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
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0f5dbb5a-0012-4e91-b047-5e1d9df50e63 | identifiability-of-discretized-latent | 2306.16334 | null | https://arxiv.org/abs/2306.16334v1 | https://arxiv.org/pdf/2306.16334v1.pdf | Identifiability of Discretized Latent Coordinate Systems via Density Landmarks Detection | Disentanglement aims to recover meaningful latent ground-truth factors from only the observed distribution. Identifiability provides the theoretical grounding for disentanglement to be well-founded. Unfortunately, unsupervised identifiability of independent latent factors is a theoretically proven impossibility in the ... | ['Pascal Vincent', 'Simon Lacoste-Julien', 'Kartik Ahuja', 'Vitória Barin-Pacela'] | 2023-06-28 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 5.78406230e-02 4.69780028e-01 -3.37658554e-01 -9.90676656e-02
-8.69299531e-01 -8.46416652e-01 6.54378772e-01 -4.23990756e-01
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-2.93493658e-01 7.98700392e-01 -6.38320267e-01 1.52857333... | [11.485888481140137, -0.0951782688498497] |
49a9a034-32dc-446e-92b8-9d11235587c0 | a-novel-apex-time-network-for-cross-dataset | 1904.03699 | null | https://arxiv.org/abs/1904.03699v7 | https://arxiv.org/pdf/1904.03699v7.pdf | A Novel Apex-Time Network for Cross-Dataset Micro-Expression Recognition | The automatic recognition of micro-expression has been boosted ever since the successful introduction of deep learning approaches. As researchers working on such topics are moving to learn from the nature of micro-expression, the practice of using deep learning techniques has evolved from processing the entire video cl... | ['Yu Shi', 'Xiangdong Zhou', 'Min Peng', 'Chongyang Wang', 'Tao Bi', 'Tong Chen'] | 2019-04-07 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 1.40974700e-01 -3.97072047e-01 -4.45923120e-01 -5.23770928e-01
-5.50000668e-01 -2.00551525e-01 5.32183826e-01 -1.73798144e-01
-6.23624206e-01 4.86584574e-01 4.89294045e-02 1.22190841e-01
7.69430622e-02 -4.45292920e-01 -5.69201112e-01 -1.05522108e+00
-4.42010432e-01 -2.84913152e-01 1.66347995e-02 -1.42391786... | [13.657463073730469, 1.8792810440063477] |
3a03dcd4-505c-46e7-a83b-8e388fcf9c8a | deep-dynamic-effective-connectivity | 2202.02393 | null | https://arxiv.org/abs/2202.02393v3 | https://arxiv.org/pdf/2202.02393v3.pdf | Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series | Recently, methods that represent data as a graph, such as graph neural networks (GNNs) have been successfully used to learn data representations and structures to solve classification and link prediction problems. The applications of such methods are vast and diverse, but most of the current work relies on the assumpti... | ['Sergey Plis', 'Vince Calhoun', 'Zening Fu', 'Usman Mahmood'] | 2022-02-04 | null | null | null | null | ['connectivity-estimation'] | ['graphs'] | [ 3.21124196e-01 5.93909204e-01 -4.69848216e-01 -2.39847377e-01
3.96636903e-01 -5.30114889e-01 5.71222365e-01 3.49426389e-01
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-7.65232086e-01 -8.67750704e-01 -7.65883446e-01 -4.95787710e-01
-7.91520834e-01 5.10209203e-01 6.94298837e-03 -3.68817925... | [12.333659172058105, 3.432201623916626] |
9c4d4dd9-8505-4791-965e-94717ed4df7f | making-attention-mechanisms-more-robust-and | 2104.08763 | null | https://arxiv.org/abs/2104.08763v3 | https://arxiv.org/pdf/2104.08763v3.pdf | Making Attention Mechanisms More Robust and Interpretable with Virtual Adversarial Training | Although attention mechanisms have become fundamental components of deep learning models, they are vulnerable to perturbations, which may degrade the prediction performance and model interpretability. Adversarial training (AT) for attention mechanisms has successfully reduced such drawbacks by considering adversarial p... | ['Hitoshi Iyatomi', 'Shunsuke Kitada'] | 2021-04-18 | null | null | null | null | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [ 7.73308948e-02 3.22125167e-01 -1.11756101e-01 -2.61642814e-01
-5.84414124e-01 -6.40789330e-01 4.58283603e-01 -9.79632214e-02
-2.60987639e-01 6.76254690e-01 8.78785849e-02 -5.50072908e-01
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4.45251405e-01 5.01553178e-01 -3.70495133e-02 -2.91129559... | [10.41482162475586, 7.940823078155518] |
87f8e780-c8b9-4ebd-8ae2-e59bd42be90f | unified-model-learning-for-various-neural | 2305.02777 | null | https://arxiv.org/abs/2305.02777v2 | https://arxiv.org/pdf/2305.02777v2.pdf | Unified Model Learning for Various Neural Machine Translation | Existing neural machine translation (NMT) studies mainly focus on developing dataset-specific models based on data from different tasks (e.g., document translation and chat translation). Although the dataset-specific models have achieved impressive performance, it is cumbersome as each dataset demands a model to be des... | ['Jie zhou', 'Yufeng Chen', 'Jiaan Wang', 'Jinan Xu', 'Fandong Meng', 'Yunlong Liang'] | 2023-05-04 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 3.65901887e-01 -4.39532220e-01 -5.41404724e-01 -3.88726741e-01
-1.61489558e+00 -6.30387366e-01 6.75483167e-01 -5.07410049e-01
-3.27043027e-01 9.73692715e-01 1.23552181e-01 -7.33904898e-01
3.60157192e-01 -4.12945926e-01 -8.76701891e-01 -3.11082423e-01
6.82907403e-01 1.01511073e+00 -3.01388919e-01 -6.07682765... | [11.680173873901367, 10.10707950592041] |
9670d099-c4f1-4090-b743-57ac079894e1 | wnet-a-data-driven-dual-domain-denoising | 2207.00400 | null | https://arxiv.org/abs/2207.00400v2 | https://arxiv.org/pdf/2207.00400v2.pdf | WNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layer | Deep learning based solutions are being succesfully implemented for a wide variety of applications. Most notably, clinical use-cases have gained an increased interest and have been the main driver behind some of the cutting-edge data-driven algorithms proposed in the last years. For applications like sparse-view tomogr... | ['Tobias Lasser', 'Daniela Pfeiffer', 'Franz Pfeiffer', 'Manuel Schultheiß', 'Felix C. Hofmann', 'Theodor Cheslerean-Boghiu'] | 2022-07-01 | null | null | null | null | ['tomographic-reconstructions'] | ['medical'] | [ 4.64382142e-01 8.86175875e-03 9.95951593e-02 -5.84974885e-01
-8.60325217e-01 6.40709512e-03 3.64826858e-01 -1.08330265e-01
-5.94016075e-01 4.84308243e-01 4.65303600e-01 -9.06942934e-02
-3.85908723e-01 -6.98147953e-01 -6.86285555e-01 -9.32169795e-01
8.48120749e-02 5.72619319e-01 3.07096630e-01 -1.11119129... | [13.403046607971191, -2.5497324466705322] |
c0514c68-b15a-4b95-8242-6420c49512c1 | deep-packet-a-novel-approach-for-encrypted | 1709.02656 | null | http://arxiv.org/abs/1709.02656v3 | http://arxiv.org/pdf/1709.02656v3.pdf | Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning | Internet traffic classification has become more important with rapid growth
of current Internet network and online applications. There have been numerous
studies on this topic which have led to many different approaches. Most of
these approaches use predefined features extracted by an expert in order to
classify networ... | ['Ramin Shirali Hossein Zade', 'Mohammad Lotfollahi', 'Mahdi Jafari Siavoshani', 'Mohammdsadegh Saberian'] | 2017-09-08 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 2.15883441e-02 -3.48003060e-01 -3.01508725e-01 -3.12949836e-01
-1.25284582e-01 -6.25498950e-01 2.39478558e-01 5.91096543e-02
-4.03772384e-01 7.50822008e-01 -4.92728859e-01 -8.63761365e-01
-2.11591721e-01 -1.29717863e+00 -2.67713517e-01 -4.39774662e-01
2.93664157e-01 5.94559491e-01 5.18457830e-01 -1.62402838... | [5.088233947753906, 7.221707344055176] |
c936f8ae-6c85-4973-8f97-10af7e78ace4 | enhancing-social-network-hate-detection-using-1 | null | null | https://www.sciencedirect.com/science/article/pii/S1566253523002038?casa_token=bKYei6Rpr7QAAAAA:yxZVs7LXkANAnOA-cpZrHVty_LH0vqvloWhdi89OmHrhuJgl6_O1ph0UDnmcXvPHR6jLgDwBpEs | https://www.sciencedirect.com/science/article/abs/pii/S1566253523002038 | Enhancing Social Network Hate Detection Using Back Translation and GPT-3 Augmentations During Training and Test-Time | Social media platforms have become an essential means of communication, but they also serve as a breeding ground for hateful content. Detecting hate speech accurately is challenging due to factors such as slang and implicit hate speech. In response to these challenges, this paper presents a novel ensemble approach util... | ['L Rokach', 'S Messica', 'O Arbili', 'O Katz', 'D Presil', 'S Cohen'] | 2023-06-13 | null | null | null | information-fusion-2023-6 | ['hate-speech-normalization', 'hate-speech-detection'] | ['natural-language-processing', 'natural-language-processing'] | [-1.72334164e-02 -2.59967417e-01 -4.68146205e-02 -2.30138265e-02
-6.37433589e-01 -7.47739553e-01 6.85744762e-01 2.12834895e-01
-1.61357611e-01 4.65893716e-01 2.45837763e-01 -1.08398654e-01
2.38198340e-01 -4.23846215e-01 -4.10059273e-01 -5.07470787e-01
1.24854846e-02 -1.13848172e-01 -1.84524357e-01 -3.17919582... | [8.720803260803223, 10.551020622253418] |
419c903f-6e2d-4089-a1b0-0adc2fa72739 | fast-and-order-invariant-inference-in | 2305.16827 | null | https://arxiv.org/abs/2305.16827v1 | https://arxiv.org/pdf/2305.16827v1.pdf | Fast and Order-invariant Inference in Bayesian VARs with Non-Parametric Shocks | The shocks which hit macroeconomic models such as Vector Autoregressions (VARs) have the potential to be non-Gaussian, exhibiting asymmetries and fat tails. This consideration motivates the VAR developed in this paper which uses a Dirichlet process mixture (DPM) to model the shocks. However, we do not follow the obviou... | ['Gary Koop', 'Florian Huber'] | 2023-05-26 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-3.73740643e-01 1.09075628e-01 2.88733453e-01 -1.83662891e-01
-4.06643242e-01 -6.46763265e-01 9.67285335e-01 -2.04569355e-01
-1.14743277e-01 1.01579440e+00 4.58675086e-01 -6.29165053e-01
-3.49718541e-01 -1.05849576e+00 -4.46129829e-01 -8.32080185e-01
4.85540852e-02 8.47205102e-01 4.58190329e-02 9.94424000... | [6.1111578941345215, 3.991018772125244] |
eff53c3b-c122-444e-92be-2caa995ee1a0 | efficient-bayesian-inverse-reinforcement | null | null | https://openreview.net/forum?id=7_a6T7RGNpj | https://openreview.net/pdf?id=7_a6T7RGNpj | Efficient Bayesian Inverse Reinforcement Learning via Conditional Kernel Density Estimation | Inverse reinforcement learning (IRL) methods attempt to recover the reward function of an agent by observing its behavior. Given the large amount of uncertainty in the underlying reward function, it is often useful to model this function probabilistically, rather than estimate a single reward function. However, existin... | ['Barbara Engelhardt', 'Andrew Jones', 'Diana Cai', 'Didong Li', 'Aishwarya Mandyam'] | 2021-11-22 | null | null | null | pproximateinference-aabi-symposium-2022-2 | ['birl-cima'] | ['medical'] | [-3.46695989e-01 1.34755179e-01 -3.52039725e-01 -2.43359268e-01
-9.32564497e-01 -6.81464076e-01 7.52238691e-01 1.14410870e-01
-7.49653101e-01 1.17090321e+00 4.06863093e-02 -2.65325189e-01
-3.15234482e-01 -5.98595619e-01 -7.21171498e-01 -5.88295221e-01
-4.87234771e-01 7.21712589e-01 1.60061345e-01 1.28444433... | [4.104613780975342, 2.183779001235962] |
351521a5-3eba-45f7-9569-e44071df8458 | psdr-room-single-photo-to-scene-using | 2307.03244 | null | https://arxiv.org/abs/2307.03244v1 | https://arxiv.org/pdf/2307.03244v1.pdf | PSDR-Room: Single Photo to Scene using Differentiable Rendering | A 3D digital scene contains many components: lights, materials and geometries, interacting to reach the desired appearance. Staging such a scene is time-consuming and requires both artistic and technical skills. In this work, we propose PSDR-Room, a system allowing to optimize lighting as well as the pose and materials... | ['Shuang Zhao', 'Valentin Deschaintre', 'Thibault Groueix', 'Miloš Hašan', 'Fujun Luan', 'Kai Yan'] | 2023-07-06 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [ 6.42921269e-01 -7.87422508e-02 8.83327007e-01 -3.63917798e-01
-3.91781420e-01 -9.19773161e-01 5.31985223e-01 1.83401987e-01
7.81019852e-02 2.07168490e-01 -1.18843250e-01 -3.76656055e-01
6.78421333e-02 -7.71661162e-01 -8.37395847e-01 -1.77668363e-01
2.69191474e-01 4.41235840e-01 1.82706192e-02 -1.72539786... | [9.398191452026367, -3.1079635620117188] |
4f238350-ba28-486a-9f99-cd02dd2cc9d9 | judging-a-book-by-its-cover | 1610.09204 | null | http://arxiv.org/abs/1610.09204v3 | http://arxiv.org/pdf/1610.09204v3.pdf | Judging a Book By its Cover | Book covers communicate information to potential readers, but can that same
information be learned by computers? We propose using a deep Convolutional
Neural Network (CNN) to predict the genre of a book based on the visual clues
provided by its cover. The purpose of this research is to investigate whether
relationships... | ['Sheraz Ahmed', 'Seiichi Uchida', 'Brian Kenji Iwana', 'Syed Tahseen Raza Rizvi', 'Andreas Dengel'] | 2016-10-28 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 1.53967798e-01 -7.02624470e-02 -5.92559934e-01 -3.67843032e-01
-1.84389651e-01 -1.12457502e+00 6.08851552e-01 -7.97845200e-02
3.21447551e-01 4.65075344e-01 3.30752343e-01 -2.44704962e-01
-2.16186374e-01 -1.04764128e+00 -1.00939167e+00 -4.50106338e-02
1.91290259e-01 6.60903692e-01 -2.02130899e-01 -5.33047497... | [11.664033889770508, 2.9343316555023193] |
69f61f2b-13ec-45f4-bd43-50ce08acba78 | unsupervised-video-depth-estimation-based-on | 1909.01028 | null | https://arxiv.org/abs/1909.01028v1 | https://arxiv.org/pdf/1909.01028v1.pdf | Unsupervised Video Depth Estimation Based on Ego-motion and Disparity Consensus | Unsupervised learning based depth estimation methods have received more and more attention as they do not need vast quantities of densely labeled data for training which are touch to acquire. In this paper, we propose a novel unsupervised monocular video depth estimation method in natural scenes by taking advantage of ... | ['Guizhong Liu', 'Lingtao Zhou', 'Jiaojiao Fang'] | 2019-09-03 | null | null | null | null | ['depth-and-camera-motion', 'l2-regularization'] | ['computer-vision', 'methodology'] | [ 1.03870161e-01 9.71131697e-02 -2.36603171e-01 -5.07449985e-01
