paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
942043ba-c25a-4eed-b9ab-3cf1a2dea6e0 | two-independent-teachers-are-better-role | 2306.05745 | null | https://arxiv.org/abs/2306.05745v1 | https://arxiv.org/pdf/2306.05745v1.pdf | Two Independent Teachers are Better Role Model | Recent deep learning models have attracted substantial attention in infant brain analysis. These models have performed state-of-the-art performance, such as semi-supervised techniques (e.g., Temporal Ensembling, mean teacher). However, these models depend on an encoder-decoder structure with stacked local operators to ... | ['Kun He', 'Ahmed A. Mubarak', 'Afifa Khaled'] | 2023-06-09 | null | null | null | null | ['brain-segmentation'] | ['medical'] | [-5.22401147e-02 1.03951864e-01 -1.37861535e-01 -6.06805623e-01
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-4.96506959e-01 5.25940716e-01 -6.18727058e-02 -6.27725422e-02
-1.79197475e-01 -7.84123361e-01 -8.63499701e-01 -8.76560688e-01
-1.96940705e-01 1.44717559e-01 6.00819051e-01 2.03294843... | [14.508959770202637, -2.5510776042938232] |
b6cc63d6-37ce-434b-bc6f-7bccd7508cba | a-provably-convergent-information-bottleneck | 2102.04729 | null | https://arxiv.org/abs/2102.04729v2 | https://arxiv.org/pdf/2102.04729v2.pdf | A Provably Convergent Information Bottleneck Solution via ADMM | The Information bottleneck (IB) method enables optimizing over the trade-off between compression of data and prediction accuracy of learned representations, and has successfully and robustly been applied to both supervised and unsupervised representation learning problems. However, IB has several limitations. First, th... | ['Aly El Gamal', 'Teng-Hui Huang'] | 2021-02-09 | null | null | null | null | ['information-plane'] | ['methodology'] | [ 4.62675542e-02 4.48822323e-03 -4.35479909e-01 2.09315196e-02
-9.38868821e-01 -3.28400701e-01 -1.26898999e-03 1.65555134e-01
-4.78403211e-01 8.88475418e-01 -1.10705353e-01 -2.04121724e-01
-5.86619139e-01 -7.36940265e-01 -7.96763301e-01 -9.55826700e-01
-2.93450266e-01 4.99515146e-01 -4.22585785e-01 -3.32176350... | [6.905595302581787, 4.337372779846191] |
1649c3ce-a3c3-4bd1-bf06-9193e75e8d4a | osu_chgcg-at-semeval-2016-task-9-chinese | null | null | https://aclanthology.org/S16-1189 | https://aclanthology.org/S16-1189.pdf | OSU\_CHGCG at SemEval-2016 Task 9 : Chinese Semantic Dependency Parsing with Generalized Categorial Grammar | null | ['Lifeng Jin', 'William Schuler', 'Manjuan Duan'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['semantic-dependency-parsing'] | ['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... | [-7.362309455871582, 3.663600206375122] |
69f632db-b407-46e3-b87f-05ba17d88315 | the-effectiveness-of-a-dynamic-loss-function | 2305.10447 | null | https://arxiv.org/abs/2305.10447v1 | https://arxiv.org/pdf/2305.10447v1.pdf | The Effectiveness of a Dynamic Loss Function in Neural Network Based Automated Essay Scoring | Neural networks and in particular the attention mechanism have brought significant advances to the field of Automated Essay Scoring. Many of these systems use a regression-based model which may be prone to underfitting when the model only predicts the mean of the training data. In this paper, we present a dynamic loss ... | ['Oscar Morris'] | 2023-05-15 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [-3.24756712e-01 3.42060804e-01 -2.98956960e-01 -6.99937463e-01
-7.54930377e-01 -4.57249820e-01 8.33123177e-02 4.68844503e-01
-6.01127148e-01 9.31923151e-01 -1.80793628e-02 -3.86583716e-01
-2.57813245e-01 -7.92303562e-01 -2.33890250e-01 -3.07763875e-01
4.65421081e-01 5.75596750e-01 6.98532239e-02 -1.43908858... | [11.329463958740234, 9.37099838256836] |
451c5cea-80df-42d0-a9ff-b96235cb8641 | enhancing-pre-trained-language-model-with | 2012.15070 | null | https://arxiv.org/abs/2012.15070v1 | https://arxiv.org/pdf/2012.15070v1.pdf | Enhancing Pre-trained Language Model with Lexical Simplification | For both human readers and pre-trained language models (PrLMs), lexical diversity may lead to confusion and inaccuracy when understanding the underlying semantic meanings of given sentences. By substituting complex words with simple alternatives, lexical simplification (LS) is a recognized method to reduce such lexical... | ['Hai Zhao', 'Zhuosheng Zhang', 'Jiayi Wang', 'Rongzhou Bao'] | 2020-12-30 | null | null | null | null | ['lexical-simplification'] | ['natural-language-processing'] | [ 6.04853332e-01 3.60410184e-01 -1.13323405e-01 -6.58802509e-01
-5.87213516e-01 -2.78475076e-01 5.58308423e-01 5.06689429e-01
-6.10080421e-01 7.19984412e-01 4.37713683e-01 -3.44779521e-01
4.30942059e-01 -6.79607391e-01 -4.50516343e-01 -2.18200728e-01
7.53636897e-01 2.97972471e-01 -4.55171801e-03 -4.78580773... | [11.143444061279297, 9.123190879821777] |
b2d3600d-941e-4eeb-a035-2c7e31efc355 | an-online-dictionary-for-dialects-of-north | null | null | https://aclanthology.org/2022.eurali-1.15 | https://aclanthology.org/2022.eurali-1.15.pdf | An Online Dictionary for Dialects of North Frisian | Language is an essential part of communication and culture. Documenting, digitizing, and preserving language is a meaningful pursuit. The first author of this work is a speaker of Söl’ring which is a dialect of the North Frisian language spoken on the island of Sylt in the North Frisia region of Germany. Söl’ring is es... | ['Tanno Hüttenrauch', 'Michael Wehar'] | null | null | null | null | eurali-lrec-2022-6 | ['culture'] | ['speech'] | [-6.33466423e-01 6.81903884e-02 -6.03633560e-02 -1.29205823e-01
-7.91228294e-01 -9.63358879e-01 5.89430451e-01 4.07473266e-01
-7.04958916e-01 5.05764663e-01 5.40738583e-01 -5.07761002e-01
-2.03728266e-02 -5.38287282e-01 -2.42262572e-01 -2.24214375e-01
1.79203704e-01 3.62528265e-01 -1.95178971e-01 -7.09353685... | [10.469358444213867, 10.228668212890625] |
a55b13a1-3828-4db2-9f43-49fb6064a51b | a-comparative-study-of-feature-selection | 1902.06242 | null | http://arxiv.org/abs/1902.06242v1 | http://arxiv.org/pdf/1902.06242v1.pdf | A Comparative Study of Feature Selection Methods for Dialectal Arabic Sentiment Classification Using Support Vector Machine | Unlike other languages, the Arabic language has a morphological complexity
which makes the Arabic sentiment analysis is a challenging task. Moreover, the
presence of the dialects in the Arabic texts have made the sentiment analysis
task is more challenging, due to the absence of specific rules that govern the
writing o... | ['Omar Al-Harbi'] | 2019-02-17 | null | null | null | null | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-3.40561837e-01 -5.22480190e-01 7.68500492e-02 -4.10719693e-01
2.56864540e-02 -5.43757558e-01 7.05432177e-01 4.55776662e-01
-5.67034841e-01 7.09921300e-01 1.35684222e-01 -1.67041123e-01
-4.37036783e-01 -7.79201686e-01 2.03710154e-01 -9.94854391e-01
1.63945094e-01 1.91779822e-01 5.55333979e-02 -8.89023840... | [11.010150909423828, 6.931622505187988] |
83d5369f-38e9-4997-944d-678db8bba52e | convnext-based-neural-network-for-anti | 2209.06434 | null | https://arxiv.org/abs/2209.06434v5 | https://arxiv.org/pdf/2209.06434v5.pdf | ConvNeXt Based Neural Network for Audio Anti-Spoofing | With the rapid development of speech conversion and speech synthesis algorithms, automatic speaker verification (ASV) systems are vulnerable to spoofing attacks. In recent years, researchers had proposed a number of anti-spoofing methods based on hand-crafted features. However, using hand-crafted features rather than r... | ['Wing W. Y. Ng', 'Ying Gao', 'Weiheng Liu', 'Yitao Yang', 'Jinghui Zhong', 'Qiaowei Ma'] | 2022-09-14 | null | null | null | null | ['synthetic-speech-detection'] | ['audio'] | [ 2.70065516e-01 -1.91268101e-01 -1.48570403e-01 -2.74846613e-01
-5.05159974e-01 -2.35871196e-01 6.03523910e-01 -1.18624046e-01
-4.24741507e-01 4.02665764e-01 2.97627568e-01 -5.67058444e-01
2.01375321e-01 -4.91618097e-01 -4.86176401e-01 -8.37738037e-01
6.53481036e-02 -3.51032138e-01 1.31643012e-01 -2.45407850... | [14.098994255065918, 5.853119850158691] |
b08ab6f0-b9fd-4202-a547-f74db6ea2bea | consistent-jumpy-predictions-for-videos-and-1 | null | null | https://openreview.net/forum?id=S1gQ5sRcFm | https://openreview.net/pdf?id=S1gQ5sRcFm | Consistent Jumpy Predictions for Videos and Scenes | Stochastic video prediction models take in a sequence of image frames, and generate a sequence of consecutive future image frames. These models typically generate future frames in an autoregressive fashion, which is slow and requires the input and output frames to be consecutive. We introduce a model that overcomes the... | ['Murray Shanahan', 'S. M. Ali Eslami', 'Edward Lockhart', 'Fabio Viola', 'Marta Garnelo', 'Danilo Rezende', 'Ananya Kumar'] | 2019-05-01 | null | null | null | iclr-2019-5 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 3.38265687e-01 2.10385188e-03 -8.04910213e-02 -1.94180548e-01
-7.60949910e-01 -6.98695064e-01 6.51397407e-01 -4.55538690e-01
1.59947313e-02 5.40130258e-01 3.27868789e-01 -2.04203710e-01
4.02395308e-01 -6.49441004e-01 -1.06503105e+00 -4.90921170e-01
-9.02469233e-02 1.12414867e-01 4.65449959e-01 3.17629367... | [9.64401626586914, -2.0768940448760986] |
f43889f3-d587-4ada-a5f1-4c2914a1cb42 | small-object-detection-using-deep-learning | 2201.03243 | null | https://arxiv.org/abs/2201.03243v1 | https://arxiv.org/pdf/2201.03243v1.pdf | Small Object Detection using Deep Learning | Now a days, UAVs such as drones are greatly used for various purposes like that of capturing and target detection from ariel imagery etc. Easy access of these small ariel vehicles to public can cause serious security threats. For instance, critical places may be monitored by spies blended in public using drones. Study ... | ['Asif Ullah Khan', 'Tauseef Jamal', 'Ayesha Salar', 'Aleena Ajaz'] | 2022-01-10 | null | null | null | null | ['real-time-object-detection', 'small-object-detection'] | ['computer-vision', 'computer-vision'] | [-5.28944433e-01 -2.40615785e-01 2.39101708e-01 5.59468865e-02
-9.88955945e-02 -6.46651983e-01 5.55376232e-01 -6.86163306e-02
-7.01215863e-01 5.93015373e-01 -6.03021920e-01 -1.23078665e-02
2.56746243e-05 -9.14479733e-01 -5.10106742e-01 -7.13449180e-01
-4.40509260e-01 2.50345260e-01 8.24956357e-01 -4.14322048... | [8.519155502319336, -0.9581327438354492] |
bf563f36-0993-4e5f-840f-cd45351cffe9 | candidate-set-re-ranking-for-composed-image | 2305.16304 | null | https://arxiv.org/abs/2305.16304v2 | https://arxiv.org/pdf/2305.16304v2.pdf | Candidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal Encoder | Composed image retrieval aims to find an image that best matches a given multi-modal user query consisting of a reference image and text pair. Existing methods commonly pre-compute image embeddings over the entire corpus and compare these to a reference image embedding modified by the query text at test time. Such a pi... | ['Stephen Gould', 'Damien Teney', 'Weixuan Sun', 'Zheyuan Liu'] | 2023-05-25 | null | null | null | null | ['composed-image-retrieval'] | ['computer-vision'] | [ 3.63245308e-01 -5.01562178e-01 -3.38782400e-01 -2.92547673e-01
-1.45041800e+00 -6.41466916e-01 8.16393852e-01 3.80847335e-01
-9.82406437e-01 2.40825027e-01 4.86706384e-02 -1.04166552e-01
-5.92462085e-02 -6.26419365e-01 -6.22367144e-01 -5.66609204e-01
3.79276276e-01 6.48237467e-01 6.46032214e-01 -3.28437760... | [10.678824424743652, 1.0505117177963257] |
6ccfbaf8-0a8d-474d-823c-2103a27551be | from-semantic-categories-to-fixations-a-novel | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wang_From_Semantic_Categories_to_Fixations_A_Novel_Weakly-Supervised_Visual-Auditory_Saliency_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wang_From_Semantic_Categories_to_Fixations_A_Novel_Weakly-Supervised_Visual-Auditory_Saliency_CVPR_2021_paper.pdf | From Semantic Categories to Fixations: A Novel Weakly-Supervised Visual-Auditory Saliency Detection Approach | Thanks to the rapid advances in the deep learning techniques and the wide availability of large-scale training sets, the performances of video saliency detection models have been improving steadily and significantly. However, the deep learning based visual-audio fixation prediction is still in its infancy. At prese... | ['Hong Qin', 'Aimin Hao', 'Deng-Ping Fan', 'Chenglizhao Chen', 'Guotao Wang'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['video-saliency-detection'] | ['computer-vision'] | [ 2.59192914e-01 -1.96239829e-01 -1.68284968e-01 -7.58529454e-02
-5.82692921e-01 -1.24668978e-01 3.64262670e-01 4.25746366e-02
-5.27110219e-01 6.97698474e-01 -1.20338365e-01 5.39474823e-02
-3.92058864e-02 -4.53851640e-01 -6.65337026e-01 -7.91778684e-01
2.35291049e-01 1.91406757e-02 7.33591139e-01 -1.18300386... | [9.779098510742188, -0.2537320554256439] |
2001c0ef-f258-4152-a244-84d278251497 | effective-open-intent-classification-with-k | 2304.10220 | null | https://arxiv.org/abs/2304.10220v1 | https://arxiv.org/pdf/2304.10220v1.pdf | Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary | Open intent classification, which aims to correctly classify the known intents into their corresponding classes while identifying the new unknown (open) intents, is an essential but challenging task in dialogue systems. In this paper, we introduce novel K-center contrastive learning and adjustable decision boundary lea... | ['Benyou Wang', 'Ruifeng Xu', 'Min Yang', 'Jingjing Mu', 'Jianquan Li', 'Xiaokang Liu'] | 2023-04-20 | null | null | null | null | ['intent-classification'] | ['natural-language-processing'] | [-5.73327346e-03 2.30332315e-01 -3.88152838e-01 -6.54970884e-01
-7.03150153e-01 -8.19006085e-01 3.03675890e-01 4.71004695e-02
-3.05752665e-01 6.11781240e-01 4.37306762e-01 -4.59396720e-01
-2.50207521e-02 -5.40007591e-01 -2.13428199e-01 -6.41275227e-01
-4.59341407e-02 6.23610675e-01 6.05700575e-02 -2.39380270... | [12.395990371704102, 7.437747955322266] |
0ef722cc-62d2-496f-93b6-395338d0a7b0 | oriental-language-recognition-olr-2020 | 2107.05365 | null | https://arxiv.org/abs/2107.05365v1 | https://arxiv.org/pdf/2107.05365v1.pdf | Oriental Language Recognition (OLR) 2020: Summary and Analysis | The fifth Oriental Language Recognition (OLR) Challenge focuses on language recognition in a variety of complex environments to promote its development. The OLR 2020 Challenge includes three tasks: (1) cross-channel language identification, (2) dialect identification, and (3) noisy language identification. We choose Ca... | ['Dong Wang', 'Qingyang Hong', 'Lin Li', 'Zheng Li', 'Yiming Zhi', 'Binling Wang', 'Jing Li'] | 2021-07-05 | null | null | null | null | ['dialect-identification'] | ['natural-language-processing'] | [-1.98052764e-01 -6.88906550e-01 -1.29931241e-01 -4.59823966e-01
