paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a7f644f2-84b3-46bf-8b2d-e226661a1de6 | ultra-scalable-spectral-clustering-and | 1903.01057 | null | http://arxiv.org/abs/1903.01057v2 | http://arxiv.org/pdf/1903.01057v2.pdf | Ultra-Scalable Spectral Clustering and Ensemble Clustering | This paper focuses on scalability and robustness of spectral clustering for
extremely large-scale datasets with limited resources. Two novel algorithms are
proposed, namely, ultra-scalable spectral clustering (U-SPEC) and
ultra-scalable ensemble clustering (U-SENC). In U-SPEC, a hybrid representative
selection strategy... | ['Chee-Keong Kwoh', 'Jian-Huang Lai', 'Jian-Sheng Wu', 'Chang-Dong Wang', 'Dong Huang'] | 2019-03-04 | null | null | null | null | ['imagedocument-clustering'] | ['computer-vision'] | [-2.04295307e-01 -4.42875743e-01 -3.12129799e-02 -1.60439879e-01
-9.76105154e-01 -4.92879778e-01 -9.18009356e-02 1.82907823e-02
-4.25402448e-02 4.92711604e-01 1.54256895e-01 -8.04810151e-02
-6.08560205e-01 -8.01162183e-01 -4.98637438e-01 -1.27250934e+00
-2.87445873e-01 5.58039546e-01 3.40322226e-01 2.06232920... | [7.56129264831543, 4.743781089782715] |
d7f02ca6-2d34-43c2-b142-bc17459076bf | knowledge-augmented-language-model-prompting | 2306.04136 | null | https://arxiv.org/abs/2306.04136v1 | https://arxiv.org/pdf/2306.04136v1.pdf | Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering | Large Language Models (LLMs) are capable of performing zero-shot closed-book question answering tasks, based on their internal knowledge stored in parameters during pre-training. However, such internalized knowledge might be insufficient and incorrect, which could lead LLMs to generate factually wrong answers. Furtherm... | ['Amir Saffari', 'Alham Fikri Aji', 'Jinheon Baek'] | 2023-06-07 | null | null | null | null | ['graph-question-answering'] | ['graphs'] | [ 1.10339798e-01 8.77872884e-01 -8.99788737e-02 -1.47988558e-01
-8.32327783e-01 -6.92393482e-01 5.65245032e-01 6.46170437e-01
-5.08161008e-01 6.07543826e-01 2.96430826e-01 -4.55641419e-01
-5.75070642e-02 -1.22332561e+00 -9.50854838e-01 1.28437757e-01
6.09531105e-01 7.77919352e-01 7.56099284e-01 -5.86001158... | [10.90010929107666, 7.9243550300598145] |
9c691dde-1fee-4f3f-a0b3-789dcbd097a8 | most-multiple-object-localization-with-self | 2304.05387 | null | https://arxiv.org/abs/2304.05387v1 | https://arxiv.org/pdf/2304.05387v1.pdf | MOST: Multiple Object localization with Self-supervised Transformers for object discovery | We tackle the challenging task of unsupervised object localization in this work. Recently, transformers trained with self-supervised learning have been shown to exhibit object localization properties without being trained for this task. In this work, we present Multiple Object localization with Self-supervised Transfor... | ['Abhinav Shrivastava', 'Rama Chellappa', 'Ishan Misra', 'Sai Saketh Rambhatla'] | 2023-04-11 | null | null | null | null | ['unsupervised-object-localization', 'object-discovery', 'semi-supervised-object-detection', 'object-localization'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.63592565e-01 1.11614026e-01 -1.12131231e-01 -3.16354394e-01
-1.23712838e+00 -6.67396307e-01 6.47285163e-01 3.72142494e-01
-1.50244296e-01 4.04149115e-01 -3.01641434e-01 2.30678404e-03
1.50776178e-01 -6.28594816e-01 -1.10020494e+00 -7.62541056e-01
-2.59585708e-01 8.64829063e-01 1.18476403e+00 3.65776420... | [9.394859313964844, 0.931625485420227] |
140efed5-e877-411b-9b8e-ab57221def6a | call-for-papers-the-babylm-challenge-sample | 2301.11796 | null | https://arxiv.org/abs/2301.11796v1 | https://arxiv.org/pdf/2301.11796v1.pdf | Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus | We present the call for papers for the BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus. This shared task is intended for participants with an interest in small scale language modeling, human language acquisition, low-resource NLP, and cognitive modeling. In partnership with CoNLL an... | ['Chengxu Zhuang', 'Ethan Wilcox', 'Adina Williams', 'Aaron Mueller', 'Leshem Choshen', 'Alex Warstadt'] | 2023-01-27 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 2.10753262e-01 7.31134534e-01 -3.05340797e-01 -8.49673569e-01
-8.90347660e-01 -8.02517533e-01 6.77995324e-01 4.12712067e-01
-8.04724395e-01 4.57412750e-01 7.07915843e-01 -4.65058625e-01
2.22393215e-01 -4.03637975e-01 -1.03048062e+00 1.99557200e-01
1.41602010e-01 1.14327967e+00 1.42600134e-01 -2.77708292... | [10.870394706726074, 9.15762710571289] |
7181e27d-9dca-4dd1-b894-7a6bf704df10 | convolutional-neural-networks-with-intra | null | null | http://papers.nips.cc/paper/5634-convolutional-neural-networks-with-intra-layer-recurrent-connections-for-scene-labeling | http://papers.nips.cc/paper/5634-convolutional-neural-networks-with-intra-layer-recurrent-connections-for-scene-labeling.pdf | Convolutional Neural Networks with Intra-Layer Recurrent Connections for Scene Labeling | Scene labeling is a challenging computer vision task. It requires the use of both local discriminative features and global context information. We adopt a deep recurrent convolutional neural network (RCNN) for this task, which is originally proposed for object recognition. Different from traditional convolutional neura... | ['Ming Liang', 'Xiaolin Hu', 'Bo Zhang'] | 2015-12-01 | null | null | null | neurips-2015-12 | ['scene-labeling'] | ['computer-vision'] | [ 3.00915211e-01 -4.50235218e-01 -5.26687682e-01 -4.88079160e-01
-6.64061680e-02 -3.01615447e-01 5.22019386e-01 -3.34699243e-01
-5.64042807e-01 3.65966588e-01 3.99243504e-01 -2.87903905e-01
4.04032379e-01 -9.00676727e-01 -3.79362583e-01 -7.07221806e-01
8.40020105e-02 -4.74027872e-01 6.43999994e-01 -7.96960816... | [9.524770736694336, 0.49369657039642334] |
e7dfab64-bfc3-4738-98d6-c9872a5ed9d0 | latent-dirichlet-allocation-based | 1511.05076 | null | http://arxiv.org/abs/1511.05076v1 | http://arxiv.org/pdf/1511.05076v1.pdf | Latent Dirichlet Allocation Based Organisation of Broadcast Media Archives for Deep Neural Network Adaptation | This paper presents a new method for the discovery of latent domains in
diverse speech data, for the use of adaptation of Deep Neural Networks (DNNs)
for Automatic Speech Recognition. Our work focuses on transcription of
multi-genre broadcast media, which is often only categorised broadly in terms
of high level genres ... | ['Raymond W. M. Ng', 'Oscar Saz', 'Mortaza Doulaty', 'Thomas Hain'] | 2015-11-16 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 3.71015146e-02 -4.65238579e-02 -4.42699715e-02 -5.79162776e-01
-1.07917309e+00 -7.05274582e-01 8.28703701e-01 -2.46944562e-01
-1.28961116e-01 7.06278324e-01 8.16694677e-01 -1.02584049e-01
-1.02335833e-01 -4.35650349e-01 -5.94303608e-01 -9.67009544e-01
8.47088024e-02 8.75350893e-01 3.69701952e-01 -8.31362754... | [14.476990699768066, 6.652678489685059] |
808298b7-0437-4f2c-abe5-56ccff74385d | convolutional-mesh-regression-for-single | 1905.03244 | null | https://arxiv.org/abs/1905.03244v1 | https://arxiv.org/pdf/1905.03244v1.pdf | Convolutional Mesh Regression for Single-Image Human Shape Reconstruction | This paper addresses the problem of 3D human pose and shape estimation from a single image. Previous approaches consider a parametric model of the human body, SMPL, and attempt to regress the model parameters that give rise to a mesh consistent with image evidence. This parameter regression has been a very challenging ... | ['Nikos Kolotouros', 'Georgios Pavlakos', 'Kostas Daniilidis'] | 2019-05-08 | convolutional-mesh-regression-for-single-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Kolotouros_Convolutional_Mesh_Regression_for_Single-Image_Human_Shape_Reconstruction_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Kolotouros_Convolutional_Mesh_Regression_for_Single-Image_Human_Shape_Reconstruction_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-human-pose-and-shape-estimation', 'monocular-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-3.70939709e-02 5.09864748e-01 -2.00842977e-01 2.64306273e-02
-6.10767722e-01 -4.75507319e-01 4.06166166e-01 3.87744121e-02
-2.45766416e-01 3.22360456e-01 6.33950755e-02 1.88840091e-01
6.86594844e-02 -6.39570713e-01 -1.08324385e+00 -3.88437718e-01
-1.02123164e-01 1.25989521e+00 3.65421027e-01 -2.38110110... | [7.030370712280273, -1.2561959028244019] |
790d2fe3-b96f-4b22-a3e8-72e970f7c456 | semi-supervised-object-detection-with-object | 2212.02747 | null | https://arxiv.org/abs/2212.02747v1 | https://arxiv.org/pdf/2212.02747v1.pdf | Semi-Supervised Object Detection with Object-wise Contrastive Learning and Regression Uncertainty | Semi-supervised object detection (SSOD) aims to boost detection performance by leveraging extra unlabeled data. The teacher-student framework has been shown to be promising for SSOD, in which a teacher network generates pseudo-labels for unlabeled data to assist the training of a student network. Since the pseudo-label... | ['Tae-Kyun Kim', 'Xuepeng Shi', 'Zhixiang Chen', 'Honggyu Choi'] | 2022-12-06 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 2.98849702e-01 3.71285886e-01 -3.33231121e-01 -7.86049545e-01
-9.74460244e-01 -4.73963857e-01 4.41907704e-01 1.95445135e-01
-5.37112892e-01 4.93175030e-01 -3.19657832e-01 -2.62563564e-02
6.32021502e-02 -4.88453746e-01 -7.77637303e-01 -9.81074035e-01
4.26954538e-01 4.57657218e-01 6.72211111e-01 3.93528819... | [9.199033737182617, 1.3232842683792114] |
8ae572ae-80e3-4ae9-aeaa-2cda858d3671 | biomedical-language-models-are-robust-to-sub | 2306.17649 | null | https://arxiv.org/abs/2306.17649v3 | https://arxiv.org/pdf/2306.17649v3.pdf | Biomedical Language Models are Robust to Sub-optimal Tokenization | As opposed to general English, many concepts in biomedical terminology have been designed in recent history by biomedical professionals with the goal of being precise and concise. This is often achieved by concatenating meaningful biomedical morphemes to create new semantic units. Nevertheless, most modern biomedical l... | ['Yu Su', 'Huan Sun', 'Bernal Jiménez Gutiérrez'] | 2023-06-30 | null | null | null | null | ['entity-linking', 'named-entity-recognition-ner', 'cg'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.94317812e-01 5.56139231e-01 -3.90649766e-01 -2.95396686e-01
-1.17268002e+00 -5.37057817e-01 4.31503594e-01 8.45336974e-01
-9.53407228e-01 1.04484630e+00 3.91122162e-01 -5.34114778e-01
1.12845801e-01 -5.15987992e-01 -5.27282596e-01 -3.41222882e-01
2.73444038e-02 5.75956464e-01 -3.25594872e-01 7.13357106... | [8.537652015686035, 8.692413330078125] |
cc987243-5ed8-480f-b586-ea182c8491d4 | group-equivariant-convolutional-networks | 1602.07576 | null | http://arxiv.org/abs/1602.07576v3 | http://arxiv.org/pdf/1602.07576v3.pdf | Group Equivariant Convolutional Networks | We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a
natural generalization of convolutional neural networks that reduces sample
complexity by exploiting symmetries. G-CNNs use G-convolutions, a new type of
layer that enjoys a substantially higher degree of weight sharing than regular
convolution la... | ['Taco S. Cohen', 'Max Welling'] | 2016-02-24 | null | null | null | null | ['rotated-mnist', 'colorectal-gland-segmentation', 'breast-tumour-classification', 'multi-tissue-nucleus-segmentation'] | ['computer-vision', 'medical', 'medical', 'medical'] | [ 1.22952297e-01 4.68937427e-01 -1.25833958e-01 -5.10736227e-01
1.61905766e-01 -4.83647734e-01 8.05882752e-01 -5.09867668e-01
-7.77097940e-01 3.49759668e-01 4.59369481e-01 -5.25107682e-01
1.28988950e-02 -9.74335015e-01 -1.02861953e+00 -6.20837033e-01
-4.17037994e-01 -3.14242601e-01 -1.51918521e-02 -3.38851064... | [8.915361404418945, 2.3821351528167725] |
65bc88d7-9045-4a1c-b9ed-d29a19e6999a | prod-progressive-distillation-for-dense | 2209.13335 | null | https://arxiv.org/abs/2209.13335v3 | https://arxiv.org/pdf/2209.13335v3.pdf | PROD: Progressive Distillation for Dense Retrieval | Knowledge distillation is an effective way to transfer knowledge from a strong teacher to an efficient student model. Ideally, we expect the better the teacher is, the better the student. However, this expectation does not always come true. It is common that a better teacher model results in a bad student via distillat... | ['Nan Duan', 'Rangan Majumder', 'Daxin Jiang', 'Jingwen Lu', 'Jian Jiao', 'Anlei Dong', 'Chen Lin', 'Hang Zhang', 'Xiao Liu', 'Yeyun Gong', 'Zhenghao Lin'] | 2022-09-27 | null | null | null | null | ['natural-questions'] | ['miscellaneous'] | [-8.90145227e-02 -5.27053066e-02 -1.54117122e-01 -1.83199883e-01
-1.12118161e+00 -7.44709313e-01 5.95857620e-01 3.64059895e-01
-7.61618257e-01 9.20971215e-01 2.40892753e-01 -4.92323488e-01
-4.81797606e-01 -6.37027860e-01 -7.72697806e-01 -7.25513041e-01
1.49548575e-01 8.55912089e-01 5.71829438e-01 -2.68482894... | [11.389006614685059, 7.678753852844238] |
b20403a8-b5e8-48e5-8682-f1e5501732e0 | solving-novel-program-synthesis-problems-with | 2306.04839 | null | https://arxiv.org/abs/2306.04839v1 | https://arxiv.org/pdf/2306.04839v1.pdf | Solving Novel Program Synthesis Problems with Genetic Programming using Parametric Polymorphism | Contemporary genetic programming (GP) systems for general program synthesis have been primarily concerned with evolving programs that can manipulate values from a standard set of primitive data types and simple indexed data structures. In contrast, human programmers do not limit themselves to a small finite set of data... | ['Thomas Helmuth', 'Edward Pantridge'] | 2023-06-08 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 1.95802972e-01 3.25861514e-01 -1.52086467e-01 -2.64453799e-01
-2.28801057e-01 -8.95849943e-01 5.52254856e-01 3.11929315e-01
-5.40879630e-02 7.09494412e-01 -2.77921647e-01 -8.83412361e-01
-1.07073281e-02 -1.43634176e+00 -7.02381670e-01 -3.69281650e-01
-8.01770806e-01 4.07299221e-01 6.35732174e-01 -7.41085887... | [8.054279327392578, 7.291537284851074] |
b16b3fd2-725a-4101-98ab-4959ba726907 | mapping-and-localization-from-planar-markers | 1606.00151 | null | http://arxiv.org/abs/1606.00151v2 | http://arxiv.org/pdf/1606.00151v2.pdf | Mapping and Localization from Planar Markers | Squared planar markers are a popular tool for fast, accurate and robust
camera localization, but its use is frequently limited to a single marker, or
at most, to a small set of them for which their relative pose is known
beforehand. Mapping and localization from a large set of planar markers is yet
a scarcely treated p... | ['Rafael Medina-Carnicer', 'Enrique Yeguas-Bolivar', 'Manuel J. Marín-Jimenez', 'Rafael Muñoz-Salinas'] | 2016-06-01 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [ 1.82256073e-01 -3.63085926e-01 -7.03291371e-02 -6.33600056e-02
-7.39074409e-01 -7.55339026e-01 6.61391139e-01 4.50592488e-01
-4.87162262e-01 7.22397089e-01 -4.35966253e-01 1.71946421e-01
-6.54796362e-02 -4.72624302e-01 -8.11390519e-01 -5.49587429e-01
1.50774017e-01 7.44882524e-01 4.37976420e-01 3.93541791... | [7.831070899963379, -2.2698867321014404] |
6d0c02b6-6d90-4111-bac1-8aa719bb4304 | dual-attention-network-for-scene-segmentation | 1809.02983 | null | http://arxiv.org/abs/1809.02983v4 | http://arxiv.org/pdf/1809.02983v4.pdf | Dual Attention Network for Scene Segmentation | In this paper, we address the scene segmentation task by capturing rich
contextual dependencies based on the selfattention mechanism. Unlike previous
works that capture contexts by multi-scale features fusion, we propose a Dual
Attention Networks (DANet) to adaptively integrate local features with their
global dependen... | ['Zhiwei Fang', 'Yongjun Bao', 'Jing Liu', 'Hanqing Lu', 'Haijie Tian', 'Yong Li', 'Jun Fu'] | 2018-09-09 | dual-attention-network-for-scene-segmentation-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Fu_Dual_Attention_Network_for_Scene_Segmentation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Fu_Dual_Attention_Network_for_Scene_Segmentation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['thermal-image-segmentation'] | ['computer-vision'] | [ 9.80414897e-02 -1.84945107e-01 5.45598902e-02 -5.97381949e-01
-7.28407741e-01 -5.21357834e-01 3.72727692e-01 3.02181691e-01
-6.02519572e-01 5.47927260e-01 2.36812219e-01 7.77797550e-02
-4.26171571e-02 -7.72774398e-01 -7.42723048e-01 -7.82812476e-01
-1.19213352e-03 3.37517969e-02 6.21896684e-01 -2.31674314... | [9.568659782409668, 0.2673945724964142] |
c9494118-0f37-46d6-bd66-7b54e05e7a95 | ecg-tcn-wearable-cardiac-arrhythmia-detection | 2103.1374 | null | https://arxiv.org/abs/2103.13740v2 | https://arxiv.org/pdf/2103.13740v2.pdf | ECG-TCN: Wearable Cardiac Arrhythmia Detection with a Temporal Convolutional Network | Personalized ubiquitous healthcare solutions require energy-efficient wearable platforms that provide an accurate classification of bio-signals while consuming low average power for long-term battery-operated use. Single lead electrocardiogram (ECG) signals provide the ability to detect, classify, and even predict card... | ['Luca Benini', 'Lukas Cavigelli', 'Alessio Burrello', 'Michael Hersche', 'Xiaying Wang', 'Thorir Mar Ingolfsson'] | 2021-03-25 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [ 9.58823636e-02 -2.25645915e-01 -2.31254861e-01 -3.37268889e-01
-3.05015624e-01 -1.30846754e-01 -4.19612467e-01 1.83704615e-01
-5.96581221e-01 6.09582365e-01 -2.87809372e-01 -6.82606816e-01
8.17441866e-02 -7.66947567e-01 -4.42304224e-01 -5.11881530e-01
-4.40789431e-01 -3.00287753e-01 -2.28955805e-01 2.47606099... | [13.999740600585938, 3.1368298530578613] |
78e91f83-908c-4a85-8554-7c6fd33a4880 | privacy-enabled-biometric-search | 1708.04726 | null | http://arxiv.org/abs/1708.04726v1 | http://arxiv.org/pdf/1708.04726v1.pdf | Privacy-Enabled Biometric Search | Biometrics have a long-held hope of replacing passwords by establishing a
non-repudiated identity and providing authentication with convenience.
