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
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
221ddaee-0da7-4a10-834f-adba333ef5ab | sad-saliency-based-defenses-against | 2003.04820 | null | https://arxiv.org/abs/2003.04820v1 | https://arxiv.org/pdf/2003.04820v1.pdf | SAD: Saliency-based Defenses Against Adversarial Examples | With the rise in popularity of machine and deep learning models, there is an increased focus on their vulnerability to malicious inputs. These adversarial examples drift model predictions away from the original intent of the network and are a growing concern in practical security. In order to combat these attacks, neur... | ['Michael Geyer', 'David Patrick', 'Richard Tran', 'Amanda Fernandez'] | 2020-03-10 | null | null | null | null | ['music-genre-recognition'] | ['music'] | [ 5.41049421e-01 1.86408043e-01 1.14213616e-01 -2.22959980e-01
-6.44237161e-01 -8.31832647e-01 7.16842353e-01 3.54916066e-01
-4.05747503e-01 4.20604348e-01 1.48364410e-01 -2.65955895e-01
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-7.38132894e-02 -1.92014709e-01 6.07517183e-01 -6.37440622... | [5.530428409576416, 7.932652473449707] |
fdf6660c-0f57-4fd7-9e6e-693fb2e3e6d3 | chartqa-a-benchmark-for-question-answering | 2203.10244 | null | https://arxiv.org/abs/2203.10244v1 | https://arxiv.org/pdf/2203.10244v1.pdf | ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning | Charts are very popular for analyzing data. When exploring charts, people often ask a variety of complex reasoning questions that involve several logical and arithmetic operations. They also commonly refer to visual features of a chart in their questions. However, most existing datasets do not focus on such complex rea... | ['Enamul Hoque', 'Shafiq Joty', 'Jia Qing Tan', 'Do Xuan Long', 'Ahmed Masry'] | 2022-03-19 | null | https://aclanthology.org/2022.findings-acl.177 | https://aclanthology.org/2022.findings-acl.177.pdf | findings-acl-2022-5 | ['chart-question-answering', 'chart-question-answering'] | ['computer-code', 'computer-vision'] | [-2.94424176e-01 -9.73526447e-04 -7.36735482e-03 -3.51259649e-01
-1.03332353e+00 -1.02715743e+00 7.02640533e-01 5.90717554e-01
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-2.43047222e-01 -8.57020855e-01 -6.27160072e-01 2.50093371e-01
2.17048123e-01 4.99452382e-01 4.79445606e-01 -4.11060929... | [11.19690227508545, 2.0398027896881104] |
7b8ea859-b36a-44e6-9175-1e1dcf3a6b42 | faster-video-moment-retrieval-with-point | 2305.14017 | null | https://arxiv.org/abs/2305.14017v1 | https://arxiv.org/pdf/2305.14017v1.pdf | Faster Video Moment Retrieval with Point-Level Supervision | Video Moment Retrieval (VMR) aims at retrieving the most relevant events from an untrimmed video with natural language queries. Existing VMR methods suffer from two defects: (1) massive expensive temporal annotations are required to obtain satisfying performance; (2) complicated cross-modal interaction modules are depl... | ['Heng Tao Shen', 'Guoqing Wang', 'Yang Yang', 'Xing Xu', 'Zailei Zhou', 'Xun Jiang'] | 2023-05-23 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [ 9.09939259e-02 -5.63430667e-01 -4.68447238e-01 -1.27915770e-01
-1.29614604e+00 -3.24983448e-01 5.69803119e-01 2.31386244e-01
-6.90138757e-01 2.97325432e-01 1.37948558e-01 7.73883760e-02
-3.91483009e-01 -5.00006735e-01 -6.92079842e-01 -6.44523740e-01
1.96932517e-02 4.05424386e-01 5.76382637e-01 -1.90976605... | [10.125072479248047, 0.7333472371101379] |
1dea319c-3c77-40a6-8753-4674b3ec730e | event-based-monocular-dense-depth-estimation | 2212.02791 | null | https://arxiv.org/abs/2212.02791v1 | https://arxiv.org/pdf/2212.02791v1.pdf | Event-based Monocular Dense Depth Estimation with Recurrent Transformers | Event cameras, offering high temporal resolutions and high dynamic ranges, have brought a new perspective to address common challenges (e.g., motion blur and low light) in monocular depth estimation. However, how to effectively exploit the sparse spatial information and rich temporal cues from asynchronous events remai... | ['Yonghong Tian', 'Xiaopeng Fan', 'Jianing Li', 'Xu Liu'] | 2022-12-06 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 2.19732106e-01 -4.55738962e-01 -3.88487987e-02 -2.96601862e-01
-4.51639414e-01 -2.59910256e-01 6.39174759e-01 -3.62971753e-01
-3.09567422e-01 5.67737937e-01 2.38123670e-01 -6.45035282e-02
1.99644327e-01 -7.52019644e-01 -6.62727654e-01 -7.86564648e-01
1.98365256e-01 -2.59168893e-01 8.03971291e-01 3.16222697... | [9.109579086303711, -1.7504867315292358] |
9f7e3c1e-cd2b-44b1-8b50-ebe62c7fe390 | slideflow-deep-learning-for-digital | 2304.04142 | null | https://arxiv.org/abs/2304.04142v1 | https://arxiv.org/pdf/2304.04142v1.pdf | Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization | Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for deploying models in an interactive interface. Experimenting with different deep learning approaches... | ['Alexander T. Pearson', 'Prajval Mohan', 'Anran Li', 'Frederick M. Howard', 'Matteo Sacco', 'Andrew Srisuwananukorn', 'Emma Dyer', 'Sara Kochanny', 'James M. Dolezal'] | 2023-04-09 | null | null | null | null | ['whole-slide-images', 'histopathological-segmentation', 'histopathological-image-classification', 'multiple-instance-learning'] | ['computer-vision', 'computer-vision', 'medical', 'methodology'] | [-3.13122690e-01 -3.52887243e-01 2.86421441e-02 -4.60080266e-01
-1.20290279e+00 -5.94920456e-01 2.32860614e-02 5.86005390e-01
-4.83272374e-01 2.67018080e-01 -2.11749569e-01 -6.68878138e-01
-1.42552435e-01 -6.34541988e-01 -1.04869872e-01 -1.08276796e+00
-3.11406970e-01 6.20575190e-01 1.53009072e-01 2.98799723... | [15.096793174743652, -3.0741498470306396] |
3414ac75-3685-4411-ae4c-ee3a523f2555 | fooling-a-real-car-with-adversarial-traffic | 1907.00374 | null | https://arxiv.org/abs/1907.00374v1 | https://arxiv.org/pdf/1907.00374v1.pdf | Fooling a Real Car with Adversarial Traffic Signs | The attacks on the neural-network-based classifiers using adversarial images have gained a lot of attention recently. An adversary can purposely generate an image that is indistinguishable from a innocent image for a human being but is incorrectly classified by the neural networks. The adversarial images do not need to... | ['Alexander Kreines', 'Shachar Mendelowitz', 'Yuval Weisglass', 'Nir Morgulis'] | 2019-06-30 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 2.80569464e-01 2.81495064e-01 5.56418121e-01 -3.18717271e-01
-4.53497469e-01 -9.99245882e-01 6.16427183e-01 -4.71470803e-01
-4.43188518e-01 4.59247977e-01 -8.31327498e-01 -5.82740545e-01
2.94783384e-01 -8.90132070e-01 -1.27741945e+00 -7.82772303e-01
3.35517675e-02 3.88180315e-01 9.86274898e-01 -4.75661397... | [5.480008602142334, 7.816496849060059] |
a161baed-6ccd-44b9-95da-380edf05b31d | comparison-between-voting-classifier-and-deep | null | null | https://aclanthology.org/2020.wanlp-1.23 | https://aclanthology.org/2020.wanlp-1.23.pdf | Comparison between Voting Classifier and Deep Learning methods for Arabic Dialect Identification | In this paper, we present three methods developed for the NADI shared task on Arabic Dialect Identification for tweets. The first and the second method use respectively a machine learning model based on a Voting Classifier with words and character level features and a deep learning model at word level. The third method... | ['Gaël Lejeune', 'Ghoul Dhaou'] | null | null | null | null | coling-wanlp-2020-12 | ['dialect-identification'] | ['natural-language-processing'] | [-3.74185234e-01 1.90846361e-02 -1.23757191e-01 -4.60947692e-01
-5.75007081e-01 -4.66659218e-01 1.09417284e+00 4.38228279e-01
-8.72587323e-01 5.65743744e-01 3.97880971e-01 -4.78947461e-01
-6.77170455e-02 -1.10886347e+00 3.51741686e-02 -6.16279185e-01
-1.32746369e-01 8.01936030e-01 1.70811027e-01 -8.34307134... | [10.1629638671875, 10.670921325683594] |
86f946b8-5a37-4738-8c8f-770198e6de9e | the-voice-and-eye-gaze-behavior-of-an | null | null | https://aclanthology.org/W12-0408 | https://aclanthology.org/W12-0408.pdf | The Voice and Eye Gaze Behavior of an Imposter: Automated Interviewing and Detection for Rapid Screening at the Border | null | ['Monica Gariup', 'Aaron Elkins', 'Douglas Derrick'] | 2012-04-01 | null | null | null | ws-2012-4 | ['deception-detection'] | ['miscellaneous'] | [-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... | [-7.402584552764893, 3.623408555984497] |
6faec35b-e5b4-4d01-8ad4-55a0d3fa32ac | self-supervised-learning-by-cross-modal-audio | 1911.12667 | null | https://arxiv.org/abs/1911.12667v3 | https://arxiv.org/pdf/1911.12667v3.pdf | Self-Supervised Learning by Cross-Modal Audio-Video Clustering | Visual and audio modalities are highly correlated, yet they contain different information. Their strong correlation makes it possible to predict the semantics of one from the other with good accuracy. Their intrinsic differences make cross-modal prediction a potentially more rewarding pretext task for self-supervised l... | ['Bruno Korbar', 'Humam Alwassel', 'Du Tran', 'Dhruv Mahajan', 'Lorenzo Torresani', 'Bernard Ghanem'] | 2019-11-28 | null | http://proceedings.neurips.cc/paper/2020/hash/6f2268bd1d3d3ebaabb04d6b5d099425-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/6f2268bd1d3d3ebaabb04d6b5d099425-Paper.pdf | neurips-2020-12 | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 2.41547331e-01 6.64162543e-03 -7.57528841e-01 -3.95852268e-01
-1.04215002e+00 -4.70034808e-01 7.94270456e-01 -9.32534412e-02
-3.53465229e-01 3.35722536e-01 6.92610681e-01 2.68266678e-01
5.85197918e-02 -2.17218444e-01 -1.05550826e+00 -5.97774386e-01
-1.23517096e-01 3.87326568e-01 1.67756081e-01 2.29787663... | [9.630102157592773, 1.034551978111267] |
fcb9fa38-11ab-4ad6-9da8-c778bf02cf1c | stack-based-multi-layer-attention-for | null | null | https://aclanthology.org/D17-1175 | https://aclanthology.org/D17-1175.pdf | Stack-based Multi-layer Attention for Transition-based Dependency Parsing | Although sequence-to-sequence (seq2seq) network has achieved significant success in many NLP tasks such as machine translation and text summarization, simply applying this approach to transition-based dependency parsing cannot yield a comparable performance gain as in other state-of-the-art methods, such as stack-LSTM ... | ['Ming Zhou', 'Shujie Liu', 'Zhirui Zhang', 'Mu Li', 'Enhong Chen'] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [ 3.56946021e-01 2.71501541e-01 -5.80011189e-01 -6.58927739e-01
-1.43591201e+00 -5.85004747e-01 2.93068916e-01 3.59778225e-01
-6.50890946e-01 8.07355106e-01 7.17987895e-01 -7.95152128e-01
4.11555737e-01 -4.26053077e-01 -8.81910086e-01 -3.61313343e-01
1.45918399e-01 4.39553767e-01 3.68096590e-01 -2.05812812... | [10.414997100830078, 9.559991836547852] |
36d89537-e7cc-44df-97c8-7e2ad46e6bb1 | prompt-pre-training-with-twenty-thousand | 2304.04704 | null | https://arxiv.org/abs/2304.04704v1 | https://arxiv.org/pdf/2304.04704v1.pdf | Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition | This work proposes POMP, a prompt pre-training method for vision-language models. Being memory and computation efficient, POMP enables the learned prompt to condense semantic information for a rich set of visual concepts with over twenty-thousand classes. Once pre-trained, the prompt with a strong transferable ability ... | ['Xu sun', 'Alex Smola', 'Mu Li', 'Shuai Zheng', 'Shuai Zhang', 'Yi Zhu', 'Aston Zhang', 'Shuhuai Ren'] | 2023-04-10 | null | null | null | null | ['open-vocabulary-object-detection'] | ['computer-vision'] | [ 1.91837698e-01 2.08187446e-01 -5.90354979e-01 -2.39699274e-01
-1.18868852e+00 -8.87875259e-01 9.52210665e-01 1.43173143e-01
-6.41678870e-01 4.25350934e-01 -1.83691651e-01 -1.54808134e-01
4.87415701e-01 -7.33833790e-01 -9.87463057e-01 -6.76638424e-01
3.50111604e-01 7.39080489e-01 6.80711031e-01 1.34811968... | [10.273396492004395, 1.5546144247055054] |
8cae14a9-90c0-4e73-bce3-991cf69dedb4 | dp-fp-differentially-private-forward | 2112.14430 | null | https://arxiv.org/abs/2112.14430v1 | https://arxiv.org/pdf/2112.14430v1.pdf | DP-FP: Differentially Private Forward Propagation for Large Models | When applied to large-scale learning problems, the conventional wisdom on privacy-preserving deep learning, known as Differential Private Stochastic Gradient Descent (DP-SGD), has met with limited success due to significant performance degradation and high memory overhead when compared to the non-privacy counterpart. W... | ['Haitao Mi', 'Jian Du'] | 2021-12-29 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [ 5.83560951e-02 2.49929994e-01 1.32242795e-02 -6.36978805e-01
-1.40563297e+00 -5.98402023e-01 3.65418553e-01 2.39791244e-01
-8.83234799e-01 7.73614526e-01 1.72005087e-01 -7.15454400e-01
4.89645422e-01 -6.44225240e-01 -1.03650177e+00 -7.31834590e-01
-5.79037368e-02 -1.55161902e-01 -7.07438514e-02 2.78553158... | [5.903469562530518, 6.848443031311035] |
475708fa-97b4-44d3-b011-17e5d2e1d44f | word-translation-without-parallel-data | 1710.04087 | null | http://arxiv.org/abs/1710.04087v3 | http://arxiv.org/pdf/1710.04087v3.pdf | Word Translation Without Parallel Data | State-of-the-art methods for learning cross-lingual word embeddings have
relied on bilingual dictionaries or parallel corpora. Recent studies showed
that the need for parallel data supervision can be alleviated with
character-level information. While these methods showed encouraging results,
they are not on par with th... | ['Hervé Jégou', "Marc'Aurelio Ranzato", 'Ludovic Denoyer', 'Guillaume Lample', 'Alexis Conneau'] | 2017-10-11 | word-translation-without-parallel-data-1 | https://openreview.net/forum?id=H196sainb | https://openreview.net/pdf?id=H196sainb | iclr-2018-1 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [-2.65309304e-01 -2.42003292e-01 -7.51556039e-01 -3.72780353e-01
-8.26963246e-01 -8.56780708e-01 9.16732490e-01 2.40625307e-01
-8.82707059e-01 8.05095732e-01 3.52168351e-01 -7.48626649e-01
3.03642780e-01 -5.96774697e-01 -5.93760610e-01 -3.73977065e-01
1.74183145e-01 7.95530736e-01 -4.38353838e-03 -6.83358431... | [11.018477439880371, 10.027776718139648] |
