ['expressive-speech-synthesis'] | ['speech'] | [ 2.55509198e-01 7.19532743e-02 -2.82088578e-01 -7.15078473e-01
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fd59083e-5891-4297-967c-e892903e48f0 | dbgdgm-dynamic-brain-graph-deep-generative | 2301.11408 | null | https://arxiv.org/abs/2301.11408v1 | https://arxiv.org/pdf/2301.11408v1.pdf | DBGDGM: Dynamic Brain Graph Deep Generative Model | Graphs are a natural representation of brain activity derived from functional magnetic imaging (fMRI) data. It is well known that clusters of anatomical brain regions, known as functional connectivity networks (FCNs), encode temporal relationships which can serve as useful biomarkers for understanding brain function and dysfunction. Previous works, however, ignore the temporal dynamics of the brain and focus on static graphs. In this paper, we propose a dynamic brain graph deep generative model (DBGDGM) which simultaneously clusters brain regions into temporally evolving communities and learns dynamic unsupervised node embeddings. Specifically, DBGDGM represents brain graph nodes as embeddings sampled from a distribution over communities that evolve over time. We parameterise this community distribution using neural networks that learn from subject and node embeddings as well as past community assignments. Experiments demonstrate DBGDGM outperforms baselines in graph generation, dynamic link prediction, and is comparable for graph classification. Finally, an analysis of the learnt community distributions reveals overlap with known FCNs reported in neuroscience literature. | ['Pietro Lio', 'Nicola Toschi', 'Simeon Spasov', 'Alexander Campbell'] | 2023-01-26 | null | null | null | null | ['dynamic-link-prediction', 'graph-classification'] | ['graphs', 'graphs'] | [-1.64789334e-02 3.37194115e-01 1.06482003e-02 -3.01481485e-01
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df504d32-b687-4cfd-a91c-60338feec913 | single-stage-is-enough-multi-person-absolute | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Jin_Single-Stage_Is_Enough_Multi-Person_Absolute_3D_Pose_Estimation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Jin_Single-Stage_Is_Enough_Multi-Person_Absolute_3D_Pose_Estimation_CVPR_2022_paper.pdf | Single-Stage Is Enough: Multi-Person Absolute 3D Pose Estimation | The existing multi-person absolute 3D pose estimation methods are mainly based on two-stage paradigm, i.e., top-down or bottom-up, leading to redundant pipelines with high computation cost. We argue that it is more desirable to simplify such two-stage paradigm to a single-stage one to promote both efficiency and performance. To this end, we present an efficient single-stage solution, Decoupled Regression Model (DRM), with three distinct novelties. First, DRM introduces a new decoupled representation for 3D pose, which expresses the 2D pose in image plane and depth information of each 3D human instance via 2D center point (center of visible keypoints) and root point (denoted as pelvis), respectively. Second, to learn better feature representation for the human depth regression, DRM introduces a 2D Pose-guided Depth Query Module (PDQM) to extract the features in 2D pose regression branch, enabling the depth regression branch to perceive the scale information of instances. Third, DRM leverages a Decoupled Absolute Pose Loss (DAPL) to facilitate the absolute root depth and root-relative depth estimation, thus improving the accuracy of absolute 3D pose. Comprehensive experiments on challenging benchmarks including MuPoTS-3D and Panoptic clearly verify the superiority of our framework, which outperforms the state-of-the-art bottom-up absolute 3D pose estimation methods. | ['Jian Zhao', 'Xuecheng Nie', 'Yandong Guo', 'Yabo Xiao', 'Xiaojuan Wang', 'Chenyang Xu', 'Lei Jin'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['3d-pose-estimation'] | ['computer-vision'] | [-2.21707955e-01 3.82736996e-02 -3.25489372e-01 -4.77216244e-01
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243267cd-710b-493a-9b61-642c72f4b888 | introducing-fuzzy-layers-for-deep-learning | 2003.00880 | null | https://arxiv.org/abs/2003.00880v1 | https://arxiv.org/pdf/2003.00880v1.pdf | Introducing Fuzzy Layers for Deep Learning | Many state-of-the-art technologies developed in recent years have been influenced by machine learning to some extent. Most popular at the time of this writing are artificial intelligence methodologies that fall under the umbrella of deep learning. Deep learning has been shown across many applications to be extremely powerful and capable of handling problems that possess great complexity and difficulty. In this work, we introduce a new layer to deep learning: the fuzzy layer. Traditionally, the network architecture of neural networks is composed of an input layer, some combination of hidden layers, and an output layer. We propose the introduction of fuzzy layers into the deep learning architecture to exploit the powerful aggregation properties expressed through fuzzy methodologies, such as the Choquet and Sugueno fuzzy integrals. To date, fuzzy approaches taken to deep learning have been through the application of various fusion strategies at the decision level to aggregate outputs from state-of-the-art pre-trained models, e.g., AlexNet, VGG16, GoogLeNet, Inception-v3, ResNet-18, etc. While these strategies have been shown to improve accuracy performance for image classification tasks, none have explored the use of fuzzified intermediate, or hidden, layers. Herein, we present a new deep learning strategy that incorporates fuzzy strategies into the deep learning architecture focused on the application of semantic segmentation using per-pixel classification. Experiments are conducted on a benchmark data set as well as a data set collected via an unmanned aerial system at a U.S. Army test site for the task of automatic road segmentation, and preliminary results are promising. | ['Derek T. Anderson', 'Stanton R. Price', 'Steven R. Price'] | 2020-02-21 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 1.21604301e-01 -1.99697353e-03 5.97223267e-02 -5.79214692e-01
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60071211-8823-4810-b1fc-e5d053c924b6 | efficient-differentiable-quadratic | 2112.07464 | null | https://arxiv.org/abs/2112.07464v1 | https://arxiv.org/pdf/2112.07464v1.pdf | Efficient differentiable quadratic programming layers: an ADMM approach | Recent advances in neural-network architecture allow for seamless integration of convex optimization problems as differentiable layers in an end-to-end trainable neural network. Integrating medium and large scale quadratic programs into a deep neural network architecture, however, is challenging as solving quadratic programs exactly by interior-point methods has worst-case cubic complexity in the number of variables. In this paper, we present an alternative network layer architecture based on the alternating direction method of multipliers (ADMM) that is capable of scaling to problems with a moderately large number of variables. Backward differentiation is performed by implicit differentiation of the residual map of a modified fixed-point iteration. Simulated results demonstrate the computational advantage of the ADMM layer, which for medium scaled problems is approximately an order of magnitude faster than the OptNet quadratic programming layer. Furthermore, our novel backward-pass routine is efficient, from both a memory and computation standpoint, in comparison to the standard approach based on unrolled differentiation or implicit differentiation of the KKT optimality conditions. We conclude with examples from portfolio optimization in the integrated prediction and optimization paradigm. | ['Roy Kwon', 'Andrew Butler'] | 2021-12-14 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-2.20762581e-01 7.09001273e-02 1.34126410e-01 -4.14173454e-01
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09cfad47-38d8-449d-bfc8-e5081f299550 | flexible-domain-adaptation-for-automated | null | null | https://aclanthology.org/D15-1049 | https://aclanthology.org/D15-1049.pdf | Flexible Domain Adaptation for Automated Essay Scoring Using Correlated Linear Regression | null | ['Ph', 'Peter i', 'Kian Ming A. Chai', 'Hwee Tou Ng'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['automated-essay-scoring'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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1bda8cf7-96f8-4aad-9362-f278f743b468 | optimal-task-and-motion-planning-and | 2303.14874 | null | https://arxiv.org/abs/2303.14874v1 | https://arxiv.org/pdf/2303.14874v1.pdf | Optimal task and motion planning and execution for human-robot multi-agent systems in dynamic environments | Combining symbolic and geometric reasoning in multi-agent systems is a challenging task that involves planning, scheduling, and synchronization problems. Existing works overlooked the variability of task duration and geometric feasibility that is intrinsic to these systems because of the interaction between agents and the environment. We propose a combined task and motion planning approach to optimize sequencing, assignment, and execution of tasks under temporal and spatial variability. The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task. At the task level, timeline-based planning deals with temporal constraints, duration variability, and synergic assignment of tasks. At the action level, online motion planning plans for the actual movements dealing with environmental changes. We demonstrate the approach effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic in the shortest time possible. Compared with existing works, our approach applies to a broader range of applications and reduces the execution time of the process. | ['Nicola Pedrocchi', 'Amedeo Cesta', 'Andrea Orlandini', 'Manuel Beschi', 'Alessandro Umbrico', 'Marco Faroni'] | 2023-03-27 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 3.81425619e-01 3.02081764e-01 7.65022039e-02 1.42079845e-01
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23241c39-e6ad-4d33-a515-b318eb7698bd | labelbench-a-comprehensive-framework-for | 2306.09910 | null | https://arxiv.org/abs/2306.09910v1 | https://arxiv.org/pdf/2306.09910v1.pdf | LabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning | Labeled data are critical to modern machine learning applications, but obtaining labels can be expensive. To mitigate this cost, machine learning methods, such as transfer learning, semi-supervised learning and active learning, aim to be label-efficient: achieving high predictive performance from relatively few labeled examples. While obtaining the best label-efficiency in practice often requires combinations of these techniques, existing benchmark and evaluation frameworks do not capture a concerted combination of all such techniques. This paper addresses this deficiency by introducing LabelBench, a new computationally-efficient framework for joint evaluation of multiple label-efficient learning techniques. As an application of LabelBench, we introduce a novel benchmark of state-of-the-art active learning methods in combination with semi-supervised learning for fine-tuning pretrained vision transformers. Our benchmark demonstrates better label-efficiencies than previously reported in active learning. LabelBench's modular codebase is open-sourced for the broader community to contribute label-efficient learning methods and benchmarks. The repository can be found at: https://github.com/EfficientTraining/LabelBench. | ['Robert D Nowak', 'Kevin Jamieson', 'Simon Shaolei Du', 'Yinglun Zhu', 'Stephen Mussmann', 'Gregory Canal', 'Yifang Chen', 'Jifan Zhang'] | 2023-06-16 | null | null | null | null | ['active-learning', 'benchmarking', 'active-learning', 'benchmarking'] | ['methodology', 'miscellaneous', 'natural-language-processing', 'robots'] | [ 2.55574137e-01 6.76018000e-02 -7.55348325e-01 -6.08900964e-01
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44f06a81-8659-4849-99b2-9b982a77d3e4 | the-unknown-knowns-a-graph-based-approach-for | null | null | https://www.emerald.com/insight/content/doi/10.1108/OIR-12-2020-0562/full/html | https://www.researchgate.net/publication/350180428_The_unknown_knowns_a_graph-based_approach_for_temporal_COVID-19_literature_mining | The unknown knowns: a graph-based approach for temporal COVID-19 literature mining | Purpose
The COVID-19 pandemic has sparked a remarkable volume of research literature, and scientists are increasingly in need of intelligent tools to cut through the noise and uncover relevant research directions. As a response, the authors propose a novel framework. In this framework, the authors develop a novel weighted semantic graph model to compress the research studies efficiently. Also, the authors present two analyses on this graph to propose alternative ways to uncover additional aspects of COVID-19 research.
Design/methodology/approach
The authors construct the semantic graph using state-of-the-art natural language processing (NLP) techniques on COVID-19 publication texts (>100,000 texts). Next, the authors conduct an evolutionary analysis to capture the changes in COVID-19 research across time. Finally, the authors apply a link prediction study to detect novel COVID-19 research directions that are so far undiscovered.
Findings
Findings reveal the success of the semantic graph in capturing scientific knowledge and its evolution. Meanwhile, the prediction experiments provide 79% accuracy on returning intelligible links, showing the reliability of the methods for predicting novel connections that could help scientists discover potential new directions.
