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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
14b2435a-74af-478e-8018-ef4ff6334acb | face-recognition-under-varying-blur | 1902.10885 | null | http://arxiv.org/abs/1902.10885v1 | http://arxiv.org/pdf/1902.10885v1.pdf | Face Recognition Under Varying Blur, Illumination and Expression in an Unconstrained Environment | Face recognition system is one of the esteemed research areas in pattern
recognition and computer vision as long as its major challenges. A few
challenges in recognizing faces are blur, illumination, and varied expressions.
Blur is natural while taking photographs using cameras, mobile phones, etc.
Blur can be uniform ... | ['Anubha Pearline. S', 'Hemalatha. M'] | 2019-02-28 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 3.54108095e-01 -6.38659120e-01 1.93154648e-01 -6.08575642e-01
2.50976324e-01 -6.31944358e-01 2.11138546e-01 -1.08448219e+00
-1.00468457e-01 1.04418492e+00 1.04157135e-01 3.00468296e-01
-1.41798705e-01 -2.13226870e-01 -3.19785893e-01 -1.00437915e+00
2.73652643e-01 -1.64942577e-01 -1.23520523e-01 7.32354969... | [12.98672103881836, 0.3915238380432129] |
8e4e3037-1e79-46d9-a723-85ff1a826dec | community-detection-in-complex-networks-via | 2303.12212 | null | https://arxiv.org/abs/2303.12212v2 | https://arxiv.org/pdf/2303.12212v2.pdf | Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering | Community detection is a critical challenge in analysing real graphs, including social, transportation, citation, cybersecurity, and many other networks. This article proposes three new, general, hierarchical frameworks to deal with this task. The introduced approach supports various linkage-based clustering algorithms... | ['Marek Gagolewski', 'Grzegorz Siudem', 'Łukasz Brzozowski'] | 2023-03-21 | null | null | null | null | ['stochastic-block-model', 'community-detection'] | ['graphs', 'graphs'] | [-2.17120126e-01 3.28874076e-03 -1.17748298e-01 1.30408019e-01
-2.04134926e-01 -9.52664554e-01 9.49087083e-01 7.20084310e-01
-2.01936718e-02 7.91388750e-01 5.24860732e-02 -6.63230002e-01
-9.23906267e-01 -1.26435268e+00 -4.67357755e-01 -6.16944551e-01
-7.67789662e-01 1.11704898e+00 5.36563277e-01 -1.47455424... | [7.088634967803955, 5.893851280212402] |
27d5253a-9833-46eb-8934-17a611ff361f | multitalent-a-multi-dataset-approach-to | 2303.14444 | null | https://arxiv.org/abs/2303.14444v1 | https://arxiv.org/pdf/2303.14444v1.pdf | MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation | The medical imaging community generates a wealth of datasets, many of which are openly accessible and annotated for specific diseases and tasks such as multi-organ or lesion segmentation. Current practices continue to limit model training and supervised pre-training to one or a few similar datasets, neglecting the syne... | ['Klaus H. Maier-Hein', 'Michael Baumgartner', 'Maximilian Zenk', 'Tassilo Wald', 'Fabian Isensee', 'Constantin Ulrich'] | 2023-03-25 | null | null | null | null | ['lesion-segmentation', 'unsupervised-pre-training'] | ['medical', 'methodology'] | [ 5.5809599e-01 4.6428034e-01 -5.7189369e-01 -5.0673866e-01
-1.4483876e+00 -6.7713988e-01 2.4730283e-01 3.3269146e-01
-3.5278809e-01 4.9538502e-01 3.1361461e-01 -5.3176516e-01
-7.8179941e-02 -4.1567308e-01 -3.2168040e-01 -7.6246601e-01
-7.4377949e-03 8.8888156e-01 5.1184797e-01 1.4231068e-01
-1.9395144e-01... | [14.737102508544922, -2.259833812713623] |
c3b6d485-c01a-44a6-bf10-1b996c9f83a1 | extractive-email-thread-summarization-can-we | null | null | https://aclanthology.org/W12-1513 | https://aclanthology.org/W12-1513.pdf | Extractive email thread summarization: Can we do better than He Said She Said? | null | ['Pablo Duboue'] | 2012-05-01 | null | null | null | ws-2012-5 | ['email-thread-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... | [-7.129591941833496, 3.7573678493499756] |
1342bae8-6670-42ac-afcb-5f35ce8d5656 | jbnu-cclab-at-semeval-2022-task-12-machine | null | null | https://aclanthology.org/2022.semeval-1.231 | https://aclanthology.org/2022.semeval-1.231.pdf | JBNU-CCLab at SemEval-2022 Task 12: Machine Reading Comprehension and Span Pair Classification for Linking Mathematical Symbols to Their Descriptions | This paper describes our system in the SemEval-2022 Task 12: ‘linking mathematical symbols to their descriptions’, achieving first on the leaderboard for all the subtasks comprising named entity extraction (NER) and relation extraction (RE). Our system is a two-stage pipeline model based on SciBERT that detects symbols... | ['Seung-Hoon Na', 'Sung-Min Lee'] | null | null | null | null | semeval-naacl-2022-7 | ['joint-entity-and-relation-extraction', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.93335855e-01 5.35712779e-01 -2.10137174e-01 -4.59716707e-01
-8.05849016e-01 -6.34578407e-01 5.62593699e-01 8.01050603e-01
-5.24892926e-01 7.15947688e-01 1.81774080e-01 -7.44071364e-01
4.87508327e-02 -8.46360803e-01 -9.56240952e-01 5.88191636e-02
-1.61737368e-01 3.24071139e-01 1.67024717e-01 1.22218635... | [9.481431007385254, 8.722082138061523] |
28cb5982-74e7-4d5e-a3ea-19dcf1636582 | modeling-tag-prediction-based-on-question | 2307.01420 | null | https://arxiv.org/abs/2307.01420v1 | https://arxiv.org/pdf/2307.01420v1.pdf | Modeling Tag Prediction based on Question Tagging Behavior Analysis of CommunityQA Platform Users | In community question-answering platforms, tags play essential roles in effective information organization and retrieval, better question routing, faster response to questions, and assessment of topic popularity. Hence, automatic assistance for predicting and suggesting tags for posts is of high utility to users of suc... | ['Silviu Cucerzan', 'Nirupama Chandrasekaran', 'Michael Gamon', 'Kuntal Kumar Pal'] | 2023-07-04 | null | null | null | null | ['retrieval', 'community-question-answering', 'question-answering', 'community-question-answering'] | ['methodology', 'miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [-4.96060193e-01 3.96264046e-02 -4.54850197e-01 -4.35572654e-01
-9.71422911e-01 -6.08115375e-01 5.13120353e-01 4.66026038e-01
-2.50711232e-01 5.37717938e-01 7.56197214e-01 -3.68436456e-01
-4.15767938e-01 -7.03206241e-01 -1.67570919e-01 -2.12550700e-01
-2.97107249e-01 6.57936037e-01 9.66081202e-01 -1.49014533... | [11.430167198181152, 7.965633392333984] |
75b01158-d02d-4f9a-af92-8f2d672c5196 | change-detection-methods-for-remote-sensing | 2305.05813 | null | https://arxiv.org/abs/2305.05813v1 | https://arxiv.org/pdf/2305.05813v1.pdf | Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review | Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover monitoring. Detecting changes in remote sensing images is a compl... | ['Shiming Xiang', 'Qi Zhao', 'Zhaoyang Xu', 'Shuchang Lyu', 'Xiangtai Li', 'Yunmeng Huang', 'Guangliang Cheng'] | 2023-05-09 | null | null | null | null | ['change-detection', 'change-detection-for-remote-sensing-images'] | ['computer-vision', 'miscellaneous'] | [ 5.96354783e-01 -7.47641206e-01 -1.59599125e-01 -4.98568147e-01
-3.67450923e-01 -5.97720981e-01 5.82851470e-01 1.94133222e-01
-4.03437912e-01 5.71478128e-01 1.50346593e-03 -3.25393677e-01
-2.85395712e-01 -1.15891349e+00 -1.53113648e-01 -9.79051769e-01
-5.54129183e-01 -2.80724138e-01 -5.59403636e-02 -5.63884795... | [9.635980606079102, -1.344618320465088] |
23a23dc8-1b31-4d3b-8712-e2880d342d6c | consistent-polynomial-time-unseeded-graph | 1807.11027 | null | http://arxiv.org/abs/1807.11027v1 | http://arxiv.org/pdf/1807.11027v1.pdf | Consistent polynomial-time unseeded graph matching for Lipschitz graphons | We propose a consistent polynomial-time method for the unseeded node matching
problem for networks with smooth underlying structures. Despite widely
conjectured by the research community that the structured graph matching
problem to be significantly easier than its worst case counterpart, well-known
to be NP-hard, the ... | ['Yuan Zhang'] | 2018-07-29 | null | null | null | null | ['graphon-estimation'] | ['graphs'] | [ 4.52887952e-01 5.95542133e-01 -2.50665009e-01 -2.64710605e-01
-7.92266250e-01 -8.70704830e-01 3.32174689e-01 1.21472985e-01
-1.66439321e-02 6.81595922e-01 -2.09643289e-01 -7.86217332e-01
-7.73719192e-01 -9.22776043e-01 -6.66840672e-01 -8.02786887e-01
-6.05784297e-01 8.83821487e-01 5.99905014e-01 -2.01005936... | [6.888472557067871, 5.224370956420898] |
645248ed-d876-4f7b-9adb-5f4d932a5b95 | what-do-we-expect-from-multiple-choice-qa | 2011.10647 | null | https://arxiv.org/abs/2011.10647v1 | https://arxiv.org/pdf/2011.10647v1.pdf | What do we expect from Multiple-choice QA Systems? | The recent success of machine learning systems on various QA datasets could be interpreted as a significant improvement in models' language understanding abilities. However, using various perturbations, multiple recent works have shown that good performance on a dataset might not indicate performance that correlates we... | ['Dan Roth', 'Nitish Gupta', 'Krunal Shah'] | 2020-11-20 | null | https://aclanthology.org/2020.findings-emnlp.317 | https://aclanthology.org/2020.findings-emnlp.317.pdf | findings-of-the-association-for-computational | ['multiple-choice-qa'] | ['natural-language-processing'] | [ 1.98432788e-01 6.06507659e-01 2.27988616e-01 -7.24954009e-01
-1.21940041e+00 -7.92782903e-01 7.75115788e-01 2.12301314e-01
-5.02778172e-01 8.30185533e-01 3.99472088e-01 -9.66886818e-01
-1.57954887e-01 -7.68821716e-01 -8.66614699e-01 -2.10184902e-01
6.02815390e-01 1.02887225e+00 5.14611423e-01 -8.80346537... | [11.025590896606445, 8.02688217163086] |
f442b3de-eb41-4afb-9ce3-a0e619e89380 | mvp-multi-view-prompting-improves-aspect | 2305.12627 | null | https://arxiv.org/abs/2305.12627v1 | https://arxiv.org/pdf/2305.12627v1.pdf | MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction | Generative methods greatly promote aspect-based sentiment analysis via generating a sequence of sentiment elements in a specified format. However, existing studies usually predict sentiment elements in a fixed order, which ignores the effect of the interdependence of the elements in a sentiment tuple and the diversity ... | ['Yujiu Yang', 'Qingyan Guo', 'Zhibin Gou'] | 2023-05-22 | null | null | null | null | ['term-extraction', 'aspect-sentiment-opinion-triplet-extraction', 'aspect-based-sentiment-analysis', 'aspect-category-polarity', 'aspect-category-opinion-sentiment-quadruple', 'extract-aspect', 'extract-aspect-polarity-tuple', 'hidden-aspect-detection', 'aspect-sentiment-triplet-extraction', 'aspect-category-detection... | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.89080939e-01 -1.27559662e-01 -1.81346029e-01 -8.95688951e-01
-9.35503662e-01 -7.00724840e-01 6.89359069e-01 -1.85969561e-01
6.52052835e-02 5.28774738e-01 6.73404038e-01 -4.53743413e-02
1.54065505e-01 -7.56267667e-01 -6.48544908e-01 -5.69835603e-01
5.77361643e-01 8.47347677e-01 -3.40713978e-01 -5.64541399... | [11.50318431854248, 6.689464569091797] |
83bf675a-3919-4da8-a2c5-475de3ea5c19 | on-the-power-of-saturated-transformers-a-view | 2106.16213 | null | https://arxiv.org/abs/2106.16213v3 | https://arxiv.org/pdf/2106.16213v3.pdf | Saturated Transformers are Constant-Depth Threshold Circuits | Transformers have become a standard neural network architecture for many NLP problems, motivating theoretical analysis of their power in terms of formal languages. Recent work has shown that transformers with hard attention are quite limited in power (Hahn, 2020), as they can be simulated by constant-depth AND/OR circu... | ['Ashish Sabharwal', 'Noah A. Smith', 'William Merrill'] | 2021-06-30 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 3.96454066e-01 5.85075200e-01 -2.25533117e-02 -1.43789276e-01
-6.20481312e-01 -9.05923009e-01 3.13941687e-01 -3.43353301e-02
-2.72135586e-01 5.44043839e-01 -2.92205103e-02 -9.03962255e-01
-1.31000027e-01 -1.18560255e+00 -1.12458897e+00 -4.02881056e-01
-1.48701265e-01 6.07505977e-01 3.15476060e-01 -3.40260625... | [9.459426879882812, 7.113461494445801] |
0a99a989-526f-445a-b31d-1ae69db9e7c7 | clcc-contrastive-learning-for-color-constancy | 2106.04989 | null | https://arxiv.org/abs/2106.04989v1 | https://arxiv.org/pdf/2106.04989v1.pdf | CLCC: Contrastive Learning for Color Constancy | In this paper, we present CLCC, a novel contrastive learning framework for color constancy. Contrastive learning has been applied for learning high-quality visual representations for image classification. One key aspect to yield useful representations for image classification is to design illuminant invariant augmentat... | ['Kevin Jou', 'Yu-Lin Chang', 'Chia-Ping Chen', 'Yu-Hao Huang', 'Hsuan-Chao Chiu', 'Chia-Che Chang', 'Yi-Chen Lo'] | 2021-06-09 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Lo_CLCC_Contrastive_Learning_for_Color_Constancy_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Lo_CLCC_Contrastive_Learning_for_Color_Constancy_CVPR_2021_paper.pdf | cvpr-2021-1 | ['color-constancy'] | ['computer-vision'] | [ 1.74510762e-01 -5.19803643e-01 -1.80556774e-01 -3.60364199e-01
-7.87575841e-01 -4.90353137e-01 3.98123354e-01 -1.68233991e-01
-3.67092282e-01 5.37398756e-01 4.18418534e-02 -1.79505631e-01
2.11155951e-01 -5.88154912e-01 -9.29725170e-01 -9.08368945e-01
-5.55541255e-02 -2.55121976e-01 -2.13902220e-01 -2.45199516... | [10.471537590026855, -2.593221426010132] |
91242a31-b111-4b48-bfd1-1b568cd0391f | storytelling-of-photo-stream-with | 1606.00625 | null | http://arxiv.org/abs/1606.00625v1 | http://arxiv.org/pdf/1606.00625v1.pdf | Storytelling of Photo Stream with Bidirectional Multi-thread Recurrent Neural Network | Visual storytelling aims to generate human-level narrative language (i.e., a
