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1ba7309d-8287-489b-95e6-eb46c0b2030e | bottom-up-approaches-for-multi-person-pose | 2112.11834 | null | https://arxiv.org/abs/2112.11834v1 | https://arxiv.org/pdf/2112.11834v1.pdf | Bottom-up approaches for multi-person pose estimation and it's applications: A brief review | Human Pose Estimation (HPE) is one of the fundamental problems in computer vision. It has applications ranging from virtual reality, human behavior analysis, video surveillance, anomaly detection, self-driving to medical assistance. The main objective of HPE is to obtain the person's posture from the given input. Among... | ['Thong Duy Nguyen', 'Milan Kresović'] | 2021-12-22 | null | null | null | null | ['multi-person-pose-estimation'] | ['computer-vision'] | [ 2.28117057e-03 -2.09082127e-01 1.00633673e-01 -2.30220795e-01
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-1.87005818e-01 6.76261723e-01 2.02513203e-01 -4.61935610... | [7.058037281036377, -0.9709742665290833] |
0be430a5-4feb-422d-a509-196e7d3d3964 | a-two-stage-method-for-non-extreme-value-salt | 2206.05520 | null | https://arxiv.org/abs/2206.05520v2 | https://arxiv.org/pdf/2206.05520v2.pdf | A Two-stage Method for Non-extreme Value Salt-and-Pepper Noise Removal | There are several previous methods based on neural network can have great performance in denoising salt and pepper noise. However, those methods are based on a hypothesis that the value of salt and pepper noise is exactly 0 and 255. It is not true in the real world. The result of those methods deviate sharply when the ... | ['Bing Zeng', 'Yike Liu', 'Renwei Yang'] | 2022-06-11 | null | null | null | null | ['salt-and-pepper-noise-removal'] | ['computer-vision'] | [ 2.17246667e-01 -5.81482708e-01 1.75754428e-01 -2.86224514e-01
3.59966755e-02 -2.45931879e-01 2.43294481e-02 -8.87708738e-02
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2.10176557e-01 -5.98022461e-01 5.71347117e-01 -5.64770162... | [11.288561820983887, -2.4656546115875244] |
d095a7c0-b433-4a10-9a48-36f4e2de82de | sibblings-similarity-driven-building-block | 2306.04817 | null | https://arxiv.org/abs/2306.04817v1 | https://arxiv.org/pdf/2306.04817v1.pdf | SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States | Interpretable methods for extracting meaningful building blocks (BBs) underlying multi-dimensional time series are vital for discovering valuable insights in complex systems. Existing techniques, however, encounter limitations that restrict their applicability to real-world systems, like reliance on orthogonality assum... | ['Adam S. Charles', 'Gal Mishne', 'Noga Mudrik'] | 2023-06-07 | null | null | null | null | ['dictionary-learning'] | ['methodology'] | [ 1.19133763e-01 -6.91430271e-01 -6.33617878e-01 -1.79073662e-01
-3.71504098e-01 -8.35708499e-01 5.53626955e-01 5.78377843e-01
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-1.03031814e+00 7.61540756e-02 1.40330583e-01 -2.95876712... | [6.9464111328125, 3.3612661361694336] |
703e9687-a3f2-4000-93ae-beb87bc14984 | ovarnet-towards-open-vocabulary-object | 2301.09506 | null | https://arxiv.org/abs/2301.09506v1 | https://arxiv.org/pdf/2301.09506v1.pdf | OvarNet: Towards Open-vocabulary Object Attribute Recognition | In this paper, we consider the problem of simultaneously detecting objects and inferring their visual attributes in an image, even for those with no manual annotations provided at the training stage, resembling an open-vocabulary scenario. To achieve this goal, we make the following contributions: (i) we start with a n... | ['Weidi Xie', 'Jianqi Chen', 'Yan Gao', 'Xu Tang', 'Yao Hu', 'XiaoLong Jiang', 'Keyan Chen'] | 2023-01-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_OvarNet_Towards_Open-Vocabulary_Object_Attribute_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_OvarNet_Towards_Open-Vocabulary_Object_Attribute_Recognition_CVPR_2023_paper.pdf | cvpr-2023-1 | ['open-vocabulary-object-detection', 'open-vocabulary-attribute-detection'] | ['computer-vision', 'computer-vision'] | [ 4.21472728e-01 3.37329805e-01 -3.60325783e-01 -5.79867303e-01
-8.01180363e-01 -8.49169254e-01 7.45367289e-01 2.11230755e-01
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-3.74755710e-02 8.15007091e-01 -1.03414478e-02 2.14671925... | [9.999810218811035, 1.7878491878509521] |
50dd84b0-1ce3-445c-8ba8-b118437cac03 | learning-hierarchical-dynamics-with-spatial | null | null | https://dl.acm.org/doi/abs/10.1145/3503161.3548322 | https://dl.acm.org/doi/abs/10.1145/3503161.3548322 | Learning Hierarchical Dynamics with Spatial Adjacency for Image Enhancement | In various real-world image enhancement applications, the degradations are always non-uniform or non-homogeneous and diverse, which challenges most deep networks with fixed parameters during the inference phase. Inspired by the dynamic deep networks that adapt the model structures or parameters conditioned on the input... | ['WangMeng Zuo', 'Wenjian Wang', 'Jiaying Liu', 'Wenqi Ren', 'Bin Wang', 'Yudong Liang'] | 2022-08-10 | null | null | null | acmmm-2022-8 | ['image-dehazing', 'rain-removal', 'image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.65327632e-01 -5.25557995e-01 3.51883750e-03 -2.59460509e-01
-3.47196907e-01 -4.21379149e-01 4.26996887e-01 -1.69932976e-01
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-2.28750139e-01 -8.91183615e-01 -6.40050054e-01 -1.31529987e+00
-4.27225754e-02 -7.00723410e-01 3.48355353e-01 -3.27370048... | [11.003433227539062, -2.2082693576812744] |
d0022905-961c-4a43-af19-7ab2187d2223 | discover-and-cure-concept-aware-mitigation-of | 2305.00650 | null | https://arxiv.org/abs/2305.00650v2 | https://arxiv.org/pdf/2305.00650v2.pdf | Discover and Cure: Concept-aware Mitigation of Spurious Correlation | Deep neural networks often rely on spurious correlations to make predictions, which hinders generalization beyond training environments. For instance, models that associate cats with bed backgrounds can fail to predict the existence of cats in other environments without beds. Mitigating spurious correlations is crucial... | ['James Zou', 'Linjun Zhang', 'Mert Yuksekgonul', 'Shirley Wu'] | 2023-05-01 | null | null | null | null | ['object-recognition', 'skin-lesion-classification'] | ['computer-vision', 'medical'] | [ 1.39091656e-01 2.32011974e-01 -2.80967325e-01 -6.28123343e-01
7.01270590e-04 -3.55605066e-01 2.28831172e-01 1.31241187e-01
-6.65452033e-02 8.12477052e-01 -1.59310639e-01 -4.43224460e-01
-5.03179848e-01 -6.36349261e-01 -8.41592312e-01 -7.14639425e-01
-1.85302541e-01 2.82601029e-01 -7.83264041e-02 1.09801121... | [8.9321928024292, 5.453731060028076] |
e6739af3-61af-48d7-bb7b-fde6a0150ea2 | de-pacrr-exploring-layers-inside-the-pacrr | 1706.08746 | null | http://arxiv.org/abs/1706.08746v2 | http://arxiv.org/pdf/1706.08746v2.pdf | DE-PACRR: Exploring Layers Inside the PACRR Model | Recent neural IR models have demonstrated deep learning's utility in ad-hoc
information retrieval. However, deep models have a reputation for being black
boxes, and the roles of a neural IR model's components may not be obvious at
first glance. In this work, we attempt to shed light on the inner workings of a
recently ... | ['Kai Hui', 'Andrew Yates'] | 2017-06-27 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [ 5.08248173e-02 3.02309453e-01 -3.66739810e-01 -3.49682480e-01
-6.33766472e-01 -5.02600849e-01 7.26314604e-01 1.88035518e-01
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-5.07773161e-01 -5.08082569e-01 -4.11498070e-01 -5.31794846e-01
-2.91359484e-01 2.83534646e-01 -1.90569550e-01 -6.93833709... | [11.455577850341797, 7.5906548500061035] |
d98d7ed0-59e4-495d-980b-5cb1ab8586a0 | real-time-video-quality-representation | 1602.00489 | null | http://arxiv.org/abs/1602.00489v2 | http://arxiv.org/pdf/1602.00489v2.pdf | Real Time Video Quality Representation Classification of Encrypted HTTP Adaptive Video Streaming - the Case of Safari | The increasing popularity of HTTP adaptive video streaming services has
dramatically increased bandwidth requirements on operator networks, which
attempt to shape their traffic through Deep Packet Inspection (DPI). However,
Google and certain content providers have started to encrypt their video
services. As a result, ... | ['Ofir Pele', 'Ofer Hadar', 'Ofir Trabelsi', 'Ran Dubin', 'Itay Richman', 'Amit Dvir'] | 2016-02-01 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 2.80614763e-01 -2.71677434e-01 -3.76586229e-01 -3.66063803e-01
-7.73043692e-01 -1.03512418e+00 7.89046213e-02 -1.03614427e-01
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-3.50005388e-01 3.45579058e-01 8.00774992e-01 8.32573324... | [5.072526931762695, 7.24387788772583] |
a4fdd04e-f9e3-4fe3-95ba-46f16ff855e2 | multi-grained-chinese-word-segmentation-with | null | null | https://aclanthology.org/2020.coling-main.183 | https://aclanthology.org/2020.coling-main.183.pdf | Multi-grained Chinese Word Segmentation with Weakly Labeled Data | In contrast with the traditional single-grained word segmentation (SWS), where a sentence corresponds to a single word sequence, multi-grained Chinese word segmentation (MWS) aims to segment a sentence into multiple word sequences to preserve all words of different granularities. Due to the lack of manually annotated M... | ['Min Zhang', 'Bowei Zou', 'Zhenghua Li', 'Chen Gong'] | 2020-12-01 | null | null | null | coling-2020-8 | ['chinese-word-segmentation'] | ['natural-language-processing'] | [ 3.22818190e-01 2.02576861e-01 -3.02006572e-01 -5.32068193e-01
-1.02631390e+00 -5.82794726e-01 1.82204545e-01 2.61448205e-01
-8.80737543e-01 7.63536930e-01 2.68725723e-01 -5.47589183e-01
4.29664463e-01 -6.92780316e-01 -7.01500893e-01 -4.54360515e-01
4.41434562e-01 5.21562040e-01 7.05009997e-01 -3.74066114... | [9.997364044189453, 10.10886287689209] |
bf85c76a-a4a7-4c59-842c-e5c5086b7c37 | emotion-recognition-in-audio-and-video-using | 2006.08129 | null | https://arxiv.org/abs/2006.08129v1 | https://arxiv.org/pdf/2006.08129v1.pdf | Emotion Recognition in Audio and Video Using Deep Neural Networks | Humans are able to comprehend information from multiple domains for e.g. speech, text and visual. With advancement of deep learning technology there has been significant improvement of speech recognition. Recognizing emotion from speech is important aspect and with deep learning technology emotion recognition has impro... | ['Mandeep Singh', 'Yuan Fang'] | 2020-06-15 | null | null | null | null | ['video-emotion-recognition', 'multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'computer-vision', 'speech'] | [-3.32921416e-01 -3.38009655e-01 1.79933965e-01 -5.20787835e-01
-3.19259316e-01 -4.29102272e-01 2.71119118e-01 -3.32905203e-02
-4.55981880e-01 6.12116337e-01 4.38239396e-01 -9.12578255e-02
2.30151609e-01 -3.63757551e-01 -2.24207386e-01 -2.74333745e-01
4.78496477e-02 3.33161056e-02 -2.73981512e-01 -3.25661421... | [13.484542846679688, 5.448517322540283] |
07ba10f3-6569-4f71-95b8-aac16cbb8c76 | edge-augmentation-for-large-scale-sketch | 2202.13164 | null | https://arxiv.org/abs/2202.13164v2 | https://arxiv.org/pdf/2202.13164v2.pdf | Edge Augmentation for Large-Scale Sketch Recognition without Sketches | This work addresses scaling up the sketch classification task into a large number of categories. Collecting sketches for training is a slow and tedious process that has so far precluded any attempts to large-scale sketch recognition. We overcome the lack of training sketch data by exploiting labeled collections of natu... | ['Ondrej Chum', 'Giorgos Tolias', 'Nikos Efthymiadis'] | 2022-02-26 | null | null | null | null | ['sketch-recognition'] | ['computer-vision'] | [ 2.87373155e-01 -2.89744467e-01 -1.76094770e-01 -4.72012609e-01
-3.68297756e-01 -8.52000296e-01 8.53810608e-01 -2.11870268e-01
-4.89546895e-01 5.53805292e-01 4.78570955e-03 -1.58011615e-01
1.45017803e-01 -1.00153410e+00 -6.19582236e-01 -3.54305953e-01
3.56655642e-02 4.38456684e-01 1.63880274e-01 -7.89978802... | [11.768340110778809, 0.44448143243789673] |
7a7df4ba-1abe-41c2-8923-326ecd2bda4b | a-simple-approach-to-jointly-rank-passages | 2109.10497 | null | https://arxiv.org/abs/2109.10497v2 | https://arxiv.org/pdf/2109.10497v2.pdf | A Simple Approach to Jointly Rank Passages and Select Relevant Sentences in the OBQA Context | In the open book question answering (OBQA) task, selecting the relevant passages and sentences from distracting information is crucial to reason the answer to a question. HotpotQA dataset is designed to teach and evaluate systems to do both passage ranking and sentence selection. Many existing frameworks use separate m... | ['Chitta Baral', 'Shuguang Chen', 'Man Luo'] | 2021-09-22 | null | https://aclanthology.org/2022.naacl-srw.23 | https://aclanthology.org/2022.naacl-srw.23.pdf | naacl-acl-2022-7 | ['passage-ranking'] | ['natural-language-processing'] | [-1.70229405e-01 -1.11576907e-01 2.96810180e-01 -5.03109753e-01
-1.43314946e+00 -7.23941088e-01 5.67730725e-01 2.90703237e-01
-5.80046117e-01 7.73349285e-01 3.89335394e-01 -1.20118544e-01
-2.51135170e-01 -6.18872523e-01 -4.96779770e-01 -3.65382701e-01
2.30723143e-01 5.98195374e-01 9.23998356e-01 -7.51493454... | [11.370942115783691, 8.027669906616211] |
fa48258a-0cd7-497a-b2fc-eba75a6c5817 | wac-a-corpus-of-wikipedia-conversations-for | 2003.06190 | null | https://arxiv.org/abs/2003.06190v1 | https://arxiv.org/pdf/2003.06190v1.pdf | WAC: A Corpus of Wikipedia Conversations for Online Abuse Detection | With the spread of online social networks, it is more and more difficult to monitor all the user-generated content. Automating the moderation process of the inappropriate exchange content on Internet has thus become a priority task. Methods have been proposed for this purpose, but it can be challenging to find a suitab... | ['Georges Linares', 'Richard Dufour', 'Vincent Labatut', 'Noé Cecillon'] | 2020-03-13 | wac-a-corpus-of-wikipedia-conversations-for-1 | https://aclanthology.org/2020.lrec-1.173 | https://aclanthology.org/2020.lrec-1.173.pdf | lrec-2020-5 | ['abuse-detection'] | ['natural-language-processing'] | [-4.70495634e-02 2.24764515e-02 -1.16531372e-01 -1.76782325e-01