Originality/value
To the authors’ knowledge, this is the first study to propose a holistic framework that includes encoding the scientific knowledge in a semantic graph, demonstrates an evolutionary examination of past and ongoing research and offers scientists with tools to generate new hypotheses and research directions through predictive modeling and deep machine learning techniques. | ['Lamia Benhiba', 'Aqil Assalil', 'Runia Roy', 'Ulya Bayram'] | 2021-03-23 | null | null | null | online-information-review-2021-3 | ['literature-mining'] | ['natural-language-processing'] | [-9.22344849e-02 7.46081844e-02 -8.10246825e-01 2.02705562e-01
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f96b4933-3187-468a-b6b9-f5e6152b9fe1 | a-thousand-words-are-worth-more-than-a | 2201.05299 | null | https://arxiv.org/abs/2201.05299v1 | https://arxiv.org/pdf/2201.05299v1.pdf | A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering | Outside-knowledge visual question answering (OK-VQA) requires the agent to comprehend the image, make use of relevant knowledge from the entire web, and digest all the information to answer the question. Most previous works address the problem by first fusing the image and question in the multi-modal space, which is inflexible for further fusion with a vast amount of external knowledge. In this paper, we call for a paradigm shift for the OK-VQA task, which transforms the image into plain text, so that we can enable knowledge passage retrieval, and generative question-answering in the natural language space. This paradigm takes advantage of the sheer volume of gigantic knowledge bases and the richness of pre-trained language models. A Transform-Retrieve-Generate framework (TRiG) framework is proposed, which can be plug-and-played with alternative image-to-text models and textual knowledge bases. Experimental results show that our TRiG framework outperforms all state-of-the-art supervised methods by at least 11.1% absolute margin. | ['Prem Natarajan', 'Ying Nian Wu', 'Aishwarya Reganti', 'Govind Thattai', 'Qing Ping', 'Feng Gao'] | 2022-01-14 | null | null | null | null | ['generative-question-answering'] | ['natural-language-processing'] | [ 1.74127176e-01 3.82155955e-01 -1.93241891e-02 -1.10126004e-01
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54f98915-8872-4731-88ae-bd4af4414a0b | sar-generalization-of-physiological-agility | 2307.03716 | null | https://arxiv.org/abs/2307.03716v1 | https://arxiv.org/pdf/2307.03716v1.pdf | SAR: Generalization of Physiological Agility and Dexterity via Synergistic Action Representation | Learning effective continuous control policies in high-dimensional systems, including musculoskeletal agents, remains a significant challenge. Over the course of biological evolution, organisms have developed robust mechanisms for overcoming this complexity to learn highly sophisticated strategies for motor control. What accounts for this robust behavioral flexibility? Modular control via muscle synergies, i.e. coordinated muscle co-contractions, is considered to be one putative mechanism that enables organisms to learn muscle control in a simplified and generalizable action space. Drawing inspiration from this evolved motor control strategy, we use physiologically accurate human hand and leg models as a testbed for determining the extent to which a Synergistic Action Representation (SAR) acquired from simpler tasks facilitates learning more complex tasks. We find in both cases that SAR-exploiting policies significantly outperform end-to-end reinforcement learning. Policies trained with SAR were able to achieve robust locomotion on a wide set of terrains with high sample efficiency, while baseline approaches failed to learn meaningful behaviors. Additionally, policies trained with SAR on a multiobject manipulation task significantly outperformed (>70% success) baseline approaches (<20% success). Both of these SAR-exploiting policies were also found to generalize zero-shot to out-of-domain environmental conditions, while policies that did not adopt SAR failed to generalize. Finally, we establish the generality of SAR on broader high-dimensional control problems using a robotic manipulation task set and a full-body humanoid locomotion task. To the best of our knowledge, this investigation is the first of its kind to present an end-to-end pipeline for discovering synergies and using this representation to learn high-dimensional continuous control across a wide diversity of tasks. | ['Vikash Kumar', 'Vittorio Caggiano', 'Cameron Berg'] | 2023-07-07 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 2.49083340e-01 1.90852061e-01 -2.16552347e-01 2.06784025e-01
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525c0f7a-f2de-46cd-ae0c-947b04d2bdba | investigating-lexical-sharing-in-multilingual | 2305.03207 | null | https://arxiv.org/abs/2305.03207v1 | https://arxiv.org/pdf/2305.03207v1.pdf | Investigating Lexical Sharing in Multilingual Machine Translation for Indian Languages | Multilingual language models have shown impressive cross-lingual transfer ability across a diverse set of languages and tasks. To improve the cross-lingual ability of these models, some strategies include transliteration and finer-grained segmentation into characters as opposed to subwords. In this work, we investigate lexical sharing in multilingual machine translation (MT) from Hindi, Gujarati, Nepali into English. We explore the trade-offs that exist in translation performance between data sampling and vocabulary size, and we explore whether transliteration is useful in encouraging cross-script generalisation. We also verify how the different settings generalise to unseen languages (Marathi and Bengali). We find that transliteration does not give pronounced improvements and our analysis suggests that our multilingual MT models trained on original scripts seem to already be robust to cross-script differences even for relatively low-resource languages | ['Rachel Bawden', 'Sonal Sannigrahi'] | 2023-05-04 | null | null | null | null | ['transliteration', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [-1.82297137e-02 -3.29591453e-01 -3.51551175e-01 -5.52740812e-01
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3644a67b-4708-44b8-bf99-32fbf3757ba5 | adaptive-multi-scale-online-likelihood | 2303.13696 | null | https://arxiv.org/abs/2303.13696v1 | https://arxiv.org/pdf/2303.13696v1.pdf | Adaptive Multi-scale Online Likelihood Network for AI-assisted Interactive Segmentation | Existing interactive segmentation methods leverage automatic segmentation and user interactions for label refinement, significantly reducing the annotation workload compared to manual annotation. However, these methods lack quick adaptability to ambiguous and noisy data, which is a challenge in CT volumes containing lung lesions from COVID-19 patients. In this work, we propose an adaptive multi-scale online likelihood network (MONet) that adaptively learns in a data-efficient online setting from both an initial automatic segmentation and user interactions providing corrections. We achieve adaptive learning by proposing an adaptive loss that extends the influence of user-provided interaction to neighboring regions with similar features. In addition, we propose a data-efficient probability-guided pruning method that discards uncertain and redundant labels in the initial segmentation to enable efficient online training and inference. Our proposed method was evaluated by an expert in a blinded comparative study on COVID-19 lung lesion annotation task in CT. Our approach achieved 5.86% higher Dice score with 24.67% less perceived NASA-TLX workload score than the state-of-the-art. Source code is available at: https://github.com/masadcv/MONet-MONAILabel | ['Tom Vercauteren', "Jan D'hooge", 'Jan Deprest', 'Sarim Ather', 'Indrajeet Mandal', 'Helena Williams', 'Muhammad Asad'] | 2023-03-23 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [ 1.55354232e-01 1.25681087e-01 -3.12875479e-01 -5.89851439e-01
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e2932e6f-c4f8-4b79-befd-59b60a750a63 | direct-speech-to-speech-translation-without | 2212.05805 | null | https://arxiv.org/abs/2212.05805v1 | https://arxiv.org/pdf/2212.05805v1.pdf | Direct Speech-to-speech Translation without Textual Annotation using Bottleneck Features | Speech-to-speech translation directly translates a speech utterance to another between different languages, and has great potential in tasks such as simultaneous interpretation. State-of-art models usually contains an auxiliary module for phoneme sequences prediction, and this requires textual annotation of the training dataset. We propose a direct speech-to-speech translation model which can be trained without any textual annotation or content information. Instead of introducing an auxiliary phoneme prediction task in the model, we propose to use bottleneck features as intermediate training objectives for our model to ensure the translation performance of the system. Experiments on Mandarin-Cantonese speech translation demonstrate the feasibility of the proposed approach and the performance can match a cascaded system with respect of translation and synthesis qualities. | ['Zejun Ma', 'Xiang Yin', 'Junjie Pan', 'Junhui Zhang'] | 2022-12-12 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [ 5.76242566e-01 2.25054204e-01 -3.60806912e-01 -5.88090241e-01
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68cdf3f6-8504-40fe-9533-ee1daf03545b | dual-path-attention-is-all-you-need-for-audio | 2207.04213 | null | https://arxiv.org/abs/2207.04213v2 | https://arxiv.org/pdf/2207.04213v2.pdf | Dual-Path Cross-Modal Attention for better Audio-Visual Speech Extraction | Audio-visual target speech extraction, which aims to extract a certain speaker's speech from the noisy mixture by looking at lip movements, has made significant progress combining time-domain speech separation models and visual feature extractors (CNN). One problem of fusing audio and video information is that they have different time resolutions. Most current research upsamples the visual features along the time dimension so that audio and video features are able to align in time. However, we believe that lip movement should mostly contain long-term, or phone-level information. Based on this assumption, we propose a new way to fuse audio-visual features. We observe that for DPRNN \cite{dprnn}, the interchunk dimension's time resolution could be very close to the time resolution of video frames. Like \cite{sepformer}, the LSTM in DPRNN is replaced by intra-chunk and inter-chunk self-attention, but in the proposed algorithm, inter-chunk attention incorporates the visual features as an additional feature stream. This prevents the upsampling of visual cues, resulting in more efficient audio-visual fusion. The result shows we achieve superior results compared with other time-domain based audio-visual fusion models. | ['Mark Hasegawa-Johnson', 'Xulin Fan', 'Zhongweiyang Xu'] | 2022-07-09 | null | null | null | null | ['speech-separation', 'speech-extraction'] | ['speech', 'speech'] | [-1.83295142e-02 -2.22213030e-01 -3.10791850e-01 -1.15869626e-01
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7a936479-7ab0-496c-b2b2-adc4a784c83e | image-generation-from-scene-graphs | 1804.01622 | null | http://arxiv.org/abs/1804.01622v1 | http://arxiv.org/pdf/1804.01622v1.pdf | Image Generation from Scene Graphs | To truly understand the visual world our models should be able not only to
recognize images but also generate them. To this end, there has been exciting
recent progress on generating images from natural language descriptions. These
methods give stunning results on limited domains such as descriptions of birds
or flowers, but struggle to faithfully reproduce complex sentences with many
objects and relationships. To overcome this limitation we propose a method for
generating images from scene graphs, enabling explicitly reasoning about
objects and their relationships. Our model uses graph convolution to process
input graphs, computes a scene layout by predicting bounding boxes and
segmentation masks for objects, and converts the layout to an image with a
cascaded refinement network. The network is trained adversarially against a
pair of discriminators to ensure realistic outputs. We validate our approach on
Visual Genome and COCO-Stuff, where qualitative results, ablations, and user
studies demonstrate our method's ability to generate complex images with
multiple objects. | ['Li Fei-Fei', 'Justin Johnson', 'Agrim Gupta'] | 2018-04-04 | image-generation-from-scene-graphs-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Johnson_Image_Generation_From_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Johnson_Image_Generation_From_CVPR_2018_paper.pdf | cvpr-2018-6 | ['layout-to-image-generation', 'image-generation-from-scene-graphs'] | ['computer-vision', 'computer-vision'] | [ 6.36192739e-01 5.75083256e-01 3.79223526e-01 -5.30672610e-01
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6cebb484-58e8-4f02-aae1-a1b8c795a492 | training-compute-optimal-large-language | 2203.15556 | null | https://arxiv.org/abs/2203.15556v1 | https://arxiv.org/pdf/2203.15556v1.pdf | Training Compute-Optimal Large Language Models | We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence of the recent focus on scaling language models whilst keeping the amount of training data constant. By training over \nummodels language models ranging from 70 million to over 16 billion parameters on 5 to 500 billion tokens, we find that for compute-optimal training, the model size and the number of training tokens should be scaled equally: for every doubling of model size the number of training tokens should also be doubled. We test this hypothesis by training a predicted compute-optimal model, \chinchilla, that uses the same compute budget as \gopher but with 70B parameters and 4$\times$ more more data. \chinchilla uniformly and significantly outperforms \Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of downstream evaluation tasks. This also means that \chinchilla uses substantially less compute for fine-tuning and inference, greatly facilitating downstream usage. As a highlight, \chinchilla reaches a state-of-the-art average accuracy of 67.5\% on the MMLU benchmark, greater than a 7\% improvement over \gopher. | ['Laurent SIfre', 'Oriol Vinyals', 'Jack W. Rae', 'Erich Elsen', 'Karen Simonyan', 'Simon Osindero', 'Aurelia Guy', 'Bogdan Damoc', 'George van den Driessche', 'Katie Millican', 'Eric Noland', 'Tom Hennigan', 'Aidan Clark', 'Johannes Welbl', 'Lisa Anne Hendricks', 'Diego de Las Casas', 'Eliza Rutherford', 'Trevor Cai', 'Elena Buchatskaya', 'Arthur Mensch', 'Sebastian Borgeaud', 'Jordan Hoffmann'] | 2022-03-29 | null | null | null | null | ['logical-fallacy-detection', 'multi-task-language-understanding', 'moral-permissibility', 'sentence-ambiguity', 'human-organs-senses-multiple-choice', 'similarities-abstraction', 'misconceptions', 'epistemic-reasoning', 'movie-recommendation', 'known-unknowns', 'logic-grid-puzzle', 'physics-mc', 'sports-understanding', 'dark-humor-detection', 'implicatures', 'fantasy-reasoning', 'figure-of-speech-detection', 'word-sense-disambiguation', 'english-proverbs', 'multiple-choice-qa', 'intent-recognition', 'phrase-relatedness', 'timedial', 'irony-identification', 'lambada', 'empirical-judgments', 'gre-reading-comprehension', 'riddle-sense', 'disambiguation-q', 'ruin-names', 'understanding-fables', 'movie-dialog-same-or-different', 'discourse-marker-prediction', 'mathematical-reasoning', 'snarks', 'formal-fallacies-syllogisms-negation', 'nonsense-words-grammar', 'implicit-relations', 'question-selection', 'hyperbaton', 'metaphor-boolean', 'penguins-in-a-table', 'analogical-similarity', 'temporal-sequences', 'physical-intuition', 'identify-odd-metapor', 'crash-blossom', 'presuppositions-as-nli', 'entailed-polarity', 'evaluating-information-essentiality', 'odd-one-out', 'anachronisms', 'mathematical-induction', 'reasoning-about-colored-objects', 'causal-judgment', 'analytic-entailment', 'logical-fallacy-detection', 'date-understanding', 'crass-ai', 'logical-args', 'winowhy', 'strategyqa', 'novel-concepts', 'logical-sequence'] | ['methodology', 'methodology', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'reasoning', 'reasoning', 'reasoning', 'reasoning', 'reasoning', 'reasoning', 'reasoning', 'reasoning', 'reasoning', 'reasoning', 'reasoning', 'reasoning', 'reasoning', 'reasoning', 'reasoning', 'reasoning', 'reasoning', 'reasoning', 'reasoning', 'reasoning', 'reasoning', 'reasoning', 'reasoning', 'reasoning'] | [-5.93069732e-01 1.23002373e-01 -2.65323997e-01 -3.82368118e-01