natural paragraph with multiple sentences) from a photo streams. A typical
photo story consists of a global timeline with multi-thread local storylines,
where each storyline occurs in one different scene. Such complex structure
leads to large ... | ['Yu Liu', 'Jianlong Fu', 'Chang Wen Chen', 'Tao Mei'] | 2016-06-02 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 3.68363947e-01 -5.38919959e-03 -2.90369809e-01 -2.46268719e-01
-6.98596597e-01 -2.78006941e-01 8.48587930e-01 -3.34421396e-01
1.04993045e-01 7.54867435e-01 7.85481274e-01 -1.33842990e-01
4.61707622e-01 -6.60018146e-01 -9.67989206e-01 -6.84924722e-01
4.75301385e-01 7.92645141e-02 3.17851424e-01 -1.90553874... | [11.135492324829102, 0.6791940927505493] |
9ed876e0-6bfe-4e16-9a02-6730d1b2195d | rpbert-a-text-image-relation-propagation | 2102.02967 | null | https://arxiv.org/abs/2102.02967v1 | https://arxiv.org/pdf/2102.02967v1.pdf | RpBERT: A Text-image Relation Propagation-based BERT Model for Multimodal NER | Recently multimodal named entity recognition (MNER) has utilized images to improve the accuracy of NER in tweets. However, most of the multimodal methods use attention mechanisms to extract visual clues regardless of whether the text and image are relevant. Practically, the irrelevant text-image pairs account for a lar... | ['Fangsheng Weng', 'Yindu Su', 'Kai Zhang', 'Jiquan Wang', 'Lin Sun'] | 2021-02-05 | null | null | null | null | ['multi-modal-named-entity-recognition'] | ['natural-language-processing'] | [-8.64545554e-02 -2.45445654e-01 9.19870436e-02 -4.46926415e-01
-8.79988790e-01 -5.04792333e-01 6.99680269e-01 1.04453757e-01
-1.13035083e+00 5.36637902e-01 2.21110150e-01 -8.98049176e-02
3.93852293e-01 -5.35771549e-01 -8.25194657e-01 -6.91658080e-01
3.98482740e-01 2.54469365e-01 3.95756960e-01 -3.16409498... | [10.853692054748535, 1.5467121601104736] |
26d2dd45-ffa1-4bdb-8675-70ad6aa7f1b2 | real-time-instance-segmentation-of-surgical | 2111.04911 | null | https://arxiv.org/abs/2111.04911v2 | https://arxiv.org/pdf/2111.04911v2.pdf | Real-time Instance Segmentation of Surgical Instruments using Attention and Multi-scale Feature Fusion | Precise instrument segmentation aid surgeons to navigate the body more easily and increase patient safety. While accurate tracking of surgical instruments in real-time plays a crucial role in minimally invasive computer-assisted surgeries, it is a challenging task to achieve, mainly due to 1) complex surgical environme... | ['Sharib Ali', 'Leonardo Chang', 'Gilberto Ochoa-Ruiz', 'Juan Carlos Angeles-Ceron'] | 2021-11-09 | null | null | null | null | ['real-time-instance-segmentation'] | ['computer-vision'] | [ 1.80493712e-01 2.44616523e-01 -2.60525733e-01 -6.32190555e-02
-1.24708319e+00 -6.79473639e-01 1.41535506e-01 3.08737814e-01
-8.22952390e-01 4.29866850e-01 1.74360096e-01 -6.28001988e-01
-2.29462415e-01 -8.09707493e-02 -7.71849871e-01 -3.40486974e-01
-2.26977512e-01 6.59522533e-01 2.28410393e-01 -7.26556331... | [13.98672103881836, -3.192314386367798] |
a553065e-03e7-4054-858c-3f21ddd299aa | mt3-multi-task-multitrack-music-transcription-1 | 2111.03017 | null | https://arxiv.org/abs/2111.03017v4 | https://arxiv.org/pdf/2111.03017v4.pdf | MT3: Multi-Task Multitrack Music Transcription | Automatic Music Transcription (AMT), inferring musical notes from raw audio, is a challenging task at the core of music understanding. Unlike Automatic Speech Recognition (ASR), which typically focuses on the words of a single speaker, AMT often requires transcribing multiple instruments simultaneously, all while prese... | ['Jesse Engel', 'Curtis Hawthorne', 'Ethan Manilow', 'Ian Simon', 'Josh Gardner'] | 2021-11-04 | mt3-multi-task-multitrack-music-transcription | https://openreview.net/forum?id=iMSjopcOn0p | https://openreview.net/pdf?id=iMSjopcOn0p | iclr-2022-4 | ['music-transcription'] | ['music'] | [ 5.01475036e-01 -3.38839740e-01 -6.49813637e-02 -3.46319121e-03
-1.59776950e+00 -1.17980778e+00 1.40049592e-01 -2.92443246e-01
-8.43404084e-02 3.26704890e-01 4.96835679e-01 -1.67953730e-01
-1.58309758e-01 -2.85565376e-01 -7.44458258e-01 -5.25627851e-01
6.18755445e-02 5.67839086e-01 -4.03429389e-01 -3.06060851... | [15.829805374145508, 5.418605804443359] |
7136499c-ebbf-4bf2-91e7-19ebc96858b7 | factkg-fact-verification-via-reasoning-on | 2305.06590 | null | https://arxiv.org/abs/2305.06590v2 | https://arxiv.org/pdf/2305.06590v2.pdf | FactKG: Fact Verification via Reasoning on Knowledge Graphs | In real world applications, knowledge graphs (KG) are widely used in various domains (e.g. medical applications and dialogue agents). However, for fact verification, KGs have not been adequately utilized as a knowledge source. KGs can be a valuable knowledge source in fact verification due to their reliability and broa... | ['Edward Choi', 'James Thorne', 'Yohan Jo', 'Yeonsu Kwon', 'Sungjin Park', 'Jiho Kim'] | 2023-05-11 | null | null | null | null | ['fact-verification'] | ['natural-language-processing'] | [-3.29330921e-01 6.46866500e-01 -8.60746145e-01 -3.07140440e-01
-5.21609604e-01 -8.46513808e-01 7.59879112e-01 7.95045793e-01
1.33683592e-01 1.01147437e+00 5.99889219e-01 -7.21594095e-01
-2.57952571e-01 -1.23088753e+00 -7.62653589e-01 -4.12748493e-02
1.78468022e-02 3.62968177e-01 6.26275778e-01 -5.43250620... | [9.417784690856934, 8.322187423706055] |
32ade26c-7f3b-4fd5-bd21-777531d18dc1 | intrinsic-image-transformation-via-scale | 1805.10253 | null | http://arxiv.org/abs/1805.10253v1 | http://arxiv.org/pdf/1805.10253v1.pdf | Intrinsic Image Transformation via Scale Space Decomposition | We introduce a new network structure for decomposing an image into its
intrinsic albedo and shading. We treat this as an image-to-image transformation
problem and explore the scale space of the input and output. By expanding the
output images (albedo and shading) into their Laplacian pyramid components, we
develop a mu... | ['Zicheng Liao', 'Chengyi Zhang', 'Lechao Cheng'] | 2018-05-25 | intrinsic-image-transformation-via-scale-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Cheng_Intrinsic_Image_Transformation_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Cheng_Intrinsic_Image_Transformation_CVPR_2018_paper.pdf | cvpr-2018-6 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 5.84057331e-01 -1.37951136e-01 3.05238396e-01 -4.67235267e-01
-4.44069386e-01 -5.63310564e-01 4.81553555e-01 -8.22543979e-01
-2.38844201e-01 1.52902976e-01 1.66223288e-01 -1.84845522e-01
3.06694180e-01 -7.20366120e-01 -9.58496630e-01 -8.98460269e-01
-1.44552827e-01 -2.60329153e-02 1.35059385e-02 -2.58102268... | [9.744157791137695, -2.8713788986206055] |
974f979d-d54a-472d-b1f3-8d436604f602 | learning-robust-self-attention-features-for | 2305.06273 | null | https://arxiv.org/abs/2305.06273v1 | https://arxiv.org/pdf/2305.06273v1.pdf | Learning Robust Self-attention Features for Speech Emotion Recognition with Label-adaptive Mixup | Speech Emotion Recognition (SER) is to recognize human emotions in a natural verbal interaction scenario with machines, which is considered as a challenging problem due to the ambiguous human emotions. Despite the recent progress in SER, state-of-the-art models struggle to achieve a satisfactory performance. We propose... | ['Dazhi Jiang', 'Lichao Zhang', 'Lei Kang'] | 2023-05-07 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [ 1.69904694e-01 -9.23310816e-02 3.21551450e-02 -6.66109979e-01
-6.71113074e-01 2.78203301e-02 5.03579319e-01 -1.42713383e-01
-6.66331351e-01 6.13204420e-01 2.35023931e-01 1.44023925e-01
4.62787360e-01 -3.28576453e-02 -2.90061265e-01 -4.98617291e-01
1.56146303e-01 4.66911107e-01 -4.00430858e-02 -2.62547165... | [13.406700134277344, 5.790063381195068] |
b0f4daa6-96d9-4a7a-9b32-1b4a3a25c581 | divergence-aware-federated-self-supervised-1 | 2204.04385 | null | https://arxiv.org/abs/2204.04385v1 | https://arxiv.org/pdf/2204.04385v1.pdf | Divergence-aware Federated Self-Supervised Learning | Self-supervised learning (SSL) is capable of learning remarkable representations from centrally available data. Recent works further implement federated learning with SSL to learn from rapidly growing decentralized unlabeled images (e.g., from cameras and phones), often resulted from privacy constraints. Extensive atte... | ['Shuai Zhang', 'Yonggang Wen', 'Weiming Zhuang'] | 2022-04-09 | divergence-aware-federated-self-supervised | https://openreview.net/forum?id=oVE1z8NlNe | https://openreview.net/pdf?id=oVE1z8NlNe | iclr-2022-4 | ['federated-unsupervised-learning'] | ['methodology'] | [-1.10625632e-01 3.01482771e-02 -4.80492532e-01 -5.32177091e-01
-7.73934424e-01 -6.42843008e-01 6.06678784e-01 -1.82771370e-01
-4.18423772e-01 9.05306518e-01 9.10716951e-02 -2.35702321e-01
-2.81891793e-01 -3.42387736e-01 -9.37084079e-01 -9.04885292e-01
-2.34070376e-01 4.26882982e-01 -7.31571242e-02 2.02299610... | [5.823480129241943, 6.311390399932861] |
fa6d9654-7c08-4082-af8e-c6c6a7e67f75 | semi-blind-and-l1-robust-system | 2008.08758 | null | http://arxiv.org/abs/2008.08758v1 | http://arxiv.org/pdf/2008.08758v1.pdf | Semi-Blind and l1 Robust System Identification for Anemia Management | Chronic diseases such as cancer, diabetes, heart diseases, chronic kidney
disease (CKD) require a drug management system that ensures a stable and robust
output of the patient's condition in response to drug dosage. In the case of
CKD, the patients suffer from the deficiency of red blood cell count and
external human r... | [] | 2020-08-20 | null | null | null | null | ['blood-cell-count'] | ['computer-vision'] | [-6.07102662e-02 -1.09323114e-01 -1.91712335e-01 -3.08476359e-01
-1.44629702e-01 2.14666245e-03 1.70461535e-02 5.56453586e-01
-2.07925245e-01 9.58277166e-01 5.74971922e-02 -2.04200611e-01
-2.89202899e-01 -6.89327955e-01 -4.27247211e-02 -7.43410587e-01
4.59307581e-02 5.67960382e-01 -4.39912587e-01 -1.01384506... | [14.046663284301758, 3.0205862522125244] |
f0e67321-72a1-4b39-aad2-918b977b076b | anomaly-clustering-grouping-images-into | 2112.11573 | null | https://arxiv.org/abs/2112.11573v2 | https://arxiv.org/pdf/2112.11573v2.pdf | Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types | We study anomaly clustering, grouping data into coherent clusters of anomaly types. This is different from anomaly detection that aims to divide anomalies from normal data. Unlike object-centered image clustering, anomaly clustering is particularly challenging as anomalous patterns are subtle and local. We present a si... | ['Tomas Pfister', 'Chen-Yu Lee', 'Chun-Liang Li', 'Jinsung Yoon', 'Kihyuk Sohn'] | 2021-12-21 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [-1.80843160e-01 -8.47209990e-02 3.37533653e-01 -5.07960439e-01
-7.29729354e-01 -4.75773811e-01 5.32973170e-01 6.35239363e-01
-1.29676551e-01 -1.20720722e-01 1.16141170e-01 1.79429606e-01
-3.54983300e-01 -6.40634358e-01 -4.88154888e-01 -1.17044699e+00
-6.64061427e-01 4.47598189e-01 1.55295506e-01 3.13715972... | [7.635928630828857, 2.301877737045288] |
db4adfde-b998-44ba-ac62-c083bd819854 | transformer-based-image-generation-from-scene | 2303.04634 | null | https://arxiv.org/abs/2303.04634v1 | https://arxiv.org/pdf/2303.04634v1.pdf | Transformer-based Image Generation from Scene Graphs | Graph-structured scene descriptions can be efficiently used in generative models to control the composition of the generated image. Previous approaches are based on the combination of graph convolutional networks and adversarial methods for layout prediction and image generation, respectively. In this work, we show how... | ['Concetto Spampinato', 'Simone Palazzo', 'Renato Sortino'] | 2023-03-08 | null | null | null | null | ['image-generation-from-scene-graphs'] | ['computer-vision'] | [ 3.19213212e-01 3.29568118e-01 4.14787889e-01 8.62262095e-04
-6.02977574e-01 -7.88534582e-01 7.51800656e-01 -8.49040300e-02
1.32265732e-01 5.44806004e-01 8.91153067e-02 -2.07853451e-01
1.38052121e-01 -1.13995349e+00 -1.28634799e+00 -7.42834330e-01
2.06254765e-01 3.86022091e-01 1.38181020e-02 -1.32116675... | [11.492849349975586, -0.40016135573387146] |
99a38446-5646-4e42-b68e-f67245967262 | you-only-look-once-unified-real-time-object | 1506.02640 | null | http://arxiv.org/abs/1506.02640v5 | http://arxiv.org/pdf/1506.02640v5.pdf | You Only Look Once: Unified, Real-Time Object Detection | We present YOLO, a new approach to object detection. Prior work on object
detection repurposes classifiers to perform detection. Instead, we frame object
detection as a regression problem to spatially separated bounding boxes and
associated class probabilities. A single neural network predicts bounding boxes
and class ... | ['Ali Farhadi', 'Santosh Divvala', 'Ross Girshick', 'Joseph Redmon'] | 2015-06-08 | you-only-look-once-unified-real-time-object-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Redmon_You_Only_Look_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Redmon_You_Only_Look_CVPR_2016_paper.pdf | cvpr-2016-6 | ['object-counting'] | ['computer-vision'] | [-1.57727152e-01 -1.18209809e-01 -5.24630100e-02 -2.43758395e-01
-7.53507257e-01 -5.66957474e-01 5.66647291e-01 -1.02383969e-02
-7.36976027e-01 1.11807562e-01 -2.72960365e-01 -1.82702407e-01
6.28031969e-01 -8.26200783e-01 -7.89382398e-01 -4.07381684e-01
-2.73522884e-01 4.26081181e-01 1.00476813e+00 5.04831830... | [9.029928207397461, 0.6209631562232971] |
