paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a52cb6ff-97bc-48d4-8827-e864666f5bf8 | unsupervised-learning-of-foreground-object | 1808.04593 | null | http://arxiv.org/abs/1808.04593v1 | http://arxiv.org/pdf/1808.04593v1.pdf | Unsupervised learning of foreground object detection | Unsupervised learning poses one of the most difficult challenges in computer
vision today. The task has an immense practical value with many applications in
artificial intelligence and emerging technologies, as large quantities of
unlabeled videos can be collected at relatively low cost. In this paper, we
address the u... | ['Marius Leordeanu', 'Simion-Vlad Bogolin', 'Ioana Croitoru'] | 2018-08-14 | null | null | null | null | ['object-discovery-in-videos'] | ['computer-vision'] | [ 7.50106990e-01 2.64700830e-01 -2.30221942e-01 -3.26072156e-01
-4.49472129e-01 -3.05891871e-01 3.90867651e-01 3.03807437e-01
-6.53481781e-01 5.98704636e-01 -2.59161949e-01 -1.50390312e-01
-1.15764156e-01 -6.69854164e-01 -9.76669133e-01 -8.60115767e-01
-8.09417069e-02 6.40487850e-01 9.81616557e-01 2.44729757... | [9.372625350952148, 0.6844771504402161] |
e13d04a7-729f-436c-8f7e-32e143df3383 | a-study-of-the-complexity-and-accuracy-of | 1811.11787 | null | http://arxiv.org/abs/1811.11787v1 | http://arxiv.org/pdf/1811.11787v1.pdf | A Study of the Complexity and Accuracy of Direction of Arrival Estimation Methods Based on GCC-PHAT for a Pair of Close Microphones | This paper investigates the accuracy of various Generalized Cross-Correlation
with Phase Transform (GCC-PHAT) methods for a close pair of microphones. We
investigate interpolation-based methods and also propose another approach based
on Singular Value Decomposition (SVD). All investigated methods are implemented
in C c... | [] | 2018-11-28 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [-5.20762801e-02 -4.78097796e-01 2.75884241e-01 -5.56234606e-02
-1.03257072e+00 -4.19094354e-01 1.75710380e-01 4.56656478e-02
-3.21476430e-01 9.53863263e-01 -1.64078400e-02 -5.20633817e-01
-3.29537868e-01 -4.48862433e-01 -2.01769769e-01 -8.84741306e-01
-3.99989516e-01 -2.53596097e-01 2.90389955e-01 -2.30714887... | [15.158811569213867, 5.62785005569458] |
315caf8d-bb93-44c5-a266-dff1b54b9a9d | operational-neural-networks-for-efficient | 2303.16636 | null | https://arxiv.org/abs/2303.16636v1 | https://arxiv.org/pdf/2303.16636v1.pdf | Operational Neural Networks for Efficient Hyperspectral Single-Image Super-Resolution | Hyperspectral Imaging is a crucial tool in remote sensing which captures far more spectral information than standard color images. However, the increase in spectral information comes at the cost of spatial resolution. Super-resolution is a popular technique where the goal is to generate a high-resolution version of a g... | ['Nour Aburaed', 'Mehmet Yamac', 'Serkan Kiranyaz', 'Moncef Gabbouj', 'Stephen Marshall', 'Paul Murray', 'Alexander Ulrichsen'] | 2023-03-29 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 6.67012632e-01 -3.02485079e-01 2.64473975e-01 -4.17342424e-01
-4.22421634e-01 -4.10390705e-01 4.42279756e-01 -4.08300281e-01
-3.46153766e-01 9.12844956e-01 -2.92504672e-02 -1.41009882e-01
-3.84402782e-01 -1.13664627e+00 -6.25990331e-01 -1.02835536e+00
-3.34511288e-02 -1.94122031e-01 6.39823452e-02 -6.24184489... | [10.185128211975098, -1.953848958015442] |
a5d4b58a-865c-41cb-9d4d-2ff28964668b | test-time-adaptation-with-perturbation | 2304.12764 | null | https://arxiv.org/abs/2304.12764v1 | https://arxiv.org/pdf/2304.12764v1.pdf | Test-Time Adaptation with Perturbation Consistency Learning | Currently, pre-trained language models (PLMs) do not cope well with the distribution shift problem, resulting in models trained on the training set failing in real test scenarios. To address this problem, the test-time adaptation (TTA) shows great potential, which updates model parameters to suit the test data at the t... | ['Min Zhang', 'Hai Ye', 'Juntao Li', 'Yixin Ji', 'Yi Su'] | 2023-04-25 | null | null | null | null | ['pseudo-label'] | ['miscellaneous'] | [-3.30174975e-02 -3.41162123e-02 -4.02848005e-01 -3.84254575e-01
-1.10186219e+00 -5.87628722e-01 6.31803036e-01 -2.01027051e-01
-1.73904151e-01 8.91521513e-01 -2.09035844e-01 -5.31980038e-01
9.32242051e-02 -5.01189768e-01 -8.72690499e-01 -8.02338183e-01
5.67834377e-02 7.78568804e-01 3.38588476e-01 -2.93647170... | [9.80423355102539, 3.161245107650757] |
79ed1a54-84a7-44b6-8e87-5061115e3bdc | perturbation-checklists-for-evaluating-nlg | 2109.05771 | null | https://arxiv.org/abs/2109.05771v1 | https://arxiv.org/pdf/2109.05771v1.pdf | Perturbation CheckLists for Evaluating NLG Evaluation Metrics | Natural Language Generation (NLG) evaluation is a multifaceted task requiring assessment of multiple desirable criteria, e.g., fluency, coherency, coverage, relevance, adequacy, overall quality, etc. Across existing datasets for 6 NLG tasks, we observe that the human evaluation scores on these multiple criteria are oft... | ['Mitesh M. Khapra', 'Sreyas Mohan', 'Dev Yashpal Sheth', 'Tanay Dixit', 'Ananya B. Sai'] | 2021-09-13 | null | https://aclanthology.org/2021.emnlp-main.575 | https://aclanthology.org/2021.emnlp-main.575.pdf | emnlp-2021-11 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 3.03489387e-01 2.62662560e-01 -7.05430866e-04 -3.67818743e-01
-7.78342009e-01 -9.82665002e-01 8.10427189e-01 6.61274731e-01
-4.36258763e-01 8.55899334e-01 3.36937934e-01 -3.95766079e-01
-3.65189672e-01 -6.54900193e-01 -1.65439516e-01 -1.76877588e-01
3.46218705e-01 5.83461225e-01 1.19918361e-01 -3.08119327... | [11.699055671691895, 9.050028800964355] |
c4cc76d7-d134-4b81-9c99-34420cccf76c | i3d-transformer-architectures-with-input | 2303.07624 | null | https://arxiv.org/abs/2303.07624v1 | https://arxiv.org/pdf/2303.07624v1.pdf | I3D: Transformer architectures with input-dependent dynamic depth for speech recognition | Transformer-based end-to-end speech recognition has achieved great success. However, the large footprint and computational overhead make it difficult to deploy these models in some real-world applications. Model compression techniques can reduce the model size and speed up inference, but the compressed model has a fixe... | ['Shinji Watanabe', 'Jaesong Lee', 'Yifan Peng'] | 2023-03-14 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 2.37492099e-01 6.71142340e-02 -3.28138024e-01 -5.65278172e-01
-7.79274344e-01 -4.14393455e-01 3.44246984e-01 -3.04697067e-01
-3.80570978e-01 3.54616672e-01 2.90574908e-01 -1.04493093e+00
2.59118080e-02 -7.68463254e-01 -6.93401992e-01 -3.72635663e-01
-5.61524928e-02 4.11934882e-01 3.61958146e-01 -5.88582009... | [8.761962890625, 3.5463738441467285] |
7dc9bada-6d0d-4105-bd83-0aa09b610509 | encrypted-internet-traffic-classification | 2101.09818 | null | https://arxiv.org/abs/2101.09818v2 | https://arxiv.org/pdf/2101.09818v2.pdf | Encrypted Internet traffic classification using a supervised Spiking Neural Network | Internet traffic recognition is an essential tool for access providers since recognizing traffic categories related to different data packets transmitted on a network help them define adapted priorities. That means, for instance, high priority requirements for an audio conference and low ones for a file transfer, to en... | ['Timothée Masquelier', 'Saeed Bagheri Shouraki', 'Romain Zimmer', 'Carlos Aguilar-Melchor', 'Florian Delpech', 'Ali Rasteh'] | 2021-01-24 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 3.21422458e-01 -3.19418252e-01 -3.33556682e-01 -1.82092577e-01
-1.19153015e-01 -4.00374413e-01 3.78977269e-01 1.96445942e-01
-7.10898638e-01 9.16973948e-01 -5.89230478e-01 -6.55394077e-01
-3.06258023e-01 -9.15185928e-01 -6.29408062e-01 -8.31172168e-01
-8.53040516e-02 1.94873765e-01 6.91573560e-01 -2.32404806... | [5.064972877502441, 7.26100492477417] |
82725b0a-e95b-49a9-8ac9-34675f16e308 | fedward-flexible-federated-backdoor-defense | 2307.00356 | null | https://arxiv.org/abs/2307.00356v1 | https://arxiv.org/pdf/2307.00356v1.pdf | Fedward: Flexible Federated Backdoor Defense Framework with Non-IID Data | Federated learning (FL) enables multiple clients to collaboratively train deep learning models while considering sensitive local datasets' privacy. However, adversaries can manipulate datasets and upload models by injecting triggers for federated backdoor attacks (FBA). Existing defense strategies against FBA consider ... | ['Yujie Lin', 'Ximeng Liu', 'Zhiwei Zheng', 'Fuyi Wang', 'Zekai Chen'] | 2023-07-01 | null | null | null | null | ['clustering'] | ['methodology'] | [-3.02679688e-01 -2.77888209e-01 -5.40670343e-02 -2.93684453e-01
-8.04941535e-01 -1.08069217e+00 4.58669722e-01 -5.21872699e-01
-3.12488198e-01 5.06348312e-01 -2.23146930e-01 -6.10616922e-01
-3.65295261e-01 -7.79398739e-01 -6.82863295e-01 -1.17455971e+00
-1.98120847e-01 6.85407370e-02 7.67609701e-02 -7.24702002... | [5.764414310455322, 7.1522440910339355] |
47c67e5a-6ccf-4a66-8058-61d1a2ef4260 | ultra-large-alignments-using-phylogeny-aware | 1504.01142 | null | http://arxiv.org/abs/1504.01142v1 | http://arxiv.org/pdf/1504.01142v1.pdf | Ultra-large alignments using Phylogeny-aware Profiles | Many biological questions, including the estimation of deep evolutionary
histories and the detection of remote homology between protein sequences, rely
upon multiple sequence alignments (MSAs) and phylogenetic trees of large
datasets. However, accurate large-scale multiple sequence alignment is very
difficult, especial... | ['Nam-phuong Nguyen', 'Keerthana Kumar', 'Tandy Warnow', 'Siavash Mirarab'] | 2015-04-05 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 4.76587921e-01 -3.28913212e-01 -3.63410115e-01 -2.30418324e-01
-1.05197990e+00 -7.80910015e-01 1.40207738e-01 2.22188756e-01
-4.23190445e-01 1.32024801e+00 -2.34816179e-01 -6.10676944e-01
2.11845726e-01 -3.19092005e-01 -7.03195989e-01 -1.09169459e+00
1.53329559e-02 8.95817697e-01 6.04464352e-01 -1.49833754... | [4.8284173011779785, 5.2508625984191895] |
1b9c80ba-131d-4b41-9952-c807af120def | single-stage-3d-geometry-preserving-depth-1 | 2306.02878 | null | https://arxiv.org/abs/2306.02878v1 | https://arxiv.org/pdf/2306.02878v1.pdf | Single-Stage 3D Geometry-Preserving Depth Estimation Model Training on Dataset Mixtures with Uncalibrated Stereo Data | Nowadays, robotics, AR, and 3D modeling applications attract considerable attention to single-view depth estimation (SVDE) as it allows estimating scene geometry from a single RGB image. Recent works have demonstrated that the accuracy of an SVDE method hugely depends on the diversity and volume of the training data. H... | ['Anton Konushin', 'Mikhail Artemyev', 'Anna Vorontsova', 'Mikhail Romanov', 'Nikolay Patakin'] | 2023-06-05 | single-stage-3d-geometry-preserving-depth | http://openaccess.thecvf.com//content/CVPR2022/html/Patakin_Single-Stage_3D_Geometry-Preserving_Depth_Estimation_Model_Training_on_Dataset_Mixtures_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Patakin_Single-Stage_3D_Geometry-Preserving_Depth_Estimation_Model_Training_on_Dataset_Mixtures_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-reconstruction'] | ['computer-vision'] | [ 2.38618627e-01 8.27815905e-02 2.75193751e-01 -2.06183881e-01
-7.91692078e-01 -6.39974356e-01 3.67829442e-01 -3.50249708e-01
-2.29422122e-01 4.22017872e-01 -2.39683583e-01 -8.80171657e-02
-2.94263475e-02 -9.79356587e-01 -1.20960951e+00 -7.23760128e-01
3.24971199e-01 7.49520957e-01 3.39655489e-01 -2.73802936... | [8.636092185974121, -2.8672704696655273] |
39869557-07f4-4fc2-a3d7-641f56c18828 | 171010324 | 1710.10324 | null | http://arxiv.org/abs/1710.10324v3 | http://arxiv.org/pdf/1710.10324v3.pdf | Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties | The use of machine learning methods for accelerating the design of
crystalline materials usually requires manually constructed feature vectors or
complex transformation of atom coordinates to input the crystal structure,
which either constrains the model to certain crystal types or makes it
difficult to provide chemica... | ['Tian Xie', 'Jeffrey C. Grossman'] | 2017-10-27 | crystal-graph-convolutional-neural-networks | null | null | phys-rev-lett-2017-10 | ['formation-energy'] | ['miscellaneous'] | [ 9.69033688e-02 1.21897804e-02 -5.34011841e-01 -4.79691088e-01
-3.81465018e-01 -3.88430268e-01 3.63560617e-01 2.52172112e-01
-1.54975310e-01 1.12973094e+00 2.02041760e-01 -4.37779754e-01
-5.00250384e-02 -1.17062771e+00 -9.37149763e-01 -1.02589715e+00
-2.02600062e-01 7.96379864e-01 -1.92938969e-01 -2.61179924... | [5.174755573272705, 5.469464302062988] |
b6ae70c2-1dd0-4dc2-b2d4-b1b66c0cf59d | knowledge-based-conversational-search | 1912.06859 | null | https://arxiv.org/abs/1912.06859v1 | https://arxiv.org/pdf/1912.06859v1.pdf | Knowledge-based Conversational Search | Conversational interfaces that allow for intuitive and comprehensive access to digitally stored information remain an ambitious goal. In this thesis, we lay foundations for designing conversational search systems by analyzing the requirements and proposing concrete solutions for automating some of the basic components ... | ['Svitlana Vakulenko'] | 2019-12-14 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 1.69010296e-01 5.24873972e-01 1.11733072e-01 -2.42623597e-01
-2.77339637e-01 -8.11890960e-01 8.12508643e-01 4.27601278e-01
-3.95950109e-01 6.69575512e-01 4.48880196e-01 -5.95786035e-01
-8.83914471e-01 -8.17081630e-01 2.15828955e-01 3.31690498e-02
2.77648300e-01 7.69841015e-01 5.13612390e-01 -1.06930721... | [12.231697082519531, 7.814239978790283] |
