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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a3fd1c66-ca89-4cc6-bb56-4c968450660c | learning-robust-feature-representations-for | 2005.12466 | null | https://arxiv.org/abs/2005.12466v1 | https://arxiv.org/pdf/2005.12466v1.pdf | Learning Robust Feature Representations for Scene Text Detection | Scene text detection based on deep neural networks have progressed substantially over the past years. However, previous state-of-the-art methods may still fall short when dealing with challenging public benchmarks because the performances of algorithm are determined by the robust features extraction and components in n... | ['Taejang Park', 'Sihwan Kim'] | 2020-05-26 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 3.14891860e-02 -1.36288688e-01 -7.04362392e-02 -6.99417353e-01
-1.06463695e+00 -1.81041397e-02 6.96771324e-01 -1.46696931e-02
-6.51951253e-01 6.56315863e-01 1.82776637e-02 1.67182133e-01
-1.88807011e-01 -7.39890814e-01 -7.37950027e-01 -8.31488073e-01
1.34582773e-01 3.43599528e-01 1.01752348e-01 2.21268654... | [9.632173538208008, 2.955577850341797] |
21377bda-e981-41a8-957f-ec866c7ec5f2 | a-shallow-triple-stream-three-dimensional-cnn | 1902.03634 | null | https://arxiv.org/abs/1902.03634v2 | https://arxiv.org/pdf/1902.03634v2.pdf | Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition | In the recent year, state-of-the-art for facial micro-expression recognition have been significantly advanced by deep neural networks. The robustness of deep learning has yielded promising performance beyond that of traditional handcrafted approaches. Most works in literature emphasized on increasing the depth of netwo... | ['Yen-Chang Huang', 'Huai-Qian Khor', 'Sze-Teng Liong', 'John See', 'Y. S. Gan'] | 2019-02-10 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 1.74799666e-03 -3.06028187e-01 -1.50663361e-01 -7.33279586e-01
-2.29966626e-01 2.48630494e-02 4.41128314e-01 -4.84756321e-01
-5.52770674e-01 6.33974195e-01 9.11773592e-02 2.41597354e-01
-5.99874035e-02 -4.62129056e-01 -3.06883126e-01 -8.12907875e-01
-3.91308963e-01 -3.22955519e-01 -3.19157809e-01 -3.61365974... | [13.615650177001953, 1.716415524482727] |
b2581bbc-ef85-4ddd-92fb-6607141b53b8 | a-reverse-jensen-inequality-result-with | 2111.06676 | null | https://arxiv.org/abs/2111.06676v1 | https://arxiv.org/pdf/2111.06676v1.pdf | A Reverse Jensen Inequality Result with Application to Mutual Information Estimation | The Jensen inequality is a widely used tool in a multitude of fields, such as for example information theory and machine learning. It can be also used to derive other standard inequalities such as the inequality of arithmetic and geometric means or the H\"older inequality. In a probabilistic setting, the Jensen inequal... | ['Rafael F. Schaefer', 'Rick Fritschek', 'Benedikt Groß', 'Gerhard Wunder'] | 2021-11-12 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 2.04417169e-01 2.63375103e-01 -2.95660853e-01 -3.58078063e-01
-5.42577684e-01 -6.12519622e-01 1.68296516e-01 3.48676026e-01
-4.77373242e-01 9.42452550e-01 -1.71523422e-01 -4.79835868e-01
-5.05210280e-01 -6.47780955e-01 -5.71107805e-01 -1.00781083e+00
-1.03576243e-01 3.15378904e-01 2.79481202e-01 -1.35613665... | [7.293030738830566, 4.191829204559326] |
a23cdb44-216f-41b0-9271-ce7323f7fa0e | multimodal-review-generation-with-privacy-and | null | null | https://aclanthology.org/2020.coling-main.37 | https://aclanthology.org/2020.coling-main.37.pdf | Multimodal Review Generation with Privacy and Fairness Awareness | Users express their opinions towards entities (e.g., restaurants) via online reviews which can be in diverse forms such as text, ratings, and images. Modeling reviews are advantageous for user behavior understanding which, in turn, supports various user-oriented tasks such as recommendation, sentiment analysis, and rev... | ['Lili Jiang', 'Duc-Trong Le', 'Thanh-Son Nguyen', 'Xuan-Son Vu'] | 2020-12-01 | null | null | null | coling-2020-8 | ['review-generation'] | ['natural-language-processing'] | [-1.68687910e-01 3.43079269e-01 -5.95967829e-01 -7.71304071e-01
-4.85926181e-01 -5.74148357e-01 5.87142289e-01 3.79045159e-01
-3.85206491e-01 6.13437176e-01 5.94614327e-01 -1.94644362e-01
2.99794614e-01 -8.91133964e-01 -3.76583397e-01 -3.58414322e-01
4.53633219e-01 -1.03210032e-01 -6.62677586e-01 -4.38148975... | [11.334941864013672, 6.741359233856201] |
12b57a11-1fbe-4465-a338-3c5ad5477cf0 | text-compression-aided-transformer-encoding | 2102.05951 | null | https://arxiv.org/abs/2102.05951v1 | https://arxiv.org/pdf/2102.05951v1.pdf | Text Compression-aided Transformer Encoding | Text encoding is one of the most important steps in Natural Language Processing (NLP). It has been done well by the self-attention mechanism in the current state-of-the-art Transformer encoder, which has brought about significant improvements in the performance of many NLP tasks. Though the Transformer encoder may effe... | ['Eiichiro Sumita', 'Masao Utiyama', 'Kehai Chen', 'Rui Wang', 'Hai Zhao', 'Zhuosheng Zhang', 'Zuchao Li'] | 2021-02-11 | null | null | null | null | ['text-compression'] | ['natural-language-processing'] | [ 5.46029150e-01 2.51371235e-01 -2.91735798e-01 -4.75928664e-01
-9.48326290e-01 -1.51937276e-01 1.04904187e+00 5.90136826e-01
-6.49594009e-01 5.17397404e-01 1.00133634e+00 -3.87656838e-01
2.25145295e-01 -9.06999588e-01 -8.90603662e-01 -3.99347007e-01
4.01427299e-01 7.59551942e-01 1.96361408e-01 -4.04530525... | [12.000572204589844, 9.19987678527832] |
968593ed-fd09-4eb9-8131-42bedf3e7794 | isotachophoresis-applied-to-chemical | 1708.08298 | null | http://arxiv.org/abs/1708.08298v1 | http://arxiv.org/pdf/1708.08298v1.pdf | Isotachophoresis applied to chemical reactions | This review discusses research developments and applications of
isotachophoresis (ITP) to the initiation, control, and acceleration of chemical
reactions, emphasizing reactions involving biomolecular reactants such as
nucleic acids, proteins, and live cells. ITP is a versatile technique which
requires no specific geome... | [] | 2017-08-28 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 4.69031602e-01 -5.79633892e-01 -3.23243588e-02 2.17491657e-01
-2.70337909e-01 -1.14443290e+00 5.49597681e-01 6.12461329e-01
-5.81699848e-01 9.85363364e-01 -1.52038753e-01 -6.25652015e-01
3.39299411e-01 -5.98618925e-01 -3.73638093e-01 -1.16147935e+00
6.52553663e-02 5.34074843e-01 4.09844011e-01 8.10759962... | [13.797813415527344, -3.1168060302734375] |
5d26ffb8-af31-40c3-b714-46757ca62eae | fine-grained-3d-shape-classification-with | 2005.12541 | null | https://arxiv.org/abs/2005.12541v2 | https://arxiv.org/pdf/2005.12541v2.pdf | Fine-Grained 3D Shape Classification with Hierarchical Part-View Attentions | Fine-grained 3D shape classification is important for shape understanding and analysis, which poses a challenging research problem. However, the studies on the fine-grained 3D shape classification have rarely been explored, due to the lack of fine-grained 3D shape benchmarks. To address this issue, we first introduce a... | ['Matthias Zwicker', 'Yu-Shen Liu', 'Xinhai Liu', 'Zhizhong Han'] | 2020-05-26 | null | null | null | null | ['semantic-part-detection', '3d-shape-retrieval'] | ['computer-vision', 'computer-vision'] | [-3.43630642e-01 -1.97423771e-01 -4.60019708e-02 -4.56841856e-01
-6.00390971e-01 -6.13781631e-01 6.26367986e-01 -1.82882205e-01
4.49543029e-01 1.52543671e-02 5.94756782e-01 9.92703959e-02
-9.72743481e-02 -9.62329507e-01 -6.56088114e-01 -7.46485472e-01
3.09990674e-01 5.44744015e-01 4.22188610e-01 -4.72442955... | [8.108911514282227, -3.7533674240112305] |
ca9cd343-b149-4148-90b4-a9d715bd5fb7 | from-adversarial-arms-race-to-model-centric | 2305.18503 | null | https://arxiv.org/abs/2305.18503v1 | https://arxiv.org/pdf/2305.18503v1.pdf | From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework | Textual adversarial attacks can discover models' weaknesses by adding semantic-preserved but misleading perturbations to the inputs. The long-lasting adversarial attack-and-defense arms race in Natural Language Processing (NLP) is algorithm-centric, providing valuable techniques for automatic robustness evaluation. How... | ['Heng Ji', 'Maosong Sun', 'Zhiyuan Liu', 'Hui Xue', 'Longtao Huang', 'Bo Yuan', 'Ning Shi', 'Hanlu Wu', 'Dehan Kong', 'Lifan Yuan', 'Ganqu Cui', 'Hongcheng Gao', 'Yangyi Chen'] | 2023-05-29 | null | null | null | null | ['adversarial-attack'] | ['adversarial'] | [ 8.36603343e-02 -3.38166565e-01 -4.10610400e-02 -1.65883467e-01
-1.06996393e+00 -1.26567876e+00 8.26625466e-01 -1.61766469e-01
-2.06567466e-01 5.25319278e-01 1.89025775e-01 -5.50062180e-01
-8.49340111e-02 -8.61958683e-01 -5.80167592e-01 -4.54699069e-01
6.88239979e-03 7.38452896e-02 8.46344903e-02 -3.39649022... | [5.9862060546875, 8.04868221282959] |
4568ae36-f749-4be4-8f16-e48cf0ede626 | risk-assessment-of-lymph-node-metastases-in | 2305.10041 | null | https://arxiv.org/abs/2305.10041v1 | https://arxiv.org/pdf/2305.10041v1.pdf | Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approach | Assessing the pre-operative risk of lymph node metastases in endometrial cancer patients is a complex and challenging task. In principle, machine learning and deep learning models are flexible and expressive enough to capture the dynamics of clinical risk assessment. However, in this setting we are limited to observati... | ['Fabio Stella', 'Marco Scutari', 'Casper Reijnen', 'Hanny Pijnenborg', 'Peter J. F. Lucas', 'Alice Bernasconi', 'Alessio Zanga'] | 2023-05-17 | null | null | null | null | ['imputation', 'causal-discovery', 'imputation', 'selection-bias', 'imputation'] | ['computer-vision', 'knowledge-base', 'miscellaneous', 'natural-language-processing', 'time-series'] | [ 3.71818304e-01 4.24628109e-01 -8.79194081e-01 -3.86264235e-01
-6.33186400e-01 -1.43055946e-01 3.95241529e-01 4.43908244e-01
-5.89964747e-01 1.10828555e+00 8.04833293e-01 -7.08737969e-01
-7.55955398e-01 -9.70433295e-01 -5.73609710e-01 -6.10191703e-01
-4.04012620e-01 4.53179359e-01 -3.74364913e-01 1.68360397... | [7.987527370452881, 5.509975433349609] |
283cf69f-d3d7-44c9-8e61-6c5f931b267e | automatic-liver-segmentation-using-an | 1707.08037 | null | http://arxiv.org/abs/1707.08037v1 | http://arxiv.org/pdf/1707.08037v1.pdf | Automatic Liver Segmentation Using an Adversarial Image-to-Image Network | Automatic liver segmentation in 3D medical images is essential in many
clinical applications, such as pathological diagnosis of hepatic diseases,
surgical planning, and postoperative assessment. However, it is still a very
challenging task due to the complex background, fuzzy boundary, and various
appearance of liver. ... | ['S. Kevin Zhou', 'Sasa Grbic', 'Dong Yang', 'Mingqing Chen', 'Dimitris Metaxas', 'Daguang Xu', 'Dorin Comaniciu', 'Bogdan Georgescu'] | 2017-07-25 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-1.23720609e-01 -9.71135944e-02 2.21895903e-01 -2.54874468e-01
-5.35735607e-01 -6.63135946e-01 3.26352358e-01 1.08830839e-01
-3.57033461e-01 3.99048537e-01 1.98385090e-01 -5.04151762e-01
3.09977740e-01 -7.36641526e-01 -4.79669183e-01 -8.83632720e-01
-3.05868357e-01 5.06811023e-01 3.40412617e-01 1.42061979... | [14.512892723083496, -2.6564908027648926] |
f9e57379-8905-4de7-9559-ed0f137dd15b | leveraging-experience-in-lifelong-multi-agent | 2202.04382 | null | https://arxiv.org/abs/2202.04382v3 | https://arxiv.org/pdf/2202.04382v3.pdf | Leveraging Experience in Lifelong Multi-Agent Pathfinding | In Lifelong Multi-Agent Path Finding (L-MAPF) a team of agents performs a stream of tasks consisting of multiple locations to be visited by the agents on a shared graph while avoiding collisions with one another. L-MAPF is typically tackled by partitioning it into multiple consecutive, and hence similar, "one-shot" MAP... | ['Oren Salzman', 'Kiril Solovey', 'Nitzan Madar'] | 2022-02-09 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 6.17342219e-02 4.19565350e-01 -1.74137220e-01 1.41564801e-01
-8.26196790e-01 -7.79009104e-01 5.60083270e-01 5.53835928e-01
-6.10159993e-01 1.03651714e+00 1.22984879e-01 -1.69877350e-01
-8.17319036e-01 -1.03157508e+00 -6.33948982e-01 -4.90026295e-01
-5.30266523e-01 1.20853436e+00 7.83094585e-01 -5.65205991... | [4.964629173278809, 1.764493703842163] |
d78d70da-92ce-4554-a832-94742486e7a3 | action-anticipation-with-rbf | 1911.07806 | null | https://arxiv.org/abs/1911.07806v3 | https://arxiv.org/pdf/1911.07806v3.pdf | Action Anticipation with RBF Kernelized Feature Mapping RNN | We introduce a novel Recurrent Neural Network-based algorithm for future video feature generation and action anticipation called feature mapping RNN. Our novel RNN architecture builds upon three effective principles of machine learning, namely parameter sharing, Radial Basis Function kernels and adversarial training. U... | ['Yuge Shi', 'Richard Hartley', 'Basura Fernando'] | 2019-11-18 | action-anticipation-with-rbf-kernelized | http://openaccess.thecvf.com/content_ECCV_2018/html/Yuge_Shi_Action_Anticipation_with_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Yuge_Shi_Action_Anticipation_with_ECCV_2018_paper.pdf | eccv-2018-9 | ['action-anticipation'] | ['computer-vision'] | [ 4.68980938e-01 5.10896683e-01 -3.55406880e-01 -4.31001425e-01
-5.50872862e-01 -1.99588135e-01 6.18724883e-01 -6.18997931e-01
-4.83455420e-01 7.80234993e-01 7.53441691e-01 -6.11106902e-02
1.33819297e-01 -5.77889621e-01 -1.13974404e+00 -3.94456834e-01
-3.49727392e-01 4.67487983e-02 3.69529426e-02 -2.63918400... | [8.119577407836914, 0.4516393542289734] |
db005b53-cda3-43d6-a330-e532ca274c1c | two-decades-of-colorization-and | 2204.13322 | null | https://arxiv.org/abs/2204.13322v2 | https://arxiv.org/pdf/2204.13322v2.pdf | Two Decades of Colorization and Decolorization for Images and Videos | Colorization is a computer-aided process, which aims to give color to a gray image or video. It can be used to enhance black-and-white images, including black-and-white photos, old-fashioned films, and scientific imaging results. On the contrary, decolorization is to convert a color image or video into a grayscale one.... | ['Shiguang Liu'] | 2022-04-28 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 6.34412289e-01 -6.22499287e-01 -3.15760933e-02 1.61064550e-01
