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a2555879-c8de-4e98-a179-878e911fed53 | honestbait-headline-generation-via-faithful | null | null | https://openreview.net/forum?id=xfBbBwOkwgQ | https://openreview.net/pdf?id=xfBbBwOkwgQ | HonestBait: Headline Generation via Faithful Forward Reference | Current methods for generating attractive headlines often learn directly from data, which bases attractiveness on the number of user clicks and views. Although clicks or views do reflect user interest, they can fail to reveal how much interest is raised by the writing style and how much is caused by the event or topic ... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['headline-generation'] | ['natural-language-processing'] | [-1.03789873e-01 4.92946893e-01 -1.22007251e-01 -4.79107320e-01
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1.79386407e-01 1.16021246e-01 2.71566898e-01 -6.10163510... | [12.082602500915527, 9.104093551635742] |
02a22902-03e2-44be-8ff2-35be27fe45c2 | give-me-more-feedback-annotating-argument | null | null | https://aclanthology.org/P18-1058 | https://aclanthology.org/P18-1058.pdf | Give Me More Feedback: Annotating Argument Persuasiveness and Related Attributes in Student Essays | While argument persuasiveness is one of the most important dimensions of argumentative essay quality, it is relatively little studied in automated essay scoring research. Progress on scoring argument persuasiveness is hindered in part by the scarcity of annotated corpora. We present the first corpus of essays that are ... | ['Nishant Gurrapadi', 'Vincent Ng', 'Zixuan Ke', 'Winston Carlile'] | 2018-07-01 | null | null | null | acl-2018-7 | ['automated-essay-scoring'] | ['natural-language-processing'] | [ 1.39524117e-01 7.27524817e-01 -5.75681448e-01 -4.67191398e-01
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7.78503299e-01 5.08227706e-01 1.28279313e-01 -3.61731172... | [11.239754676818848, 9.263590812683105] |
6c082703-f58d-4a46-bb08-d05f1dd6cb85 | structured-and-natural-responses-co | null | null | https://dl.acm.org/doi/abs/10.1145/3477495.3532063 | https://yecchen.github.io/paper/RERG_mm22.pdf | Structured and Natural Responses Co-generation for Conversational Search | Generating fluent and informative natural responses while maintaining representative internal states for search optimization is critical for conversational search systems. Existing approaches either 1) predict structured dialog acts first and then generate natural response; or 2) map conversation context to natural res... | ['Tat-Seng Chua', 'Wei Ji', 'Fuli Feng', 'Lizi Liao', 'Chenchen Ye'] | 2022-07-07 | null | null | null | acm-sigir-conference-on-research-and | ['conversational-search'] | ['natural-language-processing'] | [ 4.41424489e-01 5.44030011e-01 -3.31404805e-01 -6.09423041e-01
-7.79784620e-01 -4.36606973e-01 1.10447776e+00 -4.21561807e-01
-2.66982347e-01 1.01716602e+00 8.92944336e-01 -6.91188276e-02
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4.05386150e-01 1.04141819e+00 -9.88413580e-03 -4.54902321... | [12.784639358520508, 8.148518562316895] |
fa5ef8ad-bea8-4af3-a80d-0772d05d8bd6 | face-hallucination-using-linear-models-of | 1512.06009 | null | http://arxiv.org/abs/1512.06009v1 | http://arxiv.org/pdf/1512.06009v1.pdf | Face Hallucination using Linear Models of Coupled Sparse Support | Most face super-resolution methods assume that low-resolution and
high-resolution manifolds have similar local geometrical structure, hence learn
local models on the lowresolution manifolds (e.g. sparse or locally linear
embedding models), which are then applied on the high-resolution manifold.
However, the low-resolut... | ['Christine Guillemot', 'Reuben Farrugia'] | 2015-12-18 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 1.41122535e-01 1.44640788e-01 -6.97648749e-02 -2.17788950e-01
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-5.30213937e-02 3.43790889e-01 5.15153594e-02 -1.72809198... | [12.822114944458008, -0.01242329552769661] |
1cb35f3a-48f8-4d56-812c-2caa60124f74 | a-novel-dual-dense-connection-network-for | 2203.02723 | null | https://arxiv.org/abs/2203.02723v1 | https://arxiv.org/pdf/2203.02723v1.pdf | A Novel Dual Dense Connection Network for Video Super-resolution | Video super-resolution (VSR) refers to the reconstruction of high-resolution (HR) video from the corresponding low-resolution (LR) video. Recently, VSR has received increasing attention. In this paper, we propose a novel dual dense connection network that can generate high-quality super-resolution (SR) results. The inp... | ['Yonggui Zhu', 'Guofang Li'] | 2022-03-05 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 2.54795134e-01 -4.36734110e-01 -1.05165571e-01 -2.34115973e-01
-5.55037677e-01 1.47505373e-01 2.20563605e-01 -6.17593706e-01
-1.99263930e-01 1.01342881e+00 4.77833211e-01 3.34408820e-01
-1.86056271e-01 -8.55546594e-01 -5.31972885e-01 -5.43894708e-01
4.48418297e-02 -2.71280140e-01 6.22201681e-01 -2.21781477... | [11.0361328125, -1.8403072357177734] |
e78c07da-cebb-450d-9125-5648669b2831 | directional-skip-gram-explicitly | null | null | https://aclanthology.org/N18-2028 | https://aclanthology.org/N18-2028.pdf | Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings | In this paper, we present directional skip-gram (DSG), a simple but effective enhancement of the skip-gram model by explicitly distinguishing left and right context in word prediction. In doing so, a direction vector is introduced for each word, whose embedding is thus learned by not only word co-occurrence patterns in... | ['Yan Song', 'Jing Li', 'Shuming Shi', 'Haisong Zhang'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['learning-word-embeddings'] | ['methodology'] | [ 8.46363083e-02 1.16128917e-03 -4.92690295e-01 -5.42847753e-01
-4.62420404e-01 -6.92765296e-01 6.91933751e-01 3.85650158e-01
-6.23550355e-01 4.71777856e-01 8.66937339e-01 -6.68384552e-01
2.03005895e-01 -6.46766484e-01 -1.26083091e-01 -6.18015110e-01
-1.25555873e-01 4.04987335e-01 3.60506743e-01 -4.30145055... | [10.50367259979248, 8.635720252990723] |
7da74691-b9d7-4bcf-b50a-1161b76d4ef4 | mfnet-towards-real-time-semantic-segmentation | null | null | https://ieeexplore.ieee.org/abstract/document/8206396 | https://ieeexplore.ieee.org/abstract/document/8206396 | MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes | This work addresses the semantic segmentation of images of street scenes for autonomous vehicles based on a new RGB-Thermal dataset, which is also introduced in this paper. An increasing interest in self-driving vehicles has brought the adaptation of semantic segmentation to self-driving systems. However, recent resear... | ['Tatsuya Harada', 'Yoshitaka Ushiku', 'Takumi Karasawa', 'Kohei Watanabe', 'Qishen Ha'] | 2017-12-14 | null | null | null | ieee-rsj-international-conference-on-6 | ['real-time-semantic-segmentation', 'thermal-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.40819049e-01 -1.57084465e-01 2.37516478e-01 -6.63351715e-01
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3.22729141e-01 9.33062583e-02 4.33206618e-01 -4.30156052... | [9.003979682922363, -1.5247900485992432] |
aa100656-0afd-458d-8ea2-16818c140ef3 | combining-shallow-and-linguistically | null | null | https://aclanthology.org/W13-1726 | https://aclanthology.org/W13-1726.pdf | Combining Shallow and Linguistically Motivated Features in Native Language Identification | null | ['Sowmya Vajjala', 'Detmar Meurers', 'Serhiy Bykh', 'Julia Krivanek'] | 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
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-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.335831642150879, 3.6701674461364746] |
6f65de30-a51a-4e7c-b1f6-fffe55d295a3 | vision-language-transformer-and-query | 2108.05565 | null | https://arxiv.org/abs/2108.05565v1 | https://arxiv.org/pdf/2108.05565v1.pdf | Vision-Language Transformer and Query Generation for Referring Segmentation | In this work, we address the challenging task of referring segmentation. The query expression in referring segmentation typically indicates the target object by describing its relationship with others. Therefore, to find the target one among all instances in the image, the model must have a holistic understanding of th... | ['Xudong Jiang', 'Suchen Wang', 'Chang Liu', 'Henghui Ding'] | 2021-08-12 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Ding_Vision-Language_Transformer_and_Query_Generation_for_Referring_Segmentation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Ding_Vision-Language_Transformer_and_Query_Generation_for_Referring_Segmentation_ICCV_2021_paper.pdf | iccv-2021-1 | ['generalized-referring-expression-segmentation', 'referring-expression-segmentation'] | ['computer-vision', 'computer-vision'] | [ 0.132929 0.0541526 -0.31166387 -0.45031774 -0.9523509 -0.41902527
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0.39320803 -0.35384807 0.31703204 0.26099378 -1.2658937 0.46654597
0.9239249 1.0339515 0.7... | [10.277024269104004, 1.260948657989502] |
2800eaf7-81b7-4101-9860-df3f65dfaade | vqa-and-visual-reasoning-an-overview-of | 2212.13296 | null | https://arxiv.org/abs/2212.13296v1 | https://arxiv.org/pdf/2212.13296v1.pdf | VQA and Visual Reasoning: An Overview of Recent Datasets, Methods and Challenges | Artificial Intelligence (AI) and its applications have sparked extraordinary interest in recent years. This achievement can be ascribed in part to advances in AI subfields including Machine Learning (ML), Computer Vision (CV), and Natural Language Processing (NLP). Deep learning, a sub-field of machine learning that em... | ['Yuezhou Dong', 'Zaharaddeen Karami Lawal', 'Ke Qin', 'Hailin Wang', 'Jim Wilson Owusu', 'Rufai Yusuf Zakari'] | 2022-12-26 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 2.46514797e-01 -8.48994777e-02 -4.01368737e-01 -4.15660203e-01
-3.45612466e-01 -4.76211101e-01 9.81671095e-01 1.93581983e-01
-4.23506230e-01 4.36604649e-01 2.84220070e-01 -2.47455016e-01
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1.09848700e-01 4.49337602e-01 4.17777747e-02 -2.75415838... | [10.32007122039795, 1.7171820402145386] |
d15aff47-3276-4432-b09a-dd60e27dcf44 | an-analysis-of-quantile-temporal-difference | 2301.04462 | null | https://arxiv.org/abs/2301.04462v2 | https://arxiv.org/pdf/2301.04462v2.pdf | An Analysis of Quantile Temporal-Difference Learning | We analyse quantile temporal-difference learning (QTD), a distributional reinforcement learning algorithm that has proven to be a key component in several successful large-scale applications of reinforcement learning. Despite these empirical successes, a theoretical understanding of QTD has proven elusive until now. Un... | ['Will Dabney', 'Marc G. Bellemare', 'Karl Tuyls', 'Anna Harutyunyan', 'Georg Ostrovski', 'Yunhao Tang', 'Mohammad Gheshlaghi Azar', 'Rémi Munos', 'Mark Rowland'] | 2023-01-11 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.67301977e-01 1.93351492e-01 -2.18940377e-01 1.23585150e-01
-1.06282711e+00 -5.95099926e-01 4.85954642e-01 2.66945362e-01
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-5.74499905e-01 -6.31138980e-01 -9.03675675e-01 -1.06946659e+00
-5.41645527e-01 5.11732161e-01 1.32929400e-01 -1.17163159... | [4.158573627471924, 2.6037561893463135] |
82f7fa58-1b56-4122-9bd8-ec038197a9d4 | beyond-a-gaussian-denoiser-residual-learning | 1608.03981 | null | http://arxiv.org/abs/1608.03981v1 | http://arxiv.org/pdf/1608.03981v1.pdf | Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising | Discriminative model learning for image denoising has been recently
attracting considerable attentions due to its favorable denoising performance.
