paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
34ac490c-e281-431e-a875-06981ace4392 | rethinking-translation-memory-augmented | 2306.06948 | null | https://arxiv.org/abs/2306.06948v1 | https://arxiv.org/pdf/2306.06948v1.pdf | Rethinking Translation Memory Augmented Neural Machine Translation | This paper rethinks translation memory augmented neural machine translation (TM-augmented NMT) from two perspectives, i.e., a probabilistic view of retrieval and the variance-bias decomposition principle. The finding demonstrates that TM-augmented NMT is good at the ability of fitting data (i.e., lower bias) but is mor... | ['Rui Wang', 'Shuming Shi', 'Zhirui Zhang', 'Lemao Liu', 'Guoping Huang', 'Hongkun Hao'] | 2023-06-12 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 4.05098855e-01 -2.66616583e-01 -6.81865811e-01 -5.21741398e-02
-1.21208763e+00 -2.04908416e-01 9.49769676e-01 -5.90770841e-01
-4.03900445e-01 8.06268930e-01 2.85619497e-01 -8.20697665e-01
-1.16103590e-01 -4.33498591e-01 -9.10238385e-01 -7.55554795e-01
4.42228019e-01 7.76880383e-01 -2.03159779e-01 -5.17095685... | [11.634973526000977, 10.160614013671875] |
b9a2e528-8768-4ab1-98cf-3e66202a265b | convolutional-tensor-train-lstm-for-long-term | null | null | https://openreview.net/forum?id=Hkee1JBKwB | https://openreview.net/pdf?id=Hkee1JBKwB | Convolutional Tensor-Train LSTM for Long-Term Video Prediction | Long-term video prediction is highly challenging since it entails simultaneously capturing spatial and temporal information across a long range of image frames.Standard recurrent models are ineffective since they are prone to error propagation and cannot effectively capture higher-order correlations. A potential soluti... | ['Animashree Anandkumar', 'Jan Kautz', 'Furong Huang', 'Wonmin Byeon', 'Jiahao Su'] | 2019-09-25 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [-1.92173824e-01 -6.20735288e-01 -2.58885264e-01 -1.19569853e-01
-7.10438430e-01 -2.89086908e-01 5.73053360e-01 -1.98014110e-01
-3.16687465e-01 3.94700557e-01 5.29568970e-01 -3.41806740e-01
-3.19526136e-01 -5.12686253e-01 -7.55235434e-01 -6.28679037e-01
-5.13665795e-01 2.01229677e-01 3.21635306e-01 -4.81081344... | [8.842601776123047, 0.4941946864128113] |
bb2fc990-de09-4d08-8876-d45998698769 | fourier-transformer-fast-long-range-modeling | 2305.15099 | null | https://arxiv.org/abs/2305.15099v1 | https://arxiv.org/pdf/2305.15099v1.pdf | Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator | The transformer model is known to be computationally demanding, and prohibitively costly for long sequences, as the self-attention module uses a quadratic time and space complexity with respect to sequence length. Many researchers have focused on designing new forms of self-attention or introducing new parameters to ov... | ['Zhouhan Lin', 'Jingwen Leng', 'Xinbing Wang', 'Jingcheng Yin', 'Minwei Feng', 'Meng Yang', 'Ziwei He'] | 2023-05-24 | null | null | null | null | ['abstractive-text-summarization', 'long-range-modeling', 'open-domain-question-answering', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.97485501e-01 -1.01497367e-01 2.24270746e-01 -3.83987904e-01
-1.00099266e+00 -5.50934315e-01 5.00044405e-01 -2.74853706e-01
-5.81170619e-01 7.02982545e-01 2.74333149e-01 -4.40607101e-01
6.22423925e-02 -6.85783863e-01 -9.45894003e-01 -8.76611650e-01
7.40862042e-02 4.42164361e-01 1.38454810e-01 -2.60044128... | [10.889935493469238, 6.582707405090332] |
48dcb424-d409-44eb-a55e-ab59ce665d97 | exploiting-social-information-in-grounded | null | null | https://aclanthology.org/P12-1093 | https://aclanthology.org/P12-1093.pdf | Exploiting Social Information in Grounded Language Learning via Grammatical Reduction | null | ['Mark Johnson', 'Michael Frank', 'Katherine Demuth'] | 2012-07-01 | null | null | null | acl-2012-7 | ['grounded-language-learning'] | ['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... | [-7.320960521697998, 3.691803455352783] |
eb2184a5-1f4f-4c5b-8c26-165c9beed047 | coactseg-learning-from-heterogeneous-data-for | 2307.04513 | null | https://arxiv.org/abs/2307.04513v1 | https://arxiv.org/pdf/2307.04513v1.pdf | CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentation | New lesion segmentation is essential to estimate the disease progression and therapeutic effects during multiple sclerosis (MS) clinical treatments. However, the expensive data acquisition and expert annotation restrict the feasibility of applying large-scale deep learning models. Since single-time-point samples with a... | ['Jianfei Cai', 'Winston Chong', 'Bjoern Picker', 'Hengcan Shi', 'Zhonghua Wu', 'Yicheng Wu'] | 2023-07-10 | null | null | null | null | ['lesion-segmentation'] | ['medical'] | [ 1.17400460e-01 -3.62469614e-01 -6.82352662e-01 -6.49776399e-01
-1.02753055e+00 -3.11932564e-01 4.54381913e-01 -9.48925018e-02
-4.96788204e-01 8.59713674e-01 -7.12250471e-02 -2.92020351e-01
-2.80031711e-01 -4.41718638e-01 -5.78735054e-01 -9.03935254e-01
-1.37474313e-01 7.66987085e-01 3.67765576e-01 1.08123176... | [14.434042930603027, -2.0264604091644287] |
0c29cf14-f038-4ee3-88aa-0996d33d5938 | frame-wise-cross-modal-match-for-video-moment | 2009.10434 | null | https://arxiv.org/abs/2009.10434v2 | https://arxiv.org/pdf/2009.10434v2.pdf | Frame-wise Cross-modal Matching for Video Moment Retrieval | Video moment retrieval targets at retrieving a moment in a video for a given language query. The challenges of this task include 1) the requirement of localizing the relevant moment in an untrimmed video, and 2) bridging the semantic gap between textual query and video contents. To tackle those problems, early approach... | ['IEEE', 'Meng Liu', 'Jihua Zhu', 'Haoyu Tang', 'Zhiyong Cheng', 'Zan Gao', 'Member'] | 2020-09-22 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [ 2.75719345e-01 -4.67247158e-01 -3.96076769e-01 -1.06777877e-01
-9.74544585e-01 -3.03263903e-01 6.74543560e-01 4.77794483e-02
-4.37167972e-01 3.12261194e-01 2.76780218e-01 2.37893492e-01
-1.09359026e-01 -4.23620641e-01 -6.02239668e-01 -5.61841488e-01
-4.33307998e-02 3.67382611e-03 5.78992844e-01 -1.08122148... | [10.189934730529785, 0.7134169340133667] |
c3b3cb0d-5003-497d-a25d-d69a0c0273f3 | tree-stack-lstm-in-transition-based | null | null | https://aclanthology.org/K18-2012 | https://aclanthology.org/K18-2012.pdf | Tree-Stack LSTM in Transition Based Dependency Parsing | We introduce tree-stack LSTM to model state of a transition based parser with recurrent neural networks. Tree-stack LSTM does not use any parse tree based or hand-crafted features, yet performs better than models with these features. We also develop new set of embeddings from raw features to enhance the performance. Th... | ['Erenay Dayan{\\i}k', '{\\"O}mer K{\\i}rnap', 'Deniz Yuret'] | 2018-10-01 | null | null | null | conll-2018-10 | ['transition-based-dependency-parsing', 'morphological-tagging'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.35906720e-01 3.82725924e-01 -1.14058010e-01 -5.89922786e-01
-1.14841211e+00 -6.72977507e-01 2.13985294e-01 2.18706608e-01
-7.84040391e-01 4.16675031e-01 7.04175770e-01 -9.95349228e-01
5.46635687e-01 -8.32465172e-01 -9.92044866e-01 -1.58791304e-01
-2.69667268e-01 3.95251572e-01 5.23006201e-01 -2.50055969... | [10.377205848693848, 9.549481391906738] |
4b54bc51-e941-432d-9407-d02be092acb7 | a-survey-of-deep-graph-clustering-taxonomy | 2211.12875 | null | https://arxiv.org/abs/2211.12875v2 | https://arxiv.org/pdf/2211.12875v2.pdf | A Survey of Deep Graph Clustering: Taxonomy, Challenge, and Application | Graph clustering, which aims to divide the nodes in the graph into several distinct clusters, is a fundamental and challenging task. In recent years, deep graph clustering methods have been increasingly proposed and achieved promising performance. However, the corresponding survey paper is scarce and it is imminent to ... | ['Xinwang Liu', 'Stan Z. Li', 'Wenxuan Tu', 'Ke Liang', 'Xihong Yang', 'Xifeng Guo', 'Siwei Wang', 'Sihang Zhou', 'Jun Xia', 'Yue Liu'] | 2022-11-23 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-5.13954878e-01 3.19843516e-02 -2.23467097e-01 -1.20669603e-01
