paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d4eb8fb9-684e-4870-a1ee-f2b5b15a1f1b | low-latency-sequence-to-sequence-speech | 2005.11185 | null | https://arxiv.org/abs/2005.11185v2 | https://arxiv.org/pdf/2005.11185v2.pdf | Low-Latency Sequence-to-Sequence Speech Recognition and Translation by Partial Hypothesis Selection | Encoder-decoder models provide a generic architecture for sequence-to-sequence tasks such as speech recognition and translation. While offline systems are often evaluated on quality metrics like word error rates (WER) and BLEU, latency is also a crucial factor in many practical use-cases. We propose three latency reduc... | ['Jan Niehues', 'Gerasimos Spanakis', 'Danni Liu'] | 2020-05-22 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 3.78959388e-01 2.06882611e-01 -8.53425562e-02 -3.55515689e-01
-1.63990963e+00 -6.72785938e-01 4.73495871e-01 3.43345255e-01
-7.86949039e-01 7.76058137e-01 1.40196338e-01 -8.84348094e-01
4.06631261e-01 -2.56365716e-01 -8.41362357e-01 -3.15246314e-01
8.30408260e-02 6.35066330e-01 3.85730416e-01 -2.99836714... | [14.464800834655762, 7.032690525054932] |
15107676-c39f-433e-b8d1-e3de88d40e01 | st-mfnet-mini-knowledge-distillation-driven | 2302.08455 | null | https://arxiv.org/abs/2302.08455v2 | https://arxiv.org/pdf/2302.08455v2.pdf | ST-MFNet Mini: Knowledge Distillation-Driven Frame Interpolation | Currently, one of the major challenges in deep learning-based video frame interpolation (VFI) is the large model sizes and high computational complexity associated with many high performance VFI approaches. In this paper, we present a distillation-based two-stage workflow for obtaining compressed VFI models which perfo... | ['David R. Bull', 'Nantheera Anantrasirichai', 'Fan Zhang', 'Duolikun Danier', 'Crispian Morris'] | 2023-02-16 | null | null | null | null | ['video-frame-interpolation'] | ['computer-vision'] | [ 0.3056803 0.18613818 -0.3226866 -0.08599804 -0.5854744 0.12763354
0.50958943 -0.16702968 -0.539411 0.9283939 -0.01948026 -0.5329595
-0.01200223 -0.5231773 -1.0744643 -0.32698914 -0.07097666 0.33525616
0.20377061 0.03461755 0.12624504 0.2734691 -1.4386399 0.31767377
1.0485402 1.2848457 0.2... | [10.67928409576416, -1.3576750755310059] |
4c6f75df-2f59-434b-94c5-cf475f039f6d | a-novel-bi-hemispheric-discrepancy-model-for-1 | 1906.01704 | null | http://arxiv.org/abs/1906.01704v1 | http://arxiv.org/pdf/1906.01704v1.pdf | A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognition | The neuroscience study has revealed the discrepancy of emotion expression
between left and right hemispheres of human brain. Inspired by this study, in
this paper, we propose a novel bi-hemispheric discrepancy model (BiHDM) to
learn the asymmetric differences between two hemispheres for
electroencephalograph (EEG) emot... | [] | 2019-05-11 | a-novel-bi-hemispheric-discrepancy-model-for | https://arxiv.org/abs/1906.01704 | https://arxiv.org/pdf/1906.01704 | arxiv190601704-search-help-advanced-search | ['eeg-emotion-recognition'] | ['miscellaneous'] | [-5.31941056e-02 -1.59118310e-01 4.46813852e-01 -7.18735933e-01
-1.98072061e-01 -3.85985136e-01 2.26849273e-01 -6.61805332e-01
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-1.55329242e-01 -3.05838495e-01 -4.24972147e-01 -2.40836769... | [13.12378215789795, 3.499345064163208] |
c64a4cba-e27f-445c-a748-ae3d01024ae1 | exploiting-neighborhood-structural-features | 2302.05114 | null | https://arxiv.org/abs/2302.05114v1 | https://arxiv.org/pdf/2302.05114v1.pdf | Exploiting Neighborhood Structural Features for Change Detection | In this letter, a novel method for change detection is proposed using neighborhood structure correlation. Because structure features are insensitive to the intensity differences between bi-temporal images, we perform the correlation analysis on structure features rather than intensity information. First, we extract the... | ['Yuanxin Ye', 'Jianwei Fan', 'Ming Hao', 'Bai Zhu', 'Peizhen Yang', 'Zhiqiang Han', 'Mengmeng Wang'] | 2023-02-10 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 5.91772854e-01 -8.40877950e-01 -1.28045022e-01 -3.34106207e-01
-1.70359910e-01 -1.33061051e-01 3.85258228e-01 1.58904612e-01
-4.23178732e-01 5.53062141e-01 1.17470130e-01 -1.82234938e-03
-2.06749424e-01 -1.00653815e+00 -1.05389848e-01 -1.02152252e+00
1.28796488e-01 -4.35919821e-01 7.02382326e-01 -1.03530914... | [9.991923332214355, -1.031110167503357] |
620db457-de38-4a52-bf7d-9920336d67f7 | transferable-deep-metric-learning-for | 2302.06523 | null | https://arxiv.org/abs/2302.06523v1 | https://arxiv.org/pdf/2302.06523v1.pdf | Transferable Deep Metric Learning for Clustering | Clustering in high dimension spaces is a difficult task; the usual distance metrics may no longer be appropriate under the curse of dimensionality. Indeed, the choice of the metric is crucial, and it is highly dependent on the dataset characteristics. However a single metric could be used to correctly perform clusterin... | ['Jesse Read', 'Rim Kaddah', 'Simo Alami. C'] | 2023-02-13 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 4.06255201e-02 -2.40113467e-01 3.35558467e-02 -5.79987884e-01
-6.66864693e-01 -8.60737026e-01 7.27418840e-01 4.69642341e-01
-7.83770561e-01 4.31162924e-01 4.41657426e-03 -7.59226270e-03
-6.60414577e-01 -6.66902721e-01 -2.81036139e-01 -8.29094410e-01
-1.48739889e-01 9.55244362e-01 3.75097215e-01 -5.20205460... | [9.134471893310547, 3.0916271209716797] |
d611fcf0-98da-41e6-8bc6-3d9a4e489068 | batch-prompting-efficient-inference-with | 2301.08721 | null | https://arxiv.org/abs/2301.08721v1 | https://arxiv.org/pdf/2301.08721v1.pdf | Batch Prompting: Efficient Inference with Large Language Model APIs | Performing inference on hundreds of thousands of samples with large language models (LLMs) can be computationally and financially costly. We propose batch prompting, a simple alternative prompting approach that enables the LLM to run inference in batches, instead of one sample at a time. Our method reduces both token a... | ['Tao Yu', 'Jungo Kasai', 'Zhoujun Cheng'] | 2023-01-19 | null | null | null | null | ['arithmetic-reasoning'] | ['reasoning'] | [ 2.04578191e-01 1.50512293e-01 -1.59198772e-02 -5.65687358e-01
-1.14691341e+00 -7.29798198e-01 7.04552889e-01 3.08885485e-01
-8.63251805e-01 6.36741579e-01 2.29829594e-01 -7.41031170e-01
-1.56183749e-01 -7.77439296e-01 -8.28371584e-01 -1.38791770e-01
2.51843363e-01 6.94562852e-01 -1.01025030e-03 5.14499843... | [9.77253246307373, 7.4462714195251465] |
9c198a98-2454-4b2b-8c14-49466992d6bc | feature-extraction-of-text-for-deep-learning | 2010.05496 | null | https://arxiv.org/abs/2010.05496v2 | https://arxiv.org/pdf/2010.05496v2.pdf | Feature Extraction of Text for Deep Learning Algorithms: Application on Fake News Detection | Feature extraction is an important process of machine learning and deep learning, as the process make algorithms function more efficiently, and also accurate. In natural language processing used in deception detection such as fake news detection, several ways of feature extraction in statistical aspect had been introdu... | ['HyeonJun Kim'] | 2020-10-12 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [-2.29942888e-01 -1.05023518e-01 -2.40909934e-01 -4.61932868e-01
-2.34359317e-02 -5.06202638e-01 8.61175179e-01 5.11546969e-01
-4.10284877e-01 7.54876018e-01 3.39315236e-01 -4.65026051e-01
1.76955894e-01 -1.18645215e+00 -6.66593075e-01 -6.05272055e-01
-2.94162072e-02 2.04395384e-01 -1.67448252e-01 -5.89304209... | [8.143367767333984, 10.244889259338379] |
75811b2f-08da-4e94-ae1a-aa8258044781 | multi-hop-reading-comprehension-across-2 | 2006.06478 | null | https://arxiv.org/abs/2006.06478v2 | https://arxiv.org/pdf/2006.06478v2.pdf | Multi-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network | Multi-hop reading comprehension across multiple documents attracts much attention recently. In this paper, we propose a novel approach to tackle this multi-hop reading comprehension problem. Inspired by human reasoning processing, we construct a path-based reasoning graph from supporting documents. This graph can combi... | ['Wei Xu', 'Yongliang Shen', 'Zeyun Tang', 'Weiming Lu', 'Xinyin Ma', 'Jiale Yu'] | 2020-06-11 | null | null | null | null | ['multi-hop-reading-comprehension'] | ['natural-language-processing'] | [ 2.67278165e-01 4.76121008e-01 -1.50784850e-01 -3.42005014e-01
-7.90970385e-01 -4.27394181e-01 4.64896441e-01 9.41116869e-01
-5.38772106e-01 6.05089068e-01 6.58302784e-01 -6.35447383e-01
-4.98584151e-01 -1.19730270e+00 -6.84718609e-01 -1.82717845e-01
5.94212651e-01 3.16964805e-01 9.81429160e-01 -6.25191808... | [10.849823951721191, 7.939654350280762] |
14768aa6-40c4-4fc7-b51f-792d7b2888f1 | inference-of-a-rumor-s-source-in-the | 2205.12125 | null | https://arxiv.org/abs/2205.12125v1 | https://arxiv.org/pdf/2205.12125v1.pdf | Inference of a Rumor's Source in the Independent Cascade Model | We consider the so-called Independent Cascade Model for rumor spreading or epidemic processes popularized by Kempe et al.\ [2003]. In this model, a small subset of nodes from a network are the source of a rumor. In discrete time steps, each informed node "infects" each of its uninformed neighbors with probability $p$. ... | ['Malin Rau', 'Lena Krieg', 'Dominik Kaaser', 'Max Hahn-Klimroth', 'Petra Berenbrink'] | 2022-05-24 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 1.55174816e-02 4.76608604e-01 -3.86584669e-01 7.01232627e-03
1.34100184e-01 -5.37554801e-01 4.45320189e-01 2.46431544e-01
-3.30089539e-01 8.40720475e-01 -2.12633461e-01 -4.50102985e-01
-3.69852304e-01 -1.17700005e+00 -5.96652985e-01 -7.90237129e-01
-8.30838621e-01 1.06992924e+00 1.31235734e-01 -3.23860496... | [6.677347660064697, 5.073212146759033] |
94b8ea10-3d13-43c7-9e9e-2372312982b0 | unsupervised-low-light-image-enhancement | 2306.02082 | null | https://arxiv.org/abs/2306.02082v1 | https://arxiv.org/pdf/2306.02082v1.pdf | Unsupervised Low Light Image Enhancement Using SNR-Aware Swin Transformer | Image captured under low-light conditions presents unpleasing artifacts, which debilitate the performance of feature extraction for many upstream visual tasks. Low-light image enhancement aims at improving brightness and contrast, and further reducing noise that corrupts the visual quality. Recently, many image restora... | ['Yanzeng Gao', 'Zihan Huang', 'Yueen Hou', 'Jiahui Tang', 'Zhijian Luo'] | 2023-06-03 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement', 'image-restoration'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.31698966e-01 -5.58884501e-01 2.18855917e-01 -3.00276279e-01
-5.26183665e-01 -2.69432366e-01 3.27939272e-01 -2.00988382e-01
-2.34080359e-01 8.17030191e-01 1.70116037e-01 -9.10464525e-02
-1.37269303e-01 -7.55411923e-01 -5.07031262e-01 -1.23485386e+00
4.95989978e-01 -7.61566699e-01 -2.96833925e-02 -2.17649356... | [10.817492485046387, -2.504446268081665] |
8cbd803e-8b26-4355-adb5-7c1441ef10ca | asvspoof-2019-spoofing-countermeasures-for | 2102.05889 | null | https://arxiv.org/abs/2102.05889v1 | https://arxiv.org/pdf/2102.05889v1.pdf | ASVspoof 2019: spoofing countermeasures for the detection of synthesized, converted and replayed speech | The ASVspoof initiative was conceived to spearhead research in anti-spoofing for automatic speaker verification (ASV). This paper describes the third in a series of bi-annual challenges: ASVspoof 2019. With the challenge database and protocols being described elsewhere, the focus of this paper is on results and the top... | ['Kong Aik Lee', 'Junichi Yamagishi', 'Md Sahidullah', 'Héctor Delgado', 'Massimiliano Todisco', 'Ville Vestman', 'Tomi Kinnunen', 'Nicholas Evans', 'Xin Wang', 'Andreas Nautsch'] | 2021-02-11 | null | null | null | null | ['voice-anti-spoofing'] | ['audio'] | [ 1.35911867e-01 -9.23002884e-02 2.46494696e-01 -3.35508287e-01
-1.20505166e+00 -7.80908823e-01 9.10560966e-01 3.72305103e-02
-3.51217479e-01 3.39546621e-01 6.72183633e-01 -6.62073314e-01
1.15881953e-02 1.23735242e-01 -5.02507031e-01 -5.89000523e-01
-3.14826593e-02 1.22130595e-01 -1.03724107e-01 -6.27050400... | [14.188016891479492, 5.991428852081299] |
9605487c-caf9-4547-b2c9-b650c6811dc7 | lexical-complexity-prediction-an-overview | 2303.04851 | null | https://arxiv.org/abs/2303.04851v1 | https://arxiv.org/pdf/2303.04851v1.pdf | Lexical Complexity Prediction: An Overview | The occurrence of unknown words in texts significantly hinders reading comprehension. To improve accessibility for specific target populations, computational modelling has been applied to identify complex words in texts and substitute them for simpler alternatives. In this paper, we present an overview of computational... | ['Matthew Shardlow', 'Marcos Zampieri', 'Kai North'] | 2023-03-08 | null | null | null | null | ['lexical-complexity-prediction', 'reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.26058602e-01 2.29197755e-01 -3.45053613e-01 -2.70969093e-01
-4.69447047e-01 -5.28392076e-01 3.78364861e-01 8.76301646e-01
-1.02367473e+00 7.96218455e-01 5.68960369e-01 -7.48047233e-01
-2.36010760e-01 -6.64445221e-01 -4.45625961e-01 -1.02727693e-02
4.01103824e-01 4.91623670e-01 -2.16224208e-01 -5.29661894... | [10.888261795043945, 10.337769508361816] |
3e0648a8-bfbe-4896-a5df-d18c55f20c64 | construction-of-segmentation-and-part-of | null | null | https://aclanthology.org/2022.lt4hala-1.23 | https://aclanthology.org/2022.lt4hala-1.23.pdf | Construction of Segmentation and Part of Speech Annotation Model in Ancient Chinese | Among the four civilizations in the world with the longest history, only Chinese civilization has been inherited and never interrupted for 5000 years. An important factor is that the Chinese nation has the fine tradition of sorting out classics. Recording history with words, inheriting culture through continuous collat... | ['Zhuying Z. Xia', 'Huyin H. Xie', 'Qinyu C. Chang', 'Longjie Jiang'] | null | null | null | null | lt4hala-lrec-2022-6 | ['culture'] | ['speech'] | [-3.55324298e-01 -2.32256711e-01 -2.14151844e-01 -3.51384670e-01