4f6343d2-e708-4343-a076-cc22563f8f35 | evolutionary-multiparty-distance-minimization | 2207.13390 | null | https://arxiv.org/abs/2207.13390v1 | https://arxiv.org/pdf/2207.13390v1.pdf | Evolutionary Multiparty Distance Minimization | In the field of evolutionary multiobjective optimization, the decision maker (DM) concerns conflicting objectives. In the real-world applications, there usually exist more than one DM and each DM concerns parts of these objectives. Multiparty multiobjective optimization problems (MPMOPs) are proposed to depict the MOP ... | ['Yuhui Shi', 'Yatong Chang', 'Xin Lin', 'Wenjian Luo', 'Zeneng She'] | 2022-07-27 | null | null | null | null | ['multiobjective-optimization'] | ['methodology'] | [-1.06964936e-03 -2.19355926e-01 2.83437520e-01 -3.16064298e-01
-2.13280976e-01 -4.29461211e-01 5.02368249e-02 2.50283808e-01
-2.83236235e-01 1.06056046e+00 -1.79424152e-01 2.50929780e-02
-8.86597633e-01 -9.20649230e-01 -2.79888123e-01 -9.33403790e-01
1.62298717e-02 9.14060712e-01 -2.12184727e-01 -5.77717364... | [5.706081390380859, 3.518502950668335] |
9c362038-ec0d-4fca-9364-3d1856c4cadc | on-the-relation-between-color-image-denoising | 1704.01372 | null | http://arxiv.org/abs/1704.01372v1 | http://arxiv.org/pdf/1704.01372v1.pdf | On the Relation between Color Image Denoising and Classification | Large amount of image denoising literature focuses on single channel images
and often experimentally validates the proposed methods on tens of images at
most. In this paper, we investigate the interaction between denoising and
classification on large scale dataset. Inspired by classification models, we
propose a novel ... | ['Luc van Gool', 'Radu Timofte', 'Jiqing Wu', 'Zhiwu Huang'] | 2017-04-05 | null | null | null | null | ['color-image-denoising'] | ['computer-vision'] | [ 2.64165998e-01 -3.81730437e-01 3.16194922e-01 -4.47843313e-01
-8.01579773e-01 -5.24076581e-01 4.46876526e-01 8.26807413e-03
-6.62965000e-01 7.00005710e-01 1.98692411e-01 -4.41727377e-02
-1.19232886e-01 -9.32162285e-01 -8.97371411e-01 -9.50171351e-01
-1.64377257e-01 -3.59870404e-01 -3.57986763e-02 -3.78636688... | [11.444655418395996, -2.310603141784668] |
60803432-652d-4727-9f82-b3b1c0161fbe | deliberate-self-attention-network-with | 2009.09112 | null | https://arxiv.org/abs/2009.09112v2 | https://arxiv.org/pdf/2009.09112v2.pdf | An Interpretable and Uncertainty Aware Multi-Task Framework for Multi-Aspect Sentiment Analysis | In recent years, several online platforms have seen a rapid increase in the number of review systems that request users to provide aspect-level feedback. Document-level Multi-aspect Sentiment Classification (DMSC), where the goal is to predict the ratings/sentiment from a review at an individual aspect level, has becom... | ['Chandan K. Reddy', 'Tian Shi', 'Ping Wang'] | 2020-09-18 | null | null | null | null | ['extract-aspect'] | ['natural-language-processing'] | [-2.50262003e-02 2.91186690e-01 -1.21871367e-01 -7.33454108e-01
-1.24666119e+00 -3.69118690e-01 5.93451977e-01 3.71328205e-01
-3.30229908e-01 5.37690580e-01 6.96873188e-01 -3.68487358e-01
-2.35741287e-02 -5.32968402e-01 -8.16485226e-01 -3.01846802e-01
3.65701646e-01 5.34878075e-01 -1.41613513e-01 -4.46789324... | [11.390952110290527, 6.660897731781006] |
7235a44a-ded2-4cc2-abc5-81501e220f65 | skeleton-image-representation-for-3d-action | 1909.05704 | null | https://arxiv.org/abs/1909.05704v1 | https://arxiv.org/pdf/1909.05704v1.pdf | Skeleton Image Representation for 3D Action Recognition based on Tree Structure and Reference Joints | In the last years, the computer vision research community has studied on how to model temporal dynamics in videos to employ 3D human action recognition. To that end, two main baseline approaches have been researched: (i) Recurrent Neural Networks (RNNs) with Long-Short Term Memory (LSTM); and (ii) skeleton image repres... | ['William Robson Schwartz', 'François Brémond', 'Carlos Caetano'] | 2019-09-11 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 2.75835484e-01 -2.58332584e-03 -5.04421473e-01 -2.03326762e-01
-1.41745701e-01 8.47394019e-02 8.62765729e-01 -4.79869843e-01
-4.92527246e-01 3.32977831e-01 3.88224214e-01 -2.72469260e-02
-2.26359777e-02 -6.24825418e-01 -7.42438257e-01 -8.33679020e-01
-1.18581839e-01 1.39252424e-01 4.96813953e-01 -2.12245375... | [7.87412166595459, 0.37952521443367004] |
ee13c83a-dd7d-4430-b386-8ea60f15907e | a-brief-overview-of-physics-inspired | 2201.12810 | null | https://arxiv.org/abs/2201.12810v1 | https://arxiv.org/pdf/2201.12810v1.pdf | A Brief Overview of Physics-inspired Metaheuristic Optimization Techniques | Metaheuristic algorithms are methods devised to efficiently solve computationally challenging optimization problems. Researchers have taken inspiration from various natural and physical processes alike to formulate meta-heuristics that have successfully provided near-optimal or optimal solutions to several engineering ... | ['Rishav Pramanik', 'Aritra Marik', 'Soumitri Chattopadhyay'] | 2022-01-30 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [ 3.51082124e-02 -1.00350522e-01 -1.36249781e-01 3.13901097e-01
-2.71175474e-01 -2.26628914e-01 5.17550051e-01 3.62482518e-01
9.30595491e-03 1.19385457e+00 -4.28811103e-01 -1.01550095e-01
-1.01981199e+00 -8.48706961e-01 -2.58999556e-01 -1.16512012e+00
-4.40311342e-01 6.31707191e-01 -1.58468455e-01 -6.01605773... | [5.704746723175049, 3.6207938194274902] |
6b7937bc-a331-4744-b482-26defef4641c | symbolic-discovery-of-optimization-algorithms | 2302.06675 | null | https://arxiv.org/abs/2302.06675v4 | https://arxiv.org/pdf/2302.06675v4.pdf | Symbolic Discovery of Optimization Algorithms | We present a method to formulate algorithm discovery as program search, and apply it to discover optimization algorithms for deep neural network training. We leverage efficient search techniques to explore an infinite and sparse program space. To bridge the large generalization gap between proxy and target tasks, we al... | ['Quoc V. Le', 'Yifeng Lu', 'Cho-Jui Hsieh', 'Thang Luong', 'Xuanyi Dong', 'Hieu Pham', 'Yao Liu', 'Kaiyuan Wang', 'Esteban Real', 'Da Huang', 'Chen Liang', 'Xiangning Chen'] | 2023-02-13 | null | null | null | null | ['zero-shot-transfer-image-classification'] | ['computer-vision'] | [-1.33393914e-01 -2.48150215e-01 -3.93311113e-01 -3.85463148e-01
-7.89254665e-01 -6.43462300e-01 3.92434627e-01 -4.31189872e-02
-9.18434143e-01 3.29738230e-01 -5.20001531e-01 -7.01285899e-01
-1.25954032e-01 -5.15913188e-01 -1.07710552e+00 -4.96960253e-01
-1.32575601e-01 4.10497367e-01 1.05163880e-01 -1.75218493... | [8.62681770324707, 3.2374868392944336] |
81267d09-02f2-4452-98b0-d068b3299c22 | regularized-graph-structure-learning-with | 2210.06126 | null | https://arxiv.org/abs/2210.06126v1 | https://arxiv.org/pdf/2210.06126v1.pdf | Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting | Multivariate time-series forecasting is a critical task for many applications, and graph time-series network is widely studied due to its capability to capture the spatial-temporal correlation simultaneously. However, most existing works focus more on learning with the explicit prior graph structure, while ignoring pot... | ['Alex Liu', 'Liang Wang', 'Yan Huang', 'Jianguo Li', 'Weichen Yu', 'Ting Li', 'Hongyuan Yu'] | 2022-10-12 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [-5.67010641e-02 1.83081239e-01 -2.46505708e-01 -4.61782992e-01
-2.13105172e-01 -3.30667704e-01 5.67192852e-01 9.92103145e-02
2.01248020e-01 3.90786916e-01 4.78025407e-01 -3.08622152e-01
-2.19463512e-01 -8.59112620e-01 -7.20400214e-01 -8.49446177e-01
-2.89439678e-01 7.21604526e-02 -1.94297820e-01 -1.18091978... | [6.876657009124756, 2.935842752456665] |
4b3e4eb8-a012-485c-bac5-d120e9fa700b | alert-calm-down-there-is-nothing-to-worry | null | null | https://aclanthology.org/L14-1566 | https://aclanthology.org/L14-1566.pdf | Alert!... Calm Down, There is Nothing to Worry About. Warning and Soothing Speech Synthesis. | Presence of appropriate acoustic cues of affective features in the synthesized speech can be a prerequisite for the proper evaluation of the semantic content by the message recipient. In the recent work the authors have focused on the research of expressive speech synthesis capable of generating naturally sounding synt... | ["R{\\'o}bert Sabo", "Mari{\\'a}n Ritomsk{\\'y}", "Mari{\\'a}n Trnka", 'Sakhia Darjaa', 'Milan Rusko'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['expressive-speech-synthesis'] | ['speech'] | [ 1.88630596e-01 3.84564131e-01 4.26812500e-01 -6.88461959e-01
-4.89769608e-01 -4.71179783e-01 7.41643250e-01 1.59625858e-01
-2.17824668e-01 6.60846591e-01 5.15971005e-01 -2.00881227e-03
-1.87962770e-01 -5.08149803e-01 -2.78196726e-02 -6.65395796e-01
4.03406382e-01 3.38851333e-01 -1.25977144e-01 -6.69829011... | [14.714045524597168, 6.55550479888916] |
933ba739-ed24-4b92-8b5a-b386f051b017 | learning-subpocket-prototypes-for | 2305.13997 | null | https://arxiv.org/abs/2305.13997v1 | https://arxiv.org/pdf/2305.13997v1.pdf | Learning Subpocket Prototypes for Generalizable Structure-based Drug Design | Generating molecules with high binding affinities to target proteins (a.k.a. structure-based drug design) is a fundamental and challenging task in drug discovery. Recently, deep generative models have achieved remarkable success in generating 3D molecules conditioned on the protein pocket. However, most existing method... | ['Qi Liu', 'Zaixi Zhang'] | 2023-05-22 | null | null | null | null | ['drug-discovery', '3d-molecule-generation'] | ['medical', 'medical'] | [ 2.99753517e-01 1.24020956e-01 -3.72918278e-01 -1.74554661e-01
-7.36693323e-01 -5.59875965e-01 4.72334087e-01 1.24060676e-01
9.95231271e-02 1.42109561e+00 1.92520082e-01 -3.99683088e-01
4.96651828e-02 -8.75739872e-01 -1.31554341e+00 -1.02842963e+00
-2.90082637e-02 9.09711480e-01 2.07707658e-01 -2.69982755... | [4.968233108520508, 5.720600128173828] |
fd0ae046-6d51-4be8-9d86-8c07b747e5ff | generalizable-wireless-navigation-through | 2306.06766 | null | https://arxiv.org/abs/2306.06766v1 | https://arxiv.org/pdf/2306.06766v1.pdf | Generalizable Wireless Navigation through Physics-Informed Reinforcement Learning in Wireless Digital Twin | The growing focus on indoor robot navigation utilizing wireless signals has stemmed from the capability of these signals to capture high-resolution angular and temporal measurements. However, employing end-to-end generic reinforcement learning (RL) for wireless indoor navigation (WIN) in initially unknown environments ... | ['Quanyan Zhu', 'Sundeep Rangan', 'Yaqi Hu', 'Haozhe Lei', 'Tao Li', 'Mingsheng Yin'] | 2023-06-11 | null | null | null | null | ['robot-navigation'] | ['robots'] | [ 1.50692746e-01 6.87147230e-02 1.62211969e-01 -2.77982831e-01
-1.04541838e+00 -4.51954395e-01 4.28885520e-01 5.03330976e-02
-5.38996875e-01 1.23372543e+00 -1.39889479e-01 -4.75537360e-01
-1.07221389e+00 -1.05766511e+00 -1.01976883e+00 -8.07613075e-01
-8.90558720e-01 3.44193608e-01 -9.57396068e-03 -3.78386021... | [5.856173515319824, 1.063976764678955] |
17b8572c-308e-4bd6-b2a5-e254ad471d4e | root-aligned-smiles-for-molecular | 2203.11444 | null | https://arxiv.org/abs/2203.11444v5 | https://arxiv.org/pdf/2203.11444v5.pdf | Root-aligned SMILES: A Tight Representation for Chemical Reaction Prediction | Chemical reaction prediction, involving forward synthesis and retrosynthesis prediction, is a fundamental problem in organic synthesis. A popular computational paradigm formulates synthesis prediction as a sequence-to-sequence translation problem, where the typical SMILES is adopted for molecule representations. Howeve... | ['Shaolun Yao', 'Mingli Song', 'Tingjun Hou', 'Min Wu', 'Lingxiang Jia', 'Tiantao Liu', 'Zunlei Feng', 'Jie Song', 'Zipeng Zhong'] | 2022-03-22 | null | null | null | null | ['chemical-reaction-prediction', 'retrosynthesis'] | ['medical', 'medical'] | [ 6.62169755e-01 -2.53644288e-02 -4.89856392e-01 -1.11744821e-01
-3.36583704e-01 -8.82416487e-01 7.59529769e-01 4.04224068e-01
-7.61973066e-03 9.90518093e-01 2.06374303e-01 -6.33593619e-01
2.08479583e-01 -9.04447913e-01 -1.01044297e+00 -9.68666852e-01
2.80149102e-01 1.26969054e-01 8.24416578e-02 -2.90554285... | [4.534318447113037, 6.090055465698242] |
31163c99-424b-428f-a21e-3fa3d51309ef | robust-kalman-filters-with-unknown-covariance | 2110.08740 | null | https://arxiv.org/abs/2110.08740v3 | https://arxiv.org/pdf/2110.08740v3.pdf | Robust Kalman filters with unknown covariance of multiplicative noise | In this paper, state and noise covariance estimation problems for linear system with unknown multiplicative noise are considered. The measurement likelihood is modelled as a mixture of two Gaussian distributions and a Student's t distribution, respectively. The unknown covariance of multiplicative noise is modelled as ... | ['Ziyang Meng', 'Xingkai Yu'] | 2021-10-17 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [-1.04953477e-03 2.52502412e-02 -1.51121125e-01 5.21309078e-02
-4.83502239e-01 -4.02030945e-01 4.98695016e-01 -2.95228094e-01
-3.27640206e-01 9.12506282e-01 -2.21070275e-01 -2.71821767e-01
-7.60707736e-01 -5.34509003e-01 -4.57485229e-01 -1.14958549e+00
5.62313087e-02 -4.52517681e-02 2.17580184e-01 2.66767800... | [5.508355617523193, 2.6841487884521484] |
79e23f55-06a4-424c-9237-cde3153071c7 | crossing-roads-of-federated-learning-and | 2304.08602 | null | https://arxiv.org/abs/2304.08602v1 | https://arxiv.org/pdf/2304.08602v1.pdf | Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives | Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require huge amounts of data to achieve acceptable performance leading in most cases to risks of privacy leakage... | ['Wilfried Elmenreich', 'Wathiq Mansoor', 'Fodil Fadli', 'Faycal Bensaali', 'Abbes Amira', 'Yassine Himeur', 'Roumaysa Bousselidj', 'Hafsa Bousbiat'] | 2023-04-17 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-1.96226507e-01 3.69135067e-02 -1.92491740e-01 -6.25892758e-01
