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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
52bc7a60-bbe5-4ce8-8a35-e2bed6a47d21 | 190412580 | 1904.12580 | null | http://arxiv.org/abs/1904.12580v1 | http://arxiv.org/pdf/1904.12580v1.pdf | Twitter Sentiment Analysis using Distributed Word and Sentence Representation | An important part of the information gathering and data analysis is to find
out what people think about, either a product or an entity. Twitter is an
opinion rich social networking site. The posts or tweets from this data can be
used for mining people's opinions. The recent surge of activity in this area
can be attribu... | ['Dr. N V Subba Reddy', 'Dwarampudi Mahidhar Reddy'] | 2019-04-01 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-1.89836010e-01 2.23051775e-02 -2.87200898e-01 -7.02671230e-01
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4.29320373e-02 3.21681291e-01 5.86660206e-03 -6.49621546... | [11.103130340576172, 7.058303356170654] |
fbd9e1a6-cee9-4e21-9a3d-dcffb2848ae6 | meta-learning-for-relative-density-ratio | 2107.00801 | null | https://arxiv.org/abs/2107.00801v1 | https://arxiv.org/pdf/2107.00801v1.pdf | Meta-Learning for Relative Density-Ratio Estimation | The ratio of two probability densities, called a density-ratio, is a vital quantity in machine learning. In particular, a relative density-ratio, which is a bounded extension of the density-ratio, has received much attention due to its stability and has been used in various applications such as outlier detection and da... | ['Yasuhiro Fujiwara', 'Tomoharu Iwata', 'Atsutoshi Kumagai'] | 2021-07-02 | null | http://proceedings.neurips.cc/paper/2021/hash/ff49cc40a8890e6a60f40ff3026d2730-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/ff49cc40a8890e6a60f40ff3026d2730-Paper.pdf | neurips-2021-12 | ['density-ratio-estimation'] | ['methodology'] | [-1.00362308e-01 -1.22641288e-01 -2.59626418e-01 -4.26552951e-01
-1.02175307e+00 -1.16025046e-01 2.22097129e-01 4.37874138e-01
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-6.69522136e-02 4.35852170e-01 3.10976729e-02 8.33441317... | [7.708803176879883, 3.448460817337036] |
4d95743c-7820-4a36-a1de-4097e84201f8 | ml-decoder-scalable-and-versatile | 2111.12933 | null | https://arxiv.org/abs/2111.12933v2 | https://arxiv.org/pdf/2111.12933v2.pdf | ML-Decoder: Scalable and Versatile Classification Head | In this paper, we introduce ML-Decoder, a new attention-based classification head. ML-Decoder predicts the existence of class labels via queries, and enables better utilization of spatial data compared to global average pooling. By redesigning the decoder architecture, and using a novel group-decoding scheme, ML-Decode... | ['Asaf Noy', 'Emanuel Ben-Baruch', 'Avi Ben-Cohen', 'Gilad Sharir', 'Tal Ridnik'] | 2021-11-25 | null | null | null | null | ['multi-label-zero-shot-learning', 'fine-grained-image-classification'] | ['computer-vision', 'computer-vision'] | [-5.12030013e-02 9.70766768e-02 -3.63832235e-01 -4.06974345e-01
-1.27515030e+00 -4.44656968e-01 3.79571885e-01 1.36315733e-01
-6.13298714e-01 6.01611853e-01 8.79272353e-03 -1.56848639e-01
4.82851774e-01 -7.38239229e-01 -7.66688824e-01 -3.16006750e-01
1.76436931e-01 4.70503420e-01 7.03524768e-01 -1.80025131... | [9.541581153869629, 0.8973336815834045] |
31b171e3-f497-4ff7-8883-421bdd5237bf | mb-hgcn-a-hierarchical-graph-convolutional | 2306.10679 | null | https://arxiv.org/abs/2306.10679v1 | https://arxiv.org/pdf/2306.10679v1.pdf | MB-HGCN: A Hierarchical Graph Convolutional Network for Multi-behavior Recommendation | Collaborative filtering-based recommender systems that rely on a single type of behavior often encounter serious sparsity issues in real-world applications, leading to unsatisfactory performance. Multi-behavior Recommendation (MBR) is a method that seeks to learn user preferences, represented as vector embeddings, from... | ['Yuxin Peng', 'Fuming Sun', 'Jing Sun', 'Zhiyong Cheng', 'Mingshi Yan'] | 2023-06-19 | null | null | null | null | ['multi-task-learning', 'collaborative-filtering'] | ['methodology', 'miscellaneous'] | [-3.89085084e-01 -5.00582337e-01 -4.87044424e-01 -5.31485319e-01
-3.56585890e-01 -3.22753370e-01 2.15106130e-01 2.72198737e-01
-2.77410179e-01 7.96360672e-02 9.56040502e-01 -2.65196860e-01
-5.33166766e-01 -7.69366562e-01 -4.10736173e-01 -6.64589524e-01
-1.02517888e-01 2.14488328e-01 -8.56761727e-03 -4.95378107... | [10.173041343688965, 5.607107162475586] |
97606a02-f653-4d33-8b97-07b2299e7cb3 | fanet-a-feedback-attention-network-for | 2103.17235 | null | https://arxiv.org/abs/2103.17235v3 | https://arxiv.org/pdf/2103.17235v3.pdf | FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation | The increase of available large clinical and experimental datasets has contributed to a substantial amount of important contributions in the area of biomedical image analysis. Image segmentation, which is crucial for any quantitative analysis, has especially attracted attention. Recent hardware advancement has led to t... | ['Sharib Ali', 'Pål Halvorsen', 'Jens Rittscher', 'Dag Johansen', 'Håvard D. Johansen', 'Michael A. Riegler', 'Debesh Jha', 'Nikhil Kumar Tomar'] | 2021-03-31 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 5.47034323e-01 9.92093235e-02 -6.51814416e-02 -6.03616893e-01
-6.58618867e-01 -1.16124868e-01 2.28539482e-01 3.10350060e-01
-5.70321143e-01 5.48324823e-01 -5.68613037e-02 -2.48820916e-01
-3.19070630e-02 -4.51397896e-01 -7.52011120e-01 -7.29187906e-01
8.51189569e-02 2.74762809e-01 4.56143290e-01 6.92810342... | [14.643306732177734, -2.55218243598938] |
4491b3df-12fe-4d1f-9f2d-02344b7ef5e5 | unfolding-of-crumpled-thin-sheets | 2102.09995 | null | https://arxiv.org/abs/2102.09995v1 | https://arxiv.org/pdf/2102.09995v1.pdf | Unfolding of Crumpled Thin Sheets | Crumpled thin sheets are complex fractal structures whose physical properties are influenced by a hierarchy of ridges. In this Letter, we report experiments that measure the stress-strain relation and show the coexistence of phases in the stretching of crumpled surfaces. The pull stress showed a change from a linear Ho... | ['Marcelo A F Gomes', 'Francisco C B Leal'] | 2021-02-19 | null | null | null | null | ['stress-strain-relation'] | ['miscellaneous'] | [ 3.42192613e-02 -3.65611538e-02 -1.31804764e-01 4.75600362e-03
1.78190082e-01 -4.27738458e-01 5.35294831e-01 5.36182858e-02
6.64192587e-02 9.27780271e-01 2.91907996e-01 -5.92712834e-02
-6.14074230e-01 -9.21256483e-01 -7.41500556e-01 -1.27261901e+00
-2.98766077e-01 3.65056634e-01 7.56535947e-01 -8.58961225... | [5.707936763763428, 4.159407615661621] |
ed8cae55-2197-44f3-8fa5-303d88f76bee | sepmark-deep-separable-watermarking-for | 2305.06321 | null | https://arxiv.org/abs/2305.06321v1 | https://arxiv.org/pdf/2305.06321v1.pdf | SepMark: Deep Separable Watermarking for Unified Source Tracing and Deepfake Detection | Malicious Deepfakes have led to a sharp conflict over distinguishing between genuine and forged faces. Although many countermeasures have been developed to detect Deepfakes ex-post, undoubtedly, passive forensics has not considered any preventive measures for the pristine face before foreseeable manipulations. To compl... | ['Bo Ou', 'Xin Liao', 'Xiaoshuai Wu'] | 2023-05-10 | null | null | null | null | ['deepfake-detection', 'face-swapping'] | ['computer-vision', 'computer-vision'] | [ 4.42156881e-01 2.49437839e-01 -2.05513209e-01 1.86906248e-01
-5.24766922e-01 -8.86940002e-01 5.85119426e-01 -7.36697689e-02
-1.03676662e-01 4.57833558e-01 -6.91166669e-02 -1.64175808e-01
1.76647663e-01 -8.74392569e-01 -4.88817364e-01 -9.39161718e-01
-8.25320631e-02 -2.23642990e-01 3.76133233e-01 8.84873420... | [12.730891227722168, 1.075208306312561] |
89b6ba84-a133-4434-9de2-4483b5a28168 | online-parameter-estimation-and-change-point | 2302.04407 | null | https://arxiv.org/abs/2302.04407v2 | https://arxiv.org/pdf/2302.04407v2.pdf | Bayesian Non-parametric Hidden Markov Model for Agile Radar Pulse Sequences Streaming Analysis | Multi-function radars (MFRs) are sophisticated types of sensors with the capabilities of complex agile inter-pulse modulation implementation and dynamic work mode scheduling. The developments in MFRs pose great challenges to modern electronic reconnaissance systems or radar warning receivers for recognition and inferen... | ['Shafei Wang', 'Mengtao Zhu', 'Yunjie Li', 'Jiadi Bao'] | 2023-02-09 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 5.91292143e-01 -4.15901780e-01 1.06368728e-01 -5.12416005e-01
-8.61064136e-01 -3.47926021e-01 5.74207723e-01 -4.60302591e-01
-2.48558134e-01 5.73409319e-01 -1.76897228e-01 -4.17972863e-01
-6.81376040e-01 -5.72784901e-01 -2.94183433e-01 -9.59473312e-01
-2.80779094e-01 7.16254652e-01 4.85266447e-01 -5.32938354... | [6.487788677215576, 1.4679806232452393] |
f9a2c7bd-1ddf-4ebf-90da-e83d2a1e52f6 | a-clustering-based-framework-for-classifying | 2106.11823 | null | https://arxiv.org/abs/2106.11823v1 | https://arxiv.org/pdf/2106.11823v1.pdf | A Clustering-based Framework for Classifying Data Streams | The non-stationary nature of data streams strongly challenges traditional machine learning techniques. Although some solutions have been proposed to extend traditional machine learning techniques for handling data streams, these approaches either require an initial label set or rely on specialized design parameters. Th... | ['Edward Tunstel', 'Abenezer Girma', 'Mrinmoy Sarkar', 'Abdollah Homaifar', 'Xuyang Yan'] | 2021-06-22 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 2.15995237e-01 -4.39073086e-01 -5.24787724e-01 -6.64816320e-01
-4.45605338e-01 -4.66066748e-01 4.38877106e-01 8.40431213e-01
-4.17187989e-01 4.70572978e-01 -3.13466042e-01 -4.39857431e-02
-3.79028618e-01 -9.28106427e-01 -6.36067241e-02 -8.31585526e-01
-5.31019449e-01 6.50474310e-01 5.41367292e-01 2.69659340... | [7.448049545288086, 3.0783212184906006] |
13b04639-8556-4581-86ce-9fb84e81dcf6 | a-bandit-approach-to-online-pricing-for | 2302.06953 | null | https://arxiv.org/abs/2302.06953v1 | https://arxiv.org/pdf/2302.06953v1.pdf | A Bandit Approach to Online Pricing for Heterogeneous Edge Resource Allocation | Edge Computing (EC) offers a superior user experience by positioning cloud resources in close proximity to end users. The challenge of allocating edge resources efficiently while maximizing profit for the EC platform remains a sophisticated problem, especially with the added complexity of the online arrival of resource... | ['Vijay K. Bhargava', 'Duong Tung Nguyen', 'Lele Wang', 'Duong Thuy Anh Nguyen', 'Jiaming Cheng'] | 2023-02-14 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [-5.52294612e-01 -2.79609501e-01 -7.19505847e-01 6.48701563e-02
-8.78313959e-01 -6.36241257e-01 -4.26800847e-02 1.35864660e-01
-3.98831278e-01 1.18167901e+00 -1.63885191e-01 -6.43260181e-01
-8.08320403e-01 -8.11512768e-01 -5.86401582e-01 -8.23880851e-01
-3.30002785e-01 7.10994244e-01 -1.60813749e-01 3.01778853... | [4.614864349365234, 3.2723469734191895] |
bdd2096f-0252-4f0f-b0e8-4a788b1be8a9 | validation-of-a-deep-learning-mammography | 1911.00364 | null | https://arxiv.org/abs/1911.00364v1 | https://arxiv.org/pdf/1911.00364v1.pdf | Validation of a deep learning mammography model in a population with low screening rates | A key promise of AI applications in healthcare is in increasing access to quality medical care in under-served populations and emerging markets. However, deep learning models are often only trained on data from advantaged populations that have the infrastructure and resources required for large-scale data collection. I... | ['Greg Sorensen', 'Bill Lotter', 'Yaping Wu', 'Hongna Tan', 'Meiyun Wang', 'Kevin Wu', 'Eric Wu'] | 2019-11-01 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 3.13154072e-01 3.95259112e-01 -4.61654305e-01 -6.22672319e-01
-1.07250142e+00 8.00886229e-02 1.83985621e-01 4.53583390e-01
-5.97730041e-01 5.89380741e-01 5.25360584e-01 -8.78921747e-01
-2.81008840e-01 -8.77080679e-01 -9.58092630e-01 -4.94219363e-01
-3.95205170e-01 7.12935209e-01 -2.53305167e-01 2.15213299... | [15.144877433776855, -2.383979082107544] |
d8becd92-ab79-43ac-afa2-a42b0cf93e91 | on-the-state-of-the-art-in-authorship | 2209.06869 | null | https://arxiv.org/abs/2209.06869v2 | https://arxiv.org/pdf/2209.06869v2.pdf | On the State of the Art in Authorship Attribution and Authorship Verification | Despite decades of research on authorship attribution (AA) and authorship verification (AV), inconsistent dataset splits/filtering and mismatched evaluation methods make it difficult to assess the state of the art. In this paper, we present a survey of the fields, resolve points of confusion, introduce Valla that stand... | ['Zachary C. Lipton', 'Bhuwan Dhingra', 'Jacob Tyo'] | 2022-09-14 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [ 1.15930205e-02 -2.90476322e-01 -5.00161350e-01 -2.92322159e-01
-9.70445216e-01 -8.74504685e-01 8.87965202e-01 2.76233166e-01
