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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2c25c152-acee-441f-9576-03a4f57fdb5f | fuzzy-expert-system-for-stock-portfolio | 2204.13385 | null | https://arxiv.org/abs/2204.13385v2 | https://arxiv.org/pdf/2204.13385v2.pdf | Fuzzy Expert System for Stock Portfolio Selection: An Application to Bombay Stock Exchange | Selection of proper stocks, before allocating investment ratios, is always a crucial task for the investors. Presence of many influencing factors in stock performance have motivated researchers to adopt various Artificial Intelligence (AI) techniques to make this challenging task easier. In this paper a novel fuzzy exp... | ['Rupak Bhattacharyya', 'Seema Sarkar', 'Gour Sundar Mitra Thakur'] | 2022-04-28 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-2.00231194e-01 -1.57402039e-01 2.55618393e-01 -1.10241622e-01
2.80588895e-01 -7.92433858e-01 3.90610099e-01 1.40996754e-01
-5.15086174e-01 1.03319776e+00 -1.66241363e-01 -5.11335611e-01
-9.26401913e-01 -1.11557364e+00 6.04812466e-02 -5.56578636e-01
3.17477226e-01 7.09498882e-01 2.57042795e-01 -5.77144980... | [5.241966724395752, 3.816481351852417] |
bced6dac-5484-42f2-85b5-a2837cd9be7a | how-far-is-language-model-from-100-few-shot | 2307.00186 | null | https://arxiv.org/abs/2307.00186v1 | https://arxiv.org/pdf/2307.00186v1.pdf | How far is Language Model from 100% Few-shot Named Entity Recognition in Medical Domain | Recent advancements in language models (LMs) have led to the emergence of powerful models such as Small LMs (e.g., T5) and Large LMs (e.g., GPT-4). These models have demonstrated exceptional capabilities across a wide range of tasks, such as name entity recognition (NER) in the general domain. (We define SLMs as pre-tr... | ['Rui Zhang', 'Mingchen Li'] | 2023-07-01 | null | null | null | null | ['few-shot-ner', 'cg'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.52944000e-02 2.90036023e-01 -1.43962666e-01 -5.35778403e-02
-9.91350234e-01 -1.02571681e-01 4.24158275e-01 3.62449676e-01
-8.48122656e-01 6.41190171e-01 3.83966327e-01 -4.12747771e-01
-5.09825587e-01 -7.26264358e-01 -4.30615634e-01 -4.50113058e-01
1.69582173e-01 4.36723888e-01 1.63785264e-01 -3.69257927... | [8.57150936126709, 8.849961280822754] |
16d5fdfb-0c02-4fe7-a27d-ab36b58e8735 | face-fast-accurate-and-context-aware-audio | 2303.03666 | null | https://arxiv.org/abs/2303.03666v1 | https://arxiv.org/pdf/2303.03666v1.pdf | Face: Fast, Accurate and Context-Aware Audio Annotation and Classification | This paper presents a context-aware framework for feature selection and classification procedures to realize a fast and accurate audio event annotation and classification. The context-aware design starts with exploring feature extraction techniques to find an appropriate combination to select a set resulting in remarka... | ['Saeed Bagheri Shouraki', 'Hoda Mohammadzade', 'M. Mehrdad Morsali'] | 2023-03-07 | null | null | null | null | ['audio-classification', 'environmental-sound-classification', 'sound-classification'] | ['audio', 'audio', 'audio'] | [ 2.02125385e-01 -2.80778669e-02 3.68188322e-02 -4.86048013e-01
-1.24412477e+00 -4.11103755e-01 3.20682488e-02 5.92219710e-01
-3.46667975e-01 5.93968391e-01 2.04425976e-01 2.25448206e-01
-6.16380453e-01 -6.37324512e-01 -1.06535472e-01 -7.15766847e-01
-3.54435354e-01 1.36950627e-01 7.29672834e-02 1.09905869... | [15.624749183654785, 5.22084379196167] |
ffefc513-01e3-4ea6-bc18-7e2a123cb995 | neural-ordinary-differential-equations | 1806.07366 | null | https://arxiv.org/abs/1806.07366v5 | https://arxiv.org/pdf/1806.07366v5.pdf | Neural Ordinary Differential Equations | We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network. The output of the network is computed using a black-box differential equation solver. These continuous-depth models have constan... | ['Yulia Rubanova', 'David Duvenaud', 'Jesse Bettencourt', 'Ricky T. Q. Chen'] | 2018-06-19 | neural-ordinary-differential-equations-1 | http://papers.nips.cc/paper/7892-neural-ordinary-differential-equations | http://papers.nips.cc/paper/7892-neural-ordinary-differential-equations.pdf | neurips-2018-12 | ['multivariate-time-series-imputation'] | ['time-series'] | [-2.54693359e-01 2.90161550e-01 -6.05012812e-02 -6.04509674e-02
-3.16991597e-01 -8.88094008e-01 6.15517437e-01 -5.25890946e-01
-3.87865901e-01 7.55975008e-01 -2.25900009e-01 -7.22172856e-01
-4.66610007e-02 -8.84638667e-01 -5.18694639e-01 -7.42938221e-01
-4.71350402e-01 6.73406899e-01 -1.08948641e-01 8.25925991... | [6.591987133026123, 3.436844825744629] |
80d28c27-5e14-4179-9869-3257b691efce | provable-multi-instance-deep-auc-maximization | 2305.08040 | null | https://arxiv.org/abs/2305.08040v4 | https://arxiv.org/pdf/2305.08040v4.pdf | Provable Multi-instance Deep AUC Maximization with Stochastic Pooling | This paper considers a novel application of deep AUC maximization (DAM) for multi-instance learning (MIL), in which a single class label is assigned to a bag of instances (e.g., multiple 2D slices of a CT scan for a patient). We address a neglected yet non-negligible computational challenge of MIL in the context of DAM... | ['Dixian Zhu', 'Tianbao Yang', 'Xiaodong Wu', 'Milan Sonka', 'Yaxing Wang', 'Zhi Chen', 'Bokun Wang'] | 2023-05-14 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 3.89641851e-01 1.94368333e-01 -1.61770880e-02 -6.12007022e-01
-1.60523605e+00 -1.12255089e-01 3.23882326e-02 3.07843447e-01
-6.55053735e-01 9.21182513e-01 1.43832844e-02 -1.15917087e-01
-2.16857746e-01 -6.85892701e-01 -1.06645298e+00 -1.02657592e+00
5.86657934e-02 5.68573475e-01 6.85001761e-02 3.78724426... | [14.439160346984863, -2.0214924812316895] |
b44e1b18-cf8d-4c09-b81d-c9c3222b84c1 | performance-aware-approximation-of-global | 2303.11923 | null | https://arxiv.org/abs/2303.11923v1 | https://arxiv.org/pdf/2303.11923v1.pdf | Performance-aware Approximation of Global Channel Pruning for Multitask CNNs | Global channel pruning (GCP) aims to remove a subset of channels (filters) across different layers from a deep model without hurting the performance. Previous works focus on either single task model pruning or simply adapting it to multitask scenario, and still face the following problems when handling multitask prunin... | ['Bin Wang', 'Jiayuan Fan', 'Tao Chen', 'Bo Zhang', 'Hancheng Ye'] | 2023-03-21 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 5.46477973e-01 -2.25778952e-01 -1.67944565e-01 -2.44250610e-01
-5.35514951e-01 -1.17757179e-01 -1.16866417e-02 2.76139975e-01
-5.04934072e-01 7.03587890e-01 -5.67669943e-02 -3.48508537e-01
-3.06859553e-01 -5.77885926e-01 -7.66496778e-01 -7.03368783e-01
-3.84122953e-02 -5.32550998e-02 8.34667683e-01 9.61836576... | [8.63611888885498, 3.0218727588653564] |
a67ade42-5846-42d1-9742-57975680e7d4 | using-deepfake-technologies-for-word-emphasis | 2305.07791 | null | https://arxiv.org/abs/2305.07791v1 | https://arxiv.org/pdf/2305.07791v1.pdf | Using Deepfake Technologies for Word Emphasis Detection | In this work, we consider the task of automated emphasis detection for spoken language. This problem is challenging in that emphasis is affected by the particularities of speech of the subject, for example the subject accent, dialect or voice. To address this task, we propose to utilize deep fake technology to produce ... | ['Lee-Ad Gottlieb', 'Eran Kaufman'] | 2023-05-12 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 3.26654285e-01 2.41452292e-01 2.58545280e-01 -1.90658972e-01
-5.04796982e-01 -7.50535488e-01 5.59264243e-01 -1.47485957e-01
-2.30560824e-01 6.04571044e-01 4.78891551e-01 -4.07220334e-01
3.66426826e-01 -3.02744627e-01 -2.01328650e-01 -6.58243060e-01
2.26867586e-01 1.48588628e-01 2.82843322e-01 -3.97438437... | [14.660669326782227, 6.4081315994262695] |
5e0aacbc-8a36-424d-a00b-c83ef6c39123 | contactart-learning-3d-interaction-priors-for | 2305.01618 | null | https://arxiv.org/abs/2305.01618v1 | https://arxiv.org/pdf/2305.01618v1.pdf | ContactArt: Learning 3D Interaction Priors for Category-level Articulated Object and Hand Poses Estimation | We propose a new dataset and a novel approach to learning hand-object interaction priors for hand and articulated object pose estimation. We first collect a dataset using visual teleoperation, where the human operator can directly play within a physical simulator to manipulate the articulated objects. We record the dat... | ['Xiaolong Wang', 'Varun Jampani', 'Deqing Sun', 'Yuzhe Qin', 'Jiashun Wang', 'Zehao Zhu'] | 2023-05-02 | null | null | null | null | ['hand-pose-estimation'] | ['computer-vision'] | [-1.93413302e-01 3.51311229e-02 -2.89109945e-01 -1.28458560e-01
-5.97384930e-01 -7.52456307e-01 3.18790257e-01 -7.06026554e-01
-1.58487797e-01 4.49334681e-01 2.87461579e-01 1.06480375e-01
7.23747164e-02 -2.17006266e-01 -7.74512887e-01 -5.23434758e-01
9.20471773e-02 1.22887647e+00 2.99504429e-01 6.08293787... | [6.446499347686768, -0.9875685572624207] |
c06d3ec2-d518-4ee5-9d7c-00ab4951991a | fisr-deep-joint-frame-interpolation-and-super | 1912.07213 | null | https://arxiv.org/abs/1912.07213v2 | https://arxiv.org/pdf/1912.07213v2.pdf | FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-scale Temporal Loss | Super-resolution (SR) has been widely used to convert low-resolution legacy videos to high-resolution (HR) ones, to suit the increasing resolution of displays (e.g. UHD TVs). However, it becomes easier for humans to notice motion artifacts (e.g. motion judder) in HR videos being rendered on larger-sized display devices... | ['Munchurl Kim', 'Soo Ye Kim', 'Jihyong Oh'] | 2019-12-16 | null | null | null | null | ['space-time-video-super-resolution'] | ['computer-vision'] | [ 5.26055455e-01 -3.01165432e-01 -6.36513457e-02 -1.20703794e-01
-8.20137143e-01 -1.96788609e-01 2.33210072e-01 -4.60757494e-01
-2.99584210e-01 7.72659600e-01 1.39231324e-01 -2.04233110e-01
-2.36534420e-02 -5.93488812e-01 -6.78230286e-01 -5.74712217e-01
-4.30350155e-02 -6.31458521e-01 5.71132362e-01 -1.38111800... | [11.020593643188477, -1.9612853527069092] |
53a1aa50-c7e9-4951-bb64-eeff4aed4bb2 | detection-of-sub-cellular-changes-by-use-of-l | 2103.13484 | null | https://arxiv.org/abs/2103.13484v2 | https://arxiv.org/pdf/2103.13484v2.pdf | Continuous monitoring of plant sub-cellular structural changes for plant and crop diseases detection by use of Intelligent Laser Speckle Classification (AI) technique | The continuous online monitoring of early signs of plant and crop diseases, at their early stages before a potential spread, is of high importance and necessitates multi-disciplinary techniques. Within this study a proposed technique achieves this goal by exploiting laser physics, textural image analysis, and AI for Sh... | ['Ahmet Orun'] | 2021-03-23 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 5.35670578e-01 -5.57477809e-02 -1.37437701e-01 3.16531301e-01
-1.40473144e-02 -6.28315210e-01 2.04590812e-01 4.51453120e-01
1.64682195e-01 8.18016827e-01 -5.98361492e-01 -1.36619851e-01
-5.61644793e-01 -1.27637315e+00 -1.23507574e-01 -1.22726810e+00
-6.34003282e-02 4.58394736e-01 5.46019137e-01 -1.33166522... | [9.189830780029297, -1.6011667251586914] |
0fad5b1f-a4c6-4d3d-be90-950d167970a9 | contrastive-energy-prediction-for-exact | 2304.12824 | null | https://arxiv.org/abs/2304.12824v2 | https://arxiv.org/pdf/2304.12824v2.pdf | Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning | Guided sampling is a vital approach for applying diffusion models in real-world tasks that embeds human-defined guidance during the sampling procedure. This paper considers a general setting where the guidance is defined by an (unnormalized) energy function. The main challenge for this setting is that the intermediate ... | ['Jun Zhu', 'Chongxuan Li', 'Hang Su', 'Jianfei Chen', 'Huayu Chen', 'Cheng Lu'] | 2023-04-25 | null | null | null | null | ['d4rl'] | ['robots'] | [ 2.03221187e-01 2.01664209e-01 -3.62969607e-01 -7.64429569e-02
-1.03799880e+00 -4.06971246e-01 8.99341226e-01 -6.65570647e-02
-3.69745612e-01 6.29155636e-01 1.22600637e-01 -2.51777261e-01
1.94086302e-02 -6.78472698e-01 -9.47000742e-01 -8.50943744e-01
-3.00328415e-02 3.58765155e-01 9.80363488e-02 -1.78324386... | [4.144830226898193, 2.127885103225708] |
07ca06d3-db23-4060-8b28-255a1ea6d549 | vector-based-representation-is-the-key-a | 2305.18063 | null | https://arxiv.org/abs/2305.18063v1 | https://arxiv.org/pdf/2305.18063v1.pdf | Vector-based Representation is the Key: A Study on Disentanglement and Compositional Generalization | Recognizing elementary underlying concepts from observations (disentanglement) and generating novel combinations of these concepts (compositional generalization) are fundamental abilities for humans to support rapid knowledge learning and generalize to new tasks, with which the deep learning models struggle. Towards hu... | ['Nanning Zheng', 'Yan Lu', 'Cuiling Lan', 'Yuwang Wang', 'Tao Yang'] | 2023-05-29 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 1.98694751e-01 -1.24367699e-01 -1.96648300e-01 -1.68379620e-01
9.09113064e-02 -6.89614415e-01 9.64459777e-01 1.17241561e-01
-3.04712594e-01 7.02844381e-01 2.88370192e-01 -6.20027818e-02
-5.17130673e-01 -9.17002618e-01 -3.03129077e-01 -9.92893696e-01
-9.88449603e-02 3.25679213e-01 -3.34704995e-01 -5.45251727... | [9.232056617736816, 4.860423564910889] |
d1c84c53-44a0-4bc7-98a8-39c20216a786 | a-probabilistic-end-to-end-task-oriented | 2009.08115 | null | https://arxiv.org/abs/2009.08115v3 | https://arxiv.org/pdf/2009.08115v3.pdf | A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning | Structured belief states are crucial for user goal tracking and database query in task-oriented dialog systems. However, training belief trackers often requires expensive turn-level annotations of every user utterance. In this paper we aim at alleviating the reliance on belief state labels in building end-to-end dialog... | ['Yichi Zhang', 'Huixin Wang', 'Junlan Feng', 'Zhijian Ou'] | 2020-09-17 | null | https://aclanthology.org/2020.emnlp-main.740 | https://aclanthology.org/2020.emnlp-main.740.pdf | emnlp-2020-11 | ['end-to-end-dialogue-modelling'] | ['natural-language-processing'] | [-2.54549861e-01 7.06485033e-01 -4.03089553e-01 -8.53078663e-01
