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58027378-45e6-49cf-81ef-0356437cbb56 | impact-of-position-bias-on-language-models-in | 2304.13567 | null | https://arxiv.org/abs/2304.13567v2 | https://arxiv.org/pdf/2304.13567v2.pdf | Technical Report on Token Position Bias in Transformers | Language Models (LMs) have shown state-of-the-art performance in Natural Language Processing (NLP) tasks. Downstream tasks such as Named Entity Recognition (NER) or Part-of-Speech (POS) tagging are known to suffer from data imbalance issues, specifically in terms of the ratio of positive to negative examples, and class... | ['Jelena Mitrović', 'Michael Granitzer', 'Mehdi Ben Amor'] | 2023-04-26 | null | null | null | null | ['part-of-speech-tagging', 'named-entity-recognition-ner'] | ['natural-language-processing', 'natural-language-processing'] | [-2.03369051e-01 1.22743413e-01 -2.92889833e-01 -4.34292257e-01
-1.11136639e+00 -5.99596858e-01 3.85150224e-01 5.38776457e-01
-8.65365028e-01 9.40683961e-01 1.62021801e-01 -6.21592224e-01
1.87857509e-01 -7.11099744e-01 -7.34099567e-01 -4.04411942e-01
-9.19130668e-02 4.69447106e-01 1.96800843e-01 -3.87065224... | [9.867215156555176, 9.532880783081055] |
0be85a35-8851-456f-b55c-82f989e4e915 | toward-more-accurate-and-generalizable-brain | 2306.05255 | null | https://arxiv.org/abs/2306.05255v1 | https://arxiv.org/pdf/2306.05255v1.pdf | Toward more accurate and generalizable brain deformation estimators for traumatic brain injury detection with unsupervised domain adaptation | Machine learning head models (MLHMs) are developed to estimate brain deformation for early detection of traumatic brain injury (TBI). However, the overfitting to simulated impacts and the lack of generalizability caused by distributional shift of different head impact datasets hinders the broad clinical applications of... | ['David B. Camarillo', 'Michael M. Zeineh', 'Olivier Gevaert', 'Enora Le Flao', 'Nicholas J. Cecchi', 'Yuzhe Liu', 'Jiawei Sun', 'Xianghao Zhan'] | 2023-06-08 | null | null | null | null | ['unsupervised-domain-adaptation'] | ['methodology'] | [-1.51358217e-01 4.27107327e-02 -1.01776801e-01 -1.22855254e-01
-1.15203154e+00 -1.38404146e-01 2.15156674e-01 -8.57021064e-02
-5.78637242e-01 7.64378130e-01 7.26410687e-01 7.67566590e-03
-2.42471486e-01 -6.31608665e-01 -7.80997634e-01 -8.00160170e-01
-3.34384650e-01 8.33424985e-01 3.50813895e-01 -1.89302951... | [14.077093124389648, -1.8916213512420654] |
def87520-38a0-4dc4-84e5-ff5cd8612ba9 | screenplay-summarization-using-latent | 2004.12727 | null | https://arxiv.org/abs/2004.12727v1 | https://arxiv.org/pdf/2004.12727v1.pdf | Screenplay Summarization Using Latent Narrative Structure | Most general-purpose extractive summarization models are trained on news articles, which are short and present all important information upfront. As a result, such models are biased on position and often perform a smart selection of sentences from the beginning of the document. When summarizing long narratives, which h... | ['Lea Frermann', 'Frank Keller', 'Pinelopi Papalampidi', 'Mirella Lapata'] | 2020-04-27 | screenplay-summarization-using-latent-1 | https://aclanthology.org/2020.acl-main.174 | https://aclanthology.org/2020.acl-main.174.pdf | acl-2020-6 | ['turning-point-identification', 'extractive-document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.11650586e-01 3.85260105e-01 -5.96235514e-01 -2.24453852e-01
-1.14955819e+00 -1.03995633e+00 9.94502962e-01 7.16065526e-01
-2.39241421e-01 7.68221080e-01 1.48638391e+00 1.06676966e-01
-2.47396082e-02 -5.36543012e-01 -5.59245229e-01 -1.98468611e-01
9.73289087e-02 4.03923035e-01 1.08808791e-02 -1.39956221... | [12.453764915466309, 9.503264427185059] |
41b8d437-96da-4b95-ae62-70126d359809 | revisiting-neural-retrieval-on-accelerators | 2306.04039 | null | https://arxiv.org/abs/2306.04039v1 | https://arxiv.org/pdf/2306.04039v1.pdf | Revisiting Neural Retrieval on Accelerators | Retrieval finds a small number of relevant candidates from a large corpus for information retrieval and recommendation applications. A key component of retrieval is to model (user, item) similarity, which is commonly represented as the dot product of two learned embeddings. This formulation permits efficient inference,... | ['Xing Liu', 'Fu Li', 'Zheng Yan', 'Xiao Sun', 'Yueming Wang', 'Zhaojie Gong', 'Jiaqi Zhai'] | 2023-06-06 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [-1.26059160e-01 -6.83357656e-01 -4.25714552e-01 -4.68825921e-02
-1.05945718e+00 -6.50206029e-01 7.89105594e-01 6.79481745e-01
-6.54149592e-01 4.11796123e-01 3.27659994e-01 -4.10900414e-01
-7.13843942e-01 -7.25107968e-01 -7.01976836e-01 -5.59281409e-01
-3.92413855e-01 1.02400136e+00 4.08464760e-01 -3.71657997... | [11.397966384887695, 7.553837299346924] |
66da2ffe-d93a-47c1-8484-ee78d6c6ee30 | highrisk-prediction-from-electronic-medical | 1712.00010 | null | http://arxiv.org/abs/1712.00010v1 | http://arxiv.org/pdf/1712.00010v1.pdf | Highrisk Prediction from Electronic Medical Records via Deep Attention Networks | Predicting highrisk vascular diseases is a significant issue in the medical
domain. Most predicting methods predict the prognosis of patients from
pathological and radiological measurements, which are expensive and require
much time to be analyzed. Here we propose deep attention models that predict
the onset of the hig... | ['Jung-Woo Ha', 'Yun-Geun Lee', 'Jin Joo Park', 'You Jin Kim', 'Jeong Whun Kim', 'Borim Ryu'] | 2017-11-30 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 3.76275294e-02 1.73406303e-01 -2.41052091e-01 -4.40090865e-01
-8.41620445e-01 2.27022439e-01 2.81874478e-01 6.11458361e-01
-4.62413639e-01 8.11519325e-01 6.34489000e-01 -5.96018374e-01
-3.84924710e-01 -1.01486313e+00 -3.54293078e-01 -3.47623855e-01
-6.91235244e-01 6.51641011e-01 7.33092874e-02 -3.52835655... | [7.949476718902588, 6.413298606872559] |
f79a2421-3a6e-4fea-89cd-2e8612dd3e78 | class-adaptive-threshold-and-negative-class | 2305.01884 | null | https://arxiv.org/abs/2305.01884v1 | https://arxiv.org/pdf/2305.01884v1.pdf | Class adaptive threshold and negative class guided noisy annotation robust Facial Expression Recognition | The hindering problem in facial expression recognition (FER) is the presence of inaccurate annotations referred to as noisy annotations in the datasets. These noisy annotations are present in the datasets inherently because the labeling is subjective to the annotator, clarity of the image, etc. Recent works use sample ... | ['S Balasubramanian', 'Bobbili Veerendra Raj Kumar', 'Badveeti Naveen Siva Kumar', 'Darshan Gera'] | 2023-05-03 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 3.01746190e-01 2.01108456e-01 -6.65913671e-02 -7.21012473e-01
-1.07764196e+00 -4.67217624e-01 -2.08071470e-02 -1.12101465e-01
-5.48075676e-01 1.10369754e+00 -2.16267779e-02 2.46562168e-01
3.69740844e-01 -4.69816387e-01 -5.83198071e-01 -9.40625548e-01
2.08506584e-01 4.08004344e-01 1.48469776e-01 -6.47311211... | [13.596339225769043, 1.6502774953842163] |
1718b2b7-4e9a-4626-9574-7484c95fb8d3 | gans-and-alternative-methods-of-synthetic | 2306.01469 | null | https://arxiv.org/abs/2306.01469v1 | https://arxiv.org/pdf/2306.01469v1.pdf | GANs and alternative methods of synthetic noise generation for domain adaption of defect classification of Non-destructive ultrasonic testing | This work provides a solution to the challenge of small amounts of training data in Non-Destructive Ultrasonic Testing for composite components. It was demonstrated that direct simulation alone is ineffective at producing training data that was representative of the experimental domain due to poor noise reconstruction.... | ['Charalampos Loukas', 'Tom OHare', 'Charles MacLeod', 'Christopher MacKinnon', 'Ehsan Mohseni', 'S. Gareth Pierce', 'Shaun McKnight'] | 2023-06-02 | null | null | null | null | ['synthetic-data-generation', 'hyperparameter-optimization', 'synthetic-data-generation'] | ['medical', 'methodology', 'miscellaneous'] | [ 5.94596922e-01 3.42777222e-01 7.89259017e-01 -2.16091439e-01
-1.26109827e+00 -2.22206056e-01 4.06847656e-01 -8.71464089e-02
-2.46792257e-01 5.91589868e-01 -3.31940234e-01 -1.97252288e-01
-4.52824920e-01 -9.28986251e-01 -8.70030999e-01 -1.09357488e+00
-2.29584709e-01 5.56047082e-01 2.66986668e-01 -2.51162767... | [12.019292831420898, -0.6216424107551575] |
899e7a2b-7f6e-4a67-ac85-841a0075366f | ltcr-long-text-chinese-rumor-detection | 2306.07201 | null | https://arxiv.org/abs/2306.07201v2 | https://arxiv.org/pdf/2306.07201v2.pdf | LTCR: Long-Text Chinese Rumor Detection Dataset | False information can spread quickly on social media, negatively influencing the citizens' behaviors and responses to social events. To better detect all of the fake news, especially long texts which are harder to find completely, a Long-Text Chinese Rumor detection dataset named LTCR is proposed. The LTCR dataset prov... | ['Ying Shen', 'Guian Fang', 'Mengsha Liu', 'Ziyang Ma'] | 2023-06-12 | null | null | null | null | ['misinformation', 'fake-news-detection'] | ['miscellaneous', 'natural-language-processing'] | [-4.22549039e-01 4.27373089e-02 -6.70589626e-01 6.84401467e-02
-6.57874048e-01 -4.77983296e-01 8.99216890e-01 3.86271268e-01
-2.13397294e-01 9.00706053e-01 6.00061357e-01 -3.20301414e-01
6.11032665e-01 -9.05117452e-01 -4.84015107e-01 -3.07771444e-01
3.57109517e-01 1.88685358e-01 4.90972728e-01 -6.91035748... | [8.140060424804688, 10.26241683959961] |
56db144d-56d6-4cea-a414-719f323a2b80 | dense-reconstruction-of-transparent-objects | 2105.09993 | null | https://arxiv.org/abs/2105.09993v1 | https://arxiv.org/pdf/2105.09993v1.pdf | Dense Reconstruction of Transparent Objects by Altering Incident Light Paths Through Refraction | This paper addresses the problem of reconstructing the surface shape of transparent objects. The difficulty of this problem originates from the viewpoint dependent appearance of a transparent object, which quickly makes reconstruction methods tailored for diffuse surfaces fail disgracefully. In this paper, we introduce... | ['Miaomiao Liu', 'Kwan-Yee K. Wong', 'Kai Han'] | 2021-05-20 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 6.82020605e-01 1.06978863e-01 7.37542510e-01 -2.93675184e-01
3.92700061e-02 -3.73401850e-01 4.46666777e-01 -3.33448529e-01
-2.71314651e-01 5.08458316e-01 -4.85987842e-01 -9.91327465e-02
-3.57111916e-02 -9.87679899e-01 -5.70827365e-01 -9.55610812e-01
1.94732994e-01 9.31067526e-01 6.13359928e-01 -3.49003598... | [9.674100875854492, -2.996441602706909] |
8937aeca-29f4-453b-bc80-bd66797d985a | private-eye-on-the-limits-of-textual-screen | 2205.03971 | null | https://arxiv.org/abs/2205.03971v3 | https://arxiv.org/pdf/2205.03971v3.pdf | Private Eye: On the Limits of Textual Screen Peeking via Eyeglass Reflections in Video Conferencing | Using mathematical modeling and human subjects experiments, this research explores the extent to which emerging webcams might leak recognizable textual and graphical information gleaming from eyeglass reflections captured by webcams. The primary goal of our work is to measure, compute, and predict the factors, limits, ... | ['Kevin Fu', 'Shilin Xiao', 'Wenyuan Xu', 'Shivan Prasad', 'Chen Yan', 'Yan Long'] | 2022-05-08 | null | null | null | null | ['multi-frame-super-resolution'] | ['computer-vision'] | [ 3.39568704e-01 2.84942426e-02 1.02154307e-01 1.39417455e-01
-9.64737058e-01 -1.34288597e+00 5.85917294e-01 -3.89567524e-01
-2.60601521e-01 8.21167529e-02 6.57890365e-02 -9.49887276e-01
8.57994184e-02 -4.04548705e-01 -8.16135466e-01 -1.93432853e-01
1.77311450e-01 -6.26024663e-01 4.24058735e-01 8.60787705... | [12.574363708496094, 1.1008154153823853] |
d2e5f83e-7822-434d-b20b-efd639566504 | efficient-image-retargeting-for-high-dynamic | 1305.4544 | null | http://arxiv.org/abs/1305.4544v1 | http://arxiv.org/pdf/1305.4544v1.pdf | Efficient Image Retargeting for High Dynamic Range Scenes | Most of the real world scenes have a very high dynamic range (HDR). The
mobile phone cameras and the digital cameras available in markets are limited
in their capability in both the range and spatial resolution. Same argument can
be posed about the limited dynamic range display devices which also differ in
the spatial ... | ['Shanmuganathan Raman', 'Puneet Sharma', 'Govind Salvi'] | 2013-05-20 | null | null | null | null | ['image-retargeting'] | ['computer-vision'] | [ 7.92787194e-01 -2.47649074e-01 3.18174362e-01 2.47028545e-02
-4.26982254e-01 -7.22969115e-01 5.23194015e-01 -3.48294199e-01
-3.35590541e-01 6.90930367e-01 5.27936742e-02 -2.46266991e-01
-2.34972328e-01 -8.11097205e-01 -4.21858519e-01 -8.10502291e-01
3.31106395e-01 -5.29596396e-02 7.00336576e-01 -4.06283379... | [10.845681190490723, -2.4518094062805176] |
199278d0-7c9c-4334-a15a-a59f6252814b | single-stage-heavy-tailed-food-classification | 2307.00182 | null | https://arxiv.org/abs/2307.00182v1 | https://arxiv.org/pdf/2307.00182v1.pdf | Single-Stage Heavy-Tailed Food Classification | Deep learning based food image classification has enabled more accurate nutrition content analysis for image-based dietary assessment by predicting the types of food in eating occasion images. However, there are two major obstacles to apply food classification in real life applications. First, real life food images are... | ['Fengqing Zhu', 'Jiangpeng He'] | 2023-07-01 | null | null | null | null | ['classification-1', 'nutrition'] | ['methodology', 'miscellaneous'] | [ 1.75285414e-01 -3.67138773e-01 -7.39909410e-01 -6.43519521e-01
-5.42278886e-01 -3.22321296e-01 -1.03745156e-03 8.67499530e-01
-2.15278849e-01 3.23637933e-01 3.83300811e-01 -9.14402530e-02
2.44242370e-01 -1.02275860e+00 -8.22962880e-01 -6.95877314e-01
-1.55273959e-01 2.49933168e-01 1.11889407e-01 6.47923127... | [11.555815696716309, 4.383572578430176] |
0fc9c99f-a99c-4ba3-98e8-0928f9858c8b | tsgm-a-flexible-framework-for-generative | 2305.11567 | null | https://arxiv.org/abs/2305.11567v1 | https://arxiv.org/pdf/2305.11567v1.pdf | TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series | Temporally indexed data are essential in a wide range of fields and of interest to machine learning researchers. Time series data, however, are often scarce or highly sensitive, which precludes the sharing of data between researchers and industrial organizations and the application of existing and new data-intensive ML... | ['Samuel Kaski', 'Letizia Iannucci', 'Alexander Nikitin'] | 2023-05-19 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [-2.01605096e-01 -3.27521652e-01 6.49806950e-03 -2.20157847e-01
