paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6579fc6c-2f80-4b12-9368-22725200f725 | rethinking-gnn-based-entity-alignment-on | 2304.03468 | null | https://arxiv.org/abs/2304.03468v2 | https://arxiv.org/pdf/2304.03468v2.pdf | Rethinking GNN-based Entity Alignment on Heterogeneous Knowledge Graphs: New Datasets and A New Method | The development of knowledge graph (KG) applications has led to a rising need for entity alignment (EA) between heterogeneous KGs that are extracted from various sources. Recently, graph neural networks (GNNs) have been widely adopted in EA tasks due to GNNs' impressive ability to capture structure information. However... | ['HuaWei Shen', 'Zixuan Li', 'Fei Sun', 'Yuanzhuo Wang', 'Fenglong Su', 'Yinghan Shen', 'Chengjin Xu', 'Xuhui Jiang'] | 2023-04-07 | null | null | null | null | ['entity-alignment', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing'] | [-1.22409895e-01 1.95557088e-01 -3.77926588e-01 3.72925284e-03
-6.14871383e-02 -3.72753471e-01 4.13717121e-01 3.07703733e-01
-1.82916462e-01 8.97451282e-01 1.84560731e-01 -2.92744398e-01
-5.64698398e-01 -1.21514797e+00 -6.50188267e-01 -7.04668939e-01
-3.56293023e-01 5.44449091e-01 2.58179277e-01 -4.31312829... | [8.703712463378906, 7.959947109222412] |
86e9e96e-9026-46da-b1e9-162b85889536 | learning-axioms-to-compute-verifiable | null | null | https://openreview.net/forum?id=PkqwRo2wjuW | https://openreview.net/pdf?id=PkqwRo2wjuW | Learning Axioms to Compute Verifiable Symbolic Expression Equivalence Proofs Using Graph-to-Sequence Networks | We target the problem of proving the semantic equivalence between two complex expressions represented as typed trees, and demonstrate our system on expressions from a rich multi-type symbolic language for linear algebra. We propose the first graph-to-sequence deep learning system to generate axiomatic proofs of equival... | ['Theo Barolett', 'Louis-Noel Pouchet', 'Steven James Kommrusch'] | 2021-01-01 | null | null | null | null | ['graph-to-sequence'] | ['natural-language-processing'] | [ 2.91346222e-01 4.38654333e-01 -3.66184711e-01 -2.54138023e-01
-8.52293015e-01 -1.01421630e+00 2.57502317e-01 1.23889886e-01
3.88325274e-01 7.63920605e-01 -1.24772742e-01 -1.71947074e+00
2.97461953e-02 -1.27092838e+00 -1.59533119e+00 3.72157931e-01
-9.20681596e-01 2.65850306e-01 3.74342084e-01 -3.71677279... | [8.822938919067383, 7.160538196563721] |
d63df5b2-efc8-4ee6-bf2b-064343e94701 | difficulty-aware-simulator-for-open-set | 2207.10024 | null | https://arxiv.org/abs/2207.10024v1 | https://arxiv.org/pdf/2207.10024v1.pdf | Difficulty-Aware Simulator for Open Set Recognition | Open set recognition (OSR) assumes unknown instances appear out of the blue at the inference time. The main challenge of OSR is that the response of models for unknowns is totally unpredictable. Furthermore, the diversity of open set makes it harder since instances have different difficulty levels. Therefore, we presen... | ['Jae-Pil Heo', 'Cheol-Ho Cho', 'Hyun Seok Seong', 'Junho Park', 'WonJun Moon'] | 2022-07-20 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [-1.22759305e-01 2.54816413e-01 4.03510220e-02 -2.28261024e-01
-1.02046156e+00 -9.81484771e-01 4.25875723e-01 -5.99356949e-01
1.76375717e-01 9.40047681e-01 -2.05963403e-01 -1.29046455e-01
5.80959991e-02 -8.17487121e-01 -8.42177033e-01 -5.89167416e-01
4.10544813e-01 4.05357748e-01 -2.16403052e-01 -3.55740756... | [5.753663063049316, 7.829673767089844] |
56b74c59-fd70-45d1-8d16-a145fa1583e9 | neural-message-passing-with-edge-updates-for | 1806.03146 | null | http://arxiv.org/abs/1806.03146v1 | http://arxiv.org/pdf/1806.03146v1.pdf | Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials | Neural message passing on molecular graphs is one of the most promising
methods for predicting formation energy and other properties of molecules and
materials. In this work we extend the neural message passing model with an edge
update network which allows the information exchanged between atoms to depend
on the hidde... | ['Mikkel N. Schmidt', 'Peter Bjørn Jørgensen', 'Karsten Wedel Jacobsen'] | 2018-06-08 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [ 1.35239527e-01 1.14055410e-01 -3.44791323e-01 -3.14886481e-01
-7.24947527e-02 -1.70057699e-01 6.71862960e-01 9.91871178e-01
-4.55316186e-01 1.21202660e+00 1.81341134e-02 -5.12693703e-01
-1.66366547e-01 -1.38337183e+00 -9.11805511e-01 -9.59143639e-01
-5.64805627e-01 4.15509254e-01 2.90640652e-01 -2.82262146... | [5.298842906951904, 5.611733436584473] |
33dd9dd8-d454-42bf-87a8-5ccf2c0953d6 | fm-vit-flexible-modal-vision-transformers-for | 2305.03277 | null | https://arxiv.org/abs/2305.03277v1 | https://arxiv.org/pdf/2305.03277v1.pdf | FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofing | The availability of handy multi-modal (i.e., RGB-D) sensors has brought about a surge of face anti-spoofing research. However, the current multi-modal face presentation attack detection (PAD) has two defects: (1) The framework based on multi-modal fusion requires providing modalities consistent with the training input,... | ['Guodong Guo', 'Stan Z. Li', 'Du Zhang', 'Zhen Lei', 'Yanyan Liang', 'Jun Wan', 'Chenxu Zhao', 'Zitong Yu', 'Zichang Tan', 'Ajian Liu'] | 2023-05-05 | null | null | null | null | ['face-presentation-attack-detection', 'face-anti-spoofing'] | ['computer-vision', 'computer-vision'] | [ 3.14361930e-01 -3.03295672e-01 -1.61388442e-01 -6.10749051e-02
-1.10066664e+00 -4.49463993e-01 6.87033117e-01 -5.88376641e-01
4.61953804e-02 2.74972469e-01 2.08827034e-01 -2.69006401e-01
-1.96507633e-01 -7.99470365e-01 -6.57876372e-01 -1.00509048e+00
3.60267878e-01 2.19667882e-01 2.28749827e-01 -3.90061915... | [13.074335098266602, 1.1980525255203247] |
e59d199a-31a5-4fae-9298-bdd3f1cd2c39 | xihe-a-3d-vision-based-lighting-estimation | 2106.15280 | null | https://arxiv.org/abs/2106.15280v1 | https://arxiv.org/pdf/2106.15280v1.pdf | Xihe: A 3D Vision-based Lighting Estimation Framework for Mobile Augmented Reality | Omnidirectional lighting provides the foundation for achieving spatially-variant photorealistic 3D rendering, a desirable property for mobile augmented reality applications. However, in practice, estimating omnidirectional lighting can be challenging due to limitations such as partial panoramas of the rendering positio... | ['Tian Guo', 'Yiqin Zhao'] | 2021-05-30 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 5.87299466e-02 -6.26272202e-01 2.26388022e-01 -2.16700807e-01
-5.46801448e-01 -5.52055478e-01 4.55845445e-01 -3.62051517e-01
-2.74827540e-01 2.69044012e-01 -4.69205715e-02 -7.40312636e-01
2.92943597e-01 -9.72194493e-01 -8.62652838e-01 -2.91537017e-01
-8.89879689e-02 2.72293061e-01 1.69408739e-01 -2.02740341... | [9.454816818237305, -2.81457257270813] |
630973bf-6164-45a2-9d30-cb378747fab7 | infrared-and-visible-image-fusion-via-dual | 2210.11018 | null | https://arxiv.org/abs/2210.11018v2 | https://arxiv.org/pdf/2210.11018v2.pdf | An Attention-Guided and Wavelet-Constrained Generative Adversarial Network for Infrared and Visible Image Fusion | The GAN-based infrared and visible image fusion methods have gained ever-increasing attention due to its effectiveness and superiority. However, the existing methods adopt the global pixel distribution of source images as the basis for discrimination, which fails to focus on the key modality information. Moreover, the ... | ['Xin Yang', 'Jing Li', 'Hongtao Huo', 'Renhua Wang', 'Xiaowen Liu'] | 2022-10-20 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 3.97536099e-01 -5.67919195e-01 1.94911882e-01 -9.10013542e-02
-8.53297353e-01 -2.90326595e-01 3.04983944e-01 -5.29086053e-01
-8.39168206e-02 5.22648215e-01 4.88508761e-01 6.17273636e-02
-1.98567465e-01 -8.53247046e-01 -1.90184966e-01 -1.31646430e+00
9.11473691e-01 -4.62594241e-01 -9.00917128e-02 -3.68181556... | [10.55003833770752, -1.8890713453292847] |
9704b123-f2cd-456d-bbba-893cf98f0a15 | schemaless-queries-over-document-tables-with | 1911.09356 | null | https://arxiv.org/abs/1911.09356v1 | https://arxiv.org/pdf/1911.09356v1.pdf | Schemaless Queries over Document Tables with Dependencies | Unstructured enterprise data such as reports, manuals and guidelines often contain tables. The traditional way of integrating data from these tables is through a two-step process of table detection/extraction and mapping the table layouts to an appropriate schema. This can be an expensive process. In this paper we show... | ['Cristina Cornelio', 'Mustafa Canim', 'Mariano Rodrigez Muro', 'Arun Iyengar', 'Ryan Musa'] | 2019-11-21 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [-1.07642680e-01 4.77402240e-01 -7.32942224e-02 -4.73754734e-01
-6.96132898e-01 -1.06838965e+00 3.73947233e-01 1.07849967e+00
-2.99245656e-01 8.31766605e-01 2.02648848e-01 -4.03025091e-01
-5.19949377e-01 -1.50150645e+00 -5.53354740e-01 3.22088301e-01
1.06817767e-01 8.35870147e-01 8.93787563e-01 -5.61842322... | [9.180909156799316, 7.821672439575195] |
82419feb-3511-4b9a-ab30-b8f7d105c255 | on-the-use-of-the-gram-matrix-for | 2306.12949 | null | https://arxiv.org/abs/2306.12949v1 | https://arxiv.org/pdf/2306.12949v1.pdf | On the use of the Gram matrix for multivariate functional principal components analysis | Dimension reduction is crucial in functional data analysis (FDA). The key tool to reduce the dimension of the data is functional principal component analysis. Existing approaches for functional principal component analysis usually involve the diagonalization of the covariance operator. With the increasing size and comp... | ['Norma Bargary', 'Andrew J. Simpkin', 'Edward Gunning', 'Steven Golovkine'] | 2023-06-22 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 8.63320157e-02 -4.12684143e-01 7.92750418e-02 -6.77120164e-02
-3.38601977e-01 -9.19071317e-01 -1.12933345e-01 1.60701469e-01
-2.89646208e-01 4.12173718e-01 1.44904673e-01 -4.60471690e-01
-5.88871956e-01 -1.63876697e-01 -3.79955560e-01 -6.99261069e-01
-1.89547777e-01 -7.84882233e-02 -3.85044903e-01 1.03212424... | [7.566555500030518, 4.411421775817871] |
77de3c6f-16e0-44cd-a3e3-118740719f2b | reconstruction-regularized-deep-metric | 2007.13547 | null | https://arxiv.org/abs/2007.13547v1 | https://arxiv.org/pdf/2007.13547v1.pdf | Reconstruction Regularized Deep Metric Learning for Multi-label Image Classification | In this paper, we present a novel deep metric learning method to tackle the multi-label image classification problem. In order to better learn the correlations among images features, as well as labels, we attempt to explore a latent space, where images and labels are embedded via two unique deep neural networks, respec... | ['Peng Gao', 'Changsheng Li', 'Kai Zheng', 'Chong Liu', 'Lixin Duan'] | 2020-07-27 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 2.06716821e-01 -1.34358436e-01 -8.23681280e-02 -7.86299109e-01
-6.17630303e-01 -4.19380873e-01 2.30931193e-01 2.71331906e-01
-5.48068941e-01 2.63892055e-01 1.10059552e-01 3.58558625e-01
-2.87989169e-01 -7.48540461e-01 -6.40918970e-01 -9.46900964e-01
3.14251125e-01 3.83169323e-01 -1.26751512e-01 3.84194613... | [9.73007583618164, 3.983647346496582] |
8a47fe4b-752a-43af-a172-744f300d7128 | moprd-a-multidisciplinary-open-peer-review | 2212.04972 | null | https://arxiv.org/abs/2212.04972v1 | https://arxiv.org/pdf/2212.04972v1.pdf | MOPRD: A multidisciplinary open peer review dataset | Open peer review is a growing trend in academic publications. Public access to peer review data can benefit both the academic and publishing communities. It also serves as a great support to studies on review comment generation and further to the realization of automated scholarly paper review. However, most of the exi... | ['Xiaodong Shi', 'Yidong Chen', 'Zhangping Zhou', 'Jiaxin Song', 'Jialiang Lin'] | 2022-12-09 | null | null | null | null | ['review-generation', 'comment-generation'] | ['natural-language-processing', 'natural-language-processing'] | [-2.55973071e-01 1.66685089e-01 -8.78229380e-01 2.34721135e-02
-8.33629668e-01 -3.74647737e-01 7.64829636e-01 4.73707914e-01
-9.19501185e-02 9.55923736e-01 1.86638087e-01 -6.94351137e-01
-2.41232812e-01 -7.56093442e-01 -3.49329472e-01 -1.87961414e-01
8.20223093e-01 3.00569862e-01 1.50703728e-01 -1.08084984... | [12.23670768737793, 9.542953491210938] |
