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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
26c8f01c-f1b1-4f67-929a-c6833ab3484b | lipper-synthesizing-thy-speech-using-multi | 1907.01367 | null | https://arxiv.org/abs/1907.01367v1 | https://arxiv.org/pdf/1907.01367v1.pdf | Lipper: Synthesizing Thy Speech using Multi-View Lipreading | Lipreading has a lot of potential applications such as in the domain of surveillance and video conferencing. Despite this, most of the work in building lipreading systems has been limited to classifying silent videos into classes representing text phrases. However, there are multiple problems associated with making lip... | ['Yifang Yin', 'Rajiv Ratn Shah', 'Khwaja Mohd. Salik', 'Roger Zimmermann', 'Yaman Kumar', 'Rohit Jain'] | 2019-06-28 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 1.95237204e-01 -6.34360081e-03 -2.48105645e-01 -3.67476434e-01
-9.56067085e-01 -5.08076310e-01 6.89875782e-01 -1.37264729e-01
-2.06504852e-01 3.60849798e-01 4.68501300e-01 -3.83073807e-01
2.24955037e-01 -8.68411139e-02 -5.57259321e-01 -4.74921972e-01
2.97205061e-01 1.07864022e-01 4.23292011e-01 -9.48893204... | [14.326654434204102, 5.030131816864014] |
7a700a9d-e211-4afd-a095-3465655f5cee | sonicverse-a-multisensory-simulation-platform | 2306.00923 | null | https://arxiv.org/abs/2306.00923v1 | https://arxiv.org/pdf/2306.00923v1.pdf | Sonicverse: A Multisensory Simulation Platform for Embodied Household Agents that See and Hear | Developing embodied agents in simulation has been a key research topic in recent years. Exciting new tasks, algorithms, and benchmarks have been developed in various simulators. However, most of them assume deaf agents in silent environments, while we humans perceive the world with multiple senses. We introduce Sonicve... | ['Jiajun Wu', 'Li Fei-Fei', 'Silvio Savarese', 'Fei Xia', 'Chengshu Li', 'Zhuzhu Wang', 'Gokul Dharan', 'Hao Li', 'Ruohan Gao'] | 2023-06-01 | null | null | null | null | ['visual-navigation'] | ['robots'] | [-3.57358873e-01 -1.63489267e-01 7.33435690e-01 2.36605220e-02
-7.52432942e-01 -6.44896090e-01 5.92966855e-01 -2.02799767e-01
-6.49347067e-01 4.02681172e-01 9.69421864e-02 -9.31866989e-02
3.10259908e-01 -8.63406718e-01 -8.05238008e-01 -5.20665646e-01
-3.57147694e-01 6.19912565e-01 5.13090134e-01 -9.92098451... | [4.442207336425781, 0.7960724234580994] |
60cd1740-8063-46fe-a81e-119482ab80a6 | galois-monodromy-groups-for-decomposing | 2105.04460 | null | https://arxiv.org/abs/2105.04460v1 | https://arxiv.org/pdf/2105.04460v1.pdf | Galois/monodromy groups for decomposing minimal problems in 3D reconstruction | We consider Galois/monodromy groups arising in computer vision applications, with a view towards building more efficient polynomial solvers. The Galois/monodromy group allows us to decide when a given problem decomposes into algebraic subproblems, and whether or not it has any symmetries. Tools from numerical algebraic... | ['Margaret H. Regan', 'Tomas Pajdla', 'Viktor Korotynskiy', 'Timothy Duff'] | 2021-05-10 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 2.08119303e-01 3.07849616e-01 9.90115106e-03 -7.60145113e-02
-5.37289083e-01 -7.87483692e-01 6.00739837e-01 -2.88035065e-01
-6.42072177e-03 4.89908069e-01 -1.33086726e-01 -1.24309361e-01
-3.55159074e-01 -4.79115844e-01 -8.00274611e-01 -8.46701086e-01
-1.78214550e-01 7.61021495e-01 -1.23737231e-01 -4.97708797... | [8.00554370880127, -2.274226665496826] |
8032859e-e1cf-4e0c-a8a9-af6f5486459d | universe-points-representation-learning-for | 2212.00780 | null | https://arxiv.org/abs/2212.00780v2 | https://arxiv.org/pdf/2212.00780v2.pdf | Universe Points Representation Learning for Partial Multi-Graph Matching | Many challenges from natural world can be formulated as a graph matching problem. Previous deep learning-based methods mainly consider a full two-graph matching setting. In this work, we study the more general partial matching problem with multi-graph cycle consistency guarantees. Building on a recent progress in deep ... | ['Florian Bernard', 'Frank R. Schmidt', 'Zhakshylyk Nurlanov'] | 2022-12-01 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 1.10362865e-01 1.64666578e-01 -4.47791010e-01 -3.11123610e-01
-6.98411822e-01 -4.24407572e-01 9.41105962e-01 5.02139688e-01
-1.18385434e-01 1.40355468e-01 1.12397350e-01 -8.77922177e-02
-2.98829228e-01 -1.20795524e+00 -1.06559074e+00 -2.02463597e-01
-9.81911048e-02 7.30671287e-01 5.06251216e-01 -3.58992010... | [7.146459579467773, 6.306248664855957] |
0921fdcc-6798-42e8-a350-7f38851870ec | moving-object-detection-in-time-lapse-or | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Shakeri_Moving_Object_Detection_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Shakeri_Moving_Object_Detection_ICCV_2017_paper.pdf | Moving Object Detection in Time-Lapse or Motion Trigger Image Sequences Using Low-Rank and Invariant Sparse Decomposition | Low-rank and sparse representation based methods have attracted wide attention in background subtraction and moving object detection, where moving objects in the scene are modeled as pixel-wise sparse outliers. Since in real scenarios moving objects are also structurally sparse, recently researchers have attempted to e... | ['Moein Shakeri', 'Hong Zhang'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['moving-object-detection'] | ['computer-vision'] | [ 6.60899103e-01 -8.61738324e-01 5.11795208e-02 -2.62390345e-01
-6.04552269e-01 -3.25244248e-01 3.55679840e-01 -2.98902541e-01
-1.68406263e-01 7.65390635e-01 2.94957429e-01 1.87277511e-01
-6.34782687e-02 -3.52064520e-01 -7.75596023e-01 -9.53182578e-01
-1.22732021e-01 -2.25241989e-01 3.38674635e-01 1.56320050... | [9.02426815032959, -0.8147104978561401] |
df9cbdc5-344f-40ed-bb58-a6845adf9c93 | stochastic-learning-of-multi-instance | 1609.00817 | null | http://arxiv.org/abs/1609.00817v1 | http://arxiv.org/pdf/1609.00817v1.pdf | Stochastic Learning of Multi-Instance Dictionary for Earth Mover's Distance based Histogram Comparison | Dictionary plays an important role in multi-instance data representation. It
maps bags of instances to histograms. Earth mover's distance (EMD) is the most
effective histogram distance metric for the application of multi-instance
retrieval. However, up to now, there is no existing multi-instance dictionary
learning met... | ['Ru-Ze Liang', 'Jihong Fan'] | 2016-09-03 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [-8.2056925e-02 -2.9419515e-01 -3.6674222e-01 -3.3760107e-01
-1.1146600e+00 -2.5426850e-01 1.8122363e-01 9.5216489e-01
-5.0520372e-01 6.0071445e-01 -4.1262735e-02 -7.2122574e-02
-3.7376615e-01 -1.0037881e+00 -4.9008131e-01 -1.1819712e+00
1.6929182e-01 6.9600308e-01 2.3779434e-01 -1.9574808e-01
3.6012626e-01... | [11.336073875427246, 0.9671360850334167] |
56a14a4c-27e7-427e-b404-97ccd3f2c99b | a-neural-approach-to-kgqa-via-sparql | null | null | https://openreview.net/forum?id=SshQlYuME_ | https://openreview.net/pdf?id=SshQlYuME_ | A Neural Approach to KGQA via SPARQL Silhouette Generation | Semantic parsing is a predominant approach to solve the Knowledge Graph Question Answering (KGQA) task where, natural language question is translated into a logic form such as SPARQL. Semantic parsing based solutions are mostly modular/pipelined where, noise introduced by the upstream modules for entit... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['graph-question-answering'] | ['graphs'] | [ 1.67946607e-01 6.96209371e-01 2.48991922e-01 -3.66937160e-01
-1.15443730e+00 -7.84221053e-01 1.27617553e-01 4.10247803e-01
-3.83210093e-01 8.12206507e-01 -5.52754402e-02 -5.44152796e-01
-1.47699893e-01 -1.47239923e+00 -1.22296858e+00 -3.26377511e-01
9.43048671e-02 7.63727248e-01 4.10176665e-01 -5.12530386... | [10.011497497558594, 7.842283725738525] |
c46add53-97f0-4a01-9665-51933c6877a5 | preventing-information-leakage-with-neural | 1912.08421 | null | https://arxiv.org/abs/1912.08421v2 | https://arxiv.org/pdf/1912.08421v2.pdf | Learning to Prevent Leakage: Privacy-Preserving Inference in the Mobile Cloud | Powered by machine learning services in the cloud, numerous learning-driven mobile applications are gaining popularity in the market. As deep learning tasks are mostly computation-intensive, it has become a trend to process raw data on devices and send the deep neural network (DNN) features to the cloud, where the feat... | ['Wei Wang', 'YiXuan Wang', 'CongCong Li', 'Shuang Zhang', 'Bo Li', 'Quanshi Zhang', 'Liyao Xiang'] | 2019-12-18 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [ 9.92803425e-02 1.70785651e-01 -2.41403952e-01 -5.31444550e-01
-6.00304723e-01 -9.88681197e-01 2.94413596e-01 -4.19508852e-03
-7.23875046e-01 8.11652541e-01 -4.07364853e-02 -5.90597034e-01
1.75381109e-01 -9.60670114e-01 -9.50980604e-01 -8.73144507e-01
7.04842433e-02 1.25423893e-01 7.64250979e-02 4.10814136... | [5.8408427238464355, 6.780174255371094] |
e2ce7e2d-4a13-4907-be72-7f8f123d4fb2 | frame-recurrent-video-super-resolution | 1801.04590 | null | http://arxiv.org/abs/1801.04590v4 | http://arxiv.org/pdf/1801.04590v4.pdf | Frame-Recurrent Video Super-Resolution | Recent advances in video super-resolution have shown that convolutional
neural networks combined with motion compensation are able to merge information
from multiple low-resolution (LR) frames to generate high-quality images.
Current state-of-the-art methods process a batch of LR frames to generate a
single high-resolu... | ['Matthew Brown', 'Raviteja Vemulapalli', 'Mehdi S. M. Sajjadi'] | 2018-01-14 | frame-recurrent-video-super-resolution-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Sajjadi_Frame-Recurrent_Video_Super-Resolution_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Sajjadi_Frame-Recurrent_Video_Super-Resolution_CVPR_2018_paper.pdf | cvpr-2018-6 | ['multi-frame-super-resolution'] | ['computer-vision'] | [ 6.26978159e-01 -2.44234920e-01 -2.08633710e-02 -8.72076675e-02
-9.46247876e-01 -1.71148747e-01 4.27321315e-01 -4.24005747e-01
-4.68512595e-01 9.40109313e-01 2.68094927e-01 3.24118793e-01
6.06402867e-02 -7.03926444e-01 -6.71978474e-01 -6.60290360e-01
8.68743137e-02 -2.47536197e-01 7.16241241e-01 -2.15336829... | [11.002467155456543, -1.9012022018432617] |
cc11a34a-7db6-4203-87ba-8101c28e1423 | data-generation-for-satellite-image | 2205.14418 | null | https://arxiv.org/abs/2205.14418v1 | https://arxiv.org/pdf/2205.14418v1.pdf | Data Generation for Satellite Image Classification Using Self-Supervised Representation Learning | Supervised deep neural networks are the-state-of-the-art for many tasks in the remote sensing domain, against the fact that such techniques require the dataset consisting of pairs of input and label, which are rare and expensive to collect in term of both manpower and resources. On the other hand, there are abundance o... | ['Rachada Kongkachandra', 'Pokpong Songmuang', 'Wasit Limprasert', 'Sarun Gulyanon'] | 2022-05-28 | null | null | null | null | ['satellite-image-classification'] | ['computer-vision'] | [ 4.08549160e-01 -1.01291701e-01 -1.09977908e-01 -4.17927146e-01
-3.63687605e-01 -4.73674148e-01 5.55340767e-01 2.48137349e-03
-3.96958143e-01 8.33198488e-01 -2.09031478e-01 -3.29962432e-01
3.63327600e-02 -1.13125491e+00 -6.03070974e-01 -9.89380717e-01
2.05917194e-01 2.31915459e-01 6.40500113e-02 -1.35007352... | [9.795287132263184, -1.3802329301834106] |
9e2b3527-ba38-4b06-9d00-87802d7512fe | voxgraf-fast-3d-aware-image-synthesis-with | 2206.07695 | null | https://arxiv.org/abs/2206.07695v3 | https://arxiv.org/pdf/2206.07695v3.pdf | VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Grids | State-of-the-art 3D-aware generative models rely on coordinate-based MLPs to parameterize 3D radiance fields. While demonstrating impressive results, querying an MLP for every sample along each ray leads to slow rendering. Therefore, existing approaches often render low-resolution feature maps and process them with an ... | ['Andreas Geiger', 'Yiyi Liao', 'Michael Niemeyer', 'Axel Sauer', 'Katja Schwarz'] | 2022-06-15 | null | null | null | null | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 4.28739250e-01 -7.13730007e-02 3.94503504e-01 -2.58150280e-01
-6.68499231e-01 -6.26408517e-01 9.59426165e-01 -1.31020576e-01
3.40775065e-02 7.25854695e-01 4.24894068e-04 -2.54653066e-01
1.29394114e-01 -1.36133635e+00 -8.19738925e-01 -7.27038026e-01
1.22181572e-01 7.23514855e-01 1.62392572e-01 6.63890541... | [9.277024269104004, -3.1850013732910156] |
ad70c211-95be-4c85-b5de-8476230a3a67 | a-read-write-memory-network-for-movie-story | 1709.09345 | null | http://arxiv.org/abs/1709.09345v4 | http://arxiv.org/pdf/1709.09345v4.pdf | A Read-Write Memory Network for Movie Story Understanding | We propose a novel memory network model named Read-Write Memory Network
(RWMN) to perform question and answering tasks for large-scale, multimodal
movie story understanding. The key focus of our RWMN model is to design the
read network and the write network that consist of multiple convolutional
layers, which enable me... | ['Ji-Sung Kim', 'Sang-ho Lee', 'Seil Na', 'Gunhee Kim'] | 2017-09-27 | a-read-write-memory-network-for-movie-story-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Na_A_Read-Write_Memory_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Na_A_Read-Write_Memory_ICCV_2017_paper.pdf | iccv-2017-10 | ['video-story-qa'] | ['computer-vision'] | [-1.12142459e-01 1.65681526e-01 -3.11872333e-01 -2.04622343e-01
-5.08159816e-01 -6.18396223e-01 8.64293039e-01 2.11978704e-02
-3.50292146e-01 4.35514688e-01 8.76965225e-01 -2.58202195e-01
2.82262146e-01 -9.96074378e-01 -9.00661349e-01 -4.06982660e-01
4.36602443e-01 6.92902744e-01 4.53529537e-01 -3.04966867... | [11.277138710021973, 7.816256523132324] |
c6ed5289-24b1-475c-81ca-5f61c12844e3 | fast-determinantal-point-processes-via | 1811.03717 | null | http://arxiv.org/abs/1811.03717v2 | http://arxiv.org/pdf/1811.03717v2.pdf | Fast determinantal point processes via distortion-free intermediate sampling | Given a fixed $n\times d$ matrix $\mathbf{X}$, where $n\gg d$, we study the