-8.77173431e-03 -4.72916245e-01 2.54177034e-01 -2.60061264e-01
-6.43421173e-01 7.13924348e-01 -2.13308185e-01 -4.31763947e-01
3.14097017e-01 -7.48079658e-01 -2.91764766e-01 -9.91129756e-01
3.95111620e-01 -4.97264057e-01 1.86250165e-01 4.18161601... | [10.845096588134766, -2.4383151531219482] |
d29b0cb3-21d8-4119-86cf-a5c2c292fe42 | twin-two-stage-interest-network-for-lifelong | 2302.02352 | null | https://arxiv.org/abs/2302.02352v2 | https://arxiv.org/pdf/2302.02352v2.pdf | TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou | Life-long user behavior modeling, i.e., extracting a user's hidden interests from rich historical behaviors in months or even years, plays a central role in modern CTR prediction systems. Conventional algorithms mostly follow two cascading stages: a simple General Search Unit (GSU) for fast and coarse search over tens ... | ['Kun Gai', 'Yang song', 'Yanan Niu', 'Dewei Leng', 'Yiqun Hui', 'Jing Lu', 'Lin Guan', 'Xiaoxue Zang', 'Zhiyi Fu', 'Chenbin Zhang', 'Jianxin Chang'] | 2023-02-05 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [ 3.54190581e-02 -5.85089087e-01 -6.06450915e-01 -2.19841048e-01
-6.22940242e-01 -1.65936261e-01 -5.15004657e-02 -2.67441850e-02
-3.38749468e-01 5.36078632e-01 2.08637878e-01 -3.20033997e-01
-2.70410746e-01 -6.06898069e-01 -4.62063074e-01 -5.80314100e-01
2.05232576e-02 3.13298851e-01 5.59228539e-01 -4.41249639... | [10.1506986618042, 5.535309791564941] |
3def96db-03fd-4f2c-b9f3-664585ba5fbe | clip-rr-improved-clip-network-for-relation | 2302.06350 | null | https://arxiv.org/abs/2302.06350v2 | https://arxiv.org/pdf/2302.06350v2.pdf | VITR: Augmenting Vision Transformers with Relation-Focused Learning for Cross-Modal Information Retrieval | Relation-focused cross-modal information retrieval focuses on retrieving information based on relations expressed in user queries, and it is particularly important in information retrieval applications and next-generation search engines. While pre-trained networks like Contrastive Language-Image Pre-training (CLIP) hav... | ['Georgina Cosma', 'Yan Gong'] | 2023-02-13 | null | null | null | null | ['cross-modal-information-retrieval'] | ['miscellaneous'] | [ 1.72261581e-01 -2.41095766e-01 -3.94638360e-01 -2.33434349e-01
-1.18647468e+00 -4.01044250e-01 9.34071422e-01 7.39063844e-02
-4.00425255e-01 3.13410014e-01 2.48070881e-01 -1.01214414e-02
-5.69271028e-01 -7.07185268e-01 -5.03069937e-01 -5.12162209e-01
2.32110620e-01 5.73374867e-01 3.20301026e-01 -4.04462099... | [10.850663185119629, 1.229317545890808] |
057ef931-bd32-4887-881c-a4e4b5f15599 | semeval-2010-task-13-tempeval-2 | null | null | https://aclanthology.org/S10-1010/ | https://aclanthology.org/S10-1010.pdf | SemEval-2010 Task 13: TempEval-2 | Tempeval-2 comprises evaluation tasks for time expressions, events and temporal relations, the latter of which was split up in four sub tasks, motivated by the notion that smaller subtasks would make both data preparation and temporal relation extraction easier. Manually annotated data were provided for six languages: ... | ['James Pustejovsky', 'Tommaso Caselli', 'Roser Saurí', 'Marc Verhagen'] | 2010-07-01 | null | null | null | proceedings-of-the-5th-international-workshop | ['temporal-relation-extraction', 'temporal-relation-classification', 'temporal-tagging'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-2.53712952e-01 3.01456600e-01 -3.29232514e-01 -5.53961396e-01
-4.27457958e-01 -1.05371058e+00 1.09745324e+00 5.60661435e-01
-7.13672578e-01 1.08144188e+00 7.58508921e-01 -3.46252829e-01
-2.25106135e-01 -4.15926099e-01 1.86266692e-03 -2.13434864e-02
-8.17337990e-01 2.96964347e-01 3.34952831e-01 -1.38961270... | [9.100245475769043, 9.238439559936523] |
15ac2b7b-f056-4e60-bdba-3352a405c087 | multilegalsbd-a-multilingual-legal-sentence | 2305.01211 | null | https://arxiv.org/abs/2305.01211v1 | https://arxiv.org/pdf/2305.01211v1.pdf | MultiLegalSBD: A Multilingual Legal Sentence Boundary Detection Dataset | Sentence Boundary Detection (SBD) is one of the foundational building blocks of Natural Language Processing (NLP), with incorrectly split sentences heavily influencing the output quality of downstream tasks. It is a challenging task for algorithms, especially in the legal domain, considering the complex and different s... | ['Joel Niklaus', 'Matthias Stürmer', 'Tobias Brugger'] | 2023-05-02 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 2.86624655e-02 9.62627605e-02 -2.01068506e-01 -5.02502501e-01
-1.43269730e+00 -8.47078025e-01 4.00182307e-01 1.94389880e-01
-6.31197691e-01 1.01070774e+00 5.78043520e-01 -9.09397066e-01
4.96021777e-01 -3.89160246e-01 -6.25229776e-01 -4.10354286e-02
1.09246708e-01 5.38597584e-01 4.64921921e-01 -5.46953022... | [10.496269226074219, 9.724899291992188] |
511ebc1f-cdf1-432b-95c5-66a0899aca5d | multi-level-second-order-few-shot-learning | 2201.05916 | null | https://arxiv.org/abs/2201.05916v1 | https://arxiv.org/pdf/2201.05916v1.pdf | Multi-level Second-order Few-shot Learning | We propose a Multi-level Second-order (MlSo) few-shot learning network for supervised or unsupervised few-shot image classification and few-shot action recognition. We leverage so-called power-normalized second-order base learner streams combined with features that express multiple levels of visual abstraction, and we ... | ['Piotr Koniusz', 'Hongdong Li', 'Hongguang Zhang'] | 2022-01-15 | null | null | null | null | ['few-shot-action-recognition', 'unsupervised-few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 1.54131025e-01 -3.05281371e-01 -4.85075057e-01 -4.40752178e-01
-6.53949082e-01 -1.53258303e-02 5.76977432e-01 -6.20479928e-04
-5.44000924e-01 3.95858616e-01 3.60138744e-01 2.76532143e-01
5.75642399e-02 -9.29464877e-01 -6.40213728e-01 -4.58430916e-01
-3.63671571e-01 -1.75238490e-01 7.61725068e-01 -2.56638199... | [9.873152732849121, 2.679337739944458] |
977ef510-c22d-4ea7-8517-2c7f455908e9 | cross-modal-causal-relational-reasoning-for | 2207.12647 | null | https://arxiv.org/abs/2207.12647v8 | https://arxiv.org/pdf/2207.12647v8.pdf | Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering | Existing visual question answering methods often suffer from cross-modal spurious correlations and oversimplified event-level reasoning processes that fail to capture event temporality, causality, and dynamics spanning over the video. In this work, to address the task of event-level visual question answering, we propos... | ['Liang Lin', 'Guanbin Li', 'Yang Liu'] | 2022-07-26 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [-2.55219311e-01 -1.70869738e-01 -3.31005365e-01 -4.57266957e-01
-7.36039340e-01 -6.46524727e-01 1.07875502e+00 3.30493689e-01
2.79103518e-01 2.84221858e-01 8.90456200e-01 -4.24307495e-01
-4.52536494e-01 -6.12701774e-01 -8.23210657e-01 -3.58981699e-01
-1.91770434e-01 7.14550242e-02 3.87983948e-01 -2.56198384... | [10.49211597442627, 1.269737958908081] |
97231c0e-8f77-4791-ad3d-e908a5780921 | text-to-image-editing-by-image-information | 2305.17489 | null | https://arxiv.org/abs/2305.17489v1 | https://arxiv.org/pdf/2305.17489v1.pdf | Text-to-image Editing by Image Information Removal | Diffusion models have demonstrated impressive performance in text-guided image generation. To leverage the knowledge of text-guided image generation models in image editing, current approaches either fine-tune the pretrained models using the input image (e.g., Imagic) or incorporate structure information as additional ... | ['Bryan A. Plummer', 'Jacob Zhiyuan Fang', 'Jian Zheng', 'Zhongping Zhang'] | 2023-05-27 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 6.51659429e-01 1.35835305e-01 5.89493141e-02 -3.01161617e-01
-5.28342009e-01 -6.75209761e-01 5.27126491e-01 -2.74125993e-01
-3.76165390e-01 6.92725122e-01 1.62355065e-01 3.73937190e-02
2.60532171e-01 -9.87346113e-01 -1.02003324e+00 -6.86643302e-01
5.67849457e-01 1.86066553e-02 1.06064968e-01 -3.62938613... | [11.500995635986328, -0.5424659848213196] |
e3103f65-6498-4677-9974-aa099bae53d3 | the-change-that-matters-in-discourse-parsing | null | null | https://openreview.net/forum?id=KkF3IHfdT_z | https://openreview.net/pdf?id=KkF3IHfdT_z | The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error | Discourse analysis allows us to attain high-level inferences of a text document beyond the sentence-level. However, currently the performance of discourse models is very low on texts outside of the training distribution's coverage. There is need for a measure that can inform us to what extent our model generalizes from... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['discourse-parsing'] | ['natural-language-processing'] | [ 3.28193218e-01 4.72629189e-01 -5.32360673e-01 -5.89489162e-01
-1.04668629e+00 -7.91687250e-01 8.99107933e-01 3.93676907e-01
-4.97335583e-01 1.17455435e+00 6.85551643e-01 -4.91433173e-01
-1.16866045e-01 -6.22517526e-01 -7.70868719e-01 -5.19160688e-01
1.94729894e-01 6.85160041e-01 3.62370253e-01 -2.36134589... | [10.941591262817383, 9.054040908813477] |
72115840-a083-4d9c-b22a-66e95bd7db58 | deep-state-space-models-for-time-series | null | null | http://papers.nips.cc/paper/8004-deep-state-space-models-for-time-series-forecasting | http://papers.nips.cc/paper/8004-deep-state-space-models-for-time-series-forecasting.pdf | Deep State Space Models for Time Series Forecasting | We present a novel approach to probabilistic time series forecasting that combines state space models with deep learning. By parametrizing a per-time-series linear state space model with a jointly-learned recurrent neural network, our method retains desired properties of state space models such as data efficiency and i... | ['Syama Sundar Rangapuram', 'Matthias W. Seeger', 'Jan Gasthaus', 'Tim Januschowski', 'Yuyang Wang', 'Lorenzo Stella'] | 2018-12-01 | null | null | null | neurips-2018-12 | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-1.03218660e-01 -2.90269926e-02 -4.66739476e-01 -4.25735831e-01
-8.80665541e-01 -6.66301250e-01 1.17698526e+00 1.55786425e-02
-7.40885958e-02 6.83311701e-01 4.64344531e-01 -9.29918349e-01
-2.65847117e-01 -6.43347085e-01 -7.94827998e-01 -6.72819912e-01
-6.58785820e-01 4.13167089e-01 -1.39198795e-01 -3.44597548... | [6.946745872497559, 3.2085914611816406] |
361f8aba-ba82-4945-b58e-6df39a411008 | novel-hybrid-dnn-approaches-for-speaker | 2112.13353 | null | https://arxiv.org/abs/2112.13353v1 | https://arxiv.org/pdf/2112.13353v1.pdf | Novel Hybrid DNN Approaches for Speaker Verification in Emotional and Stressful Talking Environments | In this work, we conducted an empirical comparative study of the performance of text-independent speaker verification in emotional and stressful environments. This work combined deep models with shallow architecture, which resulted in novel hybrid classifiers. Four distinct hybrid models were utilized: deep neural netw... | ['Kemal Polat', 'Adi Alhudhaif', 'Ashraf Elnagar', 'Nawel Nemmour', 'Ali Bou Nassif', 'Ismail Shahin'] | 2021-12-26 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [-5.42785048e-01 -8.60000700e-02 2.08875865e-01 -4.01238412e-01
-5.89780867e-01 -2.59933084e-01 3.02386642e-01 -3.52872223e-01
-2.59525269e-01 3.90632361e-01 2.11167604e-01 -3.34174842e-01
2.16551170e-01 -2.47086316e-01 -1.46725461e-01 -9.19857442e-01
-1.17221154e-01 2.53054760e-02 -4.44494903e-01 -2.77234316... | [14.338117599487305, 6.016212463378906] |
c6f06ed6-0a30-4fe5-ae59-00a7d18aa968 | multi-view-reasoning-consistent-contrastive | 2210.11694 | null | https://arxiv.org/abs/2210.11694v1 | https://arxiv.org/pdf/2210.11694v1.pdf | Multi-View Reasoning: Consistent Contrastive Learning for Math Word Problem | Math word problem solver requires both precise relation reasoning about quantities in the text and reliable generation for the diverse equation. Current sequence-to-tree or relation extraction methods regard this only from a fixed view, struggling to simultaneously handle complex semantics and diverse equations. Howeve... | ['Weiming Lu', 'Qingpeng Nong', 'Zeqi Tan', 'Xiaoxia Cheng', 'Yanna Ma', 'Yongliang Shen', 'Wenqi Zhang'] | 2022-10-21 | null | null | null | null | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 1.36082485e-01 1.18177772e-01 -2.74541527e-01 -5.50191462e-01
-1.09777570e+00 -9.94281709e-01 7.55556405e-01 1.10904731e-01
1.44703493e-01 7.50028610e-01 4.63234723e-01 -3.70155305e-01
-1.74957529e-01 -1.17413199e+00 -6.23676240e-01 -3.74158025e-02
4.90907788e-01 8.65510523e-01 -1.01224393e-01 -5.56939662... | [9.715309143066406, 7.459100723266602] |
00694f27-b70b-4349-99ab-8eab1c8b247a | continual-few-shot-intent-detection | null | null | https://aclanthology.org/2022.coling-1.26 | https://aclanthology.org/2022.coling-1.26.pdf | Continual Few-shot Intent Detection | Intent detection is at the core of task-oriented dialogue systems. Existing intent detection systems are typically trained with a large amount of data over a predefined set of intent classes. However, newly emerged intents in multiple domains are commonplace in the real world. And it is time-consuming and impractical f... | ['Yin Zhang', 'Ji Zhang', 'Xing Gao', 'Qianglong Chen', 'Yuchen Zhai', 'Guodun Li'] | null | null | null | null | coling-2022-10 | ['task-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 4.75096196e-01 6.23158738e-02 -1.93463638e-01 -5.52506030e-01
-6.51835203e-01 -3.57842147e-01 6.52985156e-01 1.32519886e-01
-7.33928978e-01 1.03823316e+00 3.64506781e-01 -2.39840686e-01
9.22925174e-02 -3.23910862e-01 -2.41252780e-01 -3.41329932e-01
-2.02776064e-04 5.65041959e-01 3.67324769e-01 -4.11280513... | [12.185718536376953, 7.6198625564575195] |
