paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
474b02c3-0a26-4279-9c2a-8864fa503b73 | microsoft-ai-challenge-india-2018-learning-to | 1906.06056 | null | https://arxiv.org/abs/1906.06056v1 | https://arxiv.org/pdf/1906.06056v1.pdf | Microsoft AI Challenge India 2018: Learning to Rank Passages for Web Question Answering with Deep Attention Networks | This paper describes our system for The Microsoft AI Challenge India 2018: Ranking Passages for Web Question Answering. The system uses the biLSTM network with co-attention mechanism between query and passage representations. Additionally, we use self attention on embeddings to increase the lexical coverage by allowing... | ['Chaitanya Sai Alaparthi'] | 2019-06-14 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-2.62882888e-01 6.35263231e-03 -4.57297787e-02 -2.84752280e-01
-1.42053890e+00 -5.76703370e-01 6.95706010e-01 4.70833510e-01
-1.01744854e+00 5.49098492e-01 6.96657300e-01 -5.43449402e-01
1.71948686e-01 -8.01500857e-01 -8.62160087e-01 2.85733901e-02
-9.19871777e-02 3.07418823e-01 6.15225434e-01 -5.02965510... | [11.329193115234375, 7.890735149383545] |
dc1bbf47-d87a-4535-8a57-d26ed8691446 | bowler-a-neural-approach-to-extractive-text | null | null | https://aclanthology.org/Y18-1017 | https://aclanthology.org/Y18-1017.pdf | BoWLer: A neural approach to extractive text summarization | null | ['Manish Shrivastava', 'Pranav Dhakras'] | null | null | null | null | paclic-2018-12 | ['extractive-document-summarization'] | ['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... | [-7.216468811035156, 3.598571300506592] |
c44bc9dd-ff51-4e1b-89cb-1225f52b6fc6 | musicbert-symbolic-music-understanding-with | 2106.05630 | null | https://arxiv.org/abs/2106.05630v1 | https://arxiv.org/pdf/2106.05630v1.pdf | MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training | Symbolic music understanding, which refers to the understanding of music from the symbolic data (e.g., MIDI format, but not audio), covers many music applications such as genre classification, emotion classification, and music pieces matching. While good music representations are beneficial for these applications, the ... | ['Tie-Yan Liu', 'Tao Qin', 'Zeqian Ju', 'Rui Wang', 'Xu Tan', 'Mingliang Zeng'] | 2021-06-10 | null | https://aclanthology.org/2021.findings-acl.70 | https://aclanthology.org/2021.findings-acl.70.pdf | findings-acl-2021-8 | ['genre-classification'] | ['computer-vision'] | [ 3.85749102e-01 -2.23222986e-01 -2.94035494e-01 -2.73398936e-01
-6.11274898e-01 -8.72742653e-01 8.33280832e-02 -3.28375995e-02
-6.06520176e-02 2.15158716e-01 3.34142834e-01 -1.19514935e-01
-4.76142704e-01 -5.24781764e-01 -4.89173353e-01 -2.70800292e-01
-4.59379032e-02 1.82634190e-01 -2.92935967e-01 -3.49670708... | [15.898619651794434, 5.411362648010254] |
0c951a68-60ab-4345-821a-f82e13749aba | tod-da-towards-boosting-the-robustness-of | 2112.12441 | null | https://arxiv.org/abs/2112.12441v1 | https://arxiv.org/pdf/2112.12441v1.pdf | TOD-DA: Towards Boosting the Robustness of Task-oriented Dialogue Modeling on Spoken Conversations | Task-oriented dialogue systems have been plagued by the difficulties of obtaining large-scale and high-quality annotated conversations. Furthermore, most of the publicly available datasets only include written conversations, which are insufficient to reflect actual human behaviors in practical spoken dialogue systems. ... | ['Haifeng Wang', 'Hua Wu', 'Fan Wang', 'Shuqi Sun', 'Jian Xie', 'Xiyuan Zhang', 'Qiang Ju', 'Liankai Huang', 'Huang He', 'Siqi Bao', 'Yingzhan Lin', 'Dongfeng He', 'Xinxian Huang', 'Xin Tian'] | 2021-12-23 | null | null | null | null | ['spoken-dialogue-systems'] | ['speech'] | [ 5.18972985e-02 3.22582155e-01 5.87973557e-02 -5.75017631e-01
-6.64280772e-01 -4.70310420e-01 9.19433355e-01 -3.14913720e-01
-2.58430332e-01 7.12594151e-01 8.71659279e-01 -3.58174741e-01
3.62531215e-01 -2.50217378e-01 6.27605692e-02 -4.24422383e-01
2.02729329e-01 9.17078018e-01 -1.85447499e-01 -7.48043418... | [12.799704551696777, 7.928284645080566] |
18a6ce31-051d-413b-836d-36f8d197c670 | separating-facts-from-fiction-linguistic | null | null | https://aclanthology.org/P17-2102 | https://aclanthology.org/P17-2102.pdf | Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter | Pew research polls report 62 percent of U.S. adults get news on social media (Gottfried and Shearer, 2016). In a December poll, 64 percent of U.S. adults said that {``}made-up news{''} has caused a {``}great deal of confusion{''} about the facts of current events (Barthel et al., 2016). Fabricated stories in social med... | ['Jin Yea Jang', 'Kyle Shaffer', 'Svitlana Volkova', 'Nathan Hodas'] | 2017-07-01 | null | null | null | acl-2017-7 | ['deception-detection'] | ['miscellaneous'] | [-1.18715845e-01 2.95327276e-01 -6.32112503e-01 -4.99242634e-01
-4.36860383e-01 -5.13977706e-01 1.10445154e+00 9.45780873e-01
-6.08835578e-01 9.08246279e-01 1.08572054e+00 -5.35082400e-01
8.50428194e-02 -9.33220088e-01 -4.46484953e-01 -2.35033631e-02
3.50967079e-01 5.17981276e-02 -2.85585485e-02 -5.78527093... | [8.581302642822266, 10.273999214172363] |
e24b3788-8e53-4a47-80e3-0ab67bd12821 | time-will-tell-new-outlooks-and-a-baseline | 2210.02443 | null | https://arxiv.org/abs/2210.02443v1 | https://arxiv.org/pdf/2210.02443v1.pdf | Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection | While recent camera-only 3D detection methods leverage multiple timesteps, the limited history they use significantly hampers the extent to which temporal fusion can improve object perception. Observing that existing works' fusion of multi-frame images are instances of temporal stereo matching, we find that performance... | ['Wei Zhan', 'Masayoshi Tomizuka', 'Kris Kitani', 'Kurt Keutzer', 'Shijia Yang', 'Chenfeng Xu', 'Jinhyung Park'] | 2022-10-05 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 2.83342540e-01 -3.70116383e-01 -2.52987623e-01 -1.61630794e-01
-1.02718031e+00 -8.22701395e-01 8.07914019e-01 -1.25064790e-01
-4.02427256e-01 5.03170252e-01 2.91877627e-01 7.37349689e-02
-9.40066427e-02 -8.31668019e-01 -7.87883639e-01 -6.69797242e-01
4.31916793e-04 4.49403748e-02 8.17941487e-01 -1.18629508... | [8.401569366455078, -2.228623628616333] |
61d0e99e-35c3-4140-8c4b-ffb1dcf2378e | proceedings-of-the-4th-international-workshop-2 | 2211.13285 | null | https://arxiv.org/abs/2211.13285v1 | https://arxiv.org/pdf/2211.13285v1.pdf | Proceedings of the 4th International Workshop on Reading Music Systems | The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relev... | ['Elona Shatri', 'Alexander Pacha', 'Jorge Calvo-Zaragoza'] | 2022-11-23 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [ 3.87803137e-01 -4.67159003e-01 -1.32238135e-01 2.77526081e-01
-8.39178801e-01 -1.07207441e+00 1.89017847e-01 -9.23499092e-02
-5.34065552e-02 -1.91968605e-01 2.81907886e-01 -3.57777090e-03
-7.99565196e-01 -3.11198920e-01 -1.88211054e-02 -2.26325899e-01
2.27194563e-01 5.34802914e-01 1.49478197e-01 -6.20694719... | [16.041709899902344, 5.194366455078125] |
494e785a-1a12-4253-af1d-2e5686353f38 | zeroavatar-zero-shot-3d-avatar-generation | 2305.16411 | null | https://arxiv.org/abs/2305.16411v1 | https://arxiv.org/pdf/2305.16411v1.pdf | ZeroAvatar: Zero-shot 3D Avatar Generation from a Single Image | Recent advancements in text-to-image generation have enabled significant progress in zero-shot 3D shape generation. This is achieved by score distillation, a methodology that uses pre-trained text-to-image diffusion models to optimize the parameters of a 3D neural presentation, e.g. Neural Radiance Field (NeRF). While ... | ['Serena Yeung', 'Zeyu Wang', 'Zhenzhen Weng'] | 2023-05-25 | null | null | null | null | ['3d-shape-generation', 'image-to-3d'] | ['computer-vision', 'computer-vision'] | [ 3.12746555e-01 4.42005962e-01 4.26466674e-01 -3.99207287e-02
-5.23773611e-01 -3.73799741e-01 7.98489451e-01 -2.25206941e-01
3.13162394e-02 4.42032069e-01 5.59748769e-01 2.50488490e-01
1.89012051e-01 -7.96011686e-01 -8.74627411e-01 -7.85413682e-01
4.01597440e-01 6.71438158e-01 6.82478100e-02 -3.66850346... | [9.412849426269531, -3.2164573669433594] |
dc55ca9e-0e4f-4cae-9ce0-94c701388b76 | automated-audio-captioning-by-fine-tuning | null | null | https://dcase.community/documents/workshop2021/proceedings/DCASE2021Workshop_Gontier_57.pdf | https://dcase.community/documents/workshop2021/proceedings/DCASE2021Workshop_Gontier_57.pdf | AUTOMATED AUDIO CAPTIONING BY FINE-TUNING BART WITH AUDIOSET TAGS | utomated audio captioning is the multimodal task of describing
environmental audio recordings with fluent natural language. Most
current methods utilize pre-trained analysis models to extract rele-
vant semantic content from the audio input. However, prior infor-
mation on language modeling is rarely introduced, an... | ['Christophe Cerisara', 'Romain Serizel', 'F ́elix Gontier'] | 2021-11-15 | null | null | null | dcase-workshop-2021-11 | ['audio-captioning'] | ['audio'] | [ 2.79593796e-01 4.23542708e-02 -6.39001504e-02 -3.72009456e-01
-1.54907513e+00 -5.21989405e-01 2.83619702e-01 1.20853111e-01
-4.94049698e-01 5.56735158e-01 8.45076978e-01 8.47605690e-02
3.98562163e-01 -3.47884387e-01 -9.76305842e-01 -2.67637223e-01
-1.43892944e-01 4.17392194e-01 -4.79039401e-02 -1.13735072... | [15.28309440612793, 4.953114032745361] |
70b6fa2c-11ed-4795-a922-880f60740b7f | online-k-means-clustering | 1909.06861 | null | https://arxiv.org/abs/1909.06861v1 | https://arxiv.org/pdf/1909.06861v1.pdf | Online k-means Clustering | We study the problem of online clustering where a clustering algorithm has to assign a new point that arrives to one of $k$ clusters. The specific formulation we use is the $k$-means objective: At each time step the algorithm has to maintain a set of k candidate centers and the loss incurred is the squared distance bet... | ['Vincent Cohen-Addad', 'Benjamin Guedj', 'Guy Rom', 'Varun Kanade'] | 2019-09-15 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [-1.98778361e-01 2.69129962e-01 1.79958418e-01 -4.61213201e-01
-1.16154063e+00 -8.97411108e-01 -4.87457782e-01 5.57136714e-01
-9.10611629e-01 4.62141484e-01 -5.46019495e-01 -4.51167911e-01
-8.20605934e-01 -8.56704533e-01 -1.03425241e+00 -9.15364444e-01
-5.96118212e-01 9.88148034e-01 6.31013960e-02 9.68084931... | [6.628669261932373, 4.837262153625488] |
c6ae49ac-0640-41ce-924e-a48f9e19f9d3 | the-midi-degradation-toolkit-symbolic-music | 2010.00059 | null | https://arxiv.org/abs/2010.00059v1 | https://arxiv.org/pdf/2010.00059v1.pdf | The MIDI Degradation Toolkit: Symbolic Music Augmentation and Correction | In this paper, we introduce the MIDI Degradation Toolkit (MDTK), containing functions which take as input a musical excerpt (a set of notes with pitch, onset time, and duration), and return a "degraded" version of that excerpt with some error (or errors) introduced. Using the toolkit, we create the Altered and Corrupte... | ['Kazuyoshi Yoshii', 'James Owers', 'Andrew McLeod'] | 2020-09-30 | null | null | null | null | ['music-transcription'] | ['music'] | [ 5.51245511e-01 -1.33823290e-01 3.41329247e-01 -1.46853015e-01
-1.18171251e+00 -1.16981006e+00 3.12233537e-01 1.87578909e-02
-1.14609212e-01 4.25289333e-01 3.96058738e-01 -1.56842172e-01
-1.61038876e-01 -2.51618892e-01 -4.89789963e-01 -5.36869586e-01
-8.54362845e-02 3.91011387e-01 2.56786913e-01 -1.05686218... | [15.727798461914062, 5.740323066711426] |
1a67469f-08e3-4370-b8f0-3b168e9170b8 | fast-hybrid-image-retargeting | 2203.13595 | null | https://arxiv.org/abs/2203.13595v1 | https://arxiv.org/pdf/2203.13595v1.pdf | Fast Hybrid Image Retargeting | Image retargeting changes the aspect ratio of images while aiming to preserve content and minimise noticeable distortion. Fast and high-quality methods are particularly relevant at present, due to the large variety of image and display aspect ratios. We propose a retargeting method that quantifies and limits warping di... | ['Timothy Smith', 'Oleg Muraveynyk', 'Daniel Valdez-Balderas'] | 2022-03-25 | null | null | null | null | ['image-retargeting'] | ['computer-vision'] | [ 9.03778553e-01 5.45974523e-02 1.37925074e-01 -1.59106150e-01
-6.09500527e-01 -6.90504670e-01 8.15797210e-01 4.33740586e-01
-4.79736239e-01 3.08614373e-01 1.91354588e-01 2.28978302e-02
1.37553280e-02 -7.45325446e-01 -8.02993596e-01 -3.48983914e-01
1.78955987e-01 -2.55034454e-02 9.30139244e-01 -3.73933345... | [10.94039535522461, -1.0172843933105469] |
b29e091c-b78b-4677-bfd1-3174099c4404 | learning-to-generate-equitable-text-in | 2307.04303 | null | https://arxiv.org/abs/2307.04303v1 | https://arxiv.org/pdf/2307.04303v1.pdf | Learning to Generate Equitable Text in Dialogue from Biased Training Data | The ingrained principles of fairness in a dialogue system's decision-making process and generated responses are crucial for user engagement, satisfaction, and task achievement. Absence of equitable and inclusive principles can hinder the formation of common ground, which in turn negatively impacts the overall performan... | ['Malihe Alikhani', 'Anthony Sicilia'] | 2023-07-10 | null | null | null | null | ['fairness', 'fairness', 'text-generation', 'decision-making'] | ['computer-vision', 'miscellaneous', 'natural-language-processing', 'reasoning'] | [ 2.67042488e-01 8.93235028e-01 -3.38203669e-01 -5.22602499e-01
-5.32491148e-01 -4.69159484e-01 7.46453166e-01 9.97118875e-02
-4.95831609e-01 1.25976121e+00 5.19905925e-01 -4.53786582e-01
-3.84040759e-04 -7.19722569e-01 -9.71280560e-02 -7.69987106e-02
4.66354012e-01 6.67602599e-01 -4.98035043e-01 -6.47954881... | [12.663613319396973, 8.155471801757812] |
