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b1e0604b-291f-48a5-bab0-616a6ee547bd | deep-generative-models-for-decision-making | 2306.08810 | null | https://arxiv.org/abs/2306.08810v1 | https://arxiv.org/pdf/2306.08810v1.pdf | Deep Generative Models for Decision-Making and Control | Deep model-based reinforcement learning methods offer a conceptually simple approach to the decision-making and control problem: use learning for the purpose of estimating an approximate dynamics model, and offload the rest of the work to classical trajectory optimization. However, this combination has a number of empi... | ['Michael Janner'] | 2023-06-15 | null | null | null | null | ['image-inpainting', 'model-based-reinforcement-learning'] | ['computer-vision', 'reasoning'] | [ 4.50362749e-02 1.41234249e-01 -4.99482423e-01 1.45672336e-01
-5.77397645e-01 -3.65243554e-01 8.14637959e-01 -2.58096337e-01
-1.59484565e-01 1.07288361e+00 4.18349504e-02 -5.21765172e-01
-7.56367266e-01 -7.41129577e-01 -3.77068609e-01 -9.24056232e-01
2.03205153e-01 6.55314803e-01 -3.16597551e-01 -2.84444034... | [4.190046787261963, 2.147531509399414] |
0211c9c7-83c3-474b-879b-0902d1fcd130 | automating-vitiligo-skin-lesion-segmentation | 1912.08350 | null | https://arxiv.org/abs/1912.08350v1 | https://arxiv.org/pdf/1912.08350v1.pdf | Automating Vitiligo Skin Lesion Segmentation Using Convolutional Neural Networks | For several skin conditions such as vitiligo, accurate segmentation of lesions from skin images is the primary measure of disease progression and severity. Existing methods for vitiligo lesion segmentation require manual intervention. Unfortunately, manual segmentation is time and labor-intensive, as well as irreproduc... | ['Makena Low', 'Priyanka Raina'] | 2019-12-16 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 6.83914602e-01 1.74977079e-01 -3.21689367e-01 -1.37507126e-01
-8.72066498e-01 -4.95097488e-01 9.96498093e-02 2.68274873e-01
-5.24056077e-01 5.62092006e-01 -2.75056511e-01 -3.95591110e-01
1.09903164e-01 -9.43232238e-01 -3.58515590e-01 -8.15149784e-01
1.30055025e-01 5.46250880e-01 3.50355208e-01 -2.53122821... | [15.611638069152832, -2.9559383392333984] |
91c9960c-7209-438d-81f2-ba13d42616da | causal-discovery-performance-of-chatgpt-in | 2301.13819 | null | https://arxiv.org/abs/2301.13819v2 | https://arxiv.org/pdf/2301.13819v2.pdf | Causal-Discovery Performance of ChatGPT in the context of Neuropathic Pain Diagnosis | ChatGPT has demonstrated exceptional proficiency in natural language conversation, e.g., it can answer a wide range of questions while no previous large language models can. Thus, we would like to push its limit and explore its ability to answer causal discovery questions by using a medical benchmark (Tu et al. 2019) i... | ['Cheng Zhang', 'Chao Ma', 'Ruibo Tu'] | 2023-01-24 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [-2.00670004e-01 8.60570431e-01 -7.38294184e-01 -1.33316472e-01
-8.09889197e-01 -3.92607301e-01 8.84329677e-01 2.52186686e-01
-9.19240415e-02 1.48900652e+00 9.98621047e-01 -1.16017044e+00
-5.31791270e-01 -7.30711281e-01 -3.56425196e-01 -1.48942068e-01
-2.86067784e-01 7.51347005e-01 3.00573409e-01 -4.20700610... | [9.293963432312012, 8.025586128234863] |
dd98a329-eb84-40aa-b363-f2df2d1bfc3e | emotion-recognition-techniques-with-rule | 2103.00658 | null | https://arxiv.org/abs/2103.00658v1 | https://arxiv.org/pdf/2103.00658v1.pdf | Emotion recognition techniques with rule based and machine learning approaches | Emotion recognition using digital image processing is a multifarious task because facial emotions depend on warped facial features as well as on gender, age, and culture. Furthermore, there are several factors such as varied illumination and intricate settings that increase complexity in facial emotion recognition. In ... | ['Babar Hussian', 'Aasma Aslam'] | 2021-02-28 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 2.72849441e-01 -3.91331375e-01 -3.31476107e-02 -4.89547938e-01
-3.64571176e-02 -4.48926002e-01 3.63883048e-01 -8.33481178e-02
-4.72771138e-01 6.17725313e-01 8.64400994e-03 6.19319864e-02
1.77471980e-01 -4.38481092e-01 -4.68421169e-02 -8.02307606e-01
1.19570062e-01 -6.51832998e-01 9.25370827e-02 -2.72354901... | [13.272515296936035, 0.8868977427482605] |
3af71392-1c9f-40b2-b60c-874c5dbc9f31 | deep-level-set-for-box-supervised-instance | 2112.03451 | null | https://arxiv.org/abs/2112.03451v1 | https://arxiv.org/pdf/2112.03451v1.pdf | Deep Level Set for Box-supervised Instance Segmentation in Aerial Images | Box-supervised instance segmentation has recently attracted lots of research efforts while little attention is received in aerial image domain. In contrast to the general object collections, aerial objects have large intra-class variances and inter-class similarity with complex background. Moreover, there are many tiny... | ['Jianke Zhu', 'Wenyu Liu', 'Yijie Chen', 'Wentong Li'] | 2021-12-07 | null | null | null | null | ['box-supervised-instance-segmentation'] | ['computer-vision'] | [ 1.43495247e-01 5.71395233e-02 -6.87011927e-02 -5.61787784e-01
-7.25369990e-01 -4.69814271e-01 1.81811184e-01 -2.13145912e-02
-3.16100597e-01 5.19865572e-01 -4.92623746e-01 1.36424974e-01
-3.11334610e-01 -8.14946353e-01 -6.09679639e-01 -1.04196680e+00
-4.80671637e-02 6.03228867e-01 6.03855729e-01 -1.15138656... | [9.621332168579102, 0.28875720500946045] |
c393a15e-c3c9-4aab-b357-3bce28ff454a | combined-learning-of-salient-local | 1303.02783 | null | http://arxiv.org/abs/1303.2783v1 | http://arxiv.org/pdf/1303.2783v1.pdf | Combined Learning of Salient Local Descriptors and Distance Metrics for Image Set Face Verification | In contrast to comparing faces via single exemplars, matching sets of face
images increases robustness and discrimination performance. Recent image set
matching approaches typically measure similarities between subspaces or
manifolds, while representing faces in a rigid and holistic manner. Such
representations are eas... | ['Mehrtash T. Harandi', 'Conrad Sanderson', 'Yongkang Wong', 'Brian C. Lovell'] | 2013-03-12 | null | null | null | null | ['set-matching'] | ['computer-vision'] | [ 2.85399761e-02 -4.82245147e-01 -2.28565097e-01 -7.03054607e-01
-5.05517542e-01 -5.70555210e-01 8.91246259e-01 1.47073800e-02
-1.39960513e-01 2.56204665e-01 1.39071688e-01 4.03962076e-01
-6.94562137e-01 -5.97890019e-01 -3.51865083e-01 -8.11896741e-01
-2.58200735e-01 4.58084494e-01 -4.28309254e-02 -2.24772081... | [13.135575294494629, 0.5601373314857483] |
a7555109-d2db-4379-b3cf-1feefe0d1f74 | energy-efficient-vehicular-edge-computing | 2301.13460 | null | https://arxiv.org/abs/2301.13460v1 | https://arxiv.org/pdf/2301.13460v1.pdf | Energy-Efficient Vehicular Edge Computing with One-by-one Access Scheme | With the advent of ever-growing vehicular applications, vehicular edge computing (VEC) has been a promising solution to augment the computing capacity of future smart vehicles. The ultimate challenge to fulfill the quality of service (QoS) is increasingly prominent with constrained computing and communication resources... | ['Joonhyuk Kang', 'Seongah Jeong', 'Youngsu Jang'] | 2023-01-31 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-8.40103179e-02 -2.95449141e-02 -6.70898974e-01 -3.15713547e-02
-1.82439119e-01 -3.84589851e-01 2.97764093e-01 -2.98846304e-01
-3.30796897e-01 9.92321372e-01 -3.95801514e-01 -6.84675336e-01
1.85966324e-02 -7.81329930e-01 -3.38263035e-01 -1.02137613e+00
-2.68852055e-01 3.14369291e-01 8.77038985e-02 -4.35568020... | [5.841587543487549, 1.6014593839645386] |
d27ced44-ff71-4969-b5ac-dd4566224780 | asyncval-a-toolkit-for-asynchronously | 2202.12510 | null | https://arxiv.org/abs/2202.12510v2 | https://arxiv.org/pdf/2202.12510v2.pdf | Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints during Training | The process of model checkpoint validation refers to the evaluation of the performance of a model checkpoint executed on a held-out portion of the training data while learning the hyperparameters of the model, and is used to avoid over-fitting and determine when the model has converged so as to stop training. A simple ... | ['Guido Zuccon', 'Shengyao Zhuang'] | 2022-02-25 | null | null | null | null | ['natural-questions', 'passage-retrieval'] | ['miscellaneous', 'natural-language-processing'] | [-1.99156076e-01 -3.50020409e-01 -8.14844072e-02 -3.42942357e-01
-1.13444543e+00 -7.73289919e-01 6.23242557e-01 5.68099141e-01
-6.70040965e-01 5.36877990e-01 -3.56418163e-01 -7.40270555e-01
1.73122600e-01 -6.97697997e-01 -8.78539979e-01 -5.87354243e-01
3.74669395e-03 9.57128644e-01 1.12926945e-01 -2.31371745... | [8.609722137451172, 3.6960935592651367] |
c3a644aa-993b-4422-9601-53356cbf385e | agconv-adaptive-graph-convolution-on-3d-point | 2206.04665 | null | https://arxiv.org/abs/2206.04665v2 | https://arxiv.org/pdf/2206.04665v2.pdf | AGConv: Adaptive Graph Convolution on 3D Point Clouds | Convolution on 3D point clouds is widely researched yet far from perfect in geometric deep learning. The traditional wisdom of convolution characterises feature correspondences indistinguishably among 3D points, arising an intrinsic limitation of poor distinctive feature learning. In this paper, we propose Adaptive Gra... | ['Jing Qin', 'Jun Wang', 'Yanwen Guo', 'Xuefeng Yan', 'Jingbo Qiu', 'Zhe Zhu', 'Zhilei Chen', 'Huajian Si', 'Fei Hu', 'Haoran Zhou', 'Zeyong Wei', 'Mingqiang Wei'] | 2022-06-09 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [-4.14699167e-01 -2.78606325e-01 2.06565350e-01 -1.85497493e-01
-4.04341221e-01 -5.96201956e-01 5.63063383e-01 -2.91699357e-02
-1.34756848e-01 8.29676092e-02 -2.47890785e-01 -3.49732220e-01
-2.88053691e-01 -9.16872382e-01 -8.66897166e-01 -5.67707717e-01
-7.20631378e-03 4.53968585e-01 1.65147632e-01 -2.36743391... | [7.927119731903076, -3.5872700214385986] |
7d9bf0aa-86a8-4dcd-b126-75b927da5ddd | lidar-iris-for-loop-closure-detection | 1912.03825 | null | https://arxiv.org/abs/1912.03825v3 | https://arxiv.org/pdf/1912.03825v3.pdf | LiDAR Iris for Loop-Closure Detection | In this paper, a global descriptor for a LiDAR point cloud, called LiDAR Iris, is proposed for fast and accurate loop-closure detection. A binary signature image can be obtained for each point cloud after several LoG-Gabor filtering and thresholding operations on the LiDAR-Iris image representation. Given two point clo... | ['Jian Yang', 'Cheng-Zhong Xu', 'Ying Wang', 'Hui Kong', 'Sanjay Sarma', 'Zezhou Sun'] | 2019-12-09 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [ 4.32592660e-01 -5.18236578e-01 -4.34699744e-01 -2.12971509e-01
-8.05039465e-01 -6.58855855e-01 4.67574894e-01 2.57812947e-01
-3.81563544e-01 1.49493972e-02 -5.02634466e-01 -2.59459674e-01
-2.66882062e-01 -6.14436805e-01 -5.55263042e-01 -4.23388094e-01
-7.08335638e-02 5.78983963e-01 1.88729241e-01 1.77072033... | [7.567543983459473, -2.501615047454834] |
a11dc51c-bd4b-4f8c-8de8-faf996f40c97 | learning-to-predict-indoor-illumination-from | 1704.00090 | null | http://arxiv.org/abs/1704.00090v3 | http://arxiv.org/pdf/1704.00090v3.pdf | Learning to Predict Indoor Illumination from a Single Image | We propose an automatic method to infer high dynamic range illumination from
a single, limited field-of-view, low dynamic range photograph of an indoor
scene. In contrast to previous work that relies on specialized image capture,
user input, and/or simple scene models, we train an end-to-end deep neural
network that di... | ['Jean-François Lalonde', 'Christian Gagné', 'Kalyan Sunkavalli', 'Marc-André Gardner', 'Ersin Yumer', 'Xiaohui Shen', 'Emiliano Gambaretto'] | 2017-04-01 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 4.16460127e-01 -2.72991627e-01 5.29125512e-01 -7.17631459e-01
-6.00559413e-01 -7.96606421e-01 3.67034048e-01 -1.15209259e-01
-3.66630763e-01 4.47901160e-01 2.20258236e-01 -2.56135523e-01
3.51343751e-01 -7.91331351e-01 -1.11641264e+00 -3.46853405e-01
3.91014278e-01 2.06345305e-01 1.87498733e-01 -4.05726545... | [9.736108779907227, -2.959409475326538] |
bb0ba082-383f-4d20-9301-8252467c01d7 | response-to-moffat-s-comment-on-towards | 2212.11735 | null | https://arxiv.org/abs/2212.11735v1 | https://arxiv.org/pdf/2212.11735v1.pdf | Response to Moffat's Comment on "Towards Meaningful Statements in IR Evaluation: Mapping Evaluation Measures to Interval Scales" | Moffat recently commented on our previous work. Our work focused on how laying the foundations of our evaluation methodology into the theory of measurement can improve our knowledge and understanding of the evaluation measures we use in IR and how it can shed light on the different types of scales adopted by our evalua... | ['Norbert Fuhr', 'Nicola Ferro', 'Marco Ferrante'] | 2022-12-22 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 2.36020610e-01 2.53896534e-01 -2.88441867e-01 -5.46883345e-01
-4.78767931e-01 -6.35250270e-01 5.86530685e-01 4.22463566e-01
-5.14671087e-01 5.30758977e-01 6.02679133e-01 -8.36784244e-01
-7.82092214e-01 -7.75994658e-01 -5.64141572e-01 -3.27958077e-01
2.74162710e-01 2.25022569e-01 8.46310705e-02 -4.14205641... | [10.000914573669434, 8.437910079956055] |
68e31b29-1033-400e-a7ea-505730136b66 | condnet-conditional-classifier-for-scene | 2109.10322 | null | https://arxiv.org/abs/2109.10322v1 | https://arxiv.org/pdf/2109.10322v1.pdf | CondNet: Conditional Classifier for Scene Segmentation | The fully convolutional network (FCN) has achieved tremendous success in dense visual recognition tasks, such as scene segmentation. The last layer of FCN is typically a global classifier (1x1 convolution) to recognize each pixel to a semantic label. We empirically show that this global classifier, ignoring the intra-c... | ['Nong Sang', 'Changxin Gao', 'Yuanjie Shao', 'Changqian Yu'] | 2021-09-21 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 4.15252864e-01 5.49314693e-02 -2.45809197e-01 -5.34443617e-01
