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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
8f78e8de-7b48-44c9-a393-6a8c437233db | knowledge-guided-open-attribute-value | 2010.09189 | null | https://arxiv.org/abs/2010.09189v1 | https://arxiv.org/pdf/2010.09189v1.pdf | Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning | Open attribute value extraction for emerging entities is an important but challenging task. A lot of previous works formulate the problem as a \textit{question-answering} (QA) task. While the collections of articles from web corpus provide updated information about the emerging entities, the retrieved texts can be nois... | ['Yanghua Xiao', 'Suo Feng', 'Rui Song', 'Sheng Zhang', 'Ye Liu'] | 2020-10-19 | null | https://aclanthology.org/2020.emnlp-main.693 | https://aclanthology.org/2020.emnlp-main.693.pdf | emnlp-2020-11 | ['attribute-value-extraction'] | ['natural-language-processing'] | [-1.72230303e-01 5.05964100e-01 -4.16388750e-01 -2.39735126e-01
-1.43915021e+00 -6.03056669e-01 6.80277869e-02 5.55377007e-01
-5.60871124e-01 1.34101820e+00 4.34316397e-01 7.17811510e-02
-2.38602147e-01 -1.24232137e+00 -8.21254075e-01 -2.72657394e-01
-1.05830403e-02 5.20188808e-01 2.96681076e-01 -4.32020992... | [10.350749015808105, 8.102991104125977] |
f40ee9e2-4b20-460b-8114-b7549ac2fee9 | generating-high-quality-3dmpcs-by-adaptive | 2305.06777 | null | https://arxiv.org/abs/2305.06777v1 | https://arxiv.org/pdf/2305.06777v1.pdf | Generating high-quality 3DMPCs by adaptive data acquisition and NeREF-based reflectance correction to facilitate efficient plant phenotyping | Non-destructive assessments of plant phenotypic traits using high-quality three-dimensional (3D) and multispectral data can deepen breeders' understanding of plant growth and allow them to make informed managerial decisions. However, subjective viewpoint selection and complex illumination effects under natural light co... | ['Haiyan Cen', 'Jiangpeng Zhu', 'Xuqi Lu', 'YuTao Shen', 'Mengqi Lv', 'Ruiming Du', 'Zhihong Ma', 'Pengyao Xie'] | 2023-05-11 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 2.41337225e-01 -3.77496660e-01 2.05594957e-01 3.19507383e-02
-2.68652141e-01 -1.01240587e+00 -1.88496143e-01 2.55041689e-01
2.40107283e-01 5.69678605e-01 -7.28624701e-01 -4.42373931e-01
-5.69269180e-01 -1.15056086e+00 -2.04120368e-01 -1.12371862e+00
2.17550546e-01 1.92429081e-01 2.75714159e-01 -2.11848333... | [9.126110076904297, -1.6343046426773071] |
955e3825-a239-4f3f-81fa-3d8a1d6193ec | deep-semantic-ranking-based-hashing-for-multi | 1501.06272 | null | http://arxiv.org/abs/1501.06272v2 | http://arxiv.org/pdf/1501.06272v2.pdf | Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval | With the rapid growth of web images, hashing has received increasing
interests in large scale image retrieval. Research efforts have been devoted to
learning compact binary codes that preserve semantic similarity based on
labels. However, most of these hashing methods are designed to handle simple
binary similarity. Th... | ['Tieniu Tan', 'Liang Wang', 'Fang Zhao', 'Yongzhen Huang'] | 2015-01-26 | deep-semantic-ranking-based-hashing-for-multi-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Zhao_Deep_Semantic_Ranking_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Zhao_Deep_Semantic_Ranking_2015_CVPR_paper.pdf | cvpr-2015-6 | ['multi-label-image-retrieval'] | ['computer-vision'] | [ 9.64344144e-02 -2.73911059e-01 -2.11255223e-01 -7.09766567e-01
-1.31005991e+00 -2.70611286e-01 4.05451268e-01 6.49332404e-01
-5.54266810e-01 4.40608174e-01 6.39394373e-02 3.08434129e-01
-3.35105628e-01 -8.29224706e-01 -6.86174572e-01 -9.09606636e-01
-3.15636285e-02 3.06750238e-01 1.78248525e-01 1.59530062... | [11.344804763793945, 0.9494827389717102] |
d393902b-6673-4e9e-893d-baeb7b898643 | joint-learning-of-answer-selection-and-answer | 1911.09801 | null | https://arxiv.org/abs/1911.09801v1 | https://arxiv.org/pdf/1911.09801v1.pdf | Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering | Community question answering (CQA) gains increasing popularity in both academy and industry recently. However, the redundancy and lengthiness issues of crowdsourced answers limit the performance of answer selection and lead to reading difficulties and misunderstandings for community users. To solve these problems, we t... | ['Yaliang Li', 'Yuexiang Xie', 'Daoyuan Chen', 'Min Yang', 'Ying Shen', 'Yang Deng', 'Wai Lam'] | 2019-11-22 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [-2.40823608e-02 9.78671536e-02 3.45895886e-02 -7.71759078e-02
-1.53727067e+00 -5.95994413e-01 4.04583961e-01 6.46257460e-01
-3.29072863e-01 9.52785790e-01 8.11652482e-01 -1.77083507e-01
-2.41390780e-01 -8.01258743e-01 -2.08724841e-01 -2.50694126e-01
3.11463356e-01 5.44079900e-01 4.51126605e-01 -6.44237041... | [11.657644271850586, 8.254738807678223] |
b2e361e7-a9f8-4075-9c7a-7717b308a871 | the-whole-is-greater-than-the-sum-of-its | null | null | https://aclanthology.org/W18-0522 | https://aclanthology.org/W18-0522.pdf | The Whole is Greater than the Sum of its Parts: Towards the Effectiveness of Voting Ensemble Classifiers for Complex Word Identification | In this paper, we present an effective system using voting ensemble classifiers to detect contextually complex words for non-native English speakers. To make the final decision, we channel a set of eight calibrated classifiers based on lexical, size and vocabulary features and train our model with annotated datasets co... | ['', 'eep', 'S Mathias', 'Pushpak Bhattacharyya', 'Jayashree Aan Gajjam', 'Nikhil Wani'] | 2018-06-01 | null | null | null | ws-2018-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-2.02792436e-01 -1.38025824e-02 -2.08157703e-01 -7.40710616e-01
-1.06611824e+00 -8.00072789e-01 7.82433093e-01 1.86010584e-01
-9.48989153e-01 8.34043264e-01 2.83112586e-01 -5.58465064e-01
2.30870008e-01 -4.65478987e-01 -3.22063208e-01 -2.06303000e-01
1.32744789e-01 3.42764229e-01 2.68202454e-01 -5.78778028... | [10.47040843963623, 10.494050979614258] |
f9520bd7-742f-4855-84ba-a848b534ff01 | atlas-a-dataset-and-benchmark-for-e-commerce | 1908.08984 | null | https://arxiv.org/abs/1908.08984v1 | https://arxiv.org/pdf/1908.08984v1.pdf | Atlas: A Dataset and Benchmark for E-commerce Clothing Product Categorization | In E-commerce, it is a common practice to organize the product catalog using product taxonomy. This enables the buyer to easily locate the item they are looking for and also to explore various items available under a category. Product taxonomy is a tree structure with 3 or more levels of depth and several leaf nodes. P... | ['Venkatesh Umaashankar', 'Girish Shanmugam S', 'Aditi Prakash'] | 2019-08-12 | null | null | null | null | ['product-categorization'] | ['miscellaneous'] | [-9.90171134e-02 -4.24689829e-01 -7.19824612e-01 -6.90196633e-01
-1.95548698e-01 -1.09107304e+00 3.03632587e-01 4.08645153e-01
-8.66633728e-02 2.60301288e-02 3.16116214e-01 -2.69165218e-01
4.11255434e-02 -7.96778917e-01 -4.42129344e-01 -3.77485484e-01
-1.49467021e-01 3.58552754e-01 1.17576100e-01 -1.51896462... | [9.906390190124512, 6.143749713897705] |
755048a8-90b6-4bff-92fc-3d75f1f04f27 | focusedcleaner-sanitizing-poisoned-graphs-for | 2210.13815 | null | https://arxiv.org/abs/2210.13815v1 | https://arxiv.org/pdf/2210.13815v1.pdf | FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification | Recently, a lot of research attention has been devoted to exploring Web security, a most representative topic is the adversarial robustness of graph mining algorithms. Especially, a widely deployed adversarial attacks formulation is the graph manipulation attacks by modifying the relational data to mislead the Graph Ne... | ['Kai Zhou', 'Liang Tong', 'Yulin Zhu'] | 2022-10-25 | null | null | null | null | ['graph-mining'] | ['graphs'] | [ 2.87981510e-01 3.39770913e-01 -3.57749939e-01 3.52087468e-01
-4.82996225e-01 -8.90062809e-01 4.63064790e-01 4.60391968e-01
-9.56344977e-02 3.96752298e-01 -1.37857944e-01 -6.56124175e-01
-1.97252795e-01 -1.16648781e+00 -9.60245728e-01 -8.28093231e-01
-3.82760823e-01 3.21530521e-01 5.71111381e-01 -3.50676030... | [6.165030002593994, 7.30071496963501] |
aae5efcd-f9d2-46be-ac6b-ca425b227121 | inharmonious-region-localization-via | 2210.02036 | null | https://arxiv.org/abs/2210.02036v1 | https://arxiv.org/pdf/2210.02036v1.pdf | Inharmonious Region Localization via Recurrent Self-Reasoning | Synthetic images created by image editing operations are prevalent, but the color or illumination inconsistency between the manipulated region and background may make it unrealistic. Thus, it is important yet challenging to localize the inharmonious region to improve the quality of synthetic image. Inspired by the clas... | ['Liqing Zhang', 'Jing Liang', 'Li Niu', 'Penghao Wu'] | 2022-10-05 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 4.71813351e-01 1.43265560e-01 1.79596677e-01 -1.05503373e-01
-5.04459977e-01 -5.00971258e-01 4.36231852e-01 -2.85943329e-01
-1.68481529e-01 3.97819012e-01 6.83717132e-02 3.35207582e-02
2.39389226e-01 -6.11312151e-01 -7.68630266e-01 -8.18098843e-01
5.66255033e-01 -6.16575871e-03 3.04291457e-01 -1.43189862... | [11.26550006866455, -1.163069725036621] |
04e45bab-027b-46b0-95a3-f109466f5747 | a-compact-neural-architecture-for-visual | 1910.06840 | null | https://arxiv.org/abs/1910.06840v3 | https://arxiv.org/pdf/1910.06840v3.pdf | A Hybrid Compact Neural Architecture for Visual Place Recognition | State-of-the-art algorithms for visual place recognition, and related visual navigation systems, can be broadly split into two categories: computer-science-oriented models including deep learning or image retrieval-based techniques with minimal biological plausibility, and neuroscience-oriented dynamical networks that ... | ['Andrew B. Barron', 'Luis Hernandez-Nunez', 'Marvin Chancán', 'Ajay Narendra', 'Michael Milford'] | 2019-10-15 | null | null | null | null | ['sequential-place-learning', 'sequential-place-recognition'] | ['robots', 'robots'] | [-9.61506963e-02 -2.53191113e-01 1.30338609e-01 -4.00160030e-02
1.64242730e-01 -6.13832951e-01 1.20400870e+00 2.63947505e-03
-8.90435100e-01 6.79627955e-01 6.48197830e-02 -1.36507496e-01
-4.61479515e-01 -6.33307576e-01 -6.21614873e-01 -8.60436440e-01
-6.54780984e-01 3.02412868e-01 6.07459784e-01 -3.33742678... | [7.646579265594482, -1.7880743741989136] |
fa666467-4ae8-42f4-af34-093c7f6b48a3 | bi-matching-mechanism-to-combat-the-long-tail | null | null | https://openreview.net/forum?id=FvmGqUEuh7 | https://openreview.net/pdf?id=FvmGqUEuh7 | Bi-Matching Mechanism to Combat the Long Tail of Word Sense Disambiguation | The long tail phenomenon of word sense distribution in linguistics causes the Word Sense Disambiguation (WSD) task to face a serious polarization of word sense distribution, that is, Most Frequent Senses (MFSs) with huge sample sizes and Long Tail Senses (LTSs) with small sample sizes. The single matching mechanism mod... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-2.08657235e-01 -2.17563570e-01 -3.25746834e-01 -2.79126555e-01
-4.94812250e-01 -3.80423099e-01 5.10702193e-01 3.26353222e-01
-8.71027589e-01 6.26963973e-01 2.97095448e-01 -3.19366008e-01
-2.25666724e-02 -6.91620886e-01 2.75000900e-01 -7.01637685e-01
2.63600618e-01 3.01734120e-01 4.27674860e-01 -7.92149901... | [10.222604751586914, 9.077654838562012] |
7a73676e-9f9d-4a49-b683-6ed4a8d4d96b | cin-enhancing-topological-message-passing | 2306.03561 | null | https://arxiv.org/abs/2306.03561v1 | https://arxiv.org/pdf/2306.03561v1.pdf | CIN++: Enhancing Topological Message Passing | Graph Neural Networks (GNNs) have demonstrated remarkable success in learning from graph-structured data. However, they face significant limitations in expressive power, struggling with long-range interactions and lacking a principled approach to modeling higher-order structures and group interactions. Cellular Isomorp... | ['Pietro Liò', 'Cristian Bodnar', 'Francesco Ceccarelli', 'Teodora Reu', 'Lorenzo Giusti'] | 2023-06-06 | null | null | null | null | ['graph-regression', 'graph-classification'] | ['graphs', 'graphs'] | [ 1.90330535e-01 2.71901965e-01 -1.10680051e-01 7.17250630e-02
2.48066232e-01 -5.99021196e-01 9.84347999e-01 8.45277607e-01
-3.48161429e-01 8.51264119e-01 5.17009050e-02 -6.15859509e-01
-4.77039546e-01 -1.32590318e+00 -1.15618217e+00 -7.55173206e-01
-6.85708165e-01 6.04953408e-01 5.05855918e-01 -4.96135086... | [6.714847564697266, 6.0554890632629395] |
67246739-96b9-46ef-8736-85956e2d3785 | attention-mechanism-transformers-bert-and-gpt | null | null | https://osf.io/m6gcn | https://osf.io/m6gcn/download | Attention Mechanism, Transformers, BERT, and GPT: Tutorial and Survey | This is a tutorial and survey paper on the attention mechanism, transformers, BERT, and GPT. We first explain attention mechanism, sequence-to-sequence model without and with attention, self-attention, and attention in different areas such as natural language processing and computer vision. Then, we explain transformer... | ['Ali Ghodsi', 'Benyamin Ghojogh'] | 2020-11-17 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 1.80175155e-01 3.76505017e-01 -5.12840375e-02 -7.45869875e-02
-6.26882195e-01 -2.96975642e-01 5.00795126e-01 -3.01624328e-01
1.21401764e-01 6.63411617e-01 7.36314535e-01 -4.73476350e-01
2.34775111e-01 -7.61301994e-01 -6.68646276e-01 -7.36724019e-01
1.66603237e-01 4.61304218e-01 2.14087054e-01 -3.81855339... | [11.072929382324219, 6.952631950378418] |
67f4d1c1-9fb3-4564-9319-b26e268e2d31 | towards-unified-prompt-tuning-for-few-shot-1 | 2205.05313 | null | https://arxiv.org/abs/2205.05313v1 | https://arxiv.org/pdf/2205.05313v1.pdf | Towards Unified Prompt Tuning for Few-shot Text Classification | Prompt-based fine-tuning has boosted the performance of Pre-trained Language Models (PLMs) on few-shot text classification by employing task-specific prompts. Yet, PLMs are unfamiliar with prompt-style expressions during pre-training, which limits the few-shot learning performance on downstream tasks. It would be desir... | ['Ming Gao', 'Songfang Huang', 'Qiuhui Shi', 'Fei Yang', 'Minghui Qiu', 'Chuanqi Tan', 'Fuli Luo', 'Chengyu Wang', 'Jianing Wang'] | 2022-05-11 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 3.86747599e-01 -2.55589969e-02 -3.73139799e-01 -7.02183723e-01
-8.95392299e-01 -5.38464367e-01 7.22929835e-01 1.89432949e-01
-9.27875280e-01 4.52556223e-01 5.67289829e-01 -1.85747787e-01
1.37444139e-01 -3.65257323e-01 -3.20474207e-01 -2.81680614e-01
4.59627360e-01 5.98145068e-01 4.41528797e-01 -4.96262372... | [10.873193740844727, 8.054939270019531] |
0b99c98d-3cf3-45e8-b7fe-0f1206564ce8 | mastering-the-exploration-exploitation-trade | 2305.08624 | null | https://arxiv.org/abs/2305.08624v1 | https://arxiv.org/pdf/2305.08624v1.pdf | Mastering the exploration-exploitation trade-off in Bayesian Optimization | Gaussian Process based Bayesian Optimization is a well-known sample efficient sequential strategy for globally optimizing black-box, expensive, and multi-extremal functions. The role of the Gaussian Process is to provide a probabilistic approximation of the unknown function, depending on the sequentially collected obse... | ['Antonio Candelieri'] | 2023-05-15 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [ 1.40086770e-01 -7.57239908e-02 -1.21911354e-01 -9.38182026e-02
-5.04885316e-01 -6.64921343e-01 6.07449055e-01 4.36494768e-01
-7.82247603e-01 9.16903257e-01 -1.50638834e-01 -6.27371892e-02
-9.84186888e-01 -7.61363208e-01 -3.05672020e-01 -1.16746151e+00
-1.24798305e-01 1.05252111e+00 2.23403677e-01 -7.35476147... | [6.0307111740112305, 3.6468684673309326] |