-1.46913469e+00 -7.85994291e-01 7.41037250e-01 -4.11902815e-01
-8.89100850e-01 5.71355999e-01 1.91027999e-01 -5.88055015e-01
4.96793419e-01 -9.67897773e-02 -2.82815009e-01 -5.70507765e-01
9.32166576e-02 3.29061359e-01 -1.45633385e-01 -4.22897160... | [14.200414657592773, 6.656313419342041] |
c318c88c-cef5-4c11-ab50-76e5017a38ec | blcufight-at-semeval-2021-task-10-novel | null | null | https://aclanthology.org/2021.semeval-1.43 | https://aclanthology.org/2021.semeval-1.43.pdf | BLCUFIGHT at SemEval-2021 Task 10: Novel Unsupervised Frameworks For Source-Free Domain Adaptation | Domain adaptation assumes that samples from source and target domains are freely accessible during a training phase. However, such assumption is rarely plausible in the real-world and may causes data-privacy issues, especially when the label of the source domain can be a sensitive attribute as an identifier. SemEval-20... | ['Pengyuan Liu', 'Yixiang Liu', 'Yi Wu', 'Weikang Wang'] | 2021-08-01 | null | null | null | semeval-2021 | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 2.61760622e-01 1.23649396e-01 -5.56829691e-01 -7.65919924e-01
-5.87187946e-01 -6.45868421e-01 6.43922746e-01 -5.01090661e-02
-7.47593582e-01 1.41883409e+00 -2.24060535e-01 -1.68557495e-01
1.47495553e-01 -3.92970234e-01 -6.33317649e-01 -5.06522119e-01
3.21153671e-01 8.77350748e-01 2.33234286e-01 -1.23043969... | [10.374521255493164, 3.1968319416046143] |
fab0c4bf-f980-4d81-ab63-8a409e07e2f4 | bayesian-fusion-for-infrared-and-visible | 2005.05839 | null | https://arxiv.org/abs/2005.05839v1 | https://arxiv.org/pdf/2005.05839v1.pdf | Bayesian Fusion for Infrared and Visible Images | Infrared and visible image fusion has been a hot issue in image fusion. In this task, a fused image containing both the gradient and detailed texture information of visible images as well as the thermal radiation and highlighting targets of infrared images is expected to be obtained. In this paper, a novel Bayesian fus... | ['Chun-Xia Zhang', 'Junmin Liu', 'Jiangshe Zhang', 'Zixiang Zhao', 'Shuang Xu'] | 2020-05-12 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 6.17680490e-01 -4.42726016e-01 2.55780160e-01 -4.16565239e-01
-8.40652525e-01 -1.90596685e-01 4.37656432e-01 -3.39272350e-01
-1.34587049e-01 6.63127303e-01 -9.79707092e-02 -8.52994174e-02
-1.78887025e-01 -6.91443920e-01 -2.52500683e-01 -1.37370670e+00
6.91474438e-01 -3.90418768e-02 5.35887070e-02 -3.79588231... | [10.563213348388672, -1.9487202167510986] |
ae23e0f9-a088-49e4-8c74-230e71fb64e8 | laneaf-robust-multi-lane-detection-with-1 | 2212.11533 | null | https://arxiv.org/abs/2212.11533v5 | https://arxiv.org/pdf/2212.11533v5.pdf | Multi Lane Detection | Lane detection is a long-standing task and a basic module in autonomous driving. The task is to detect the lane of the current driving road, and provide relevant information such as the ID, direction, curvature, width, length, with visualization. Our work is based on CNN backbone DLA-34, along with Affinity Fields, aim... | ['Fei Wu', 'Luoyu Chen'] | 2022-12-22 | null | null | null | null | ['lane-detection'] | ['computer-vision'] | [-1.35801062e-01 -1.64818943e-01 -3.18577826e-01 -6.72939062e-01
-1.30356610e-01 -5.25540650e-01 3.13937008e-01 -1.33278713e-01
-5.28603733e-01 4.94985789e-01 2.64111459e-01 -9.32962894e-01
1.55517057e-01 -9.14097667e-01 -6.39612436e-01 -6.47534966e-01
-3.31700593e-02 3.47697474e-02 7.79324055e-01 -5.73468268... | [8.042351722717285, -1.4236348867416382] |
b5d33892-2bcb-4266-a9e6-72f17fb9eb54 | detecting-oriented-text-in-natural-images-by | 1703.06520 | null | http://arxiv.org/abs/1703.06520v3 | http://arxiv.org/pdf/1703.06520v3.pdf | Detecting Oriented Text in Natural Images by Linking Segments | Most state-of-the-art text detection methods are specific to horizontal Latin
text and are not fast enough for real-time applications. We introduce Segment
Linking (SegLink), an oriented text detection method. The main idea is to
decompose text into two locally detectable elements, namely segments and links.
A segment ... | ['Serge Belongie', 'Baoguang Shi', 'Xiang Bai'] | 2017-03-19 | detecting-oriented-text-in-natural-images-by-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Shi_Detecting_Oriented_Text_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Shi_Detecting_Oriented_Text_CVPR_2017_paper.pdf | cvpr-2017-7 | ['curved-text-detection'] | ['computer-vision'] | [ 2.67239183e-01 1.16272934e-01 -2.35209346e-01 1.58060029e-01
-7.93023348e-01 -8.05557609e-01 7.31693983e-01 6.56559348e-01
-4.19037819e-01 1.46635726e-01 3.60373929e-02 -1.90375775e-01
6.07428432e-01 -5.04793406e-01 -8.14044416e-01 -1.89050063e-01
1.08156763e-01 5.29025376e-01 9.32207167e-01 2.55140215... | [12.006707191467285, 2.354170799255371] |
e9cff06e-3893-4621-9780-bb3b1852114a | dreamdecompiler-improved-bayesian-program | 2306.07856 | null | https://arxiv.org/abs/2306.07856v1 | https://arxiv.org/pdf/2306.07856v1.pdf | DreamDecompiler: Improved Bayesian Program Learning by Decompiling Amortised Knowledge | Solving program induction problems requires searching through an enormous space of possibilities. DreamCoder is an inductive program synthesis system that, whilst solving problems, learns to simplify search in an iterative wake-sleep procedure. The cost of search is amortised by training a neural search policy, reducin... | ['N. Siddharth', 'Christopher G. Lucas', 'Alessandro B. Palmarini'] | 2023-06-13 | null | null | null | null | ['program-synthesis', 'program-induction'] | ['computer-code', 'computer-code'] | [ 2.61247844e-01 2.86578149e-01 -7.61947393e-01 -3.00861150e-01
-7.69136965e-01 -8.89801919e-01 3.10011119e-01 1.15855835e-01
-5.63892424e-01 7.81800032e-01 2.21408278e-01 -8.68277669e-01
-7.94081017e-02 -1.04488885e+00 -1.12606609e+00 -1.46409258e-01
-7.51386359e-02 3.94571513e-01 -8.87030959e-02 -8.38517100... | [8.726836204528809, 7.239196300506592] |
12455bff-b18f-4fe8-ba15-fea948347e93 | progressive-purification-for-instance | 2206.00830 | null | https://arxiv.org/abs/2206.00830v2 | https://arxiv.org/pdf/2206.00830v2.pdf | Progressive Purification for Instance-Dependent Partial Label Learning | Partial label learning (PLL) aims to train multiclass classifiers from the examples each annotated with a set of candidate labels where a fixed but unknown candidate label is correct. In the last few years, the instance-independent generation process of candidate labels has been extensively studied, on the basis of whi... | ['Xin Geng', 'Congyu Qiao', 'Biao Liu', 'Jiaqi Lv', 'Ning Xu'] | 2022-06-02 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 4.61197674e-01 3.36285740e-01 -3.56477559e-01 -5.17013013e-01
-1.04812372e+00 -3.24315399e-01 2.88106203e-01 4.38149542e-01
-5.25077403e-01 1.10505879e+00 -6.41007483e-01 -1.11263909e-03
-3.90681118e-01 -7.68356919e-01 -7.40919769e-01 -1.12742984e+00
1.47014856e-01 8.20575774e-01 2.52498567e-01 3.82825315... | [9.259793281555176, 4.068922519683838] |
3f333000-5331-4926-9730-b63e38314940 | simultaneous-face-detection-and-360-degree | 2111.11604 | null | https://arxiv.org/abs/2111.11604v1 | https://arxiv.org/pdf/2111.11604v1.pdf | Simultaneous face detection and 360 degree headpose estimation | With many practical applications in human life, including manufacturing surveillance cameras, analyzing and processing customer behavior, many researchers are noticing face detection and head pose estimation on digital images. A large number of proposed deep learning models have state-of-the-art accuracy such as YOLO, ... | ['Long Tran Quoc', 'Duc Tran Minh', 'Tuan Nguyen Dinh', 'Linh Nguyen Viet', 'Hoang Nguyen Viet'] | 2021-11-23 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-4.18348372e-01 5.99229597e-02 -4.57989424e-03 -3.94128621e-01
-3.60542208e-01 -3.81505370e-01 2.35281169e-01 -6.46058857e-01
-2.82735199e-01 2.30381295e-01 -2.10256688e-02 6.35487884e-02
8.04559048e-03 -3.57288033e-01 -6.14418864e-01 -7.56879926e-01
-1.89274014e-03 6.54650927e-01 1.28672749e-01 -9.75375529... | [13.63985538482666, 0.3038301467895508] |
e6445bba-9317-4f5c-a1fc-edb346d8f9a4 | learning-rigidity-in-dynamic-scenes-with-a | 1804.04259 | null | http://arxiv.org/abs/1804.04259v2 | http://arxiv.org/pdf/1804.04259v2.pdf | Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation | Estimation of 3D motion in a dynamic scene from a temporal pair of images is
a core task in many scene understanding problems. In real world applications, a
dynamic scene is commonly captured by a moving camera (i.e., panning, tilting
or hand-held), increasing the task complexity because the scene is observed
from diff... | ['Zhaoyang Lv', 'Deqing Sun', 'Jan Kautz', 'James M. Rehg', 'Kihwan Kim', 'Alejandro Troccoli'] | 2018-04-12 | learning-rigidity-in-dynamic-scenes-with-a-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Zhaoyang_Lv_Learning_Rigidity_in_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhaoyang_Lv_Learning_Rigidity_in_ECCV_2018_paper.pdf | eccv-2018-9 | ['scene-flow-estimation'] | ['computer-vision'] | [ 5.23617566e-01 -2.17618212e-01 1.00189127e-01 -4.73964997e-02
-1.93867654e-01 -8.26814473e-01 4.71617579e-01 -4.90403414e-01
-4.19336706e-01 2.94224292e-01 1.39639452e-01 -4.38004695e-02
5.54718226e-02 -5.49589038e-01 -7.82603264e-01 -6.57469034e-01
5.36719598e-02 5.12469351e-01 6.25001490e-01 6.21845163... | [8.590841293334961, -2.0555076599121094] |
1c0601a9-9b25-4043-bded-dcf0b6df0d96 | multigbs-a-multi-layer-graph-approach-to | 2008.11908 | null | https://arxiv.org/abs/2008.11908v2 | https://arxiv.org/pdf/2008.11908v2.pdf | MultiGBS: A multi-layer graph approach to biomedical summarization | Automatic text summarization methods generate a shorter version of the input text to assist the reader in gaining a quick yet informative gist. Existing text summarization methods generally focus on a single aspect of text when selecting sentences, causing the potential loss of essential information. In this study, we ... | ['Nasser Ghadiri', 'Ensieh Davoodijam', 'Fabio Rinaldi', 'Maryam Lotfi Shahreza'] | 2020-08-27 | null | null | null | null | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 4.66057032e-01 6.73031926e-01 -7.77313933e-02 -1.42454384e-02
-5.06142735e-01 -4.92384285e-01 6.41261637e-01 1.21210182e+00
-2.11663052e-01 6.93188131e-01 9.94631231e-01 9.47751030e-02
-6.05728090e-01 -8.16788137e-01 4.29817215e-02 -1.68555781e-01
1.02638841e-01 5.79524934e-01 3.66464406e-01 -5.63081563... | [12.42379093170166, 9.558388710021973] |
25eb6aab-db73-4cd3-870c-2fdd63fc9c01 | seq-nms-for-video-object-detection | 1602.08465 | null | http://arxiv.org/abs/1602.08465v3 | http://arxiv.org/pdf/1602.08465v3.pdf | Seq-NMS for Video Object Detection | Video object detection is challenging because objects that are easily
detected in one frame may be difficult to detect in another frame within the
same clip. Recently, there have been major advances for doing object detection
in a single image. These methods typically contain three phases: (i) object
proposal generatio... | ['Honghui Shi', 'Prajit Ramachandran', 'Wei Han', 'Tom Le Paine', 'Thomas S. Huang', 'Pooya Khorrami', 'Shuicheng Yan', 'Mohammad Babaeizadeh', 'Jianan Li'] | 2016-02-26 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 4.40239489e-01 -5.33834636e-01 6.65587513e-03 -2.91577607e-01
-8.27047110e-01 -5.50338447e-01 5.32305658e-01 2.44250402e-01
-8.06122482e-01 3.75200272e-01 -9.18722451e-02 9.95498672e-02
4.46199745e-01 -3.95677567e-01 -1.05099893e+00 -4.65969473e-01
-2.20841408e-01 4.37990353e-02 1.39963984e+00 2.14236453... | [8.663561820983887, -0.21679627895355225] |
dc755bc0-eaca-4730-913a-0e3225ace153 | fine-grained-image-to-image-transformation | 2001.03856 | null | https://arxiv.org/abs/2001.03856v2 | https://arxiv.org/pdf/2001.03856v2.pdf | Fine-grained Image-to-Image Transformation towards Visual Recognition | Existing image-to-image transformation approaches primarily focus on synthesizing visually pleasing data. Generating images with correct identity labels is challenging yet much less explored. It is even more challenging to deal with image transformation tasks with large deformation in poses, viewpoints, or scales while... | ['Lin Ma', 'Wenhan Luo', 'Jiebo Luo', 'Yixuan Zhang', 'Yutong He', 'Wei Xiong'] | 2020-01-12 | fine-grained-image-to-image-transformation-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Xiong_Fine-Grained_Image-to-Image_Transformation_Towards_Visual_Recognition_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Xiong_Fine-Grained_Image-to-Image_Transformation_Towards_Visual_Recognition_CVPR_2020_paper.pdf | cvpr-2020-6 | ['fine-grained-image-recognition'] | ['computer-vision'] | [ 5.27391195e-01 6.37267977e-02 -1.40831739e-01 -3.36444736e-01
-5.06732702e-01 -6.64059699e-01 6.63496792e-01 -7.34014452e-01
-5.80753312e-02 6.26408696e-01 4.74266708e-02 1.50539935e-01
9.86202583e-02 -1.03741074e+00 -1.02836490e+00 -9.72164273e-01
7.07122326e-01 2.04489067e-01 -1.33315161e-01 -2.60038078... | [12.003471374511719, -0.316432923078537] |
4639b897-6aff-4376-8c63-3964f381fc9a | qascore-an-unsupervised-unreferenced-metric | 2210.04320 | null | https://arxiv.org/abs/2210.04320v1 | https://arxiv.org/pdf/2210.04320v1.pdf | QAScore -- An Unsupervised Unreferenced Metric for the Question Generation Evaluation | Question Generation (QG) aims to automate the task of composing questions for a passage with a set of chosen answers found within the passage. In recent years, the introduction of neural generation models has resulted in substantial improvements of automatically generated questions in terms of quality, especially compa... | ['Yvette Graham', 'Liting Zhou', 'Gareth Jones', 'Chenyang Lyu', 'Tianbo Ji'] | 2022-10-09 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [-1.85411185e-01 7.44978338e-02 4.98371482e-01 -2.55008221e-01
-1.34707785e+00 -6.43028140e-01 6.61875427e-01 4.44687635e-01
-6.81646585e-01 8.03334951e-01 5.31795144e-01 -2.35144392e-01
-6.94114566e-02 -9.96226788e-01 -2.18686745e-01 -3.37974399e-01
4.67573285e-01 4.76598710e-01 2.83347964e-01 -6.40719593... | [11.548017501831055, 8.253650665283203] |
