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values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5d6a2638-c858-469c-967a-96e894a8ef2e | deepgd-a-multi-objective-black-box-test | 2303.04878 | null | https://arxiv.org/abs/2303.04878v1 | https://arxiv.org/pdf/2303.04878v1.pdf | DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks | Deep neural networks (DNNs) are widely used in various application domains such as image processing, speech recognition, and natural language processing. However, testing DNN models may be challenging due to the complexity and size of their input domain. Particularly, testing DNN models often requires generating or exp... | ['Lionel Briand', 'Mahboubeh Dadkhah', 'Manel Abdellatif', 'Zohreh Aghababaeyan'] | 2023-03-08 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 3.50122362e-01 -4.70874757e-02 -2.73656845e-01 -5.27002096e-01
-7.02668428e-01 -5.39115071e-01 -5.46058938e-02 -2.09898382e-01
-1.23277016e-01 1.20269990e+00 -3.99782270e-01 -6.64147556e-01
-4.94940728e-01 -1.01522934e+00 -9.93571937e-01 -5.95340192e-01
1.24547616e-01 9.31186736e-01 3.36445838e-01 3.37875903... | [6.567193031311035, 7.649109840393066] |
e5a16158-93f8-4e2a-be6c-2f4e5b177700 | semi-supervised-acoustic-model-training-for-1 | 1906.08647 | null | https://arxiv.org/abs/1906.08647v2 | https://arxiv.org/pdf/1906.08647v2.pdf | Semi-supervised acoustic model training for five-lingual code-switched ASR | This paper presents recent progress in the acoustic modelling of under-resourced code-switched (CS) speech in multiple South African languages. We consider two approaches. The first constructs separate bilingual acoustic models corresponding to language pairs (English-isiZulu, English-isiXhosa, English-Setswana and Eng... | ['Febe De Wet', 'Emre Yilmaz', 'Thomas Niesler', 'Astik Biswas', 'Ewald van der Westhuizen'] | 2019-06-20 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [-1.03787452e-01 -1.65375546e-02 2.19863057e-01 -3.37828696e-01
-1.28067076e+00 -6.32020354e-01 5.20204961e-01 -1.59929678e-01
-7.26764202e-01 4.44867730e-01 2.32267737e-01 -7.71942616e-01
2.18161732e-01 -4.07446325e-01 -6.44966006e-01 -5.41558087e-01
4.82528284e-03 8.05216670e-01 1.50202185e-01 -4.23391581... | [14.369363784790039, 6.921812057495117] |
8327fbf2-4d26-440a-806f-6cf00fe2e3e1 | autocolor-learned-light-power-control-for | 2305.01611 | null | https://arxiv.org/abs/2305.01611v1 | https://arxiv.org/pdf/2305.01611v1.pdf | AutoColor: Learned Light Power Control for Multi-Color Holograms | Multi-color holograms rely on simultaneous illumination from multiple light sources. These multi-color holograms could utilize light sources better than conventional single-color holograms and can improve the dynamic range of holographic displays. In this letter, we introduce \projectname, the first learned method for ... | ['Kaan Akşit', 'Qi Sun', 'Hakan Urey', 'Koray Kavaklı', 'Yicheng Zhan'] | 2023-05-02 | null | null | null | null | ['monocular-depth-estimation'] | ['computer-vision'] | [ 2.35876501e-01 -1.37482002e-01 5.71769059e-01 -3.52047265e-01
-1.10866666e+00 -4.43133831e-01 4.28805292e-01 -6.72049403e-01
-4.04558629e-01 8.90698075e-01 8.13547596e-02 -1.07244626e-01
4.25968915e-01 -1.15409899e+00 -8.93015742e-01 -6.96530938e-01
6.37023747e-01 6.19521856e-01 -4.10253443e-02 3.48392785... | [9.807981491088867, -2.7479333877563477] |
0c7adbea-dd02-4e2f-8b4f-38a51317a572 | mots-multi-object-tracking-and-segmentation | 1902.03604 | null | http://arxiv.org/abs/1902.03604v2 | http://arxiv.org/pdf/1902.03604v2.pdf | MOTS: Multi-Object Tracking and Segmentation | This paper extends the popular task of multi-object tracking to multi-object
tracking and segmentation (MOTS). Towards this goal, we create dense
pixel-level annotations for two existing tracking datasets using a
semi-automatic annotation procedure. Our new annotations comprise 65,213 pixel
masks for 977 distinct objec... | ['Berin Balachandar Gnana Sekar', 'Jonathon Luiten', 'Bastian Leibe', 'Paul Voigtlaender', 'Michael Krause', 'Andreas Geiger', 'Aljosa Osep'] | 2019-02-10 | mots-multi-object-tracking-and-segmentation-2 | http://openaccess.thecvf.com/content_CVPR_2019/html/Voigtlaender_MOTS_Multi-Object_Tracking_and_Segmentation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Voigtlaender_MOTS_Multi-Object_Tracking_and_Segmentation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['multi-object-tracking-and-segmentation'] | ['computer-vision'] | [ 6.90651536e-02 -3.58273923e-01 -2.18015015e-01 -2.71652073e-01
-9.72470045e-01 -8.44200432e-01 5.28474450e-01 -1.07544489e-01
-5.57496548e-01 7.05297649e-01 -2.82149464e-02 -2.04618812e-01
5.75859010e-01 -3.58771354e-01 -8.56255531e-01 -3.57410282e-01
4.71991338e-02 4.48439538e-01 9.42775667e-01 2.42391288... | [6.393340110778809, -1.9906085729599] |
f4c5752d-48b7-4cb8-9237-11dc9b3fa565 | a-copy-mechanism-for-handling-knowledge-base | 2211.10271 | null | https://arxiv.org/abs/2211.10271v1 | https://arxiv.org/pdf/2211.10271v1.pdf | A Copy Mechanism for Handling Knowledge Base Elements in SPARQL Neural Machine Translation | Neural Machine Translation (NMT) models from English to SPARQL are a promising development for SPARQL query generation. However, current architectures are unable to integrate the knowledge base (KB) schema and handle questions on knowledge resources, classes, and properties unseen during training, rendering them unusab... | ['Samuel Reyd', 'Amal Zouaq', 'Rose Hirigoyen'] | 2022-11-18 | null | null | null | null | ['nmt'] | ['computer-code'] | [-3.59523520e-02 6.83519304e-01 -1.21633828e-01 -5.11433601e-01
-1.01407862e+00 -5.76852858e-01 6.23882115e-01 1.86861619e-01
-6.62180662e-01 1.18916678e+00 2.54163951e-01 -2.59390146e-01
-1.24203472e-03 -1.51538575e+00 -1.32780516e+00 1.67321056e-01
3.89795423e-01 1.01348948e+00 4.77077246e-01 -7.16896534... | [10.184653282165527, 7.903359889984131] |
360482b1-b033-4656-a98b-54caf8f6a508 | explainability-aware-one-point-attack-for | 2110.04158 | null | https://arxiv.org/abs/2110.04158v3 | https://arxiv.org/pdf/2110.04158v3.pdf | Explainability-Aware One Point Attack for Point Cloud Neural Networks | With the proposition of neural networks for point clouds, deep learning has started to shine in the field of 3D object recognition while researchers have shown an increased interest to investigate the reliability of point cloud networks by adversarial attacks. However, most of the existing studies aim to deceive humans... | ['Helena Kotthaus', 'Hanxiao Tan'] | 2021-10-08 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-2.64164865e-01 1.84029117e-01 1.11101851e-01 -8.55904371e-02
-2.76212186e-01 -1.04913092e+00 6.47322655e-01 -5.55759203e-03
1.10856406e-01 2.74271250e-01 -6.69398725e-01 -6.66500449e-01
-1.47970423e-01 -8.71542156e-01 -1.29567134e+00 -6.38850868e-01
-4.35027838e-01 4.06056285e-01 1.41472638e-01 -3.00865322... | [7.700451850891113, -4.474695682525635] |
552dc2f6-e104-41e6-95e9-3adbb25f9167 | topic-guided-variational-auto-encoder-for | null | null | https://aclanthology.org/N19-1015 | https://aclanthology.org/N19-1015.pdf | Topic-Guided Variational Auto-Encoder for Text Generation | We propose a topic-guided variational auto-encoder (TGVAE) model for text generation. Distinct from existing variational auto-encoder (VAE) based approaches, which assume a simple Gaussian prior for latent code, our model specifies the prior as a Gaussian mixture model (GMM) parametrized by a neural topic module. Each ... | ['Lawrence Carin', 'Dinghan Shen', 'Guoyin Wang', 'Wenlin Wang', 'Changyou Chen', 'Zhe Gan', 'Hongteng Xu', 'Ruiyi Zhang'] | 2019-06-01 | null | null | null | naacl-2019-6 | ['conditional-text-generation'] | ['natural-language-processing'] | [ 8.40214267e-02 5.80609620e-01 -1.20432273e-01 -3.38060230e-01
-1.10864532e+00 -5.07797956e-01 1.14684045e+00 -4.51118797e-01
2.06758067e-01 7.23854959e-01 6.75654948e-01 -2.31937706e-01
5.69005787e-01 -1.00692415e+00 -8.81400168e-01 -7.49850214e-01
4.52678561e-01 6.45600200e-01 -2.66535759e-01 -7.91664273... | [11.841216087341309, 9.167567253112793] |
1dc5120b-07ee-4ca2-b703-7273d206af08 | spectroscopic-and-photometric-redshift | 2101.02532 | null | https://arxiv.org/abs/2101.02532v1 | https://arxiv.org/pdf/2101.02532v1.pdf | Spectroscopic and Photometric Redshift Estimation by Neural Networks For the China Space Station Optical Survey (CSS-OS) | The estimation of spectroscopic and photometric redshifts (spec-z and photo-z) is crucial for future cosmological surveys. It can directly affect several powerful measurements of the Universe, e.g. weak lensing and galaxy clustering. In this work, we explore the accuracies of spec-z and photo-z that can be obtained in ... | ['Liping Fu', 'Zuhui Fan', 'Valeria Amaro', 'Xuelei Chen', 'Ye Cao', 'Xin Zhang', 'Xian-Min Meng', 'Yan Gong', 'Xingchen Zhou'] | 2021-01-07 | null | null | null | null | ['photometric-redshift-estimation'] | ['miscellaneous'] | [-3.67133021e-01 -1.20515600e-01 3.47264737e-01 -6.74131691e-01
-8.31554651e-01 -4.70725298e-01 9.01645422e-01 -4.80838001e-01
-4.18759644e-01 8.00098240e-01 -9.99114066e-02 -2.82600462e-01
-3.97302032e-01 -1.25817335e+00 -5.56447208e-01 -1.12697005e+00
-6.30325899e-02 5.20901084e-01 5.90183556e-01 1.93438366... | [7.401675224304199, 3.247619867324829] |
09fd9fac-2332-47f5-ac8e-d1bd700c9b2b | communication-efficient-federated-learning-6 | 2104.12416 | null | https://arxiv.org/abs/2104.12416v1 | https://arxiv.org/pdf/2104.12416v1.pdf | Communication-Efficient Federated Learning with Dual-Side Low-Rank Compression | Federated learning (FL) is a promising and powerful approach for training deep learning models without sharing the raw data of clients. During the training process of FL, the central server and distributed clients need to exchange a vast amount of model information periodically. To address the challenge of communicatio... | ['Khaled B. Letaief', 'Jun Zhang', 'Xianghao Yu', 'Zhefeng Qiao'] | 2021-04-26 | null | null | null | null | ['low-rank-compression'] | ['computer-code'] | [-2.28653893e-01 -3.01593006e-01 -2.95821577e-01 -5.95162392e-01
-9.10068810e-01 -3.48173112e-01 4.08678293e-01 -2.58291304e-01
-4.54176307e-01 6.43647850e-01 2.13805940e-02 -5.05768597e-01
-3.65570754e-01 -8.88308942e-01 -8.97086143e-01 -7.96790838e-01
1.03956267e-01 6.97966635e-01 -6.70978278e-02 3.58536541... | [5.91277551651001, 5.997523307800293] |
4a061838-d8da-4069-9ab0-10513e737a8d | cubifae-3d-monocular-camera-space | 2006.04080 | null | https://arxiv.org/abs/2006.04080v2 | https://arxiv.org/pdf/2006.04080v2.pdf | CubifAE-3D: Monocular Camera Space Cubification for Auto-Encoder based 3D Object Detection | We introduce a method for 3D object detection using a single monocular image. Starting from a synthetic dataset, we pre-train an RGB-to-Depth Auto-Encoder (AE). The embedding learnt from this AE is then used to train a 3D Object Detector (3DOD) CNN which is used to regress the parameters of 3D object poses after the en... | ['Punarjay Chakravarty', 'Shubham Shrivastava'] | 2020-06-07 | null | null | null | null | ['3d-object-detection-from-monocular-images'] | ['computer-vision'] | [-2.97448546e-01 3.78065437e-01 9.80558526e-03 -3.78365219e-01
-5.73264122e-01 -5.83374023e-01 5.98184526e-01 -5.69872797e-01
-6.23672307e-01 1.51280075e-01 -3.11504066e-01 -2.38915995e-01
5.21378577e-01 -7.18546450e-01 -1.31406367e+00 -6.59518838e-01
-2.23146193e-02 9.77418244e-01 5.06467521e-01 2.09210053... | [7.799881458282471, -2.536126136779785] |
9af5d2db-86fd-4bb7-a8e3-782525d17b29 | glancenets-interpretabile-leak-proof-concept | 2205.15612 | null | https://arxiv.org/abs/2205.15612v2 | https://arxiv.org/pdf/2205.15612v2.pdf | GlanceNets: Interpretabile, Leak-proof Concept-based Models | There is growing interest in concept-based models (CBMs) that combine high-performance and interpretability by acquiring and reasoning with a vocabulary of high-level concepts. A key requirement is that the concepts be interpretable. Existing CBMs tackle this desideratum using a variety of heuristics based on unclear n... | ['Stefano Teso', 'Andrea Passerini', 'Emanuele Marconato'] | 2022-05-31 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 5.10410309e-01 8.09358418e-01 -4.06516284e-01 -5.37493229e-01
-4.48641002e-01 -8.07920575e-01 8.12964141e-01 4.88821387e-01
2.85673086e-02 4.55551893e-01 4.37231243e-01 -5.68086207e-01
-4.19363618e-01 -8.74666631e-01 -7.45650351e-01 -1.80175185e-01
-4.45853621e-02 8.87693524e-01 -2.50137001e-01 -3.20769250... | [9.407384872436523, 6.793144226074219] |
239396c5-e7ab-4a35-8387-e838aaa4d629 | revisiting-gaussian-neurons-for-online | 2205.00920 | null | https://arxiv.org/abs/2205.00920v2 | https://arxiv.org/pdf/2205.00920v2.pdf | Revisiting Gaussian Neurons for Online Clustering with Unknown Number of Clusters | Despite the recent success of artificial neural networks, more biologically plausible learning methods may be needed to resolve the weaknesses of backpropagation trained models such as catastrophic forgetting and adversarial attacks. Although these weaknesses are not specifically addressed, a novel local learning rule ... | ['Ole Christian Eidheim'] | 2022-05-02 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [ 2.19640657e-01 2.16234773e-01 7.50582889e-02 -2.16935664e-01
9.32425186e-02 -3.55799913e-01 4.68957633e-01 -4.68220599e-02
-8.57065558e-01 9.29227233e-01 -3.39805156e-01 -1.53580144e-01
-2.78592587e-01 -6.06951833e-01 -8.17986727e-01 -1.20981169e+00
-2.42788449e-01 1.92665413e-01 4.94038552e-01 9.27470624... | [8.481191635131836, 3.212310314178467] |