complexity of sampling from a distribution over all subsets of rows where the
probability of a subset is proportional to the squared volume of the
parallelepiped spanned by the rows (a.k.a. a determinantal point process). In
this task, it is im... | ['Michał Dereziński'] | 2018-11-08 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 4.41354454e-01 -4.33158036e-03 2.16142207e-01 4.91971746e-02
-7.82860756e-01 -7.83707261e-01 5.30028343e-01 4.93801296e-01
-6.64953470e-01 6.69825852e-01 -2.24721566e-01 -4.07165319e-01
-4.00260210e-01 -1.21651661e+00 -8.84135127e-01 -1.10135651e+00
-1.87252387e-01 9.64949310e-01 1.53836831e-01 -1.44245148... | [6.604231357574463, 4.709583759307861] |
8a1f5ede-22c8-4b6e-ba57-7ca42ee8913f | long-term-conversation-analysis-exploring | 2306.16071 | null | https://arxiv.org/abs/2306.16071v1 | https://arxiv.org/pdf/2306.16071v1.pdf | Long-term Conversation Analysis: Exploring Utility and Privacy | The analysis of conversations recorded in everyday life requires privacy protection. In this contribution, we explore a privacy-preserving feature extraction method based on input feature dimension reduction, spectral smoothing and the low-cost speaker anonymization technique based on McAdams coefficient. We assess the... | ['Joerg Bitzer', 'Patrick A. Naylor', 'Jule Pohlhausen', 'Francesco Nespoli'] | 2023-06-28 | null | null | null | null | ['action-detection', 'activity-detection', 'dimensionality-reduction', 'speech-recognition', 'speaker-diarization', 'speaker-verification'] | ['computer-vision', 'computer-vision', 'methodology', 'speech', 'speech', 'speech'] | [ 3.74191076e-01 3.80243361e-01 1.25307962e-01 -6.25975370e-01
-8.11352313e-01 -8.92985225e-01 6.59910798e-01 9.05148685e-02
-5.41113555e-01 8.72939646e-01 4.88712788e-01 -3.45133960e-01
-2.08039522e-01 -3.30360413e-01 -1.95393220e-01 -7.66461790e-01
-2.09122986e-01 -2.19958887e-01 -7.56948441e-02 2.91315559... | [13.937080383300781, 5.874355792999268] |
224e50e0-57ee-4008-9976-e893ce0be9c0 | the-aircraft-context-dataset-understanding | null | null | https://openaccess.thecvf.com/content/ICCV2021W/AOTW/html/Steininger_The_Aircraft_Context_Dataset_Understanding_and_Optimizing_Data_Variability_in_ICCVW_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021W/AOTW/papers/Steininger_The_Aircraft_Context_Dataset_Understanding_and_Optimizing_Data_Variability_in_ICCVW_2021_paper.pdf | The Aircraft Context Dataset: Understanding and Optimizing Data Variability in Aerial Domains | Despite their increasing demand for assistant and autonomous systems, the recent shift towards data-driven approaches has hardly reached aerial domains, partly due to a lack of specific training and test data. We introduce the Aircraft Context Dataset, a composition of two inter-compatible large-scale and versatile ima... | ['Christoph Sulzbachner', 'Andreas Kriegler', 'Julia Simon', 'Verena Widhalm', 'Daniel Steininger'] | 2021-10-17 | null | null | null | proceedings-of-the-ieee-cvf-international | ['robust-object-detection', 'small-object-detection', 'fine-grained-image-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.40174437e-01 -1.22838765e-01 -8.89242906e-03 -5.48622727e-01
-4.40048873e-01 -1.07547951e+00 1.02713656e+00 2.24117830e-01
-4.97151494e-01 7.33002484e-01 -1.72686085e-01 -1.45769849e-01
-6.58060730e-01 -4.10775155e-01 -5.03870785e-01 -5.92509270e-01
-2.17972249e-01 7.92752266e-01 5.07224619e-01 -3.26674998... | [8.042759895324707, -1.1248924732208252] |
374af84b-e2eb-49e0-bedc-7cec21a1aa9c | exploring-domain-invariant-parameters-for | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Exploring_Domain-Invariant_Parameters_for_Source_Free_Domain_Adaptation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Exploring_Domain-Invariant_Parameters_for_Source_Free_Domain_Adaptation_CVPR_2022_paper.pdf | Exploring Domain-Invariant Parameters for Source Free Domain Adaptation | Source-free domain adaptation (SFDA) newly emerges to transfer the relevant knowledge of a well-trained source model to an unlabeled target domain, which is critical in various privacy-preserving scenarios. Most existing methods focus on learning the domain-invariant representations depending solely on the target d... | ['Yilong Yin', 'Yongshun Gong', 'Zhongyi Han', 'Fan Wang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 4.64965582e-01 -1.23759523e-01 -6.00494862e-01 -4.51974720e-01
-9.67036545e-01 -8.25107574e-01 8.87868524e-01 -6.61926493e-02
-2.55698711e-01 1.07407546e+00 3.08392555e-01 -5.65777682e-02
-2.00013056e-01 -5.31494737e-01 -6.15575016e-01 -1.01441717e+00
3.17523509e-01 4.34741497e-01 2.41473526e-01 -3.43669713... | [10.37838363647461, 3.1559929847717285] |
26f4f866-be64-4451-8466-c9319f35903d | a-comparative-study-of-conversion-aided | null | null | https://aclanthology.org/W14-4507 | https://aclanthology.org/W14-4507.pdf | A Comparative Study of Conversion Aided Methods for WordNet Sentence Textual Similarity | null | ['Muhidin Mohamed', 'Mourad Oussalah'] | 2014-08-01 | null | null | null | ws-2014-8 | ['text-clustering'] | ['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... | [-7.138737201690674, 3.687035083770752] |
ba370f2b-2c40-4d6f-9a17-e08754950ede | investigating-the-importance-of-shape | 2002.03779 | null | https://arxiv.org/abs/2002.03779v2 | https://arxiv.org/pdf/2002.03779v2.pdf | Investigating the Importance of Shape Features, Color Constancy, Color Spaces and Similarity Measures in Open-Ended 3D Object Recognition | Despite the recent success of state-of-the-art 3D object recognition approaches, service robots are frequently failed to recognize many objects in real human-centric environments. For these robots, object recognition is a challenging task due to the high demand for accurate and real-time response under changing and unp... | ['Wessel van der Rest', 'Jits Schilperoort', 'S. Hamidreza Kasaei', 'Maryam Ghorbani'] | 2020-02-10 | null | null | null | null | ['3d-object-recognition', 'color-constancy'] | ['computer-vision', 'computer-vision'] | [-2.36858204e-02 -6.19469523e-01 1.75100923e-01 -3.25257957e-01
-1.02672741e-01 -8.33365440e-01 4.41537172e-01 8.20923895e-02
-5.02294481e-01 3.58334154e-01 -6.50515795e-01 -9.91302803e-02
-2.97481298e-01 -5.72977304e-01 -5.06646693e-01 -8.20124507e-01
-6.41491711e-02 6.95257425e-01 3.30003798e-01 -9.67109650... | [7.613922119140625, -1.3060818910598755] |
91cf0252-50ee-42b4-b68f-cbcd7c9d1a43 | book2movie-aligning-video-scenes-with-book | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Tapaswi_Book2Movie_Aligning_Video_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Tapaswi_Book2Movie_Aligning_Video_2015_CVPR_paper.pdf | Book2Movie: Aligning Video Scenes With Book Chapters | Film adaptations of novels often visually display in a few shots what is described in many pages of the source novel. In this paper we present a new problem: to align book chapters with video scenes. Such an alignment facilitates finding differences between the adaptation and the original source, and also acts as a bas... | ['Rainer Stiefelhagen', 'Martin Bauml', 'Makarand Tapaswi'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['video-alignment'] | ['computer-vision'] | [ 4.12318438e-01 -1.94632098e-01 -2.32850656e-01 -3.53239596e-01
-1.06577241e+00 -9.86299276e-01 9.05255139e-01 2.15131044e-01
-1.72995254e-01 5.19409478e-01 9.95589435e-01 3.76711667e-01
1.94043443e-02 -3.44248980e-01 -6.60058498e-01 -2.44507357e-01
3.08333457e-01 3.52525771e-01 4.90987659e-01 -2.44539842... | [10.704846382141113, 0.6417894959449768] |
8adec524-4058-4e8d-ab56-b059ca901730 | linguistically-informed-self-attention-for-1 | null | null | https://openreview.net/forum?id=Bk7GGldiz | https://openreview.net/pdf?id=Bk7GGldiz | Linguistically-Informed Self-Attention for Semantic Role Labeling | The current state-of-the-art end-to-end semantic role labeling (SRL) model is a deep neural network architecture with no explicit linguistic features.
However, prior work has shown that gold syntax trees can dramatically improve SRL, suggesting that neural network models could see great improvements from explicit mode... | ['Anonymous'] | 2018-04-08 | null | null | null | null | ['predicate-detection', 'semantic-role-labeling'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.43462080e-01 8.44218969e-01 -3.44492674e-01 -9.72815692e-01
-1.34939659e+00 -6.95214868e-01 2.66038090e-01 2.78284580e-01
-6.43117964e-01 7.53730297e-01 7.15303063e-01 -4.55326498e-01
1.92275137e-01 -5.90088904e-01 -1.10623085e+00 -2.29061127e-01
4.61351611e-02 8.55253637e-01 3.22014779e-01 -4.05215204... | [10.451192855834961, 9.442742347717285] |
19bdc023-e5aa-4f44-b0f3-c2ba39cd3f4c | lstm-cf-unifying-context-modeling-and-fusion | 1604.05000 | null | http://arxiv.org/abs/1604.05000v3 | http://arxiv.org/pdf/1604.05000v3.pdf | LSTM-CF: Unifying Context Modeling and Fusion with LSTMs for RGB-D Scene Labeling | Semantic labeling of RGB-D scenes is crucial to many intelligent applications
including perceptual robotics. It generates pixelwise and fine-grained label
maps from simultaneously sensed photometric (RGB) and depth channels. This
paper addresses this problem by i) developing a novel Long Short-Term Memorized
Context Fu... | ['Liang Lin', 'Hui Cheng', 'Yukang Gan', 'Xiaodan Liang', 'Yizhou Yu', 'Zhen Li'] | 2016-04-18 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 4.43350703e-01 -9.10431445e-02 8.47286582e-02 -8.93692791e-01
-8.41506660e-01 -4.12525535e-01 5.97871602e-01 2.27023941e-02
-5.54033577e-01 6.21597230e-01 1.52267674e-02 7.21717775e-02
1.31491736e-01 -1.17210448e+00 -9.51442003e-01 -9.09627914e-01
3.64809871e-01 1.43154217e-02 4.73541677e-01 2.56072562... | [9.380117416381836, -1.1819326877593994] |
24f62278-5b57-4810-90ab-e7ca38628949 | context-empowered-visual-attention-prediction | 2210.16933 | null | https://arxiv.org/abs/2210.16933v1 | https://arxiv.org/pdf/2210.16933v1.pdf | Context-empowered Visual Attention Prediction in Pedestrian Scenarios | Effective and flexible allocation of visual attention is key for pedestrians who have to navigate to a desired goal under different conditions of urgency and safety preferences. While automatic modelling of pedestrian attention holds great promise to improve simulations of pedestrian behavior, current saliency predicti... | ['Christian Mueller', 'Ahmed Abouelazm', 'Nils Lipp', 'Lorena Hell', 'Philipp Mueller', 'Igor Vozniak'] | 2022-10-30 | null | null | null | null | ['saliency-prediction'] | ['computer-vision'] | [ 6.59960210e-02 9.79506746e-02 -2.55750954e-01 -3.66744041e-01
-5.30590773e-01 -7.90361315e-02 4.49269325e-01 3.13844323e-01
-5.19765496e-01 6.86579645e-01 6.42899275e-01 -2.32234314e-01
1.90623522e-01 -4.30040509e-01 -6.80018961e-01 -2.94755846e-01
-1.35063171e-01 1.73210561e-01 5.85687518e-01 -5.33752978... | [6.292829513549805, 0.6111217141151428] |
33dad568-1ede-4e00-b2d0-c2d084d95e73 | adaptive-message-quantization-and | 2306.01381 | null | https://arxiv.org/abs/2306.01381v1 | https://arxiv.org/pdf/2306.01381v1.pdf | Adaptive Message Quantization and Parallelization for Distributed Full-graph GNN Training | Distributed full-graph training of Graph Neural Networks (GNNs) over large graphs is bandwidth-demanding and time-consuming. Frequent exchanges of node features, embeddings and embedding gradients (all referred to as messages) across devices bring significant communication overhead for nodes with remote neighbors on ot... | ['Chuan Wu', 'Juntao Zhao', 'Borui Wan'] | 2023-06-02 | null | null | null | null | ['quantization'] | ['methodology'] | [-2.73501929e-02 2.31435239e-01 -3.71115029e-01 -3.55669379e-01
-5.07415175e-01 -4.09802079e-01 1.73560940e-02 4.65635985e-01
-6.38519645e-01 5.22236645e-01 -6.52014971e-01 -8.79699707e-01
-2.39780471e-01 -1.01173913e+00 -8.98385167e-01 -7.50662684e-01
-6.22974098e-01 3.99212003e-01 4.33570385e-01 1.60243288... | [6.981847763061523, 5.699334621429443] |
23e8da33-6aa2-4dae-aeaf-9733b89ca7df | towards-better-validity-dispersion-based | 1906.01308 | null | https://arxiv.org/abs/1906.01308v1 | https://arxiv.org/pdf/1906.01308v1.pdf | Towards better Validity: Dispersion based Clustering for Unsupervised Person Re-identification | Person re-identification aims to establish the correct identity correspondences of a person moving through a non-overlapping multi-camera installation. Recent advances based on deep learning models for this task mainly focus on supervised learning scenarios where accurate annotations are assumed to be available for eac... | ['Guodong Ding', 'Salman Khan', 'Fatih Porikli', 'Zhenmin Tang', 'Jian Zhang'] | 2019-06-04 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-1.69910371e-01 -4.42804933e-01 5.48008643e-02 -5.27604640e-01
-4.68566686e-01 -5.26917696e-01 4.30470586e-01 3.61379802e-01
-5.19739330e-01 3.81719112e-01 7.76961669e-02 4.25733067e-02
-4.62609857e-01 -6.09418333e-01 -4.33090538e-01 -9.39444721e-01
-2.88615674e-02 7.13946819e-01 -3.03327709e-01 2.13583007... | [14.769647598266602, 1.0612694025039673] |
543deea0-066c-43e6-820f-c3e79a254938 | english-language-spelling-correction-as-an | null | null | https://aclanthology.org/2022.lrec-1.48 | https://aclanthology.org/2022.lrec-1.48.pdf | English Language Spelling Correction as an Information Retrieval Task Using Wikipedia Search Statistics | Spelling correction utilities have become commonplace during the writing process, however, many spelling correction utilities suffer due to the size and quality of dictionaries available to aid correction. Many terms, acronyms, and morphological variations of terms are often missing, leaving potential spelling errors u... | ['Markus Hofmann', 'Kyle Goslin'] | null | null | null | null | lrec-2022-6 | ['spelling-correction'] | ['natural-language-processing'] | [ 4.25888926e-01 -3.92887414e-01 5.23998998e-02 1.85924117e-02
-5.56934536e-01 -9.75799441e-01 6.71044827e-01 9.20432031e-01