3b980a21-1061-4331-b21b-447937ef719e | improving-low-resource-named-entity | null | null | https://aclanthology.org/2020.acl-main.523 | https://aclanthology.org/2020.acl-main.523.pdf | Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling | Exploiting sentence-level labels, which are easy to obtain, is one of the plausible methods to improve low-resource named entity recognition (NER), where token-level labels are costly to annotate. Current models for jointly learning sentence and token labeling are limited to binary classification. We present a joint mo... | ['Sharifah Mahani Aljunied', 'Canasai Kruengkrai', 'Thien Hai Nguyen', 'Lidong Bing'] | 2020-07-01 | null | null | null | acl-2020-6 | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [-4.56061512e-02 1.76681146e-01 -5.74050546e-01 -8.18207681e-01
-1.28112721e+00 -8.80273402e-01 2.31756166e-01 4.04780954e-01
-9.40621376e-01 9.96954381e-01 1.69839218e-01 -5.27194738e-01
5.58873653e-01 -6.16650045e-01 -6.67168319e-01 -3.06159824e-01
2.84995914e-01 2.34547809e-01 -2.49541014e-01 -3.70289199... | [9.86950969696045, 9.731375694274902] |
67a44b4d-6045-431b-b45b-da46ad596d6b | deepsportlab-a-unified-framework-for-ball | 2112.00627 | null | https://arxiv.org/abs/2112.00627v1 | https://arxiv.org/pdf/2112.00627v1.pdf | DeepSportLab: a Unified Framework for Ball Detection, Player Instance Segmentation and Pose Estimation in Team Sports Scenes | This paper presents a unified framework to (i) locate the ball, (ii) predict the pose, and (iii) segment the instance mask of players in team sports scenes. Those problems are of high interest in automated sports analytics, production, and broadcast. A common practice is to individually solve each problem by exploiting... | ['Christophe De Vleeschouwer', 'Amirafshar Moshtaghpour', 'Niels Sayez', 'Maxime Istasse', 'Gabriel Van Zandycke', 'Seyed Abolfazl Ghasemzadeh'] | 2021-12-01 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [-1.86722845e-01 -1.09341756e-01 -1.86129317e-01 -7.96934590e-02
-6.95552826e-01 -5.52162468e-01 2.87781805e-01 1.25901714e-01
-5.83974719e-01 2.29159489e-01 -2.98284553e-02 3.60494703e-01
9.42124147e-03 -7.35105336e-01 -7.26272047e-01 -5.98073959e-01
5.56498282e-02 9.02896762e-01 9.45815504e-01 -3.18029076... | [7.291167736053467, -0.7957663536071777] |
02344553-5ce2-4222-b770-3f2975ef570f | sentence-level-discourse-parsing-as-text-to | null | null | https://openreview.net/forum?id=MzT29q_1ez8 | https://openreview.net/pdf?id=MzT29q_1ez8 | Sentence-Level Discourse Parsing as Text-to-Text Generation | Previous studies have made great advances in RST discourse parsing through neural frameworks or efficient features, but they split the parsing process into two subtasks and heavily depended on gold segmentation. In this paper, we introduce an end-to-end method for sentence-level RST discourse parsing via transforming i... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['discourse-segmentation', 'discourse-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.54846168e-01 9.28908885e-01 -1.86659634e-01 -4.46554452e-01
-1.32498312e+00 -7.28655517e-01 6.69573963e-01 6.02242276e-02
-4.27798897e-01 9.77633476e-01 5.85136831e-01 -6.51824594e-01
5.62002540e-01 -1.01515782e+00 -5.44644296e-01 -3.23804885e-01
3.16791773e-01 3.55208695e-01 6.00870013e-01 -3.83305520... | [10.772645950317383, 9.388235092163086] |
e3e508f8-4fad-4de2-8800-507fa3490a89 | bayling-bridging-cross-lingual-alignment-and | 2306.10968 | null | https://arxiv.org/abs/2306.10968v2 | https://arxiv.org/pdf/2306.10968v2.pdf | BayLing: Bridging Cross-lingual Alignment and Instruction Following through Interactive Translation for Large Language Models | Large language models (LLMs) have demonstrated remarkable prowess in language understanding and generation. Advancing from foundation LLMs to instructionfollowing LLMs, instruction tuning plays a vital role in aligning LLMs to human preferences. However, the existing LLMs are usually focused on English, leading to infe... | ['Yang Feng', 'Xilin Chen', 'Yunji Chen', 'Shangtong Gui', 'Mengyu Bu', 'Langlin Huang', 'Yan Zhou', 'Zhengrui Ma', 'Zhuocheng Zhang', 'Qingkai Fang', 'Shaolei Zhang'] | 2023-06-19 | null | null | null | null | ['text-generation', 'instruction-following'] | ['natural-language-processing', 'natural-language-processing'] | [-1.94591895e-01 -1.62161112e-01 -4.38619524e-01 -3.76559585e-01
-1.35197067e+00 -7.38733590e-01 2.77678281e-01 -2.23880991e-01
-4.07367617e-01 5.78843772e-01 -6.73146173e-03 -1.22083032e+00
3.26277256e-01 -6.39785290e-01 -9.04308021e-01 -5.10019958e-02
1.22081615e-01 8.28883886e-01 1.62889436e-01 -7.70759881... | [10.781756401062012, 8.41832447052002] |
609c7cb0-f45d-463c-9216-58de13301c40 | feature-space-selection-and-combination-for | null | null | https://aclanthology.org/W13-1712 | https://aclanthology.org/W13-1712.pdf | Feature Space Selection and Combination for Native Language Identification | null | ["Serge L{\\'e}ger", 'Marine Carpuat', 'Cyril Goutte'] | 2013-06-01 | null | null | null | ws-2013-6 | ['native-language-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.251829147338867, 3.7175402641296387] |
88c99951-1bf6-4a35-8abb-fc68cc168d08 | flexible-option-learning-1 | 2112.03097 | null | https://arxiv.org/abs/2112.03097v1 | https://arxiv.org/pdf/2112.03097v1.pdf | Flexible Option Learning | Temporal abstraction in reinforcement learning (RL), offers the promise of improving generalization and knowledge transfer in complex environments, by propagating information more efficiently over time. Although option learning was initially formulated in a way that allows updating many options simultaneously, using of... | ['Doina Precup', 'Martin Klissarov'] | 2021-12-06 | flexible-option-learning | http://proceedings.neurips.cc/paper/2021/hash/24cceab7ffc1118f5daaace13c670885-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/24cceab7ffc1118f5daaace13c670885-Paper.pdf | neurips-2021-12 | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 1.19770274e-01 2.32216433e-01 -5.57458818e-01 -3.68669555e-02
-7.68649876e-01 -7.58158624e-01 7.35080957e-01 1.35558426e-01
-8.60804200e-01 1.38547742e+00 3.05857033e-01 -2.34263912e-01
-5.16598463e-01 -8.09089065e-01 -3.79931688e-01 -6.59620225e-01
-4.12211865e-01 5.77867746e-01 2.75438398e-01 -3.14625263... | [4.023139476776123, 1.8146657943725586] |
13138563-e0ca-465e-85df-1a69578c4bb3 | boss-bottom-up-cross-modal-semantic | 2207.04211 | null | https://arxiv.org/abs/2207.04211v1 | https://arxiv.org/pdf/2207.04211v1.pdf | BOSS: Bottom-up Cross-modal Semantic Composition with Hybrid Counterfactual Training for Robust Content-based Image Retrieval | Content-Based Image Retrieval (CIR) aims to search for a target image by concurrently comprehending the composition of an example image and a complementary text, which potentially impacts a wide variety of real-world applications, such as internet search and fashion retrieval. In this scenario, the input image serves a... | ['Yueting Zhuang', 'Siliang Tang', 'Juncheng Li', 'Shengyu Zhang', 'Haochen Shi', 'Mengze Li', 'Jiannan Guo', 'Wenqiao Zhang'] | 2022-07-09 | null | null | null | null | ['content-based-image-retrieval', 'semantic-composition'] | ['computer-vision', 'natural-language-processing'] | [ 5.89568377e-01 6.26179129e-02 -2.68563420e-01 -2.28216320e-01
-6.72725558e-01 -9.23412740e-01 1.12731147e+00 2.39849374e-01
-6.21668041e-01 3.92673314e-01 2.46548489e-01 -3.76324475e-01
-3.39075416e-01 -7.27865815e-01 -8.41937900e-01 -7.23746777e-01
4.78391200e-01 3.79960716e-01 -6.40755845e-03 -5.04046261... | [10.86546516418457, 1.6393169164657593] |
05c1be73-4e75-48e4-b709-7a55df28db9a | a-contrast-adaptive-method-for-simultaneous | 2005.05135 | null | https://arxiv.org/abs/2005.05135v2 | https://arxiv.org/pdf/2005.05135v2.pdf | A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis | Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients. The method integrates a novel model for white matter lesions into a previously validated generative model for whole-brain... | ['Koen van Leemput', 'Mark Mühlau', 'Dominik S. Meier', 'Oula Puonti', 'Jens Wuerfel', 'Hartwig R. Siebner', 'Stefano Cerri'] | 2020-05-11 | null | null | null | null | ['3d-medical-imaging-segmentation', 'brain-image-segmentation', 'brain-lesion-segmentation-from-mri'] | ['medical', 'medical', 'medical'] | [ 3.92476827e-01 -9.36658755e-02 3.04440349e-01 -5.88874221e-01
-7.73931265e-01 -5.62356651e-01 3.49238098e-01 -3.72476578e-02
-7.04750299e-01 5.51292539e-01 6.43172190e-02 -3.80605966e-01
-1.76683143e-01 -3.32488716e-01 -2.24528119e-01 -5.52564383e-01
-6.03935122e-01 1.21991932e+00 5.85041285e-01 1.53788745... | [14.060797691345215, -2.191800594329834] |
8324bf74-8911-4311-8b2c-d6d1602700cf | pushing-the-limits-of-simple-pipelines-for | 2204.07305 | null | https://arxiv.org/abs/2204.07305v1 | https://arxiv.org/pdf/2204.07305v1.pdf | Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference | Few-shot learning (FSL) is an important and topical problem in computer vision that has motivated extensive research into numerous methods spanning from sophisticated meta-learning methods to simple transfer learning baselines. We seek to push the limits of a simple-but-effective pipeline for more realistic and practic... | ['Timothy M. Hospedales', 'Minyoung Kim', 'Jan Stühmer', 'Da Li', 'Shell Xu Hu'] | 2022-04-15 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Hu_Pushing_the_Limits_of_Simple_Pipelines_for_Few-Shot_Learning_External_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Hu_Pushing_the_Limits_of_Simple_Pipelines_for_Few-Shot_Learning_External_CVPR_2022_paper.pdf | cvpr-2022-1 | ['few-shot-image-classification'] | ['computer-vision'] | [ 3.12728882e-01 3.01186163e-02 -2.33121365e-01 -4.40129310e-01
-7.94654608e-01 -1.95435524e-01 8.73810768e-01 -1.11127652e-01
-6.79080784e-01 4.50925380e-01 1.62037715e-01 -1.68692976e-01
-4.76673711e-03 -6.10585392e-01 -8.26336145e-01 -6.19915724e-01
7.71331266e-02 4.20816153e-01 6.69165850e-01 -4.62533921... | [9.992427825927734, 2.6408016681671143] |
58a52f1a-96c2-46e8-b5b5-606f7c509e7c | model-based-image-signal-processors-via | 2201.03210 | null | https://arxiv.org/abs/2201.03210v1 | https://arxiv.org/pdf/2201.03210v1.pdf | Model-Based Image Signal Processors via Learnable Dictionaries | Digital cameras transform sensor RAW readings into RGB images by means of their Image Signal Processor (ISP). Computational photography tasks such as image denoising and colour constancy are commonly performed in the RAW domain, in part due to the inherent hardware design, but also due to the appealing simplicity of no... | ['Eduardo Pérez-Pellitero', 'Aleš Leonardis', 'Matteo Maggioni', 'Steven McDonagh', 'Marcos V. Conde'] | 2022-01-10 | null | null | null | null | ['color-constancy', 'raw-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 7.49707401e-01 3.43707949e-01 -4.38175462e-02 -4.93220627e-01
-7.53840446e-01 -5.31958759e-01 7.42327809e-01 -2.43751809e-01
-5.71000636e-01 6.35339856e-01 8.01954791e-03 -1.70448214e-01
-1.63101047e-01 -6.09601557e-01 -1.03618193e+00 -7.88057685e-01
2.65723467e-01 2.95383155e-01 -1.73050240e-01 -3.90631855... | [9.732640266418457, -2.681690216064453] |
62f15c65-4433-47a1-81ea-e0be53db93b4 | aca-net-towards-lightweight-speaker | 2305.12121 | null | https://arxiv.org/abs/2305.12121v1 | https://arxiv.org/pdf/2305.12121v1.pdf | ACA-Net: Towards Lightweight Speaker Verification using Asymmetric Cross Attention | In this paper, we propose ACA-Net, a lightweight, global context-aware speaker embedding extractor for Speaker Verification (SV) that improves upon existing work by using Asymmetric Cross Attention (ACA) to replace temporal pooling. ACA is able to distill large, variable-length sequences into small, fixed-sized latents... | ['Bin Ma', 'Eng Siong Chng', 'Shengkui Zhao', 'Chongjia Ni', 'Trung Hieu Nguyen', 'Yukun Ma', 'Chong Zhang', 'Dianwen Ng', 'Tuan Truong', 'Jia Qi Yip'] | 2023-05-20 | null | null | null | null | ['speaker-verification'] | ['speech'] | [ 8.18847492e-02 -1.14009358e-01 -9.86815691e-02 -6.69683397e-01
-1.25188708e+00 -6.63152874e-01 4.39062834e-01 -2.85860360e-01
-5.83944499e-01 4.83605236e-01 5.97237051e-01 -4.25385237e-01
3.70121628e-01 -1.05969585e-01 -6.10478997e-01 -7.07546294e-01
-4.39457804e-01 -6.65223040e-03 1.74089253e-01 -9.16880146... | [14.410572052001953, 6.0985283851623535] |
0590627b-257d-41b0-9393-e0a4a0bb99c0 | the-memad-submission-to-the-wmt18-multimodal | 1808.10802 | null | http://arxiv.org/abs/1808.10802v2 | http://arxiv.org/pdf/1808.10802v2.pdf | The MeMAD Submission to the WMT18 Multimodal Translation Task | This paper describes the MeMAD project entry to the WMT Multimodal Machine
Translation Shared Task.