-2.06011385e-01 -5.06438076e-01 2.07005903e-01 3.43446344e-01
1.57944039e-02 1.68337569e-01 -1.07585818e-01 -1.37378454e-01
-3.18082929e-01 -8.02922070e-01 -1.28170177e-01 -1.03056204e+00
-4.66575235e-01 5.81089914e-01 -1.27635943e-02 8.88948701... | [7.293485641479492, 5.956362247467041] |
fd404f10-05fe-45f9-bbcf-cb79865ec6ae | live-video-comment-generation-based-on | 1808.04091 | null | http://arxiv.org/abs/1808.04091v1 | http://arxiv.org/pdf/1808.04091v1.pdf | Live Video Comment Generation Based on Surrounding Frames and Live Comments | In this paper, we propose the task of live comment generation. Live comments
are a new form of comments on videos, which can be regarded as a mixture of
comments and chats. A high-quality live comment should be not only relevant to
the video, but also interactive with other users. In this work, we first
construct a new... | ['Damai Dai'] | 2018-08-13 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [ 1.74239695e-01 7.46287405e-02 1.12168908e-01 -5.04412055e-01
-1.01373613e+00 -4.85516816e-01 8.23276281e-01 -2.07955658e-01
-4.59228419e-02 7.75570631e-01 1.02555525e+00 -2.99790762e-02
8.49548340e-01 -2.27284744e-01 -3.39223981e-01 -5.66023827e-01
4.05703366e-01 1.48837734e-02 3.52381527e-01 -6.82929456... | [10.741358757019043, 0.6763034462928772] |
490dc13f-8cb5-438e-945c-10e20b35a370 | synthetic-sample-selection-for-generalized | 2304.02846 | null | https://arxiv.org/abs/2304.02846v1 | https://arxiv.org/pdf/2304.02846v1.pdf | Synthetic Sample Selection for Generalized Zero-Shot Learning | Generalized Zero-Shot Learning (GZSL) has emerged as a pivotal research domain in computer vision, owing to its capability to recognize objects that have not been seen during training. Despite the significant progress achieved by generative techniques in converting traditional GZSL to fully supervised learning, they te... | ['Shreyank N Gowda'] | 2023-04-06 | null | null | null | null | ['zero-shot-action-recognition', 'generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 4.60728586e-01 -1.32944867e-01 -2.61721611e-01 -3.10324520e-01
-9.36690331e-01 -2.71817386e-01 8.57997537e-01 -9.90609825e-02
-1.61030844e-01 7.33979940e-01 -1.73273340e-01 1.51994780e-01
-1.11621797e-01 -8.27101231e-01 -6.09897316e-01 -9.49656367e-01
2.29864776e-01 3.02578509e-01 2.42888376e-01 -5.60562424... | [9.937841415405273, 2.9018869400024414] |
323de0ae-33ee-4324-87a1-dde329e3fcd0 | a-comparative-study-of-machine-learning-6 | 2307.00361 | null | https://arxiv.org/abs/2307.00361v1 | https://arxiv.org/pdf/2307.00361v1.pdf | A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact | In the context of Industry 4.0, the use of artificial intelligence (AI) and machine learning for anomaly detection is being hampered by high computational requirements and associated environmental effects. This study seeks to address the demands of high-performance machine learning models with environmental sustainabil... | ['Alejandro Echeverría Rey', 'Rubén García Maezo', 'Carlos Martí-González', 'Álvaro Huertas-García'] | 2023-07-01 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [ 6.46433175e-01 -7.64787942e-02 -3.39379907e-01 1.32968843e-01
-2.67452508e-01 -5.30258119e-01 6.38889372e-01 4.35758859e-01
-2.87827432e-01 4.96409267e-01 -3.33846509e-01 -8.66786182e-01
-7.14594781e-01 -6.45805836e-01 -2.37169147e-01 -9.70382452e-01
-1.49625421e-01 -1.03762120e-01 -3.42829794e-01 3.12258273... | [6.250392436981201, 3.634669303894043] |
3f38662a-4d14-4df9-8d48-4a5f7eb9ef93 | co-salient-object-detection-with-co | 2303.07670 | null | https://arxiv.org/abs/2303.07670v1 | https://arxiv.org/pdf/2303.07670v1.pdf | Co-Salient Object Detection with Co-Representation Purification | Co-salient object detection (Co-SOD) aims at discovering the common objects in a group of relevant images. Mining a co-representation is essential for locating co-salient objects. Unfortunately, the current Co-SOD method does not pay enough attention that the information not related to the co-salient object is included... | ['Ming-Ming Cheng', 'Xing Sun', 'Zheng Lin', 'Zhao Zhang', 'Ziyue Zhu'] | 2023-03-14 | null | null | null | null | ['co-saliency-detection'] | ['computer-vision'] | [-7.08431331e-03 -1.20904967e-01 -2.64517635e-01 4.52877879e-02
-7.67502904e-01 -5.11027239e-02 4.65956837e-01 6.70750618e-01
-1.57021657e-01 2.22650915e-01 5.35012662e-01 2.10747421e-01
-2.75834471e-01 -7.47372746e-01 -5.93780220e-01 -6.52996778e-01
-1.40326977e-01 -1.23862728e-01 4.95310992e-01 3.59006487... | [9.753531455993652, -0.1449088752269745] |
f2da0e59-9e91-492f-9649-3c124f4cff26 | exploring-and-exploiting-multi-granularity | 2208.08750 | null | https://arxiv.org/abs/2208.08750v1 | https://arxiv.org/pdf/2208.08750v1.pdf | Exploring and Exploiting Multi-Granularity Representations for Machine Reading Comprehension | Recently, the attention-enhanced multi-layer encoder, such as Transformer, has been extensively studied in Machine Reading Comprehension (MRC). To predict the answer, it is common practice to employ a predictor to draw information only from the final encoder layer which generates the coarse-grained representations of t... | ['Chenyu You', 'Nuo Chen'] | 2022-08-18 | null | null | null | null | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 2.14367434e-01 1.90845668e-01 -5.60062751e-02 -2.62331665e-01
-5.31950772e-01 -3.50344777e-01 2.88232207e-01 3.30683023e-01
-2.12128267e-01 5.76557934e-01 6.43184900e-01 -3.76507640e-01
8.58137831e-02 -9.93038356e-01 -9.04437840e-01 -6.67928517e-01
2.03260511e-01 4.37933244e-02 3.63994092e-01 -4.81354356... | [11.314998626708984, 8.199069023132324] |
06650a30-6363-4b9c-a647-fe97289a7cc9 | hybrid-code-networks-using-a-convolutional | 1907.12162 | null | https://arxiv.org/abs/1907.12162v1 | https://arxiv.org/pdf/1907.12162v1.pdf | Hybrid Code Networks using a convolutional neural network as an input layer achieves higher turn accuracy | The dialogue management is a task of conversational artificial intelligence. The goal of the dialogue manager is to select the appropriate response to the conversational partner conditioned by the input message and recent dialogue state. Hybrid Code Networks is one of the models of dialogue managers, which uses an aver... | ['Petr Marek'] | 2019-07-28 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [-3.28233778e-01 5.68770349e-01 -1.06849736e-02 -8.11664581e-01
1.62325241e-02 -3.44831765e-01 9.42881286e-01 -1.66641325e-01
-4.34363186e-01 7.34470844e-01 7.63698995e-01 -4.45842326e-01
2.47008249e-01 -8.14323008e-01 2.16731861e-01 -3.72936070e-01
-1.08272485e-01 9.01036680e-01 9.47914869e-02 -9.30432141... | [12.891552925109863, 7.916103363037109] |
e497bdcf-2245-4fcc-b2b1-05b631351541 | synthetic-data-generation-method-for-data | 2301.04338 | null | https://arxiv.org/abs/2301.04338v2 | https://arxiv.org/pdf/2301.04338v2.pdf | Synthetic data generation method for data-free knowledge distillation in regression neural networks | Knowledge distillation is the technique of compressing a larger neural network, known as the teacher, into a smaller neural network, known as the student, while still trying to maintain the performance of the larger neural network as much as possible. Existing methods of knowledge distillation are mostly applicable for... | ['Keng-Hwee Chiam', 'Tianxun Zhou'] | 2023-01-11 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 5.44382513e-01 7.52823591e-01 -1.29116982e-01 -1.99929982e-01
-4.13860530e-01 -6.71505332e-01 4.86930758e-01 9.93251652e-02
-5.06545901e-01 1.16179490e+00 -3.04395795e-01 -2.97135890e-01
2.71068722e-01 -1.21432304e+00 -1.06527066e+00 -8.63397956e-01