-7.53591120e-01 -7.66351700e-01 7.24260867e-01 2.69031823e-01
-1.48578286e-01 8.59219432e-01 -1.37161627e-01 -4.35648829e-01
-2.53374726e-01 -9.30216968e-01 -3.08114290e-01 -6.79616868e-01
-2.82710075e-01 6.99911416e-01 2.73040116e-01 -8.69345814... | [7.2125630378723145, 3.6036620140075684] |
7cf827bc-f7b5-4975-9b0f-ff032e4ec1a4 | ultrahyperbolic-knowledge-graph-embeddings | 2206.00449 | null | https://arxiv.org/abs/2206.00449v1 | https://arxiv.org/pdf/2206.00449v1.pdf | Ultrahyperbolic Knowledge Graph Embeddings | Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry due to its superior capability for representing hierarchies. The topological structures of real-world KGs, however, are rather heterogeneous, i.e., a KG is composed of multiple distinct hierarchies and non-hierarchical graph structures. Th... | ['Steffen Staab', 'Chuan Zhou', 'Shirui Pan', 'Chengjin Xu', 'Mojtaba Nayyeri', 'Shichao Zhu', 'Bo Xiong'] | 2022-06-01 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-4.48901057e-01 5.06151795e-01 1.57855481e-01 -1.25847146e-01
-5.22304438e-02 -6.25483155e-01 6.16873324e-01 4.26478721e-02
2.27390721e-01 1.34535894e-01 4.34653521e-01 -9.06787962e-02
-5.21852612e-01 -1.07508409e+00 -3.44838649e-01 -7.44810104e-01
-4.44967330e-01 4.47371930e-01 2.56636649e-01 -4.08211023... | [8.606475830078125, 7.729087829589844] |
89800d38-e8ab-472f-86db-f403b1f1ae24 | extreme-multi-label-learning-for-semantic | 2106.12657 | null | https://arxiv.org/abs/2106.12657v1 | https://arxiv.org/pdf/2106.12657v1.pdf | Extreme Multi-label Learning for Semantic Matching in Product Search | We consider the problem of semantic matching in product search: given a customer query, retrieve all semantically related products from a huge catalog of size 100 million, or more. Because of large catalog spaces and real-time latency constraints, semantic matching algorithms not only desire high recall but also need t... | ['Inderjit S. Dhillon', 'Japinder Singh', 'Vyacheslav Ievgrafov', 'Nikhil Shandilya', 'Qie Hu', 'Kedarnath Kolluri', 'Kai Zhong', 'Jiong Zhang', 'Choon-Hui Teo', 'Hsiang-Fu Yu', 'Daniel Jiang', 'Wei-Cheng Chang'] | 2021-06-23 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 1.21487178e-01 -2.60181576e-01 -8.65813494e-01 -5.48204243e-01
-1.00552833e+00 -7.79365599e-01 2.38636628e-01 4.31241989e-01
-3.88406754e-01 -1.30594522e-01 -1.42990127e-02 -4.27958310e-01
-4.70660090e-01 -1.15368390e+00 -5.27026892e-01 -6.12096721e-03
1.48469746e-01 1.12673223e+00 2.49563605e-01 -2.15527222... | [11.234721183776855, 7.330539703369141] |
c171a573-7a85-4b48-9542-7a7f1ff7aa24 | first-session-adaptation-a-strong-replay-free | 2303.13199 | null | https://arxiv.org/abs/2303.13199v1 | https://arxiv.org/pdf/2303.13199v1.pdf | First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning | In Class-Incremental Learning (CIL) an image classification system is exposed to new classes in each learning session and must be updated incrementally. Methods approaching this problem have updated both the classification head and the feature extractor body at each session of CIL. In this work, we develop a baseline m... | ['Richard E. Turner', 'Rahaf Aljundi', 'Daniel Olmeda Reino', 'Yuriko Kobe', 'Aristeidis Panos'] | 2023-03-23 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [ 4.52588201e-01 -5.38734868e-02 -4.66053635e-01 -4.01972592e-01
-7.39719570e-01 -2.88923591e-01 7.47300386e-01 2.45060965e-01
-7.14853942e-01 5.93999088e-01 -3.20216082e-02 -1.02109507e-01
-2.61244833e-01 -5.22429943e-01 -7.42994726e-01 -7.05032647e-01
-1.38462827e-01 4.77909148e-01 5.65375626e-01 -1.58962727... | [9.87344741821289, 3.083477735519409] |
0111d12d-ced4-4b66-8a3a-536462e9cb7b | unleashing-the-power-of-visual-prompting-at | 2212.10556 | null | https://arxiv.org/abs/2212.10556v2 | https://arxiv.org/pdf/2212.10556v2.pdf | Unleashing the Power of Visual Prompting At the Pixel Level | This paper presents a simple and effective visual prompting method for adapting pre-trained models to downstream recognition tasks. Our method includes two key designs. First, rather than directly adding together the prompt and the image, we treat the prompt as an extra and independent learnable component. We show that... | ['Cihang Xie', 'Yuyin Zhou', 'Alan Yuille', 'Huiyu Wang', 'Chen Wei', 'Xianhang Li', 'Junyang Wu'] | 2022-12-20 | null | null | null | null | ['visual-prompting'] | ['computer-vision'] | [ 2.89372444e-01 -3.21776532e-02 -3.48001182e-01 -4.53292668e-01
-1.12351632e+00 -1.04492092e+00 7.32644320e-01 -3.33238184e-01
-4.51158375e-01 6.90521479e-01 3.35332006e-01 -3.65623325e-01
2.62311280e-01 -2.52897114e-01 -1.01913083e+00 -7.12059796e-01
3.45577776e-01 1.01408958e-01 1.27843395e-01 -1.82479963... | [10.221307754516602, 2.1849350929260254] |
70de9844-dfad-4f3e-9aa7-f9f80b92a24c | voice2mesh-cross-modal-3d-face-model | 2104.10299 | null | https://arxiv.org/abs/2104.10299v1 | https://arxiv.org/pdf/2104.10299v1.pdf | Voice2Mesh: Cross-Modal 3D Face Model Generation from Voices | This work focuses on the analysis that whether 3D face models can be learned from only the speech inputs of speakers. Previous works for cross-modal face synthesis study image generation from voices. However, image synthesis includes variations such as hairstyles, backgrounds, and facial textures, that are arguably irr... | ['Ulrich Neumann', 'Chin-Cheng Hsu', 'Ke Xu', 'Cho-Ying Wu'] | 2021-04-21 | null | null | null | null | ['face-model'] | ['computer-vision'] | [ 9.12431106e-02 7.24697769e-01 -2.56771734e-03 -2.76729494e-01
-7.47081220e-01 -4.93474633e-01 5.78697979e-01 -6.98102534e-01
1.34685293e-01 5.01559615e-01 4.52875197e-01 -1.11055195e-01
3.00445203e-02 -6.66191697e-01 -7.96313107e-01 -6.27883255e-01
1.34724617e-01 2.38721967e-01 -2.94898152e-01 -7.60581866... | [12.92284870147705, -0.313340425491333] |
0c464d0c-540a-43e7-a482-063398c8449c | multi-2oie-multilingual-open-information-1 | null | null | https://aclanthology.org/2020.findings-emnlp.99 | https://aclanthology.org/2020.findings-emnlp.99.pdf | Multi\^2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT | In this paper, we propose Multi$^2$OIE, which performs open information extraction (open IE) by combining BERT with multi-head attention. Our model is a sequence-labeling system with an efficient and effective argument extraction method. We use a query, key, and value setting inspired by the Multimodal Transformer to r... | ['Pilsung Kang', 'Yukyung Lee', 'Youngbin Ro'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['open-information-extraction'] | ['natural-language-processing'] | [ 1.35268671e-02 2.12883130e-01 -6.06281698e-01 2.77121142e-02
-1.40955245e+00 -9.91197646e-01 7.07503319e-01 3.41855437e-01
-1.04265320e+00 1.12875974e+00 3.06728274e-01 -8.55263710e-01
6.76371902e-02 -7.75799274e-01 -1.14846003e+00 5.19656278e-02
2.21193373e-01 7.59777546e-01 1.35552004e-01 -5.08128941... | [9.881592750549316, 8.936219215393066] |
f0ee7bb7-5162-442c-84b6-d7dd46b7f5d7 | towards-flow-graph-prediction-of-open-domain | 2305.19497 | null | https://arxiv.org/abs/2305.19497v1 | https://arxiv.org/pdf/2305.19497v1.pdf | Towards Flow Graph Prediction of Open-Domain Procedural Texts | Machine comprehension of procedural texts is essential for reasoning about the steps and automating the procedures. However, this requires identifying entities within a text and resolving the relationships between the entities. Previous work focused on the cooking domain and proposed a framework to convert a recipe tex... | ['Shinsuke Mori', 'Hirotaka Kameko', 'Keisuke Shirai'] | 2023-05-31 | null | null | null | null | ['reading-comprehension'] | ['natural-language-processing'] | [ 1.45855457e-01 4.63831484e-01 -1.87451035e-01 -4.13059443e-01
-2.47429401e-01 -7.20092177e-01 3.79167795e-01 5.57088792e-01
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-1.29101619e-01 -1.31760550e+00 -7.83560514e-01 1.26293391e-01
1.25326216e-01 3.69997293e-01 4.33925152e-01 -3.98284018... | [9.83618450164795, 8.073934555053711] |
a559fd21-2b0e-4bc7-87fd-0ecbbab95985 | evaluating-methods-for-extraction-of-aspect | null | null | https://aclanthology.org/2022.lrec-1.407 | https://aclanthology.org/2022.lrec-1.407.pdf | Evaluating Methods for Extraction of Aspect Terms in Opinion Texts in Portuguese - the Challenges of Implicit Aspects | One of the challenges of aspect-based sentiment analysis is the implicit mention of aspects. These are more difficult to identify and may require world knowledge to do so. In this work, we evaluate frequency-based, hybrid, and machine learning methods, including the use of the pre-trained BERT language model, in the ta... | ['Thiago Alexandre Salgueiro Pardo', 'Mateus Machado'] | null | null | null | null | lrec-2022-6 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-9.46451095e-04 4.60972399e-01 -4.53090072e-01 -1.96032673e-01
-4.35418576e-01 -8.60824585e-01 9.60162580e-01 8.89893234e-01
-5.33323288e-01 5.78905940e-01 4.83192176e-01 -3.17756921e-01
-4.24850166e-01 -7.48272717e-01 -1.57337606e-01 -4.67829227e-01
2.05718353e-01 5.93248487e-01 2.96072457e-02 -7.16302872... | [11.264972686767578, 6.8067522048950195] |
613e5362-3579-4ccd-9ee6-3236c67c9505 | supervised-visual-attention-for-simultaneous | 2201.09324 | null | https://arxiv.org/abs/2201.09324v2 | https://arxiv.org/pdf/2201.09324v2.pdf | Supervised Visual Attention for Simultaneous Multimodal Machine Translation | Recently, there has been a surge in research in multimodal machine translation (MMT), where additional modalities such as images are used to improve translation quality of textual systems. A particular use for such multimodal systems is the task of simultaneous machine translation, where visual context has been shown t... | ['Lucia Specia', 'Ozan Caglayan', 'Veneta Haralampieva'] | 2022-01-23 | null | null | null | null | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 4.35202777e-01 -3.76122333e-02 -4.10251737e-01 -1.14975259e-01
-1.10257912e+00 -7.00142205e-01 1.06467187e+00 -9.08689864e-04
-2.59738892e-01 6.39913678e-01 3.01235884e-01 -7.38426805e-01
5.12067199e-01 -1.84149921e-01 -8.01052928e-01 -4.84392285e-01
5.11532009e-01 5.67492127e-01 -5.66663705e-02 -4.03838903... | [11.439160346984863, 1.4904249906539917] |
442058fe-911d-4d64-9083-adce28209e8b | face-recognition-accuracy-across-demographics | 2206.01881 | null | https://arxiv.org/abs/2206.01881v2 | https://arxiv.org/pdf/2206.01881v2.pdf | Face Recognition Accuracy Across Demographics: Shining a Light Into the Problem | We explore varying face recognition accuracy across demographic groups as a phenomenon partly caused by differences in face illumination. We observe that for a common operational scenario with controlled image acquisition, there is a large difference in face region brightness between African-American and Caucasian, and... | ['Kevin W. Bowyer', 'Michael C. King', 'K. S. Krishnapriya', 'Vítor Albiero', 'Haiyu Wu'] | 2022-06-04 | null | null | null | null | ['unsupervised-face-recognition'] | ['computer-vision'] | [ 4.04324055e-01 -1.05530895e-01 8.90904814e-02 -7.05559373e-01
-2.49120682e-01 -4.27164614e-01 3.77804786e-01 -4.21275795e-01
-2.34127954e-01 3.97606403e-01 3.94943804e-02 -1.77852511e-01
6.73368797e-02 -7.19937384e-01 -4.94833082e-01 -7.69736648e-01
3.25863987e-01 -1.35355294e-01 -2.72639662e-01 6.50023073... | [13.035174369812012, 1.1558433771133423] |
66f65283-82e5-4966-85d3-8a92770172c7 | always-valid-risk-monitoring-for-online | 2211.10363 | null | https://arxiv.org/abs/2211.10363v1 | https://arxiv.org/pdf/2211.10363v1.pdf | Always Valid Risk Monitoring for Online Matrix Completion | Always-valid concentration inequalities are increasingly used as performance measures for online statistical learning, notably in the learning of generative models and supervised learning. Such inequality advances the online learning algorithms design by allowing random, adaptively chosen sample sizes instead of a fixe... | ['Wenjie Li', 'Chi-Hua Wang'] | 2022-11-18 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 3.23832817e-02 1.17384037e-02 -1.94401935e-01 -1.60957679e-01
-1.02647674e+00 -5.15125096e-01 2.32827470e-01 2.70347953e-01
-4.80815589e-01 7.90132582e-01 -1.49252981e-01 -5.53171754e-01
-5.79841793e-01 -5.19775927e-01 -1.25189805e+00 -9.54969227e-01
-5.26567280e-01 5.31456232e-01 -1.50188535e-01 2.13556319... | [6.833423614501953, 4.419346809387207] |
d5ee8b6d-2a55-45a0-8589-c63d158186f7 | elementwise-language-representation | 2302.13475 | null | https://arxiv.org/abs/2302.13475v1 | https://arxiv.org/pdf/2302.13475v1.pdf | Elementwise Language Representation | We propose a new technique for computational language representation called elementwise embedding, in which a material (semantic unit) is abstracted into a horizontal concatenation of lower-dimensional element (character) embeddings. While elements are always characters, materials are arbitrary levels of semantic units... | ['Jeeeun Kim', 'Dunam Kim'] | 2023-02-27 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [ 5.48935056e-01 1.55322626e-01 -3.22799832e-01 -1.25175431e-01
-7.49271631e-01 -7.10057259e-01 4.68073159e-01 4.91991520e-01
-7.61074662e-01 5.97685635e-01 1.49014935e-01 -8.36515844e-01
-3.82811087e-03 -1.02897537e+00 -6.72934592e-01 -5.30193090e-01
-1.70480758e-01 2.80106634e-01 -2.92188842e-02 -2.98628062... | [10.594846725463867, 8.63782787322998] |
02969134-978f-48a8-80c8-413671c4cfac | towards-protein-protein-docking-with | 1605.09266 | null | http://arxiv.org/abs/1605.09266v1 | http://arxiv.org/pdf/1605.09266v1.pdf | Towards protein-protein docking with significant structural changes using CABS-dock | The protein-protein interactions (PPIs) are crucial for understanding the
majority of cellular processes. PPIs play important role in gene transcription
regulation, cellular signaling, molecular basis of immune response and more.