78ff3ebb-34af-4c38-b77b-9f40d0b82118 | image2stylegan-how-to-embed-images-into-the | 1904.03189 | null | https://arxiv.org/abs/1904.03189v2 | https://arxiv.org/pdf/1904.03189v2.pdf | Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space? | We propose an efficient algorithm to embed a given image into the latent space of StyleGAN. This embedding enables semantic image editing operations that can be applied to existing photographs. Taking the StyleGAN trained on the FFHQ dataset as an example, we show results for image morphing, style transfer, and express... | ['Peter Wonka', 'Rameen Abdal', 'Yipeng Qin'] | 2019-04-05 | image2stylegan-how-to-embed-images-into-the-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Abdal_Image2StyleGAN_How_to_Embed_Images_Into_the_StyleGAN_Latent_Space_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Abdal_Image2StyleGAN_How_to_Embed_Images_Into_the_StyleGAN_Latent_Space_ICCV_2019_paper.pdf | iccv-2019-10 | ['image-morphing'] | ['computer-vision'] | [ 6.75599575e-01 5.37678480e-01 -1.12639554e-01 -4.90102917e-01
-1.33129239e-01 -9.53810453e-01 8.52794945e-01 -4.26572382e-01
-1.63577870e-01 4.44739699e-01 4.08929378e-01 -1.42949879e-01
2.98934758e-01 -9.32110131e-01 -9.35840428e-01 -6.19330406e-01
2.34915361e-01 1.23268694e-01 -1.20155461e-01 -5.41129634... | [11.781255722045898, -0.29270240664482117] |
a91c1f46-872e-4f52-8607-b9080c4c9f01 | learning-homographic-disambiguation | 2304.05860 | null | https://arxiv.org/abs/2304.05860v2 | https://arxiv.org/pdf/2304.05860v2.pdf | Learning Homographic Disambiguation Representation for Neural Machine Translation | Homographs, words with the same spelling but different meanings, remain challenging in Neural Machine Translation (NMT). While recent works leverage various word embedding approaches to differentiate word sense in NMT, they do not focus on the pivotal components in resolving ambiguities of homographs in NMT: the hidden... | ['Qun Liu', 'Wei Peng', 'Weixuan Wang'] | 2023-04-12 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 4.90464151e-01 1.35260403e-01 -3.57343316e-01 -1.73164502e-01
-8.27987850e-01 -7.39579141e-01 8.08420360e-01 -6.90734759e-02
-2.84867138e-01 8.29572260e-01 5.52998304e-01 -5.39014578e-01
-9.55114290e-02 -8.29346776e-01 -7.60739088e-01 -6.41988993e-01
3.58922392e-01 5.45776784e-01 -3.10984671e-01 -7.09813297... | [11.539843559265137, 10.09378433227539] |
0d40ee99-2659-41b6-bd91-db76b7c65172 | compressive-mr-fingerprinting-reconstruction | 2006.15271 | null | https://arxiv.org/abs/2006.15271v3 | https://arxiv.org/pdf/2006.15271v3.pdf | Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations | Consistency of the predictions with respect to the physical forward model is pivotal for reliably solving inverse problems. This consistency is mostly un-controlled in the current end-to-end deep learning methodologies proposed for the Magnetic Resonance Fingerprinting (MRF) problem. To address this, we propose ProxNet... | ['Mohammad Golbabaee', 'Mike E. Davies', 'Dongdong Chen'] | 2020-06-27 | null | null | null | null | ['de-aliasing', 'magnetic-resonance-fingerprinting'] | ['computer-vision', 'medical'] | [-1.74925238e-01 1.17601901e-01 -3.36521119e-01 -5.72548211e-01
-1.02701104e+00 -1.27184018e-02 4.03854460e-01 -3.71398836e-01
-2.31992483e-01 7.69357204e-01 4.65554476e-01 -3.60266268e-01
-4.25835609e-01 -5.31604111e-01 -9.31205153e-01 -7.11216033e-01
-2.47939095e-01 4.08335865e-01 -5.60527854e-02 4.92659174... | [13.526860237121582, -2.395923137664795] |
61fc1389-7a5a-415d-b7ba-52dba9c0f214 | scalable-weight-reparametrization-for | 2302.13435 | null | https://arxiv.org/abs/2302.13435v1 | https://arxiv.org/pdf/2302.13435v1.pdf | Scalable Weight Reparametrization for Efficient Transfer Learning | This paper proposes a novel, efficient transfer learning method, called Scalable Weight Reparametrization (SWR) that is efficient and effective for multiple downstream tasks. Efficient transfer learning involves utilizing a pre-trained model trained on a larger dataset and repurposing it for downstream tasks with the a... | ['Simyung Chang', 'Seunghan Yang', 'Jun-Tae Lee', 'Byeonggeun Kim'] | 2023-02-26 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 1.97672799e-01 -9.69293863e-02 -3.55881929e-01 -2.58371532e-01
-1.08201742e+00 -2.32840002e-01 5.24845839e-01 -9.13056657e-02
-8.96671653e-01 6.11095548e-01 1.04146570e-01 -4.67176616e-01
1.81043241e-02 -5.54828167e-01 -7.45019674e-01 -7.00071156e-01
2.24918216e-01 5.67216814e-01 1.56977296e-01 -1.24297790... | [10.127636909484863, 2.4715464115142822] |
e08e27bd-0f25-4393-aedf-143a31883b6d | aerial-imagery-pile-burn-detection-using-deep | 2012.14036 | null | https://arxiv.org/abs/2012.14036v1 | https://arxiv.org/pdf/2012.14036v1.pdf | Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset | Wildfires are one of the costliest and deadliest natural disasters in the US, causing damage to millions of hectares of forest resources and threatening the lives of people and animals. Of particular importance are risks to firefighters and operational forces, which highlights the need for leveraging technology to mini... | ['Erik Blasch', 'Peter Z Fulé', 'Liming Zheng', 'Abolfazl Razi', 'Fatemeh Afghah', 'Alireza Shamsoshoara'] | 2020-12-28 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 7.45491326e-01 -7.62719572e-01 -2.05054343e-01 -3.05385655e-03
-2.14917004e-01 -6.83700740e-01 3.94961327e-01 -2.14488178e-01
-8.03881407e-01 7.33768642e-01 -9.81812272e-03 -3.97070438e-01
-2.07275853e-01 -1.28372645e+00 -1.89689934e-01 -8.69454324e-01
-4.08219844e-01 4.21711169e-02 1.14192560e-01 -1.54785961... | [9.181795120239258, -1.2553658485412598] |
c8e687cb-82a0-45fc-800a-7ad300a82a92 | nc-dre-leveraging-non-entity-clue-information | 2204.00255 | null | https://arxiv.org/abs/2204.00255v1 | https://arxiv.org/pdf/2204.00255v1.pdf | NC-DRE: Leveraging Non-entity Clue Information for Document-level Relation Extraction | Document-level relation extraction (RE), which requires reasoning on multiple entities in different sentences to identify complex inter-sentence relations, is more challenging than sentence-level RE. To extract the complex inter-sentence relations, previous studies usually employ graph neural networks (GNN) to perform ... | ['Yidong Cheng', 'Liang Zhang'] | 2022-04-01 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 1.17680803e-01 6.60845995e-01 -3.98392558e-01 -3.98979366e-01
-6.58769786e-01 -4.94615346e-01 5.34541070e-01 6.51456654e-01
-3.09355527e-01 7.14154065e-01 5.43080747e-01 -6.93821847e-01
3.66675295e-02 -1.29809904e+00 -7.50851452e-01 1.55109644e-03
6.94551840e-02 5.97225904e-01 2.20448464e-01 -3.72742504... | [9.20391845703125, 8.587726593017578] |
7dc18a35-29a0-41c2-87d1-d69309d9a706 | detector-in-detector-multi-level-analysis-for | 1902.07017 | null | http://arxiv.org/abs/1902.07017v1 | http://arxiv.org/pdf/1902.07017v1.pdf | Detector-in-Detector: Multi-Level Analysis for Human-Parts | Vision-based person, hand or face detection approaches have achieved
incredible success in recent years with the development of deep convolutional
neural network (CNN). In this paper, we take the inherent correlation between
the body and body parts into account and propose a new framework to boost up
the detection perf... | ['Qing Song', 'Xiaojie Li', 'Lu Yang', 'Fuqiang Zhou'] | 2019-02-19 | null | null | null | null | ['body-detection', 'hand-detection'] | ['computer-vision', 'computer-vision'] | [-1.90324351e-01 5.51032349e-02 3.67316268e-02 -1.45525694e-01
-2.82151669e-01 -8.39532465e-02 3.80618483e-01 -3.89632910e-01
-3.86579752e-01 1.67817861e-01 2.00662166e-01 4.91796941e-01
4.65626121e-01 -7.21156061e-01 -5.11054516e-01 -6.66739225e-01
2.11866781e-01 4.47280943e-01 6.22769654e-01 -7.63326045... | [7.950474739074707, -0.5548710823059082] |
e7789942-85fd-4ee9-a2dd-a48782acd153 | mrgan360-multi-stage-recurrent-generative | 2303.08525 | null | https://arxiv.org/abs/2303.08525v1 | https://arxiv.org/pdf/2303.08525v1.pdf | MRGAN360: Multi-stage Recurrent Generative Adversarial Network for 360 Degree Image Saliency Prediction | Thanks to the ability of providing an immersive and interactive experience, the uptake of 360 degree image content has been rapidly growing in consumer and industrial applications. Compared to planar 2D images, saliency prediction for 360 degree images is more challenging due to their high resolutions and spherical vie... | ['Wei Xiang', 'Rong Quan', 'Xinlang Chen', 'Pan Gao'] | 2023-03-15 | null | null | null | null | ['saliency-prediction'] | ['computer-vision'] | [ 2.97067434e-01 1.54140577e-01 1.47288514e-03 -2.57158995e-01
-2.15741210e-02 3.09778452e-02 3.39805573e-01 -2.30776280e-01
-9.28544328e-02 2.49391034e-01 2.02319145e-01 -1.90674469e-01
1.72604188e-01 -6.75868511e-01 -6.24706149e-01 -5.04369497e-01
2.08116531e-01 -3.03862631e-01 6.27716184e-01 -2.81600207... | [9.78117847442627, -0.39375096559524536] |
50eb6a86-c533-4f02-baf2-f2bbcca3c875 | crab-learning-certifiably-fair-predictive | 2212.10839 | null | https://arxiv.org/abs/2212.10839v2 | https://arxiv.org/pdf/2212.10839v2.pdf | Consistent Range Approximation for Fair Predictive Modeling | This paper proposes a novel framework for certifying the fairness of predictive models trained on biased data. It draws from query answering for incomplete and inconsistent databases to formulate the problem of consistent range approximation (CRA) of fairness queries for a predictive model on a target population. The f... | ['Babak Salimi', 'Sainyam Galhotra', 'Nazanin Sabri', 'Jiongli Zhu'] | 2022-12-21 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [-1.94321200e-01 5.21291614e-01 -8.79635155e-01 -1.03452039e+00
-1.24004102e+00 -3.41165334e-01 4.77967143e-01 3.68296385e-01
-3.21721226e-01 1.08425987e+00 -5.29769324e-02 -4.53957498e-01
-4.21709657e-01 -9.72087026e-01 -7.25109339e-01 -1.45698950e-01
9.28310826e-02 1.14985573e+00 -1.15960337e-01 -7.57475644... | [8.871914863586426, 5.302847862243652] |
b4c02f68-9a04-47f9-8832-e933f7cf7b50 | a-residual-solver-and-its-unfolding-neural | 2009.03477 | null | https://arxiv.org/abs/2009.03477v1 | https://arxiv.org/pdf/2009.03477v1.pdf | A Residual Solver and Its Unfolding Neural Network for Total Variation Regularized Models | This paper proposes to solve the Total Variation regularized models by finding the residual between the input and the unknown optimal solution. After analyzing a previous method, we developed a new iterative algorithm, named as Residual Solver, which implicitly solves the model in gradient domain. We theoretically prov... | ['Yuanhao Gong'] | 2020-09-08 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 2.87356496e-01 9.55217704e-02 2.59833872e-01 -1.25198111e-01
-3.65018815e-01 1.14467843e-02 -5.03783561e-02 -4.96061444e-01
-3.93124521e-01 9.04991806e-01 -1.70320123e-01 2.01221276e-02
-2.66150266e-01 -5.13062418e-01 -7.95235753e-01 -1.00974560e+00
1.10311508e-01 -9.64129791e-02 -9.18439105e-02 -3.26764137... | [11.651649475097656, -2.524151086807251] |
7affb89d-23db-4989-9009-2e5d3fea5044 | noise-aware-physics-informed-machine-learning | 2206.12901 | null | https://arxiv.org/abs/2206.12901v5 | https://arxiv.org/pdf/2206.12901v5.pdf | Noise-aware Physics-informed Machine Learning for Robust PDE Discovery | This work is concerned with discovering the governing partial differential equation (PDE) of a physical system. Existing methods have demonstrated the PDE identification from finite observations but failed to maintain satisfying results against noisy data, partly owing to suboptimal estimated derivatives and found PDE ... | ['Takashi Morita', 'Ken-ichi Fukui', 'Masayuki Numao', 'Pongpisit Thanasutives'] | 2022-06-26 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 2.38410041e-01 -2.32231587e-01 1.93170354e-01 2.60770936e-02
-9.39979672e-01 -4.95403796e-01 3.82425725e-01 -3.77477616e-01
6.66891634e-02 1.12033927e+00 -5.32509643e-04 -5.76372668e-02
-8.82747829e-01 -4.96322423e-01 -7.42300808e-01 -1.24416721e+00
-1.49094984e-01 7.48920500e-01 -7.09004328e-02 -8.80503282... | [6.513397216796875, 3.510263442993164] |
5f36755a-5af0-4857-b69a-a57ac3962354 | iterative-signal-processing-for-integrated | 2306.05235 | null | https://arxiv.org/abs/2306.05235v1 | https://arxiv.org/pdf/2306.05235v1.pdf | Iterative Signal Processing for Integrated Sensing and Communication Systems | Integrated sensing and communication (ISAC), with sensing and communication sharing the same wireless resources and hardware, has the advantages of high spectrum efficiency and low hardware cost, which is regarded as one of the key technologies of the fifth generation advanced (5G-A) and sixth generation (6G) mobile co... | ['Zhiyong Feng', 'Huici Wu', 'Kaifeng Han', 'Wangjun Jiang', 'Hanyang Qu', 'Zhiqing Wei'] | 2023-06-08 | null | null | null | null | ['quantization'] | ['methodology'] | [ 7.02018857e-01 -3.60637039e-01 -3.97010833e-01 1.81889653e-01
-4.16137993e-01 -1.98318213e-01 1.61454007e-01 -5.17981173e-03
-2.59387374e-01 4.35085833e-01 1.28801502e-02 -4.76183116e-01
-6.36151195e-01 -7.72162735e-01 1.38403550e-01 -1.00165391e+00
-4.57549691e-01 -4.22344476e-01 4.87940386e-02 -3.93421873... | [6.347332954406738, 1.2148429155349731] |
753b08b3-bdee-4c39-8c1c-01228e0ca500 | video-enhancement-with-task-oriented-flow | 1711.09078 | null | https://arxiv.org/abs/1711.09078v3 | https://arxiv.org/pdf/1711.09078v3.pdf | Video Enhancement with Task-Oriented Flow | Many video enhancement algorithms rely on optical flow to register frames in a video sequence. Precise flow estimation is however intractable; and optical flow itself is often a sub-optimal representation for particular video processing tasks. In this paper, we propose task-oriented flow (TOFlow), a motion representati... | ['Baian Chen', 'Jiajun Wu', 'Donglai Wei', 'William T. Freeman', 'Tianfan Xue'] | 2017-11-24 | null | null | null | null | ['video-denoising', 'video-enhancement'] | ['computer-vision', 'computer-vision'] | [ 2.70924568e-01 -7.42396533e-01 -4.65695500e-01 -3.31875473e-01
-5.36860824e-01 -3.15096378e-01 3.24930012e-01 -5.15404403e-01
-5.00306606e-01 7.61642814e-01 5.21088481e-01 -5.99751174e-02
1.39607102e-01 -4.55567390e-01 -8.33572984e-01 -5.42038560e-01
-4.73899871e-01 -1.36771634e-01 2.48449355e-01 8.44021067... | [10.719393730163574, -1.4765853881835938] |
dd37a252-3bc1-4103-9eb1-a3f4e43a9934 | action-unit-memory-network-for-weakly | 2104.14135 | null | https://arxiv.org/abs/2104.14135v1 | https://arxiv.org/pdf/2104.14135v1.pdf | Action Unit Memory Network for Weakly Supervised Temporal Action Localization | Weakly supervised temporal action localization aims to detect and localize actions in untrimmed videos with only video-level labels during training. However, without frame-level annotations, it is challenging to achieve localization completeness and relieve background interference. In this paper, we present an Action U... | ['Yongdong Zhang', 'Feng Wu', 'Tao Mei', 'Jingen Liu', 'Wenfei Yang', 'Tianzhu Zhang', 'Wang Luo'] | 2021-04-29 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Luo_Action_Unit_Memory_Network_for_Weakly_Supervised_Temporal_Action_Localization_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Luo_Action_Unit_Memory_Network_for_Weakly_Supervised_Temporal_Action_Localization_CVPR_2021_paper.pdf | cvpr-2021-1 | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 2.69967288e-01 -4.71236520e-02 -8.79039049e-01 -1.42467201e-01