36b3f43c-f3e7-4a37-bc21-b796ed57ef4c | search-and-learn-improving-semantic-coverage-1 | 2112.02770 | null | https://arxiv.org/abs/2112.02770v1 | https://arxiv.org/pdf/2112.02770v1.pdf | Search and Learn: Improving Semantic Coverage for Data-to-Text Generation | Data-to-text generation systems aim to generate text descriptions based on input data (often represented in the tabular form). A typical system uses huge training samples for learning the correspondence between tables and texts. However, large training sets are expensive to obtain, limiting the applicability of these a... | ['Lili Mou', 'Andreas Dengel', 'Zi Xuan Zhang', 'Shailza Jolly'] | 2021-12-06 | search-and-learn-improving-semantic-coverage | https://openreview.net/forum?id=brOPxR6Bav | https://openreview.net/pdf?id=brOPxR6Bav | null | ['data-to-text-generation'] | ['natural-language-processing'] | [ 2.05787733e-01 4.54588264e-01 -5.37699103e-01 -3.95342350e-01
-1.31024611e+00 -4.17874426e-01 6.35107756e-01 1.71591699e-01
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5.82841523e-02 -1.30580342e+00 -8.58815551e-01 -9.79081839e-02
7.09474564e-01 8.54579866e-01 8.37115869e-02 -4.83864665... | [11.660916328430176, 8.811751365661621] |
3159835b-ed29-4d08-ba3e-3faeb9ff033d | adaptive-hierarchical-similarity-metric | 2111.00006 | null | https://arxiv.org/abs/2111.00006v1 | https://arxiv.org/pdf/2111.00006v1.pdf | Adaptive Hierarchical Similarity Metric Learning with Noisy Labels | Deep Metric Learning (DML) plays a critical role in various machine learning tasks. However, most existing deep metric learning methods with binary similarity are sensitive to noisy labels, which are widely present in real-world data. Since these noisy labels often cause severe performance degradation, it is crucial to... | ['Heng Huang', 'Cheng Deng', 'Lei Luo', 'Jiexi Yan'] | 2021-10-29 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [-0.09870818 -0.5802549 0.0411524 -0.8830792 -0.87624 -0.34917134
0.24438809 0.3142202 -0.48337328 0.64628476 0.13838565 -0.03420069
-0.673165 -0.8137064 -0.21633571 -0.89420724 0.16939202 0.2985468
0.2398484 -0.23420365 0.25370023 0.36049047 -1.3312131 -0.09389382
1.176731 1.4264355 0.0... | [9.441994667053223, 3.2249670028686523] |
84c45e86-4e73-4bbf-a727-57fe122c6052 | weakly-and-semi-supervised-panoptic | 1808.03575 | null | http://arxiv.org/abs/1808.03575v3 | http://arxiv.org/pdf/1808.03575v3.pdf | Weakly- and Semi-Supervised Panoptic Segmentation | We present a weakly supervised model that jointly performs both semantic- and
instance-segmentation -- a particularly relevant problem given the substantial
cost of obtaining pixel-perfect annotation for these tasks. In contrast to many
popular instance segmentation approaches based on object detectors, our method
does... | ['Philip H. S. Torr', 'Anurag Arnab', 'Qizhu Li'] | 2018-08-10 | weakly-and-semi-supervised-panoptic-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Anurag_Arnab_Weakly-_and_Semi-Supervised_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Anurag_Arnab_Weakly-_and_Semi-Supervised_ECCV_2018_paper.pdf | eccv-2018-9 | ['weakly-supervised-panoptic-segmentation', 'weakly-supervised-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.24871361e-01 6.98910952e-01 -3.97236884e-01 -5.68868101e-01
-1.29542077e+00 -6.86824322e-01 7.13902116e-01 2.44803295e-01
-5.22740245e-01 7.86012173e-01 -1.99199915e-01 -3.34655568e-02
9.06453133e-02 -7.95058966e-01 -9.56080675e-01 -7.08526552e-01
1.90825596e-01 7.71277964e-01 7.16182232e-01 8.23099986... | [9.492303848266602, 0.5123147368431091] |
7f6b62e2-3b5e-4106-97b9-4257904ea8f9 | entity-relative-position-representation-based | null | null | https://aclanthology.org/2020.ccl-1.89 | https://aclanthology.org/2020.ccl-1.89.pdf | Entity Relative Position Representation based Multi-head Selection for Joint Entity and Relation Extraction | Joint entity and relation extraction has received increasing interests recently, due to the capability of utilizing the interactions between both steps. Among existing studies, the Multi-Head Selection (MHS) framework is efficient in extracting entities and relations simultaneously. However, the method is weak for its ... | ['Zhoujun Li', 'Yunbo Cao', 'Zhao Yan', 'Tianyang Zhao'] | null | null | null | null | ccl-2020-10 | ['relation-classification', 'joint-entity-and-relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.35907406e-02 1.78723693e-01 -5.74212849e-01 -5.82516372e-01
-9.54794705e-01 -3.95662606e-01 7.11319745e-01 4.79983002e-01
-4.84093755e-01 6.96410418e-01 3.57908607e-01 -1.91524044e-01
-1.36181846e-01 -8.54920506e-01 -6.42623961e-01 -5.17332733e-01
-6.39821813e-02 7.93068856e-02 3.21481258e-01 -2.20067471... | [9.275659561157227, 8.686155319213867] |
b0996466-dd76-4142-8be2-be5826184146 | exploration-of-the-search-space-of-gaussian | 2303.05561 | null | https://arxiv.org/abs/2303.05561v1 | https://arxiv.org/pdf/2303.05561v1.pdf | Exploration of the search space of Gaussian graphical models for paired data | We consider the problem of learning a Gaussian graphical model in the case where the observations come from two dependent groups sharing the same variables. We focus on a family of coloured Gaussian graphical models specifically suited for the paired data problem. Commonly, graphical models are ordered by the submodel ... | ['Dung Ngoc Nguyen', 'Alberto Roverato'] | 2023-03-09 | null | null | null | null | ['efficient-exploration'] | ['methodology'] | [ 3.78639907e-01 3.08054745e-01 2.31960677e-02 -4.29685563e-01
-2.60432005e-01 -4.76376176e-01 7.64441669e-01 1.51352152e-01
-6.22334719e-01 7.05779910e-01 -6.34170994e-02 -2.90072799e-01
-5.38603961e-01 -7.14006960e-01 -6.22507572e-01 -1.03347683e+00
-4.02651668e-01 8.35166514e-01 1.17357217e-01 2.64539242... | [7.091087818145752, 4.911638259887695] |
0c2a1735-8a51-4fe6-afd8-7b33963d5002 | discriminative-feature-alignment | 2006.12770 | null | https://arxiv.org/abs/2006.12770v5 | https://arxiv.org/pdf/2006.12770v5.pdf | Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment | In this study, we focus on the unsupervised domain adaptation problem where an approximate inference model is to be learned from a labeled data domain and expected to generalize well to an unlabeled data domain. The success of unsupervised domain adaptation largely relies on the cross-domain feature alignment. Previous... | ['Jing Wang', 'Jianzhe Lin', 'Clarence W. de Silva', 'Leonid Sigal', 'Jiahong Chen'] | 2020-06-23 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 2.65767485e-01 -5.23499213e-02 -5.37055850e-01 -7.43720770e-01
-7.11213946e-01 -4.36622113e-01 5.96570671e-01 -1.68786839e-01
-1.00098372e-01 9.74184155e-01 2.92749733e-01 2.98073500e-01
-2.75690466e-01 -5.86178422e-01 -6.81340098e-01 -8.05119812e-01
6.27041399e-01 5.17098725e-01 3.46428938e-02 9.93276909... | [10.406590461730957, 3.170940637588501] |
a590099d-495f-4231-888b-538508173f64 | a-neural-state-space-model-approach-to | 2305.16932 | null | https://arxiv.org/abs/2305.16932v1 | https://arxiv.org/pdf/2305.16932v1.pdf | A Neural State-Space Model Approach to Efficient Speech Separation | In this work, we introduce S4M, a new efficient speech separation framework based on neural state-space models (SSM). Motivated by linear time-invariant systems for sequence modeling, our SSM-based approach can efficiently model input signals into a format of linear ordinary differential equations (ODEs) for representa... | ['Eng Siong Chng', 'Pin-Jui Ku', 'Yuchen Hu', 'Kai Li', 'Chao-Han Huck Yang', 'Chen Chen'] | 2023-05-26 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 1.68167483e-02 -1.39531597e-01 2.97746565e-02 -1.18444398e-01
-1.09039533e+00 -5.10401905e-01 6.20034993e-01 -3.85820657e-01
-3.40117484e-01 4.28270340e-01 1.51964396e-01 -6.99673593e-01
-1.39690816e-01 2.19736382e-01 -5.97079217e-01 -7.55706728e-01
-6.16574250e-02 3.87326509e-01 5.72353937e-02 -1.31689683... | [14.876596450805664, 6.097800254821777] |
b6219249-79a4-4673-8edf-d98dd10b3a00 | towards-robust-aspect-based-sentiment | 2306.13971 | null | https://arxiv.org/abs/2306.13971v1 | https://arxiv.org/pdf/2306.13971v1.pdf | Towards Robust Aspect-based Sentiment Analysis through Non-counterfactual Augmentations | While state-of-the-art NLP models have demonstrated excellent performance for aspect based sentiment analysis (ABSA), substantial evidence has been presented on their lack of robustness. This is especially manifested as significant degradation in performance when faced with out-of-distribution data. Recent solutions th... | ['Jingbo Zhu', 'Tong Xiao', 'Pranava Madhyastha', 'Chunyang Xiao', 'Kaikai An', 'Yan Ding', 'Xinyu Liu'] | 2023-06-24 | null | null | null | null | ['sentiment-analysis', 'aspect-based-sentiment-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.44315112e-01 2.21950054e-01 -4.41613883e-01 -4.53430563e-01
-1.12423539e+00 -8.30312788e-01 1.32388759e+00 3.84169936e-01
-4.20113683e-01 6.60106361e-01 8.93043697e-01 -3.44700426e-01
-2.29824841e-01 -6.27602518e-01 -7.67285287e-01 -3.85452569e-01
2.10402668e-01 3.97970647e-01 -6.86898455e-02 -6.73741579... | [11.283243179321289, 6.89691686630249] |
588aa7de-428f-4301-81bf-113635031c76 | a-bayesian-3d-multi-view-multi-object | 2001.04118 | null | https://arxiv.org/abs/2001.04118v4 | https://arxiv.org/pdf/2001.04118v4.pdf | A Bayesian Filter for Multi-view 3D Multi-object Tracking with Occlusion Handling | This paper proposes an online multi-camera multi-object tracker that only requires monocular detector training, independent of the multi-camera configurations, allowing seamless extension/deletion of cameras without retraining effort. The proposed algorithm has a linear complexity in the total number of detections acro... | ['Sven Nordholm', 'Du Yong Kim', 'Ba Tuong Vo', 'Jonah Ong', 'Ba Ngu Vo'] | 2020-01-13 | null | null | null | null | ['occlusion-handling', '3d-multi-object-tracking'] | ['computer-vision', 'computer-vision'] | [-7.31090978e-02 -6.74264431e-01 6.60835579e-02 1.80342287e-01
-7.21651971e-01 -1.11509824e+00 5.10947227e-01 -1.32684186e-01
-4.48255479e-01 4.09147769e-01 -2.74999350e-01 -1.45180479e-01
-1.70112103e-01 -5.51178493e-02 -8.18294287e-01 -5.75996101e-01
6.50018528e-02 6.42107844e-01 1.01033604e+00 4.25499052... | [6.495640754699707, -2.072140693664551] |
c7dc3a44-331d-4f4e-bf91-1114b6e646fb | towards-open-vocabulary-object-detection | 2111.09452 | null | https://arxiv.org/abs/2111.09452v3 | https://arxiv.org/pdf/2111.09452v3.pdf | Open Vocabulary Object Detection with Pseudo Bounding-Box Labels | Despite great progress in object detection, most existing methods work only on a limited set of object categories, due to the tremendous human effort needed for bounding-box annotations of training data. To alleviate the problem, recent open vocabulary and zero-shot detection methods attempt to detect novel object cate... | ['Caiming Xiong', 'Wenhao Liu', 'ran Xu', 'Junnan Li', 'Juan Carlos Niebles', 'Chen Xing', 'Mingfei Gao'] | 2021-11-18 | null | null | null | null | ['open-vocabulary-object-detection'] | ['computer-vision'] | [ 1.68999135e-01 2.09640935e-01 -1.75167695e-01 -4.56992298e-01
-1.16261053e+00 -9.19980884e-01 6.57477796e-01 1.93844303e-01
-5.32161474e-01 4.12741393e-01 -1.00455277e-01 -4.20705788e-03
5.32372117e-01 -5.64147353e-01 -9.35149074e-01 -3.04542750e-01
1.88084185e-01 5.95707536e-01 8.88033926e-01 -8.13622102... | [9.537271499633789, 1.4344860315322876] |
5386ae0a-4b70-4ae1-9ef1-9d73ccd17429 | will-it-blend-mixing-training-paradigms | 2209.08966 | null | https://arxiv.org/abs/2209.08966v2 | https://arxiv.org/pdf/2209.08966v2.pdf | Will It Blend? Mixing Training Paradigms & Prompting for Argument Quality Prediction | This paper describes our contributions to the Shared Task of the 9th Workshop on Argument Mining (2022). Our approach uses Large Language Models for the task of Argument Quality Prediction. We perform prompt engineering using GPT-3, and also investigate the training paradigms multi-task learning, contrastive learning, ... | ['Selene Báez Santamaría', 'Lea Krause', 'Urja Khurana', 'Myrthe Reuver', 'Michiel van der Meer'] | 2022-09-19 | null | https://aclanthology.org/2022.argmining-1.8 | https://aclanthology.org/2022.argmining-1.8.pdf | argmining-acl-2022-10 | ['argument-mining'] | ['natural-language-processing'] | [-1.28054559e-01 8.18747699e-01 -8.70877862e-01 -3.34967762e-01
-1.35611522e+00 -4.49666113e-01 8.92398596e-01 8.83190870e-01
-4.53313202e-01 9.46332157e-01 6.00977719e-01 -1.06514072e+00
-5.87404072e-01 -6.30967438e-01 -7.47765481e-01 8.68953317e-02
2.36062761e-02 1.17044199e+00 2.65408605e-01 -4.46071684... | [9.601283073425293, 9.616650581359863] |
3764058d-ffef-4130-a423-fb35198c264d | utilizing-bidirectional-encoder | 2011.07208 | null | https://arxiv.org/abs/2011.07208v1 | https://arxiv.org/pdf/2011.07208v1.pdf | Utilizing Bidirectional Encoder Representations from Transformers for Answer Selection | Pre-training a transformer-based model for the language modeling task in a large dataset and then fine-tuning it for downstream tasks has been found very useful in recent years. One major advantage of such pre-trained language models is that they can effectively absorb the context of each word in a sentence. However, f... | ['Jimmy Xiangji Huang', 'Enamul Hoque', 'Md Tahmid Rahman Laskar'] | 2020-11-14 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [ 6.91169053e-02 1.73981100e-01 1.68891326e-02 -4.77021128e-01
-1.26108384e+00 -4.94221121e-01 6.06015027e-01 2.47181371e-01
-4.96985197e-01 6.23445630e-01 6.08535349e-01 -8.06550384e-01
5.74384518e-02 -9.24126208e-01 -5.61338127e-01 -2.50304580e-01
1.62630782e-01 6.58856034e-01 4.47115690e-01 -6.32523835... | [11.198731422424316, 8.309313774108887] |
a5557b4e-8496-42c5-b43b-7f8288b5a8b1 | ai4opt-ai-institute-for-advances-in | 2307.02671 | null | https://arxiv.org/abs/2307.02671v1 | https://arxiv.org/pdf/2307.02671v1.pdf | AI4OPT: AI Institute for Advances in Optimization | This article is a short introduction to AI4OPT, the NSF AI Institute for Advances in Optimization. AI4OPT fuses AI and Optimization, inspired by end-use cases in supply chains, energy systems, chip design and manufacturing, and sustainable food systems. AI4OPT also applies its "teaching the teachers" philosophy to prov... | ['Kevin Dalmeijer', 'Pascal Van Hentenryck'] | 2023-07-05 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [-1.24649502e-01 1.72033846e-01 -6.17399633e-01 1.64954122e-02
1.85508117e-01 -3.58966619e-01 -3.97905223e-02 -2.78456528e-02
4.23248500e-01 4.91160452e-01 -4.19045389e-02 -3.85879457e-01
-5.75135052e-01 -6.29098654e-01 -9.10409749e-01 -3.87641758e-01
-1.49186566e-01 1.92350015e-01 -6.56052232e-01 -4.10492033... | [5.754880428314209, 3.4158477783203125] |