a05c2494-623e-4f2e-bf37-a38092af856a | end-to-end-high-accuracy-license-plate | 2202.10277 | null | https://arxiv.org/abs/2202.10277v1 | https://arxiv.org/pdf/2202.10277v1.pdf | End-to-End High Accuracy License Plate Recognition Based on Depthwise Separable Convolution Networks | Automatic license plate recognition plays a crucial role in modern transportation systems such as for traffic monitoring and vehicle violation detection. In real-world scenarios, license plate recognition still faces many challenges and is impaired by unpredictable interference such as weather or lighting conditions. M... | ['Wen-Kai Tai', 'Zheng-Yi Shen', 'Hong-Yang Shih', 'Song-Ren Wang'] | 2022-02-21 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 5.38567044e-02 -9.78567779e-01 -1.37081593e-01 -3.42571974e-01
-7.54919767e-01 -5.48417509e-01 4.51310873e-01 -5.20965099e-01
-6.24418378e-01 7.14404941e-01 -5.57881117e-01 -2.41851032e-01
1.13816977e-01 -7.51721919e-01 -7.83334434e-01 -6.45396352e-01
2.41052702e-01 4.16269988e-01 9.57966030e-01 -2.84531713... | [9.843086242675781, -4.914684295654297] |
81d82453-b8fd-47ef-9fc5-36600ab092f0 | zero-shot-classification-by-logical-reasoning | 2211.03252 | null | https://arxiv.org/abs/2211.03252v2 | https://arxiv.org/pdf/2211.03252v2.pdf | Zero-Shot Classification by Logical Reasoning on Natural Language Explanations | Humans can classify data of an unseen category by reasoning on its language explanations. This ability is owing to the compositional nature of language: we can combine previously seen attributes to describe the new category. For example, we might describe a sage thrasher as "it has a slim straight relatively short bill... | ['Heng Ji', 'Xinya Du', 'Hengzhi Pei', 'Chi Han'] | 2022-11-07 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 2.71839231e-01 7.03617573e-01 -4.63195056e-01 -7.11121619e-01
-5.25572598e-01 -5.49271166e-01 8.74595106e-01 5.08626759e-01
-1.08497199e-02 4.13835913e-01 5.96632659e-01 -6.64184451e-01
-1.41131818e-01 -8.55991066e-01 -5.94436467e-01 -2.50636667e-01
3.60658973e-01 5.20143032e-01 1.37209818e-01 -2.89848536... | [9.575538635253906, 6.759335517883301] |
5c55f6b5-aff0-477d-9a49-6ac2b8342920 | learn-to-solve-algebra-word-problems-using | null | null | https://aclanthology.org/D15-1096 | https://aclanthology.org/D15-1096.pdf | Learn to Solve Algebra Word Problems Using Quadratic Programming | null | ['Li-Wei Chen', 'Shuaixiang Dai', 'Lipu Zhou'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [-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... | [-7.248019218444824, 3.773810625076294] |
9bb1bd38-5b85-4017-a20e-74d507fb530b | automated-mobile-attention-kpconv-networks | null | null | https://openreview.net/forum?id=VZC5Lzyl0le | https://openreview.net/pdf?id=VZC5Lzyl0le | Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor | Kernel Point Convolution (KPConv) achieves cutting-edge performance on 3D point cloud applications. Unfortunately, the large size of KPConv network limits its usage in mobile scenarios. In addition, we observe that KPConv ignores the kernel relationship and treats each kernel point equally when formulating neighbor-ker... | ['Yiran Chen', 'Hai Li', 'Feng Yan', 'Mingyuan Ma', 'Tunhou Zhang'] | 2021-09-29 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [-3.84895861e-01 -2.77187258e-01 -3.30564976e-01 -2.04260588e-01
-3.95357102e-01 -4.17235672e-01 8.04043189e-02 -2.10208386e-01
-1.98482975e-01 -3.26334573e-02 1.89302508e-02 -8.89463484e-01
-2.94928551e-01 -8.89242709e-01 -9.73173559e-01 -2.65806675e-01
9.21077654e-02 2.09067613e-01 2.93223828e-01 -1.79866314... | [7.883116722106934, -3.585314989089966] |
58062e39-ff7c-4965-b720-aeeef65e9a64 | unsupervised-image-semantic-segmentation | 2210.11810 | null | https://arxiv.org/abs/2210.11810v1 | https://arxiv.org/pdf/2210.11810v1.pdf | Unsupervised Image Semantic Segmentation through Superpixels and Graph Neural Networks | Unsupervised image segmentation is an important task in many real-world scenarios where labelled data is of scarce availability. In this paper we propose a novel approach that harnesses recent advances in unsupervised learning using a combination of Mutual Information Maximization (MIM), Neural Superpixel Segmentation ... | ['Eran Treister', 'Nir Ben Zikri', 'Moshe Eliasof'] | 2022-10-21 | null | null | null | null | ['unsupervised-semantic-segmentation', 'superpixels'] | ['computer-vision', 'computer-vision'] | [ 5.66612840e-01 5.66225171e-01 3.49709429e-02 -3.55892569e-01
-4.29442525e-01 -3.92133087e-01 6.32163882e-01 2.61003733e-01
-6.53485298e-01 6.14547133e-01 8.62417519e-02 -2.02148169e-01
-2.56100237e-01 -7.07216084e-01 -8.12107682e-01 -5.48912108e-01
-6.80802464e-02 5.43464422e-01 6.81769073e-01 -4.69826721... | [9.57740592956543, 0.5221666693687439] |
ed0d4274-ee1c-4afd-87b2-4725b2cfd390 | sentemo-a-multilingual-adaptive-platform-for | null | null | https://aclanthology.org/2022.wassa-1.5 | https://aclanthology.org/2022.wassa-1.5.pdf | SentEMO: A Multilingual Adaptive Platform for Aspect-based Sentiment and Emotion Analysis | In this paper, we present the SentEMO platform, a tool that provides aspect-based sentiment analysis and emotion detection of unstructured text data such as reviews, emails and customer care conversations. Currently, models have been trained for five domains and one general domain and are implemented in a pipeline appr... | ['Veronique Hoste', 'Olivier Parent', 'Pranaydeep Singh', 'Els Lefever', 'Cynthia Van Hee', 'Orphee De Clercq', 'Ellen De Geyndt'] | null | null | null | null | wassa-acl-2022-5 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-1.09714255e-01 2.94872731e-01 -1.26129776e-01 -8.23939681e-01
-2.67247558e-01 -7.54212439e-01 5.95979810e-01 6.43213809e-01
1.26053095e-01 7.06567690e-02 4.55133766e-01 -3.11043710e-01
3.46913904e-01 -7.46441066e-01 -7.37044364e-02 -2.79631466e-01
5.45415819e-01 3.42718720e-01 -9.33060050e-02 -6.13560915... | [11.284825325012207, 6.832795143127441] |
a100440e-38df-45ea-883c-ea9247ac3522 | exploiting-rich-syntax-for-better-knowledge | 2107.07940 | null | https://arxiv.org/abs/2107.07940v1 | https://arxiv.org/pdf/2107.07940v1.pdf | Exploiting Rich Syntax for Better Knowledge Base Question Answering | Recent studies on Knowledge Base Question Answering (KBQA) have shown great progress on this task via better question understanding. Previous works for encoding questions mainly focus on the word sequences, but seldom consider the information from syntactic trees.In this paper, we propose an approach to learn syntax-ba... | ['Min Zhang', 'Wenliang Chen', 'Muhua Zhu', 'Yonghui Jia', 'Pengju Zhang'] | 2021-07-16 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [ 3.56084332e-02 -4.32448313e-02 -1.38567641e-01 -7.16881633e-01
-1.03223097e+00 -6.70829296e-01 1.40369877e-01 3.30417812e-01
-3.53433132e-01 6.24062300e-01 6.97084606e-01 -8.42200518e-01
-1.84139207e-01 -1.23227751e+00 -7.58056223e-01 -1.14058554e-01
1.41430333e-01 4.00731802e-01 7.99829125e-01 -7.30693758... | [10.657815933227539, 7.922436237335205] |
378e4d4e-4a25-40b3-856b-06d17119d9ca | effects-of-word-frequency-based-pre-and-post | 2009.11436 | null | https://arxiv.org/abs/2009.11436v1 | https://arxiv.org/pdf/2009.11436v1.pdf | Effects of Word-frequency based Pre- and Post- Processings for Audio Captioning | The system we used for Task 6 (Automated Audio Captioning)of the Detection and Classification of Acoustic Scenes and Events(DCASE) 2020 Challenge combines three elements, namely, dataaugmentation, multi-task learning, and post-processing, for audiocaptioning. The system received the highest evaluation scores, butwhich ... | ['Kunio Kashino', 'Yasunori Ohishi', 'Yuma Koizumi', 'Daiki Takeuchi', 'Noboru Harada'] | 2020-09-24 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 3.00819188e-01 -1.03049256e-01 2.98161507e-01 -2.16706291e-01
-1.73480785e+00 -7.02198803e-01 5.60605884e-01 2.41804570e-01
-5.91622829e-01 4.55911875e-01 5.03418028e-01 -1.12641402e-01
5.15921926e-03 -9.65310559e-02 -7.54600823e-01 -3.61967266e-01
-1.50568679e-01 2.18125686e-01 2.00501248e-01 -7.84070790... | [15.14358901977539, 5.140678405761719] |
8d22891a-6414-48e1-bc22-6f4c2cf2222d | corpus-lexicography-in-a-wider-context | null | null | https://aclanthology.org/R19-1041 | https://aclanthology.org/R19-1041.pdf | Corpus Lexicography in a Wider Context | This paper describes a set of tools that offers comprehensive solutions for corpus lexicography. The tools perform a range of tasks, including construction of corpus lexicon, integrating information from external dictionaries, internal analysis of the lexicon, and lexical analysis of the corpus. The set of tools is par... | ['Chen Gafni'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['lexical-analysis'] | ['natural-language-processing'] | [-3.33929360e-01 -1.61141291e-01 -3.30888063e-01 -2.15378359e-01
-2.18565181e-01 -8.02863419e-01 5.04542887e-01 3.15730006e-01
-4.50115472e-01 6.77734613e-01 3.84039283e-01 -6.49478137e-01
1.38387114e-01 -6.46347940e-01 1.73093364e-01 -3.68829638e-01
4.00925785e-01 8.44429016e-01 1.69239536e-01 -7.38634288... | [10.31023120880127, 10.097131729125977] |
df63461c-bb99-4831-9afc-a9c0139f3cda | a-systematic-literature-review-on-the-use-of | 2009.06520 | null | https://arxiv.org/abs/2009.06520v2 | https://arxiv.org/pdf/2009.06520v2.pdf | A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research | An increasingly popular set of techniques adopted by software engineering (SE) researchers to automate development tasks are those rooted in the concept of Deep Learning (DL). The popularity of such techniques largely stems from their automated feature engineering capabilities, which aid in modeling software artifacts.... | ['Nathan Cooper', 'Kevin Moran', 'Denys Poshyvanyk', 'David Nader Palacio', 'Cody Watson'] | 2020-09-14 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [ 3.81179564e-02 1.31989792e-01 -4.15574193e-01 -1.84836298e-01
-2.85080582e-01 -3.34129035e-01 5.02585709e-01 8.39022547e-03
2.40273297e-01 2.13426381e-01 -4.88593020e-02 -4.94054198e-01
-4.55012560e-01 -6.26743972e-01 -5.86023390e-01 -9.95865837e-02
-4.46601957e-02 -6.47351369e-02 -9.99508649e-02 -1.28571317... | [7.82426643371582, 7.564787864685059] |
90ce1ec0-71c5-4872-b080-6e462724c900 | investigating-deep-learning-benchmarks-for | 2204.04420 | null | https://arxiv.org/abs/2204.04420v1 | https://arxiv.org/pdf/2204.04420v1.pdf | Investigating Deep Learning Benchmarks for Electrocardiography Signal Processing | In recent years, deep learning has witnessed its blossom in the field of Electrocardiography (ECG) processing, outperforming traditional signal processing methods in various tasks, for example, classification, QRS detection, wave delineation. Although many neural architectures have been proposed in the literature, ther... | ['Kang Jingsu', 'Wen Hao'] | 2022-04-09 | null | null | null | null | ['ecg-qrs-detection', 'ecg-classification', 'ecg-wave-delineation', 'atrial-fibrillation-detection', 'electrocardiography-ecg'] | ['medical', 'medical', 'medical', 'medical', 'methodology'] | [ 8.56479034e-02 -2.85440475e-01 1.61227584e-01 -4.17782098e-01
-6.21954203e-01 -3.96140575e-01 -1.67380199e-01 3.90425503e-01
-4.51222569e-01 5.58661342e-01 -6.50156885e-02 -4.12267655e-01
-4.41464305e-01 -5.90151191e-01 -1.00587986e-01 -8.59329998e-01
-6.19177163e-01 3.18013132e-01 -3.13117355e-01 -1.43680409... | [14.329315185546875, 3.266768455505371] |
b0e26bf0-aee6-4f29-ad88-7d8c7eaae5b9 | towards-a-broad-coverage-named-entity | 2201.12219 | null | https://arxiv.org/abs/2201.12219v2 | https://arxiv.org/pdf/2201.12219v2.pdf | Towards a Broad Coverage Named Entity Resource: A Data-Efficient Approach for Many Diverse Languages | Parallel corpora are ideal for extracting a multilingual named entity (MNE) resource, i.e., a dataset of names translated into multiple languages. Prior work on extracting MNE datasets from parallel corpora required resources such as large monolingual corpora or word aligners that are unavailable or perform poorly for ... | ['Hinrich Schütze', 'Philipp Dufter', 'Ayyoob Imani', 'Silvia Severini'] | 2022-01-28 | null | https://aclanthology.org/2022.lrec-1.417 | https://aclanthology.org/2022.lrec-1.417.pdf | lrec-2022-6 | ['transliteration'] | ['natural-language-processing'] | [-1.40112624e-01 -5.99817233e-03 -8.30215096e-01 -1.89057410e-01
-9.98021603e-01 -1.13866603e+00 6.43484354e-01 1.62236305e-04
-8.24525177e-01 1.16603255e+00 5.34434021e-01 -9.08853173e-01
4.24808323e-01 -6.62400544e-01 -7.65493631e-01 1.26365900e-01
1.38450503e-01 1.15281570e+00 -7.07542300e-02 -4.45092261... | [9.99817943572998, 9.519619941711426] |
5ff67d3d-197f-48f5-8698-49c20484798e | exploration-in-deep-reinforcement-learning-a-1 | 2205.00824 | null | https://arxiv.org/abs/2205.00824v1 | https://arxiv.org/pdf/2205.00824v1.pdf | Exploration in Deep Reinforcement Learning: A Survey | This paper reviews exploration techniques in deep reinforcement learning. Exploration techniques are of primary importance when solving sparse reward problems. In sparse reward problems, the reward is rare, which means that the agent will not find the reward often by acting randomly. In such a scenario, it is challengi... | ['Hyondong Oh', 'Minwoo Kim', 'Lilian Weng', 'Pawel Ladosz'] | 2022-05-02 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-2.01410770e-01 1.41282991e-01 -6.25909388e-01 8.76225978e-02
-3.85422647e-01 -3.33151042e-01 5.38270950e-01 -6.45592213e-02
-6.28864646e-01 1.51059258e+00 -1.22979574e-01 -1.07696466e-02
-4.14380312e-01 -5.78378439e-01 -3.78927350e-01 -1.09240162e+00
-6.73617125e-01 4.21457976e-01 6.18837327e-02 -4.37634498... | [3.9191527366638184, 1.8477587699890137] |