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80a984b3-44c5-4001-aa30-6be2e25d5890 | mvstylizer-an-efficient-edge-assisted-video | 2005.11630 | null | https://arxiv.org/abs/2005.11630v2 | https://arxiv.org/pdf/2005.11630v2.pdf | MVStylizer: An Efficient Edge-Assisted Video Photorealistic Style Transfer System for Mobile Phones | Recent research has made great progress in realizing neural style transfer of images, which denotes transforming an image to a desired style. Many users start to use their mobile phones to record their daily life, and then edit and share the captured images and videos with other users. However, directly applying existi... | ['Yiran Chen', 'Chunpeng Wu', 'Ang Li', 'Bin Ni'] | 2020-05-24 | null | null | null | null | ['video-style-transfer'] | ['computer-vision'] | [ 2.69796759e-01 -3.96901160e-01 -3.45001183e-02 -1.44072786e-01
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3.55818093e-01 -1.64696574e-02 2.43802845e-01 -4.63660024... | [10.840279579162598, -1.0301084518432617] |
5cc530c3-1643-4fb6-9063-13778c613b57 | look-closer-to-see-better-recurrent-attention | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Fu_Look_Closer_to_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Fu_Look_Closer_to_CVPR_2017_paper.pdf | Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition | Recognizing fine-grained categories (e.g., bird species) is difficult due to the challenges of discriminative region localization and fine-grained feature learning. Existing approaches predominantly solve these challenges independently, while neglecting the fact that region detection and fine-grained feature learning a... | ['Jianlong Fu', 'Heliang Zheng', 'Tao Mei'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['fine-grained-image-recognition'] | ['computer-vision'] | [-1.53067559e-01 -1.51457772e-01 -1.47956446e-01 -4.57137585e-01
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-6.76854700e-02 2.32049674e-01 5.67887485e-01 5.90016805... | [9.566117286682129, 2.0185577869415283] |
7c682290-605f-49d3-b596-333d708930cb | on-the-fairness-of-swarm-learning-in-skin | 2109.12176 | null | https://arxiv.org/abs/2109.12176v1 | https://arxiv.org/pdf/2109.12176v1.pdf | On the Fairness of Swarm Learning in Skin Lesion Classification | in healthcare. However, the existing AI model may be biased in its decision marking. The bias induced by data itself, such as collecting data in subgroups only, can be mitigated by including more diversified data. Distributed and collaborative learning is an approach to involve training models in massive, heterogeneous... | ['Xiaoxiao Li', 'Yifan Wu', 'Di Fan'] | 2021-09-24 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [-1.68457150e-01 7.64954761e-02 -4.65741754e-01 -1.82748884e-01
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-2.52136320e-01 5.05649805e-01 -1.14518091e-01 4.51240800... | [6.154947757720947, 6.435591220855713] |
9618edca-2edb-4515-a1a6-978c98d9e5d7 | image-quality-assessment-for-machine-learning | 2203.14258 | null | https://arxiv.org/abs/2203.14258v1 | https://arxiv.org/pdf/2203.14258v1.pdf | Image quality assessment for machine learning tasks using meta-reinforcement learning | In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability. When the task is performed using machine learning algorithms, such as a neural-network-based task predictor for image classification or segmentation, the perfor... | ['Yipeng Hu', 'Dean C. Barratt', 'J. Alison Noble', 'Geoffrey A. Sonn', 'Richard E. Fan', 'Mirabela Rusu', 'Qianye Yang', 'Zachary M. C. Baum', 'Vasilis Stavrinides', 'Yunguan Fu', 'Shaheer U. Saeed'] | 2022-03-27 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 8.63997042e-01 3.82266462e-01 -1.03512034e-01 -5.08686602e-01
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-7.94293061e-02 5.28844297e-01 1.08974455e-02 2.10333616... | [14.605314254760742, -2.0668468475341797] |
608f503b-593c-4a70-8ab5-4ae609082eea | a-robustly-optimized-bmrc-for-aspect | null | null | https://aclanthology.org/2022.naacl-main.20 | https://aclanthology.org/2022.naacl-main.20.pdf | A Robustly Optimized BMRC for Aspect Sentiment Triplet Extraction | Aspect sentiment triplet extraction (ASTE) is a challenging subtask in aspect-based sentiment analysis. It aims to explore the triplets of aspects, opinions and sentiments with complex correspondence from the context. The bidirectional machine reading comprehension (BMRC), can effectively deal with ASTE task, but sever... | ['Zuhe Li', 'Kaiwen Li', 'Shu Liu'] | null | null | null | null | naacl-2022-7 | ['aspect-based-sentiment-analysis', 'machine-reading-comprehension', 'aspect-sentiment-triplet-extraction'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.28992248e-01 -1.15218610e-02 -2.84906507e-01 -6.81948721e-01
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6.10279799e-01 5.45280039e-01 4.06533092e-01 -4.18839186... | [11.600790977478027, 6.530228614807129] |
f3ed7856-0528-478f-8a61-3a1f78f7fc07 | mutual-wasserstein-discrepancy-minimization | 2301.12197 | null | https://arxiv.org/abs/2301.12197v2 | https://arxiv.org/pdf/2301.12197v2.pdf | Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation | Self-supervised sequential recommendation significantly improves recommendation performance by maximizing mutual information with well-designed data augmentations. However, the mutual information estimation is based on the calculation of Kullback Leibler divergence with several limitations, including asymmetrical estim... | ['Philip S Yu', 'Hao Peng', 'Zhiwei Liu', 'Ziwei Fan'] | 2023-01-28 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 1.67372841e-02 -1.65544674e-01 -2.61884093e-01 -4.82555538e-01
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-1.84326693e-01 1.98947191e-01 -1.09120227e-01 -2.01683447... | [10.02226448059082, 5.548157691955566] |
5139907f-9023-4be0-b7a9-3060b98cc907 | a-machine-learning-model-for-stock-market | 1402.7351 | null | http://arxiv.org/abs/1402.7351v1 | http://arxiv.org/pdf/1402.7351v1.pdf | A Machine Learning Model for Stock Market Prediction | Stock market prediction is the act of trying to determine the future value of
a company stock or other financial instrument traded on a financial exchange. | ['Mustafa Abdul Salam', 'Omar S. Soliman', 'Osman Hegazy'] | 2014-02-28 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-6.33701742e-01 1.26430020e-01 -3.66918266e-01 -1.36451527e-01
2.74660707e-01 -7.08443820e-01 8.91899109e-01 -3.33094060e-01
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-7.26412460e-02 3.99725169e-01 4.59557801e-01 -3.20186079... | [4.529783248901367, 4.189852714538574] |
0be63c24-85f5-4e76-855c-72d87f49c41b | understanding-mcmc-dynamics-as-flows-on-the | 1902.00282 | null | https://arxiv.org/abs/1902.00282v3 | https://arxiv.org/pdf/1902.00282v3.pdf | Understanding MCMC Dynamics as Flows on the Wasserstein Space | It is known that the Langevin dynamics used in MCMC is the gradient flow of the KL divergence on the Wasserstein space, which helps convergence analysis and inspires recent particle-based variational inference methods (ParVIs). But no more MCMC dynamics is understood in this way. In this work, by developing novel conce... | ['Jun Zhu', 'Chang Liu', 'Jingwei Zhuo'] | 2019-02-01 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [-3.55018407e-01 -3.18809241e-01 2.87872583e-01 -8.81447569e-02
1.10334292e-01 -3.62168640e-01 7.84803152e-01 -3.30391198e-01
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-2.48247713e-01 5.57146668e-01 2.77986079e-01 -2.83339530... | [7.0061492919921875, 3.9679691791534424] |
ddc0021e-d8ad-4847-8a54-eabfcdc48c80 | wdc-products-a-multi-dimensional-entity | 2301.09521 | null | https://arxiv.org/abs/2301.09521v2 | https://arxiv.org/pdf/2301.09521v2.pdf | WDC Products: A Multi-Dimensional Entity Matching Benchmark | The difficulty of an entity matching task depends on a combination of multiple factors such as the amount of corner-case pairs, the fraction of entities in the test set that have not been seen during training, and the size of the development set. Current entity matching benchmarks usually represent single points in the... | ['Christian Bizer', 'Reng Chiz Der', 'Ralph Peeters'] | 2023-01-23 | null | null | null | null | ['data-integration', 'entity-resolution'] | ['knowledge-base', 'natural-language-processing'] | [-5.78201748e-02 -1.03958227e-01 -1.98578268e-01 -4.17418808e-01
-8.48559976e-01 -9.07155335e-01 8.82578373e-01 5.21576643e-01
-8.82462800e-01 5.18785775e-01 -5.50035387e-02 -3.19575697e-01
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-1.12208083e-01 9.04800773e-01 2.91312844e-01 -5.69640160... | [9.517043113708496, 8.489513397216797] |
9a387290-739b-481a-9dd7-9af1a2ba7754 | texttt-tasksource-structured-dataset | 2301.05948 | null | https://arxiv.org/abs/2301.05948v3 | https://arxiv.org/pdf/2301.05948v3.pdf | tasksource: A Dataset Harmonization Framework for Streamlined NLP Multi-Task Learning and Evaluation | The HuggingFace Datasets Hub hosts thousands of datasets, offering exciting opportunities for language model training and evaluation. However, datasets for a specific task type often have different schemas, making harmonization challenging. Multi-task training or evaluation necessitates manual work to fit data into tas... | ['Damien Sileo'] | 2023-01-14 | null | null | null | null | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 6.28242791e-02 -7.69835263e-02 -3.61561835e-01 -7.85841823e-01
-1.14673769e+00 -9.31574404e-01 4.55361247e-01 3.66570026e-01
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4.94551361e-01 7.42526352e-01 -1.83646947e-01 -1.24297768... | [10.610642433166504, 9.834792137145996] |
fe024b64-4f7b-479c-bfa1-2b3ecc8d748b | tcube-domain-agnostic-neural-time-series | 2110.05633 | null | https://arxiv.org/abs/2110.05633v1 | https://arxiv.org/pdf/2110.05633v1.pdf | TCube: Domain-Agnostic Neural Time-series Narration | The task of generating rich and fluent narratives that aptly describe the characteristics, trends, and anomalies of time-series data is invaluable to the sciences (geology, meteorology, epidemiology) or finance (trades, stocks, or sales and inventory). The efforts for time-series narration hitherto are domain-specific ... | ['Naren Ramakrishnan', 'John S. Brownstein', 'Mandar Sharma'] | 2021-10-11 | null | null | null | null | ['epidemiology'] | ['medical'] | [-4.45641205e-02 2.64195681e-01 3.79418060e-02 -7.96132535e-02
-7.77294517e-01 -9.17207241e-01 9.00023103e-01 2.20203511e-02
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-4.62080628e-01 2.04102531e-01 -3.91074568e-01 -7.29771793... | [11.13805866241455, 8.827312469482422] |
b07fdbb3-3148-4d3c-8767-ccb378b374f3 | on-universal-black-box-domain-adaptation | 2104.04665 | null | https://arxiv.org/abs/2104.04665v1 | https://arxiv.org/pdf/2104.04665v1.pdf | On Universal Black-Box Domain Adaptation | In this paper, we study an arguably least restrictive setting of domain adaptation in a sense of practical deployment, where only the interface of source model is available to the target domain, and where the label-space relations between the two domains are allowed to be different and unknown. We term such a setting a... | ['Kui Jia', 'Changxing Ding', 'Hui Tang', 'Yabin Zhang', 'Bin Deng'] | 2021-04-10 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [ 5.38189650e-01 3.21408570e-01 -6.97353005e-01 -4.68848765e-01
-9.43624556e-01 -9.07387257e-01 7.14998841e-01 1.64900303e-01
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1.26051530e-01 6.30834341e-01 2.85035878e-01 -1.32067040... | [10.341670989990234, 3.221713066101074] |
0f6735b6-bf48-4e12-ba73-2801bd21f620 | towards-identifying-alternative | null | null | https://aclanthology.org/2022.coling-1.70 | https://aclanthology.org/2022.coling-1.70.pdf | Towards Identifying Alternative-Lexicalization Signals of Discourse Relations | The task of shallow discourse parsing in the Penn Discourse Treebank (PDTB) framework has traditionally been restricted to identifying those relations that are signaled by a discourse connective (“explicit”) and those that have no signal at all (“implicit”). The third type, the more flexible group of “AltLex” realizati... | ['Manfred Stede', 'René Knaebel'] | null | null | null | null | coling-2022-10 | ['discourse-parsing'] | ['natural-language-processing'] | [ 2.66197801e-01 9.52833831e-01 -2.39852950e-01 -4.93056476e-01
-6.77204132e-01 -7.33764291e-01 9.10581291e-01 6.93250537e-01
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1.06829159e-01 4.81084585e-01 6.81517065e-01 -3.77231181... | [10.687607765197754, 9.395936012268066] |
3b9e3fd0-3d97-4c33-91fc-e8c2a032fea1 | xlent-mining-a-large-cross-lingual-entity | 2104.08597 | null | https://arxiv.org/abs/2104.08597v2 | https://arxiv.org/pdf/2104.08597v2.pdf | XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment | Cross-lingual named-entity lexica are an important resource to multilingual NLP tasks such as machine translation and cross-lingual wikification. While knowledge bases contain a large number of entities in high-resource languages such as English and French, corresponding entities for lower-resource languages are often ... | ['Adithya Renduchintala', 'Philipp Koehn', 'Francisco Guzmán', 'James Cross', 'Ahmed El-Kishky'] | 2021-04-17 | null | https://aclanthology.org/2021.emnlp-main.814 | https://aclanthology.org/2021.emnlp-main.814.pdf | emnlp-2021-11 | ['multilingual-nlp'] | ['natural-language-processing'] | [-4.31115299e-01 -6.46054223e-02 -7.41752803e-01 -3.50925922e-01
-1.58252180e+00 -1.22832108e+00 3.41229051e-01 2.56968111e-01
-8.31501544e-01 1.38996375e+00 6.45426214e-01 -3.20839673e-01