-1.15056801e+00 -8.31564009e-01 6.77298367e-01 -1.84404388e-01
-3.68679047e-01 6.80522561e-01 5.16236782e-01 -2.51124918e-01
4.50278968e-01 -3.39927942e-01 -4.52894777e-01 -2.35964239e-01
3.79344463e-01 7.74754226e-01 3.10577065e-01 -5.41017830... | [12.779973030090332, 7.853922367095947] |
6b3b244b-e9ca-449c-9033-f48aafaad827 | dense-fully-convolutional-network-for-skin | 1712.10207 | null | https://arxiv.org/abs/1712.10207v4 | https://arxiv.org/pdf/1712.10207v4.pdf | Dense Pooling layers in Fully Convolutional Network for Skin Lesion Segmentation | One of the essential tasks in medical image analysis is segmentation and accurate detection of borders. Lesion segmentation in skin images is an essential step in the computerized detection of skin cancer. However, many of the state-of-the-art segmentation methods have deficiencies in their border detection phase. In t... | ['Nader Karimi', 'Ebrahim Nasr-Esfahani', 'Mohammad H. Jafari', 'Kayvan Najarian', 'James S. Wrobel', 'Shima Rafiei', 'Shadrokh Samavi', 'S. M. Reza Soroushmehr'] | 2017-12-29 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 5.90450406e-01 8.44050199e-02 -2.63688803e-01 -1.44463345e-01
-4.64896262e-01 -3.20778221e-01 1.72299325e-01 2.15717584e-01
-6.86158657e-01 5.56849182e-01 -3.61446917e-01 -1.60193488e-01
1.29130349e-01 -8.33137333e-01 -5.34696169e-02 -8.03617656e-01
2.61709571e-01 1.30168684e-02 9.46066558e-01 -1.41692027... | [15.582956314086914, -3.0372767448425293] |
94f28d4b-1b82-4d82-87a4-d7a400c8c509 | efficient-ensembles-of-graph-neural-networks | null | null | https://openreview.net/forum?id=lTiW8Jet8t | https://openreview.net/pdf?id=lTiW8Jet8t | Efficient Ensembles of Graph Neural Networks | Graph Neural Networks (GNNs) have enabled the power of deep learning to be applied to inputs beyond the Euclidean domain, with applications ranging from social networks and product recommendation engines to the life sciences. GNNs, like other classes of machine learning models, benefit from ensemble learning, wherein m... | ['Anand Raghunathan', 'Jacob R. Stevens', 'Amrit Nagarajan'] | 2021-09-29 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 2.96166658e-01 1.11504301e-01 -6.06223382e-02 -1.67899951e-01
-6.17274940e-02 -4.08985734e-01 2.08656132e-01 4.80723202e-01
-2.56902426e-01 7.01243222e-01 -3.29262257e-01 -6.22615695e-01
-2.93238878e-01 -1.50425386e+00 -9.74401891e-01 -4.95090783e-01
-4.07078236e-01 1.86971247e-01 -2.07488965e-02 -2.11863011... | [6.981621265411377, 6.0033135414123535] |
5cb3819e-a4d1-44b9-9a35-a132218cbedc | entity-and-evidence-guided-relation | 2008.12283 | null | https://arxiv.org/abs/2008.12283v1 | https://arxiv.org/pdf/2008.12283v1.pdf | Entity and Evidence Guided Relation Extraction for DocRED | Document-level relation extraction is a challenging task which requires reasoning over multiple sentences in order to predict relations in a document. In this paper, we pro-pose a joint training frameworkE2GRE(Entity and Evidence Guided Relation Extraction)for this task. First, we introduce entity-guided sequences as i... | ['Guangtao Wang', 'Tengyu Ma', 'Jing Huang', 'Kevin Huang'] | 2020-08-27 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 1.40874296e-01 9.48631167e-01 -4.57148999e-01 -2.89935887e-01
-9.52315688e-01 -5.80689490e-01 9.22105849e-01 8.26602995e-01
-4.23011720e-01 9.69142199e-01 4.02620137e-01 -5.67898691e-01
-2.85990953e-01 -8.61662090e-01 -9.88774538e-01 1.53955771e-02
-3.31105530e-01 8.20950449e-01 5.38173854e-01 -2.18965188... | [9.369006156921387, 8.671226501464844] |
97bd2316-e98c-484a-8b40-8c5a83717549 | language-free-training-for-zero-shot-video | 2210.12977 | null | https://arxiv.org/abs/2210.12977v1 | https://arxiv.org/pdf/2210.12977v1.pdf | Language-free Training for Zero-shot Video Grounding | Given an untrimmed video and a language query depicting a specific temporal moment in the video, video grounding aims to localize the time interval by understanding the text and video simultaneously. One of the most challenging issues is an extremely time- and cost-consuming annotation collection, including video capti... | ['Kwanghoon Sohn', 'Seongheon Park', 'Jiyoung Lee', 'Jungin Park', 'Dahye Kim'] | 2022-10-24 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [ 3.02432120e-01 2.57229954e-02 -5.57096124e-01 -2.45055765e-01
-8.71345699e-01 -6.52979791e-01 4.85006213e-01 -1.76466450e-01
-3.54772985e-01 5.54782450e-01 4.68545407e-02 -2.94705868e-01
2.33359665e-01 -4.74955857e-01 -1.22544193e+00 -3.70794624e-01
2.87437835e-03 1.24928355e-01 3.03284079e-01 1.47724245... | [10.049249649047852, 0.7395883798599243] |
8d4c14d5-2474-4a01-a151-16c689c028ee | hymo-vulnerability-detection-in-smart | 2304.13103 | null | https://arxiv.org/abs/2304.13103v1 | https://arxiv.org/pdf/2304.13103v1.pdf | HyMo: Vulnerability Detection in Smart Contracts using a Novel Multi-Modal Hybrid Model | With blockchain technology rapidly progress, the smart contracts have become a common tool in a number of industries including finance, healthcare, insurance and gaming. The number of smart contracts has multiplied, and at the same time, the security of smart contracts has drawn considerable attention due to the moneta... | ['Jafar Tahmoresnezhad', 'Mohammad Khodadadi'] | 2023-04-25 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-1.34448871e-01 -2.83868730e-01 -1.03920951e-01 1.25058427e-01
-7.56668270e-01 -9.59266961e-01 8.35408270e-01 4.23478037e-02
-1.45827487e-01 3.56252313e-01 8.33871841e-01 -9.39626157e-01
2.13672131e-01 -1.02278388e+00 -2.58787900e-01 -9.05337036e-01
1.03015795e-01 6.39971852e-01 2.39792448e-02 -6.43969774... | [6.81016206741333, 7.253413677215576] |
303588f4-02d4-4964-a219-812fb6b79214 | airrl-a-reinforcement-learning-approach-to | 2003.12205 | null | https://arxiv.org/abs/2003.12205v1 | https://arxiv.org/pdf/2003.12205v1.pdf | AirRL: A Reinforcement Learning Approach to Urban Air Quality Inference | Urban air pollution has become a major environmental problem that threatens public health. It has become increasingly important to infer fine-grained urban air quality based on existing monitoring stations. One of the challenges is how to effectively select some relevant stations for air quality inference. In this pape... | ['Cunxiang Yin', 'Jinchang Luo', 'JiaWei He', 'Xiaohui Wu', 'Huiqiang Zhong'] | 2020-03-27 | null | null | null | null | ['air-quality-inference'] | ['miscellaneous'] | [ 7.65166432e-02 -4.90602255e-01 -1.77920207e-01 -2.14956343e-01
-1.01428235e+00 -3.01972210e-01 2.69950211e-01 2.74645329e-01
-4.75283116e-01 1.17187595e+00 2.42089316e-01 -5.72110176e-01
-6.09030068e-01 -1.69356596e+00 -6.93639040e-01 -7.74723470e-01
3.63012284e-01 4.71094966e-01 3.29587132e-01 9.64211151... | [6.276653289794922, 2.500866174697876] |
54b832e9-e5ef-440d-8f3f-bd419d749969 | explain-to-me-salience-based-explainability | 2303.11969 | null | https://arxiv.org/abs/2303.11969v2 | https://arxiv.org/pdf/2303.11969v2.pdf | Explain To Me: Salience-Based Explainability for Synthetic Face Detection Models | The performance of convolutional neural networks has continued to improve over the last decade. At the same time, as model complexity grows, it becomes increasingly more difficult to explain model decisions. Such explanations may be of critical importance for reliable operation of human-machine pairing setups, or for m... | ['Adam Czajka', 'Kevin Bowyer', 'Timothy Kelley', 'Christopher Sweet', 'Jacob Piland', 'Aidan Boyd', 'Patrick Tinsley', 'Colton Crum'] | 2023-03-21 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 4.40605819e-01 2.75257140e-01 -1.44559711e-01 -4.10445243e-01
-2.28235483e-01 -2.71805316e-01 7.60608077e-01 4.79447305e-01
-3.13925564e-01 5.13474643e-01 3.44818868e-02 -1.27298996e-01
-2.59964794e-01 -4.60155249e-01 -7.73306370e-01 -5.95155656e-01
-3.49326879e-01 2.45057732e-01 8.51799324e-02 -2.75119632... | [9.939043045043945, 2.1740570068359375] |
1eb63d36-db9e-4703-8ce3-8a77c9230d2f | date-dual-attentive-tree-aware-embedding-for | null | null | https://dl.acm.org/doi/10.1145/3394486.3403339 | https://dl.acm.org/doi/pdf/10.1145/3394486.3403339 | DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection | Intentional manipulation of invoices that lead to undervaluation of trade goods is the most common type of customs fraud to avoid ad valorem duties and taxes. To secure government revenue without interrupting legitimate trade flows, customs administrations around the world strive to develop ways to detect illicit trade... | ['Cheng-Te Li', 'Yu-Che Tsai', 'Karandeep Singh', 'Etim Ibok', 'Yeonsoo Choi', 'Sundong Kim', 'Meeyoung Cha'] | 2020-08-23 | null | null | null | kdd-2020-8 | ['value-prediction', 'multi-target-regression'] | ['computer-code', 'miscellaneous'] | [-4.18016404e-01 -1.41008884e-01 -7.47260213e-01 -3.12133431e-01
-6.12652779e-01 -1.11993515e+00 5.87069035e-01 3.72948676e-01
-4.22447324e-01 4.36341584e-01 4.35133129e-01 -1.17821968e+00
-2.71955192e-01 -1.10202134e+00 -5.17749310e-01 -3.94336939e-01
-1.93427548e-01 7.69650161e-01 -5.08282125e-01 -3.62895876... | [7.336564064025879, 5.875798225402832] |
5c62b8b3-6c90-4531-b316-3631390f4a13 | noise-robust-morphological-disambiguation-for | null | null | https://aclanthology.org/N18-1087 | https://aclanthology.org/N18-1087.pdf | Noise-Robust Morphological Disambiguation for Dialectal Arabic | User-generated text tends to be noisy with many lexical and orthographic inconsistencies, making natural language processing (NLP) tasks more challenging. The challenging nature of noisy text processing is exacerbated for dialectal content, where in addition to spelling and lexical differences, dialectal text is charac... | ['er', 'Alex Erdmann', 'Nizar Habash', 'Nasser Zalmout'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['lexical-normalization', 'morphological-disambiguation', 'morphological-tagging'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.59002244e-02 -2.85439901e-02 2.16284543e-01 -4.57800299e-01
-1.22898614e+00 -9.03099477e-01 1.35047957e-01 6.73784494e-01
-8.43741059e-01 8.85409594e-01 5.36454439e-01 -2.49759525e-01
2.07059562e-01 -7.91083395e-01 -4.45470273e-01 -6.16801918e-01
5.99640831e-02 6.09280229e-01 -1.13715805e-01 -5.77373207... | [10.433847427368164, 10.161334991455078] |
f41d3e79-7bc4-426b-a498-d2681f4ffe2c | robust-recovery-for-stochastic-block-models | 2111.08568 | null | https://arxiv.org/abs/2111.08568v1 | https://arxiv.org/pdf/2111.08568v1.pdf | Robust recovery for stochastic block models | We develop an efficient algorithm for weak recovery in a robust version of the stochastic block model. The algorithm matches the statistical guarantees of the best known algorithms for the vanilla version of the stochastic block model. In this sense, our results show that there is no price of robustness in the stochast... | ['David Steurer', 'Rajai Nasser', "Tommaso d'Orsi", 'Jingqiu Ding'] | 2021-11-16 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 5.00885248e-01 8.41350257e-02 -1.78417832e-01 2.15778574e-01
-1.24732625e+00 -7.58709192e-01 5.50441444e-01 7.91398659e-02
-2.48293146e-01 6.79014742e-01 2.31415227e-01 -4.38785851e-01
-6.21263206e-01 -5.39478064e-01 -9.90546703e-01 -1.43438900e+00
-2.98647672e-01 3.79947990e-01 1.87805757e-01 -4.67455596... | [6.912405014038086, 4.694620609283447] |
9ca024c8-3607-4ae4-8045-fbe8e92c0837 | efficient-deep-learning-for-stereo-matching | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Luo_Efficient_Deep_Learning_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Luo_Efficient_Deep_Learning_CVPR_2016_paper.pdf | Efficient Deep Learning for Stereo Matching | In the past year, convolutional neural networks have been shown to perform extremely well for stereo estimation. However, current architectures rely on siamese networks which exploit concatenation followed by further processing layers, requiring a minute of GPU computation per image pair. In contrast, in this paper we ... | ['Raquel Urtasun', 'Wenjie Luo', 'Alexander G. Schwing'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['stereo-matching'] | ['computer-vision'] | [ 1.28031611e-01 -1.58014223e-01 2.04477042e-01 -3.67932647e-01
-6.12249672e-01 -4.52298403e-01 8.85284960e-01 6.68308511e-03
-9.38941300e-01 4.86620873e-01 4.70551774e-02 -1.35303557e-01
2.65974611e-01 -9.83831584e-01 -7.69989252e-01 -3.24827969e-01
3.56919356e-02 3.74332964e-01 4.28253770e-01 -4.04324025... | [8.847197532653809, -2.1265339851379395] |
1d2e7e32-cbe4-4b21-ba8b-2d46336e19ed | detecting-and-simulating-artifacts-in-gan | 1907.06515 | null | https://arxiv.org/abs/1907.06515v2 | https://arxiv.org/pdf/1907.06515v2.pdf | Detecting and Simulating Artifacts in GAN Fake Images | To detect GAN generated images, conventional supervised machine learning algorithms require collection of a number of real and fake images from the targeted GAN model. However, the specific model used by the attacker is often unavailable. To address this, we propose a GAN simulator, AutoGAN, which can simulate the arti... | ['Shih-Fu Chang', 'Xu Zhang', 'Svebor Karaman'] | 2019-07-15 | null | null | null | null | ['gan-image-forensics'] | ['computer-vision'] | [ 6.67744160e-01 3.27569455e-01 2.98467219e-01 3.33431393e-01
-9.25810099e-01 -8.72506559e-01 6.90575123e-01 -4.98359770e-01
2.24705949e-01 4.66541409e-01 -3.60542089e-01 -1.98247224e-01
7.49767244e-01 -8.90499830e-01 -1.19305992e+00 -8.06309760e-01
2.57773250e-01 1.44474149e-01 1.02504350e-01 8.42805654... | [12.385367393493652, 1.0355428457260132] |
736b4286-bf87-4e0e-b0f4-15d4b8233450 | conflict-based-search-for-connected-multi | 2006.03280 | null | https://arxiv.org/abs/2006.03280v1 | https://arxiv.org/pdf/2006.03280v1.pdf | Conflict-Based Search for Connected Multi-Agent Path Finding | We study a variant of the multi-agent path finding problem (MAPF) in which agents are required to remain connected to each other and to a designated base. This problem has applications in search and rescue missions where the entire execution must be monitored by a human operator. We re-visit the conflict-based search a... | ['François Schwarzentruber', 'Ocan Sankur', 'Arthur Queffelec'] | 2020-06-05 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 7.95360878e-02 1.64578080e-01 -2.35573709e-01 -5.12544066e-02