467c8480-335c-4bbd-a68a-8417cb8a6fb8 | diet-networks-thin-parameters-for-fat | 1611.09340 | null | http://arxiv.org/abs/1611.09340v3 | http://arxiv.org/pdf/1611.09340v3.pdf | Diet Networks: Thin Parameters for Fat Genomics | Learning tasks such as those involving genomic data often poses a serious
challenge: the number of input features can be orders of magnitude larger than
the number of training examples, making it difficult to avoid overfitting, even
when using the known regularization techniques. We focus here on tasks in which
the inp... | ['Marie-Pierre Dubé', 'Marc-André Legault', 'Pierre Luc Carrier', 'Akram Erraqabi', 'Yoshua Bengio', 'Tristan Sylvain', 'Julie G. Hussin', 'Etienne Dejoie', 'Alex Auvolat', 'Adriana Romero'] | 2016-11-28 | null | null | null | null | ['parameter-prediction'] | ['miscellaneous'] | [ 4.21860695e-01 1.98159441e-01 -1.31625757e-02 -6.32431567e-01
-5.65487623e-01 -3.99756074e-01 2.64845073e-01 4.34116870e-01
-6.04997396e-01 7.63855755e-01 -1.32000491e-01 -4.22479123e-01
-3.28603923e-01 -8.85744572e-01 -1.04841411e+00 -1.04101551e+00
-1.85452193e-01 7.17213035e-01 -5.80647364e-02 3.84911858... | [8.265966415405273, 4.472285747528076] |
925abaad-c04f-4e7e-a720-ac71d8d6ed18 | full-reconstruction-of-non-stationary-strand | 1610.05057 | null | http://arxiv.org/abs/1610.05057v2 | http://arxiv.org/pdf/1610.05057v2.pdf | Full Reconstruction of Non-Stationary Strand-Symmetric Models on Rooted Phylogenies | Understanding the evolutionary relationship among species is of fundamental
importance to the biological sciences. The location of the root in any
phylogenetic tree is critical as it gives an order to evolutionary events. None
of the popular models of nucleotide evolution used in likelihood or Bayesian
methods are able... | [] | 2016-11-13 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 8.30628574e-01 -2.42883861e-01 -4.29693609e-01 -1.64883405e-01
-2.60807902e-01 -7.43778646e-01 4.32283610e-01 1.68803185e-01
-5.55529237e-01 1.13891566e+00 -2.16268525e-01 -9.78903949e-01
1.30979000e-02 -2.41643772e-01 -5.79114676e-01 -9.68136847e-01
-1.82297274e-01 7.85553336e-01 6.56031132e-01 1.88798662... | [4.865238189697266, 5.160327434539795] |
039004cb-3ea9-4c75-ad19-61d09a682277 | t-cvae-transformer-based-conditioned | null | null | https://www.ijcai.org/proceedings/2019/727 | https://www.ijcai.org/proceedings/2019/0727.pdf | T-CVAE: Transformer-Based Conditioned Variational Autoencoder for Story Completion | Story completion is a very challenging task of generating the missing plot for an incomplete story, which requires not only understanding but also inference of the given contextual clues. In this paper, we present a novel conditional variational autoencoder based on Transformer for missing plot generation. Our model us... | ['Xiaojun Wan', 'Tianming Wang'] | 2019-07-01 | null | null | null | international-joint-conference-on-artificial-2 | ['story-completion'] | ['natural-language-processing'] | [-3.46250758e-02 4.51313972e-01 -1.23668574e-01 -2.60055751e-01
-8.85357201e-01 -4.29687887e-01 7.80486465e-01 -8.83595049e-02
3.77431780e-01 1.01608479e+00 1.05714464e+00 1.18779860e-01
6.11419678e-02 -9.40208912e-01 -1.03506804e+00 -4.22503710e-01
5.42889059e-01 5.76711059e-01 -1.92387000e-01 -8.44324455... | [11.210152626037598, 0.6681820750236511] |
9b68bec6-78be-4c7e-b2db-ac11177bbc22 | vesr-net-the-winning-solution-to-youku-video | 2003.02115 | null | https://arxiv.org/abs/2003.02115v1 | https://arxiv.org/pdf/2003.02115v1.pdf | VESR-Net: The Winning Solution to Youku Video Enhancement and Super-Resolution Challenge | This paper introduces VESR-Net, a method for video enhancement and super-resolution (VESR). We design a separate non-local module to explore the relations among video frames and fuse video frames efficiently, and a channel attention residual block to capture the relations among feature maps for video frame reconstructi... | ['Chaowei Shan', 'Zhibo Chen', 'Sen Liu', 'Xu Tan', 'Jiale Chen'] | 2020-03-04 | null | null | null | null | ['video-enhancement'] | ['computer-vision'] | [ 3.09339035e-02 -3.12702179e-01 -1.47683837e-03 -1.97711885e-01
-9.18891191e-01 4.18979377e-02 3.45122427e-01 -6.46757722e-01
-3.97517443e-01 6.34693325e-01 7.03445435e-01 1.22960895e-01
7.14724213e-02 -4.61216986e-01 -8.05105746e-01 -1.36573762e-01
-2.43940860e-01 -3.76243085e-01 5.15977621e-01 -6.36491656... | [11.094188690185547, -1.9214117527008057] |
24ab638b-bdd3-4d87-92a3-0c969e75763b | c3-cross-instance-guided-contrastive | 2211.07136 | null | https://arxiv.org/abs/2211.07136v2 | https://arxiv.org/pdf/2211.07136v2.pdf | C3: Cross-instance guided Contrastive Clustering | Clustering is the task of gathering similar data samples into clusters without using any predefined labels. It has been widely studied in machine learning literature, and recent advancements in deep learning have revived interest in this field. Contrastive clustering (CC) models are a staple of deep clustering in which... | ['Narges Armanfard', 'Hadi Hojjati', 'Mohammadreza Sadeghi'] | 2022-11-14 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [-7.59236962e-02 -2.61155814e-01 -6.05398640e-02 -6.85875893e-01
-6.39733374e-01 -2.90117979e-01 7.00893641e-01 3.82303476e-01
-4.83864307e-01 3.23176712e-01 -2.12988615e-01 2.25087598e-01
-3.01281214e-01 -6.20384395e-01 -5.30648947e-01 -9.88513231e-01
-3.07172328e-01 5.10541618e-01 -1.44631714e-02 2.61840343... | [9.222664833068848, 3.3098089694976807] |
8870fc0c-7348-40b2-ae9d-1649f49eb6f9 | meta-learning-for-few-shot-camera-adaptive | 1811.11788 | null | http://arxiv.org/abs/1811.11788v2 | http://arxiv.org/pdf/1811.11788v2.pdf | Formulating Camera-Adaptive Color Constancy as a Few-shot Meta-Learning Problem | Digital camera pipelines employ color constancy methods to estimate an
unknown scene illuminant, in order to re-illuminate images as if they were
acquired under an achromatic light source. Fully-supervised learning approaches
exhibit state-of-the-art estimation accuracy with camera-specific labelled
training imagery. R... | ['Ales Leonardis', 'Sarah Parisot', 'Steven McDonagh', 'Zhenguo Li', 'Xing Zhang', 'Gregory Slabaugh', 'Fengwei Zhou'] | 2018-11-28 | null | null | null | null | ['color-constancy', 'few-shot-camera-adaptive-color-constancy', 'few-shot-camera-adaptive-color-constancy'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 5.54946661e-01 -5.91551781e-01 7.32005537e-02 -4.17914718e-01
-8.25535357e-01 -1.01447082e+00 6.65924668e-01 -8.31572786e-02
-6.77144885e-01 4.55287576e-01 -3.08932304e-01 1.56046957e-01
1.57608375e-01 -1.41350240e-01 -9.22626853e-01 -7.60703564e-01
3.96054298e-01 1.96559563e-01 3.35764706e-01 2.49049991... | [10.12796401977539, -2.6218650341033936] |
6c269f65-7566-4c8f-8f12-7782085bf7d3 | meta-optimizing-semantic-evolutionary-search | null | null | https://wiki.opencog.org/w/Meta-Optimizing_Semantic_Evolutionary_Search | http://metacog.org/papers/gecco07b_full.pdf | Meta-Optimizing Semantic Evolutionary Search | I present MOSES (meta-optimizing semantic evolutionary search), a new probabilistic modeling (estimation of distribution) approach to program evolution. Distributions are not estimated over the entire space of programs. Rather, a novel representation-building procedure that exploits domain knowledge is used to dynamica... | ['Moshe Looks'] | 2017-07-11 | null | null | null | association-for-computing-machinery-acm-2017 | ['problem-decomposition'] | ['miscellaneous'] | [ 1.95550650e-01 9.10057649e-02 -5.98329127e-01 -2.38146544e-01
-4.53471601e-01 -6.43523872e-01 3.20027113e-01 -1.33525385e-02
-9.22031924e-02 6.95613980e-01 -7.99981207e-02 -3.96987766e-01
-6.92078710e-01 -8.77912223e-01 -3.88201058e-01 -6.24901056e-01
-1.99581146e-01 9.34537053e-01 4.96744990e-01 -3.82562339... | [8.100727081298828, 7.253157615661621] |
75a41892-fc4f-4ada-89e4-29f487bac339 | time-in-a-box-advancing-knowledge-graph | 2111.06854 | null | https://arxiv.org/abs/2111.06854v1 | https://arxiv.org/pdf/2111.06854v1.pdf | Time in a Box: Advancing Knowledge Graph Completion with Temporal Scopes | Almost all statements in knowledge bases have a temporal scope during which they are valid. Hence, knowledge base completion (KBC) on temporal knowledge bases (TKB), where each statement \textit{may} be associated with a temporal scope, has attracted growing attention. Prior works assume that each statement in a TKB \t... | ['Gengchen Mai', 'Rui Zhu', 'Bo Yan', 'Krzysztof Janowic', 'Ling Cai'] | 2021-11-12 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [-3.48314464e-01 2.43811607e-02 -9.69718099e-01 -5.04168630e-01
-3.52367818e-01 -8.50207746e-01 5.88732302e-01 5.81710279e-01
-1.36492342e-01 1.06575632e+00 2.62706161e-01 -5.62564135e-01
-5.42501926e-01 -1.40116370e+00 -8.62303853e-01 -2.98170388e-01
-3.49990815e-01 5.67500472e-01 8.39152992e-01 -4.55052227... | [8.533204078674316, 7.928412437438965] |
94c44b8e-562d-499d-b19f-f21cdbad3ee6 | quantitative-dynamics-of-design-thinking-and | 2306.10971 | null | https://arxiv.org/abs/2306.10971v1 | https://arxiv.org/pdf/2306.10971v1.pdf | Quantitative dynamics of design thinking and creativity perspectives in company context | This study is intended to provide in-depth insights into how design thinking and creativity issues are understood and possibly evolve in the course of design discussions in a company context. For that purpose, we use the seminar transcripts of the Design Thinking Research Symposium 12 (DTRS12) dataset "Tech-centred Des... | ['Danko D. Georgiev', 'Georgi V. Georgiev'] | 2023-06-19 | null | null | null | null | ['word-similarity'] | ['natural-language-processing'] | [-1.03758477e-01 -7.54763633e-02 3.55648547e-02 -5.30410558e-02
4.13141817e-01 -8.08558643e-01 6.11629009e-01 3.18020046e-01
-2.19270796e-01 -3.83609891e-01 1.04268241e+00 -5.99499464e-01
-1.06130815e+00 -7.78987527e-01 2.76954383e-01 -2.90383726e-01
5.98307252e-01 6.56267762e-01 -3.49631011e-01 -4.53535229... | [9.16716480255127, 6.486791133880615] |
a2982b83-7fff-463f-860a-5130c3591ec7 | mining-false-positive-examples-for-text-based | 2303.08466 | null | https://arxiv.org/abs/2303.08466v1 | https://arxiv.org/pdf/2303.08466v1.pdf | Mining False Positive Examples for Text-Based Person Re-identification | Text-based person re-identification (ReID) aims to identify images of the targeted person from a large-scale person image database according to a given textual description. However, due to significant inter-modal gaps, text-based person ReID remains a challenging problem. Most existing methods generally rely heavily on... | ['Changxing Ding', 'Zhiyin Shao', 'Wenhao Xu'] | 2023-03-15 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 5.81451319e-02 -4.91750807e-01 -6.93758652e-02 -4.80978221e-01
-9.32134569e-01 -3.50693196e-01 6.98765218e-01 4.44269218e-02
-8.44394505e-01 7.30460584e-01 2.62253940e-01 9.93291959e-02
-9.91558097e-03 -5.48460305e-01 -4.37670320e-01 -6.81842327e-01
5.77086620e-02 3.54251832e-01 8.49085525e-02 -1.79982498... | [14.771620750427246, 0.8925073742866516] |
4bd7c213-8938-4ef2-bb5e-a9a93994c753 | an-investigation-of-noise-in-morphological | 2305.16581 | null | https://arxiv.org/abs/2305.16581v1 | https://arxiv.org/pdf/2305.16581v1.pdf | An Investigation of Noise in Morphological Inflection | With a growing focus on morphological inflection systems for languages where high-quality data is scarce, training data noise is a serious but so far largely ignored concern. We aim at closing this gap by investigating the types of noise encountered within a pipeline for truly unsupervised morphological paradigm comple... | ['Katharina Kann', 'Miikka Silfverberg', 'Garrett Nicolai', 'Changbing Yang', 'Adam Wiemerslage'] | 2023-05-26 | null | null | null | null | ['morphological-inflection'] | ['natural-language-processing'] | [ 2.37484857e-01 -8.98021385e-02 7.56266490e-02 -3.66129130e-01
-8.76870036e-01 -7.62667596e-01 5.03791749e-01 6.66943610e-01
-8.67760718e-01 2.55996615e-01 5.86792529e-01 -5.53350449e-01
3.11136454e-01 -7.19734728e-01 -8.43225956e-01 -3.25034529e-01
8.79488885e-02 3.89261663e-01 2.10244000e-01 -1.59859046... | [10.698877334594727, 9.755813598632812] |
4e90913e-7638-446e-af70-6e2a9ff80bdb | dataset-creation-pipeline-for-camera-based | 2303.01468 | null | https://arxiv.org/abs/2303.01468v1 | https://arxiv.org/pdf/2303.01468v1.pdf | Dataset Creation Pipeline for Camera-Based Heart Rate Estimation | Heart rate is one of the most vital health metrics which can be utilized to investigate and gain intuitions into various human physiological and psychological information. Estimating heart rate without the constraints of contact-based sensors thus presents itself as a very attractive field of research as it enables wel... | ['Peter Corcoran', 'Joseph Lemley', 'Amr Elrasad', 'Mohamed Moustafa'] | 2023-03-02 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 5.73602259e-01 -2.23297521e-01 1.28384857e-02 -6.80974305e-01
-3.16832870e-01 -1.14248283e-01 4.98828590e-02 4.56386060e-01
-4.78744298e-01 4.54111487e-01 -2.24630117e-01 1.20124906e-01
1.05122156e-01 -2.87368834e-01 -1.27103865e-01 -6.95455194e-01
8.12022686e-02 -2.01521274e-02 -2.99955755e-01 3.69547546... | [13.874894142150879, 2.838090419769287] |
01e19d9f-b666-4439-b44d-ac536d7ab409 | tablelab-an-interactive-table-extraction | 2102.08445 | null | https://arxiv.org/abs/2102.08445v1 | https://arxiv.org/pdf/2102.08445v1.pdf | TableLab: An Interactive Table Extraction System with Adaptive Deep Learning | Table extraction from PDF and image documents is a ubiquitous task in the real-world. Perfect extraction quality is difficult to achieve with one single out-of-box model due to (1) the wide variety of table styles, (2) the lack of training data representing this variety and (3) the inherent ambiguity and subjectivity o... | ['Yunyao Li', 'Douglas Burdick', 'Nancy Xin Ru Wang'] | 2021-02-16 | null | null | null | null | ['table-extraction'] | ['miscellaneous'] | [ 1.03608035e-01 -3.69249433e-02 -1.18671037e-01 -3.25136304e-01
-1.17619824e+00 -1.06104934e+00 3.00127983e-01 5.48263252e-01
-2.84395844e-01 3.78533334e-01 1.35179758e-01 -2.41302982e-01