2.05546528e-01 -6.09531760e-01 -9.15083230e-01 7.01723471e-02
1.96544200e-01 7.94579566e-01 1.13493325e-02 -1.34014562... | [9.714239120483398, 9.209619522094727] |
97a23e1f-d215-4a68-9404-11213c45ea89 | fusion-of-global-and-local-knowledge-for | 2302.11051 | null | https://arxiv.org/abs/2302.11051v1 | https://arxiv.org/pdf/2302.11051v1.pdf | Fusion of Global and Local Knowledge for Personalized Federated Learning | Personalized federated learning, as a variant of federated learning, trains customized models for clients using their heterogeneously distributed data. However, it is still inconclusive about how to design personalized models with better representation of shared global knowledge and personalized pattern. To bridge the ... | ['DaCheng Tao', 'Weiwei Lin', 'Yan Sun', 'Li Shen', 'Tiansheng Huang'] | 2023-02-21 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-3.41342360e-01 -5.30481189e-02 -6.26188219e-01 -4.60832119e-01
-1.17824554e+00 -4.63400841e-01 -8.11477676e-02 -4.34232086e-01
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-5.94681501e-01 -6.06220543e-01 -8.99303734e-01 -8.98179114e-01
1.53852552e-01 4.68205184e-01 -2.80845106e-01 1.53125197... | [5.7876081466674805, 6.2401227951049805] |
2ee10a66-189f-4df8-9844-b4942f7c8fcd | constituency-lattice-encoding-for-aspect-term | null | null | https://aclanthology.org/2020.coling-main.73 | https://aclanthology.org/2020.coling-main.73.pdf | Constituency Lattice Encoding for Aspect Term Extraction | One of the remaining challenges for aspect term extraction in sentiment analysis resides in the extraction of phrase-level aspect terms, which is non-trivial to determine the boundaries of such terms. In this paper, we aim to address this issue by incorporating the span annotations of constituents of a sentence to leve... | ['Qinliang Su', 'Weizhou Shen', 'Xiaojun Quan', 'Kun Li', 'Yunyi Yang'] | 2020-12-01 | null | null | null | coling-2020-8 | ['aspect-term-extraction-and-sentiment'] | ['natural-language-processing'] | [ 2.27245241e-01 4.07526314e-01 -3.45945388e-01 -3.65380973e-01
-7.99667180e-01 -5.06399155e-01 6.49405837e-01 2.41784289e-01
-4.88969028e-01 6.93365753e-01 4.62414443e-01 -4.07888651e-01
4.36659098e-01 -8.04891825e-01 -4.48430389e-01 -4.86267626e-01
1.84396937e-01 -2.27261279e-02 3.55489701e-02 -2.85141200... | [11.45783519744873, 6.677940368652344] |
fb0b1bce-da60-4f48-acb7-59451237df42 | image-based-navigation-in-real-world | 2202.01069 | null | https://arxiv.org/abs/2202.01069v1 | https://arxiv.org/pdf/2202.01069v1.pdf | Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models, Benchmark and Efficient Evaluation | Navigating complex indoor environments requires a deep understanding of the space the robotic agent is acting into to correctly inform the navigation process of the agent towards the goal location. In recent learning-based navigation approaches, the scene understanding and navigation abilities of the agent are achieved... | ['Giovanni Maria Farinella', 'Corrado Santoro', 'Luigi Gulino', 'Antonino Furnari', 'Marco Rosano'] | 2022-02-02 | null | null | null | null | ['pointgoal-navigation'] | ['robots'] | [-1.59755334e-01 6.32678047e-02 4.67513859e-01 -2.57990152e-01
-2.12441653e-01 -8.07819664e-01 7.81308413e-01 -3.49928886e-02
-7.41941273e-01 8.11358392e-01 -2.18445897e-01 -4.98315483e-01
-2.26793036e-01 -1.02055800e+00 -9.90373194e-01 -4.50734168e-01
-5.15974343e-01 6.81588054e-01 2.67720073e-01 -6.88589036... | [4.714552402496338, 0.8670093417167664] |
bd044cec-c786-4a24-a238-831b826b33e3 | implicit-pdf-non-parametric-representation-of | 2106.05965 | null | https://arxiv.org/abs/2106.05965v2 | https://arxiv.org/pdf/2106.05965v2.pdf | Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold | Single image pose estimation is a fundamental problem in many vision and robotics tasks, and existing deep learning approaches suffer by not completely modeling and handling: i) uncertainty about the predictions, and ii) symmetric objects with multiple (sometimes infinite) correct poses. To this end, we introduce a met... | ['Ameesh Makadia', 'Srikumar Ramalingam', 'Varun Jampani', 'Carlos Esteves', 'Kieran Murphy'] | 2021-06-10 | implicit-pdf-non-parametric-representation-of-1 | https://implicit-pdf.github.io/ | https://arxiv.org/pdf/2106.05965.pdf | null | ['3d-pose-estimation', '3d-rotation-estimation'] | ['computer-vision', 'computer-vision'] | [-1.42987281e-01 2.71954417e-01 -7.34273763e-03 -3.50556433e-01
-9.89897072e-01 -8.12547982e-01 7.15132535e-01 -9.61548164e-02
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-1.31698549e-01 1.42643034e+00 1.52174726e-01 9.49174687... | [7.412978649139404, -2.0515732765197754] |
b9594021-fbaf-4784-8ffb-5cbfba0cbca6 | natural-language-inspired-approach-for | null | null | https://aclanthology.org/W12-1015 | https://aclanthology.org/W12-1015.pdf | Natural Language Inspired Approach for Handwritten Text Line Detection in Legacy Documents | null | ["ro H{\\'e}ctor", 'Alej Toselli', 'Vicente Bosch', 'Enrique Vidal'] | 2012-04-01 | null | null | null | ws-2012-4 | ['document-layout-analysis', 'line-detection'] | ['computer-vision', 'computer-vision'] | [-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... | [-7.243827819824219, 3.7572431564331055] |
ab17c088-c456-4401-8cd3-1b947cc09a3d | interactive-feature-fusion-for-end-to-end | 2110.05267 | null | https://arxiv.org/abs/2110.05267v2 | https://arxiv.org/pdf/2110.05267v2.pdf | Interactive Feature Fusion for End-to-End Noise-Robust Speech Recognition | Speech enhancement (SE) aims to suppress the additive noise from a noisy speech signal to improve the speech's perceptual quality and intelligibility. However, the over-suppression phenomenon in the enhanced speech might degrade the performance of downstream automatic speech recognition (ASR) task due to the missing la... | ['Eng Siong Chng', 'Chen Chen', 'Nana Hou', 'Yuchen Hu'] | 2021-10-11 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 5.51796913e-01 5.95909208e-02 2.49505341e-01 -3.92745972e-01
-1.13326645e+00 -6.25755638e-02 3.71086031e-01 -3.93184930e-01
-5.32280743e-01 5.12388647e-01 9.93696928e-01 -4.48854923e-01
-5.33950864e-04 -1.93580743e-02 -2.97788739e-01 -9.30104256e-01
2.26397589e-01 -7.67954707e-01 -1.07153736e-01 -4.71815974... | [14.813948631286621, 6.09888219833374] |
0ceb8140-b86d-45a0-b006-6ea0f42da1da | on-the-predictive-accuracy-of-neural-temporal | 2306.17066 | null | https://arxiv.org/abs/2306.17066v2 | https://arxiv.org/pdf/2306.17066v2.pdf | On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data | Temporal Point Processes (TPPs) serve as the standard mathematical framework for modeling asynchronous event sequences in continuous time. However, classical TPP models are often constrained by strong assumptions, limiting their ability to capture complex real-world event dynamics. To overcome this limitation, research... | ['Souhaib Ben Taieb', 'Tanguy Bosser'] | 2023-06-29 | null | null | null | null | ['point-processes'] | ['methodology'] | [ 2.52793223e-01 -2.91266829e-01 -4.99271214e-01 -2.19764963e-01
-7.15582311e-01 -5.74996114e-01 6.56242132e-01 3.94443244e-01
-3.14536184e-01 5.41488051e-01 2.95130193e-01 -6.14952624e-01
-2.05808684e-01 -7.26899266e-01 -9.08509731e-01 -4.57016051e-01
-2.94876873e-01 3.99046689e-01 4.73934948e-01 1.07651353... | [7.084506511688232, 3.402465581893921] |
28be6ae6-c147-423f-89b1-0f9705352500 | systematic-generalization-on-gscan-with | 2009.05552 | null | https://arxiv.org/abs/2009.05552v2 | https://arxiv.org/pdf/2009.05552v2.pdf | Systematic Generalization on gSCAN with Language Conditioned Embedding | Systematic Generalization refers to a learning algorithm's ability to extrapolate learned behavior to unseen situations that are distinct but semantically similar to its training data. As shown in recent work, state-of-the-art deep learning models fail dramatically even on tasks for which they are designed when the tes... | ['Raymond J. Mooney', 'Tong Gao', 'Qi Huang'] | 2020-09-11 | null | https://aclanthology.org/2020.aacl-main.49 | https://aclanthology.org/2020.aacl-main.49.pdf | asian-chapter-of-the-association-for | ['systematic-generalization'] | ['reasoning'] | [ 2.09647477e-01 2.97815830e-01 -1.58580050e-01 -8.03640604e-01
-5.39598405e-01 -5.26085138e-01 7.70211101e-01 3.95192027e-01
-8.55895400e-01 4.95892107e-01 2.43723944e-01 -5.66752493e-01
-8.54476020e-02 -9.68043208e-01 -1.24843323e+00 -2.56312877e-01
-4.54343349e-01 8.14525902e-01 5.66403210e-01 -5.15569270... | [9.643350601196289, 6.992115020751953] |
76128ac4-1c70-4206-9f6f-44d46e4d2685 | clarinet-a-music-retrieval-system | 2210.12648 | null | https://arxiv.org/abs/2210.12648v2 | https://arxiv.org/pdf/2210.12648v2.pdf | Clarinet: A Music Retrieval System | A MIDI based approach for music recognition is proposed and implemented in this paper. Our Clarinet music retrieval system is designed to search piano MIDI files with high recall and speed. We design a novel melody extraction algorithm that improves recall results by more than 10%. We also implement 3 algorithms for re... | ['Siddhant Sharma', 'Rohan Sharma', 'Kshitij Alwadhi'] | 2022-10-23 | null | null | null | null | ['melody-extraction'] | ['music'] | [ 1.68029591e-03 -5.93123913e-01 -3.70670080e-01 1.07304394e-01
-1.29990375e+00 -1.00997591e+00 3.90869796e-01 1.52387917e-01
-3.10968012e-01 4.92219627e-01 4.77517962e-01 2.46335804e-01
-7.90362597e-01 -3.90085101e-01 2.27968216e-01 -3.09741169e-01
-1.67011887e-01 2.69974768e-01 2.75135696e-01 -5.05913854... | [15.922022819519043, 5.2631425857543945] |
3c5912a3-9d09-49e4-a3a2-213dc86e0925 | dreampaint-few-shot-inpainting-of-e-commerce | 2305.01257 | null | https://arxiv.org/abs/2305.01257v1 | https://arxiv.org/pdf/2305.01257v1.pdf | DreamPaint: Few-Shot Inpainting of E-Commerce Items for Virtual Try-On without 3D Modeling | We introduce DreamPaint, a framework to intelligently inpaint any e-commerce product on any user-provided context image. The context image can be, for example, the user's own image for virtual try-on of clothes from the e-commerce catalog on themselves, the user's room image for virtual try-on of a piece of furniture f... | ['Ismail B. Tutar', 'Amir Tavanaei', 'Suren Kumar', 'Karim Bouyarmane', 'Mehmet Saygin Seyfioglu'] | 2023-05-02 | null | null | null | null | ['virtual-try-on'] | ['computer-vision'] | [ 2.94654995e-01 9.96260494e-02 1.13453396e-01 -1.66698486e-01
-5.83590686e-01 -8.06338906e-01 3.05967480e-01 -1.62077501e-01
-8.99160653e-02 1.11203440e-01 1.71359386e-02 -2.51726985e-01
1.50932163e-01 -6.88203812e-01 -8.00129890e-01 -6.77262247e-01
4.61410999e-01 3.90097260e-01 6.90783411e-02 -3.38048398... | [9.354759216308594, -2.6932122707366943] |
e69f9bd1-201a-4c38-82da-f8156e1812c0 | detecting-kissing-scenes-in-a-database-of | 1906.01843 | null | https://arxiv.org/abs/1906.01843v1 | https://arxiv.org/pdf/1906.01843v1.pdf | Detecting Kissing Scenes in a Database of Hollywood Films | Detecting scene types in a movie can be very useful for application such as video editing, ratings assignment, and personalization. We propose a system for detecting kissing scenes in a movie. This system consists of two components. The first component is a binary classifier that predicts a binary label (i.e. kissing o... | ['Amir Ziai'] | 2019-06-05 | null | null | null | null | ['kiss-detection'] | ['computer-vision'] | [-6.91317543e-02 -3.60120803e-01 -2.19254807e-01 -6.79451287e-01
-6.71350062e-01 -8.50539923e-01 4.45451587e-01 2.37835288e-01
-4.05856520e-02 1.16729818e-01 1.21492818e-01 1.30604550e-01
5.11134751e-02 -6.64528072e-01 -8.09969485e-01 -3.46515507e-01
-3.73794436e-01 1.14434483e-02 5.42248666e-01 -2.98671991... | [9.844571113586426, 0.5497163534164429] |
38bc8c1c-cdc0-4b01-aa38-032bc9f043a8 | perceptual-learned-source-channel-coding-for | 2205.13120 | null | https://arxiv.org/abs/2205.13120v1 | https://arxiv.org/pdf/2205.13120v1.pdf | Perceptual Learned Source-Channel Coding for High-Fidelity Image Semantic Transmission | As one novel approach to realize end-to-end wireless image semantic transmission, deep learning-based joint source-channel coding (deep JSCC) method is emerging in both deep learning and communication communities. However, current deep JSCC image transmission systems are typically optimized for traditional distortion m... | ['Kai Niu', 'Dekun Zhou', 'Zhongwei Si', 'Jincheng Dai', 'Sixian Wang', 'Jun Wang'] | 2022-05-26 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 3.09669465e-01 7.38132149e-02 -9.12147388e-02 -2.39525452e-01
-7.22041070e-01 -8.72663707e-02 2.24557117e-01 -1.40957683e-01
-4.34954196e-01 7.57224083e-01 1.87565684e-01 -1.93735376e-01
6.70553818e-02 -7.41321087e-01 -7.32090473e-01 -7.06112742e-01
-4.66130853e-01 -5.82876146e-01 3.26939791e-01 -1.50581196... | [11.336111068725586, -1.7464947700500488] |
e4b5d05b-cdf2-41cc-bdc5-8435ff507751 | group-r-cnn-for-weakly-semi-supervised-object | 2205.05920 | null | https://arxiv.org/abs/2205.05920v1 | https://arxiv.org/pdf/2205.05920v1.pdf | Group R-CNN for Weakly Semi-supervised Object Detection with Points | We study the problem of weakly semi-supervised object detection with points (WSSOD-P), where the training data is combined by a small set of fully annotated images with bounding boxes and a large set of weakly-labeled images with only a single point annotated for each instance. The core of this task is to train a point... | ['Kai Chen', 'Aojun Zhou', 'Xinjiang Wang', 'Liyang Liu', 'Zhuoran Yu', 'Shilong Zhang'] | 2022-05-12 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhang_Group_R-CNN_for_Weakly_Semi-Supervised_Object_Detection_With_Points_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhang_Group_R-CNN_for_Weakly_Semi-Supervised_Object_Detection_With_Points_CVPR_2022_paper.pdf | cvpr-2022-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [-6.76035956e-02 3.45416129e-01 -4.06802833e-01 -5.21086693e-01