-5.05551040e-01 -2.21640036e-01 3.84098262e-01 -3.61475945e-01
-5.71683347e-01 7.36238718e-01 2.95736670e-01 1.38847262e-01
2.80750781e-01 -2.94767886e-01 -9.02234316e-01 -7.94905126e-01
-3.59720886e-01 -1.66940108e-01 7.98420787e-01 2.78734237... | [8.411396980285645, 0.536492645740509] |
012c3997-5381-42ab-ba5f-47b00df5a381 | generalizable-re-identification-from-videos | 2211.03663 | null | https://arxiv.org/abs/2211.03663v2 | https://arxiv.org/pdf/2211.03663v2.pdf | Generalizable Re-Identification from Videos with Cycle Association | In this paper, we are interested in learning a generalizable person re-identification (re-ID) representation from unlabeled videos. Compared with 1) the popular unsupervised re-ID setting where the training and test sets are typically under the same domain, and 2) the popular domain generalization (DG) re-ID setting wh... | ['Liang Zheng', 'Shengjin Wang', 'YaLi Li', 'Yifan Sun', 'Jingwei Zhang', 'Zhaopeng Dou', 'Zhongdao Wang'] | 2022-11-07 | null | null | null | null | ['generalizable-person-re-identification'] | ['computer-vision'] | [ 2.62433216e-02 -2.59063214e-01 -4.70813781e-01 -2.89061755e-01
-5.33892870e-01 -6.37614131e-01 6.86891079e-01 -2.63787150e-01
-5.40906310e-01 9.22158062e-01 2.12088645e-01 2.68995345e-01
-1.40513610e-02 -4.23301935e-01 -7.17447758e-01 -4.53738660e-01
-5.37877567e-02 7.88367629e-01 3.51092629e-02 -3.72979492... | [14.741125106811523, 1.0589454174041748] |
c6c93fbc-b13d-49e6-a1e9-67c69ff5a978 | quantum-algorithms-applied-to-satellite | 2302.07181 | null | https://arxiv.org/abs/2302.07181v1 | https://arxiv.org/pdf/2302.07181v1.pdf | Quantum algorithms applied to satellite mission planning for Earth observation | Earth imaging satellites are a crucial part of our everyday lives that enable global tracking of industrial activities. Use cases span many applications, from weather forecasting to digital maps, carbon footprint tracking, and vegetation monitoring. However, there are also limitations; satellites are difficult to manuf... | ['Alexey Melnikov', 'Markus Pflitsch', 'Mohammad Kordzanganeh', 'Egor Barashov', 'Miras Koblan', 'Daria Lemtiuzhnikova', 'Karan Pinto', 'Saaketh Rayaprolu', 'Sergei Iudin', 'Igor Tokarev', 'Serge Rainjonneau'] | 2023-02-14 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [ 4.40496683e-01 -3.39626335e-02 -2.47637004e-01 1.67312939e-02
-8.01287115e-01 -5.21696806e-01 3.28221738e-01 -1.75058126e-01
-4.67444718e-01 1.16037714e+00 -3.93009394e-01 -3.48529756e-01
-6.81296468e-01 -8.09485555e-01 -7.29652166e-01 -1.09046185e+00
-4.93477196e-01 8.19937289e-01 -2.51729786e-01 -5.31319976... | [5.601892471313477, 4.766911029815674] |
c8758889-6d08-48db-83ff-89efbb6dab2e | tailoring-to-the-tails-risk-measures-for-fine | 2208.03066 | null | https://arxiv.org/abs/2208.03066v2 | https://arxiv.org/pdf/2208.03066v2.pdf | Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity | Expected risk minimization (ERM) is at the core of many machine learning systems. This means that the risk inherent in a loss distribution is summarized using a single number - its average. In this paper, we propose a general approach to construct risk measures which exhibit a desired tail sensitivity and may replace t... | ['Robert C. Williamson', 'Christian Fröhlich'] | 2022-08-05 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [ 1.01559833e-01 3.70898128e-01 1.31831452e-01 -4.01894331e-01
-8.20396781e-01 -5.52048624e-01 6.00996614e-01 3.90188873e-01
-6.33976519e-01 8.55913877e-01 -9.14439932e-02 -1.32550195e-01
-4.86953020e-01 -9.38573003e-01 -5.37984550e-01 -9.99830782e-01
-3.04827243e-01 2.00771570e-01 -9.16955471e-02 -3.50041270... | [7.223840236663818, 4.048754692077637] |
49f1ca4e-33e4-4bc2-8234-d0d5adc29648 | an-asr-based-tutor-for-learning-to-read-how | 2306.04190 | null | https://arxiv.org/abs/2306.04190v1 | https://arxiv.org/pdf/2306.04190v1.pdf | An ASR-Based Tutor for Learning to Read: How to Optimize Feedback to First Graders | The interest in employing automatic speech recognition (ASR) in applications for reading practice has been growing in recent years. In a previous study, we presented an ASR-based Dutch reading tutor application that was developed to provide instantaneous feedback to first-graders learning to read. We saw that ASR has p... | ['Helmer Strik', 'Catia Cucchiarini', 'Ferdy Hubers', 'Cristian Tejedor-Garcia', 'Yu Bai'] | 2023-06-07 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 2.90278465e-01 4.19960529e-01 7.34471753e-02 -4.08977121e-01
-9.80399549e-01 -7.16740966e-01 6.70145512e-01 7.72695243e-01
-7.53312111e-01 6.78019226e-01 5.38531601e-01 -8.15063477e-01
-2.46611387e-01 -3.62789899e-01 -3.37549657e-01 -2.25033965e-02
6.54487669e-01 3.20363373e-01 7.24246860e-01 -7.20928252... | [11.907395362854004, 8.56702995300293] |
7d91a860-5244-4328-a2ce-56df911c155b | imputing-missing-observations-with-time | 2201.05634 | null | https://arxiv.org/abs/2201.05634v1 | https://arxiv.org/pdf/2201.05634v1.pdf | Imputing Missing Observations with Time Sliced Synthetic Minority Oversampling Technique | We present a simple yet novel time series imputation technique with the goal of constructing an irregular time series that is uniform across every sample in a data set. Specifically, we fix a grid defined by the midpoints of non-overlapping bins (dubbed "slices") of observation times and ensure that each sample has val... | ['Jennifer Hadlock', 'Qi Wei', 'Sevda Molani', 'Andrew Baumgartner'] | 2022-01-14 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [ 1.60237581e-01 1.86094232e-02 -3.11533719e-01 -4.33686197e-01
-1.12920320e+00 -4.48141277e-01 2.30826005e-01 1.82628274e-01
-2.00325102e-01 9.56381083e-01 3.87440026e-01 -4.45749238e-02
-4.44490463e-01 -7.64640749e-01 -8.07729423e-01 -8.65712285e-01
-3.71292442e-01 6.48776054e-01 -5.80624700e-01 1.81174930... | [7.105462074279785, 3.341601848602295] |
7ad7db10-85a9-441b-ac21-fcf56b084915 | position-agnostic-multi-microphone-speech | 2010.11875 | null | https://arxiv.org/abs/2010.11875v2 | https://arxiv.org/pdf/2010.11875v2.pdf | Scene-Agnostic Multi-Microphone Speech Dereverberation | Neural networks (NNs) have been widely applied in speech processing tasks, and, in particular, those employing microphone arrays. Nevertheless, most existing NN architectures can only deal with fixed and position-specific microphone arrays. In this paper, we present an NN architecture that can cope with microphone arra... | ['Sharon Gannot', 'Haggai Maron', 'Ethan Fetaya', 'Yochai Yemini'] | 2020-10-22 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 3.81874591e-01 -3.38428795e-01 7.52691925e-01 -2.89073408e-01
-9.01012778e-01 -4.87599373e-01 4.35951561e-01 -2.62020856e-01
-5.75484991e-01 3.82963985e-01 4.69379485e-01 -6.10589266e-01
-4.15596306e-01 -4.15604770e-01 -7.01890349e-01 -9.01137471e-01
-1.39821082e-01 8.42195451e-02 6.31953180e-02 -3.84859830... | [15.090444564819336, 5.823320388793945] |
07a4d152-f88a-43a0-a8a1-5823c5bf6407 | evaluating-the-utility-of-document-embedding | 1907.08184 | null | https://arxiv.org/abs/1907.08184v1 | https://arxiv.org/pdf/1907.08184v1.pdf | Evaluating the Utility of Document Embedding Vector Difference for Relation Learning | Recent work has demonstrated that vector offsets obtained by subtracting pretrained word embedding vectors can be used to predict lexical relations with surprising accuracy. Inspired by this finding, in this paper, we extend the idea to the document level, in generating document-level embeddings, calculating the distan... | ['Timothy Baldwin', 'Jingyuan Zhang'] | 2019-07-18 | null | null | null | null | ['document-embedding'] | ['methodology'] | [ 1.82990789e-01 5.09882212e-01 -2.91168034e-01 -3.91906768e-01
-3.55457276e-01 -6.21590257e-01 9.28284407e-01 8.61218154e-01
-7.93608189e-01 4.66111869e-01 7.68354237e-01 -5.17857909e-01
-4.00343418e-01 -8.50975096e-01 -2.93197408e-02 -3.09765786e-01
-2.15894952e-01 5.55089056e-01 2.41624430e-01 -6.09646082... | [10.533355712890625, 8.768109321594238] |
d6cd2540-8562-4eba-ba1f-97e2a25b512c | assisting-clinical-decisions-for-scarcely | 2307.03315 | null | https://arxiv.org/abs/2307.03315v1 | https://arxiv.org/pdf/2307.03315v1.pdf | Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation | Extracorporeal membrane oxygenation (ECMO) is an essential life-supporting modality for COVID-19 patients who are refractory to conventional therapies. However, the proper treatment decision has been the subject of significant debate and it remains controversial about who benefits from this scarcely available and techn... | ['Chenyang Lu', 'Philip Payne', 'Hanqing Yang', 'Neel Shah', 'Hanyang Liu', 'Ziqi Xu', 'Ahmed Sameh Said', 'Bing Xue'] | 2023-07-06 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 4.29746732e-02 7.37980232e-02 -8.90585899e-01 -3.70328486e-01
-5.47401547e-01 -2.20773295e-01 2.96430200e-01 -2.05924101e-02
-2.16809571e-01 1.15474975e+00 8.14483643e-01 -3.04402769e-01
-4.89222050e-01 -6.88645720e-01 -4.29727703e-01 -1.17822707e+00
2.54382759e-01 1.09211838e+00 -8.42581391e-01 3.38241756... | [7.980721950531006, 5.5434794425964355] |
b96bf4d2-ef86-4f3a-a2ab-6b85887fd3ea | illinois-math-solver-math-reasoning-on-the | null | null | https://aclanthology.org/N16-3011 | https://aclanthology.org/N16-3011.pdf | Illinois Math Solver: Math Reasoning on the Web | null | ['Subhro Roy', 'Dan Roth'] | 2016-06-01 | null | null | null | naacl-2016-6 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.2480998039245605, 3.8105437755584717] |
81ae52cf-6ce5-43d1-a046-bc3abd2d85f5 | on-crowdsourcing-design-with-comparison | 2306.01538 | null | https://arxiv.org/abs/2306.01538v1 | https://arxiv.org/pdf/2306.01538v1.pdf | On Crowdsourcing-design with Comparison Category Rating for Evaluating Speech Enhancement Algorithms | Speech enhancement techniques improve the quality or the intelligibility of an audio signal by removing unwanted noise. It is used as preprocessing in numerous applications such as speech recognition, hearing aids, broadcasting and telephony. The evaluation of such algorithms often relies on reference-based objective m... | ['Sneha Das', 'Line H. Clemmensen', 'Clément Laroche', 'Angélica S. Z. Suárez'] | 2023-06-02 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 2.22424977e-03 -1.14475951e-01 5.14828026e-01 -4.04274821e-01
-9.92489219e-01 -6.02844596e-01 3.16741675e-01 6.40641749e-01
-8.89487684e-01 6.38787329e-01 6.13002598e-01 -2.78625160e-01
-2.55722135e-01 -3.54093283e-01 7.86144957e-02 -5.33505738e-01
1.85075268e-01 -4.46449742e-02 2.54288495e-01 -6.04487002... | [15.113118171691895, 5.701748847961426] |
e87c2e41-5361-4ffb-b583-af831a55c781 | binary-domain-generalization-for-sparsifying | 2306.13515 | null | https://arxiv.org/abs/2306.13515v1 | https://arxiv.org/pdf/2306.13515v1.pdf | Binary domain generalization for sparsifying binary neural networks | Binary neural networks (BNNs) are an attractive solution for developing and deploying deep neural network (DNN)-based applications in resource constrained devices. Despite their success, BNNs still suffer from a fixed and limited compression factor that may be explained by the fact that existing pruning methods for ful... | ['Maria A. Zuluaga', 'Francesco Galati', 'Riccardo Schiavone'] | 2023-06-23 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [ 3.23872060e-01 1.57181285e-02 -2.90172428e-01 -6.40945315e-01
-6.48940355e-02 7.03957006e-02 1.35526806e-01 8.97947233e-04
-7.12341845e-01 8.45643759e-01 -3.11896831e-01 -4.49107409e-01
-5.08035541e-01 -1.04434204e+00 -5.73408544e-01 -6.06423378e-01
-3.09539717e-02 3.76954168e-01 4.65808094e-01 -2.49201581... | [8.53123950958252, 3.0515730381011963] |
18bd4fdf-e1f9-45ba-9e61-27c9d5b5f48a | on-the-impact-of-voice-anonymization-on | 2304.02181 | null | https://arxiv.org/abs/2304.02181v1 | https://arxiv.org/pdf/2304.02181v1.pdf | On the Impact of Voice Anonymization on Speech-Based COVID-19 Detection | With advances seen in deep learning, voice-based applications are burgeoning, ranging from personal assistants, affective computing, to remote disease diagnostics. As the voice contains both linguistic and paralinguistic information (e.g., vocal pitch, intonation, speech rate, loudness), there is growing interest in vo... | ['Tiago H. Falk', 'Carolyn Côté-Lussier', 'Mohamed Imoussaïne-Aïkous', 'Yi Zhu'] | 2023-04-05 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [-4.39700484e-02 1.14285082e-01 8.42508897e-02 -5.22849381e-01
-8.29074502e-01 -7.38549709e-01 3.02419960e-01 2.73044050e-01
-3.81734073e-01 5.70158899e-01 7.66618192e-01 -8.19968656e-02
-7.12247714e-02 -3.35866213e-01 -2.16803581e-01 -4.81425017e-01
-1.54919103e-01 2.45075509e-01 -6.30440235e-01 8.67597237... | [14.016050338745117, 5.878878116607666] |
93022461-c0cf-41f4-a38d-3d3ccd5216cf | hm-net-a-regression-network-for-object-center | 2110.09881 | null | https://arxiv.org/abs/2110.09881v2 | https://arxiv.org/pdf/2110.09881v2.pdf | HM-Net: A Regression Network for Object Center Detection and Tracking on Wide Area Motion Imagery | Wide Area Motion Imagery (WAMI) yields high-resolution images with a large number of extremely small objects. Target objects have large spatial displacements throughout consecutive frames. This nature of WAMI images makes object tracking and detection challenging. In this paper, we present our deep neural network-based... | ['Bahadir Gunturk', 'H. Fatih Ugurdag', 'Hasan F. Ates', 'Hakki Motorcu'] | 2021-10-19 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 3.09149534e-01 -3.36816669e-01 1.01706833e-01 2.46191360e-02
-4.31187540e-01 -3.89532566e-01 5.42473078e-01 -9.17831808e-02
-7.62666464e-01 5.82958281e-01 -3.98670226e-01 3.94590711e-03
3.81186426e-01 -9.04102504e-01 -9.04074132e-01 -8.68408382e-01
-4.34686929e-01 8.45852196e-02 1.23728132e+00 -1.94125175... | [6.4963788986206055, -2.062049627304077] |