-6.05078697e-01 -1.53253868e-01 4.61474210e-01 -5.28602183e-01
-6.02199793e-01 8.38265359e-01 1.59811288e-01 9.83586907e-02
3.05638105e-01 -6.72116399e-01 -8.05895388e-01 -8.32980394e-01
2.74119079e-01 7.25617856e-02 5.40992975e-01 -7.31792906... | [8.687439918518066, -2.3690743446350098] |
7fe002d9-c667-4ad5-8005-e6bc3b83b110 | measuring-the-impact-of-programming-language | 2302.01973 | null | https://arxiv.org/abs/2302.01973v3 | https://arxiv.org/pdf/2302.01973v3.pdf | Measuring The Impact Of Programming Language Distribution | Current benchmarks for evaluating neural code models focus on only a small subset of programming languages, excluding many popular languages such as Go or Rust. To ameliorate this issue, we present the BabelCode framework for execution-based evaluation of any benchmark in any language. BabelCode enables new investigati... | ['Rishabh Singh', 'Michele Catasta', 'Jacob Austin', 'Jonathan Malmaud', 'Joshua Howland', 'Jeffrey Hui', 'Xavier Garcia', 'Kefan Xiao', 'Gabriel Orlanski'] | 2023-02-03 | null | null | null | null | ['code-translation'] | ['computer-code'] | [-2.99113005e-01 -1.30484596e-01 -2.29409695e-01 -2.33840525e-01
-1.49877703e+00 -8.55565846e-01 4.10070688e-01 -3.35420631e-02
-5.94028413e-01 5.40002823e-01 1.06555939e-01 -7.77137339e-01
2.09457412e-01 -7.32916236e-01 -1.10953093e+00 -2.81780690e-01
-1.07694261e-01 3.59021395e-01 1.01662362e-02 -5.61484173... | [7.756524562835693, 7.843461513519287] |
68ee990d-9a23-4b23-be1e-a70c761af1be | tempel-linking-dynamically-evolving-and-newly | 2302.02500 | null | https://arxiv.org/abs/2302.02500v1 | https://arxiv.org/pdf/2302.02500v1.pdf | TempEL: Linking Dynamically Evolving and Newly Emerging Entities | In our continuously evolving world, entities change over time and new, previously non-existing or unknown, entities appear. We study how this evolutionary scenario impacts the performance on a well established entity linking (EL) task. For that study, we introduce TempEL, an entity linking dataset that consists of time... | ['Isabelle Augenstein', 'Chris Develder', 'Thomas Demeester', 'Johannes Deleu', 'Lucie-Aimee Kaffee', 'Klim Zaporojets'] | 2023-02-05 | null | null | null | null | ['entity-disambiguation'] | ['natural-language-processing'] | [-5.53665876e-01 3.24859113e-01 -3.17386925e-01 1.53442517e-01
-5.45576692e-01 -1.06909740e+00 1.03257167e+00 1.01950824e+00
-9.08681810e-01 1.38157296e+00 1.63425893e-01 3.81627120e-02
-1.79022193e-01 -1.14992058e+00 -9.43229139e-01 -2.33531550e-01
-5.86931944e-01 8.55622947e-01 5.20428598e-01 -4.15533960... | [9.414104461669922, 8.815353393554688] |
655b5d4b-e5cd-42d9-af27-d6c6a4468590 | regularized-policy-iteration | null | null | http://papers.nips.cc/paper/3445-regularized-policy-iteration | http://papers.nips.cc/paper/3445-regularized-policy-iteration.pdf | Regularized Policy Iteration | In this paper we consider approximate policy-iteration-based reinforcement learning algorithms. In order to implement a flexible function approximation scheme we propose the use of non-parametric methods with regularization, providing a convenient way to control the complexity of the function approximator. We propose t... | ['Csaba Szepesvári', 'Shie Mannor', 'Mohammad Ghavamzadeh', 'Amir M. Farahmand'] | 2008-12-01 | null | null | null | neurips-2008-12 | ['l2-regularization'] | ['methodology'] | [-2.09435672e-01 2.26744235e-01 -2.86161900e-01 1.35953659e-02
-8.85079443e-01 -2.91501760e-01 5.70829809e-01 -4.22959030e-02
-9.46213484e-01 1.39268482e+00 -1.49328068e-01 -4.30502623e-01
-3.24354619e-01 -5.25617778e-01 -8.26166213e-01 -7.45451331e-01
-3.41834933e-01 4.26282376e-01 6.17344081e-02 -2.89383173... | [4.293369770050049, 2.477541446685791] |
161d2a46-9c3f-4363-93cc-37c6294835b6 | morphing-and-sampling-network-for-dense-point | 1912.00280 | null | https://arxiv.org/abs/1912.00280v1 | https://arxiv.org/pdf/1912.00280v1.pdf | Morphing and Sampling Network for Dense Point Cloud Completion | 3D point cloud completion, the task of inferring the complete geometric shape from a partial point cloud, has been attracting attention in the community. For acquiring high-fidelity dense point clouds and avoiding uneven distribution, blurred details, or structural loss of existing methods' results, we propose a novel ... | ['Shi-Min Hu', 'Sheng Yang', 'Lu Sheng', 'Jing Shao', 'Minghua Liu'] | 2019-11-30 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [ 7.23124146e-02 -2.79460456e-02 1.44404694e-01 -2.62797356e-01
-9.61899817e-01 -4.20954585e-01 5.46126902e-01 1.36439204e-01
-3.43821757e-02 5.73192358e-01 -3.23394209e-01 -2.98549943e-02
-1.46250606e-01 -9.01212096e-01 -9.15906608e-01 -5.06473899e-01
9.11591426e-02 1.07136524e+00 5.39791048e-01 1.03824779... | [8.283730506896973, -3.3663623332977295] |
26a30416-0161-4163-87c6-bf32d09c9f20 | the-stem-ecr-dataset-grounding-scientific | 2003.01006 | null | https://arxiv.org/abs/2003.01006v4 | https://arxiv.org/pdf/2003.01006v4.pdf | The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources | We introduce the STEM (Science, Technology, Engineering, and Medicine) Dataset for Scientific Entity Extraction, Classification, and Resolution, version 1.0 (STEM-ECR v1.0). The STEM-ECR v1.0 dataset has been developed to provide a benchmark for the evaluation of scientific entity extraction, classification, and resolu... | ['Sören Auer', "Jennifer D'Souza", 'Arthur Brack', 'Anett Hoppe', 'Ralph Ewerth', 'Mohamad Yaser Jaradeh'] | 2020-03-02 | the-stem-ecr-dataset-grounding-scientific-1 | https://aclanthology.org/2020.lrec-1.268 | https://aclanthology.org/2020.lrec-1.268.pdf | lrec-2020-5 | ['entity-extraction'] | ['natural-language-processing'] | [-1.33515045e-01 2.96394914e-01 -2.95064479e-01 2.34995335e-01
-9.06593621e-01 -8.18141818e-01 8.62382650e-01 1.02275646e+00
-6.42263651e-01 1.22026336e+00 2.82235831e-01 -2.60525763e-01
-8.62520456e-01 -7.06896842e-01 -6.38623953e-01 -4.95769501e-01
1.05405569e-01 9.17171896e-01 -8.51327926e-02 -2.27053285... | [8.874235153198242, 8.606270790100098] |
bc167495-0017-4725-a84b-95f43bb2830f | flying-guide-dog-walkable-path-discovery-for | 2108.07007 | null | https://arxiv.org/abs/2108.07007v1 | https://arxiv.org/pdf/2108.07007v1.pdf | Flying Guide Dog: Walkable Path Discovery for the Visually Impaired Utilizing Drones and Transformer-based Semantic Segmentation | Lacking the ability to sense ambient environments effectively, blind and visually impaired people (BVIP) face difficulty in walking outdoors, especially in urban areas. Therefore, tools for assisting BVIP are of great importance. In this paper, we propose a novel "flying guide dog" prototype for BVIP assistance using d... | ['Rainer Stiefelhagen', 'Kailun Yang', 'Constantin Seibold', 'Jiaming Zhang', 'Xinyu Luo', 'Chang Chen', 'Haobin Tan'] | 2021-08-16 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [-2.54007310e-01 -3.95500898e-01 9.72305983e-02 -2.51487970e-01
1.51157126e-01 -4.56971675e-01 8.50459337e-02 -3.76577616e-01
-2.88671941e-01 5.37131071e-01 -1.18333045e-02 -6.27103031e-01
2.52498657e-01 -9.89849746e-01 -9.81730074e-02 -5.54008245e-01
4.36526507e-01 -1.91206798e-01 6.52444184e-01 -5.81725299... | [7.810545921325684, -1.536839485168457] |
86ee4861-4c98-47c1-bd57-ede7fe104950 | the-atilf-llf-system-for-parseme-shared-task | null | null | https://aclanthology.org/W17-1717 | https://aclanthology.org/W17-1717.pdf | The ATILF-LLF System for Parseme Shared Task: a Transition-based Verbal Multiword Expression Tagger | We describe the ATILF-LLF system built for the MWE 2017 Shared Task on automatic identification of verbal multiword expressions. We participated in the closed track only, for all the 18 available languages. Our system is a robust greedy transition-based system, in which MWE are identified through a MERGE transition. Th... | ['C', 'Marie ito', 'Hazem Al Saied', 'Matthieu Constant'] | 2017-04-01 | null | null | null | ws-2017-4 | ['lexical-analysis'] | ['natural-language-processing'] | [-2.67368913e-01 -1.96899757e-01 -4.80655670e-01 -5.41580975e-01
-1.14169943e+00 -9.73753989e-01 5.17382324e-01 3.71847093e-01
-1.08128071e+00 7.18410969e-01 5.60731888e-01 -5.73965013e-01
-1.62173107e-01 -2.04957038e-01 -1.25694007e-01 -2.35820144e-01
-4.59548011e-02 5.95293522e-01 -2.51888096e-01 -2.69690245... | [10.535764694213867, 10.4058198928833] |
85901c72-56f4-4c72-a083-a2c03dc3b484 | probabilistic-spatial-distribution-prior | 2111.09006 | null | https://arxiv.org/abs/2111.09006v2 | https://arxiv.org/pdf/2111.09006v2.pdf | Probabilistic Spatial Distribution Prior Based Attentional Keypoints Matching Network | Keypoints matching is a pivotal component for many image-relevant applications such as image stitching, visual simultaneous localization and mapping (SLAM), and so on. Both handcrafted-based and recently emerged deep learning-based keypoints matching methods merely rely on keypoints and local features, while losing sig... | ['Zhengguo Li', 'Fanghong Guo', 'Weihai Chen', 'Xingming Wu', 'Jingmeng Liu', 'Xiaoming Zhao'] | 2021-11-17 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 4.62437840e-03 -1.72167718e-01 -4.17348385e-01 -2.97038078e-01
-2.93727696e-01 -1.45316243e-01 7.35057890e-01 7.74168968e-02
-5.66050828e-01 4.06830907e-01 4.26828042e-02 -9.14485231e-02
-3.89362365e-01 -6.10895634e-01 -9.13064539e-01 -5.52857280e-01
2.79552251e-01 1.50251210e-01 2.04648599e-02 -4.23590168... | [7.597728729248047, -2.058556318283081] |
19a13e25-b872-4aa8-83ab-ad1695ae1a23 | learning-to-rank-meets-language-boosting | 2306.13856 | null | https://arxiv.org/abs/2306.13856v1 | https://arxiv.org/pdf/2306.13856v1.pdf | Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification | We present a novel language-driven ordering alignment method for ordinal classification. The labels in ordinal classification contain additional ordering relations, making them prone to overfitting when relying solely on training data. Recent developments in pre-trained vision-language models inspire us to leverage the... | ['Zhaofeng He', 'Ran He', 'Chunshui Cao', 'Huaibo Huang', 'Peipei Li', 'Rui Wang'] | 2023-06-24 | null | null | null | null | ['age-estimation', 'learning-to-rank', 'classification-1', 'learning-to-rank', 'age-estimation'] | ['computer-vision', 'graphs', 'methodology', 'miscellaneous', 'miscellaneous'] | [ 3.43721718e-01 -4.44015898e-02 -2.77620733e-01 -7.97410309e-01
-7.45715201e-01 -3.69943202e-01 8.64082098e-01 1.95233926e-01
-6.72603488e-01 1.28817111e-01 5.71745634e-01 -7.90221617e-02
-1.17807485e-01 -3.29583228e-01 -4.14626151e-01 -5.09833336e-01
2.54639268e-01 8.44758153e-02 -3.31371814e-01 -2.99247298... | [10.740347862243652, 1.4427660703659058] |
c3829660-f1d8-404c-8b8a-606b1fe0e7da | an-easy-to-use-and-robust-approach-for-the | 2211.01147 | null | https://arxiv.org/abs/2211.01147v1 | https://arxiv.org/pdf/2211.01147v1.pdf | An Easy-to-use and Robust Approach for the Differentially Private De-Identification of Clinical Textual Documents | Unstructured textual data is at the heart of healthcare systems. For obvious privacy reasons, these documents are not accessible to researchers as long as they contain personally identifiable information. One way to share this data while respecting the legislative framework (notably GDPR or HIPAA) is, within the medica... | ['David Laiymani', 'Jean-François Couchot', 'Yakini Tchouka'] | 2022-11-02 | null | null | null | null | ['de-identification'] | ['natural-language-processing'] | [ 4.61215675e-01 5.03133714e-01 -5.66807576e-02 -2.02116057e-01
-8.12897742e-01 -8.95456791e-01 2.98965514e-01 8.80760014e-01
-8.48100305e-01 1.00012672e+00 3.99180681e-01 -4.11117762e-01
-2.51566201e-01 -6.73162460e-01 -1.78708553e-01 -6.81247115e-01
2.70498872e-01 7.04403639e-01 -3.44392136e-02 1.88652739... | [6.753940105438232, 7.0240325927734375] |
b3d88514-8aeb-4677-88e8-bf16ec76998a | active-scene-learning | 1903.02832 | null | http://arxiv.org/abs/1903.02832v1 | http://arxiv.org/pdf/1903.02832v1.pdf | Active Scene Learning | Sketch recognition allows natural and efficient interaction in pen-based
interfaces. A key obstacle to building accurate sketch recognizers has been the
difficulty of creating large amounts of annotated training data. Several
authors have attempted to address this issue by creating synthetic data, and by
building tools... | ['Tevfik Metin Sezgin', 'Erelcan Yanik'] | 2019-03-07 | null | null | null | null | ['sketch-recognition'] | ['computer-vision'] | [ 4.64559644e-01 2.95904338e-01 -3.03800911e-01 -3.53916407e-01
-8.93133104e-01 -8.60722542e-01 6.05639815e-01 9.31714177e-02
-2.69610733e-01 7.56735086e-01 9.32519976e-03 -1.10313259e-01
-3.66963521e-02 -7.18728960e-01 -4.34660345e-01 -5.30573308e-01
3.47338796e-01 9.96326625e-01 4.62986559e-01 8.10938701... | [9.501727104187012, 0.6844214200973511] |
e9ea93c9-3e1b-4d97-b0b5-c1c108325943 | similarity-learning-for-cover-song | null | null | https://ieeexplore.ieee.org/abstract/document/9053257 | https://ieeexplore.ieee.org/abstract/document/9053257 | SIMILARITY LEARNING FOR COVER SONG IDENTIFICATION USING CROSS-SIMILARITY MATRICES OF MULTI-LEVEL DEEP SEQUENCES | In recent years, several deep learning models have been proposed for cover song identification and they have been designed to learn fixed-length feature vectors for music tracks. However, the aspect of temporal progression of music, which is important for measuring the melody similarity between two tracks, is not well ... | ['Xiaoou Chen', 'Chaoya Jiang', 'Deshun Yang'] | 2020-05-14 | null | null | null | null | ['cover-song-identification'] | ['music'] | [-5.50812669e-03 -9.23762321e-01 -1.03081256e-01 -2.82247186e-01