-3.54955345e-01 -5.97595334e-01 6.21655107e-01 -3.53372276e-01
-8.35875034e-01 1.07589042e+00 6.77607894e-01 -4.79672283e-01
2.03299612e-01 -8.18614900e-01 1.61766317e-02 -5.89311242e-01
1.81761961e-02 6.52401984e-01 -2.87843291e-02 -5.36603510... | [10.45710277557373, 10.114514350891113] |
89765475-942c-4b66-a3e0-5eea461cfb79 | a-hypergraph-based-machine-learning-ensemble | 2211.03933 | null | https://arxiv.org/abs/2211.03933v2 | https://arxiv.org/pdf/2211.03933v2.pdf | A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection System | Network intrusion detection systems (NIDS) to detect malicious attacks continue to meet challenges. NIDS are often developed offline while they face auto-generated port scan infiltration attempts, resulting in a significant time lag from adversarial adaption to NIDS response. To address these challenges, we use hypergr... | ['Nathaniel D. Bastian', 'Mark M. Bailey', 'Thomas D. Pike', 'Zong-Zhi Lin'] | 2022-11-08 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 2.78003335e-01 -2.73441195e-01 3.78424451e-02 -2.13077486e-01
4.44314368e-02 -7.90515125e-01 7.97841132e-01 -2.07601666e-01
-4.00238395e-01 5.38499713e-01 -6.28567517e-01 -8.51686478e-01
-3.96633774e-01 -1.09561098e+00 -1.15699582e-01 -3.43457639e-01
-4.54740644e-01 9.79192853e-01 8.40661585e-01 -1.29232377... | [5.353024482727051, 7.335224151611328] |
da45c2ca-8078-416b-9f45-8104d0323c1d | werewolf-among-us-a-multimodal-dataset-for | 2212.08279 | null | https://arxiv.org/abs/2212.08279v1 | https://arxiv.org/pdf/2212.08279v1.pdf | Werewolf Among Us: A Multimodal Dataset for Modeling Persuasion Behaviors in Social Deduction Games | Persuasion modeling is a key building block for conversational agents. Existing works in this direction are limited to analyzing textual dialogue corpus. We argue that visual signals also play an important role in understanding human persuasive behaviors. In this paper, we introduce the first multimodal dataset for mod... | ['Diyi Yang', 'James M. Rehg', 'Shirley Anugrah Hayati', 'Wenqi Jia', 'Fiona Ryan', 'Aryan Pariani', 'Miao Liu', 'Hongxin Zhang', 'Bolin Lai'] | 2022-12-16 | null | null | null | null | ['persuasion-strategies'] | ['computer-vision'] | [ 3.57833683e-01 4.87257749e-01 -1.83172315e-01 -3.66491318e-01
-5.26327610e-01 -6.65945828e-01 1.17908454e+00 2.44462751e-02
-4.13199872e-01 7.89858937e-01 8.78056347e-01 -7.40774632e-01
7.35727474e-02 -6.41942859e-01 -2.30870172e-01 -3.31426144e-01
1.87664315e-01 3.07267487e-01 5.88871771e-03 -7.00750709... | [12.841485023498535, 7.906250476837158] |
fb7775a1-5ffc-48ca-88e5-6c8881a8f5eb | enhancing-mapless-trajectory-prediction | 2306.14177 | null | https://arxiv.org/abs/2306.14177v1 | https://arxiv.org/pdf/2306.14177v1.pdf | Enhancing Mapless Trajectory Prediction through Knowledge Distillation | Scene information plays a crucial role in trajectory forecasting systems for autonomous driving by providing semantic clues and constraints on potential future paths of traffic agents. Prevalent trajectory prediction techniques often take high-definition maps (HD maps) as part of the inputs to provide scene knowledge. ... | ['Jianru Xue', 'Lei Bai', 'Pu Zhang', 'Yuning Wang'] | 2023-06-25 | null | null | null | null | ['trajectory-prediction', 'trajectory-forecasting'] | ['computer-vision', 'computer-vision'] | [-5.20227961e-02 2.36312836e-01 -5.01853168e-01 -6.37675107e-01
-5.60540736e-01 -4.64960277e-01 6.46293879e-01 9.06106904e-02
-2.36003056e-01 8.35640550e-01 5.38149998e-02 -7.26587296e-01
-2.69660503e-01 -1.19755185e+00 -9.05924499e-01 -4.69700724e-01
1.59068301e-01 7.41729200e-01 9.83470559e-01 -3.61577421... | [5.933268070220947, 0.9448404908180237] |
dab6bb65-7655-48b3-ac3b-a5b15a0bdef1 | a-proposal-for-multimodal-emotion-recognition | null | null | https://www.mdpi.com/2076-3417/12/1/327 | https://www.mdpi.com/2076-3417/12/1/327/pdf | A proposal for Multimodal Emotion Recognition using aural transformers and Action Units on RAVDESS dataset | Emotion recognition is attracting the attention of the research community due to its multiple applications in different fields, such as medicine or autonomous driving. In this paper, we proposed an automatic emotion recognizer system that consisted of a speech emotion recognizer (SER) and a facial emotion recognizer (F... | ['Fernando Fernández-Martínez', 'Juan M. Montero', 'Zoraida Callejas', 'David Griol', 'Ricardo Kleinlein', 'Cristina Luna-Jiménez'] | 2021-12-30 | null | null | null | applied-sciences-journal-2021-12 | ['facial-emotion-recognition', 'multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'computer-vision', 'speech'] | [-3.25390883e-03 1.85496897e-01 2.00053304e-01 -4.03983384e-01
-1.38569757e-01 -1.29623339e-01 4.10063535e-01 -1.00512661e-01
-7.70233572e-01 5.63804030e-01 4.22783606e-02 2.43774414e-01
2.16816261e-01 -3.50226820e-01 -6.12511039e-01 -7.15189517e-01
-1.44563481e-01 -1.79473519e-01 9.97396111e-02 -4.95472550... | [13.364147186279297, 5.017551898956299] |
86fc2b08-194d-43a6-860a-90d5d4554cea | tgif-a-new-dataset-and-benchmark-on-animated | 1604.02748 | null | http://arxiv.org/abs/1604.02748v2 | http://arxiv.org/pdf/1604.02748v2.pdf | TGIF: A New Dataset and Benchmark on Animated GIF Description | With the recent popularity of animated GIFs on social media, there is need
for ways to index them with rich metadata. To advance research on animated GIF
understanding, we collected a new dataset, Tumblr GIF (TGIF), with 100K
animated GIFs from Tumblr and 120K natural language descriptions obtained via
crowdsourcing. T... | ['Liangliang Cao', 'Yuncheng Li', 'Jiebo Luo', 'Joel Tetreault', 'Alejandro Jaimes', 'Yale Song', 'Larry Goldberg'] | 2016-04-10 | tgif-a-new-dataset-and-benchmark-on-animated-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Li_TGIF_A_New_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Li_TGIF_A_New_CVPR_2016_paper.pdf | cvpr-2016-6 | ['video-description'] | ['computer-vision'] | [ 2.43773967e-01 -1.72189698e-01 -2.98185647e-01 -4.83109087e-01
-1.06605673e+00 -9.32524562e-01 8.89745772e-01 -2.12297007e-01
-3.53549540e-01 7.69551754e-01 6.90767109e-01 1.85458601e-04
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-9.24712121e-02 4.90406781e-01 2.83118188e-01 -4.04716671... | [10.692038536071777, 0.9362819790840149] |
d7c936fd-c336-416e-85a9-014dd22dd74c | sport-task-fine-grained-action-detection-and | 2301.13576 | null | https://arxiv.org/abs/2301.13576v1 | https://arxiv.org/pdf/2301.13576v1.pdf | Sport Task: Fine Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2022 | Sports video analysis is a widespread research topic. Its applications are very diverse, like events detection during a match, video summary, or fine-grained movement analysis of athletes. As part of the MediaEval 2022 benchmarking initiative, this task aims at detecting and classifying subtle movements from sport vide... | ['Julien Morlier', 'Laurent Mascarilla', 'Renaud Péteri', 'Jenny Benois-Pineau', 'Boris Mansencal', 'Jordan Calandre', 'Pierre-Etienne Martin'] | 2023-01-31 | null | null | null | null | ['fine-grained-action-detection'] | ['computer-vision'] | [ 4.78246570e-01 -3.71226609e-01 -5.17736018e-01 8.58116373e-02
-5.55353761e-01 -7.01686263e-01 6.05170429e-01 7.48093240e-03
-6.41835272e-01 5.24760842e-01 5.62536955e-01 2.65823483e-01
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-2.46428683e-01 -9.30524915e-02 8.24036777e-01 -1.56177253... | [7.767632007598877, 0.16224876046180725] |
43608be2-3306-4a0e-8730-fc5ea97e7bc0 | chatvideo-a-tracklet-centric-multimodal-and | 2304.14407 | null | https://arxiv.org/abs/2304.14407v2 | https://arxiv.org/pdf/2304.14407v2.pdf | ChatVideo: A Tracklet-centric Multimodal and Versatile Video Understanding System | Existing deep video models are limited by specific tasks, fixed input-output spaces, and poor generalization capabilities, making it difficult to deploy them in real-world scenarios. In this paper, we present our vision for multimodal and versatile video understanding and propose a prototype system, \system. Our system... | ['Yu-Gang Jiang', 'Zuxuan Wu', 'Lu Yuan', 'Xiyang Dai', 'Chong Luo', 'Dongdong Chen', 'Junke Wang'] | 2023-04-27 | null | null | null | null | ['video-understanding'] | ['computer-vision'] | [-2.48034313e-01 -4.90404427e-01 -3.04512322e-01 -1.41321316e-01
-2.95611531e-01 -8.09928954e-01 4.81535435e-01 -3.81132215e-01
-1.65207401e-01 3.91495734e-01 1.91587120e-01 -1.24815702e-01
2.04220593e-01 -4.45333481e-01 -7.16401160e-01 -4.20124948e-01
7.29191750e-02 -9.29317810e-03 5.81640780e-01 -2.53866732... | [9.942973136901855, 0.757461667060852] |
15591718-2a1b-4492-93fa-404387798eeb | pacanet-a-study-on-cyclegan-with-transfer | 2301.13082 | null | https://arxiv.org/abs/2301.13082v5 | https://arxiv.org/pdf/2301.13082v5.pdf | PaCaNet: A Study on CycleGAN with Transfer Learning for Diversifying Fused Chinese Painting and Calligraphy | AI-Generated Content (AIGC) has recently gained a surge in popularity, powered by its high efficiency and consistency in production, and its capability of being customized and diversified. The cross-modality nature of the representation learning mechanism in most AIGC technology allows for more freedom and flexibility ... | ['Yingfang Yuan', 'Yisheng Yuan', 'Yue Wang', 'Wei Pang', 'Yang Xu', 'Zhang Luo', 'Huajun Bai', 'Zuhao Yang'] | 2023-01-30 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 3.81185025e-01 -5.12785651e-02 1.03892468e-01 -8.53614509e-02
-2.95981258e-01 -7.05520630e-01 7.90553331e-01 -6.47919774e-01
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-3.86720560e-02 -1.02065110e+00 -5.97580969e-01 -8.67778480e-01
2.77985632e-01 1.77756950e-01 -2.78557509e-01 -5.09903729... | [11.763496398925781, -0.4837616980075836] |
e5817af7-f89b-4952-a8a1-cd440bd036b2 | vision-transformer-with-super-token-sampling | 2211.11167 | null | https://arxiv.org/abs/2211.11167v1 | https://arxiv.org/pdf/2211.11167v1.pdf | Vision Transformer with Super Token Sampling | Vision transformer has achieved impressive performance for many vision tasks. However, it may suffer from high redundancy in capturing local features for shallow layers. Local self-attention or early-stage convolutions are thus utilized, which sacrifice the capacity to capture long-range dependency. A challenge then ar... | ['Tieniu Tan', 'Ran He', 'Jie Cao', 'Xiaoqiang Zhou', 'Huaibo Huang'] | 2022-11-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Vision_Transformer_With_Super_Token_Sampling_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Vision_Transformer_With_Super_Token_Sampling_CVPR_2023_paper.pdf | cvpr-2023-1 | ['superpixels'] | ['computer-vision'] | [ 8.02224502e-02 -9.24513564e-02 -1.73570752e-01 -3.92575413e-01
-6.26967609e-01 -2.42155552e-01 4.49675620e-01 -5.68604730e-02
-6.07510388e-01 3.61790121e-01 1.78542435e-02 -1.68729141e-01
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3.23468715e-01 1.19801529e-01 4.64542061e-01 1.78856567... | [9.594328880310059, 0.4926674962043762] |
de5ece4c-8d1e-49f9-826b-df829aa6e39f | foc-osod-focus-on-classification-one-shot | null | null | https://openreview.net/forum?id=r7qgus1bZ2 | https://openreview.net/pdf?id=r7qgus1bZ2 | FOC OSOD: Focus on Classification One-Shot Object Detection | One-shot object detection (OSOD) aims at detecting all instances that are consistent with the category of the single reference image. OSOD achieves object detection by comparing the query image and the reference image. We observe that the essential problem behind the limited performance of OSOD is that OSOD generates a... | ['Yu Zhang', 'SABA GHORBANI BARZEGAR', 'Huaijin Pi', 'Hanqing Yang'] | 2021-01-01 | null | null | null | null | ['one-shot-object-detection'] | ['computer-vision'] | [-8.76877755e-02 -9.21793580e-02 -2.35218361e-01 -1.57265827e-01
-1.21251798e+00 -2.41850644e-01 6.27332091e-01 -5.86314872e-02
-6.63279235e-01 3.01641226e-01 -1.90742224e-01 4.03515875e-01
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1.64630666e-01 1.52300596e-01 9.35879409e-01 -1.21134438... | [9.37417984008789, 1.2021145820617676] |
49027efe-0ed5-4608-b023-1d275f159f4f | fast-and-effective-adaptation-of-facial | 1909.12158 | null | https://arxiv.org/abs/1909.12158v2 | https://arxiv.org/pdf/1909.12158v2.pdf | Fast and Effective Adaptation of Facial Action Unit Detection Deep Model | Detecting facial action units (AU) is one of the fundamental steps in automatic recognition of facial expression of emotions and cognitive states. Though there have been a variety of approaches proposed for this task, most of these models are trained only for the specific target AUs, and as such they fail to easily ada... | ['Vladimir Pavlovic', 'Ognjen Rudovic', 'Maja Pantic', 'Mihee Lee'] | 2019-09-26 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 4.52627540e-01 7.23666921e-02 6.57995492e-02 -5.52749395e-01
-4.92272228e-01 -3.44490141e-01 5.98511636e-01 -2.60624915e-01
-3.78382206e-01 2.90695369e-01 -4.72756058e-01 3.40916574e-01
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4.41176966e-02 3.56193125e-01 1.12141944e-01 -1.97040334... | [13.601908683776855, 1.66022789478302] |
7df860fe-3e88-49ce-934e-9ad2951d44da | reducing-crowdsourcing-to-graphon-estimation | 1703.08085 | null | https://arxiv.org/abs/1703.08085v4 | https://arxiv.org/pdf/1703.08085v4.pdf | Reducing Crowdsourcing to Graphon Estimation, Statistically | Inferring the correct answers to binary tasks based on multiple noisy answers in an unsupervised manner has emerged as the canonical question for micro-task crowdsourcing or more generally aggregating opinions. In graphon estimation, one is interested in estimating edge intensities or probabilities between nodes using ... | ['Christina Lee Yu', 'Devavrat Shah'] | 2017-03-23 | null | null | null | null | ['graphon-estimation'] | ['graphs'] | [ 2.92030841e-01 5.50593913e-01 1.38890639e-01 -2.37464860e-01
-1.15819228e+00 -9.37439263e-01 5.81718385e-01 5.26300848e-01