-7.08931923e-01 7.85718322e-01 1.17671616e-01 -5.56141198e-01
-2.22427249e-02 -2.64486551e-01 -4.01706964e-01 -3.57960701e-01
2.23120153e-01 7.99339116e-01 -1.50542721e-01 -1.12769715... | [9.574641227722168, 10.543054580688477] |
a4683762-066c-494e-9e63-218ea685a06c | musical-information-extraction-from-the | 2204.03166 | null | https://arxiv.org/abs/2204.03166v1 | https://arxiv.org/pdf/2204.03166v1.pdf | Musical Information Extraction from the Singing Voice | Music information retrieval is currently an active research area that addresses the extraction of musically important information from audio signals, and the applications of such information. The extracted information can be used for search and retrieval of music in recommendation systems, or to aid musicological studi... | ['Preeti Rao'] | 2022-04-07 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [ 4.52519715e-01 -5.32023787e-01 -8.55667591e-02 -2.59958766e-02
-9.14088428e-01 -8.52218091e-01 1.28094569e-01 2.03504607e-01
-6.23951592e-02 3.60695630e-01 3.66525799e-01 2.75654435e-01
-7.98865914e-01 -3.58154118e-01 2.24159025e-02 -8.35373938e-01
-1.94750488e-01 1.57319516e-01 6.92094266e-02 -3.16059411... | [15.926301956176758, 5.257668972015381] |
d00c056f-22f3-4f44-b7c7-74d758bca655 | data-augmentation-for-learning-bilingual-word | 2006.00262 | null | https://arxiv.org/abs/2006.00262v3 | https://arxiv.org/pdf/2006.00262v3.pdf | Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings | Unsupervised cross-lingual word embedding (CLWE) methods learn a linear transformation matrix that maps two monolingual embedding spaces that are separately trained with monolingual corpora. This method relies on the assumption that the two embedding spaces are structurally similar, which does not necessarily hold true... | ['Ryokan Ri', 'Yoshimasa Tsuruoka', 'Sosuke Nishikawa'] | 2020-05-30 | null | https://aclanthology.org/2021.acl-srw.17 | https://aclanthology.org/2021.acl-srw.17.pdf | acl-2021-5 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [-1.01002164e-01 2.44462311e-01 -4.26633149e-01 -1.85015246e-01
-9.37203705e-01 -9.05395687e-01 9.30527151e-01 1.47485211e-01
-6.14534080e-01 7.25101233e-01 6.10526025e-01 -5.61966062e-01
1.67676091e-01 -5.12537181e-01 -6.78045034e-01 -4.73863035e-01
1.71117589e-01 8.50753248e-01 -1.89397916e-01 -5.44043541... | [11.062424659729004, 10.100898742675781] |
dbfb724b-ab28-4a99-a6ed-610e09d81647 | online-continuous-hyperparameter-optimization | 2302.09440 | null | https://arxiv.org/abs/2302.09440v2 | https://arxiv.org/pdf/2302.09440v2.pdf | Online Continuous Hyperparameter Optimization for Contextual Bandits | In stochastic contextual bandits, an agent sequentially makes actions from a time-dependent action set based on past experience to minimize the cumulative regret. Like many other machine learning algorithms, the performance of bandits heavily depends on their multiple hyperparameters, and theoretically derived paramete... | ['Thomas C. M. Lee', 'Cho-Jui Hsieh', 'Yue Kang'] | 2023-02-18 | null | null | null | null | ['thompson-sampling', 'multi-armed-bandits'] | ['methodology', 'miscellaneous'] | [ 1.01297587e-01 -2.32736930e-01 -8.05542767e-01 -5.46200834e-02
-1.12516403e+00 -8.28856766e-01 2.81850964e-01 -1.00831375e-01
-4.17604595e-01 1.24859452e+00 -2.35692203e-01 -7.50053287e-01
-7.23040462e-01 -8.23256671e-01 -9.22351837e-01 -1.10942984e+00
1.34115713e-02 8.35631192e-01 -6.38665780e-02 6.17114604... | [4.562877655029297, 3.251453399658203] |
9b8e5cc1-acb8-46b6-81bf-ea49776555fb | cross-modal-weighting-network-for-rgb-d | 2007.04901 | null | https://arxiv.org/abs/2007.04901v1 | https://arxiv.org/pdf/2007.04901v1.pdf | Cross-Modal Weighting Network for RGB-D Salient Object Detection | Depth maps contain geometric clues for assisting Salient Object Detection (SOD). In this paper, we propose a novel Cross-Modal Weighting (CMW) strategy to encourage comprehensive interactions between RGB and depth channels for RGB-D SOD. Specifically, three RGB-depth interaction modules, named CMW-L, CMW-M and CMW-H, a... | ['Haibin Ling', 'Gongyang Li', 'Yang Wang', 'Linwei Ye', 'Zhi Liu'] | 2020-07-09 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2864_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123620647.pdf | eccv-2020-8 | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [-1.04143173e-01 1.34313971e-01 4.44468763e-03 -5.29452145e-01
-1.01337516e+00 -5.81105202e-02 5.15545130e-01 1.55036166e-01
-3.04174155e-01 2.65020549e-01 4.41291124e-01 3.07377037e-02
-3.11279148e-02 -8.24651539e-01 -6.50975585e-01 -6.83451056e-01
1.51467294e-01 -1.27417505e-01 8.84183645e-01 -4.49449420... | [9.705093383789062, -0.7834990620613098] |
b6af5f07-dd61-4f0f-9641-9c92b1d897ea | ultrafast-non-destructive-measurement-of-the | 2012.12069 | null | https://arxiv.org/abs/2012.12069v1 | https://arxiv.org/pdf/2012.12069v1.pdf | Ultrafast non-destructive measurement of the quantum state of light using free electrons | Since the birth of quantum optics, the measurement of quantum states of nonclassical light has been of tremendous importance for advancement in the field. To date, conventional detectors such as photomultipliers, avalanche photodiodes, and superconducting nanowires, all rely at their core on linear excitation of bound ... | ['Ido Kaminer', "Avi Pe'er", 'Eliahu Cohen', 'Raphael Dahan', 'Aviv Karnieli', 'Alexey Gorlach'] | 2020-12-22 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 4.83432055e-01 -3.17295820e-01 2.86214113e-01 2.66167875e-02
-2.91249961e-01 -7.45162666e-01 5.95817387e-01 -1.16833158e-01
-1.01315629e+00 8.97187054e-01 -5.08862793e-01 -2.73416132e-01
2.18850777e-01 -1.14865816e+00 -4.61460859e-01 -1.22230637e+00
2.67492205e-01 6.32353604e-01 4.77431387e-01 -3.78149212... | [5.624950408935547, 4.822749614715576] |
12eb224a-0309-4d77-9924-b397b89bbfc7 | assessing-the-potential-of-classical-q | 1810.06078 | null | http://arxiv.org/abs/1810.06078v1 | http://arxiv.org/pdf/1810.06078v1.pdf | Assessing the Potential of Classical Q-learning in General Game Playing | After the recent groundbreaking results of AlphaGo and AlphaZero, we have
seen strong interests in deep reinforcement learning and artificial general
intelligence (AGI) in game playing. However, deep learning is
resource-intensive and the theory is not yet well developed. For small games,
simple classical table-based Q... | ['Michael Emmerich', 'Hui Wang', 'Aske Plaat'] | 2018-10-14 | null | null | null | null | ['board-games'] | ['playing-games'] | [-5.47566414e-01 -7.89215639e-02 -2.30167821e-01 3.21733296e-01
-8.64236772e-01 -5.23302853e-01 1.07595801e-01 1.02685310e-01
-6.98221266e-01 1.23092198e+00 -2.74393618e-01 -7.79385448e-01
-5.76593697e-01 -1.16732204e+00 -7.82577276e-01 -5.95910013e-01
-5.16858280e-01 5.02298236e-01 2.28224397e-01 -8.06419611... | [3.5614798069000244, 1.5287065505981445] |
ae87ab2d-eece-4e04-919c-55bf0f7dc3e5 | image-outpainting-and-harmonization-using | 1912.10960 | null | https://arxiv.org/abs/1912.10960v2 | https://arxiv.org/pdf/1912.10960v2.pdf | Image Outpainting and Harmonization using Generative Adversarial Networks | Although the inherently ambiguous task of predicting what resides beyond all four edges of an image has rarely been explored before, we demonstrate that GANs hold powerful potential in producing reasonable extrapolations. Two outpainting methods are proposed that aim to instigate this line of research: the first approa... | ['Basile Van Hoorick'] | 2019-12-23 | null | null | null | null | ['image-outpainting'] | ['computer-vision'] | [ 6.69885933e-01 4.62507069e-01 1.12457991e-01 -2.70456433e-01
-2.16461301e-01 -3.72516781e-01 9.88155127e-01 -9.54740718e-02
-5.33506386e-02 9.44163561e-01 3.73633474e-01 -1.30576521e-01
1.59793019e-01 -8.86046648e-01 -7.61555910e-01 -7.11381853e-01
3.21371555e-01 -3.50560322e-02 1.15907706e-01 -1.96966827... | [11.49843692779541, -0.6803439855575562] |
1a4233a6-10a1-44ab-9ead-34e127d96cfd | predictive-incrementality-by-experimentation | 2304.06828 | null | https://arxiv.org/abs/2304.06828v1 | https://arxiv.org/pdf/2304.06828v1.pdf | Predictive Incrementality by Experimentation (PIE) for Ad Measurement | We present a novel approach to causal measurement for advertising, namely to use exogenous variation in advertising exposure (RCTs) for a subset of ad campaigns to build a model that can predict the causal effect of ad campaigns that were run without RCTs. This approach -- Predictive Incrementality by Experimentation (... | ['Florian Zettelmeyer', 'Robert Moakler', 'Brett R. Gordon'] | 2023-04-13 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 4.31042969e-01 4.07581657e-01 -1.33828521e+00 -5.06363213e-01
-1.21052527e+00 -6.97334468e-01 1.01117015e+00 3.62768948e-01
-4.53618318e-01 7.39279509e-01 8.21169853e-01 -1.13746655e+00
-1.76758051e-01 -9.10827100e-01 -1.33965790e+00 -5.54087758e-02
-9.01688710e-02 3.06095719e-01 -5.30360313e-03 1.95845217... | [8.208035469055176, 5.406731128692627] |
6d76232c-f45b-42c0-a3d1-2b9b7483a9ad | let-2d-diffusion-model-know-3d-consistency | 2303.07937 | null | https://arxiv.org/abs/2303.07937v3 | https://arxiv.org/pdf/2303.07937v3.pdf | Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation | Text-to-3D generation has shown rapid progress in recent days with the advent of score distillation, a methodology of using pretrained text-to-2D diffusion models to optimize neural radiance field (NeRF) in the zero-shot setting. However, the lack of 3D awareness in the 2D diffusion models destabilizes score distillati... | ['Seungryong Kim', 'Jiyoung Lee', 'Jin-Hwa Kim', 'Junho Kim', 'Hyeonsu Kim', 'Jaehoon Ko', 'Min-Seop Kwak', 'Wooseok Jang', 'Junyoung Seo'] | 2023-03-14 | null | null | null | null | ['single-view-3d-reconstruction', 'text-to-3d'] | ['computer-vision', 'computer-vision'] | [ 1.06569618e-01 1.82954386e-01 5.32839745e-02 -2.60318637e-01
-9.19376254e-01 -5.00001609e-01 9.01703000e-01 -2.64955968e-01
2.61323780e-01 2.29257926e-01 8.07722270e-01 7.24293292e-02
-1.39994517e-01 -8.98131728e-01 -5.16557455e-01 -6.09942496e-01
4.13940400e-01 4.36332643e-01 2.74073124e-01 -2.53447175... | [9.34990119934082, -3.168170213699341] |
3841a2a0-e638-4ce0-adf3-5bb1192aad66 | data-augmentation-for-low-resource-dialogue | null | null | https://openreview.net/forum?id=8KHmFphT9BA | https://openreview.net/pdf?id=8KHmFphT9BA | Data Augmentation for Low-Resource Dialogue Summarization | We present DADS, a novel Data Augmentation technique for low-resource Dialogue Summarization. Our method generates synthetic examples by replacing sections of text from both the input dialogue and summary while preserving the augmented summary to correspond to a viable summary for the augmented dialogue. We utilize pre... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['meeting-summarization'] | ['natural-language-processing'] | [ 5.10931671e-01 9.76717293e-01 -2.70963442e-02 -3.34236294e-01
-1.39770997e+00 -7.01232553e-01 1.00017679e+00 3.94852191e-01
-2.97462910e-01 1.36898041e+00 1.27839696e+00 -9.11939610e-03
3.62259060e-01 -3.54960293e-01 -1.75332278e-01 -1.29662275e-01
3.89196903e-01 9.33597982e-01 -2.63667673e-01 -4.33092117... | [12.411702156066895, 9.166397094726562] |
b5ef67a0-0e07-4d1d-8dd7-22c9f24e64c8 | meta-learning-for-mixed-linear-regression | 2002.08936 | null | https://arxiv.org/abs/2002.08936v1 | https://arxiv.org/pdf/2002.08936v1.pdf | Meta-learning for mixed linear regression | In modern supervised learning, there are a large number of tasks, but many of them are associated with only a small amount of labeled data. These include data from medical image processing and robotic interaction. Even though each individual task cannot be meaningfully trained in isolation, one seeks to meta-learn acro... | ['Zhao Song', 'Sewoong Oh', 'Raghav Somani', 'Weihao Kong', 'Sham Kakade'] | 2020-02-20 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/6124-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/6124-Paper.pdf | icml-2020-1 | ['small-data'] | ['computer-vision'] | [ 4.27447051e-01 2.85135329e-01 -2.02127337e-01 -2.69559294e-01
-8.32740724e-01 -2.26308793e-01 3.34263891e-01 5.19061722e-02
-7.98578918e-01 7.87877023e-01 -1.51942074e-01 -1.28730118e-01
-5.93176186e-01 -2.81795382e-01 -7.66602218e-01 -8.98154020e-01
-2.79584795e-01 5.53950965e-01 -5.23591787e-02 -1.44097790... | [9.3399019241333, 3.5517420768737793] |
d36ad80b-7e88-4161-ae7e-a6bf02af5931 | in-defense-of-sparsity-based-face-recognition | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Deng_In_Defense_of_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Deng_In_Defense_of_2013_CVPR_paper.pdf | In Defense of Sparsity Based Face Recognition | The success of sparse representation based classification (SRC) has largely boosted the research of sparsity based face recognition in recent years. A prevailing view is that the sparsity based face recognition performs well only when the training images have been carefully controlled and the number of samples per clas... | ['Weihong Deng', 'Jiani Hu', 'Jun Guo'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 4.32544053e-01 -3.92360747e-01 -3.45027328e-01 -5.24457514e-01