cfd9c055-370d-4654-85bf-3d564ebbd4bc | vipformer-efficient-vision-and-pointcloud | 2303.14376 | null | https://arxiv.org/abs/2303.14376v1 | https://arxiv.org/pdf/2303.14376v1.pdf | ViPFormer: Efficient Vision-and-Pointcloud Transformer for Unsupervised Pointcloud Understanding | Recently, a growing number of work design unsupervised paradigms for point cloud processing to alleviate the limitation of expensive manual annotation and poor transferability of supervised methods. Among them, CrossPoint follows the contrastive learning framework and exploits image and point cloud data for unsupervise... | ['Deying Li', 'Xuewei Bai', 'Xudong Cai', 'Yongcai Wang', 'Hongyu Sun'] | 2023-03-25 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [ 7.48933703e-02 -9.67421457e-02 -3.18165123e-02 -3.65747124e-01
-7.48105228e-01 -6.57976329e-01 4.05180216e-01 2.02363372e-01
-2.54469573e-01 5.78018576e-02 -5.79208076e-01 -1.03878617e-01
-2.26762630e-02 -6.93712592e-01 -7.90274262e-01 -7.50840604e-01
2.51888514e-01 6.01603746e-01 5.30385315e-01 1.20126083... | [8.026222229003906, -3.171055555343628] |
3120d4cb-1719-44fe-bc8e-27d2eaf06fe5 | flexible-dataset-distillation-learn-labels | 2006.08572 | null | https://arxiv.org/abs/2006.08572v3 | https://arxiv.org/pdf/2006.08572v3.pdf | Flexible Dataset Distillation: Learn Labels Instead of Images | We study the problem of dataset distillation - creating a small set of synthetic examples capable of training a good model. In particular, we study the problem of label distillation - creating synthetic labels for a small set of real images, and show it to be more effective than the prior image-based approach to datase... | ['Timothy Hospedales', 'Yongxin Yang', 'Ondrej Bohdal'] | 2020-06-15 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 6.17818117e-01 4.72019762e-01 1.25668198e-01 -5.91428936e-01
-9.39102113e-01 -7.87521124e-01 8.85685384e-01 -2.35797331e-01
-6.77452683e-01 1.08096433e+00 5.34857661e-02 -1.19005196e-01
1.82791144e-01 -5.76556802e-01 -1.02211845e+00 -6.54769838e-01
2.80603319e-01 1.06718159e+00 -4.16556865e-01 -7.89913833... | [10.946633338928223, 0.5435497164726257] |
ab45ccf9-2761-44c0-b6ee-2feef24b1788 | age-and-gender-classification-with-small | null | null | https://koreascience.kr/article/JAKO202209464471634.page | https://koreascience.kr/article/JAKO202209464471634.pdf | Age and Gender Classification with Small Scale CNN | Artificial intelligence is getting a crucial part of our lives with its incredible benefits. Machines outperform humans
in recognizing objects in images, particularly in classifying people into correct age and gender groups. In this respect,
age and gender classification has been one of the hot topics among computer ... | ['J. H. Yoo', 'H. Yoo', 'U. Jamoliddin'] | 2022-02-28 | null | null | null | journal-of-the-kiecs-2022-2 | ['age-and-gender-classification'] | ['computer-vision'] | [-4.07548547e-01 2.14857538e-03 -2.12329209e-01 -6.78830385e-01
3.63448650e-01 -9.00982991e-02 6.01339579e-01 2.34493405e-01
-7.40855217e-01 6.59114003e-01 -1.49008140e-01 -1.74535617e-01
-6.69907108e-02 -1.07857144e+00 -9.71458703e-02 -6.43746436e-01
-3.26143252e-03 6.41456127e-01 -2.50065863e-01 -1.38663203... | [13.554365158081055, 0.9853693842887878] |
beee4495-15b8-4245-9276-87c97fcf6fbd | dynamic-multistep-reasoning-based-on-video | null | null | https://aclanthology.org/2022.naacl-main.286 | https://aclanthology.org/2022.naacl-main.286.pdf | Dynamic Multistep Reasoning based on Video Scene Graph for Video Question Answering | Existing video question answering (video QA) models lack the capacity for deep video understanding and flexible multistep reasoning. We propose for video QA a novel model which performs dynamic multistep reasoning between questions and videos. It creates video semantic representation based on the video scene graph comp... | ['Yong Zhu', 'Hong Liu', 'Yajuan Lyu', 'Zhifan Feng', 'Xiangdong Wang', 'Wenbin Jiang', 'Jianguo Mao'] | null | null | null | null | naacl-2022-7 | ['video-question-answering'] | ['computer-vision'] | [-6.63028508e-02 -2.59155091e-02 3.81443761e-02 -3.39699507e-01
-6.75775766e-01 -5.35241961e-01 3.94159228e-01 -1.96181193e-01
-4.15258063e-03 3.75197172e-01 5.82353234e-01 -3.95668745e-01
-3.10746670e-01 -6.44925773e-01 -8.67808223e-01 -1.22679643e-01
3.70056301e-01 4.44973588e-01 7.18243062e-01 -4.51682895... | [10.430496215820312, 1.079901933670044] |
1157d2a6-a97b-4ac9-8b5f-5e566116af18 | wronging-a-right-generating-better-errors-to-1 | 1810.00668 | null | http://arxiv.org/abs/1810.00668v1 | http://arxiv.org/pdf/1810.00668v1.pdf | Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection | Grammatical error correction, like other machine learning tasks, greatly
benefits from large quantities of high quality training data, which is
typically expensive to produce. While writing a program to automatically
generate realistic grammatical errors would be difficult, one could learn the
distribution of naturally... | ['Riedel Sebastian', 'Stenetorp Pontus', 'Kasewa Sudhanshu'] | 2018-09-26 | wronging-a-right-generating-better-errors-to | https://aclanthology.org/D18-1541 | https://aclanthology.org/D18-1541.pdf | emnlp-2018-10 | ['grammatical-error-detection'] | ['natural-language-processing'] | [ 5.74875593e-01 4.90559846e-01 4.13904369e-01 -6.95564091e-01
-1.46833789e+00 -4.58163738e-01 3.51816684e-01 2.92168498e-01
-5.25970638e-01 1.15320349e+00 3.36383432e-02 -7.34165370e-01
7.04919279e-01 -6.48922801e-01 -1.28387558e+00 -6.17677905e-02
2.44093552e-01 7.39611804e-01 -1.41960725e-01 -3.99205089... | [11.18777847290039, 10.275540351867676] |
ed03b7db-b927-4e7b-b577-b2d3d8e80dbb | scientific-evidence-extraction | 2110.00061 | null | https://arxiv.org/abs/2110.00061v3 | https://arxiv.org/pdf/2110.00061v3.pdf | PubTables-1M: Towards comprehensive table extraction from unstructured documents | Recently, significant progress has been made applying machine learning to the problem of table structure inference and extraction from unstructured documents. However, one of the greatest challenges remains the creation of datasets with complete, unambiguous ground truth at scale. To address this, we develop a new, mor... | ['Robin Abraham', 'Rohith Pesala', 'Brandon Smock'] | 2021-09-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Smock_PubTables-1M_Towards_Comprehensive_Table_Extraction_From_Unstructured_Documents_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Smock_PubTables-1M_Towards_Comprehensive_Table_Extraction_From_Unstructured_Documents_CVPR_2022_paper.pdf | cvpr-2022-1 | ['table-recognition', 'table-detection', 'table-extraction'] | ['computer-vision', 'miscellaneous', 'miscellaneous'] | [ 3.29321951e-01 8.82260427e-02 -4.67164069e-01 -3.10491621e-01
-1.48590374e+00 -9.53989625e-01 4.46738094e-01 7.43184626e-01
1.28168643e-01 7.22436428e-01 1.12098619e-01 -3.15461785e-01
1.12900540e-01 -8.29914451e-01 -9.41021621e-01 -2.21230149e-01
2.15904370e-01 6.42949700e-01 2.07582369e-01 1.78959534... | [9.634026527404785, 7.8473219871521] |
bc0d5172-986e-47a1-af7c-689e61f19ec2 | region-based-contrastive-pretraining-for | 2305.05598 | null | https://arxiv.org/abs/2305.05598v1 | https://arxiv.org/pdf/2305.05598v1.pdf | Region-based Contrastive Pretraining for Medical Image Retrieval with Anatomic Query | We introduce a novel Region-based contrastive pretraining for Medical Image Retrieval (RegionMIR) that demonstrates the feasibility of medical image retrieval with similar anatomical regions. RegionMIR addresses two major challenges for medical image retrieval i) standardization of clinically relevant searching criteri... | ['Ivan Tarapov', 'Javier Alvarez-Valle', 'Fernando Pérez-García', 'Ozan Oktay', 'Jameson Merkow', 'Alberto Santamaria-Pang', 'Ho Hin Lee'] | 2023-05-09 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval', 'anatomy'] | ['computer-vision', 'medical', 'miscellaneous'] | [ 3.88362348e-01 -1.68492392e-01 -6.65030003e-01 -2.95155764e-01
-1.73952830e+00 -7.76886642e-01 2.69866288e-01 5.78451335e-01
-7.34261274e-01 4.88978207e-01 3.68241191e-01 -3.77257243e-02
-7.81296790e-01 -5.71004391e-01 -4.80609268e-01 -7.73244560e-01
-2.11070657e-01 4.88653183e-01 -1.19500071e-01 1.42327949... | [14.418212890625, -1.6064180135726929] |
be2a98e5-c762-4e7c-a5b1-0d2306b4f1a3 | crossatnet-a-novel-cross-attention-based | 2104.09918 | null | https://arxiv.org/abs/2104.09918v1 | https://arxiv.org/pdf/2104.09918v1.pdf | CrossATNet - A Novel Cross-Attention Based Framework for Sketch-Based Image Retrieval | We propose a novel framework for cross-modal zero-shot learning (ZSL) in the context of sketch-based image retrieval (SBIR). Conventionally, the SBIR schema mainly considers simultaneous mappings among the two image views and the semantic side information. Therefore, it is desirable to consider fine-grained classes mai... | ['Mihai Datcu', 'Avik Bhattacharya', 'Biplab Banerjee', 'Ushasi Chaudhuri'] | 2021-04-20 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 1.67303100e-01 -2.40321115e-01 -2.48864219e-01 -2.96636134e-01
-9.10131276e-01 -5.25539279e-01 6.98215663e-01 -1.70690596e-01
-8.14621374e-02 4.70963359e-01 -6.87435865e-02 1.49843544e-01
-5.86247385e-01 -1.08134651e+00 -7.17531204e-01 -9.29783523e-01
4.79934961e-01 4.56869036e-01 1.29924908e-01 -2.37301141... | [11.641742706298828, 0.712063729763031] |
2b8f7a0c-817e-4a7b-b96e-244cc6b96ffb | deep-node-ranking-an-algorithm-for-structural | 1902.03964 | null | https://arxiv.org/abs/1902.03964v6 | https://arxiv.org/pdf/1902.03964v6.pdf | Deep Node Ranking for Neuro-symbolic Structural Node Embedding and Classification | Network node embedding is an active research subfield of complex network analysis. This paper contributes a novel approach to learning network node embeddings and direct node classification using a node ranking scheme coupled with an autoencoder-based neural network architecture. The main advantages of the proposed Dee... | ['Nada Lavrač', 'Marko Robnik-Šikonja', 'Blaž Škrlj', 'Jan Kralj', 'Janez Konc'] | 2019-02-11 | null | null | null | null | ['structural-node-embedding'] | ['graphs'] | [-1.26052245e-01 4.03125316e-01 -4.21145797e-01 -3.14389139e-01
6.64754063e-02 -4.01502252e-01 6.97125375e-01 4.74527448e-01
-4.64555413e-01 5.82038343e-01 -6.43092170e-02 -5.89431167e-01
-6.93179846e-01 -1.32693708e+00 -2.97029465e-01 -6.94678962e-01
-8.54747653e-01 7.98416078e-01 2.44857103e-01 -3.89480114... | [7.0467023849487305, 6.278100490570068] |
a295451f-beb8-4201-900c-73a01fc008c4 | bmd-a-general-class-balanced-multicentric | 2204.02811 | null | https://arxiv.org/abs/2204.02811v2 | https://arxiv.org/pdf/2204.02811v2.pdf | BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy for Source-free Domain Adaptation | Source-free Domain Adaptation (SFDA) aims to adapt a pre-trained source model to the unlabeled target domain without accessing the well-labeled source data, which is a much more practical setting due to the data privacy, security, and transmission issues. To make up for the absence of source data, most existing methods... | ['DaCheng Tao', 'wei he', 'Zhijun Li', 'Jing Zhang', 'Guang Chen', 'Sanqing Qu'] | 2022-04-06 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 2.04117492e-01 -1.03803709e-01 -5.14443457e-01 -5.93154848e-01
-7.86005020e-01 -6.33773029e-01 3.98934841e-01 3.74761894e-02
-2.98942715e-01 8.43824327e-01 -2.14928165e-01 -5.92616433e-03
1.15380682e-01 -7.64068305e-01 -6.34821177e-01 -8.38492572e-01
3.42298359e-01 5.16506374e-01 3.61735910e-01 1.44755438... | [10.404645919799805, 3.0598461627960205] |
7ce95271-1d76-4315-ae68-624782065afa | exot-exit-aware-object-tracker-for-safe | 2306.05262 | null | https://arxiv.org/abs/2306.05262v1 | https://arxiv.org/pdf/2306.05262v1.pdf | EXOT: Exit-aware Object Tracker for Safe Robotic Manipulation of Moving Object | Current robotic hand manipulation narrowly operates with objects in predictable positions in limited environments. Thus, when the location of the target object deviates severely from the expected location, a robot sometimes responds in an unexpected way, especially when it operates with a human. For safe robot operatio... | ['Byoung-Tak Zhang', 'Dong-Sig Han', 'Minji Kim', 'Hye Jung Yoon', 'Hyunseo Kim'] | 2023-06-08 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [-1.05092399e-01 -1.70703262e-01 -1.94110498e-01 -3.89018166e-03
-4.40940678e-01 -6.78945243e-01 2.23343968e-01 -4.35181856e-01
-3.59805763e-01 2.62185723e-01 -4.05205339e-01 -1.57926232e-01
6.59394041e-02 -1.90588266e-01 -9.29237723e-01 -7.12069750e-01
-1.00312166e-01 5.56617320e-01 7.11201072e-01 1.63165659... | [6.448986053466797, -1.1267964839935303] |
15ba72f4-a173-4df4-975b-11f8f15f01f2 | image-steganography-based-on-iteratively | 2101.05209 | null | https://arxiv.org/abs/2101.05209v1 | https://arxiv.org/pdf/2101.05209v1.pdf | Image Steganography based on Iteratively Adversarial Samples of A Synchronized-directions Sub-image | Nowadays a steganography has to face challenges of both feature based staganalysis and convolutional neural network (CNN) based steganalysis. In this paper, we present a novel steganography scheme denoted as ITE-SYN (based on ITEratively adversarial perturbations onto a SYNchronized-directions sub-image), by which secu... | ['Jiwu Huang', 'Weixuan Tang', 'Bin Li', 'Shunquan Tan', 'Xinghong Qin'] | 2021-01-13 | null | null | null | null | ['steganalysis', 'image-steganography'] | ['computer-vision', 'computer-vision'] | [ 9.6573603e-01 2.3387757e-01 1.1756521e-01 1.4794651e-01
-2.9071929e-02 -7.1409172e-01 2.9870453e-01 -4.0500885e-01
-4.0117258e-01 5.1261657e-01 -3.5508761e-01 -2.4281046e-01
3.1823003e-01 -1.0786027e+00 -6.7780548e-01 -1.0613914e+00
-1.6404410e-01 -2.2923100e-01 5.6985396e-01 -5.6866413e-01
3.3243307e-01... | [4.293829441070557, 8.056559562683105] |
ddc7c3fa-b378-42d8-a20e-3cb1ea359aa1 | skeleton-based-zero-shot-action-recognition | 1911.11344 | null | https://arxiv.org/abs/1911.11344v1 | https://arxiv.org/pdf/1911.11344v1.pdf | Skeleton based Zero Shot Action Recognition in Joint Pose-Language Semantic Space | How does one represent an action? How does one describe an action that we have never seen before? Such questions are addressed by the Zero Shot Learning paradigm, where a model is trained on only a subset of classes and is evaluated on its ability to correctly classify an example from a class it has never seen before. ... | ['Afshaan Mazagonwalla', 'Bhavan Jasani'] | 2019-11-26 | null | null | null | null | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 5.04147410e-01 2.05684960e-01 -4.75144029e-01 -5.56516469e-01