fe40a396-b629-413a-a843-82fbcf1814af | deep-tiny-network-for-recognition-oriented | 2106.04852 | null | https://arxiv.org/abs/2106.04852v1 | https://arxiv.org/pdf/2106.04852v1.pdf | Deep Tiny Network for Recognition-Oriented Face Image Quality Assessment | Face recognition has made significant progress in recent years due to deep convolutional neural networks (CNN). In many face recognition (FR) scenarios, face images are acquired from a sequence with huge intra-variations. These intra-variations, which are mainly affected by the low-quality face images, cause instabilit... | ['Dongsheng Li', 'Zhaoning Zhang', 'Heng Yang', 'Min Liu', 'Baoyun Peng'] | 2021-06-09 | null | null | null | null | ['face-image-quality', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 9.72486734e-02 -6.87186182e-01 1.23159237e-01 -7.81348050e-01
-7.01320052e-01 -1.28168121e-01 3.65892500e-01 -6.31161809e-01
-1.71182677e-01 5.89279056e-01 -3.67431939e-02 1.75572172e-01
-4.55059618e-01 -7.75924206e-01 -5.85402071e-01 -6.73149526e-01
2.09513213e-02 1.58242285e-02 -7.12560266e-02 -2.05131382... | [12.956868171691895, 0.5963693261146545] |
2f6841cc-7400-47e9-a723-6697427b48a0 | learning-with-labeling-induced-abstentions | null | null | http://proceedings.neurips.cc/paper/2021/hash/689041c2baed0f6d91050495d632d6e0-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/689041c2baed0f6d91050495d632d6e0-Paper.pdf | Learning with Labeling Induced Abstentions | Consider a setting where we wish to automate an expensive task with a machine learning algorithm using a limited labeling resource. In such settings, examples routed for labeling are often out of scope for the machine learning algorithm. For example, in a spam detection setting, human reviewers not only provide labeled... | ['Afshin Rostamizadeh', 'Giulia Desalvo', 'Kareem Amin'] | 2021-12-01 | null | https://openreview.net/forum?id=-1OkHh56c2m | https://openreview.net/pdf?id=-1OkHh56c2m | neurips-2021-12 | ['spam-detection'] | ['natural-language-processing'] | [ 4.48616803e-01 5.19568384e-01 -5.48153937e-01 -5.80798209e-01
-1.18234110e+00 -9.96873200e-01 5.88138163e-01 3.90180081e-01
-6.18962348e-01 6.44915998e-01 -2.49704033e-01 -7.12580085e-01
-1.63226090e-02 -6.82956398e-01 -6.54397964e-01 -6.42827094e-01
2.10911244e-01 9.27442014e-01 2.00749233e-01 3.56001377... | [9.13507080078125, 4.148754119873047] |
f604e248-ffc0-4f58-b3c7-b8e453fc5d91 | what-do-patients-say-about-their-disease | 2305.04905 | null | https://arxiv.org/abs/2305.04905v1 | https://arxiv.org/pdf/2305.04905v1.pdf | What Do Patients Say About Their Disease Symptoms? Deep Multilabel Text Classification With Human-in-the-Loop Curation for Automatic Labeling of Patient Self Reports of Problems | The USA Food and Drug Administration has accorded increasing importance to patient-reported problems in clinical and research settings. In this paper, we explore one of the largest online datasets comprising 170,141 open-ended self-reported responses (called "verbatims") from patients with Parkinson's (PwPs) to questio... | ['Ira Shoulson', 'Vikram Ramanarayanan', 'Abhishek Hosamath', 'Lakshmi Arbatti'] | 2023-05-08 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 9.83969569e-02 2.52837986e-01 -8.59507859e-01 -5.32509625e-01
-1.33773661e+00 -7.20989645e-01 -7.47128502e-02 6.53461158e-01
-6.32430494e-01 9.59337533e-01 8.04623187e-01 -1.90343797e-01
-2.95870215e-01 -3.62116814e-01 2.00004913e-02 1.16167432e-02
-1.74158111e-01 9.58216012e-01 2.53446311e-01 -1.59619823... | [8.543041229248047, 8.684863090515137] |
d69bdbf6-a1b7-46f5-92fa-05dff866c491 | sentence-incremental-neural-coreference | 2305.16947 | null | https://arxiv.org/abs/2305.16947v1 | https://arxiv.org/pdf/2305.16947v1.pdf | Sentence-Incremental Neural Coreference Resolution | We propose a sentence-incremental neural coreference resolution system which incrementally builds clusters after marking mention boundaries in a shift-reduce method. The system is aimed at bridging two recent approaches at coreference resolution: (1) state-of-the-art non-incremental models that incur quadratic complexi... | ['Mark Steedman', 'Shay B. Cohen', 'Matt Grenander'] | 2023-05-26 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 3.05472106e-01 4.30934280e-01 -4.17916238e-01 -4.68706548e-01
-1.43765187e+00 -8.23934257e-01 7.10814476e-01 6.04258366e-02
-9.00355697e-01 7.52092421e-01 6.79906428e-01 -2.70338953e-01
-2.13582963e-01 -3.53922606e-01 -8.76377642e-01 -1.87717125e-01
-1.77560136e-01 1.25758255e+00 3.07651341e-01 -4.52026963... | [9.294904708862305, 9.545530319213867] |
e01d4186-687a-4326-a2d5-876a9b3fe532 | have-my-arguments-been-replied-to-argument | null | null | https://aclanthology.org/2022.acl-short.4 | https://aclanthology.org/2022.acl-short.4.pdf | Have my arguments been replied to? Argument Pair Extraction as Machine Reading Comprehension | Argument pair extraction (APE) aims to automatically mine argument pairs from two interrelated argumentative documents. Existing studies typically identify argument pairs indirectly by predicting sentence-level relations between two documents, neglecting the modeling of the holistic argument-level interactions. Towards... | ['Ruifeng Xu', 'Qinglin Zhu', 'Jingyi Sun', 'Jianzhu Bao'] | null | null | null | null | acl-2022-5 | ['argument-pair-extraction-ape', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.08971059e-01 8.07915092e-01 -2.85013735e-01 -2.53734648e-01
-1.10425389e+00 -5.73134720e-01 1.06518674e+00 9.52507496e-01
-3.43797922e-01 6.23632550e-01 4.73842084e-01 -8.66135836e-01
-4.73883748e-01 -8.80245984e-01 -7.52810955e-01 -2.99752831e-01
1.08619355e-01 7.55372107e-01 3.22651654e-01 -3.69874060... | [9.798008918762207, 9.275716781616211] |
71470572-8ae1-4647-8a43-bdd5385f879d | self-supervised-learning-of-3d-human-pose | 1903.02330 | null | http://arxiv.org/abs/1903.02330v2 | http://arxiv.org/pdf/1903.02330v2.pdf | Self-Supervised Learning of 3D Human Pose using Multi-view Geometry | Training accurate 3D human pose estimators requires large amount of 3D
ground-truth data which is costly to collect. Various weakly or self supervised
pose estimation methods have been proposed due to lack of 3D data.
Nevertheless, these methods, in addition to 2D ground-truth poses, require
either additional supervisi... | ['Salih Karagoz', 'Muhammed Kocabas', 'Emre Akbas'] | 2019-03-06 | self-supervised-learning-of-3d-human-pose-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Kocabas_Self-Supervised_Learning_of_3D_Human_Pose_Using_Multi-View_Geometry_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Kocabas_Self-Supervised_Learning_of_3D_Human_Pose_Using_Multi-View_Geometry_CVPR_2019_paper.pdf | cvpr-2019-6 | ['weakly-supervised-3d-human-pose-estimation'] | ['computer-vision'] | [-1.72599658e-01 8.12691748e-02 -2.11542130e-01 -4.43880498e-01
-8.01600099e-01 -5.64429760e-01 4.16900933e-01 -9.73762497e-02
-3.76199633e-01 6.45015180e-01 1.85955271e-01 3.82402718e-01
1.94310278e-01 -3.98738265e-01 -1.00634742e+00 -4.84374583e-01
7.47587904e-02 9.51680541e-01 2.83124566e-01 -2.53496647... | [7.011940956115723, -0.9501915574073792] |
0660adc1-5b66-4a80-8483-0ba7c57e1343 | temposum-evaluating-the-temporal | 2305.01951 | null | https://arxiv.org/abs/2305.01951v1 | https://arxiv.org/pdf/2305.01951v1.pdf | TempoSum: Evaluating the Temporal Generalization of Abstractive Summarization | Recent pre-trained language models (PLMs) achieve promising results in existing abstractive summarization datasets. However, existing summarization benchmarks overlap in time with the standard pre-training corpora and finetuning datasets. Hence, the strong performance of PLMs may rely on the parametric knowledge that i... | ['Lidia S. Chao', 'Shudong Liu', 'Yanming Sun', 'Zhaocong Li', 'Xuebo Liu', 'Derek F. Wong', 'Hou Pong Chan', 'Chi Seng Cheang'] | 2023-05-03 | null | null | null | null | ['abstractive-text-summarization', 'text-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.08631098e-01 1.89287990e-01 -5.52379012e-01 -2.40810633e-01
-7.97368824e-01 -5.02510607e-01 6.71960115e-01 5.66689789e-01
-3.93982530e-01 1.02043343e+00 8.88007760e-01 8.93433578e-03
8.16275179e-02 -7.46686578e-01 -7.42419302e-01 -2.34130248e-01
2.18488835e-03 2.96910614e-01 2.24567413e-01 -3.25571597... | [12.41802978515625, 9.408263206481934] |
1651ae81-80b4-414a-bfdf-c82008a0080a | near-lossless-deep-feature-compression-for | 1804.09963 | null | http://arxiv.org/abs/1804.09963v2 | http://arxiv.org/pdf/1804.09963v2.pdf | Near-Lossless Deep Feature Compression for Collaborative Intelligence | Collaborative intelligence is a new paradigm for efficient deployment of deep
neural networks across the mobile-cloud infrastructure. By dividing the network
between the mobile and the cloud, it is possible to distribute the
computational workload such that the overall energy and/or latency of the
system is minimized. ... | ['Ivan V. Bajic', 'Hyomin Choi'] | 2018-04-26 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [-1.17072120e-01 -1.44608527e-01 1.19751222e-01 -2.42660165e-01
-1.38309732e-01 -2.82678932e-01 8.93051326e-02 -1.40465751e-01
-6.35995626e-01 3.81993771e-01 -8.76725018e-02 -3.22563738e-01
-1.33182615e-01 -1.04735184e+00 -6.47544503e-01 -8.76441121e-01
-4.60621677e-02 1.27308235e-01 1.58882678e-01 1.08962394... | [8.455527305603027, 2.8344573974609375] |
78d5486d-e233-483a-af81-535e63960f57 | ign-implicit-generative-networks | 2206.05860 | null | https://arxiv.org/abs/2206.05860v2 | https://arxiv.org/pdf/2206.05860v2.pdf | IGN : Implicit Generative Networks | In this work, we build recent advances in distributional reinforcement learning to give a state-of-art distributional variant of the model based on the IQN. We achieve this by using the GAN model's generator and discriminator function with the quantile regression to approximate the full quantile value for the state-act... | ['Jianfen Zhang', 'Zhijun Yan', 'Feiyu Han', 'Tianyi Wu', 'Haozheng Luo'] | 2022-06-13 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-4.42857653e-01 2.36250401e-01 -3.82556587e-01 -3.23627144e-01
-1.41886401e+00 -6.87064469e-01 8.47129345e-01 -4.40640897e-01
-6.48089170e-01 1.33149731e+00 3.51460785e-01 -7.63559461e-01
-2.48019621e-01 -9.67793107e-01 -8.01799655e-01 -7.99313664e-01
-3.08605760e-01 1.07453489e+00 -1.31449327e-01 -5.23906350... | [4.071872234344482, 2.5417487621307373] |
9b6afa2c-6f14-433b-9196-fdc5cb12606e | imbalanced-classification-in-faulty-turbine | 2301.04049 | null | https://arxiv.org/abs/2301.04049v1 | https://arxiv.org/pdf/2301.04049v1.pdf | Imbalanced Classification In Faulty Turbine Data: New Proximal Policy Optimization | There is growing importance to detecting faults and implementing the best methods in industrial and real-world systems. We are searching for the most trustworthy and practical data-based fault detection methods proposed by artificial intelligence applications. In this paper, we propose a framework for fault detection b... | ['Arash Ghahremani', 'Mostafa Yari', 'Mahdi Aliyari Shoorehdeli', 'Mohammad Hossein Modirrousta'] | 2023-01-10 | null | null | null | null | ['imbalanced-classification', 'fault-detection'] | ['miscellaneous', 'miscellaneous'] | [-2.30521217e-01 -1.25125095e-01 -1.29372239e-01 -8.25325865e-03
-3.03345948e-01 7.42292181e-02 3.44006270e-02 5.04238248e-01
-3.15003455e-01 1.09733021e+00 -5.50610006e-01 -2.36487612e-01
-4.99778539e-01 -1.05391455e+00 -6.45240545e-01 -3.78139019e-01
-4.28886622e-01 4.55847889e-01 5.07224262e-01 -2.22875357... | [6.98496675491333, 2.444140672683716] |
c5483975-84ed-4f7f-970a-4098def0db82 | constructing-a-meta-learner-for-unsupervised | 2304.11438 | null | https://arxiv.org/abs/2304.11438v1 | https://arxiv.org/pdf/2304.11438v1.pdf | Constructing a meta-learner for unsupervised anomaly detection | Unsupervised anomaly detection (AD) is critical for a wide range of practical applications, from network security to health and medical tools. Due to the diversity of problems, no single algorithm has been found to be superior for all AD tasks. Choosing an algorithm, otherwise known as the Algorithm Selection Problem (... | ['Andrew McCarren', 'Suzanne Little', 'Małgorzata Gutowska'] | 2023-04-22 | null | null | null | null | ['automl'] | ['methodology'] | [ 2.18831211e-01 -2.53188372e-01 -1.72079965e-01 -2.22347364e-01
-5.13170660e-01 3.18136154e-04 6.87949061e-01 6.26673341e-01
-5.73344707e-01 4.35146213e-01 -1.97470903e-01 -2.55056441e-01
-5.75047314e-01 -7.07113862e-01 -3.27514499e-01 -7.02765167e-01
-1.16455041e-01 3.20452482e-01 1.35213539e-01 1.96580708... | [8.305335998535156, 4.232251167297363] |
8ecf5a20-d03d-4d29-98d4-80ba27572985 | decoupling-visual-semantic-feature-learning | 2111.12351 | null | https://arxiv.org/abs/2111.12351v1 | https://arxiv.org/pdf/2111.12351v1.pdf | Decoupling Visual-Semantic Feature Learning for Robust Scene Text Recognition | Semantic information has been proved effective in scene text recognition. Most existing methods tend to couple both visual and semantic information in an attention-based decoder. As a result, the learning of semantic features is prone to have a bias on the limited vocabulary of the training set, which is called vocabul... | ['Wenyu Liu', 'Yongpan Wang', 'Qi Zheng', 'Bohan Li', 'Changxu Cheng'] | 2021-11-24 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 2.58439898e-01 -3.10612798e-01 -2.19112337e-01 -5.47064424e-01
-5.32919765e-01 -2.05185592e-01 7.78025210e-01 -4.07134136e-03
-5.05659163e-01 3.31814110e-01 4.07070458e-01 4.43123542e-02
4.89854008e-01 -6.78978503e-01 -7.50228941e-01 -7.06309199e-01
9.41273391e-01 3.36662233e-01 4.22379225e-01 -2.07560688... | [11.751325607299805, 2.113184928894043] |
cd60566d-a56d-4e3b-9d24-f202177d2d4c | kinnews-and-kirnews-benchmarking-cross | 2010.12174 | null | https://arxiv.org/abs/2010.12174v1 | https://arxiv.org/pdf/2010.12174v1.pdf | KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi | Recent progress in text classification has been focused on high-resource languages such as English and Chinese. For low-resource languages, amongst them most African languages, the lack of well-annotated data and effective preprocessing, is hindering the progress and the transfer of successful methods. In this paper, w... | ['Li Huang', 'Julia Kreutzer', 'Hong Qu', 'Rubungo Andre Niyongabo'] | 2020-10-23 | null | https://aclanthology.org/2020.coling-main.480 | https://aclanthology.org/2020.coling-main.480.pdf | coling-2020-8 | ['news-classification'] | ['natural-language-processing'] | [-2.72794992e-01 -1.25680894e-01 -5.34843922e-01 -4.34751779e-01