Moreover, a disruption of hese mechanisms is frequently postulated as a
possible cause of ... | [] | 2016-05-30 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 1.48593470e-01 -1.96575925e-01 -2.04052255e-01 -6.51454777e-02
-2.04524621e-01 -6.26548111e-01 1.77399874e-01 5.76221883e-01
-5.12667596e-01 1.32938206e+00 -3.83304775e-01 -3.94839108e-01
8.44963416e-02 -6.57388091e-01 -7.82944322e-01 -1.22516418e+00
1.68062076e-01 6.01540446e-01 4.86002326e-01 -2.21788242... | [4.780991077423096, 5.348245620727539] |
749530ff-d22c-4130-9d40-399bc769f66f | geometric-matrix-completion-via-sylvester | 2206.09477 | null | https://arxiv.org/abs/2206.09477v1 | https://arxiv.org/pdf/2206.09477v1.pdf | Geometric Matrix Completion via Sylvester Multi-Graph Neural Network | Despite the success of the Sylvester equation empowered methods on various graph mining applications, such as semi-supervised label learning and network alignment, there also exists several limitations. The Sylvester equation's inability of modeling non-linear relations and the inflexibility of tuning towards different... | ['Hanghang Tong', 'Fei Wang', 'Changhe Yuan', 'Boxin Du'] | 2022-06-19 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 3.88800979e-01 2.58910805e-01 -4.10860091e-01 -3.26478630e-01
-3.52556735e-01 -1.26139000e-01 4.80823815e-01 -3.69891003e-02
-3.15811366e-01 6.19138062e-01 7.40273148e-02 -5.19667566e-01
-4.98051047e-01 -6.74030364e-01 -5.39039433e-01 -3.96277428e-01
-4.87806588e-01 6.74739003e-01 -2.78254449e-01 -1.55361116... | [7.2095465660095215, 6.230625629425049] |
742b6f2d-b849-4c30-b82a-83651892e57d | dynamic-algorithms-for-matroid-submodular | 2306.00959 | null | https://arxiv.org/abs/2306.00959v1 | https://arxiv.org/pdf/2306.00959v1.pdf | Dynamic Algorithms for Matroid Submodular Maximization | Submodular maximization under matroid and cardinality constraints are classical problems with a wide range of applications in machine learning, auction theory, and combinatorial optimization. In this paper, we consider these problems in the dynamic setting where (1) we have oracle access to a monotone submodular functi... | ['Morteza Monemizadeh', 'Peyman Jabbarzade', 'Mohammadtaghi Hajiaghayi', 'Samira Goudarzi', 'Leyla Biabani', 'Kiarash Banihashem'] | 2023-06-01 | null | null | null | null | ['combinatorial-optimization', 'open-question'] | ['methodology', 'natural-language-processing'] | [ 2.19718456e-01 2.98613280e-01 -3.32368076e-01 -8.46178606e-02
-8.15472424e-01 -1.06678224e+00 -4.11213845e-01 3.46425146e-01
-9.14381027e-01 7.30262458e-01 -6.10261381e-01 -6.27928436e-01
-6.77389562e-01 -1.31005490e+00 -1.18406951e+00 -9.10132825e-01
-6.10718727e-01 9.71445978e-01 8.89651924e-02 -3.39982092... | [6.567547798156738, 4.8372273445129395] |
9a5f5a48-f221-49a0-b53c-b908f7d19261 | bangla-handwritten-digit-recognition-and | 2103.07905 | null | https://arxiv.org/abs/2103.07905v1 | https://arxiv.org/pdf/2103.07905v1.pdf | Bangla Handwritten Digit Recognition and Generation | Handwritten digit or numeral recognition is one of the classical issues in the area of pattern recognition and has seen tremendous advancement because of the recent wide availability of computing resources. Plentiful works have already done on English, Arabic, Chinese, Japanese handwritten script. Some work on Bangla a... | ['Md Fahim Sikder'] | 2021-03-14 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 8.77235979e-02 -1.58537850e-01 4.42681164e-01 -3.56546760e-01
-1.95169643e-01 -4.54656035e-01 8.46781194e-01 -4.32687253e-01
-3.05218786e-01 1.07483280e+00 9.22082439e-02 -3.93155485e-01
3.84624712e-02 -9.70721960e-01 -2.46869639e-01 -7.01258481e-01
3.19958240e-01 4.15261954e-01 1.12574674e-01 -3.73765320... | [11.849250793457031, 2.677915573120117] |
2e3bf60e-da47-4002-843c-5b1d40a4bff9 | generalized-approach-to-matched-filtering | 2104.03961 | null | https://arxiv.org/abs/2104.03961v3 | https://arxiv.org/pdf/2104.03961v3.pdf | Generalized Approach to Matched Filtering using Neural Networks | Gravitational wave science is a pioneering field with rapidly evolving data analysis methodology currently assimilating and inventing deep learning techniques. The bulk of the sophisticated flagship searches of the field rely on the time-tested matched filtering principle within their core. In this paper, we make a key... | ['Szabolcs Márka', 'Zsuzsa Márka', 'John Wright', 'Imre Bartos', 'Doğa Veske', 'Robert E. Colgan', 'Mariam Avagyan', 'Jingkai Yan'] | 2021-04-08 | null | null | null | null | ['gravitational-wave-detection'] | ['miscellaneous'] | [-3.99758518e-02 -1.02884814e-01 1.74895689e-01 -3.64561439e-01
-4.48565423e-01 -6.04058683e-01 8.28747690e-01 -4.16303515e-01
-5.40268362e-01 4.45942283e-01 -1.18682414e-01 -5.02589941e-01
-5.88043928e-01 -1.03280163e+00 -6.53612614e-01 -9.70774233e-01
-2.69884467e-01 6.08933091e-01 3.65446478e-01 -4.75166559... | [7.589334487915039, 3.1244821548461914] |
f69739d7-fab2-49ac-ba65-3411a3fe22ac | how-well-do-self-supervised-models-transfer | 2011.13377 | null | https://arxiv.org/abs/2011.13377v2 | https://arxiv.org/pdf/2011.13377v2.pdf | How Well Do Self-Supervised Models Transfer? | Self-supervised visual representation learning has seen huge progress recently, but no large scale evaluation has compared the many models now available. We evaluate the transfer performance of 13 top self-supervised models on 40 downstream tasks, including many-shot and few-shot recognition, object detection, and dens... | ['Timothy M. Hospedales', 'Henry Gouk', 'Linus Ericsson'] | 2020-11-26 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Ericsson_How_Well_Do_Self-Supervised_Models_Transfer_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Ericsson_How_Well_Do_Self-Supervised_Models_Transfer_CVPR_2021_paper.pdf | cvpr-2021-1 | ['fine-grained-image-recognition', 'classifier-calibration', 'surface-normals-estimation', 'classifier-calibration'] | ['computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous'] | [ 4.81756330e-01 2.05079079e-01 -3.54068339e-01 -4.83580649e-01
-7.31423259e-01 -2.29902148e-01 1.02910483e+00 2.31354758e-01
-5.20784676e-01 6.06601954e-01 4.21182722e-01 6.12266501e-03
-1.10975794e-01 -4.18391198e-01 -7.45148838e-01 -7.56247818e-01
-4.61480319e-02 5.58265150e-01 5.48074007e-01 -1.50529742... | [9.840818405151367, 2.600034713745117] |
1f404b4b-8048-419d-bcea-5af080725d06 | self-supervised-rf-signal-representation | 2207.03046 | null | https://arxiv.org/abs/2207.03046v2 | https://arxiv.org/pdf/2207.03046v2.pdf | Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning | Deep learning (DL) finds rich applications in the wireless domain to improve spectrum awareness. Typically, DL models are either randomly initialized following a statistical distribution or pretrained on tasks from other domains in the form of transfer learning without accounting for the unique characteristics of wirel... | ['Ender Ayanoglu', 'Yalin E. Sagduyu', 'Mehmet Can Ertem', 'Serdar Boztas', 'Kemal Davaslioglu'] | 2022-07-07 | null | null | null | null | ['automatic-modulation-recognition'] | ['time-series'] | [ 8.62515509e-01 1.57505661e-01 -6.22562408e-01 -5.72559357e-01
-1.01966941e+00 -3.46656770e-01 5.48963070e-01 -2.58186519e-01
-2.77786344e-01 8.63811731e-01 -3.01089045e-02 -4.29486275e-01
-3.72386664e-01 -7.62735605e-01 -7.46826768e-01 -6.37740731e-01
-3.50773275e-01 1.41345069e-01 -1.76624238e-01 -1.51948169... | [6.557858943939209, 1.5024254322052002] |
c06a4d97-c714-450b-8d8a-35dff77a0bd9 | a-morphological-analyzer-for-gulf-arabic | null | null | https://aclanthology.org/W17-1305 | https://aclanthology.org/W17-1305.pdf | A Morphological Analyzer for Gulf Arabic Verbs | We present CALIMAGLF, a Gulf Arabic morphological analyzer currently covering over 2,600 verbal lemmas. We describe in detail the process of building the analyzer starting from phonetic dictionary entries to fully inflected orthographic paradigms and associated lexicon and orthographic variants. We evaluate the coverag... | ['Salam Khalifa', 'Sara Hassan', 'Nizar Habash'] | 2017-04-01 | null | null | null | ws-2017-4 | ['morphological-tagging'] | ['natural-language-processing'] | [-2.93904305e-01 -2.87491500e-01 4.58955318e-02 -2.60030001e-01
-7.82886386e-01 -1.56951928e+00 2.53852516e-01 6.27669990e-01
-8.07337701e-01 8.77110660e-01 1.45777449e-01 -8.87605846e-01
-7.87962973e-02 -8.25740337e-01 -2.67443389e-01 -3.68735909e-01
3.13737720e-01 1.00887215e+00 9.74409357e-02 -9.19456959... | [10.37196159362793, 10.423514366149902] |
f2c05455-05d9-4108-8f3c-3783db4a482f | needle-match-reliable-patch-matching-under | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Lotan_Needle-Match_Reliable_Patch_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Lotan_Needle-Match_Reliable_Patch_CVPR_2016_paper.pdf | Needle-Match: Reliable Patch Matching Under High Uncertainty | Reliable patch-matching forms the basis for many algorithms (super-resolution, denoising, inpainting, etc.) However, when the image quality deteriorates (by noise, blur or geometric distortions), the reliability of patch-matching deteriorates as well. Matched patches in the degraded image, do not necessarily imply sim... | ['Michal Irani', 'Or Lotan'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['patch-matching'] | ['computer-vision'] | [ 5.57692885e-01 -3.11446488e-02 3.32453161e-01 2.82685369e-01
-7.76582301e-01 -4.78229940e-01 2.95486212e-01 -1.35370836e-01
3.22977811e-01 9.79864240e-01 3.06633145e-01 3.94855142e-01
-2.59921551e-01 -8.42443407e-01 -6.96970522e-01 -9.93645847e-01
4.75646704e-02 8.41401294e-02 6.95061386e-01 -4.54072595... | [11.287572860717773, -2.2264842987060547] |
9365008f-f179-401c-804a-4cb108fa2c72 | dueqnet-dual-equivariance-network-in-outdoor | 2302.13577 | null | https://arxiv.org/abs/2302.13577v1 | https://arxiv.org/pdf/2302.13577v1.pdf | DuEqNet: Dual-Equivariance Network in Outdoor 3D Object Detection for Autonomous Driving | Outdoor 3D object detection has played an essential role in the environment perception of autonomous driving. In complicated traffic situations, precise object recognition provides indispensable information for prediction and planning in the dynamic system, improving self-driving safety and reliability. However, with t... | ['Xian Wei', 'Arafat Al-Jawari', 'Hai Lan', 'JiaMing Lei', 'Xihao Wang'] | 2023-02-27 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [-3.81931007e-01 -2.85481185e-01 1.21004656e-01 -3.64713252e-01
-1.01999350e-01 -3.01211476e-01 4.96127635e-01 -2.05831498e-01
-3.72468054e-01 -8.41146186e-02 -3.01951349e-01 -4.14234161e-01
-1.02429770e-01 -8.87551665e-01 -7.55855918e-01 -7.84409642e-01
1.09030768e-01 3.80548164e-02 7.91826665e-01 -7.02275336... | [7.965858459472656, -2.008829355239868] |
b2684b3b-827f-45ab-9a6f-35be6f876286 | parameter-level-soft-masking-for-continual | 2306.14775 | null | https://arxiv.org/abs/2306.14775v1 | https://arxiv.org/pdf/2306.14775v1.pdf | Parameter-Level Soft-Masking for Continual Learning | Existing research on task incremental learning in continual learning has primarily focused on preventing catastrophic forgetting (CF). Although several techniques have achieved learning with no CF, they attain it by letting each task monopolize a sub-network in a shared network, which seriously limits knowledge transfe... | ['Bing Liu', 'Gyuhak Kim', 'Zixuan Ke', 'Chihiro Ono', 'Mori Kurokawa', 'Tatsuya Konishi'] | 2023-06-26 | null | null | null | null | ['incremental-learning', 'transfer-learning'] | ['methodology', 'miscellaneous'] | [ 3.08638304e-01 1.69481024e-01 -1.76185414e-01 1.90331116e-01
1.28822150e-02 -4.82525617e-01 1.92813680e-01 -3.53413261e-02
-7.36687899e-01 1.14728856e+00 -1.36500970e-01 -4.41582620e-01
-4.51106906e-01 -6.36630237e-01 -7.75943637e-01 -8.12338531e-01
-9.62232724e-02 2.42666900e-01 7.21716762e-01 -8.39317590... | [9.740456581115723, 3.478543519973755] |
2553d820-9046-4c3a-8289-f9c037da8324 | qcri-at-semeval-2023-task-3-news-genre | 2305.03336 | null | https://arxiv.org/abs/2305.03336v1 | https://arxiv.org/pdf/2305.03336v1.pdf | QCRI at SemEval-2023 Task 3: News Genre, Framing and Persuasion Techniques Detection using Multilingual Models | Misinformation spreading in mainstream and social media has been misleading users in different ways. Manual detection and verification efforts by journalists and fact-checkers can no longer cope with the great scale and quick spread of misleading information. This motivated research and industry efforts to develop syst... | ['Firoj Alam', 'Preslav Nakov', 'Rabindra Nath Nandi', 'Ahmed Oumar El-Shangiti', 'Maram Hasanain'] | 2023-05-05 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [-7.19207749e-02 1.39520779e-01 -6.08722642e-02 -3.23000729e-01
-1.15190148e+00 -1.00029707e+00 1.08038688e+00 4.39019829e-01
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2.80098498e-01 -3.93549889e-01 -6.43704832e-01 7.93429464e-02
1.78093374e-01 7.13129163e-01 6.87351584e-01 -7.33774483... | [8.31370735168457, 10.097750663757324] |
d8445cdd-97e4-42d0-a009-21961820d9dd | robust-general-and-low-complexity-acoustic | 2210.08610 | null | https://arxiv.org/abs/2210.08610v1 | https://arxiv.org/pdf/2210.08610v1.pdf | Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context | In this paper, we present a comprehensive analysis of Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording from its acoustic signature. In particular, we firstly propose an inception-based and low footprint ASC model, referred to as the ASC baseline. The proposed ASC baseline is ... | ['Phu X. Nguyen', 'Canh Vu', 'Khoa Tran', 'Alexander Schindler', 'Anahid Jalali', 'Dusan Salovic', 'Lam Pham'] | 2022-10-16 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 2.76236832e-01 -5.97407401e-01 7.98009157e-01 -8.46711919e-02
-7.91584373e-01 -2.65234411e-01 3.18456262e-01 9.22047794e-02
-2.80432552e-01 1.26264781e-01 2.84747511e-01 -2.94920772e-01
-1.53145581e-01 -3.67660493e-01 -6.16384983e-01 -6.60268724e-01
-4.08508569e-01 -4.43170369e-01 4.20290828e-01 -7.73081481... | [15.164155006408691, 5.206484317779541] |
42091ca1-e52e-4b7d-b840-b18c1e91f8f5 | rethinking-deep-policy-gradients-via-state | null | null | https://openreview.net/forum?id=USDowdRKXmN | https://openreview.net/pdf?id=USDowdRKXmN | Rethinking Deep Policy Gradients via State-Wise Policy Improvement | Deep policy gradient is one of the major frameworks in reinforcement learning, and it has been shown to improve parameterized policies across various tasks and environments. However, recent studies show that the key components of the deep policy gradient methods, such as gradient estimation, value prediction, and optim... | ['I-Chen Wu', 'Ting Han Wei', 'Ping-Chun Hsieh', 'Kai-Chun Hu'] | 2020-10-19 | null | null | null | neurips-workshop-icbinb-2020-12 | ['value-prediction', 'policy-gradient-methods'] | ['computer-code', 'methodology'] | [-1.00714266e-01 -1.27379060e-01 -8.78930032e-01 -1.84751660e-01