-1.42117071e+00 -5.36662102e-01 5.68371594e-01 2.39595518e-01
-5.74760616e-01 3.54525924e-01 -1.56639174e-01 -2.42793206e-02
2.86624044e-01 -7.18752921e-01 -1.13313508e+00 -6.29739046e-01
6.28609583e-02 6.54139817e-01 6.29790664e-01 -1.28929615... | [9.179252624511719, 0.8887239098548889] |
d4c8b6c6-9e36-480f-bb33-9e19d7a22fe5 | very-low-resource-sentence-alignment-luhya | null | null | https://aclanthology.org/2022.loresmt-1.1 | https://aclanthology.org/2022.loresmt-1.1.pdf | Very Low Resource Sentence Alignment: Luhya and Swahili | Language-agnostic sentence embeddings generated by pre-trained models such as LASER and LaBSE are attractive options for mining large datasets to produce parallel corpora for low-resource machine translation. We test LASER and LaBSE in extracting bitext for two related low-resource African languages: Luhya and Swahili.... | ['Bruce Bassett', 'Everlyn Chimoto'] | null | null | null | null | loresmt-coling-2022-10 | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [-9.29199014e-05 1.46636199e-02 -2.66291678e-01 -5.31758010e-01
-1.48914576e+00 -6.81490421e-01 6.78826213e-01 2.53067285e-01
-8.63434672e-01 9.30686295e-01 6.73764408e-01 -7.79019833e-01
2.28125229e-01 -6.62029684e-01 -6.96766675e-01 -3.28263134e-01
2.41508111e-02 8.34941506e-01 -3.89990479e-01 -4.91785765... | [11.338045120239258, 10.223871231079102] |
352bc5cf-dd0d-4ad8-86b5-23825f7cb20e | object-guided-instance-segmentation-with | 2106.07159 | null | https://arxiv.org/abs/2106.07159v1 | https://arxiv.org/pdf/2106.07159v1.pdf | Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images | Instance segmentation is of great importance for many biological applications, such as study of neural cell interactions, plant phenotyping, and quantitatively measuring how cells react to drug treatment. In this paper, we propose a novel box-based instance segmentation method. Box-based instance segmentation methods c... | ['Dimitris N. Metaxas', 'Daniel J. Hoeppner', 'Wei Fan', 'Lianyi Han', 'Hui Qu', 'Qiaoying Huang', 'Bo Liu', 'Hui Tang', 'Pengxiang Wu', 'Jingru Yi'] | 2021-06-14 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 1.97265416e-01 -3.03152855e-02 -2.20926270e-01 -3.48665863e-01
-4.64791119e-01 -3.92219931e-01 2.27688834e-01 4.87391055e-01
-1.21154338e-01 5.01734436e-01 -3.58273327e-01 1.04935028e-01
2.04417948e-02 -1.03323376e+00 -6.31783664e-01 -8.91213953e-01
1.63633212e-01 6.39241815e-01 8.88825059e-01 1.99576512... | [9.651213645935059, 0.19423963129520416] |
80893968-863c-4582-980c-066cab3285e3 | dynamic-community-detection-via-adversarial | 2207.03580 | null | https://arxiv.org/abs/2207.03580v1 | https://arxiv.org/pdf/2207.03580v1.pdf | Dynamic Community Detection via Adversarial Temporal Graph Representation Learning | Dynamic community detection has been prospered as a powerful tool for quantifying changes in dynamic brain network connectivity patterns by identifying strongly connected sets of nodes. However, as the network science problems and network data to be processed become gradually more sophisticated, it awaits a better meth... | ['Shuqiang Wang', 'Yanyan Shen', 'Changhong Jing', 'Changwei Gong'] | 2022-06-29 | null | null | null | null | ['dynamic-community-detection'] | ['graphs'] | [ 1.60726324e-01 1.91632174e-02 7.62426704e-02 -4.29768749e-02
2.04151526e-01 -4.90657061e-01 3.86875480e-01 -1.21822096e-01
-1.57200202e-01 3.46301138e-01 8.80224779e-02 -1.83790103e-01
-5.94169855e-01 -8.04240942e-01 -3.25636506e-01 -7.69033849e-01
-8.63276601e-01 2.48510584e-01 3.34982365e-01 -1.01792865... | [12.363520622253418, 3.4165263175964355] |
c0df1007-68af-4426-8639-d3dd69dd6dad | a-comprehensive-survey-on-video-frame | null | null | https://linkspringer.53yu.com/article/10.1007/s00371-020-02016-y | https://linkspringer.53yu.com/article/10.1007/s00371-020-02016-y | A comprehensive survey on video frame interpolation techniques | Video frame interpolation is an important area in the computer vision research activities for video post-processing, surveillance, and video restoration tasks. It aims toward increasing the frame rate of a video sequence by calculating intermittent frames between consecutive input frames. This ensures extra smooth, cle... | ['Kshitija Pandya & Ashray Aggarwal', 'Disha Varshney', 'Anil Singh Parihar'] | 2021-01-04 | null | null | null | the-visual-computer-2021-1 | ['video-restoration'] | ['computer-vision'] | [ 2.95513719e-01 -6.83894873e-01 -3.85581821e-01 -8.88849795e-02
-3.78715664e-01 -1.13153338e-01 4.16410923e-01 -4.38148916e-01
-3.06076944e-01 8.56992126e-01 1.50103912e-01 -2.67084479e-01
2.43373811e-01 -3.84071141e-01 -8.59194756e-01 -9.40700889e-01
-3.44296455e-01 -3.25077474e-01 4.02891934e-01 -1.12957984... | [10.734855651855469, -1.4487426280975342] |
fd2f7861-e19f-4132-8a56-091d51f3adf8 | contrastmotion-self-supervised-scene-motion | 2304.12589 | null | https://arxiv.org/abs/2304.12589v1 | https://arxiv.org/pdf/2304.12589v1.pdf | ContrastMotion: Self-supervised Scene Motion Learning for Large-Scale LiDAR Point Clouds | In this paper, we propose a novel self-supervised motion estimator for LiDAR-based autonomous driving via BEV representation. Different from usually adopted self-supervised strategies for data-level structure consistency, we predict scene motion via feature-level consistency between pillars in consecutive frames, which... | ['Yuexin Ma', 'Ji Zhang', 'Yandong Guo', 'Xinge Zhu', 'Hui Zhou', 'Xiangze Jia'] | 2023-04-25 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [ 4.04355638e-02 -3.68640721e-01 -4.53364879e-01 -8.58154476e-01
-6.45399809e-01 -2.56848156e-01 4.95287359e-01 -3.20359796e-01
-4.81056362e-01 8.01975250e-01 -1.11626036e-01 8.03346038e-02
-2.13283554e-01 -7.82650590e-01 -8.80010426e-01 -7.44136870e-01
1.99885026e-01 2.68645257e-01 7.16875017e-01 -5.60114011... | [8.351264953613281, -2.1813957691192627] |
68879b09-1df3-4d13-8285-7bea4a4861a9 | onegan-simultaneous-unsupervised-learning-of | 1912.13471 | null | https://arxiv.org/abs/1912.13471v2 | https://arxiv.org/pdf/1912.13471v2.pdf | OneGAN: Simultaneous Unsupervised Learning of Conditional Image Generation, Foreground Segmentation, and Fine-Grained Clustering | We present a method for simultaneously learning, in an unsupervised manner, (i) a conditional image generator, (ii) foreground extraction and segmentation, (iii) clustering into a two-level class hierarchy, and (iv) object removal and background completion, all done without any use of annotation. The method combines a ... | ['Yaniv Benny', 'Lior Wolf'] | 2019-12-31 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5448_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123710511.pdf | eccv-2020-8 | ['foreground-segmentation'] | ['computer-vision'] | [ 9.35970008e-01 4.09865528e-01 1.55106723e-01 2.99102310e-02
-8.97553742e-01 -6.92630351e-01 8.26151371e-01 -1.17004335e-01
-4.14291620e-01 8.76291990e-01 -3.37796658e-01 -1.33622333e-01
3.47219080e-01 -7.70244420e-01 -8.62859249e-01 -1.38420475e+00
2.82723814e-01 9.36675251e-01 6.36581242e-01 5.27562141... | [10.222686767578125, 0.1145353764295578] |
b3f33ba7-144f-48e3-b57b-f148efb937cf | person-retrieval-in-surveillance-using | 2105.02414 | null | https://arxiv.org/abs/2105.02414v1 | https://arxiv.org/pdf/2105.02414v1.pdf | Person Retrieval in Surveillance Using Textual Query: A Review | Recent advancement of research in biometrics, computer vision, and natural language processing has discovered opportunities for person retrieval from surveillance videos using textual query. The prime objective of a surveillance system is to locate a person using a description, e.g., a short woman with a pink t-shirt a... | ['Mehul S Raval', 'Hiren Galiyawala'] | 2021-05-06 | null | null | null | null | ['person-retrieval'] | ['computer-vision'] | [ 1.64316788e-01 -5.41109264e-01 -1.57555014e-01 -5.74701488e-01
-5.62780440e-01 -8.90495181e-01 7.93430626e-01 2.02860415e-01
-3.71496856e-01 6.23431683e-01 5.30211963e-02 3.48575890e-01
-2.02359006e-01 -5.19533992e-01 -1.73070356e-01 -7.37127125e-01
2.37686545e-01 3.80696148e-01 -3.26977342e-01 -1.85515061... | [14.563446044921875, 0.9080914855003357] |
60466315-50b7-4ffe-ab91-b0ee6d434c79 | unified-multimodal-pre-training-and-prompt | 2112.05587 | null | https://arxiv.org/abs/2112.05587v2 | https://arxiv.org/pdf/2112.05587v2.pdf | Unified Multimodal Pre-training and Prompt-based Tuning for Vision-Language Understanding and Generation | Most existing vision-language pre-training methods focus on understanding tasks and use BERT-like objectives (masked language modeling and image-text matching) during pretraining. Although they perform well in many understanding downstream tasks, e.g., visual question answering, image-text retrieval and visual entailme... | ['Yu-Gang Jiang', 'Jingjing Chen', 'Wenhan Xiong', 'Zuxuan Wu', 'Tianyi Liu'] | 2021-12-10 | null | null | null | null | ['visual-entailment'] | ['reasoning'] | [ 5.46705842e-01 2.68290490e-01 -3.36639993e-02 -4.70331341e-01
-6.80348992e-01 -5.28536260e-01 1.18133593e+00 -5.06261736e-02
-5.17738938e-01 5.06243527e-01 1.99119449e-01 -4.41421807e-01
9.04017091e-02 -7.57027268e-01 -9.49092090e-01 -4.44743872e-01
5.78234494e-01 6.62608922e-01 2.10823312e-01 -2.57345051... | [10.766263008117676, 1.5862948894500732] |
465441b7-6e46-40b4-b3b1-e4deb0ed3a20 | improving-accuracy-without-losing | 2212.06620 | null | https://arxiv.org/abs/2212.06620v1 | https://arxiv.org/pdf/2212.06620v1.pdf | Improving Accuracy Without Losing Interpretability: A ML Approach for Time Series Forecasting | In time series forecasting, decomposition-based algorithms break aggregate data into meaningful components and are therefore appreciated for their particular advantages in interpretability. Recent algorithms often combine machine learning (hereafter ML) methodology with decomposition to improve prediction accuracy. How... | ['ZuoJun Max Shen', 'Hao Hu', 'Yongzhi Qi', 'Jianshen Zhang', 'Zhengxin Shi', 'Yiqi Sun'] | 2022-12-13 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-1.76006276e-02 -1.72830656e-01 -4.06086683e-01 -3.18931848e-01
-4.60484087e-01 -5.55238664e-01 3.76725733e-01 9.55818743e-02
3.23814839e-01 5.48540652e-01 1.47469327e-01 -7.34095037e-01
-4.41743672e-01 -8.50125730e-01 -3.17446649e-01 -9.17414188e-01
-1.15934849e-01 3.16654146e-01 -7.62000740e-01 -3.53554696... | [6.559615135192871, 2.965211868286133] |
30fd6066-bc4f-4276-85dd-4ede15570a13 | temporal-viewpoint-transportation-plan-for | 2210.16820 | null | https://arxiv.org/abs/2210.16820v1 | https://arxiv.org/pdf/2210.16820v1.pdf | Temporal-Viewpoint Transportation Plan for Skeletal Few-shot Action Recognition | We propose a Few-shot Learning pipeline for 3D skeleton-based action recognition by Joint tEmporal and cAmera viewpoiNt alIgnmEnt (JEANIE). To factor out misalignment between query and support sequences of 3D body joints, we propose an advanced variant of Dynamic Time Warping which jointly models each smooth path betwe... | ['Piotr Koniusz', 'Lei Wang'] | 2022-10-30 | null | null | null | null | ['few-shot-action-recognition'] | ['computer-vision'] | [ 2.55327344e-01 -3.04261800e-02 -4.16809082e-01 -2.70568162e-01
-7.32158422e-01 -1.39943376e-01 5.85184574e-01 -5.39886057e-01
-3.82149696e-01 1.96966827e-01 5.61231375e-01 3.68704200e-01
1.08847551e-01 -6.58356026e-02 -8.47316742e-01 -5.35877526e-01
-4.22815561e-01 4.02784258e-01 5.07135689e-01 -1.25404805... | [7.663495063781738, 0.027440015226602554] |
8d17edcd-ad4c-43a9-8326-96254b7326b6 | multi-object-representation-learning-with | 1903.00450 | null | https://arxiv.org/abs/1903.00450v3 | https://arxiv.org/pdf/1903.00450v3.pdf | Multi-Object Representation Learning with Iterative Variational Inference | Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic generalization abilities. Yet most work on representation learning focuses on feature learning without even considering multiple objects, or treats segmentation as an (often supervised) preprocess... | ['Matthew Botvinick', 'Daniel Zoran', 'Raphaël Lopez Kaufman', 'Klaus Greff', 'Chris Burgess', 'Alexander Lerchner', 'Nick Watters', 'Rishabh Kabra', 'Loic Matthey'] | 2019-03-01 | null | null | null | null | ['unsupervised-object-segmentation', 'systematic-generalization'] | ['computer-vision', 'reasoning'] | [ 6.73767030e-01 4.40441310e-01 -1.62745744e-01 -4.80681121e-01
-8.38799775e-01 -7.38267601e-01 7.66597927e-01 9.45449322e-02
-2.79773355e-01 6.24508083e-01 3.04494590e-01 -1.80729747e-01
-7.89829493e-02 -7.89245963e-01 -9.37021136e-01 -7.38644540e-01
5.18838577e-02 7.98692465e-01 -1.51090762e-02 8.98671895... | [9.946212768554688, 0.4309048354625702] |
e5a7723e-3a5f-4788-aa16-0d9ab48cd0c7 | skin-lesion-segmentation-u-nets-versus | 1710.01248 | null | http://arxiv.org/abs/1710.01248v1 | http://arxiv.org/pdf/1710.01248v1.pdf | Skin Lesion Segmentation: U-Nets versus Clustering | Many automatic skin lesion diagnosis systems use segmentation as a
preprocessing step to diagnose skin conditions because skin lesion shape,
border irregularity, and size can influence the likelihood of malignancy. This
paper presents, examines and compares two different approaches to skin lesion
segmentation. The firs... | ['Shivam Kalra', 'Kevin Michael', 'H. R. Tizhoosh', 'Bill S. Lin'] | 2017-09-27 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 5.63345671e-01 -1.07094022e-02 -5.17180860e-02 -2.83706129e-01