-6.28512979e-01 7.79180586e-01 2.00213253e-01 -3.42294186e-01
-5.47064364e-01 -5.32151461e-01 -5.25681019e-01 -8.48030373e-02
5.99858105e-01 6.43461525e-01 2.92987853e-01 -6.02443457... | [10.892062187194824, 10.607683181762695] |
24a9bb42-d7cd-4d9b-8141-9ff74a9e3f19 | epic-fields-marrying-3d-geometry-and-video | 2306.08731 | null | https://arxiv.org/abs/2306.08731v1 | https://arxiv.org/pdf/2306.08731v1.pdf | EPIC Fields: Marrying 3D Geometry and Video Understanding | Neural rendering is fuelling a unification of learning, 3D geometry and video understanding that has been waiting for more than two decades. Progress, however, is still hampered by a lack of suitable datasets and benchmarks. To address this gap, we introduce EPIC Fields, an augmentation of EPIC-KITCHENS with 3D camera ... | ['Andrea Vedaldi', 'Dima Damen', 'Diane Larlus', 'Iro Laina', 'David Fouhey', 'Zhifan Zhu', 'Ahmad Darkhalil', 'Vadim Tschernezki'] | 2023-06-14 | null | null | null | null | ['neural-rendering', 'video-understanding'] | ['computer-vision', 'computer-vision'] | [ 4.21978325e-01 1.77644715e-01 -1.01293273e-01 -3.19724143e-01
-6.85896695e-01 -7.71635890e-01 7.68741906e-01 -5.67619562e-01
-4.31987315e-01 3.83526236e-01 4.20921862e-01 -2.32271254e-01
1.13286473e-01 -5.85112154e-01 -1.23276198e+00 -4.52970415e-01
1.32158205e-01 5.89911878e-01 1.44169018e-01 -7.06471279... | [8.547784805297852, -1.8715797662734985] |
1ecb441a-3f96-47d7-bdbf-e2de5f8b551b | complex-logical-reasoning-over-knowledge | 2305.01157 | null | https://arxiv.org/abs/2305.01157v2 | https://arxiv.org/pdf/2305.01157v2.pdf | Complex Logical Reasoning over Knowledge Graphs using Large Language Models | Reasoning over knowledge graphs (KGs) is a challenging task that requires a deep understanding of the complex relationships between entities and the underlying logic of their relations. Current approaches rely on learning geometries to embed entities in vector space for logical query operations, but they suffer from su... | ['Chandan K. Reddy', 'Nurendra Choudhary'] | 2023-05-02 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [-1.27250567e-01 5.29574335e-01 -4.92021233e-01 -3.35607857e-01
-6.54809654e-01 -6.40226543e-01 6.71617568e-01 8.81095290e-01
-7.36330748e-02 3.80820423e-01 2.04271764e-01 -7.41923988e-01
-4.90759343e-01 -1.55053055e+00 -9.57794726e-01 1.43628135e-01
-3.12694967e-01 6.67936146e-01 6.01989567e-01 -4.12627727... | [9.1358003616333, 7.695498466491699] |
5598e8cb-fe55-401b-b1f4-48d56d02c991 | inducing-point-allocation-for-sparse-gaussian | 2301.10123 | null | https://arxiv.org/abs/2301.10123v2 | https://arxiv.org/pdf/2301.10123v2.pdf | Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation | Sparse Gaussian Processes are a key component of high-throughput Bayesian Optimisation (BO) loops; however, we show that existing methods for allocating their inducing points severely hamper optimisation performance. By exploiting the quality-diversity decomposition of Determinantal Point Processes, we propose the firs... | ['Victor Picheny', 'Sebastian W. Ober', 'Henry B. Moss'] | 2023-01-24 | null | null | null | null | ['point-processes', 'bayesian-optimisation'] | ['methodology', 'methodology'] | [ 3.33327740e-01 2.59894967e-01 4.83261757e-02 -1.26338884e-01
-1.21753860e+00 -5.01008570e-01 8.66429567e-01 5.59328385e-02
-2.67850637e-01 6.70404494e-01 1.85027674e-01 -4.73211378e-01
-7.75031090e-01 -5.19727886e-01 -5.83700120e-01 -1.19131088e+00
-1.12446018e-01 8.83682072e-01 1.55538157e-01 1.21274628... | [6.389586448669434, 3.8425145149230957] |
9dbe6d3d-c591-4f82-958a-563a7d6d064d | what-when-and-where-self-supervised-spatio | 2303.16990 | null | https://arxiv.org/abs/2303.16990v1 | https://arxiv.org/pdf/2303.16990v1.pdf | What, when, and where? -- Self-Supervised Spatio-Temporal Grounding in Untrimmed Multi-Action Videos from Narrated Instructions | Spatio-temporal grounding describes the task of localizing events in space and time, e.g., in video data, based on verbal descriptions only. Models for this task are usually trained with human-annotated sentences and bounding box supervision. This work addresses this task from a multimodal supervision perspective, prop... | ['Hilde Kuehne', 'James Glass', 'Rogerio Feris', 'Shih-Fu Chang', 'Samuel Thomas', 'Daniel Kondermann', 'Andrew Rouditchenko', 'Nina Shvetsova', 'Brian Chen'] | 2023-03-29 | null | null | null | null | ['spatio-temporal-video-grounding'] | ['computer-vision'] | [ 3.15182090e-01 -1.22716971e-01 -4.56172079e-01 -4.69100833e-01
-1.26638854e+00 -7.57366776e-01 7.62356937e-01 5.68965793e-01
-4.10249770e-01 6.07666254e-01 8.85367692e-01 -1.52521715e-01
-1.78983882e-01 -2.90663928e-01 -1.14844263e+00 -5.45664907e-01
-1.45600855e-01 9.63089690e-02 4.91912723e-01 -2.15901151... | [10.057876586914062, 0.7600241303443909] |
8defa087-4682-4264-a302-239e57072a8e | mathematical-capabilities-of-chatgpt | 2301.13867 | null | https://arxiv.org/abs/2301.13867v1 | https://arxiv.org/pdf/2301.13867v1.pdf | Mathematical Capabilities of ChatGPT | We investigate the mathematical capabilities of ChatGPT by testing it on publicly available datasets, as well as hand-crafted ones, and measuring its performance against other models trained on a mathematical corpus, such as Minerva. We also test whether ChatGPT can be a useful assistant to professional mathematicians ... | ['Julius Berner', 'Alexis Chevalier', 'Philipp Christian Petersen', 'Thomas Lukasiewicz', 'Tommaso Salvatori', 'Ryan-Rhys Griffiths', 'Luca Pinchetti', 'Simon Frieder'] | 2023-01-31 | null | null | null | null | ['selection-bias', 'elementary-mathematics'] | ['natural-language-processing', 'reasoning'] | [-2.48873636e-01 2.86447525e-01 -1.80018023e-02 -4.44148034e-02
-8.32660675e-01 -8.61932993e-01 7.78266251e-01 6.48185372e-01
-4.08924967e-01 6.96435392e-01 -1.83450997e-01 -1.30967176e+00
-5.25042951e-01 -1.18756294e+00 -9.09233570e-01 -1.05440527e-01
-2.99269576e-02 5.79186618e-01 8.87323543e-02 -2.33920112... | [9.622729301452637, 7.317354679107666] |
411a1c35-797a-4773-89ac-6414a6410565 | vonda-a-framework-for-ontology-based-dialogue | 1910.00340 | null | https://arxiv.org/abs/1910.00340v1 | https://arxiv.org/pdf/1910.00340v1.pdf | VOnDA: A Framework for Ontology-Based Dialogue Management | We present VOnDA, a framework to implement the dialogue management functionality in dialogue systems. Although domain-independent, VOnDA is tailored towards dialogue systems with a focus on social communication, which implies the need of long-term memory and high user adaptivity. For these systems, which are used in he... | ['Christophe Biwer', 'Anna Welker', 'Bernd Kiefer'] | 2019-10-01 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [-6.32321954e-01 1.05267560e+00 -1.74632519e-01 -3.76322687e-01
2.29854994e-02 -4.76080716e-01 7.17303097e-01 7.33743489e-01
-6.36849523e-01 9.74252820e-01 5.73761821e-01 -7.39931166e-02
-2.80673325e-01 -1.05431306e+00 2.17204511e-01 -9.19280276e-02
1.54818937e-01 4.40301746e-01 6.01995528e-01 -9.00161743... | [12.86268424987793, 7.975837230682373] |
014187f8-2200-4d14-adea-cd1e4d1db602 | c-cnn-contourlet-convolutional-neural | null | null | https://ieeexplore.ieee.org/document/9145825 | https://sci-hub.mksa.top/10.1109/TNNLS.2020.3007412 | C-CNN: Contourlet Convolutional Neural Networks | Extracting effective features is always a challenging problem for texture classification because of the uncertainty of scales and the clutter of textural patterns. For texture classification, spectral analysis is traditionally employed in the frequency domain. Recent studies have shown the potential of convolutional ne... | ['Shuyuan Yang', 'Fang Liu', 'Lingling Li', 'Xu Liu', 'Licheng Jiao', 'Mengkun Liu'] | 2020-07-21 | null | null | null | ieee-transactions-on-neural-networks-and-5 | ['texture-classification'] | ['computer-vision'] | [ 2.35655144e-01 -5.85956872e-01 1.08667284e-01 -3.23073626e-01
-5.18900156e-01 4.34488468e-02 4.55180943e-01 -1.16679169e-01
-3.87226641e-01 7.11247504e-01 -4.75424267e-02 5.39309718e-02
-6.52272642e-01 -1.04339528e+00 -2.14117184e-01 -1.11248589e+00
-3.91482234e-01 -2.74343401e-01 3.15844938e-02 -4.76403236... | [10.138111114501953, -0.704339325428009] |
34fcc7c1-560f-42d8-8d8a-3d0e857611f7 | magicpony-learning-articulated-3d-animals-in | 2211.12497 | null | https://arxiv.org/abs/2211.12497v3 | https://arxiv.org/pdf/2211.12497v3.pdf | MagicPony: Learning Articulated 3D Animals in the Wild | We consider the problem of predicting the 3D shape, articulation, viewpoint, texture, and lighting of an articulated animal like a horse given a single test image as input. We present a new method, dubbed MagicPony, that learns this predictor purely from in-the-wild single-view images of the object category, with minim... | ['Andrea Vedaldi', 'Christian Rupprecht', 'Tomas Jakab', 'Ruining Li', 'Shangzhe Wu'] | 2022-11-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_MagicPony_Learning_Articulated_3D_Animals_in_the_Wild_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_MagicPony_Learning_Articulated_3D_Animals_in_the_Wild_CVPR_2023_paper.pdf | cvpr-2023-1 | ['viewpoint-estimation'] | ['computer-vision'] | [ 2.29714662e-01 2.90720254e-01 2.33274177e-01 -3.86286706e-01
-6.01805925e-01 -8.47314596e-01 7.21254289e-01 -2.62917072e-01
-3.99212949e-02 2.42333665e-01 3.89504805e-02 2.21408844e-01
1.20937169e-01 -4.36484069e-01 -1.17099798e+00 -3.71744692e-01
3.75105888e-01 1.07630861e+00 3.97639841e-01 -6.87858164... | [8.263776779174805, -2.8350346088409424] |
40fdf84a-fcd9-41a6-8ad9-e35ae4fa1752 | an-empirical-study-of-finding-similar | 2111.08322 | null | https://arxiv.org/abs/2111.08322v1 | https://arxiv.org/pdf/2111.08322v1.pdf | An Empirical Study of Finding Similar Exercises | Education artificial intelligence aims to profit tasks in the education domain such as intelligent test paper generation and consolidation exercises where the main technique behind is how to match the exercises, known as the finding similar exercises(FSE) problem. Most of these approaches emphasized their model abiliti... | ['Xihua Li', 'Tongwen Huang'] | 2021-11-16 | null | null | null | null | ['paper-generation'] | ['natural-language-processing'] | [ 4.16417062e-01 -3.64052951e-01 1.32444099e-01 -3.62793207e-01
-6.07653975e-01 -6.59048200e-01 8.79440829e-02 -3.92715409e-02
-5.16686857e-01 1.06784689e+00 1.05974443e-01 -2.74799854e-01
-6.77317262e-01 -8.90013039e-01 -7.05917656e-01 -2.27043033e-01
5.67535400e-01 2.29510546e-01 1.24015965e-01 -5.00508547... | [10.043954849243164, 7.278232574462891] |
0ddbb0f7-ae8c-4232-a811-653c40f9a679 | efficient-hybrid-modeling-and-sorption-model | 2303.13555 | null | https://arxiv.org/abs/2303.13555v3 | https://arxiv.org/pdf/2303.13555v3.pdf | Efficient hybrid modeling and sorption model discovery for non-linear advection-diffusion-sorption systems: A systematic scientific machine learning approach | This study presents a systematic machine learning approach for creating efficient hybrid models and discovering sorption uptake models in non-linear advection-diffusion-sorption systems. It demonstrates an effective method to train these complex systems using gradient based optimizers, adjoint sensitivity analysis, and... | ['Chris Rackauckas', 'Idelfonso B. R. Nogueira', 'Ana Mafalda Ribeiro', 'Carine M. Rebello', 'Erbet Costa', 'Vinicius V. Santana'] | 2023-03-22 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 3.37613449e-02 -5.49348295e-01 -1.38981476e-01 1.58546083e-02
-4.56894606e-01 -2.77184814e-01 1.58579364e-01 6.14442110e-01
-5.24760067e-01 1.23769152e+00 -4.86988634e-01 -7.16055751e-01
-9.65904593e-01 -6.06445253e-01 -8.30213785e-01 -1.01576388e+00
-7.14718103e-01 8.23695064e-01 -3.49550128e-01 -1.07930338... | [6.383156776428223, 3.381610870361328] |
aa6cf3ed-7785-4b5a-8cbc-f61aad191df9 | multimodal-web-navigation-with-instruction | 2305.11854 | null | https://arxiv.org/abs/2305.11854v1 | https://arxiv.org/pdf/2305.11854v1.pdf | Multimodal Web Navigation with Instruction-Finetuned Foundation Models | The progress of autonomous web navigation has been hindered by the dependence on billions of exploratory interactions via online reinforcement learning, and domain-specific model designs that make it difficult to leverage generalization from rich out-of-domain data. In this work, we study data-driven offline training f... | ['Izzeddin Gur', 'Shixiang Shane Gu', 'Yutaka Matsuo', 'Kuang-Huei Lee', 'Ofir Nachum', 'Hiroki Furuta'] | 2023-05-19 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [-5.20012043e-02 9.34242979e-02 -3.19941759e-01 -3.08379322e-01
-9.82650578e-01 -1.03061426e+00 6.97163582e-01 -1.97329193e-01
-6.48069978e-01 5.20641565e-01 2.27013841e-01 -8.49154949e-01
1.45568892e-01 -5.08168221e-01 -1.39054227e+00 -1.88090712e-01
-1.46647066e-01 5.44975638e-01 4.62075055e-01 -3.12243581... | [4.411139011383057, 0.8343736529350281] |
6f8949a2-afca-4dca-8f03-f32891ab766a | going-denser-with-open-vocabulary-part | 2305.11173 | null | https://arxiv.org/abs/2305.11173v1 | https://arxiv.org/pdf/2305.11173v1.pdf | Going Denser with Open-Vocabulary Part Segmentation | Object detection has been expanded from a limited number of categories to open vocabulary. Moving forward, a complete intelligent vision system requires understanding more fine-grained object descriptions, object parts. In this paper, we propose a detector with the ability to predict both open-vocabulary objects and th... | ['Zhicheng Yan', 'Saining Xie', 'Ping Luo', 'Fanyi Xiao', 'Chenchen Zhu', 'Shoufa Chen', 'Peize Sun'] | 2023-05-18 | null | null | null | null | ['semantic-correspondence'] | ['computer-vision'] | [ 1.76880807e-01 2.06708044e-01 -1.71865240e-01 -8.28812242e-01
-1.00997734e+00 -7.20912814e-01 4.78617340e-01 -1.42454952e-01
-2.54538417e-01 2.46282071e-01 -1.13329589e-01 5.37504405e-02
4.24684346e-01 -6.72232151e-01 -1.11648107e+00 -2.66361237e-01
2.33872458e-01 7.28635132e-01 1.01278174e+00 -2.27410585... | [9.532453536987305, 1.3039400577545166] |