27c63198-1daf-45ea-a7a9-4cb28fded4f5 | transfer-to-a-low-resource-language-via-close | 2304.08823 | null | https://arxiv.org/abs/2304.08823v1 | https://arxiv.org/pdf/2304.08823v1.pdf | Transfer to a Low-Resource Language via Close Relatives: The Case Study on Faroese | Multilingual language models have pushed state-of-the-art in cross-lingual NLP transfer. The majority of zero-shot cross-lingual transfer, however, use one and the same massively multilingual transformer (e.g., mBERT or XLM-R) to transfer to all target languages, irrespective of their typological, etymological, and phy... | ['Ivan Vulić', 'Goran Glavaš', 'Annika Simonsen', 'Vésteinn Snæbjarnarson'] | 2023-04-18 | null | null | null | null | ['zero-shot-cross-lingual-transfer', 'named-entity-recognition-ner', 'cross-lingual-transfer', 'xlm-r'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-2.52533644e-01 -2.60436416e-01 -4.73949552e-01 -2.95254618e-01
-1.07675099e+00 -1.03943455e+00 5.03441811e-01 2.40961224e-01
-1.01629162e+00 1.10003603e+00 4.26404119e-01 -5.88540614e-01
2.19913363e-01 -4.58645910e-01 -7.39342630e-01 -1.44427627e-01
2.49358192e-01 7.18012214e-01 2.08417565e-01 -5.89223683... | [10.778732299804688, 9.899868965148926] |
43ee1dc5-6a89-4bc4-9186-d2ec8f340ccd | good-news-vs-bad-news-what-are-they-talking | null | null | https://aclanthology.org/R17-1044 | https://aclanthology.org/R17-1044.pdf | Good News vs. Bad News: What are they talking about? | Today{'}s massive news streams demand the automate analysis which is provided by various online news explorers. However, most of them do not provide sentiment analysis. The main problem of sentiment analysis of news is the differences between the writers and readers attitudes to the news text. News can be good or bad b... | ['Olga Kanishcheva', 'Victoria Bobicev'] | 2017-09-01 | null | null | null | ranlp-2017-9 | ['text-annotation'] | ['natural-language-processing'] | [-3.85416150e-01 1.65834770e-01 -2.71307975e-01 -6.56280518e-01
-1.62247583e-01 -1.01806748e+00 7.11978316e-01 6.30981147e-01
-5.96023321e-01 8.95421565e-01 7.65294909e-01 -1.84865013e-01
4.38465089e-01 -7.11996555e-01 -2.08834603e-01 -4.79059637e-01
6.55353606e-01 4.35116023e-01 1.80081129e-01 -7.29142308... | [11.083269119262695, 6.9499125480651855] |
a8ac150b-f4ec-4c27-8c4a-8a22f32f7f95 | pager-progressive-attribute-guided-extendable | 2206.00162 | null | https://arxiv.org/abs/2206.00162v2 | https://arxiv.org/pdf/2206.00162v2.pdf | PAGER: Progressive Attribute-Guided Extendable Robust Image Generation | This work presents a generative modeling approach based on successive subspace learning (SSL). Unlike most generative models in the literature, our method does not utilize neural networks to analyze the underlying source distribution and synthesize images. The resulting method, called the progressive attribute-guided e... | ['C. -C. Jay Kuo', 'Zohreh Azizi'] | 2022-06-01 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 6.05626225e-01 3.03722918e-02 -7.38338828e-02 -2.27228194e-01
-9.81073260e-01 -4.94200706e-01 8.45479071e-01 -7.77018547e-01
4.44243886e-02 1.01639807e+00 3.26756269e-01 3.29250954e-02
7.34058395e-02 -9.22961414e-01 -7.84932375e-01 -8.85476172e-01
3.51758093e-01 3.01969618e-01 -2.65757918e-01 1.13294609... | [11.58603572845459, -0.5429757237434387] |
4f1dd253-45f0-475d-8d8f-bea445ceddae | cl4ac-a-contrastive-loss-for-audio-captioning | 2107.09990 | null | https://arxiv.org/abs/2107.09990v3 | https://arxiv.org/pdf/2107.09990v3.pdf | CL4AC: A Contrastive Loss for Audio Captioning | Automated Audio captioning (AAC) is a cross-modal translation task that aims to use natural language to describe the content of an audio clip. As shown in the submissions received for Task 6 of the DCASE 2021 Challenges, this problem has received increasing interest in the community. The existing AAC systems are usuall... | ['Wenwu Wang', 'Mark D. Plumbley', 'H Lilian Tang', 'Tom Ko', 'Xinhao Mei', 'Qiushi Huang', 'Xubo Liu'] | 2021-07-21 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 6.96200430e-01 1.48901224e-01 1.19282585e-02 -2.63115764e-01
-1.39653146e+00 -4.26844269e-01 4.60631609e-01 -4.22427356e-02
1.19682692e-01 5.99748492e-01 8.75521421e-01 2.88215399e-01
2.76665926e-01 -9.30995345e-02 -8.26887429e-01 -5.36455095e-01
2.75034398e-01 2.52989411e-01 -3.08386594e-01 9.21852291... | [15.258832931518555, 4.912491798400879] |
f5461dcc-6579-416d-8954-b0d04911be5d | domain-adaptation-for-semantic-segmentation | 1810.07911 | null | http://arxiv.org/abs/1810.07911v2 | http://arxiv.org/pdf/1810.07911v2.pdf | Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training | Recent deep networks achieved state of the art performance on a variety of
semantic segmentation tasks. Despite such progress, these models often face
challenges in real world `wild tasks' where large difference between labeled
training/source data and unseen test/target data exists. In particular, such
difference is o... | ['Yang Zou', 'Zhiding Yu', 'Jinsong Wang', 'B. V. K. Vijaya Kumar'] | 2018-10-18 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 6.56756222e-01 3.01732749e-01 -2.60967523e-01 -5.97421348e-01
-8.99663746e-01 -5.11608958e-01 5.61260879e-01 -3.09260469e-02
-6.19961679e-01 8.29793155e-01 -1.00218348e-01 2.28959303e-02
-6.59581199e-02 -6.50997937e-01 -6.65153861e-01 -9.07675445e-01
5.75258136e-01 7.09908307e-01 4.40089583e-01 1.51843518... | [9.636284828186035, 1.3823785781860352] |
ca4fd891-346d-4239-a967-e4f1f79c67c7 | criminal-investigation-tracker-with-suspect | 2302.10423 | null | https://arxiv.org/abs/2302.10423v1 | https://arxiv.org/pdf/2302.10423v1.pdf | Criminal Investigation Tracker with Suspect Prediction using Machine Learning | An automated approach to identifying offenders in Sri Lanka would be better than the current system. Obtaining information from eyewitnesses is one of the less reliable approaches and procedures still in use today. Automated criminal identification has the ability to save lives, notwithstanding Sri Lankan culture's lac... | ['Pradeepa Bandara', 'D. D. G. Delgasdeniya', 'S. P. S. Mandula', 'Erandika Lakmali', 'R. A. T. M. Rajapaksha', 'S. J. Dilmini'] | 2023-02-21 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [ 4.22333598e-01 -4.30302352e-01 -2.16738567e-01 -3.77394885e-01
-3.34734857e-01 -7.56190300e-01 1.63937107e-01 2.08628982e-01
-5.74866951e-01 5.59295356e-01 -3.47842835e-02 -6.44203365e-01
-4.08906490e-01 -9.37148809e-01 -2.97885891e-02 -5.82293987e-01
1.72797069e-01 3.91726464e-01 -3.16169634e-02 5.04615083... | [13.033305168151855, 0.976281464099884] |
e0b33119-331d-41ac-a66e-b93e55e9c5e9 | residual-generation-using-physically-based | 2008.04644 | null | https://arxiv.org/abs/2008.04644v1 | https://arxiv.org/pdf/2008.04644v1.pdf | Residual Generation Using Physically-Based Grey-Box Recurrent Neural Networks For Engine Fault Diagnosis | Data-driven fault diagnosis is complicated by unknown fault classes and limited training data from different fault realizations. In these situations, conventional multi-class classification approaches are not suitable for fault diagnosis. One solution is the use of anomaly classifiers that are trained using only nomina... | ['Daniel Jung'] | 2020-08-11 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [ 2.34974056e-01 9.87614617e-02 2.50693798e-01 -9.56699103e-02
-1.44351244e-01 -5.49785793e-03 2.26058826e-01 1.91486642e-01
6.47576690e-01 4.96002734e-01 -4.36178833e-01 -6.74978614e-01
-7.28367686e-01 -7.46117175e-01 -2.81238407e-01 -8.51033390e-01
-1.39592513e-01 7.48682201e-01 1.59167469e-01 -4.90932554... | [6.681512355804443, 2.4642674922943115] |
97b3b318-cf50-4290-9a7b-0fe2792b7933 | sequential-quantiles-via-hermite-series | 1507.05073 | null | http://arxiv.org/abs/1507.05073v2 | http://arxiv.org/pdf/1507.05073v2.pdf | Sequential Quantiles via Hermite Series Density Estimation | Sequential quantile estimation refers to incorporating observations into
quantile estimates in an incremental fashion thus furnishing an online estimate
of one or more quantiles at any given point in time. Sequential quantile
estimation is also known as online quantile estimation. This area is relevant
to the analysis ... | ['Melvin Varughese', 'Iain Macdonald', 'Michael Stephanou'] | 2015-07-17 | null | null | null | null | ['sequential-distribution-function-estimation', 'sequential-quantile-estimation', 'data-summarization'] | ['miscellaneous', 'miscellaneous', 'miscellaneous'] | [-2.48842046e-01 -2.86916941e-01 3.95361967e-02 -4.57746893e-01
-9.80018020e-01 -8.02656233e-01 5.37227243e-02 9.24374163e-01
-3.38171303e-01 7.24327743e-01 -1.55579820e-01 -3.84805113e-01
-5.67924500e-01 -1.02086866e+00 -6.60203338e-01 -4.96352077e-01
-5.48258185e-01 6.24635100e-01 3.09763730e-01 -1.42567560... | [7.048947811126709, 3.9317073822021484] |
fd3c1399-eafd-4cab-ba92-93706d70b985 | speed-experimental-design-for-policy | 2301.12357 | null | https://arxiv.org/abs/2301.12357v2 | https://arxiv.org/pdf/2301.12357v2.pdf | SPEED: Experimental Design for Policy Evaluation in Linear Heteroscedastic Bandits | In this paper, we study the problem of optimal data collection for policy evaluation in linear bandits. In policy evaluation, we are given a target policy and asked to estimate the expected reward it will obtain when executed in a multi-armed bandit environment. Our work is the first work that focuses on such optimal d... | ['Robert Nowak', 'Josiah Hanna', 'Qiaomin Xie', 'Subhojyoti Mukherjee'] | 2023-01-29 | null | null | null | null | ['experimental-design'] | ['methodology'] | [ 4.59485725e-02 7.67709017e-02 -1.06781304e+00 -3.16494077e-01
-1.42525220e+00 -8.78278375e-01 3.14897835e-01 3.86932045e-02
-8.88637722e-01 1.27091956e+00 3.22055966e-01 -1.11851430e+00
-7.00366795e-01 -4.38160807e-01 -1.15559494e+00 -8.14899266e-01
-1.49826556e-02 8.68104160e-01 -3.93350512e-01 6.12064362... | [4.487074375152588, 3.2356362342834473] |
b885bb73-3a93-4b8c-9c25-07824f7a1716 | vidit-virtual-image-dataset-for-illumination | 2005.05460 | null | https://arxiv.org/abs/2005.05460v2 | https://arxiv.org/pdf/2005.05460v2.pdf | VIDIT: Virtual Image Dataset for Illumination Transfer | Deep image relighting is gaining more interest lately, as it allows photo enhancement through illumination-specific retouching without human effort. Aside from aesthetic enhancement and photo montage, image relighting is valuable for domain adaptation, whether to augment datasets for training or to normalize input test... | ['Sabine Süsstrunk', 'Majed El Helou', 'Ruofan Zhou', 'Johan Barthas'] | 2020-05-11 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 7.03656137e-01 -1.91438407e-01 3.00764948e-01 -3.58028620e-01
-5.28420746e-01 -7.19006717e-01 8.42271328e-01 6.67008460e-02
-3.90519798e-01 9.03090239e-01 1.63243726e-01 -3.16816159e-02
2.51731634e-01 -7.18446195e-01 -8.22339952e-01 -7.33360946e-01
2.08507538e-01 3.58155042e-01 5.56964763e-02 -2.75687069... | [10.47054386138916, -2.375222682952881] |
bec1bd31-b930-4ae8-b943-8d30ad5928a7 | transferable-adversarial-robustness-for | 2306.04064 | null | https://arxiv.org/abs/2306.04064v1 | https://arxiv.org/pdf/2306.04064v1.pdf | Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings | Research on adversarial robustness is primarily focused on image and text data. Yet, many scenarios in which lack of robustness can result in serious risks, such as fraud detection, medical diagnosis, or recommender systems often do not rely on images or text but instead on tabular data. Adversarial robustness in tabul... | ['Nicolas Flammarion', 'Carmela Troncoso', 'Maksym Andriushchenko', 'Klim Kireev'] | 2023-06-06 | null | null | null | null | ['adversarial-robustness', 'medical-diagnosis', 'fraud-detection'] | ['adversarial', 'medical', 'miscellaneous'] | [ 2.33377054e-01 1.31826416e-01 -1.64070111e-02 -1.66875809e-01
-8.65775704e-01 -1.01827800e+00 6.59918070e-01 2.06808001e-01
-1.82718202e-01 7.38353908e-01 1.09231390e-01 -3.61390591e-01
-1.61727399e-01 -1.18384016e+00 -9.84552145e-01 -8.10944498e-01
3.52156796e-02 4.16454285e-01 -8.98588821e-02 -4.48956072... | [5.610775947570801, 7.906092643737793] |
b9c4000f-3fa3-4a7a-8364-41edac576a68 | hardware-software-co-design-with-adc-less-in | 2211.02167 | null | https://arxiv.org/abs/2211.02167v2 | https://arxiv.org/pdf/2211.02167v2.pdf | Hardware/Software co-design with ADC-Less In-memory Computing Hardware for Spiking Neural Networks | Spiking Neural Networks (SNNs) are bio-plausible models that hold great potential for realizing energy-efficient implementations of sequential tasks on resource-constrained edge devices. However, commercial edge platforms based on standard GPUs are not optimized to deploy SNNs, resulting in high energy and latency. Whi... | ['Kaushik Roy', 'Utkarsh Saxena', 'Adarsh Kumar Kosta', 'Marco Paul E. Apolinario'] | 2022-11-03 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 3.19334745e-01 -3.76550466e-01 -6.77581578e-02 1.15182422e-01
5.88996485e-02 -3.36792290e-01 2.98966706e-01 2.54339844e-01
-9.86464024e-01 5.46975970e-01 -4.32856858e-01 -3.61187071e-01
1.54958963e-01 -7.83841491e-01 -7.45813727e-01 -6.26178920e-01
-6.16458356e-02 -1.46949872e-01 5.48035324e-01 1.15671247... | [8.29780101776123, 2.485051155090332] |
a259a3dc-70ae-45b4-9b53-6e21e757889b | expanding-the-text-classification-toolbox | 1903.09878 | null | http://arxiv.org/abs/1903.09878v2 | http://arxiv.org/pdf/1903.09878v2.pdf | Expanding the Text Classification Toolbox with Cross-Lingual Embeddings | Most work in text classification and Natural Language Processing (NLP)
focuses on English or a handful of other languages that have text corpora of
hundreds of millions of words. This is creating a new version of the digital
divide: the artificial intelligence (AI) divide. Transfer-based approaches,
such as Cross-Lingu... | ["Meryem M'hamdi", 'Michael Baeriswyl', 'Robert West', 'Claudiu Musat', 'Andreea Hossmann'] | 2019-03-23 | null | null | null | null | ['multilingual-word-embeddings'] | ['methodology'] | [-1.97706386e-01 -2.03259513e-01 -6.76989555e-01 -2.53828406e-01
-7.42778480e-01 -5.58291674e-01 1.03545129e+00 6.37187898e-01
-8.26982737e-01 3.47542882e-01 8.64957392e-01 -7.02487648e-01
8.81546065e-02 -6.37336552e-01 -1.96563259e-01 -4.51596528e-01
1.03966407e-01 9.09245610e-01 -3.13102394e-01 -3.89846444... | [10.54782485961914, 9.639232635498047] |
957da8b1-7fbc-4dc5-92d3-e320b6216a3b | multi-instance-learning-for-end-to-end | 1903.02652 | null | http://arxiv.org/abs/1903.02652v1 | http://arxiv.org/pdf/1903.02652v1.pdf | Multi-Instance Learning for End-to-End Knowledge Base Question Answering | End-to-end training has been a popular approach for knowledge base question
answering (KBQA). However, real world applications often contain answers of
varied quality for users' questions. It is not appropriate to treat all
available answers of a user question equally.