Convenience drives consumers toward biometrics-based access management
solutions. Unlike passwords, biometrics cannot be script-injected; however,
biometric data is considered... | ['Scott Streit', 'Stephen Suffian', 'Brian Streit'] | 2017-08-16 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 3.50005537e-01 -1.63132519e-01 -1.64969668e-01 -4.97194320e-01
-4.09288138e-01 -1.11338866e+00 3.07855248e-01 3.00284952e-01
-8.54043245e-01 6.44466579e-01 -3.71002406e-01 -6.41896486e-01
-6.83539137e-02 -9.97027755e-01 -1.94843173e-01 -7.20651269e-01
1.52055144e-01 1.37534693e-01 -2.77563661e-01 -2.43891496... | [13.12362289428711, 1.2147568464279175] |
96fdb2e2-5c79-4c2a-9ddf-3b6d4e5574a2 | neural-parameter-calibration-for-large-scale | 2209.13565 | null | https://arxiv.org/abs/2209.13565v3 | https://arxiv.org/pdf/2209.13565v3.pdf | Neural parameter calibration for large-scale multi-agent models | Computational models have become a powerful tool in the quantitative sciences to understand the behaviour of complex systems that evolve in time. However, they often contain a potentially large number of free parameters whose values cannot be obtained from theory but need to be inferred from data. This is especially th... | ['Mark Girolami', 'Grigorios A. Pavliotis', 'Thomas Gaskin'] | 2022-09-27 | null | null | null | null | ['epidemiology'] | ['medical'] | [-2.45704744e-02 -1.16389796e-01 2.08214894e-01 1.67192891e-01
-4.19004947e-01 -5.44457018e-01 6.95298672e-01 8.23414996e-02
-7.44426191e-01 1.00075769e+00 -3.10591180e-02 -6.60926700e-01
-4.68663931e-01 -8.31543863e-01 -4.57612544e-01 -7.98575222e-01
-5.20490885e-01 1.08103561e+00 4.58343215e-02 -3.45478743... | [6.064451217651367, 4.335069179534912] |
aab1645b-b1ba-441a-aadd-f519272e6997 | syntax-representation-in-word-embeddings-and | 2010.01063 | null | https://arxiv.org/abs/2010.01063v1 | https://arxiv.org/pdf/2010.01063v1.pdf | Syntax Representation in Word Embeddings and Neural Networks -- A Survey | Neural networks trained on natural language processing tasks capture syntax even though it is not provided as a supervision signal. This indicates that syntactic analysis is essential to the understating of language in artificial intelligence systems. This overview paper covers approaches of evaluating the amount of sy... | ['David Mareček', 'Tomasz Limisiewicz'] | 2020-10-02 | null | null | null | null | ['syntax-representation'] | ['natural-language-processing'] | [-2.12881733e-02 3.25644672e-01 -7.62268901e-01 -7.48226702e-01
-4.68938291e-01 -6.70427620e-01 7.66557753e-01 6.33822978e-02
-8.65337610e-01 7.79695332e-01 5.91677606e-01 -1.03219688e+00
2.99015135e-01 -5.44904411e-01 -7.10811675e-01 -5.75614609e-02
1.25730708e-01 6.82657778e-01 -4.60641503e-01 -5.28387845... | [11.000838279724121, 9.785962104797363] |
90031198-f047-4b8d-98b1-5f6132ad7a74 | translate-to-adapt-rgb-d-scene-recognition | 2103.14672 | null | https://arxiv.org/abs/2103.14672v2 | https://arxiv.org/pdf/2103.14672v2.pdf | Multi-Modal RGB-D Scene Recognition Across Domains | Scene recognition is one of the basic problems in computer vision research with extensive applications in robotics. When available, depth images provide helpful geometric cues that complement the RGB texture information and help to identify discriminative scene image features. Depth sensing technology developed fast in... | ['Tatiana Tommasi', 'Silvia Bucci', 'Andrea Ferreri'] | 2021-03-26 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 6.88819051e-01 -3.52541596e-01 -1.85802966e-01 -4.48403120e-01
-4.91464913e-01 -6.73539579e-01 7.14180291e-01 5.81643954e-02
-4.78710502e-01 4.24016148e-01 -1.50185466e-01 2.85571311e-02
-3.30960602e-01 -7.22479880e-01 -4.89226699e-01 -1.06213140e+00
4.70159888e-01 2.91613609e-01 3.55724961e-01 -4.20081541... | [8.290055274963379, -2.3077173233032227] |
982993e9-9a9a-48ec-9320-1e40c712c5b0 | supervised-contrastive-learning-for-accented | 2107.00921 | null | https://arxiv.org/abs/2107.00921v1 | https://arxiv.org/pdf/2107.00921v1.pdf | Supervised Contrastive Learning for Accented Speech Recognition | Neural network based speech recognition systems suffer from performance degradation due to accented speech, especially unfamiliar accents. In this paper, we study the supervised contrastive learning framework for accented speech recognition. To build different views (similar "positive" data samples) for contrastive lea... | ['Wei Han', 'Ziang Yang', 'Hantao Huang', 'Tao Han'] | 2021-07-02 | null | null | null | null | ['accented-speech-recognition'] | ['speech'] | [ 2.49061942e-01 2.80104112e-02 -9.01342705e-02 -5.47883093e-01
-1.09624648e+00 -2.27584615e-01 7.37035513e-01 -3.55068147e-01
-5.05907178e-01 7.81555533e-01 6.42964542e-01 -2.81145424e-01
2.44600743e-01 -1.42889455e-01 -3.48652750e-01 -7.90376306e-01
8.87175649e-02 1.83555916e-01 -1.26600310e-01 -3.80598158... | [14.510257720947266, 6.474651336669922] |
cd2b3ce0-49f4-4472-b7d1-dbb96c4a1e21 | approaches-and-applications-of-early | 2005.02595 | null | https://arxiv.org/abs/2005.02595v2 | https://arxiv.org/pdf/2005.02595v2.pdf | Approaches and Applications of Early Classification of Time Series: A Review | Early classification of time series has been extensively studied for minimizing class prediction delay in time-sensitive applications such as healthcare and finance. A primary task of an early classification approach is to classify an incomplete time series as soon as possible with some desired level of accuracy. Recen... | ['Tanima Dutta', 'Hari Prabhat Gupta', 'Bhaskar Biswas', 'Ashish Gupta'] | 2020-05-06 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [ 4.63623106e-01 -6.09588504e-01 -3.52404773e-01 -4.01763678e-01
-3.29216719e-01 -3.11689794e-01 1.95148349e-01 5.73122561e-01
-5.26872836e-02 5.70589304e-01 -6.17123425e-01 -2.11693943e-01
-5.92570543e-01 -5.96094310e-01 3.41666453e-02 -9.47400331e-01
-4.79636610e-01 1.38665885e-01 1.62596568e-01 -8.56727883... | [7.22817850112915, 3.137441396713257] |
d5a96f10-500f-4a2d-a2a4-71c35841b0d7 | time-aware-relational-graph-attention-network | null | null | https://openreview.net/forum?id=ShtJLsF7cbb | https://openreview.net/pdf?id=ShtJLsF7cbb | Time-aware Relational Graph Attention Network for Temporal Knowledge Graph Embeddings | Embedding-based representation learning approaches for knowledge graphs (KGs) have been mostly designed for static data. However, many KGs involve temporal data, which creates the need for new representation learning approaches that can characterize and reason over time. In this work, we propose a Time-aware Relational... | ['Jens Lehmann', 'Fenglong Su', 'Chengjin Xu'] | 2021-09-29 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-5.49313903e-01 9.03970152e-02 -5.09081066e-01 -4.05886352e-01
-8.48551467e-03 -4.48022842e-01 6.69130385e-01 8.00811708e-01
-3.48317236e-01 3.30268770e-01 4.62549508e-01 -3.53338659e-01
-5.35284162e-01 -1.23999918e+00 -5.47489703e-01 -4.94135261e-01
-6.15438104e-01 5.70450068e-01 2.50365615e-01 -2.20950216... | [8.578947067260742, 7.880882740020752] |
030072e4-5b0f-486f-b275-0581ad89a2a6 | graphonomy-universal-image-parsing-via-graph | 2101.1062 | null | https://arxiv.org/abs/2101.10620v1 | https://arxiv.org/pdf/2101.10620v1.pdf | Graphonomy: Universal Image Parsing via Graph Reasoning and Transfer | Prior highly-tuned image parsing models are usually studied in a certain domain with a specific set of semantic labels and can hardly be adapted into other scenarios (e.g., sharing discrepant label granularity) without extensive re-training. Learning a single universal parsing model by unifying label annotations from d... | ['Xiaodan Liang', 'Meng Wang', 'Ke Gong', 'Yiming Gao', 'Liang Lin'] | 2021-01-26 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 3.78938138e-01 3.89851213e-01 -3.90458345e-01 -6.24570310e-01
-5.93408048e-01 -9.38217998e-01 6.69746101e-01 3.29698354e-01
-1.56578273e-01 6.24277055e-01 6.86054602e-02 -1.53828725e-01
-2.93390274e-01 -1.18655562e+00 -7.74819613e-01 -5.82732677e-01
2.11107641e-01 6.86435282e-01 4.41517234e-01 4.43311296... | [9.855098724365234, 1.3299022912979126] |
5404a666-8e9b-49c9-a56c-24be1af00650 | towards-safe-continuing-task-reinforcement | 2102.12585 | null | https://arxiv.org/abs/2102.12585v1 | https://arxiv.org/pdf/2102.12585v1.pdf | Towards Safe Continuing Task Reinforcement Learning | Safety is a critical feature of controller design for physical systems. When designing control policies, several approaches to guarantee this aspect of autonomy have been proposed, such as robust controllers or control barrier functions. However, these solutions strongly rely on the model of the system being available ... | ['Santiago Paternain', 'Luiz F. O. Chamon', 'Miguel Calvo-Fullana'] | 2021-02-24 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 2.42197827e-01 5.08664608e-01 -3.43554646e-01 1.65794522e-01
-3.90787929e-01 -6.56568170e-01 6.21104598e-01 2.74420977e-01
-4.97165143e-01 1.15437222e+00 -4.29777056e-01 -4.89237130e-01
-5.78115880e-01 -5.34268260e-01 -7.39589214e-01 -9.53298986e-01
-3.24061990e-01 1.31499857e-01 5.04585028e-01 -3.43125761... | [4.740832805633545, 2.093233346939087] |
fbb2d805-e633-4136-991e-93c305bc3b19 | natural-logic-guided-autoregressive-multi-hop | 2212.05276 | null | https://arxiv.org/abs/2212.05276v1 | https://arxiv.org/pdf/2212.05276v1.pdf | Natural Logic-guided Autoregressive Multi-hop Document Retrieval for Fact Verification | A key component of fact verification is thevevidence retrieval, often from multiple documents. Recent approaches use dense representations and condition the retrieval of each document on the previously retrieved ones. The latter step is performed over all the documents in the collection, requiring storing their dense r... | ['Andreas Vlachos', 'Rami Aly'] | 2022-12-10 | null | null | null | null | ['fact-verification'] | ['natural-language-processing'] | [ 2.88028777e-01 -5.92755601e-02 -3.78748536e-01 -8.21438897e-03
-1.29092276e+00 -8.23997974e-01 9.23955083e-01 6.29511833e-01
-4.31903571e-01 8.62907648e-01 -6.44963905e-02 -4.15465295e-01
-4.60275054e-01 -1.02992666e+00 -8.18951786e-01 -5.34986198e-01
-7.24350214e-02 6.70915902e-01 5.25237620e-01 -2.34858155... | [11.49457836151123, 7.623379707336426] |
fa82566c-5a7d-4f73-b2c9-6dda99e087c1 | conjectures-tests-and-proofs-an-overview-of | 2109.03721 | null | https://arxiv.org/abs/2109.03721v1 | https://arxiv.org/pdf/2109.03721v1.pdf | Conjectures, Tests and Proofs: An Overview of Theory Exploration | A key component of mathematical reasoning is the ability to formulate interesting conjectures about a problem domain at hand. In this paper, we give a brief overview of a theory exploration system called QuickSpec, which is able to automatically discover interesting conjectures about a given set of functions. QuickSpec... | ['Nicholas Smallbone', 'Moa Johansson'] | 2021-09-07 | null | null | null | null | ['automated-theorem-proving', 'mathematical-reasoning', 'automated-theorem-proving'] | ['miscellaneous', 'natural-language-processing', 'reasoning'] | [ 2.70865589e-01 6.15960956e-01 -1.28696173e-01 9.81941074e-02
-6.56500638e-01 -8.60469699e-01 4.38069016e-01 2.86650509e-01
5.36923468e-01 9.55397606e-01 -3.37118477e-01 -1.40306020e+00
-2.17514619e-01 -1.07815790e+00 -8.92936587e-01 2.11380459e-02
-7.00245619e-01 6.04996085e-01 4.59974408e-01 -2.16274545... | [8.817025184631348, 6.894043445587158] |
c77d849a-c984-472c-9653-67f50c88f246 | synthetic-demographic-data-generation-for | 2306.17109 | null | https://arxiv.org/abs/2306.17109v1 | https://arxiv.org/pdf/2306.17109v1.pdf | Synthetic Demographic Data Generation for Card Fraud Detection Using GANs | Using machine learning models to generate synthetic data has become common in many fields. Technology to generate synthetic transactions that can be used to detect fraud is also growing fast. Generally, this synthetic data contains only information about the transaction, such as the time, place, and amount of money. It... | ['John Hawkin', 'Charles Robertson', 'Xianta Jiang', 'Terrence Tricco', 'Shuo Wang'] | 2023-06-29 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation', 'fraud-detection'] | ['medical', 'miscellaneous', 'miscellaneous'] | [-2.39588302e-02 1.70332775e-01 -4.55354387e-03 -4.92112607e-01
-2.56638110e-01 -1.17539197e-01 5.41027784e-01 3.24782342e-01
-3.19818437e-01 1.03747702e+00 4.73950319e-02 -1.03410870e-01
7.57924497e-01 -1.61998057e+00 -7.75580287e-01 -5.28237164e-01
1.26238927e-01 7.80127525e-01 -2.74488717e-01 -5.42096198... | [8.852909088134766, 4.281287670135498] |
efc1889e-d704-4173-b794-f04546378e35 | defx-at-semeval-2020-task-6-joint-extraction | 2103.1709 | null | https://arxiv.org/abs/2103.17090v1 | https://arxiv.org/pdf/2103.17090v1.pdf | Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction | Definition Extraction systems are a valuable knowledge source for both humans and algorithms. In this paper we describe our submissions to the DeftEval shared task (SemEval-2020 Task 6), which is evaluated on an English textbook corpus. We provide a detailed explanation of our system for the joint extraction of definit... | ['Leonhard Hennig', 'Robert Schwarzenberg', 'Christoph Alt', 'Marc Hübner'] | 2021-03-31 | null | https://aclanthology.org/2020.semeval-1.92 | https://aclanthology.org/2020.semeval-1.92.pdf | semeval-2020 | ['definition-extraction'] | ['natural-language-processing'] | [ 2.87693232e-01 4.56296772e-01 -1.69834509e-01 -4.56604511e-01