In this paper, we take one step forward by investigating the construction of
feed-forward denoising convolutional neural networks (DnCNNs) to embrace the
progress in very de... | ['Yunjin Chen', 'WangMeng Zuo', 'Lei Zhang', 'Kai Zhang', 'Deyu Meng'] | 2016-08-13 | null | null | null | null | ['color-image-denoising', 'image-deblocking', 'jpeg-artifact-correction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.53795284e-01 -3.68793070e-01 3.35029751e-01 -3.97538632e-01
-7.62585402e-01 -2.33082417e-02 4.87093061e-01 -2.60162354e-01
-5.01586735e-01 4.53689843e-01 2.67389506e-01 -8.97173807e-02
6.53247237e-02 -7.44791448e-01 -7.24356115e-01 -1.31460726e+00
2.90277034e-01 -1.43386841e-01 -9.88763124e-02 -3.52791965... | [11.454205513000488, -2.368751287460327] |
1e4e1aa2-11a9-4ab7-97d4-796e125b11b8 | red-deep-recurrent-neural-networks-for-sleep | 2005.07795 | null | https://arxiv.org/abs/2005.07795v2 | https://arxiv.org/pdf/2005.07795v2.pdf | RED: Deep Recurrent Neural Networks for Sleep EEG Event Detection | The brain electrical activity presents several short events during sleep that can be observed as distinctive micro-structures in the electroencephalogram (EEG), such as sleep spindles and K-complexes. These events have been associated with biological processes and neurological disorders, making them a research topic in... | ['Pablo A. Estévez', 'Nicolás I. Tapia'] | 2020-05-15 | null | null | null | null | ['k-complex-detection', 'spindle-detection', 'sleep-micro-event-detection'] | ['medical', 'medical', 'medical'] | [ 2.03901365e-01 -2.51599461e-01 2.29042277e-01 -1.37654379e-01
-3.11851025e-01 -5.38127244e-01 4.38677579e-01 3.38501811e-01
-6.32011890e-01 6.97650671e-01 -5.94154894e-02 -2.25211903e-01
-2.09185839e-01 -4.14033085e-01 -1.88729808e-01 -8.03755999e-01
-2.66715705e-01 -2.26630226e-01 2.40423143e-01 2.00486910... | [13.47847843170166, 3.5158638954162598] |
fbf872f3-2252-42f0-8ea7-eda3a51428f0 | simcvd-simple-contrastive-voxel-wise | 2108.06227 | null | https://arxiv.org/abs/2108.06227v4 | https://arxiv.org/pdf/2108.06227v4.pdf | SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation | Automated segmentation in medical image analysis is a challenging task that requires a large amount of manually labeled data. However, most existing learning-based approaches usually suffer from limited manually annotated medical data, which poses a major practical problem for accurate and robust medical image segmenta... | ['James S. Duncan', 'Lawrence Staib', 'Ruihan Zhao', 'Yuan Zhou', 'Chenyu You'] | 2021-08-13 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 4.58845824e-01 3.65842193e-01 -2.40800247e-01 -6.01922154e-01
-9.60614979e-01 -4.42126751e-01 3.56517345e-01 3.39293897e-01
-6.24725282e-01 6.45560324e-01 1.06616415e-01 -2.38987491e-01
1.61762848e-01 -4.80651826e-01 -7.23888636e-01 -8.01307499e-01
-3.33426520e-02 6.94203258e-01 2.73114353e-01 1.53128020... | [14.567431449890137, -2.215947151184082] |
2aaba9e3-824c-4f0e-9a72-ceab746cc6aa | babyai-first-steps-towards-grounded-language | 1810.08272 | null | https://arxiv.org/abs/1810.08272v4 | https://arxiv.org/pdf/1810.08272v4.pdf | BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning | Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and scientific reasons, but given the poor data efficiency of the current learning methods, this goal may require substantial research efforts. Here, we introduce the BabyAI research platform to ... | ['Maxime Chevalier-Boisvert', 'Thien Huu Nguyen', 'Chitwan Saharia', 'Salem Lahlou', 'Yoshua Bengio', 'Lucas Willems', 'Dzmitry Bahdanau'] | 2018-10-18 | babyai-a-platform-to-study-the-sample | https://openreview.net/forum?id=rJeXCo0cYX | https://openreview.net/pdf?id=rJeXCo0cYX | iclr-2019-5 | ['grounded-language-learning'] | ['natural-language-processing'] | [ 3.37236464e-01 7.44435310e-01 1.42916217e-01 -3.20699245e-01
-6.96708620e-01 -7.77646720e-01 9.56915140e-01 1.20139077e-01
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1.79259013e-02 -7.60438859e-01 -9.21691239e-01 -3.35434884e-01
1.99564472e-02 1.03587484e+00 5.88585734e-02 -4.03726071... | [4.154476165771484, 1.183275580406189] |
12eed9c5-9443-4fb0-b3b9-a37f0f8ecdd6 | design-considerations-for-hypothesis | 2211.09711 | null | https://arxiv.org/abs/2211.09711v1 | https://arxiv.org/pdf/2211.09711v1.pdf | Design Considerations For Hypothesis Rejection Modules In Spoken Language Understanding Systems | Spoken Language Understanding (SLU) systems typically consist of a set of machine learning models that operate in conjunction to produce an SLU hypothesis. The generated hypothesis is then sent to downstream components for further action. However, it is desirable to discard an incorrect hypothesis before sending it dow... | ['Shankar Ananthakrishnan', 'Rahul Gupta', 'Aman Alok'] | 2022-10-31 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 4.76185411e-01 6.96416557e-01 6.90030605e-02 -6.85904801e-01
-1.01551747e+00 -6.17518723e-01 6.89420581e-01 3.20991367e-01
-2.24694908e-01 5.73078752e-01 2.75036037e-01 -4.77539361e-01
1.06238902e-01 -6.89519227e-01 -4.60394114e-01 -2.72175848e-01
1.13351539e-01 4.66897905e-01 4.70412344e-01 -4.30176646... | [13.964408874511719, 6.940930366516113] |
2af0bdc0-46c4-45d0-be16-5e251b7dba4f | learning-based-symbolic-abstractions-for | 2004.01879 | null | https://arxiv.org/abs/2004.01879v4 | https://arxiv.org/pdf/2004.01879v4.pdf | Learning-based Symbolic Abstractions for Nonlinear Control Systems | Symbolic models or abstractions are known to be powerful tools for the control design of cyber-physical systems (CPSs) with logic specifications. In this paper, we investigate a novel learning-based approach to the construction of symbolic models for nonlinear control systems. In particular, the symbolic model is const... | ['Dimos Dimarogonas', 'Toshimitsu Ushio', 'Masako Kishida', 'Adnane Saoud', 'Kazumune Hashimoto'] | 2020-04-04 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 2.79545456e-01 4.47405308e-01 -3.52381200e-01 1.03878111e-01
-2.44993076e-01 -4.90907311e-01 4.90121484e-01 1.94908142e-01
1.54824346e-01 8.63426149e-01 -5.23732066e-01 -7.52855599e-01
-5.55964887e-01 -7.79376090e-01 -7.98862875e-01 -7.17869341e-01
-4.44229603e-01 9.05979201e-02 2.43307918e-01 -1.23678014... | [4.855257034301758, 2.2578728199005127] |
e9a9ea3f-1fe3-4dd3-b0d0-0f3cb9dad4d5 | sgdraw-scene-graph-drawing-interface-using | 2211.16697 | null | https://arxiv.org/abs/2211.16697v2 | https://arxiv.org/pdf/2211.16697v2.pdf | SGDraw: Scene Graph Drawing Interface Using Object-Oriented Representation | Scene understanding is an essential and challenging task in computer vision. To provide the visually fundamental graphical structure of an image, the scene graph has received increased attention due to its powerful semantic representation. However, it is difficult to draw a proper scene graph for image retrieval, image... | ['Haoran Xie', 'Xi Yang', 'Chia-Ming Chang', 'Xusheng Du', 'Tianyu Zhang'] | 2022-11-30 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 2.77478158e-01 -2.29390338e-02 3.19939166e-01 -3.62675250e-01
1.09914154e-01 -4.82973456e-01 4.95598257e-01 6.23101830e-01
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-1.13009214e-01 -1.09354055e+00 -5.08328021e-01 -2.49905676e-01
4.63974983e-01 2.54075378e-01 6.02778614e-01 -2.54605085... | [10.37491512298584, 1.4823120832443237] |
134394cb-6627-43fe-9772-4c26c56165e3 | palm-scaling-language-modeling-with-pathways-1 | 2204.02311 | null | https://arxiv.org/abs/2204.02311v5 | https://arxiv.org/pdf/2204.02311v5.pdf | PaLM: Scaling Language Modeling with Pathways | Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale o... | ['Noah Fiedel', 'Slav Petrov', 'Jeff Dean', 'Douglas Eck', 'Kathy Meier-Hellstern', 'Jason Wei', 'Michele Catasta', 'Orhan Firat', 'Mark Diaz', 'Brennan Saeta', 'Xuezhi Wang', 'Zongwei Zhou', 'Katherine Lee', 'Oleksandr Polozov', 'Rewon Child', 'Erica Moreira', 'Aitor Lewkowycz', 'Marie Pellat', 'Thanumalayan Sankarana... | 2022-04-05 | palm-scaling-language-modeling-with-pathways | https://storage.googleapis.com/pathways-language-model/PaLM-paper.pdf | https://storage.googleapis.com/pathways-language-model/PaLM-paper.pdf | google-research-2022-4 | ['multi-task-language-understanding', 'auto-debugging', 'known-unknowns', 'logic-grid-puzzle', 'hindu-knowledge', 'multiple-choice-qa', 'cross-lingual-question-answering', 'winowhy', 'strategyqa', 'novel-concepts'] | ['methodology', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'natural-language-processing', 'natural-language-processing', 'reasoning', 'reasoning', 'reasoning'] | [ 5.06676510e-02 1.36355637e-02 -3.48319530e-01 -1.80230886e-01
-1.21400595e+00 -5.61600447e-01 7.38347590e-01 6.87867776e-02
-4.05505151e-01 8.75557601e-01 3.21181476e-01 -4.21873391e-01
7.01257363e-02 -9.50211644e-01 -1.03737986e+00 -2.32740372e-01
8.74051377e-02 6.25864804e-01 2.13280976e-01 -5.73515654... | [10.752700805664062, 8.132765769958496] |
8b15db5d-089f-4f28-8f74-4c43198fff6c | joint-system-wise-optimization-for-pipeline | 2106.04835 | null | https://arxiv.org/abs/2106.04835v1 | https://arxiv.org/pdf/2106.04835v1.pdf | Joint System-Wise Optimization for Pipeline Goal-Oriented Dialog System | Recent work (Takanobu et al., 2020) proposed the system-wise evaluation on dialog systems and found that improvement on individual components (e.g., NLU, policy) in prior work may not necessarily bring benefit to pipeline systems in system-wise evaluation. To improve the system-wise performance, in this paper, we propo... | ['Tengyu Ma', 'Xiaodong He', 'BoWen Zhou', 'Jing Huang', 'Zichuan Lin'] | 2021-06-09 | null | null | null | null | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-2.47561589e-01 3.53697658e-01 -1.83058053e-01 -5.19051909e-01
-1.08388853e+00 -9.19727087e-01 9.14456189e-01 -2.53977537e-01
-7.95670986e-01 7.91733444e-01 3.96784097e-01 -5.79441428e-01
2.08396778e-01 -1.74487770e-01 -3.19022357e-01 -3.65328461e-01
3.32988232e-01 1.03974378e+00 5.22931218e-01 -5.05661845... | [12.774024963378906, 8.00940990447998] |
bd5e63a1-6b6c-487d-bbdc-6b36d2610f64 | improving-selective-visual-question-answering-1 | 2306.08751 | null | https://arxiv.org/abs/2306.08751v1 | https://arxiv.org/pdf/2306.08751v1.pdf | Improving Selective Visual Question Answering by Learning from Your Peers | Despite advances in Visual Question Answering (VQA), the ability of models to assess their own correctness remains underexplored. Recent work has shown that VQA models, out-of-the-box, can have difficulties abstaining from answering when they are wrong. The option to abstain, also called Selective Prediction, is highly... | ['Marcus Rohrbach', 'Matthieu Cord', 'Xinlei Chen', 'Stefan Scherer', 'Ramakrishna Vedantam', 'Rishabh Maheshwary', 'Spencer Whitehead', 'Corentin Dancette'] | 2023-06-14 | improving-selective-visual-question-answering | http://openaccess.thecvf.com//content/CVPR2023/html/Dancette_Improving_Selective_Visual_Question_Answering_by_Learning_From_Your_Peers_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Dancette_Improving_Selective_Visual_Question_Answering_by_Learning_From_Your_Peers_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-question-answering-1', 'question-answering'] | ['computer-vision', 'natural-language-processing'] | [ 5.02831023e-03 6.44139051e-01 1.37029037e-01 -6.77200258e-01
-1.19376361e+00 -8.44419718e-01 2.00481847e-01 -1.76722482e-02
-3.65974456e-01 6.52009189e-01 -9.22735706e-02 -6.98440135e-01
9.47562754e-02 -4.47582126e-01 -8.13072145e-01 -1.75043374e-01
3.01103801e-01 6.66834772e-01 2.77462751e-01 -2.65257955... | [11.243090629577637, 8.00083065032959] |
c81f6bbd-b9a6-407d-8508-28cccb77c493 | a-practical-system-based-on-cnn-blstm-network | null | null | https://ieeexplore.ieee.org/abstract/document/9420620 | https://ieeexplore.ieee.org/abstract/document/9420620 | A practical system based on CNN-BLSTM network for accurate classification of ECG heartbeats of MIT-BIH imbalanced dataset | ECG beats have a key role in the reduction of fatality rate arising from cardiovascular diseases (CVDs) by using Arrhythmia diagnosis computer-aided systems and get the important information from patient cardiac conditions to the specialist. However, the accuracy and speed of arrhythmia diagnosis are challenging in ECG... | ['mb dowlatshahi', 'armin shoughi'] | 2021-05-07 | null | null | null | 26th-international-computer-conference | ['ecg-classification'] | ['medical'] | [ 1.95559040e-02 -4.23351467e-01 3.84689495e-02 -2.27899656e-01
-4.22517031e-01 -2.19692990e-01 -4.57692266e-01 2.68762618e-01
-1.99501961e-01 7.27453113e-01 -2.05175400e-01 -5.40988088e-01
-2.95602888e-01 -5.83786666e-01 2.47124657e-02 -6.58462286e-01
-2.48951897e-01 4.29680318e-01 -3.32939804e-01 3.64321005... | [14.302411079406738, 3.285748243331909] |
59c8db77-3b45-4b90-ab56-2648000308ef | improved-multiple-image-based-reflection | 2208.04679 | null | https://arxiv.org/abs/2208.04679v2 | https://arxiv.org/pdf/2208.04679v2.pdf | Improved Multiple-Image-Based Reflection Removal Algorithm Using Deep Neural Networks | When imaging through a semi-reflective medium such as glass, the reflection of another scene can often be found in the captured images. It degrades the quality of the images and affects their subsequent analyses. In this paper, a novel deep neural network approach for solving the reflection problem in imaging is presen... | ['Daniel P. K. Lun', 'Yuk-Hee Chan', 'Tingtian Li'] | 2022-08-09 | null | null | null | null | ['reflection-removal'] | ['computer-vision'] | [ 7.14687467e-01 -9.57661718e-02 5.49468100e-01 -1.18387423e-01
-3.91427875e-01 -1.89964920e-01 2.58339167e-01 -4.82459933e-01
-4.08332616e-01 6.07068658e-01 -2.11146876e-01 -8.02568346e-02
2.72873133e-01 -8.86508584e-01 -6.58531666e-01 -1.12730801e+00
5.35495162e-01 2.25908179e-02 3.27294886e-01 1.57087326... | [10.48293685913086, -2.6911652088165283] |
80536980-29dd-49f5-a357-b1a4a36a47d7 | a-survey-of-quantum-cognitively-inspired | 2306.03608 | null | https://arxiv.org/abs/2306.03608v1 | https://arxiv.org/pdf/2306.03608v1.pdf | A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models | Quantum theory, originally proposed as a physical theory to describe the motions of microscopic particles, has been applied to various non-physics domains involving human cognition and decision-making that are inherently uncertain and exhibit certain non-classical, quantum-like characteristics. Sentiment analysis is a ... | ['Dawei Song', 'Yazhou Zhang', 'Benyou Wang', 'Qiuchi Li', 'Yaochen Liu'] | 2023-06-06 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [ 1.87242543e-03 -5.21874763e-02 2.18793988e-01 -3.18275452e-01
-1.83644250e-01 -4.99717861e-01 7.54525304e-01 4.61738199e-01