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-6.49618581e-02 -6.98752999e-01 -6.91172183e-01 -6.97881639e-01
2.42262006e-01 8.09391737e-01 5.10602653e-01 -3.20710897... | [9.632292747497559, 4.688546657562256] |
fbd4b0d7-7ac8-4a01-85ea-4a80b2e7449f | a-symmetric-local-search-network-for-emotion | null | null | https://aclanthology.org/2020.coling-main.12 | https://aclanthology.org/2020.coling-main.12.pdf | A Symmetric Local Search Network for Emotion-Cause Pair Extraction | Emotion-cause pair extraction (ECPE) is a new task which aims at extracting the potential clause pairs of emotions and corresponding causes in a document. To tackle this task, a two-step method was proposed by previous study which first extracted emotion clauses and cause clauses individually, then paired the emotion a... | ['Qing Gu', 'Hua Yu', 'Yafeng Yin', 'Zhiwei Jiang', 'Zifeng Cheng'] | 2020-12-01 | null | null | null | coling-2020-8 | ['emotion-cause-pair-extraction'] | ['natural-language-processing'] | [ 3.43638033e-01 3.80154550e-01 -2.03183591e-01 -4.34772998e-01
-7.70963430e-01 -4.53386158e-01 5.12559235e-01 4.05824445e-02
-2.02040270e-01 6.24846399e-01 1.26909971e-01 2.01565381e-02
-2.30098516e-01 -8.11159670e-01 -2.74491996e-01 -5.69876909e-01
-9.59380791e-02 3.89473587e-01 2.27640092e-01 3.00767012... | [12.626951217651367, 6.211202144622803] |
f04509c8-fcc6-4217-a94a-e51aa088c49e | clone-seeker-effective-code-clone-search | 2106.03042 | null | https://arxiv.org/abs/2106.03042v1 | https://arxiv.org/pdf/2106.03042v1.pdf | Clone-Seeker: Effective Code Clone Search Using Annotations | Source code search plays an important role in software development, e.g. for exploratory development or opportunistic reuse of existing code from a code base. Often, exploration of different implementations with the same functionality is needed for tasks like automated software transplantation, software diversification... | ['Mark van den Brand', 'Hamid Abdul Basit', 'Önder Babur', 'Muhammad Hammad'] | 2021-06-06 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-1.38894394e-01 -1.15835935e-01 -6.06806576e-01 -7.22127780e-02
-8.82235587e-01 -7.87250638e-01 3.15099746e-01 6.31871104e-01
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-2.08029114e-02 -6.53406799e-01 -5.24746001e-01 -4.05038744e-02
1.58645764e-01 3.72786298e-02 6.93666518e-01 -1.24303401... | [7.525604248046875, 8.140523910522461] |
8146e6bf-12e0-47ef-a970-d84b759273e8 | raat-relation-augmented-attention-transformer | 2206.03377 | null | https://arxiv.org/abs/2206.03377v1 | https://arxiv.org/pdf/2206.03377v1.pdf | RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction | In document-level event extraction (DEE) task, event arguments always scatter across sentences (across-sentence issue) and multiple events may lie in one document (multi-event issue). In this paper, we argue that the relation information of event arguments is of great significance for addressing the above two issues, a... | ['Bo Ren', 'Di Yin', 'Zhuoxuan Jiang', 'Yuan Liang'] | 2022-06-07 | null | https://aclanthology.org/2022.naacl-main.367 | https://aclanthology.org/2022.naacl-main.367.pdf | naacl-2022-7 | ['document-level-event-extraction'] | ['natural-language-processing'] | [ 1.29200518e-01 1.12319402e-01 -4.77080345e-01 -4.96155292e-01
-1.32321095e+00 -4.50414509e-01 8.25627744e-01 4.29534048e-01
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-1.85924530e-01 -8.95447254e-01 -8.68819833e-01 -3.69321316e-01
1.81809384e-02 2.12329760e-01 3.66777837e-01 -1.31112352... | [9.04732608795166, 9.142507553100586] |
c0225991-4273-40fe-b5ae-21f634f1dd8f | learning-binary-features-online-from-motion | 1601.03821 | null | http://arxiv.org/abs/1601.03821v2 | http://arxiv.org/pdf/1601.03821v2.pdf | Learning Binary Features Online from Motion Dynamics for Incremental Loop-Closure Detection and Place Recognition | This paper proposes a simple yet effective approach to learn visual features
online for improving loop-closure detection and place recognition, based on
bag-of-words frameworks. The approach learns a codeword in bag-of-words model
from a pair of matched features from two consecutive frames, such that the
codeword has t... | ['Mason J. Lilly', 'Guangcong Zhang', 'Patricio A. Vela'] | 2016-01-15 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [ 2.45467588e-01 -3.18551868e-01 -5.29579282e-01 -4.03935343e-01
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-2.73492157e-01 -4.18935418e-01 -8.97197843e-01 -7.59115100e-01
-4.52450067e-01 -1.11364886e-01 4.10444945e-01 5.28312773... | [7.846816539764404, -1.9453561305999756] |
82e06137-a6bb-4468-ab63-2d5edf23a0dd | reformulating-zero-shot-action-recognition | null | null | http://proceedings.neurips.cc/paper/2021/hash/d6539d3b57159babf6a72e106beb45bd-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/d6539d3b57159babf6a72e106beb45bd-Paper.pdf | Reformulating Zero-shot Action Recognition for Multi-label Actions | The goal of zero-shot action recognition (ZSAR) is to classify action classes which were not previously seen during training. Traditionally, this is achieved by training a network to map, or regress, visual inputs to a semantic space where a nearest neighbor classifier is used to select the closest target class. We arg... | ['Mubarak Shah', 'Yogesh Rawat', 'Kevin Duarte', 'Alec Kerrigan'] | 2021-12-01 | null | https://openreview.net/forum?id=mHHU6KWQ1ci | https://openreview.net/pdf?id=mHHU6KWQ1ci | neurips-2021-12 | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 6.86023533e-01 -2.57398814e-01 -3.07513028e-01 -6.09767556e-01
-1.10385370e+00 -4.78218436e-01 6.09530509e-01 3.32068279e-02
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-1.25487685e-01 -5.10006309e-01 -6.12809062e-01 -5.73669553e-01
-1.54601038e-02 4.58807051e-01 5.62335372e-01 7.70072117... | [8.491657257080078, 0.89389967918396] |
ca566611-74bc-4fa1-95f9-638d7f9eb965 | som-ncscm-an-efficient-neural-chinese | null | null | https://aclanthology.org/2021.emnlp-main.33 | https://aclanthology.org/2021.emnlp-main.33.pdf | SOM-NCSCM : An Efficient Neural Chinese Sentence Compression Model Enhanced with Self-Organizing Map | Sentence Compression (SC), which aims to shorten sentences while retaining important words that express the essential meanings, has been studied for many years in many languages, especially in English. However, improvements on Chinese SC task are still quite few due to several difficulties: scarce of parallel corpora, ... | ['Cungen Cao', 'Yanan Cao', 'Jicun Li', 'Yu Liu', 'Shi Wang', 'Kangli Zi'] | null | null | null | null | emnlp-2021-11 | ['sentence-compression'] | ['natural-language-processing'] | [ 3.06341887e-01 -2.38588318e-01 2.57262170e-01 -6.34626746e-01
-6.72233999e-01 -6.16637319e-02 1.35571510e-01 1.30926594e-01
-6.41105831e-01 7.56659508e-01 5.67847192e-01 -2.62317747e-01
4.71196324e-03 -8.23495507e-01 -2.18761444e-01 -7.48668849e-01
3.06993634e-01 3.05809081e-01 3.40215385e-01 -4.53853756... | [10.887847900390625, 9.310729026794434] |
fef47d9e-1a36-40e7-b442-7f9e37703787 | an-off-the-grid-approach-to-multi-compartment | 2011.11193 | null | https://arxiv.org/abs/2011.11193v1 | https://arxiv.org/pdf/2011.11193v1.pdf | An off-the-grid approach to multi-compartment magnetic resonance fingerprinting | We propose a novel numerical approach to separate multiple tissue compartments in image voxels and to estimate quantitatively their nuclear magnetic resonance (NMR) properties and mixture fractions, given magnetic resonance fingerprinting (MRF) measurements. The number of tissues, their types or quantitative properties... | ['Clarice Poon', 'Mohammad Golbabaee'] | 2020-11-23 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 3.71067405e-01 -2.40352601e-02 8.86816382e-02 -2.66314447e-01
-7.63396561e-01 -2.79388368e-01 3.10364425e-01 4.00466844e-02
-2.30014294e-01 9.90488112e-01 1.92398369e-01 5.65282181e-02
-3.80213439e-01 -2.46866569e-01 -6.72155797e-01 -1.18663204e+00
-6.04642332e-01 8.89448345e-01 -2.85910107e-02 2.51336128... | [13.442456245422363, -2.418820381164551] |
9afe063b-b907-4a6c-b250-cb41ad94771c | adaptive-re-ranking-with-a-corpus-graph | 2208.08942 | null | https://arxiv.org/abs/2208.08942v1 | https://arxiv.org/pdf/2208.08942v1.pdf | Adaptive Re-Ranking with a Corpus Graph | Search systems often employ a re-ranking pipeline, wherein documents (or passages) from an initial pool of candidates are assigned new ranking scores. The process enables the use of highly-effective but expensive scoring functions that are not suitable for use directly in structures like inverted indices or approximate... | ['Craig Macdonald', 'Nicola Tonellotto', 'Sean MacAvaney'] | 2022-08-18 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [ 1.04368180e-01 -1.88288346e-01 -1.89513221e-01 -5.86708030e-03
-1.25301588e+00 -1.07284737e+00 6.72500670e-01 7.95621276e-01
-5.93194366e-01 7.22495377e-01 4.62021410e-01 -2.57385880e-01
-8.44959676e-01 -9.60262239e-01 -3.72421414e-01 -2.48888239e-01
-1.21532403e-01 1.06503129e+00 9.01909411e-01 -4.53019202... | [11.459677696228027, 7.534677982330322] |
78a75cd7-855f-4a39-8bcb-8169a687643a | from-dictations-to-clinical-reports-using | null | null | https://aclanthology.org/N18-3015 | https://aclanthology.org/N18-3015.pdf | From dictations to clinical reports using machine translation | A typical workflow to document clinical encounters entails dictating a summary, running speech recognition, and post-processing the resulting text into a formatted letter. Post-processing entails a host of transformations including punctuation restoration, truecasing, marking sections and headers, converting dates and ... | ['David Suendermann-Oeft', 'a', 'Wael Salloum', 'Najmeh Sadoughi', 'Mark Miller', 'Gregory Finley', 'Nico Axtmann', 'Michael Brenndoerfer', 'Erik Edwards', 'Am Robinson'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['punctuation-restoration'] | ['natural-language-processing'] | [ 8.97620857e-01 2.89092392e-01 -5.34303449e-02 -7.03689039e-01
-1.25487387e+00 -7.09002018e-01 4.76914108e-01 1.17712319e+00
-7.41590798e-01 8.52995396e-01 4.57221329e-01 -1.11613584e+00
-1.00649670e-01 -1.36876956e-01 -4.80511636e-01 -1.99262083e-01
1.46484897e-01 4.61566716e-01 -6.79748505e-02 1.38663307... | [8.617256164550781, 8.649327278137207] |
0b7a9405-c7ed-4efe-a120-91b64b00083a | defending-against-poisoning-attacks-in-open | 2212.10002 | null | https://arxiv.org/abs/2212.10002v2 | https://arxiv.org/pdf/2212.10002v2.pdf | Defending Against Misinformation Attacks in Open-Domain Question Answering | Recent work in open-domain question answering (ODQA) has shown that adversarial poisoning of the search collection can cause large drops in accuracy for production systems. However, little to no work has proposed methods to defend against these attacks. To do so, we rely on the intuition that redundant information ofte... | ['Benjamin Van Durme', 'Dawn Lawrie', 'Nathaniel Weir', 'Aleem Khan', 'Orion Weller'] | 2022-12-20 | null | null | null | null | ['data-poisoning', 'open-domain-question-answering'] | ['adversarial', 'natural-language-processing'] | [-1.31190792e-01 2.52560794e-01 1.39550030e-01 -7.87040964e-02
-1.51425648e+00 -1.19472039e+00 5.47503114e-01 4.12458360e-01
-4.86860782e-01 8.62612486e-01 3.13190490e-01 -5.07415175e-01
-1.85448378e-02 -8.55498612e-01 -9.39792156e-01 -3.28401357e-01
2.48019293e-01 7.95655429e-01 9.35736656e-01 -6.70730472... | [11.118722915649414, 7.987025737762451] |
4e77a6a7-6263-42d2-a92d-9cee25217736 | advancing-from-predictive-maintenance-to | 2009.00351 | null | https://arxiv.org/abs/2009.00351v1 | https://arxiv.org/pdf/2009.00351v1.pdf | Advancing from Predictive Maintenance to Intelligent Maintenance with AI and IIoT | As Artificial Intelligent (AI) technology advances and increasingly large amounts of data become readily available via various Industrial Internet of Things (IIoT) projects, we evaluate the state of the art of predictive maintenance approaches and propose our innovative framework to improve the current practice. The pa... | ['Antonio R. Paiva', 'Haining Zheng', 'Chris S. Gurciullo'] | 2020-09-01 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [-5.06266296e-01 -7.24264141e-03 -1.63539976e-01 -1.76893681e-01
-3.56888235e-01 4.27556515e-01 1.75653338e-01 -3.53854559e-02
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-7.61761963e-01 -1.23646593e+00 -6.67219102e-01 -7.47259259e-01
-3.90942305e-01 1.35987854e+00 2.08325684e-01 -2.11974531... | [6.781292915344238, 2.454103946685791] |
dc8e9de2-e441-4200-95dc-b1373e456b0a | mpsa-densenet-a-novel-deep-learning-model-for | 2306.08798 | null | https://arxiv.org/abs/2306.08798v1 | https://arxiv.org/pdf/2306.08798v1.pdf | MPSA-DenseNet: A novel deep learning model for English accent classification | This paper presents three innovative deep learning models for English accent classification: Multi-DenseNet, PSA-DenseNet, and MPSE-DenseNet, that combine multi-task learning and the PSA module attention mechanism with DenseNet. We applied these models to data collected from six dialects of English across native Englis... | ['Ton Viet Ta', 'Linh Thi Hoai Nguyen', 'Tianyu Song'] | 2023-06-15 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [-6.06775463e-01 -2.60788739e-01 -1.21037802e-02 -6.49322331e-01
-8.01509559e-01 -7.12348163e-01 2.82957852e-01 -1.35802031e-01
-8.70494127e-01 1.02387202e+00 5.78567922e-01 -5.93834400e-01
-7.22856745e-02 -6.50654554e-01 -3.08964252e-01 -4.03623641e-01
-1.15750648e-01 7.56071806e-01 -3.28072459e-01 -6.04207754... | [14.296006202697754, 6.754585266113281] |
1e36dd42-e12b-4605-823b-3b9c8c521973 | probing-representations-learned-by-multimodal | 1908.11125 | null | https://arxiv.org/abs/1908.11125v1 | https://arxiv.org/pdf/1908.11125v1.pdf | Probing Representations Learned by Multimodal Recurrent and Transformer Models | Recent literature shows that large-scale language modeling provides excellent reusable sentence representations with both recurrent and self-attentive architectures. However, there has been less clarity on the commonalities and differences in the representational properties induced by the two architectures. It also has... | ['Jindřich Libovický', 'Pranava Madhyastha'] | 2019-08-29 | null | null | null | null | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 4.40094948e-01 1.66633263e-01 -4.27077115e-01 -2.85052627e-01