-9.19943601e-02 -7.57880092e-01 -6.08048439e-01 -1.70789778e-01
1.96952149e-01 9.23676074e-01 2.14070916e-01 -1.22347943... | [11.690034866333008, 2.918715000152588] |
947e86d3-9016-4233-8de2-4354be2f5622 | dictionary-learning-under-symmetries-via | 2305.19557 | null | https://arxiv.org/abs/2305.19557v1 | https://arxiv.org/pdf/2305.19557v1.pdf | Dictionary Learning under Symmetries via Group Representations | The dictionary learning problem can be viewed as a data-driven process to learn a suitable transformation so that data is sparsely represented directly from example data. In this paper, we examine the problem of learning a dictionary that is invariant under a pre-specified group of transformations. Natural settings inc... | ['Brendan K. Y. Tan', 'Zhuohang Feng', 'Yong Sheng Soh', 'Aaron Y. R. Low', 'Subhroshekhar Ghosh'] | 2023-05-31 | null | null | null | null | ['pose-estimation', 'object-tracking', 'multi-object-tracking', 'dimensionality-reduction'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [ 3.50043982e-01 2.37011716e-01 -1.61582083e-01 3.74929160e-02
-4.03407454e-01 -6.00167096e-01 8.29706073e-01 -1.36980057e-01
-2.86287636e-01 4.51090395e-01 2.62673825e-01 -1.74855635e-01
-4.54758912e-01 -6.39064312e-01 -6.31235957e-01 -1.34776688e+00
-4.32112552e-02 6.35774136e-01 -3.34434420e-01 -5.72929919... | [7.162046909332275, 4.5020575523376465] |
ab057473-cd92-4caa-b9e0-a1c47af47e92 | high-resolution-synthetic-rgb-d-datasets-for | 2305.01732 | null | https://arxiv.org/abs/2305.01732v1 | https://arxiv.org/pdf/2305.01732v1.pdf | High-Resolution Synthetic RGB-D Datasets for Monocular Depth Estimation | Accurate depth maps are essential in various applications, such as autonomous driving, scene reconstruction, point-cloud creation, etc. However, monocular-depth estimation (MDE) algorithms often fail to provide enough texture & sharpness, and also are inconsistent for homogeneous scenes. These algorithms mostly use CNN... | ['Sunil Jaiswal', 'Philipp Slusallek', 'Klaus Illgner-Fehns', 'Noshaba Cheema', 'Aakash Rajpal'] | 2023-05-02 | null | null | null | null | ['monocular-depth-estimation'] | ['computer-vision'] | [ 3.55430514e-01 -2.25846380e-01 2.30775386e-01 -5.61148763e-01
-8.20730329e-01 -1.35646030e-01 5.28363347e-01 -2.51282096e-01
-1.95206195e-01 8.42010677e-01 -9.92644504e-02 4.08716546e-03
1.12397932e-01 -1.35799778e+00 -9.18698013e-01 -6.29938900e-01
3.61502618e-01 4.81248379e-01 4.62127000e-01 -1.36964679... | [8.93803596496582, -2.5309174060821533] |
6566d850-0edd-461a-8f40-672ed04cfe63 | iterative-self-transfer-learning-a-general | 2306.08700 | null | https://arxiv.org/abs/2306.08700v1 | https://arxiv.org/pdf/2306.08700v1.pdf | Iterative self-transfer learning: A general methodology for response time-history prediction based on small dataset | There are numerous advantages of deep neural network surrogate modeling for response time-history prediction. However, due to the high cost of refined numerical simulations and actual experiments, the lack of data has become an unavoidable bottleneck in practical applications. An iterative self-transfer learningmethod ... | ['Yuli Huang', 'Yifan Fei', 'Xinzheng Lu', 'Yongjia Xu'] | 2023-06-14 | null | null | null | null | ['pseudo-label'] | ['miscellaneous'] | [-1.47079732e-02 -2.88547009e-01 -3.95458072e-01 -2.70341873e-01
-6.65109754e-01 2.02234790e-01 3.45956296e-01 -1.89830706e-01
-2.10435227e-01 1.15767181e+00 -5.08975565e-01 -5.24511755e-01
-3.22301239e-01 -7.09934473e-01 -6.85646117e-01 -1.09308493e+00
7.94010609e-02 4.78880376e-01 8.69955346e-02 -1.21125452... | [6.5574049949646, 3.2697370052337646] |
e4773412-12c4-49da-a284-052a0c944386 | a-bert-based-dual-embedding-model-for-chinese | 2011.02378 | null | https://arxiv.org/abs/2011.02378v1 | https://arxiv.org/pdf/2011.02378v1.pdf | A BERT-based Dual Embedding Model for Chinese Idiom Prediction | Chinese idioms are special fixed phrases usually derived from ancient stories, whose meanings are oftentimes highly idiomatic and non-compositional. The Chinese idiom prediction task is to select the correct idiom from a set of candidate idioms given a context with a blank. We propose a BERT-based dual embedding model ... | ['Jing Jiang', 'Minghuan Tan'] | 2020-11-04 | null | https://aclanthology.org/2020.coling-main.113 | https://aclanthology.org/2020.coling-main.113.pdf | coling-2020-8 | ['cloze-test'] | ['natural-language-processing'] | [ 7.04302862e-02 -1.14908449e-01 -5.44972718e-01 -4.31638718e-01
-5.75939596e-01 -7.66534150e-01 7.48104155e-01 -1.19613759e-01
-2.74816126e-01 2.16333821e-01 8.74533713e-01 -3.97954494e-01
1.72704346e-02 -7.92599022e-01 -1.25510260e-01 -5.71212053e-01
1.27161071e-01 8.01761150e-01 1.93587109e-01 -4.90492791... | [10.840168952941895, 9.444941520690918] |
d8c01fc9-e233-4574-ac64-06644ab51169 | caption-anything-interactive-image | 2305.02677 | null | https://arxiv.org/abs/2305.02677v3 | https://arxiv.org/pdf/2305.02677v3.pdf | Caption Anything: Interactive Image Description with Diverse Multimodal Controls | Controllable image captioning is an emerging multimodal topic that aims to describe the image with natural language following human purpose, $\textit{e.g.}$, looking at the specified regions or telling in a particular text style. State-of-the-art methods are trained on annotated pairs of input controls and output capti... | ['Shanshan Zhao', 'Mingqi Gao', 'Zhe Li', 'Yunlong Tang', 'Hao Zheng', 'Junjie Fei', 'Jinrui Zhang', 'Teng Wang'] | 2023-05-04 | null | null | null | null | ['controllable-image-captioning', 'instruction-following'] | ['computer-vision', 'natural-language-processing'] | [ 2.27490515e-01 2.78641939e-01 -5.93792796e-01 -4.87699926e-01
-8.20374072e-01 -9.29633498e-01 7.86863148e-01 1.60268042e-02
-1.38969675e-01 4.90393162e-01 5.71839571e-01 -5.75032651e-01
3.99710834e-01 -1.11112162e-01 -9.78659749e-01 -2.61364043e-01
5.37055850e-01 4.29356188e-01 -1.20339617e-01 -5.82658827... | [10.939449310302734, 1.562026858329773] |
825ea851-0ea1-426a-b486-cd4af537a7a1 | a-survey-on-graph-classification-and-link | 2307.00865 | null | https://arxiv.org/abs/2307.00865v1 | https://arxiv.org/pdf/2307.00865v1.pdf | A Survey on Graph Classification and Link Prediction based on GNN | Traditional convolutional neural networks are limited to handling Euclidean space data, overlooking the vast realm of real-life scenarios represented as graph data, including transportation networks, social networks, and reference networks. The pivotal step in transferring convolutional neural networks to graph data an... | ['Quan Wen', 'Juan Chen', 'Xingyu Liu'] | 2023-07-03 | null | null | null | null | ['node-classification', 'link-prediction', 'graph-classification'] | ['graphs', 'graphs', 'graphs'] | [-1.03549756e-01 5.16230464e-01 -2.17619970e-01 -1.70421749e-01
6.36699498e-01 -2.37334073e-01 3.73669803e-01 3.92829567e-01
-1.89870477e-01 3.06839496e-01 4.69025001e-02 -8.55142534e-01
-3.46855760e-01 -1.53675604e+00 -5.02495050e-01 -2.57816255e-01
-7.05652833e-01 2.19379678e-01 -5.05832620e-02 -3.80438507... | [7.054903984069824, 6.294227123260498] |
84612906-7c57-4b11-8333-01011d6cbf7e | boosting-language-models-reasoning-with-chain | 2306.06427 | null | https://arxiv.org/abs/2306.06427v1 | https://arxiv.org/pdf/2306.06427v1.pdf | Boosting Language Models Reasoning with Chain-of-Knowledge Prompting | Recently, Chain-of-Thought (CoT) prompting has delivered success on complex reasoning tasks, which aims at designing a simple prompt like ``Let's think step by step'' or multiple in-context exemplars with well-designed rationales to elicit Large Language Models (LLMs) to generate intermediate reasoning steps. However, ... | ['Ming Gao', 'Xiang Li', 'Nuo Chen', 'Qiushi Sun', 'Jianing Wang'] | 2023-06-10 | null | null | null | null | ['arithmetic-reasoning'] | ['reasoning'] | [ 1.66652799e-01 6.08411729e-01 1.79193646e-01 -4.68840539e-01
-3.97993475e-01 -4.96907413e-01 6.67068183e-01 1.21649183e-01
-5.86128421e-03 5.99839211e-01 4.86611009e-01 -6.13950431e-01
-2.19520777e-01 -8.41227829e-01 -6.52096450e-01 -2.85891950e-01
5.97424507e-01 2.01700851e-01 1.38687551e-01 -5.25835574... | [9.601309776306152, 7.513340473175049] |
6036b831-0a6c-4924-b9e8-99f5204257c3 | structure-amplification-on-multi-layer | 2108.00127 | null | https://arxiv.org/abs/2108.00127v1 | https://arxiv.org/pdf/2108.00127v1.pdf | Structure Amplification on Multi-layer Stochastic Block Models | Much of the complexity of social, biological, and engineered systems arises from a network of complex interactions connecting many basic components. Network analysis tools have been successful at uncovering latent structure termed communities in such networks. However, some of the most interesting structure can be diff... | ['John E. Hopcroft', 'Bart Selman', 'Jialu Bao', 'Kun He', 'Xiaodong Xin'] | 2021-07-31 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 4.80403185e-01 7.57124364e-01 -1.15551151e-01 4.45163250e-01
2.36015752e-01 -9.53464448e-01 1.74615219e-01 -9.11191665e-03
6.56292319e-01 7.21787274e-01 3.88159335e-01 -5.81184566e-01
-4.12361026e-01 -8.47656250e-01 -7.70917237e-01 -9.92192030e-01
-9.19978321e-01 1.14439785e-01 4.48429406e-01 -1.02537856... | [6.929213523864746, 5.230286121368408] |
972371ac-4fa0-44d0-a0cd-91d22e872c85 | a-new-ensemble-learning-framework-for-3d | 1812.03945 | null | http://arxiv.org/abs/1812.03945v1 | http://arxiv.org/pdf/1812.03945v1.pdf | A New Ensemble Learning Framework for 3D Biomedical Image Segmentation | 3D image segmentation plays an important role in biomedical image analysis.
Many 2D and 3D deep learning models have achieved state-of-the-art segmentation
performance on 3D biomedical image datasets. Yet, 2D and 3D models have their
own strengths and weaknesses, and by unifying them together, one may be able to
achiev... | ['Danny Z. Chen', 'Peixian Liang', 'Yizhe Zhang', 'Lin Yang', 'Zhuo Zhao', 'Hao Zheng', 'Chaoli Wang'] | 2018-12-10 | null | null | null | null | ['3d-medical-imaging-segmentation'] | ['medical'] | [ 1.54375374e-01 7.86584243e-02 -2.11784586e-01 -5.04503012e-01
-8.37558389e-01 -3.09215486e-01 2.31691346e-01 -4.61903214e-03
-6.95968151e-01 4.58543479e-01 -1.72179744e-01 -4.82136577e-01
1.72043785e-01 -5.93118966e-01 -6.66539133e-01 -7.93065310e-01
5.27980328e-02 4.03299153e-01 3.21396917e-01 -5.93699664... | [14.625872611999512, -2.252593755722046] |
02a322b4-0322-47ef-8a68-8766163f2965 | new-methods-metrics-for-lfqa-tasks | 2112.13432 | null | https://arxiv.org/abs/2112.13432v1 | https://arxiv.org/pdf/2112.13432v1.pdf | New Methods & Metrics for LFQA tasks | Long-form question answering (LFQA) tasks require retrieving the documents pertinent to a query, using them to form a paragraph-length answer. Despite considerable progress in LFQA modeling, fundamental issues impede its progress: i) train/validation/test dataset overlap, ii) absence of automatic metrics and iii) gener... | ['Prasanna Kumar', 'Pablo Bertorello', 'Vladimir Blagojevic', 'Suchismit Mahapatra'] | 2021-12-26 | null | null | null | null | ['long-form-question-answering'] | ['natural-language-processing'] | [ 3.02572072e-01 3.52584213e-01 -1.43031821e-01 -4.44941431e-01
-1.73760712e+00 -1.07072437e+00 7.84031332e-01 3.28570902e-01
-3.34093094e-01 1.38653636e+00 5.85567176e-01 -8.40324521e-01
-3.37081999e-01 -8.08170319e-01 -6.49686694e-01 6.68163523e-02
2.64143199e-01 8.53566170e-01 3.05077970e-01 -4.28340822... | [11.356440544128418, 8.075859069824219] |
aa6695e3-e77d-405b-841f-e395c717857f | inexpensive-cost-optimized-measurement | 1705.09879 | null | http://arxiv.org/abs/1705.09879v1 | http://arxiv.org/pdf/1705.09879v1.pdf | Inexpensive Cost-Optimized Measurement Proposal for Sequential Model-Based Diagnosis | In this work we present strategies for (optimal) measurement selection in
model-based sequential diagnosis. In particular, assuming a set of leading
diagnoses being given, we show how queries (sets of measurements) can be
computed and optimized along two dimensions: expected number of queries and
cost per query. By mea... | ['Wolfgang Schmid', 'Konstantin Schekotihin', 'Patrick Rodler'] | 2017-05-28 | null | null | null | null | ['sequential-diagnosis'] | ['medical'] | [ 4.43559945e-01 5.87413609e-01 -6.51868209e-02 -3.36047560e-01
-1.12548053e+00 -4.62382048e-01 2.93072283e-01 6.55783534e-01
-3.71812195e-01 6.44660354e-01 -3.72318923e-01 -3.68210196e-01
-8.86863351e-01 -9.83746529e-01 -3.86786580e-01 -6.18544877e-01
-1.80622488e-01 1.38953185e+00 6.47477984e-01 1.64203793... | [5.461527347564697, 2.7795939445495605] |
621942d2-a948-42f9-9af5-063edad2aefc | auxiliary-learning-induced-graph | null | null | https://openreview.net/forum?id=9QffERDO_rJ | https://openreview.net/pdf?id=9QffERDO_rJ | Auxiliary learning induced graph convolutional networks | In this article, we propose a novel auxiliary learning induced graph convolutional network in a multi-task fashion. Specifically, both the link prediction and pseudo label generation are used as two auxiliary tasks to complement the primary task of node classification. Those two auxiliary tasks are jointly trained with... | ['Yongjian Wu', 'Feiyue Huang', 'Baochang Zhang', 'Ling Shao', 'Rongrong Ji', 'Taisong Jin', 'Gengchen Duan'] | 2021-05-21 | null | null | null | neurips-2021-12 | ['auxiliary-learning'] | ['methodology'] | [ 2.73369640e-01 5.25142491e-01 -8.04119170e-01 -9.69640017e-02
-4.77643430e-01 -2.30373308e-01 8.88556480e-01 3.87258351e-01
-1.76847294e-01 9.49133992e-01 -1.02482982e-01 -7.21504748e-01
-6.41471818e-02 -8.16863060e-01 -6.69911802e-01 -6.67667449e-01
-3.14359009e-01 5.84118783e-01 2.15815023e-01 -4.13704999... | [7.298776626586914, 6.318594455718994] |