-1.93766758e-01 -9.88363147e-01 5.04608750e-01 6.96490288e-01
-9.17623937e-01 1.45508313e+00 -9.90945473e-02 -1.64751142e-01
-1.09226048e+00 -1.13473010e+00 -2.82779038e-01 -6.86216056e-01
-8.36110234e-01 1.40974045e+00 8.05402517e-01 -8.96609306... | [4.937862873077393, 1.7267073392868042] |
eb89581c-5022-48f8-9f06-3340402b902f | depth-adaptive-computational-policies-for | 1801.00508 | null | http://arxiv.org/abs/1801.00508v1 | http://arxiv.org/pdf/1801.00508v1.pdf | Depth-Adaptive Computational Policies for Efficient Visual Tracking | Current convolutional neural networks algorithms for video object tracking
spend the same amount of computation for each object and video frame. However,
it is harder to track an object in some frames than others, due to the varying
amount of clutter, scene complexity, amount of motion, and object's
distinctiveness aga... | ['Katerina Fragkiadaki', 'Chris Ying'] | 2018-01-01 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [-5.29385172e-02 -3.80833179e-01 -4.18627828e-01 -1.80170491e-01
-6.02599919e-01 -6.82406008e-01 2.98577040e-01 -1.24749809e-03
-9.32064235e-01 2.33744055e-01 -1.20816521e-01 2.90148985e-02
1.77315295e-01 -5.79747558e-01 -1.04502249e+00 -4.94926363e-01
-3.21960121e-01 2.86316723e-01 8.65778029e-01 2.75873542... | [8.954293251037598, -0.19921939074993134] |
af6b2960-f49a-4c51-9746-da3ec44be886 | yolo-and-mask-r-cnn-for-vehicle-number-plate | 2207.13165 | null | https://arxiv.org/abs/2207.13165v2 | https://arxiv.org/pdf/2207.13165v2.pdf | YOLO and Mask R-CNN for Vehicle Number Plate Identification | License plate scanners have grown in popularity in parking lots during the past few years. In order to quickly identify license plates, traditional plate recognition devices used in parking lots employ a fixed source of light and shooting angles. For skewed angles, such as license plate images taken with ultra-wide ang... | ['Siddharth Ganjoo'] | 2022-07-26 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [-1.21058479e-01 -3.88271034e-01 2.01024771e-01 -2.55337000e-01
-1.66802660e-01 -6.56529248e-01 3.87402564e-01 -8.40622723e-01
-5.41000068e-01 5.04746675e-01 -5.07853627e-01 -4.63842034e-01
3.39824766e-01 -6.31129742e-01 -4.17179316e-01 -6.72854245e-01
4.23709452e-01 5.44956267e-01 5.19510865e-01 -4.50461864... | [9.819615364074707, -4.968945503234863] |
e78a0002-bb72-4cb5-a633-2d7729511b52 | evaluating-counterfactual-explanations-using | 2301.02499 | null | https://arxiv.org/abs/2301.02499v1 | https://arxiv.org/pdf/2301.02499v1.pdf | Evaluating counterfactual explanations using Pearl's counterfactual method | Counterfactual explanations (CEs) are methods for generating an alternative scenario that produces a different desirable outcome. For example, if a student is predicted to fail a course, then counterfactual explanations can provide the student with alternate ways so that they would be predicted to pass. The application... | ['Bevan I. Smith'] | 2023-01-06 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 1.62570924e-01 6.54399216e-01 -6.06645286e-01 -2.98243254e-01
-3.40147883e-01 -5.99505067e-01 1.02837110e+00 1.79561540e-01
-4.58535463e-01 1.44514489e+00 5.39653420e-01 -1.19873071e+00
-4.56149697e-01 -8.85164082e-01 -8.75922978e-01 -4.44192529e-01
4.12910990e-02 3.36130023e-01 -1.50802091e-01 2.97820661... | [8.563305854797363, 5.629896640777588] |
52440856-4cc7-4ced-a167-6178b9705601 | interventional-and-counterfactual-inference | 2302.00860 | null | https://arxiv.org/abs/2302.00860v2 | https://arxiv.org/pdf/2302.00860v2.pdf | Interventional and Counterfactual Inference with Diffusion Models | We consider the problem of answering observational, interventional, and counterfactual queries in a causally sufficient setting where only observational data and the causal graph are available. Utilizing the recent developments in diffusion models, we introduce diffusion-based causal models (DCM) to learn causal mechan... | ['Shiva Prasad Kasiviswanathan', 'Patrick Blöbaum', 'Patrick Chao'] | 2023-02-02 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 6.10231400e-01 6.24248922e-01 -1.20084918e+00 -2.74099320e-01
-8.71277153e-01 -4.87407953e-01 1.03068912e+00 2.33841240e-01
2.69869845e-02 1.17978656e+00 1.30837691e+00 -7.95579135e-01
-4.29388672e-01 -1.08118927e+00 -1.14323902e+00 -3.59036356e-01
-5.65280199e-01 3.57469976e-01 -4.69033957e-01 1.71762988... | [8.108266830444336, 5.42254638671875] |
06ea026e-3db4-4c38-a15e-bdea4528c6c6 | survey-on-software-isp-methods-based-on-deep | 2305.11994 | null | https://arxiv.org/abs/2305.11994v2 | https://arxiv.org/pdf/2305.11994v2.pdf | ISP meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing | The entire Image Signal Processor (ISP) of a camera relies on several processes to transform the data from the Color Filter Array (CFA) sensor, such as demosaicing, denoising, and enhancement. These processes can be executed either by some hardware or via software. In recent years, Deep Learning has emerged as one solu... | ['Claudio Filipi Gonçalves dos Santos', 'Rodolfo Coelho Dalapicola', 'Mayara Costa Regazio', 'Bruno Melo de Souza', 'Lucas Borges Rondon', 'Iago Oliveira Lima', 'Guilherme Augusto Bileki', 'Leonardo Tadeu Lopes', 'Wladimir Barroso Guedes de Araújo Neto', 'Rodrigo Reis Arrais', 'Jhessica Victoria Santos da Silva', 'Math... | 2023-05-19 | null | null | null | null | ['demosaicking'] | ['computer-vision'] | [ 2.44056195e-01 -3.59969705e-01 5.63508987e-01 -4.58023012e-01
-3.73420209e-01 -4.28320497e-01 3.32056373e-01 -2.41496846e-01
-6.14428341e-01 2.69789189e-01 -1.55688897e-01 -2.10093081e-01
1.37065828e-01 -7.77703881e-01 -6.50225580e-01 -9.47762430e-01
6.51889294e-02 -6.83613941e-02 4.16048318e-01 -2.85978224... | [11.374403953552246, -2.3213202953338623] |
7f7bce46-ed8d-4261-9afc-e64110340640 | aec-in-a-netshell-on-target-and-topology | 2103.09007 | null | https://arxiv.org/abs/2103.09007v1 | https://arxiv.org/pdf/2103.09007v1.pdf | AEC in a NetShell: On Target and Topology Choices for FCRN Acoustic Echo Cancellation | Acoustic echo cancellation (AEC) algorithms have a long-term steady role in signal processing, with approaches improving the performance of applications such as automotive hands-free systems, smart home and loudspeaker devices, or web conference systems. Just recently, very first deep neural network (DNN)-based approac... | ['Tim Fingscheidt', 'Ernst Seidel', 'Jan Franzen'] | 2021-03-16 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 2.28354558e-01 -1.16607778e-01 6.97270155e-01 -9.55925435e-02
-8.16010177e-01 -1.79604560e-01 5.80587149e-01 -1.60476983e-01
-6.00199640e-01 4.14609283e-01 4.93507832e-01 -3.03564459e-01
-4.19037253e-01 -3.43167752e-01 -3.29042315e-01 -7.79872656e-01
-2.13495776e-01 -4.40201722e-02 2.35679194e-01 -5.92866898... | [15.050735473632812, 5.930543422698975] |
054d3260-562f-4c2a-8e64-3ced295fec4e | scene-coordinate-regression-with-angle-based | 1808.04999 | null | http://arxiv.org/abs/1808.04999v2 | http://arxiv.org/pdf/1808.04999v2.pdf | Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera Relocalization | Image-based camera relocalization is an important problem in computer vision
and robotics. Recent works utilize convolutional neural networks (CNNs) to
regress for pixels in a query image their corresponding 3D world coordinates in
the scene. The final pose is then solved via a RANSAC-based optimization scheme
using th... | ['Juho Kannala', 'Xiaotian Li', 'Juha Ylioinas', 'Jakob Verbeek'] | 2018-08-15 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [-7.40239546e-02 -1.94846153e-01 -2.03956962e-01 -4.22682434e-01
-3.84613186e-01 -4.74923283e-01 1.99369252e-01 -4.01763678e-01
-6.70868576e-01 4.89465624e-01 -8.06817710e-02 5.13552837e-02
1.83516100e-01 -7.95985520e-01 -1.00635016e+00 -5.35851419e-01
7.61330605e-01 2.48413011e-01 8.87376666e-02 -8.07750002... | [7.952687740325928, -2.2922425270080566] |
edd97948-735e-47a6-aee3-308a8d66a5e3 | mul-gad-a-semi-supervised-graph-anomaly | 2212.05478 | null | https://arxiv.org/abs/2212.05478v1 | https://arxiv.org/pdf/2212.05478v1.pdf | Mul-GAD: a semi-supervised graph anomaly detection framework via aggregating multi-view information | Anomaly detection is defined as discovering patterns that do not conform to the expected behavior. Previously, anomaly detection was mostly conducted using traditional shallow learning techniques, but with little improvement. As the emergence of graph neural networks (GNN), graph anomaly detection has been greatly deve... | ['Jingzhang Sun', 'Chunjie Cao', 'Zhiyuan Liu'] | 2022-12-11 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [-1.65324524e-01 -1.51498273e-01 -1.28336370e-01 -2.58731134e-02
-1.46019623e-01 -4.45739925e-01 7.52324998e-01 7.23992467e-01
-1.04832217e-01 2.84246504e-01 -8.21027905e-02 -2.03498632e-01
-2.80174792e-01 -1.11886859e+00 -4.07451779e-01 -5.86918175e-01
-3.26277316e-01 1.72185391e-01 4.08427328e-01 -2.57176369... | [6.690584659576416, 5.826527118682861] |
50d53c77-b637-4f09-8e32-5cbdf267d03a | why-so-pessimistic-estimating-uncertainties-1 | 2205.13703 | null | https://arxiv.org/abs/2205.13703v1 | https://arxiv.org/pdf/2205.13703v1.pdf | Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters | Motivated by the success of ensembles for uncertainty estimation in supervised learning, we take a renewed look at how ensembles of $Q$-functions can be leveraged as the primary source of pessimism for offline reinforcement learning (RL). We begin by identifying a critical flaw in a popular algorithmic choice used by m... | ['Ofir Nachum', 'Shixiang Shane Gu', 'Seyed Kamyar Seyed Ghasemipour'] | 2022-05-27 | null | null | null | null | ['d4rl'] | ['robots'] | [-1.30740389e-01 3.27629566e-01 -1.79104745e-01 -4.04128313e-01
-1.16158426e+00 -8.45654428e-01 5.29508233e-01 2.09284890e-02
-6.58562124e-01 1.19519353e+00 3.35900113e-02 -6.07539177e-01
-2.69235730e-01 -4.80270535e-01 -8.80209923e-01 -6.71502590e-01
-4.44528311e-01 4.58762795e-01 -2.43779257e-01 -2.57702738... | [4.171448707580566, 2.429891586303711] |
43d9f557-3651-43e6-a99c-e3d08eaf227e | sparsity-based-morphological-identification | 2301.06538 | null | https://arxiv.org/abs/2301.06538v1 | https://arxiv.org/pdf/2301.06538v1.pdf | Sparsity based morphological identification of heartbeats | The electrocardiogram (ECG) is one of the most common primary tests to evaluate the health of the heart. Reliable automatic interpretation of ECG records is crucial to the goal of improving public health. It can enable a safe inexpensive monitoring. This work presents a new methodology for morphological identification ... | ['Amadou Sidi Watt', 'Khalil Battikh', 'Laura Rebollo-Neira'] | 2023-01-16 | null | null | null | null | ['classification'] | ['methodology'] | [ 4.42961991e-01 1.40257284e-01 1.23937331e-01 -3.32420677e-01
-3.20866525e-01 -5.38055241e-01 1.25832513e-01 5.54986596e-01
-2.46078789e-01 6.59635007e-01 -3.28051411e-02 -3.18578482e-01
-4.95832950e-01 -4.93389785e-01 3.83361951e-02 -8.19467247e-01
-3.07511896e-01 6.39182031e-01 -2.37257063e-01 2.13055476... | [14.206507682800293, 3.2180705070495605] |
4dc202b6-a584-4c56-8928-72e5e1d12df9 | towards-ontology-reshaping-for-kg-generation | 2209.11067 | null | https://arxiv.org/abs/2209.11067v1 | https://arxiv.org/pdf/2209.11067v1.pdf | Towards Ontology Reshaping for KG Generation with User-in-the-Loop: Applied to Bosch Welding | Knowledge graphs (KG) are used in a wide range of applications. The automation of KG generation is very desired due to the data volume and variety in industries. One important approach of KG generation is to map the raw data to a given KG schema, namely a domain ontology, and construct the entities and properties accor... | ['Evgeny Kharlamov', 'Egor V. Kostylev', 'Gong Cheng', 'Jieying Chen', 'Baifan Zhou', 'Dongzhuoran Zhou'] | 2022-09-22 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [ 2.46809214e-01 5.09029686e-01 -1.86778456e-01 -3.73363137e-01
6.76283613e-02 -4.84393388e-01 3.92714590e-01 3.88839781e-01
-3.91439348e-02 5.84925175e-01 -5.94585063e-03 -2.06942484e-01
-7.49920189e-01 -1.51487410e+00 -3.84771645e-01 -3.20391476e-01
1.60723343e-01 7.86946118e-01 5.91737688e-01 -4.94811922... | [9.105035781860352, 7.814087390899658] |
cb56c813-b90a-48fc-a25e-5a03a13caf9c | em-decipherment-for-large-vocabularies | null | null | https://aclanthology.org/P14-2123 | https://aclanthology.org/P14-2123.pdf | EM Decipherment for Large Vocabularies | null | ['Hermann Ney', 'Malte Nuhn'] | 2014-06-01 | null | null | null | acl-2014-6 | ['decipherment'] | ['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... | [-7.410221099853516, 3.768235921859741] |
0ebe6476-6120-4a8b-ad05-30f0b4609e29 | automatic-parameter-tying-in-neural-networks | null | null | https://openreview.net/forum?id=HkinqfbAb | https://openreview.net/pdf?id=HkinqfbAb | Automatic Parameter Tying in Neural Networks | Recently, there has been growing interest in methods that perform neural network compression, namely techniques that attempt to substantially reduce the size of a neural network without significant reduction in performance. However, most existing methods are post-processing approaches in that they take a learned neural... | ['Vibhav Gogate', 'Nicholas Ruozzi', 'Yibo Yang'] | 2018-01-01 | null | null | null | iclr-2018-1 | ['l2-regularization'] | ['methodology'] | [ 5.60902476e-01 1.85930163e-01 -3.11451674e-01 -5.96653223e-01
-4.59565341e-01 -3.90040874e-01 3.71648580e-01 2.40414903e-01
-8.55426013e-01 7.30767787e-01 -3.08952779e-02 -3.00154567e-01
-3.86451066e-01 -8.89891505e-01 -1.11240053e+00 -6.29613280e-01
7.74702281e-02 4.80777949e-01 2.58626699e-01 1.20463230... | [8.517987251281738, 3.2845938205718994] |
d02f8cd4-5fd5-4874-963d-3929569a4583 | cryo-electron-microscopy-image-analysis-using | 1904.07772 | null | http://arxiv.org/abs/1904.07772v1 | http://arxiv.org/pdf/1904.07772v1.pdf | Cryo-Electron Microscopy Image Analysis Using Multi-Frequency Vector Diffusion Maps | Cryo-electron microscopy (EM) single particle reconstruction is an entirely
general technique for 3D structure determination of macromolecular complexes.