b32254d0-a33b-4c6a-b8a6-d25687696c2d | automated-multi-process-ctc-detection-using | 2109.12709 | null | https://arxiv.org/abs/2109.12709v1 | https://arxiv.org/pdf/2109.12709v1.pdf | Automated Multi-Process CTC Detection using Deep Learning | Circulating Tumor Cells (CTCs) bear great promise as biomarkers in tumor prognosis. However, the process of identification and later enumeration of CTCs require manual labor, which is error-prone and time-consuming. The recent developments in object detection via Deep Learning using Mask-RCNNs and wider availability of... | ['Andrew F. Laine', 'Kam W. Leong', 'Elena Ivanova'] | 2021-09-26 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 3.04828465e-01 -4.84098941e-01 1.40921578e-01 -2.74328794e-02
-9.66352403e-01 -5.29539585e-01 5.80529213e-01 6.64668560e-01
-1.13664865e+00 9.08172786e-01 -2.97136784e-01 -3.40760678e-01
4.33292449e-01 -5.23216784e-01 7.05073103e-02 -1.25756598e+00
6.26620650e-02 7.20775723e-01 2.35068575e-01 4.02246565... | [15.06197452545166, -3.109299421310425] |
88064a41-2590-4c35-9072-16e3ebec8fd3 | siamese-instance-search-for-tracking | 1605.05863 | null | http://arxiv.org/abs/1605.05863v1 | http://arxiv.org/pdf/1605.05863v1.pdf | Siamese Instance Search for Tracking | In this paper we present a tracker, which is radically different from
state-of-the-art trackers: we apply no model updating, no occlusion detection,
no combination of trackers, no geometric matching, and still deliver
state-of-the-art tracking performance, as demonstrated on the popular online
tracking benchmark (OTB) ... | ['Arnold W. M. Smeulders', 'Efstratios Gavves', 'Ran Tao'] | 2016-05-19 | siamese-instance-search-for-tracking-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Tao_Siamese_Instance_Search_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Tao_Siamese_Instance_Search_CVPR_2016_paper.pdf | cvpr-2016-6 | ['geometric-matching', 'instance-search'] | ['computer-vision', 'computer-vision'] | [-2.85009474e-01 -1.47909582e-01 -2.19687372e-01 2.87656456e-01
-6.36951506e-01 -8.53306174e-01 6.91066027e-01 1.33374622e-02
-5.92829585e-01 5.68022251e-01 -2.03669935e-01 1.49017170e-01
1.38834277e-02 -2.13308752e-01 -1.05882061e+00 -7.10589707e-01
-4.74218547e-01 5.45006990e-01 9.03340876e-01 -1.87630802... | [6.379839897155762, -2.0713748931884766] |
601a86d9-c295-4330-b1d9-d7cba3ae0e3b | sensing-and-recognizing-surface-textures | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Li_Sensing_and_Recognizing_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Li_Sensing_and_Recognizing_2013_CVPR_paper.pdf | Sensing and Recognizing Surface Textures Using a GelSight Sensor | Sensing surface textures by tou ch is a valuable was difficult to build capability for robots. Until recently it a a compliant sensor with high sennnsitivity and high resolution. The GelSight sensor is cooompliant and offers sensitivity and resolution exceeding that of the human fingertips. This opens the possibility o... | ['Rui Li', 'Edward H. Adelson'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['material-recognition'] | ['computer-vision'] | [ 3.51547003e-01 -1.43419495e-02 2.13570714e-01 -2.35396042e-01
-2.06494555e-01 -3.95998091e-01 2.84616500e-01 -2.30824545e-01
-1.57299235e-01 5.27346611e-01 -3.93988371e-01 1.15884259e-01
-2.87538081e-01 -1.19091892e+00 -6.05312049e-01 -7.39601970e-01
1.39621019e-01 4.85755742e-01 8.46189320e-01 -4.96665269... | [8.549230575561523, -2.3831825256347656] |
25670457-bc87-4d15-ab00-70eca23b667f | accelerated-convergence-of-nesterov-s | 2306.08109 | null | https://arxiv.org/abs/2306.08109v1 | https://arxiv.org/pdf/2306.08109v1.pdf | Accelerated Convergence of Nesterov's Momentum for Deep Neural Networks under Partial Strong Convexity | Current state-of-the-art analyses on the convergence of gradient descent for training neural networks focus on characterizing properties of the loss landscape, such as the Polyak-Lojaciewicz (PL) condition and the restricted strong convexity. While gradient descent converges linearly under such conditions, it remains a... | ['Anastasios Kyrillidis', 'Fangshuo Liao'] | 2023-06-13 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [-2.33728856e-01 2.91006356e-01 -2.95594543e-01 -3.43101680e-01
-1.89204082e-01 -4.69564289e-01 1.26681209e-01 7.71277845e-02
-7.18428731e-01 9.16087925e-01 -2.35250160e-01 -6.92009807e-01
-4.01802301e-01 -4.18983728e-01 -9.57239032e-01 -6.99714661e-01
-3.03387970e-01 3.69305164e-01 -6.91361651e-02 -4.35185105... | [7.775157451629639, 3.795847177505493] |
94918315-3798-48a8-9e7f-cc9379845dd8 | a-transformer-based-user-satisfaction | 2212.03817 | null | https://arxiv.org/abs/2212.03817v1 | https://arxiv.org/pdf/2212.03817v1.pdf | A Transformer-Based User Satisfaction Prediction for Proactive Interaction Mechanism in DuerOS | Recently, spoken dialogue systems have been widely deployed in a variety of applications, serving a huge number of end-users. A common issue is that the errors resulting from noisy utterances, semantic misunderstandings, or lack of knowledge make it hard for a real system to respond properly, possibly leading to an uns... | ['Jian Xie', 'Xuyun Zhang', 'Chuheng Zhang', 'Xiaonan He', 'Wei Shen'] | 2022-12-05 | null | null | null | null | ['spoken-dialogue-systems'] | ['speech'] | [ 1.54749781e-01 2.57079720e-01 9.39492807e-02 -7.89055169e-01
-7.28322625e-01 -6.65621161e-01 5.96040845e-01 2.22527102e-01
-4.91953582e-01 5.80616832e-01 3.97933275e-01 -4.65689242e-01
2.49137461e-01 -8.09075296e-01 4.82130721e-02 -2.32173800e-01
5.23477137e-01 7.76587009e-01 2.85359442e-01 -8.40391815... | [12.587700843811035, 7.8753180503845215] |
49114284-9b22-4f9a-ae81-48cb3eaf2c59 | a-distant-supervision-corpus-for-extracting | 2204.06584 | null | https://arxiv.org/abs/2204.06584v1 | https://arxiv.org/pdf/2204.06584v1.pdf | A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes | We introduce ChemDisGene, a new dataset for training and evaluating multi-class multi-label document-level biomedical relation extraction models. Our dataset contains 80k biomedical research abstracts labeled with mentions of chemicals, diseases, and genes, portions of which human experts labeled with 18 types of biome... | ['Andrew McCallum', 'Michaela Torkar', 'Sunil Mohan', 'Dongxu Zhang'] | 2022-04-13 | null | https://aclanthology.org/2022.lrec-1.116 | https://aclanthology.org/2022.lrec-1.116.pdf | lrec-2022-6 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 1.66612878e-01 8.70459676e-01 -7.80104756e-01 -3.29313964e-01
-1.20877481e+00 -7.48824000e-01 5.55958569e-01 1.23414242e+00
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-1.42102167e-01 -6.41138673e-01 -8.45293462e-01 -4.84901488e-01
-1.24629252e-01 8.56596112e-01 -2.90994436e-01 1.33578748... | [8.5772066116333, 8.747515678405762] |
ff9e6925-1e60-4b0e-85eb-79e19f0f6484 | dual-feature-warping-based-motion-model | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Li_Dual-Feature_Warping-Based_Motion_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Li_Dual-Feature_Warping-Based_Motion_ICCV_2015_paper.pdf | Dual-Feature Warping-Based Motion Model Estimation | To break down the geometry assumptions of traditional motion models (e.g., homography, affine), warping-based motion model recently becomes very popular and is adopted in many latest applications (e.g., image stitching, video stabilization). With high degrees of freedom, the accuracy of model heavily relies on data-ter... | ['Long Quan', 'Jian Sun', 'Shiwei Li', 'Lu Yuan'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['image-stitching', 'video-stabilization'] | ['computer-vision', 'computer-vision'] | [ 1.98262647e-01 -5.52697837e-01 -2.73760617e-01 9.87657756e-02
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3.53927284e-01 -3.46200317e-02 6.14293873e-01 -4.67281371... | [9.20557689666748, -2.35117244720459] |
d6db20f4-3d77-420f-a030-87d285212056 | scalable-and-explainable-1-bit-matrix | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/16863 | https://ojs.aaai.org/index.php/AAAI/article/view/16863/16670 | Scalable and Explainable 1-Bit Matrix Completion via Graph Signal Learning | One-bit matrix completion is an important class of positiveunlabeled (PU) learning problems where the observations consist of only positive examples, eg, in top-N recommender systems. For the first time, we show that 1-bit matrix completion can be formulated as the problem of recovering clean graph signals from noise-c... | ['Xiaokang Yang', 'Hanchi Huang', 'Junchi Yan', 'Dongsheng Li', 'Chao Chen'] | 2021-05-18 | null | null | null | aaai-2021-5 | ['collaborative-ranking'] | ['graphs'] | [ 3.47620636e-01 6.36737868e-02 -3.30669284e-01 3.53801288e-02
-1.00874591e+00 -5.23733795e-01 2.79146016e-01 -7.30061978e-02
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-4.82659936e-01 -5.60986936e-01 -1.06993914e+00 -7.35530794e-01
-3.68323416e-01 2.40843371e-01 -2.38865301e-01 -3.28395963... | [6.991121768951416, 4.841805458068848] |
20f5b554-0a31-4e43-9664-ab9d1bdf204f | improving-pedestrian-attribute-recognition | 1910.04562 | null | https://arxiv.org/abs/1910.04562v1 | https://arxiv.org/pdf/1910.04562v1.pdf | Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization | Pedestrian attribute recognition has been an emerging research topic in the area of video surveillance. To predict the existence of a particular attribute, it is demanded to localize the regions related to the attribute. However, in this task, the region annotations are not available. How to carve out these attribute-r... | ['Zhao-Xiang Zhang', 'Lu Sheng', 'Chufeng Tang', 'Xiaolin Hu'] | 2019-10-10 | improving-pedestrian-attribute-recognition-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Tang_Improving_Pedestrian_Attribute_Recognition_With_Weakly-Supervised_Multi-Scale_Attribute-Specific_Localization_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Tang_Improving_Pedestrian_Attribute_Recognition_With_Weakly-Supervised_Multi-Scale_Attribute-Specific_Localization_ICCV_2019_paper.pdf | iccv-2019-10 | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [ 8.58480949e-03 5.02758026e-02 -3.48222733e-01 -8.48192155e-01
-7.05424607e-01 -2.72652626e-01 5.64273119e-01 3.43342692e-01
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8.47029239e-02 2.97127366e-01 6.53453887e-01 -4.21585031... | [14.312480926513672, 1.050398826599121] |
33505626-bd87-4aa3-ae0d-2182aa408552 | evaluating-various-tokenizers-for-arabic-text | 2106.07540 | null | https://arxiv.org/abs/2106.07540v2 | https://arxiv.org/pdf/2106.07540v2.pdf | Evaluating Various Tokenizers for Arabic Text Classification | The first step in any NLP pipeline is to split the text into individual tokens. The most obvious and straightforward approach is to use words as tokens. However, given a large text corpus, representing all the words is not efficient in terms of vocabulary size. In the literature, many tokenization algorithms have emerg... | ['Irfan Ahmad', 'Mustafa Ghaleb', 'Maged S. Al-shaibani', 'Zaid Alyafeai'] | 2021-06-14 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [-1.06253698e-01 -1.46335304e-01 -3.09804827e-01 -2.72322863e-01
-5.58701634e-01 -8.93749714e-01 7.52891600e-01 8.22040379e-01
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5.20418346e-01 5.59855223e-01 1.49232447e-01 -5.38536966... | [10.300537109375, 9.846599578857422] |
a49c94c4-e92f-4bd4-a93d-bc811acf9d03 | mgadn-a-multi-task-graph-anomaly-detection | 2211.12141 | null | https://arxiv.org/abs/2211.12141v2 | https://arxiv.org/pdf/2211.12141v2.pdf | MGADN: A Multi-task Graph Anomaly Detection Network for Multivariate Time Series | Anomaly detection of time series, especially multivariate time series(time series with multiple sensors), has been focused on for several years. Though existing method has achieved great progress, there are several challenging problems to be solved. Firstly, existing method including neural network only concentrate on ... | ['Xiaochen Sun', 'Weixuan Xiong'] | 2022-11-22 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [ 1.10559061e-01 -2.45560363e-01 1.25624716e-01 -2.50789583e-01
-3.99286002e-02 -1.82957411e-01 4.34371829e-01 3.34333599e-01
-2.45781958e-01 5.49351931e-01 1.58276439e-01 -3.36030126e-01
-1.70172736e-01 -1.00906157e+00 -7.89848208e-01 -6.21353209e-01
-5.36794782e-01 2.03441322e-01 9.33274850e-02 -3.98413002... | [7.094484806060791, 2.794710159301758] |
e653e34f-16bb-4ec2-94f9-9904b4af18cf | a-deep-visual-correspondence-embedding-model | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Chen_A_Deep_Visual_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Chen_A_Deep_Visual_ICCV_2015_paper.pdf | A Deep Visual Correspondence Embedding Model for Stereo Matching Costs | This paper presents a data-driven matching cost for stereo matching. A novel deep visual correspondence embedding model is trained via Convolutional Neural Network on a large set of stereo images with ground truth disparities. This deep embedding model leverages appearance data to learn visual similarity relationships ... | ['Xun Sun', 'Liang Wang', 'Chang Huang', 'Yinan Yu', 'Zhuoyuan Chen'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['stereo-matching'] | ['computer-vision'] | [ 1.02155723e-01 -2.13061959e-01 -3.13043505e-01 -6.15356863e-01
-8.15214634e-01 -4.19452369e-01 5.96250653e-01 -1.25079334e-01
-4.01243716e-01 3.51118118e-01 3.59362245e-01 9.60471630e-02
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1.19156912e-01 2.80732989e-01 2.06691533e-01 -9.91007537... | [8.513336181640625, -2.2102251052856445] |
ed3bf35c-4cd9-4886-a222-9ffd6e9daa0f | deep-content-user-embedding-model-for-music | 1807.06786 | null | http://arxiv.org/abs/1807.06786v1 | http://arxiv.org/pdf/1807.06786v1.pdf | Deep Content-User Embedding Model for Music Recommendation | Recently deep learning based recommendation systems have been actively
explored to solve the cold-start problem using a hybrid approach. However, the
majority of previous studies proposed a hybrid model where collaborative
filtering and content-based filtering modules are independently trained. The