-1.49417460e-01 -4.38993573e-01 7.90902734e-01 -3.75919119e-02
-6.73006058e-01 1.38224936e+00 2.57768154e-01 -7.24697888e-01
-3.92590553e-01 -6.59157395e-01 -6.69998825e-01 -8.79953742e-01
-4.53685261e-02 -7.61998519e-02 1.00169778e-02 -2.96939284... | [4.125992298126221, 2.3500053882598877] |
71572295-416a-4ffc-ad13-2df1a5053d16 | dont-throw-those-morphological-analyzers-away | null | null | https://aclanthology.org/D17-1073 | https://aclanthology.org/D17-1073.pdf | Don't Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic | This paper presents a model for Arabic morphological disambiguation based on Recurrent Neural Networks (RNN). We train Long Short-Term Memory (LSTM) cells in several configurations and embedding levels to model the various morphological features. Our experiments show that these models outperform state-of-the-art system... | ['Nizar Habash', 'Nasser Zalmout'] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['morphological-disambiguation', 'morphological-tagging'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.15592539e-01 -5.98821826e-02 2.41507009e-01 -2.63531953e-01
-1.02289629e+00 -7.38563716e-01 2.68942863e-01 7.03038752e-01
-8.58043492e-01 3.36007386e-01 7.46637508e-02 -6.33818328e-01
5.11068590e-02 -9.84142542e-01 -4.39907879e-01 -5.52457392e-01
-3.50612730e-01 5.01690388e-01 -4.55903858e-02 -7.23499596... | [10.471071243286133, 10.174099922180176] |
269d74f4-6efd-4bed-9443-4fd84ec37012 | a-cognitive-approach-based-on-the-actionable | 2011.09554 | null | https://arxiv.org/abs/2011.09554v1 | https://arxiv.org/pdf/2011.09554v1.pdf | A Cognitive Approach based on the Actionable Knowledge Graph for supporting Maintenance Operations | In the era of Industry 4.0, cognitive computing and its enabling technologies (Artificial Intelligence, Machine Learning, etc.) allow to define systems able to support maintenance by providing relevant information, at the right time, retrieved from structured companies' databases, and unstructured documents, like techn... | ['Francesco Orciuoli', 'Domenico Marino', 'Vincenzo Loia', 'Mariacristina Gallo', 'Giuseppe Fenza'] | 2020-11-18 | null | null | null | null | ['outdoor-positioning'] | ['miscellaneous'] | [ 3.58154535e-01 3.76855768e-02 -6.54179215e-01 -2.80354470e-01
-3.95728648e-02 -5.44658780e-01 5.28103352e-01 5.87690711e-01
-5.90201244e-02 6.23933792e-01 3.22458357e-01 -3.91090512e-01
-9.72687185e-01 -9.75710988e-01 -1.97357818e-01 -2.02132925e-01
3.26462567e-01 2.43448004e-01 2.59068400e-01 -2.16682643... | [8.541095733642578, 5.942290306091309] |
3f687141-e89b-4ce4-9287-c04cc71922d8 | app-net-auxiliary-point-based-push-and-pull | 2205.00847 | null | https://arxiv.org/abs/2205.00847v2 | https://arxiv.org/pdf/2205.00847v2.pdf | APP-Net: Auxiliary-point-based Push and Pull Operations for Efficient Point Cloud Classification | Aggregating neighbor features is essential for point cloud classification. In the existing work, each point in the cloud may inevitably be selected as the neighbors of multiple aggregation centers, as all centers will gather neighbor features from the whole point cloud independently. Thus each point has to participate ... | ['LiMin Wang', 'Gangshan Wu', 'Youxin Chen', 'Chunxu Liu', 'Tao Lu'] | 2022-05-02 | null | null | null | null | ['3d-point-cloud-classification', '3d-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-4.19594795e-01 -3.39869589e-01 -5.00909574e-02 -2.62663871e-01
-6.25704527e-01 -5.25008261e-01 1.67419180e-01 4.52435225e-01
-1.77035436e-01 4.26265776e-01 -5.44723332e-01 -2.65726864e-01
5.53364567e-02 -1.29863775e+00 -7.85324216e-01 -6.96648836e-01
-1.29885584e-01 5.88321030e-01 5.73043108e-01 1.62322327... | [7.915178298950195, -3.426178216934204] |
59b38cc5-06a4-4ede-ac57-09f56333ca37 | generative-adversarial-network-in-medical | 1809.07294 | null | https://arxiv.org/abs/1809.07294v4 | https://arxiv.org/pdf/1809.07294v4.pdf | Generative Adversarial Network in Medical Imaging: A Review | Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function. The adversarial loss brought by the discriminator provides a clever way of incorporating unlabeled samples into traini... | ['Paul Babyn', 'Ekta Walia', 'Xin Yi'] | 2018-09-19 | null | null | null | null | ['medical-image-generation'] | ['medical'] | [ 6.67030811e-01 3.29792947e-01 -2.84357995e-01 -2.73975551e-01
-7.15434670e-01 -5.16441464e-01 5.64145088e-01 1.21796004e-01
-4.21732843e-01 7.76180744e-01 8.69422629e-02 -2.86622852e-01
1.53639853e-01 -7.78478265e-01 -4.89713669e-01 -9.67323661e-01
-2.89387554e-02 4.38701630e-01 7.25407712e-03 3.61636057... | [14.113710403442383, -1.9847356081008911] |
f6a6b0ed-4e08-4e4b-9ce0-a1a311032c17 | improving-zero-and-few-shot-generalization-in | 2205.12673 | null | https://arxiv.org/abs/2205.12673v2 | https://arxiv.org/pdf/2205.12673v2.pdf | InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning | Instruction tuning is an emergent paradigm in NLP wherein natural language instructions are leveraged with language models to induce zero-shot performance on unseen tasks. Instructions have been shown to enable good performance on unseen tasks and datasets in both large and small language models. Dialogue is an especia... | ['Jeffrey P. Bigham', 'Maxine Eskenazi', 'Shikib Mehri', 'Yi-Ting Yeh', 'Cathy Jiao', 'Prakhar Gupta'] | 2022-05-25 | null | null | null | null | ['dialogue-evaluation', 'open-domain-dialog'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.73922265e-01 3.83761019e-01 -1.40278280e-01 -5.55613339e-01
-6.88037217e-01 -6.12752140e-01 1.15047181e+00 1.09515272e-01
-5.07998526e-01 9.38384235e-01 6.15044773e-01 -3.11690718e-01
1.12333201e-01 -5.91389418e-01 -2.15253845e-01 -2.89065868e-01
8.25958401e-02 8.20625782e-01 5.66851906e-02 -1.02921724... | [12.726268768310547, 7.943490505218506] |
d9e4f663-2e3d-4e69-ad42-1cb2fb77a06a | nfts-to-mars-multi-attention-recommender | 2306.10053 | null | https://arxiv.org/abs/2306.10053v1 | https://arxiv.org/pdf/2306.10053v1.pdf | NFTs to MARS: Multi-Attention Recommender System for NFTs | Recommender systems have become essential tools for enhancing user experiences across various domains. While extensive research has been conducted on recommender systems for movies, music, and e-commerce, the rapidly growing and economically significant Non-Fungible Token (NFT) market remains underexplored. The unique ... | ['YongJae lee', 'Joohwan Hong', 'Yejin Kim', 'Youngbin Lee', 'Seonmi Kim'] | 2023-06-13 | null | null | null | null | ['graph-attention', 'multi-task-learning'] | ['graphs', 'methodology'] | [-3.07740986e-01 -4.11687791e-01 -6.14777505e-01 -1.88823298e-01
-5.89436173e-01 -6.19138360e-01 7.65570641e-01 -1.12203501e-01
-1.45795643e-01 2.76960969e-01 7.61674106e-01 -6.42497420e-01
-3.55337888e-01 -5.51041722e-01 -6.47254944e-01 -3.18739504e-01
-1.02663852e-01 6.73594356e-01 -3.66370410e-01 -6.58737004... | [10.086334228515625, 5.617682456970215] |
7edd5daa-ff07-41f9-853b-b60d2991368f | potter-pooling-attention-transformer-for | 2303.13357 | null | https://arxiv.org/abs/2303.13357v1 | https://arxiv.org/pdf/2303.13357v1.pdf | POTTER: Pooling Attention Transformer for Efficient Human Mesh Recovery | Transformer architectures have achieved SOTA performance on the human mesh recovery (HMR) from monocular images. However, the performance gain has come at the cost of substantial memory and computational overhead. A lightweight and efficient model to reconstruct accurate human mesh is needed for real-world applications... | ['Chen Chen', 'Guo-Jun Qi', 'Xianpeng Liu', 'Ce Zheng'] | 2023-03-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zheng_POTTER_Pooling_Attention_Transformer_for_Efficient_Human_Mesh_Recovery_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zheng_POTTER_Pooling_Attention_Transformer_for_Efficient_Human_Mesh_Recovery_CVPR_2023_paper.pdf | cvpr-2023-1 | ['human-mesh-recovery'] | ['computer-vision'] | [-1.02377027e-01 3.01181134e-02 5.86086214e-02 1.65327042e-02
-9.90406394e-01 2.91765839e-01 7.55354539e-02 -4.02595639e-01
-3.97961527e-01 5.03623009e-01 2.72427946e-01 2.11919069e-01
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1.55008301e-01 3.78419042e-01 2.17943519e-01 -1.05098516... | [7.2000532150268555, -1.1487627029418945] |
f304f7db-2ae4-482d-95d2-520a7d895e86 | adversarial-alignment-of-multilingual-models | 2005.09392 | null | https://arxiv.org/abs/2005.09392v1 | https://arxiv.org/pdf/2005.09392v1.pdf | Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text | Although temporal tagging is still dominated by rule-based systems, there have been recent attempts at neural temporal taggers. However, all of them focus on monolingual settings. In this paper, we explore multilingual methods for the extraction of temporal expressions from text and investigate adversarial training for... | ['Jannik Strötgen', 'Heike Adel', 'Anastasiia Iurshina', 'Lukas Lange'] | 2020-05-19 | adversarial-alignment-of-multilingual-models-1 | https://aclanthology.org/2020.repl4nlp-1.14 | https://aclanthology.org/2020.repl4nlp-1.14.pdf | ws-2020-7 | ['temporal-tagging'] | ['natural-language-processing'] | [-2.00731099e-01 6.00945130e-02 -4.66855198e-01 -3.16968173e-01
-7.71543860e-01 -1.03521872e+00 9.44052756e-01 -4.10509080e-01
-6.93376243e-01 1.04934299e+00 1.53900325e-01 -4.66860563e-01
3.30994606e-01 -3.93682331e-01 -5.67343831e-01 -5.19315958e-01
-3.50991040e-01 3.90036136e-01 4.41514343e-01 -3.70438933... | [10.044873237609863, 9.352069854736328] |
7e4fabd5-f91f-4130-93ea-3ed3fed2aa37 | damo-yolo-a-report-on-real-time-object | 2211.15444 | null | https://arxiv.org/abs/2211.15444v4 | https://arxiv.org/pdf/2211.15444v4.pdf | DAMO-YOLO : A Report on Real-Time Object Detection Design | In this report, we present a fast and accurate object detection method dubbed DAMO-YOLO, which achieves higher performance than the state-of-the-art YOLO series. DAMO-YOLO is extended from YOLO with some new technologies, including Neural Architecture Search (NAS), efficient Reparameterized Generalized-FPN (RepGFPN), a... | ['Xiuyu Sun', 'Yuan Zhang', 'Yilun Huang', 'Weihua Chen', 'Yiqi Jiang', 'Xianzhe Xu'] | 2022-11-23 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-2.51289785e-01 -3.67343515e-01 2.17870280e-01 -1.66366532e-01
-3.82478654e-01 -2.40750283e-01 -2.66612368e-03 -2.80606151e-01
-6.49450481e-01 6.38112009e-01 -1.83516830e-01 -1.22237630e-01
-4.45674872e-03 -6.81044638e-01 -6.59198284e-01 -7.29298949e-01
-2.07372591e-01 1.71210244e-01 7.95128644e-01 -1.55517086... | [8.704943656921387, -0.2918030619621277] |
addbf691-3d3d-4b3d-ac76-efde7e386c5e | m-2-dar-multi-view-multi-scale-driver-action | 2305.08877 | null | https://arxiv.org/abs/2305.08877v1 | https://arxiv.org/pdf/2305.08877v1.pdf | M$^2$DAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformer | Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal. In this paper, we present a multi-view, multi-scale framework for naturalistic driving action recognition and localization in untrimmed video... | ['Ziran Wang', 'Zihao Li', 'Rohit Gupta', 'Kyungtae Han', 'Amr Abdelraouf', 'Liangqi Yuan', 'Yunsheng Ma'] | 2023-05-13 | null | null | null | null | ['action-recognition-in-videos'] | ['computer-vision'] | [ 1.47416353e-01 -1.69570178e-01 -2.92590767e-01 -4.11725372e-01
-1.19444346e+00 -3.32313567e-01 5.35375834e-01 -4.45042044e-01
-2.59510159e-01 4.54868019e-01 5.73574126e-01 -1.27931386e-01
-1.90577790e-01 -3.58603686e-01 -5.39788961e-01 -6.84725285e-01
1.48370132e-01 3.70932673e-03 5.12467086e-01 -3.19679409... | [7.995996475219727, 0.3267695903778076] |
5a32c5a8-660e-422f-96f4-b09479d19bdd | lmr-a-large-scale-multi-reference-dataset-for | 2303.04970 | null | https://arxiv.org/abs/2303.04970v1 | https://arxiv.org/pdf/2303.04970v1.pdf | LMR: A Large-Scale Multi-Reference Dataset for Reference-based Super-Resolution | It is widely agreed that reference-based super-resolution (RefSR) achieves superior results by referring to similar high quality images, compared to single image super-resolution (SISR). Intuitively, the more references, the better performance. However, previous RefSR methods have all focused on single-reference image ... | ['Zhaoxiang Zhang', 'Errui Ding', 'Dongliang He', 'Xin Li', 'Lin Zhang'] | 2023-03-09 | null | null | null | null | ['reference-based-super-resolution'] | ['computer-vision'] | [ 3.07044238e-01 -4.99296844e-01 -1.86834008e-01 -4.22071785e-01
-1.40883172e+00 -1.63181558e-01 4.29865718e-01 -5.27498960e-01
-1.51089624e-01 8.79764318e-01 4.25006568e-01 3.62029880e-01
-2.32264951e-01 -6.83457434e-01 -5.10404170e-01 -6.25493646e-01
3.40404242e-01 4.84916531e-02 4.00676221e-01 -4.38669413... | [10.9736328125, -2.0942904949188232] |
739dcca1-2980-4798-ac14-a67d68d91fee | on-the-liveliness-of-artificial-life | 2302.10196 | null | https://arxiv.org/abs/2302.10196v1 | https://arxiv.org/pdf/2302.10196v1.pdf | On the Liveliness of Artificial Life | There has been on-going philosophical debate on whether artificial life models, also known as digital organisms, are truly alive. The main difficulty appears to be finding an encompassing and definite definition of life. By examining similarities and differences in recent definitions of life, we define life as "any sys... | ['Maurice HT Ling', 'Yong Zher Koh'] | 2023-02-19 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 3.23232889e-01 4.70979512e-01 2.87490994e-01 2.94395655e-01
5.77805579e-01 -9.91700590e-01 8.44045699e-01 8.21718350e-02
-9.06370431e-02 8.20228815e-01 -1.13819288e-02 -4.20082122e-01
-5.72431833e-02 -1.12019241e+00 -5.35755515e-01 -1.02832770e+00
-8.53975117e-02 2.31492504e-01 2.39216745e-01 -4.31259930... | [5.592956066131592, 4.153954982757568] |
fd3f2294-7bc4-499d-97bf-b51e4f526779 | learning-how-to-be-robust-deep-polynomial | 1804.06504 | null | http://arxiv.org/abs/1804.06504v2 | http://arxiv.org/pdf/1804.06504v2.pdf | Learning how to be robust: Deep polynomial regression | Polynomial regression is a recurrent problem with a large number of
applications. In computer vision it often appears in motion analysis. Whatever
the application, standard methods for regression of polynomial models tend to
deliver biased results when the input data is heavily contaminated by outliers.