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38735e38-cb52-4ec9-aca4-61e3824438b1 | rnn-dbscan-a-density-based-clustering | null | null | https://ieeexplore.ieee.org/document/8240674 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8240674 | RNN-DBSCAN: A Density-Based Clustering Algorithm Using Reverse Nearest Neighbor Density Estimates | A new density-based clustering algorithm, RNN-DBSCAN, is presented which uses reverse nearest neighbor counts as an estimate of observation density. Clustering is performed using a DBSCAN-like approach based on k nearest neighbor graph traversals through dense observations. RNN-DBSCAN is preferable to the popular density-based clustering algorithm DBSCAN in two aspects. First, problem complexity is reduced to the use of a single parameter (choice of k nearest neighbors), and second, an improved ability for handling large variations in cluster density (heterogeneous density). The superiority of RNN-DBSCAN is demonstrated on several artificial and real-world datasets with respect to prior work on reverse nearest neighbor based clustering approaches (RECORD, IS-DBSCAN, and ISB-DBSCAN) along with DBSCAN and OPTICS. Each of these clustering approaches is described by a common graph-based interpretation wherein clusters of dense observations are defined as connected components, along with a discussion on their computational complexity. Heuristics for RNN-DBSCAN parameter selection are presented, and the effects of k on RNN-DBSCAN clusterings discussed. Additionally, with respect to scalability, an approximate version of RNN-DBSCAN is presented leveraging an existing approximate k nearest neighbor technique. | ['Krzysztof Cios', 'Avory Bryant'] | 2017-12-27 | null | null | null | null | ['3d-multi-person-pose-estimation-absolute'] | ['computer-vision'] | [-3.02572042e-01 -2.70522594e-01 -2.11708769e-01 -5.81790328e-01
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f666c6a3-4ab1-45b2-8ace-8fd967c89b88 | federated-stochastic-bandit-learning-with | 2303.17043 | null | https://arxiv.org/abs/2303.17043v1 | https://arxiv.org/pdf/2303.17043v1.pdf | Federated Stochastic Bandit Learning with Unobserved Context | We study the problem of federated stochastic multi-arm contextual bandits with unknown contexts, in which M agents are faced with different bandits and collaborate to learn. The communication model consists of a central server and the agents share their estimates with the central server periodically to learn to choose optimal actions in order to minimize the total regret. We assume that the exact contexts are not observable and the agents observe only a distribution of the contexts. Such a situation arises, for instance, when the context itself is a noisy measurement or based on a prediction mechanism. Our goal is to develop a distributed and federated algorithm that facilitates collaborative learning among the agents to select a sequence of optimal actions so as to maximize the cumulative reward. By performing a feature vector transformation, we propose an elimination-based algorithm and prove the regret bound for linearly parametrized reward functions. Finally, we validated the performance of our algorithm and compared it with another baseline approach using numerical simulations on synthetic data and on the real-world movielens dataset. | ['Shana Moothedath', 'Jiabin Lin'] | 2023-03-29 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 2.67209671e-02 -1.90574631e-01 -2.12260216e-01 -2.97966123e-01
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afb40ed0-3d2b-4edd-9d0f-e0f8aeabbc1d | influencer-detection-with-dynamic-graph | 2211.09664 | null | https://arxiv.org/abs/2211.09664v1 | https://arxiv.org/pdf/2211.09664v1.pdf | Influencer Detection with Dynamic Graph Neural Networks | Leveraging network information for prediction tasks has become a common practice in many domains. Being an important part of targeted marketing, influencer detection can potentially benefit from incorporating dynamic network representation. In this work, we investigate different dynamic Graph Neural Networks (GNNs) configurations for influencer detection and evaluate their prediction performance using a unique corporate data set. We show that using deep multi-head attention in GNN and encoding temporal attributes significantly improves performance. Furthermore, our empirical evaluation illustrates that capturing neighborhood representation is more beneficial that using network centrality measures. | ['Cristián Bravo', 'Monique Snoeck', 'Bart Baesens', 'Alejandro Correa Bahnsen', 'Hernan Garcia', 'María Óskarsdóttir', 'Emiliano Penaloza', 'Elena Tiukhova'] | 2022-11-15 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 1.08971409e-01 2.38772169e-01 -7.80688703e-01 -3.38247418e-01
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64d7cffe-a640-421a-b704-08184cc339e6 | removing-distortion-effects-in-music-using | 2202.01664 | null | https://arxiv.org/abs/2202.01664v3 | https://arxiv.org/pdf/2202.01664v3.pdf | Distortion Audio Effects: Learning How to Recover the Clean Signal | Given the recent advances in music source separation and automatic mixing, removing audio effects in music tracks is a meaningful step toward developing an automated remixing system. This paper focuses on removing distortion audio effects applied to guitar tracks in music production. We explore whether effect removal can be solved by neural networks designed for source separation and audio effect modeling. Our approach proves particularly effective for effects that mix the processed and clean signals. The models achieve better quality and significantly faster inference compared to state-of-the-art solutions based on sparse optimization. We demonstrate that the models are suitable not only for declipping but also for other types of distortion effects. By discussing the results, we stress the usefulness of multiple evaluation metrics to assess different aspects of reconstruction in distortion effect removal. | ['Yuki Mitsufuji', 'Yuichiro Koyama', 'Stefan Uhlich', 'Marco A. Martínez Ramírez', 'Giorgio Fabbro', 'Johannes Imort'] | 2022-02-03 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 3.34601820e-01 -3.87360722e-01 1.17036723e-01 1.33289933e-01
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69dc201c-9827-4a38-bd5a-a4cf46a6a8b3 | hybrid-aco-ci-algorithm-for-beam-design | 2303.16908 | null | https://arxiv.org/abs/2303.16908v1 | https://arxiv.org/pdf/2303.16908v1.pdf | Hybrid ACO-CI Algorithm for Beam Design problems | A range of complicated real-world problems have inspired the development of several optimization methods. Here, a novel hybrid version of the Ant colony optimization (ACO) method is developed using the sample space reduction technique of the Cohort Intelligence (CI) Algorithm. The algorithm is developed, and accuracy is tested by solving 35 standard benchmark test functions. Furthermore, the constrained version of the algorithm is used to solve two mechanical design problems involving stepped cantilever beams and I-section beams. The effectiveness of the proposed technique of solution is evaluated relative to contemporary algorithmic approaches that are already in use. The results show that our proposed hybrid ACO-CI algorithm will take lesser number of iterations to produce the desired output which means lesser computational time. For the minimization of weight of stepped cantilever beam and deflection in I-section beam a proposed hybrid ACO-CI algorithm yielded best results when compared to other existing algorithms. The proposed work could be investigate for variegated real world applications encompassing domains of engineering, combinatorial and health care problems. | ['Aayushi Singh', 'Abhinav Anand', 'Kaustubh Dhamankar', 'Ayush Khedkar', 'Mandar S Sapre', 'Ishaan R Kale'] | 2023-03-29 | null | null | null | null | ['cantilever-beam'] | ['miscellaneous'] | [ 6.60668075e-01 1.41701594e-01 3.30170125e-01 -1.03963409e-02
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bc274042-5691-4ea3-b414-3ce665f71678 | renewable-energy-management-in-smart-home-1 | 2307.01622 | null | https://arxiv.org/abs/2307.01622v2 | https://arxiv.org/pdf/2307.01622v2.pdf | Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network | Smart home energy management systems help the distribution grid operate more efficiently and reliably, and enable effective penetration of distributed renewable energy sources. These systems rely on robust forecasting, optimization, and control/scheduling algorithms that can handle the uncertain nature of demand and renewable generation. This paper proposes an advanced ML algorithm, called Recurrent Trend Predictive Neural Network based Forecast Embedded Scheduling (rTPNN-FES), to provide efficient residential demand control. rTPNN-FES is a novel neural network architecture that simultaneously forecasts renewable energy generation and schedules household appliances. By its embedded structure, rTPNN-FES eliminates the utilization of separate algorithms for forecasting and scheduling and generates a schedule that is robust against forecasting errors. This paper also evaluates the performance of the proposed algorithm for an IoT-enabled smart home. The evaluation results reveal that rTPNN-FES provides near-optimal scheduling $37.5$ times faster than the optimization while outperforming state-of-the-art forecasting techniques. | ['Cüneyt Güzeliş', 'Emrah Biyik', 'Onur Çopur', 'Mert Nakıp'] | 2023-07-04 | renewable-energy-management-in-smart-home | https://www.sciencedirect.com/science/article/abs/pii/S0306261923003781?dgcid=author | https://www.sciencedirect.com/science/article/abs/pii/S0306261923003781?dgcid=author | applied-energy-2023-3 | ['management', 'energy-management'] | ['miscellaneous', 'time-series'] | [-3.32828999e-01 -2.43777797e-01 -1.81754678e-01 -5.21555007e-01
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60a10bea-bddc-4e16-8d6f-f807905b37e1 | smirl-surprise-minimizing-rl-in-entropic | null | null | https://openreview.net/forum?id=H1lDbaVYvH | https://openreview.net/pdf?id=H1lDbaVYvH | SMiRL: Surprise Minimizing RL in Entropic Environments | All living organisms struggle against the forces of nature to carve out niches where
they can maintain relative stasis. We propose that such a search for order amidst
chaos might offer a unifying principle for the emergence of useful behaviors in
artificial agents. We formalize this idea into an unsupervised reinforcement learning
method called surprise minimizing RL (SMiRL). SMiRL trains an agent with the
objective of maximizing the probability of observed states under a model trained on
all previously seen states. The resulting agents acquire several proactive behaviors
to seek and maintain stable states such as balancing and damage avoidance, that
are closely tied to the affordances of the environment and its prevailing sources
of entropy, such as winds, earthquakes, and other agents. We demonstrate that
our surprise minimizing agents can successfully play Tetris, Doom, and control
a humanoid to avoid falls, without any task-specific reward supervision. We
further show that SMiRL can be used as an unsupervised pre-training objective
that substantially accelerates subsequent reward-driven learning | ['Sergey Levine', 'Chelsea Finn', 'Dinesh Jayaraman', 'Coline Devin', 'Daniel Geng', 'Glen Berseth'] | 2019-09-25 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 8.58404338e-02 2.16871783e-01 -1.04509071e-01 6.21779971e-02
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88702415-28db-41f8-80a1-eb2ae0ca41b2 | provably-efficient-bayesian-optimization-with | 2306.06844 | null | https://arxiv.org/abs/2306.06844v1 | https://arxiv.org/pdf/2306.06844v1.pdf | Provably Efficient Bayesian Optimization with Unbiased Gaussian Process Hyperparameter Estimation | Gaussian process (GP) based Bayesian optimization (BO) is a powerful method for optimizing black-box functions efficiently. The practical performance and theoretical guarantees associated with this approach depend on having the correct GP hyperparameter values, which are usually unknown in advance and need to be estimated from the observed data. However, in practice, these estimations could be incorrect due to biased data sampling strategies commonly used in BO. This can lead to degraded performance and break the sub-linear global convergence guarantee of BO. To address this issue, we propose a new BO method that can sub-linearly converge to the global optimum of the objective function even when the true GP hyperparameters are unknown in advance and need to be estimated from the observed data. Our method uses a multi-armed bandit technique (EXP3) to add random data points to the BO process, and employs a novel training loss function for the GP hyperparameter estimation process that ensures unbiased estimation from the observed data. We further provide theoretical analysis of our proposed method. Finally, we demonstrate empirically that our method outperforms existing approaches on various synthetic and real-world problems. | ['Anton Van Den Hengel', 'Hongyu Zhang', 'Vu Nguyen', 'Huong Ha'] | 2023-06-12 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [-1.15238361e-01 -1.81518629e-01 -2.32607514e-01 -1.63687587e-01
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954e2464-84ea-4605-a496-a3837806833f | team-ruc-aim3-technical-report-at-activitynet | 2006.07896 | null | https://arxiv.org/abs/2006.07896v1 | https://arxiv.org/pdf/2006.07896v1.pdf | Team RUC_AIM3 Technical Report at Activitynet 2020 Task 2: Exploring Sequential Events Detection for Dense Video Captioning | Detecting meaningful events in an untrimmed video is essential for dense video captioning. In this work, we propose a novel and simple model for event sequence generation and explore temporal relationships of the event sequence in the video. The proposed model omits inefficient two-stage proposal generation and directly generates event boundaries conditioned on bi-directional temporal dependency in one pass. Experimental results show that the proposed event sequence generation model can generate more accurate and diverse events within a small number of proposals. For the event captioning, we follow our previous work to employ the intra-event captioning models into our pipeline system. The overall system achieves state-of-the-art performance on the dense-captioning events in video task with 9.894 METEOR score on the challenge testing set. | ['Shi-Zhe Chen', 'Yuqing Song', 'Yida Zhao', 'Qin Jin'] | 2020-06-14 | team-ruc-aim3-technical-report-at-activitynet-2 | https://arxiv.org/abs/2006.07896 | https://arxiv.org/pdf/2006.07896.pdf | null | ['dense-captioning', 'dense-video-captioning'] | ['computer-vision', 'computer-vision'] | [ 3.30467016e-01 6.15811208e-03 -9.77770705e-03 -5.33754826e-01