-4.06192631e-01 -3.49771023e-01 8.60289633e-01 -1.40287643e-02
-4.13938522e-01 4.37390625e-01 7.51078010e-01 4.74115223e-01
3.16707306e-02 -8.03148210e-01 -7.36565173e-01 -4.86403167e-01
-4.97771688e-02 9.32077587e-01 4.18867409e-01 -3.15229565... | [8.465161323547363, 0.8416976928710938] |
6707f7a4-7132-4e5b-8171-aa56c54e46c6 | pareto-front-identification-with-regret | 2306.00096 | null | https://arxiv.org/abs/2306.00096v1 | https://arxiv.org/pdf/2306.00096v1.pdf | Pareto Front Identification with Regret Minimization | We consider Pareto front identification for linear bandits (PFILin) where the goal is to identify a set of arms whose reward vectors are not dominated by any of the others when the mean reward vector is a linear function of the context. PFILin includes the best arm identification problem and multi-objective active lear... | ['Assaf Zeevi', 'Garud Iyengar', 'Wonyoung Kim'] | 2023-05-31 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 6.65823445e-02 -7.57191852e-02 -7.75191009e-01 -1.05590120e-01
-1.13511586e+00 -1.10452604e+00 -2.08265066e-01 2.17129290e-01
-6.05530322e-01 8.89027178e-01 -1.92747727e-01 -5.16603827e-01
-9.56209838e-01 -7.21541524e-01 -7.57850468e-01 -9.94431436e-01
-4.12545592e-01 7.80009449e-01 -2.35948265e-01 1.41196549... | [4.568022727966309, 3.337733507156372] |
984ac012-58ea-4a1b-8136-cd6315d4999c | exploiting-all-samples-in-low-resource | 2111.06971 | null | https://arxiv.org/abs/2111.06971v1 | https://arxiv.org/pdf/2111.06971v1.pdf | Exploiting all samples in low-resource sentence classification: early stopping and initialization parameters | In low resource settings, deep neural models have often shown lower performance due to overfitting. The primary method to solve the overfitting problem is to generalize model parameters. To this end, many researchers have depended on large external resources with various manipulation techniques. In this study, we discu... | ['Hyunju Lee', 'HongSeok Choi'] | 2021-11-12 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [ 5.77740967e-02 -3.08752775e-01 -3.45771343e-01 -6.24117255e-01
-5.01085341e-01 -2.27738395e-01 1.90178618e-01 -3.18824947e-02
-9.26709354e-01 8.37916970e-01 -2.63769150e-01 -2.67779976e-01
7.00048753e-04 -7.03057766e-01 -4.64534551e-01 -7.11030066e-01
3.06855857e-01 1.79777518e-01 1.97537333e-01 5.57247363... | [9.103506088256836, 3.951772451400757] |
c7af6f68-a6e3-484c-b90b-ad8dd83c1f44 | learning-to-combine-instructions-in-llvm | 2202.12379 | null | https://arxiv.org/abs/2202.12379v1 | https://arxiv.org/pdf/2202.12379v1.pdf | Learning to Combine Instructions in LLVM Compiler | Instruction combiner (IC) is a critical compiler optimization pass, which replaces a sequence of instructions with an equivalent and optimized instruction sequence at basic block level. There can be thousands of instruction-combining patterns which need to be frequently updated as new coding idioms/applications and nov... | ['Dibyendu Das', 'Sandya Mannarswamy'] | 2022-02-22 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [ 3.91974300e-01 -1.89828441e-01 -7.49276698e-01 -4.43609029e-01
-8.24584961e-01 -5.98831117e-01 1.47281334e-01 1.13690831e-01
-3.70036751e-01 4.11840230e-01 3.41288924e-01 -1.19598556e+00
6.97163165e-01 -6.91863716e-01 -1.18216443e+00 1.17772758e-01
-3.37313041e-02 9.35216025e-02 -1.02751575e-01 -4.39232558... | [7.805032253265381, 7.5995659828186035] |
23e8b2fb-4c81-4821-90d4-8729fd42cf3a | read-before-generate-faithful-long-form | 2203.00343 | null | https://arxiv.org/abs/2203.00343v1 | https://arxiv.org/pdf/2203.00343v1.pdf | Read before Generate! Faithful Long Form Question Answering with Machine Reading | Long-form question answering (LFQA) aims to generate a paragraph-length answer for a given question. While current work on LFQA using large pre-trained model for generation are effective at producing fluent and somewhat relevant content, one primary challenge lies in how to generate a faithful answer that has less hall... | ['Pascale Fung', 'Qun Liu', 'Xin Jiang', 'Lifeng Shang', 'Jindi Zhang', 'Xiaoguang Li', 'Dan Su'] | 2022-03-01 | null | https://aclanthology.org/2022.findings-acl.61 | https://aclanthology.org/2022.findings-acl.61.pdf | findings-acl-2022-5 | ['long-form-question-answering'] | ['natural-language-processing'] | [ 1.08449504e-01 6.06375277e-01 2.46828943e-01 -4.61879700e-01
-1.55398047e+00 -6.31272435e-01 9.33300495e-01 3.67139690e-02
-1.47227570e-01 1.09020066e+00 1.06982076e+00 -2.22846150e-01
3.37498009e-01 -9.32374358e-01 -6.74456775e-01 8.95831883e-02
6.71767294e-01 9.45583284e-01 1.00932933e-01 -8.61180365... | [11.485321998596191, 8.158233642578125] |
e03e8c94-40c9-4130-bc34-a737a6f3fe4d | multi-channel-transformers-for-multi | 2009.00299 | null | https://arxiv.org/abs/2009.00299v1 | https://arxiv.org/pdf/2009.00299v1.pdf | Multi-channel Transformers for Multi-articulatory Sign Language Translation | Sign languages use multiple asynchronous information channels (articulators), not just the hands but also the face and body, which computational approaches often ignore. In this paper we tackle the multi-articulatory sign language translation task and propose a novel multi-channel transformer architecture. The proposed... | ['Simon Hadfield', 'Oscar Koller', 'Richard Bowden', 'Necati Cihan Camgoz'] | 2020-09-01 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 3.38595331e-01 1.50156943e-02 -1.74731508e-01 -1.50037974e-01
-9.04066503e-01 -1.05689299e+00 1.08771813e+00 -5.00068367e-01
-6.59249187e-01 7.49849677e-01 7.66191185e-01 -1.18895501e-01
4.64019887e-02 -4.23921376e-01 -6.29230440e-01 -3.59636754e-01
2.28013590e-01 6.60458326e-01 1.26746103e-01 -2.45474905... | [9.216373443603516, -6.543091297149658] |
82a7f2d9-94d6-47f3-b040-05c2da6289b6 | inherently-interpretable-multi-label | 2303.00500 | null | https://arxiv.org/abs/2303.00500v1 | https://arxiv.org/pdf/2303.00500v1.pdf | Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals | Interpretability is essential for machine learning algorithms in high-stakes application fields such as medical image analysis. However, high-performing black-box neural networks do not provide explanations for their predictions, which can lead to mistrust and suboptimal human-ML collaboration. Post-hoc explanation tec... | ['Christian F. Baumgartner', 'Lisa M. Koch', 'Andreas Maier', 'Stefano Woerner', 'Susu Sun'] | 2023-03-01 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 5.77488184e-01 1.02951252e+00 -7.32586026e-01 -8.03976059e-01
-6.10812783e-01 -3.42453569e-01 3.12931687e-01 4.88602847e-01
-1.90771855e-02 9.94891584e-01 3.57107848e-01 -9.03998733e-01
-3.43693882e-01 -2.86871821e-01 -6.67788804e-01 -3.18297386e-01
3.27601492e-01 6.36197984e-01 -4.59966749e-01 3.89966995... | [8.596667289733887, 5.715461730957031] |
1f41588a-4d60-46a1-af31-bf7528177b35 | one-class-svm-on-siamese-neural-network | 2304.08058 | null | https://arxiv.org/abs/2304.08058v1 | https://arxiv.org/pdf/2304.08058v1.pdf | One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities | Anomaly detection remains a challenging task in neuroimaging when little to no supervision is available and when lesions can be very small or with subtle contrast. Patch-based representation learning has shown powerful representation capacities when applied to industrial or medical imaging and outlier detection methods... | ['Carole Lartizien', 'Robin Trombetta', 'Nicolas Pinon'] | 2023-04-17 | null | null | null | null | ['outlier-detection'] | ['methodology'] | [ 3.22658122e-01 1.76161062e-02 1.92787454e-01 -2.61198014e-01
-7.74800122e-01 1.31423429e-01 6.41786039e-01 3.98993462e-01
-4.83951688e-01 4.62760687e-01 3.71166505e-02 -1.04395546e-01
-1.59947142e-01 -1.79722890e-01 -6.86128497e-01 -8.12340021e-01
-6.66939497e-01 7.45480835e-01 3.26444030e-01 2.20721796... | [7.682313442230225, 2.2269413471221924] |
70571ae3-fad1-45a0-8676-c74202819704 | a-scalable-method-for-quantifying-the-role-of | null | null | https://aclanthology.org/W19-5934 | https://aclanthology.org/W19-5934.pdf | A Scalable Method for Quantifying the Role of Pitch in Conversational Turn-Taking | Pitch has long been held as an important signalling channel when planning and deploying speech in conversation, and myriad studies have been undertaken to determine the extent to which it actually plays this role. Unfortunately, these studies have required considerable human investment in data preparation and analysis,... | ['Kornel Laskowski', 'Mattias Heldner', 'Marcin Wlodarczak'] | 2019-09-01 | null | null | null | ws-2019-9 | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 3.37065935e-01 1.94432050e-01 -2.82217771e-01 -3.50693911e-01
-6.89626217e-01 -8.98843765e-01 9.31218803e-01 3.83840501e-01
-3.78854692e-01 5.77409685e-01 7.19829917e-01 -5.13948262e-01
-1.43648982e-01 -4.08760309e-01 2.16672882e-01 -6.11387849e-01
-1.41078785e-01 4.03862417e-01 1.72097787e-01 -3.44420880... | [14.249629020690918, 6.710218906402588] |
64e3dbfb-5d6f-4ef0-abcd-733bfac51c89 | spectral-embedding-for-dynamic-networks-with | 2106.01282 | null | https://arxiv.org/abs/2106.01282v2 | https://arxiv.org/pdf/2106.01282v2.pdf | Spectral embedding for dynamic networks with stability guarantees | We consider the problem of embedding a dynamic network, to obtain time-evolving vector representations of each node, which can then be used to describe changes in behaviour of individual nodes, communities, or the entire graph. Given this open-ended remit, we argue that two types of stability in the spatio-temporal pos... | ['Patrick Rubin-Delanchy', 'Andrew Jones', 'Ian Gallagher'] | 2021-06-02 | null | http://proceedings.neurips.cc/paper/2021/hash/5446f217e9504bc593ad9dcf2ec88dda-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/5446f217e9504bc593ad9dcf2ec88dda-Paper.pdf | neurips-2021-12 | ['stochastic-block-model'] | ['graphs'] | [ 2.48865440e-01 2.60342032e-01 -2.18718704e-02 8.54533389e-02
2.11088225e-01 -9.34766233e-01 8.91276777e-01 3.95016998e-01
-1.03559978e-01 4.95425314e-01 4.50825423e-01 -3.76196384e-01
-7.59406209e-01 -1.00566483e+00 -3.35336387e-01 -8.84533823e-01
-7.35408902e-01 3.65751505e-01 1.78330094e-01 -2.20319048... | [7.061751365661621, 5.410226821899414] |
7e7f13df-3609-4a74-965b-8346d5632713 | deep-probabilistic-kernels-for-sample | 1910.05858 | null | https://arxiv.org/abs/1910.05858v2 | https://arxiv.org/pdf/1910.05858v2.pdf | Deep Kernels with Probabilistic Embeddings for Small-Data Learning | Gaussian Processes (GPs) are known to provide accurate predictions and uncertainty estimates even with small amounts of labeled data by capturing similarity between data points through their kernel function. However traditional GP kernels are not very effective at capturing similarity between high dimensional data poin... | ['T. Yong-Jin Han', 'Gauri Joshi', 'Chaitanya Dwivedi', 'Ankur Mallick', 'Bhavya Kailkhura'] | 2019-10-13 | null | null | null | null | ['small-data'] | ['computer-vision'] | [-2.84555584e-01 2.33599152e-02 -2.50232369e-01 -6.03296101e-01
-8.82759869e-01 -3.55533302e-01 7.15670884e-01 3.82463843e-01
-3.73286724e-01 4.16354567e-01 9.95039493e-02 -2.16219411e-03
-3.24403673e-01 -8.95626664e-01 -8.61553729e-01 -7.11993039e-01
-2.48677507e-01 8.26856196e-01 4.54356611e-01 4.70924318... | [7.403618812561035, 3.7359812259674072] |
342d75d8-c1bb-47e1-9e01-9492a98cd07c | fragment-graphical-variational-autoencoding | 1910.13325 | null | https://arxiv.org/abs/1910.13325v2 | https://arxiv.org/pdf/1910.13325v2.pdf | Fragment Graphical Variational AutoEncoding for Screening Molecules with Small Data | In the majority of molecular optimization tasks, predictive machine learning (ML) models are limited due to the unavailability and cost of generating big experimental datasets on the specific task. To circumvent this limitation, ML models are trained on big theoretical datasets or experimental indicators of molecular s... | ['Henning Sirringhaus', 'Gareth Conduit', 'Ivan Dimov', 'Lorena Marañón', 'Ian E. Jacobs', 'John Armitage', 'Iyad Nasrallah', 'Dimitrios Simatos', 'Mark Nikolka', 'Malgorzata Nguyen', 'Leszek J. Spalek', 'Christian B. Nielsen', 'Iain McCulloch', 'Guillaume Schweicher'] | 2019-10-21 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 4.93473619e-01 -5.69437444e-02 -3.84173512e-01 -3.30381602e-01
-7.61641741e-01 -6.02337301e-01 4.09623027e-01 6.52449489e-01
-2.76445508e-01 1.28544497e+00 -2.33546048e-01 -6.59585238e-01
-2.86264569e-01 -9.68190014e-01 -1.00909579e+00 -8.01568925e-01
1.00765740e-02 6.27765954e-01 -1.64262980e-01 -1.94154456... | [5.153909206390381, 5.498390197753906] |
cc0dd151-ccbc-41b0-9fff-7a42bc7bb1a2 | heat-a-highly-efficient-and-affordable | 2304.07334 | null | https://arxiv.org/abs/2304.07334v2 | https://arxiv.org/pdf/2304.07334v2.pdf | HEAT: A Highly Efficient and Affordable Training System for Collaborative Filtering Based Recommendation on CPUs | Collaborative filtering (CF) has been proven to be one of the most effective techniques for recommendation. Among all CF approaches, SimpleX is the state-of-the-art method that adopts a novel loss function and a proper number of negative samples. However, there is no work that optimizes SimpleX on multi-core CPUs, lead... | ['Dingwen Tao', 'Yuxiong He', 'Shuaiwen Leon Song', 'Xiaodong Yu', 'Jonathan Soifer', 'Jiannan Tian', 'Baixi Sun', 'Shaden Smith', 'Chengming Zhang'] | 2023-04-14 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-2.78268337e-01 -8.39574993e-01 -1.28869817e-01 -2.29393423e-01