-3.31261694e-01 5.92522025e-02 5.29063344e-01 -2.66475409e-01
-8.39791447e-03 6.73612058e-01 1.38380915e-01 2.00705484e-01
-3.56053412e-01 -6.36812806e-01 -3.66418809e-01 -1.12083673e+00
7.48568028e-02 1.36235341e-01 -3.76170188e-01 -2.70069629... | [12.502020835876465, 0.4135721027851105] |
ec1e8d35-4cf1-4ee6-bf41-fba13ead8ec4 | feature-embedding-by-template-matching-as-a | 2210.00992 | null | https://arxiv.org/abs/2210.00992v1 | https://arxiv.org/pdf/2210.00992v1.pdf | Feature Embedding by Template Matching as a ResNet Block | Convolution blocks serve as local feature extractors and are the key to success of the neural networks. To make local semantic feature embedding rather explicit, we reformulate convolution blocks as feature selection according to the best matching kernel. In this manner, we show that typical ResNet blocks indeed perfor... | ['A. Aydin Alatan', 'Yeti Z. Gurbuz', 'Ada Gorgun'] | 2022-10-03 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 6.36018440e-02 -1.87894665e-02 -4.62672621e-01 -6.87956870e-01
-7.63986290e-01 -6.04390502e-01 8.30318153e-01 -5.74978627e-02
-7.09387779e-01 3.60657483e-01 5.07242441e-01 -5.52095659e-02
-6.61272183e-02 -7.82368183e-01 -9.17536497e-01 -7.17218697e-01
1.13872916e-01 9.14669689e-03 7.49596721e-03 -1.75007001... | [9.333013534545898, 2.141580820083618] |
ef89a023-4e8e-484b-b490-73ee9fd961a2 | robust-sparse-mean-estimation-via-incremental | 2305.15276 | null | https://arxiv.org/abs/2305.15276v1 | https://arxiv.org/pdf/2305.15276v1.pdf | Robust Sparse Mean Estimation via Incremental Learning | In this paper, we study the problem of robust sparse mean estimation, where the goal is to estimate a $k$-sparse mean from a collection of partially corrupted samples drawn from a heavy-tailed distribution. Existing estimators face two critical challenges in this setting. First, they are limited by a conjectured comput... | ['Wei Hu', 'Salar Fattahi', 'Yinghui He', 'Rui Ray Chen', 'Jianhao Ma'] | 2023-05-24 | null | null | null | null | ['incremental-learning'] | ['methodology'] | [ 9.27924514e-02 6.84665143e-02 -2.05003873e-01 -1.34291276e-01
-1.55612493e+00 -4.65108544e-01 -2.38463342e-01 -4.42409255e-02
-3.63468498e-01 8.34188640e-01 -5.06879427e-02 -1.17510855e-01
-3.26938480e-01 -5.73779821e-01 -1.10338962e+00 -1.07296419e+00
-3.72022688e-01 3.40532102e-02 -4.16920722e-01 1.66388944... | [6.7449750900268555, 4.5610880851745605] |
a1df6ebf-d86f-4013-b153-401de157b8ff | transfer-and-active-learning-for-dissonance | 2305.02459 | null | https://arxiv.org/abs/2305.02459v2 | https://arxiv.org/pdf/2305.02459v2.pdf | Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge | While transformer-based systems have enabled greater accuracies with fewer training examples, data acquisition obstacles still persist for rare-class tasks -- when the class label is very infrequent (e.g. < 5% of samples). Active learning has in general been proposed to alleviate such challenges, but choice of selectio... | ['H. Andrew Schwartz', 'Christian Luhmann', 'Jonah Luby', 'Xiaoran Liu', 'Syeda Mahwish', 'Swanie Juhng', 'Vasudha Varadarajan'] | 2023-05-03 | null | null | null | null | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 5.07104099e-01 2.41183117e-01 -3.36471945e-01 -5.54792702e-01
-1.06120741e+00 -5.05156815e-01 6.77484930e-01 5.34755945e-01
-8.65603745e-01 7.76893735e-01 6.76008463e-02 -3.92229617e-01
-5.27251422e-01 -5.52549660e-01 -3.63207787e-01 -4.69504446e-01
-4.79396433e-02 9.40904021e-01 4.22429204e-01 -1.62225097... | [9.63863754272461, 4.317963600158691] |
82018f1b-2203-43c5-a15f-6bc8606f8b37 | estimating-risk-aware-flexibility-areas-for | 2301.00564 | null | https://arxiv.org/abs/2301.00564v1 | https://arxiv.org/pdf/2301.00564v1.pdf | Estimating Risk-Aware Flexibility Areas for EV Charging Pools via Stochastic AC-OPF | This paper introduces a stochastic AC-OPF (SOPF) for the flexibility management of electric vehicle (EV) charging pools in distribution networks under uncertainty. The SOPF considers discrete utility functions from charging pools as a compensation mechanism for eventual energy not served to their charging tasks. An app... | ['Johann L. Hurink', 'Gerwin Hoogsteen', 'Maria Vlasiou', 'Pedro P. Vergara', 'Nataly Banol Arias', 'Juan S. Giraldo'] | 2023-01-02 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-4.29720730e-01 5.44394612e-01 -2.04086870e-01 -4.29256529e-01
-4.31093484e-01 -9.64113116e-01 2.85943449e-01 1.31753385e-01
2.51915678e-02 1.05845165e+00 -7.34432191e-02 -1.68776482e-01
-8.67206395e-01 -1.02238011e+00 -4.89408493e-01 -1.05237973e+00
9.58784893e-02 9.78655398e-01 -2.12722957e-01 -1.00381441... | [5.638200759887695, 2.371795892715454] |
5a5c83ea-690f-4a8b-8ce1-53cb014b3a1f | hsc-rocket-an-interactive-dialogue-assistant | null | null | https://openreview.net/forum?id=tiuszgEsW9 | https://openreview.net/pdf?id=tiuszgEsW9 | HSC-Rocket: An interactive dialogue assistant to make agents composing service better through human feedback | Facing the current dynamic service environment, fast and efficient service composition has attracted great attention in recent years. Users prefer to express their personal requirements based on natural language, and their real-time feedback could better reflect the effect of service composition to a great extent. Cons... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['service-composition'] | ['miscellaneous'] | [-1.22809455e-01 -3.91531706e-01 2.55444981e-02 -7.87503779e-01
-2.78699934e-01 -1.82203114e-01 1.99463099e-01 -3.98151457e-01
-5.24717331e-01 2.33151183e-01 6.18243873e-01 -1.70504063e-01
-1.01163626e-01 -6.81233883e-01 2.53792316e-01 -6.65238380e-01
3.22240919e-01 4.74485129e-01 1.21524453e-01 -7.80442119... | [13.005383491516113, 6.177823543548584] |
d47d62dc-ea19-4b02-9489-6bc33429d669 | progrest-prototypical-graph-regression-soft | 2210.03745 | null | https://arxiv.org/abs/2210.03745v2 | https://arxiv.org/pdf/2210.03745v2.pdf | ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Prediction | In this work, we propose the novel Prototypical Graph Regression Self-explainable Trees (ProGReST) model, which combines prototype learning, soft decision trees, and Graph Neural Networks. In contrast to other works, our model can be used to address various challenging tasks, including compound property prediction. In ... | ['Tomasz Danel', 'Daniel Dobrowolski', 'Dawid Rymarczyk'] | 2022-10-07 | null | null | null | null | ['graph-regression', 'molecular-property-prediction'] | ['graphs', 'miscellaneous'] | [ 7.70543873e-01 5.81025898e-01 -9.03876364e-01 -2.66450137e-01
-2.72980720e-01 -3.08265805e-01 4.06335354e-01 5.21121204e-01
4.03712481e-01 9.61309373e-01 2.83005219e-02 -8.88903916e-01
-2.99737155e-01 -6.92990303e-01 -7.45581567e-01 -5.00046194e-01
-4.63879816e-02 6.75688028e-01 8.47576419e-04 -1.13453150... | [5.21448278427124, 5.928685188293457] |
37b24651-d1c9-495c-b685-aece151a7f77 | laugh-betrays-you-learning-robust-speaker | 2210.16028 | null | https://arxiv.org/abs/2210.16028v2 | https://arxiv.org/pdf/2210.16028v2.pdf | Laugh Betrays You? Learning Robust Speaker Representation From Speech Containing Non-Verbal Fragments | The success of automatic speaker verification shows that discriminative speaker representations can be extracted from neutral speech. However, as a kind of non-verbal voice, laughter should also carry speaker information intuitively. Thus, this paper focuses on exploring speaker verification about utterances containing... | ['Ming Li', 'Zhenyi Zhu', 'Huahua Cui', 'Xiaoyi Qin', 'Yuke Lin'] | 2022-10-28 | null | null | null | null | ['speaker-verification'] | ['speech'] | [-9.13331062e-02 8.53837579e-02 -1.79569095e-01 -6.23163342e-01
-1.24192977e+00 -5.29158950e-01 4.86288637e-01 -4.04290795e-01
-1.20329551e-01 4.36038613e-01 5.66404045e-01 -1.76302835e-01
6.25461817e-01 -4.75986376e-02 -3.97507757e-01 -8.00739706e-01
9.90479141e-02 -2.79249717e-02 -1.62513539e-01 -2.61777788... | [14.445487022399902, 6.082090377807617] |
410d548d-b803-425f-8aa8-91d5ebf572b4 | evaluating-bayesian-model-visualisations | 2201.03604 | null | https://arxiv.org/abs/2201.03604v1 | https://arxiv.org/pdf/2201.03604v1.pdf | Evaluating Bayesian Model Visualisations | Probabilistic models inform an increasingly broad range of business and policy decisions ultimately made by people. Recent algorithmic, computational, and software framework development progress facilitate the proliferation of Bayesian probabilistic models, which characterise unobserved parameters by their joint distri... | ['John H. Williamson', 'Sebastian Stein'] | 2022-01-10 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 2.20899716e-01 5.09106576e-01 -1.90566123e-01 -6.69723690e-01
-4.07853603e-01 -7.57024467e-01 7.83542037e-01 6.46135747e-01
-5.62282324e-01 5.21611869e-01 7.66957402e-01 -1.40669131e+00
-5.83180606e-01 -5.73383987e-01 -3.24583054e-02 -2.92546421e-01
1.22993082e-01 6.27207935e-01 -4.75471839e-02 3.49424750... | [8.744012832641602, 5.861914157867432] |
238ff60e-6d20-46b7-b1bf-3b8cfc423176 | human-pose-estimation-with-parsing-induced | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Nie_Human_Pose_Estimation_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Nie_Human_Pose_Estimation_CVPR_2018_paper.pdf | Human Pose Estimation With Parsing Induced Learner | Human pose estimation still faces various difficulties in challenging scenarios. Human parsing, as a closely related task, can provide valuable cues for better pose estimation, which however has not been fully exploited. In this paper, we propose a novel Parsing Induced Learner to exploit parsing information to effecti... | ['Shuicheng Yan', 'Yiming Zuo', 'Jiashi Feng', 'Xuecheng Nie'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['human-parsing'] | ['computer-vision'] | [ 1.49721101e-01 1.88686267e-01 -3.99413079e-01 -6.71729505e-01
-1.40526211e+00 -4.29251820e-01 1.83147326e-01 -1.92211986e-01
-4.47742999e-01 4.57795888e-01 4.23692644e-01 3.38919789e-01
3.16154361e-01 -2.03673169e-01 -9.00131881e-01 -4.77419823e-01
8.74271020e-02 7.36489654e-01 4.08785373e-01 1.33316115... | [7.6242289543151855, -0.5509968996047974] |
c3596677-4075-4c43-ad85-f4285a61462b | damix-density-aware-data-augmentation-for | 2109.12544 | null | https://arxiv.org/abs/2109.12544v2 | https://arxiv.org/pdf/2109.12544v2.pdf | DAMix: A Density-Aware Mixup Augmentation for Single Image Dehazing under Domain Shift | Deep learning-based methods have achieved considerable success on single image dehazing in recent years. However, these methods are often subject to performance degradation when domain shifts are confronted. Specifically, haze density gaps exist among the existing datasets, often resulting in poor performance when thes... | ['Tsung-Nan Lin', 'Chia-Ming Chang'] | 2021-09-26 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 1.77730456e-01 -1.49823561e-01 3.16762865e-01 -2.47329265e-01
-6.36651099e-01 -2.41303697e-01 5.31215131e-01 3.32556292e-02
-4.26516891e-01 7.93230116e-01 1.83008403e-01 6.40197024e-02
-3.76011990e-02 -9.99204397e-01 -8.06026220e-01 -1.03460312e+00
2.52788812e-01 3.68456602e-01 4.71591473e-01 -3.55690032... | [10.929132461547852, -3.0752744674682617] |
9d08a894-920a-46c2-94d2-6dae725d3657 | deep-learning-based-edm-subgenre | 2110.08862 | null | https://arxiv.org/abs/2110.08862v1 | https://arxiv.org/pdf/2110.08862v1.pdf | Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features | Along with the evolution of music technology, a large number of styles, or "subgenres," of Electronic Dance Music(EDM) have emerged in recent years. While the classification task of distinguishing between EDM and non-EDM has been often studied in the context of music genre classification, little work has been done on t... | ['Yi-Hsuan Yang', 'Bo-Yu Chen', 'Wei-Han Hsu'] | 2021-10-17 | null | null | null | null | ['genre-classification', 'music-auto-tagging'] | ['computer-vision', 'music'] | [ 4.34895568e-02 -3.84060085e-01 -1.42510772e-01 -5.58708534e-02
-5.08128822e-01 -6.59276307e-01 6.01594210e-01 1.41039759e-01
-3.55511516e-01 4.70763296e-01 4.99096572e-01 1.95713907e-01
-4.94464636e-01 -6.57077730e-01 -1.64547890e-01 -4.75284994e-01
-1.66242719e-01 4.95169163e-01 -6.34174934e-03 -2.95186311... | [15.86679458618164, 5.235988616943359] |
e0b15770-5af8-47ca-865e-b34ddf085764 | activity-recognition-from-videos-with | 1505.00581 | null | http://arxiv.org/abs/1505.00581v1 | http://arxiv.org/pdf/1505.00581v1.pdf | Activity recognition from videos with parallel hypergraph matching on GPUs | In this paper, we propose a method for activity recognition from videos based
on sparse local features and hypergraph matching. We benefit from special
properties of the temporal domain in the data to derive a sequential and fast
graph matching algorithm for GPUs.