-1.12671173e+00 -4.37589973e-01 1.14981484e+00 3.14435333e-01
-6.01118617e-02 3.43674541e-01 8.69613469e-01 -3.08438152e-01
1.63547501e-01 -5.50665498e-01 -6.57002211e-01 -3.25374663e-01
4.96498287e-01 2.53740162e-01 -4.27836239e-01 -2.91077405... | [11.173471450805664, 1.564185619354248] |
5324c612-d2db-4670-b48a-834d05c8b334 | imenet-joint-3d-semantic-scene-completion-and | 2106.15413 | null | https://arxiv.org/abs/2106.15413v1 | https://arxiv.org/pdf/2106.15413v1.pdf | IMENet: Joint 3D Semantic Scene Completion and 2D Semantic Segmentation through Iterative Mutual Enhancement | 3D semantic scene completion and 2D semantic segmentation are two tightly correlated tasks that are both essential for indoor scene understanding, because they predict the same semantic classes, using positively correlated high-level features. Current methods use 2D features extracted from early-fused RGB-D images for ... | ['Rui Huang', 'Laiyan Ding', 'Jie Li'] | 2021-06-29 | null | null | null | null | ['3d-semantic-scene-completion', '2d-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.76601195e-01 1.89669743e-01 1.63486451e-01 -6.41771734e-01
-5.24077535e-01 -2.57951736e-01 4.60499704e-01 -4.88750972e-02
-3.09463680e-01 3.60447377e-01 2.53571182e-01 8.03348143e-03
6.68554381e-02 -8.17165256e-01 -5.82059562e-01 -5.12829840e-01
3.11912358e-01 3.52257520e-01 8.15437615e-01 -1.83867827... | [8.459839820861816, -2.860056161880493] |
af1934c0-c35d-4cf4-b629-0f69d05dc431 | diffsrl-learning-dynamic-aware-state | 2110.12352 | null | https://arxiv.org/abs/2110.12352v2 | https://arxiv.org/pdf/2110.12352v2.pdf | DiffSRL: Learning Dynamical State Representation for Deformable Object Manipulation with Differentiable Simulator | Dynamic state representation learning is an important task in robot learning. Latent space that can capture dynamics related information has wide application in areas such as accelerating model free reinforcement learning, closing the simulation to reality gap, as well as reducing the motion planning complexity. Howeve... | ['Jia Pan', 'Tingxiang Fan', 'Shang Wen Yao', 'Jialong Li', 'Yunhao Liu', 'Sirui Chen'] | 2021-10-24 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [-2.52046406e-01 8.11814144e-02 -4.49164212e-01 -3.56100611e-02
-6.06179595e-01 -5.86868703e-01 8.06857228e-01 -1.67912543e-01
-5.48370779e-01 6.29610479e-01 3.76763582e-01 -3.31998646e-01
-1.43404588e-01 -5.38391948e-01 -9.89140451e-01 -5.34510732e-01
-5.59770584e-01 8.66182923e-01 3.45212132e-01 -6.42381251... | [4.495150566101074, 1.1038001775741577] |
c904e994-c7ee-467d-909f-f5724ede563a | thinkminers-disorder-recognition-using | null | null | https://aclanthology.org/S14-2116 | https://aclanthology.org/S14-2116.pdf | ThinkMiners: Disorder Recognition using Conditional Random Fields and Distributional Semantics | null | ['Avinesh PVS', 'Joy Mustafi', 'Ashish Mungi', 'Ankur Parikh', 'Lalit Agarwalla'] | 2014-08-01 | null | null | null | semeval-2014-8 | ['clinical-concept-extraction'] | ['medical'] | [-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... | [-7.241811275482178, 3.688123941421509] |
a3f91c1f-82b5-4869-b06e-f982f6453858 | rethinking-the-aligned-and-misaligned | 2108.12176 | null | https://arxiv.org/abs/2108.12176v5 | https://arxiv.org/pdf/2108.12176v5.pdf | Rethinking the Misalignment Problem in Dense Object Detection | Object detection aims to localize and classify the objects in a given image, and these two tasks are sensitive to different object regions. Therefore, some locations predict high-quality bounding boxes but low classification scores, and some locations are quite the opposite. A misalignment exists between the two tasks,... | ['Zihao Huang', 'Degang Sun', 'Junxing Ren', 'Bo Meng', 'Min Li', 'Yang Yang'] | 2021-08-27 | null | null | null | null | ['dense-object-detection'] | ['computer-vision'] | [-1.06516711e-01 -2.06752375e-01 -1.64166778e-01 -4.61088747e-01
-9.23688829e-01 -3.48579794e-01 5.69680393e-01 -1.62211120e-01
-7.26031244e-01 4.84289825e-01 -1.14604183e-01 1.23489596e-01
1.11709282e-01 -5.81890762e-01 -8.53537381e-01 -8.38098943e-01
-6.34285761e-03 3.47293824e-01 6.83212519e-01 -1.67357642... | [8.945087432861328, 0.2533133029937744] |
8e73a27e-f298-4269-8753-d271298e11f4 | social-cost-of-carbon-what-do-the-numbers | 2001.08935 | null | https://arxiv.org/abs/2001.08935v3 | https://arxiv.org/pdf/2001.08935v3.pdf | Social Cost of Carbon: What Do the Numbers Really Mean? | Social cost of carbon (SCC) is estimated by integrated assessment models (IAM) and is widely used by government agencies to value climate policy impacts. While there is an ongoing debate about obtained numerical estimates and related uncertainties, little attention has been paid so far to the SCC calculation method its... | ['Michael Obersteiner', 'Alexey Smirnov', 'Nikolay Khabarov'] | 2020-01-24 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [ 1.26200825e-01 2.38917217e-01 -1.61478356e-01 1.67510077e-01
-9.24472958e-02 -6.29064798e-01 9.54019547e-01 2.69658417e-01
-5.15588582e-01 8.51425886e-01 2.52586305e-01 -1.07976758e+00
-5.71089149e-01 -1.07379472e+00 -2.77279824e-01 -9.01236832e-01
3.68685126e-01 2.38153592e-01 -1.04535222e-01 -2.44494706... | [5.615076065063477, 3.782487154006958] |
2852743c-306a-4e58-b90a-83da16c0a532 | learning-cross-image-object-semantic-relation | 2207.00784 | null | https://arxiv.org/abs/2207.00784v1 | https://arxiv.org/pdf/2207.00784v1.pdf | Learning Cross-Image Object Semantic Relation in Transformer for Few-Shot Fine-Grained Image Classification | Few-shot fine-grained learning aims to classify a query image into one of a set of support categories with fine-grained differences. Although learning different objects' local differences via Deep Neural Networks has achieved success, how to exploit the query-support cross-image object semantic relations in Transformer... | ['Botian Shi', 'Jiayuan Fan', 'Tao Chen', 'Baopu Li', 'Jiakang Yuan', 'Bo Zhang'] | 2022-07-02 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 2.27765039e-01 -9.66542140e-02 -4.26699311e-01 -5.49803853e-01
-7.85014749e-01 -1.58249378e-01 5.34105480e-01 1.82146534e-01
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-5.94646633e-01 -1.23499608e+00 -6.80685103e-01 -5.96680939e-01
1.13138497e-01 3.81976932e-01 6.76997244e-01 -3.62255961... | [9.692957878112793, 1.98551607131958] |
e7060067-b543-4d93-8d8b-5c57cace8955 | layoutdiffusion-controllable-diffusion-model | 2303.17189 | null | https://arxiv.org/abs/2303.17189v1 | https://arxiv.org/pdf/2303.17189v1.pdf | LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generation | Recently, diffusion models have achieved great success in image synthesis. However, when it comes to the layout-to-image generation where an image often has a complex scene of multiple objects, how to make strong control over both the global layout map and each detailed object remains a challenging task. In this paper,... | ['Xi Li', 'Ying Shan', 'Zhongang Qi', 'XueWei Li', 'Xianpan Zhou', 'Guangcong Zheng'] | 2023-03-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zheng_LayoutDiffusion_Controllable_Diffusion_Model_for_Layout-to-Image_Generation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zheng_LayoutDiffusion_Controllable_Diffusion_Model_for_Layout-to-Image_Generation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['layout-to-image-generation'] | ['computer-vision'] | [-6.81567043e-02 -1.65248200e-01 7.45379627e-02 -8.50656256e-02
-4.14653271e-01 -4.85920221e-01 5.04217565e-01 -3.96778714e-03
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-2.30862796e-01 -9.34380829e-01 -7.21608877e-01 -8.73962998e-01
2.84541577e-01 1.02140918e-01 3.72377455e-01 -2.31413350... | [11.387269973754883, -0.7685655355453491] |
79687516-2469-44c0-b687-ded41e9a45ed | improving-diversity-and-reducing-redundancy | null | null | https://ieeexplore.ieee.org/abstract/document/9206644 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9206644 | Improving Diversity and Reducing Redundancy in Paragraph Captions | The purpose of an image paragraph captioning
model is to produce detailed descriptions of the source images.
Generally, paragraph captioning models use encoder-decoder
based architectures similar to the standard image captioning
models. The encoder is a CNN based model, and the decoder is a
LSTM or GRU. The standa... | ['and Pushpak Bhattacharyya', 'Sriparna Saha', 'Chandresh S.', 'Kanani'] | 2020-07-19 | null | null | null | international-joint-conference-on-neural-2 | ['dense-captioning'] | ['computer-vision'] | [ 5.56800902e-01 4.61035877e-01 -7.04349130e-02 -3.79181325e-01
-7.39462256e-01 -4.33213651e-01 7.30801225e-01 -1.44227035e-03
-1.82993263e-01 1.03830767e+00 6.90720916e-01 -8.48777816e-02
5.40272117e-01 -4.13832814e-01 -1.22003627e+00 -5.89371443e-01
2.27057621e-01 4.06755507e-01 9.84295458e-02 -2.91427493... | [11.027884483337402, 1.0619678497314453] |
4bf73653-dbcd-44d3-b613-cd1463cbc0c6 | graph-convolutional-networks-based-word | 1809.04283 | null | https://arxiv.org/abs/1809.04283v4 | https://arxiv.org/pdf/1809.04283v4.pdf | Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks | Word embeddings have been widely adopted across several NLP applications. Most existing word embedding methods utilize sequential context of a word to learn its embedding. While there have been some attempts at utilizing syntactic context of a word, such methods result in an explosion of the vocabulary size. In this pa... | ['Shikhar Vashishth', 'Partha Talukdar', 'Manik Bhandari', 'Chiranjib Bhattacharyya', 'Prateek Yadav', 'Piyush Rai'] | 2018-09-12 | incorporating-syntactic-and-semantic | https://aclanthology.org/P19-1320 | https://aclanthology.org/P19-1320.pdf | acl-2019-7 | ['learning-word-embeddings'] | ['methodology'] | [-2.62618452e-01 -1.36257052e-01 -5.74223459e-01 -3.44743937e-01
-1.88246638e-01 -3.56026381e-01 4.96609747e-01 2.60591567e-01
-8.87018204e-01 4.95327622e-01 6.12262368e-01 -4.88168150e-01
6.15680665e-02 -1.00852716e+00 -1.61637470e-01 -3.99852842e-01
1.59369126e-01 5.52238198e-03 1.12882473e-01 -3.25825691... | [10.496020317077637, 8.636876106262207] |
bd52b4a6-d298-4977-99b1-381238b7e9c5 | optimizing-video-prediction-via-video-frame-1 | 2206.13454 | null | https://arxiv.org/abs/2206.13454v1 | https://arxiv.org/pdf/2206.13454v1.pdf | Optimizing Video Prediction via Video Frame Interpolation | Video prediction is an extrapolation task that predicts future frames given past frames, and video frame interpolation is an interpolation task that estimates intermediate frames between two frames. We have witnessed the tremendous advancement of video frame interpolation, but the general video prediction in the wild i... | ['Qifeng Chen', 'Qiang Wen', 'Yue Wu'] | 2022-06-27 | optimizing-video-prediction-via-video-frame | http://openaccess.thecvf.com//content/CVPR2022/html/Wu_Optimizing_Video_Prediction_via_Video_Frame_Interpolation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wu_Optimizing_Video_Prediction_via_Video_Frame_Interpolation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-prediction'] | ['computer-vision'] | [ 1.51741549e-01 -3.84832285e-02 -3.21153134e-01 -3.66984218e-01
-5.47380567e-01 -7.43588358e-02 4.27238077e-01 -4.05166209e-01
-2.05697969e-01 7.77307987e-01 1.78046316e-01 -1.75096631e-01
4.02744621e-01 -5.73496699e-01 -1.22604620e+00 -5.48656046e-01
2.58758874e-03 -1.76508084e-01 6.70332134e-01 -1.63655967... | [10.537529945373535, -1.0658124685287476] |
d7234b4a-7e4f-49d5-a81f-9c6856a67265 | efficient-bayesian-travel-time-tomography | 2307.04228 | null | https://arxiv.org/abs/2307.04228v1 | https://arxiv.org/pdf/2307.04228v1.pdf | Efficient Bayesian travel-time tomography with geologically-complex priors using sensitivity-informed polynomial chaos expansion and deep generative networks | Monte Carlo Markov Chain (MCMC) methods commonly confront two fundamental challenges: the accurate characterization of the prior distribution and the efficient evaluation of the likelihood. In the context of Bayesian studies on tomography, principal component analysis (PCA) can in some cases facilitate the straightforw... | ['Niklas Linde', 'Stefano Marelli', 'Shiran Levy', 'Macarena Amaya', 'Giovanni Angelo Meles'] | 2023-07-09 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 1.20638765e-01 -3.91559511e-01 4.29281622e-01 3.06947790e-02
-8.92603397e-01 -4.99357820e-01 8.53632033e-01 -2.62137856e-02
-3.04316819e-01 7.78501749e-01 -5.90920486e-02 -4.22713548e-01
-6.35515630e-01 -9.27037597e-01 -4.48131889e-01 -1.14387810e+00
1.13333061e-01 1.01381743e+00 -3.92242223e-01 1.60934553... | [6.812584400177002, 3.6402971744537354] |
1cf9aca9-1960-490d-a7e4-e8770f288dc0 | medical-federated-model-with-mixture-of | 2306.14483 | null | https://arxiv.org/abs/2306.14483v1 | https://arxiv.org/pdf/2306.14483v1.pdf | Medical Federated Model with Mixture of Personalized and Sharing Components | Although data-driven methods usually have noticeable performance on disease diagnosis and treatment, they are suspected of leakage of privacy due to collecting data for model training. Recently, federated learning provides a secure and trustable alternative to collaboratively train model without any exchange of medical... | ['Kunlun He', 'Xinwang Liu', 'Qinghe Liu', 'Yawei Zhao'] | 2023-06-26 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [-7.26531893e-02 8.98894221e-02 -5.61831772e-01 -5.88919342e-01
-9.67107296e-01 -3.34951729e-01 6.14866614e-02 1.67413279e-01
-1.97619066e-01 6.43436372e-01 2.93695360e-01 -4.81529385e-01
-1.92786857e-01 -8.34532857e-01 -4.73670065e-01 -9.84506369e-01