-6.37850285e-01 -5.61638534e-01 4.22080934e-01 2.34500274e-01
-4.73024964e-01 4.45620328e-01 3.31801087e-01 -8.68614733e-01
2.02068631e-02 -1.06932151e+00 -4.66356248e-01 -7.30565488e-01
-1.13376901e-01 2.44247362e-01 3.68836999e-01 -2.00887889... | [8.432901382446289, 3.4005234241485596] |
5cf20bf9-519e-460f-8e29-ce2c55725a23 | semantic-aware-contrastive-learning-for-more | 2301.07919 | null | https://arxiv.org/abs/2301.07919v1 | https://arxiv.org/pdf/2301.07919v1.pdf | Semantic-aware Contrastive Learning for More Accurate Semantic Parsing | Since the meaning representations are detailed and accurate annotations which express fine-grained sequence-level semtantics, it is usually hard to train discriminative semantic parsers via Maximum Likelihood Estimation (MLE) in an autoregressive fashion. In this paper, we propose a semantic-aware contrastive learning ... | ['Le Sun', 'Xianpei Han', 'Bo Chen', 'Chunlei Xin', 'Shan Wu'] | 2023-01-19 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 6.70813799e-01 1.10642791e-01 -2.01614574e-01 -9.51043189e-01
-1.46613967e+00 -6.80550933e-01 4.39679176e-01 1.10212699e-01
-5.55762291e-01 5.86910963e-01 4.29334342e-01 -9.85159501e-02
3.79689671e-02 -5.27033985e-01 -8.85805428e-01 -7.30899632e-01
1.43001169e-01 6.13652349e-01 -1.46089755e-02 -7.75263309... | [10.759471893310547, 8.790302276611328] |
607f45ca-8d99-4ccc-98df-d7e1a76797e1 | exarn-self-attending-rnn-for-target-speaker | 2212.01106 | null | https://arxiv.org/abs/2212.01106v2 | https://arxiv.org/pdf/2212.01106v2.pdf | ExARN: self-attending RNN for target speaker extraction | Target speaker extraction is to extract the target speaker, specified by enrollment utterance, in an environment with other competing speakers. Therefore, the task needs to solve two problems, speaker identification and separation, at the same time. In this paper, we combine self-attention and Recurrent Neural Networks... | ['Xueliang Zhang', 'Shulin He', 'Pengjie Shen'] | 2022-12-02 | null | null | null | null | ['target-speaker-extraction', 'speaker-identification'] | ['audio', 'speech'] | [ 3.48832458e-01 7.79006854e-02 -1.39231116e-01 -5.29237747e-01
-1.10508955e+00 -1.82409957e-01 2.44452760e-01 -5.90105712e-01
-9.84296501e-02 5.79713523e-01 5.51923275e-01 -2.30998680e-01
3.01780552e-01 -7.67839700e-02 8.73718858e-02 -8.22579205e-01
1.47623032e-01 1.58064604e-01 -3.48340660e-01 -3.01031113... | [14.515252113342285, 6.055954456329346] |
190f9c39-c6a8-4515-b816-51e229cfedc9 | cross-attentive-pooling-for-speaker | 2008.05983 | null | https://arxiv.org/abs/2008.05983v2 | https://arxiv.org/pdf/2008.05983v2.pdf | Cross attentive pooling for speaker verification | The goal of this paper is text-independent speaker verification where utterances come from 'in the wild' videos and may contain irrelevant signal. While speaker verification is naturally a pair-wise problem, existing methods to produce the speaker embeddings are instance-wise. In this paper, we propose Cross Attentive ... | [] | 2020-08-13 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 2.29009479e-01 -1.20551199e-01 -9.42999497e-02 -1.02491295e+00
-1.50429356e+00 -5.21067262e-01 6.13934517e-01 -2.65385568e-01
-3.70317489e-01 3.58327419e-01 8.15645576e-01 1.34980917e-01
1.62747741e-01 1.25491107e-02 -5.54500759e-01 -8.37361813e-01
-1.40178502e-01 -6.78968057e-02 -3.69292237e-02 -2.23089918... | [14.355745315551758, 6.065332889556885] |
2233c474-5c53-41bf-a82c-da8005921fba | building-a-corpus-for-biomedical-relation | 2306.08403 | null | https://arxiv.org/abs/2306.08403v1 | https://arxiv.org/pdf/2306.08403v1.pdf | Building a Corpus for Biomedical Relation Extraction of Species Mentions | We present a manually annotated corpus, Species-Species Interaction, for extracting meaningful binary relations between species, in biomedical texts, at sentence level, with a focus on the gut microbiota. The corpus leverages PubTator to annotate species in full-text articles after evaluating different Named Entity Rec... | ['Samuel Chaffron', 'Solen Quiniou', 'Oumaima El Khettari'] | 2023-06-14 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [ 5.38472772e-01 -8.43823254e-02 -7.18472123e-01 1.87167525e-01
-3.49439412e-01 -9.66869414e-01 5.68221986e-01 1.41041112e+00
-5.56263506e-01 1.30091763e+00 5.53804636e-01 -3.81797135e-01
-1.88282251e-01 -5.12691498e-01 -6.38872683e-01 -6.98742926e-01
-7.37783790e-01 2.85007566e-01 -2.17490673e-01 -2.50795752... | [8.443771362304688, 8.756741523742676] |
6417c529-fc67-4021-b611-e503213e746a | deep-reinforcement-learning-for-surgical | 1806.08089 | null | http://arxiv.org/abs/1806.08089v1 | http://arxiv.org/pdf/1806.08089v1.pdf | Deep Reinforcement Learning for Surgical Gesture Segmentation and Classification | Recognition of surgical gesture is crucial for surgical skill assessment and
efficient surgery training. Prior works on this task are based on either
variant graphical models such as HMMs and CRFs, or deep learning models such as
Recurrent Neural Networks and Temporal Convolutional Networks. Most of the
current approac... | ['Tingting Jiang', 'Daochang Liu'] | 2018-06-21 | null | null | null | null | ['surgical-gesture-recognition'] | ['medical'] | [ 4.08952355e-01 5.05070627e-01 -4.85203117e-01 -4.53110725e-01
-7.35145152e-01 -2.51371026e-01 4.14741904e-01 2.27112636e-01
-7.93191314e-01 6.63224518e-01 2.98318386e-01 -3.63374293e-01
-8.41124207e-02 -3.05400103e-01 -5.57790637e-01 -7.64367521e-01
2.53693815e-02 4.77111161e-01 3.54177386e-01 7.18045160... | [14.060250282287598, -3.341071605682373] |
eec73d30-b835-4e9a-9f10-c2459b7219c5 | generative-adversarial-networks-based-on | null | null | https://aclanthology.org/2021.rocling-1.36 | https://aclanthology.org/2021.rocling-1.36.pdf | Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications | We propose the mixed-attention-based Generative Adversarial Network (named maGAN), and apply it for citation intent classification in scientific publication. We select domain-specific training data, propose a mixed-attention mechanism, and employ generative adversarial network architecture for pre-training language mod... | ['Lung-Hao Lee', 'Chao-Yi Chen', 'Yuh-Shyang Wang'] | null | null | null | null | rocling-2021-10 | ['citation-intent-classification'] | ['natural-language-processing'] | [-1.56671539e-01 2.82526553e-01 -2.00238094e-01 -3.72326165e-01
-1.04338598e+00 -4.49953079e-01 1.00940013e+00 -4.59709823e-01
-4.39689040e-01 1.03517449e+00 3.96790832e-01 -7.73633063e-01
1.50005594e-01 -8.88737798e-01 -9.06555235e-01 -3.97202641e-01
3.87575120e-01 4.59310353e-01 -2.98131436e-01 9.74458456... | [10.262409210205078, 8.15455436706543] |
02c815a8-95f5-4edb-9eb8-94373c4c79cc | incorporating-orientations-into-end-to-end | 2103.05846 | null | https://arxiv.org/abs/2103.05846v1 | https://arxiv.org/pdf/2103.05846v1.pdf | Incorporating Orientations into End-to-end Driving Model for Steering Control | In this paper, we present a novel end-to-end deep neural network model for autonomous driving that takes monocular image sequence as input, and directly generates the steering control angle. Firstly, we model the end-to-end driving problem as a local path planning process. Inspired by the environmental representation i... | ['Jianfeng Lu', 'Zhenbo Song', 'Peng Wan'] | 2021-03-10 | null | null | null | null | ['steering-control'] | ['computer-vision'] | [ 5.35104014e-02 -1.82156786e-02 -2.33323470e-01 -1.25526285e+00
-3.69262755e-01 -3.66324782e-01 5.27206421e-01 -6.36137962e-01
-5.86656570e-01 7.27651894e-01 6.11858629e-02 -5.59988856e-01
-7.19713373e-03 -1.09665537e+00 -8.92614067e-01 -5.56694627e-01
1.57179937e-01 3.20187062e-01 3.83628219e-01 -5.57204366... | [8.042851448059082, -1.593914270401001] |
7fd4e16e-1e34-4629-b1c0-0c63268e569d | challenges-in-designing-games-with-a-purpose | null | null | https://aclanthology.org/2021.hcinlp-1.10 | https://aclanthology.org/2021.hcinlp-1.10.pdf | Challenges in Designing Games with a Purpose for Abusive Language Annotation | In this paper we discuss several challenges related to the development of a 3D game, whose goal is to raise awareness on cyberbullying while collecting linguistic annotation on offensive language. The game is meant to be used by teenagers, thus raising a number of issues that need to be tackled during development. For ... | ['Sara Tonelli', 'Federico Bonetti'] | null | null | null | null | eacl-hcinlp-2021-4 | ['abusive-language'] | ['natural-language-processing'] | [-1.55483603e-01 6.30759776e-01 1.06036499e-01 -1.73033684e-01
-2.33312264e-01 -4.95302260e-01 6.77905902e-02 5.58454633e-01
-5.17994046e-01 4.71464932e-01 2.26461813e-01 -4.53324849e-03
-2.23498657e-01 -7.83179045e-01 -1.59303367e-01 -2.27369457e-01
1.85677130e-02 1.81536272e-01 3.44739407e-01 -4.49843675... | [8.737125396728516, 10.46005630493164] |
85bd7830-94ba-4e4d-8220-f77df62d6f57 | polytrack-tracking-with-bounding-polygons | 2111.01606 | null | https://arxiv.org/abs/2111.01606v1 | https://arxiv.org/pdf/2111.01606v1.pdf | PolyTrack: Tracking with Bounding Polygons | In this paper, we present a novel method called PolyTrack for fast multi-object tracking and segmentation using bounding polygons. Polytrack detects objects by producing heatmaps of their center keypoint. For each of them, a rough segmentation is done by computing a bounding polygon over each instance instead of the tr... | ['Nicolas Saunier', 'Guillaume-Alexandre Bilodeau', 'Hughes Perreault', 'Gaspar Faure'] | 2021-11-02 | null | null | null | null | ['multi-object-tracking-and-segmentation'] | ['computer-vision'] | [-1.21121161e-01 -1.33966774e-01 -4.53632027e-02 -2.86818087e-01
-5.16418040e-01 -7.89805770e-01 4.07763541e-01 1.86466873e-01
-5.07303774e-01 3.09638619e-01 -4.90621209e-01 -8.55867341e-02
3.94794554e-01 -8.62109303e-01 -8.11240673e-01 -5.07660925e-01
2.94002779e-02 6.48841262e-01 1.25333273e+00 1.95816278... | [7.052350997924805, -1.940996766090393] |
06bc52aa-6cb8-4776-b5a8-916ad0ee47f7 | causal-reinforcement-learning-a-survey | 2307.01452 | null | https://arxiv.org/abs/2307.01452v1 | https://arxiv.org/pdf/2307.01452v1.pdf | Causal Reinforcement Learning: A Survey | Reinforcement learning is an essential paradigm for solving sequential decision problems under uncertainty. Despite many remarkable achievements in recent decades, applying reinforcement learning methods in the real world remains challenging. One of the main obstacles is that reinforcement learning agents lack a fundam... | ['Chengqi Zhang', 'Guodong Long', 'Jing Jiang', 'Zhihong Deng'] | 2023-07-04 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [ 2.27056116e-01 1.82428584e-01 -7.51891136e-01 -2.63416439e-01
-4.76141453e-01 -5.53957820e-01 7.27114141e-01 3.23034912e-01
-3.55959296e-01 1.37845254e+00 5.19131087e-02 -2.99155325e-01
-8.73456120e-01 -9.25868332e-01 -6.86113119e-01 -6.94151700e-01
-5.46634853e-01 4.06966895e-01 1.30580977e-01 -4.18220401... | [3.9748973846435547, 1.745336890220642] |
037216d5-94a1-44b7-b3d6-d135a92d78bb | using-spatio-temporal-dual-stream-network | 2305.02719 | null | https://arxiv.org/abs/2305.02719v2 | https://arxiv.org/pdf/2305.02719v2.pdf | Using Spatio-Temporal Dual-Stream Network with Self-Supervised Learning for Lung Tumor Classification on Radial Probe Endobronchial Ultrasound Video | The purpose of this study is to develop a computer-aided diagnosis system for classifying benign and malignant lung lesions, and to assist physicians in real-time analysis of radial probe endobronchial ultrasound (EBUS) videos. During the biopsy process of lung cancer, physicians use real-time ultrasound images to find... | ['Yun-Chien Cheng', 'Chin-Wen Chen', 'Ching-Kai Lin'] | 2023-05-04 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 3.56283523e-02 -3.47188503e-01 -1.04081258e-01 8.36918354e-02
-5.12801766e-01 -3.85979623e-01 9.49981362e-02 -1.54644996e-01
-4.76292104e-01 2.46346533e-01 -1.91949680e-01 -6.64882600e-01
-4.83097702e-01 -7.48921335e-01 -9.39399078e-02 -1.03356385e+00
-2.90805310e-01 4.24878627e-01 6.50078177e-01 3.09486896... | [15.23033618927002, -2.116334915161133] |
d1ccbb38-ffde-4379-a427-bce6c44fb668 | instruct2act-mapping-multi-modality | 2305.11176 | null | https://arxiv.org/abs/2305.11176v3 | https://arxiv.org/pdf/2305.11176v3.pdf | Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model | Foundation models have made significant strides in various applications, including text-to-image generation, panoptic segmentation, and natural language processing. This paper presents Instruct2Act, a framework that utilizes Large Language Models to map multi-modal instructions to sequential actions for robotic manipul... | ['Hongsheng Li', 'Peng Gao', 'Yu Qiao', 'Hao Dong', 'Zhengkai Jiang', 'Siyuan Huang'] | 2023-05-18 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 2.37813070e-01 7.64782727e-02 -5.47347307e-01 -2.54435003e-01
-6.82885945e-01 -7.56781578e-01 6.55139804e-01 1.62200462e-02
-2.89551169e-01 2.81053275e-01 4.16602306e-02 -4.28657591e-01
-2.69597709e-01 -6.97797775e-01 -9.36646521e-01 -3.20661217e-01
1.73280701e-01 4.74740028e-01 4.77355689e-01 -3.86210352... | [4.543042182922363, 0.767650842666626] |
13e83562-5c49-429c-8513-d92f2f84f18f | how-well-can-text-to-image-generative-models | 2210.15230 | null | https://arxiv.org/abs/2210.15230v1 | https://arxiv.org/pdf/2210.15230v1.pdf | How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions? | Text-to-image generative models have achieved unprecedented success in generating high-quality images based on natural language descriptions. However, it is shown that these models tend to favor specific social groups when prompted with neutral text descriptions (e.g., 'a photo of a lawyer'). Following Zhao et al. (202... | ['Kai-Wei Chang', 'Masoud Monajatipoor', 'Da Yin', 'Hritik Bansal'] | 2022-10-27 | null | null | null | null | ['culture'] | ['speech'] | [ 3.85827094e-01 5.89360178e-01 -9.29298252e-02 -5.19852519e-01