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42a0c3e0-812f-477f-b04b-ee405d6214dd | jointly-learning-to-label-sentences-and | 1811.05949 | null | http://arxiv.org/abs/1811.05949v1 | http://arxiv.org/pdf/1811.05949v1.pdf | Jointly Learning to Label Sentences and Tokens | Learning to construct text representations in end-to-end systems can be
difficult, as natural languages are highly compositional and task-specific
annotated datasets are often limited in size. Methods for directly supervising
language composition can allow us to guide the models based on existing
knowledge, regularizing them towards more robust and interpretable
representations. In this paper, we investigate how objectives at different
granularities can be used to learn better language representations and we
propose an architecture for jointly learning to label sentences and tokens. The
predictions at each level are combined together using an attention mechanism,
with token-level labels also acting as explicit supervision for composing
sentence-level representations. Our experiments show that by learning to
perform these tasks jointly on multiple levels, the model achieves substantial
improvements for both sentence classification and sequence labeling. | ['Anders Søgaard', 'Marek Rei'] | 2018-11-14 | null | null | null | null | ['grammatical-error-detection'] | ['natural-language-processing'] | [ 5.36050558e-01 4.00447279e-01 -4.81780052e-01 -8.21696043e-01
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29e808a6-0e3f-4805-889f-f2bebb413dec | how-to-use-reinforcement-learning-to | 2305.02485 | null | https://arxiv.org/abs/2305.02485v2 | https://arxiv.org/pdf/2305.02485v2.pdf | How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 1: A Paradigmatic Theory | In face of the pressing need of decarbonization in the power sector, the re-design of electricity market is necessary as a Marco-level approach to accommodate the high penetration of renewable generations, and to achieve power system operation security, economic efficiency, and environmental friendliness. However, existing market design methodologies suffer from the lack of coordination among energy spot market (ESM), ancillary service market (ASM) and financial market (FM), i.e., the "joint market", and the lack of reliable simulation-based verification. To tackle these deficiencies, this two-part paper develops a paradigmatic theory and detailed methods of the joint market design using reinforcement-learning (RL)-based simulation. In Part 1, the theory and framework of this novel market design philosophy are proposed. First, the controversial market design options while designing the joint market are summarized as the targeted research questions. Second, the Markov game model is developed to describe the bidding game in the joint market, incorporating the market design options to be determined. Third, a framework of deploying multiple types of RL algorithms to simulate the market model is developed. Finally, several market operation performance indicators are proposed to validate the market design based on the simulation results. | ['Shiwei Xia', 'Bin Zhou', 'Ka Wing Chan', 'Siqi Bu', 'Ziqing Zhu'] | 2023-05-04 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [-8.30344677e-01 -2.40148887e-01 -3.93407196e-01 3.03431690e-01
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0e25fdc0-9701-4275-a0a6-a8503ee77951 | toward-accurate-and-reliable-iris | 2110.10334 | null | https://arxiv.org/abs/2110.10334v2 | https://arxiv.org/pdf/2110.10334v2.pdf | Toward Accurate and Reliable Iris Segmentation Using Uncertainty Learning | Iris segmentation is a deterministic part of the iris recognition system. Unreliable segmentation of iris regions especially the limbic area is still the bottleneck problem, which impedes more accurate recognition. To make further efforts on accurate and reliable iris segmentation, we propose a bilateral self-attention module and design Bilateral Transformer (BiTrans) with hierarchical architecture by exploring spatial and visual relationships. The bilateral self-attention module adopts a spatial branch to capture spatial contextual information without resolution reduction and a visual branch with a large receptive field to extract the visual contextual features. BiTrans actively applies convolutional projections and cross-attention to improve spatial perception and hierarchical feature fusion. Besides, Iris Segmentation Uncertainty Learning is developed to learn the uncertainty map according to prediction discrepancy. With the estimated uncertainty, a weighting scheme and a regularization term are designed to reduce predictive uncertainty. More importantly, the uncertainty estimate reflects the reliability of the segmentation predictions. Experimental results on three publicly available databases demonstrate that the proposed approach achieves better segmentation performance using 20% FLOPs of the SOTA IrisParseNet. | ['Zhenan Sun', 'Ran He', 'Min Ren', 'Yunlong Wang', 'Muyi Sun', 'Huaibo Huang', 'Jianze Wei'] | 2021-10-20 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 1.13060340e-01 9.99407768e-02 -2.94953048e-01 -6.55295312e-01
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dea2bc93-2db5-4f86-82b7-03f73fabf771 | learning-graph-normalization-for-graph-neural | 2009.11746 | null | https://arxiv.org/abs/2009.11746v1 | https://arxiv.org/pdf/2009.11746v1.pdf | Learning Graph Normalization for Graph Neural Networks | Graph Neural Networks (GNNs) have attracted considerable attention and have emerged as a new promising paradigm to process graph-structured data. GNNs are usually stacked to multiple layers and the node representations in each layer are computed through propagating and aggregating the neighboring node features with respect to the graph. By stacking to multiple layers, GNNs are able to capture the long-range dependencies among the data on the graph and thus bring performance improvements. To train a GNN with multiple layers effectively, some normalization techniques (e.g., node-wise normalization, batch-wise normalization) are necessary. However, the normalization techniques for GNNs are highly task-relevant and different application tasks prefer to different normalization techniques, which is hard to know in advance. To tackle this deficiency, in this paper, we propose to learn graph normalization by optimizing a weighted combination of normalization techniques at four different levels, including node-wise normalization, adjacency-wise normalization, graph-wise normalization, and batch-wise normalization, in which the adjacency-wise normalization and the graph-wise normalization are newly proposed in this paper to take into account the local structure and the global structure on the graph, respectively. By learning the optimal weights, we are able to automatically select a single best or a best combination of multiple normalizations for a specific task. We conduct extensive experiments on benchmark datasets for different tasks, including node classification, link prediction, graph classification and graph regression, and confirm that the learned graph normalization leads to competitive results and that the learned weights suggest the appropriate normalization techniques for the specific task. Source code is released here https://github.com/cyh1112/GraphNormalization. | ['Chun-Guang Li', 'Xin Tang', 'Yihao Chen', 'Xianbiao Qi', 'Rong Xiao'] | 2020-09-24 | null | https://openreview.net/forum?id=oLltLS5F9R | https://openreview.net/pdf?id=oLltLS5F9R | null | ['graph-regression'] | ['graphs'] | [ 1.32319376e-01 3.94736975e-02 -3.16879869e-01 -5.52592039e-01
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8339b16b-eabd-4cea-8843-f0d1a1bfc51c | large-language-models-are-implicitly-topic | 2301.11916 | null | https://arxiv.org/abs/2301.11916v2 | https://arxiv.org/pdf/2301.11916v2.pdf | Large Language Models Are Implicitly Topic Models: Explaining and Finding Good Demonstrations for In-Context Learning | In recent years, pre-trained large language models have demonstrated remarkable efficiency in achieving an inference-time few-shot learning capability known as in-context learning. However, existing literature has highlighted the sensitivity of this capability to the selection of few-shot demonstrations. The underlying mechanisms by which this capability arises from regular language model pretraining objectives remain poorly understood. In this study, we aim to examine the in-context learning phenomenon through a Bayesian lens, viewing large language models as topic models that implicitly infer task-related information from demonstrations. On this premise, we propose an algorithm for selecting optimal demonstrations from a set of annotated data and demonstrate a significant 12.5% improvement relative to the random selection baseline, averaged over eight GPT2 and GPT3 models on eight different real-world text classification datasets. Our empirical findings support our hypothesis that large language models implicitly infer a latent concept variable. | ['Mark Steyvers', 'Michael Saxon', 'William Yang Wang', 'Wanrong Zhu', 'Xinyi Wang'] | 2023-01-27 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [ 2.87069321e-01 2.54845828e-01 -5.69182277e-01 -4.57163781e-01
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da24a793-9701-4062-86c8-95fc7554ae6f | online-heart-rate-prediction-using | 1807.04667 | null | http://arxiv.org/abs/1807.04667v1 | http://arxiv.org/pdf/1807.04667v1.pdf | Online Heart Rate Prediction using Acceleration from a Wrist Worn Wearable | In this paper we study the prediction of heart rate from acceleration using a
wrist worn wearable. Although existing photoplethysmography (PPG) heart rate
sensors provide reliable measurements, they use considerably more energy than
accelerometers and have a major impact on battery life of wearable devices. By
using energy-efficient accelerometers to predict heart rate, significant energy
savings can be made. Further, we are interested in understanding patient
recovery after a heart rate intervention, where we expect a variation in heart
rate over time. Therefore, we propose an online approach to tackle the concept
as time passes. We evaluate the methods on approximately 4 weeks of free living
data from three patients over a number of months. We show that our approach can
achieve good predictive performance (e.g., 2.89 Mean Absolute Error) while
using the PPG heart rate sensor infrequently (e.g., 20.25% of the samples). | ['Raul Santos-Rodriguez', 'Ryan McConville', 'Gareth Archer', 'Robert Piechocki', 'Ian Craddock', 'Herman ter Horst', 'James Pope'] | 2018-06-25 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 3.29390556e-01 1.98265746e-01 -2.57092237e-01 -2.52593666e-01
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39486a34-2c0a-463b-a640-c5f0f3875644 | self-distillation-for-gaussian-process | 2304.02641 | null | https://arxiv.org/abs/2304.02641v1 | https://arxiv.org/pdf/2304.02641v1.pdf | Self-Distillation for Gaussian Process Regression and Classification | We propose two approaches to extend the notion of knowledge distillation to Gaussian Process Regression (GPR) and Gaussian Process Classification (GPC); data-centric and distribution-centric. The data-centric approach resembles most current distillation techniques for machine learning, and refits a model on deterministic predictions from the teacher, while the distribution-centric approach, re-uses the full probabilistic posterior for the next iteration. By analyzing the properties of these approaches, we show that the data-centric approach for GPR closely relates to known results for self-distillation of kernel ridge regression and that the distribution-centric approach for GPR corresponds to ordinary GPR with a very particular choice of hyperparameters. Furthermore, we demonstrate that the distribution-centric approach for GPC approximately corresponds to data duplication and a particular scaling of the covariance and that the data-centric approach for GPC requires redefining the model from a Binomial likelihood to a continuous Bernoulli likelihood to be well-specified. To the best of our knowledge, our proposed approaches are the first to formulate knowledge distillation specifically for Gaussian Process models. | ['Lars Nørvang Andersen', 'Kenneth Borup'] | 2023-04-05 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-2.70274356e-02 2.71485835e-01 -9.29248556e-02 -3.23873997e-01
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44165090-2ccb-4b92-8705-277d78dfc47c | end-to-end-neural-networks-for-subvocal | null | null | https://www.semanticscholar.org/paper/End-to-end-neural-networks-for-subvocal-speech-Rosello-Toman/6676c8ce28ee02e785a1f1136112beee94a5c7e1 | https://web.stanford.edu/class/cs224s/project/reports_2017/Pol_Rosello.pdf | End-to-end neural networks for subvocal speech recognition | Subvocalization is a phenomenon observed while subjects read or think, characterized by involuntary facial and laryngeal muscle movements. By measuring this muscle activity using surface electromyography (EMG), it may be possible to perform automatic speech recognition (ASR) and enable silent, handsfree human-computer interfaces. In our work, we describe the first approach toward end-to-end, session-independent subvocal speech recognition by leveraging character-level recurrent neural networks (RNNs) and the connectionist temporal classification loss (CTC). We attempt to address challenges posed by a lack of data, including poor generalization, through data augmentation of electromyographic signals, a specialized multi-modal architecture, and regularization. We show results indicating reasonable qualitative performance on test set utterances, and describe promising avenues for future work in this direction. | ['Nipun Agarwala', 'Pamela Toman', 'Pol Rosello'] | 2017-06-11 | null | null | null | cs-224s-2017-6 | ['electromyography-emg'] | ['medical'] | [ 7.05768645e-01 2.13866875e-01 -4.60909218e-01 -2.43089601e-01
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39a47255-3f8b-4392-b9c7-f060dd2459c5 | a-versatile-deep-learning-based-protein | 2307.01066 | null | https://arxiv.org/abs/2307.01066v1 | https://arxiv.org/pdf/2307.01066v1.pdf | A versatile deep learning-based protein-ligand interaction prediction model for accurate binding affinity scoring and virtual screening | Protein--ligand interaction (PLI) prediction is critical in drug discovery, aiding the identification and enhancement of molecules that effectively bind to target proteins. Despite recent advances in deep learning-based PLI prediction, developing a versatile model capable of accurate binding affinity scoring and virtual screening in PLI prediction is an ongoing challenge. This is primarily due to the lack of structure--affinity data, resulting in low model generalization ability. We here propose a viable solution to this challenge by introducing a novel data augmentation strategy along with a physics-informed neural network. The resulting model exhibits significant improvement in both scoring and screening capabilities. Its performance was compared to task-specific deep learning-based PLI prediction models, confirming its versatility. Notably, it even outperformed computationally expensive molecular dynamics simulations as well as the other deep learning models in a derivative benchmark while maintaining sufficiently high performance in virtual screening. This underscores the potential of this approach in drug discovery, demonstrating its applicability to both binding affinity scoring and virtual screening. | ['Woo Youn Kim', 'Jaechang Lim', 'Sang-Yeon Hwang', 'Seokhyun Moon'] | 2023-07-03 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 3.73269945e-01 -3.19998920e-01 -2.29008257e-01 -1.32744342e-01