84363d50-cdea-4786-8a0f-c5e20bb659aa | does-vision-accelerate-hierarchical | 2302.00667 | null | https://arxiv.org/abs/2302.00667v1 | https://arxiv.org/pdf/2302.00667v1.pdf | Does Vision Accelerate Hierarchical Generalization of Neural Language Learners? | Neural language models (LMs) are arguably less data-efficient than humans -- why does this gap occur? In this study, we hypothesize that this gap stems from the learners' accessibility to modalities other than text, specifically, vision. We conducted two complementary experiments (using noisy, realistic data and a simp... | ['Tatsuki Kuribayashi'] | 2023-02-01 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [-8.93595815e-02 9.57206562e-02 1.21155344e-01 -2.19755813e-01
-6.94989145e-01 -6.86617613e-01 5.97058535e-01 4.50807631e-01
-1.09935033e+00 5.35787344e-01 3.38989824e-01 -7.36398101e-01
4.16364782e-02 -5.69500685e-01 -8.83419037e-01 -4.42588657e-01
3.51044655e-01 1.82233617e-01 1.37390837e-01 -3.27644706... | [10.113667488098145, 8.507742881774902] |
737e9f0f-25fa-47a5-b91d-90aa095f6502 | enhanced-distribution-modelling-via-augmented | 2306.02731 | null | https://arxiv.org/abs/2306.02731v1 | https://arxiv.org/pdf/2306.02731v1.pdf | Enhanced Distribution Modelling via Augmented Architectures For Neural ODE Flows | While the neural ODE formulation of normalizing flows such as in FFJORD enables us to calculate the determinants of free form Jacobians in O(D) time, the flexibility of the transformation underlying neural ODEs has been shown to be suboptimal. In this paper, we present AFFJORD, a neural ODE-based normalizing flow which... | ['Marco Lorenzi', 'Etrit Haxholli'] | 2023-06-05 | null | null | null | null | ['density-estimation'] | ['methodology'] | [-4.40837324e-01 7.61561692e-02 1.78940073e-01 -2.39157006e-01
1.90384969e-01 -6.77851856e-01 8.07244003e-01 -4.62875724e-01
-5.64812899e-01 7.87148714e-01 2.93768257e-01 -4.43605095e-01
-1.72677830e-01 -4.79647279e-01 -7.48620570e-01 -7.18320191e-01
-2.62249231e-01 1.72480062e-01 -1.24045454e-01 -3.43823314... | [7.0681538581848145, 3.7107388973236084] |
7a267f7f-affe-490e-ae08-f3639ff57f35 | simple-text-detoxification-by-identifying-a | 2112.08346 | null | https://arxiv.org/abs/2112.08346v1 | https://arxiv.org/pdf/2112.08346v1.pdf | Simple Text Detoxification by Identifying a Linear Toxic Subspace in Language Model Embeddings | Large pre-trained language models are often trained on large volumes of internet data, some of which may contain toxic or abusive language. Consequently, language models encode toxic information, which makes the real-world usage of these language models limited. Current methods aim to prevent toxic features from appear... | ['Yangfeng Ji', 'Mohit Sudhakar', 'Andrew Wang'] | 2021-12-15 | null | null | null | null | ['abusive-language'] | ['natural-language-processing'] | [ 2.37970129e-01 -1.48306310e-01 -1.49452522e-01 7.92071372e-02
-6.06795728e-01 -9.17222619e-01 7.00937212e-01 -3.00999600e-02
-2.31519863e-01 7.28503406e-01 6.27850056e-01 -3.09206367e-01
1.04618110e-01 -6.22460008e-01 -5.96824527e-01 -8.31586182e-01
2.09529977e-02 1.84805185e-01 -2.28112593e-01 -3.11730534... | [11.557031631469727, 9.312578201293945] |
4fda3822-d63e-4561-8327-892ecc3259a1 | grounded-language-learning-from-video | null | null | https://aclanthology.org/P13-1006 | https://aclanthology.org/P13-1006.pdf | Grounded Language Learning from Video Described with Sentences | null | ['Haonan Yu', 'Jeffrey Mark Siskind'] | 2013-08-01 | null | null | null | acl-2013-8 | ['grounded-language-learning'] | ['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... | [-7.266048908233643, 3.689340353012085] |
07a59574-6c2c-4196-85b8-9561f3731f82 | figaro-generating-symbolic-music-with-fine | 2201.10936 | null | https://arxiv.org/abs/2201.10936v3 | https://arxiv.org/pdf/2201.10936v3.pdf | FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control | Generating music with deep neural networks has been an area of active research in recent years. While the quality of generated samples has been steadily increasing, most methods are only able to exert minimal control over the generated sequence, if any. We propose the self-supervised description-to-sequence task, which... | ['Thomas Hofmann', 'Yannic Kilcher', 'Luca Biggio', 'Dimitri von Rütte'] | 2022-01-26 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 6.22220039e-01 1.63961411e-01 -3.13911945e-01 -7.32591897e-02
-1.07306755e+00 -8.36534142e-01 9.56328213e-01 -1.51216805e-01
-3.95219401e-02 9.80877161e-01 5.74546337e-01 3.92835498e-01
-7.98680633e-02 -8.94016743e-01 -1.07052088e+00 -7.31660068e-01
7.49725010e-03 9.08212841e-01 -2.00916559e-01 -4.75907952... | [15.91377067565918, 5.661720275878906] |
9894312d-1d1c-4d31-a23d-cd7be6223b9d | counting-to-explore-and-generalize-in-text | 1806.11525 | null | http://arxiv.org/abs/1806.11525v2 | http://arxiv.org/pdf/1806.11525v2.pdf | Counting to Explore and Generalize in Text-based Games | We propose a recurrent RL agent with an episodic exploration mechanism that
helps discovering good policies in text-based game environments. We show
promising results on a set of generated text-based games of varying difficulty
where the goal is to collect a coin located at the end of a chain of rooms. In
contrast to p... | ['Marc-Alexandre Côté', 'Xingdi Yuan', 'Alessandro Sordoni', 'Romain Laroche', 'Remi Tachet des Combes', 'Adam Trischler', 'Matthew Hausknecht'] | 2018-06-29 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [-2.11110443e-01 4.31442976e-01 -2.86622234e-02 2.38896698e-01
-6.49320543e-01 -6.16597354e-01 5.33385634e-01 -8.21874663e-02
-6.79454029e-01 1.40347552e+00 3.14180285e-01 -3.45813006e-01
-3.39076608e-01 -8.92174184e-01 -4.58167583e-01 -5.92492044e-01
-4.46703881e-01 1.08378410e+00 1.04214288e-01 -7.75718868... | [3.7916176319122314, 1.5787612199783325] |
c185bd7a-cd30-4970-97ff-847236b51747 | ltrc-mup-2022-multi-perspective-scientific | null | null | https://aclanthology.org/2022.sdp-1.35 | https://aclanthology.org/2022.sdp-1.35.pdf | LTRC @MuP 2022: Multi-Perspective Scientific Document Summarization Using Pre-trained Generation Models | The MuP-2022 shared task focuses on multiperspective scientific document summarization. Given a scientific document, with multiple reference summaries, our goal was to develop a model that can produce a generic summary covering as many aspects of the document as covered by all of its reference summaries. This paper des... | ['Manish Shrivastava', 'Nirmal Surange', 'Ashok Urlana'] | null | null | null | null | sdp-coling-2022-10 | ['scientific-article-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.03750452e-01 3.83380592e-01 -3.69030088e-01 -7.74634955e-03
-1.80660617e+00 -1.08670831e+00 9.49506283e-01 4.30316955e-01
2.34304164e-02 1.32071459e+00 1.03115559e+00 -1.22950077e-01
-2.19200090e-01 -2.97575921e-01 -6.31321192e-01 -3.38507593e-01
1.40669063e-01 6.38341665e-01 -1.14474729e-01 -1.21510796... | [12.406808853149414, 9.536107063293457] |
f00675fc-3a91-4f56-b669-959030e6a7ce | mastering-nordschleife-a-comprehensive-race | 2306.16088 | null | https://arxiv.org/abs/2306.16088v1 | https://arxiv.org/pdf/2306.16088v1.pdf | Mastering Nordschleife -- A comprehensive race simulation for AI strategy decision-making in motorsports | In the realm of circuit motorsports, race strategy plays a pivotal role in determining race outcomes. This strategy focuses on the timing of pit stops, which are necessary due to fuel consumption and tire performance degradation. The objective of race strategy is to balance the advantages of pit stops, such as tire rep... | ['David Klotz', 'Max Boettinger'] | 2023-06-28 | null | null | null | null | ['decision-making'] | ['reasoning'] | [-5.91484345e-02 -9.45451483e-02 -7.08341718e-01 8.34789574e-02
-5.33924460e-01 -5.63918769e-01 4.58132565e-01 -5.27602881e-02
-8.06867480e-01 9.01125968e-01 9.04398113e-02 -1.02276254e+00
-8.66839468e-01 -8.83351326e-01 -4.11825120e-01 -6.05990946e-01
-8.17770734e-02 6.26912713e-01 1.50875691e-02 -4.68622357... | [4.789804458618164, 1.7871798276901245] |
ef15bf62-59ef-4d96-a163-a46386e2c343 | deep-traffic-sign-detection-and-recognition | 2008.00962 | null | https://arxiv.org/abs/2008.00962v1 | https://arxiv.org/pdf/2008.00962v1.pdf | Deep Traffic Sign Detection and Recognition Without Target Domain Real Images | Deep learning has been successfully applied to several problems related to autonomous driving, often relying on large databases of real target-domain images for proper training. The acquisition of such real-world data is not always possible in the self-driving context, and sometimes their annotation is not feasible. Mo... | ['Thiago Oliveira-Santos', 'Claudine Badue', 'Alberto F. de Souza', 'Thiago M. Paixão', 'Lucas Tabelini', 'Rodrigo Berriel', 'Nicu Sebe'] | 2020-07-30 | null | null | null | null | ['traffic-sign-detection'] | ['computer-vision'] | [ 2.55431086e-01 -5.40259629e-02 2.84226704e-02 -3.48662049e-01
-7.95632422e-01 -1.49676427e-01 7.04349875e-01 -3.31650466e-01
-8.06898296e-01 7.84674525e-01 -4.78792369e-01 -2.48734072e-01
1.29996985e-01 -7.40125656e-01 -8.13861310e-01 -7.40713000e-01
4.19759035e-01 8.07392418e-01 5.19296348e-01 -4.11466300... | [7.9833550453186035, -0.9008894562721252] |
1be93c7b-d2bc-457d-842d-ed053e55ec30 | migrant-laborer-s-optimization-mechanism | 2306.08829 | null | https://arxiv.org/abs/2306.08829v1 | https://arxiv.org/pdf/2306.08829v1.pdf | Migrant Laborer's Optimization Mechanism Under Employment Permit System(EPS): Introducing and Analyzing 'Skill-Relevance-Self Selection' Model | Migrant laborers subject to ROK's Employment Permit System(EPS) must strike a balance between host country's high wage and 'Depreciation of skill-relevance entailed by immigration', whilst taking account of the 'migration costs'. This study modelizes the optimization mechanism of migrant workers and the firms hiring th... | ['Sunghyun Cho', 'Yejin Lim', 'Kwonhyung Lee'] | 2023-06-15 | null | null | null | null | ['jurisprudence'] | ['miscellaneous'] | [-2.67161757e-01 3.26073229e-01 -1.02789009e+00 3.84060532e-01
2.18166113e-02 -4.87863958e-01 4.17707711e-01 -1.39549479e-01
-7.18339622e-01 9.73213911e-01 4.40980941e-01 -1.26017606e+00
-7.24822223e-01 -8.15574229e-01 -5.21703213e-02 -6.20500147e-01
-1.12800494e-01 3.36613774e-01 -5.62038541e-01 -6.43973589... | [7.9259257316589355, 5.218021392822266] |
0592ba6f-47e0-4c57-99fa-624bba8e73f8 | altitude-loss-optimal-glides-in-engine | 2304.06499 | null | https://arxiv.org/abs/2304.06499v1 | https://arxiv.org/pdf/2304.06499v1.pdf | Altitude-Loss Optimal Glides in Engine Failure Emergencies -- Accounting for Ground Obstacles and Wind | Engine failure is a recurring emergency in General Aviation and fixed-wing UAVs, often requiring the pilot or remote operator to carry out carefully planned glides to safely reach a candidate landing strip. We tackle the problem of minimizing the altitude loss of a thrustless aircraft flying towards a designated target... | ['Nahum Shimkin', 'Aharon Bar-Gill', 'Daniel Segal'] | 2023-04-13 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [ 1.56624869e-01 4.32930402e-02 2.91208088e-01 2.38439903e-01
-1.22919455e-01 -1.08307540e+00 2.07259282e-01 1.47290707e-01
-3.82546604e-01 8.47597480e-01 -4.94878590e-01 -9.64310169e-01
-9.47997928e-01 -8.21894228e-01 -4.21957672e-01 -5.09313703e-01
-8.40061903e-01 4.38183874e-01 1.64849713e-01 -8.11227620... | [5.160151958465576, 1.9639641046524048] |
4a687c40-5304-45cf-84c2-8894f47bc8ff | an-effective-system-for-multi-format | 2108.06957 | null | https://arxiv.org/abs/2108.06957v1 | https://arxiv.org/pdf/2108.06957v1.pdf | An Effective System for Multi-format Information Extraction | The multi-format information extraction task in the 2021 Language and Intelligence Challenge is designed to comprehensively evaluate information extraction from different dimensions. It consists of an multiple slots relation extraction subtask and two event extraction subtasks that extract events from both sentence-lev... | ['Feiliang Ren', 'Xiaofeng Zhao', 'Shujuan Yin', 'Longhui Zhang', 'Yaduo Liu'] | 2021-08-16 | null | null | null | null | ['document-level-event-extraction'] | ['natural-language-processing'] | [ 1.19505696e-01 4.00824934e-01 -3.54727864e-01 -2.78889745e-01
-1.36295021e+00 -5.90978503e-01 7.28125215e-01 5.57919145e-01
-6.41176462e-01 7.91924775e-01 5.48716724e-01 -3.57571721e-01
2.67331693e-02 -7.95291543e-01 -5.47305763e-01 -2.47978419e-01
-3.51840910e-03 2.93118834e-01 4.97242242e-01 -1.72845662... | [9.074853897094727, 9.160219192504883] |
1fc1c185-54a0-432c-abad-a5585a55988e | system-status-aware-adaptive-network-for | 2303.15742 | null | https://arxiv.org/abs/2303.15742v2 | https://arxiv.org/pdf/2303.15742v2.pdf | System-status-aware Adaptive Network for Online Streaming Video Understanding | Recent years have witnessed great progress in deep neural networks for real-time applications. However, most existing works do not explicitly consider the general case where the device's state and the available resources fluctuate over time, and none of them investigate or address the impact of varying computational re... | ['Jun Liu', 'Zhipeng Fan', 'Jia Gong', 'Lin Geng Foo'] | 2023-03-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Foo_System-Status-Aware_Adaptive_Network_for_Online_Streaming_Video_Understanding_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Foo_System-Status-Aware_Adaptive_Network_for_Online_Streaming_Video_Understanding_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-understanding'] | ['computer-vision'] | [-5.69357611e-02 -3.07835519e-01 -4.65284616e-01 -4.44489419e-01