-1.09982543e-01 -3.20588380e-01 6.76582754e-01 -1.80352315e-01
-5.42464018e-01 4.84864444e-01 -2.31895953e-01 -3.56027931e-01
-1.10648818e-01 -8.09095562e-01 -7.89628506e-01 -9.00988042e-01
9.60476771e-02 1.96775630e-01 7.02718616e-01 2.02907577... | [9.454669952392578, 1.9335832595825195] |
5d6e6af9-ce5e-4a18-9c4f-d2cf83593a07 | real-time-indoor-scene-reconstruction-with | 1812.03015 | null | http://arxiv.org/abs/1812.03015v1 | http://arxiv.org/pdf/1812.03015v1.pdf | Real-time Indoor Scene Reconstruction with RGBD and Inertia Input | Camera motion estimation is a key technique for 3D scene reconstruction and
Simultaneous localization and mapping (SLAM). To make it be feasibly achieved,
previous works usually assume slow camera motions, which limits its usage in
many real cases. We propose an end-to-end 3D reconstruction system which
combines color,... | ['Zunjie Zhu', 'Feng Xu'] | 2018-12-07 | null | null | null | null | ['3d-scene-reconstruction', 'indoor-scene-reconstruction'] | ['computer-vision', 'computer-vision'] | [-1.05058163e-01 -6.89283371e-01 -6.04201853e-02 -3.18881661e-01
-6.08451426e-01 -6.77715957e-01 5.63346028e-01 -2.85012782e-01
-4.95469272e-01 2.73828626e-01 5.50570106e-03 -8.82506296e-02
2.59020198e-02 -7.81974673e-01 -7.94739723e-01 -4.75973308e-01
4.49010342e-01 3.90502661e-01 4.90079939e-01 7.81331025... | [7.505223274230957, -2.2569618225097656] |
2b050a85-498a-4eb0-b759-f4edde5c0f3d | atomistic-calculations-of-charged-point | 2102.01016 | null | https://arxiv.org/abs/2102.01016v2 | https://arxiv.org/pdf/2102.01016v2.pdf | Atomistic calculations of charged point defects at grain boundaries in SrTiO$_3$ | Oxygen vacancies have been identified to play an important role in accelerating grain growth in polycrystalline perovskite-oxide ceramics. In order to advance the fundamental understanding of growth mechanisms at the atomic scale, classical atomistic simulations were carried out to investigate the atomistic structures ... | ['Christian Elsässer', 'Daniel F. Urban', 'Daniel Mutter', 'Cong Tao'] | 2021-02-01 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [ 1.47311032e-01 3.64453569e-02 3.80493992e-04 -2.80375510e-01
-2.68253922e-01 2.93640554e-01 1.17453136e-01 3.71756822e-01
-4.24120128e-01 1.18001914e+00 -1.69666305e-01 1.49363652e-01
2.82601751e-02 -1.03905427e+00 -6.68395340e-01 -1.48439324e+00
-1.60529047e-01 9.74751353e-01 4.24582273e-01 -3.26883256... | [5.3594536781311035, 4.90732479095459] |
fd609662-9d3e-4863-bb79-fb178f329f07 | attacking-and-defending-deep-learning-based | 2211.08291 | null | https://arxiv.org/abs/2211.08291v1 | https://arxiv.org/pdf/2211.08291v1.pdf | Attacking and Defending Deep-Learning-Based Off-Device Wireless Positioning Systems | Localization services for wireless devices play an increasingly important role and a plethora of emerging services and applications already rely on precise position information. Widely used on-device positioning methods, such as the global positioning system, enable accurate outdoor positioning and provide the users wi... | ['Christoph Studer', 'Jakob Hoydis', 'K. Pavan Srinath', 'Maximilian Arnold', 'Emre Gönültaş', 'Pengzhi Huang'] | 2022-11-15 | null | null | null | null | ['outdoor-positioning'] | ['miscellaneous'] | [-1.09315291e-02 1.65493160e-01 -4.10941243e-01 -3.09842974e-01
-6.38673365e-01 -1.19833040e+00 -8.66235718e-02 -8.10114667e-02
-5.11735618e-01 8.53465497e-01 -1.26719862e-01 -8.81730080e-01
-2.84145534e-01 -6.68915570e-01 -3.97269666e-01 -8.66196513e-01
-3.41092288e-01 -2.07603239e-02 -2.88180143e-01 1.04422294... | [6.391132354736328, 0.9222306609153748] |
641e7d20-82ce-4cd8-a8f6-e0359ac08192 | safe-reinforcement-learning-with-self | 2304.08897 | null | https://arxiv.org/abs/2304.08897v2 | https://arxiv.org/pdf/2304.08897v2.pdf | An adaptive safety layer with hard constraints for safe reinforcement learning in multi-energy management systems | Safe reinforcement learning (RL) with hard constraint guarantees is a promising optimal control direction for multi-energy management systems. It only requires the environment-specific constraint functions itself a priori and not a complete model (i.e. plant, disturbance and noise models, and prediction models for stat... | ['Maarten Messagie', 'Ann Nowé', 'Rüdiger Franke', 'Muhammad Andy Putratama', 'Glenn Ceusters'] | 2023-04-18 | null | null | null | null | ['energy-management'] | ['time-series'] | [ 6.43876866e-02 3.22355002e-01 -3.87671441e-01 1.38971508e-01
-6.83118701e-01 -6.73228383e-01 5.50752997e-01 2.02608183e-01
-3.54434252e-01 1.27646112e+00 -2.64603168e-01 -4.09538150e-01
-4.73652244e-01 -7.20760822e-01 -6.98779941e-01 -9.89399910e-01
-2.08876863e-01 4.26065832e-01 -1.52401656e-01 -2.08386257... | [5.227841854095459, 2.4435436725616455] |
3931a542-fdfc-411a-be4c-c8666a0089c7 | how-good-is-your-tokenizer-on-the-monolingual | 2012.15613 | null | https://arxiv.org/abs/2012.15613v2 | https://arxiv.org/pdf/2012.15613v2.pdf | How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models | In this work, we provide a systematic and comprehensive empirical comparison of pretrained multilingual language models versus their monolingual counterparts with regard to their monolingual task performance. We study a set of nine typologically diverse languages with readily available pretrained monolingual models on ... | ['Iryna Gurevych', 'Sebastian Ruder', 'Ivan Vulić', 'Jonas Pfeiffer', 'Phillip Rust'] | 2020-12-31 | null | https://aclanthology.org/2021.acl-long.243 | https://aclanthology.org/2021.acl-long.243.pdf | acl-2021-5 | ['pretrained-multilingual-language-models'] | ['natural-language-processing'] | [-3.90922844e-01 -3.00042093e-01 -5.49063623e-01 -2.04122111e-01
-1.01909935e+00 -1.25439131e+00 8.42655003e-01 2.15884537e-01
-1.03085613e+00 9.57330644e-01 4.58364546e-01 -9.74305212e-01
2.29491085e-01 -3.28403682e-01 -8.32899451e-01 -3.64676803e-01
3.05036962e-01 7.39745140e-01 -7.72187859e-02 -4.61786777... | [10.891134262084961, 9.981413841247559] |
9c7ec19d-2ca4-4e36-9ce3-6dbf19b7e90d | clinically-inspired-multi-agent-transformers | 2210.13889 | null | https://arxiv.org/abs/2210.13889v1 | https://arxiv.org/pdf/2210.13889v1.pdf | Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multimodal Data | Deep neural networks are often applied to medical images to automate the problem of medical diagnosis. However, a more clinically relevant question that practitioners usually face is how to predict the future trajectory of a disease. Current methods for prognosis or disease trajectory forecasting often require domain k... | ['Aleksei Tiulpin', 'Simo Saarakkala', 'Matthew B. Blaschko', 'Huy Hoang Nguyen'] | 2022-10-25 | null | null | null | null | ['trajectory-forecasting', 'disease-trajectory-forecasting'] | ['computer-vision', 'medical'] | [ 3.91393676e-02 -8.77433643e-02 -2.53654569e-01 -5.79385400e-01
-1.02387762e+00 -1.27400964e-01 3.93870801e-01 1.88279942e-01
-3.34643036e-01 7.80311644e-01 3.48218411e-01 -3.07281107e-01
-3.86849016e-01 -5.63317716e-01 -4.99597698e-01 -9.31866884e-01
-1.65612340e-01 9.92860436e-01 1.18709087e-01 1.33148029... | [14.902478218078613, -1.9632627964019775] |
01b67603-92cc-47c2-9d95-90b34b5067a3 | mect-multi-metadata-embedding-based-cross | 2107.05418 | null | https://arxiv.org/abs/2107.05418v1 | https://arxiv.org/pdf/2107.05418v1.pdf | MECT: Multi-Metadata Embedding based Cross-Transformer for Chinese Named Entity Recognition | Recently, word enhancement has become very popular for Chinese Named Entity Recognition (NER), reducing segmentation errors and increasing the semantic and boundary information of Chinese words. However, these methods tend to ignore the information of the Chinese character structure after integrating the lexical inform... | ['ZhenHua Feng', 'Xiaoning Song', 'Shuang Wu'] | 2021-07-12 | null | https://aclanthology.org/2021.acl-long.121 | https://aclanthology.org/2021.acl-long.121.pdf | acl-2021-5 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-1.72461197e-01 -5.27267933e-01 -6.46205693e-02 -2.53613591e-01
-5.64860761e-01 -7.03920007e-01 2.93289393e-01 1.57205448e-01
-8.92744303e-01 4.30264264e-01 6.38434827e-01 -1.05892994e-01
2.54179329e-01 -7.94051468e-01 -1.79915264e-01 -6.74309790e-01
4.96808648e-01 -7.54324272e-02 1.88437372e-01 -1.24164686... | [9.891426086425781, 9.853864669799805] |
9a79b216-d3e8-4e9d-8f10-852101c61efb | interactive-object-segmentation-in-3d-point | 2204.07183 | null | https://arxiv.org/abs/2204.07183v2 | https://arxiv.org/pdf/2204.07183v2.pdf | Interactive Object Segmentation in 3D Point Clouds | We propose an interactive approach for 3D instance segmentation, where users can iteratively collaborate with a deep learning model to segment objects in a 3D point cloud directly. Current methods for 3D instance segmentation are generally trained in a fully-supervised fashion, which requires large amounts of costly tr... | ['Konrad Schindler', 'Siyu Tang', 'Ekin Celikkan', 'Theodora Kontogianni'] | 2022-04-14 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 1.60456613e-01 2.05504909e-01 -6.73620626e-02 -4.95029807e-01
-4.02532309e-01 -9.35677469e-01 2.36169651e-01 5.69018796e-02
-3.24721992e-01 5.82199357e-02 -8.55597198e-01 -5.22885442e-01
4.05125856e-01 -7.33380258e-01 -7.46406913e-01 -2.16187701e-01
1.09981969e-01 1.08001506e+00 7.49964595e-01 -1.65947393... | [8.086259841918945, -3.0363729000091553] |
0361fa99-2869-451a-9a56-f3af3cf14c8d | a-survey-on-neural-open-information | 2205.11725 | null | https://arxiv.org/abs/2205.11725v2 | https://arxiv.org/pdf/2205.11725v2.pdf | A Survey on Neural Open Information Extraction: Current Status and Future Directions | Open Information Extraction (OpenIE) facilitates domain-independent discovery of relational facts from large corpora. The technique well suits many open-world natural language understanding scenarios, such as automatic knowledge base construction, open-domain question answering, and explicit reasoning. Thanks to the ra... | ['Yongbin Li', 'Haiyang Yu', 'Jian Sun', 'Jingyang Li', 'Cheng Long', 'Aixin Sun', 'Bowen Yu', 'Shaowen Zhou'] | 2022-05-24 | null | null | null | null | ['open-information-extraction'] | ['natural-language-processing'] | [-4.43891615e-01 8.51447344e-01 -5.50385714e-01 -4.56715763e-01
-7.17061341e-01 -6.14901245e-01 2.82565981e-01 2.76241839e-01
-2.81060070e-01 1.14156711e+00 3.01769495e-01 -3.97222340e-01
-5.51277161e-01 -1.15096569e+00 -8.28101039e-01 -1.18655853e-01
-1.65009335e-01 8.07359993e-01 6.78209960e-02 -5.34176707... | [9.792695045471191, 8.369087219238281] |
639340eb-b096-489e-9dae-e022267d2bd7 | coherence-and-diversity-through-noise-self | 2302.02780 | null | https://arxiv.org/abs/2302.02780v1 | https://arxiv.org/pdf/2302.02780v1.pdf | Coherence and Diversity through Noise: Self-Supervised Paraphrase Generation via Structure-Aware Denoising | In this paper, we propose SCANING, an unsupervised framework for paraphrasing via controlled noise injection. We focus on the novel task of paraphrasing algebraic word problems having practical applications in online pedagogy as a means to reduce plagiarism as well as ensure understanding on the part of the student ins... | ['Vikram Goyal', 'Mukesh Mohania', 'Venktesh V.', 'Rishabh Gupta'] | 2023-02-06 | null | null | null | null | ['paraphrase-generation', 'memorization', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing', 'natural-language-processing'] | [ 4.56458569e-01 9.38397199e-02 2.90932469e-02 -9.34231430e-02
-8.32492948e-01 -1.03108370e+00 6.08747184e-01 4.36656713e-01
-3.58542740e-01 4.39084977e-01 5.66409528e-01 -5.22494197e-01
-3.49615037e-01 -8.49307716e-01 -8.37804973e-01 -5.15380263e-01
8.28361869e-01 3.15869987e-01 3.04875355e-02 -5.67953825... | [11.512219429016113, 9.203950881958008] |
2eebf657-702c-4776-bfcd-66f3ae87e1af | towards-demystifying-dimensions-of-source | 2008.13064 | null | https://arxiv.org/abs/2008.13064v3 | https://arxiv.org/pdf/2008.13064v3.pdf | Towards Demystifying Dimensions of Source Code Embeddings | Source code representations are key in applying machine learning techniques for processing and analyzing programs. A popular approach in representing source code is neural source code embeddings that represents programs with high-dimensional vectors computed by training deep neural networks on a large volume of program... | ['Md. Rafiqul Islam Rabin', 'Omprakash Gnawali', 'Mohammad Amin Alipour', 'Arjun Mukherjee'] | 2020-08-29 | null | null | null | null | ['method-name-prediction'] | ['natural-language-processing'] | [-4.68883604e-01 1.22330844e-01 -3.79161358e-01 -3.30711186e-01
-3.36051106e-01 -7.00256407e-01 4.93176699e-01 5.98808765e-01
-2.02756613e-01 5.23247942e-02 6.27456307e-01 -7.10448980e-01
5.35285845e-02 -8.03974628e-01 -7.72516549e-01 -2.64732808e-01
-3.81105214e-01 -8.96157995e-02 -7.10410625e-02 -3.36723000... | [7.506735801696777, 7.861898899078369] |
c0b2fb24-7a6b-4a8c-b204-12dfd1729a23 | learning-to-infer | null | null | https://openreview.net/forum?id=B1Z3W-b0W | https://openreview.net/pdf?id=B1Z3W-b0W | Learning to Infer | Inference models, which replace an optimization-based inference procedure with a learned model, have been fundamental in advancing Bayesian deep learning, the most notable example being variational auto-encoders (VAEs). In this paper, we propose iterative inference models, which learn how to optimize a variational lowe... | ['Yisong Yue', 'Joseph Marino', 'Stephan Mandt'] | 2018-01-01 | null | null | null | iclr-2018-1 | ['inference-optimization'] | ['audio'] | [ 3.43366601e-02 3.03456545e-01 -4.12154496e-01 -6.48975372e-01
-7.70369172e-01 -3.59083742e-01 8.93643379e-01 -3.71696770e-01
-3.51090223e-01 1.01745665e+00 3.00640136e-01 -4.24699038e-01
-3.83812845e-01 -6.96126342e-01 -1.12942004e+00 -6.84922218e-01
5.62080968e-05 6.39837682e-01 -1.48773402e-01 2.13055760... | [7.001053333282471, 3.9602222442626953] |