3.74863386e-01 7.30626285e-01 1.39684588e-01 -1.39270425... | [9.559248924255371, 3.362666130065918] |
90ce70d0-623e-4b4a-8335-3b444b54269a | improved-deep-learning-baselines-for-ubuntu | 1510.03753 | null | http://arxiv.org/abs/1510.03753v2 | http://arxiv.org/pdf/1510.03753v2.pdf | Improved Deep Learning Baselines for Ubuntu Corpus Dialogs | This paper presents results of our experiments for the next utterance ranking
on the Ubuntu Dialog Corpus -- the largest publicly available multi-turn dialog
corpus. First, we use an in-house implementation of previously reported models
to do an independent evaluation using the same data. Second, we evaluate the
perfor... | ['Jan Kleindienst', 'Rudolf Kadlec', 'Martin Schmid'] | 2015-10-13 | null | null | null | null | ['conversational-response-selection'] | ['natural-language-processing'] | [-2.20260099e-01 4.55690682e-01 -4.66554165e-02 -9.53582823e-01
-1.12478387e+00 -5.03232300e-01 6.51399791e-01 2.31394339e-02
-5.87194383e-01 9.41229522e-01 8.53166759e-01 -5.18175781e-01
2.07861021e-01 -2.25053743e-01 -2.43126005e-01 -8.56952667e-02
-2.24737704e-01 1.08114529e+00 5.74072361e-01 -9.21834648... | [12.77357006072998, 7.891060829162598] |
d0786ad2-589e-4e3e-8095-ee20c3082dc1 | exploiting-abstract-meaning-representation | 2305.17050 | null | https://arxiv.org/abs/2305.17050v1 | https://arxiv.org/pdf/2305.17050v1.pdf | Exploiting Abstract Meaning Representation for Open-Domain Question Answering | The Open-Domain Question Answering (ODQA) task involves retrieving and subsequently generating answers from fine-grained relevant passages within a database. Current systems leverage Pretrained Language Models (PLMs) to model the relationship between questions and passages. However, the diversity in surface form expres... | ['Yue Zhang', 'Zheng Zhang', 'Xuefeng Bai', 'Xiangkun Hu', 'Qipeng Guo', 'Zhikun Xu', 'Cunxiang Wang'] | 2023-05-26 | null | null | null | null | ['natural-questions', 'triviaqa', 'open-domain-question-answering'] | ['miscellaneous', 'miscellaneous', 'natural-language-processing'] | [ 6.56453073e-02 2.76142389e-01 1.73814237e-01 -2.34942704e-01
-1.34374321e+00 -7.07087517e-01 5.73533356e-01 5.48627019e-01
-2.63898790e-01 5.38760960e-01 6.14750922e-01 -5.05889535e-01
-9.86162573e-02 -9.50244427e-01 -6.30604982e-01 2.12780654e-01
1.50159404e-01 5.35156727e-01 5.01366973e-01 -8.49932194... | [10.902263641357422, 7.998457908630371] |
25df81e4-72fc-46b6-b448-09aafb914b3a | non-local-spatial-propagation-network-for | 2007.10042 | null | https://arxiv.org/abs/2007.10042v1 | https://arxiv.org/pdf/2007.10042v1.pdf | Non-Local Spatial Propagation Network for Depth Completion | In this paper, we propose a robust and efficient end-to-end non-local spatial propagation network for depth completion. The proposed network takes RGB and sparse depth images as inputs and estimates non-local neighbors and their affinities of each pixel, as well as an initial depth map with pixel-wise confidences. The ... | ['Chi-Kuei Liu', 'Kyungdon Joo', 'Jinsun Park', 'In So Kweon', 'Zhe Hu'] | 2020-07-20 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1810_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580120.pdf | eccv-2020-8 | ['stereo-lidar-fusion'] | ['computer-vision'] | [ 2.64235169e-01 6.35313764e-02 -3.00499558e-01 -7.13844538e-01
-6.36268735e-01 -2.17907622e-01 2.65026629e-01 2.19205216e-01
-6.32818103e-01 7.30042040e-01 2.71165282e-01 2.73615181e-01
-2.09047735e-01 -9.49315608e-01 -5.97163498e-01 -7.19958961e-01
9.46049287e-04 4.40358669e-01 6.88528359e-01 1.33924097... | [8.85837459564209, -2.480748176574707] |
d8ef0f89-2573-45ad-a854-847100c88ad6 | semi-supervised-text-classification-via-self | 2109.15300 | null | https://arxiv.org/abs/2109.15300v1 | https://arxiv.org/pdf/2109.15300v1.pdf | Semi-Supervised Text Classification via Self-Pretraining | We present a neural semi-supervised learning model termed Self-Pretraining. Our model is inspired by the classic self-training algorithm. However, as opposed to self-training, Self-Pretraining is threshold-free, it can potentially update its belief about previously labeled documents, and can cope with the semantic drif... | ['Negin Karisani', 'Payam Karisani'] | 2021-09-30 | null | null | null | null | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [ 2.96914309e-01 2.81353980e-01 -6.03052914e-01 -7.21662521e-01
-4.61932302e-01 -4.02640402e-01 6.61909878e-01 3.45890492e-01
-5.81662834e-01 9.64633465e-01 -4.71892320e-02 -1.84713319e-01
1.90522879e-01 -6.23666465e-01 -7.47611105e-01 -7.77489603e-01
-5.56930155e-02 9.27581906e-01 4.08948451e-01 1.22010134... | [9.665337562561035, 3.902024745941162] |
7b0eb972-a6ce-4163-8ab7-501d3fd1f7c9 | generative-adversarial-networks-a-survey-and | 1906.01529 | null | https://arxiv.org/abs/1906.01529v6 | https://arxiv.org/pdf/1906.01529v6.pdf | Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy | Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably their most significant impact has been in the area of computer vision where great advances have been made in challenges such as plausible image generation, image-to-image translation, facial attribute manipulation and s... | ['Zhengwei Wang', 'Tomas E. Ward', 'Qi She'] | 2019-06-04 | null | null | null | null | ['image-quality-estimation'] | ['computer-vision'] | [ 8.46843064e-01 2.71799684e-01 -4.90291696e-03 -2.96661317e-01
-8.99388790e-01 -4.42217320e-01 7.34301150e-01 -5.59108675e-01
-2.82466203e-01 8.57420564e-01 -7.20583946e-02 -4.92257625e-02
1.40788078e-01 -6.28490090e-01 -4.16980475e-01 -9.22775209e-01
2.99811244e-01 1.74753085e-01 -3.58097553e-01 -2.68053532... | [11.81147289276123, -0.3007112741470337] |
ed6dea4f-2423-41f2-862e-58caa7418708 | hierarchical-attention-learning-of-scene-flow | 2010.05762 | null | https://arxiv.org/abs/2010.05762v1 | https://arxiv.org/pdf/2010.05762v1.pdf | Hierarchical Attention Learning of Scene Flow in 3D Point Clouds | Scene flow represents the 3D motion of every point in the dynamic environments. Like the optical flow that represents the motion of pixels in 2D images, 3D motion representation of scene flow benefits many applications, such as autonomous driving and service robot. This paper studies the problem of scene flow estimatio... | ['Hesheng Wang', 'Zhe Liu', 'Xinrui Wu', 'Guangming Wang'] | 2020-10-12 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-3.03391218e-01 -4.37499076e-01 -9.63205546e-02 -3.82073134e-01
1.75629705e-01 -2.87188794e-02 3.31883460e-01 -1.64779916e-01
-6.91088617e-01 5.35670877e-01 2.05803975e-01 -1.80487722e-01
-9.24366191e-02 -9.92189109e-01 -5.85632503e-01 -7.20507085e-01
6.91710552e-03 2.37999722e-01 5.18129766e-01 -3.66268724... | [8.573638916015625, -2.0935323238372803] |
bc50d0ad-50b9-46d6-9381-f2e50d8bf9da | towards-open-world-object-detection | 2103.02603 | null | https://arxiv.org/abs/2103.02603v2 | https://arxiv.org/pdf/2103.02603v2.pdf | Towards Open World Object Detection | Humans have a natural instinct to identify unknown object instances in their environments. The intrinsic curiosity about these unknown instances aids in learning about them, when the corresponding knowledge is eventually available. This motivates us to propose a novel computer vision problem called: `Open World Object ... | ['Vineeth N Balasubramanian', 'Fahad Shahbaz Khan', 'Salman Khan', 'K J Joseph'] | 2021-03-03 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Joseph_Towards_Open_World_Object_Detection_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Joseph_Towards_Open_World_Object_Detection_CVPR_2021_paper.pdf | cvpr-2021-1 | ['open-world-object-detection'] | ['computer-vision'] | [ 3.11716437e-01 3.37683588e-01 -1.49294995e-02 -2.54313648e-01
-7.81432331e-01 -8.56779099e-01 6.03874564e-01 3.29732150e-01
-6.48271561e-01 6.08043313e-01 -2.41080478e-01 1.38864502e-01
-1.73952132e-01 -4.39918995e-01 -9.36818898e-01 -7.41162002e-01