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778fa43a-5fff-4aac-a849-cd76ad2f7d5a | batchgnn-efficient-cpu-based-distributed-gnn | 2306.13814 | null | https://arxiv.org/abs/2306.13814v1 | https://arxiv.org/pdf/2306.13814v1.pdf | BatchGNN: Efficient CPU-Based Distributed GNN Training on Very Large Graphs | We present BatchGNN, a distributed CPU system that showcases techniques that can be used to efficiently train GNNs on terabyte-sized graphs. It reduces communication overhead with macrobatching in which multiple minibatches' subgraph sampling and feature fetching are batched into one communication relay to reduce redundant feature fetches when input features are static. BatchGNN provides integrated graph partitioning and native GNN layer implementations to improve runtime, and it can cache aggregated input features to further reduce sampling overhead. BatchGNN achieves an average $3\times$ speedup over DistDGL on three GNN models trained on OGBN graphs, outperforms the runtimes reported by distributed GPU systems $P^3$ and DistDGLv2, and scales to a terabyte-sized graph. | ['Bo Wu', 'Ke Ding', 'Rita Brugarolas Brufau', 'Loc Hoang'] | 2023-06-23 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [-2.52078325e-01 3.49031985e-01 -2.73795635e-01 -3.86340618e-01
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815618d8-f3c6-4420-9cce-8e8d5250f6c0 | spatio-temporal-tendency-reasoning-for-human | 2210.03659 | null | https://arxiv.org/abs/2210.03659v2 | https://arxiv.org/pdf/2210.03659v2.pdf | Spatio-temporal Tendency Reasoning for Human Body Pose and Shape Estimation from Videos | In this paper, we present a spatio-temporal tendency reasoning (STR) network for recovering human body pose and shape from videos. Previous approaches have focused on how to extend 3D human datasets and temporal-based learning to promote accuracy and temporal smoothing. Different from them, our STR aims to learn accurate and natural motion sequences in an unconstrained environment through temporal and spatial tendency and to fully excavate the spatio-temporal features of existing video data. To this end, our STR learns the representation of features in the temporal and spatial dimensions respectively, to concentrate on a more robust representation of spatio-temporal features. More specifically, for efficient temporal modeling, we first propose a temporal tendency reasoning (TTR) module. TTR constructs a time-dimensional hierarchical residual connection representation within a video sequence to effectively reason temporal sequences' tendencies and retain effective dissemination of human information. Meanwhile, for enhancing the spatial representation, we design a spatial tendency enhancing (STE) module to further learns to excite spatially time-frequency domain sensitive features in human motion information representations. Finally, we introduce integration strategies to integrate and refine the spatio-temporal feature representations. Extensive experimental findings on large-scale publically available datasets reveal that our STR remains competitive with the state-of-the-art on three datasets. Our code are available at https://github.com/Changboyang/STR.git. | ['Lei Lin', 'Pan Li', 'Kehua Ma', 'Hu Cao', 'Suping Wu', 'Boyang Zhang'] | 2022-10-07 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [-2.62280941e-01 -2.50627220e-01 -2.64454305e-01 -2.14897722e-01
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0b629eb7-3aac-46fa-b83b-a2089029365e | learning-multi-modal-class-specific-tokens | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_Learning_Multi-Modal_Class-Specific_Tokens_for_Weakly_Supervised_Dense_Object_Localization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_Learning_Multi-Modal_Class-Specific_Tokens_for_Weakly_Supervised_Dense_Object_Localization_CVPR_2023_paper.pdf | Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization | Weakly supervised dense object localization (WSDOL) relies generally on Class Activation Mapping (CAM), which exploits the correlation between the class weights of the image classifier and the pixel-level features. Due to the limited ability to address intra-class variations, the image classifier cannot properly associate the pixel features, leading to inaccurate dense localization maps. In this paper, we propose to explicitly construct multi-modal class representations by leveraging the Contrastive Language-Image Pre-training (CLIP), to guide dense localization. More specifically, we propose a unified transformer framework to learn two-modalities of class-specific tokens, i.e., class-specific visual and textual tokens. The former captures semantics from the target visual data while the latter exploits the class-related language priors from CLIP, providing complementary information to better perceive the intra-class diversities. In addition, we propose to enrich the multi-modal class-specific tokens with sample-specific contexts comprising visual context and image-language context. This enables more adaptive class representation learning, which further facilitates dense localization. Extensive experiments show the superiority of the proposed method for WSDOL on two multi-label datasets, i.e., PASCAL VOC and MS COCO, and one single-label dataset, i.e., OpenImages. Our dense localization maps also lead to the state-of-the-art weakly supervised semantic segmentation (WSSS) results on PASCAL VOC and MS COCO. | ['Dan Xu', 'Farid Boussaid', 'Mohammed Bennamoun', 'Wanli Ouyang', 'Lian Xu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['object-localization', 'weakly-supervised-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.88000941e-01 -9.53681991e-02 -5.29135764e-01 -5.99864304e-01
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f6d21799-703c-4d73-910f-7168f88db8e0 | dual-objective-fine-tuning-of-bert-for-entity | null | null | https://doi.org/10.14778/3467861.3467878 | http://vldb.org/pvldb/vol14/p1913-peeters.pdf | Dual-Objective Fine-Tuning of BERT for Entity Matching | An increasing number of data providers have adopted shared numbering schemes such as GTIN, ISBN, DUNS, or ORCID numbers for identifying entities in the respective domain. This means for data integration that shared identifiers are often available for a subset of the entity descriptions to be integrated while such identifiers are not available for others. The challenge in these settings is to learn a matcher for entity descriptions without identifiers using the entity descriptions containing identifiers as training data. The task can be approached by learning a binary classifier which distinguishes pairs of entity descriptions for the same real-world entity from descriptions of different entities. The task can also be modeled as a multi-class classification problem by learning classifiers for identifying descriptions of individual entities. We present a dual-objective training method for BERT, called JointBERT, which combines binary matching and multi-class classification, forcing the model to predict the entity identifier for each entity description in a training pair in addition to the match/non-match decision. Our evaluation across five entity matching benchmark datasets shows that dual-objective training can increase the matching performance for seen products by 1% to 5% F1 compared to single-objective Transformer-based methods, given that enough training data is available for both objectives. In order to gain a deeper understanding of the strengths and weaknesses of the proposed method, we compare JointBERT to several other BERT-based matching methods as well as baseline systems along a set of specific matching challenges. This evaluation shows that JointBERT, given enough training data for both objectives, outperforms the other methods on tasks involving seen products, while it underperforms for unseen products. Using a combination of LIME explanations and domain-specific word classes, we analyze the matching decisions of the different deep learning models and conclude that BERT-based models are better at focusing on relevant word classes compared to RNN-based models. | ['Christian Bizer', 'Ralph Peeters'] | 2021-06-01 | null | null | null | proceedings-of-the-vldb-endowment-2021-6 | ['data-integration', 'entity-resolution'] | ['knowledge-base', 'natural-language-processing'] | [ 4.58095782e-02 2.61299223e-01 -6.23783827e-01 -6.63044989e-01
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59c10008-a7d2-46cb-9918-291af3890040 | data-augmentation-for-recommender-system-a | 2306.13050 | null | https://arxiv.org/abs/2306.13050v1 | https://arxiv.org/pdf/2306.13050v1.pdf | Data augmentation for recommender system: A semi-supervised approach using maximum margin matrix factorization | Collaborative filtering (CF) has become a popular method for developing recommender systems (RS) where ratings of a user for new items is predicted based on her past preferences and available preference information of other users. Despite the popularity of CF-based methods, their performance is often greatly limited by the sparsity of observed entries. In this study, we explore the data augmentation and refinement aspects of Maximum Margin Matrix Factorization (MMMF), a widely accepted CF technique for the rating predictions, which have not been investigated before. We exploit the inherent characteristics of CF algorithms to assess the confidence level of individual ratings and propose a semi-supervised approach for rating augmentation based on self-training. We hypothesize that any CF algorithm's predictions with low confidence are due to some deficiency in the training data and hence, the performance of the algorithm can be improved by adopting a systematic data augmentation strategy. We iteratively use some of the ratings predicted with high confidence to augment the training data and remove low-confidence entries through a refinement process. By repeating this process, the system learns to improve prediction accuracy. Our method is experimentally evaluated on several state-of-the-art CF algorithms and leads to informative rating augmentation, improving the performance of the baseline approaches. | ['Arun K Pujari', 'Vikas Kumar', 'Venkateswara Rao Kagita', 'Shamal Shaikh'] | 2023-06-22 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [ 2.88111210e-01 4.46548797e-02 -5.87656558e-01 -5.51299691e-01
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79193c13-1207-4e28-a5e5-0cc6eee84b1c | photometric-mesh-optimization-for-video | 1903.08642 | null | http://arxiv.org/abs/1903.08642v1 | http://arxiv.org/pdf/1903.08642v1.pdf | Photometric Mesh Optimization for Video-Aligned 3D Object Reconstruction | In this paper, we address the problem of 3D object mesh reconstruction from
RGB videos. Our approach combines the best of multi-view geometric and
data-driven methods for 3D reconstruction by optimizing object meshes for
multi-view photometric consistency while constraining mesh deformations with a
shape prior. We pose this as a piecewise image alignment problem for each mesh
face projection. Our approach allows us to update shape parameters from the
photometric error without any depth or mask information. Moreover, we show how
to avoid a degeneracy of zero photometric gradients via rasterizing from a
virtual viewpoint. We demonstrate 3D object mesh reconstruction results from
both synthetic and real-world videos with our photometric mesh optimization,
which is unachievable with either na\"ive mesh generation networks or
traditional pipelines of surface reconstruction without heavy manual
post-processing. | ['Matthew Fisher', 'Chen-Hsuan Lin', 'Bryan C. Russell', 'Eli Shechtman', 'Vladimir G. Kim', 'Simon Lucey', 'Oliver Wang'] | 2019-03-20 | photometric-mesh-optimization-for-video-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Lin_Photometric_Mesh_Optimization_for_Video-Aligned_3D_Object_Reconstruction_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Lin_Photometric_Mesh_Optimization_for_Video-Aligned_3D_Object_Reconstruction_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-object-reconstruction'] | ['computer-vision'] | [ 3.80121589e-01 1.99547142e-01 5.10161638e-01 -3.58584613e-01
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b39ad97e-fb5a-4318-b365-8fff6e6661cd | the-devil-is-in-the-details-a-diagnostic | 2105.05332 | null | https://arxiv.org/abs/2105.05332v2 | https://arxiv.org/pdf/2105.05332v2.pdf | The DEVIL is in the Details: A Diagnostic Evaluation Benchmark for Video Inpainting | Quantitative evaluation has increased dramatically among recent video inpainting work, but the video and mask content used to gauge performance has received relatively little attention. Although attributes such as camera and background scene motion inherently change the difficulty of the task and affect methods differently, existing evaluation schemes fail to control for them, thereby providing minimal insight into inpainting failure modes. To address this gap, we propose the Diagnostic Evaluation of Video Inpainting on Landscapes (DEVIL) benchmark, which consists of two contributions: (i) a novel dataset of videos and masks labeled according to several key inpainting failure modes, and (ii) an evaluation scheme that samples slices of the dataset characterized by a fixed content attribute, and scores performance on each slice according to reconstruction, realism, and temporal consistency quality. By revealing systematic changes in performance induced by particular characteristics of the input content, our challenging benchmark enables more insightful analysis into video inpainting methods and serves as an invaluable diagnostic tool for the field. Our code and data are available at https://github.com/MichiganCOG/devil . | ['Jason J. Corso', 'Ryan Szeto'] | 2021-05-11 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Szeto_The_DEVIL_Is_in_the_Details_A_Diagnostic_Evaluation_Benchmark_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Szeto_The_DEVIL_Is_in_the_Details_A_Diagnostic_Evaluation_Benchmark_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-inpainting'] | ['computer-vision'] | [ 2.84565806e-01 -6.90258622e-01 -3.10456961e-01 -9.65942666e-02
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3435afd8-13d1-40cf-bbb4-681b75017382 | dropout-sampling-for-robust-object-detection | 1710.06677 | null | http://arxiv.org/abs/1710.06677v2 | http://arxiv.org/pdf/1710.06677v2.pdf | Dropout Sampling for Robust Object Detection in Open-Set Conditions | Dropout Variational Inference, or Dropout Sampling, has been recently
proposed as an approximation technique for Bayesian Deep Learning and evaluated
for image classification and regression tasks. This paper investigates the
utility of Dropout Sampling for object detection for the first time. We
demonstrate how label uncertainty can be extracted from a state-of-the-art
object detection system via Dropout Sampling. We evaluate this approach on a
large synthetic dataset of 30,000 images, and a real-world dataset captured by
a mobile robot in a versatile campus environment. We show that this uncertainty