0e6fe21e-d11b-40c4-9bcd-fd4ec9cfdb87 | hyperbolic-manifold-regression | 2005.13885 | null | https://arxiv.org/abs/2005.13885v1 | https://arxiv.org/pdf/2005.13885v1.pdf | Hyperbolic Manifold Regression | Geometric representation learning has recently shown great promise in several machine learning settings, ranging from relational learning to language processing and generative models. In this work, we consider the problem of performing manifold-valued regression onto an hyperbolic space as an intermediate component for... | ['Gian Maria Marconi', 'Carlo Ciliberto', 'Lorenzo Rosasco'] | 2020-05-28 | null | null | null | null | ['taxonomy-expansion'] | ['natural-language-processing'] | [ 4.23765704e-02 7.66365588e-01 -1.42959848e-01 -2.57076770e-01
-9.03386831e-01 -5.02260089e-01 5.76504707e-01 1.88881516e-01
-1.62215322e-01 1.71848685e-01 1.75338671e-01 -5.17068684e-01
-5.69938779e-01 -8.79765689e-01 -4.42971617e-01 -9.52951968e-01
-2.18971774e-01 4.93124545e-01 -3.36724780e-02 -7.16070607... | [8.053925514221191, 4.119668483734131] |
865a5273-d93d-443f-b88d-96554469364f | automated-reasoning-in-non-classical-logics | 2202.09836 | null | https://arxiv.org/abs/2202.09836v1 | https://arxiv.org/pdf/2202.09836v1.pdf | Automated Reasoning in Non-classical Logics in the TPTP World | Non-classical logics are used in a wide spectrum of disciplines, including artificial intelligence, computer science, mathematics, and philosophy. The de-facto standard infrastructure for automated theorem proving, the TPTP World, currently supports only classical logics. Similar standards for non-classical logic reaso... | ['Christoph Benzmüller', 'Geoff Sutcliffe', 'Tobias Gleißner', 'David Fuenmayor', 'Alexander Steen'] | 2022-02-20 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [-2.09216446e-01 4.42551643e-01 -4.78552788e-01 -2.44607687e-01
-2.17992365e-01 -9.65503275e-01 8.07741940e-01 3.65593806e-02
-6.43718392e-02 1.26628304e+00 -1.96128145e-01 -1.09499061e+00
-4.18515086e-01 -1.35189712e+00 -2.48661518e-01 -1.16576537e-01
-8.50714669e-02 6.43071651e-01 8.10234070e-01 -4.85299438... | [8.748467445373535, 6.808951377868652] |
ca471b4d-cc92-4ef4-b3d8-168a6fc479bf | a-generalization-of-vit-mlp-mixer-to-graphs | 2212.13350 | null | https://arxiv.org/abs/2212.13350v2 | https://arxiv.org/pdf/2212.13350v2.pdf | A Generalization of ViT/MLP-Mixer to Graphs | Graph Neural Networks (GNNs) have shown great potential in the field of graph representation learning. Standard GNNs define a local message-passing mechanism which propagates information over the whole graph domain by stacking multiple layers. This paradigm suffers from two major limitations, over-squashing and poor lo... | ['Xavier Bresson', 'Yann Lecun', 'Adam Perold', 'Thomas Laurent', 'Bryan Hooi', 'Xiaoxin He'] | 2022-12-27 | null | null | null | null | ['graph-regression'] | ['graphs'] | [ 9.47755203e-02 1.80188745e-01 -3.21787477e-01 -1.75514430e-01
-4.13251698e-01 -4.46577251e-01 7.40843892e-01 4.75637227e-01
-3.16771239e-01 5.26104271e-01 -1.27973169e-01 -5.35405040e-01
-3.81773829e-01 -1.19350052e+00 -9.57004070e-01 -6.21026337e-01
-4.27727729e-01 4.08177108e-01 3.43393326e-01 -3.11361760... | [6.963223934173584, 6.283302307128906] |
c440a733-35f5-4164-999c-c70d4be3935d | multiplex-graph-neural-network-for-extractive | 2108.12870 | null | https://arxiv.org/abs/2108.12870v2 | https://arxiv.org/pdf/2108.12870v2.pdf | Multiplex Graph Neural Network for Extractive Text Summarization | Extractive text summarization aims at extracting the most representative sentences from a given document as its summary. To extract a good summary from a long text document, sentence embedding plays an important role. Recent studies have leveraged graph neural networks to capture the inter-sentential relationship (e.g.... | ['Hanghang Tong', 'Wei Fan', 'Tao Yang', 'Zeyu You', 'Baoyu Jing'] | 2021-08-29 | null | https://aclanthology.org/2021.emnlp-main.11 | https://aclanthology.org/2021.emnlp-main.11.pdf | emnlp-2021-11 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 3.84491950e-01 3.34174961e-01 -2.37028122e-01 -3.73549759e-01
-5.04422247e-01 -3.91568720e-01 6.72697306e-01 7.08473802e-01
-1.31635547e-01 6.01643622e-01 1.16526258e+00 -1.53516665e-01
-2.01455727e-02 -8.41507554e-01 -7.26106942e-01 -1.46961123e-01
7.36748502e-02 3.12487707e-02 6.21534772e-02 -4.43108469... | [12.640192985534668, 9.577865600585938] |
e086aca4-9950-41df-83cc-e0a92feb79b6 | multimodal-and-explainable-internet-meme | 2212.05612 | null | https://arxiv.org/abs/2212.05612v3 | https://arxiv.org/pdf/2212.05612v3.pdf | Multimodal and Explainable Internet Meme Classification | In the current context where online platforms have been effectively weaponized in a variety of geo-political events and social issues, Internet memes make fair content moderation at scale even more difficult. Existing work on meme classification and tracking has focused on black-box methods that do not explicitly consi... | ['Luca Luceri', 'Zhivar Sourati', 'Riccardo Tommasini', 'Alain Mermoud', 'Hông-Ân Sandlin', 'Filip Ilievski', 'Abhinav Kumar Thakur'] | 2022-12-11 | null | null | null | null | ['explainable-models', 'hate-speech-detection', 'meme-classification'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [-2.09522918e-02 -1.06079699e-02 -1.89063624e-01 1.39339948e-02
-2.46048883e-01 -8.28875959e-01 1.08933222e+00 6.35347962e-01
-1.09533682e-01 1.42640635e-01 5.96985221e-01 -4.14662153e-01
-1.42166078e-01 -4.78470504e-01 -1.39242604e-01 -8.17255527e-02
1.68319702e-01 1.67858332e-01 1.47329375e-01 -4.59132135... | [8.508186340332031, 10.649076461791992] |
9f3017e1-61aa-4c7d-bcb0-fce798a90cae | 2305-14655 | 2305.14655 | null | https://arxiv.org/abs/2305.14655v1 | https://arxiv.org/pdf/2305.14655v1.pdf | Learning Survival Distribution with Implicit Survival Function | Survival analysis aims at modeling the relationship between covariates and event occurrence with some untracked (censored) samples. In implementation, existing methods model the survival distribution with strong assumptions or in a discrete time space for likelihood estimation with censorship, which leads to weak gener... | ['Bo Yan', 'Weimin Tan', 'Yu Ling'] | 2023-05-24 | null | null | null | null | ['numerical-integration', 'survival-analysis'] | ['miscellaneous', 'miscellaneous'] | [-2.83021092e-01 -2.74709553e-01 -7.38950253e-01 -8.12750041e-01
-9.05745983e-01 -1.22838564e-01 1.25510052e-01 1.84075400e-01
-3.39006186e-01 1.35067952e+00 3.70786726e-01 -5.79002082e-01
-2.86176413e-01 -8.28861654e-01 -3.81649107e-01 -6.74085855e-01
-4.94261980e-01 4.59696084e-01 -2.49358535e-01 2.96794564... | [7.805413722991943, 5.583540916442871] |
eda6132e-6c91-4fa3-940e-ea5255be986f | an-intriguing-property-of-geophysics | 2204.13731 | null | https://arxiv.org/abs/2204.13731v2 | https://arxiv.org/pdf/2204.13731v2.pdf | An Intriguing Property of Geophysics Inversion | Inversion techniques are widely used to reconstruct subsurface physical properties (e.g., velocity, conductivity) from surface-based geophysical measurements (e.g., seismic, electric/magnetic (EM) data). The problems are governed by partial differential equations (PDEs) like the wave or Maxwell's equations. Solving geo... | ['Youzuo Lin', 'Zicheng Liu', 'Peng Jin', 'Shihang Feng', 'Yinpeng Chen', 'Yinan Feng'] | 2022-04-28 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [ 2.39679396e-01 -7.32261762e-02 3.83163810e-01 -3.06531787e-01
-7.56506741e-01 -2.26116613e-01 5.28551936e-01 -2.85573572e-01
-5.21208644e-01 7.83467650e-01 -1.61286861e-01 -6.41763747e-01
-3.17263693e-01 -1.01200473e+00 -1.16274750e+00 -1.06372619e+00
-9.86244604e-02 4.12334681e-01 6.58824071e-02 -3.50009501... | [6.852907180786133, 2.5395312309265137] |
7e4772dc-1a67-41fb-9638-3a3504be34bd | spatio-temporal-contrastive-learning-enhanced | 2209.11461 | null | https://arxiv.org/abs/2209.11461v2 | https://arxiv.org/pdf/2209.11461v2.pdf | Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendation | Session-based recommendation (SBR) systems aim to utilize the user's short-term behavior sequence to predict the next item without the detailed user profile. Most recent works try to model the user preference by treating the sessions as between-item transition graphs and utilize various graph neural networks (GNNs) to ... | ['Yang Wang', 'Guangyong Chen', 'Ting Guo', 'Boyu Li', 'Jiezhong Qiu', 'Xin Liu', 'Benyou Wang', 'Zhongwei Wan'] | 2022-09-23 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [-1.23123839e-01 -4.68030035e-01 -4.88969475e-01 -4.04939920e-01
-2.62922913e-01 -4.39361155e-01 5.52045047e-01 3.08691114e-01
-2.63582468e-01 1.14260152e-01 5.44214785e-01 -4.32014257e-01
-6.86631858e-01 -7.71474004e-01 -4.93193865e-01 -6.32775486e-01
-5.63053191e-01 -7.10103586e-02 3.14300954e-01 -6.83979273... | [10.203490257263184, 5.609766960144043] |
fce62aaf-aa69-4058-8d73-ae1ab06a9859 | feature-selection-approaches-for-optimising | 2212.13369 | null | https://arxiv.org/abs/2212.13369v1 | https://arxiv.org/pdf/2212.13369v1.pdf | Feature Selection Approaches for Optimising Music Emotion Recognition Methods | The high feature dimensionality is a challenge in music emotion recognition. There is no common consensus on a relation between audio features and emotion. The MER system uses all available features to recognize emotion; however, this is not an optimal solution since it contains irrelevant data acting as noise. In this... | ['Gengfa Fang', 'Haiyan Lu', 'Sam Ferguson', 'Le Cai'] | 2022-12-27 | null | null | null | null | ['music-emotion-recognition'] | ['music'] | [ 3.21952254e-01 -4.82675940e-01 1.00453787e-01 -4.27677065e-01
-6.32928729e-01 -4.59662348e-01 1.58897176e-01 -3.18809092e-01
-2.97096550e-01 5.59172571e-01 3.72848630e-01 1.46616027e-01
-5.89319408e-01 -5.77040434e-01 4.30582911e-02 -6.54842734e-01
9.65312868e-02 7.91454464e-02 -1.54081419e-01 -3.78783733... | [15.846609115600586, 5.194525241851807] |
c634dc18-0e9a-4f78-a806-5ee704b650b3 | construction-d-un-systeme-de-recommandation | 2306.03247 | null | https://arxiv.org/abs/2306.03247v1 | https://arxiv.org/pdf/2306.03247v1.pdf | Construction d'un système de recommandation basé sur des contraintes via des graphes de connaissances | Knowledge graphs in RDF model entities and their relations using ontologies, and have gained popularity for information modeling. In recommender systems, knowledge graphs help represent more links and relationships between users and items. Constraint-based recommender systems leverage deep recommendation knowledge to i... | ['Philippe Gouspillou', 'Marie-Hélène Abel', 'Ngoc Luyen Le'] | 2023-06-05 | null | null | null | null | ['knowledge-graphs'] | ['knowledge-base'] | [-7.81518698e-01 4.55538094e-01 -1.15581930e+00 -6.84045672e-01
2.77212530e-01 -5.54019272e-01 2.76964337e-01 5.52830100e-01
-5.49066029e-02 5.79892397e-01 6.70843840e-01 -5.21290712e-02
-8.41732860e-01 -1.38557708e+00 -2.94900864e-01 3.21789265e-01
-2.51307398e-01 7.54597247e-01 4.91357356e-01 -7.24201441... | [9.99104118347168, 5.84326171875] |
4878d44b-fffa-420d-aef1-31edec877ee6 | doodlenet-double-deeplab-enhanced-feature | 2204.10266 | null | https://arxiv.org/abs/2204.10266v1 | https://arxiv.org/pdf/2204.10266v1.pdf | DooDLeNet: Double DeepLab Enhanced Feature Fusion for Thermal-color Semantic Segmentation | In this paper we present a new approach for feature fusion between RGB and LWIR Thermal images for the task of semantic segmentation for driving perception. We propose DooDLeNet, a double DeepLab architecture with specialized encoder-decoders for thermal and color modalities and a shared decoder for final segmentation.... | ['Catherine Wacongne', 'Lucien Martin-Gaffé', 'Oriel Frigo'] | 2022-04-21 | null | null | null | null | ['thermal-image-segmentation'] | ['computer-vision'] | [ 6.54748827e-02 -4.57913309e-01 2.85701137e-02 -1.05026507e+00
-1.00626707e+00 -3.49381655e-01 3.79813641e-01 -3.35606605e-01
-8.10727179e-01 2.24374175e-01 -3.81450087e-01 -3.43767971e-01
4.06213582e-01 -5.47367156e-01 -4.95270073e-01 -7.24491715e-01
4.07343626e-01 3.11583746e-02 7.10855246e-01 -1.66652411... | [9.12325668334961, -1.5716617107391357] |
6f2baca9-a730-4c08-a071-b1a789818e78 | dive-into-the-power-of-neuronal-heterogeneity | 2305.11484 | null | https://arxiv.org/abs/2305.11484v1 | https://arxiv.org/pdf/2305.11484v1.pdf | Dive into the Power of Neuronal Heterogeneity | The biological neural network is a vast and diverse structure with high neural heterogeneity. Conventional Artificial Neural Networks (ANNs) primarily focus on modifying the weights of connections through training while modeling neurons as highly homogenized entities and lacking exploration of neural heterogeneity. Onl... | ['Yi Zeng', 'Yang Li', 'Yiting Dong', 'Dongcheng Zhao', 'Guobin Shen'] | 2023-05-19 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 3.13651592e-01 -1.00942291e-01 1.38344929e-01 1.09699890e-01
2.37363175e-01 -2.47295067e-01 4.54581618e-01 -2.93058425e-01
-7.22942412e-01 1.10597563e+00 -2.47547776e-01 6.74803555e-02
-2.76649922e-01 -6.19760215e-01 -9.64581847e-01 -1.38483036e+00
-2.25486327e-02 3.28445226e-01 4.76904213e-01 -3.34280938... | [8.055468559265137, 2.8377957344055176] |
3cbe6eeb-6507-43bd-a1ea-f81bf87e243f | knowledge-integration-networks-for-action | 2002.07471 | null | https://arxiv.org/abs/2002.07471v1 | https://arxiv.org/pdf/2002.07471v1.pdf | Knowledge Integration Networks for Action Recognition | In this work, we propose Knowledge Integration Networks (referred as KINet) for video action recognition. KINet is capable of aggregating meaningful context features which are of great importance to identifying an action, such as human information and scene context. We design a three-branch architecture consisting of a... | ['Li-Min Wang', 'Matthew R. Scott', 'Shiwen Zhang', 'Weilin Huang', 'Sheng Guo'] | 2020-02-18 | null | null | null | null | ['scene-recognition', 'human-parsing'] | ['computer-vision', 'computer-vision'] | [ 3.29819560e-01 -2.32295945e-01 -4.44611460e-01 -2.95847237e-01