We propose adapting the Transformer neural machine translation (NMT)
architecture to a multi-modal setting. In this paper, we also describe the
preliminary experiments with text-only translation systems leading us up t... | ['Raúl Vázquez', 'Jörg Tiedemann', 'Mats Sjöberg', 'Phu Pham', 'Stig-Arne Grönroos', 'Raphael Troncy', 'Mikko Kurimo', 'Jorma Laaksonen', 'Benoit Huet', 'Umut Sulubacak', 'Bernard Merialdo'] | 2018-08-31 | the-memad-submission-to-the-wmt18-multimodal-1 | https://aclanthology.org/W18-6439 | https://aclanthology.org/W18-6439.pdf | ws-2018-10 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 1.94699451e-01 -1.55383721e-01 -1.89842284e-01 -2.54333377e-01
-1.66506779e+00 -7.82428622e-01 9.20938671e-01 -5.02428353e-01
-6.92816913e-01 8.71237636e-01 4.62013751e-01 -7.48097062e-01
1.10950403e-01 -2.61730313e-01 -6.36079133e-01 -3.84242237e-01
4.84398097e-01 9.76346672e-01 -6.25284165e-02 -5.89741230... | [11.477081298828125, 1.528331995010376] |
4425c818-1319-48e1-9b6a-33c6fbc63bc7 | a-60-ghz-radar-sensor-for-micron-scale-motion | 2107.10993 | null | https://arxiv.org/abs/2107.10993v1 | https://arxiv.org/pdf/2107.10993v1.pdf | A 60-GHz Radar Sensor for Micron-Scale Motion Detection | A compact, continuous-wave, mmWave radar sensor is developed for non-contact detection of micron-scale motions. This board-integrated radar system consists of a pair of mmWave transmitter and receiver, two series-fed microstrip patch arrays, an IF subsystem, and a microcontroller. Working at 60-GHz frequency, this supe... | ['Lixin Ran', 'Jie Wang', 'Bin Zhang', 'Chengkai Zhu', 'Marcel Balle'] | 2021-07-23 | null | null | null | null | ['motion-detection', 'contact-detection'] | ['computer-vision', 'robots'] | [ 2.60011673e-01 -7.31152445e-02 2.47861147e-01 -1.91317230e-01
-6.93314433e-01 -4.19786364e-01 1.70497626e-01 -4.87820446e-01
-4.70733911e-01 6.17878139e-01 -3.21313947e-01 -3.20928067e-01
-4.39957917e-01 -6.47631884e-01 1.79396674e-01 -8.67938280e-01
-4.37666208e-01 2.12058529e-01 -1.22084230e-01 -8.88564810... | [6.384033679962158, 1.2190046310424805] |
521ab34e-b3d0-4d8d-aed1-258a13c05055 | vectorfusion-text-to-svg-by-abstracting-pixel | 2211.11319 | null | https://arxiv.org/abs/2211.11319v1 | https://arxiv.org/pdf/2211.11319v1.pdf | VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models | Diffusion models have shown impressive results in text-to-image synthesis. Using massive datasets of captioned images, diffusion models learn to generate raster images of highly diverse objects and scenes. However, designers frequently use vector representations of images like Scalable Vector Graphics (SVGs) for digita... | ['Pieter Abbeel', 'Amber Xie', 'Ajay Jain'] | 2022-11-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jain_VectorFusion_Text-to-SVG_by_Abstracting_Pixel-Based_Diffusion_Models_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jain_VectorFusion_Text-to-SVG_by_Abstracting_Pixel-Based_Diffusion_Models_CVPR_2023_paper.pdf | cvpr-2023-1 | ['text-to-3d'] | ['computer-vision'] | [ 4.61222261e-01 2.05934942e-01 -1.36241123e-01 -2.63668895e-01
-7.35537589e-01 -6.70999169e-01 9.28278923e-01 -5.58547735e-01
8.11719820e-02 6.37428761e-01 6.88942134e-01 -3.23481709e-01
4.75729048e-01 -8.90337944e-01 -9.14150715e-01 -3.17834169e-01
4.76499677e-01 5.15430391e-01 -1.43413484e-01 -4.35515940... | [11.386665344238281, -0.28776681423187256] |
a45df01d-0f23-4679-a2a4-129b9a5f9da4 | multivent-multilingual-videos-of-events-with | 2307.03153 | null | https://arxiv.org/abs/2307.03153v1 | https://arxiv.org/pdf/2307.03153v1.pdf | MultiVENT: Multilingual Videos of Events with Aligned Natural Text | Everyday news coverage has shifted from traditional broadcasts towards a wide range of presentation formats such as first-hand, unedited video footage. Datasets that reflect the diverse array of multimodal, multilingual news sources available online could be used to teach models to benefit from this shift, but existing... | ['Benjamin Van Durme', 'Reno Kriz', 'David Etter', 'Kate Sanders'] | 2023-07-06 | null | null | null | null | ['video-retrieval', 'retrieval', 'information-retrieval'] | ['computer-vision', 'methodology', 'natural-language-processing'] | [-3.31787080e-01 -2.50125736e-01 -7.47146428e-01 -2.90136933e-01
-1.49828780e+00 -8.83750260e-01 1.08801568e+00 2.12054431e-01
-4.06041682e-01 6.18144989e-01 1.28492165e+00 6.07665768e-03
-5.91840632e-02 -4.58422810e-01 -1.24378037e+00 1.77550204e-02
-8.40245858e-02 4.21572328e-02 2.22951993e-01 -5.77094674... | [10.473855018615723, 0.9087481498718262] |
13b2c341-ed11-41d7-b269-dcadf780dd04 | boundary-content-graph-neural-network-for | 2008.01432 | null | https://arxiv.org/abs/2008.01432v1 | https://arxiv.org/pdf/2008.01432v1.pdf | Boundary Content Graph Neural Network for Temporal Action Proposal Generation | Temporal action proposal generation plays an important role in video action understanding, which requires localizing high-quality action content precisely. However, generating temporal proposals with both precise boundaries and high-quality action content is extremely challenging. To address this issue, we propose a no... | ['Yunhai Tong', 'Junhui Liu', 'Yingying Wang', 'Yueran Bai', 'Yang Yang', 'Qiyue Liu'] | 2020-08-04 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6021_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123730120.pdf | eccv-2020-8 | ['action-understanding', 'temporal-action-proposal-generation'] | ['computer-vision', 'computer-vision'] | [ 3.18359435e-01 5.10571264e-02 -4.31189716e-01 -2.05928177e-01
-7.95048177e-02 -5.98734394e-02 5.97982705e-01 1.83285445e-01
-2.57189840e-01 5.90912521e-01 5.90718985e-01 1.31437525e-01
-8.06312934e-02 -7.61224389e-01 -5.30904412e-01 -5.31531930e-01
-1.49123654e-01 5.05203661e-03 1.11471236e+00 -1.43974662... | [8.400177001953125, 0.5516021251678467] |
dda4f50a-aa3f-4148-b091-56ef3e1dbbc7 | relative-acoustic-features-for-distance | 2212.01306 | null | https://arxiv.org/abs/2212.01306v1 | https://arxiv.org/pdf/2212.01306v1.pdf | Relative Acoustic Features for Distance Estimation in Smart-Homes | Any audio recording encapsulates the unique fingerprint of the associated acoustic environment, namely the background noise and reverberation. Considering the scenario of a room equipped with a fixed smart speaker device with one or more microphones and a wearable smart device (watch, glasses or smartphone), we employe... | ['Patrick A. Naylor', 'Daniel Barreda', 'Francesco Nespoli'] | 2022-12-02 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 5.01875699e-01 -1.98764443e-01 8.79918933e-01 -2.06805319e-01
-1.31055677e+00 -4.52849805e-01 2.62930572e-01 2.46535435e-01
-1.16054997e-01 2.55926788e-01 6.64677918e-01 6.31154925e-02
-5.04484653e-01 -4.42861289e-01 -4.10655677e-01 -1.09359443e+00
-3.90224904e-01 6.11541234e-02 -2.29687020e-01 1.26209170... | [15.092889785766602, 5.808326721191406] |
1804d334-6505-4cc2-b49a-7f01839286b0 | tcfimt-temporal-counterfactual-forecasting | 2212.08890 | null | https://arxiv.org/abs/2212.08890v1 | https://arxiv.org/pdf/2212.08890v1.pdf | TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspective | Determining causal effects of temporal multi-intervention assists decision-making. Restricted by time-varying bias, selection bias, and interactions of multiple interventions, the disentanglement and estimation of multiple treatment effects from individual temporal data is still rare. To tackle these challenges, we pro... | ['Xiangnan Feng', 'Changjie Fan', 'Tangjie Lv', 'Yu Ding', 'Runze Wu', 'Wei Huang', 'Mingming Gong', 'Yu Xiong', 'Zhipeng Hu', 'Guifeng Wang', 'Pengfei Xi'] | 2022-12-17 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 5.96477270e-01 -1.75737321e-01 -1.12895274e+00 -3.81847590e-01
-8.34425449e-01 -5.73834062e-01 9.13252354e-01 1.12537118e-02
-3.47718000e-01 1.14326036e+00 8.52992237e-01 -6.78065658e-01
-3.54532272e-01 -6.02475286e-01 -7.56198049e-01 -6.89243853e-01
-3.84762585e-01 2.65640557e-01 -3.09636176e-01 1.93581596... | [8.057377815246582, 5.375694751739502] |
fae57213-f030-4d59-90aa-032680c1950b | fully-convolutional-multi-scale-residual | 1801.05173 | null | http://arxiv.org/abs/1801.05173v1 | http://arxiv.org/pdf/1801.05173v1.pdf | Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers | Deep fully convolutional neural network (FCN) based architectures have shown
great potential in medical image segmentation. However, such architectures
usually have millions of parameters and inadequate number of training samples
leading to over-fitting and poor generalization. In this paper, we present a
novel highly ... | ['Ganapathy Krishnamurthi', 'Mahendra Khened', 'Varghese Alex Kollerathu'] | 2018-01-16 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 2.06652492e-01 1.19366311e-01 2.45935246e-01 -4.63500082e-01
-7.35017598e-01 -4.08679903e-01 2.49838471e-01 1.68179005e-01
-5.87865949e-01 7.55855381e-01 -2.02016339e-01 -4.72921938e-01
-2.72202998e-01 -5.95333278e-01 -1.82796568e-01 -5.49372315e-01
-1.49541855e-01 8.87293816e-01 6.42426834e-02 1.89627454... | [14.28218936920166, -2.452603816986084] |
9c523629-4cbd-4daf-ae25-814d3a3b6a8f | beyond-static-features-for-temporally | 2011.08627 | null | https://arxiv.org/abs/2011.08627v4 | https://arxiv.org/pdf/2011.08627v4.pdf | Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video | Despite the recent success of single image-based 3D human pose and shape estimation methods, recovering temporally consistent and smooth 3D human motion from a video is still challenging. Several video-based methods have been proposed; however, they fail to resolve the single image-based methods' temporal inconsistency... | ['Ju Yong Chang', 'Kyoung Mu Lee', 'Gyeongsik Moon', 'Hongsuk Choi'] | 2020-11-17 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Choi_Beyond_Static_Features_for_Temporally_Consistent_3D_Human_Pose_and_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Choi_Beyond_Static_Features_for_Temporally_Consistent_3D_Human_Pose_and_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-human-pose-and-shape-estimation'] | ['computer-vision'] | [-2.65009642e-01 -5.50311446e-01 -1.16555020e-01 -1.38575554e-01
-6.04499698e-01 -2.53977060e-01 2.05690786e-01 -2.51614779e-01
-1.77272260e-01 7.51816809e-01 2.01129138e-01 2.13579446e-01
3.93676870e-02 -4.74049687e-01 -5.54806232e-01 -4.85863090e-01
-7.39847124e-02 3.63354623e-01 6.21788919e-01 -2.44128957... | [7.374253749847412, -0.8089503645896912] |
4aded5bf-5f44-4569-bff7-dc34f7ee22ff | efficient-visual-fault-detection-for-freight | 2307.00701 | null | https://arxiv.org/abs/2307.00701v1 | https://arxiv.org/pdf/2307.00701v1.pdf | Efficient Visual Fault Detection for Freight Train Braking System via Heterogeneous Self Distillation in the Wild | Efficient visual fault detection of freight trains is a critical part of ensuring the safe operation of railways under the restricted hardware environment. Although deep learning-based approaches have excelled in object detection, the efficiency of freight train fault detection is still insufficient to apply in real-wo... | ['Guodong Sun', 'Mingying Li', 'Yang Zhou', 'Huilin Pan', 'Yang Zhang'] | 2023-07-03 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-1.44756019e-01 -1.72483310e-01 -1.11474514e-01 -3.01666170e-01
-9.62827265e-01 -1.68859795e-01 -8.50498825e-02 8.67506023e-03
-4.65872705e-01 6.95468426e-01 -4.07933772e-01 -3.21060121e-01
-1.46527350e-01 -8.10558796e-01 -9.00651515e-01 -8.92260075e-01
-9.53050703e-02 2.47999787e-01 8.60168219e-01 -7.51029849... | [8.7566556930542, -0.5728165507316589] |
662d6863-c1bd-413f-93cd-cf22dae102dd | an-encoder-decoder-based-framework-for-hindi | null | null | https://link.springer.com/article/10.1007/s11042-021-11106-5 | https://link.springer.com/article/10.1007/s11042-021-11106-5 | An encoder-decoder based framework for hindi image caption generation | In recent times, research activity on image caption generation has attracted several researchers. The present work attempt to address the problem of Hindi image caption generation using Hindi Visual genome dataset. Hindi is the official and most spoken language in India. In a linguistically diverse country like India, ... | ['Sivaji Bandyopadhyay', 'Thoudam Doren Singh', 'Alok Singh'] | 2021-07-09 | null | null | null | multimedia-tools-and-applications-2021-7 | ['hindi-image-captioning'] | ['computer-vision'] | [ 5.87484956e-01 5.78004122e-02 2.70534605e-01 -4.04597670e-01
-9.41214085e-01 -5.00549197e-01 8.61803830e-01 -1.96164891e-01
-3.09934795e-01 1.18313515e+00 3.92281353e-01 -2.66811788e-01
6.58956587e-01 -6.23550713e-01 -1.11617851e+00 -6.33992434e-01
2.96382725e-01 2.22836390e-01 -9.43343714e-02 -2.78963596... | [11.019847869873047, 1.0448918342590332] |
c00c991c-ccb2-4ef7-b079-458bda8f3bb5 | personalized-review-generation-by-expanding | null | null | https://aclanthology.org/P18-2112 | https://aclanthology.org/P18-2112.pdf | Personalized Review Generation By Expanding Phrases and Attending on Aspect-Aware Representations | In this paper, we focus on the problem of building assistive systems that can help users to write reviews. We cast this problem using an encoder-decoder framework that generates personalized reviews by expanding short phrases (e.g. review summaries, product titles) provided as input to the system. We incorporate aspect... | ['Jianmo Ni', 'Julian McAuley'] | 2018-07-01 | null | null | null | acl-2018-7 | ['review-generation'] | ['natural-language-processing'] | [ 3.80041271e-01 9.44072962e-01 -4.86360520e-01 -6.73708975e-01
-8.86787295e-01 -1.47771388e-01 9.15583551e-01 1.49219513e-01
-7.74754584e-02 9.57794428e-01 1.04632521e+00 -5.46830893e-02
3.68500710e-01 -8.77860904e-01 -5.97206116e-01 -1.38440713e-01
5.12141109e-01 6.63929522e-01 -5.60664773e-01 -6.25877082... | [12.026719093322754, 8.875667572021484] |
7c2b3224-4c76-41fc-b34c-4a41146e445c | dtg-ssod-dense-teacher-guidance-for-semi | 2207.05536 | null | https://arxiv.org/abs/2207.05536v1 | https://arxiv.org/pdf/2207.05536v1.pdf | DTG-SSOD: Dense Teacher Guidance for Semi-Supervised Object Detection | The Mean-Teacher (MT) scheme is widely adopted in semi-supervised object detection (SSOD). In MT, the sparse pseudo labels, offered by the final predictions of the teacher (e.g., after Non Maximum Suppression (NMS) post-processing), are adopted for the dense supervision for the student via hand-crafted label assignment... | ['Shanshan Zhang', 'Ding Liang', 'Yichao Wu', 'Yujie Wang', 'Xiang Li', 'Gang Li'] | 2022-07-12 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 4.44025069e-01 5.26273727e-01 -1.76588133e-01 -4.38945204e-01