However, because the images are taken at low electron dose, it is extremely
hard to visualize the individual particle with low contrast and high noise
level. In this ... | ['Zhizhen Zhao', 'Yifeng Fan'] | 2019-04-16 | null | null | null | null | ['cryogenic-electron-microscopy-cryo-em'] | ['computer-vision'] | [ 3.73937309e-01 -5.38809121e-01 7.57321715e-01 -1.08986825e-01
-5.19223988e-01 -4.43716496e-01 5.61265767e-01 2.39426434e-01
-4.86073524e-01 7.16674030e-01 -1.54805392e-01 -4.16551232e-02
-1.85712248e-01 -4.79909867e-01 -5.33848464e-01 -1.14176929e+00
1.53761134e-01 5.95879138e-01 1.23561904e-01 2.69461237... | [13.251298904418945, -3.0353035926818848] |
d8dfb577-635d-404a-bac7-726b02ff2f8c | multi-stage-framework-with-refinement-based | null | null | https://openreview.net/forum?id=z7KsNClgofB | https://openreview.net/pdf?id=z7KsNClgofB | Multi-Stage Framework with Refinement based Point Set Registration for Unsupervised Bi-Lingual Word Alignment | Cross-lingual alignment of word embeddings play an important role in knowledge transfer across languages, for improving machine translation and other multi-lingual applications. Current unsupervised approaches rely on learning structure preserving linear transformations using adversarial networks and refinement strateg... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['word-similarity'] | ['natural-language-processing'] | [ 2.65851747e-02 -3.99447262e-01 -4.22549963e-01 -2.98222899e-01
-1.20121908e+00 -1.03314114e+00 7.13579714e-01 6.91958666e-02
-5.71244657e-01 5.90564907e-01 3.02823544e-01 -5.33489406e-01
1.22528449e-01 -4.89308745e-01 -7.37358391e-01 -7.33682096e-01
2.54779488e-01 7.74275362e-01 -1.83859304e-01 -4.76825953... | [11.112120628356934, 10.105268478393555] |
d3777594-b1b2-4ad1-abda-cbc1172238ab | assessing-dialogue-systems-with-distribution | 2105.02573 | null | https://arxiv.org/abs/2105.02573v3 | https://arxiv.org/pdf/2105.02573v3.pdf | Assessing Dialogue Systems with Distribution Distances | An important aspect of developing dialogue systems is how to evaluate and compare the performance of different systems. Existing automatic evaluation metrics are based on turn-level quality evaluation and use average scores for system-level comparison. In this paper, we propose to measure the performance of a dialogue ... | ['Lemao Liu', 'Defu Lian', 'Huayang Li', 'Deng Cai', 'Yahui Liu', 'Jiannan Xiang'] | 2021-05-06 | null | https://aclanthology.org/2021.findings-acl.193 | https://aclanthology.org/2021.findings-acl.193.pdf | findings-acl-2021-8 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-3.25559109e-01 1.50894597e-01 8.52463916e-02 -6.06260896e-01
-8.69603038e-01 -9.23719704e-01 1.14890265e+00 4.60711449e-01
-3.51440430e-01 9.95876431e-01 7.33494878e-01 -1.82718590e-01
-6.96602911e-02 -5.90713799e-01 4.55136836e-01 -1.65217608e-01
2.14223817e-01 6.21482849e-01 3.51693362e-01 -7.75307238... | [12.856679916381836, 8.161293983459473] |
b80779e9-59f9-4b58-b080-9ab67fde9f45 | distinguishability-calibration-to-in-context | 2302.06198 | null | https://arxiv.org/abs/2302.06198v3 | https://arxiv.org/pdf/2302.06198v3.pdf | Distinguishability Calibration to In-Context Learning | Recent years have witnessed increasing interests in prompt-based learning in which models can be trained on only a few annotated instances, making them suitable in low-resource settings. When using prompt-based learning for text classification, the goal is to use a pre-trained language model (PLM) to predict a missing ... | ['Lin Gui', 'Yulan He', 'Li Qian', 'Yanran Li', 'Hanqi Yan', 'Hongjing Li'] | 2023-02-13 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 1.23551689e-01 -2.47101132e-02 -2.20767468e-01 -5.24672925e-01
-7.97835112e-01 -6.99424863e-01 7.69062757e-01 5.54636538e-01
-5.21413267e-01 3.01668286e-01 2.91257918e-01 -1.95762828e-01
-1.75838873e-01 -8.12782705e-01 -4.12576735e-01 -6.80044055e-01
3.94284278e-01 4.54467773e-01 -2.40912903e-02 1.38425663... | [10.295755386352539, 6.960758686065674] |
35d4daea-487c-4b57-b342-e6658b291f00 | polka-lines-learning-structured-illumination | 2011.13117 | null | https://arxiv.org/abs/2011.13117v2 | https://arxiv.org/pdf/2011.13117v2.pdf | Polka Lines: Learning Structured Illumination and Reconstruction for Active Stereo | Active stereo cameras that recover depth from structured light captures have become a cornerstone sensor modality for 3D scene reconstruction and understanding tasks across application domains. Existing active stereo cameras project a pseudo-random dot pattern on object surfaces to extract disparity independently of ob... | ['Felix Heide', 'Seung-Hwan Baek'] | 2020-11-26 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Baek_Polka_Lines_Learning_Structured_Illumination_and_Reconstruction_for_Active_Stereo_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Baek_Polka_Lines_Learning_Structured_Illumination_and_Reconstruction_for_Active_Stereo_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 6.56992972e-01 1.24043236e-02 2.82489240e-01 -4.89140034e-01
-6.79068923e-01 -5.37634254e-01 4.12972391e-01 -6.28136933e-01
-4.09713775e-01 4.99238729e-01 3.47822756e-01 2.50790529e-02
-1.12524390e-01 -4.81140256e-01 -9.88098145e-01 -9.67903674e-01
6.47633851e-01 3.25812072e-01 2.26518214e-01 1.60989597... | [9.375216484069824, -2.730773448944092] |
ecd0f20d-50a3-45c7-b166-9f1080ea0ffa | test-time-fast-adaptation-for-dynamic-scene | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Chi_Test-Time_Fast_Adaptation_for_Dynamic_Scene_Deblurring_via_Meta-Auxiliary_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Chi_Test-Time_Fast_Adaptation_for_Dynamic_Scene_Deblurring_via_Meta-Auxiliary_Learning_CVPR_2021_paper.pdf | Test-Time Fast Adaptation for Dynamic Scene Deblurring via Meta-Auxiliary Learning | In this paper, we tackle the problem of dynamic scene deblurring. Most existing deep end-to-end learning approaches adopt the same generic model for all unseen test images. These solutions are sub-optimal, as they fail to utilize the internal information within a specific image. On the other hand, a self-supervised... | ['Jin Tang', 'Yuanhao Yu', 'Yang Wang', 'Zhixiang Chi'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['auxiliary-learning'] | ['methodology'] | [ 2.18034536e-03 -3.50150645e-01 -8.92826542e-02 -2.15552256e-01
-7.88145423e-01 -3.89366657e-01 4.30465609e-01 -4.67834294e-01
-4.92260963e-01 4.86317515e-01 1.83747455e-01 2.44511967e-03
2.58214712e-01 -4.87178445e-01 -7.54414856e-01 -9.47269022e-01
4.57872480e-01 6.11320324e-02 2.73154765e-01 2.41180230... | [11.495757102966309, -2.5158350467681885] |
d1141a2e-281f-4a72-b7b5-86ff2340468c | self-supervised-representations-improve-end | 2006.12124 | null | https://arxiv.org/abs/2006.12124v2 | https://arxiv.org/pdf/2006.12124v2.pdf | Self-Supervised Representations Improve End-to-End Speech Translation | End-to-end speech-to-text translation can provide a simpler and smaller system but is facing the challenge of data scarcity. Pre-training methods can leverage unlabeled data and have been shown to be effective on data-scarce settings. In this work, we explore whether self-supervised pre-trained speech representations c... | ['Juan Pino', 'Changhan Wang', 'Jiatao Gu', 'Anne Wu'] | 2020-06-22 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 1.95861697e-01 1.91898614e-01 -5.49438357e-01 -5.18722534e-01
-1.52528858e+00 -6.43944800e-01 7.71066189e-01 -4.28931206e-01
-4.57965881e-01 8.37125123e-01 7.51136899e-01 -7.94927061e-01
5.18046737e-01 -2.91635752e-01 -7.25630760e-01 -2.10268974e-01
4.17589903e-01 8.06583643e-01 -2.24635080e-01 -4.40989792... | [14.479103088378906, 7.139556407928467] |
02f2be1a-a74e-4e26-a4ff-1940e7c525d6 | multi-task-audio-source-separation | 2107.06467 | null | https://arxiv.org/abs/2107.06467v1 | https://arxiv.org/pdf/2107.06467v1.pdf | Multi-Task Audio Source Separation | The audio source separation tasks, such as speech enhancement, speech separation, and music source separation, have achieved impressive performance in recent studies. The powerful modeling capabilities of deep neural networks give us hope for more challenging tasks. This paper launches a new multi-task audio source sep... | ['Xiaorui Wang', 'Feng Deng', 'Chenxing Li', 'Lu Zhang'] | 2021-07-14 | null | null | null | null | ['audio-source-separation', 'music-source-separation'] | ['audio', 'music'] | [ 2.45199472e-01 -7.53341556e-01 2.06555873e-01 9.41184722e-03
-1.29518473e+00 -3.94592285e-01 3.67813855e-01 -2.69807160e-01
-5.48337847e-02 4.57123339e-01 4.91215289e-01 6.62977174e-02
-2.27583125e-01 -2.62280833e-02 -2.62831450e-01 -1.02133965e+00
5.66171780e-02 -1.94132522e-01 9.34692845e-02 -1.12194844... | [14.995284080505371, 5.818770408630371] |
06812e5e-831b-4bc8-bca3-298c9ce235b1 | non-linearities-improve-originet-based-on | 2005.07991 | null | https://arxiv.org/abs/2005.07991v1 | https://arxiv.org/pdf/2005.07991v1.pdf | Non-Linearities Improve OrigiNet based on Active Imaging for Micro Expression Recognition | Micro expression recognition (MER)is a very challenging task as the expression lives very short in nature and demands feature modeling with the involvement of both spatial and temporal dynamics. Existing MER systems exploit CNN networks to spot the significant features of minor muscle movements and subtle changes. Howe... | ['Monu Verma', 'Santosh Kumar Vipparthi', 'Girdhari Singh'] | 2020-05-16 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 1.88113943e-01 -2.26669878e-01 -2.25183547e-01 -5.13616264e-01
-1.05487816e-01 -1.92032710e-01 3.31530511e-01 -5.48657835e-01
-5.46540856e-01 5.96910059e-01 1.01570040e-01 4.69646335e-01
-2.16302320e-01 -4.93332267e-01 -7.30407894e-01 -9.43653941e-01
-3.27246815e-01 -4.90689754e-01 9.96944457e-02 -6.63418472... | [13.635526657104492, 1.7515418529510498] |
ce434b1b-feac-417f-934f-184c84d98449 | groupvit-semantic-segmentation-emerges-from | 2202.11094 | null | https://arxiv.org/abs/2202.11094v5 | https://arxiv.org/pdf/2202.11094v5.pdf | GroupViT: Semantic Segmentation Emerges from Text Supervision | Grouping and recognition are important components of visual scene understanding, e.g., for object detection and semantic segmentation. With end-to-end deep learning systems, grouping of image regions usually happens implicitly via top-down supervision from pixel-level recognition labels. Instead, in this paper, we prop... | ['Xiaolong Wang', 'Jan Kautz', 'Thomas Breuel', 'Wonmin Byeon', 'Sifei Liu', 'Shalini De Mello', 'Jiarui Xu'] | 2022-02-22 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Xu_GroupViT_Semantic_Segmentation_Emerges_From_Text_Supervision_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xu_GroupViT_Semantic_Segmentation_Emerges_From_Text_Supervision_CVPR_2022_paper.pdf | cvpr-2022-1 | ['unsupervised-semantic-segmentation-with'] | ['computer-vision'] | [ 4.60078508e-01 3.02424014e-01 -2.11424440e-01 -6.57957137e-01
-8.19537401e-01 -5.74003220e-01 4.53570753e-01 3.44910920e-02
-6.15896106e-01 2.51556635e-01 -2.26755023e-01 -4.27780151e-01
4.62215930e-01 -7.42175043e-01 -1.25099504e+00 -4.26123202e-01
3.42760146e-01 5.57258189e-01 6.64180696e-01 1.86831966... | [9.694416999816895, 0.7219445109367371] |
fd41703c-d339-40b9-b717-3a9cfe20eb7a | joint-convolutional-neural-pyramid-for-depth | 1801.00968 | null | http://arxiv.org/abs/1801.00968v1 | http://arxiv.org/pdf/1801.00968v1.pdf | Joint convolutional neural pyramid for depth map super-resolution | High-resolution depth map can be inferred from a low-resolution one with the
guidance of an additional high-resolution texture map of the same scene.