-3.14767212e-01 7.49839425e-01 3.67860019e-01 2.34731779... | [9.65168285369873, 1.9541254043579102] |
8efe4af2-9649-4a17-ad9f-661a0c20bfab | non-intrusive-load-monitoring-based-on-self | 2210.04176 | null | https://arxiv.org/abs/2210.04176v1 | https://arxiv.org/pdf/2210.04176v1.pdf | Non-intrusive Load Monitoring based on Self-supervised Learning | Deep learning models for non-intrusive load monitoring (NILM) tend to require a large amount of labeled data for training. However, it is difficult to generalize the trained models to unseen sites due to different load characteristics and operating patterns of appliances between data sets. For addressing such problems,... | ['Yixin Yu', 'Wenpeng Luan', 'Mingjun Zhong', 'Bochao Zhao', 'Shuyi Chen'] | 2022-10-09 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 2.07580477e-02 3.98856513e-02 -3.98538142e-01 -7.38344193e-01
-8.25404584e-01 -3.08779925e-01 3.94896626e-01 -1.66952521e-01
1.33392587e-01 8.76148164e-01 1.81498945e-01 -1.82483330e-01
-2.53330264e-02 -1.00215495e+00 -4.89763051e-01 -8.49878848e-01
-1.87961251e-01 6.13690615e-01 -3.06651741e-01 -1.05933426... | [16.05364418029785, 7.570494651794434] |
7c6717b7-e891-44f8-8309-2c0fa0337f54 | modelling-high-level-mathematical-reasoning | 2006.09265 | null | https://arxiv.org/abs/2006.09265v2 | https://arxiv.org/pdf/2006.09265v2.pdf | IsarStep: a Benchmark for High-level Mathematical Reasoning | A well-defined benchmark is essential for measuring and accelerating research progress of machine learning models. In this paper, we present a benchmark for high-level mathematical reasoning and study the reasoning capabilities of neural sequence-to-sequence models. We build a non-synthetic dataset from the largest rep... | ['Lei Yu', 'Wenda Li', 'Yuhuai Wu', 'Lawrence C. Paulson'] | 2020-06-13 | isarstep-a-benchmark-for-high-level | https://openreview.net/forum?id=Pzj6fzU6wkj | https://openreview.net/pdf?id=Pzj6fzU6wkj | iclr-2021-1 | ['mathematical-proofs', 'mathematical-reasoning'] | ['miscellaneous', 'natural-language-processing'] | [ 3.53985697e-01 4.06994164e-01 -2.55978435e-01 -7.20516816e-02
-8.04414332e-01 -9.57181811e-01 8.37574899e-01 1.67680845e-01
1.99908078e-01 9.80552375e-01 -3.26000266e-02 -1.40054023e+00
-1.61025763e-01 -1.09756386e+00 -1.47946429e+00 1.05029270e-01
-8.93481821e-02 3.18123758e-01 6.78829178e-02 -1.77316695... | [9.072474479675293, 7.111479759216309] |
49982ba5-ab7b-4a2f-bd65-2f17d7d6f4f8 | cluster-based-mention-typing-for-named-entity | 2109.11389 | null | https://arxiv.org/abs/2109.11389v1 | https://arxiv.org/pdf/2109.11389v1.pdf | Cluster-based Mention Typing for Named Entity Disambiguation | An entity mention in text such as "Washington" may correspond to many different named entities such as the city "Washington D.C." or the newspaper "Washington Post." The goal of named entity disambiguation is to identify the mentioned named entity correctly among all possible candidates. If the type (e.g. location or p... | ['Arzucan Özgür', 'Arda Çelebi'] | 2021-09-23 | null | null | null | null | ['entity-disambiguation'] | ['natural-language-processing'] | [-5.30172586e-01 2.70639986e-01 -4.67555791e-01 -5.21480918e-01
-5.12126863e-01 -7.40380108e-01 6.92980170e-01 8.34995627e-01
-6.69870436e-01 9.11427796e-01 3.73654664e-01 -2.69215614e-01
1.74524565e-03 -1.19607842e+00 -5.80605268e-01 -5.16219079e-01
-2.17783883e-01 9.33882833e-01 5.21594524e-01 -8.17485899... | [9.58000373840332, 9.182506561279297] |
0845e47c-37e3-4fea-bc57-30619ea62a33 | tag2pix-line-art-colorization-using-text-tag | 1908.05840 | null | https://arxiv.org/abs/1908.05840v1 | https://arxiv.org/pdf/1908.05840v1.pdf | Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss | Line art colorization is expensive and challenging to automate. A GAN approach is proposed, called Tag2Pix, of line art colorization which takes as input a grayscale line art and color tag information and produces a quality colored image. First, we present the Tag2Pix line art colorization dataset. A generator network ... | ['Ho Young Jhoo', 'Hyunsu Kim', 'Sungjoo Yoo', 'Eunhyeok Park'] | 2019-08-16 | tag2pix-line-art-colorization-using-text-tag-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Kim_Tag2Pix_Line_Art_Colorization_Using_Text_Tag_With_SECat_and_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Kim_Tag2Pix_Line_Art_Colorization_Using_Text_Tag_With_SECat_and_ICCV_2019_paper.pdf | iccv-2019-10 | ['line-art-colorization'] | ['computer-vision'] | [ 3.32422942e-01 1.24304570e-01 1.62127867e-01 -3.83470684e-01
-6.66977048e-01 -9.88549531e-01 5.43165028e-01 -3.92033935e-01
-1.83539540e-01 6.29685104e-01 -2.30137169e-01 -1.48496762e-01
3.25251073e-01 -1.11254525e+00 -9.43716586e-01 -6.98249280e-01
7.17820168e-01 5.32601416e-01 1.90853775e-02 1.12732120... | [11.487502098083496, -0.8639295101165771] |
df5f04ce-25e9-4f7a-9992-ea80312b0998 | kronecker-decomposition-for-knowledge-graph | 2205.06560 | null | https://arxiv.org/abs/2205.06560v1 | https://arxiv.org/pdf/2205.06560v1.pdf | Kronecker Decomposition for Knowledge Graph Embeddings | Knowledge graph embedding research has mainly focused on learning continuous representations of entities and relations tailored towards the link prediction problem. Recent results indicate an ever increasing predictive ability of current approaches on benchmark datasets. However, this effectiveness often comes with the... | ['Axel-Cyrille Ngonga Ngomo', 'Julian Lienen', 'Caglar Demir'] | 2022-05-13 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-7.92744979e-02 2.40450457e-01 -2.70686746e-01 5.14527969e-02
-1.23531498e-01 -6.92147136e-01 6.01874352e-01 3.10498506e-01
-4.05527145e-01 4.55328554e-01 2.57913709e-01 -4.75952923e-01
-4.05741036e-01 -1.04966688e+00 -6.10630870e-01 -5.30206621e-01
-3.14665854e-01 1.03638679e-01 8.17634761e-02 -1.08114794... | [8.64687728881836, 7.7732439041137695] |
8ab7e956-9674-479e-b7d9-66b9a450886f | imprecise-bayesian-neural-networks | 2302.09656 | null | https://arxiv.org/abs/2302.09656v2 | https://arxiv.org/pdf/2302.09656v2.pdf | Imprecise Bayesian Neural Networks | Uncertainty quantification and robustness to distribution shifts are important goals in machine learning and artificial intelligence. Although Bayesian neural networks (BNNs) allow for uncertainty in the predictions to be assessed, different sources of uncertainty are indistinguishable. We present imprecise Bayesian ne... | ['Insup Lee', 'Oleg Sokolsky', 'Radoslav Ivanov', 'Vivian Lin', 'Kuk Jin Jang', 'Souradeep Dutta', 'Michele Caprio'] | 2023-02-19 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [ 1.94659859e-01 5.99145234e-01 -1.63054168e-01 -6.81272805e-01
-5.21168053e-01 -4.29377556e-01 9.54416990e-01 2.90655136e-01
-5.18698394e-01 1.28533554e+00 5.02911722e-03 -3.07941645e-01
-8.56832504e-01 -9.72618222e-01 -9.71404910e-01 -8.93970072e-01
-2.63529629e-01 7.56553352e-01 3.15424532e-01 2.02120915... | [7.415647506713867, 3.855130195617676] |
a3bddcea-5eac-4ef1-a619-9b7689f75201 | the-role-of-learning-regime-architecture-and | null | null | https://openreview.net/forum?id=3r034NfDKnL | https://openreview.net/pdf?id=3r034NfDKnL | The Role of Learning Regime, Architecture and Dataset Structure on Systematic Generalization in Simple Neural Networks | Humans often systematically generalize in situations where standard deep neural networks do not. Empirical studies have shown that the learning procedure and network architecture can influence systematicity in deep networks, but the underlying reasons for this influence remain unclear. Here we theoretically study the a... | ['Andrew M Saxe', 'Benjamin Rosman', 'Richard Klein', 'Devon Jarvis'] | 2021-09-29 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 2.27220505e-01 3.45125616e-01 -6.71536475e-02 -1.00259587e-01
2.52241582e-01 -9.77527738e-01 5.70817292e-01 -1.25159817e-02
-5.28572083e-01 5.41486979e-01 9.36502293e-02 -3.46735984e-01