Traditionally, graphs and hypergraphs are frequently ... | ['Bülent Sankur', 'Christian Wolf', 'Eric Lombardi', 'Oya Celiktutan'] | 2015-05-04 | null | null | null | null | ['set-matching', 'hypergraph-matching'] | ['computer-vision', 'graphs'] | [ 2.74840057e-01 -2.01777101e-01 -4.20544744e-02 -9.69457477e-02
-3.30321461e-01 -5.00204742e-01 2.50879645e-01 5.55167317e-01
-3.79391998e-01 3.76101673e-01 -2.88444072e-01 1.00772614e-02
-2.48533934e-01 -8.86130333e-01 -5.54471612e-01 -7.90126145e-01
-2.02256396e-01 6.83079541e-01 3.16310585e-01 1.58582747... | [8.26082992553711, -1.766132116317749] |
6ff76458-32bc-4ec6-9946-24a350d66b04 | contour-detection-using-cost-sensitive | 1412.6857 | null | http://arxiv.org/abs/1412.6857v5 | http://arxiv.org/pdf/1412.6857v5.pdf | Contour Detection Using Cost-Sensitive Convolutional Neural Networks | We address the problem of contour detection via per-pixel classifications of
edge point. To facilitate the process, the proposed approach leverages with
DenseNet, an efficient implementation of multiscale convolutional neural
networks (CNNs), to extract an informative feature vector for each pixel and
uses an SVM class... | ['Tyng-Luh Liu', 'Jyh-Jing Hwang'] | 2014-12-22 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 3.85134101e-01 -7.24531636e-02 -1.51698932e-01 -1.11076601e-01
-8.98690283e-01 -4.43420738e-01 4.01340246e-01 1.93268389e-01
-4.93231058e-01 3.19293559e-01 -2.22175736e-02 -2.99334198e-01
2.63335496e-01 -1.17903221e+00 -6.99550092e-01 -5.54625988e-01
-2.09741712e-01 -7.37253875e-02 5.98120868e-01 -1.04188465... | [9.501449584960938, 0.15697214007377625] |
f2ab91f0-0c38-4272-aca6-ff6c9f84c4c0 | a-joint-3d-2d-based-method-for-free-space | 1711.02144 | null | http://arxiv.org/abs/1711.02144v3 | http://arxiv.org/pdf/1711.02144v3.pdf | A Joint 3D-2D based Method for Free Space Detection on Roads | In this paper, we address the problem of road segmentation and free space
detection in the context of autonomous driving. Traditional methods either use
3-dimensional (3D) cues such as point clouds obtained from LIDAR, RADAR or
stereo cameras or 2-dimensional (2D) cues such as lane markings, road
boundaries and object ... | ['Suvam Patra', 'Subhashis Banerjee', 'Shashank Yadav', 'Pranjal Maheshwari', 'Chetan Arora'] | 2017-11-06 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 4.20363873e-01 5.84638258e-03 -7.47427642e-02 -4.59537894e-01
-4.84228879e-01 -4.70055223e-01 7.01872289e-01 7.30410814e-02
-4.33959275e-01 7.11676240e-01 -3.60373825e-01 -4.72809047e-01
-5.12830690e-02 -1.26115692e+00 -8.85658085e-01 -4.32238400e-01
8.63856822e-02 7.03234315e-01 7.33309686e-01 -3.62939507... | [8.122638702392578, -1.9938879013061523] |
015aab7f-571e-4999-a117-056b43d9c29e | text2tex-text-driven-texture-synthesis-via | 2303.11396 | null | https://arxiv.org/abs/2303.11396v1 | https://arxiv.org/pdf/2303.11396v1.pdf | Text2Tex: Text-driven Texture Synthesis via Diffusion Models | We present Text2Tex, a novel method for generating high-quality textures for 3D meshes from the given text prompts. Our method incorporates inpainting into a pre-trained depth-aware image diffusion model to progressively synthesize high resolution partial textures from multiple viewpoints. To avoid accumulating inconsi... | ['Matthias Nießner', 'Sergey Tulyakov', 'Hsin-Ying Lee', 'Yawar Siddiqui', 'Dave Zhenyu Chen'] | 2023-03-20 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 5.55454969e-01 8.35397616e-02 1.53640993e-02 -1.73418820e-01
-7.74962604e-01 -5.79150736e-01 5.80803692e-01 -5.48107266e-01
5.01918674e-01 7.10757196e-01 4.56058860e-01 1.00875333e-01
4.50706393e-01 -1.04543293e+00 -6.43471539e-01 -4.95709300e-01
7.17987418e-01 6.02951765e-01 3.85078430e-01 3.62573080... | [9.351314544677734, -3.0780296325683594] |
853a7ef9-5cd5-486a-84c4-328758cf0db6 | fer-former-multi-modal-transformer-for-facial | 2303.12997 | null | https://arxiv.org/abs/2303.12997v1 | https://arxiv.org/pdf/2303.12997v1.pdf | FER-former: Multi-modal Transformer for Facial Expression Recognition | The ever-increasing demands for intuitive interactions in Virtual Reality has triggered a boom in the realm of Facial Expression Recognition (FER). To address the limitations in existing approaches (e.g., narrow receptive fields and homogenous supervisory signals) and further cement the capacity of FER tools, a novel m... | ['Li Liu', 'Yonggang Lu', 'Minglun Gong', 'Mingjie Wang', 'Yande Li'] | 2023-03-23 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 3.19874197e-01 2.00137436e-01 -2.61172920e-01 -6.08750463e-01
-5.06714463e-01 -2.01374993e-01 7.61256218e-01 -3.30464303e-01
-1.80093050e-01 5.77989042e-01 1.91404536e-01 1.96651444e-01
-2.83470452e-01 -4.19125706e-01 -4.78234529e-01 -7.54451573e-01
2.29235843e-01 -1.35355338e-01 -4.80641462e-02 -5.84799707... | [13.578737258911133, 1.6064237356185913] |
e19de147-c77d-4485-9a56-6b9d8a10ecbe | light-vqa-a-multi-dimensional-quality | 2305.09512 | null | https://arxiv.org/abs/2305.09512v1 | https://arxiv.org/pdf/2305.09512v1.pdf | Light-VQA: A Multi-Dimensional Quality Assessment Model for Low-Light Video Enhancement | Recently, Users Generated Content (UGC) videos becomes ubiquitous in our daily lives. However, due to the limitations of photographic equipments and techniques, UGC videos often contain various degradations, in which one of the most visually unfavorable effects is the underexposure. Therefore, corresponding video enhan... | ['Guangtao Zhai', 'Tao Tan', 'Xunchu Zhou', 'Yixuan Gao', 'Xiaohong Liu', 'Yunlong Dong'] | 2023-05-16 | null | null | null | null | ['video-quality-assessment', 'video-enhancement', 'video-quality-assessment'] | ['computer-vision', 'computer-vision', 'time-series'] | [-4.47493186e-03 -7.60181129e-01 6.41036630e-02 -3.04690033e-01
-5.90125501e-01 -1.72546908e-01 2.54759431e-01 -2.63981402e-01
-3.41269970e-01 6.44754887e-01 2.94573635e-01 -2.51776725e-03
-5.04240282e-02 -9.29156959e-01 -6.96087301e-01 -8.95602345e-01
1.95777193e-01 -7.14707732e-01 2.86510944e-01 -3.57342243... | [11.554951667785645, -1.859817385673523] |
789cc368-543c-4b8e-a211-cf7d6862ac74 | risk-averse-contextual-multi-armed-bandit | 2206.12463 | null | https://arxiv.org/abs/2206.12463v1 | https://arxiv.org/pdf/2206.12463v1.pdf | Risk-averse Contextual Multi-armed Bandit Problem with Linear Payoffs | In this paper we consider the contextual multi-armed bandit problem for linear payoffs under a risk-averse criterion. At each round, contexts are revealed for each arm, and the decision maker chooses one arm to pull and receives the corresponding reward. In particular, we consider mean-variance as the risk criterion, a... | ['Enlu Zhou', 'Yuhao Wang', 'Yifan Lin'] | 2022-06-24 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 4.26863320e-02 2.83203185e-01 -4.01568830e-01 -2.46074200e-02
-9.69142139e-01 -9.23291862e-01 -3.12166363e-01 1.77284017e-01
-1.01606262e+00 9.28078592e-01 -3.29648763e-01 -8.91222894e-01
-1.11141181e+00 -8.99074614e-01 -6.18562341e-01 -9.77509201e-01
-4.48427260e-01 3.48319590e-01 -7.84398168e-02 5.37872240... | [4.550400733947754, 3.230031728744507] |
1078771f-4202-4c25-879f-b58e1f08c181 | self-supervised-transformers-for-unsupervised | 2202.11539 | null | https://arxiv.org/abs/2202.11539v2 | https://arxiv.org/pdf/2202.11539v2.pdf | Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut | Transformers trained with self-supervised learning using self-distillation loss (DINO) have been shown to produce attention maps that highlight salient foreground objects. In this paper, we demonstrate a graph-based approach that uses the self-supervised transformer features to discover an object from an image. Visual ... | ['Dominique Vaufreydaz', 'James Crowley', 'Yuan Yuan', 'Shell Hu', 'Xi Shen', 'Yangtao Wang'] | 2022-02-23 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Self-Supervised_Transformers_for_Unsupervised_Object_Discovery_Using_Normalized_Cut_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Self-Supervised_Transformers_for_Unsupervised_Object_Discovery_Using_Normalized_Cut_CVPR_2022_paper.pdf | cvpr-2022-1 | ['single-object-discovery'] | ['computer-vision'] | [ 5.73392868e-01 5.05126834e-01 -1.44267038e-01 -2.07712993e-01
-6.82189763e-01 -4.22195882e-01 4.18640435e-01 3.03733706e-01
-2.52419829e-01 4.62474018e-01 -3.33038345e-02 6.55971915e-02
1.01940006e-01 -6.45331800e-01 -8.78464580e-01 -6.69171393e-01
-1.22194983e-01 2.84147501e-01 1.20308709e+00 2.90518184... | [9.473724365234375, 0.650446355342865] |
0a7a91fe-013c-423d-b3de-62f8c62a2681 | deep-metric-multi-view-hashing-for-multimedia | 2304.06358 | null | https://arxiv.org/abs/2304.06358v1 | https://arxiv.org/pdf/2304.06358v1.pdf | Deep Metric Multi-View Hashing for Multimedia Retrieval | Learning the hash representation of multi-view heterogeneous data is an important task in multimedia retrieval. However, existing methods fail to effectively fuse the multi-view features and utilize the metric information provided by the dissimilar samples, leading to limited retrieval precision. Current methods utiliz... | ['Lingfang Zeng', 'Yongli Cheng', 'Yu Cui', 'Xiaohu Ruan', 'Zhangmin Huang', 'Jian Zhu'] | 2023-04-13 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [-4.36376274e-01 -1.04001951e+00 -4.07091081e-01 -3.06778580e-01
-1.80496395e+00 -7.08042622e-01 5.88609993e-01 4.33369488e-01
-3.59503329e-01 3.65908772e-01 5.95905602e-01 3.70674908e-01
-1.08742908e-01 -8.35313380e-01 -5.89570224e-01 -9.36062098e-01
-1.66541100e-01 4.08698082e-01 3.31692070e-01 -4.64465469... | [11.34001350402832, 0.934941291809082] |
ac48bed2-585a-4713-beca-9e8b6d5d6bdd | high-resolution-peak-demand-estimation-using | 2203.03342 | null | https://arxiv.org/abs/2203.03342v2 | https://arxiv.org/pdf/2203.03342v2.pdf | High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks | This paper covers predicting high-resolution electricity peak demand features given lower-resolution data. This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future high-resolution peak loads when the high-resolution data is no longer available. That question is partic... | ['Florian Ziel', 'Michał Narajewski', 'Jonathan Berrisch'] | 2022-03-07 | null | null | null | null | ['additive-models'] | ['methodology'] | [-1.59325063e-01 5.04820701e-03 -1.86995864e-01 -4.81691301e-01
-8.15043271e-01 -3.39968801e-01 7.88525820e-01 2.03137770e-01
-1.48419440e-01 1.23167002e+00 1.36753529e-01 -2.46959537e-01
-8.66377354e-01 -1.35498726e+00 -3.46646577e-01 -9.08019304e-01
-5.83804607e-01 6.92277074e-01 -5.52737236e-01 -3.80338997... | [6.132876396179199, 2.83687162399292] |
cbf0a7de-c9de-4884-be8d-afeac15b3897 | challenges-in-gaussian-processes-for-non | 2211.13018 | null | https://arxiv.org/abs/2211.13018v1 | https://arxiv.org/pdf/2211.13018v1.pdf | Challenges in Gaussian Processes for Non Intrusive Load Monitoring | Non-intrusive load monitoring (NILM) or energy disaggregation aims to break down total household energy consumption into constituent appliances. Prior work has shown that providing an energy breakdown can help people save up to 15\% of energy. In recent years, deep neural networks (deep NNs) have made remarkable progre... | ['Nipun Batra', 'Zeel B Patel', 'Gautam Vashishtha', 'Aadesh Desai'] | 2022-11-18 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-7.59544075e-02 3.31800908e-01 -6.25000298e-02 -4.26347136e-01
-7.49062836e-01 -4.17999148e-01 6.16191268e-01 4.82650623e-02
3.32191363e-02 8.73238683e-01 3.22327852e-01 -3.15873384e-01
-3.38851899e-01 -1.06821656e+00 -5.47924459e-01 -7.49408662e-01
-1.98612176e-02 6.24093592e-01 -4.88652021e-01 3.71586472... | [16.063385009765625, 7.577278137207031] |
9e0b24dc-38ee-4a1d-a0a0-63844dc0f681 | whoi-plankton-a-large-scale-fine-grained | 1510.00745 | null | http://arxiv.org/abs/1510.00745v1 | http://arxiv.org/pdf/1510.00745v1.pdf | WHOI-Plankton- A Large Scale Fine Grained Visual Recognition Benchmark Dataset for Plankton Classification | Planktonic organisms are of fundamental importance to marine ecosystems: they
form the basis of the food web, provide the link between the atmosphere and the
deep ocean, and influence global-scale biogeochemical cycles. Scientists are
increasingly using imaging-based technologies to study these creatures in their
natur... | ['Heidi M. Sosik', 'Eric C. Orenstein', 'Emily E. Peacock', 'Oscar Beijbom'] | 2015-10-02 | null | null | null | null | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 2.98980437e-02 -5.86643696e-01 5.31679511e-01 -7.45565891e-02
-2.81519741e-01 -8.87342989e-01 5.68603098e-01 5.47593720e-02
-7.59774745e-01 6.66297495e-01 1.65650412e-01 -2.18358472e-01
-3.81480977e-02 -6.84517026e-01 -4.71498221e-01 -9.74101186e-01
-3.08979750e-01 4.09904122e-01 4.83411372e-01 3.49730030... | [8.460147857666016, -1.2531999349594116] |
e1fa35ff-320d-49e6-a2c4-e75274c8d1ef | generalized-few-shot-semantic-segmentation-1 | 2112.10982 | null | https://arxiv.org/abs/2112.10982v3 | https://arxiv.org/pdf/2112.10982v3.pdf | Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning | Generalized few-shot semantic segmentation was introduced to move beyond only evaluating few-shot segmentation models on novel classes to include testing their ability to remember base classes. While the current state-of-the-art approach is based on meta-learning, it performs poorly and saturates in learning after obse... | ['Danna Gurari', 'Scott Cohen', 'Brian Price', 'Yinan Zhao', 'Josh Myers-Dean'] | 2021-12-21 | null | null | null | null | ['generalized-few-shot-semantic-segmentation'] | ['computer-vision'] | [ 3.38421613e-01 2.80322552e-01 -3.09414595e-01 -4.24005091e-01
-1.05272877e+00 -5.10091901e-01 6.80819333e-01 2.03805134e-01
-7.32454002e-01 6.43004239e-01 2.57316418e-02 -1.02363043e-02
-1.95094720e-01 -7.02680767e-01 -7.51527131e-01 -4.41511005e-01
-5.28834462e-02 7.98783839e-01 1.23211432e+00 -1.24735564... | [9.667263984680176, 1.875821590423584] |
a5500343-a9df-4f54-af96-cdded167cb54 | adversarial-training-a-simple-and-efficient | null | null | https://openreview.net/forum?id=EO4VJGAllb | https://openreview.net/pdf?id=EO4VJGAllb | Adversarial Training: A simple and efficient technique to Improving NLP Robustness | NLP models are shown to be prone to adversarial attacks which undermines their robustness, i.e. a small perturbation to the input text can fool an NLP model to incorrectly classify text. In this study, we present a new Adversarial Text Generation technique that, given an input text, generates adversarial texts through ... | ['marwan omar'] | 2021-09-29 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 6.67568803e-01 5.80837727e-01 2.23837897e-01 -3.56566280e-01