This paper proposes a novel approach based on mu... | ['Qiong Zhang', 'Yifan He', 'Luo Si', 'Mengxi Wei'] | 2019-03-06 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-3.93977553e-01 8.31749290e-02 1.09189734e-01 -6.37520850e-01
-1.81673133e+00 -7.36296415e-01 9.71157625e-02 5.38376570e-01
-7.58110166e-01 1.09444189e+00 3.40596139e-01 -3.04614931e-01
-1.52381599e-01 -1.03020537e+00 -7.09722877e-01 -3.27262312e-01
5.43883443e-01 1.10671151e+00 8.00073028e-01 -7.23528445... | [11.262449264526367, 8.036029815673828] |
edd4e4fb-4944-434b-a51d-92734180cf55 | masked-conditional-video-diffusion-for | 2205.09853 | null | https://arxiv.org/abs/2205.09853v4 | https://arxiv.org/pdf/2205.09853v4.pdf | MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation | Video prediction is a challenging task. The quality of video frames from current state-of-the-art (SOTA) generative models tends to be poor and generalization beyond the training data is difficult. Furthermore, existing prediction frameworks are typically not capable of simultaneously handling other video-related tasks... | ['Christopher Pal', 'Alexia Jolicoeur-Martineau', 'Vikram Voleti'] | 2022-05-19 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 1.34598106e-01 -3.28046918e-01 3.75236059e-03 -2.57178664e-01
-8.60144138e-01 -3.00781012e-01 7.80670404e-01 -5.86318374e-01
-8.84390026e-02 7.34010994e-01 2.69478589e-01 -4.10504133e-01
4.95635539e-01 -8.58834386e-01 -1.22875595e+00 -7.20547259e-01
-2.70111829e-01 1.26302361e-01 3.72948438e-01 -8.59773904... | [10.733820915222168, -0.7174512147903442] |
45fb54a9-98d8-42d9-92d6-47518adffcb8 | joint-3d-scene-reconstruction-and-class | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Hane_Joint_3D_Scene_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Hane_Joint_3D_Scene_2013_CVPR_paper.pdf | Joint 3D Scene Reconstruction and Class Segmentation | Both image segmentation and dense 3D modeling from images represent an intrinsically ill-posed problem. Strong regularizers are therefore required to constrain the solutions from being 'too noisy'. Unfortunately, these priors generally yield overly smooth reconstructions and/or segmentations in certain regions whereas ... | ['Andrea Cohen', 'Roland Angst', 'Marc Pollefeys', 'Christopher Zach', 'Christian Hane'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 5.53648353e-01 2.64605135e-01 -1.26762152e-01 -4.61423069e-01
-7.31803834e-01 -3.20294976e-01 4.61254179e-01 -1.37253165e-01
-2.35166863e-01 3.81544054e-01 -3.21559757e-02 3.97589915e-02
-7.84324780e-02 -5.47294140e-01 -6.59812450e-01 -8.76180053e-01
2.93647438e-01 5.41669846e-01 2.60108471e-01 1.34853810... | [9.064848899841309, -3.051060199737549] |
4cc84a1c-b451-4877-b672-f81ddf803373 | unsupervised-meta-learning-for-few-shot-image | 1811.11819 | null | https://arxiv.org/abs/1811.11819v2 | https://arxiv.org/pdf/1811.11819v2.pdf | Unsupervised Meta-Learning For Few-Shot Image Classification | Few-shot or one-shot learning of classifiers requires a significant inductive bias towards the type of task to be learned. One way to acquire this is by meta-learning on tasks similar to the target task. In this paper, we propose UMTRA, an algorithm that performs unsupervised, model-agnostic meta-learning for classific... | ['Ladislau Bölöni', 'Siavash Khodadadeh', 'Mubarak Shah'] | 2018-11-28 | unsupervised-meta-learning-for-few-shot-image-1 | http://papers.nips.cc/paper/9203-unsupervised-meta-learning-for-few-shot-image-classification | http://papers.nips.cc/paper/9203-unsupervised-meta-learning-for-few-shot-image-classification.pdf | neurips-2019-12 | ['unsupervised-few-shot-learning', 'unsupervised-few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 6.04853988e-01 2.96863765e-01 -4.71550465e-01 -4.73407596e-01
-8.34621549e-01 -2.34707907e-01 1.02853024e+00 3.84205341e-01
-5.98237514e-01 7.10154057e-01 -1.28749967e-01 2.49402644e-03
-1.04782293e-02 -8.38124394e-01 -7.23872721e-01 -7.12620020e-01
1.82086423e-01 8.07896495e-01 4.01493609e-01 -1.49258584... | [9.942405700683594, 3.0413849353790283] |
ea881f7d-d844-4fb9-95c0-3bf397f389cc | adversarial-semi-supervised-audio-source | 1711.00048 | null | http://arxiv.org/abs/1711.00048v2 | http://arxiv.org/pdf/1711.00048v2.pdf | Adversarial Semi-Supervised Audio Source Separation applied to Singing Voice Extraction | The state of the art in music source separation employs neural networks
trained in a supervised fashion on multi-track databases to estimate the
sources from a given mixture. With only few datasets available, often extensive
data augmentation is used to combat overfitting. Mixing random tracks, however,
can even reduce... | ['Sebastian Ewert', 'Simon Dixon', 'Daniel Stoller'] | 2017-10-31 | null | null | null | null | ['audio-source-separation', 'music-source-separation'] | ['audio', 'music'] | [ 4.89963889e-01 8.33586082e-02 7.17204586e-02 1.60194308e-01
-1.09938884e+00 -9.27046537e-01 3.17214340e-01 -1.67595088e-01
-1.29292160e-01 8.17163408e-01 1.77670032e-01 1.08374227e-02
-3.45785588e-01 -3.88161868e-01 -7.68708885e-01 -8.27740192e-01
3.10135838e-02 4.65879261e-01 -1.39909431e-01 -2.70387501... | [15.537339210510254, 5.5534563064575195] |
7f94d40f-ec45-4154-a09a-48ba505d8c51 | coordinating-cross-modal-distillation-for | 2211.16712 | null | https://arxiv.org/abs/2211.16712v1 | https://arxiv.org/pdf/2211.16712v1.pdf | Coordinating Cross-modal Distillation for Molecular Property Prediction | In recent years, molecular graph representation learning (GRL) has drawn much more attention in molecular property prediction (MPP) problems. The existing graph methods have demonstrated that 3D geometric information is significant for better performance in MPP. However, accurate 3D structures are often costly and time... | ['Meng Liu', 'Yingyi Zhang', 'Lei Shen', 'Ruixin Zhang', 'Nan Zhang', 'Hao Zhang'] | 2022-11-30 | null | null | null | null | ['graph-regression', 'molecular-property-prediction'] | ['graphs', 'miscellaneous'] | [ 1.45662636e-01 -1.21573612e-01 -4.44404423e-01 -5.21551780e-02
-4.39335495e-01 -3.50860655e-01 3.32894087e-01 4.68907863e-01
-2.70482302e-01 1.06145811e+00 -1.62998334e-01 -6.32850409e-01
-7.54458308e-02 -8.34594607e-01 -9.34101462e-01 -9.55546975e-01
-1.82159007e-01 4.34645474e-01 9.03878361e-02 -2.08864003... | [5.116727828979492, 5.826181888580322] |
0881f095-ffae-480d-b0f1-31865d3c1d9f | network-modeling-and-pathway-inference-from | 1810.00839 | null | http://arxiv.org/abs/1810.00839v1 | http://arxiv.org/pdf/1810.00839v1.pdf | Network Modeling and Pathway Inference from Incomplete Data ("PathInf") | In this work, we developed a network inference method from incomplete data
("PathInf") , as massive and non-uniformly distributed missing values is a
common challenge in practical problems. PathInf is a two-stages inference
model. In the first stage, it applies a data summarization model based on
maximum likelihood to ... | ['Quanzheng Li', 'Xing Wang', 'Nan Wu', 'Qitian Chen', 'Xiang Li', 'Ning Guo'] | 2018-10-01 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 3.96934956e-01 5.37859797e-01 -6.17909133e-01 -1.92106694e-01
-3.39207649e-01 -1.68927118e-01 2.69832760e-01 3.30284059e-01
-4.37171496e-02 1.24581385e+00 5.41612208e-01 -6.27709806e-01
-1.06433082e+00 -9.73471165e-01 -4.69241470e-01 -1.04198372e+00
-2.05970332e-01 9.32326019e-01 5.71424142e-02 1.50101244... | [7.7691192626953125, 5.319459438323975] |
0c616e5f-591f-4ef9-9f91-5f3049f8fca6 | ampose-alternatively-mixed-global-local | 2210.04216 | null | https://arxiv.org/abs/2210.04216v4 | https://arxiv.org/pdf/2210.04216v4.pdf | AMPose: Alternatively Mixed Global-Local Attention Model for 3D Human Pose Estimation | The graph convolutional networks (GCNs) have been applied to model the physically connected and non-local relations among human joints for 3D human pose estimation (HPE). In addition, the purely Transformer-based models recently show promising results in video-based 3D HPE. However, the single-frame method still needs ... | ['PeiYuan Wu', 'Yunwei Chiu', 'Hongxin Lin'] | 2022-10-09 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [-4.85582203e-01 1.74772710e-01 7.35225007e-02 -7.40686432e-02
-1.19700752e-01 -3.06528658e-02 3.53276938e-01 -3.87043089e-01
-1.67898938e-01 3.78637880e-01 2.12801501e-01 1.92828581e-01
-2.09021166e-01 -8.26392412e-01 -8.46976638e-01 -5.11357903e-01
-3.15418005e-01 5.77088356e-01 5.50697505e-01 -3.30295742... | [7.153324127197266, -0.5972241759300232] |
122dffed-124e-486c-a67c-fcbfbc594b27 | dialogue-discourse-aware-graph-convolutional | 2012.03502 | null | https://arxiv.org/abs/2012.03502v2 | https://arxiv.org/pdf/2012.03502v2.pdf | Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization | Meeting summarization is a challenging task due to its dynamic interaction nature among multiple speakers and lack of sufficient training data. Existing methods view the meeting as a linear sequence of utterances while ignoring the diverse relations between each utterance. Besides, the limited labeled data further hind... | ['Xinwei Geng', 'Bing Qin', 'Xiaocheng Feng', 'Xiachong Feng'] | 2020-12-07 | null | null | null | null | ['meeting-summarization'] | ['natural-language-processing'] | [ 3.10959518e-01 8.17085445e-01 -1.25576958e-01 -6.61417484e-01
-7.34833241e-01 -3.63580555e-01 7.58783579e-01 2.02949658e-01
-8.58229399e-03 5.71587503e-01 9.53471661e-01 -2.67636508e-01
3.72520179e-01 -5.73325098e-01 -5.82936406e-01 -7.86665231e-02
1.11200869e-01 6.94842160e-01 8.20430070e-02 -5.50673485... | [12.512981414794922, 8.178709983825684] |
9449d857-cad6-4650-bf51-bac08f8cd6cd | corefi-a-crowd-sourcing-suite-for-coreference | 2010.02588 | null | https://arxiv.org/abs/2010.02588v1 | https://arxiv.org/pdf/2010.02588v1.pdf | CoRefi: A Crowd Sourcing Suite for Coreference Annotation | Coreference annotation is an important, yet expensive and time consuming, task, which often involved expert annotators trained on complex decision guidelines. To enable cheaper and more efficient annotation, we present CoRefi, a web-based coreference annotation suite, oriented for crowdsourcing. Beyond the core corefer... | ['Ido Dagan', 'Arie Cattan', 'Aaron Bornstein'] | 2020-10-06 | null | https://aclanthology.org/2020.emnlp-demos.27 | https://aclanthology.org/2020.emnlp-demos.27.pdf | emnlp-2020-11 | ['cross-document-coreference-resolution'] | ['natural-language-processing'] | [-2.46753469e-01 6.45897150e-01 -2.35246539e-01 -3.10901310e-02
-1.30011153e+00 -1.32485926e+00 4.36977684e-01 1.43453524e-01
-3.36000204e-01 8.28285813e-01 5.98901331e-01 -6.46500811e-02
1.94716044e-02 -1.59351349e-01 -3.96933287e-01 -2.67500013e-01
3.31622928e-01 1.38130713e+00 5.51242232e-01 -4.82242495... | [9.36915111541748, 9.2487154006958] |
0e9f56de-fca0-4136-bca1-bd70f433733f | loop-closure-detection-based-on-object-level | 2304.05146 | null | https://arxiv.org/abs/2304.05146v2 | https://arxiv.org/pdf/2304.05146v2.pdf | Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency | Visual simultaneous localization and mapping (SLAM) systems face challenges in detecting loop closure under the circumstance of large viewpoint changes. In this paper, we present an object-based loop closure detection method based on the spatial layout and semanic consistency of the 3D scene graph. Firstly, we propose ... | ['Fei Wen', 'Rendong Ying', 'Xiang Chen', 'Haochen Niu', 'Peilin Liu', 'Xingwu Ji'] | 2023-04-11 | null | null | null | null | ['simultaneous-localization-and-mapping', 'loop-closure-detection', 'graph-matching'] | ['computer-vision', 'computer-vision', 'graphs'] | [-1.95768133e-01 -3.89575899e-01 -7.28648379e-02 -3.95454556e-01
-3.05286139e-01 -7.20946074e-01 4.35153931e-01 4.72224206e-01
-9.43446532e-02 6.14223890e-02 -5.99372983e-02 8.27593058e-02
-3.13234985e-01 -6.35276973e-01 -7.76380837e-01 -1.76314160e-01
1.04758605e-01 6.74032748e-01 6.73695922e-01 3.29907089... | [7.309749603271484, -2.303487539291382] |
fd0f8a0e-4f9a-425a-a217-5113c47013b6 | unsupervised-machine-learning-framework-for | 2208.01439 | null | https://arxiv.org/abs/2208.01439v3 | https://arxiv.org/pdf/2208.01439v3.pdf | Unsupervised machine learning framework for discriminating major variants of concern during COVID-19 | Due to the high mutation rate of the virus, the COVID-19 pandemic evolved rapidly. Certain variants of the virus, such as Delta and Omicron, emerged with altered viral properties leading to severe transmission and death rates. These variants burdened the medical systems worldwide with a major impact to travel, producti... | ['Seshadri Vasan', 'Laurence O. W. Wilson', 'Pranjal Singh', 'Vinti Agarwal', 'Tom Blau', 'Mingyue Kang', 'Chaarvi Bansal', 'Rohitash Chandra'] | 2022-08-01 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [ 2.22023860e-01 -5.37714362e-01 2.57631063e-01 4.21511047e-02
