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da89219b-9797-42d6-94dc-facab9d7197f | nndetection-for-intracranial-aneurysms | 2305.13398 | null | https://arxiv.org/abs/2305.13398v1 | https://arxiv.org/pdf/2305.13398v1.pdf | nnDetection for Intracranial Aneurysms Detection and Localization | Intracranial aneurysms are a commonly occurring and life-threatening condition, affecting approximately 3.2% of the general population. Consequently, detecting these aneurysms plays a crucial role in their management. Lesion detection involves the simultaneous localization and categorization of abnormalities within med... | ['Chengcheng Zhu', 'Mahmud Mossa-Basha', 'Shaojun Xia', 'Negar Firoozeh', 'Maysam Orouskhani'] | 2023-05-22 | null | null | null | null | ['medical-object-detection'] | ['computer-vision'] | [-5.40746868e-01 -1.36184096e-01 1.89770192e-01 -5.32421112e-01
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-9.15699482e-01 6.80891931e-01 1.52296990e-01 2.73496747... | [14.477801322937012, -2.2161643505096436] |
245c203a-228e-48b7-b2cd-9b7a58fa9e22 | m3fas-an-accurate-and-robust-multimodal | 2301.12831 | null | https://arxiv.org/abs/2301.12831v2 | https://arxiv.org/pdf/2301.12831v2.pdf | M3FAS: An Accurate and Robust MultiModal Mobile Face Anti-Spoofing System | Face presentation attacks (FPA), also known as face spoofing, have brought increasing concerns to the public through various malicious applications, such as financial fraud and privacy leakage. Therefore, safeguarding face recognition systems against FPA is of utmost importance. Although existing learning-based face an... | ['Haoliang Li', 'Anderson Rocha', 'Shiqi Wang', 'Yibing Liu', 'Kexin Zheng', 'Chenqi Kong'] | 2023-01-30 | null | null | null | null | ['face-anti-spoofing'] | ['computer-vision'] | [ 3.14018816e-01 -3.19652617e-01 -9.35488120e-02 -1.76431820e-01
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1.30324125e-01 -2.59583533e-01 2.44512334e-01 -1.91241339... | [13.077737808227539, 1.1708847284317017] |
f7a7d885-938c-4d56-80c3-da6108bce0e4 | the-impact-of-subword-pooling-strategy-for | 2302.11365 | null | https://arxiv.org/abs/2302.11365v2 | https://arxiv.org/pdf/2302.11365v2.pdf | Impact of Subword Pooling Strategy on Cross-lingual Event Detection | Pre-trained multilingual language models (e.g., mBERT, XLM-RoBERTa) have significantly advanced the state-of-the-art for zero-shot cross-lingual information extraction. These language models ubiquitously rely on word segmentation techniques that break a word into smaller constituent subwords. Therefore, all word labeli... | ['Elizabeth Boschee', 'Scott Miller', 'Chris Jenkins', 'Steven Fincke', 'Shantanu Agarwal'] | 2023-02-22 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [-1.96034864e-01 -2.95162201e-01 -2.45368496e-01 -2.67831624e-01
-1.24713600e+00 -7.94234812e-01 6.62270844e-01 5.02847552e-01
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9.82323885e-02 -1.03023089e-02 1.74110815e-01 -9.94864181... | [10.230374336242676, 9.893712997436523] |
5b5eb4a8-22bc-443f-b064-3efcac6100e9 | topic-ontologies-for-arguments | 2301.09759 | null | https://arxiv.org/abs/2301.09759v1 | https://arxiv.org/pdf/2301.09759v1.pdf | Topic Ontologies for Arguments | Many computational argumentation tasks, like stance classification, are topic-dependent: the effectiveness of approaches to these tasks significantly depends on whether the approaches were trained on arguments from the same topics as those they are tested on. So, which are these topics that researchers train approaches... | ['Martin Potthast', 'Benno Stein', 'Johannes Kiesel', 'Yamen Ajjour'] | 2023-01-23 | null | null | null | null | ['topic-coverage'] | ['natural-language-processing'] | [-1.04058616e-01 7.78309226e-01 -8.75658393e-01 -5.65451384e-02
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3.27788562e-01 1.05610240e+00 6.14529133e-01 -6.98620081... | [9.29742431640625, 9.738357543945312] |
f77ea7f8-5119-4d2d-9ab1-601a7218f5ec | svdiff-compact-parameter-space-for-diffusion | 2303.11305 | null | https://arxiv.org/abs/2303.11305v4 | https://arxiv.org/pdf/2303.11305v4.pdf | SVDiff: Compact Parameter Space for Diffusion Fine-Tuning | Diffusion models have achieved remarkable success in text-to-image generation, enabling the creation of high-quality images from text prompts or other modalities. However, existing methods for customizing these models are limited by handling multiple personalized subjects and the risk of overfitting. Moreover, their la... | ['Feng Yang', 'Dimitris Metaxas', 'Peyman Milanfar', 'Han Zhang', 'Yinxiao Li', 'Ligong Han'] | 2023-03-20 | null | null | null | null | ['text-based-image-editing'] | ['computer-vision'] | [ 2.44645774e-01 -1.75975502e-01 -7.01311529e-02 -3.09918731e-01
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783db7d4-0407-40c1-b8ae-7632b5e1d121 | bridging-the-gap-between-sign-language | null | null | https://aclanthology.org/W15-5102 | https://aclanthology.org/W15-5102.pdf | Bridging the gap between sign language machine translation and sign language animation using sequence classification | null | ['Matt Huenerfauth', 'Sarah Ebling'] | 2015-09-01 | null | null | null | ws-2015-9 | ['sign-language-translation'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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9221d922-a67c-48d5-94a8-db217fda655d | unsupervised-instance-discriminative-learning | 2206.13016 | null | https://arxiv.org/abs/2206.13016v1 | https://arxiv.org/pdf/2206.13016v1.pdf | Unsupervised Instance Discriminative Learning for Depression Detection from Speech Signals | Major Depressive Disorder (MDD) is a severe illness that affects millions of people, and it is critical to diagnose this disorder as early as possible. Detecting depression from voice signals can be of great help to physicians and can be done without any invasive procedure. Since relevant labelled data are scarce, we p... | ['Abeer Alwan', 'Jonathan Flint', 'Vijay Ravi', 'Jinhan Wang'] | 2022-06-27 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [-3.76724428e-03 -9.37278196e-02 -2.77376503e-01 -6.27409160e-01
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-3.78886491e-01 3.53869617e-01 -1.73652261e-01 -1.39284521... | [14.219378471374512, 6.095572471618652] |
3119dd66-ef66-4b80-a81e-931e570ef0a0 | exploiting-rigidity-constraints-for-lidar | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Dong_Exploiting_Rigidity_Constraints_for_LiDAR_Scene_Flow_Estimation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Dong_Exploiting_Rigidity_Constraints_for_LiDAR_Scene_Flow_Estimation_CVPR_2022_paper.pdf | Exploiting Rigidity Constraints for LiDAR Scene Flow Estimation | Previous LiDAR scene flow estimation methods, especially recurrent neural networks, usually suffer from structure distortion in challenging cases, such as sparse reflection and motion occlusions. In this paper, we propose a novel optimization method based on a recurrent neural network to predict LiDAR scene flow in... | ['Zhiwei Xiong', 'Xiaoyan Sun', 'HanLin Li', 'Yueyi Zhang', 'Guanting Dong'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['scene-flow-estimation'] | ['computer-vision'] | [ 1.63240999e-01 -1.63114473e-01 -3.52519870e-01 -5.32024622e-01
-6.12291753e-01 -3.57505172e-01 4.57359403e-01 -5.17727733e-01
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3.71111035e-01 6.16613150e-01 -3.28892693e-02 -2.40153968... | [8.508454322814941, -2.0702407360076904] |
971fe461-7100-471f-ae75-e6131a6053f0 | relaxing-instrument-exclusion-with-common | 2301.02052 | null | https://arxiv.org/abs/2301.02052v2 | https://arxiv.org/pdf/2301.02052v2.pdf | Relaxing Instrument Exogeneity with Common Confounders | Instruments can be used to identify causal effects in the presence of unobserved confounding, under the famous relevance and exogeneity (unconfoundedness and exclusion) assumptions. As exogeneity is difficult to justify and to some degree untestable, it often invites criticism in applications. Hoping to alleviate this ... | ['Christian Tien'] | 2023-01-05 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [-2.21875057e-01 5.29779214e-03 -9.75648761e-01 -2.21397921e-01
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-8.50137591e-01 -6.52244925e-01 -7.82332122e-01 -5.63735366e-01
-7.26441070e-02 4.57435697e-01 -6.84351265e-01 3.54149222... | [7.9434814453125, 5.104445934295654] |
ce90d043-e117-4c29-9f98-31652182dc55 | the-effects-of-political-martyrdom-on | 2305.18004 | null | https://arxiv.org/abs/2305.18004v1 | https://arxiv.org/pdf/2305.18004v1.pdf | The Effects of Political Martyrdom on Election Results: The Assassination of Abe | In developed nations assassinations are rare and thus the impact of such acts on the electoral and political landscape is understudied. In this paper, we focus on Twitter data to examine the effects of Japan's former Primer Minister Abe's assassination on the Japanese House of Councillors elections in 2022. We utilize ... | ['Miu Nicole Takagi'] | 2023-05-29 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-2.92668074e-01 2.50408232e-01 -3.89298707e-01 -2.78522938e-01
-4.02280003e-01 -4.07566696e-01 1.21566403e+00 4.81636435e-01
-8.60340118e-01 8.94389987e-01 1.24162757e+00 -9.48615372e-01
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3.07849050e-01 -3.79575305e-02 -5.87011993e-01 -8.27932477... | [8.806431770324707, 9.932908058166504] |
b8f65a60-c402-4ab2-8a12-6e8291872dff | re-centric-recommendations-for-the | 2306.01774 | null | https://arxiv.org/abs/2306.01774v1 | https://arxiv.org/pdf/2306.01774v1.pdf | RE-centric Recommendations for the Development of Trustworthy(er) Autonomous Systems | Complying with the EU AI Act (AIA) guidelines while developing and implementing AI systems will soon be mandatory within the EU. However, practitioners lack actionable instructions to operationalise ethics during AI systems development. A literature review of different ethical guidelines revealed inconsistencies in the... | ['Christian Berger', 'Jennifer Horkoff', 'Beatriz Cabrero-Daniel', 'Krishna Ronanki'] | 2023-05-29 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [ 2.74343491e-01 5.80052137e-01 1.15677036e-01 -5.13126135e-01
-1.57914311e-02 -5.55430651e-01 4.82514739e-01 2.35740587e-01
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-3.97794038e-01 -3.70464057e-01 -3.94396782e-01 -1.55262411e-01
7.21207023e-01 8.05064738e-02 -3.04471999e-01 -2.23368078... | [9.060908317565918, 6.346818923950195] |
28085484-cc08-47a9-aec0-57257b24f35d | scale-invariant-scale-channel-networks-deep | 2106.06418 | null | https://arxiv.org/abs/2106.06418v2 | https://arxiv.org/pdf/2106.06418v2.pdf | Scale-invariant scale-channel networks: Deep networks that generalise to previously unseen scales | The ability to handle large scale variations is crucial for many real world visual tasks. A straightforward approach for handling scale in a deep network is to process an image at several scales simultaneously in a set of scale channels. Scale invariance can then, in principle, be achieved by using weight sharing betwe... | ['Tony Lindeberg', 'Ylva Jansson'] | 2021-06-11 | null | null | null | null | ['scale-generalisation'] | ['computer-vision'] | [ 4.99587715e-01 -2.01283749e-02 4.26842183e-01 -4.15796727e-01
-1.75652310e-01 -8.70328248e-01 5.83756089e-01 -1.45607382e-01
-8.47940743e-01 5.73266864e-01 -2.21890792e-01 -7.13349134e-02
-3.03784907e-01 -7.22644746e-01 -7.66007483e-01 -5.97942412e-01
-3.38958204e-01 9.93881598e-02 9.29690003e-01 -4.72557336... | [9.218524932861328, 2.3135428428649902] |
42421043-6d5f-4ec2-81cf-a717d9af27e5 | mesh-sort-simple-and-effective-of-location | 2302.14415 | null | https://arxiv.org/abs/2302.14415v3 | https://arxiv.org/pdf/2302.14415v3.pdf | Mesh-SORT: Simple and effective location-wise tracker with lost management strategies | Multi-Object Tracking (MOT) has gained extensive attention in recent years due to its potential applications in traffic and pedestrian detection. We note that tracking by detection may suffer from errors generated by noise detectors, such as an imprecise bounding box before the occlusions, and observed that in most tra... | ['ZongTan Li'] | 2023-02-28 | null | null | null | null | ['pedestrian-detection', 'human-detection'] | ['computer-vision', 'computer-vision'] | [ 5.46337850e-02 -2.74351001e-01 6.73335418e-02 -7.65615106e-02
-6.24604106e-01 -4.38826948e-01 4.97641832e-01 9.98182669e-02
-5.61788678e-01 8.29429507e-01 -2.43564427e-01 -4.28065658e-02
1.28422186e-01 -7.00201631e-01 -8.82080257e-01 -6.12550616e-01
-7.84372464e-02 4.55743015e-01 1.20492160e+00 2.29900941... | [6.522732257843018, -1.9880743026733398] |
f5c9dafa-4557-4123-a521-af51973f9b1d | enhancing-adversarial-training-via | 2306.14275 | null | https://arxiv.org/abs/2306.14275v3 | https://arxiv.org/pdf/2306.14275v3.pdf | Enhancing Adversarial Training via Reweighting Optimization Trajectory | Despite the fact that adversarial training has become the de facto method for improving the robustness of deep neural networks, it is well-known that vanilla adversarial training suffers from daunting robust overfitting, resulting in unsatisfactory robust generalization. A number of approaches have been proposed to add... | ['Mykola Pechenizkiy', 'Yulong Pei', 'Lu Yin', 'Vlaod Menkovski', 'Li Shen', 'Meng Fang', 'Tianlong Chen', 'Shiwei Liu', 'Tianjin Huang'] | 2023-06-25 | null | null | null | null | ['adversarial-robustness'] | ['adversarial'] | [ 2.43168883e-03 -9.78555083e-02 1.41692504e-01 -3.18268597e-01
-9.67143416e-01 -7.33682692e-01 3.60081345e-01 -3.55336696e-01
-5.81622243e-01 8.40021133e-01 -1.87192112e-02 -4.95515645e-01