end-to-end approach ... | ['Jang-Yeon Park', 'Jiyoung Park', 'Juhan Nam', 'Jongpil Lee', 'Kyungyun Lee'] | 2018-07-18 | null | null | null | null | ['music-auto-tagging'] | ['music'] | [-2.31299326e-01 -3.97902846e-01 -9.82793272e-02 -5.08938372e-01
-7.37227857e-01 -4.13797110e-01 4.55755234e-01 -3.57044429e-01
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3.51272583e-01 1.25526816e-01 1.48884043e-01 -3.14887941... | [10.193756103515625, 5.581940174102783] |
ab1168e6-8034-4255-94ec-0e76b7d7a061 | provably-efficient-iterated-cvar | 2307.02842 | null | https://arxiv.org/abs/2307.02842v1 | https://arxiv.org/pdf/2307.02842v1.pdf | Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation | Risk-sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk. In this paper, we investigate a novel risk-sensitive RL formulation with an Iterated Conditional Value-at-Risk (CVaR) objective under linear and general function approximations. This new formulation, named IC... | ['Longbo Huang', 'Desheng Wu', 'Siwei Wang', 'Pihe Hu', 'Yihan Du', 'Yu Chen'] | 2023-07-06 | null | null | null | null | ['reinforcement-learning-1'] | ['methodology'] | [-1.02239706e-01 1.64322332e-01 -2.89226890e-01 -1.47863656e-01
-1.28077590e+00 -4.14112121e-01 -2.31189430e-02 2.96310365e-01
-9.65426326e-01 8.64435375e-01 -2.43441626e-01 -3.96157891e-01
-8.04814696e-01 -9.92214620e-01 -6.44816875e-01 -8.90002906e-01
-8.27123284e-01 1.85740635e-01 7.33937249e-02 -3.56724709... | [4.3804030418396, 2.9429492950439453] |
4d459eff-34ff-4d7e-bae3-589c760f78c6 | machine-learning-of-percolation-models-using | 2207.03368 | null | https://arxiv.org/abs/2207.03368v2 | https://arxiv.org/pdf/2207.03368v2.pdf | Machine learning of percolation models using graph convolutional neural networks | Percolation is an important topic in climate, physics, materials science, epidemiology, finance, and so on. Prediction of percolation thresholds with machine learning methods remains challenging. In this paper, we build a powerful graph convolutional neural network to study the percolation in both supervised and unsupe... | ['Wanzhou Zhang', 'Youjin Deng', 'Lirong Zhang', 'Hua Tian'] | 2022-07-07 | null | null | null | null | ['epidemiology'] | ['medical'] | [-1.52471513e-01 -3.77798565e-02 1.34820836e-02 -2.01006029e-02
-1.16851613e-01 -5.44515908e-01 3.89747024e-01 5.40344357e-01
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-1.25039995e-01 -1.50729632e+00 -7.01894403e-01 -1.09127128e+00
-4.84512657e-01 4.35860783e-01 4.63759899e-01 -1.90270975... | [6.936287879943848, 6.186816692352295] |
84eb362f-3201-4fb6-bdae-6c735aade93d | portfolio-optimization-on-nifty-thematic | 2202.02723 | null | https://arxiv.org/abs/2202.02723v1 | https://arxiv.org/pdf/2202.02723v1.pdf | Portfolio Optimization on NIFTY Thematic Sector Stocks Using an LSTM Model | Portfolio optimization has been a broad and intense area of interest for quantitative and statistical finance researchers and financial analysts. It is a challenging task to design a portfolio of stocks to arrive at the optimized values of the return and risk. This paper presents an algorithmic approach for designing o... | ['Sidra Mehtab', 'Saikat Mondal', 'Jaydip Sen'] | 2022-02-06 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-4.67553943e-01 -1.16652653e-01 -3.19737159e-02 -2.29665771e-01
-6.56042099e-01 -7.83367991e-01 5.68964541e-01 -3.19089085e-01
-2.69473344e-01 6.54298723e-01 6.93723142e-01 -6.73400700e-01
-5.75835466e-01 -1.09780097e+00 -2.03739598e-01 -4.97746825e-01
-2.74239689e-01 2.76828796e-01 -2.11760253e-01 -1.51512146... | [4.5859904289245605, 4.098278999328613] |
233c15e1-dbe1-4476-8a97-73c01223e9dc | visual-recognition-driven-image-restoration | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Visual_Recognition-Driven_Image_Restoration_for_Multiple_Degradation_With_Intrinsic_Semantics_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Visual_Recognition-Driven_Image_Restoration_for_Multiple_Degradation_With_Intrinsic_Semantics_CVPR_2023_paper.pdf | Visual Recognition-Driven Image Restoration for Multiple Degradation With Intrinsic Semantics Recovery | Deep image recognition models suffer a significant performance drop when applied to low-quality images since they are trained on high-quality images. Although many studies have investigated to solve the issue through image restoration or domain adaptation, the former focuses on visual quality rather than recognitio... | ['Feng Zhao', 'Jinghao Zhang', 'Hu Yu', 'Man Zhou', 'Jiahao Chang', 'Jie Huang', 'Zizheng Yang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['person-re-identification', 'image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 5.09970188e-01 -4.95751768e-01 1.30665861e-02 -5.38842499e-01
-8.82844746e-01 -1.66077495e-01 4.96105343e-01 -4.66356635e-01
-2.54923284e-01 4.57681328e-01 5.07651389e-01 1.83112361e-02
-1.64420068e-01 -6.33686304e-01 -7.09695756e-01 -6.64978266e-01
6.42597854e-01 -9.10656154e-02 -2.73165941e-01 -2.42837071... | [11.821181297302246, -1.8223915100097656] |
66f1ef2f-9c51-40b1-b20c-c470346c0e85 | neural-unification-for-logic-reasoning-over | 2109.08460 | null | https://arxiv.org/abs/2109.08460v1 | https://arxiv.org/pdf/2109.08460v1.pdf | Neural Unification for Logic Reasoning over Natural Language | Automated Theorem Proving (ATP) deals with the development of computer programs being able to show that some conjectures (queries) are a logical consequence of a set of axioms (facts and rules). There exists several successful ATPs where conjectures and axioms are formally provided (e.g. formalised as First Order Logic... | ['Vanessa Lopez Garcia', 'Marco Luca Sbodio', 'Hoang Thanh Lam', 'Gabriele Picco'] | 2021-09-17 | null | https://aclanthology.org/2021.findings-emnlp.331 | https://aclanthology.org/2021.findings-emnlp.331.pdf | findings-emnlp-2021-11 | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 3.24489802e-01 7.69591212e-01 -7.42477179e-03 -3.64300191e-01
-5.28435767e-01 -5.78041434e-01 8.87798846e-01 2.24490970e-01
-4.36264165e-02 9.37956512e-01 -4.17911351e-01 -1.09906924e+00
-2.77934581e-01 -1.45989883e+00 -1.27935660e+00 -4.12712879e-02
-2.11911067e-01 6.18090749e-01 5.57075024e-01 -3.69737297... | [8.955480575561523, 7.070331573486328] |
753e2e56-30c8-4df0-9b5f-facb36dfb20d | cross-modal-image-fusion-theory-guided-by | 1912.10718 | null | https://arxiv.org/abs/1912.10718v1 | https://arxiv.org/pdf/1912.10718v1.pdf | Cross-Modal Image Fusion Theory Guided by Subjective Visual Attention | The human visual perception system has very strong robustness and contextual awareness in a variety of image processing tasks. This robustness and the perception ability of contextual awareness is closely related to the characteristics of multi-task auxiliary learning and subjective attention of the human visual percep... | ['Yanning Zhang', 'Xinbo Zhao', 'Aiqing Fang'] | 2019-12-23 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 1.55444995e-01 -4.23606724e-01 2.11325943e-01 -1.17874309e-01
-1.98253661e-01 1.37502118e-03 3.57815385e-01 -2.25475147e-01
-6.14198208e-01 3.15683246e-01 1.40108138e-01 -2.32224795e-03
-1.67732328e-01 -4.55088407e-01 -3.25730175e-01 -9.54182506e-01
5.06265819e-01 -4.65597838e-01 3.82059991e-01 -3.73487175... | [10.52833080291748, -1.8320146799087524] |
2b76c896-6632-42c2-a96e-04c838884c19 | prompt-as-triggers-for-backdoor-attack | 2305.01219 | null | https://arxiv.org/abs/2305.01219v4 | https://arxiv.org/pdf/2305.01219v4.pdf | Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models | The prompt-based learning paradigm, which bridges the gap between pre-training and fine-tuning, achieves state-of-the-art performance on several NLP tasks, particularly in few-shot settings. Despite being widely applied, prompt-based learning is vulnerable to backdoor attacks. Textual backdoor attacks are designed to i... | ['Jie Fu', 'Junbo Zhao', 'Luu Anh Tuan', 'Jinming Wen', 'Shuai Zhao'] | 2023-05-02 | null | null | null | null | ['backdoor-attack', 'few-shot-text-classification'] | ['adversarial', 'natural-language-processing'] | [ 8.62303078e-02 -3.63362283e-01 -5.46466708e-01 -1.29922450e-01
-1.37516153e+00 -1.30849373e+00 8.13671827e-01 4.66633558e-01
-5.04064918e-01 4.38344628e-01 -2.14072056e-02 -5.11595368e-01
3.06335241e-01 -7.48489738e-01 -7.72139430e-01 -7.46558011e-01
1.63544267e-01 1.99803799e-01 3.30850095e-01 -2.63241231... | [5.977257251739502, 7.850648403167725] |
44f51b78-1eba-4dd4-ba94-707f8c7f41a3 | simon-dravidianlangtech-eacl2021-meme | null | null | https://aclanthology.org/2021.dravidianlangtech-1.41 | https://aclanthology.org/2021.dravidianlangtech-1.41.pdf | Simon @ DravidianLangTech-EACL2021: Meme Classification for Tamil with BERT | In this paper, we introduce the system for the task of meme classification for Tamil, submitted by our team. In today’s society, social media has become an important platform for people to communicate. We use social media to share information about ourselves and express our views on things. It has gradually developed a... | ['Qinyu Que'] | null | null | null | null | eacl-dravidianlangtech-2021-4 | ['meme-classification'] | ['natural-language-processing'] | [-6.84471190e-01 -1.88188046e-01 -5.49804121e-02 -1.26692787e-01
-1.02777883e-01 -3.40308428e-01 6.02713108e-01 5.42272806e-01
-3.86791110e-01 6.32338166e-01 5.49402177e-01 4.78205271e-02
6.58518612e-01 -8.34109843e-01 -5.77996448e-02 -3.32764655e-01
5.27243257e-01 -4.31238152e-02 8.18547755e-02 -1.13061261... | [8.567641258239746, 10.687816619873047] |
75cee697-eb38-4aa8-9103-c562f9377ef5 | guidelines-for-the-regularization-of-gammas | 2205.07260 | null | https://arxiv.org/abs/2205.07260v1 | https://arxiv.org/pdf/2205.07260v1.pdf | Guidelines for the Regularization of Gammas in Batch Normalization for Deep Residual Networks | L2 regularization for weights in neural networks is widely used as a standard training trick. However, L2 regularization for gamma, a trainable parameter of batch normalization, remains an undiscussed mystery and is applied in different ways depending on the library and practitioner. In this paper, we study whether L2 ... | ['Sang Woo Kim', 'Wonseok Jeong', 'Dong Gu Lee', 'Hyeonah Jang', 'Hyeyeon Choi', 'Bum Jun Kim'] | 2022-05-15 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-7.26423860e-02 1.93066761e-01 -2.13532954e-01 -5.34080565e-01
-2.71939367e-01 -5.17663121e-01 5.18578947e-01 -2.46000379e-01
-5.58056414e-01 7.44300246e-01 1.14086494e-01 -4.49777752e-01
-1.08932309e-01 -5.45797944e-01 -6.61737561e-01 -8.57289135e-01
3.60338897e-01 -1.00457057e-01 3.62577826e-01 -1.78776667... | [8.415690422058105, 3.577867031097412] |
c0798fa9-4f5b-46cd-ac35-6db11d658cd4 | zeroth-order-hard-thresholding-gradient-error | 2210.05279 | null | https://arxiv.org/abs/2210.05279v1 | https://arxiv.org/pdf/2210.05279v1.pdf | Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity | $\ell_0$ constrained optimization is prevalent in machine learning, particularly for high-dimensional problems, because it is a fundamental approach to achieve sparse learning. Hard-thresholding gradient descent is a dominant technique to solve this problem. However, first-order gradients of the objective function may ... | ['Bin Gu', 'Xiao-Tong Yuan', 'Huimin Wu', 'Hualin Zhang', 'William de Vazelhes'] | 2022-10-11 | null | null | null | null | ['sparse-learning', 'portfolio-optimization'] | ['methodology', 'time-series'] | [-4.00821492e-02 -2.69004613e-01 -1.78991079e-01 -1.76220313e-01
-1.01201892e+00 -6.27972364e-01 2.74105221e-02 -7.87623599e-02
-4.88612145e-01 8.23208869e-01 -1.61805972e-01 -4.32502151e-01
-4.27389264e-01 -7.44354069e-01 -7.94904530e-01 -1.08553994e+00
-1.67383417e-01 1.94799528e-01 2.24737469e-02 -3.54622334... | [6.704378604888916, 4.422868728637695] |
d9d3c349-3810-4b9e-b89e-0f2a393d7447 | residual-force-control-for-agile-human | 2006.07364 | null | https://arxiv.org/abs/2006.07364v2 | https://arxiv.org/pdf/2006.07364v2.pdf | Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis | Reinforcement learning has shown great promise for synthesizing realistic human behaviors by learning humanoid control policies from motion capture data. However, it is still very challenging to reproduce sophisticated human skills like ballet dance, or to stably imitate long-term human behaviors with complex transitio... | ['Ye Yuan', 'Kris Kitani'] | 2020-06-12 | null | http://proceedings.neurips.cc/paper/2020/hash/f76a89f0cb91bc419542ce9fa43902dc-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/f76a89f0cb91bc419542ce9fa43902dc-Paper.pdf | neurips-2020-12 | ['humanoid-control'] | ['robots'] | [-1.75458163e-01 1.83102086e-01 -2.20205173e-01 4.30697739e-01
-3.81279081e-01 -5.91674924e-01 6.21457100e-01 -6.68880701e-01
-4.53624010e-01 9.87769246e-01 1.20593473e-01 -1.04967661e-01
5.38021810e-02 -5.24945617e-01 -9.96668339e-01 -7.76936591e-01
-3.51783819e-02 6.49786115e-01 3.94749790e-01 -6.63720787... | [4.977664470672607, 0.7964155077934265] |
3d9846d3-48b6-41a7-94ba-e6d062919176 | on-the-universality-of-graph-neural-networks | 2105.13099 | null | https://arxiv.org/abs/2105.13099v2 | https://arxiv.org/pdf/2105.13099v2.pdf | On the Universality of Graph Neural Networks on Large Random Graphs | We study the approximation power of Graph Neural Networks (GNNs) on latent position random graphs. In the large graph limit, GNNs are known to converge to certain "continuous" models known as c-GNNs, which directly enables a study of their approximation power on random graph models. In the absence of input node feature... | ['Samuel Vaiter', 'Alberto Bietti', 'Nicolas Keriven'] | 2021-05-27 | null | http://proceedings.neurips.cc/paper/2021/hash/38181d991caac98be8fb2ecb8bd0f166-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/38181d991caac98be8fb2ecb8bd0f166-Paper.pdf | neurips-2021-12 | ['stochastic-block-model'] | ['graphs'] | [ 1.86365217e-01 6.32364035e-01 -2.06028476e-01 -1.54837640e-02