-5.49131393e-01 -7.91746914e-01 8.49341869e-01 6.47139549e-01
-4.86508399e-01 8.19798827e-01 3.85750622e-01 -1.86147094e-01
-4.19942215e-02 -4.95550632e-01 -2.36119911e-01 -5.05806088e-01
-1.28390685e-01 4.88186598e-01 4.06662196e-01 -4.71946001... | [8.601151466369629, 10.374651908874512] |
a4f73cdb-1719-44c2-808c-2e9b8f336097 | the-geometry-of-multilingual-language-model | 2205.10964 | null | https://arxiv.org/abs/2205.10964v2 | https://arxiv.org/pdf/2205.10964v2.pdf | The Geometry of Multilingual Language Model Representations | We assess how multilingual language models maintain a shared multilingual representation space while still encoding language-sensitive information in each language. Using XLM-R as a case study, we show that languages occupy similar linear subspaces after mean-centering, evaluated based on causal effects on language mod... | ['Benjamin K. Bergen', 'Zhuowen Tu', 'Tyler A. Chang'] | 2022-05-22 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [-5.51729441e-01 -1.05519213e-01 -6.59558058e-01 -3.93927336e-01
-7.83586085e-01 -1.18682837e+00 8.84561658e-01 2.60223359e-01
-6.25517845e-01 3.13931525e-01 1.05379760e+00 -5.01592934e-01
-2.82022525e-02 -3.80583078e-01 -5.54218709e-01 -4.18472499e-01
-2.71352857e-01 4.04492915e-01 -3.78734231e-01 -3.65227282... | [10.916180610656738, 9.875699996948242] |
b00f755c-8bde-4c4b-b3d6-b41692c2d3ef | review-on-dna-strand-algebra-and-its | 1903.04260 | null | https://arxiv.org/abs/1903.04260v2 | https://arxiv.org/pdf/1903.04260v2.pdf | Review on DNA Strand Algebra and its Application | Several technological limitations of traditional silicon based computing are leading towards the paradigm shift, from silicon to carbon, in computational world. Among the unconventional modes of computing evolved in past several decades, DNA computing has been considered to be quite promising in solving computational a... | ['Kumar S. Ray', 'Mandrita Mondal'] | 2019-03-04 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 5.64621806e-01 4.93099719e-01 2.58150309e-01 4.60621640e-02
2.95413464e-01 -9.67017174e-01 1.33959150e+00 4.60153192e-01
-3.94245565e-01 6.23729706e-01 9.89657938e-02 -1.03793228e+00
-2.50725657e-01 -1.03520060e+00 -4.55442637e-01 -8.25774312e-01
-2.02136561e-01 4.73121226e-01 4.92145807e-01 -2.82155901... | [5.7572407722473145, 4.640433311462402] |
ef5b92a2-79e7-4190-83a3-7031a1e843bf | standoff-tracking-using-dnn-based-mpc-with | 2212.10945 | null | https://arxiv.org/abs/2212.10945v1 | https://arxiv.org/pdf/2212.10945v1.pdf | Standoff Tracking Using DNN-Based MPC with Implementation on FPGA | This work studies the standoff tracking problem to drive an unmanned aerial vehicle (UAV) to slide on a desired circle over a moving target at a constant height. We propose a novel Lyapunov guidance vector (LGV) field with tunable convergence rates for the UAV's trajectory planning and a deep neural network (DNN)-based... | ['Shiji Song', 'Keyou You', 'Xingchen Li', 'Fei Dong'] | 2022-12-21 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [ 2.73440629e-02 1.67831883e-01 -3.47591281e-01 1.99379697e-01
2.14371040e-01 -8.88006330e-01 3.39828342e-01 -2.41354987e-01
-2.12682888e-01 8.44898582e-01 -9.90053475e-01 -9.97881770e-01
-3.43675375e-01 -6.94858193e-01 -8.60772014e-01 -7.55003572e-01
-5.35932660e-01 3.05253658e-02 1.35168180e-01 -4.64860290... | [4.933393478393555, 2.063222885131836] |
5fdd845c-0e11-4ac7-a2ef-4f268f227674 | metal-artifact-reduction-in-2d-ct-images-with | 2109.13483 | null | https://arxiv.org/abs/2109.13483v1 | https://arxiv.org/pdf/2109.13483v1.pdf | Metal Artifact Reduction in 2D CT Images with Self-supervised Cross-domain Learning | The presence of metallic implants often introduces severe metal artifacts in the X-ray CT images, which could adversely influence clinical diagnosis or dose calculation in radiation therapy. In this work, we present a novel deep-learning-based approach for metal artifact reduction (MAR). In order to alleviate the need ... | ['Lei Xing', 'Wei Zhao', 'Hongyi Ren', 'Xiaomeng Li', 'Zhicheng Zhang', 'Lequan Yu'] | 2021-09-28 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 4.96600896e-01 1.30228832e-01 2.72299677e-01 -1.84283987e-01
-9.46491539e-01 -2.81195324e-02 1.64216429e-01 -3.20874929e-01
-2.24051625e-01 6.94541335e-01 3.33972871e-01 -1.95244417e-01
-2.41381273e-01 -8.01824868e-01 -8.69220912e-01 -8.92390907e-01
4.19576287e-01 2.15039879e-01 3.64122212e-01 2.79548168... | [13.508736610412598, -2.5503976345062256] |
40147488-89f3-4697-8bf6-7707387c95fe | causal-based-supervision-of-attention-in | 2305.13115 | null | https://arxiv.org/abs/2305.13115v1 | https://arxiv.org/pdf/2305.13115v1.pdf | Causal-Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice towards Powerful Attention | In recent years, attention mechanisms have demonstrated significant potential in the field of graph representation learning. However, while variants of attention-based GNNs are setting new benchmarks for numerous real-world datasets, recent works have pointed out that their induced attentions are less robust and genera... | ['Xuan Song', 'Shi Han', 'Qiang Fu', 'Lun Du', 'Jiyuan Chen', 'Hongjun Wang'] | 2023-05-22 | null | null | null | null | ['graph-representation-learning'] | ['methodology'] | [ 1.25785619e-01 3.88347685e-01 -3.68997753e-01 -2.50123858e-01
-3.08817416e-01 -1.48946181e-01 6.09981358e-01 1.31336942e-01
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-4.01252598e-01 -9.57907677e-01 -7.75164306e-01 -5.13601124e-01
-1.99629560e-01 4.29058135e-01 1.72325253e-01 -3.70260745... | [7.198451519012451, 6.1893815994262695] |
58d11aa5-4918-430e-b4d3-df2cd5e05156 | active-learning-for-multilingual-semantic | 2301.12920 | null | https://arxiv.org/abs/2301.12920v3 | https://arxiv.org/pdf/2301.12920v3.pdf | Active Learning for Multilingual Semantic Parser | Current multilingual semantic parsing (MSP) datasets are almost all collected by translating the utterances in the existing datasets from the resource-rich language to the target language. However, manual translation is costly. To reduce the translation effort, this paper proposes the first active learning procedure fo... | ['Gholamreza Haffari', 'Zhuang Li'] | 2023-01-30 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 1.34337798e-01 3.77005547e-01 -7.31624901e-01 -7.78499722e-01
-1.68138087e+00 -7.77602255e-01 1.20197572e-01 -1.21160284e-01
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2.38356948e-01 -7.94138014e-01 -8.50034416e-01 -4.31416124e-01
4.91508037e-01 8.51530015e-01 3.85413289e-01 -2.59744644... | [11.256261825561523, 10.061027526855469] |
06e3c1c7-d219-4af0-bde5-9d3c2d77e900 | zero-shot-causal-learning | 2301.12292 | null | https://arxiv.org/abs/2301.12292v2 | https://arxiv.org/pdf/2301.12292v2.pdf | Zero-shot causal learning | Predicting how different interventions will causally affect a specific individual is important in a variety of domains such as personalized medicine, public policy, and online marketing. There are a large number of methods to predict the effect of an existing intervention based on historical data from individuals who r... | ['Jure Leskovec', 'Sara Oblak', 'Michihiro Yasunaga', 'Anja Šurina', 'Yining Chen', 'Yusuf Roohani', 'Michael Moor', 'Hamed Nilforoshan'] | 2023-01-28 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 6.62474573e-01 1.50578737e-01 -9.52166021e-01 -1.89363226e-01
-7.70305634e-01 -2.74705917e-01 7.43704498e-01 6.60139561e-01
-2.21166208e-01 1.10682917e+00 7.47914910e-01 -4.84161168e-01
-3.50185513e-01 -9.06455159e-01 -1.17243242e+00 -5.78257859e-01
-1.62804946e-01 5.77473223e-01 -1.51653007e-01 5.57596125... | [8.0846586227417, 5.495830535888672] |
ed3c58f6-a40e-4480-9571-7c35b4081ef6 | music-generation-using-an-lstm | 2203.12105 | null | https://arxiv.org/abs/2203.12105v1 | https://arxiv.org/pdf/2203.12105v1.pdf | Music Generation Using an LSTM | Over the past several years, deep learning for sequence modeling has grown in popularity. To achieve this goal, LSTM network structures have proven to be very useful for making predictions for the next output in a series. For instance, a smartphone predicting the next word of a text message could use an LSTM. We sought... | ['Alexander Neuwirth', 'Reagan Strelow', 'David Hunger', 'Kevin Adams', 'Lucas Gral', 'Michael Conner'] | 2022-03-23 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 4.83314842e-01 2.31111851e-02 4.36621755e-02 -6.64828625e-03
-7.68233716e-01 -4.64509159e-01 4.89230216e-01 -3.43528211e-01
-5.83964884e-02 7.34651268e-01 7.09758103e-01 -3.53494465e-01
1.17426679e-01 -7.31640279e-01 -7.22608805e-01 -3.54478836e-01
-2.18256205e-01 7.67846033e-02 -4.88899648e-01 -3.42521191... | [15.926700592041016, 5.510984420776367] |
09f6c39e-2912-4793-991e-13e52af2cf00 | u-dudonet-unpaired-dual-domain-network-for-ct | 2103.04552 | null | https://arxiv.org/abs/2103.04552v1 | https://arxiv.org/pdf/2103.04552v1.pdf | U-DuDoNet: Unpaired dual-domain network for CT metal artifact reduction | Recently, both supervised and unsupervised deep learning methods have been widely applied on the CT metal artifact reduction (MAR) task. Supervised methods such as Dual Domain Network (Du-DoNet) work well on simulation data; however, their performance on clinical data is limited due to domain gap. Unsupervised methods ... | ['S. Kevin Zhou', 'Cheng Peng', 'Jiajun Fu', 'Yuanyuan Lyu'] | 2021-03-08 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 2.95916677e-01 -1.41058583e-04 3.93756963e-02 -3.17055553e-01
-9.18988287e-01 -8.69459137e-02 1.68491676e-01 -1.62738204e-01
-1.86395556e-01 8.65790069e-01 6.04982138e-01 -8.32642689e-02
-3.13445091e-01 -5.33706844e-01 -8.16614151e-01 -7.67914712e-01
3.28484103e-02 1.23682640e-01 7.22556934e-02 1.18421465... | [13.526665687561035, -2.531423568725586] |
a0135a54-6968-49ff-abf9-0da5a0d7507c | adversarial-attacks-on-audio-source | 2010.03164 | null | https://arxiv.org/abs/2010.03164v3 | https://arxiv.org/pdf/2010.03164v3.pdf | Adversarial attacks on audio source separation | Despite the excellent performance of neural-network-based audio source separation methods and their wide range of applications, their robustness against intentional attacks has been largely neglected. In this work, we reformulate various adversarial attack methods for the audio source separation problem and intensively... | ['Yuki Mitsufuji', 'Shota Inoue', 'Naoya Takahashi'] | 2020-10-07 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 6.16658449e-01 -2.93130040e-01 2.43731381e-04 2.60552853e-01
-1.09145713e+00 -9.88866389e-01 2.39095598e-01 -1.12473689e-01
-9.95303094e-02 4.75427896e-01 1.84374571e-01 -3.85075867e-01
-3.31439227e-01 -6.17713809e-01 -5.27070045e-01 -1.07597148e+00
-4.36684132e-01 -5.03273487e-01 1.39610514e-01 -7.06804842... | [14.016773223876953, 5.831924915313721] |
b2c4adce-d052-432e-b536-164198048e0f | alto-a-large-scale-dataset-for-uav-visual | 2207.12317 | null | https://arxiv.org/abs/2207.12317v1 | https://arxiv.org/pdf/2207.12317v1.pdf | ALTO: A Large-Scale Dataset for UAV Visual Place Recognition and Localization | We present the ALTO dataset, a vision-focused dataset for the development and benchmarking of Visual Place Recognition and Localization methods for Unmanned Aerial Vehicles. The dataset is composed of two long (approximately 150km and 260km) trajectories flown by a helicopter over Ohio and Pennsylvania, and it includes... | ['Sebastian Scherer', 'Howie Choset', 'Ji Zhang', 'Peng Yin', 'Ivan Cisneros'] | 2022-07-19 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-3.49991083e-01 -6.29285932e-01 -3.93896520e-01 -1.82600096e-01
-1.21288322e-01 -1.11634040e+00 7.90399790e-01 3.86263393e-02
-4.67097253e-01 5.69328249e-01 2.99380068e-02 -4.35122848e-01
-2.01812778e-02 -7.79020667e-01 -4.23565894e-01 -3.59243095e-01
-3.40774298e-01 3.49077106e-01 3.03206563e-01 -4.36316550... | [7.3542962074279785, -1.977980613708496] |
23e42ff8-00ae-4667-91b9-313c6be60aea | line-graph-enhanced-amr-to-text-generation | null | null | https://aclanthology.org/2020.acl-main.67 | https://aclanthology.org/2020.acl-main.67.pdf | Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks | Efficient structure encoding for graphs with labeled edges is an important yet challenging point in many graph-based models. This work focuses on AMR-to-text generation {--} A graph-to-sequence task aiming to recover natural language from Abstract Meaning Representations (AMR). Existing graph-to-sequence approaches gen... | ['Kai Yu', 'Ruisheng Cao', 'Zhi Chen', 'Yanbin Zhao', 'Su Zhu', 'Lu Chen'] | 2020-07-01 | null | null | null | acl-2020-6 | ['graph-to-sequence'] | ['natural-language-processing'] | [ 6.64266467e-01 6.66740239e-01 -3.65990072e-01 -2.47297123e-01
-3.47886711e-01 -3.97545457e-01 6.63307130e-01 3.43611658e-01
1.16850346e-01 5.35646856e-01 6.57136977e-01 -7.20786154e-01
2.45763421e-01 -1.31842828e+00 -8.66021931e-01 -1.22934945e-01
-1.17367186e-01 3.64952087e-01 -2.10294113e-01 -6.10708356... | [10.299758911132812, 8.3108549118042] |
ded1fb3c-10c0-4471-acd2-34f820a7647b | cross-modal-interaction-networks-for-query | 1906.02497 | null | https://arxiv.org/abs/1906.02497v2 | https://arxiv.org/pdf/1906.02497v2.pdf | Cross-Modal Interaction Networks for Query-Based Moment Retrieval in Videos | Query-based moment retrieval aims to localize the most relevant moment in an untrimmed video according to the given natural language query. Existing works often only focus on one aspect of this emerging task, such as the query representation learning, video context modeling or multi-modal fusion, thus fail to develop a... | ['Zhenxin Xiao', 'Zhou Zhao', 'Zhijie Lin', 'Zhu Zhang'] | 2019-06-06 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [ 5.00031784e-02 -6.10152781e-01 -4.90588665e-01 -3.65569025e-01