-8.40989202e-02 -7.61728108e-01 -9.03007090e-01 -7.21644342e-01
-1.62159845e-01 -1.78672910e-01 1.36590451e-01 -5.18815815... | [5.603680610656738, 7.899422645568848] |
26dbecc4-9075-413c-941a-9b9dc6973787 | semantics-preserving-sketch-embedding-for | 2211.13015 | null | https://arxiv.org/abs/2211.13015v2 | https://arxiv.org/pdf/2211.13015v2.pdf | Semantics-Preserving Sketch Embedding for Face Generation | With recent advances in image-to-image translation tasks, remarkable progress has been witnessed in generating face images from sketches. However, existing methods frequently fail to generate images with details that are semantically and geometrically consistent with the input sketch, especially when various decoration... | ['Xiaoyan Sun', 'Zihan Chen', 'Chi Zhang', 'Chaoqun Wang', 'Xuejin Chen', 'Binxin Yang'] | 2022-11-23 | null | null | null | null | ['face-generation'] | ['computer-vision'] | [ 4.33897465e-01 3.18516977e-02 -3.16637039e-01 -6.63800597e-01
-3.54930758e-01 -5.95722675e-01 8.04315209e-01 -5.12616515e-01
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4.24956352e-01 2.03325495e-01 -2.42796123e-01 -2.94035703... | [11.865050315856934, 0.18135066330432892] |
2937c15b-cee2-405f-88cd-36fc84b27b89 | graph-sparsification-for-gcn-towards-optimal | 2306.01725 | null | https://arxiv.org/abs/2306.01725v1 | https://arxiv.org/pdf/2306.01725v1.pdf | Graph Sparsification for GCN Towards Optimal Crop Yield Predictions | In agronomics, predicting crop yield at a per field/county granularity is important for farmers to minimize uncertainty and plan seeding for the next crop cycle. While state-of-the-art prediction techniques employ graph convolutional nets (GCN) to predict future crop yields given relevant features and crop yields of pr... | ['Tim Eadie', 'Gene Cheung', 'Saghar Bagheri'] | 2023-06-02 | null | null | null | null | ['crop-yield-prediction', 'crop-yield-prediction'] | ['computer-vision', 'miscellaneous'] | [ 2.28819221e-01 3.72839272e-01 -3.59060735e-01 -3.34885009e-02
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-3.96634161e-01 -1.54413247e+00 -1.00128937e+00 -6.62419260e-01
-7.07597375e-01 2.75221048e-03 -4.67564762e-02 -4.39326286... | [9.34361457824707, -1.5169011354446411] |
1249eebf-3955-4f7c-b15d-0bc90c42a08e | towards-designing-a-chatgpt-conversational | 2304.09866 | null | https://arxiv.org/abs/2304.09866v1 | https://arxiv.org/pdf/2304.09866v1.pdf | Towards Designing a ChatGPT Conversational Companion for Elderly People | Loneliness and social isolation are serious and widespread problems among older people, affecting their physical and mental health, quality of life, and longevity. In this paper, we propose a ChatGPT-based conversational companion system for elderly people. The system is designed to provide companionship and help reduc... | ['Hend Al-Khalifa', 'Abeer Alessa'] | 2023-04-18 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [-3.50311697e-01 9.35697556e-01 1.19020678e-01 -3.43431979e-01
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1.17456436e-01 -5.51300168e-01 2.25553617e-01 -1.79911748e-01
-1.06709965e-01 9.48040411e-02 -1.01832211e-01 -5.85354090... | [12.843080520629883, 7.7910566329956055] |
893c9d14-7ff5-48ad-8dba-1ed5834e14bf | probing-schema-linking-information-from-pre | null | null | https://openreview.net/forum?id=uKGVHs4EMy | https://openreview.net/pdf?id=uKGVHs4EMy | Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing | The importance of building text-to-SQL parsers which can be applied to new databases has long been acknowledged, and a critical step to achieve this goal is schema linking, i.e., properly recognizing mentions of unseen columns or tables when generating SQLs. In this work, we propose a novel framework to elicit relation... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['text-to-sql'] | ['computer-code'] | [ 2.38538906e-01 6.36791706e-01 -4.65461403e-01 -4.82571989e-01
-1.17018330e+00 -1.04304779e+00 6.69781864e-01 7.53127217e-01
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-1.24998674e-01 -1.30508590e+00 -1.13706529e+00 1.53989837e-01
-3.24003585e-02 8.97601604e-01 4.38595116e-01 -3.49883765... | [9.519392967224121, 8.118215560913086] |
7c1a616b-8445-4824-aa13-196917f2de3e | winter-wheat-crop-yield-prediction-on | 2306.11946 | null | https://arxiv.org/abs/2306.11946v1 | https://arxiv.org/pdf/2306.11946v1.pdf | Winter Wheat Crop Yield Prediction on Multiple Heterogeneous Datasets using Machine Learning | Winter wheat is one of the most important crops in the United Kingdom, and crop yield prediction is essential for the nation's food security. Several studies have employed machine learning (ML) techniques to predict crop yield on a county or farm-based level. The main objective of this study is to predict winter wheat ... | ['Prof. Mohand Tahar Kechadi', 'Dr. David Lillis', 'Yogesh Bansal'] | 2023-06-20 | null | null | null | null | ['crop-yield-prediction', 'crop-yield-prediction'] | ['computer-vision', 'miscellaneous'] | [ 1.00936718e-01 -3.49102229e-01 -6.82955503e-01 -2.46404976e-01
-4.23477292e-01 -5.04143775e-01 2.58178353e-01 9.01383162e-01
-2.34272648e-02 9.69957829e-01 -1.66294277e-01 -8.07933509e-01
-3.44321400e-01 -1.58868361e+00 -5.48208475e-01 -7.69611657e-01
-1.72175735e-01 -1.56066984e-01 5.77326268e-02 -4.30820614... | [9.361527442932129, -1.5976794958114624] |
9709fd56-4c90-4ce9-8ae7-54957e7bb478 | object-counting-and-instance-segmentation | 1903.02494 | null | https://arxiv.org/abs/1903.02494v2 | https://arxiv.org/pdf/1903.02494v2.pdf | Object Counting and Instance Segmentation with Image-level Supervision | Common object counting in a natural scene is a challenging problem in computer vision with numerous real-world applications. Existing image-level supervised common object counting approaches only predict the global object count and rely on additional instance-level supervision to also determine object locations. We pro... | ['Guolei Sun', 'Hisham Cholakkal', 'Fahad Shahbaz Khan', 'Ling Shao'] | 2019-03-06 | object-counting-and-instance-segmentation-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Cholakkal_Object_Counting_and_Instance_Segmentation_With_Image-Level_Supervision_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Cholakkal_Object_Counting_and_Instance_Segmentation_With_Image-Level_Supervision_CVPR_2019_paper.pdf | cvpr-2019-6 | ['object-counting', 'image-level-supervised-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.95550278e-01 -2.75808007e-01 -5.01645923e-01 -5.79188168e-01
-8.23594809e-01 -4.73029524e-01 5.45053661e-01 4.12516713e-01
-8.95652235e-01 5.86461246e-01 -5.80018222e-01 1.78667475e-02
1.78976774e-01 -8.59779477e-01 -9.66705084e-01 -3.41561317e-01
3.18149358e-01 9.85233068e-01 6.44007862e-01 5.72899401... | [9.054641723632812, 0.4598081409931183] |
92e06fbf-87b8-47b2-8498-e3af351b2920 | when-does-return-conditioned-supervised | 2206.01079 | null | https://arxiv.org/abs/2206.01079v3 | https://arxiv.org/pdf/2206.01079v3.pdf | When does return-conditioned supervised learning work for offline reinforcement learning? | Several recent works have proposed a class of algorithms for the offline reinforcement learning (RL) problem that we will refer to as return-conditioned supervised learning (RCSL). RCSL algorithms learn the distribution of actions conditioned on both the state and the return of the trajectory. Then they define a policy... | ['Joan Bruna', 'Romain Laroche', 'Jacob Buckman', 'Alberto Bietti', 'David Brandfonbrener'] | 2022-06-02 | null | null | null | null | ['d4rl'] | ['robots'] | [-1.07130203e-02 2.06131816e-01 -6.51371777e-01 -8.51162821e-02
-7.36548901e-01 -5.99043965e-01 7.66727805e-01 2.29406074e-01
-6.07556224e-01 1.22029686e+00 -5.19399121e-02 -6.58527970e-01
-7.07306743e-01 -6.99321270e-01 -1.00497341e+00 -8.44616294e-01
-8.12243283e-01 4.85120028e-01 2.22596332e-01 -1.58761039... | [4.234809875488281, 2.2541377544403076] |
ccbbd1ee-e17c-4589-89df-579c96ff627a | outfin-a-multi-device-and-multi-modal-dataset | 2205.14921 | null | https://arxiv.org/abs/2205.14921v1 | https://arxiv.org/pdf/2205.14921v1.pdf | OutFin, a multi-device and multi-modal dataset for outdoor localization based on the fingerprinting approach | In recent years, fingerprint-based positioning has gained researchers attention since it is a promising alternative to the Global Navigation Satellite System and cellular network-based localization in urban areas. Despite this, the lack of publicly available datasets that researchers can use to develop, evaluate, and c... | ['Mohammad H. Mahoor', 'Fahad Alhomayani'] | 2022-05-30 | null | null | null | null | ['outdoor-localization'] | ['robots'] | [-7.37843812e-02 -4.40949649e-01 -3.63781780e-01 -3.67029637e-01
-5.82094073e-01 -6.75567925e-01 3.51451129e-01 2.19814330e-01
-3.44590724e-01 1.03264093e+00 5.90825826e-03 -4.24266994e-01
-2.35163391e-01 -1.17925179e+00 -4.71250117e-01 -5.40072978e-01
9.58124734e-03 3.58904973e-02 1.65652171e-01 -2.20081341... | [6.375899791717529, 0.9599154591560364] |
4477ee06-af68-4e2a-922c-399e52be245f | rucola-russian-corpus-of-linguistic | 2210.12814 | null | https://arxiv.org/abs/2210.12814v1 | https://arxiv.org/pdf/2210.12814v1.pdf | RuCoLA: Russian Corpus of Linguistic Acceptability | Linguistic acceptability (LA) attracts the attention of the research community due to its many uses, such as testing the grammatical knowledge of language models and filtering implausible texts with acceptability classifiers. However, the application scope of LA in languages other than English is limited due to the lac... | ['Ekaterina Artemova', 'Ivan Smurov', 'Alena Pestova', 'Max Ryabinin', 'Tatiana Shamardina', 'Vladislav Mikhailov'] | 2022-10-23 | null | null | null | null | ['linguistic-acceptability'] | ['natural-language-processing'] | [-5.89644313e-02 2.86036134e-01 1.08968183e-01 -8.09630990e-01
-1.37387145e+00 -8.44685853e-01 4.02162135e-01 4.49022204e-01
-5.15797257e-01 7.94229448e-01 2.32338414e-01 -7.85233617e-01
2.48001050e-02 -7.80922174e-01 -7.56215215e-01 -1.47754729e-01
3.35569650e-01 5.07312834e-01 -1.71599999e-01 -4.72026318... | [10.760862350463867, 9.598322868347168] |
452c5896-1fa4-42d2-bf43-d6b9e24ba53a | federated-multi-view-learning-for-private | 2105.01603 | null | https://arxiv.org/abs/2105.01603v1 | https://arxiv.org/pdf/2105.01603v1.pdf | Federated Multi-View Learning for Private Medical Data Integration and Analysis | Along with the rapid expansion of information technology and digitalization of health data, there is an increasing concern on maintaining data privacy while garnering the benefits in medical field. Two critical challenges are identified: Firstly, medical data is naturally distributed across multiple local sites, making... | ['Lifang He', 'Yong Chen', 'Lichao Sun', 'Hao Peng', 'Sicong Che'] | 2021-05-04 | null | null | null | null | ['multi-view-learning', 'data-integration'] | ['computer-vision', 'knowledge-base'] | [-9.04686525e-02 -2.62469407e-02 -3.96250606e-01 -3.24491620e-01
-8.08697045e-01 -7.52571106e-01 3.33332151e-01 5.97376943e-01
-4.07001644e-01 5.37754476e-01 4.29946601e-01 -3.85876745e-01
-3.71402174e-01 -7.52793729e-01 -4.83124763e-01 -8.36597860e-01
-1.88300550e-01 9.20499563e-02 -9.37329680e-02 1.39981493... | [6.121460437774658, 6.472764015197754] |
48ddbbe8-1033-43aa-99a5-058da8a8bc75 | rangenet-fast-and-accurate-lidar-semantic | null | null | http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/milioto2019iros.pdf | http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/milioto2019iros.pdf | RangeNet++: Fast and Accurate LiDAR Semantic Segmentation | Perception in autonomous vehicles is often carried out through a suite of different sensing modalities. Given the massive amount of openly available labeled RGB data and the advent of high-quality deep learning algorithms for image-based recognition, high-level semantic perception tasks are pre-dominantly solved using ... | ['Jens Behley', 'Ignacio Vizzo', 'Cyrill Stachniss', 'Andres Milioto'] | 2019-11-04 | null | null | null | ieeersj-international-conference-on | ['robust-3d-semantic-segmentation', 'lidar-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.13148195e-01 -1.22951522e-01 4.39983271e-02 -7.17402458e-01
-9.01104689e-01 -5.81346810e-01 5.70951045e-01 5.87261766e-02
-8.00197601e-01 3.66030812e-01 -6.27556324e-01 -3.94250602e-01
2.45294288e-01 -1.05634499e+00 -1.10386884e+00 -5.74838221e-01
3.62205595e-01 6.15664542e-01 6.38838708e-01 -1.83142155... | [8.361729621887207, -2.453817129135132] |
12e4a94b-4348-44c3-8d47-e6694d6a37ac | recursive-deep-learning-framework-for | 2301.10874 | null | https://arxiv.org/abs/2301.10874v1 | https://arxiv.org/pdf/2301.10874v1.pdf | Recursive deep learning framework for forecasting the decadal world economic outlook | Gross domestic product (GDP) is the most widely used indicator in macroeconomics and the main tool for measuring a country's economic ouput. Due to the diversity and complexity of the world economy, a wide range of models have been used, but there are challenges in making decadal GDP forecasts given unexpected changes ... | ['Rohitash Chandra', 'John Hawkins', 'Rodney Beard', 'Tianyi Wang'] | 2023-01-25 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [-9.26921189e-01 -3.11863065e-01 -1.11761011e-01 6.00526072e-02
-1.50042996e-01 -4.58888739e-01 1.08497488e+00 9.82188806e-03
-2.85968989e-01 1.12173343e+00 6.04826152e-01 -1.03790188e+00
2.22987011e-01 -1.09049988e+00 -2.15258896e-01 -6.65494502e-01
-5.05712211e-01 3.47650260e-01 -3.39023530e-01 -4.28054124... | [6.32613468170166, 3.0599498748779297] |