-7.13953733e-01 -3.00596029e-01 4.65495139e-01 -1.14813410e-01
-6.22126877e-01 4.54257071e-01 3.59523386e-01 -1.78068146e-01
1.41438156e-01 -6.93668842e-01 -8.16086769e-01 -6.60876274e-01
-7.17544407e-02 1.34021863e-01 6.59676135e-01 1.62927315... | [8.5090970993042, 0.609455406665802] |
d3036f88-99b2-47cc-a870-722ba2a4517c | a-comparative-evaluation-of-heart-rate | 2005.11101 | null | https://arxiv.org/abs/2005.11101v1 | https://arxiv.org/pdf/2005.11101v1.pdf | A Comparative Evaluation of Heart Rate Estimation Methods using Face Videos | This paper presents a comparative evaluation of methods for remote heart rate estimation using face videos, i.e., given a video sequence of the face as input, methods to process it to obtain a robust estimation of the subjects heart rate at each moment. Four alternatives from the literature are tested, three based in h... | ['Javier Hernandez-Ortega', 'Julian Fierrez', 'Aythami Morales', 'David Diaz'] | 2020-05-22 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [-1.65058747e-02 1.74670681e-01 -9.02225971e-02 -4.41261441e-01
-2.71371514e-01 -4.07478139e-02 2.20245123e-01 -3.05791169e-01
-7.06044674e-01 6.33865654e-01 -2.03344822e-01 -1.22441828e-01
7.58513957e-02 -4.34367746e-01 -2.46538952e-01 -1.00512493e+00
-1.82254568e-01 2.64910191e-01 -2.21013069e-01 1.01266317... | [13.858908653259277, 2.666558265686035] |
d7bb7467-6f64-4bcd-819f-93b960cbb882 | gender-bias-evaluation-in-luganda-english | null | null | https://aclanthology.org/2022.amta-research.21 | https://aclanthology.org/2022.amta-research.21.pdf | Gender bias Evaluation in Luganda-English Machine Translation | We have seen significant growth in the area of building Natural Language Processing (NLP) tools for African languages. However, the evaluation of gender bias in the machine translation systems for African languages is not yet thoroughly investigated. This is due to the unavailability of explicit text data available for... | ['Eric Peter Wairagala'] | null | null | null | null | amta-2022-9 | ['embeddings-evaluation'] | ['natural-language-processing'] | [-3.08792800e-01 2.10562080e-01 -4.90489691e-01 -6.48617208e-01
-2.34061450e-01 -5.82550228e-01 1.22632766e+00 2.57879615e-01
-8.72541130e-01 9.54717398e-01 3.39190096e-01 -6.60714686e-01
1.47274107e-01 -7.90150940e-01 -2.10783288e-01 -6.37231469e-01
3.69908810e-01 1.09100795e+00 -5.01095951e-01 -7.21119106... | [9.407405853271484, 10.254837036132812] |
a44e8ed4-4674-4c9d-a8d3-8875466d4f55 | taming-diffusion-models-for-audio-driven-co | 2303.09119 | null | https://arxiv.org/abs/2303.09119v2 | https://arxiv.org/pdf/2303.09119v2.pdf | Taming Diffusion Models for Audio-Driven Co-Speech Gesture Generation | Animating virtual avatars to make co-speech gestures facilitates various applications in human-machine interaction. The existing methods mainly rely on generative adversarial networks (GANs), which typically suffer from notorious mode collapse and unstable training, thus making it difficult to learn accurate audio-gest... | ['Lequan Yu', 'Ziwei Liu', 'Rui Qian', 'Xuanyu Liu', 'Xian Liu', 'Lingting Zhu'] | 2023-03-16 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhu_Taming_Diffusion_Models_for_Audio-Driven_Co-Speech_Gesture_Generation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhu_Taming_Diffusion_Models_for_Audio-Driven_Co-Speech_Gesture_Generation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['gesture-generation'] | ['robots'] | [ 4.64200117e-02 -3.74684483e-02 -7.38129243e-02 -5.32199182e-02
-1.03254771e+00 -4.11067039e-01 7.68374503e-01 -6.98686123e-01
9.10982117e-02 3.88326287e-01 5.80361068e-01 6.76939711e-02
2.02984009e-02 -5.74160278e-01 -4.56101537e-01 -1.13295472e+00
-7.65714096e-03 1.41565412e-01 2.63011120e-02 -2.67076969... | [5.738527297973633, -0.19869668781757355] |
23df0298-f293-4a53-aee8-bb9b1542a2c0 | cross-task-transfer-for-multimodal-aerial | 2005.08449 | null | https://arxiv.org/abs/2005.08449v2 | https://arxiv.org/pdf/2005.08449v2.pdf | Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition | Aerial scene recognition is a fundamental task in remote sensing and has recently received increased interest. While the visual information from overhead images with powerful models and efficient algorithms yields considerable performance on scene recognition, it still suffers from the variation of ground objects, ligh... | ['Xuhong LI', 'Xiaoxiang Zhu', 'Liping Jing', 'Lichao Mou', 'Dong Chen', 'Pu Jin', 'Di Hu', 'Dejing Dou'] | 2020-05-18 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4513_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123690069.pdf | eccv-2020-8 | ['scene-recognition'] | ['computer-vision'] | [ 7.52272248e-01 -6.57180071e-01 2.56428719e-01 -3.68478209e-01
-5.33660471e-01 -6.21568918e-01 5.80047488e-01 3.68167996e-01
-3.14856708e-01 4.46239114e-01 2.66098708e-01 4.40750495e-02
-2.96867341e-01 -8.69930804e-01 -5.17842650e-01 -8.61828983e-01
2.09399208e-01 -4.86261487e-01 1.01054788e-01 -2.04600524... | [9.796740531921387, 1.7832187414169312] |
d2469126-0801-4739-80bf-625f437d81a2 | cell-nuclei-classification-in | 2202.10177 | null | https://arxiv.org/abs/2202.10177v1 | https://arxiv.org/pdf/2202.10177v1.pdf | Cell nuclei classification in histopathological images using hybrid OLConvNet | Computer-aided histopathological image analysis for cancer detection is a major research challenge in the medical domain. Automatic detection and classification of nuclei for cancer diagnosis impose a lot of challenges in developing state of the art algorithms due to the heterogeneity of cell nuclei and data set variab... | ['Satish Kumar Singh', 'Suvidha Tripathi'] | 2022-02-21 | null | null | null | null | ['nuclei-classification'] | ['medical'] | [-1.00133851e-01 2.42827669e-01 -1.48919418e-01 -2.28684992e-01
-2.86486477e-01 -3.78925443e-01 3.43938917e-01 3.91310036e-01
-7.77199149e-01 8.57572615e-01 -1.76604196e-01 -6.45130455e-01
-3.27733606e-01 -8.46001923e-01 -3.48780274e-01 -8.82580340e-01
-9.69069079e-02 3.12652141e-01 2.94489503e-01 -2.63151288... | [15.056147575378418, -2.7994613647460938] |
8d84a33b-3231-4e67-89c1-7b4801575451 | from-perception-to-programs-regularize | 2206.05922 | null | https://arxiv.org/abs/2206.05922v2 | https://arxiv.org/pdf/2206.05922v2.pdf | From Perception to Programs: Regularize, Overparameterize, and Amortize | Toward combining inductive reasoning with perception abilities, we develop techniques for neurosymbolic program synthesis where perceptual input is first parsed by neural nets into a low-dimensional interpretable representation, which is then processed by a synthesized program. We explore several techniques for relaxin... | ['Kevin Ellis', 'Hao Tang'] | 2022-06-13 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 4.49920356e-01 6.46390736e-01 -3.94397378e-01 -5.07393360e-01
-6.88182414e-01 -8.05639446e-01 5.89420080e-01 1.14535183e-01
-4.99038130e-01 5.51406085e-01 2.15221524e-01 -7.37892091e-01
-7.51805678e-02 -9.65184152e-01 -1.07205451e+00 -1.64860934e-01
-1.79727420e-01 6.47582650e-01 -2.19027519e-01 1.37548432... | [8.75537395477295, 7.16160249710083] |
297aae60-2ac7-4910-8ce9-5c8b02fc2b1c | semi-supervised-learning-made-simple-with-1 | 2306.07483 | null | https://arxiv.org/abs/2306.07483v1 | https://arxiv.org/pdf/2306.07483v1.pdf | Semi-supervised learning made simple with self-supervised clustering | Self-supervised learning models have been shown to learn rich visual representations without requiring human annotations. However, in many real-world scenarios, labels are partially available, motivating a recent line of work on semi-supervised methods inspired by self-supervised principles. In this paper, we propose a... | ['Elisa Ricci', 'Moin Nabi', 'Julien Mairal', 'Xavier Alameda-Pineda', 'Karteek Alahari', 'Pietro Astolfi', 'Enrico Fini'] | 2023-06-13 | semi-supervised-learning-made-simple-with | http://openaccess.thecvf.com//content/CVPR2023/html/Fini_Semi-Supervised_Learning_Made_Simple_With_Self-Supervised_Clustering_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Fini_Semi-Supervised_Learning_Made_Simple_With_Self-Supervised_Clustering_CVPR_2023_paper.pdf | cvpr-2023-1 | ['clustering'] | ['methodology'] | [ 1.66610613e-01 4.84744221e-01 -5.54444313e-01 -7.53771782e-01
-7.52821207e-01 -6.03947580e-01 8.95794332e-01 3.89809102e-01
-6.34013534e-01 7.94971049e-01 3.73681188e-02 -4.78295051e-02
-2.36241687e-02 -3.08148891e-01 -6.30349696e-01 -7.79438436e-01
-2.06967726e-01 6.71386182e-01 1.04889147e-01 2.91983724... | [9.438148498535156, 2.8539044857025146] |
4093db2a-a187-4a88-b6ae-4a1783865896 | cross-lingual-and-cross-domain-transfer | null | null | https://aclanthology.org/2022.lrec-1.68 | https://aclanthology.org/2022.lrec-1.68.pdf | Cross-lingual and Cross-domain Transfer Learning for Automatic Term Extraction from Low Resource Data | Automatic Term Extraction (ATE) is a key component for domain knowledge understanding and an important basis for further natural language processing applications. Even with persistent improvements, ATE still exhibits weak results exacerbated by small training data inherent to specialized domain corpora. Recently, trans... | ['Beatrice Daille', 'Florian Boudin', 'Merieme Bouhandi', 'Amir Hazem'] | null | null | null | null | lrec-2022-6 | ['term-extraction'] | ['natural-language-processing'] | [ 3.35805677e-02 -1.04397923e-01 -6.25682890e-01 -4.64020163e-01
-1.12672246e+00 -6.71089530e-01 7.67962337e-01 9.32529047e-02
-6.70479298e-01 9.17522132e-01 1.70018539e-01 -5.59080303e-01
-1.57381073e-01 -6.68102682e-01 -6.67960346e-01 -3.48238349e-01
-2.47796208e-01 7.23497927e-01 -7.23158345e-02 -4.95083153... | [10.363790512084961, 9.182446479797363] |
fa75cbb4-dfcb-4976-9d73-49c844abbe74 | normalized-compression-distance-of-multisets | 1212.5711 | null | http://arxiv.org/abs/1212.5711v4 | http://arxiv.org/pdf/1212.5711v4.pdf | Normalized Compression Distance of Multisets with Applications | Normalized compression distance (NCD) is a parameter-free, feature-free,
alignment-free, similarity measure between a pair of finite objects based on
compression. However, it is not sufficient for all applications. We propose an
NCD of finite multisets (a.k.a. multiples) of finite objects that is also a
metric. Previou... | ['Andrew R. Cohen', 'Paul M. B. Vitanyi'] | 2012-12-22 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 6.69766128e-01 -4.71485615e-01 1.28359005e-01 -3.33860159e-01
-6.16180301e-01 -6.25192523e-01 6.04879022e-01 5.72429836e-01
-7.03676462e-01 9.59263861e-01 -8.38707089e-02 -2.09524602e-01
-8.31342578e-01 -7.27199495e-01 -4.82125849e-01 -8.81748855e-01
-1.93058938e-01 6.15824342e-01 3.47753465e-01 -1.18714638... | [7.258962154388428, 3.812659740447998] |
82ad8a97-c28f-4223-81da-05e88d1d9b7d | uv-gan-adversarial-facial-uv-map-completion | 1712.04695 | null | http://arxiv.org/abs/1712.04695v1 | http://arxiv.org/pdf/1712.04695v1.pdf | UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition | Recently proposed robust 3D face alignment methods establish either dense or
sparse correspondence between a 3D face model and a 2D facial image. The use of
these methods presents new challenges as well as opportunities for facial
texture analysis. In particular, by sampling the image using the fitted model,
a facial U... | ['Yuxiang Zhou', 'Shiyang Cheng', 'Niannan Xue', 'Stefanos Zafeiriou', 'Jiankang Deng'] | 2017-12-13 | uv-gan-adversarial-facial-uv-map-completion-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Deng_UV-GAN_Adversarial_Facial_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Deng_UV-GAN_Adversarial_Facial_CVPR_2018_paper.pdf | cvpr-2018-6 | ['robust-face-recognition'] | ['computer-vision'] | [ 3.49566847e-01 2.30275750e-01 1.86492592e-01 -6.40813529e-01
-9.07841504e-01 -5.17964184e-01 4.58028823e-01 -7.53885210e-01
5.96913062e-02 3.18051368e-01 -3.34594816e-01 1.23022676e-01
2.08524078e-01 -7.71065712e-01 -1.15768695e+00 -5.75846076e-01
1.36945173e-01 5.20323694e-01 -4.33926344e-01 -2.71966726... | [13.081192970275879, 0.04916268214583397] |
7d0bedd1-ab14-4f79-88c6-9dfc1b4bb549 | an-energy-based-model-for-neuro-symbolic | 2110.01639 | null | https://arxiv.org/abs/2110.01639v1 | https://arxiv.org/pdf/2110.01639v1.pdf | An energy-based model for neuro-symbolic reasoning on knowledge graphs | Machine learning on graph-structured data has recently become a major topic in industry and research, finding many exciting applications such as recommender systems and automated theorem proving. We propose an energy-based graph embedding algorithm to characterize industrial automation systems, integrating knowledge fr... | ['Josep Soler Garrido', 'Dominik Dold'] | 2021-10-04 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 4.83228445e-01 4.40582335e-01 -2.62302935e-01 1.02593973e-01
5.22468537e-02 -5.46099961e-01 6.69025540e-01 1.03201544e+00
2.20081881e-01 5.16797245e-01 -1.94678470e-01 -8.21981847e-01
-6.03765070e-01 -1.13401890e+00 -5.56382596e-01 -6.85238898e-01
-5.53409278e-01 2.06837550e-01 1.44973904e-01 -2.75435388... | [7.288451194763184, 2.977374315261841] |
1c14415c-b0b4-48f1-a8bf-38401f7c8aa6 | class-distribution-aware-pseudo-labeling-for | 2305.02795 | null | https://arxiv.org/abs/2305.02795v2 | https://arxiv.org/pdf/2305.02795v2.pdf | Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning | Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data. However, in the context of semi-supervised multi-label learning (SSMLL), conventional pseudo-labeling methods encounter difficulties when dealing with instances associated with multiple labels and an unknown label count. These... | ['Hao-Zhe Liu', 'Sheng-Jun Huang', 'Masashi Sugiyama', 'Gang Niu', 'Jia-Hao Xiao', 'Ming-Kun Xie'] | 2023-05-04 | null | null | null | null | ['multi-label-learning', 'pseudo-label'] | ['methodology', 'miscellaneous'] | [ 5.79812467e-01 1.61063656e-01 -6.45079315e-01 -7.28606105e-01