-1.02780378e+00 -4.32291120e-01 6.98255062e-01 2.64401808e-02
-4.04863626e-01 1.44681916e-01 8.29273939e-01 7.44419098e-02
-3.09337109e-01 -3.41407180e-01 -6.33038402e-01 -5.30694485e-01
-3.36600579e-02 1.33821201e-02 5.33636689e-01 -2.33549803... | [10.308823585510254, 0.9614923000335693] |
a82464fe-e311-4fcb-90fc-341edeff8c97 | epution-at-semeval-2018-task-2-emoji | null | null | https://aclanthology.org/S18-1071 | https://aclanthology.org/S18-1071.pdf | EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption | This paper describes our approach, called EPUTION, for the open trial of the SemEval- 2018 Task 2, Multilingual Emoji Prediction. The task relates to using social media {---} more precisely, Twitter {---} with its aim to predict the most likely associated emoji of a tweet. Our solution for this text classification prob... | ['Liyuan Zhou', 'Tom Gedeon', 'Hanna Suominen', 'Qiongkai Xu'] | 2018-06-01 | null | null | null | semeval-2018-6 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-2.57338136e-01 1.89731762e-01 -1.35864317e-01 -5.62143266e-01
-3.52863520e-01 -1.30148515e-01 5.56616485e-01 1.41140342e-01
-6.62562490e-01 8.03801477e-01 3.28241318e-01 -3.01835895e-01
2.54865587e-01 -5.76742530e-01 -6.25237823e-01 -1.09204493e-01
-1.06764838e-01 4.50128078e-01 4.31372561e-02 -3.33851218... | [9.10595989227295, 10.335111618041992] |
a77ecb32-9867-4750-9e6b-e3e3e93a8666 | 3d-fully-convolutional-neural-networks-with | 2102.07280 | null | https://arxiv.org/abs/2102.07280v2 | https://arxiv.org/pdf/2102.07280v2.pdf | 3D Fully Convolutional Neural Networks with Intersection Over Union Loss for Crop Mapping from Multi-Temporal Satellite Images | Information on cultivated crops is relevant for a large number of food security studies. Different scientific efforts are dedicated to generating this information from remote sensing images by means of machine learning methods. Unfortunately, these methods do not take account of the spatial-temporal relationships inher... | ['Alfred Stein', 'Mariana Belgiu', 'Sina Mohammadi'] | 2021-02-15 | null | null | null | null | ['security-studies'] | ['miscellaneous'] | [ 3.56054455e-01 -1.27873391e-01 -3.09014887e-01 -4.42669958e-01
-3.08556437e-01 -6.28871858e-01 4.85061616e-01 3.20193350e-01
-3.83346885e-01 7.83019364e-01 -2.77589053e-01 -7.93416977e-01
-3.50537509e-01 -1.39581621e+00 -8.09329867e-01 -7.47650623e-01
-2.76881218e-01 -1.20689757e-01 -1.56916007e-01 -3.33638251... | [9.448062896728516, -1.579661250114441] |
6ce09e4f-8f7d-4613-bb07-c778119023b9 | texture-classification-using-block-intensity | 2002.01154 | null | https://arxiv.org/abs/2002.01154v1 | https://arxiv.org/pdf/2002.01154v1.pdf | Texture Classification using Block Intensity and Gradient Difference (BIGD) Descriptor | In this paper, we present an efficient and distinctive local descriptor, namely block intensity and gradient difference (BIGD). In an image patch, we randomly sample multi-scale block pairs and utilize the intensity and gradient differences of pairwise blocks to construct the local BIGD descriptor. The random sampling ... | ['Yuting Hu', 'Ghassan AlRegib', 'Zhen Wang'] | 2020-02-04 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [-1.02091972e-02 -9.65123534e-01 -3.70549589e-01 -2.91673005e-01
-1.03326392e+00 -1.39984444e-01 7.29660511e-01 1.66919142e-01
-1.75592259e-01 4.33934093e-01 2.62936354e-02 1.58878788e-01
-3.13732415e-01 -7.42150366e-01 -1.77839234e-01 -1.29949498e+00
-2.91917861e-01 2.76339576e-02 5.49867034e-01 -1.21626586... | [10.432111740112305, -0.33212170004844666] |
e854b298-c8c8-4905-858b-a427cbd54eb8 | bert-based-classification-system-for | 2109.02975 | null | https://arxiv.org/abs/2109.02975v1 | https://arxiv.org/pdf/2109.02975v1.pdf | BERT based classification system for detecting rumours on Twitter | The role of social media in opinion formation has far-reaching implications in all spheres of society. Though social media provide platforms for expressing news and views, it is hard to control the quality of posts due to the sheer volumes of posts on platforms like Twitter and Facebook. Misinformation and rumours have... | ['Amitava Datta', 'Ghulam Mubashar Hassan', 'Rini Anggrainingsih'] | 2021-09-07 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-1.72761485e-01 1.48744807e-01 -1.33087590e-01 -1.79212019e-01
-5.78432381e-02 -3.86605978e-01 9.95077729e-01 9.38505948e-01
-4.70100313e-01 8.18651557e-01 6.10615849e-01 -3.80204529e-01
3.48429471e-01 -1.13143861e+00 -9.18365270e-02 -3.84387851e-01
-4.51851673e-02 1.04566924e-01 2.73259044e-01 -9.03659582... | [8.270001411437988, 10.097012519836426] |
13c3e948-d889-4510-a962-df561d2a7c34 | end-to-end-trainable-attentive-decoder-for | null | null | https://aclanthology.org/E17-2119 | https://aclanthology.org/E17-2119.pdf | End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification | We address fine-grained entity classification and propose a novel attention-based recurrent neural network (RNN) encoder-decoder that generates paths in the type hierarchy and can be trained end-to-end. We show that our model performs better on fine-grained entity classification than prior work that relies on flat or l... | ['Hinrich Sch{\\"u}tze', 'Ulli Waltinger', 'Sanjeev Karn'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['morphological-tagging'] | ['natural-language-processing'] | [-3.27135444e-01 6.26319289e-01 -3.82280141e-01 -5.53569615e-01
-8.06257427e-01 -4.14415449e-01 4.80708867e-01 3.23488384e-01
-4.64703411e-01 9.78054643e-01 9.14389849e-01 -5.87616682e-01
1.81016803e-01 -1.14488041e+00 -9.22053277e-01 1.91125691e-01
-1.34344876e-01 6.36948824e-01 1.43668398e-01 -2.53578573... | [9.725520133972168, 9.42722225189209] |
798c221b-77bb-46f0-9de0-b99373751158 | chairs-towards-full-body-articulated-human | 2212.10621 | null | https://arxiv.org/abs/2212.10621v2 | https://arxiv.org/pdf/2212.10621v2.pdf | Full-Body Articulated Human-Object Interaction | Fine-grained capturing of 3D HOI boosts human activity understanding and facilitates downstream visual tasks, including action recognition, holistic scene reconstruction, and human motion synthesis. Despite its significance, existing works mostly assume that humans interact with rigid objects using only a few body part... | ['Yixin Chen', 'Zhiyuan Zhang', 'Siyuan Huang', 'Yixin Zhu', 'He Wang', 'Jieming Cui', 'Zhexuan Cao', 'Tengyu Liu', 'Nan Jiang'] | 2022-12-20 | null | null | null | null | ['human-object-interaction-detection'] | ['computer-vision'] | [ 3.16280164e-02 1.64344132e-01 -6.20234273e-02 4.35804687e-02
-2.81608105e-01 -5.95297456e-01 5.25227666e-01 -6.14666224e-01
-3.63096595e-02 3.33808482e-01 6.86279237e-01 2.27238595e-01
1.42763972e-01 -3.36226016e-01 -9.14838135e-01 -2.83347338e-01
-5.82082085e-02 1.14572716e+00 1.74951151e-01 -1.81029812... | [6.9067864418029785, -0.8814417123794556] |
9e08f62b-003c-47c0-b94b-ef5abb642ce8 | ax-mabsa-a-framework-for-extremely-weakly | 2211.03837 | null | https://arxiv.org/abs/2211.03837v1 | https://arxiv.org/pdf/2211.03837v1.pdf | AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment Analysis | Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health. Prior works in this area are primarily based on supervised methods, with a few techniques using weak supervision limited to predicting a single aspect category per review sen... | ['Mingxue Wang', 'Sourav Dutta', 'Walid Magdy', 'Sabyasachi Kamila'] | 2022-11-07 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 6.38631284e-01 2.81668931e-01 -6.15682542e-01 -8.07526529e-01
-9.09049511e-01 -5.91091394e-01 8.29291284e-01 4.55625564e-01
-5.41946113e-01 6.48694754e-01 3.26299906e-01 -4.01277244e-01
4.92518067e-01 -6.71422601e-01 -2.55095989e-01 -4.02511150e-01
5.40597618e-01 3.26077729e-01 6.20082617e-02 -5.28110385... | [11.332261085510254, 6.7481842041015625] |
0b5f839b-dddb-422e-897e-87eb71f6f778 | mixed-type-wafer-classification-for-low | 2303.13974 | null | https://arxiv.org/abs/2303.13974v1 | https://arxiv.org/pdf/2303.13974v1.pdf | Mixed-Type Wafer Classification For Low Memory Devices Using Knowledge Distillation | Manufacturing wafers is an intricate task involving thousands of steps. Defect Pattern Recognition (DPR) of wafer maps is crucial for determining the root cause of production defects, which may further provide insight for yield improvement in wafer foundry. During manufacturing, various defects may appear standalone in... | ['Srivatsan K', 'Anurima Dey', 'Nitish Shukla'] | 2023-03-24 | null | null | null | null | ['type'] | ['speech'] | [ 3.02213252e-01 4.38325182e-02 -2.15988055e-01 -4.70352054e-01
-7.02884734e-01 -5.10776877e-01 7.46409819e-02 5.03746271e-01
2.79247910e-01 4.09986377e-01 -5.25413215e-01 -6.19545579e-01
-5.07513024e-02 -7.71622777e-01 -7.84029186e-01 -4.47026402e-01
2.75056422e-01 8.23113918e-01 2.67106500e-02 7.63463303... | [7.3041768074035645, 2.0023796558380127] |
484e6e4b-c66b-4a0e-a397-ffba5a87afbd | unsupervised-feature-learning-for | 1904.04221 | null | https://arxiv.org/abs/1904.04221v2 | https://arxiv.org/pdf/1904.04221v2.pdf | Unsupervised Feature Learning for Environmental Sound Classification Using Weighted Cycle-Consistent Generative Adversarial Network | In this paper we propose a novel environmental sound classification approach incorporating unsupervised feature learning from codebook via spherical $K$-Means++ algorithm and a new architecture for high-level data augmentation. The audio signal is transformed into a 2D representation using a discrete wavelet transform ... | ['Alessandro Lameiras Koerich', 'Patrick Cardinal', 'Mohammad Esmaeilpour'] | 2019-04-08 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 3.38757008e-01 -1.31473094e-01 5.37851989e-01 -2.35773996e-01
-8.21422279e-01 -4.23032820e-01 5.11699498e-01 -8.00998658e-02
-4.58236575e-01 3.08131278e-01 1.72531664e-01 -8.96220505e-02
9.77678448e-02 -1.00152898e+00 -8.16117764e-01 -9.47759032e-01
-4.55653310e-01 6.35800585e-02 1.32931665e-01 -2.48674732... | [15.191203117370605, 5.246728897094727] |
facb32f4-91a3-46af-81cd-360eabd23f87 | dshgt-dual-supervisors-heterogeneous-graph | 2306.01376 | null | https://arxiv.org/abs/2306.01376v1 | https://arxiv.org/pdf/2306.01376v1.pdf | DSHGT: Dual-Supervisors Heterogeneous Graph Transformer -- A pioneer study of using heterogeneous graph learning for detecting software vulnerabilities | Vulnerability detection is a critical problem in software security and attracts growing attention both from academia and industry. Traditionally, software security is safeguarded by designated rule-based detectors that heavily rely on empirical expertise, requiring tremendous effort from software experts to generate ru... | ['Xi Zheng', 'Jun Yin', 'Xiaowei Huang', 'Xin Chen', 'Yuzhe Tian', 'Jianping Zhang', 'Rui Xu', 'Tiehua Zhang'] | 2023-06-02 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [ 9.44399610e-02 1.21116325e-01 -5.06677449e-01 4.29846300e-03
-4.87071484e-01 -7.89330721e-01 3.42808664e-01 6.06048167e-01
7.01625198e-02 7.38036186e-02 -1.25940964e-01 -1.16062939e+00
5.26640303e-02 -1.02909279e+00 -7.08652377e-01 -8.32355469e-02
-4.87164140e-01 -2.03497469e-01 5.62400699e-01 -3.54846776... | [7.158489227294922, 7.770480155944824] |
d99076b6-fd59-461c-9206-cb93a366939b | a-modified-pinn-approach-for-identifiable | 2208.01169 | null | https://arxiv.org/abs/2208.01169v2 | https://arxiv.org/pdf/2208.01169v2.pdf | A Modified PINN Approach for Identifiable Compartmental Models in Epidemiology with Applications to COVID-19 | A variety of approaches using compartmental models have been used to study the COVID-19 pandemic and the usage of machine learning methods with these models has had particularly notable success. We present here an approach toward analyzing accessible data on Covid-19's U.S. development using a variation of the "Physics... | ['HongKun Zhang', 'Panayotis G. Kevrekidis', 'Connor M Kennedy', 'Haoran Hu'] | 2022-08-01 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 2.55424738e-01 -2.11902246e-01 -2.40222305e-01 -1.95548072e-01
-7.71164238e-01 -3.64962399e-01 6.66864753e-01 2.16725603e-01
-7.10213482e-01 1.13875580e+00 2.38991454e-01 -8.30815971e-01
-7.51870990e-01 -7.81174600e-01 -5.15260637e-01 -8.88768792e-01
-2.13881105e-01 9.58948076e-01 -1.24503843e-01 -3.82631123... | [6.037579536437988, 4.327662467956543] |
fa48525f-2807-43ad-975b-e24617c48577 | face-reconstruction-with-variational | 2112.02139 | null | https://arxiv.org/abs/2112.02139v1 | https://arxiv.org/pdf/2112.02139v1.pdf | Face Reconstruction with Variational Autoencoder and Face Masks | Variational AutoEncoders (VAE) employ deep learning models to learn a continuous latent z-space that is subjacent to a high-dimensional observed dataset. With that, many tasks are made possible, including face reconstruction and face synthesis. In this work, we investigated how face masks can help the training of VAEs ... | ['Eric A. Antonelo', 'Rafael S. Toledo'] | 2021-12-03 | null | null | null | null | ['face-reconstruction'] | ['computer-vision'] | [ 8.62175301e-02 5.55925131e-01 1.55408204e-01 -3.38663846e-01
-6.76700324e-02 -2.39002839e-01 7.90725410e-01 -3.76693875e-01
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-2.34924644e-01 -8.25725973e-01 -1.01781547e+00 -1.09674144e+00
2.18190834e-01 2.95247376e-01 -5.27908355e-02 1.60375778... | [12.496417999267578, -0.1027742400765419] |
3d0b7474-acaa-42f0-a820-e4a7565c064c | comparative-study-of-pre-trained-bert-models | 2305.15722 | null | https://arxiv.org/abs/2305.15722v2 | https://arxiv.org/pdf/2305.15722v2.pdf | Comparative Study of Pre-Trained BERT Models for Code-Mixed Hindi-English Data | The term "Code Mixed" refers to the use of more than one language in the same text. This phenomenon is predominantly observed on social media platforms, with an increasing amount of adaptation as time goes on. It is critical to detect foreign elements in a language and process them correctly, as a considerable number o... | ['Raviraj Joshi', 'Gauri Takawane', 'Abhishek Phaltankar', 'Varad Patwardhan', 'Aryan Patil'] | 2023-05-25 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-4.45464164e-01 -1.43790856e-01 2.97527850e-01 -3.52166593e-01