9b5b517d-2426-46d5-8d64-6151ee7e0b7e | integrating-deep-features-for-material | 1511.06522 | null | http://arxiv.org/abs/1511.06522v6 | http://arxiv.org/pdf/1511.06522v6.pdf | Integrating Deep Features for Material Recognition | We propose a method for integration of features extracted using deep
representations of Convolutional Neural Networks (CNNs) each of which is
learned using a different image dataset of objects and materials for material
recognition. Given a set of representations of multiple pre-trained CNNs, we
first compute activatio... | ['Yan Zhang', 'Takayuki Okatani', 'Mete Ozay', 'Xing Liu'] | 2015-11-20 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [ 1.93164960e-01 -2.18167230e-01 -1.53346136e-01 -4.76651281e-01
-8.10792983e-01 -1.87309608e-01 6.55793488e-01 2.77497172e-02
-4.34275627e-01 5.90162456e-01 5.29490001e-02 4.70568627e-01
-3.92949164e-01 -1.11098468e+00 -1.14173567e+00 -8.00616741e-01
-1.78020403e-01 2.62499720e-01 6.11111410e-02 3.32599968... | [9.654777526855469, 1.8687775135040283] |
a2fa6cb0-d37c-44d8-9f48-dec3b65569d4 | coloristanet-for-photorealistic-video-style | 2212.09247 | null | https://arxiv.org/abs/2212.09247v2 | https://arxiv.org/pdf/2212.09247v2.pdf | ColoristaNet for Photorealistic Video Style Transfer | Photorealistic style transfer aims to transfer the artistic style of an image onto an input image or video while keeping photorealism. In this paper, we think it's the summary statistics matching scheme in existing algorithms that leads to unrealistic stylization. To avoid employing the popular Gram loss, we propose a ... | ['Weifeng Ge', 'Wenqiang Zhang', 'Yingtao Zhang', 'Boan He', 'Ruize Xu', 'Xiaowen Qiu'] | 2022-12-19 | null | null | null | null | ['video-style-transfer'] | ['computer-vision'] | [ 4.72878784e-01 -3.86732548e-01 9.59167853e-02 -3.96522969e-01
-3.90033387e-02 -6.57805741e-01 5.47764778e-01 -7.48159707e-01
-2.85628825e-01 7.37071574e-01 2.19473526e-01 2.69055609e-02
3.79134238e-01 -7.77761757e-01 -7.43105650e-01 -7.24653125e-01
8.43595624e-01 -1.24241665e-01 9.54682752e-02 -3.19008261... | [11.507486343383789, -0.6756829619407654] |
05d984f1-db5d-41fd-9672-529259154532 | universal-differentiable-renderer-for | 2003.09852 | null | https://arxiv.org/abs/2003.09852v3 | https://arxiv.org/pdf/2003.09852v3.pdf | Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance | In this work we address the challenging problem of multiview 3D surface reconstruction. We introduce a neural network architecture that simultaneously learns the unknown geometry, camera parameters, and a neural renderer that approximates the light reflected from the surface towards the camera. The geometry is represen... | ['Yoni Kasten', 'Dror Moran', 'Yaron Lipman', 'Meirav Galun', 'Matan Atzmon', 'Lior Yariv', 'Ronen Basri'] | 2020-03-22 | null | http://proceedings.neurips.cc/paper/2020/hash/1a77befc3b608d6ed363567685f70e1e-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/1a77befc3b608d6ed363567685f70e1e-Paper.pdf | neurips-2020-12 | ['3d-shape-representation'] | ['computer-vision'] | [ 4.99699652e-01 -1.76170617e-02 4.83731776e-01 -3.68004054e-01
-6.43811822e-01 -3.68939877e-01 5.45893312e-01 -4.59556073e-01
-4.62405607e-02 9.49754193e-02 -6.34798631e-02 -1.33624136e-01
4.11370933e-01 -8.77158344e-01 -1.20090389e+00 -4.37392056e-01
2.49903589e-01 6.92625463e-01 1.61731709e-02 1.86207108... | [9.224832534790039, -3.1154415607452393] |
6fe2893a-6cda-41b4-9548-ab1396476b1b | a-survey-on-biomedical-text-summarization | 2304.08763 | null | https://arxiv.org/abs/2304.08763v1 | https://arxiv.org/pdf/2304.08763v1.pdf | A Survey on Biomedical Text Summarization with Pre-trained Language Model | The exponential growth of biomedical texts such as biomedical literature and electronic health records (EHRs), provides a big challenge for clinicians and researchers to access clinical information efficiently. To address the problem, biomedical text summarization has been proposed to support clinical information retri... | ['Sophia Ananiadou', 'Benyou Wang', 'Zheheng Luo', 'Qianqian Xie'] | 2023-04-18 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [ 5.42449057e-01 2.21982747e-01 -5.03062725e-01 -2.56150782e-01
-1.20625985e+00 -2.31304526e-01 3.15797389e-01 1.16034079e+00
-3.67106378e-01 1.03402126e+00 9.48950291e-01 -4.13762443e-02
-1.19793281e-01 -3.18989933e-01 -3.60459313e-02 -6.77692652e-01
-1.55631034e-02 5.04792511e-01 -1.77885145e-01 5.88113442... | [12.188308715820312, 9.500997543334961] |
45f384d4-d1af-4e81-93fb-02df5500b775 | compositional-diversity-in-visual-concept | 2305.19374 | null | https://arxiv.org/abs/2305.19374v1 | https://arxiv.org/pdf/2305.19374v1.pdf | Compositional diversity in visual concept learning | Humans leverage compositionality to efficiently learn new concepts, understanding how familiar parts can combine together to form novel objects. In contrast, popular computer vision models struggle to make the same types of inferences, requiring more data and generalizing less flexibly than people do. Here, we study th... | ['Brenden M. Lake', 'Reuben Feinman', 'Yanli Zhou'] | 2023-05-30 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 2.64365673e-01 2.50337064e-01 3.60811017e-02 -4.08389777e-01
1.01042204e-01 -8.53259504e-01 8.86948287e-01 2.57051408e-01
-7.78594464e-02 1.94488153e-01 2.82862633e-01 -2.01825961e-01
-1.57515138e-01 -8.42552662e-01 -8.29891086e-01 -4.98211920e-01
-8.60260352e-02 7.42296398e-01 3.06307584e-01 -1.74687862... | [9.562214851379395, 6.845407485961914] |
ea79e23e-8636-4386-be31-86af3c19bcd2 | adversarial-transfer-learning-for-chinese | null | null | https://aclanthology.org/D18-1017 | https://aclanthology.org/D18-1017.pdf | Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism | Named entity recognition (NER) is an important task in natural language processing area, which needs to determine entities boundaries and classify them into pre-defined categories. For Chinese NER task, there is only a very small amount of annotated data available. Chinese NER task and Chinese word segmentation (CWS) t... | ['Yubo Chen', 'Pengfei Cao', 'Jun Zhao', 'Shengping Liu', 'Kang Liu'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [ 9.78101045e-02 -1.96811512e-01 -9.13471058e-02 -5.32848001e-01
-7.48251796e-01 -8.40124488e-01 3.03791076e-01 9.32295248e-02
-1.00363350e+00 9.01553452e-01 3.25889140e-01 -4.39174265e-01
4.80984360e-01 -8.63647640e-01 -5.08860350e-01 -3.72712672e-01
1.56052411e-01 1.97966069e-01 5.31210005e-01 -7.72375315... | [9.809525489807129, 9.854578018188477] |
e028eef3-2a06-4273-a910-efd1b36cf7a4 | deformable-graph-convolutional-networks | 2112.14438 | null | https://arxiv.org/abs/2112.14438v1 | https://arxiv.org/pdf/2112.14438v1.pdf | Deformable Graph Convolutional Networks | Graph neural networks (GNNs) have significantly improved the representation power for graph-structured data. Despite of the recent success of GNNs, the graph convolution in most GNNs have two limitations. Since the graph convolution is performed in a small local neighborhood on the input graph, it is inherently incapab... | ['Hyunwoo J. Kim', 'Jihwan Park', 'Sungdong Yoo', 'Jinyoung Park'] | 2021-12-29 | null | null | null | null | ['node-classification-on-non-homophilic'] | ['graphs'] | [-1.85525000e-01 1.75300911e-01 -1.15311123e-01 -3.38860095e-01
2.59646684e-01 -7.02691078e-01 4.51893628e-01 3.52253735e-01
-9.52592939e-02 3.03920209e-01 1.65001988e-01 -2.81419992e-01
-2.08567232e-01 -1.53835130e+00 -6.11374140e-01 -9.34349954e-01
-4.83791202e-01 6.02803051e-01 1.95028245e-01 -2.81855613... | [7.075328350067139, 6.221421718597412] |
87580b98-9b30-44d0-b298-34adf1a77787 | porter-5-fast-state-of-the-art-ab-initio | null | null | https://doi.org/10.1101/289033 | https://www.biorxiv.org/content/early/2018/10/05/289033.full.pdf | Porter 5: fast, state-of-the-art ab initio prediction of protein secondary structure in 3 and 8 classes | Motivation: Although secondary structure predictors have been developed for decades, current ab initio methods have still some way to go to reach their theoretical limits. Moreover, the continuous effort towards harnessing ever-expanding data sets and more sophisticated, deeper Machine Learning techniques, has not come... | ['Mirko Torrisi', 'Gianluca Pollastri', 'Manaz Kaleel'] | 2018-10-05 | null | null | null | biorxiv-2018-10 | ['protein-secondary-structure-prediction'] | ['medical'] | [ 1.30982352e-02 -9.74272043e-02 -3.88910830e-01 -4.47350740e-01
-1.32846010e+00 -5.77004790e-01 4.54378039e-01 3.16309363e-01
-3.94539058e-01 1.43786025e+00 8.84909183e-02 -7.54447401e-01
4.83067222e-02 -3.74982148e-01 -5.41401267e-01 -1.04162431e+00
-8.98151845e-02 8.15709531e-01 4.46570575e-01 -3.51445019... | [4.774402618408203, 5.513051509857178] |
a42e4157-1b6c-4e40-8c1c-3ee52d822a24 | flexible-sampling-for-long-tailed-skin-lesion | 2204.03161 | null | https://arxiv.org/abs/2204.03161v1 | https://arxiv.org/pdf/2204.03161v1.pdf | Flexible Sampling for Long-tailed Skin Lesion Classification | Most of the medical tasks naturally exhibit a long-tailed distribution due to the complex patient-level conditions and the existence of rare diseases. Existing long-tailed learning methods usually treat each class equally to re-balance the long-tailed distribution. However, considering that some challenging classes may... | ['ZongYuan Ge', 'Paul Bonnington', 'Xin Wang', 'Xin Zhao', 'Zhen Yu', 'Lin Wang', 'Yicheng Wu', 'Lie Ju'] | 2022-04-07 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 2.46685103e-01 -2.47215822e-01 -7.03255236e-01 -6.73553586e-01
-1.03806376e+00 -1.07784666e-01 3.07869107e-01 2.70923495e-01
-4.82617706e-01 8.86593044e-01 2.73812674e-02 -2.73076236e-01
-4.95294243e-01 -6.67011738e-01 -5.05776227e-01 -1.07125556e+00
1.14691094e-01 7.60345995e-01 5.16063690e-01 3.37941498... | [15.290389060974121, -2.608619213104248] |
d6f396c2-8fbc-40bd-8de9-872b26a8713c | interaction-compass-multi-label-zero-shot | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Huynh_Interaction_Compass_Multi-Label_Zero-Shot_Learning_of_Human-Object_Interactions_via_Spatial_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Huynh_Interaction_Compass_Multi-Label_Zero-Shot_Learning_of_Human-Object_Interactions_via_Spatial_ICCV_2021_paper.pdf | Interaction Compass: Multi-Label Zero-Shot Learning of Human-Object Interactions via Spatial Relations | We study the problem of multi-label zero-shot recognition in which labels are in the form of human-object interactions (combinations of actions on objects), each image may contain multiple interactions and some interactions do not have training images. We propose a novel compositional learning framework that decoup... | ['Ehsan Elhamifar', 'Dat Huynh'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['multi-label-zero-shot-learning'] | ['computer-vision'] | [ 3.80890667e-01 3.97644863e-02 -2.46127352e-01 -4.63250577e-01
-8.04119766e-01 -6.32339001e-01 6.19589925e-01 -5.14207259e-02
-1.93220630e-01 5.93193948e-01 2.37198219e-01 1.34062409e-01
-1.36875078e-01 -5.95782697e-01 -1.11086023e+00 -9.10414457e-01
1.97310112e-02 5.65916419e-01 5.31141102e-01 1.87218353... | [9.165985107421875, 1.0153249502182007] |
0feddc9e-dfd3-4cde-9b41-4e919d46cad2 | a-fusion-model-towards-a-virtual-physical-and | 2305.09992 | null | https://arxiv.org/abs/2305.09992v1 | https://arxiv.org/pdf/2305.09992v1.pdf | A Fusion Model: Towards a Virtual, Physical and Cognitive Integration and its Principles | Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), digital twin, Metaverse and other related digital technologies have attracted much attention in recent years. These new emerging technologies are changing the world significantly. This research introduces a fusion model, i.e. Fusion Universe (FU), where ... | ['Sanghyuk Lee', 'Yifan Lu', 'Yun Xue', 'Hao Lan Zhang'] | 2023-05-17 | null | null | null | null | ['mixed-reality'] | ['computer-vision'] | [-3.18170369e-01 -3.31182152e-01 2.15228066e-01 3.14824671e-01
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-2.33656272e-01 -1.26180601e+00 -1.55065119e-01 -9.11919102e-02
-9.47591588e-02 -4.77123380e-01 5.79653263e-01 -9.13825631... | [8.202964782714844, -1.5629075765609741] |
1da0bbc0-4ca3-47be-a7a5-eb18f16a63e2 | cross-lingual-alzheimer-s-disease-detection | 2303.07650 | null | https://arxiv.org/abs/2303.07650v1 | https://arxiv.org/pdf/2303.07650v1.pdf | Cross-lingual Alzheimer's Disease detection based on paralinguistic and pre-trained features | We present our submission to the ICASSP-SPGC-2023 ADReSS-M Challenge Task, which aims to investigate which acoustic features can be generalized and transferred across languages for Alzheimer's Disease (AD) prediction. The challenge consists of two tasks: one is to classify the speech of AD patients and healthy individu... | ['Wei-Qiang Zhang', 'Jinpeng Li', 'Yu Pu', 'Xuchu Chen'] | 2023-03-14 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [ 5.18944487e-03 1.71053752e-01 3.56125504e-01 -6.43755734e-01
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-3.60650927e-01 4.60886389e-01 4.75095548e-02 -1.62683979... | [13.906970977783203, 5.361222743988037] |
9a89efc9-c180-4757-a357-e5cdda065fd0 | fine-tashkeel-finetuning-byte-level-models | 2303.14588 | null | https://arxiv.org/abs/2303.14588v1 | https://arxiv.org/pdf/2303.14588v1.pdf | Fine-Tashkeel: Finetuning Byte-Level Models for Accurate Arabic Text Diacritization | Most of previous work on learning diacritization of the Arabic language relied on training models from scratch. In this paper, we investigate how to leverage pre-trained language models to learn diacritization. We finetune token-free pre-trained multilingual models (ByT5) to learn to predict and insert missing diacriti... | ['Rami Al-Rfou', 'Gheith Abandah', 'Bashar Al-Rfooh'] | 2023-03-25 | null | null | null | null | ['feature-engineering', 'arabic-text-diacritization'] | ['methodology', 'natural-language-processing'] | [ 1.35063142e-01 1.23402931e-01 -1.64122477e-01 -4.55054402e-01