Moreover, the p... | ['Patrick Perez', 'Patrick Bouthemy', 'Juan-Manuel Perez-Rua', 'Tomas Crivelli'] | 2018-04-17 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [ 1.38659984e-01 9.73260626e-02 -9.67966691e-02 -1.63558602e-01
-1.12106204e+00 -4.36775148e-01 4.37308639e-01 -5.83905727e-02
-3.42741400e-01 5.30667067e-01 3.67783941e-02 -1.65020928e-01
-2.88801156e-02 -7.06382543e-02 -1.26447284e+00 -8.49093676e-01
1.40382927e-02 2.74728924e-01 1.15003802e-01 -3.80750686... | [10.42286205291748, -1.2867143154144287] |
b846df75-988c-403a-842d-363b0bfb51c3 | a-cramer-distance-perspective-on-non-crossing | 2110.00535 | null | https://arxiv.org/abs/2110.00535v2 | https://arxiv.org/pdf/2110.00535v2.pdf | A Cramér Distance perspective on Quantile Regression based Distributional Reinforcement Learning | Distributional reinforcement learning (DRL) extends the value-based approach by approximating the full distribution over future returns instead of the mean only, providing a richer signal that leads to improved performances. Quantile Regression (QR) based methods like QR-DQN project arbitrary distributions into a param... | ['Nicolas Bondoux', 'Alix Lhéritier'] | 2021-10-01 | null | https://openreview.net/forum?id=weBSeGTv0i | https://openreview.net/pdf?id=weBSeGTv0i | neurips-2021-12 | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.36468568e-01 3.01830888e-01 -3.73145729e-01 -4.58424747e-01
-9.56269503e-01 -7.11382031e-01 3.72441411e-01 3.94758463e-01
-6.22447371e-01 1.04440010e+00 -1.67213865e-02 -4.59169030e-01
-6.73394680e-01 -9.38251555e-01 -9.15255547e-01 -8.34299207e-01
-5.39815128e-01 4.56083328e-01 -7.99612328e-02 -2.47845903... | [4.137958526611328, 2.6024088859558105] |
4c582116-97c3-423d-9630-d10f50511b90 | a-self-attention-guided-multi-scale-gradient | 2210.06334 | null | https://arxiv.org/abs/2210.06334v2 | https://arxiv.org/pdf/2210.06334v2.pdf | A Self-attention Guided Multi-scale Gradient GAN for Diversified X-ray Image Synthesis | Imbalanced image datasets are commonly available in the domain of biomedical image analysis. Biomedical images contain diversified features that are significant in predicting targeted diseases. Generative Adversarial Networks (GANs) are utilized to address the data limitation problem via the generation of synthetic ima... | ["Ruairi O'Reilly", 'Mubashir Husain Rehmani', 'Muhammad Muneeb Saad'] | 2022-10-09 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 3.59123766e-01 5.97026683e-02 2.18946636e-01 -4.01783474e-02
-9.31345701e-01 -1.95503563e-01 4.21306401e-01 -3.54402393e-01
2.60481844e-03 9.81413901e-01 1.65579110e-01 2.77002039e-03
-4.82982621e-02 -7.98465073e-01 -7.73439348e-01 -1.02431071e+00
2.12276861e-01 4.86225076e-02 -1.04363903e-01 -4.19499159... | [14.016707420349121, -2.001892328262329] |
9a2d9163-1e30-49fd-9a17-5e13470793d6 | faithful-to-the-original-fact-aware-neural | 1711.04434 | null | http://arxiv.org/abs/1711.04434v1 | http://arxiv.org/pdf/1711.04434v1.pdf | Faithful to the Original: Fact Aware Neural Abstractive Summarization | Unlike extractive summarization, abstractive summarization has to fuse
different parts of the source text, which inclines to create fake facts. Our
preliminary study reveals nearly 30% of the outputs from a state-of-the-art
neural summarization system suffer from this problem. While previous
abstractive summarization a... | ['Furu Wei', 'Ziqiang Cao', 'Wenjie Li', 'Sujian Li'] | 2017-11-13 | null | null | null | null | ['summarization'] | ['natural-language-processing'] | [ 4.25779164e-01 9.71221149e-01 -2.95890987e-01 -1.28879339e-01
-1.22886956e+00 -5.92607439e-01 6.38849854e-01 4.56548125e-01
-1.99113563e-01 1.27358496e+00 1.10278642e+00 -1.68932751e-01
4.78721142e-01 -7.04084694e-01 -1.06558585e+00 -1.83097288e-01
4.74413097e-01 4.56448942e-01 -1.66013241e-01 -5.25495172... | [12.308525085449219, 9.33747386932373] |
13f4345d-3924-4490-bfae-f98a49b8e50d | investigating-robustness-of-dialog-models-to | 2110.00687 | null | https://arxiv.org/abs/2110.00687v1 | https://arxiv.org/pdf/2110.00687v1.pdf | Investigating Robustness of Dialog Models to Popular Figurative Language Constructs | Humans often employ figurative language use in communication, including during interactions with dialog systems. Thus, it is important for real-world dialog systems to be able to handle popular figurative language constructs like metaphor and simile. In this work, we analyze the performance of existing dialog models in... | ['Taylor Berg-Kirkpatrick', 'Eduard Hovy', 'Varun Gangal', 'Harsh Jhamtani'] | 2021-10-01 | null | https://aclanthology.org/2021.emnlp-main.592 | https://aclanthology.org/2021.emnlp-main.592.pdf | emnlp-2021-11 | ['open-domain-dialog'] | ['natural-language-processing'] | [-0.24671592 0.39816138 0.23645288 -0.5178073 -0.07096735 -1.1066654
1.1250987 0.18496531 -0.18666522 0.6638656 0.6963275 -0.6960911
0.16649085 -0.7827582 0.01609207 0.24439907 0.23170331 0.8245817
0.22819589 -1.0371041 0.2213707 0.3886814 -0.9502544 0.7027994
0.45672315 0.15672596 0.2978... | [12.875751495361328, 8.018468856811523] |
79c68806-7bb5-48f3-9553-64d9b6ded42d | matsci-nlp-evaluating-scientific-language | 2305.08264 | null | https://arxiv.org/abs/2305.08264v1 | https://arxiv.org/pdf/2305.08264v1.pdf | MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling | We present MatSci-NLP, a natural language benchmark for evaluating the performance of natural language processing (NLP) models on materials science text. We construct the benchmark from publicly available materials science text data to encompass seven different NLP tasks, including conventional NLP tasks like named ent... | ['Bang Liu', 'Santiago Miret', 'Yu Song'] | 2023-05-14 | null | null | null | null | ['relation-classification'] | ['natural-language-processing'] | [ 2.55888373e-01 2.08992928e-01 -3.06281477e-01 -2.84647077e-01
-1.42526698e+00 -9.88678634e-01 7.57053435e-01 4.78696585e-01
-5.45140088e-01 7.93556392e-01 4.68080461e-01 -5.85907102e-01
-2.19035119e-01 -8.75021935e-01 -1.28241098e+00 -3.99291277e-01
5.61594784e-01 8.48006308e-01 -4.80168536e-02 4.81295325... | [9.909339904785156, 8.488085746765137] |
d2009e98-c2fc-4637-8d0f-9ea465f420e2 | short-term-electricity-load-forecasting-using | 2305.10559 | null | https://arxiv.org/abs/2305.10559v1 | https://arxiv.org/pdf/2305.10559v1.pdf | Short-Term Electricity Load Forecasting Using the Temporal Fusion Transformer: Effect of Grid Hierarchies and Data Sources | Recent developments related to the energy transition pose particular challenges for distribution grids. Hence, precise load forecasts become more and more important for effective grid management. Novel modeling approaches such as the Transformer architecture, in particular the Temporal Fusion Transformer (TFT), have em... | ['Konstantin Hopf', 'Felix Haag', 'Elena Giacomazzi'] | 2023-05-17 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-2.96648353e-01 -2.56011426e-01 1.12462915e-01 -2.19362646e-01
-6.38062358e-01 -4.69951659e-01 8.37860286e-01 1.60436809e-01
3.93928707e-01 9.07145858e-01 3.12465250e-01 -6.27872646e-01
-4.15381491e-01 -1.15839493e+00 -1.72799584e-02 -1.05771971e+00
-5.91031432e-01 3.94164741e-01 -3.51592332e-01 -4.76439148... | [6.149807929992676, 2.812535285949707] |
4567a63c-6f9d-4a58-8058-586a05e67eac | unsupervised-domain-adaptation-of-a | 2011.11499 | null | https://arxiv.org/abs/2011.11499v1 | https://arxiv.org/pdf/2011.11499v1.pdf | Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model | Recent research indicates that pretraining cross-lingual language models on large-scale unlabeled texts yields significant performance improvements over various cross-lingual and low-resource tasks. Through training on one hundred languages and terabytes of texts, cross-lingual language models have proven to be effecti... | ['Rui Yan', 'Lidong Bing', 'Hwee Tou Ng', 'Hai Ye', 'Ruidan He', 'Juntao Li'] | 2020-11-23 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [-4.76857156e-01 -5.34447670e-01 -5.72328150e-01 -6.06347620e-01
-1.35778284e+00 -8.50036860e-01 6.52472734e-01 -1.32717937e-01
-5.81970930e-01 7.24526286e-01 2.68730730e-01 -3.07079047e-01
4.09635186e-01 -4.29700851e-01 -6.96658075e-01 -2.24628732e-01
1.41763791e-01 6.07966423e-01 -2.19781935e-01 -1.72742143... | [10.959637641906738, 9.88442611694336] |
2cb897df-02c6-4f32-922a-65971ad5c2ac | spelling-correction-with-denoising | 2105.05977 | null | https://arxiv.org/abs/2105.05977v1 | https://arxiv.org/pdf/2105.05977v1.pdf | Spelling Correction with Denoising Transformer | We present a novel method of performing spelling correction on short input strings, such as search queries or individual words. At its core lies a procedure for generating artificial typos which closely follow the error patterns manifested by humans. This procedure is used to train the production spelling correction mo... | ['Hector Urdiales', 'Alex Kuznetsov'] | 2021-05-12 | null | null | null | null | ['spelling-correction', 'bangla-spelling-error-correction'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.42043209e-01 -4.40110080e-02 3.10462080e-02 -1.55237122e-02
-7.17714369e-01 -9.51697528e-01 8.10052216e-01 6.88077137e-02
-4.81329262e-01 8.15808475e-01 1.08224958e-01 -7.13788152e-01
1.24426343e-01 -8.46340716e-01 -8.43347192e-01 -2.05439314e-01
7.62005746e-01 1.03766096e+00 1.12975143e-01 -8.80376935... | [11.17873477935791, 10.112317085266113] |
1473d47a-b6ce-40c1-be48-3ae6418ba4cd | blind-extraction-of-target-speech-source | 2111.03482 | null | https://arxiv.org/abs/2111.03482v2 | https://arxiv.org/pdf/2111.03482v2.pdf | Target Speech Extraction: Independent Vector Extraction Guided by Supervised Speaker Identification | This manuscript proposes a novel robust procedure for the extraction of a speaker of interest (SOI) from a mixture of audio sources. The estimation of the SOI is performed via independent vector extraction (IVE). Since the blind IVE cannot distinguish the target source by itself, it is guided towards the SOI via frame-... | ['Jindrich Zdansky', 'Jaroslav Cmejla', 'Tomas Kounovsky', 'Zbynek Koldovsky', 'Jakub Jansky', 'Jiri Malek'] | 2021-11-05 | null | null | null | null | ['speech-extraction', 'speaker-identification'] | ['speech', 'speech'] | [ 3.71167153e-01 -2.54637539e-01 5.53395748e-01 -6.31290302e-02
-1.19132853e+00 -7.12063313e-01 4.13744837e-01 1.72124624e-01
-4.64871705e-01 5.96909642e-01 1.60693318e-01 -2.73242921e-01
-9.02453810e-02 -1.05765492e-01 -5.70527256e-01 -1.10229266e+00
-6.54030815e-02 3.58700097e-01 1.21892663e-02 2.32510060... | [14.83825969696045, 5.781285285949707] |
2cea51e1-7814-492c-ba3d-da99ed06fb9d | minimum-levels-of-interpretability-for | 2307.00660 | null | https://arxiv.org/abs/2307.00660v1 | https://arxiv.org/pdf/2307.00660v1.pdf | Minimum Levels of Interpretability for Artificial Moral Agents | As artificial intelligence (AI) models continue to scale up, they are becoming more capable and integrated into various forms of decision-making systems. For models involved in moral decision-making, also known as artificial moral agents (AMA), interpretability provides a way to trust and understand the agent's interna... | ['Cosmin Badea', 'Avish Vijayaraghavan'] | 2023-07-02 | null | null | null | null | ['decision-making'] | ['reasoning'] | [-9.10672843e-02 7.76838303e-01 -2.77946144e-01 -5.26778162e-01
3.75006825e-01 -5.57668149e-01 8.43075812e-01 4.52015132e-01
-4.54401702e-01 6.46797299e-01 6.85941428e-02 -6.30374849e-01
-2.19334111e-01 -5.38835645e-01 -1.98555261e-01 -2.88475573e-01
1.65077895e-02 7.32262492e-01 -4.27729189e-01 -3.24981093... | [8.958528518676758, 6.204000949859619] |
87822b0e-9451-420a-9930-ef538f82118e | multi-template-temporal-siamese-network-for | 2211.13812 | null | https://arxiv.org/abs/2211.13812v1 | https://arxiv.org/pdf/2211.13812v1.pdf | Multi-Template Temporal Siamese Network for Long-Term Object Tracking | Siamese Networks are one of most popular visual object tracking methods for their high speed and high accuracy tracking ability as long as the target is well identified. However, most Siamese Network based trackers use the first frame as the ground truth of an object and fail when target appearance changes significantl... | ['Won-Sook Lee', 'Ali Sekhavati'] | 2022-11-24 | null | null | null | null | ['visual-object-tracking'] | ['computer-vision'] | [-3.57331663e-01 -5.01794338e-01 -3.28858078e-01 1.45302013e-01
-1.34464994e-01 -8.88366938e-01 5.67181289e-01 -2.19920442e-01
-7.14903235e-01 7.47513294e-01 -2.74679899e-01 2.82266110e-01
4.88967523e-02 -4.37582344e-01 -6.44390047e-01 -8.49702358e-01
-3.63734096e-01 4.36707616e-01 1.10865974e+00 -3.41053642... | [6.438023090362549, -2.0490269660949707] |
b9d2896c-9ff9-4448-8099-9a20564ef32e | retinexflow-for-ct-metal-artifact-reduction | 2306.10520 | null | https://arxiv.org/abs/2306.10520v1 | https://arxiv.org/pdf/2306.10520v1.pdf | RetinexFlow for CT metal artifact reduction | Metal artifacts is a major challenge in computed tomography (CT) imaging, significantly degrading image quality and making accurate diagnosis difficult. However, previous methods either require prior knowledge of the location of metal implants, or have modeling deviations with the mechanism of artifact formation, which... | ['Dong Liang', 'Kun Shang', 'Yinsheng Li', 'Ce Wang', 'Jiandong Su'] | 2023-06-18 | null | null | null | null | ['metal-artifact-reduction', 'computed-tomography-ct'] | ['medical', 'methodology'] | [ 3.32708389e-01 -2.17006356e-01 1.81330562e-01 -8.85369256e-02