d45f6ffb-f124-4e81-ad02-1cd98328309d | sparsifying-sparse-representations-for | 2112.09628 | null | https://arxiv.org/abs/2112.09628v1 | https://arxiv.org/pdf/2112.09628v1.pdf | Sparsifying Sparse Representations for Passage Retrieval by Top-$k$ Masking | Sparse lexical representation learning has demonstrated much progress in improving passage retrieval effectiveness in recent models such as DeepImpact, uniCOIL, and SPLADE. This paper describes a straightforward yet effective approach for sparsifying lexical representations for passage retrieval, building on SPLADE by ... | ['Jimmy Lin', 'Xueguang Ma', 'Jheng-Hong Yang'] | 2021-12-17 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [-2.78240621e-01 -2.64533460e-01 -9.27877605e-01 -3.49452421e-02
-1.50668383e+00 -4.62561071e-01 7.00310349e-01 3.94322366e-01
-5.96931696e-01 8.25644433e-01 8.88497293e-01 -2.82215863e-01
-2.13612810e-01 -9.83556569e-01 -5.19078016e-01 -3.79475057e-01
-1.92575201e-01 3.46967399e-01 3.43274564e-01 -6.15816891... | [11.47475814819336, 7.665626525878906] |
2f0be9f2-56b4-4517-846f-daca9ffe9c1f | cognifnn-a-fuzzy-neural-network-framework-for | 2009.11485 | null | https://arxiv.org/abs/2009.11485v2 | https://arxiv.org/pdf/2009.11485v2.pdf | CogniFNN: A Fuzzy Neural Network Framework for Cognitive Word Embedding Evaluation | Word embeddings can reflect the semantic representations, and the embedding qualities can be comprehensively evaluated with human natural reading-related cognitive data sources. In this paper, we proposed the CogniFNN framework, which is the first attempt at using fuzzy neural networks to extract non-linear and non-sta... | ['Son Tran', 'Xinping Liu', 'Zehong Cao'] | 2020-09-24 | null | null | null | null | ['embeddings-evaluation'] | ['natural-language-processing'] | [-1.76352318e-02 -1.68642119e-01 7.20353350e-02 -4.60968286e-01
-8.26631635e-02 -3.75187010e-01 6.05487168e-01 4.77581888e-01
-1.07050550e+00 4.31268871e-01 3.29701096e-01 -2.38268867e-01
-4.98414397e-01 -9.11452949e-01 -1.56418636e-01 -2.11226106e-01
-8.39357674e-02 5.90862371e-02 2.64671952e-01 -3.92024100... | [10.555442810058594, 8.75291633605957] |
0813e515-f436-48c1-9e61-9e7a11892e9d | l3cube-mahaner-a-marathi-named-entity | 2204.06029 | null | https://arxiv.org/abs/2204.06029v1 | https://arxiv.org/pdf/2204.06029v1.pdf | L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models | Named Entity Recognition (NER) is a basic NLP task and finds major applications in conversational and search systems. It helps us identify key entities in a sentence used for the downstream application. NER or similar slot filling systems for popular languages have been heavily used in commercial applications. In this ... | ['Raviraj Joshi', 'Onkar Litake', 'Maithili Sabane', 'Aparna Ranade', 'Parth Patil'] | 2022-04-12 | null | https://aclanthology.org/2022.wildre-1.6 | https://aclanthology.org/2022.wildre-1.6.pdf | wildre-lrec-2022-6 | ['slot-filling'] | ['natural-language-processing'] | [-6.40937686e-01 -8.49405602e-02 9.72017720e-02 -4.17622805e-01
-8.49666119e-01 -6.28856778e-01 7.37080872e-01 9.54324603e-02
-9.12781298e-01 1.30599582e+00 5.66337883e-01 -3.59597802e-01
2.22193047e-01 -6.84019446e-01 -3.90629143e-01 -4.08553660e-01
-6.93457052e-02 9.36890900e-01 2.05727890e-01 -5.94056010... | [9.847987174987793, 9.753747940063477] |
07632cb1-f3d9-4f0f-9162-3516f29bca77 | joint-patch-and-multi-label-learning-for | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Zhao_Joint_Patch_and_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Zhao_Joint_Patch_and_2015_CVPR_paper.pdf | Joint Patch and Multi-Label Learning for Facial Action Unit Detection | The face is one of the most powerful channel of non-verbal communication. The most commonly used taxonomy to describe facial behaviour is the Facial Action Coding System (FACS). FACS segments the visible effects of facial muscle activation into 30+ action units (AUs). AUs, which may occur alone and in thousands of co... | ['Fernando de la Torre', 'Wen-Sheng Chu', 'Jeffrey F. Cohn', 'Kaili Zhao', 'Honggang Zhang'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 2.86253035e-01 -4.92496639e-02 -6.58012748e-01 -5.81260622e-01
-7.54853606e-01 -4.45041984e-01 5.58849156e-01 -1.94959685e-01
-1.25967488e-01 4.41045523e-01 3.50224465e-01 4.31930423e-01
1.73776746e-01 -1.73532695e-01 -3.25517595e-01 -7.76859224e-01
-2.71054357e-01 5.98290488e-02 -3.66097003e-01 2.06524972... | [13.547693252563477, 1.839769959449768] |
6ada8d7c-1e20-467c-9fcc-6b194f413d3f | crossfire-camera-relocalization-on-self | 2303.04869 | null | https://arxiv.org/abs/2303.04869v1 | https://arxiv.org/pdf/2303.04869v1.pdf | CROSSFIRE: Camera Relocalization On Self-Supervised Features from an Implicit Representation | Beyond novel view synthesis, Neural Radiance Fields are useful for applications that interact with the real world. In this paper, we use them as an implicit map of a given scene and propose a camera relocalization algorithm tailored for this representation. The proposed method enables to compute in real-time the precis... | ['Arnaud de La Fortelle', 'Bogdan Stanciulescu', 'Dzmitry Tsishkou', 'Moussab Bennehar', 'Nathan Piasco', 'Arthur Moreau'] | 2023-03-08 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [ 0.50188833 -0.14466757 0.20843048 -0.42174032 -0.20004676 -0.83341914
0.6956419 0.12380278 -0.6176952 0.5150645 -0.42728972 -0.02642272
-0.12342906 -1.0724458 -0.7434122 -0.5731233 0.59831315 0.6511739
0.32128444 -0.3011477 0.24453445 0.9829918 -1.7531868 -0.07609244
0.5332249 1.1036916 0.6... | [9.164239883422852, -2.859923839569092] |
0be31ecc-4cb4-4793-9854-e5fa99147ab5 | acoustic-prosodic-and-lexical-cues-to | null | null | https://aclanthology.org/2020.tacl-1.14 | https://aclanthology.org/2020.tacl-1.14.pdf | Acoustic-Prosodic and Lexical Cues to Deception and Trust: Deciphering How People Detect Lies | Humans rarely perform better than chance at lie detection. To better understand human perception of deception, we created a game framework, LieCatcher, to collect ratings of perceived deception using a large corpus of deceptive and truthful interviews. We analyzed the acoustic-prosodic and linguistic characteristics of... | ['Julia Hirschberg', 'M', 'Xi (Leslie) Chen', 'Michelle Levine', 'Sarah Ita Levitan', 'Marko ic'] | 2020-01-01 | null | null | null | tacl-2020-1 | ['deception-detection'] | ['miscellaneous'] | [-2.48659968e-01 2.27625147e-01 8.40185285e-02 -8.02134633e-01
-9.72343445e-01 -1.02096987e+00 6.64145708e-01 1.30127937e-01
-5.23027718e-01 6.99656069e-01 3.88061941e-01 -3.94005120e-01
5.12164116e-01 -6.64888173e-02 -1.15678780e-01 -2.99777478e-01
3.28072667e-01 2.39494875e-01 -2.01545641e-01 -3.70497286... | [8.195710182189941, 10.373002052307129] |
a6443cbe-b36e-4d5b-9d48-a3c64b39a315 | sire-separate-intra-and-inter-sentential | 2106.01709 | null | https://arxiv.org/abs/2106.01709v1 | https://arxiv.org/pdf/2106.01709v1.pdf | SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction | Document-level relation extraction has attracted much attention in recent years. It is usually formulated as a classification problem that predicts relations for all entity pairs in the document. However, previous works indiscriminately represent intra- and inter-sentential relations in the same way, confounding the di... | ['Baobao Chang', 'Yuting Wu', 'Shuang Zeng'] | 2021-06-03 | null | https://aclanthology.org/2021.findings-acl.47 | https://aclanthology.org/2021.findings-acl.47.pdf | findings-acl-2021-8 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-2.22884506e-01 8.00936699e-01 -6.46498621e-01 -5.64185023e-01
-1.70139465e-02 -5.32090247e-01 6.32689416e-01 4.38116103e-01
3.74236137e-01 8.05664182e-01 2.06275865e-01 -6.96143448e-01
-4.90271360e-01 -1.51656187e+00 -5.43287575e-01 1.79976776e-01
-1.52256116e-01 6.76245987e-01 6.27905965e-01 -3.39552134... | [9.168468475341797, 8.26472282409668] |
70adb4e8-f9a0-42d2-be96-71d1723a6347 | proceedings-37th-international-conference-on | 2109.07914 | null | https://arxiv.org/abs/2109.07914v1 | https://arxiv.org/pdf/2109.07914v1.pdf | Proceedings 37th International Conference on Logic Programming (Technical Communications) | ICLP is the premier international event for presenting research in logic programming. Contributions to ICLP 2021 were sought in all areas of logic programming, including but not limited to: Foundations: Semantics, Formalisms, Nonmonotonic reasoning, Knowledge representation. Languages issues: Concurrency, Objects, Coor... | ['Neng-Fa Zhou', 'Joost Vennekens', 'Gian Luca Pozzato', 'Paul Fodor', 'Carmine Dodaro', 'Veronica Dahl', 'Alex Brik', 'Bart Bogaerts', 'Yanhong Annie Liu', 'Andrea Formisano'] | 2021-09-15 | null | null | null | null | ['data-integration', 'automated-theorem-proving', 'automated-theorem-proving'] | ['knowledge-base', 'miscellaneous', 'reasoning'] | [-5.05414188e-01 3.78073305e-01 -2.09567592e-01 -4.69560742e-01
3.47748071e-01 -1.01903987e+00 5.22836030e-01 9.77803349e-01
2.08568469e-01 1.21925557e+00 -8.04623142e-02 -5.64950526e-01
-8.79243970e-01 -1.08922732e+00 -2.70348340e-01 -7.91401137e-03
-4.83214825e-01 1.27600098e+00 8.09365153e-01 -3.21222425... | [8.667410850524902, 6.7690839767456055] |
386de307-1c50-43ee-8dbb-3c68ade85927 | rdcnet-instance-segmentation-with-a | 2010.00991 | null | https://arxiv.org/abs/2010.00991v1 | https://arxiv.org/pdf/2010.00991v1.pdf | RDCNet: Instance segmentation with a minimalist recurrent residual network | Instance segmentation is a key step for quantitative microscopy. While several machine learning based methods have been proposed for this problem, most of them rely on computationally complex models that are trained on surrogate tasks. Building on recent developments towards end-to-end trainable instance segmentation, ... | ['Markus Rempfler', 'Prisca Liberali', 'Antoine H. F. M. Peters', 'Gustavo de Medeiros', 'Raphael Ortiz'] | 2020-10-02 | null | null | null | null | ['nuclear-segmentation'] | ['medical'] | [ 7.76482046e-01 4.82358754e-01 -5.54850698e-02 -4.73372400e-01
-9.88848507e-01 -7.25382090e-01 4.56220180e-01 -1.06628165e-02
-4.28494960e-01 6.69339359e-01 -2.80158311e-01 -6.01502657e-01
-1.05910271e-01 -3.85617018e-01 -8.66496623e-01 -9.55574453e-01
-1.81364566e-02 8.12198818e-01 4.16856587e-01 1.36905804... | [14.424206733703613, -3.0785269737243652] |
92f29b81-81ee-43b1-93ff-c6906ed8f894 | deep-attention-model-for-triage-of-emergency | 1804.03240 | null | http://arxiv.org/abs/1804.03240v1 | http://arxiv.org/pdf/1804.03240v1.pdf | Deep Attention Model for Triage of Emergency Department Patients | Optimization of patient throughput and wait time in emergency departments
(ED) is an important task for hospital systems. For that reason, Emergency
Severity Index (ESI) system for patient triage was introduced to help guide
manual estimation of acuity levels, which is used by nurses to rank the
patients and organize h... | ['Ivan Stojkovic', 'Daniel Del Portal', 'Djordje Gligorijevic', 'Wayne Satz', 'Jelena Stojanovic', 'Zoran Obradovic', 'Kathrin Schreyer'] | 2018-03-28 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-7.95222968e-02 1.68014929e-01 -2.92047262e-02 -2.92656749e-01
-7.01845229e-01 -8.11926201e-02 -2.54692614e-01 1.03904891e+00
-7.86071539e-01 6.88782394e-01 7.41841376e-01 -7.14352310e-01
-7.54887581e-01 -7.37592876e-01 -3.74419838e-02 -3.60411555e-01
-2.24099234e-01 1.06630635e+00 -5.22655785e-01 -1.64509972... | [7.944104194641113, 6.260335445404053] |
0a34cbb3-fc63-4709-bdc9-6985b4f11ac0 | investigating-prompting-techniques-for-zero | 2306.09996 | null | https://arxiv.org/abs/2306.09996v1 | https://arxiv.org/pdf/2306.09996v1.pdf | Investigating Prompting Techniques for Zero- and Few-Shot Visual Question Answering | Visual question answering (VQA) is a challenging task that requires the ability to comprehend and reason with visual information. While recent vision-language models have made strides, they continue to struggle with zero-shot VQA, particularly in handling complex compositional questions and adapting to new domains i.e.... | ['Aishwarya Agrawal', 'Le Zhang', 'Rabiul Awal'] | 2023-06-16 | null | null | null | null | ['visual-question-answering-1', 'image-captioning', 'question-answering'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 1.55252770e-01 1.45320773e-01 1.23449109e-01 -3.54736507e-01
-7.20515490e-01 -6.69494748e-01 8.11958492e-01 2.20754460e-01
-4.61792648e-01 2.35082135e-01 5.28540313e-01 -6.23791397e-01
-1.21408165e-01 -5.50206363e-01 -6.66576207e-01 -2.02009425e-01
5.53722799e-01 1.67040378e-01 4.50105280e-01 -4.79915529... | [10.753294944763184, 1.7335312366485596] |
e3e89c92-06be-4380-836f-0221b069c0ec | aiai-at-finsbd-task-sentence-boundary | null | null | https://aclanthology.org/W19-5514 | https://aclanthology.org/W19-5514.pdf | aiai at FinSBD task: Sentence Boundary Detection in Noisy Texts From Financial Documents Using Deep Attention Model | null | ['Zi Jun Peng', 'Ke Tian'] | 2019-08-01 | null | null | null | ws-2019-8 | ['deep-attention', 'deep-attention'] | ['computer-vision', '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... | [-7.316308498382568, 3.643272876739502] |
763ed97d-edbb-4d11-b745-c03a2853482e | cell-free-data-power-control-via-scalable | 2212.10299 | null | https://arxiv.org/abs/2212.10299v1 | https://arxiv.org/pdf/2212.10299v1.pdf | Cell-Free Data Power Control Via Scalable Multi-Objective Bayesian Optimisation | Cell-free multi-user multiple input multiple output networks are a promising alternative to classical cellular architectures, since they have the potential to provide uniform service quality and high resource utilisation over the entire coverage area of the network. To realise this potential, previous works have develo... | ['Hugo Tullberg', 'Gábor Fodor', 'Sergey S. Tambovskiy'] | 2022-12-20 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 4.01594728e-01 3.41598988e-01 -1.65699989e-01 1.58427745e-01
-3.05724770e-01 -2.87479699e-01 4.56067353e-01 -2.14213356e-01
-4.63293314e-01 1.68526256e+00 -9.21109924e-04 -5.10958195e-01
-7.93527126e-01 -9.69488978e-01 -2.58189682e-02 -1.08847582e+00
-3.75039816e-01 5.79807699e-01 -4.70509902e-02 -6.09585121... | [6.032399654388428, 1.59402334690094] |
49dc5bce-9d00-4f8c-b234-8a911f3aaddb | scalable-and-compact-3d-action-recognition | 1711.10290 | null | http://arxiv.org/abs/1711.10290v1 | http://arxiv.org/pdf/1711.10290v1.pdf | Scalable and Compact 3D Action Recognition with Approximated RBF Kernel Machines | Despite the recent deep learning (DL) revolution, kernel machines still
remain powerful methods for action recognition. DL has brought the use of large
datasets and this is typically a problem for kernel approaches, which are not
scaling up efficiently due to kernel Gram matrices. Nevertheless, kernel
methods are still... | ['Jacopo Cavazza', 'Vittorio Murino', 'Pietro Morerio'] | 2017-11-28 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [-1.62317261e-01 9.33415592e-02 -2.55670130e-01 -9.68973935e-02
-3.07910860e-01 -3.74407947e-01 5.68446457e-01 3.07551414e-01
-6.31447375e-01 8.38534892e-01 7.05605000e-02 -3.87915671e-01
-4.05081600e-01 -7.54393816e-01 -6.08515084e-01 -7.76280701e-01
-1.48044392e-01 3.55308145e-01 4.32349414e-01 1.05972648... | [7.691080093383789, 4.056509017944336] |
a71c307b-8969-4d1d-ba07-eb4b7607d3c9 | sar-image-despeckling-through-convolutional | 1704.00275 | null | http://arxiv.org/abs/1704.00275v2 | http://arxiv.org/pdf/1704.00275v2.pdf | SAR image despeckling through convolutional neural networks | In this paper we investigate the use of discriminative model learning through
Convolutional Neural Networks (CNNs) for SAR image despeckling. The network
uses a residual learning strategy, hence it does not recover the filtered
image, but the speckle component, which is then subtracted from the noisy one.