can be utilized to increase object detection performance under the open-set
conditions that are typically encountered in robotic vision. A Dropout Sampling
network is shown to achieve a 12.3% increase in recall (for the same precision
score as a standard network) and a 15.1% increase in precision (for the same
recall score as the standard network). | ['Niko Sünderhauf', 'Lachlan Nicholson', 'Dimity Miller', 'Feras Dayoub'] | 2017-10-18 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [ 2.38446712e-01 1.65636614e-01 -9.11606997e-02 -3.36613059e-01
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5209801e-0783-417b-83c9-964573f0b034 | global-ecg-classification-by-self-operational | 2204.03768 | null | https://arxiv.org/abs/2204.03768v2 | https://arxiv.org/pdf/2204.03768v2.pdf | Global ECG Classification by Self-Operational Neural Networks with Feature Injection | Objective: Global (inter-patient) ECG classification for arrhythmia detection over Electrocardiogram (ECG) signal is a challenging task for both humans and machines. The main reason is the significant variations of both normal and arrhythmic ECG patterns among patients. Automating this process with utmost accuracy is, therefore, highly desirable due to the advent of wearable ECG sensors. However, even with numerous deep learning approaches proposed recently, there is still a notable gap in the performance of global and patient-specific ECG classification performances. This study proposes a novel approach to narrow this gap and propose a real-time solution with shallow and compact 1D Self-Organized Operational Neural Networks (Self-ONNs). Methods: In this study, we propose a novel approach for inter-patient ECG classification using a compact 1D Self-ONN by exploiting morphological and timing information in heart cycles. We used 1D Self-ONN layers to automatically learn morphological representations from ECG data, enabling us to capture the shape of the ECG waveform around the R peaks. We further inject temporal features based on RR interval for timing characterization. The classification layers can thus benefit from both temporal and learned features for the final arrhythmia classification. Results: Using the MIT-BIH arrhythmia benchmark database, the proposed method achieves the highest classification performance ever achieved, i.e., 99.21% precision, 99.10% recall, and 99.15% F1-score for normal (N) segments; 82.19% precision, 82.50% recall, and 82.34% F1-score for the supra-ventricular ectopic beat (SVEBs); and finally, 94.41% precision, 96.10% recall, and 95.2% F1-score for the ventricular-ectopic beats (VEBs). | ['Moncef Gabbouj', 'Serkan Kiranyaz', 'Muhammad Uzair Zahid'] | 2022-04-07 | null | null | null | null | ['arrhythmia-detection', 'ecg-classification'] | ['medical', 'medical'] | [ 0.2759255 -0.12362531 0.04840629 -0.2540696 -0.71276814 -0.6250859
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c667b2ca-0f96-4a34-afef-a62390ff27e4 | maskbev-joint-object-detection-and-footprint | 2307.01864 | null | https://arxiv.org/abs/2307.01864v1 | https://arxiv.org/pdf/2307.01864v1.pdf | MaskBEV: Joint Object Detection and Footprint Completion for Bird's-eye View 3D Point Clouds | Recent works in object detection in LiDAR point clouds mostly focus on predicting bounding boxes around objects. This prediction is commonly achieved using anchor-based or anchor-free detectors that predict bounding boxes, requiring significant explicit prior knowledge about the objects to work properly. To remedy these limitations, we propose MaskBEV, a bird's-eye view (BEV) mask-based object detector neural architecture. MaskBEV predicts a set of BEV instance masks that represent the footprints of detected objects. Moreover, our approach allows object detection and footprint completion in a single pass. MaskBEV also reformulates the detection problem purely in terms of classification, doing away with regression usually done to predict bounding boxes. We evaluate the performance of MaskBEV on both SemanticKITTI and KITTI datasets while analyzing the architecture advantages and limitations. | ['Philippe Giguère', 'François Pomerleau', 'Jean-Michel Fortin', 'William Guimont-Martin'] | 2023-07-04 | null | null | null | null | ['object-detection'] | ['computer-vision'] | [ 3.79240997e-02 4.80838232e-02 -6.54283836e-02 -6.64547741e-01
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80de6dbb-98ff-4320-9c04-3c31b1127f0d | learning-new-tasks-from-a-few-examples-with | 2210.17437 | null | https://arxiv.org/abs/2210.17437v2 | https://arxiv.org/pdf/2210.17437v2.pdf | Learning New Tasks from a Few Examples with Soft-Label Prototypes | It has been experimentally demonstrated that humans are able to learn in a manner that allows them to make predictions on categories for which they have not seen any examples (Malaviya et al., 2022). Sucholutsky and Schonlau (2020) have recently presented a machine learning approach that aims to do the same. They utilise synthetically generated data and demonstrate that it is possible to achieve sub-linear scaling and develop models that can learn to recognise N classes from M training samples where M is less than N - aka less-than-one shot learning. Their method was, however, defined for univariate or simple multivariate data (Sucholutsky et al., 2021). We extend it to work on large, high-dimensional and real-world datasets and empirically validate it in this new and challenging setting. We apply this method to learn previously unseen NLP tasks from very few examples (4, 8 or 16). We first generate compact, sophisticated less-than-one shot representations called soft-label prototypes which are fitted on training data, capturing the distribution of different classes across the input domain space. We then use a modified k-Nearest Neighbours classifier to demonstrate that soft-label prototypes can classify data competitively, even outperforming much more computationally complex few-shot learning methods. | ['Helen Yannakoudakis', 'Ekaterina Shutova', 'Avyav Kumar Singh'] | 2022-10-31 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 5.46548247e-01 4.19792026e-01 -2.72738904e-01 -5.97168088e-01
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16fa46d2-92a4-4e54-8eab-4e702e1a8ca9 | chipformer-transferable-chip-placement-via | 2306.14744 | null | https://arxiv.org/abs/2306.14744v1 | https://arxiv.org/pdf/2306.14744v1.pdf | ChiPFormer: Transferable Chip Placement via Offline Decision Transformer | Placement is a critical step in modern chip design, aiming to determine the positions of circuit modules on the chip canvas. Recent works have shown that reinforcement learning (RL) can improve human performance in chip placement. However, such an RL-based approach suffers from long training time and low transfer ability in unseen chip circuits. To resolve these challenges, we cast the chip placement as an offline RL formulation and present ChiPFormer that enables learning a transferable placement policy from fixed offline data. ChiPFormer has several advantages that prior arts do not have. First, ChiPFormer can exploit offline placement designs to learn transferable policies more efficiently in a multi-task setting. Second, ChiPFormer can promote effective finetuning for unseen chip circuits, reducing the placement runtime from hours to minutes. Third, extensive experiments on 32 chip circuits demonstrate that ChiPFormer achieves significantly better placement quality while reducing the runtime by 10x compared to recent state-of-the-art approaches in both public benchmarks and realistic industrial tasks. The deliverables are released at https://sites.google.com/view/chipformer/home. | ['Ping Luo', 'Jianye Hao', 'Bin Wang', 'Zhentao Tang', 'Jinxin Liu', 'Yao Lai'] | 2023-06-26 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.04087675e-01 9.04622674e-02 -5.55979609e-01 -1.69518813e-01
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bbe38e70-da62-440b-8d19-61ff595574bf | stereo-video-reconstruction-without-explicit | 2109.08227 | null | https://arxiv.org/abs/2109.08227v1 | https://arxiv.org/pdf/2109.08227v1.pdf | Stereo Video Reconstruction Without Explicit Depth Maps for Endoscopic Surgery | We introduce the task of stereo video reconstruction or, equivalently, 2D-to-3D video conversion for minimally invasive surgical video. We design and implement a series of end-to-end U-Net-based solutions for this task by varying the input (single frame vs. multiple consecutive frames), loss function (MSE, MAE, or perceptual losses), and network architecture. We evaluate these solutions by surveying ten experts - surgeons who routinely perform endoscopic surgery. We run two separate reader studies: one evaluating individual frames and the other evaluating fully reconstructed 3D video played on a VR headset. In the first reader study, a variant of the U-Net that takes as input multiple consecutive video frames and outputs the missing view performs best. We draw two conclusions from this outcome. First, motion information coming from multiple past frames is crucial in recreating stereo vision. Second, the proposed U-Net variant can indeed exploit such motion information for solving this task. The result from the second study further confirms the effectiveness of the proposed U-Net variant. The surgeons reported that they could successfully perceive depth from the reconstructed 3D video clips. They also expressed a clear preference for the reconstructed 3D video over the original 2D video. These two reader studies strongly support the usefulness of the proposed task of stereo reconstruction for minimally invasive surgical video and indicate that deep learning is a promising approach to this task. Finally, we identify two automatic metrics, LPIPS and DISTS, that are strongly correlated with expert judgement and that could serve as proxies for the latter in future studies. | ['Eric Oermann', 'Doug Kondziolka', 'Kyunghyun Cho', 'Jesse Swanson', 'Annika Brundyn'] | 2021-09-16 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 1.99875116e-01 4.62776005e-01 -2.95593739e-01 -1.00592658e-01
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3e133533-e529-4260-9419-f10333f445d1 | learning-transparent-object-matting | 1907.11544 | null | https://arxiv.org/abs/1907.11544v1 | https://arxiv.org/pdf/1907.11544v1.pdf | Learning Transparent Object Matting | This paper addresses the problem of image matting for transparent objects. Existing approaches often require tedious capturing procedures and long processing time, which limit their practical use. In this paper, we formulate transparent object matting as a refractive flow estimation problem, and propose a deep learning framework, called TOM-Net, for learning the refractive flow. Our framework comprises two parts, namely a multi-scale encoder-decoder network for producing a coarse prediction, and a residual network for refinement. At test time, TOM-Net takes a single image as input, and outputs a matte (consisting of an object mask, an attenuation mask and a refractive flow field) in a fast feed-forward pass. As no off-the-shelf dataset is available for transparent object matting, we create a large-scale synthetic dataset consisting of $178K$ images of transparent objects rendered in front of images sampled from the Microsoft COCO dataset. We also capture a real dataset consisting of $876$ samples using $14$ transparent objects and $60$ background images. Besides, we show that our method can be easily extended to handle the cases where a trimap or a background image is available.Promising experimental results have been achieved on both synthetic and real data, which clearly demonstrate the effectiveness of our approach. | ['Guan-Ying Chen', 'Kwan-Yee K. Wong', 'Kai Han'] | 2019-07-25 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 4.15491670e-01 -9.30654705e-02 4.89082068e-01 -4.06122267e-01
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fa5d3861-5e7c-43ac-b27b-41603a45c288 | a-dynamic-reduction-network-for-point-clouds | 2003.08013 | null | https://arxiv.org/abs/2003.08013v1 | https://arxiv.org/pdf/2003.08013v1.pdf | A Dynamic Reduction Network for Point Clouds | Classifying whole images is a classic problem in machine learning, and graph neural networks are a powerful methodology to learn highly irregular geometries. It is often the case that certain parts of a point cloud are more important than others when determining overall classification. On graph structures this started by pooling information at the end of convolutional filters, and has evolved to a variety of staged pooling techniques on static graphs. In this paper, a dynamic graph formulation of pooling is introduced that removes the need for predetermined graph structure. It achieves this by dynamically learning the most important relationships between data via an intermediate clustering. The network architecture yields interesting results considering representation size and efficiency. It also adapts easily to a large number of tasks from image classification to energy regression in high energy particle physics. | ['Thomas Klijnsma', 'Shamik Ghosh', 'Lindsey Gray'] | 2020-03-18 | null | null | null | null | ['superpixel-image-classification'] | ['computer-vision'] | [-3.79704009e-03 2.30482906e-01 -1.01185031e-01 -3.99146169e-01
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19b460d5-0eda-4d67-8eb4-9a9a30623ce0 | entity-extraction-with-knowledge-from-web | 1911.09373 | null | https://arxiv.org/abs/1911.09373v1 | https://arxiv.org/pdf/1911.09373v1.pdf | Entity Extraction with Knowledge from Web Scale Corpora | Entity extraction is an important task in text mining and natural language processing. A popular method for entity extraction is by comparing substrings from free text against a dictionary of entities. In this paper, we present several techniques as a post-processing step for improving the effectiveness of the existing entity extraction technique. These techniques utilise models trained with the web-scale corpora which makes our techniques robust and versatile. Experiments show that our techniques bring a notable improvement on efficiency and effectiveness. | ['Zeyu Huang', 'Rui Zhang', 'Zeyi Wen'] | 2019-11-21 | null | null | null | null | ['entity-extraction'] | ['natural-language-processing'] | [ 5.12649827e-02 2.00533211e-01 -4.19722229e-01 -3.56334895e-01
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2948a276-dc5e-42ce-869c-5dc6bb9ff664 | on-the-noise-sensitivity-of-the-randomized | 2305.17435 | null | https://arxiv.org/abs/2305.17435v2 | https://arxiv.org/pdf/2305.17435v2.pdf | On the Noise Sensitivity of the Randomized SVD | The randomized singular value decomposition (R-SVD) is a popular sketching-based algorithm for efficiently computing the partial SVD of a large matrix. When the matrix is low-rank, the R-SVD produces its partial SVD exactly; but when the rank is large, it only yields an approximation. Motivated by applications in data science and principal component analysis (PCA), we analyze the R-SVD under a low-rank signal plus noise measurement model; specifically, when its input is a spiked random matrix. The singular values produced by the R-SVD are shown to exhibit a BBP-like phase transition: when the SNR exceeds a certain detectability threshold, that depends on the dimension reduction factor, the largest singular value is an outlier; below the threshold, no outlier emerges from the bulk of singular values. We further compute asymptotic formulas for the overlap between the ground truth signal singular vectors and the approximations produced by the R-SVD. Dimensionality reduction has the adverse affect of amplifying the noise in a highly nonlinear manner. Our results demonstrate the statistical advantage -- in both signal detection and estimation -- of the R-SVD over more naive sketched PCA variants; the advantage is especially dramatic when the sketching dimension is small. Our analysis is asymptotically exact, and substantially more fine-grained than existing operator-norm error bounds for the R-SVD, which largely fail to give meaningful error estimates in the moderate SNR regime. It applies for a broad family of sketching matrices previously considered in the literature, including Gaussian i.i.d. sketches, random projections, and the sub-sampled Hadamard transform, among others. Lastly, we derive an optimal singular value shrinker for singular values and vectors obtained through the R-SVD, which may be useful for applications in matrix denoising. | ['Elad Romanov'] | 2023-05-27 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 4.68061984e-01 1.54535184e-02 1.57913655e-01 4.53392982e-01