-7.30689168e-01 -6.29333928e-02 1.09255448e-01 -3.84886146e-01
-9.07403901e-02 4.16454285e-01 4.91331249e-01 -3.20932940e-02
-3.37124199e-01 -5.34086168e-01 -4.40481305e-01 -8.15398157e-01
1.09600082e-01 -2.55880002e-02 1.36666834e-01 1.25870392... | [13.508604049682617, -2.558328628540039] |
308a95ba-d1f5-4e2a-a205-384a2270659c | low-rank-time-frequency-synthesis | null | null | http://papers.nips.cc/paper/5522-low-rank-time-frequency-synthesis | http://papers.nips.cc/paper/5522-low-rank-time-frequency-synthesis.pdf | Low-Rank Time-Frequency Synthesis | Many single-channel signal decomposition techniques rely on a low-rank factorization of a time-frequency transform. In particular, nonnegative matrix factorization (NMF) of the spectrogram -- the (power) magnitude of the short-time Fourier transform (STFT) -- has been considered in many audio applications. In this sett... | ['Cédric Févotte', 'Matthieu Kowalski'] | 2014-12-01 | null | null | null | neurips-2014-12 | ['audio-signal-processing'] | ['audio'] | [ 5.47559917e-01 -7.33040720e-02 3.80475491e-01 1.98040791e-02
-8.16963732e-01 -6.81707501e-01 4.26352054e-01 -3.08272809e-01
-2.98575103e-01 5.98013222e-01 6.10713065e-01 -2.19337016e-01
-5.95616996e-01 -3.63962561e-01 -4.47108120e-01 -1.05876148e+00
-7.25349486e-02 -4.63844985e-02 -2.77960747e-01 -4.25604224... | [15.450004577636719, 5.5705885887146] |
9c257ee8-7a1c-4cd8-9245-b2a12cccc619 | transition-propagation-graph-neural-networks | 2304.07501 | null | https://arxiv.org/abs/2304.07501v1 | https://arxiv.org/pdf/2304.07501v1.pdf | Transition Propagation Graph Neural Networks for Temporal Networks | Researchers of temporal networks (e.g., social networks and transaction networks) have been interested in mining dynamic patterns of nodes from their diverse interactions. Inspired by recently powerful graph mining methods like skip-gram models and Graph Neural Networks (GNNs), existing approaches focus on generating t... | ['Chun Chen', 'Ji Zhao', 'Xinyu Wang', 'Xingen Wang', 'Mingli Song', 'Yunzhi Hao', 'Tianli Zhang', 'Zunlei Feng', 'Tongya Zheng'] | 2023-04-15 | null | null | null | null | ['graph-mining', 'memorization'] | ['graphs', 'natural-language-processing'] | [-2.50037938e-01 1.72571614e-01 -5.70030630e-01 -1.51940733e-01
3.05453658e-01 -3.13192338e-01 5.86187422e-01 3.07358384e-01
-6.77273124e-02 4.34598297e-01 3.42296623e-02 -6.67525232e-01
-4.20041800e-01 -1.22204244e+00 -5.38435400e-01 -4.16599065e-01
-9.00995255e-01 4.57835197e-01 4.58307236e-01 -3.40351194... | [7.233029842376709, 5.972745418548584] |
6818b0ed-44ba-41cc-b883-09d89f906f4f | physics-informed-neural-networks-for-9 | 2207.14230 | null | https://arxiv.org/abs/2207.14230v1 | https://arxiv.org/pdf/2207.14230v1.pdf | Physics-informed neural networks for diffraction tomography | We propose a physics-informed neural network as the forward model for tomographic reconstructions of biological samples. We demonstrate that by training this network with the Helmholtz equation as a physical loss, we can predict the scattered field accurately. It will be shown that a pretrained network can be fine-tune... | ['Demetri Psaltis', 'Ahmed B. Ayoub', 'Carlo Gigli', 'Amirhossein Saba'] | 2022-07-28 | null | null | null | null | ['tomographic-reconstructions'] | ['medical'] | [ 4.80478257e-01 -6.94841426e-03 4.82501000e-01 -7.36350358e-01
-6.03459179e-01 2.37831876e-01 -1.85510062e-03 -4.81759429e-01
-5.82169652e-01 1.14836991e+00 -1.02715425e-01 -3.03714365e-01
-2.81802982e-01 -8.85299146e-01 -1.07687187e+00 -1.08884943e+00
-2.06013739e-01 8.16417933e-01 1.85631126e-01 -3.17915171... | [12.58035659790039, -2.624521255493164] |
be55c91e-67d0-4e3b-b929-c1600de78c1b | the-pose-knows-video-forecasting-by | 1705.00053 | null | http://arxiv.org/abs/1705.00053v1 | http://arxiv.org/pdf/1705.00053v1.pdf | The Pose Knows: Video Forecasting by Generating Pose Futures | Current approaches in video forecasting attempt to generate videos directly
in pixel space using Generative Adversarial Networks (GANs) or Variational
Autoencoders (VAEs). However, since these approaches try to model all the
structure and scene dynamics at once, in unconstrained settings they often
generate uninterpret... | ['Martial Hebert', 'Abhinav Gupta', 'Kenneth Marino', 'Jacob Walker'] | 2017-04-28 | the-pose-knows-video-forecasting-by-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Walker_The_Pose_Knows_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Walker_The_Pose_Knows_ICCV_2017_paper.pdf | iccv-2017-10 | ['human-pose-forecasting'] | ['computer-vision'] | [ 4.22259808e-01 6.26185954e-01 9.93024260e-02 -3.38868886e-01
-5.29087663e-01 -5.88148415e-01 8.92913401e-01 -8.45006466e-01
3.58757526e-02 8.79234195e-01 5.12845218e-01 2.38437485e-03
6.68034554e-01 -9.85528111e-01 -1.37970674e+00 -6.57754242e-01
2.57581860e-01 5.82691491e-01 1.74135953e-01 -2.64080524... | [10.731184959411621, -0.649075984954834] |
9f846c9e-1e59-498c-b11f-f0773eec2b17 | designing-game-of-theorems | 1906.08549 | null | https://arxiv.org/abs/1906.08549v1 | https://arxiv.org/pdf/1906.08549v1.pdf | Designing Game of Theorems | "Theorem proving is similar to the game of Go. So, we can probably improve our provers using deep learning, like DeepMind built the super-human computer Go program, AlphaGo." Such optimism has been observed among participants of AITP2017. But is theorem proving really similar to Go? In this paper, we first identify the... | ['Yutaka Nagashima'] | 2019-06-20 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-5.93817532e-01 4.14844632e-01 -1.05457031e-03 -1.16008617e-01
-3.65115464e-01 -7.25849509e-01 3.13569099e-01 1.69398859e-02
-4.22686636e-02 1.14335573e+00 -1.42572418e-01 -1.09619677e+00
-1.71911776e-01 -1.16125500e+00 -1.17302918e+00 -3.65684450e-01
-2.61552185e-01 8.77103209e-01 4.74066883e-01 -7.73293018... | [8.918739318847656, 7.043548107147217] |
94ec8ba7-54f7-425e-9e55-b439f2d4ba22 | time-series-classification-using-the-hidden | 1506.05085 | null | http://arxiv.org/abs/1506.05085v2 | http://arxiv.org/pdf/1506.05085v2.pdf | Time Series Classification using the Hidden-Unit Logistic Model | We present a new model for time series classification, called the hidden-unit
logistic model, that uses binary stochastic hidden units to model latent
structure in the data. The hidden units are connected in a chain structure that
models temporal dependencies in the data. Compared to the prior models for time
series cl... | ['Laurens van der Maaten', 'Hamdi Dibeklioğlu', 'Wenjie Pei', 'David M. J. Tax'] | 2015-06-16 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 5.37223458e-01 9.56336483e-02 -7.97998130e-01 -6.52528524e-01
-7.66867697e-02 -1.78491641e-02 8.33693087e-01 -5.11182487e-01
-4.01806496e-02 4.03636396e-01 5.78716993e-02 -2.84209281e-01
3.73082340e-01 -4.74581748e-01 -2.97634691e-01 -1.02239537e+00
-4.69752342e-01 2.47237742e-01 3.44550729e-01 1.95432901... | [8.491016387939453, 1.0129456520080566] |
0639852c-2629-4522-880e-12e659e2bacb | towards-understanding-generalization-of-macro | 2305.05248 | null | https://arxiv.org/abs/2305.05248v2 | https://arxiv.org/pdf/2305.05248v2.pdf | Towards Understanding Generalization of Macro-AUC in Multi-label Learning | Macro-AUC is the arithmetic mean of the class-wise AUCs in multi-label learning and is commonly used in practice. However, its theoretical understanding is far lacking. Toward solving it, we characterize the generalization properties of various learning algorithms based on the corresponding surrogate losses w.r.t. Macr... | ['Yilong Yin', 'Chongxuan Li', 'Guoqiang Wu'] | 2023-05-09 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 1.27185658e-01 8.33928883e-02 -4.73759770e-01 -5.66392839e-01
-9.47336435e-01 -4.39199686e-01 -6.98385462e-02 7.95180917e-01
-4.38792855e-01 1.13864362e+00 -5.02605975e-01 -1.28641158e-01
-7.61026442e-01 -6.07731044e-01 -7.21807063e-01 -1.11558843e+00
-9.68478471e-02 4.41428930e-01 1.93682779e-02 3.62688005... | [9.00698184967041, 4.195162296295166] |
579c3a6c-adc6-4e26-bba0-0bad25ba4322 | a-boosting-algorithm-for-positive-unlabeled | 2205.09485 | null | https://arxiv.org/abs/2205.09485v4 | https://arxiv.org/pdf/2205.09485v4.pdf | A Boosting Algorithm for Positive-Unlabeled Learning | Positive-unlabeled (PU) learning deals with binary classification problems when only positive (P) and unlabeled (U) data are available. Many recent PU methods are based on neural networks, but little has been done to develop boosting algorithms for PU learning, despite boosting algorithms' strong performance on many fu... | ['Weitong Chen', 'Miao Xu', 'Nan Ye', 'Chenhao Zhang', 'Mingzhe Zhang', 'Yawen Zhao'] | 2022-05-19 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 2.47030072e-02 -2.74518609e-01 -8.73656631e-01 -4.93083000e-01
-7.17151701e-01 -4.08765763e-01 1.98199809e-01 5.87101936e-01
-5.49679458e-01 1.08907223e+00 -1.05332263e-01 -6.55730605e-01
2.98260659e-01 -1.26766694e+00 -7.01041281e-01 -9.17849004e-01
-9.48405713e-02 3.60930175e-01 3.27619940e-01 -2.48484418... | [9.619516372680664, 3.526097297668457] |
65bcea58-999f-46f4-ab8f-0e2936e20db2 | meta-temporal-point-processes | 2301.12023 | null | https://arxiv.org/abs/2301.12023v1 | https://arxiv.org/pdf/2301.12023v1.pdf | Meta Temporal Point Processes | A temporal point process (TPP) is a stochastic process where its realization is a sequence of discrete events in time. Recent work in TPPs model the process using a neural network in a supervised learning framework, where a training set is a collection of all the sequences. In this work, we propose to train TPPs in a m... | ['Gabriel L. Oliveira', 'Frederick Tung', 'Mohamed Osama Ahmed', 'Wonho Bae'] | 2023-01-27 | null | null | null | null | ['point-processes'] | ['methodology'] | [ 4.85078156e-01 -9.54609830e-03 -1.54773235e-01 -2.43512854e-01
-6.59391940e-01 -5.15794933e-01 1.33537757e+00 2.75169760e-01
-5.26101999e-02 5.50024629e-01 4.74838823e-01 5.70544489e-02
-1.75958961e-01 -1.07314813e+00 -9.32464242e-01 -9.04709697e-01
-3.25693816e-01 8.80801797e-01 5.39665341e-01 1.07765220... | [8.474377632141113, 5.676767826080322] |
c12bd267-019d-42a7-85d6-b0624c1694ee | mindstorms-in-natural-language-based | 2305.17066 | null | https://arxiv.org/abs/2305.17066v1 | https://arxiv.org/pdf/2305.17066v1.pdf | Mindstorms in Natural Language-Based Societies of Mind | Both Minsky's "society of mind" and Schmidhuber's "learning to think" inspire diverse societies of large multimodal neural networks (NNs) that solve problems by interviewing each other in a "mindstorm." Recent implementations of NN-based societies of minds consist of large language models (LLMs) and other NN-based expe... | ['Jürgen Schmidhuber', 'Bernard Ghanem', 'Deng-Ping Fan', 'Mengmeng Xu', 'Yuhui Wang', 'Wenyi Wang', 'Aleksandar Stanić', 'Weimin Shi', 'Imanol Schlag', 'Aditya Ramesh', 'Piotr Piękos', 'Jinjie Mai', 'Shuming Liu', 'Guohao Li', 'Bing Li', 'Louis Kirsch', 'Kazuki Irie', 'Vincent Herrmann', 'Hasan Abed Al Kader Hammoud',... | 2023-05-26 | null | null | null | null | ['image-captioning'] | ['computer-vision'] | [ 1.16959317e-02 8.36141229e-01 3.15811992e-01 -4.57963757e-02
6.40233904e-02 -5.00307262e-01 1.06336153e+00 -2.54717410e-01
-5.31402051e-01 7.60495365e-01 3.36656660e-01 -3.51560652e-01
4.62739579e-02 -7.17208922e-01 -5.91934443e-01 -6.48959279e-01
2.29838528e-02 8.82600069e-01 -1.34616747e-01 -8.77344489... | [4.369327545166016, 1.5192407369613647] |
76d606f2-2331-4dfc-bc2a-99e44ffb306f | unsupervised-translation-of-german-lower-1 | null | null | https://aclanthology.org/2021.wmt-1.104 | https://aclanthology.org/2021.wmt-1.104.pdf | Unsupervised Translation of German–Lower Sorbian: Exploring Training and Novel Transfer Methods on a Low-Resource Language | This paper describes the methods behind the systems submitted by the University of Groningen for the WMT 2021 Unsupervised Machine Translation task for German–Lower Sorbian (DE–DSB): a high-resource language to a low-resource one. Our system uses a transformer encoder-decoder architecture in which we make three changes... | ['Gertjan van Noord', 'Antonio Toral', 'Ahmet Üstün', 'Lukas Edman'] | null | null | null | null | wmt-emnlp-2021-11 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [-1.16837539e-01 1.65955946e-01 -3.02252531e-01 -4.09496248e-01
-1.39422524e+00 -8.98631155e-01 1.02828074e+00 -9.87471640e-02
-7.30475068e-01 1.32988584e+00 3.65369767e-01 -8.32030833e-01
2.71601081e-01 -4.30945486e-01 -7.29692757e-01 -1.99545667e-01
3.70945454e-01 1.22927296e+00 -2.01430231e-01 -8.68986368... | [11.494739532470703, 10.336833000183105] |
5f43468e-05e1-4fcf-b5e7-4e1d4b850fab | beyond-gauss-image-set-matching-on-the | 1507.08711 | null | http://arxiv.org/abs/1507.08711v1 | http://arxiv.org/pdf/1507.08711v1.pdf | Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs | State-of-the-art image-set matching techniques typically implicitly model
each image-set with a Gaussian distribution. Here, we propose to go beyond
these representations and model image-sets as probability distribution
functions (PDFs) using kernel density estimators. To compare and match
image-sets, we exploit Csisza... | ['Mathieu Salzmann', 'Mehrtash Harandi', 'Mahsa Baktashmotlagh'] | 2015-07-31 | beyond-gauss-image-set-matching-on-the-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Harandi_Beyond_Gauss_Image-Set_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Harandi_Beyond_Gauss_Image-Set_ICCV_2015_paper.pdf | iccv-2015-12 | ['set-matching', 'supervised-dimensionality-reduction'] | ['computer-vision', 'computer-vision'] | [-1.12027273e-01 -3.49594146e-01 -1.78237081e-01 -5.55973649e-01