-2.93539196e-01 -5.21628201e-01 2.16174141e-01 8.36583018e-01
-7.90451586e-01 5.79003513e-01 -5.42945445e-01 -4.46073592e-01
-1.55193120e-01 -1.17118919e+00 -1.36156172e-01 -9.49458659e-01
2.68596172e-01 4.75345910e-01 4.42592174e-01 1.75510272... | [15.560223579406738, -3.014772891998291] |
5dc8ba6d-8d82-4bed-82a9-a914d8fac98a | a-classification-of-g-invariant-shallow | 2205.09219 | null | https://arxiv.org/abs/2205.09219v5 | https://arxiv.org/pdf/2205.09219v5.pdf | A Classification of $G$-invariant Shallow Neural Networks | When trying to fit a deep neural network (DNN) to a $G$-invariant target function with $G$ a group, it only makes sense to constrain the DNN to be $G$-invariant as well. However, there can be many different ways to do this, thus raising the problem of ``$G$-invariant neural architecture design'': What is the optimal $G... | ['James Ostrowski', 'Devanshu Agrawal'] | 2022-05-18 | null | null | null | null | ['classification'] | ['methodology'] | [ 1.33830577e-01 5.90900362e-01 7.69826397e-02 -8.47294256e-02
4.67981584e-03 -6.94013774e-01 3.16763729e-01 -3.37470949e-01
-1.75348088e-01 2.72223383e-01 2.68194694e-02 -7.03380287e-01
-4.58957046e-01 -1.04159319e+00 -8.81923378e-01 -1.05657244e+00
-5.87849200e-01 3.37994963e-01 5.60689755e-02 -6.20097101... | [8.245593070983887, 3.347116231918335] |
fc82b7d5-0b26-48bc-a0cb-4d9965ea9941 | on-testing-and-comparing-fair-classifiers | 2302.05906 | null | https://arxiv.org/abs/2302.05906v1 | https://arxiv.org/pdf/2302.05906v1.pdf | On Testing and Comparing Fair classifiers under Data Bias | In this paper, we consider a theoretical model for injecting data bias, namely, under-representation and label bias (Blum & Stangl, 2019). We theoretically and empirically study its effect on the accuracy and fairness of fair classifiers. Theoretically, we prove that the Bayes optimal group-aware fair classifier on the... | ['Rajiv Ratn Shah', 'Amit Deshpande', 'Mohit Sharma'] | 2023-02-12 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-2.09641993e-01 1.92275465e-01 -5.84090114e-01 -8.98594260e-01
-3.35070044e-01 -7.02745378e-01 6.40344083e-01 2.84447610e-01
-8.06751132e-01 1.08033609e+00 -3.03074624e-02 -4.79026407e-01
2.92116180e-02 -6.59984112e-01 -4.82904375e-01 -5.02031386e-01
-1.68247029e-01 3.71533722e-01 -2.54890770e-01 6.47458155... | [8.93353271484375, 5.301453113555908] |
2845a672-ac0d-45d1-bd6c-eaeebcec52f4 | ur-funny-a-multimodal-language-dataset-for | 1904.06618 | null | http://arxiv.org/abs/1904.06618v1 | http://arxiv.org/pdf/1904.06618v1.pdf | UR-FUNNY: A Multimodal Language Dataset for Understanding Humor | Humor is a unique and creative communicative behavior displayed during social
interactions. It is produced in a multimodal manner, through the usage of words
(text), gestures (vision) and prosodic cues (acoustic). Understanding humor
from these three modalities falls within boundaries of multimodal language; a
recent r... | ['Md. Kamrul Hasan', 'Md. Iftekhar Tanveer', 'Louis-Philippe Morency', 'Jianyuan Zhong', 'Wasifur Rahman', 'Mohammed', 'Hoque', 'Amir Zadeh'] | 2019-04-14 | ur-funny-a-multimodal-language-dataset-for-1 | https://aclanthology.org/D19-1211 | https://aclanthology.org/D19-1211.pdf | ijcnlp-2019-11 | ['humor-detection'] | ['natural-language-processing'] | [-1.29850432e-01 -8.09029490e-02 -9.65358466e-02 6.93910336e-03
-9.75890085e-02 -6.99606538e-01 1.12534344e+00 6.93274662e-02
3.77756134e-02 5.83780944e-01 1.17742884e+00 1.92524791e-01
3.67475748e-01 -3.02880198e-01 1.82252601e-01 -4.58887279e-01
1.72847390e-01 1.96416199e-01 -2.33534664e-01 -7.14688599... | [8.922321319580078, 10.962395668029785] |
d80f83ad-95fb-4c31-9e40-1126a39ca380 | neural-machine-translation-with-synchronous | null | null | https://aclanthology.org/2021.acl-srw.33 | https://aclanthology.org/2021.acl-srw.33.pdf | Neural Machine Translation with Synchronous Latent Phrase Structure | It is reported that grammatical information is useful for machine translation (MT) task. However, the annotation of grammatical information requires the highly human resources. Furthermore, it is not trivial to adapt grammatical information to MT since grammatical annotation usually adapts tokenization standards which ... | ['Taro Watanabe', 'Shintaro Harada'] | 2021-08-01 | null | null | null | acl-2021-5 | ['constituency-parsing'] | ['natural-language-processing'] | [ 2.14871287e-01 3.64335597e-01 -3.27587456e-01 -6.84150279e-01
-9.18941438e-01 -5.54832101e-01 2.73192346e-01 1.45823225e-01
-3.96688104e-01 9.92710769e-01 2.49319866e-01 -6.31159484e-01
4.11426812e-01 -5.58216333e-01 -9.81257737e-01 -4.07261580e-01
3.39379817e-01 4.98302549e-01 -5.89056648e-02 -4.01704788... | [11.515207290649414, 10.204795837402344] |
5225cd68-0d24-4d9f-9b4f-34a89a6d216c | self-supervised-learning-for-modeling-gamma | 2302.07700 | null | https://arxiv.org/abs/2302.07700v1 | https://arxiv.org/pdf/2302.07700v1.pdf | Self-Supervised Learning for Modeling Gamma-ray Variability in Blazars | Blazars are active galactic nuclei with relativistic jets pointed almost directly at Earth. Blazars are characterized by strong, apparently stochastic flux variability at virtually all observed wavelengths and timescales, from minutes to years, the physical origin of which is still poorly understood. In the high-energy... | ['Aryeh Brill'] | 2023-02-15 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-3.00454378e-01 -3.77783418e-01 -8.75058174e-02 -4.35825855e-01
-6.86398327e-01 -7.24397600e-01 1.05877900e+00 -2.24410743e-01
-1.48560643e-01 8.40156376e-01 1.09098546e-01 -6.46157324e-01
-5.18975377e-01 -8.12498271e-01 -6.28548861e-01 -1.22895133e+00
-1.71693444e-01 9.94049788e-01 -1.00831434e-01 -1.81629479... | [7.533499717712402, 3.172065496444702] |
c73fcb17-2f7e-4efb-8203-ced5efcbf61e | optimality-of-graph-scanning-statistic-for | 2102.05821 | null | https://arxiv.org/abs/2102.05821v1 | https://arxiv.org/pdf/2102.05821v1.pdf | Optimality of Graph Scanning Statistic for Online Community Detection | Sequential change-point detection for graphs is a fundamental problem for streaming network data types and has wide applications in social networks and power systems. Given fixed vertices and a sequence of random graphs, the objective is to detect the change-point where the underlying distribution of the random graph c... | ['Yao Xie', 'Liyan Xie'] | 2021-02-11 | null | null | null | null | ['online-community-detection'] | ['graphs'] | [ 4.25352424e-01 7.31722638e-02 -3.49956095e-01 1.18666761e-01
-1.55922949e-01 -7.44237959e-01 3.31547797e-01 6.00427270e-01
6.00386970e-02 6.37563646e-01 -4.78256643e-01 -3.25049609e-01
-4.44576353e-01 -1.21729553e+00 -6.68363154e-01 -8.06939483e-01
-8.13735306e-01 4.31132942e-01 5.03145814e-01 2.40614533... | [6.8934454917907715, 5.115460395812988] |
0f3ab87e-30fd-44dd-8eab-d2a15b8b6286 | explaining-neural-matrix-factorization-with | 2010.05516 | null | https://arxiv.org/abs/2010.05516v4 | https://arxiv.org/pdf/2010.05516v4.pdf | Explaining Neural Matrix Factorization with Gradient Rollback | Explaining the predictions of neural black-box models is an important problem, especially when such models are used in applications where user trust is crucial. Estimating the influence of training examples on a learned neural model's behavior allows us to identify training examples most responsible for a given predict... | ['Mathias Niepert', 'Timo Sztyler', 'Carolin Lawrence'] | 2020-10-12 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion', 'influence-approximation'] | ['graphs', 'knowledge-base', 'methodology'] | [-8.20986629e-02 3.38588119e-01 -5.53887069e-01 -3.76939476e-01
-1.01425223e-01 -3.81037235e-01 3.23652655e-01 2.30258405e-01
-3.10729086e-01 8.08886111e-01 -1.14299327e-01 -3.91902238e-01
-3.05697590e-01 -8.83286178e-01 -1.29911721e+00 -6.14012539e-01
-2.22916037e-01 4.18951482e-01 1.47538453e-01 -2.73120046... | [8.343616485595703, 4.613187313079834] |
ae196abd-8a79-40f3-abf6-7bae93a2d1cb | an-in-depth-exploration-of-person-re | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_An_In-Depth_Exploration_of_Person_Re-Identification_and_Gait_Recognition_in_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_An_In-Depth_Exploration_of_Person_Re-Identification_and_Gait_Recognition_in_CVPR_2023_paper.pdf | An In-Depth Exploration of Person Re-Identification and Gait Recognition in Cloth-Changing Conditions | The target of person re-identification (ReID) and gait recognition is consistent, that is to match the target pedestrian under surveillance cameras. For the cloth-changing problem, video-based ReID is rarely studied due to the lack of a suitable cloth-changing benchmark, and gait recognition is often researched und... | ['Yao Zhao', 'Yongzhen Huang', 'Xu Liu', 'Chunshui Cao', 'Chunjie Zhang', 'Saihui Hou', 'Weijia Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['person-re-identification', 'gait-recognition'] | ['computer-vision', 'computer-vision'] | [-1.28466681e-01 -9.12740409e-01 -5.16381152e-02 -1.78318933e-01
-1.43140823e-01 -3.90890032e-01 4.26123649e-01 -3.41941684e-01
-3.50725263e-01 7.81319141e-01 8.31887051e-02 1.84156969e-01
1.44521266e-01 -5.89749157e-01 -5.11464357e-01 -1.00531375e+00
7.89847632e-04 5.33454567e-02 2.04697743e-01 -2.86118448... | [14.39841079711914, 1.2551623582839966] |
27b738c9-78b0-45c3-bc1e-b1b51e2dc8ca | unsupervised-neural-machine-translation | 1710.11041 | null | http://arxiv.org/abs/1710.11041v2 | http://arxiv.org/pdf/1710.11041v2.pdf | Unsupervised Neural Machine Translation | In spite of the recent success of neural machine translation (NMT) in
standard benchmarks, the lack of large parallel corpora poses a major practical
problem for many language pairs. There have been several proposals to alleviate
this issue with, for instance, triangulation and semi-supervised learning
techniques, but ... | ['Mikel Artetxe', 'Kyunghyun Cho', 'Gorka Labaka', 'Eneko Agirre'] | 2017-10-30 | unsupervised-neural-machine-translation-2 | https://openreview.net/forum?id=Sy2ogebAW | https://openreview.net/pdf?id=Sy2ogebAW | iclr-2018-1 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 5.17367758e-02 7.00683072e-02 -1.16667956e-01 -3.85751784e-01
-1.37442148e+00 -5.68270504e-01 8.70216548e-01 -1.09123550e-01
-8.41016352e-01 9.35803175e-01 1.67624772e-01 -5.56698084e-01
3.30130696e-01 -5.91512918e-01 -9.28491652e-01 -5.11697888e-01
4.11251545e-01 8.01754475e-01 -1.40973508e-01 -5.99469900... | [11.57430648803711, 10.268221855163574] |
2fc9e92b-8c99-43c2-b8cb-f398c4407d68 | efficient-sequence-transduction-by-jointly | 2304.06795 | null | https://arxiv.org/abs/2304.06795v2 | https://arxiv.org/pdf/2304.06795v2.pdf | Efficient Sequence Transduction by Jointly Predicting Tokens and Durations | This paper introduces a novel Token-and-Duration Transducer (TDT) architecture for sequence-to-sequence tasks. TDT extends conventional RNN-Transducer architectures by jointly predicting both a token and its duration, i.e. the number of input frames covered by the emitted token. This is achieved by using a joint networ... | ['Boris Ginsburg', 'Shinji Watanabe', 'He Huang', 'Somshubra Majumdar', 'Fei Jia', 'Hainan Xu'] | 2023-04-13 | null | null | null | null | ['intent-classification', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.71268022e-01 3.57099563e-01 -2.54594147e-01 -5.02306938e-01
-1.19200075e+00 -6.32215381e-01 6.14815831e-01 -1.56044886e-01
-3.85977834e-01 4.37568665e-01 3.54607999e-01 -8.53199720e-01
7.17595041e-01 -4.89336818e-01 -7.32643425e-01 -4.23856765e-01
4.35498767e-02 7.19840169e-01 2.25159153e-01 1.34798467... | [14.51679515838623, 6.973802089691162] |
801e8ad2-8a72-4c8b-93b1-1e739c951df8 | noise-tolerant-learning-for-audio-visual | 2205.07611 | null | https://arxiv.org/abs/2205.07611v2 | https://arxiv.org/pdf/2205.07611v2.pdf | Noise-Tolerant Learning for Audio-Visual Action Recognition | Recently, video recognition is emerging with the help of multi-modal learning, which focuses on integrating multiple modalities to improve the performance or robustness of a model. Although various multi-modal learning methods have been proposed and offer remarkable recognition results, almost all of these methods rely... | ['Yan Chen', 'Feng Tian', 'Kaiyao Miao', 'Minnan Luo', 'Qinghua Zheng', 'Haochen Han'] | 2022-05-16 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 4.29601222e-01 -6.09742284e-01 4.18103809e-06 -1.55160442e-01
-1.03788042e+00 -2.15928212e-01 5.88347793e-01 -2.75293946e-01
-4.39923853e-01 4.70950603e-01 3.92590851e-01 4.30014372e-01
-4.12144721e-01 -5.50768375e-01 -5.87792456e-01 -1.00377834e+00
4.43081886e-01 -7.87315518e-02 3.58455300e-01 1.32926106... | [11.049518585205078, 1.2246301174163818] |
f7d1f3c0-25da-4225-85e7-79b0453a772c | acoustic-scene-clustering-using-joint | 2306.05621 | null | https://arxiv.org/abs/2306.05621v1 | https://arxiv.org/pdf/2306.05621v1.pdf | Acoustic Scene Clustering Using Joint Optimization of Deep Embedding Learning and Clustering Iteration | Recent efforts have been made on acoustic scene classification in the audio signal processing community. In contrast, few studies have been conducted on acoustic scene clustering, which is a newly emerging problem. Acoustic scene clustering aims at merging the audio recordings of the same class of acoustic scene into a... | ['Qianhua He', 'Yuhan Zhang', 'Wucheng Wang', 'Mingle Liu', 'Yanxiong Li'] | 2023-06-09 | null | null | null | null | ['audio-signal-processing', 'acoustic-scene-classification', 'scene-classification'] | ['audio', 'audio', 'computer-vision'] | [ 9.58290473e-02 -3.96001130e-01 5.52929938e-01 -6.95176601e-01