Recently, deep neural networks with large receptive fields are shown to benefit
applications such as image completion. Our insight is that super resolution is
similar to ... | ['Yan Zheng', 'Xianyi Zhu', 'Xiang Cao', 'Renzhi Yang', 'Yi Xiao'] | 2018-01-03 | null | null | null | null | ['depth-map-super-resolution'] | ['computer-vision'] | [ 6.13427520e-01 3.49907637e-01 1.28637061e-01 -3.91574144e-01
-7.69419014e-01 -1.76496476e-01 2.71521896e-01 -3.73168945e-01
-9.32148620e-02 7.54310191e-01 4.23585951e-01 4.04976010e-01
1.09068938e-01 -1.23667800e+00 -1.10822356e+00 -7.12074518e-01
1.58939332e-01 1.48406014e-01 8.14536929e-01 -5.31708479... | [9.858637809753418, -2.392747640609741] |
d626123e-eb92-4608-a143-71d9c764b3a2 | gio-gradient-information-optimization-for | 2306.11670 | null | https://arxiv.org/abs/2306.11670v1 | https://arxiv.org/pdf/2306.11670v1.pdf | GIO: Gradient Information Optimization for Training Dataset Selection | It is often advantageous to train models on a subset of the available train examples, because the examples are of variable quality or because one would like to train with fewer examples, without sacrificing performance. We present Gradient Information Optimization (GIO), a scalable, task-agnostic approach to this data ... | ['Christopher Potts', 'Dante Everaert'] | 2023-06-20 | null | null | null | null | ['machine-translation', 'spelling-correction'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.93536091e-01 -1.50218561e-01 -5.02798200e-01 -6.47840023e-01
-1.37375879e+00 -7.87088752e-01 7.52646089e-01 4.01125140e-02
-7.03554034e-01 9.07491088e-01 -1.00217871e-01 -5.32215059e-01
-2.22642794e-01 -3.28618288e-01 -7.20865309e-01 -5.26976168e-01
1.66383639e-01 1.11774135e+00 1.58323213e-01 -1.00660235... | [9.513284683227539, 3.4852070808410645] |
afbdf0db-f854-46ff-9301-de2bb3c0d2b3 | a-review-of-sentiment-analysis-research-in | 2005.12240 | null | https://arxiv.org/abs/2005.12240v1 | https://arxiv.org/pdf/2005.12240v1.pdf | A review of sentiment analysis research in Arabic language | Sentiment analysis is a task of natural language processing which has recently attracted increasing attention. However, sentiment analysis research has mainly been carried out for the English language. Although Arabic is ramping up as one of the most used languages on the Internet, only a few studies have focused on Ar... | ['Habib OUNELLI', 'Erik Cambria', 'Oumaima Oueslati', 'Moez Ben HajHmida'] | 2020-05-25 | null | null | null | null | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-5.36336452e-02 -1.52552038e-01 -4.26634014e-01 -5.07769227e-01
-4.36011910e-01 -7.24670887e-01 6.50573611e-01 5.60603499e-01
-7.30829179e-01 5.95684409e-01 2.06927717e-01 -5.18487751e-01
2.80357659e-01 -6.92834198e-01 -3.78659777e-02 -3.78268272e-01
2.92190433e-01 3.02688777e-01 -1.03290588e-01 -9.86312509... | [11.021263122558594, 6.877188205718994] |
82d90016-b886-410a-b927-ad0ed548efd2 | fdti-fine-grained-deep-traffic-inference-with | 2306.10945 | null | https://arxiv.org/abs/2306.10945v1 | https://arxiv.org/pdf/2306.10945v1.pdf | FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched Graph | This paper proposes the fine-grained traffic prediction task (e.g. interval between data points is 1 minute), which is essential to traffic-related downstream applications. Under this setting, traffic flow is highly influenced by traffic signals and the correlation between traffic nodes is dynamic. As a result, the tra... | ['Hua Wei', 'Guanjie Zheng', 'Chumeng Liang', 'Zhanyu Liu'] | 2023-06-19 | null | null | null | null | ['traffic-prediction'] | ['time-series'] | [-3.50158840e-01 -3.31562102e-01 -4.30227071e-01 -3.85639191e-01
-8.14425871e-02 -1.18336082e-01 5.23416817e-01 -4.04751688e-01
1.50269225e-01 8.38856339e-01 -6.88703433e-02 -8.17319751e-01
-4.62284833e-01 -1.33341825e+00 -8.13543081e-01 -5.27283132e-01
-2.36640483e-01 6.56292617e-01 6.35940909e-01 -4.21680748... | [6.466664791107178, 2.0357916355133057] |
6c0fca20-a1eb-48fb-bf4e-4582eaf3f67d | cascaded-zoom-in-detector-for-high-resolution | 2303.08747 | null | https://arxiv.org/abs/2303.08747v1 | https://arxiv.org/pdf/2303.08747v1.pdf | Cascaded Zoom-in Detector for High Resolution Aerial Images | Detecting objects in aerial images is challenging because they are typically composed of crowded small objects distributed non-uniformly over high-resolution images. Density cropping is a widely used method to improve this small object detection where the crowded small object regions are extracted and processed in high... | ['Marco Pedersoli', 'Eric Granger', 'Akhil Meethal'] | 2023-03-15 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 2.13500470e-01 -2.22010270e-01 2.82841265e-01 7.23731518e-02
-2.17523873e-01 -5.17140925e-01 5.21914482e-01 2.15430006e-01
-7.67754018e-01 6.18074834e-01 -4.44783330e-01 1.60304278e-01
2.04275101e-01 -1.00528383e+00 -5.70645690e-01 -1.07419300e+00
-1.35836661e-01 6.17846787e-01 1.05078030e+00 6.95501193... | [8.685348510742188, -0.7300403118133545] |
b1f0c602-d024-4644-8975-86f20b599b5a | the-role-of-user-profile-for-fake-news | 1904.13355 | null | http://arxiv.org/abs/1904.13355v1 | http://arxiv.org/pdf/1904.13355v1.pdf | The Role of User Profile for Fake News Detection | Consuming news from social media is becoming increasingly popular. Social
media appeals to users due to its fast dissemination of information, low cost,
and easy access. However, social media also enables the widespread of fake
news. Because of the detrimental societal effects of fake news, detecting fake
news has attr... | ['Huan Liu', 'Reza Zafarani', 'Kai Shu', 'Suhang Wang', 'Xinyi Zhou'] | 2019-04-30 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [-2.80128896e-01 9.69915763e-02 -6.72786713e-01 -1.58197403e-01
-2.55473167e-01 -6.30002260e-01 8.96925092e-01 6.40838444e-01
-1.29016832e-01 6.03952944e-01 4.29401875e-01 -1.49249166e-01
2.28722081e-01 -9.25589442e-01 -3.38117421e-01 -1.69001937e-01
3.73927169e-02 -1.14480898e-01 2.64364868e-01 -5.80034077... | [8.133410453796387, 10.265463829040527] |
82821d53-f9b2-4124-b83a-195caadd8cf5 | deep-active-inference-for-pixel-based | 2109.04155 | null | https://arxiv.org/abs/2109.04155v1 | https://arxiv.org/pdf/2109.04155v1.pdf | Deep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing Problem | Despite the potential of active inference for visual-based control, learning the model and the preferences (priors) while interacting with the environment is challenging. Here, we study the performance of a deep active inference (dAIF) agent on OpenAI's car racing benchmark, where there is no access to the car's state.... | ['Pablo Lanillos', 'Niels van Hoeffelen'] | 2021-09-09 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-9.61500108e-02 6.20849133e-01 -6.13483250e-01 -3.31742704e-01
-7.95705140e-01 -4.32096809e-01 9.51532066e-01 6.50648624e-02
-7.03811646e-01 8.15779805e-01 3.97353381e-01 -2.62043923e-01
-8.96880217e-03 -6.03579938e-01 -1.03674817e+00 -6.44353509e-01
-1.80483952e-01 9.23233986e-01 2.46028289e-01 -1.50176629... | [4.253448009490967, 1.498718500137329] |
08b8d549-0a7a-4e8d-a264-83a226890042 | q-tod-a-query-driven-task-oriented-dialogue | 2210.07564 | null | https://arxiv.org/abs/2210.07564v1 | https://arxiv.org/pdf/2210.07564v1.pdf | Q-TOD: A Query-driven Task-oriented Dialogue System | Existing pipelined task-oriented dialogue systems usually have difficulties adapting to unseen domains, whereas end-to-end systems are plagued by large-scale knowledge bases in practice. In this paper, we introduce a novel query-driven task-oriented dialogue system, namely Q-TOD. The essential information from the dial... | ['Hua Wu', 'Shuqi Sun', 'Huang He', 'Fan Wang', 'Siqi Bao', 'Mengfei Song', 'Yingzhan Lin', 'Xin Tian'] | 2022-10-14 | null | null | null | null | ['task-oriented-dialogue-systems'] | ['natural-language-processing'] | [-2.60630399e-01 3.57087076e-01 -1.33937582e-01 -2.55134076e-01
-1.18325162e+00 -8.77882183e-01 7.05449224e-01 3.23310792e-02
-7.22240388e-01 9.79395330e-01 6.03804111e-01 -2.49487534e-02
7.01193660e-02 -5.61486125e-01 -3.08659703e-01 -2.71311164e-01
3.40813339e-01 9.42534864e-01 6.91847742e-01 -9.06382740... | [12.249588966369629, 8.037284851074219] |
b10c91f6-2ea3-47e3-8395-5c42ac67832a | rate-splitting-multiple-access-for-joint | 2104.08180 | null | https://arxiv.org/abs/2104.08180v1 | https://arxiv.org/pdf/2104.08180v1.pdf | Rate-Splitting Multiple Access for Joint Radar-Communications with Low-Resolution DACs | In this paper, we introduce the design of a multi-antenna Joint Radar-Communication (JRC) system with Rate Splitting Multiple Access (RSMA) and low resolution Digital-to-Analog Converter (DAC) units. Using RSMA, the communication messages are split into private and common parts, then precoded and quantized before trans... | ['Christos Masouros', 'Bruno Clerckx', 'Aryan Kaushik', 'Onur Dizdar'] | 2021-04-16 | null | null | null | null | ['joint-radar-communication'] | ['robots'] | [ 7.49351442e-01 -1.10060997e-01 -1.37326568e-01 -3.09112102e-01
-9.66354430e-01 -4.22445655e-01 4.89345372e-01 -5.63779056e-01
-3.01776379e-01 8.92471790e-01 3.01781595e-01 -4.82197911e-01
-7.72660136e-01 -8.34849954e-01 -9.23019499e-02 -6.87677026e-01
-4.40032333e-01 -3.01591549e-02 -5.20342469e-01 3.15588452... | [6.405088424682617, 1.2365617752075195] |
2d45279d-576e-470d-a015-27ae0850ac16 | growing-and-serving-large-open-domain | 2305.09464 | null | https://arxiv.org/abs/2305.09464v1 | https://arxiv.org/pdf/2305.09464v1.pdf | Growing and Serving Large Open-domain Knowledge Graphs | Applications of large open-domain knowledge graphs (KGs) to real-world problems pose many unique challenges. In this paper, we present extensions to Saga our platform for continuous construction and serving of knowledge at scale. In particular, we describe a pipeline for training knowledge graph embeddings that powers ... | ['Chiraag Sumanth', 'Theodoros Rekatsinas', 'Jeffrey Pound', 'Ali Mousavi', 'Umar Farooq Minhas', 'Yunyao Li', 'JP Lacerda', 'Ihab F. Ilyas'] | 2023-05-16 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings', 'fact-verification', 'entity-linking'] | ['graphs', 'methodology', 'natural-language-processing', 'natural-language-processing'] | [-6.99805439e-01 8.32933843e-01 -7.86649346e-01 -1.34461731e-01
-8.64536107e-01 -1.11062765e+00 4.14682567e-01 7.64786124e-01
-1.28563181e-01 8.84294987e-01 6.51632667e-01 -1.10314831e-01
-6.20323241e-01 -1.32977855e+00 -8.28198016e-01 2.27329120e-01
-9.10684019e-02 9.37381387e-01 8.41162920e-01 -3.78837407... | [9.1265230178833, 8.140043258666992] |
09bfe8de-66f6-437d-b419-07ebfdb9e8ed | multi-objective-reinforcement-learning-with | 1406.3497 | null | http://arxiv.org/abs/1406.3497v2 | http://arxiv.org/pdf/1406.3497v2.pdf | Multi-objective Reinforcement Learning with Continuous Pareto Frontier Approximation Supplementary Material | This document contains supplementary material for the paper "Multi-objective
Reinforcement Learning with Continuous Pareto Frontier Approximation",
published at the Twenty-Ninth AAAI Conference on Artificial Intelligence
(AAAI-15). The paper is about learning a continuous approximation of the Pareto
frontier in Multi-O... | ['Matteo Pirotta', 'Marcello Restelli', 'Simone Parisi'] | 2014-06-13 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-3.02292965e-02 6.62514195e-02 -2.58745313e-01 -1.28021568e-01
-9.57288563e-01 -5.39549828e-01 3.27097714e-01 3.21709394e-01
-7.52672136e-01 1.32314575e+00 1.33349448e-02 -3.10124218e-01
-7.01665282e-01 -5.63375175e-01 -7.86468446e-01 -8.32228959e-01
-8.71127993e-02 8.48730087e-01 -2.39451346e-03 -4.75285724... | [4.299102306365967, 2.364431858062744] |
66088790-96db-4124-9f09-3a534bcf867d | joint-engagement-classification-using-video | 2212.14128 | null | https://arxiv.org/abs/2212.14128v1 | https://arxiv.org/pdf/2212.14128v1.pdf | Joint Engagement Classification using Video Augmentation Techniques for Multi-person Human-robot Interaction | Affect understanding capability is essential for social robots to autonomously interact with a group of users in an intuitive and reciprocal way. However, the challenge of multi-person affect understanding comes from not only the accurate perception of each user's affective state (e.g., engagement) but also the recogni... | ['Hae Won Park', 'Cynthia Breazeal', 'Sharifa Alghowinem', 'Huili Chen', 'Yubin Kim'] | 2022-12-28 | null | null | null | null | ['face-swapping', 'video-understanding'] | ['computer-vision', 'computer-vision'] | [ 1.80555329e-01 4.56637114e-01 2.25711703e-01 -5.98632693e-01
-3.86437029e-01 -3.15686792e-01 6.87251687e-01 7.97330216e-02
-8.56123120e-02 3.34877998e-01 4.38912004e-01 6.06014132e-01
1.57409146e-01 -3.14328134e-01 -5.44409931e-01 -5.99226773e-01
-3.32933128e-01 6.60708010e-01 -5.85391998e-01 -2.60850847... | [13.407364845275879, 2.2124762535095215] |
bcdedc75-547f-40fa-a6bf-2dfe0af15b5c | question-rewriting-for-conversational | 2004.14652 | null | https://arxiv.org/abs/2004.14652v3 | https://arxiv.org/pdf/2004.14652v3.pdf | Question Rewriting for Conversational Question Answering | Conversational question answering (QA) requires the ability to correctly interpret a question in the context of previous conversation turns. We address the conversational QA task by decomposing it into question rewriting and question answering subtasks. The question rewriting (QR) subtask is specifically designed to re... | ['Zhucheng Tu', 'Raviteja Anantha', 'Svitlana Vakulenko', 'Shayne Longpre'] | 2020-04-30 | null | null | null | null | ['question-rewriting'] | ['natural-language-processing'] | [ 3.01294953e-01 4.72341001e-01 6.94807172e-01 -4.61140692e-01
-1.76576495e+00 -1.10819530e+00 9.85381901e-01 6.08199872e-02
-3.78123224e-01 8.92204046e-01 9.72044766e-01 -7.30644405e-01
-1.04531296e-01 -5.50309539e-01 -5.18033981e-01 -1.05815284e-01
3.09163719e-01 9.14828479e-01 3.63591403e-01 -1.09035349... | [11.973424911499023, 7.991094589233398] |