-7.48502314e-01 -8.72305751e-01 1.10187149e+00 2.57925332e-01
-8.52322340e-01 1.18860447e+00 1.16886802e-01 -5.17062783e-01
-3.96937281e-01 -6.76156461e-01 -2.35304818e-01 1.44895211e-01
1.52431488e-01 8.55523944e-01 3.58637601e-01 -4.80431437... | [9.786608695983887, 8.939358711242676] |
0397bec6-7ae3-4e93-b3d5-ed80d22bb51e | breaking-the-limit-of-graph-neural-networks | 2106.06586 | null | https://arxiv.org/abs/2106.06586v1 | https://arxiv.org/pdf/2106.06586v1.pdf | Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns | Graph neural networks (GNNs) have achieved tremendous success on multiple graph-based learning tasks by fusing network structure and node features. Modern GNN models are built upon iterative aggregation of neighbor's/proximity features by message passing. Its prediction performance has been shown to be strongly bounded... | ['Jianzhu Ma', 'Pan Li', 'Jennifer Neville', 'Vinith Budde', 'Susheel Suresh'] | 2021-06-11 | null | null | null | null | ['node-classification-on-non-homophilic'] | ['graphs'] | [ 7.86781460e-02 -4.54562604e-02 -4.38799530e-01 -2.52774805e-01
-3.99498828e-02 -8.06183875e-01 7.27695107e-01 7.24496067e-01
-1.85148433e-01 5.19067466e-01 -5.70615791e-02 -3.22775304e-01
-6.56947911e-01 -1.37887478e+00 -7.11614668e-01 -9.12029564e-01
-5.47109842e-01 5.13726532e-01 1.69894025e-01 -2.72908986... | [6.929956912994385, 6.098354816436768] |
ff5ff21d-1719-4514-a5a5-ca26e90a81b5 | optimal-transport-based-identity-matching-for | 2209.12172 | null | https://arxiv.org/abs/2209.12172v1 | https://arxiv.org/pdf/2209.12172v1.pdf | Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition | Identity-invariant facial expression recognition (FER) has been one of the challenging computer vision tasks. Since conventional FER schemes do not explicitly address the inter-identity variation of facial expressions, their neural network models still operate depending on facial identity. This paper proposes to quanti... | ['Byung Cheol Song', 'Daeha Kim'] | 2022-09-25 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [-5.02487533e-02 -2.80484110e-01 1.25818828e-03 -6.25554085e-01
-5.07622421e-01 -5.00298321e-01 2.49871537e-01 -4.59309578e-01
-2.66676396e-01 4.96980071e-01 -2.78705388e-01 1.58791646e-01
-2.88942695e-01 -6.02840960e-01 -4.97905314e-01 -7.20068097e-01
-3.75219993e-02 1.53613552e-01 -2.00505912e-01 -3.25459033... | [13.615278244018555, 1.5245046615600586] |
a5dde0da-bb15-4b62-bc1c-437647a975da | localizing-moments-in-long-video-via | 2302.13372 | null | https://arxiv.org/abs/2302.13372v1 | https://arxiv.org/pdf/2302.13372v1.pdf | Localizing Moments in Long Video Via Multimodal Guidance | The recent introduction of the large-scale long-form MAD dataset for language grounding in videos has enabled researchers to investigate the performance of current state-of-the-art methods in the long-form setup, with unexpected findings. In fact, current grounding methods alone fail at tackling this challenging task a... | ['Bernard Ghanem', 'Alberto Mario Ceballos-Arroyo', 'Fabian Caba Heilbron', 'Mattia Soldan', 'Wayner Barrios'] | 2023-02-26 | null | null | null | null | ['video-grounding', 'video-understanding', 'natural-language-moment-retrieval', 'natural-language-visual-grounding'] | ['computer-vision', 'computer-vision', 'computer-vision', 'reasoning'] | [ 2.93767810e-01 1.64126232e-01 -8.55618268e-02 -1.37933373e-01
-1.13280177e+00 -6.35682821e-01 6.52496934e-01 6.71912357e-02
-4.68814433e-01 3.87474656e-01 4.67329949e-01 -2.39546597e-01
1.13272421e-01 -3.46935838e-01 -8.68100762e-01 -3.22302371e-01
-4.48762700e-02 4.74287570e-01 4.52849060e-01 -3.66355062... | [10.249101638793945, 0.7987715601921082] |
76eaa639-44b6-4672-a9ed-a0b70d29ad1c | improve-document-embedding-for-text | 2006.00572 | null | https://arxiv.org/abs/2006.00572v1 | https://arxiv.org/pdf/2006.00572v1.pdf | Improve Document Embedding for Text Categorization Through Deep Siamese Neural Network | Due to the increasing amount of data on the internet, finding a highly-informative, low-dimensional representation for text is one of the main challenges for efficient natural language processing tasks including text classification. This representation should capture the semantic information of the text while retaining... | ['Hadi Veisi', 'Erfaneh Gharavi'] | 2020-05-31 | null | null | null | null | ['document-embedding', 'text-categorization'] | ['methodology', 'natural-language-processing'] | [-0.06163628 -0.09410255 -0.26591522 -0.62888914 -0.8051893 -0.16521281
0.95697916 0.64822304 -0.4282201 0.39360455 0.88595223 -0.01655727
-0.35017645 -0.66912806 -0.35810277 -0.45762295 0.05968419 0.8041328
0.18420334 -0.15960604 0.7606781 0.36496252 -1.6340264 0.73077357
0.64264065 1.1831114 0.2... | [10.470613479614258, 6.925899982452393] |
dd11fc49-51dc-458f-b594-0574c93fee4d | machine-learning-based-classification-of-3 | 2212.04684 | null | https://arxiv.org/abs/2212.04684v1 | https://arxiv.org/pdf/2212.04684v1.pdf | Machine Learning-based Classification of Birds through Birdsong | Audio sound recognition and classification is used for many tasks and applications including human voice recognition, music recognition and audio tagging. In this paper we apply Mel Frequency Cepstral Coefficients (MFCC) in combination with a range of machine learning models to identify (Australian) birds from publicly... | ['Richard O. Sinnott', 'Yueying Chang'] | 2022-12-09 | null | null | null | null | ['audio-tagging'] | ['audio'] | [-3.73402763e-05 -6.63486123e-01 1.34238794e-01 -2.18101680e-01
-6.88220024e-01 -7.48643339e-01 1.98776871e-01 2.27058813e-01
-6.56025887e-01 3.56644124e-01 4.68277693e-01 1.40688438e-02
1.55867487e-02 -2.98031151e-01 6.72946274e-02 -3.64363194e-01
-5.69765329e-01 -7.41103012e-03 2.62879521e-01 -2.68084943... | [15.285284996032715, 5.309927463531494] |
09205b87-4c49-4da9-aba9-446021dafa33 | are-semantically-coherent-topic-models-useful | null | null | https://aclanthology.org/P13-2027 | https://aclanthology.org/P13-2027.pdf | Are Semantically Coherent Topic Models Useful for Ad Hoc Information Retrieval? | null | ['Eric SanJuan', 'Romain Deveaud', 'Patrice Bellot'] | 2013-08-01 | null | null | null | acl-2013-8 | ['ad-hoc-information-retrieval'] | ['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... | [-7.294112205505371, 3.5921502113342285] |
0796eef3-46d9-4463-a3be-21912abf5af6 | borch-a-deep-universal-probabilistic | 2209.06168 | null | https://arxiv.org/abs/2209.06168v1 | https://arxiv.org/pdf/2209.06168v1.pdf | Borch: A Deep Universal Probabilistic Programming Language | Ever since the Multilayered Perceptron was first introduced the connectionist community has struggled with the concept of uncertainty and how this could be represented in these types of models. This past decade has seen a lot of effort in trying to join the principled approach of probabilistic modeling with the scalabl... | ['Michael Green', 'Johan Gudmundsson', 'Lewis Belcher'] | 2022-09-13 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [-3.33999723e-01 4.88747030e-01 2.40461789e-02 -7.66469896e-01
-6.07910216e-01 -3.68724525e-01 6.49926066e-01 1.81141242e-01
-4.46623504e-01 6.59815133e-01 3.25980693e-01 -6.12195075e-01
-3.13548207e-01 -7.20492125e-01 -6.45407557e-01 -5.76016963e-01
-1.49414688e-01 8.26107204e-01 1.56791151e-01 2.98514545... | [7.267086029052734, 3.873002529144287] |
abcb4735-b5a1-403b-91fe-bf289b2a1d0f | learning-to-distill-the-essence-vector | 1611.07206 | null | http://arxiv.org/abs/1611.07206v1 | http://arxiv.org/pdf/1611.07206v1.pdf | Learning to Distill: The Essence Vector Modeling Framework | In the context of natural language processing, representation learning has
emerged as a newly active research subject because of its excellent performance
in many applications. Learning representations of words is a pioneering study
in this school of research. However, paragraph (or sentence and document)
embedding lea... | ['Hsin-Min Wang', 'Shih-Hung Liu', 'Kuan-Yu Chen', 'Berlin Chen'] | 2016-11-22 | learning-to-distill-the-essence-vector-2 | https://aclanthology.org/C16-1035 | https://aclanthology.org/C16-1035.pdf | coling-2016-12 | ['document-embedding'] | ['methodology'] | [ 3.67455840e-01 2.00507417e-01 -2.41693631e-01 -3.76818240e-01
-5.84234715e-01 -1.74254715e-01 7.68290699e-01 6.28600180e-01
-2.89076179e-01 5.74522376e-01 7.69967735e-01 -5.75364009e-02
7.56860599e-02 -8.05235445e-01 -4.09216553e-01 -7.58566499e-01
4.93698925e-01 -1.97378874e-01 -1.68626994e-01 -2.56261438... | [11.2532377243042, 8.84883975982666] |
0d0bce0c-0722-43f6-97c5-4231d5a90cb0 | carl-g-clustering-accelerated-representation | 2306.06936 | null | https://arxiv.org/abs/2306.06936v1 | https://arxiv.org/pdf/2306.06936v1.pdf | CARL-G: Clustering-Accelerated Representation Learning on Graphs | Self-supervised learning on graphs has made large strides in achieving great performance in various downstream tasks. However, many state-of-the-art methods suffer from a number of impediments, which prevent them from realizing their full potential. For instance, contrastive methods typically require negative sampling,... | ['Evangelos E. Papalexakis', 'Neil Shah', 'Tong Zhao', 'Yozen Liu', 'Uday Singh Saini', 'William Shiao'] | 2023-06-12 | null | null | null | null | ['clustering', 'graph-representation-learning'] | ['methodology', 'methodology'] | [ 2.37419471e-01 1.27322808e-01 -6.27558112e-01 -3.52870107e-01
-8.34619224e-01 -5.81897259e-01 6.11305892e-01 8.44392002e-01
-2.69701153e-01 3.37513894e-01 -7.55298045e-03 -3.58885705e-01
-4.85520571e-01 -8.89684618e-01 -5.59911788e-01 -7.88566768e-01
-5.12338340e-01 7.01786339e-01 2.47086072e-03 -9.68230739... | [7.119868755340576, 6.088598728179932] |
747739fc-27b3-49b2-838f-c7f2f9770065 | antifragile-and-robust-heteroscedastic | null | null | https://openreview.net/forum?id=B1lTqgSFDH | https://openreview.net/pdf?id=B1lTqgSFDH | Antifragile and Robust Heteroscedastic Bayesian Optimisation | Bayesian Optimisation is an important decision-making tool for high-stakes applications in drug discovery and materials design. An oft-overlooked modelling consideration however is the representation of input-dependent or heteroscedastic aleatoric uncertainty. The cost of misrepresenting this uncertainty as being h... | ['Alpha A. Lee', 'Alexander A. Aldrick', 'Miguel Garcia-Ortegon', 'Ryan Rhys-Griffiths'] | 2019-09-25 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 5.38674653e-01 2.39074066e-01 2.21094772e-01 -2.06809893e-01
-9.51388299e-01 -5.21138370e-01 8.39748025e-01 1.97464183e-01
-6.60385251e-01 1.03488934e+00 1.14366077e-01 -6.87392235e-01
-9.19951618e-01 -5.08785546e-01 -6.68729961e-01 -1.05576003e+00
2.64268190e-01 9.25441027e-01 -1.07517382e-02 3.98185879... | [6.232745170593262, 3.7715375423431396] |
02d21bd2-cfaa-4b30-85e9-2d9a074b1e04 | learn-what-not-to-learn-action-elimination | 1809.02121 | null | http://arxiv.org/abs/1809.02121v3 | http://arxiv.org/pdf/1809.02121v3.pdf | Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning | Learning how to act when there are many available actions in each state is a
challenging task for Reinforcement Learning (RL) agents, especially when many
of the actions are redundant or irrelevant. In such cases, it is sometimes
easier to learn which actions not to take. In this work, we propose the
Action-Elimination... | ['Matan Haroush', 'Daniel J. Mankowitz', 'Tom Zahavy', 'Shie Mannor', 'Nadav Merlis'] | 2018-09-06 | learn-what-not-to-learn-action-elimination-1 | http://papers.nips.cc/paper/7615-learn-what-not-to-learn-action-elimination-with-deep-reinforcement-learning | http://papers.nips.cc/paper/7615-learn-what-not-to-learn-action-elimination-with-deep-reinforcement-learning.pdf | neurips-2018-12 | ['text-based-games'] | ['playing-games'] | [ 1.36475116e-01 9.98681486e-02 -1.48452669e-01 -4.48233262e-03
-3.87342453e-01 -4.50640261e-01 4.91309226e-01 -1.11062646e-01
-8.88995111e-01 1.33584392e+00 2.58745790e-01 -4.00429845e-01
-1.99014381e-01 -8.42678070e-01 -6.98832273e-01 -7.70512044e-01
-1.85955837e-01 5.61835229e-01 2.78156906e-01 -6.58368886... | [3.8863189220428467, 1.7092944383621216] |
790a8e83-4c53-489c-bb36-84fe32a4ea36 | tasnet-surpassing-ideal-time-frequency | 1809.07454 | null | https://arxiv.org/abs/1809.07454v3 | https://arxiv.org/pdf/1809.07454v3.pdf | Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation | Single-channel, speaker-independent speech separation methods have recently seen great progress. However, the accuracy, latency, and computational cost of such methods remain insufficient. The majority of the previous methods have formulated the separation problem through the time-frequency representation of the mixed ... | ['Nima Mesgarani', 'Yi Luo'] | 2018-09-20 | null | null | null | null | ['music-source-separation', 'speaker-separation'] | ['music', 'speech'] | [ 7.15674758e-02 -4.52225059e-01 1.85515121e-01 -3.11424106e-01
-1.01260078e+00 -5.15229344e-01 1.80383772e-01 -1.44874871e-01
-3.26228797e-01 3.35404724e-01 1.76615015e-01 -2.51869828e-01
-2.76951462e-01 -1.28627583e-01 -1.72172412e-01 -9.18846250e-01
-2.69174933e-01 -1.70318171e-01 1.61342680e-01 -1.99138656... | [14.92463493347168, 5.915769100189209] |