4.07265089e-02 -7.08002567e-01 6.66085184e-01 1.11628778e-01
4.01925892e-02 6.56550944e-01 -7.55951330e-02 -5.63375056e-01
-4.95818526e-01 -1.17437768e+00 -1.12719822e+00 -7.84129858e-01
-7.25168705e-01 8.32590878e-01 1.90051377e-01 -3.43084186... | [6.861679553985596, 6.137794494628906] |
700ab3fa-82ac-4bf0-8f5e-67224a0828aa | german-arabic-speech-to-speech-translation | null | null | https://aclanthology.org/2020.wanlp-1.1 | https://aclanthology.org/2020.wanlp-1.1.pdf | German-Arabic Speech-to-Speech Translation for Psychiatric Diagnosis | In this paper we present the natural language processing components of our German-Arabic speech-to-speech translation system which is being deployed in the context of interpretation during psychiatric, diagnostic interviews. For this purpose we have built a pipe-lined speech-to-speech translation system consisting of a... | ['Alexander Waibel', 'Sebastian Stüker', 'M. Amin Cheragui', 'Moritz Behr', 'Mohammed Mediani', 'Juan Hussain'] | null | null | null | null | coling-wanlp-2020-12 | ['speech-to-speech-translation'] | ['speech'] | [ 3.42177361e-01 6.56423569e-01 6.01828754e-01 -7.22266197e-01
-1.07412088e+00 -3.72349888e-01 6.21794105e-01 9.70570650e-03
-5.21111488e-01 5.05169690e-01 4.91296083e-01 -7.62898445e-01
1.63757905e-01 -3.59238029e-01 7.78409764e-02 -4.10770714e-01
2.50203848e-01 1.51477277e+00 -3.28612812e-02 -5.83893895... | [14.36467170715332, 7.072574615478516] |
ef8d8ef3-3290-4fc4-86cf-fa2104ae4536 | training-robust-tree-ensembles-for-security | 1912.01149 | null | https://arxiv.org/abs/1912.01149v5 | https://arxiv.org/pdf/1912.01149v5.pdf | Cost-Aware Robust Tree Ensembles for Security Applications | There are various costs for attackers to manipulate the features of security classifiers. The costs are asymmetric across features and to the directions of changes, which cannot be precisely captured by existing cost models based on $L_p$-norm robustness. In this paper, we utilize such domain knowledge to increase the ... | ['Yizheng Chen', 'Shiqi Wang', 'Suman Jana', 'Asaf Cidon', 'Weifan Jiang'] | 2019-12-03 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-8.03801641e-02 -3.80779535e-01 -2.28105068e-01 -3.24056119e-01
-7.87872851e-01 -8.10934603e-01 3.16241622e-01 1.17753908e-01
-3.61891955e-01 4.94992256e-01 -2.12730706e-01 -6.99503243e-01
-1.48998737e-01 -1.10321879e+00 -6.54192865e-01 -7.33374596e-01
-7.76369348e-02 3.78769115e-02 4.13457572e-01 -2.93685466... | [5.743680000305176, 7.545100212097168] |
4cda4ea5-a699-4ccf-bfc0-22d7ede94cc7 | amicron-a-framework-for-generating | 2306.13149 | null | https://arxiv.org/abs/2306.13149v1 | https://arxiv.org/pdf/2306.13149v1.pdf | AmicroN: A Framework for Generating Annotations for Human Activity Recognition with Granular Micro-Activities | Efficient human activity recognition (HAR) using sensor data needs a significant volume of annotated data. The growing volume of unlabelled sensor data has challenged conventional practices for gathering HAR annotations with human-in-the-loop approaches, often leading to the collection of shallower annotations. These s... | ['Sandip Chakraborty', 'Bivas Mitra', 'Soumyajit Chatterjee'] | 2023-06-22 | null | null | null | null | ['activity-recognition', 'human-activity-recognition', 'change-point-detection', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'time-series', 'time-series'] | [ 4.43520427e-01 3.08051080e-01 -1.38322636e-01 -3.26676697e-01
-8.68487597e-01 -4.86139059e-01 6.30763233e-01 5.32814980e-01
-4.34178799e-01 7.59580493e-01 7.06927299e-01 1.88828155e-01
-5.90049513e-02 -6.70783699e-01 -6.54803336e-01 -5.47022283e-01
-2.04824448e-01 4.58986908e-02 4.34702665e-01 -2.92800516... | [7.898184299468994, 0.8634730577468872] |
ab1025c9-02d7-44f4-9353-29fd3e947776 | designing-strong-baselines-for-ternary-neural | 2306.17442 | null | https://arxiv.org/abs/2306.17442v1 | https://arxiv.org/pdf/2306.17442v1.pdf | Designing strong baselines for ternary neural network quantization through support and mass equalization | Deep neural networks (DNNs) offer the highest performance in a wide range of applications in computer vision. These results rely on over-parameterized backbones, which are expensive to run. This computational burden can be dramatically reduced by quantizing (in either data-free (DFQ), post-training (PTQ) or quantizatio... | ['Kevin Bailly', 'Arnaud Dapogny', 'Edouard Yvinec'] | 2023-06-30 | null | null | null | null | ['quantization'] | ['methodology'] | [ 3.92699659e-01 3.57078388e-02 -8.94952342e-02 -4.53726798e-01
-5.42659998e-01 -4.93639439e-01 4.90909278e-01 3.25106919e-01
-8.18924725e-01 7.79044569e-01 -2.39067927e-01 -6.20061696e-01
-2.12426081e-01 -1.07018626e+00 -7.75799572e-01 -8.88548613e-01
3.32447924e-02 2.89229035e-01 2.64845341e-01 -1.26244754... | [8.578161239624023, 3.102931022644043] |
0ba6b611-a971-4871-9456-30abf632986c | inharmonious-region-localization-by | 2209.15368 | null | https://arxiv.org/abs/2209.15368v1 | https://arxiv.org/pdf/2209.15368v1.pdf | Inharmonious Region Localization by Magnifying Domain Discrepancy | Inharmonious region localization aims to localize the region in a synthetic image which is incompatible with surrounding background. The inharmony issue is mainly attributed to the color and illumination inconsistency produced by image editing techniques. In this work, we tend to transform the input image to another co... | ['Teng Long', 'Fengjun Guo', 'Penghao Wu', 'Li Niu', 'Jing Liang'] | 2022-09-30 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 9.28057134e-02 2.40921900e-02 2.37662680e-02 6.25731796e-02
-3.07548434e-01 -6.43647075e-01 3.83043379e-01 -3.80770355e-01
-1.45953938e-01 6.33508563e-01 -2.04380557e-01 -1.12103321e-01
1.79466262e-01 -6.83041573e-01 -6.84066176e-01 -7.50525296e-01
4.45340335e-01 3.69929112e-02 3.71832877e-01 -3.04833829... | [11.233134269714355, -1.2293360233306885] |
2e959b5d-82b5-4e95-8440-eafd88d744eb | solving-statistical-mechanics-using | 1809.10606 | null | http://arxiv.org/abs/1809.10606v2 | http://arxiv.org/pdf/1809.10606v2.pdf | Solving Statistical Mechanics Using Variational Autoregressive Networks | We propose a general framework for solving statistical mechanics of systems
with finite size. The approach extends the celebrated variational mean-field
approaches using autoregressive neural networks, which support direct sampling
and exact calculation of normalized probability of configurations. It computes
variation... | ['Dian Wu', 'Pan Zhang', 'Lei Wang'] | 2018-09-27 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [-7.24269915e-03 2.09606029e-02 1.46758050e-01 -7.26191029e-02
-5.85314333e-01 -2.50782490e-01 9.53783393e-01 -4.47388738e-01
-5.06414115e-01 1.37633598e+00 -4.04102169e-02 -1.63302183e-01
-2.35899925e-01 -9.10470903e-01 -1.00596809e+00 -1.41850805e+00
-3.27121586e-01 8.34450364e-01 -1.44761115e-01 -3.63836765... | [5.745877742767334, 4.8124589920043945] |
4df51ef3-05b5-49ce-945b-49e8a5ded9a2 | using-local-knowledge-graph-construction-to | 1910.08435 | null | https://arxiv.org/abs/1910.08435v1 | https://arxiv.org/pdf/1910.08435v1.pdf | Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs | Query-based open-domain NLP tasks require information synthesis from long and diverse web results. Current approaches extractively select portions of web text as input to Sequence-to-Sequence models using methods such as TF-IDF ranking. We propose constructing a local graph structured knowledge base for each query, whi... | ['Claire Gardent', 'Chloe Braud', 'Angela Fan', 'Antoine Bordes'] | 2019-10-18 | using-local-knowledge-graph-construction-to-1 | https://aclanthology.org/D19-1428 | https://aclanthology.org/D19-1428.pdf | ijcnlp-2019-11 | ['long-form-question-answering'] | ['natural-language-processing'] | [ 7.77943313e-01 5.98975122e-01 -7.78238058e-01 -2.46152461e-01
-1.78609228e+00 -1.13414025e+00 5.01601279e-01 5.63347042e-01
-2.53074110e-01 8.64647210e-01 8.98294091e-01 -3.81743014e-01
-4.14028674e-01 -1.06347930e+00 -1.04939890e+00 3.37676108e-02
-8.51170123e-02 1.14187527e+00 4.08030301e-01 -3.31381619... | [11.269171714782715, 7.928714275360107] |
540d2d36-8b48-440a-a7e1-334fa98eeefd | continual-multimodal-knowledge-graph | 2305.08698 | null | https://arxiv.org/abs/2305.08698v1 | https://arxiv.org/pdf/2305.08698v1.pdf | Continual Multimodal Knowledge Graph Construction | Multimodal Knowledge Graph Construction (MMKC) refers to the process of creating a structured representation of entities and relationships through multiple modalities such as text, images, videos, etc. However, existing MMKC models have limitations in handling the introduction of new entities and relations due to the d... | ['Ningyu Zhang', 'Huajun Chen', 'Luo Si', 'Yongheng Wang', 'Shumin Deng', 'Tongtong Wu', 'Xiaohan Wang', 'Jintian Zhang', 'Xiang Chen'] | 2023-05-15 | null | null | null | null | ['graph-construction', 'relation-extraction'] | ['graphs', 'natural-language-processing'] | [-7.35532399e-03 6.04971610e-02 -4.85723734e-01 2.19340220e-01
-6.62103474e-01 -5.46707869e-01 6.19100273e-01 4.70140964e-01
-2.80878663e-01 9.51993704e-01 2.17997715e-01 -2.34605044e-01
-4.68325257e-01 -8.36449027e-01 -9.52528656e-01 -5.12637436e-01
-2.27785021e-01 4.30147141e-01 2.56249905e-01 -3.15941215... | [8.629716873168945, 7.692735195159912] |
f8ce0ccd-8ece-4864-a24e-7a8a0dd66f8e | learning-character-agnostic-motion-for-motion | 1905.01680 | null | https://arxiv.org/abs/1905.01680v1 | https://arxiv.org/pdf/1905.01680v1.pdf | Learning Character-Agnostic Motion for Motion Retargeting in 2D | Analyzing human motion is a challenging task with a wide variety of applications in computer vision and in graphics. One such application, of particular importance in computer animation, is the retargeting of motion from one performer to another. While humans move in three dimensions, the vast majority of human motions... | ['Daniel Cohen-Or', 'Rundi Wu', 'Kfir Aberman', 'Dani Lischinski', 'Baoquan Chen'] | 2019-05-05 | null | null | null | null | ['motion-retargeting'] | ['computer-vision'] | [ 3.24443281e-01 -2.30648011e-01 -1.66860551e-01 1.55265123e-01
-4.00655478e-01 -8.59656155e-01 6.69739485e-01 -3.66773546e-01
-5.07516921e-01 2.85849452e-01 2.61491239e-01 3.19672748e-02
2.90732086e-01 -3.76429975e-01 -8.16075981e-01 -6.04830682e-01
-1.99722946e-02 2.75619864e-01 3.93480420e-01 -2.52941877... | [7.4815850257873535, -0.6860932111740112] |
333286e1-d0ca-444d-8912-7c997cef431d | macro-average-rare-types-are-important-too | 2104.05700 | null | https://arxiv.org/abs/2104.05700v1 | https://arxiv.org/pdf/2104.05700v1.pdf | Macro-Average: Rare Types Are Important Too | While traditional corpus-level evaluation metrics for machine translation (MT) correlate well with fluency, they struggle to reflect adequacy. Model-based MT metrics trained on segment-level human judgments have emerged as an attractive replacement due to strong correlation results. These models, however, require poten... | ['Jonathan May', 'Constantine Lignos', 'Weiqiu You', 'Thamme Gowda'] | 2021-04-12 | null | https://aclanthology.org/2021.naacl-main.90 | https://aclanthology.org/2021.naacl-main.90.pdf | naacl-2021-4 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [ 2.90424168e-01 -4.21280526e-02 -8.73248816e-01 -6.41383648e-01
-1.51374662e+00 -9.07916129e-01 1.04370654e+00 3.30545038e-01
-5.79414666e-01 9.24608350e-01 4.53190863e-01 -8.83877695e-01
3.52839157e-02 -3.33600581e-01 -3.87610227e-01 -1.45395532e-01
5.29124737e-01 7.94332564e-01 -3.04990083e-01 -2.72011161... | [11.489341735839844, 10.129912376403809] |
309fb648-9704-4d8b-99a5-2697f939e27d | image-clustering-with-optimization-algorithms | null | null | https://doi.org/10.3390/e20040296 | https://www.mdpi.com/1099-4300/20/4/296/htm | Image Clustering with Optimization Algorithms and Color Space | In image clustering, it is desired that pixels assigned in the same class must be the same or similar. In other words, the homogeneity of a cluster must be high. In gray scale image segmentation, the specified goal is achieved by increasing the number of thresholds. However, the determination of multiple thresholds is ... | ['Mohammad-Reza Feizi-Derakhshi', 'Recep Demirci', 'Taymaz Rahkar Farshi'] | 2018-04-18 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 3.17284286e-01 -3.97725761e-01 2.09670842e-01 -1.16442584e-01
6.22485066e-03 -4.46198225e-01 5.96031919e-02 4.62095767e-01
-5.16508460e-01 6.58073545e-01 -5.74978292e-01 -1.99865699e-01
-1.88945815e-01 -1.06689763e+00 -1.40298977e-01 -1.01595461e+00
1.26795620e-01 4.90923285e-01 5.80665708e-01 1.88516125... | [9.460335731506348, -1.5424836874008179] |
4e636306-fa0d-4293-9077-f11157e23df0 | detection-of-clouds-in-multiple-wind-velocity | 2105.03535 | null | https://arxiv.org/abs/2105.03535v3 | https://arxiv.org/pdf/2105.03535v3.pdf | Detection of Clouds in Multiple Wind Velocity Fields using Ground-based Infrared Sky Images | Horizontal atmospheric wind shear causes wind velocity fields to have different directions and speeds. In images of clouds acquired using ground-based sky imagers, clouds may be moving in different wind layers. To increase the performance of an intra-hour global solar irradiance forecasting algorithm, it is important t... | ['Manel Martínez-Ramón', 'Guillermo Terrén-Serrano'] | 2021-05-07 | null | null | null | null | ['solar-irradiance-forecasting'] | ['time-series'] | [-1.89811990e-01 -1.00970459e+00 -1.17235206e-01 -1.70277819e-01
2.84446888e-02 -7.06877947e-01 7.87651777e-01 -1.87220529e-01
-1.47906989e-01 5.06038487e-01 -2.69997567e-01 -3.48583013e-01
-1.55049235e-01 -7.78128564e-01 -5.22001944e-02 -1.36850750e+00
1.71504140e-01 5.00223935e-01 2.55089015e-01 1.64749682... | [9.656396865844727, -1.759629487991333] |