e8acfa73-d4d0-47b3-a797-38553418e8e0 | two-languages-are-better-than-one-bilingual | null | null | https://aclanthology.org/2022.coling-1.176 | https://aclanthology.org/2022.coling-1.176.pdf | Two Languages Are Better than One: Bilingual Enhancement for Chinese Named Entity Recognition | Chinese Named Entity Recognition (NER) has continued to attract research attention. However, most existing studies only explore the internal features of the Chinese language but neglect other lingual modal features. Actually, as another modal knowledge of the Chinese language, English contains rich prompts about entiti... | ['Jian Wang', 'Hongfei Lin', 'Yuanyuan Sun', 'Zhizheng Wang', 'Zhihao Yang', 'Jinzhong Ning'] | null | null | null | null | coling-2022-10 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-4.16425675e-01 -3.65090102e-01 -1.31653339e-01 -4.73449260e-01
-6.18094325e-01 -6.07858658e-01 5.51465392e-01 -1.67212814e-01
-8.36089194e-01 8.08988750e-01 8.19355249e-01 -3.20577651e-01
3.44554514e-01 -7.58567870e-01 -4.98319536e-01 -4.63483781e-01
4.50324655e-01 1.51611552e-01 -9.73545983e-02 -4.68164414... | [9.811388969421387, 9.747503280639648] |
e62af787-7e45-4f27-9a72-bf355eec49ec | conviformers-convolutionally-guided-vision | 2208.08900 | null | https://arxiv.org/abs/2208.08900v2 | https://arxiv.org/pdf/2208.08900v2.pdf | Conviformers: Convolutionally guided Vision Transformer | Vision transformers are nowadays the de-facto choice for image classification tasks. There are two broad categories of classification tasks, fine-grained and coarse-grained. In fine-grained classification, the necessity is to discover subtle differences due to the high level of similarity between sub-classes. Such dist... | ['Ivań Felipe Rodríguez', 'Thomas Serre', 'Thomas Fel', 'Mohit Vaishnav'] | 2022-08-17 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 3.68837327e-01 -3.16850305e-01 2.79311866e-01 -1.14480175e-01
-1.81049153e-01 -8.24945152e-01 4.62936014e-01 3.48367691e-02
-2.13905036e-01 5.60334384e-01 -2.96987504e-01 -4.41119522e-01
-3.34697098e-01 -1.17886364e+00 -4.04241204e-01 -6.11071110e-01
2.09864944e-01 2.97972798e-01 3.21886063e-01 -1.98867410... | [9.633136749267578, 2.0327093601226807] |
9ce12623-fa83-49cf-8d44-50a161b949c3 | exploiting-feature-diversity-for-make-up | 2208.06179 | null | https://arxiv.org/abs/2208.06179v1 | https://arxiv.org/pdf/2208.06179v1.pdf | Exploiting Feature Diversity for Make-up Temporal Video Grounding | This technical report presents the 3rd winning solution for MTVG, a new task introduced in the 4-th Person in Context (PIC) Challenge at ACM MM 2022. MTVG aims at localizing the temporal boundary of the step in an untrimmed video based on a textual description. The biggest challenge of this task is the fi ne-grained vi... | ['Ruizhi Qiao', 'Chen Wu', 'Sunan He', 'Taian Guo', 'Wei Wen', 'Xiujun Shu'] | 2022-08-12 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [ 3.32145989e-01 -2.82442003e-01 -3.93416375e-01 -4.31867003e-01
-7.71886826e-01 -4.64243650e-01 8.28455210e-01 5.72197400e-02
-5.53534567e-01 6.06675923e-01 9.21063840e-01 2.05086365e-01
1.33116450e-02 -2.10472167e-01 -5.65492511e-01 -2.06169456e-01
-7.84457400e-02 4.55234908e-02 1.03434347e-01 -2.53377974... | [8.445093154907227, 0.5660225749015808] |
4da52274-ea85-4a6e-8de7-9c1fc4fb9903 | the-generic-holdout-preventing-false | 1809.05596 | null | http://arxiv.org/abs/1809.05596v1 | http://arxiv.org/pdf/1809.05596v1.pdf | The Generic Holdout: Preventing False-Discoveries in Adaptive Data Science | Adaptive data analysis has posed a challenge to science due to its ability to
generate false hypotheses on moderately large data sets. In general, with
non-adaptive data analyses (where queries to the data are generated without
being influenced by answers to previous queries) a data set containing $n$
samples may suppo... | ['Jarosław Błasiok', 'Preetum Nakkiran'] | 2018-09-14 | null | null | null | null | ['holdout-set'] | ['computer-vision'] | [ 1.82936653e-01 2.76631445e-01 -1.86215729e-01 -2.61865169e-01
-7.56884933e-01 -8.41295302e-01 3.12218010e-01 4.31709379e-01
-6.44945025e-01 7.29385138e-01 -1.78721413e-01 -7.05122292e-01
-7.01192498e-01 -1.07009554e+00 -8.17682445e-01 -7.99394488e-01
-1.14966281e-01 8.96797121e-01 2.24803641e-01 -1.20837763... | [7.73859977722168, 4.7084641456604] |
aa697d43-4a5c-4189-b8d3-a6c0c6cdef30 | humans-need-not-label-more-humans-occlusion | 2210.03686 | null | https://arxiv.org/abs/2210.03686v1 | https://arxiv.org/pdf/2210.03686v1.pdf | Humans need not label more humans: Occlusion Copy & Paste for Occluded Human Instance Segmentation | Modern object detection and instance segmentation networks stumble when picking out humans in crowded or highly occluded scenes. Yet, these are often scenarios where we require our detectors to work well. Many works have approached this problem with model-centric improvements. While they have been shown to work to some... | ['Minhoe Hur', 'Dezhao Huang', 'Evan Ling'] | 2022-10-07 | null | null | null | null | ['human-instance-segmentation'] | ['computer-vision'] | [ 3.53485048e-01 4.82956201e-01 -8.47461149e-02 -4.68563139e-01
-7.35081732e-01 -2.85851300e-01 6.90084577e-01 4.90599088e-02
-6.46832466e-01 5.59569180e-01 -1.40500933e-01 -2.06745118e-01
1.71085209e-01 -4.27850962e-01 -8.57765138e-01 -4.77021873e-01
2.45698243e-01 9.15160000e-01 7.70176768e-01 -2.23940492... | [9.325706481933594, 0.34449198842048645] |
b0f2ab6b-35b8-4316-95cc-7c239611adf6 | improving-fine-grain-segmentation-via | 2210.03879 | null | https://arxiv.org/abs/2210.03879v2 | https://arxiv.org/pdf/2210.03879v2.pdf | Improving Data-Efficient Fossil Segmentation via Model Editing | Most computer vision research focuses on datasets containing thousands of images of commonplace objects. However, many high-impact datasets, such as those in medicine and the geosciences, contain fine-grain objects that require domain-expert knowledge to recognize and are time-consuming to collect and annotate. As a re... | ['Ruth Fong', 'Adam Maloof', 'Ryan Manzuk', 'Indu Panigrahi'] | 2022-10-08 | null | null | null | null | ['model-editing'] | ['natural-language-processing'] | [ 6.49725020e-01 2.86478907e-01 2.30396003e-01 -3.56129944e-01
-7.29394853e-01 -9.03920710e-01 4.55595642e-01 8.03183690e-02
-7.23003983e-01 4.99852598e-01 -2.48659864e-01 -4.11804974e-01
2.13813320e-01 -8.53282452e-01 -1.07469153e+00 -5.59953868e-01
2.31216431e-01 6.92525029e-01 5.28398454e-01 -3.53098549... | [9.607952117919922, 0.962203860282898] |
c879b720-83c1-4ec2-af66-c7c6231e7b92 | blurry-video-frame-interpolation | 2002.12259 | null | https://arxiv.org/abs/2002.12259v1 | https://arxiv.org/pdf/2002.12259v1.pdf | Blurry Video Frame Interpolation | Existing works reduce motion blur and up-convert frame rate through two separate ways, including frame deblurring and frame interpolation. However, few studies have approached the joint video enhancement problem, namely synthesizing high-frame-rate clear results from low-frame-rate blurry inputs. In this paper, we prop... | ['Zhiyong Gao', 'Wenbo Bao', 'Li Chen', 'Xiongkuo Min', 'Wang Shen', 'Guangtao Zhai'] | 2020-02-27 | blurry-video-frame-interpolation-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Shen_Blurry_Video_Frame_Interpolation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Shen_Blurry_Video_Frame_Interpolation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['video-enhancement'] | ['computer-vision'] | [ 3.57864141e-01 -5.19439042e-01 -1.49044562e-02 -9.85404626e-02
-3.86503518e-01 -2.37941474e-01 3.06358457e-01 -7.14247584e-01
-3.17766070e-01 8.56511295e-01 4.08439845e-01 -1.47767529e-01
1.61689058e-01 -5.39348364e-01 -5.90322137e-01 -6.66136980e-01
2.49553800e-01 -9.09123540e-01 4.42869693e-01 -5.24981273... | [11.220183372497559, -2.215895652770996] |
34bf95a2-85f0-46ec-ac13-6a9a1c883f5e | a-robustness-evaluation-framework-for | null | null | https://aclanthology.org/2022.argmining-1.16 | https://aclanthology.org/2022.argmining-1.16.pdf | A Robustness Evaluation Framework for Argument Mining | Standard practice for evaluating the performance of machine learning models for argument mining is to report different metrics such as accuracy or F1. However, little is usually known about the model’s stability and consistency when deployed in real-world settings. In this paper, we propose a robustness evaluation fram... | ['Oana Cocarascu', 'Matteo Fortier', 'Mehmet Sofi'] | null | null | null | null | argmining-acl-2022-10 | ['argument-mining'] | ['natural-language-processing'] | [ 2.96278358e-01 5.78319430e-01 -6.22994840e-01 -3.59842330e-01
-9.99466062e-01 -7.95122862e-01 1.02989447e+00 1.01319456e+00
-5.94574034e-01 8.49769115e-01 5.37571847e-01 -1.13152742e+00
-5.44922590e-01 -6.97954059e-01 -6.69343829e-01 -1.97157547e-01
1.00783510e-02 4.26323891e-01 2.57475197e-01 -1.47157833... | [9.534215927124023, 9.600945472717285] |
203bce20-cdee-4b88-9f10-12ff419e17fa | unsupervised-learning-of-multi-frame-optical | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Joel_Janai_Unsupervised_Learning_of_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Joel_Janai_Unsupervised_Learning_of_ECCV_2018_paper.pdf | Unsupervised Learning of Multi-Frame Optical Flow with Occlusions | Learning optical flow with neural networks is hampered by the need for obtaining training data with associated ground truth. Unsupervised learning is a promising direction, yet the performance of current unsupervised methods is still limited. In particular, the lack of proper occlusion handling in commonly used data te... | ['Michael Black', 'Anurag Ranjan', 'Joel Janai', 'Fatma Guney', 'Andreas Geiger'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['occlusion-handling'] | ['computer-vision'] | [ 1.22252032e-01 -2.15361789e-01 -4.91958141e-01 -3.94468874e-01
-3.41312617e-01 -4.93428171e-01 5.69306552e-01 4.14237529e-02
-4.03506279e-01 1.12201965e+00 3.25252444e-01 -1.52497441e-02
-1.98188335e-01 -4.93728489e-01 -5.67586660e-01 -7.25289643e-01
9.28555131e-02 6.56287670e-02 5.54901883e-02 1.02912381... | [8.803592681884766, -1.782378911972046] |
62e2c1d9-49c2-48de-925e-ffbcd1832eca | kagnet-knowledge-aware-graph-networks-for | 1909.02151 | null | https://arxiv.org/abs/1909.02151v1 | https://arxiv.org/pdf/1909.02151v1.pdf | KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning | Commonsense reasoning aims to empower machines with the human ability to make presumptions about ordinary situations in our daily life. In this paper, we propose a textual inference framework for answering commonsense questions, which effectively utilizes external, structured commonsense knowledge graphs to perform exp... | ['Bill Yuchen Lin', 'Xiang Ren', 'Xinyue Chen', 'Jamin Chen'] | 2019-09-04 | kagnet-knowledge-aware-graph-networks-for-1 | https://aclanthology.org/D19-1282 | https://aclanthology.org/D19-1282.pdf | ijcnlp-2019-11 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [ 2.90298700e-01 1.04351544e+00 -3.58502008e-02 -3.41728359e-01
-2.78361320e-01 -3.59530091e-01 6.53643191e-01 3.22282940e-01
3.22104506e-02 6.00606680e-01 4.32392627e-01 -6.69009864e-01
-2.64847111e-02 -1.56455028e+00 -9.02921975e-01 2.59943604e-01
3.04627746e-01 8.71912003e-01 3.42710078e-01 -6.38920844... | [9.968637466430664, 8.041258811950684] |
e5e978fb-22d1-4dc2-bd33-4ab5e1156f2f | towards-ground-truth-for-single-image | 2206.10779 | null | https://arxiv.org/abs/2206.10779v2 | https://arxiv.org/pdf/2206.10779v2.pdf | Not Just Streaks: Towards Ground Truth for Single Image Deraining | We propose a large-scale dataset of real-world rainy and clean image pairs and a method to remove degradations, induced by rain streaks and rain accumulation, from the image. As there exists no real-world dataset for deraining, current state-of-the-art methods rely on synthetic data and thus are limited by the sim2real... | ['Achuta Kadambi', 'Alex Wong', 'Stefano Soatto', 'Suya You', 'Celso de Melo', 'Chethan Chinder Chandrappa', 'Arnold Pfahnl', 'Akira Suzuki', 'Ethan Yang', 'Howard Zhang', 'Yunhao Ba'] | 2022-06-22 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [-1.09054856e-02 -5.70571363e-01 4.31282908e-01 -6.97746158e-01
-9.66627419e-01 -3.70965213e-01 1.67206451e-01 -5.44881463e-01
-5.60314879e-02 1.18262172e+00 -5.24156131e-02 -2.01141626e-01
1.97864264e-01 -8.72751713e-01 -9.80512083e-01 -1.06231439e+00
-3.41607720e-01 1.30851135e-01 -8.84587318e-02 -4.55543220... | [10.907051086425781, -3.239934206008911] |
d428af48-766b-478d-890e-d0817b5b4252 | computational-efficient-deep-neural-network | 2011.12082 | null | https://arxiv.org/abs/2011.12082v2 | https://arxiv.org/pdf/2011.12082v2.pdf | Computational efficient deep neural network with difference attention maps for facial action unit detection | In this paper, we propose a computational efficient end-to-end training deep neural network (CEDNN) model and spatial attention maps based on difference images. Firstly, the difference image is generated by image processing. Then five binary images of difference images are obtained using different thresholds, which are... | ['Meichen Liu', 'Kejun Wang', 'Chenhui Wang', 'Jing Chen'] | 2020-11-24 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 5.47193829e-03 -4.61123139e-01 2.02640012e-01 -2.11647213e-01
1.54216483e-03 4.25538011e-02 -8.38654190e-02 -2.32498527e-01
-8.57766390e-01 3.57795149e-01 -2.11442158e-01 -1.01444893e-01
2.08513923e-02 -1.19961846e+00 -4.78014499e-01 -8.14666569e-01
2.47782305e-01 -1.58887893e-01 8.36001992e-01 -2.17384085... | [9.225232124328613, -0.5527082681655884] |
2cf784e4-2ceb-48e6-9c1f-e5d44c89a9ad | interpreting-black-box-predictions-using | 1810.10118 | null | http://arxiv.org/abs/1810.10118v1 | http://arxiv.org/pdf/1810.10118v1.pdf | Interpreting Black Box Predictions using Fisher Kernels | Research in both machine learning and psychology suggests that salient
examples can help humans to interpret learning models. To this end, we take a
novel look at black box interpretation of test predictions in terms of training
examples. Our goal is to ask `which training examples are most responsible for
a given set ... | ['Oluwasanmi Koyejo', 'Joydeep Ghosh', 'Been Kim', 'Rajiv Khanna'] | 2018-10-23 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 4.98290539e-01 6.75177515e-01 -2.97504216e-01 -7.87475288e-01
-8.21274102e-01 -5.87140739e-01 4.80153292e-01 4.95139331e-01
-3.26095670e-01 9.30080414e-01 5.12010492e-02 -3.36362481e-01
-6.79119110e-01 -6.33519471e-01 -9.62322891e-01 -6.66704178e-01