6334ce61-26d6-44b3-8a1c-4214c1e3bb8f | personalized-federated-learning-with-2 | 2108.01903 | null | https://arxiv.org/abs/2108.01903v3 | https://arxiv.org/pdf/2108.01903v3.pdf | Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application | While machine learning techniques are being applied to various fields for their exceptional ability to find complex relations in large datasets, the strengthening of regulations on data ownership and privacy is causing increasing difficulty in its application to medical data. In light of this, Federated Learning has re... | ['Tai-Myoung Chung', 'Hong Jin Jeon', 'Han Young Yu', 'Ah Young Kim', 'Eun-Hye Jang', 'Hyejun Jeong', 'Ha Min Son', 'Joo Hun Yoo'] | 2021-08-04 | null | null | null | null | ['severity-prediction', 'heart-rate-variability'] | ['computer-vision', 'medical'] | [-6.74133375e-02 5.49177349e-01 -3.66554469e-01 -7.90540457e-01
-4.10124242e-01 -3.77859235e-01 -8.08856916e-03 3.02529365e-01
-2.83413529e-01 9.02494967e-01 1.98841080e-01 -4.27389830e-01
-5.95446110e-01 -8.46033752e-01 -3.76843810e-01 -7.04105675e-01
-2.72526026e-01 4.19964254e-01 -5.30678570e-01 2.73089826... | [6.152388095855713, 6.4823713302612305] |
545fa4ae-314c-489c-959d-eb5b341bec9a | bayesian-neural-network-inference-via | 2209.02188 | null | https://arxiv.org/abs/2209.02188v1 | https://arxiv.org/pdf/2209.02188v1.pdf | Bayesian Neural Network Inference via Implicit Models and the Posterior Predictive Distribution | We propose a novel approach to perform approximate Bayesian inference in complex models such as Bayesian neural networks. The approach is more scalable to large data than Markov Chain Monte Carlo, it embraces more expressive models than Variational Inference, and it does not rely on adversarial training (or density rat... | ['Daniel Edward Pagendam', 'Joel Janek Dabrowski'] | 2022-09-06 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 1.72958493e-01 5.73822141e-01 7.47594088e-02 -4.39615369e-01
-1.01627111e+00 -3.88877451e-01 1.04632676e+00 -2.55046725e-01
-4.87506270e-01 1.21740556e+00 -3.62861827e-02 -5.19064963e-01
-2.35318825e-01 -8.52500498e-01 -1.11708629e+00 -1.05488980e+00
8.06519166e-02 9.43566322e-01 2.10805818e-01 1.48033485... | [6.981794834136963, 3.834427833557129] |
a6d6342d-3a6a-470a-af9c-f5233343689c | ucl-dehaze-towards-real-world-image-dehazing | 2205.01871 | null | https://arxiv.org/abs/2205.01871v1 | https://arxiv.org/pdf/2205.01871v1.pdf | UCL-Dehaze: Towards Real-world Image Dehazing via Unsupervised Contrastive Learning | While the wisdom of training an image dehazing model on synthetic hazy data can alleviate the difficulty of collecting real-world hazy/clean image pairs, it brings the well-known domain shift problem. From a different yet new perspective, this paper explores contrastive learning with an adversarial training effort to l... | ['Jing Qin', 'Mingqiang Wei', 'Wenhan Yang', 'Haoran Xie', 'Fu Lee Wang', 'Xuefeng Yan', 'Yongzhen Wang'] | 2022-05-04 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 3.16418409e-01 -2.33594939e-01 3.25821966e-01 -2.25932240e-01
-7.84924686e-01 -3.12640667e-01 4.59803760e-01 -3.12107354e-01
-3.41155946e-01 8.39309573e-01 9.47845429e-02 -2.13545725e-01
4.72442098e-02 -8.68320942e-01 -8.84929478e-01 -1.22029352e+00
2.34111547e-01 -2.22910702e-01 -2.27700118e-02 -4.29928035... | [10.931320190429688, -3.1396548748016357] |
89301d1b-ac34-4b51-82c7-15cfee40b0a1 | visda-2021-competition-universal-domain | 2107.11011 | null | https://arxiv.org/abs/2107.11011v1 | https://arxiv.org/pdf/2107.11011v1.pdf | VisDA-2021 Competition Universal Domain Adaptation to Improve Performance on Out-of-Distribution Data | Progress in machine learning is typically measured by training and testing a model on the same distribution of data, i.e., the same domain. This over-estimates future accuracy on out-of-distribution data. The Visual Domain Adaptation (VisDA) 2021 competition tests models' ability to adapt to novel test distributions an... | ['Ben Usman', 'Piotr Teterwak', 'Kuniaki Saito', 'Kate Saenko', 'Samarth Mishra', 'Donghyun Kim', 'Dan Hendrycks', 'Dina Bashkirova'] | 2021-07-23 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [ 2.30866641e-01 -3.25129062e-01 -1.81011111e-01 -6.71321929e-01
-9.48054314e-01 -9.72042143e-01 7.14338839e-01 -5.64249679e-02
-5.25469601e-01 9.75825608e-01 4.56429087e-02 2.76424754e-02
1.68650210e-01 -2.81658143e-01 -7.94734299e-01 -4.95221496e-01
-2.46971268e-02 8.16295445e-01 3.83583248e-01 1.05087884... | [10.12208080291748, 2.7065601348876953] |
72033dd7-b88e-454e-b126-ef9fc5887a17 | automated-linear-time-detection-and-quality | 1701.0194 | null | http://arxiv.org/abs/1701.01940v1 | http://arxiv.org/pdf/1701.01940v1.pdf | Automated Linear-Time Detection and Quality Assessment of Superpixels in Uncalibrated True- or False-Color RGB Images | Capable of automated near real time superpixel detection and quality
assessment in an uncalibrated monitor typical red green blue (RGB) image,
depicted in either true or false colors, an original low level computer vision
(CV) lightweight computer program, called RGB Image Automatic Mapper (RGBIAM),
is designed and imp... | ['Stefan Lang', 'Dirk Tiede', 'Andrea Baraldi'] | 2017-01-08 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 4.11310762e-01 -2.98575144e-02 4.79497433e-01 -2.18532875e-01
-9.09795105e-01 -9.75381911e-01 4.80074912e-01 1.17444836e-01
-4.44542140e-01 5.82757235e-01 -6.36400998e-01 -6.03163600e-01
-1.01273425e-01 -1.20172977e+00 -7.56926596e-01 -8.91995490e-01
1.71539441e-01 5.22012532e-01 2.43662894e-01 1.22881392... | [10.239108085632324, -2.4366538524627686] |
33912360-1bad-4c4c-b884-a127c4b490dc | hifacegan-face-renovation-via-collaborative | 2005.05005 | null | https://arxiv.org/abs/2005.05005v2 | https://arxiv.org/pdf/2005.05005v2.pdf | HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment | Existing face restoration researches typically relies on either the degradation prior or explicit guidance labels for training, which often results in limited generalization ability over real-world images with heterogeneous degradations and rich background contents. In this paper, we investigate the more challenging an... | ['Pan Wang', 'Wen Gao', 'Chang Liu', 'Shanshe Wang', 'Lingbo Yang', 'Siwei Ma', 'Peiran Ren'] | 2020-05-11 | null | null | null | null | ['face-hallucination', 'blind-face-restoration'] | ['computer-vision', 'computer-vision'] | [ 7.38362134e-01 -1.09634340e-01 2.32797831e-01 -2.93937653e-01
-5.38069606e-01 -2.03652844e-01 6.92373872e-01 -5.75666666e-01
-7.73946196e-02 7.52094209e-01 4.60871875e-01 9.11756456e-02
-1.00608282e-02 -4.67724711e-01 -4.72544312e-01 -9.92558956e-01
2.16462240e-01 -1.50819421e-01 -1.06316373e-01 -4.40012872... | [12.826501846313477, -0.1687726080417633] |
2d23ce78-5575-49dc-a5d8-bec16abf575d | smoothed-q-learning | 2303.08631 | null | https://arxiv.org/abs/2303.08631v1 | https://arxiv.org/pdf/2303.08631v1.pdf | Smoothed Q-learning | In Reinforcement Learning the Q-learning algorithm provably converges to the optimal solution. However, as others have demonstrated, Q-learning can also overestimate the values and thereby spend too long exploring unhelpful states. Double Q-learning is a provably convergent alternative that mitigates some of the overes... | ['David Barber'] | 2023-03-15 | null | null | null | null | ['q-learning'] | ['methodology'] | [-4.90276247e-01 4.05977190e-01 -4.10738409e-01 1.01579584e-01
-1.20606971e+00 -7.87814975e-01 2.85622567e-01 2.11922228e-01
-7.38137245e-01 1.50037348e+00 1.48048550e-02 -7.49433577e-01
-2.46928275e-01 -6.73688650e-01 -5.32642424e-01 -7.45996535e-01
-5.33216558e-02 4.97559637e-01 1.47986248e-01 -8.34503919... | [4.153663635253906, 2.3353660106658936] |
91288513-1538-4dac-a9dd-e7e087259bcf | crossnet-an-end-to-end-reference-based-super | 1807.10547 | null | http://arxiv.org/abs/1807.10547v1 | http://arxiv.org/pdf/1807.10547v1.pdf | CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping | The Reference-based Super-resolution (RefSR) super-resolves a low-resolution
(LR) image given an external high-resolution (HR) reference image, where the
reference image and LR image share similar viewpoint but with significant
resolution gap x8. Existing RefSR methods work in a cascaded way such as patch
matching foll... | ['Lu Fang', 'Mengqi Ji', 'Haitian Zheng', 'Yebin Liu', 'Haoqian Wang'] | 2018-07-27 | crossnet-an-end-to-end-reference-based-super-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Haitian_Zheng_CrossNet_An_End-to-end_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Haitian_Zheng_CrossNet_An_End-to-end_ECCV_2018_paper.pdf | eccv-2018-9 | ['reference-based-super-resolution', 'patch-matching'] | ['computer-vision', 'computer-vision'] | [ 5.71343482e-01 -1.11668937e-01 2.80490667e-02 -3.36563438e-01
-1.62311184e+00 -2.98124731e-01 4.39199477e-01 -6.06281221e-01
-2.62060672e-01 7.39233136e-01 4.07128632e-01 2.27774903e-01
-4.62668389e-02 -8.07049692e-01 -8.76659274e-01 -5.91177166e-01
2.44340077e-01 -1.67618081e-01 5.86698472e-01 -3.91086370... | [10.923954010009766, -2.078573703765869] |
bacabdc5-5626-4f5e-800c-284553a4ad29 | bootstrapped-q-learning-with-context-relevant | 2009.11896 | null | https://arxiv.org/abs/2009.11896v1 | https://arxiv.org/pdf/2009.11896v1.pdf | Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games | We show that Reinforcement Learning (RL) methods for solving Text-Based Games (TBGs) often fail to generalize on unseen games, especially in small data regimes. To address this issue, we propose Context Relevant Episodic State Truncation (CREST) for irrelevant token removal in observation text for improved generalizati... | ['Ryuki Tachibana', 'Daiki Kimura', 'Asim Munawar', 'Subhajit Chaudhury', 'Michiaki Tatsubori', 'Kartik Talamadupula'] | 2020-09-24 | null | https://aclanthology.org/2020.emnlp-main.241 | https://aclanthology.org/2020.emnlp-main.241.pdf | emnlp-2020-11 | ['text-based-games'] | ['playing-games'] | [ 2.25895092e-01 1.85423851e-01 -2.73304045e-01 1.18823268e-01
-1.02196395e+00 -5.97619116e-01 5.19450903e-01 1.93390310e-01
-9.66632128e-01 1.40253592e+00 -5.95415086e-02 -5.27646244e-01
-2.69599855e-01 -8.14305723e-01 -5.63154876e-01 -6.77684069e-01
7.79026002e-02 8.99355292e-01 4.88099664e-01 -7.09459960... | [3.933366537094116, 1.432818055152893] |
3cdd0154-cba6-485b-afdd-3e79e6a498e8 | ps4-a-next-generation-dataset-for-protein | null | null | https://www.biorxiv.org/content/10.1101/2023.02.28.530456v1 | https://www.biorxiv.org/content/10.1101/2023.02.28.530456v1.full.pdf+html | PS4: a Next-Generation Dataset for Protein Single Sequence Secondary Structure Prediction | Protein secondary structure prediction is a subproblem of protein folding. A lightweight algorithm capable of accurately predicting secondary structure from only the protein residue sequence could therefore provide a useful input for tertiary structure prediction, alleviating the reliance on MSA typically seen in today... | ['Omar Peracha'] | 2023-03-01 | null | null | null | biorxiv-preprint-2023-3 | ['protein-secondary-structure-prediction', 'protein-folding'] | ['medical', 'natural-language-processing'] | [ 3.13087732e-01 -1.93059966e-02 -1.98931351e-01 -3.85650277e-01
-7.91310251e-01 -8.29122484e-01 9.12462398e-02 3.80175442e-01
-3.32070917e-01 1.19194996e+00 1.84669673e-01 -7.90451467e-01
2.16440618e-01 -4.07042742e-01 -6.67328835e-01 -8.61435056e-01
8.08662847e-02 5.98553717e-01 3.41537148e-01 -1.67804062... | [4.769915580749512, 5.519698143005371] |
9d8d08b4-01d4-4161-a931-d916a0fe2356 | learning-object-affordance-with-contact-and | 2210.09245 | null | https://arxiv.org/abs/2210.09245v3 | https://arxiv.org/pdf/2210.09245v3.pdf | Contact2Grasp: 3D Grasp Synthesis via Hand-Object Contact Constraint | 3D grasp synthesis generates grasping poses given an input object. Existing works tackle the problem by learning a direct mapping from objects to the distributions of grasping poses. However, because the physical contact is sensitive to small changes in pose, the high-nonlinear mapping between 3D object representation ... | ['Qi Ye', 'Jiming Chen', 'Yuchi Huo', 'Xiang Li', 'Yang Zhou', 'Xinzhuo Lin', 'Haoming Li'] | 2022-10-17 | null | null | null | null | ['grasp-generation', 'robotic-grasping'] | ['computer-vision', 'robots'] | [ 2.31491670e-01 1.29209995e-01 -2.57001609e-01 -2.93094337e-01
-7.41168141e-01 -8.02298069e-01 3.67491007e-01 -1.49762603e-02
2.28510480e-02 4.51060385e-01 -1.49691924e-02 2.34841377e-01
-2.71475047e-01 -9.78263080e-01 -1.15014613e+00 -6.84204578e-01
-1.49231434e-01 9.63314831e-01 2.41468370e-01 -8.23236555... | [5.802511692047119, -0.8546320796012878] |
ec7f5223-b5fe-4bee-a5a2-a04b5f9234f8 | malcom-generating-malicious-comments-to | 2009.01048 | null | https://arxiv.org/abs/2009.01048v2 | https://arxiv.org/pdf/2009.01048v2.pdf | MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models | In recent years, the proliferation of so-called "fake news" has caused much disruptions in society and weakened the news ecosystem. Therefore, to mitigate such problems, researchers have developed state-of-the-art models to auto-detect fake news on social media using sophisticated data science and machine learning tech... | ['Thai Le', 'Suhang Wang', 'Dongwon Lee'] | 2020-09-01 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [-2.27825537e-01 2.73016512e-01 7.21868593e-03 -2.05501974e-01
-5.80094516e-01 -9.66826558e-01 1.04878008e+00 -7.37146959e-02