-8.36855769e-01 -5.24404883e-01 7.65873194e-01 1.65142834e-01
-4.74393725e-01 4.06548291e-01 -1.86892584e-01 -2.18213111e-01
-7.98308402e-02 -4.65592921e-01 -8.18349004e-01 -1.05966079e+00
4.37169433e-01 6.66801870e-01 6.01728082e-01 9.21840966... | [9.1890287399292, 1.415969967842102] |
2341c774-03c4-4c0b-bc0a-ef4c69b62578 | lifting-interpretability-performance-trade | 2002.04267 | null | https://arxiv.org/abs/2002.04267v1 | https://arxiv.org/pdf/2002.04267v1.pdf | Lifting Interpretability-Performance Trade-off via Automated Feature Engineering | Complex black-box predictive models may have high performance, but lack of interpretability causes problems like lack of trust, lack of stability, sensitivity to concept drift. On the other hand, achieving satisfactory accuracy of interpretable models require more time-consuming work related to feature engineering. Can... | ['Alicja Gosiewska', 'Przemyslaw Biecek'] | 2020-02-11 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [-1.07630618e-01 6.96894884e-01 -1.30482718e-01 -9.12667751e-01
-4.61423725e-01 -5.42636096e-01 2.73393840e-01 2.61222124e-02
1.58537135e-01 9.97939825e-01 -3.72253056e-03 -4.28188741e-01
-4.62750494e-01 -6.49969757e-01 -8.61779571e-01 -1.84238881e-01
-7.30797695e-03 8.07204366e-01 8.32030848e-02 -1.56350583... | [8.763635635375977, 5.914841175079346] |
6fdbf615-1212-44b3-8272-4aac09e5f825 | instant-volumetric-head-avatars | 2211.12499 | null | https://arxiv.org/abs/2211.12499v2 | https://arxiv.org/pdf/2211.12499v2.pdf | Instant Volumetric Head Avatars | We present Instant Volumetric Head Avatars (INSTA), a novel approach for reconstructing photo-realistic digital avatars instantaneously. INSTA models a dynamic neural radiance field based on neural graphics primitives embedded around a parametric face model. Our pipeline is trained on a single monocular RGB portrait vi... | ['Justus Thies', 'Timo Bolkart', 'Wojciech Zielonka'] | 2022-11-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zielonka_Instant_Volumetric_Head_Avatars_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zielonka_Instant_Volumetric_Head_Avatars_CVPR_2023_paper.pdf | cvpr-2023-1 | ['face-model'] | ['computer-vision'] | [ 1.97969787e-02 3.61667812e-01 6.25945389e-01 -3.07276875e-01
-7.20740139e-01 -7.06374109e-01 5.09844065e-01 -5.98611534e-01
2.09731050e-02 2.70379871e-01 2.32766736e-02 1.07410245e-01
6.28029883e-01 -6.36084974e-01 -9.34992373e-01 -3.40732753e-01
7.30113611e-02 6.64260745e-01 8.92074183e-02 -2.14854732... | [12.800180435180664, -0.46898651123046875] |
3f0e97a7-e366-4ba5-8036-374497190cd8 | machine-learning-based-missing-values | 2306.06338 | null | https://arxiv.org/abs/2306.06338v1 | https://arxiv.org/pdf/2306.06338v1.pdf | Machine Learning Based Missing Values Imputation in Categorical Datasets | This study explored the use of machine learning algorithms for predicting and imputing missing values in categorical datasets. We focused on ensemble models that use the error correction output codes (ECOC) framework, including SVM-based and KNN-based ensemble models, as well as an ensemble classifier that combines SVM... | ['Arshad Khan', 'Asfandyar Khan', 'Majid Khan', 'Laila iftikhar', 'Muhammad Ishaq'] | 2023-06-10 | null | null | null | null | ['imputation', 'imputation', 'imputation'] | ['computer-vision', 'miscellaneous', 'time-series'] | [ 2.32630953e-01 -1.14189267e-01 -3.04560006e-01 -8.54213893e-01
-7.04215109e-01 -2.18931109e-01 1.83201626e-01 3.78161490e-01
-1.58885196e-01 1.05510414e+00 4.28823441e-01 -4.30160999e-01
-5.21646857e-01 -7.72358954e-01 -4.82901573e-01 -6.93898737e-01
6.09345175e-02 4.80161279e-01 -5.08775830e-01 2.39133276... | [7.9527997970581055, 4.996739387512207] |
c50f5729-5cef-48df-a48e-745850117325 | ir-lpr-large-scale-of-iranian-license-plate | 2209.04680 | null | https://arxiv.org/abs/2209.04680v1 | https://arxiv.org/pdf/2209.04680v1.pdf | IR-LPR: Large Scale of Iranian License Plate Recognition Dataset | Object detection has always been practical. There are so many things in our world that recognizing them can not only increase our automatic knowledge of the surroundings, but can also be lucrative for those interested in starting a new business. One of these attractive objects is the license plate (LP). In addition to ... | ['Mohammad Ali Keyvanrad', 'Mahdi Naghibi', 'Mohammad Mohsen Talaie', 'Seyyede Mahila Moghadami', 'Melika Sabaghian', 'Mahdi Rahmani'] | 2022-09-10 | null | null | null | null | ['license-plate-recognition', 'license-plate-detection'] | ['computer-vision', 'computer-vision'] | [-1.72164515e-01 -7.65124142e-01 8.25318173e-02 -1.10876866e-01
-4.84497577e-01 -5.80013156e-01 4.03890789e-01 -4.36439037e-01
-4.72560018e-01 6.62032425e-01 -2.30024606e-01 -1.95748836e-01
2.60141194e-01 -1.05708230e+00 -3.94736290e-01 -8.63681853e-01
3.80908310e-01 3.58168542e-01 7.13725746e-01 -2.72092402... | [9.82477855682373, -4.961678504943848] |
ef6f4483-9b51-4442-acd1-e461f15bf6ff | multilevel-regression-with-poststratification | 2009.06615 | null | https://arxiv.org/abs/2009.06615v1 | https://arxiv.org/pdf/2009.06615v1.pdf | Multilevel regression with poststratification for the national level Viber/Street poll on the 2020 presidential election in Belarus | Independent sociological polls are forbidden in Belarus. Online polls performed without sound scientific rigour do not yield representative results. Yet, both inside and outside Belarus it is of great importance to obtain precise estimates of the ratings of all candidates. These ratings could function as reliable proxi... | ['Ales Zahorski'] | 2020-09-14 | null | null | null | null | ['pre-election-ratings-estimation'] | ['reasoning'] | [-5.85638762e-01 6.54252052e-01 -4.06499207e-01 -3.60258609e-01
-8.70273113e-01 -4.83256638e-01 8.74281943e-01 2.87562191e-01
-8.31231594e-01 1.29413235e+00 5.17310262e-01 -9.49089408e-01
-1.72172487e-01 -1.00985312e+00 -4.78474706e-01 -5.99260688e-01
5.52809417e-01 5.20876944e-01 -1.64283827e-01 -3.44787180... | [7.678923606872559, 4.948190212249756] |
e02a2908-5ed4-48d2-b09b-88f8c40bb2fb | funny3-at-semeval-2020-task-7-humor-detection | null | null | https://aclanthology.org/2020.semeval-1.132 | https://aclanthology.org/2020.semeval-1.132.pdf | Funny3 at SemEval-2020 Task 7: Humor Detection of Edited Headlines with LSTM and TFIDF Neural Network System | This paper presents a neural network system where we participate in the first task of SemEval-2020 shared task 7 {``}Assessing the Funniness of Edited News Headlines{''}. Our target is to create to neural network model that can predict the funniness of edited headlines. We build our model using a combination of LSTM an... | ['Kuan Tang', 'Xuefeng Luo'] | 2020-12-01 | null | null | null | semeval-2020 | ['humor-detection'] | ['natural-language-processing'] | [-4.03511226e-02 6.63272798e-01 7.12445304e-02 -7.26804554e-01
-7.11297333e-01 -1.66955635e-01 1.06003165e+00 7.15029761e-02
-8.56167614e-01 7.98193634e-01 8.39196920e-01 -2.64705330e-01
-1.13414004e-01 -5.96961915e-01 -8.46397758e-01 1.16475061e-01
-6.20483868e-02 4.24577475e-01 9.34683718e-03 -7.42436230... | [8.761234283447266, 10.831469535827637] |
38839726-2928-4706-ad63-39a2f3c28dec | learning-crosslingual-word-embeddings-without | 1606.09403 | null | http://arxiv.org/abs/1606.09403v1 | http://arxiv.org/pdf/1606.09403v1.pdf | Learning Crosslingual Word Embeddings without Bilingual Corpora | Crosslingual word embeddings represent lexical items from different languages
in the same vector space, enabling transfer of NLP tools. However, previous
attempts had expensive resource requirements, difficulty incorporating
monolingual data or were unable to handle polysemy. We address these drawbacks
in our method wh... | ['Long Duong', 'Steven Bird', 'Trevor Cohn', 'Tengfei Ma', 'Hiroshi Kanayama'] | 2016-06-30 | learning-crosslingual-word-embeddings-without-1 | https://aclanthology.org/D16-1136 | https://aclanthology.org/D16-1136.pdf | emnlp-2016-11 | ['cross-lingual-document-classification'] | ['natural-language-processing'] | [-5.16981304e-01 -5.74529469e-01 -8.32676589e-01 -1.90790460e-01
-1.03996897e+00 -9.80691850e-01 7.76256382e-01 2.18065694e-01
-9.67781126e-01 7.90153205e-01 4.88115370e-01 -8.61225843e-01
2.80615419e-01 -4.82300133e-01 -3.38206828e-01 -2.11951151e-01
1.43672526e-01 8.61002326e-01 -3.10052633e-01 -3.78247112... | [10.883501052856445, 9.893138885498047] |
74fa342d-0d6a-456c-bee9-34f3ae829475 | a-local-machine-learning-approach-for | 2302.10810 | null | https://arxiv.org/abs/2302.10810v1 | https://arxiv.org/pdf/2302.10810v1.pdf | A Local Machine Learning Approach for Fingerprint-based Indoor Localization | Machine learning (ML) solutions to indoor localization problems have become popular in recent years due to high positioning accuracy and low cost of implementation. This paper proposes a novel local nonparametric approach for solving localizations from high-dimensional Received Signal Strength Indicator (RSSI) values. ... | ['Haiqing Xu', 'Xiao Meng', 'Brian Evans', 'Nora Agah'] | 2023-02-21 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-4.09480184e-02 -2.87145972e-01 -2.00403363e-01 -6.57119930e-01
-9.69158888e-01 -6.53391480e-01 4.07908142e-01 3.29325944e-01
-5.64903736e-01 1.39755046e+00 -1.07178897e-01 -5.35653412e-01
-7.34981298e-01 -1.06214821e+00 -7.25612879e-01 -8.81400585e-01
-2.75026143e-01 3.48477811e-01 6.49977624e-02 1.28246486... | [6.408503532409668, 0.9654938578605652] |
0b57cffb-11d9-4567-9718-5247196b3f34 | cycle-self-training-for-semi-supervised | 2207.05334 | null | https://arxiv.org/abs/2207.05334v1 | https://arxiv.org/pdf/2207.05334v1.pdf | Cycle Self-Training for Semi-Supervised Object Detection with Distribution Consistency Reweighting | Recently, many semi-supervised object detection (SSOD) methods adopt teacher-student framework and have achieved state-of-the-art results. However, the teacher network is tightly coupled with the student network since the teacher is an exponential moving average (EMA) of the student, which causes a performance bottlene... | ['Peng Wu', 'Feng Dai', 'Chunpeng Wu', 'Bo wang', 'Bin Chen', 'Hao liu'] | 2022-07-12 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 6.24059588e-02 4.07139540e-01 -3.06948692e-01 -4.68652576e-01
-7.17499495e-01 -3.85334104e-01 3.18765342e-01 1.63417205e-01
-6.45861983e-01 6.12875879e-01 -4.51912850e-01 -3.90349537e-01
-9.06244814e-02 -8.33607435e-01 -8.60285342e-01 -8.96536767e-01
3.39042366e-01 4.07396048e-01 6.71274662e-01 1.66373998... | [9.462078094482422, 3.072057008743286] |
f998e9fc-20a6-48e2-ab10-c14139336bab | mobilestereonet-towards-lightweight-deep | 2108.09770 | null | https://arxiv.org/abs/2108.09770v1 | https://arxiv.org/pdf/2108.09770v1.pdf | MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matching | Recent methods in stereo matching have continuously improved the accuracy using deep models. This gain, however, is attained with a high increase in computation cost, such that the network may not fit even on a moderate GPU. This issue raises problems when the model needs to be deployed on resource-limited devices. For... | ['Andreas Zell', 'Rafia Rahim', 'Samuel Woerz', 'Faranak Shamsafar'] | 2021-08-22 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [-4.30309735e-02 -1.95081290e-02 -6.96757296e-03 -3.24843973e-01
-2.16518596e-01 -2.86692381e-01 4.18372363e-01 -2.34159142e-01
-6.16361976e-01 3.86290878e-01 -8.25590193e-02 -5.92744291e-01
2.23194450e-01 -9.78751719e-01 -7.69542873e-01 -3.36192161e-01
3.84817421e-01 1.03802405e-01 4.03407395e-01 -6.50534257... | [8.9719877243042, -2.318681240081787] |
bc1dbf87-55d4-465d-abaf-e248c9382585 | learning-monocular-depth-in-dynamic-scenes | 2102.02629 | null | https://arxiv.org/abs/2102.02629v1 | https://arxiv.org/pdf/2102.02629v1.pdf | Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency | We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion and depth in a monocular camera setup without supervision. Our technical contributions are three-fold. First, we highlight the fundamental difference between inverse and forward projection while... | ['In So Kweon', 'Stephen Lin', 'Sunghoon Im', 'Seokju Lee'] | 2021-02-04 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 2.74062678e-02 -5.88689670e-02 -1.48355439e-01 -4.96055484e-01
-7.63893366e-01 -6.68271542e-01 5.74948668e-01 -7.94972420e-01
-2.11060464e-01 4.66969371e-01 8.26365724e-02 5.82266450e-02
3.11147332e-01 -3.31536472e-01 -9.26014185e-01 -5.31024396e-01
2.78664589e-01 4.80779797e-01 4.34918314e-01 2.02985018... | [8.4895658493042, -2.007967233657837] |
3b5d57fa-7f01-450c-b87b-44de578c6f26 | solving-math-word-problems-with-process-and | 2211.14275 | null | https://arxiv.org/abs/2211.14275v1 | https://arxiv.org/pdf/2211.14275v1.pdf | Solving math word problems with process- and outcome-based feedback | Recent work has shown that asking language models to generate reasoning steps improves performance on many reasoning tasks. When moving beyond prompting, this raises the question of how we should supervise such models: outcome-based approaches which supervise the final result, or process-based approaches which supervis... | ['Irina Higgins', 'Geoffrey Irving', 'Antonia Creswell', 'Lisa Wang', 'Noah Siegel', 'Francis Song', 'Ramana Kumar', 'Nate Kushman', 'Jonathan Uesato'] | 2022-11-25 | null | null | null | null | ['gsm8k', 'arithmetic-reasoning'] | ['natural-language-processing', 'reasoning'] | [ 2.82353342e-01 7.90469110e-01 1.31348133e-01 -7.87377954e-01
-9.12417352e-01 -7.35033333e-01 5.61757684e-01 5.28320789e-01
-5.84305763e-01 8.84744883e-01 1.79573029e-01 -9.03584242e-01
-4.09044355e-01 -9.98733580e-01 -7.34830141e-01 -2.02164829e-01
3.97699177e-01 8.37674201e-01 3.22310388e-01 -3.72780085... | [9.73537826538086, 7.386620998382568] |
136d480f-72ea-4987-aded-176ad01f80d2 | motion-corrected-multishot-mri-reconstruction | 1902.07430 | null | https://arxiv.org/abs/1902.07430v6 | https://arxiv.org/pdf/1902.07430v6.pdf | Motion Corrected Multishot MRI Reconstruction Using Generative Networks with Sensitivity Encoding | Multishot Magnetic Resonance Imaging (MRI) is a promising imaging modality that can produce a high-resolution image with relatively less data acquisition time. The downside of multishot MRI is that it is very sensitive to subject motion and even small amounts of motion during the scan can produce artifacts in the final... | ['Siddique Latif', 'Muhammad Usman', 'Muhammad Umar Farooq', 'Junaid Qadir', 'Muhammad Asim'] | 2019-02-20 | null | null | null | null | ['motion-correction-in-multishot-mri'] | ['medical'] | [ 4.86233562e-01 -3.05596918e-01 2.91897565e-01 -2.27473408e-01