3.60005647e-02 2.19034135e-01 -9.34585780e-02 3.27666014... | [6.062697887420654, 6.486255645751953] |
a7434222-cd61-4f81-b373-0f5f55f2a859 | samo-speaker-attractor-multi-center-one-class | 2211.02718 | null | https://arxiv.org/abs/2211.02718v1 | https://arxiv.org/pdf/2211.02718v1.pdf | SAMO: Speaker Attractor Multi-Center One-Class Learning for Voice Anti-Spoofing | Voice anti-spoofing systems are crucial auxiliaries for automatic speaker verification (ASV) systems. A major challenge is caused by unseen attacks empowered by advanced speech synthesis technologies. Our previous research on one-class learning has improved the generalization ability to unseen attacks by compacting the... | ['Zhiyao Duan', 'You Zhang', 'Siwen Ding'] | 2022-11-04 | null | null | null | null | ['voice-anti-spoofing', 'speaker-verification'] | ['audio', 'speech'] | [-2.12726370e-01 1.63982704e-01 -2.85011560e-01 -1.11239217e-01
-7.66169727e-01 -7.04230607e-01 4.96958792e-01 -1.50856301e-01
-3.81090008e-02 2.15821147e-01 5.90149760e-01 -6.30578935e-01
-1.05225988e-01 -1.03041679e-01 -3.48706275e-01 -8.39658082e-01
-1.58071369e-01 3.34048629e-01 -5.25568314e-02 -4.48751360... | [14.085243225097656, 5.900129318237305] |
33537b04-e104-445e-b3c3-66b6fef81bfc | hawkes-process-based-on-controlled | 2305.07031 | null | https://arxiv.org/abs/2305.07031v2 | https://arxiv.org/pdf/2305.07031v2.pdf | Hawkes Process Based on Controlled Differential Equations | Hawkes processes are a popular framework to model the occurrence of sequential events, i.e., occurrence dynamics, in several fields such as social diffusion. In real-world scenarios, the inter-arrival time among events is irregular. However, existing neural network-based Hawkes process models not only i) fail to captur... | ['Noseong Park', 'Seungji Kook', 'Minju Jo'] | 2023-05-09 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [-9.35879201e-02 -1.93113878e-01 9.63642001e-02 2.70110399e-01
-3.41023654e-02 -1.68442726e-01 8.56761873e-01 3.32874715e-01
-4.21838671e-01 6.39075637e-01 4.17351127e-02 -3.02809715e-01
-3.88614684e-01 -1.22044718e+00 -6.74918354e-01 -7.91294336e-01
-4.56133068e-01 6.22751594e-01 2.95975924e-01 -1.55070171... | [6.906551837921143, 3.450338840484619] |
fe942554-777c-4980-8704-812d3840eb07 | simulating-liquids-with-graph-networks | 2203.07895 | null | https://arxiv.org/abs/2203.07895v1 | https://arxiv.org/pdf/2203.07895v1.pdf | Simulating Liquids with Graph Networks | Simulating complex dynamics like fluids with traditional simulators is computationally challenging. Deep learning models have been proposed as an efficient alternative, extending or replacing parts of traditional simulators. We investigate graph neural networks (GNNs) for learning fluid dynamics and find that their gen... | ['Nils Thuerey', 'Philipp Holl', 'Jonathan Klimesch'] | 2022-03-14 | null | null | null | null | ['liquid-simulation', 'physical-simulations'] | ['miscellaneous', 'miscellaneous'] | [-2.92661458e-01 -9.61473659e-02 2.59807289e-01 -6.48521408e-02
3.47405434e-01 -6.96484745e-01 6.96993649e-01 7.81887099e-02
-4.84082639e-01 9.41148579e-01 -1.90423265e-01 -8.33565533e-01
-2.08846867e-01 -9.64898467e-01 -9.54628050e-01 -8.02815378e-01
-6.94084287e-01 7.16174603e-01 4.63470638e-01 -7.10204720... | [6.454824447631836, 3.460477113723755] |
bc13a816-1712-4dde-8632-344a5ff2d0ed | embracing-compact-and-robust-architectures | 2305.12236 | null | https://arxiv.org/abs/2305.12236v1 | https://arxiv.org/pdf/2305.12236v1.pdf | Embracing Compact and Robust Architectures for Multi-Exposure Image Fusion | In recent years, deep learning-based methods have achieved remarkable progress in multi-exposure image fusion. However, existing methods rely on aligned image pairs, inevitably generating artifacts when faced with device shaking in real-world scenarios. Moreover, these learning-based methods are built on handcrafted ar... | ['Risheng Liu', 'Xin Fan', 'Guanyao Wu', 'JinYuan Liu', 'Zhu Liu'] | 2023-05-20 | null | null | null | null | ['multi-exposure-image-fusion', 'architecture-search'] | ['computer-vision', 'methodology'] | [ 3.34848195e-01 -6.46469653e-01 1.73172474e-01 -2.51938432e-01
-8.45742881e-01 -3.66474986e-01 3.45875710e-01 -1.78328544e-01
-4.43275034e-01 5.17969787e-01 4.62719519e-03 -8.02870691e-02
-2.19199926e-01 -7.07897365e-01 -8.09769630e-01 -9.20351684e-01
3.35917503e-01 -3.14257741e-01 3.79383117e-02 -2.93948621... | [10.864858627319336, -1.9377655982971191] |
14578a2d-9656-4ce5-8899-ce4ce69c847b | efficient-global-2d-3d-matching-for-camera | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Liu_Efficient_Global_2D-3D_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Liu_Efficient_Global_2D-3D_ICCV_2017_paper.pdf | Efficient Global 2D-3D Matching for Camera Localization in a Large-Scale 3D Map | Given an image of a street scene in a city, this paper develops a new method that can quickly and precisely pinpoint at which location (as well as viewing direction) the image was taken, against a pre-stored large-scale 3D point-cloud map of the city. We adopt the recently developed 2D-3D direct feature matching framew... | ['Yuchao Dai', 'Liu Liu', 'Hongdong Li'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['3d-feature-matching', 'camera-localization'] | ['computer-vision', 'computer-vision'] | [-2.89887041e-02 -5.15127122e-01 1.52653560e-01 -2.06544027e-01
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-1.87189147e-01 7.52622485e-01 8.54317427e-01 -2.23368153... | [7.625156402587891, -2.3459184169769287] |
88e48a45-1281-4716-99cd-d804e0c06894 | self-supervised-learning-framework-for-remote | 2107.07695 | null | https://arxiv.org/abs/2107.07695v2 | https://arxiv.org/pdf/2107.07695v2.pdf | Self-supervised Representation Learning Framework for Remote Physiological Measurement Using Spatiotemporal Augmentation Loss | Recent advances in supervised deep learning methods are enabling remote measurements of photoplethysmography-based physiological signals using facial videos. The performance of these supervised methods, however, are dependent on the availability of large labelled data. Contrastive learning as a self-supervised method h... | ['Jinman Kim', 'Euijoon Ahn', 'Hao Wang'] | 2021-07-16 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 7.06437171e-01 -3.50533426e-01 -1.23275593e-01 -4.50757295e-01
-8.86500835e-01 -2.16198951e-01 4.75375235e-01 -4.09075260e-01
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-4.16653186e-01 -2.46221080e-01 -2.95250535e-01 -2.20606960... | [13.854339599609375, 2.629767656326294] |
807dc55b-e69e-48f8-aa13-7b9c1f319cf7 | efficient-passive-membership-inference-attack | 2111.0043 | null | https://arxiv.org/abs/2111.00430v1 | https://arxiv.org/pdf/2111.00430v1.pdf | Efficient passive membership inference attack in federated learning | In cross-device federated learning (FL) setting, clients such as mobiles cooperate with the server to train a global machine learning model, while maintaining their data locally. However, recent work shows that client's private information can still be disclosed to an adversary who just eavesdrops the messages exchange... | ['Giovanni Neglia', 'Chuan Xu', 'Oualid Zari'] | 2021-10-31 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [-1.24643348e-01 1.58242464e-01 -3.92446756e-01 -3.75679910e-01
-1.25043130e+00 -1.22459614e+00 1.72863439e-01 -2.77334489e-02
-4.98239696e-01 8.58231664e-01 -2.85660744e-01 -9.19630706e-01
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-1.32413790e-01 5.61268151e-01 4.14858162e-01 3.98817301... | [5.805443286895752, 6.824004173278809] |
82d9b3c4-db22-4aea-b5bf-0e6dc61f9eba | multilingual-distributed-representations | 1312.6173 | null | http://arxiv.org/abs/1312.6173v4 | http://arxiv.org/pdf/1312.6173v4.pdf | Multilingual Distributed Representations without Word Alignment | Distributed representations of meaning are a natural way to encode covariance
relationships between words and phrases in NLP. By overcoming data sparsity
problems, as well as providing information about semantic relatedness which is
not available in discrete representations, distributed representations have
proven usef... | ['Karl Moritz Hermann', 'Phil Blunsom'] | 2013-12-20 | null | null | null | null | ['cross-lingual-document-classification'] | ['natural-language-processing'] | [ 1.12216242e-01 1.34497866e-01 -4.18314099e-01 -7.64492154e-01
-1.09191716e+00 -8.04276466e-01 7.30363727e-01 6.15315080e-01
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-1.52719617e-01 -8.13744724e-01 -6.34444475e-01 -5.60138583e-01
1.51482418e-01 7.10551381e-01 -2.62245446e-01 -4.89329219... | [10.884676933288574, 9.663629531860352] |
a828a2df-7434-43ac-a92c-149505503a68 | open-domain-suggestion-mining-leveraging-fine | 2007.04297 | null | https://arxiv.org/abs/2007.04297v2 | https://arxiv.org/pdf/2007.04297v2.pdf | Open Domain Suggestion Mining Leveraging Fine-Grained Analysis | Suggestion mining tasks are often semantically complex and lack sophisticated methodologies that can be applied to real-world data. The presence of suggestions across a large diversity of domains and the absence of large labelled and balanced datasets render this task particularly challenging to deal with. In an attemp... | ['Tanishq Goel', 'Sonika Dahiya', 'Shivang Chopra', 'Shreya Singal'] | 2020-06-27 | null | null | null | null | ['suggestion-mining'] | ['natural-language-processing'] | [ 4.03000824e-02 4.50274289e-01 -2.80543774e-01 -3.04030627e-01
-9.68381882e-01 -7.98460245e-01 1.06497908e+00 4.74197835e-01
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-3.75880718e-01 -5.99602163e-01 -2.64879495e-01 9.95620489e-02
5.63138165e-02 5.58819294e-01 4.01973695e-01 -3.59472156... | [10.469626426696777, 8.402549743652344] |
0aac38d5-37fa-4fec-bc61-e161a68b1261 | codes-a-distribution-shift-benchmark-dataset | 2206.0548 | null | https://arxiv.org/abs/2206.05480v2 | https://arxiv.org/pdf/2206.05480v2.pdf | CodeS: Towards Code Model Generalization Under Distribution Shift | Distribution shift has been a longstanding challenge for the reliable deployment of deep learning (DL) models due to unexpected accuracy degradation. Although DL has been becoming a driving force for large-scale source code analysis in the big code era, limited progress has been made on distribution shift analysis and ... | ['Yves Le Traon', 'Mike Papadakis', 'Lei Ma', 'Maxime Cordy', 'Xiaofei Xie', 'Yuejun Guo', 'Qiang Hu'] | 2022-06-11 | null | null | null | null | ['code-classification'] | ['computer-code'] | [-3.66407990e-01 -3.18155885e-01 -4.35764134e-01 -3.33711296e-01
-7.81213582e-01 -7.84645796e-01 5.00962019e-01 4.24799532e-01
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-2.70171195e-01 1.94896445e-01 3.18186194e-01 4.10728939... | [7.624568939208984, 7.933331489562988] |
8a95f1e5-d2ee-4d7f-b820-044580aaed0e | motion-r3-fast-and-accurate-motion-annotation | 2304.01672 | null | https://arxiv.org/abs/2304.01672v1 | https://arxiv.org/pdf/2304.01672v1.pdf | Motion-R3: Fast and Accurate Motion Annotation via Representation-based Representativeness Ranking | In this paper, we follow a data-centric philosophy and propose a novel motion annotation method based on the inherent representativeness of motion data in a given dataset. Specifically, we propose a Representation-based Representativeness Ranking R3 method that ranks all motion data in a given dataset according to thei... | ['Yipeng Qin', 'Andreas Aristidou', 'Yazhan Zhang', 'Zijiao Zeng', 'Kai Wang', 'Fengyi Fang', 'Shihui Guo', 'Tianxiang Ren', 'Jubo Yu'] | 2023-04-04 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [ 7.37446884e-04 -3.69706929e-01 -7.37836540e-01 -3.28681141e-01
-6.47028148e-01 -2.82023728e-01 5.33834755e-01 9.61550977e-03
-2.59131581e-01 2.70337254e-01 9.49591756e-01 2.66613543e-01
-5.49033523e-01 -4.32278514e-01 -3.77435803e-01 -5.12331247e-01
-8.65732133e-02 3.11341345e-01 4.68516290e-01 -2.14378655... | [8.440022468566895, 0.5560885071754456] |
3b23c515-4119-4565-bbe9-1f7fceec3da9 | barlow-twins-self-supervised-learning-via | 2103.0323 | null | https://arxiv.org/abs/2103.03230v3 | https://arxiv.org/pdf/2103.03230v3.pdf | Barlow Twins: Self-Supervised Learning via Redundancy Reduction | Self-supervised learning (SSL) is rapidly closing the gap with supervised methods on large computer vision benchmarks. A successful approach to SSL is to learn embeddings which are invariant to distortions of the input sample. However, a recurring issue with this approach is the existence of trivial constant solutions.... | ['Stéphane Deny', 'Yann Lecun', 'Ishan Misra', 'Li Jing', 'Jure Zbontar'] | 2021-03-04 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 2.26122797e-01 1.51371136e-01 -4.94079888e-02 -3.25569451e-01
-1.82424650e-01 -4.45048809e-01 7.04374850e-01 2.13064328e-01
-7.32128263e-01 4.01722014e-01 3.51150110e-02 3.82806882e-02
-4.14836437e-01 -5.49796402e-01 -7.95591950e-01 -8.29695761e-01
-2.78466791e-01 4.47606444e-01 2.76257247e-01 -3.14937025... | [9.298491477966309, 2.845961332321167] |
5856e751-c04c-4d3d-ae30-57096931ba76 | bilingual-topic-models-for-comparable-corpora | 2111.15278 | null | https://arxiv.org/abs/2111.15278v1 | https://arxiv.org/pdf/2111.15278v1.pdf | Bilingual Topic Models for Comparable Corpora | Probabilistic topic models like Latent Dirichlet Allocation (LDA) have been previously extended to the bilingual setting. A fundamental modeling assumption in several of these extensions is that the input corpora are in the form of document pairs whose constituent documents share a single topic distribution. However, t... | ['Marianne Clausel', 'Massih-Reza Amini', 'Georgios Balikas'] | 2021-11-30 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-4.43841100e-01 1.91533878e-01 -2.73247451e-01 -5.69410622e-01
-9.94898200e-01 -7.57861376e-01 1.16579771e+00 3.96374643e-01
-4.44682151e-01 5.85185528e-01 5.96874893e-01 -1.06162347e-01
-2.29705602e-01 -7.69606531e-01 -5.21545827e-01 -7.87494719e-01
