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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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2cab964e-118c-4b14-8e0d-2ebbd497f782 | sql-to-text-generation-with-graph-to-sequence | 1809.05255 | null | http://arxiv.org/abs/1809.05255v2 | http://arxiv.org/pdf/1809.05255v2.pdf | SQL-to-Text Generation with Graph-to-Sequence Model | Previous work approaches the SQL-to-text generation task using vanilla
Seq2Seq models, which may not fully capture the inherent graph-structured
information in SQL query. In this paper, we first introduce a strategy to
represent the SQL query as a directed graph and then employ a graph-to-sequence
model to encode the g... | ['Lingfei Wu', 'Kun Xu', 'Vadim Sheinin', 'Zhiguo Wang', 'Yansong Feng'] | 2018-09-14 | sql-to-text-generation-with-graph-to-sequence-1 | https://aclanthology.org/D18-1112 | https://aclanthology.org/D18-1112.pdf | emnlp-2018-10 | ['sql-to-text', 'graph-to-sequence'] | ['computer-code', 'natural-language-processing'] | [ 1.91254765e-01 3.66225541e-01 -3.56878906e-01 -7.43091881e-01
-6.79144979e-01 -7.91036427e-01 5.29553771e-01 2.95125902e-01
6.32012039e-02 2.16051295e-01 7.53889680e-01 -7.63382673e-01
2.54613370e-01 -1.32406175e+00 -8.78167629e-01 2.40618721e-01
-2.03238487e-01 4.63349849e-01 2.74943262e-01 -5.43014169... | [9.997551918029785, 7.888462066650391] |
834e2e36-81ea-4f65-998a-6ab48551540a | stagewise-unsupervised-domain-adaptation-with | 2108.12611 | null | https://arxiv.org/abs/2108.12611v1 | https://arxiv.org/pdf/2108.12611v1.pdf | Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images | Road segmentation from remote sensing images is a challenging task with wide ranges of application potentials. Deep neural networks have advanced this field by leveraging the power of large-scale labeled data, which, however, are extremely expensive and time-consuming to acquire. One solution is to use cheap available ... | ['DaCheng Tao', 'Jing Zhang', 'Meng Lan', 'Lefei Zhang'] | 2021-08-28 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 5.13307989e-01 6.29297122e-02 -7.65534416e-02 -4.28366303e-01
-8.63830149e-01 -6.22074604e-01 4.24452424e-01 -3.82719606e-01
-2.31429398e-01 8.62841129e-01 -2.20470533e-01 -1.32165626e-01
6.11096323e-02 -1.24375832e+00 -8.29098880e-01 -9.01660740e-01
5.87315619e-01 5.64407349e-01 4.28766012e-01 -1.90515801... | [9.705803871154785, 1.1767354011535645] |
966203d6-3cd7-4a6c-94fe-03043a01f58f | random-embeddings-and-linear-regression-can | 2104.14661 | null | https://arxiv.org/abs/2104.14661v1 | https://arxiv.org/pdf/2104.14661v1.pdf | Random Embeddings and Linear Regression can Predict Protein Function | Large self-supervised models pretrained on millions of protein sequences have recently gained popularity in generating embeddings of protein sequences for protein function prediction. However, the absence of random baselines makes it difficult to conclude whether pretraining has learned useful information for protein f... | ['Alan M. Moses', 'Alex X. Lu', 'Tianyu Lu'] | 2021-04-25 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 4.50013697e-01 3.45750570e-01 -2.22079709e-01 -6.39104843e-01
-5.35433233e-01 -7.71825433e-01 3.63751560e-01 5.20521760e-01
-4.88196999e-01 1.20748079e+00 3.28361988e-01 -3.76319230e-01
3.05662781e-01 -4.89795923e-01 -1.06913996e+00 -9.25655842e-01
-1.74291655e-01 7.89718151e-01 2.57487744e-01 1.58258695... | [4.705158710479736, 5.657131671905518] |
e5cc0efc-dfd2-4f41-a523-92ed3a97ffd5 | multi-subregion-based-correlation-filter-bank | 1603.07604 | null | http://arxiv.org/abs/1603.07604v1 | http://arxiv.org/pdf/1603.07604v1.pdf | Multi-Subregion Based Correlation Filter Bank for Robust Face Recognition | In this paper, we propose an effective feature extraction algorithm, called
Multi-Subregion based Correlation Filter Bank (MS-CFB), for robust face
recognition. MS-CFB combines the benefits of global-based and local-based
feature extraction algorithms, where multiple correlation filters correspond-
ing to different fac... | ['David Suter', 'Yan Yan', 'Hanzi Wang'] | 2016-03-24 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [-2.99457759e-01 -7.60593176e-01 -2.40344226e-01 -4.56015229e-01
-5.56287527e-01 -1.99807972e-01 2.20128685e-01 -5.04779279e-01
7.68106757e-03 4.99940962e-01 7.69979581e-02 7.62498751e-02
-5.66434920e-01 -7.03931451e-01 -8.98153111e-02 -9.51880336e-01
-2.60239571e-01 -1.97267190e-01 1.54091278e-02 -6.27226830... | [12.810249328613281, 0.5357569456100464] |
d60aeec4-8435-4a34-bdac-9890af32c8f2 | inducing-positive-perspectives-with-text | 2204.02952 | null | https://arxiv.org/abs/2204.02952v1 | https://arxiv.org/pdf/2204.02952v1.pdf | Inducing Positive Perspectives with Text Reframing | Sentiment transfer is one popular example of a text style transfer task, where the goal is to reverse the sentiment polarity of a text. With a sentiment reversal comes also a reversal in meaning. We introduce a different but related task called positive reframing in which we neutralize a negative point of view and gene... | ['Diyi Yang', 'Anthony Zhang', 'Minzhi Li', 'Caleb Ziems'] | 2022-04-06 | null | https://aclanthology.org/2022.acl-long.257 | https://aclanthology.org/2022.acl-long.257.pdf | acl-2022-5 | ['text-style-transfoer'] | ['natural-language-processing'] | [ 6.68409348e-01 4.67273533e-01 -2.67857879e-01 -8.68923604e-01
-5.06298006e-01 -8.39761376e-01 9.37038839e-01 -7.74964541e-02
-3.63469422e-01 1.14212704e+00 1.00196671e+00 -2.59965867e-01
4.96155053e-01 -6.70851588e-01 -6.85861766e-01 -2.09787443e-01
7.94745922e-01 4.54998463e-01 -4.18143511e-01 -8.96488607... | [11.640022277832031, 9.49643611907959] |
b358c1d9-1ac8-4d6a-afda-e75ec17e7310 | yolino-generic-single-shot-polyline-detection | 2103.14420 | null | https://arxiv.org/abs/2103.14420v2 | https://arxiv.org/pdf/2103.14420v2.pdf | YOLinO: Generic Single Shot Polyline Detection in Real Time | The detection of polylines is usually either bound to branchless polylines or formulated in a recurrent way, prohibiting their use in real-time systems. We propose an approach that builds upon the idea of single shot object detection. Reformulating the problem of polyline detection as a bottom-up composition of small l... | ['Christoph Stiller', 'Jan-Hendrik Pauls', 'Philipp Skudlik', 'Annika Meyer'] | 2021-03-26 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 3.13651711e-01 5.12338663e-03 1.82743162e-01 -9.74280573e-03
-5.44041634e-01 -9.76212084e-01 6.80592239e-01 6.30017459e-01
-2.92556435e-01 6.54604793e-01 -4.95079815e-01 -5.45799911e-01
2.14110181e-01 -8.85667920e-01 -5.62515497e-01 -2.87617236e-01
-1.02293268e-01 7.25638807e-01 1.13009512e+00 -4.06389266... | [8.260549545288086, -1.5837814807891846] |
46989914-19a6-4dc4-91c0-9d7d86051abc | hilo-exploiting-high-low-frequency-relations | 2303.15994 | null | https://arxiv.org/abs/2303.15994v1 | https://arxiv.org/pdf/2303.15994v1.pdf | HiLo: Exploiting High Low Frequency Relations for Unbiased Panoptic Scene Graph Generation | Panoptic Scene Graph generation (PSG) is a recently proposed task in image scene understanding that aims to segment the image and extract triplets of subjects, objects and their relations to build a scene graph. This task is particularly challenging for two reasons. First, it suffers from a long-tail problem in its rel... | ['Holger Caesar', 'Miaojing Shi', 'Zijian Zhou'] | 2023-03-28 | null | null | null | null | ['scene-graph-generation', 'panoptic-scene-graph-generation'] | ['computer-vision', 'computer-vision'] | [ 5.13140500e-01 3.52180660e-01 -2.14358672e-01 -5.38556695e-01
-4.20473337e-01 -3.54272604e-01 9.30128574e-01 1.90134734e-01
-2.07377031e-01 5.00903666e-01 2.60879666e-01 -2.78063476e-01
-2.02269122e-01 -8.19754004e-01 -8.05558145e-01 -5.77411473e-01
2.14395970e-02 6.99819922e-01 5.72966754e-01 -1.67001739... | [10.358417510986328, 1.704214096069336] |
b6161aa3-41ef-4857-b86c-6ef8442997c8 | inducing-alignment-structure-with-gated-graph | 2010.07668 | null | https://arxiv.org/abs/2010.07668v2 | https://arxiv.org/pdf/2010.07668v2.pdf | Inducing Alignment Structure with Gated Graph Attention Networks for Sentence Matching | Sentence matching is a fundamental task of natural language processing with various applications. Most recent approaches adopt attention-based neural models to build word- or phrase-level alignment between two sentences. However, these models usually ignore the inherent structure within the sentences and fail to consid... | ['Yuanchao Liu', 'Le Hu', 'Peng Cui'] | 2020-10-15 | null | null | null | null | ['paraphrase-identification'] | ['natural-language-processing'] | [ 3.46517533e-01 1.28582001e-01 -3.06099355e-01 -6.66583180e-01
-5.11656940e-01 -2.12301850e-01 3.82886320e-01 6.53475046e-01
-2.72790194e-01 1.69254556e-01 6.51413977e-01 -6.55847132e-01
5.15122600e-02 -9.93632913e-01 -6.19061589e-01 -3.02216504e-02
2.69028276e-01 1.60991728e-01 -3.56432311e-02 -4.21082377... | [11.025927543640137, 8.635985374450684] |
44ad5df0-4d59-4bc1-8add-b97d2e16f8e1 | adversarially-guided-subgoal-generation-for | 2201.09635 | null | https://arxiv.org/abs/2201.09635v4 | https://arxiv.org/pdf/2201.09635v4.pdf | State-Conditioned Adversarial Subgoal Generation | Hierarchical reinforcement learning (HRL) proposes to solve difficult tasks by performing decision-making and control at successively higher levels of temporal abstraction. However, off-policy HRL often suffers from the problem of a non-stationary high-level policy since the low-level policy is constantly changing. In ... | ['Joni-Kristian Kämäräinen', 'Tinghuai Wang', 'Joni Pajarinen', 'Vivienne Huiling Wang'] | 2022-01-24 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 2.02990249e-01 3.15957129e-01 -4.01372433e-01 2.16305256e-01
-7.90502787e-01 -7.53451288e-01 8.38087618e-01 1.63654983e-01
-6.94822431e-01 1.18280292e+00 -5.72224781e-02 -3.77497464e-01
-1.08157456e-01 -7.86192238e-01 -8.04585099e-01 -9.67534542e-01
-3.89737248e-01 4.12960440e-01 4.49024677e-01 -4.75384414... | [4.139705657958984, 1.6977524757385254] |
0f6859eb-016c-4d65-8cd3-fec0732b9e94 | estimation-of-bivariate-structural-causal | 2109.02521 | null | https://arxiv.org/abs/2109.02521v1 | https://arxiv.org/pdf/2109.02521v1.pdf | Estimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flows | One major drawback of state-of-the-art artificial intelligence is its lack of explainability. One approach to solve the problem is taking causality into account. Causal mechanisms can be described by structural causal models. In this work, we propose a method for estimating bivariate structural causal models using a co... | ['Bin Yang', 'Alexander Bartler', 'Felix Wiewel', 'Nico Reick'] | 2021-09-06 | null | null | null | null | ['normalising-flows'] | ['methodology'] | [ 2.28180319e-01 3.15571219e-01 -4.08637613e-01 -2.11084321e-01
-5.77046514e-01 -4.95243371e-01 9.98371542e-01 2.04795897e-01
7.45295063e-02 1.22121382e+00 5.55801630e-01 -6.83445454e-01
-7.30918169e-01 -9.55079973e-01 -9.61234152e-01 -6.80344403e-01
-2.94323981e-01 7.65958786e-01 8.32811520e-02 2.77478039... | [7.794707775115967, 5.279311656951904] |
847f4d65-24a2-432b-a7f9-dac56ff6ce07 | a-quantitative-review-on-language-model | 2306.01768 | null | https://arxiv.org/abs/2306.01768v1 | https://arxiv.org/pdf/2306.01768v1.pdf | A Quantitative Review on Language Model Efficiency Research | Language models (LMs) are being scaled and becoming powerful. Improving their efficiency is one of the core research topics in neural information processing systems. Tay et al. (2022) provided a comprehensive overview of efficient Transformers that have become an indispensable staple in the field of NLP. However, in th... | ['Lingbo Tong', 'Hy Dang', 'Meng Jiang'] | 2023-05-28 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 2.82679293e-02 -1.13594504e-02 -3.12045068e-01 -1.77318722e-01
-6.42321587e-01 -6.06194556e-01 6.30731285e-01 -1.75231934e-01
-9.14467573e-01 6.51162922e-01 1.37734815e-01 -6.86331451e-01
-1.84438750e-01 -3.71357113e-01 -7.39828706e-01 -5.94842494e-01
2.60229826e-01 4.99048501e-01 2.24008366e-01 -2.07865670... | [10.979520797729492, 6.683862209320068] |
ff26cc40-191c-4a48-9672-c65ccc793a9b | rdmnet-reliable-dense-matching-based-point | 2303.18084 | null | https://arxiv.org/abs/2303.18084v1 | https://arxiv.org/pdf/2303.18084v1.pdf | RDMNet: Reliable Dense Matching Based Point Cloud Registration for Autonomous Driving | Point cloud registration is an important task in robotics and autonomous driving to estimate the ego-motion of the vehicle. Recent advances following the coarse-to-fine manner show promising potential in point cloud registration. However, existing methods rely on good superpoint correspondences, which are hard to be ob... | ['Bin Dai', 'Junhao Xiao', 'Wenbang Deng', 'Huimin Lu', 'Xieyuanli Chen', 'Chenghao Shi'] | 2023-03-31 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-2.51471043e-01 -3.70680034e-01 -2.42720738e-01 -4.18841630e-01
-7.82790124e-01 -4.36367452e-01 7.49775171e-01 4.12073322e-02
-2.93094933e-01 3.62642705e-01 -2.19198123e-01 1.73529863e-01
-3.06663364e-01 -8.75250340e-01 -1.02081251e+00 -4.79701757e-01
8.30724183e-03 7.91369975e-01 5.31355560e-01 -5.94107330... | [7.685057640075684, -2.8810861110687256] |
398b5abf-8ca3-4e80-b644-eabc7c6f39ec | multimodal-image-registration-using-laplacian | null | null | https://www.sciencedirect.com/science/article/pii/S1566253517305316 | https://www.sciencedirect.com/science/article/pii/S1566253517305316/pdfft?md5=47e182707e90578060ddb97372c97a26&pid=1-s2.0-S1566253517305316-main.pdf | Multimodal image registration using Laplacian commutators | The fusion and combination of images from multiple modalities is important in many applications. Typically,
this process consists of the alignment of the images and the combination of the complementary information.