-3.96522433e-01 -4.68146265e-01 6.42713368e-01 -1.28052324e-01
-2.55848318e-01 1.42536879e-01 -2.57559001e-01 8.17333758e-02
-5.09869576e-01 -8.34891677e-01 -6.22735560e-01 -9.52667415e-01
-1.18126482e-01 3.85206848e-01 -9.95643996e-03 -1.26300126... | [8.015192985534668, 3.7190709114074707] |
0e09a01a-977b-4e16-b9f4-c33c729320a6 | hymer-a-hybrid-machine-learning-framework-for | 1909.08074 | null | https://arxiv.org/abs/1909.08074v3 | https://arxiv.org/pdf/1909.08074v3.pdf | MER-SDN: Machine Learning Framework for Traffic Aware Energy Efficient Routing in SDN | Software Defined Networking (SDN) achieves programmability of a network through separation of the control and data planes. It enables flexibility in network management and control. Energy efficiency is one of the challenging global problems which has both economic and environmental impact. A massive amount of informati... | ['Oznur Ozkasap', 'Beakal Gizachew Assefa'] | 2019-08-27 | null | null | null | null | ['traffic-classification', 'parameter-prediction'] | ['miscellaneous', 'miscellaneous'] | [ 3.46536525e-02 -1.60762891e-01 -6.91875637e-01 -4.02986795e-01
2.32466772e-01 -4.76343215e-01 8.88931751e-02 -2.72643715e-01
-9.57992300e-03 1.16761267e+00 -4.77085441e-01 -6.11357272e-01
-5.82591474e-01 -7.82183290e-01 -1.05395876e-01 -5.45996666e-01
-4.53635931e-01 9.18807626e-01 4.69383389e-01 1.57291576... | [5.83573579788208, 1.757981538772583] |
da664aea-46dd-429a-9012-a87c3d61825b | online-visual-multi-object-tracking-via | 1611.06011 | null | http://arxiv.org/abs/1611.06011v2 | http://arxiv.org/pdf/1611.06011v2.pdf | Online Visual Multi-Object Tracking via Labeled Random Finite Set Filtering | This paper proposes an online visual multi-object tracking algorithm using a
top-down Bayesian formulation that seamlessly integrates state estimation,
track management, clutter rejection, occlusion and mis-detection handling into
a single recursion. This is achieved by modeling the multi-object state as
labeled random... | ['Ba-Tuong Vo', 'Ba-Ngu Vo', 'Du Yong Kim'] | 2016-11-18 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 5.34387343e-02 -4.16613787e-01 -8.45916495e-02 4.50254045e-02
-4.41255122e-01 -6.72269225e-01 5.16782820e-01 2.85216391e-01
-5.32521605e-01 7.35725880e-01 -2.39317253e-01 -1.18444659e-01
-1.93166077e-01 -4.56163973e-01 -8.02503169e-01 -5.80433249e-01
-1.00037515e-01 7.54577041e-01 9.20866191e-01 4.48396325... | [6.580289363861084, -1.959050178527832] |
1b0531a5-151d-43f6-822a-76fffd1fa37f | racai-gec-a-hybrid-approach-to-grammatical | null | null | https://aclanthology.org/W14-1705 | https://aclanthology.org/W14-1705.pdf | RACAI GEC -- A hybrid approach to Grammatical Error Correction | null | ['Ionut Paul V{\\u{a}}duva', 'Tiberiu Boro{\\textcommabelow{s}}', 'Verginica Barbu Mititelu', 'Stefan Daniel Dumitrescu', 'Adrian Zafiu'] | 2014-06-01 | null | null | null | ws-2014-6 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.23227071762085, 3.811842918395996] |
50221572-b8a3-4629-b49b-54ae708328c0 | on-the-effectiveness-of-knowledge-graph | 2206.00983 | null | https://arxiv.org/abs/2206.00983v2 | https://arxiv.org/pdf/2206.00983v2.pdf | On the Effectiveness of Knowledge Graph Embeddings: a Rule Mining Approach | We study the effectiveness of Knowledge Graph Embeddings (KGE) for knowledge graph (KG) completion with rule mining. More specifically, we mine rules from KGs before and after they have been completed by a KGE to compare possible differences in the rules extracted. We apply this method to classical KGEs approaches, in ... | ['Ana Ozaki', 'Ricardo Guimarães', 'Johanna Jøsang'] | 2022-06-02 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-2.38519982e-01 6.55162632e-01 -3.05422515e-01 -1.76697206e-02
3.23598444e-01 -5.64525068e-01 4.95101720e-01 6.88832104e-01
-2.42736533e-01 7.23835886e-01 9.26197246e-02 -4.50525641e-01
-6.94731712e-01 -1.34968472e+00 -7.22070873e-01 -7.23623186e-02
-4.13158178e-01 5.20077705e-01 5.97602844e-01 -3.09122115... | [8.814497947692871, 7.858809947967529] |
2c9949ff-6164-4829-9085-0ae236baeb90 | learning-a-predictive-model-for-music-using | 1709.08842 | null | http://arxiv.org/abs/1709.08842v1 | http://arxiv.org/pdf/1709.08842v1.pdf | Learning a Predictive Model for Music Using PULSE | Predictive models for music are studied by researchers of algorithmic
composition, the cognitive sciences and machine learning. They serve as base
models for composition, can simulate human prediction and provide a
multidisciplinary application domain for learning algorithms. A particularly
well established and constan... | ['Jonas Langhabel'] | 2017-09-26 | null | null | null | null | ['music-modeling'] | ['music'] | [ 3.32280070e-01 -3.79824549e-01 -1.39949962e-01 -4.42414954e-02
-5.05448222e-01 -6.91251457e-01 6.31766915e-01 -2.88941637e-02
-1.82621300e-01 4.73314404e-01 2.23239124e-01 1.59084350e-02
-6.76925838e-01 -4.98770267e-01 -3.44290346e-01 -5.48764765e-01
-3.08822066e-01 7.85343528e-01 2.58728117e-02 -4.47217196... | [15.946097373962402, 5.3287506103515625] |
acc2a2b4-3c32-4293-b30a-986691121abb | seed-driven-document-ranking-for-systematic | 2112.04090 | null | https://arxiv.org/abs/2112.04090v1 | https://arxiv.org/pdf/2112.04090v1.pdf | Seed-driven Document Ranking for Systematic Reviews: A Reproducibility Study | Screening or assessing studies is critical to the quality and outcomes of a systematic review. Typically, a Boolean query retrieves the set of studies to screen. As the set of studies retrieved is unordered, screening all retrieved studies is usually required for high-quality systematic reviews. Screening prioritisatio... | ['Guido Zuccon', 'Ahmed Mourad', 'Harrisen Scells', 'Shuai Wang'] | 2021-12-08 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [ 3.41147840e-01 -5.11613190e-02 -5.72701335e-01 -1.59116685e-02
-1.00944734e+00 -9.33543324e-01 4.39734817e-01 5.46694994e-01
-5.00223219e-01 7.07063496e-01 5.18661261e-01 -9.05250967e-01
-5.71685672e-01 -6.54156029e-01 -7.85701275e-01 -1.85214095e-02
1.24106161e-01 2.19506249e-01 4.73565489e-01 3.38586599... | [8.77573013305664, 8.44133472442627] |
54cf93b4-8295-49ef-ad73-071f297b5ad5 | language-models-are-multilingual-chain-of | 2210.03057 | null | https://arxiv.org/abs/2210.03057v1 | https://arxiv.org/pdf/2210.03057v1.pdf | Language Models are Multilingual Chain-of-Thought Reasoners | We evaluate the reasoning abilities of large language models in multilingual settings. We introduce the Multilingual Grade School Math (MGSM) benchmark, by manually translating 250 grade-school math problems from the GSM8K dataset (Cobbe et al., 2021) into ten typologically diverse languages. We find that the ability t... | ['Jason Wei', 'Dipanjan Das', 'Denny Zhou', 'Sebastian Ruder', 'Yi Tay', 'Hyung Won Chung', 'Soroush Vosoughi', 'Suraj Srivats', 'Xuezhi Wang', 'Markus Freitag', 'Mirac Suzgun', 'Freda Shi'] | 2022-10-06 | null | null | null | null | ['gsm8k'] | ['natural-language-processing'] | [-5.19801497e-01 2.42231503e-01 -3.13604511e-02 -2.16080382e-01
-9.00991976e-01 -1.18018055e+00 6.81584239e-01 3.24345440e-01
-3.43554497e-01 8.92984033e-01 3.93681258e-01 -8.79975617e-01
-4.98984873e-01 -1.25992608e+00 -8.54134977e-01 -4.20685783e-02
6.02766037e-01 6.76600039e-01 -1.94755286e-01 -5.49080670... | [9.784581184387207, 7.395485877990723] |
d204017d-1f7d-441c-ac26-36e4b3f69254 | unsupervised-extraction-of-market-moving | 2001.09466 | null | https://arxiv.org/abs/2001.09466v3 | https://arxiv.org/pdf/2001.09466v3.pdf | From Stock Prediction to Financial Relevance: Repurposing Attention Weights to Assess News Relevance Without Manual Annotations | We present a method to automatically identify financially relevant news using stock price movements and news headlines as input. The method repurposes the attention weights of a neural network initially trained to predict stock prices to assign a relevance score to each headline, eliminating the need for manually label... | ['Johannes Hoffart', 'Luciano Del Corro'] | 2020-01-26 | null | https://aclanthology.org/2021.econlp-1.6 | https://aclanthology.org/2021.econlp-1.6.pdf | emnlp-econlp-2021-11 | ['stock-price-prediction', 'stock-prediction'] | ['time-series', 'time-series'] | [-5.85151076e-01 3.17380250e-01 -7.41134465e-01 -3.06922793e-01
-7.43549645e-01 -7.58640468e-01 8.24696481e-01 3.53388280e-01
-6.04920566e-01 9.19728398e-01 1.14438426e+00 -4.31822121e-01
-2.18824148e-02 -1.07906294e+00 -6.66293204e-01 -8.31729472e-02
-1.53533667e-01 6.18371129e-01 1.55670047e-01 -4.57256496... | [4.419174671173096, 4.291524887084961] |
4334d5f1-0850-4435-a1b6-422579e681a7 | on-the-convergence-of-decentralized-federated | 2303.10695 | null | https://arxiv.org/abs/2303.10695v1 | https://arxiv.org/pdf/2303.10695v1.pdf | On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing | Decentralized learning and optimization is a central problem in control that encompasses several existing and emerging applications, such as federated learning. While there exists a vast literature on this topic and most methods centered around the celebrated average-consensus paradigm, less attention has been devoted ... | ['Stanislaw H /. Zak', 'Abolfazl Hashemi', 'Antesh Upadhyay', 'Vishnu Pandi Chellapandi'] | 2023-03-19 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-4.70415324e-01 -7.28362352e-02 6.14439622e-02 -8.44763443e-02
-4.33296323e-01 -5.20521343e-01 7.47949779e-01 1.64126530e-01
-4.51234996e-01 9.36711311e-01 1.54288381e-01 -5.66971265e-02
-2.75482357e-01 -6.71750426e-01 -7.16077387e-01 -1.32437348e+00
-5.72053134e-01 5.01503125e-02 -6.64481297e-02 -2.49583006... | [6.1687235832214355, 4.985624313354492] |
5fc6076e-d68e-4db7-a047-23ca9449da54 | a-multi-modal-approach-to-single-modal-visual | 2305.06179 | null | https://arxiv.org/abs/2305.06179v2 | https://arxiv.org/pdf/2305.06179v2.pdf | A Multi-modal Approach to Single-modal Visual Place Classification | Visual place classification from a first-person-view monocular RGB image is a fundamental problem in long-term robot navigation. A difficulty arises from the fact that RGB image classifiers are often vulnerable to spatial and appearance changes and degrade due to domain shifts, such as seasonal, weather, and lighting d... | ['Kenta Tsukahara', 'Kanji Tanaka', 'Tomoya Iwasaki'] | 2023-05-10 | null | null | null | null | ['monocular-depth-estimation', 'robot-navigation'] | ['computer-vision', 'robots'] | [ 3.05671006e-01 -3.89394134e-01 8.01972970e-02 -7.43911803e-01
-8.64378631e-01 -6.84675157e-01 7.80058265e-01 -2.73335706e-02
-6.24890268e-01 7.92663991e-01 1.12755619e-01 9.47760865e-02
7.88696781e-02 -6.87506914e-01 -7.16273010e-01 -7.82222450e-01
5.11215210e-01 5.48398755e-02 1.32337973e-01 -1.46279648... | [8.307612419128418, -2.332390308380127] |
85673192-a9bd-4be3-85bb-3898359e419d | learning-to-jointly-generate-and-separate | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Ma_Learning_to_Jointly_Generate_and_Separate_Reflections_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Ma_Learning_to_Jointly_Generate_and_Separate_Reflections_ICCV_2019_paper.pdf | Learning to Jointly Generate and Separate Reflections | Existing learning-based single image reflection removal methods using paired training data have fundamental limitations about the generalization capability on real-world reflections due to the limited variations in training pairs. In this work, we propose to jointly generate and separate reflections within a weakly-sup... | [' Ling-Yu Duan', ' Alex C. Kot', ' Boxin Shi', ' Renjie Wan', 'Daiqian Ma'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['reflection-removal'] | ['computer-vision'] | [ 7.18344212e-01 -2.79758405e-02 2.94178277e-01 -1.93538308e-01
-1.26357937e+00 -2.71245629e-01 7.63886929e-01 -6.85360610e-01
-1.77018136e-01 6.55537546e-01 2.18687534e-01 -2.83592552e-01
3.42284143e-02 -6.83449924e-01 -1.03264225e+00 -1.12204611e+00
3.21939409e-01 -6.50275424e-02 -5.07544614e-02 -2.42138639... | [10.589333534240723, -2.765544891357422] |
5766955e-bb57-4b9f-b828-77496b539c7b | spatial-temporal-anomaly-detection-for-sensor | 2212.07757 | null | https://arxiv.org/abs/2212.07757v1 | https://arxiv.org/pdf/2212.07757v1.pdf | Spatial-Temporal Anomaly Detection for Sensor Attacks in Autonomous Vehicles | Time-of-flight (ToF) distance measurement devices such as ultrasonics, LiDAR and radar are widely used in autonomous vehicles for environmental perception, navigation and assisted braking control. Despite their relative importance in making safer driving decisions, these devices are vulnerable to multiple attack types ... | ['David Wallom', 'Devki Jha', 'Martin Higgins'] | 2022-12-15 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 7.69434497e-02 -1.55150250e-01 1.27318934e-01 -3.13252419e-01
-1.51375130e-01 -6.85887098e-01 7.77034283e-01 3.27670604e-01
-8.07575107e-01 6.57326281e-01 -5.82623243e-01 -9.13613856e-01
-1.78049698e-01 -1.06013548e+00 -5.63729823e-01 -6.34755433e-01
-2.01467514e-01 -3.13502252e-02 1.13467968e+00 -9.72858816... | [5.328400611877441, 7.341818332672119] |