Training is c... | ['L. Verdoliva', 'G. Poggi', 'D. Cozzolino', 'G. Chierchia'] | 2017-04-02 | null | null | null | null | ['sar-image-despeckling'] | ['computer-vision'] | [ 6.60543501e-01 -1.86365351e-01 4.20752555e-01 -1.69095263e-01
-8.19527745e-01 -1.90934375e-01 6.03233457e-01 -3.66672784e-01
-7.98488617e-01 9.86692488e-01 2.93631136e-01 -9.34343040e-02
-3.02953959e-01 -7.56787896e-01 -4.38482076e-01 -1.05880451e+00
-4.12677079e-02 7.64991120e-02 -4.78613488e-02 -2.97077149... | [10.47329330444336, -2.2197964191436768] |
a33363cf-9db0-4bf7-ad16-a31f00a21012 | landscape-learning-for-neural-network | 2206.09027 | null | https://arxiv.org/abs/2206.09027v1 | https://arxiv.org/pdf/2206.09027v1.pdf | Landscape Learning for Neural Network Inversion | Many machine learning methods operate by inverting a neural network at inference time, which has become a popular technique for solving inverse problems in computer vision, robotics, and graphics. However, these methods often involve gradient descent through a highly non-convex loss landscape, causing the optimization ... | ['Carl Vondrick', 'Hao Wang', 'Purva Tendulkar', 'Chengzhi Mao', 'Ruoshi Liu'] | 2022-06-17 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 3.43767405e-01 1.50210530e-01 -1.21823363e-02 -4.45295274e-01
-7.78015792e-01 -5.35513163e-01 5.34274101e-01 -7.05929101e-01
-3.40917796e-01 7.98099935e-01 6.54741228e-02 -2.97629476e-01
2.39275441e-01 -7.64770627e-01 -9.68511224e-01 -5.46007276e-01
1.99843004e-01 7.13489354e-01 -3.31592292e-01 -2.55278319... | [11.583789825439453, -0.541548490524292] |
9bf5285e-cb26-4104-ba2c-aebf185e0da9 | text-spotting-transformers | 2204.01918 | null | https://arxiv.org/abs/2204.01918v1 | https://arxiv.org/pdf/2204.01918v1.pdf | Text Spotting Transformers | In this paper, we present TExt Spotting TRansformers (TESTR), a generic end-to-end text spotting framework using Transformers for text detection and recognition in the wild. TESTR builds upon a single encoder and dual decoders for the joint text-box control point regression and character recognition. Other than most ex... | ['Zhuowen Tu', 'Subarna Tripathi', 'Yongwen Su', 'Xiang Zhang'] | 2022-04-05 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhang_Text_Spotting_Transformers_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhang_Text_Spotting_Transformers_CVPR_2022_paper.pdf | cvpr-2022-1 | ['text-spotting'] | ['computer-vision'] | [ 5.79849303e-01 -2.88807541e-01 7.47542381e-02 -3.46063614e-01
-1.17223752e+00 -6.97788537e-01 5.89355588e-01 3.05182248e-01
-3.16393077e-01 2.31636375e-01 -2.20378816e-01 -6.50958359e-01
1.07473634e-01 -7.34222889e-01 -7.12040961e-01 -3.56817603e-01
1.50955528e-01 8.80564928e-01 4.74901825e-01 -2.25457788... | [11.959741592407227, 2.205212116241455] |
2b2e065a-d7a7-47a7-83d7-7d65325cbefc | monkey-business-reinforcement-learning-meets | 2202.13706 | null | https://arxiv.org/abs/2202.13706v1 | https://arxiv.org/pdf/2202.13706v1.pdf | Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network Embedding | In this article, we consider the Virtual Network Embedding (VNE) problem for 5G networks slicing. This problem requires to allocate multiple Virtual Networks (VN) on a substrate virtualized physical network while maximizing among others, resource utilization, maximum number of placed VNs and network operator's benefit.... | ['Badii Jouaber', 'Hind Castel', 'Andrea Araldo', 'Massinissa Ait Aba', 'Maxime Elkael'] | 2022-02-28 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-2.58445501e-01 3.38958740e-01 -7.05215812e-01 5.42113781e-02
-2.28137244e-02 -7.45696783e-01 8.77118111e-02 -2.62234539e-01
-2.76874989e-01 1.38672352e+00 -3.52600724e-01 -8.02036643e-01
-4.63591427e-01 -1.04371178e+00 -5.61501801e-01 -6.61018133e-01
-5.98361671e-01 1.03541791e+00 4.90838289e-01 -9.22021121... | [5.8247809410095215, 1.745381474494934] |
2124bb21-cab7-4524-963f-a06a5a8b4423 | leveraging-reaction-aware-substructures-for | 2204.05919 | null | https://arxiv.org/abs/2204.05919v4 | https://arxiv.org/pdf/2204.05919v4.pdf | Leveraging Reaction-aware Substructures for Retrosynthesis Analysis | Retrosynthesis analysis is a critical task in organic chemistry central to many important industries. Previously, various machine learning approaches have achieved promising results on this task by representing output molecules as strings and autoregressively decoded token-by-token with generative models. Text generati... | ['Li Tan', 'Junren Li', 'Lei Fang', 'Jian-Guang Lou', 'Ming Zhao'] | 2022-04-12 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 8.11758876e-01 1.56491920e-01 -5.48994303e-01 -1.54118240e-01
-7.02611625e-01 -7.74356604e-01 9.87330914e-01 7.44430900e-01
-1.22730292e-01 1.25034225e+00 3.88534874e-01 -4.86751378e-01
4.05837804e-01 -9.96841431e-01 -7.09489226e-01 -9.74364817e-01
1.79732963e-01 3.06885421e-01 9.81548131e-02 -3.49317014... | [4.545088291168213, 6.077699184417725] |
f5af63b4-1392-4f2c-8ae8-fda9db98a618 | phone-duration-modeling-for-speaker-age | 2109.01568 | null | https://arxiv.org/abs/2109.01568v1 | https://arxiv.org/pdf/2109.01568v1.pdf | Phone Duration Modeling for Speaker Age Estimation in Children | Automatic inference of important paralinguistic information such as age from speech is an important area of research with numerous spoken language technology based applications. Speaker age estimation has applications in enabling personalization and age-appropriate curation of information and content. However, research... | ['Shrikanth Narayanan', 'Catherine Lord', 'Somer Bishop', 'Prashanth Gurunath Shivakumar'] | 2021-09-03 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [ 5.74106947e-02 7.96337351e-02 -3.12154830e-01 -9.04854596e-01
-7.81202376e-01 -3.19562137e-01 3.91229421e-01 6.11763835e-01
-5.07244468e-01 3.48860741e-01 6.11949563e-01 2.23837444e-03
-3.30105945e-02 -4.32483226e-01 -2.37735823e-01 -7.52409816e-01
-1.91059664e-01 3.77206326e-01 -1.13214150e-01 1.57369435... | [14.237159729003906, 6.1906561851501465] |
98ad7f61-23cc-4ffb-84d5-32af83542f73 | lightweight-attentional-feature-fusion-for | 2112.01832 | null | https://arxiv.org/abs/2112.01832v3 | https://arxiv.org/pdf/2112.01832v3.pdf | Lightweight Attentional Feature Fusion: A New Baseline for Text-to-Video Retrieval | In this paper we revisit feature fusion, an old-fashioned topic, in the new context of text-to-video retrieval. Different from previous research that considers feature fusion only at one end, let it be video or text, we aim for feature fusion for both ends within a unified framework. We hypothesize that optimizing the ... | ['Xirong Li', 'Jianfeng Dong', 'Fangming Zhou', 'Ziyue Wang', 'Aozhu Chen', 'Fan Hu'] | 2021-12-03 | null | null | null | null | ['ad-hoc-video-search'] | ['computer-vision'] | [ 4.06774096e-02 -6.37550831e-01 -3.41220766e-01 -4.42146391e-01
-1.27458692e+00 -5.93308449e-01 1.09324276e+00 2.30836064e-01
-6.07821882e-01 3.95333529e-01 4.60308999e-01 -2.98945755e-02
-3.60135108e-01 -6.16489872e-02 -3.88493627e-01 -5.83044946e-01
5.79584725e-02 3.01613510e-01 1.17658906e-01 -1.40689552... | [10.434393882751465, 0.8952713012695312] |
1059480c-8623-40f8-a6ac-afd4a65f8cc3 | a-multi-camera-unsupervised-domain-adaptation | null | null | https://iplab.dmi.unict.it/OBJ-MDA/ | https://www.sciencedirect.com/science/article/abs/pii/S1077314222000911?CMX_ID=&SIS_ID=&dgcid=STMJ_AUTH_SERV_PUBLISHED&utm_acid=170381815&utm_campaign=STMJ_AUTH_SERV_PUBLISHED&utm_in=DM270343&utm_medium=email&utm_source=AC_ | A Multi Camera Unsupervised Domain Adaptation Pipeline for Object Detection in Cultural Sites through Adversarial Learning and Self-Training | Object detection algorithms allow to enable many interesting applications which can be implemented in different devices, such as smartphones and wearable devices. In the context of a cultural site, implementing these algorithms in a wearable device, such as a pair of smart glasses, allow to enable the use of augmented ... | ['Giovanni Maria Farinella', 'Antonino Furnari', 'Giovanni Pasqualino'] | 2022-09-01 | null | null | null | computer-vision-and-image-understanding-cviu-1 | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 4.96545881e-01 9.97934192e-02 3.42177413e-02 -2.10152537e-01
-4.16716069e-01 -6.26952410e-01 5.15836000e-01 -2.93818675e-02
-4.73240644e-01 5.89840233e-01 -2.94030279e-01 1.38574257e-01
1.23150893e-01 -8.37600946e-01 -9.07168269e-01 -4.11796689e-01
3.48571569e-01 7.01298177e-01 5.37248075e-01 -2.22104326... | [7.808539867401123, -0.8371108770370483] |
230c71ec-dc69-46ac-b717-626d101abba0 | exploring-paracrawl-for-document-level-neural | 2304.10216 | null | https://arxiv.org/abs/2304.10216v1 | https://arxiv.org/pdf/2304.10216v1.pdf | Exploring Paracrawl for Document-level Neural Machine Translation | Document-level neural machine translation (NMT) has outperformed sentence-level NMT on a number of datasets. However, document-level NMT is still not widely adopted in real-world translation systems mainly due to the lack of large-scale general-domain training data for document-level NMT. We examine the effectiveness o... | ['Josef van Genabith', 'Jingyi Zhang', 'Yusser Al Ghussin'] | 2023-04-20 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 2.93665439e-01 -1.65648416e-01 -5.81502676e-01 -1.94878101e-01
-1.62497962e+00 -8.79989624e-01 8.75431657e-01 8.46867263e-02
-6.43788338e-01 1.23187816e+00 3.74394506e-01 -8.08565795e-01
1.99633762e-01 -5.59623480e-01 -1.16588521e+00 -2.21456692e-01
4.55386072e-01 1.23067462e+00 -1.13167241e-01 -6.86604917... | [11.580273628234863, 10.360323905944824] |
83379da4-dc38-4b00-9b4b-d5850cc1c5eb | unified-discrete-diffusion-for-simultaneous | 2211.14842 | null | https://arxiv.org/abs/2211.14842v1 | https://arxiv.org/pdf/2211.14842v1.pdf | Unified Discrete Diffusion for Simultaneous Vision-Language Generation | The recently developed discrete diffusion models perform extraordinarily well in the text-to-image task, showing significant promise for handling the multi-modality signals. In this work, we harness these traits and present a unified multimodal generation model that can conduct both the "modality translation" and "mult... | ['Ponnuthurai N. Suganthan', 'DaCheng Tao', 'Zuopeng Yang', 'Chaoyue Wang', 'Tat-Jen Cham', 'Heliang Zheng', 'Chuanxia Zheng', 'Minghui Hu'] | 2022-11-27 | null | null | null | null | ['multimodal-generation'] | ['natural-language-processing'] | [ 3.54827225e-01 -1.25998193e-02 -8.99769459e-03 -1.85038671e-01
-1.15805507e+00 -3.48423749e-01 1.29382586e+00 -4.43249077e-01
-1.94822803e-01 6.88603759e-01 4.57205504e-01 -1.21641517e-01
-1.43241752e-02 -6.82634711e-01 -6.14888966e-01 -8.88033628e-01
4.88154978e-01 3.39367718e-01 -1.83785900e-01 -3.96215886... | [11.316173553466797, 0.3204880654811859] |
fb8a1405-555a-43e1-a70c-b6278780c0af | few-shot-learning-with-siamese-networks-and | null | null | https://openreview.net/forum?id=za_XIJLkkB8 | https://openreview.net/pdf?id=za_XIJLkkB8 | Few-Shot Learning with Siamese Networks and Label Tuning | We study the problem of building text classifiers with little or no training data, commonly known as zero and few-shot text classification.