-1.08847499e+00 -8.94936144e-01 7.23765612e-01 4.53889370e-01
-9.87308085e-01 8.17184567e-01 8.30739200e-01 -7.35510111e-01
2.47145757e-01 -5.54324090e-01 -3.55581284e-01 -3.55954945e-01
1.27914861e-01 6.49255455e-01 -2.15456486e-01 -2.16541648... | [10.572056770324707, 9.901747703552246] |
76a25769-31ea-4f76-b37d-bafd3e99011c | underwater-ranker-learn-which-is-better-and | 2208.06857 | null | https://arxiv.org/abs/2208.06857v2 | https://arxiv.org/pdf/2208.06857v2.pdf | Underwater Ranker: Learn Which Is Better and How to Be Better | In this paper, we present a ranking-based underwater image quality assessment (UIQA) method, abbreviated as URanker. The URanker is built on the efficient conv-attentional image Transformer. In terms of underwater images, we specially devise (1) the histogram prior that embeds the color distribution of an underwater im... | ['Chongyi Li', 'Weidong Zhang', 'Zhi Chai', 'Linghao Han', 'Xin Jin', 'Ruiqi Wu', 'Chunle Guo'] | 2022-08-14 | null | null | null | null | ['uie'] | ['computer-vision'] | [ 1.76556751e-01 -1.86646059e-01 6.53984308e-01 -4.73666579e-01
-9.05767202e-01 -2.85640061e-01 5.29347658e-02 -2.01761529e-01
-6.14251256e-01 3.35646957e-01 3.95596802e-01 6.18451647e-02
-2.83275694e-01 -8.58622849e-01 -8.47588956e-01 -9.62805212e-01
-3.90722543e-01 -3.24654877e-01 4.32605565e-01 -6.25168741... | [10.693957328796387, -3.527132272720337] |
25223227-ebf2-4f42-bd46-291df8e3a040 | convergence-theory-of-generalized-distributed | 2207.10969 | null | https://arxiv.org/abs/2207.10969v2 | https://arxiv.org/pdf/2207.10969v2.pdf | Convergence Theory of Generalized Distributed Subgradient Method with Random Quantization | The distributed subgradient method (DSG) is a widely discussed algorithm to cope with large-scale distributed optimization problems in the arising machine learning applications. Most exisiting works on DSG focus on ideal communication between the cooperative agents such that the shared information between agents is exa... | ['Yong Ren', 'Jun Du', 'Zhaoyue Xia'] | 2022-07-22 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-6.19594716e-02 2.94602126e-01 -2.55325854e-01 -2.91628927e-01
-8.27948928e-01 -3.98696333e-01 3.89463641e-02 3.78560632e-01
-6.82644606e-01 1.27975738e+00 -3.02311927e-01 -1.53065726e-01
-3.92340273e-01 -8.90031815e-01 -5.77351332e-01 -1.32094967e+00
-5.30386746e-01 1.02184638e-01 -2.82108009e-01 -1.02058180... | [6.166580677032471, 4.959129810333252] |
15ff5e53-835d-45b0-a6fb-a6183df8cbb2 | hierarchical-modeling-of-molecular-energies | 1710.00017 | null | http://arxiv.org/abs/1710.00017v1 | http://arxiv.org/pdf/1710.00017v1.pdf | Hierarchical modeling of molecular energies using a deep neural network | We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN)
to model molecular properties from datasets of quantum calculations. Inspired
by a many-body expansion, HIP-NN decomposes properties, such as energy, as a
sum over hierarchical terms. These terms are generated from a neural network--a
composit... | ['Nicholas Lubbers', 'Kipton Barros', 'Justin S. Smith'] | 2017-09-29 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [ 2.38074884e-01 1.76793322e-01 -2.52584428e-01 -3.62593353e-01
-7.82219648e-01 -3.86393577e-01 6.02235138e-01 6.60385013e-01
-3.35756391e-01 1.13563776e+00 3.35796475e-01 -6.08392358e-01
-7.07184598e-02 -1.25559855e+00 -1.24124062e+00 -1.08552289e+00
-4.14843857e-01 6.30694568e-01 -2.09279303e-02 -2.34259695... | [5.31348180770874, 5.450888156890869] |
3d62d8b9-1fe1-4ed6-89b7-00b0472249a7 | discriminative-appearance-modeling-with-multi | 2101.12159 | null | https://arxiv.org/abs/2101.12159v1 | https://arxiv.org/pdf/2101.12159v1.pdf | Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking | In multi-object tracking, the tracker maintains in its memory the appearance and motion information for each object in the scene. This memory is utilized for finding matches between tracks and detections and is updated based on the matching result. Many approaches model each target in isolation and lack the ability to ... | ['James M. Rehg', 'Mazen Alotaibi', 'Li Fuxin', 'Chanho Kim'] | 2021-01-28 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Kim_Discriminative_Appearance_Modeling_With_Multi-Track_Pooling_for_Real-Time_Multi-Object_Tracking_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Kim_Discriminative_Appearance_Modeling_With_Multi-Track_Pooling_for_Real-Time_Multi-Object_Tracking_CVPR_2021_paper.pdf | cvpr-2021-1 | ['real-time-multi-object-tracking'] | ['computer-vision'] | [-1.33815885e-01 -4.75600719e-01 -2.45792344e-01 5.47877848e-02
-7.65879929e-01 -8.24787617e-01 4.66375262e-01 2.78646559e-01
-6.10831082e-01 5.23619950e-01 -1.55586556e-01 2.06643060e-01
1.38052061e-01 -4.71648604e-01 -1.01577926e+00 -5.11538744e-01
-1.01423703e-01 5.26023924e-01 1.05933511e+00 4.19211179... | [6.378493309020996, -2.0497119426727295] |
8a2a1c76-0e6e-44b6-9691-1ca827958ef7 | no-spare-parts-sharing-part-detectors-for | 1510.04908 | null | http://arxiv.org/abs/1510.04908v2 | http://arxiv.org/pdf/1510.04908v2.pdf | No Spare Parts: Sharing Part Detectors for Image Categorization | This work aims for image categorization using a representation of distinctive
parts. Different from existing part-based work, we argue that parts are
naturally shared between image categories and should be modeled as such. We
motivate our approach with a quantitative and qualitative analysis by
backtracking where selec... | ['Cees G. M. Snoek', 'Jan C. van Gemert', 'Pascal Mettes'] | 2015-10-16 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 3.50774050e-01 2.06112385e-01 -2.81167418e-01 -6.58363283e-01
-4.26618785e-01 -7.19747484e-01 8.80703151e-01 4.58139509e-01
-2.32448488e-01 4.36068289e-02 5.00218689e-01 2.78787047e-01
-2.06192359e-01 -6.21048868e-01 -5.50388277e-01 -5.88582933e-01
1.31632537e-01 4.76642966e-01 5.17896116e-01 -2.52730101... | [9.500438690185547, 1.13686203956604] |
84a6648c-f71d-43be-aa73-0d0b9bf7e4ac | bandit-social-learning-exploration-under | 2302.07425 | null | https://arxiv.org/abs/2302.07425v3 | https://arxiv.org/pdf/2302.07425v3.pdf | Bandit Social Learning: Exploration under Myopic Behavior | We study social learning dynamics where the agents collectively follow a simple multi-armed bandit protocol. Agents arrive sequentially, choose arms and receive associated rewards. Each agent observes the full history (arms and rewards) of the previous agents, and there are no private signals. While collectively the ag... | ['Aleksandrs Slivkins', 'Suho Shin', 'Mohammadtaghi Hajiaghayi', 'Kiarash Banihashem'] | 2023-02-15 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [-2.33320206e-01 5.74667156e-01 -8.27616155e-01 -1.67425215e-01
-7.55426526e-01 -1.01876867e+00 3.01515162e-01 6.08201362e-02
-6.08589470e-01 1.23974180e+00 4.00831997e-02 -4.16360736e-01
-6.07461989e-01 -6.28462553e-01 -8.82611394e-01 -9.24337745e-01
-2.98486084e-01 8.98989201e-01 -3.34428310e-01 -1.41452253... | [4.438459396362305, 3.2105484008789062] |
ba54cba2-aab5-47ed-b131-4e3970607dc1 | sofamyroom-a-fast-and-multiplatform-shoebox | 2106.12992 | null | https://arxiv.org/abs/2106.12992v1 | https://arxiv.org/pdf/2106.12992v1.pdf | SofaMyRoom: a fast and multiplatform "shoebox" room simulator for binaural room impulse response dataset generation | This paper introduces a shoebox room simulator able to systematically generate synthetic datasets of binaural room impulse responses (BRIRs) given an arbitrary set of head-related transfer functions (HRTFs). The evaluation of machine hearing algorithms frequently requires BRIR datasets in order to simulate the acoustic... | ['Federico Avanzini', 'Michele Geronazzo', 'Daniele Bianchi', 'Roberto Barumerli'] | 2021-06-24 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [-3.40277642e-01 -3.46140057e-01 1.30064273e+00 -4.44538504e-01
-1.30354178e+00 -6.61205769e-01 1.89113945e-01 3.23358849e-02
-3.35043192e-01 6.80422068e-01 1.85402140e-01 -4.30175215e-01
-2.05049053e-01 -5.77018738e-01 -5.69104612e-01 -7.72809803e-01
-2.20826715e-01 3.24336082e-01 2.55318224e-01 -3.86085153... | [15.14090633392334, 5.847815990447998] |
af46d40b-413c-49ab-bc2a-ddc6913183a2 | augmenting-task-oriented-dialogue-systems | 2210.13344 | null | https://arxiv.org/abs/2210.13344v1 | https://arxiv.org/pdf/2210.13344v1.pdf | Augmenting Task-Oriented Dialogue Systems with Relation Extraction | The standard task-oriented dialogue pipeline uses intent classification and slot-filling to interpret user utterances. While this approach can handle a wide range of queries, it does not extract the information needed to handle more complex queries that contain relationships between slots. We propose integration of rel... | ['Jonathan K. Kummerfeld', 'Kevin Leach', 'Zhenguo Chen', 'Andrew Lee'] | 2022-10-24 | null | null | null | null | ['intent-classification', 'slot-filling', 'task-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.05823109e-01 1.02437067e+00 -1.45980209e-01 -6.61767900e-01
-5.62259138e-01 -7.49814391e-01 7.52714515e-01 6.15732133e-01
-4.18720514e-01 9.45144773e-01 7.08773851e-01 -6.46919668e-01
2.88167112e-02 -8.31355393e-01 2.17957854e-01 3.65611881e-01
2.99131814e-02 9.31865990e-01 8.16137493e-01 -9.65308964... | [12.641746520996094, 7.762259483337402] |
3f023d06-6ec9-46ed-8d53-1c1a8a1311cb | metric-learning-for-image-registration | 1904.09524 | null | http://arxiv.org/abs/1904.09524v1 | http://arxiv.org/pdf/1904.09524v1.pdf | Metric Learning for Image Registration | Image registration is a key technique in medical image analysis to estimate
deformations between image pairs. A good deformation model is important for
high-quality estimates. However, most existing approaches use ad-hoc
deformation models chosen for mathematical convenience rather than to capture
observed data variati... | ['Francois-Xavier Vialard', 'Marc Niethammer', 'Roland Kwitt'] | 2019-04-21 | metric-learning-for-image-registration-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Niethammer_Metric_Learning_for_Image_Registration_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Niethammer_Metric_Learning_for_Image_Registration_CVPR_2019_paper.pdf | cvpr-2019-6 | ['deformable-medical-image-registration', 'diffeomorphic-medical-image-registration'] | ['medical', 'medical'] | [ 7.71073028e-02 2.21000209e-01 -3.61064345e-01 -7.86453068e-01
-1.00317192e+00 -5.79122186e-01 5.37649035e-01 1.37156278e-01
-5.17504156e-01 1.93036050e-01 4.64737952e-01 1.31607890e-01
-1.74143270e-01 -7.75363088e-01 -7.27703691e-01 -6.86142266e-01
-2.33892262e-01 6.09613359e-01 -2.80725881e-02 -2.16069728... | [13.96570873260498, -2.5605995655059814] |
7da50679-c919-478a-85e7-66afee9e35aa | bi-link-bridging-inductive-link-predictions | 2210.14463 | null | https://arxiv.org/abs/2210.14463v1 | https://arxiv.org/pdf/2210.14463v1.pdf | Bi-Link: Bridging Inductive Link Predictions from Text via Contrastive Learning of Transformers and Prompts | Inductive knowledge graph completion requires models to comprehend the underlying semantics and logic patterns of relations. With the advance of pretrained language models, recent research have designed transformers for link prediction tasks. However, empirical studies show that linearizing triples affects the learning... | ['Mobarakol Islam', 'Shihao Liang', 'Bohua Peng'] | 2022-10-26 | null | null | null | null | ['inductive-knowledge-graph-completion'] | ['knowledge-base'] | [-1.40414327e-01 7.84201622e-01 -8.25563967e-01 -4.84654933e-01
-1.46113455e-01 -5.50064445e-01 6.10652804e-01 1.97950438e-01
6.42044395e-02 8.49867344e-01 4.66337830e-01 -6.69923365e-01
-5.23624361e-01 -1.38318920e+00 -1.22505379e+00 2.28783265e-01
-3.33965749e-01 9.25070524e-01 4.87424940e-01 -5.70904315... | [8.906241416931152, 7.869927406311035] |
16718075-4f02-4020-be30-2fc5a3811cfb | how-well-do-multi-hop-reading-comprehension-1 | 2210.05208 | null | https://arxiv.org/abs/2210.05208v1 | https://arxiv.org/pdf/2210.05208v1.pdf | How Well Do Multi-hop Reading Comprehension Models Understand Date Information? | Several multi-hop reading comprehension datasets have been proposed to resolve the issue of reasoning shortcuts by which questions can be answered without performing multi-hop reasoning. However, the ability of multi-hop models to perform step-by-step reasoning when finding an answer to a comparison question remains un... | ['Akiko Aizawa', 'Saku Sugawara', 'Xanh Ho'] | 2022-10-11 | null | null | null | null | ['multi-hop-reading-comprehension'] | ['natural-language-processing'] | [ 1.90544754e-01 4.56478745e-01 2.25686207e-01 -6.89113379e-01
-1.22785246e+00 -8.89889956e-01 4.45468545e-01 5.52387238e-01
-4.43426937e-01 5.04629135e-01 3.07563297e-03 -8.32221508e-01
-4.97970402e-01 -1.12049580e+00 -9.69630718e-01 1.03986859e-01
3.30911368e-01 7.69211769e-01 4.76067811e-01 -8.26376677... | [10.99449634552002, 7.931440353393555] |
1465c786-81ef-4c3a-b95a-7058c2d402c0 | a-review-of-testing-object-based-environment | 2102.08460 | null | https://arxiv.org/abs/2102.08460v1 | https://arxiv.org/pdf/2102.08460v1.pdf | A Review of Testing Object-Based Environment Perception for Safe Automated Driving | Safety assurance of automated driving systems must consider uncertain environment perception. This paper reviews literature addressing how perception testing is realized as part of safety assurance. We focus on testing for verification and validation purposes at the interface between perception and planning, and struct... | ['Lutz Eckstein', 'Maike Scholtes', 'Michael Hoss'] | 2021-02-16 | null | null | null | null | ['sensor-modeling'] | ['computer-vision'] | [ 3.53169352e-01 3.49330723e-01 -5.82356080e-02 -6.09286845e-01
-4.87181365e-01 -8.91151190e-01 5.52164495e-01 5.30555487e-01
-1.24840371e-01 4.52598542e-01 -1.76743716e-01 -8.82172585e-01
-6.16574883e-01 -7.62174189e-01 -5.44497073e-01 -2.09641859e-01
1.27267361e-01 1.20808408e-01 5.99620283e-01 -4.26923960... | [5.533448696136475, 1.3370546102523804] |