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4c4c1cfe-9c39-4dae-a86f-47e5c0e98796 | c-1-loss-learn-to-classify-c-classes-of | null | null | https://openreview.net/forum?id=6kruvdT0yfY | https://openreview.net/pdf?id=6kruvdT0yfY | C+1 Loss: Learn to Classify C Classes of Interest and the Background Class Differentially | There is one kind of problem all around the classification area, where we want to classify C+1 classes of samples, including C semantically deterministic classes which we call classes of interest and the (C+1)th semantically undeterministic class which we call background class. In spite of most classification algorithm use softmax-based cross-entropy loss to supervise the classifier training process without differentiating the background class from the classes of interest, it is unreasonable as each of the classes of interest has its own inherent characteristics, but the background class dosen’t. We figure out that the background class should be treated differently from the classes of interest during training. Motivated by this, firstly we define the C+1 classification problem. Then, we propose three properties that a good C+1 classifier should have: basic discriminability, compactness and background margin. Based on them we define a uniform general C+1 loss, composed of three parts, driving the C+1 classifier to satisfy those properties. Finally, we instantialize a C+1 loss and experiment it in semantic segmentation, human parsing and object detection tasks. The proposed approach shows its superiority over the traditional cross-entropy loss. | ['ShiLiang Pu', 'Jun Che', 'Yi Lu', 'Mengyu Ye', 'Xile Shen', 'Changhuai Chen'] | 2021-09-29 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 5.23390055e-01 4.79202509e-01 -2.32247770e-01 -6.45659804e-01
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fea3392c-e00b-4ee5-be3e-a0e0865dcb57 | better-quality-estimation-for-low-resource | 2203.08259 | null | https://arxiv.org/abs/2203.08259v1 | https://arxiv.org/pdf/2203.08259v1.pdf | Better Quality Estimation for Low Resource Corpus Mining | Quality Estimation (QE) models have the potential to change how we evaluate and maybe even train machine translation models. However, these models still lack the robustness to achieve general adoption. We show that State-of-the-art QE models, when tested in a Parallel Corpus Mining (PCM) setting, perform unexpectedly bad due to a lack of robustness to out-of-domain examples. We propose a combination of multitask training, data augmentation and contrastive learning to achieve better and more robust QE performance. We show that our method improves QE performance significantly in the MLQE challenge and the robustness of QE models when tested in the Parallel Corpus Mining setup. We increase the accuracy in PCM by more than 0.80, making it on par with state-of-the-art PCM methods that use millions of sentence pairs to train their models. In comparison, we use a thousand times less data, 7K parallel sentences in total, and propose a novel low resource PCM method. | ['Derry Wijaya', 'Jiho Lee', 'Muhammed Yusuf Kocyigit'] | 2022-03-15 | null | https://aclanthology.org/2022.findings-acl.45 | https://aclanthology.org/2022.findings-acl.45.pdf | findings-acl-2022-5 | ['parallel-corpus-mining'] | ['natural-language-processing'] | [ 1.07177302e-01 -2.39322662e-01 8.48508924e-02 -2.50542998e-01
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fde20a44-1fc4-4a0a-9ab7-fd86f2f80f8e | unsupervised-model-personalization-while | 2003.13296 | null | https://arxiv.org/abs/2003.13296v1 | https://arxiv.org/pdf/2003.13296v1.pdf | Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem | This work investigates the task of unsupervised model personalization, adapted to continually evolving, unlabeled local user images. We consider the practical scenario where a high capacity server interacts with a myriad of resource-limited edge devices, imposing strong requirements on scalability and local data privacy. We aim to address this challenge within the continual learning paradigm and provide a novel Dual User-Adaptation framework (DUA) to explore the problem. This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device, with desirable properties regarding scalability and privacy constraints. First, on the server, we introduce incremental learning of task-specific expert models, subsequently aggregated using a concealed unsupervised user prior. Aggregation avoids retraining, whereas the user prior conceals sensitive raw user data, and grants unsupervised adaptation. Second, local user-adaptation incorporates a domain adaptation point of view, adapting regularizing batch normalization parameters to the user data. We explore various empirical user configurations with different priors in categories and a tenfold of transforms for MIT Indoor Scene recognition, and classify numbers in a combined MNIST and SVHN setup. Extensive experiments yield promising results for data-driven local adaptation and elicit user priors for server adaptation to depend on the model rather than user data. Hence, although user-adaptation remains a challenging open problem, the DUA framework formalizes a principled foundation for personalizing both on server and user device, while maintaining privacy and scalability. | ['Gregory Slabaugh', 'Matthias De Lange', 'Xu Jia', 'Tinne Tuytelaars', 'Ales Leonardis', 'Sarah Parisot'] | 2020-03-30 | unsupervised-model-personalization-while-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/De_Lange_Unsupervised_Model_Personalization_While_Preserving_Privacy_and_Scalability_An_Open_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/De_Lange_Unsupervised_Model_Personalization_While_Preserving_Privacy_and_Scalability_An_Open_CVPR_2020_paper.pdf | cvpr-2020-6 | ['scene-recognition'] | ['computer-vision'] | [ 4.66928631e-01 2.69515323e-03 -1.95826322e-01 -5.78001380e-01
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56d4c21b-5c09-475d-812a-cc3a88a71fd7 | a-unified-approach-to-lane-change-intention | 2304.13732 | null | https://arxiv.org/abs/2304.13732v1 | https://arxiv.org/pdf/2304.13732v1.pdf | A Unified Approach to Lane Change Intention Recognition and Driving Status Prediction through TCN-LSTM and Multi-Task Learning Models | Lane change (LC) is a continuous and complex operation process. Accurately detecting and predicting LC processes can help traffic participants better understand their surrounding environment, recognize potential LC safety hazards, and improve traffic safety. This present paper focuses on LC processes, developing an LC intention recognition (LC-IR) model and an LC status prediction (LC-SP) model. A novel ensemble temporal convolutional network with Long Short-Term Memory units (TCN-LSTM) is first proposed to capture long-range dependencies in sequential data. Then, three multi-task models (MTL-LSTM, MTL-TCN, MTL-TCN -LSTM) are developed to capture the intrinsic relationship among output indicators. Furthermore, a unified modeling framework for LC intention recognition and driving status prediction (LC-IR-SP) is developed. To validate the performance of the proposed models, a total number of 1023 vehicle trajectories is extracted from the CitySim dataset. The Pearson coefficient is employed to determine the related indicators. The results indicate that using150 frames as input length, the TCN-LSTM model with 96.67% accuracy outperforms TCN and LSTM models in LC intention classification and provides more balanced results for each class. Three proposed multi-tasking learning models provide markedly increased performance compared to corresponding single-task models, with an average reduction of 24.24% and 22.86% in the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), respectively. The developed LC-IR-SP model has promising applications for autonomous vehicles to identity lane change behaviors, calculate a real-time traffic conflict index and improve vehicle control strategies. | ['Qiaojun Xiang', 'Ou Zheng', 'Xin Gu', 'Mohamed Abdel-Aty', 'Renteng Yuan'] | 2023-04-25 | null | null | null | null | ['intent-detection'] | ['natural-language-processing'] | [ 7.78702721e-02 -6.13801599e-01 -6.20637655e-01 -3.96769404e-01
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6451baf4-f323-4512-ac4d-6d2f422c3b76 | bidirectional-transition-based-dependency | null | null | https://aaai.org/ojs/index.php/AAAI/article/view/4733 | https://aaai.org/ojs/index.php/AAAI/article/view/4733/4611 | Bidirectional Transition-Based Dependency Parsing | Transition-based dependency parsing is a fast and effective approach for dependency parsing. Traditionally, a transitionbased dependency parser processes an input sentence and predicts a sequence of parsing actions in a left-to-right manner. During this process, an early prediction error may negatively impact the prediction of subsequent actions. In this paper, we propose a simple framework for bidirectional transitionbased parsing. During training, we learn a left-to-right parser and a right-to-left parser separately. To parse a sentence, we perform joint decoding with the two parsers. We propose three joint decoding algorithms that are based on joint scoring, dual decomposition, and dynamic oracle respectively. Empirical results show that our methods lead to competitive parsing accuracy and our method based on dynamic oracle consistently achieves the best performance. | ['Kewei Tu', 'Yunzhe Yuan', 'Yong Jiang'] | 2019-07-17 | null | null | null | aaai-2019-7 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [ 2.91932732e-01 2.38501921e-01 -3.23487371e-01 -8.57330918e-01
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8e5cecf1-e0ba-4911-b583-709c7e753d10 | classifying-temporal-relations-with-rich | null | null | https://aclanthology.org/N13-1112 | https://aclanthology.org/N13-1112.pdf | Classifying Temporal Relations with Rich Linguistic Knowledge | null | ["Jennifer D{'}Souza", 'Vincent Ng'] | 2013-06-01 | null | null | null | naacl-2013-6 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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a4c954fe-a5cb-4907-841a-aa7c27d7126e | learning-representation-over-dynamic-graph | 2106.01678 | null | https://arxiv.org/abs/2106.01678v1 | https://arxiv.org/pdf/2106.01678v1.pdf | Learning Representation over Dynamic Graph using Aggregation-Diffusion Mechanism | Representation learning on graphs that evolve has recently received significant attention due to its wide application scenarios, such as bioinformatics, knowledge graphs, and social networks. The propagation of information in graphs is important in learning dynamic graph representations, and most of the existing methods achieve this by aggregation. However, relying only on aggregation to propagate information in dynamic graphs can result in delays in information propagation and thus affect the performance of the method. To alleviate this problem, we propose an aggregation-diffusion (AD) mechanism that actively propagates information to its neighbor by diffusion after the node updates its embedding through the aggregation mechanism. In experiments on two real-world datasets in the dynamic link prediction task, the AD mechanism outperforms the baseline models that only use aggregation to propagate information. We further conduct extensive experiments to discuss the influence of different factors in the AD mechanism. | ['Zhongjie Wang', 'Xiaofei Xu', 'Zhiying Tu', 'Mingyi Liu'] | 2021-06-03 | null | null | null | null | ['dynamic-link-prediction'] | ['graphs'] | [-3.73964272e-02 1.96504071e-01 -4.83960152e-01 -1.08212747e-01
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fe7289fe-c9df-4e1e-9fd4-aae9dcfba86c | interactive-instance-based-evaluation-of | null | null | https://aclanthology.org/D18-2020 | https://aclanthology.org/D18-2020.pdf | Interactive Instance-based Evaluation of Knowledge Base Question Answering | Most approaches to Knowledge Base Question Answering are based on semantic parsing. In this paper, we present a tool that aids in debugging of question answering systems that construct a structured semantic representation for the input question. Previous work has largely focused on building question answering interfaces or evaluation frameworks that unify multiple data sets. The primary objective of our system is to enable interactive debugging of model predictions on individual instances (questions) and to simplify manual error analysis. Our interactive interface helps researchers to understand the shortcomings of a particular model, qualitatively analyze the complete pipeline and compare different models. A set of sit-by sessions was used to validate our interface design. | ['Iryna Gurevych', 'Daniil Sorokin'] | 2018-11-01 | null | null | null | emnlp-2018-11 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-1.85738981e-01 7.12838173e-01 1.33789301e-01 -7.71575570e-01
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bed841a0-8fdb-4c5c-af13-7b4f003dfb2b | hegel-hypergraph-transformer-for-long | 2210.04126 | null | https://arxiv.org/abs/2210.04126v1 | https://arxiv.org/pdf/2210.04126v1.pdf | HEGEL: Hypergraph Transformer for Long Document Summarization | Extractive summarization for long documents is challenging due to the extended structured input context. The long-distance sentence dependency hinders cross-sentence relations modeling, the critical step of extractive summarization. This paper proposes HEGEL, a hypergraph neural network for long document summarization by capturing high-order cross-sentence relations. HEGEL updates and learns effective sentence representations with hypergraph transformer layers and fuses different types of sentence dependencies, including latent topics, keywords coreference, and section structure. We validate HEGEL by conducting extensive experiments on two benchmark datasets, and experimental results demonstrate the effectiveness and efficiency of HEGEL. | ['Jiawei Zhang', 'Xiao Liu', 'Haopeng Zhang'] | 2022-10-09 | null | null | null | null | ['extractive-summarization'] | ['natural-language-processing'] | [ 2.88457841e-01 6.05554044e-01 -4.99802232e-01 -2.02632129e-01