-1.20998979e+00 -6.80346727e-01 3.34074199e-01 5.88016808e-01
-3.16844642e-01 1.04122901e+00 -4.71684784e-01 -9.65809524e-02
-6.59864172e-02 -4.97513831e-01 -6.04216754e-01 -1.01995182e+00
4.61819679e-01 4.79387343e-01 7.23778307e-02 5.26469827... | [9.362951278686523, 4.01597261428833] |
de725bef-0c1e-4f0d-aae1-d21b77cd8417 | mderank-a-masked-document-embedding-rank | 2110.06651 | null | https://arxiv.org/abs/2110.06651v3 | https://arxiv.org/pdf/2110.06651v3.pdf | MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction | Keyphrase extraction (KPE) automatically extracts phrases in a document that provide a concise summary of the core content, which benefits downstream information retrieval and NLP tasks. Previous state-of-the-art (SOTA) methods select candidate keyphrases based on the similarity between learned representations of the c... | ['Xin Cao', 'Wei Wang', 'Bing Li', 'Shiliang Zhang', 'Chong Deng', 'Wen Wang', 'Qian Chen', 'Linhan Zhang'] | 2021-10-13 | null | https://aclanthology.org/2022.findings-acl.34 | https://aclanthology.org/2022.findings-acl.34.pdf | findings-acl-2022-5 | ['document-embedding'] | ['methodology'] | [-9.22361538e-02 -6.38493150e-03 -5.87912500e-01 1.80556089e-01
-1.19712698e+00 -6.10491455e-01 9.16075289e-01 6.78412199e-01
-5.63282430e-01 3.87750149e-01 7.96365917e-01 -2.29165107e-01
-4.54125732e-01 -6.93706274e-01 -5.74544072e-01 -5.50390065e-01
-3.25332791e-01 3.37827116e-01 3.95797402e-01 -2.15359524... | [12.222155570983887, 8.85249137878418] |
934d9f95-43dc-47a0-8d3c-935c2febe69a | throttling-poisson-processes | null | null | http://papers.nips.cc/paper/4025-throttling-poisson-processes | http://papers.nips.cc/paper/4025-throttling-poisson-processes.pdf | Throttling Poisson Processes | We study a setting in which Poisson processes generate sequences of decision-making events. The optimization goal is allowed to depend on the rate of decision outcomes; the rate may depend on a potentially long backlog of events and decisions. We model the problem as a Poisson process with a throttling policy that enfo... | ['Michael Brückner', 'Peter Haider', 'Uwe Dick', 'Tobias Scheffer', 'Thomas Vanck'] | 2010-12-01 | null | null | null | neurips-2010-12 | ['abuse-detection'] | ['natural-language-processing'] | [ 1.57081753e-01 -3.03802192e-01 -3.25662792e-01 -2.53930271e-01
-5.53498924e-01 -6.01588964e-01 4.82559055e-01 5.89056373e-01
-1.02991068e+00 5.90085924e-01 -1.45306617e-01 -6.36497378e-01
1.98328555e-01 -8.70759010e-01 -5.75218379e-01 -6.35111570e-01
-6.34366393e-01 9.00375843e-01 3.21745545e-01 3.27247679... | [4.4743242263793945, 3.178588390350342] |
abe77bcc-20e4-4055-8232-8c137e77530f | macsaar-at-semeval-2016-task-11-zipfian-and | null | null | https://aclanthology.org/S16-1155 | https://aclanthology.org/S16-1155.pdf | MacSaar at SemEval-2016 Task 11: Zipfian and Character Features for ComplexWord Identification | null | ['Josef van Genabith', 'Marcos Zampieri', 'Liling Tan'] | 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... | [-7.40109395980835, 3.6928813457489014] |
25f1b871-fe2d-4df1-955c-fac0ed0ed082 | understanding-the-capabilities-of-large | 2305.16151 | null | https://arxiv.org/abs/2305.16151v1 | https://arxiv.org/pdf/2305.16151v1.pdf | Understanding the Capabilities of Large Language Models for Automated Planning | Automated planning is concerned with developing efficient algorithms to generate plans or sequences of actions to achieve a specific goal in a given environment. Emerging Large Language Models (LLMs) can answer questions, write high-quality programming code, and predict protein folding, showcasing their versatility in ... | ['Andrea Loreggia', 'Francesco Fabiano', 'Lior Horesh', 'Biplav Srivastava', 'Francesca Rossi', 'Keerthiram Murugesan', 'Bharath Muppasani', 'Vishal Pallagani'] | 2023-05-25 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [ 4.91533220e-01 6.00213170e-01 -2.67814666e-01 -2.04443783e-01
-6.15775347e-01 -5.23081899e-01 6.72401011e-01 4.09920007e-01
-1.73790351e-01 8.61426532e-01 5.68427980e-01 -5.09022534e-01
-2.66052991e-01 -1.00934207e+00 -6.39315665e-01 -1.90680087e-01
-1.71759859e-01 6.95158303e-01 5.16338311e-02 -4.21472490... | [4.302672863006592, 1.1118580102920532] |
4e106037-ba94-41fd-bc4c-664ec533519b | improving-chinese-grammatical-error-detection | null | null | https://aclanthology.org/2022.findings-acl.233 | https://aclanthology.org/2022.findings-acl.233.pdf | Improving Chinese Grammatical Error Detection via Data augmentation by Conditional Error Generation | Chinese Grammatical Error Detection(CGED) aims at detecting grammatical errors in Chinese texts. One of the main challenges for CGED is the lack of annotated data. To alleviate this problem, previous studies proposed various methods to automatically generate more training samples, which can be roughly categorized into ... | ['TingHao Yu', 'Shengkang Song', 'Tao Yang', 'Huihui Cai', 'Shulin Liu', 'Tianchi Yue'] | null | null | null | null | findings-acl-2022-5 | ['grammatical-error-detection'] | ['natural-language-processing'] | [ 2.29853362e-01 -4.62090820e-02 6.61319673e-01 -4.20093089e-01
-6.60930574e-01 -1.39607146e-01 6.92908317e-02 1.63684309e-01
-4.07636732e-01 8.06781530e-01 2.99476206e-01 -3.37043285e-01
5.29106855e-01 -9.19587195e-01 -6.51765108e-01 -4.15126801e-01
5.59292436e-01 2.82700241e-01 8.65699444e-03 -3.95184487... | [10.9917631149292, 10.779138565063477] |
c19c67c4-3024-40a8-8cfb-831f294ce628 | toward-multi-agent-reinforcement-learning-for | 2305.08723 | null | https://arxiv.org/abs/2305.08723v1 | https://arxiv.org/pdf/2305.08723v1.pdf | Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control | Event-triggered communication and control provide high control performance in networked control systems without overloading the communication network. However, most approaches require precise mathematical models of the system dynamics, which may not always be available. Model-free learning of communication and control ... | ['Dominik Baumann', 'Sebastian Trimpe', 'Lukas Kesper'] | 2023-05-15 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-1.77679598e-01 -1.76626295e-02 -3.12981904e-01 2.24895850e-02
-4.05978322e-01 -4.21799600e-01 5.68539917e-01 4.65038091e-01
-5.55549622e-01 1.56641448e+00 -2.94831038e-01 -3.91712129e-01
-5.24541318e-01 -8.33797574e-01 -3.92319173e-01 -8.44525397e-01
-7.22553194e-01 6.34419322e-01 1.23693980e-01 -1.90404743... | [4.001095771789551, 2.2108500003814697] |
11fde43e-b067-4cac-bcf6-415d6516215c | mer-gcn-micro-expression-recognition-based-on | 2004.08915 | null | https://arxiv.org/abs/2004.08915v1 | https://arxiv.org/pdf/2004.08915v1.pdf | MER-GCN: Micro Expression Recognition Based on Relation Modeling with Graph Convolutional Network | Micro-Expression (ME) is the spontaneous, involuntary movement of a face that can reveal the true feeling. Recently, increasing researches have paid attention to this field combing deep learning techniques. Action units (AUs) are the fundamental actions reflecting the facial muscle movements and AU detection has been a... | ['Wen-Huang Cheng', 'Hong-Han Shuai', 'Hong-Xia Xie', 'Ling Lo'] | 2020-04-19 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 5.9352990e-02 2.8235134e-01 -1.7879961e-01 -6.4764053e-01
2.4559048e-01 2.5158726e-02 3.9082149e-01 -3.7397444e-01
-7.8128114e-02 2.0611800e-01 -2.5662687e-02 1.8009916e-01
8.7391764e-02 -7.2650182e-01 -5.2705675e-01 -7.0314330e-01
-2.0577955e-01 -1.8061031e-01 -3.2999715e-01 -5.9212160e-01
-4.5286559e-02... | [13.647160530090332, 1.6395379304885864] |
1ac70edd-96f8-4e6e-93e3-56e62a407cd1 | dna-inspired-online-behavioral-modeling-and | 1602.00110 | null | http://arxiv.org/abs/1602.00110v1 | http://arxiv.org/pdf/1602.00110v1.pdf | DNA-inspired online behavioral modeling and its application to spambot detection | We propose a strikingly novel, simple, and effective approach to model online
user behavior: we extract and analyze digital DNA sequences from user online
actions and we use Twitter as a benchmark to test our proposal. We obtain an
incisive and compact DNA-inspired characterization of user actions. Then, we
apply stand... | ['Maurizio Tesconi', 'Angelo Spognardi', 'Marinella Petrocchi', 'Stefano Cresci', 'Roberto Di Pietro'] | 2016-01-30 | null | null | null | null | ['dna-analysis'] | ['medical'] | [ 1.74441203e-01 -2.40314752e-01 -4.64658767e-01 -7.37318099e-02
-2.88834184e-01 -9.57493067e-01 9.72057760e-01 5.32394469e-01
-6.37113094e-01 6.14924788e-01 8.97448603e-03 -5.60854137e-01
5.05579971e-02 -9.98776138e-01 -3.88649225e-01 -5.77584982e-01
1.17211767e-01 6.54170454e-01 5.34288049e-01 -1.67809486... | [8.188316345214844, 10.200197219848633] |
5d7222d3-e852-4f07-95fe-5ae1071518f2 | open-world-continual-learning-unifying | 2304.10038 | null | https://arxiv.org/abs/2304.10038v1 | https://arxiv.org/pdf/2304.10038v1.pdf | Open-World Continual Learning: Unifying Novelty Detection and Continual Learning | As AI agents are increasingly used in the real open world with unknowns or novelties, they need the ability to (1) recognize objects that (i) they have learned and (ii) detect items that they have not seen or learned before, and (2) learn the new items incrementally to become more and more knowledgeable and powerful. (... | ['Bing Liu', 'Zixuan Ke', 'Tatsuya Konishi', 'Changnan Xiao', 'Gyuhak Kim'] | 2023-04-20 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [ 5.92015423e-02 9.63714719e-02 -4.28375304e-01 -6.18101005e-03
-4.87973958e-01 -5.28853536e-01 7.07158864e-01 2.73796737e-01
-3.80081326e-01 8.44113708e-01 -1.56865641e-01 -8.93364400e-02
-3.75454873e-01 -7.31647551e-01 -1.04153180e+00 -6.06147051e-01
-4.30629760e-01 7.12645292e-01 4.58050966e-01 -3.61802876... | [9.855354309082031, 3.3109638690948486] |
5a26b0ba-61f7-4f7b-bc24-d449bcc1cd75 | lemmatization-and-morphological-tagging-in | null | null | https://aclanthology.org/L16-1239 | https://aclanthology.org/L16-1239.pdf | Lemmatization and Morphological Tagging in German and Latin: A Comparison and a Survey of the State-of-the-art | This paper relates to the challenge of morphological tagging and lemmatization in morphologically rich languages by example of German and Latin. We focus on the question what a practitioner can expect when using state-of-the-art solutions out of the box. Moreover, we contrast these with old(er) methods and implementati... | ['er', 'R{\\"u}diger Gleim', 'Alex Mehler', 'Steffen Eger'] | 2016-05-01 | lemmatization-and-morphological-tagging-in-1 | https://aclanthology.org/L16-1239 | https://aclanthology.org/L16-1239.pdf | lrec-2016-5 | ['morphological-tagging'] | ['natural-language-processing'] | [-9.47610661e-02 3.08079690e-01 -3.40799652e-02 -4.47244942e-01
-9.74173248e-01 -1.19365036e+00 5.21160066e-01 6.73623800e-01
-8.92477453e-01 6.20349288e-01 5.03535926e-01 -5.79020560e-01
9.35207084e-02 -7.22998559e-01 -2.52911747e-01 -3.84267181e-01
1.38590902e-01 8.51870775e-01 6.04496859e-02 -2.12548465... | [10.38170337677002, 10.02133560180664] |
8acecbdd-cb69-4d21-99dc-0f93d5f25f59 | global-relation-modeling-and-refinement-for | 2303.14888 | null | https://arxiv.org/abs/2303.14888v1 | https://arxiv.org/pdf/2303.14888v1.pdf | Global Relation Modeling and Refinement for Bottom-Up Human Pose Estimation | In this paper, we concern on the bottom-up paradigm in multi-person pose estimation (MPPE). Most previous bottom-up methods try to consider the relation of instances to identify different body parts during the post processing, while ignoring to model the relation among instances or environment in the feature learning p... | ['Jianqin Yin', 'Ruoqi Yin'] | 2023-03-27 | null | null | null | null | ['multi-person-pose-estimation'] | ['computer-vision'] | [-6.85119182e-02 -3.35190415e-01 9.64428410e-02 -3.55659306e-01
-6.22489393e-01 -1.71142310e-01 3.91557425e-01 4.36453484e-02
-5.18182755e-01 3.63876075e-01 5.03877640e-01 6.35925412e-01
-6.82345554e-02 -8.09953570e-01 -7.76771903e-01 -4.14750993e-01
1.66793689e-02 5.39760709e-01 3.33479613e-01 -3.75390738... | [7.198568344116211, -0.7396367788314819] |
e79d5ad0-3ce3-4cbc-8020-37a5b094f521 | low-cost-and-high-performance-data | 2101.02353 | null | https://arxiv.org/abs/2101.02353v1 | https://arxiv.org/pdf/2101.02353v1.pdf | Low-cost and high-performance data augmentation for deep-learning-based skin lesion classification | Although deep convolutional neural networks (DCNNs) have achieved significant accuracy in skin lesion classification comparable or even superior to those of dermatologists, practical implementation of these models for skin cancer screening in low resource settings is hindered by their limitations in computational cost ... | ['Ronald X. Xu', 'Peng Yao', 'Zhihong Zhang', 'Peng Liu', 'Chi Zhang', 'Liang Xu', 'Honghong Liu', 'Pengfei Shao', 'Fan Zhang', 'Mengjuan Xu', 'Shuwei Shen'] | 2021-01-07 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 6.30604327e-01 6.71146438e-02 -3.66721898e-01 -1.20136119e-01
-7.03287303e-01 -3.00337821e-01 2.84461886e-01 5.31224906e-01
-9.32377636e-01 5.62358975e-01 -2.79309094e-01 -5.02151728e-01
-2.36390695e-01 -9.69844341e-01 -2.66840249e-01 -7.24487424e-01
2.79774696e-01 2.73071676e-01 3.28226119e-01 8.56784731... | [15.660407066345215, -2.9455208778381348] |