In this work, we focused on the former part and propose a multimodal image distance measure based on th... | ['Gemma Piellaa', 'Miguel Ángel González Ballester a', 'Veronika A. Zimmer a'] | 2019-09-09 | null | null | null | journal-2019-9 | ['image-registration'] | ['computer-vision'] | [ 4.36896473e-01 -1.86577231e-01 2.58640379e-01 -2.31016323e-01
-3.78007442e-01 -7.68716693e-01 7.31847703e-01 4.79817182e-01
-7.07787216e-01 3.14186335e-01 1.46384418e-01 -5.58647364e-02
-5.14618099e-01 -5.54307699e-01 -3.31161231e-01 -8.97727132e-01
7.29661509e-02 3.48226964e-01 -6.19267412e-02 -3.70773613... | [7.858721733093262, 4.179197311401367] |
373aa430-7917-4d2e-9205-ec808a00915e | monolayout-amodal-scene-layout-from-a-single | 2002.08394 | null | https://arxiv.org/abs/2002.08394v1 | https://arxiv.org/pdf/2002.08394v1.pdf | MonoLayout: Amodal scene layout from a single image | In this paper, we address the novel, highly challenging problem of estimating the layout of a complex urban driving scenario. Given a single color image captured from a driving platform, we aim to predict the bird's-eye view layout of the road and other traffic participants. The estimated layout should reason beyond wh... | ['Krishna Murthy Jatavallabhula', 'Shubhika Garg', 'N. Sai Shankar', 'Swapnil Daga', 'K. Madhava Krishna', 'Kaustubh Mani'] | 2020-02-19 | null | null | null | null | ['amodal-layout-estimation'] | ['computer-vision'] | [ 2.23932564e-01 1.25639319e-01 2.53083795e-01 -3.86993587e-01
-9.85906780e-01 -6.99154556e-01 4.68077183e-01 -3.57461095e-01
-1.54275388e-01 5.93407512e-01 2.03669965e-01 -4.28956389e-01
3.54959756e-01 -4.01266366e-01 -1.26647294e+00 -5.12808621e-01
2.65317589e-01 1.71165392e-01 2.13951035e-03 -3.50777134... | [8.226177215576172, -2.098496675491333] |
32d41eb1-dbce-4bca-b8ea-cda616ea9fc0 | cronos-colorization-and-contrastive-learning | 2211.10354 | null | https://arxiv.org/abs/2211.10354v4 | https://arxiv.org/pdf/2211.10354v4.pdf | CRONOS: Colorization and Contrastive Learning for Device-Free NLoS Human Presence Detection using Wi-Fi CSI | In recent years, the demand for pervasive smart services and applications has increased rapidly. Device-free human detection through sensors or cameras has been widely adopted, but it comes with privacy issues as well as misdetection for motionless people. To address these drawbacks, channel state information (CSI) cap... | ['Chia-Che Hsieh', 'Li-Hsiang Shen', 'Kai-Ten Feng', 'An-Hung Hsiao'] | 2022-11-07 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 2.45244324e-01 -7.07135081e-01 -8.33404064e-02 1.17577547e-02
-7.16831267e-01 -3.27481717e-01 4.12279546e-01 -2.86575347e-01
-3.81295472e-01 9.45917428e-01 6.37990236e-02 -2.76359409e-01
-2.18478844e-01 -5.10358691e-01 -7.27744699e-02 -1.09720457e+00
-1.42508924e-01 -2.30078787e-01 -3.91025953e-02 1.26472982... | [6.702964782714844, 0.6988778114318848] |
42d09201-43fa-4adb-9d59-3c74a451446e | vlsp-2021-shared-task-vietnamese-machine | 2203.11400 | null | https://arxiv.org/abs/2203.11400v3 | https://arxiv.org/pdf/2203.11400v3.pdf | VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension | One of the emerging research trends in natural language understanding is machine reading comprehension (MRC) which is the task to find answers to human questions based on textual data. Existing Vietnamese datasets for MRC research concentrate solely on answerable questions. However, in reality, questions can be unanswe... | ['Ngan Luu-Thuy Nguyen', 'Son T. Luu', 'Tin Van Huynh', 'Luan Thanh Nguyen', 'Son Quoc Tran', 'Kiet Van Nguyen'] | 2022-03-22 | null | null | null | null | ['vietnamese-datasets'] | ['natural-language-processing'] | [ 3.71266127e-01 3.20363581e-01 2.21713871e-01 -4.64978933e-01
-1.42628765e+00 -8.90714049e-01 3.81859750e-01 3.25457394e-01
-6.89532757e-01 8.32182348e-01 4.61903721e-01 -9.19609666e-01
1.54397458e-01 -7.67856479e-01 -7.39449203e-01 -3.71304457e-03
4.70900744e-01 7.89804935e-01 2.51674891e-01 -7.79551685... | [11.38839054107666, 8.24990463256836] |
5e5ac9ca-8e59-41ef-b467-b784e831ef57 | istego100k-large-scale-image-steganalysis | 1911.05542 | null | https://arxiv.org/abs/1911.05542v1 | https://arxiv.org/pdf/1911.05542v1.pdf | IStego100K: Large-scale Image Steganalysis Dataset | In order to promote the rapid development of image steganalysis technology, in this paper, we construct and release a multivariable large-scale image steganalysis dataset called IStego100K. It contains 208,104 images with the same size of 1024*1024. Among them, 200,000 images (100,000 cover-stego image pairs) are divid... | ['Yongfeng Huang', 'Sai Ma', 'Ke Wang', 'Xianfeng Zhao', 'Zhongliang Yang', 'Xiangui Kang'] | 2019-11-13 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 1.93425670e-01 -2.57333100e-01 3.44250202e-02 2.52738565e-01
-1.00797251e-01 -4.11193579e-01 1.88653380e-01 -5.58243811e-01
-1.24079578e-01 5.43305814e-01 -2.01762766e-01 -6.48211658e-01
2.77181894e-01 -1.17552042e+00 -4.99660492e-01 -9.69817936e-01
-3.47549587e-01 -1.53633833e-01 4.00656044e-01 -4.39572424... | [4.299421787261963, 8.05489730834961] |
0902aee1-e850-4c5e-aab3-392502b3e572 | value-function-estimation-using-conditional | 2306.07290 | null | https://arxiv.org/abs/2306.07290v1 | https://arxiv.org/pdf/2306.07290v1.pdf | Value function estimation using conditional diffusion models for control | A fairly reliable trend in deep reinforcement learning is that the performance scales with the number of parameters, provided a complimentary scaling in amount of training data. As the appetite for large models increases, it is imperative to address, sooner than later, the potential problem of running out of high-quali... | ['Josh Susskind', 'Alexander Toshev', 'Devon Hjelm', 'Miguel Angel Bautista', 'Walter Talbott', 'Bogdan Mazoure'] | 2023-06-09 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-4.00534682e-02 1.70011386e-01 -1.91858098e-01 -1.17898561e-01
-7.28497863e-01 -7.17656612e-01 5.03297031e-01 1.70431808e-01
-7.44096458e-01 1.19694269e+00 -2.14858979e-01 -4.45240408e-01
-2.99950063e-01 -6.35164022e-01 -9.78439212e-01 -7.46893585e-01
-5.99606454e-01 6.48473263e-01 2.13712618e-01 -4.58302885... | [4.37475061416626, 1.5866563320159912] |
4d00b3c3-d6df-4ff8-8e88-38a7b102088e | probabilistic-forecast-based-portfolio | 2305.09474 | null | https://arxiv.org/abs/2305.09474v1 | https://arxiv.org/pdf/2305.09474v1.pdf | Probabilistic Forecast-based Portfolio Optimization of Electricity Demand at Low Aggregation Levels | In the effort to achieve carbon neutrality through a decentralized electricity market, accurate short-term load forecasting at low aggregation levels has become increasingly crucial for various market participants' strategies. Accurate probabilistic forecasts at low aggregation levels can improve peer-to-peer energy sh... | ['Kwangwon Ahn', 'Hokyun Kim', 'Fotios Petropoulos', 'Ran Li', 'Jooyoung Jeon', 'Estêvão Alvarenga', 'Jungyeon Park'] | 2023-04-18 | null | null | null | null | ['load-forecasting', 'portfolio-optimization'] | ['miscellaneous', 'time-series'] | [-5.99739194e-01 -5.22419363e-02 -3.01977955e-02 -3.85985523e-01
-7.34448433e-01 -6.84832752e-01 6.21407568e-01 3.83227468e-01
3.22553098e-01 9.03475523e-01 5.69182098e-01 -4.46855426e-01
-4.39496309e-01 -1.20140553e+00 1.19650781e-01 -9.08451140e-01
2.52147694e-03 8.90059948e-01 -1.79206461e-01 1.10354036... | [6.106678485870361, 2.864527702331543] |
0c4fc125-e4c4-4581-a48c-53e530676781 | agss-vos-attention-guided-single-shot-video | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Lin_AGSS-VOS_Attention_Guided_Single-Shot_Video_Object_Segmentation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Lin_AGSS-VOS_Attention_Guided_Single-Shot_Video_Object_Segmentation_ICCV_2019_paper.pdf | AGSS-VOS: Attention Guided Single-Shot Video Object Segmentation | Most video object segmentation approaches process objects separately. This incurs high computational cost when multiple objects exist. In this paper, we propose AGSS-VOS to segment multiple objects in one feed-forward path via instance-agnostic and instance-specific modules. Information from the two modules is fused vi... | [' Jiaya Jia', ' Xiaojuan Qi', 'Huaijia Lin'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 1.40755614e-02 8.42406154e-02 -5.08856535e-01 -5.57204962e-01
-8.85484219e-01 -3.45459461e-01 8.74678791e-02 -2.36774594e-01
-6.43216312e-01 4.06379312e-01 -4.27639067e-01 7.11676031e-02
3.45078349e-01 -4.90297735e-01 -1.14994276e+00 -1.90875411e-01
1.02354296e-01 5.68857193e-01 8.72707546e-01 4.56102759... | [9.210573196411133, -0.07717674970626831] |
47e29b76-47c3-4aa5-a2b1-439ab6b5ce75 | unsupervised-mandarin-cantonese-machine | 2301.03971 | null | https://arxiv.org/abs/2301.03971v1 | https://arxiv.org/pdf/2301.03971v1.pdf | Unsupervised Mandarin-Cantonese Machine Translation | Advancements in unsupervised machine translation have enabled the development of machine translation systems that can translate between languages for which there is not an abundance of parallel data available. We explored unsupervised machine translation between Mandarin Chinese and Cantonese. Despite the vast number o... | ['Shibingfeng Zhang', 'Yifan Wang', 'Averie Ho Zoen So', 'Valentina Fajardo Diaz', 'Megan Dare'] | 2023-01-10 | null | null | null | null | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 8.47341269e-02 -1.72899857e-01 -4.91807997e-01 -3.51401001e-01
-1.16504753e+00 -8.17113459e-01 8.43984723e-01 2.90475115e-02
-5.27112484e-01 1.04917645e+00 5.06779909e-01 -8.78753841e-01
5.27476907e-01 -4.64087605e-01 -4.94829625e-01 -3.08263212e-01
3.65191847e-01 7.46741951e-01 -1.72276109e-01 -5.30016005... | [11.446245193481445, 10.387828826904297] |
d5dd2e66-4cff-4e5c-9658-cb4131868a3b | solving-inefficiency-of-self-supervised | 2104.08760 | null | https://arxiv.org/abs/2104.08760v3 | https://arxiv.org/pdf/2104.08760v3.pdf | Solving Inefficiency of Self-supervised Representation Learning | Self-supervised learning (especially contrastive learning) has attracted great interest due to its huge potential in learning discriminative representations in an unsupervised manner. Despite the acknowledged successes, existing contrastive learning methods suffer from very low learning efficiency, e.g., taking about t... | ['Philip H. S. Torr', 'Liang Lin', 'Guangcong Wang', 'Keze Wang', 'Guangrun Wang'] | 2021-04-18 | solving-inefficiency-of-self-supervised-1 | http://openaccess.thecvf.com//content/ICCV2021/html/Wang_Solving_Inefficiency_of_Self-Supervised_Representation_Learning_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_Solving_Inefficiency_of_Self-Supervised_Representation_Learning_ICCV_2021_paper.pdf | iccv-2021-1 | ['self-supervised-image-classification', 'self-supervised-person-re-identification'] | ['computer-vision', 'computer-vision'] | [ 6.05494790e-02 -2.17361987e-01 -4.19114888e-01 -5.31790137e-01
-9.38427269e-01 -4.57758427e-01 4.81713623e-01 1.65596247e-01
-5.68024814e-01 6.18849516e-01 -3.13848794e-01 1.57790389e-02
-9.39809978e-02 -5.63066781e-01 -6.30764365e-01 -1.00445950e+00
-1.56486884e-01 5.12738705e-01 1.85617409e-03 4.61277701... | [9.438186645507812, 3.2231547832489014] |
ad96a55a-7e84-42bb-871f-e935a8990039 | testing-for-coefficient-randomness-in-local | 2301.04853 | null | https://arxiv.org/abs/2301.04853v2 | https://arxiv.org/pdf/2301.04853v2.pdf | Testing for Coefficient Randomness in Local-to-Unity Autoregressions | In this study, we propose a test for the coefficient randomness in autoregressive models where the autoregressive coefficient is local to unity, which is empirically relevant given the results of earlier studies. Under this specification, we theoretically analyze the effect of the correlation between the random coeffic... | ['Mikihito Nishi'] | 2023-01-12 | null | null | null | null | ['unity'] | ['computer-vision'] | [-1.29513159e-01 -7.16211721e-02 -3.67387325e-01 -2.07877159e-01
-3.45829040e-01 -6.87139153e-01 3.90240014e-01 -1.70355678e-01
-2.64959753e-01 8.05806935e-01 1.75098568e-01 -7.53942728e-01
-6.66067362e-01 -8.96726251e-01 -5.71293175e-01 -7.02708244e-01
-2.77388394e-01 1.33422837e-01 2.51823008e-01 2.86244061... | [6.624338626861572, 4.306291103363037] |
f5deed9e-34f4-45b2-963e-9b28594f9a58 | jcse-contrastive-learning-of-japanese | 2301.08193 | null | https://arxiv.org/abs/2301.08193v1 | https://arxiv.org/pdf/2301.08193v1.pdf | JCSE: Contrastive Learning of Japanese Sentence Embeddings and Its Applications | Contrastive learning is widely used for sentence representation learning. Despite this prevalence, most studies have focused exclusively on English and few concern domain adaptation for domain-specific downstream tasks, especially for low-resource languages like Japanese, which are characterized by insufficient target ... | ['Kimiaki Shirahama', 'Hisashi Handa', 'Zihao Chen'] | 2023-01-19 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings', 'semantic-textual-similarity'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 4.79046613e-01 8.26485232e-02 -4.62550372e-02 -2.85542846e-01
-9.55675960e-01 -2.05012932e-01 4.56074029e-01 3.29733044e-01
-8.54841888e-01 8.61218750e-01 6.95275843e-01 -2.46248826e-01
-1.20830499e-02 -7.53520429e-01 -1.75206318e-01 -5.66763639e-01
3.47926497e-01 3.38473409e-01 1.91285670e-01 -7.04357982... | [10.882515907287598, 8.594298362731934] |
8dfdd0ef-7287-45a7-ab83-1f0533579130 | tma-temporal-motion-aggregation-for-event | 2303.11629 | null | https://arxiv.org/abs/2303.11629v1 | https://arxiv.org/pdf/2303.11629v1.pdf | TMA: Temporal Motion Aggregation for Event-based Optical Flow | Event cameras have the ability to record continuous and detailed trajectories of objects with high temporal resolution, thereby providing intuitive motion cues for optical flow estimation. Nevertheless, most existing learning-based approaches for event optical flow estimation directly remould the paradigm of convention... | ['Changjun Jiang', 'Alois Knoll', 'Zhijun Li', 'Yanping Zhang', 'Sanqing Qu', 'Guang Chen', 'Haotian Liu'] | 2023-03-21 | null | null | null | null | ['event-based-optical-flow'] | ['computer-vision'] | [ 2.22791415e-02 -5.88160217e-01 -2.91603386e-01 -5.73469028e-02
-2.26376206e-01 -4.53042001e-01 5.90896428e-01 1.26733050e-01
-2.65098304e-01 7.38958538e-01 5.33155203e-01 -1.78026646e-01