-8.29437852e-01 -6.89380944e-01 5.94488144e-01 5.10692179e-01
-4.45905477e-01 3.63200188e-01 5.92638738e-02 -2.82874644e-01
4.52639401e-01 -3.70315611e-01 -4.02008176e-01 -3.05703938e-01
3.59735191e-02 2.87169158e-01 -9.15020406e-02 -7.68393099... | [9.247820854187012, 10.455854415893555] |
0c0753a3-3736-483e-9e0f-52849c4d33ea | mer-2023-multi-label-learning-modality | 2304.08981 | null | https://arxiv.org/abs/2304.08981v1 | https://arxiv.org/pdf/2304.08981v1.pdf | MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning | Over the past few decades, multimodal emotion recognition has made remarkable progress with the development of deep learning. However, existing technologies are difficult to meet the demand for practical applications. To improve the robustness, we launch a Multimodal Emotion Recognition Challenge (MER 2023) to motivate... | ['JianHua Tao', 'Björn W. Schuller', 'Guoying Zhao', 'Erik Cambria', 'Meng Wang', 'Jiangyan Yi', 'Bin Liu', 'Ye Liu', 'Jinming Zhao', 'Licai Sun', 'Haiyang Sun', 'Zheng Lian'] | 2023-04-18 | null | null | null | null | ['multimodal-emotion-recognition', 'multi-label-learning', 'multimodal-emotion-recognition'] | ['computer-vision', 'methodology', 'speech'] | [-1.37446463e-01 -4.48665082e-01 1.37609690e-01 -5.87320983e-01
-1.17565775e+00 -4.63960975e-01 2.57097542e-01 -1.57384202e-01
-7.03739405e-01 4.00289446e-01 9.41962972e-02 2.61591405e-01
5.78831434e-01 -1.96514383e-01 -5.74484944e-01 -5.39214373e-01
-1.10633232e-01 -2.28754848e-01 -4.02816206e-01 -2.50552773... | [13.255877494812012, 5.129270076751709] |
01c0eaa7-737c-4b16-bc5e-3ce4a8746b22 | metamorphosis-task-oriented-privacy-cognizant | 2305.07815 | null | https://arxiv.org/abs/2305.07815v1 | https://arxiv.org/pdf/2305.07815v1.pdf | MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning | With the growth of computer vision applications, deep learning, and edge computing contribute to ensuring practical collaborative intelligence (CI) by distributing the workload among edge devices and the cloud. However, running separate single-task models on edge devices is inefficient regarding the required computatio... | ['Anupam Das', 'Md Yusuf Sarwar Uddin', 'Zhouyu Li', 'Md Adnan Arefeen'] | 2023-05-13 | null | null | null | null | ['scene-understanding', 'edge-computing'] | ['computer-vision', 'time-series'] | [ 2.65613317e-01 4.01288383e-02 8.64467695e-02 -5.45998216e-01
-7.20408142e-01 -7.51046777e-01 4.49566603e-01 -2.28890374e-01
-4.22568470e-01 5.29182613e-01 -2.65321761e-01 -2.57496238e-01
7.58536085e-02 -8.13756108e-01 -8.76161993e-01 -8.95118296e-01
1.87415481e-01 9.67256445e-03 -1.62216112e-01 3.08238775... | [5.868804931640625, 6.754838943481445] |
2d540731-1484-463f-93ed-05ba7c7f5042 | a-lightweight-nms-free-framework-for-real | 2205.12458 | null | https://arxiv.org/abs/2205.12458v1 | https://arxiv.org/pdf/2205.12458v1.pdf | A Lightweight NMS-free Framework for Real-time Visual Fault Detection System of Freight Trains | Real-time vision-based system of fault detection (RVBS-FD) for freight trains is an essential part of ensuring railway transportation safety. Most existing vision-based methods still have high computational costs based on convolutional neural networks. The computational cost is mainly reflected in the backbone, neck, a... | ['Yang Zhang', 'Ye Hu', 'Bo Wu', 'Huilin Pan', 'Yang Zhou', 'Guodong Sun'] | 2022-05-25 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-9.83583629e-02 -5.48348844e-01 3.34225416e-01 -1.45303771e-01
-5.80836892e-01 1.14273980e-01 1.22202158e-01 -3.11335754e-02
-6.44982100e-01 1.54955238e-01 -3.82608086e-01 -2.81896532e-01
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3.10939103e-01 -7.31651112e-03 1.16891444e+00 -1.48126587... | [8.730669975280762, -0.4907466471195221] |
316fae90-d33c-4388-8cae-30b3f996ec5e | guided-mcmc-for-sparse-bayesian-models-to | null | null | https://openreview.net/forum?id=Yc64t25hseP | https://openreview.net/pdf?id=Yc64t25hseP | GUIDED MCMC FOR SPARSE BAYESIAN MODELS TO DETECT RARE EVENTS IN IMAGES SANS LABELED DATA | Detection of rare events in images is a challenging task because of two main problems, the first problem is the lack of labeled data for rare category class and the second problem is a highly imbalanced data problem. Training models in this scenario becomes hard. Unsupervised methods do not apply as we need to detect r... | ['Mrinal Das', 'Gaurav Jain'] | 2021-09-29 | null | null | null | null | ['unsupervised-image-classification'] | ['computer-vision'] | [ 5.01059890e-01 8.04529041e-02 -2.45169476e-02 -3.78558487e-01
-6.69906557e-01 -2.27753185e-02 6.15696013e-01 6.43440068e-01
-6.61195874e-01 8.47118318e-01 -2.96303719e-01 -2.24861473e-01
-4.38076854e-01 -8.57145369e-01 -4.17109102e-01 -9.95326579e-01
1.31866589e-01 9.32953835e-01 5.74272275e-01 2.27872923... | [8.453564643859863, 2.2599825859069824] |
b1d9d19f-a681-42f8-9ae1-2abf4e5aca80 | visible-thermal-uav-tracking-a-large-scale | 2204.04120 | null | https://arxiv.org/abs/2204.04120v1 | https://arxiv.org/pdf/2204.04120v1.pdf | Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline | With the popularity of multi-modal sensors, visible-thermal (RGB-T) object tracking is to achieve robust performance and wider application scenarios with the guidance of objects' temperature information. However, the lack of paired training samples is the main bottleneck for unlocking the power of RGB-T tracking. Since... | ['Xiang Ruan', 'Huchuan Lu', 'Dong Wang', 'Jie Zhao', 'Pengyu Zhang'] | 2022-04-08 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhang_Visible-Thermal_UAV_Tracking_A_Large-Scale_Benchmark_and_New_Baseline_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhang_Visible-Thermal_UAV_Tracking_A_Large-Scale_Benchmark_and_New_Baseline_CVPR_2022_paper.pdf | cvpr-2022-1 | ['rgb-t-tracking'] | ['computer-vision'] | [-3.63935456e-02 -8.10184956e-01 -3.11096609e-02 -2.39442542e-01
-8.25013638e-01 -8.84185553e-01 3.92662078e-01 -4.91750866e-01
-3.76132339e-01 5.27522326e-01 -3.60616833e-01 -3.76057066e-02
6.06718846e-02 -4.82297719e-01 -7.00173318e-01 -1.08767748e+00
6.56049028e-02 1.47011176e-01 6.05145633e-01 -1.93611294... | [6.448387622833252, -2.1819286346435547] |
96316df3-74c6-4b58-82b5-b6d8dbad32a6 | random-walk-on-multiple-networks | 2307.01637 | null | https://arxiv.org/abs/2307.01637v1 | https://arxiv.org/pdf/2307.01637v1.pdf | Random Walk on Multiple Networks | Random Walk is a basic algorithm to explore the structure of networks, which can be used in many tasks, such as local community detection and network embedding. Existing random walk methods are based on single networks that contain limited information. In contrast, real data often contain entities with different types ... | ['Xiang Zhang', 'Xiao Liu', 'Jun Huan', 'Xiong Yu', 'Yaowei Yan', 'Yuchen Bian', 'Dongsheng Luo'] | 2023-07-04 | null | null | null | null | ['link-prediction', 'local-community-detection', 'community-detection', 'network-embedding'] | ['graphs', 'graphs', 'graphs', 'methodology'] | [-1.50375634e-01 6.70470819e-02 -5.94204843e-01 -1.30681455e-01
2.44931281e-01 -5.70319712e-01 4.59227294e-01 5.02416730e-01
-2.04945013e-01 7.51524329e-01 1.77112117e-01 -2.83758640e-01
-5.75605333e-01 -1.52312696e+00 -2.19264314e-01 -4.75962251e-01
-7.43600607e-01 7.44380414e-01 6.93270445e-01 -5.56844249... | [7.226092338562012, 5.949730396270752] |
15d5e3bc-5437-41b4-9edd-e462b8b9430e | joint-discriminative-and-generative-learning | 1904.07223 | null | https://arxiv.org/abs/1904.07223v3 | https://arxiv.org/pdf/1904.07223v3.pdf | Joint Discriminative and Generative Learning for Person Re-identification | Person re-identification (re-id) remains challenging due to significant intra-class variations across different cameras. Recently, there has been a growing interest in using generative models to augment training data and enhance the invariance to input changes. The generative pipelines in existing methods, however, sta... | ['Xiaodong Yang', 'Zhiding Yu', 'Jan Kautz', 'Yi Yang', 'Zhedong Zheng', 'Liang Zheng'] | 2019-04-15 | joint-discriminative-and-generative-learning-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Zheng_Joint_Discriminative_and_Generative_Learning_for_Person_Re-Identification_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zheng_Joint_Discriminative_and_Generative_Learning_for_Person_Re-Identification_CVPR_2019_paper.pdf | cvpr-2019-6 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 2.28796706e-01 -1.13520086e-01 2.02907532e-01 -5.88368654e-01
-6.05844140e-01 -5.35470009e-01 8.19958806e-01 -2.19282299e-01
-3.94137889e-01 4.59080428e-01 3.39368880e-01 3.34883749e-01
3.67138952e-01 -6.61177099e-01 -7.19084620e-01 -6.99599743e-01
5.22686839e-01 4.66978639e-01 -1.64207906e-01 7.51087293... | [14.706496238708496, 1.004082441329956] |
fd59246d-ae84-42a4-b8f1-0c85215d8171 | image-coding-via-perceptually-inspired-graph | 2303.01674 | null | https://arxiv.org/abs/2303.01674v1 | https://arxiv.org/pdf/2303.01674v1.pdf | Image Coding via Perceptually Inspired Graph Learning | Most codec designs rely on the mean squared error (MSE) as a fidelity metric in rate-distortion optimization, which allows to choose the optimal parameters in the transform domain but may fail to reflect perceptual quality. Alternative distortion metrics, such as the structural similarity index (SSIM), can be computed ... | ['Antonio Ortega', 'Eduardo Pavez', 'Samuel Fernández-Menduiña'] | 2023-03-03 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 5.88927746e-01 -2.94046968e-01 -2.73769259e-01 -4.02888447e-01
-7.27741122e-01 -4.92645115e-01 3.71986449e-01 3.18879455e-01
-2.30954230e-01 5.03502011e-01 2.30934322e-01 -3.14560801e-01
-3.09885353e-01 -6.97451770e-01 -6.38615310e-01 -5.54764211e-01
-1.51403844e-01 -2.34326854e-01 5.40157437e-01 -1.42829478... | [11.44328498840332, -1.8280086517333984] |
c9300839-ff15-4005-99e8-c9f74be46f51 | complex-query-answering-with-neural-link-1 | 2011.03459 | null | https://arxiv.org/abs/2011.03459v4 | https://arxiv.org/pdf/2011.03459v4.pdf | Complex Query Answering with Neural Link Predictors | Neural link predictors are immensely useful for identifying missing edges in large scale Knowledge Graphs. However, it is still not clear how to use these models for answering more complex queries that arise in a number of domains, such as queries using logical conjunctions ($\land$), disjunctions ($\lor$) and existent... | ['Michael Cochez', 'Pasquale Minervini', 'Daniel Daza', 'Erik Arakelyan'] | 2020-11-06 | complex-query-answering-with-neural-link | https://openreview.net/forum?id=Mos9F9kDwkz | https://openreview.net/pdf?id=Mos9F9kDwkz | iclr-2021-1 | ['complex-query-answering'] | ['knowledge-base'] | [ 1.63819984e-01 4.28004324e-01 -3.77695084e-01 -2.61808902e-01
-1.07548845e+00 -6.54727757e-01 2.24761397e-01 5.27230978e-01
-4.21948373e-01 1.09529650e+00 -1.59901217e-01 -6.09606445e-01
-5.15059531e-01 -1.20001781e+00 -1.31290829e+00 -2.82217935e-02
-2.22045407e-01 1.02909625e+00 3.18728864e-01 -3.90262872... | [9.413775444030762, 7.690106391906738] |
67b51468-7a1e-4b68-a1b6-ee5c92d1edaa | a-convolutional-neural-network-of-low | 2301.09861 | null | https://arxiv.org/abs/2301.09861v4 | https://arxiv.org/pdf/2301.09861v4.pdf | A convolutional neural network of low complexity for tumor anomaly detection | The automated detection of cancerous tumors has attracted interest mainly during the last decade, due to the necessity of early and efficient diagnosis that will lead to the most effective possible treatment of the impending risk. Several machine learning and artificial intelligence methodologies has been employed aimi... | ['Dimitrios-Panagiotis Papageorgiou', 'Pantelis Dogoulis', 'Vasileios E. Papageorgiou'] | 2023-01-24 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 3.58911425e-01 5.57633162e-01 1.13348506e-01 -2.79485315e-01
-4.56143171e-01 -1.54743334e-02 8.36274683e-01 5.81136048e-01
-9.17601407e-01 9.06766295e-01 -2.75517642e-01 -3.87548476e-01
-3.62049758e-01 -6.06072426e-01 -3.44033182e-01 -8.53145599e-01
-2.64310271e-01 5.10890186e-01 3.48373540e-02 1.06596850... | [15.18183422088623, -2.688488721847534] |
bb4de3ca-01d3-42e3-95ac-dabfe1db554d | evaluating-inter-bilingual-semantic-parsing | 2304.13005 | null | https://arxiv.org/abs/2304.13005v2 | https://arxiv.org/pdf/2304.13005v2.pdf | Evaluating Inter-Bilingual Semantic Parsing for Indian Languages | Despite significant progress in Natural Language Generation for Indian languages (IndicNLP), there is a lack of datasets around complex structured tasks such as semantic parsing. One reason for this imminent gap is the complexity of the logical form, which makes English to multilingual translation difficult. The proces... | ['Anoop Kunchukuttan', 'Vivek Gupta', 'Divyanshu Aggarwal'] | 2023-04-25 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 2.42320850e-01 3.34943026e-01 -1.00013480e-01 -5.97509444e-01
-1.37849915e+00 -1.07060635e+00 3.13854754e-01 -7.69732371e-02
-3.73638213e-01 1.34511983e+00 4.12906796e-01 -6.59700572e-01
2.15988889e-01 -8.16420853e-01 -9.31367457e-01 4.99970913e-02
4.21186358e-01 9.48229969e-01 1.02363609e-01 -6.48425639... | [10.769173622131348, 9.661900520324707] |
a984734e-91a3-40c8-9a34-3f022a136491 | deep-learning-based-vulnerability-detection-1 | 2212.01254 | null | https://arxiv.org/abs/2212.01254v1 | https://arxiv.org/pdf/2212.01254v1.pdf | Deep-Learning-based Vulnerability Detection in Binary Executables | The identification of vulnerabilities is an important element in the software development life cycle to ensure the security of software. While vulnerability identification based on the source code is a well studied field, the identification of vulnerabilities on basis of a binary executable without the corresponding so... | ['Dominik Binder', 'Andreas Schaad'] | 2022-11-25 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [ 1.31311059e-01 -4.58768010e-03 1.60759836e-01 -9.02138203e-02