-9.42924917e-01 -1.90740138e-01 5.32859921e-01 -2.15003267e-01
-6.24144375e-01 7.81915367e-01 2.36911267e-01 -5.34450114e-02
-2.32380971e-01 -5.40474772e-01 -4.81937855e-01 -1.00386584e+00
8.04158673e-03 3.64732474e-01 5.20533502e-01 -9.59151015... | [13.59910774230957, -2.420337438583374] |
58cf9032-2e26-494f-83d8-baf4a27ce3b2 | fast-interpretable-greedy-tree-sums-figs | 2201.11931 | null | https://arxiv.org/abs/2201.11931v3 | https://arxiv.org/pdf/2201.11931v3.pdf | Fast Interpretable Greedy-Tree Sums | Modern machine learning has achieved impressive prediction performance, but often sacrifices interpretability, a critical consideration in high-stakes domains such as medicine. In such settings, practitioners often use highly interpretable decision tree models, but these suffer from inductive bias against additive stru... | ['Bin Yu', 'Aaron Kornblith', 'Matthew Epland', 'Omer Ronen', 'James Duncan', 'Abhineet Agarwal', 'Keyan Nasseri', 'Chandan Singh', 'Yan Shuo Tan'] | 2022-01-28 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 4.75649208e-01 7.70311058e-01 -6.87221169e-01 -6.34852767e-01
-8.10336113e-01 -6.44509733e-01 7.37450719e-02 -6.86312001e-03
3.77969816e-02 1.17298591e+00 2.69529909e-01 -1.01275170e+00
-6.29234433e-01 -8.45332026e-01 -7.42099643e-01 -5.67250371e-01
-1.12919793e-01 7.65790999e-01 -4.23140734e-01 1.19572669... | [8.35748291015625, 5.536640167236328] |
e35a5860-8baf-491e-8774-04625ea5f977 | learning-to-synthesize-volumetric-meshes-from | 2203.15155 | null | https://arxiv.org/abs/2203.15155v1 | https://arxiv.org/pdf/2203.15155v1.pdf | Learning to Synthesize Volumetric Meshes from Vision-based Tactile Imprints | Vision-based tactile sensors typically utilize a deformable elastomer and a camera mounted above to provide high-resolution image observations of contacts. Obtaining accurate volumetric meshes for the deformed elastomer can provide direct contact information and benefit robotic grasping and manipulation. This paper foc... | ['Jeroen van Baar', 'Masayoshi Tomizuka', 'Siddarth Jain', 'Xinghao Zhu'] | 2022-03-29 | null | null | null | null | ['image-augmentation', 'robotic-grasping'] | ['computer-vision', 'robots'] | [ 8.83402884e-01 1.82646796e-01 3.10902387e-01 -3.86034608e-01
-1.61315039e-01 -4.67507005e-01 1.41269475e-01 -4.68934715e-01
-1.57130644e-01 6.44815505e-01 -5.01745820e-01 3.84370625e-01
-2.49168605e-01 -8.78993094e-01 -1.29189122e+00 -3.85328531e-01
6.23503290e-02 8.13668072e-01 3.17807406e-01 -3.79559360... | [5.882495880126953, -0.8743159770965576] |
a17aba57-6034-4017-abd8-5d877bd6b6db | combinatorial-neural-bandits | 2306.00242 | null | https://arxiv.org/abs/2306.00242v1 | https://arxiv.org/pdf/2306.00242v1.pdf | Combinatorial Neural Bandits | We consider a contextual combinatorial bandit problem where in each round a learning agent selects a subset of arms and receives feedback on the selected arms according to their scores. The score of an arm is an unknown function of the arm's feature. Approximating this unknown score function with deep neural networks, ... | ['Min-hwan Oh', 'Kyuwook Chai', 'TaeHyun Hwang'] | 2023-05-31 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 1.77830160e-01 3.78827304e-01 -6.56558752e-01 -2.82770902e-01
-1.29351151e+00 -7.92997420e-01 -1.06854893e-01 -7.87980109e-03
-6.70098960e-01 9.50437903e-01 -4.40774679e-01 -7.74622262e-01
-1.07945108e+00 -8.98641586e-01 -1.21620977e+00 -9.91688490e-01
-3.46479088e-01 7.30922937e-01 -1.99762255e-01 3.56755257... | [4.641561508178711, 3.3936212062835693] |
411817b4-c996-4baf-a7ef-6959f0ac6087 | randomized-low-rank-dynamic-mode | 1512.03526 | null | http://arxiv.org/abs/1512.03526v1 | http://arxiv.org/pdf/1512.03526v1.pdf | Randomized Low-Rank Dynamic Mode Decomposition for Motion Detection | This paper introduces a fast algorithm for randomized computation of a
low-rank Dynamic Mode Decomposition (DMD) of a matrix. Here we consider this
matrix to represent the development of a spatial grid through time e.g. data
from a static video source. DMD was originally introduced in the fluid
mechanics community, but... | ['N. Benjamin Erichson', 'Carl Donovan'] | 2015-12-11 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 3.53587538e-01 -7.74396777e-01 6.51101589e-01 1.64262667e-01
-3.42718422e-01 -5.38270772e-01 6.97885931e-01 -1.47022441e-01
-5.59322178e-01 4.67725545e-01 -1.66935772e-02 -3.27221930e-01
1.04261972e-01 -5.16014218e-01 -3.94067317e-01 -1.11738908e+00
-2.86258489e-01 2.33003870e-01 6.87004089e-01 1.08650498... | [8.94058609008789, -0.8865518569946289] |
61418cfd-8054-4fb3-a712-20b5a10b90f6 | cost-aware-learning-of-relevant-contextual | 2305.14120 | null | https://arxiv.org/abs/2305.14120v2 | https://arxiv.org/pdf/2305.14120v2.pdf | Cost-aware learning of relevant contextual variables within Bayesian optimization | Contextual Bayesian Optimization (CBO) is a powerful framework for optimizing black-box, expensive-to-evaluate functions with respect to design variables, while simultaneously efficiently integrating relevant contextual information regarding the environment, such as experimental conditions. However, in many practical s... | ['Samuel Kaski', 'Louis Filstroff', 'Sabina Sloman', 'Armi Tiihonen', 'S. T. John', 'Ayush Bharti', 'Julien Martinelli'] | 2023-05-23 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [ 6.55790687e-01 -3.83352369e-01 -1.88844368e-01 -3.26208860e-01
-1.16798651e+00 -6.85526311e-01 4.32765275e-01 3.22451353e-01
-5.29953599e-01 9.05066788e-01 6.19918182e-02 -4.26129758e-01
-5.92506111e-01 -4.51388359e-01 -7.52672315e-01 -9.12214994e-01
-3.51164266e-02 2.26260290e-01 2.05817912e-03 1.59372995... | [6.271945476531982, 3.8784542083740234] |
c2f1b1c8-2850-4c34-a623-6333a8b433a7 | swingnn-rethinking-permutation-invariance-in | 2307.01646 | null | https://arxiv.org/abs/2307.01646v1 | https://arxiv.org/pdf/2307.01646v1.pdf | SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation | Diffusion models based on permutation-equivariant networks can learn permutation-invariant distributions for graph data. However, in comparison to their non-invariant counterparts, we have found that these invariant models encounter greater learning challenges since 1) their effective target distributions exhibit more ... | ['Lele Wang', 'Renjie Liao', 'Yang song', 'Zhengyang Liang', 'Qi Yan'] | 2023-07-04 | null | null | null | null | ['graph-generation'] | ['graphs'] | [ 5.28954089e-01 1.28065899e-01 -6.62879348e-02 -2.40983829e-01
-7.75700331e-01 -5.56038082e-01 5.28138578e-01 -2.84405529e-01
-1.64539695e-01 7.93652594e-01 1.63702860e-01 -4.56125110e-01
-3.23150098e-01 -8.22007179e-01 -9.16341186e-01 -1.23633790e+00
-3.70609730e-01 3.28819752e-01 6.69892691e-03 -1.30295902... | [6.933570384979248, 6.1373443603515625] |
5020dfd6-3f72-49a6-a30c-abcbef816fe4 | knowledge-graph-question-answering-using | 2103.06752 | null | https://arxiv.org/abs/2103.06752v2 | https://arxiv.org/pdf/2103.06752v2.pdf | Knowledge Graph Question Answering using Graph-Pattern Isomorphism | Knowledge Graph Question Answering (KGQA) systems are based on machine learning algorithms, requiring thousands of question-answer pairs as training examples or natural language processing pipelines that need module fine-tuning. In this paper, we present a novel QA approach, dubbed TeBaQA. Our approach learns to answer... | ['Ricardo Usbeck', 'Axel-Cyrille Ngonga Ngomo', 'Hardik Topiwala', 'Diego Moussallem', 'Rricha Jalota', 'Daniel Vollmers'] | 2021-03-11 | null | null | null | null | ['graph-question-answering'] | ['graphs'] | [-2.92839974e-01 3.49506706e-01 2.43712589e-01 -5.00467837e-01
-1.17496693e+00 -9.34840500e-01 4.75592613e-01 6.78670049e-01
-3.30264479e-01 4.66988713e-01 5.54994233e-02 -7.23351896e-01
-3.13053071e-01 -1.41104281e+00 -7.96569943e-01 2.58966386e-01
2.39192732e-02 1.24396598e+00 9.66671586e-01 -7.71754563... | [10.298283576965332, 7.8740129470825195] |
38ac1a01-71b5-4eee-9cf4-f9e8b1e852da | pygod-a-python-library-for-graph-outlier | 2204.12095 | null | https://arxiv.org/abs/2204.12095v1 | https://arxiv.org/pdf/2204.12095v1.pdf | PyGOD: A Python Library for Graph Outlier Detection | PyGOD is an open-source Python library for detecting outliers on graph data. As the first comprehensive library of its kind, PyGOD supports a wide array of leading graph-based methods for node-, edge-, subgraph-, and graph-level outlier detection, under a unified, well-documented API designed for use by both researcher... | ['Philip S. Yu', 'Zhihao Jia', 'George H. Chen', 'Kai Shu', 'Hao Peng', 'Canyu Chen', 'Kaize Ding', 'Ruitong Zhang', 'Xiyang Hu', 'Xueying Ding', 'Yue Zhao', 'Yingtong Dou', 'Kay Liu'] | 2022-04-26 | null | null | null | null | ['graph-outlier-detection'] | ['graphs'] | [-6.97232366e-01 -6.35913387e-02 -1.92724213e-01 -3.47805172e-02
-4.41179216e-01 -5.61499119e-01 2.55267560e-01 6.55562460e-01
2.87711501e-01 3.00275773e-01 3.00841667e-02 -6.06260419e-01
1.69390403e-02 -1.10529983e+00 -4.01598364e-01 -1.42713457e-01
-5.74949086e-01 3.20158869e-01 4.06739384e-01 1.82638168... | [6.874274253845215, 5.6822004318237305] |
03186375-c15e-4f6d-9cec-4b14532cdf75 | how-to-train-your-dragan-a-task-oriented | 2211.10065 | null | https://arxiv.org/abs/2211.10065v1 | https://arxiv.org/pdf/2211.10065v1.pdf | How to train your draGAN: A task oriented solution to imbalanced classification | The long-standing challenge of building effective classification models for small and imbalanced datasets has seen little improvement since the creation of the Synthetic Minority Over-sampling Technique (SMOTE) over 20 years ago. Though GAN based models seem promising, there has been a lack of purpose built architectur... | ['Anh Tuan Luu', 'Andri Ashfahani', 'Leon O. Guertler'] | 2022-11-18 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 1.08674772e-01 2.70079553e-01 -4.39303517e-01 -5.10640442e-01
-9.21189308e-01 -2.64972419e-01 6.83165789e-01 -1.81348659e-02
4.97976951e-02 1.11555398e+00 2.61574805e-01 -1.50477886e-01
5.85608035e-02 -9.43649709e-01 -4.62668747e-01 -5.84425986e-01
2.62431085e-01 8.52918565e-01 -2.39942417e-01 -3.87621313... | [8.856377601623535, 4.335042476654053] |
42c9bf6e-7abd-45a4-b546-6a89a8520afc | simulation-algorithms-for-markovian-and-non | 2302.02812 | null | https://arxiv.org/abs/2302.02812v1 | https://arxiv.org/pdf/2302.02812v1.pdf | Simulation algorithms for Markovian and non-Markovian epidemics | Researchers have employed stochastic simulations to determine the validity of their theoretical findings and to study analytically intractable spreading dynamics. In both cases, the correctness and efficiency of the simulation algorithm are of paramount importance. We prove in this article that the Next Reaction Method... | ['Guohao Dou'] | 2023-02-06 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 7.52186254e-02 -1.96468174e-01 1.77280039e-01 3.29059601e-01
5.11544682e-02 -5.48162103e-01 4.93773520e-01 1.37649819e-01
-7.71478713e-01 9.91479576e-01 -2.87173808e-01 -8.02380919e-01
-5.22946179e-01 -7.63074398e-01 -1.92488894e-01 -7.27802575e-01
-6.66047871e-01 6.20015860e-01 4.58833337e-01 -4.74297941... | [5.975308895111084, 4.404853820800781] |
037871a7-47b5-4284-8bb2-f109293436d1 | liar-liar-pants-on-fire-a-new-benchmark-1 | null | null | https://aclanthology.org/P17-2067 | https://aclanthology.org/P17-2067.pdf | ``Liar, Liar Pants on Fire'': A New Benchmark Dataset for Fake News Detection | Automatic fake news detection is a challenging problem in deception detection, and it has tremendous real-world political and social impacts. However, statistical approaches to combating fake news has been dramatically limited by the lack of labeled benchmark datasets. In this paper, we present LIAR: a new, publicly av... | ['William Yang Wang'] | 2017-07-01 | null | null | null | acl-2017-7 | ['deception-detection'] | ['miscellaneous'] | [-1.56271011e-02 -9.55954101e-03 -7.92847276e-01 -3.11325908e-01
-1.02470446e+00 -8.15588415e-01 1.12642348e+00 3.75666827e-01
-1.67679951e-01 8.95206034e-01 7.73552775e-01 -5.62157631e-01
5.27305841e-01 -7.74241865e-01 -9.87186491e-01 -2.08909482e-01
2.91956007e-01 3.58134300e-01 1.11029841e-01 -7.10928738... | [8.167875289916992, 10.230916023254395] |
370bc393-2853-4a96-845f-1c8cc2fe7c37 | a-cosine-similarity-based-method-for-out-of | 2306.14920 | null | https://arxiv.org/abs/2306.14920v1 | https://arxiv.org/pdf/2306.14920v1.pdf | A Cosine Similarity-based Method for Out-of-Distribution Detection | The ability to detect OOD data is a crucial aspect of practical machine learning applications. In this work, we show that cosine similarity between the test feature and the typical ID feature is a good indicator of OOD data. We propose Class Typical Matching (CTM), a post hoc OOD detection algorithm that uses a cosine ... | ['Hoang Thanh-Tung', 'Khoa D Doan', 'Thanh Nguyen-Tang', 'The-Anh Ta', 'Nguyen Hung-Quang', 'Nguyen Ngoc-Hieu'] | 2023-06-23 | null | null | null | null | ['out-of-distribution-detection'] | ['computer-vision'] | [-9.19692442e-02 -7.55462945e-02 -5.09382427e-01 -5.36446095e-01
-5.19024432e-01 -4.38329577e-01 9.89195764e-01 7.05066025e-01
9.45089310e-02 -4.02965024e-03 1.42532196e-02 2.04638597e-02
-1.56137869e-01 -7.50266969e-01 -1.99863881e-01 -9.04900655e-02
-5.89385808e-01 5.82918823e-01 7.91667044e-01 9.35030058... | [9.193190574645996, 3.1607542037963867] |
87c8a7fa-3585-4d78-921b-879b1224a23d | deep-learning-for-smile-recognition | 1602.00172 | null | http://arxiv.org/abs/1602.00172v2 | http://arxiv.org/pdf/1602.00172v2.pdf | Deep Learning For Smile Recognition | Inspired by recent successes of deep learning in computer vision, we propose
a novel application of deep convolutional neural networks to facial expression
recognition, in particular smile recognition. A smile recognition test accuracy
of 99.45% is achieved for the Denver Intensity of Spontaneous Facial Action
(DISFA) ... | ['Patrick O. Glauner'] | 2016-01-30 | null | null | null | null | ['smile-recognition'] | ['computer-vision'] | [ 7.93147609e-02 -1.32871538e-01 -1.02576390e-01 -6.09126568e-01
-3.07550937e-01 -1.84495281e-02 5.80816090e-01 -4.19784635e-01
-5.03181458e-01 6.10418260e-01 -1.79621339e-01 -9.63363424e-02
1.78963065e-01 -4.32631433e-01 -3.18358392e-01 -9.10679162e-01