1.47622108e-01 6.06244385e-01 1.56872913e-01 1.05549932... | [8.792282104492188, 5.716201305389404] |
bc029ef1-9bb4-4e61-a10b-4cba400c4a8a | water-filling-an-efficient-algorithm-for | 1904.09763 | null | https://arxiv.org/abs/1904.09763v2 | https://arxiv.org/pdf/1904.09763v2.pdf | Water-Filling: An Efficient Algorithm for Digitized Document Shadow Removal | In this paper, we propose a novel algorithm to rectify illumination of the digitized documents by eliminating shading artifacts. Firstly, a topographic surface of an input digitized document is created using luminance value of each pixel. Then the shading artifact on the document is estimated by simulating an immersion... | ['Changick Kim', 'Seungjun Jung', 'Muhammad Abul Hasan'] | 2019-04-22 | null | null | null | null | ['shadow-removal'] | ['computer-vision'] | [ 7.20952749e-01 -4.68836099e-01 7.39669681e-01 -2.31581867e-01
-2.02234119e-01 -5.45920253e-01 6.35034800e-01 -3.20854455e-01
-1.79580107e-01 5.68028629e-01 3.70020643e-02 -4.42315713e-02
1.58602431e-01 -6.18447065e-01 -4.44110274e-01 -7.07330465e-01
5.10977685e-01 -9.05842483e-02 2.89967000e-01 1.09744906... | [10.646778106689453, -2.8209621906280518] |
f350c785-4da8-43d0-9e72-36163f738d6d | egocentric-deep-multi-channel-audio-visual | 2201.01928 | null | https://arxiv.org/abs/2201.01928v1 | https://arxiv.org/pdf/2201.01928v1.pdf | Egocentric Deep Multi-Channel Audio-Visual Active Speaker Localization | Augmented reality devices have the potential to enhance human perception and enable other assistive functionalities in complex conversational environments. Effectively capturing the audio-visual context necessary for understanding these social interactions first requires detecting and localizing the voice activities of... | ['Vamsi Krishna Ithapu', 'Calvin Murdock', 'Hao Jiang'] | 2022-01-06 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Jiang_Egocentric_Deep_Multi-Channel_Audio-Visual_Active_Speaker_Localization_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Jiang_Egocentric_Deep_Multi-Channel_Audio-Visual_Active_Speaker_Localization_CVPR_2022_paper.pdf | cvpr-2022-1 | ['active-speaker-localization', 'audio-visual-active-speaker-detection'] | ['audio', 'computer-vision'] | [ 9.47398972e-03 -2.14256257e-01 2.54747957e-01 4.99928594e-02
-8.68249297e-01 -4.52205747e-01 1.93660796e-01 -3.77262652e-01
-1.86538965e-01 5.01338065e-01 4.95898813e-01 1.20759688e-01
2.73239881e-01 -3.73255834e-02 -3.64945054e-01 -7.22571671e-01
4.16587889e-02 -1.04802333e-01 3.43274027e-01 1.27320573... | [14.536835670471191, 5.204346179962158] |
19b75955-8c43-4422-a2e0-a297974e7ad4 | analysis-of-face-detection-face-landmarking | 2207.06478 | null | https://arxiv.org/abs/2207.06478v1 | https://arxiv.org/pdf/2207.06478v1.pdf | Analysis of face detection, face landmarking, and face recognition performance with masked face images | Face recognition has become an essential task in our lives. However, the current COVID-19 pandemic has led to the widespread use of face masks. The effect of wearing face masks is currently an understudied issue. The aim of this paper is to analyze face detection, face landmarking, and face recognition performance with... | ['Ožbej Golob'] | 2022-06-03 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [-4.93511604e-03 -1.54337779e-01 6.60222545e-02 -5.61984420e-01
-2.31193915e-01 -5.76925874e-01 3.11096996e-01 -4.01204407e-01
-4.12157416e-01 2.82505095e-01 -2.72385199e-02 -4.69178110e-02
3.00896615e-01 -4.73016024e-01 -4.25304472e-01 -6.27624035e-01
-1.94174424e-01 6.01998158e-02 -2.27948785e-01 1.04758069... | [13.321727752685547, 0.799431562423706] |
6854ae08-3cc8-4231-a265-31a320d10188 | csdr-bert-a-pre-trained-scientific-dataset | 2301.12700 | null | https://arxiv.org/abs/2301.12700v3 | https://arxiv.org/pdf/2301.12700v3.pdf | CSDR-BERT: a pre-trained scientific dataset match model for Chinese Scientific Dataset Retrieval | As the number of open and shared scientific datasets on the Internet increases under the open science movement, efficiently retrieving these datasets is a crucial task in information retrieval (IR) research. In recent years, the development of large models, particularly the pre-training and fine-tuning paradigm, which ... | ['XiaoFeng Wang', 'Jian Wang', 'Xunxun Gu', 'Meng Wang', 'Yingfei Wang', 'Jianping Liu', 'Xintao Chu'] | 2023-01-30 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [ 1.79574471e-02 -8.15386549e-02 -1.94270283e-01 -4.09592897e-01
-1.12929583e+00 -6.16384149e-01 6.85045421e-01 3.45273286e-01
-9.54603970e-01 5.16827404e-01 3.40440094e-01 -2.87171453e-01
-3.69700730e-01 -6.67198837e-01 -5.71344316e-01 -2.58713812e-01
1.26754433e-01 7.26729929e-01 1.00305721e-01 -2.06571296... | [11.330060958862305, 7.793327331542969] |
bbd0eedf-d542-4d9d-ac43-03348c21251c | graph-driven-generative-models-for | 1911.08709 | null | https://arxiv.org/abs/1911.08709v1 | https://arxiv.org/pdf/1911.08709v1.pdf | Graph-Driven Generative Models for Heterogeneous Multi-Task Learning | We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework. The proposed model is based on the fact that heterogeneous learning tasks, which correspond to different generative processes, often rely on data with a shared graph structure. Accordingly, our ... | ['Zhe Gan', 'Wenlin Wang', 'Lawrence Carin', 'Qian Yang', 'Liqun Chen', 'Bai Li', 'Wenqi Wang', 'Hongteng Xu', 'Guoyin Wang'] | 2019-11-20 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [-8.68424922e-02 3.46657932e-01 -1.55334488e-01 -1.34296253e-01
-4.80118454e-01 -3.07457358e-01 7.07594395e-01 2.12739244e-01
4.43329476e-02 5.72094262e-01 5.68961084e-01 -1.79963231e-01
-1.76838800e-01 -1.00757372e+00 -7.53226161e-01 -8.70154738e-01
2.72676975e-01 8.20888340e-01 -1.42631814e-01 1.11838788... | [7.347444534301758, 6.202232360839844] |
32dbef74-9982-4dbe-a49a-e9f17c77ad72 | carl-a-benchmark-for-contextual-and-adaptive | 2110.02102 | null | https://arxiv.org/abs/2110.02102v2 | https://arxiv.org/pdf/2110.02102v2.pdf | CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning | While Reinforcement Learning has made great strides towards solving ever more complicated tasks, many algorithms are still brittle to even slight changes in their environment. This is a limiting factor for real-world applications of RL. Although the research community continuously aims at improving both robustness and ... | ['Marius Lindauer', 'Frank Hutter', 'Bodo Rosenhahn', 'André Biedenkapp', 'Frederik Schubert', 'Theresa Eimer', 'Carolin Benjamins'] | 2021-10-05 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [ 2.05700427e-01 -2.17484906e-01 -3.27553600e-01 3.29212286e-02
-6.91900134e-01 -8.65143478e-01 8.16376030e-01 2.44002789e-02
-5.47342300e-01 1.32180107e+00 -7.97445551e-02 -3.09234411e-01
-3.70100588e-01 -4.79038388e-01 -6.66703939e-01 -1.00282669e+00
-4.03497487e-01 5.38986206e-01 2.59722501e-01 -7.34941065... | [4.039078712463379, 1.7787187099456787] |
0bf3e59c-1a9e-4f19-a73d-de50db6049b2 | adaptive-streaming-perception-using-deep | 2106.05665 | null | https://arxiv.org/abs/2106.05665v2 | https://arxiv.org/pdf/2106.05665v2.pdf | Learning Runtime Decisions for Adaptive Real-Time Perception | Real-time perception requires planned resource utilization. Computational planning in real-time perception is governed by two considerations -- accuracy and latency. There exist run-time decisions (e.g. choice of input resolution) that induce tradeoffs affecting performance on a given hardware, arising from intrinsic (... | ['Aditya Singh', 'Vaibhav Balloli', 'Tanuja Ganu', 'Akshay Nambi', 'Anurag Ghosh'] | 2021-06-10 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [ 3.50580871e-01 -1.85693145e-01 -3.26456368e-01 -4.93234128e-01
-5.72155595e-01 -5.84052920e-01 5.79475820e-01 2.20220745e-01
-6.56587362e-01 3.06867301e-01 2.15121627e-01 -4.65446830e-01
-1.43875197e-01 -7.56155133e-01 -7.36615539e-01 -5.97383678e-01
-1.83042347e-01 4.17715788e-01 4.63120788e-01 -4.92458791... | [5.235624313354492, 2.93346905708313] |
c7c5252f-0bdc-47e6-a4fa-05f556c702ae | multi-scale-local-temporal-similarity-fusion | 2107.12762 | null | https://arxiv.org/abs/2107.12762v1 | https://arxiv.org/pdf/2107.12762v1.pdf | Multi-Scale Local-Temporal Similarity Fusion for Continuous Sign Language Recognition | Continuous sign language recognition (cSLR) is a public significant task that transcribes a sign language video into an ordered gloss sequence. It is important to capture the fine-grained gloss-level details, since there is no explicit alignment between sign video frames and the corresponding glosses. Among the past wo... | ['Xiaohui Hu', 'Bin Wang', 'Jianwei Cui', 'Mengyi Zhao', 'Yao Du', 'Zhi Cui', 'Pan Xie'] | 2021-07-27 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 9.72395539e-02 -8.20320189e-01 -1.45468712e-01 -5.85527956e-01
-7.53700018e-01 -3.20228159e-01 4.85319704e-01 -5.68683207e-01
-6.61878288e-01 1.77224323e-01 7.90352881e-01 8.88995528e-02
-6.16755597e-02 -5.09827197e-01 -5.57360411e-01 -8.43606055e-01
-1.67960301e-02 -5.15485220e-02 6.74585879e-01 -1.75762728... | [9.21903133392334, -6.490591049194336] |
922e2c1b-a95b-460c-ba7c-422d164b0687 | large-scale-unsupervised-semantic | 2106.03149 | null | https://arxiv.org/abs/2106.03149v3 | https://arxiv.org/pdf/2106.03149v3.pdf | Large-scale Unsupervised Semantic Segmentation | Empowered by large datasets, e.g., ImageNet, unsupervised learning on large-scale data has enabled significant advances for classification tasks. However, whether the large-scale unsupervised semantic segmentation can be achieved remains unknown. There are two major challenges: i) we need a large-scale benchmark for as... | ['ShangHua Gao', 'Philip Torr', 'Junwei Han', 'Ming-Ming Cheng', 'Ming-Hsuan Yang', 'Zhong-Yu Li'] | 2021-06-06 | null | null | null | null | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 3.36079478e-01 1.90191761e-01 -3.11405689e-01 -4.92523521e-01
-9.73573506e-01 -6.35604799e-01 2.07449570e-01 -2.20109731e-01
-5.44453681e-01 5.93171775e-01 9.19807926e-02 1.96308754e-02
5.17334379e-02 -5.94625056e-01 -7.43248701e-01 -6.95643365e-01
2.40413278e-01 6.69347882e-01 6.13984227e-01 6.00968003... | [9.592916488647461, 0.7916719317436218] |
cae80d87-e120-419d-a7c4-3e85fa040676 | knowledge-assembly-semi-supervised-multi-task | 2306.08839 | null | https://arxiv.org/abs/2306.08839v1 | https://arxiv.org/pdf/2306.08839v1.pdf | Knowledge Assembly: Semi-Supervised Multi-Task Learning from Multiple Datasets with Disjoint Labels | In real-world scenarios we often need to perform multiple tasks simultaneously. Multi-Task Learning (MTL) is an adequate method to do so, but usually requires datasets labeled for all tasks. We propose a method that can leverage datasets labeled for only some of the tasks in the MTL framework. Our work, Knowledge Assem... | ['Tae-hoon Kim', 'Minhyeong Yu', 'Philipp Benz', 'Federica Spinola'] | 2023-06-15 | null | null | null | null | ['person-re-identification', 'pedestrian-attribute-recognition', 'multi-task-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 1.44979700e-01 1.67693496e-01 -6.92985430e-02 -7.39174962e-01
-1.04943097e+00 -5.85598588e-01 6.26711547e-01 1.09052323e-01
-8.42188597e-01 1.04045594e+00 -2.95242891e-02 4.01438549e-02
1.10105224e-01 -5.88994384e-01 -1.05168879e+00 -4.35318559e-01
2.92022854e-01 8.39879990e-01 5.92268296e-02 1.80255398... | [14.747150421142578, 1.0500743389129639] |
3568a57b-86bd-4fc9-b379-14ac47c3ec12 | render-and-compare-cross-view-6-dof | 2302.06287 | null | https://arxiv.org/abs/2302.06287v1 | https://arxiv.org/pdf/2302.06287v1.pdf | Render-and-Compare: Cross-View 6 DoF Localization from Noisy Prior | Despite the significant progress in 6-DoF visual localization, researchers are mostly driven by ground-level benchmarks. Compared with aerial oblique photography, ground-level map collection lacks scalability and complete coverage. In this work, we propose to go beyond the traditional ground-level setting and exploit t... | ['Maojun Zhang', 'Yu Liu', 'Rouwan Wu', 'Juelin Zhu', 'Yuxiang Liu', 'Xiaoya Cheng', 'Shen Yan'] | 2023-02-13 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [ 1.68929957e-02 -3.65592182e-01 -1.17292576e-01 -4.78916198e-01
-1.19566453e+00 -1.27721965e+00 5.97759426e-01 -2.71573097e-01
-3.67547750e-01 5.14980614e-01 3.00248321e-02 -1.80862799e-01
2.27696270e-01 -4.96514827e-01 -9.38717782e-01 -3.43828827e-01
7.30742700e-03 3.89003158e-01 4.78920579e-01 -1.85471058... | [7.6434783935546875, -2.276855230331421] |
d537fd33-c45c-4f3f-8066-d34033939d9e | domain-specific-language-model-pretraining | 2007.15779 | null | https://arxiv.org/abs/2007.15779v6 | https://arxiv.org/pdf/2007.15779v6.pdf | Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing | Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from ... | ['Hoifung Poon', 'Xiaodong Liu', 'Yu Gu', 'Tristan Naumann', 'Naoto Usuyama', 'Jianfeng Gao', 'Hao Cheng', 'Robert Tinn', 'Michael Lucas'] | 2020-07-31 | null | null | null | null | ['participant-intervention-comparison-outcome', 'continual-pretraining', 'drug-drug-interaction-extraction', 'pico'] | ['medical', 'methodology', 'natural-language-processing', 'natural-language-processing'] | [ 1.35527447e-01 2.41231665e-01 -4.28933799e-01 -6.09462440e-01
-9.73935246e-01 -4.93375599e-01 2.95027345e-01 3.28615040e-01
-8.38936388e-01 9.00026977e-01 4.30813074e-01 -5.26568055e-01
1.14331916e-01 -5.14892697e-01 -7.68481314e-01 -3.42636257e-01
7.48142526e-02 7.23775804e-01 -1.09128594e-01 -2.03310475... | [8.631025314331055, 8.674267768859863] |
599137ec-22de-409d-9d74-88ad61ae6b94 | few-shot-novel-concept-learning-for-semantic | null | null | https://aclanthology.org/2021.findings-emnlp.177 | https://aclanthology.org/2021.findings-emnlp.177.pdf | Few-Shot Novel Concept Learning for Semantic Parsing | Humans are capable of learning novel concepts from very few examples; in contrast, state-of-the-art machine learning algorithms typically need thousands of examples to do so. In this paper, we propose an algorithm for learning novel concepts by representing them as programs over existing concepts. This way the concept ... | ['Dan Roth', 'Osbert Bastani', 'Soham Dan'] | null | null | null | null | findings-emnlp-2021-11 | ['novel-concepts'] | ['reasoning'] | [ 5.35081267e-01 6.68853700e-01 -9.47408378e-02 -6.69512630e-01