20f6fa0e-fa73-4a94-8e37-e9593fb6098c | active-object-localization-in-visual | 1607.00548 | null | http://arxiv.org/abs/1607.00548v1 | http://arxiv.org/pdf/1607.00548v1.pdf | Active Object Localization in Visual Situations | We describe a method for performing active localization of objects in
instances of visual situations. A visual situation is an abstract
concept---e.g., "a boxing match", "a birthday party", "walking the dog",
"waiting for a bus"---whose image instantiations are linked more by their
common spatial and semantic structure... | ['Anthony D. Rhodes', 'Melanie Mitchell', 'Max H. Quinn'] | 2016-07-02 | null | null | null | null | ['active-object-localization'] | ['computer-vision'] | [ 2.00273022e-01 -1.81909531e-01 -2.17484713e-01 -7.43158817e-01
-9.03631628e-01 -8.03383648e-01 6.57731712e-01 6.39149725e-01
-7.29874909e-01 4.85364258e-01 2.41207957e-01 -8.24223608e-02
-2.09583610e-01 -4.40485686e-01 -7.10825861e-01 -6.89623952e-01
-2.81153977e-01 5.73038757e-01 9.84302044e-01 7.36927167... | [9.688435554504395, 0.8389241099357605] |
769d2f12-a6d7-4da8-832a-c1018ab2242a | noise2music-text-conditioned-music-generation | 2302.03917 | null | https://arxiv.org/abs/2302.03917v2 | https://arxiv.org/pdf/2302.03917v2.pdf | Noise2Music: Text-conditioned Music Generation with Diffusion Models | We introduce Noise2Music, where a series of diffusion models is trained to generate high-quality 30-second music clips from text prompts. Two types of diffusion models, a generator model, which generates an intermediate representation conditioned on text, and a cascader model, which generates high-fidelity audio condit... | ['Zhifeng Chen', 'Wei Han', 'William Chan', 'Quoc V. Le', 'Jesse Engel', 'Christian Frank', 'Jiahui Yu', 'Zhishuai Zhang', 'Zhengdong Zhang', 'Nanxin Chen', 'Andy Ly', 'Timo I. Denk', 'Tao Wang', 'Daniel S. Park', 'Qingqing Huang'] | 2023-02-08 | null | null | null | null | ['text-to-music-generation', 'music-generation', 'music-generation', 'text-to-music-generation'] | ['audio', 'audio', 'music', 'music'] | [ 1.12305604e-01 8.23625252e-02 1.70386329e-01 5.89826237e-03
-9.52852488e-01 -7.24358559e-01 6.73552096e-01 1.69351891e-01
-7.11457804e-02 1.83603629e-01 9.06744540e-01 9.37785730e-02
2.09435880e-01 -8.32409382e-01 -5.82277477e-01 -3.95235658e-01
-5.63261157e-04 3.74549598e-01 3.51249725e-02 -4.99185652... | [15.685785293579102, 5.709201335906982] |
95784d10-ef23-413e-b062-4ca934db6c77 | effectiveness-of-artificial-intelligence-in | 2107.01031 | null | https://arxiv.org/abs/2107.01031v1 | https://arxiv.org/pdf/2107.01031v1.pdf | Effectiveness of Artificial Intelligence in Stock Market Prediction based on Machine Learning | This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are empl... | ['Jin Liu', 'Kang K. Yen', 'Sohrab Mokhtari'] | 2021-06-30 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-4.74224299e-01 1.88112497e-01 -5.52071035e-01 1.86623245e-01
-3.02852213e-01 -5.27301550e-01 9.82122540e-01 3.22200596e-01
-3.60978276e-01 7.26414979e-01 1.52354792e-01 -6.03457689e-01
1.76154271e-01 -1.16840994e+00 -3.78194571e-01 -5.92893004e-01
1.41539216e-01 1.92602217e-01 9.62629728e-03 -6.06346667... | [4.487812042236328, 4.32722806930542] |
aea4e81c-4a0c-4194-9bf1-7ee37baa8219 | fec-fast-euclidean-clustering-for-point-cloud | 2208.07678 | null | https://arxiv.org/abs/2208.07678v2 | https://arxiv.org/pdf/2208.07678v2.pdf | FEC: Fast Euclidean Clustering for Point Cloud Segmentation | Segmentation from point cloud data is essential in many applications such as remote sensing, mobile robots, or autonomous cars. However, the point clouds captured by the 3D range sensor are commonly sparse and unstructured, challenging efficient segmentation. In this paper, we present a fast solution to point cloud ins... | ['Yizhen Lao', 'Huiqing Zhang', 'Yifei Xue', 'Yancheng Wang', 'Yu Cao'] | 2022-08-16 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 1.38001740e-01 -1.74614936e-01 1.03251435e-01 -2.60771155e-01
-6.12379253e-01 -6.57289922e-01 4.23859030e-01 2.88650125e-01
-4.34009165e-01 3.01320583e-01 -7.10412741e-01 -5.89715242e-01
-5.57205528e-02 -1.01718140e+00 -7.20776618e-01 -5.13554990e-01
-1.20297268e-01 9.03812706e-01 7.55008221e-01 -1.28950655... | [7.987198352813721, -2.9096577167510986] |
65863011-3ad5-454a-8ca7-0a40c7143cc2 | multi-branch-learning-for-weakly-labeled | 2002.09661 | null | http://arxiv.org/abs/2002.09661v1 | http://arxiv.org/pdf/2002.09661v1.pdf | Multi-Branch Learning for Weakly-Labeled Sound Event Detection | There are two sub-tasks implied in the weakly-supervised SED: audio tagging
and event boundary detection. Current methods which combine multi-task learning
with SED requires annotations both for these two sub-tasks. Since there are
only annotations for audio tagging available in weakly-supervised SED, we
design multipl... | [] | 2020-02-22 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 5.86803108e-02 1.38477907e-01 -3.95567119e-01 -2.31748551e-01
-1.72841322e+00 -7.59538293e-01 4.42602903e-01 9.84630082e-03
-5.83869219e-01 7.89944947e-01 5.69236040e-01 -1.81835368e-01
-1.74769089e-02 -2.06779405e-01 -6.24363303e-01 -5.37981629e-01
-3.30976337e-01 1.64515719e-01 6.94547772e-01 2.59203702... | [15.220064163208008, 5.119710922241211] |
5afe0773-c767-486f-9d1b-df5cb065bc9a | federated-generalized-face-presentation | 2104.06595 | null | https://arxiv.org/abs/2104.06595v2 | https://arxiv.org/pdf/2104.06595v2.pdf | Federated Generalized Face Presentation Attack Detection | Face presentation attack detection plays a critical role in the modern face recognition pipeline. A face presentation attack detection model with good generalization can be obtained when it is trained with face images from different input distributions and different types of spoof attacks. In reality, training data (bo... | ['Vishal M. Patel', 'Pong C. Yuen', 'Pramuditha Perera', 'Rui Shao'] | 2021-04-14 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [-9.81394108e-03 -3.41708153e-01 -1.51139259e-01 -2.86302269e-01
-6.57082856e-01 -1.05094469e+00 4.06644136e-01 -2.48274475e-01
-1.87470410e-02 4.20338273e-01 -1.13660753e-01 -2.25461453e-01
-1.67485401e-01 -8.95774961e-01 -5.25090575e-01 -1.04641056e+00
-3.32386255e-01 3.76615047e-01 2.24291667e-01 -4.57454398... | [13.055647850036621, 1.164618968963623] |
89a448dc-c835-4a87-9a98-a2224b7e3059 | visual-semantic-re-ranker-for-text-spotting | 1810.09776 | null | http://arxiv.org/abs/1810.09776v2 | http://arxiv.org/pdf/1810.09776v2.pdf | Visual Semantic Re-ranker for Text Spotting | Many current state-of-the-art methods for text recognition are based on
purely local information and ignore the semantic correlation between text and
its surrounding visual context. In this paper, we propose a post-processing
approach to improve the accuracy of text spotting by using the semantic
relation between the t... | ['Lluís Padró', 'Francesc Moreno-Noguer', 'Ahmed Sabir'] | 2018-10-23 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 5.31324089e-01 -2.94490248e-01 2.17858136e-01 -5.53533137e-01
-4.73176658e-01 -4.20821428e-01 1.00220764e+00 6.11407697e-01
-7.09202230e-01 2.47587472e-01 2.76453197e-01 -8.12935159e-02
6.25848025e-02 -7.12351799e-01 -4.44183797e-01 -3.97138506e-01
7.72145808e-01 7.20117748e-01 5.56387067e-01 -1.76275298... | [11.813593864440918, 2.3151469230651855] |
20459f42-4bd8-40ec-ac79-a3d08545f4f3 | attention-is-all-you-need | 1706.03762 | null | http://arxiv.org/abs/1706.03762v5 | http://arxiv.org/pdf/1706.03762v5.pdf | Attention Is All You Need | The dominant sequence transduction models are based on complex recurrent or
convolutional neural networks in an encoder-decoder configuration. The best
performing models also connect the encoder and decoder through an attention
mechanism. We propose a new simple network architecture, the Transformer, based
solely on at... | ['Niki Parmar', 'Lukasz Kaiser', 'Llion Jones', 'Illia Polosukhin', 'Noam Shazeer', 'Jakob Uszkoreit', 'Ashish Vaswani', 'Aidan N. Gomez'] | 2017-06-12 | attention-is-all-you-need-1 | http://papers.nips.cc/paper/7181-attention-is-all-you-need | http://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf | neurips-2017-12 | ['few-shot-3d-point-cloud-classification', 'multimodal-machine-translation'] | ['computer-vision', 'natural-language-processing'] | [ 5.18665493e-01 6.75925910e-02 -1.89653426e-01 -2.36462414e-01
-1.12294602e+00 -6.87849939e-01 6.92425549e-01 -8.38492587e-02
-5.59977591e-01 7.95091927e-01 1.65601760e-01 -1.05568659e+00
6.00617886e-01 -7.51512110e-01 -1.24871874e+00 -4.04750079e-01
1.55891672e-01 7.21104681e-01 -2.41016708e-02 -6.32229090... | [10.880220413208008, 7.416637420654297] |
09091eac-d737-495a-9cc9-af0e6bf8d279 | learning-a-deep-generative-model-like-a | 2011.11063 | null | https://arxiv.org/abs/2011.11063v1 | https://arxiv.org/pdf/2011.11063v1.pdf | Learning a Deep Generative Model like a Program: the Free Category Prior | Humans surpass the cognitive abilities of most other animals in our ability to "chunk" concepts into words, and then combine the words to combine the concepts. In this process, we make "infinite use of finite means", enabling us to learn new concepts quickly and nest concepts within each-other. While program induction ... | ['Eli Sennesh'] | 2020-11-22 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 2.77398795e-01 2.87398309e-01 -6.43625632e-02 -5.66070318e-01
-1.92862511e-01 -6.99842930e-01 8.69187593e-01 4.73199368e-01
-4.59030390e-01 2.91266829e-01 -5.34895472e-02 -1.04515398e+00
1.10424899e-01 -1.23698199e+00 -7.09676206e-01 -2.39955351e-01
-2.91390985e-01 6.76376760e-01 2.92801201e-01 -3.77459377... | [8.95400333404541, 7.038023471832275] |
8f662d38-17d1-42ca-b008-715892f7181a | hierarchical-graph-matching-networks-for-deep | null | null | https://openreview.net/forum?id=rkeqn1rtDH | https://openreview.net/pdf?id=rkeqn1rtDH | Hierarchical Graph Matching Networks for Deep Graph Similarity Learning | While the celebrated graph neural networks yields effective representations for individual nodes of a graph, there has been relatively less success in extending to deep graph similarity learning.
Recent work has considered either global-level graph-graph interactions or low-level node-node interactions, ignoring the r... | ['Shouling Ji', 'Chunming Wu', 'Fangli Xu', 'Tengfei Ma', 'Saizhuo Wang', 'Lingfei Wu', 'Xiang Ling'] | 2019-09-25 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [-8.10265318e-02 4.14731324e-01 -8.04835930e-02 -4.10437465e-01
-3.69270474e-01 -3.92695248e-01 6.32161677e-01 6.91778123e-01
1.05983503e-02 -1.42048985e-01 1.02157881e-02 -2.03180552e-01
-1.67317763e-01 -1.28733921e+00 -7.31366396e-01 -4.94003326e-01
-5.03052473e-01 6.55398726e-01 2.59152800e-01 -2.60516256... | [7.112646579742432, 6.341628074645996] |
d9442f67-61f5-4a67-870c-5c32d1b6587b | indicbart-a-pre-trained-model-for-natural | 2109.02903 | null | https://arxiv.org/abs/2109.02903v2 | https://arxiv.org/pdf/2109.02903v2.pdf | IndicBART: A Pre-trained Model for Indic Natural Language Generation | In this paper, we study pre-trained sequence-to-sequence models for a group of related languages, with a focus on Indic languages. We present IndicBART, a multilingual, sequence-to-sequence pre-trained model focusing on 11 Indic languages and English. IndicBART utilizes the orthographic similarity between Indic scripts... | ['Pratyush Kumar', 'Mitesh M. Khapra', 'Ratish Puduppully', 'Anoop Kunchukuttan', 'Himani Shrotriya', 'Raj Dabre'] | 2021-09-07 | indicbart-a-pre-trained-model-for-indic-1 | https://aclanthology.org/2022.findings-acl.145 | https://aclanthology.org/2022.findings-acl.145.pdf | findings-acl-2022-5 | ['extreme-summarization'] | ['natural-language-processing'] | [ 3.72942537e-01 3.71450745e-02 -3.96039754e-01 -4.51089621e-01
-1.21531904e+00 -9.09146309e-01 5.79409242e-01 -2.03874543e-01
-5.63359320e-01 1.07285738e+00 4.03671265e-01 -9.07579601e-01
4.93228465e-01 -1.89602181e-01 -1.07809019e+00 9.63251218e-02
2.48923942e-01 9.66310084e-01 -6.20500892e-02 -4.77231473... | [11.619880676269531, 10.313365936279297] |
399844e5-cffd-4f3a-b641-4313352ad385 | factored-neus-reconstructing-surfaces | 2305.17929 | null | https://arxiv.org/abs/2305.17929v1 | https://arxiv.org/pdf/2305.17929v1.pdf | Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects | We develop a method that recovers the surface, materials, and illumination of a scene from its posed multi-view images. In contrast to prior work, it does not require any additional data and can handle glossy objects or bright lighting. It is a progressive inverse rendering approach, which consists of three stages. Fir... | ['Yiqun Wang', 'Peter Wonka', 'Evgeny Burnaev', 'Savva Ignatyev', 'Oleg Voynov', 'Ivan Skorokhodov', 'Yue Fan'] | 2023-05-29 | null | null | null | null | ['inverse-rendering'] | ['computer-vision'] | [ 7.75332451e-01 -4.37540978e-01 6.30254567e-01 -4.94568288e-01
-5.98145247e-01 -6.49924755e-01 5.50837159e-01 -7.11121321e-01
-2.56795492e-02 6.22594297e-01 2.81497352e-02 -3.72761860e-02
1.54503351e-02 -7.74630070e-01 -6.49341822e-01 -1.15798712e+00
5.24004817e-01 2.92746693e-01 1.32541686e-01 1.86980888... | [9.825186729431152, -3.0112452507019043] |
ba1b993c-5f58-4cf4-b940-530884a6e90e | unsupervised-discovery-of-object-landmarks-as | 1804.04412 | null | http://arxiv.org/abs/1804.04412v1 | http://arxiv.org/pdf/1804.04412v1.pdf | Unsupervised Discovery of Object Landmarks as Structural Representations | Deep neural networks can model images with rich latent representations, but
they cannot naturally conceptualize structures of object categories in a
human-perceptible way. This paper addresses the problem of learning object
structures in an image modeling process without supervision. We propose an