-2.25464150e-01 -6.71382487e-01 1.04928637e+00 -1.70016184e-01
-3.05332899e-01 8.54474902e-01 6.90468609e-01 -1.40719235e-01
1.79298714e-01 -8.95804167e-01 -8.21506917e-01 -6.55154228e-01
5.34229279e-01 2.37794831e-01 -4.89571750e-01 -3.11011851... | [12.087080955505371, 0.9470548033714294] |
7a20dab2-f9c9-40b7-9784-787965850883 | in-situ-3d-spatiotemporal-measurement-of | 2209.01164 | null | https://arxiv.org/abs/2209.01164v3 | https://arxiv.org/pdf/2209.01164v3.pdf | In Situ 3D Spatiotemporal Measurement of Soluble Biomarkers in Organoid Culture | Advanced cell culture techniques such as 3D bio-printing and hydrogel-based cell embedding techniques harbor many new and exciting opportunities to study cells in environments that closely recapitulate in-vivo conditions. Researchers often study these environments using fluorescence microscopy to visualize the protein ... | ['W. Gregory Sawyer', 'Jack E Famiglietti', 'Eric O McGhee', 'Alexander J McGhee'] | 2022-09-02 | null | null | null | null | ['culture'] | ['speech'] | [ 1.89403445e-02 -5.79885066e-01 8.11098740e-02 4.36898887e-01
-7.58236587e-01 -6.97391987e-01 2.84765065e-01 5.49249232e-01
-4.76725787e-01 9.44403052e-01 -4.85426746e-02 -1.01607665e-01
6.51058853e-02 -6.37228966e-01 -5.29734313e-01 -1.20837784e+00
-2.76869118e-01 6.76537275e-01 3.13184381e-01 1.77912906... | [13.727827072143555, -3.0993454456329346] |
28a7ebeb-f865-4558-a33c-3a064db29061 | improving-automatic-source-code-summarization | 1811.07234 | null | http://arxiv.org/abs/1811.07234v1 | http://arxiv.org/pdf/1811.07234v1.pdf | Improving Automatic Source Code Summarization via Deep Reinforcement Learning | Code summarization provides a high level natural language description of the
function performed by code, as it can benefit the software maintenance, code
categorization and retrieval. To the best of our knowledge, most
state-of-the-art approaches follow an encoder-decoder framework which encodes
the code into a hidden ... | ['Yao Wan', 'Philip S. Yu', 'Jian Wu', 'Guandong Xu', 'Zhou Zhao', 'Haochao Ying', 'Min Yang'] | 2018-11-17 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 1.94113314e-01 2.92286783e-01 -3.99026692e-01 -1.81600571e-01
-8.26221347e-01 -3.35642308e-01 2.27829888e-01 2.45904431e-01
-5.99760823e-02 6.19847596e-01 5.03836632e-01 -4.18414891e-01
3.66235912e-01 -6.86304271e-01 -1.02754474e+00 -3.77604753e-01
-2.66978405e-02 -2.98651517e-03 2.68677801e-01 -1.38637275... | [7.671348571777344, 7.89597225189209] |
54d8bdd0-9096-4cf8-b7d5-14d9e3871407 | saliency-detection-with-moving-camera-via | 2111.01681 | null | https://arxiv.org/abs/2111.01681v1 | https://arxiv.org/pdf/2111.01681v1.pdf | Saliency detection with moving camera via background model completion | To detect saliency in video is a fundamental step in many computer vision systems. Saliency is the significant target(s) in the video. The object of interest is further analyzed for high-level applications. The segregation of saliency and the background can be made if they exhibit different visual cues. Therefore, sali... | ['Kwok-Leung Chan', 'Yupei Zhang'] | 2021-10-30 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 7.50134170e-01 -3.52056146e-01 -9.91457477e-02 7.98819065e-02
-3.35063934e-01 -2.24650681e-01 2.23188266e-01 -1.02773190e-01
-2.65918523e-01 6.65182292e-01 -1.43217191e-01 -5.83210066e-02
5.76443791e-01 -6.03338182e-01 -8.83844137e-01 -9.37030673e-01
2.82258123e-01 -1.00879088e-01 1.12206471e+00 -1.05659693... | [9.160443305969238, -0.4844220280647278] |
237e4eae-43de-4387-b032-7e7a480c3b36 | frigate-frugal-spatio-temporal-forecasting-on | 2306.08277 | null | https://arxiv.org/abs/2306.08277v1 | https://arxiv.org/pdf/2306.08277v1.pdf | FRIGATE: Frugal Spatio-temporal Forecasting on Road Networks | Modelling spatio-temporal processes on road networks is a task of growing importance. While significant progress has been made on developing spatio-temporal graph neural networks (Gnns), existing works are built upon three assumptions that are not practical on real-world road networks. First, they assume sensing on eve... | ['Sayan Ranu', 'Hariprasad Kodamana', 'Mridul Gupta'] | 2023-06-14 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [ 2.73739785e-01 3.47619176e-01 -1.99491516e-01 -7.04451501e-02
-1.04592763e-01 -3.91208619e-01 6.71114862e-01 3.43219608e-01
-5.21588981e-01 8.63773048e-01 1.57304123e-01 -5.94033957e-01
-5.73338091e-01 -1.55422926e+00 -1.00013757e+00 -5.34412444e-01
-6.84141874e-01 5.04014254e-01 5.70395231e-01 -3.30239654... | [6.459964752197266, 2.058907985687256] |
0ae85a04-3b52-41b8-8b1b-6d7da3f95642 | keyphrase-generation-with-gans-in-low | null | null | https://aclanthology.org/2020.sustainlp-1.12 | https://aclanthology.org/2020.sustainlp-1.12.pdf | Keyphrase Generation with GANs in Low-Resources Scenarios | Keyphrase Generation is the task of predicting Keyphrases (KPs), short phrases that summarize the semantic meaning of a given document. Several past studies provided diverse approaches to generate Keyphrases for an input document. However, all of these approaches still need to be trained on very large datasets. In this... | ['Carlo Tasso', 'Giuseppe Serra', 'Beatrice Portelli', 'Saida S.Mohamed', 'Giuseppe Lancioni'] | null | null | null | null | emnlp-sustainlp-2020-11 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 3.77767026e-01 3.34963143e-01 -2.51522452e-01 2.23664150e-01
-1.30881667e+00 -9.49554145e-01 1.26477468e+00 3.78687650e-01
-3.78817499e-01 1.09241056e+00 3.44457835e-01 -1.81301743e-01
2.74738550e-01 -9.23624337e-01 -8.24374318e-01 -5.75879753e-01
2.89179951e-01 6.53609157e-01 2.40476847e-01 -4.61889476... | [12.272200584411621, 8.910719871520996] |
3cc59e96-a330-4153-ad91-289b7b07e50c | table-retrieval-does-not-necessitate-table | null | null | https://openreview.net/forum?id=Z1Vgh4I_5ur | https://openreview.net/pdf?id=Z1Vgh4I_5ur | Table Retrieval Does Not Necessitate Table-specific Model Design | Tables are an important form of structured data for both human and machine readers alike, providing answers to questions that cannot, or cannot easily, be found in texts. Recent work designs special models and trains for table-related tasks such as table-based question answering and table retrieval. Though effective, t... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['table-retrieval'] | ['natural-language-processing'] | [-7.22019151e-02 9.36399847e-02 -3.66076007e-02 -1.64335132e-01
-1.54523039e+00 -9.52487826e-01 4.56316501e-01 7.88325787e-01
-4.87914830e-01 5.57624459e-01 7.22277403e-01 -5.75824440e-01
-2.62557477e-01 -1.06243563e+00 -9.35066998e-01 -9.70050246e-02
3.18631560e-01 1.14002442e+00 4.86602843e-01 -9.06754434... | [10.157649993896484, 7.945359230041504] |
9ef0d0fa-ccac-4542-a049-eb46f25460e8 | raytran-3d-pose-estimation-and-shape | 2203.13296 | null | https://arxiv.org/abs/2203.13296v2 | https://arxiv.org/pdf/2203.13296v2.pdf | RayTran: 3D pose estimation and shape reconstruction of multiple objects from videos with ray-traced transformers | We propose a transformer-based neural network architecture for multi-object 3D reconstruction from RGB videos. It relies on two alternative ways to represent its knowledge: as a global 3D grid of features and an array of view-specific 2D grids. We progressively exchange information between the two with a dedicated bidi... | ['Vittorio Ferrari', 'Stefan Popov', 'Kevis-Kokitsi Maninis', 'Michał J. Tyszkiewicz'] | 2022-03-24 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [ 7.34555721e-02 6.70632273e-02 2.05088988e-01 -4.16592032e-01
-9.13821816e-01 -5.50855696e-01 3.93303394e-01 -2.82666415e-01
-2.70410269e-01 4.18868586e-02 9.57576782e-02 -2.14967623e-01
1.08470425e-01 -5.57969630e-01 -1.31447864e+00 -3.28888059e-01
2.40061179e-01 1.08983612e+00 5.56399584e-01 -3.56292538... | [7.62324857711792, -2.569611072540283] |
3d3183d0-649c-4112-b40e-9cca933d3fac | priberam-compressive-summarization-corpus-a | null | null | https://aclanthology.org/L14-1193 | https://aclanthology.org/L14-1193.pdf | Priberam Compressive Summarization Corpus: A New Multi-Document Summarization Corpus for European Portuguese | In this paper, we introduce the Priberam Compressive Summarization Corpus, a new multi-document summarization corpus for European Portuguese. The corpus follows the format of the summarization corpora for English in recent DUC and TAC conferences. It contains 80 manually chosen topics referring to events occurred betwe... | ["Cl{\\'a}udia Pinto", "Andr{\\'e} F. T. Martins", 'Pedro Mendes', 'Miguel B. Almeida', 'Mariana S. C. Almeida', 'Helena Figueira'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['sentence-compression'] | ['natural-language-processing'] | [ 6.34973824e-01 5.43844938e-01 -2.06039548e-01 -4.76290882e-02
-1.46140528e+00 -8.07797730e-01 8.27207625e-01 7.14842796e-01
-5.31946182e-01 1.28995430e+00 1.29213655e+00 6.75044805e-02
-5.62630296e-02 -2.40910873e-01 -4.46793377e-01 -4.44411516e-01
2.11612694e-02 5.70277572e-01 6.76793158e-02 -2.49201059... | [12.46518611907959, 9.526551246643066] |
7e57d063-e072-4169-9f54-ea854b1e0014 | cause-effect-inference-in-location-scale | 2301.12930 | null | https://arxiv.org/abs/2301.12930v2 | https://arxiv.org/pdf/2301.12930v2.pdf | Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing | A fundamental problem of causal discovery is cause-effect inference, learning the correct causal direction between two random variables. Significant progress has been made through modelling the effect as a function of its cause and a noise term, which allows us to leverage assumptions about the generating function clas... | ['Oliver Schulte', 'Xiangyu Sun'] | 2023-01-26 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 4.15348589e-01 -2.07827300e-01 -4.58421707e-01 -3.52451861e-01
-8.75681520e-01 -6.43244028e-01 6.90433800e-01 1.22610658e-01
-1.98654249e-01 9.41857219e-01 4.57949102e-01 -6.24825358e-01
-5.46828330e-01 -7.88770556e-01 -9.87848938e-01 -6.54426277e-01
-2.09479064e-01 3.35010141e-01 -1.04776137e-01 4.15734321... | [7.842119216918945, 5.24251651763916] |
08194ab3-17af-496b-a153-0ca2a64e908d | improved-neural-text-attribute-transfer-with | 1711.09395 | null | http://arxiv.org/abs/1711.09395v2 | http://arxiv.org/pdf/1711.09395v2.pdf | Improved Neural Text Attribute Transfer with Non-parallel Data | Text attribute transfer using non-parallel data requires methods that can
perform disentanglement of content and linguistic attributes. In this work, we
propose multiple improvements over the existing approaches that enable the
encoder-decoder framework to cope with the text attribute transfer from
non-parallel data. W... | ['Cicero Nogueira dos santos', 'Igor Melnyk', 'Inkit Padhi', 'Kahini Wadhawan', 'Abhishek Kumar'] | 2017-11-26 | null | null | null | null | ['text-attribute-transfer'] | ['natural-language-processing'] | [ 4.74642217e-01 1.80194169e-01 -3.09539080e-01 -9.55280066e-01
-1.13258564e+00 -5.13272166e-01 1.15147161e+00 5.13985038e-01
-8.82961750e-01 1.00792766e+00 4.47432578e-01 -6.47916496e-02
3.43674600e-01 -6.07260823e-01 -7.28996515e-01 -2.74023890e-01
1.90965891e-01 1.00981927e+00 -5.91943823e-02 -3.38889360... | [11.294747352600098, 9.546908378601074] |
72e442e4-16ac-4e83-9005-584b5a819dfb | uncertainty-driven-6d-pose-estimation-of | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Brachmann_Uncertainty-Driven_6D_Pose_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Brachmann_Uncertainty-Driven_6D_Pose_CVPR_2016_paper.pdf | Uncertainty-Driven 6D Pose Estimation of Objects and Scenes From a Single RGB Image | In recent years, the task of estimating the 6D pose of object instances and complete scenes, i.e. camera localization, from a single input image has received considerable attention. Consumer RGB-D cameras have made this feasible, even for difficult, texture-less objects and scenes. In this work, we show that a single R... | ['Carsten Rother', 'Michael Ying Yang', 'Frank Michel', 'Eric Brachmann', 'Stefan Gumhold', 'Alexander Krull'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['camera-localization'] | ['computer-vision'] | [ 1.40947074e-01 -2.08853245e-01 1.06249996e-01 -5.61733663e-01
-1.11141896e+00 -1.00524318e+00 3.57278258e-01 -2.56823525e-02
-4.77856904e-01 3.40180755e-01 -1.96379364e-01 9.84084699e-03
2.24329621e-01 -2.00693011e-01 -9.32966650e-01 -6.88098609e-01
3.56877565e-01 7.99421787e-01 4.24119592e-01 5.02825677... | [7.601783752441406, -2.554365634918213] |
f79eba9e-1805-40f7-b1a7-6db5ac07b67d | eeg-cortical-source-feature-based-hand | 2304.06321 | null | https://arxiv.org/abs/2304.06321v1 | https://arxiv.org/pdf/2304.06321v1.pdf | EEG Cortical Source Feature based Hand Kinematics Decoding using Residual CNN-LSTM Neural Network | Motor kinematics decoding (MKD) using brain signal is essential to develop Brain-computer interface (BCI) system for rehabilitation or prosthesis devices. Surface electroencephalogram (EEG) signal has been widely utilized for MKD. However, kinematic decoding from cortical sources is sparsely explored. In this work, the... | ['Lalan Kumar', 'Anant Jain'] | 2023-04-13 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 9.36344191e-02 -3.09895664e-01 -1.02668904e-01 -2.21598614e-02
-6.01834178e-01 -3.47528867e-02 3.39159936e-01 -5.13234735e-01
-5.50531447e-01 1.01593471e+00 4.76672679e-01 -3.14849168e-02
-3.01107198e-01 -3.46761525e-01 -7.83953190e-01 -7.47011960e-01
-4.06459033e-01 -2.39736274e-01 -1.83576196e-01 -7.01727644... | [12.989822387695312, 3.4111857414245605] |