183045e5-615c-4276-aefc-7a646b076e93 | a-global-context-mechanism-for-sequence | 2305.19928 | null | https://arxiv.org/abs/2305.19928v4 | https://arxiv.org/pdf/2305.19928v4.pdf | Supplementary Features of BiLSTM for Enhanced Sequence Labeling | Sequence labeling tasks require the computation of sentence representations for each word within a given sentence. A prevalent method incorporates a Bi-directional Long Short-Term Memory (BiLSTM) layer to enhance the sequence structure information. However, empirical evidence Li (2020) suggests that the capacity of BiL... | ['Hongguang Sun', 'Kun Shen', 'Conglei Xu'] | 2023-05-31 | null | null | null | null | ['sentiment-analysis', 'part-of-speech-tagging', 'named-entity-recognition-ner', 'chinese-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.45117199e-01 -7.11067691e-02 -7.01410025e-02 -5.83432198e-01
-7.76218474e-01 -7.80614376e-01 3.03111941e-01 2.67641425e-01
-6.92847669e-01 9.24416721e-01 4.74535286e-01 -4.17859584e-01
7.43438244e-01 -6.68423474e-01 -4.84713376e-01 -4.47786003e-01
3.13210458e-01 -2.43873730e-01 2.63375521e-01 -2.88262546... | [10.035993576049805, 9.517949104309082] |
7b1979df-0153-46d8-b1e6-f10a90968265 | mordecai-3-a-neural-geoparser-and-event | 2303.13675 | null | https://arxiv.org/abs/2303.13675v1 | https://arxiv.org/pdf/2303.13675v1.pdf | Mordecai 3: A Neural Geoparser and Event Geocoder | Mordecai3 is a new end-to-end text geoparser and event geolocation system. The system performs toponym resolution using a new neural ranking model to resolve a place name extracted from a document to its entry in the Geonames gazetteer. It also performs event geocoding, the process of linking events reported in text wi... | ['Andrew Halterman'] | 2023-03-23 | null | null | null | null | ['toponym-resolution'] | ['natural-language-processing'] | [-4.15117949e-01 2.00186819e-01 -5.29920869e-02 -2.66712397e-01
-1.28650701e+00 -7.14530945e-01 1.05960381e+00 9.41613793e-01
-8.33958149e-01 8.12910438e-01 9.83724952e-01 9.52236354e-02
-2.64828473e-01 -1.17221987e+00 -7.16788173e-01 -1.61429211e-01
-6.22603185e-02 9.69230592e-01 3.35626453e-01 -2.05229968... | [9.3195161819458, 9.070025444030762] |
15a5f77d-100c-4aca-891c-f74edb27564a | tool-flank-wear-prediction-using-high | 2212.13905 | null | https://arxiv.org/abs/2212.13905v1 | https://arxiv.org/pdf/2212.13905v1.pdf | Tool flank wear prediction using high-frequency machine data from industrial edge device | Tool flank wear monitoring can minimize machining downtime costs while increasing productivity and product quality. In some industrial applications, only a limited level of tool wear is allowed to attain necessary tolerances. It may become challenging to monitor a limited level of tool wear in the data collected from t... | ['I. Lazoglu', 'E. Emekli', 'U. Uresin', 'T. Pehlivan', 'G. Burun', 'M. R. Chehrehzad', 'C. Besirova', 'G. Kecibas', 'D. Bilgili'] | 2022-12-12 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 2.04693705e-01 -5.78649700e-01 -1.61108553e-01 -7.05744550e-02
1.21775351e-01 1.45878553e-01 -2.17902631e-01 -1.21555917e-01
4.77896743e-02 3.45236808e-01 -7.97105014e-01 1.00609593e-01
-6.11571372e-01 -3.85672301e-01 -3.31803679e-01 -5.09001255e-01
7.56896064e-02 2.21267879e-01 9.13541019e-02 -1.67967767... | [6.8292107582092285, 2.318666696548462] |
bdc8e31d-ec1e-4695-87da-72fb471b3223 | end-to-end-training-of-neural-retrievers-for | 2101.00408 | null | https://arxiv.org/abs/2101.00408v2 | https://arxiv.org/pdf/2101.00408v2.pdf | End-to-End Training of Neural Retrievers for Open-Domain Question Answering | Recent work on training neural retrievers for open-domain question answering (OpenQA) has employed both supervised and unsupervised approaches. However, it remains unclear how unsupervised and supervised methods can be used most effectively for neural retrievers. In this work, we systematically study retriever pre-trai... | ['Bryan Catanzaro', 'William L Hamilton', 'Wei Ping', 'Neel Kant', 'Mohammad Shoeybi', 'Mostofa Patwary', 'Devendra Singh Sachan'] | 2021-01-02 | null | https://aclanthology.org/2021.acl-long.519 | https://aclanthology.org/2021.acl-long.519.pdf | acl-2021-5 | ['triviaqa'] | ['miscellaneous'] | [ 2.12146193e-01 2.34302863e-01 4.26130323e-03 -2.37388030e-01
-1.75832176e+00 -7.91374624e-01 5.73839605e-01 3.53799343e-01
-6.65934920e-01 5.62532067e-01 3.39503884e-01 -5.11036515e-01
-4.82908100e-01 -6.95165634e-01 -7.86662459e-01 -2.64240921e-01
1.49779767e-01 8.85630071e-01 6.15523636e-01 -7.01525092... | [11.331021308898926, 7.952814102172852] |
9fffca76-78a1-4464-94ec-193c1000d91d | on-the-effectiveness-of-image-manipulation | 2304.09414 | null | https://arxiv.org/abs/2304.09414v1 | https://arxiv.org/pdf/2304.09414v1.pdf | On the Effectiveness of Image Manipulation Detection in the Age of Social Media | Image manipulation detection algorithms designed to identify local anomalies often rely on the manipulated regions being ``sufficiently'' different from the rest of the non-tampered regions in the image. However, such anomalies might not be easily identifiable in high-quality manipulations, and their use is often based... | ['Walter J. Scheirer', 'Jason Schlessman', 'Grant Jensen', 'Daniel Moreira', 'Priscila Saboia', 'Rosaura G. VidalMata'] | 2023-04-19 | null | null | null | null | ['image-manipulation-detection', 'image-manipulation'] | ['computer-vision', 'computer-vision'] | [ 6.93598807e-01 -4.51875597e-01 2.32997239e-01 3.52157503e-02
-4.31648254e-01 -5.63082457e-01 7.74851024e-01 6.96594715e-01
-1.76176742e-01 2.32906397e-02 -3.73225778e-01 -1.47212774e-01
4.98954952e-02 -7.77311504e-01 -8.86005700e-01 -7.94913471e-01
-3.33067119e-01 -9.44742234e-04 4.58684921e-01 -2.49419108... | [12.25423526763916, 1.0311890840530396] |
be5e9f32-ca2b-4d7c-b21a-e91b3b74cf6e | radar-based-respiratory-rate-monitoring-in | 2203.05075 | null | https://arxiv.org/abs/2203.05075v2 | https://arxiv.org/pdf/2203.05075v2.pdf | Radar-based Respiratory Rate Monitoring in Standing Position | Estimating human vital signs in a contactless non-invasive method using radar provides a convenient method in the medical field to conduct several health checkups easily and quickly. In addition to monitoring while sitting and sleeping, the standing position has aroused interest for both the industrial and medical fiel... | ['Urs Schneider', 'Christoph Wasser', 'Dominik Alscher', 'Marco F. Huber', 'Omar Metwally', 'Tassneem Helal', 'Fady Aziz'] | 2022-03-09 | null | null | null | null | ['respiratory-rate-estimation'] | ['medical'] | [ 4.11535114e-01 -1.00777522e-01 1.17228344e-01 -3.01721022e-02
-4.20367837e-01 -1.40811494e-02 -5.99118359e-02 -3.09260469e-02
-5.56195378e-01 9.99069750e-01 -1.06360644e-01 -3.81706767e-02
-3.28126341e-01 -2.22918808e-01 2.49445975e-01 -9.07848001e-01
-2.15082243e-02 3.72103900e-01 2.79884756e-04 7.93463923... | [13.915900230407715, 2.984098196029663] |
748df93e-79cb-4d05-963b-7d26ee2a19f7 | learning-robust-visual-representations-using | 1906.04547 | null | https://arxiv.org/abs/1906.04547v1 | https://arxiv.org/pdf/1906.04547v1.pdf | Learning robust visual representations using data augmentation invariance | Deep convolutional neural networks trained for image object categorization have shown remarkable similarities with representations found across the primate ventral visual stream. Yet, artificial and biological networks still exhibit important differences. Here we investigate one such property: increasing invariance to ... | ['Peter König', 'Alex Hernández-García', 'Tim C. Kietzmann'] | 2019-06-11 | null | https://openreview.net/forum?id=B1elqkrKPH | https://openreview.net/pdf?id=B1elqkrKPH | null | ['object-categorization'] | ['computer-vision'] | [ 6.37650013e-01 1.48005694e-01 -1.32941594e-02 -6.22939587e-01
3.34867805e-01 -5.80045283e-01 8.91476691e-01 1.11919761e-01
-1.01649904e+00 2.45213985e-01 1.77841693e-01 -1.89315885e-01
-2.65320063e-01 -4.89633322e-01 -8.36539209e-01 -7.04756200e-01
-1.03096096e-02 -8.27936009e-02 2.82307006e-02 -1.57497838... | [9.656601905822754, 2.385835886001587] |
8691f741-4b12-4769-a88b-9193a636caa3 | a-reranking-model-for-discourse-segmentation | null | null | https://aclanthology.org/W12-1623 | https://aclanthology.org/W12-1623.pdf | A Reranking Model for Discourse Segmentation using Subtree Features | null | ['Nguyen Le Minh', 'Ngo Xuan Bach', 'Akira Shimazu'] | 2012-07-01 | null | null | null | ws-2012-7 | ['discourse-segmentation'] | ['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... | [-7.394907474517822, 3.655766248703003] |
559add4e-5df0-4ba8-b6f9-d5b05f91d843 | knowledge-aware-deep-framework-for | 2106.03455 | null | https://arxiv.org/abs/2106.03455v2 | https://arxiv.org/pdf/2106.03455v2.pdf | Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition | Deep learning techniques have shown their superior performance in dermatologist clinical inspection. Nevertheless, melanoma diagnosis is still a challenging task due to the difficulty of incorporating the useful dermatologist clinical knowledge into the learning process. In this paper, we propose a novel knowledge-awar... | ['Jun Liu', 'Yuqian Zhao', 'Henghui Ding', 'Xudong Jiang', 'XiaoHong Wang'] | 2021-06-07 | null | null | null | null | ['melanoma-diagnosis', 'skin-lesion-segmentation', 'clinical-knowledge'] | ['computer-vision', 'medical', 'miscellaneous'] | [ 4.76771861e-01 -3.13485175e-01 -1.98594689e-01 -3.13979566e-01
-9.73808348e-01 -4.00303423e-01 3.19073915e-01 1.61051065e-01
-4.90670383e-01 5.00764012e-01 -4.68501374e-02 -1.70733228e-01
-3.93687010e-01 -6.51365161e-01 2.54243854e-02 -1.36676824e+00
6.50296628e-01 -1.89379171e-01 1.19889088e-01 2.79907674... | [15.64020824432373, -2.9317216873168945] |
dc8c8142-53d5-4caa-b9d4-b66f44851755 | watching-the-news-towards-videoqa-models-that | 2211.05588 | null | https://arxiv.org/abs/2211.05588v1 | https://arxiv.org/pdf/2211.05588v1.pdf | Watching the News: Towards VideoQA Models that can Read | Video Question Answering methods focus on commonsense reasoning and visual cognition of objects or persons and their interactions over time. Current VideoQA approaches ignore the textual information present in the video. Instead, we argue that textual information is complementary to the action and provides essential co... | ['C. V. Jawahar', 'Dimosthenis Karatzas', 'Minesh Mathew', 'Soumya Jahagirdar'] | 2022-11-10 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [ 9.97341275e-02 -8.87744203e-02 6.63824379e-02 -7.51194060e-01
-8.14226985e-01 -7.35757530e-01 6.72528505e-01 4.61296178e-02
-4.67877984e-01 6.13193929e-01 6.98646426e-01 -2.40836680e-01
2.08472833e-01 -6.03823781e-01 -9.85343993e-01 -1.33914456e-01
2.51509905e-01 1.02310367e-01 3.23465228e-01 -6.09929204... | [10.456852912902832, 1.002577304840088] |
2084d044-fb1c-40f7-9d7e-d4050864995d | text2shape-deep-retrieval-model-generating | 2302.06341 | null | https://arxiv.org/abs/2302.06341v1 | https://arxiv.org/pdf/2302.06341v1.pdf | Text2shape Deep Retrieval Model: Generating Initial Cases for Mechanical Part Redesign under the Context of Case-Based Reasoning | Retrieving the similar solutions from the historical case base for new design requirements is the first step in mechanical part redesign under the context of case-based reasoning. However, the manual retrieving method has the problem of low efficiency when the case base is large. Additionally, it is difficult for simpl... | ['Pingyu Jiang', 'Wentao Yong', 'Maolin Yang', 'Tianshuo Zang'] | 2023-02-13 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-3.52178693e-01 -1.79466575e-01 -2.00786129e-01 -1.88014984e-01
-7.10360706e-01 -4.35375124e-01 1.79508049e-02 -7.68719055e-03
4.31184262e-01 2.28631243e-01 1.88683018e-01 -9.00173262e-02
-7.39640236e-01 -1.01147318e+00 -5.15755415e-01 -4.35465395e-01
3.88033837e-01 6.99052870e-01 -1.45343676e-01 -5.75721204... | [5.927919387817383, 3.1253557205200195] |
5a25dbfa-a5a4-4ee5-936f-68c5f42644f4 | metricprompt-prompting-model-as-a-relevance | 2306.08892 | null | https://arxiv.org/abs/2306.08892v1 | https://arxiv.org/pdf/2306.08892v1.pdf | MetricPrompt: Prompting Model as a Relevance Metric for Few-shot Text Classification | Prompting methods have shown impressive performance in a variety of text mining tasks and applications, especially few-shot ones. Despite the promising prospects, the performance of prompting model largely depends on the design of prompt template and verbalizer. In this work, we propose MetricPrompt, which eases verbal... | ['Wanxiang Che', 'Weinan Zhang', 'Hongyuan Dong'] | 2023-06-15 | null | null | null | null | ['classification-1', 'text-classification', 'few-shot-text-classification'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 3.94386441e-01 -1.68474931e-02 -6.35680318e-01 -4.87457275e-01
-1.09132659e+00 -1.61770746e-01 9.40387666e-01 6.27649248e-01
-8.04591238e-01 4.93779391e-01 5.63407362e-01 -2.33123437e-01
-1.41196534e-01 -3.73499662e-01 1.33206144e-01 -2.84992903e-01
6.41134381e-01 6.26289368e-01 4.24678594e-01 -4.29449230... | [10.730785369873047, 7.755161285400391] |
2c7d1b89-40c5-48cf-86be-733351ef1823 | esresnet-environmental-sound-classification | 2004.07301 | null | https://arxiv.org/abs/2004.07301v1 | https://arxiv.org/pdf/2004.07301v1.pdf | ESResNet: Environmental Sound Classification Based on Visual Domain Models | Environmental Sound Classification (ESC) is an active research area in the audio domain and has seen a lot of progress in the past years. However, many of the existing approaches achieve high accuracy by relying on domain-specific features and architectures, making it harder to benefit from advances in other fields (e.... | ['Jörn Hees', 'Andrey Guzhov', 'Federico Raue', 'Andreas Dengel'] | 2020-04-15 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 1.71533182e-01 -3.52359474e-01 3.73979092e-01 -2.61640221e-01
-8.56338084e-01 -5.89915693e-01 5.28024256e-01 -1.74133956e-01
-6.54071152e-01 5.99533379e-01 3.24515730e-01 -1.11883253e-01
-2.19657093e-01 -5.90250731e-01 -5.26432693e-01 -6.61146998e-01
-1.78407773e-01 7.39031062e-02 4.85359907e-01 -4.29774612... | [15.215742111206055, 5.12770938873291] |