-4.36585486e-01 5.54071605e-01 3.14008221e-02 -1.30130902e-01
-3.99000674e-01 -1.20314884e+00 -2.70087928e-01 -8.27919245e-01
1.02041990e-01 3.72762412e-01 -2.21570760e-01 -6.91635489... | [5.585886001586914, 4.995668411254883] |
8b5f1138-b5ec-436d-8ee1-b58ba162c873 | is-bert-blind-exploring-the-effect-of-vision | 2303.12513 | null | https://arxiv.org/abs/2303.12513v1 | https://arxiv.org/pdf/2303.12513v1.pdf | Is BERT Blind? Exploring the Effect of Vision-and-Language Pretraining on Visual Language Understanding | Most humans use visual imagination to understand and reason about language, but models such as BERT reason about language using knowledge acquired during text-only pretraining. In this work, we investigate whether vision-and-language pretraining can improve performance on text-only tasks that involve implicit visual re... | ['Hadar Averbuch-Elor', 'Michael Fiman', 'Morris Alper'] | 2023-03-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Alper_Is_BERT_Blind_Exploring_the_Effect_of_Vision-and-Language_Pretraining_on_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Alper_Is_BERT_Blind_Exploring_the_Effect_of_Vision-and-Language_Pretraining_on_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 2.12691814e-01 5.59381962e-01 -1.81580052e-01 -2.32745200e-01
-5.73431373e-01 -6.89527392e-01 9.57564771e-01 1.27176926e-01
-7.90101230e-01 3.90397668e-01 3.95534784e-01 -9.45628107e-01
1.47909522e-01 -4.49066073e-01 -1.10525596e+00 -3.83509606e-01
3.14583033e-01 5.85037172e-01 -9.24493223e-02 -1.70468107... | [10.837738037109375, 1.7375761270523071] |
fa618b00-d035-466a-a13c-77dfbf1131d9 | spatio-temporal-point-processes-with-deep-non | 2211.11179 | null | https://arxiv.org/abs/2211.11179v1 | https://arxiv.org/pdf/2211.11179v1.pdf | Spatio-temporal point processes with deep non-stationary kernels | Point process data are becoming ubiquitous in modern applications, such as social networks, health care, and finance. Despite the powerful expressiveness of the popular recurrent neural network (RNN) models for point process data, they may not successfully capture sophisticated non-stationary dependencies in the data d... | ['Yao Xie', 'Xiuyuan Cheng', 'Zheng Dong'] | 2022-11-21 | null | null | null | null | ['point-processes'] | ['methodology'] | [-2.93719135e-02 -3.53210092e-01 8.95149484e-02 -1.80266351e-01
-3.53507251e-01 -2.17278972e-01 7.26697206e-01 2.22136423e-01
-4.47550327e-01 6.41561985e-01 4.15807724e-01 -3.89871091e-01
-5.03116131e-01 -8.68339360e-01 -8.14919651e-01 -8.74375820e-01
-3.41128170e-01 4.99177039e-01 1.51891872e-01 1.36772245... | [6.919344902038574, 3.3782198429107666] |
0a38a76a-be6c-4c2b-b119-52d758c59add | trueteacher-learning-factual-consistency | 2305.11171 | null | https://arxiv.org/abs/2305.11171v1 | https://arxiv.org/pdf/2305.11171v1.pdf | TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models | Factual consistency evaluation is often conducted using Natural Language Inference (NLI) models, yet these models exhibit limited success in evaluating summaries. Previous work improved such models with synthetic training data. However, the data is typically based on perturbed human-written summaries, which often diffe... | ['Idan Szpektor', 'Chen Elkind', 'Roee Aharoni', 'Jonathan Herzig', 'Zorik Gekhman'] | 2023-05-18 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 3.73282395e-02 5.74668407e-01 -3.42828840e-01 -3.62571925e-01
-1.70848882e+00 -7.15634167e-01 1.30167150e+00 2.04032332e-01
-5.50782494e-02 1.54013956e+00 5.49858510e-01 -3.21588218e-01
2.38269404e-01 -6.80617511e-01 -1.14734972e+00 -2.49823332e-01
2.81864345e-01 1.07362092e+00 -9.51678604e-02 -1.10872760... | [11.567138671875, 8.883318901062012] |
f06f6915-377d-48f9-919f-3e6dfa3e72c4 | pixel-objectness | 1701.05349 | null | http://arxiv.org/abs/1701.05349v2 | http://arxiv.org/pdf/1701.05349v2.pdf | Pixel Objectness | We propose an end-to-end learning framework for generating foreground object
segmentations. Given a single novel image, our approach produces pixel-level
masks for all "object-like" regions---even for object categories never seen
during training. We formulate the task as a structured prediction problem of
assigning for... | ['Kristen Grauman', 'Suyog Dutt Jain', 'Bo Xiong'] | 2017-01-19 | null | null | null | null | ['image-retargeting', 'foreground-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.01047218e+00 5.45414865e-01 -1.57926697e-02 -4.88983065e-01
-1.13665593e+00 -8.10633659e-01 5.79750240e-01 -1.53553262e-01
-4.31391269e-01 5.67730784e-01 -2.55981803e-01 -3.16234112e-01
4.45679486e-01 -6.89079821e-01 -1.38473499e+00 -6.01362348e-01
6.53611273e-02 6.38009429e-01 8.44171107e-01 2.71132022... | [9.643096923828125, 0.4301162362098694] |
20e9f3ac-cf42-4256-be16-143fdc82c3e1 | medical-entity-corpus-with-pico-elements-and | null | null | https://aclanthology.org/L18-1044 | https://aclanthology.org/L18-1044.pdf | Medical Entity Corpus with PICO elements and Sentiment Analysis | null | ['Michael Andersson', 'Markus Zlabinger', 'Linda Andersson', 'Allan Hanbury', 'Vanessa Quasnik', 'Jon Brassey'] | 2018-05-01 | medical-entity-corpus-with-pico-elements-and-1 | https://aclanthology.org/L18-1044 | https://aclanthology.org/L18-1044.pdf | lrec-2018-5 | ['pico'] | ['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... | [-7.318605422973633, 3.6143624782562256] |
0aac044c-d62e-4586-9b32-6cab6e2cdf3f | training-neural-networks-to-have-brain-like | 1905.10679 | null | https://arxiv.org/abs/1905.10679v4 | https://arxiv.org/pdf/1905.10679v4.pdf | Improved object recognition using neural networks trained to mimic the brain's statistical properties | The current state-of-the-art object recognition algorithms, deep convolutional neural networks (DCNNs), are inspired by the architecture of the mammalian visual system, and are capable of human-level performance on many tasks. However, even these algorithms make errors. As they are trained for object recognition tasks,... | ['Haoyan Xu', 'Alona Fyshe', 'Joel Zylberberg', 'Callie Federer'] | 2019-05-25 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 3.53625447e-01 1.10857949e-01 6.11798130e-02 -5.18065393e-01
2.31135860e-01 -4.59815055e-01 7.87202001e-01 -4.19764183e-02
-6.69236958e-01 4.93581206e-01 -2.22460598e-01 -3.51692200e-01
-8.50489810e-02 -8.64472210e-01 -9.05755877e-01 -7.81528056e-01
-1.70420967e-02 2.16289520e-01 4.64228243e-01 3.27954739... | [9.811328887939453, 2.3696019649505615] |
22562bc7-7783-48dc-94e2-da5e2f677757 | inter-patient-ecg-classification-with | 1810.04121 | null | http://arxiv.org/abs/1810.04121v1 | http://arxiv.org/pdf/1810.04121v1.pdf | Inter-Patient ECG Classification with Convolutional and Recurrent Neural Networks | The recent advances in ECG sensor devices provide opportunities for user
self-managed auto-diagnosis and monitoring services over the internet. This
imposes the requirements for generic ECG classification methods that are
inter-patient and device independent. In this paper, we present our work on
using the densely conn... | [] | 2018-09-27 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 2.39954606e-01 -1.21461906e-01 2.66393006e-01 -3.73630881e-01
-6.90069258e-01 -3.91638488e-01 -1.04741991e-01 3.42840016e-01
-3.11270088e-01 8.79772365e-01 -2.57312030e-01 -4.82898295e-01
-3.79321814e-01 -4.89118725e-01 -1.42780051e-01 -6.06519997e-01
-3.33565235e-01 4.49101567e-01 -3.12136531e-01 -8.25878158... | [14.282831192016602, 3.281224012374878] |
37977ff4-14ef-4024-9ee5-668cb55f507a | chance-constrained-ac-optimal-power-flow-for | 2207.09520 | null | https://arxiv.org/abs/2207.09520v1 | https://arxiv.org/pdf/2207.09520v1.pdf | Chance-Constrained AC Optimal Power Flow for Unbalanced Distribution Grids | The growing penetration of distributed energy resources (DERs) is leading to continually changing operating conditions, which need to be managed efficiently by distribution grid operators. The intermittent nature of DERs such as solar photovoltaic (PV) systems as well as load forecasting errors not only increase uncert... | ['Line A. Roald', 'Ashley M. Hou', 'Kshitij Girigoudar'] | 2022-07-19 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-1.04260504e-01 -1.24500208e-01 6.40784651e-02 -4.38527167e-02
-4.27333474e-01 -1.03476703e+00 2.30099514e-01 4.91694510e-01
2.47531638e-01 1.43744826e+00 -1.24267772e-01 -2.62821436e-01
-7.76792407e-01 -8.16767991e-01 -8.14169422e-02 -1.04051149e+00
-4.50985730e-01 4.52361494e-01 -3.01204056e-01 2.29251832... | [5.700668811798096, 2.55151629447937] |
70a98ab7-2245-4e76-98f1-5ba13cab5ba0 | bag-of-tricks-for-efficient-text | 1607.01759 | null | http://arxiv.org/abs/1607.01759v3 | http://arxiv.org/pdf/1607.01759v3.pdf | Bag of Tricks for Efficient Text Classification | This paper explores a simple and efficient baseline for text classification.
Our experiments show that our fast text classifier fastText is often on par
with deep learning classifiers in terms of accuracy, and many orders of
magnitude faster for training and evaluation. We can train fastText on more
than one billion wo... | ['Edouard Grave', 'Armand Joulin', 'Piotr Bojanowski', 'Tomas Mikolov'] | 2016-07-06 | bag-of-tricks-for-efficient-text-1 | https://aclanthology.org/E17-2068 | https://aclanthology.org/E17-2068.pdf | eacl-2017-4 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-3.56484711e-01 -3.36783201e-01 -3.50413531e-01 -8.19586575e-01
-8.58839571e-01 -8.38765085e-01 7.91489065e-01 6.77897930e-01
-9.23440993e-01 8.13939273e-01 3.01914234e-02 -8.71640325e-01
2.86195278e-01 -8.75211060e-01 -5.20594299e-01 -3.52755964e-01
1.66539446e-01 7.89491892e-01 2.57950187e-01 -1.51309416... | [10.66331958770752, 7.754453182220459] |
36e93352-3d49-4655-890d-76719a9d4c8f | robust-reflection-removal-with-flash-only | 2211.02914 | null | https://arxiv.org/abs/2211.02914v1 | https://arxiv.org/pdf/2211.02914v1.pdf | Robust Reflection Removal with Flash-only Cues in the Wild | We propose a simple yet effective reflection-free cue for robust reflection removal from a pair of flash and ambient (no-flash) images. The reflection-free cue exploits a flash-only image obtained by subtracting the ambient image from the corresponding flash image in raw data space. The flash-only image is equivalent t... | ['Qifeng Chen', 'Xudong Jiang', 'Chenyang Lei'] | 2022-11-05 | null | null | null | null | ['reflection-removal'] | ['computer-vision'] | [ 7.84501314e-01 -2.91538179e-01 5.20090640e-01 -8.77530798e-02
-1.02274168e+00 -5.10424614e-01 2.33843461e-01 -6.11459017e-01
-3.02509040e-01 4.68304217e-01 2.23451927e-01 -3.83259505e-01
3.34645629e-01 -4.07907218e-01 -8.97369146e-01 -9.72978830e-01
3.76009732e-01 -7.05771387e-01 3.88503551e-01 -1.22963481... | [10.490581512451172, -2.7066938877105713] |
3c394843-17a7-4d0c-9881-7f920194b9c1 | variational-f-divergence-and-derangements-for | 2305.20025 | null | https://arxiv.org/abs/2305.20025v1 | https://arxiv.org/pdf/2305.20025v1.pdf | Variational $f$-Divergence and Derangements for Discriminative Mutual Information Estimation | The accurate estimation of the mutual information is a crucial task in various applications, including machine learning, communications, and biology, since it enables the understanding of complex systems. High-dimensional data render the task extremely challenging due to the amount of data to be processed and the prese... | ['Andrea M. Tonello', 'Nicola Novello', 'Nunzio A. Letizia'] | 2023-05-31 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 2.39427909e-01 -2.74664372e-01 1.21388108e-01 -5.45778573e-01
-6.01452410e-01 -2.75727242e-01 6.97316527e-01 2.48988971e-01
-7.02599943e-01 9.17017579e-01 -1.98448658e-01 -2.84668915e-02
-5.47987580e-01 -6.72582090e-01 -7.78309882e-01 -9.45981622e-01
-2.01041520e-01 2.87128985e-01 3.89225155e-01 1.76891372... | [7.280550479888916, 3.961782693862915] |
6588dabd-e8e3-4183-b50e-29838312c6f0 | natcat-weakly-supervised-text-classification | 2009.14335 | null | https://arxiv.org/abs/2009.14335v2 | https://arxiv.org/pdf/2009.14335v2.pdf | NatCat: Weakly Supervised Text Classification with Naturally Annotated Resources | We describe NatCat, a large-scale resource for text classification constructed from three data sources: Wikipedia, Stack Exchange, and Reddit. NatCat consists of document-category pairs derived from manual curation that occurs naturally within online communities. To demonstrate its usefulness, we build general purpose ... | ['Karl Stratos', 'Zewei Chu', 'Kevin Gimpel'] | 2020-09-29 | null | https://openreview.net/forum?id=kmVA04ltlG_ | https://openreview.net/pdf?id=kmVA04ltlG_ | akbc-2021-10 | ['text-categorization'] | ['natural-language-processing'] | [-3.66016001e-01 -3.20101887e-01 -1.80107117e-01 -1.87340498e-01
-7.09243476e-01 -9.02173340e-01 1.27338624e+00 6.94803894e-01
-6.80187345e-01 6.48652136e-01 5.65598488e-01 -3.98503393e-01
-1.31599903e-01 -6.56952441e-01 -9.31932107e-02 4.94210832e-02
-1.26886874e-01 4.81518984e-01 4.46242094e-01 -2.61774033... | [9.456633567810059, 9.122856140136719] |
205233cc-c18d-4565-9717-3232a8b442ec | hypothesis-testing-for-equality-of-latent | 2105.10838 | null | https://arxiv.org/abs/2105.10838v2 | https://arxiv.org/pdf/2105.10838v2.pdf | Hypothesis Testing for Equality of Latent Positions in Random Graphs | We consider the hypothesis testing problem that two vertices $i$ and $j$ of a generalized random dot product graph have the same latent positions, possibly up to scaling. Special cases of this hypothesis test include testing whether two vertices in a stochastic block model or degree-corrected stochastic block model gra... | ['Minh Tang', 'Xinjie Du'] | 2021-05-23 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 1.54737264e-01 8.29199553e-02 -3.48149538e-01 -6.01997301e-02
-3.41947973e-02 -5.34619272e-01 3.58307093e-01 3.84086758e-01
-6.11705519e-02 6.45083070e-01 -2.80697376e-01 -6.35189652e-01
-9.08934295e-01 -1.07204843e+00 -5.47138572e-01 -9.91951525e-01
-6.92696810e-01 7.22205281e-01 2.40537927e-01 1.78065345... | [6.923585891723633, 5.172764778137207] |
e0161232-7989-414a-929c-9c0df8b0622f | social-processes-self-supervised-forecasting | 2107.13576 | null | https://arxiv.org/abs/2107.13576v3 | https://arxiv.org/pdf/2107.13576v3.pdf | Social Processes: Self-Supervised Meta-Learning over Conversational Groups for Forecasting Nonverbal Social Cues | Free-standing social conversations constitute a yet underexplored setting for human behavior forecasting. While the task of predicting pedestrian trajectories has received much recent attention, an intrinsic difference between these settings is how groups form and disband. Evidence from social psychology suggests that ... | ['Marco Loog', 'Hayley Hung', 'Chirag Raman'] | 2021-07-28 | null | https://openreview.net/forum?id=qcjOWDHAc4J | https://openreview.net/pdf?id=qcjOWDHAc4J | neurips-2021-12 | ['social-cue-forecasting', 'human-behavior-forecasting'] | ['time-series', 'time-series'] | [ 1.53224975e-01 1.96761806e-02 -1.80660367e-01 -5.51828325e-01