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-4.38644364e-02 3.59330565e-01 2.53743261e-01 -9.11808014... | [10.768723487854004, 10.244183540344238] |
1a6db66a-21b9-4dce-a4d3-2a33fc305c6a | applying-automated-machine-translation-to | 2301.03141 | null | https://arxiv.org/abs/2301.03141v1 | https://arxiv.org/pdf/2301.03141v1.pdf | Applying Automated Machine Translation to Educational Video Courses | We studied the capability of automated machine translation in the online video education space by automatically translating Khan Academy videos with state of the art translation models and applying Text-to-Speech synthesis to build engaging videos in target languages. We also analyzed and established a reliable transla... | ['Linden Wang'] | 2023-01-09 | null | null | null | null | ['text-to-speech-synthesis'] | ['speech'] | [ 8.03626105e-02 1.40519366e-01 -3.11188251e-01 -2.54720390e-01
-1.65370822e+00 -8.71683002e-01 4.11164284e-01 -1.98260441e-01
-1.63146839e-01 9.02992666e-01 3.85036618e-01 -6.84697270e-01
9.70382616e-02 -3.99170697e-01 -1.18077302e+00 8.05978701e-02
3.63168299e-01 5.43396652e-01 -1.19077131e-01 -4.84846652... | [14.48471450805664, 7.17218017578125] |
fa357f42-95f3-494d-9a99-57f7870868f7 | successive-projection-algorithm-robust-to | 1908.04109 | null | https://arxiv.org/abs/1908.04109v1 | https://arxiv.org/pdf/1908.04109v1.pdf | Successive Projection Algorithm Robust to Outliers | The successive projection algorithm (SPA) is a fast algorithm to tackle separable nonnegative matrix factorization (NMF). Given a nonnegative data matrix $X$, SPA identifies an index set $\mathcal{K}$ such that there exists a nonnegative matrix $H$ with $X \approx X(:,\mathcal{K})H$. SPA has been successfully used as a... | ['Nicolas Gillis'] | 2019-08-12 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 6.98912740e-01 -5.15521288e-01 1.82024449e-01 3.09571717e-03
-8.71647894e-01 -6.46699429e-01 1.25849783e-01 -7.36546190e-03
-3.99206519e-01 5.32805979e-01 -1.59577176e-01 -4.49246705e-01
-1.03612614e+00 -8.45829070e-01 -3.67885172e-01 -1.31640387e+00
-5.47213964e-02 2.59677291e-01 -4.41867262e-01 -8.50025266... | [10.061166763305664, -1.9949190616607666] |
161bb0e0-7acf-498d-a7d8-f76c029515d8 | on-the-role-of-conceptualization-in | 2003.03239 | null | https://arxiv.org/abs/2003.03239v2 | https://arxiv.org/pdf/2003.03239v2.pdf | On the Role of Conceptualization in Commonsense Knowledge Graph Construction | Commonsense knowledge graphs (CKGs) like Atomic and ASER are substantially different from conventional KGs as they consist of much larger number of nodes formed by loosely-structured text, which, though, enables them to handle highly diverse queries in natural language related to commonsense, leads to unique challenges... | ['Mutian He', 'Kun Xu', 'Yangqiu Song', 'Dong Yu'] | 2020-03-06 | null | null | null | null | ['triple-classification'] | ['graphs'] | [ 2.41225958e-01 8.21734309e-01 -3.13380539e-01 -1.21119857e-01
-3.98003876e-01 -8.28610897e-01 7.98681259e-01 4.60067153e-01
-1.96350599e-03 1.10762417e+00 4.93319035e-01 -2.40944579e-01
-2.48224378e-01 -1.33996177e+00 -8.98774385e-01 -3.71706754e-01
-7.99556896e-02 9.23157156e-01 1.77606091e-01 -4.88039941... | [9.657221794128418, 8.16015911102295] |
79a96d58-3134-4efb-bddd-85f8af1af292 | zero-1-to-3-zero-shot-one-image-to-3d-object | 2303.11328 | null | https://arxiv.org/abs/2303.11328v1 | https://arxiv.org/pdf/2303.11328v1.pdf | Zero-1-to-3: Zero-shot One Image to 3D Object | We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image. To perform novel view synthesis in this under-constrained setting, we capitalize on the geometric priors that large-scale diffusion models learn about natural images. Our conditional diffusion model uses ... | ['Carl Vondrick', 'Sergey Zakharov', 'Pavel Tokmakov', 'Basile Van Hoorick', 'Rundi Wu', 'Ruoshi Liu'] | 2023-03-20 | null | null | null | null | ['single-view-3d-reconstruction', 'image-to-3d'] | ['computer-vision', 'computer-vision'] | [ 4.43605721e-01 3.42333347e-01 -1.03859568e-03 -3.17553848e-01
-5.69191933e-01 -8.81063938e-01 9.51381624e-01 -8.17060947e-01
-9.91285741e-02 2.23571584e-01 1.80044428e-01 1.01307712e-01
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5.41598082e-01 6.76031053e-01 3.91600847e-01 5.23593687... | [9.255990982055664, -3.115262508392334] |
da1e5078-e64d-4b12-be80-34d20743a662 | hit-a-hierarchically-fused-deep-attention | 2105.14600 | null | https://arxiv.org/abs/2105.14600v1 | https://arxiv.org/pdf/2105.14600v1.pdf | HIT: A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language Representation | Understanding linguistics and morphology of resource-scarce code-mixed texts remains a key challenge in text processing. Although word embedding comes in handy to support downstream tasks for low-resource languages, there are plenty of scopes in improving the quality of language representation particularly for code-mix... | ['Md Shad Akhtar', 'Tanmoy Chakraborty', 'Sourabh Kumar Bhattacharjee', 'Ayan Sengupta'] | 2021-05-30 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-3.19755450e-03 -3.60281765e-02 -9.61971506e-02 -4.25270945e-01
-1.06371117e+00 -6.29510641e-01 4.10995960e-01 3.36634606e-01
-6.18795812e-01 3.89566302e-01 5.75321853e-01 -7.65905619e-01
3.82821709e-02 -5.42362213e-01 -4.13423866e-01 -3.53918493e-01
7.15431422e-02 2.86322206e-01 -1.28967976e-02 -6.15530849... | [10.649577140808105, 9.695432662963867] |
847d9597-f05e-492a-a689-2a253dcd5ed0 | document-enhancement-using-visibility | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Kligler_Document_Enhancement_Using_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Kligler_Document_Enhancement_Using_CVPR_2018_paper.pdf | Document Enhancement Using Visibility Detection | This paper re-visits classical problems in document enhancement. Rather than proposing a new algorithm for a specific problem, we introduce a novel general approach. The key idea is to modify any state- of-the-art algorithm, by providing it with new information (input), improving its own results. Interestingly, this in... | ['Sagi Katz', 'Netanel Kligler', 'Ayellet Tal'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['document-enhancement'] | ['computer-vision'] | [ 8.05518925e-01 2.38588095e-01 2.63856649e-01 -3.36153284e-02
-2.94530272e-01 -7.88859665e-01 7.00107038e-01 2.46013194e-01
-8.39731842e-02 1.81687176e-01 -1.56628609e-01 -5.95271766e-01
-1.64096206e-01 -9.43518817e-01 -3.91104907e-01 -1.05074024e+00
-2.72822920e-02 3.79083723e-01 6.21992946e-01 -6.00801170... | [10.841329574584961, -2.6225645542144775] |
ca920c60-7773-48c2-bb6f-03b669f46edc | accelerated-functional-brain-aging-in-major | 2205.04871 | null | https://arxiv.org/abs/2205.04871v1 | https://arxiv.org/pdf/2205.04871v1.pdf | Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants | Major depressive disorder (MDD) is one of the most common mental health conditions that has been intensively investigated for its association with brain atrophy and mortality. Recent studies reveal that the deviation between the predicted and the chronological age can be a marker of accelerated brain aging to character... | ['Tao Jia', 'Jiang Qiu', 'Wenyu Chen', 'YunSong Luo'] | 2022-05-08 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-1.09935842e-01 -3.52332331e-02 -2.53617465e-01 -5.89284301e-01
-4.50244188e-01 2.06086218e-01 2.02229232e-01 2.66314238e-01
-7.65016973e-01 7.36595750e-01 1.00446669e-02 -3.73827726e-01
-1.16601847e-01 -7.86434829e-01 -4.17788953e-01 -4.30057585e-01
-6.95115924e-01 2.52067417e-01 -4.23060477e-01 -1.86338499... | [14.106755256652832, -1.547048807144165] |
a887fba9-a137-4e18-ac34-1d5bb04a89cb | combinatory-chemistry-towards-a-simple-model | 2003.07916 | null | https://arxiv.org/abs/2003.07916v2 | https://arxiv.org/pdf/2003.07916v2.pdf | Combinatory Chemistry: Towards a Simple Model of Emergent Evolution | An explanatory model for the emergence of evolvable units must display emerging structures that (1) preserve themselves in time (2) self-reproduce and (3) tolerate a certain amount of variation when reproducing. To tackle this challenge, here we introduce Combinatory Chemistry, an Algorithmic Artificial Chemistry based... | ['Germán Kruszewski', 'Tomas Mikolov'] | 2020-03-17 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 3.81042242e-01 4.15592045e-01 6.02162033e-02 4.38270390e-01
1.02389967e+00 -1.05125487e+00 1.11954224e+00 5.38999774e-02
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-1.83283865e-01 -1.03117466e+00 -6.87986851e-01 -1.13004708e+00
-4.58548456e-01 6.48603380e-01 3.84780258e-01 -5.89721084... | [5.606098175048828, 4.154456615447998] |
3cc3e71a-26c0-4556-a006-73c1e8890949 | every-time-i-fire-a-conversational-designer-1 | null | null | https://aclanthology.org/2022.lrec-1.15 | https://aclanthology.org/2022.lrec-1.15.pdf | Every time I fire a conversational designer, the performance of the dialogue system goes down | Incorporating handwritten domain scripts into neural-based task-oriented dialogue systems may be an effective way to reduce the need for large sets of annotated dialogues. In this paper, we investigate how the use of domain scripts written by conversational designers affects the performance of neural-based dialogue sys... | ['Fabio Massimo Zanzotto', 'Raniero Romagnoli', 'Andrea Favalli', 'Cristina Giannone', 'Samir Salman', 'Michele Mastromattei', 'Giancarlo Xompero'] | null | null | null | null | lrec-2022-6 | ['task-oriented-dialogue-systems'] | ['natural-language-processing'] | [-5.27141020e-02 7.27103829e-01 3.89146060e-01 -6.98422730e-01
-2.11987257e-01 -6.29925251e-01 7.19899356e-01 -3.33216190e-01
-6.25681520e-01 9.25926805e-01 5.12261212e-01 -6.00616693e-01
7.22494647e-02 -7.16802120e-01 -3.62542063e-01 1.55249566e-01
3.20325255e-01 9.70486939e-01 9.44875479e-02 -9.60295677... | [12.918716430664062, 7.984976768493652] |
5e988ae3-233b-4c33-bf02-9713aa8c8249 | 3d-point-cloud-generative-adversarial-network | 1905.06292 | null | https://arxiv.org/abs/1905.06292v2 | https://arxiv.org/pdf/1905.06292v2.pdf | 3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions | In this paper, we propose a novel generative adversarial network (GAN) for 3D point clouds generation, which is called tree-GAN. To achieve state-of-the-art performance for multi-class 3D point cloud generation, a tree-structured graph convolution network (TreeGCN) is introduced as a generator for tree-GAN. Because Tre... | ['Dong Wook Shu', 'Sung Woo Park', 'Junseok Kwon'] | 2019-05-15 | 3d-point-cloud-generative-adversarial-network-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Shu_3D_Point_Cloud_Generative_Adversarial_Network_Based_on_Tree_Structured_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Shu_3D_Point_Cloud_Generative_Adversarial_Network_Based_on_Tree_Structured_ICCV_2019_paper.pdf | iccv-2019-10 | ['point-cloud-generation'] | ['computer-vision'] | [-5.88531382e-02 2.06373528e-01 2.30690405e-01 -1.43979192e-01
-7.85777867e-01 -5.48671246e-01 6.18512750e-01 -4.07810807e-01
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3.79584163e-01 -1.57397425e+00 -1.00691569e+00 -5.64523697e-01
1.85964733e-01 5.03948212e-01 7.59459063e-02 -1.58848971... | [8.852622032165527, -3.6910488605499268] |
f01cab74-cdee-4563-b47a-269cabe1d354 | neural-pruning-via-growing-regularization-1 | 2012.09243 | null | https://arxiv.org/abs/2012.09243v2 | https://arxiv.org/pdf/2012.09243v2.pdf | Neural Pruning via Growing Regularization | Regularization has long been utilized to learn sparsity in deep neural network pruning. However, its role is mainly explored in the small penalty strength regime. In this work, we extend its application to a new scenario where the regularization grows large gradually to tackle two central problems of pruning: pruning s... | ['Yun Fu', 'Yulun Zhang', 'Can Qin', 'Huan Wang'] | 2020-12-16 | neural-pruning-via-growing-regularization | https://openreview.net/forum?id=o966_Is_nPA | https://openreview.net/pdf?id=o966_Is_nPA | iclr-2021-1 | ['l2-regularization'] | ['methodology'] | [ 2.12026402e-01 5.42820338e-03 -3.27347338e-01 -3.13397467e-01
-3.77631575e-01 -1.81181490e-01 1.41909555e-01 2.15063125e-01
-8.32220316e-01 6.91531003e-01 -6.05018102e-02 -2.38844037e-01
-3.25475127e-01 -5.76625228e-01 -7.76379108e-01 -7.88881719e-01
-1.53897554e-01 1.01594269e-01 3.65783632e-01 -2.74180919... | [8.653255462646484, 3.282565116882324] |
1f1c9755-4bb7-45b1-87b6-2c0de8d23f24 | distant-supervision-for-relation-extraction | null | null | https://aclanthology.org/D15-1203 | https://aclanthology.org/D15-1203.pdf | Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks | null | ['Daojian Zeng', 'Yubo Chen', 'Jun Zhao', 'Kang Liu'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['relationship-extraction-distant-supervised'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.255461692810059, 3.7228293418884277] |
24249f5c-814c-483b-84a3-a099d5e51410 | robust-constrained-hyperspectral-unmixing | 2302.08247 | null | https://arxiv.org/abs/2302.08247v1 | https://arxiv.org/pdf/2302.08247v1.pdf | Robust Constrained Hyperspectral Unmixing Using Reconstructed-Image Regularization | Hyperspectral (HS) unmixing is the process of decomposing an HS image into material-specific spectra (endmembers) and their spatial distributions (abundance maps). Existing unmixing methods have two limitations with respect to noise robustness. First, if the input HS image is highly noisy, even if the balance between s... | ['Shunsuke Ono', 'Yuki Nagamatsu', 'Kazuki Naganuma'] | 2023-02-16 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 6.89159870e-01 -5.92922986e-01 8.92907754e-02 -4.39682007e-02