In recent years, an approach based on neural textual entailment models has been found to give strong results on a diverse range of tasks.
In this work, we show that with proper pre... | ['Anonymous'] | 2021-10-16 | null | null | null | acl-arr-october-2021-10 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 2.94546634e-01 4.74499464e-02 -3.83081466e-01 -6.65387869e-01
-7.87398100e-01 -4.90491807e-01 8.88801038e-01 4.90731567e-01
-9.19426978e-01 5.99722683e-01 1.35094956e-01 -1.65867895e-01
4.97572161e-02 -6.24046445e-01 -5.57527959e-01 -5.53739965e-01
3.15511405e-01 5.81581354e-01 3.36294770e-01 -1.43158570... | [10.68674087524414, 7.891918182373047] |
93b1bbc9-7a08-4744-ba45-7373ece3b9e0 | ai-based-software-for-lung-nodule-detection | 2206.10912 | null | https://arxiv.org/abs/2206.10912v1 | https://arxiv.org/pdf/2206.10912v1.pdf | AI-based software for lung nodule detection in chest X-rays -- Time for a second reader approach? | Objectives: To compare artificial intelligence (AI) as a second reader in detecting lung nodules on chest X-rays (CXR) versus radiologists of two binational institutions, and to evaluate AI performance when using two different modes: automated versus assisted (additional remote radiologist review). Methods: The CXR pub... | ['Dirk Pickuth', 'Jūratė Dementavičienė', 'Artūras Samuilis', 'Jonas Ražanskas', 'Jonas Bialopetravičius', 'Vytautas Naujalis', 'Neringa Bielskienė', 'Darius Barušauskas', 'Emil Johnson Jeyakumar', 'Sebastian Huettinger', 'Naglis Ramanauskas', 'Susanne Ohlmann-Knafo'] | 2022-06-22 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 1.97610676e-01 4.64771032e-01 -1.83792278e-01 1.24761440e-01
-1.00413954e+00 -6.69533432e-01 2.46653140e-01 2.67803937e-01
-6.20100021e-01 4.32660401e-01 -4.13505621e-02 -1.00461853e+00
-4.56804633e-01 -6.99347138e-01 -4.63532180e-01 -6.67331040e-01
-3.85599174e-02 8.91789615e-01 5.99333346e-01 6.12493575... | [15.383484840393066, -2.0883796215057373] |
142ee38c-c394-40a8-9e77-d127430bf9cd | the-effects-of-noisy-labels-on-deep | 1706.02361 | null | http://arxiv.org/abs/1706.02361v3 | http://arxiv.org/pdf/1706.02361v3.pdf | The Effects of Noisy Labels on Deep Convolutional Neural Networks for Music Tagging | Deep neural networks (DNN) have been successfully applied to music
classification including music tagging. However, there are several open
questions regarding the training, evaluation, and analysis of DNNs. In this
article, we investigate specific aspects of neural networks, the effects of
noisy labels, to deepen our u... | ['Kyunghyun Cho', 'Keunwoo Choi', 'Mark Sandler', 'George Fazekas'] | 2017-06-07 | null | null | null | null | ['music-classification'] | ['music'] | [ 3.91667545e-01 -1.63973123e-01 -1.12137780e-01 -3.22090775e-01
-6.85023367e-01 -9.48537946e-01 2.76873112e-01 1.32379413e-01
-5.86931884e-01 3.23284119e-01 4.28107053e-01 -6.08338267e-02
-4.03146625e-01 -3.97072285e-01 -5.25441587e-01 -5.20423532e-01
-2.28471130e-01 3.31038266e-01 -1.22151703e-01 -8.19360018... | [15.7643461227417, 5.240870475769043] |
95c00b43-46a1-4455-be2e-e2ec217e78e0 | a-confidence-based-partial-label-learning | 2305.12485 | null | https://arxiv.org/abs/2305.12485v1 | https://arxiv.org/pdf/2305.12485v1.pdf | A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition | Existing models for named entity recognition (NER) are mainly based on large-scale labeled datasets, which always obtain using crowdsourcing. However, it is hard to obtain a unified and correct label via majority voting from multiple annotators for NER due to the large labeling space and complexity of this task. To add... | ['Ying Shan', 'Jin Ma', 'Xuanjing Huang', 'Tao Gui', 'Qi Zhang', 'Yuanbin Wu', 'Xiao Wang', 'Qunxi Zhu', 'Jie zhou', 'Limao Xiong'] | 2023-05-21 | null | null | null | null | ['partial-label-learning', 'named-entity-recognition-ner'] | ['methodology', 'natural-language-processing'] | [-2.96737999e-01 1.59016281e-01 -9.26664397e-02 -6.70538068e-01
-1.47692907e+00 -8.76034617e-01 3.43174011e-01 2.39391088e-01
-9.96723354e-01 1.07447505e+00 1.68148473e-01 8.44872966e-02
5.14653563e-01 -3.94152105e-01 -5.12057662e-01 -5.23250759e-01
4.75753307e-01 6.60849988e-01 4.59971398e-01 4.48033363... | [9.6200532913208, 4.713494777679443] |
f3a475b2-dd59-424f-8b6f-c544e14fd13d | auxiliary-multimodal-lstm-for-audio-visual | 1701.04224 | null | http://arxiv.org/abs/1701.04224v2 | http://arxiv.org/pdf/1701.04224v2.pdf | Auxiliary Multimodal LSTM for Audio-visual Speech Recognition and Lipreading | The Aduio-visual Speech Recognition (AVSR) which employs both the video and
audio information to do Automatic Speech Recognition (ASR) is one of the
application of multimodal leaning making ASR system more robust and accuracy.
The traditional models usually treated AVSR as inference or projection but
strict prior limit... | ['Weijun Ji', 'Chunlin Tian'] | 2017-01-16 | null | null | null | null | ['lipreading', 'audio-visual-speech-recognition'] | ['computer-vision', 'speech'] | [-3.65068138e-01 -2.76924640e-01 -1.72513068e-01 -1.62565365e-01
-4.42758977e-01 -8.11369121e-02 8.09617400e-01 -4.09388125e-01
-5.22694051e-01 6.62202001e-01 4.58740711e-01 -5.39861977e-01
1.46520242e-01 -4.53925848e-01 -4.23182696e-01 -8.26821744e-01
2.51145095e-01 3.79967630e-01 1.55120715e-01 -4.52771544... | [13.923480987548828, 5.414465427398682] |
0be6a72a-950c-448c-8d34-e629fd1e21cc | three-things-everyone-should-know-to-improve | null | null | https://ieeexplore.ieee.org/document/6248018 | https://ieeexplore.ieee.org/document/6248018 | Three things everyone should know to improve object retrieval | The objective of this work is object retrieval in large scale image datasets, where the object is specified by an image query and retrieval should be immediate at run time in the manner of Video Google [28]. We make the following three contributions: (i) a new method to compare SIFT descriptors (RootSIFT) which yields ... | ['Andrew Zisserman', 'Relja Arandjelović'] | 2012-06-16 | null | null | null | cvpr-2012-6 | ['image-augmentation', 'image-matching'] | ['computer-vision', 'computer-vision'] | [ 2.12500647e-01 -7.11872339e-01 -3.70726466e-01 -1.94853008e-01
-1.20508850e+00 -8.53084743e-01 8.37382376e-01 2.01935604e-01
-5.54505050e-01 3.35753143e-01 1.31173963e-02 -3.42297144e-02
-4.48920071e-01 -6.92213595e-01 -6.02357149e-01 -4.17759269e-01
-4.31053728e-01 5.84587157e-01 5.52555561e-01 -3.88810456... | [10.651185989379883, 0.4356978237628937] |
00b9706e-6c44-4725-93b0-fc4e9539a0ef | mean-shift-mask-transformer-for-unseen-object | 2211.11679 | null | https://arxiv.org/abs/2211.11679v2 | https://arxiv.org/pdf/2211.11679v2.pdf | Mean Shift Mask Transformer for Unseen Object Instance Segmentation | Segmenting unseen objects from images is a critical perception skill that a robot needs to acquire. In robot manipulation, it can facilitate a robot to grasp and manipulate unseen objects. Mean shift clustering is a widely used method for image segmentation tasks. However, the traditional mean shift clustering algorith... | ['Yu Xiang', 'Nicholas Ruozzi', 'Yuqiao Chen', 'Yangxiao Lu'] | 2022-11-21 | null | null | null | null | ['unseen-object-instance-segmentation', 'robot-manipulation'] | ['computer-vision', 'robots'] | [ 7.63008818e-02 2.24587657e-02 -1.22069595e-02 -4.79324877e-01
-3.91936570e-01 -6.79103851e-01 3.56070817e-01 -2.12815732e-01
-5.20270050e-01 9.43846703e-02 -5.99482238e-01 -1.15955472e-01
-9.47566330e-03 -4.12376791e-01 -1.12890542e+00 -8.06363881e-01
1.75275072e-01 9.35677946e-01 2.97861248e-01 1.05766002... | [7.906572341918945, -2.819173812866211] |
c9d9595b-9b26-4f65-8983-41255716114c | modelling-emotion-dynamics-in-song-lyrics | 2210.09434 | null | https://arxiv.org/abs/2210.09434v1 | https://arxiv.org/pdf/2210.09434v1.pdf | Modelling Emotion Dynamics in Song Lyrics with State Space Models | Most previous work in music emotion recognition assumes a single or a few song-level labels for the whole song. While it is known that different emotions can vary in intensity within a song, annotated data for this setup is scarce and difficult to obtain. In this work, we propose a method to predict emotion dynamics in... | ['Daniel Beck', 'Yingjin Song'] | 2022-10-17 | null | null | null | null | ['music-emotion-recognition'] | ['music'] | [ 1.92195177e-01 -4.62126881e-01 -1.13063157e-01 -4.57823515e-01
-8.28037202e-01 -9.01419938e-01 5.75794160e-01 -2.84510374e-01
-2.18759835e-01 5.39985478e-01 3.68594795e-01 7.87680447e-02
1.33813873e-01 -3.01752210e-01 -5.63915431e-01 -5.34738481e-01
-1.29533544e-01 1.40278593e-01 -2.66750723e-01 -2.67378151... | [15.897311210632324, 5.358955383300781] |
7ba2d491-7697-464d-8d3e-c0faa9c4f24d | workflow-discovery-from-dialogues-in-the-low | 2205.11690 | null | https://arxiv.org/abs/2205.11690v2 | https://arxiv.org/pdf/2205.11690v2.pdf | Workflow Discovery from Dialogues in the Low Data Regime | Text-based dialogues are now widely used to solve real-world problems. In cases where solution strategies are already known, they can sometimes be codified into workflows and used to guide humans or artificial agents through the task of helping clients. We introduce a new problem formulation that we call Workflow Disco... | ['Chris Pal', 'Pau Rodriguez', 'David Vazquez', 'Issam Laradji', 'Stefania Raimondo', 'Amine El Hattami'] | 2022-05-24 | null | null | null | null | ['workflow-discovery'] | ['natural-language-processing'] | [ 5.30750096e-01 1.57315552e-01 1.61793485e-01 -5.32968879e-01
-8.14179778e-01 -9.69019532e-01 1.09940958e+00 -5.63838519e-02
-5.05611956e-01 9.41545248e-01 6.28284335e-01 -3.97455245e-01
-2.89810926e-01 -4.65717673e-01 -2.90110558e-01 -4.00775999e-01
4.65502329e-02 9.39827800e-01 5.32722354e-01 -4.36737001... | [12.870532989501953, 7.934365272521973] |
58cfa6d2-882d-4b6d-89f1-3c623992ecbf | interactive-molecular-discovery-with-natural | 2306.11976 | null | https://arxiv.org/abs/2306.11976v1 | https://arxiv.org/pdf/2306.11976v1.pdf | Interactive Molecular Discovery with Natural Language | Natural language is expected to be a key medium for various human-machine interactions in the era of large language models. When it comes to the biochemistry field, a series of tasks around molecules (e.g., property prediction, molecule mining, etc.) are of great significance while having a high technical threshold. Br... | ['Zhiyuan Liu', 'Guotong Xie', 'Maosong Sun', 'Xingzhi Sun', 'Haishen Yao', 'Cheng Yang', 'Jiarui Liu', 'Shipeng Wang', 'Bangchen Yin', 'Zheni Zeng'] | 2023-06-21 | null | null | null | null | ['property-prediction'] | ['medical'] | [ 3.40377957e-01 3.31688374e-02 -1.86607987e-01 -1.46070078e-01
-5.35107553e-01 -8.17708790e-01 6.73632085e-01 4.18680757e-01
-1.49824902e-01 1.35062313e+00 3.38506430e-01 -6.62350178e-01
-8.64110980e-03 -9.99965072e-01 -8.71465266e-01 -9.96223629e-01
-5.32451831e-02 2.95188040e-01 9.34802443e-02 -3.95705372... | [5.029621601104736, 5.87459659576416] |
1b3eaba3-052c-4abb-8312-953551b4d135 | mist-multiple-instance-spatial-transformer | 1811.10725 | null | https://arxiv.org/abs/1811.10725v5 | https://arxiv.org/pdf/1811.10725v5.pdf | MIST: Multiple Instance Spatial Transformer Network | We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts. The network learns to extract the most significant top-K patches, and feeds these patches to a task-specific ... | ['Simon Kornblith', 'Yuhe Jin', 'Andrea Tagliasacchi', 'Kwang Moo Yi', 'Baptiste Angles'] | 2018-11-26 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 5.94236791e-01 3.14750999e-01 -1.99975103e-01 -2.32864380e-01
-1.03813469e+00 -4.17711049e-01 2.23849922e-01 -4.02499624e-02
-4.55183834e-01 4.05379087e-01 -1.20147526e-01 -1.73072144e-01
-3.78901362e-02 -5.57128966e-01 -1.23603928e+00 -7.75268078e-01
4.20439430e-02 4.94017571e-01 1.57009155e-01 1.65865123... | [9.567768096923828, 1.3605122566223145] |
41c5bfc4-ee2a-47de-89e6-dcf7374e2c51 | fusion-of-sentiment-and-asset-price | 2203.05673 | null | https://arxiv.org/abs/2203.05673v1 | https://arxiv.org/pdf/2203.05673v1.pdf | Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization | The fusion of public sentiment data in the form of text with stock price prediction is a topic of increasing interest within the financial community. However, the research literature seldom explores the application of investor sentiment in the Portfolio Selection problem. This paper aims to unpack and develop an enhanc... | ['Terence L. Van Zyl', 'Mufhumudzi Muthivhi'] | 2022-03-10 | null | null | null | null | ['portfolio-optimization', 'stock-price-prediction'] | ['time-series', 'time-series'] | [-5.52528165e-03 -8.74745473e-02 -3.49157065e-01 -5.39443016e-01
-7.36567438e-01 -6.19467616e-01 5.23525298e-01 -9.71487723e-03
-2.32318982e-01 4.67527211e-01 6.13073647e-01 -4.01769370e-01
-5.47186434e-01 -1.17315269e+00 -4.76846397e-01 -6.18964076e-01
3.73431176e-01 2.58496910e-01 -3.52458298e-01 -4.89473671... | [4.408894062042236, 4.11757230758667] |
791dd8c6-28ce-43a4-b30f-57f02937cb13 | data-roaming-and-early-fusion-for-composed | 2303.09429 | null | https://arxiv.org/abs/2303.09429v1 | https://arxiv.org/pdf/2303.09429v1.pdf | Data Roaming and Early Fusion for Composed Image Retrieval | We study the task of Composed Image Retrieval (CoIR), where a query is composed of two modalities, image and text, extending the user's expression ability. Previous methods typically address this task by a separate encoding of each query modality, followed by late fusion of the extracted features. In this paper, we pro... | ['Dani Lischinski', 'Nir Darshan', 'Rami Ben-Ari', 'Matan Levy'] | 2023-03-16 | null | null | null | null | ['composed-image-retrieval'] | ['computer-vision'] | [ 5.19575715e-01 -3.14158112e-01 -2.84995288e-01 -2.99413830e-01
-1.40144801e+00 -7.50662386e-01 1.06331849e+00 8.24281424e-02
-6.79077446e-01 5.62409222e-01 3.66324306e-01 4.29631509e-02
-2.46729642e-01 -3.13828647e-01 -7.82996595e-01 -5.26343226e-01
2.52413243e-01 2.97991157e-01 2.12876111e-01 -5.01499534... | [10.900561332702637, 1.0861140489578247] |