-8.21843982e-01 -6.32267773e-01 1.26719564e-01 8.42504874e-02
-3.69330406e-01 2.40151584e-01 4.14369494e-01 1.94823384e-01
-4.38112348e-01 -6.63254082e-01 -3.67938936e-01 -4.63951886e-01
8.44135135e-02 1.87684759e-01 -6.34835614e-03 -1.39297083... | [15.809880256652832, 5.181699752807617] |
03089506-9290-4a43-9762-76284fde6f18 | crystalbox-future-based-explanations-for-drl | 2302.13483 | null | https://arxiv.org/abs/2302.13483v2 | https://arxiv.org/pdf/2302.13483v2.pdf | CrystalBox: Future-Based Explanations for DRL Network Controllers | Lack of explainability is a key factor limiting the practical adoption of high-performant Deep Reinforcement Learning (DRL) controllers. Explainable RL for networking hitherto used salient input features to interpret a controller's behavior. However, these feature-based solutions do not completely explain the controlle... | ['Nina Narodytska', 'Sangeetha Abdu Jyothi', 'Sagar Patel'] | 2023-02-27 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-2.14283690e-01 5.61391890e-01 -4.64070380e-01 -3.39940548e-01
-2.27177650e-01 -4.29334223e-01 1.24790594e-01 5.47947688e-03
-5.42678759e-02 9.45108652e-01 2.37990603e-01 -8.34459841e-01
-6.25445545e-01 -4.94250417e-01 -4.94861871e-01 -2.02390656e-01
-6.63953006e-01 1.95039496e-01 6.81535108e-03 -4.80084777... | [4.3569560050964355, 1.8469208478927612] |
5b483eb3-ff1c-4832-b34b-5a96076d7c45 | on-evaluating-the-adversarial-robustness-of | 2306.14217 | null | https://arxiv.org/abs/2306.14217v1 | https://arxiv.org/pdf/2306.14217v1.pdf | On Evaluating the Adversarial Robustness of Semantic Segmentation Models | Achieving robustness against adversarial input perturbation is an important and intriguing problem in machine learning. In the area of semantic image segmentation, a number of adversarial training approaches have been proposed as a defense against adversarial perturbation, but the methodology of evaluating the robustne... | ['Mark Jelasity', 'Levente Halmosi'] | 2023-06-25 | null | null | null | null | ['adversarial-robustness'] | ['adversarial'] | [ 6.42536104e-01 3.39771807e-01 2.96647370e-01 -1.56192794e-01
-8.57184172e-01 -1.05189872e+00 7.36116707e-01 6.17515296e-02
-3.61658156e-01 5.95791519e-01 -4.21431780e-01 -4.56775546e-01
-9.68839303e-02 -7.40153730e-01 -1.01767182e+00 -8.54195535e-01
-9.92970616e-02 3.68622005e-01 6.52305424e-01 -4.47170734... | [5.7534356117248535, 7.703568935394287] |
b0999e23-c45d-4ee8-a48d-19971504235c | understand-waiting-time-in-transaction-fee | 2305.02552 | null | https://arxiv.org/abs/2305.02552v1 | https://arxiv.org/pdf/2305.02552v1.pdf | Understand Waiting Time in Transaction Fee Mechanism: An Interdisciplinary Perspective | Blockchain enables peer-to-peer transactions in cyberspace without a trusted third party. The rapid growth of Ethereum and smart contract blockchains generally calls for well-designed Transaction Fee Mechanisms (TFMs) to allocate limited storage and computation resources. However, existing research on TFMs must conside... | ['Fan Zhang', 'Luyao Zhang'] | 2023-05-04 | null | null | null | null | ['causal-inference', 'computer-security', 'causal-inference'] | ['knowledge-base', 'miscellaneous', 'miscellaneous'] | [-4.12348270e-01 -1.44692481e-01 -8.18191648e-01 1.31392241e-01
-3.38489622e-01 -1.02644360e+00 8.30262125e-01 1.89132243e-02
-2.08007425e-01 7.30287433e-01 3.36119711e-01 -1.25480282e+00
-1.50431335e-01 -8.73033285e-01 -7.93479323e-01 -6.32988691e-01
-4.73935723e-01 1.97140202e-01 -8.91981646e-02 1.82244748... | [4.792539119720459, 4.126256942749023] |
fe8fd2c7-4ed1-49f8-9782-a550b249c4c6 | a-shared-task-on-multimodal-machine | null | null | https://aclanthology.org/W16-2346 | https://aclanthology.org/W16-2346.pdf | A Shared Task on Multimodal Machine Translation and Crosslingual Image Description | null | ["Khalil Sima{'}an", 'Lucia Specia', 'Stella Frank', 'Desmond Elliott'] | 2016-08-01 | null | null | null | ws-2016-8 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.395374298095703, 3.786696434020996] |
80bdc156-c331-4468-a70b-0033074c8b38 | reinforcement-learning-for-instance | null | null | https://openreview.net/forum?id=6EWOVxvJ5FI | https://openreview.net/pdf?id=6EWOVxvJ5FI | Reinforcement learning for instance segmentation with high-level priors | Instance segmentation is an important computer vision problem which remains challenging despite impressive recent advances due to deep learning-based methods. Given sufficient training data, fully supervised methods can yield excellent performance, but annotation of ground-truth data remains a major bottleneck, especia... | ['Anna Kreshuk', 'Constantin Pape', 'Maria Leptin', 'Sourabh Bhide', 'Edgar Kaziakhmedov', 'Paul Hilt'] | 2021-05-21 | null | null | null | neurips-2021-12 | ['graph-partitioning'] | ['graphs'] | [ 3.55634391e-01 5.44822991e-01 -2.82437325e-01 -4.85577822e-01
-7.58910596e-01 -4.08032447e-01 2.67438948e-01 3.42544675e-01
-6.53692126e-01 9.12934124e-01 -4.62213725e-01 -9.42789465e-02
-3.09324898e-02 -6.39466047e-01 -7.91451871e-01 -8.62765551e-01
1.27542883e-01 8.48097622e-01 3.67015868e-01 1.27728516... | [14.604175567626953, -2.072942018508911] |
8bf8cb24-fb06-448e-9512-ea4bfcf3be9d | channel-estimation-for-underwater-visible | 2303.07248 | null | https://arxiv.org/abs/2303.07248v1 | https://arxiv.org/pdf/2303.07248v1.pdf | Channel Estimation for Underwater Visible Light Communication: A Sparse Learning Perspective | The underwater propagation environment for visible light signals is affected by complex factors such as absorption, shadowing, and reflection, making it very challengeable to achieve effective underwater visible light communication (UVLC) channel estimation. It is difficult for the UVLC channel to be sparse represented... | ['Sicong Liu', 'Younan Mou'] | 2023-03-13 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 4.91651267e-01 -2.01457679e-01 4.85936373e-01 -1.47398546e-01
-5.72727859e-01 -9.50043574e-02 9.03194994e-02 -7.11510852e-02
-3.95961791e-01 7.61574268e-01 3.26517582e-01 -1.67034388e-01
-6.41257986e-02 -8.17846954e-01 -7.44866729e-01 -1.13660085e+00
-2.34233037e-01 -3.67592216e-01 -6.61138371e-02 -2.74627924... | [10.707524299621582, -3.3947317600250244] |
0be136a4-f0ce-427f-8de2-c5f06b1700ae | algorithme-em-regularise | 2307.01955 | null | https://arxiv.org/abs/2307.01955v1 | https://arxiv.org/pdf/2307.01955v1.pdf | Algorithme EM régularisé | Expectation-Maximization (EM) algorithm is a widely used iterative algorithm for computing maximum likelihood estimate when dealing with Gaussian Mixture Model (GMM). When the sample size is smaller than the data dimension, this could lead to a singular or poorly conditioned covariance matrix and, thus, to performance ... | ['Frederic Pascal', 'Matthieu Jonkcheere', 'Pierre Houdouin'] | 2023-07-04 | null | null | null | null | ['clustering'] | ['methodology'] | [ 6.05936125e-02 1.53553262e-01 5.21284193e-02 -1.95269018e-01
-6.57706499e-01 -2.37913415e-01 2.98951209e-01 -8.27630535e-02
-6.91288531e-01 8.63822341e-01 -2.65178680e-01 -2.77199566e-01
-2.99287736e-01 -3.59152853e-01 -2.04457164e-01 -7.98841476e-01
-9.10670459e-02 7.96465933e-01 -3.71966213e-02 5.78614533... | [7.340121746063232, 4.269839763641357] |
fd63024f-a486-4615-b98a-219eb1d465c5 | artificial-neural-networks-for-finger-vein | 2208.13341 | null | https://arxiv.org/abs/2208.13341v1 | https://arxiv.org/pdf/2208.13341v1.pdf | Artificial Neural Networks for Finger Vein Recognition: A Survey | Finger vein recognition is an emerging biometric recognition technology. Different from the other biometric features on the body surface, the venous vascular tissue of the fingers is buried deep inside the skin. Due to this advantage, finger vein recognition is highly stable and private. They are almost impossible to b... | ['Jinghua Zhang', 'Chen Li', 'Siliang He', 'Wanxia Deng', 'PengFei Liu', 'Renye Zhang', 'Yimin Yin'] | 2022-08-29 | null | null | null | null | ['finger-vein-recognition'] | ['computer-vision'] | [ 2.12781653e-01 -2.46769950e-01 -3.08984965e-01 -1.94301188e-01
3.66857618e-01 -7.12333024e-01 2.20531523e-01 -5.44123948e-01
-4.58721876e-01 6.69545412e-01 -2.82011509e-01 -1.86259702e-01
6.13139756e-02 -1.02197063e+00 3.48164998e-02 -7.92500496e-01
2.04715669e-01 2.69875093e-03 -6.47894815e-02 2.23965547... | [13.052458763122559, 1.0341206789016724] |
d1dd1fea-f088-459b-b0ac-eaebb878312f | label-free-liver-tumor-segmentation | 2303.14869 | null | https://arxiv.org/abs/2303.14869v1 | https://arxiv.org/pdf/2303.14869v1.pdf | Label-Free Liver Tumor Segmentation | We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intriguing advantages: (I) realistic in shape and texture, which even medical professionals can confuse with real tumors; (II) effective for train... | ['Zongwei Zhou', 'Alan Yuille', 'Jieneng Chen', 'Shuwen Sun', 'Junfei Xiao', 'Yixiong Chen', 'Qixin Hu'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hu_Label-Free_Liver_Tumor_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hu_Label-Free_Liver_Tumor_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['tumor-segmentation'] | ['computer-vision'] | [ 1.17475100e-01 7.94856369e-01 1.79534908e-02 -1.67893708e-01
-7.83492088e-01 -3.55837643e-01 4.45717305e-01 2.15498090e-01
4.75337841e-02 5.54584980e-01 1.95320949e-01 -4.94700938e-01
3.40644926e-01 -7.61596084e-01 -4.15105045e-01 -7.95987010e-01
-3.76347452e-01 1.01593971e+00 2.62199789e-01 1.43504813... | [14.625000953674316, -2.3880162239074707] |
b7d8e99d-9bdb-4d93-bb4f-462dd5ce514c | exploiting-implicit-rigidity-constraints-via | 2303.02454 | null | https://arxiv.org/abs/2303.02454v2 | https://arxiv.org/pdf/2303.02454v2.pdf | Exploiting Implicit Rigidity Constraints via Weight-Sharing Aggregation for Scene Flow Estimation from Point Clouds | Scene flow estimation, which predicts the 3D motion of scene points from point clouds, is a core task in autonomous driving and many other 3D vision applications. Existing methods either suffer from structure distortion due to ignorance of rigid motion consistency or require explicit pose estimation and 3D object segme... | ['Xin Yang', 'Cheng Chi', 'Yun Wang'] | 2023-03-04 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-2.81087756e-01 -2.83981502e-01 -1.52159110e-01 -5.37383378e-01
-1.88496649e-01 -5.99647403e-01 4.07137483e-01 -3.43867362e-01
-4.52930599e-01 3.71773630e-01 8.34028237e-03 -1.87940806e-01
3.18908989e-02 -7.69493401e-01 -7.17599809e-01 -4.40480500e-01
1.28880933e-01 3.50103468e-01 8.48618507e-01 -2.44071200... | [8.425508499145508, -2.0794005393981934] |
7620fc9e-7d04-4081-9e02-ce4063da7709 | pencil-deep-learning-with-noisy-labels | 2202.08436 | null | https://arxiv.org/abs/2202.08436v1 | https://arxiv.org/pdf/2202.08436v1.pdf | PENCIL: Deep Learning with Noisy Labels | Deep learning has achieved excellent performance in various computer vision tasks, but requires a lot of training examples with clean labels. It is easy to collect a dataset with noisy labels, but such noise makes networks overfit seriously and accuracies drop dramatically. To address this problem, we propose an end-to... | ['Jianxin Wu', 'Guo-Hua Wang', 'Kun Yi'] | 2022-02-17 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 0.17685907 -0.2138053 -0.05941239 -0.7608482 -0.803599 -0.61485445
0.3950215 -0.13819215 -0.62024474 0.7932058 -0.1849032 0.0235788
0.02963863 -0.45603448 -0.662473 -0.91547185 0.49740195 0.49555188
0.06624597 0.13347761 -0.24687687 0.11168811 -1.1840142 -0.03429516
0.6177374 1.0935805 0.1... | [9.38278865814209, 3.8765013217926025] |
0aef6e26-4d48-4a88-8cb9-a9dd9b1cba87 | read-attend-and-comment-a-deep-architecture | 1909.11974 | null | https://arxiv.org/abs/1909.11974v3 | https://arxiv.org/pdf/1909.11974v3.pdf | Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation | Automatic news comment generation is a new testbed for techniques of natural language generation. In this paper, we propose a "read-attend-comment" procedure for news comment generation and formalize the procedure with a reading network and a generation network. The reading network comprehends a news article and distil... | ['Zhoujun Li', 'Ze Yang', 'Wei Wu', 'Can Xu'] | 2019-09-26 | read-attend-and-comment-a-deep-architecture-2 | https://aclanthology.org/D19-1512 | https://aclanthology.org/D19-1512.pdf | ijcnlp-2019-11 | ['comment-generation'] | ['natural-language-processing'] | [ 2.35044390e-01 9.72614884e-01 -3.16170454e-01 -5.66217661e-01
-1.17030907e+00 -5.81190050e-01 1.06894207e+00 3.82017285e-01
-2.01535538e-01 1.04299784e+00 9.66684222e-01 -2.37473845e-01
1.61773935e-01 -6.90960646e-01 -5.91438472e-01 -4.97296333e-01
1.18077107e-01 7.64463723e-01 -5.31621911e-02 -3.80356640... | [12.29116153717041, 9.23512077331543] |
decfb259-6239-4faf-9f3f-7cdcf1908540 | enhanced-boundary-learning-for-glass-like | 2103.15734 | null | https://arxiv.org/abs/2103.15734v2 | https://arxiv.org/pdf/2103.15734v2.pdf | Enhanced Boundary Learning for Glass-like Object Segmentation | Glass-like objects such as windows, bottles, and mirrors exist widely in the real world. Sensing these objects has many applications, including robot navigation and grasping. However, this task is very challenging due to the arbitrary scenes behind glass-like objects. This paper aims to solve the glass-like object segm... | ['Lubin Weng', 'Véronique Prinet', 'Gaofeng Meng', 'Yunhai Tong', 'Jianping Shi', 'Guangliang Cheng', 'Xiangtai Li', 'Hao He'] | 2021-03-29 | null | http://openaccess.thecvf.com//content/ICCV2021/html/He_Enhanced_Boundary_Learning_for_Glass-Like_Object_Segmentation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/He_Enhanced_Boundary_Learning_for_Glass-Like_Object_Segmentation_ICCV_2021_paper.pdf | iccv-2021-1 | ['thermal-image-segmentation'] | ['computer-vision'] | [-6.09925240e-02 2.33757019e-01 5.08193932e-02 -5.18332720e-01