35fdf62f-6f8c-4417-b4a1-028616762fc4 | can-chatgpt-assess-human-personalities-a | 2303.01248 | null | https://arxiv.org/abs/2303.01248v2 | https://arxiv.org/pdf/2303.01248v2.pdf | Can ChatGPT Assess Human Personalities? A General Evaluation Framework | Large Language Models (LLMs) especially ChatGPT have produced impressive results in various areas, but their potential human-like psychology is still largely unexplored. Existing works study the virtual personalities of LLMs but rarely explore the possibility of analyzing human personalities via LLMs. This paper presen... | ['Chunyan Miao', 'Cyril Leung', 'Haocong Rao'] | 2023-03-01 | null | null | null | null | ['answer-generation'] | ['natural-language-processing'] | [-2.66142815e-01 1.82500228e-01 -1.87697922e-04 -5.99388897e-01
-5.78244627e-01 -4.98743236e-01 4.99078304e-01 1.10473973e-03
-5.98951817e-01 7.22046673e-01 1.72034264e-01 -4.60780948e-01
-1.70574948e-01 -8.01285446e-01 -1.79281577e-01 -4.47752088e-01
2.74983257e-01 3.80727470e-01 2.03002304e-01 -1.30724996... | [11.765214920043945, 8.17653751373291] |
0b265114-dd4b-4994-beac-ab5d30134bdd | falcon-fast-visual-concept-learning-by-1 | 2203.16639 | null | https://arxiv.org/abs/2203.16639v1 | https://arxiv.org/pdf/2203.16639v1.pdf | FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations | We present a meta-learning framework for learning new visual concepts quickly, from just one or a few examples, guided by multiple naturally occurring data streams: simultaneously looking at images, reading sentences that describe the objects in the scene, and interpreting supplemental sentences that relate the novel c... | ['Joshua B. Tenenbaum', 'Chuang Gan', 'Ziqi Wang', 'Jiayuan Mao', 'Lingjie Mei'] | 2022-03-30 | falcon-fast-visual-concept-learning-by | https://openreview.net/forum?id=htWIlvDcY8 | https://openreview.net/pdf?id=htWIlvDcY8 | iclr-2022-4 | ['novel-concepts'] | ['reasoning'] | [ 3.54723960e-01 2.31321201e-01 4.00225334e-02 -6.70887828e-01
-6.26327336e-01 -6.96686566e-01 6.31424665e-01 5.94331563e-01
-1.61892906e-01 4.59441066e-01 2.44453296e-01 -5.92153333e-02
-1.34340059e-02 -7.85510480e-01 -1.12782252e+00 -5.01898944e-01
-4.49545905e-02 4.54305559e-01 7.79377595e-02 1.20473407... | [10.223636627197266, 1.9073407649993896] |
4134c2ef-1a51-4ea3-8727-db9f624c8d0c | small-text-active-learning-for-text | 2107.10314 | null | https://arxiv.org/abs/2107.10314v6 | https://arxiv.org/pdf/2107.10314v6.pdf | Small-Text: Active Learning for Text Classification in Python | We introduce small-text, an easy-to-use active learning library, which offers pool-based active learning for single- and multi-label text classification in Python. It features numerous pre-implemented state-of-the-art query strategies, including some that leverage the GPU. Standardized interfaces allow the combination ... | ['Martin Potthast', 'Andreas Niekler', 'Lydia Müller', 'Christopher Schröder'] | 2021-07-21 | null | null | null | null | ['classification'] | ['methodology'] | [ 2.02802964e-03 -3.48439097e-01 -4.85726476e-01 -3.83454889e-01
-1.23279178e+00 -9.76483107e-01 6.86581254e-01 6.66461587e-01
-7.39224672e-01 6.35167241e-01 -2.39746869e-01 -4.10801619e-01
1.69409830e-02 -6.20390356e-01 -3.38660628e-01 -7.51105726e-01
3.02012920e-01 8.17426383e-01 4.94038850e-01 -2.87011433... | [9.635421752929688, 4.255773544311523] |
6855060b-5ae4-470f-9018-936d7a18cc6c | vatex-a-large-scale-high-quality-multilingual | 1904.03493 | null | https://arxiv.org/abs/1904.03493v3 | https://arxiv.org/pdf/1904.03493v3.pdf | VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research | We present a new large-scale multilingual video description dataset, VATEX, which contains over 41,250 videos and 825,000 captions in both English and Chinese. Among the captions, there are over 206,000 English-Chinese parallel translation pairs. Compared to the widely-used MSR-VTT dataset, VATEX is multilingual, large... | ['Yuan-Fang Wang', 'Lei LI', 'Xin Wang', 'William Yang Wang', 'Junkun Chen', 'Jiawei Wu'] | 2019-04-06 | vatex-a-large-scale-high-quality-multilingual-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_VaTeX_A_Large-Scale_High-Quality_Multilingual_Dataset_for_Video-and-Language_Research_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_VaTeX_A_Large-Scale_High-Quality_Multilingual_Dataset_for_Video-and-Language_Research_ICCV_2019_paper.pdf | iccv-2019-10 | ['video-description'] | ['computer-vision'] | [-1.01159662e-01 -6.33348823e-01 -6.01067424e-01 -2.78129429e-01
-1.32492614e+00 -8.70341063e-01 6.07729793e-01 -4.38804388e-01
-3.09766650e-01 9.54084635e-01 5.16703367e-01 -1.81927949e-01
7.62315512e-01 -2.02430397e-01 -9.82777119e-01 -3.16232383e-01
1.98759213e-01 4.65251058e-01 -3.94708067e-02 -1.35333478... | [10.811234474182129, 1.0890910625457764] |
dc6ef5a2-b6e2-4ea0-9ff8-9877f8638815 | model-based-trajectory-stitching-for-improved-1 | 2212.04280 | null | https://arxiv.org/abs/2212.04280v1 | https://arxiv.org/pdf/2212.04280v1.pdf | Model-based trajectory stitching for improved behavioural cloning and its applications | Behavioural cloning (BC) is a commonly used imitation learning method to infer a sequential decision-making policy from expert demonstrations. However, when the quality of the data is not optimal, the resulting behavioural policy also performs sub-optimally once deployed. Recently, there has been a surge in offline rei... | ['Giovanni Montana', 'Charles A. Hepburn'] | 2022-12-08 | null | null | null | null | ['d4rl'] | ['robots'] | [ 3.59126091e-01 2.49516934e-01 -4.70696449e-01 -4.10995744e-02
-8.38356316e-01 -7.60511696e-01 9.53350425e-01 2.38895655e-01
-7.24310398e-01 1.31603110e+00 1.27740771e-01 -5.06965995e-01
-3.88469636e-01 -5.42212367e-01 -1.15290916e+00 -7.95313418e-01
-4.85682875e-01 7.02544630e-01 5.35056770e-01 -1.24643967... | [4.1694536209106445, 2.019341230392456] |
7832eb73-43de-4859-8415-80c8ca8fabf1 | stationary-diffusion-state-neural-estimation | 2112.01334 | null | https://arxiv.org/abs/2112.01334v1 | https://arxiv.org/pdf/2112.01334v1.pdf | Stationary Diffusion State Neural Estimation for Multiview Clustering | Although many graph-based clustering methods attempt to model the stationary diffusion state in their objectives, their performance limits to using a predefined graph. We argue that the estimation of the stationary diffusion state can be achieved by gradient descent over neural networks. We specifically design the Stat... | ['Kun Zhan', 'Yixuan Ma', 'Zhuolin Liao', 'Chenghua Liu'] | 2021-12-02 | null | null | null | null | ['multiview-learning'] | ['computer-vision'] | [-1.84893444e-01 4.36394304e-01 -3.97705615e-01 -3.90545070e-01
-2.67076701e-01 -2.98209608e-01 4.68825519e-01 1.46679938e-01
1.47045171e-02 2.03221917e-01 7.51524121e-02 7.98464119e-02
-3.08483660e-01 -7.55236804e-01 -7.14661300e-01 -8.38709593e-01
-1.22708879e-01 4.71330851e-01 1.10852636e-01 1.26903623... | [7.51605749130249, 5.942156791687012] |
d9b92c43-f5b8-45c8-8e81-3bc8239bafee | a-real-time-wrong-way-vehicle-detection-based | 2210.10226 | null | https://arxiv.org/abs/2210.10226v1 | https://arxiv.org/pdf/2210.10226v1.pdf | A Real-Time Wrong-Way Vehicle Detection Based on YOLO and Centroid Tracking | Wrong-way driving is one of the main causes of road accidents and traffic jam all over the world. By detecting wrong-way vehicles, the number of accidents can be minimized and traffic jam can be reduced. With the increasing popularity of real-time traffic management systems and due to the availability of cheaper camera... | ['Muhammad Ahsan Ullah', 'Amit Mazumder Ami', 'Zillur Rahman'] | 2022-10-19 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [-1.73531160e-01 -5.79325140e-01 -3.15245569e-01 -1.52529553e-01
-1.37468129e-01 -5.03315628e-01 4.09285754e-01 -2.62590677e-01
-6.61880255e-01 4.57681686e-01 -2.66208291e-01 -4.68442738e-01
-4.68696654e-02 -8.17319214e-01 -5.19986749e-01 -7.32614458e-01
3.33840549e-01 1.05871201e-01 1.25572729e+00 -1.08673900... | [7.966064929962158, -1.1080280542373657] |
7018b60b-d8b4-4af2-9a9c-a883a08ee3be | bayesian-optimization-of-expensive-nested | 2306.05150 | null | https://arxiv.org/abs/2306.05150v1 | https://arxiv.org/pdf/2306.05150v1.pdf | Bayesian Optimization of Expensive Nested Grey-Box Functions | We consider the problem of optimizing a grey-box objective function, i.e., nested function composed of both black-box and white-box functions. A general formulation for such grey-box problems is given, which covers the existing grey-box optimization formulations as special cases. We then design an optimism-driven algor... | ['Colin N. Jones', 'Bratislav Svetozarevic', 'Yuning Jiang', 'Wenjie Xu'] | 2023-06-08 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [ 6.46146461e-02 3.08139771e-01 -1.78565174e-01 -5.09596705e-01
-1.16002226e+00 -5.16727149e-01 1.11588642e-01 2.71706395e-02
-4.87539709e-01 8.09529006e-01 -1.64664071e-02 -3.42294335e-01
-6.31812274e-01 -4.64688897e-01 -7.92719662e-01 -1.06045794e+00
-2.76292980e-01 5.63713431e-01 -2.05786414e-02 -1.59441799... | [6.473679065704346, 4.118503570556641] |
37deb794-3569-4104-8406-1ec708078452 | contextualized-embeddings-based-transformer | null | null | https://aclanthology.org/2020.lrec-1.676 | https://aclanthology.org/2020.lrec-1.676.pdf | Contextualized Embeddings based Transformer Encoder for Sentence Similarity Modeling in Answer Selection Task | Word embeddings that consider context have attracted great attention for various natural language processing tasks in recent years. In this paper, we utilize contextualized word embeddings with the transformer encoder for sentence similarity modeling in the answer selection task. We present two different approaches (fe... | ['Md Tahmid Rahman Laskar', 'Enamul Hoque', 'Jimmy Xiangji Huang'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['answer-selection'] | ['natural-language-processing'] | [-1.57437012e-01 -3.12275231e-01 -1.70730412e-01 -6.28045619e-01
-7.56256163e-01 -2.49489844e-01 4.90249068e-01 5.20743787e-01
-7.90874839e-01 3.03441286e-01 7.58207977e-01 -4.42337215e-01
-1.79909900e-01 -9.44125354e-01 -3.08500320e-01 -2.07604989e-01
3.85483831e-01 3.20535868e-01 4.89568949e-01 -6.22030616... | [10.958894729614258, 8.546529769897461] |
43416a15-83aa-4427-9dd5-bedb88a1542d | adversarially-learned-one-class-classifier | 1802.09088 | null | http://arxiv.org/abs/1802.09088v2 | http://arxiv.org/pdf/1802.09088v2.pdf | Adversarially Learned One-Class Classifier for Novelty Detection | Novelty detection is the process of identifying the observation(s) that
differ in some respect from the training observations (the target class). In
reality, the novelty class is often absent during training, poorly sampled or
not well defined. Therefore, one-class classifiers can efficiently model such
problems. Howev... | ['Mohammad Sabokrou', 'Mahmood Fathy', 'Ehsan Adeli', 'Mohammad Khalooei'] | 2018-02-25 | adversarially-learned-one-class-classifier-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Sabokrou_Adversarially_Learned_One-Class_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Sabokrou_Adversarially_Learned_One-Class_CVPR_2018_paper.pdf | cvpr-2018-6 | ['one-class-classifier'] | ['methodology'] | [ 1.60203815e-01 6.70837760e-02 1.60102576e-01 -3.20683926e-01
-5.31792521e-01 -2.65586823e-01 4.90419269e-01 1.20617807e-01
-2.73555428e-01 3.78977090e-01 -1.51794612e-01 1.64882224e-02
3.96642275e-02 -5.24824500e-01 -1.10549402e+00 -9.57060099e-01
-1.26681328e-01 5.66386640e-01 1.14169024e-01 -2.60928161... | [7.685258388519287, 2.2852025032043457] |
3d1c7394-94eb-4d51-a22d-200c6b1478a9 | absformer-transformer-based-model-for | 2306.04787 | null | https://arxiv.org/abs/2306.04787v1 | https://arxiv.org/pdf/2306.04787v1.pdf | Absformer: Transformer-based Model for Unsupervised Multi-Document Abstractive Summarization | Multi-document summarization (MDS) refers to the task of summarizing the text in multiple documents into a concise summary. The generated summary can save the time of reading many documents by providing the important content in the form of a few sentences. Abstractive MDS aims to generate a coherent and fluent summary ... | ['Huseyin Uzunalioglu', 'Mohamed Trabelsi'] | 2023-06-07 | null | null | null | null | ['abstractive-text-summarization', 'multi-document-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.86611211e-01 4.75104511e-01 6.39488548e-02 -4.14073110e-01
-1.56417894e+00 -5.14806449e-01 1.01204693e+00 5.65538585e-01
-1.77206486e-01 8.67597938e-01 9.33647752e-01 2.39006169e-02
1.88392609e-01 -4.31379884e-01 -7.05902100e-01 -4.57535774e-01
3.03715676e-01 9.35576439e-01 1.19343802e-01 -1.42022058... | [12.516444206237793, 9.347265243530273] |
d2b103b4-1c9b-49ac-b15d-28d38047f780 | unsupervised-keyphrase-extraction-via-1 | 2203.07640 | null | https://arxiv.org/abs/2203.07640v2 | https://arxiv.org/pdf/2203.07640v2.pdf | Unsupervised Keyphrase Extraction via Interpretable Neural Networks | Keyphrase extraction aims at automatically extracting a list of "important" phrases representing the key concepts in a document. Prior approaches for unsupervised keyphrase extraction resorted to heuristic notions of phrase importance via embedding clustering or graph centrality, requiring extensive domain expertise. O... | ['Yulia Tsvetkov', 'Svitlana Volkova', 'Maria Glenski', 'Emily Saldanha', 'Vidhisha Balachandran', 'Rishabh Joshi'] | 2022-03-15 | null | null | null | null | ['keyphrase-extraction'] | ['natural-language-processing'] | [ 1.52304634e-01 6.07944608e-01 -6.69775426e-01 2.71211416e-01
-7.54630864e-01 -7.89241731e-01 1.16302884e+00 1.23286033e+00
-4.06661153e-01 7.33708799e-01 9.66615617e-01 -5.40839374e-01
-3.57976228e-01 -8.60855043e-01 -3.73771876e-01 -6.01297975e-01
-3.59828949e-01 5.99440515e-01 1.71725407e-01 -4.22143377... | [12.182626724243164, 8.90673828125] |