-1.12699404e-01 -8.22027028e-01 7.38956630e-01 4.47467089e-01
-3.71043503e-01 6.52669370e-01 3.38078916e-01 -4.74234670e-01
-5.19584179e-01 -5.14287353e-01 1.36241645e-01 -1.14132130e+00
-5.03342271e-01 1.00116420e+00 -3.80881906e-01 -1.83631748... | [5.0355987548828125, 5.229383945465088] |
60c7cbd5-b137-4769-8aa6-fb610439c8bf | covariance-regression-with-random-forests | 2209.08173 | null | https://arxiv.org/abs/2209.08173v3 | https://arxiv.org/pdf/2209.08173v3.pdf | Covariance regression with random forests | Capturing the conditional covariances or correlations among the elements of a multivariate response vector based on covariates is important to various fields including neuroscience, epidemiology and biomedicine. We propose a new method called Covariance Regression with Random Forests (CovRegRF) to estimate the covarian... | ['Aurelie Labbe', 'Denis Larocque', 'Cansu Alakus'] | 2022-09-16 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 0.5142719 -0.12711503 -0.24231252 -0.7449912 -0.45839885 -0.01769425
0.46986362 0.0791059 -0.31017244 0.9519614 0.39447033 -0.42116126
-0.545911 -0.8506434 -0.56825066 -0.894358 -0.20333152 0.4112067
-0.0903176 0.32232797 0.20941842 0.3396842 -1.1185379 -0.37258145
0.9801147 0.55692637 0.3... | [7.727998733520508, 4.947638034820557] |
1954a9be-004b-4d2a-9c3e-ebbf636232c7 | diffusion-policies-as-an-expressive-policy | 2208.06193 | null | https://arxiv.org/abs/2208.06193v2 | https://arxiv.org/pdf/2208.06193v2.pdf | Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning | Offline reinforcement learning (RL), which aims to learn an optimal policy using a previously collected static dataset, is an important paradigm of RL. Standard RL methods often perform poorly in this regime due to the function approximation errors on out-of-distribution actions. While a variety of regularization metho... | ['Mingyuan Zhou', 'Jonathan J Hunt', 'Zhendong Wang'] | 2022-08-12 | null | null | null | null | ['d4rl'] | ['robots'] | [-2.59866774e-01 1.14908054e-01 -7.82791436e-01 -5.49769513e-02
-1.00029230e+00 -4.52777952e-01 8.07447135e-01 -1.13628186e-01
-5.44002056e-01 1.08489835e+00 5.41359961e-01 -3.40196460e-01
-3.30688685e-01 -7.25495577e-01 -8.62876892e-01 -1.01272500e+00
1.16647534e-01 5.74587882e-01 -2.79287323e-02 -2.64626652... | [4.0703043937683105, 2.3204474449157715] |
125c0945-151c-4a29-8b73-e7ea8994656b | video-to-video-synthesis | 1808.06601 | null | http://arxiv.org/abs/1808.06601v2 | http://arxiv.org/pdf/1808.06601v2.pdf | Video-to-Video Synthesis | We study the problem of video-to-video synthesis, whose goal is to learn a
mapping function from an input source video (e.g., a sequence of semantic
segmentation masks) to an output photorealistic video that precisely depicts
the content of the source video. While its image counterpart, the
image-to-image synthesis pro... | ['Jun-Yan Zhu', 'Ming-Yu Liu', 'Ting-Chun Wang', 'Jan Kautz', 'Andrew Tao', 'Guilin Liu', 'Bryan Catanzaro'] | 2018-08-20 | video-to-video-synthesis-1 | http://papers.nips.cc/paper/7391-video-to-video-synthesis | http://papers.nips.cc/paper/7391-video-to-video-synthesis.pdf | neurips-2018-12 | ['video-to-video-synthesis'] | ['computer-vision'] | [ 7.12799847e-01 -7.70029724e-02 -5.54601997e-02 3.56910154e-02
-9.37162757e-01 -8.63465726e-01 8.41823280e-01 -7.58032560e-01
1.11778274e-01 6.61019921e-01 2.19264135e-01 -9.40125883e-02
3.75771791e-01 -6.84453011e-01 -1.42835486e+00 -6.01578236e-01
3.33243728e-01 9.68806222e-02 2.48843491e-01 -1.89397007... | [10.856088638305664, -0.614187479019165] |
dc0c02b5-c77f-49ba-adb4-cf23f1159668 | classification-of-audio-segments-in-call | 2106.02422 | null | https://arxiv.org/abs/2106.02422v1 | https://arxiv.org/pdf/2106.02422v1.pdf | Classification of Audio Segments in Call Center Recordings using Convolutional Recurrent Neural Networks | Detailed statistical analysis of call center recordings is critical in the customer relationship management point of view. With the recent advances in artificial intelligence, many tasks regarding the calculation of call statistics are now performed automatically. This work proposes a neural network framework where the... | ['Şükrü Ozan'] | 2021-06-04 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [-1.21680081e-01 4.04674672e-02 2.17509434e-01 -5.60091496e-01
-7.51071274e-01 -3.66513640e-01 4.70643997e-01 2.75283188e-01
-6.55733407e-01 4.91344661e-01 8.90070423e-02 -1.60601154e-01
-1.78582683e-01 -6.58448040e-01 -4.13381070e-01 -6.19316041e-01
-1.27654135e-01 9.22013283e-01 -3.31869453e-01 -3.50864410... | [15.784043312072754, 5.242916584014893] |
17e2c512-5261-4d25-97e2-95c2deba8bb7 | re-ranking-via-metric-fusion-for-object | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Bai_Re-Ranking_via_Metric_Fusion_for_Object_Retrieval_and_Person_Re-Identification_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Bai_Re-Ranking_via_Metric_Fusion_for_Object_Retrieval_and_Person_Re-Identification_CVPR_2019_paper.pdf | Re-Ranking via Metric Fusion for Object Retrieval and Person Re-Identification | This work studies the unsupervised re-ranking procedure for object retrieval and person re-identification with a specific concentration on an ensemble of multiple metrics (or similarities). While the re-ranking step is involved by running a diffusion process on the underlying data manifolds, the fusion step can leverag... | [' Longin Jan Latecki', ' Philip H.S. Torr', ' Peng Tang', 'Song Bai'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['3d-shape-retrieval'] | ['computer-vision'] | [ 1.61938474e-01 -3.65256041e-01 -3.16577516e-02 -4.49165069e-02
-6.65913999e-01 -7.28587270e-01 9.88163710e-01 2.71689653e-01
-4.16961581e-01 2.40656957e-01 1.07617296e-01 2.08479792e-01
-8.67080212e-01 -5.53596377e-01 -4.32724357e-02 -1.01645529e+00
-4.15867493e-02 4.93030876e-01 9.38612502e-03 -2.84601003... | [11.019030570983887, 0.939283549785614] |
460075e8-d0a7-4780-85b6-d66623b3653b | multi-person-3d-pose-estimation-in-crowded | 2007.10986 | null | https://arxiv.org/abs/2007.10986v1 | https://arxiv.org/pdf/2007.10986v1.pdf | Multi-person 3D Pose Estimation in Crowded Scenes Based on Multi-View Geometry | Epipolar constraints are at the core of feature matching and depth estimation in current multi-person multi-camera 3D human pose estimation methods. Despite the satisfactory performance of this formulation in sparser crowd scenes, its effectiveness is frequently challenged under denser crowd circumstances mainly due to... | ['He Chen', 'Gim Hee Lee', 'Pengfei Li', 'Pengfei Guo', 'Gregory Chirikjian'] | 2020-07-21 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/672_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480545.pdf | eccv-2020-8 | ['3d-multi-person-pose-estimation'] | ['computer-vision'] | [ 9.40634757e-02 -1.63697109e-01 3.49639654e-01 -1.13538444e-01
-5.43262899e-01 -2.08822712e-01 6.46146119e-01 1.60265751e-02
-5.60576856e-01 5.46732724e-01 4.99181956e-01 4.75727588e-01
-7.57426098e-02 -4.19872224e-01 -5.36256194e-01 -4.80880916e-01
3.52018833e-01 6.11179531e-01 3.07168424e-01 -3.14480811... | [7.071255683898926, -0.9983372688293457] |
1d3a8d53-05c4-45ba-860f-68a7707a7b06 | adaptive-unimodal-cost-volume-filtering-for | 1909.03751 | null | https://arxiv.org/abs/1909.03751v2 | https://arxiv.org/pdf/1909.03751v2.pdf | Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching | State-of-the-art deep learning based stereo matching approaches treat disparity estimation as a regression problem, where loss function is directly defined on true disparities and their estimated ones. However, disparity is just a byproduct of a matching process modeled by cost volume, while indirectly learning cost vo... | ['Zhiwei Li', 'Kun Yu', 'Youmin Zhang', 'Yimin Chen', 'Suihanjin Yu', 'Xiao Bai', 'Kuiyuan Yang'] | 2019-09-09 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [-1.73143908e-01 -1.93600550e-01 -1.48295030e-01 -5.49066663e-01
-1.04787242e+00 -2.30735675e-01 5.22900403e-01 -1.30783051e-01
-6.50451183e-01 7.85141647e-01 2.25620300e-01 -1.24527469e-01
1.69811696e-01 -8.84254992e-01 -1.03073967e+00 -5.35513222e-01
1.63819604e-02 3.60590547e-01 2.19520256e-01 1.40996231... | [8.791584014892578, -2.2831223011016846] |
efcc6e66-c896-4eba-a4e7-8165340663bd | interpretable-neural-embeddings-with-sparse | 2306.14135 | null | https://arxiv.org/abs/2306.14135v1 | https://arxiv.org/pdf/2306.14135v1.pdf | Interpretable Neural Embeddings with Sparse Self-Representation | Interpretability benefits the theoretical understanding of representations. Existing word embeddings are generally dense representations. Hence, the meaning of latent dimensions is difficult to interpret. This makes word embeddings like a black-box and prevents them from being human-readable and further manipulation. M... | ['Hao Zhu', 'Minxue Xia'] | 2023-06-25 | null | null | null | null | ['dictionary-learning', 'word-embeddings'] | ['methodology', 'methodology'] | [-6.45515621e-02 2.97682911e-01 -6.42017782e-01 -5.02026558e-01
-4.12975580e-01 -5.10688424e-01 5.61048985e-01 4.30164695e-01
-4.60586220e-01 3.87963533e-01 7.59464383e-01 -5.19545019e-01
2.21571140e-02 -6.37206793e-01 -3.15980852e-01 -3.96860063e-01
1.19342431e-01 4.81119215e-01 -3.94701362e-01 -2.39963174... | [10.509124755859375, 8.646236419677734] |
980a18c5-8131-43eb-aa1e-a5b2b4e94a73 | learning-object-permanence-from-video | 2003.10469 | null | https://arxiv.org/abs/2003.10469v4 | https://arxiv.org/pdf/2003.10469v4.pdf | Learning Object Permanence from Video | Object Permanence allows people to reason about the location of non-visible objects, by understanding that they continue to exist even when not perceived directly. Object Permanence is critical for building a model of the world, since objects in natural visual scenes dynamically occlude and contain each-other. Intensiv... | ['Gal Chechik', 'Amir Globerson', 'Aviv Shamsian', 'Ofri Kleinfeld'] | 2020-03-23 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2481_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123610035.pdf | eccv-2020-8 | ['video-object-tracking'] | ['computer-vision'] | [ 1.80352420e-01 -2.91299373e-01 6.04502819e-02 -2.48657644e-01
-9.13784578e-02 -7.41488159e-01 6.77709222e-01 1.33598745e-01
-5.25949538e-01 4.92009968e-01 4.95557114e-02 -1.25407204e-01
6.99209124e-02 -3.69716763e-01 -1.29757798e+00 -7.38220751e-01
-1.98630348e-01 2.26801708e-01 5.92392623e-01 2.32306466... | [9.95349407196045, 1.1168246269226074] |
e3bf2203-381c-457d-b5a2-9af80a74524c | a-fast-geometric-regularizer-to-mitigate | 2212.07350 | null | https://arxiv.org/abs/2212.07350v1 | https://arxiv.org/pdf/2212.07350v1.pdf | A Fast Geometric Regularizer to Mitigate Event Collapse in the Contrast Maximization Framework | Event cameras are emerging vision sensors and their advantages are suitable for various applications such as autonomous robots. Contrast maximization (CMax), which provides state-of-the-art accuracy on motion estimation using events, may suffer from an overfitting problem called event collapse. Prior works are computat... | ['Guillermo Gallego', 'Yoshimitsu Aoki', 'Shintaro Shiba'] | 2022-12-14 | null | null | null | null | ['event-based-vision', 'event-based-motion-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.74056828e-01 -1.78035423e-01 -1.39009342e-01 -1.00124285e-01
-5.46845615e-01 -2.42256522e-01 6.33481562e-01 1.12402383e-02
-7.45593846e-01 4.15460587e-01 -1.79273576e-01 -1.37649268e-01
5.60164414e-02 -7.06350029e-01 -7.10813224e-01 -7.61512637e-01
-9.45025310e-02 -3.87221910e-02 8.48102987e-01 1.15514621... | [8.450549125671387, -1.3897593021392822] |
d5599aea-f129-4662-8324-9efca252ea37 | eeg-aided-boosting-of-single-lead-ecg-based | 2211.13125 | null | https://arxiv.org/abs/2211.13125v1 | https://arxiv.org/pdf/2211.13125v1.pdf | EEG aided boosting of single-lead ECG based sleep staging with Deep Knowledge Distillation | An electroencephalogram (EEG) signal is currently accepted as a standard for automatic sleep staging. Lately, Near-human accuracy in automated sleep staging has been achievable by Deep Learning (DL) based approaches, enabling multi-fold progress in this area. However, An extensive and expensive clinical setup is requir... | ['Mohanasankar Sivaprakasam', 'Preejith SP', 'Sricharan V', 'Vaibhav Joshi'] | 2022-11-18 | null | null | null | null | ['sleep-staging', 'eeg-based-sleep-staging', 'ecg-based-sleep-staging'] | ['medical', 'time-series', 'time-series'] | [ 6.92365617e-02 6.20853491e-02 -9.49248746e-02 -4.70820218e-01