-4.39100713e-01 -5.23622990e-01 1.88174263e-01 -1.58713803e-01
-5.78417219e-02 2.29496285e-01 -3.97189289e-01 -7.36741051e-02
1.90827832e-01 -8.97707939e-01 -7.89544642e-01 -6.12841308e-01
2.82859541e-02 4.83456343e-01 7.36885250e-01 -2.34963909... | [8.039192199707031, -2.9115357398986816] |
773f38b8-2b70-4551-8343-d71e2adfc815 | isolation-kernel-and-its-effect-on-svm | null | null | https://dl.acm.org/doi/abs/10.1145/3219819.3219990 | https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/kdd18.pdf | Isolation Kernel and Its Effect on SVM | This paper investigates data dependent kernels that are derived directly from data. This has been an outstanding issue for about two decades which hampered the development of kernel-based methods. We introduce Isolation Kernel which is solely dependent on data distribution, requiring neither class information nor expli... | ['Zhi-Hua Zhou', 'Yue Zhu', 'Kai Ming Ting'] | 2018-07-19 | null | null | null | proceedings-of-the-24th-acm-sigkdd-1 | ['classification'] | ['methodology'] | [ 7.37902075e-02 -1.76774412e-01 -5.65447628e-01 -6.20325208e-01
-2.77551442e-01 -7.06154823e-01 5.04743099e-01 1.24915875e-01
-4.89896446e-01 8.28274310e-01 -1.94740176e-01 -6.60687149e-01
-4.08554226e-01 -9.33366001e-01 -4.64382559e-01 -8.60064507e-01
-1.65881723e-01 7.41977319e-02 5.85650504e-01 -7.54908621... | [7.8984856605529785, 4.003172874450684] |
bff659a6-b3f8-4205-ae2c-4b7867dfcab5 | a-two-stage-real-image-deraining-method-for | 2305.07979 | null | https://arxiv.org/abs/2305.07979v1 | https://arxiv.org/pdf/2305.07979v1.pdf | A Two-Stage Real Image Deraining Method for GT-RAIN Challenge CVPR 2023 Workshop UG$^{\textbf{2}}$+ Track 3 | In this technical report, we briefly introduce the solution of our team HUST\li VIE for GT-Rain Challenge in CVPR 2023 UG$^{2}$+ Track 3. In this task, we propose an efficient two-stage framework to reconstruct a clear image from rainy frames. Firstly, a low-rank based video deraining method is utilized to generate pse... | ['Luxin Yan', 'Yi Chang', 'Yi Li', 'Xiaoxiong Wang', 'Xueyao Xiao', 'Yun Guo'] | 2023-05-13 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 2.49965996e-01 -3.31817955e-01 6.18819833e-01 -5.53470194e-01
-9.78439331e-01 -3.61270934e-01 7.07430691e-02 -4.96114522e-01
-3.46645474e-01 6.97515249e-01 -8.50760490e-02 -3.08236599e-01
1.48067906e-01 -7.67011583e-01 -7.50109434e-01 -8.94525707e-01
-1.29100293e-01 -2.94619557e-02 7.67131373e-02 -3.56450111... | [10.946830749511719, -3.245706796646118] |
b71a4c00-d5e7-4394-bc52-49cfcbde04d4 | practical-first-order-bayesian-optimization | 2306.10815 | null | https://arxiv.org/abs/2306.10815v1 | https://arxiv.org/pdf/2306.10815v1.pdf | Practical First-Order Bayesian Optimization Algorithms | First Order Bayesian Optimization (FOBO) is a sample efficient sequential approach to find the global maxima of an expensive-to-evaluate black-box objective function by suitably querying for the function and its gradient evaluations. Such methods assume Gaussian process (GP) models for both, the function and its gradie... | ['Tejas Bodas', 'Prabuchandran K. J.', 'Kushagra Khatwani', 'Aryan Chollera', 'Utkarsh Prakash'] | 2023-06-19 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [-9.21505094e-02 -2.47084379e-01 -2.42902920e-01 -1.85923576e-01
-1.16936553e+00 -5.37535012e-01 4.75572944e-01 5.35913229e-01
-6.71161771e-01 8.03370714e-01 -3.28023106e-01 -1.17997780e-01
-6.83889806e-01 -7.05780029e-01 -9.87223685e-01 -1.07856596e+00
-3.24276716e-01 7.95990109e-01 4.89802837e-01 -1.82302052... | [6.184355735778809, 3.7685484886169434] |
c56dcfc0-f005-4d44-90ab-4001eedefb59 | midmed-towards-mixed-type-dialogues-for | 2306.02923 | null | https://arxiv.org/abs/2306.02923v2 | https://arxiv.org/pdf/2306.02923v2.pdf | MidMed: Towards Mixed-Type Dialogues for Medical Consultation | Most medical dialogue systems assume that patients have clear goals (medicine querying, surgical operation querying, etc.) before medical consultation. However, in many real scenarios, due to the lack of medical knowledge, it is usually difficult for patients to determine clear goals with all necessary slots. In this p... | ['Shaoting Zhang', 'Xiaofan Zhang', 'Kui Xue', 'Haitao Leng', 'Chuan Wang', 'Zeming Liu', 'Xiaoming Shi'] | 2023-06-05 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [ 5.63664399e-02 1.22502875e+00 4.81390543e-02 -4.22484368e-01
-9.66344714e-01 -5.53125799e-01 6.53738976e-01 4.23161954e-01
-2.70468444e-01 1.24023819e+00 8.88557494e-01 -6.09618127e-01
-1.52340278e-01 -4.71268564e-01 1.43430650e-01 -2.76398242e-01
3.67150486e-01 1.26661921e+00 1.96374953e-01 -6.91995323... | [12.441972732543945, 8.359033584594727] |
0c1f9470-8aee-4574-97eb-be266f435551 | a-large-scale-dataset-for-biomedical | 2211.12124 | null | https://arxiv.org/abs/2211.12124v1 | https://arxiv.org/pdf/2211.12124v1.pdf | A Large-Scale Dataset for Biomedical Keyphrase Generation | Keyphrase generation is the task consisting in generating a set of words or phrases that highlight the main topics of a document. There are few datasets for keyphrase generation in the biomedical domain and they do not meet the expectations in terms of size for training generative models. In this paper, we introduce kp... | ['Beatrice Daille', 'Florian Boudin', 'Mael Houbre'] | 2022-11-22 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 3.14607590e-01 4.49782103e-01 -1.99495286e-01 9.80750006e-03
-1.08232403e+00 -4.96096194e-01 9.91129279e-01 5.41942060e-01
-2.24184155e-01 1.45561600e+00 8.43056083e-01 -3.94098878e-01
-8.50855708e-02 -7.21536160e-01 -8.64069462e-01 -6.72293484e-01
1.65388823e-01 8.08726370e-01 7.98527375e-02 -1.58454418... | [8.671980857849121, 8.788402557373047] |
f3ec7ff5-43ae-4769-8c43-f124a8ed904c | water-from-two-rocks-maximizing-the-mutual | 1802.08887 | null | http://arxiv.org/abs/1802.08887v3 | http://arxiv.org/pdf/1802.08887v3.pdf | Water from Two Rocks: Maximizing the Mutual Information | We build a natural connection between the learning problem, co-training, and
forecast elicitation without verification (related to peer-prediction) and
address them simultaneously using the same information theoretic approach.
In co-training/multiview learning, the goal is to aggregate two views of data
into a predic... | ['Grant Schoenebeck', 'Yuqing Kong'] | 2018-02-24 | null | null | null | null | ['multiview-learning'] | ['computer-vision'] | [ 3.31719726e-01 7.01482594e-01 -5.22044539e-01 -7.58493006e-01
-1.27925754e+00 -6.61347568e-01 7.09823191e-01 -8.78018811e-02
-2.55852669e-01 8.63676012e-01 3.49819928e-01 -1.51899755e-01
-5.11019111e-01 -4.55510974e-01 -8.56067061e-01 -7.90360689e-01
2.20252723e-01 9.46584940e-01 -3.16426605e-01 2.18728930... | [4.667985916137695, 3.3426458835601807] |
0f798dd7-3868-4dd8-bf75-d22a1f80e42f | evaluation-datasets-for-cross-lingual | null | null | https://aclanthology.org/2021.ranlp-main.59 | https://aclanthology.org/2021.ranlp-main.59.pdf | Evaluation Datasets for Cross-lingual Semantic Textual Similarity | Semantic textual similarity (STS) systems estimate the degree of the meaning similarity between two sentences. Cross-lingual STS systems estimate the degree of the meaning similarity between two sentences, each in a different language. State-of-the-art algorithms usually employ a strongly supervised, resource-rich appr... | ['Pavel Kral', 'Tomáš Hercig'] | null | null | https://aclanthology.org/2021.ranlp-1.59 | https://aclanthology.org/2021.ranlp-1.59.pdf | ranlp-2021-9 | ['cross-lingual-semantic-textual-similarity'] | ['natural-language-processing'] | [-5.07358946e-02 -9.91831124e-02 -2.11089388e-01 -8.24547648e-01
-8.57996881e-01 -7.89136410e-01 8.09298337e-01 5.33026516e-01
-5.96360028e-01 5.45199275e-01 5.35226226e-01 -1.23154186e-01
3.31387103e-01 -5.02978384e-01 -2.16495246e-01 -3.12439829e-01
3.23926955e-01 6.88742399e-01 4.36330557e-01 -7.07107723... | [10.88592529296875, 9.668302536010742] |
2fb5c8e3-4d9c-4dcc-bc33-0195d5f4edf4 | proposal-learning-for-semi-supervised-object | 2001.05086 | null | https://arxiv.org/abs/2001.05086v2 | https://arxiv.org/pdf/2001.05086v2.pdf | Proposal Learning for Semi-Supervised Object Detection | In this paper, we focus on semi-supervised object detection to boost performance of proposal-based object detectors (a.k.a. two-stage object detectors) by training on both labeled and unlabeled data. However, it is non-trivial to train object detectors on unlabeled data due to the unavailability of ground truth labels.... | ['ran Xu', 'Peng Tang', 'Chetan Ramaiah', 'Caiming Xiong', 'Yan Wang'] | 2020-01-15 | null | null | null | null | ['robust-object-detection', 'semi-supervised-object-detection'] | ['computer-vision', 'computer-vision'] | [-5.98241808e-03 1.99398011e-01 -2.71292508e-01 -6.21911287e-01
-1.21458232e+00 -4.57140297e-01 6.18822157e-01 3.31345677e-01
-6.78434074e-01 2.38510385e-01 -2.71055788e-01 -5.71926236e-02
3.73529077e-01 -5.78324854e-01 -8.19867015e-01 -6.98811769e-01
2.52540946e-01 6.21410072e-01 9.46787596e-01 7.30398148... | [9.22641372680664, 1.1779273748397827] |
496a6edd-17fd-4cd8-b9af-3343e95128ca | on-the-proper-treatment-of-quantifiers-in | null | null | https://aclanthology.org/W15-0119 | https://aclanthology.org/W15-0119.pdf | On the Proper Treatment of Quantifiers in Probabilistic Logic Semantics | null | ['Katrin Erk', 'Islam Beltagy'] | 2015-04-01 | null | null | null | ws-2015-4 | ['fine-grained-opinion-analysis'] | ['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... | [-7.393303871154785, 3.7764434814453125] |
d7ccc8ec-0dd6-4ec2-aa86-5645e31e2303 | videnn-deep-blind-video-denoising | 1904.10898 | null | http://arxiv.org/abs/1904.10898v1 | http://arxiv.org/pdf/1904.10898v1.pdf | ViDeNN: Deep Blind Video Denoising | We propose ViDeNN: a CNN for Video Denoising without prior knowledge on the
noise distribution (blind denoising). The CNN architecture uses a combination
of spatial and temporal filtering, learning to spatially denoise the frames
first and at the same time how to combine their temporal information, handling
objects mot... | ['Jan van Gemert', 'Michele Claus'] | 2019-04-24 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 9.93694216e-02 -4.95868713e-01 3.94219726e-01 -4.01362002e-01
-4.21747684e-01 -5.83996832e-01 5.21652520e-01 -3.69295403e-02
-8.09940755e-01 5.66650808e-01 2.37828061e-01 1.37173161e-01
-1.04618885e-01 -6.85152352e-01 -7.91406035e-01 -9.42570806e-01
-1.44727036e-01 -8.44246075e-02 6.70198858e-01 -2.93079853... | [11.442286491394043, -2.1945457458496094] |
e75718d0-952e-4176-8894-9906aa5d580d | gpt-nas-neural-architecture-search-with-the | 2305.05351 | null | https://arxiv.org/abs/2305.05351v2 | https://arxiv.org/pdf/2305.05351v2.pdf | GPT-NAS: Evolutionary Neural Architecture Search with the Generative Pre-Trained Model | Neural Architecture Search (NAS) has emerged as one of the effective methods to design the optimal neural network architecture automatically. Although neural architectures have achieved human-level performances in several tasks, few of them are obtained from the NAS method. The main reason is the huge search space of n... | ['Chenwei Tang', 'Wentao Feng', 'Jiancheng Lv', 'Xianggen Liu', 'Caiyang Yu'] | 2023-05-09 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 1.10531628e-01 2.32098833e-01 3.21058109e-02 -2.57183075e-01
-5.62330425e-01 -5.72825670e-01 3.04304689e-01 -5.59495747e-01
-3.74714524e-01 1.90023646e-01 1.00533448e-01 -3.81100893e-01
-1.97763115e-01 -6.89978361e-01 -9.06160831e-01 -7.11135328e-01
4.73319083e-01 7.62794733e-01 -5.80768473e-02 -2.15140015... | [8.583045959472656, 3.295969247817993] |
9005d1aa-e72a-4c50-ba51-746d111593de | drg-net-interactive-joint-learning-of-multi | 2212.14615 | null | https://arxiv.org/abs/2212.14615v1 | https://arxiv.org/pdf/2212.14615v1.pdf | DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Grading | Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease gr... | ['Daniel Sonntag', 'Pengtao Xie', 'Ngan Le', 'Ngoc T. T. Than', 'Hans-Juergen Profitlich', 'Michael Barz', 'Binh T. Nguyen', 'Triet A. Nguyen', 'Mai T. N. Truong', 'Duy M. H. Nguyen', 'Hasan Md Tusfiqur'] | 2022-12-30 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [ 6.83065131e-02 4.75582272e-01 -8.57707039e-02 -6.12768352e-01
-8.44132602e-01 -3.22987616e-01 1.67449042e-02 -1.67997868e-03
-1.53905496e-01 6.56836092e-01 2.47033462e-01 -3.47582936e-01
-1.03964530e-01 -8.06365788e-01 -5.84722698e-01 -5.60326159e-01
2.33705759e-01 3.06123883e-01 2.47457832e-01 2.17157230... | [15.785286903381348, -3.95110821723938] |
40ac134a-a914-46a6-a901-e1543b39838f | investigating-the-effectiveness-of-chatgpt-in | 2306.06331 | null | https://arxiv.org/abs/2306.06331v1 | https://arxiv.org/pdf/2306.06331v1.pdf | Investigating the Effectiveness of ChatGPT in Mathematical Reasoning and Problem Solving: Evidence from the Vietnamese National High School Graduation Examination | This study offers a complete analysis of ChatGPT's mathematics abilities in responding to multiple-choice questions for the Vietnamese National High School Graduation Examination (VNHSGE) on a range of subjects and difficulty levels. The dataset included 250 questions divided into four levels: knowledge (K), comprehens... | ['Ngoc-Bich Le', 'Xuan-Quy Dao'] | 2023-06-10 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [-7.94470549e-01 -5.74372150e-02 6.38442561e-02 -1.60736546e-01
-1.09583139e+00 -9.90593135e-01 -1.21869799e-02 7.80742586e-01
-3.94965947e-01 5.61673522e-01 -2.48170719e-01 -1.24333203e+00
-7.44275630e-01 -1.20972240e+00 -6.29712522e-01 -1.66976109e-01
-6.01036064e-02 2.58523524e-01 1.27137437e-01 -7.01138198... | [9.967108726501465, 7.390214443206787] |