-6.04006350e-01 -4.98878390e-01 2.20272601e-01 -1.99961469e-01
-7.40489438e-02 7.81068861e-01 2.35793039e-01 -8.33513588e-02
-2.50156999e-01 -8.18212688e-01 -5.03702581e-01 -1.29728758e+00
3.13788086e-01 7.08030835e-02 -2.47816876e-01 -1.62960291... | [10.053078651428223, -2.090796709060669] |
7a3a5e70-a0b5-4ddc-a42e-601f99fef0f2 | portfolio-optimization-with-relative-tail | 2303.12209 | null | https://arxiv.org/abs/2303.12209v2 | https://arxiv.org/pdf/2303.12209v2.pdf | Portfolio Optimization with Relative Tail Risk | This paper proposes analytic forms of portfolio CoVaR and CoCVaR on the normal tempered stable market model. Since CoCVaR captures the relative risk of the portfolio with respect to a benchmark return, we apply it to the relative portfolio optimization. Moreover, we derive analytic forms for the marginal contribution t... | ['Young Shin Kim'] | 2023-03-21 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-5.49654067e-01 -1.73496887e-01 3.70545059e-01 7.84650594e-02
-6.51209712e-01 -9.94747818e-01 5.11285484e-01 -1.93088979e-01
-1.83820948e-01 6.98774219e-01 -4.20115143e-02 -5.57105124e-01
-6.10967219e-01 -1.00748110e+00 -3.23676050e-01 -7.67642856e-01
3.97585239e-03 3.28183055e-01 -8.26988295e-02 -1.02315165... | [4.94803524017334, 3.9578661918640137] |
6e3e3d32-8f34-44f7-9cc7-e1a68a9b68a0 | fine-grained-object-semantic-understanding | 1912.12577 | null | https://arxiv.org/abs/1912.12577v2 | https://arxiv.org/pdf/1912.12577v2.pdf | Human Correspondence Consensus for 3D Object Semantic Understanding | Semantic understanding of 3D objects is crucial in many applications such as object manipulation. However, it is hard to give a universal definition of point-level semantics that everyone would agree on. We observe that people have a consensus on semantic correspondences between two areas from different objects, but ar... | ['Yang You', 'Chengkun Li', 'Zhoujun Cheng', 'Cewu Lu', 'Yujing Lou', 'Weiming Wang', 'Lizhuang Ma', 'Liangwei Li'] | 2019-12-29 | human-correspondence-consensus-for-3d-object | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4107_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123670494.pdf | eccv-2020-8 | ['3d-feature-matching', '3d-point-cloud-matching'] | ['computer-vision', 'computer-vision'] | [-6.20767251e-02 2.39816420e-02 -6.63082078e-02 -6.70224130e-01
-5.22322953e-01 -6.78962052e-01 6.37358427e-01 4.98315156e-01
-3.52460265e-01 1.13806419e-01 7.92482942e-02 2.43902519e-01
-3.10431212e-01 -1.05786204e+00 -8.05194199e-01 -3.82957220e-01
3.10172290e-01 9.05170321e-01 4.88496900e-01 -2.94476092... | [8.014567375183105, -3.23715877532959] |
e3d3c10d-4eea-4ad4-8fa0-c0ea51c909e0 | learning-visual-question-answering-by | 1808.00300 | null | http://arxiv.org/abs/1808.00300v1 | http://arxiv.org/pdf/1808.00300v1.pdf | Learning Visual Question Answering by Bootstrapping Hard Attention | Attention mechanisms in biological perception are thought to select subsets
of perceptual information for more sophisticated processing which would be
prohibitive to perform on all sensory inputs. In computer vision, however,
there has been relatively little exploration of hard attention, where some
information is sele... | ['Adam Santoro', 'Mateusz Malinowski', 'Carl Doersch', 'Peter Battaglia'] | 2018-08-01 | learning-visual-question-answering-by-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Mateusz_Malinowski_Learning_Visual_Question_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Mateusz_Malinowski_Learning_Visual_Question_ECCV_2018_paper.pdf | eccv-2018-9 | ['hard-attention'] | ['methodology'] | [ 5.32315135e-01 1.83851346e-01 2.82260656e-01 -4.55736369e-01
-5.73748648e-01 -7.02997565e-01 5.63629091e-01 5.92630029e-01
-8.40289176e-01 6.26866400e-01 3.53569537e-01 -2.77269602e-01
-3.49852741e-01 -5.67848742e-01 -5.11030853e-01 -6.13219917e-01
5.57491602e-03 3.94598246e-01 5.74127197e-01 -3.51323813... | [10.0269775390625, 1.8408575057983398] |
93da8949-066f-492f-a75c-ee3ee96cd510 | imitation-learning-via-differentiable-physics | 2206.04873 | null | https://arxiv.org/abs/2206.04873v1 | https://arxiv.org/pdf/2206.04873v1.pdf | Imitation Learning via Differentiable Physics | Existing imitation learning (IL) methods such as inverse reinforcement learning (IRL) usually have a double-loop training process, alternating between learning a reward function and a policy and tend to suffer long training time and high variance. In this work, we identify the benefits of differentiable physics simulat... | ['Zhongwen Xu', 'Xiao Ma', 'Siwei Chen'] | 2022-06-10 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [-1.03131412e-02 -8.95083044e-03 -2.47122526e-01 1.69661418e-01
-3.29171240e-01 -6.29280686e-01 5.05339682e-01 -5.76280579e-02
-5.93717754e-01 8.40260088e-01 -4.17857438e-01 -3.59391868e-01
-4.37235177e-01 -6.13103330e-01 -1.14929605e+00 -7.83715069e-01
-2.28794098e-01 6.05865359e-01 4.46626097e-01 -3.41417938... | [4.419600009918213, 1.4445416927337646] |
3465bb42-027a-40a9-b5d1-dfc1af124477 | improving-sonar-image-patch-matching-via-deep | 1709.02150 | null | http://arxiv.org/abs/1709.02150v1 | http://arxiv.org/pdf/1709.02150v1.pdf | Improving Sonar Image Patch Matching via Deep Learning | Matching sonar images with high accuracy has been a problem for a long time,
as sonar images are inherently hard to model due to reflections, noise and
viewpoint dependence. Autonomous Underwater Vehicles require good sonar image
matching capabilities for tasks such as tracking, simultaneous localization and
mapping (S... | ['Matias Valdenegro-Toro'] | 2017-09-07 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 2.23437428e-01 1.50639471e-02 2.70814151e-01 -7.37094104e-01
-8.50378633e-01 -4.39511955e-01 6.62212610e-01 4.33832109e-01
-1.00551307e+00 4.62655634e-01 -2.78573334e-01 -9.50509906e-02
-3.33057910e-01 -1.04836595e+00 -9.19875741e-01 -4.71381068e-01
-4.77135032e-01 4.09521341e-01 2.73555636e-01 -2.97572643... | [7.9690752029418945, -1.7656896114349365] |
4f0a4969-8f76-429f-ba8e-c60d875f800a | an-annotated-instance-segmentation-xxl-ct | 2212.08639 | null | https://arxiv.org/abs/2212.08639v1 | https://arxiv.org/pdf/2212.08639v1.pdf | An annotated instance segmentation XXL-CT dataset from a historic airplane | The Me 163 was a Second World War fighter airplane and a result of the German air force secret developments. One of these airplanes is currently owned and displayed in the historic aircraft exhibition of the Deutsches Museum in Munich, Germany. To gain insights with respect to its history, design and state of preservat... | ['Thomas Wittenberg', 'Michael Salamon', 'Stefan Gerth', 'Andreas Hempfer', 'Nils Reims', 'Roland Gruber'] | 2022-12-16 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [ 2.16910884e-01 2.20090136e-01 1.74780920e-01 -1.77905113e-01
-3.33007127e-01 -5.51544547e-01 1.37667239e-01 5.63833356e-01
-3.87050986e-01 6.42884910e-01 -2.33270466e-01 -4.74044204e-01
-3.68554622e-01 -7.93737233e-01 -1.83539122e-01 -5.22001088e-01
-5.59041262e-01 1.30527985e+00 5.70845127e-01 -8.50342363... | [13.982095718383789, -2.6482956409454346] |
e7a5853a-0e6e-4ff4-9ca8-dcec7e2068c7 | hissnet-sound-event-detection-and-speaker | 2303.07538 | null | https://arxiv.org/abs/2303.07538v1 | https://arxiv.org/pdf/2303.07538v1.pdf | HiSSNet: Sound Event Detection and Speaker Identification via Hierarchical Prototypical Networks for Low-Resource Headphones | Modern noise-cancelling headphones have significantly improved users' auditory experiences by removing unwanted background noise, but they can also block out sounds that matter to users. Machine learning (ML) models for sound event detection (SED) and speaker identification (SID) can enable headphones to selectively pa... | ['Huang', 'Chuan-Che', 'Shuo Zhang', 'Jeremy Kemmerer', 'Mohammad Rasool Izadi', 'Berker Banar', 'N Shashaank'] | 2023-03-13 | null | null | null | null | ['sound-event-detection', 'speaker-identification'] | ['audio', 'speech'] | [-7.43380636e-02 -3.22182506e-01 2.74454296e-01 -2.56366521e-01
-8.96118343e-01 -3.09887379e-01 -1.29959360e-01 9.51874927e-02
-3.60006511e-01 2.85154670e-01 2.21245736e-01 -3.49151194e-01
8.16381574e-02 -6.14643872e-01 -3.36627185e-01 -2.15301380e-01
-1.67710811e-01 1.26382038e-01 7.13565409e-01 -1.52955428... | [14.908876419067383, 5.927661418914795] |
19254e3b-a012-4eae-a78b-4c2b95606b2e | respect-reinforcement-learning-based-edge | 2304.04716 | null | https://arxiv.org/abs/2304.04716v1 | https://arxiv.org/pdf/2304.04716v1.pdf | RESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUs | Deep neural networks (DNNs) have substantial computational and memory requirements, and the compilation of its computational graphs has a great impact on the performance of resource-constrained (e.g., computation, I/O, and memory-bound) edge computing systems. While efficient execution of their computational graph requ... | ['Cunxi Yu', 'Daniel Robinson', 'Yingjie Li', 'Jiaqi Yin'] | 2023-04-10 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-6.62063658e-02 1.87711760e-01 -9.49642137e-02 -3.27320874e-01
-8.99564028e-02 -1.96017891e-01 -3.40468772e-02 -8.58203247e-02
-8.14895391e-01 6.32611096e-01 -4.53585595e-01 -6.61322176e-01
-1.97626323e-01 -1.08876109e+00 -1.04991055e+00 -4.92466152e-01
-2.92658716e-01 7.15444624e-01 1.82119027e-01 5.64707853... | [7.15974760055542, 5.405590534210205] |
30b10d1c-3f2c-4416-9ec8-c34ce5455caa | cross-view-tracking-for-multi-human-3d-pose | 2003.03972 | null | https://arxiv.org/abs/2003.03972v3 | https://arxiv.org/pdf/2003.03972v3.pdf | Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS | Estimating 3D poses of multiple humans in real-time is a classic but still challenging task in computer vision. Its major difficulty lies in the ambiguity in cross-view association of 2D poses and the huge state space when there are multiple people in multiple views. In this paper, we present a novel solution for multi... | ['Shuang Liu', 'Haizhou Ai', 'Zijie Zhuang', 'Long Chen', 'Rui Chen'] | 2020-03-09 | cross-view-tracking-for-multi-human-3d-pose-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Cross-View_Tracking_for_Multi-Human_3D_Pose_Estimation_at_Over_100_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_Cross-View_Tracking_for_Multi-Human_3D_Pose_Estimation_at_Over_100_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-multi-person-pose-estimation'] | ['computer-vision'] | [-3.76503229e-01 -6.23910129e-01 8.90289843e-02 -1.35543168e-01
-6.33786559e-01 -7.80535638e-01 2.34402210e-01 -2.50908792e-01
-6.48138583e-01 4.56379116e-01 -9.57755670e-02 4.31366414e-01
1.68858573e-01 -1.48598179e-01 -7.14106858e-01 -3.90620142e-01
-8.83407518e-02 6.93712890e-01 5.06033242e-01 -9.51161981... | [7.081691741943359, -1.0655995607376099] |
8c0965dc-b8fb-4393-a206-a0ecd1f4467e | augmenting-deep-learning-adaptation-for | 2307.00883 | null | https://arxiv.org/abs/2307.00883v1 | https://arxiv.org/pdf/2307.00883v1.pdf | Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding | Deep learning advancements have revolutionized scalable classification in many domains including computer vision. However, when it comes to wearable-based classification and domain adaptation, existing computer vision-based deep learning architectures and pretrained models trained on thousands of labeled images for mon... | ['Mohammad Arif Ul Alam', 'Md Mahmudur Rahman', 'Yidong Zhu'] | 2023-07-03 | null | null | null | null | ['activity-recognition', 'image-augmentation'] | ['computer-vision', 'computer-vision'] | [ 5.87725699e-01 -3.28053921e-01 -3.22363466e-01 -5.42470753e-01
-6.05137229e-01 -1.94351315e-01 3.87455851e-01 3.13934356e-01
-6.76196575e-01 5.71170151e-01 5.02599239e-01 7.70094842e-02
-3.93467285e-02 -6.89235985e-01 -7.02687919e-01 -4.72899765e-01
-3.26382101e-01 -3.83189231e-01 2.43759770e-02 -1.71169356... | [7.578712463378906, 0.8140469789505005] |
9fbaebf0-2f23-4d5e-a855-36fba2cea208 | recognition-of-instrument-tissue-interactions | 2007.05405 | null | https://arxiv.org/abs/2007.05405v1 | https://arxiv.org/pdf/2007.05405v1.pdf | Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action Triplets | Recognition of surgical activity is an essential component to develop context-aware decision support for the operating room. In this work, we tackle the recognition of fine-grained activities, modeled as action triplets <instrument, verb, target> representing the tool activity. To this end, we introduce a new laparosco... | ['Cristians Gonzalez', 'Tong Yu', 'Didier Mutter', 'Pietro Mascagni', 'Nicolas Padoy', 'Jacques Marescaux', 'Chinedu Innocent Nwoye'] | 2020-07-10 | null | null | null | null | ['action-triplet-recognition', 'weakly-supervised-action-localization'] | ['computer-vision', 'computer-vision'] | [ 3.87019008e-01 1.87908307e-01 -3.91907126e-01 -3.15329820e-01
-6.79624617e-01 -8.02159190e-01 6.08100474e-01 8.55159089e-02
-3.44834417e-01 2.20998451e-01 6.93216026e-01 -1.50919005e-01
-2.10627675e-01 -3.80173355e-01 -7.81538069e-01 -5.82806826e-01
-1.43286794e-01 1.86140165e-01 -6.80768713e-02 -8.53538290... | [14.093537330627441, -3.3978044986724854] |