-7.17053592e-01 -9.71248448e-01 -9.99845564e-01 -6.76904023e-01
-2.92586088e-01 4.10558939e-01 8.49334300e-02 -2.35291362... | [8.22217082977295, 3.4949724674224854] |
21129f3e-5b34-4190-bc3e-2c008d74c1f8 | menuai-restaurant-food-recommendation-system | 2210.08266 | null | https://arxiv.org/abs/2210.08266v1 | https://arxiv.org/pdf/2210.08266v1.pdf | MenuAI: Restaurant Food Recommendation System via a Transformer-based Deep Learning Model | Food recommendation system has proven as an effective technology to provide guidance on dietary choices, and this is especially important for patients suffering from chronic diseases. Unlike other multimedia recommendations, such as books and movies, food recommendation task is highly relied on the context at the momen... | ['Benny Lo', 'Jiachuan Peng', 'Peilun Shi', 'Jianing Qiu', 'Frank Po Wen Lo', 'Xinwei Ju'] | 2022-10-15 | null | null | null | null | ['food-recommendation'] | ['miscellaneous'] | [-1.97332539e-02 -5.41652262e-01 -5.20125568e-01 -4.80519384e-01
-1.75340965e-01 -5.49274743e-01 4.39879075e-02 7.36541927e-01
-2.11720407e-01 1.00911528e-01 4.86557156e-01 2.26772204e-02
-1.31202966e-01 -1.14304197e+00 -3.91397327e-01 -6.88728333e-01
-3.10537927e-02 1.19894922e-01 1.40206710e-01 -3.43306959... | [11.552659034729004, 4.422238826751709] |
201a1647-fc6a-42b7-b29c-98b8199055f9 | douzero-mastering-doudizhu-with-self-play | 2106.06135 | null | https://arxiv.org/abs/2106.06135v1 | https://arxiv.org/pdf/2106.06135v1.pdf | DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning | Games are abstractions of the real world, where artificial agents learn to compete and cooperate with other agents. While significant achievements have been made in various perfect- and imperfect-information games, DouDizhu (a.k.a. Fighting the Landlord), a three-player card game, is still unsolved. DouDizhu is a very ... | ['Ji Liu', 'Xia Hu', 'Xiangru Lian', 'Sheng Zhang', 'Wenye Ma', 'Jingru Xie', 'Daochen Zha'] | 2021-06-11 | null | null | null | null | ['game-of-poker', 'card-games'] | ['playing-games', 'playing-games'] | [-4.80268747e-01 -2.83650696e-01 1.00835465e-01 3.50964725e-01
-6.02375984e-01 -7.40064502e-01 8.52207899e-01 -2.52976328e-01
-5.49292922e-01 1.40659857e+00 -1.24158219e-01 -4.71392542e-01
-1.55982941e-01 -1.23574483e+00 -7.01023817e-01 -8.09023082e-01
-4.29705203e-01 1.16752219e+00 4.05416846e-01 -9.37190533... | [3.5195438861846924, 1.484937071800232] |
f889c07b-f3a4-4da5-bce1-278debc43727 | sepp-similarity-estimation-of-predicted | 2110.05748 | null | https://arxiv.org/abs/2110.05748v2 | https://arxiv.org/pdf/2110.05748v2.pdf | SEPP: Similarity Estimation of Predicted Probabilities for Defending and Detecting Adversarial Text | There are two cases describing how a classifier processes input text, namely, misclassification and correct classification. In terms of misclassified texts, a classifier handles the texts with both incorrect predictions and adversarial texts, which are generated to fool the classifier, which is called a victim. Both ty... | ['Shinsaku Kiyomoto', 'Kazuhide Fukushima', 'Seira Hidano', 'Hoang-Quoc Nguyen-Son'] | 2021-10-12 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 5.97781718e-01 3.71638834e-01 2.71080732e-01 -1.64604068e-01
-4.66354817e-01 -1.01007867e+00 6.98847651e-01 5.72320044e-01
3.19971927e-02 8.07888150e-01 -1.86587617e-01 -3.18028301e-01
1.83541477e-01 -1.09514511e+00 -7.11643517e-01 -7.89910734e-01
1.03639193e-01 5.08076906e-01 4.82298642e-01 -3.29500943... | [5.858665466308594, 7.955933570861816] |
2d5e476a-efaa-4171-8380-d07c90fede86 | soa-nlp-lt-edi-acl2022-an-ensemble-model-for | null | null | https://aclanthology.org/2022.ltedi-1.31 | https://aclanthology.org/2022.ltedi-1.31.pdf | SOA_NLP@LT-EDI-ACL2022: An Ensemble Model for Hope Speech Detection from YouTube Comments | Language should be accommodating of equality and diversity as a fundamental aspect of communication. The language of internet users has a big impact on peer users all over the world. On virtual platforms such as Facebook, Twitter, and YouTube, people express their opinions in different languages. People respect others’... | ['Pradeep Roy', 'Sunil Saumya', 'Abhinav Kumar'] | null | null | null | null | ltedi-acl-2022-5 | ['hope-speech-detection'] | ['natural-language-processing'] | [-5.46092153e-01 1.03359595e-02 -1.01637459e+00 -3.23918879e-01
-1.31158620e-01 -2.33166352e-01 6.13463700e-01 5.00895739e-01
-5.92525721e-01 9.34519351e-01 6.76559210e-01 -4.94950145e-01
-9.58964601e-02 -6.56122267e-01 3.78099263e-01 -4.17501599e-01
2.91815728e-01 -3.54419611e-02 -2.93463141e-01 -5.68970919... | [8.910080909729004, 10.671156883239746] |
5f38c9bd-daa4-4f04-81a8-fded043da9c7 | exploring-anisotropy-and-outliers-in | 2306.00458 | null | https://arxiv.org/abs/2306.00458v2 | https://arxiv.org/pdf/2306.00458v2.pdf | Exploring Anisotropy and Outliers in Multilingual Language Models for Cross-Lingual Semantic Sentence Similarity | Previous work has shown that the representations output by contextual language models are more anisotropic than static type embeddings, and typically display outlier dimensions. This seems to be true for both monolingual and multilingual models, although much less work has been done on the multilingual context. Why the... | ['Alexander Fraser', 'Jindřich Libovický', 'Alina Fastowski', 'Katharina Hämmerl'] | 2023-06-01 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [-2.42291495e-01 1.12192713e-01 -4.18872833e-02 -3.58831644e-01
-7.80013084e-01 -7.68238842e-01 1.03180099e+00 3.26229572e-01
-7.00616479e-01 4.30329114e-01 9.43094909e-01 -5.32082438e-01
1.91755164e-02 -5.16565442e-01 -6.43142223e-01 -6.41110599e-01
1.12256585e-02 4.67995286e-01 9.57987383e-02 -3.50439221... | [10.861401557922363, 9.865848541259766] |
16dbcf51-bb73-45fe-91b5-0114684fd4aa | rada-robust-adversarial-data-augmentation-for | 2112.02469 | null | https://arxiv.org/abs/2112.02469v1 | https://arxiv.org/pdf/2112.02469v1.pdf | RADA: Robust Adversarial Data Augmentation for Camera Localization in Challenging Weather | Camera localization is a fundamental and crucial problem for many robotic applications. In recent years, using deep-learning for camera-based localization has become a popular research direction. However, they lack robustness to large domain shifts, which can be caused by seasonal or illumination changes between traini... | ['Andrew Markham', 'Niki Trigon', 'Chris Xiaoxuan Lu', 'Muhamad Risqi U. Saputra', 'Jialu Wang'] | 2021-12-05 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-2.21416652e-02 -2.21624196e-01 -1.39476508e-02 -1.67747006e-01
-6.23562276e-01 -8.37221682e-01 7.98782647e-01 -1.65615916e-01
-7.08637476e-01 7.31928289e-01 6.49176538e-02 -1.76107988e-01
3.77648622e-01 -4.63831335e-01 -1.06187940e+00 -9.54237461e-01
2.52057910e-01 2.59965926e-01 2.95912683e-01 -2.65554130... | [7.877261161804199, -2.0593552589416504] |
7d6d0135-9246-419b-902b-302c665dbfe5 | a-comparative-study-of-western-and-chinese | 2002.09021 | null | https://arxiv.org/abs/2002.09021v1 | https://arxiv.org/pdf/2002.09021v1.pdf | A Comparative Study of Western and Chinese Classical Music based on Soundscape Models | Whether literally or suggestively, the concept of soundscape is alluded in both modern and ancient music. In this study, we examine whether we can analyze and compare Western and Chinese classical music based on soundscape models. We addressed this question through a comparative study. Specifically, corpora of Western ... | ['Yi-Hsuan Yang', 'Jianyu Fan', 'Philippe Pasquier', 'Kui Dong'] | 2020-02-20 | null | null | null | null | ['music-emotion-recognition'] | ['music'] | [-5.37221655e-02 -4.57280487e-01 2.14139134e-01 -2.25142002e-01
-6.80260241e-01 -9.62953627e-01 4.51039493e-01 1.41792176e-02
-6.54514730e-01 4.33501184e-01 3.66826504e-01 2.00999796e-01
-1.24407001e-01 -4.32786047e-01 -2.60554969e-01 -3.39499056e-01