7.61452988e-02 1.03758109e+00 1.73635036e-01 -1.30886346... | [10.474815368652344, 6.998488426208496] |
75d6d5f9-247f-44e9-a415-603054ade746 | layered-rgbd-scene-flow-estimation | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Sun_Layered_RGBD_Scene_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Sun_Layered_RGBD_Scene_2015_CVPR_paper.pdf | Layered RGBD Scene Flow Estimation | As consumer depth sensors become widely available, estimating scene flow from RGBD sequences has received increasing attention. Although the depth information allows the recovery of 3D motion from a single view, it poses new challenges. In particular, depth boundaries are not well-aligned with RGB image edges and there... | ['Deqing Sun', 'Erik B. Sudderth', 'Hanspeter Pfister'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['scene-flow-estimation'] | ['computer-vision'] | [ 1.36076346e-01 -1.62563235e-01 -3.01440328e-01 -3.74515027e-01
-4.00605828e-01 -6.32596850e-01 7.52657875e-02 -3.60298634e-01
-3.86316329e-01 5.29243529e-01 2.03814402e-01 -2.29969442e-01
2.78121680e-01 -6.79957509e-01 -3.91430706e-01 -7.08187163e-01
2.04789415e-01 1.10860631e-01 6.12065554e-01 1.66840523... | [8.551708221435547, -2.084460973739624] |
a78f175e-99e0-4b2d-904a-8a4e7b9c3565 | how-effective-are-neural-networks-for-fixing | 2305.18607 | null | https://arxiv.org/abs/2305.18607v1 | https://arxiv.org/pdf/2305.18607v1.pdf | How Effective Are Neural Networks for Fixing Security Vulnerabilities | Security vulnerability repair is a difficult task that is in dire need of automation. Two groups of techniques have shown promise: (1) large code language models (LLMs) that have been pre-trained on source code for tasks such as code completion, and (2) automated program repair (APR) techniques that use deep learning (... | ['Sameena Shah', 'Petr Babkin', 'Lin Tan', 'Jordan Davis', 'Thibaud Lutellier', 'Hung Viet Pham', 'Nan Jiang', 'Yi Wu'] | 2023-05-29 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-2.55161911e-01 4.48996164e-02 -4.22979504e-01 5.84078440e-03
-1.23337567e+00 -1.13703871e+00 1.52929798e-01 3.62950414e-01
1.45469323e-01 1.89017966e-01 1.71952188e-01 -1.48824263e+00
1.04847923e-01 -7.74116099e-01 -1.03583598e+00 2.12181926e-01
-5.85386634e-01 -4.35563564e-01 5.33508122e-01 -5.09636343... | [7.106083393096924, 7.781507968902588] |
74888ab7-012c-4e24-93fc-ec4ebe4ceb77 | spoken-language-understanding-for | 2212.10728 | null | https://arxiv.org/abs/2212.10728v1 | https://arxiv.org/pdf/2212.10728v1.pdf | Spoken Language Understanding for Conversational AI: Recent Advances and Future Direction | When a human communicates with a machine using natural language on the web and online, how can it understand the human's intention and semantic context of their talk? This is an important AI task as it enables the machine to construct a sensible answer or perform a useful action for the human. Meaning is represented at... | ['Josiah Poon', 'Henry Weld', 'Siqu Long', 'Soyeon Caren Han'] | 2022-12-21 | null | null | null | null | ['spoken-language-understanding', 'intent-detection', 'intent-classification', 'slot-filling', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 4.26478118e-01 6.02728486e-01 -3.02964598e-01 -7.10988998e-01
-6.88048303e-01 -4.79971021e-01 5.97429335e-01 6.28624931e-02
-2.51638502e-01 4.99969751e-01 5.99871755e-01 -7.69990861e-01
2.36914024e-01 -8.14521253e-01 -3.58551919e-01 -1.52755797e-01
1.59891888e-01 7.04804420e-01 -3.23847570e-02 -5.28764904... | [12.535622596740723, 7.459218502044678] |
190d7bd8-7622-4a12-94b9-38050a497597 | informing-the-design-of-spoken-conversational | null | null | https://openreview.net/forum?id=rJgGxq1_z4 | https://openreview.net/pdf?id=rJgGxq1_z4 | Informing the Design of Spoken Conversational Search | We conducted a laboratory-based observational study where pairs of people performed search tasks communicating verbally. Examination of the discourse allowed commonly used interactions to be identified for Spoken Conversational Search (SCS). We compared the interactions to existing models of search behaviour. We find t... | ['Mark Sanderson', 'Hideo Joho', 'Lawrence Cavedon', 'Damiano Spina', 'Johanne R. Trippas'] | 2019-01-12 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 1.56351298e-01 2.64253736e-01 -1.72831044e-01 -4.32696998e-01
-5.62246561e-01 -6.30434275e-01 9.96288419e-01 2.04826161e-01
-5.69472253e-01 4.86052603e-01 8.65814447e-01 -5.85297942e-01
-5.27161419e-01 -1.25351325e-02 2.34465584e-01 -3.93036529e-02
-2.91096449e-01 6.99212730e-01 3.64382893e-01 -2.81633317... | [12.291970252990723, 7.782953262329102] |
1e825799-6dbf-4a77-a496-e24f9d950b02 | image-difference-captioning-with-pre-training | 2202.04298 | null | https://arxiv.org/abs/2202.04298v1 | https://arxiv.org/pdf/2202.04298v1.pdf | Image Difference Captioning with Pre-training and Contrastive Learning | The Image Difference Captioning (IDC) task aims to describe the visual differences between two similar images with natural language. The major challenges of this task lie in two aspects: 1) fine-grained visual differences that require learning stronger vision and language association and 2) high-cost of manual annotati... | ['Qin Jin', 'Weiying Wang', 'Linli Yao'] | 2022-02-09 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 7.74428919e-02 -2.92232126e-01 -2.38674089e-01 -6.06155753e-01
-6.51961803e-01 -5.47784567e-01 6.46216691e-01 2.72866767e-02
-5.25550604e-01 5.19071162e-01 2.70642966e-01 -9.16341245e-02
2.40302578e-01 -3.33260983e-01 -6.33595824e-01 -4.03963834e-01
4.20361280e-01 1.69040114e-01 3.34518068e-02 -1.63391218... | [10.795832633972168, 1.4321283102035522] |
05d91110-5322-46c9-89e3-8c84aa6d9e20 | espnet-onnx-bridging-a-gap-between-research | 2209.09756 | null | https://arxiv.org/abs/2209.09756v2 | https://arxiv.org/pdf/2209.09756v2.pdf | ESPnet-ONNX: Bridging a Gap Between Research and Production | In the field of deep learning, researchers often focus on inventing novel neural network models and improving benchmarks. In contrast, application developers are interested in making models suitable for actual products, which involves optimizing a model for faster inference and adapting a model to various platforms (e.... | ['Shinji Watanabe', 'Tomoki Hayashi', 'Yosuke Higuchi', 'Masao Someki'] | 2022-09-20 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [-2.70760804e-01 6.59673065e-02 -3.18035930e-01 -7.21534252e-01
-4.23679113e-01 -4.37303871e-01 2.33694669e-02 -1.95005193e-01
-2.86836088e-01 2.14885548e-01 -3.70920002e-02 -8.01472306e-01
4.06299531e-01 -7.21299410e-01 -7.32547700e-01 -2.10608140e-01
2.21081987e-01 4.02164608e-01 -3.37809995e-02 -1.63372651... | [14.096797943115234, 6.405102729797363] |
caa1cede-39bb-4406-976c-83dcdb1c193b | better-context-makes-better-code-language | 2306.00381 | null | https://arxiv.org/abs/2306.00381v1 | https://arxiv.org/pdf/2306.00381v1.pdf | Better Context Makes Better Code Language Models: A Case Study on Function Call Argument Completion | Pretrained code language models have enabled great progress towards program synthesis. However, common approaches only consider in-file local context and thus miss information and constraints imposed by other parts of the codebase and its external dependencies. Existing code completion benchmarks also lack such context... | ['George Karypis', 'Sheng Zha', 'Leonard Lausen', 'Jinman Zhao', 'Hengzhi Pei'] | 2023-06-01 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [-3.26359011e-02 8.18329584e-03 -6.02759480e-01 -7.02039659e-01
-7.83135176e-01 -8.86371195e-01 2.86166906e-01 2.77726620e-01
-6.28646761e-02 3.76205921e-01 2.83964306e-01 -9.34310377e-01
3.61040980e-01 -9.25449550e-01 -8.98597836e-01 8.22474808e-02
6.45472258e-02 -6.67202845e-03 2.21772105e-01 -3.99741158... | [7.74192476272583, 7.806286334991455] |
c606824f-d82b-49ee-ab22-ced964e48bed | risks-and-benefits-of-using-a-commercially | 1903.04907 | null | http://arxiv.org/abs/1903.04907v2 | http://arxiv.org/pdf/1903.04907v2.pdf | Risks and Benefits of Using a Commercially Available Ventricular Assist Device for Failing Fontan Cavopulmonary Support: A Modeling Investigation | Fontan patients often develop circulatory failure and are in desperate need
of a therapeutic solution. A blood pump surgically placed in the cavopulmonary
pathway can substitute the function of the absent sub-pulmonary ventricle by
generating a mild pressure boost. However, there is currently no commercially
available ... | [] | 2019-04-18 | null | null | null | null | ['circulatory-failure'] | ['medical'] | [-3.78834635e-01 2.57585078e-01 9.70133319e-02 4.61228579e-01
5.01221836e-01 -9.87392068e-01 7.73019493e-02 1.13366798e-01
-3.56379926e-01 5.70855260e-01 1.99649036e-01 -1.21297550e+00
-1.33758932e-01 -5.65777123e-01 -7.33602792e-02 -7.48710692e-01
-2.62252567e-03 5.88411927e-01 3.53945851e-01 1.67562827... | [14.101117134094238, 3.0230000019073486] |
9b799b9a-5e04-4b8d-8ece-11565b5208ce | improving-sample-diversity-of-a-pre-trained | 1910.0476 | null | https://arxiv.org/abs/1910.04760v4 | https://arxiv.org/pdf/1910.04760v4.pdf | A cost-effective method for improving and re-purposing large, pre-trained GANs by fine-tuning their class-embeddings | Large, pre-trained generative models have been increasingly popular and useful to both the research and wider communities. Specifically, BigGANs a class-conditional Generative Adversarial Networks trained on ImageNet---achieved excellent, state-of-the-art capability in generating realistic photos. However, fine-tuning ... | ['Michael A. Alcorn', 'Qi Li', 'Long Mai', 'Anh Nguyen'] | 2019-10-10 | null | null | null | null | ['model-editing'] | ['natural-language-processing'] | [ 3.93625498e-01 2.64064699e-01 2.67422229e-01 -1.04668066e-01
-7.95431376e-01 -5.24977624e-01 7.10341990e-01 -6.85318947e-01
-1.28616795e-01 9.56888318e-01 4.79163863e-02 -2.45434597e-01
4.00322378e-01 -1.12584150e+00 -1.03844976e+00 -8.57162237e-01
2.42402226e-01 4.03442115e-01 -1.41351342e-01 -2.43498951... | [11.606107711791992, -0.4059349298477173] |
703a19ac-1514-4e90-add9-4254b115f78b | leveraging-dependency-forest-for-neural-1 | 1911.04123 | null | https://arxiv.org/abs/1911.04123v2 | https://arxiv.org/pdf/1911.04123v2.pdf | Leveraging Dependency Forest for Neural Medical Relation Extraction | Medical relation extraction discovers relations between entity mentions in text, such as research articles. For this task, dependency syntax has been recognized as a crucial source of features. Yet in the medical domain, 1-best parse trees suffer from relatively low accuracies, diminishing their usefulness. We investig... | ['Yue Zhang', 'Mo Yu', 'Jinsong Su', 'Zhiguo Wang', 'Linfeng Song', 'Daniel Gildea'] | 2019-11-11 | leveraging-dependency-forest-for-neural | https://aclanthology.org/D19-1020 | https://aclanthology.org/D19-1020.pdf | ijcnlp-2019-11 | ['medical-relation-extraction'] | ['medical'] | [ 1.99467734e-01 6.04273200e-01 -6.11871719e-01 -4.16290849e-01
-8.53301227e-01 -2.17588052e-01 3.25971693e-01 5.47922969e-01
-4.97862101e-01 1.08326054e+00 3.82239074e-01 -6.71596587e-01
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-3.16689134e-01 5.42111993e-01 5.59133172e-01 3.93687896... | [8.77941608428955, 8.754480361938477] |
79912db1-71f9-4936-a1e7-5cff98f2a022 | end-to-end-measure-for-text-recognition | 1908.09584 | null | https://arxiv.org/abs/1908.09584v1 | https://arxiv.org/pdf/1908.09584v1.pdf | End-To-End Measure for Text Recognition | Measuring the performance of text recognition and text line detection engines is an important step to objectively compare systems and their configuration. There exist well-established measures for both tasks separately. However, there is no sophisticated evaluation scheme to measure the quality of a combined text line ... | ['Svenja Leifert', 'Tobias Grüning', 'Roger Labahn', 'Gundram Leifert'] | 2019-08-26 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 1.80312201e-01 -3.10203582e-01 6.21871464e-02 -3.83739501e-01
-5.41300595e-01 -5.82234561e-01 7.67508447e-01 4.49877024e-01
-7.09350944e-01 3.96175027e-01 -2.57930726e-01 -3.08811188e-01
-3.69308703e-02 -6.58961117e-01 -3.24806422e-01 -6.07755899e-01
6.14965737e-01 5.37913322e-01 4.31561768e-01 -1.33960545... | [11.815350532531738, 2.6262943744659424] |
2411c5ca-c9a5-4248-923c-1d0c1a8c624a | context-aware-document-embedding | 1707.01521 | null | http://arxiv.org/abs/1707.01521v1 | http://arxiv.org/pdf/1707.01521v1.pdf | Context Aware Document Embedding | Recently, doc2vec has achieved excellent results in different tasks. In this
paper, we present a context aware variant of doc2vec. We introduce a novel
weight estimating mechanism that generates weights for each word occurrence
according to its contribution in the context, using deep neural networks. Our
context aware ... | ['Junfeng Hu', 'Zhaocheng Zhu'] | 2017-07-05 | null | null | null | null | ['document-embedding'] | ['methodology'] | [-4.70638752e-01 1.79544643e-01 -2.42262781e-01 -3.09172511e-01
-3.83871883e-01 -3.37751210e-01 1.07527578e+00 1.93877652e-01
-7.13307798e-01 7.70701766e-01 1.06179976e+00 -2.04259411e-01
-1.81364641e-01 -1.01305592e+00 -2.35251307e-01 -5.49105167e-01
-7.16912076e-02 5.29102564e-01 1.26898944e-01 -4.90865767... | [10.5711669921875, 8.479058265686035] |
f74dc857-d5cf-439d-9759-de683ad73799 | softflow-probabilistic-framework-for | 2006.04604 | null | https://arxiv.org/abs/2006.04604v4 | https://arxiv.org/pdf/2006.04604v4.pdf | SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds | Flow-based generative models are composed of invertible transformations between two random variables of the same dimension. Therefore, flow-based models cannot be adequately trained if the dimension of the data distribution does not match that of the underlying target distribution. In this paper, we propose SoftFlow, a... | ['Joun Yeop Lee', 'Woo Hyun Kang', 'Nam Soo Kim', 'Hyeongju Kim', 'Hyeonseung Lee'] | 2020-06-08 | null | http://proceedings.neurips.cc/paper/2020/hash/bdbca288fee7f92f2bfa9f7012727740-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf | neurips-2020-12 | ['point-cloud-generation'] | ['computer-vision'] | [-3.39986831e-01 -2.35503484e-02 9.60380863e-03 -8.24259594e-02