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cd60709e-9099-4c21-81cd-ce23f529e9ff | causal-inference-using-linear-time-varying | 2012.13025 | null | https://arxiv.org/abs/2012.13025v3 | https://arxiv.org/pdf/2012.13025v3.pdf | Causal Inference from Slowly Varying Nonstationary Processes | Causal inference from observational data following the restricted structural causal model (SCM) framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms, such as non-Gaussianity or nonlinearity. This methodology can be adapted to stationary time series, yet inferring causal relationships from nonstationary time series remains a challenging task. In this work, we propose a new class of restricted SCM, via a time-varying filter and stationary noise, and exploit the asymmetry from nonstationarity for causal identification in both bivariate and network settings. We propose efficient procedures by leveraging powerful estimates of the bivariate evolutionary spectra for slowly varying processes. Various synthetic and real datasets that involve high-order and non-smooth filters are evaluated to demonstrate the effectiveness of our proposed methodology. | ['Yu Xiang', 'Kang Du'] | 2020-12-23 | null | null | null | null | ['causal-identification'] | ['reasoning'] | [ 3.93811375e-01 -4.54286933e-01 -2.36987203e-01 -2.17437625e-01
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078331c5-1836-4e66-a30d-6c8bd1db5077 | exploring-automated-essay-scoring-for | 1706.03335 | null | http://arxiv.org/abs/1706.03335v3 | http://arxiv.org/pdf/1706.03335v3.pdf | Exploring Automated Essay Scoring for Nonnative English Speakers | Automated Essay Scoring (AES) has been quite popular and is being widely
used. However, lack of appropriate methodology for rating nonnative English
speakers' essays has meant a lopsided advancement in this field. In this paper,
we report initial results of our experiments with nonnative AES that learns
from manual evaluation of nonnative essays. For this purpose, we conducted an
exercise in which essays written by nonnative English speakers in test
environment were rated both manually and by the automated system designed for
the experiment. In the process, we experimented with a few features to learn
about nuances linked to nonnative evaluation. The proposed methodology of
automated essay evaluation has yielded a correlation coefficient of 0.750 with
the manual evaluation. | ['Amber Nigam'] | 2017-06-11 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [-3.23988289e-01 -1.23913743e-01 2.48078272e-01 -7.00986207e-01
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41fcefeb-117f-4776-96d2-5c8373a59720 | counterfactual-explanations-for-models-of | 2111.05711 | null | https://arxiv.org/abs/2111.05711v1 | https://arxiv.org/pdf/2111.05711v1.pdf | Counterfactual Explanations for Models of Code | Machine learning (ML) models play an increasingly prevalent role in many software engineering tasks. However, because most models are now powered by opaque deep neural networks, it can be difficult for developers to understand why the model came to a certain conclusion and how to act upon the model's prediction. Motivated by this problem, this paper explores counterfactual explanations for models of source code. Such counterfactual explanations constitute minimal changes to the source code under which the model "changes its mind". We integrate counterfactual explanation generation to models of source code in a real-world setting. We describe considerations that impact both the ability to find realistic and plausible counterfactual explanations, as well as the usefulness of such explanation to the user of the model. In a series of experiments we investigate the efficacy of our approach on three different models, each based on a BERT-like architecture operating over source code. | ['Satish Chandra', 'Vijayaraghavan Murali', 'Isil Dillig', 'Jürgen Cito'] | 2021-11-10 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 1.91504553e-01 1.00961888e+00 -2.73788780e-01 -3.56823921e-01
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6f1e42cd-bab5-47ad-bb0a-d0ec00e65daf | real-time-bearing-fault-diagnosis-based-on | 2304.09100 | null | https://arxiv.org/abs/2304.09100v1 | https://arxiv.org/pdf/2304.09100v1.pdf | Real Time Bearing Fault Diagnosis Based on Convolutional Neural Network and STM32 Microcontroller | With the rapid development of big data and edge computing, many researchers focus on improving the accuracy of bearing fault classification using deep learning models, and implementing the deep learning classification model on limited resource platforms such as STM32. To this end, this paper realizes the identification of bearing fault vibration signal based on convolutional neural network, the fault identification accuracy of the optimised model can reach 98.9%. In addition, this paper successfully applies the convolutional neural network model to STM32H743VI microcontroller, the running time of each diagnosis is 19ms. Finally, a complete real-time communication framework between the host computer and the STM32 is designed, which can perfectly complete the data transmission through the serial port and display the diagnosis results on the TFT-LCD screen. | ['Wenhao Liao'] | 2023-04-14 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-4.83800203e-01 -5.10138154e-01 3.54932427e-01 6.98782653e-02
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97910397-c069-441c-9858-1ad58fce4c5f | reload-reinforcement-learning-with-optimistic | 2302.01275 | null | https://arxiv.org/abs/2302.01275v2 | https://arxiv.org/pdf/2302.01275v2.pdf | ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs | In recent years, Reinforcement Learning (RL) has been applied to real-world problems with increasing success. Such applications often require to put constraints on the agent's behavior. Existing algorithms for constrained RL (CRL) rely on gradient descent-ascent, but this approach comes with a caveat. While these algorithms are guaranteed to converge on average, they do not guarantee last-iterate convergence, i.e., the current policy of the agent may never converge to the optimal solution. In practice, it is often observed that the policy alternates between satisfying the constraints and maximizing the reward, rarely accomplishing both objectives simultaneously. Here, we address this problem by introducing Reinforcement Learning with Optimistic Ascent-Descent (ReLOAD), a principled CRL method with guaranteed last-iterate convergence. We demonstrate its empirical effectiveness on a wide variety of CRL problems including discrete MDPs and continuous control. In the process we establish a benchmark of challenging CRL problems. | ['Tom Zahavy', 'Satinder Singh', 'Sebastian Flennerhag', 'Vivek Veeriah', "Brendan O'Donoghue", 'Ted Moskovitz'] | 2023-02-02 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-6.51695952e-02 -3.15130353e-02 -5.97142756e-01 1.20985880e-01
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f089251c-db67-4c6e-a8cc-323f81dc8671 | medical-image-synthesis-for-data-augmentation | 1807.10225 | null | http://arxiv.org/abs/1807.10225v2 | http://arxiv.org/pdf/1807.10225v2.pdf | Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks | Data diversity is critical to success when training deep learning models.
Medical imaging data sets are often imbalanced as pathologic findings are
generally rare, which introduces significant challenges when training deep
learning models. In this work, we propose a method to generate synthetic
abnormal MRI images with brain tumors by training a generative adversarial
network using two publicly available data sets of brain MRI. We demonstrate two
unique benefits that the synthetic images provide. First, we illustrate
improved performance on tumor segmentation by leveraging the synthetic images
as a form of data augmentation. Second, we demonstrate the value of generative
models as an anonymization tool, achieving comparable tumor segmentation
results when trained on the synthetic data versus when trained on real subject
data. Together, these results offer a potential solution to two of the largest
challenges facing machine learning in medical imaging, namely the small
incidence of pathological findings, and the restrictions around sharing of
patient data. | ['Neil A. Tenenholtz', 'Katherine Andriole', 'Hoo-chang Shin', 'Matthew L Senjem', 'Mark Michalski', 'Jameson K Rogers', 'Jeffrey L Gunter', 'Christopher G Schwarz'] | 2018-07-26 | null | null | null | null | ['medical-image-generation'] | ['medical'] | [ 5.11561334e-01 7.13493407e-01 -8.24492946e-02 -6.91785455e-01
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a70297b1-74f1-4ee3-b93f-90871eb6ef90 | listen-only-to-me-how-well-can-target-speech | 2204.04811 | null | https://arxiv.org/abs/2204.04811v2 | https://arxiv.org/pdf/2204.04811v2.pdf | Listen only to me! How well can target speech extraction handle false alarms? | Target speech extraction (TSE) extracts the speech of a target speaker in a mixture given auxiliary clues characterizing the speaker, such as an enrollment utterance. TSE addresses thus the challenging problem of simultaneously performing separation and speaker identification. There has been much progress in extraction performance following the recent development of neural networks for speech enhancement and separation. Most studies have focused on processing mixtures where the target speaker is actively speaking. However, the target speaker is sometimes silent in practice, i.e., inactive speaker (IS). A typical TSE system will tend to output a signal in IS cases, causing false alarms. It is a severe problem for the practical deployment of TSE systems. This paper aims at understanding better how well TSE systems can handle IS cases. We consider two approaches to deal with IS, (1) training a system to directly output zero signals or (2) detecting IS with an extra speaker verification module. We perform an extensive experimental comparison of these schemes in terms of extraction performance and IS detection using the LibriMix dataset and reveal their pros and cons. | ['Tomohiro Nakatani', 'Hiroshi Sato', 'Katerina Zmolikova', 'Tsubasa Ochiai', 'Keisuke Kinoshita', 'Marc Delcroix'] | 2022-04-11 | null | null | null | null | ['speech-extraction', 'speaker-identification'] | ['speech', 'speech'] | [ 5.49844384e-01 2.14693785e-01 -2.35356335e-02 -1.07887775e-01
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1e09b40a-fe03-492c-a709-e3f6e0d8651f | exploiting-rich-syntactic-information-for | 1808.07624 | null | http://arxiv.org/abs/1808.07624v1 | http://arxiv.org/pdf/1808.07624v1.pdf | Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model | Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a
sequential LSTM, to extract word order features while neglecting other valuable
syntactic information such as dependency graph or constituent trees. In this
paper, we first propose to use the \textit{syntactic graph} to represent three
types of syntactic information, i.e., word order, dependency and constituency
features. We further employ a graph-to-sequence model to encode the syntactic
graph and decode a logical form. Experimental results on benchmark datasets
show that our model is comparable to the state-of-the-art on Jobs640, ATIS and
Geo880. Experimental results on adversarial examples demonstrate the robustness
of the model is also improved by encoding more syntactic information. | ['Li-Wei Chen', 'Lingfei Wu', 'Kun Xu', 'Vadim Sheinin', 'Mo Yu', 'Zhiguo Wang'] | 2018-08-23 | exploiting-rich-syntactic-information-for-1 | https://aclanthology.org/D18-1110 | https://aclanthology.org/D18-1110.pdf | emnlp-2018-10 | ['graph-to-sequence'] | ['natural-language-processing'] | [ 1.46944389e-01 2.19953641e-01 -2.09008574e-01 -7.61361420e-01
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242efaa8-70c9-464f-9403-2c1361b80887 | knowledge-embedded-representation-learning | 1807.00505 | null | http://arxiv.org/abs/1807.00505v1 | http://arxiv.org/pdf/1807.00505v1.pdf | Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition | Humans can naturally understand an image in depth with the aid of rich
knowledge accumulated from daily lives or professions. For example, to achieve
fine-grained image recognition (e.g., categorizing hundreds of subordinate
categories of birds) usually requires a comprehensive visual concept
organization including category labels and part-level attributes. In this work,
we investigate how to unify rich professional knowledge with deep neural
network architectures and propose a Knowledge-Embedded Representation Learning
(KERL) framework for handling the problem of fine-grained image recognition.
Specifically, we organize the rich visual concepts in the form of knowledge
graph and employ a Gated Graph Neural Network to propagate node message through
the graph for generating the knowledge representation. By introducing a novel
gated mechanism, our KERL framework incorporates this knowledge representation
into the discriminative image feature learning, i.e., implicitly associating
the specific attributes with the feature maps. Compared with existing methods
of fine-grained image classification, our KERL framework has several appealing
properties: i) The embedded high-level knowledge enhances the feature
representation, thus facilitating distinguishing the subtle differences among
subordinate categories. ii) Our framework can learn feature maps with a
meaningful configuration that the highlighted regions finely accord with the
nodes (specific attributes) of the knowledge graph. Extensive experiments on
the widely used Caltech-UCSD bird dataset demonstrate the superiority of our
KERL framework over existing state-of-the-art methods. | ['Liang Lin', 'Yang Wu', 'Xiaonan Luo', 'Tianshui Chen', 'Riquan Chen'] | 2018-07-02 | null | null | null | null | ['fine-grained-image-recognition'] | ['computer-vision'] | [ 9.89936441e-02 4.44886871e-02 -2.28481010e-01 -5.78327179e-01
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00182bf1-2b93-4015-9ecf-71263808d4fe | eeg-emotion-recognition-using-dynamical-graph | null | null | https://doi.org/10.1109/taffc.2018.2817622 | https://pdfs.semanticscholar.org/3378/0d6c82a0c060a9a35fd07effbd2fbb3e5b82.pdf?_ga=2.118713866.2007402716.1567967863-1098133910.1548150455 | EEG emotion recognition using dynamical graph convolutional neural networks | In this paper, a multichannel EEG emotion recognition method based on a novel dynamical graph convolutional neural networks (DGCNN) is proposed. The basic idea of the proposed EEG emotion recognition method is to use a graph to model the multichannel EEG features and then perform EEG emotion classification based on this model. Different from the traditional graph convolutional neural networks (GCNN) methods, however, the proposed DGCNN method can dynamically learn the intrinsic relationship between different electroencephalogram (EEG) channels, represented by an adjacency matrix, via training a neural network so as to benefit for more discriminative EEG feature extraction. Then, the learned adjacency matrix is used for learning more discriminative features for improving the EEG emotion recognition. We conduct extensive experiments on the SJTU emotion EEG dataset (SEED) and DREAMER dataset. The experimental results demonstrate that the proposed method achieves better recognition performance than the state-of-the-art methods, in which the average recognition accuracy of 90.4\% is achieved for subject dependent experiment while 79.95\% for subject independent cross-validation one on the SEED database, and the average accuracies of 86.23\%, 84.54\% and 85.02\% are respectively obtained for valence, arousal and dominance classifications on the DREAMER database. | ['Peng Song', 'Zhen Cui', 'Wenming Zheng', 'Zhenyang Zhang'] | 2018-03-21 | null | null | null | ieee-transactions-on-affective-computing-2018 | ['eeg-emotion-recognition'] | ['miscellaneous'] | [-3.40964757e-02 -2.21303850e-01 3.43294889e-01 -4.31698322e-01
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