-5.76881886e-01 -7.23166823e-01 4.18444484e-01 6.57329559e-01
-2.62322307e-01 2.16053560e-01 -2.37540573e-01 -9.29193199e-01
-1.65977761e-01 -1.06329060e+00 -3.11659753e-01 -5.94081819e-01
-2.10833147e-01 7.70295709e-02 1.21901274e-01 -3.84653121... | [7.062564849853516, 7.774383068084717] |
833446a8-cfed-4b9a-a979-b7d1181d29fb | galaxy-image-deconvolution-for-weak | 2211.01567 | null | https://arxiv.org/abs/2211.01567v3 | https://arxiv.org/pdf/2211.01567v3.pdf | Galaxy Image Deconvolution for Weak Gravitational Lensing with Unrolled Plug-and-Play ADMM | Removing optical and atmospheric blur from galaxy images significantly improves galaxy shape measurements for weak gravitational lensing and galaxy evolution studies. This ill-posed linear inverse problem is usually solved with deconvolution algorithms enhanced by regularisation priors or deep learning. We introduce a ... | ['Emma Alexander', 'Tianao Li'] | 2022-11-03 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 5.93769713e-04 -1.48383398e-02 8.31527948e-01 -1.69938520e-01
-5.23481190e-01 -8.28270555e-01 9.81849074e-01 -7.33138978e-01
-5.53418458e-01 7.42141962e-01 4.40656900e-01 -4.07862127e-01
-4.00059432e-01 -4.08522695e-01 -6.78155661e-01 -1.16450846e+00
-1.11577265e-01 4.99815047e-01 2.36652732e-01 1.05305091... | [11.638808250427246, -2.724001884460449] |
0a4d890b-1eec-40b3-9212-ffe455ba801c | ego-vehicle-speed-estimation-using-3d | 2212.05432 | null | https://arxiv.org/abs/2212.05432v1 | https://arxiv.org/pdf/2212.05432v1.pdf | Ego Vehicle Speed Estimation using 3D Convolution with Masked Attention | Speed estimation of an ego vehicle is crucial to enable autonomous driving and advanced driver assistance technologies. Due to functional and legacy issues, conventional methods depend on in-car sensors to extract vehicle speed through the Controller Area Network bus. However, it is desirable to have modular systems th... | ['Thariq Khalid', 'Athul M. Mathew'] | 2022-12-11 | null | null | null | null | ['vehicle-speed-estimation'] | ['computer-vision'] | [-3.41410637e-01 -2.31095552e-02 -2.26517305e-01 -4.86501426e-01
-1.26324192e-01 -4.05845016e-01 4.77016807e-01 -6.53080106e-01
-5.23811281e-01 2.86237568e-01 -2.48104230e-01 -7.30215669e-01
3.55110586e-01 -5.92996299e-01 -8.06344867e-01 -3.06959242e-01
3.27813685e-01 -2.00307220e-01 3.51750612e-01 -3.61720622... | [7.935519695281982, -1.2891311645507812] |
c3de6181-72a2-40c3-b858-7e280bfa0985 | imface-a-nonlinear-3d-morphable-face-model | 2203.14510 | null | https://arxiv.org/abs/2203.14510v2 | https://arxiv.org/pdf/2203.14510v2.pdf | ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations | Precise representations of 3D faces are beneficial to various computer vision and graphics applications. Due to the data discretization and model linearity, however, it remains challenging to capture accurate identity and expression clues in current studies. This paper presents a novel 3D morphable face model, namely I... | ['Liming Chen', 'Di Huang', 'Hongyu Yang', 'Mingwu Zheng'] | 2022-03-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zheng_ImFace_A_Nonlinear_3D_Morphable_Face_Model_With_Implicit_Neural_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zheng_ImFace_A_Nonlinear_3D_Morphable_Face_Model_With_Implicit_Neural_CVPR_2022_paper.pdf | cvpr-2022-1 | ['face-model'] | ['computer-vision'] | [ 8.80654305e-02 1.29870951e-01 -1.60431728e-01 -6.44396842e-01
-2.31498063e-01 -3.99664670e-01 4.86232460e-01 -5.87703228e-01
2.19956025e-01 4.24812287e-01 9.37360823e-02 1.71367958e-01
-4.66959132e-03 -7.48391986e-01 -4.76102978e-01 -5.85003495e-01
-6.63109943e-02 1.47714645e-01 -4.67963070e-01 -2.74070770... | [12.952497482299805, -0.07988473027944565] |
7ba5dbaa-9d6a-4e03-b6c5-602a89617060 | a-novel-ecg-denoising-scheme-using-the | 2207.11819 | null | https://arxiv.org/abs/2207.11819v1 | https://arxiv.org/pdf/2207.11819v1.pdf | A Novel ECG Denoising Scheme Using the Ensemble Kalman Filter | Monitoring of electrocardiogram (ECG) provides vital information as well as any cardiovascular anomalies. Recent advances in the technology of wearable electronics have enabled compact devices to acquire personal physiological signals in the home setting; however, signals are usually contaminated with high level noise.... | ['Hung Cao', 'Tadesse Ghirmai', 'Manoj Vishwanath', 'Michael P. H. Lau', 'Samir Malhotra', 'Daniel Jilani', 'Hoang Vuong', 'Sadaf Sarafan'] | 2022-07-24 | null | null | null | null | ['ecg-denoising'] | ['medical'] | [ 3.57713193e-01 -4.31237668e-01 4.93851304e-01 -2.14069158e-01
-5.10204434e-01 -2.75833100e-01 -1.18268162e-01 1.83741361e-01
-5.26296377e-01 1.16717255e+00 1.14424646e-01 -1.78402156e-01
-4.71893966e-01 -2.31397852e-01 -3.36342342e-02 -9.36580300e-01
-3.72501761e-01 -6.19176209e-01 -1.98648334e-01 5.67489602... | [14.044398307800293, 3.0761325359344482] |
d2272059-ddc6-4c10-9fa3-52b7a70702d9 | bidirectional-attention-flow-for-machine | 1611.01603 | null | http://arxiv.org/abs/1611.01603v6 | http://arxiv.org/pdf/1611.01603v6.pdf | Bidirectional Attention Flow for Machine Comprehension | Machine comprehension (MC), answering a query about a given context
paragraph, requires modeling complex interactions between the context and the
query. Recently, attention mechanisms have been successfully extended to MC.
Typically these methods use attention to focus on a small portion of the
context and summarize it... | ['Ali Farhadi', 'Minjoon Seo', 'Hannaneh Hajishirzi', 'Aniruddha Kembhavi'] | 2016-11-05 | null | null | null | null | ['cloze-test'] | ['natural-language-processing'] | [ 7.06652030e-02 -1.34085402e-01 -8.73952061e-02 -6.00775063e-01
-1.09355378e+00 -5.32499969e-01 5.18904150e-01 5.73382556e-01
-4.49967265e-01 5.99814892e-01 8.10487390e-01 -4.58438843e-01
4.66894284e-02 -6.26774371e-01 -7.13256776e-01 -1.96110979e-01
9.40909088e-02 2.91736990e-01 5.37389219e-01 -2.14560047... | [11.239821434020996, 8.176207542419434] |
15e9d9c2-1b3f-4f22-8662-6091be2310a1 | unsupervised-extractive-summarization-with | 2211.04698 | null | https://arxiv.org/abs/2211.04698v1 | https://arxiv.org/pdf/2211.04698v1.pdf | Unsupervised Extractive Summarization with Heterogeneous Graph Embeddings for Chinese Document | In the scenario of unsupervised extractive summarization, learning high-quality sentence representations is essential to select salient sentences from the input document. Previous studies focus more on employing statistical approaches or pre-trained language models (PLMs) to extract sentence embeddings, while ignoring ... | ['Di Yin', 'Siyu An', 'Ye Liu', 'Chen Lin'] | 2022-11-09 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings', 'unsupervised-extractive-summarization', 'extractive-summarization'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.95982057e-01 5.60813725e-01 -4.11016196e-01 -1.58868313e-01
-7.21257448e-01 -5.38333058e-01 8.62482369e-01 8.06648791e-01
-4.10695195e-01 5.91953516e-01 1.24755394e+00 -1.14101633e-01
-7.32165650e-02 -7.56630301e-01 -3.96417677e-01 -4.21832174e-01
-1.28505602e-01 1.85730204e-01 3.85702327e-02 -4.45898205... | [12.5450439453125, 9.582670211791992] |
ad48b55c-0749-4816-b972-90ae784ec41b | neural-extractive-summarization-with | null | null | https://aclanthology.org/2020.emnlp-main.295 | https://aclanthology.org/2020.emnlp-main.295.pdf | Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network | Sentence-level extractive text summarization is substantially a node classification task of network mining, adhering to the informative components and concise representations. There are lots of redundant phrases between extracted sentences, but it is difficult to model them exactly by the general supervised methods. Pr... | ['Shi Wang', 'Cong Cao', 'Fang Fang', 'Hengzhu Tang', 'Yanan Cao', 'Ruipeng Jia'] | null | null | null | null | emnlp-2020-11 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 4.42260146e-01 5.56871533e-01 -5.19726276e-01 -4.59921151e-01
-5.17126799e-01 -1.81001291e-01 4.18761522e-01 7.66519308e-01
-7.33961677e-03 9.25564587e-01 1.15325177e+00 1.05324797e-02
-1.56299517e-01 -8.52504194e-01 -4.59440976e-01 -3.48436594e-01
-2.04270348e-01 2.17947647e-01 2.95589268e-01 -5.17588735... | [12.563172340393066, 9.534866333007812] |
fd7ae812-47a5-46f8-9248-ce03bb5ba80f | a-hybrid-persian-sentiment-analysis-framework | 1909.13568 | null | https://arxiv.org/abs/1909.13568v1 | https://arxiv.org/pdf/1909.13568v1.pdf | A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks | Social media hold valuable, vast and unstructured information on public opinion that can be utilized to improve products and services. The automatic analysis of such data, however, requires a deep understanding of natural language. Current sentiment analysis approaches are mainly based on word co-occurrence frequencies... | ['Kia Dashtipour', 'Bin Kong', 'Amir Hussain', 'Mandar Gogate', 'Jingpeng Li', 'Fengling Jiang'] | 2019-09-30 | null | null | null | null | ['persian-sentiment-anlysis'] | ['natural-language-processing'] | [ 1.32809639e-01 -9.14536566e-02 -5.60286343e-01 -5.83750367e-01
-7.26653710e-02 -8.11138511e-01 5.71515739e-01 8.27656746e-01
-2.99102992e-01 7.46236861e-01 1.41875207e-01 -6.50021255e-01
5.57040647e-02 -1.10896814e+00 -3.14823776e-01 -5.90750873e-01
-4.50914577e-02 2.33173579e-01 -1.69311315e-01 -8.41427863... | [11.165939331054688, 7.006159782409668] |
59d020a5-51f9-4b3b-b9d6-bc5cc4ce5388 | smoothnets-optimizing-cnn-architecture-design | 2205.04095 | null | https://arxiv.org/abs/2205.04095v1 | https://arxiv.org/pdf/2205.04095v1.pdf | SmoothNets: Optimizing CNN architecture design for differentially private deep learning | The arguably most widely employed algorithm to train deep neural networks with Differential Privacy is DPSGD, which requires clipping and noising of per-sample gradients. This introduces a reduction in model utility compared to non-private training. Empirically, it can be observed that this accuracy degradation is stro... | ['Georgios Kaissis', 'Daniel Rueckert', 'Alexander Ziller', 'Nicolas W. Remerscheid'] | 2022-05-09 | null | null | null | null | ['image-classification-with-dp'] | ['computer-vision'] | [ 1.41719386e-01 1.73699677e-01 1.09049208e-01 -4.69012946e-01
-7.36325681e-01 -5.05756497e-01 6.48739517e-01 -1.16037086e-01
-9.82479095e-01 8.68802249e-01 -3.88856053e-01 -3.79810244e-01
-6.05905382e-03 -5.41791797e-01 -9.34928715e-01 -8.68744671e-01
-2.82298028e-01 -4.70211841e-02 1.18819639e-01 6.35763183... | [5.890960216522217, 6.95286750793457] |
2eeeab83-18e8-4d0b-b367-245382c08b27 | label-embedding-for-image-classification | 1503.08677 | null | http://arxiv.org/abs/1503.08677v2 | http://arxiv.org/pdf/1503.08677v2.pdf | Label-Embedding for Image Classification | Attributes act as intermediate representations that enable parameter sharing
between classes, a must when training data is scarce. We propose to view
attribute-based image classification as a label-embedding problem: each class
is embedded in the space of attribute vectors. We introduce a function that
measures the com... | ['Cordelia Schmid', 'Zeynep Akata', 'Florent Perronnin', 'Zaid Harchaoui'] | 2015-03-30 | null | null | null | null | ['zero-shot-action-recognition', 'multi-label-zero-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 4.62361991e-01 2.76620686e-01 -7.56567717e-01 -9.33792591e-01
-6.46103144e-01 -4.30444926e-01 1.02386904e+00 4.23235536e-01
-5.90345144e-01 6.95110440e-01 2.36861035e-01 1.42969757e-01
-1.37676597e-01 -8.43020737e-01 -6.62083983e-01 -8.39637518e-01
-4.49142791e-02 7.30072320e-01 2.90068518e-03 -3.19341682... | [9.973225593566895, 2.3623909950256348] |
d1408da0-8cf4-4999-9ff8-0bc55256639b | two-stage-autoencoder-neural-network-for-3d | 2306.04987 | null | https://arxiv.org/abs/2306.04987v2 | https://arxiv.org/pdf/2306.04987v2.pdf | Convolutional Recurrent Neural Network with Attention for 3D Speech Enhancement | 3D speech enhancement can effectively improve the auditory experience and plays a crucial role in augmented reality technology. However, traditional convolutional-based speech enhancement methods have limitations in extracting dynamic voice information. In this paper, we incorporate a dual-path recurrent neural network... | ['Mou Wang', 'Jianfeng Chen', 'Yafei Jia', 'Siwei Huang', 'Jisheng Bai', 'Han Yin'] | 2023-06-08 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [-5.61522394e-02 -1.27121434e-01 1.78415582e-01 -1.08376145e-01
-1.07505214e+00 5.61100505e-02 1.21432729e-01 -3.77683222e-01
-4.85935032e-01 3.44769120e-01 7.98590600e-01 -2.81302512e-01
5.09728119e-02 -4.09826547e-01 -5.01039445e-01 -3.19946736e-01
-5.59840947e-02 -7.08392859e-01 8.22022334e-02 -4.12260205... | [14.941435813903809, 5.935564041137695] |
e3924672-5318-417c-ab83-474d3b9b16de | conan-a-complementary-neighboring-based | null | null | https://aclanthology.org/2020.coling-main.177 | https://aclanthology.org/2020.coling-main.177.pdf | CoNAN: A Complementary Neighboring-based Attention Network for Referring Expression Generation | Daily scenes are complex in the real world due to occlusion, undesired lighting conditions, etc. Although humans handle those complicated environments well, they evoke challenges for machine learning systems to identify and describe the target without ambiguity. Most previous research focuses on mining discriminating f... | ['Jialin Wu', 'Hanbin Ko', 'Jungjun Kim'] | 2020-12-01 | null | null | null | coling-2020-8 | ['referring-expression-generation'] | ['computer-vision'] | [-4.84971553e-02 -2.07162142e-01 -2.66239375e-01 -7.71382272e-01
-3.50366950e-01 -5.27691066e-01 6.16350889e-01 2.23308980e-01
-3.27581257e-01 4.89299476e-01 3.76040816e-01 4.46834624e-01