-1.05536483e-01 -7.61269152e-01 -4.21940953e-01 -5.18714726e-01
-2.92545587e-01 -1.90766066e-01 7.08800793e-01 2.30906472... | [8.873272895812988, -1.4418452978134155] |
55e85234-7981-4415-87c3-29a599f787ce | latent-heterogeneous-graph-network-for | 2208.13669 | null | https://arxiv.org/abs/2208.13669v1 | https://arxiv.org/pdf/2208.13669v1.pdf | Latent Heterogeneous Graph Network for Incomplete Multi-View Learning | Multi-view learning has progressed rapidly in recent years. Although many previous studies assume that each instance appears in all views, it is common in real-world applications for instances to be missing from some views, resulting in incomplete multi-view data. To tackle this problem, we propose a novel Latent Heter... | ['QinGhua Hu', 'Shuai Zhao', 'Binyuan Hui', 'Meng Cao', 'Yu Wang', 'Xinjie Yao', 'Pengfei Zhu'] | 2022-08-29 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-2.48719957e-02 1.32669464e-01 -5.91700017e-01 -5.56683123e-01
-3.91856194e-01 -5.49524665e-01 4.84205186e-01 -1.03432968e-01
2.34212711e-01 6.27377510e-01 1.87202811e-01 1.25486061e-01
-1.51381746e-01 -9.82874811e-01 -4.45730656e-01 -7.14116991e-01
2.95620024e-01 4.60201442e-01 1.17324784e-01 1.55435771... | [8.430338859558105, 4.54019832611084] |
814ca9ef-a3c1-4466-b1b9-f429f8b9a1a5 | reintel-challenge-2020-a-comparative-study-of | 2109.12777 | null | https://arxiv.org/abs/2109.12777v1 | https://arxiv.org/pdf/2109.12777v1.pdf | ReINTEL Challenge 2020: A Comparative Study of Hybrid Deep Neural Network for Reliable Intelligence Identification on Vietnamese SNSs | The overwhelming abundance of data has created a misinformation crisis. Unverified sensationalism that is designed to grab the readers' short attention span, when crafted with malice, has caused irreparable damage to our society's structure. As a result, determining the reliability of an article has become a crucial ta... | ['Ta Minh Thanh', 'Ngoc N. Tran', 'Quang Huu Pham', 'Huy Quang Dao', 'Tam Minh Nguyen', 'Tung Tien Bui', 'Hoang Viet Trinh'] | 2021-09-27 | null | https://aclanthology.org/2020.vlsp-1.2 | https://aclanthology.org/2020.vlsp-1.2.pdf | vlsp-2020-12 | ['reliable-intelligence-identification'] | ['natural-language-processing'] | [ 7.12698177e-02 1.18966185e-01 -5.55549920e-01 -6.81037679e-02
-1.31828356e+00 -1.02341318e+00 7.11810470e-01 4.43217337e-01
-6.46458209e-01 6.29393637e-01 4.83466834e-01 -8.44370484e-01
-5.31023704e-02 -3.58957171e-01 -8.19216907e-01 -2.13770330e-01
3.35285366e-01 1.74661145e-01 -5.38247265e-02 -2.67906517... | [8.291589736938477, 10.157402992248535] |
7c143f33-1f53-46d8-84d7-d833e6e96051 | a-deep-convolutional-network-for-seismic-shot | 1912.01148 | null | https://arxiv.org/abs/1912.01148v1 | https://arxiv.org/pdf/1912.01148v1.pdf | A Deep Convolutional Network for Seismic Shot-Gather Image Quality Classification | Deep Learning-based models such as Convolutional Neural Networks, have led to significant advancements in several areas of computing applications. Seismogram quality assurance is a relevant Geophysics task, since in the early stages of seismic processing, we are required to identify and fix noisy sail lines. In this wo... | ['André Bulcão', 'Sérgio Colcher', 'Ruy Luiz Milidiú', 'Antonio José Grandson Busson', 'Eduardo Betine Bucker', 'Bruno Pereira Dias'] | 2019-12-03 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [-2.67450005e-01 -2.48553738e-01 5.05433083e-01 -4.50889140e-01
-1.05719650e+00 -3.02230924e-01 1.64678022e-01 6.26547575e-01
-5.31357050e-01 4.23474282e-01 2.91364282e-01 -1.43550396e-01
-2.07082585e-01 -1.09371316e+00 -5.89336693e-01 -6.61264837e-01
-5.05023181e-01 2.52080470e-01 2.15454563e-01 -1.53450489... | [6.927068710327148, 2.532076835632324] |
56da9c99-f9a4-4ec8-92c7-d0cc86cddec8 | dynamic-routing-on-deep-neural-network-for | 1808.05744 | null | http://arxiv.org/abs/1808.05744v1 | http://arxiv.org/pdf/1808.05744v1.pdf | Dynamic Routing on Deep Neural Network for Thoracic Disease Classification and Sensitive Area Localization | We present and evaluate a new deep neural network architecture for automatic
thoracic disease detection on chest X-rays. Deep neural networks have shown
great success in a plethora of visual recognition tasks such as image
classification and object detection by stacking multiple layers of
convolutional neural networks ... | ['Mingchen Gao', 'Yan Shen'] | 2018-08-17 | null | null | null | null | ['thoracic-disease-classification'] | ['computer-vision'] | [ 2.17907250e-01 3.72080743e-01 -2.36493886e-01 -5.63929081e-01
-7.79451966e-01 -4.55467790e-01 3.63991439e-01 4.98293974e-02
-4.22031671e-01 3.75108093e-01 2.21481025e-01 -8.01130414e-01
-1.52438805e-01 -4.90919173e-01 -7.14027524e-01 -3.91101301e-01
-1.63692757e-01 5.52399397e-01 4.63894904e-01 1.73117667... | [15.222672462463379, -2.1414401531219482] |
dfa87af5-7489-43bf-bf59-3b1fe8a5f41e | rcp-recurrent-closest-point-for-scene-flow | 2205.11028 | null | https://arxiv.org/abs/2205.11028v2 | https://arxiv.org/pdf/2205.11028v2.pdf | RCP: Recurrent Closest Point for Scene Flow Estimation on 3D Point Clouds | 3D motion estimation including scene flow and point cloud registration has drawn increasing interest. Inspired by 2D flow estimation, recent methods employ deep neural networks to construct the cost volume for estimating accurate 3D flow. However, these methods are limited by the fact that it is difficult to define a s... | ['Ping Tan', 'Siyu Zhu', 'Zuozhuo Dai', 'Weihao Yuan', 'Chengzhou Tang', 'Xiaodong Gu'] | 2022-05-23 | null | null | null | null | ['point-cloud-registration', 'scene-flow-estimation'] | ['computer-vision', 'computer-vision'] | [-4.41894740e-01 -7.37801135e-01 7.88096152e-03 -1.27153188e-01
-1.69725895e-01 -4.08722788e-01 4.14325178e-01 -1.31425962e-01
-5.90160847e-01 3.38264108e-01 -3.65254208e-02 -1.88382745e-01
-2.12222263e-02 -8.67191195e-01 -5.04236042e-01 -5.35564721e-01
-2.39149436e-01 4.17248428e-01 7.25529075e-01 -2.35700950... | [8.51385498046875, -2.0967350006103516] |
fd86f73c-6d43-47d5-b88d-f712b6c3c922 | distinguishing-healthy-ageing-from-dementia-a | 2108.08214 | null | https://arxiv.org/abs/2108.08214v1 | https://arxiv.org/pdf/2108.08214v1.pdf | Distinguishing Healthy Ageing from Dementia: a Biomechanical Simulation of Brain Atrophy using Deep Networks | Biomechanical modeling of tissue deformation can be used to simulate different scenarios of longitudinal brain evolution. In this work,we present a deep learning framework for hyper-elastic strain modelling of brain atrophy, during healthy ageing and in Alzheimer's Disease. The framework directly models the effects of ... | ['Emma C. Robinson', 'M. Jorge Cardoso', 'Cher Bass', 'Kara Garcia', 'Carole H. Sudre', 'Mariana da Silva'] | 2021-08-18 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 1.40681397e-02 4.20867741e-01 1.35096967e-01 -5.24219036e-01
-1.25439376e-01 5.97770624e-02 5.16001701e-01 -2.02667505e-01
-7.67599821e-01 8.20669949e-01 5.74051261e-01 -2.48004168e-01
-3.41943145e-01 -5.51245570e-01 -5.73187172e-01 -5.41865706e-01
-9.91670251e-01 8.80741715e-01 2.40955621e-01 -3.34762126... | [14.046807289123535, -1.9753661155700684] |
591e120a-21fe-4fe7-ac34-21569745a9f1 | contrast-with-reconstruct-contrastive-3d | 2302.02318 | null | https://arxiv.org/abs/2302.02318v2 | https://arxiv.org/pdf/2302.02318v2.pdf | Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining | Mainstream 3D representation learning approaches are built upon contrastive or generative modeling pretext tasks, where great improvements in performance on various downstream tasks have been achieved. However, we find these two paradigms have different characteristics: (i) contrastive models are data-hungry that suffe... | ['Li Yi', 'Kaisheng Ma', 'Xiangyu Zhang', 'Zheng Ge', 'Guofan Fan', 'Runpei Dong', 'Zekun Qi'] | 2023-02-05 | null | null | null | null | ['3d-point-cloud-classification', '3d-point-cloud-linear-classification', 'zero-shot-transfer-3d-point-cloud', 'few-shot-3d-point-cloud-classification'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.40254572e-02 1.85666531e-01 -1.18048191e-01 -4.84549701e-01
-7.77411997e-01 -4.83428121e-01 8.47550392e-01 -3.88714939e-01
-1.58000540e-03 2.45059684e-01 3.64088088e-01 -4.55153853e-01
-1.99538935e-02 -7.14798570e-01 -9.02064979e-01 -7.88466454e-01
4.66178715e-01 6.00637913e-01 1.54605538e-01 -2.41634890... | [8.154353141784668, -3.3508810997009277] |
1c1ea75d-b7cd-4d3f-9d1d-fc9ff3ab3096 | learned-dual-view-reflection-removal | 2010.00702 | null | https://arxiv.org/abs/2010.00702v1 | https://arxiv.org/pdf/2010.00702v1.pdf | Learned Dual-View Reflection Removal | Traditional reflection removal algorithms either use a single image as input, which suffers from intrinsic ambiguities, or use multiple images from a moving camera, which is inconvenient for users. We instead propose a learning-based dereflection algorithm that uses stereo images as input. This is an effective trade-of... | ['Tianfan Xue', 'Feng Liu', 'Rahul Garg', 'Neal Wadhwa', 'Jonathan T. Barron', 'Xuaner Cecilia Zhang', 'Simon Niklaus'] | 2020-10-01 | null | null | null | null | ['reflection-removal'] | ['computer-vision'] | [ 4.47336674e-01 -1.89458966e-01 1.53657109e-01 -3.17781150e-01
-8.02618444e-01 -7.89576292e-01 8.41365755e-01 -5.57832658e-01
-1.39808729e-01 4.81388003e-01 3.67708772e-01 -2.51002103e-01
1.49144351e-01 -6.45319343e-01 -6.10443830e-01 -5.44428945e-01
4.48623955e-01 1.56929463e-01 3.32585871e-01 -2.13096663... | [9.439273834228516, -2.7382559776306152] |
ee943aef-18ea-4fbf-be51-9d9ff944e76c | mvfst-rl-an-asynchronous-rl-framework-for | 1910.04054 | null | https://arxiv.org/abs/1910.04054v4 | https://arxiv.org/pdf/1910.04054v4.pdf | MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions | Effective network congestion control strategies are key to keeping the Internet (or any large computer network) operational. Network congestion control has been dominated by hand-crafted heuristics for decades. Recently, ReinforcementLearning (RL) has emerged as an alternative to automatically optimize such control str... | ['Sebastian Riedel', 'Joelle Pineau', 'Mike Rabbat', 'Heinrich Küttler', 'Alexander H. Miller', 'Tim Rocktäschel', 'Olivier Delalleau', 'Nantas Nardelli', 'Viswanath Sivakumar'] | 2019-10-09 | null | null | null | null | ['network-congestion-control'] | ['miscellaneous'] | [-2.01619402e-01 1.65522084e-01 -7.73099482e-01 -1.08571738e-01
-4.56821471e-01 -5.78012824e-01 3.28617543e-01 -1.00146979e-01
-7.22519696e-01 1.23552322e+00 -1.13368463e-02 -1.17056882e+00
-1.29958078e-01 -6.80208743e-01 -4.72362190e-01 -4.45528775e-01
-6.55373335e-01 4.94698286e-01 5.85929036e-01 -3.83943260... | [4.871982574462891, 1.7398751974105835] |
4af87302-1546-4433-a982-d1a5e3743b72 | robust-category-level-3d-pose-estimation-from | 2305.16124 | null | https://arxiv.org/abs/2305.16124v1 | https://arxiv.org/pdf/2305.16124v1.pdf | Robust Category-Level 3D Pose Estimation from Synthetic Data | Obtaining accurate 3D object poses is vital for numerous computer vision applications, such as 3D reconstruction and scene understanding. However, annotating real-world objects is time-consuming and challenging. While synthetically generated training data is a viable alternative, the domain shift between real and synth... | ['Adam Kortylewski', 'Alan Yuille', 'Xiaoding Yuan', 'Angtian Wang', 'Wufei Ma', 'Jiahao Yang'] | 2023-05-25 | null | null | null | null | ['3d-pose-estimation', '3d-reconstruction', 'inverse-rendering', 'scene-understanding'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 4.70991850e-01 2.97308356e-01 7.50436038e-02 -2.64205933e-01
-9.27500427e-01 -5.23187041e-01 6.91418529e-01 -2.04696506e-01
-3.48160654e-01 5.62044501e-01 -3.42811912e-01 -3.71020450e-03
5.02062887e-02 -7.70217359e-01 -1.15498364e+00 -2.74762481e-01
1.27807781e-01 1.14438367e+00 5.75240493e-01 -1.86913863... | [8.063577651977539, -2.7633731365203857] |
1d059602-aae4-4683-bd9e-947e82fec12b | improving-few-shot-image-classification-using | 2207.03133 | null | https://arxiv.org/abs/2207.03133v1 | https://arxiv.org/pdf/2207.03133v1.pdf | Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions | Humans can obtain the knowledge of novel visual concepts from language descriptions, and we thus use the few-shot image classification task to investigate whether a machine learning model can have this capability. Our proposed model, LIDE (Learning from Image and DEscription), has a text decoder to generate the descrip... | ['Shuichi Nishioka', 'Kyosuke Nishida', 'Kosuke Nishida'] | 2022-07-07 | null | https://aclanthology.org/2022.findings-naacl.106 | https://aclanthology.org/2022.findings-naacl.106.pdf | findings-naacl-2022-7 | ['few-shot-image-classification'] | ['computer-vision'] | [ 1.82948232e-01 2.32691690e-01 -3.84146601e-01 -6.57674611e-01
-6.99033737e-01 -1.44407406e-01 1.03434336e+00 -1.01969447e-02
-2.35691905e-01 5.55912495e-01 3.71451169e-01 1.53133720e-01
3.11352491e-01 -5.05387068e-01 -7.15662003e-01 -3.51178497e-01
9.93625820e-02 2.75050431e-01 1.96861595e-01 1.52011201... | [10.159409523010254, 2.247260093688965] |
96f565e3-c4f4-427c-89e9-8acebf000832 | an-open-source-tool-for-longitudinal-whole | 2207.04534 | null | https://arxiv.org/abs/2207.04534v2 | https://arxiv.org/pdf/2207.04534v2.pdf | An Open-Source Tool for Longitudinal Whole-Brain and White Matter Lesion Segmentation | In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans. It builds upon an existing whole-brain segmentation method that can handle multi-contrast data and robustly analyze images with white matter lesions. This method is here extended with subject-specific la... | ['Koen van Leemput', 'Mark Mühlau', 'Hartwig R. Siebner', 'Henrik Lundell', 'Andrew Hoopes', 'Douglas N. Greve', 'Stefano Cerri'] | 2022-07-10 | null | null | null | null | ['3d-medical-imaging-segmentation', 'brain-image-segmentation', 'brain-segmentation', 'brain-lesion-segmentation-from-mri'] | ['medical', 'medical', 'medical', 'medical'] | [ 1.81200746e-02 -1.06987812e-01 -1.07310101e-01 -6.78546607e-01
-6.49353087e-01 -4.16217923e-01 5.64089119e-01 2.91487932e-01
-8.23255956e-01 8.17215919e-01 1.67544857e-01 -1.70803770e-01
-5.38508654e-01 -3.88844281e-01 -1.23348534e-01 -6.65357232e-01
-8.51929724e-01 8.61339331e-01 6.59400165e-01 1.57854021... | [14.044203758239746, -2.230473041534424] |