8607749e-3e4e-40b5-8521-f981a779ee84 | learning-generative-embeddings-using-an | 2209.00372 | null | https://arxiv.org/abs/2209.00372v1 | https://arxiv.org/pdf/2209.00372v1.pdf | Learning Generative Embeddings using an Optimal Subsampling Policy for Tensor Sketching | Data tensors of orders 3 and greater are routinely being generated. These data collections are increasingly huge and growing. They are either tensor fields (e.g., images, videos, geographic data) in which each location of data contains important information or permutation invariant general tensors (e.g., unsupervised l... | ['Rochan Avlur', 'Taemin Heo', 'Chandrajit Bajaj'] | 2022-09-01 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [-1.86141133e-01 4.01729159e-02 -4.31472361e-01 -7.46843740e-02
-5.19087017e-01 -1.06721997e+00 8.39411914e-01 -1.81043535e-01
1.20300643e-01 3.42458814e-01 9.48835075e-01 -2.20872164e-01
-7.25823462e-01 -5.32692373e-01 -5.86670935e-01 -5.90178788e-01
-6.21041775e-01 9.44461763e-01 -7.90609121e-02 2.75360912... | [7.189509868621826, 4.691577911376953] |
095eaf86-95ce-4e25-bfc1-b51fb9249b14 | transfer-learning-based-detection-of-diabetic | 1905.07203 | null | https://arxiv.org/abs/1905.07203v2 | https://arxiv.org/pdf/1905.07203v2.pdf | Transfer Learning based Detection of Diabetic Retinopathy from Small Dataset | Annotated training data insufficiency remains to be one of the challenges of applying deep learning in medical data classification problems. Transfer learning from an already trained deep convolutional network can be used to reduce the cost of training from scratch and to train with small training data for deep learnin... | ['Misgina Tsighe Hagos', 'Shri Kant'] | 2019-05-17 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [ 2.20034420e-01 3.51617306e-01 -8.27242509e-02 -6.03552043e-01
-4.75125194e-01 -1.76568985e-01 3.55588347e-01 1.83038235e-01
-8.06351960e-01 4.62415010e-01 1.25073761e-01 -5.50801516e-01
-1.90846458e-01 -7.57587850e-01 -6.72150195e-01 -5.27606785e-01
2.30362103e-03 5.15883148e-01 1.67018458e-01 -3.39421004... | [14.95710563659668, -2.4315807819366455] |
ac9e1e7f-3139-4ca1-842c-e31e4331ed67 | super-prompting-utilizing-model-independent | 2204.11922 | null | https://arxiv.org/abs/2204.11922v1 | https://arxiv.org/pdf/2204.11922v1.pdf | Super-Prompting: Utilizing Model-Independent Contextual Data to Reduce Data Annotation Required in Visual Commonsense Tasks | Pre-trained language models have shown excellent results in few-shot learning scenarios using in-context learning. Although it is impressive, the size of language models can be prohibitive to make them usable in on-device applications, such as sensors or smartphones. With smaller language models, task-specific data ann... | ['Marek Z. Reformat', 'Navid Rezaei'] | 2022-04-25 | null | null | null | null | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 2.17421308e-01 1.45560995e-01 -3.42762798e-01 -4.29925233e-01
-6.92137539e-01 -4.95102257e-01 5.83308458e-01 2.53934592e-01
-4.24235553e-01 5.91461182e-01 1.52055085e-01 -3.56237501e-01
-1.44515922e-02 -8.92555594e-01 -3.94027472e-01 -2.20513463e-01
2.86547959e-01 3.51603776e-01 5.86725295e-01 -3.26766878... | [10.697067260742188, 7.9060378074646] |
020dfdde-d058-4aff-891b-f9fd5baf6dd2 | safety-of-autonomous-vehicles-a-survey-on | 2305.17941 | null | https://arxiv.org/abs/2305.17941v1 | https://arxiv.org/pdf/2305.17941v1.pdf | Safety of autonomous vehicles: A survey on Model-based vs. AI-based approaches | The growing advancements in Autonomous Vehicles (AVs) have emphasized the critical need to prioritize the absolute safety of AV maneuvers, especially in dynamic and unpredictable environments or situations. This objective becomes even more challenging due to the uniqueness of every traffic situation/condition. To cope ... | ['Lounis Adouane', 'Dimia Iberraken'] | 2023-05-29 | null | null | null | null | ['autonomous-vehicles'] | ['computer-vision'] | [ 6.23801388e-02 3.23749065e-01 -3.20563018e-01 -9.70171988e-02
1.51370913e-01 -3.98568749e-01 9.02516663e-01 -6.79039657e-02
-3.23669940e-01 7.95062542e-01 -3.73710543e-01 -8.39571536e-01
-5.98142862e-01 -9.51337814e-01 -2.62951612e-01 -6.75581455e-01
1.10628024e-01 3.34399670e-01 4.27143961e-01 -8.18896174... | [5.6361494064331055, 1.347004771232605] |
c6c2dc28-730d-4816-b370-8ae1de749471 | perceptual-losses-for-real-time-style | 1603.08155 | null | http://arxiv.org/abs/1603.08155v1 | http://arxiv.org/pdf/1603.08155v1.pdf | Perceptual Losses for Real-Time Style Transfer and Super-Resolution | We consider image transformation problems, where an input image is
transformed into an output image. Recent methods for such problems typically
train feed-forward convolutional neural networks using a \emph{per-pixel} loss
between the output and ground-truth images. Parallel work has shown that
high-quality images can ... | ['Li Fei-Fei', 'Alexandre Alahi', 'Justin Johnson'] | 2016-03-27 | null | null | null | null | ['nuclear-segmentation'] | ['medical'] | [ 7.88231611e-01 3.33007723e-01 2.43865281e-01 -6.15815580e-01
-9.93106604e-01 -3.96401852e-01 6.16099298e-01 -2.49918580e-01
-6.76772237e-01 6.35649741e-01 1.86806291e-01 -9.47867259e-02
2.80383795e-01 -9.24330235e-01 -1.19656658e+00 -3.91338766e-01
2.22062290e-01 1.81567445e-01 1.56058624e-01 -5.41235209... | [11.510997772216797, -0.6817985773086548] |
124df5ee-f179-4882-b36c-0568f315c695 | semeval-2012-task-7-choice-of-plausible | null | null | https://aclanthology.org/S12-1052 | https://aclanthology.org/S12-1052.pdf | SemEval-2012 Task 7: Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning | null | ['Andrew Gordon', 'Zornitsa Kozareva', 'Melissa Roemmele'] | 2012-07-01 | null | null | null | semeval-2012-7 | ['commonsense-causal-reasoning'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.195794582366943, 3.7270984649658203] |
e82d27f3-7f35-4234-bd78-b9eb40e4a3bb | exploiting-social-relations-and-sentiment-for | null | null | https://aclanthology.org/D14-1120 | https://aclanthology.org/D14-1120.pdf | Exploiting Social Relations and Sentiment for Stock Prediction | null | ['Sinno Jialin Pan', 'Jianfeng Si', 'Huayi Li', 'Bing Liu', 'Qing Li', 'Arjun Mukherjee'] | 2014-10-01 | null | null | null | emnlp-2014-10 | ['stock-market-prediction', 'stock-prediction'] | ['time-series', 'time-series'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.2714338302612305, 3.610337495803833] |
8dd7ea04-2e8c-4dff-af79-19e29ec91d7d | a-benchmark-study-of-contrastive-learning-for | 2210.12314 | null | https://arxiv.org/abs/2210.12314v1 | https://arxiv.org/pdf/2210.12314v1.pdf | A Benchmark Study of Contrastive Learning for Arabic Social Meaning | Contrastive learning (CL) brought significant progress to various NLP tasks. Despite this progress, CL has not been applied to Arabic NLP to date. Nor is it clear how much benefits it could bring to particular classes of tasks such as those involved in Arabic social meaning (e.g., sentiment analysis, dialect identifica... | ['Laks V. S. Lakshmanan', 'Muhammad Abdul-Mageed', 'AbdelRahim Elmadany', 'El Moatez Billah Nagoudi', 'Md Tawkat Islam Khondaker'] | 2022-10-22 | null | null | null | null | ['dialect-identification'] | ['natural-language-processing'] | [ 1.13600641e-01 3.45245898e-02 -1.86825752e-01 -3.32314223e-01
-1.04940116e+00 -9.98993754e-01 6.60697877e-01 4.41650152e-01
-5.92305124e-01 7.27859855e-01 2.66269088e-01 -3.69597793e-01
-6.60934970e-02 -5.06352544e-01 -4.49838758e-01 -5.97217858e-01
-2.19416581e-02 5.15855730e-01 -2.55813450e-01 -6.61773562... | [11.00700569152832, 9.795551300048828] |
6ec5f0ff-9ec9-47ad-bea7-e156640b48b2 | a-deep-learning-approach-with-an-attention | 1805.05036 | null | http://arxiv.org/abs/1805.05036v1 | http://arxiv.org/pdf/1805.05036v1.pdf | A Deep Learning Approach with an Attention Mechanism for Automatic Sleep Stage Classification | Automatic sleep staging is a challenging problem and state-of-the-art
algorithms have not yet reached satisfactory performance to be used instead of
manual scoring by a sleep technician. Much research has been done to find good
feature representations that extract the useful information to correctly
classify each epoch... | ['Martin Längkvist', 'Amy Loutfi'] | 2018-05-14 | null | null | null | null | ['sleep-staging', 'automatic-sleep-stage-classification'] | ['medical', 'medical'] | [ 1.57984480e-01 1.09946094e-01 -9.05456468e-02 -7.24605620e-01
-1.54616490e-01 4.51289602e-02 1.25351161e-01 2.00201824e-01
-6.71923399e-01 7.40853906e-01 3.05764019e-01 4.16463763e-02
-3.36670697e-01 -4.80413973e-01 9.48283374e-02 -8.68751585e-01
-4.58914340e-02 6.12796366e-01 3.39076310e-01 -2.41893589... | [13.518266677856445, 3.5039796829223633] |
0142505b-94c7-4dd2-9dd7-cbd492391f66 | balancing-between-over-weighting-and-under | 1604.04007 | null | http://arxiv.org/abs/1604.04007v1 | http://arxiv.org/pdf/1604.04007v1.pdf | Balancing Between Over-Weighting and Under-Weighting in Supervised Term Weighting | Supervised term weighting could improve the performance of text
categorization. A way proven to be effective is to give more weight to terms
with more imbalanced distributions across categories. This paper shows that
supervised term weighting should not just assign large weights to imbalanced
terms, but should also con... | ['Gu Xiaodong', 'Wu Haibing'] | 2016-04-14 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [ 4.55666259e-02 -2.87630577e-02 -5.39016426e-01 -6.00373089e-01
-3.86417240e-01 -3.31462383e-01 5.18341720e-01 5.19475698e-01
-5.79071641e-01 4.91676569e-01 5.35235107e-01 -1.92345798e-01
-1.89808920e-01 -9.33367908e-01 -1.09651983e-01 -7.66253591e-01
-9.67986509e-02 -3.34051512e-02 4.04916316e-01 -3.55346054... | [10.479713439941406, 7.276558876037598] |
f60ef3c2-e0b9-4712-9cd9-b13b332f094e | splal-similarity-based-pseudo-labeling-with | 2307.04610 | null | https://arxiv.org/abs/2307.04610v1 | https://arxiv.org/pdf/2307.04610v1.pdf | SPLAL: Similarity-based pseudo-labeling with alignment loss for semi-supervised medical image classification | Medical image classification is a challenging task due to the scarcity of labeled samples and class imbalance caused by the high variance in disease prevalence. Semi-supervised learning (SSL) methods can mitigate these challenges by leveraging both labeled and unlabeled data. However, SSL methods for medical image clas... | ['Pravendra Singh', 'Suruchi Kumari', 'Divyansh Agarwal', 'Pranaw Raj', 'Md Junaid Mahmood'] | 2023-07-10 | null | null | null | null | ['skin-lesion-classification', 'medical-image-classification', 'semi-supervised-medical-image-classification', 'classification-1'] | ['medical', 'medical', 'medical', 'methodology'] | [ 4.53977287e-01 -9.27596986e-02 -6.43369615e-01 -5.60242414e-01
-1.22308731e+00 -2.47284070e-01 4.25336957e-01 3.75104934e-01
-3.83728385e-01 7.69033015e-01 -5.88377677e-02 -1.62425786e-01
-2.71472689e-02 -3.47395211e-01 -3.97869945e-01 -8.75743151e-01
2.15055957e-01 1.92813814e-01 9.53665301e-02 4.05506283... | [15.0922212600708, -2.4067208766937256] |
0ee4bd36-4953-4542-98d9-344fb9fda67c | self-paced-contrastive-learning-with-hybrid | 2006.02713 | null | https://arxiv.org/abs/2006.02713v2 | https://arxiv.org/pdf/2006.02713v2.pdf | Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID | Domain adaptive object re-ID aims to transfer the learned knowledge from the labeled source domain to the unlabeled target domain to tackle the open-class re-identification problems. Although state-of-the-art pseudo-label-based methods have achieved great success, they did not make full use of all valuable information ... | ['Hongsheng Li', 'Dapeng Chen', 'Rui Zhao', 'Yixiao Ge', 'Feng Zhu'] | 2020-06-04 | null | http://proceedings.neurips.cc/paper/2020/hash/821fa74b50ba3f7cba1e6c53e8fa6845-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/821fa74b50ba3f7cba1e6c53e8fa6845-Paper.pdf | neurips-2020-12 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 3.34405184e-01 -2.18963972e-03 -6.63119793e-01 -4.10244852e-01
-9.67372358e-01 -5.17116308e-01 7.45119214e-01 2.98174918e-01
-2.95640558e-01 7.88617969e-01 -1.64699614e-01 2.95275450e-01
-2.51629651e-01 -5.15602589e-01 -6.64105117e-01 -8.62904787e-01
1.28718987e-01 1.10411012e+00 3.81016552e-01 1.45395055... | [10.241888999938965, 2.9809975624084473] |
135393ff-6a12-4ff4-884d-97b70983bcd7 | logic-rules-powered-knowledge-graph-embedding | 1903.03772 | null | http://arxiv.org/abs/1903.03772v1 | http://arxiv.org/pdf/1903.03772v1.pdf | Logic Rules Powered Knowledge Graph Embedding | Large scale knowledge graph embedding has attracted much attention from both
academia and industry in the field of Artificial Intelligence. However, most
existing methods concentrate solely on fact triples contained in the given
knowledge graph. Inspired by the fact that logic rules can provide a flexible
and declarati... | ['Pengwei Wang', 'Nisansa de Silva', 'Lianwen Jin', 'Fangzhao Wu', 'Dejing Dou'] | 2019-03-09 | null | null | null | null | ['triple-classification'] | ['graphs'] | [-1.43776983e-01 3.66636217e-01 -5.32705784e-01 -4.05424714e-01
-1.92283958e-01 -4.60099608e-01 3.94847602e-01 2.40072325e-01
-2.10777536e-01 7.18132317e-01 1.29406944e-01 -4.77483690e-01
-4.95513707e-01 -1.47873664e+00 -9.78141665e-01 -3.02083969e-01
1.20413443e-02 2.05450609e-01 5.02047479e-01 -3.61566484... | [8.83074951171875, 7.819396495819092] |
779a2855-b005-46af-9d9a-21c1ca859c35 | 4dhumanoutfit-a-multi-subject-4d-dataset-of | 2306.07399 | null | https://arxiv.org/abs/2306.07399v1 | https://arxiv.org/pdf/2306.07399v1.pdf | 4DHumanOutfit: a multi-subject 4D dataset of human motion sequences in varying outfits exhibiting large displacements | This work presents 4DHumanOutfit, a new dataset of densely sampled spatio-temporal 4D human motion data of different actors, outfits and motions. The dataset is designed to contain different actors wearing different outfits while performing different motions in each outfit. In this way, the dataset can be seen as a cub... | ['Stefanie Wuhrer', 'Anilkumar Swamy', 'Gregory Rogez', 'Rim Rekik', 'Sergi Pujades', 'Julien Pansiot', 'Mathieu Marsot', 'Vincent Leroy', 'Christophe Legras', 'Martin Humenberger', 'Jean-Sebastien Franco', 'Edmond Boyer', 'Laurence Boissieux', 'Matthieu Armando'] | 2023-06-12 | null | null | null | null | ['virtual-try-on'] | ['computer-vision'] | [-2.89024532e-01 -2.04398707e-01 3.64034176e-02 -2.68685408e-02