-8.40325356e-02 -5.06402910e-01 -3.57468516e-01 -7.70659924e-01
1.95693791e-01 2.44050696e-01 -3.58561128e-02 -3.87604833... | [10.039467811584473, 1.8852379322052002] |
6497fd96-27e4-4d58-9ae8-9d2e31673df9 | semi-supervised-learning-from-street-view | 2307.02574 | null | https://arxiv.org/abs/2307.02574v1 | https://arxiv.org/pdf/2307.02574v1.pdf | Semi-supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation | Accurate building height estimation is key to the automatic derivation of 3D city models from emerging big geospatial data, including Volunteered Geographical Information (VGI). However, an automatic solution for large-scale building height estimation based on low-cost VGI data is currently missing. The fast developmen... | ['Martin Werner', 'Alexander Zipf', 'Hongchao Fan', 'Gefei Kong', 'Gabriel Dax', 'Zhendong Yuan', 'Hao Li'] | 2023-07-05 | null | null | null | null | ['object-detection', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 1.54088512e-01 2.67595649e-01 3.92786235e-01 -4.45290476e-01
-8.54769349e-01 -1.10743947e-01 7.06026971e-01 6.04057372e-01
-5.24418831e-01 8.72519255e-01 -2.47559603e-02 -4.07968134e-01
-2.06452891e-01 -1.68664515e+00 -7.37817168e-01 -5.99389553e-01
-1.72609910e-01 9.45555449e-01 3.56474280e-01 -2.52364308... | [8.956365585327148, -1.9258956909179688] |
952a043c-d0c1-4a9b-b218-281d18549f0a | a-robust-pedestrian-detection-approach-for | 2210.10489 | null | https://arxiv.org/abs/2210.10489v1 | https://arxiv.org/pdf/2210.10489v1.pdf | A Robust Pedestrian Detection Approach for Autonomous Vehicles | Nowadays, utilizing Advanced Driver-Assistance Systems (ADAS) has absorbed a huge interest as a potential solution for reducing road traffic issues. Despite recent technological advances in such systems, there are still many inquiries that need to be overcome. For instance, ADAS requires accurate and real-time detectio... | ['Asadollah Shahbahrami', 'Ali Tourani', 'Bahareh Ghari'] | 2022-10-19 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [-2.35853344e-01 -2.39066422e-01 2.06816196e-01 -4.16190922e-01
-6.19109690e-01 -2.12476462e-01 4.62540478e-01 -1.99323017e-02
-8.06141496e-01 6.52688861e-01 -4.51848239e-01 -4.40417141e-01
5.07297635e-01 -7.26949692e-01 -5.19148290e-01 -6.82890892e-01
3.78379971e-01 1.30520603e-02 9.96641278e-01 -4.90839511... | [7.9616618156433105, -0.8374708890914917] |
9bd8fcb2-90a1-4a0e-b095-b6f20dcf23c1 | optimizing-neural-networks-through-activation | 2304.03374 | null | https://arxiv.org/abs/2304.03374v1 | https://arxiv.org/pdf/2304.03374v1.pdf | Optimizing Neural Networks through Activation Function Discovery and Automatic Weight Initialization | Automated machine learning (AutoML) methods improve upon existing models by optimizing various aspects of their design. While present methods focus on hyperparameters and neural network topologies, other aspects of neural network design can be optimized as well. To further the state of the art in AutoML, this dissertat... | ['Garrett Bingham'] | 2023-04-06 | null | null | null | null | ['automl'] | ['methodology'] | [ 1.54315010e-01 2.44666502e-01 -5.40562630e-01 -4.56170410e-01
-4.48636591e-01 -5.48210204e-01 1.66676387e-01 -9.04136449e-02
-3.60694975e-01 7.11178184e-01 -3.16146873e-02 -2.65949816e-01
-4.40635949e-01 -4.06005234e-01 -5.13882995e-01 -7.25699544e-01
-1.73601165e-01 4.78756964e-01 -2.82586277e-01 -1.42795041... | [8.548623085021973, 3.3467648029327393] |
2c8f45c5-25d3-49a5-9697-818e15a92d90 | generative-adversarial-networks-for-image | 2204.04707 | null | https://arxiv.org/abs/2204.04707v2 | https://arxiv.org/pdf/2204.04707v2.pdf | Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review | In agricultural image analysis, optimal model performance is keenly pursued for better fulfilling visual recognition tasks (e.g., image classification, segmentation, object detection and localization), in the presence of challenges with biological variability and unstructured environments. Large-scale, balanced and gro... | ['Yanbo Huang', 'Yuzhen Lu', 'Dong Chen', 'Ebenezer Olaniyi'] | 2022-04-10 | null | null | null | null | ['plant-phenotyping', 'image-augmentation'] | ['computer-vision', 'computer-vision'] | [ 7.87279129e-01 2.89067198e-02 -1.65769443e-01 -7.33432025e-02
-2.23537743e-01 -9.37954426e-01 3.24061096e-01 4.13415611e-01
7.04339966e-02 5.13655543e-01 -4.33574289e-01 -5.99504352e-01
2.34447956e-01 -9.96302724e-01 -7.88736224e-01 -1.02345908e+00
2.12747380e-01 1.64696127e-01 -3.41640025e-01 -2.57211000... | [9.158320426940918, -1.5244324207305908] |
4a00d991-63da-4bcc-ba06-3e1e5441556d | deep-reinforcement-learning-for-unsupervised | 1801.00054 | null | http://arxiv.org/abs/1801.00054v3 | http://arxiv.org/pdf/1801.00054v3.pdf | Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward | Video summarization aims to facilitate large-scale video browsing by
producing short, concise summaries that are diverse and representative of
original videos. In this paper, we formulate video summarization as a
sequential decision-making process and develop a deep summarization network
(DSN) to summarize videos. DSN ... | ['Tao Xiang', 'Yu Qiao', 'Kaiyang Zhou'] | 2017-12-29 | null | null | null | null | ['unsupervised-video-summarization', 'supervised-video-summarization'] | ['computer-vision', 'computer-vision'] | [ 2.64177948e-01 -2.60503348e-02 -4.81180340e-01 -5.05741179e-01
-8.32130671e-01 -3.29101294e-01 5.47733426e-01 2.12447599e-01
-3.25403899e-01 9.30876970e-01 7.90246844e-01 1.57631665e-01
2.10007787e-01 -4.88884062e-01 -9.07322645e-01 -5.87997556e-01
-1.13771804e-01 2.41524801e-01 2.87133783e-01 3.51773709... | [10.477422714233398, 0.4199371337890625] |
2fd9da36-03fd-45ee-bc9e-657a1ba1232b | fair-lending-needs-explainable-models-for | 1809.04684 | null | http://arxiv.org/abs/1809.04684v1 | http://arxiv.org/pdf/1809.04684v1.pdf | Fair lending needs explainable models for responsible recommendation | The financial services industry has unique explainability and fairness
challenges arising from compliance and ethical considerations in credit
decisioning. These challenges complicate the use of model machine learning and
artificial intelligence methods in business decision processes. | ['Jiahao Chen'] | 2018-09-12 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [-1.34220526e-01 5.27702868e-01 -7.34305441e-01 -8.81774187e-01
3.37942958e-01 -2.18870223e-01 5.50934255e-01 -4.88938950e-02
-3.45929444e-01 6.71558321e-01 1.92249656e-01 -1.10442781e+00
-3.15113217e-01 -5.85229099e-01 1.10955015e-01 -1.53551757e-01
8.59487057e-02 7.50025570e-01 -8.01902533e-01 1.06612593... | [8.78154182434082, 5.664770603179932] |
42fabae5-31f9-4b59-b80e-2f0399ccda7c | scriptworld-text-based-environment-for | 2307.03906 | null | https://arxiv.org/abs/2307.03906v1 | https://arxiv.org/pdf/2307.03906v1.pdf | ScriptWorld: Text Based Environment For Learning Procedural Knowledge | Text-based games provide a framework for developing natural language understanding and commonsense knowledge about the world in reinforcement learning based agents. Existing text-based environments often rely on fictional situations and characters to create a gaming framework and are far from real-world scenarios. In t... | ['Ashutosh Modi', 'Umang Pandey', 'Areeb Ahmad', 'Abhinav Joshi'] | 2023-07-08 | null | null | null | null | ['natural-language-understanding', 'text-based-games'] | ['natural-language-processing', 'playing-games'] | [-2.14713752e-01 6.06955774e-03 2.55174756e-01 -1.64087236e-01
-2.11075857e-01 -6.26829207e-01 9.90183949e-01 -3.87667656e-01
-4.08611804e-01 8.09712589e-01 3.10052663e-01 -4.36080605e-01
2.93776989e-01 -1.11508632e+00 -6.72504544e-01 -3.48273814e-02
-7.41969943e-02 7.45017886e-01 2.79202402e-01 -1.08585238... | [3.8920204639434814, 1.2813148498535156] |
2bbce5e4-5f03-4d46-a7df-c2251acdea19 | x-trans2cap-cross-modal-knowledge-transfer | 2203.00843 | null | https://arxiv.org/abs/2203.00843v3 | https://arxiv.org/pdf/2203.00843v3.pdf | X-Trans2Cap: Cross-Modal Knowledge Transfer using Transformer for 3D Dense Captioning | 3D dense captioning aims to describe individual objects by natural language in 3D scenes, where 3D scenes are usually represented as RGB-D scans or point clouds. However, only exploiting single modal information, e.g., point cloud, previous approaches fail to produce faithful descriptions. Though aggregating 2D feature... | ['Shuguang Cui', 'Zhen Li', 'Guanbin Li', 'Yao Guo', 'Yinghong Liao', 'Xu Yan', 'Zhihao Yuan'] | 2022-03-02 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yuan_X-Trans2Cap_Cross-Modal_Knowledge_Transfer_Using_Transformer_for_3D_Dense_Captioning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yuan_X-Trans2Cap_Cross-Modal_Knowledge_Transfer_Using_Transformer_for_3D_Dense_Captioning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['dense-captioning', '3d-dense-captioning'] | ['computer-vision', 'computer-vision'] | [-1.27466366e-01 4.04114604e-01 -3.09159577e-01 -4.91473675e-01
-1.07658863e+00 -6.40323162e-01 7.43943632e-01 5.99717684e-02
-8.21912810e-02 4.93818194e-01 -5.53892180e-02 -2.79075652e-02
2.64848135e-02 -8.41537058e-01 -1.33053350e+00 -6.85630620e-01
4.31018710e-01 9.28522050e-01 -2.04324480e-02 -1.42173305... | [8.229923248291016, -3.2383687496185303] |
7a54a15d-ad16-4bd5-8295-4751c7f473c9 | learning-analytical-posterior-probability-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Fang_Learning_Analytical_Posterior_Probability_for_Human_Mesh_Recovery_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Fang_Learning_Analytical_Posterior_Probability_for_Human_Mesh_Recovery_CVPR_2023_paper.pdf | Learning Analytical Posterior Probability for Human Mesh Recovery | Despite various probabilistic methods for modeling the uncertainty and ambiguity in human mesh recovery, their overall precision is limited because existing formulations for joint rotations are either not constrained to SO(3) or difficult to learn for neural networks. To address such an issue, we derive a novel ana... | ['Weidong Zhang', 'Jiefeng Li', 'Qing Shuai', 'Yinghui Fan', 'Kang Chen', 'Qi Fang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['human-mesh-recovery'] | ['computer-vision'] | [ 1.13753617e-01 2.26136804e-01 -4.39358771e-01 -1.68178007e-01
-1.12378502e+00 -1.98053703e-01 1.94229826e-01 -3.98615748e-01
1.68966223e-02 6.27355635e-01 6.15350425e-01 4.34370972e-02
-3.06672156e-01 -6.53978646e-01 -8.52408350e-01 -4.69483614e-01
5.69519885e-02 8.03688049e-01 1.79080844e-01 5.83637680... | [7.086912631988525, -1.0712344646453857] |
e8af4cb2-5f1c-40f8-9769-aa6e99966992 | a-novel-heap-based-pilot-assignment-for-full | 2007.04787 | null | https://arxiv.org/abs/2007.04787v1 | https://arxiv.org/pdf/2007.04787v1.pdf | A Novel Heap-based Pilot Assignment for Full Duplex Cell-Free Massive MIMO with Zero-Forcing | This paper investigates the combined benefits of full-duplex (FD) and cell-free massive multiple-input multipleoutput (CF-mMIMO), where a large number of distributed access points (APs) having FD capability simultaneously serve numerous uplink and downlink user equipments (UEs) on the same time-frequency resources. To ... | ['Oh-Soon Shin', 'Björn Ottersten', 'Symeon Chatzinotas', 'Shree Krishna Sharma', 'Octavia A. Dobre', 'Van-Dinh Nguyen', 'Hieu V. Nguyen'] | 2020-07-08 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [-5.78833483e-02 2.31355369e-01 -3.33288983e-02 4.65696752e-01
-7.19490111e-01 -5.25217831e-01 -2.10200876e-01 -4.26387399e-01
-4.46513742e-02 1.57944202e+00 -2.56229222e-01 -8.39436948e-01
-3.93557042e-01 -6.61971688e-01 -3.49535257e-01 -1.14107680e+00
-1.40249699e-01 -1.08510643e-01 -3.07383150e-01 2.57371664... | [6.181392669677734, 1.4286829233169556] |
9e31b00a-19cf-4a41-8cb8-b8c4b1c114ce | tdnn-a-two-stage-deep-neural-network-for | null | null | https://aclanthology.org/P18-1100 | https://aclanthology.org/P18-1100.pdf | TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring | Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To ... | ['Le Sun', 'Ben He', 'Kai Hui', 'Cancan Jin'] | 2018-07-01 | null | null | null | acl-2018-7 | ['automated-essay-scoring'] | ['natural-language-processing'] | [ 7.17706755e-02 -1.42793894e-01 -1.22211568e-01 -7.83119977e-01
-1.11802864e+00 -5.38649678e-01 3.16344917e-01 4.54587638e-01
-6.26478314e-01 5.31255484e-01 3.02743524e-01 -1.32691786e-01
6.62260130e-02 -5.52718461e-01 -1.15510948e-01 -4.28792655e-01
6.30508900e-01 5.68937421e-01 -6.43560067e-02 -4.85935718... | [11.294940948486328, 9.319923400878906] |
b7d6396f-db4e-4fe7-b331-bcb874e9a4f4 | collaborative-receptive-field-learning | 1402.0170 | null | http://arxiv.org/abs/1402.0170v1 | http://arxiv.org/pdf/1402.0170v1.pdf | Collaborative Receptive Field Learning | The challenge of object categorization in images is largely due to arbitrary
translations and scales of the foreground objects. To attack this difficulty,
we propose a new approach called collaborative receptive field learning to
extract specific receptive fields (RF's) or regions from multiple images, and
the selected... | ['Shu Kong', 'Qiang Yang', 'Zhuolin Jiang'] | 2014-02-02 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 3.25316519e-01 -3.18304598e-01 -7.36753121e-02 -6.97562575e-01
-7.30173290e-01 -4.85718340e-01 3.17459613e-01 -8.22648183e-02
-2.45313376e-01 3.82342875e-01 1.80468373e-02 2.52663583e-01
-6.59585774e-01 -7.81127870e-01 -6.76366806e-01 -9.76736248e-01
-1.93154812e-01 1.23239132e-02 6.95548952e-01 1.55642509... | [9.77925968170166, 1.9640717506408691] |
1e017724-cc9a-4fc3-80c1-b7a8c94901a2 | um-checker-a-hybrid-system-for-english | null | null | https://aclanthology.org/W13-3605 | https://aclanthology.org/W13-3605.pdf | UM-Checker: A Hybrid System for English Grammatical Error Correction | null | ['Long-Yue Wang', 'Xiaodong Zeng', 'Lidia S. Chao', 'Junwen Xing', 'Derek F. Wong'] | 2013-08-01 | null | null | null | ws-2013-8 | ['grammatical-error-detection'] | ['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... | [-7.285709857940674, 3.7072830200195312] |