75e951c1-833d-4bd5-b400-758b1ad05717 | supervision-and-source-domain-impact-on | 2005.08629 | null | https://arxiv.org/abs/2005.08629v1 | https://arxiv.org/pdf/2005.08629v1.pdf | Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study | As many algorithms depend on a suitable representation of data, learning unique features is considered a crucial task. Although supervised techniques using deep neural networks have boosted the performance of representation learning, the need for a large set of labeled data limits the application of such methods. As an... | ['Sobhan Shafiei', 'Milad Sikaroudi', 'Mark Crowley', 'H. R. Tizhoosh', 'Benyamin Ghojogh', 'Amir Safarpoor'] | 2020-05-10 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [ 5.38474798e-01 -6.30734935e-02 -1.31063201e-02 -6.10699236e-01
-1.14511299e+00 -2.30870619e-01 5.69447160e-01 7.78454602e-01
-6.99931026e-01 6.54281259e-01 1.05615586e-01 9.03853122e-03
-5.61628938e-01 -7.58248627e-01 -3.36726338e-01 -9.88437533e-01
-1.63969204e-01 7.41994798e-01 9.28689018e-02 1.34584773... | [14.955490112304688, -2.648197650909424] |
6458f29d-a027-43a1-931e-a50e5bbcca05 | online-map-vectorization-for-autonomous | 2306.10502 | null | https://arxiv.org/abs/2306.10502v1 | https://arxiv.org/pdf/2306.10502v1.pdf | Online Map Vectorization for Autonomous Driving: A Rasterization Perspective | Vectorized high-definition (HD) map is essential for autonomous driving, providing detailed and precise environmental information for advanced perception and planning. However, current map vectorization methods often exhibit deviations, and the existing evaluation metric for map vectorization lacks sufficient sensitivi... | ['Zuoguan Wang', 'Shijian Lu', 'Yang Xue', 'Zhipeng Luo', 'Yilin Song', 'Shuang Wu', 'Jiahao Lin', 'Gongjie Zhang'] | 2023-06-18 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [ 1.81129515e-01 3.81922908e-02 -2.27138489e-01 -8.42402160e-01
-6.09192729e-01 -5.23693502e-01 7.08441377e-01 2.43847489e-01
-4.25407976e-01 4.92553204e-01 2.75215030e-01 -5.38362145e-01
-1.18137412e-01 -1.35172415e+00 -8.98973584e-01 -3.50813180e-01
3.71140055e-02 2.57220149e-01 4.29711580e-01 -7.02887118... | [7.831470966339111, -1.8311703205108643] |
df3b1602-ca8d-45b0-8ef1-990271b5bfc4 | opi-at-semeval-2022-task-10-transformer-based | null | null | https://aclanthology.org/2022.semeval-1.190 | https://aclanthology.org/2022.semeval-1.190.pdf | OPI at SemEval-2022 Task 10: Transformer-based Sequence Tagging with Relation Classification for Structured Sentiment Analysis | This paper presents our solution for SemEval-2022 Task 10: Structured Sentiment Analysis. The solution consisted of two modules: the first for sequence tagging and the second for relation classification. In both modules we used transformer-based language models. In addition to utilizing language models specific to each... | ['Rafał Poświata'] | null | null | null | null | semeval-naacl-2022-7 | ['relation-classification'] | ['natural-language-processing'] | [-7.97454417e-02 4.66562808e-01 -1.75058424e-01 -2.82321751e-01
-8.88093293e-01 -7.85344899e-01 7.67692089e-01 4.91252661e-01
-6.33773983e-01 9.54543769e-01 1.52629152e-01 -4.79344577e-01
2.67284840e-01 -5.27441323e-01 -5.69507837e-01 -2.14081183e-01
8.25471953e-02 5.30731022e-01 1.71904564e-01 -4.33760732... | [10.232494354248047, 9.579453468322754] |
7e171036-89ad-4f36-b409-f87dd0f6f858 | conditionally-optimistic-exploration-for | 2303.09032 | null | https://arxiv.org/abs/2303.09032v1 | https://arxiv.org/pdf/2303.09032v1.pdf | Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning | Efficient exploration is critical in cooperative deep Multi-Agent Reinforcement Learning (MARL). In this paper, we propose an exploration method that efficiently encourages cooperative exploration based on the idea of the theoretically justified tree search algorithm UCT (Upper Confidence bounds applied to Trees). The ... | ['Janarthanan Rajendran', 'Sarath Chandar', 'Chenjun Xiao', 'Yangchen Pan', 'Xutong Zhao'] | 2023-03-16 | null | null | null | null | ['efficient-exploration'] | ['methodology'] | [-3.37846249e-01 6.19681776e-01 -6.16139472e-01 1.14845820e-02
-6.91864252e-01 -3.34447533e-01 4.75156784e-01 3.68189484e-01
-7.72436142e-01 1.21015060e+00 2.12888584e-01 -4.55034971e-01
-3.57292622e-01 -9.31557596e-01 -6.70811117e-01 -1.14841270e+00
-8.50751698e-01 1.12046111e+00 7.13931620e-02 -2.81494737... | [3.815430164337158, 1.7887576818466187] |
8c26fbfb-033b-43be-a63d-c51709db94e5 | dialoguetrm-exploring-the-intra-and-inter | 2010.07637 | null | https://arxiv.org/abs/2010.07637v1 | https://arxiv.org/pdf/2010.07637v1.pdf | DialogueTRM: Exploring the Intra- and Inter-Modal Emotional Behaviors in the Conversation | Emotion Recognition in Conversations (ERC) is essential for building empathetic human-machine systems. Existing studies on ERC primarily focus on summarizing the context information in a conversation, however, ignoring the differentiated emotional behaviors within and across different modalities. Designing appropriate ... | ['Jianping Shen', 'Xuan Li', 'Weiguo Gao', 'Xiaojie Wang', 'Guang Liu', 'Qi Sun', 'Yuzhao Mao'] | 2020-10-15 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-2.20448092e-01 -1.65813521e-01 -5.16958907e-02 -7.15200186e-01
-7.10240185e-01 -2.80367970e-01 4.47876066e-01 -1.58231720e-01
-3.01111728e-01 4.44136590e-01 7.46297240e-01 3.02636921e-01
-4.13436927e-02 -3.32005948e-01 9.38852057e-02 -7.33653009e-01
2.67246604e-01 3.00679415e-01 -2.47096747e-01 -5.37069201... | [13.023531913757324, 6.019354343414307] |
70236dbd-e97c-4505-99d6-6fcc19091326 | unsupervised-conversation-disentanglement | 2109.03199 | null | https://arxiv.org/abs/2109.03199v1 | https://arxiv.org/pdf/2109.03199v1.pdf | Unsupervised Conversation Disentanglement through Co-Training | Conversation disentanglement aims to separate intermingled messages into detached sessions, which is a fundamental task in understanding multi-party conversations. Existing work on conversation disentanglement relies heavily upon human-annotated datasets, which are expensive to obtain in practice. In this work, we expl... | ['Xiaodan Zhu', 'Zhan Shi', 'Hui Liu'] | 2021-09-07 | null | https://aclanthology.org/2021.emnlp-main.181 | https://aclanthology.org/2021.emnlp-main.181.pdf | emnlp-2021-11 | ['conversation-disentanglement'] | ['natural-language-processing'] | [ 5.16093731e-01 5.74150026e-01 -2.42533579e-01 -6.63347781e-01
-1.08482695e+00 -5.87613404e-01 9.00587738e-01 -4.37665917e-02
-2.82131106e-01 8.64327312e-01 5.69428384e-01 -2.66061932e-01
6.72507286e-02 -5.60555160e-01 -4.44046080e-01 -8.34423423e-01
9.12553295e-02 8.27254534e-01 -8.84087011e-02 -3.00373673... | [12.591572761535645, 7.833738803863525] |
f93924dc-11ab-4754-80d6-72da809d199f | cascadepsp-toward-class-agnostic-and-very | 2005.02551 | null | https://arxiv.org/abs/2005.02551v1 | https://arxiv.org/pdf/2005.02551v1.pdf | CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement | State-of-the-art semantic segmentation methods were almost exclusively trained on images within a fixed resolution range. These segmentations are inaccurate for very high-resolution images since using bicubic upsampling of low-resolution segmentation does not adequately capture high-resolution details along object boun... | ['Chi-Keung Tang', 'Yu-Wing Tai', 'Jihoon Chung', 'Ho Kei Cheng'] | 2020-05-06 | cascadepsp-toward-class-agnostic-and-very-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Cheng_CascadePSP_Toward_Class-Agnostic_and_Very_High-Resolution_Segmentation_via_Global_and_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Cheng_CascadePSP_Toward_Class-Agnostic_and_Very_High-Resolution_Segmentation_via_Global_and_CVPR_2020_paper.pdf | cvpr-2020-6 | ['scene-parsing'] | ['computer-vision'] | [ 6.47649169e-01 3.73053581e-01 -7.29341991e-03 -4.19793665e-01
-1.27405179e+00 -5.43878436e-01 2.91151345e-01 -5.78664504e-02
-5.94465971e-01 5.79859197e-01 -2.57683098e-01 1.36707157e-01
7.51340985e-02 -1.14386642e+00 -1.00020266e+00 -4.67242748e-01
4.65829819e-01 7.77814686e-01 1.04131687e+00 -1.17665745... | [9.550901412963867, 0.2900802791118622] |
3d7c4acf-5a99-4045-b7d7-cd6efd6816b5 | learning-human-compatible-representations-for | 2303.04809 | null | https://arxiv.org/abs/2303.04809v1 | https://arxiv.org/pdf/2303.04809v1.pdf | Learning Human-Compatible Representations for Case-Based Decision Support | Algorithmic case-based decision support provides examples to help human make sense of predicted labels and aid human in decision-making tasks. Despite the promising performance of supervised learning, representations learned by supervised models may not align well with human intuitions: what models consider as similar ... | ['Chenhao Tan', 'Yuxin Chen', 'Shi Feng', 'Chacha Chen', 'Yizhou Tian', 'Han Liu'] | 2023-03-06 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 5.59742808e-01 2.53156513e-01 -3.54669452e-01 -1.13931727e+00
-6.27796292e-01 -6.40494049e-01 5.65684795e-01 7.80265391e-01
-3.76639605e-01 5.00884116e-01 2.46552199e-01 -3.49603355e-01
-4.02680308e-01 -5.69699585e-01 -2.55982935e-01 -2.42326230e-01
3.38436775e-02 7.61359453e-01 -1.57055929e-01 -1.41600877... | [9.454412460327148, 6.261760711669922] |
414fefc9-dfba-4b62-84da-60b9a588f365 | improving-road-segmentation-in-challenging | 2205.14112 | null | https://arxiv.org/abs/2205.14112v1 | https://arxiv.org/pdf/2205.14112v1.pdf | Improving Road Segmentation in Challenging Domains Using Similar Place Priors | Road segmentation in challenging domains, such as night, snow or rain, is a difficult task. Most current approaches boost performance using fine-tuning, domain adaptation, style transfer, or by referencing previously acquired imagery. These approaches share one or more of three significant limitations: a reliance on la... | ['Michael Milford', 'Thierry Peynot', 'Ming Xu', 'Sourav Garg', 'Connor Malone'] | 2022-05-27 | null | null | null | null | ['road-segementation', 'visual-place-recognition'] | ['computer-vision', 'computer-vision'] | [ 5.70224106e-01 1.01572294e-02 -7.12198243e-02 -6.60002768e-01
-9.19028640e-01 -8.58126581e-01 7.00311720e-01 6.85605854e-02
-6.48240805e-01 1.04462492e+00 1.15386024e-01 -4.24027562e-01
-3.70006502e-01 -9.51976895e-01 -9.27530289e-01 -4.38308835e-01
-2.43098065e-01 8.86323392e-01 6.85671687e-01 -3.55886966... | [9.102132797241211, -1.4590619802474976] |
a8b4a135-8595-4731-81c5-a79deb7cbe69 | unsupervised-noise-adaptation-using-data | 2302.11981 | null | https://arxiv.org/abs/2302.11981v1 | https://arxiv.org/pdf/2302.11981v1.pdf | Unsupervised Noise adaptation using Data Simulation | Deep neural network based speech enhancement approaches aim to learn a noisy-to-clean transformation using a supervised learning paradigm. However, such a trained-well transformation is vulnerable to unseen noises that are not included in training set. In this work, we focus on the unsupervised noise adaptation problem... | ['Eng Siong Chng', 'Linhui Sun', 'Heqing Zou', 'Yuchen Hu', 'Chen Chen'] | 2023-02-23 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 5.57258666e-01 -2.48987097e-02 3.55565131e-01 -4.70623046e-01
-1.35892260e+00 -5.96951485e-01 4.79766458e-01 -5.10258377e-01
-5.02201498e-01 8.67123902e-01 4.33663458e-01 -1.49359494e-01
2.38536343e-01 -6.03156090e-01 -7.82604098e-01 -1.01606083e+00
5.42045712e-01 -1.52784362e-01 -1.85964316e-01 -5.60882747... | [14.91696548461914, 6.206473350524902] |
195853bb-b624-4933-846b-2a8ad07be9d4 | target-based-surrogates-for-stochastic | 2302.02607 | null | https://arxiv.org/abs/2302.02607v2 | https://arxiv.org/pdf/2302.02607v2.pdf | Target-based Surrogates for Stochastic Optimization | We consider minimizing functions for which it is expensive to compute the (possibly stochastic) gradient. Such functions are prevalent in reinforcement learning, imitation learning and adversarial training. Our target optimization framework uses the (expensive) gradient computation to construct surrogate functions in a... | ['Nicolas Le Roux', 'Mark Schmidt', 'Reza Babanezhad', 'Sharan Vaswani', 'Jonathan Wilder Lavington'] | 2023-02-06 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 5.94248548e-02 2.67948031e-01 -1.91753373e-01 -3.20942312e-01
-1.19217229e+00 -7.37043858e-01 3.68310422e-01 -5.15679866e-02
-8.69289637e-01 7.92951405e-01 -2.27922499e-01 -2.82194942e-01
-2.22600028e-01 -7.16440558e-01 -1.31901062e+00 -8.53195667e-01
-2.29820222e-01 2.87278026e-01 -2.22797796e-01 -1.91218168... | [6.744833469390869, 4.109294891357422] |
c3fbc28c-56b6-42a9-8984-fd2aadab54cc | a-comparison-of-the-word-similarity | null | null | https://aclanthology.org/2021.triton-1.14 | https://aclanthology.org/2021.triton-1.14.pdf | A Comparison of the Word Similarity Measurement in English-Arabic Translation Memory Segment Retrieval Including an Inflectional Affix Intervention | The aim of this paper is to investigate the similarity measurement approach of translation memory (TM) in five representative computer-aided translation (CAT) tools when retrieving inflectional verb-variation sentences in Arabic to English translation. In English, inflectional affixes in verbs include suffixes only; un... | ['Khaled Ben Milad'] | null | null | null | null | triton-2021-7 | ['word-similarity'] | ['natural-language-processing'] | [ 4.45435911e-01 -3.18237692e-02 -2.97517985e-01 -2.37653092e-01
-6.10655427e-01 -1.01748538e+00 6.24547124e-01 2.81367898e-01
-6.19586766e-01 8.14407527e-01 3.79883647e-01 -9.75126028e-01
-6.88737035e-02 -7.51882374e-01 -5.77417910e-01 -4.65557605e-01
3.87206256e-01 7.07937777e-01 6.99733477e-03 -6.27539396... | [11.249367713928223, 10.212360382080078] |
45bb9543-0fba-4fb7-ae99-eb83ec1c5a9b | distance-weighted-graph-neural-networks-on | 2008.03601 | null | https://arxiv.org/abs/2008.03601v2 | https://arxiv.org/pdf/2008.03601v2.pdf | Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics | Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGPA-based first layer of real-time data filtering at the CERN Large Hadr... | ['Nhan Tran', 'Jennifer Ngadiuba', 'Gerrit Van Onsem', 'Sergo Jindariani', 'Philip Harris', 'Kinga Wozniak', 'Abhijay Gupta', 'Yutaro Iiyama', 'Marcel Rieger', 'Kevin Pedro', 'Jan Kieseler', 'Giuseppe Di Guglielmo', 'Edward Kreinar', 'Zhenbin Wu', 'Vladimir Loncar', 'Shah Rukh Qasim', 'Mia Liu', 'Maurizio Pierini', 'Ja... | 2020-08-08 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [ 1.59898903e-02 1.87868476e-01 -1.57864943e-01 -7.48529911e-01