5a870447-de76-484c-9b9f-6b6f3f4ea86d | systems-challenges-for-trustworthy-embodied | 2201.03413 | null | https://arxiv.org/abs/2201.03413v2 | https://arxiv.org/pdf/2201.03413v2.pdf | Systems Challenges for Trustworthy Embodied Systems | A new generation of increasingly autonomous and self-learning embodied systems is about to be developed. When deploying embodied systems into a real-life context we face various engineering challenges, as it is crucial to coordinate the behavior of embodied systems in a beneficial manner, ensure their compatibility wit... | ['Harald Rueß'] | 2022-01-10 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [-2.39693537e-01 2.05350190e-01 5.20423234e-01 -3.03256541e-01
-3.70676927e-02 -7.02705085e-01 8.61543059e-01 4.55965847e-02
-1.37788653e-01 7.69531250e-01 1.89907074e-01 -1.08089678e-01
-4.77078736e-01 -4.91645813e-01 -3.59676093e-01 -2.67892867e-01
-3.04067791e-01 4.19533908e-01 -4.79015559e-02 -7.52577305... | [8.930144309997559, 6.2303009033203125] |
94e30143-e07c-47e1-b96e-4f3a1f71a6fa | analysing-mixed-initiatives-and-search | 2109.05955 | null | https://arxiv.org/abs/2109.05955v1 | https://arxiv.org/pdf/2109.05955v1.pdf | Analysing Mixed Initiatives and Search Strategies during Conversational Search | Information seeking conversations between users and Conversational Search Agents (CSAs) consist of multiple turns of interaction. While users initiate a search session, ideally a CSA should sometimes take the lead in the conversation by obtaining feedback from the user by offering query suggestions or asking for query ... | ['Nick Craswel', 'Paul Thomas', 'Evangelos Kanoulas', 'Hamed Zamani', 'Leif Azzopardi', 'Mohammad Aliannejadi'] | 2021-09-13 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 2.29542747e-01 2.48544544e-01 -1.09411843e-01 -2.48976827e-01
-1.02195740e+00 -1.01940000e+00 1.14142501e+00 3.71014297e-01
-8.06844175e-01 4.47487354e-01 5.74078321e-01 -5.70294499e-01
-5.95847130e-01 -4.05042499e-01 -1.08631186e-01 -5.53784549e-01
1.51100397e-01 9.39193308e-01 3.91025186e-01 -4.77142662... | [12.223397254943848, 7.78875732421875] |
a745dcf5-5300-4c03-bbd3-0f5b33fa2cdd | hunting-for-spammers-detecting-evolved | 1512.02573 | null | http://arxiv.org/abs/1512.02573v2 | http://arxiv.org/pdf/1512.02573v2.pdf | Hunting for Spammers: Detecting Evolved Spammers on Twitter | Once an email problem, spam has nowadays branched into new territories with
disruptive effects. In particular, spam has established itself over the recent
years as a ubiquitous, annoying, and sometimes threatening aspect of online
social networks. Due to its prevalent existence, many works have tackled spam
on Twitter ... | ['Alaboodi Saad', 'El-Mawass Nour'] | 2015-12-15 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-1.08648904e-01 -1.73137471e-01 1.26585111e-01 -2.14522555e-01
-2.10550413e-01 -7.41473794e-01 1.08735287e+00 3.23290914e-01
-3.24131221e-01 7.52698123e-01 1.68969750e-01 -3.61972481e-01
8.22097138e-02 -1.02002537e+00 5.40064313e-02 -5.99194467e-01
-2.91436221e-02 6.02357149e-01 8.21195662e-01 -8.86323452... | [7.909165859222412, 10.057793617248535] |
7ff75174-e469-4d3c-9c9b-d0ea6974ece8 | grounding-language-representation-with-visual | null | null | https://openreview.net/forum?id=Mdn3eM7VHFn | https://openreview.net/pdf?id=Mdn3eM7VHFn | Grounding Language Representation with Visual Object Information via Cross Modal Pretraining | Previous studies of visual grounded language learning use a convolutional neural network (CNN) to extract features from the whole image for grounding with the sentence description. However, this approach has two main drawbacks: (i) the whole image usually contains more objects and backgrounds than the sentence itself; ... | ['Tho Quan', 'Anh Tuan Luu', 'Cong-Duy T Nguyen'] | 2021-09-29 | null | null | null | null | ['grounded-language-learning'] | ['natural-language-processing'] | [ 4.30092700e-02 -4.01205830e-02 -1.84278682e-01 -5.14937162e-01
-6.08155429e-01 -3.84636283e-01 8.98853302e-01 1.22197539e-01
-4.11469311e-01 3.63435358e-01 4.67348456e-01 -1.18836932e-01
2.77042508e-01 -1.03949821e+00 -1.12437630e+00 -6.26298666e-01
3.42347473e-01 3.57652307e-01 4.17483449e-01 -6.21152699... | [10.705766677856445, 1.589190125465393] |
2ee30179-1286-459b-89e7-f296313fcf4a | webcaricature-a-benchmark-for-caricature | 1703.03230 | null | http://arxiv.org/abs/1703.03230v4 | http://arxiv.org/pdf/1703.03230v4.pdf | WebCaricature: a benchmark for caricature recognition | Studying caricature recognition is fundamentally important to understanding
of face perception. However, little research has been conducted in the computer
vision community, largely due to the shortage of suitable datasets. In this
paper, a new caricature dataset is built, with the objective to facilitate
research in c... | ['Yang Gao', 'Wenbin Li', 'Jing Huo', 'Yinghuan Shi', 'Hujun Yin'] | 2017-03-09 | null | null | null | null | ['caricature'] | ['computer-vision'] | [ 2.53344089e-01 -3.57550383e-01 -2.40397394e-01 -6.24826133e-01
-4.53765213e-01 -5.05723774e-01 7.01141298e-01 -5.70222020e-01
2.05999210e-01 4.19894904e-01 -1.52060285e-01 1.10325269e-01
-1.85910061e-01 -4.50579852e-01 -5.70994258e-01 -8.94950926e-01
-7.26882294e-02 1.77048668e-01 -3.72575462e-01 -1.12462848... | [13.212163925170898, 0.7032554745674133] |
5bdc8eec-5158-4551-a7ff-46817ee0f64e | augmentation-techniques-analysis-with-removal | null | null | https://www.researchgate.net/profile/Allena-Venkata-Sai-Abhishek-2/publication/364345327_Augmentation_Techniques_Analysis_with_Removal_of_Class_Imbalance_Using_PyTorch_for_Intel_Scene_Dataset/links/634d3f1612cbac6a3ed4a645/Augmentation-Techniques-Analysis-with-Removal-of-Class-Imbalance-Using-PyTorch-for-Intel-Scene-Da... | https://www.researchgate.net/profile/Allena-Venkata-Sai-Abhishek-2/publication/364345327_Augmentation_Techniques_Analysis_with_Removal_of_Class_Imbalance_Using_PyTorch_for_Intel_Scene_Dataset/links/634d3f1612cbac6a3ed4a645/Augmentation-Techniques-Analysis-with-Removal-of-Class-Imbalance-Using-PyTorch-for-Intel-Scene-Da... | Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset | although best-in-class AI can deliver extraordinary outcomes in experimentation, data scientists struggle to duplicate these outcomes on actual-world data. It's nothing unexpected-actual data mirrors the messy world that made it, containing many biases and gaps. A painful element of real data is that it tends to be imb... | ['Anusha Kanukolanu', 'Dr. S Phani Kumar', 'Allena Venkata Sai Abhishek'] | 2022-05-01 | null | null | null | international-journal-of-innovative-research | ['image-manipulation-detection', 'image-smoothing', 'image-matting', 'image-variation', 'image-stitching', 'image-augmentation', 'image-manipulation', 'image-morphing', 'roi-based-image-generation', 'image-cropping', 'detecting-image-manipulation', 'data-visualization', 'data-visualization'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'methodology', 'miscellaneous'] | [ 3.08126658e-01 2.74351060e-01 -4.30359632e-01 -5.60810685e-01
-4.18189466e-01 -3.14288735e-01 3.08941990e-01 2.92902261e-01
-2.77057678e-01 7.92617023e-01 3.38112146e-01 -2.29166433e-01
-6.73996508e-02 -9.31193888e-01 -6.51805878e-01 -5.26411653e-01
8.04257840e-02 7.14482069e-01 -2.04935834e-01 -6.04123175... | [8.807780265808105, 4.3252272605896] |
04b97981-bf55-4889-a961-1119340b5996 | deep-ordinal-regression-with-label-diversity | 2006.15864 | null | https://arxiv.org/abs/2006.15864v1 | https://arxiv.org/pdf/2006.15864v1.pdf | Deep Ordinal Regression with Label Diversity | Regression via classification (RvC) is a common method used for regression problems in deep learning, where the target variable belongs to a set of continuous values. By discretizing the target into a set of non-overlapping classes, it has been shown that training a classifier can improve neural network accuracy compar... | ["Mark O'Connor", 'Magnus Oskarsson', 'Axel Berg'] | 2020-06-29 | null | null | null | null | ['head-pose-estimation', 'historical-color-image-dating'] | ['computer-vision', 'computer-vision'] | [ 5.19980669e-01 1.03150770e-01 -4.38875228e-01 -6.30730748e-01
-9.52035069e-01 -4.68653023e-01 5.40488958e-01 1.76704124e-01
-4.45905834e-01 9.47824240e-01 -2.71141231e-01 -4.43538219e-01
-2.27132425e-01 -7.23560929e-01 -8.18918049e-01 -6.43987298e-01
1.05660483e-01 6.25194490e-01 -3.78718041e-03 -1.26291737... | [8.875709533691406, 3.4338619709014893] |
93974c99-e64e-4ad4-be4f-9a782d13a2cf | counterfactual-inference-for-consumer-choice | 1906.02635 | null | https://arxiv.org/abs/1906.02635v1 | https://arxiv.org/pdf/1906.02635v1.pdf | Counterfactual Inference for Consumer Choice Across Many Product Categories | This paper proposes a method for estimating consumer preferences among discrete choices, where the consumer chooses at most one product in a category, but selects from multiple categories in parallel. The consumer's utility is additive in the different categories. Her preferences about product attributes as well as her... | ['David Blei', 'Susan Athey', 'Francisco R. Ruiz', 'Rob Donnelly'] | 2019-06-06 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [-3.82479250e-01 -1.50902376e-01 -9.31041121e-01 -6.67246401e-01
-7.94182062e-01 -8.36062610e-01 4.58849341e-01 4.93490428e-01
-5.97512245e-01 5.01766682e-01 4.98233557e-01 -3.24627817e-01
-2.54696935e-01 -9.45310652e-01 -6.52034521e-01 -5.26277602e-01
-3.23540628e-01 7.66986132e-01 -1.05444923e-01 -1.81638867... | [9.143209457397461, 5.503706455230713] |
2623fee8-e905-4c96-89ec-5cdbbeb0334c | fine-tuned-neural-models-for-propaganda | 1910.09702 | null | https://arxiv.org/abs/1910.09702v1 | https://arxiv.org/pdf/1910.09702v1.pdf | Fine-Tuned Neural Models for Propaganda Detection at the Sentence and Fragment levels | This paper presents the CUNLP submission for the NLP4IF 2019 shared-task on FineGrained Propaganda Detection. Our system finished 5th out of 26 teams on the sentence-level classification task and 5th out of 11 teams on the fragment-level classification task based on our scores on the blind test set. We present our mode... | ['Tariq Alhindi', 'Smaranda Muresan', 'Jonas Pfeiffer'] | 2019-10-22 | fine-tuned-neural-models-for-propaganda-1 | https://aclanthology.org/D19-5013 | https://aclanthology.org/D19-5013.pdf | ws-2019-11 | ['propaganda-detection'] | ['natural-language-processing'] | [ 1.99403018e-02 1.89528629e-01 -1.49412453e-01 -3.17576900e-02
-1.32686615e+00 -6.96718097e-01 1.12558401e+00 4.04054016e-01
-9.23198402e-01 6.82244122e-01 9.57426488e-01 -6.30153894e-01
-2.07378119e-01 -2.01332137e-01 -2.88155198e-01 -3.14904720e-01
-1.12217635e-01 2.59013563e-01 7.58552849e-02 -4.40706074... | [8.462806701660156, 10.679157257080078] |
31d6c939-a65b-4312-b98d-78d28d57f543 | oberta-improving-sparse-transfer-learning-via | 2303.17612 | null | https://arxiv.org/abs/2303.17612v3 | https://arxiv.org/pdf/2303.17612v3.pdf | oBERTa: Improving Sparse Transfer Learning via improved initialization, distillation, and pruning regimes | In this paper, we introduce the range of oBERTa language models, an easy-to-use set of language models which allows Natural Language Processing (NLP) practitioners to obtain between 3.8 and 24.3 times faster models without expertise in model compression. Specifically, oBERTa extends existing work on pruning, knowledge ... | ['ChengXiang Zhai', 'Mark Kurtz', 'Alexandre Marques', 'Daniel Campos'] | 2023-03-30 | null | null | null | null | ['model-compression'] | ['methodology'] | [-2.68466901e-02 3.09455395e-01 -4.34906870e-01 -5.01099944e-01
-1.02139354e+00 -7.68564641e-01 6.43932641e-01 3.50247651e-01
-7.90222824e-01 6.41438067e-01 2.23899364e-01 -8.22997928e-01
-4.06907588e-01 -7.33258605e-01 -7.99714029e-01 -1.04659423e-01
-2.91063059e-02 9.47545350e-01 1.41245827e-01 -1.39034614... | [10.808128356933594, 8.08434009552002] |
9e18b825-a309-4411-8496-1e7d018226fd | unimelb-at-semeval-2019-task-12-multi-model | null | null | https://aclanthology.org/S19-2231 | https://aclanthology.org/S19-2231.pdf | UniMelb at SemEval-2019 Task 12: Multi-model combination for toponym resolution | This paper describes our submission to SemEval-2019 Task 12 on toponym resolution over scientific articles. We train separate NER models for toponym detection over text extracted from tables vs. text from the body of the paper, and train another auxiliary model to eliminate misdetected toponyms. For toponym disambiguat... | ['Martin Tomko', 'Timothy Baldwin', 'Minghan Wang', 'Haonan Li', 'Maria Vasardani'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['toponym-resolution'] | ['natural-language-processing'] | [ 4.61048586e-03 3.23152930e-01 -2.33593345e-01 -5.75398803e-02
-1.15877581e+00 -9.01838779e-01 6.57786250e-01 1.06900275e+00
-8.97825181e-01 1.06151366e+00 -3.89762111e-02 2.58941855e-03
-4.39338237e-01 -4.82225060e-01 -6.24390960e-01 -2.00365275e-01
2.79900640e-01 9.53982413e-01 -5.14217429e-02 -1.11191608... | [9.208452224731445, 9.114164352416992] |
39da45a8-6d77-403a-b749-89d654667db4 | arguably-at-comma-icon-detection-of | null | null | https://aclanthology.org/2021.icon-multigen.7 | https://aclanthology.org/2021.icon-multigen.7.pdf | ARGUABLY at ComMA@ICON: Detection of Multilingual Aggressive, Gender Biased, and Communally Charged Tweets Using Ensemble and Fine-Tuned IndicBERT | The proliferation in Social Networking has increased offensive language, aggression, and hate-speech detection, which has drawn the focus of the NLP community. However, people’s difference in perception makes it difficult to distinguish between acceptable content and aggressive/hateful content, thus making it harder to... | ['Jatin Bedi', 'Prabsimran Kaur', 'Guneet Kohli'] | null | null | null | null | icon-2021-12 | ['transliteration'] | ['natural-language-processing'] | [-6.04330003e-01 -3.26396455e-03 1.49245560e-01 -4.63086069e-01
1.16265461e-01 -3.92020941e-01 8.15462708e-01 4.09330457e-01
-6.47579432e-01 6.51347399e-01 5.92399716e-01 5.38669433e-03
-2.38917246e-01 -7.21611202e-01 2.29722574e-01 -4.22217339e-01