-6.98597372e-01 -1.45423210e+00 8.95142555e-01 -9.58282724e-02
-3.41413952e-02 9.78827059e-01 1.47697315e-01 -6.53822362e-01
6.40394628e-01 -1.17864621e+00 -1.00677025e+00 -3.35643440e-01
4.50824320e-01 5.62962115e-01 -1.50196224e-01 -6.25962973... | [6.003640174865723, 8.117290496826172] |
10e6ae0d-870b-40e6-be3c-ae299d142810 | neural-sentence-embedding-models-for-semantic | 2110.15708 | null | https://arxiv.org/abs/2110.15708v1 | https://arxiv.org/pdf/2110.15708v1.pdf | Neural sentence embedding models for semantic similarity estimation in the biomedical domain | BACKGROUND: In this study, we investigated the efficacy of current state-of-the-art neural sentence embedding models for semantic similarity estimation of sentences from biomedical literature. We trained different neural embedding models on 1.7 million articles from the PubMed Open Access dataset, and evaluated them ba... | ['Matthias Samwald', 'Asan Agibetov', 'Hong Xu', 'Kathrin Blagec'] | 2021-10-01 | null | null | null | null | ['sentence-embeddings-for-biomedical-texts', 'sentence-embeddings-for-biomedical-texts'] | ['methodology', 'natural-language-processing'] | [ 2.91090161e-01 3.50970894e-01 -2.42096167e-02 -4.07665581e-01
-7.02516377e-01 -1.02067880e-01 4.46907729e-01 1.15848470e+00
-1.11752617e+00 7.98333406e-01 4.15640205e-01 3.47592272e-02
-6.53666377e-01 -8.29028308e-01 -5.57394266e-01 -3.58017147e-01
-2.90321469e-01 7.65007675e-01 2.16995195e-01 -5.33505499... | [8.542733192443848, 8.726874351501465] |
f6a8a6e1-dcb0-428d-a9fe-a6d56862ac1a | genetic-algorithm-optimized-long-short-term | null | null | https://www.mdpi.com/2071-1050/10/10/3765 | https://www.mdpi.com/2071-1050/10/10/3765/pdf?version=1539866453 | Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction | With recent advances in computing technology, massive amounts of data and information are being constantly accumulated. Especially in the field of finance, we have great opportunities to create useful insights by analyzing that information, because the financial market produces a tremendous amount of real-time data, in... | ['Kyung-shik Shin', 'Hyejung Chung'] | 2018-10-18 | null | null | null | sustainability-2018-10 | ['stock-market-prediction'] | ['time-series'] | [-4.67764586e-01 -7.03064859e-01 -4.06682901e-02 3.71656679e-02
-1.33776784e-01 -3.76955718e-01 4.00575817e-01 -8.30395520e-02
-4.18165058e-01 7.49079168e-01 -1.34349063e-01 -6.18177414e-01
-2.89775193e-01 -1.17095971e+00 -2.39528462e-01 -7.31041551e-01
-4.94536549e-01 2.51872957e-01 2.40588248e-01 -3.04321498... | [4.514476299285889, 4.1891326904296875] |
e144f2f2-147d-409d-936a-802e3e3b2de3 | word-representation-models-for | 1606.04217 | null | http://arxiv.org/abs/1606.04217v1 | http://arxiv.org/pdf/1606.04217v1.pdf | Word Representation Models for Morphologically Rich Languages in Neural Machine Translation | Dealing with the complex word forms in morphologically rich languages is an
open problem in language processing, and is particularly important in
translation. In contrast to most modern neural systems of translation, which
discard the identity for rare words, in this paper we propose several
architectures for learning ... | ['Gholamreza Haffari', 'Ekaterina Vylomova', 'Trevor Cohn', 'Xuanli He'] | 2016-06-14 | word-representation-models-for-1 | https://aclanthology.org/W17-4115 | https://aclanthology.org/W17-4115.pdf | ws-2017-9 | ['hard-attention'] | ['methodology'] | [ 3.01264286e-01 -1.20696239e-02 -5.38998187e-01 -3.30778688e-01
-1.20855153e+00 -8.27200353e-01 6.66063190e-01 1.52526006e-01
-6.79848731e-01 1.04923069e+00 5.90735018e-01 -8.34854245e-01
4.20624167e-01 -7.79325604e-01 -7.95977294e-01 -2.80847132e-01
3.95273656e-01 8.72055948e-01 -4.74649221e-01 -5.81826389... | [11.435956001281738, 10.105379104614258] |
7f5dfed1-dc46-4e4b-bc2e-fc27db3143ba | dc-net-divide-and-conquer-for-salient-object | 2305.14955 | null | https://arxiv.org/abs/2305.14955v2 | https://arxiv.org/pdf/2305.14955v2.pdf | DC-Net: Divide-and-Conquer for Salient Object Detection | In this paper, we introduce Divide-and-Conquer into the salient object detection (SOD) task to enable the model to learn prior knowledge that is for predicting the saliency map. We design a novel network, Divide-and-Conquer Network (DC-Net) which uses two encoders to solve different subtasks that are conducive to predi... | ['Abdulmotaleb Elsaddik', 'Xuebin Qin', 'JiaYi Zhu'] | 2023-05-24 | null | null | null | null | ['salient-object-detection-1'] | ['computer-vision'] | [ 1.52444914e-01 1.96193531e-02 1.18816696e-01 -4.59351510e-01
-4.93721515e-01 -2.05489695e-02 4.18732353e-02 -6.49410337e-02
-5.54484010e-01 5.76973557e-01 1.52674124e-01 -5.55209536e-03
1.57055512e-01 -8.05276752e-01 -9.66568112e-01 -5.69381952e-01
-1.01724789e-01 -2.47813091e-01 1.06794310e+00 -2.45031118... | [9.66601848602295, -0.46006789803504944] |
c3a7fc21-6e44-4903-ae3b-f8250636323f | vqn-variable-quantization-noise-for-neural | null | null | https://openreview.net/forum?id=VbtRUvpzht | https://openreview.net/pdf?id=VbtRUvpzht | VQN: Variable Quantization Noise for Neural Network Compression | Quantization refers to a set of methods that compress a neural network by representing its parameters with fewer bits. However, applying quantization to a neural network after training often leads to severe performance regressions. Quantization Aware Training (QAT) addresses this problem by applying simulated training-... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 5.79145610e-01 -7.88452923e-02 -1.94701388e-01 -2.93952525e-01
-7.75136590e-01 -5.47923148e-01 6.16746545e-01 1.30649164e-01
-9.88101900e-01 6.92766428e-01 -3.96472737e-02 -6.37753487e-01
-2.50517726e-01 -9.33124840e-01 -8.87782812e-01 -6.84155881e-01
-1.83697715e-01 3.04362029e-01 3.36518377e-01 -8.23225419... | [8.638632774353027, 3.1267735958099365] |
dbabf9a3-69aa-409c-9060-69f610d45846 | label-only-membership-inference-attacks | 2007.14321 | null | https://arxiv.org/abs/2007.14321v3 | https://arxiv.org/pdf/2007.14321v3.pdf | Label-Only Membership Inference Attacks | Membership inference attacks are one of the simplest forms of privacy leakage for machine learning models: given a data point and model, determine whether the point was used to train the model. Existing membership inference attacks exploit models' abnormal confidence when queried on their training data. These attacks d... | ['Christopher A. Choquette-Choo', 'Nicolas Papernot', 'Florian Tramer', 'Nicholas Carlini'] | 2020-07-28 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 3.87475312e-01 2.68251568e-01 -2.98559129e-01 -4.48127359e-01
-1.01971412e+00 -1.41716588e+00 5.41697383e-01 4.99594003e-01
-3.76333386e-01 5.95778465e-01 -4.89624441e-01 -7.98464835e-01
4.10162238e-03 -9.00383830e-01 -1.07079756e+00 -7.50713944e-01
-3.41263443e-01 3.30894738e-01 1.13939755e-02 2.07093000... | [5.898436546325684, 7.196834564208984] |
b0c5eaeb-cb03-4718-a9fd-39fb2eddad15 | benchmarking-data-driven-surrogate-simulators | null | null | https://openreview.net/forum?id=-or413Lh_aF | https://openreview.net/pdf?id=-or413Lh_aF | Benchmarking Data-driven Surrogate Simulators for Artificial Electromagnetic Materials | Artificial electromagnetic materials (AEMs), including metamaterials, derive their electromagnetic properties from geometry rather than chemistry. With the appropriate geometric design, AEMs have achieved exotic properties not realizable with conventional materials (e.g., cloaking or negative refractive index). However... | ['Jordan Malof', 'Willie Padilla', 'Vahid Tarokh', 'Mohammadreza Soltani', 'Omar Khatib', 'Simiao Ren*', 'Juncheng Dong*', 'Yang Deng*'] | 2021-11-06 | null | null | null | neurips-2021-11 | ['neural-network-simulation'] | ['computer-code'] | [ 3.74053448e-01 -1.67775586e-01 5.55060685e-01 -2.44785830e-01
-2.35959381e-01 -5.55540681e-01 7.06779301e-01 -1.15806118e-01
-1.97859153e-01 6.99535370e-01 -8.88429284e-02 -7.48557508e-01
-1.27283365e-01 -9.39927876e-01 -8.45747709e-01 -1.11676633e+00
-1.16608091e-01 1.26322731e-01 1.17872301e-02 -1.51381105... | [8.150787353515625, 2.4381513595581055] |
e25761b6-72dc-4913-92ad-db217ef8c40f | permutation-aware-action-segmentation-via | 2305.19478 | null | https://arxiv.org/abs/2305.19478v2 | https://arxiv.org/pdf/2305.19478v2.pdf | Permutation-Aware Action Segmentation via Unsupervised Frame-to-Segment Alignment | This paper presents an unsupervised transformer-based framework for temporal activity segmentation which leverages not only frame-level cues but also segment-level cues. This is in contrast with previous methods which often rely on frame-level information only. Our approach begins with a frame-level prediction module w... | ['M. Zeeshan Zia', 'Andrey Konin', 'Anas Zafar', 'Muhammad Naufil', 'Muhammad Ahmed', 'Ahmed Mehmood', 'Quoc-Huy Tran'] | 2023-05-31 | null | null | null | null | ['action-segmentation'] | ['computer-vision'] | [ 6.54085159e-01 7.58433864e-02 -6.22436821e-01 -5.44731021e-01
-1.15390611e+00 -6.41957641e-01 4.87199038e-01 1.38388455e-01
-3.60881776e-01 5.21825790e-01 4.01154518e-01 -1.62458122e-01
2.48031422e-01 -6.00947797e-01 -8.20544124e-01 -6.41230285e-01
-8.95037279e-02 1.49041221e-01 5.90482473e-01 2.93894053... | [8.528831481933594, 0.5458881258964539] |
684a2df1-a2f7-4ac1-ba54-7fa4e79fdcf1 | vitol-vision-transformer-for-weakly | 2204.06772 | null | https://arxiv.org/abs/2204.06772v1 | https://arxiv.org/pdf/2204.06772v1.pdf | ViTOL: Vision Transformer for Weakly Supervised Object Localization | Weakly supervised object localization (WSOL) aims at predicting object locations in an image using only image-level category labels. Common challenges that image classification models encounter when localizing objects are, (a) they tend to look at the most discriminative features in an image that confines the localizat... | ['Rahul Tallamraju', 'Abhay Rawat', 'Sourav Lakhotia', 'Saurav Gupta'] | 2022-04-14 | null | null | null | null | ['weakly-supervised-object-localization'] | ['computer-vision'] | [-1.19586520e-01 -1.93632971e-02 -2.90151715e-01 -4.18316394e-01
-9.98654246e-01 -6.45417929e-01 5.36052227e-01 1.85590371e-01
-5.38774729e-01 4.71463084e-01 8.34554881e-02 9.94709879e-02
2.74572283e-01 -3.69685888e-01 -1.08986950e+00 -6.87907934e-01
-1.80240087e-02 1.07014522e-01 6.02179825e-01 1.41337454... | [9.619916915893555, 0.9192532300949097] |
597a88e7-7095-44d1-bd7b-985e217f173f | sparsity-based-feature-selection-for | 2201.02008 | null | https://arxiv.org/abs/2201.02008v1 | https://arxiv.org/pdf/2201.02008v1.pdf | Sparsity-based Feature Selection for Anomalous Subgroup Discovery | Anomalous pattern detection aims to identify instances where deviation from normalcy is evident, and is widely applicable across domains. Multiple anomalous detection techniques have been proposed in the state of the art. However, there is a common lack of a principled and scalable feature selection method for efficien... | ['Skyler Speakman', 'Aisha Walcott-Bryant', 'Vibha Anand', "Isaiah Onando Mulang'", 'Charles Wachira', 'Catherine Wanjiru', 'William Ogallo', 'Girmaw Abebe Tadesse'] | 2022-01-06 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 3.98091316e-01 -3.02977711e-01 -1.96108088e-01 -5.26845992e-01
-8.72400463e-01 -1.60470232e-01 2.44583473e-01 7.56910324e-01
-4.62142229e-02 5.75149596e-01 2.01868102e-01 -1.35841966e-01
-6.33546054e-01 -5.76796174e-01 -1.30359873e-01 -4.86699700e-01
-3.11464131e-01 2.83781499e-01 1.85437784e-01 4.63745967... | [7.563826560974121, 2.984264373779297] |
1bd2b8cc-65cd-491b-928a-1ac34bdcb783 | a-hybrid-attention-mechanism-for-weakly | 2101.00545 | null | https://arxiv.org/abs/2101.00545v3 | https://arxiv.org/pdf/2101.00545v3.pdf | A Hybrid Attention Mechanism for Weakly-Supervised Temporal Action Localization | Weakly supervised temporal action localization is a challenging vision task due to the absence of ground-truth temporal locations of actions in the training videos. With only video-level supervision during training, most existing methods rely on a Multiple Instance Learning (MIL) framework to predict the start and end ... | ['Richard Radke', 'Chengjiang Long', 'Ashraful Islam'] | 2021-01-03 | null | null | null | null | ['weakly-supervised-temporal-action', 'hard-attention'] | ['computer-vision', 'methodology'] | [ 3.33920270e-01 -1.22821569e-01 -5.11350930e-01 -1.16224997e-01
-7.25593448e-01 -1.91758588e-01 5.75601280e-01 -2.30984643e-01
-5.13200223e-01 5.43690443e-01 2.10838661e-01 1.02279186e-01
3.06520104e-01 -4.19478565e-01 -8.86885047e-01 -9.54505801e-01
4.87866029e-02 9.97850299e-02 9.50783193e-01 1.60947517... | [8.524348258972168, 0.5306925177574158] |
07e42fcb-74f4-4145-b899-47f1222957e0 | dcu-uva-multimodal-mt-system-report | null | null | https://aclanthology.org/W16-2359 | https://aclanthology.org/W16-2359.pdf | DCU-UvA Multimodal MT System Report | null | ['Iacer Calixto', 'Stella Frank', 'Desmond Elliott'] | 2016-08-01 | null | null | null | ws-2016-8 | ['multimodal-machine-translation'] | ['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... | [-7.385758876800537, 3.673095941543579] |
0ed1cc61-f246-4d2f-868d-ae554738625b | decouvrir-de-nouvelles-classes-dans-des | 2211.16352 | null | https://arxiv.org/abs/2211.16352v1 | https://arxiv.org/pdf/2211.16352v1.pdf | Découvrir de nouvelles classes dans des données tabulaires | In Novel Class Discovery (NCD), the goal is to find new classes in an unlabeled set given a labeled set of known but different classes. While NCD has recently gained attention from the community, no framework has yet been proposed for heterogeneous tabular data, despite being a very common representation of data. In th... | ['Vincent Lemaire', 'Alexandre Reiffers-Masson', 'Sandrine Vaton', 'Stéphane Gosselin', 'Joachim Flocon-Cholet', 'Colin Troisemaine'] | 2022-11-28 | null | null | null | null | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 4.56287444e-01 1.31176353e-01 -5.14726400e-01 -6.17421091e-01
-1.09806943e+00 -7.35961914e-01 4.45956439e-01 3.97239566e-01
1.15995063e-02 1.14697003e+00 -1.55211225e-01 -3.51344496e-02
-4.20869291e-01 -7.40119278e-01 -5.44106960e-01 -9.91695642e-01
1.13455072e-01 1.29610991e+00 8.62963572e-02 3.03107888... | [9.597137451171875, 3.0471394062042236] |