38f05644-3d2b-49ad-9abd-e99139aefb3e | counterfactual-explanations-and-predictive | 2306.03980 | null | https://arxiv.org/abs/2306.03980v1 | https://arxiv.org/pdf/2306.03980v1.pdf | Counterfactual Explanations and Predictive Models to Enhance Clinical Decision-Making in Schizophrenia using Digital Phenotyping | Clinical practice in psychiatry is burdened with the increased demand for healthcare services and the scarce resources available. New paradigms of health data powered with machine learning techniques could open the possibility to improve clinical workflow in critical stages of clinical assessment and treatment in psych... | ['Omar Costilla-Reyes', 'Francisco Gomez', 'Juan Sebastian Canas'] | 2023-06-06 | null | null | null | null | ['change-point-detection'] | ['time-series'] | [ 4.78951931e-01 5.41680276e-01 -1.29700989e-01 -6.79173410e-01
-2.85013944e-01 -2.29427695e-01 3.35494488e-01 8.16274285e-01
-2.39966780e-01 9.31198478e-01 3.61124873e-01 -5.28378189e-01
-6.94471657e-01 -5.33909917e-01 -9.07474607e-02 -5.43032527e-01
-3.47356528e-01 7.22856760e-01 -5.88602722e-01 2.14545920... | [8.272087097167969, 5.831803798675537] |
058ca04e-54dd-4c00-8101-a1162f51a000 | detecting-anomalies-within-time-series-using | 2202.03944 | null | https://arxiv.org/abs/2202.03944v2 | https://arxiv.org/pdf/2202.03944v2.pdf | Detecting Anomalies within Time Series using Local Neural Transformations | We develop a new method to detect anomalies within time series, which is essential in many application domains, reaching from self-driving cars, finance, and marketing to medical diagnosis and epidemiology. The method is based on self-supervised deep learning that has played a key role in facilitating deep anomaly dete... | ['Maja Rudolph', 'Stephan Mandt', 'Steffen Staab', 'Decky Aspandi Latif', 'Marius Kloft', 'Chen Qiu', 'Tim Schneider'] | 2022-02-08 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 1.37451753e-01 -1.24518998e-01 2.55271107e-01 -2.38611847e-01
-4.30343807e-01 -5.28318107e-01 7.87412167e-01 3.59380126e-01
-3.11681367e-02 1.06300190e-01 5.11249341e-03 -7.15210795e-01
-2.03637213e-01 -8.58331800e-01 -8.98721099e-01 -6.81836247e-01
-6.43026710e-01 2.01316942e-02 1.15140162e-01 -2.74652779... | [7.46690559387207, 2.567391872406006] |
711ba897-64ce-44ad-9a24-caa0399a9f7e | fairr-faithful-and-robust-deductive-reasoning | null | null | https://openreview.net/forum?id=0DR74NlWR7u | https://openreview.net/pdf?id=0DR74NlWR7u | FaiRR: Faithful and Robust Deductive Reasoning over Natural Language | Transformers have been shown to be able to perform deductive reasoning on a logical rulebase containing rules and statements written in natural language. Recent works show that such models can also produce the reasoning steps (i.e., the proof graph) that emulate the model's logical reasoning process. Currently, these b... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['fact-selection'] | ['natural-language-processing'] | [ 3.01948518e-01 1.01384687e+00 -2.48268303e-02 -9.24963132e-02
-2.94287711e-01 -8.47978771e-01 1.18967187e+00 8.84159580e-02
3.90408307e-01 9.32482660e-01 2.33377308e-01 -9.05891597e-01
-3.34004223e-01 -1.45247972e+00 -1.14873064e+00 -2.11976409e-01
8.07252973e-02 6.78325117e-01 5.96566617e-01 -3.09651434... | [9.177820205688477, 7.169439315795898] |
9ad72de5-c5b2-488e-823f-fab00f7ec856 | data-driven-deep-supervision-for-skin-lesion | 2209.01527 | null | https://arxiv.org/abs/2209.01527v1 | https://arxiv.org/pdf/2209.01527v1.pdf | Data-Driven Deep Supervision for Skin Lesion Classification | Automatic classification of pigmented, non-pigmented, and depigmented non-melanocytic skin lesions have garnered lots of attention in recent years. However, imaging variations in skin texture, lesion shape, depigmentation contrast, lighting condition, etc. hinder robust feature extraction, affecting classification accu... | ['Danny Z. Chen', 'X. Sharon Hu', 'Tianyu Zhang', 'Li Zhang', 'Yizhe Zhang', 'Suraj Mishra'] | 2022-09-04 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 6.85979307e-01 3.82471317e-03 -1.64316863e-01 -4.11383569e-01
-3.28804344e-01 -4.08021986e-01 4.95719761e-01 -4.16796245e-02
-3.57650310e-01 5.87699115e-01 -2.22785324e-02 -1.75784156e-01
-4.45086211e-02 -4.72112775e-01 -4.77098882e-01 -1.00533080e+00
2.37664118e-01 -5.31573474e-01 2.07157075e-01 -7.30309188... | [15.662674903869629, -2.9580752849578857] |
f6b210dd-36ff-4f3a-98d7-07f9bab44eef | benchmarking-the-robustness-of-lidar-semantic | 2301.00970 | null | https://arxiv.org/abs/2301.00970v2 | https://arxiv.org/pdf/2301.00970v2.pdf | Benchmarking the Robustness of LiDAR Semantic Segmentation Models | When using LiDAR semantic segmentation models for safety-critical applications such as autonomous driving, it is essential to understand and improve their robustness with respect to a large range of LiDAR corruptions. In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models ... | ['Dengxin Dai', 'Shuguang Cui', 'Zhen Li', 'Chaoda Zheng', 'Xu Yan'] | 2023-01-03 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 3.09778363e-01 -2.46553481e-01 -1.35112539e-01 -5.32795966e-01
-8.93794298e-01 -5.56320727e-01 3.86881351e-01 9.41221267e-02
-2.20182016e-01 4.97519255e-01 -9.61361974e-02 -4.00880605e-01
-3.48101765e-01 -9.20208931e-01 -8.94268930e-01 -5.33479810e-01
1.34396791e-01 2.06303060e-01 5.04460514e-01 -3.54564786... | [7.9230828285217285, -2.359726667404175] |
c0fca77e-7729-449f-98a5-8f6bb05a0e1f | self-supervised-learning-from-images-with-a | 2301.08243 | null | https://arxiv.org/abs/2301.08243v3 | https://arxiv.org/pdf/2301.08243v3.pdf | Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture | This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations. We introduce the Image-based Joint-Embedding Predictive Architecture (I-JEPA), a non-generative approach for self-supervised learning from images. The idea behind I-JEPA is simple: ... | ['Nicolas Ballas', 'Yann Lecun', 'Michael Rabbat', 'Pascal Vincent', 'Piotr Bojanowski', 'Ishan Misra', 'Quentin Duval', 'Mahmoud Assran'] | 2023-01-19 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Assran_Self-Supervised_Learning_From_Images_With_a_Joint-Embedding_Predictive_Architecture_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Assran_Self-Supervised_Learning_From_Images_With_a_Joint-Embedding_Predictive_Architecture_CVPR_2023_paper.pdf | cvpr-2023-1 | ['object-counting'] | ['computer-vision'] | [ 4.67743874e-01 4.10388917e-01 -1.84388578e-01 -3.39736491e-01
-8.77012730e-01 -3.11540812e-01 7.96021223e-01 -8.99061635e-02
-3.66062790e-01 3.83416384e-01 4.27860767e-01 -2.93005854e-01
3.21151227e-01 -8.74118507e-01 -1.16641152e+00 -7.45562732e-01
-9.54029709e-03 5.38725793e-01 3.33249360e-01 1.34394109... | [9.766432762145996, 1.4235448837280273] |
845a2504-951c-4e0e-9da8-85f1a9ee08c0 | automatically-bounding-the-taylor-remainder | 2212.11429 | null | https://arxiv.org/abs/2212.11429v2 | https://arxiv.org/pdf/2212.11429v2.pdf | Automatically Bounding the Taylor Remainder Series: Tighter Bounds and New Applications | We present a new algorithm for automatically bounding the Taylor remainder series. In the special case of a scalar function $f: \mathbb{R} \to \mathbb{R}$, our algorithm takes as input a reference point $x_0$, trust region $[a, b]$, and integer $k \ge 1$, and returns an interval $I$ such that $f(x) - \sum_{i=0}^{k-1} \... | ['Joshua V. Dillon', 'Matthew Streeter'] | 2022-12-22 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-2.84459412e-01 3.02381031e-02 -1.76562667e-01 -3.47553045e-01
-9.66390789e-01 -6.62439346e-01 -1.15003794e-01 1.27545163e-01
-7.29361594e-01 9.60510731e-01 -6.73661470e-01 -7.69974232e-01
-1.38395578e-01 -8.32738519e-01 -8.81628990e-01 -7.73235977e-01
-6.91943586e-01 3.05481195e-01 -2.18190506e-01 -5.15924037... | [7.338155269622803, 3.673576593399048] |
2f9d3a2e-f5aa-47c2-b71a-4ab1ec6f1532 | utterance-level-aggregation-for-speaker | 1902.10107 | null | https://arxiv.org/abs/1902.10107v2 | https://arxiv.org/pdf/1902.10107v2.pdf | Utterance-level Aggregation For Speaker Recognition In The Wild | The objective of this paper is speaker recognition "in the wild"-where utterances may be of variable length and also contain irrelevant signals. Crucial elements in the design of deep networks for this task are the type of trunk (frame level) network, and the method of temporal aggregation. We propose a powerful speake... | ['Weidi Xie', 'Joon Son Chung', 'Arsha Nagrani', 'Andrew Zisserman'] | 2019-02-26 | utterance-level-aggregation-for-speaker-1 | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 2.25628857e-02 -9.96335000e-02 8.16264674e-02 -7.97751129e-01
-8.61515343e-01 -3.63067478e-01 5.43049037e-01 -3.07103485e-01
-5.27619123e-01 4.05919731e-01 4.19210017e-01 -3.63335878e-01
1.45760819e-01 -2.45485276e-01 -5.13535202e-01 -7.87970841e-01
-2.98459500e-01 8.81781131e-02 9.15080160e-02 -3.16697866... | [14.430797576904297, 6.077672004699707] |
c85e8d79-4822-4f0c-9d64-3f4be0bf218a | correspondence-on-acmg-statement-acmg-sf-v3-0 | 2203.04684 | null | https://arxiv.org/abs/2203.04684v1 | https://arxiv.org/pdf/2203.04684v1.pdf | Correspondence on ACMG STATEMENT: ACMG SF v3.0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG) by Miller et al | We were interested to read the recent update on recommendations for reporting of secondary findings in clinical sequencing1, and the accompanying updated list of genes in which secondary findings should be sought (ACMG SF v3.0)2. Though the authors discuss challenges around incomplete penetrance in considerable detail,... | ['James S. Ware', 'Declan P. O Regan', 'Thomas R. Lumbers', 'Angharad Roberts', 'Nicola Whiffin', 'Antonio de Marvao', 'Matthew Edwards', 'Katherine Josephs', 'Albert Henry', 'Sean L. Zheng', 'Kathryn A. McGurk'] | 2022-03-09 | null | null | null | null | ['medical-genetics'] | ['miscellaneous'] | [ 6.43147469e-01 3.20576727e-01 -3.76562685e-01 -3.56521875e-01
-7.00889289e-01 -7.92087018e-01 -4.98939008e-02 5.66533804e-01
-4.16446477e-01 1.06118274e+00 4.15148824e-01 -8.47212434e-01
-4.93469745e-01 -2.56196916e-01 -4.73846346e-01 -3.56384397e-01
-2.02135623e-01 4.09699351e-01 1.08628504e-01 2.41313159... | [7.952182769775391, 5.639594554901123] |
2a4a2cb9-0ecf-4ffe-a143-2bcfa0531cfe | safe-imitation-learning-of-nonlinear-model | 2212.02941 | null | https://arxiv.org/abs/2212.02941v1 | https://arxiv.org/pdf/2212.02941v1.pdf | Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots | Flexible robots may overcome the industry's major problems: safe human-robot collaboration and increased load-to-mass ratio. However, oscillations and high dimensional state space complicate the control of flexible robots. This work investigates nonlinear model predictive control (NMPC) of flexible robots -- for simult... | ['Jan Swevers', 'Moritz Diehl', 'Rudolf Reiter', 'Shamil Mamedov'] | 2022-12-06 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [ 9.15084183e-02 7.17743397e-01 -2.44200855e-01 3.42701524e-01
-2.41655529e-01 -6.39861763e-01 2.88793683e-01 -4.07595247e-01
-2.78521955e-01 7.94983566e-01 -5.51217794e-01 -2.67795116e-01
-5.17145634e-01 -3.61548543e-01 -9.83896613e-01 -1.02274120e+00
-1.10617213e-01 6.39252603e-01 5.09383567e-02 -3.16179097... | [4.882218837738037, 1.5986902713775635] |
78f324f9-e8dd-41d6-8610-e2913393f69b | kernel-functional-optimisation | null | null | http://proceedings.neurips.cc/paper/2021/hash/251e16a2aac0ca4847adf561483381bf-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/251e16a2aac0ca4847adf561483381bf-Paper.pdf | Kernel Functional Optimisation | Traditional methods for kernel selection rely on parametric kernel functions or a combination thereof and although the kernel hyperparameters are tuned, these methods often provide sub-optimal results due to the limitations induced by the parametric forms. In this paper, we propose a novel formulation for kernel select... | ['Svetha Venkatesh', 'Sunil Gupta', 'Santu Rana', 'Alistair Shilton', 'Arun Kumar Anjanapura Venkatesh'] | 2021-12-01 | null | https://openreview.net/forum?id=zDtFO9vohmF | https://openreview.net/pdf?id=zDtFO9vohmF | neurips-2021-12 | ['bayesian-optimisation'] | ['methodology'] | [-3.75959836e-02 -4.92487907e-01 -3.06106955e-01 -3.06642711e-01
-6.16772950e-01 -4.75205868e-01 4.40154940e-01 -7.13735968e-02
-4.51642334e-01 9.11185801e-01 -3.81515265e-01 -2.48103738e-01
-5.38144052e-01 -6.62809134e-01 -4.42368090e-01 -1.01560879e+00
-1.55061379e-01 3.95309329e-01 5.41845977e-01 1.75456494... | [7.661070346832275, 4.178031921386719] |
814c48d2-d115-4f55-8ea8-7f4244440534 | what-makes-a-language-easy-to-deep-learn | 2302.12239 | null | https://arxiv.org/abs/2302.12239v1 | https://arxiv.org/pdf/2302.12239v1.pdf | What makes a language easy to deep-learn? | Neural networks drive the success of natural language processing. A fundamental property of natural languages is their compositional structure, allowing us to describe new meanings systematically. However, neural networks notoriously struggle with systematic generalization and do not necessarily benefit from a composit... | ['Limor Raviv', 'Yoav Ram', 'Lukas Galke'] | 2023-02-23 | null | null | null | null | ['memorization', 'systematic-generalization'] | ['natural-language-processing', 'reasoning'] | [ 1.94574758e-01 3.78834419e-02 2.16360062e-01 -1.19061351e-01
5.45801640e-01 -7.52247691e-01 1.01673841e+00 3.05456489e-01
-7.31242299e-01 5.83811224e-01 4.88845021e-01 -4.39909607e-01
-9.16432962e-02 -1.05681944e+00 -5.93262613e-01 -6.28793418e-01
-4.61842597e-01 4.71612573e-01 -8.01007673e-02 -6.26745462... | [4.529013633728027, 1.6490544080734253] |
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