5b8c33cc-7a60-4021-908c-388a8b723b6f | diverse-human-motion-prediction-via-gumbel | 2207.07351 | null | https://arxiv.org/abs/2207.07351v1 | https://arxiv.org/pdf/2207.07351v1.pdf | Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary Space | Diverse human motion prediction aims at predicting multiple possible future pose sequences from a sequence of observed poses. Previous approaches usually employ deep generative networks to model the conditional distribution of data, and then randomly sample outcomes from the distribution. While different results can be... | ['Guiqing Li', 'Qing Zhang', 'Chengjiang Long', 'Yongwei Nie', 'Lingwei Dang'] | 2022-07-15 | null | null | null | null | ['human-pose-forecasting'] | ['computer-vision'] | [ 1.11958936e-01 -9.59184989e-02 -4.49034721e-01 -5.09808004e-01
-1.19187522e+00 -3.50290895e-01 7.21630156e-01 -5.39268255e-01
-3.20639044e-01 1.05497205e+00 3.48506302e-01 1.21212743e-01
9.50486287e-02 -8.08517337e-01 -1.04681957e+00 -8.24765444e-01
7.34350830e-02 9.61865008e-01 1.97105318e-01 -5.58531806... | [7.243173599243164, -0.11432895064353943] |
f5d33768-844b-463b-b86f-7a7c60f82b2b | panoocc-unified-occupancy-representation-for | 2306.10013 | null | https://arxiv.org/abs/2306.10013v1 | https://arxiv.org/pdf/2306.10013v1.pdf | PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation | Comprehensive modeling of the surrounding 3D world is key to the success of autonomous driving. However, existing perception tasks like object detection, road structure segmentation, depth & elevation estimation, and open-set object localization each only focus on a small facet of the holistic 3D scene understanding ta... | ['Zhaoxiang Zhang', 'Lue Fan', 'Xingyu Liao', 'Yuntao Chen', 'Yuqi Wang'] | 2023-06-16 | null | null | null | null | ['panoptic-segmentation', 'object-localization', 'scene-understanding'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 7.22204968e-02 -3.11330527e-01 -1.24785542e-01 -3.84481817e-01
-8.41126621e-01 -5.80680072e-01 5.15246689e-01 1.49738938e-01
-3.04313153e-01 2.60029376e-01 -1.75746724e-01 -3.34973425e-01
-1.25155523e-01 -9.36332405e-01 -6.16727948e-01 -5.63607216e-01
2.59452045e-01 5.20783126e-01 6.32074177e-01 -5.99796288... | [8.195631980895996, -2.4120066165924072] |
7f8afb45-03ef-4f8f-94e2-056cd4530214 | machine-learning-models-in-stock-market | 2202.09359 | null | https://arxiv.org/abs/2202.09359v1 | https://arxiv.org/pdf/2202.09359v1.pdf | Machine Learning Models in Stock Market Prediction | The paper focuses on predicting the Nifty 50 Index by using 8 Supervised Machine Learning Models. The techniques used for empirical study are Adaptive Boost (AdaBoost), k-Nearest Neighbors (kNN), Linear Regression (LR), Artificial Neural Network (ANN), Random Forest (RF), Stochastic Gradient Descent (SGD), Support Vect... | ['Gurjeet Singh'] | 2022-02-06 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-5.17246008e-01 -2.63319880e-01 -2.29380220e-01 -4.04673457e-01
4.66968864e-02 -7.64698505e-01 7.77012348e-01 1.48768112e-01
-6.02542698e-01 1.07345784e+00 1.78590387e-01 -8.60512137e-01
-3.21913749e-01 -1.16175139e+00 -9.22888294e-02 -4.42486614e-01
-5.93349457e-01 8.85884106e-01 6.10700667e-01 -3.71845663... | [4.52291202545166, 4.2203850746154785] |
c9de2156-e857-4825-8503-c3b7534e3a4e | joint-tone-mapping-and-denoising-of-thermal | 2305.00691 | null | https://arxiv.org/abs/2305.00691v1 | https://arxiv.org/pdf/2305.00691v1.pdf | Joint tone mapping and denoising of thermal infrared images via multi-scale Retinex and multi-task learning | Cameras digitize real-world scenes as pixel intensity values with a limited value range given by the available bits per pixel (bpp). High Dynamic Range (HDR) cameras capture those luminance values in higher resolution through an increase in the number of bpp. Most displays, however, are limited to 8 bpp. Naive HDR comp... | ['Michael Teutsch', 'Gabriel Eilertsen', 'Daniel König', 'Axel Gödrich'] | 2023-05-01 | null | null | null | null | ['video-enhancement', 'tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 9.35676455e-01 -4.16925281e-01 -8.34222957e-02 -3.57061207e-01
-8.08954120e-01 -5.42454496e-02 3.92102689e-01 -4.39365774e-01
-6.84643209e-01 7.09140539e-01 2.08843842e-01 3.71419825e-02
-5.62469251e-02 -9.42945182e-01 -1.03536832e+00 -9.66520488e-01
3.52075428e-01 -3.03685546e-01 1.74970597e-01 -3.78370494... | [10.815171241760254, -2.236598253250122] |
4c9fdfb5-1199-4643-9f22-eb8d9490a845 | class-attention-transfer-based-knowledge | 2304.12777 | null | https://arxiv.org/abs/2304.12777v1 | https://arxiv.org/pdf/2304.12777v1.pdf | Class Attention Transfer Based Knowledge Distillation | Previous knowledge distillation methods have shown their impressive performance on model compression tasks, however, it is hard to explain how the knowledge they transferred helps to improve the performance of the student network. In this work, we focus on proposing a knowledge distillation method that has both high in... | ['Xiaodong Lin', 'Hui Li', 'Haonan Yan', 'Ziyao Guo'] | 2023-04-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Guo_Class_Attention_Transfer_Based_Knowledge_Distillation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Guo_Class_Attention_Transfer_Based_Knowledge_Distillation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['model-compression'] | ['methodology'] | [ 3.92239466e-02 4.12285030e-01 -3.43834579e-01 -3.47776204e-01
-4.13874000e-01 -6.46168590e-01 3.95276070e-01 1.50110602e-01
-4.27902639e-01 7.15849876e-01 2.53456861e-01 -5.81585228e-01
-3.27230960e-01 -9.32189941e-01 -1.05700743e+00 -4.98358071e-01
2.52733022e-01 3.55352789e-01 2.84465820e-01 -7.36577883... | [9.44192886352539, 3.2203290462493896] |
caa854fa-2163-4a9b-a952-4e30a584179b | eventgraph-event-extraction-as-semantic-graph | 2210.08646 | null | https://arxiv.org/abs/2210.08646v1 | https://arxiv.org/pdf/2210.08646v1.pdf | EventGraph: Event Extraction as Semantic Graph Parsing | Event extraction involves the detection and extraction of both the event triggers and corresponding event arguments. Existing systems often decompose event extraction into multiple subtasks, without considering their possible interactions. In this paper, we propose EventGraph, a joint framework for event extraction, wh... | ['Lilja Øvrelid', 'Samia Touileb', 'David Samuel', 'Huiling You'] | 2022-10-16 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 3.12099129e-01 4.74846303e-01 -2.29985535e-01 -3.39888930e-01
-8.85254979e-01 -9.05705810e-01 1.02047086e+00 9.90202606e-01
-4.20079768e-01 6.70258641e-01 6.18851244e-01 -3.50610524e-01
6.50841137e-03 -1.02139628e+00 -6.13116145e-01 -9.18208733e-02
-4.52484906e-01 3.89529765e-01 8.30519557e-01 2.44920611... | [9.037820816040039, 9.188619613647461] |
eaae584a-6196-46e8-b545-fd4c573a049d | viesum-how-robust-are-transformer-based | 2110.04257 | null | https://arxiv.org/abs/2110.04257v1 | https://arxiv.org/pdf/2110.04257v1.pdf | VieSum: How Robust Are Transformer-based Models on Vietnamese Summarization? | Text summarization is a challenging task within natural language processing that involves text generation from lengthy input sequences. While this task has been widely studied in English, there is very limited research on summarization for Vietnamese text. In this paper, we investigate the robustness of transformer-bas... | ['Hieu Tran', 'Alec Peltekian', 'James Anibal', 'Long Phan', 'Hieu Nguyen'] | 2021-10-08 | null | null | null | null | ['vietnamese-datasets'] | ['natural-language-processing'] | [ 7.28474677e-01 3.87027174e-01 -2.74712235e-01 -3.46681744e-01
-1.23033643e+00 -4.60089296e-01 6.78766131e-01 2.00424358e-01
-4.91495222e-01 1.16080213e+00 1.11891484e+00 -3.42489600e-01
4.35112417e-01 -4.33642983e-01 -7.29594886e-01 -1.52303979e-01
1.90148428e-01 3.16278815e-01 -4.03119996e-02 -5.61420381... | [12.393059730529785, 9.563234329223633] |
b7c1f504-3877-4070-afc1-9060128e547f | mrn-multiplexed-routing-network-for | 2305.14758 | null | https://arxiv.org/abs/2305.14758v1 | https://arxiv.org/pdf/2305.14758v1.pdf | MRN: Multiplexed Routing Network for Incremental Multilingual Text Recognition | Traditional Multilingual Text Recognition (MLTR) usually targets a fixed set of languages and thus struggles to handle newly added languages or adapt to ever-changing class distributions. In this paper, we introduce the Incremental Multilingual Text Recognition (IMLTR) task in the incremental learning setting, where ne... | ['Yu-Gang Jiang', 'Wei zhang', 'Bingchen Huang', 'Zhineng Chen', 'Tianlun Zheng'] | 2023-05-24 | null | null | null | null | ['incremental-learning'] | ['methodology'] | [ 3.34674835e-01 -4.97353196e-01 -5.29678404e-01 -5.21195889e-01
-1.11176848e+00 -5.34257948e-01 5.36455989e-01 1.52930260e-01
-7.86787510e-01 7.90537655e-01 -4.24130261e-02 -6.13423884e-01
2.79101372e-01 -4.04700369e-01 -7.65923142e-01 -5.58137119e-01
1.27546251e-01 7.63326049e-01 8.11234340e-02 -4.60214354... | [13.852737426757812, 7.053201675415039] |
1338cc8e-327d-4f7a-ad7e-309e8d231c97 | on-the-generalizability-of-neural-program | 2008.01566 | null | https://arxiv.org/abs/2008.01566v3 | https://arxiv.org/pdf/2008.01566v3.pdf | On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations | With the prevalence of publicly available source code repositories to train deep neural network models, neural program models can do well in source code analysis tasks such as predicting method names in given programs that cannot be easily done by traditional program analysis techniques. Although such neural program mo... | ['Ke Wang', 'Md Rafiqul Islam Rabin', 'Lingxiao Jiang', 'Nghi D. Q. Bui', 'Mohammad Amin Alipour', 'Yijun Yu'] | 2020-07-31 | null | null | null | null | ['method-name-prediction'] | ['natural-language-processing'] | [ 8.42430815e-02 1.16626054e-01 -4.93091822e-01 -5.99548340e-01
-2.04135239e-01 -5.35634756e-01 1.48050308e-01 3.06342930e-01
-3.18281725e-02 7.62051567e-02 3.30180764e-01 -9.59750950e-01
2.65872508e-01 -9.93208468e-01 -1.27310157e+00 -7.48941861e-03
-1.21963657e-01 -4.38906029e-02 2.09616885e-01 -3.89645070... | [7.568301200866699, 7.795657634735107] |
6775e14e-b152-4f89-96f5-5e83c4897366 | myocardial-infarction-detection-from-ecg-a | 2302.13011 | null | https://arxiv.org/abs/2302.13011v1 | https://arxiv.org/pdf/2302.13011v1.pdf | Myocardial Infarction Detection from ECG: A Gramian Angular Field-based 2D-CNN Approach | This paper presents a novel method for myocardial infarction (MI) detection using lead II of electrocardiogram (ECG). Under our proposed method, we first clean the noisy ECG signals using db4 wavelet, followed by an R-peak detection algorithm to segment the ECG signals into beats. We then translate the ECG timeseries d... | ['Muhammad Mahboob Ur Rahman', 'Kashif Riaz', 'Muhammad Farooq', 'Saqib Riaz', 'Rehan Hafiz', 'Asim Yousuf'] | 2023-02-25 | null | null | null | null | ['myocardial-infarction-detection'] | ['medical'] | [ 5.01082897e-01 -3.11102986e-01 3.21015358e-01 -1.53617799e-01
-6.28841579e-01 -4.95666653e-01 -1.18691392e-01 1.25563294e-01
-4.94736433e-01 5.36953747e-01 -2.66253591e-01 -4.89097744e-01
-3.09637576e-01 -7.01106846e-01 -2.65135169e-01 -8.40762377e-01
-6.05796039e-01 -7.33434781e-02 -2.05807745e-01 1.15056708... | [14.313314437866211, 3.248304843902588] |
7265804d-547a-4263-a8b3-e1bf5563ebd7 | nsp-ner-a-prompt-based-learner-for-few-shot | null | null | https://openreview.net/forum?id=8CRReJ4AgsG | https://openreview.net/pdf?id=8CRReJ4AgsG | NSP-NER: A Prompt-based Learner for Few-shot NER Driven by Next Sentence Prediction | Recently, prompt-based learning has achieved great success in few-shot learning. This paradigm does better integrate pre-training and downstream tasks and mine the knowledge inherent in the pre-train language model itself. Most research on prompt-learning has been conducted, but the application of prompt-based learning... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['few-shot-ner'] | ['natural-language-processing'] | [ 1.96401715e-01 8.63851458e-02 -3.11391711e-01 -4.45005298e-01
-9.57710981e-01 -4.66603905e-01 4.67691094e-01 3.65721673e-01
-8.62845063e-01 7.61636019e-01 5.99388003e-01 -1.98033258e-01
1.88398689e-01 -8.35025489e-01 -4.39255953e-01 -3.58846903e-01
1.90007657e-01 2.10689366e-01 6.57525361e-01 -5.08673251... | [9.868803024291992, 9.1788911819458] |
5dd96297-d368-49f4-afbc-195e33761f3a | classifying-segmenting-and-tracking-object | 1912.04573 | null | https://arxiv.org/abs/1912.04573v4 | https://arxiv.org/pdf/1912.04573v4.pdf | Classifying, Segmenting, and Tracking Object Instances in Video with Mask Propagation | We introduce a method for simultaneously classifying, segmenting and tracking object instances in a video sequence. Our method, named MaskProp, adapts the popular Mask R-CNN to video by adding a mask propagation branch that propagates frame-level object instance masks from each video frame to all the other frames in a ... | ['Gedas Bertasius', 'Lorenzo Torresani'] | 2019-12-10 | classifying-segmenting-and-tracking-object-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Bertasius_Classifying_Segmenting_and_Tracking_Object_Instances_in_Video_with_Mask_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Bertasius_Classifying_Segmenting_and_Tracking_Object_Instances_in_Video_with_Mask_CVPR_2020_paper.pdf | cvpr-2020-6 | ['video-instance-segmentation'] | ['computer-vision'] | [ 2.98984736e-01 7.68493637e-02 -5.25139093e-01 -3.56119603e-01
-9.72470105e-01 -8.59486222e-01 3.10357630e-01 -2.08717957e-02
-4.49551493e-01 5.09237111e-01 -7.38479719e-02 1.31977797e-02
2.51974076e-01 -3.35120469e-01 -1.26150310e+00 -3.50423843e-01
-4.07385200e-01 4.02267069e-01 8.60689580e-01 4.23387825... | [9.142114639282227, -0.14747285842895508] |