-7.02047721e-02 -4.51588511e-01 9.19571459e-01 1.26309380e-01
-2.12187096e-01 7.95427322e-01 7.61790574e-01 -1.92060247e-01
-1.71889246e-01 -5.17490029e-01 -6.61165595e-01 -6.15162790e-01
-2.36094818e-01 5.47621787e-01 1.95345283e-01 -4.39458162... | [6.111873626708984, 0.8041769862174988] |
e7716f13-f84c-4396-a18e-69c6fa8c2d74 | few-shot-incremental-learning-in-the-context | 2207.00693 | null | https://arxiv.org/abs/2207.00693v1 | https://arxiv.org/pdf/2207.00693v1.pdf | Few-shot incremental learning in the context of solar cell quality inspection | In industry, Deep Neural Networks have shown high defect detection rates surpassing other more traditional manual feature engineering based proposals. This has been achieved mainly through supervised training where a great amount of data is required in order to learn good classification models. However, such amount of ... | ['Luka Eciolaza', 'Julen Balzategui'] | 2022-07-01 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 4.13817465e-01 2.60401130e-01 1.96430326e-01 -3.78161579e-01
1.57158062e-01 -1.21402703e-01 2.98284441e-01 5.10312259e-01
-1.61312670e-01 8.32506776e-01 -5.19473910e-01 -1.27134696e-01
-2.53083408e-01 -1.12203109e+00 -7.13021874e-01 -7.57525504e-01
1.42054632e-02 4.73408103e-01 3.31466049e-01 -2.90227860... | [7.32025146484375, 1.989988088607788] |
0ae784d0-1e1a-45f8-9648-2ac32752ff2d | multiview-detection-with-cardboard-human | 2207.02013 | null | https://arxiv.org/abs/2207.02013v5 | https://arxiv.org/pdf/2207.02013v5.pdf | Multiview Detection with Cardboard Human Modeling | Multiview detection uses multiple calibrated cameras with overlapping fields of views to locate occluded pedestrians. In this field, existing methods typically adopt a ``human modeling - aggregation'' strategy. To find robust pedestrian representations, some intuitively incorporate 2D perception results from each frame... | ['Chuong Nguyen', 'Liang Zheng', 'Zicheng Duan', 'Jiahao Ma'] | 2022-07-05 | null | null | null | null | ['multiview-detection'] | ['computer-vision'] | [-2.35830247e-01 -2.44526327e-01 3.63219827e-02 -2.94838190e-01
-5.04765093e-01 -4.24028069e-01 4.52648252e-01 9.57200229e-02
-1.11548200e-01 4.18864071e-01 1.96813270e-01 2.43077464e-02
6.63836598e-01 -9.66641426e-01 -7.45337486e-01 -4.77587998e-01
4.57072914e-01 4.56782550e-01 6.64895594e-01 -2.90509276... | [7.437475681304932, -1.1120645999908447] |
6e799f1f-ba76-4fab-a2bc-06ff5a479271 | bilingual-lexicon-induction-through | 1907.10761 | null | https://arxiv.org/abs/1907.10761v1 | https://arxiv.org/pdf/1907.10761v1.pdf | Bilingual Lexicon Induction through Unsupervised Machine Translation | A recent research line has obtained strong results on bilingual lexicon induction by aligning independently trained word embeddings in two languages and using the resulting cross-lingual embeddings to induce word translation pairs through nearest neighbor or related retrieval methods. In this paper, we propose an alter... | ['Mikel Artetxe', 'Gorka Labaka', 'Eneko Agirre'] | 2019-07-24 | bilingual-lexicon-induction-through-1 | https://aclanthology.org/P19-1494 | https://aclanthology.org/P19-1494.pdf | acl-2019-7 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 3.53877270e-03 -1.58605039e-01 -6.15500510e-01 -1.54350415e-01
-1.25409484e+00 -9.21615601e-01 9.32354510e-01 2.47987673e-01
-9.62516725e-01 7.82104850e-01 3.55914623e-01 -5.55945635e-01
2.23664761e-01 -8.23357105e-01 -7.82480597e-01 -5.55810034e-01
5.11460066e-01 8.88370275e-01 -2.43479405e-02 -4.55838978... | [11.131728172302246, 10.046674728393555] |
e77075d3-a04a-41cb-b123-e2acc4d1ed09 | vad-vectorized-scene-representation-for | 2303.12077 | null | https://arxiv.org/abs/2303.12077v2 | https://arxiv.org/pdf/2303.12077v2.pdf | VAD: Vectorized Scene Representation for Efficient Autonomous Driving | Autonomous driving requires a comprehensive understanding of the surrounding environment for reliable trajectory planning. Previous works rely on dense rasterized scene representation (e.g., agent occupancy and semantic map) to perform planning, which is computationally intensive and misses the instance-level structure... | ['Xinggang Wang', 'Chang Huang', 'Wenyu Liu', 'Qian Zhang', 'Helong Zhou', 'Jiajie Chen', 'Bencheng Liao', 'Qing Xu', 'Shaoyu Chen', 'Bo Jiang'] | 2023-03-21 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-1.26245946e-01 7.95010999e-02 -2.31781676e-01 -3.77802759e-01
-7.38977253e-01 -4.61732626e-01 7.87811995e-01 1.50931552e-01
-6.64707422e-01 6.42022192e-01 1.89562082e-01 -7.72558808e-01
1.73046999e-02 -1.29843819e+00 -8.08384597e-01 -4.57832664e-01
-4.17780519e-01 6.72023535e-01 7.96786427e-01 -5.97051382... | [5.796955585479736, 0.8261553645133972] |
c36cf722-677c-4716-b86b-0a843e8d2301 | on-the-convergence-rates-of-policy-gradient | 2201.07443 | null | https://arxiv.org/abs/2201.07443v2 | https://arxiv.org/pdf/2201.07443v2.pdf | On the Convergence Rates of Policy Gradient Methods | We consider infinite-horizon discounted Markov decision problems with finite state and action spaces and study the convergence rates of the projected policy gradient method and a general class of policy mirror descent methods, all with direct parametrization in the policy space. First, we develop a theory of weak gradi... | ['Lin Xiao'] | 2022-01-19 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-2.99467589e-03 4.70268875e-01 -5.27418017e-01 -7.68936649e-02
-8.64760458e-01 -7.10462928e-01 5.87615848e-01 -1.79290727e-01
-7.84444034e-01 1.21547318e+00 3.81560415e-01 -8.70637178e-01
-2.07149088e-01 -4.10758764e-01 -7.86533773e-01 -9.26090658e-01
-1.35936916e-01 4.74395156e-01 -4.35694866e-02 -2.09234640... | [4.286829471588135, 2.5814247131347656] |
6f8344c5-04fb-4908-afc2-2ca777a1f950 | prescriptive-pca-dimensionality-reduction-for | 2306.02223 | null | https://arxiv.org/abs/2306.02223v1 | https://arxiv.org/pdf/2306.02223v1.pdf | Prescriptive PCA: Dimensionality Reduction for Two-stage Stochastic Optimization | In this paper, we consider the alignment between an upstream dimensionality reduction task of learning a low-dimensional representation of a set of high-dimensional data and a downstream optimization task of solving a stochastic program parameterized by said representation. In this case, standard dimensionality reducti... | ['Ho-Yin Mak', 'Long He'] | 2023-06-04 | null | null | null | null | ['dimensionality-reduction', 'stochastic-optimization'] | ['methodology', 'methodology'] | [ 0.2142745 0.21819872 -0.03273612 -0.36545214 -0.85724413 -0.66222894
0.24029934 0.13278192 -0.3276058 0.4550105 0.45135847 -0.31756172
-0.7593534 -0.73404866 -0.5349427 -0.9334162 -0.03719681 0.87467074
-0.6653923 -0.14399347 0.11482874 0.5868443 -1.2225933 -0.2126238
0.9174971 0.82003117 0.0... | [7.124890327453613, 4.181656360626221] |
42d23a2b-2a01-47ff-872f-f5b4e9b8da85 | pointacl-adversarial-contrastive-learning-for | 2209.06971 | null | https://arxiv.org/abs/2209.06971v1 | https://arxiv.org/pdf/2209.06971v1.pdf | PointACL:Adversarial Contrastive Learning for Robust Point Clouds Representation under Adversarial Attack | Despite recent success of self-supervised based contrastive learning model for 3D point clouds representation, the adversarial robustness of such pre-trained models raised concerns. Adversarial contrastive learning (ACL) is considered an effective way to improve the robustness of pre-trained models. In contrastive lear... | ['Chunming Qiao', 'Junsong Yuan', 'Bai Chen', 'Lu Cheng', 'Yatong An', 'Junxuan Huang'] | 2022-09-14 | null | null | null | null | ['3d-classification'] | ['computer-vision'] | [ 1.30431712e-01 3.14227223e-01 1.61760598e-01 -1.54960990e-01
-9.21557546e-01 -8.87251198e-01 8.26494992e-01 -3.79225522e-01
-2.91835368e-01 5.91662586e-01 -2.08916724e-01 -3.51129889e-01
2.42590010e-01 -1.18509519e+00 -1.37496269e+00 -6.04016185e-01
-8.43989626e-02 6.17273629e-01 2.26520211e-01 -5.71438134... | [7.901355266571045, -4.263686656951904] |
ec611185-1838-40da-96d2-bcfa705d69e8 | asbert-siamese-and-triplet-network-embedding | 2104.08558 | null | https://arxiv.org/abs/2104.08558v1 | https://arxiv.org/pdf/2104.08558v1.pdf | ASBERT: Siamese and Triplet network embedding for open question answering | Answer selection (AS) is an essential subtask in the field of natural language processing with an objective to identify the most likely answer to a given question from a corpus containing candidate answer sentences. A common approach to address the AS problem is to generate an embedding for each candidate sentence and ... | ['Olabanji Shonibare'] | 2021-04-17 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [ 4.15575117e-01 -1.04450680e-01 1.56857893e-01 -5.62610269e-01
-1.17775714e+00 -5.46609223e-01 4.71842378e-01 6.64240956e-01
-9.03557003e-01 3.56900930e-01 4.33633655e-01 -3.25811952e-01
-4.03003931e-01 -7.06629753e-01 -3.30106169e-01 -3.26936513e-01
1.37526989e-01 6.08886659e-01 3.72281700e-01 -5.76588929... | [11.248127937316895, 8.120339393615723] |
0af4f8d6-a14d-4909-bb23-eca99c73c017 | multi-agent-path-finding-with-prioritized | 2202.03634 | null | https://arxiv.org/abs/2202.03634v2 | https://arxiv.org/pdf/2202.03634v2.pdf | Multi-Agent Path Finding with Prioritized Communication Learning | Multi-agent pathfinding (MAPF) has been widely used to solve large-scale real-world problems, e.g., automation warehouses. The learning-based, fully decentralized framework has been introduced to alleviate real-time problems and simultaneously pursue optimal planning policy. However, existing methods might generate sig... | ['Xiangfeng Wang', 'Hongyuan Zha', 'Wenzhe Tan', 'Bo Jin', 'Hongjun Chen', 'Wenhao Li'] | 2022-02-08 | null | null | null | null | ['pico', 'multi-agent-path-finding'] | ['natural-language-processing', 'playing-games'] | [-2.54995227e-01 4.06940371e-01 -1.89307213e-01 1.68608874e-02
-5.07771492e-01 -3.60856086e-01 4.89192516e-01 4.63371307e-01
-5.60715497e-01 1.30508101e+00 -4.79732193e-02 -2.90307641e-01
-7.33205140e-01 -1.12296557e+00 -5.83085477e-01 -7.97750294e-01
-6.00965798e-01 1.06082940e+00 5.68552256e-01 -6.80190146... | [4.725723743438721, 1.67091965675354] |
a713a258-b1e1-4f12-bf88-a7ed7e4a462c | text-gestalt-stroke-aware-scene-text-image | 2112.08171 | null | https://arxiv.org/abs/2112.08171v1 | https://arxiv.org/pdf/2112.08171v1.pdf | Text Gestalt: Stroke-Aware Scene Text Image Super-Resolution | In the last decade, the blossom of deep learning has witnessed the rapid development of scene text recognition. However, the recognition of low-resolution scene text images remains a challenge. Even though some super-resolution methods have been proposed to tackle this problem, they usually treat text images as general... | ['xiangyang xue', 'Bin Li', 'jianqi ma', 'Haiyang Yu', 'Jingye Chen'] | 2021-12-13 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 5.25788724e-01 -2.03147724e-01 3.76907596e-03 -3.41197848e-01
-3.52748841e-01 -3.65899444e-01 8.08180153e-01 -2.85114080e-01
-1.54571995e-01 3.17523062e-01 4.08187807e-01 -6.61253855e-02
-4.68962938e-02 -9.87561584e-01 -5.66817760e-01 -7.51439333e-01
6.73703432e-01 2.06730768e-01 3.43123853e-01 -3.46491009... | [11.461540222167969, -1.6381542682647705] |
a77095eb-9491-45dc-be81-343031728d71 | procan-progressive-growing-channel-attentive | 2010.15417 | null | https://arxiv.org/abs/2010.15417v3 | https://arxiv.org/pdf/2010.15417v3.pdf | ProCAN: Progressive Growing Channel Attentive Non-Local Network for Lung Nodule Classification | Lung cancer classification in screening computed tomography (CT) scans is one of the most crucial tasks for early detection of this disease. Many lives can be saved if we are able to accurately classify malignant/cancerous lung nodules. Consequently, several deep learning based models have been proposed recently to cla... | ['Maxine Tan', 'Kelvin Shak', 'Mundher Al-Shabi'] | 2020-10-29 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [ 3.02675933e-01 1.49267375e-01 -3.01446229e-01 -1.88041613e-01
-9.26004708e-01 -3.23124081e-01 5.16944289e-01 4.98506092e-02
-5.61740279e-01 6.05786502e-01 3.53397392e-02 -4.63765800e-01
-1.73235223e-01 -6.74636424e-01 -5.25135100e-01 -8.73404562e-01
9.29526389e-02 7.20607579e-01 6.46883190e-01 2.25180343... | [15.39923095703125, -2.18733811378479] |
1aed10ce-a049-41e6-8d97-b858e5592593 | fact-check-worthiness-detection-as-positive | 2003.02736 | null | https://arxiv.org/abs/2003.02736v2 | https://arxiv.org/pdf/2003.02736v2.pdf | Claim Check-Worthiness Detection as Positive Unlabelled Learning | As the first step of automatic fact checking, claim check-worthiness detection is a critical component of fact checking systems. There are multiple lines of research which study this problem: check-worthiness ranking from political speeches and debates, rumour detection on Twitter, and citation needed detection from Wi... | ['Dustin Wright', 'Isabelle Augenstein'] | 2020-03-05 | null | https://aclanthology.org/2020.findings-emnlp.43 | https://aclanthology.org/2020.findings-emnlp.43.pdf | findings-of-the-association-for-computational | ['rumour-detection'] | ['natural-language-processing'] | [ 4.35780793e-01 3.31596732e-01 -4.58434939e-01 -9.66986716e-02
-1.27826107e+00 -8.16038489e-01 1.16189599e+00 1.05514538e+00
-2.86957979e-01 9.17278051e-01 4.08111721e-01 -7.54105747e-01
-2.28138939e-01 -7.52578974e-01 -6.16748512e-01 -2.26390138e-01
3.89854044e-01 7.11009800e-01 6.09073400e-01 -4.36485291... | [8.61080265045166, 9.874982833862305] |
65c51ae3-5dc4-4d51-b8df-738a64e83bcf | unleashing-realistic-air-quality-forecasting | 2306.13948 | null | https://arxiv.org/abs/2306.13948v1 | https://arxiv.org/pdf/2306.13948v1.pdf | Unleashing Realistic Air Quality Forecasting: Introducing the Ready-to-Use PurpleAirSF Dataset | Air quality forecasting has garnered significant attention recently, with data-driven models taking center stage due to advancements in machine learning and deep learning models. However, researchers face challenges with complex data acquisition and the lack of open-sourced datasets, hindering efficient model validatio... | ['Hakim Hacid', 'Michele Baldo', 'Wenbin Li', 'Jingwei Zuo'] | 2023-06-24 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-1.07140034e-01 -8.57413173e-01 -1.78493395e-01 -4.66726214e-01
-7.37579048e-01 -4.02624756e-01 6.35267556e-01 5.37904799e-01
-2.84418106e-01 6.93778038e-01 3.53731334e-01 -3.84086907e-01
-4.80254292e-01 -1.15362942e+00 -4.05760825e-01 -6.95232153e-01