ef3dc062-12f8-45c3-9b2b-9d547154943a | hierarchical-representations-and-explicit | 2108.01176 | null | https://arxiv.org/abs/2108.01176v2 | https://arxiv.org/pdf/2108.01176v2.pdf | Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networks | Representations are crucial for a robot to learn effective navigation policies. Recent work has shown that mid-level perceptual abstractions, such as depth estimates or 2D semantic segmentation, lead to more effective policies when provided as observations in place of raw sensor data (e.g., RGB images). However, such p... | ['Luca Carlone', 'J. Daniel Griffith', 'Nathan Hughes', 'Lisa Peng', 'Zachary Ravichandran'] | 2021-08-02 | null | null | null | null | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 7.87840337e-02 4.16091025e-01 -3.61618429e-01 -3.30318362e-01
-4.71412838e-01 -4.65160847e-01 6.74071372e-01 3.86585295e-01
-5.59689701e-01 3.36573869e-01 4.26577538e-01 -2.52290249e-01
-9.31769833e-02 -8.74896169e-01 -9.74207282e-01 -4.23023194e-01
-2.24489316e-01 4.38787878e-01 1.89641997e-01 -1.78012341... | [4.57877254486084, 0.6187423467636108] |
d9e700fe-9775-42e9-98f7-3c945752911f | l-verse-bidirectional-generation-between | 2111.11133 | null | https://arxiv.org/abs/2111.11133v10 | https://arxiv.org/pdf/2111.11133v10.pdf | L-Verse: Bidirectional Generation Between Image and Text | Far beyond learning long-range interactions of natural language, transformers are becoming the de-facto standard for many vision tasks with their power and scalability. Especially with cross-modal tasks between image and text, vector quantized variational autoencoders (VQ-VAEs) are widely used to make a raw RGB image i... | ['Kyunghoon Bae', 'Honglak Lee', 'Seung Hwan Kim', 'Soonyoung Lee', 'Yewon Seo', 'Sangyun Kim', 'Sihaeng Lee', 'Gwangmo Song', 'TaeHoon Kim'] | 2021-11-22 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Kim_L-Verse_Bidirectional_Generation_Between_Image_and_Text_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Kim_L-Verse_Bidirectional_Generation_Between_Image_and_Text_CVPR_2022_paper.pdf | cvpr-2022-1 | ['zero-shot-text-to-image-generation'] | ['natural-language-processing'] | [ 2.39320889e-01 2.22411454e-01 2.49002159e-01 -3.24157923e-01
-1.01920807e+00 -6.11304700e-01 1.33708251e+00 -7.64754951e-01
-2.80874997e-01 5.78718662e-01 2.75175124e-01 -2.09269613e-01
3.78814787e-01 -7.65459895e-01 -1.34004545e+00 -8.43550444e-01
6.35317922e-01 6.81822956e-01 -1.56822009e-03 -4.84408587... | [11.03407096862793, 1.2136867046356201] |
f95bb49f-7c76-46e5-b5e9-5ac42319324b | make-an-audio-text-to-audio-generation-with | 2301.12661 | null | https://arxiv.org/abs/2301.12661v1 | https://arxiv.org/pdf/2301.12661v1.pdf | Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models | Large-scale multimodal generative modeling has created milestones in text-to-image and text-to-video generation. Its application to audio still lags behind for two main reasons: the lack of large-scale datasets with high-quality text-audio pairs, and the complexity of modeling long continuous audio data. In this work, ... | ['Zhou Zhao', 'Xiang Yin', 'Jinglin Liu', 'Zhenhui Ye', 'Mingze Li', 'Luping Liu', 'Yi Ren', 'Dongchao Yang', 'Jiawei Huang', 'Rongjie Huang'] | 2023-01-30 | null | null | null | null | ['audio-generation', 'video-generation', 'text-to-video-generation'] | ['audio', 'computer-vision', 'natural-language-processing'] | [ 3.15253764e-01 -1.87035743e-02 9.91943628e-02 -1.88315123e-01
-1.56357002e+00 -4.48249847e-01 6.02378666e-01 -1.07879117e-01
-4.45061326e-02 5.93120039e-01 4.80638146e-01 -1.10441722e-01
-1.51020795e-01 -6.42485499e-01 -7.15710580e-01 -6.81119680e-01
-2.98015419e-02 3.31728905e-01 -1.57328054e-01 -3.57124716... | [15.478331565856934, 5.532462120056152] |
878c9b8e-04db-4c1a-a847-821b515681e1 | medfmc-a-real-world-dataset-and-benchmark-for | 2306.09579 | null | https://arxiv.org/abs/2306.09579v1 | https://arxiv.org/pdf/2306.09579v1.pdf | MedFMC: A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification | Foundation models, often pre-trained with large-scale data, have achieved paramount success in jump-starting various vision and language applications. Recent advances further enable adapting foundation models in downstream tasks efficiently using only a few training samples, e.g., in-context learning. Yet, the applicat... | ['Shaoting Zhang', 'Yu Qiao', 'Kang Li', 'Jie Zhao', 'Qi Duan', 'Tian Shen', 'Junjun He', 'Jun Shen', 'Xiangyu Gao', 'Xiaoqiang Liu', 'Qian Da', 'Mengzhang Li', 'Lilong Wang', 'Xiaosong Wang', 'Dequan Wang'] | 2023-06-16 | null | null | null | null | ['medical-image-classification', 'diabetic-retinopathy-grading'] | ['medical', 'medical'] | [ 5.29801965e-01 -5.23650311e-02 -3.47961396e-01 -5.86256981e-01
-1.15718269e+00 -3.76759559e-01 2.75178432e-01 4.49190617e-01
-5.75881541e-01 4.42617387e-01 1.51693150e-01 -5.69172323e-01
-2.11157009e-01 -3.77867788e-01 -7.60704398e-01 -6.56400144e-01
-1.76711962e-01 3.01615983e-01 1.76512256e-01 2.12122172... | [14.851948738098145, -2.4271295070648193] |
4e808bd1-20e0-4225-8673-bb8f07a6d669 | quadtree-driven-lossy-event-compression | 2005.00974 | null | https://arxiv.org/abs/2005.00974v2 | https://arxiv.org/pdf/2005.00974v2.pdf | Lossy Event Compression based on Image-derived Quad Trees and Poisson Disk Sampling | With several advantages over conventional RGB cameras, event cameras have provided new opportunities for tackling visual tasks under challenging scenarios with fast motion, high dynamic range, and/or power constraint. Yet unlike image/video compression, the performance of event compression algorithm is far from satisfy... | ['Zihao W. Wang', 'Srutarshi Banerjee', 'Aggelos Katsaggelos', 'Henry H. Chopp', 'Oliver Cossairt'] | 2020-05-03 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 8.12292457e-01 -5.64065516e-01 -1.65130869e-01 -2.80241072e-01
-6.88847959e-01 -4.08740669e-01 3.70171994e-01 5.16815543e-01
-6.39371574e-01 7.61584342e-01 1.48082033e-01 1.46325082e-01
-2.25836873e-01 -1.03671575e+00 -6.48345709e-01 -1.00534582e+00
-3.67289513e-01 1.64584145e-01 6.69510543e-01 5.53514123... | [8.983074188232422, -1.5410114526748657] |
ea5f4d6c-5003-492b-a4a1-2d4c2c8a188b | temporal-information-extraction-from-clinical | null | null | https://aclanthology.org/E17-2117 | https://aclanthology.org/E17-2117.pdf | Temporal information extraction from clinical text | In this paper, we present a method for temporal relation extraction from clinical narratives in French and in English. We experiment on two comparable corpora, the MERLOT corpus and the THYME corpus, and show that a common approach can be used for both languages. | ["Aur{\\'e}lie N{\\'e}v{\\'e}ol", 'Olivier Ferret', 'Xavier Tannier', 'Julien Tourille'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['temporal-relation-extraction', 'temporal-information-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.19504291e-02 3.99677306e-01 -6.18927598e-01 -2.47309312e-01
-7.81024575e-01 -4.47129667e-01 7.99253225e-01 2.81253010e-01
-7.26351082e-01 1.32618570e+00 5.46673417e-01 -4.16369379e-01
-2.68750936e-01 -3.52935106e-01 2.51772642e-01 -1.87788367e-01
-1.16833888e-01 5.70204377e-01 5.42280316e-01 -3.97706240... | [8.571464538574219, 8.991735458374023] |
cedc9ade-3071-4f19-9458-6e0441b5a1f1 | mist-multiple-instance-self-training | 2104.01633 | null | https://arxiv.org/abs/2104.01633v1 | https://arxiv.org/pdf/2104.01633v1.pdf | MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection | Weakly supervised video anomaly detection (WS-VAD) is to distinguish anomalies from normal events based on discriminative representations. Most existing works are limited in insufficient video representations. In this work, we develop a multiple instance self-training framework (MIST)to efficiently refine task-specific... | ['Wei-Shi Zheng', 'Fa-Ting Hong', 'Jia-Chang Feng'] | 2021-04-04 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Feng_MIST_Multiple_Instance_Self-Training_Framework_for_Video_Anomaly_Detection_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Feng_MIST_Multiple_Instance_Self-Training_Framework_for_Video_Anomaly_Detection_CVPR_2021_paper.pdf | cvpr-2021-1 | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 4.11682487e-01 -1.46397933e-01 -1.46218613e-01 -6.14837289e-01
-1.05624354e+00 -2.24294379e-01 4.91744101e-01 7.39235803e-02
-2.28395090e-01 4.57877606e-01 2.89197594e-01 7.29042962e-02
4.03858006e-01 -3.43791932e-01 -9.40694273e-01 -6.09881997e-01
-1.96269572e-01 1.03099950e-01 3.01275730e-01 1.06059954... | [7.851104259490967, 1.5926729440689087] |
4aaa129e-6b6d-4b9e-b47d-12a27fa3b330 | transtrack-multiple-object-tracking-with | 2012.15460 | null | https://arxiv.org/abs/2012.15460v2 | https://arxiv.org/pdf/2012.15460v2.pdf | TransTrack: Multiple Object Tracking with Transformer | In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple object tracking problems. TransTrack leverages the transformer architecture, which is an attention-based query-key mechanism. It applies object features from the previous frame as a query of the current frame and introduces a set o... | ['Ping Luo', 'Changhu Wang', 'Zehuan Yuan', 'Enze Xie', 'Jinkun Cao', 'Rufeng Zhang', 'Yi Jiang', 'Peize Sun'] | 2020-12-31 | null | null | null | null | ['multiple-object-tracking-with-transformer'] | ['computer-vision'] | [-2.52421945e-01 -5.40594220e-01 -4.06248242e-01 -5.47786355e-02
-9.69415724e-01 -5.02909184e-01 5.00867069e-01 -1.67087734e-01
-5.03870010e-01 4.92311627e-01 -1.26491398e-01 3.81893814e-02
1.06485486e-01 -6.02962196e-01 -8.63156021e-01 -5.58100879e-01
1.43096549e-02 5.77854037e-01 1.08003342e+00 6.32546097... | [6.3188157081604, -2.0747413635253906] |
54e11af7-0d07-4643-bc72-a133182a9a59 | poster-v2-a-simpler-and-stronger-facial | 2301.12149 | null | https://arxiv.org/abs/2301.12149v2 | https://arxiv.org/pdf/2301.12149v2.pdf | POSTER++: A simpler and stronger facial expression recognition network | Facial expression recognition (FER) plays an important role in a variety of real-world applications such as human-computer interaction. POSTER achieves the state-of-the-art (SOTA) performance in FER by effectively combining facial landmark and image features through two-stream pyramid cross-fusion design. However, the ... | ['Aibin Huang', 'Binling Nie', 'Yuanqi Chang', 'Xuesong Yin', 'Rui Xu', 'Jiawei Mao'] | 2023-01-28 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [-4.53410707e-02 -3.48692298e-01 3.82322110e-02 -5.77516258e-01
-7.76329815e-01 1.78536654e-01 1.69382274e-01 -3.92960757e-02
-7.12654471e-01 3.16095203e-01 1.56494360e-02 4.76695597e-03
2.95964807e-01 -6.76511109e-01 -5.32869220e-01 -5.52460015e-01
-1.53218225e-01 -2.99270272e-01 2.43121460e-01 -4.57227260... | [13.538862228393555, 1.4715653657913208] |
d30b3a8c-0507-4510-b79b-d99220c9f42a | multimodal-interactive-lung-lesion | 2301.09914 | null | https://arxiv.org/abs/2301.09914v1 | https://arxiv.org/pdf/2301.09914v1.pdf | Multimodal Interactive Lung Lesion Segmentation: A Framework for Annotating PET/CT Images based on Physiological and Anatomical Cues | Recently, deep learning enabled the accurate segmentation of various diseases in medical imaging. These performances, however, typically demand large amounts of manual voxel annotations. This tedious process for volumetric data becomes more complex when not all required information is available in a single imaging doma... | ['Rainer Stiefelhagen', 'Jens Kleesiek', 'Constantin Seibold', 'Lars Heiliger', 'Moon Kim', 'Zdravko Marinov', 'Philipp Kataliakos', 'Tobias Schlumberger', 'Verena Jasmin Hallitschke'] | 2023-01-24 | null | null | null | null | ['interactive-segmentation', 'user-simulation'] | ['computer-vision', 'natural-language-processing'] | [-5.89661747e-02 2.10968122e-01 1.01507433e-01 -7.63420224e-01
-9.53337967e-01 -7.84649074e-01 -9.27903503e-03 3.55449617e-01
-8.08236241e-01 6.66135430e-01 -1.23749413e-01 -5.88804305e-01
1.98701054e-01 -5.27027428e-01 -4.45011765e-01 -5.10006011e-01
1.86279286e-02 6.93748295e-01 4.39722866e-01 2.21334085... | [14.672104835510254, -2.2943010330200195] |
a3dcea54-c619-4300-81ca-fb580e853a36 | motion-guided-3d-pose-estimation-from-videos | 2004.13985 | null | https://arxiv.org/abs/2004.13985v1 | https://arxiv.org/pdf/2004.13985v1.pdf | Motion Guided 3D Pose Estimation from Videos | We propose a new loss function, called motion loss, for the problem of monocular 3D Human pose estimation from 2D pose. In computing motion loss, a simple yet effective representation for keypoint motion, called pairwise motion encoding, is introduced. We design a new graph convolutional network architecture, U-shaped ... | ['Yuanjun Xiong', 'Jingbo Wang', 'Sijie Yan', 'Dahua Lin'] | 2020-04-29 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2054_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580749.pdf | eccv-2020-8 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-3.41620862e-01 -2.88054854e-01 -4.97160465e-01 -1.18576132e-01
-6.23302937e-01 -4.21703279e-01 4.60448921e-01 -5.25174201e-01
-5.28625846e-01 5.50415397e-01 4.96118426e-01 -2.76592728e-02
2.86030889e-01 -3.57862234e-01 -9.38328028e-01 -3.25320214e-01
-4.27160293e-01 2.52389073e-01 2.45698750e-01 -1.94646046... | [7.237828731536865, -0.48091065883636475] |
1cb186ed-0081-4c0a-a8cd-8e6731c349c0 | deep-long-short-term-memory-adaptive | 1711.08016 | null | http://arxiv.org/abs/1711.08016v1 | http://arxiv.org/pdf/1711.08016v1.pdf | Deep Long Short-Term Memory Adaptive Beamforming Networks For Multichannel Robust Speech Recognition | Far-field speech recognition in noisy and reverberant conditions remains a
challenging problem despite recent deep learning breakthroughs. This problem is
commonly addressed by acquiring a speech signal from multiple microphones and
performing beamforming over them. In this paper, we propose to use a recurrent
neural n... | ['Hakan Erdogan', 'John R. Hershey', 'Zhong Meng', 'Shinji Watanabe'] | 2017-11-21 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 2.13110119e-01 -5.40562749e-01 8.53974164e-01 -5.93477190e-01
-1.27036572e+00 -4.96645629e-01 1.67359874e-01 -6.46499634e-01
-4.18668866e-01 4.51784223e-01 7.29871154e-01 -4.86622870e-01
1.60435531e-02 -2.66113549e-01 -8.11375916e-01 -9.45260882e-01
-2.85324693e-01 -7.73472413e-02 -2.12300941e-01 -7.27867708... | [14.944487571716309, 5.979240417480469] |
eac10370-3985-4215-a6ae-6174e8c8f684 | joint-vertebrae-identification-and | 1812.03500 | null | http://arxiv.org/abs/1812.03500v1 | http://arxiv.org/pdf/1812.03500v1.pdf | Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long-Range Contextual Information | Automatic vertebrae identification and localization from arbitrary CT images
is challenging. Vertebrae usually share similar morphological appearance.