-3.29709262e-01 1.90220997e-01 -6.22506261e-01 -2.04409346... | [13.57028579711914, 1.7985819578170776] |
8ee4bb24-d047-4193-a476-0f494c6c27e2 | a-comparative-study-of-pre-trained-speech-and | 2304.11472 | null | https://arxiv.org/abs/2304.11472v1 | https://arxiv.org/pdf/2304.11472v1.pdf | A Comparative Study of Pre-trained Speech and Audio Embeddings for Speech Emotion Recognition | Pre-trained models (PTMs) have shown great promise in the speech and audio domain. Embeddings leveraged from these models serve as inputs for learning algorithms with applications in various downstream tasks. One such crucial task is Speech Emotion Recognition (SER) which has a wide range of applications, including dyn... | ['Rajesh Sharma', 'Arun Balaji Buduru', 'Orchid Chetia Phukan'] | 2023-04-22 | null | null | null | null | ['speaker-recognition', 'speech-emotion-recognition'] | ['speech', 'speech'] | [-1.51922181e-01 -4.77306321e-02 -5.33865299e-03 -4.51880842e-01
-5.93168080e-01 -3.53113830e-01 4.33970153e-01 3.26852083e-01
-4.80008364e-01 8.68529603e-02 4.99909759e-01 -4.84323770e-01
-9.44474861e-02 -3.72541279e-01 -7.94781819e-02 -4.81007457e-01
-2.65896142e-01 3.44203621e-01 -2.17119887e-01 -5.00372708... | [13.771109580993652, 5.885767459869385] |
e23d3a33-4418-480c-a822-ba2d39e7c5e8 | how-multi-is-multi-document-summarization | 2210.12688 | null | https://arxiv.org/abs/2210.12688v1 | https://arxiv.org/pdf/2210.12688v1.pdf | How "Multi" is Multi-Document Summarization? | The task of multi-document summarization (MDS) aims at models that, given multiple documents as input, are able to generate a summary that combines disperse information, originally spread across these documents. Accordingly, it is expected that both reference summaries in MDS datasets, as well as system summaries, woul... | ['Ido Dagan', 'Ori Ernst', 'Arie Cattan', 'Ruben Wolhandler'] | 2022-10-23 | null | null | null | null | ['document-summarization'] | ['natural-language-processing'] | [ 1.55588686e-01 3.54960591e-01 -1.60177499e-01 -1.25372425e-01
-1.33840847e+00 -1.01663947e+00 1.06320512e+00 9.79031026e-01
-1.83419496e-01 8.68466616e-01 1.04527771e+00 -1.57929286e-01
-3.21394682e-01 -7.18854308e-01 -4.83814657e-01 -4.19589758e-01
5.37222885e-02 5.87540507e-01 1.76602930e-01 -1.85394213... | [12.422185897827148, 9.427741050720215] |
bdaad125-0364-4eca-9a04-716649e051ab | writer-aware-cnn-for-parsimonious-hmm-based | 1812.09809 | null | https://arxiv.org/abs/1812.09809v2 | https://arxiv.org/pdf/1812.09809v2.pdf | Writer-Aware CNN for Parsimonious HMM-Based Offline Handwritten Chinese Text Recognition | Recently, the hybrid convolutional neural network hidden Markov model (CNN-HMM) has been introduced for offline handwritten Chinese text recognition (HCTR) and has achieved state-of-the-art performance. However, modeling each of the large vocabulary of Chinese characters with a uniform and fixed number of hidden states... | ['Jia-Ming Wang', 'Zi-Rui Wang', 'Jun Du'] | 2018-12-24 | null | null | null | null | ['handwritten-chinese-text-recognition', 'handwritten-chinese-text-recognition'] | ['computer-vision', 'natural-language-processing'] | [ 1.00669883e-01 -4.63623136e-01 -3.84845704e-01 -3.65897447e-01
-4.01906669e-01 -2.05896348e-01 2.20086351e-01 -5.71766138e-01
-4.91367429e-01 2.95661390e-01 1.61224574e-01 -5.76449275e-01
3.98178995e-01 -4.57098097e-01 -4.02772725e-01 -8.05809200e-01
5.54195285e-01 4.48399872e-01 2.02162966e-01 -1.45887822... | [11.959891319274902, 2.400590181350708] |
232e44ff-4f4e-4359-9176-fd9985583e76 | deam-dialogue-coherence-evaluation-using-amr | 2203.09711 | null | https://arxiv.org/abs/2203.09711v1 | https://arxiv.org/pdf/2203.09711v1.pdf | DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations | Automatic evaluation metrics are essential for the rapid development of open-domain dialogue systems as they facilitate hyper-parameter tuning and comparison between models. Although recently proposed trainable conversation-level metrics have shown encouraging results, the quality of the metrics is strongly dependent o... | ['Nanyun Peng', 'Aram Galstyan', 'Nuan Wen', 'Sarik Ghazarian'] | 2022-03-18 | null | https://aclanthology.org/2022.acl-long.57 | https://aclanthology.org/2022.acl-long.57.pdf | acl-2022-5 | ['dialogue-evaluation', 'coherence-evaluation'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.04077676e-01 6.12670958e-01 4.30145860e-02 -5.08960545e-01
-5.56141257e-01 -7.82091141e-01 1.19505703e+00 1.33607596e-01
-3.25836390e-01 9.45822418e-01 7.56898582e-01 -1.51208594e-01
1.15431190e-01 -6.59954250e-01 1.64857171e-02 -3.88226807e-01
6.73582405e-02 7.31514573e-01 1.90007724e-02 -8.37782919... | [12.706530570983887, 8.14530086517334] |
9a2024b1-e828-432e-92ce-594c65eb9cef | estimates-for-the-branching-factors-of-atari | 2107.02385 | null | https://arxiv.org/abs/2107.02385v2 | https://arxiv.org/pdf/2107.02385v2.pdf | Estimates for the Branching Factors of Atari Games | The branching factor of a game is the average number of new states reachable from a given state. It is a widely used metric in AI research on board games, but less often computed or discussed for videogames. This paper provides estimates for the branching factors of 103 Atari 2600 games, as implemented in the Arcade Le... | ['Mark J. Nelson'] | 2021-07-06 | null | null | null | null | ['board-games'] | ['playing-games'] | [-2.33835727e-01 2.43251994e-01 1.08004939e-02 1.85755998e-01
-5.75274169e-01 -9.34287250e-01 6.99257314e-01 1.31761700e-01
-9.09452617e-01 1.02350354e+00 4.66959290e-02 -7.98401535e-01
-1.68293923e-01 -1.02809370e+00 -3.99246275e-01 -5.65970480e-01
-6.20538235e-01 6.71152472e-01 9.11404312e-01 -4.71634954... | [3.4676663875579834, 1.4844986200332642] |
507960a9-aa19-4891-aa49-da5c37c3ee39 | an-effective-optimization-method-for-neural | null | null | https://aclanthology.org/2020.wat-1.2 | https://aclanthology.org/2020.wat-1.2.pdf | An Effective Optimization Method for Neural Machine Translation: The Case of English-Persian Bilingually Low-Resource Scenario | In this paper, we propose a useful optimization method for low-resource Neural Machine Translation (NMT) by investigating the effectiveness of multiple neural network optimization algorithms. Our results confirm that applying the proposed optimization method on English-Persian translation can exceed translation quality... | ['Raul Aranovich', 'Benyamin Ahmadnia'] | null | null | null | null | aacl-wat-2020-12 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 5.00294685e-01 1.24954499e-01 -6.45874441e-01 -1.88517779e-01
-1.05556965e+00 -2.03129277e-01 5.38709879e-01 -3.36378902e-01
-9.39373493e-01 1.35248399e+00 1.60523206e-01 -1.04962182e+00
1.52021304e-01 -3.15972269e-01 -7.09016800e-01 -2.47383863e-01
6.11487806e-01 1.01101351e+00 -6.77893996e-01 -4.65109795... | [11.582918167114258, 10.349382400512695] |
a203344a-4e97-49ba-ba65-a6b2fdede169 | instance-incremental-scene-graph-generation | 2302.10425 | null | https://arxiv.org/abs/2302.10425v1 | https://arxiv.org/pdf/2302.10425v1.pdf | Instance-incremental Scene Graph Generation from Real-world Point Clouds via Normalizing Flows | This work introduces a new task of instance-incremental scene graph generation: Given an empty room of the point cloud, representing it as a graph and automatically increasing novel instances. A graph denoting the object layout of the scene is finally generated. It is an important task since it helps to guide the inser... | ['Pengxiang Ding', 'Jinghang Xu', 'Jianqin Yin', 'Chao Qi'] | 2023-02-21 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 2.10317001e-01 2.90883332e-01 5.65518618e-01 -2.72415757e-01
-4.85951960e-01 -4.87521619e-01 6.86698437e-01 6.15825541e-02
7.66638741e-02 1.19264916e-01 -2.45035812e-01 -2.69422561e-01
-4.48349863e-02 -1.19476283e+00 -1.07389915e+00 -4.99874085e-01
-2.51851767e-01 1.06212401e+00 3.74308944e-01 1.07799977... | [8.736649513244629, -3.334881544113159] |
98bbc33c-2060-4c8b-a22b-67b7f89f5cda | prediction-calibration-for-generalized-few | 2210.08290 | null | https://arxiv.org/abs/2210.08290v1 | https://arxiv.org/pdf/2210.08290v1.pdf | Prediction Calibration for Generalized Few-shot Semantic Segmentation | Generalized Few-shot Semantic Segmentation (GFSS) aims to segment each image pixel into either base classes with abundant training examples or novel classes with only a handful of (e.g., 1-5) training images per class. Compared to the widely studied Few-shot Semantic Segmentation FSS, which is limited to segmenting nov... | ['Tao Xiang', 'Yi-Zhe Song', 'Da Li', 'Sen He', 'Zhihe Lu'] | 2022-10-15 | null | null | null | null | ['generalized-few-shot-semantic-segmentation'] | ['computer-vision'] | [ 6.88735783e-01 1.48943901e-01 -2.25539893e-01 -4.99291927e-01
-8.45418334e-01 -1.46362111e-01 3.10528010e-01 1.98824748e-01
-5.69512963e-01 5.89917898e-01 -4.37536418e-01 -1.16730288e-01
2.76623238e-02 -8.91419172e-01 -9.28527117e-01 -7.97904491e-01
2.97741324e-01 1.83201700e-01 7.83063352e-01 -1.00339621... | [9.46563720703125, 1.556931495666504] |
86808383-9461-40b2-8304-0d421a930778 | on-the-fly-strategy-adaptation-for-ad-hoc | 2203.08015 | null | https://arxiv.org/abs/2203.08015v1 | https://arxiv.org/pdf/2203.08015v1.pdf | On-the-fly Strategy Adaptation for ad-hoc Agent Coordination | Training agents in cooperative settings offers the promise of AI agents able to interact effectively with humans (and other agents) in the real world. Multi-agent reinforcement learning (MARL) has the potential to achieve this goal, demonstrating success in a series of challenging problems. However, whilst these advanc... | ['Stephen J. Roberts', 'Jack Parker-Holder', 'Jaleh Zand'] | 2022-03-08 | null | null | null | null | ['game-of-hanabi'] | ['playing-games'] | [ 8.13781843e-02 1.56231463e-01 3.96190017e-01 3.95250954e-02
-7.32916951e-01 -5.99819779e-01 9.89394665e-01 1.10104956e-01
-1.02155864e+00 1.28241682e+00 -2.89074928e-01 1.54038042e-01
-3.38470399e-01 -8.11390758e-01 -4.72487807e-01 -1.19141030e+00
-4.89771038e-01 1.43385422e+00 5.35715997e-01 -6.43161714... | [3.7625820636749268, 1.9438815116882324] |
9cfc68dd-508e-4675-a790-cbccf721d966 | development-and-evaluation-of-an-online-home | 2304.11770 | null | https://arxiv.org/abs/2304.11770v1 | https://arxiv.org/pdf/2304.11770v1.pdf | Development and Evaluation of an Online Home Energy Management Strategy for Load Coordination in Smart Homes with Renewable Energy Sources | In this paper, a real time implementable load coordination strategy is developed for the optimization of electric demands in a smart home. The strategy minimizes the electricity cost to the home owner, while limiting the disruptions associated with the deferring of flexible power loads. A multi-objective nonlinear mixe... | ['Stephanie Stockar', 'Rachel Blaser', 'Prasad Dev Hanumalagutti', 'Mithun Goutham', 'Cory Miller', 'Xiaoling Chen'] | 2023-04-23 | null | null | null | null | ['energy-management'] | ['time-series'] | [-1.60721824e-01 3.33372325e-01 7.01827630e-02 1.25350744e-01
-1.30342603e-01 -6.20626390e-01 1.75963506e-01 2.34702304e-01
1.49998352e-01 1.04703152e+00 4.02526222e-02 1.49035260e-01
-8.92606080e-01 -7.86159992e-01 -7.00252652e-02 -1.17531180e+00
-1.99116841e-01 6.37552738e-01 -5.71083128e-01 -2.43516818... | [5.667477607727051, 2.4964075088500977] |
73368113-947f-4369-9d03-420c624cd915 | predicting-affinity-ties-in-a-surname-network | 2306.01218 | null | https://arxiv.org/abs/2306.01218v1 | https://arxiv.org/pdf/2306.01218v1.pdf | Predicting affinity ties in a surname network | From administrative registers of last names in Santiago, Chile, we create a surname affinity network that encodes socioeconomic data. This network is a multi-relational graph with nodes representing surnames and edges representing the prevalence of interactions between surnames by socioeconomic decile. We model the pre... | ['Naim Bro', 'Marcelo Mendoza'] | 2023-06-02 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [-4.20849323e-01 7.07360029e-01 -6.87832952e-01 -2.53547907e-01
1.59528330e-01 -4.53628480e-01 7.72998750e-01 5.49583733e-01
-3.07083189e-01 9.69400287e-01 1.24607420e+00 -4.47111040e-01
-7.75341988e-01 -1.70116496e+00 -5.45379877e-01 -8.99576992e-02
-4.45404798e-01 6.75325334e-01 -3.16250861e-01 -5.32530785... | [7.201411247253418, 6.1627092361450195] |
73a74ff2-972e-406a-af07-28047888a8fc | interpretable-bangla-sarcasm-detection-using | 2303.12772 | null | https://arxiv.org/abs/2303.12772v1 | https://arxiv.org/pdf/2303.12772v1.pdf | Interpretable Bangla Sarcasm Detection using BERT and Explainable AI | A positive phrase or a sentence with an underlying negative motive is usually defined as sarcasm that is widely used in today's social media platforms such as Facebook, Twitter, Reddit, etc. In recent times active users in social media platforms are increasing dramatically which raises the need for an automated NLP-bas... | ['Md. Golam Rabiul Alam', 'Sudipta Mondal', 'Elizabeth Antora Modhu', 'Zeba Tahsin Hossain', 'Tasnim Sakib Apon', 'Ramisa Anan'] | 2023-03-22 | null | null | null | null | ['sarcasm-detection'] | ['natural-language-processing'] | [-1.07060038e-01 3.23506713e-01 -3.88847381e-01 -3.89547676e-01
-3.61063808e-01 -3.81948948e-01 5.16925633e-01 4.44119781e-01
-2.26455361e-01 6.26093268e-01 5.71104586e-01 -3.41761500e-01
1.41352415e-01 -5.54592729e-01 -1.19419686e-01 -4.24257338e-01
7.21144080e-01 1.62855521e-01 -1.17530607e-01 -6.88594341... | [9.125528335571289, 10.558584213256836] |
ce454b4a-58de-4ab8-b7c2-ca7321bae8c5 | reconstruction-and-analysis-of-negatively | 2211.05489 | null | https://arxiv.org/abs/2211.05489v1 | https://arxiv.org/pdf/2211.05489v1.pdf | Reconstruction and analysis of negatively buoyant jets with interpretable machine learning | In this paper, negatively inclined buoyant jets, which appear during the discharge of wastewater from processes such as desalination, are observed. To minimize harmful effects and assess environmental impact, a detailed numerical investigation is necessary. The selection of appropriate geometry and working conditions f... | ['Lado Kranjčević', 'Ante Sikirica', 'Luka Grbčić', 'Marta Alvir'] | 2022-11-10 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [-2.26714373e-01 -3.43868077e-01 1.82446197e-01 -1.30910128e-01
5.68498254e-01 -5.66710472e-01 4.49761689e-01 4.38262373e-01
-1.48949385e-01 1.08692539e+00 1.08261488e-01 -7.34646082e-01
-7.29184985e-01 -8.24463069e-01 -1.69627577e-01 -8.31083179e-01
-3.77383560e-01 1.99438855e-01 -2.46932536e-01 -3.99765491... | [6.304340839385986, 3.1476974487304688] |