autoencoding formulat... | ['Yijun Luo', 'Yuting Zhang', 'Zhiyuan He', 'Yijie Guo', 'Honglak Lee', 'Yixin Jin'] | 2018-04-12 | unsupervised-discovery-of-object-landmarks-as-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Unsupervised_Discovery_of_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Unsupervised_Discovery_of_CVPR_2018_paper.pdf | cvpr-2018-6 | ['unsupervised-facial-landmark-detection'] | ['computer-vision'] | [ 2.23768115e-01 5.99577069e-01 -3.27110171e-01 -8.10442924e-01
-4.28673595e-01 -6.16968632e-01 5.68305373e-01 -2.77022179e-02
-7.18777180e-02 3.04347813e-01 2.35353842e-01 -3.14554386e-02
-9.91788879e-02 -1.01007330e+00 -1.21703935e+00 -6.29305720e-01
-3.19604343e-03 6.91952229e-01 -3.02961648e-01 1.92041501... | [9.96692943572998, 0.8669626116752625] |
2a7f058a-11a4-4fb9-8cbc-bb5a17d718c3 | sumbt-slot-utterance-matching-for-universal | 1907.07421 | null | https://arxiv.org/abs/1907.07421v1 | https://arxiv.org/pdf/1907.07421v1.pdf | SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Tracking | In goal-oriented dialog systems, belief trackers estimate the probability distribution of slot-values at every dialog turn. Previous neural approaches have modeled domain- and slot-dependent belief trackers, and have difficulty in adding new slot-values, resulting in lack of flexibility of domain ontology configuration... | ['Tae-Yoon Kim', 'Jinsik Lee', 'Hwaran Lee'] | 2019-07-17 | sumbt-slot-utterance-matching-for-universal-1 | https://aclanthology.org/P19-1546 | https://aclanthology.org/P19-1546.pdf | acl-2019-7 | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-4.62664872e-01 7.94568896e-01 -4.58716959e-01 -7.70731390e-01
-8.49061430e-01 -4.06525910e-01 7.49553382e-01 2.15025723e-01
-3.11942190e-01 9.88135874e-01 4.94870961e-01 -2.00106293e-01
-4.86775069e-03 -6.56277180e-01 -2.21397504e-01 -2.69564033e-01
1.34151235e-01 1.33157301e+00 6.67773187e-01 -7.87254930... | [12.761067390441895, 7.814013481140137] |
ded4a645-84dc-41ab-9056-f56a8fb7538c | understanding-cross-domain-few-shot-learning | 2202.01339 | null | https://arxiv.org/abs/2202.01339v3 | https://arxiv.org/pdf/2202.01339v3.pdf | Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty | Cross-domain few-shot learning (CD-FSL) has drawn increasing attention for handling large differences between the source and target domains--an important concern in real-world scenarios. To overcome these large differences, recent works have considered exploiting small-scale unlabeled data from the target domain during... | ['Se-Young Yun', 'Hwanjun Song', 'Jin-Hwa Kim', 'Namgyu Ho', 'Sungnyun Kim', 'Jaehoon Oh'] | 2022-02-01 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 2.35776931e-01 -9.04668048e-02 -6.43058062e-01 -4.93342906e-01
-8.73139679e-01 -6.18581712e-01 5.11107326e-01 2.31258899e-01
-4.94551092e-01 7.20286608e-01 1.33744687e-01 -1.57762077e-02
-1.46696031e-01 -6.45964622e-01 -3.62420380e-01 -3.28753799e-01
1.11452244e-01 5.84001839e-01 6.21654510e-01 -3.60030264... | [10.144083976745605, 3.108905076980591] |
101624ab-196f-4754-aaf5-3474e72c233f | fine-grained-sentence-functions-for-short | 1907.10302 | null | https://arxiv.org/abs/1907.10302v3 | https://arxiv.org/pdf/1907.10302v3.pdf | Fine-Grained Sentence Functions for Short-Text Conversation | Sentence function is an important linguistic feature referring to a user's purpose in uttering a specific sentence. The use of sentence function has shown promising results to improve the performance of conversation models. However, there is no large conversation dataset annotated with sentence functions. In this work,... | ['Xiaojiang Liu', 'Jun Gao', 'Shuming Shi', 'Wei Bi'] | 2019-07-24 | fine-grained-sentence-functions-for-short-1 | https://aclanthology.org/P19-1389 | https://aclanthology.org/P19-1389.pdf | acl-2019-7 | ['short-text-conversation'] | ['natural-language-processing'] | [ 2.93350607e-01 1.08466774e-01 1.98047891e-01 -9.54645634e-01
-9.99781668e-01 -6.15783751e-01 6.93094611e-01 -2.04197451e-01
-6.82200938e-02 8.25439095e-01 6.97520137e-01 -3.87587637e-01
3.25485379e-01 -6.94626987e-01 -4.62255985e-01 -2.90436834e-01
3.84571850e-01 6.78022027e-01 9.14262757e-02 -4.19053912... | [12.658395767211914, 7.864465713500977] |
cab0db00-7f83-4548-9cf2-c34d2f9fb222 | learnt-deep-hyperparameter-selection-in | 2302.14516 | null | https://arxiv.org/abs/2302.14516v1 | https://arxiv.org/pdf/2302.14516v1.pdf | Learnt Deep Hyperparameter selection in Adversarial Training for compressed video enhancement with perceptual critic | Image based Deep Feature Quality Metrics (DFQMs) have been shown to better correlate with subjective perceptual scores over traditional metrics. The fundamental focus of these DFQMs is to exploit internal representations from a large scale classification network as the metric feature space. Previously, no attention has... | ['Anil Kokaram', 'Darren Ramsook'] | 2023-02-28 | null | null | null | null | ['video-enhancement'] | ['computer-vision'] | [ 4.94089305e-01 2.78210461e-01 2.45122656e-01 -3.06886613e-01
-7.41952956e-01 -2.39830837e-01 5.29334843e-01 -1.63536891e-02
-6.05449557e-01 5.22522271e-01 3.94371361e-01 1.46810502e-01
-2.39716724e-01 -6.80120885e-01 -5.28297007e-01 -8.03861022e-01
-1.06867142e-01 -3.18591475e-01 2.82642603e-01 -1.86638534... | [11.73973560333252, -1.4900919198989868] |
55237073-bcad-4b3e-9e68-2ab76935612e | active-ensemble-deep-learning-for | 2006.15771 | null | https://arxiv.org/abs/2006.15771v1 | https://arxiv.org/pdf/2006.15771v1.pdf | Active Ensemble Deep Learning for Polarimetric Synthetic Aperture Radar Image Classification | Although deep learning has achieved great success in image classification tasks, its performance is subject to the quantity and quality of training samples. For classification of polarimetric synthetic aperture radar (PolSAR) images, it is nearly impossible to annotate the images from visual interpretation. Therefore, ... | ['Sheng-Jie Liu', 'Qian Shi', 'Haowen Luo'] | 2020-06-29 | null | null | null | null | ['multiview-learning'] | ['computer-vision'] | [ 4.38451357e-02 2.39328831e-01 -2.38079876e-01 -5.95813155e-01
-1.09302938e+00 -3.80750507e-01 4.67729509e-01 -5.66800572e-02
-5.10643125e-01 1.02495480e+00 -6.39579967e-02 -2.13879272e-01
-2.32267410e-01 -5.95203817e-01 -3.27684492e-01 -1.43011463e+00
-1.36204123e-01 6.84155345e-01 -1.06618516e-01 3.82825397... | [9.884526252746582, -1.4096084833145142] |
90ce70bd-5ff4-436c-8b01-5b329746c684 | neural-network-for-low-memory-iot-devices-and | 2006.02824 | null | https://arxiv.org/abs/2006.02824v2 | https://arxiv.org/pdf/2006.02824v2.pdf | Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map | This study presents a neural network which uses filters based on logistic mapping (LogNNet). LogNNet has a feedforward network structure, but possesses the properties of reservoir neural networks. The input weight matrix, set by a recurrent logistic mapping, forms the kernels that transform the input space to the highe... | ['Andrei Velichko'] | 2020-06-04 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-5.73495589e-03 -4.14072722e-01 8.12055841e-02 -2.78182570e-02
9.85024631e-01 -1.90429270e-01 3.84169102e-01 -1.31319240e-01
-8.34007919e-01 6.23076737e-01 -3.14577639e-01 -5.15075266e-01
-3.51126432e-01 -1.31420970e+00 -3.87925655e-01 -9.49057043e-01
-2.07404181e-01 1.09393783e-01 4.13496107e-01 -3.92443568... | [8.241424560546875, 2.618175983428955] |
1c7043be-e209-4557-8817-6f9b9952171d | direct-molecular-conformation-generation-1 | 2202.01356 | null | https://arxiv.org/abs/2202.01356v2 | https://arxiv.org/pdf/2202.01356v2.pdf | Direct Molecular Conformation Generation | Molecular conformation generation aims to generate three-dimensional coordinates of all the atoms in a molecule and is an important task in bioinformatics and pharmacology. Previous methods usually first predict the interatomic distances, the gradients of interatomic distances or the local structures (e.g., torsion ang... | ['Haiguang Liu', 'Yusong Wang', 'Tie-Yan Liu', 'Houqiang Li', 'Tao Qin', 'Wengang Zhou', 'Tong Wang', 'Shufang Xie', 'Lijun Wu', 'Chang Liu', 'Yingce Xia', 'Jinhua Zhu'] | 2022-02-03 | direct-molecular-conformation-generation | https://openreview.net/forum?id=kcrIligNnl | https://openreview.net/pdf?id=kcrIligNnl | null | ['molecular-docking'] | ['medical'] | [-1.22043388e-02 -1.03033647e-01 -3.91439170e-01 -3.30130011e-01
-6.68232977e-01 -6.12408698e-01 2.49242380e-01 2.61285424e-01
-1.88669741e-01 1.39840639e+00 1.70698971e-01 -3.44368845e-01
1.30795553e-01 -7.16879487e-01 -8.68806362e-01 -1.21640456e+00
-3.50374877e-02 4.04736698e-01 5.62092029e-02 -3.02474380... | [4.934269905090332, 5.632669448852539] |
2aaaca51-9722-472d-b81a-c6e498b3ca9c | uncertainty-aware-ab3dmot-by-variational-3d | 2302.05923 | null | https://arxiv.org/abs/2302.05923v1 | https://arxiv.org/pdf/2302.05923v1.pdf | Uncertainty-Aware AB3DMOT by Variational 3D Object Detection | Autonomous driving needs to rely on high-quality 3D object detection to ensure safe navigation in the world. Uncertainty estimation is an effective tool to provide statistically accurate predictions, while the associated detection uncertainty can be used to implement a more safe navigation protocol or include the user ... | ['Alexandros Iosifidis', 'Illia Oleksiienko'] | 2023-02-12 | null | null | null | null | ['3d-object-tracking'] | ['computer-vision'] | [-3.11832875e-01 2.16008097e-01 2.60811061e-01 -2.96477646e-01
-5.63144445e-01 -2.96710908e-01 8.43875945e-01 2.14402437e-01
-1.00365639e+00 6.18710816e-01 -2.92853862e-01 -2.44003475e-01
-4.16811928e-02 -7.55346715e-01 -1.11637318e+00 -7.28139043e-01
7.44494647e-02 7.41701543e-01 7.11106479e-01 1.11638337... | [7.752443313598633, -1.1653450727462769] |
29ba31f7-922d-41c8-90c6-bf636ae52ac4 | iterative-prompt-learning-for-unsupervised | 2303.17569 | null | https://arxiv.org/abs/2303.17569v1 | https://arxiv.org/pdf/2303.17569v1.pdf | Iterative Prompt Learning for Unsupervised Backlit Image Enhancement | We propose a novel unsupervised backlit image enhancement method, abbreviated as CLIP-LIT, by exploring the potential of Contrastive Language-Image Pre-Training (CLIP) for pixel-level image enhancement. We show that the open-world CLIP prior not only aids in distinguishing between backlit and well-lit images, but also ... | ['Chen Change Loy', 'Ruicheng Feng', 'Shangchen Zhou', 'Chongyi Li', 'Zhexin Liang'] | 2023-03-30 | null | null | null | null | ['image-enhancement', 'image-manipulation'] | ['computer-vision', 'computer-vision'] | [ 7.22230494e-01 -3.53582591e-01 -5.29666990e-02 -4.09276038e-01
-1.08231378e+00 -4.39464867e-01 4.65404689e-01 -5.60074374e-02
-5.06563604e-01 5.57760358e-01 2.75599927e-01 -7.02113211e-02
-3.14389795e-01 -6.12925947e-01 -7.80371547e-01 -8.32539856e-01
1.94071516e-01 -3.17754388e-01 1.32662743e-01 -5.72380200... | [11.102770805358887, -2.0267996788024902] |
37eeb63b-7c2f-40b5-8581-fa7f54245ef1 | fast-unsupervised-dependency-parsing-with-arc | null | null | https://aclanthology.org/W12-0701 | https://aclanthology.org/W12-0701.pdf | Fast Unsupervised Dependency Parsing with Arc-Standard Transitions | null | ['Mohammad Sadegh Rasooli', 'Heshaam Faili'] | 2012-04-01 | null | null | null | ws-2012-4 | ['dependency-grammar-induction', 'unsupervised-dependency-parsing'] | ['natural-language-processing', '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... | [-7.294206142425537, 3.827517509460449] |
307ce0b7-7667-4ef9-926c-5cd646a627c9 | heterogeneous-graph-contrastive-learning-for | 2303.00995 | null | https://arxiv.org/abs/2303.00995v1 | https://arxiv.org/pdf/2303.00995v1.pdf | Heterogeneous Graph Contrastive Learning for Recommendation | Graph Neural Networks (GNNs) have become powerful tools in modeling graph-structured data in recommender systems. However, real-life recommendation scenarios usually involve heterogeneous relationships (e.g., social-aware user influence, knowledge-aware item dependency) which contains fruitful information to enhance th... | ['Ronghua Luo', 'Yong Xu', 'Wei Wei', 'Lianghao Xia', 'Chao Huang', 'Mengru Chen'] | 2023-03-02 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [-1.46546111e-01 -2.33192965e-02 -7.34558225e-01 -3.10725659e-01
4.89787161e-02 -3.46271217e-01 3.09222817e-01 2.20982462e-01
-4.92644161e-02 5.41647196e-01 6.16492093e-01 -8.98385420e-02
-6.10243797e-01 -1.05937898e+00 -4.80968237e-01 -4.99688089e-01
-4.57030647e-02 4.72672731e-01 2.42243752e-01 -6.88721359... | [10.2185697555542, 5.613656044006348] |
1fe2557e-c3e2-42b3-9018-90a335c51771 | editing-language-model-based-knowledge-graph | 2301.10405 | null | https://arxiv.org/abs/2301.10405v4 | https://arxiv.org/pdf/2301.10405v4.pdf | Editing Language Model-based Knowledge Graph Embeddings | Recently decades have witnessed the empirical success of framing Knowledge Graph (KG) embeddings via language models. However, language model-based KG embeddings are usually deployed as static artifacts, which are challenging to modify without re-training after deployment. To address this issue, we propose a new task o... | ['Huajun Chen', 'Wei Guo', 'Feiyu Xiong', 'Zelin Dai', 'Bozhong Tian', 'Ningyu Zhang', 'Siyuan Cheng'] | 2023-01-25 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-4.07123417e-01 4.49297428e-01 -2.14116544e-01 -1.53561696e-01
-4.73086596e-01 -4.99917388e-01 4.93825197e-01 1.81799963e-01
-6.32102787e-01 7.08912790e-01 5.31476378e-01 -3.18190485e-01
-8.17103460e-02 -9.62359846e-01 -9.59335208e-01 -1.36083871e-01
1.78761911e-02 3.79720837e-01 1.49107426e-01 -3.09767514... | [8.910368919372559, 7.949681282043457] |
eb920e09-d6e3-4e39-b5fe-6f7f20f7f696 | sample-prior-guided-robust-model-learning-to | 2112.01197 | null | https://arxiv.org/abs/2112.01197v3 | https://arxiv.org/pdf/2112.01197v3.pdf | Sample Prior Guided Robust Model Learning to Suppress Noisy Labels | Imperfect labels are ubiquitous in real-world datasets and seriously harm the model performance. Several recent effective methods for handling noisy labels have two key steps: 1) dividing samples into cleanly labeled and wrongly labeled sets by training loss, 2) using semi-supervised methods to generate pseudo-labels f... | ['Tiejun Huang', 'Mengting Li', 'Yi Chen', 'Chuang Zhu', 'Wenkai Chen'] | 2021-12-02 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 3.21799278e-01 2.84764320e-02 1.85837433e-01 -8.90847504e-01