359dc3cd-55bd-4405-be6a-4f7e54b0d32d | juno-jump-start-reinforcement-learning-based | 2205.08422 | null | https://arxiv.org/abs/2205.08422v1 | https://arxiv.org/pdf/2205.08422v1.pdf | JUNO: Jump-Start Reinforcement Learning-based Node Selection for UWB Indoor Localization | Ultra-Wideband (UWB) is one of the key technologies empowering the Internet of Thing (IoT) concept to perform reliable, energy-efficient, and highly accurate monitoring, screening, and localization in indoor environments. Performance of UWB-based localization systems, however, can significantly degrade because of Non L... | ['Arash Mohammadi', 'Ming Hou', 'Zohreh Hajiakhondi-Meybodi'] | 2022-05-06 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-5.19110076e-02 -1.17588721e-01 -1.71798140e-01 3.98536436e-02
-8.07951331e-01 -4.37518597e-01 2.43722931e-01 4.33306098e-01
-3.63841861e-01 1.22298717e+00 -3.29434127e-01 -5.65875351e-01
-4.87108558e-01 -1.01921535e+00 -4.98741925e-01 -9.94367957e-01
-3.83351296e-01 9.54077318e-02 2.33711123e-01 2.34048087... | [6.260490894317627, 1.1292407512664795] |
b0d239f5-7282-46df-9169-7a7bc3105101 | quo-vadis-is-trajectory-forecasting-the-key | 2210.07681 | null | https://arxiv.org/abs/2210.07681v2 | https://arxiv.org/pdf/2210.07681v2.pdf | Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking? | Recent developments in monocular multi-object tracking have been very successful in tracking visible objects and bridging short occlusion gaps, mainly relying on data-driven appearance models. While we have significantly advanced short-term tracking performance, bridging longer occlusion gaps remains elusive: state-of-... | ['Laura Leal-Taixé', 'Aljoša Ošep', 'Vladimir Yugay', 'Patrick Dendorfer'] | 2022-10-14 | null | null | null | null | ['trajectory-forecasting'] | ['computer-vision'] | [-5.34234226e-01 -3.98883224e-01 -1.49671182e-01 4.23502736e-02
-4.32277292e-01 -9.26436305e-01 6.30744815e-01 -1.00543164e-01
-4.11274970e-01 8.18977416e-01 -1.29582420e-01 -4.14983392e-01
-7.88576305e-02 -3.02070707e-01 -7.13216662e-01 -5.16852558e-01
-2.08549008e-01 5.37554741e-01 8.66506338e-01 3.81805636... | [6.385349750518799, -2.048044204711914] |
82d8e87b-2292-46a9-987b-ed19e749361b | video-saliency-detection-by-3d-convolutional | 1807.04514 | null | http://arxiv.org/abs/1807.04514v1 | http://arxiv.org/pdf/1807.04514v1.pdf | Video Saliency Detection by 3D Convolutional Neural Networks | Different from salient object detection methods for still images, a key
challenging for video saliency detection is how to extract and combine spatial
and temporal features. In this paper, we present a novel and effective approach
for salient object detection for video sequences based on 3D convolutional
neural network... | ['Yuming Fang', 'Guanqun Ding'] | 2018-07-12 | null | null | null | null | ['video-saliency-detection'] | ['computer-vision'] | [ 1.81107685e-01 -3.93548965e-01 -1.46475881e-01 -2.23813150e-02
-2.35945433e-01 4.55164611e-02 2.43207023e-01 -3.09089512e-01
-2.83166319e-01 6.07431114e-01 4.49774712e-01 -2.97227083e-03
2.09500864e-01 -2.65289247e-01 -8.48053694e-01 -4.69153345e-01
-1.99174017e-01 -4.77568358e-01 1.25621676e+00 -8.62183049... | [9.741941452026367, -0.3159821629524231] |
a259b78a-f04c-498b-b3bb-f609acbc0e04 | hierarchical-neural-program-synthesis | 2303.06018 | null | https://arxiv.org/abs/2303.06018v1 | https://arxiv.org/pdf/2303.06018v1.pdf | Hierarchical Neural Program Synthesis | Program synthesis aims to automatically construct human-readable programs that satisfy given task specifications, such as input/output pairs or demonstrations. Recent works have demonstrated encouraging results in a variety of domains, such as string transformation, tensor manipulation, and describing behaviors of embo... | ['Shao-Hua Sun', 'Joseph J. Lim', 'Jesse Zhang', 'Ryan Lindeborg', 'Linghan Zhong'] | 2023-03-09 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 4.59784269e-01 6.13517910e-02 -1.44433603e-01 -4.54002976e-01
-4.72403824e-01 -9.23659980e-01 5.59591234e-01 4.75374088e-02
-1.76526442e-01 3.40593815e-01 6.14652038e-02 -5.90877235e-01
4.27138358e-01 -1.03674936e+00 -1.21817899e+00 -4.20581490e-01
2.16804847e-01 1.14088237e-01 1.23261034e-01 -1.09051019... | [7.8884758949279785, 7.711095333099365] |
a79ada20-ea00-4171-a7cc-042dfa743e17 | shape-from-texture-using-locally-scaled-point | 1311.7401 | null | http://arxiv.org/abs/1311.7401v1 | http://arxiv.org/pdf/1311.7401v1.pdf | Shape from Texture using Locally Scaled Point Processes | Shape from texture refers to the extraction of 3D information from 2D images
with irregular texture. This paper introduces a statistical framework to learn
shape from texture where convex texture elements in a 2D image are represented
through a point process. In a first step, the 2D image is preprocessed to
generate a ... | ['Christoph Schnörr', 'Alex Lenkoski', 'Eva-Maria Didden', 'Thordis Linda Thorarinsdottir'] | 2013-11-28 | null | null | null | null | ['shape-from-texture'] | ['computer-vision'] | [ 6.65476024e-01 4.31481212e-01 9.85279828e-02 -2.82948494e-01
-5.44908702e-01 -2.25166619e-01 9.09998775e-01 -1.70754820e-01
-4.98550273e-02 2.12059796e-01 6.56474903e-02 2.74913460e-01
-1.22777581e-01 -1.01218748e+00 -7.44166195e-01 -1.17720950e+00
2.34462991e-01 1.02295864e+00 1.54685616e-01 1.25081360... | [8.875306129455566, -3.3635849952697754] |
0fc40938-435e-43ee-84df-709ec59bcf66 | learning-semantic-representations-for-the | 1312.0482 | null | http://arxiv.org/abs/1312.0482v1 | http://arxiv.org/pdf/1312.0482v1.pdf | Learning Semantic Representations for the Phrase Translation Model | This paper presents a novel semantic-based phrase translation model. A pair
of source and target phrases are projected into continuous-valued vector
representations in a low-dimensional latent semantic space, where their
translation score is computed by the distance between the pair in this new
space. The projection is... | ['Wen-tau Yih', 'Xiaodong He', 'Li Deng', 'Jianfeng Gao'] | 2013-11-28 | null | null | null | null | ['learning-semantic-representations'] | ['methodology'] | [ 3.25968027e-01 -5.82190380e-02 -6.55170500e-01 -3.79722953e-01
-1.43747449e+00 -4.78066772e-01 8.48079622e-01 -4.73603494e-02
-5.01923978e-01 9.23621118e-01 4.67660546e-01 -4.25986201e-01
3.15024883e-01 -6.85180724e-01 -8.72679770e-01 -6.53220654e-01
4.02186841e-01 1.01478720e+00 -3.22897494e-01 -2.66012341... | [11.615616798400879, 10.278844833374023] |
357ac170-6cab-44e2-a8ab-6f1a6f989582 | monocular-3d-multi-person-pose-estimation-by | 2104.01797 | null | https://arxiv.org/abs/2104.01797v2 | https://arxiv.org/pdf/2104.01797v2.pdf | Monocular 3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks | In monocular video 3D multi-person pose estimation, inter-person occlusion and close interactions can cause human detection to be erroneous and human-joints grouping to be unreliable. Existing top-down methods rely on human detection and thus suffer from these problems. Existing bottom-up methods do not use human detec... | ['Robby T. Tan', 'Bo Yang', 'Bo wang', 'Yu Cheng'] | 2021-04-05 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Cheng_Monocular_3D_Multi-Person_Pose_Estimation_by_Integrating_Top-Down_and_Bottom-Up_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Cheng_Monocular_3D_Multi-Person_Pose_Estimation_by_Integrating_Top-Down_and_Bottom-Up_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-multi-person-pose-estimation-absolute', '3d-multi-person-pose-estimation-root-relative', '3d-multi-person-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.16946436e-01 -1.72122649e-03 3.45585532e-02 -2.10840479e-01
-5.87116361e-01 -4.29696560e-01 4.24619555e-01 -3.14912319e-01
-4.83724624e-01 5.19925535e-01 4.08317715e-01 5.46895146e-01
2.71138340e-01 -6.91345513e-01 -6.23260915e-01 -3.08598787e-01
8.25794507e-03 5.54906964e-01 6.03788614e-01 -2.01005444... | [7.0492448806762695, -0.8966325521469116] |
3c73c170-84df-474b-bca6-ebbec9dc49d7 | characterization-of-graphs-for-protein | 1407.8033 | null | http://arxiv.org/abs/1407.8033v5 | http://arxiv.org/pdf/1407.8033v5.pdf | Characterization of graphs for protein structure modeling and recognition of solubility | This paper deals with the relations among structural, topological, and
chemical properties of the E.Coli proteome from the vantage point of the
solubility/aggregation propensity of proteins. Each E.Coli protein is initially
represented according to its known folded 3D shape. This step consists in
representing the avail... | ['Alessandro Giuliani', 'Alireza Sadeghian', 'Lorenzo Livi'] | 2014-07-30 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 1.47007808e-01 1.28565744e-01 -1.67766362e-02 -1.41405314e-01
3.30809802e-02 -7.74261594e-01 6.45364642e-01 8.25178802e-01
-3.05750847e-01 7.89360166e-01 -5.44566065e-02 -3.77259284e-01
-5.05835176e-01 -7.73806810e-01 -5.41226149e-01 -1.11446762e+00
-5.94421387e-01 6.97095037e-01 3.03283483e-01 -3.17974865... | [4.989155292510986, 5.3171515464782715] |
f072e057-9546-4f02-a2df-7a5b9325328e | learning-integrable-dynamics-with-action | 2211.15338 | null | https://arxiv.org/abs/2211.15338v1 | https://arxiv.org/pdf/2211.15338v1.pdf | Learning Integrable Dynamics with Action-Angle Networks | Machine learning has become increasingly popular for efficiently modelling the dynamics of complex physical systems, demonstrating a capability to learn effective models for dynamics which ignore redundant degrees of freedom. Learned simulators typically predict the evolution of the system in a step-by-step manner with... | ['Shirley Ho', 'Tess Smidt', 'Miles Cranmer', 'Arthur Kosmala', 'Ameya Daigavane'] | 2022-11-24 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-3.26851398e-01 9.87416059e-02 3.33173424e-02 8.23753476e-02
-3.82156342e-01 -4.07948941e-01 6.39507830e-01 -1.45728484e-01
-3.35665375e-01 1.32425928e+00 -7.09978998e-01 -2.15086132e-01
-3.83349121e-01 -9.95159209e-01 -8.49165499e-01 -8.96016419e-01
-4.98355478e-01 4.88301039e-01 8.41217339e-02 -7.71832168... | [6.531064510345459, 3.466583251953125] |
e43ef4f1-0af7-49ab-8bad-e74b6b26506d | predictive-inference-with-feature-conformal | 2210.00173 | null | https://arxiv.org/abs/2210.00173v4 | https://arxiv.org/pdf/2210.00173v4.pdf | Predictive Inference with Feature Conformal Prediction | Conformal prediction is a distribution-free technique for establishing valid prediction intervals. Although conventionally people conduct conformal prediction in the output space, this is not the only possibility. In this paper, we propose feature conformal prediction, which extends the scope of conformal prediction to... | ['Yang Yuan', 'Yang Gao', 'Yoshua Bengio', 'Dinghuai Zhang', 'Chuan Wen', 'Jiaye Teng'] | 2022-10-01 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 8.59366357e-02 4.32090491e-01 -3.77831787e-01 -7.60421515e-01
-9.94458973e-01 -5.45425177e-01 1.03595054e+00 1.17317416e-01
7.05744475e-02 7.09203601e-01 3.50096017e-01 -4.43734884e-01
-4.66678053e-01 -1.09284747e+00 -8.65088820e-01 -5.47300696e-01
3.86767127e-02 7.62596369e-01 2.38734603e-01 1.14277713... | [7.956416606903076, 4.150622367858887] |
c8eae135-2cbe-43a1-a83b-5c96e5e51570 | meci-a-multilingual-dataset-for-event | null | null | https://aclanthology.org/2022.coling-1.206 | https://aclanthology.org/2022.coling-1.206.pdf | MECI: A Multilingual Dataset for Event Causality Identification | Event Causality Identification (ECI) is the task of detecting causal relations between events mentioned in the text. Although this task has been extensively studied for English materials, it is under-explored for many other languages. A major reason for this issue is the lack of multilingual datasets that provide consi... | ['Thien Huu Nguyen', 'Franck Dernoncourt', 'Minh Van Nguyen', 'Amir Pouran Ben Veyseh', 'Viet Dac Lai'] | null | null | null | null | coling-2022-10 | ['event-causality-identification'] | ['natural-language-processing'] | [-3.52641463e-01 1.81898556e-03 -5.43472886e-01 -2.11128861e-01
-7.03858733e-01 -6.12627685e-01 9.97314811e-01 2.71036446e-01
-3.71944696e-01 1.22896266e+00 6.33573115e-01 -4.11498934e-01
1.66534763e-02 -7.24192977e-01 -8.73268068e-01 -1.58227131e-01
-1.24365263e-01 3.83088112e-01 1.31403193e-01 1.06546640... | [9.132208824157715, 9.210675239562988] |
e9825a58-1a99-4a2a-aa1a-3b2107a344ff | exploiting-intrinsic-stochasticity-of-real | 2304.06056 | null | https://arxiv.org/abs/2304.06056v1 | https://arxiv.org/pdf/2304.06056v1.pdf | Exploiting Intrinsic Stochasticity of Real-Time Simulation to Facilitate Robust Reinforcement Learning for Robot Manipulation | Simulation is essential to reinforcement learning (RL) before implementation in the real world, especially for safety-critical applications like robot manipulation. Conventionally, RL agents are sensitive to the discrepancies between the simulation and the real world, known as the sim-to-real gap. The application of do... | ['Homayoun Najjaran', 'Dean Richert', 'Zengjie Zhang', 'Amir M. Soufi Enayati', 'Ram Dershan'] | 2023-04-12 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [-3.52103449e-02 1.41361773e-01 -1.41778171e-01 1.53857470e-01
-3.92652780e-01 -4.51023281e-01 5.82080007e-01 -1.69711798e-01
-6.14434600e-01 9.70579803e-01 -4.41465497e-01 -5.08615732e-01
-6.60494626e-01 -7.10973263e-01 -9.00097787e-01 -8.14311087e-01
-6.32068872e-01 7.03275025e-01 2.68283635e-01 -6.82111263... | [4.758749008178711, 1.720940351486206] |
3c8846aa-b29d-4161-8981-309e502e51b0 | multimodal-fusion-via-teacher-student-network | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/16430 | https://ojs.aaai.org/index.php/AAAI/article/view/16430/16237 | Multimodal Fusion via Teacher-Student Network for Indoor Action Recognition | Indoor action recognition plays an important role in modern
society, such as intelligent healthcare in large mobile cabin
hospitals. With the wide usage of depth sensors like Kinect,
multimodal information including skeleton and RGB modalities
brings a promising way to improve the performance.