-5.23469597e-02 1.45412043e-01 -1.95548341e-01 1.07362799... | [6.260385990142822, 2.542649745941162] |
b5ab33e5-06ec-4759-94c1-a530bbb84001 | a-large-scale-multimodal-dataset-of-human | 2303.08295 | null | https://arxiv.org/abs/2303.08295v1 | https://arxiv.org/pdf/2303.08295v1.pdf | A large-scale multimodal dataset of human speech recognition | Nowadays, non-privacy small-scale motion detection has attracted an increasing amount of research in remote sensing in speech recognition. These new modalities are employed to enhance and restore speech information from speakers of multiple types of data. In this paper, we propose a dataset contains 7.5 GHz Channel Imp... | ['Muhammad Imran', 'Qammer H. Abbasi', 'Daniele Faccio', 'Kevin Chetty', 'Wenda Li', 'Zikang Zhang', 'Haobo Li', 'Chong Tang', 'Yao Ge'] | 2023-03-15 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 3.34020793e-01 -2.97307402e-01 -2.29832958e-02 -4.01606500e-01
-1.22887504e+00 -2.74393052e-01 4.67316777e-01 -7.55237281e-01
-5.26622951e-01 5.46104491e-01 6.10608101e-01 -3.72917235e-01
-2.58098751e-01 -5.34098744e-01 8.59270245e-02 -1.19395578e+00
-3.90015962e-03 -2.67327458e-01 -2.55200595e-01 9.26032364... | [15.059425354003906, 5.765749454498291] |
a57d0c10-4c35-4d1b-9a4d-a32919705bb4 | walking-your-lidog-a-journey-through-multiple | 2304.11705 | null | https://arxiv.org/abs/2304.11705v1 | https://arxiv.org/pdf/2304.11705v1.pdf | Walking Your LiDOG: A Journey Through Multiple Domains for LiDAR Semantic Segmentation | The ability to deploy robots that can operate safely in diverse environments is crucial for developing embodied intelligent agents. As a community, we have made tremendous progress in within-domain LiDAR semantic segmentation. However, do these methods generalize across domains? To answer this question, we design the f... | ['Laura Leal-Taixé', 'Elisa Ricci', 'Aljoša Ošep', 'Cristiano Saltori'] | 2023-04-23 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 1.36416599e-01 3.98684561e-01 3.03770248e-02 -5.65114915e-01
-6.86784863e-01 -8.24937761e-01 3.62876981e-01 -7.98485056e-02
-6.71231866e-01 6.27823949e-01 -3.55524749e-01 -1.24658316e-01
8.57878104e-03 -8.53860438e-01 -1.22018862e+00 -2.35951126e-01
-1.73729107e-01 9.30474162e-01 5.33445597e-01 -3.67881238... | [8.124849319458008, -2.566190242767334] |
cc2d8d09-0868-445f-b24a-3c72e926b835 | retrieval-of-boost-invariant-symbolic | 2306.13496 | null | https://arxiv.org/abs/2306.13496v1 | https://arxiv.org/pdf/2306.13496v1.pdf | Retrieval of Boost Invariant Symbolic Observables via Feature Importance | Deep learning approaches for jet tagging in high-energy physics are characterized as black boxes that process a large amount of information from which it is difficult to extract key distinctive observables. In this proceeding, we present an alternative to deep learning approaches, Boost Invariant Polynomials, which ena... | ['Francesco Romeo', 'Christoph Ortner', 'Ilyes Batatia', 'Jose M Munoz'] | 2023-06-23 | null | null | null | null | ['jet-tagging', 'retrieval'] | ['graphs', 'methodology'] | [-4.57289040e-01 -4.41538393e-02 -9.31185856e-02 -2.90930718e-01
-8.59507263e-01 -6.32851124e-01 8.35354626e-01 4.05093908e-01
-4.16640341e-01 8.12423348e-01 -6.71785846e-02 -4.05095726e-01
-6.50142610e-01 -7.38059938e-01 -4.12111759e-01 -1.11461616e+00
-4.60282117e-01 8.91524553e-01 3.37025493e-01 -2.07769871... | [15.69823932647705, 2.91747784614563] |
0b6ee4f3-1a0d-4613-b8f4-62c713f3f282 | boost-video-frame-interpolation-via-motion | 2306.13933 | null | https://arxiv.org/abs/2306.13933v1 | https://arxiv.org/pdf/2306.13933v1.pdf | Boost Video Frame Interpolation via Motion Adaptation | Video frame interpolation (VFI) is a challenging task that aims to generate intermediate frames between two consecutive frames in a video. Existing learning-based VFI methods have achieved great success, but they still suffer from limited generalization ability due to the limited motion distribution of training dataset... | ['Yanfeng Wang', 'Ya zhang', 'Weidi Xie', 'Xiaoyun Zhang', 'HaoNing Wu'] | 2023-06-24 | null | null | null | null | ['video-frame-interpolation', 'motion-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.08065420e-01 -4.94324863e-01 -4.91671413e-01 -2.14869514e-01
-5.74074388e-01 -3.64171863e-01 5.29179275e-01 -5.51769376e-01
-1.58285841e-01 7.29572952e-01 1.48404226e-01 -3.31001163e-01
2.55475849e-01 -4.08671409e-01 -9.64077830e-01 -5.11130869e-01
-8.57656524e-02 -6.66007400e-02 6.74746215e-01 2.27027182... | [10.701504707336426, -1.3455008268356323] |
c8317cb7-4113-42b1-a7b6-b504b850e887 | moving-beyond-word-lists-towards-abstractive | 2211.05599 | null | https://arxiv.org/abs/2211.05599v1 | https://arxiv.org/pdf/2211.05599v1.pdf | Moving beyond word lists: towards abstractive topic labels for human-like topics of scientific documents | Topic models represent groups of documents as a list of words (the topic labels). This work asks whether an alternative approach to topic labeling can be developed that is closer to a natural language description of a topic than a word list. To this end, we present an approach to generating human-like topic labels usin... | ['Domenic Rosati'] | 2022-10-28 | null | null | null | null | ['topic-models', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.60041824e-01 8.01480651e-01 -2.40743518e-01 -5.33726990e-01
-8.35732341e-01 -5.56249678e-01 1.02809179e+00 8.52715313e-01
-7.33026043e-02 5.27164042e-01 1.10632432e+00 -4.24957484e-01
-4.33035284e-01 -7.93981493e-01 -1.22302130e-01 -4.40775841e-01
1.25756919e-01 9.12506759e-01 3.36601466e-01 -8.15053433... | [10.456543922424316, 7.219030380249023] |
1a6ba7f2-4f17-4885-999e-b1e687f8bdd7 | another-dead-end-for-morphological-tags | 2305.15119 | null | https://arxiv.org/abs/2305.15119v1 | https://arxiv.org/pdf/2305.15119v1.pdf | Another Dead End for Morphological Tags? Perturbed Inputs and Parsing | The usefulness of part-of-speech tags for parsing has been heavily questioned due to the success of word-contextualized parsers. Yet, most studies are limited to coarse-grained tags and high quality written content; while we know little about their influence when it comes to models in production that face lexical error... | ['David Vilares', 'Alberto Muñoz-Ortiz'] | 2023-05-24 | null | null | null | null | ['adversarial-attack'] | ['adversarial'] | [ 3.08672160e-01 5.93467772e-01 2.35967934e-01 -1.74324781e-01
-1.03001010e+00 -1.05896854e+00 3.91419351e-01 3.61624569e-01
-6.16447628e-01 7.22328782e-01 3.10334712e-01 -1.04947710e+00
4.47287977e-01 -8.08056653e-01 -9.60775256e-01 -5.97276330e-01
8.76460969e-02 4.17850405e-01 6.76537514e-01 -3.00992578... | [10.497384071350098, 9.651650428771973] |
10b6621a-f0f6-4bc2-9499-d378e8f7d3b7 | sketch-specific-data-augmentation-for | 1910.06038 | null | https://arxiv.org/abs/1910.06038v2 | https://arxiv.org/pdf/1910.06038v2.pdf | Sketch-Specific Data Augmentation for Freehand Sketch Recognition | Sketch recognition remains a significant challenge due to the limited training data and the substantial intra-class variance of freehand sketches for the same object. Conventional methods for this task often rely on the availability of the temporal order of sketch strokes, additional cues acquired from different modali... | ['Sicheng Zhao', 'Xiaoshuai Sun', 'Ying Zheng', 'Fatih Porikli', 'Hongxun Yao', 'Shengping Zhang'] | 2019-10-14 | null | null | null | null | ['sketch-based-image-retrieval', 'sketch-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.43243709e-01 -3.49825174e-01 -1.50918603e-01 -2.23956838e-01
-6.11588717e-01 -5.93710661e-01 1.00117362e+00 -3.48769516e-01
-3.36076826e-01 4.35025394e-01 -4.96650152e-02 7.86603093e-02
-2.68807739e-01 -7.97110379e-01 -6.64261937e-01 -6.21819556e-01
1.77022457e-01 4.25824702e-01 1.68020502e-01 -4.33354080... | [11.720267295837402, 0.4515213072299957] |
0e1601e7-2be1-43f0-8742-9dc521a81b3e | situational-object-boundary-detection | 1504.06434 | null | http://arxiv.org/abs/1504.06434v1 | http://arxiv.org/pdf/1504.06434v1.pdf | Situational Object Boundary Detection | Intuitively, the appearance of true object boundaries varies from image to
image. Hence the usual monolithic approach of training a single boundary
predictor and applying it to all images regardless of their content is bound to
be suboptimal. In this paper we therefore propose situational object boundary
detection: We ... | ['Vittorio Ferrari', 'Jasper Uijlings'] | 2015-04-24 | situational-object-boundary-detection-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Uijlings_Situational_Object_Boundary_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Uijlings_Situational_Object_Boundary_2015_CVPR_paper.pdf | cvpr-2015-6 | ['contour-detection'] | ['computer-vision'] | [ 5.42595923e-01 1.20729059e-01 -3.55486929e-01 -3.18713665e-01
-8.26987922e-01 -8.62786472e-01 5.09112000e-01 8.51365998e-02
-4.25001711e-01 3.32354814e-01 -4.51671839e-01 -4.55002904e-01
4.08941537e-01 -5.42969584e-01 -7.57739544e-01 -4.36773330e-01
2.76595265e-01 7.05777705e-01 9.14343178e-01 2.62883097... | [9.491044044494629, 0.34098103642463684] |
566d41a6-b755-4c3a-985c-c8ca21762875 | factuality-enhanced-language-models-for-open | 2206.04624 | null | https://arxiv.org/abs/2206.04624v3 | https://arxiv.org/pdf/2206.04624v3.pdf | Factuality Enhanced Language Models for Open-Ended Text Generation | Pretrained language models (LMs) are susceptible to generate text with nonfactual information. In this work, we measure and improve the factual accuracy of large-scale LMs for open-ended text generation. We design the FactualityPrompts test set and metrics to measure the factuality of LM generations. Based on that, we ... | ['Pascale Fung', 'Bryan Catanzaro', 'Mohammad Shoeybi', 'Mostofa Patwary', 'Peng Xu', 'Wei Ping', 'Nayeon Lee'] | 2022-06-09 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [-1.98595092e-01 6.33395910e-01 -2.81692356e-01 -1.64233416e-01
-9.70219851e-01 -6.44444585e-01 9.17881787e-01 -5.59463874e-02
-2.61163235e-01 1.49192345e+00 5.95660388e-01 -5.37144899e-01
-7.05553219e-02 -1.32757509e+00 -1.18946898e+00 -3.14616382e-01
1.49035841e-01 3.67193609e-01 -1.26518488e-01 -4.14279014... | [11.749735832214355, 8.900555610656738] |
7535c2ec-b958-4b11-b687-be650ecfe5be | binary-classifier-calibration-non-parametric | 1401.3390 | null | http://arxiv.org/abs/1401.3390v1 | http://arxiv.org/pdf/1401.3390v1.pdf | Binary Classifier Calibration: Non-parametric approach | Accurate calibration of probabilistic predictive models learned is critical
for many practical prediction and decision-making tasks. There are two main
categories of methods for building calibrated classifiers. One approach is to
develop methods for learning probabilistic models that are well-calibrated, ab
initio. The... | ['Gregory F. Cooper', 'Milos Hauskrecht', 'Mahdi Pakdaman Naeini'] | 2014-01-14 | null | null | null | null | ['classifier-calibration', 'classifier-calibration'] | ['computer-vision', 'miscellaneous'] | [ 2.95847416e-01 -3.44866887e-02 -1.87257469e-01 -7.87175834e-01
-9.85439420e-01 -5.21452844e-01 5.84006310e-01 3.97572607e-01
-3.25660527e-01 9.86176729e-01 -3.85644197e-01 -4.77228522e-01
-3.60244840e-01 -1.10955203e+00 -7.51397133e-01 -9.28962529e-01
1.14326596e-01 7.11605012e-01 5.83390832e-01 1.37255535... | [8.074762344360352, 4.195536136627197] |
2396cd92-21cc-48f5-9381-06243089be4f | towards-reliable-online-clickbait-video | 1907.07604 | null | https://arxiv.org/abs/1907.07604v2 | https://arxiv.org/pdf/1907.07604v2.pdf | Towards Reliable Online Clickbait Video Detection: A Content-Agnostic Approach | Online video sharing platforms (e.g., YouTube, Vimeo) have become an increasingly popular paradigm for people to consume video contents. Clickbait video, whose content clearly deviates from its title/thumbnail, has emerged as a critical problem on online video sharing platforms. Current clickbait detection solutions th... | ['Dong Wang', 'Daniel Zhang', 'Lanyu Shang', 'Shuyue Lai', 'Michael Wang'] | 2019-07-17 | null | null | null | null | ['clickbait-detection'] | ['natural-language-processing'] | [-8.25945288e-02 -6.48746669e-01 -7.12769270e-01 -8.60580243e-03
-9.51899111e-01 -9.97998178e-01 5.82885981e-01 1.09611504e-01
-1.61199495e-01 3.42347145e-01 8.22833329e-02 -3.80431086e-01
3.64285350e-01 -1.99145511e-01 -7.73380876e-01 -2.47147828e-01
1.59576505e-01 -3.32751632e-01 9.84889865e-01 2.56803602... | [7.733821868896484, 9.7260160446167] |
e37e4031-e1b0-4a89-9b17-66c1b3dd5ced | m3ke-a-massive-multi-level-multi-subject | 2305.10263 | null | https://arxiv.org/abs/2305.10263v2 | https://arxiv.org/pdf/2305.10263v2.pdf | M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models | Large language models have recently made tremendous progress in a variety of aspects, e.g., cross-task generalization, instruction following. Comprehensively evaluating the capability of large language models in multiple tasks is of great importance. In this paper, we propose M3KE, a Massive Multi-Level Multi-Subject K... | ['Deyi Xiong', 'Qun Liu', 'Xiaowen Su', 'Qingqing Lyu', 'Peiyi Zhang', 'Jianxiang Peng', 'Shuting Zhang', 'Xiaohan Peng', 'Tianyu Dong', 'Linhao Yu', 'Yuqi Ren', 'Renren Jin', 'Chuang Liu'] | 2023-05-17 | null | null | null | null | ['multiple-choice-qa', 'instruction-following'] | ['natural-language-processing', 'natural-language-processing'] | [-6.41936421e-01 -5.35781682e-01 -2.18911752e-01 -6.55868500e-02
-1.20329309e+00 -7.04921722e-01 3.10703337e-01 -3.25020310e-03
-7.86395967e-01 9.82029319e-01 7.37814531e-02 -4.65989560e-01
-1.26229748e-01 -6.42161369e-01 -6.43610835e-01 -3.53477061e-01
2.89074183e-01 3.13137680e-01 4.42967325e-01 -3.85450006... | [10.70329475402832, 8.774886131286621] |
df8b0144-c252-4aec-922f-7878c12061be | dry-focus-and-transcribe-end-to-end | 1904.09049 | null | http://arxiv.org/abs/1904.09049v3 | http://arxiv.org/pdf/1904.09049v3.pdf | An Investigation of End-to-End Multichannel Speech Recognition for Reverberant and Mismatch Conditions | Sequence-to-sequence (S2S) modeling is becoming a popular paradigm for
automatic speech recognition (ASR) because of its ability to jointly optimize
all the conventional ASR components in an end-to-end (E2E) fashion. This report
investigates the ability of E2E ASR from standard close-talk to far-field
applications by e... | ['Shinji Watanabe', 'Dung Tran', 'Aswin Shanmugam Subramanian', 'Toru Taniguchi', 'Yuya Fujita', 'Xiaofei Wang'] | 2019-04-19 | null | null | null | null | ['noisy-speech-recognition'] | ['speech'] | [ 2.03406155e-01 -2.16917440e-01 8.50088775e-01 -6.26790583e-01
-1.48735452e+00 -5.55064082e-01 4.18140858e-01 -4.66270864e-01
-5.72786808e-01 4.55477893e-01 6.40837193e-01 -6.78523362e-01