06bd410f-af9d-42bb-be9b-ce0280a160f5 | guided-image-to-image-translation-with-bi-1 | 1910.11328 | null | https://arxiv.org/abs/1910.11328v1 | https://arxiv.org/pdf/1910.11328v1.pdf | Guided Image-to-Image Translation with Bi-Directional Feature Transformation | We address the problem of guided image-to-image translation where we translate an input image into another while respecting the constraints provided by an external, user-provided guidance image. Various conditioning methods for leveraging the given guidance image have been explored, including input concatenation , feat... | ['Jia-Bin Huang', 'Badour AlBahar'] | 2019-10-24 | guided-image-to-image-translation-with-bi | http://openaccess.thecvf.com/content_ICCV_2019/html/AlBahar_Guided_Image-to-Image_Translation_With_Bi-Directional_Feature_Transformation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/AlBahar_Guided_Image-to-Image_Translation_With_Bi-Directional_Feature_Transformation_ICCV_2019_paper.pdf | iccv-2019-10 | ['pose-transfer'] | ['computer-vision'] | [ 9.13714230e-01 2.38872059e-02 -2.72080809e-01 -5.51205218e-01
-6.96790814e-01 -6.34625256e-01 9.31699991e-01 -2.88636416e-01
-5.60480118e-01 4.97830063e-01 2.84328401e-01 -1.31776184e-01
1.73533991e-01 -4.85141963e-01 -8.18935394e-01 -6.47461414e-01
5.17962694e-01 5.76392561e-02 -1.55785242e-02 -3.62777002... | [11.484156608581543, -0.5198706388473511] |
7a428192-c4a8-483d-9c8d-24dae7d8c690 | improving-cloze-test-performance-of-language | null | null | https://aclanthology.org/C14-1091 | https://aclanthology.org/C14-1091.pdf | Improving Cloze Test Performance of Language Learners Using Web N-Grams | null | ['Anna Beyer', 'Benno Stein', 'Matthias Hagen', 'Martin Potthast'] | 2014-08-01 | improving-cloze-test-performance-of-language-1 | https://aclanthology.org/C14-1091 | https://aclanthology.org/C14-1091.pdf | coling-2014-8 | ['cloze-test'] | ['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... | [-7.267706871032715, 3.652627468109131] |
ac9a932b-77d0-4feb-8c70-b0baaa0692f6 | ontology-aware-token-embeddings-for | 1705.02925 | null | http://arxiv.org/abs/1705.02925v1 | http://arxiv.org/pdf/1705.02925v1.pdf | Ontology-Aware Token Embeddings for Prepositional Phrase Attachment | Type-level word embeddings use the same set of parameters to represent all
instances of a word regardless of its context, ignoring the inherent lexical
ambiguity in language. Instead, we embed semantic concepts (or synsets) as
defined in WordNet and represent a word token in a particular context by
estimating a distrib... | ['Waleed Ammar', 'Pradeep Dasigi', 'Chris Dyer', 'Eduard Hovy'] | 2017-05-08 | ontology-aware-token-embeddings-for-1 | https://aclanthology.org/P17-1191 | https://aclanthology.org/P17-1191.pdf | acl-2017-7 | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [-1.83323443e-01 1.03360474e-01 -4.92912799e-01 -5.18522859e-01
-4.98850584e-01 -6.26010120e-01 5.41056335e-01 7.88494468e-01
-9.72445965e-01 5.57465911e-01 6.41791403e-01 -2.61007309e-01
2.01771289e-01 -1.01278663e+00 -5.02623260e-01 -4.18752670e-01
-1.94367245e-01 4.75472450e-01 1.83522508e-01 -2.66327947... | [10.413700103759766, 8.92280101776123] |
4c59d790-0ab8-469f-bd30-a2fb5c379832 | mean-absorption-estimation-from-room-impulse | 2109.00393 | null | https://arxiv.org/abs/2109.00393v1 | https://arxiv.org/pdf/2109.00393v1.pdf | Mean absorption estimation from room impulse responses using virtually supervised learning | In the context of building acoustics and the acoustic diagnosis of an existing room, this paper introduces and investigates a new approach to estimate mean absorption coefficients solely from a room impulse response (RIR). This inverse problem is tackled via virtually-supervised learning, namely, the RIR-to-absorption ... | ['Diego Di Carlo', 'Antoine Deleforge', 'Cédric Foy'] | 2021-09-01 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 5.56942403e-01 -7.79066095e-03 9.71722126e-01 -4.69808996e-01
-1.00955224e+00 -1.91415906e-01 8.58919844e-02 2.30184644e-02
-4.76944536e-01 6.58247828e-01 1.71703756e-01 -3.90074193e-01
-5.38239002e-01 -6.75031185e-01 -5.59846282e-01 -1.15861702e+00
-3.92478853e-01 1.03721701e-01 -2.60863602e-01 -4.24430341... | [15.159894943237305, 5.767819404602051] |
c164e75f-aaef-4cac-9d24-a052e91e6fa0 | matching-entropy-based-disparity-estimation | 2210.15948 | null | https://arxiv.org/abs/2210.15948v2 | https://arxiv.org/pdf/2210.15948v2.pdf | Matching entropy based disparity estimation from light field | A major challenge for matching-based depth estimation is to prevent mismatches in occlusion and smooth regions. An effective matching window satisfying three characteristics: texture richness, disparity consistency and anti-occlusion should be able to prevent mismatches to some extent. According to these characteristic... | ['Jun Qiu', 'Xing Zhao', 'Di He', 'Chang Liu', 'Ligen Shi'] | 2022-10-28 | null | null | null | null | ['disparity-estimation'] | ['computer-vision'] | [ 2.58504152e-01 -5.80581427e-01 -9.81719643e-02 -3.60470325e-01
-1.79323912e-01 1.80407956e-01 8.95817950e-02 -4.49888706e-02
-3.30622554e-01 4.90164340e-01 1.95325091e-01 1.12700559e-01
-1.40716165e-01 -1.13885832e+00 -7.26392791e-02 -9.45689201e-01
4.26219791e-01 -1.09291933e-01 7.56565392e-01 -7.08245933... | [9.265759468078613, -2.4644501209259033] |
aec32c4b-3a07-40ad-889b-9698ffad193d | improving-siem-for-critical-scada-water | 1904.05724 | null | http://arxiv.org/abs/1904.05724v1 | http://arxiv.org/pdf/1904.05724v1.pdf | Improving SIEM for Critical SCADA Water Infrastructures Using Machine Learning | Network Control Systems (NAC) have been used in many industrial processes.
They aim to reduce the human factor burden and efficiently handle the complex
process and communication of those systems. Supervisory control and data
acquisition (SCADA) systems are used in industrial, infrastructure and facility
processes (e.g... | ['Xavier Bellekens', 'Hanan Hindy', 'David Brosset', 'Amar Seeam', 'Ethan Bayne'] | 2019-03-06 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [ 2.60506839e-01 -1.91114590e-01 3.41987789e-01 4.34107631e-02
4.51359034e-01 -5.05861402e-01 6.97240293e-01 9.01232719e-01
4.98776659e-02 4.18271303e-01 -5.11770487e-01 -6.76468849e-01
-4.46246296e-01 -9.19066966e-01 -2.82558560e-01 -8.09628010e-01
-3.03714871e-01 2.35057309e-01 7.48548031e-01 -1.64559309... | [6.304579734802246, 2.603804588317871] |
cea53249-c594-4f3d-ba9e-46fc48f86df1 | prototypes-as-explanation-for-time-series | 2307.01601 | null | https://arxiv.org/abs/2307.01601v1 | https://arxiv.org/pdf/2307.01601v1.pdf | Prototypes as Explanation for Time Series Anomaly Detection | Detecting abnormal patterns that deviate from a certain regular repeating pattern in time series is essential in many big data applications. However, the lack of labels, the dynamic nature of time series data, and unforeseeable abnormal behaviors make the detection process challenging. Despite the success of recent dee... | ['Emmanuel Müller', 'Carsten Jentsch', 'Bin Li'] | 2023-07-04 | null | null | null | null | ['anomaly-detection', 'time-series-anomaly-detection'] | ['methodology', 'time-series'] | [ 3.81309092e-02 9.61806178e-02 -4.76983674e-02 -3.82604659e-01
2.85919756e-01 -6.28561437e-01 5.36553741e-01 5.05922139e-01
4.41452265e-01 4.40623425e-02 1.35893881e-01 -8.35586548e-01
-2.92910963e-01 -6.93959355e-01 -3.42872649e-01 -4.95804816e-01
-7.08779633e-01 2.04458162e-01 -4.18524677e-03 -2.82117844... | [7.510058879852295, 2.562950849533081] |
76092173-e94f-4846-8407-ed76be14ce9a | how-self-supervised-learning-can-be-used-for | 2108.04893 | null | https://arxiv.org/abs/2108.04893v6 | https://arxiv.org/pdf/2108.04893v6.pdf | How Self-Supervised Learning Can be Used for Fine-Grained Head Pose Estimation? | The cost of head pose labeling is the main challenge of improving the fine-grained Head Pose Estimation (HPE). Although Self-Supervised Learning (SSL) can be a solution to the lack of huge amounts of labeled data, its efficacy for fine-grained HPE is not yet fully explored. This study aims to assess the usage of SSL in... | ['Seyedehsamaneh Shojaeilangari', 'Sasan Karamizadeh', 'Farzaneh Esmaili', 'Ebrahim Mousavi', 'Mahdi Pourmirzaei'] | 2021-08-10 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-1.00960601e-02 4.39159840e-01 -6.85250610e-02 -4.56627846e-01
-9.22265530e-01 1.72734596e-02 5.11205733e-01 4.51305099e-02
-9.16714728e-01 9.32158470e-01 3.93876493e-01 1.04647405e-01
-1.60383016e-01 -5.73450565e-01 -8.75869930e-01 -8.98825943e-01
4.23591062e-02 7.44496882e-01 3.65652353e-01 -4.26967829... | [13.699713706970215, 0.2798609137535095] |
1cb5fd30-8172-494d-aee0-b25d8768c861 | a-brief-summary-of-interactions-between-meta | 2103.00845 | null | https://arxiv.org/abs/2103.00845v2 | https://arxiv.org/pdf/2103.00845v2.pdf | A Brief Summary of Interactions Between Meta-Learning and Self-Supervised Learning | This paper briefly reviews the connections between meta-learning and self-supervised learning. Meta-learning can be applied to improve model generalization capability and to construct general AI algorithms. Self-supervised learning utilizes self-supervision from original data and extracts higher-level generalizable fea... | ['Huimin Peng'] | 2021-03-01 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 4.11421478e-01 4.62725520e-01 -7.12215543e-01 -7.85260499e-01
-4.27842349e-01 -2.77391165e-01 8.15350235e-01 3.17684263e-01
-3.50028604e-01 8.04289877e-01 -1.43748417e-01 2.94710070e-01
-1.85518220e-01 -9.49356437e-01 -6.42274797e-01 -5.51861703e-01
-2.49079298e-02 7.87307978e-01 -1.82815775e-01 -2.36955374... | [9.852741241455078, 3.059251070022583] |
3836b908-d652-40e1-b36a-9fe1ed6df6aa | learning-generative-vision-transformer-with-1 | 2112.13528 | null | https://arxiv.org/abs/2112.13528v1 | https://arxiv.org/pdf/2112.13528v1.pdf | Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction | Vision transformer networks have shown superiority in many computer vision tasks. In this paper, we take a step further by proposing a novel generative vision transformer with latent variables following an informative energy-based prior for salient object detection. Both the vision transformer network and the energy-ba... | ['Ping Li', 'Nick Barnes', 'Jianwen Xie', 'Jing Zhang'] | 2021-12-27 | learning-generative-vision-transformer-with | http://proceedings.neurips.cc/paper/2021/hash/8289889263db4a40463e3f358bb7c7a1-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/8289889263db4a40463e3f358bb7c7a1-Paper.pdf | neurips-2021-12 | ['rgb-d-salient-object-detection', 'thermal-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.87002212e-01 1.26901120e-01 1.17828190e-01 -3.66568953e-01
-3.83016944e-01 1.51563548e-02 7.72685587e-01 -3.67783666e-01
-3.12298745e-01 6.78111911e-01 2.02201843e-01 1.34031564e-01
-1.80612579e-02 -8.25449765e-01 -8.59107912e-01 -1.00112557e+00
5.67403674e-01 4.49948311e-01 5.76591194e-01 1.93996638... | [10.034191131591797, -0.3265591263771057] |
6318534f-a1a6-4c70-a79b-9358e7683145 | learning-to-repeat-fine-grained-action | 1702.06054 | null | https://arxiv.org/abs/1702.06054v2 | https://arxiv.org/pdf/1702.06054v2.pdf | Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning | Reinforcement Learning algorithms can learn complex behavioral patterns for sequential decision making tasks wherein an agent interacts with an environment and acquires feedback in the form of rewards sampled from it. Traditionally, such algorithms make decisions, i.e., select actions to execute, at every single time s... | ['Aravind Srinivas', 'Balaraman Ravindran', 'Sahil Sharma'] | 2017-02-20 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-2.44930804e-01 5.51666282e-02 -3.99898589e-01 -1.17003970e-01
-3.75251412e-01 -6.27050936e-01 8.38198304e-01 2.04240650e-01
-9.83482718e-01 1.12615097e+00 9.77995321e-02 -4.90968555e-01
-1.11031272e-01 -7.15615690e-01 -6.92186177e-01 -7.94326127e-01
-4.23812211e-01 7.99679518e-01 2.01933146e-01 -3.30891073... | [4.0495219230651855, 1.8154367208480835] |
73360ccd-88d3-4dd5-95fc-014b97017e22 | the-effect-of-balancing-methods-on-model | 2307.00157 | null | https://arxiv.org/abs/2307.00157v1 | https://arxiv.org/pdf/2307.00157v1.pdf | The Effect of Balancing Methods on Model Behavior in Imbalanced Classification Problems | Imbalanced data poses a significant challenge in classification as model performance is affected by insufficient learning from minority classes. Balancing methods are often used to address this problem. However, such techniques can lead to problems such as overfitting or loss of information. This study addresses a more... | ['Przemysław Biecek', 'Mustafa Cavus', 'Adrian Stando'] | 2023-06-30 | null | null | null | null | ['explainable-artificial-intelligence', 'imbalanced-classification'] | ['computer-vision', 'miscellaneous'] | [ 1.05620913e-01 -1.69733495e-01 -6.38934016e-01 -6.39883697e-01
-4.23214793e-01 -1.41432822e-01 2.40458816e-01 5.30839562e-01
-2.42939487e-01 1.08333743e+00 -4.09382321e-02 -4.28280294e-01
-2.84055322e-01 -8.12883496e-01 -6.10385180e-01 -7.75936127e-01
2.76612699e-01 3.24774653e-01 9.01062600e-03 -2.52367184... | [8.730539321899414, 4.307992458343506] |
3e6a73cf-f83f-4989-9467-b63b78a093bc | tlpg-tracker-joint-learning-of-target | null | null | https://www.ijcai.org/proceedings/2020/0099 | https://www.ijcai.org/proceedings/2020/0099.pdf | TLPG-Tracker: Joint Learning of Target Localization and Proposal Generation for Visual Tracking. | Target localization and proposal generation are two essential subtasks in generic visual tracking, and it is a challenge to address both the two efficiently. In this paper, we propose an efficient two-stage architecture which makes full use of the complementarity of two subtasks to achieve robust localization and high-... | ['Feng Du', 'Linglong Qiu', 'Anna Wang', 'Ziyu Liu', 'Zhi Zhang', 'Siyuan Li'] | 2020-06-01 | null | null | null | international-joint-conference-on-artificial-7 | ['visual-tracking'] | ['computer-vision'] | [-2.83040971e-01 -2.60633707e-01 -2.24001467e-01 -1.76113188e-01