Because of pathology and the arbitrary field-of-view of CT scans, one can
hardly rely on the existence of some anchor vertebrae or parametric methods to
model the appear... | ['Jiebo Luo', 'Haofu Liao', 'Addisu Mesfin'] | 2018-12-09 | null | null | null | null | ['joint-vertebrae-identification-and'] | ['medical'] | [-2.28993759e-01 -2.08603665e-01 -2.76193678e-01 -3.10603887e-01
-1.03698564e+00 -2.92038053e-01 1.59229547e-01 1.01417992e-02
-4.27556425e-01 2.77841926e-01 2.31271490e-01 -1.12763099e-01
-3.52537990e-01 -5.74346483e-01 -7.90273666e-01 -7.07225382e-01
9.56947580e-02 6.48723543e-01 5.40171504e-01 -9.25556049... | [14.684100151062012, -2.2649431228637695] |
54343750-771f-4180-8b80-f985ce6acf3e | mlrg-deep-curvature | 1912.09656 | null | https://arxiv.org/abs/1912.09656v2 | https://arxiv.org/pdf/1912.09656v2.pdf | Deep Curvature Suite | We present MLRG Deep Curvature suite, a PyTorch-based, open-source package for analysis and visualisation of neural network curvature and loss landscape. Despite of providing rich information into properties of neural network and useful for a various designed tasks, curvature information is still not made sufficient us... | ['Xingchen Wan', 'Timur Garipov', 'Diego Granziol'] | 2019-12-20 | null | https://openreview.net/forum?id=86t2GlfzFo | https://openreview.net/pdf?id=86t2GlfzFo | null | ['misconceptions'] | ['miscellaneous'] | [-1.62811249e-01 1.61850184e-01 1.83557943e-01 -5.61580837e-01
-2.10600078e-01 -4.24100280e-01 4.64901567e-01 -1.26669765e-01
-6.07390702e-01 8.19816530e-01 -6.20493293e-03 -8.04505408e-01
-4.87715214e-01 -2.34767467e-01 -5.98228514e-01 -1.14742482e+00
-5.94752550e-01 -7.19492789e-03 3.59140813e-01 -2.87518114... | [7.945828437805176, 3.581557512283325] |
d09c454f-a061-4d54-a20f-264714b7db26 | a-causal-based-framework-for-multimodal | 2008.02171 | null | https://arxiv.org/abs/2008.02171v1 | https://arxiv.org/pdf/2008.02171v1.pdf | A Causal-based Framework for Multimodal Multivariate Time Series Validation Enhanced by Unsupervised Deep Learning as an Enabler for Industry 4.0 | An advanced conceptual validation framework for multimodal multivariate time series defines a multi-level contextual anomaly detection ranging from an univariate context definition, to a multimodal abstract context representation learnt by an Autoencoder from heterogeneous data (images, time series, sounds, etc.) assoc... | ['Cedric Schockaert'] | 2020-08-05 | null | null | null | null | ['contextual-anomaly-detection'] | ['miscellaneous'] | [ 6.51001573e-01 1.91352606e-01 2.34307438e-01 -7.04768822e-02
-3.59238744e-01 -3.67385775e-01 9.11894441e-01 9.06283677e-01
2.83442497e-01 3.58325481e-01 3.94505620e-01 -5.66375017e-01
-9.14867401e-01 -1.03898859e+00 -6.62684619e-01 -9.20597494e-01
-5.93906105e-01 3.63976091e-01 -3.58270049e-01 -1.98178947... | [7.086830139160156, 2.66949462890625] |
c8eb23c5-ecd0-4ba2-8ca6-a8366d54b42b | direct-simultaneous-speech-to-speech | 2110.08250 | null | https://arxiv.org/abs/2110.08250v2 | https://arxiv.org/pdf/2110.08250v2.pdf | Direct Simultaneous Speech-to-Speech Translation with Variational Monotonic Multihead Attention | We present a direct simultaneous speech-to-speech translation (Simul-S2ST) model, Furthermore, the generation of translation is independent from intermediate text representations. Our approach leverages recent progress on direct speech-to-speech translation with discrete units, in which a sequence of discrete represent... | ['Phillip Koehn', 'Juan Pino', 'Wei-Ning Hsu', 'Peng-Jen Chen', 'Yun Tang', 'Ann Lee', 'Danni Liu', 'Hongyu Gong', 'Xutai Ma'] | 2021-10-15 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [ 4.01315480e-01 3.27391237e-01 -2.45236441e-01 -2.90890515e-01
-1.68239236e+00 -7.43415236e-01 1.10448015e+00 -3.68561417e-01
-9.23660398e-02 9.36383247e-01 6.63081467e-01 -7.76827991e-01
6.60257339e-01 -3.80041122e-01 -1.09269702e+00 -5.83838105e-01
4.44412082e-01 6.95215642e-01 -1.97515458e-01 -2.80027300... | [14.550394058227539, 7.110716342926025] |
e9cd64a6-0845-4244-8288-12a816db636b | flat-latent-manifolds-for-music-improvisation | 2202.12243 | null | https://arxiv.org/abs/2202.12243v3 | https://arxiv.org/pdf/2202.12243v3.pdf | Flat Latent Manifolds for Human-machine Co-creation of Music | The use of machine learning in artistic music generation leads to controversial discussions of the quality of art, for which objective quantification is nonsensical. We therefore consider a music-generating algorithm as a counterpart to a human musician, in a setting where reciprocal interplay is to lead to new experie... | ['Patrick van der Smagt', 'Luciano Pinna', 'Mathis Nitschke', 'Francesco Ferroni', 'Djalel Benbouzid', 'Nutan Chen'] | 2022-02-23 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 2.89178878e-01 6.47243679e-01 2.99317569e-01 1.02051713e-01
-4.18501616e-01 -8.12692761e-01 1.01716959e+00 -4.90682214e-01
-2.37650815e-02 4.75580752e-01 4.92921323e-01 1.51418373e-01
-2.96313584e-01 -7.68224061e-01 -8.71348739e-01 -8.44821215e-01
9.02065448e-03 3.87108296e-01 -4.38130438e-01 -4.60999042... | [15.854843139648438, 5.608773708343506] |
0fac4010-87cc-4353-99c3-d61f41dda9dd | graphsaint-graph-sampling-based-inductive | 1907.04931 | null | https://arxiv.org/abs/1907.04931v4 | https://arxiv.org/pdf/1907.04931v4.pdf | GraphSAINT: Graph Sampling Based Inductive Learning Method | Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer sampling techniques to alleviate the "neighbor explosion" problem during minibatch training. We propose GraphSAINT, a graph sampling based ... | ['Ajitesh Srivastava', 'Viktor Prasanna', 'Rajgopal Kannan', 'Hongkuan Zhou', 'Hanqing Zeng'] | 2019-07-10 | null | https://openreview.net/forum?id=BJe8pkHFwS | https://openreview.net/pdf?id=BJe8pkHFwS | iclr-2020-1 | ['graph-sampling'] | ['graphs'] | [-9.36560705e-02 5.95906854e-01 -4.09577191e-01 -4.90505248e-01
-5.91731310e-01 -5.61367452e-01 3.62594336e-01 2.17432261e-01
2.68738549e-02 9.25737679e-01 -6.02085143e-03 -2.25435749e-01
-2.24328369e-01 -1.15300786e+00 -1.14254248e+00 -7.03218758e-01
-4.54907984e-01 5.02344429e-01 3.30421060e-01 5.28494157... | [6.976623058319092, 6.296162128448486] |
dd370890-665a-40c1-a1f8-5b000ed1e77b | optimal-graph-filters-for-clustering | 2211.04634 | null | https://arxiv.org/abs/2211.04634v1 | https://arxiv.org/pdf/2211.04634v1.pdf | Optimal Graph Filters for Clustering Attributed Graphs | Many real-world systems can be represented as graphs where the different entities are presented by nodes and their interactions by edges. An important task in studying large datasets is graph clustering. While there has been a lot of work on graph clustering using the connectivity between the nodes, many real-world net... | ['Selin Aviyente', 'Meiby Ortiz-Bouza'] | 2022-11-09 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-1.15553454e-01 4.16539073e-01 1.89759210e-01 -5.19643843e-01
1.53648823e-01 -3.91244709e-01 5.03498971e-01 7.46890962e-01
-2.92414814e-01 5.21899052e-02 -2.30926252e-03 -4.75526899e-02
-5.86702526e-01 -9.93305624e-01 -5.71233690e-01 -4.45747644e-01
-5.37560821e-01 5.80213487e-01 1.96155116e-01 1.28224507... | [7.171375274658203, 6.0998101234436035] |
05ada3e2-b9e4-4500-8caf-652fe9d4a84b | hrel-filter-pruning-based-on-high-relevance | 2202.10716 | null | https://arxiv.org/abs/2202.10716v1 | https://arxiv.org/pdf/2202.10716v1.pdf | HRel: Filter Pruning based on High Relevance between Activation Maps and Class Labels | This paper proposes an Information Bottleneck theory based filter pruning method that uses a statistical measure called Mutual Information (MI). The MI between filters and class labels, also called \textit{Relevance}, is computed using the filter's activation maps and the annotations. The filters having High Relevance ... | ['SH Shabbeer Basha', 'Shiv Ram Dubey', 'Mrinmoy Ghorai', 'CH Sarvani'] | 2022-02-22 | null | null | null | null | ['information-plane'] | ['methodology'] | [-1.05215590e-02 1.53403208e-01 4.01228994e-01 -1.99378714e-01
2.20274609e-02 -2.62935191e-01 -3.90012786e-02 1.75281599e-01
-9.26998496e-01 9.13368702e-01 -2.54104018e-01 -4.37370718e-01
-5.08421123e-01 -1.10885823e+00 -5.54334462e-01 -4.41929072e-01
-1.28527343e-01 -7.32936338e-02 6.43099785e-01 -1.93953753... | [8.559946060180664, 3.0246477127075195] |
99a26a20-840d-4931-a740-3efe387edf06 | take-the-hint-improving-arabic-diacritization | 2306.03557 | null | https://arxiv.org/abs/2306.03557v1 | https://arxiv.org/pdf/2306.03557v1.pdf | Take the Hint: Improving Arabic Diacritization with Partially-Diacritized Text | Automatic Arabic diacritization is useful in many applications, ranging from reading support for language learners to accurate pronunciation predictor for downstream tasks like speech synthesis. While most of the previous works focused on models that operate on raw non-diacritized text, production systems can gain accu... | ['Mohammad Zeineldeen', 'Nick Rossenbach', 'Mattia Di Gangi', 'Parnia Bahar'] | 2023-06-06 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 3.52242976e-01 3.43981206e-01 -2.13291466e-01 -3.97531211e-01
-1.06020224e+00 -7.96360970e-01 6.71522319e-01 3.39025617e-01
-5.46944678e-01 8.24068189e-01 3.76786828e-01 -9.96628702e-01
3.34291935e-01 -6.71490610e-01 -7.80276537e-01 -3.72509271e-01
2.31903493e-01 6.33440673e-01 5.45690298e-01 -7.74830639... | [10.860761642456055, 10.28982162475586] |
f7b72f2d-354f-452b-8ba5-ee0b4c8b63dd | automated-topical-component-extraction-using-1 | 2008.01809 | null | https://arxiv.org/abs/2008.01809v1 | https://arxiv.org/pdf/2008.01809v1.pdf | Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring | While automated essay scoring (AES) can reliably grade essays at scale, automated writing evaluation (AWE) additionally provides formative feedback to guide essay revision. However, a neural AES typically does not provide useful feature representations for supporting AWE. This paper presents a method for linking AWE an... | ['Haoran Zhang', 'Diane Litman'] | 2020-08-04 | automated-topical-component-extraction-using | https://aclanthology.org/2020.acl-main.759 | https://aclanthology.org/2020.acl-main.759.pdf | acl-2020-6 | ['automated-essay-scoring', 'automated-writing-evaluation'] | ['natural-language-processing', 'natural-language-processing'] | [-5.39897159e-02 1.68714561e-02 -3.91778089e-02 -3.84067923e-01
-1.04736710e+00 -7.78634250e-01 6.38061643e-01 6.99559212e-01
-5.09780049e-01 8.38286817e-01 6.06326342e-01 -3.78027201e-01
-3.40685278e-01 -7.54701912e-01 -2.52837509e-01 -4.57565933e-02
6.42932534e-01 1.56853959e-01 -1.83410242e-01 -3.08248132... | [11.300117492675781, 9.338724136352539] |
8257483b-35e1-4996-8477-923ed9404a45 | auto-weighted-multi-view-feature-selection | 2104.04906 | null | https://arxiv.org/abs/2104.04906v1 | https://arxiv.org/pdf/2104.04906v1.pdf | Auto-weighted Multi-view Feature Selection with Graph Optimization | In this paper, we focus on the unsupervised multi-view feature selection which tries to handle high dimensional data in the field of multi-view learning. Although some graph-based methods have achieved satisfactory performance, they ignore the underlying data structure across different views. Besides, their pre-defined... | ['Xuelong Li', 'Mulin Chen', 'Xu Jiang', 'Qi Wang'] | 2021-04-11 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-1.00090824e-01 -4.50102806e-01 -2.16713682e-01 -3.33473146e-01
-4.62746263e-01 -2.19648153e-01 2.07474247e-01 -1.38993442e-01
4.13546860e-02 2.42911443e-01 4.38509375e-01 3.23950648e-01
-7.21850514e-01 -7.75431752e-01 -1.45812500e-02 -9.19489145e-01
2.61787057e-01 1.05853364e-01 2.78669029e-01 -2.35808060... | [8.206299781799316, 4.628712177276611] |
72794340-b69b-49f2-9d8b-dc0c660b7df3 | generalizing-hand-segmentation-in-egocentric | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Cai_Generalizing_Hand_Segmentation_in_Egocentric_Videos_With_Uncertainty-Guided_Model_Adaptation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Cai_Generalizing_Hand_Segmentation_in_Egocentric_Videos_With_Uncertainty-Guided_Model_Adaptation_CVPR_2020_paper.pdf | Generalizing Hand Segmentation in Egocentric Videos With Uncertainty-Guided Model Adaptation | Although the performance of hand segmentation in egocentric videos has been significantly improved by using CNNs, it still remains a challenging issue to generalize the trained models to new domains, e.g., unseen environments. In this work, we solve the hand segmentation generalization problem without requiring segment... | [' Yoichi Sato', ' Feng Lu', 'Minjie Cai'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['hand-segmentation'] | ['computer-vision'] | [ 1.34415075e-01 -5.28959557e-03 -1.66973129e-01 -3.85632128e-01
-3.20170671e-01 -5.95590234e-01 1.14698648e-01 -6.94355011e-01
-2.32950285e-01 7.53788352e-01 1.31088406e-01 2.79550433e-01
-1.44968191e-02 -7.77430356e-01 -7.87686169e-01 -7.61524439e-01
5.79540491e-01 5.98705053e-01 4.09758359e-01 1.93825662... | [9.538701057434082, 1.2870944738388062] |
efdeb7dd-681c-4b04-b9ed-1a5ccf9e961c | opensr-open-modality-speech-recognition-via | 2306.06410 | null | https://arxiv.org/abs/2306.06410v1 | https://arxiv.org/pdf/2306.06410v1.pdf | OpenSR: Open-Modality Speech Recognition via Maintaining Multi-Modality Alignment | Speech Recognition builds a bridge between the multimedia streaming (audio-only, visual-only or audio-visual) and the corresponding text transcription. However, when training the specific model of new domain, it often gets stuck in the lack of new-domain utterances, especially the labeled visual utterances. To break th... | ['Zhou Zhao', 'Xinyu Duan', 'Wang Lin', 'Linjun Li', 'Tao Jin', 'Xize Cheng'] | 2023-06-10 | null | null | null | null | ['visual-speech-recognition', 'audio-visual-speech-recognition'] | ['speech', 'speech'] | [ 3.08231205e-01 -6.96701035e-02 -3.49190801e-01 -2.38314003e-01
-1.64587200e+00 -4.86418158e-01 3.03789109e-01 -3.31901729e-01
-3.04453999e-01 5.34875631e-01 2.51970828e-01 -3.21485341e-01
3.27527732e-01 -3.33822191e-01 -8.56263459e-01 -7.31705725e-01
4.99208421e-01 2.62865782e-01 3.34258348e-01 -1.58548951... | [14.297340393066406, 5.130397796630859] |
cd61dc2e-a5df-40f1-93b2-bd68b450e8da | skillgpt-a-restful-api-service-for-skill | 2304.11060 | null | https://arxiv.org/abs/2304.11060v1 | https://arxiv.org/pdf/2304.11060v1.pdf | SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language Model | We present SkillGPT, a tool for skill extraction and standardization (SES) from free-style job descriptions and user profiles with an open-source Large Language Model (LLM) as backbone. Most previous methods for similar tasks either need supervision or rely on heavy data-preprocessing and feature engineering. Directly ... | ['Tijl De Bie', 'Bo Kang', 'Nan Li'] | 2023-04-17 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 1.73952147e-01 1.36954561e-01 -2.93540478e-01 -4.12762523e-01