-6.47918805e-02 -4.31963384e-01 2.32950062e-01 -7.06943078e-03
-5.42780101e-01 5.55643559e-01 -5.65031528e-01 -5.23749292e-01
-2.66199578e-02 -5.07979631e-01 -9.02487934e-01 -6.86844647e-01
-2.11321518e-01 6.15569174e-01 7.36650348e-01 -5.94841354... | [8.127653121948242, 2.5700855255126953] |
446f0ae8-f63d-408f-9eaf-9df8a3bedd92 | pre-clustering-point-clouds-of-crop-fields | 2107.10950 | null | https://arxiv.org/abs/2107.10950v2 | https://arxiv.org/pdf/2107.10950v2.pdf | Pre-Clustering Point Clouds of Crop Fields Using Scalable Methods | In order to apply the recent successes of machine learning and automated plant phenotyping on a large scale using agricultural robotics, efficient and general algorithms must be designed to intelligently split crop fields into small, yet actionable, portions that can then be processed by more complex algorithms. In thi... | ['Nikolaos Papanikolopoulos', 'Henry J. Nelson'] | 2021-07-22 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 3.73242378e-01 4.72599454e-02 -2.40972579e-01 -2.05494389e-01
1.92917287e-02 -7.33933747e-01 9.76421088e-02 6.27118170e-01
-3.84865403e-02 5.57794690e-01 -7.07782626e-01 -5.72100401e-01
-6.07887447e-01 -1.04125953e+00 -4.16877747e-01 -8.00122976e-01
-1.84043884e-01 8.36930156e-01 5.52811265e-01 -1.89961597... | [9.124791145324707, -1.5136395692825317] |
bbc1766a-712e-415d-87b8-75497ca43c56 | are-large-language-models-good-evaluators-for | 2305.13091 | null | https://arxiv.org/abs/2305.13091v1 | https://arxiv.org/pdf/2305.13091v1.pdf | Are Large Language Models Good Evaluators for Abstractive Summarization? | Human evaluations are often required for abstractive summary evaluations to give fairer judgments. However, they are often time-consuming, costly, inconsistent, and non-reproducible. To overcome these challenges, we explore the potential of using an out-of-the-box LLM (i.e. "gpt-3.5-turbo") for summarization evaluation... | ['Lidong Bing', 'Yang You', 'Liying Cheng', 'Chenhui Shen'] | 2023-05-22 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [-2.65100598e-01 -2.31712341e-01 -3.54791939e-01 -5.26225626e-01
-1.27117491e+00 -9.97588992e-01 4.20478493e-01 5.75744510e-01
-5.28512776e-01 6.68422222e-01 5.95095992e-01 -3.53481501e-01
-2.03101680e-01 -2.99345344e-01 -3.81823163e-04 -1.48866326e-01
2.54000694e-01 1.85617685e-01 1.97099715e-01 -3.11659306... | [11.880840301513672, 9.079368591308594] |
2e5a2911-2286-4c27-b6c9-f3be497439fb | lgpma-complicated-table-structure-recognition | 2105.06224 | null | https://arxiv.org/abs/2105.06224v3 | https://arxiv.org/pdf/2105.06224v3.pdf | LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment | Table structure recognition is a challenging task due to the various structures and complicated cell spanning relations. Previous methods handled the problem starting from elements in different granularities (rows/columns, text regions), which somehow fell into the issues like lossy heuristic rules or neglect of empty ... | ['Fei Wu', 'Wenming Tan', 'Wenqi Ren', 'Yi Niu', 'ShiLiang Pu', 'Peng Zhang', 'Zhanzhan Cheng', 'Zaisheng Li', 'Liang Qiao'] | 2021-05-13 | null | null | null | null | ['table-recognition'] | ['computer-vision'] | [ 5.36436252e-02 -3.00701290e-01 -4.49979812e-01 -1.04747415e-01
-1.00970042e+00 -6.02793634e-01 1.45373538e-01 4.43295926e-01
7.72628486e-02 7.90238500e-01 3.57775152e-01 4.45577428e-02
-3.56427543e-02 -8.75991106e-01 -6.39340401e-01 -7.17197359e-01
9.37011838e-02 5.99956870e-01 5.84044397e-01 -1.38443872... | [11.715700149536133, 3.040271043777466] |
cd0b632a-d5b5-4adc-aed2-a87f78378d55 | residual-attention-based-network-for | 2107.08425 | null | https://arxiv.org/abs/2107.08425v1 | https://arxiv.org/pdf/2107.08425v1.pdf | Residual Attention Based Network for Automatic Classification of Phonation Modes | Phonation mode is an essential characteristic of singing style as well as an important expression of performance. It can be classified into four categories, called neutral, breathy, pressed and flow. Previous studies used voice quality features and feature engineering for classification. While deep learning has achieve... | ['Wei Li', 'Yiliang Jiang', 'Xiaoheng Sun'] | 2021-07-18 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [-7.87499398e-02 -5.43972194e-01 -2.06211403e-01 1.18032888e-01
-5.24065733e-01 -3.49602163e-01 2.69333422e-01 -3.07800800e-01
-2.38196895e-01 3.26634079e-01 3.70034754e-01 1.02652662e-01
-2.36393243e-01 -5.68655670e-01 -4.00965028e-02 -7.51669228e-01
5.16479313e-02 2.24942937e-02 -9.81910825e-02 -2.68108547... | [15.812041282653809, 5.277843475341797] |
a3ec2b38-3bc8-46be-bda1-2748f5e90b8e | mental-arithmetic-task-classification-with | 2209.11767 | null | https://arxiv.org/abs/2209.11767v2 | https://arxiv.org/pdf/2209.11767v2.pdf | Mental arithmetic task classification with convolutional neural network based on spectral-temporal features from EEG | In recent years, neuroscientists have been interested to the development of brain-computer interface (BCI) devices. Patients with motor disorders may benefit from BCIs as a means of communication and for the restoration of motor functions. Electroencephalography (EEG) is one of most used for evaluating the neuronal act... | ['Stephane Perrey', 'Jacky Montmain', 'Gérard Dray', 'Binbin Xu', 'Zaineb Ajra'] | 2022-09-26 | null | null | null | null | ['mental-arithmetic-task'] | ['medical'] | [-1.48055609e-02 -1.61715850e-01 3.28150779e-01 -5.69212735e-02
-7.00112358e-02 1.11013271e-01 4.15991992e-01 -3.46525997e-01
-7.79449224e-01 1.19990766e+00 -3.44722383e-02 -9.92724597e-02
-4.03095901e-01 -3.37058634e-01 -3.65635991e-01 -8.32864642e-01
-3.86911958e-01 1.63339600e-01 2.94965357e-01 -3.61250430... | [13.080723762512207, 3.4244930744171143] |
09478734-98e9-49e9-a9d5-8555b06a1356 | document-level-event-factuality-1 | null | null | https://aclanthology.org/2022.coling-1.231 | https://aclanthology.org/2022.coling-1.231.pdf | Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning | Document-level Event Factuality Identification (DEFI) predicts the factuality of a specific event based on a document from which the event can be derived, which is a fundamental and crucial task in Natural Language Processing (NLP). However, most previous studies only considered sentence-level task and did not adopt do... | ['Guodong Zhou', 'Qiaoming Zhu', 'Peifeng Li', 'Heng Zhang', 'Zhong Qian'] | null | null | null | null | coling-2022-10 | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 4.79363412e-01 1.43837348e-01 -3.39797318e-01 -2.79871851e-01
-1.04103661e+00 -5.00792146e-01 9.73015964e-01 6.91828072e-01
-7.95704067e-01 1.02321851e+00 6.43610537e-01 -4.16171134e-01
-1.50570218e-02 -8.73844326e-01 -8.33388269e-01 -2.91202307e-01
2.19948471e-01 3.27846497e-01 2.58133978e-01 -2.91985035... | [9.22923469543457, 9.103246688842773] |
15294011-f762-4518-bc1c-9d965a4a2501 | musika-fast-infinite-waveform-music | 2208.08706 | null | https://arxiv.org/abs/2208.08706v1 | https://arxiv.org/pdf/2208.08706v1.pdf | Musika! Fast Infinite Waveform Music Generation | Fast and user-controllable music generation could enable novel ways of composing or performing music. However, state-of-the-art music generation systems require large amounts of data and computational resources for training, and are slow at inference. This makes them impractical for real-time interactive use. In this w... | ['Jan Schlüter', 'Marco Pasini'] | 2022-08-18 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 2.43688449e-01 -7.52538219e-02 2.05418125e-01 2.77830154e-01
-8.71139407e-01 -1.09231603e+00 4.62669551e-01 -5.06579101e-01
1.30349606e-01 5.87125838e-01 5.66921309e-02 -1.60138398e-01
2.04114035e-01 -9.37310398e-01 -6.41885400e-01 -7.59191036e-01
9.27270949e-02 5.92062235e-01 -3.02372277e-01 -3.29612911... | [15.78361701965332, 5.7667155265808105] |
8ec02935-a7fa-481c-994d-923d99726dea | gh-feat-learning-versatile-generative | 2301.05315 | null | https://arxiv.org/abs/2301.05315v1 | https://arxiv.org/pdf/2301.05315v1.pdf | GH-Feat: Learning Versatile Generative Hierarchical Features from GANs | Recent years witness the tremendous success of generative adversarial networks (GANs) in synthesizing photo-realistic images. GAN generator learns to compose realistic images and reproduce the real data distribution. Through that, a hierarchical visual feature with multi-level semantics spontaneously emerges. In this w... | ['Bolei Zhou', 'Ceyuan Yang', 'Jiapeng Zhu', 'Yujun Shen', 'Yinghao Xu'] | 2023-01-12 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 5.61988592e-01 3.90176088e-01 1.41792325e-02 -4.91993457e-01
-8.47403944e-01 -6.73717499e-01 7.84864664e-01 -4.67342347e-01
2.44611830e-01 6.75019443e-01 9.15780365e-02 1.38728783e-01
-8.79771113e-02 -1.01461470e+00 -1.22458673e+00 -7.84263313e-01
4.61946964e-01 4.50521588e-01 -1.94389150e-01 -3.01556319... | [11.623066902160645, -0.41631606221199036] |
64874b0f-5f70-4b6d-b2ca-adb6dae7cbae | rcurrency-live-digital-asset-trading-using-a | 2106.06972 | null | https://arxiv.org/abs/2106.06972v1 | https://arxiv.org/pdf/2106.06972v1.pdf | RCURRENCY: Live Digital Asset Trading Using a Recurrent Neural Network-based Forecasting System | Consistent alpha generation, i.e., maintaining an edge over the market, underpins the ability of asset traders to reliably generate profits. Technical indicators and trading strategies are commonly used tools to determine when to buy/hold/sell assets, yet these are limited by the fact that they operate on known values.... | ['Jan S. Rellermeyer', 'Hugo Kooijman', 'Ashay Somai', 'Ralph van Gurp', 'Yapeng Jasper Hu'] | 2021-06-13 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [-4.39080179e-01 -3.51029903e-01 -6.26493618e-02 4.91934009e-02
-1.82616308e-01 -7.30684102e-01 3.99029464e-01 -1.21796876e-01
-2.59195387e-01 1.06073821e+00 -4.12794501e-01 -4.78415996e-01
-2.44113803e-01 -1.24492729e+00 -3.00813586e-01 -6.57352984e-01
-3.53205442e-01 6.93335712e-01 2.18925148e-01 -6.93839848... | [4.485717296600342, 3.994112253189087] |
a094f0dc-1b93-4759-8e9d-de62d1a9cede | hypernetworks-for-zero-shot-transfer-in | 2211.15457 | null | https://arxiv.org/abs/2211.15457v2 | https://arxiv.org/pdf/2211.15457v2.pdf | Hypernetworks for Zero-shot Transfer in Reinforcement Learning | In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objective and data from a set of near-optimal RL solutions for training tasks. This work relates to meta RL, contextual RL, and transfer learning, with a particular focus on zero-shot p... | ['David Meger', 'Gregory Dudek', 'Francois Robert Hogan', 'Charlotte Morissette', 'Sahand Rezaei-Shoshtari'] | 2022-11-28 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 2.73700655e-01 4.04055297e-01 -4.42342579e-01 -6.81709349e-02
-1.15071368e+00 -4.49570388e-01 7.89272785e-01 -2.41609469e-01
-6.99069083e-01 1.11344230e+00 1.49204865e-01 -4.04933468e-02
-2.20597222e-01 -3.34429473e-01 -1.00394094e+00 -7.58421838e-01
-2.10766241e-01 9.49653506e-01 -5.59304319e-02 -3.53357047... | [4.153185844421387, 1.774062991142273] |
f29df373-5ba0-4d83-9a97-46e6c270be79 | alignerf-high-fidelity-neural-radiance-fields | 2211.09682 | null | https://arxiv.org/abs/2211.09682v1 | https://arxiv.org/pdf/2211.09682v1.pdf | AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training | Neural Radiance Fields (NeRFs) are a powerful representation for modeling a 3D scene as a continuous function. Though NeRF is able to render complex 3D scenes with view-dependent effects, few efforts have been devoted to exploring its limits in a high-resolution setting. Specifically, existing NeRF-based methods face s... | ['Tianfan Xue', 'Zhangyang Wang', 'Jonathan T. Barron', 'Dejia Xu', 'Ben Mildenhall', 'Peter Hedman', 'Yifan Jiang'] | 2022-11-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jiang_AligNeRF_High-Fidelity_Neural_Radiance_Fields_via_Alignment-Aware_Training_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jiang_AligNeRF_High-Fidelity_Neural_Radiance_Fields_via_Alignment-Aware_Training_CVPR_2023_paper.pdf | cvpr-2023-1 | ['camera-calibration'] | ['computer-vision'] | [ 3.11429858e-01 -2.51946837e-01 2.81617671e-01 -4.89825755e-01
-7.29067683e-01 -3.68641227e-01 5.30735970e-01 -4.49261606e-01
-2.80420482e-01 5.97264230e-01 2.29812741e-01 -2.80292362e-01
-2.08946422e-01 -9.25896585e-01 -9.43664610e-01 -6.05129063e-01
2.89137177e-02 4.03569676e-02 1.31537348e-01 -1.20419353... | [9.702332496643066, -2.8014869689941406] |
3853a2fe-2dd5-494a-b352-ecbf4103c2e1 | deep-data-flow-analysis-1 | 2012.01470 | null | https://arxiv.org/abs/2012.01470v1 | https://arxiv.org/pdf/2012.01470v1.pdf | Deep Data Flow Analysis | Compiler architects increasingly look to machine learning when building heuristics for compiler optimization. The promise of automatic heuristic design, freeing the compiler engineer from the complex interactions of program, architecture, and other optimizations, is alluring. However, most machine learning methods cann... | ["Michael O'Boyle", 'Torsten Hoefler', 'Tal Ben-Nun', 'Zacharias Fisches', 'Hugh Leather', 'Chris Cummins'] | 2020-11-21 | deep-data-flow-analysis | https://openreview.net/forum?id=SPhswbiXpJQ | https://openreview.net/pdf?id=SPhswbiXpJQ | null | ['compiler-optimization'] | ['computer-code'] | [-1.12643346e-01 1.49964206e-02 -1.12955344e+00 -6.13623381e-01