214415ec-fea5-41bf-90e6-7d74d99c4142 | acnet-attention-based-network-to-exploit | 1905.10089 | null | https://arxiv.org/abs/1905.10089v1 | https://arxiv.org/pdf/1905.10089v1.pdf | ACNet: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation | Compared to RGB semantic segmentation, RGBD semantic segmentation can achieve better performance by taking depth information into consideration. However, it is still problematic for contemporary segmenters to effectively exploit RGBD information since the feature distributions of RGB and depth (D) images vary significa... | ['Kaiwei Wang', 'Lei Fei', 'Kailun Yang', 'Xinxin Hu'] | 2019-05-24 | null | null | null | null | ['thermal-image-segmentation'] | ['computer-vision'] | [ 6.43664300e-02 2.05396235e-01 -2.01366216e-01 -2.99419403e-01
-6.98570251e-01 -3.82931858e-01 2.56288439e-01 -1.87298283e-01
-5.27963042e-01 3.73729914e-01 9.08102244e-02 -2.16641322e-01
3.20380896e-01 -9.43172991e-01 -5.47397614e-01 -8.38224053e-01
3.20899338e-01 5.77814765e-02 5.51328421e-01 -1.10778719... | [9.430039405822754, -1.086272954940796] |
815f3375-70e0-4e19-90e7-5082e19ba00c | the-spoken-language-understanding-media | null | null | https://aclanthology.org/2022.lrec-1.171 | https://aclanthology.org/2022.lrec-1.171.pdf | The Spoken Language Understanding MEDIA Benchmark Dataset in the Era of Deep Learning: data updates, training and evaluation tools | With the emergence of neural end-to-end approaches for spoken language understanding (SLU), a growing number of studies have been presented during these last three years on this topic. The major part of these works addresses the spoken language understanding domain through a simple task like speech intent detection. In... | ['Yannick Estève', 'Bassam Jabaian', 'Sahar Ghannay', 'Nathalie Camelin', 'Salima Mdhaffar', 'Antoine Caubrière', 'Valentin Pelloin', 'Gaëlle Laperrière'] | null | null | null | null | lrec-2022-6 | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 3.31001654e-02 4.46060628e-01 2.60023445e-01 -6.85907125e-01
-7.73826361e-01 -4.98160630e-01 8.55077863e-01 4.41920832e-02
-7.23358631e-01 8.49404573e-01 7.05419421e-01 -3.76018733e-02
1.75263122e-01 -3.68044794e-01 -4.25935656e-01 -4.26381171e-01
1.52499437e-01 7.88736224e-01 2.19775572e-01 -4.45390612... | [13.966066360473633, 6.896762371063232] |
b5673255-831c-4c99-ab6b-56703bcd76e1 | semi-supervised-vocabulary-informed-learning | 1604.07093 | null | http://arxiv.org/abs/1604.07093v1 | http://arxiv.org/pdf/1604.07093v1.pdf | Semi-supervised Vocabulary-informed Learning | Despite significant progress in object categorization, in recent years, a
number of important challenges remain, mainly, ability to learn from limited
labeled data and ability to recognize object classes within large, potentially
open, set of labels. Zero-shot learning is one way of addressing these
challenges, but it ... | ['Yanwei Fu', 'Leonid Sigal'] | 2016-04-24 | semi-supervised-vocabulary-informed-learning-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Fu_Semi-Supervised_Vocabulary-Informed_Learning_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Fu_Semi-Supervised_Vocabulary-Informed_Learning_CVPR_2016_paper.pdf | cvpr-2016-6 | ['object-categorization'] | ['computer-vision'] | [ 3.68966222e-01 2.19328552e-01 -5.71036696e-01 -7.18410015e-01
-7.17298687e-01 -6.00980759e-01 5.67293644e-01 1.91228092e-01
-3.47993672e-01 5.15991688e-01 1.39622331e-01 1.72868416e-01
-3.91998142e-01 -6.90478206e-01 -4.77878004e-01 -6.41640484e-01
3.76472734e-02 8.82857680e-01 1.34761095e-01 2.47259866... | [9.976364135742188, 2.496323585510254] |
da1840f5-5f87-46b0-a0d1-4016987ae10d | adaptive-bayesian-beamforming-for-imaging-by | 2212.03824 | null | https://arxiv.org/abs/2212.03824v2 | https://arxiv.org/pdf/2212.03824v2.pdf | Adaptive Bayesian Beamforming for Imaging by Marginalizing the Speed of Sound | Imaging methods based on array signal processing often require a fixed propagation speed of the medium, or speed of sound (SoS) for methods based on acoustic signals. The resolution of the images formed using these methods is strongly affected by the assumed SoS, which, due to multipath, nonlinear propagation, and non-... | ['Jason F. Ralph', 'Simon Maskell', 'Kyurae Kim'] | 2022-12-07 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 5.60509384e-01 -5.25202096e-01 1.06126177e+00 -1.90390095e-01
-8.71694863e-01 -6.69264913e-01 2.85781771e-01 -3.11335832e-01
-6.47605181e-01 4.82563883e-01 3.19258153e-01 -2.41461083e-01
-8.04790735e-01 -5.83938897e-01 -3.97590846e-01 -1.33563507e+00
-3.80624592e-01 1.13283126e-02 1.01800069e-01 1.29740030... | [6.554741859436035, 1.3102574348449707] |
1addf3b5-9153-4f21-b67e-aa3dc6bf24ae | an-audio-video-deep-and-transfer-learning | 2010.03692 | null | https://arxiv.org/abs/2010.03692v3 | https://arxiv.org/pdf/2010.03692v3.pdf | An Audio-Video Deep and Transfer Learning Framework for Multimodal Emotion Recognition in the wild | In this paper, we present our contribution to ABAW facial expression challenge. We report the proposed system and the official challenge results adhering to the challenge protocol. Using end-to-end deep learning and benefiting from transfer learning approaches, we reached a test set challenge performance measure of 42.... | ['Wolfgang Minker', 'Alexey Karpov', 'Maxim Markitantov', 'Heysem Kaya', 'Elena Ryumina', 'Denis Dresvyanskiy'] | 2020-10-07 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-6.11823872e-02 1.86056614e-01 1.03916049e-01 -9.70317304e-01
-9.57571566e-01 -2.12415025e-01 3.81913602e-01 -7.44220376e-01
-5.91480732e-01 7.34119534e-01 -1.57115180e-02 1.78938061e-01
3.20744693e-01 -1.49279058e-01 -2.96773911e-01 -2.79316425e-01
-6.41414106e-01 1.90394133e-01 -4.09023225e-01 -8.14336896... | [13.580923080444336, 1.8527828454971313] |
0511e436-243d-427b-b979-06a0a1a09e3e | latincy-synthetic-trained-pipelines-for-latin | 2305.04365 | null | https://arxiv.org/abs/2305.04365v1 | https://arxiv.org/pdf/2305.04365v1.pdf | LatinCy: Synthetic Trained Pipelines for Latin NLP | This paper introduces LatinCy, a set of trained general purpose Latin-language "core" pipelines for use with the spaCy natural language processing framework. The models are trained on a large amount of available Latin data, including all five of the Latin Universal Dependency treebanks, which have been preprocessed to ... | ['Patrick J. Burns'] | 2023-05-07 | null | null | null | null | ['lemmatization', 'morphological-tagging'] | ['natural-language-processing', 'natural-language-processing'] | [-3.67769241e-01 1.79938465e-01 -4.91575867e-01 -4.86722559e-01
-1.03421640e+00 -1.00153339e+00 4.78881747e-01 5.57437181e-01
-7.79849112e-01 7.94133902e-01 4.79001999e-01 -6.96166813e-01
2.69999385e-01 -5.75602174e-01 -1.45576447e-01 -3.44424069e-01
1.13215566e-01 7.46851087e-01 -1.54847354e-02 -4.59872298... | [10.373059272766113, 10.04860782623291] |
f574cee1-598f-47d1-81e7-11b1a37f90f5 | signed-latent-factors-for-spamming-activity | 2209.13814 | null | https://arxiv.org/abs/2209.13814v1 | https://arxiv.org/pdf/2209.13814v1.pdf | Signed Latent Factors for Spamming Activity Detection | Due to the increasing trend of performing spamming activities (e.g., Web spam, deceptive reviews, fake followers, etc.) on various online platforms to gain undeserved benefits, spam detection has emerged as a hot research issue. Previous attempts to combat spam mainly employ features related to metadata, user behaviors... | ['Yuli Liu'] | 2022-09-28 | null | null | null | null | ['activity-detection', 'spam-detection'] | ['computer-vision', 'natural-language-processing'] | [ 1.57237679e-01 -1.73945412e-01 -4.88976359e-01 -2.92551428e-01
-3.42546940e-01 -3.28732103e-01 7.78394043e-01 9.14190114e-02
8.89451578e-02 5.67640305e-01 1.71552449e-02 -3.94421786e-01
-5.47970891e-01 -8.99479747e-01 -4.85729486e-01 -3.81589144e-01
-2.81679899e-01 4.14587438e-01 7.15568006e-01 -2.51401037... | [7.858124732971191, 10.045507431030273] |
7aeb302f-b23f-4380-9c50-ea626e45a9ff | upper-middle-and-lower-region-learning-for | 2002.04023 | null | https://arxiv.org/abs/2002.04023v2 | https://arxiv.org/pdf/2002.04023v2.pdf | Upper, Middle and Lower Region Learning for Facial Action Unit Detection | Facial action units (AUs) detection is fundamental to facial expression analysis. As AU occurs only in a small area of the face, region-based learning has been widely recognized useful for AU detection. Most region-based studies focus on a small region where the AU occurs. Focusing on a specific region helps eliminate ... | ['Yao Xia'] | 2020-02-10 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection', 'hard-attention'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 1.27240241e-01 1.61874015e-02 -2.60553837e-01 -2.10505888e-01
-6.93864405e-01 -1.42129630e-01 2.43673250e-01 -2.11204395e-01
-2.75854975e-01 1.06334493e-01 9.96592641e-02 3.13858867e-01
4.40769970e-01 -7.87917078e-01 -5.31408012e-01 -8.27411354e-01
1.47568574e-02 -1.96594372e-01 1.46820158e-01 -2.96076506... | [13.623274803161621, 1.5846842527389526] |
0924f860-4775-45e5-b43f-9eaab84308df | an-open-source-tool-for-negation-detection-a | null | null | https://aclanthology.org/W17-1810 | https://aclanthology.org/W17-1810.pdf | An open-source tool for negation detection: a maximum-margin approach | This paper presents an open-source toolkit for negation detection. It identifies negation cues and their corresponding scope in either raw or parsed text using maximum-margin classification. The system design draws on best practice from the existing literature on negation detection, aiming for a simple and portable sys... | ['Lilja {\\O}vrelid', 'Martine Enger', 'Erik Velldal'] | 2017-04-01 | null | null | null | ws-2017-4 | ['negation-detection'] | ['natural-language-processing'] | [ 3.19517791e-01 1.64935350e-01 -6.65521085e-01 -6.68188810e-01
-8.36358070e-01 -7.08930075e-01 2.19130859e-01 5.82385659e-01
-8.66729558e-01 7.23430574e-01 1.23852059e-01 -8.17303121e-01
2.88726807e-01 -5.05900741e-01 -1.75874308e-01 -9.11278501e-02
7.83205181e-02 1.64874613e-01 2.74124593e-01 -7.68114150... | [10.444069862365723, 9.289193153381348] |
5fc4cd18-0a7a-4bd5-a95a-e9da0bc11d99 | technical-report-for-ego4d-long-term-action | 2307.01467 | null | https://arxiv.org/abs/2307.01467v1 | https://arxiv.org/pdf/2307.01467v1.pdf | Technical Report for Ego4D Long Term Action Anticipation Challenge 2023 | In this report, we describe the technical details of our approach for the Ego4D Long-Term Action Anticipation Challenge 2023. The aim of this task is to predict a sequence of future actions that will take place at an arbitrary time or later, given an input video. To accomplish this task, we introduce three improvements... | ['Yuji Sato', 'Noriyuki Kugo', 'Kosuke Ono', 'Tatsuya Ishibashi'] | 2023-07-04 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 4.16071922e-01 1.69197142e-01 -3.62988859e-01 -7.07368910e-01
-8.55367959e-01 -4.29205954e-01 9.21895325e-01 -1.89127371e-01
-5.07781863e-01 7.76488841e-01 1.21987867e+00 3.06809116e-02
2.04276770e-01 -3.21828932e-01 -7.85789251e-01 -3.13963264e-01
-4.74860251e-01 2.99340010e-01 1.86756238e-01 -3.38670388... | [8.020672798156738, 0.5566210150718689] |
cd89dc5a-5a20-46af-80ca-aa0d4193df1a | improving-commonsense-causal-reasoning-by | 2101.04966 | null | https://arxiv.org/abs/2101.04966v1 | https://arxiv.org/pdf/2101.04966v1.pdf | Improving Commonsense Causal Reasoning by Adversarial Training and Data Augmentation | Determining the plausibility of causal relations between clauses is a commonsense reasoning task that requires complex inference ability. The general approach to this task is to train a large pretrained language model on a specific dataset. However, the available training data for the task is often scarce, which leads ... | ['Ignacio Iacobacci', 'Philip John Gorinski', 'Ieva Staliūnaitė'] | 2021-01-13 | null | null | null | null | ['commonsense-causal-reasoning'] | ['natural-language-processing'] | [ 5.58793902e-01 7.43330717e-01 -1.15896866e-01 -4.09296125e-01
-8.00559700e-01 -6.83285177e-01 1.17234302e+00 2.73485333e-01
-2.38376632e-01 1.03489995e+00 7.88030386e-01 -4.68737185e-01
-1.35492504e-01 -9.61934924e-01 -8.33740652e-01 -4.90234315e-01
3.62056540e-03 6.91138327e-01 1.14851534e-01 -6.02435648... | [9.950328826904297, 8.081490516662598] |
826b6957-4377-4a95-985d-e4dadb71b9da | structured-sentiment-analysis-as-transition | 2305.05311 | null | https://arxiv.org/abs/2305.05311v1 | https://arxiv.org/pdf/2305.05311v1.pdf | Structured Sentiment Analysis as Transition-based Dependency Parsing | Structured sentiment analysis (SSA) aims to automatically extract people's opinions from a text in natural language and adequately represent that information in a graph structure. One of the most accurate methods for performing SSA was recently proposed and consists of approaching it as a dependency parsing task. Altho... | ['Daniel Fernández-González'] | 2023-05-09 | null | null | null | null | ['dependency-parsing', 'transition-based-dependency-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.27666485e-01 5.06249726e-01 -6.80484846e-02 -5.60709417e-01
-7.03180790e-01 -8.32144141e-01 4.49346989e-01 7.30500758e-01
-3.43084246e-01 5.02792537e-01 1.44787028e-01 -8.38987887e-01
7.21051171e-02 -9.67446327e-01 -5.60251892e-01 -8.06407258e-02
-1.15139857e-01 5.13988495e-01 2.59494692e-01 -4.20081258... | [11.339653015136719, 6.925233364105225] |
c6667757-a576-4b83-bc32-cf67ffbded9d | online-attentive-kernel-based-temporal | 2201.09065 | null | https://arxiv.org/abs/2201.09065v1 | https://arxiv.org/pdf/2201.09065v1.pdf | Online Attentive Kernel-Based Temporal Difference Learning | With rising uncertainty in the real world, online Reinforcement Learning (RL) has been receiving increasing attention due to its fast learning capability and improving data efficiency. However, online RL often suffers from complex Value Function Approximation (VFA) and catastrophic interference, creating difficulty for... | ['Yang Gao', 'Shaokang Dong', 'Huihui Wang', 'Shangdong Yang', 'Xingguo Chen', 'Guang Yang'] | 2022-01-22 | null | null | null | null | ['acrobot'] | ['playing-games'] | [-2.37467259e-01 -2.09322110e-01 -1.58427179e-01 -1.32598624e-01