-1.80304736e-01 6.67080462e-01 -4.71224077e-02 -5.80206275e-01
8.66366208e-01 -8.77849519e-01 -9.70278323e-01 -1.22421429e-01
4.19503272e-01 2.40241885e-01 4.21854109e-01 -7.32662320... | [8.135107040405273, 10.228588104248047] |
cdd03311-5f57-44c2-a4f9-9bb7d4e4c801 | landmark-aware-and-part-based-ensemble | 2104.11274 | null | https://arxiv.org/abs/2104.11274v2 | https://arxiv.org/pdf/2104.11274v2.pdf | Landmark-Aware and Part-based Ensemble Transfer Learning Network for Facial Expression Recognition from Static images | Facial Expression Recognition from static images is a challenging problem in computer vision applications. Convolutional Neural Network (CNN), the state-of-the-art method for various computer vision tasks, has had limited success in predicting expressions from faces having extreme poses, illumination, and occlusion con... | ['Tapan K. Gandhi', 'Rohan Wadhawan'] | 2021-04-22 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [ 1.63036641e-02 -2.17740908e-01 -1.36559337e-01 -6.69044256e-01
-3.05069208e-01 -3.07064444e-01 3.34740013e-01 -6.24235630e-01
-4.63054955e-01 6.29501343e-01 -2.10511118e-01 9.96295139e-02
-6.25767559e-02 -4.06211078e-01 -4.96825546e-01 -9.60565269e-01
-1.70189291e-01 -9.45321620e-02 -3.88538480e-01 -3.45820755... | [13.582588195800781, 1.7392829656600952] |
4acced89-f79c-468a-be42-f4b634794cab | uncertainty-driven-trajectory-truncation-for | 2304.0466 | null | https://arxiv.org/abs/2304.04660v1 | https://arxiv.org/pdf/2304.04660v1.pdf | Uncertainty-driven Trajectory Truncation for Model-based Offline Reinforcement Learning | Equipped with the trained environmental dynamics, model-based offline reinforcement learning (RL) algorithms can often successfully learn good policies from fixed-sized datasets, even some datasets with poor quality. Unfortunately, however, it can not be guaranteed that the generated samples from the trained dynamics m... | ['Xiu Li', 'Le Wan', 'Jun Yang', 'Jiangpeng Yan', 'Xiaoteng Ma', 'Jiafei Lyu', 'Junjie Zhang'] | 2023-04-10 | null | null | null | null | ['offline-rl', 'd4rl'] | ['playing-games', 'robots'] | [-4.87652749e-01 2.96704695e-02 -4.35251743e-01 1.38889104e-01
-9.24247861e-01 -8.54647219e-01 4.53620017e-01 5.49213327e-02
-4.09588993e-01 1.32685018e+00 -2.59476304e-01 -5.67625880e-01
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8623a753-a2e4-456e-9eec-324e48b2233b | boundary-aware-transformers-for-skin-lesion | 2110.03864 | null | https://arxiv.org/abs/2110.03864v1 | https://arxiv.org/pdf/2110.03864v1.pdf | Boundary-aware Transformers for Skin Lesion Segmentation | Skin lesion segmentation from dermoscopy images is of great importance for improving the quantitative analysis of skin cancer. However, the automatic segmentation of melanoma is a very challenging task owing to the large variation of melanoma and ambiguous boundaries of lesion areas. While convolutional neutral network... | ['Jing Qin', 'Lei Zhu', 'Qichao Zhou', 'Liansheng Wang', 'Lan Wei', 'Jiacheng Wang'] | 2021-10-08 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 5.10165513e-01 -1.49247870e-02 -4.28149194e-01 -2.88878381e-01
-7.07312942e-01 -3.53475690e-01 6.06598914e-01 1.93960845e-01
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4.26208824e-01 -6.95637837e-02 4.33448166e-01 -3.35432768... | [15.608097076416016, -2.913339138031006] |
f7b5331b-3532-4080-b7fa-a60dcb4996d9 | best-answer-prediction-in-q-a-sites-using | 2212.08475 | null | https://arxiv.org/abs/2212.08475v1 | https://arxiv.org/pdf/2212.08475v1.pdf | Best-Answer Prediction in Q&A Sites Using User Information | Community Question Answering (CQA) sites have spread and multiplied significantly in recent years. Sites like Reddit, Quora, and Stack Exchange are becoming popular amongst people interested in finding answers to diverse questions. One practical way of finding such answers is automatically predicting the best candidate... | ['Takayuki Ito', 'Kai Yoshino', 'Ahmed Moustafa', 'Rafik Hadfi'] | 2022-12-15 | null | null | null | null | ['community-question-answering', 'community-question-answering'] | ['miscellaneous', 'natural-language-processing'] | [-4.65368390e-01 -1.25547096e-01 1.27261337e-02 -3.13822508e-01
-6.87248826e-01 -6.57846749e-01 4.89039540e-01 9.05267715e-01
-4.19866174e-01 4.28155899e-01 5.28176010e-01 -1.78539380e-01
-5.62296152e-01 -8.44891131e-01 5.68618141e-02 -1.45131171e-01
-1.97612830e-02 1.24379382e-01 8.80641878e-01 -6.31881058... | [11.456694602966309, 8.054780960083008] |
0e311248-ad93-4ff7-9985-8c10b9eff50e | semeval-2020-task-6-definition-extraction | 2008.13694 | null | https://arxiv.org/abs/2008.13694v1 | https://arxiv.org/pdf/2008.13694v1.pdf | SemEval-2020 Task 6: Definition extraction from free text with the DEFT corpus | Research on definition extraction has been conducted for well over a decade, largely with significant constraints on the type of definitions considered. In this work, we present DeftEval, a SemEval shared task in which participants must extract definitions from free text using a term-definition pair corpus that reflect... | ['Nicholas A. Miller', 'Carl Dockhorn', 'Franck Dernoncourt', 'Sasha Spala'] | 2020-08-31 | null | https://aclanthology.org/2020.semeval-1.41 | https://aclanthology.org/2020.semeval-1.41.pdf | semeval-2020 | ['definition-extraction'] | ['natural-language-processing'] | [ 8.37972045e-01 1.60698116e-01 -4.26450849e-01 -6.32863224e-01
-7.97090232e-01 -1.10827374e+00 9.11492884e-01 5.72118044e-01
-6.53013229e-01 1.28393114e+00 3.46195132e-01 -6.98042929e-01
-1.96315855e-01 -6.26078486e-01 -7.37745985e-02 -1.05852865e-01
1.97450295e-01 2.98104733e-01 -1.00577455e-02 -3.73337746... | [9.913338661193848, 8.938593864440918] |
5c161b05-bc8c-456d-858d-74c446afd116 | context-modeling-with-evidence-filter-for | 2010.02649 | null | https://arxiv.org/abs/2010.02649v1 | https://arxiv.org/pdf/2010.02649v1.pdf | Context Modeling with Evidence Filter for Multiple Choice Question Answering | Multiple-Choice Question Answering (MCQA) is a challenging task in machine reading comprehension. The main challenge in MCQA is to extract "evidence" from the given context that supports the correct answer. In the OpenbookQA dataset, the requirement of extracting "evidence" is particularly important due to the mutual i... | ['Jing Jiang', 'Wei Jing', 'Hao Zhang', 'Sicheng Yu'] | 2020-10-06 | null | null | null | null | ['multiple-choice-qa'] | ['natural-language-processing'] | [ 3.67829174e-01 3.04850280e-01 1.55282095e-01 -6.10327482e-01
-1.07468379e+00 -6.26314282e-01 3.38764697e-01 6.32774591e-01
-4.42539632e-01 1.06336832e+00 4.82491285e-01 -6.52705669e-01
-4.12317812e-01 -5.65104723e-01 -7.90202916e-01 -1.89551979e-01
2.05782056e-01 3.48840594e-01 6.86273038e-01 -5.76823950... | [11.253783226013184, 8.068102836608887] |
95a2d4a8-a9ff-48bb-acc6-b125f7e74feb | multi-view-integration-learning-for | 2101.09986 | null | https://arxiv.org/abs/2101.09986v2 | https://arxiv.org/pdf/2101.09986v2.pdf | Multi-view Integration Learning for Irregularly-sampled Clinical Time Series | Electronic health record (EHR) data is sparse and irregular as it is recorded at irregular time intervals, and different clinical variables are measured at each observation point. In this work, we propose a multi-view features integration learning from irregular multivariate time series data by self-attention mechanism... | ['Heung-Il Suk', 'Eunji Jun', 'Yurim Lee'] | 2021-01-25 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [ 1.19937494e-01 5.99898305e-03 -3.13914031e-01 -5.24487853e-01
-9.69241679e-01 -1.81880444e-01 1.78047433e-01 6.93659365e-01
-2.98567295e-01 8.32303822e-01 7.69593954e-01 -1.19298562e-01
-2.85755932e-01 -6.30967796e-01 -9.16246176e-01 -6.17376566e-01
-2.61664003e-01 5.19771039e-01 -8.07776749e-01 -8.75227712... | [7.902946949005127, 6.232944965362549] |
a9e7769b-1e9b-4da3-a865-2037e888b807 | fully-convolutional-networks-for-chip-wise | 1910.02451 | null | https://arxiv.org/abs/1910.02451v1 | https://arxiv.org/pdf/1910.02451v1.pdf | Fully Convolutional Networks for Chip-wise Defect Detection Employing Photoluminescence Images | Efficient quality control is inevitable in the manufacturing of light-emitting diodes (LEDs). Because defective LED chips may be traced back to different causes, a time and cost-intensive electrical and optical contact measurement is employed. Fast photoluminescence measurements, on the other hand, are commonly used to... | ['Maike Lorena Stern', 'Martin Schellenberger'] | 2019-10-06 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 3.95975560e-01 -1.13109998e-01 2.36891404e-01 -3.76425922e-01
-6.21521473e-01 -4.52373207e-01 2.43420482e-01 9.45225433e-02
-1.29022583e-01 7.60796905e-01 -6.85882032e-01 -1.17535107e-01
8.87217745e-02 -9.52665031e-01 -5.54509103e-01 -9.76052999e-01
4.91618723e-01 6.57574177e-01 1.11326225e-01 7.30557218... | [7.295166969299316, 1.9130761623382568] |
f4956b97-fa0a-49bc-9d8a-e224f24f1834 | idea-net-dynamic-3d-point-cloud-interpolation | 2203.1159 | null | https://arxiv.org/abs/2203.11590v1 | https://arxiv.org/pdf/2203.11590v1.pdf | IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment | This paper investigates the problem of temporally interpolating dynamic 3D point clouds with large non-rigid deformation. We formulate the problem as estimation of point-wise trajectories (i.e., smooth curves) and further reason that temporal irregularity and under-sampling are two major challenges. To tackle the chall... | ['Ying He', 'Yixuan Yuan', 'Junhui Hou', 'Qijian Zhang', 'Yue Qian', 'Yiming Zeng'] | 2022-03-22 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zeng_IDEA-Net_Dynamic_3D_Point_Cloud_Interpolation_via_Deep_Embedding_Alignment_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zeng_IDEA-Net_Dynamic_3D_Point_Cloud_Interpolation_via_Deep_Embedding_Alignment_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-point-cloud-interpolation'] | ['computer-vision'] | [-3.87829095e-01 -2.79612273e-01 -9.77202412e-03 -1.22956261e-01
-8.76348138e-01 -4.88873124e-01 5.40507555e-01 -1.21645540e-01
-1.11339398e-01 4.30137485e-01 1.58820972e-02 -2.16765907e-02
-8.64050463e-02 -5.68945169e-01 -1.11612141e+00 -6.05044305e-01
-3.07927728e-01 4.00667638e-01 2.01254562e-01 -1.60522357... | [8.536781311035156, -2.155642032623291] |
0d89d680-e387-443d-bc7e-aebc6dd6d632 | creating-pro-level-ai-for-real-time-fighting | 1904.03821 | null | https://arxiv.org/abs/1904.03821v3 | https://arxiv.org/pdf/1904.03821v3.pdf | Creating Pro-Level AI for a Real-Time Fighting Game Using Deep Reinforcement Learning | Reinforcement learning combined with deep neural networks has performed remarkably well in many genres of games recently. It has surpassed human-level performance in fixed game environments and turn-based two player board games. However, to the best of our knowledge, current research has yet to produce a result that ha... | ['Hyoil Lee', 'Seongho Son', 'Inseok Oh', 'Seungeun Rho', 'Sangbin Moon', 'Jinyun Chung'] | 2019-04-08 | null | null | null | null | ['board-games'] | ['playing-games'] | [-1.33496791e-01 -6.19763657e-02 -3.49945277e-02 3.74968420e-03
-4.18214738e-01 -4.05916274e-01 2.54176736e-01 -5.98208234e-02
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-4.77983057e-01 -9.80630517e-01 -3.15406978e-01 -3.94050717e-01
-5.08940160e-01 7.61616528e-01 5.50473750e-01 -1.23148429... | [3.4871182441711426, 1.4848140478134155] |
a514d489-73c2-4ae7-8dd3-34058a22c8b9 | modeling-the-trade-off-of-privacy | 2303.10435 | null | https://arxiv.org/abs/2303.10435v1 | https://arxiv.org/pdf/2303.10435v1.pdf | Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images | A computer vision system using low-resolution image sensors can provide intelligent services (e.g., activity recognition) but preserve unnecessary visual privacy information from the hardware level. However, preserving visual privacy and enabling accurate machine recognition have adversarial needs on image resolution. ... | ['Yuanchun Shi', 'Shwetak Patel', 'Chun Yu', 'Yukang Yan', 'Xuhai Xu', 'Xueyang Wang', 'Yan Kong', 'Xin Yi', 'Zirui Cheng', 'Yuntao Wang'] | 2023-03-18 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 7.09825337e-01 -1.05274830e-03 -1.03129640e-01 -5.91156840e-01
-6.76796257e-01 -5.33631146e-01 5.72622359e-01 -1.69619724e-01
-7.52660394e-01 5.46635389e-01 2.39943355e-01 -8.38989317e-02
8.20780545e-02 -6.96830571e-01 -8.25218379e-01 -7.45703042e-01
1.11785484e-02 -5.84493041e-01 -1.27839759e-01 2.92996973... | [12.745426177978516, 0.815586268901825] |
123a5577-f20f-4fff-82f6-1507207441f1 | mass-masked-sequence-to-sequence-pre-training | 1905.0245 | null | https://arxiv.org/abs/1905.02450v5 | https://arxiv.org/pdf/1905.02450v5.pdf | MASS: Masked Sequence to Sequence Pre-training for Language Generation | Pre-training and fine-tuning, e.g., BERT, have achieved great success in language understanding by transferring knowledge from rich-resource pre-training task to the low/zero-resource downstream tasks. Inspired by the success of BERT, we propose MAsked Sequence to Sequence pre-training (MASS) for the encoder-decoder ba... | ['Tie-Yan Liu', 'Jianfeng Lu', 'Xu Tan', 'Tao Qin', 'Kaitao Song'] | 2019-05-07 | null | null | null | null | ['unsupervised-machine-translation', 'conversational-response-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.84271014e-01 4.90352809e-01 -2.51241773e-01 -3.41882735e-01