24ea09ad-55eb-42da-bfd0-e8a612be5083 | class-specific-anchoring-proposal-for-3d | 1907.09081 | null | https://arxiv.org/abs/1907.09081v1 | https://arxiv.org/pdf/1907.09081v1.pdf | Class-specific Anchoring Proposal for 3D Object Recognition in LIDAR and RGB Images | Detecting objects in a two-dimensional setting is often insufficient in the context of real-life applications where the surrounding environment needs to be accurately recognized and oriented in three-dimension (3D), such as in the case of autonomous driving vehicles. Therefore, accurately and efficiently detecting obje... | ['Humayun Irshad', 'Amir Hossein Raffiee'] | 2019-07-22 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-1.32208064e-01 7.44378865e-02 6.97189569e-02 -9.26142633e-02
-3.88430744e-01 -4.37507570e-01 4.57573503e-01 3.27790260e-01
-4.26175952e-01 1.76328912e-01 -4.74488497e-01 -3.03407073e-01
-2.57796198e-01 -7.89095283e-01 -5.55589736e-01 -7.53919363e-01
-1.51149303e-01 7.24657416e-01 1.00956535e+00 -1.22094832... | [7.871452331542969, -1.1031352281570435] |
f92a7b64-b496-49b4-9817-d162d16cadf6 | no-more-reviewer-2-subverting-automatic-paper | 2303.14443 | null | https://arxiv.org/abs/2303.14443v1 | https://arxiv.org/pdf/2303.14443v1.pdf | No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning | The number of papers submitted to academic conferences is steadily rising in many scientific disciplines. To handle this growth, systems for automatic paper-reviewer assignments are increasingly used during the reviewing process. These systems use statistical topic models to characterize the content of submissions and ... | ['Konrad Rieck', 'Thorsten Holz', 'Doreen Riepel', 'Jonas Möller', 'Erwin Quiring', 'Thorsten Eisenhofer'] | 2023-03-25 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [ 1.48613602e-01 1.54304847e-01 3.93241458e-02 -5.50003611e-02
-9.73260999e-01 -1.34156120e+00 4.89650011e-01 1.90411910e-01
-3.70773792e-01 8.43390763e-01 -6.09084606e-01 -4.87918258e-01
-1.51782379e-01 -7.46872127e-01 -9.67983603e-01 -4.43434864e-01
2.17176840e-01 7.00260580e-01 3.43725383e-01 1.61988229... | [5.942873477935791, 7.886735439300537] |
8dcfcd43-3cfd-4c4b-931f-e0308d90dc4a | learning-to-automatically-solve-algebra-word | null | null | https://aclanthology.org/P14-1026 | https://aclanthology.org/P14-1026.pdf | Learning to Automatically Solve Algebra Word Problems | null | ['Nate Kushman', 'Luke Zettlemoyer', 'Regina Barzilay', 'Yoav Artzi'] | 2014-06-01 | null | null | null | acl-2014-6 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [-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... | [-7.2327189445495605, 3.5093634128570557] |
480b4d94-f840-4026-ab92-c0e184616772 | mutual-information-estimation-as-a-difference | null | null | https://openreview.net/forum?id=J7FaSJw-xCM | https://openreview.net/pdf?id=J7FaSJw-xCM | Mutual Information Estimation as a Difference of Entropies for Unsupervised Representation Learning | Contrastive loss has been successfully exploited in the latest visual unsupervised representation learning methods. Contrastive loss is based on a lower-bound estimation of mutual information where its known limitations include batch size dependency expressed as $O(log (n))$. It is also commonly known as negative sampl... | ['Wonjong Rhee', 'Jaeill Kim'] | 2021-09-29 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 3.41732353e-01 2.58328170e-01 2.93958616e-02 -6.87205791e-02
-6.78655267e-01 -4.34185535e-01 4.67879087e-01 3.52071226e-01
-7.88464189e-01 8.93454254e-01 -3.37395489e-01 -2.04596207e-01
-3.53676468e-01 -7.21267819e-01 -6.39303625e-01 -9.79701877e-01
-5.73122621e-01 2.28857666e-01 2.45600015e-01 -1.77884847... | [7.554877281188965, 3.958195447921753] |
70b96eda-f02a-401d-946d-858d5dd791c7 | towards-robust-ranker-for-text-retrieval | 2206.08063 | null | https://arxiv.org/abs/2206.08063v1 | https://arxiv.org/pdf/2206.08063v1.pdf | Towards Robust Ranker for Text Retrieval | A ranker plays an indispensable role in the de facto 'retrieval & rerank' pipeline, but its training still lags behind -- learning from moderate negatives or/and serving as an auxiliary module for a retriever. In this work, we first identify two major barriers to a robust ranker, i.e., inherent label noises caused by a... | ['Daxin Jiang', 'Binxing Jiao', 'Guodong Long', 'Can Xu', 'Chongyang Tao', 'Xiubo Geng', 'Tao Shen', 'Yucheng Zhou'] | 2022-06-16 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [ 6.22096211e-02 -3.43913287e-01 -1.46285862e-01 -1.44982159e-01
-1.82185924e+00 -8.83103549e-01 6.83572233e-01 -1.10225417e-02
-5.06712198e-01 9.82188344e-01 3.29494536e-01 -1.04554519e-01
-2.25042373e-01 -5.07687569e-01 -7.84919381e-01 -8.43558788e-01
1.49250776e-01 1.18274271e+00 4.75066721e-01 -8.10959637... | [11.45163631439209, 7.591207027435303] |
b2e87300-7204-48b4-bdc5-312b92018413 | what-would-elsa-do-freezing-layers-during | 1911.03090 | null | https://arxiv.org/abs/1911.03090v1 | https://arxiv.org/pdf/1911.03090v1.pdf | What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning | Pretrained transformer-based language models have achieved state of the art across countless tasks in natural language processing. These models are highly expressive, comprising at least a hundred million parameters and a dozen layers. Recent evidence suggests that only a few of the final layers need to be fine-tuned f... | ['Raphael Tang', 'Jaejun Lee', 'Jimmy Lin'] | 2019-11-08 | null | null | null | null | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 1.17030501e-01 6.14320338e-02 7.37421215e-02 -8.03706467e-01
-9.59830225e-01 -7.98134387e-01 6.76619530e-01 2.27256075e-01
-9.46585059e-01 5.13087928e-01 4.19408888e-01 -5.83499014e-01
-1.05189653e-02 -5.96131921e-01 -7.15285301e-01 -1.41548276e-01
3.47309411e-01 5.92459381e-01 2.15871930e-01 -7.22067773... | [10.702439308166504, 8.694705963134766] |
5c94bca3-1dbf-4d08-8316-6b0c34a3aeab | cns-net-conservative-novelty-synthesizing | 2305.01236 | null | https://arxiv.org/abs/2305.01236v1 | https://arxiv.org/pdf/2305.01236v1.pdf | CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set Scenario | We study the challenging task of malware recognition on both known and novel unknown malware families, called malware open-set recognition (MOSR). Previous works usually assume the malware families are known to the classifier in a close-set scenario, i.e., testing families are the subset or at most identical to trainin... | ['Yuanyuan Xu', 'Yuxia Sun', 'Shiheng Ma', 'Song Guo', 'Jingcai Guo'] | 2023-05-02 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 3.28867525e-01 -4.83717799e-01 -1.65286541e-01 -2.06791729e-01
-2.50890374e-01 -8.66292357e-01 4.99040186e-01 -2.50720624e-02
1.47413209e-01 7.92742968e-01 -6.15397096e-01 -6.88374639e-01
2.03395754e-01 -7.93227792e-01 -9.38039482e-01 -7.40782559e-01
-8.82702172e-02 4.20068771e-01 3.68795365e-01 -1.13172874... | [14.391059875488281, 9.63888168334961] |
bc2e28a2-c0bc-4d90-b605-a2de8b463a16 | a-multi-modal-method-for-satire-detection | 2010.06671 | null | https://arxiv.org/abs/2010.06671v1 | https://arxiv.org/pdf/2010.06671v1.pdf | A Multi-Modal Method for Satire Detection using Textual and Visual Cues | Satire is a form of humorous critique, but it is sometimes misinterpreted by readers as legitimate news, which can lead to harmful consequences. We observe that the images used in satirical news articles often contain absurd or ridiculous content and that image manipulation is used to create fictional scenarios. While ... | ['David A. Broniatowski', 'Pedram Hosseini', 'Or Levi', 'Lily Li'] | 2020-10-13 | null | https://aclanthology.org/2020.nlp4if-1.4 | https://aclanthology.org/2020.nlp4if-1.4.pdf | nlp4if-coling-2020-12 | ['image-forensics', 'satire-detection'] | ['computer-vision', 'natural-language-processing'] | [ 3.33711296e-01 -7.63280615e-02 1.16074823e-01 -8.55281055e-02
-8.08075428e-01 -9.84687924e-01 1.23182547e+00 -5.02470466e-05
-3.79199326e-01 2.38236859e-01 8.09152007e-01 -4.57658470e-01
5.95521867e-01 -5.82717955e-01 -8.07197630e-01 -4.25838709e-01
7.49715030e-01 3.56107801e-01 1.18937530e-01 -7.87633836... | [8.20261001586914, 10.25905990600586] |
34c2b0e8-fbdc-4ee5-963e-9c3c413ed7a1 | adbert-an-effective-few-shot-learning | null | null | https://aclanthology.org/2022.wnut-1.19 | https://aclanthology.org/2022.wnut-1.19.pdf | AdBERT: An Effective Few Shot Learning Framework for Aligning Tweets to Superbowl Advertisements | The tremendous increase in social media usage for sharing Television (TV) experiences has provided a unique opportunity in the Public Health and Marketing sectors to understand viewer engagement and attitudes through viewer-generated content on social media. However, this opportunity also comes with associated technica... | ['Jaideep Srivastava', 'Jisu Huh', 'Maral Abdollahi', 'Roopana Chenchu', 'Debarati Das'] | null | null | null | null | coling-wnut-2022-10 | ['marketing'] | ['miscellaneous'] | [ 1.13806836e-01 2.94961095e-01 -3.28534037e-01 -6.78206027e-01
-9.88976717e-01 -3.77206415e-01 7.26769447e-01 4.73803133e-01
-2.85190612e-01 3.66151065e-01 5.82118511e-01 -4.03350405e-02
-3.45653594e-02 -1.00260806e+00 -5.99084795e-01 -2.15663791e-01
-1.24797739e-01 4.60791379e-01 5.41284919e-01 -7.36425281... | [10.264885902404785, 6.605899333953857] |
ebee31a2-462d-4bfc-8d46-e91831f6be1c | factual-and-informative-review-generation-for | 2209.12613 | null | https://arxiv.org/abs/2209.12613v2 | https://arxiv.org/pdf/2209.12613v2.pdf | Factual and Informative Review Generation for Explainable Recommendation | Recent models can generate fluent and grammatical synthetic reviews while accurately predicting user ratings. The generated reviews, expressing users' estimated opinions towards related products, are often viewed as natural language 'rationales' for the jointly predicted rating. However, previous studies found that exi... | ['Bodhisattwa Prasad Majumder', 'Julian McAuley', 'Sameer Singh', 'Zhouhang Xie'] | 2022-09-12 | null | null | null | null | ['review-generation'] | ['natural-language-processing'] | [ 1.01593271e-01 1.04619622e+00 -2.14366361e-01 -7.59719372e-01
-1.00699651e+00 -6.91368520e-01 6.61128819e-01 6.90329894e-02
2.27171868e-01 1.09880698e+00 8.66882980e-01 -1.31762803e-01
4.07620132e-01 -6.30167723e-01 -6.74941480e-01 -2.29934119e-02
4.31580693e-01 6.56647861e-01 -5.06080806e-01 -5.40381312... | [11.952038764953613, 8.794425964355469] |
a06faee4-a3d1-42d7-bc86-6ead19e8166e | optimization-based-eye-tracking-using | 2303.04997 | null | https://arxiv.org/abs/2303.04997v1 | https://arxiv.org/pdf/2303.04997v1.pdf | Optimization-Based Eye Tracking using Deflectometric Information | Eye tracking is an important tool with a wide range of applications in Virtual, Augmented, and Mixed Reality (VR/AR/MR) technologies. State-of-the-art eye tracking methods are either reflection-based and track reflections of sparse point light sources, or image-based and exploit 2D features of the acquired eye image. I... | ['Florian Willomitzer', 'Oliver Cossairt', 'Jiazhang Wang', 'Tianfu Wang'] | 2023-03-09 | null | null | null | null | ['mixed-reality', 'inverse-rendering'] | ['computer-vision', 'computer-vision'] | [ 2.40934432e-01 -1.53547257e-01 5.17811120e-01 -1.06909297e-01
-4.48374450e-01 -2.62571871e-01 3.30315560e-01 -4.70309466e-01
-3.57536852e-01 5.17544091e-01 -4.91199642e-02 -3.58362377e-01
1.32216334e-01 -3.71702790e-01 -7.27388620e-01 -5.38324177e-01
3.79680365e-01 1.50341406e-01 2.38820732e-01 -4.70456868... | [9.529472351074219, -2.9463565349578857] |
f902f257-f34a-4352-85c0-f231bf625250 | interpretable-battery-cycle-life-range | 2204.12420 | null | https://arxiv.org/abs/2204.12420v2 | https://arxiv.org/pdf/2204.12420v2.pdf | Interpretable Battery Cycle Life Range Prediction Using Early Degradation Data at Cell Level | Battery cycle life prediction using early degradation data has many potential applications throughout the battery product life cycle. For that reason, various data-driven methods have been proposed for point prediction of battery cycle life with minimum knowledge of the battery degradation mechanisms. However, managing... | ['Sebastien Gros', 'Torsten Wik', 'Faisal Altaf', 'Yang Su', 'Huang Zhang'] | 2022-04-26 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 2.26517528e-01 -1.55335531e-01 -4.17090565e-01 -5.52078545e-01
-6.21560097e-01 -4.92120624e-01 3.03416759e-01 4.19715703e-01
-7.34429806e-02 1.21873164e+00 -1.52817499e-02 -6.56597853e-01
-8.35256100e-01 -8.93718362e-01 -6.73778713e-01 -9.09157693e-01
-4.23421934e-02 4.34199810e-01 1.75033048e-01 -2.94008292... | [6.413240909576416, 2.862715721130371] |
71f6869e-7643-40c2-a933-3b9ec6cbc9ec | an-empirical-study-of-pre-trained-language | 2303.10368 | null | https://arxiv.org/abs/2303.10368v1 | https://arxiv.org/pdf/2303.10368v1.pdf | An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question Answering | Large-scale pre-trained language models (PLMs) such as BERT have recently achieved great success and become a milestone in natural language processing (NLP). It is now the consensus of the NLP community to adopt PLMs as the backbone for downstream tasks. In recent works on knowledge graph question answering (KGQA), BER... | ['Zafar Ali', 'Jeff Z. Pan', 'Jiaoyan Chen', 'Dehai Min', 'Guilin Qi', 'Yike Wu', 'Nan Hu'] | 2023-03-18 | null | null | null | null | ['graph-question-answering'] | ['graphs'] | [-3.71524692e-01 3.96802366e-01 -1.76409721e-01 -3.35702807e-01
-1.04823792e+00 -7.58168519e-01 5.90156972e-01 3.50704581e-01
-5.79631805e-01 8.55478168e-01 3.78884017e-01 -5.23350894e-01
-1.89979747e-01 -1.27777898e+00 -8.42905283e-01 -3.08733761e-01
1.04843974e-01 9.86700475e-01 6.67847574e-01 -7.51797497... | [10.513479232788086, 7.960515975952148] |
2c73c34d-077f-4baa-85a7-66eadad25c34 | learning-from-video-and-text-via-large-scale | 1707.09074 | null | http://arxiv.org/abs/1707.09074v1 | http://arxiv.org/pdf/1707.09074v1.pdf | Learning from Video and Text via Large-Scale Discriminative Clustering | Discriminative clustering has been successfully applied to a number of
weakly-supervised learning tasks. Such applications include person and action
recognition, text-to-video alignment, object co-segmentation and colocalization
in videos and images. One drawback of discriminative clustering, however, is