-9.63444486e-02 -1.94683038e-02 -8.95967036e-02 -3.91379327... | [15.856063842773438, 5.188298225402832] |
7bca421b-40af-443e-9fa8-9f8885f7f247 | sts-ccl-spatial-temporal-synchronous | 2307.02507 | null | https://arxiv.org/abs/2307.02507v1 | https://arxiv.org/pdf/2307.02507v1.pdf | STS-CCL: Spatial-Temporal Synchronous Contextual Contrastive Learning for Urban Traffic Forecasting | Efficiently capturing the complex spatiotemporal representations from large-scale unlabeled traffic data remains to be a challenging task. In considering of the dilemma, this work employs the advanced contrastive learning and proposes a novel Spatial-Temporal Synchronous Contextual Contrastive Learning (STS-CCL) model.... | ['Jichao Bi', 'Fengji Luo', 'Kaixiang Yang', 'Lincan Li'] | 2023-07-05 | null | null | null | null | ['contrastive-learning', 'contrastive-learning'] | ['computer-vision', 'methodology'] | [ 1.54194802e-01 -3.78300786e-01 -3.63851875e-01 -3.96197170e-01
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-2.37546653e-01 1.98987275e-01 6.32911026e-01 -5.93005896... | [6.499320983886719, 2.0869951248168945] |
683cde65-f00c-496c-889b-9ba6c3eba2ce | regularized-training-of-nearest-neighbor | 2109.08249 | null | https://arxiv.org/abs/2109.08249v1 | https://arxiv.org/pdf/2109.08249v1.pdf | Regularized Training of Nearest Neighbor Language Models | Including memory banks in a natural language processing architecture increases model capacity by equipping it with additional data at inference time. In this paper, we build upon $k$NN-LM \citep{khandelwal20generalization}, which uses a pre-trained language model together with an exhaustive $k$NN search through the tra... | ['Josh Susskind', 'Shuangfei Zhai', 'Walter Talbott', 'Jean-Francois Ton'] | 2021-09-16 | null | https://aclanthology.org/2022.naacl-srw.4 | https://aclanthology.org/2022.naacl-srw.4.pdf | naacl-acl-2022-7 | ['l2-regularization'] | ['methodology'] | [ 2.32174490e-02 2.72794217e-01 -2.59245280e-02 -4.30444568e-01
-9.22675908e-01 -4.64148164e-01 4.15942311e-01 2.48868003e-01
-1.13203537e+00 3.76101017e-01 5.18540777e-02 -7.85734296e-01
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-2.28725113e-02 4.34121221e-01 2.79068649e-01 -1.34334788... | [10.716266632080078, 8.797856330871582] |
bda18350-bff9-4204-9caf-6acca65effe7 | local-guided-global-paired-similarity | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Choi_Local-Guided_Global_Paired_Similarity_Representation_for_Visual_Reinforcement_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Choi_Local-Guided_Global_Paired_Similarity_Representation_for_Visual_Reinforcement_Learning_CVPR_2023_paper.pdf | Local-Guided Global: Paired Similarity Representation for Visual Reinforcement Learning | Recent vision-based reinforcement learning (RL) methods have found extracting high-level features from raw pixels with self-supervised learning to be effective in learning policies. However, these methods focus on learning global representations of images, and disregard local spatial structures present in the conse... | ['Dongbo Min', 'Kwanghoon Sohn', 'Sangryul Jeon', 'Wonil Song', 'Hunsang Lee', 'Hyesong Choi'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['atari-games'] | ['playing-games'] | [ 1.79649353e-01 -7.54972994e-02 -5.23990393e-01 -3.82227391e-01
-5.53182185e-01 -1.65592562e-02 8.95342469e-01 -1.09098613e-01
-2.48395070e-01 6.91919506e-01 3.23099911e-01 2.30539873e-01
-1.81973517e-01 -7.85777271e-01 -9.13360715e-01 -8.55541170e-01
-2.78021336e-01 2.66058981e-01 4.98474807e-01 -1.98840633... | [8.615937232971191, 0.4875110983848572] |
dbe069bd-7930-4c6d-81b1-600510c30501 | contrastive-identity-aware-learning-for-multi | 2211.12712 | null | https://arxiv.org/abs/2211.12712v2 | https://arxiv.org/pdf/2211.12712v2.pdf | Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition | Value Decomposition (VD) aims to deduce the contributions of agents for decentralized policies in the presence of only global rewards, and has recently emerged as a powerful credit assignment paradigm for tackling cooperative Multi-Agent Reinforcement Learning (MARL) problems. One of the main challenges in VD is to pro... | ['Mingli Song', 'Zunlei Feng', 'Tongtian Zhu', 'KaiXuan Chen', 'Tongya Zheng', 'Jie Song', 'Yihe Zhou', 'Shunyu Liu'] | 2022-11-23 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-4.65744972e-01 1.43921033e-01 -6.92700505e-01 6.10507689e-02
-3.58948976e-01 -5.70481479e-01 8.90241802e-01 1.02123983e-01
-3.98546785e-01 9.75222707e-01 2.38933533e-01 -1.17513128e-02
-5.23821115e-01 -6.89474881e-01 -4.07050073e-01 -1.11810124e+00
-3.62459928e-01 6.93235397e-01 1.20364301e-01 -5.97957671... | [3.7517197132110596, 2.057478904724121] |
b466df66-4e4d-4360-bde5-99a309f18a7c | image-as-a-foreign-language-beit-pretraining | 2208.10442 | null | https://arxiv.org/abs/2208.10442v2 | https://arxiv.org/pdf/2208.10442v2.pdf | Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks | A big convergence of language, vision, and multimodal pretraining is emerging. In this work, we introduce a general-purpose multimodal foundation model BEiT-3, which achieves state-of-the-art transfer performance on both vision and vision-language tasks. Specifically, we advance the big convergence from three aspects: ... | ['Furu Wei', 'Subhojit Som', 'Saksham Singhal', 'Owais Khan Mohammed', 'Kriti Aggarwal', 'Qiang Liu', 'Zhiliang Peng', 'Johan Bjorck', 'Li Dong', 'Hangbo Bao', 'Wenhui Wang'] | 2022-08-22 | null | null | null | null | ['visual-reasoning', 'zero-shot-cross-modal-retrieval', 'visual-reasoning'] | ['computer-vision', 'miscellaneous', 'reasoning'] | [-6.93727583e-02 4.30830643e-02 -1.64008029e-02 -4.31578666e-01
-1.14818752e+00 -6.43954992e-01 7.98942983e-01 -4.19756807e-02
-6.42772257e-01 1.70912966e-01 1.96652450e-02 -4.53497142e-01
4.18145567e-01 -3.52500439e-01 -1.06979394e+00 -2.97024608e-01
3.37307304e-01 6.45031035e-01 1.17333077e-01 -3.52166504... | [10.848350524902344, 1.6063101291656494] |
a76e69da-8ab0-43a2-8340-0fdd8af1f777 | eagle-large-scale-dataset-for-vehicle | 2007.06124 | null | https://arxiv.org/abs/2007.06124v3 | https://arxiv.org/pdf/2007.06124v3.pdf | EAGLE: Large-scale Vehicle Detection Dataset in Real-World Scenarios using Aerial Imagery | Multi-class vehicle detection from airborne imagery with orientation estimation is an important task in the near and remote vision domains with applications in traffic monitoring and disaster management. In the last decade, we have witnessed significant progress in object detection in ground imagery, but it is still in... | ['Reza Bahmanyar', 'Seyed Majid Azimi', 'Franz Kurz', 'Corenin Henry'] | 2020-07-12 | null | null | null | null | ['shadow-removal'] | ['computer-vision'] | [ 3.07810664e-01 -6.71849251e-01 1.69885457e-01 -4.60500658e-01
-4.59902495e-01 -6.61824167e-01 4.53844875e-01 -1.72502056e-01
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-7.95984417e-02 -9.07224357e-01 -4.66263413e-01 -8.69625747e-01
-2.74248809e-01 5.83518386e-01 8.50090623e-01 -4.68876183... | [8.710943222045898, -0.7993441224098206] |
465c2d9d-eaee-4c11-91bd-7ecd1651e439 | overlap-guided-gaussian-mixture-models-for | 2210.09836 | null | https://arxiv.org/abs/2210.09836v1 | https://arxiv.org/pdf/2210.09836v1.pdf | Overlap-guided Gaussian Mixture Models for Point Cloud Registration | Probabilistic 3D point cloud registration methods have shown competitive performance in overcoming noise, outliers, and density variations. However, registering point cloud pairs in the case of partial overlap is still a challenge. This paper proposes a novel overlap-guided probabilistic registration approach that comp... | ['Nicu Sebe', 'Elisa Ricci', 'Jian Zhang', 'Cristiano Saltori', 'Fabio Poiesi', 'Guofeng Mei'] | 2022-10-17 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-3.17283481e-01 -3.72655332e-01 1.33489162e-01 -2.22826988e-01