-5.01892090e-01 -7.81999290e-01 7.20767915e-01 -4.28069413e-01
1.46600097e-01 7.43049145e-01 1.60551801e-01 -2.64311492e-01
1.06463172e-01 -1.16580367e+00 -9.47501302e-01 -6.98997736e-01
2.11919382e-01 8.09251845e-01 3.22562009e-02 1.68085247... | [8.883990287780762, -3.645747661590576] |
adf78b9d-49c4-49fa-be99-22f3ea0acd11 | tstnn-two-stage-transformer-based-neural | 2103.09963 | null | https://arxiv.org/abs/2103.09963v1 | https://arxiv.org/pdf/2103.09963v1.pdf | TSTNN: Two-stage Transformer based Neural Network for Speech Enhancement in the Time Domain | In this paper, we propose a transformer-based architecture, called two-stage transformer neural network (TSTNN) for end-to-end speech denoising in the time domain. The proposed model is composed of an encoder, a two-stage transformer module (TSTM), a masking module and a decoder. The encoder maps input noisy speech int... | ['Wei-Ping Zhu', 'Bengbeng He', 'Kai Wang'] | 2021-03-18 | null | null | null | null | ['speech-denoising'] | ['speech'] | [ 4.37343240e-01 4.98633943e-02 2.69716024e-01 -4.03529704e-01
-9.26363826e-01 1.54623250e-02 3.00407439e-01 -4.94019806e-01
-3.09964061e-01 1.57209173e-01 4.23165739e-01 -4.16064352e-01
2.72437632e-01 -5.31118929e-01 -4.83454704e-01 -8.15316975e-01
1.46863252e-01 -2.46394783e-01 2.82833338e-01 -2.93949783... | [14.855613708496094, 5.9887776374816895] |
594ad5da-a29f-48f8-b15e-b39b7ed390a7 | a-cnn-toolbox-for-skin-cancer-classification | 1908.08187 | null | https://arxiv.org/abs/1908.08187v1 | https://arxiv.org/pdf/1908.08187v1.pdf | A CNN toolbox for skin cancer classification | We describe a software toolbox for the configuration of deep neural networks in the domain of skin cancer classification. The implemented software architecture allows developers to quickly set up new convolutional neural network (CNN) architectures and hyper-parameter configurations. At the same time, the user interfac... | ['Fabrizio Nunnari', 'Daniel Sonntag'] | 2019-08-21 | null | null | null | null | ['skin-cancer-classification'] | ['medical'] | [ 2.35137537e-01 8.88088569e-02 2.08853520e-02 -3.98342013e-01
-9.08184350e-02 -6.07113302e-01 1.56021476e-01 8.08026195e-02
-7.44282901e-01 1.77283198e-01 -4.19471771e-01 -6.27893984e-01
-1.30014688e-01 -8.33375454e-01 -1.30169451e-01 -4.92172867e-01
3.02991197e-02 5.16876802e-02 3.48134607e-01 -2.74585932... | [15.669193267822266, -2.979501724243164] |
b22092db-782b-4d85-ac41-d58f7614b28a | bagging-regional-classification-activation | 2207.07818 | null | https://arxiv.org/abs/2207.07818v1 | https://arxiv.org/pdf/2207.07818v1.pdf | Bagging Regional Classification Activation Maps for Weakly Supervised Object Localization | Classification activation map (CAM), utilizing the classification structure to generate pixel-wise localization maps, is a crucial mechanism for weakly supervised object localization (WSOL). However, CAM directly uses the classifier trained on image-level features to locate objects, making it prefers to discern global ... | ['Yanye Lu', 'Yunfei You', 'Lujia Jin', 'Qian Chen', 'Lei Zhu'] | 2022-07-16 | null | null | null | null | ['weakly-supervised-object-localization'] | ['computer-vision'] | [-8.19050614e-03 -1.31150082e-01 -5.55350780e-01 -4.53901559e-01
-1.28795624e+00 -7.26162136e-01 6.56659484e-01 7.61608481e-02
-4.85034734e-01 4.55312192e-01 1.62351150e-02 -2.46728450e-01
7.52825961e-02 -6.26220644e-01 -9.66264546e-01 -9.91858244e-01
1.09935179e-01 1.76623662e-03 6.47433281e-01 2.00426262... | [9.556374549865723, 0.9453558325767517] |
e137f196-23b6-44df-b535-adae7d66dae5 | boosting-the-generalization-capability-in | 2108.05028 | null | https://arxiv.org/abs/2108.05028v2 | https://arxiv.org/pdf/2108.05028v2.pdf | Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder | State of the art (SOTA) few-shot learning (FSL) methods suffer significant performance drop in the presence of domain differences between source and target datasets. The strong discrimination ability on the source dataset does not necessarily translate to high classification accuracy on the target dataset. In this work... | ['Juwei Lu', 'Peng Dai', 'Qiong Zhang', 'Hanwen Liang'] | 2021-08-11 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Liang_Boosting_the_Generalization_Capability_in_Cross-Domain_Few-Shot_Learning_via_Noise-Enhanced_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liang_Boosting_the_Generalization_Capability_in_Cross-Domain_Few-Shot_Learning_via_Noise-Enhanced_ICCV_2021_paper.pdf | iccv-2021-1 | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 2.57763416e-01 -3.24469239e-01 -1.07839689e-01 -4.80069309e-01
-6.05233073e-01 -3.25096875e-01 4.74150211e-01 -3.22792888e-01
-3.17006499e-01 7.02821791e-01 2.65062749e-01 3.91919136e-01
-3.73034000e-01 -8.26103568e-01 -5.18017709e-01 -6.90058172e-01
2.96462089e-01 2.36200407e-01 3.65569741e-01 -2.89714187... | [10.012916564941406, 2.986598491668701] |
b7ff9cdf-bf3b-4b1c-b4b6-16e959c6cc58 | towards-lightweight-cross-domain-sequential | 2302.03221 | null | https://arxiv.org/abs/2302.03221v1 | https://arxiv.org/pdf/2302.03221v1.pdf | Towards Lightweight Cross-domain Sequential Recommendation via External Attention-enhanced Graph Convolution Network | Cross-domain Sequential Recommendation (CSR) is an emerging yet challenging task that depicts the evolution of behavior patterns for overlapped users by modeling their interactions from multiple domains. Existing studies on CSR mainly focus on using composite or in-depth structures that achieve significant improvement ... | ['Xinhua Wang', 'Liancheng Xu', 'Lei Guo', 'Huichuan Duan', 'Jinyu Zhang'] | 2023-02-07 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [ 4.78577055e-02 -5.72350860e-01 -2.20088229e-01 -3.43355149e-01
-4.95452017e-01 -2.30425522e-01 2.11149961e-01 -2.73978319e-02
-2.36066595e-01 3.94021392e-01 4.86169904e-01 -2.68918425e-01
-3.71738702e-01 -8.20693970e-01 -7.93139219e-01 -5.13677299e-01
-1.81679130e-01 -2.35666987e-02 2.23569110e-01 -4.83085990... | [10.144558906555176, 5.5530900955200195] |
5e2e5da5-8e6b-49d2-b3e6-2a0bec18e3f5 | discovering-transferable-forensic-features | 2208.11342 | null | https://arxiv.org/abs/2208.11342v1 | https://arxiv.org/pdf/2208.11342v1.pdf | Discovering Transferable Forensic Features for CNN-generated Images Detection | Visual counterfeits are increasingly causing an existential conundrum in mainstream media with rapid evolution in neural image synthesis methods. Though detection of such counterfeits has been a taxing problem in the image forensics community, a recent class of forensic detectors -- universal detectors -- are able to s... | ['Ngai-Man Cheung', 'Alexander Binder', 'Ngoc-Trung Tran', 'Keshigeyan Chandrasegaran'] | 2022-08-24 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 1.62880182e-01 -2.99482077e-01 -2.08377823e-01 5.19479886e-02
-8.39920342e-01 -6.33079648e-01 6.83552682e-01 1.68940198e-04
-2.29329333e-01 4.13600415e-01 8.80358219e-02 -4.61677194e-01
-5.27244732e-02 -5.08037925e-01 -8.57885957e-01 -5.19986510e-01
-1.65767163e-01 -1.28235206e-01 5.35483181e-01 -1.15750641... | [12.37857437133789, 1.0274885892868042] |
17ee1fd4-a3fa-4298-8bdb-887e059224b8 | unsupervised-hierarchical-semantic | 2204.11432 | null | https://arxiv.org/abs/2204.11432v1 | https://arxiv.org/pdf/2204.11432v1.pdf | Unsupervised Hierarchical Semantic Segmentation with Multiview Cosegmentation and Clustering Transformers | Unsupervised semantic segmentation aims to discover groupings within and across images that capture object and view-invariance of a category without external supervision. Grouping naturally has levels of granularity, creating ambiguity in unsupervised segmentation. Existing methods avoid this ambiguity and treat it as ... | ['Stella X. Yu', 'Xudong Wang', 'Yunhui Guo', 'Jyh-Jing Hwang', 'Tsung-Wei Ke'] | 2022-04-25 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ke_Unsupervised_Hierarchical_Semantic_Segmentation_With_Multiview_Cosegmentation_and_Clustering_Transformers_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ke_Unsupervised_Hierarchical_Semantic_Segmentation_With_Multiview_Cosegmentation_and_Clustering_Transformers_CVPR_2022_paper.pdf | cvpr-2022-1 | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 2.22691372e-01 1.30601630e-01 -3.38130802e-01 -7.73611009e-01
-6.69767916e-01 -9.43081498e-01 5.76822937e-01 3.56854737e-01
-2.82879751e-02 -1.40962541e-01 5.19923031e-01 8.67263079e-02
-1.26183197e-01 -6.34738386e-01 -5.71745813e-01 -5.21842718e-01
-1.80747211e-02 4.71506923e-01 5.47410309e-01 2.49219850... | [9.550002098083496, 0.7656238675117493] |
a9870123-079b-4f22-9d2b-8d38517c752d | deep-patch-visual-odometry | 2208.04726 | null | https://arxiv.org/abs/2208.04726v2 | https://arxiv.org/pdf/2208.04726v2.pdf | Deep Patch Visual Odometry | We propose Deep Patch Visual Odometry (DPVO), a new deep learning system for monocular Visual Odometry (VO). DPVO uses a novel recurrent network architecture designed for tracking image patches across time. Recent approaches to VO have significantly improved the state-of-the-art accuracy by using deep networks to predi... | ['Jia Deng', 'Lahav Lipson', 'Zachary Teed'] | 2022-08-08 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-5.15850902e-01 -1.61450192e-01 -4.63517547e-01 3.76955755e-02
-2.75625050e-01 -2.69439965e-01 4.13922459e-01 -2.29562998e-01
-3.00546944e-01 4.72965330e-01 3.09176385e-01 -1.52483672e-01
1.77340254e-01 -7.38568842e-01 -9.84715044e-01 -3.12578857e-01
-2.60053277e-01 3.42937797e-01 4.24774885e-01 -1.38602048... | [8.481270790100098, -2.0686161518096924] |
7d4860d7-da2f-44eb-931a-ece06c41050d | safe-exploration-by-solving-early-terminated | 2107.042 | null | https://arxiv.org/abs/2107.04200v1 | https://arxiv.org/pdf/2107.04200v1.pdf | Safe Exploration by Solving Early Terminated MDP | Safe exploration is crucial for the real-world application of reinforcement learning (RL). Previous works consider the safe exploration problem as Constrained Markov Decision Process (CMDP), where the policies are being optimized under constraints. However, when encountering any potential dangers, human tends to stop i... | ['Bolei Zhou', 'Bo Dai', 'Jiadong Guo', 'Zhenghao Peng', 'Meng Fang', 'Ziping Xu', 'Hao Sun'] | 2021-07-09 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-9.51460078e-02 5.63153148e-01 -5.98478854e-01 2.26001382e-01
-8.84556949e-01 -5.02364576e-01 4.90724802e-01 4.87429984e-02
-8.39140892e-01 1.22336066e+00 2.86476631e-02 -6.01345062e-01
-1.48597956e-01 -7.29305148e-01 -5.53960085e-01 -1.02751529e+00
-4.69617665e-01 3.04869145e-01 2.01330945e-01 -4.20923457... | [4.498925685882568, 2.142439842224121] |
214e26e7-d2aa-49e8-99da-dd53535d0b02 | investigation-of-applying-quantum-neural | 2210.03882 | null | https://arxiv.org/abs/2210.03882v2 | https://arxiv.org/pdf/2210.03882v2.pdf | Investigation of Applying Quantum Neural Network of Early-Stage Breast Cancer Detection | Due to the heavy burden on medical institutes and computer-aided image diagnostics (CAD) have been gaining importance in diagnostic medicine to aid the medical staff to attain better service for the patients. Breast cancer is a fatal disease that can be treated successfully if it is detected early. Quantum neural netwo... | ['Muazez Al Ali', 'Amjad Y. Sahib', 'Musaddiq Al Ali'] | 2022-10-08 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 4.92651999e-01 1.98629186e-01 -4.02634352e-01 -6.76843822e-02
-6.16779327e-01 1.80795521e-01 1.71068102e-01 4.67782915e-01
-5.88086188e-01 8.29733670e-01 -2.52334446e-01 -6.89445972e-01
1.22776376e-02 -1.19434607e+00 -2.11492896e-01 -7.22775578e-01
-2.04195991e-01 4.99736905e-01 2.87957966e-01 -3.97798777... | [15.262072563171387, -2.6707282066345215] |
3e88144e-4354-482e-a588-3bf1fd749058 | easy-things-first-installments-improve | null | null | https://aclanthology.org/P16-1058 | https://aclanthology.org/P16-1058.pdf | Easy Things First: Installments Improve Referring Expression Generation for Objects in Photographs | null | ['Sina Zarrie{\\ss}', 'David Schlangen'] | 2016-08-01 | null | null | null | acl-2016-8 | ['referring-expression-generation'] | ['computer-vision'] | [-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... | [-7.443027019500732, 3.5593693256378174] |
e44bc3c3-b3bc-4ee7-b002-9e8f61b7d857 | trajectory-user-linking-is-easier-than-you | 2212.07081 | null | https://arxiv.org/abs/2212.07081v1 | https://arxiv.org/pdf/2212.07081v1.pdf | Trajectory-User Linking Is Easier Than You Think | Trajectory-User Linking (TUL) is a relatively new mobility classification task in which anonymous trajectories are linked to the users who generated them. With applications ranging from personalized recommendations to criminal activity detection, TUL has received increasing attention over the past five years. While res... | ['Kyle Mede', 'Alameen Najjar'] | 2022-12-14 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [-2.95848638e-01 -9.68808010e-02 -6.71105981e-01 -2.92337716e-01
-5.55854499e-01 -7.97335386e-01 8.93733859e-01 5.63189447e-01
-4.15786594e-01 9.32356477e-01 4.21841323e-01 -5.77691972e-01
-2.91555107e-01 -9.51871514e-01 -6.50945783e-01 -1.92211196e-01
-6.19090676e-01 6.21125221e-01 1.95147917e-01 -2.07148269... | [6.560232162475586, 2.1096320152282715] |
34e5d0e1-04ae-4ceb-9937-da1b7cd670c0 | the-singularity-controversy-part-i-lessons | 1601.05977 | null | http://arxiv.org/abs/1601.05977v2 | http://arxiv.org/pdf/1601.05977v2.pdf | The Singularity Controversy, Part I: Lessons Learned and Open Questions: Conclusions from the Battle on the Legitimacy of the Debate | This report seeks to inform policy makers on the nature and the merit of the
arguments for and against the concerns associated with a potential
technological singularity.