-1.42201185e-01 7.98630640e-02 -6.25547647e-01 -6.73081458e-01
-1.17897578e-01 -2.04899758e-02 -1.66740939e-01 -7.55422533... | [14.952703475952148, 6.027982711791992] |
3e0306a2-9d29-4e41-b3ed-e2af8618509a | multi-scale-knowledge-distillation-for | 2204.09931 | null | https://arxiv.org/abs/2204.09931v2 | https://arxiv.org/pdf/2204.09931v2.pdf | Learning to Purification for Unsupervised Person Re-identification | Unsupervised person re-identification is a challenging and promising task in computer vision. Nowadays unsupervised person re-identification methods have achieved great progress by training with pseudo labels. However, how to purify feature and label noise is less explicitly studied in the unsupervised manner. To purif... | ['DaCheng Tao', 'Jing Zhang', 'Xiang Zhang', 'Xiao Teng', 'Long Lan'] | 2022-04-21 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 1.05612926e-01 -2.76541591e-01 1.11858353e-01 -4.84607309e-01
-5.35247982e-01 -4.50798333e-01 4.48968738e-01 2.07929853e-02
-6.98790014e-01 5.70454061e-01 1.79388061e-01 3.69646460e-01
-8.91362876e-02 -5.85114360e-01 -4.77863461e-01 -1.00696313e+00
4.16097105e-01 5.58854818e-01 -1.70096800e-01 1.26525819... | [14.80541706085205, 1.0621559619903564] |
0130168f-e37f-4a21-b382-7399f7f3995c | an-approach-to-solving-the-abstraction-and | 2306.03553 | null | https://arxiv.org/abs/2306.03553v1 | https://arxiv.org/pdf/2306.03553v1.pdf | An Approach to Solving the Abstraction and Reasoning Corpus (ARC) Challenge | We utilise the power of Large Language Models (LLMs), in particular GPT4, to be prompt engineered into performing an arbitrary task. Here, we give the model some human priors via text, along with some typical procedures for solving the ARC tasks, and ask it to generate the i) broad description of the input-output relat... | ['Tan John Chong Min'] | 2023-06-06 | null | null | null | null | ['visual-question-answering-1'] | ['computer-vision'] | [ 1.79084376e-01 8.34147274e-01 5.10946155e-01 -1.10855468e-01
-9.60973322e-01 -9.22167480e-01 8.16491961e-01 -6.28667325e-02
-2.54883289e-01 5.64948857e-01 1.14877202e-01 -8.15154552e-01
-6.02030575e-01 -6.43942416e-01 -5.69655955e-01 -3.50511193e-01
-1.37119606e-01 1.16993070e+00 3.72658163e-01 -2.10553020... | [4.274390697479248, 1.1087110042572021] |
f14e95c9-2740-4107-91c5-f6a5f8950f8b | time-to-die-death-prediction-in-dota-2-using | 1906.03939 | null | https://arxiv.org/abs/1906.03939v1 | https://arxiv.org/pdf/1906.03939v1.pdf | Time to Die: Death Prediction in Dota 2 using Deep Learning | Esports have become major international sports with hundreds of millions of spectators. Esports games generate massive amounts of telemetry data. Using these to predict the outcome of esports matches has received considerable attention, but micro-predictions, which seek to predict events inside a match, is as yet unkno... | ['James Alfred Walker', 'Ryan Spick', 'Simon Demediuk', 'Anders Drachen', 'Adam Katona', 'Victoria Hodge', 'Florian Block'] | 2019-05-21 | null | null | null | null | ['dota-2'] | ['playing-games'] | [-2.78861791e-01 -5.71170300e-02 -9.93572772e-02 -1.78955719e-02
-9.83287096e-01 -6.57739997e-01 2.54268050e-01 4.34050679e-01
-8.19650292e-01 7.23316371e-01 4.56294775e-01 5.62485354e-03
-7.67044052e-02 -1.08077371e+00 -5.41314900e-01 -2.08722383e-01
-3.70466381e-01 6.89027071e-01 6.65200830e-01 -7.69290328... | [6.631248950958252, 0.35377177596092224] |
7ee0fbb4-96cc-4581-a600-1b2d86fd4c69 | rotated-object-detection-via-scale-invariant | 2204.00840 | null | https://arxiv.org/abs/2204.00840v2 | https://arxiv.org/pdf/2204.00840v2.pdf | Rotated Object Detection via Scale-invariant Mahalanobis Distance in Aerial Images | Rotated object detection in aerial images is a meaningful yet challenging task as objects are densely arranged and have arbitrary orientations. The eight-parameter (coordinates of box vectors) methods in rotated object detection usually use ln-norm losses (L1 loss, L2 loss, and smooth L1 loss) as loss functions. As ln-... | ['Yi Liu', 'Ruijie Wu', 'Wei Guo', 'Siyang Wen'] | 2022-04-02 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-2.28224173e-01 -3.02195400e-01 -5.01793111e-03 -1.95684910e-01
-4.62994188e-01 -5.13523519e-01 1.23486131e-01 -2.35059544e-01
-5.79373360e-01 4.82985258e-01 -3.37832659e-01 -1.53544417e-03
-3.73150259e-01 -6.49809122e-01 -5.64599574e-01 -6.59448564e-01
-3.93361688e-01 1.82437360e-01 7.32655346e-01 -1.89104825... | [8.684552192687988, -0.7592396140098572] |
4b1a8e6b-d76f-45c7-81d8-77b37a18f322 | concra-a-convolutional-neural-network-code | 2009.01959 | null | https://arxiv.org/abs/2009.01959v1 | https://arxiv.org/pdf/2009.01959v1.pdf | CoNCRA: A Convolutional Neural Network Code Retrieval Approach | Software developers routinely search for code using general-purpose search engines. However, these search engines cannot find code semantically unless it has an accompanying description. We propose a technique for semantic code search: A Convolutional Neural Network approach to code retrieval (CoNCRA). Our technique ai... | ['Marco A. Gerosa', 'Marcelo de Rezende Martins'] | 2020-09-03 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-2.47521177e-01 -8.96399692e-02 -1.99024335e-01 1.28721505e-01
-8.76132607e-01 -6.62365496e-01 2.30120748e-01 5.93370140e-01
-2.22574770e-01 -1.53501019e-01 3.73224378e-01 -6.58672452e-01
-3.97879988e-01 -6.80420995e-01 -6.66039646e-01 6.32793754e-02
-1.18867114e-01 -2.07969710e-01 4.97300982e-01 -4.04976845... | [7.527677059173584, 8.083995819091797] |
e4d5e4d1-9d09-485b-a19d-2ebd02fd9425 | learning-spatiotemporal-frequency-transformer | 2208.03012 | null | https://arxiv.org/abs/2208.03012v1 | https://arxiv.org/pdf/2208.03012v1.pdf | Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution | Compressed video super-resolution (VSR) aims to restore high-resolution frames from compressed low-resolution counterparts. Most recent VSR approaches often enhance an input frame by borrowing relevant textures from neighboring video frames. Although some progress has been made, there are grand challenges to effectivel... | ['Dongmei Fu', 'Jianlong Fu', 'Huan Yang', 'Zhongwei Qiu'] | 2022-08-05 | null | null | null | null | ['video-super-resolution', 'video-enhancement'] | ['computer-vision', 'computer-vision'] | [ 6.58754051e-01 -4.31876361e-01 -3.99347186e-01 -8.64779055e-02
-1.13978851e+00 -1.34664282e-01 2.95127988e-01 -4.35519427e-01
1.17108300e-01 7.60241389e-01 7.01855123e-01 1.32504106e-01
-1.11388145e-02 -7.87377954e-01 -9.74756718e-01 -7.46905863e-01
1.89469494e-02 -5.97464383e-01 4.27958459e-01 -3.83989513... | [11.097932815551758, -1.9728692770004272] |
68fbaca1-68ac-421b-a8a3-965191ee440d | comparison-of-the-performance-of-machine | null | null | https://www.jphres.org/index.php/jphres/article/view/1677 | https://www.jphres.org/index.php/jphres/article/view/1677 | Comparison of the performance of machine learning algorithms in breast cancer screening and detection: A protocol | BACKGROUND:
Breast Cancer (BC) is a known global crisis. The World Health Organization reports a global 2.09 million incidences and 627,000 deaths in 2018 relating to BC. The traditional BC screening method in developed countries is mammography, whilst developing countries employ breast self-examination and clinical b... | ['Yashik Singh', 'Zakia Salod'] | 2019-12-04 | null | null | null | journal-of-public-health-research-2019-12 | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 2.94108123e-01 -1.46663964e-01 -5.87997496e-01 -2.51906812e-01
-6.58340096e-01 -1.97802514e-01 5.92042923e-01 7.00529456e-01
-3.58804673e-01 9.59283650e-01 1.03545338e-01 -1.10697651e+00
-3.08054179e-01 -1.04561496e+00 -1.80195093e-01 -8.55871201e-01
-3.13172162e-01 5.51509738e-01 3.26993585e-01 -1.17761679... | [15.285818099975586, -2.7199935913085938] |
35ff9556-95af-499d-a20b-3916a8b933cd | improving-automatic-skin-lesion-segmentation | 1807.08392 | null | http://arxiv.org/abs/1807.08392v2 | http://arxiv.org/pdf/1807.08392v2.pdf | Improving Automatic Skin Lesion Segmentation using Adversarial Learning based Data Augmentation | Segmentation of skin lesions is considered as an important step in computer
aided diagnosis (CAD) for automated melanoma diagnosis. In recent years,
segmentation methods based on fully convolutional networks (FCN) have achieved
great success in general images. This success is primarily due to the
leveraging of large la... | ['Jinman Kim', 'Lei Bi', 'Dagan Feng'] | 2018-07-23 | null | null | null | null | ['melanoma-diagnosis', 'skin-lesion-segmentation'] | ['computer-vision', 'medical'] | [ 7.30084002e-01 1.71461791e-01 9.87238213e-02 -2.96761185e-01
-8.53780508e-01 -5.52293837e-01 5.25577426e-01 -1.01396419e-01
-3.12923580e-01 5.66522360e-01 1.22630961e-01 -5.53734116e-02
-7.53955767e-02 -8.76662254e-01 -5.43915927e-01 -7.68660188e-01
2.67039150e-01 3.46165806e-01 2.46740475e-01 -3.61235917... | [15.27465534210205, -2.7162668704986572] |
8e4ff511-f664-417a-94fa-3975f237d268 | uncertainty-aware-deep-co-training-for-semi | 2111.11629 | null | https://arxiv.org/abs/2111.11629v2 | https://arxiv.org/pdf/2111.11629v2.pdf | Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentation | Semi-supervised learning has made significant strides in the medical domain since it alleviates the heavy burden of collecting abundant pixel-wise annotated data for semantic segmentation tasks. Existing semi-supervised approaches enhance the ability to extract features from unlabeled data with prior knowledge obtained... | ['Chiu-Wing Sham', 'Xingwei Wang', 'Jialei Chen', 'Haoyu Xie', 'Chong Fu', 'Xu Zheng'] | 2021-11-23 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 3.52937490e-01 4.33413118e-01 -5.31731248e-01 -8.17543983e-01
-7.70977199e-01 -1.12190358e-01 1.17774479e-01 9.74999517e-02
-5.86759090e-01 1.03181696e+00 2.11687712e-03 -3.57085876e-02
-1.32081807e-01 -6.11817360e-01 -6.33382440e-01 -9.63427186e-01
4.27876115e-01 3.84019673e-01 1.64420947e-01 2.62007505... | [14.699736595153809, -2.010460376739502] |
472c8657-e046-4f37-aa1d-488a5e24dc95 | reactive-exploration-to-cope-with-non | 2207.05742 | null | https://arxiv.org/abs/2207.05742v2 | https://arxiv.org/pdf/2207.05742v2.pdf | Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning | In lifelong learning, an agent learns throughout its entire life without resets, in a constantly changing environment, as we humans do. Consequently, lifelong learning comes with a plethora of research problems such as continual domain shifts, which result in non-stationary rewards and environment dynamics. These non-s... | ['Sepp Hochreiter', 'Hamid Eghbal-zadeh', 'Angela Bitto-Nemling', 'Vihang Patil', 'Marius-Constantin Dinu', 'Fabian Paischer', 'Thomas Schmied', 'Christian Steinparz'] | 2022-07-12 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-3.56868565e-01 -2.70497948e-01 -4.20944154e-01 9.10092071e-02
-3.68163794e-01 -6.22418940e-01 5.82333028e-01 1.01955451e-01
-5.98934591e-01 1.44542575e+00 -1.80466250e-01 -1.78073272e-01
-3.93609852e-01 -6.78626895e-01 -6.27701879e-01 -9.80545163e-01
-4.95378882e-01 6.81115150e-01 3.07565272e-01 -4.23704565... | [4.048673152923584, 2.231920003890991] |
c5cca409-4b6d-48e2-845a-6e56daa0d505 | robust-monopoly-regulation | 1910.04260 | null | https://arxiv.org/abs/1910.04260v1 | https://arxiv.org/pdf/1910.04260v1.pdf | Robust Monopoly Regulation | We study the regulation of a monopolistic firm using a robust-design approach. We solve for the policy that minimizes the regulator's worst-case regret, where the regret is the difference between his complete-information payoff minus his realized payoff. When the regulator's payoff is consumers' surplus, it is optimal ... | ['Eran Shmaya', 'Yingni Guo'] | 2019-10-09 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 1.04174607e-01 8.86084735e-01 -5.65902472e-01 1.14517421e-01
-8.48719239e-01 -7.34413743e-01 -2.85727262e-01 1.69635177e-01
-4.70359355e-01 7.90824354e-01 4.21688765e-01 -6.18908405e-01
-4.82509732e-01 -6.58353686e-01 -3.59510869e-01 -8.92816484e-01
8.33097771e-02 -6.13186276e-03 -5.28929830e-01 1.12640135... | [4.429364204406738, 3.1739656925201416] |
13978621-ce8c-43d3-b535-9e8c2115efc3 | converging-measures-and-an-emergent-model-a | 2303.13799 | null | https://arxiv.org/abs/2303.13799v1 | https://arxiv.org/pdf/2303.13799v1.pdf | Converging Measures and an Emergent Model: A Meta-Analysis of Human-Automation Trust Questionnaires | A significant challenge to measuring human-automation trust is the amount of construct proliferation, models, and questionnaires with highly variable validation. However, all agree that trust is a crucial element of technological acceptance, continued usage, fluency, and teamwork. Herein, we synthesize a consensus mode... | ['Karen M. Feigh', 'Yosef S. Razin'] | 2023-03-24 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [-6.45559192e-01 3.99367251e-02 -3.81158710e-01 -3.82865608e-01
2.34727059e-02 -4.05731887e-01 1.59340709e-01 5.28444946e-01
-3.40728253e-01 3.88905525e-01 2.17274457e-01 -5.04181087e-01
-2.76306242e-01 -1.90366834e-01 -4.39357847e-01 3.38509604e-02
2.02623338e-01 -1.42481595e-01 -4.00811166e-01 -4.01822835... | [9.062834739685059, 6.284859657287598] |
1869b4ed-4261-4688-acff-e02e87a63268 | raft-a-real-world-few-shot-text | 2109.14076 | null | https://arxiv.org/abs/2109.14076v3 | https://arxiv.org/pdf/2109.14076v3.pdf | RAFT: A Real-World Few-Shot Text Classification Benchmark | Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? Existing benchmarks are not designed to measure progress in applied s... | ['Andreas Stuhlmüller', 'Michael Noetel', 'Alexis Carlier', 'Paul Sedille', 'Carolyn Ashurst', 'Emmie Hine', 'C. Jess Riedel', 'Pegah Maham', 'Abhishek Thakur', 'Lewis Tunstall', 'Eli Lifland', 'Neel Alex'] | 2021-09-28 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 2.44615808e-01 2.34890953e-01 -2.36276105e-01 -3.60058695e-01
-1.00309706e+00 -5.07466555e-01 8.32999766e-01 2.66987700e-02
-8.25961411e-01 7.90506780e-01 3.58039349e-01 -4.17351186e-01
-2.02315822e-02 -4.57491070e-01 -2.82971710e-01 -1.60640135e-01
9.03541222e-02 1.07267082e+00 4.72226202e-01 -6.94771588... | [11.126886367797852, 8.325722694396973] |
19023167-cf76-4a29-98fe-d964af2c1157 | sfmlearner-learning-monocular-depth-ego | 1812.08370 | null | http://arxiv.org/abs/1812.08370v1 | http://arxiv.org/pdf/1812.08370v1.pdf | SfMLearner++: Learning Monocular Depth & Ego-Motion using Meaningful Geometric Constraints | Most geometric approaches to monocular Visual Odometry (VO) provide robust
pose estimates, but sparse or semi-dense depth estimates. Off late, deep
methods have shown good performance in generating dense depths and VO from
monocular images by optimizing the photometric consistency between images.