However, existing me... | ['Keith C.C. Chan', 'Yan Liu', 'Bruce X.B. Yu'] | 2021-05-18 | null | null | null | association-for-the-advancement-of-artificial-3 | ['action-recognition-in-videos-2'] | ['computer-vision'] | [ 2.53410161e-01 -1.67087048e-01 -1.26914129e-01 -4.16027576e-01
-8.32502246e-01 -5.72962500e-02 5.09438396e-01 -2.27502808e-01
-3.71541023e-01 7.20835745e-01 2.74425119e-01 -2.73736089e-01
-1.57337874e-01 -7.64629602e-01 -6.03380144e-01 -1.01900578e+00
4.66666073e-01 1.10899679e-01 3.61520082e-01 -1.06171407... | [7.871856212615967, 0.4184073209762573] |
8ebad10d-5d18-4616-b840-5cb584ae2205 | discriminating-known-from-unknown-objects-via | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_Discriminating_Known_From_Unknown_Objects_via_Structure-Enhanced_Recurrent_Variational_AutoEncoder_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_Discriminating_Known_From_Unknown_Objects_via_Structure-Enhanced_Recurrent_Variational_AutoEncoder_CVPR_2023_paper.pdf | Discriminating Known From Unknown Objects via Structure-Enhanced Recurrent Variational AutoEncoder | Discriminating known from unknown objects is an important essential ability for human beings. To simulate this ability, a task of unsupervised out-of-distribution object detection (OOD-OD) is proposed to detect the objects that are never-seen-before during model training, which is beneficial for promoting the safe ... | ['Cheng Deng', 'Aming Wu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['object-localization'] | ['computer-vision'] | [-8.09450224e-02 -1.74047187e-01 -7.40944222e-02 -1.14122041e-01
-6.22219801e-01 -2.30045885e-01 4.76687670e-01 -3.74060631e-01
-1.04537308e-01 2.48165831e-01 -8.29032958e-02 -1.40803844e-01
2.51813401e-02 -5.48983455e-01 -6.35547042e-01 -8.77165556e-01
1.24381170e-01 1.09801263e-01 3.74756396e-01 5.06137311... | [9.351404190063477, 1.487152099609375] |
2ca69883-9763-45c3-9a61-7e9ddf7ff5f2 | do-llms-understand-user-preferences | 2305.06474 | null | https://arxiv.org/abs/2305.06474v1 | https://arxiv.org/pdf/2305.06474v1.pdf | Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction | Large Language Models (LLMs) have demonstrated exceptional capabilities in generalizing to new tasks in a zero-shot or few-shot manner. However, the extent to which LLMs can comprehend user preferences based on their previous behavior remains an emerging and still unclear research question. Traditionally, Collaborative... | ['Derek Zhiyuan Cheng', 'Ed Chi', 'Lichan Hong', 'Maheswaran Sathiamoorthy', 'Nikhil Mehta', 'Jianmo Ni', 'Wang-Cheng Kang'] | 2023-05-10 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-1.97808787e-01 -5.01571894e-01 -4.70081538e-01 -6.20746732e-01
-4.47508335e-01 -6.16209805e-01 6.33007884e-01 1.72457755e-01
-5.46676040e-01 4.13307041e-01 3.01989764e-01 -4.74095255e-01
-3.84447902e-01 -5.95810413e-01 -1.93514913e-01 -1.88483268e-01
-1.13184921e-01 4.76724654e-01 2.71387339e-01 -6.82416260... | [10.117950439453125, 5.697439193725586] |
9d8f8cd5-9e00-4eed-8ee0-769f07a22606 | saac-safe-reinforcement-learning-as-an | 2204.09424 | null | https://arxiv.org/abs/2204.09424v2 | https://arxiv.org/pdf/2204.09424v2.pdf | SAAC: Safe Reinforcement Learning as an Adversarial Game of Actor-Critics | Although Reinforcement Learning (RL) is effective for sequential decision-making problems under uncertainty, it still fails to thrive in real-world systems where risk or safety is a binding constraint. In this paper, we formulate the RL problem with safety constraints as a non-zero-sum game. While deployed with maximum... | ['Debabrota Basu', 'Yannis Flet-Berliac'] | 2022-04-20 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-1.13426045e-01 6.76208138e-01 -2.75505632e-01 2.21598342e-01
-9.56664920e-01 -7.78324425e-01 5.59433579e-01 1.66757286e-01
-7.81955361e-01 1.10817254e+00 -1.18639395e-02 -6.61071777e-01
-4.53678876e-01 -6.08262002e-01 -4.52211589e-01 -1.11767459e+00
-3.73573869e-01 3.98904562e-01 -1.58168197e-01 -3.79127860... | [4.337841510772705, 2.394789457321167] |
e2f34e17-acba-46e4-b23a-4f295680e23c | objectseeker-certifiably-robust-object | 2202.01811 | null | https://arxiv.org/abs/2202.01811v2 | https://arxiv.org/pdf/2202.01811v2.pdf | ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking | Object detectors, which are widely deployed in security-critical systems such as autonomous vehicles, have been found vulnerable to patch hiding attacks. An attacker can use a single physically-realizable adversarial patch to make the object detector miss the detection of victim objects and undermine the functionality ... | ['Prateek Mittal', 'Saeed Mahloujifar', 'Alexander Valtchanov', 'Chong Xiang'] | 2022-02-03 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [ 4.73389417e-01 2.66784191e-01 -7.25167468e-02 2.03098834e-01
-9.73941863e-01 -1.38482201e+00 4.84598666e-01 8.21010023e-02
-2.25910500e-01 2.90058076e-01 -5.49680650e-01 -7.83515453e-01
3.52790684e-01 -8.84093881e-01 -1.24800694e+00 -8.60963643e-01
-4.35847878e-01 -2.00875744e-01 7.78134644e-01 -1.55837595... | [5.555655002593994, 7.919543743133545] |
3e521974-8391-42eb-ad23-6447152a2ee3 | a-technical-report-for-iccv-2021-vipriors-re | 2109.15164 | null | https://arxiv.org/abs/2109.15164v1 | https://arxiv.org/pdf/2109.15164v1.pdf | A Technical Report for ICCV 2021 VIPriors Re-identification Challenge | Person re-identification has always been a hot and challenging task. This paper introduces our solution for the re-identification track in VIPriors Challenge 2021. In this challenge, the difficulty is how to train the model from scratch without any pretrained weight. In our method, we show use state-of-the-art data pro... | ['Yue Lin', 'Yunbo Peng', 'Cen Liu'] | 2021-09-30 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [-3.79932225e-02 -4.76972550e-01 8.10359567e-02 -4.12564844e-01
-9.66295183e-01 -4.62676257e-01 5.66707194e-01 -1.26101628e-01
-9.41256642e-01 6.81088448e-01 2.24726379e-01 2.66545951e-01
-5.30923605e-02 -3.02832842e-01 -4.52630788e-01 -3.17299426e-01
2.55936123e-02 6.44033730e-01 1.06293842e-01 -3.40524703... | [14.718932151794434, 0.9128021597862244] |
4fab06ee-738d-48a9-9592-4a6e6488c019 | assessing-the-coherence-modeling-capabilities | null | null | https://openreview.net/forum?id=NkOTih-TP7T | https://openreview.net/pdf?id=NkOTih-TP7T | Assessing the Coherence Modeling Capabilities of Pretrained Transformer-based Language Models | The task of ordering a shuffled set of sentences into a coherent text is used to evaluate the capacity of a model to understand causal and temporal relations between entities and events. Recent approaches rely on pretrained Transformer-based models, but it remains unknown whether the differences between them, such as s... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['sentence-ordering'] | ['natural-language-processing'] | [-3.71205062e-01 3.52575451e-01 -1.02570906e-01 -7.64007688e-01
-6.55263901e-01 -6.09562397e-01 9.91631925e-01 5.37059486e-01
-6.08038068e-01 8.39436650e-01 8.98126304e-01 -5.39237440e-01
-2.05120057e-01 -6.18867338e-01 -6.97523654e-01 -5.55772111e-02
-3.58263820e-01 6.72240436e-01 8.26042518e-02 -6.08517945... | [11.525938987731934, 9.132550239562988] |
fda500cc-af4e-4c95-8beb-9f3396739def | trahgr-few-shot-learning-for-hand-gesture | 2203.16336 | null | https://arxiv.org/abs/2203.16336v2 | https://arxiv.org/pdf/2203.16336v2.pdf | TraHGR: Transformer for Hand Gesture Recognition via ElectroMyography | Deep learning-based Hand Gesture Recognition (HGR) via surface Electromyogram (sEMG) signals has recently shown significant potential for development of advanced myoelectric-controlled prosthesis. Existing deep learning approaches, typically, include only one model as such can hardly maintain acceptable generalization ... | ['Arash Mohammadi', 'Amir Asif', 'Elahe Rahimian', 'Soheil Zabihi'] | 2022-03-28 | null | null | null | null | ['hand-gesture-recognition', 'gesture-recognition'] | ['computer-vision', 'computer-vision'] | [ 8.59606266e-02 -1.28580704e-01 1.65732354e-02 5.70487268e-02
-6.65144086e-01 -2.28355467e-01 4.02008295e-01 -7.84392118e-01
-5.54827988e-01 7.07555950e-01 8.80073309e-02 1.01604275e-02
-3.61405224e-01 -3.32446694e-01 -5.01693666e-01 -9.68333662e-01
-3.42757225e-01 2.91992635e-01 -5.47007620e-02 -1.70646027... | [6.837733268737793, 0.12616433203220367] |
70e37572-3613-47bc-9bf5-e1e97dcf0e74 | cine-cardiac-mri-motion-artifact-reduction | 2006.12700 | null | https://arxiv.org/abs/2006.12700v1 | https://arxiv.org/pdf/2006.12700v1.pdf | Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network | Cine cardiac magnetic resonance imaging (MRI) is widely used for diagnosis of cardiac diseases thanks to its ability to present cardiovascular features in excellent contrast. As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' ... | ['Debiao Li', 'Qing Lyu', 'Hongming Shan', 'Ge Wang', 'Yibin Xie'] | 2020-06-23 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 4.59343404e-01 -4.79442656e-01 3.08563281e-02 -2.48373926e-01
-1.02700055e+00 -5.02584204e-02 1.18282743e-01 -7.28987083e-02
-4.52979654e-01 5.89149952e-01 2.99688697e-01 -1.09850526e-01
-1.82923034e-01 -3.64974350e-01 -1.13982186e-01 -9.35407937e-01
-3.88602406e-01 -3.82902250e-02 2.36394092e-01 2.35479251... | [13.65608024597168, -2.465764045715332] |
51a70ed8-782f-4ce4-9ac7-efbf7f462aee | path-neural-networks-expressive-and-accurate | 2306.05955 | null | https://arxiv.org/abs/2306.05955v1 | https://arxiv.org/pdf/2306.05955v1.pdf | Path Neural Networks: Expressive and Accurate Graph Neural Networks | Graph neural networks (GNNs) have recently become the standard approach for learning with graph-structured data. Prior work has shed light into their potential, but also their limitations. Unfortunately, it was shown that standard GNNs are limited in their expressive power. These models are no more powerful than the 1-... | ['Michalis Vazirgiannis', 'Johannes Lutzeyer', 'Giannis Nikolentzos', 'Gaspard Michel'] | 2023-06-09 | null | null | null | null | ['graph-regression', 'graph-classification'] | ['graphs', 'graphs'] | [ 3.74228418e-01 4.96771365e-01 -4.75092769e-01 -1.27718270e-01
-2.05065534e-01 -9.86839533e-01 3.81130159e-01 3.17552388e-01
-1.91530645e-01 7.87255704e-01 -1.19977511e-01 -9.42959607e-01
-6.54486179e-01 -1.27677095e+00 -8.89679968e-01 -5.73297262e-01
-1.05491614e+00 8.00541222e-01 3.12025905e-01 -2.60781109... | [6.863509178161621, 6.24064826965332] |
10b455b6-cd35-4f53-84f6-ff183a633d03 | on-the-compactness-efficiency-and | 1707.01992 | null | http://arxiv.org/abs/1707.01992v1 | http://arxiv.org/pdf/1707.01992v1.pdf | On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task | Deep convolutional neural networks are powerful tools for learning visual
representations from images. However, designing efficient deep architectures to
analyse volumetric medical images remains challenging. This work investigates
efficient and flexible elements of modern convolutional networks such as
dilated convolu... | ['Lucas Fidon', 'Guotai Wang', 'Tom Vercauteren', 'Sebastien Ourselin', 'Wenqi Li', 'M. Jorge Cardoso'] | 2017-07-06 | null | null | null | null | ['3d-medical-imaging-segmentation', 'volumetric-medical-image-segmentation'] | ['medical', 'medical'] | [ 2.38091081e-01 8.19709957e-01 -1.72980458e-01 -5.72981775e-01
-7.77540743e-01 -1.11643687e-01 2.68409520e-01 5.26940897e-02
-7.51988292e-01 9.02053297e-01 6.19183481e-03 -4.62086529e-01
1.40308693e-01 -5.49741089e-01 -9.96365547e-01 -6.30793691e-01
-5.48786402e-01 8.10816526e-01 2.11512014e-01 1.55128837... | [14.518417358398438, -2.5107359886169434] |
154e19db-7129-45d7-a8ff-d0097c78d81c | constrained-multi-task-learning-for-automated | null | null | https://aclanthology.org/P16-1075 | https://aclanthology.org/P16-1075.pdf | Constrained Multi-Task Learning for Automated Essay Scoring | null | ['Ted Briscoe', 'Ronan Cummins', 'Meng Zhang'] | 2016-08-01 | null | null | null | acl-2016-8 | ['automated-essay-scoring'] | ['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... | [-7.400345802307129, 3.7519710063934326] |
398e6e48-c603-4e10-8322-4f4471858f2b | noise-to-norm-reconstruction-for-industrial | 2307.02836 | null | https://arxiv.org/abs/2307.02836v1 | https://arxiv.org/pdf/2307.02836v1.pdf | Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization | Anomaly detection has a wide range of applications and is especially important in industrial quality inspection. Currently, many top-performing anomaly-detection models rely on feature-embedding methods. However, these methods do not perform well on datasets with large variations in object locations. Reconstruction-bas... | ['Jun Gong', 'Ruiyan Zhuang', 'Zhiyu Sun', 'Shiqi Deng'] | 2023-07-06 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [-6.94213361e-02 -4.87210661e-01 2.86526352e-01 -2.70094723e-01
-5.47478497e-01 8.80649537e-02 2.95247585e-01 3.72560620e-01
6.22153049e-03 6.21737540e-02 -2.84680188e-01 -2.35692188e-01
-2.23087177e-01 -7.82949984e-01 -4.05007422e-01 -9.48725998e-01
-1.15347490e-01 -1.18519170e-02 4.42698210e-01 -1.45302653... | [7.545282363891602, 2.0608460903167725] |
5932251a-0154-4c72-8eb6-951d78c9d965 | flow-per-instance-personalized-federated | 2211.15281 | null | https://arxiv.org/abs/2211.15281v1 | https://arxiv.org/pdf/2211.15281v1.pdf | Flow: Per-Instance Personalized Federated Learning Through Dynamic Routing | Personalization in Federated Learning (FL) aims to modify a collaboratively trained global model according to each client. Current approaches to personalization in FL are at a coarse granularity, i.e. all the input instances of a client use the same personalized model. This ignores the fact that some instances are more... | ['Hui Guan', 'Sunav Choudhary', 'Kunjal Panchal'] | 2022-11-28 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-3.86300117e-01 -3.15952450e-02 -5.94769716e-01 -7.51554549e-01
-6.72330499e-01 -7.36420453e-01 4.04443413e-01 -1.43522888e-01
-2.24024445e-01 7.33632982e-01 4.05070394e-01 -1.67940453e-01
-2.01110974e-01 -6.54807627e-01 -6.81190729e-01 -6.69802368e-01
-1.45127410e-02 7.23601639e-01 5.35736978e-01 1.63229525... | [5.821394920349121, 6.261359214782715] |