-5.61743915e-01 -1.04792096e-01 2.87205458e-01 8.90461132e-02
-4.81098741e-01 9.68616068e-01 2.76963681e-01 -2.31233630e-02
-4.87384915e-01 -5.28821945e-01 -6.73712790e-01 -7.33177066e-01
-4.40470874e-01 2.63371766e-01 -4.34680935e-03 -3.80625844... | [4.037319660186768, 2.1975035667419434] |
8164b4d0-b149-4a3f-a4bd-c576d21d708a | deep-double-incomplete-multi-view-multi-label | null | null | https://ieeexplore.ieee.org/abstract/document/10086538 | https://ieeexplore.ieee.org/abstract/document/10086538 | Deep Double Incomplete Multi-view Multi-label Learning with Incomplete Labels and Missing Views | View missing and label missing are two challenging problems in the applications of multi-view multi-label classification scenery. In the past years, many efforts have been made to address the incomplete multi-view learning or incomplete multi-label learning problem. However, few works can simultaneously handle the chal... | ['Yong Xu', 'Ke Yan', 'Lunke Fei', 'Yicheng Liu', 'Shijie Deng', 'Chengliang Liu', 'Jie Wen'] | 2023-03-23 | null | null | null | ieee-transactions-on-neural-networks-and-14 | ['multi-view-learning', 'multi-label-learning'] | ['computer-vision', 'methodology'] | [ 4.35144007e-01 -3.42691898e-01 -4.31080312e-01 -7.25465357e-01
-1.13953280e+00 -3.37619811e-01 2.85692245e-01 -8.12363476e-02
-1.72840729e-01 5.06179392e-01 1.78062215e-01 2.17752874e-01
-1.85158849e-02 -4.51977044e-01 -2.94930190e-01 -9.96422768e-01
7.19599783e-01 3.02016586e-01 1.50976673e-01 1.94759056... | [8.692351341247559, 4.457998752593994] |
dcdbc98f-a39f-4ef1-be9a-091230482cde | licamgait-gait-recognition-in-the-wild-by | 2211.12371 | null | https://arxiv.org/abs/2211.12371v1 | https://arxiv.org/pdf/2211.12371v1.pdf | LiCamGait: Gait Recognition in the Wild by Using LiDAR and Camera Multi-modal Visual Sensors | LiDAR can capture accurate depth information in large-scale scenarios without the effect of light conditions, and the captured point cloud contains gait-related 3D geometric properties and dynamic motion characteristics. We make the first attempt to leverage LiDAR to remedy the limitation of view-dependent and light-se... | ['Yuexin Ma', 'Jingyi Yu', 'Jingya Wang', 'Lan Xu', 'Peishan Cong', 'Xiao Han'] | 2022-11-22 | null | null | null | null | ['gait-recognition-in-the-wild', 'gait-recognition'] | ['computer-vision', 'computer-vision'] | [-1.57372192e-01 -1.03000033e+00 -1.33941174e-01 -2.57624477e-01
-6.99275911e-01 -4.24254924e-01 2.57004350e-01 -1.95707351e-01
-2.73567557e-01 5.05507946e-01 -1.22931942e-01 3.02341610e-01
1.18882107e-02 -9.99478996e-01 -2.19141483e-01 -6.11526668e-01
2.27842126e-02 4.97281820e-01 7.60602593e-01 -2.48416170... | [7.847390174865723, -1.6351096630096436] |
9127fbc6-3114-41e3-ad20-e8872cb70d74 | an-analysis-of-parallelized-motion-masking | 1702.05156 | null | http://arxiv.org/abs/1702.05156v1 | http://arxiv.org/pdf/1702.05156v1.pdf | An Analysis of Parallelized Motion Masking Using Dual-Mode Single Gaussian Models | Motion detection in video is important for a number of applications and
fields. In video surveillance, motion detection is an essential accompaniment
to activity recognition for early warning systems. Robotics also has much to
gain from motion detection and segmentation, particularly in high speed motion
tracking for t... | ['Peter Henderson', 'Matthew Vertescher'] | 2017-02-16 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 4.00739908e-01 -5.91925025e-01 -1.16396900e-02 1.14224963e-01
-2.49293476e-01 -5.36255836e-01 4.78654891e-01 -3.58334817e-02
-6.87291920e-01 3.97708237e-01 -9.18320119e-02 -5.78693986e-01
3.06524456e-01 -6.86279356e-01 -4.07679021e-01 -9.33663428e-01
-1.24450587e-02 1.66340228e-02 1.09999800e+00 1.61669478... | [8.893012046813965, -0.9285058975219727] |
164fb04f-13fd-4235-ae6c-59da3fa37b53 | sparql-as-a-foreign-language | 1708.07624 | null | https://arxiv.org/abs/1708.07624v2 | https://arxiv.org/pdf/1708.07624v2.pdf | SPARQL as a Foreign Language | In the last years, the Linked Data Cloud has achieved a size of more than 100 billion facts pertaining to a multitude of domains. However, accessing this information has been significantly challenging for lay users. Approaches to problems such as Question Answering on Linked Data and Link Discovery have notably played ... | ['André Valdestilhas', 'Gustavo Publio', 'Diego Esteves', 'Edgard Marx', 'Ciro Baron Neto', 'Tommaso Soru', 'Diego Moussallem'] | 2017-08-25 | null | null | null | null | ['graph-question-answering', 'knowledge-base-question-answering'] | ['graphs', 'natural-language-processing'] | [ 1.69239506e-01 6.17455661e-01 -2.68466473e-01 -5.79455435e-01
-8.18249047e-01 -6.47355020e-01 5.11583507e-01 8.00183773e-01
-3.71004850e-01 8.82548749e-01 3.49338293e-01 -3.72092992e-01
-3.29436392e-01 -1.41374898e+00 -1.08295918e+00 1.44117460e-01
-3.59073997e-01 1.11713493e+00 1.59245625e-01 -8.83597791... | [10.170804023742676, 7.928186416625977] |
76ca6e47-265e-4e0c-a90e-8e5c2cf606f3 | morphganformer-transformer-based-face | 2302.09404 | null | https://arxiv.org/abs/2302.09404v1 | https://arxiv.org/pdf/2302.09404v1.pdf | MorphGANFormer: Transformer-based Face Morphing and De-Morphing | Semantic face image manipulation has received increasing attention in recent years. StyleGAN-based approaches to face morphing are among the leading techniques; however, they often suffer from noticeable blurring and artifacts as a result of the uniform attention in the latent feature space. In this paper, we propose t... | ['Guo-Jun Qi', 'Xin Li', 'Xudong Liu', 'Na Zhang'] | 2023-02-18 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 8.45018208e-01 2.54653573e-01 9.02109221e-02 -1.14649773e-01
-5.52534282e-01 -5.80011070e-01 6.83608294e-01 -5.89195430e-01
-9.77603421e-02 6.27090871e-01 -7.23472163e-02 -1.40028089e-01
-2.78119534e-01 -9.10082161e-01 -4.63608086e-01 -1.18803048e+00
8.63351002e-02 -8.99870619e-02 -4.31414336e-01 -3.32076848... | [12.648545265197754, 0.014508020132780075] |
675f5ab5-55d2-4f75-8a7d-5b6522044978 | adaptive-graph-representation-learning-and | 2101.07034 | null | https://arxiv.org/abs/2101.07034v3 | https://arxiv.org/pdf/2101.07034v3.pdf | AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing | Face parsing infers a pixel-wise label to each facial component, which has drawn much attention recently. Previous methods have shown their success in face parsing, which however overlook the correlation among facial components. As a matter of fact, the component-wise relationship is a critical clue in discriminating a... | ['Tao Mei', 'Hailin Shi', 'Yinglu Liu', 'Wei Hu', 'Gusi Te'] | 2021-01-18 | null | null | null | null | ['face-parsing', 'human-parsing'] | ['computer-vision', 'computer-vision'] | [ 4.31964248e-01 4.87461269e-01 -4.93290275e-02 -8.20492744e-01
-5.32074690e-01 -3.61729592e-01 3.12961191e-01 -7.04494189e-04
9.75338817e-02 2.13374600e-01 1.42437607e-01 1.13714442e-01
-1.25910938e-01 -1.02537870e+00 -6.98879838e-01 -7.69091249e-01
1.62160143e-01 2.69278407e-01 1.03866100e-01 7.11164698... | [13.375650405883789, 0.6445926427841187] |
aab80ef6-cb03-45f7-82ec-da8e1654a5da | speaker-turn-modeling-for-dialogue-act | 2109.05056 | null | https://arxiv.org/abs/2109.05056v1 | https://arxiv.org/pdf/2109.05056v1.pdf | Speaker Turn Modeling for Dialogue Act Classification | Dialogue Act (DA) classification is the task of classifying utterances with respect to the function they serve in a dialogue. Existing approaches to DA classification model utterances without incorporating the turn changes among speakers throughout the dialogue, therefore treating it no different than non-interactive w... | ['Mohammad Soleymani', 'Kristina Lerman', 'Leili Tavabi', 'Zihao He'] | 2021-09-10 | null | https://aclanthology.org/2021.findings-emnlp.185 | https://aclanthology.org/2021.findings-emnlp.185.pdf | findings-emnlp-2021-11 | ['dialogue-act-classification'] | ['natural-language-processing'] | [-6.67399839e-02 5.02369761e-01 -1.42477661e-01 -7.88554311e-01
-3.87337148e-01 -7.88464367e-01 1.22289491e+00 1.01792477e-01
-1.98747501e-01 4.15935993e-01 1.09020090e+00 -2.47600213e-01
5.31943023e-01 -7.50563562e-01 -5.76578453e-02 -5.50922394e-01
2.83413857e-01 6.10818088e-01 6.23691920e-03 -6.95747375... | [12.760807037353516, 7.723708152770996] |
7e41475a-2f94-44e2-9a8a-954292721a7e | think-rationally-about-what-you-see | 2305.03503 | null | https://arxiv.org/abs/2305.03503v1 | https://arxiv.org/pdf/2305.03503v1.pdf | Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction | Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role. Previous works either focus on how to leverage the entity information (e.g., entity types, entity verbalization) to inference relations, but ign... | ['Philip S. Yu', 'Irwin King', 'Chenwei Zhang', 'Zhaochen Hong', 'Xuming Hu'] | 2023-05-02 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [ 4.42446947e-01 5.72703958e-01 -5.28905571e-01 -5.77735722e-01
-6.46111012e-01 -4.75297362e-01 2.89806873e-01 3.98124605e-01
-4.58338022e-01 1.02930081e+00 8.22426498e-01 -2.39748016e-01
-1.65897414e-01 -8.78219783e-01 -4.33869034e-01 -3.99886131e-01
3.16848814e-01 8.26988891e-02 1.49565458e-01 -2.09465802... | [9.338018417358398, 8.660040855407715] |
c4c8ee44-6f59-493c-ac3e-759e7d5c8885 | learning-by-example-fast-reliability-aware | 2104.06255 | null | https://arxiv.org/abs/2104.06255v1 | https://arxiv.org/pdf/2104.06255v1.pdf | Learning by example: fast reliability-aware seismic imaging with normalizing flows | Uncertainty quantification provides quantitative measures on the reliability of candidate solutions of ill-posed inverse problems. Due to their sequential nature, Monte Carlo sampling methods require large numbers of sampling steps for accurate Bayesian inference and are often computationally infeasible for large-scale... | ['Felix J. Herrmann', 'Ali Siahkoohi'] | 2021-04-13 | null | null | null | null | ['seismic-imaging'] | ['miscellaneous'] | [ 4.57711548e-01 2.66122054e-02 4.78499055e-01 -4.52889651e-01
-1.43022430e+00 -3.42889607e-01 6.72523737e-01 -2.86772609e-01
-7.40693629e-01 8.32736254e-01 2.81069636e-01 -3.68414372e-01
-4.06502664e-01 -9.94604528e-01 -1.06941450e+00 -7.65080929e-01
-2.45858550e-01 8.98878336e-01 2.73919225e-01 1.38796911... | [6.836201190948486, 3.4852993488311768] |
568b13c4-ebd9-4a25-af82-f949b94d5eb1 | pregan-answer-oriented-passage-ranking-with | 2207.01762 | null | https://arxiv.org/abs/2207.01762v1 | https://arxiv.org/pdf/2207.01762v1.pdf | PReGAN: Answer Oriented Passage Ranking with Weakly Supervised GAN | Beyond topical relevance, passage ranking for open-domain factoid question answering also requires a passage to contain an answer (answerability). While a few recent studies have incorporated some reading capability into a ranker to account for answerability, the ranker is still hindered by the noisy nature of the trai... | ['Xiaohui Yan', 'Lixin Zou', 'Hao Jiang', 'Yutao Zhu', 'Jian-Yun Nie', 'Pan Du'] | 2022-07-05 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [ 1.92795649e-01 3.59445602e-01 5.08510582e-02 -2.60426730e-01
-1.50391936e+00 -9.01417077e-01 6.95033252e-01 4.26276028e-01
-2.80857980e-01 1.02503395e+00 6.22198939e-01 -3.63598198e-01
-1.78116843e-01 -1.18097723e+00 -8.65220785e-01 -2.53152519e-01
4.00216937e-01 8.27566385e-01 7.42635131e-01 -6.80107415... | [11.427125930786133, 8.04638957977295] |
8b01e23a-2b28-4229-9427-502992629541 | multilingual-neural-machine-translation-2 | 2306.12693 | null | https://arxiv.org/abs/2306.12693v1 | https://arxiv.org/pdf/2306.12693v1.pdf | Multilingual Neural Machine Translation System for Indic to Indic Languages | This paper gives an Indic-to-Indic (IL-IL) MNMT baseline model for 11 ILs implemented on the Samanantar corpus and analyzed on the Flores-200 corpus. All the models are evaluated using the BLEU score. In addition, the languages are classified under three groups namely East Indo- Aryan (EI), Dravidian (DR), and West Ind... | ['Asif Ekbal', 'Bidyut Kr. Patra', 'Tapas Kumar Mishra', 'Divyajyoti Panda', 'Sudhansu Bala Das'] | 2023-06-22 | null | null | null | null | ['machine-translation', 'transliteration'] | ['natural-language-processing', 'natural-language-processing'] | [-1.98059663e-01 -1.60818726e-01 -3.41458797e-01 -2.22708732e-02
-6.98274016e-01 -7.18233228e-01 9.19525325e-01 -1.55543819e-01
-7.68271983e-01 8.23069692e-01 4.49806958e-01 -8.15412164e-01
-1.53898194e-01 -4.83000219e-01 -4.13218915e-01 -5.35169959e-01
4.61086810e-01 7.28479028e-01 4.80914116e-02 -4.64208305... | [11.330211639404297, 10.304800987243652] |
d4ea5276-f29c-46af-8246-ea5a234bd5a7 | comparing-ptb-and-ud-information-for-pdtb | null | null | https://aclanthology.org/2020.jeptalnrecital-recital.10 | https://aclanthology.org/2020.jeptalnrecital-recital.10.pdf | Comparing PTB and UD information for PDTB discourseconnective identification | Our work on the automatic detection of English discourse connectives in the Penn Discourse Treebank (PDTB) shows that syntactic information from the Universal Dependencies (UD) framework is a viable alternative to that from the Penn Treebank (PTB) framework. In fact, we found minor increases when comparing between the ... | ['Kelvin Han', 'Srilakshmi Balard', 'Phyllicia Leavitt'] | 2020-06-01 | null | null | null | jeptalnrecital-2020-6 | ['discourse-parsing'] | ['natural-language-processing'] | [-3.41598429e-02 6.83526158e-01 -4.46344048e-01 -4.20853376e-01
-1.16988528e+00 -8.36852729e-01 7.43508220e-01 7.67086446e-01
-5.02870381e-01 1.17307806e+00 8.45781028e-01 -1.00673020e+00
9.49655622e-02 -6.48139119e-01 -2.53360420e-01 -5.63687921e-01