-1.29200673e+00 -3.74507964e-01 3.42763901e-01 -2.01836362e-01
-4.88049865e-01 9.70694721e-01 2.74056911e-01 5.02358675e-01
1.26050875e-01 -7.42005467e-01 -8.38403106e-01 -1.19827986e+00
6.34509683e-01 4.63249385e-01 -1.16601558e-02 1.90425873... | [9.416366577148438, 3.901991128921509] |
cc759f17-99d9-48e6-ba16-8326d88acb6d | the-effect-of-dependency-representation | null | null | https://aclanthology.org/U14-1002 | https://aclanthology.org/U14-1002.pdf | The Effect of Dependency Representation Scheme on Syntactic Language Modelling | null | ['Mark Johnson', 'John Pate', 'Sunghwan Kim'] | 2014-11-01 | the-effect-of-dependency-representation-1 | https://aclanthology.org/U14-1002 | https://aclanthology.org/U14-1002.pdf | alta-2014-11 | ['transition-based-dependency-parsing'] | ['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... | [-7.38780403137207, 3.750426769256592] |
980a5dec-1a3c-4e49-a846-4d7fe3bdebb2 | dialogue-act-classification-for-augmentative | null | null | https://aclanthology.org/2021.nlp4posimpact-1.12 | https://aclanthology.org/2021.nlp4posimpact-1.12.pdf | Dialogue Act Classification for Augmentative and Alternative Communication | Augmentative and Alternative Communication (AAC) devices and applications are intended to make it easier for individuals with complex communication needs to participate in conversations. However, these devices have low adoption and retention rates. We review prior work with text recommendation systems that have not bee... | ['E. Margaret Perkoff'] | null | null | null | null | acl-nlp4posimpact-2021-8 | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 5.34900486e-01 5.67931652e-01 -2.76932865e-01 -5.83586693e-01
-6.63480222e-01 -3.22939575e-01 8.51387441e-01 1.07080542e-01
-4.64567453e-01 1.00056827e+00 1.31210077e+00 -6.03318334e-01
2.62156725e-01 -2.46009648e-01 2.97791928e-01 1.02692179e-01
2.57756263e-01 4.93152738e-01 -2.18879744e-01 -2.87941217... | [12.830787658691406, 7.91858434677124] |
b5e5bf0e-0c02-4f69-8988-349fd0919d16 | learning-generalized-zero-shot-learners-for | 2302.00275 | null | https://arxiv.org/abs/2302.00275v1 | https://arxiv.org/pdf/2302.00275v1.pdf | Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization | Image geolocalization is the challenging task of predicting the geographic coordinates of origin for a given photo. It is an unsolved problem relying on the ability to combine visual clues with general knowledge about the world to make accurate predictions across geographies. We present $\href{https://huggingface.co/ge... | ['Michal Skreta', 'Silas Alberti', 'Lukas Haas'] | 2023-02-01 | null | null | null | null | ['photo-geolocation-estimation', 'generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 1.11953706e-01 1.05624102e-01 -5.24023175e-01 -4.08875734e-01
-1.42062128e+00 -5.79687238e-01 7.73439109e-01 1.40990525e-01
-2.49821216e-01 5.71721911e-01 7.43450880e-01 -4.00631614e-02
2.33142659e-01 -9.01646078e-01 -1.06612396e+00 -3.39176774e-01
1.65421098e-01 3.01478118e-01 8.96593109e-02 -1.61400199... | [7.7120442390441895, -1.7943600416183472] |
94242f2b-7345-4b7e-9dce-d896572c4a33 | inferring-super-resolution-depth-from-a | 1912.06501 | null | https://arxiv.org/abs/1912.06501v1 | https://arxiv.org/pdf/1912.06501v1.pdf | Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach | A novel approach towards depth map super-resolution using multi-view uncalibrated photometric stereo is presented. Practically, an LED light source is attached to a commodity RGB-D sensor and is used to capture objects from multiple viewpoints with unknown motion. This non-static camera-to-object setup is described wit... | ['Lu Sang', 'Daniel Cremers', 'Bjoern Haefner'] | 2019-12-13 | null | null | null | null | ['depth-map-super-resolution'] | ['computer-vision'] | [ 4.82665628e-01 -3.65456529e-02 3.36173862e-01 -3.85612249e-01
-7.16553450e-01 -6.86317265e-01 3.45882863e-01 -5.22843361e-01
-3.74641418e-01 5.73790908e-01 -1.00583412e-01 4.69466001e-01
1.12257093e-01 -3.60986203e-01 -7.43604481e-01 -6.98758304e-01
8.56460631e-01 7.13676989e-01 1.37801155e-01 5.15505634... | [9.289168357849121, -2.8483760356903076] |
f9aed454-25fb-4bcc-bfd6-daa24519e84a | an-optical-physics-inspired-cnn-approach-for | 2105.10076 | null | https://arxiv.org/abs/2105.10076v1 | https://arxiv.org/pdf/2105.10076v1.pdf | An Optical physics inspired CNN approach for intrinsic image decomposition | Intrinsic Image Decomposition is an open problem of generating the constituents of an image. Generating reflectance and shading from a single image is a challenging task specifically when there is no ground truth. There is a lack of unsupervised learning approaches for decomposing an image into reflectance and shading ... | ['Roshan Godaliyadda', 'Vijitha Herath', 'Roshan Ragel', 'Parakrama Ekanayake', 'Suren Sritharan', 'Gihan Jayatilaka', 'Harshana Weligampola'] | 2021-05-21 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 8.86232734e-01 1.90085575e-01 5.54245234e-01 -4.35793370e-01
-6.11168981e-01 -3.21284860e-01 6.36889398e-01 -1.83086842e-01
3.49708982e-02 7.09273040e-01 5.40301651e-02 -3.47451240e-01
-1.28371879e-01 -9.43944812e-01 -7.04765677e-01 -9.83906269e-01
1.77245572e-01 2.37358436e-01 -2.40906589e-02 -2.33789235... | [10.028074264526367, -2.837038516998291] |
93c42b11-219f-4515-a383-cb9d66c325c6 | targeted-attack-on-gpt-neo-for-the-satml | 2302.07735 | null | https://arxiv.org/abs/2302.07735v1 | https://arxiv.org/pdf/2302.07735v1.pdf | Targeted Attack on GPT-Neo for the SATML Language Model Data Extraction Challenge | Previous work has shown that Large Language Models are susceptible to so-called data extraction attacks. This allows an attacker to extract a sample that was contained in the training data, which has massive privacy implications. The construction of data extraction attacks is challenging, current attacks are quite inef... | ['Arie van Deursen', 'Maliheh Izadi', 'Ali Al-Kaswan'] | 2023-02-13 | null | null | null | null | ['inference-attack', 'membership-inference-attack', 'memorization'] | ['adversarial', 'computer-vision', 'natural-language-processing'] | [ 4.66588348e-01 5.05459607e-01 -2.79255390e-01 -1.84829101e-01
-1.29013371e+00 -1.24107194e+00 5.35912037e-01 3.94545019e-01
-5.20296991e-01 7.96827972e-01 -2.01456681e-01 -7.25275576e-01
1.17486276e-01 -8.50349605e-01 -8.91307533e-01 -6.07471287e-01
6.70764744e-02 4.27085429e-01 3.57400894e-01 3.29190254... | [5.906716346740723, 7.330589771270752] |
fb03b681-c856-4c6c-b5ec-f1420cd96ce5 | a-sequential-model-for-classifying-temporal | 1707.07343 | null | http://arxiv.org/abs/1707.07343v1 | http://arxiv.org/pdf/1707.07343v1.pdf | A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events | We present a sequential model for temporal relation classification between
intra-sentence events. The key observation is that the overall syntactic
structure and compositional meanings of the multi-word context between events
are important for distinguishing among fine-grained temporal relations.
Specifically, our appr... | ['Prafulla Kumar Choubey', 'Ruihong Huang'] | 2017-07-23 | a-sequential-model-for-classifying-temporal-1 | https://aclanthology.org/D17-1190 | https://aclanthology.org/D17-1190.pdf | emnlp-2017-9 | ['temporal-relation-classification'] | ['natural-language-processing'] | [ 5.54897785e-01 -1.06096603e-01 -4.52680200e-01 -7.31497467e-01
-3.26630473e-01 -5.66849291e-01 8.73699784e-01 8.69269013e-01
-6.86876893e-01 5.37437260e-01 6.13271415e-01 -4.91914570e-01
-9.04702693e-02 -8.73621643e-01 -3.24977189e-01 -3.51666391e-01
-5.60806990e-01 2.50595480e-01 2.71697372e-01 -3.15075487... | [9.087698936462402, 9.212699890136719] |
ed8c7169-8501-4755-bcbe-1dd56e170f0c | bi-apc-bidirectional-autoregressive | 2102.06816 | null | https://arxiv.org/abs/2102.06816v1 | https://arxiv.org/pdf/2102.06816v1.pdf | Bi-APC: Bidirectional Autoregressive Predictive Coding for Unsupervised Pre-training and Its Application to Children's ASR | We present a bidirectional unsupervised model pre-training (UPT) method and apply it to children's automatic speech recognition (ASR). An obstacle to improving child ASR is the scarcity of child speech databases. A common approach to alleviate this problem is model pre-training using data from adult speech. Pre-trainin... | ['Abeer Alwan', 'Amber Afshan', 'Ruchao Fan'] | 2021-02-12 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 6.67559803e-01 6.01534545e-01 -3.40686649e-01 -5.36977351e-01
-8.06238651e-01 -2.53943235e-01 5.37701249e-01 5.57843372e-02
-5.63555717e-01 4.94790167e-01 4.78754729e-01 -5.34542203e-01
3.62254292e-01 -5.09779155e-01 -6.91808462e-01 -4.27080095e-01
2.48158321e-01 7.29979396e-01 6.87246025e-01 -1.16749540... | [14.42576789855957, 6.698174476623535] |
96f68435-b9cb-4d5b-baef-0225fcc36d11 | quantum-motion-segmentation | 2203.13185 | null | https://arxiv.org/abs/2203.13185v1 | https://arxiv.org/pdf/2203.13185v1.pdf | Quantum Motion Segmentation | Motion segmentation is a challenging problem that seeks to identify independent motions in two or several input images. This paper introduces the first algorithm for motion segmentation that relies on adiabatic quantum optimization of the objective function. The proposed method achieves on-par performance with the stat... | ['Vladislav Golyanik', 'Elisa Ricci', 'Marcel Seelbach Benkner', 'Willi Menapace', 'Federica Arrigoni'] | 2022-03-24 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 6.64807677e-01 7.41295740e-02 -3.32489848e-01 3.54398564e-02
-7.87922084e-01 -7.14794219e-01 5.84336042e-01 -3.68897647e-01
-9.04778004e-01 8.62118781e-01 -5.58578730e-01 -4.03496683e-01
-5.15349209e-02 -3.91086042e-01 -3.86197746e-01 -1.30110466e+00
1.45955617e-03 7.37549484e-01 3.85896802e-01 -2.03153998... | [5.560887813568115, 4.95700740814209] |
62f5e246-4af5-44cf-a79b-1b54f82d7798 | text-anchor-based-metric-learning-for-small | 2108.05516 | null | https://arxiv.org/abs/2108.05516v1 | https://arxiv.org/pdf/2108.05516v1.pdf | Text Anchor Based Metric Learning for Small-footprint Keyword Spotting | Keyword Spotting (KWS) remains challenging to achieve the trade-off between small footprint and high accuracy. Recently proposed metric learning approaches improved the generalizability of models for the KWS task, and 1D-CNN based KWS models have achieved the state-of-the-arts (SOTA) in terms of model size. However, fo... | ['Yuexian Zou', 'Nuo Chen', 'Rongzhi Gu', 'Li Wang'] | 2021-08-12 | null | null | null | null | ['small-footprint-keyword-spotting'] | ['speech'] | [-3.62947285e-01 -2.10734934e-01 -1.73994198e-01 -6.35154724e-01
-1.39499211e+00 -5.07743917e-02 2.76436597e-01 -2.09603265e-01
-4.26113933e-01 2.35431850e-01 2.98710734e-01 -4.42985952e-01
-5.49950562e-02 -2.56842226e-01 -5.39417148e-01 -5.02267122e-01
-1.60082906e-01 2.31340993e-03 2.96444654e-01 -1.81509808... | [14.338940620422363, 6.3025803565979] |
c09d2c9b-533e-4c30-b6a9-7989c8c40173 | explainable-and-position-aware-learning-in | 2306.08198 | null | https://arxiv.org/abs/2306.08198v1 | https://arxiv.org/pdf/2306.08198v1.pdf | Explainable and Position-Aware Learning in Digital Pathology | Encoding whole slide images (WSI) as graphs is well motivated since it makes it possible for the gigapixel resolution WSI to be represented in its entirety for the purpose of graph learning. To this end, WSIs can be broken into smaller patches that represent the nodes of the graph. Then, graph-based learning methods ca... | ['Nasim Yahyasoltani', 'Milan Aryal'] | 2023-06-14 | null | null | null | null | ['whole-slide-images', 'graph-attention', 'graph-learning', 'graph-classification'] | ['computer-vision', 'graphs', 'graphs', 'graphs'] | [ 2.72373348e-01 8.42964888e-01 -3.22998852e-01 -1.06440581e-01
-2.34273031e-01 -1.89893976e-01 4.51584578e-01 7.17339396e-01
1.90494761e-01 4.74616140e-01 2.13324174e-01 -3.89428467e-01
-3.01279575e-01 -1.12156260e+00 -6.30823255e-01 -8.07878554e-01
-1.17850177e-01 2.31124669e-01 4.25302446e-01 -2.14426607... | [15.05794906616211, -2.918736696243286] |
50d6d4f6-01a3-4e94-84fb-286f5144b1bd | is-depth-really-necessary-for-salient-object | 2006.00269 | null | https://arxiv.org/abs/2006.00269v2 | https://arxiv.org/pdf/2006.00269v2.pdf | Is Depth Really Necessary for Salient Object Detection? | Salient object detection (SOD) is a crucial and preliminary task for many computer vision applications, which have made progress with deep CNNs. Most of the existing methods mainly rely on the RGB information to distinguish the salient objects, which faces difficulties in some complex scenarios. To solve this, many rec... | ['Jia-Wei Zhao', 'Yifan Zhao', 'Xiaowu Chen', 'Jia Li'] | 2020-05-30 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 1.79878220e-01 -2.02033333e-02 -1.30667701e-01 -3.89066666e-01
-5.07400215e-01 -6.88137710e-02 3.20768118e-01 -6.49632663e-02
-7.60545254e-01 4.41351086e-01 4.46004048e-02 -1.98212937e-01
8.23240206e-02 -8.90658081e-01 -7.36309111e-01 -1.05714130e+00
4.14188653e-01 -4.48225401e-02 9.94150698e-01 -3.91094804... | [9.649646759033203, -0.8404189348220825] |
82bbf272-1e19-4f8b-a446-fe063d8d636c | on-the-efficacy-of-sampling-adapters | 2307.03749 | null | https://arxiv.org/abs/2307.03749v1 | https://arxiv.org/pdf/2307.03749v1.pdf | On the Efficacy of Sampling Adapters | Sampling is a common strategy for generating text from probabilistic models, yet standard ancestral sampling often results in text that is incoherent or ungrammatical. To alleviate this issue, various modifications to a model's sampling distribution, such as nucleus or top-k sampling, have been introduced and are now u... | ['Ryan Cotterell', 'Ethan G. Wilcox', 'Luca Malagutti', 'Tiago Pimentel', 'Clara Meister'] | 2023-07-07 | null | null | null | null | ['text-generation'] | ['natural-language-processing'] | [ 3.79129529e-01 4.13491637e-01 -2.48954892e-01 -2.90405273e-01