Despite being intuitiv... | ['Vignesh Prasad', 'Brojeshwar Bhowmick'] | 2018-12-20 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-2.15658881e-02 2.26376176e-01 1.42959245e-02 -5.70749998e-01
-6.45305634e-01 -4.54712063e-01 7.65320361e-01 -2.28551418e-01
-5.72439790e-01 8.64294410e-01 1.52617171e-01 -2.37105805e-02
2.64064260e-02 -6.37857556e-01 -1.00636697e+00 -5.69909930e-01
2.47524709e-01 7.68797040e-01 2.42805481e-01 7.33388215... | [8.572245597839355, -2.5295369625091553] |
f0ecbf7f-cc31-4aca-8325-14ba51dd82d9 | ckconv-continuous-kernel-convolution-for | 2102.02611 | null | https://arxiv.org/abs/2102.02611v3 | https://arxiv.org/pdf/2102.02611v3.pdf | CKConv: Continuous Kernel Convolution For Sequential Data | Conventional neural architectures for sequential data present important limitations. Recurrent networks suffer from exploding and vanishing gradients, small effective memory horizons, and must be trained sequentially. Convolutional networks are unable to handle sequences of unknown size and their memory horizon must be... | ['Mark Hoogendoorn', 'Jakub M. Tomczak', 'Erik J. Bekkers', 'Anna Kuzina', 'David W. Romero'] | 2021-02-04 | ckconv-continuous-kernel-convolution-for-1 | https://openreview.net/forum?id=8FhxBtXSl0 | https://openreview.net/pdf?id=8FhxBtXSl0 | iclr-2022-4 | ['sequential-image-classification'] | ['computer-vision'] | [ 8.70859176e-02 -2.20958829e-01 -2.00583830e-01 -2.96904962e-03
-1.06589265e-01 -6.94897056e-01 4.72626299e-01 -1.56481415e-01
-9.18557882e-01 6.77025557e-01 -4.58237946e-01 -5.51009059e-01
-3.10019076e-01 -6.13173664e-01 -8.43145669e-01 -6.22913718e-01
-4.81044441e-01 2.49702692e-01 4.26890880e-01 -3.39278928... | [7.721799373626709, 3.4045538902282715] |
55a7d349-19de-40b5-9226-78b1f8ec15df | gait-cycle-reconstruction-and-human | 2206.13395 | null | https://arxiv.org/abs/2206.13395v1 | https://arxiv.org/pdf/2206.13395v1.pdf | Gait Cycle Reconstruction and Human Identification from Occluded Sequences | Gait-based person identification from videos captured at surveillance sites using Computer Vision-based techniques is quite challenging since these walking sequences are usually corrupted with occlusion, and a complete cycle of gait is not always available. In this work, we propose an effective neural network-based mod... | ['Pratik Chattopadhyay', 'Jinesh Jain', 'Manav Mukesh Jain', 'Abhishek Paul'] | 2022-06-20 | null | null | null | null | ['gait-recognition', 'person-identification', 'occlusion-handling'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.73466146e-01 -4.04892206e-01 1.18100472e-01 -2.58917302e-01
-4.91845697e-01 2.39250407e-01 2.82639533e-01 -5.64172626e-01
-5.33933580e-01 9.75773096e-01 2.63896435e-01 2.40665823e-01
2.30436951e-01 -7.74092317e-01 -5.78225553e-01 -9.08502340e-01
-4.16217595e-01 1.77277341e-01 -1.44765377e-01 1.41030192... | [14.268715858459473, 1.4436912536621094] |
2237f093-f876-4d12-af43-af34dfd6442a | diverse-trajectory-forecasting-with | 1907.04967 | null | https://arxiv.org/abs/1907.04967v2 | https://arxiv.org/pdf/1907.04967v2.pdf | Diverse Trajectory Forecasting with Determinantal Point Processes | The ability to forecast a set of likely yet diverse possible future behaviors of an agent (e.g., future trajectories of a pedestrian) is essential for safety-critical perception systems (e.g., autonomous vehicles). In particular, a set of possible future behaviors generated by the system must be diverse to account for ... | ['Ye Yuan', 'Kris Kitani'] | 2019-07-11 | null | https://openreview.net/forum?id=ryxnY3NYPS | https://openreview.net/pdf?id=ryxnY3NYPS | iclr-2020-1 | ['human-pose-forecasting'] | ['computer-vision'] | [-7.30462372e-02 -1.96758490e-02 -1.55044183e-01 -4.71696466e-01
-6.69857264e-01 -5.39520919e-01 8.66912127e-01 -2.24092737e-01
-1.11516081e-01 7.43544281e-01 3.60692799e-01 -2.24373773e-01
-2.17377558e-01 -9.92199361e-01 -9.91789222e-01 -9.83657539e-01
-1.03778057e-01 7.07422137e-01 1.25186741e-01 -2.26104304... | [6.430457592010498, 0.7949286699295044] |
b89a91e7-b370-4cec-a1b6-49b1ee84e86f | enabling-efficiency-precision-trade-offs-for | 2106.00730 | null | https://arxiv.org/abs/2106.00730v2 | https://arxiv.org/pdf/2106.00730v2.pdf | Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification | Extreme multi-label classification (XMC) aims to learn a model that can tag data points with a subset of relevant labels from an extremely large label set. Real world e-commerce applications like personalized recommendations and product advertising can be formulated as XMC problems, where the objective is to predict fo... | ['Inderjit S. Dhillon', 'Sujay Sanghavi', 'Kedarnath Kolluri', 'Daniel L. Jiang', 'Tavor Z. Baharav'] | 2021-06-01 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 2.39122733e-01 -3.43165509e-02 -3.47684860e-01 -6.19430304e-01
-1.15988839e+00 -9.02872801e-01 3.02444547e-01 6.27809882e-01
-3.23065937e-01 3.21029723e-01 -2.78241396e-01 -6.38882339e-01
-4.62453455e-01 -6.02010429e-01 -6.58630073e-01 -5.80018699e-01
-3.88724536e-01 9.86256540e-01 9.96778533e-02 2.41510794... | [9.427563667297363, 4.440122127532959] |
79a776dc-13df-475f-9299-c7d25a768b9a | bias-in-conversational-search-the-double | 2010.10409 | null | https://arxiv.org/abs/2010.10409v1 | https://arxiv.org/pdf/2010.10409v1.pdf | Bias in Conversational Search: The Double-Edged Sword of the Personalized Knowledge Graph | Conversational AI systems are being used in personal devices, providing users with highly personalized content. Personalized knowledge graphs (PKGs) are one of the recently proposed methods to store users' information in a structured form and tailor answers to their liking. Personalization, however, is prone to amplify... | ['Arjen P. de Vries', 'Faegheh Hasibi', 'Emma J. Gerritse'] | 2020-10-20 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-2.40674242e-01 4.39992517e-01 -5.33566475e-01 -5.69553316e-01
-1.50476784e-01 -5.50374568e-01 4.55177456e-01 2.54147917e-01
-3.04918826e-01 8.16483140e-01 7.41659701e-01 -7.60840029e-02
-3.49534929e-01 -6.98355854e-01 5.54793924e-02 -3.08335185e-01
2.29435846e-01 7.61102736e-01 -8.30499455e-02 -6.56584382... | [12.257837295532227, 7.762327671051025] |
fe86d86d-097c-49c8-a1de-07569d7c7b73 | xlid-lexica-cross-lingual-linked-data-lexica | null | null | https://aclanthology.org/L14-1232 | https://aclanthology.org/L14-1232.pdf | xLiD-Lexica: Cross-lingual Linked Data Lexica | In this paper, we introduce our cross-lingual linked data lexica, called xLiD-Lexica, which are constructed by exploiting the multilingual Wikipedia and linked data resources from Linked Open Data (LOD). We provide the cross-lingual groundings of linked data resources from LOD as RDF data, which can be easily integrate... | ['Michael F{\\"a}rber', 'Achim Rettinger', 'Lei Zhang'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['cross-lingual-entity-linking', 'text-annotation'] | ['natural-language-processing', 'natural-language-processing'] | [-7.95237124e-01 5.39777339e-01 -3.60682517e-01 -4.31477606e-01
-9.94790077e-01 -9.73701000e-01 6.90760374e-01 9.78222132e-01
-3.86951357e-01 1.12631214e+00 6.78473532e-01 -5.35755828e-02
-3.64712119e-01 -1.40040183e+00 -7.53051758e-01 3.41009736e-01
4.68009012e-03 6.86507761e-01 6.16378129e-01 -6.59732938... | [9.405694961547852, 8.482860565185547] |
4f36b777-03c4-4eff-b98b-08eaf7234980 | towards-a-query-optimal-and-time-efficient | 2106.10374 | null | https://arxiv.org/abs/2106.10374v1 | https://arxiv.org/pdf/2106.10374v1.pdf | Towards a Query-Optimal and Time-Efficient Algorithm for Clustering with a Faulty Oracle | Motivated by applications in crowdsourced entity resolution in database, signed edge prediction in social networks and correlation clustering, Mazumdar and Saha [NIPS 2017] proposed an elegant theoretical model for studying clustering with a faulty oracle. In this model, given a set of $n$ items which belong to $k$ unk... | ['Jiapeng Zhang', 'Pan Peng'] | 2021-06-18 | null | null | null | null | ['stochastic-block-model', 'entity-resolution'] | ['graphs', 'natural-language-processing'] | [-1.38089523e-01 3.09204072e-01 -1.28776327e-01 -8.44319072e-03
-1.21352839e+00 -8.68330002e-01 -2.42367581e-01 5.45496941e-01
-5.74573874e-01 7.22452700e-01 -4.92471606e-01 -3.70059073e-01
-6.86657846e-01 -1.26689327e+00 -1.01529038e+00 -8.48079383e-01
-5.87592423e-01 1.02140081e+00 4.21148509e-01 -1.42373621... | [6.783349514007568, 5.000453948974609] |
554d94fb-d3d5-41af-b366-8046a1c64bcd | gait-recognition-in-the-wild-with-dense-3d | 2204.02569 | null | https://arxiv.org/abs/2204.02569v1 | https://arxiv.org/pdf/2204.02569v1.pdf | Gait Recognition in the Wild with Dense 3D Representations and A Benchmark | Existing studies for gait recognition are dominated by 2D representations like the silhouette or skeleton of the human body in constrained scenes. However, humans live and walk in the unconstrained 3D space, so projecting the 3D human body onto the 2D plane will discard a lot of crucial information like the viewpoint, ... | ['Tao Mei', 'Chenggang Yan', 'Lingxiao He', 'Wu Liu', 'Xinchen Liu', 'Jinkai Zheng'] | 2022-04-06 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zheng_Gait_Recognition_in_the_Wild_With_Dense_3D_Representations_and_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zheng_Gait_Recognition_in_the_Wild_With_Dense_3D_Representations_and_CVPR_2022_paper.pdf | cvpr-2022-1 | ['gait-recognition-in-the-wild'] | ['computer-vision'] | [-2.72907972e-01 -4.51790154e-01 -1.35416791e-01 -2.12938013e-03
-5.06605208e-02 -2.08918348e-01 2.62733281e-01 -6.37799323e-01
-1.17399618e-01 2.66733021e-01 4.41047132e-01 2.85805196e-01
2.64254689e-01 -5.54148734e-01 -3.52553904e-01 -8.59350622e-01
-2.83339292e-01 5.46208918e-01 1.08500943e-01 -1.98989153... | [14.283770561218262, 1.4142816066741943] |
5f7086b1-a5ba-4c67-ad8a-f45eeadb2344 | describing-unseen-videos-via-multi | 2008.07935 | null | https://arxiv.org/abs/2008.07935v2 | https://arxiv.org/pdf/2008.07935v2.pdf | Describing Unseen Videos via Multi-Modal Cooperative Dialog Agents | With the arising concerns for the AI systems provided with direct access to abundant sensitive information, researchers seek to develop more reliable AI with implicit information sources. To this end, in this paper, we introduce a new task called video description via two multi-modal cooperative dialog agents, whose ul... | ['Yu Wu', 'Yi Yang', 'Ye Zhu', 'Yan Yan'] | 2020-08-18 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4257_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123680154.pdf | eccv-2020-8 | ['video-description'] | ['computer-vision'] | [-1.47044092e-01 4.20609444e-01 -2.37265140e-01 -3.10323536e-01
-8.21653366e-01 -6.35742188e-01 4.21910375e-01 -3.58413130e-01
-4.60450351e-01 8.70440781e-01 2.65619308e-01 3.71123821e-01
1.54857934e-01 -3.91885936e-01 -1.72396719e-01 -7.02980697e-01
-6.29624575e-02 7.02731490e-01 6.62140131e-01 -5.17154813... | [10.840975761413574, 1.126764178276062] |
b6953196-3888-4148-8ae3-7c859c532000 | semantic-aware-graph-matching-mechanism-for | 2304.11275 | null | https://arxiv.org/abs/2304.11275v1 | https://arxiv.org/pdf/2304.11275v1.pdf | Semantic-Aware Graph Matching Mechanism for Multi-Label Image Recognition | Multi-label image recognition aims to predict a set of labels that present in an image. The key to deal with such problem is to mine the associations between image contents and labels, and further obtain the correct assignments between images and their labels. In this paper, we treat each image as a bag of instances, a... | ['Yang Wang', 'Songhe Feng', 'Yanan Wu'] | 2023-04-21 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 7.91288912e-01 -1.05580300e-01 -5.30062795e-01 -6.65314257e-01
-8.01717162e-01 -2.23870009e-01 3.86139423e-01 3.58995378e-01
5.71334325e-02 2.19778493e-01 -1.09220974e-01 2.73759156e-01
-4.42106843e-01 -7.39406407e-01 -3.85344416e-01 -7.77718365e-01
3.86404455e-01 4.05821204e-01 -8.74568336e-03 4.10081923... | [9.746611595153809, 4.0315680503845215] |
72775b62-6f66-4119-8e7e-4178c0554173 | channel-attention-networks-for-robust-mr | 2012.01241 | null | https://arxiv.org/abs/2012.01241v1 | https://arxiv.org/pdf/2012.01241v1.pdf | Channel Attention Networks for Robust MR Fingerprinting Matching | Magnetic Resonance Fingerprinting (MRF) enables simultaneous mapping of multiple tissue parameters such as T1 and T2 relaxation times. The working principle of MRF relies on varying acquisition parameters pseudo-randomly, so that each tissue generates its unique signal evolution during scanning. Even though MRF provide... | ['Ilkay Oksuz', 'Devrim Unay', 'Andrew P. King', 'Claudia Prieto', 'Gastao Cruz', 'Eda Ozgu Ersoy', 'Ebru Navruz', 'Refik Soyak'] | 2020-12-02 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 6.20591700e-01 3.54579203e-02 1.09058537e-01 -3.47250611e-01