0f68b1a1-1fab-402c-a346-b5ba341f6735 | hyperparameter-selection-for-the-discrete | 2109.13651 | null | https://arxiv.org/abs/2109.13651v2 | https://arxiv.org/pdf/2109.13651v2.pdf | Hyperparameter selection for Discrete Mumford-Shah | This work focuses on a parameter-free joint piecewise smooth image denoising and contour detection. Formulated as the minimization of a discrete Mumford-Shah functional and estimated via a theoretically grounded alternating minimization scheme, the bottleneck of such a variational approach lies in the need to fine-tune... | ['Patrice Abry', 'Nelly Pustelnik', 'Barbara Pascal', 'Charles-Gérard Lucas'] | 2021-09-28 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 3.02678168e-01 2.62446880e-01 4.55178320e-01 -2.13492364e-01
-1.05681098e+00 -4.22093540e-01 3.00193667e-01 5.51204562e-01
-7.58981168e-01 6.49368525e-01 -4.51544076e-01 -6.14636317e-02
-1.79046944e-01 -6.59076154e-01 -5.55952668e-01 -8.79714310e-01
-1.40170425e-01 4.23223048e-01 1.78775504e-01 -1.91060409... | [7.3445539474487305, 3.7905707359313965] |
b9a7f648-811d-4ba0-a95c-c9c3db20c70d | exact-bayesian-inference-on-discrete-models | 2305.17058 | null | https://arxiv.org/abs/2305.17058v1 | https://arxiv.org/pdf/2305.17058v1.pdf | Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach | We present an exact Bayesian inference method for discrete statistical models, which can find exact solutions to many discrete inference problems, even with infinite support and continuous priors. To express such models, we introduce a probabilistic programming language that supports discrete and continuous sampling, d... | ['Luke Ong', 'Andrzej S. Murawski', 'Fabian Zaiser'] | 2023-05-26 | null | null | null | null | ['bayesian-inference', 'probabilistic-programming'] | ['methodology', 'methodology'] | [-4.87508513e-02 -2.82510668e-02 -3.61524910e-01 -4.11979407e-01
-9.41758156e-01 -7.98689783e-01 1.10014749e+00 1.95957929e-01
-1.98422134e-01 1.14941168e+00 -2.69090563e-01 -6.37954831e-01
-3.23651910e-01 -1.23085058e+00 -5.71315229e-01 -5.67331195e-01
-3.48104566e-01 1.26697016e+00 2.58937180e-01 3.08009595... | [7.133294582366943, 4.358574867248535] |
9aacd44c-9040-4c35-a74a-219781b9c366 | binbert-binary-code-understanding-with-a-fine | 2208.06692 | null | https://arxiv.org/abs/2208.06692v1 | https://arxiv.org/pdf/2208.06692v1.pdf | BinBert: Binary Code Understanding with a Fine-tunable and Execution-aware Transformer | A recent trend in binary code analysis promotes the use of neural solutions based on instruction embedding models. An instruction embedding model is a neural network that transforms sequences of assembly instructions into embedding vectors. If the embedding network is trained such that the translation from code to vect... | ['Leonardo Querzoni', 'Giuseppe A. Di Luna', 'Marco Mormando', 'Fiorella Artuso'] | 2022-08-13 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [ 2.13204607e-01 1.61729872e-01 -4.84103471e-01 -4.69920814e-01
-2.19322622e-01 -6.34265184e-01 5.52556217e-01 2.64038324e-01
-2.90026575e-01 2.27537602e-01 2.90177435e-01 -9.22035813e-01
2.67563164e-01 -8.27008843e-01 -1.18516254e+00 -3.88328999e-01
-7.39479214e-02 4.59895074e-01 3.42907906e-01 -6.21676087... | [7.465741157531738, 7.789315223693848] |
da354bf3-5b3b-4cd2-a82a-52f438c3ba43 | a-nonconvex-projection-method-for-robust-pca | 1805.07962 | null | https://arxiv.org/abs/1805.07962v2 | https://arxiv.org/pdf/1805.07962v2.pdf | A Nonconvex Projection Method for Robust PCA | Robust principal component analysis (RPCA) is a well-studied problem with the goal of decomposing a matrix into the sum of low-rank and sparse components. In this paper, we propose a nonconvex feasibility reformulation of RPCA problem and apply an alternating projection method to solve it. To the best of our knowledge,... | ['Peter Richtárik', 'Filip Hanzely', 'Aritra Dutta'] | 2018-05-21 | null | null | null | null | ['shadow-removal'] | ['computer-vision'] | [ 3.64586025e-01 -3.54639024e-01 2.13893563e-01 7.45126829e-02
-9.61460829e-01 -4.83446270e-01 4.51948494e-01 -5.94677627e-01
-3.80403288e-02 5.94248652e-01 1.91540003e-01 -1.91637784e-01
-2.90804982e-01 -6.45534471e-02 -5.03113687e-01 -9.86263275e-01
5.03299423e-02 5.01927376e-01 -5.88445477e-02 2.06049487... | [7.572827339172363, 4.384336948394775] |
c41653d0-5153-4ba2-a95d-e95f4c71f972 | lesionseg-semantic-segmentation-of-skin | 1703.03372 | null | http://arxiv.org/abs/1703.03372v3 | http://arxiv.org/pdf/1703.03372v3.pdf | LesionSeg: Semantic segmentation of skin lesions using Deep Convolutional Neural Network | We present a method for skin lesion segmentation for the ISIC 2017 Skin
Lesion Segmentation Challenge. Our approach is based on a Fully Convolutional
Network architecture which is trained end to end, from scratch, on a limited
dataset. Our semantic segmentation architecture utilizes several recent
innovations in partic... | ['Dhanesh Ramachandram', 'Terrance DeVries'] | 2017-03-09 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 7.76913702e-01 7.13639557e-01 6.30814396e-03 -3.76829147e-01
-8.62252116e-01 -3.39492500e-01 3.41514617e-01 6.36448059e-03
-6.37372077e-01 4.69854206e-01 1.36769712e-01 -4.09766197e-01
7.02994466e-02 -5.84777951e-01 -6.64036095e-01 -4.63908702e-01
4.61915620e-02 1.97872013e-01 6.81240320e-01 -9.47021618... | [15.526383399963379, -2.907862901687622] |
f70f7402-aa1c-4bb2-b316-1808db75786f | lotr-face-landmark-localization-using | 2109.10057 | null | https://arxiv.org/abs/2109.10057v3 | https://arxiv.org/pdf/2109.10057v3.pdf | LOTR: Face Landmark Localization Using Localization Transformer | This paper presents a novel Transformer-based facial landmark localization network named Localization Transformer (LOTR). The proposed framework is a direct coordinate regression approach leveraging a Transformer network to better utilize the spatial information in the feature map. An LOTR model consists of three main ... | ['Benjaphan Sommana', 'Nakarin Sritrakool', 'Samuel W. F. Earp', 'Aubin Samacoits', 'Ankush Ganguly', 'Pavit Noinongyao', 'Sanjana Jain', 'Ukrit Watchareeruetai'] | 2021-09-21 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [-1.58106804e-01 -6.07123896e-02 -1.87345892e-01 -7.38788068e-01
-1.06754684e+00 -3.87615934e-02 6.40755296e-01 -3.74249011e-01
-2.55118310e-01 3.24283719e-01 1.35381706e-02 1.45934090e-01
-5.62654436e-02 -4.81867850e-01 -8.35370123e-01 -6.81380510e-01
-1.13215901e-01 5.05170107e-01 -1.72402754e-01 -1.55413002... | [13.480321884155273, 0.3984692692756653] |
4899ab7f-e8a2-4247-903a-02ac8c83c1af | the-brain-tumor-segmentation-brats-challenge-1 | 2305.08992 | null | https://arxiv.org/abs/2305.08992v1 | https://arxiv.org/pdf/2305.08992v1.pdf | The Brain Tumor Segmentation (BraTS) Challenge 2023: Local Synthesis of Healthy Brain Tissue via Inpainting | A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition time series typically starts with a scan that is already pathological. This poses problems, as many algorithms are designed to analyze healthy... | ['Bjoern Menze', 'Marie Piraud', 'Ivan Ezhov', 'Benedikt Wiestler', 'Daniel Rueckert', 'Spyridon Bakas', 'Koen van Leemput', 'Juan Eugenio Iglesias', 'Mariam Aboian', 'Udunna Anazodo', 'Jake Albrecht', 'Andras Jakab', 'Priscila Crivellaro', 'Errol Colak', 'Javier Villanueva-Meyer', 'Soonmee Cha', 'Christopher Hess', 'J... | 2023-05-15 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation', 'anatomy'] | ['computer-vision', 'medical', 'miscellaneous'] | [ 5.56948066e-01 3.01755518e-01 1.43690392e-01 -5.03656447e-01
-8.10665488e-01 -2.50234485e-01 2.55502492e-01 9.68040079e-02
-5.58151722e-01 7.62448668e-01 1.00708008e-01 -1.91147566e-01
7.04000741e-02 -5.68632297e-02 -2.80984402e-01 -5.62514186e-01
-3.42583686e-01 4.64427650e-01 4.38387282e-02 1.66402236... | [14.164701461791992, -2.3011817932128906] |
823f17c8-2a84-4fb5-a917-dafd5695c004 | paris-personalized-activity-recommendation | 2110.13745 | null | https://arxiv.org/abs/2110.13745v1 | https://arxiv.org/pdf/2110.13745v1.pdf | PARIS: Personalized Activity Recommendation for Improving Sleep Quality | The quality of sleep has a deep impact on people's physical and mental health. People with insufficient sleep are more likely to report physical and mental distress, activity limitation, anxiety, and pain. Moreover, in the past few years, there has been an explosion of applications and devices for activity monitoring a... | ['Jaideep Srivastava', 'Louis Kazaglis', 'Abhiraj Mohan', 'Saksham Goel', 'Meghna Singh'] | 2021-10-26 | null | null | null | null | ['sleep-quality-prediction', 'time-series-clustering'] | ['medical', 'time-series'] | [-2.37295732e-01 -4.54215296e-02 -8.24841440e-01 -3.32038581e-01
2.31341660e-01 4.69750492e-03 -1.91789791e-01 3.74568880e-01
-2.52222680e-02 6.73902452e-01 7.50419259e-01 1.23871028e-01
-3.38251293e-01 -7.50280619e-01 2.54773736e-01 -7.21821666e-01
1.06995694e-01 -2.27881283e-01 -2.52534121e-01 -6.26946893... | [13.568575859069824, 3.4121179580688477] |
a2a45996-7ef8-49c5-bc0d-37c7a447dd9c | explainable-interpretable-trustworthy-ai-for | 2301.06676 | null | https://arxiv.org/abs/2301.06676v1 | https://arxiv.org/pdf/2301.06676v1.pdf | Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life | Machine learning (ML) and Artificial Intelligence (AI) are increasingly used in energy and engineering systems, but these models must be fair, unbiased, and explainable. It is critical to have confidence in AI's trustworthiness. ML techniques have been useful in predicting important parameters and improving model perfo... | ['Syed B. Alam', 'Souvik Chakraborty', 'Md Nazmus Sakib', 'Bader Almutairi', 'Kazuma Kobayashi'] | 2023-01-17 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [-1.73261017e-01 4.56752360e-01 -2.34452650e-01 -3.54182035e-01
-1.72225222e-01 -2.28063092e-01 2.99006313e-01 3.29966843e-01
6.12930477e-01 8.34108591e-01 -3.29672515e-01 -7.52760410e-01
-5.39132476e-01 -8.26557517e-01 -5.88112354e-01 -6.01500213e-01
-5.32905683e-02 7.09223509e-01 -3.11714262e-01 2.10587811... | [6.735660076141357, 2.806462049484253] |
7ad416ce-4dc8-48fe-b82e-b7beef7ab5ac | unsupervised-domain-adaptation-by | 1409.7495 | null | http://arxiv.org/abs/1409.7495v2 | http://arxiv.org/pdf/1409.7495v2.pdf | Unsupervised Domain Adaptation by Backpropagation | Top-performing deep architectures are trained on massive amounts of labeled
data. In the absence of labeled data for a certain task, domain adaptation
often provides an attractive option given that labeled data of similar nature
but from a different domain (e.g. synthetic images) are available. Here, we
propose a new a... | ['Yaroslav Ganin', 'Victor Lempitsky'] | 2014-09-26 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 2.06794038e-01 1.37361854e-01 -1.11798398e-01 -7.03841329e-01
-4.53203797e-01 -6.44326985e-01 8.72574687e-01 1.13109879e-01
-8.51879001e-01 1.02750933e+00 -3.64932716e-02 -1.84930354e-01
1.49916887e-01 -7.23441303e-01 -9.34097052e-01 -6.54270291e-01
3.06561708e-01 9.30545747e-01 4.58301604e-01 -5.09739339... | [9.879157066345215, 2.572402238845825] |
3cd956a8-426e-4df4-b041-289bba350b72 | lidar-guided-stereo-matching-with-a-spatial | 2202.09953 | null | https://arxiv.org/abs/2202.09953v2 | https://arxiv.org/pdf/2202.09953v2.pdf | LiDAR-guided Stereo Matching with a Spatial Consistency Constraint | The complementary fusion of light detection and ranging (LiDAR) data and image data is a promising but challenging task for generating high-precision and high-density point clouds. This study proposes an innovative LiDAR-guided stereo matching approach called LiDAR-guided stereo matching (LGSM), which considers the spa... | ['Yongxiang Yao', 'Yi Wan', 'Xu Huang', 'Xinyi Liu', 'Siyuan Zou', 'Yongjun Zhang'] | 2022-02-21 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 3.37757409e-01 -4.91453588e-01 2.59062834e-02 -3.77795935e-01
-5.85641265e-01 -2.38139480e-01 2.90439814e-01 5.00119058e-03
-3.83329511e-01 5.41200817e-01 -4.07301813e-01 -6.97741583e-02
-1.40627846e-01 -1.37226272e+00 -5.93457401e-01 -5.16003370e-01
2.31968731e-01 5.67397535e-01 6.97976530e-01 -1.53675586... | [8.386634826660156, -2.5854620933532715] |
0d5ba94b-773d-402f-8290-e54f7288e6a6 | combining-gcn-and-transformer-for-chinese | 2105.09085 | null | https://arxiv.org/abs/2105.09085v3 | https://arxiv.org/pdf/2105.09085v3.pdf | Combining GCN and Transformer for Chinese Grammatical Error Detection | This paper describes our system at NLPTEA-2020 Task: Chinese Grammatical Error Diagnosis (CGED). The goal of CGED is to diagnose four types of grammatical errors: word selection (S), redundant words (R), missing words (M), and disordered words (W). The automatic CGED system contains two parts including error detection ... | ['Jinhong Zhang'] | 2021-05-19 | null | null | null | null | ['grammatical-error-detection'] | ['natural-language-processing'] | [-1.79004461e-01 3.13798875e-01 4.55112278e-01 -3.07464123e-01
-8.06115687e-01 1.25460953e-01 -1.01963893e-01 6.99898422e-01
-5.75316548e-01 5.24522305e-01 4.85791415e-01 -6.61572695e-01
3.04759800e-01 -7.09685922e-01 -4.56295073e-01 9.40340459e-02
-1.05430067e-01 5.36004841e-01 2.57753819e-01 -4.57980156... | [11.147329330444336, 10.897653579711914] |
3934bbc7-8983-4559-a27b-ed05bcfc7d54 | cross-market-product-recommendation | 2109.05929 | null | https://arxiv.org/abs/2109.05929v1 | https://arxiv.org/pdf/2109.05929v1.pdf | Cross-Market Product Recommendation | We study the problem of recommending relevant products to users in relatively resource-scarce markets by leveraging data from similar, richer in resource auxiliary markets. We hypothesize that data from one market can be used to improve performance in another. Only a few studies have been conducted in this area, partly... | ['James Allan', 'Evangelos Kanoulas', 'Ali Vardasbi', 'Mohammad Aliannejadi', 'Hamed Bonab'] | 2021-09-13 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 1.02626368e-01 -4.41017747e-01 -7.98009157e-01 -4.18023348e-01
-8.75843108e-01 -9.57674146e-01 4.83856410e-01 1.97518617e-01
-4.52495486e-01 4.88672704e-01 2.94591755e-01 -4.36724037e-01
-6.43326342e-02 -6.68656111e-01 -9.27048147e-01 -2.44306520e-01
2.03321859e-01 7.13464200e-01 1.32831689e-02 -4.75708157... | [10.090882301330566, 5.68186092376709] |
9de503bf-fe3f-4dbc-8c5e-797ce0455326 | discovering-outstanding-subgroup-lists-for | 2006.09186 | null | https://arxiv.org/abs/2006.09186v1 | https://arxiv.org/pdf/2006.09186v1.pdf | Discovering outstanding subgroup lists for numeric targets using MDL | The task of subgroup discovery (SD) is to find interpretable descriptions of subsets of a dataset that stand out with respect to a target attribute. To address the problem of mining large numbers of redundant subgroups, subgroup set discovery (SSD) has been proposed. State-of-the-art SSD methods have their limitations ... | ['Thomas Bäck', 'Peter Grünwald', 'Hugo M. Proença', 'Matthijs van Leeuwen'] | 2020-06-16 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 4.99248356e-01 4.33586776e-01 -5.71922958e-01 -5.59220493e-01