-8.83936048e-01 -1.17560454e-01 2.48326510e-01 3.02489370e-01
-6.17043614e-01 -1.33778483e-01 1.51444584e-01 3.40630889e-01
-3.36822599e-01 -5.01360178e-01 -3.86409312e-01 -1.10423124e+00
-1.61892235e-01 1.43233955e-01 6.22373186e-02 4.14951921... | [14.98033618927002, 5.018987655639648] |
1b94db3d-29ec-44c1-89cf-da29827e1e8a | a-conditional-generative-chatbot-using | 2306.02074 | null | https://arxiv.org/abs/2306.02074v1 | https://arxiv.org/pdf/2306.02074v1.pdf | A Conditional Generative Chatbot using Transformer Model | A Chatbot serves as a communication tool between a human user and a machine to achieve an appropriate answer based on the human input. In more recent approaches, a combination of Natural Language Processing and sequential models are used to build a generative Chatbot. The main challenge of these models is their sequent... | ['Razieh Rastgoo', 'Kourosh Kiani', 'Nura Esfandiari'] | 2023-06-03 | null | null | null | null | ['chatbot', 'chatbot', 'answer-generation'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 2.13682711e-01 4.58048195e-01 6.77801907e-01 -3.20342451e-01
-7.85646737e-01 -6.28922701e-01 8.32612157e-01 -3.73159684e-02
-3.95302773e-01 7.30585039e-01 -8.40728953e-02 -1.48166999e-01
3.08259487e-01 -8.42253506e-01 -2.98107386e-01 -5.36467612e-01
6.23362660e-01 7.74894536e-01 5.25704622e-01 -5.37221789... | [12.553214073181152, 8.362336158752441] |
9bf7ebd8-2464-4923-aba4-8370b6de2caa | dual-adaptive-transformations-for-weakly | 2207.09084 | null | https://arxiv.org/abs/2207.09084v1 | https://arxiv.org/pdf/2207.09084v1.pdf | Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation | Weakly supervised point cloud segmentation, i.e. semantically segmenting a point cloud with only a few labeled points in the whole 3D scene, is highly desirable due to the heavy burden of collecting abundant dense annotations for the model training. However, existing methods remain challenging to accurately segment 3D ... | ['Chen Qian', 'Jianfei Cai', 'Guosheng Lin', 'Yicheng Wu', 'Zhonghua Wu'] | 2022-07-19 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 1.60705179e-01 3.67326438e-01 -3.54586124e-01 -4.37186927e-01
-1.06595623e+00 -9.17647839e-01 4.22784835e-01 8.91655460e-02
-1.02666251e-01 2.29458570e-01 -4.84622389e-01 -4.37844664e-01
2.68727273e-01 -6.32377446e-01 -1.21445382e+00 -6.05051696e-01
2.34579921e-01 8.87864649e-01 5.09538054e-01 -5.30767255... | [8.060037612915039, -3.1489734649658203] |
4f586d01-1e65-45bf-8fd2-0ea716b28a47 | geolayoutlm-geometric-pre-training-for-visual | 2304.10759 | null | https://arxiv.org/abs/2304.10759v1 | https://arxiv.org/pdf/2304.10759v1.pdf | GeoLayoutLM: Geometric Pre-training for Visual Information Extraction | Visual information extraction (VIE) plays an important role in Document Intelligence. Generally, it is divided into two tasks: semantic entity recognition (SER) and relation extraction (RE). Recently, pre-trained models for documents have achieved substantial progress in VIE, particularly in SER. However, most of the e... | ['Cong Yao', 'Qi Zheng', 'Changxu Cheng', 'Chuwei Luo'] | 2023-04-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Luo_GeoLayoutLM_Geometric_Pre-Training_for_Visual_Information_Extraction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Luo_GeoLayoutLM_Geometric_Pre-Training_for_Visual_Information_Extraction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['document-ai', 'semantic-entity-labeling', 'key-information-extraction'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.87390327e-01 9.45202932e-02 -2.25963295e-01 -2.18373224e-01
-7.37916946e-01 -6.93306983e-01 9.06526685e-01 4.16836441e-01
-4.31863993e-01 5.08984089e-01 2.52928734e-01 -2.83033341e-01
-7.21517131e-02 -1.02373123e+00 -7.47511685e-01 -5.40235162e-01
1.12357482e-01 5.37263215e-01 1.43188804e-01 -3.41376245... | [11.307236671447754, 2.3741140365600586] |
cb0e5f33-6454-4b06-87e7-ec2ddfd87a76 | target-speaker-voice-activity-detection-via | 2210.16127 | null | https://arxiv.org/abs/2210.16127v3 | https://arxiv.org/pdf/2210.16127v3.pdf | Target-Speaker Voice Activity Detection via Sequence-to-Sequence Prediction | Target-speaker voice activity detection is currently a promising approach for speaker diarization in complex acoustic environments. This paper presents a novel Sequence-to-Sequence Target-Speaker Voice Activity Detection (Seq2Seq-TSVAD) method that can efficiently address the joint modeling of large-scale speakers and ... | ['Ming Li', 'Xiaoyi Qin', 'Yucong Zhang', 'Weiqing Wang', 'Ming Cheng'] | 2022-10-28 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 1.78946182e-01 -2.65132934e-01 -1.80331901e-01 -2.59667963e-01
-1.89470589e+00 -5.57042956e-01 5.22015512e-01 -5.92233002e-01
-3.16227406e-01 4.45899576e-01 4.57280517e-01 -1.02254026e-01
2.82734066e-01 2.20472917e-01 -5.34070916e-02 -7.84952044e-01
-2.69452751e-01 3.64942312e-01 1.96987525e-01 9.23219472... | [14.593777656555176, 6.128042221069336] |
44a1814e-60da-4c64-9162-3e5e883641e2 | goal-representations-for-instruction | 2307.00117 | null | https://arxiv.org/abs/2307.00117v1 | https://arxiv.org/pdf/2307.00117v1.pdf | Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control | Our goal is for robots to follow natural language instructions like "put the towel next to the microwave." But getting large amounts of labeled data, i.e. data that contains demonstrations of tasks labeled with the language instruction, is prohibitive. In contrast, obtaining policies that respond to image goals is much... | ['Sergey Levine', 'Anca Dragan', 'Andrey Kolobov', 'Mihai Jalobeanu', 'Ching-An Cheng', 'Philippe Hansen-Estruch', 'Homer Walke', 'Kuan Fang', 'Andre He', 'Vivek Myers'] | 2023-06-30 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 1.52317882e-01 2.33361349e-01 -7.56105706e-02 -6.92327797e-01
-6.29497170e-01 -8.26024413e-01 7.81949461e-01 -1.48338184e-01
-7.91039348e-01 5.51008880e-01 2.44778410e-01 -5.43428838e-01
3.37278903e-01 -4.40481156e-01 -9.86535907e-01 -5.36467135e-01
9.73317213e-03 6.03093684e-01 6.45165369e-02 -3.56390774... | [4.436043739318848, 0.8198786377906799] |
cc96e4a4-4bff-4646-aa96-9a392726a457 | low-dose-ct-image-denoising-using-parallel | 2005.06724 | null | https://arxiv.org/abs/2005.06724v1 | https://arxiv.org/pdf/2005.06724v1.pdf | Low-Dose CT Image Denoising Using Parallel-Clone Networks | Deep neural networks have a great potential to improve image denoising in low-dose computed tomography (LDCT). Popular ways to increase the network capacity include adding more layers or repeating a modularized clone model in a sequence. In such sequential architectures, the noisy input image and end output image are c... | ['Guobao Wang', 'Siqi Li'] | 2020-05-14 | null | null | null | null | ['medical-image-denoising'] | ['computer-vision'] | [ 7.22203404e-02 -1.78293243e-01 1.58034459e-01 -4.43329841e-01
-6.86410785e-01 1.94750074e-02 1.56146660e-01 2.21402496e-02
-1.01956379e+00 6.58793867e-01 -9.32315551e-03 1.34885516e-02
8.98067355e-02 -6.45714223e-01 -6.70710921e-01 -1.05308092e+00
6.58386350e-02 1.51476741e-01 3.69787604e-01 -9.12069306... | [13.41404914855957, -2.5185165405273438] |
88f390a4-2dba-41e3-a24e-c2f22f72f90a | event-driven-query-expansion | 2012.12065 | null | https://arxiv.org/abs/2012.12065v1 | https://arxiv.org/pdf/2012.12065v1.pdf | Event-Driven Query Expansion | A significant number of event-related queries are issued in Web search. In this paper, we seek to improve retrieval performance by leveraging events and specifically target the classic task of query expansion. We propose a method to expand an event-related query by first detecting the events related to it. Then, we der... | ['Kira Radinsky', 'Ido Guy', 'Guy D. Rosin'] | 2020-12-22 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [ 8.20663720e-02 -3.97689313e-01 -5.43658257e-01 -2.47143924e-01
-1.38328946e+00 -6.60700023e-01 9.54472721e-01 7.16198683e-01
-9.51967597e-01 3.70748103e-01 8.27129245e-01 -4.83473726e-02
-3.51980209e-01 -9.18857336e-01 -5.42806089e-01 -1.14500649e-01
-2.19753549e-01 4.88468528e-01 7.34105229e-01 -4.40762818... | [11.525793075561523, 7.600564479827881] |
26be0717-5af3-464a-b84e-82acb1f9d22c | conical-classification-for-computationally | 2111.00375 | null | https://arxiv.org/abs/2111.00375v1 | https://arxiv.org/pdf/2111.00375v1.pdf | Conical Classification For Computationally Efficient One-Class Topic Determination | As the Internet grows in size, so does the amount of text based information that exists. For many application spaces it is paramount to isolate and identify texts that relate to a particular topic. While one-class classification would be ideal for such analysis, there is a relative lack of research regarding efficient ... | ['Sameer Khanna'] | 2021-10-31 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 3.20975155e-01 -2.85080254e-01 -3.71154457e-01 -1.56644896e-01
-7.43795097e-01 -9.72519457e-01 8.54076743e-01 6.82830095e-01
-3.96462679e-01 4.81411487e-01 2.38197863e-01 -9.17668939e-01
-5.00481963e-01 -8.41960371e-01 -4.33857329e-02 -4.45099503e-01
-4.23801169e-02 7.63645887e-01 3.38195622e-01 -9.00344402... | [10.259610176086426, 7.437196254730225] |
6c01d413-315f-41dd-841b-503dc9b4c03d | toward-estimating-personal-well-being-using | 1910.10082 | null | https://arxiv.org/abs/1910.10082v1 | https://arxiv.org/pdf/1910.10082v1.pdf | Toward estimating personal well-being using voice | Estimating personal well-being draws increasing attention particularly from healthcare and pharmaceutical industries. We propose an approach to estimate personal well-being in terms of various measurements such as anxiety, sleep quality and mood using voice. With clinically validated questionnaires to score those measu... | ["Henry O'Connell", 'Samuel Kim', 'Namhee Kwon'] | 2019-10-22 | null | null | null | null | ['sleep-quality-prediction'] | ['medical'] | [-3.28541249e-01 4.04508054e-01 -6.61019921e-01 -8.29709411e-01
-1.00662029e+00 3.69041152e-02 -1.05380818e-01 5.70489824e-01
-3.42788458e-01 1.02588928e+00 7.89643168e-01 3.27727556e-01
-2.83205420e-01 -7.23510087e-01 2.14444071e-01 -3.46928447e-01
-9.74807590e-02 4.97008115e-02 -8.70043278e-01 -5.04572988... | [13.685545921325684, 3.1962039470672607] |
245bca76-ad12-4cfe-a54e-5ca16131900e | rethinking-image-cropping-exploring-diverse | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Jia_Rethinking_Image_Cropping_Exploring_Diverse_Compositions_From_Global_Views_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Jia_Rethinking_Image_Cropping_Exploring_Diverse_Compositions_From_Global_Views_CVPR_2022_paper.pdf | Rethinking Image Cropping: Exploring Diverse Compositions From Global Views | Existing image cropping works mainly use anchor evaluation methods or coordinate regression methods. However, it is difficult for pre-defined anchors to cover good crops globally, and the regression methods ignore the cropping diversity. In this paper, we regard image cropping as a set prediction problem. A set of ... | ['Ran He', 'Chaoyou Fu', 'Huaibo Huang', 'Gengyun Jia'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['image-cropping'] | ['computer-vision'] | [ 3.23094606e-01 3.97061259e-02 -7.47557342e-01 -3.89114559e-01
-6.63593173e-01 -4.65569645e-01 2.54239023e-01 1.96738139e-01
1.47634208e-01 6.12795830e-01 1.05297931e-01 1.04859009e-01
-1.19554155e-01 -1.20008349e+00 -8.43811333e-01 -9.78115201e-01
1.83975011e-01 2.39894614e-02 2.88127124e-01 -4.37992722... | [11.295357704162598, -1.1273329257965088] |
ab957e90-402e-4c4e-9a0c-868587ef56be | visualbackprop-for-learning-using-privileged | 1805.09474 | null | http://arxiv.org/abs/1805.09474v1 | http://arxiv.org/pdf/1805.09474v1.pdf | VisualBackProp for learning using privileged information with CNNs | In many machine learning applications, from medical diagnostics to autonomous
driving, the availability of prior knowledge can be used to improve the
predictive performance of learning algorithms and incorporate `physical,'
`domain knowledge,' or `common sense' concepts into training of machine
learning systems as well... | ['Devansh Bisla', 'Anna Choromanska'] | 2018-05-24 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 4.92612571e-01 6.07730925e-01 -2.48420402e-01 -4.15085346e-01
-1.44245557e-03 -3.58103186e-01 4.11659837e-01 4.37887833e-02
-5.05646110e-01 7.55545318e-01 -5.31744540e-01 -5.58291316e-01
-3.39477897e-01 -8.54571402e-01 -9.63124335e-01 -9.73775685e-01
1.34159345e-02 2.76708812e-01 4.03527588e-01 -3.78124639... | [9.491340637207031, 2.490180253982544] |
d383a79e-1e6e-4cb3-b635-1821da1e7ac8 | learning-the-spectrogram-temporal-resolution | 2210.01719 | null | https://arxiv.org/abs/2210.01719v2 | https://arxiv.org/pdf/2210.01719v2.pdf | Learning the Spectrogram Temporal Resolution for Audio Classification | The audio spectrogram is a time-frequency representation that has been widely used for audio classification. The temporal resolution of a spectrogram depends on hop size. Previous works generally assume the hop size should be a constant value such as ten milliseconds. However, a fixed hop size or resolution is not alwa... | ['Mark D. Plumbley', 'Wenwu Wang', 'Qiuqiang Kong', 'Xubo Liu', 'Haohe Liu'] | 2022-10-04 | null | null | null | null | ['classification'] | ['methodology'] | [ 7.76570588e-02 -5.29296756e-01 -5.40340953e-02 -1.06070831e-01
-1.12694311e+00 -4.43372160e-01 6.20661452e-02 2.54885167e-01
-4.27256793e-01 4.69249964e-01 1.56205773e-01 -6.97195381e-02
-2.79628724e-01 -5.61304152e-01 -2.30230302e-01 -4.66913074e-01
-4.10349309e-01 -2.02464774e-01 6.04554474e-01 -2.50507798... | [15.38515567779541, 5.22957181930542] |
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