-2.46557370e-01 -2.47835666e-01 3.17064732e-01 7.35579014e-01
-5.92564166e-01 5.27145147e-01 -4.03560996e-01 -7.55337596e-01
-4.18214053e-01 -9.93642688e-01 -6.36200249e-01 -4.05770868e-01
-1.90044895e-01 1.02141726e+00 4.97418582e-01 -1.09255547... | [15.693459510803223, 2.9197418689727783] |
e12df3d5-7b43-47e1-934b-37bbaf5de6c6 | gradient-induced-co-saliency-detection | 2004.13364 | null | https://arxiv.org/abs/2004.13364v3 | https://arxiv.org/pdf/2004.13364v3.pdf | Gradient-Induced Co-Saliency Detection | Co-saliency detection (Co-SOD) aims to segment the common salient foreground in a group of relevant images. In this paper, inspired by human behavior, we propose a gradient-induced co-saliency detection (GICD) method. We first abstract a consensus representation for the grouped images in the embedding space; then, by c... | ['Ming-Ming Cheng', 'Jun Xu', 'Zhao Zhang', 'Wenda Jin'] | 2020-04-28 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1615_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123570443.pdf | eccv-2020-8 | ['co-saliency-detection'] | ['computer-vision'] | [ 3.50177318e-01 8.27094764e-02 -2.62026966e-01 -1.45843923e-01
-6.75867558e-01 -1.36892289e-01 3.96018028e-01 -2.77355239e-02
4.42333473e-03 2.71302253e-01 2.83783048e-01 1.77478656e-01
3.68832261e-03 -4.47793633e-01 -8.46836030e-01 -5.88701963e-01
1.04549915e-01 -6.31236807e-02 8.84599447e-01 7.15589849... | [9.765983581542969, -0.30893179774284363] |
ba358bd9-6223-4086-a388-d24ff9884d7e | kert-automatic-extraction-and-ranking-of | 1306.0271 | null | http://arxiv.org/abs/1306.0271v1 | http://arxiv.org/pdf/1306.0271v1.pdf | KERT: Automatic Extraction and Ranking of Topical Keyphrases from Content-Representative Document Titles | We introduce KERT (Keyphrase Extraction and Ranking by Topic), a framework
for topical keyphrase generation and ranking. By shifting from the
unigram-centric traditional methods of unsupervised keyphrase extraction to a
phrase-centric approach, we are able to directly compare and rank phrases of
different lengths. We c... | ['Jiawei Han', 'Marina Danilevsky', 'Nihit Desai', 'Jingyi Guo', 'Chi Wang'] | 2013-06-03 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 2.40601555e-01 -3.84093001e-02 -5.81678748e-01 3.23650360e-01
-1.17499769e+00 -9.78844643e-01 1.25895631e+00 1.07860565e+00
-6.27586901e-01 8.44400942e-01 8.38874280e-01 -3.41681182e-01
-4.20499951e-01 -7.87460625e-01 -4.68049139e-01 -2.60183245e-01
-2.61016548e-01 3.50626290e-01 4.57423538e-01 -1.72276840... | [12.224503517150879, 8.902961730957031] |
d2bd3404-4143-48d0-b5f3-21113673662c | seismic-inverse-modeling-method-based-on | 2106.04197 | null | https://arxiv.org/abs/2106.04197v1 | https://arxiv.org/pdf/2106.04197v1.pdf | Seismic Inverse Modeling Method based on Generative Adversarial Network | Seismic inverse modeling is a common method in reservoir prediction and it plays a vital role in the exploration and development of oil and gas. Conventional seismic inversion method is difficult to combine with complicated and abstract knowledge on geological mode and its uncertainty is difficult to be assessed. The p... | ['LiXin Wang', 'Mei Chen', 'JiaGen Hou', 'YanShu Yin', 'Pengfei Xie'] | 2021-06-08 | null | null | null | null | ['seismic-inversion'] | ['miscellaneous'] | [ 1.47286281e-01 1.31210431e-01 2.86147654e-01 -2.42743924e-01
-4.92225170e-01 -1.32070780e-01 6.73020661e-01 -6.19483352e-01
-6.97222874e-02 1.03371048e+00 4.70792323e-01 -1.17288969e-01
-1.33111656e-01 -1.24615979e+00 -7.90705323e-01 -8.66916358e-01
-3.50949005e-03 8.64665627e-01 1.69366091e-01 -3.63555461... | [6.873014450073242, 2.5849812030792236] |
5004a3bd-e339-44eb-90b2-87eb08fb83a6 | exploring-dense-retrieval-for-dialogue | 2110.06612 | null | https://arxiv.org/abs/2110.06612v3 | https://arxiv.org/pdf/2110.06612v3.pdf | Exploring Dense Retrieval for Dialogue Response Selection | Recent progress in deep learning has continuously improved the accuracy of dialogue response selection. In particular, sophisticated neural network architectures are leveraged to capture the rich interactions between dialogue context and response candidates. While remarkably effective, these models also bring in a stee... | ['Xian-Ling Mao', 'Heyan Huang', 'Yixuan Su', 'Yan Wang', 'Deng Cai', 'Tian Lan'] | 2021-10-13 | null | null | null | null | ['conversational-response-selection'] | ['natural-language-processing'] | [ 1.99539542e-01 -9.86445323e-02 -1.83832258e-01 -6.25566661e-01
-1.55424881e+00 -7.92502761e-01 8.60460222e-01 1.29828379e-01
-9.13845122e-01 7.41524398e-01 5.66994071e-01 -1.46982491e-01
1.29637390e-01 -5.24080276e-01 -3.10816675e-01 -1.36068121e-01
2.13934258e-01 9.45173085e-01 2.98127651e-01 -7.43019640... | [12.313982009887695, 7.903771877288818] |
c21b91fd-dbe2-4874-813c-1b702e02fe4f | amodal-panoptic-segmentation | 2202.11542 | null | https://arxiv.org/abs/2202.11542v1 | https://arxiv.org/pdf/2202.11542v1.pdf | Amodal Panoptic Segmentation | Humans have the remarkable ability to perceive objects as a whole, even when parts of them are occluded. This ability of amodal perception forms the basis of our perceptual and cognitive understanding of our world. To enable robots to reason with this capability, we formulate and propose a novel task that we name amoda... | ['Abhinav Valada', 'Rohit Mohan'] | 2022-02-23 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Mohan_Amodal_Panoptic_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Mohan_Amodal_Panoptic_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['amodal-panoptic-segmentation'] | ['computer-vision'] | [ 4.57681030e-01 1.38680845e-01 2.23026946e-02 -5.74368358e-01
-4.96357918e-01 -9.15679097e-01 7.49929965e-01 1.26841024e-01
-1.17899023e-01 2.30577707e-01 -8.79716408e-03 -2.76635438e-01
6.59162328e-02 -8.32186282e-01 -1.05467391e+00 -7.38466859e-01
-3.06158159e-02 6.28357589e-01 2.00892866e-01 -2.96681225... | [9.532483100891113, 0.4175122380256653] |
4dd1a7cf-80c7-4bad-87bf-650843ad86a9 | optical-flow-based-branch-segmentation-for | 2202.13050 | null | https://arxiv.org/abs/2202.13050v1 | https://arxiv.org/pdf/2202.13050v1.pdf | Optical flow-based branch segmentation for complex orchard environments | Machine vision is a critical subsystem for enabling robots to be able to perform a variety of tasks in orchard environments. However, orchards are highly visually complex environments, and computer vision algorithms operating in them must be able to contend with variable lighting conditions and background noise. Past w... | ['Joseph R. Davidson', 'Cindy Grimm', 'Alexander You'] | 2022-02-26 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 2.53891945e-01 -4.21633571e-01 2.12082863e-01 -4.48550224e-01
1.61039904e-01 -1.05207956e+00 9.92830172e-02 -1.21937536e-01
-4.57368881e-01 4.22080189e-01 -8.00498009e-01 -7.86742151e-01
4.11249876e-01 -8.20302546e-01 -7.25536168e-01 -5.65146625e-01
-2.92188466e-01 4.92999643e-01 4.50989693e-01 -1.93331778... | [9.047700881958008, -1.5964323282241821] |
292f9642-0ab7-44f1-831a-9330b17e4dc8 | remote-sensing-change-detection-based-on | null | null | https://www.mdpi.com/2072-4292/13/15/3053 | https://www.mdpi.com/2072-4292/13/15/3053 | Remote Sensing Change Detection Based on Multidirectional Adaptive Feature Fusion and Perceptual Similarity | Remote sensing change detection (RSCD) is an important yet challenging task in Earth observation. The booming development of convolutional neural networks (CNNs) in computer vision raises new possibilities for RSCD, and many recent RSCD methods have introduced CNNs to achieve promising improvements in performance. In t... | ['Yang Luo', 'Shicai Wei', 'Xinyue Chen', 'Chunbo Luo', 'Jialang Xu'] | 2021-08-03 | null | null | null | remote-sensing-2021-8 | ['change-detection', 'change-detection-for-remote-sensing-images'] | ['computer-vision', 'miscellaneous'] | [ 3.23485613e-01 -5.35961330e-01 4.16941613e-01 -4.87104535e-01
-1.82189107e-01 -2.36810997e-01 6.81939244e-01 3.52425575e-01
-4.54230785e-01 4.52046871e-01 3.82381797e-01 -1.74011528e-01
-4.69896764e-01 -1.38768721e+00 -4.93020147e-01 -7.35883176e-01
-6.50085956e-02 -5.41652262e-01 6.93369567e-01 -6.61585867... | [9.774853706359863, -1.328477144241333] |
5012e9d5-572c-416b-91a5-70bee0c727ff | material-recognition-in-the-wild-with-the | 1412.0623 | null | http://arxiv.org/abs/1412.0623v2 | http://arxiv.org/pdf/1412.0623v2.pdf | Material Recognition in the Wild with the Materials in Context Database | Recognizing materials in real-world images is a challenging task. Real-world
materials have rich surface texture, geometry, lighting conditions, and
clutter, which combine to make the problem particularly difficult. In this
paper, we introduce a new, large-scale, open dataset of materials in the wild,
the Materials in ... | ['Paul Upchurch', 'Sean Bell', 'Kavita Bala', 'Noah Snavely'] | 2014-12-01 | material-recognition-in-the-wild-with-the-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Bell_Material_Recognition_in_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Bell_Material_Recognition_in_2015_CVPR_paper.pdf | cvpr-2015-6 | ['material-recognition'] | ['computer-vision'] | [ 7.58011758e-01 -3.26657593e-01 -6.44011982e-03 -2.65800297e-01
-1.03151035e+00 -5.05417347e-01 3.32473814e-01 -3.69073730e-03
-2.06772611e-02 3.83374631e-01 -2.04915911e-01 -5.07863015e-02
8.76087919e-02 -1.28554118e+00 -1.34113073e+00 -7.78645277e-01
-1.56472102e-02 5.17188489e-01 5.95760405e-01 2.71254689... | [10.148627281188965, -0.1746099889278412] |
c9d5b565-9d0c-4734-9d85-a1fb576c92a7 | chq-summ-a-dataset-for-consumer-healthcare | 2206.06581 | null | https://arxiv.org/abs/2206.06581v2 | https://arxiv.org/pdf/2206.06581v2.pdf | CHQ-Summ: A Dataset for Consumer Healthcare Question Summarization | The quest for seeking health information has swamped the web with consumers' health-related questions. Generally, consumers use overly descriptive and peripheral information to express their medical condition or other healthcare needs, contributing to the challenges of natural language understanding. One way to address... | ['Dina Demner-Fushman', 'Deepak Gupta', 'Shweta Yadav'] | 2022-06-14 | null | null | null | null | ['community-question-answering', 'community-question-answering'] | ['miscellaneous', 'natural-language-processing'] | [ 2.97872633e-01 6.48126900e-01 -6.33141279e-01 -3.28676820e-01
-1.47854912e+00 -3.16857100e-01 2.92395651e-01 1.36136115e+00
-3.51466805e-01 5.61797321e-01 1.37546992e+00 -1.53177083e-01
-8.51571932e-02 -5.09736001e-01 -1.78061128e-01 -1.12643592e-01
2.43540123e-01 4.41178739e-01 2.14608029e-01 -6.24084890... | [8.678024291992188, 8.792025566101074] |
c67f5edd-8c80-4257-95a0-9d9c39cb0d0f | codegen2-lessons-for-training-llms-on | 2305.02309 | null | https://arxiv.org/abs/2305.02309v1 | https://arxiv.org/pdf/2305.02309v1.pdf | CodeGen2: Lessons for Training LLMs on Programming and Natural Languages | Large language models (LLMs) have demonstrated remarkable abilities in representation learning for program synthesis and understanding tasks. The quality of the learned representations appears to be dictated by the neural scaling laws as a function of the number of model parameters and observations, while imposing uppe... | ['Yingbo Zhou', 'Silvio Savarese', 'Caiming Xiong', 'Hiroaki Hayashi', 'Erik Nijkamp'] | 2023-05-03 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [-3.65355425e-02 1.05269097e-01 -6.86395407e-01 -3.20845932e-01
-8.07664275e-01 -4.70031649e-01 5.77400684e-01 4.73510846e-02
-2.55896412e-02 3.93472344e-01 2.42289796e-01 -9.67026830e-01
1.80506065e-01 -6.90203309e-01 -1.29060686e+00 -3.29699874e-01
-1.92761526e-01 1.89448416e-01 6.77281059e-03 -9.23399404... | [8.055951118469238, 7.667320728302002] |
99c3f810-1260-4de5-ad15-f6c47e23ef73 | neurohex-a-deep-q-learning-hex-agent | 1604.07097 | null | http://arxiv.org/abs/1604.07097v2 | http://arxiv.org/pdf/1604.07097v2.pdf | Neurohex: A Deep Q-learning Hex Agent | DeepMind's recent spectacular success in using deep convolutional neural nets
and machine learning to build superhuman level agents --- e.g. for Atari games
via deep Q-learning and for the game of Go via Reinforcement Learning ---
raises many questions, including to what extent these methods will succeed in
other domai... | ['Gautham Vasan', 'Kenny Young', 'Ryan Hayward'] | 2016-04-24 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-5.15465200e-01 3.79129499e-01 -9.90559012e-02 2.04083368e-01
-4.36565459e-01 -3.12879294e-01 1.57788634e-01 -4.21678901e-01
-9.76323247e-01 1.17640734e+00 -1.56073451e-01 -5.65254450e-01
-2.82983094e-01 -8.66826952e-01 -8.89988124e-01 -5.98409474e-01
-4.74024683e-01 7.77498484e-01 2.37825081e-01 -1.22398460... | [3.5632095336914062, 1.5383628606796265] |
e93cda20-038a-42bd-bc67-507bda22bf26 | norefer-a-referenceless-quality-metric-for | 2306.12577 | null | https://arxiv.org/abs/2306.12577v1 | https://arxiv.org/pdf/2306.12577v1.pdf | NoRefER: a Referenceless Quality Metric for Automatic Speech Recognition via Semi-Supervised Language Model Fine-Tuning with Contrastive Learning | This paper introduces NoRefER, a novel referenceless quality metric for automatic speech recognition (ASR) systems. Traditional reference-based metrics for evaluating ASR systems require costly ground-truth transcripts. NoRefER overcomes this limitation by fine-tuning a multilingual language model for pair-wise ranking... | ['Ahmet Gunduz', 'Mohamed El-Badrashiny', 'Golara Javadi', 'Thiago Ferreira', 'Kamer Ali Yuksel'] | 2023-06-21 | null | null | null | null | ['contrastive-learning', 'contrastive-learning', 'automatic-speech-recognition'] | ['computer-vision', 'methodology', 'speech'] | [ 2.15256035e-01 -1.49263829e-01 -4.06016290e-01 -6.19173765e-01
-1.90361929e+00 -6.46930218e-01 5.42891741e-01 2.36160040e-01
-5.47774136e-01 6.05117798e-01 4.33558315e-01 -4.71999168e-01
-2.48543650e-01 -1.11521274e-01 -4.10944581e-01 -2.74912357e-01
-8.46887603e-02 6.75375104e-01 3.31563577e-02 -4.56138402... | [14.420495986938477, 6.752904415130615] |