-6.75760090e-01 -6.44724965e-01 6.04810834e-01 -4.77900133e-02
-4.88686889e-01 5.16524017e-01 9.21025649e-02 7.77446199e-03
2.10904121e-01 -3.24207693e-01 -5.07707834e-01 -4.35868323e-01
-1.33606285e-01 2.54230291e-01 9.34444547e-01 -2.51927637... | [6.322630882263184, -2.129281997680664] |
a6c4527f-f3de-43f7-ae3d-7d25824a0e5e | a-fast-maximum-k-plex-algorithm-parameterized | 2306.13258 | null | https://arxiv.org/abs/2306.13258v1 | https://arxiv.org/pdf/2306.13258v1.pdf | A Fast Maximum $k$-Plex Algorithm Parameterized by the Degeneracy Gap | Given a graph, the $k$-plex is a vertex set in which each vertex is not adjacent to at most $k-1$ other vertices in the set. The maximum $k$-plex problem, which asks for the largest $k$-plex from a given graph, is an important but computationally challenging problem in applications like graph search and community detec... | ['Mingyu Xiao', 'Chunyu Luo', 'Yi Zhou', 'Zhengren Wang'] | 2023-06-23 | null | null | null | null | ['community-detection'] | ['graphs'] | [-8.09405074e-02 3.32856506e-01 -1.67290643e-01 2.28338629e-01
-4.97165352e-01 -7.87942052e-01 -4.42356199e-01 4.62879479e-01
-2.57362932e-01 5.68619192e-01 -8.28855395e-01 -5.94145834e-01
-6.00044906e-01 -1.31719065e+00 -7.67194331e-01 -6.13079011e-01
-9.02796328e-01 7.49758720e-01 5.59889078e-01 -5.91160133... | [6.865138053894043, 5.182799816131592] |
454a957a-d059-4717-a7db-fef34258fbf3 | glitch-in-the-matrix-a-large-scale-benchmark | 2305.01979 | null | https://arxiv.org/abs/2305.01979v2 | https://arxiv.org/pdf/2305.01979v2.pdf | "Glitch in the Matrix!": A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localization | Most deepfake detection methods focus on detecting spatial and/or spatio-temporal changes in facial attributes. This is because available benchmark datasets contain mostly visual-only modifications. However, a sophisticated deepfake may include small segments of audio or audio-visual manipulations that can completely c... | ['Munawar Hayat', 'Kalin Stefanov', 'Abhinav Dhall', 'Tom Gedeon', 'Shreya Ghosh', 'Zhixi Cai'] | 2023-05-03 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 4.37395014e-02 -4.43292439e-01 -8.68636593e-02 -1.50165334e-01
-6.50869429e-01 -4.83767778e-01 4.61927563e-01 -3.31001967e-01
-3.00651486e-03 2.55062252e-01 3.81546795e-01 5.96694350e-02
2.85980105e-01 -4.09005940e-01 -8.07363153e-01 -6.76060498e-01
1.54882640e-01 -4.05321538e-01 3.17338586e-01 -1.79527432... | [13.011922836303711, 1.2071019411087036] |
bc19ce5e-7b31-4d3f-b39f-7184ec9b4b41 | endtimes-at-semeval-2021-task-7-detecting-and | null | null | https://aclanthology.org/2021.semeval-1.172 | https://aclanthology.org/2021.semeval-1.172.pdf | EndTimes at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with BERT and Ensembles | This paper describes Humor-BERT, a set of BERT Large based models that we used in the SemEval-2021 Task 7: Detecting and Rating Humor and Offense. It presents pre and post processing techniques, variable threshold learning, meta learning and Ensemble approach to solve various sub-tasks that were part of the challenge. ... | ['Karan Mangla', 'Chirag Singh', 'Chandan Kumar Pandey'] | 2021-08-01 | null | null | null | semeval-2021 | ['humor-detection'] | ['natural-language-processing'] | [-5.63835800e-01 1.45065054e-01 1.34381935e-01 2.59916216e-01
-4.48167026e-01 -3.49162847e-01 7.92997777e-01 3.64793926e-01
-2.32142121e-01 1.04492974e+00 9.21050370e-01 -6.56834617e-02
-1.38352394e-01 -3.96166712e-01 1.39968172e-02 -1.48259282e-01
2.76773989e-01 6.22335315e-01 1.05381601e-01 -9.18667495... | [8.87362289428711, 11.067334175109863] |
308126f5-bdb4-47a4-ba18-f364ec7981bd | vanishing-point-guided-natural-image | 2004.02478 | null | https://arxiv.org/abs/2004.02478v1 | https://arxiv.org/pdf/2004.02478v1.pdf | Vanishing Point Guided Natural Image Stitching | Recently, works on improving the naturalness of stitching images gain more and more extensive attention. Previous methods suffer the failures of severe projective distortion and unnatural rotation, especially when the number of involved images is large or images cover a very wide field of view. In this paper, we propos... | ['Yahui Liu', 'Kai Chen', 'Jian Yao', 'Yinxuan Li', 'Jingmin Tu', 'Li Li'] | 2020-04-06 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 4.37643319e-01 -2.17707023e-01 -2.40029860e-02 5.23922034e-02
-3.60223353e-01 -5.53245127e-01 6.19935930e-01 -3.80659401e-01
-1.11024685e-01 3.70291501e-01 2.35100776e-01 1.36479497e-01
3.66474525e-03 -5.40695012e-01 -5.46850502e-01 -7.28312194e-01
3.02868336e-01 2.56348312e-01 5.56589365e-01 -5.42965949... | [9.355016708374023, -2.352206230163574] |
0fbc7dca-3f77-4eed-86cf-0b8f4a09fefc | an-end-to-end-ocr-framework-for-robust-arabic | 2208.11484 | null | https://arxiv.org/abs/2208.11484v2 | https://arxiv.org/pdf/2208.11484v2.pdf | An End-to-End OCR Framework for Robust Arabic-Handwriting Recognition using a Novel Transformers-based Model and an Innovative 270 Million-Words Multi-Font Corpus of Classical Arabic with Diacritics | This research is the second phase in a series of investigations on developing an Optical Character Recognition (OCR) of Arabic historical documents and examining how different modeling procedures interact with the problem. The first research studied the effect of Transformers on our custom-built Arabic dataset. One of ... | ['Amr S. Ghoneim', 'Anas Salah', 'Salma Jamal', 'Ahmed Elbehery', 'Ali Ashraf', 'Omar Mohamed', 'Aly Mostafa'] | 2022-08-20 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 1.88797504e-01 -2.87976056e-01 4.75690395e-01 -3.37325662e-01
-3.93944919e-01 -6.02975368e-01 7.73699641e-01 -2.15799630e-01
-8.86325240e-01 1.90719873e-01 4.10525426e-02 -4.04514641e-01
4.41025943e-01 -7.75188982e-01 -7.50697851e-01 -4.85519260e-01
2.38656029e-01 3.30082148e-01 8.97281542e-02 -2.45150909... | [11.8574857711792, 2.5076544284820557] |
18042053-1e00-4294-be5f-e148b163dcb5 | the-cross-evaluation-of-machine-learning | 2203.04686 | null | https://arxiv.org/abs/2203.04686v1 | https://arxiv.org/pdf/2203.04686v1.pdf | The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems | Enhancing Network Intrusion Detection Systems (NIDS) with supervised Machine Learning (ML) is tough. ML-NIDS must be trained and evaluated, operations requiring data where benign and malicious samples are clearly labelled. Such labels demand costly expert knowledge, resulting in a lack of real deployments, as well as o... | ['Mauro Conti', 'Luca Pajola', 'Giovanni Apruzzese'] | 2022-03-09 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 1.06234536e-01 -7.17077684e-03 -3.44391376e-01 -5.95818043e-01
-3.01361442e-01 -7.72727907e-01 9.81358111e-01 2.81167403e-02
-5.23308635e-01 7.86048591e-01 -6.35496914e-01 -7.29909241e-01
-3.44850481e-01 -5.53489089e-01 -4.59162354e-01 -3.36153299e-01
-6.44617736e-01 7.07924068e-01 4.39665556e-01 -6.77916184... | [5.272898197174072, 7.229483127593994] |
274afadb-92d7-4e8d-8a65-ff11573ea21e | to-fit-or-not-to-fit-model-based-face | 2106.09614 | null | https://arxiv.org/abs/2106.09614v3 | https://arxiv.org/pdf/2106.09614v3.pdf | Robust Model-based Face Reconstruction through Weakly-Supervised Outlier Segmentation | In this work, we aim to enhance model-based face reconstruction by avoiding fitting the model to outliers, i.e. regions that cannot be well-expressed by the model such as occluders or make-up. The core challenge for localizing outliers is that they are highly variable and difficult to annotate. To overcome this challen... | ['Adam Kortylewski', 'Bernhard Egger', 'Thomas Vetter', 'Andreas Morel-Forster', 'Chunlu Li'] | 2021-06-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Robust_Model-Based_Face_Reconstruction_Through_Weakly-Supervised_Outlier_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Robust_Model-Based_Face_Reconstruction_Through_Weakly-Supervised_Outlier_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-face-reconstruction', 'face-model', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.59519070e-01 3.36584151e-01 1.73653379e-01 -5.11084497e-01
-6.19200647e-01 -2.58568078e-01 1.70655504e-01 -4.33797777e-01
-1.16904244e-01 2.25283802e-01 -2.87979729e-02 3.06775987e-01
2.62478650e-01 -3.78776729e-01 -1.10693645e+00 -6.03776515e-01
3.37385893e-01 5.55252254e-01 -1.63653284e-01 1.47312567... | [13.352090835571289, 0.29314765334129333] |
cbfb145a-bed3-4ac0-a07c-3fe97e8d8e2c | are-random-decompositions-all-we-need-in-high | 2301.12844 | null | https://arxiv.org/abs/2301.12844v2 | https://arxiv.org/pdf/2301.12844v2.pdf | Are Random Decompositions all we need in High Dimensional Bayesian Optimisation? | Learning decompositions of expensive-to-evaluate black-box functions promises to scale Bayesian optimisation (BO) to high-dimensional problems. However, the success of these techniques depends on finding proper decompositions that accurately represent the black-box. While previous works learn those decompositions based... | ['Haitham Bou-Ammar', 'Juliusz Ziomek'] | 2023-01-30 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [-2.01117188e-01 3.16851199e-01 -2.33580723e-01 -1.64793059e-01
-1.32194436e+00 -5.46037376e-01 5.91511607e-01 -3.89246047e-02
-3.24640006e-01 8.15130949e-01 2.28579506e-01 -4.14908051e-01
-6.68390572e-01 -5.30436218e-01 -7.98468411e-01 -1.09572613e+00
-2.08587706e-01 1.10349834e+00 2.15967819e-01 1.27082378... | [6.402853488922119, 3.8478591442108154] |
3b202a37-b9c8-4dc7-9b63-849bdeb50275 | benchmarking-the-human-brain-against | 2305.14363 | null | https://arxiv.org/abs/2305.14363v1 | https://arxiv.org/pdf/2305.14363v1.pdf | Benchmarking the human brain against computational architectures | The human brain has inspired novel concepts complementary to classical and quantum computing architectures, such as artificial neural networks and neuromorphic computers, but it is not clear how their performances compare. Here we report a new methodological framework for benchmarking cognitive performance based on sol... | ['Philip Walther', 'Catherine Schuman', 'Céline van Valkenhoef'] | 2023-05-15 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 3.69424522e-01 1.68996483e-01 5.53246915e-01 -7.67984241e-02
-3.82003412e-02 -3.95472586e-01 8.59415591e-01 -2.23466530e-02
-1.04824984e+00 8.18828285e-01 -3.43352377e-01 -3.50655802e-03
-4.32472765e-01 -1.11535621e+00 -4.86243248e-01 -8.12551618e-01
-1.49309859e-01 3.51176560e-01 3.00416648e-01 -3.91847640... | [5.571905612945557, 4.973240852355957] |
76389909-4fac-493a-b3c6-a057cb637b3b | ns3-neuro-symbolic-semantic-code-search | 2205.10674 | null | https://arxiv.org/abs/2205.10674v2 | https://arxiv.org/pdf/2205.10674v2.pdf | NS3: Neuro-Symbolic Semantic Code Search | Semantic code search is the task of retrieving a code snippet given a textual description of its functionality. Recent work has been focused on using similarity metrics between neural embeddings of text and code. However, current language models are known to struggle with longer, compositional text, and multi-step reas... | ['Xiang Ren', 'Luis Garcia', 'Christophe Hauser', 'Miltiadis Allamanis', 'Anna Hakhverdyan', 'Shushan Arakelyan'] | 2022-05-21 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [ 2.22019732e-01 9.74179730e-02 -1.24554142e-01 -4.02052224e-01
-9.02395785e-01 -6.61543190e-01 4.99963135e-01 6.81948781e-01
-3.57506245e-01 -5.15659787e-02 5.60851634e-01 -6.22131228e-01
-1.85804337e-01 -6.05064273e-01 -5.71712971e-01 1.34412363e-01
2.93035805e-02 4.05448943e-01 4.78579044e-01 -1.77129060... | [7.521047115325928, 8.077773094177246] |
0ee63d0e-5f23-44c6-8ee5-af0ae28b782b | shape-robust-text-detection-with-progressive | 1806.02559 | null | http://arxiv.org/abs/1806.02559v1 | http://arxiv.org/pdf/1806.02559v1.pdf | Shape Robust Text Detection with Progressive Scale Expansion Network | The challenges of shape robust text detection lie in two aspects: 1) most
existing quadrangular bounding box based detectors are difficult to locate
texts with arbitrary shapes, which are hard to be enclosed perfectly in a
rectangle; 2) most pixel-wise segmentation-based detectors may not separate the
text instances th... | ['Ruo-Ze Liu', 'Jian Yang', 'Wenhai Wang', 'Tong Lu', 'Xiang Li', 'Wenbo Hou'] | 2018-06-07 | null | null | null | null | ['curved-text-detection'] | ['computer-vision'] | [-7.63122458e-03 -7.59424493e-02 2.72828974e-02 -4.62704487e-02
-8.50061834e-01 -6.86972439e-01 5.62389731e-01 1.97221592e-01
-2.73698658e-01 7.99883008e-02 -1.59315695e-03 -1.76780269e-01
2.62407772e-02 -7.87337720e-01 -5.73340178e-01 -6.56846225e-01
1.16168573e-01 7.63171196e-01 8.11359048e-01 -1.52891889... | [12.08283519744873, 2.2830724716186523] |
66b8ac2e-e65c-4e47-a39d-18cccd7695bb | uncertainty-aware-score-distribution-learning-1 | 2006.07665 | null | https://arxiv.org/abs/2006.07665v1 | https://arxiv.org/pdf/2006.07665v1.pdf | Uncertainty-aware Score Distribution Learning for Action Quality Assessment | Assessing action quality from videos has attracted growing attention in recent years. Most existing approaches usually tackle this problem based on regression algorithms, which ignore the intrinsic ambiguity in the score labels caused by multiple judges or their subjective appraisals. To address this issue, we propose ... | ['Jie zhou', 'Yansong Tang', 'Jiwen Lu', 'Danyang Zhang', 'Zanlin Ni', 'Ying Wu', 'Jiahuan Zhou'] | 2020-06-13 | uncertainty-aware-score-distribution-learning | http://openaccess.thecvf.com/content_CVPR_2020/html/Tang_Uncertainty-Aware_Score_Distribution_Learning_for_Action_Quality_Assessment_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Tang_Uncertainty-Aware_Score_Distribution_Learning_for_Action_Quality_Assessment_CVPR_2020_paper.pdf | cvpr-2020-6 | ['action-quality-assessment'] | ['computer-vision'] | [ 8.78368765e-02 -1.38204247e-01 -5.10020614e-01 -6.43628597e-01
-1.18297505e+00 -3.92827213e-01 2.13306516e-01 1.90823153e-01
-3.38665903e-01 7.86401212e-01 6.20395124e-01 2.17758119e-01