-8.06137383e-01 -9.81301844e-01 8.28850091e-01 1.82506979e-01
-2.47470960e-01 1.05453050e+00 6.23385608e-01 -4.05855238e-01
-2.87853256e-02 -1.10354054e+00 -5.59988379e-01 -5.75009346e-01
5.10821819e-01 6.45187080e-01 1.63664281e-01 -3.37307423... | [11.518348693847656, 9.262063980102539] |
360333ba-a2dc-491b-83b4-e7519ee90d54 | point-to-voxel-knowledge-distillation-for-1 | 2206.02099 | null | https://arxiv.org/abs/2206.02099v1 | https://arxiv.org/pdf/2206.02099v1.pdf | Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation | This article addresses the problem of distilling knowledge from a large teacher model to a slim student network for LiDAR semantic segmentation. Directly employing previous distillation approaches yields inferior results due to the intrinsic challenges of point cloud, i.e., sparsity, randomness and varying density. To ... | ['Yikang Li', 'Chen Change Loy', 'Yuexin Ma', 'Xinge Zhu', 'Yuenan Hou'] | 2022-06-05 | point-to-voxel-knowledge-distillation-for | http://openaccess.thecvf.com//content/CVPR2022/html/Hou_Point-to-Voxel_Knowledge_Distillation_for_LiDAR_Semantic_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Hou_Point-to-Voxel_Knowledge_Distillation_for_LiDAR_Semantic_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['lidar-semantic-segmentation'] | ['computer-vision'] | [-3.11149247e-02 1.04857266e-01 -4.28980052e-01 -3.55990678e-01
-8.71214390e-01 -4.99140799e-01 3.99577856e-01 1.04508683e-01
-2.44538546e-01 7.20675230e-01 -1.66789964e-01 -7.62659758e-02
-3.03954512e-01 -8.77490640e-01 -1.12350976e+00 -8.04604769e-01
2.16112211e-01 1.01500618e+00 5.83310187e-01 1.69749305... | [8.071383476257324, -3.201228380203247] |
4358f131-d0a5-4554-9f57-5977dbfbd3d0 | flow-packet-hybrid-traffic-classification-for | 2105.00074 | null | https://arxiv.org/abs/2105.00074v1 | https://arxiv.org/pdf/2105.00074v1.pdf | Flow-Packet Hybrid Traffic Classification for Class-Aware Network Routing | Network traffic classification using machine learning techniques has been widely studied. Most existing schemes classify entire traffic flows, but there are major limitations to their practicality. At a network router, the packets need to be processed with minimum delay, so the classifier cannot wait until the end of t... | ['Ilijc Albanese', 'Ali Tizghadam', 'Ben Liang', 'Sayantan Chowdhury'] | 2021-04-30 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 1.28040656e-01 -3.74101162e-01 -6.61818326e-01 -5.00800788e-01
-1.33710057e-01 -5.05014956e-01 -4.53389399e-02 8.10721982e-03
-1.53376803e-01 7.19711006e-01 -8.37271690e-01 -1.05859935e+00
-1.17420889e-01 -1.21703577e+00 -2.75282890e-01 -5.62271297e-01
-2.68243939e-01 5.93865454e-01 8.52097511e-01 2.18182847... | [5.04296875, 7.206131458282471] |
33ed2055-4c82-4b49-bff4-9b3906bb9ffb | polito-iit-cini-submission-to-the-epic | 2209.04525 | null | https://arxiv.org/abs/2209.04525v1 | https://arxiv.org/pdf/2209.04525v1.pdf | PoliTO-IIT-CINI Submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition | In this report, we describe the technical details of our submission to the EPIC-Kitchens-100 Unsupervised Domain Adaptation (UDA) Challenge in Action Recognition. To tackle the domain-shift which exists under the UDA setting, we first exploited a recent Domain Generalization (DG) technique, called Relative Norm Alignme... | ['Barbara Caputo', 'Giuseppe Averta', 'Gabriele Trivigno', 'Gabriele Goletto', 'Mirco Planamente'] | 2022-09-09 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 4.93435889e-01 5.41589409e-02 5.42900749e-02 -2.66914636e-01
-7.25784540e-01 -7.25828111e-01 8.30859244e-01 1.20453639e-02
-6.41958773e-01 8.70398641e-01 4.13562089e-01 3.24794874e-02
3.12019896e-04 -5.01226008e-01 -9.27113891e-01 -8.30396473e-01
6.34044111e-02 3.97956759e-01 3.11253428e-01 -4.19988155... | [10.042427062988281, 2.762042284011841] |
c3594bff-af56-4ca5-ad64-54a6e5fc525d | discovering-dialog-structure-graph-for-open | 2012.15543 | null | https://arxiv.org/abs/2012.15543v1 | https://arxiv.org/pdf/2012.15543v1.pdf | Discovering Dialog Structure Graph for Open-Domain Dialog Generation | Learning interpretable dialog structure from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dialog generation. In this paper, we conduct unsupervised discovery of dialog structure from chitchat corpora, and then leverage it to facilitat... | ['Ting Liu', 'Wanxiang Che', 'Hua Wu', 'Zheng-Yu Niu', 'Haifeng Wang', 'Zeyang Lei', 'Jun Xu'] | 2020-12-31 | null | null | null | null | ['open-domain-dialog'] | ['natural-language-processing'] | [-1.10480502e-01 9.08525348e-01 -2.84171663e-02 -7.43468285e-01
-4.71126765e-01 -8.17379832e-01 4.87900943e-01 -3.80599797e-02
2.95585692e-01 6.41416192e-01 9.56943691e-01 -5.05337179e-01
1.49526387e-01 -8.28241825e-01 -3.82067353e-01 -2.42184654e-01
9.09492671e-02 9.53910530e-01 1.19556576e-01 -8.61265123... | [12.672529220581055, 8.055683135986328] |
2cb63d07-a8c8-4d60-a5e9-b39374d1c48f | cross-modal-attribute-insertions-for | 2306.11065 | null | https://arxiv.org/abs/2306.11065v1 | https://arxiv.org/pdf/2306.11065v1.pdf | Cross-Modal Attribute Insertions for Assessing the Robustness of Vision-and-Language Learning | The robustness of multimodal deep learning models to realistic changes in the input text is critical for their applicability to important tasks such as text-to-image retrieval and cross-modal entailment. To measure robustness, several existing approaches edit the text data, but do so without leveraging the cross-modal ... | ['Srijan Kumar', 'Gaurav Verma', 'Shivaen Ramshetty'] | 2023-06-19 | null | null | null | null | ['multimodal-deep-learning'] | ['natural-language-processing'] | [ 1.61087975e-01 -2.97143310e-01 1.82442948e-01 -4.89806056e-01
-1.37463140e+00 -1.11950994e+00 6.54644668e-01 2.12172210e-01
-6.26770556e-01 2.47937649e-01 8.66353586e-02 -1.45094112e-01
1.79756969e-01 -1.93739533e-01 -1.13642991e+00 -5.89536190e-01
2.34354034e-01 4.61403191e-01 -4.30835783e-02 -2.40992740... | [10.921427726745605, 1.3728008270263672] |
2ba0c0f7-ad41-4ae2-9eb8-86425ba93342 | queryinst-parallelly-supervised-mask-query | 2105.01928 | null | https://arxiv.org/abs/2105.01928v3 | https://arxiv.org/pdf/2105.01928v3.pdf | Instances as Queries | Recently, query based object detection frameworks achieve comparable performance with previous state-of-the-art object detectors. However, how to fully leverage such frameworks to perform instance segmentation remains an open problem. In this paper, we present QueryInst (Instances as Queries), a query based instance se... | ['Wenyu Liu', 'Bin Feng', 'Ying Shan', 'Chen Fang', 'Yu Li', 'Xinggang Wang', 'Shusheng Yang', 'Yuxin Fang'] | 2021-05-05 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Fang_Instances_As_Queries_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Fang_Instances_As_Queries_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-instance-segmentation'] | ['computer-vision'] | [-1.89533368e-01 -6.22428656e-02 -4.79596317e-01 -2.94032127e-01
-1.27181172e+00 -7.65848637e-01 3.33392411e-01 2.76948288e-02
-5.75319171e-01 3.44225407e-01 -4.51310068e-01 -2.36106992e-01
1.79149359e-01 -5.66437066e-01 -8.99806380e-01 -3.26698482e-01
1.22316137e-01 6.08424008e-01 1.04078174e+00 1.23881094... | [9.256860733032227, -0.05629350244998932] |
d5aa5863-243e-46a4-9e22-eeb40543fe00 | human-judgement-as-a-compass-to-navigate | 2204.07549 | null | https://arxiv.org/abs/2204.07549v1 | https://arxiv.org/pdf/2204.07549v1.pdf | Human Judgement as a Compass to Navigate Automatic Metrics for Formality Transfer | Although text style transfer has witnessed rapid development in recent years, there is as yet no established standard for evaluation, which is performed using several automatic metrics, lacking the possibility of always resorting to human judgement. We focus on the task of formality transfer, and on the three aspects t... | ['Malvina Nissim', 'Antonio Toral', 'Jiali Mao', 'Huiyuan Lai'] | 2022-04-15 | null | https://aclanthology.org/2022.humeval-1.9 | https://aclanthology.org/2022.humeval-1.9.pdf | humeval-acl-2022-5 | ['text-style-transfoer'] | ['natural-language-processing'] | [ 3.21667522e-01 8.06955621e-02 2.48477887e-02 -3.55892032e-01
-5.69218218e-01 -7.67821014e-01 9.65441942e-01 4.41598207e-01
-7.97887325e-01 7.68778980e-01 4.82338905e-01 -3.42354417e-01
-3.69552284e-01 -4.08874124e-01 -1.48270214e-02 -1.59853041e-01
3.16025734e-01 6.21125281e-01 3.30535233e-01 -4.80130821... | [11.329492568969727, 9.661521911621094] |
a828228a-117c-4aa2-bc9a-6b9d11ebd149 | active-speaker-detection-as-a-multi-objective | 2106.03821 | null | https://arxiv.org/abs/2106.03821v2 | https://arxiv.org/pdf/2106.03821v2.pdf | Active Speaker Detection as a Multi-Objective Optimization with Uncertainty-based Multimodal Fusion | It is now well established from a variety of studies that there is a significant benefit from combining video and audio data in detecting active speakers. However, either of the modalities can potentially mislead audiovisual fusion by inducing unreliable or deceptive information. This paper outlines active speaker dete... | ['Frederic Precioso', 'Charles Bouveyron', 'Leela K. Gudupudi', 'Laurent Pilati', 'Baptiste Pouthier'] | 2021-06-07 | null | null | null | null | ['audio-visual-active-speaker-detection'] | ['computer-vision'] | [ 2.52433568e-01 1.12558194e-01 4.42722924e-02 -3.98669988e-01
-2.11224484e+00 -6.57994747e-01 8.81103098e-01 1.92829236e-01
-3.66322428e-01 7.92600930e-01 4.16963428e-01 3.05877686e-01
-2.51644850e-01 2.26687025e-02 -5.52556515e-01 -1.04678500e+00
-3.45416144e-02 3.10020357e-01 4.03555483e-02 4.11876701... | [14.395960807800293, 5.169036865234375] |
ac613c18-f3dd-40c7-9fcf-609eeb1edcf1 | a-survey-on-few-shot-class-incremental | 2304.08130 | null | https://arxiv.org/abs/2304.08130v1 | https://arxiv.org/pdf/2304.08130v1.pdf | A Survey on Few-Shot Class-Incremental Learning | Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup easily leads... | ['Prayag Tiwari', 'Xin Ning', 'Hang Ran', 'Weijun Li', 'Lusi Li', 'Songsong Tian'] | 2023-04-17 | null | null | null | null | ['class-incremental-learning', 'few-shot-class-incremental-learning'] | ['computer-vision', 'methodology'] | [ 3.35371464e-01 -1.09427884e-01 -4.53618497e-01 -1.62573442e-01
-3.71019781e-01 -3.39168727e-01 4.12783414e-01 1.11570947e-01
-5.94586909e-01 5.99161685e-01 -2.73419589e-01 -8.52127746e-02
-4.63023812e-01 -7.73157537e-01 -6.44675672e-01 -9.19109583e-01
7.46285245e-02 2.93412060e-01 3.97727460e-01 9.62884575... | [9.885627746582031, 3.3180413246154785] |
95f3313f-f360-4d34-9097-f9f2cb2a0f75 | screening-covid-19-cases-using-deep-neural | null | null | https://www.ijariit.com/manuscript/screening-covid-19-cases-using-deep-neural-networks-with-x-ray-images/ | https://www.ijariit.com/manuscripts/v6i3/V6I3-1626.pdf | Screening COVID-19 cases using Deep Neural Networks with X-ray images | descriptionThe novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected pat... | ['Tarit Sengupta'] | 2020-11-24 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [ 4.47717980e-02 -3.03040862e-01 -9.35440045e-03 -4.81201597e-02
-3.16603124e-01 -5.44123530e-01 1.58187792e-01 3.12316746e-01
-6.46472692e-01 6.74011469e-01 -1.67724714e-01 -8.73821080e-01
-1.18340693e-01 -8.11639369e-01 -2.16070682e-01 -7.49957263e-01
-1.74832612e-01 8.17500532e-01 2.29743034e-01 2.02836599... | [15.56712532043457, -1.697908878326416] |
31a8d993-85dc-417a-8446-26b72b898b6f | on-learning-the-structure-of-clusters-in | 2212.14345 | null | https://arxiv.org/abs/2212.14345v1 | https://arxiv.org/pdf/2212.14345v1.pdf | On Learning the Structure of Clusters in Graphs | Graph clustering is a fundamental problem in unsupervised learning, with numerous applications in computer science and in analysing real-world data. In many real-world applications, we find that the clusters have a significant high-level structure. This is often overlooked in the design and analysis of graph clustering... | ['Peter Macgregor'] | 2022-12-29 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 2.50584900e-01 1.41437531e-01 -3.33648384e-01 -2.90161669e-01
9.94710028e-02 -7.53856242e-01 4.54036713e-01 6.58704698e-01
-3.27305049e-01 4.43746656e-01 -2.58947760e-01 -6.17266238e-01
-5.79489768e-01 -8.66566479e-01 -3.71820003e-01 -7.80068755e-01
-7.37633348e-01 9.29013968e-01 2.58540809e-01 1.72522098... | [7.039214134216309, 5.30800199508667] |
14be7a27-8e4b-4ee8-9620-f15cf3764137 | legal-prompting-teaching-a-language-model-to | 2212.01326 | null | https://arxiv.org/abs/2212.01326v2 | https://arxiv.org/pdf/2212.01326v2.pdf | Legal Prompting: Teaching a Language Model to Think Like a Lawyer | Large language models that are capable of zero or few-shot prompting approaches have given rise to the new research area of prompt engineering. Recent advances showed that for example Chain-of-Thought (CoT) prompts can improve arithmetic or common sense tasks significantly. We explore how such approaches fare with lega... | ['Frank Schilder', 'Lee Quartey', 'FangYi Yu'] | 2022-12-02 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [-1.30165026e-01 4.21243429e-01 -2.29891688e-01 -5.03393769e-01
-1.14758956e+00 -5.53883374e-01 9.95772243e-01 3.98271352e-01
-5.28335989e-01 8.54173958e-01 4.62220103e-01 -9.66268063e-01
-5.95461905e-01 -6.58210576e-01 -7.29335189e-01 -3.58616225e-02
4.05805171e-01 4.26215351e-01 6.03878736e-01 -5.97898722... | [9.733686447143555, 7.417537212371826] |
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