fdff8f50-0365-481a-bb56-6b1b8b9adb51 | an-event-based-algorithm-for-simultaneous-6 | 2301.00618 | null | https://arxiv.org/abs/2301.00618v2 | https://arxiv.org/pdf/2301.00618v2.pdf | An Event-based Algorithm for Simultaneous 6-DOF Camera Pose Tracking and Mapping | Compared to regular cameras, Dynamic Vision Sensors or Event Cameras can output compact visual data based on a change in the intensity in each pixel location asynchronously. In this paper, we study the application of current image-based SLAM techniques to these novel sensors. To this end, the information in adaptively ... | ['Mohammad Reza Ahmadzadeh', 'Masoud Dayani Najafabadi'] | 2023-01-02 | null | null | null | null | ['pose-tracking'] | ['computer-vision'] | [ 3.89333516e-01 -3.94004196e-01 3.78658652e-01 -4.18791443e-01
-4.61140782e-01 -7.67113984e-01 7.91995823e-01 2.31592938e-01
-9.40687656e-01 6.23047471e-01 -4.91257496e-02 1.69142872e-01
1.77103564e-01 -5.85330904e-01 -1.02489173e+00 -4.58114535e-01
3.29375267e-03 7.46035039e-01 1.00538135e+00 1.69204980... | [8.011356353759766, -1.7959837913513184] |
6d59a1a6-c740-40fb-b050-1d7308646207 | scene-labeling-with-contextual-hierarchical | 1402.0595 | null | http://arxiv.org/abs/1402.0595v1 | http://arxiv.org/pdf/1402.0595v1.pdf | Scene Labeling with Contextual Hierarchical Models | Scene labeling is the problem of assigning an object label to each pixel. It
unifies the image segmentation and object recognition problems. The importance
of using contextual information in scene labeling frameworks has been widely
realized in the field. We propose a contextual framework, called contextual
hierarchica... | ['Tolga Tasdizen', 'Mojtaba Seyedhosseini'] | 2014-02-04 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 7.05801845e-01 2.40001231e-02 -3.65994573e-01 -4.69061077e-01
-8.19397092e-01 -2.96094149e-01 4.32361782e-01 2.03322873e-01
-5.64816654e-01 4.11818683e-01 -1.28397673e-01 -1.56874940e-01
1.29126772e-01 -9.66054738e-01 -8.08199823e-01 -6.95734262e-01
4.70091142e-02 3.51320654e-01 9.40613925e-01 1.89323843... | [9.541169166564941, 0.3514177203178406] |
5d8a39b1-7542-47a3-b30e-624bed89e546 | identification-of-truth-and-deception-in-text | null | null | https://aclanthology.org/W12-0415 | https://aclanthology.org/W12-0415.pdf | Identification of Truth and Deception in Text: Application of Vector Space Model to Rhetorical Structure Theory | null | ['Victoria L. Rubin', 'Tatiana Vashchilko'] | 2012-04-01 | null | null | null | ws-2012-4 | ['deception-detection'] | ['miscellaneous'] | [-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... | [-7.3262529373168945, 3.696417808532715] |
7db87f36-407e-4b89-91ab-8fd2949d7805 | timebalance-temporally-invariant-and | 2303.16268 | null | https://arxiv.org/abs/2303.16268v1 | https://arxiv.org/pdf/2303.16268v1.pdf | TimeBalance: Temporally-Invariant and Temporally-Distinctive Video Representations for Semi-Supervised Action Recognition | Semi-Supervised Learning can be more beneficial for the video domain compared to images because of its higher annotation cost and dimensionality. Besides, any video understanding task requires reasoning over both spatial and temporal dimensions. In order to learn both the static and motion related features for the semi... | ['Mubarak Shah', 'Chen Chen', 'Mamshad Nayeem Rizve', 'Ishan Rajendrakumar Dave'] | 2023-03-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Dave_TimeBalance_Temporally-Invariant_and_Temporally-Distinctive_Video_Representations_for_Semi-Supervised_Action_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Dave_TimeBalance_Temporally-Invariant_and_Temporally-Distinctive_Video_Representations_for_Semi-Supervised_Action_Recognition_CVPR_2023_paper.pdf | cvpr-2023-1 | ['action-recognition-in-videos', 'video-understanding'] | ['computer-vision', 'computer-vision'] | [ 1.66375875e-01 -3.10048163e-01 -6.44414067e-01 -4.73653972e-01
-5.22929549e-01 -4.93911892e-01 6.49680197e-01 -7.99375400e-02
-5.81939399e-01 4.33693260e-01 2.31141403e-01 -4.47316840e-02
-8.73650014e-02 -4.88264441e-01 -6.52194679e-01 -8.48386288e-01
3.17157879e-02 1.51822776e-01 5.23351431e-01 9.23716649... | [8.656427383422852, 0.7125102877616882] |
95aada56-4c32-46b1-84e5-45ba5dbb16fb | spatial-graph-convolutional-neural-network | 2112.06033 | null | https://arxiv.org/abs/2112.06033v1 | https://arxiv.org/pdf/2112.06033v1.pdf | Spatial Graph Convolutional Neural Network via Structured Subdomain Adaptation and Domain Adversarial Learning for Bearing Fault Diagnosis | Unsupervised domain adaptation (UDA) has shown remarkable results in bearing fault diagnosis under changing working conditions in recent years. However, most UDA methods do not consider the geometric structure of the data. Furthermore, the global domain adaptation technique is commonly applied, which ignores the relati... | ['Amin Ramezani', 'Mohammad TH Beheshti', 'Mohammadreza Kavianpour', 'Mohammadreza Ghorvei'] | 2021-12-11 | null | null | null | null | ['subdomain-adaptation'] | ['methodology'] | [-1.61494702e-01 7.52360746e-02 1.99699104e-01 -9.24170613e-02
-2.71452188e-01 1.11797908e-02 2.00250447e-01 -1.64041892e-02
1.53468162e-01 8.33796740e-01 1.48147404e-01 -1.57866448e-01
-3.19509089e-01 -1.12008524e+00 -5.76894760e-01 -9.60818231e-01
-1.85009420e-01 6.96314454e-01 2.46966824e-01 -6.85429752... | [7.435885429382324, 1.9410667419433594] |
423e6d41-931e-459d-9f00-1b40be6c2386 | evolving-differentiable-gene-regulatory | 1807.05948 | null | http://arxiv.org/abs/1807.05948v1 | http://arxiv.org/pdf/1807.05948v1.pdf | Evolving Differentiable Gene Regulatory Networks | Over the past twenty years, artificial Gene Regulatory Networks (GRNs) have
shown their capacity to solve real-world problems in various domains such as
agent control, signal processing and artificial life experiments. They have
also benefited from new evolutionary approaches and improvements to dynamic
which have incr... | ['Hervé Luga', 'Sylvain Cussat-Blanc', 'Dennis G Wilson', 'Kyle Harrington'] | 2018-07-16 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 4.12167132e-01 8.24011117e-03 1.34272248e-01 -2.29834929e-01
1.20007902e-01 -5.45520008e-01 7.04356313e-01 2.74133645e-02
-5.79042137e-01 1.04331863e+00 -1.64912447e-01 -5.17819345e-01
-1.18564822e-01 -8.18718314e-01 -5.22568882e-01 -7.53756166e-01
-4.11377311e-01 6.34047627e-01 1.01588763e-01 -7.64837384... | [8.248494148254395, 3.2532453536987305] |
48de0cc7-ec30-4f7e-ae53-3de668963a25 | multilevel-memetic-hypergraph-partitioning | 2204.03730 | null | https://arxiv.org/abs/2204.03730v1 | https://arxiv.org/pdf/2204.03730v1.pdf | Multilevel Memetic Hypergraph Partitioning with Greedy Recombination | The Hypergraph Partitioning (HGP) problem is a well-studied problem that finds applications in a variety of domains. The literature on the HGP problem has heavily focused on developing fast heuristic approaches. In several application domains, such as the VLSI design and database migration planning, the quality of the ... | ['Bugra Caskurlu', 'Utku Umur Acikalin'] | 2022-04-07 | null | null | null | null | ['hypergraph-partitioning'] | ['graphs'] | [ 1.02848187e-01 1.19972512e-01 -3.61789733e-01 1.17983082e-02
-4.45656508e-01 -2.06528574e-01 -2.28105381e-01 4.54141289e-01
-3.26309741e-01 1.25858915e+00 -6.53351724e-01 -2.22331613e-01
-6.23673379e-01 -1.32700396e+00 -6.61039650e-01 -7.92486191e-01
-2.99260736e-01 1.03831303e+00 3.62522393e-01 -2.97489822... | [5.732307434082031, 3.7105331420898438] |
e288157f-d80a-4f9c-b7cd-3a8a1342b732 | dense-transformer-networks-for-brain-electron | null | null | https://doi.org/10.24963/ijcai.2019/401 | https://www.ijcai.org/proceedings/2019/0401.pdf | Dense Transformer Networks for Brain Electron Microscopy Image Segmentation | The key idea of current deep learning methods for dense prediction is to apply a model on a regular patch centered on each pixel to make pixel-wise predictions. These methods are limited in the sense that the patches are determined by network architecture instead of learned from data. In this work, we propose the dense... | ['Jun Li', 'Yongjun Chen', 'Lei Cai', 'Shuiwang Ji', 'Ian Davidson'] | 2019-08-10 | null | null | null | twenty-eighth-international-joint-conference | ['electron-microscopy-image-segmentation'] | ['computer-vision'] | [ 4.68715757e-01 5.87510526e-01 -3.07098101e-03 -4.13365960e-01
-5.62510550e-01 -1.70160547e-01 3.13194871e-01 -2.77188748e-01
-9.11274403e-02 5.32675087e-01 1.52750388e-01 2.93479152e-02
2.80687302e-01 -9.10054445e-01 -1.20732772e+00 -8.30645204e-01
2.38052025e-01 4.58422840e-01 5.21602094e-01 1.27097189... | [9.92547607421875, 0.40678808093070984] |
4b9ced61-6d57-4714-84df-4e23bd53464b | improving-neural-rst-parsing-model-with | null | null | https://aclanthology.org/2021.naacl-main.127 | https://aclanthology.org/2021.naacl-main.127.pdf | Improving Neural RST Parsing Model with Silver Agreement Subtrees | Most of the previous Rhetorical Structure Theory (RST) parsing methods are based on supervised learning such as neural networks, that require an annotated corpus of sufficient size and quality. However, the RST Discourse Treebank (RST-DT), the benchmark corpus for RST parsing in English, is small due to the costly anno... | ['Masaaki Nagata', 'Manabu Okumura', 'Hidetaka Kamigaito', 'Tsutomu Hirao', 'Naoki Kobayashi'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['discourse-parsing'] | ['natural-language-processing'] | [ 2.53625035e-01 1.05470634e+00 -4.01980370e-01 -4.19330150e-01
-1.31375837e+00 -5.67685843e-01 4.56512690e-01 2.17861369e-01
-3.94778341e-01 8.81076634e-01 3.76652598e-01 -6.15683615e-01
2.68569946e-01 -9.12330508e-01 -8.85309458e-01 -5.04234552e-01
2.28515882e-02 6.73039377e-01 5.19024372e-01 -3.76244038... | [10.740249633789062, 9.412056922912598] |
68091a29-719d-4317-9d43-90578ff92a51 | joint-encoding-of-appearance-and-motion | 2002.03982 | null | https://arxiv.org/abs/2002.03982v2 | https://arxiv.org/pdf/2002.03982v2.pdf | Self-Supervised Joint Encoding of Motion and Appearance for First Person Action Recognition | Wearable cameras are becoming more and more popular in several applications, increasing the interest of the research community in developing approaches for recognizing actions from the first-person point of view. An open challenge in egocentric action recognition is that videos lack detailed information about the main ... | ['Barbara Caputo', 'Andrea Bottino', 'Mirco Planamente'] | 2020-02-10 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 1.51792318e-01 -1.17472701e-01 -2.85681188e-01 -2.00781584e-01
1.85665265e-02 -4.72454220e-01 8.37026715e-01 2.40141153e-01
-4.84724969e-01 3.26731205e-01 4.35035080e-01 3.32206041e-01
2.22474709e-02 -5.43783903e-01 -6.06666863e-01 -6.70683920e-01
-3.74183431e-03 1.52097881e-01 5.35434127e-01 -1.51437744... | [8.086004257202148, 0.38144272565841675] |
9ea4afb1-b883-4b4a-bd43-9596da5487cb | rumour-detection-via-zero-shot-cross-lingual | 2109.12773 | null | https://arxiv.org/abs/2109.12773v1 | https://arxiv.org/pdf/2109.12773v1.pdf | Rumour Detection via Zero-shot Cross-lingual Transfer Learning | Most rumour detection models for social media are designed for one specific language (mostly English). There are over 40 languages on Twitter and most languages lack annotated resources to build rumour detection models. In this paper we propose a zero-shot cross-lingual transfer learning framework that can adapt a rumo... | ['Jey Han Lau', 'Xiuzhen Zhang', 'Lin Tian'] | 2021-09-27 | null | null | null | null | ['rumour-detection', 'pretrained-multilingual-language-models'] | ['natural-language-processing', 'natural-language-processing'] | [-4.08451855e-01 -1.38773233e-01 -6.25078261e-01 -2.24891141e-01
-9.73093867e-01 -2.66974121e-01 1.06667936e+00 9.80592594e-02
-3.89128029e-01 8.31600368e-01 3.49647164e-01 -4.14252758e-01
9.16062713e-01 -8.67284954e-01 -6.76391423e-01 1.51447849e-02
-5.36784232e-02 7.13855743e-01 4.93248075e-01 -8.20498109... | [8.21949577331543, 10.142399787902832] |
ae35293a-e3d3-4f0f-8d6e-9d39d42a8778 | effective-email-spam-detection-system-using | 2012.14430 | null | https://arxiv.org/abs/2012.14430v1 | https://arxiv.org/pdf/2012.14430v1.pdf | Effective Email Spam Detection System using Extreme Gradient Boosting | The popularity, cost-effectiveness and ease of information exchange that electronic mails offer to electronic device users has been plagued with the rising number of unsolicited or spam emails. Driven by the need to protect email users from this growing menace, research in spam email filtering/detection systems has bei... | ['Afolabi Kazeem', 'Siti Mariyam Shamsuddin', 'Sunday O. Olatunji', 'Shafaatunnur Hasan', 'Ismail B. Mustapha'] | 2020-12-27 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-4.33634743e-02 -3.41550320e-01 -4.46863063e-02 -6.33903563e-01
-1.69429734e-01 -4.31354612e-01 9.39807355e-01 2.26327479e-01
-4.69902605e-01 7.50952721e-01 -5.51679246e-02 -6.29817903e-01
-2.02843398e-01 -7.01320767e-01 1.45209417e-01 -4.31497991e-01
1.77796319e-01 4.99920696e-01 5.47615945e-01 -4.20139670... | [7.851080417633057, 10.016915321350098] |
747e1946-e6af-47e3-a07a-5c2bb0feae6d | on-the-generalization-of-gan-image-forensics | 1902.11153 | null | https://arxiv.org/abs/1902.11153v2 | https://arxiv.org/pdf/1902.11153v2.pdf | On the generalization of GAN image forensics | Recently the GAN generated face images are more and more realistic with high-quality, even hard for human eyes to detect. On the other hand, the forensics community keeps on developing methods to detect these generated fake images and try to guarantee the credibility of visual contents. Although researchers have develo... | ['Xinsheng Xuan', 'Jing Dong', 'Bo Peng', 'Wei Wang'] | 2019-02-27 | null | null | null | null | ['gan-image-forensics', 'image-forensics'] | ['computer-vision', 'computer-vision'] | [ 1.28005728e-01 2.09226355e-01 1.31486043e-01 -1.85539499e-01
-3.82455170e-01 -3.92235994e-01 4.93240267e-01 -4.08880979e-01
-1.00800030e-01 7.18460739e-01 -3.48623902e-01 -1.86143041e-01
3.97718191e-01 -9.94136155e-01 -5.60901165e-01 -7.34310150e-01
2.57151216e-01 2.04649180e-01 7.14323521e-02 7.41436556... | [12.516412734985352, 1.0436843633651733] |