-7.41150320e-01 -6.43686950e-01 4.25789326e-01 4.91836578e-01
-4.70054038e-02 8.84174168e-01 2.19965324e-01 -1.72255248e-01
-1.13682640e+00 -6.76326036e-01 -4.10251737e-01 -7.90220380e-01
-3.13599557e-01 9.91063595e-01 3.55173409e-01 -8.08872953... | [7.772242069244385, 4.836526870727539] |
39ad5e69-231d-4727-8796-763d1cd55a10 | relation-aware-graph-attention-network-for | 1903.12314 | null | https://arxiv.org/abs/1903.12314v3 | https://arxiv.org/pdf/1903.12314v3.pdf | Relation-Aware Graph Attention Network for Visual Question Answering | In order to answer semantically-complicated questions about an image, a Visual Question Answering (VQA) model needs to fully understand the visual scene in the image, especially the interactive dynamics between different objects. We propose a Relation-aware Graph Attention Network (ReGAT), which encodes each image into... | ['Yu Cheng', 'Linjie Li', 'Jingjing Liu', 'Zhe Gan'] | 2019-03-29 | relation-aware-graph-attention-network-for-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Li_Relation-Aware_Graph_Attention_Network_for_Visual_Question_Answering_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Li_Relation-Aware_Graph_Attention_Network_for_Visual_Question_Answering_ICCV_2019_paper.pdf | iccv-2019-10 | ['implicit-relations'] | ['natural-language-processing'] | [-1.51600823e-01 3.74000102e-01 -5.08861654e-02 -5.61298668e-01
-3.86079431e-01 -7.33860731e-01 5.85781515e-01 3.19687992e-01
1.21945232e-01 1.18333586e-02 3.03377062e-01 -5.61614931e-01
3.23197320e-02 -1.02588129e+00 -9.41040039e-01 -2.24952310e-01
-1.32816300e-01 7.28048861e-01 6.48473561e-01 -5.25601149... | [10.601436614990234, 1.7065343856811523] |
d54a6514-6b94-4b8c-90e0-b8676211091a | deep-learning-for-channel-coding-via-neural | 1903.02865 | null | http://arxiv.org/abs/1903.02865v1 | http://arxiv.org/pdf/1903.02865v1.pdf | Deep Learning for Channel Coding via Neural Mutual Information Estimation | End-to-end deep learning for communication systems, i.e., systems whose
encoder and decoder are learned, has attracted significant interest recently,
due to its performance which comes close to well-developed classical
encoder-decoder designs. However, one of the drawbacks of current learning
approaches is that a diffe... | ['Gerhard Wunder', 'Rick Fritschek', 'Rafael F. Schaefer'] | 2019-03-07 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 2.47583747e-01 3.23357224e-01 -1.22600839e-01 -2.23064885e-01
-1.02553272e+00 -1.89534992e-01 4.60529566e-01 1.68551192e-01
-4.30177271e-01 9.09723759e-01 -3.58057842e-02 -3.73617470e-01
5.80269806e-02 -7.22692430e-01 -1.03862214e+00 -9.15499747e-01
-6.03954541e-03 3.52340281e-01 -2.83222258e-01 1.63542153... | [6.448606014251709, 1.553215503692627] |
1bcb18a3-fc0d-4b36-842c-1464b22fd2ee | bio-plausible-unsupervised-delay-learning-for | 2011.09380 | null | https://arxiv.org/abs/2011.09380v1 | https://arxiv.org/pdf/2011.09380v1.pdf | Bio-plausible Unsupervised Delay Learning for Extracting Temporal Features in Spiking Neural Networks | The plasticity of the conduction delay between neurons plays a fundamental role in learning. However, the exact underlying mechanisms in the brain for this modulation is still an open problem. Understanding the precise adjustment of synaptic delays could help us in developing effective brain-inspired computational mode... | ['Mohammad Ganjtabesh', 'Alireza Nadafian'] | 2020-11-18 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [ 2.91995376e-01 -5.14956534e-01 6.17211591e-03 -1.34352446e-01
4.51622665e-01 -4.98113394e-01 2.85159260e-01 8.04154724e-02
-6.94861770e-01 9.94805276e-01 -5.96467078e-01 -2.85865754e-01
-5.63985825e-01 -7.51241267e-01 -8.91779244e-01 -1.25241530e+00
-3.14912766e-01 -1.82548493e-01 8.18491995e-01 -3.72665435... | [8.148247718811035, 2.581660270690918] |
e18153aa-0ab6-4eea-9d66-33ef29a8172b | disto-evaluating-textual-distractors-for | 2304.04881 | null | https://arxiv.org/abs/2304.04881v1 | https://arxiv.org/pdf/2304.04881v1.pdf | DISTO: Evaluating Textual Distractors for Multi-Choice Questions using Negative Sampling based Approach | Multiple choice questions (MCQs) are an efficient and common way to assess reading comprehension (RC). Every MCQ needs a set of distractor answers that are incorrect, but plausible enough to test student knowledge. Distractor generation (DG) models have been proposed, and their performance is typically evaluated using ... | ['Alona Fyshe', 'Bilal Ghanem'] | 2023-04-10 | null | null | null | null | ['distractor-generation', 'reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [-2.93068498e-01 3.67097855e-01 -3.32267731e-02 -2.02296317e-01
-1.07135057e+00 -8.69702637e-01 6.48252845e-01 7.84450054e-01
-4.29147094e-01 7.99898863e-01 3.10883611e-01 -6.13000870e-01
-1.87597916e-01 -5.85517287e-01 -4.67582315e-01 -1.43635780e-01
6.45433903e-01 6.03931665e-01 6.42660201e-01 -4.75455672... | [11.506062507629395, 8.2000150680542] |
50f7b7a1-6799-4e33-ab7a-377a2732c7ba | test-time-adaptation-for-nighttime-color | 2307.04470 | null | https://arxiv.org/abs/2307.04470v1 | https://arxiv.org/pdf/2307.04470v1.pdf | Test-Time Adaptation for Nighttime Color-Thermal Semantic Segmentation | The ability to scene understanding in adverse visual conditions, e.g., nighttime, has sparked active research for RGB-Thermal (RGB-T) semantic segmentation. However, it is essentially hampered by two critical problems: 1) the day-night gap of RGB images is larger than that of thermal images, and 2) the class-wise perfo... | ['Lin Wang', 'Athanasios Vasilakos', 'Jinjing Zhu', 'Guoyang Zhao', 'Weiming Zhang', 'Yexin Liu'] | 2023-07-10 | null | null | null | null | ['semantic-segmentation', 'scene-understanding'] | ['computer-vision', 'computer-vision'] | [ 1.69053003e-01 -3.12005401e-01 2.13551641e-01 -5.31275392e-01
-1.05065703e+00 -4.80028450e-01 2.93276966e-01 -2.92991012e-01
-3.46254945e-01 6.60214722e-01 -3.47221404e-01 -5.02781093e-01
-7.09872246e-02 -6.86974227e-01 -7.28528857e-01 -1.27035224e+00
3.78832102e-01 2.07413793e-01 4.14364010e-01 -2.10737392... | [9.288281440734863, -1.2468208074569702] |
4e97f217-ae83-46d8-a290-01ec001463b2 | quantitative-trading-using-deep-q-learning | 2304.06037 | null | https://arxiv.org/abs/2304.06037v1 | https://arxiv.org/pdf/2304.06037v1.pdf | Quantitative Trading using Deep Q Learning | Reinforcement learning (RL) is a branch of machine learning that has been used in a variety of applications such as robotics, game playing, and autonomous systems. In recent years, there has been growing interest in applying RL to quantitative trading, where the goal is to make profitable trades in financial markets. T... | ['Soumyadip Sarkar'] | 2023-04-03 | null | null | null | null | ['q-learning'] | ['methodology'] | [-4.02761966e-01 -4.64660488e-02 -2.64698505e-01 -1.70448720e-01
-4.90044087e-01 -6.71473920e-01 7.09557116e-01 8.31518099e-02
-5.33067703e-01 6.81094110e-01 -2.11405233e-01 -5.77474058e-01
-9.10023600e-02 -1.24519491e+00 -3.40621948e-01 -4.35428381e-01
-4.19183820e-01 5.53727806e-01 3.70030463e-01 -5.20747006... | [4.409470081329346, 3.8739280700683594] |
cb7295a0-f3f4-4f41-9d60-8e8b2268edda | exploring-long-short-range-temporal | 2208.03754 | null | https://arxiv.org/abs/2208.03754v2 | https://arxiv.org/pdf/2208.03754v2.pdf | Exploring Long & Short Range Temporal Information for Learned Video Compression | Learned video compression methods have gained a variety of interest in the video coding community since they have matched or even exceeded the rate-distortion (RD) performance of traditional video codecs. However, many current learning-based methods are dedicated to utilizing short-range temporal information, thus limi... | ['Zhenzhong Chen', 'Huairui Wang'] | 2022-08-07 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 3.23887259e-01 -2.88946867e-01 -4.93448585e-01 -2.10747182e-01
-4.31684256e-01 -1.90943956e-01 2.53186733e-01 -1.79505393e-01
-2.37300545e-01 5.70523858e-01 5.74339926e-01 6.19798489e-02
-2.78396726e-01 -6.51612878e-01 -6.02175236e-01 -7.51024127e-01
-2.17735291e-01 -4.00174052e-01 5.74615359e-01 -2.23071352... | [11.133696556091309, -1.6423882246017456] |
92124e46-1fd0-450c-9d29-4ed0cff32a92 | self-supervised-transformer-architecture-for | 2302.02025 | null | https://arxiv.org/abs/2302.02025v1 | https://arxiv.org/pdf/2302.02025v1.pdf | Self-Supervised Transformer Architecture for Change Detection in Radio Access Networks | Radio Access Networks (RANs) for telecommunications represent large agglomerations of interconnected hardware consisting of hundreds of thousands of transmitting devices (cells). Such networks undergo frequent and often heterogeneous changes caused by network operators, who are seeking to tune their system parameters f... | ['Gregory Dudek', 'Xue Liu', 'Di wu', 'Wei-Di Chang', 'Dmitriy Rivkin', 'Igor Kozlov'] | 2023-02-03 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [-1.66629195e-01 -3.19253534e-01 -3.15436780e-01 -2.82283366e-01
-3.67261559e-01 -7.76150346e-01 3.38640511e-01 -2.98452750e-02
1.61858708e-01 9.48020041e-01 6.41432479e-02 -7.09101737e-01
-4.49441940e-01 -6.45430326e-01 -5.33970535e-01 -5.42891979e-01
-6.64795637e-01 9.99290287e-01 -5.40395156e-02 -2.43674323... | [5.989805698394775, 1.6549605131149292] |
b91ce872-57b0-4acc-9014-183e2acc0b2b | transferrable-operative-difficulty-assessment | 1906.04934 | null | https://arxiv.org/abs/1906.04934v2 | https://arxiv.org/pdf/1906.04934v2.pdf | Transferrable Operative Difficulty Assessment in Robot-assisted Teleoperation: A Domain Adaptation Approach | Providing an accurate and efficient assessment of operative difficulty is important for designing robot-assisted teleoperation interfaces that are easy and natural for human operators to use. In this paper, we aim to develop a data-driven approach to numerically characterize the operative difficulty demand of complex t... | ['Ziheng Wang', 'Jie Zhang', 'Cong Feng', 'Ann Majewicz Fey'] | 2019-06-12 | null | null | null | null | ['steering-control'] | ['computer-vision'] | [ 2.09614888e-01 1.03048600e-01 -2.78243244e-01 -1.04757652e-01
-8.02331150e-01 -5.97822726e-01 7.65738785e-02 -7.06589967e-02
-7.13574231e-01 4.69962299e-01 3.39661986e-01 -5.04409373e-01
-7.29194403e-01 7.88298547e-02 -4.52179492e-01 -4.02950734e-01
-1.12220697e-01 1.93141118e-01 -2.06358939e-01 -4.51123625... | [6.449454307556152, 0.3023439645767212] |
55883a53-fa15-49b6-9eff-0cf90d322b4e | early-life-imprints-the-hierarchy-of-t-cell | 2007.11113 | null | https://arxiv.org/abs/2007.11113v1 | https://arxiv.org/pdf/2007.11113v1.pdf | Early life imprints the hierarchy of T cell clone sizes | The adaptive immune system responds to pathogens by selecting clones of cells with specific receptors. While clonal selection in response to particular antigens has been studied in detail, it is unknown how a lifetime of exposures to many antigens collectively shape the immune repertoire. Here, through mathematical mod... | ['Andreas Mayer', 'Jonathan Desponds', 'Maximilian Nguyen', 'Mario U. Gaimann'] | 2020-07-21 | null | null | null | null | ['human-aging'] | ['miscellaneous'] | [ 4.99469995e-01 -3.98951203e-01 -2.86791831e-01 -3.07965249e-01
-2.11549848e-02 -5.44710994e-01 3.69369954e-01 5.57021916e-01
-6.32153213e-01 7.21559286e-01 1.97637424e-01 -4.42314833e-01
-1.98552310e-01 -8.86426926e-01 -6.48879468e-01 -9.57395554e-01
-2.86882997e-01 1.03053975e+00 3.07872385e-01 -3.51109713... | [5.087234973907471, 4.899930477142334] |
86cede29-25c5-4bab-ab74-f4d23f6a55f4 | reinforcement-learning-guided-multi-objective | 2303.01042 | null | https://arxiv.org/abs/2303.01042v1 | https://arxiv.org/pdf/2303.01042v1.pdf | Reinforcement Learning Guided Multi-Objective Exam Paper Generation | To reduce the repetitive and complex work of instructors, exam paper generation (EPG) technique has become a salient topic in the intelligent education field, which targets at generating high-quality exam paper automatically according to instructor-specified assessment criteria. The current advances utilize the ability... | ['Kun Liang', 'Yimeng Ren', 'Xiankun Zhang', 'Hao Peng', 'Lihong Wang', 'Xuexiong Luo', 'Yuhu Shang'] | 2023-03-02 | null | null | null | null | ['knowledge-tracing', 'paper-generation'] | ['miscellaneous', 'natural-language-processing'] | [-8.10985640e-02 -9.09971371e-02 -2.30400801e-01 -2.42069870e-01
-8.00154328e-01 -7.53082573e-01 -2.84526974e-01 2.70044893e-01
-5.09429388e-02 8.89917850e-01 -2.39261240e-01 -6.76377177e-01
-1.01275289e+00 -1.06943583e+00 -6.01902008e-01 -3.15417320e-01
5.67116499e-01 3.40663195e-01 1.70895800e-01 -2.42656186... | [10.138620376586914, 7.165647983551025] |
e44ef50b-1615-482a-8c8c-614f177313ec | transfer-learning-of-fmri-dynamics | 1911.06813 | null | https://arxiv.org/abs/1911.06813v1 | https://arxiv.org/pdf/1911.06813v1.pdf | Transfer Learning of fMRI Dynamics | As a mental disorder progresses, it may affect brain structure, but brain function expressed in brain dynamics is affected much earlier. Capturing the moment when brain dynamics express the disorder is crucial for early diagnosis. The traditional approach to this problem via training classifiers either proceeds from ha... | ['Zening Fu', 'Sergey Plis', 'Md Mahfuzur Rahman', 'Alex Fedorov', 'Usman Mahmood'] | 2019-11-16 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 1.71388105e-01 -3.56176123e-02 -9.33271721e-02 -6.16762757e-01
-3.76413733e-01 -3.84014159e-01 4.71012503e-01 1.47176042e-01
-3.94281775e-01 7.23847091e-01 1.61797106e-01 -2.01225117e-01
-4.62541968e-01 -5.70836306e-01 -2.42208004e-01 -6.53692901e-01
-7.81249046e-01 8.24031055e-01 -1.67709157e-01 -9.15121064... | [12.512178421020508, 3.3019936084747314] |
4190a5fa-97c6-4645-9afe-204d1552f164 | a-comparative-analysis-of-expected-and | 1901.11084 | null | http://arxiv.org/abs/1901.11084v2 | http://arxiv.org/pdf/1901.11084v2.pdf | A Comparative Analysis of Expected and Distributional Reinforcement Learning | Since their introduction a year ago, distributional approaches to
reinforcement learning (distributional RL) have produced strong results
relative to the standard approach which models expected values (expected RL).