-1.43310964e+00 -3.43093306e-01 7.64909863e-01 -2.15065718e-01
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7.22582817e-01 -7.50221193e-01 -9.40225720e-01 -3.63782316e-01
3.79009426e-01 7.14346588e-01 -2.53741711e-01 -6.05371714... | [11.834897994995117, 9.170478820800781] |
0fb46715-0d8c-4efe-8950-518787c3219c | on-the-benefits-of-3d-pose-and-tracking-for | 2304.01199 | null | https://arxiv.org/abs/2304.01199v1 | https://arxiv.org/pdf/2304.01199v1.pdf | On the Benefits of 3D Pose and Tracking for Human Action Recognition | In this work we study the benefits of using tracking and 3D poses for action recognition. To achieve this, we take the Lagrangian view on analysing actions over a trajectory of human motion rather than at a fixed point in space. Taking this stand allows us to use the tracklets of people to predict their actions. In thi... | ['Jitendra Malik', 'Christoph Feichtenhofer', 'Angjoo Kanazawa', 'Georgios Pavlakos', 'Jathushan Rajasegaran'] | 2023-04-03 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Rajasegaran_On_the_Benefits_of_3D_Pose_and_Tracking_for_Human_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Rajasegaran_On_the_Benefits_of_3D_Pose_and_Tracking_for_Human_CVPR_2023_paper.pdf | cvpr-2023-1 | ['action-recognition-in-videos'] | ['computer-vision'] | [ 3.78910489e-02 6.47315830e-02 -7.82418028e-02 -1.45553559e-01
-6.82854593e-01 -5.39399028e-01 9.58049834e-01 -1.37867257e-01
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-2.09195882e-01 5.83596706e-01 4.00281101e-01 -1.31980121... | [7.25062370300293, -0.5724424719810486] |
ec1dec23-b448-48a0-bbf2-67c788686f8a | human-preferences-as-dueling-bandits | 2204.10362 | null | https://arxiv.org/abs/2204.10362v1 | https://arxiv.org/pdf/2204.10362v1.pdf | Human Preferences as Dueling Bandits | The dramatic improvements in core information retrieval tasks engendered by neural rankers create a need for novel evaluation methods. If every ranker returns highly relevant items in the top ranks, it becomes difficult to recognize meaningful differences between them and to build reusable test collections. Several rec... | ['Pablo Castells', 'Ellen M. Voorhees', 'Nick Craswell', 'Charles L. A. Clarke', 'Chengxi Luo', 'Xinyi Yan'] | 2022-04-21 | null | null | null | null | ['online-ranker-evaluation'] | ['miscellaneous'] | [ 3.21239941e-02 -2.78824985e-01 -5.48989952e-01 -7.19997823e-01
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-8.80574703e-01 -6.27642274e-01 -5.37500143e-01 -5.19550383e-01
-3.35718691e-01 1.09829938e+00 2.98921108e-01 -1.84535626... | [11.456686973571777, 7.5400776863098145] |
091b5ebd-1753-4766-aa42-7b6b2f26a7e9 | analysis-of-oversampling-in-uplink-massive | 2306.17697 | null | https://arxiv.org/abs/2306.17697v1 | https://arxiv.org/pdf/2306.17697v1.pdf | Analysis of Oversampling in Uplink Massive MIMO-OFDM with Low-Resolution ADCs | Low-resolution analog-to-digital converters (ADCs) have emerged as an efficient solution for massive multiple-input multiple-output (MIMO) systems to reap high data rates with reasonable power consumption and hardware complexity. In this paper, we analyze the performance of oversampling in uplink massive MIMO orthogona... | ['Markku Juntti', 'Italo Atzeni', 'Nhan Thanh Nguyen', 'Mengyuan Ma'] | 2023-06-30 | null | null | null | null | ['quantization'] | ['methodology'] | [ 3.53292495e-01 -2.55780756e-01 -4.25427139e-01 3.26071292e-01
-7.74338305e-01 -5.58598578e-01 3.78957480e-01 2.26435825e-01
-3.97999734e-01 9.87074196e-01 7.04605430e-02 -5.80115736e-01
-4.73223627e-01 -6.56686783e-01 -3.05017799e-01 -7.24493623e-01
-2.29633793e-01 -5.30339122e-01 -2.83485055e-01 -1.75260648... | [6.383587837219238, 1.3327473402023315] |
cf95f8ef-8c74-4728-89a9-a8cc3c8e50ba | agi-agent-safety-by-iteratively-improving-the | 2007.05411 | null | https://arxiv.org/abs/2007.05411v1 | https://arxiv.org/pdf/2007.05411v1.pdf | AGI Agent Safety by Iteratively Improving the Utility Function | While it is still unclear if agents with Artificial General Intelligence (AGI) could ever be built, we can already use mathematical models to investigate potential safety systems for these agents. We present an AGI safety layer that creates a special dedicated input terminal to support the iterative improvement of an A... | ['Koen Holtman'] | 2020-07-10 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [ 2.80575097e-01 1.17986274e+00 4.01169807e-02 -9.38840676e-04
3.45372744e-02 -8.97224545e-01 8.97444963e-01 3.16499956e-02
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-4.68536735e-01 -9.74868715e-01 -7.42291689e-01 -6.13040864e-01
-4.22530383e-01 5.83778203e-01 2.82227308e-01 -2.45619893... | [4.396336078643799, 2.0723371505737305] |
ce3cd1e9-1111-40b8-804c-0045edaa1fcb | color-image-edge-detection-using-multi-scale | 2208.07503 | null | https://arxiv.org/abs/2208.07503v1 | https://arxiv.org/pdf/2208.07503v1.pdf | Color Image Edge Detection using Multi-scale and Multi-directional Gabor filter | In this paper, a color edge detection method is proposed where the multi-scale Gabor filter are used to obtain edges from input color images. The main advantage of the proposed method is that high edge detection accuracy is attained while maintaining good noise robustness. The proposed method consists of three aspects:... | ['Jinni Chen', 'Jie Ren', 'Weichuan Zhang', 'Yuandong Bi', 'Yunhong Li'] | 2022-08-16 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [-4.21941392e-02 -8.38904202e-01 7.30071962e-02 2.31023759e-01
-2.29655579e-02 -4.44342285e-01 -3.00055370e-03 -2.20350295e-01
-7.16328025e-01 4.67726111e-01 -9.29621011e-02 -2.17233270e-01
1.05467765e-02 -9.96540248e-01 -8.31602141e-02 -7.88029552e-01
8.68134722e-02 -6.85622990e-01 7.45989919e-01 -1.08912118... | [10.91262149810791, -2.426302909851074] |
c7661d54-ebbc-4a14-b43d-d8f963d00334 | hyperbolic-temporal-knowledge-graph | 2106.04311 | null | https://arxiv.org/abs/2106.04311v1 | https://arxiv.org/pdf/2106.04311v1.pdf | Hyperbolic Temporal Knowledge Graph Embeddings with Relational and Time Curvatures | Knowledge Graph (KG) completion has been excessively studied with a massive number of models proposed for the Link Prediction (LP) task. The main limitation of such models is their insensitivity to time. Indeed, the temporal aspect of stored facts is often ignored. To this end, more and more works consider time as a pa... | ['Johannes Heinecke', 'Lina Rojas-Barahona', 'Sebastien Montella'] | 2021-06-08 | null | https://aclanthology.org/2021.findings-acl.292 | https://aclanthology.org/2021.findings-acl.292.pdf | findings-acl-2021-8 | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-3.45695883e-01 2.72612810e-01 -4.86821979e-01 -3.04023802e-01
-2.00310096e-01 -4.53337044e-01 7.69435644e-01 3.18346083e-01
-1.99800193e-01 6.01496994e-01 -9.95260552e-02 -4.09155667e-01
-4.68201429e-01 -1.10284138e+00 -8.86902750e-01 -5.78163266e-01
-5.20342529e-01 4.34740901e-01 5.02771914e-01 -4.21308756... | [8.593626976013184, 7.901104927062988] |
46562339-06c5-46c3-a284-ca3cd769599a | efficient-unsupervised-video-object | 2211.05364 | null | https://arxiv.org/abs/2211.05364v2 | https://arxiv.org/pdf/2211.05364v2.pdf | Efficient Unsupervised Video Object Segmentation Network Based on Motion Guidance | Due to the problem of performance constraints of unsupervised video object detection, its large-scale application is limited. In response to this pain point, we propose another excellent method to solve this problematic point. By incorporating motion characterization in unsupervised video object detection, detection ac... | ['Liqiang Zhu', 'Chao Hu'] | 2022-11-10 | null | null | null | null | ['video-object-segmentation', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.18253238e-01 -5.55433273e-01 -1.82171538e-01 -1.12720281e-01
-3.08995485e-01 -1.77663323e-02 3.13291699e-01 -4.00513411e-01
-5.41919768e-01 1.58220068e-01 4.56808209e-02 2.91772485e-01
1.40482392e-02 -7.21323669e-01 -3.84409249e-01 -9.69590127e-01
1.45405710e-01 -2.80857086e-01 1.01456463e+00 2.52612419... | [9.190481185913086, -0.3291400969028473] |
62ccf789-891e-47af-bccc-3aabc1c1ba02 | humans-as-light-bulbs-3d-human-reconstruction | 2305.01652 | null | https://arxiv.org/abs/2305.01652v1 | https://arxiv.org/pdf/2305.01652v1.pdf | Humans as Light Bulbs: 3D Human Reconstruction from Thermal Reflection | The relatively hot temperature of the human body causes people to turn into long-wave infrared light sources. Since this emitted light has a larger wavelength than visible light, many surfaces in typical scenes act as infrared mirrors with strong specular reflections. We exploit the thermal reflections of a person onto... | ['Carl Vondrick', 'Ruoshi Liu'] | 2023-05-02 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_Humans_As_Light_Bulbs_3D_Human_Reconstruction_From_Thermal_Reflection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_Humans_As_Light_Bulbs_3D_Human_Reconstruction_From_Thermal_Reflection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-human-reconstruction'] | ['computer-vision'] | [ 4.21695054e-01 -3.66846565e-03 6.01656854e-01 -1.56088427e-01
1.11624740e-01 -4.62784231e-01 7.68207967e-01 -1.17017210e+00
-1.28158748e-01 2.52999455e-01 2.23350868e-01 3.28387529e-01
6.01068616e-01 -7.99068034e-01 -4.16634202e-01 -7.44849920e-01
6.98271275e-01 6.62039757e-01 3.88134345e-02 -4.23852578... | [9.812159538269043, -3.004401922225952] |
9374a4c8-0ba7-4a4c-b75b-a8634222ddd3 | one-for-all-one-stage-referring-expression | 2208.00361 | null | https://arxiv.org/abs/2208.00361v3 | https://arxiv.org/pdf/2208.00361v3.pdf | One for All: One-stage Referring Expression Comprehension with Dynamic Reasoning | Referring Expression Comprehension (REC) is one of the most important tasks in visual reasoning that requires a model to detect the target object referred by a natural language expression. Among the proposed pipelines, the one-stage Referring Expression Comprehension (OSREC) has become the dominant trend since it merge... | ['Peng Wang', 'Rui Niu', 'Zhongzhen Huang', 'Zhimin Wei', 'Zhipeng Zhang'] | 2022-07-31 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 2.57585704e-01 1.77921504e-01 -1.60550237e-01 -5.85697711e-01
-3.97999525e-01 -5.48877478e-01 6.94865227e-01 2.04526439e-01
-3.75600278e-01 3.11717302e-01 2.12900698e-01 -3.46315473e-01
-9.49540921e-03 -9.80453253e-01 -6.56070292e-01 -3.24573129e-01
3.42598557e-01 6.02328718e-01 7.43031025e-01 -4.38356608... | [10.424715995788574, 1.4257980585098267] |
7b5efd11-1e7a-4303-8f08-247cb021de49 | no-reference-image-quality-assessment-by | 2108.04165 | null | https://arxiv.org/abs/2108.04165v3 | https://arxiv.org/pdf/2108.04165v3.pdf | No-Reference Image Quality Assessment by Hallucinating Pristine Features | In this paper, we propose a no-reference (NR) image quality assessment (IQA) method via feature level pseudo-reference (PR) hallucination. The proposed quality assessment framework is grounded on the prior models of natural image statistical behaviors and rooted in the view that the perceptually meaningful features cou... | ['Zhu Li', 'Shiqi Wang', 'Hanwei Zhu', 'Chenqi Kong', 'Lingyu Zhu', 'Baoliang Chen'] | 2021-08-09 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 2.80796498e-01 -2.16093376e-01 -1.65470004e-01 -5.34896314e-01
-1.05349314e+00 -6.77557588e-02 5.73794246e-01 -1.89769655e-01
-9.42399651e-02 5.48010707e-01 3.19310665e-01 2.09419101e-01
-4.29269612e-01 -7.01541603e-01 -5.60841501e-01 -9.89487350e-01
2.64662728e-02 -3.81749541e-01 -3.77648056e-01 -1.14178114... | [11.902091979980469, -1.86527419090271] |
b7d3025d-69ec-43e1-aeaa-0f93a761e2be | emotion-cause-pair-extraction-as-sequence | null | null | https://aclanthology.org/2020.emnlp-main.289 | https://aclanthology.org/2020.emnlp-main.289.pdf | Emotion-Cause Pair Extraction as Sequence Labeling Based on A Novel Tagging Scheme | The task of emotion-cause pair extraction deals with finding all emotions and the corresponding causes in unannotated emotion texts. Most recent studies are based on the likelihood of Cartesian product among all clause candidates, resulting in a high computational cost. Targeting this issue, we regard the task as a seq... | ['Ruifeng Xu', 'Jianzhu Bao', 'Chuang Fan', 'Chaofa Yuan'] | null | null | null | null | emnlp-2020-11 | ['emotion-cause-pair-extraction'] | ['natural-language-processing'] | [ 2.43499309e-01 3.76311600e-01 -3.43333125e-01 -3.38552654e-01
-7.63687849e-01 -6.57443464e-01 3.38378221e-01 4.78444010e-01
-5.06515741e-01 6.92562461e-01 6.00059815e-02 -2.71033365e-02
-1.45655215e-01 -5.89392781e-01 -3.98949683e-01 -5.52880168e-01
-2.04023749e-01 2.56912589e-01 4.27942835e-02 -1.03249680... | [12.611377716064453, 6.167050361633301] |
43c518e0-64fa-48db-90eb-4b21d019ea19 | revisiting-grammatical-error-correction | 2211.01635 | null | https://arxiv.org/abs/2211.01635v1 | https://arxiv.org/pdf/2211.01635v1.pdf | Revisiting Grammatical Error Correction Evaluation and Beyond | Pretraining-based (PT-based) automatic evaluation metrics (e.g., BERTScore and BARTScore) have been widely used in several sentence generation tasks (e.g., machine translation and text summarization) due to their better correlation with human judgments over traditional overlap-based methods. Although PT-based methods h... | ['Min Zhang', 'Heyan Huang', 'Xuebo Liu', 'Peiyuan Gong'] | 2022-11-03 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [ 1.10821404e-01 1.51511803e-01 7.14310408e-02 -4.00272548e-01