-8.65507901e-01 -5.51576912e-01 6.68396175e-01 4.05724972e-01
-4.97741878e-01 8.04291308e-01 3.20844382e-01 -5.22332907e-01
-3.96011710e-01 -3.54010224e-01 -5.81348166e-02 -1.29293799e-01
1.91305667e-01 8.36476505e-01 2.58718636e-02 -4.62806225... | [12.726411819458008, 7.994873523712158] |
3fe7e3f5-5e0a-4058-8d04-93e7518fbba0 | attention-based-multi-modal-new-product-sales | null | null | https://dl.acm.org/doi/10.1145/3394486.3403362 | https://dl.acm.org/doi/pdf/10.1145/3394486.3403362 | Attention based Multi-Modal New Product Sales Time-series Forecasting | Trend driven retail industries such as fashion, launch substantial new products every season. In such a scenario, an accurate demand forecast for these newly launched products is vital for efficient downstream supply chain planning like assortment planning and stock allocation. While classical time-series forecasting a... | ['Vikas Raykar', 'Satyam Dwivedi', 'SURYA SHRAVAN KUMAR SAJJA', 'Sumanta Mukherjee', 'Kushagra Manglik', 'Vijay Ekambaram'] | 2020-08-23 | null | null | null | acm-sigkdd-international-conference-on-3 | ['new-product-sales-forecasting', 'short-observation-new-product-sales'] | ['time-series', 'time-series'] | [-1.58221200e-01 -3.00299168e-01 -9.19664085e-01 -9.94096458e-01
-6.28576040e-01 -7.90392518e-01 6.82423413e-01 5.02260149e-01
-1.58161119e-01 2.84200877e-01 6.54121339e-01 -3.42332363e-01
-1.16039298e-01 -7.10941195e-01 -6.86164439e-01 -3.67501825e-01
-5.41993454e-02 8.48332942e-01 -4.06402111e-01 -4.07129496... | [7.1470794677734375, 2.902189016342163] |
6d22ec18-a90a-4ae4-9c21-55d4a08a709b | adversarial-attacks-on-machine-learning | 2002.09565 | null | https://arxiv.org/abs/2002.09565v4 | https://arxiv.org/pdf/2002.09565v4.pdf | Adversarial Attacks on Machine Learning Systems for High-Frequency Trading | Algorithmic trading systems are often completely automated, and deep learning is increasingly receiving attention in this domain. Nonetheless, little is known about the robustness properties of these models. We study valuation models for algorithmic trading from the perspective of adversarial machine learning. We intro... | ['Avi Schwarzschild', 'Tom Goldstein', 'Micah Goldblum', 'Ankit B. Patel'] | 2020-02-21 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-2.00465783e-01 1.85428560e-01 -1.49131306e-02 -1.25960290e-01
-4.27898139e-01 -1.39010203e+00 6.93715036e-01 8.63728598e-02
-2.87786573e-01 6.04976356e-01 -3.74264985e-01 -7.96568513e-01
1.07853167e-01 -9.67825413e-01 -8.50134313e-01 -3.71085882e-01
-5.30877829e-01 5.81791699e-01 6.21392615e-02 -1.80891335... | [5.69517707824707, 7.610881328582764] |
431eb38f-ac34-4bf2-91e2-4cb5a4c02b97 | privacy-preserving-representations-are-not | 2305.04603 | null | https://arxiv.org/abs/2305.04603v1 | https://arxiv.org/pdf/2305.04603v1.pdf | Privacy-Preserving Representations are not Enough -- Recovering Scene Content from Camera Poses | Visual localization is the task of estimating the camera pose from which a given image was taken and is central to several 3D computer vision applications. With the rapid growth in the popularity of AR/VR/MR devices and cloud-based applications, privacy issues are becoming a very important aspect of the localization pr... | ['Zuzana Kukelova', 'Fredrik Kahl', 'Torsten Sattler', 'Kunal Chelani'] | 2023-05-08 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [-3.38222831e-02 -3.90277356e-01 -6.84285397e-03 -2.43045717e-01
-8.25024128e-01 -1.32227135e+00 3.14960837e-01 3.68983924e-01
-5.39957583e-01 2.96950042e-01 -4.78492707e-01 -4.78272259e-01
1.93039551e-01 -6.66075587e-01 -8.68086338e-01 -8.31366718e-01
-1.96011424e-01 4.00756985e-01 3.29194307e-01 1.46651119... | [7.55098819732666, -2.1384921073913574] |
0ea99eaa-be6c-4cb7-9f85-8022148fe19e | gleam-greedy-learning-for-large-scale | 2207.08393 | null | https://arxiv.org/abs/2207.08393v1 | https://arxiv.org/pdf/2207.08393v1.pdf | GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction | Unrolled neural networks have recently achieved state-of-the-art accelerated MRI reconstruction. These networks unroll iterative optimization algorithms by alternating between physics-based consistency and neural-network based regularization. However, they require several iterations of a large neural network to handle ... | ['Mert Pilanci', 'Morteza Mardani', 'John M Pauly', 'Shreyas Vasanawala', 'Christopher M Sandino', 'Arjun D Desai', 'Tolga Ergen', 'Arda Sahiner', 'Batu Ozturkler'] | 2022-07-18 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 1.18744366e-01 -3.08264215e-02 -1.74608409e-01 -5.52563965e-01
-9.16328728e-01 -2.97010923e-03 -1.24321310e-02 5.94008863e-02
-9.82110977e-01 4.34744239e-01 3.10163736e-01 -6.04992390e-01
3.19449529e-02 -3.34232211e-01 -8.60585272e-01 -6.95209801e-01
-8.10695410e-01 4.57878739e-01 2.33799919e-01 2.40791187... | [13.500245094299316, -2.381458044052124] |
73e117ff-26b5-4332-ad2e-0ef565b8fde3 | real-time-object-detection-yolov1-re | 2305.17786 | null | https://arxiv.org/abs/2305.17786v1 | https://arxiv.org/pdf/2305.17786v1.pdf | Real-time Object Detection: YOLOv1 Re-Implementation in PyTorch | Real-time object detection is a crucial problem to solve when in comes to computer vision systems that needs to make appropriate decision based on detection in a timely manner. I have chosen the YOLO v1 architecture to implement it using PyTorch framework, with goal to familiarize with entire object detection pipeline ... | ['Michael Shenoda'] | 2023-05-28 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-2.83893608e-02 -4.05656546e-01 3.21754754e-01 -3.44733387e-01
-1.41996428e-01 -4.59724844e-01 4.10855114e-01 -1.09223559e-01
-6.63787186e-01 5.24875782e-02 -4.18579012e-01 -5.12781143e-01
1.11688957e-01 -6.16514921e-01 -3.32509518e-01 -3.79708976e-01
5.21003678e-02 1.20447673e-01 1.14599144e+00 -2.19427198... | [8.541923522949219, -0.501251757144928] |
ab191fde-1f2f-42bd-b37a-1cf3732e52f4 | a-model-based-solution-to-the-offline-multi | 2305.17198 | null | https://arxiv.org/abs/2305.17198v1 | https://arxiv.org/pdf/2305.17198v1.pdf | A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem | Training multiple agents to coordinate is an important problem with applications in robotics, game theory, economics, and social sciences. However, most existing Multi-Agent Reinforcement Learning (MARL) methods are online and thus impractical for real-world applications in which collecting new interactions is costly o... | ['Amy Zhang', 'Derek Nowrouzezahrai', 'Jakob Foerster', 'Paul Barde'] | 2023-05-26 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-2.98015475e-01 1.87171504e-01 -2.55277067e-01 3.37939650e-01
-6.62627697e-01 -6.59018934e-01 8.07102382e-01 4.42659497e-01
-6.68794096e-01 1.39611888e+00 -1.50485784e-01 -2.94347674e-01
-5.18211484e-01 -5.58096647e-01 -7.65768886e-01 -7.51237869e-01
-4.06648219e-01 9.31627512e-01 -2.24532932e-02 -5.67107022... | [3.7752108573913574, 2.0318379402160645] |
3c6d963f-3104-4ca8-89f5-1788fbab2684 | weakly-supervised-regional-and-temporal | 2204.00379 | null | https://arxiv.org/abs/2204.00379v1 | https://arxiv.org/pdf/2204.00379v1.pdf | Weakly Supervised Regional and Temporal Learning for Facial Action Unit Recognition | Automatic facial action unit (AU) recognition is a challenging task due to the scarcity of manual annotations. To alleviate this problem, a large amount of efforts has been dedicated to exploiting various weakly supervised methods which leverage numerous unlabeled data. However, many aspects with regard to some unique ... | ['ShiLiang Pu', 'Chunmao Wang', 'Qiang Li', 'Jingjing Wang', 'Jingwei Yan'] | 2022-04-01 | null | null | null | null | ['facial-action-unit-detection'] | ['computer-vision'] | [ 1.95209637e-01 1.27223834e-01 -6.42381489e-01 -3.04217845e-01
-5.71274936e-01 -1.45285517e-01 3.63714933e-01 -7.12833226e-01
-1.23371691e-01 6.71383381e-01 5.34295440e-01 4.19604957e-01
7.43438303e-02 -4.29789126e-01 -5.10344326e-01 -1.07347751e+00
1.68168500e-01 -1.92803532e-01 -9.80989542e-03 -1.71753302... | [13.623042106628418, 1.5677108764648438] |
a9de318d-8be3-4621-95d5-65362f3955cf | sequencer-sequence-to-sequence-learning-for | 1901.01808 | null | https://arxiv.org/abs/1901.01808v3 | https://arxiv.org/pdf/1901.01808v3.pdf | SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair | This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning. We devise, implement, and evaluate a system, called SequenceR, for fixing bugs based on sequence-to-sequence learning on source code. This approach uses the copy mechanism to overcome the unlimited vocabulary probl... | ['Martin Monperrus', 'Louis-Noël Pouchet', 'Zimin Chen', 'Steve Kommrusch', 'Denys Poshyvanyk', 'Michele Tufano'] | 2018-12-24 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-1.78497970e-01 -1.53619170e-01 -2.64932126e-01 -2.81182110e-01
-1.38157988e+00 -8.10349762e-01 -4.08958405e-01 3.06176633e-01
2.82133579e-01 5.25451422e-01 2.02079322e-02 -7.34039903e-01
3.56218852e-02 -5.60711741e-01 -1.23259580e+00 8.56051818e-02
-3.49537522e-01 -9.86859873e-02 4.51101929e-01 -3.98100197... | [7.628413677215576, 7.717782497406006] |
72ee643d-9a09-47b6-9267-cef90e517f47 | continual-learning-in-open-vocabulary | 2307.01430 | null | https://arxiv.org/abs/2307.01430v1 | https://arxiv.org/pdf/2307.01430v1.pdf | Continual Learning in Open-vocabulary Classification with Complementary Memory Systems | We introduce a method for flexible continual learning in open-vocabulary image classification, drawing inspiration from the complementary learning systems observed in human cognition. We propose a "tree probe" method, an adaption of lazy learning principles, which enables fast learning from new examples with competitiv... | ['Derek Hoiem', 'Yao Xiao', 'Weijie Lyu', 'Zhen Zhu'] | 2023-07-04 | null | null | null | null | ['continual-learning'] | ['methodology'] | [ 3.75582665e-01 1.05483569e-01 -4.21887815e-01 -6.45841360e-01
-8.78283858e-01 -3.39034528e-01 6.40911937e-01 3.06332409e-01
-5.40404499e-01 8.00189972e-01 -4.50653881e-02 -1.75755844e-02
-3.87575626e-01 -6.17032945e-01 -7.87682176e-01 -5.10090470e-01
-2.19955817e-01 6.40040100e-01 6.39084399e-01 8.56868178... | [9.911911010742188, 3.145718812942505] |
705e5b98-04ce-445a-929d-f645e533ed13 | a-new-generation-of-perspective-api-efficient | 2202.11176 | null | https://arxiv.org/abs/2202.11176v1 | https://arxiv.org/pdf/2202.11176v1.pdf | A New Generation of Perspective API: Efficient Multilingual Character-level Transformers | On the world wide web, toxic content detectors are a crucial line of defense against potentially hateful and offensive messages. As such, building highly effective classifiers that enable a safer internet is an important research area. Moreover, the web is a highly multilingual, cross-cultural community that develops i... | ['Lucy Vasserman', 'Donald Metzler', 'Jai Gupta', 'Jeffrey Sorensen', 'Yi Tay', 'Vinh Q. Tran', 'Alyssa Lees'] | 2022-02-22 | null | null | null | null | ['toxic-comment-classification'] | ['natural-language-processing'] | [-2.55220354e-01 -6.49550915e-01 -4.07258749e-01 4.42237109e-02
-9.24469233e-01 -1.11751747e+00 7.66970515e-01 2.58715928e-01
-3.28628808e-01 6.07028008e-01 3.24823022e-01 -5.88947594e-01
2.45218843e-01 -4.85364646e-01 -4.96480316e-01 -2.82102883e-01
-1.31798415e-02 -8.82017910e-02 3.10911179e-01 -5.73921740... | [8.801642417907715, 10.562004089355469] |
527ba3e8-9989-4afa-9e75-a278185b1107 | tile2vec-unsupervised-representation-learning | 1805.02855 | null | http://arxiv.org/abs/1805.02855v2 | http://arxiv.org/pdf/1805.02855v2.pdf | Tile2Vec: Unsupervised representation learning for spatially distributed data | Geospatial analysis lacks methods like the word vector representations and
pre-trained networks that significantly boost performance across a wide range
of natural language and computer vision tasks. To fill this gap, we introduce
Tile2Vec, an unsupervised representation learning algorithm that extends the
distribution... | ['Sherrie Wang', 'George Azzari', 'Stefano Ermon', 'Neal Jean', 'David Lobell', 'Anshul Samar'] | 2018-05-08 | null | null | null | null | ['visual-analogies'] | ['computer-vision'] | [ 6.29293360e-03 -1.81624100e-01 -1.91203147e-01 -4.76120234e-01
-4.83684123e-01 -6.27469540e-01 1.18240547e+00 5.57969868e-01
-3.68209273e-01 4.99434859e-01 1.07963729e+00 -7.79974818e-01
-4.82246839e-02 -1.11822736e+00 -4.33182329e-01 -4.52074260e-01
-2.84026980e-01 -1.86874419e-02 3.06772604e-03 -2.24527687... | [10.523207664489746, 2.1367335319519043] |
d4cd5b5c-acbc-4e2b-aabd-b62e87601138 | learning-predictive-representations-for | 2003.05436 | null | https://arxiv.org/abs/2003.05436v1 | https://arxiv.org/pdf/2003.05436v1.pdf | Learning Predictive Representations for Deformable Objects Using Contrastive Estimation | Using visual model-based learning for deformable object manipulation is challenging due to difficulties in learning plannable visual representations along with complex dynamic models. In this work, we propose a new learning framework that jointly optimizes both the visual representation model and the dynamics model usi... | ['Pieter Abbeel', 'Wilson Yan', 'Lerrel Pinto', 'Ashwin Vangipuram'] | 2020-03-11 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [ 1.67885140e-01 1.78246126e-01 -4.18179691e-01 9.61140394e-02
-5.52981138e-01 -9.39257801e-01 5.47592819e-01 -1.73309535e-01
-1.47993237e-01 6.16321385e-01 8.65828916e-02 6.91747591e-02
-1.32960364e-01 -4.20451283e-01 -1.29573143e+00 -4.90674078e-01
-3.81399214e-01 1.04583168e+00 2.09711298e-01 -1.50966555... | [4.835115432739258, 0.4424383342266083] |
c4d89199-0029-478b-9c58-bcfd05df75b4 | occlusion-handling-using-semantic | 1707.09603 | null | http://arxiv.org/abs/1707.09603v1 | http://arxiv.org/pdf/1707.09603v1.pdf | Occlusion Handling using Semantic Segmentation and Visibility-Based Rendering for Mixed Reality | Real-time occlusion handling is a major problem in outdoor mixed reality
system because it requires great computational cost mainly due to the
complexity of the scene. Using only segmentation, it is difficult to accurately
render a virtual object occluded by complex objects such as trees, bushes etc.