-8.26516688e-01 -8.74598145e-01 2.29346842e-01 5.84639132e-01
5.75221404e-02 3.46001834e-01 4.94278580e-01 -1.42537558e+00
1.71255156e-01 -7.55307138e-01 -9.01241243e-01 -9.90661047e-03
-2.47130811e-01 2.22565129e-01 -2.45358303e-01 -3.37036431... | [7.784160614013672, 7.541549205780029] |
0fd50d3c-59b8-4fea-8b2d-077a9444aa74 | squarified-treemaps | null | null | https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.36.6685 | https://www.win.tue.nl/~vanwijk/stm.pdf | Squarified Treemaps | An extension to the treemap method for the visualization of hierarchical information, such as directory structures and organization structures, is presented. The standard treemap method often gives thin, elongated rectangles. As a result, rectangles are difficult to compare and to select. A new method is presented to g... | ['Jarke J. van Wijk', 'Kees Huizing', 'Mark Bruls'] | 1999-10-01 | null | null | null | ieee-tcvg-symposium-on-visualization-1999-10 | ['tree-map-layout'] | ['graphs'] | [-3.29121679e-01 2.79554874e-01 1.37418762e-01 -2.77489573e-01
1.52465835e-01 -7.47775495e-01 2.54430294e-01 8.04460824e-01
2.69677311e-01 9.14813042e-01 5.10221958e-01 -8.43591332e-01
-2.05253765e-01 -7.79484451e-01 2.70047486e-02 -1.72604352e-01
-3.81033838e-01 1.31102011e-01 3.68956506e-01 -2.94834524... | [7.986160755157471, 4.6995368003845215] |
3384146f-3ccb-4e34-8b7f-f41f226ecdb0 | wildfire-forecasting-with-satellite-images | 2208.09411 | null | https://arxiv.org/abs/2208.09411v2 | https://arxiv.org/pdf/2208.09411v2.pdf | Wildfire Forecasting with Satellite Images and Deep Generative Model | Wildfire forecasting has been one of the most critical tasks that humanities want to thrive. It plays a vital role in protecting human life. Wildfire prediction, on the other hand, is difficult because of its stochastic and chaotic properties. We tackled the problem by interpreting a series of wildfire images as a vide... | ['Chris Schmidt', 'Sang Truong', 'Thai-Nam Hoang'] | 2022-08-19 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 3.14273566e-01 -2.64403135e-01 -9.11055040e-03 -1.78864107e-01
-1.96061119e-01 -2.95816392e-01 8.98829401e-01 -3.79315346e-01
-2.18906939e-01 6.23165846e-01 5.30997574e-01 -3.26186299e-01
-7.17602819e-02 -6.08517468e-01 -5.63413322e-01 -9.17104125e-01
-3.12901765e-01 1.61763728e-01 4.76573110e-01 -1.47525787... | [8.523469924926758, 0.24992859363555908] |
36c2a4ed-84df-4377-91de-bde7e151c32d | the-second-place-solution-for-the-4th-large | 2206.12035 | null | https://arxiv.org/abs/2206.12035v1 | https://arxiv.org/pdf/2206.12035v1.pdf | The Second Place Solution for The 4th Large-scale Video Object Segmentation Challenge--Track 3: Referring Video Object Segmentation | The referring video object segmentation task (RVOS) aims to segment object instances in a given video referred by a language expression in all video frames. Due to the requirement of understanding cross-modal semantics within individual instances, this task is more challenging than the traditional semi-supervised video... | ['Hongbin Wang', 'Yuchen Hu', 'Fengliang Qi', 'Feng Zhang', 'Bo Yan', 'Zhuang Li', 'Leilei Cao'] | 2022-06-24 | null | null | null | null | ['semi-supervised-video-object-segmentation', 'referring-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.80957961e-01 1.30076140e-01 -5.23776174e-01 -4.32469666e-01
-1.13180685e+00 -5.37000477e-01 5.21730721e-01 -3.69876802e-01
-4.44580942e-01 5.24363041e-01 5.86362034e-02 3.60821225e-02
3.03343326e-01 -1.10115342e-01 -9.90947843e-01 -3.26405644e-01
-7.81908855e-02 3.76663536e-01 5.80460131e-01 1.59238696... | [9.354310035705566, 0.23505689203739166] |
8bab734b-f2fc-4da6-a517-ceab8dda0618 | brain-captioning-decoding-human-brain | 2305.11560 | null | https://arxiv.org/abs/2305.11560v1 | https://arxiv.org/pdf/2305.11560v1.pdf | Brain Captioning: Decoding human brain activity into images and text | Every day, the human brain processes an immense volume of visual information, relying on intricate neural mechanisms to perceive and interpret these stimuli. Recent breakthroughs in functional magnetic resonance imaging (fMRI) have enabled scientists to extract visual information from human brain activity patterns. In ... | ['Nicola Toschi', 'Rufin VanRullen', 'Tommaso Boccato', 'Furkan Ozcelik', 'Matteo Ferrante'] | 2023-05-19 | null | null | null | null | ['style-transfer', 'image-reconstruction', 'brain-decoding', 'brain-decoding'] | ['computer-vision', 'computer-vision', 'medical', 'miscellaneous'] | [ 6.15290999e-01 1.23098217e-01 8.39553624e-02 -5.44627488e-01
-3.93086106e-01 -5.41038096e-01 9.01547432e-01 -3.64021689e-01
-4.94411886e-01 6.33858144e-01 6.42524183e-01 -3.35374214e-02
2.33432591e-01 -5.43336749e-01 -1.00054324e+00 -6.35727525e-01
2.58592796e-02 3.87765944e-01 2.72102207e-02 9.49693620... | [10.72939395904541, 2.50056791305542] |
a92146d3-5d75-4c36-bf0c-de580550f1fc | human-pose-forecasting-via-deep-markov-models | 1707.09240 | null | http://arxiv.org/abs/1707.09240v2 | http://arxiv.org/pdf/1707.09240v2.pdf | Human Pose Forecasting via Deep Markov Models | Human pose forecasting is an important problem in computer vision with
applications to human-robot interaction, visual surveillance, and autonomous
driving. Usually, forecasting algorithms use 3D skeleton sequences and are
trained to forecast for a few milliseconds into the future. Long-range
forecasting is challenging... | ['Tengda Han', 'Stephen Gould', 'Sam Toyer', 'Anoop Cherian'] | 2017-07-24 | null | null | null | null | ['human-pose-forecasting'] | ['computer-vision'] | [-1.11971488e-02 1.22580856e-01 2.73244858e-01 -5.47793388e-01
-4.99099553e-01 -2.29630142e-01 7.66607583e-01 -3.59098911e-01
-2.33806700e-01 5.34183919e-01 3.91819894e-01 3.07633191e-01
4.56931368e-02 -3.97203773e-01 -8.95351291e-01 -3.74445111e-01
-9.51189771e-02 8.73226345e-01 3.88807268e-03 -2.59271085... | [7.219430446624756, -0.4590565860271454] |
9dce6776-f91d-4d36-a861-13297452ff66 | im-sorry-dave-im-afraid-i-cant-do-that-deep-q | 1910.02078 | null | https://arxiv.org/abs/1910.02078v4 | https://arxiv.org/pdf/1910.02078v4.pdf | I'm sorry Dave, I'm afraid I can't do that, Deep Q-learning from forbidden action | The use of Reinforcement Learning (RL) is still restricted to simulation or to enhance human-operated systems through recommendations. Real-world environments (e.g. industrial robots or power grids) are generally designed with safety constraints in mind implemented in the shape of valid actions masks or contingency con... | ['Olivier Pietquin', 'Mathieu Seurin', 'Philippe Preux'] | 2019-10-04 | null | null | null | null | ['industrial-robots'] | ['robots'] | [ 1.14047162e-01 4.23039883e-01 -3.40248674e-01 -7.52610341e-02
-1.41152978e-01 -5.78973591e-01 6.56221330e-01 2.34361634e-01
-6.85990870e-01 1.25212002e+00 -5.51127493e-01 -4.76575911e-01
-4.28152323e-01 -8.65324378e-01 -7.16984689e-01 -8.65572035e-01
-4.02637064e-01 5.63474417e-01 3.19766730e-01 -2.18083262... | [4.64918327331543, 1.9725013971328735] |
401324c3-acd3-4f6d-b0ba-7487281adbc8 | unveiling-class-labeling-structure-for | 2010.04873 | null | https://arxiv.org/abs/2010.04873v1 | https://arxiv.org/pdf/2010.04873v1.pdf | Unveiling Class-Labeling Structure for Universal Domain Adaptation | As a more practical setting for unsupervised domain adaptation, Universal Domain Adaptation (UDA) is recently introduced, where the target label set is unknown. One of the big challenges in UDA is how to determine the common label set shared by source and target domains, as there is simply no labeling available in the ... | ['Haifeng Hu', 'Xiaofu Wu', 'Zhen Yang', 'Yueming Yin'] | 2020-10-10 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [ 1.90612912e-01 4.46468405e-02 -3.33330572e-01 -3.68587315e-01
-1.02771628e+00 -5.99891961e-01 4.04344141e-01 1.68812156e-01
-3.62602234e-01 8.02381754e-01 -1.10674754e-01 5.35515277e-03
-2.35880464e-01 -7.79988587e-01 -7.19594181e-01 -1.01511204e+00
3.71554762e-01 6.91898644e-01 4.21761960e-01 -7.58113386... | [10.302326202392578, 3.214747190475464] |
cd9d8105-bb55-4dd1-8173-e92a1ff7811c | piven-a-deep-neural-network-for-prediction | 2006.05139 | null | https://arxiv.org/abs/2006.05139v3 | https://arxiv.org/pdf/2006.05139v3.pdf | PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction | Improving the robustness of neural nets in regression tasks is key to their application in multiple domains. Deep learning-based approaches aim to achieve this goal either by improving their prediction of specific values (i.e., point prediction), or by producing prediction intervals (PIs) that quantify uncertainty. We ... | ['Lior Rokach', 'Gilad Katz', 'Eli Simhayev'] | 2020-06-09 | null | https://openreview.net/forum?id=qn_gk5j3PJ | https://openreview.net/pdf?id=qn_gk5j3PJ | null | ['value-prediction'] | ['computer-code'] | [ 1.23012647e-01 3.58245730e-01 -4.20954555e-01 -6.68498993e-01
-9.87974644e-01 -4.92663115e-01 4.49678034e-01 3.19355190e-01
-3.30987185e-01 8.76517415e-01 1.01295255e-01 -3.22189838e-01
-3.35700780e-01 -1.02601087e+00 -1.15073276e+00 -5.40323377e-01
-3.24309736e-01 5.02542615e-01 3.85172635e-01 -4.11231294... | [7.72099494934082, 3.9024717807769775] |
89957304-5839-4175-aa73-f999837e4bde | imbalanced-classification-via-a-tabular | 2204.08683 | null | https://arxiv.org/abs/2204.08683v1 | https://arxiv.org/pdf/2204.08683v1.pdf | Imbalanced Classification via a Tabular Translation GAN | When presented with a binary classification problem where the data exhibits severe class imbalance, most standard predictive methods may fail to accurately model the minority class. We present a model based on Generative Adversarial Networks which uses additional regularization losses to map majority samples to corresp... | ['Amir Averbuch', 'Ofir Lindenbaum', 'Yoav Tulpan', 'Moshe Salhov', 'Jonathan Gradstein'] | 2022-04-19 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 4.57913429e-01 2.36617550e-01 -8.65867138e-01 -7.72909701e-01
-8.66695523e-01 -2.90060848e-01 5.63329101e-01 2.28203133e-01
2.63486020e-02 1.47722423e+00 -4.15535085e-02 -6.77712485e-02
-2.04356909e-02 -1.19374800e+00 -5.32741666e-01 -8.10393274e-01
4.13542658e-01 7.76256561e-01 -1.69808850e-01 -2.48376697... | [8.837447166442871, 4.2053446769714355] |
43619435-f180-4e8d-a17e-4da7696c1c5c | simara-a-database-for-key-value-information | 2304.13606 | null | https://arxiv.org/abs/2304.13606v1 | https://arxiv.org/pdf/2304.13606v1.pdf | SIMARA: a database for key-value information extraction from full pages | We propose a new database for information extraction from historical handwritten documents. The corpus includes 5,393 finding aids from six different series, dating from the 18th-20th centuries. Finding aids are handwritten documents that contain metadata describing older archives. They are stored in the National Archi... | ['Christopher Kermorvant', 'Jean-François Moufflet', 'Mélodie Boillet', 'Solène Tarride'] | 2023-04-26 | null | null | null | null | ['handwriting-recognition', 'named-entity-recognition-ner', 'key-information-extraction'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [-4.73401174e-02 -1.92617446e-01 -4.83188808e-01 -1.53096274e-01
-9.51227427e-01 -9.16232288e-01 8.40984046e-01 2.43451118e-01
-6.92015290e-01 8.30468535e-01 3.60921860e-01 -2.00716704e-01
-1.39114365e-01 -7.03278542e-01 -5.05544186e-01 -4.73125041e-01
-2.50556301e-02 8.17906201e-01 4.51961398e-01 2.63314135... | [10.156596183776855, 10.288704872131348] |
59e1a7be-4484-485c-ba92-6f180276b230 | speechglue-how-well-can-self-supervised | 2306.08374 | null | https://arxiv.org/abs/2306.08374v1 | https://arxiv.org/pdf/2306.08374v1.pdf | SpeechGLUE: How Well Can Self-Supervised Speech Models Capture Linguistic Knowledge? | Self-supervised learning (SSL) for speech representation has been successfully applied in various downstream tasks, such as speech and speaker recognition. More recently, speech SSL models have also been shown to be beneficial in advancing spoken language understanding tasks, implying that the SSL models have the poten... | ['Yukinori Honma', 'Marc Delcroix', 'Taichi Asami', 'Yusuke Ijima', 'Tomohiro Tanaka', 'Kohei Matsuura', 'Takafumi Moriya', 'Takanori Ashihara'] | 2023-06-14 | null | null | null | null | ['spoken-language-understanding', 'speaker-recognition', 'spoken-language-understanding'] | ['natural-language-processing', 'speech', 'speech'] | [ 2.61267781e-01 6.14682794e-01 -3.94659311e-01 -8.40338290e-01
-9.44265962e-01 -6.03746593e-01 9.92070317e-01 1.31589323e-01
-2.40071774e-01 4.24150586e-01 6.53740466e-01 -6.87696695e-01
2.19267890e-01 -3.04994076e-01 -7.21837521e-01 -2.68314391e-01
-4.24541086e-02 3.97345603e-01 -8.18818510e-02 -3.92867863... | [14.053046226501465, 6.987002372741699] |
1330747d-2c2e-4d19-96d1-55c8d68e3e53 | structured-learning-for-temporal-relation | null | null | https://aclanthology.org/E17-1108 | https://aclanthology.org/E17-1108.pdf | Structured Learning for Temporal Relation Extraction from Clinical Records | We propose a scalable structured learning model that jointly predicts temporal relations between events and temporal expressions (TLINKS), and the relation between these events and the document creation time (DCTR). We employ a structured perceptron, together with integer linear programming constraints for document-lev... | ['Marie-Francine Moens', 'Artuur Leeuwenberg'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['temporal-relation-extraction', 'temporal-information-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.52675489e-02 9.07894447e-02 -1.02430904e+00 -6.21184468e-01