its limited sc... | ['Piotr Bojanowski', 'Jean-Baptiste Alayrac', 'Antoine Miech', 'Ivan Laptev', 'Josef Sivic'] | 2017-07-27 | learning-from-video-and-text-via-large-scale-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Miech_Learning_From_Video_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Miech_Learning_From_Video_ICCV_2017_paper.pdf | iccv-2017-10 | ['weakly-supervised-action-recognition', 'video-alignment'] | ['computer-vision', 'computer-vision'] | [ 4.00610596e-01 -4.16856170e-01 -3.60288948e-01 -5.70020378e-01
-8.90725315e-01 -6.56512201e-01 4.93821830e-01 -2.83293496e-03
-5.93896985e-01 5.20916939e-01 3.14540625e-01 1.62074283e-01
-2.37805724e-01 -1.20561505e-02 -4.14006442e-01 -8.18210363e-01
-1.31374016e-01 5.94776690e-01 4.04546529e-01 4.08988655... | [8.6232271194458, 0.3817332983016968] |
11871f1f-f56f-4f37-9973-d3abb5b7f5ce | bl-research-at-semeval-2022-task-8-using | null | null | https://aclanthology.org/2022.semeval-1.173 | https://aclanthology.org/2022.semeval-1.173.pdf | BL.Research at SemEval-2022 Task 8: Using various Semantic Information to evaluate document-level Semantic Textual Similarity | This paper presents our system for document-level semantic textual similarity (STS) evaluation at SemEval-2022 Task 8: “Multilingual News Article Similarity”. The semantic information used is obtained by using different semantic models ranging from the extraction of key terms and named entities to the document classifi... | ['Youssef Miloudi', 'Christophe Bortolaso', 'Mokhtar Boumedyen Billami', 'Camille Gosse', 'Karim Boutamine', 'Mohamed Mehdi Kandi', 'Sebastien Dufour'] | null | null | null | null | semeval-naacl-2022-7 | ['document-classification'] | ['natural-language-processing'] | [ 2.53721252e-02 2.08943635e-01 -8.72176364e-02 -5.00546396e-01
-8.71989429e-01 -5.97719371e-01 1.26841617e+00 1.19291615e+00
-8.12106729e-01 6.78515017e-01 8.97054851e-01 1.82243988e-01
-3.36325675e-01 -4.49520171e-01 -3.35204333e-01 -8.37851875e-03
2.55455881e-01 7.53676891e-01 5.21960914e-01 -7.37973928... | [10.904192924499512, 9.313958168029785] |
fa7e47ee-b4d5-442f-8e3a-c8d3743a9aca | octree-guided-cnn-with-spherical-kernels-for | 1903.00343 | null | http://arxiv.org/abs/1903.00343v1 | http://arxiv.org/pdf/1903.00343v1.pdf | Octree guided CNN with Spherical Kernels for 3D Point Clouds | We propose an octree guided neural network architecture and spherical
convolutional kernel for machine learning from arbitrary 3D point clouds. The
network architecture capitalizes on the sparse nature of irregular point
clouds, and hierarchically coarsens the data representation with space
partitioning. At the same ti... | ['Huan Lei', 'Ajmal Mian', 'Naveed Akhtar'] | 2019-02-28 | octree-guided-cnn-with-spherical-kernels-for-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Lei_Octree_Guided_CNN_With_Spherical_Kernels_for_3D_Point_Clouds_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Lei_Octree_Guided_CNN_With_Spherical_Kernels_for_3D_Point_Clouds_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-object-classification', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [-2.79965073e-01 -3.43340635e-02 -1.73127893e-02 -3.21581990e-01
-1.37751326e-01 -5.22177279e-01 5.97901702e-01 2.34650075e-01
-2.91754752e-01 5.82195260e-03 -2.84385234e-01 -2.47928023e-01
-3.97516489e-01 -1.14842296e+00 -9.75539863e-01 -4.77652639e-01
-4.22301233e-01 7.82338560e-01 4.20390606e-01 -1.73988193... | [7.943143367767334, -3.648979425430298] |
3cb17e4e-af01-434d-a9e6-8a372b915908 | ditto-in-the-house-building-articulation | 2302.01295 | null | https://arxiv.org/abs/2302.01295v1 | https://arxiv.org/pdf/2302.01295v1.pdf | Ditto in the House: Building Articulation Models of Indoor Scenes through Interactive Perception | Virtualizing the physical world into virtual models has been a critical technique for robot navigation and planning in the real world. To foster manipulation with articulated objects in everyday life, this work explores building articulation models of indoor scenes through a robot's purposeful interactions in these sce... | ['Yuke Zhu', 'Zhenyu Jiang', 'Cheng-Chun Hsu'] | 2023-02-02 | null | null | null | null | ['robot-navigation'] | ['robots'] | [-5.24124615e-02 4.22251999e-01 -3.19163352e-02 -2.13591173e-01
-2.32566014e-01 -6.66833162e-01 5.78241765e-01 -6.30767941e-02
1.71201676e-01 3.35418820e-01 3.46858233e-01 -4.86157566e-01
-4.46957722e-02 -7.71900177e-01 -7.77211070e-01 -1.08352192e-01
-3.11457813e-01 8.98955524e-01 4.06759381e-01 -4.31907475... | [4.782026290893555, 0.5165302753448486] |
963bb0b5-8824-47bf-8875-50a692f78aec | cycnn-a-rotation-invariant-cnn-using-polar | 2007.10588 | null | https://arxiv.org/abs/2007.10588v1 | https://arxiv.org/pdf/2007.10588v1.pdf | CyCNN: A Rotation Invariant CNN using Polar Mapping and Cylindrical Convolution Layers | Deep Convolutional Neural Networks (CNNs) are empirically known to be invariant to moderate translation but not to rotation in image classification. This paper proposes a deep CNN model, called CyCNN, which exploits polar mapping of input images to convert rotation to translation. To deal with the cylindrical property ... | ['Jaejin Lee', 'Hyungmo Kim', 'Wooekun Jung', 'Jinpyo Kim'] | 2020-07-21 | null | null | null | null | ['rotated-mnist'] | ['computer-vision'] | [-1.24213777e-01 1.20706499e-01 -3.01904052e-01 -3.15544903e-01
1.76584020e-01 -6.65839434e-01 7.49677062e-01 -5.87643743e-01
-6.63349032e-01 4.10216510e-01 9.66363773e-02 -5.90992332e-01
2.81778872e-01 -7.85024166e-01 -1.06225193e+00 -5.37869036e-01
1.45325094e-01 -8.52479860e-02 2.19865143e-01 -1.35856792... | [8.968255996704102, 2.322899341583252] |
4f7a100f-d576-4ca3-a63c-6e9dc2104bda | any-speaker-adaptive-text-to-speech-synthesis | 2211.09383 | null | https://arxiv.org/abs/2211.09383v2 | https://arxiv.org/pdf/2211.09383v2.pdf | Grad-StyleSpeech: Any-speaker Adaptive Text-to-Speech Synthesis with Diffusion Models | There has been a significant progress in Text-To-Speech (TTS) synthesis technology in recent years, thanks to the advancement in neural generative modeling. However, existing methods on any-speaker adaptive TTS have achieved unsatisfactory performance, due to their suboptimal accuracy in mimicking the target speakers' ... | ['Sung Ju Hwang', 'Dongchan Min', 'Minki Kang'] | 2022-11-17 | null | null | null | null | ['text-to-speech-synthesis'] | ['speech'] | [ 1.18108466e-03 3.56090404e-02 1.63335335e-02 -5.44189990e-01
-1.24417305e+00 -4.87566471e-01 7.68282533e-01 -6.29429460e-01
5.70301414e-02 5.18774211e-01 7.36465037e-01 -4.48989719e-01
5.58241546e-01 -2.12291017e-01 -4.49911475e-01 -7.11493850e-01
4.59908038e-01 5.55126011e-01 1.22685134e-01 -4.08043057... | [14.934053421020508, 6.562429428100586] |
a1592792-1364-42b9-8087-0d5f4ef5be41 | genas-neural-architecture-search-with-better | 2305.08611 | null | https://arxiv.org/abs/2305.08611v2 | https://arxiv.org/pdf/2305.08611v2.pdf | GeNAS: Neural Architecture Search with Better Generalization | Neural Architecture Search (NAS) aims to automatically excavate the optimal network architecture with superior test performance. Recent neural architecture search (NAS) approaches rely on validation loss or accuracy to find the superior network for the target data. In this paper, we investigate a new neural architectur... | ['Youngjoon Yoo', 'Dongyoon Han', 'Geondo Park', 'Joonsang Yu', 'JoonHyun Jeong'] | 2023-05-15 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-1.35029852e-03 9.72453132e-02 -9.77520347e-02 -4.05674934e-01
-5.59447289e-01 -5.73886812e-01 1.55762687e-01 -1.20726079e-01
-3.83243889e-01 4.97123003e-01 -2.23485008e-01 -3.94378811e-01
-5.87843120e-01 -6.73988819e-01 -7.01913297e-01 -6.99539721e-01
-4.41252850e-02 4.48781043e-01 2.39376739e-01 -2.64948029... | [8.54012680053711, 3.2540669441223145] |
7897cffc-c1ce-4736-b74d-3829d72e5834 | theoretical-analysis-on-the-efficiency-of | 2306.10023 | null | https://arxiv.org/abs/2306.10023v1 | https://arxiv.org/pdf/2306.10023v1.pdf | Theoretical Analysis on the Efficiency of Interleaved Comparisons | This study presents a theoretical analysis on the efficiency of interleaving, an efficient online evaluation method for rankings. Although interleaving has already been applied to production systems, the source of its high efficiency has not been clarified in the literature. Therefore, this study presents a theoretical... | ['Makoto P. Kato', 'Hajime Morita', 'Kojiro Iizuka'] | 2023-05-31 | null | null | null | null | ['user-simulation'] | ['natural-language-processing'] | [-2.22902030e-01 -3.63527894e-01 -3.36830825e-01 -8.12217668e-02
-1.39500663e-01 -8.56219351e-01 2.29847729e-01 2.94948846e-01
-3.95590663e-01 7.41473258e-01 -2.45585993e-01 -8.13594222e-01
-6.54122472e-01 -7.16725826e-01 -6.68125629e-01 -1.10510632e-01
-5.64210594e-01 4.59230423e-01 5.00313997e-01 -2.43758261... | [9.738710403442383, 5.830741882324219] |
a5274c86-992a-499f-a149-2cd4e7e097b0 | a-full-image-full-resolution-end-to-end | 1909.06751 | null | https://arxiv.org/abs/1909.06751v1 | https://arxiv.org/pdf/1909.06751v1.pdf | A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection | Due to limited computational and memory resources, current deep learning models accept only rather small images in input, calling for preliminary image resizing. This is not a problem for high-level vision problems, where discriminative features are barely affected by resizing. On the contrary, in image forensics, resi... | ['Luisa Verdoliva', 'Diego Gragnaniello', 'Giovanni Poggi', 'Francesco Marra'] | 2019-09-15 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 3.36969137e-01 3.61329205e-02 1.68429524e-01 -1.56753749e-01
-7.40437150e-01 -3.50338489e-01 4.96424288e-01 2.61806577e-01
-9.42979693e-01 4.52477515e-01 -1.72874376e-01 -2.87465751e-01
1.44239381e-01 -6.90826774e-01 -9.46982324e-01 -7.39108920e-01
1.67362273e-01 2.63504069e-02 3.01401287e-01 3.97340171... | [12.357213973999023, 0.9480480551719666] |
316dd4bf-c5b5-4c34-960b-00d70bc09600 | the-change-that-matters-in-discourse-parsing-1 | 2203.11317 | null | https://arxiv.org/abs/2203.11317v1 | https://arxiv.org/pdf/2203.11317v1.pdf | The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error | Discourse analysis allows us to attain inferences of a text document that extend beyond the sentence-level. The current performance of discourse models is very low on texts outside of the training distribution's coverage, diminishing the practical utility of existing models. There is need for a measure that can inform ... | ['Malihe Alikhani', 'Seong Jae Hwang', 'Anthony Sicilia', 'Katherine Atwell'] | 2022-03-21 | null | https://aclanthology.org/2022.findings-acl.68 | https://aclanthology.org/2022.findings-acl.68.pdf | findings-acl-2022-5 | ['discourse-parsing'] | ['natural-language-processing'] | [ 3.28056514e-01 4.57049012e-01 -5.91503263e-01 -5.67179799e-01
-1.00010121e+00 -8.33975136e-01 8.79782319e-01 3.78324807e-01
-5.26656508e-01 1.20330381e+00 6.57415211e-01 -5.61460495e-01
-1.47208989e-01 -6.06043994e-01 -7.74196506e-01 -5.50813377e-01
1.85630187e-01 6.56723380e-01 3.39714080e-01 -2.57774383... | [10.99146842956543, 9.076759338378906] |
ea27eaaa-e393-491c-ad79-2622bb8dad9c | one-shot-object-localization-in-medical | 2012.07043 | null | https://arxiv.org/abs/2012.07043v2 | https://arxiv.org/pdf/2012.07043v2.pdf | Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images | Deep learning networks have shown promising performance for accurate object localization in medial images, but require large amount of annotated data for supervised training, which is expensive and expertise burdensome. To address this problem, we present a one-shot framework for organ and landmark localization in volu... | ['Guotai Wang', 'Shaoting Zhang', 'Hao Fu', 'Ran Gu', 'Wei Xu', 'Wenhui Lei'] | 2020-12-13 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [-8.21518451e-02 2.49295443e-01 -2.09795877e-01 -4.11581904e-01
-1.01311004e+00 -3.27019453e-01 2.98657030e-01 3.45805705e-01
-3.55726898e-01 3.96215290e-01 -3.78992371e-02 5.09655885e-02
5.16439788e-02 -8.31947863e-01 -7.74881184e-01 -8.70308280e-01
-1.31665939e-03 7.79196560e-01 5.20203233e-01 2.24919766... | [14.788803100585938, -2.30372953414917] |
81f19c1b-8bab-4e08-93ff-4b343536a5a3 | what-is-wrong-with-you-leveraging-user | 2203.13927 | null | https://arxiv.org/abs/2203.13927v1 | https://arxiv.org/pdf/2203.13927v1.pdf | What is wrong with you?: Leveraging User Sentiment for Automatic Dialog Evaluation | Accurate automatic evaluation metrics for open-domain dialogs are in high demand. Existing model-based metrics for system response evaluation are trained on human annotated data, which is cumbersome to collect. In this work, we propose to use information that can be automatically extracted from the next user utterance,... | ['Dilek Hakkani-Tur', 'Yang Liu', 'Alexandros Papangelis', 'Behnam Hedayatnia', 'Sarik Ghazarian'] | 2022-03-25 | null | https://aclanthology.org/2022.findings-acl.331 | https://aclanthology.org/2022.findings-acl.331.pdf | findings-acl-2022-5 | ['dialogue-evaluation', 'open-domain-dialog'] | ['natural-language-processing', 'natural-language-processing'] | [-1.44309610e-01 3.90452981e-01 -1.36530250e-01 -9.02862132e-01
-1.02464092e+00 -9.65181410e-01 5.32495797e-01 3.84221584e-01
-5.13745606e-01 8.87429237e-01 5.93010008e-01 -2.79733986e-01
4.60690171e-01 -4.12802190e-01 1.99495535e-02 -8.30257088e-02
4.65244323e-01 8.11731219e-01 2.51955748e-01 -7.32226968... | [12.855216026306152, 7.957503318786621] |
f2ebd3ee-9a71-4711-9d51-0c993081e21b | towards-end-to-end-handwritten-document | 2209.15362 | null | https://arxiv.org/abs/2209.15362v2 | https://arxiv.org/pdf/2209.15362v2.pdf | Towards End-to-end Handwritten Document Recognition | Handwritten text recognition has been widely studied in the last decades for its numerous applications. Nowadays, the state-of-the-art approach consists in a three-step process. The document is segmented into text lines, which are then ordered and recognized. However, this three-step approach has many drawbacks. The th... | ['Denis Coquenet'] | 2022-09-30 | null | null | null | null | ['handwriting-recognition', 'handwritten-document-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.24108928e-01 -2.32534096e-01 1.79089844e-01 -4.87992108e-01
-4.77922469e-01 -6.93323851e-01 8.42732966e-01 3.36493522e-01
-5.92870593e-01 6.07975245e-01 -1.63764432e-01 -2.43717924e-01
-3.18259448e-01 -6.89364910e-01 -6.18369639e-01 -4.80280012e-01
2.52444148e-01 7.51490951e-01 4.70242858e-01 1.19178481... | [11.711353302001953, 2.5666182041168213] |
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