b440cc54-513d-4ff3-b55b-59d6ba33eb64 | medlocker-a-transferable-adversarial | 2303.09858 | null | https://arxiv.org/abs/2303.09858v2 | https://arxiv.org/pdf/2303.09858v2.pdf | MedLocker: A Transferable Adversarial Watermarking for Preventing Unauthorized Analysis of Medical Image Dataset | The collection of medical image datasets is a demanding and laborious process that requires significant resources. Furthermore, these medical datasets may contain personally identifiable information, necessitating measures to ensure that unauthorized access is prevented. Failure to do so could violate the intellectual ... | ['Shiji Zhao', 'Huazhu Fu', 'Xingxing Wei', 'Bangzheng Pu'] | 2023-03-17 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [ 8.66702914e-01 4.04053450e-01 -1.52669698e-01 3.99691388e-02
-4.36755419e-01 -9.97762620e-01 7.06321076e-02 4.04406905e-01
-3.00200731e-01 3.54031444e-01 -1.27935603e-01 -7.10432529e-01
8.17825124e-02 -8.10373247e-01 -7.19891489e-01 -5.51024973e-01
6.36328384e-02 -2.18124956e-01 2.42126569e-01 3.44183594... | [6.014008045196533, 7.067345142364502] |
b7df76b1-d461-4330-a0c6-14ec4c3c1bea | chinese-word-segmentation-with-heterogeneous | 2201.08975 | null | https://arxiv.org/abs/2201.08975v1 | https://arxiv.org/pdf/2201.08975v1.pdf | Chinese Word Segmentation with Heterogeneous Graph Neural Network | In recent years, deep learning has achieved significant success in the Chinese word segmentation (CWS) task. Most of these methods improve the performance of CWS by leveraging external information, e.g., words, sub-words, syntax. However, existing approaches fail to effectively integrate the multi-level linguistic info... | ['Qi Su', 'Jun Wang', 'Xuemei Tang'] | 2022-01-22 | null | null | null | null | ['chinese-word-segmentation'] | ['natural-language-processing'] | [-9.04890224e-02 5.12115024e-02 -4.37390417e-01 -3.50409806e-01
-6.50184453e-01 -6.17883801e-01 1.51543334e-01 8.49558040e-02
-6.09549344e-01 4.70129669e-01 1.94421276e-01 -6.70356572e-01
3.75533968e-01 -9.51156139e-01 -4.68488783e-01 -2.57911801e-01
4.63602483e-01 1.95611700e-01 5.87458849e-01 -3.21579427... | [10.021080017089844, 10.125632286071777] |
3460079e-6a69-4f60-9248-ad423fecc8a1 | relational-representation-learning-in | 2205.02411 | null | https://arxiv.org/abs/2205.02411v1 | https://arxiv.org/pdf/2205.02411v1.pdf | Relational Representation Learning in Visually-Rich Documents | Relational understanding is critical for a number of visually-rich documents (VRDs) understanding tasks. Through multi-modal pre-training, recent studies provide comprehensive contextual representations and exploit them as prior knowledge for downstream tasks. In spite of their impressive results, we observe that the w... | ['Bo Ren', 'Yinsong Liu', 'Deqiang Jiang', 'Yunfei Wu', 'Haoyu Cao', 'Yiqing Hu', 'Yan Zheng', 'Xin Li'] | 2022-05-05 | null | null | null | null | ['key-information-extraction'] | ['natural-language-processing'] | [ 1.57241791e-01 2.06358075e-01 -5.65427423e-01 -2.85668224e-01
-3.01661700e-01 -6.80223405e-01 9.11054075e-01 6.79170132e-01
2.83317864e-01 3.69569004e-01 5.84790349e-01 -7.29506791e-01
-6.75353825e-01 -9.44189787e-01 -6.29723310e-01 -1.56252041e-01
1.58518199e-02 4.50182080e-01 2.10581109e-01 -6.33358240... | [9.175243377685547, 8.216641426086426] |
a64c8ceb-32cd-4872-bb60-b26e71c3f725 | promptclass-weakly-supervised-text | 2305.13723 | null | https://arxiv.org/abs/2305.13723v1 | https://arxiv.org/pdf/2305.13723v1.pdf | PromptClass: Weakly-Supervised Text Classification with Prompting Enhanced Noise-Robust Self-Training | Recently proposed weakly-supervised text classification settings train a classifier using the label name of each target class as the only supervision. Such weakly-supervised settings have been gaining increasing attention since they can largely reduce human annotation efforts compared to fully-supervised and semi-super... | ['Jiawei Han', 'Yu Zhang', 'Yu Meng', 'Minhao Jiang', 'Yunyi Zhang'] | 2023-05-23 | null | null | null | null | ['pseudo-label', 'sentiment-analysis'] | ['miscellaneous', 'natural-language-processing'] | [ 7.69970655e-01 7.42372870e-02 -3.96746218e-01 -8.72219324e-01
-8.96528602e-01 -6.32955432e-01 7.53424823e-01 4.29532051e-01
-6.77844942e-01 6.73248827e-01 1.02625497e-01 -2.59482831e-01
4.44747269e-01 -6.44892871e-01 -5.35545111e-01 -5.45991957e-01
6.50584042e-01 6.16570473e-01 2.31954157e-01 -2.34482586... | [10.666293144226074, 7.3118367195129395] |
885fc4d8-9124-48e9-a457-13f610ee839c | dpw-sdnet-dual-pixel-wavelet-domain-deep-cnns | 1805.10558 | null | http://arxiv.org/abs/1805.10558v1 | http://arxiv.org/pdf/1805.10558v1.pdf | DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images | JPEG is one of the widely used lossy compression methods. JPEG-compressed
images usually suffer from compression artifacts including blocking and
blurring, especially at low bit-rates. Soft decoding is an effective solution
to improve the quality of compressed images without changing codec or
introducing extra coding b... | ['Shuhua Xiong', 'Xiaohai He', 'Truong Q. Nguyen', 'Linbo Qing', 'Honggang Chen'] | 2018-05-27 | null | null | null | null | ['jpeg-artifact-correction'] | ['computer-vision'] | [ 7.80459702e-01 -3.47367138e-01 -1.43135667e-01 -3.62523884e-01
-5.38253307e-01 3.18099469e-01 1.93078592e-01 -1.40387475e-01
-3.89437199e-01 4.74668413e-01 4.03725863e-01 -7.65907317e-02
1.92749292e-01 -9.69199717e-01 -8.71857643e-01 -6.51791573e-01
-4.87553217e-02 -3.78779799e-01 4.14818913e-01 -1.57464236... | [11.366656303405762, -1.644039511680603] |
6599816d-bfac-4653-993b-b2561a8220fa | vinafood21-a-novel-dataset-for-evaluating | 2108.02929 | null | https://arxiv.org/abs/2108.02929v1 | https://arxiv.org/pdf/2108.02929v1.pdf | VinaFood21: A Novel Dataset for Evaluating Vietnamese Food Recognition | Vietnam is such an attractive tourist destination with its stunning and pristine landscapes and its top-rated unique food and drink. Among thousands of Vietnamese dishes, foreigners and native people are interested in easy-to-eat tastes and easy-to-do recipes, along with reasonable prices, mouthwatering flavors, and po... | ['Khang Nguyen', 'Kiet Van Nguyen', 'Nguyen D. Vo', 'Ngoc Ho', 'Vi Nguyen', 'Dung Vo', 'Thuan Q. Nguyen', 'Thuan Trong Nguyen'] | 2021-08-06 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [-7.09554195e-01 -4.51872230e-01 -3.63328964e-01 -4.57119912e-01
-3.98249090e-01 -6.17646694e-01 1.73506558e-01 4.39741105e-01
-4.54401195e-01 6.21860683e-01 3.03841770e-01 1.43464925e-02
3.42205882e-01 -1.15403092e+00 -6.74042165e-01 -7.56057918e-01
-3.38525802e-01 9.01423171e-02 -4.15384203e-01 -7.33407915... | [11.553814888000488, 4.384588241577148] |
13e181cf-4c27-4804-81ea-67a463827505 | memory-efficient-fine-tuning-of-compressed | 2305.14152 | null | https://arxiv.org/abs/2305.14152v1 | https://arxiv.org/pdf/2305.14152v1.pdf | Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization | Parameter-efficient fine-tuning (PEFT) methods have emerged to mitigate the prohibitive cost of full fine-tuning large language models (LLMs). Nonetheless, the enormous size of LLMs impedes routine deployment. To address the issue, we present Parameter-Efficient and Quantization-aware Adaptation (PEQA), a novel quantiz... | ['Dongsoo Lee', 'Se Jung Kwon', 'Kang Min Yoo', 'Joonsuk Park', 'Sungdong Kim', 'Jung Hyun Lee', 'Jeonghoon Kim'] | 2023-05-23 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 1.70483232e-01 -1.68089673e-01 -2.34051466e-01 -4.18363333e-01
-1.09150398e+00 -6.13585174e-01 5.28062880e-01 1.77329883e-01
-7.93404579e-01 7.16802299e-01 2.02417478e-01 -5.10922134e-01
9.25068334e-02 -6.29670858e-01 -8.60592246e-01 -5.12849867e-01
1.62408486e-01 5.57139516e-01 1.84021890e-01 -2.16940030... | [8.74825382232666, 3.5931732654571533] |
6555ce4e-5b27-4a4b-9a67-ab78554bb3fd | grounded-situation-recognition | 2003.12058 | null | https://arxiv.org/abs/2003.12058v1 | https://arxiv.org/pdf/2003.12058v1.pdf | Grounded Situation Recognition | We introduce Grounded Situation Recognition (GSR), a task that requires producing structured semantic summaries of images describing: the primary activity, entities engaged in the activity with their roles (e.g. agent, tool), and bounding-box groundings of entities. GSR presents important technical challenges: identify... | ['Ali Farhadi', 'Aniruddha Kembhavi', 'Sarah Pratt', 'Mark Yatskar', 'Luca Weihs'] | 2020-03-26 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1987_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490307.pdf | eccv-2020-8 | ['grounded-situation-recognition', 'situation-recognition'] | ['computer-vision', 'computer-vision'] | [ 5.20053148e-01 3.98708403e-01 -2.95744419e-01 -4.33845997e-01
-1.23224866e+00 -7.70868063e-01 7.31833041e-01 4.14504439e-01
-2.16175184e-01 5.67195952e-01 9.03778553e-01 1.67683568e-02
-1.68756574e-01 -4.04610544e-01 -8.89539480e-01 -2.09281474e-01
-1.78991988e-01 4.38594639e-01 2.35015586e-01 -1.17829688... | [10.415332794189453, 1.2995322942733765] |
7fe2aad9-65d1-4eaf-8d10-e6a3de92d19e | black-lscdiscovery-shared-task-ualberta-at | null | null | https://aclanthology.org/2022.lchange-1.19 | https://aclanthology.org/2022.lchange-1.19.pdf | black[LSCDiscovery shared task] UAlberta at LSCDiscovery: Lexical Semantic Change Detection via Word Sense Disambiguation | We describe our two systems for the shared task on Lexical Semantic Change Discovery in Spanish. For binary change detection, we frame the task as a word sense disambiguation (WSD) problem. We derive sense frequency distributions for target words in both old and modern corpora. We assume that the word semantics have ch... | ['Grzegorz Kondrak', 'Spencer von der Ohe', 'Daniela Teodorescu'] | null | null | null | null | lchange-acl-2022-5 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 1.50423735e-01 -2.03571290e-01 -4.31396216e-01 -2.47771412e-01
-7.23035991e-01 -8.65602732e-01 9.99095857e-01 7.44218469e-01
-1.12887073e+00 8.80445242e-01 5.05495310e-01 -3.45190644e-01
3.10220450e-01 -7.66352296e-01 -3.18007618e-01 -5.42206585e-01
8.23414326e-02 3.41636568e-01 4.61614549e-01 -5.49341977... | [10.28369140625, 8.998906135559082] |
317bb9ee-a844-43f0-9113-3e4ff84d35bc | speech-synthesis-with-mixed-emotions | 2208.05890 | null | https://arxiv.org/abs/2208.05890v3 | https://arxiv.org/pdf/2208.05890v3.pdf | Speech Synthesis with Mixed Emotions | Emotional speech synthesis aims to synthesize human voices with various emotional effects. The current studies are mostly focused on imitating an averaged style belonging to a specific emotion type. In this paper, we seek to generate speech with a mixture of emotions at run-time. We propose a novel formulation that mea... | ['Haizhou Li', 'B. W. Schuller', 'Rajib Rana', 'Berrak Sisman', 'Kun Zhou'] | 2022-08-11 | null | null | null | null | ['emotional-speech-synthesis'] | ['speech'] | [ 2.51296580e-01 -2.22945940e-02 7.03091770e-02 -4.07986224e-01
-5.44501066e-01 -5.52176654e-01 6.70723021e-01 -3.44018608e-01
-3.39475200e-02 6.10512495e-01 4.29766864e-01 6.30297810e-02
1.96283340e-01 -4.33297455e-01 -2.61253744e-01 -6.16131723e-01
3.33820313e-01 2.99855918e-01 -4.42137122e-01 -2.56961912... | [14.613619804382324, 6.45572566986084] |
3d79021f-5fac-402f-81db-71d4e8b29421 | qa-driven-zero-shot-slot-filling-with-weak | null | null | https://aclanthology.org/2021.acl-short.83 | https://aclanthology.org/2021.acl-short.83.pdf | QA-Driven Zero-shot Slot Filling with Weak Supervision Pretraining | Slot-filling is an essential component for building task-oriented dialog systems. In this work, we focus on the zero-shot slot-filling problem, where the model needs to predict slots and their values, given utterances from new domains without training on the target domain. Prior methods directly encode slot description... | ['Yuan Zhang', 'Panupong Pasupat', 'Dian Yu', 'Qi Li', 'Luheng He', 'Xinya Du'] | 2021-08-01 | null | null | null | acl-2021-5 | ['zero-shot-slot-filling'] | ['natural-language-processing'] | [ 3.42235655e-01 8.83960903e-01 -1.50389820e-01 -7.85018146e-01
-1.17591143e+00 -5.09717524e-01 5.06831169e-01 -1.24415793e-01
-2.67963231e-01 1.22090709e+00 3.50534469e-01 -4.45845455e-01
5.49321055e-01 -7.71396458e-01 -5.19766688e-01 -8.19019377e-02
4.26818818e-01 1.23827767e+00 4.55458790e-01 -7.77502060... | [12.608088493347168, 7.522870063781738] |
45a861da-18f1-4928-a7bd-ebbc0b57072d | translating-robot-skills-learning | null | null | https://openreview.net/forum?id=NPJ5zWk_IQj | https://openreview.net/pdf?id=NPJ5zWk_IQj | Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots | In this paper, we explore how we can endow robots with the ability to learn correspondences between their own skills, and those of morphologically different robots in different domains, in an entirely unsupervised manner. We make the insight that different morphological robots use similar task strategies to solve simil... | ['Jean Oh', 'Stuart Anderson', 'Vikash Kumar', 'Aravind Rajeswaran', 'Yixin Lin', 'Tanmay Shankar'] | 2021-09-29 | null | null | null | null | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 3.61398280e-01 3.81776899e-01 1.91645309e-01 -5.13775587e-01
-4.14030612e-01 -1.13993239e+00 6.73384786e-01 8.36071372e-02
-3.68522078e-01 6.55644774e-01 2.00140804e-01 3.88866439e-02
-3.13012004e-01 -5.48654914e-01 -9.58378613e-01 -3.80128622e-01
2.91329384e-01 9.41769719e-01 1.70745440e-02 -4.01768386... | [4.387297630310059, 0.9698885083198547] |
2cdfaea4-89f3-48db-a542-41e4810108b5 | swiden-convolutional-neural-networks-for | 1607.08764 | null | http://arxiv.org/abs/1607.08764v1 | http://arxiv.org/pdf/1607.08764v1.pdf | SwiDeN : Convolutional Neural Networks For Depiction Invariant Object Recognition | Current state of the art object recognition architectures achieve impressive
performance but are typically specialized for a single depictive style (e.g.