-3.23289424e-01 -6.50516093e-01 8.22939992e-01 -5.06220281e-01
-2.80570447e-01 1.45517200e-01 6.88206911e-01 3.04366738e-01
2.14106098e-01 -4.11946803e-01 -5.80391347e-01 -4.46224630e-01
-8.68340861e-03 5.20366251e-01 1.02938570e-01 -4.02414203... | [7.202971935272217, -0.7425932884216309] |
d042c308-a1e3-4358-b6c6-38f8bd49537c | multi-task-pre-training-of-modular-prompt-for | 2210.07565 | null | https://arxiv.org/abs/2210.07565v3 | https://arxiv.org/pdf/2210.07565v3.pdf | Multitask Pre-training of Modular Prompt for Chinese Few-Shot Learning | Prompt tuning is a parameter-efficient approach to adapting pre-trained language models to downstream tasks. Although prompt tuning has been shown to match the performance of full model tuning when training data is sufficient, it tends to struggle in few-shot learning settings. In this paper, we present Multi-task Pre-... | ['Xuanjing Huang', 'Xipeng Qiu', 'Qin Zhu', 'Zhengfu He', 'Tianxiang Sun'] | 2022-10-14 | null | null | null | null | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 5.54748416e-01 2.80796856e-01 -8.84366706e-02 -5.38701594e-01
-1.03811979e+00 -6.12060010e-01 5.78456819e-01 3.01078916e-01
-6.90946400e-01 5.01433194e-01 5.62450290e-01 -5.72129607e-01
2.07235426e-01 -5.94093919e-01 -5.07153571e-01 -3.63376558e-01
4.17211562e-01 3.70211899e-01 6.76712990e-01 -7.18155444... | [10.894769668579102, 8.12389087677002] |
a61b2286-bb05-432d-b2b7-fd13b54ee47c | ongoing-eeg-artifact-correction-using-blind | 2306.16910 | null | https://arxiv.org/abs/2306.16910v1 | https://arxiv.org/pdf/2306.16910v1.pdf | Ongoing EEG artifact correction using blind source separation | Objective: Analysis of the electroencephalogram (EEG) for epileptic spike and seizure detection or brain-computer interfaces can be severely hampered by the presence of artifacts. The aim of this study is to describe and evaluate a fast automatic algorithm for ongoing correction of artifacts in continuous EEG recording... | ['Nobukazu Nakasato', 'Kazutaka Jin', 'Yosuke Kakisaka', 'Rie Tsuda', 'Rie Sakuraba', 'Takafumi Sato', 'Izumi Itabashi', 'Kanoko Kozawa', 'Suguru Asagi', 'Harald Bornfleth', 'Arndt Ebert', 'Toshiyuki Taura', 'Yano Shumpei', 'Yoshiaki Nakao', 'Nicole Ille'] | 2023-06-29 | null | null | null | null | ['seizure-detection', 'eeg', 'eeg'] | ['medical', 'methodology', 'time-series'] | [ 3.31945837e-01 -4.17574972e-01 7.29906023e-01 -2.00160854e-02
-7.71142662e-01 -6.37526214e-01 5.93610331e-02 4.60063964e-01
-4.75195944e-01 1.08527720e+00 1.31701171e-01 -5.39656021e-02
-5.22900701e-01 -7.28014261e-02 -4.53736246e-01 -6.09170079e-01
-5.41856527e-01 -1.07957549e-01 1.60399422e-01 8.54762048... | [13.23723316192627, 3.344759225845337] |
db0dcbfa-c2ad-49f3-96e5-876c80fc524c | multi-label-zero-shot-human-action | 1709.05107 | null | http://arxiv.org/abs/1709.05107v3 | http://arxiv.org/pdf/1709.05107v3.pdf | Multi-Label Zero-Shot Human Action Recognition via Joint Latent Ranking Embedding | Human action recognition refers to automatic recognizing human actions from a
video clip. In reality, there often exist multiple human actions in a video
stream. Such a video stream is often weakly-annotated with a set of relevant
human action labels at a global level rather than assigning each label to a
specific vide... | ['Ke Chen', 'Qian Wang'] | 2017-09-15 | null | null | null | null | ['multi-label-zero-shot-learning'] | ['computer-vision'] | [ 6.31479502e-01 -2.24338502e-01 -4.71116513e-01 -2.71300137e-01
-8.06324005e-01 -2.25147083e-01 5.53255856e-01 3.84177640e-02
-4.78941172e-01 4.62096751e-01 4.32080120e-01 2.87842542e-01
-1.87735721e-01 -5.28085053e-01 -4.39546734e-01 -8.82866740e-01
1.57759577e-01 1.56282246e-01 3.33975434e-01 1.63410172... | [8.530386924743652, 0.7880268096923828] |
bd6016f9-2316-454f-bc00-1fce6b2409d6 | qu-brats-miccai-brats-2020-challenge-on | 2112.10074 | null | https://arxiv.org/abs/2112.10074v2 | https://arxiv.org/pdf/2112.10074v2.pdf | QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results | Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and po... | ['Mikhail Milchenko1', 'Marc-Andre Weber', 'Tommy Lofstedt', 'Ilyess Zemmoura', 'Sarahi Rosas-Gonzalez', 'Lin-min Pei', 'Yuan-han Mo', 'Hadrien Reynaud', 'Pablo Arbelaez', 'Catalina Gomez', 'Katrin Datwyler', 'Tal Arbel', 'Yarin Gal', 'Spyridon Bakas', 'Bjoern Menze', 'Christos Davatzikos', 'Justin Kirby', 'John Freyma... | 2021-12-19 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [-1.93603233e-01 6.97518826e-01 -3.14518183e-01 -5.76365769e-01
-1.51985955e+00 -5.73310614e-01 4.41871405e-01 7.36707389e-01
-4.21334267e-01 1.03804958e+00 6.13863707e-01 -7.11723566e-01
-3.85168761e-01 -5.18581450e-01 -6.62250578e-01 -5.48020005e-01
1.06816985e-01 8.68913114e-01 2.42453367e-02 5.44783354... | [14.429834365844727, -2.0568931102752686] |
99be6800-628a-41f0-b0e8-e3bb176e0331 | an-intrinsic-entropy-model-for-exchange | 2205.01386 | null | https://arxiv.org/abs/2205.01386v1 | https://arxiv.org/pdf/2205.01386v1.pdf | An Intrinsic Entropy Model for Exchange-Traded Securities | This article introduces an intrinsic entropy model that can be used as an indicator to gauge investor interest in a given exchange-traded security, along with the state of the general market corroborated by individual security trade data. Although the syntagma of intrinsic entropy might sound somehow pleonastic, since ... | ['Marcel Ausloos', 'Titus-Felix Furtuna', 'Ion Smeureanu', 'Claudiu Vinte'] | 2022-05-03 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-4.08283085e-01 1.36853188e-01 -3.08377296e-01 -1.47190854e-01
2.63599306e-02 -7.58490860e-01 8.41437995e-01 2.80485749e-01
-5.01084626e-01 6.48391008e-01 7.07568675e-02 -6.79949105e-01
-3.89524072e-01 -1.08613193e+00 -2.79332697e-01 -7.37194419e-01
-1.45432376e-03 3.93241912e-01 2.73386892e-02 -4.29179639... | [4.619071006774902, 4.120868682861328] |
e62ec370-ea74-4da8-aadb-a715025a2544 | mtrnet-a-generic-scene-text-eraser | 1903.04092 | null | https://arxiv.org/abs/1903.04092v3 | https://arxiv.org/pdf/1903.04092v3.pdf | MTRNet: A Generic Scene Text Eraser | Text removal algorithms have been proposed for uni-lingual scripts with regular shapes and layouts. However, to the best of our knowledge, a generic text removal method which is able to remove all or user-specified text regions regardless of font, script, language or shape is not available. Developing such a generic te... | ['Sridha Sridharan', 'Sabesan Sivapalan', 'Rui Zeng', 'Clinton Fookes', 'Simon Denman', 'Osman Tursun'] | 2019-03-11 | null | null | null | null | ['curved-text-detection'] | ['computer-vision'] | [ 5.89915812e-01 -3.33477974e-01 6.07098043e-01 -4.91498560e-02
-6.75889194e-01 -6.95971668e-01 6.56709015e-01 -2.46806860e-01
-2.62284786e-01 3.41533482e-01 -5.56091666e-02 -2.72922546e-01
4.82500196e-01 -5.19428015e-01 -8.90682995e-01 -5.46074033e-01
4.15186971e-01 6.20209634e-01 2.86587805e-01 -3.38014573... | [11.901041984558105, 2.0926928520202637] |
d46a446c-659c-42fa-b7c8-7a59e3889267 | breaking-down-the-ontology-alignment-task | 1805.12402 | null | http://arxiv.org/abs/1805.12402v1 | http://arxiv.org/pdf/1805.12402v1.pdf | Breaking-down the Ontology Alignment Task with a Lexical Index and Neural Embeddings | Large ontologies still pose serious challenges to state-of-the-art ontology
alignment systems. In the paper we present an approach that combines a lexical
index, a neural embedding model and locality modules to effectively divide an
input ontology matching task into smaller and more tractable matching
(sub)tasks. We ha... | ['Valerie Cross', 'Ernesto Jimenez-Ruiz', 'Matthias Samwald', 'Asan Agibetov'] | 2018-05-31 | null | null | null | null | ['ontology-matching'] | ['knowledge-base'] | [ 1.40757084e-01 3.77733022e-01 -3.74935508e-01 -4.60483134e-01
-3.60622048e-01 -1.51860401e-01 6.70919657e-01 6.72472417e-01
-6.62830353e-01 3.22290540e-01 6.22039676e-01 -1.75828904e-01
-6.29116118e-01 -7.33087480e-01 -2.77587384e-01 2.14282960e-01
-1.23122953e-01 1.16759646e+00 3.17962497e-01 -8.23800564... | [9.181114196777344, 8.171034812927246] |
4fbaf602-923f-4349-b703-d5d34fdb3df6 | effect-of-adaptive-and-fixed-shared-steering | 2106.03364 | null | https://arxiv.org/abs/2106.03364v1 | https://arxiv.org/pdf/2106.03364v1.pdf | Effect of Adaptive and Fixed Shared Steering Control on Distracted Driver Behavior | Driver distraction is a well-known cause for traffic collisions worldwide. Studies have indicated that shared steering control, which actively provides haptic guidance torque on the steering wheel, effectively improves the performance of distracted drivers. Recently, adaptive shared steering control based on the physio... | ['Kimihiko Nakano', 'Bo Yang', 'Edric John Cruz Nacpil', 'Satoshi Suga', 'Zheng Wang'] | 2021-06-07 | null | null | null | null | ['steering-control'] | ['computer-vision'] | [-3.11388016e-01 2.78811753e-01 -2.18232602e-01 -1.66800439e-01
-1.10516101e-01 -4.45148349e-01 1.72573090e-01 -1.39117450e-01
-8.85456502e-01 5.12191117e-01 2.20775396e-01 -7.33958185e-01
-3.93606663e-01 -1.84373707e-01 -3.22121769e-01 -5.86322308e-01
3.01684082e-01 -4.09751505e-01 2.85883427e-01 -6.14121616... | [5.754054069519043, 1.083006739616394] |
4033c675-1c91-44ae-9a7f-3adbfaf5b734 | swem-towards-real-time-video-object-1 | 2208.10128 | null | https://arxiv.org/abs/2208.10128v1 | https://arxiv.org/pdf/2208.10128v1.pdf | SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-Maximization | Matching-based methods, especially those based on space-time memory, are significantly ahead of other solutions in semi-supervised video object segmentation (VOS). However, continuously growing and redundant template features lead to an inefficient inference. To alleviate this, we propose a novel Sequential Weighted Ex... | ['Wei Liu', 'Wenhao Jiang', 'Chun Yuan', 'Ziyu Wang', 'Maomao Li', 'Tianyu Yang', 'Zhihui Lin'] | 2022-08-22 | swem-towards-real-time-video-object | http://openaccess.thecvf.com//content/CVPR2022/html/Lin_SWEM_Towards_Real-Time_Video_Object_Segmentation_With_Sequential_Weighted_Expectation-Maximization_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lin_SWEM_Towards_Real-Time_Video_Object_Segmentation_With_Sequential_Weighted_Expectation-Maximization_CVPR_2022_paper.pdf | cvpr-2022-1 | ['semi-supervised-video-object-segmentation'] | ['computer-vision'] | [ 1.05715036e-01 -2.93785155e-01 -2.07362890e-01 -5.21952212e-01
-4.92256194e-01 -1.67900115e-01 -5.22728376e-02 -2.58259952e-01
-5.67889690e-01 4.76286829e-01 -3.66424620e-01 -1.38464421e-01
-1.74482763e-01 -7.27974653e-01 -6.83272958e-01 -6.66134894e-01
2.35818446e-01 1.00258537e-01 6.50567770e-01 3.99099350... | [9.166088104248047, -0.12747687101364136] |
d9ceca1c-4504-4e8c-9f54-91cd0a7b53b0 | instance-based-counterfactual-explanations | 2009.13211 | null | https://arxiv.org/abs/2009.13211v2 | https://arxiv.org/pdf/2009.13211v2.pdf | Instance-based Counterfactual Explanations for Time Series Classification | In recent years, there has been a rapidly expanding focus on explaining the predictions made by black-box AI systems that handle image and tabular data. However, considerably less attention has been paid to explaining the predictions of opaque AI systems handling time series data. In this paper, we advance a novel mode... | ['Eoin Delaney', 'Mark T. Keane', 'Derek Greene'] | 2020-09-28 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 4.96570915e-01 6.50913239e-01 -4.16150481e-01 -6.51398003e-01
-4.93427336e-01 -5.53061306e-01 9.17280912e-01 9.84876081e-02
2.72502333e-01 1.09492123e+00 3.31954837e-01 -8.00573945e-01
-5.45591533e-01 -8.69830012e-01 -6.74734294e-01 -3.34361136e-01
-4.10283864e-01 4.00112629e-01 -1.56617269e-01 -2.28966236... | [8.742711067199707, 5.6408891677856445] |
83cebba7-90b4-48b9-93ea-deefee83495c | glot500-scaling-multilingual-corpora-and | 2305.12182 | null | https://arxiv.org/abs/2305.12182v2 | https://arxiv.org/pdf/2305.12182v2.pdf | Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages | The NLP community has mainly focused on scaling Large Language Models (LLMs) vertically, i.e., making them better for about 100 languages. We instead scale LLMs horizontally: we create, through continued pretraining, Glot500-m, an LLM that covers 511 predominantly low-resource languages. An important part of this effor... | ['Ayyoob Imani', 'Hinrich Schütze', 'François Yvon', 'André F. T. Martins', 'Helmut Schmid', 'Chunlan Ma', 'Nora Kassner', 'Masoud Jalili Sabet', 'Silvia Severini', 'Amir Hossein Kargaran', 'Peiqin Lin'] | 2023-05-20 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [-4.49194938e-01 3.18424441e-02 -5.52648723e-01 -2.20535189e-01
-1.27219367e+00 -8.85131478e-01 5.97325861e-01 1.02409698e-01
-6.83355570e-01 8.01146209e-01 7.34944522e-01 -7.79222250e-01
2.89596617e-01 -6.07910275e-01 -7.15152979e-01 1.16744516e-02
3.99823785e-01 7.83673108e-01 -3.33610386e-01 -3.93304139... | [10.874897003173828, 9.83910083770752] |