-1.24840009e+00 -4.36316937e-01 5.38557351e-01 3.50016415e-01
-5.36380351e-01 9.90905762e-01 3.40249538e-01 -4.00068432e-01
1.04150817e-01 -5.22022605e-01 -6.22158825e-01 -4.45641667e-01
8.31362903e-02 5.58885992e-01 3.09779756e-02 -4.70183462... | [11.180749893188477, 10.479741096496582] |
622f58fe-c660-43ae-a699-683663cb4efa | pancreas-segmentation-in-ct-and-mri-images | 1803.11303 | null | http://arxiv.org/abs/1803.11303v1 | http://arxiv.org/pdf/1803.11303v1.pdf | Pancreas Segmentation in CT and MRI Images via Domain Specific Network Designing and Recurrent Neural Contextual Learning | Automatic pancreas segmentation in radiology images, eg., computed tomography
(CT) and magnetic resonance imaging (MRI), is frequently required by
computer-aided screening, diagnosis, and quantitative assessment. Yet pancreas
is a challenging abdominal organ to segment due to the high inter-patient
anatomical variabili... | ['Lin Yang', 'Le Lu', 'Jinzheng Cai', 'Fuyong Xing'] | 2018-03-30 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [ 2.04574406e-01 3.16744596e-01 -1.10271700e-01 -5.59414208e-01
-8.64664733e-01 -2.94780284e-01 -9.27660242e-03 2.28010803e-01
-4.07240719e-01 3.14828485e-01 3.43933403e-01 -4.11745727e-01
9.29026380e-02 -6.70010269e-01 -9.36578810e-01 -7.31805027e-01
-3.75287235e-01 4.06768888e-01 2.55939722e-01 2.63509542... | [14.535605430603027, -2.6708078384399414] |
0a61ab97-868b-4cc2-909e-704a7418c1dc | estimating-network-edge-probabilities-by | 1509.08588 | null | http://arxiv.org/abs/1509.08588v3 | http://arxiv.org/pdf/1509.08588v3.pdf | Estimating network edge probabilities by neighborhood smoothing | The estimation of probabilities of network edges from the observed adjacency
matrix has important applications to predicting missing links and network
denoising. It has usually been addressed by estimating the graphon, a function
that determines the matrix of edge probabilities, but this is ill-defined
without strong a... | ['Yuan Zhang', 'Ji Zhu', 'Elizaveta Levina'] | 2015-09-29 | null | null | null | null | ['graphon-estimation'] | ['graphs'] | [ 2.68470734e-01 4.85107958e-01 -1.05682157e-01 -1.95757776e-01
-1.79599762e-01 -3.89261663e-01 2.25987867e-01 1.95728213e-01
-2.20214520e-02 7.19282269e-01 -6.19677268e-02 -6.13321066e-01
-5.45654416e-01 -9.51970696e-01 -7.06618428e-01 -6.81786239e-01
-8.21824074e-01 5.04563451e-01 4.76942807e-01 -9.38084498... | [6.959219932556152, 5.365571022033691] |
c301b6ec-28ea-4db2-a80a-a0a4d5db94af | structformer-joint-unsupervised-induction-of-1 | 2012.00857 | null | https://arxiv.org/abs/2012.00857v3 | https://arxiv.org/pdf/2012.00857v3.pdf | StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling | There are two major classes of natural language grammar -- the dependency grammar that models one-to-one correspondences between words and the constituency grammar that models the assembly of one or several corresponded words. While previous unsupervised parsing methods mostly focus on only inducing one class of gramma... | ['Aaron Courville', 'Donald Metzler', 'Dara Bahri', 'Che Zheng', 'Yi Tay', 'Yikang Shen'] | 2020-12-01 | structformer-joint-unsupervised-induction-of | https://aclanthology.org/2021.acl-long.559 | https://aclanthology.org/2021.acl-long.559.pdf | acl-2021-5 | ['constituency-parsing', 'unsupervised-dependency-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.28258929e-01 6.73073709e-01 -2.26691142e-01 -8.41084838e-01
-6.79115593e-01 -5.52953720e-01 4.80106056e-01 2.23805755e-01
-7.75607070e-03 3.08993280e-01 5.46734869e-01 -5.39368272e-01
3.23817611e-01 -1.06275713e+00 -6.20209932e-01 -3.88587505e-01
2.02636078e-01 3.91976178e-01 1.19525708e-01 -3.56955588... | [10.326236724853516, 9.545710563659668] |
fe4c5b54-b533-49fe-9a2d-9afd237a0505 | light-weighted-cnn-attention-based | 2210.15119 | null | https://arxiv.org/abs/2210.15119v1 | https://arxiv.org/pdf/2210.15119v1.pdf | Light-weighted CNN-Attention based architecture for Hand Gesture Recognition via ElectroMyography | Advancements in Biological Signal Processing (BSP) and Machine-Learning (ML) models have paved the path for development of novel immersive Human-Machine Interfaces (HMI). In this context, there has been a surge of significant interest in Hand Gesture Recognition (HGR) utilizing Surface-Electromyogram (sEMG) signals. Th... | ['Arash Mohammadi', 'Amir Asif', 'Elahe Rahimian', 'Soheil Zabihi'] | 2022-10-27 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition', 'mixed-reality'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 4.17927086e-01 -2.96926081e-01 -2.94149518e-02 -2.05527529e-01
-4.49678510e-01 -2.11868472e-02 2.04403862e-01 -4.21864212e-01
-9.95622039e-01 8.00481558e-01 1.20882966e-01 -8.99581835e-02
-1.48554131e-01 -4.68260318e-01 -4.58121002e-01 -6.98086381e-01
-2.29797512e-01 -2.24105775e-01 -2.84573250e-02 -1.74472705... | [6.81045389175415, 0.13164184987545013] |
a6d6fb2d-0286-42a0-a0f7-679dc2b797e8 | neural-embedding-allocation-distributed | 1909.04702 | null | https://arxiv.org/abs/1909.04702v1 | https://arxiv.org/pdf/1909.04702v1.pdf | Neural Embedding Allocation: Distributed Representations of Topic Models | Word embedding models such as the skip-gram learn vector representations of words' semantic relationships, and document embedding models learn similar representations for documents. On the other hand, topic models provide latent representations of the documents' topical themes. To get the benefits of these representati... | ['Kamrun Naher Keya', 'James R. Foulds', 'Yannis Papanikolaou'] | 2019-09-10 | null | null | null | null | ['document-embedding'] | ['methodology'] | [-6.03464127e-01 3.17010224e-01 -6.49023771e-01 -1.96525887e-01
-5.40037930e-01 -1.96261987e-01 1.05849373e+00 1.34361580e-01
1.18215643e-01 2.30859667e-01 1.09073794e+00 -2.59415895e-01
-1.73423976e-01 -8.96409750e-01 -1.74140066e-01 -5.88935137e-01
-3.77751768e-01 7.21343637e-01 -6.47059307e-02 4.56770360... | [10.455467224121094, 6.980878829956055] |
9a77ef92-3c1f-4331-aeda-b1b73f6210be | the-2023-video-similarity-dataset-and | 2306.09489 | null | https://arxiv.org/abs/2306.09489v1 | https://arxiv.org/pdf/2306.09489v1.pdf | The 2023 Video Similarity Dataset and Challenge | This work introduces a dataset, benchmark, and challenge for the problem of video copy detection and localization. The problem comprises two distinct but related tasks: determining whether a query video shares content with a reference video ("detection"), and additionally temporally localizing the shared content within... | ['Matthijs Douze', 'Giorgos Tolias', 'Symeon Papadopoulos', 'Akshay Gupta', 'Sugosh Nagavara Ravindra', 'Gheorghe Postelnicu', 'Hiral Patel', 'Giorgos Kordopatis-Zilos', 'Ed Pizzi'] | 2023-06-15 | null | null | null | null | ['video-similarity'] | ['computer-vision'] | [ 3.67102474e-01 -6.44974113e-01 -4.56912458e-01 -2.89389081e-02
-1.28345168e+00 -9.62397099e-01 5.62102854e-01 8.89296383e-02
-3.77168775e-01 2.66429245e-01 1.41991496e-01 1.79809723e-02
1.56805247e-01 -1.57572329e-01 -1.04555500e+00 -6.21952534e-01
-6.17981195e-01 1.67397812e-01 6.20971680e-01 2.65940934... | [10.149237632751465, 0.588818371295929] |
0ef22a8d-449b-4cd4-84bc-1b4775310762 | equivariant-muzero | 2302.04798 | null | https://arxiv.org/abs/2302.04798v1 | https://arxiv.org/pdf/2302.04798v1.pdf | Equivariant MuZero | Deep reinforcement learning repeatedly succeeds in closed, well-defined domains such as games (Chess, Go, StarCraft). The next frontier is real-world scenarios, where setups are numerous and varied. For this, agents need to learn the underlying rules governing the environment, so as to robustly generalise to conditions... | ['George Papamakarios', 'Théophane Weber', 'Andreea Deac'] | 2023-02-09 | null | null | null | null | ['starcraft'] | ['playing-games'] | [-6.14836998e-02 -1.05386212e-01 8.21942762e-02 1.37574494e-01
-1.98234230e-01 -1.03237975e+00 7.89005518e-01 -1.14408396e-02
-5.68969965e-01 8.07797551e-01 1.57552540e-01 -1.41324580e-01
-5.09678543e-01 -9.91268575e-01 -9.58979487e-01 -7.82876134e-01
-5.06423414e-01 5.63404739e-01 2.18095616e-01 -9.33013201... | [4.005280017852783, 1.7001630067825317] |
c555c592-0094-48f5-8159-0df1278ea8bc | generative-models-for-pose-transfer | 1806.0907 | null | http://arxiv.org/abs/1806.09070v1 | http://arxiv.org/pdf/1806.09070v1.pdf | Generative Models for Pose Transfer | We investigate nearest neighbor and generative models for transferring pose
between persons. We take in a video of one person performing a sequence of
actions and attempt to generate a video of another person performing the same
actions. Our generative model (pix2pix) outperforms k-NN at both generating
corresponding f... | ['Alexander Li', 'Gokul Swamy', 'Patrick Chao'] | 2018-06-24 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 3.63163352e-01 -5.26069291e-03 4.83169973e-01 -4.98463094e-01
-1.01711702e+00 -8.10935080e-01 8.31306875e-01 -6.01605654e-01
-1.27356812e-01 4.50243145e-01 4.67301875e-01 3.04070413e-01
2.80127227e-01 -4.81395423e-01 -7.50825286e-01 -4.24217910e-01
-1.56035826e-01 7.36989915e-01 1.97710693e-01 2.09351957... | [7.10809850692749, -0.9027119874954224] |
299ed45c-8940-423a-8218-acf86d72e51c | approximate-bayesian-optimisation-for-neural | 2108.12461 | null | https://arxiv.org/abs/2108.12461v2 | https://arxiv.org/pdf/2108.12461v2.pdf | Approximate Bayesian Optimisation for Neural Networks | A body of work has been done to automate machine learning algorithm to highlight the importance of model choice. Automating the process of choosing the best forecasting model and its corresponding parameters can result to improve a wide range of real-world applications. Bayesian optimisation (BO) uses a blackbox optimi... | ['Irina Rish', 'Nadhir Hassen'] | 2021-08-27 | null | null | null | null | ['density-ratio-estimation', 'bayesian-optimisation'] | ['methodology', 'methodology'] | [ 1.64514378e-01 -1.38307035e-01 -1.39701933e-01 -3.39996129e-01
-7.20618844e-01 -4.02194709e-01 7.88056970e-01 7.84704611e-02
-4.53450263e-01 1.03304875e+00 -2.66592324e-01 -3.72680604e-01
-1.01065683e+00 -7.34518647e-01 -6.57919466e-01 -1.05210888e+00
-1.48362562e-01 8.77931774e-01 -1.58164188e-01 1.26664788... | [6.2590813636779785, 3.659233570098877] |
a2631750-13b3-4546-9141-2587621b87c9 | distribution-estimation-and-change-point | 2211.14577 | null | https://arxiv.org/abs/2211.14577v2 | https://arxiv.org/pdf/2211.14577v2.pdf | Distribution estimation and change-point estimation for time series via DNN-based GANs | The generative adversarial networks (GANs) have recently been applied to estimating the distribution of independent and identically distributed data, and have attracted a lot of research attention. In this paper, we use the blocking technique to demonstrate the effectiveness of GANs for estimating the distribution of s... | ['Qiuran Yao', 'Lihu Xu', 'Zhijie Xiao', 'Yingjun Mo', 'Jianya Lu'] | 2022-11-26 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [ 3.08650862e-02 -2.59621769e-01 1.62970945e-01 -2.55125642e-01
-9.26772237e-01 -5.21943927e-01 3.70997965e-01 -5.67636967e-01
-2.55130798e-01 1.00761306e+00 1.07695833e-01 -1.42157063e-01
-9.25484523e-02 -7.66692162e-01 -8.60484838e-01 -1.01343894e+00
-3.54701549e-01 3.95306528e-01 -4.22317922e-01 2.47022748... | [6.991392135620117, 3.250979423522949] |
d8ba2e9c-f5af-4759-8386-48226907d7ca | unsupervised-deep-language-and-dialect | null | null | https://aclanthology.org/2020.coling-main.141 | https://aclanthology.org/2020.coling-main.141.pdf | Unsupervised Deep Language and Dialect Identification for Short Texts | Automatic Language Identification (LI) or Dialect Identification (DI) of short texts of closely related languages or dialects, is one of the primary steps in many natural language processing pipelines. Language identification is considered a solved task in many cases; however, in the case of very closely related langua... | ['John P. McCrae', 'Theodorus Fransen', 'Bharathi Raja Chakravarthi', 'Rajdeep Sarkar', 'Koustava Goswami'] | 2020-12-01 | null | null | null | coling-2020-8 | ['dialect-identification'] | ['natural-language-processing'] | [-1.80981293e-01 -2.75219113e-01 -3.74299251e-02 -6.28007650e-01
-5.63412070e-01 -8.86842668e-01 8.05902958e-01 5.40177703e-01
-7.00672328e-01 2.29479343e-01 5.47960401e-01 -4.46540177e-01
1.24784023e-01 -4.68379617e-01 -2.39631325e-01 -4.64962453e-01
9.20714289e-02 1.18419611e+00 -2.36107782e-02 -2.25170046... | [10.60677433013916, 10.151123046875] |
5f8a753e-7502-4b9d-84b9-f82fba2def91 | event-based-frame-interpolation-with-ad-hoc | 2301.05191 | null | https://arxiv.org/abs/2301.05191v1 | https://arxiv.org/pdf/2301.05191v1.pdf | Event-Based Frame Interpolation with Ad-hoc Deblurring | The performance of video frame interpolation is inherently correlated with the ability to handle motion in the input scene. Even though previous works recognize the utility of asynchronous event information for this task, they ignore the fact that motion may or may not result in blur in the input video to be interpolat... | ['Luc van Gool', 'Kaiwei Wang', 'Qi Jiang', 'Kai Zhang', 'JieZhang Cao', 'Peng Sun', 'Jingyun Liang', 'Christos Sakaridis', 'Lei Sun'] | 2023-01-12 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sun_Event-Based_Frame_Interpolation_With_Ad-Hoc_Deblurring_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sun_Event-Based_Frame_Interpolation_With_Ad-Hoc_Deblurring_CVPR_2023_paper.pdf | cvpr-2023-1 | ['deblurring', 'video-frame-interpolation'] | ['computer-vision', 'computer-vision'] | [ 2.40593046e-01 -6.42311931e-01 -2.00620651e-01 -1.76556975e-01