881ae911-f2e4-41ba-abe1-09eee44bb496 | a-study-on-the-impact-of-face-image-quality | 2307.02679 | null | https://arxiv.org/abs/2307.02679v1 | https://arxiv.org/pdf/2307.02679v1.pdf | A Study on the Impact of Face Image Quality on Face Recognition in the Wild | Deep learning has received increasing interests in face recognition recently. Large quantities of deep learning methods have been proposed to handle various problems appeared in face recognition. Quite a lot deep methods claimed that they have gained or even surpassed human-level face verification performance in certai... | ['Na Zhang'] | 2023-07-05 | null | null | null | null | ['face-image-quality', 'face-recognition', 'face-verification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-3.11635107e-01 -4.54541355e-01 1.38986841e-01 -8.66302431e-01
-4.38444167e-01 -7.97280520e-02 4.25904304e-01 -7.45520830e-01
-2.60545045e-01 4.19848889e-01 -3.41541618e-02 -1.23122133e-01
-3.43236923e-01 -8.25784385e-01 -5.57509363e-01 -8.16209376e-01
1.23417951e-01 2.06220806e-01 -5.39174378e-01 -4.48223025... | [13.128747940063477, 0.7689566612243652] |
affc66e9-3f95-4531-99b3-1e4fac7c690f | fast-capsnet-for-lung-cancer-screening | 1806.07416 | null | http://arxiv.org/abs/1806.07416v1 | http://arxiv.org/pdf/1806.07416v1.pdf | Fast CapsNet for Lung Cancer Screening | Lung cancer is the leading cause of cancer-related deaths in the past several
years. A major challenge in lung cancer screening is the detection of lung
nodules from computed tomography (CT) scans. State-of-the-art approaches in
automated lung nodule classification use deep convolutional neural networks
(CNNs). However... | ['Aryan Mobiny', 'Hien Van Nguyen'] | 2018-06-19 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [ 7.18269050e-02 2.27420971e-01 -6.46155894e-01 -1.09663531e-01
-7.86718011e-01 -3.48432571e-01 7.65470788e-02 -3.25813711e-01
-3.40935647e-01 6.13238573e-01 -1.72409844e-02 -8.21023226e-01
1.72603354e-01 -1.08860624e+00 -6.99493647e-01 -3.19836348e-01
-3.92710231e-02 3.76266152e-01 7.38449574e-01 3.16301197... | [15.358817100524902, -2.1904876232147217] |
7488f0ab-663a-4c0c-a149-7700050cdd41 | microbert-effective-training-of-low-resource | 2212.12510 | null | https://arxiv.org/abs/2212.12510v2 | https://arxiv.org/pdf/2212.12510v2.pdf | MicroBERT: Effective Training of Low-resource Monolingual BERTs through Parameter Reduction and Multitask Learning | Transformer language models (TLMs) are critical for most NLP tasks, but they are difficult to create for low-resource languages because of how much pretraining data they require. In this work, we investigate two techniques for training monolingual TLMs in a low-resource setting: greatly reducing TLM size, and complemen... | ['Amir Zeldes', 'Luke Gessler'] | 2022-12-23 | null | null | null | null | ['dependency-parsing', 'part-of-speech-tagging'] | ['natural-language-processing', 'natural-language-processing'] | [-1.79176152e-01 2.81921625e-01 -4.06352878e-01 -5.49815834e-01
-1.70615828e+00 -9.56500649e-01 6.05750561e-01 2.81963408e-01
-7.47651041e-01 8.12390029e-01 3.96399885e-01 -9.32013571e-01
6.13689840e-01 -3.46490860e-01 -8.48141491e-01 -2.45751590e-01
2.68647969e-01 6.95890069e-01 9.49601978e-02 -2.05653012... | [10.604690551757812, 9.945411682128906] |
11201c92-6afa-4fe4-ac45-1593397e74a9 | a-multi-task-architecture-on-relevance-based | 1906.06849 | null | https://arxiv.org/abs/1906.06849v1 | https://arxiv.org/pdf/1906.06849v1.pdf | A Multi-Task Architecture on Relevance-based Neural Query Translation | We describe a multi-task learning approach to train a Neural Machine Translation (NMT) model with a Relevance-based Auxiliary Task (RAT) for search query translation. The translation process for Cross-lingual Information Retrieval (CLIR) task is usually treated as a black box and it is performed as an independent step.... | ['James Allan', 'Sheikh Muhammad Sarwar', 'Hamed Bonab'] | 2019-06-17 | a-multi-task-architecture-on-relevance-based-1 | https://aclanthology.org/P19-1639 | https://aclanthology.org/P19-1639.pdf | acl-2019-7 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [ 0.34814474 -0.00827679 -0.8019403 -0.217744 -2.1594117 -0.7046905
0.9279424 0.01156545 -0.77692384 0.84404355 0.5480962 -0.7285159
0.23137264 -0.22573134 -1.1188165 -0.39131266 0.70899814 1.4351768
-0.21988598 -0.7036958 0.0273435 -0.1026575 -0.37028828 0.8080456
0.92471164 0.6026642 0.1983... | [11.561169624328613, 10.02457046508789] |
88b74c80-7635-462f-86cf-9245d67923c1 | dreamdiffusion-generating-high-quality-images | 2306.16934 | null | https://arxiv.org/abs/2306.16934v2 | https://arxiv.org/pdf/2306.16934v2.pdf | DreamDiffusion: Generating High-Quality Images from Brain EEG Signals | This paper introduces DreamDiffusion, a novel method for generating high-quality images directly from brain electroencephalogram (EEG) signals, without the need to translate thoughts into text. DreamDiffusion leverages pre-trained text-to-image models and employs temporal masked signal modeling to pre-train the EEG enc... | ['Yan-Pei Cao', 'Ying Shan', 'Chun Yuan', 'Yixiao Ge', 'Xintao Wang', 'Yunpeng Bai'] | 2023-06-29 | null | null | null | null | ['image-generation', 'eeg', 'eeg'] | ['computer-vision', 'methodology', 'time-series'] | [ 2.53048778e-01 8.75739828e-02 3.06028754e-01 -6.63004756e-01
-7.86666095e-01 -3.40722591e-01 6.75327778e-01 -1.40498698e-01
-4.46752042e-01 8.29436183e-01 4.14542735e-01 -9.80293006e-02
1.34082392e-01 -3.90325904e-01 -8.41879904e-01 -7.77099609e-01
1.02766842e-01 -1.66964963e-01 -5.16472757e-01 1.10301554... | [10.833364486694336, 2.5213890075683594] |
48dd7d9a-7d74-4f1c-8a57-bfbe761070f7 | pose-guided-image-generation-from-misaligned | 2202.00843 | null | https://arxiv.org/abs/2202.00843v1 | https://arxiv.org/pdf/2202.00843v1.pdf | Pose Guided Image Generation from Misaligned Sources via Residual Flow Based Correction | Generating new images with desired properties (e.g. new view/poses) from source images has been enthusiastically pursued recently, due to its wide range of potential applications. One way to ensure high-quality generation is to use multiple sources with complementary information such as different views of the same obje... | ['Kun Zhou', 'Yin Yang', 'Tianjia Shao', 'He Wang', 'Jiawei Lu'] | 2022-02-02 | null | null | null | null | ['pose-guided-image-generation'] | ['computer-vision'] | [ 3.30086827e-01 -1.68719411e-01 9.56085324e-02 -3.95468056e-01
-2.27320313e-01 -6.39991522e-01 7.49093175e-01 -4.38552350e-01
-1.71826761e-02 7.22870946e-01 3.65784615e-01 4.33820873e-01
6.42495081e-02 -6.60393238e-01 -6.15007043e-01 -7.51244664e-01
4.19605613e-01 1.78297997e-01 3.46392632e-01 -2.75346398... | [9.30114459991455, -2.6328470706939697] |
c7e92911-3937-45c3-b020-6638e9abc5ab | diverse-single-image-generation-with | 2102.04780 | null | https://arxiv.org/abs/2102.04780v4 | https://arxiv.org/pdf/2102.04780v4.pdf | Diverse Single Image Generation with Controllable Global Structure | Image generation from a single image using generative adversarial networks is quite interesting due to the realism of generated images. However, recent approaches need improvement for such realistic and diverse image generation, when the global context of the image is important such as in face, animal, and architectura... | ['Ranga Rodrigo', 'Chamira Edussooriya', 'Sutharsan Mahendren'] | 2021-02-09 | null | null | null | null | ['single-image-generation'] | ['computer-vision'] | [ 2.83774942e-01 2.04694927e-01 4.17081326e-01 -5.19719832e-02
-3.78207803e-01 -4.46462721e-01 7.13350594e-01 -2.13292390e-01
-2.40865126e-01 1.18787754e+00 4.52425815e-02 2.18337655e-01
1.29339948e-01 -1.08933473e+00 -9.07460153e-01 -9.30785179e-01
5.05400002e-02 2.69505531e-01 2.86035687e-01 -3.28732997... | [11.697854042053223, -0.47702357172966003] |
745f536f-9804-4e1d-afb5-5b7be270ba92 | fasterx-real-time-object-detection-based-on | 2209.03157 | null | https://arxiv.org/abs/2209.03157v1 | https://arxiv.org/pdf/2209.03157v1.pdf | FasterX: Real-Time Object Detection Based on Edge GPUs for UAV Applications | Real-time object detection on Unmanned Aerial Vehicles (UAVs) is a challenging issue due to the limited computing resources of edge GPU devices as Internet of Things (IoT) nodes. To solve this problem, in this paper, we propose a novel lightweight deep learning architectures named FasterX based on YOLOX model for real-... | ['JunYi', 'Huan Luo', 'Yiwen Long', 'Rui Hu', 'Xuanlin Min', 'Wei Zhou'] | 2022-09-07 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-3.62848252e-01 -4.28441226e-01 1.03982575e-01 -2.56205529e-01
2.83505261e-01 -3.19270998e-01 -4.32796823e-03 -2.30483264e-01
-6.36053205e-01 3.63140851e-01 -3.70010346e-01 -3.38807046e-01
8.18803236e-02 -9.89721835e-01 -6.78924799e-01 -6.52962744e-01
-1.72382742e-02 -1.91480592e-01 6.12778068e-01 1.36092203... | [8.816258430480957, -0.3906027674674988] |
4345cf6e-2400-4923-81ac-6ecc60c4535d | some-strategies-to-capture-karaka-yogyata | 2201.01700 | null | https://arxiv.org/abs/2201.01700v1 | https://arxiv.org/pdf/2201.01700v1.pdf | Some Strategies to Capture Karaka-Yogyata with Special Reference to apadana | In today's digital world language technology has gained importance. Several softwares, have been developed and are available in the field of computational linguistics. Such tools play a crucial role in making classical language texts easily accessible. Some Indian philosophical schools have contributed towards various ... | ['Malhar Kulkarni', 'Diptesh Kanojia', 'Swaraja Salaskar'] | 2022-01-05 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-7.20463321e-02 3.14089417e-01 1.88971728e-01 -2.12219834e-01
-6.10992722e-02 -6.25873208e-01 7.13855624e-01 5.10513425e-01
-5.04785240e-01 7.15303600e-01 2.05920741e-01 -6.64475918e-01
-6.34128928e-01 -8.27792168e-01 -5.37572801e-03 -3.84895027e-01
3.36115211e-01 5.09186149e-01 3.92585009e-01 -8.26255679... | [9.99087905883789, 9.286465644836426] |
17f06fe6-a732-4839-bb72-dd16e4c13ade | adapting-multi-lingual-asr-models-for | 2305.18747 | null | https://arxiv.org/abs/2305.18747v1 | https://arxiv.org/pdf/2305.18747v1.pdf | Adapting Multi-Lingual ASR Models for Handling Multiple Talkers | State-of-the-art large-scale universal speech models (USMs) show a decent automatic speech recognition (ASR) performance across multiple domains and languages. However, it remains a challenge for these models to recognize overlapped speech, which is often seen in meeting conversations. We propose an approach to adapt U... | ['Michael Zeng', 'Yanmin Qian', 'Takuya Yoshioka', 'Dongmei Wang', 'Naoyuki Kanda', 'Zhuo Chen', 'Yao Qian', 'Chenda Li'] | 2023-05-30 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [-1.77920341e-01 5.66874072e-02 -1.76419108e-03 -6.53687477e-01
-1.37327492e+00 -5.18552959e-01 6.38149083e-01 -1.34504855e-01
-3.35531533e-01 4.38322395e-01 2.58570790e-01 -5.92495501e-01
5.32901645e-01 -2.44911052e-02 -7.01900244e-01 -4.43031818e-01
-9.95648280e-02 7.81668007e-01 2.73151785e-01 -3.56436700... | [14.547231674194336, 6.522904872894287] |
34881ecc-ce83-45d8-8fa5-458e4c156891 | exploring-attention-mechanisms-for-multimodal | 2306.07115 | null | https://arxiv.org/abs/2306.07115v1 | https://arxiv.org/pdf/2306.07115v1.pdf | Exploring Attention Mechanisms for Multimodal Emotion Recognition in an Emergency Call Center Corpus | The emotion detection technology to enhance human decision-making is an important research issue for real-world applications, but real-life emotion datasets are relatively rare and small. The experiments conducted in this paper use the CEMO, which was collected in a French emergency call center. Two pre-trained models ... | ['Laurence Devillers', 'Lori Lamel', 'Théo Deschamps-Berger'] | 2023-06-12 | null | null | null | null | ['multimodal-emotion-recognition', 'speech-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech', 'speech'] | [ 2.43469626e-01 2.40218360e-02 2.44351611e-01 -5.01711905e-01
-1.08951199e+00 -3.01685967e-02 5.82278609e-01 3.44761848e-01
-6.16782367e-01 8.07714283e-01 5.87703109e-01 9.46075916e-02
-3.21293138e-02 -3.34521502e-01 -1.81991875e-01 -7.07565546e-01
9.75613892e-02 2.50480354e-01 -4.02117610e-01 -5.47241390... | [13.270339012145996, 5.364918231964111] |
75d697eb-d464-4aef-8417-120575fe1130 | predicting-the-next-action-by-modeling-the | 2209.05044 | null | https://arxiv.org/abs/2209.05044v4 | https://arxiv.org/pdf/2209.05044v4.pdf | Predicting the Next Action by Modeling the Abstract Goal | The problem of anticipating human actions is an inherently uncertain one. However, we can reduce this uncertainty if we have a sense of the goal that the actor is trying to achieve. Here, we present an action anticipation model that leverages goal information for the purpose of reducing the uncertainty in future predic... | ['Basura Fernando', 'Debaditya Roy'] | 2022-09-12 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 1.62893429e-01 2.69963771e-01 -9.47664306e-02 -4.63554978e-01
-9.64133739e-01 -4.47445840e-01 7.39166617e-01 -2.98750401e-01
-4.47148502e-01 5.99050641e-01 5.62518120e-01 7.12487176e-02
-9.12116989e-02 -2.90501833e-01 -8.68903339e-01 -5.92330754e-01
1.67667102e-02 2.76883125e-01 -1.26855865e-01 -2.36708567... | [7.972569465637207, 0.5973602533340454] |
02ef6d85-aac6-40af-8e54-4bafb47fc946 | convolutional-neural-network-hyperparameters | null | null | https://www.semanticscholar.org/paper/Convolutional-Neural-Network-Hyperparameters-for-Vulpe-Grigora%C5%9Fi-Grigore/fe344427eafecc60a1ba29beb87a46e91b7c1420#related-papers | https://ieeexplore.ieee.org/document/9425073 | Convolutional Neural Network Hyperparameters optimization for Facial Emotion Recognition | This paper presents a method of optimizing the hyperparameters of a convolutional neural network in order to increase accuracy in the context of facial emotion recognition. The optimal hyperparameters of the network were determined by generating and training models based on Random Search algorithm applied on a search s... | ['Ovidiu Grigore', 'Adrian Vulpe-Grigorași'] | 2021-03-25 | null | null | null | 12th-international-symposium-on-advanced | ['facial-emotion-recognition'] | ['computer-vision'] | [ 1.12446569e-01 2.91960686e-01 1.07843064e-01 -7.71536171e-01
-2.75370657e-01 -1.45621762e-01 2.46147424e-01 -4.88296688e-01