-8.05502355e-01 -6.41728163e-01 4.92566615e-01 7.41081715e-01
-5.26408851e-01 5.82384706e-01 -9.87150520e-02 -3.62122476e-01
1.80769116e-01 -1.28538132e+00 -1.26170766e+00 -4.53454375e-01
-1.87525302e-01 6.95477724e-01 4.94730473e-01 -1.79463461... | [10.505653381347656, 8.979100227355957] |
8856f9a7-f8aa-4f6b-bb3a-34d65dab23d9 | an-analysis-of-annotated-corpora-for-emotion | null | null | https://aclanthology.org/C18-1179 | https://aclanthology.org/C18-1179.pdf | An Analysis of Annotated Corpora for Emotion Classification in Text | Several datasets have been annotated and published for classification of emotions. They differ in several ways: (1) the use of different annotation schemata (e. g., discrete label sets, including joy, anger, fear, or sadness or continuous values including valence, or arousal), (2) the domain, and, (3) the file formats.... | ['Laura-Ana-Maria Bostan', 'Roman Klinger'] | 2018-08-01 | an-analysis-of-annotated-corpora-for-emotion-1 | https://aclanthology.org/C18-1179 | https://aclanthology.org/C18-1179.pdf | coling-2018-8 | ['cross-corpus'] | ['computer-vision'] | [ 3.19415554e-02 -4.81600761e-02 -2.40005150e-01 -7.81103909e-01
-2.64670432e-01 -7.45539367e-01 6.01517320e-01 5.55447102e-01
-4.33204055e-01 7.44665623e-01 2.38774866e-01 8.17507058e-02
-1.97362006e-01 -5.05095303e-01 -4.84793857e-02 -4.82650936e-01
1.57383934e-01 5.82565486e-01 -1.17049776e-01 -3.66034716... | [12.66522216796875, 6.388296604156494] |
07137853-1de4-4509-94b6-8874859d0d73 | rendering-nighttime-image-via-cascaded-color | 2204.08970 | null | https://arxiv.org/abs/2204.08970v2 | https://arxiv.org/pdf/2204.08970v2.pdf | Rendering Nighttime Image Via Cascaded Color and Brightness Compensation | Image signal processing (ISP) is crucial for camera imaging, and neural networks (NN) solutions are extensively deployed for daytime scenes. The lack of sufficient nighttime image dataset and insights on nighttime illumination characteristics poses a great challenge for high-quality rendering using existing NN ISPs. To... | ['Zhan Ma', 'Si Yi', 'Zhihao LI'] | 2022-04-19 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 2.95307845e-01 -4.82849628e-01 2.29035795e-01 -6.24990761e-01
-7.03889549e-01 -6.71240687e-01 4.18131679e-01 -7.49476194e-01
-3.87510091e-01 5.79130828e-01 1.31684929e-01 -5.44206321e-01
4.23590727e-02 -4.85090464e-01 -5.66053689e-01 -6.92614317e-01
3.16478521e-01 -3.23092103e-01 -7.54186977e-03 -3.79844695... | [10.741889953613281, -2.498948097229004] |
1effe3b7-cab2-480d-bd41-6600c2c5dcaa | system-log-parsing-a-survey | 2212.14277 | null | https://arxiv.org/abs/2212.14277v1 | https://arxiv.org/pdf/2212.14277v1.pdf | System Log Parsing: A Survey | Modern information and communication systems have become increasingly challenging to manage. The ubiquitous system logs contain plentiful information and are thus widely exploited as an alternative source for system management. As log files usually encompass large amounts of raw data, manually analyzing them is laborio... | ['Fabio Pianese', 'Chung Shue Chen', 'Myriana Rifai', 'Gabriele Castellano', 'Han Qiu', 'Tianzhu Zhang'] | 2022-12-29 | null | null | null | null | ['log-parsing'] | ['computer-code'] | [-6.86332881e-02 -3.08769763e-01 -3.95403832e-01 -3.42743218e-01
-7.30558753e-01 -8.15240324e-01 7.19768181e-02 6.26038432e-01
-4.75116372e-02 4.42017525e-01 -1.48926735e-01 -7.64601946e-01
2.54111644e-03 -6.32943153e-01 -1.31415054e-01 -1.64000750e-01
-3.90539646e-01 4.41304773e-01 3.69561613e-01 -1.33644715... | [7.997892379760742, 6.886466026306152] |
af6b4218-339a-46e8-bdd7-6ec548cd0ca2 | urvos-unified-referring-video-object | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2327_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123600205.pdf | URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark | We propose a unified referring video object segmentation network (URVOS). URVOS takes a video and a referring expression as inputs, and estimates the {object masks} referred by the given language expression in the whole video frames. Our algorithm addresses the challenging problem by performing language-based object se... | ['Joon-Young Lee', 'Seonguk Seo', 'Bohyung Han'] | null | null | null | null | eccv-2020-8 | ['one-shot-visual-object-segmentation', 'referring-expression-segmentation', 'referring-video-object-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.35456428e-01 -4.27313149e-02 -5.21968484e-01 -4.00174737e-01
-1.06134403e+00 -5.47928572e-01 2.01467425e-01 -6.06698632e-01
-4.09354925e-01 4.32589918e-01 1.45121804e-02 -3.78600806e-02
3.87293071e-01 -3.84581596e-01 -1.03967655e+00 -1.74367771e-01
4.15499151e-01 2.35623419e-01 4.42363262e-01 2.40430281... | [9.475698471069336, 0.32337403297424316] |
e00f500c-df34-40cb-858c-2c84042c8344 | sface-sigmoid-constrained-hypersphere-loss-1 | 2205.12010 | null | https://arxiv.org/abs/2205.12010v1 | https://arxiv.org/pdf/2205.12010v1.pdf | SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition | Deep face recognition has achieved great success due to large-scale training databases and rapidly developing loss functions. The existing algorithms devote to realizing an ideal idea: minimizing the intra-class distance and maximizing the inter-class distance. However, they may neglect that there are also low quality ... | ['Dongchao Wen', 'Xian Li', 'Dongyue Zhao', 'Jiani Hu', 'Weihong Deng', 'Yaoyao Zhong'] | 2022-05-24 | sface-sigmoid-constrained-hypersphere-loss | https://ieeexplore.ieee.org/document/9318547 | https://ieeexplore.ieee.org/document/9318547 | ieee-transactions-on-image-processing-2021-1 | ['robust-face-recognition'] | ['computer-vision'] | [-2.16111302e-01 -1.71382234e-01 -2.84430720e-02 -8.63954425e-01
-2.90071338e-01 -6.95219263e-03 3.85717392e-01 -3.78435582e-01
-4.20749277e-01 5.39330184e-01 -1.46369830e-01 4.90187705e-02
-3.42287153e-01 -9.07393634e-01 -5.49035549e-01 -9.13488269e-01
-6.76406845e-02 1.87703550e-01 -6.05208799e-02 -1.69985220... | [13.182503700256348, 0.7688756585121155] |
78de69a4-8706-4f3a-96d3-13ae6b9dd717 | can-predicate-argument-relationships-be | null | null | https://aclanthology.org/2021.law-1.5 | https://aclanthology.org/2021.law-1.5.pdf | Can predicate-argument relationships be extracted from UD trees? | In this paper we investigate the possibility of extracting predicate-argument relations from UD trees (and enhanced UD graphs). Con- cretely, we apply UD parsers on an En- glish question answering/semantic-role label- ing data set (FitzGerald et al., 2018) and check if the annotations reflect the relations in the resul... | ['Stergios Chatzikyriakidis', 'Jean-Philippe Bernardy', 'Adam Ek'] | null | null | null | null | emnlp-law-dmr-2021-11 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 1.12519152e-01 1.09074914e+00 -7.04586357e-02 -3.80594492e-01
-9.37307835e-01 -1.11641777e+00 4.21948165e-01 7.18697608e-01
-2.31733978e-01 8.46834958e-01 4.28286135e-01 -7.39597797e-01
-5.16504288e-01 -1.02994776e+00 -8.47849131e-01 -2.91759707e-02
4.58341897e-01 7.42921591e-01 4.97326314e-01 -4.48798805... | [10.10348129272461, 9.083333015441895] |
3590178b-e881-4b98-8142-11dd038f5fb0 | efficient-transformer-based-method-for-remote | 2103.00208 | null | https://arxiv.org/abs/2103.00208v3 | https://arxiv.org/pdf/2103.00208v3.pdf | Remote Sensing Image Change Detection with Transformers | Modern change detection (CD) has achieved remarkable success by the powerful discriminative ability of deep convolutions. However, high-resolution remote sensing CD remains challenging due to the complexity of objects in the scene. Objects with the same semantic concept may show distinct spectral characteristics at dif... | ['Zhenwei Shi', 'Zipeng Qi', 'Hao Chen'] | 2021-02-27 | null | null | null | null | ['building-change-detection-for-remote-sensing'] | ['miscellaneous'] | [ 4.07011241e-01 -4.68757778e-01 1.68353036e-01 -4.26061720e-01
-7.73483515e-01 -5.40724456e-01 7.74293542e-01 1.69107635e-02
-3.39748681e-01 4.41536069e-01 3.64856362e-01 -1.80357605e-01
-1.84806213e-02 -1.16370893e+00 -8.93951833e-01 -8.58813345e-01
-9.53436457e-03 -7.06770048e-02 3.27245802e-01 -2.80095875... | [9.677633285522461, -1.3343946933746338] |
569db09b-db76-4cfa-a48c-0f40eaf0d3c1 | multilingual-offensive-lexicon-annotated-with | null | null | https://openreview.net/forum?id=WCBAn7V584l | https://openreview.net/pdf?id=WCBAn7V584l | Multilingual offensive lexicon annotated with contextual information | Online hate speech and offensive comments detection is not a trivial research problem since pragmatic (contextual) factors influence what is considered offensive. Moreover, offensive terms are hardly found in classical lexical resources such as wordnets, sentiment, and emotion lexicons. In this paper, we embrace the ch... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['abusive-language'] | ['natural-language-processing'] | [ 7.38107637e-02 1.77805915e-01 -5.54838181e-01 9.03189834e-03
-5.15079558e-01 -1.14954066e+00 5.90262592e-01 7.18844295e-01
-5.94363868e-01 8.93249154e-01 5.35063624e-01 3.92764546e-02
4.51222777e-01 -2.57266313e-01 1.49626955e-01 -3.09594482e-01
3.90868276e-01 8.97178501e-02 -2.06783995e-01 -5.71904480... | [8.777693748474121, 10.5396728515625] |
b9e32a5a-7e20-4275-8259-6b8edd385e20 | reinforcement-learning-with-imbalanced | null | null | https://aclanthology.org/2020.findings-emnlp.202 | https://aclanthology.org/2020.findings-emnlp.202.pdf | Reinforcement Learning with Imbalanced Dataset for Data-to-Text Medical Report Generation | Automated generation of medical reports that describe the findings in the medical images helps radiologists by alleviating their workload. Medical report generation system should generate correct and concise reports. However, data imbalance makes it difficult to train models accurately. Medical datasets are commonly im... | ['Keigo Nakamura', 'Tomoko Ohkuma', 'Motoki Taniguchi', 'Yuki Tagawa', 'Norihisa Nakano', 'Ryuji Kano', 'Tomoki Taniguchi', 'Yohei Momoki', 'Ryota Ozaki', 'Toru Nishino'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['medical-report-generation'] | ['medical'] | [ 3.09382200e-01 4.58363175e-01 -2.47710064e-01 -7.82537162e-01
-1.12458718e+00 -2.41753280e-01 8.50436091e-02 4.26281333e-01
-1.59480393e-01 8.13061714e-01 9.90925133e-02 -4.41446453e-01
9.90642142e-03 -9.69822526e-01 -6.29699767e-01 -4.56538558e-01
1.76528007e-01 6.93020701e-01 -5.47688454e-02 2.22622350... | [15.04706859588623, -1.3892362117767334] |
b3217278-5ddd-42d6-829d-e59224a02a3b | fairness-for-workers-who-pull-the-arms-an | 2303.00799 | null | https://arxiv.org/abs/2303.00799v1 | https://arxiv.org/pdf/2303.00799v1.pdf | Fairness for Workers Who Pull the Arms: An Index Based Policy for Allocation of Restless Bandit Tasks | Motivated by applications such as machine repair, project monitoring, and anti-poaching patrol scheduling, we study intervention planning of stochastic processes under resource constraints. This planning problem has previously been modeled as restless multi-armed bandits (RMAB), where each arm is an intervention-depend... | ['Milind Tambe', 'Susobhan Ghosh', 'Paula Rodriguez Diaz', 'Jackson A. Killian', 'Arpita Biswas'] | 2023-03-01 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 4.59262848e-01 3.60320032e-01 -8.21903884e-01 -8.87838677e-02
-6.28571630e-01 -3.63074809e-01 3.17628592e-01 6.88976124e-02
-5.44991851e-01 1.05531394e+00 1.54769540e-01 -5.48271477e-01
-6.83900177e-01 -5.69456279e-01 -6.01388872e-01 -8.42587411e-01
1.32660389e-01 1.13931215e+00 -2.84695197e-02 2.33086482... | [4.454455852508545, 3.1877329349517822] |
fefa1af0-8af0-46b2-afa9-261453c7e8e7 | an-improved-neural-baseline-for-temporal | 1909.00429 | null | https://arxiv.org/abs/1909.00429v1 | https://arxiv.org/pdf/1909.00429v1.pdf | An Improved Neural Baseline for Temporal Relation Extraction | Determining temporal relations (e.g., before or after) between events has been a challenging natural language understanding task, partly due to the difficulty to generate large amounts of high-quality training data. Consequently, neural approaches have not been widely used on it, or showed only moderate improvements. T... | ['Qiang Ning', 'Dan Roth', 'Sanjay Subramanian'] | 2019-09-01 | an-improved-neural-baseline-for-temporal-1 | https://aclanthology.org/D19-1642 | https://aclanthology.org/D19-1642.pdf | ijcnlp-2019-11 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 3.45260948e-02 2.57426873e-02 -6.12397969e-01 -5.48203945e-01
-8.30466509e-01 -3.12258124e-01 9.17332470e-01 3.34641576e-01
-8.65591168e-01 8.08897376e-01 5.25108695e-01 -2.08585098e-01
4.65182960e-02 -9.80907500e-01 -7.26830900e-01 -4.62762028e-01
-2.15472341e-01 5.76160908e-01 4.25108045e-01 -2.39558622... | [9.207568168640137, 9.203386306762695] |
a82b9f5f-efb5-48c0-96d5-84ee8a28e28b | box-aware-feature-enhancement-for-single | 2108.04728 | null | https://arxiv.org/abs/2108.04728v2 | https://arxiv.org/pdf/2108.04728v2.pdf | Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds | Current 3D single object tracking approaches track the target based on a feature comparison between the target template and the search area. However, due to the common occlusion in LiDAR scans, it is non-trivial to conduct accurate feature comparisons on severe sparse and incomplete shapes. In this work, we exploit the... | ['Shuguang Cui', 'Zhen Li', 'Wei zhang', 'Weibing Zhao', 'Jiantao Gao', 'Xu Yan', 'Chaoda Zheng'] | 2021-08-10 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zheng_Box-Aware_Feature_Enhancement_for_Single_Object_Tracking_on_Point_Clouds_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zheng_Box-Aware_Feature_Enhancement_for_Single_Object_Tracking_on_Point_Clouds_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-single-object-tracking'] | ['computer-vision'] | [-7.97678903e-02 -4.21975434e-01 -1.50806829e-01 -2.44089544e-01
-9.72566545e-01 -7.70861149e-01 7.45846510e-01 1.61839709e-01
-2.72301853e-01 3.10968697e-01 -1.23320799e-02 7.55611286e-02
-1.28314435e-01 -6.35120034e-01 -6.11365438e-01 -5.86822569e-01
9.55020636e-02 4.65866745e-01 6.77264988e-01 8.90517607... | [6.635537624359131, -2.3381707668304443] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.