ee8e7418-53b8-41ca-9c51-8cbe570ac6ab | utopia-universally-trainable-optimal | 2306.16549 | null | https://arxiv.org/abs/2306.16549v1 | https://arxiv.org/pdf/2306.16549v1.pdf | UTOPIA: Universally Trainable Optimal Prediction Intervals Aggregation | Uncertainty quantification for prediction is an intriguing problem with significant applications in various fields, such as biomedical science, economic studies, and weather forecasts. Numerous methods are available for constructing prediction intervals, such as quantile regression and conformal predictions, among othe... | ['Debarghya Mukherjee', 'Jiawei Ge', 'Jianqing Fan'] | 2023-06-28 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 8.89617428e-02 3.48422825e-01 -4.61136460e-01 -4.28122044e-01
-9.65297222e-01 -4.87255812e-01 2.46552423e-01 5.21131933e-01
6.47883043e-02 1.39665771e+00 -1.16136082e-01 -5.31594992e-01
-5.47664881e-01 -9.53484595e-01 -7.61019647e-01 -6.93736672e-01
-7.65193477e-02 3.86464953e-01 3.58887352e-02 1.70903414... | [7.319485187530518, 3.9352009296417236] |
f91c5ced-95ce-4345-bf97-bfa58f4827dd | on-the-transferability-of-whisper-based | 2305.14546 | null | https://arxiv.org/abs/2305.14546v1 | https://arxiv.org/pdf/2305.14546v1.pdf | On the Transferability of Whisper-based Representations for "In-the-Wild" Cross-Task Downstream Speech Applications | Large self-supervised pre-trained speech models have achieved remarkable success across various speech-processing tasks. The self-supervised training of these models leads to universal speech representations that can be used for different downstream tasks, ranging from automatic speech recognition (ASR) to speaker iden... | ['Tiago Falk', 'Boxing Chen', 'Mehdi Rezagholizadeh', 'Anderson Avila', 'Arthur Pimentel', 'Heitor Guimaraes', 'Marzieh Tahaei', 'Vamsikrishna Chemudupati'] | 2023-05-23 | null | null | null | null | ['automatic-speech-recognition', 'speaker-identification'] | ['speech', 'speech'] | [ 2.66355157e-01 7.83560798e-02 1.53329775e-01 -6.12946272e-01
-1.15594196e+00 -4.72926915e-01 7.29101300e-01 -4.26191948e-02
-2.40350440e-01 3.08563322e-01 7.08142638e-01 -5.77046573e-01
3.19612563e-01 -5.26850522e-02 -6.41643703e-01 -7.59273827e-01
-3.29033434e-02 3.17279607e-01 2.99127221e-01 -5.79676449... | [14.588850021362305, 6.405691623687744] |
68f3d854-6dea-412f-aa49-dc7d7ca838d0 | focusing-on-targets-for-improving-weakly | 2302.11252 | null | https://arxiv.org/abs/2302.11252v1 | https://arxiv.org/pdf/2302.11252v1.pdf | Focusing On Targets For Improving Weakly Supervised Visual Grounding | Weakly supervised visual grounding aims to predict the region in an image that corresponds to a specific linguistic query, where the mapping between the target object and query is unknown in the training stage. The state-of-the-art method uses a vision language pre-training model to acquire heatmaps from Grad-CAM, whic... | ['Nao Mishima', 'Viet-Quoc Pham'] | 2023-02-22 | null | null | null | null | ['visual-grounding', 'dependency-parsing'] | ['computer-vision', 'natural-language-processing'] | [ 3.17024320e-01 2.40654215e-01 -5.64274192e-01 -3.99355143e-01
-8.96211505e-01 -5.77032745e-01 7.75553346e-01 1.31850913e-01
-3.21616501e-01 2.94125646e-01 2.56405264e-01 -1.91469118e-01
3.87142420e-01 -8.91520679e-01 -9.13909256e-01 -4.89348888e-01
4.63376373e-01 4.93140161e-01 7.14359283e-01 -1.54174820... | [10.457552909851074, 1.3901453018188477] |
806d97de-b80d-4fcd-be9e-2d45b20d0ba6 | how-to-choose-pretrained-handwriting | 2305.02593 | null | https://arxiv.org/abs/2305.02593v1 | https://arxiv.org/pdf/2305.02593v1.pdf | How to Choose Pretrained Handwriting Recognition Models for Single Writer Fine-Tuning | Recent advancements in Deep Learning-based Handwritten Text Recognition (HTR) have led to models with remarkable performance on both modern and historical manuscripts in large benchmark datasets. Nonetheless, those models struggle to obtain the same performance when applied to manuscripts with peculiar characteristics,... | ['Rita Cucchiara', 'Christopher Kermorvant', 'Silvia Cascianelli', 'Vittorio Pippi'] | 2023-05-04 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 1.36070237e-01 -1.51352957e-01 1.74785331e-01 -3.30565214e-01
-8.62744510e-01 -7.32021749e-01 9.22700405e-01 2.28350610e-01
-6.09316707e-01 1.20427728e+00 -1.04698144e-01 -9.53667089e-02
-2.08681241e-01 -7.76878774e-01 -8.14881802e-01 -5.71877778e-01
1.86564341e-01 9.71790195e-01 -2.96974648e-02 -3.35522920... | [11.830981254577637, 2.4362642765045166] |
ab087558-b461-48d9-b2f6-3d793630c0ff | comparing-heterogeneous-visual-gestures-for | 1805.02948 | null | http://arxiv.org/abs/1805.02948v1 | http://arxiv.org/pdf/1805.02948v1.pdf | Comparing heterogeneous visual gestures for measuring the diversity of visual speech signals | Visual lip gestures observed whilst lipreading have a few working
definitions, the most common two are; `the visual equivalent of a phoneme' and
`phonemes which are indistinguishable on the lips'. To date there is no formal
definition, in part because to date we have not established a two-way
relationship or mapping be... | ['Helen L. Bear', 'Richard Harvey'] | 2018-05-08 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 1.97034851e-02 -1.36750728e-01 -2.94281393e-01 -3.37488472e-01
-7.13963389e-01 -7.68791735e-01 8.31200600e-01 -1.54808104e-01
-3.68493468e-01 3.15690726e-01 7.92532980e-01 -4.39689547e-01
-5.44244573e-02 -6.00311421e-02 -2.42474958e-01 -7.00791299e-01
2.62266070e-01 2.29079440e-01 1.92235142e-01 9.09051374... | [14.301506996154785, 4.994369029998779] |
a145d974-12c9-43f3-afb9-cd25cc4cb6a9 | rethinking-embedding-coupling-in-pre-trained-1 | 2010.12821 | null | https://arxiv.org/abs/2010.12821v1 | https://arxiv.org/pdf/2010.12821v1.pdf | Rethinking embedding coupling in pre-trained language models | We re-evaluate the standard practice of sharing weights between input and output embeddings in state-of-the-art pre-trained language models. We show that decoupled embeddings provide increased modeling flexibility, allowing us to significantly improve the efficiency of parameter allocation in the input embedding of mul... | ['Sebastian Ruder', 'Melvin Johnson', 'Henry Tsai', 'Thibault Févry', 'Hyung Won Chung'] | 2020-10-24 | rethinking-embedding-coupling-in-pre-trained | https://openreview.net/forum?id=xpFFI_NtgpW | https://openreview.net/pdf?id=xpFFI_NtgpW | iclr-2021-1 | ['cross-lingual-natural-language-inference', 'cross-lingual-question-answering', 'cross-lingual-ner'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.36397511e-01 3.89848024e-01 -1.65689379e-01 -4.59059238e-01
-4.94567871e-01 -9.13980484e-01 7.44244874e-01 6.11061901e-02
-7.58864522e-01 3.30558449e-01 6.46682978e-01 -7.03696728e-01
3.01069945e-01 -7.57899284e-01 -7.64536619e-01 -2.22142652e-01
1.32935941e-01 5.02543867e-01 1.40682057e-01 -3.19739014... | [10.685928344726562, 8.644671440124512] |
a61cbed3-6534-47da-8480-f789878542c2 | revisiting-quantization-error-in-face | null | null | https://openaccess.thecvf.com/content/ICCV2021W/MFR/html/Lan_Revisting_Quantization_Error_in_Face_Alignment_ICCVW_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021W/MFR/papers/Lan_Revisting_Quantization_Error_in_Face_Alignment_ICCVW_2021_paper.pdf | Revisiting Quantization Error in Face Alignment | Recently, heatmap regression models have become the mainstream in locating facial landmarks. To keep com- putation affordable and reduce memory usage, the whole procedure involves downsampling from the raw image to the output heatmap. However, how much impact will the quantization error introduced by downsampling bring... | ['Jian Cheng', 'Qinghao Hu', 'Xing Lan'] | 2021-09-13 | null | null | null | iccv-workshop-2021-9 | ['face-alignment'] | ['computer-vision'] | [ 7.41231740e-02 9.53859463e-02 -2.87691027e-01 -5.32597065e-01
-7.56433606e-01 -1.46972284e-01 3.53562862e-01 -2.08780706e-01
-9.48674008e-02 4.79614705e-01 2.29278758e-01 2.84708124e-02
1.35861978e-01 -7.61453152e-01 -6.41965330e-01 -7.45168626e-01
1.28941685e-01 7.35514760e-02 -3.14951420e-01 -1.43271297... | [13.455855369567871, 0.46133100986480713] |
a9614d4d-f330-4eca-82df-992fcebc7e87 | eddynet-a-deep-neural-network-for-pixel-wise | 1711.03954 | null | http://arxiv.org/abs/1711.03954v1 | http://arxiv.org/pdf/1711.03954v1.pdf | EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies | This work presents EddyNet, a deep learning based architecture for automated
eddy detection and classification from Sea Surface Height (SSH) maps provided
by the Copernicus Marine and Environment Monitoring Service (CMEMS). EddyNet is
a U-Net like network that consists of a convolutional encoder-decoder followed
by a p... | ['Pierre Tandeo', 'Ronan Fablet', 'Redouane Lguensat', 'Ge Chen', 'Evan Mason', 'Miao Sun'] | 2017-11-10 | null | null | null | null | ['oceanic-eddy-classification'] | ['miscellaneous'] | [ 7.81212226e-02 3.45842983e-03 4.72806484e-01 -7.70817876e-01
-4.25777674e-01 -5.26138723e-01 7.66568899e-01 1.56601459e-01
-7.95770168e-01 9.87328351e-01 1.53211862e-01 -5.73284686e-01
1.58769578e-01 -1.09806061e+00 -8.06685328e-01 -8.22758496e-01
-3.06049496e-01 1.68743208e-01 3.76699686e-01 -1.54697493... | [9.504558563232422, -1.4738759994506836] |
84ebe409-47d5-4448-bf2f-2db2292ac4d2 | learning-by-distilling-context | 2209.15189 | null | https://arxiv.org/abs/2209.15189v1 | https://arxiv.org/pdf/2209.15189v1.pdf | Learning by Distilling Context | Language models significantly benefit from context tokens, such as prompts or scratchpads. They perform better when prompted with informative instructions, and they acquire new reasoning capabilities by generating a scratch-pad before predicting the final answers. However, they do not \textit{internalize} these perform... | ['Ruiqi Zhong', 'Dan Klein', 'Charlie Snell'] | 2022-09-30 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 5.59258819e-01 3.56708288e-01 -2.74339110e-01 -4.69425827e-01
-9.64925408e-01 -7.31367826e-01 4.37975138e-01 -3.59032303e-03
-5.86326838e-01 7.96933949e-01 1.13013990e-01 -1.04894841e+00
2.46120632e-01 -8.68815005e-01 -9.80254889e-01 -3.97077084e-01
9.11699422e-03 4.92230922e-01 1.77842170e-01 -4.20081168... | [9.726201057434082, 7.512149810791016] |
7d0fda95-6175-43e3-8c63-b9dbffaa85d3 | multiple-input-multiple-output-fusion-network | null | null | https://ieeexplore.ieee.org/document/9413509 | https://ieeexplore.ieee.org/document/9413509 | Multiple-Input Multiple-Output Fusion Network For Generalized Zero-Shot Learning | Generalized zero-shot learning (GZSL) has attracted consid-
erable attention recently, which trains models with data from
seen classes and tests on data from both seen and unseen
classes. Most of the existing methods attempt to find a map-
ping from visual space to semantic space, such mapping can
easily result in... | ['Feng Xia', 'Xu Yuan', 'Zhikui Chen', 'Guangze Wang', 'Fangming Zhong∗'] | 2021-05-13 | null | null | null | ieee-2021-5 | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 2.86968470e-01 -5.95185235e-02 -5.03223091e-02 -6.15695238e-01
-7.77330101e-01 -2.49146521e-01 6.89312398e-01 1.24897666e-01
-2.12250486e-01 7.02518880e-01 1.93135440e-03 1.78709716e-01
-1.95079446e-02 -1.04011011e+00 -6.48674965e-01 -6.40871286e-01
3.81801814e-01 3.70451719e-01 5.06118894e-01 -8.56572986... | [9.96700382232666, 2.451399803161621] |
8291dacd-452b-43e8-bcf6-be0ba49eea95 | cvxnets-learnable-convex-decomposition | 1909.05736 | null | https://arxiv.org/abs/1909.05736v4 | https://arxiv.org/pdf/1909.05736v4.pdf | CvxNet: Learnable Convex Decomposition | Any solid object can be decomposed into a collection of convex polytopes (in short, convexes). When a small number of convexes are used, such a decomposition can be thought of as a piece-wise approximation of the geometry. This decomposition is fundamental in computer graphics, where it provides one of the most common ... | ['Soroosh Yazdani', 'Sofien Bouaziz', 'Andrea Tagliasacchi', 'Kyle Genova', 'Geoffrey Hinton', 'Boyang Deng'] | 2019-09-12 | cvxnet-learnable-convex-decomposition | http://openaccess.thecvf.com/content_CVPR_2020/html/Deng_CvxNet_Learnable_Convex_Decomposition_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Deng_CvxNet_Learnable_Convex_Decomposition_CVPR_2020_paper.pdf | cvpr-2020-6 | ['image-to-3d'] | ['computer-vision'] | [ 1.98145472e-02 4.18123931e-01 2.03808248e-01 -3.55076730e-01
-2.18267918e-01 -6.87003076e-01 7.28307068e-01 2.63493657e-01
1.65383324e-01 4.06420201e-01 -9.79873985e-02 -1.65097639e-01
5.15186228e-02 -1.32569063e+00 -1.15665889e+00 -5.36909878e-01
-1.75412908e-01 1.37660348e+00 4.67703491e-02 -4.77029681... | [8.631021499633789, -3.662874937057495] |
7d53a619-0fd0-40ef-94fd-cd02a808abf4 | bygpt5-end-to-end-style-conditioned-poetry | 2212.10474 | null | https://arxiv.org/abs/2212.10474v2 | https://arxiv.org/pdf/2212.10474v2.pdf | ByGPT5: End-to-End Style-conditioned Poetry Generation with Token-free Language Models | State-of-the-art poetry generation systems are often complex. They either consist of task-specific model pipelines, incorporate prior knowledge in the form of manually created constraints, or both. In contrast, end-to-end models would not suffer from the overhead of having to model prior knowledge and could learn the n... | ['Steffen Eger', 'Jonas Belouadi'] | 2022-12-20 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 1.83026850e-01 3.59567046e-01 2.08887279e-01 -2.43161529e-01
-1.03757787e+00 -7.79221117e-01 7.49948382e-01 -6.86536878e-02
-5.52058160e-01 9.13495719e-01 4.14656043e-01 -4.45929736e-01
3.77418369e-01 -7.12011874e-01 -6.68629646e-01 -1.12364419e-01
4.25556093e-01 9.85445082e-01 -2.42045093e-02 -5.30266941... | [11.539545059204102, 9.48111629486084] |
50ff4a95-bead-499f-93b1-192e77cc55de | one-shot-video-object-segmentation | 1611.05198 | null | http://arxiv.org/abs/1611.05198v4 | http://arxiv.org/pdf/1611.05198v4.pdf | One-Shot Video Object Segmentation | This paper tackles the task of semi-supervised video object segmentation,
i.e., the separation of an object from the background in a video, given the
mask of the first frame. We present One-Shot Video Object Segmentation (OSVOS),
based on a fully-convolutional neural network architecture that is able to
successively tr... | ['Luc van Gool', 'Laura Leal-Taixé', 'Jordi Pont-Tuset', 'Kevis-Kokitsi Maninis', 'Sergi Caelles', 'Daniel Cremers'] | 2016-11-16 | one-shot-video-object-segmentation-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Caelles_One-Shot_Video_Object_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Caelles_One-Shot_Video_Object_CVPR_2017_paper.pdf | cvpr-2017-7 | ['foreground-segmentation'] | ['computer-vision'] | [ 5.71118534e-01 1.90265536e-01 -7.92051032e-02 -3.58635962e-01