-8.85254741e-01 5.89145541e-01 -2.48197153e-01 2.32561320e-01
-2.21382841e-01 -5.03659964e-01 -4.80345428e-01 -7.05003202e-01
-2.70964831e-01 1.06984787e-01 -4.06156927e-01 -2.20825989... | [13.52357292175293, 1.7722920179367065] |
04f12b82-9601-4602-a8c1-ea7d220c6ff6 | horizonnet-learning-room-layout-with-1d | 1901.03861 | null | http://arxiv.org/abs/1901.03861v2 | http://arxiv.org/pdf/1901.03861v2.pdf | HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentation | We present a new approach to the problem of estimating the 3D room layout
from a single panoramic image. We represent room layout as three 1D vectors
that encode, at each image column, the boundary positions of floor-wall and
ceiling-wall, and the existence of wall-wall boundary. The proposed network,
HorizonNet, train... | ['Hwann-Tzong Chen', 'Chi-Wei Hsiao', 'Min Sun', 'Cheng Sun'] | 2019-01-12 | horizonnet-learning-room-layout-with-1d-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_HorizonNet_Learning_Room_Layout_With_1D_Representation_and_Pano_Stretch_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Sun_HorizonNet_Learning_Room_Layout_With_1D_Representation_and_Pano_Stretch_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-room-layouts-from-a-single-rgb-panorama'] | ['computer-vision'] | [ 4.61046815e-01 1.68330848e-01 5.80763482e-02 -4.81795043e-01
-6.65060103e-01 -6.79402053e-01 6.47494912e-01 2.99035132e-01
-2.51319468e-01 3.54391694e-01 5.35897791e-01 -5.43086946e-01
-2.30312437e-01 -8.25783908e-01 -9.96357679e-01 -4.20641035e-01
-1.41417369e-01 4.88795400e-01 -6.75460398e-02 -1.34494543... | [8.712353706359863, -2.823868751525879] |
2141143e-f91c-49c6-971c-9fd8620dcf93 | stdan-deformable-attention-network-for-space | 2203.06841 | null | https://arxiv.org/abs/2203.06841v2 | https://arxiv.org/pdf/2203.06841v2.pdf | STDAN: Deformable Attention Network for Space-Time Video Super-Resolution | The target of space-time video super-resolution (STVSR) is to increase the spatial-temporal resolution of low-resolution (LR) and low frame rate (LFR) videos. Recent approaches based on deep learning have made significant improvements, but most of them only use two adjacent frames, that is, short-term features, to synt... | ['Qingmin Liao', 'Wenming Yang', 'Yapeng Tian', 'Xiaoyu Xiang', 'Hai Wang'] | 2022-03-14 | null | null | null | null | ['space-time-video-super-resolution', 'video-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 1.64195925e-01 -1.91149175e-01 -3.14683914e-01 -2.99551308e-01
-8.01749170e-01 -9.53377262e-02 4.22437876e-01 -7.58506000e-01
-2.78694272e-01 8.15496981e-01 6.59467816e-01 -2.84911357e-02
5.47031425e-02 -6.85788214e-01 -9.08572912e-01 -6.34082913e-01
2.20638558e-01 -3.39108348e-01 3.87920558e-01 -2.18509808... | [11.012740135192871, -1.8241175413131714] |
32cf65f0-ee99-4fcb-9093-94aa508f013d | transformer-based-program-synthesis-for-low | 2205.09246 | null | https://arxiv.org/abs/2205.09246v1 | https://arxiv.org/pdf/2205.09246v1.pdf | Transformer-based Program Synthesis for Low-Data Environments | Recent advancements in large pre-trained transformer models (GPT2/3, T5) have found use in program synthesis to generate programs that satisfy a set of input/output examples. However, these models perform poorly on long-horizon and low-data tasks, and often don't seem to understand the semantics of the languages they g... | ['Jack Roper'] | 2022-05-18 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 3.10485095e-01 4.54498529e-01 -4.77954447e-01 -4.61221397e-01
-8.28077197e-01 -7.50585854e-01 5.52125812e-01 3.74578387e-01
2.75045693e-01 4.30907309e-01 1.99668422e-01 -9.09567952e-01
3.44476193e-01 -1.40332818e+00 -1.07820654e+00 2.23712906e-01
3.77395167e-03 5.91281831e-01 3.93365175e-01 -4.48966354... | [8.194644927978516, 7.468592166900635] |
c646874c-440b-44b2-9cfa-e5e9a693de70 | grounded-image-captioning-in-top-down-view | 2306.07490 | null | https://arxiv.org/abs/2306.07490v2 | https://arxiv.org/pdf/2306.07490v2.pdf | Top-Down Viewing for Weakly Supervised Grounded Image Captioning | Weakly supervised grounded image captioning (WSGIC) aims to generate the caption and ground (localize) predicted object words in the input image without using bounding box supervision. Recent two-stage solutions mostly apply a bottom-up pipeline: (1) first apply an off-the-shelf object detector to encode the input imag... | ['Kim-Hui Yap', 'Suchen Wang', 'Chen Cai'] | 2023-06-13 | null | null | null | null | ['image-captioning'] | ['computer-vision'] | [ 3.69578898e-01 7.22593486e-01 -2.37962529e-01 -5.42340994e-01
-1.18394983e+00 -4.48684454e-01 3.49861592e-01 9.35325846e-02
-1.77912846e-01 4.51085925e-01 2.11538300e-01 -9.46119726e-02
5.42766929e-01 -8.62812400e-01 -1.36881888e+00 -5.67817688e-01
5.14367163e-01 7.15238929e-01 3.33613783e-01 -2.50801325... | [10.481401443481445, 1.4133226871490479] |
2541dd3a-edab-494d-a3b4-a35593a49172 | simplicial-complex-based-point-correspondence | 2007.02381 | null | https://arxiv.org/abs/2007.02381v3 | https://arxiv.org/pdf/2007.02381v3.pdf | Simplicial Complex based Point Correspondence between Images warped onto Manifolds | Recent increase in the availability of warped images projected onto a manifold (e.g., omnidirectional spherical images), coupled with the success of higher-order assignment methods, has sparked an interest in the search for improved higher-order matching algorithms on warped images due to projection. Although currently... | ['Charu Sharma', 'Manohar Kaul'] | 2020-07-05 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6515_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123740052.pdf | eccv-2020-8 | ['hypergraph-matching'] | ['graphs'] | [ 6.05399430e-01 1.17298573e-01 1.53612718e-01 -1.29889891e-01
-6.99769497e-01 -8.64933431e-01 6.59714580e-01 -2.77187824e-01
2.80714612e-02 4.01687920e-01 1.30191207e-01 -2.33501121e-02
-4.47503775e-01 -7.76372671e-01 -7.97578573e-01 -5.07220507e-01
4.15492021e-02 6.68430090e-01 1.92799926e-01 -3.96777630... | [8.013882637023926, -2.293696403503418] |
bc0f23e3-9f81-430d-b119-be0a69c65b5c | audiopalm-a-large-language-model-that-can | 2306.12925 | null | https://arxiv.org/abs/2306.12925v1 | https://arxiv.org/pdf/2306.12925v1.pdf | AudioPaLM: A Large Language Model That Can Speak and Listen | We introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models, PaLM-2 [Anil et al., 2023] and AudioLM [Borsos et al., 2022], into a unified multimodal architecture that can process and generate text and speech with applications includ... | ['Christian Frank', 'Lukas Zilka', 'Zhishuai Zhang', 'Yu Zhang', 'Neil Zeghidour', 'Vicky Zayats', 'Yongqiang Wang', 'Jiahui Yu', 'Damien Vincent', 'Mihajlo Velimirović', 'Alexandru Tudor', 'Marco Tagliasacchi', 'Ramanovich', 'Michelle Tadmor', 'Matt Sharifi', 'Johan Schalkwyk', 'Tara Sainath', 'Danny Rozenberg', 'Jame... | 2023-06-22 | null | null | null | null | ['speech-to-text-translation', 'speech-to-speech-translation'] | ['natural-language-processing', 'speech'] | [ 1.74440518e-01 3.38723689e-01 6.13451190e-02 -3.94079268e-01
-1.44934011e+00 -7.87597656e-01 6.82931602e-01 -2.61789203e-01
-1.87428653e-01 3.65464091e-01 6.68318987e-01 -6.50073886e-01
4.45126563e-01 -3.64885300e-01 -7.10839331e-01 -2.38300711e-01
2.79198468e-01 6.78683281e-01 -1.01061255e-01 -5.09953678... | [14.571606636047363, 6.965937614440918] |
4a209ad3-189e-4c45-b8d5-0e0b1b5400b8 | difficulty-aware-distractor-generation-for | null | null | https://aclanthology.org/U19-1021 | https://aclanthology.org/U19-1021.pdf | Difficulty-aware Distractor Generation for Gap-Fill Items | null | ['John Lee', 'Chak Yan Yeung', 'Benjamin Tsou'] | 2019-04-01 | null | null | null | alta-2019-4 | ['distractor-generation'] | ['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... | [-7.237696170806885, 3.719326972961426] |
9059ca66-8acc-411b-8632-231abcf401d1 | bev-dc-bird-s-eye-view-assisted-training-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhou_BEVDC_Birds-Eye_View_Assisted_Training_for_Depth_Completion_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhou_BEVDC_Birds-Eye_View_Assisted_Training_for_Depth_Completion_CVPR_2023_paper.pdf | BEV@DC: Bird's-Eye View Assisted Training for Depth Completion | Depth completion plays a crucial role in autonomous driving, in which cameras and LiDARs are two complementary sensors. Recent approaches attempt to exploit spatial geometric constraints hidden in LiDARs to enhance image-guided depth completion. However, only low efficiency and poor generalization can be achieved. ... | ['Zhen Li', 'Shuguang Cui', 'Gangming Zhao', 'Jin Huang', 'Yuankai Lin', 'Yinghong Liao', 'Xu Yan', 'Wending Zhou'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['depth-completion'] | ['computer-vision'] | [ 7.83761889e-02 2.67123710e-02 -3.08485270e-01 -6.65876150e-01
-8.49169493e-01 -4.01648283e-01 7.19466209e-01 -9.43085998e-02
-5.40471137e-01 4.17728275e-01 -5.80441058e-02 -3.70917618e-01
1.36852726e-01 -1.03253567e+00 -1.04887676e+00 -5.91883540e-01
4.43560690e-01 4.57461029e-01 4.63050514e-01 -2.27025285... | [8.56987190246582, -2.666487216949463] |
07c3a4fe-ec6f-481b-8725-55a34f643253 | hardc-a-novel-ecg-based-heartbeat | 2303.06020 | null | https://arxiv.org/abs/2303.06020v1 | https://arxiv.org/pdf/2303.06020v1.pdf | HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN | In this paper have developed a novel hybrid hierarchical attention-based bidirectional recurrent neural network with dilated CNN (HARDC) method for arrhythmia classification. This solves problems that arise when traditional dilated convolutional neural network (CNN) models disregard the correlation between contexts and... | ['Mohammad Ali Moni', 'Julian M. W. Quinn', 'Pietro Lio', 'Shahadat Uddin', 'Sunjida Sultana', 'Khondokar Fida Hasan', 'Md Shofiqul Islam'] | 2023-03-06 | null | null | null | null | ['heartbeat-classification'] | ['medical'] | [ 3.91031951e-01 -3.79935578e-02 2.85247326e-01 -1.99398622e-01
-1.00613344e+00 -2.51042426e-01 3.39443907e-02 -7.32762506e-03
-2.82175809e-01 7.85372376e-01 1.13830879e-01 -3.07134688e-01
-1.38599232e-01 -6.19226992e-01 -4.32573736e-01 -8.08094859e-01
-1.27402395e-01 -7.93723390e-02 -2.51527637e-01 -1.42211840... | [14.283663749694824, 3.2619521617889404] |
85dbed2f-baf3-4efb-936a-8f3bf974ea74 | distillpose-lightweight-camera-localization | 2108.03819 | null | https://arxiv.org/abs/2108.03819v1 | https://arxiv.org/pdf/2108.03819v1.pdf | DistillPose: Lightweight Camera Localization Using Auxiliary Learning | We propose a lightweight retrieval-based pipeline to predict 6DOF camera poses from RGB images. Our pipeline uses a convolutional neural network (CNN) to encode a query image as a feature vector. A nearest neighbor lookup finds the pose-wise nearest database image. A siamese convolutional neural network regresses the r... | ['Slobodan Ilic', 'Mai Bui', 'Yehya Abouelnaga'] | 2021-08-09 | null | null | null | null | ['camera-localization', 'auxiliary-learning'] | ['computer-vision', 'methodology'] | [ 5.91635667e-02 -2.56396621e-01 -3.99641126e-01 -6.62324727e-01
-1.12206399e+00 -5.56132078e-01 2.65594006e-01 1.15281139e-02
-7.73561239e-01 2.05296323e-01 9.72726122e-02 7.60003105e-02
-6.37594461e-02 -9.70761955e-01 -1.17712843e+00 -3.79798591e-01
1.32405430e-01 4.07341510e-01 3.11628103e-01 1.65677909... | [7.698017120361328, -2.17336368560791] |
dde9cdce-dbcb-49d0-8475-07047b4fecd6 | learning-representations-in-model-free | 1810.10096 | null | http://arxiv.org/abs/1810.10096v3 | http://arxiv.org/pdf/1810.10096v3.pdf | Learning Representations in Model-Free Hierarchical Reinforcement Learning | Common approaches to Reinforcement Learning (RL) are seriously challenged by
large-scale applications involving huge state spaces and sparse delayed reward
feedback. Hierarchical Reinforcement Learning (HRL) methods attempt to address
this scalability issue by learning action selection policies at multiple levels
of te... | ['David C. Noelle', 'Jacob Rafati'] | 2018-10-23 | null | https://openreview.net/forum?id=S1gDCiCqtQ | https://openreview.net/pdf?id=S1gDCiCqtQ | null | ['montezumas-revenge'] | ['playing-games'] | [ 1.47488505e-01 1.66499019e-01 -2.82000005e-01 -3.65075730e-02
-6.93939388e-01 -6.86180353e-01 7.56295264e-01 5.88318348e-01
-7.84517348e-01 1.13946915e+00 9.19646323e-02 1.89107787e-02
-3.28461319e-01 -8.28855515e-01 -8.00344884e-01 -6.09549403e-01
-7.27759957e-01 5.61042249e-01 6.66454852e-01 -5.86875141... | [4.104305744171143, 1.5860481262207031] |
75b10880-274f-4869-818f-4ffc536f6737 | neural-network-pruning-for-real-time-polyp | 2306.13203 | null | https://arxiv.org/abs/2306.13203v1 | https://arxiv.org/pdf/2306.13203v1.pdf | Neural Network Pruning for Real-time Polyp Segmentation | Computer-assisted treatment has emerged as a viable application of medical imaging, owing to the efficacy of deep learning models. Real-time inference speed remains a key requirement for such applications to help medical personnel. Even though there generally exists a trade-off between performance and model size, impre... | ['Binod Bhattarai', 'Bibek Panthi', 'Sudarshan Regmi', 'Pranav Poudel', 'Suman Sapkota'] | 2023-06-22 | null | null | null | null | ['network-pruning'] | ['methodology'] | [ 5.15151322e-01 2.60647118e-01 1.07111134e-01 -4.41112995e-01
-5.58807671e-01 -1.76482782e-01 8.22009668e-02 5.36632895e-01
-8.45631480e-01 5.04743516e-01 -1.91038966e-01 -5.26321352e-01
-3.77202153e-01 -6.45137668e-01 -5.64411759e-01 -6.40509963e-01
-1.31105721e-01 9.36035663e-02 3.64176273e-01 8.40608105... | [8.607951164245605, 3.0934510231018066] |
324d258b-1584-47af-bd05-b291db27c58a | temporal-information-extraction-by-predicting | 1808.09401 | null | http://arxiv.org/abs/1808.09401v1 | http://arxiv.org/pdf/1808.09401v1.pdf | Temporal Information Extraction by Predicting Relative Time-lines | The current leading paradigm for temporal information extraction from text
consists of three phases: (1) recognition of events and temporal expressions,