14a985c1-2c39-49ac-8f3c-9c14a4c1ec29 | provably-doubly-accelerated-federated | 2210.13277 | null | https://arxiv.org/abs/2210.13277v3 | https://arxiv.org/pdf/2210.13277v3.pdf | Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Communication Compression | In federated learning, a large number of users are involved in a global learning task, in a collaborative way. They alternate local computations and two-way communication with a distant orchestrating server. Communication, which can be slow and costly, is the main bottleneck in this setting. To reduce the communication... | ['Ivan Agarský', 'Peter Richtárik', 'Laurent Condat'] | 2022-10-24 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-4.50230002e-01 -2.84855068e-01 -1.81483403e-01 -1.45345941e-01
-8.17614675e-01 -6.46015286e-01 3.60060990e-01 5.16821921e-01
-7.32129872e-01 6.27314270e-01 1.50662974e-01 -4.43114519e-01
-2.00508416e-01 -9.08106923e-01 -6.11032963e-01 -9.03674185e-01
-2.49935046e-01 5.97455442e-01 -9.33374986e-02 9.58235040... | [6.20983362197876, 5.107778072357178] |
0813f877-921d-4692-8b37-10114d77bd64 | know-thy-corpus-robust-methods-for-digital | 2003.06389 | null | https://arxiv.org/abs/2003.06389v1 | https://arxiv.org/pdf/2003.06389v1.pdf | Know thy corpus! Robust methods for digital curation of Web corpora | This paper proposes a novel framework for digital curation of Web corpora in order to provide robust estimation of their parameters, such as their composition and the lexicon. In recent years language models pre-trained on large corpora emerged as clear winners in numerous NLP tasks, but no proper analysis of the corpo... | ['Serge Sharoff'] | 2020-03-13 | know-thy-corpus-robust-methods-for-digital-1 | https://aclanthology.org/2020.lrec-1.298 | https://aclanthology.org/2020.lrec-1.298.pdf | lrec-2020-5 | ['genre-classification'] | ['computer-vision'] | [-6.93240315e-02 2.17664167e-01 -2.00862840e-01 3.17962840e-03
-1.14269722e+00 -9.96285021e-01 1.21308231e+00 7.13243067e-01
-5.62387884e-01 7.90479362e-01 6.70653999e-01 -3.34803522e-01
-4.65390831e-01 -6.30276680e-01 -5.07430315e-01 -7.23487854e-01
1.18409969e-01 7.46754587e-01 3.10207069e-01 -3.42713565... | [9.85293960571289, 9.648719787597656] |
0e33eb82-778c-498f-9f98-b35acc1c5f19 | hdr-plenoxels-self-calibrating-high-dynamic | 2208.06787 | null | https://arxiv.org/abs/2208.06787v2 | https://arxiv.org/pdf/2208.06787v2.pdf | HDR-Plenoxels: Self-Calibrating High Dynamic Range Radiance Fields | We propose high dynamic range (HDR) radiance fields, HDR-Plenoxels, that learn a plenoptic function of 3D HDR radiance fields, geometry information, and varying camera settings inherent in 2D low dynamic range (LDR) images. Our voxel-based volume rendering pipeline reconstructs HDR radiance fields with only multi-view ... | ['Tae-Hyun Oh', 'Moon Ye-Bin', 'Kim Yu-Ji', 'Kim Jun-Seong'] | 2022-08-14 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 2.92957760e-02 -4.99446422e-01 2.37472430e-01 -3.77499968e-01
-7.89225757e-01 -8.64118636e-01 3.75900865e-01 -7.52772331e-01
-7.41242170e-02 4.09022689e-01 5.34400493e-02 -1.03927054e-01
1.58523858e-01 -1.04876292e+00 -8.10182571e-01 -4.48608339e-01
2.39958227e-01 5.40002346e-01 2.97734022e-01 -7.34487623... | [9.60166072845459, -3.0587897300720215] |
59022517-ab47-425e-8e45-600c4c00642c | low-rank-factorization-of-determinantal-point | 1602.05436 | null | http://arxiv.org/abs/1602.05436v1 | http://arxiv.org/pdf/1602.05436v1.pdf | Low-Rank Factorization of Determinantal Point Processes for Recommendation | Determinantal point processes (DPPs) have garnered attention as an elegant
probabilistic model of set diversity. They are useful for a number of subset
selection tasks, including product recommendation. DPPs are parametrized by a
positive semi-definite kernel matrix. In this work we present a new method for
learning th... | ['Mike Gartrell', 'Noam Koenigstein', 'Ulrich Paquet'] | 2016-02-17 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 8.41476768e-03 -3.66899043e-01 -7.78268695e-01 -3.52996707e-01
-1.00517261e+00 -9.35472906e-01 7.09371507e-01 1.89721510e-01
-1.40664214e-02 4.80160117e-01 4.58348215e-01 -4.13140059e-01
-4.61677551e-01 -5.38864195e-01 -7.35885918e-01 -7.63717890e-01
-1.85054913e-01 6.88660860e-01 3.20950985e-01 -2.07322910... | [9.520500183105469, 5.40716552734375] |
0778d480-2d75-4c3c-93b4-222f46ff3418 | electramed-a-new-pre-trained-language | 2104.09585 | null | https://arxiv.org/abs/2104.09585v1 | https://arxiv.org/pdf/2104.09585v1.pdf | ELECTRAMed: a new pre-trained language representation model for biomedical NLP | The overwhelming amount of biomedical scientific texts calls for the development of effective language models able to tackle a wide range of biomedical natural language processing (NLP) tasks. The most recent dominant approaches are domain-specific models, initialized with general-domain textual data and then trained o... | ['Carlotta Orsenigo', 'Giulio Mantoan', 'Giacomo Miolo'] | 2021-04-19 | null | null | null | null | ['drug-drug-interaction-extraction', 'medical-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.83536962e-01 2.88739800e-01 -6.28940910e-02 -4.20451999e-01
-8.93528044e-01 -2.09949717e-01 7.43211150e-01 5.82249582e-01
-9.35586691e-01 1.02003062e+00 4.71963845e-02 -2.41904512e-01
-3.67940575e-01 -5.62707782e-01 -5.86855531e-01 -6.05741918e-01
-6.93831369e-02 9.14711893e-01 2.30163828e-01 -2.16743127... | [8.570206642150879, 8.70952033996582] |
973dcc40-a952-48a6-9524-db9aa6a69e91 | to-adapt-or-to-fine-tune-a-case-study-on | 2208.14559 | null | https://arxiv.org/abs/2208.14559v1 | https://arxiv.org/pdf/2208.14559v1.pdf | To Adapt or to Fine-tune: A Case Study on Abstractive Summarization | Recent advances in the field of abstractive summarization leverage pre-trained language models rather than train a model from scratch. However, such models are sluggish to train and accompanied by a massive overhead. Researchers have proposed a few lightweight alternatives such as smaller adapters to mitigate the drawb... | ['Pinzhen Chen', 'Zheng Zhao'] | 2022-08-30 | null | https://aclanthology.org/2022.ccl-1.73 | https://aclanthology.org/2022.ccl-1.73.pdf | ccl-2022-10 | ['pretrained-multilingual-language-models'] | ['natural-language-processing'] | [ 2.01743454e-01 1.61702916e-01 -2.95578748e-01 -1.77049696e-01
-1.22161329e+00 -6.70532525e-01 7.31636524e-01 4.10744846e-01
-7.96976388e-01 7.85748720e-01 7.30025947e-01 -5.57366788e-01
1.47601172e-01 -3.23833674e-01 -6.64514959e-01 -2.37858489e-01
2.15670481e-01 4.41039920e-01 -6.19432069e-02 -3.51619840... | [11.624166488647461, 9.06070613861084] |
a79d64fd-2b7d-429a-b8d0-d079e0dc73f1 | decoder-only-or-encoder-decoder-interpreting | 2304.04052 | null | https://arxiv.org/abs/2304.04052v1 | https://arxiv.org/pdf/2304.04052v1.pdf | Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decoder | The sequence-to-sequence (seq2seq) task aims at generating the target sequence based on the given input source sequence. Traditionally, most of the seq2seq task is resolved by the Encoder-Decoder framework which requires an encoder to encode the source sequence and a decoder to generate the target text. Recently, a bun... | ['Nigel Collier', 'Zhiyuan Liu', 'Shengding Hu', 'Anthony Man-Cho So', 'Qian Yu', 'Wai Lam', 'Zihao Fu'] | 2023-04-08 | null | null | null | null | ['data-to-text-generation'] | ['natural-language-processing'] | [ 6.36879563e-01 4.92430955e-01 -1.32037133e-01 -7.77618140e-02
-7.45847821e-01 -4.67159867e-01 6.88874185e-01 -1.17023075e-02
-8.07165578e-02 8.28935266e-01 5.91797650e-01 -5.56702137e-01
3.66129756e-01 -6.32536352e-01 -8.11784863e-01 -4.62398142e-01
4.59247589e-01 3.46680582e-01 -9.02231559e-02 -5.54772079... | [11.948740005493164, 9.133220672607422] |
5c12bfe3-d567-4da3-aded-66a004ca061d | style-controllable-speech-driven-gesture | null | null | https://diglib.eg.org/handle/10.1111/cgf13946 | https://onlinelibrary.wiley.com/doi/epdf/10.1111/cgf.13946 | Style-Controllable Speech-Driven Gesture Synthesis Using Normalising Flows | Automatic synthesis of realistic gestures promises to transform the fields of animation, avatars and communicative agents. In off‐line applications, novel tools can alter the role of an animator to that of a director, who provides only high‐level input for the desired animation; a learned network then translates these ... | ['Taras Kucherenko', 'Gustav Eje Henter', 'Simon Alexanderson', 'Jonas Beskow'] | 2020-05-25 | null | null | null | computer-graphics-forum-2020-5 | ['normalising-flows', 'gesture-generation'] | ['methodology', 'robots'] | [ 6.62104860e-02 4.64737833e-01 -3.83492038e-02 -3.54250163e-01
-5.47130585e-01 -7.58830845e-01 1.10005915e+00 -8.69272709e-01
-1.59215461e-02 5.14840126e-01 6.65879965e-01 -7.87421390e-02
3.48493367e-01 -6.40404642e-01 -4.73908722e-01 -5.57181895e-01
9.38665420e-02 5.41374147e-01 1.84929855e-02 -7.13278294... | [5.613417148590088, -0.08190403878688812] |
35506d7c-a87a-4666-b2bc-fc8d63c62568 | finrl-a-deep-reinforcement-learning-library | 2011.09607 | null | https://arxiv.org/abs/2011.09607v2 | https://arxiv.org/pdf/2011.09607v2.pdf | FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance | As deep reinforcement learning (DRL) has been recognized as an effective approach in quantitative finance, getting hands-on experiences is attractive to beginners. However, to train a practical DRL trading agent that decides where to trade, at what price, and what quantity involves error-prone and arduous development a... | ['Christina Dan Wang', 'Bowen Xiao', 'Liuqing Yang', 'Runjia Zhang', 'Qian Chen', 'Hongyang Yang', 'Xiao-Yang Liu'] | 2020-11-19 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-1.27289474e+00 -3.70286882e-01 -3.34993631e-01 -1.49827018e-01
-8.00978959e-01 -9.16840315e-01 3.30113590e-01 -1.44176707e-01
-4.31210279e-01 9.11787808e-01 -4.25853759e-01 -8.81486654e-01
-1.14790499e-01 -9.93890584e-01 -5.45934379e-01 -3.30540925e-01
-3.65049571e-01 7.51843870e-01 1.63263068e-01 -5.06908953... | [4.428737640380859, 3.945425510406494] |
d3ab078b-5930-4eec-844e-41df931a713a | masakhapos-part-of-speech-tagging-for | 2305.13989 | null | https://arxiv.org/abs/2305.13989v1 | https://arxiv.org/pdf/2305.13989v1.pdf | MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African Languages | In this paper, we present MasakhaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. We discuss the challenges in annotating POS for these languages using the UD (universal dependencies) guidelines. We conducted extensive POS baseline experiments using conditional random field ... | ['Dietrich Klakow', 'Muhammad Abdullahi', 'Aliyu Yusuf', 'Chinedu Uchechukwu', 'Seydou Traore', 'Apelete Agbolo', 'Patrick Mizha', 'Kudzai Gotosa', 'Ester Chimhenga', 'Emile Niyomutabazi', 'Théogène Musabeyezu', 'Marien Nahimana', 'Olanrewaju Samuel', 'Idris Akinade', 'Tolulope Adelani', 'Gratien Atindogbe', 'Ikechukwu... | 2023-05-23 | null | null | null | null | ['part-of-speech-tagging', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [-3.25847328e-01 2.75902753e-03 -4.98047382e-01 -3.29900712e-01
-1.12140512e+00 -1.18616247e+00 3.66429478e-01 2.43892953e-01
-6.59580231e-01 1.18428707e+00 3.46223384e-01 -5.69662571e-01
2.91688681e-01 -3.97125214e-01 -7.53482521e-01 -5.11171162e-01
3.65095586e-02 1.02942538e+00 3.25044781e-01 -3.46409440... | [10.473433494567871, 9.968659400939941] |
3c121926-49ff-472c-b6f1-d259e4a22ecc | counterfactual-mean-embedding-a-kernel-method | 1805.08845 | null | https://arxiv.org/abs/1805.08845v4 | https://arxiv.org/pdf/1805.08845v4.pdf | Counterfactual Mean Embeddings | Counterfactual inference has become a ubiquitous tool in online advertisement, recommendation systems, medical diagnosis, and econometrics. Accurate modeling of outcome distributions associated with different interventions -- known as counterfactual distributions -- is crucial for the success of these applications. In ... | ['Sorawit Saengkyongam', 'Sanparith Marukatat', 'Motonobu Kanagawa', 'Krikamol Muandet'] | 2018-05-22 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 2.13207424e-01 2.18821570e-01 -3.37225884e-01 -1.57517463e-01
-5.22700548e-01 -3.10035825e-01 5.36915481e-01 4.74879332e-02
-3.41826916e-01 1.00020814e+00 4.76416320e-01 -5.29319048e-01
-4.55826223e-01 -9.21994925e-01 -9.74559665e-01 -7.76403487e-01
-5.54912388e-01 -1.29169868e-02 -3.85392278e-01 2.93060601... | [8.019953727722168, 5.302900791168213] |
e3733395-ae0b-4ba4-9420-f8f5dc0d6e54 | unsupervised-learning-of-parsimonious-general | 1704.03507 | null | http://arxiv.org/abs/1704.03507v2 | http://arxiv.org/pdf/1704.03507v2.pdf | Unsupervised Learning of Parsimonious General-Purpose Embeddings for User and Location Modelling | Many social network applications depend on robust representations of
spatio-temporal data. In this work, we present an embedding model based on
feed-forward neural networks which transforms social media check-ins into dense
feature vectors encoding geographic, temporal, and functional aspects for
modelling places, neig... | ['Eickhoff Carsten', 'Yang Jing'] | 2018-01-22 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [-3.60861301e-01 -2.90373266e-01 -2.68660754e-01 -3.66063893e-01
-2.91153669e-01 -1.84276938e-01 7.79764593e-01 5.20539761e-01
-3.33027661e-01 3.88599426e-01 7.88220227e-01 -5.23563623e-01
-3.25813890e-01 -1.10996270e+00 -2.82181889e-01 -2.40667596e-01
-4.64806348e-01 -2.28363588e-01 1.16503514e-01 -3.12632680... | [6.6155686378479, 2.09202241897583] |
af539d5e-fe8e-4a43-87c7-b75564f7b6f1 | making-a-spiking-net-work-robust-brain-like | 2208.01204 | null | https://arxiv.org/abs/2208.01204v2 | https://arxiv.org/pdf/2208.01204v2.pdf | Making a Spiking Net Work: Robust brain-like unsupervised machine learning | The surge in interest in Artificial Intelligence (AI) over the past decade has been driven almost exclusively by advances in Artificial Neural Networks (ANNs). While ANNs set state-of-the-art performance for many previously intractable problems, the use of global gradient descent necessitates large datasets and computa... | ['Tara J. Hamilton', 'Allen Cheung', 'Chip Essam', 'Andrew Wabnitz', 'Peter G. Stratton'] | 2022-08-02 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [ 2.33720616e-01 -6.28239885e-02 2.09240824e-01 -2.06201568e-01