-3.74558657e-01 -5.77361465e-01 1.10751748e+00 7.56781578e-01
-3.21875483e-01 5.45128584e-01 4.57330734e-01 -3.39089483e-01
-6.42625809e-01 -6.12036347e-01 -6.72062755e-01 -3.62303644e-01
-1.05391383e+00 5.79624414e-01 2.26300806e-01 2.11504743... | [9.084505081176758, 9.159429550170898] |
026d5353-cb4e-4afc-87ff-92009936c718 | point-cloud-sampling-via-graph-balancing-and | 2103.06153 | null | https://arxiv.org/abs/2103.06153v1 | https://arxiv.org/pdf/2103.06153v1.pdf | Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment | 3D point cloud (PC) -- a collection of discrete geometric samples of a physical object's surface -- is typically large in size, which entails expensive subsequent operations like viewpoint image rendering and object recognition. Leveraging on recent advances in graph sampling, we propose a fast PC sub-sampling algorith... | ['Ivan Bajic', 'Gene Cheung', 'Chinthaka Dinesh'] | 2021-03-10 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 4.86067504e-01 5.15279830e-01 2.61855900e-01 1.52486712e-01
-1.07945538e+00 -3.74132305e-01 -1.57282755e-01 5.12880366e-03
-1.17180841e-02 3.56583744e-01 -5.07950187e-01 -2.91316479e-01
-4.79898304e-01 -1.11468947e+00 -8.33171964e-01 -7.87881911e-01
-5.71146607e-01 3.61206800e-01 9.56599265e-02 -8.60933363... | [6.651403903961182, 4.755599498748779] |
92e990ef-1b1e-429a-8be2-82425943b619 | apollocar3d-a-large-3d-car-instance | 1811.12222 | null | http://arxiv.org/abs/1811.12222v2 | http://arxiv.org/pdf/1811.12222v2.pdf | ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving | Autonomous driving has attracted remarkable attention from both industry and
academia. An important task is to estimate 3D properties(e.g.translation,
rotation and shape) of a moving or parked vehicle on the road. This task, while
critical, is still under-researched in the computer vision community -
partially owing to... | ['Peng Wang', 'Rui Zhu', 'Ruigang Yang', 'Chenye Guan', 'Hao Su', 'Yuchao Dai', 'Xibin Song', 'Hongdong Li', 'Dingfu Zhou'] | 2018-11-29 | apollocar3d-a-large-3d-car-instance-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Song_ApolloCar3D_A_Large_3D_Car_Instance_Understanding_Benchmark_for_Autonomous_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Song_ApolloCar3D_A_Large_3D_Car_Instance_Understanding_Benchmark_for_Autonomous_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-car-instance-understanding'] | ['computer-vision'] | [-2.36412048e-01 1.19825862e-01 -2.42871091e-01 -5.34137189e-01
-9.73266602e-01 -7.99053788e-01 7.52945602e-01 -3.50181341e-01
-3.97269338e-01 1.45485193e-01 -3.25009108e-01 -4.78106380e-01
2.80495852e-01 -5.13309538e-01 -1.12215042e+00 -3.60506833e-01
1.94957972e-01 9.93921757e-01 5.10587037e-01 -3.04618239... | [7.900210380554199, -2.4062044620513916] |
09df160a-d757-4489-9a9a-366a70ad2b81 | a-survey-on-conversational-search-and | 2211.15328 | null | https://arxiv.org/abs/2211.15328v1 | https://arxiv.org/pdf/2211.15328v1.pdf | A Survey on Conversational Search and Applications in Biomedicine | This paper aims to provide a radical rundown on Conversation Search (ConvSearch), an approach to enhance the information retrieval method where users engage in a dialogue for the information-seeking tasks. In this survey, we predominantly focused on the human interactive characteristics of the ConvSearch systems, highl... | ['Jiho Noh', 'Gowtham Reddy Gadireddy', 'Naga Sai Krishna Adatrao'] | 2022-11-28 | null | null | null | null | ['conversational-search', 'dialogue-management'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.11664140e-01 1.02533317e+00 -3.40878755e-01 -2.57533789e-01
-6.31663203e-01 -3.86930496e-01 6.77154243e-01 4.24347341e-01
-1.59950152e-01 6.27644658e-01 7.61364341e-01 -6.90883279e-01
-7.26557136e-01 -3.47056448e-01 3.51250499e-01 -3.36236417e-01
1.59298792e-01 6.08172894e-01 -1.09717965e-01 -8.33321095... | [12.227093696594238, 7.840612888336182] |
5873fb9f-098d-418d-bf97-51fa242b6651 | together-we-make-sense-learning-meta-sense | 2305.19092 | null | https://arxiv.org/abs/2305.19092v1 | https://arxiv.org/pdf/2305.19092v1.pdf | Together We Make Sense -- Learning Meta-Sense Embeddings from Pretrained Static Sense Embeddings | Sense embedding learning methods learn multiple vectors for a given ambiguous word, corresponding to its different word senses. For this purpose, different methods have been proposed in prior work on sense embedding learning that use different sense inventories, sense-tagged corpora and learning methods. However, not a... | ['Danushka Bollegala', 'Yi Zhou', 'Haochen Luo'] | 2023-05-30 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 3.12471241e-01 -1.21760763e-01 -4.59322244e-01 -2.69789308e-01
-7.48075128e-01 -8.41307282e-01 7.23501921e-01 6.35603786e-01
-8.07458639e-01 6.46377087e-01 8.49157691e-01 -1.74784571e-01
-1.94566458e-01 -7.80681014e-01 6.19264916e-02 -6.48999095e-01
3.64729881e-01 2.63339728e-01 2.47320443e-01 -8.07389081... | [10.361430168151855, 8.975935935974121] |
2c65f50f-1e43-4128-82b5-f6e755c8ee57 | looking-and-listening-audio-guided-text | 2306.03482 | null | https://arxiv.org/abs/2306.03482v1 | https://arxiv.org/pdf/2306.03482v1.pdf | Looking and Listening: Audio Guided Text Recognition | Text recognition in the wild is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest vision and language processing are effective for scene text recognition. Yet, solving edit errors such as add, delete, or replace is still the main challenge for existing approaches. In... | ['Xiang Bai', 'Yuliang Liu', 'Xing Sun', 'Deqiang Jiang', 'Enming Zhang', 'Biao Yang', 'MingYu Liu', 'Wenwen Yu'] | 2023-06-06 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 4.70852673e-01 -5.91680527e-01 6.70886263e-02 -4.17834252e-01
-6.77448094e-01 -4.16478604e-01 4.99646097e-01 1.39611021e-01
-3.93189132e-01 2.68358231e-01 2.89527148e-01 -2.82542229e-01
3.38077307e-01 -4.27082866e-01 -7.62416244e-01 -6.38781607e-01
6.29537821e-01 1.23097181e-01 1.59960479e-01 -6.02286905... | [11.935359001159668, 2.242252826690674] |
bc059a30-41d7-42af-b537-3421e878623e | tinydet-accurate-small-object-detection-in | 2304.03428 | null | https://arxiv.org/abs/2304.03428v1 | https://arxiv.org/pdf/2304.03428v1.pdf | TinyDet: Accurate Small Object Detection in Lightweight Generic Detectors | Small object detection requires the detection head to scan a large number of positions on image feature maps, which is extremely hard for computation- and energy-efficient lightweight generic detectors. To accurately detect small objects with limited computation, we propose a two-stage lightweight detection framework w... | ['Xinggang Wang', 'Wenyu Liu', 'Yuan Li', 'Qian Zhang', 'Jiemin Fang', 'Tianheng Cheng', 'Shaoyu Chen'] | 2023-04-07 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [-1.23536587e-01 -3.39920908e-01 -1.96578816e-01 -7.73383863e-03
-7.50228226e-01 -2.60541677e-01 1.22446746e-01 1.32915825e-01
-7.00672805e-01 7.76326060e-02 -2.32306018e-01 -2.03480616e-01
3.44589561e-01 -8.28073740e-01 -8.57300162e-01 -5.34628689e-01
-1.74924105e-01 2.02471152e-01 9.98744130e-01 1.60755172... | [8.728198051452637, -0.4084008038043976] |
bae4da74-2fec-4687-81a5-3ff97d642153 | fine-grained-emotional-paraphrasing-along | 2212.03297 | null | https://arxiv.org/abs/2212.03297v1 | https://arxiv.org/pdf/2212.03297v1.pdf | Fine-Grained Emotional Paraphrasing along Emotion Gradients | Paraphrase generation, a.k.a. paraphrasing, is a common and important task in natural language processing. Emotional paraphrasing, which changes the emotion embodied in a piece of text while preserving its meaning, has many potential applications, e.g., moderating online dialogues and preventing cyberbullying. We intro... | ['Justin Xie'] | 2022-10-30 | null | null | null | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 6.79706559e-02 -1.39681876e-01 -2.19595045e-01 -6.65904462e-01
-9.43168998e-01 -1.02122319e+00 4.52797949e-01 1.58780620e-01
-8.40317681e-02 7.49558449e-01 7.71739662e-01 1.52399093e-01
1.55155912e-01 -4.80012447e-01 -6.58291757e-01 -3.71002316e-01
7.38801301e-01 3.27032626e-01 -4.52887625e-01 -7.04156756... | [11.578081130981445, 9.317960739135742] |
546b73d4-6576-4d09-8ba9-0cca687d6a60 | progressive-contour-regression-for-arbitrary | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Dai_Progressive_Contour_Regression_for_Arbitrary-Shape_Scene_Text_Detection_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Dai_Progressive_Contour_Regression_for_Arbitrary-Shape_Scene_Text_Detection_CVPR_2021_paper.pdf | Progressive Contour Regression for Arbitrary-Shape Scene Text Detection | State-of-the-art scene text detection methods usually model the text instance with local pixels or components from the bottom-up perspective and, therefore, are sensitive to noises and dependent on the complicated heuristic post-processing especially for arbitrary-shape texts. To relieve these two issues, instead, ... | ['Xiaochun Cao', 'Hua Zhang', 'Sanyi Zhang', 'Pengwen Dai'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['scene-text-detection'] | ['computer-vision'] | [ 1.42467454e-01 -1.43687531e-01 1.10444270e-01 -1.33350924e-01
-3.17743510e-01 -3.79275292e-01 4.85095710e-01 2.59757787e-01
-2.42600083e-01 3.59801382e-01 5.41166440e-02 -6.40797392e-02
1.92227602e-01 -7.91502416e-01 -2.75614470e-01 -8.21469903e-01
5.51294684e-01 7.04746366e-01 1.02921247e+00 -2.15125993... | [12.108331680297852, 2.317290782928467] |
5bfa72ac-9bb4-49cc-b964-bd0cf20230f0 | unsupervised-deformable-medical-image | 2004.07624 | null | https://arxiv.org/abs/2004.07624v1 | https://arxiv.org/pdf/2004.07624v1.pdf | Unsupervised Deformable Medical Image Registration via Pyramidal Residual Deformation Fields Estimation | Deformation field estimation is an important and challenging issue in many medical image registration applications. In recent years, deep learning technique has become a promising approach for simplifying registration problems, and has been gradually applied to medical image registration. However, most existing deep le... | ['Yujia Zhou', 'Zhentai Lu', 'Yuhang Sun', 'Yi Wu', 'Wei Yang', 'Lei Zhao', 'Yaqin Liu', 'Shumao Pang', 'Qianjin Feng', 'Jun Cheng'] | 2020-04-16 | null | null | null | null | ['deformable-medical-image-registration'] | ['medical'] | [ 5.90129010e-02 -4.57396954e-01 1.23909153e-01 -2.40387335e-01
-6.59241676e-01 2.90127546e-02 2.70531088e-01 -1.65928304e-01
-6.60988271e-01 5.46072781e-01 2.17148170e-01 4.57808107e-01
-2.92909026e-01 -1.07463646e+00 -3.34509134e-01 -1.07164204e+00
-1.97191790e-01 2.50533342e-01 5.84964991e-01 -2.85442412... | [14.118069648742676, -2.558147430419922] |
fdd26851-4566-45c1-b512-dc54ba0d7689 | softgroup-scalable-3d-instance-segmentation | 2209.08263 | null | https://arxiv.org/abs/2209.08263v2 | https://arxiv.org/pdf/2209.08263v2.pdf | Scalable SoftGroup for 3D Instance Segmentation on Point Clouds | This paper considers a network referred to as SoftGroup for accurate and scalable 3D instance segmentation. Existing state-of-the-art methods produce hard semantic predictions followed by grouping to obtain instance segmentation results. However, the errors stemming from hard decisions propagate into grouping that resu... | ['Chang D. Yoo', 'Junyeong Kim', 'Thanh Nguyen', 'Tung M. Luu', 'Kookhoi Kim', 'Thang Vu'] | 2022-09-17 | null | null | null | null | ['3d-instance-segmentation-1', 'panoptic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.41105908e-01 2.59536922e-01 -1.82230741e-01 -4.81365830e-01
-8.48904014e-01 -3.41145128e-01 6.45549819e-02 1.19338252e-01
-3.40706646e-01 6.07253969e-01 -4.43856925e-01 -1.86684966e-01
4.60052490e-02 -1.04581964e+00 -8.65909219e-01 -6.03051305e-01
1.54904639e-02 4.48347181e-01 9.08630431e-01 9.78183225... | [9.28647232055664, -0.36317571997642517] |
39c7a6d6-740e-4a25-aae6-a0e50af919ac | patch-similarity-aware-data-free-quantization | 2203.02250 | null | https://arxiv.org/abs/2203.02250v3 | https://arxiv.org/pdf/2203.02250v3.pdf | Patch Similarity Aware Data-Free Quantization for Vision Transformers | Vision transformers have recently gained great success on various computer vision tasks; nevertheless, their high model complexity makes it challenging to deploy on resource-constrained devices. Quantization is an effective approach to reduce model complexity, and data-free quantization, which can address data privacy ... | ['Qingyi Gu', 'Junrui Xiao', 'Mengjuan Chen', 'Liping Ma', 'Zhikai Li'] | 2022-03-04 | null | null | null | null | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 9.60047469e-02 -1.47914767e-01 -1.40484750e-01 -4.39478785e-01
-5.36780953e-01 -2.90711015e-01 3.96904498e-01 -3.28214586e-01
-2.02175856e-01 2.24472553e-01 -3.86486910e-02 -4.49480802e-01
-9.31485891e-02 -6.05977416e-01 -5.13630629e-01 -8.62278938e-01
5.31432331e-01 1.80485044e-02 3.60092968e-02 1.60370721... | [8.716876029968262, 2.9762425422668457] |
03d3afca-5048-4dc2-80b7-5a25d91194f1 | collagan-collaborative-gan-for-missing-image | 1901.09764 | null | http://arxiv.org/abs/1901.09764v3 | http://arxiv.org/pdf/1901.09764v3.pdf | CollaGAN : Collaborative GAN for Missing Image Data Imputation | In many applications requiring multiple inputs to obtain a desired output, if
any of the input data is missing, it often introduces large amounts of bias.