Part I describes the lessons learned from our investigation of the subject,
separating the argu-ments of merit from the fallacies and misconception... | ['Amnon H. Eden'] | 2016-01-22 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 5.27597725e-01 3.70110244e-01 -3.14934105e-01 -1.36505082e-01
-1.57758340e-01 -9.13895726e-01 7.85148978e-01 4.26708758e-01
-2.22520083e-01 5.83829582e-01 6.15967274e-01 -1.52428532e+00
-4.12024051e-01 -2.88848519e-01 -7.06818342e-01 -5.69599926e-01
1.98005661e-01 -4.27306771e-01 7.97321945e-02 -1.36733428... | [8.910475730895996, 6.597665309906006] |
34ce236b-8670-49b4-9b80-081b7f5c65e8 | probability-calibration-for-knowledge-graph-1 | 1912.1 | null | https://arxiv.org/abs/1912.10000v2 | https://arxiv.org/pdf/1912.10000v2.pdf | Probability Calibration for Knowledge Graph Embedding Models | Knowledge graph embedding research has overlooked the problem of probability calibration. We show popular embedding models are indeed uncalibrated. That means probability estimates associated to predicted triples are unreliable. We present a novel method to calibrate a model when ground truth negatives are not availabl... | ['Luca Costabello', 'Pedro Tabacof'] | 2019-12-20 | null | https://openreview.net/forum?id=S1g8K1BFwS | https://openreview.net/pdf?id=S1g8K1BFwS | iclr-2020-1 | ['calibration-for-link-prediction'] | ['graphs'] | [-1.47197738e-01 7.03910589e-01 -5.95448613e-01 -2.02295005e-01
-6.43137813e-01 -5.73071718e-01 8.56083274e-01 2.22605929e-01
-3.44206661e-01 1.05987382e+00 5.50696962e-02 -3.57590944e-01
-3.84650826e-01 -1.12731338e+00 -1.02325761e+00 -4.01090175e-01
1.02253985e-02 1.15332007e+00 4.80547220e-01 -1.94210008... | [8.729150772094727, 7.678472995758057] |
d4280bd1-4bcc-4ada-9c40-9ca5bec8cef6 | contour-based-interactive-segmentation | 2302.06353 | null | https://arxiv.org/abs/2302.06353v1 | https://arxiv.org/pdf/2302.06353v1.pdf | Contour-based Interactive Segmentation | Recent advances in interactive segmentation (IS) allow speeding up and simplifying image editing and labeling greatly. The majority of modern IS approaches accept user input in the form of clicks. However, using clicks may require too many user interactions, especially when selecting small objects, minor parts of an ob... | ['Anton Konushin', 'Anna Vorontsova', 'Polina Popenova', 'Danil Galeev'] | 2023-02-13 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [ 3.41949582e-01 -2.11373687e-01 -4.83458824e-02 -4.33910549e-01
-4.49957669e-01 -9.40850914e-01 3.11719626e-01 5.89860737e-01
-8.67508888e-01 4.28031296e-01 -5.00153184e-01 -5.76902330e-01
1.79626822e-01 -6.62473500e-01 -4.99341935e-01 -3.09946477e-01
3.14965606e-01 4.11582619e-01 9.82351899e-01 4.98873256... | [9.421625137329102, -0.06974874436855316] |
9e64ddf3-2f1b-4233-9159-752c66f7f148 | multi-level-cross-modal-interaction-network | 2007.14352 | null | https://arxiv.org/abs/2007.14352v2 | https://arxiv.org/pdf/2007.14352v2.pdf | Multi-level Cross-modal Interaction Network for RGB-D Salient Object Detection | Depth cues with affluent spatial information have been proven beneficial in boosting salient object detection (SOD), while the depth quality directly affects the subsequent SOD performance. However, it is inevitable to obtain some low-quality depth cues due to limitations of its acquisition devices, which can inhibit t... | ['Bi-Yuan Liu', 'Yun-Zhi Yang', 'Huai-Xin Chen', 'Zhou Huang', 'Tao Zhou'] | 2020-07-10 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [-2.03129694e-01 -3.26797366e-01 -2.20508844e-01 -2.26288453e-01
-5.16153216e-01 2.22664341e-01 1.98624194e-01 -8.61345232e-02
-4.43624049e-01 2.87008941e-01 2.96793461e-01 1.08128101e-01
-1.55462369e-01 -1.03956032e+00 -4.65769857e-01 -8.61457288e-01
2.11384133e-01 -4.67674047e-01 1.05655479e+00 -4.30812359... | [9.683695793151855, -0.8703410029411316] |
50101c63-c9a7-456e-bfc4-e0de707a801a | nqe-n-ary-query-embedding-for-complex-query | 2211.13469 | null | https://arxiv.org/abs/2211.13469v3 | https://arxiv.org/pdf/2211.13469v3.pdf | NQE: N-ary Query Embedding for Complex Query Answering over Hyper-Relational Knowledge Graphs | Complex query answering (CQA) is an essential task for multi-hop and logical reasoning on knowledge graphs (KGs). Currently, most approaches are limited to queries among binary relational facts and pay less attention to n-ary facts (n>=2) containing more than two entities, which are more prevalent in the real world. Mo... | ['Kaiyang Wan', 'Xueyuan Lin', 'Zichen Tang', 'Tianyu Yao', 'Yikai Guo', 'Gengxian Zhou', 'Yuhao Yang', 'Haihong E', 'Haoran Luo'] | 2022-11-24 | null | null | null | null | ['complex-query-answering', 'logical-reasoning'] | ['knowledge-base', 'reasoning'] | [-4.01619822e-01 5.16353287e-02 -5.34463942e-01 -4.89057660e-01
-4.46587414e-01 -5.05433440e-01 2.49181405e-01 5.43715239e-01
-2.86393195e-01 7.42939055e-01 7.83897266e-02 -5.49937129e-01
-7.43941963e-01 -1.86408901e+00 -8.46167684e-01 -6.46387860e-02
-1.40583590e-01 9.10973728e-01 5.69414854e-01 -7.91612685... | [9.122954368591309, 7.666326999664307] |
0a4ce2e3-3a4e-412d-8e1f-17fc41e61e09 | refining-data-for-text-generation | null | null | https://aclanthology.org/2020.ccl-1.82 | https://aclanthology.org/2020.ccl-1.82.pdf | Refining Data for Text Generation | Recent work on data-to-text generation has made progress under the neural encoder-decoder architectures. However, the data input size is often enormous, while not all data records are important for text generation and inappropriate input may bring noise into the final output. To solve this problem, we propose a two-ste... | ['Sujian Li', 'Tianyi Li', 'Qianying Liu', 'Wenyu Guan'] | null | null | null | null | ccl-2020-10 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 6.36255145e-01 3.75132322e-01 -3.77411813e-01 -3.83091629e-01
-7.58054733e-01 -2.48542055e-01 6.09892905e-01 4.45279777e-01
-4.80234683e-01 1.02863538e+00 7.30320811e-01 -2.34509438e-01
-3.80656049e-02 -1.06450284e+00 -5.86765110e-01 -3.59958410e-01
4.08636719e-01 6.26169622e-01 -5.01287431e-02 -1.65794566... | [11.767364501953125, 8.889494895935059] |
ca4e8351-2b71-42a5-ab24-4c3a332a25ff | paraphrase-generation-via-adversarial | null | null | https://aclanthology.org/2020.wnut-1.32 | https://aclanthology.org/2020.wnut-1.32.pdf | Paraphrase Generation via Adversarial Penalizations | Paraphrase generation is an important problem in Natural Language Processing that has been addressed with neural network-based approaches recently. This paper presents an adversarial framework to address the paraphrase generation problem in English. Unlike previous methods, we employ the discriminator output as penaliz... | ['Jose Ochoa-Luna', 'Gerson Vizcarra'] | null | null | null | null | emnlp-wnut-2020-11 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 3.31647277e-01 -5.14256358e-02 -2.16625690e-01 -2.64017969e-01
-1.06480706e+00 -7.12961674e-01 8.61388981e-01 -1.25086069e-01
-8.42474699e-01 1.04885554e+00 5.95513582e-01 -1.90739095e-01
3.96530002e-01 -8.72830212e-01 -8.23308408e-01 -3.09197366e-01
6.53068364e-01 2.20436886e-01 1.45273417e-01 -5.51098764... | [11.756755828857422, 9.220707893371582] |
99aa3c96-8076-4ad4-af22-f636fdeb080c | discriminative-nearest-neighbor-few-shot | 2010.13009 | null | https://arxiv.org/abs/2010.13009v1 | https://arxiv.org/pdf/2010.13009v1.pdf | Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference | Intent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill. Few-shot learning is attracting much attention to mitigate data scarcity, but OOS detection becomes even more challenging. In this paper, we present a simple ye... | ['Caiming Xiong', 'Richard Socher', 'Philip S. Yu', 'Yao Wan', 'Chien-Sheng Wu', 'Wenhao Liu', 'Kazuma Hashimoto', 'Jian-Guo Zhang'] | 2020-10-25 | null | https://aclanthology.org/2020.emnlp-main.411 | https://aclanthology.org/2020.emnlp-main.411.pdf | emnlp-2020-11 | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-3.78632988e-03 2.51066275e-02 -6.24453843e-01 -6.44603550e-01
-9.11443591e-01 -4.14497584e-01 7.53174961e-01 2.08283484e-01
-6.01820588e-01 2.76987523e-01 6.84657216e-01 -3.68077978e-02
3.52027104e-03 -6.15955949e-01 -3.58952135e-02 -2.12387711e-01
1.40097708e-01 6.33492112e-01 2.25490659e-01 -4.69198644... | [12.188029289245605, 7.585257530212402] |
28af6799-5174-48ae-a437-0369be6a7a92 | technical-report-assisting-backdoor-federated | 2207.12327 | null | https://arxiv.org/abs/2207.12327v1 | https://arxiv.org/pdf/2207.12327v1.pdf | Technical Report: Assisting Backdoor Federated Learning with Whole Population Knowledge Alignment | Due to the distributed nature of Federated Learning (FL), researchers have uncovered that FL is vulnerable to backdoor attacks, which aim at injecting a sub-task into the FL without corrupting the performance of the main task. Single-shot backdoor attack achieves high accuracy on both the main task and backdoor sub-tas... | ['Tao Shu', 'Xueyang Hu', 'Tian Liu'] | 2022-07-25 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [-1.45196348e-01 -2.40527496e-01 -2.08471894e-01 1.83094397e-01
-7.78056681e-01 -9.73253429e-01 6.77400649e-01 -4.81768250e-02
-4.75134581e-01 7.77799547e-01 -1.41471848e-01 -5.14769971e-01
-7.75194913e-02 -8.01668584e-01 -1.05202496e+00 -1.04922080e+00
-1.30463436e-01 3.05127591e-01 2.65286535e-01 3.53692211... | [5.739367485046387, 7.348329067230225] |
f2c6dc35-a269-4d2f-a471-b8ae63827a1a | density-map-distillation-for-incremental | 2304.05255 | null | https://arxiv.org/abs/2304.05255v1 | https://arxiv.org/pdf/2304.05255v1.pdf | Density Map Distillation for Incremental Object Counting | We investigate the problem of incremental learning for object counting, where a method must learn to count a variety of object classes from a sequence of datasets. A na\"ive approach to incremental object counting would suffer from catastrophic forgetting, where it would suffer from a dramatic performance drop on previ... | ['Joost Van de Weijer', 'Chenshen Wu'] | 2023-04-11 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 2.79156119e-01 -3.27011019e-01 -5.28005362e-02 -3.21322709e-01
-5.64624965e-01 -2.06521153e-01 5.24029970e-01 2.57538110e-01
-9.89458144e-01 1.18005097e+00 4.60327305e-02 -7.75446147e-02
1.23334512e-01 -6.37513220e-01 -1.02986395e+00 -6.07712626e-01
-5.06418087e-02 5.25992513e-01 5.45846045e-01 4.22013640... | [9.751349449157715, 3.279282569885254] |
2059b2fb-2bee-4609-b863-b774e79b355c | high-fidelity-point-cloud-completion-with-low | 2112.11271 | null | https://arxiv.org/abs/2112.11271v2 | https://arxiv.org/pdf/2112.11271v2.pdf | High-Fidelity Point Cloud Completion with Low-Resolution Recovery and Noise-Aware Upsampling | Completing an unordered partial point cloud is a challenging task. Existing approaches that rely on decoding a latent feature to recover the complete shape, often lead to the completed point cloud being over-smoothing, losing details, and noisy. Instead of decoding a whole shape, we propose to decode and refine a low-r... | ['Lin Gao', 'Ling-Xiao Zhang', 'Chun-Peng Li', 'Bo wang', 'Ren-Wu Li'] | 2021-12-21 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [ 2.61680424e-01 6.61488622e-02 4.24215138e-01 -7.02341050e-02
-1.06613851e+00 -3.45034391e-01 4.37008798e-01 1.04808785e-01
6.37930483e-02 6.76511586e-01 -5.88598363e-02 2.47753039e-01
-1.97257451e-03 -1.13388240e+00 -9.67856288e-01 -6.91662490e-01
3.35112154e-01 7.00613141e-01 2.53659099e-01 3.11431382... | [8.413784980773926, -3.44325590133667] |
6032c74d-ac58-4afc-9f30-4ef952b31b35 | supervised-speech-representation-learning-for | 2106.00531 | null | https://arxiv.org/abs/2106.00531v2 | https://arxiv.org/pdf/2106.00531v2.pdf | Supervised Speech Representation Learning for Parkinson's Disease Classification | Recently proposed automatic pathological speech classification techniques use unsupervised auto-encoders to obtain a high-level abstract representation of speech. Since these representations are learned based on reconstructing the input, there is no guarantee that they are robust to pathology-unrelated cues such as spe... | ['Ina Kodrasi', 'Parvaneh Janbakhshi'] | 2021-06-01 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 5.21476150e-01 5.98365247e-01 -8.83800685e-02 -6.09290898e-01
-1.13962758e+00 -2.22196475e-01 3.87636006e-01 -7.67298788e-02
-1.40717342e-01 5.59631586e-01 8.04514885e-01 1.79647899e-03
2.58647859e-01 -3.92459124e-01 -4.85053331e-01 -6.91059113e-01
-9.99800563e-02 3.17184955e-01 -7.46182650e-02 -3.11101880... | [14.475842475891113, 6.311915397644043] |
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