photos only, sketches only). In this paper, we present SwiDeN : our
Convolutional Neural Network (CNN) architecture which recognizes objects
regardless of how they ar... | ['Srinivas S. S. Kruthiventi', 'Venkatesh Babu R', 'Ravi Kiran Sarvadevabhatla', 'Shiv Surya'] | 2016-07-29 | null | null | null | null | ['depiction-invariant-object-recognition'] | ['computer-vision'] | [ 3.66649181e-01 -4.39669698e-01 -1.14704400e-01 -5.62309325e-01
1.28950030e-01 -9.93732154e-01 1.03505635e+00 -5.22841632e-01
-1.23577058e-01 3.30777258e-01 -3.95602770e-02 -3.21046382e-01
5.10557555e-02 -8.52040946e-01 -8.51489782e-01 -3.93233865e-01
2.22826183e-01 4.83814895e-01 1.82936236e-01 -2.53274798... | [11.653627395629883, 0.40152353048324585] |
d25f2828-2300-4255-98e5-a30e1ab5394e | follow-the-timeline-generating-abstractive | 2301.00867 | null | https://arxiv.org/abs/2301.00867v1 | https://arxiv.org/pdf/2301.00867v1.pdf | Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order | Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline. Unlike traditional document summarization, timeline summarization needs to model the time series inf... | ['Rui Yan', 'Xiangliang Zhang', 'Xin Gao', 'Dongyan Zhao', 'Zhangming Chan', 'Shen Gao', 'Mingzhe Li', 'Xiuying Chen'] | 2023-01-02 | null | null | null | null | ['timeline-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.34641963e-01 1.82805851e-01 -2.42302567e-01 -2.55679846e-01
-9.33542311e-01 -4.74358410e-01 8.26903522e-01 7.89796710e-01
-3.18488270e-01 8.99496436e-01 1.34199440e+00 -7.16691241e-02
-1.45952538e-01 -7.67948925e-01 -7.86095262e-01 -1.40604302e-01
-1.99392229e-01 2.99786597e-01 2.90005296e-01 -2.55633324... | [12.558239936828613, 9.504176139831543] |
dad60fb9-97a8-4fb0-a669-f3250f0d947f | predictive-compliance-monitoring-in-process | 2205.05446 | null | https://arxiv.org/abs/2205.05446v3 | https://arxiv.org/pdf/2205.05446v3.pdf | Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directions | Business process compliance is a key area of business process management and aims at ensuring that processes obey to compliance constraints such as regulatory constraints or business rules imposed on them. Process compliance can be checked during process design time based on verification of process models and at runtim... | ['Janik-Vasily Benzin', 'Karolin Winter', 'Stefanie Rinderle-Ma'] | 2022-05-10 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 1.00016177e+00 5.58646798e-01 -3.83611888e-01 -5.03200650e-01
-1.07440785e-01 -5.20365834e-01 9.72283483e-01 7.36160278e-01
1.39131129e-01 2.09378108e-01 3.45724165e-01 -4.32104051e-01
-8.86812925e-01 -1.13742304e+00 1.99038088e-02 1.65785551e-01
2.77884662e-01 8.96971583e-01 4.71860558e-01 1.74781516... | [8.599291801452637, 6.074399948120117] |
eba872eb-d2c6-4895-8340-a0afa1b2f3f1 | hypertree-proof-search-for-neural-theorem | 2205.11491 | null | https://arxiv.org/abs/2205.11491v1 | https://arxiv.org/pdf/2205.11491v1.pdf | HyperTree Proof Search for Neural Theorem Proving | We propose an online training procedure for a transformer-based automated theorem prover. Our approach leverages a new search algorithm, HyperTree Proof Search (HTPS), inspired by the recent success of AlphaZero. Our model learns from previous proof searches through online training, allowing it to generalize to domains... | ['Timothée Lacroix', 'Aurélien Rodriguez', 'Gabriel Ebner', 'Amaury Hayat', 'Xavier Martinet', 'Thibaut Lavril', 'Marie-Anne Lachaux', 'Guillaume Lample'] | 2022-05-23 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 1.41254038e-01 5.73288262e-01 -6.15367532e-01 -9.26030800e-03
-1.18206489e+00 -1.07761323e+00 5.08253992e-01 1.79061815e-01
2.59769522e-02 1.18609679e+00 -3.82868528e-01 -1.34655821e+00
-3.45306933e-01 -9.11822855e-01 -1.43055928e+00 1.34844020e-01
-4.30620968e-01 7.55557060e-01 5.69990158e-01 -1.68239146... | [8.94991683959961, 7.0665364265441895] |
3563b6eb-fa5e-4ec3-8f48-0cee476063d6 | ssncse-nlp-lt-edi-acl2022-hope-speech | null | null | https://aclanthology.org/2022.ltedi-1.30 | https://aclanthology.org/2022.ltedi-1.30.pdf | SSNCSE_NLP@LT-EDI-ACL2022:Hope Speech Detection for Equality, Diversity and Inclusion using sentence transformers | In recent times, applications have been developed to regulate and control the spread of negativity and toxicity on online platforms. The world is filled with serious problems like political & religious conflicts, wars, pandemics, and offensive hate speech is the last thing we desire. Our task was to classify a text int... | ['Senthil Kumar B', 'Thenmozhi Durairaj', 'Josephine Varsha', 'Dhanya Srinivasan', 'Bharathi B'] | null | null | null | null | ltedi-acl-2022-5 | ['hope-speech-detection'] | ['natural-language-processing'] | [-6.11584425e-01 2.56450567e-02 -3.81145477e-01 1.87121212e-01
-5.24771512e-01 -6.90945923e-01 9.97357666e-01 2.28833169e-01
-3.52793306e-01 1.07743943e+00 7.91405559e-01 -4.98199344e-01
-1.61444530e-01 -4.59788233e-01 1.29618160e-02 -4.26440746e-01
5.99364098e-03 2.78277189e-01 -1.38286073e-02 -7.88456917... | [8.926660537719727, 10.646751403808594] |
f146067d-8bec-47a6-b06a-fde0a93355b6 | autoencoders-for-real-time-suep-detection | 2306.13595 | null | https://arxiv.org/abs/2306.13595v2 | https://arxiv.org/pdf/2306.13595v2.pdf | Autoencoders for Real-Time SUEP Detection | Confining dark sectors with pseudo-conformal dynamics can produce Soft Unclustered Energy Patterns, or SUEPs, at the Large Hadron Collider: the production of dark quarks in proton-proton collisions leading to a dark shower and the high-multiplicity production of dark hadrons. The final experimental signature is spheric... | ['Syed Hasan', 'Maurzio Pierini', 'Benedikt Maier', 'Nadezda Chernyavskaya', 'Simranjit Singh Chhibra'] | 2023-06-23 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [-3.60339403e-01 4.56469879e-02 2.55935937e-01 -3.37370694e-01
-3.69205385e-01 -1.77242085e-01 8.40543389e-01 4.96870205e-02
-7.97620595e-01 5.43982506e-01 -1.11616842e-01 -4.66729224e-01
2.87292004e-01 -7.65311182e-01 -8.78463626e-01 -1.10136700e+00
-1.38086258e-02 1.27057183e+00 4.72921461e-01 4.75095436... | [15.689948081970215, 2.92404842376709] |
4398dc2d-c245-4c3f-8494-f2414f687248 | uncovering-convolutional-neural-network | 1904.08771 | null | http://arxiv.org/abs/1904.08771v1 | http://arxiv.org/pdf/1904.08771v1.pdf | Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation | Machine learning-based imaging diagnostics has recently reached or even
superseded the level of clinical experts in several clinical domains. However,
classification decisions of a trained machine learning system are typically
non-transparent, a major hindrance for clinical integration, error tracking or
knowledge disc... | ['John-Dylan Haynes', 'René M. Giess', 'Klemens Ruprecht', 'Judith Bellmann-Strobl', 'Martin Weygandt', 'Susanna Asseyer', 'Kerstin Ritter', 'Joseph Kuchling', 'Friedemann Paul', 'Fabian Eitel', 'Emily Soehler', 'Alexander U. Brandt', 'Michael Scheel'] | 2019-04-18 | null | null | null | null | ['holdout-set'] | ['computer-vision'] | [ 3.75621855e-01 2.61812061e-01 -1.41138524e-01 -5.47960877e-01
-7.02013671e-01 -2.52647847e-01 5.13426185e-01 5.14082611e-01
-6.82039499e-01 7.19170451e-01 1.39450207e-01 -5.38210988e-01
-5.32616377e-01 -5.67764819e-01 -4.22931701e-01 -7.14524269e-01
-7.70652235e-01 8.12735021e-01 1.39821693e-01 2.61800081... | [14.210542678833008, -1.8864550590515137] |
6facb969-7da9-4900-a68e-cd389b45ed7a | directionally-constrained-fully-convolutional | 1908.06673 | null | https://arxiv.org/abs/1908.06673v1 | https://arxiv.org/pdf/1908.06673v1.pdf | Directionally Constrained Fully Convolutional Neural Network For Airborne Lidar Point Cloud Classification | Point cloud classification plays an important role in a wide range of airborne light detection and ranging (LiDAR) applications, such as topographic mapping, forest monitoring, power line detection, and road detection. However, due to the sensor noise, high redundancy, incompleteness, and complexity of airborne LiDAR s... | ['Tianhe Chi', 'Lina Yang', 'Congcong Wen', 'Xiang Li', 'Ling Peng'] | 2019-08-19 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 2.19647348e-01 -4.13005859e-01 -2.86215581e-02 -6.46916032e-01
-5.14878809e-01 -4.28266138e-01 5.21890521e-01 8.95739347e-02
-4.54230309e-01 2.50353038e-01 -3.41102004e-01 -5.10968089e-01
-2.44533777e-01 -1.27973545e+00 -8.20050061e-01 -3.34757745e-01
-6.86371103e-02 3.75106812e-01 2.78187126e-01 -1.06708907... | [8.02676010131836, -3.026393413543701] |
0f457811-bf99-4f48-b2d1-68af4d845322 | mpc-bert-a-pre-trained-language-model-for | 2106.01541 | null | https://arxiv.org/abs/2106.01541v1 | https://arxiv.org/pdf/2106.01541v1.pdf | MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding | Recently, various neural models for multi-party conversation (MPC) have achieved impressive improvements on a variety of tasks such as addressee recognition, speaker identification and response prediction. However, these existing methods on MPC usually represent interlocutors and utterances individually and ignore the ... | ['Daxin Jiang', 'Xiubo Geng', 'Can Xu', 'Zhen-Hua Ling', 'Chongyang Tao', 'Jia-Chen Gu'] | 2021-06-03 | null | https://aclanthology.org/2021.acl-long.285 | https://aclanthology.org/2021.acl-long.285.pdf | acl-2021-5 | ['speaker-identification'] | ['speech'] | [ 2.88837522e-01 3.51239532e-01 -3.15098703e-01 -9.67053533e-01
-1.38665235e+00 -4.88109231e-01 4.89014238e-01 -1.18953504e-01
2.54683018e-01 3.44224989e-01 8.94203067e-01 -3.79162133e-01
2.54143566e-01 4.01512720e-02 -2.13037997e-01 -7.55895078e-01
-6.34990409e-02 6.87277913e-01 -4.22705570e-03 -2.84502625... | [12.58296012878418, 7.720943450927734] |
6ad18787-1b4f-43f9-98e3-cc4771798e60 | seal-semantic-frame-execution-and | 2303.14067 | null | https://arxiv.org/abs/2303.14067v1 | https://arxiv.org/pdf/2303.14067v1.pdf | SEAL: Semantic Frame Execution And Localization for Perceiving Afforded Robot Actions | Recent advances in robotic mobile manipulation have spurred the expansion of the operating environment for robots from constrained workspaces to large-scale, human environments. In order to effectively complete tasks in these spaces, robots must be able to perceive, reason, and execute over a diversity of affordances, ... | ['Odest Chadwicke Jenkins', 'Matthew Shannon', 'Daksh Narang', 'Cameron Kisailus'] | 2023-03-24 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 3.99893582e-01 3.15209478e-01 -1.36674598e-01 -3.61371905e-01
-3.35717291e-01 -6.22128427e-01 8.72355402e-01 8.81926715e-02
-3.20354939e-01 5.69993615e-01 2.66831160e-01 -3.41619730e-01
-6.26764536e-01 -6.61273420e-01 -7.08725274e-01 -1.90598071e-01
-3.31304133e-01 4.43305761e-01 4.19227064e-01 -5.72251558... | [4.541940689086914, 0.8160803914070129] |
6f258263-847f-4128-bfa9-8c496fa46a41 | sparse-fuse-dense-towards-high-quality-3d-1 | 2203.09780 | null | https://arxiv.org/abs/2203.09780v2 | https://arxiv.org/pdf/2203.09780v2.pdf | Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion | Current LiDAR-only 3D detection methods inevitably suffer from the sparsity of point clouds. Many multi-modal methods are proposed to alleviate this issue, while different representations of images and point clouds make it difficult to fuse them, resulting in suboptimal performance. In this paper, we present a novel mu... | ['Deng Cai', 'Haifeng Liu', 'Chengqi Deng', 'Chenxi Huang', 'Liang Xie', 'Honghui Yang', 'Liang Peng', 'Xiaopei Wu'] | 2022-03-18 | sparse-fuse-dense-towards-high-quality-3d | http://openaccess.thecvf.com//content/CVPR2022/html/Wu_Sparse_Fuse_Dense_Towards_High_Quality_3D_Detection_With_Depth_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wu_Sparse_Fuse_Dense_Towards_High_Quality_3D_Detection_With_Depth_CVPR_2022_paper.pdf | cvpr-2022-1 | ['depth-completion'] | ['computer-vision'] | [-1.76981091e-01 -4.89408821e-01 5.19414805e-02 -6.18292205e-02
-1.02428436e+00 -6.27412021e-01 6.50985241e-01 -8.44512880e-02
-1.49473533e-01 2.45784938e-01 -9.17555988e-02 -2.99416631e-01
8.80384073e-02 -1.05751133e+00 -7.41446018e-01 -5.74890018e-01
2.63132334e-01 4.60714787e-01 4.59380150e-01 -3.91104698... | [7.82658576965332, -2.7329671382904053] |
18ef2e93-bb13-437e-8109-0cbfd46c16e3 | meta-federated-reinforcement-learning-for | 2307.02900 | null | https://arxiv.org/abs/2307.02900v2 | https://arxiv.org/pdf/2307.02900v2.pdf | Meta Federated Reinforcement Learning for Distributed Resource Allocation | In cellular networks, resource allocation is usually performed in a centralized way, which brings huge computation complexity to the base station (BS) and high transmission overhead. This paper explores a distributed resource allocation method that aims to maximize energy efficiency (EE) while ensuring the quality of s... | ['Xiaoming Tao', 'Zhijin Qin', 'Zelin Ji'] | 2023-07-06 | null | null | null | null | ['meta-learning', 'federated-learning'] | ['methodology', 'methodology'] | [-3.38588029e-01 -5.50620183e-02 -3.94006789e-01 7.50658587e-02
-4.72465575e-01 -3.58721882e-01 -1.39967099e-01 -7.06722820e-03
-3.31808269e-01 1.22995281e+00 -2.28108361e-01 -3.98943007e-01
-4.18696910e-01 -1.15961325e+00 -1.38972938e-01 -1.36232138e+00
-3.76953423e-01 5.40194064e-02 -5.08224726e-01 3.38890613... | [5.94134521484375, 1.6367100477218628] |
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