2.01046497e-01 4.16136295e-01 -1.44680053e-01 -7.72774220... | [8.816947937011719, 10.633091926574707] |
344e79dc-86bf-4671-988c-0d8eb0790295 | proceedings-of-the-13th-international-9 | 2112.14770 | null | https://arxiv.org/abs/2112.14770v1 | https://arxiv.org/pdf/2112.14770v1.pdf | Proceedings of the 13th International Conference on Automated Deduction in Geometry | Automated Deduction in Geometry (ADG) is a forum to exchange ideas and views, to present research results and progress, and to demonstrate software tools at the intersection between geometry and automated deduction. Relevant topics include (but are not limited to): polynomial algebra, invariant and coordinate-free meth... | ['Zoltán Kovács', 'Predrag Janičić'] | 2021-12-28 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [-4.26028520e-01 3.55471343e-01 3.60188931e-01 2.35233635e-01
-3.48894268e-01 -8.10237229e-01 6.22185051e-01 4.26362514e-01
8.42320248e-02 7.31384695e-01 -2.59808511e-01 -8.03411067e-01
-6.87726736e-01 -1.09684551e+00 -5.90525985e-01 -3.28226209e-01
-7.10617959e-01 6.40676618e-01 1.07269406e-01 -7.57701322... | [8.77348804473877, 6.787961006164551] |
1fea07c9-e53a-485a-b391-0f3293059b3b | deepspectrumlite-a-power-efficient-transfer | 2104.11629 | null | https://arxiv.org/abs/2104.11629v1 | https://arxiv.org/pdf/2104.11629v1.pdf | DeepSpectrumLite: A Power-Efficient Transfer Learning Framework for Embedded Speech and Audio Processing from Decentralised Data | Deep neural speech and audio processing systems have a large number of trainable parameters, a relatively complex architecture, and require a vast amount of training data and computational power. These constraints make it more challenging to integrate such systems into embedded devices and utilise them for real-time, r... | ['Björn W. Schuller', 'Sandra Ottl', 'Maurice Gerczuk', 'Tobias Hübner', 'Shahin Amiriparian'] | 2021-04-23 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 1.61941588e-01 -1.27881125e-01 1.17947310e-01 -3.20120573e-01
-9.44420099e-01 -5.30616879e-01 1.47596881e-01 -2.15885848e-01
-5.56068301e-01 2.53403544e-01 -3.16763461e-01 -6.88696444e-01
8.99344161e-02 -4.80613232e-01 -8.66859555e-01 -5.33842802e-01
-1.75411075e-01 1.77479357e-01 1.23193897e-01 -4.83975112... | [14.916571617126465, 5.566709041595459] |
43f6a82d-6079-432a-b6cf-1c9df360ef52 | caption-feature-space-regularization-for | 2204.08409 | null | https://arxiv.org/abs/2204.08409v1 | https://arxiv.org/pdf/2204.08409v1.pdf | Caption Feature Space Regularization for Audio Captioning | Audio captioning aims at describing the content of audio clips with human language. Due to the ambiguity of audio, different people may perceive the same audio differently, resulting in caption disparities (i.e., one audio may correlate to several captions with diverse semantics). For that, general audio captioning mod... | ['Yuan Dong', 'Zhanyu Ma', 'Ruoyi Du', 'Hong Yu', 'Yiming Zhang'] | 2022-04-18 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 3.63759637e-01 2.14658044e-02 -2.58326419e-02 -4.57546532e-01
-1.15635443e+00 -2.87757635e-01 1.87299415e-01 -9.85093694e-03
-5.65412156e-02 6.62534773e-01 7.96949923e-01 3.48214120e-01
1.69214785e-01 -2.73122430e-01 -9.35589135e-01 -7.70344377e-01
1.99880660e-01 2.46110260e-01 -3.05888951e-02 -4.52220216... | [15.259134292602539, 4.888879299163818] |
0582eae6-77a1-4716-ba20-669d705fd3c9 | efficient-generative-modeling-of-protein | 2103.03292 | null | https://arxiv.org/abs/2103.03292v3 | https://arxiv.org/pdf/2103.03292v3.pdf | Efficient generative modeling of protein sequences using simple autoregressive models | Generative models emerge as promising candidates for novel sequence-data driven approaches to protein design, and for the extraction of structural and functional information about proteins deeply hidden in rapidly growing sequence databases. Here we propose simple autoregressive models as highly accurate but computatio... | ['Martin Weigt', 'Francesco Zamponi', 'Andrea Pagnani', 'Guido Uguzzoni', 'Jeanne Trinquier'] | 2021-03-04 | null | null | null | null | ['protein-design'] | ['medical'] | [ 4.07047898e-01 2.31794834e-01 2.71311760e-01 -3.28373045e-01
-9.09301460e-01 -6.56407714e-01 3.51925343e-01 -6.14890121e-02
-5.84090054e-01 1.30169415e+00 -6.17963932e-02 -6.00307524e-01
-2.43794397e-02 -6.50034964e-01 -8.51339161e-01 -1.29787886e+00
-8.98325518e-02 7.21482396e-01 -4.59731705e-02 -3.78185034... | [4.714311599731445, 5.5777268409729] |
50bc9f24-c84f-4947-b137-b13dc287647d | joint-acoustic-echo-cancellation-and-blind | 2205.06473 | null | https://arxiv.org/abs/2205.06473v2 | https://arxiv.org/pdf/2205.06473v2.pdf | Joint Acoustic Echo Cancellation and Blind Source Extraction based on Independent Vector Extraction | We describe a joint acoustic echo cancellation (AEC) and blind source extraction (BSE) approach for multi-microphone acoustic frontends. The proposed algorithm blindly estimates AEC and beamforming filters by maximizing the statistical independence of a non-Gaussian source of interest and a stationary Gaussian backgrou... | ['Walter Kellermann', 'Zbyněk Koldovský', 'Thomas Haubner'] | 2022-05-13 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 1.16654217e-01 -4.48765129e-01 1.02085471e+00 -2.83063531e-01
-1.50493121e+00 -5.56846678e-01 3.99940014e-01 -1.74176350e-01
-7.19053030e-01 4.09450263e-01 5.53651989e-01 -1.45006344e-01
-2.40814209e-01 2.49047596e-02 -5.31496346e-01 -9.39399004e-01
-3.22802395e-01 -3.44297439e-02 1.99239224e-01 3.46920311... | [15.135476112365723, 5.764640808105469] |
1295625b-d370-4c69-802f-ca1b5d36ae24 | tsk-fuzzy-system-towards-few-labeled | 2110.05610 | null | https://arxiv.org/abs/2110.05610v3 | https://arxiv.org/pdf/2110.05610v3.pdf | TSK Fuzzy System Towards Few Labeled Incomplete Multi-View Data Classification | Data collected by multiple methods or from multiple sources is called multi-view data. To make full use of the multi-view data, multi-view learning plays an increasingly important role. Traditional multi-view learning methods rely on a large number of labeled and completed multi-view data. However, it is expensive and ... | ['Shitong Wang', 'Kup-Sze Choi', 'Te Zhang', 'Qiongdan Lou', 'Zhaohong Deng', 'Wei zhang'] | 2021-10-08 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-9.51270610e-02 -3.21049422e-01 -4.88734394e-01 -4.62126017e-01
-5.87976694e-01 -5.53188980e-01 1.61202878e-01 -9.61634740e-02
8.46879855e-02 6.63776755e-01 8.02151263e-02 1.51791573e-01
-3.17236096e-01 -7.43949175e-01 -4.11792994e-01 -8.13854814e-01
8.14598203e-01 7.21145868e-01 5.93413822e-02 -1.21166401... | [8.294673919677734, 4.626969814300537] |
1015d850-f0bc-4b52-acb8-469fd82ca056 | explaining-knowledge-graph-embedding-via | null | null | https://openreview.net/forum?id=RCyHECZIUFb | https://openreview.net/pdf?id=RCyHECZIUFb | Explaining Knowledge Graph Embedding via Latent Rule Learning | Knowledge Graph Embeddings (KGEs) embed entities and relations into continuous vector space following certain assumption, and are a powerful tools for representation learning of knowledge graphs. However, following vector space assumptions makes KGE a one step reasoner that directly predict final results without reason... | ['Huajun Chen', 'Yushan Zhu', 'Zezhong Xu', 'Mingyang Chen', 'Wen Zhang'] | 2021-09-29 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-2.37040028e-01 1.37518156e+00 -8.86188745e-01 -3.76176715e-01
7.91819990e-02 -2.27793753e-01 5.74059188e-01 4.06388074e-01
4.39159602e-01 7.12151647e-01 5.18569469e-01 -8.51733923e-01
-6.13987744e-01 -1.29379880e+00 -9.20948625e-01 -3.69399823e-02
-2.28093550e-01 8.74586523e-01 1.18183233e-01 -3.25849205... | [8.870599746704102, 7.76581335067749] |
2100ba09-4a18-4d13-8da6-b9f0b0ac84e9 | how-to-prevent-the-poor-performance-clients | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Qu_How_To_Prevent_the_Poor_Performance_Clients_for_Personalized_Federated_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qu_How_To_Prevent_the_Poor_Performance_Clients_for_Personalized_Federated_CVPR_2023_paper.pdf | How To Prevent the Poor Performance Clients for Personalized Federated Learning? | Personalized federated learning (pFL) collaboratively trains personalized models, which provides a customized model solution for individual clients in the presence of heterogeneous distributed local data. Although many recent studies have applied various algorithms to enhance personalization in pFL, they mainly foc... | ['Lixing Chen', 'Chengchao Shen', 'Rui Duan', 'Xiao Han', 'Xingyu Li', 'Zhe Qu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['personalized-federated-learning', 'generalization-bounds'] | ['methodology', 'methodology'] | [-2.03076243e-01 -1.15600802e-01 -4.14445966e-01 -4.94743347e-01
-1.16360414e+00 -3.17439944e-01 2.04916075e-01 -1.46052778e-01
-6.32439852e-02 7.94019103e-01 3.47046256e-01 4.29326808e-03
-5.68186820e-01 -6.71640933e-01 -8.57667863e-01 -1.22178388e+00
4.09775376e-02 6.60892069e-01 1.02620780e-01 1.33736217... | [5.824272632598877, 6.27525520324707] |
697fb346-2d14-4610-8b7b-8c0fe84cf2dd | a-survey-of-embodied-ai-from-simulator-to | 2103.04918 | null | https://arxiv.org/abs/2103.04918v8 | https://arxiv.org/pdf/2103.04918v8.pdf | A Survey of Embodied AI: From Simulators to Research Tasks | There has been an emerging paradigm shift from the era of "internet AI" to "embodied AI", where AI algorithms and agents no longer learn from datasets of images, videos or text curated primarily from the internet. Instead, they learn through interactions with their environments from an egocentric perception similar to ... | ['Hui Li Tan', 'Jiafei Duan', 'Cheston Tan', 'Hongyuan Zhu', 'Samson Yu'] | 2021-03-08 | null | null | null | null | ['embodied-question-answering'] | ['computer-vision'] | [ 1.58905089e-01 3.61357033e-01 2.60071427e-01 -6.73554465e-02
-1.61077991e-01 -6.82561994e-01 8.91471684e-01 -1.61967084e-01
-6.90865636e-01 5.94045460e-01 2.72991359e-01 -4.21798617e-01
-3.02096099e-01 -5.88993251e-01 -6.50919318e-01 -4.79909837e-01
-4.25607204e-01 2.71439731e-01 -2.61473745e-01 -7.33610332... | [4.48035192489624, 0.726983904838562] |
fd689272-ff6b-4817-8fda-2df6dc0fda62 | rankmix-data-augmentation-for-weakly | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_RankMix_Data_Augmentation_for_Weakly_Supervised_Learning_of_Classifying_Whole_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_RankMix_Data_Augmentation_for_Weakly_Supervised_Learning_of_Classifying_Whole_CVPR_2023_paper.pdf | RankMix: Data Augmentation for Weakly Supervised Learning of Classifying Whole Slide Images With Diverse Sizes and Imbalanced Categories | Whole Slide Images (WSIs) are usually gigapixel in size and lack pixel-level annotations. The WSI datasets are also imbalanced in categories. These unique characteristics, significantly different from the ones in natural images, pose the challenge of classifying WSI images as a kind of weakly supervise learning pro... | ['Chun-Shien Lu', 'Yuan-Chih Chen'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['whole-slide-images'] | ['computer-vision'] | [ 8.83241296e-01 2.54438460e-01 -5.32804489e-01 -6.04188740e-01
-1.00747728e+00 -5.21134734e-01 6.82580769e-01 3.59623164e-01
-3.16936225e-01 7.79276192e-01 2.80132174e-01 -5.66238388e-02
-1.61498442e-01 -3.66342723e-01 -6.53996766e-01 -1.09347963e+00
1.27521828e-01 2.98068047e-01 2.74021775e-01 -1.18409224... | [15.09763240814209, -2.778594970703125] |
5b904b9c-d8fc-427b-bb7e-a8f3467d3f87 | sharp-sparsity-and-hidden-activation-replay | 2305.18563 | null | https://arxiv.org/abs/2305.18563v1 | https://arxiv.org/pdf/2305.18563v1.pdf | SHARP: Sparsity and Hidden Activation RePlay for Neuro-Inspired Continual Learning | Deep neural networks (DNNs) struggle to learn in dynamic environments since they rely on fixed datasets or stationary environments. Continual learning (CL) aims to address this limitation and enable DNNs to accumulate knowledge incrementally, similar to human learning. Inspired by how our brain consolidates memories, a... | ['Constantine Dovrolis', 'Jean Michael Moorman', 'Mustafa Burak Gurbuz'] | 2023-05-29 | null | null | null | null | ['class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology'] | [ 1.30678862e-01 -1.27941251e-01 -1.38920128e-01 -1.83316782e-01
-5.96313588e-02 -5.67235827e-01 7.14866579e-01 -9.49671045e-02
-6.32240117e-01 9.61967289e-01 8.38693008e-02 -6.60271272e-02
-1.96696043e-01 -7.98150182e-01 -1.11095941e+00 -7.80592322e-01
-6.03908161e-03 2.66985238e-01 4.57955986e-01 -8.77739191... | [9.850516319274902, 3.4058480262756348] |
53880652-b1b2-4a1a-9552-f381910b661b | exploring-sparse-expert-models-and-beyond | 2105.15082 | null | https://arxiv.org/abs/2105.15082v5 | https://arxiv.org/pdf/2105.15082v5.pdf | M6-T: Exploring Sparse Expert Models and Beyond | Mixture-of-Experts (MoE) models can achieve promising results with outrageous large amount of parameters but constant computation cost, and thus it has become a trend in model scaling. Still it is a mystery how MoE layers bring quality gains by leveraging the parameters with sparse activation. In this work, we investig... | ['Hongxia Yang', 'Jingren Zhou', 'Lin Qu', 'Wei Lin', 'Di Zhang', 'Yong Li', 'Jiamang Wang', 'Jie Zhang', 'Ang Wang', 'Xianyan Jia', 'Le Jiang', 'Chang Zhou', 'Rui Men', 'Junyang Lin', 'An Yang'] | 2021-05-31 | null | null | null | null | ['2048'] | ['playing-games'] | [-4.49818820e-01 -5.20078652e-02 -3.02209795e-01 -3.27074707e-01
-6.99313283e-01 -4.07646894e-01 1.63859352e-01 -3.16121787e-01
-6.85606360e-01 5.60270011e-01 -2.04551760e-02 -4.01146591e-01
-5.03373668e-02 -5.42257309e-01 -8.52612376e-01 -5.56571364e-01
2.98615415e-02 7.61346936e-01 3.56031537e-01 -8.69586766... | [8.751225471496582, 3.4969873428344727] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.