-1.23112714e+00 -4.98007357e-01 6.84458375e-01 1.14145234e-01
-2.40033790e-01 9.20556635e-02 -3.59258831e-01 3.44147384e-02
-6.40489310e-02 -7.53555715e-01 -5.79339325e-01 -7.22945273e-01
1.12191945e-01 1.17914474e+00 5.12844205e-01 2.48119459... | [7.7567458152771, -2.9714651107788086] |
dc5b5e55-470c-4a31-9cfc-1fb88e3880bf | pokemonchat-auditing-chatgpt-for-pokemon | 2306.03024 | null | https://arxiv.org/abs/2306.03024v1 | https://arxiv.org/pdf/2306.03024v1.pdf | PokemonChat: Auditing ChatGPT for Pokémon Universe Knowledge | The recently released ChatGPT model demonstrates unprecedented capabilities in zero-shot question-answering. In this work, we probe ChatGPT for its conversational understanding and introduce a conversational framework (protocol) that can be adopted in future studies. The Pok\'emon universe serves as an ideal testing gr... | ['Ilias Chalkidis', 'Jiaang Li', 'Laura Cabello'] | 2023-06-05 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [-1.31388620e-01 3.95024329e-01 3.19639206e-01 -1.17690139e-01
-6.77751184e-01 -9.11487818e-01 7.16090024e-01 4.57825810e-02
-4.23257440e-01 9.07639086e-01 4.08192247e-01 -6.22540176e-01
-3.53334486e-01 -8.95856261e-01 -4.84960824e-02 -2.63048381e-01
-3.69154245e-01 7.88370132e-01 2.52264142e-01 -1.02065861... | [12.636487007141113, 8.050992965698242] |
7c3a2a79-002a-4931-b30d-79e93c04a48a | stock-price-prediction-using-dynamic-neural | 2306.12969 | null | https://arxiv.org/abs/2306.12969v1 | https://arxiv.org/pdf/2306.12969v1.pdf | Stock Price Prediction using Dynamic Neural Networks | This paper will analyze and implement a time series dynamic neural network to predict daily closing stock prices. Neural networks possess unsurpassed abilities in identifying underlying patterns in chaotic, non-linear, and seemingly random data, thus providing a mechanism to predict stock price movements much more prec... | ['David Noel'] | 2023-06-18 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-8.42554629e-01 -6.67747080e-01 -1.85960412e-01 -7.46255666e-02
5.11917353e-01 -7.50414193e-01 5.80606997e-01 -4.33743179e-01
-3.09866369e-01 8.81826699e-01 2.22133636e-01 -7.76074052e-01
-4.04395103e-01 -9.58715558e-01 -1.35243936e-02 -6.43397927e-01
-5.70341349e-01 1.08887926e-01 -1.72690824e-01 -7.27132082... | [4.499557018280029, 4.214079856872559] |
1863a131-51ed-4579-8f66-841d5e06af99 | juewu-mc-playing-minecraft-with-sample | 2112.04907 | null | https://arxiv.org/abs/2112.04907v1 | https://arxiv.org/pdf/2112.04907v1.pdf | JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning | Learning rational behaviors in open-world games like Minecraft remains to be challenging for Reinforcement Learning (RL) research due to the compound challenge of partial observability, high-dimensional visual perception and delayed reward. To address this, we propose JueWu-MC, a sample-efficient hierarchical RL approa... | ['Wei Yang', 'Qiang Fu', 'Deheng Ye', 'Jianing Shi', 'Junyou Li', 'Zichuan Lin'] | 2021-12-07 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 5.76186739e-02 2.20619753e-01 -5.29215991e-01 2.39104986e-01
-9.11953092e-01 -6.15250826e-01 5.70977867e-01 -2.48297989e-01
-6.97374582e-01 9.56285834e-01 3.19219679e-01 -2.79095292e-01
-1.88695356e-01 -4.45562422e-01 -8.70534301e-01 -7.51113296e-01
-2.31712222e-01 4.99169916e-01 2.08500475e-01 -4.24098670... | [4.134886741638184, 1.633744716644287] |
9f5cda5c-1e56-447c-a82f-3a04f11f5753 | syllable-discovery-and-cross-lingual | 2305.11435 | null | https://arxiv.org/abs/2305.11435v1 | https://arxiv.org/pdf/2305.11435v1.pdf | Syllable Discovery and Cross-Lingual Generalization in a Visually Grounded, Self-Supervised Speech Mode | In this paper, we show that representations capturing syllabic units emerge when training a self-supervised speech model with a visually-grounded training objective. We demonstrate that a nearly identical model architecture (HuBERT) trained with a masked language modeling loss does not exhibit this same ability, sugges... | ['David Harwath', 'Abdelrahman Mohamed', 'Okko Räsänen', 'Shang-Wen Li', 'Puyuan Peng'] | 2023-05-19 | null | null | null | null | ['visual-grounding'] | ['computer-vision'] | [ 3.59924406e-01 6.11737251e-01 4.08836603e-02 -2.38713816e-01
-7.10155189e-01 -8.26646745e-01 6.77225649e-01 1.70950681e-01
-3.06551099e-01 2.88162857e-01 1.95653960e-01 -6.83946013e-01
4.55956250e-01 -3.93209130e-01 -7.43914127e-01 -5.87870419e-01
-1.79275319e-01 7.96226561e-01 4.35631305e-01 -2.19665334... | [14.468788146972656, 6.609226703643799] |
e699f86b-a0f6-4260-87c0-559cac01585d | echocardiography-segmentation-using-neural | 2306.09687 | null | https://arxiv.org/abs/2306.09687v1 | https://arxiv.org/pdf/2306.09687v1.pdf | Echocardiography Segmentation Using Neural ODE-based Diffeomorphic Registration Field | Convolutional neural networks (CNNs) have recently proven their excellent ability to segment 2D cardiac ultrasound images. However, the majority of attempts to perform full-sequence segmentation of cardiac ultrasound videos either rely on models trained only on keyframe images or fail to maintain the topology over time... | ['Long Tran Quoc', 'Hieu Pham Huy', 'Phi Nguyen Van'] | 2023-06-16 | null | null | null | null | ['image-registration'] | ['computer-vision'] | [-8.65696277e-03 1.87207699e-01 1.98910862e-01 -3.09516311e-01
-5.41671693e-01 -5.19334078e-01 1.17859073e-01 1.56370297e-01
-5.08422494e-01 4.47247714e-01 -3.06280792e-01 -2.56952345e-01
-3.03066373e-01 -4.31571275e-01 -6.69445753e-01 -7.09653676e-01
-5.86416245e-01 3.38914841e-01 2.92262137e-01 3.67963687... | [13.998424530029297, -2.5073139667510986] |
d11bc8aa-8b88-46c9-ad5b-fcbd89287778 | lapscore-language-guided-person-search-via | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wu_LapsCore_Language-Guided_Person_Search_via_Color_Reasoning_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wu_LapsCore_Language-Guided_Person_Search_via_Color_Reasoning_ICCV_2021_paper.pdf | LapsCore: Language-Guided Person Search via Color Reasoning | The key point of language-guided person search is to construct the cross-modal association between visual and textual input. Existing methods focus on designing multimodal attention mechanisms and novel cross-modal loss functions to learn such association implicitly. We propose a representation learning method for ... | ['Shuguang Cui', 'Changqing Zou', 'Guanbin Li', 'Xiaoguang Han', 'Zizheng Yan', 'Yushuang Wu'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['person-search'] | ['computer-vision'] | [ 4.30597961e-02 -1.63697556e-01 -2.36261383e-01 -5.13865709e-01
-9.17320549e-01 -5.53644419e-01 7.43415236e-01 -2.45598584e-01
-4.82877553e-01 4.29082721e-01 3.96155685e-01 -2.59270728e-01
2.18990013e-01 -5.32712221e-01 -6.87861562e-01 -5.54007351e-01
4.32773083e-01 5.42814076e-01 -4.00114685e-01 4.68303896... | [10.879855155944824, 1.4269344806671143] |
d86d5687-db24-42e4-b729-3b63ab672f31 | end-to-end-offline-speech-translation-system | null | null | https://aclanthology.org/2020.iwslt-1.7 | https://aclanthology.org/2020.iwslt-1.7.pdf | End-to-End Offline Speech Translation System for IWSLT 2020 using Modality Agnostic Meta-Learning | In this paper, we describe the system submitted to the IWSLT 2020 Offline Speech Translation Task. We adopt the Transformer architecture coupled with the meta-learning approach to build our end-to-end Speech-to-Text Translation (ST) system. Our meta-learning approach tackles the data scarcity of the ST task by leveragi... | ['Sangha Kim', 'Sathish Reddy Indurthi', 'Mohd Abbas Zaidi', 'Nikhil Kumar Lakumarapu', 'Hou Jeung Han', 'Beomseok Lee'] | 2020-07-01 | null | null | null | ws-2020-7 | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 3.51893216e-01 2.84681439e-01 -2.37827122e-01 -3.40927631e-01
-1.77986455e+00 -5.44985950e-01 9.94211376e-01 -1.48768902e-01
-4.08185065e-01 9.64308739e-01 4.40895915e-01 -9.85819161e-01