-6.51541948e-01 -4.91638899e-01 5.60012758e-01 -4.44348633e-01
-3.83118510e-01 7.22822905e-01 5.73481977e-01 -2.88227767e-01
1.22445181e-01 -3.98073077e-01 -9.81072903e-01 -6.83162391e-01
-5.20633347e-02 -1.83389813e-01 3.89077246e-01 1.29089057... | [11.190403938293457, -2.167649269104004] |
94a4be13-c8b3-4a67-bfe1-5a652ae52da7 | classification-and-self-supervised-regression | 2210.14253 | null | https://arxiv.org/abs/2210.14253v1 | https://arxiv.org/pdf/2210.14253v1.pdf | Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks | Interpretation of electrocardiography (ECG) signals is required for diagnosing cardiac arrhythmia. Recently, machine learning techniques have been applied for automated computer-aided diagnosis. Machine learning tasks can be divided into regression and classification. Regression can be used for noise and artifacts remo... | ['Ru-San Tan', 'U Rajendra Acharya', 'Özal Yıldırım', 'Paweł Pławiak', 'Wojciech Masarczyk', 'Przemysław Głomb', 'Bartosz Grabowski'] | 2022-10-25 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 3.01575273e-01 4.03111018e-02 1.29498959e-01 -6.02624238e-01
-1.13496971e+00 -3.34862500e-01 -3.56711805e-01 1.75805658e-01
-2.63243794e-01 1.01100397e+00 -3.22307646e-01 -4.95694816e-01
-3.08659136e-01 -3.19191396e-01 -1.24639675e-01 -7.38354862e-01
-1.32104933e-01 4.46504444e-01 -5.22158682e-01 1.50140777... | [14.311202049255371, 3.298381805419922] |
3ba75bff-e4ee-4f52-822b-00e0dbb8a2f4 | compilergym-robust-performant-compiler | 2109.08267 | null | https://arxiv.org/abs/2109.08267v2 | https://arxiv.org/pdf/2109.08267v2.pdf | CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research | Interest in applying Artificial Intelligence (AI) techniques to compiler optimizations is increasing rapidly, but compiler research has a high entry barrier. Unlike in other domains, compiler and AI researchers do not have access to the datasets and frameworks that enable fast iteration and development of ideas, and ge... | ['Hugh Leather', 'Yuandong Tian', 'Benoit Steiner', 'Olivier Teytaud', 'Jia Liu', 'Somya Jain', 'Sahir Gomez', 'Jason Ansel', 'Brandon Cui', 'Jiadong Guo', 'Bram Wasti', 'Chris Cummins'] | 2021-09-17 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [-3.16821873e-01 -3.11086714e-01 -4.04706627e-01 -3.99561077e-01
-4.29163486e-01 -7.31592298e-01 2.70501167e-01 2.04725668e-01
-2.54783779e-01 3.19793880e-01 1.37360707e-01 -9.72681999e-01
2.65791178e-01 -8.19931567e-01 -6.87083423e-01 -1.88042089e-01
-1.56145230e-01 6.54994965e-01 -2.22104564e-01 -5.64793646... | [7.792873382568359, 7.457464218139648] |
aa88bbee-e338-4fac-b0ba-a06af2b3704c | what-can-human-sketches-do-for-object | 2303.15149 | null | https://arxiv.org/abs/2303.15149v1 | https://arxiv.org/pdf/2303.15149v1.pdf | What Can Human Sketches Do for Object Detection? | Sketches are highly expressive, inherently capturing subjective and fine-grained visual cues. The exploration of such innate properties of human sketches has, however, been limited to that of image retrieval. In this paper, for the first time, we cultivate the expressiveness of sketches but for the fundamental vision t... | ['Yi-Zhe Song', 'Tao Xiang', 'Subhadeep Koley', 'Aneeshan Sain', 'Ayan Kumar Bhunia', 'Pinaki Nath Chowdhury'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chowdhury_What_Can_Human_Sketches_Do_for_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chowdhury_What_Can_Human_Sketches_Do_for_Object_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 3.29348058e-01 4.78836633e-02 -6.73448816e-02 -2.78791487e-01
-5.74433267e-01 -1.01485550e+00 8.24799180e-01 -2.74216801e-01
-2.30154052e-01 1.38067126e-01 -3.52853648e-02 -2.94828653e-01
-3.59574258e-02 -7.85530210e-01 -1.00727487e+00 -4.68844265e-01
6.93975240e-02 5.20791471e-01 3.52025062e-01 -2.05837399... | [11.654770851135254, 0.5219424366950989] |
8b2d7b2e-d97e-4428-bde3-53499a658d08 | throwing-away-data-improves-worst-class-error | 2205.11672 | null | https://arxiv.org/abs/2205.11672v2 | https://arxiv.org/pdf/2205.11672v2.pdf | Why does Throwing Away Data Improve Worst-Group Error? | When facing data with imbalanced classes or groups, practitioners follow an intriguing strategy to achieve best results. They throw away examples until the classes or groups are balanced in size, and then perform empirical risk minimization on the reduced training set. This opposes common wisdom in learning theory, whe... | ['Martin Arjovsky', 'Kartik Ahuja', 'Kamalika Chaudhuri', 'David Lopez-Paz'] | 2022-05-23 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 1.53431743e-01 3.33629966e-01 -5.27474940e-01 -5.47820032e-01
-7.63536215e-01 -5.45174778e-01 1.15375891e-01 4.03766960e-01
-4.04119432e-01 8.11682165e-01 -1.00568242e-01 -6.22290552e-01
-4.34916645e-01 -7.44879186e-01 -7.43809342e-01 -1.05846047e+00
-8.34185630e-02 3.95401686e-01 -9.65032354e-03 9.12878439... | [8.477668762207031, 4.493873596191406] |
e4fb9399-7a51-4930-86d6-81f91ac81bb5 | an-automated-end-to-end-deep-learning-based | 2305.00046 | null | https://arxiv.org/abs/2305.00046v1 | https://arxiv.org/pdf/2305.00046v1.pdf | An automated end-to-end deep learning-based framework for lung cancer diagnosis by detecting and classifying the lung nodules | Lung cancer is a leading cause of cancer-related deaths worldwide, and early detection is crucial for improving patient outcomes. Nevertheless, early diagnosis of cancer is a major challenge, particularly in low-resource settings where access to medical resources and trained radiologists is limited. The objective of th... | ['Samiul Based Shuvo'] | 2023-04-28 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [ 7.32841715e-02 4.54167984e-02 -4.27073896e-01 1.87653393e-01
-8.86334121e-01 -4.55439448e-01 2.59787560e-01 6.73278198e-02
-5.81181586e-01 3.40390682e-01 -1.38759062e-01 -6.17152572e-01
-1.17637902e-01 -9.03965414e-01 -3.50285858e-01 -7.68544197e-01
1.14214629e-01 5.63621998e-01 6.72724903e-01 5.38927972... | [15.422245025634766, -2.1526052951812744] |
f4dda5bd-f09d-4857-ad1d-2c30f94f019f | in-sample-policy-iteration-for-offline | 2306.05726 | null | https://arxiv.org/abs/2306.05726v1 | https://arxiv.org/pdf/2306.05726v1.pdf | In-Sample Policy Iteration for Offline Reinforcement Learning | Offline reinforcement learning (RL) seeks to derive an effective control policy from previously collected data. To circumvent errors due to inadequate data coverage, behavior-regularized methods optimize the control policy while concurrently minimizing deviation from the data collection policy. Nevertheless, these meth... | ['Zhaopeng Meng', 'Yan Zheng', 'Chenjun Xiao', 'Yi Ma', 'Xiaohan Hu'] | 2023-06-09 | null | null | null | null | ['offline-rl', 'd4rl'] | ['playing-games', 'robots'] | [ 1.68385189e-02 1.41264305e-01 -8.89943600e-01 -4.49993946e-02
-1.27079475e+00 -6.21833026e-01 1.86415181e-01 2.30536625e-01
-8.80351603e-01 1.24788690e+00 5.26534393e-02 -3.74964982e-01
-3.23730737e-01 -4.56917346e-01 -1.09667170e+00 -7.65510678e-01
-1.80994183e-01 6.71519816e-01 -1.22170351e-01 7.97059163... | [4.056641578674316, 2.283149480819702] |
548d49d5-e5ad-4f0f-bcd6-bad8444c3d53 | fast-and-efficient-calculations-of-structural | 1711.05866 | null | http://arxiv.org/abs/1711.05866v2 | http://arxiv.org/pdf/1711.05866v2.pdf | Fast and Efficient Calculations of Structural Invariants of Chirality | Chirality plays an important role in physics, chemistry, biology, and other
fields. It describes an essential symmetry in structure. However, chirality
invariants are usually complicated in expression or difficult to evaluate. In
this paper, we present five general three-dimensional chirality invariants
based on the ge... | ['Hanlin Mo', 'You Hao', 'He Zhang', 'Shirui Li', 'Hua Li'] | 2017-10-20 | null | null | null | null | ['symmetry-detection'] | ['computer-vision'] | [-9.66678634e-02 -3.78554434e-01 -2.83148736e-01 4.42837253e-02
1.21305175e-01 -6.53322995e-01 8.46462607e-01 3.03897839e-02
-1.34890854e-01 6.15917802e-01 3.24310750e-01 -3.17039490e-01
-4.96476054e-01 -8.27322781e-01 -6.59692958e-02 -1.01650894e+00
-4.06494081e-01 4.92951721e-01 6.13922477e-01 -5.00782669... | [9.307497024536133, -1.7858189344406128] |
3119cafa-3c14-4c82-8435-1d9ffba188f2 | decentralized-structural-rnn-for-robot-crowd | 2011.0482 | null | https://arxiv.org/abs/2011.04820v3 | https://arxiv.org/pdf/2011.04820v3.pdf | Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning | Safe and efficient navigation through human crowds is an essential capability for mobile robots. Previous work on robot crowd navigation assumes that the dynamics of all agents are known and well-defined. In addition, the performance of previous methods deteriorates in partially observable environments and environments... | ['Katherine Driggs-Campbell', 'Neeloy Chakraborty', 'Weihang Liang', 'Peixin Chang', 'Shuijing Liu'] | 2020-11-09 | null | null | null | null | ['social-navigation'] | ['robots'] | [-4.80217665e-01 2.74220526e-01 4.78871167e-01 8.55101389e-04
-1.33227244e-01 -2.79167801e-01 5.37245870e-01 -4.45452929e-01
-1.00757825e+00 1.26466489e+00 8.40758458e-02 -3.83413583e-01
2.92792231e-01 -6.68036342e-01 -7.97130406e-01 -6.87209427e-01
-3.91653329e-01 8.25866997e-01 7.32189178e-01 -1.06766629... | [4.748933792114258, 0.9750598669052124] |
42e8d627-0fcf-4a7f-a22e-34d8cfe15da2 | misnn-multiple-imputation-via-semi-parametric | 2305.01794 | null | https://arxiv.org/abs/2305.01794v1 | https://arxiv.org/pdf/2305.01794v1.pdf | MISNN: Multiple Imputation via Semi-parametric Neural Networks | Multiple imputation (MI) has been widely applied to missing value problems in biomedical, social and econometric research, in order to avoid improper inference in the downstream data analysis. In the presence of high-dimensional data, imputation models that include feature selection, especially $\ell_1$ regularized reg... | ['Qi Long', 'Yiliang Zhang', 'Zongyu Dai', 'Zhiqi Bu'] | 2023-05-02 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 2.58560598e-01 -3.58156085e-01 -5.53040028e-01 -6.03217840e-01
-6.56531632e-01 -1.39250634e-02 -6.20879866e-02 -7.29683861e-02
-2.82593608e-01 1.28582132e+00 3.04015458e-01 -2.09561691e-01
-5.82715511e-01 -7.67555654e-01 -6.48369193e-01 -8.93904984e-01
4.01037969e-02 4.36081350e-01 -7.63014317e-01 9.56085138... | [7.614885330200195, 4.852677822113037] |
0b1a06d3-5820-4488-beb1-319fdd2f6988 | dont-let-notes-be-misunderstood-a-negation | null | null | https://aclanthology.org/W16-0310 | https://aclanthology.org/W16-0310.pdf | Don't Let Notes Be Misunderstood: A Negation Detection Method for Assessing Risk of Suicide in Mental Health Records | null | ['Harry Dean', 'Rina Dutta', 'Anika Oellrich', 'Maria Liakata', 'George Gkotsis', 'Sumithra Velupillai'] | 2016-06-01 | null | null | null | ws-2016-6 | ['negation-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... | [-7.282026767730713, 3.754664897918701] |
42d43e20-9e13-49ca-b742-c0027458c5e6 | memc-net-motion-estimation-and-motion | 1810.08768 | null | https://arxiv.org/abs/1810.08768v2 | https://arxiv.org/pdf/1810.08768v2.pdf | MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement | Motion estimation (ME) and motion compensation (MC) have been widely used for classical video frame interpolation systems over the past decades. Recently, a number of data-driven frame interpolation methods based on convolutional neural networks have been proposed. However, existing learning based methods typically est... | ['Ming-Hsuan Yang', 'Wei-Sheng Lai', 'Zhiyong Gao', 'Wenbo Bao', 'Xiaoyun Zhang'] | 2018-10-20 | null | null | null | null | ['video-enhancement'] | ['computer-vision'] | [ 2.61399269e-01 -5.78139007e-01 -1.11225285e-01 -3.43839288e-01
-6.18725538e-01 -3.02171931e-02 6.61637723e-01 -1.91668585e-01
-6.56682670e-01 8.94751489e-01 1.60908684e-01 -2.88558640e-02
1.91376582e-01 -6.41902566e-01 -7.91540384e-01 -7.24129617e-01
1.46294922e-01 -2.89704055e-01 3.59025478e-01 -1.38737604... | [10.821403503417969, -1.503053903579712] |
bf92804d-9e61-4eaf-b70e-1c6bb7a02e3c | epik-eliminating-multi-model-pipelines-with | 2211.1492 | null | https://arxiv.org/abs/2211.14920v1 | https://arxiv.org/pdf/2211.14920v1.pdf | EPIK: Eliminating multi-model Pipelines with Knowledge-distillation | Real-world tasks are largely composed of multiple models, each performing a sub-task in a larger chain of tasks, i.e., using the output from a model as input for another model in a multi-model pipeline. A model like MATRa performs the task of Crosslingual Transliteration in two stages, using English as an intermediate ... | ['Anshuman Dash', 'Yash Raj', 'Bhavesh Laddagiri'] | 2022-11-27 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [ 9.57157165e-02 3.10418576e-01 9.88193080e-02 -4.13683832e-01
-1.36687684e+00 -7.42472291e-01 5.56416154e-01 -3.27696294e-01
-7.47669041e-01 5.74047327e-01 8.24106485e-02 -7.54035115e-01
3.57295245e-01 -2.82403111e-01 -8.26505244e-01 -3.50961208e-01
6.90423906e-01 1.00223291e+00 1.60960928e-01 -6.15271777... | [11.636188507080078, 10.300738334655762] |
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