-7.75503099e-01 -1.76475689e-01 2.43979871e-01 6.55206665e-02
-4.90830630e-01 7.29604602e-01 -9.00072791e-03 -3.18202078e-02
-3.28167498e-01 -4.82199311e-01 -8.15101147e-01 -8.99355054e-01
-2.47222662e-01 1.46307185e-01 3.21145117e-01 1.05642535... | [13.609705924987793, -2.404474973678589] |
3b840650-dd71-463e-affc-5be981d39f25 | soft-prompt-tuning-for-large-language-models | 2306.04735 | null | https://arxiv.org/abs/2306.04735v1 | https://arxiv.org/pdf/2306.04735v1.pdf | Soft-prompt Tuning for Large Language Models to Evaluate Bias | Prompting large language models has gained immense popularity in recent years due to the advantage of producing good results even without the need for labelled data. However, this requires prompt tuning to get optimal prompts that lead to better model performances. In this paper, we explore the use of soft-prompt tunin... | ['Faiza Khan Khattak', 'Laleh Seyyed-Kalantari', 'Deval Pandya', 'Sevil Zanjani Miyandoab', 'David Emerson', 'Jacob-Junqi Tian'] | 2023-06-07 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-5.76954335e-03 1.97432950e-01 -2.07727224e-01 -8.90542328e-01
-5.56562483e-01 -6.58808291e-01 6.67005241e-01 4.80286598e-01
-6.00098073e-01 4.70747322e-01 3.48976851e-01 -4.81157333e-01
1.01898260e-01 -5.73911965e-01 -5.73878169e-01 -5.52743018e-01
1.35236591e-01 3.57173979e-01 1.80292979e-01 -2.11586520... | [9.797323226928711, 7.808390140533447] |
41680c12-9b57-4037-b164-07f916611468 | motion-inductive-self-supervised-object | 2210.00221 | null | https://arxiv.org/abs/2210.00221v1 | https://arxiv.org/pdf/2210.00221v1.pdf | Motion-inductive Self-supervised Object Discovery in Videos | In this paper, we consider the task of unsupervised object discovery in videos. Previous works have shown promising results via processing optical flows to segment objects. However, taking flow as input brings about two drawbacks. First, flow cannot capture sufficient cues when objects remain static or partially occlud... | ['Qi Tian', 'Hongkai Xiong', 'Xiaopeng Zhang', 'Rui Qian', 'Yabo Chen', 'Weidi Xie', 'Shuangrui Ding'] | 2022-10-01 | null | null | null | null | ['object-discovery', 'unsupervised-object-segmentation', 'object-discovery-in-videos'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.17113936e-01 -1.98023617e-01 -7.81049579e-02 -1.48190230e-01
-1.86806381e-01 -5.57439864e-01 3.24730456e-01 -1.33919239e-01
-5.05608201e-01 7.62821555e-01 -2.71416381e-02 3.78608704e-02
6.73624650e-02 -6.55948102e-01 -7.44087875e-01 -6.45518363e-01
-2.46890366e-01 1.06322847e-01 9.30454195e-01 3.09401125... | [9.142990112304688, -0.3020530343055725] |
b0deb7ec-6a37-41b4-80a9-cffc00154406 | microscopic-nuclei-classification | 1811.03447 | null | http://arxiv.org/abs/1811.03447v1 | http://arxiv.org/pdf/1811.03447v1.pdf | Microscopic Nuclei Classification, Segmentation and Detection with improved Deep Convolutional Neural Network (DCNN) Approaches | Due to cellular heterogeneity, cell nuclei classification, segmentation, and
detection from pathological images are challenging tasks. In the last few
years, Deep Convolutional Neural Networks (DCNN) approaches have been shown
state-of-the-art (SOTA) performance on histopathological imaging in different
studies. In thi... | ['Md Zahangir Alom', 'Tarek M. Taha', 'Vijayan K. Asari', 'Chris Yakopcic'] | 2018-11-08 | null | null | null | null | ['nuclei-classification'] | ['medical'] | [ 2.14578927e-01 -1.19298421e-01 -2.89704204e-01 5.16240448e-02
-9.91598547e-01 -5.12891889e-01 3.15242559e-01 4.21931475e-01
-6.63242519e-01 9.30691898e-01 9.35752094e-02 -4.65957046e-01
1.21015497e-01 -7.54044950e-01 -6.54308274e-02 -1.37760758e+00
-5.70408031e-02 3.36215347e-01 3.06456506e-01 1.49109541... | [15.09542465209961, -3.036947011947632] |
e0c42457-92bd-4963-913f-65e86169c45a | icitris-causal-representation-learning-for | 2206.06169 | null | https://arxiv.org/abs/2206.06169v2 | https://arxiv.org/pdf/2206.06169v2.pdf | Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems | Causal representation learning is the task of identifying the underlying causal variables and their relations from high-dimensional observations, such as images. Recent work has shown that one can reconstruct the causal variables from temporal sequences of observations under the assumption that there are no instantaneo... | ['Efstratios Gavves', 'Taco Cohen', 'Yuki M. Asano', 'Sindy Löwe', 'Sara Magliacane', 'Phillip Lippe'] | 2022-06-13 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [ 5.22402406e-01 1.40861114e-02 -6.69244826e-01 -1.41331494e-01
-2.01788396e-01 -6.68710351e-01 7.89352834e-01 -1.27953384e-02
2.00586826e-01 8.79588485e-01 6.10267222e-01 -5.84248066e-01
-7.07378805e-01 -7.27583230e-01 -9.71290529e-01 -6.40918255e-01
-9.24663544e-01 3.80549490e-01 -1.48365080e-01 3.15345675... | [7.823699474334717, 5.247799873352051] |
a42e0d9a-8758-427a-9d89-ce5a5206c0ae | can-chatgpt-and-bard-generate-aligned | 2304.05372 | null | https://arxiv.org/abs/2304.05372v1 | https://arxiv.org/pdf/2304.05372v1.pdf | Can ChatGPT and Bard Generate Aligned Assessment Items? A Reliability Analysis against Human Performance | ChatGPT and Bard are AI chatbots based on Large Language Models (LLM) that are slated to promise different applications in diverse areas. In education, these AI technologies have been tested for applications in assessment and teaching. In assessment, AI has long been used in automated essay scoring and automated item g... | ['Abdolvahab Khademi'] | 2023-04-09 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [-5.17058611e-01 2.24683881e-01 2.17840467e-02 -1.85670093e-01
-7.93958783e-01 -7.33791947e-01 4.08665925e-01 2.71401346e-01
-4.59023327e-01 6.45623863e-01 -4.07815538e-02 -4.72234309e-01
-5.63555419e-01 -5.46293557e-01 4.82815914e-02 -1.27838835e-01
4.37088102e-01 8.35902393e-01 3.30497891e-01 -2.76549906... | [11.288352012634277, 9.270907402038574] |
21f94e70-4eae-4e3f-88cc-5ffeeb3eb6a3 | controllable-deep-melody-generation-via | 2109.00663 | null | https://arxiv.org/abs/2109.00663v1 | https://arxiv.org/pdf/2109.00663v1.pdf | Controllable deep melody generation via hierarchical music structure representation | Recent advances in deep learning have expanded possibilities to generate music, but generating a customizable full piece of music with consistent long-term structure remains a challenge. This paper introduces MusicFrameworks, a hierarchical music structure representation and a multi-step generative process to create a ... | ['Roger B. Dannenberg', 'Celso Gomes', 'Zeyu Jin', 'Shuqi Dai'] | 2021-09-02 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 1.99235559e-01 -8.56954083e-02 1.80344909e-01 6.82090446e-02
-7.16019869e-01 -1.14370298e+00 3.91551554e-01 -2.76165962e-01
-6.27669543e-02 6.43921554e-01 5.25581777e-01 -4.02466916e-02
-4.67869669e-01 -8.99858117e-01 -4.53737587e-01 -5.69234729e-01
8.04034695e-02 3.84849191e-01 -1.61480457e-02 -7.00466633... | [16.056365966796875, 5.5233893394470215] |
bd358b7a-52c1-44e9-aa3f-027d29e905f7 | uncertainty-aware-multi-view-representation | 2201.05776 | null | https://arxiv.org/abs/2201.05776v1 | https://arxiv.org/pdf/2201.05776v1.pdf | Uncertainty-Aware Multi-View Representation Learning | Learning from different data views by exploring the underlying complementary information among them can endow the representation with stronger expressive ability. However, high-dimensional features tend to contain noise, and furthermore, the quality of data usually varies for different samples (even for different views... | ['QinGhua Hu', 'Changqing Zhang', 'Zongbo Han', 'Yu Geng'] | 2022-01-15 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-1.22011460e-01 -3.64505649e-02 -2.29269385e-01 -5.90801120e-01
-8.89023781e-01 -5.31625271e-01 3.75451833e-01 -1.06288485e-01
1.28685206e-01 4.68551695e-01 6.60101533e-01 5.99821270e-01
-3.78786534e-01 -9.19885397e-01 -4.69521582e-01 -1.02826369e+00
4.09668833e-01 3.69362056e-01 -8.80712196e-02 6.33352995... | [8.485212326049805, 4.563709735870361] |
6517e182-474e-498a-b8ea-c7dcb0f93a91 | transformers-generalize-deepsets-and-can-be-1 | null | null | https://openreview.net/forum?id=scn3RYn1DYx | https://openreview.net/pdf?id=scn3RYn1DYx | Transformers Generalize DeepSets and Can be Extended to Graphs & Hypergraphs | We present a generalization of Transformers to any-order permutation invariant data (sets, graphs, and hypergraphs). We begin by observing that Transformers generalize DeepSets, or first-order (set-input) permutation invariant MLPs. Then, based on recently characterized higher-order invariant MLPs, we extend the concep... | ['Seunghoon Hong', 'Saeyoon Oh', 'Jinwoo Kim'] | 2021-05-21 | null | null | null | neurips-2021-12 | ['graph-regression'] | ['graphs'] | [ 3.76431555e-01 4.71439391e-01 -1.15219578e-01 -2.17053697e-01
-7.21651912e-01 -5.49194455e-01 2.68426239e-01 2.90425509e-01
-3.20195466e-01 4.98824209e-01 -7.27246106e-02 -7.88844407e-01
-5.64635515e-01 -1.25426054e+00 -1.39241183e+00 -7.05325425e-01
-8.53538454e-01 5.89622498e-01 9.00460333e-02 -5.27542308... | [6.950056552886963, 6.284908294677734] |
ca7a050a-bfe9-401a-b687-d475583538ae | optimal-transport-for-offline-imitation | 2303.13971 | null | https://arxiv.org/abs/2303.13971v1 | https://arxiv.org/pdf/2303.13971v1.pdf | Optimal Transport for Offline Imitation Learning | With the advent of large datasets, offline reinforcement learning (RL) is a promising framework for learning good decision-making policies without the need to interact with the real environment. However, offline RL requires the dataset to be reward-annotated, which presents practical challenges when reward engineering ... | ['Marc Peter Deisenroth', 'Edward Grefenstette', 'samuel cohen', 'Zhengyao Jiang', 'Yicheng Luo'] | 2023-03-24 | null | null | null | null | ['offline-rl', 'd4rl'] | ['playing-games', 'robots'] | [-2.13771060e-01 1.30452320e-01 -5.62798202e-01 -2.37182230e-01
-9.91637111e-01 -9.54721928e-01 4.06759262e-01 3.13856691e-01
-7.26120234e-01 9.55574572e-01 9.27850790e-03 -4.62720543e-01
1.51452199e-02 -5.30739427e-01 -1.01366079e+00 -5.43841839e-01
-3.11330736e-01 5.85955799e-01 1.61826238e-01 2.55451053... | [4.237392902374268, 1.6066488027572632] |
5ed66b8f-21c7-44da-9b43-880cf02e5d51 | diffusion-hyperfeatures-searching-through | 2305.14334 | null | https://arxiv.org/abs/2305.14334v1 | https://arxiv.org/pdf/2305.14334v1.pdf | Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence | Diffusion models have been shown to be capable of generating high-quality images, suggesting that they could contain meaningful internal representations. Unfortunately, the feature maps that encode a diffusion model's internal information are spread not only over layers of the network, but also over diffusion timesteps... | ['Trevor Darrell', 'Aleksander Holynski', 'Dong Huk Park', 'Lisa Dunlap', 'Grace Luo'] | 2023-05-23 | null | null | null | null | ['semantic-correspondence'] | ['computer-vision'] | [ 1.18474394e-01 8.42362922e-03 5.38971461e-02 -4.00485456e-01
-1.01080585e+00 -5.71515560e-01 1.15909493e+00 -3.70571278e-02
-2.66595274e-01 4.51456070e-01 4.51125264e-01 2.18035847e-01
-2.22048119e-01 -1.03328443e+00 -5.82845509e-01 -8.74056220e-01
-1.13250479e-01 4.07808095e-01 3.44307125e-01 -2.89543808... | [11.026674270629883, 0.06942462176084518] |
08ba64a0-a623-46a8-ae1a-e8ee1e278ed9 | improving-3d-aware-image-synthesis-with-a | 2209.15637 | null | https://arxiv.org/abs/2209.15637v1 | https://arxiv.org/pdf/2209.15637v1.pdf | Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator | 3D-aware image synthesis aims at learning a generative model that can render photo-realistic 2D images while capturing decent underlying 3D shapes. A popular solution is to adopt the generative adversarial network (GAN) and replace the generator with a 3D renderer, where volume rendering with neural radiance field (NeR... | ['Dit-yan Yeung', 'Qifeng Chen', 'Deli Zhao', 'Yujun Shen', 'Yinghao Xu', 'Zifan Shi'] | 2022-09-30 | null | null | null | null | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 2.73471713e-01 4.27729011e-01 1.12689771e-01 -1.12632588e-01
-7.54023433e-01 -8.01470399e-01 7.50361741e-01 -6.15312338e-01
1.11897565e-01 6.57179117e-01 -1.24515638e-01 -2.20309243e-01
3.15861762e-01 -1.23002875e+00 -9.34271276e-01 -9.65368986e-01
4.99440581e-01 4.94633198e-01 -2.77750909e-01 -3.86504263... | [9.297171592712402, -3.24824857711792] |
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