(2) recognition of temporal relations among them, and (3) time-line
construction from the temporal relations. In contrast to the first two phases,
the last phase, time... | ['Marie-Francine Moens', 'Artuur Leeuwenberg'] | 2018-08-28 | temporal-information-extraction-by-predicting-1 | https://aclanthology.org/D18-1155 | https://aclanthology.org/D18-1155.pdf | emnlp-2018-10 | ['temporal-information-extraction'] | ['natural-language-processing'] | [ 4.35568064e-01 2.24305555e-01 -1.77287504e-01 -3.49229813e-01
-6.08742535e-01 -8.23039174e-01 1.20361340e+00 6.38694227e-01
-4.55549777e-01 5.97638130e-01 4.75515053e-02 -4.43637878e-01
-3.76347363e-01 -6.79574251e-01 -3.90505970e-01 -2.87266344e-01
-5.06618977e-01 3.95503968e-01 6.34684980e-01 -1.45563141... | [9.122038841247559, 9.25343132019043] |
98973367-e4a3-47c3-863f-17266ca77d8e | grounding-counterfactual-explanation-of-image | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kim_Grounding_Counterfactual_Explanation_of_Image_Classifiers_to_Textual_Concept_Space_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kim_Grounding_Counterfactual_Explanation_of_Image_Classifiers_to_Textual_Concept_Space_CVPR_2023_paper.pdf | Grounding Counterfactual Explanation of Image Classifiers to Textual Concept Space | Concept-based explanation aims to provide concise and human-understandable explanations of an image classifier. However, existing concept-based explanation methods typically require a significant amount of manually collected concept-annotated images. This is costly and runs the risk of human biases being involved i... | ['Tara Taghavi', 'Jaeyoung Do', 'Seunghak Yu', 'Sungjin Lee', 'Jinoh Oh', 'Siwon Kim'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['counterfactual-explanation'] | ['miscellaneous'] | [ 5.83783865e-01 7.42244661e-01 -3.16235930e-01 -7.74059892e-01
-6.00220919e-01 -2.63819665e-01 1.01312685e+00 1.67679518e-01
-2.22404629e-01 6.53784752e-01 5.91319442e-01 -4.82016206e-01
-2.23987728e-01 -6.34404302e-01 -7.42691398e-01 -4.63184774e-01
2.84492761e-01 5.74507594e-01 -3.64466131e-01 1.45313218... | [8.949581146240234, 5.5840373039245605] |
bedfbd7b-0423-4a86-ab23-4df947d8c8df | offlangone-dravidianlangtech-eacl2021 | null | null | https://aclanthology.org/2021.dravidianlangtech-1.19 | https://aclanthology.org/2021.dravidianlangtech-1.19.pdf | OFFLangOne@DravidianLangTech-EACL2021: Transformers with the Class Balanced Loss for Offensive Language Identification in Dravidian Code-Mixed text. | The intensity of online abuse has increased in recent years. Automated tools are being developed to prevent the use of hate speech and offensive content. Most of the technologies use natural language and machine learning tools to identify offensive text. In a multilingual society, where code-mixing is a norm, the hate ... | ['Radhika Mamidi', 'Suman Dowlagar'] | null | null | null | null | eacl-dravidianlangtech-2021-4 | ['transliteration'] | ['natural-language-processing'] | [-8.88826400e-02 -4.01568320e-03 -1.31760389e-01 1.39794111e-01
-4.54972297e-01 -9.40941632e-01 6.85376346e-01 4.35587764e-01
-2.47239798e-01 5.31944811e-01 2.55549252e-01 -4.21458483e-01
6.13540486e-02 -3.17960799e-01 -2.18475163e-01 -3.78309101e-01
-2.36984696e-02 6.42985478e-02 -1.48015589e-01 -5.11699021... | [8.855508804321289, 10.612669944763184] |
4e34c46e-c0b7-4ee2-8c1e-6d4f12a3f7c0 | pick-processing-key-information-extraction | 2004.07464 | null | https://arxiv.org/abs/2004.07464v3 | https://arxiv.org/pdf/2004.07464v3.pdf | PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks | Computer vision with state-of-the-art deep learning models has achieved huge success in the field of Optical Character Recognition (OCR) including text detection and recognition tasks recently. However, Key Information Extraction (KIE) from documents as the downstream task of OCR, having a large number of use scenarios... | ['Wenwen Yu', 'Xianbiao Qi', 'Rong Xiao', 'Ping Gong', 'Ning Lu'] | 2020-04-16 | null | null | null | null | ['key-information-extraction'] | ['natural-language-processing'] | [ 1.06801257e-01 -6.02624357e-01 -1.48843020e-01 -2.11758256e-01
-4.65629697e-01 -7.93461025e-01 4.99818712e-01 2.77044773e-01
-3.22042257e-01 2.99643725e-01 5.38257435e-02 -3.29322785e-01
3.91135663e-02 -6.68293297e-01 -6.10266268e-01 -6.05880201e-01
1.56483546e-01 2.40943253e-01 2.51068711e-01 -1.77089497... | [11.661229133605957, 2.3574225902557373] |
0b8ce6a9-2a07-423d-a218-8c170920e267 | uturku-drug-named-entity-recognition-and-drug | null | null | https://aclanthology.org/S13-2108 | https://aclanthology.org/S13-2108.pdf | UTurku: Drug Named Entity Recognition and Drug-Drug Interaction Extraction Using SVM Classification and Domain Knowledge | null | ['Jari Bj{\\"o}rne', 'Tapio Salakoski', 'Suwisa Kaewphan'] | 2013-06-01 | null | null | null | semeval-2013-6 | ['drug-drug-interaction-extraction'] | ['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... | [-7.226303577423096, 3.47792911529541] |
1173f79d-bf4f-4e56-bbdd-0b467aa97aeb | a-static-evaluation-of-code-completion-by | 2306.03203 | null | https://arxiv.org/abs/2306.03203v1 | https://arxiv.org/pdf/2306.03203v1.pdf | A Static Evaluation of Code Completion by Large Language Models | Large language models trained on code have shown great potential to increase productivity of software developers. Several execution-based benchmarks have been proposed to evaluate functional correctness of model-generated code on simple programming problems. Nevertheless, it is expensive to perform the same evaluation ... | ['Bing Xiang', 'Dan Roth', 'Sudipta Sengupta', 'Parminder Bhatia', 'Baishakhi Ray', 'Murali Krishna Ramanathan', 'Xiaopeng Li', 'Rob Kwiatkowski', 'Zijian Wang', 'Yuchen Tian', 'Varun Kumar', 'Hantian Ding'] | 2023-06-05 | null | null | null | null | ['code-generation'] | ['computer-code'] | [-1.71804354e-01 -5.13035208e-02 -9.70843211e-02 -4.53794301e-01
-7.17708826e-01 -5.83649814e-01 1.78359419e-01 3.64853829e-01
-1.21866740e-01 4.44627315e-01 -1.62552699e-01 -7.97036469e-01
4.07193005e-01 -8.94464850e-01 -1.00565314e+00 9.27893072e-02
1.07703634e-01 -2.78709590e-01 3.63525540e-01 -9.91534144... | [7.791095733642578, 7.772904396057129] |
ec7a6ab2-56b5-48d0-a7e8-a98573e7a486 | evaluation-of-preprocessing-techniques-for-u | 2103.14301 | null | https://arxiv.org/abs/2103.14301v1 | https://arxiv.org/pdf/2103.14301v1.pdf | Evaluation of Preprocessing Techniques for U-Net Based Automated Liver Segmentation | To extract liver from medical images is a challenging task due to similar intensity values of liver with adjacent organs, various contrast levels, various noise associated with medical images and irregular shape of liver. To address these issues, it is important to preprocess the medical images, i.e., computerized tomo... | ['Muhammad Salman Khan', 'Kaleem Nawaz Khan', 'Muhammad Islam'] | 2021-03-26 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-1.41802490e-01 -4.11732852e-01 1.17212735e-01 -4.11438167e-01
-1.77359372e-01 -5.99944293e-01 2.60507941e-01 4.64877814e-01
-5.03582299e-01 6.18176937e-01 3.50802451e-01 -5.00832379e-01
-1.27518788e-01 -7.05769122e-01 -6.95625022e-02 -9.04819071e-01
-7.38531411e-01 2.62153476e-01 2.25478530e-01 4.63744879... | [14.405684471130371, -2.7746798992156982] |
d19b4d76-947c-4ed1-a934-c217dcd75435 | attention-based-relational-graph | null | null | https://aclanthology.org/2021.eacl-main.170 | https://aclanthology.org/2021.eacl-main.170.pdf | Attention-based Relational Graph Convolutional Network for Target-Oriented Opinion Words Extraction | Target-oriented opinion words extraction (TOWE) is a subtask of aspect-based sentiment analysis (ABSA). It aims to extract the corresponding opinion words for a given opinion target in a review sentence. Intuitively, the relation between an opinion target and an opinion word mostly relies on syntactics. In this study, ... | ['Akiko Aizawa', 'An Wang', 'Junfeng Jiang'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['target-oriented-opinion-words-extraction'] | ['natural-language-processing'] | [-1.75761972e-02 1.56453475e-01 -4.12075251e-01 -7.02201486e-01
-6.52568579e-01 -6.06977582e-01 4.60686445e-01 3.02554011e-01
-6.77789748e-02 3.93416613e-01 5.53790152e-01 -5.83972812e-01
7.59248063e-02 -1.02089310e+00 -4.54416364e-01 -3.09379011e-01
2.53683269e-01 6.10928945e-02 -4.52697538e-02 -5.24494112... | [11.509889602661133, 6.626028060913086] |
bb97ff3a-ef53-48f0-ab65-3b12e7a93d38 | multi-source-fusion-and-automatic-predictor | 2108.05076 | null | https://arxiv.org/abs/2108.05076v1 | https://arxiv.org/pdf/2108.05076v1.pdf | Multi-Source Fusion and Automatic Predictor Selection for Zero-Shot Video Object Segmentation | Location and appearance are the key cues for video object segmentation. Many sources such as RGB, depth, optical flow and static saliency can provide useful information about the objects. However, existing approaches only utilize the RGB or RGB and optical flow. In this paper, we propose a novel multi-source fusion net... | ['Huchuan Lu', 'Lihe Zhang', 'Jiaxing Yang', 'Youwei Pang', 'Xiaoqi Zhao'] | 2021-08-11 | null | null | null | null | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [-5.46218194e-02 -4.04443234e-01 -2.62607306e-01 -2.86430538e-01
-5.25126398e-01 -2.63204247e-01 1.62356626e-02 -1.81213990e-01
-2.47533351e-01 6.90714180e-01 8.22355673e-02 1.50325950e-02
-1.12506784e-01 -5.58191061e-01 -5.55789769e-01 -7.43807018e-01
1.30955845e-01 -2.50352979e-01 7.38466144e-01 -6.38892725... | [9.376530647277832, -0.37394052743911743] |
2a517d97-51ba-43ad-84d0-52441e5e0856 | physical-activity-recognition-by-utilising | 2201.08688 | null | https://arxiv.org/abs/2201.08688v1 | https://arxiv.org/pdf/2201.08688v1.pdf | Physical Activity Recognition by Utilising Smartphone Sensor Signals | Human physical motion activity identification has many potential applications in various fields, such as medical diagnosis, military sensing, sports analysis, and human-computer security interaction. With the recent advances in smartphones and wearable technologies, it has become common for such devices to have embedde... | ["Nathan Clarke' Fudong Li", 'Hind Alobaidi', 'Abdulrahman Alruban'] | 2022-01-20 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 5.46867907e-01 -3.50975990e-01 -6.82798386e-01 -1.40986713e-02
-1.85014158e-01 -1.94824085e-01 3.84009928e-01 1.41897753e-01
-3.94978344e-01 7.51905918e-01 4.53714967e-01 -3.06741983e-01
-1.69875354e-01 -8.75927150e-01 7.24289566e-02 -5.04141510e-01
-1.75299868e-01 -1.76211089e-01 2.68635929e-01 5.43813109... | [7.284724235534668, 0.6203824877738953] |
b013efa1-aabb-44d8-b0e7-bd3f9b8dafbd | a-biomedical-entity-extraction-pipeline-for | 2304.08999 | null | https://arxiv.org/abs/2304.08999v1 | https://arxiv.org/pdf/2304.08999v1.pdf | A Biomedical Entity Extraction Pipeline for Oncology Health Records in Portuguese | Textual health records of cancer patients are usually protracted and highly unstructured, making it very time-consuming for health professionals to get a complete overview of the patient's therapeutic course. As such limitations can lead to suboptimal and/or inefficient treatment procedures, healthcare providers would ... | ['Mário Amorim Lopes', 'Catarina Sousa Santos', 'Alípio Jorge', 'Arian Pasquali', 'Hugo Sousa'] | 2023-04-18 | null | null | null | null | ['entity-linking'] | ['natural-language-processing'] | [ 3.99633311e-02 6.38848782e-01 -2.75561631e-01 -1.58256561e-01
-1.00712132e+00 -4.46116060e-01 2.38238573e-01 1.07468474e+00
-8.48332345e-01 1.21681476e+00 3.44365478e-01 -5.93409657e-01
-2.30837315e-01 -7.70486653e-01 -3.90876234e-01 -3.54671806e-01
2.65422374e-01 6.65187061e-01 -5.13199925e-01 1.19815163... | [8.429730415344238, 8.654623031616211] |
e6629033-4610-445f-a5d7-6bdb83a51e23 | global-to-local-neural-networks-for-document | 2009.10359 | null | https://arxiv.org/abs/2009.10359v1 | https://arxiv.org/pdf/2009.10359v1.pdf | Global-to-Local Neural Networks for Document-Level Relation Extraction | Relation extraction (RE) aims to identify the semantic relations between named entities in text. Recent years have witnessed it raised to the document level, which requires complex reasoning with entities and mentions throughout an entire document. In this paper, we propose a novel model to document-level RE, by encodi... | ['Weijian Sun', 'Difeng Wang', 'Wei Hu', 'Ermei Cao'] | 2020-09-22 | null | https://aclanthology.org/2020.emnlp-main.303 | https://aclanthology.org/2020.emnlp-main.303.pdf | emnlp-2020-11 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-8.82596150e-02 4.48810637e-01 -4.69136804e-01 -2.71525294e-01
-6.47568703e-01 -6.72151029e-01 7.89444268e-01 9.45803285e-01
-2.92165309e-01 9.07846808e-01 8.22714269e-01 -1.00010864e-01
-4.84662682e-01 -1.38169265e+00 -3.89056355e-01 -1.82145163e-01
-2.03747228e-01 5.15131831e-01 4.68941599e-01 -3.44207048... | [9.30034351348877, 8.640356063842773] |
64a734a2-d651-499a-9d8b-14754a831d01 | pose-guided-human-parsing-with-deep-learned | 1508.03881 | null | http://arxiv.org/abs/1508.03881v2 | http://arxiv.org/pdf/1508.03881v2.pdf | Pose-Guided Human Parsing with Deep Learned Features | Parsing human body into semantic regions is crucial to human-centric
analysis. In this paper, we propose a segment-based parsing pipeline that
explores human pose information, i.e. the joint location of a human model,
which improves the part proposal, accelerates the inference and regularizes the
parsing process at the... | ['Jun Zhu', 'Peng Wang', 'Alan Yuille', 'Fangting Xia'] | 2015-08-17 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 9.10065323e-02 5.85376322e-01 -4.05045778e-01 -6.67800605e-01
-1.04223895e+00 -5.89538574e-01 3.76104623e-01 1.07218839e-01
-4.70028311e-01 4.04430211e-01 5.15604496e-01 2.88357705e-01
2.87095755e-01 -7.21990764e-01 -9.24080431e-01 -2.52052337e-01
1.51812643e-01 1.04705024e+00 6.84250951e-01 -1.18408715... | [8.329459190368652, -0.20324167609214783] |