Although many techniques have been developed for imputing missing data, the
image imputation is still difficult due to complicated nature of natural
images. To addre... | ['Jong Chul Ye', 'Won-Jin Moon', 'Dongwook Lee', 'Junyoung Kim'] | 2019-01-28 | null | null | null | null | ['image-imputation'] | ['computer-vision'] | [ 7.75983870e-01 2.63230745e-02 7.99575150e-02 -4.94573891e-01
-1.00910354e+00 -5.09499431e-01 3.48522216e-01 -5.71588159e-01
-3.19635235e-02 1.22886038e+00 1.68391854e-01 -3.62861640e-04
2.29146317e-01 -7.25006282e-01 -1.16669703e+00 -8.54509175e-01
6.89455032e-01 2.66968876e-01 -5.41993797e-01 1.31438345... | [11.691781997680664, -0.44712913036346436] |
a5aa716d-678c-4ce0-a5ea-b06d1ab9e4df | revisiting-weakly-supervised-pre-training-of | 2201.08371 | null | https://arxiv.org/abs/2201.08371v2 | https://arxiv.org/pdf/2201.08371v2.pdf | Revisiting Weakly Supervised Pre-Training of Visual Perception Models | Model pre-training is a cornerstone of modern visual recognition systems. Although fully supervised pre-training on datasets like ImageNet is still the de-facto standard, recent studies suggest that large-scale weakly supervised pre-training can outperform fully supervised approaches. This paper revisits weakly-supervi... | ['Laurens van der Maaten', 'Piotr Dollár', 'Ross Girshick', 'Dhruv Mahajan', 'Raj Prateek Kosaraju', 'Bugra Gedik', 'Vinicius de Freitas Reis', 'Aaron Adcock', 'Laura Gustafson', 'Mannat Singh'] | 2022-01-20 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Singh_Revisiting_Weakly_Supervised_Pre-Training_of_Visual_Perception_Models_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Singh_Revisiting_Weakly_Supervised_Pre-Training_of_Visual_Perception_Models_CVPR_2022_paper.pdf | cvpr-2022-1 | ['fine-grained-image-classification'] | ['computer-vision'] | [ 3.21983874e-01 5.31970203e-01 -5.50910890e-01 -8.59734952e-01
-7.66143501e-01 -5.35058022e-01 1.16562128e+00 -8.06415379e-02
-7.17908263e-01 3.76589924e-01 5.06474257e-01 -3.47776949e-01
2.95613050e-01 -6.29854321e-01 -1.08444059e+00 -5.49437582e-01
-1.67535365e-01 5.63398004e-01 1.84751570e-01 -3.22503269... | [9.748556137084961, 2.443532943725586] |
f703187d-4143-44c2-aa22-166aed7b6cde | qald-9-plus-a-multilingual-dataset-for | 2202.00120 | null | https://arxiv.org/abs/2202.00120v2 | https://arxiv.org/pdf/2202.00120v2.pdf | QALD-9-plus: A Multilingual Dataset for Question Answering over DBpedia and Wikidata Translated by Native Speakers | The ability to have the same experience for different user groups (i.e., accessibility) is one of the most important characteristics of Web-based systems. The same is true for Knowledge Graph Question Answering (KGQA) systems that provide the access to Semantic Web data via natural language interface. While following o... | ['Andreas Both', 'Ricardo Usbeck', 'Dennis Diefenbach', 'Aleksandr Perevalov'] | 2022-01-31 | qald-9-plus-a-multilingual-dataset-for-1 | https://arxiv.org/abs/2202.00120 | https://arxiv.org/pdf/2202.00120.pdf | null | ['graph-question-answering'] | ['graphs'] | [-8.42581868e-01 5.66067278e-01 8.09452906e-02 -9.52104479e-02
-7.47544289e-01 -8.85340035e-01 6.28446400e-01 5.36031067e-01
-5.09080827e-01 1.06151116e+00 3.16168100e-01 -4.64952052e-01
-5.94750106e-01 -1.22891641e+00 -6.72385573e-01 -3.25938016e-02
2.98542082e-01 8.67238700e-01 4.98193443e-01 -1.01308239... | [9.73271656036377, 8.118117332458496] |
f72e7fc9-1a64-4878-bec4-bd058ec1046f | information-theoretic-hashing-for-zero-shot | 2209.12491 | null | https://arxiv.org/abs/2209.12491v1 | https://arxiv.org/pdf/2209.12491v1.pdf | Information-Theoretic Hashing for Zero-Shot Cross-Modal Retrieval | Zero-shot cross-modal retrieval (ZS-CMR) deals with the retrieval problem among heterogenous data from unseen classes. Typically, to guarantee generalization, the pre-defined class embeddings from natural language processing (NLP) models are used to build a common space. In this paper, instead of using an extra NLP mod... | ['Xinge You', 'Duanquan Xu', 'Shujian Yu', 'Yufeng Shi'] | 2022-09-26 | null | null | null | null | ['zero-shot-cross-modal-retrieval'] | ['miscellaneous'] | [ 2.07916826e-01 -1.27056018e-01 -8.55012983e-03 -3.34206551e-01
-1.00886917e+00 -4.68663037e-01 5.78617454e-01 6.41033053e-01
-3.67461592e-01 2.03614101e-01 3.02115232e-01 2.10012421e-01
-4.88067508e-01 -9.06924665e-01 -3.33342552e-01 -9.93873119e-01
3.17304656e-02 8.04065168e-02 3.64576310e-01 -2.95911163... | [11.31079387664795, 1.092328667640686] |
65275d00-f534-43b5-9f56-bf3679039372 | bottom-up-broadcast-neural-network-for-music | 1901.08928 | null | http://arxiv.org/abs/1901.08928v1 | http://arxiv.org/pdf/1901.08928v1.pdf | Bottom-up Broadcast Neural Network For Music Genre Classification | Music genre recognition based on visual representation has been successfully
explored over the last years. Recently, there has been increasing interest in
attempting convolutional neural networks (CNNs) to achieve the task. However,
most of existing methods employ the mature CNN structures proposed in image
recognition... | ['Shenglan Liu', 'Huibing Wang', 'Guochao Liu', 'Caifeng Liu', 'Lin Feng'] | 2019-01-24 | null | null | null | null | ['genre-classification', 'music-genre-recognition'] | ['computer-vision', 'music'] | [ 9.01224390e-02 -5.53963602e-01 -3.43898758e-02 -1.50334090e-01
-3.53764236e-01 -2.96255380e-01 3.42838943e-01 -1.35628521e-01
-2.89436311e-01 4.36140597e-01 8.41076300e-02 9.02008545e-03
-1.39500782e-01 -6.41688585e-01 -3.36687088e-01 -7.00406134e-01
1.17255405e-01 -3.71979237e-01 -3.22761126e-02 -2.45685875... | [15.656928062438965, 5.1664204597473145] |
60549a6f-03a0-43ba-ba87-1f541a09f4d7 | contrast-limited-adaptive-histogram | 2109.00886 | null | https://arxiv.org/abs/2109.00886v1 | https://arxiv.org/pdf/2109.00886v1.pdf | Contrast Limited Adaptive Histogram Equalization (CLAHE) Approach for Enhancement of the Microstructures of Friction Stir Welded Joints | Image processing algorithms are finding various applications in manufacturing and materials industries such as identification of cracks in the fabricated samples, calculating the geometrical properties of the given microstructure, presence of surface defects, etc. The present work deals with the application of Contrast... | ['Akshansh Mishra'] | 2021-08-15 | null | null | null | null | ['local-color-enhancement'] | ['computer-vision'] | [ 1.78628623e-01 -3.71260792e-01 5.99328995e-01 -2.28669420e-01
7.96547607e-02 3.63098527e-03 1.88621551e-01 4.83254731e-01
-5.85243523e-01 7.79309034e-01 -2.42456719e-01 2.16139913e-01
-5.60305834e-01 -8.67470920e-01 -1.22847892e-01 -9.66599524e-01
-6.75849095e-02 3.97429794e-01 6.14822745e-01 -8.11684504... | [14.8671293258667, -2.904979944229126] |
f5f33c18-38bc-4fd2-8e0c-f1d16185e619 | improving-document-clustering-by-removing | null | null | https://aclanthology.org/W17-4416 | https://aclanthology.org/W17-4416.pdf | Improving Document Clustering by Removing Unnatural Language | Technical documents contain a fair amount of unnatural language, such as tables, formulas, and pseudo-code. Unnatural language can bean important factor of confusing existing NLP tools. This paper presents an effective method of distinguishing unnatural language from natural language, and evaluates the impact of un-nat... | ['Jinho D. Choi', 'James Allan', 'Myungha Jang'] | 2017-09-01 | null | null | null | ws-2017-9 | ['document-layout-analysis'] | ['computer-vision'] | [ 1.45365368e-03 1.50749967e-01 -2.83851862e-01 -2.96455592e-01
-1.30015361e+00 -1.25504136e+00 7.51393616e-01 6.48626328e-01
-3.23854327e-01 7.41709292e-01 4.27707464e-01 -5.47167897e-01
1.27899393e-01 -4.52445567e-01 -4.84548301e-01 -3.18743944e-01
2.98953444e-01 5.31671703e-01 2.54207492e-01 9.63509604... | [9.849885940551758, 8.041025161743164] |
0adde4f2-ba8b-4b57-8445-550d20e3fe69 | mt-clinical-bert-scaling-clinical-information | 2004.10220 | null | https://arxiv.org/abs/2004.10220v1 | https://arxiv.org/pdf/2004.10220v1.pdf | MT-Clinical BERT: Scaling Clinical Information Extraction with Multitask Learning | Clinical notes contain an abundance of important but not-readily accessible information about patients. Systems to automatically extract this information rely on large amounts of training data for which their exists limited resources to create. Furthermore, they are developed dis-jointly; meaning that no information ca... | ['Bridget T. McInnes', 'Andriy Mulyar'] | 2020-04-21 | null | null | null | null | ['entity-extraction'] | ['natural-language-processing'] | [ 1.32818192e-01 2.45242998e-01 -1.09345175e-01 -2.33401254e-01
-1.34811842e+00 -5.39735794e-01 2.39939809e-01 8.67149055e-01
-5.63274264e-01 9.26740050e-01 4.28537220e-01 -6.12457931e-01
-4.79587197e-01 -5.60237646e-01 -2.47769356e-01 -3.20606172e-01
7.01391697e-03 8.17655087e-01 -1.78728729e-01 -2.17199177... | [8.525158882141113, 8.540725708007812] |
00d0c4fe-5c15-4b06-8559-707b8ffd60b3 | towards-robust-visual-information-extraction | 2102.06732 | null | https://arxiv.org/abs/2102.06732v1 | https://arxiv.org/pdf/2102.06732v1.pdf | Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution | Visual information extraction (VIE) has attracted considerable attention recently owing to its various advanced applications such as document understanding, automatic marking and intelligent education. Most existing works decoupled this problem into several independent sub-tasks of text spotting (text detection and rec... | ['Mingxiang Cai', 'Yaqiang Wu', 'Qianying Wang', 'Shuaitao Zhang', 'Jiaxin Zhang', 'Guozhi Tang', 'Lianwen Jin', 'Chongyu Liu', 'Jiapeng Wang'] | 2021-01-24 | null | null | null | null | ['3d-feature-matching', 'text-spotting'] | ['computer-vision', 'computer-vision'] | [ 4.99329537e-01 -4.28762287e-01 -6.59731105e-02 -1.09599404e-01
-6.69820845e-01 -6.51180744e-01 6.29858911e-01 1.10680811e-01
-4.83072847e-01 3.45416635e-01 -1.41311660e-02 -4.32979405e-01
-1.80859268e-02 -5.21520615e-01 -5.70443749e-01 -6.15325809e-01
6.94784701e-01 3.74218553e-01 8.87603983e-02 -8.79459232... | [11.765393257141113, 2.316797971725464] |
0f4b6635-6162-4c58-9f66-f1f243559d91 | automatic-calibration-and-error-correction | 2306.16564 | null | https://arxiv.org/abs/2306.16564v2 | https://arxiv.org/pdf/2306.16564v2.pdf | LLM Calibration and Automatic Hallucination Detection via Pareto Optimal Self-supervision | Large language models (LLMs) have demonstrated remarkable capabilities out of box for a wide range of applications, yet accuracy still remains a major growth area, especially in mission-critical domains such as biomedicine. An effective method to calibrate the confidence level on LLM responses is essential to automatic... | ['Hoifung Poon', 'J. Samuel Preston', 'Mu Wei', 'Theodore Zhao'] | 2023-06-28 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [ 5.66897810e-01 5.10772288e-01 -4.93438244e-01 -8.34649146e-01
-1.18340600e+00 -1.72355250e-01 4.76984948e-01 7.64435053e-01
-5.45320034e-01 9.98913825e-01 -1.27177373e-01 -3.99078578e-01
-4.54966187e-01 -5.63777149e-01 -7.42317736e-01 -4.23221678e-01
7.40654171e-02 8.58076155e-01 2.54106492e-01 -1.28905535... | [8.673412322998047, 8.590577125549316] |
dc157ef6-6bcd-4973-907a-34c23518005b | sequential-matching-network-a-new | 1612.01627 | null | http://arxiv.org/abs/1612.01627v2 | http://arxiv.org/pdf/1612.01627v2.pdf | Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots | We study response selection for multi-turn conversation in retrieval-based
chatbots. Existing work either concatenates utterances in context or matches a
response with a highly abstract context vector finally, which may lose
relationships among utterances or important contextual information. We propose
a sequential mat... | ['Zhoujun Li', 'Yu Wu', 'Ming Zhou', 'Wei Wu', 'Chen Xing'] | 2016-12-06 | sequential-matching-network-a-new-1 | https://aclanthology.org/P17-1046 | https://aclanthology.org/P17-1046.pdf | acl-2017-7 | ['conversational-response-selection'] | ['natural-language-processing'] | [ 4.22777206e-01 -1.43809021e-01 -1.51081949e-01 -8.13826919e-01
-1.08325005e+00 -2.88992018e-01 5.71463585e-01 1.22342356e-01
-4.83326823e-01 6.11428380e-01 9.52198565e-01 -1.73176732e-02
5.79617545e-02 -6.62703693e-01 3.79739031e-02 -3.88262987e-01
2.01833829e-01 5.55188656e-01 2.35827148e-01 -6.56259358... | [12.522381782531738, 7.8584418296813965] |
2fe358fb-ea5e-45ff-bf22-06085eca4812 | torchosr-a-pytorch-extension-package-for-open | 2305.09646 | null | https://arxiv.org/abs/2305.09646v1 | https://arxiv.org/pdf/2305.09646v1.pdf | torchosr -- a PyTorch extension package for Open Set Recognition models evaluation in Python | The article presents the torchosr package - a Python package compatible with PyTorch library - offering tools and methods dedicated to Open Set Recognition in Deep Neural Networks. The package offers two state-of-the-art methods in the field, a set of functions for handling base sets and generation of derived sets for ... | ['Pawel Ksieniewicz', 'Joanna Komorniczak'] | 2023-05-16 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [-7.37421960e-02 9.01238397e-02 3.35950851e-01 -7.44382501e-01
-1.29930884e-01 -5.46420038e-01 4.39180672e-01 -1.16772570e-01
-4.72155213e-01 9.06008899e-01 -4.29003984e-01 -2.15673700e-01
-3.51518124e-01 -9.53958154e-01 -4.18021619e-01 -7.58022010e-01
-3.44634563e-01 7.74193823e-01 3.20561498e-01 -6.08246148... | [8.995840072631836, 2.625338554382324] |
e7867e70-12ee-4648-a00a-38a1412be910 | unsupervised-domain-adaptation-for-training | 2303.12424 | null | https://arxiv.org/abs/2303.12424v1 | https://arxiv.org/pdf/2303.12424v1.pdf | Unsupervised Domain Adaptation for Training Event-Based Networks Using Contrastive Learning and Uncorrelated Conditioning | Event-based cameras offer reliable measurements for preforming computer vision tasks in high-dynamic range environments and during fast motion maneuvers. However, adopting deep learning in event-based vision faces the challenge of annotated data scarcity due to recency of event cameras. Transferring the knowledge that ... | ['Mohammad Rostami', 'Dayuan Jian'] | 2023-03-22 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 3.36528450e-01 -4.33757901e-01 -1.98386475e-01 -4.45411026e-01
-8.43332648e-01 -4.64249730e-01 7.17433751e-01 -2.34834775e-01
-1.04538119e+00 6.63250685e-01 1.97456822e-01 4.78702709e-02
-1.35759607e-01 -3.97565901e-01 -8.43859375e-01 -7.37363219e-01
-8.24804008e-02 3.14113021e-01 3.46155792e-01 9.21421573... | [8.48702335357666, -1.0044223070144653] |