1.22005716e-01 4.82787788e-01 8.02855432e-01 -6.60707414... | [10.712963104248047, 9.4678316116333] |
54399e8f-cb92-4cf4-9c79-d5eaf72d11b4 | cgua-context-guided-and-unpaired-assisted | 2203.14307 | null | https://arxiv.org/abs/2203.14307v1 | https://arxiv.org/pdf/2203.14307v1.pdf | CGUA: Context-Guided and Unpaired-Assisted Weakly Supervised Person Search | Recently, weakly supervised person search is proposed to discard human-annotated identities and train the model with only bounding box annotations. A natural way to solve this problem is to separate it into detection and unsupervised re-identification (Re-ID) steps. However, in this way, two important clues in unconstr... | ['Qinghua Zheng', 'Xiaojun Chang', 'Caixia Yan', 'Minnan Luo', 'Chengyou Jia'] | 2022-03-27 | null | null | null | null | ['person-search'] | ['computer-vision'] | [ 1.63437337e-01 -2.26384357e-01 -3.27905230e-02 -5.46316326e-01
-6.20265245e-01 -6.28500700e-01 6.73028290e-01 3.93522047e-02
-8.57173204e-01 7.15817094e-01 7.55251423e-02 6.04888797e-02
5.89231215e-02 -3.98630410e-01 -5.51616132e-01 -8.53976727e-01
2.25504071e-01 8.69285047e-01 2.01401383e-01 1.02720186... | [14.830192565917969, 1.0283209085464478] |
e8dd5a2a-6ec4-4461-b9c6-cf1dab7b38b2 | structure-representation-learning-by-jointly | null | null | https://openreview.net/forum?id=u7APnx5qszb | https://openreview.net/pdf?id=u7APnx5qszb | Structure Representation Learning by Jointly Learning to Pool and Represent | Structure representation learning is a task to provide an overall representation for a given structure (e.g., sequential text, non-sequential graph). This representation characterizes the property of that structure. Previous methods decompose the task into an element representation learning phase and a pooling phase to... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['sentence-classification'] | ['natural-language-processing'] | [ 5.36812067e-01 5.69835126e-01 6.72512576e-02 -3.23292941e-01
-4.38671470e-01 -4.40879285e-01 4.40723687e-01 8.93403530e-01
-3.21300209e-01 4.28567082e-01 4.06508774e-01 -4.12036419e-01
-2.87094861e-01 -1.14759922e+00 -5.76071262e-01 -3.39378715e-01
-1.66067317e-01 2.83571750e-01 2.19266504e-01 -3.39782000... | [10.399245262145996, 8.869352340698242] |
0decc1ea-5487-46a2-8dd6-b81ba22f5528 | searching-for-the-fakes-efficient-neural | 2306.08830 | null | https://arxiv.org/abs/2306.08830v2 | https://arxiv.org/pdf/2306.08830v2.pdf | Searching for the Fakes: Efficient Neural Architecture Search for General Face Forgery Detection | As the saying goes, "seeing is believing". However, with the development of digital face editing tools, we can no longer trust what we can see. Although face forgery detection has made promising progress, most current methods are designed manually by human experts, which is labor-consuming. In this paper, we develop an... | ['Jing Xu', 'Xin-Yue Mu', 'Xiao Jin'] | 2023-06-15 | null | null | null | null | ['deepfake-detection', 'face-swapping', 'architecture-search'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 1.45974219e-01 -2.82275289e-01 2.28382185e-01 -5.37450075e-01
-2.67399997e-01 -3.45816225e-01 3.15723568e-01 -3.74316335e-01
-7.29508400e-02 2.32505545e-01 -2.77903453e-02 -1.03438236e-01
1.15031250e-01 -7.66125023e-01 -4.04534638e-01 -4.69655395e-01
3.78423184e-01 2.08212938e-02 8.45380425e-02 -2.76176035... | [12.720137596130371, 0.7812721133232117] |
f5ee23f0-a5be-4860-928d-068fa092fdbc | optimal-operation-of-a-hydrogen-based | 2109.10754 | null | https://arxiv.org/abs/2109.10754v1 | https://arxiv.org/pdf/2109.10754v1.pdf | Optimal Operation of a Hydrogen-based Building Multi-Energy System Based on Deep Reinforcement Learning | Since hydrogen has many advantages (e.g., free pollution, extensive sources, convenient storage and transportation), hydrogen-based multi-energy systems (HMESs) have received wide attention. However, existing works on the optimal operation of HMESs neglect building thermal dynamics, which means that the flexibility of ... | ['Dong Yue', 'Chao Shen', 'Xiaohong Guan', 'Zhanbo Xu', 'Shuqi Qin', 'Liang Yu'] | 2021-09-22 | null | null | null | null | ['parameter-prediction'] | ['miscellaneous'] | [-3.90979171e-01 7.33344536e-03 3.83234993e-02 2.60468811e-01
-4.10909295e-01 -1.24227352e-01 1.12878881e-01 1.30039640e-02
-9.38754380e-02 9.67554569e-01 -2.82610178e-01 -6.72470927e-02
-4.96919513e-01 -8.53151202e-01 -6.95633590e-01 -1.24419355e+00
2.22466961e-01 2.46263176e-01 1.09365899e-02 -3.15011352... | [5.614581108093262, 2.3461010456085205] |
303d1664-a3c7-4304-96df-a9527efb19b3 | mw-gan-multi-warping-gan-for-caricature | 2001.01870 | null | https://arxiv.org/abs/2001.01870v2 | https://arxiv.org/pdf/2001.01870v2.pdf | MW-GAN: Multi-Warping GAN for Caricature Generation with Multi-Style Geometric Exaggeration | Given an input face photo, the goal of caricature generation is to produce stylized, exaggerated caricatures that share the same identity as the photo. It requires simultaneous style transfer and shape exaggeration with rich diversity, and meanwhile preserving the identity of the input. To address this challenging prob... | ['Yu-Kun Lai', 'Jing Huo', 'Yang Gao', 'Haodi Hou', 'Jing Wu'] | 2020-01-07 | null | null | null | null | ['caricature'] | ['computer-vision'] | [ 3.30263495e-01 2.48703748e-01 2.26299390e-01 -2.76298255e-01
-4.83778834e-01 -6.76131546e-01 5.18315136e-01 -7.04632044e-01
2.29842022e-01 7.35531569e-01 3.04445475e-01 2.60891080e-01
2.87654668e-01 -1.00146282e+00 -7.89577901e-01 -6.90646291e-01
7.25473166e-01 3.71684492e-01 -2.64745384e-01 -1.28722221... | [12.240525245666504, -0.359250545501709] |
fc235212-044f-48b8-bce9-6e19ce48cf64 | overview-of-the-2022-validity-and-novelty | null | null | https://aclanthology.org/2022.argmining-1.7 | https://aclanthology.org/2022.argmining-1.7.pdf | Overview of the 2022 Validity and Novelty Prediction Shared Task | This paper provides an overview of the Argument Validity and Novelty Prediction Shared Task that was organized as part of the 9th Workshop on Argument Mining (ArgMining 2022). The task focused on the prediction of the validity and novelty of a conclusion given a textual premise. Validity is defined as the degree to whi... | ['Philipp Cimiano', 'Moritz Plenz', 'Juri Opitz', 'Anette Frank', 'Philipp Heinisch'] | null | null | null | null | argmining-acl-2022-10 | ['valnov', 'argument-mining'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.24553376e-01 7.44804978e-01 -4.80473846e-01 -4.27608490e-01
-6.57910645e-01 -6.49808466e-01 7.84686625e-01 8.71200442e-01
-3.69803846e-01 8.65300357e-01 3.45195115e-01 -5.80460429e-01
-4.69610780e-01 -5.08993030e-01 -6.52085662e-01 -2.02916414e-01
1.68978885e-01 6.42822146e-01 1.08618982e-01 -2.59994119... | [9.385079383850098, 9.575627326965332] |
3079cc3b-78a3-49d9-a6bb-b1e6748ea652 | stability-and-generalization-of-stochastic-2 | 2307.03357 | null | https://arxiv.org/abs/2307.03357v1 | https://arxiv.org/pdf/2307.03357v1.pdf | Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms | Many machine learning tasks can be formulated as a stochastic compositional optimization (SCO) problem such as reinforcement learning, AUC maximization, and meta-learning, where the objective function involves a nested composition associated with an expectation. While a significant amount of studies has been devoted to... | ['Yiming Ying', 'Tianbao Yang', 'Xiyuan Wei', 'Ming Yang'] | 2023-07-07 | null | null | null | null | ['meta-learning', 'learning-theory'] | ['methodology', 'miscellaneous'] | [-3.45239788e-02 -8.61994922e-02 -1.73613161e-01 -4.30508703e-01
-6.22564018e-01 -4.51551169e-01 4.16714311e-01 2.22264051e-01
-4.23115522e-01 7.72642851e-01 -1.05981715e-01 -5.70854723e-01
-3.86582702e-01 -5.63812673e-01 -7.78292298e-01 -1.00048041e+00
-3.17347616e-01 4.25363004e-01 7.86491036e-02 -2.23286003... | [7.166384696960449, 4.077643394470215] |
0a2e83b2-2087-411c-90f0-c9cf27ecb933 | global-and-local-epidemiology-of-group-a | 2111.06498 | null | https://arxiv.org/abs/2111.06498v1 | https://arxiv.org/pdf/2111.06498v1.pdf | Global and local epidemiology of Group A Streptococcus indicates that naturally-acquired immunity is enduring and strain-specific | The bacterium Group A Streptococcus (Streptococcus pyogenes, GAS) is a human-specific pathogen and a major cause of global morbidity and mortality. Despite decades of research our knowledge of GAS infection and immunity is incomplete, hampering vaccine design and other efforts to reduce disease prevalence. Epidemiologi... | ['Nicholas Geard', 'Jodie McVernon', 'Steven Y. C. Tong', 'Mark R. Davies', 'Jukka Corander', 'Malcolm I. McDonald', 'Patricia T. Campbell', 'Jan Kokko', 'Jake A. Lacey', 'Rebecca H. Chisholm'] | 2021-11-11 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 6.14068747e-01 -5.54875433e-01 -1.33360624e-01 7.06747249e-02
-5.38369790e-02 -4.91387576e-01 2.09143445e-01 7.23806560e-01
-3.26561540e-01 4.55809146e-01 4.01614368e-01 -5.89019060e-01
-4.72293496e-01 -5.85967362e-01 -9.19238329e-01 -8.72199416e-01
-5.88148355e-01 3.31622422e-01 5.40548339e-02 -2.21975788... | [5.59096622467041, 4.536309719085693] |
730368ed-5da3-4703-bd06-697742d00202 | spatio-temporal-graph-few-shot-learning-with | 2205.13947 | null | https://arxiv.org/abs/2205.13947v2 | https://arxiv.org/pdf/2205.13947v2.pdf | Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer | Spatio-temporal graph learning is a key method for urban computing tasks, such as traffic flow, taxi demand and air quality forecasting. Due to the high cost of data collection, some developing cities have few available data, which makes it infeasible to train a well-performed model. To address this challenge, cross-ci... | ['Xinbing Wang', 'Luoyi Fu', 'Huaxiu Yao', 'Weinan Zhang', 'Xiaoying Gan', 'Bin Lu'] | 2022-05-27 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [-1.26739413e-01 -2.07086414e-01 -4.88534033e-01 -1.08997360e-01
-5.50009727e-01 -6.72594234e-02 5.59757471e-01 1.20052598e-01
-1.56720579e-02 5.34578741e-01 3.44966799e-01 -2.97534704e-01
-5.81420541e-01 -1.36273098e+00 -5.25366902e-01 -6.64838314e-01
4.75790985e-02 4.45675075e-01 6.87175870e-01 -4.31678891... | [6.487439155578613, 2.0566725730895996] |
b5e4f8fe-8d71-46b2-8985-2ba7a204c811 | end-to-end-integration-of-speech-separation | 2303.12002 | null | https://arxiv.org/abs/2303.12002v1 | https://arxiv.org/pdf/2303.12002v1.pdf | End-to-End Integration of Speech Separation and Voice Activity Detection for Low-Latency Diarization of Telephone Conversations | Recent works show that speech separation guided diarization (SSGD) is an increasingly promising direction, mainly thanks to the recent progress in speech separation. It performs diarization by first separating the speakers and then applying voice activity detection (VAD) on each separated stream. In this work we conduc... | ['Stefano Squartini', 'Alessio Brutti', 'Enrico Zovato', 'Luca Serafini', 'Samuele Cornell', 'Giovanni Morrone'] | 2023-03-21 | null | null | null | null | ['activity-detection', 'speech-separation'] | ['computer-vision', 'speech'] | [ 1.06033780e-01 1.32310838e-01 5.93944639e-02 -2.07640529e-01
-1.31668186e+00 -8.16063166e-01 7.37781823e-01 1.83365956e-01
-3.75889361e-01 5.31874657e-01 3.55809748e-01 -6.52477026e-01
-2.07896858e-01 -1.23663701e-01 -4.86790776e-01 -8.23116004e-01
-3.11258644e-01 5.65760493e-01 4.49955046e-01 -7.16605932... | [14.767156600952148, 6.135132312774658] |
89d19f37-1ba0-4a53-a5f7-8a15b8756bc3 | cola-contextualized-commonsense-causal | 2305.05191 | null | https://arxiv.org/abs/2305.05191v1 | https://arxiv.org/pdf/2305.05191v1.pdf | COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective | Detecting commonsense causal relations (causation) between events has long been an essential yet challenging task. Given that events are complicated, an event may have different causes under various contexts. Thus, exploiting context plays an essential role in detecting causal relations. Meanwhile, previous works about... | ['Simon See', 'Ginny Y. Wong', 'Yangqiu Song', 'Tianqing Fang', 'Weiqi Wang', 'Jiayao Zhang', 'Hongming Zhang', 'Quyet V. Do', 'Zhaowei Wang'] | 2023-05-09 | null | null | null | null | ['causal-inference', 'causal-inference', 'commonsense-causal-reasoning'] | ['knowledge-base', 'miscellaneous', 'natural-language-processing'] | [ 5.21835744e-01 -2.10390821e-01 -4.95038509e-01 -6.08008027e-01
-3.86293530e-01 -4.29156333e-01 9.25706267e-01 3.64851654e-01
-2.84949183e-01 8.66810203e-01 7.36845434e-01 -3.90647113e-01
-3.84371518e-03 -8.02187860e-01 -6.96110845e-01 -2.42637858e-01
7.99480677e-02 7.45690688e-02 3.12271684e-01 -1.48667723... | [9.354604721069336, 8.430838584899902] |
ede54547-89ba-4eca-b427-a7b374f4dded | seven-open-problems-in-applied-combinatorics | 2303.11464 | null | https://arxiv.org/abs/2303.11464v1 | https://arxiv.org/pdf/2303.11464v1.pdf | Seven open problems in applied combinatorics | We present and discuss seven different open problems in applied combinatorics. The application areas relevant to this compilation include quantum computing, algorithmic differentiation, topological data analysis, iterative methods, hypergraph cut algorithms, and power systems. | ['Stephen J. Young', 'Nate Veldt', 'Ignacio Segovia-Dominguez', 'Sandip Roy', 'Anthony V. Petyuk', 'Carlos Ortiz Marrero', 'Uwe Naumann', 'Bill Kay', 'Yulia R. Gel', 'José Frías', 'Yuzhou Chen', 'Ryan Bennink', 'Sinan G. Aksoy'] | 2023-03-20 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [ 2.38809049e-01 1.92018121e-01 -3.74198079e-01 5.01745105e-01
-2.22528547e-01 -8.38765562e-01 6.12044632e-01 -7.67879039e-02
-3.40391099e-02 1.20669758e+00 -1.45207018e-01 -5.75840056e-01
-5.63560307e-01 -9.47294116e-01 6.51457300e-03 -1.10552323e+00
-8.42536926e-01 9.42896008e-01 -1.70100629e-02 -6.13845825... | [5.618183135986328, 4.945427417755127] |
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