However, aside from convergence guarantees, there have been few theoretical
results investigating the re... | ['Pablo Samuel Castro', 'Clare Lyle', 'Marc G. Bellemare'] | 2019-01-30 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-1.49209395e-01 2.52749056e-01 -4.87899214e-01 -1.07547432e-01
-9.48027253e-01 -6.16325617e-01 6.33560121e-01 2.10384697e-01
-7.40506589e-01 1.32461846e+00 2.78063327e-01 -5.79697013e-01
-3.48427773e-01 -5.50313294e-01 -6.91068947e-01 -9.62795258e-01
-1.84453383e-01 4.23961997e-01 -9.92552713e-02 -2.77952075... | [4.091825008392334, 2.5718209743499756] |
901de0bd-624f-401a-bbce-6acec2c203bc | explaining-deep-convolutional-neural-networks | 1607.02444 | null | http://arxiv.org/abs/1607.02444v1 | http://arxiv.org/pdf/1607.02444v1.pdf | Explaining Deep Convolutional Neural Networks on Music Classification | Deep convolutional neural networks (CNNs) have been actively adopted in the
field of music information retrieval, e.g. genre classification, mood
detection, and chord recognition. However, the process of learning and
prediction is little understood, particularly when it is applied to
spectrograms. We introduce auralisa... | ['Keunwoo Choi', 'Mark Sandler', 'George Fazekas'] | 2016-07-08 | null | null | null | null | ['chord-recognition', 'genre-classification', 'music-classification'] | ['audio', 'computer-vision', 'music'] | [ 1.46857277e-01 -3.76231581e-01 3.43384832e-01 -2.54986018e-01
-1.94499701e-01 -7.84785807e-01 3.98731798e-01 -1.62048012e-01
-6.38657808e-02 3.16490352e-01 4.25769061e-01 2.40288556e-01
-4.41295654e-01 -6.69592738e-01 -6.91030800e-01 -8.13511074e-01
-3.79128754e-01 -1.15952179e-01 -2.29040667e-01 -4.67276722... | [15.802757263183594, 5.279950141906738] |
7082fe52-32aa-42ec-9a87-c1bbde18b8fd | mret-multi-resolution-transformer-for-video | 2303.07489 | null | https://arxiv.org/abs/2303.07489v2 | https://arxiv.org/pdf/2303.07489v2.pdf | MRET: Multi-resolution Transformer for Video Quality Assessment | No-reference video quality assessment (NR-VQA) for user generated content (UGC) is crucial for understanding and improving visual experience. Unlike video recognition tasks, VQA tasks are sensitive to changes in input resolution. Since large amounts of UGC videos nowadays are 720p or above, the fixed and relatively sma... | ['Feng Yang', 'Peyman Milanfar', 'Yilin Wang', 'Tianhao Zhang', 'Junjie Ke'] | 2023-03-13 | null | null | null | null | ['video-recognition'] | ['computer-vision'] | [-1.66676324e-02 -8.56597245e-01 -2.18989328e-01 -2.60380924e-01
-1.28056848e+00 -6.22198701e-01 2.41087914e-01 -2.65390843e-01
-1.92159295e-01 5.67637146e-01 5.96184611e-01 -1.26339784e-02
2.68752202e-02 -8.91887486e-01 -6.68839514e-01 -5.83649993e-01
-7.53922984e-02 -1.83682248e-01 7.13634014e-01 -4.70619917... | [11.644570350646973, -1.8649489879608154] |
e5f64202-ac3b-44cf-a4c1-c6a924cb37a7 | integrated-steganography-and-steganalysis | null | null | https://openreview.net/forum?id=r1Vx_oA5YQ | https://openreview.net/pdf?id=r1Vx_oA5YQ | Integrated Steganography and Steganalysis with Generative Adversarial Networks | Recently, generative adversarial network is the hotspot in research areas and industrial application areas. It's application on data generation in computer vision is most common usage. This paper extends its application to data hiding and security area. In this paper, we propose the novel framework to integrate stegano... | ['Chong Yu'] | null | null | null | null | iclr-2019-5 | ['steganalysis'] | ['computer-vision'] | [ 1.00775003e+00 6.57730401e-02 2.77190715e-01 2.41284803e-01
7.95660366e-04 -3.35068017e-01 8.54573190e-01 -8.00362885e-01
-4.26981561e-02 4.50324357e-01 -6.56675622e-02 -3.48752379e-01
4.68823850e-01 -1.12830615e+00 -6.04356110e-01 -1.21711361e+00
4.80013564e-02 -1.47631988e-01 4.18922633e-01 -4.78266329... | [4.30006217956543, 8.05612564086914] |
a9df4197-ebbc-4bfa-b306-bcc2de078ece | mmtm-multi-tasking-multi-decoder-transformer | 2206.01268 | null | https://arxiv.org/abs/2206.01268v1 | https://arxiv.org/pdf/2206.01268v1.pdf | MMTM: Multi-Tasking Multi-Decoder Transformer for Math Word Problems | Recently, quite a few novel neural architectures were derived to solve math word problems by predicting expression trees. These architectures varied from seq2seq models, including encoders leveraging graph relationships combined with tree decoders. These models achieve good performance on various MWPs datasets but perf... | ['Darshan Patel', 'Prashant Kikani', 'Amit Sheth', 'Keyur Faldu'] | 2022-06-02 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 2.81079084e-01 3.76401782e-01 -3.92873213e-02 -5.20571649e-01
-8.78999293e-01 -8.40555906e-01 3.66250277e-01 -2.35470250e-01
-3.07311773e-01 7.88162231e-01 3.16689640e-01 -5.71244478e-01
-5.83190620e-02 -1.30254400e+00 -1.02753186e+00 -2.55012363e-01
1.72258914e-02 5.90526700e-01 -1.34848028e-01 -4.78244692... | [9.984989166259766, 7.767272472381592] |
720d3944-171d-48a4-b5d5-d76cac868772 | lexical-normalization-for-code-switched-data | 2006.01175 | null | https://arxiv.org/abs/2006.01175v2 | https://arxiv.org/pdf/2006.01175v2.pdf | Lexical Normalization for Code-switched Data and its Effect on POS-tagging | Lexical normalization, the translation of non-canonical data to standard language, has shown to improve the performance of manynatural language processing tasks on social media. Yet, using multiple languages in one utterance, also called code-switching (CS), is frequently overlooked by these normalization systems, desp... | ['Özlem Çetinoğlu', 'Rob van der Goot'] | 2020-06-01 | null | null | null | null | ['lexical-normalization'] | ['natural-language-processing'] | [ 2.18741782e-02 -8.32803994e-02 -1.73277214e-01 -4.32068288e-01
-7.29633689e-01 -7.98191428e-01 6.78327799e-01 4.64801729e-01
-8.27285528e-01 5.83853304e-01 4.34983402e-01 -6.15937471e-01
4.14753377e-01 -2.64195621e-01 -3.08600485e-01 -2.58905619e-01
3.60155821e-01 4.22316939e-01 2.56644264e-02 -5.67332208... | [10.299516677856445, 10.036763191223145] |
297b3da0-96e2-4fee-95f9-bc231ceeebe5 | dygcn-dynamic-graph-embedding-with-graph | 2104.02962 | null | https://arxiv.org/abs/2104.02962v1 | https://arxiv.org/pdf/2104.02962v1.pdf | DyGCN: Dynamic Graph Embedding with Graph Convolutional Network | Graph embedding, aiming to learn low-dimensional representations (aka. embeddings) of nodes, has received significant attention recently. Recent years have witnessed a surge of efforts made on static graphs, among which Graph Convolutional Network (GCN) has emerged as an effective class of models. However, these method... | ['Mengmeng Ai', 'Liang Wang', 'Qiang Liu', 'XiaoYu Zhang', 'Shu Wu', 'Zekun Li', 'Zeyu Cui'] | 2021-04-07 | null | null | null | null | ['dynamic-graph-embedding'] | ['graphs'] | [-2.54404753e-01 8.58910307e-02 -2.05927238e-01 2.66559422e-02
1.93433151e-01 -5.15307903e-01 5.22116125e-01 4.55371112e-01
-1.49641335e-01 3.24320138e-01 2.87855625e-01 -3.91948372e-01
-1.81732804e-01 -1.26263654e+00 -3.26372594e-01 -6.99628234e-01
-4.04693931e-01 6.89270049e-02 5.15393019e-01 -2.67770201... | [7.151829242706299, 6.139838218688965] |
1bc8992f-78c6-46f3-b058-5d22e383361d | need-for-design-patterns-interoperability | 2208.12480 | null | https://arxiv.org/abs/2208.12480v1 | https://arxiv.org/pdf/2208.12480v1.pdf | Need for Design Patterns: Interoperability Issues and Modelling Challenges for Observational Data | Interoperability issues concerning observational data have gained attention in recent times. Automated data integration is important when it comes to the scientific analysis of observational data from different sources. However, it is hampered by various data interoperability issues. We focus exclusively on semantic in... | ['Friederike Klan', 'Frank Löffler', 'Trupti Padiya'] | 2022-08-26 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-1.34819970e-01 1.01048827e-01 -1.20961085e-01 -3.47789884e-01
-1.29018411e-01 -8.54360580e-01 7.81633854e-01 5.96574187e-01
-2.53149062e-01 5.22425354e-01 5.91662884e-01 -6.83404744e-01
-1.04076147e+00 -1.06894183e+00 -2.02714652e-01 -2.81520516e-01
-2.45398015e-01 4.75133747e-01 3.78180146e-01 -4.71478045... | [9.19304084777832, 7.960285663604736] |
9a8804d9-a48f-4f95-8c9d-8ce35c7cf2a1 | robust-calibrate-proxy-loss-for-deep-metric | 2304.09162 | null | https://arxiv.org/abs/2304.09162v1 | https://arxiv.org/pdf/2304.09162v1.pdf | Robust Calibrate Proxy Loss for Deep Metric Learning | The mainstream researche in deep metric learning can be divided into two genres: proxy-based and pair-based methods. Proxy-based methods have attracted extensive attention due to the lower training complexity and fast network convergence. However, these methods have limitations as the poxy optimization is done by netwo... | ['Zhixiang Liu', 'Yanling Du', 'Wei Song', 'Jian Wang', 'Xinyue Li'] | 2023-04-06 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [-2.35452279e-01 -5.56553602e-01 -2.21127763e-01 -8.37615848e-01
-1.24854159e+00 -3.54658544e-01 4.28837359e-01 2.08933294e-01
-6.17949605e-01 6.98600054e-01 2.43005715e-02 2.70850956e-01
-4.05374676e-01 -8.43575776e-01 -5.59911728e-01 -7.72485971e-01
2.77793527e-01 3.59338909e-01 1.96326911e-01 -2.32949480... | [9.387971878051758, 3.224583148956299] |
0b93610b-7a68-4f29-aa11-22a09a805283 | doppler-exploitation-in-bistatic-mmwave-radio | 2208.10204 | null | https://arxiv.org/abs/2208.10204v1 | https://arxiv.org/pdf/2208.10204v1.pdf | Doppler Exploitation in Bistatic mmWave Radio SLAM | Networks in 5G and beyond utilize millimeter wave (mmWave) radio signals, large bandwidths, and large antenna arrays, which bring opportunities in jointly localizing the user equipment and mapping the propagation environment, termed as simultaneous localization and mapping (SLAM). Existing approaches mainly rely on del... | ['Henk Wymeersch', 'Lennart Svensson', 'Mikko Valkama', 'Jukka Talvitie', 'Fan Jiang', 'Hui Chen', 'Ossi Kaltiokallio', 'Yu Ge'] | 2022-08-22 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-3.70629102e-01 -2.37896442e-01 -2.36257359e-01 -3.38155180e-02
-4.67209071e-01 -7.21020818e-01 1.58650652e-01 -3.66866678e-01
-2.37928748e-01 1.24421251e+00 -1.40012056e-01 -6.74008667e-01
-5.14874756e-01 -7.04072237e-01 -2.82077312e-01 -9.41360772e-01
-5.75229645e-01 3.11194837e-01 -2.12997556e-01 2.80390561... | [6.276594161987305, 1.1707499027252197] |
6bb92295-7b29-46c9-93ef-3085ce877eb8 | lightweight-event-based-optical-flow | 2211.13726 | null | https://arxiv.org/abs/2211.13726v3 | https://arxiv.org/pdf/2211.13726v3.pdf | Rethinking Event-based Optical Flow: Iterative Deblurring as an Alternative to Correlation Volumes | Inspired by frame-based methods, state-of-the-art event-based optical flow networks rely on the explicit construction of correlation volumes, which are expensive to compute and store, at the same time prohibiting them from estimating high-resolution flow. We observe that the spatiotemporally continuous traces of events... | ['Guido C. H. E. de Croon', 'Federico Paredes-Vallés', 'YiLun Wu'] | 2022-11-24 | null | null | null | null | ['event-based-optical-flow'] | ['computer-vision'] | [-1.94296122e-01 -3.34241331e-01 -2.85879020e-02 2.61740666e-02
-1.40051171e-01 -5.04720092e-01 5.00569463e-01 1.42701700e-01
-7.27674305e-01 9.37528133e-01 2.44809389e-01 -1.98094279e-01
-7.50330165e-02 -9.42453980e-01 -4.08891797e-01 -4.60815549e-01
-4.56844687e-01 2.93350339e-01 8.89142752e-01 2.23958358... | [8.90072250366211, -1.5647119283676147] |
9763c445-a31e-40ff-9234-9a00dcc032dc | learning-to-summarize-and-answer-questions | 2306.09922 | null | https://arxiv.org/abs/2306.09922v1 | https://arxiv.org/pdf/2306.09922v1.pdf | Learning to Summarize and Answer Questions about a Virtual Robot's Past Actions | When robots perform long action sequences, users will want to easily and reliably find out what they have done. We therefore demonstrate the task of learning to summarize and answer questions about a robot agent's past actions using natural language alone. A single system with a large language model at its core is trai... | ['Daniel Bauer', 'Iretiayo Akinola', 'Chad DeChant'] | 2023-06-16 | null | null | null | null | ['question-answering'] | ['natural-language-processing'] | [ 3.63134861e-01 6.22614861e-01 1.10037342e-01 -4.49101657e-01
-9.92851675e-01 -5.00607133e-01 7.72472084e-01 1.86115295e-01
-3.79372448e-01 7.41331875e-01 8.02577674e-01 -2.13047877e-01
3.83349717e-01 -6.05144560e-01 -8.58046412e-01 2.68256627e-02
-4.90613542e-02 6.28439903e-01 2.28675157e-01 -3.50004405... | [4.498137474060059, 0.7680226564407349] |
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