5.70147000e-02 -5.08148909e-01 4.87071902e-01 4.40447070e-02
-6.47526443e-01 9.74219799e-01 -3.57466519e-01 -2.44335189e-01
-7.03494623e-02 -5.84684491e-01 -5.21719873e-01 -1.00146914e+00
1.04124121e-01 5.05907655e-01 4.77512449e-01 -3.17980796... | [8.109894752502441, 2.6797778606414795] |
a3624c1d-8c73-4ead-b56b-02d8a410affa | weakly-supervised-temporal-action-3 | 2103.16155 | null | https://arxiv.org/abs/2103.16155v1 | https://arxiv.org/pdf/2103.16155v1.pdf | Weakly Supervised Temporal Action Localization Through Learning Explicit Subspaces for Action and Context | Weakly-supervised Temporal Action Localization (WS-TAL) methods learn to localize temporal starts and ends of action instances in a video under only video-level supervision. Existing WS-TAL methods rely on deep features learned for action recognition. However, due to the mismatch between classification and localization... | ['Gang Hua', 'Nanning Zheng', 'Junsong Yuan', 'Wei Tang', 'Le Wang', 'Ziyi Liu'] | 2021-03-30 | null | null | null | null | ['weakly-supervised-temporal-action'] | ['computer-vision'] | [ 3.52350116e-01 -3.50506604e-01 -7.18845069e-01 -2.89013773e-01
-6.83215141e-01 -3.35499763e-01 6.75476730e-01 7.98392147e-02
-3.66870642e-01 5.38887084e-01 5.39401650e-01 3.03482622e-01
-7.47158192e-04 -2.88248986e-01 -5.96404970e-01 -9.01904941e-01
-3.21195304e-01 -1.81237057e-01 5.74211299e-01 3.90100241... | [8.420928955078125, 0.6522144079208374] |
8bfa4017-5a16-4037-bdae-e3702eafe822 | hierarchical-adaptive-pooling-by-capturing | 2104.05960 | null | https://arxiv.org/abs/2104.05960v1 | https://arxiv.org/pdf/2104.05960v1.pdf | Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning | Graph neural networks (GNN) have been proven to be mature enough for handling graph-structured data on node-level graph representation learning tasks. However, the graph pooling technique for learning expressive graph-level representation is critical yet still challenging. Existing pooling methods either struggle to ca... | ['Hongzuo Xu', 'Zhiquan Lai', 'Yiming Zhang', 'Dongsheng Li', 'Songlei Jian', 'Ning Liu'] | 2021-04-13 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 2.05156058e-02 1.96908221e-01 -5.38326085e-01 -2.01378003e-01
-3.50641876e-01 -3.80246550e-01 4.31775481e-01 7.27104902e-01
-9.19695273e-02 2.93142378e-01 1.81432471e-01 -3.23075280e-02
-3.11528832e-01 -1.38272965e+00 -7.20573246e-01 -7.20523894e-01
-6.20594680e-01 1.82053611e-01 3.95675153e-01 -2.37566426... | [7.098781585693359, 6.285186767578125] |
1247aef0-80c9-43ca-a468-e23025f749b0 | foodx-251-a-dataset-for-fine-grained-food | 1907.06167 | null | https://arxiv.org/abs/1907.06167v1 | https://arxiv.org/pdf/1907.06167v1.pdf | FoodX-251: A Dataset for Fine-grained Food Classification | Food classification is a challenging problem due to the large number of categories, high visual similarity between different foods, as well as the lack of datasets for training state-of-the-art deep models. Solving this problem will require advances in both computer vision models as well as datasets for evaluating thes... | ['Serge Belongie', 'Ajay Divakaran', 'Karan Sikka', 'Weijun Wang', 'Parneet Kaur'] | 2019-07-14 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 7.53813535e-02 -4.41978157e-01 -3.16258520e-01 -7.41015017e-01
-5.09355485e-01 -9.48306620e-01 5.79521596e-01 9.32330012e-01
-3.77367884e-01 1.31970003e-01 1.89671367e-01 -2.20300108e-02
-2.53570336e-03 -7.33319223e-01 -8.46918523e-01 -3.42506737e-01
-3.10647041e-01 3.36507231e-01 -3.14417556e-02 -1.24742039... | [11.549955368041992, 4.374978065490723] |
5db3ab70-79a5-4d7f-9bea-45f40a2fa0b1 | arxiv4tgc-large-scale-datasets-for-temporal | 2306.04962 | null | https://arxiv.org/abs/2306.04962v1 | https://arxiv.org/pdf/2306.04962v1.pdf | arXiv4TGC: Large-Scale Datasets for Temporal Graph Clustering | Temporal graph clustering (TGC) is a crucial task in temporal graph learning. Its focus is on node clustering on temporal graphs, and it offers greater flexibility for large-scale graph structures due to the mechanism of temporal graph methods. However, the development of TGC is currently constrained by a significant p... | ['Xinwang Liu', 'Sihang Zhou', 'Siwei Wang', 'Yue Liu', 'Ke Liang', 'Meng Liu'] | 2023-06-08 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-6.59085035e-01 -1.46430403e-01 -4.71964210e-01 -1.12139970e-01
-3.87808323e-01 -8.60614061e-01 7.89628983e-01 5.36982238e-01
-2.55135335e-02 5.37222147e-01 -6.35967925e-02 -4.11327630e-01
-5.77119291e-01 -9.13376749e-01 -5.14149070e-01 -6.04018569e-01
-9.56228077e-01 4.82392550e-01 6.20805025e-01 -7.93030486... | [7.094300270080566, 6.023642063140869] |
faafec02-2804-4d01-9224-4843012b68e5 | finding-nano-otzi-semi-supervised-volume | 2104.01554 | null | https://arxiv.org/abs/2104.01554v1 | https://arxiv.org/pdf/2104.01554v1.pdf | Finding Nano-Ötzi: Semi-Supervised Volume Visualization for Cryo-Electron Tomography | Cryo-Electron Tomography (cryo-ET) is a new 3D imaging technique with unprecedented potential for resolving submicron structural detail. Existing volume visualization methods, however, cannot cope with its very low signal-to-noise ratio. In order to design more powerful transfer functions, we propose to leverage soft s... | ['Ivan Viola', 'Timo Ropinski', 'Sai Li', 'Peter Wonka', 'Ondřej Strnad', 'Peter Mindek', 'Dominik Engel', 'Ciril Bohak', 'Ngan Nguyen'] | 2021-04-04 | null | null | null | null | ['electron-tomography'] | ['medical'] | [ 1.48274198e-01 -5.63807972e-02 4.81422842e-01 -4.80738401e-01
-5.93911827e-01 -4.19976175e-01 4.76876140e-01 1.52268589e-01
-2.81821996e-01 8.95394504e-01 -1.67375341e-01 -5.20837307e-01
1.31928697e-01 -6.00195050e-01 -4.30098385e-01 -1.01637363e+00
-4.73434180e-01 9.39679682e-01 2.88788080e-01 -9.34760645... | [14.150124549865723, -2.895136833190918] |
f376b702-0938-40b4-9bb2-895c074e2e20 | autolaparo-a-new-dataset-of-integrated-multi | 2208.02049 | null | https://arxiv.org/abs/2208.02049v1 | https://arxiv.org/pdf/2208.02049v1.pdf | AutoLaparo: A New Dataset of Integrated Multi-tasks for Image-guided Surgical Automation in Laparoscopic Hysterectomy | Computer-assisted minimally invasive surgery has great potential in benefiting modern operating theatres. The video data streamed from the endoscope provides rich information to support context-awareness for next-generation intelligent surgical systems. To achieve accurate perception and automatic manipulation during t... | ['Yunhui Liu', 'Qi Dou', 'Tak-Hong Cheung', 'Fangxun Zhong', 'Yonghao Long', 'Bo Lu', 'Ziyi Wang'] | 2022-08-03 | null | null | null | null | ['procedure-learning'] | ['computer-vision'] | [ 1.09173194e-01 7.12735439e-03 -5.52294254e-01 -3.37061197e-01
-7.11104631e-01 -7.10404515e-01 1.52294889e-01 3.83868873e-01
-4.37817216e-01 3.13376576e-01 1.01342574e-01 -3.67449045e-01
-3.53041708e-01 -2.33780742e-01 -6.65172338e-01 -6.52118385e-01
-1.58869043e-01 3.33201796e-01 -1.44333392e-02 -1.83748290... | [14.053466796875, -3.321284770965576] |
220c8a6e-b827-4982-99d4-da3eaa698950 | runner-up-solution-to-google-universal-image | 2210.08735 | null | https://arxiv.org/abs/2210.08735v2 | https://arxiv.org/pdf/2210.08735v2.pdf | 2nd Place Solution to Google Universal Image Embedding | Image representations are a critical building block of computer vision applications. This paper presents the 2nd place solution to the Google Universal Image Embedding Competition, which is part of the ECCV2022 instance-level recognition workshops. We use the instance-level fine-grained image classification method to c... | ['Qiankun Li', 'Xiaolong Huang'] | 2022-10-17 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 6.72193840e-02 -3.59586738e-02 -3.08704346e-01 -4.85268980e-01
-9.88862991e-01 -4.09507245e-01 8.22674036e-01 7.92794451e-02
-5.57110250e-01 3.86327326e-01 -1.52552705e-02 -3.58655751e-01
2.08497241e-01 -6.49674058e-01 -7.88966775e-01 -4.49914545e-01
8.62562805e-02 -8.70053470e-02 5.35748638e-02 -2.48472109... | [9.436620712280273, 1.7329109907150269] |
fc344280-8549-491a-868b-6fc9f1648020 | semantic-role-labeling-guided-multi-turn | 2010.01417 | null | https://arxiv.org/abs/2010.01417v1 | https://arxiv.org/pdf/2010.01417v1.pdf | Semantic Role Labeling Guided Multi-turn Dialogue ReWriter | For multi-turn dialogue rewriting, the capacity of effectively modeling the linguistic knowledge in dialog context and getting rid of the noises is essential to improve its performance. Existing attentive models attend to all words without prior focus, which results in inaccurate concentration on some dispensable words... | ['Dong Yu', 'Linqi Song', 'Haisong Zhang', 'Han Wu', 'Linfeng Song', 'Haochen Tan', 'Kun Xu'] | 2020-10-03 | null | https://aclanthology.org/2020.emnlp-main.537 | https://aclanthology.org/2020.emnlp-main.537.pdf | emnlp-2020-11 | ['dialogue-rewriting'] | ['natural-language-processing'] | [-2.36978680e-02 6.00150287e-01 -5.83201870e-02 -4.51418102e-01
-4.28080350e-01 -8.37645710e-01 7.48637795e-01 9.25977677e-02
-3.42357934e-01 8.79120469e-01 8.95904422e-01 -4.76805449e-01
3.37016523e-01 -5.45189500e-01 6.53474629e-02 -8.06081221e-02
6.57035410e-01 5.29201031e-01 4.74538118e-01 -1.31874454... | [12.428603172302246, 8.0397310256958] |
fedcd317-5ecd-4567-937f-a01a5e5f0a3e | large-language-models-as-tax-attorneys-a-case | 2306.07075 | null | https://arxiv.org/abs/2306.07075v1 | https://arxiv.org/pdf/2306.07075v1.pdf | Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence | Better understanding of Large Language Models' (LLMs) legal analysis abilities can contribute to improving the efficiency of legal services, governing artificial intelligence, and leveraging LLMs to identify inconsistencies in law. This paper explores LLM capabilities in applying tax law. We choose this area of law bec... | ['Jungo Kasai', 'Jonathan H. Choi', 'Aaron Travis Lee', 'Raghav Jain', 'Meghana Bhat', 'WenTing Tao', 'Sarah B. Lawsky', 'David Karamardian', 'John J. Nay'] | 2023-06-12 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [-6.40031844e-02 5.78697979e-01 -5.07060468e-01 -2.97798485e-01
-1.16276622e+00 -8.27517867e-01 7.42924333e-01 3.77456784e-01
-4.33096886e-01 5.64111531e-01 5.86462319e-01 -1.40002859e+00
-6.97561026e-01 -7.37447500e-01 -4.47289318e-01 4.13315386e-01
2.99853414e-01 7.00560629e-01 1.22316279e-01 -6.19295776... | [9.93082332611084, 9.135957717895508] |
79a5469a-41dd-452c-b86c-0c9376406a8b | joint-chinese-word-segmentation-and-part-of-1 | null | null | https://aclanthology.org/2020.coling-main.187 | https://aclanthology.org/2020.coling-main.187.pdf | Joint Chinese Word Segmentation and Part-of-speech Tagging via Multi-channel Attention of Character N-grams | Chinese word segmentation (CWS) and part-of-speech (POS) tagging are two fundamental tasks for Chinese language processing. Previous studies have demonstrated that jointly performing them can be an effective one-step solution to both tasks and this joint task can benefit from a good modeling of contextual features such... | ['Fei Xia', 'Yan Song', 'Yuanhe Tian'] | 2020-12-01 | null | null | null | coling-2020-8 | ['chinese-word-segmentation'] | ['natural-language-processing'] | [ 2.05765665e-02 -3.31971377e-01 -2.61836946e-01 -4.27975982e-01
-8.08235407e-01 -5.33066273e-01 3.43310177e-01 2.84444064e-01
-8.62352967e-01 2.94398218e-01 4.45165217e-01 -6.00774646e-01
4.26196128e-01 -6.20492220e-01 -3.73268336e-01 -8.74208808e-01
5.03473543e-02 2.70101763e-02 5.13969481e-01 -1.02993473... | [10.065503120422363, 10.069745063781738] |
d09da08b-b9dd-4001-8f11-9d2e4003e43f | edge-computing-in-5g-cellular-networks-for | 2107.13767 | null | https://arxiv.org/abs/2107.13767v1 | https://arxiv.org/pdf/2107.13767v1.pdf | Edge computing in 5G cellular networks for real-time analysis of electrocardiography recorded with wearable textile sensors | Fifth-generation (5G) cellular networks promise higher data rates, lower latency, and large numbers of interconnected devices. Thereby, 5G will provide important steps towards unlocking the full potential of the Internet of Things (IoT). In this work, we propose a lightweight IoT platform for continuous vital sign anal... | ['Thomas M. Deserno', 'Valentin Purrucker', 'Arne Klingenberg', 'Nicolai Spicher'] | 2021-07-29 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [-1.96768671e-01 -1.51437158e-02 -2.27825925e-01 -2.51275208e-02
-2.57983118e-01 -3.96048963e-01 -3.18640321e-01 -7.85342008e-02
-1.89149268e-02 8.78048539e-01 -2.13240966e-01 -1.00581229e+00
-1.82346120e-01 -9.14806008e-01 -2.99058765e-01 -6.18486702e-01
-5.70654035e-01 2.67893702e-01 -3.64055485e-01 4.15054977... | [14.096531867980957, 3.2566916942596436] |
f3d9a4c4-c8f3-4e68-af7d-80e78219680e | license-plate-detection-and-recognition-in | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Sergio_Silva_License_Plate_Detection_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Sergio_Silva_License_Plate_Detection_ECCV_2018_paper.pdf | License Plate Detection and Recognition in Unconstrained Scenarios | Despite the large number of both commercial and academic methods for Automatic License Plate Recognition (ALPR), most existing approaches are focused on a specific license plate (LP) region (e.g. European, US, Brazilian, Taiwanese, etc.), and frequently explore datasets containing approximately frontal images. This wor... | ['Sergio Montazzolli Silva', 'Claudio Rosito Jung'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['license-plate-recognition', 'license-plate-detection'] | ['computer-vision', 'computer-vision'] | [ 1.59140706e-01 -6.76212430e-01 1.11225002e-01 -3.53982151e-01
-8.65989029e-01 -9.14546430e-01 4.89408523e-01 -7.03024209e-01
-2.50436425e-01 5.47397375e-01 -3.15613061e-01 -2.21158728e-01
2.09527642e-01 -3.67077857e-01 -8.30517113e-01 -5.04462183e-01
5.54979324e-01 3.28416139e-01 4.52758193e-01 -2.46186689... | [9.851036071777344, -4.920660018920898] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.