4769030f-d4a8-4443-86eb-54b677f4f233 | rl4real-reinforcement-learning-for-register | 2204.02013 | null | https://arxiv.org/abs/2204.02013v3 | https://arxiv.org/pdf/2204.02013v3.pdf | RL4ReAl: Reinforcement Learning for Register Allocation | We aim to automate decades of research and experience in register allocation, leveraging machine learning. We tackle this problem by embedding a multi-agent reinforcement learning algorithm within LLVM, training it with the state of the art techniques. We formalize the constraints that precisely define the problem for ... | ['Rohit Aggarwal', 'Anilava Kundu', 'Ramakrishna Upadrasta', 'Albert Cohen', 'Siddharth Jain', 'S. VenkataKeerthy'] | 2022-04-05 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-1.04260638e-01 1.40311822e-01 -1.30005693e+00 -2.14124456e-01
-6.87904298e-01 -6.07265413e-01 4.62665766e-01 -1.27969058e-02
-3.10806036e-01 8.97878230e-01 4.12078239e-02 -1.39217663e+00
3.60070825e-01 -9.03836012e-01 -9.43932593e-01 -2.82892525e-01
-3.12590271e-01 4.27107573e-01 8.85436758e-02 -6.11923337... | [7.8390703201293945, 7.519133567810059] |
a71fc672-0618-4aaa-98d8-2fb98f88bf1f | improving-word-translation-via-two-stage | null | null | https://openreview.net/forum?id=ycgOlOnbbMq | https://openreview.net/pdf?id=ycgOlOnbbMq | Improving Word Translation via Two-Stage Contrastive Learning | Word translation or bilingual lexicon induction (BLI) is a key cross-lingual task, aiming to bridge the lexical gap between different languages. In this work, we propose a robust and effective two-stage contrastive learning framework for the BLI task. As Stage C1, we propose to refine standard cross-lingual linear maps... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['multilingual-word-embeddings', 'pretrained-multilingual-language-models', 'multilingual-nlp'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 1.27893448e-01 -2.05431551e-01 -7.33232915e-01 -4.07080770e-01
-1.39631152e+00 -8.07936549e-01 8.31294358e-01 6.64878413e-02
-6.05415523e-01 7.40368545e-01 4.48929131e-01 -6.50847495e-01
2.00774893e-01 -3.34108829e-01 -9.04218197e-01 -3.44724953e-01
1.23195678e-01 6.13163531e-01 -9.44784209e-02 -5.75300753... | [11.025527000427246, 10.025758743286133] |
d2d63281-62b0-4cc2-a949-61ad7e654c7c | ambiguity-aware-multi-object-pose | 2211.00960 | null | https://arxiv.org/abs/2211.00960v1 | https://arxiv.org/pdf/2211.00960v1.pdf | Ambiguity-Aware Multi-Object Pose Optimization for Visually-Assisted Robot Manipulation | 6D object pose estimation aims to infer the relative pose between the object and the camera using a single image or multiple images. Most works have focused on predicting the object pose without associated uncertainty under occlusion and structural ambiguity (symmetricity). However, these works demand prior information... | ['Ayoung Kim', 'Jee-Hwan Ryu', 'Jeongyun Kim', 'Myung-Hwan Jeon'] | 2022-11-02 | null | null | null | null | ['object-slam', 'scene-recognition', '6d-pose-estimation', 'robot-manipulation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'robots'] | [ 1.46227047e-01 -4.52141687e-02 -2.06648245e-01 -3.78284425e-01
-5.14509916e-01 -5.50514638e-01 3.28347683e-01 -2.11716115e-01
-1.37596279e-01 3.98543239e-01 -2.31602728e-01 1.82190359e-01
-3.77908975e-01 -2.97480434e-01 -9.83918130e-01 -6.57308877e-01
3.40669274e-01 9.45183039e-01 2.70286743e-02 1.85590774... | [7.386322021484375, -2.517674446105957] |
a9229bb4-6a0c-45c8-90d4-d1b34f054af3 | layoutgpt-compositional-visual-planning-and | 2305.15393 | null | https://arxiv.org/abs/2305.15393v1 | https://arxiv.org/pdf/2305.15393v1.pdf | LayoutGPT: Compositional Visual Planning and Generation with Large Language Models | Attaining a high degree of user controllability in visual generation often requires intricate, fine-grained inputs like layouts. However, such inputs impose a substantial burden on users when compared to simple text inputs. To address the issue, we study how Large Language Models (LLMs) can serve as visual planners by ... | ['William Yang Wang', 'Xin Eric Wang', 'Sugato Basu', 'Xuehai He', 'Arjun Akula', 'Varun Jampani', 'Tsu-Jui Fu', 'Wanrong Zhu', 'Weixi Feng'] | 2023-05-24 | null | null | null | null | ['indoor-scene-synthesis'] | ['computer-vision'] | [ 1.70064092e-01 4.15424973e-01 2.80596972e-01 -3.12203079e-01
-5.92875600e-01 -9.54831243e-01 9.08855021e-01 7.13880137e-02
2.02205345e-01 5.99773943e-01 3.63650143e-01 -6.82138681e-01
2.66285717e-01 -9.33804095e-01 -8.77179861e-01 5.61856776e-02
2.95828581e-01 4.96654540e-01 -1.10889599e-01 -2.55940139... | [11.22646713256836, -0.21706973016262054] |
8a59cc5e-9f76-4619-91d7-f8e262f261d6 | a-multi-head-convolutional-neural-network-1 | 2205.15994 | null | https://arxiv.org/abs/2205.15994v1 | https://arxiv.org/pdf/2205.15994v1.pdf | A Multi-Head Convolutional Neural Network Based Non-Intrusive Load Monitoring Algorithm Under Dynamic Grid Voltage Conditions | In recent times, non-intrusive load monitoring (NILM) has emerged as an important tool for distribution-level energy management systems owing to its potential for energy conservation and management. However, load monitoring in smart building environments is challenging due to high variability of real-time load and vari... | ['T. S. Bhatti', 'B. K. Panigrahi', 'Ashu Verma', 'Lokesh Panwar', 'Himanshu Grover'] | 2022-05-31 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-5.03004611e-01 -5.12978375e-01 -3.82751487e-02 -1.98192775e-01
-2.32976183e-01 -3.08573484e-01 4.36626643e-01 1.12869740e-01
-5.75146861e-02 8.50957394e-01 -4.65781875e-02 1.86937526e-02
-2.61496782e-01 -1.04848266e+00 -4.58285585e-02 -9.86617446e-01
-3.05134326e-01 3.67558897e-01 -5.47439337e-01 -5.31755835... | [6.0285210609436035, 2.6076648235321045] |
f7d9405a-67e0-4082-8f2e-a2520553d440 | unbiased-multi-modality-guidance-for-image | 2208.11844 | null | https://arxiv.org/abs/2208.11844v1 | https://arxiv.org/pdf/2208.11844v1.pdf | Unbiased Multi-Modality Guidance for Image Inpainting | Image inpainting is an ill-posed problem to recover missing or damaged image content based on incomplete images with masks. Previous works usually predict the auxiliary structures (e.g., edges, segmentation and contours) to help fill visually realistic patches in a multi-stage fashion. However, imprecise auxiliary prio... | ['Tiejian Luo', 'Libo Zhang', 'Dawei Du', 'Yongsheng Yu'] | 2022-08-25 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 6.66305661e-01 2.12281451e-01 -9.75068510e-02 -4.30546701e-01
-1.28516006e+00 -3.14843923e-01 1.32855847e-01 -2.36952543e-01
-1.55595362e-01 7.70164549e-01 1.88041463e-01 7.93087631e-02
1.54307172e-01 -7.28702128e-01 -1.11018944e+00 -7.00088918e-01
6.03178978e-01 4.17186379e-01 3.25869501e-01 -1.82186380... | [11.273797035217285, -1.2614222764968872] |
1b8afcad-31b8-45aa-820f-db45b61cba11 | mathematical-and-preclinical-investigation-of | 2004.11325 | null | https://arxiv.org/abs/2004.11325v1 | https://arxiv.org/pdf/2004.11325v1.pdf | Mathematical and Preclinical Investigation of Respiratory Sinus Arrhythmia Effects on Cardiac Output | Respiratory sinus arrhythmia (RSA) is heart rate variability in synchrony with respiration although its functional significance not clear. The loss of sinus arrhythmia may indicate underlying heart failure or disease; therefore, there would be a great advantage of knowing how it works and affects the cardio-respiratory... | ['Sahar Rahbar'] | 2020-04-23 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [-1.86248764e-01 -5.76707013e-02 -1.58359692e-01 1.65260568e-01
7.73599565e-01 -5.25991321e-01 -1.73122823e-01 -2.69547045e-01
-3.80112120e-04 8.82739007e-01 -4.09561768e-02 -3.39100927e-01
-9.13705602e-02 -6.90200031e-01 1.18449032e-01 -7.02271640e-01
-7.95548409e-02 5.72122000e-02 -7.32150152e-02 -8.24599639... | [14.06822395324707, 3.021376609802246] |
99bc8379-fac2-4d2b-9deb-92026e092fef | camera-fingerprint-a-new-perspective-for | 1610.07728 | null | http://arxiv.org/abs/1610.07728v1 | http://arxiv.org/pdf/1610.07728v1.pdf | Camera Fingerprint: A New Perspective for Identifying User's Identity | Identifying user's identity is a key problem in many data mining
applications, such as product recommendation, customized content delivery and
criminal identification. Given a set of accounts from the same or different
social network platforms, user identification attempts to identify all accounts
belonging to the same... | ['Shikui Wei', 'Xiang Jiang', 'Xindong Wu', 'Yao Zhao', 'Ruizhen Zhao'] | 2016-10-25 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 2.78981447e-01 -4.67025459e-01 -2.89661258e-01 -3.30034822e-01
-1.42957583e-01 -8.31980884e-01 4.72995967e-01 3.44612926e-01
-2.05628276e-01 1.97946370e-01 5.89428656e-03 -1.71739161e-01
-1.40265182e-01 -7.78351486e-01 -2.74513215e-01 -5.36493242e-01
5.88811815e-01 2.22206935e-01 8.66382569e-02 2.64132291... | [14.742260932922363, 1.025586485862732] |
4c3a0606-815d-4b28-9a67-3c406c316dd8 | an-evolutionary-forest-for-regression | null | null | https://ieeexplore.ieee.org/document/9656554 | https://ieeexplore.ieee.org/document/9656554 | An Evolutionary Forest for Regression | Random forest (RF) is a type of ensemble-based machine learning method that has been applied to a variety of machine learning tasks in recent years. This article proposes an evolutionary approach to generate an oblique RF for regression problems. More specifically, our method induces an oblique RF by transforming the o... | ['Hu Zhang', 'Aimin Zhou', 'Hengzhe Zhang'] | 2021-11-20 | null | null | null | ieee-transactions-on-evolutionary-computation-2 | ['penn-machine-learning-benchmark'] | ['miscellaneous'] | [ 0.6468028 -0.44984007 -0.06936543 -0.4526189 -0.34097192 -0.0927317
0.5507391 -0.2144817 -0.2745524 1.1015406 -0.22103955 -0.23604092
-0.396027 -1.0484512 -0.19615489 -1.1953784 0.15915368 0.41521594
0.1832017 -0.18789954 0.5320397 0.5067131 -1.9274981 0.01553491
1.4429646 1.0499848 0.1... | [8.293801307678223, 4.21299934387207] |
6730c7e7-47ae-40ff-8d6e-f23b4db7b5e1 | analysis-of-resource-efficient-predictive | null | null | https://aclanthology.org/2020.sustainlp-1.18 | https://aclanthology.org/2020.sustainlp-1.18.pdf | Analysis of Resource-efficient Predictive Models for Natural Language Processing | In this paper, we presented an analyses of the resource efficient predictive models, namely Bonsai, Binary Neighbor Compression(BNC), ProtoNN, Random Forest, Naive Bayes and Support vector machine(SVM), in the machine learning field for resource constraint devices. These models try to minimize resource requirements lik... | ['Ambesh Shekhar', 'Raj Pranesh'] | null | null | https://openreview.net/forum?id=4Dowguaqed | https://openreview.net/pdf?id=4Dowguaqed | emnlp-sustainlp-2020-11 | ['news-classification'] | ['natural-language-processing'] | [ 1.63942218e-01 -1.48169160e-01 -4.89933103e-01 -5.10318220e-01
2.45018005e-01 -1.05486840e-01 7.27017939e-01 4.16933328e-01
-5.65354586e-01 1.11942589e+00 8.83277729e-02 -8.09626043e-01
-3.28950584e-01 -7.25283980e-01 -4.60283384e-02 -5.52778959e-01
1.49168000e-01 5.54870367e-01 3.11797321e-01 -1.71812907... | [8.372835159301758, 4.86386251449585] |
2cbeadda-44f1-4c6e-be7b-30143aff4774 | visual-lidar-odometry-and-mapping-with | 2304.08978 | null | https://arxiv.org/abs/2304.08978v2 | https://arxiv.org/pdf/2304.08978v2.pdf | Visual-LiDAR Odometry and Mapping with Monocular Scale Correction and Visual Bootstrapping | This paper presents a novel visual-LiDAR odometry and mapping method with low-drift characteristics. The proposed method is based on two popular approaches, ORB-SLAM and A-LOAM, with monocular scale correction and visual-bootstrapped LiDAR poses initialization modifications. The scale corrector calculates the proportio... | ['Junzheng Wang', 'Ni Ou', 'Hanyu Cai'] | 2023-04-18 | null | null | null | null | ['motion-compensation', 'visual-odometry'] | ['computer-vision', 'robots'] | [-1.72807127e-01 -3.18643123e-01 -2.89950252e-01 -4.83280301e-01
-5.93917906e-01 -5.37521422e-01 6.87276959e-01 1.64881602e-01
-6.15854383e-01 1.04857779e+00 -3.49932313e-01 -1.18375339e-01
-1.85549229e-01 -7.66244769e-01 -6.31257296e-01 -3.67802799e-01
8.93882960e-02 1.16394353e+00 5.42619407e-01 -2.22261727... | [7.353085041046143, -2.1595497131347656] |
03be7ba4-98df-4055-8324-c5c40b5d17d7 | development-of-a-realistic-crowd-simulation | 2304.13403 | null | https://arxiv.org/abs/2304.13403v1 | https://arxiv.org/pdf/2304.13403v1.pdf | Development of a Realistic Crowd Simulation Environment for Fine-grained Validation of People Tracking Methods | Generally, crowd datasets can be collected or generated from real or synthetic sources. Real data is generated by using infrastructure-based sensors (such as static cameras or other sensors). The use of simulation tools can significantly reduce the time required to generate scenario-specific crowd datasets, facilitate ... | ['Michał Staniszewski', 'Elżbieta Macioszek', 'Dominik Golba', 'Michał Cogiel', 'Bartosz Bizoń', 'Adam Cygan', 'Nicola Messina', 'Luca Ciampi', 'Agnieszka Szczęsna', 'Paweł Foszner'] | 2023-04-26 | null | null | null | null | ['multiple-people-tracking', 'unity'] | ['computer-vision', 'computer-vision'] | [-5.35407484e-01 -3.45997125e-01 5.25728047e-01 9.19713452e-02
8.15126970e-02 -5.28646350e-01 9.82817173e-01 6.47321194e-02
-7.56147146e-01 1.13473856e+00 2.16835588e-01 -1.11169733e-01
3.24466765e-01 -9.23422635e-01 -5.38354576e-01 -4.69279915e-01
-1.25989765e-01 8.36036921e-01 7.55764663e-01 -4.82086271... | [8.247453689575195, -1.1653778553009033] |