-4.61759984e-01 -4.84182507e-01 4.72182989e-01 -2.00205848e-01
-6.83237612e-01 5.42317569e-01 -4.39676136e-01 6.15931489e-02
3.43413621e-01 -3.53535593e-01 -1.10003519e+00 -8.47972989e-01
-9.63925850e-03 4.96063590e-01 1.09494007e+00 3.33915889... | [9.079479217529297, -0.20462124049663544] |
27155db6-6843-4bb7-847d-ca3cb78cd81d | 2x-faster-language-model-pre-training-via | 2305.02869 | null | https://arxiv.org/abs/2305.02869v1 | https://arxiv.org/pdf/2305.02869v1.pdf | 2x Faster Language Model Pre-training via Masked Structural Growth | Acceleration of large language model pre-training is a critical issue in present NLP research. In this paper, we focus on speeding up pre-training by progressively growing from a small Transformer structure to a large one. There are two main research problems related to progressive growth: growth schedule and growth op... | ['Yequan Wang', 'Jing Li', 'Zheng Zhang', 'Yiqun Yao'] | 2023-05-04 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 3.28200869e-02 1.08691089e-01 -3.05992186e-01 -2.04241455e-01
-4.29972827e-01 -5.44426858e-01 2.61821330e-01 3.41581047e-01
-7.20141709e-01 6.07064009e-01 -1.91617161e-01 -6.34135485e-01
-1.96651295e-01 -8.42444062e-01 -8.02362204e-01 -5.71573317e-01
-8.07570070e-02 7.11486220e-01 3.75728697e-01 -3.57178479... | [8.711196899414062, 3.6287503242492676] |
7f221c8b-c626-48b4-b885-179c112e8046 | offline-prioritized-experience-replay | 2306.05412 | null | https://arxiv.org/abs/2306.05412v2 | https://arxiv.org/pdf/2306.05412v2.pdf | Offline Prioritized Experience Replay | Offline reinforcement learning (RL) is challenged by the distributional shift problem. To address this problem, existing works mainly focus on designing sophisticated policy constraints between the learned policy and the behavior policy. However, these constraints are applied equally to well-performing and inferior act... | ['Shuicheng Yan', 'Shiji Song', 'Gao Huang', 'Xiao Ma', 'Bingyi Kang', 'Yang Yue'] | 2023-06-08 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-1.11530155e-01 -8.42691734e-02 -6.42225206e-01 -1.40456870e-01
-8.86791587e-01 -6.42124534e-01 3.55563521e-01 1.19592085e-01
-7.61397243e-01 9.67961967e-01 2.81984240e-01 -6.23809159e-01
-3.15167189e-01 -7.03838170e-01 -8.34773064e-01 -7.87578821e-01
-2.57713675e-01 2.51372218e-01 1.24719553e-01 -2.46173754... | [4.097476482391357, 2.2443418502807617] |
fffdb3bc-608c-4240-93ec-6fd39a249a29 | toward-efficient-language-model-pretraining | 2212.01853 | null | https://arxiv.org/abs/2212.01853v1 | https://arxiv.org/pdf/2212.01853v1.pdf | Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE | This technical report briefly describes our JDExplore d-team's Vega v2 submission on the SuperGLUE leaderboard. SuperGLUE is more challenging than the widely used general language understanding evaluation (GLUE) benchmark, containing eight difficult language understanding tasks, including question answering, natural la... | ['DaCheng Tao', 'Xiaoou Tang', 'Chunyan Miao', 'Xinbo Gao', 'Yixin Chen', 'Bo Du', 'Baosheng Yu', 'Juhua Liu', 'Li Shen', 'Yonggang Wen', 'Yu Qiao', 'Yibing Zhan', 'Liang Ding', 'Qihuang Zhong'] | 2022-12-04 | null | null | null | null | ['word-sense-disambiguation', 'coreference-resolution'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.93261638e-01 5.04514396e-01 -2.47875541e-01 -5.12325525e-01
-1.19529355e+00 -4.84094322e-01 5.10937214e-01 1.29127294e-01
-6.14995956e-01 7.49405146e-01 4.47534382e-01 -7.00190663e-01
-8.55381340e-02 -4.63679105e-01 -1.00237930e+00 -1.09910987e-01
-7.46841803e-02 8.50026488e-01 3.08954865e-02 -5.27069747... | [10.899674415588379, 8.311680793762207] |
9775a205-84af-4c8b-bfef-ac0c73292694 | kpconv-flexible-and-deformable-convolution | 1904.08889 | null | https://arxiv.org/abs/1904.08889v2 | https://arxiv.org/pdf/1904.08889v2.pdf | KPConv: Flexible and Deformable Convolution for Point Clouds | We present Kernel Point Convolution (KPConv), a new design of point convolution, i.e. that operates on point clouds without any intermediate representation. The convolution weights of KPConv are located in Euclidean space by kernel points, and applied to the input points close to them. Its capacity to use any number of... | ['François Goulette', 'Jean-Emmanuel Deschaud', 'Leonidas J. Guibas', 'Beatriz Marcotegui', 'Hugues Thomas', 'Charles R. Qi'] | 2019-04-18 | kpconv-flexible-and-deformable-convolution-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Thomas_KPConv_Flexible_and_Deformable_Convolution_for_Point_Clouds_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Thomas_KPConv_Flexible_and_Deformable_Convolution_for_Point_Clouds_ICCV_2019_paper.pdf | iccv-2019-10 | ['robust-3d-semantic-segmentation', '3d-part-segmentation', 'lidar-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-5.17585337e-01 8.84837061e-02 7.91845173e-02 -2.47348815e-01
1.07911900e-01 -9.74777520e-01 5.53343058e-01 -7.89137408e-02
-4.56655383e-01 3.76372129e-01 -2.01855719e-01 -3.63185078e-01
-3.40713441e-01 -1.02986109e+00 -9.80871260e-01 -5.31390011e-01
-3.12591761e-01 5.02684534e-01 6.61948740e-01 1.70287732... | [7.8590826988220215, -3.774986743927002] |
33eef2c9-a0ca-46e7-9165-7bd97cbdd1c7 | active-policy-improvement-from-multiple-black | 2306.10259 | null | https://arxiv.org/abs/2306.10259v2 | https://arxiv.org/pdf/2306.10259v2.pdf | Active Policy Improvement from Multiple Black-box Oracles | Reinforcement learning (RL) has made significant strides in various complex domains. However, identifying an effective policy via RL often necessitates extensive exploration. Imitation learning aims to mitigate this issue by using expert demonstrations to guide exploration. In real-world scenarios, one often has access... | ['Yuxin Chen', 'Matthew R. Walter', 'Chaoqi Wang', 'Takuma Yoneda', 'Xuefeng Liu'] | 2023-06-17 | null | null | null | null | ['imitation-learning'] | ['methodology'] | [-8.81026685e-02 -1.23781443e-01 -8.01577985e-01 1.19502649e-01
-1.15966201e+00 -9.69509482e-01 5.80237210e-01 -1.68657526e-01
-8.30652058e-01 1.21144783e+00 -5.13187684e-02 -6.83517814e-01
-2.22108319e-01 -3.15159529e-01 -1.08085513e+00 -7.73689091e-01
-2.12500080e-01 6.10978842e-01 -2.73155514e-02 -1.41615346... | [4.161137104034424, 1.9297186136245728] |
35707241-6dcd-4868-827f-d2bf8f3e0dbc | robot-structure-prior-guided-temporal | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tian_Robot_Structure_Prior_Guided_Temporal_Attention_for_Camera-to-Robot_Pose_Estimation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tian_Robot_Structure_Prior_Guided_Temporal_Attention_for_Camera-to-Robot_Pose_Estimation_CVPR_2023_paper.pdf | Robot Structure Prior Guided Temporal Attention for Camera-to-Robot Pose Estimation From Image Sequence | In this work, we tackle the problem of online camera-to-robot pose estimation from single-view successive frames of an image sequence, a crucial task for robots to interact with the world. The primary obstacles of this task are the robot's self-occlusions and the ambiguity of single-view images. This work demonstra... | ['Hao Dong', 'Zekai Yin', 'Jiyao Zhang', 'Yang Tian'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['robotic-grasping'] | ['robots'] | [-1.33300856e-01 1.28703490e-01 1.66284382e-01 -2.62824714e-01
-6.59464598e-01 -6.38260841e-01 4.45840210e-01 -5.17866075e-01
-6.01801574e-01 2.91844666e-01 -5.33664107e-01 1.42632440e-01
1.12352706e-01 -9.04755145e-02 -1.21388888e+00 -7.16002405e-01
9.19478759e-02 8.54653358e-01 5.09862125e-01 -1.26231313... | [7.420833110809326, -2.4990651607513428] |
06b416a3-ab0a-4850-b957-9b8cfb1bf9ca | webly-supervised-fine-grained-recognition-1 | null | null | https://www.ijcai.org/proceedings/2022/209 | https://www.ijcai.org/proceedings/2022/0209.pdf | Webly-Supervised Fine-Grained Recognition with Partial Label Learning | The task of webly-supervised fne-grained recognition is to boost recognition accuracy of classifying subordinate categories (e.g., different bird species)by utilizing freely available but noisy web data.As the label noises signifcantly hurt the network
training, it is desirable to distinguish and eliminate noisy image... | ['Jian Yang', 'Xiu-Shen Wei', 'Yang shen', 'Yu-Yan Xu'] | 2022-02-09 | null | null | null | ijcai-2022-2 | ['partial-label-learning'] | ['methodology'] | [ 2.97345996e-01 -4.68791574e-01 2.40342077e-02 -7.51588106e-01
-1.03218567e+00 -7.89599478e-01 3.68944645e-01 -2.48213559e-01
-4.71169949e-01 3.98696214e-01 1.30359968e-02 3.41990799e-01
-2.45059326e-01 -8.50562930e-01 -8.46830130e-01 -9.22176898e-01
3.92190397e-01 2.89477885e-01 7.09482357e-02 2.53091026... | [9.591560363769531, 2.629854440689087] |
cecea4eb-4785-4418-b53f-2b9169c7e985 | x-modaler-a-versatile-and-high-performance | 2108.08217 | null | https://arxiv.org/abs/2108.08217v1 | https://arxiv.org/pdf/2108.08217v1.pdf | X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics | With the rise and development of deep learning over the past decade, there has been a steady momentum of innovation and breakthroughs that convincingly push the state-of-the-art of cross-modal analytics between vision and language in multimedia field. Nevertheless, there has not been an open-source codebase in support ... | ['Tao Mei', 'Ting Yao', 'Jingwen Chen', 'Yingwei Pan', 'Yehao Li'] | 2021-08-18 | null | null | null | null | ['visual-commonsense-reasoning'] | ['reasoning'] | [-1.07944541e-01 2.53375024e-02 -2.34957412e-01 -2.25870445e-01
-8.55186880e-01 -5.92047870e-01 7.25107729e-01 -1.91943478e-02
-2.04104766e-01 1.06248371e-01 1.80554956e-01 -4.20217842e-01
9.99840349e-02 -5.86176515e-01 -8.69058132e-01 -4.00177002e-01
2.76307434e-01 3.47712040e-01 1.07840799e-01 -3.21452916... | [10.815987586975098, 1.5001206398010254] |
1aaee015-c642-4ba8-ae73-856dad6ef5c3 | a-generalized-framework-for-edge-preserving | 1907.09642 | null | https://arxiv.org/abs/1907.09642v4 | https://arxiv.org/pdf/1907.09642v4.pdf | A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing | Image smoothing is a fundamental procedure in applications of both computer vision and graphics. The required smoothing properties can be different or even contradictive among different tasks. Nevertheless, the inherent smoothing nature of one smoothing operator is usually fixed and thus cannot meet the various require... | ['Pingping Zhang', 'Yinjie Lei', 'Wei Liu', 'Xiaolin Huang', 'Jie Yang', 'Ian Reid'] | 2019-07-23 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 9.51497406e-02 -1.33462504e-01 -1.92302081e-03 -2.26115540e-01
-4.52170312e-01 -2.09375784e-01 5.22091091e-01 -1.11179247e-01
-3.12110424e-01 6.68942988e-01 -2.51106262e-01 -1.79207042e-01
-1.87210888e-01 -3.89271975e-01 -3.95140648e-01 -9.39817309e-01
2.02115193e-01 -2.86362231e-01 5.22646308e-01 -2.94412792... | [11.263587951660156, -2.5696792602539062] |
2d6d9a0d-732a-4c54-a4fd-28b7e26fa327 | memexqa-visual-memex-question-answering | 1708.01336 | null | http://arxiv.org/abs/1708.01336v1 | http://arxiv.org/pdf/1708.01336v1.pdf | MemexQA: Visual Memex Question Answering | This paper proposes a new task, MemexQA: given a collection of photos or
videos from a user, the goal is to automatically answer questions that help
users recover their memory about events captured in the collection. Towards
solving the task, we 1) present the MemexQA dataset, a large, realistic
multimodal dataset cons... | ['Yannis Kalantidis', 'Junwei Liang', 'Alexander Hauptmann', 'Sachin Farfade', 'Lu Jiang', 'Liangliang Cao'] | 2017-08-04 | null | null | null | null | ['memex-question-answering'] | ['natural-language-processing'] | [-2.79712558e-01 -1.93333507e-01 1.94317743e-01 -4.18699533e-01
-1.27328575e+00 -6.11967504e-01 4.95692194e-01 -3.51608425e-01
-6.53088093e-01 7.46407449e-01 7.13204741e-01 1.73199043e-01
3.27858388e-01 -3.17192703e-01 -8.77283335e-01 -2.09186554e-01
9.64068919e-02 5.04604280e-01 4.22116145e-02 -1.08948402... | [10.509237289428711, 1.1018955707550049] |
4a8987de-5d09-4482-beca-3c48a2b87e1f | amd-severity-prediction-and-explainability | 1907.03075 | null | https://arxiv.org/abs/1907.03075v1 | https://arxiv.org/pdf/1907.03075v1.pdf | AMD Severity Prediction And Explainability Using Image Registration And Deep Embedded Clustering | We propose a method to predict severity of age related macular degeneration (AMD) from input optical coherence tomography (OCT) images. Although there is no standard clinical severity scale for AMD, we leverage deep learning (DL) based image registration and clustering methods to identify diseased cases and predict the... | ['Dwarikanath Mahapatra'] | 2019-07-06 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [ 8.29769894e-02 -2.70484276e-02 -3.41619283e-01 -4.73434329e-01
-7.27446079e-01 -2.64001191e-01 -1.19140465e-02 -1.45362094e-01
-4.53988373e-01 8.25365484e-01 6.25445306e-01 -3.35037172e-01
-3.53995174e-01 -4.53890055e-01 3.58811170e-01 -5.28422296e-01
-4.15877588e-02 7.31931388e-01 1.25669733e-01 5.32092214... | [15.820076942443848, -3.995903253555298] |
add092f7-4697-40e2-8f98-590e1f802f25 | editing-large-language-models-problems | 2305.13172 | null | https://arxiv.org/abs/2305.13172v1 | https://arxiv.org/pdf/2305.13172v1.pdf | Editing Large Language Models: Problems, Methods, and Opportunities | Recent advancements in deep learning have precipitated the emergence of large language models (LLMs) which exhibit an impressive aptitude for understanding and producing text akin to human language. Despite the ability to train highly capable LLMs, the methodology for maintaining their relevancy and rectifying errors r... | ['Ningyu Zhang', 'Huajun Chen', 'Shumin Deng', 'Zhoubo Li', 'Siyuan Cheng', 'Bozhong Tian', 'Peng Wang', 'Yunzhi Yao'] | 2023-05-22 | null | null | null | null | ['model-editing'] | ['natural-language-processing'] | [ 5.08195817e-01 8.40136036e-02 -5.75999543e-02 -4.07987088e-01
-7.62484193e-01 -7.35182524e-01 8.28987777e-01 2.87774682e-01
-4.12933111e-01 6.50711894e-01 1.29294381e-01 -5.24687529e-01
4.54041734e-02 -4.56313312e-01 -6.56239986e-01 -3.30936790e-01
3.17656636e-01 4.05954719e-01 -2.23172039e-01 -2.70521343... | [10.888508796691895, 8.768978118896484] |
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