b3275bd1-b61a-4682-9800-469c116c92ad | hifi-wavegan-generative-adversarial-network | 2210.12740 | null | https://arxiv.org/abs/2210.12740v2 | https://arxiv.org/pdf/2210.12740v2.pdf | HiFi-WaveGAN: Generative Adversarial Network with Auxiliary Spectrogram-Phase Loss for High-Fidelity Singing Voice Generation | Entertainment-oriented singing voice synthesis (SVS) requires a vocoder to generate high-fidelity (e.g. 48kHz) audio. However, most text-to-speech (TTS) vocoders cannot work well in this scenario even if the neural vocoder for TTS has achieved significant progress. In this paper, we propose HiFi-WaveGAN which is design... | ['Xing He', 'Chang Zeng', 'Chunhui Wang'] | 2022-10-23 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [ 1.02193013e-01 -1.79242175e-02 2.12958530e-01 1.05572775e-01
-1.05564737e+00 -3.41623932e-01 1.63227007e-01 -7.92067289e-01
1.90391019e-02 6.69053972e-01 4.24842536e-01 -3.17366540e-01
1.37230039e-01 -6.55088246e-01 -7.84926176e-01 -9.32721972e-01
1.37757689e-01 -1.52340919e-01 -7.24327099e-03 -4.51495051... | [15.486860275268555, 6.195957660675049] |
6d09dc18-3af7-4df8-85b1-086545a75ca9 | mfqe-20-a-new-approach-for-multi-frame | 1902.09707 | null | https://arxiv.org/abs/1902.09707v6 | https://arxiv.org/pdf/1902.09707v6.pdf | MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video | The past few years have witnessed great success in applying deep learning to enhance the quality of compressed image/video. The existing approaches mainly focus on enhancing the quality of a single frame, not considering the similarity between consecutive frames. Since heavy fluctuation exists across compressed video f... | ['Mai Xu', 'Zulin Wang', 'Tie Liu', 'Ren Yang', 'Qunliang Xing', 'Zhenyu Guan'] | 2019-02-26 | null | null | null | null | ['video-enhancement', 'video-restoration'] | ['computer-vision', 'computer-vision'] | [ 1.97031796e-01 -3.96215469e-01 -1.50386453e-01 -9.52126384e-02
-7.00917006e-01 1.79040655e-02 1.95782632e-01 1.80264805e-02
-4.72641051e-01 5.28245866e-01 3.46027792e-01 -1.09143786e-01
-8.09589997e-02 -8.69886100e-01 -8.16669583e-01 -6.61419809e-01
-1.23760328e-01 -6.05034053e-01 5.44388354e-01 -1.67264462... | [11.311899185180664, -1.7516025304794312] |
76e1676b-7a61-48fe-9c63-de49384b56a5 | a-category-theory-framework-for-sense-systems | null | null | https://aclanthology.org/2022.gwll-1.7 | https://aclanthology.org/2022.gwll-1.7.pdf | A Category Theory Framework for Sense Systems | Sense repositories are a key component of many NLP applications that require the identification of word senses. Many sense repositories exist: a large proportion is based on lexicographic resources such as WordNet and various dictionaries, but there are others which are the product of clustering algorithms and other au... | ['Gladys Tyen', 'David Strohmaier'] | null | null | null | null | gwll-lrec-2022-6 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 2.07158074e-01 8.34632069e-02 -2.39657640e-01 -1.10728987e-01
-2.47974709e-01 -1.11103034e+00 8.41234982e-01 5.94682455e-01
-5.21071851e-01 5.72461903e-01 4.57382023e-01 -3.61149728e-01
-4.36392784e-01 -9.76621866e-01 -6.36098012e-02 -4.56527770e-01
2.26542741e-01 3.24005693e-01 4.31806356e-01 -5.94202638... | [10.231735229492188, 9.141035079956055] |
18671bf7-86ed-419a-9e66-755065d9d4bb | low-rank-matrix-completion-via-robust | 2302.11068 | null | https://arxiv.org/abs/2302.11068v1 | https://arxiv.org/pdf/2302.11068v1.pdf | Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time | Given a matrix $M\in \mathbb{R}^{m\times n}$, the low rank matrix completion problem asks us to find a rank-$k$ approximation of $M$ as $UV^\top$ for $U\in \mathbb{R}^{m\times k}$ and $V\in \mathbb{R}^{n\times k}$ by only observing a few entries masked by a binary matrix $P_{\Omega}\in \{0, 1 \}^{m\times n}$. As a part... | ['Lichen Zhang', 'Junze Yin', 'Zhao Song', 'Yuzhou Gu'] | 2023-02-21 | null | null | null | null | ['low-rank-matrix-completion', 'matrix-completion'] | ['methodology', 'methodology'] | [ 3.51565868e-01 1.92244679e-01 -9.40214545e-02 -4.41728681e-02
-1.32685566e+00 -7.79464364e-01 -2.48264670e-01 5.02159446e-02
-5.00689745e-01 6.96831286e-01 -6.05193302e-02 -6.76322699e-01
-7.94728398e-01 -8.09100866e-01 -9.30612504e-01 -7.77669311e-01
-6.16101444e-01 5.32863200e-01 -2.42652491e-01 -5.33379257... | [6.617696762084961, 4.718226909637451] |
abe0a40e-7ff8-4fe9-b7b6-65b0fbe952b4 | systematic-generalization-what-is-required | 1811.12889 | null | http://arxiv.org/abs/1811.12889v3 | http://arxiv.org/pdf/1811.12889v3.pdf | Systematic Generalization: What Is Required and Can It Be Learned? | Numerous models for grounded language understanding have been recently
proposed, including (i) generic models that can be easily adapted to any given
task and (ii) intuitively appealing modular models that require background
knowledge to be instantiated. We compare both types of models in how much they
lend themselves ... | ['Michael Noukhovitch', 'Thien Huu Nguyen', 'Harm de Vries', 'Shikhar Murty', 'Dzmitry Bahdanau', 'Aaron Courville'] | 2018-11-30 | systematic-generalization-what-is-required-1 | https://openreview.net/forum?id=HkezXnA9YX | https://openreview.net/pdf?id=HkezXnA9YX | iclr-2019-5 | ['systematic-generalization'] | ['reasoning'] | [ 6.42347187e-02 6.13645554e-01 3.66368741e-02 -4.85616833e-01
-4.67504859e-01 -1.00209630e+00 6.31204486e-01 2.42213264e-01
-1.95233822e-01 4.64698762e-01 3.03924918e-01 -4.56375182e-01
-1.94476202e-01 -8.90745461e-01 -9.55915868e-01 -1.78541183e-01
-1.22681327e-01 8.85290265e-01 2.30065659e-01 -5.32178283... | [9.563258171081543, 6.980295181274414] |
ceb689ab-adf7-4183-ae9a-d017743585b8 | deep-image-harmonization-via-domain | 1911.13239 | null | https://arxiv.org/abs/1911.13239v3 | https://arxiv.org/pdf/1911.13239v3.pdf | DoveNet: Deep Image Harmonization via Domain Verification | Image composition is an important operation in image processing, but the inconsistency between foreground and background significantly degrades the quality of composite image. Image harmonization, aiming to make the foreground compatible with the background, is a promising yet challenging task. However, the lack of hig... | ['Wenyan Cong', 'Zhixin Ling', 'Weiyuan Li', 'Li Niu', 'Jianfu Zhang', 'Liu Liu', 'Liqing Zhang'] | 2019-11-27 | dovenet-deep-image-harmonization-via-domain | http://openaccess.thecvf.com/content_CVPR_2020/html/Cong_DoveNet_Deep_Image_Harmonization_via_Domain_Verification_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Cong_DoveNet_Deep_Image_Harmonization_via_Domain_Verification_CVPR_2020_paper.pdf | cvpr-2020-6 | ['image-harmonization'] | ['computer-vision'] | [ 3.49090874e-01 -3.93114924e-01 5.80312610e-02 -1.23912826e-01
-5.97977579e-01 -7.34068751e-01 6.33583963e-01 -1.38914749e-01
-2.46471599e-01 7.05116451e-01 -3.68110910e-02 -9.57181603e-02
1.33558229e-01 -6.48844123e-01 -7.05798805e-01 -8.88346076e-01
6.84678197e-01 -7.20083416e-02 2.55280048e-01 -1.96835533... | [11.247176170349121, -1.181750774383545] |
869a74b7-dbcf-4153-a9cd-e2850e503b36 | what-do-neural-machine-translation-models | 1704.03471 | null | http://arxiv.org/abs/1704.03471v3 | http://arxiv.org/pdf/1704.03471v3.pdf | What do Neural Machine Translation Models Learn about Morphology? | Neural machine translation (MT) models obtain state-of-the-art performance
while maintaining a simple, end-to-end architecture. However, little is known
about what these models learn about source and target languages during the
training process. In this work, we analyze the representations learned by
neural MT models a... | ['Hassan Sajjad', 'Nadir Durrani', 'James Glass', 'Yonatan Belinkov', 'Fahim Dalvi'] | 2017-04-11 | what-do-neural-machine-translation-models-1 | https://aclanthology.org/P17-1080 | https://aclanthology.org/P17-1080.pdf | acl-2017-7 | ['morphological-tagging'] | ['natural-language-processing'] | [ 4.69776899e-01 3.20162803e-01 -5.65603018e-01 -4.90413725e-01
-9.97383118e-01 -8.66597295e-01 7.90771306e-01 1.89155281e-01
-4.49611723e-01 5.63527942e-01 5.76442540e-01 -7.80222356e-01
3.95435601e-01 -6.51408315e-01 -9.08973694e-01 -3.59592497e-01
2.44266555e-01 6.36115074e-01 -1.35431483e-01 -2.52616823... | [11.311836242675781, 9.833226203918457] |
1a0a8937-0555-477b-ad65-d174d6c94302 | learning-conditional-attributes-for-1 | 2305.17940 | null | https://arxiv.org/abs/2305.17940v2 | https://arxiv.org/pdf/2305.17940v2.pdf | Learning Conditional Attributes for Compositional Zero-Shot Learning | Compositional Zero-Shot Learning (CZSL) aims to train models to recognize novel compositional concepts based on learned concepts such as attribute-object combinations. One of the challenges is to model attributes interacted with different objects, e.g., the attribute ``wet" in ``wet apple" and ``wet cat" is different. ... | ['Chunhua Shen', 'Peng Wang', 'Guoqiang Liang', 'Hao Chen', 'Chenchen Jing', 'Lingqiao Liu', 'Qingsheng Wang'] | 2023-05-29 | learning-conditional-attributes-for | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Learning_Conditional_Attributes_for_Compositional_Zero-Shot_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Learning_Conditional_Attributes_for_Compositional_Zero-Shot_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 5.66829681e-01 2.17061788e-01 -1.81130707e-01 -7.63519883e-01
-7.58337915e-01 -6.39111578e-01 7.04988956e-01 2.52179980e-01
-1.32744983e-01 4.59417909e-01 2.12567806e-01 9.37267095e-02
2.80718468e-02 -1.00478685e+00 -9.19134259e-01 -8.97555709e-01
1.01368897e-01 8.59804332e-01 -1.17019847e-01 -1.67011797... | [10.15123176574707, 2.2692601680755615] |
7b75bdda-a2d7-4ff7-8f26-92af9bff058f | impact-of-spatiotemporal-heterogeneity-in | 2204.00353 | null | https://arxiv.org/abs/2204.00353v1 | https://arxiv.org/pdf/2204.00353v1.pdf | Impact of spatiotemporal heterogeneity in heat pump loads on generation and storage requirements | This paper investigates how spatiotemporal heterogeneity in inflexible residential heat pump loads affects the need for storage and generation in the electricity system under business-as-usual and low-carbon emissions budgets. Homogeneous and heterogeneous heat pump loads are generated using population-weighted average... | ['Malcolm D. McCulloch', 'Filiberto Fele', 'Claire E. Halloran'] | 2022-04-01 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-5.48388362e-01 1.49255484e-01 -3.42402995e-01 6.17302358e-02
-2.76691377e-01 -5.83946109e-01 6.96692586e-01 4.04085129e-01
7.89959431e-02 1.18685079e+00 4.43898082e-01 -5.62114358e-01
-2.99342275e-01 -1.43158460e+00 -3.15465361e-01 -1.00311077e+00
-1.29780442e-01 6.41699493e-01 -1.16829425e-01 -1.75348997... | [5.72064733505249, 2.486905574798584] |
7922f0e7-d521-4fd8-81da-3598446f2487 | multi-task-collaborative-pre-training-and | 2306.11378 | null | https://arxiv.org/abs/2306.11378v1 | https://arxiv.org/pdf/2306.11378v1.pdf | Multi-task Collaborative Pre-training and Individual-adaptive-tokens Fine-tuning: A Unified Framework for Brain Representation Learning | Structural magnetic resonance imaging (sMRI) provides accurate estimates of the brain's structural organization and learning invariant brain representations from sMRI is an enduring issue in neuroscience. Previous deep representation learning models ignore the fact that the brain, as the core of human cognitive activit... | ['Tianyi Yan', 'Gongshu Wang', 'Ning Jiang'] | 2023-06-20 | null | null | null | null | ['auxiliary-learning', 'anatomy'] | ['methodology', 'miscellaneous'] | [ 2.48356432e-01 -9.79767218e-02 -2.07510382e-01 -4.28649098e-01
-4.67651278e-01 -3.68565708e-01 5.81642687e-01 2.36061245e-01
-4.78834450e-01 6.97635412e-01 4.55303311e-01 1.30530357e-01
-7.83967793e-01 -6.38504148e-01 -5.14868200e-01 -6.80983484e-01
-1.43075645e-01 6.32125497e-01 3.69936526e-02 -1.11366391... | [12.501019477844238, 3.3158321380615234] |
d8c1fa86-b585-44aa-821e-0b87f0f58175 | label-dependencies-aware-set-prediction | 2304.07022 | null | https://arxiv.org/abs/2304.07022v1 | https://arxiv.org/pdf/2304.07022v1.pdf | Label Dependencies-aware Set Prediction Networks for Multi-label Text Classification | Multi-label text classification aims to extract all the related labels from a sentence, which can be viewed as a sequence generation problem. However, the labels in training dataset are unordered. We propose to treat it as a direct set prediction problem and don't need to consider the order of labels. Besides, in order... | ['Lv Chao', 'Sun Yalin', 'Du Xinkai', 'Han Quanjie'] | 2023-04-14 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 6.84360385e-01 1.93957798e-02 -3.14995855e-01 -7.61615694e-01
-3.70042562e-01 -6.88531220e-01 2.83817351e-01 9.92452130e-02
-2.76105791e-01 7.50539660e-01 1.52963459e-01 -7.87086189e-02
-3.19612026e-01 -8.71831954e-01 -2.05526203e-01 -8.20207119e-01
5.17704904e-01 4.78082508e-01 4.26276959e-02 -1.96195975... | [9.692999839782715, 4.191701889038086] |
ea5aa0ab-6513-4717-8dff-6a075c7dce84 | semantic-role-labeling-meets-definition | 2212.01094 | null | https://arxiv.org/abs/2212.01094v1 | https://arxiv.org/pdf/2212.01094v1.pdf | Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures | One of the common traits of past and present approaches for Semantic Role Labeling (SRL) is that they rely upon discrete labels drawn from a predefined linguistic inventory to classify predicate senses and their arguments. However, we argue this need not be the case. In this paper, we present an approach that leverages... | ['Roberto Navigli', 'Alessandro Scirè', 'Edoardo Barba', 'Simone Conia'] | 2022-12-02 | null | null | null | null | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 2.61196256e-01 5.95646322e-01 -4.96589631e-01 -7.42164850e-01
-4.18299794e-01 -1.04739559e+00 8.75775576e-01 3.82600218e-01
-3.46205443e-01 9.84545827e-01 5.72758079e-01 -6.66979134e-01
-3.26115429e-01 -6.68201029e-01 -1.81383103e-01 -2.16630131e-01
3.10028553e-01 5.13107955e-01 4.64084357e-01 -6.08203053... | [10.26990032196045, 9.264399528503418] |
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