e1c51b12-40ed-4b44-90aa-24b2835f0c4f | before-and-after-default-information-and | 2208.07163 | null | https://arxiv.org/abs/2208.07163v2 | https://arxiv.org/pdf/2208.07163v2.pdf | Before and after default: information and optimal portfolio via anticipating calculus | Default risk calculus plays a crucial role in portfolio optimization when the risky asset is under threat of bankruptcy. However, traditional stochastic control techniques are not applicable in this scenario, and additional assumptions are required to obtain the optimal solution in a before-and-after default context. W... | ["Bernardo D'Auria", 'Giulia Di Nunno', 'José A. Salmerón'] | 2022-07-05 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-3.02750133e-02 -7.92810023e-02 3.06707378e-02 4.20760922e-02
-1.55643120e-01 -6.32141590e-01 1.37908652e-01 -1.20432645e-01
-4.20919389e-01 9.59832072e-01 -3.67268324e-02 -7.33712137e-01
-4.75713789e-01 -1.13095582e+00 -1.43869475e-01 -1.05395114e+00
2.54660130e-01 1.37496114e-01 -7.37160025e-03 -6.68213665... | [4.94113302230835, 3.9389216899871826] |
796cf4c2-5740-406a-88dc-639993a560e2 | the-cloud-of-knowing-non-factive-al-ta-aknowa | null | null | https://aclanthology.org/Y16-3026 | https://aclanthology.org/Y16-3026.pdf | The Cloud of Knowing: Non-factive al-ta `know' (as a Neg-raiser) in Korean | null | ['Chungmin Lee', 'Seungjin Hong'] | 2016-10-01 | the-cloud-of-knowing-non-factive-al-ta-know | https://aclanthology.org/Y16-3026 | https://aclanthology.org/Y16-3026.pdf | paclic-2016-10 | ['rumour-detection'] | ['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... | [-7.245980739593506, 3.6938414573669434] |
a7854d2a-fc3e-48e8-aa0c-5f71ec360401 | decanus-to-legatus-synthetic-training-for-2d | 2210.02231 | null | https://arxiv.org/abs/2210.02231v1 | https://arxiv.org/pdf/2210.02231v1.pdf | Decanus to Legatus: Synthetic training for 2D-3D human pose lifting | 3D human pose estimation is a challenging task because of the difficulty to acquire ground-truth data outside of controlled environments. A number of further issues have been hindering progress in building a universal and robust model for this task, including domain gaps between different datasets, unseen actions betwe... | ['David Picard', 'Yue Zhu'] | 2022-10-05 | null | null | null | null | ['3d-pose-estimation', '3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.19215319e-01 2.44487539e-01 3.46683860e-01 -3.45271379e-01
-8.37764740e-01 -5.96616864e-01 4.78148401e-01 -4.70557272e-01
-6.27590477e-01 9.47862089e-01 2.43967742e-01 1.91920012e-01
1.26504585e-01 -2.73172319e-01 -9.26743567e-01 -1.78415790e-01
-1.38061404e-01 1.20126665e+00 3.05547655e-01 -4.71836269... | [6.9679131507873535, -1.0077049732208252] |
33035a88-f265-4599-9554-56e2dcc3c056 | semi-supervised-visual-tracking-of-marine | 2302.07344 | null | https://arxiv.org/abs/2302.07344v1 | https://arxiv.org/pdf/2302.07344v1.pdf | Semi-Supervised Visual Tracking of Marine Animals using Autonomous Underwater Vehicles | In-situ visual observations of marine organisms is crucial to developing behavioural understandings and their relations to their surrounding ecosystem. Typically, these observations are collected via divers, tags, and remotely-operated or human-piloted vehicles. Recently, however, autonomous underwater vehicles equippe... | ['Yogesh Girdhar', 'T. Aran Mooney', 'Roger Hanlon', 'Nathan E. McGuire', 'Levi Cai'] | 2023-02-14 | null | null | null | null | ['visual-tracking'] | ['computer-vision'] | [ 2.07554456e-03 -3.57493460e-01 4.83383417e-01 -3.52752566e-01
-2.30695605e-01 -8.47980797e-01 3.91623914e-01 2.65295655e-01
-1.25682116e+00 7.51367629e-01 -2.46215984e-01 1.35359466e-01
-1.87234916e-02 -6.03289843e-01 -7.88805425e-01 -9.20090437e-01
-7.84582198e-01 5.44360995e-01 9.66481626e-01 -3.37334514... | [8.083379745483398, -1.4233990907669067] |
569528e9-7ab0-403b-a0df-b98cf64484ac | self-supervised-pre-training-for-transformer | 2111.12084 | null | https://arxiv.org/abs/2111.12084v1 | https://arxiv.org/pdf/2111.12084v1.pdf | Self-Supervised Pre-Training for Transformer-Based Person Re-Identification | Transformer-based supervised pre-training achieves great performance in person re-identification (ReID). However, due to the domain gap between ImageNet and ReID datasets, it usually needs a larger pre-training dataset (e.g. ImageNet-21K) to boost the performance because of the strong data fitting ability of the transf... | ['Rong Jin', 'Hao Li', 'Fan Wang', 'Yanxin Zhou', 'Feng Ding', 'Yi Xu', 'Pichao Wang', 'Hao Luo'] | 2021-11-23 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 8.74700025e-02 -2.41345048e-01 1.96166020e-02 -6.96904898e-01
-4.77770656e-01 -2.94196129e-01 6.08724713e-01 -1.39002010e-01
-7.49747515e-01 6.97973847e-01 1.78815544e-01 -3.62808146e-02
-1.49054542e-01 -7.73867190e-01 -7.68090069e-01 -4.66948986e-01
3.78839433e-01 5.72761774e-01 1.20248996e-01 -2.03170702... | [14.781160354614258, 1.0153462886810303] |
1a325582-36c8-43cc-8e89-5cd32fc35ffa | knowledge-transfer-for-melanoma-screening | 1703.07479 | null | http://arxiv.org/abs/1703.07479v1 | http://arxiv.org/pdf/1703.07479v1.pdf | Knowledge Transfer for Melanoma Screening with Deep Learning | Knowledge transfer impacts the performance of deep learning -- the state of
the art for image classification tasks, including automated melanoma screening.
Deep learning's greed for large amounts of training data poses a challenge for
medical tasks, which we can alleviate by recycling knowledge from models
trained on d... | ['Flávia Vasques Bittencourt', 'Ramon Pires', 'Afonso Menegola', 'Sandra Avila', 'Eduardo Valle', 'Michel Fornaciali'] | 2017-03-22 | null | null | null | null | ['skin-cancer-classification'] | ['medical'] | [ 4.98285681e-01 3.26332808e-01 -3.00716162e-01 -2.06764594e-01
-9.53242362e-01 -4.55250710e-01 4.95015174e-01 4.11294959e-02
-8.38042080e-01 9.23491955e-01 2.98630446e-01 -5.66894472e-01
-1.27343029e-01 -6.69855356e-01 -9.34710622e-01 -6.81682467e-01
2.53826112e-01 3.44037563e-01 3.47638547e-01 -2.65717655... | [15.339889526367188, -2.72599458694458] |
2bed9f3d-82e1-447e-bf4d-4dc40094c4a3 | self-attentive-model-for-headline-generation | 1901.07786 | null | http://arxiv.org/abs/1901.07786v1 | http://arxiv.org/pdf/1901.07786v1.pdf | Self-Attentive Model for Headline Generation | Headline generation is a special type of text summarization task. While the
amount of available training data for this task is almost unlimited, it still
remains challenging, as learning to generate headlines for news articles
implies that the model has strong reasoning about natural language. To overcome
this issue, w... | ['Pavel Kalaidin', 'Valentin Malykh', 'Daniil Gavrilov'] | 2019-01-23 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [ 2.00179979e-01 7.64513433e-01 -3.32612813e-01 -2.84741312e-01
-1.45618677e+00 -6.95646584e-01 9.19128716e-01 3.54199767e-01
-3.25312138e-01 1.44970834e+00 1.09329724e+00 -3.15628260e-01
2.13831991e-01 -6.69349372e-01 -8.17792892e-01 -1.54574364e-01
-1.09731205e-01 7.10360050e-01 2.85114139e-01 -8.64940464... | [12.402246475219727, 9.443326950073242] |
e1662d53-738a-4023-b37e-e02bce173907 | tensorformer-normalized-matrix-attention | 2306.15989 | null | https://arxiv.org/abs/2306.15989v1 | https://arxiv.org/pdf/2306.15989v1.pdf | Tensorformer: Normalized Matrix Attention Transformer for High-quality Point Cloud Reconstruction | Surface reconstruction from raw point clouds has been studied for decades in the computer graphics community, which is highly demanded by modeling and rendering applications nowadays. Classic solutions, such as Poisson surface reconstruction, require point normals as extra input to perform reasonable results. Modern tr... | ['Kai Xu', 'Chenyang Zhu', 'Renjiao Yi', 'Zheng Qin', 'Hui Tian'] | 2023-06-28 | null | null | null | null | ['point-cloud-reconstruction'] | ['computer-vision'] | [ 9.19769928e-02 -3.00215751e-01 9.68531594e-02 -3.18338752e-01
-8.95523548e-01 -2.19896361e-01 5.23100138e-01 3.71148109e-01
-3.00226837e-01 3.76025021e-01 1.41500784e-02 -2.94210821e-01
3.14772762e-02 -1.25689542e+00 -1.09245539e+00 -5.95396578e-01
-2.92342268e-02 5.99003077e-01 2.38098100e-01 -1.38805196... | [8.012418746948242, -3.5474042892456055] |
aed6051a-71a1-49a4-8890-7da81ddd659c | a-conceptual-model-for-end-to-end-causal | 2305.16165 | null | https://arxiv.org/abs/2305.16165v1 | https://arxiv.org/pdf/2305.16165v1.pdf | A Conceptual Model for End-to-End Causal Discovery in Knowledge Tracing | In this paper, we take a preliminary step towards solving the problem of causal discovery in knowledge tracing, i.e., finding the underlying causal relationship among different skills from real-world student response data. This problem is important since it can potentially help us understand the causal relationship bet... | ['Andrew Lan', 'Aritra Ghosh', 'Hunter McNichols', 'Jaewook Lee', 'Wanyong Feng', 'Nischal Ashok Kumar'] | 2023-05-11 | null | null | null | null | ['causal-discovery', 'knowledge-tracing'] | ['knowledge-base', 'miscellaneous'] | [ 4.83831495e-01 4.59771305e-01 -3.24510515e-01 -4.73044574e-01
-5.10821760e-01 -7.18438089e-01 5.12993753e-01 2.37688795e-01
-1.94033876e-01 8.33091319e-01 6.91684246e-01 -8.02382708e-01
-1.02523911e+00 -7.92873204e-01 -1.20746648e+00 -3.88087720e-01
-7.14640990e-02 4.97652441e-01 8.80776569e-02 -4.64020133... | [10.05357551574707, 7.201754570007324] |
1b97d25d-e099-48b8-a7bb-f3c959527ceb | a-spatiotemporal-multi-channel-learning | null | null | https://ieeexplore.ieee.org/abstract/document/9106397/ | https://ieeexplore.ieee.org/abstract/document/9106397/ | A Spatiotemporal Multi-Channel Learning Framework for Automatic Modulation Recognition | Automatic modulation recognition (AMR) plays a vital role in modern communication systems. This letter proposes a novel three-stream deep learning framework to extract the features from individual and combined in-phase/quadrature (I/Q) symbols of the modulated data. The proposed framework integrates one-dimensional (1D... | ['Yang Luo', 'Gerard Parr', 'Chunbo Luo', 'Jialang Xu'] | 2020-06-02 | null | null | null | ieee-wireless-communications-letters-2020-6 | ['automatic-modulation-recognition'] | ['time-series'] | [ 5.62775910e-01 -5.81033230e-01 -4.12970424e-01 -2.44971558e-01
-1.03366840e+00 1.81877002e-01 6.56816840e-01 -2.60794640e-01
-5.22236168e-01 8.88025582e-01 4.00023125e-02 -7.92676806e-01
-5.46116292e-01 -6.74401999e-01 -1.29832730e-01 -9.84456837e-01
-7.06278741e-01 -3.83607984e-01 -2.86872000e-01 -2.37769499... | [6.479672908782959, 1.4907599687576294] |
e42f61c1-2ac0-4c49-b156-dfff597e075c | neural-topic-modeling-with-deep-mutual | 2203.06298 | null | https://arxiv.org/abs/2203.06298v1 | https://arxiv.org/pdf/2203.06298v1.pdf | Neural Topic Modeling with Deep Mutual Information Estimation | The emerging neural topic models make topic modeling more easily adaptable and extendable in unsupervised text mining. However, the existing neural topic models is difficult to retain representative information of the documents within the learnt topic representation. In this paper, we propose a neural topic model which... | ['Zheng Zhou', 'Dong Wang', 'Ning Ye', 'Guilin Qi', 'Tongtong Wu', 'Yuan-Fang Li', 'Xiaoqiu Lu', 'Kang Xu'] | 2022-03-12 | null | null | null | null | ['mutual-information-estimation', 'text-clustering', 'topic-models'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [-2.11521581e-01 2.28021711e-01 -2.54031420e-01 -5.22884190e-01
-7.33921111e-01 -1.87038869e-01 7.49183714e-01 -3.86635661e-02
6.98539168e-02 5.50309539e-01 3.60524446e-01 1.93666905e-01
-4.24472243e-01 -1.01892948e+00 -4.29846793e-01 -8.53833735e-01
-2.93063164e-01 5.62364459e-01 -5.91605417e-02 2.10778907... | [10.394994735717773, 6.921274662017822] |
752ca3a8-40f7-4c2f-8367-f0e782b067b4 | a-preference-aware-meta-optimization | 2306.14421 | null | https://arxiv.org/abs/2306.14421v1 | https://arxiv.org/pdf/2306.14421v1.pdf | A Preference-aware Meta-optimization Framework for Personalized Vehicle Energy Consumption Estimation | Vehicle Energy Consumption (VEC) estimation aims to predict the total energy required for a given trip before it starts, which is of great importance to trip planning and transportation sustainability. Existing approaches mainly focus on extracting statistically significant factors from typical trips to improve the VEC... | ['Hao liu', 'Weijia Zhang', 'Siqi Lai'] | 2023-06-26 | null | null | null | null | ['total-energy', 'memorization'] | ['miscellaneous', 'natural-language-processing'] | [-2.20566586e-01 -3.03145468e-01 -8.50401163e-01 -7.21073747e-01
-8.69108975e-01 -2.20504582e-01 2.92188406e-01 2.37397602e-04
-2.84576744e-01 5.68644345e-01 3.43620211e-01 -2.40179852e-01
-3.70643616e-01 -9.03985739e-01 -8.49112630e-01 -8.88630688e-01
2.22799182e-01 4.63612229e-02 -1.81636363e-01 -2.35835239... | [6.226416110992432, 1.8604621887207031] |
3f40446c-7090-4e8d-aa32-9bca1c05f509 | identification-explanation-and-clinical | 2301.08019 | null | https://arxiv.org/abs/2301.08019v1 | https://arxiv.org/pdf/2301.08019v1.pdf | Identification, explanation and clinical evaluation of hospital patient subtypes | We present a pipeline in which unsupervised machine learning techniques are used to automatically identify subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. With the use of state-of-the-art explainability techniques, the identified subtypes are interpreted and assigned clinic... | ['Raul Santos-Rodriguez', 'Christopher J. McWilliams', 'Alexander Hepburn', 'Christopher P. Bourdeaux', 'Michael Ambler', 'Ranjeet S. Bhamber', 'Jeffrey N. Clark', 'Enrico Werner'] | 2023-01-19 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 9.12139267e-02 9.66951013e-01 -1.73846275e-01 -5.38889468e-01
-9.47830200e-01 -4.61179167e-01 4.22205240e-01 1.10263884e+00
-2.86634535e-01 4.57197398e-01 8.81655276e-01 -6.36379659e-01
-9.67788935e-01 -2.23882586e-01 4.65064831e-02 -5.77377319e-01
-8.74271542e-02 1.22747517e+00 -4.73800063e-01 2.35240206... | [8.090123176574707, 6.521927833557129] |
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