-5.95150471e-01 -1.49068728e-01 7.24711791e-02 2.28391156e-01
-6.95630252e-01 1.05060828e+00 2.74886768e-02 -2.14696720e-01
-3.60712141e-01 -4.00181770e-01 -1.78772807e-01 -8.24831486e-01
-1.13582335e-01 3.05058211e-01 -6.15189783e-02 -8.39123949... | [13.53005313873291, 3.4997591972351074] |
62e4baa3-c63d-4c6c-8bde-582949b9627e | on-the-computational-modeling-of-meaning | 2307.04518 | null | https://arxiv.org/abs/2307.04518v1 | https://arxiv.org/pdf/2307.04518v1.pdf | On the Computational Modeling of Meaning: Embodied Cognition Intertwined with Emotion | This document chronicles this author's attempt to explore how words come to mean what they do, with a particular focus on child language acquisition and what that means for models of language understanding.\footnote{I say \emph{historical} because I synthesize the ideas based on when I discovered them and how those ide... | ['Casey Kennington'] | 2023-07-10 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 2.91522872e-02 4.00094420e-01 -2.39839152e-01 -6.08470082e-01
1.89374313e-01 -6.51170135e-01 5.91189504e-01 5.43923080e-01
-4.35603589e-01 3.96291792e-01 5.86306155e-01 -5.98330438e-01
-6.13588654e-02 -8.05914998e-01 -4.44355786e-01 -3.42826903e-01
6.16699718e-02 1.65702984e-01 -3.04682434e-01 -4.08117503... | [10.363302230834961, 8.578219413757324] |
96cd6e04-3f6c-4343-8638-d920a9f16a19 | heads-up-unsupervised-constituency-parsing | 2010.09517 | null | https://arxiv.org/abs/2010.09517v1 | https://arxiv.org/pdf/2010.09517v1.pdf | Heads-up! Unsupervised Constituency Parsing via Self-Attention Heads | Transformer-based pre-trained language models (PLMs) have dramatically improved the state of the art in NLP across many tasks. This has led to substantial interest in analyzing the syntactic knowledge PLMs learn. Previous approaches to this question have been limited, mostly using test suites or probes. Here, we propos... | ['Frank Keller', 'Reinald Kim Amplayo', 'Taeuk Kim', 'Bowen Li'] | 2020-10-19 | null | https://aclanthology.org/2020.aacl-main.43 | https://aclanthology.org/2020.aacl-main.43.pdf | asian-chapter-of-the-association-for | ['constituency-parsing'] | ['natural-language-processing'] | [ 2.58984596e-01 5.83319247e-01 -1.91335976e-01 -5.62415540e-01
-1.10876727e+00 -9.07119393e-01 6.10987961e-01 2.69652516e-01
-3.77026469e-01 7.28495598e-01 2.63910085e-01 -6.82017803e-01
2.81386346e-01 -8.67534697e-01 -8.92638564e-01 -4.36859280e-01
1.88412279e-01 7.58803010e-01 5.01816392e-01 -1.31813973... | [10.406314849853516, 9.550872802734375] |
0297bebd-52e7-41db-8b93-75393937813c | large-scale-traffic-data-imputation-with | 2301.11691 | null | https://arxiv.org/abs/2301.11691v1 | https://arxiv.org/pdf/2301.11691v1.pdf | Large-Scale Traffic Data Imputation with Spatiotemporal Semantic Understanding | Large-scale data missing is a challenging problem in Intelligent Transportation Systems (ITS). Many studies have been carried out to impute large-scale traffic data by considering their spatiotemporal correlations at a network level. In existing traffic data imputations, however, rich semantic information of a road net... | ['Zhengbing He', 'Na Xie', 'Liang Zheng', 'Lan Wu', 'Kunpeng Zhang'] | 2023-01-27 | null | null | null | null | ['traffic-data-imputation'] | ['time-series'] | [-2.92489752e-02 -1.79545596e-01 -4.91629511e-01 -5.27805448e-01
-4.09322053e-01 4.82316390e-02 3.32484394e-01 -3.76231730e-01
1.70925543e-01 1.02429676e+00 7.05217957e-01 -5.58340251e-01
-5.90861559e-01 -1.22078454e+00 -1.02338445e+00 -4.13140446e-01
1.47981033e-01 6.51377797e-01 -9.83043313e-02 -3.51343602... | [6.547829627990723, 2.0693421363830566] |
53b42db9-bb59-4376-a084-ab1b4e0961db | event-driven-two-stage-solution-to-non | 2107.12582 | null | https://arxiv.org/abs/2107.12582v1 | https://arxiv.org/pdf/2107.12582v1.pdf | Event-driven Two-stage Solution to Non-intrusive Load Monitoring | Existing methods of non-intrusive load monitoring (NILM) in literatures generally suffer from high computational complexity and/or low accuracy in identifying working household appliances. This paper proposes an event-driven Factorial Hidden Markov model (eFHMM) for multiple appliances with multiple states in a househo... | ['Zuyi Li', 'Jiayu Han', 'Wei Tian', 'Lei Yan'] | 2021-07-27 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 1.28679797e-01 -2.97643691e-01 -2.78155565e-01 -2.17345148e-01
-4.05879200e-01 -1.10100985e-01 3.26265514e-01 1.73759967e-01
3.13984573e-01 6.06795669e-01 -1.85363635e-01 -6.04880787e-02
-2.72834808e-01 -8.32012177e-01 -2.21953183e-01 -9.09490526e-01
-1.56694576e-01 4.17663336e-01 2.94486344e-01 4.31948364... | [5.998030662536621, 2.602268695831299] |
bfbffeb6-4621-4154-9e7d-7f2f9a106b48 | large-scale-real-time-personalized-similar | 2004.05716 | null | https://arxiv.org/abs/2004.05716v1 | https://arxiv.org/pdf/2004.05716v1.pdf | Large-scale Real-time Personalized Similar Product Recommendations | Similar product recommendation is one of the most common scenes in e-commerce. Many recommendation algorithms such as item-to-item Collaborative Filtering are working on measuring item similarities. In this paper, we introduce our real-time personalized algorithm to model product similarity and real-time user interests... | ['Li Chen', 'Jing Gao', 'Yan Huang', 'Dong Li', 'Zhi Liu'] | 2020-04-12 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-4.31970984e-01 -8.62113714e-01 -5.04917502e-01 -6.64178848e-01
-4.60047573e-01 -6.35131419e-01 2.27389291e-01 7.71791935e-02
-3.84083271e-01 -1.43025130e-01 5.37702918e-01 -2.49143884e-01
-3.33996207e-01 -1.07799149e+00 -2.12682620e-01 -1.47303119e-01
-1.50253743e-01 5.71647406e-01 2.62590796e-01 -6.87697887... | [10.052803993225098, 5.740522384643555] |
9d0645dc-5312-4e6f-af2b-f881ff7e4044 | sentiment-analysis-for-reinforcement-learning | 2010.02316 | null | https://arxiv.org/abs/2010.02316v1 | https://arxiv.org/pdf/2010.02316v1.pdf | Sentiment Analysis for Reinforcement Learning | While reinforcement learning (RL) has been successful in natural language processing (NLP) domains such as dialogue generation and text-based games, it typically faces the problem of sparse rewards that leads to slow or no convergence. Traditional methods that use text descriptions to extract only a state representatio... | ['Eve Fleisig', 'Ameet Deshpande'] | 2020-10-05 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [ 5.11037670e-02 3.84208739e-01 -4.64973718e-01 -4.03973937e-01
-5.00703216e-01 -6.50431097e-01 7.09382236e-01 4.25591409e-01
-6.90064728e-01 1.16841388e+00 3.98906916e-01 -2.42364630e-01
1.32387325e-01 -9.23997223e-01 -4.18239146e-01 -5.82067907e-01
-3.24793234e-02 4.26053941e-01 -2.61738390e-01 -8.20276320... | [4.058199405670166, 1.5664467811584473] |
68b309cf-be53-417e-9e73-2594bf85c29d | experience-feedback-using-representation | 2109.13027 | null | https://arxiv.org/abs/2109.13027v1 | https://arxiv.org/pdf/2109.13027v1.pdf | Experience feedback using Representation Learning for Few-Shot Object Detection on Aerial Images | This paper proposes a few-shot method based on Faster R-CNN and representation learning for object detection in aerial images. The two classification branches of Faster R-CNN are replaced by prototypical networks for online adaptation to new classes. These networks produce embeddings vectors for each generated box, whi... | ['Hanene Azzag', 'Anissa Mokraoui', 'Mustapha Lebbah', 'Pierre Le Jeune'] | 2021-09-27 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 4.90372181e-01 5.84760346e-02 -1.64510816e-01 -4.55135524e-01
-1.76941276e-01 -2.99763441e-01 4.39789861e-01 2.49909714e-01
-5.30924797e-01 4.60296035e-01 -2.00357452e-01 1.01722710e-01
-3.33655298e-01 -1.19249439e+00 -3.22802991e-01 -8.61657083e-01
-4.71319407e-01 2.62737870e-01 4.16794121e-01 -2.82414079... | [9.84738540649414, 2.3821194171905518] |
b0683fe9-86d8-4773-b797-bd3d6ec533da | nested-named-entity-recognition-via-second | 1909.02250 | null | https://arxiv.org/abs/1909.02250v3 | https://arxiv.org/pdf/1909.02250v3.pdf | Nested Named Entity Recognition via Second-best Sequence Learning and Decoding | When an entity name contains other names within it, the identification of all combinations of names can become difficult and expensive. We propose a new method to recognize not only outermost named entities but also inner nested ones. We design an objective function for training a neural model that treats the tag seque... | ['Takashi Shibuya', 'Eduard Hovy'] | 2019-09-05 | null | null | null | null | ['nested-named-entity-recognition', 'nested-mention-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-1.61035925e-01 2.38017187e-01 -1.67925730e-01 -7.70699680e-01
-7.99614429e-01 -8.76406252e-01 1.87694147e-01 1.68135434e-01
-5.82830310e-01 1.14366364e+00 -9.98490900e-02 -5.18778384e-01
1.16719101e-02 -9.75187719e-01 -8.63947988e-01 -4.17522907e-01
-3.54246795e-01 5.37869871e-01 1.45153850e-01 3.00539285... | [9.636587142944336, 9.481165885925293] |
317ab7e3-20b8-47c6-9be1-82e7da9cbe8b | class-incremental-mixture-of-gaussians-for | 2307.04094 | null | https://arxiv.org/abs/2307.04094v1 | https://arxiv.org/pdf/2307.04094v1.pdf | Class-Incremental Mixture of Gaussians for Deep Continual Learning | Continual learning models for stationary data focus on learning and retaining concepts coming to them in a sequential manner. In the most generic class-incremental environment, we have to be ready to deal with classes coming one by one, without any higher-level grouping. This requirement invalidates many previously pro... | ['Bartosz Krawczyk', 'Lukasz Korycki'] | 2023-07-09 | null | null | null | null | ['image-classification', 'continual-learning'] | ['computer-vision', 'methodology'] | [ 2.14304864e-01 -2.17882693e-01 -4.39263992e-02 -4.28582966e-01
-9.49795663e-01 -4.37861532e-01 7.87979066e-01 3.63754243e-01
-9.80323553e-01 6.11172497e-01 -1.71494097e-01 -2.63921112e-01
-4.97431338e-01 -4.93275225e-01 -8.05887878e-01 -9.06533897e-01
-8.89352486e-02 4.21434939e-01 2.46814653e-01 1.07983537... | [9.443745613098145, 2.3882265090942383] |
b07fab74-9de0-40bf-a6a2-9be73f62b984 | monocular-3d-human-pose-estimation-for-sports | 2304.04437 | null | https://arxiv.org/abs/2304.04437v1 | https://arxiv.org/pdf/2304.04437v1.pdf | Monocular 3D Human Pose Estimation for Sports Broadcasts using Partial Sports Field Registration | The filming of sporting events projects and flattens the movement of athletes in the world onto a 2D broadcast image. The pixel locations of joints in these images can be detected with high validity. Recovering the actual 3D movement of the limbs (kinematics) of the athletes requires lifting these 2D pixel locations ba... | ['Stefanie Klatt', 'Tobias Baumgartner'] | 2023-04-10 | null | null | null | null | ['camera-calibration', '3d-pose-estimation', '3d-human-pose-estimation', 'monocular-3d-human-pose-estimation', '2d-human-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-2.24800944e-01 -2.78983742e-01 -1.69989333e-01 -1.78856522e-01
-7.17310548e-01 -8.63422513e-01 2.58678794e-01 -2.84752667e-01
-4.54048753e-01 3.06406289e-01 1.37245536e-01 1.51466191e-01
7.73503855e-02 -4.03052568e-01 -1.02263474e+00 -1.71597406e-01
-9.80660915e-02 7.22733974e-01 3.77912730e-01 -3.26578349... | [7.329680442810059, -1.0851832628250122] |
db03938e-26e8-4d33-a160-735a38f91eec | systematic-visual-reasoning-through-object | 2306.02500 | null | https://arxiv.org/abs/2306.02500v1 | https://arxiv.org/pdf/2306.02500v1.pdf | Systematic Visual Reasoning through Object-Centric Relational Abstraction | Human visual reasoning is characterized by an ability to identify abstract patterns from only a small number of examples, and to systematically generalize those patterns to novel inputs. This capacity depends in large part on our ability to represent complex visual inputs in terms of both objects and relations. Recent ... | ['Jonathan D. Cohen', 'Shanka Subhra Mondal', 'Taylor W. Webb'] | 2023-06-04 | null | null | null | null | ['visual-reasoning', 'systematic-generalization', 'visual-reasoning'] | ['computer-vision', 'reasoning', 'reasoning'] | [ 3.22310030e-01 3.32593590e-01 1.36676565e-01 -4.90159810e-01
-6.11430109e-02 -7.41467953e-01 1.04126239e+00 3.86181831e-01
-3.16266268e-01 3.73119026e-01 3.08919787e-01 -3.03493261e-01
-4.55224097e-01 -7.68784881e-01 -5.64905286e-01 -3.04870218e-01
-1.49638802e-02 6.36431932e-01 4.17026579e-01 -1.86242789... | [10.581801414489746, 2.252000093460083] |
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