21f036a8-127e-4e4e-aa22-b0b747bd1141 | unsupervised-learning-of-visual-features-by | 2006.09882 | null | https://arxiv.org/abs/2006.09882v5 | https://arxiv.org/pdf/2006.09882v5.pdf | Unsupervised Learning of Visual Features by Contrasting Cluster Assignments | Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contrastive methods typically work online and rely on a large number of explicit pairwise feature comparisons, which is computationally challengi... | ['Priya Goyal', 'Julien Mairal', 'Armand Joulin', 'Piotr Bojanowski', 'Mathilde Caron', 'Ishan Misra'] | 2020-06-17 | null | http://proceedings.neurips.cc/paper/2020/hash/70feb62b69f16e0238f741fab228fec2-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/70feb62b69f16e0238f741fab228fec2-Paper.pdf | neurips-2020-12 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 1.48020476e-01 -3.84894982e-02 1.32470969e-02 -3.06272835e-01
-6.58788860e-01 -5.26577413e-01 7.89007187e-01 3.15406621e-01
-7.83043921e-01 5.80858111e-01 -1.89435482e-01 -3.70783657e-02
6.04997296e-03 -6.69039309e-01 -9.54351783e-01 -7.76767015e-01
9.36266109e-02 6.71917975e-01 2.39038184e-01 -1.28892303... | [9.38813304901123, 2.517099380493164] |
cabe27e8-743e-4de3-b806-20b4353293d5 | exposing-cross-lingual-lexical-knowledge-from | 2205.00267 | null | https://arxiv.org/abs/2205.00267v2 | https://arxiv.org/pdf/2205.00267v2.pdf | Probing Cross-Lingual Lexical Knowledge from Multilingual Sentence Encoders | Pretrained multilingual language models (LMs) can be successfully transformed into multilingual sentence encoders (SEs; e.g., LaBSE, xMPNet) via additional fine-tuning or model distillation with parallel data. However, it remains unclear how to best leverage them to represent sub-sentence lexical items (i.e., words and... | ['Anna Korhonen', 'Edoardo Maria Ponti', 'Nigel Collier', 'Fangyu Liu', 'Goran Glavaš', 'Ivan Vulić'] | 2022-04-30 | null | null | null | null | ['cross-lingual-entity-linking', 'pretrained-multilingual-language-models'] | ['natural-language-processing', 'natural-language-processing'] | [-1.20839290e-01 1.25913024e-01 -7.43156493e-01 -3.11979473e-01
-1.28631043e+00 -1.03834939e+00 4.81211185e-01 2.94781268e-01
-8.54550004e-01 1.07687593e+00 3.13270599e-01 -8.62927556e-01
3.02653074e-01 -7.32994139e-01 -1.33020568e+00 -8.58547073e-03
6.59451112e-02 5.57137430e-01 1.82094544e-01 -5.71114421... | [10.97172737121582, 9.878446578979492] |
8a2661d6-db16-4b27-b738-8094ed106398 | topological-biomarkers-for-real-time | 2211.02523 | null | https://arxiv.org/abs/2211.02523v1 | https://arxiv.org/pdf/2211.02523v1.pdf | Topological biomarkers for real-time detection of epileptic seizures | Automated seizure detection is a fundamental problem in computational neuroscience towards diagnosis and treatment's improvement of epileptic disease. We propose a real-time computational method for automated tracking and detection of epileptic seizures from raw neurophysiological recordings. Our mechanism is based on ... | ['Diego Mateos', 'Ximena Fernandez'] | 2022-11-04 | null | null | null | null | ['seizure-detection'] | ['medical'] | [ 9.37476978e-02 -3.49481136e-01 5.48684657e-01 -1.02707386e-01
-1.23986006e-01 -6.56462669e-01 5.72021902e-01 1.98015928e-01
-1.99781209e-01 5.49969673e-01 1.49945527e-01 -4.32858393e-02
-8.67065847e-01 -5.21752357e-01 -1.38031319e-01 -8.42067063e-01
-1.23182607e+00 1.89857408e-01 4.13008556e-02 -2.64796764... | [13.13943099975586, 3.5417027473449707] |
71145bfd-121b-4aea-a2a7-52c8494ec7b5 | a-joint-model-and-data-driven-method-for | 2303.17241 | null | https://arxiv.org/abs/2303.17241v1 | https://arxiv.org/pdf/2303.17241v1.pdf | A Joint Model and Data Driven Method for Distributed Estimation | This paper considers the problem of distributed estimation in wireless sensor networks (WSN), which is anticipated to support a wide range of applications such as the environmental monitoring, weather forecasting, and location estimation. To this end, we propose a joint model and data driven distributed estimation meth... | ['Chuan Huang', 'Ran Li', 'Meng He'] | 2023-03-30 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [ 1.79250345e-01 4.18086946e-02 -1.73730835e-01 -4.48077202e-01
-9.86472070e-01 -5.14407530e-02 1.58771381e-01 2.20705405e-01
-4.49255437e-01 9.16817963e-01 -2.19229713e-01 -4.40637767e-02
-5.90113223e-01 -1.06289577e+00 -6.02064133e-01 -1.38859153e+00
-4.52229559e-01 -1.03031605e-01 1.53713241e-01 4.15049568... | [6.2218098640441895, 1.5298079252243042] |
35dee570-9011-4382-9cfc-006c277952bb | explainable-representation-learning-of-small | 2306.05694 | null | https://arxiv.org/abs/2306.05694v1 | https://arxiv.org/pdf/2306.05694v1.pdf | Explainable Representation Learning of Small Quantum States | Unsupervised machine learning models build an internal representation of their training data without the need for explicit human guidance or feature engineering. This learned representation provides insights into which features of the data are relevant for the task at hand. In the context of quantum physics, training m... | ['Evert van Nieuwenburg', 'Felix Frohnert'] | 2023-06-09 | null | null | null | null | ['interpretable-machine-learning', 'feature-engineering'] | ['methodology', 'methodology'] | [ 3.77676427e-01 4.31157947e-01 -1.87152550e-01 -4.66873348e-01
-3.52830887e-01 -7.62463212e-01 8.47513378e-01 6.14979938e-02
-2.68220473e-02 6.50705755e-01 1.99239045e-01 -3.76065940e-01
-2.94915915e-01 -1.08411551e+00 -6.11499786e-01 -1.08119261e+00
-1.19725056e-01 6.07604265e-01 -5.30168474e-01 -5.20093262... | [5.633397579193115, 4.935903072357178] |
2b85e9ff-3780-4af2-8461-09b079dcd2ab | learning-comment-generation-by-leveraging | 1810.12264 | null | http://arxiv.org/abs/1810.12264v2 | http://arxiv.org/pdf/1810.12264v2.pdf | Learning Comment Generation by Leveraging User-Generated Data | Existing models on open-domain comment generation are difficult to train, and
they produce repetitive and uninteresting responses. The problem is due to
multiple and contradictory responses from a single article, and by the rigidity
of retrieval methods. To solve this problem, we propose a combined approach to
retrieva... | ['Genta Indra Winata', 'Zhaojiang Lin', 'Pascale Fung'] | 2018-10-29 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [ 3.43958944e-01 2.57298023e-01 1.25995185e-02 -1.85641989e-01
-1.54558790e+00 -6.60711229e-01 8.02492380e-01 -1.68359444e-01
-2.63687283e-01 1.16800344e+00 1.05589128e+00 6.85993880e-02
3.30478877e-01 -4.42393214e-01 -6.11259282e-01 -3.94028306e-01
6.38881743e-01 6.06338978e-01 2.34940320e-01 -6.43991709... | [12.332691192626953, 8.562629699707031] |
48af821e-4ffb-421f-95ba-4b67aba68bb5 | mycorrhiza-genotype-assignment | 2010.09483 | null | https://arxiv.org/abs/2010.09483v1 | https://arxiv.org/pdf/2010.09483v1.pdf | Mycorrhiza: Genotype Assignment usingPhylogenetic Networks | Motivation The genotype assignment problem consists of predicting, from the genotype of an individual, which of a known set of populations it originated from. The problem arises in a variety of contexts, including wildlife forensics, invasive species detection and biodiversity monitoring. Existing approaches perform we... | ['Mathieu Blanchette', 'Richard C. Hamelin', 'Jeremy Georges-Filteau'] | 2020-10-14 | null | null | null | null | ['population-assignment'] | ['medical'] | [ 6.05316877e-01 -5.12452304e-01 -3.04287434e-01 -3.57184917e-01
1.01795927e-01 -7.16536641e-01 5.97337067e-01 1.97712362e-01
-4.40876186e-01 1.11747158e+00 1.33469682e-02 -4.21970427e-01
-6.48596168e-01 -8.68043423e-01 -1.84402615e-01 -1.18994462e+00
-5.86832464e-01 7.57692754e-01 -1.89079009e-02 6.00529611... | [4.984028339385986, 5.010257720947266] |
07240c9e-8292-4427-b610-83c74b9da834 | hard-attention-for-scalable-image | 2102.10212 | null | https://arxiv.org/abs/2102.10212v2 | https://arxiv.org/pdf/2102.10212v2.pdf | Hard-Attention for Scalable Image Classification | Can we leverage high-resolution information without the unsustainable quadratic complexity to input scale? We propose Traversal Network (TNet), a novel multi-scale hard-attention architecture, which traverses image scale-space in a top-down fashion, visiting only the most informative image regions along the way. TNet o... | ['Nasir Memon', 'Paweł Korus', 'Athanasios Papadopoulos'] | 2021-02-20 | null | http://proceedings.neurips.cc/paper/2021/hash/7b7916dd2de56297aa29cccb2bbf48d4-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/7b7916dd2de56297aa29cccb2bbf48d4-Paper.pdf | neurips-2021-12 | ['deep-attention', 'hard-attention', 'deep-attention'] | ['computer-vision', 'methodology', 'natural-language-processing'] | [ 1.15900889e-01 3.32541764e-01 -3.96838002e-02 -4.94455099e-01
-1.08193672e+00 -5.77613890e-01 4.09550011e-01 4.67222445e-02
-6.49553835e-01 3.74868989e-01 1.67824537e-01 -4.76843178e-01
-2.86220014e-01 -9.85724449e-01 -1.01964927e+00 -4.61601973e-01
-4.76723671e-01 3.20117414e-01 1.98826388e-01 -2.34753296... | [9.43337631225586, 1.1026586294174194] |
5ab212fd-15e7-4a7b-9677-0e1f77c05083 | maskvit-masked-visual-pre-training-for-video | 2206.11894 | null | https://arxiv.org/abs/2206.11894v2 | https://arxiv.org/pdf/2206.11894v2.pdf | MaskViT: Masked Visual Pre-Training for Video Prediction | The ability to predict future visual observations conditioned on past observations and motor commands can enable embodied agents to plan solutions to a variety of tasks in complex environments. This work shows that we can create good video prediction models by pre-training transformers via masked visual modeling. Our a... | ['Li Fei-Fei', 'Roberto Martín-Martín', 'Jiajun Wu', 'Yunzhi Zhang', 'Stephen Tian', 'Agrim Gupta'] | 2022-06-23 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 3.41300815e-01 5.57910621e-01 -1.83338657e-01 -2.36344308e-01
-3.07508439e-01 -3.31633985e-01 9.03615534e-01 -1.70434490e-01
-3.87160569e-01 8.79941583e-01 4.75018919e-01 -2.72287667e-01
2.80857503e-01 -4.90950018e-01 -1.21190119e+00 -3.39124382e-01
-4.99855042e-01 4.58872616e-01 3.90621096e-01 -4.50249240... | [4.505895614624023, 0.7792398929595947] |
97f458a4-885d-4a36-9b1a-c8723dc5a6e9 | multimodal-deep-learning | 2301.04856 | null | https://arxiv.org/abs/2301.04856v1 | https://arxiv.org/pdf/2301.04856v1.pdf | Multimodal Deep Learning | This book is the result of a seminar in which we reviewed multimodal approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art approaches in the two subfields of Deep Learning individually. Further, modeling frameworks are discussed where one modality is transformed in... | ['Matthias Aßenmacher', 'Daniel Schalk', 'Rasmus Hvingelby', 'Christian Heumann', 'Jann Goschenhofer', 'Karol Urbanczyk', 'Rickmer Schulte', 'Maximilian Schneider', 'Nadja Sauter', 'Marco Moldovan', 'Christopher Marquardt', 'Giacomo Loss', 'Philipp Koch', 'Steffen Jauch-Walser', 'Vladana Djakovic', 'Luyang Chu', 'Cem A... | 2023-01-12 | null | null | null | null | ['multimodal-deep-learning'] | ['natural-language-processing'] | [ 3.25547695e-01 3.07008445e-01 -3.87703270e-01 -4.75143015e-01
-1.12578487e+00 -4.10990357e-01 1.08080685e+00 -1.88151732e-01
-1.40534654e-01 5.16812384e-01 5.07066905e-01 2.80667692e-01
-4.92455624e-02 -8.58745992e-01 -5.52383721e-01 -8.87008131e-01
3.86986703e-01 6.17141902e-01 -5.23539960e-01 -4.96993124... | [11.066648483276367, 1.7679256200790405] |
9f3ecfc2-7449-4436-8c1a-70af61fbc0b3 | neural-poisson-indicator-functions-for-neural | 2211.14249 | null | https://arxiv.org/abs/2211.14249v1 | https://arxiv.org/pdf/2211.14249v1.pdf | Neural Poisson: Indicator Functions for Neural Fields | Implicit neural field generating signed distance field representations (SDFs) of 3D shapes have shown remarkable progress in 3D shape reconstruction and generation. We introduce a new paradigm for neural field representations of 3D scenes; rather than characterizing surfaces as SDFs, we propose a Poisson-inspired chara... | ['Matthias Nießner', 'Angela Dai'] | 2022-11-25 | null | null | null | null | ['3d-shape-reconstruction'] | ['computer-vision'] | [ 7.81611264e-01 4.39057946e-01 2.52042234e-01 -4.91465718e-01
-5.87245405e-01 -4.14016366e-01 7.02195525e-01 1.22332193e-01
-1.05944552e-01 5.05320430e-01 1.17573008e-01 -1.76838294e-01
-4.54461366e-01 -1.30942643e+00 -1.06333387e+00 -2.66352355e-01
-1.97039336e-01 8.06012928e-01 1.67806610e-01 -2.51425803... | [8.666727066040039, -3.6627137660980225] |
746c02fb-d48a-4be6-bc8a-ba6211b8256a | crash-raw-audio-score-based-generative | 2106.07431 | null | https://arxiv.org/abs/2106.07431v1 | https://arxiv.org/pdf/2106.07431v1.pdf | CRASH: Raw Audio Score-based Generative Modeling for Controllable High-resolution Drum Sound Synthesis | In this paper, we propose a novel score-base generative model for unconditional raw audio synthesis. Our proposal builds upon the latest developments on diffusion process modeling with stochastic differential equations, which already demonstrated promising results on image generation. We motivate novel heuristics for t... | ['Gaëtan Hadjeres', 'Simon Rouard'] | 2021-06-14 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 2.70858496e-01 3.19256574e-01 4.49903458e-01 1.53377056e-01
-1.20408475e+00 -5.03341079e-01 1.04108143e+00 -3.19213241e-01
-1.85318720e-02 1.00472510e+00 4.97896463e-01 2.88103404e-03
-1.02812037e-01 -9.99692738e-01 -4.93605286e-01 -8.64300430e-01
1.58412471e-01 6.26644909e-01 1.37459889e-01 -2.59267241... | [15.59186840057373, 5.942841529846191] |
704e598b-2d70-4115-afdb-3bf5fa97df54 | sparse-bayesian-state-space-and-time-varying | 2207.12147 | null | https://arxiv.org/abs/2207.12147v1 | https://arxiv.org/pdf/2207.12147v1.pdf | Sparse Bayesian State-Space and Time-Varying Parameter Models | In this chapter, we review variance selection for time-varying parameter (TVP) models for univariate and multivariate time series within a Bayesian framework. We show how both continuous as well as discrete spike-and-slab shrinkage priors can be transferred from variable selection for regression models to variance sele... | ['Peter Knaus', 'Sylvia Frühwirth-Schnatter'] | 2022-07-25 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 2.64514178e-01 -4.58905190e-01 4.09683958e-03 -6.49395049e-01
-1.23803806e+00 -5.14670014e-01 7.50435650e-01 -2.28184924e-01
-3.16028029e-01 1.06220675e+00 -5.73362708e-02 -2.29468465e-01
-3.68616164e-01 -5.52352369e-01 -2.85653412e-01 -1.18984306e+00
-2.31604502e-01 7.65142620e-01 2.41275042e-01 3.85977924... | [6.842207431793213, 3.8604135513305664] |
b62fd6a8-5630-42c5-a20f-871b1f589c84 | slca-slow-learner-with-classifier-alignment | 2303.05118 | null | https://arxiv.org/abs/2303.05118v1 | https://arxiv.org/pdf/2303.05118v1.pdf | SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model | The goal of continual learning is to improve the performance of recognition models in learning sequentially arrived data. Although most existing works are established on the premise of learning from scratch, growing efforts have been devoted to incorporating the benefits of pre-training. However, how to adaptively expl... | ['Yunchao Wei', 'Ling Chen', 'Guoliang Kang', 'Liyuan Wang', 'Gengwei Zhang'] | 2023-03-09 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 2.84508437e-01 -2.31052428e-01 -2.72284597e-01 -5.55828273e-01