-6.46037877e-01 -5.58076084e-01 -3.11595410e-01 -7.31950283e-01
3.27410065e-02 2.06689924e-01 2.00018302e-01 -2.79637072... | [8.248050689697266, 0.6849004030227661] |
4c26e544-40e5-470e-8eed-8a37bee3d257 | uvosam-a-mask-free-paradigm-for-unsupervised | 2305.12659 | null | https://arxiv.org/abs/2305.12659v1 | https://arxiv.org/pdf/2305.12659v1.pdf | UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything Model | Unsupervised video object segmentation has made significant progress in recent years, but the manual annotation of video mask datasets is expensive and limits the diversity of available datasets. The Segment Anything Model (SAM) has introduced a new prompt-driven paradigm for image segmentation, unlocking a range of pr... | ['Siyu Zhu', 'Zuozhuo Dai', 'Shengfan Zhang', 'Zhichao Wei', 'Zhenghao Zhang'] | 2023-05-22 | null | null | null | null | ['video-object-segmentation', 'video-semantic-segmentation', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.41331136e-01 7.75042549e-02 -6.21434152e-01 -3.78462911e-01
-7.48347878e-01 -7.22636819e-01 5.91189682e-01 5.40345199e-02
-3.89269680e-01 3.73693407e-01 4.68288176e-02 -1.11160457e-01
9.58817080e-02 -2.47394145e-01 -6.43597126e-01 -5.06180346e-01
4.79920134e-02 3.18396866e-01 7.82053947e-01 3.87335926... | [9.105133056640625, -0.162195086479187] |
176df7a6-1982-4e7c-a9de-309c8bdd56cf | a-new-benchmark-for-group-distribution-shifts | 2211.00110 | null | https://arxiv.org/abs/2211.00110v1 | https://arxiv.org/pdf/2211.00110v1.pdf | A new benchmark for group distribution shifts in hand grasp regression for object manipulation. Can meta-learning raise the bar? | Understanding hand-object pose with computer vision opens the door to new applications in mixed reality, assisted living or human-robot interaction. Most methods are trained and evaluated on balanced datasets. This is of limited use in real-world applications; how do these methods perform in the wild on unknown objects... | ['Gerard Lacey', 'Théo Morales'] | 2022-10-31 | null | null | null | null | ['hand-object-pose', 'mixed-reality'] | ['computer-vision', 'computer-vision'] | [ 1.38777837e-01 4.01551872e-02 -3.55602771e-01 -4.37057942e-01
-9.72035468e-01 -5.06150603e-01 4.09445554e-01 -2.93427795e-01
-4.99605268e-01 8.23256850e-01 1.15575112e-01 1.47032216e-01
-1.90668583e-01 -7.74737671e-02 -8.96407127e-01 -7.07305968e-01
-1.84754133e-01 9.70465302e-01 3.31780314e-01 -2.66222119... | [6.917627334594727, -0.8929163217544556] |
6b9152ca-75aa-4e9d-8b2f-13b78c7ffef4 | inspherenet-a-concise-representation-and | 1912.11606 | null | https://arxiv.org/abs/1912.11606v2 | https://arxiv.org/pdf/1912.11606v2.pdf | InSphereNet: a Concise Representation and Classification Method for 3D Object | In this paper, we present an InSphereNet method for the problem of 3D object classification. Unlike previous methods that use points, voxels, or multi-view images as inputs of deep neural network (DNN), the proposed method constructs a class of more representative features named infilling spheres from signed distance f... | ['Siyu Zhang', 'Shen Cai', 'Haikuan Du', 'Hui Cao'] | 2019-12-25 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [-5.10544837e-01 -1.24626331e-01 3.54869962e-01 -4.74051714e-01
-2.26199664e-02 -4.02374506e-01 7.54720688e-01 1.57827482e-01
-4.24643427e-01 6.79765165e-01 -2.36412615e-01 -1.54293254e-01
-1.84355840e-01 -1.07325852e+00 -7.94830918e-01 -4.83740032e-01
-2.42947951e-01 4.37082469e-01 5.42093515e-01 -1.81230843... | [7.953796863555908, -3.5850234031677246] |
e7b07aea-814a-4920-ada5-1613d8c7502d | nfi-2-learning-noise-free-illuminance | 2305.10223 | null | https://arxiv.org/abs/2305.10223v1 | https://arxiv.org/pdf/2305.10223v1.pdf | NFI$_2$: Learning Noise-Free Illuminance-Interpolator for Unsupervised Low-Light Image Enhancement | Low-light situations severely restrict the pursuit of aesthetic quality in consumer photography. Although many efforts are devoted to designing heuristics, it is generally mired in a shallow spiral of tedium, such as piling up complex network architectures and empirical strategies. How to delve into the essential physi... | ['Risheng Liu', 'Xin Fan', 'Ziyu Yue', 'Jiaxin Gao', 'Xiaofeng Liu'] | 2023-05-17 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 4.33326393e-01 -3.48948315e-02 4.46729958e-02 -4.51786935e-01
-8.70621204e-02 2.46981438e-02 2.44540259e-01 -5.72382510e-01
-3.37353528e-01 7.03629255e-01 3.05403639e-02 4.66152355e-02
-3.47192019e-01 -8.89028788e-01 -6.73650324e-01 -1.13139200e+00
4.14649189e-01 -3.21004480e-01 -3.63037288e-01 -3.98428559... | [10.704581260681152, -2.55413556098938] |
cf49cd40-0067-4f1f-917a-2d1dbcd3ec87 | image-comes-dancing-with-collaborative | 2110.14147 | null | https://arxiv.org/abs/2110.14147v2 | https://arxiv.org/pdf/2110.14147v2.pdf | Image Comes Dancing with Collaborative Parsing-Flow Video Synthesis | Transferring human motion from a source to a target person poses great potential in computer vision and graphics applications. A crucial step is to manipulate sequential future motion while retaining the appearance characteristic.Previous work has either relied on crafted 3D human models or trained a separate model spe... | ['Liang Lin', 'Haoye Dong', 'Yubei Xiao', 'Xiaodan Liang', 'Zhenyu Xie', 'Bowen Wu'] | 2021-10-27 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 2.92546779e-01 -1.85477994e-02 1.88926607e-02 -8.73012990e-02
-3.19885015e-01 -4.46487069e-01 5.14479578e-01 -7.54604459e-01
-1.12825446e-01 5.49726844e-01 9.18703899e-02 1.02091230e-01
5.20398557e-01 -7.42842734e-01 -7.11941421e-01 -8.25439811e-01
1.14410289e-01 1.32234842e-01 5.66527188e-01 -1.24105506... | [10.911125183105469, -0.7910422682762146] |
0e4f3bc6-f88d-4be2-b30a-51704ee685c8 | toward-fast-and-accurate-neural-discourse | 1808.09147 | null | http://arxiv.org/abs/1808.09147v1 | http://arxiv.org/pdf/1808.09147v1.pdf | Toward Fast and Accurate Neural Discourse Segmentation | Discourse segmentation, which segments texts into Elementary Discourse Units,
is a fundamental step in discourse analysis. Previous discourse segmenters rely
on complicated hand-crafted features and are not practical in actual use. In
this paper, we propose an end-to-end neural segmenter based on BiLSTM-CRF
framework. ... | ['Jingfeng Yang', 'Yizhong Wang', 'Sujian Li'] | 2018-08-28 | toward-fast-and-accurate-neural-discourse-1 | https://aclanthology.org/D18-1116 | https://aclanthology.org/D18-1116.pdf | emnlp-2018-10 | ['discourse-segmentation'] | ['natural-language-processing'] | [ 3.60231668e-01 5.43000221e-01 -3.85018647e-01 -3.70215893e-01
-8.08484316e-01 -3.47630918e-01 7.20340490e-01 1.53576761e-01
-6.93208337e-01 8.58353674e-01 7.40420997e-01 -6.12034976e-01
4.51752812e-01 -7.62034416e-01 -5.05581439e-01 -3.07042748e-01
1.95408955e-01 3.94112110e-01 6.03806973e-01 -3.87281626... | [10.777321815490723, 9.446059226989746] |
6dbf0897-a848-42b7-800a-41e105e01776 | towards-unified-text-based-person-retrieval-a | 2306.02898 | null | https://arxiv.org/abs/2306.02898v2 | https://arxiv.org/pdf/2306.02898v2.pdf | Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark | In this paper, we introduce a large Multi-Attribute and Language Search dataset for text-based person retrieval, called MALS, and explore the feasibility of performing pre-training on both attribute recognition and image-text matching tasks in one stone. In particular, MALS contains 1,510,330 image-text pairs, which is... | ['Zhedong Zheng', 'Li Zhu', 'Yujiao Wu', 'Yaxiong Wang', 'Yinan Zhou', 'Shuyu Yang'] | 2023-06-05 | null | null | null | null | ['pedestrian-attribute-recognition', 'person-retrieval', 'nlp-based-person-retrival', 'image-text-matching', 'text-matching'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'natural-language-processing'] | [ 5.91141954e-02 -2.89957613e-01 -2.79477000e-01 -5.92726290e-01
-1.28738999e+00 -3.64350080e-01 9.67678547e-01 1.43221870e-01
-5.48449755e-01 4.86165226e-01 3.99882466e-01 2.46260077e-01
-1.37917399e-01 -6.82732284e-01 -7.12828219e-01 -6.24689698e-01
1.73416287e-01 8.23518574e-01 -1.73669815e-01 5.70898131... | [14.631180763244629, 0.9267781972885132] |
923260b6-c75f-4cb2-aca1-08d83a897908 | on-the-usefulness-of-personality-traits-in | null | null | https://aclanthology.org/2021.ranlp-main.62 | https://aclanthology.org/2021.ranlp-main.62.pdf | On the Usefulness of Personality Traits in Opinion-oriented Tasks | We use a deep bidirectional transformer to extract the Myers-Briggs personality type from user-generated data in a multi-label and multi-class classification setting. Our dataset is large and made up of three available personality datasets of various social media platforms including Reddit, Twitter, and Personality Caf... | ['Arjun Mukherjee', 'Dainis Boumber', 'Eduard Dragut', 'Marjan Hosseinia'] | null | null | https://aclanthology.org/2021.ranlp-1.62 | https://aclanthology.org/2021.ranlp-1.62.pdf | ranlp-2021-9 | ['news-classification', 'authorship-verification'] | ['natural-language-processing', 'natural-language-processing'] | [-4.62016374e-01 1.55556664e-01 -2.36531764e-01 -5.41797757e-01
-4.98725146e-01 -9.16578829e-01 7.48844385e-01 3.79154235e-01
-2.90463805e-01 6.03922486e-01 6.25025749e-01 -3.71257402e-02
-1.22267991e-01 -7.21544027e-01 -1.25576854e-01 -4.61469203e-01
1.05373509e-01 8.51180792e-01 -2.54911035e-01 -3.16688657... | [9.3975830078125, 10.344913482666016] |
b3d5d6c2-e2c0-4913-9fe8-0e7ac3318fce | simultaneous-self-supervised-reconstruction | 2210.01696 | null | https://arxiv.org/abs/2210.01696v3 | https://arxiv.org/pdf/2210.01696v3.pdf | Simultaneous self-supervised reconstruction and denoising of sub-sampled MRI data with Noisier2Noise | Most existing methods for Magnetic Resonance Imaging (MRI) reconstruction with deep learning assume that a high signal-to-noise ratio (SNR), fully sampled sampled dataset exists and use fully supervised training. In many circumstances, however, such a dataset does not exist and may be highly impractical to acquire. Rec... | ['Mark Chiew', 'Charles Millard'] | 2022-10-04 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 7.44160891e-01 1.03466049e-01 4.97653931e-02 -5.81937611e-01
-1.02617216e+00 -1.10207491e-01 4.30101573e-01 -2.24110126e-01
-5.09941459e-01 8.76049697e-01 3.36113691e-01 -2.54292846e-01
-3.99091542e-01 -5.85310161e-01 -7.48008907e-01 -1.01111698e+00
-1.85312167e-01 3.59169155e-01 7.24573731e-02 -1.71408415... | [13.370904922485352, -2.4508681297302246] |
33405817-1515-4760-abd1-ebb0e563fed5 | image-based-fire-detection-in-industrial | 2212.04786 | null | https://arxiv.org/abs/2212.04786v1 | https://arxiv.org/pdf/2212.04786v1.pdf | Image-Based Fire Detection in Industrial Environments with YOLOv4 | Fires have destructive power when they break out and affect their surroundings on a devastatingly large scale. The best way to minimize their damage is to detect the fire as quickly as possible before it has a chance to grow. Accordingly, this work looks into the potential of AI to detect and recognize fires and reduce... | ['Felix Nilsson', 'Fernando Alonso-Fernandez', 'Kevin Hernandez-Diaz', 'Joel Pålsson', 'Otto Zell'] | 2022-12-09 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 3.83917004e-01 -1.31799459e-01 2.88734496e-01 1.27003655e-01
-4.81286086e-02 -4.84173477e-01 6.62900746e-01 4.53432947e-01
-4.85986769e-01 4.95446444e-01 -1.95404723e-01 -7.29961842e-02
-9.00095850e-02 -1.15119159e+00 -3.39960158e-01 -6.83705866e-01
-1.49413019e-01 4.75590616e-01 4.86355633e-01 -2.26422161... | [9.07221508026123, -1.1584419012069702] |
d67bfa89-00fe-4ae3-8baf-f0417bda9701 | tgglines-a-robust-topological-graph-guided | 2002.12428 | null | https://arxiv.org/abs/2002.12428v1 | https://arxiv.org/pdf/2002.12428v1.pdf | TGGLines: A Robust Topological Graph Guided Line Segment Detector for Low Quality Binary Images | Line segment detection is an essential task in computer vision and image analysis, as it is the critical foundation for advanced tasks such as shape modeling and road lane line detection for autonomous driving. We present a robust topological graph guided approach for line segment detection in low quality binary images... | ['Diane Oyen', 'Liping Yang', 'Catherine Potts', 'Vijayan K. Asari', 'Ming Gong', 'Brendt Wohlberg'] | 2020-02-27 | null | null | null | null | ['line-segment-detection', 'line-detection'] | ['computer-vision', 'computer-vision'] | [ 4.91358750e-02 -1.37857750e-01 -4.68396217e-01 -5.05980924e-02
-2.97854692e-01 -6.63090467e-01 5.55947781e-01 7.34848619e-01
-1.45307556e-01 4.13439780e-01 -5.20039618e-01 -6.62545443e-01
1.98795367e-02 -9.98074889e-01 -7.45960653e-01 -2.43098602e-01
-1.54816642e-01 3.73838335e-01 1.12697995e+00 -3.30996901... | [8.272261619567871, -1.6015883684158325] |
8d5e8c6d-2361-4ace-9bc1-587c58e124d8 | inductive-graph-unlearning | 2304.03093 | null | https://arxiv.org/abs/2304.03093v2 | https://arxiv.org/pdf/2304.03093v2.pdf | Inductive Graph Unlearning | As a way to implement the "right to be forgotten" in machine learning, \textit{machine unlearning} aims to completely remove the contributions and information of the samples to be deleted from a trained model without affecting the contributions of other samples. Recently, many frameworks for machine unlearning have bee... | ['Di Wang', 'Mengdi Huai', 'Cheng-Long Wang'] | 2023-04-06 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [ 4.16670203e-01 7.21046150e-01 -3.64603877e-01 3.11964191e-02
-1.18142188e-01 -7.63103366e-01 3.86272669e-01 3.08636665e-01
5.38284108e-02 8.53284478e-01 -3.45689833e-01 -5.70296466e-01
-4.34789598e-01 -1.06701195e+00 -8.75395060e-01 -7.90275097e-01
-2.15346843e-01 6.16004646e-01 2.51861006e-01 -1.94937974... | [7.266981601715088, 6.184572219848633] |
8ef4b964-4694-4318-bd72-801b424b3efa | person-search-by-multi-scale-matching | 1807.08582 | null | http://arxiv.org/abs/1807.08582v1 | http://arxiv.org/pdf/1807.08582v1.pdf | Person Search by Multi-Scale Matching | We consider the problem of person search in unconstrained scene images.