ec48a7c2-1d99-459f-8443-8fd6fcbe4264 | heterogeneous-reconstruction-of-deformable | 2209.15121 | null | https://arxiv.org/abs/2209.15121v1 | https://arxiv.org/pdf/2209.15121v1.pdf | Heterogeneous reconstruction of deformable atomic models in Cryo-EM | Cryogenic electron microscopy (cryo-EM) provides a unique opportunity to study the structural heterogeneity of biomolecules. Being able to explain this heterogeneity with atomic models would help our understanding of their functional mechanisms but the size and ruggedness of the structural space (the space of atomic 3D... | ['Frédéric Poitevin', 'Daniel Ratner', 'Nina Miolane', 'Gordon Wetzstein', 'Bongjin Koo', 'Axel Levy', 'Julien Martel', 'Ariana Peck', 'Youssef Nashed'] | 2022-09-29 | null | null | null | null | ['cryogenic-electron-microscopy-cryo-em'] | ['computer-vision'] | [ 1.51376292e-01 1.62574738e-01 2.99471974e-01 -9.72858965e-02
-5.25175452e-01 -5.53126752e-01 6.34728730e-01 8.02103952e-02
-4.11645323e-01 9.89210069e-01 1.38442993e-01 -3.21539164e-01
5.44635020e-03 -6.28045022e-01 -1.00517094e+00 -1.33765793e+00
-2.52061725e-01 9.85526979e-01 -1.04857154e-01 -2.51553744... | [13.258028984069824, -3.068382501602173] |
e166b5f9-8c60-419a-80c3-638fe51800c9 | dime-maximizing-mutual-information-by-a | 2301.08164 | null | https://arxiv.org/abs/2301.08164v2 | https://arxiv.org/pdf/2301.08164v2.pdf | DiME: Maximizing Mutual Information by a Difference of Matrix-Based Entropies | We introduce an information-theoretic quantity with similar properties to mutual information that can be estimated from data without making explicit assumptions on the underlying distribution. This quantity is based on a recently proposed matrix-based entropy that uses the eigenvalues of a normalized Gram matrix to com... | ['Luis Gonzalo Sanchez Giraldo', 'Austin J. Brockmeier', 'Jhoan Keider Hoyos Osorio', 'Oscar Skean'] | 2023-01-19 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 1.33835509e-01 3.37683439e-01 -3.03993467e-02 -5.09141207e-01
-1.11609948e+00 -6.03132069e-01 6.02989316e-01 -1.34389717e-02
-3.18919808e-01 5.79621136e-01 3.15753907e-01 -2.25468367e-01
-5.15766203e-01 -5.24928808e-01 -3.75793546e-01 -8.40534449e-01
-4.93910998e-01 5.17438054e-01 -5.62551677e-01 2.25193068... | [7.504778861999512, 4.158319473266602] |
690ebe5f-1925-4c8c-9045-6da7d9d47040 | domain-generalization-in-deep-learning-based | 2201.11620 | null | https://arxiv.org/abs/2201.11620v2 | https://arxiv.org/pdf/2201.11620v2.pdf | Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center study | Computer-aided detection systems based on deep learning have shown great potential in breast cancer detection. However, the lack of domain generalization of artificial neural networks is an important obstacle to their deployment in changing clinical environments. In this work, we explore the domain generalization of de... | ['Karim Lekadir', 'Laura Igual', 'Oliver Diaz', 'Socayna Jouide', 'Kaisar Kushibar', 'Lidia Garrucho'] | 2022-01-27 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 3.13836277e-01 2.48055264e-01 -3.68853807e-01 -5.08412242e-01
-1.02158189e+00 -1.32510096e-01 2.32427552e-01 4.87961978e-01
-4.84388530e-01 3.46594661e-01 9.89945792e-03 -8.95414591e-01
-2.70147979e-01 -7.86561489e-01 -8.86818588e-01 -7.41373003e-01
-3.36482599e-02 7.62759805e-01 3.64805877e-01 -1.33569613... | [15.211512565612793, -2.5045886039733887] |
26e0fb40-b28f-4521-a2b7-34a9355e6c67 | exploring-resiliency-to-natural-image | 2303.09283 | null | https://arxiv.org/abs/2303.09283v1 | https://arxiv.org/pdf/2303.09283v1.pdf | Exploring Resiliency to Natural Image Corruptions in Deep Learning using Design Diversity | In this paper, we investigate the relationship between diversity metrics, accuracy, and resiliency to natural image corruptions of Deep Learning (DL) image classifier ensembles. We investigate the potential of an attribution-based diversity metric to improve the known accuracy-diversity trade-off of the typical predict... | ['Michael Paulitsch', 'Pablo Munoz', 'Rafael Rosales'] | 2023-03-15 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 1.43469078e-02 -3.56763095e-01 1.42230913e-01 -2.34782130e-01
-2.93804079e-01 -6.03618741e-01 7.31134772e-01 1.43288717e-01
-4.55941945e-01 5.66291094e-01 1.78890646e-01 -3.58328879e-01
-3.82844567e-01 -6.52512312e-01 -5.81688583e-01 -7.68210232e-01
-2.76199847e-01 -6.48263097e-02 -4.67719845e-02 -3.56958330... | [5.565266132354736, 7.7845659255981445] |
f96c570c-356d-49da-97bd-f53c1009c3fa | utilizing-domain-knowledge-in-end-to-end | 1712.00254 | null | http://arxiv.org/abs/1712.00254v1 | http://arxiv.org/pdf/1712.00254v1.pdf | Utilizing Domain Knowledge in End-to-End Audio Processing | End-to-end neural network based approaches to audio modelling are generally
outperformed by models trained on high-level data representations. In this
paper we present preliminary work that shows the feasibility of training the
first layers of a deep convolutional neural network (CNN) model to learn the
commonly-used l... | ['Lars Maaløe', 'Hendrik Purwins', 'Tycho Max Sylvester Tax', 'Jose Luis Diez Antich'] | 2017-12-01 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 1.52911723e-01 -3.43850516e-02 4.40926373e-01 -5.58147907e-01
-8.71190548e-01 -2.82400012e-01 3.64811778e-01 1.40382512e-03
-6.98799670e-01 1.69441581e-01 3.53791237e-01 -3.70360523e-01
1.43040325e-02 -4.52915281e-01 -6.35380864e-01 -1.95911169e-01
-5.01398146e-01 -2.33147535e-02 -1.44845605e-01 -3.07058185... | [15.321670532226562, 5.37695837020874] |
35c94202-5a60-41df-afd2-9a7cac6f9e30 | expanding-accurate-person-recognition-to-new | 2211.01917 | null | https://arxiv.org/abs/2211.01917v1 | https://arxiv.org/pdf/2211.01917v1.pdf | Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Dataset | Face recognition technology has advanced significantly in recent years due largely to the availability of large and increasingly complex training datasets for use in deep learning models. These datasets, however, typically comprise images scraped from news sites or social media platforms and, therefore, have limited ut... | ['David S. Bolme', 'Hector J. Santos-Villalobos', 'Scott Dolvin', 'Robert Zhang', 'Matt Yohe', 'Leanne Thompson', 'Brandon Stockwell', 'Nisha Srinivas', 'Ian Shelley', 'Christi Johnson', 'Bart Murphy', 'Matt Larson', 'Gavin Jager', 'Jim Goddard', 'Regina Ferrell', 'Andrew Duncan', 'Carl Dukes', 'Nick Burchfield', 'Seth... | 2022-11-03 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 9.73824784e-02 -5.10641992e-01 -6.56815544e-02 -5.62781036e-01
-3.81714284e-01 -2.98883796e-01 3.88968706e-01 -3.21692854e-01
-5.32921493e-01 3.93016100e-01 4.53334562e-02 5.67509681e-02
-4.47650477e-02 -7.38832951e-01 -2.26494402e-01 -5.57586908e-01
-1.97405398e-01 3.51088196e-01 -1.39249727e-01 -2.06236169... | [13.79223918914795, 1.0356782674789429] |
d833255b-edd8-4077-b14e-a11cc4edc8bd | reckon-a-28nm-sub-mm2-task-agnostic-spiking | 2208.09759 | null | https://arxiv.org/abs/2208.09759v1 | https://arxiv.org/pdf/2208.09759v1.pdf | ReckOn: A 28nm Sub-mm2 Task-Agnostic Spiking Recurrent Neural Network Processor Enabling On-Chip Learning over Second-Long Timescales | A robust real-world deployment of autonomous edge devices requires on-chip adaptation to user-, environment- and task-induced variability. Due to on-chip memory constraints, prior learning devices were limited to static stimuli with no temporal contents. We propose a 0.45-mm$^2$ spiking RNN processor enabling task-agno... | ['Giacomo Indiveri', 'Charlotte Frenkel'] | 2022-08-20 | null | null | null | null | ['gesture-recognition', 'keyword-spotting'] | ['computer-vision', 'speech'] | [ 1.93271086e-01 -1.04178354e-01 -3.49027932e-01 -2.11117297e-01
-5.60040951e-01 -5.02069116e-01 -5.67166461e-03 -1.15631476e-01
-1.12231469e+00 8.48048389e-01 -4.56385702e-01 -4.09628451e-01
1.30009368e-01 -3.94528061e-01 -6.91705823e-01 -4.60437149e-01
-2.55782604e-01 1.06472015e-01 5.08213341e-01 2.92941749... | [8.264657974243164, 2.5043129920959473] |
6552f579-f1b5-4a30-841e-384555cf397a | neural-pbir-reconstruction-of-shape-material | 2304.13445 | null | https://arxiv.org/abs/2304.13445v1 | https://arxiv.org/pdf/2304.13445v1.pdf | Neural-PBIR Reconstruction of Shape, Material, and Illumination | Reconstructing the shape and spatially varying surface appearances of a physical-world object as well as its surrounding illumination based on 2D images (e.g., photographs) of the object has been a long-standing problem in computer vision and graphics. In this paper, we introduce a robust object reconstruction pipeline... | ['Zhao Dong', 'Shuang Zhao', 'Jia-Bin Huang', 'Carl Marshall', 'Cheng Zhang', 'Kai Yan', 'Zhengqin Li', 'Guangyan Cai', 'Cheng Sun'] | 2023-04-26 | null | null | null | null | ['object-reconstruction', 'inverse-rendering'] | ['computer-vision', 'computer-vision'] | [ 5.72638154e-01 -1.33388162e-01 6.10962629e-01 -5.42259336e-01
-5.89933157e-01 -3.21439743e-01 5.06513655e-01 -2.31256694e-01
2.58168876e-02 4.06208396e-01 6.68500662e-02 -3.14777419e-02
1.32049724e-01 -7.41083026e-01 -9.38340962e-01 -5.10037541e-01
4.23402816e-01 3.74404132e-01 3.52655828e-01 1.72148496... | [9.672821044921875, -3.117753267288208] |
79d566f6-9be4-4730-a539-f018123186d6 | mert-acoustic-music-understanding-model-with | 2306.00107 | null | https://arxiv.org/abs/2306.00107v2 | https://arxiv.org/pdf/2306.00107v2.pdf | MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training | Self-supervised learning (SSL) has recently emerged as a promising paradigm for training generalisable models on large-scale data in the fields of vision, text, and speech. Although SSL has been proven effective in speech and audio, its application to music audio has yet to be thoroughly explored. This is primarily due... | ['Jie Fu', 'Yike Guo', 'Wenhao Huang', 'Yemin Shi', 'Gus Xia', 'Wenhu Chen', 'Ruibo Liu', 'Roger Dannenberg', 'Norbert Gyenge', 'Emmanouil Benetos', 'Anton Ragni', 'Chenghua Lin', 'Hanzhi Yin', 'Xingran Chen', 'Yinghao Ma', 'Ge Zhang', 'Ruibin Yuan', 'Yizhi Li'] | 2023-05-31 | null | null | null | null | ['quantization'] | ['methodology'] | [ 1.94349989e-01 -6.67676106e-02 -1.60397142e-02 -1.95746630e-01
-1.17067850e+00 -5.09345233e-01 4.88896430e-01 -2.64419019e-01
-3.30763817e-01 3.12431790e-02 2.32333601e-01 -2.17237145e-01
-1.61572248e-01 -4.31022882e-01 -7.73855925e-01 -5.76247036e-01
1.55506834e-01 3.90732855e-01 -4.12679352e-02 -1.66954204... | [15.265071868896484, 5.396842956542969] |
04738f3c-9671-4b93-ae79-2060bb5578b5 | making-the-most-of-text-semantics-to-improve | 2204.09817 | null | https://arxiv.org/abs/2204.09817v4 | https://arxiv.org/pdf/2204.09817v4.pdf | Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing | Multi-modal data abounds in biomedicine, such as radiology images and reports. Interpreting this data at scale is essential for improving clinical care and accelerating clinical research. Biomedical text with its complex semantics poses additional challenges in vision--language modelling compared to the general domain,... | ['Ozan Oktay', 'Hoifung Poon', 'Javier Alvarez-Valle', 'Aditya Nori', 'Tristan Naumann', 'Maria Wetscherek', 'Stephanie Hyland', 'Anton Schwaighofer', 'Daniel C. Castro', 'Shruthi Bannur', 'Naoto Usuyama', 'Benedikt Boecking'] | 2022-04-21 | null | null | null | null | ['pneumonia-detection', 'phrase-grounding'] | ['medical', 'natural-language-processing'] | [ 6.79879665e-01 6.12871945e-01 -4.64380682e-01 -5.80996871e-01
-1.42200315e+00 -1.89109340e-01 6.48153365e-01 5.90667963e-01
-8.87787700e-01 5.08231819e-01 7.48453140e-01 -3.61110061e-01
-8.11308250e-02 -3.14471602e-01 -7.35946298e-01 -4.84979868e-01
1.74384877e-01 1.00854385e+00 2.35644072e-01 -1.50391340... | [14.91025447845459, -1.7746554613113403] |
ec6e7500-33c5-4749-96c1-2588f19d8149 | meta-auxiliary-learning-for-low-resource | 2206.12774 | null | https://arxiv.org/abs/2206.12774v1 | https://arxiv.org/pdf/2206.12774v1.pdf | Meta Auxiliary Learning for Low-resource Spoken Language Understanding | Spoken language understanding (SLU) treats automatic speech recognition (ASR) and natural language understanding (NLU) as a unified task and usually suffers from data scarcity. We exploit an ASR and NLU joint training method based on meta auxiliary learning to improve the performance of low-resource SLU task by only ta... | ['Shilei Zhang', 'Chao Deng', 'Junlan Feng', 'Yingying Gao'] | 2022-06-26 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 7.45644748e-01 5.72347879e-01 -4.34585452e-01 -6.56740129e-01
-1.22395051e+00 -5.18132091e-01 6.04432046e-01 -2.21310303e-01
-4.58278805e-01 7.68383622e-01 4.73974943e-01 -5.95197022e-01
4.63614792e-01 -5.38778067e-01 -6.62699699e-01 -4.48269933e-01
4.64039147e-01 6.60874367e-01 5.16343266e-02 -2.01468691... | [13.798457145690918, 7.073267936706543] |
4017651d-8178-4b65-806c-deaf68b41107 | sagemix-saliency-guided-mixup-for-point | 2210.06944 | null | https://arxiv.org/abs/2210.06944v1 | https://arxiv.org/pdf/2210.06944v1.pdf | SageMix: Saliency-Guided Mixup for Point Clouds | Data augmentation is key to improving the generalization ability of deep learning models. Mixup is a simple and widely-used data augmentation technique that has proven effective in alleviating the problems of overfitting and data scarcity. Also, recent studies of saliency-aware Mixup in the image domain show that prese... | ['Hyunwoo J. Kim', 'Yunyang Xiong', 'Injae Kim', 'Minkyu Jeon', 'Sanghyeok Lee'] | 2022-10-13 | null | null | null | null | ['3d-point-cloud-classification', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [-7.94013217e-02 4.91438732e-02 -3.24297279e-01 -3.08826834e-01
-6.68945789e-01 -2.36221641e-01 3.60627800e-01 3.04433674e-01
-8.47182944e-02 2.35880449e-01 -1.12975612e-01 -3.25264931e-02
-6.51901681e-03 -6.59682453e-01 -1.10775530e+00 -6.28228724e-01
3.03759843e-01 3.96338373e-01 4.49160546e-01 -8.16467181... | [7.939978122711182, -3.4220130443573] |
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