-9.45942938e-01 -3.98741722e-01 5.86925507e-01 9.33648050e-02
-7.68115222e-01 6.11749887e-01 -2.08472490e-01 -4.59060997e-01
-1.72705546e-01 -4.65423197e-01 -8.96196842e-01 -6.33537352e-01
-4.46910225e-02 4.09463465e-01 2.83413231e-01 -1.79666832... | [9.501497268676758, 3.1954407691955566] |
991832f3-1a26-4df7-a6ea-e0a11665ab56 | neural-network-for-determining-an-asteroid | 2210.01006 | null | https://arxiv.org/abs/2210.01006v1 | https://arxiv.org/pdf/2210.01006v1.pdf | Neural network for determining an asteroid mineral composition from reflectance spectra | Chemical and mineral compositions of asteroids reflect the formation and history of our Solar System. This knowledge is also important for planetary defence and in-space resource utilisation. We aim to develop a fast and robust neural-network-based method for deriving the mineral modal and chemical compositions of sili... | ['Tomáš Kohout', 'Arto Klami', 'Antti Penttilä', 'David Korda'] | 2022-10-03 | null | null | null | null | ['type'] | ['speech'] | [-1.27380164e-02 -1.76449776e-01 5.57864942e-02 -1.44955099e-01
-1.08792670e-01 -4.49798048e-01 8.63004625e-01 -1.41628057e-01
-5.14993012e-01 7.68571734e-01 1.17872134e-02 -3.08542877e-01
-1.11798383e-01 -1.02560055e+00 -5.46796501e-01 -7.89710522e-01
7.06306472e-02 9.16968048e-01 4.48024362e-01 -6.13371909... | [7.310483932495117, 2.9457995891571045] |
c7781018-96fb-4afd-9f9c-5d338b56fde1 | sunet-a-deep-learning-architecture-for-acute | 1810.13304 | null | http://arxiv.org/abs/1810.13304v2 | http://arxiv.org/pdf/1810.13304v2.pdf | Acute and sub-acute stroke lesion segmentation from multimodal MRI | Acute stroke lesion segmentation tasks are of great clinical interest as they
can help doctors make better informed treatment decisions. Magnetic resonance
imaging (MRI) is time demanding but can provide images that are considered gold
standard for diagnosis. Automated stroke lesion segmentation can provide with
an est... | ['Xavier Lladó', 'Arnau Oliver', 'Albert Clèrigues', 'Jose Bernal', 'Sergi Valverde', 'Jordi Freixenet'] | 2018-10-31 | null | null | null | null | ['acute-stroke-lesion-segmentation', 'ischemic-stroke-lesion-segmentation', 'outcome-prediction-in-multimodal-mri'] | ['medical', 'medical', 'medical'] | [ 3.63518782e-02 -8.01870599e-02 -2.73459643e-01 -3.16897660e-01
-1.24041319e+00 -7.56380916e-01 5.07744968e-01 3.01979244e-01
-7.98424602e-01 7.34532416e-01 3.59094858e-01 -4.34395790e-01
-4.54270780e-01 -3.57887030e-01 -5.41640341e-01 -7.13012278e-01
-4.02799636e-01 9.12774086e-01 4.37759072e-01 2.02778712... | [14.246572494506836, -2.052809476852417] |
dffda702-c692-48d6-8755-f7c6ad0ada45 | upgraded-w-net-with-attention-gates-and-its | 2011.10654 | null | https://arxiv.org/abs/2011.10654v1 | https://arxiv.org/pdf/2011.10654v1.pdf | Upgraded W-Net with Attention Gates and its Application in Unsupervised 3D Liver Segmentation | Segmentation of biomedical images can assist radiologists to make a better diagnosis and take decisions faster by helping in the detection of abnormalities, such as tumors. Manual or semi-automated segmentation, however, can be a time-consuming task. Most deep learning based automated segmentation methods are supervise... | ['Andreas Nürnberger', 'Oliver Speck', 'Soumick Chatterjee', 'Dhanunjaya Mitta'] | 2020-11-20 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 1.91607371e-01 4.19911623e-01 1.56258255e-01 -7.26804614e-01
-4.42240924e-01 -9.91517976e-02 2.27457538e-01 6.43656909e-01
-7.86485374e-01 5.55911660e-01 -7.20412806e-02 -3.09517026e-01
-2.23882385e-02 -8.35950375e-01 -3.05144966e-01 -8.76437366e-01
-4.71000001e-02 6.36925578e-01 5.06837070e-01 1.59598649... | [14.451287269592285, -2.518817663192749] |
ce98b2d1-2cc1-430c-9612-44e32a8b051c | exploiting-out-of-domain-parallel-data | 1907.03060 | null | https://arxiv.org/abs/1907.03060v1 | https://arxiv.org/pdf/1907.03060v1.pdf | Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation | This paper proposes a novel multilingual multistage fine-tuning approach for low-resource neural machine translation (NMT), taking a challenging Japanese--Russian pair for benchmarking. Although there are many solutions for low-resource scenarios, such as multilingual NMT and back-translation, we have empirically confi... | ['Kenji Imamura', 'Raj Dabre', 'Atsushi Fujita', 'Aizhan Imankulova'] | 2019-07-06 | exploiting-out-of-domain-parallel-data-1 | https://aclanthology.org/W19-6613 | https://aclanthology.org/W19-6613.pdf | ws-2019-8 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 9.36678499e-02 -1.16999604e-01 -3.96218628e-01 -3.21874470e-01
-1.70809484e+00 -8.89880240e-01 7.49345958e-01 -3.19488764e-01
-8.19643676e-01 1.32281291e+00 2.45862260e-01 -8.37903321e-01
4.21698630e-01 -3.46675456e-01 -1.10606933e+00 -2.24328443e-01
5.16563416e-01 1.16247547e+00 -2.39585325e-01 -6.05762780... | [11.5382719039917, 10.310608863830566] |
5ea66e50-d574-40ed-b0bc-42366bc1c287 | does-partial-pretranslation-can-improve-low | null | null | https://aclanthology.org/2022.wat-1.10 | https://aclanthology.org/2022.wat-1.10.pdf | Does partial pretranslation can improve low ressourced-languages pairs? | We study the effects of a local and punctual pretranslation of the source corpus on the performance of a Transformer translation model. The pretranslations are performed at the morphological (morpheme translation), lexical (word translation) and morphosyntactic (numeral groups and dates) levels. We focus on small and m... | ['Raoul Blin'] | null | null | null | null | wat-2022-10 | ['word-translation'] | ['natural-language-processing'] | [ 1.33864000e-01 -1.60577208e-01 -2.48948410e-01 -3.38374227e-01
-1.15915513e+00 -1.06740141e+00 9.33300197e-01 1.74106002e-01
-7.37811625e-01 1.09514582e+00 4.13012594e-01 -1.17473686e+00
2.50032693e-01 -4.48278487e-01 -7.71025896e-01 -3.84325176e-01
3.26405853e-01 7.90329814e-01 1.85877606e-02 -5.89678347... | [11.395057678222656, 10.338879585266113] |
1840e4c8-dc71-4c93-8a30-024e403c5b36 | dag-recurrent-neural-networks-for-scene | 1509.00552 | null | http://arxiv.org/abs/1509.00552v2 | http://arxiv.org/pdf/1509.00552v2.pdf | DAG-Recurrent Neural Networks For Scene Labeling | In image labeling, local representations for image units are usually
generated from their surrounding image patches, thus long-range contextual
information is not effectively encoded. In this paper, we introduce recurrent
neural networks (RNNs) to address this issue. Specifically, directed acyclic
graph RNNs (DAG-RNNs)... | ['Bing Wang', 'Gang Wang', 'Zhen Zuo', 'Bing Shuai'] | 2015-09-02 | dag-recurrent-neural-networks-for-scene-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Shuai_DAG-Recurrent_Neural_Networks_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Shuai_DAG-Recurrent_Neural_Networks_CVPR_2016_paper.pdf | cvpr-2016-6 | ['scene-labeling'] | ['computer-vision'] | [ 1.77077681e-01 -1.51303828e-01 -5.11503577e-01 -6.54942036e-01
-2.68149674e-01 -2.05436021e-01 5.20113826e-01 2.38967985e-02
-2.58428037e-01 4.09683019e-01 6.31950676e-01 -5.57072088e-02
-6.49954518e-03 -9.82372403e-01 -7.48903632e-01 -7.02391565e-01
-4.67631668e-02 -2.97238901e-02 8.15537497e-02 1.74306612... | [9.582963943481445, 0.45079508423805237] |
3880ccfb-7dfd-47a6-ac42-8c3e4c538d0e | a-methodology-of-weed-crop-classification | 2010.14708 | null | https://arxiv.org/abs/2010.14708v2 | https://arxiv.org/pdf/2010.14708v2.pdf | Crop and weed classification based on AutoML | CNN models already play an important role in classification of crop and weed with high accuracy, more than 95% as reported in literature. However, to manually choose and fine-tune the deep learning models becomes laborious and indispensable in most traditional practices and research. Moreover, the classic objective fun... | ['Meiyu Jiang', 'Soheila Garshasbi', 'Qingguo Zhou', 'Jun Shen', 'XueTao Jiang', 'BinBin Yong'] | 2020-10-28 | null | null | null | null | ['crop-classification'] | ['miscellaneous'] | [ 1.72876477e-01 -1.04354329e-01 -2.36047298e-01 5.18455990e-02
2.61945784e-01 -7.12138355e-01 1.32425085e-01 4.41420048e-01
-4.06212866e-01 9.15904343e-01 -5.43594182e-01 -5.54507256e-01
-2.55025458e-02 -1.36347759e+00 -7.07270026e-01 -8.44103277e-01
-1.29790548e-02 1.03867285e-01 9.42435414e-02 -6.72027349... | [9.214473724365234, -1.5332504510879517] |
3cb2ca47-e32c-4dc4-aae4-2f907f579b72 | image-deblurring-by-exploring-in-depth | 2303.15198 | null | https://arxiv.org/abs/2303.15198v1 | https://arxiv.org/pdf/2303.15198v1.pdf | Image Deblurring by Exploring In-depth Properties of Transformer | Image deblurring continues to achieve impressive performance with the development of generative models. Nonetheless, there still remains a displeasing problem if one wants to improve perceptual quality and quantitative scores of recovered image at the same time. In this study, drawing inspiration from the research of t... | ['Jiayi Ma', 'Xianming Liu', 'Junjun Jiang', 'Pengwei Liang'] | 2023-03-24 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 3.40807140e-01 -3.52883220e-01 2.13653013e-01 -1.43113919e-02
-3.73050213e-01 -5.04370153e-01 5.53944051e-01 -4.11415577e-01
-3.48936953e-02 4.96366918e-01 7.15970099e-01 2.57139266e-01
-4.08929497e-01 -6.96320355e-01 -7.38280475e-01 -1.07442176e+00
3.09858888e-01 -3.43379885e-01 1.30207986e-01 -2.38812268... | [11.473614692687988, -2.3999669551849365] |
ee7db93d-628c-4f72-a2fa-be9a7da2448e | masking-orchestration-multi-task-pretraining | 2003.04994 | null | https://arxiv.org/abs/2003.04994v1 | https://arxiv.org/pdf/2003.04994v1.pdf | Masking Orchestration: Multi-task Pretraining for Multi-role Dialogue Representation Learning | Multi-role dialogue understanding comprises a wide range of diverse tasks such as question answering, act classification, dialogue summarization etc. While dialogue corpora are abundantly available, labeled data, for specific learning tasks, can be highly scarce and expensive. In this work, we investigate dialogue cont... | ['Yating Zhang', 'Tianyi Wang', 'Xiaozhong Liu', 'Qiong Zhang', 'Changlong Sun'] | 2020-02-27 | null | null | null | null | ['dialogue-understanding'] | ['natural-language-processing'] | [ 7.15988636e-01 5.63483596e-01 -2.23232299e-01 -5.20137310e-01
-5.76485813e-01 -5.28073013e-01 8.74862969e-01 4.31423962e-01
-4.52582031e-01 1.28798854e+00 9.24250960e-01 -3.14200908e-01
1.21844206e-02 -5.93839347e-01 -1.20869108e-01 -3.44766855e-01
4.31870908e-01 5.15249848e-01 5.98459654e-02 -8.00870597... | [12.652496337890625, 8.084959030151367] |
1324f7b3-be59-44ac-a919-3a4982413e90 | deepproblog-neural-probabilistic-logic-2 | 1907.08194 | null | https://arxiv.org/abs/1907.08194v2 | https://arxiv.org/pdf/1907.08194v2.pdf | Neural Probabilistic Logic Programming in DeepProbLog | We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques of the underlying probabilistic logic programming language ProbLog can be adapted for the new language. We theoretically and e... | ['Thomas Demeester', 'Sebastijan Dumančić', 'Luc De Raedt', 'Angelika Kimmig', 'Robin Manhaeve'] | 2019-07-18 | null | null | null | neurips-2018-12 | ['program-induction'] | ['computer-code'] | [-3.63748558e-02 5.63023388e-01 -5.94107568e-01 -7.27886975e-01
-4.97049212e-01 -5.02801418e-01 9.25584912e-01 2.77744472e-01
-1.28073886e-01 7.77011454e-01 7.24584758e-02 -8.03788006e-01
-3.73587966e-01 -1.42184019e+00 -1.16831481e+00 -1.79808661e-02
-5.14347970e-01 1.09645057e+00 5.57107568e-01 4.98526245... | [8.744747161865234, 7.070675849914551] |
c80ad223-08ad-4073-9d3a-5cbd9b0a596b | speech-vgg-a-deep-feature-extractor-for | 1910.09909 | null | https://arxiv.org/abs/1910.09909v5 | https://arxiv.org/pdf/1910.09909v5.pdf | Word-level Embeddings for Cross-Task Transfer Learning in Speech Processing | Recent breakthroughs in deep learning often rely on representation learning and knowledge transfer. In recent years, unsupervised and self-supervised techniques for learning speech representation were developed to foster automatic speech recognition. Up to date, most of these approaches are task-specific and designed f... | ['Milos Cernak', 'Pierre Beckmann', 'Mikolaj Kegler'] | 2019-10-22 | null | null | null | null | ['music-classification'] | ['music'] | [ 6.59657478e-01 4.45431247e-02 -2.37962212e-02 -6.06482744e-01
-1.25027418e+00 -6.07702076e-01 7.97638297e-01 8.65632743e-02
-4.33268189e-01 4.79395956e-01 4.78159666e-01 -3.96596968e-01
1.29449233e-01 -3.01019967e-01 -7.77160525e-01 -3.67849737e-01
9.12711993e-02 3.19452167e-01 2.37145647e-03 -2.04535395... | [14.455225944519043, 6.508569717407227] |
8ac0ab7e-25b0-43ff-bec4-bd5d8469e17c | warm-start-actor-critic-from-approximation | 2306.11271 | null | https://arxiv.org/abs/2306.11271v1 | https://arxiv.org/pdf/2306.11271v1.pdf | Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap | Warm-Start reinforcement learning (RL), aided by a prior policy obtained from offline training, is emerging as a promising RL approach for practical applications. Recent empirical studies have demonstrated that the performance of Warm-Start RL can be improved \textit{quickly} in some cases but become \textit{stagnant} ... | ['Junshan Zhang', 'Sen Lin', 'Hang Wang'] | 2023-06-20 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.17938319e-01 5.52976616e-02 -4.48289335e-01 1.32792130e-01
-1.05353820e+00 -6.55042648e-01 1.70851126e-01 2.15192869e-01
-7.02210009e-01 1.04152012e+00 -1.65277332e-01 -4.32208449e-01
-1.40962198e-01 -3.50766361e-01 -1.03478396e+00 -1.03000617e+00
-1.60621330e-01 1.22138023e-01 -2.07711637e-01 -3.44233572... | [4.183058738708496, 2.4601948261260986] |
9bc55ffd-8f8f-4511-91dc-6dba3fa038ff | segment-level-neural-conditional-random | null | null | https://aclanthology.org/I17-2017 | https://aclanthology.org/I17-2017.pdf | Segment-Level Neural Conditional Random Fields for Named Entity Recognition | We present Segment-level Neural CRF, which combines neural networks with a linear chain CRF for segment-level sequence modeling tasks such as named entity recognition (NER) and syntactic chunking. Our segment-level CRF can consider higher-order label dependencies compared with conventional word-level CRF. Since it is d... | ['Ikuya Yamada', 'Motoki Sato', 'Yuji Matsumoto', 'Hiroyuki Shindo'] | 2017-11-01 | segment-level-neural-conditional-random-1 | https://aclanthology.org/I17-2017 | https://aclanthology.org/I17-2017.pdf | ijcnlp-2017-11 | ['morphological-tagging'] | ['natural-language-processing'] | [ 3.08703780e-02 5.28114676e-01 -7.04711616e-01 -6.18890285e-01
-7.70575464e-01 -5.21764457e-01 -1.34019569e-01 4.04088229e-01
-8.50903928e-01 9.74666119e-01 6.18454933e-01 -7.95674801e-01
9.09688473e-01 -7.81109929e-01 -7.38339782e-01 -9.56569314e-02
-4.51754004e-01 4.19958800e-01 1.97916701e-01 2.51464516... | [9.998666763305664, 9.734158515930176] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.