Existing methods usually focus on improving the person detection accuracy to
mitigate negative effects imposed by misalignment, mis-detections, and false
alarms resulted from noisy people auto-detection. In contrast to previous
studies, we show tha... | ['Shaogang Gong', 'Xiatian Zhu', 'Xu Lan'] | 2018-07-23 | null | null | null | eccv-2018 | ['person-search'] | ['computer-vision'] | [ 2.13098690e-01 -3.41136009e-01 3.51216137e-01 -2.69237906e-01
-8.79197598e-01 -3.31712753e-01 7.41561651e-01 5.61189801e-02
-1.14151263e+00 4.74678934e-01 2.39749521e-01 5.02181649e-01
-3.29900235e-01 -6.90435827e-01 -7.22631812e-01 -3.57997924e-01
1.81002125e-01 8.74074876e-01 3.05141300e-01 -2.66036242... | [14.837800025939941, 0.8079224228858948] |
b60ff512-0105-46fd-89d6-42a756806e33 | sentiment-analysis-in-the-era-of-large | 2305.15005 | null | https://arxiv.org/abs/2305.15005v1 | https://arxiv.org/pdf/2305.15005v1.pdf | Sentiment Analysis in the Era of Large Language Models: A Reality Check | Sentiment analysis (SA) has been a long-standing research area in natural language processing. It can offer rich insights into human sentiments and opinions and has thus seen considerable interest from both academia and industry. With the advent of large language models (LLMs) such as ChatGPT, there is a great potentia... | ['Lidong Bing', 'Sinno Jialin Pan', 'Bing Liu', 'Yue Deng', 'Wenxuan Zhang'] | 2023-05-24 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [ 5.98624870e-02 -1.90534249e-01 -2.28479624e-01 -6.84110403e-01
-8.16657007e-01 -6.13287866e-01 7.18658745e-01 4.88523901e-01
-6.94859326e-01 4.78037894e-01 4.38223600e-01 -3.75377625e-01
3.76853466e-01 -5.11267424e-01 -4.59486805e-02 -5.09559512e-01
2.48144135e-01 2.34440416e-01 2.99723167e-03 -8.38874817... | [11.302867889404297, 6.899796009063721] |
e5229c6c-9ff3-4e12-9557-0f0e7ff992c5 | low-resource-quadratic-forms-for-knowledge | null | null | https://aclanthology.org/2021.sustainlp-1.1 | https://aclanthology.org/2021.sustainlp-1.1.pdf | Low Resource Quadratic Forms for Knowledge Graph Embeddings | We address the problem of link prediction between entities and relations of knowledge graphs. State of the art techniques that address this problem, while increasingly accurate, are computationally intensive. In this paper we cast link prediction as a sparse convex program whose solution defines a quadratic form that i... | ['Glenn Fung', 'Devin Conathan', 'Jeffery Kline', 'Zachary Zhou'] | null | null | null | null | emnlp-sustainlp-2021-11 | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [ 1.09638259e-01 2.19227806e-01 -5.65882087e-01 -2.45820180e-01
-7.99046040e-01 -6.52222931e-01 4.32726145e-01 5.77838957e-01
-2.94880748e-01 6.73624635e-01 -1.15144506e-01 -6.39395118e-01
-4.18298572e-01 -1.00827384e+00 -7.92943180e-01 -2.37118214e-01
-5.23384690e-01 8.66960585e-01 3.43099982e-01 -3.94378245... | [7.155229568481445, 5.5803680419921875] |
e7d6d345-16dc-473a-b82b-9c45ac157d7a | tight-and-fast-generalization-error-bound-of | 2305.07971 | null | https://arxiv.org/abs/2305.07971v1 | https://arxiv.org/pdf/2305.07971v1.pdf | Tight and fast generalization error bound of graph embedding in metric space | Recent studies have experimentally shown that we can achieve in non-Euclidean metric space effective and efficient graph embedding, which aims to obtain the vertices' representations reflecting the graph's structure in the metric space. Specifically, graph embedding in hyperbolic space has experimentally succeeded in e... | ['Kenji Yamanishi', 'Feng Tian', 'Jing Wang', 'Taiji Suzuki', 'Atsushi Nitanda', 'Atsushi Suzuki'] | 2023-05-13 | null | null | null | null | ['graph-embedding'] | ['graphs'] | [-1.64754510e-01 5.35644710e-01 6.51634634e-02 -1.23669684e-01
-4.07128602e-01 -6.11453950e-01 5.05280774e-03 1.87383458e-01
-5.57103515e-01 4.55956489e-01 -1.48154184e-01 -7.14289129e-01
-7.04124033e-01 -1.13216794e+00 -5.21507442e-01 -9.30864692e-01
-6.44919276e-01 3.29716474e-01 4.06982541e-01 -4.07821864... | [7.155984878540039, 6.0192084312438965] |
29db3411-098d-495b-94b3-b2f385eba97c | short-duration-speaker-verification-sdsv | 1912.06311 | null | https://arxiv.org/abs/1912.06311v3 | https://arxiv.org/pdf/1912.06311v3.pdf | Short-duration Speaker Verification (SdSV) Challenge 2021: the Challenge Evaluation Plan | This document describes the Short-duration Speaker Verification (SdSV) Challenge 2021. The main goal of the challenge is to evaluate new technologies for text-dependent (TD) and text-independent (TI) speaker verification (SV) in a short duration scenario. The proposed challenge evaluates SdSV with varying degree of pho... | ['Lukas Burget', 'Kong Aik Lee', 'Hossein Zeinali', 'Jahangir Alam'] | 2019-12-13 | null | null | null | null | ['text-independent-speaker-verification', 'text-dependent-speaker-verification'] | ['speech', 'speech'] | [-4.69229892e-02 -4.27862316e-01 -2.09576078e-02 -9.48840201e-01
-1.36486435e+00 -7.90556669e-01 8.81375849e-01 -2.66713321e-01
-1.59468845e-01 4.35882896e-01 3.49612862e-01 -6.56942248e-01
2.08998457e-01 2.63716072e-01 -2.62358099e-01 -7.60395288e-01
-4.98075970e-02 5.71582139e-01 -2.46127903e-01 -2.66306162... | [14.336138725280762, 6.102877616882324] |
f0eed058-5337-4b17-af0d-a21727fedb8d | computational-technologies-for-fashion | 2306.03395 | null | https://arxiv.org/abs/2306.03395v1 | https://arxiv.org/pdf/2306.03395v1.pdf | Computational Technologies for Fashion Recommendation: A Survey | Fashion recommendation is a key research field in computational fashion research and has attracted considerable interest in the computer vision, multimedia, and information retrieval communities in recent years. Due to the great demand for applications, various fashion recommendation tasks, such as personalized fashion... | ['Tat-Seng Chua', 'P. Y. Mok', 'Zhihui Lai', 'Yujuan Ding'] | 2023-06-06 | null | null | null | null | ['product-recommendation', 'information-retrieval'] | ['miscellaneous', 'natural-language-processing'] | [ 2.07789615e-01 -5.41827023e-01 -8.24192286e-01 -5.82302392e-01
-2.11330578e-01 -7.06484139e-01 1.13935307e-01 1.07880428e-01
9.90106761e-02 8.36192816e-02 6.24630332e-01 -2.14421317e-01
-4.20105517e-01 -7.19110310e-01 -2.11779460e-01 -5.28602719e-01
2.57610440e-01 -4.55645546e-02 -3.16089898e-01 -3.74585271... | [11.031770706176758, 0.25499558448791504] |
37e1f558-8b4c-4e00-bb0e-86940dfebf98 | client-selection-for-federated-policy | 2305.10978 | null | https://arxiv.org/abs/2305.10978v3 | https://arxiv.org/pdf/2305.10978v3.pdf | Client Selection for Federated Policy Optimization with Environment Heterogeneity | The development of Policy Iteration (PI) has inspired many recent algorithms for Reinforcement Learning (RL), including several policy gradient methods, that gained both theoretical soundness and empirical success on a variety of tasks. The theory of PI is rich in the context of centralized learning, but its study is s... | ['S. H. Song', 'Zhijie Xie'] | 2023-05-18 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-1.65313348e-01 6.23460300e-02 -7.34532714e-01 -1.32618964e-01
-7.77195394e-01 -2.65029699e-01 4.94223684e-01 -6.11352287e-02
-6.95250094e-01 1.25493371e+00 2.41449311e-01 -4.60574746e-01
-3.66321832e-01 -6.86391771e-01 -8.34085703e-01 -1.01065838e+00
-3.02097291e-01 7.43637800e-01 3.93271297e-02 -1.39956534... | [4.080258369445801, 2.3819589614868164] |
83025f4d-7838-4a57-8bd7-064f04992a2d | a-graph-convolution-for-signed-directed | 2208.11511 | null | https://arxiv.org/abs/2208.11511v3 | https://arxiv.org/pdf/2208.11511v3.pdf | A Graph Convolution for Signed Directed Graphs | A signed directed graph is a graph with sign and direction information on the edges. Even though signed directed graphs are more informative than unsigned or undirected graphs, they are more complicated to analyze and have received less research attention. This paper investigates a spectral graph convolution model to f... | ['Chong-Kwon Kim', 'Taewook Ko'] | 2022-08-23 | null | null | null | null | ['link-sign-prediction'] | ['graphs'] | [ 3.06192100e-01 2.02650413e-01 7.53326789e-02 -4.24507141e-01
4.30001795e-01 -6.54580534e-01 4.48828220e-01 -7.57040130e-03
-8.38699117e-02 5.98942459e-01 -1.18039632e-02 -6.81701481e-01
-4.42849547e-01 -9.12363231e-01 -4.53692704e-01 -6.45765781e-01
-6.91205978e-01 -1.07914574e-01 2.53848344e-01 -2.38586158... | [7.056813716888428, 5.960743427276611] |
d71bc929-1022-47be-92f4-d7906470144e | self-play-reinforcement-learning-for-fast | 2010.00909 | null | https://arxiv.org/abs/2010.00909v1 | https://arxiv.org/pdf/2010.00909v1.pdf | Self-Play Reinforcement Learning for Fast Image Retargeting | In this study, we address image retargeting, which is a task that adjusts input images to arbitrary sizes. In one of the best-performing methods called MULTIOP, multiple retargeting operators were combined and retargeted images at each stage were generated to find the optimal sequence of operators that minimized the di... | ['Toshihiko Yamasaki', 'Xueting Wang', 'Satoshi Kosugi', 'Nobukatsu Kajiura'] | 2020-10-02 | null | null | null | null | ['image-retargeting'] | ['computer-vision'] | [ 4.73019332e-01 -4.10885997e-02 -1.20041125e-01 7.97095522e-02
-4.13235486e-01 -4.38514322e-01 1.34291932e-01 2.17509151e-01
-8.74399245e-01 6.01841331e-01 -3.10738325e-01 -1.37445495e-01
-3.04868728e-01 -5.82974494e-01 -6.87859356e-01 -8.28299880e-01
5.91888912e-02 8.12068954e-02 7.56872058e-01 -2.83460587... | [11.161088943481445, -1.0390865802764893] |
804f7b10-08fa-40e8-b13b-0ce9462b2fa1 | findings-on-conversation-disentanglement | 2112.05346 | null | https://arxiv.org/abs/2112.05346v1 | https://arxiv.org/pdf/2112.05346v1.pdf | Findings on Conversation Disentanglement | Conversation disentanglement, the task to identify separate threads in conversations, is an important pre-processing step in multi-party conversational NLP applications such as conversational question answering and conversation summarization. Framing it as a utterance-to-utterance classification problem -- i.e. given a... | ['Jianzhong Qi', 'Jey Han Lau', 'Rongxin Zhu'] | 2021-12-10 | null | https://aclanthology.org/2021.alta-1.1 | https://aclanthology.org/2021.alta-1.1.pdf | alta-2021-12 | ['conversation-disentanglement'] | ['natural-language-processing'] | [ 6.62942708e-01 5.63197374e-01 -3.73915106e-01 -6.58770382e-01
-1.43669999e+00 -6.78226709e-01 9.43359375e-01 2.70973623e-01
-1.04674073e-02 7.88289368e-01 1.08027005e+00 -4.76384014e-01
5.03364392e-02 -3.92246753e-01 -5.10475338e-01 -6.27956986e-01
-1.81695029e-01 1.02236104e+00 1.96979917e-03 -5.42633891... | [12.61271858215332, 7.80245304107666] |
ed1d76d1-5373-40a5-9f4b-29f7e5bf42d1 | a-lexical-simplification-tool-for-promoting | null | null | https://aclanthology.org/2020.readi-1.11 | https://aclanthology.org/2020.readi-1.11.pdf | A Lexical Simplification Tool for Promoting Health Literacy | This paper presents MedSimples, an authoring tool that combines Natural Language Processing, Corpus Linguistics and Terminology to help writers to convert health-related information into a more accessible version for people with low literacy skills. MedSimples applies parsing methods associated with lexical resources t... | ["Maria Jos{\\'e} Bocorny Finatto", 'Liana Braga Paraguassu', 'Laura Berwanger', 'Gabriel Ponomarenko', 'Leonardo Zilio', 'Luis Antonio Leiva Hercules'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['lexical-simplification'] | ['natural-language-processing'] | [-1.84559748e-02 6.20508492e-01 -3.64399195e-01 -2.54303545e-01
-3.65174949e-01 -2.91662604e-01 4.89285618e-01 1.13099122e+00
-8.49023283e-01 5.98774135e-01 7.02849627e-01 -6.75217867e-01
-2.01843023e-01 -6.35602772e-01 1.40405193e-01 1.35557381e-02
3.34855258e-01 8.48983169e-01 2.63140768e-01 -5.57414532... | [10.584894180297852, 10.226747512817383] |
a94362af-4568-432f-b78d-9cce58d71d06 | weakly-supervised-joint-whole-slide | 2301.02933 | null | https://arxiv.org/abs/2301.02933v1 | https://arxiv.org/pdf/2301.02933v1.pdf | Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer | The segmentation and automatic identification of histological regions of diagnostic interest offer a valuable aid to pathologists. However, segmentation methods are hampered by the difficulty of obtaining pixel-level annotations, which are tedious and expensive to obtain for Whole-Slide images (WSI). To remedy this, we... | ['Orcun Goksel', 'Maria Gabrani', 'Behzad Bozorgtabar', 'Kevin Thandiackal', 'Zeineb Ayadi', 'Guillaume Jaume', 'Pushpak Pati'] | 2023-01-07 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 7.27887452e-01 6.08357251e-01 -4.95179981e-01 -2.96578646e-01
-1.12483859e+00 -8.49612951e-01 4.85480845e-01 7.89656281e-01
-2.80329525e-01 5.23400187e-01 -3.58676940e-01 -4.59557503e-01
-9.71529037e-02 -6.60187066e-01 -4.63005573e-01 -1.10203898e+00
2.34559109e-03 7.19558537e-01 6.28885090e-01 2.81020045... | [14.979242324829102, -2.888782024383545] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.