In this paper, we ... | ['Yasuhide Okamoto', 'Taiki Fukiage', 'Takeshi Oishi', 'Menandro Roxas', 'Tomoki Hori'] | 2017-07-30 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 3.46474797e-01 -3.39312792e-01 4.94364500e-01 -2.72244334e-01
-2.35461622e-01 -5.54023623e-01 3.05158377e-01 -1.21670976e-01
-2.49526411e-01 9.89258826e-01 -1.94097057e-01 -3.32626581e-01
3.33719701e-02 -1.26297414e+00 -3.70781839e-01 -6.08140826e-01
2.85193890e-01 6.30785406e-01 8.96663368e-01 2.08780840... | [9.16026782989502, -2.4411818981170654] |
5f19aa87-6012-4858-82fd-fbcdd595749c | fish-sounds-towards-the-evaluation-of-marine | 2201.05013 | null | https://arxiv.org/abs/2201.05013v2 | https://arxiv.org/pdf/2201.05013v2.pdf | Fish sounds: towards the evaluation of marine acoustic biodiversity through data-driven audio source separation | The marine ecosystem is changing at an alarming rate, exhibiting biodiversity loss and the migration of tropical species to temperate basins. Monitoring the underwater environments and their inhabitants is of fundamental importance to understand the evolution of these systems and implement safeguard policies. However, ... | ['Silvia Zuffi', 'Emanuele Rodolà', 'Nicola Zonca', 'Michele Mancusi'] | 2022-01-13 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 1.53200299e-01 -5.11937559e-01 6.15396261e-01 1.86290637e-01
-8.87751207e-02 -7.26221919e-01 2.18604207e-01 6.13542974e-01
-9.44261968e-01 5.81347644e-01 -6.21637590e-02 3.17592174e-02
-7.11687356e-02 -9.37050104e-01 -4.55507487e-01 -1.00008047e+00
-5.92906892e-01 1.82847187e-01 3.69191885e-01 -3.72602642... | [8.567749977111816, -1.18271803855896] |
b69ed3ea-073f-4d0f-a159-c76bac17b514 | the-drunkard-s-odometry-estimating-camera | 2306.16917 | null | https://arxiv.org/abs/2306.16917v1 | https://arxiv.org/pdf/2306.16917v1.pdf | The Drunkard's Odometry: Estimating Camera Motion in Deforming Scenes | Estimating camera motion in deformable scenes poses a complex and open research challenge. Most existing non-rigid structure from motion techniques assume to observe also static scene parts besides deforming scene parts in order to establish an anchoring reference. However, this assumption does not hold true in certain... | ['Javier Civera', 'Marc Pollefeys', 'Martin R. Oswald', 'David Recasens'] | 2023-06-29 | null | null | null | null | ['optical-flow-estimation', '6d-pose-estimation-using-rgbd', 'visual-navigation'] | ['computer-vision', 'computer-vision', 'robots'] | [-4.20009643e-02 -1.23269215e-01 6.53473511e-02 -2.06835270e-01
-5.66731691e-01 -1.03799546e+00 4.45439219e-01 -2.97978818e-01
-3.65680248e-01 6.18633091e-01 1.11012608e-01 7.83309042e-02
-5.98522089e-02 -5.94378710e-01 -8.84165525e-01 -7.85918534e-01
8.43758956e-02 6.22761071e-01 4.42310005e-01 -2.62785137... | [8.416228294372559, -2.0495898723602295] |
9ebaff42-79a7-4ce4-a0ec-4d1cd8767df9 | plujagh-at-semeval-2016-task-11-simple-system | null | null | https://aclanthology.org/S16-1146 | https://aclanthology.org/S16-1146.pdf | PLUJAGH at SemEval-2016 Task 11: Simple System for Complex Word Identification | null | ["Krzysztof Wr{\\'o}bel"] | 2016-06-01 | null | null | null | semeval-2016-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.2968339920043945, 3.6863887310028076] |
777187ae-3482-495d-a55b-82037e705873 | deformable-gans-for-pose-based-human-image | 1801.00055 | null | http://arxiv.org/abs/1801.00055v2 | http://arxiv.org/pdf/1801.00055v2.pdf | Deformable GANs for Pose-based Human Image Generation | In this paper we address the problem of generating person images conditioned
on a given pose. Specifically, given an image of a person and a target pose, we
synthesize a new image of that person in the novel pose. In order to deal with
pixel-to-pixel misalignments caused by the pose differences, we introduce
deformable... | ['Nicu Sebe', 'Stephane Lathuiliere', 'Enver Sangineto', 'Aliaksandr Siarohin'] | 2017-12-29 | deformable-gans-for-pose-based-human-image-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Siarohin_Deformable_GANs_for_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Siarohin_Deformable_GANs_for_CVPR_2018_paper.pdf | cvpr-2018-6 | ['gesture-to-gesture-translation', 'pose-transfer'] | ['computer-vision', 'computer-vision'] | [ 6.11132920e-01 3.64956111e-01 4.21880186e-01 -1.86073244e-01
-7.40959525e-01 -7.57527232e-01 7.36279309e-01 -2.10325584e-01
-5.71101964e-01 8.38477314e-01 4.64624204e-02 4.41501200e-01
2.01655179e-01 -7.75836706e-01 -1.05885470e+00 -6.98540509e-01
2.67390311e-01 7.13638961e-01 2.44405210e-01 -1.26196325... | [11.70260238647461, -0.7311621904373169] |
69234a9f-e266-4a53-aee2-0c835b52bcc8 | learning-to-do-or-learning-while-doing | 2306.03739 | null | https://arxiv.org/abs/2306.03739v1 | https://arxiv.org/pdf/2306.03739v1.pdf | Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning | Online tuning of real-world plants is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learning-based methods, such as Reinforcement Learning-trained Optimisation (RLO) and Bayesian optimisation... | ['Holger Schlarb', 'Florian Burkart', 'Thomas Vinatier', 'Frank Mayet', 'Hannes Dinter', 'Willi Kuropka', 'Erik Bründermann', 'Oliver Stein', 'Andrea Santamaria Garcia', 'Annika Eichler', 'Chenran Xu', 'Jan Kaiser'] | 2023-06-06 | null | null | null | null | ['bayesian-optimisation', 'open-question'] | ['methodology', 'natural-language-processing'] | [ 3.20156723e-01 -1.40398711e-01 -2.76206851e-01 -1.29791424e-01
-2.48063996e-01 -7.76342630e-01 4.10100788e-01 4.50731486e-01
-2.35734761e-01 7.24150538e-01 -3.18023950e-01 -8.16589653e-01
-8.10851395e-01 -7.64176846e-01 -2.67046511e-01 -1.05467999e+00
6.64240820e-03 7.81695187e-01 3.24797392e-01 -4.55211610... | [4.996670722961426, 2.378217935562134] |
827a6e52-e6b7-40a0-a917-c426ed3dcd75 | curating-a-covid-19-data-repository-and | 2005.07882 | null | https://arxiv.org/abs/2005.07882v2 | https://arxiv.org/pdf/2005.07882v2.pdf | Curating a COVID-19 data repository and forecasting county-level death counts in the United States | As the COVID-19 outbreak evolves, accurate forecasting continues to play an extremely important role in informing policy decisions. In this paper, we present our continuous curation of a large data repository containing COVID-19 information from a range of sources. We use this data to develop predictions and correspond... | ['Tiffany Tang', 'Yan Shuo Tan', 'Raaz Dwivedi', 'Briton Park', 'Yu Wang', 'Xiao Li', 'Robert Netzorg', 'Bin Yu', 'Rebecca L. Barter', 'James Duncan', 'Nick Altieri', 'Karl Kumbier', 'Chao Zhang', 'Chandan Singh'] | 2020-05-16 | null | null | null | null | ['covid-19-tracking'] | ['time-series'] | [-3.27382088e-01 -1.10875838e-01 -3.47032756e-01 -4.04358625e-01
-9.58318591e-01 -4.66930270e-01 5.36479712e-01 9.17345643e-01
-3.56089294e-01 1.00392139e+00 8.75397563e-01 -9.40620184e-01
-1.75211698e-01 -8.35713983e-01 -3.17107230e-01 -3.67927074e-01
-3.91743064e-01 3.94904256e-01 -2.62424916e-01 -7.72921070... | [6.051515579223633, 4.4234700202941895] |
73815919-85a8-4e04-b6d3-22849c0f0bd6 | iteratively-improving-speech-recognition-and | 2305.15055 | null | https://arxiv.org/abs/2305.15055v1 | https://arxiv.org/pdf/2305.15055v1.pdf | Iteratively Improving Speech Recognition and Voice Conversion | Many existing works on voice conversion (VC) tasks use automatic speech recognition (ASR) models for ensuring linguistic consistency between source and converted samples. However, for the low-data resource domains, training a high-quality ASR remains to be a challenging task. In this work, we propose a novel iterative ... | ['Onoe Naoyuki', 'Naoya Takahashi', 'Mayank Kumar Singh'] | 2023-05-24 | null | null | null | null | ['voice-conversion', 'voice-conversion', 'automatic-speech-recognition'] | ['audio', 'speech', 'speech'] | [ 3.93814653e-01 -2.53808171e-01 -1.40180081e-01 -3.71475130e-01
-1.11026406e+00 -6.69119656e-01 4.66999650e-01 -2.80544192e-01
-3.08795005e-01 6.53036535e-01 5.04928291e-01 -4.57959592e-01
3.90764683e-01 -3.37129295e-01 -5.46166301e-01 -3.94866973e-01
4.03030038e-01 3.84531230e-01 2.06313923e-01 -3.52210522... | [14.578495025634766, 6.606527805328369] |
81b8ee9d-1b89-4b40-b09b-1876ce4451bb | ego-exo-transferring-visual-representations | 2104.07905 | null | https://arxiv.org/abs/2104.07905v1 | https://arxiv.org/pdf/2104.07905v1.pdf | Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos | We introduce an approach for pre-training egocentric video models using large-scale third-person video datasets. Learning from purely egocentric data is limited by low dataset scale and diversity, while using purely exocentric (third-person) data introduces a large domain mismatch. Our idea is to discover latent signal... | ['Kristen Grauman', 'Bo Xiong', 'Tushar Nagarajan', 'Yanghao Li'] | 2021-04-16 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Li_Ego-Exo_Transferring_Visual_Representations_From_Third-Person_to_First-Person_Videos_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Li_Ego-Exo_Transferring_Visual_Representations_From_Third-Person_to_First-Person_Videos_CVPR_2021_paper.pdf | cvpr-2021-1 | ['egocentric-activity-recognition'] | ['computer-vision'] | [-2.39057407e-01 -1.64005496e-02 -5.91186285e-01 -3.47529024e-01
-4.12158281e-01 -6.61606491e-01 1.09161162e+00 -5.28798521e-01
-3.14022034e-01 4.87421930e-01 1.24617290e+00 3.27207536e-01
-1.11146495e-01 -5.21654189e-01 -1.00024962e+00 -4.11879569e-01
-3.56809288e-01 3.45036685e-01 -3.38663347e-02 -7.66521646... | [8.435591697692871, 0.5856999158859253] |
05e3e6a2-dfe5-4a3d-97a2-781dc9116361 | using-the-web-as-an-implicit-training-set | 1912.01113 | null | https://arxiv.org/abs/1912.01113v1 | https://arxiv.org/pdf/1912.01113v1.pdf | Using the Web as an Implicit Training Set: Application to Noun Compound Syntax and Semantics | An important characteristic of English written text is the abundance of noun compounds - sequences of nouns acting as a single noun, e.g., colon cancer tumor suppressor protein. While eventually mastered by domain experts, their interpretation poses a major challenge for automated analysis. Understanding noun compounds... | ['Preslav Nakov'] | 2019-11-23 | null | null | null | null | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [ 3.65775913e-01 6.88637272e-02 -6.06345892e-01 -2.92647183e-01
-6.77500308e-01 -1.00060189e+00 6.24499977e-01 8.70983422e-01
-7.00333834e-01 7.53052711e-01 4.72572356e-01 -5.74772179e-01
-1.46671131e-01 -8.81089807e-01 -6.63588762e-01 -3.81972402e-01
4.88952659e-02 7.76574433e-01 4.06886697e-01 -7.58444607... | [10.345478057861328, 9.201251029968262] |
6a4eb7ff-1a7e-47d2-9881-2209ca6469ee | dehin-a-decentralized-framework-for-embedding | 2201.02757 | null | https://arxiv.org/abs/2201.02757v1 | https://arxiv.org/pdf/2201.02757v1.pdf | DeHIN: A Decentralized Framework for Embedding Large-scale Heterogeneous Information Networks | Modeling heterogeneity by extraction and exploitation of high-order information from heterogeneous information networks (HINs) has been attracting immense research attention in recent times. Such heterogeneous network embedding (HNE) methods effectively harness the heterogeneity of small-scale HINs. However, in the rea... | ['Kai Zheng', 'Zi Huang', 'Tong Chen', 'Hongzhi Yin', 'Mubashir Imran'] | 2022-01-08 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-2.90216476e-01 5.51526666e-01 -4.72822756e-01 1.33363418e-02
-4.00657982e-01 -6.94801629e-01 3.38399738e-01 1.97312623e-01
-1.82568625e-01 4.00956929e-01 2.20920220e-01 -3.24683011e-01
-1.88998580e-01 -1.30698419e+00 -5.01141489e-01 -5.40420294e-01
-1.90180451e-01 8.65897417e-01 4.38491285e-01 -1.03061236... | [7.213281631469727, 6.221591949462891] |
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