5.47187746e-01 -3.85363307e-03 -7.74640203e-01 -2.32562676e-01
4.87864673e-01 9.65202212e-01 -1.14101827e-01 -5.39938807... | [14.48514461517334, 7.18217134475708] |
dda72c64-a1e6-4247-8962-3709b5f434b5 | measuring-annotator-agreement-generally | 2212.09503 | null | https://arxiv.org/abs/2212.09503v1 | https://arxiv.org/pdf/2212.09503v1.pdf | Measuring Annotator Agreement Generally across Complex Structured, Multi-object, and Free-text Annotation Tasks | When annotators label data, a key metric for quality assurance is inter-annotator agreement (IAA): the extent to which annotators agree on their labels. Though many IAA measures exist for simple categorical and ordinal labeling tasks, relatively little work has considered more complex labeling tasks, such as structured... | ['Matthew Lease', 'Omar Alonso', 'Alexander Braylan'] | 2022-12-15 | null | null | null | null | ['text-annotation'] | ['natural-language-processing'] | [ 2.51447737e-01 2.62314767e-01 -3.16890150e-01 -9.06782329e-01
-9.90083992e-01 -1.18683267e+00 5.55424631e-01 8.48825514e-01
-7.05058157e-01 6.25963807e-01 4.44351196e-01 -2.68282175e-01
-5.03924370e-01 8.52883682e-02 -1.03108607e-01 -2.96430349e-01
1.08685650e-01 7.63392866e-01 5.71088791e-02 1.25468701... | [9.624165534973145, 4.702820777893066] |
0abcd2e9-4e66-4a81-8856-0565c527d5b7 | pseudo-label-generation-evaluation-framework | null | null | https://ieeexplore.ieee.org/document/9506549 | https://ieeexplore.ieee.org/iel7/9506008/9506009/09506549.pdf | Pseudo-Label Generation-Evaluation Framework For Cross Domain Weakly Supervised Object Detection | Cross domain weakly supervised object detection (CDWSOD), where we can get access to instance-level annotations in the source domain while only image-level annotations are available in the target domain, adapts object detectors from label-rich to label-poor domains. It usually generates pseudo labels in the target doma... | ['Yingming Li', 'Kejie Lyu', 'Xinglu Wang', 'Shengxiong Ouyang'] | 2021-08-23 | null | null | null | ieee-international-conference-on-image-3 | ['weakly-supervised-object-detection'] | ['computer-vision'] | [ 4.78941947e-01 3.02787393e-01 -2.63817757e-01 -5.56156874e-01
-1.16159546e+00 -7.48941422e-01 3.47805738e-01 3.45346425e-03
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4.45836544e-01 6.46519899e-01 8.65752995e-01 1.77305207... | [9.2923002243042, 1.3538254499435425] |
3c881f3f-b8dd-4c16-9884-16cfbdfb30a3 | automatic-generation-of-socratic-subquestions | 2211.12835 | null | https://arxiv.org/abs/2211.12835v1 | https://arxiv.org/pdf/2211.12835v1.pdf | Automatic Generation of Socratic Subquestions for Teaching Math Word Problems | Socratic questioning is an educational method that allows students to discover answers to complex problems by asking them a series of thoughtful questions. Generation of didactically sound questions is challenging, requiring understanding of the reasoning process involved in the problem. We hypothesize that such questi... | ['Mrinmaya Sachan', 'Manu Kapur', 'Tanmay Sinha', 'Mennatallah El-Assady', 'Jakub Macina', 'Kumar Shridhar'] | 2022-11-23 | null | null | null | null | ['math-word-problem-solving', 'question-generation', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'natural-language-processing', 'reasoning', 'time-series'] | [ 1.55257151e-01 3.30539674e-01 4.24176693e-01 -3.27597678e-01
-5.63126683e-01 -9.59695876e-01 2.17699662e-01 5.95418096e-01
-2.23734871e-01 5.10986686e-01 1.55761734e-01 -1.04264116e+00
-5.04304588e-01 -1.32899761e+00 -4.98618364e-01 1.40898392e-01
2.97028959e-01 2.86202371e-01 4.22274172e-01 -7.72247314... | [10.838167190551758, 7.698014259338379] |
c33f51b4-a2e0-4606-b0fd-a363a042e940 | neural-attentive-circuits | 2210.08031 | null | https://arxiv.org/abs/2210.08031v2 | https://arxiv.org/pdf/2210.08031v2.pdf | Neural Attentive Circuits | Recent work has seen the development of general purpose neural architectures that can be trained to perform tasks across diverse data modalities. General purpose models typically make few assumptions about the underlying data-structure and are known to perform well in the large-data regime. At the same time, there has ... | ['Li Erran Li', 'Nicolas Ballas', 'Bernhard Schölkopf', 'Yoshua Bengio', 'Chris Pal', 'Francesco Locatello', 'Martin Weiss', 'Nasim Rahaman'] | 2022-10-14 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [ 8.87052715e-03 1.25944570e-01 -1.59413666e-01 -4.19364750e-01
-5.41757464e-01 -6.65027738e-01 4.96351063e-01 -5.02981097e-02
-2.83413947e-01 3.84838462e-01 1.05570674e-01 -2.44761929e-01
-5.57130240e-02 -6.94255471e-01 -1.26390707e+00 -4.95815098e-01
-8.92936215e-02 4.69639510e-01 2.07292214e-01 -2.47956008... | [9.517500877380371, 1.6928352117538452] |
99e14206-0628-4b68-84a9-3865d7bde9d4 | audio-retrieval-with-natural-language-queries-1 | 2112.09418 | null | https://arxiv.org/abs/2112.09418v2 | https://arxiv.org/pdf/2112.09418v2.pdf | Audio Retrieval with Natural Language Queries: A Benchmark Study | The objectives of this work are cross-modal text-audio and audio-text retrieval, in which the goal is to retrieve the audio content from a pool of candidates that best matches a given written description and vice versa. Text-audio retrieval enables users to search large databases through an intuitive interface: they si... | ['Samuel Albanie', 'Zeynep Akata', 'João F. Henriques', 'Andreea-Maria Oncescu', 'A. Sophia Koepke'] | 2021-12-17 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 1.44532472e-01 -7.07108617e-01 -2.01566685e-02 -1.48963362e-01
-1.86993933e+00 -9.10948575e-01 4.38603461e-01 2.63240188e-01
-2.90909857e-01 2.22596630e-01 6.22552156e-01 3.44961911e-01
-3.53900820e-01 -3.02299589e-01 -4.79145020e-01 -3.33242118e-01
-3.50322753e-01 4.57279950e-01 2.14740142e-01 -2.71369606... | [15.316697120666504, 5.010491371154785] |
076dd0e3-20cf-4aaf-8bb3-bb907f7f1add | learning-from-weights-a-cost-sensitive | 1811.12776 | null | http://arxiv.org/abs/1811.12776v2 | http://arxiv.org/pdf/1811.12776v2.pdf | Learning From Weights: A Cost-Sensitive Approach For Ad Retrieval | Retrieval models such as CLSM is trained on click-through data which treats
each clicked query-document pair as equivalent. While training on click-through
data is reasonable, this paper argues that it is sub-optimal because of its
noisy and long-tail nature (especially for sponsored search). In this paper, we
discuss ... | ['Rahul Agrawal', 'Nikit Begwani', 'Shrutendra Harsola'] | 2018-11-30 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-1.03631772e-01 -2.75595278e-01 -4.98541474e-01 -4.76162076e-01
-1.19304109e+00 -7.66474426e-01 6.27042532e-01 1.72540143e-01
-6.27828538e-01 3.98127139e-01 3.34539860e-01 -5.95473528e-01
-5.79735577e-01 -8.20352018e-01 -6.95376635e-01 -2.63976365e-01
-1.68140471e-01 5.90143740e-01 3.48995537e-01 -4.43407595... | [11.329263687133789, 7.439615726470947] |
9eed1c24-3b58-4bc3-907d-c866539146bd | refrec-pseudo-labels-refinement-via-shape | 2110.11036 | null | https://arxiv.org/abs/2110.11036v1 | https://arxiv.org/pdf/2110.11036v1.pdf | RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation | Unsupervised Domain Adaptation (UDA) for point cloud classification is an emerging research problem with relevant practical motivations. Reliance on multi-task learning to align features across domains has been the standard way to tackle it. In this paper, we take a different path and propose RefRec, the first approach... | ['Luigi Di Stefano', 'Samuele Salti', 'Pierluigi Zama Ramirez', 'Riccardo Spezialetti', 'Adriano Cardace'] | 2021-10-21 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [ 2.67231077e-01 -1.21508129e-01 -1.37215868e-01 -5.15606165e-01
-1.15458429e+00 -8.43391299e-01 7.65253127e-01 2.32957244e-01
-2.38117158e-01 4.77509439e-01 -2.59148031e-01 -9.69217718e-02
-4.66902465e-01 -6.42078996e-01 -8.49319398e-01 -7.82084286e-01
5.05936034e-02 1.17277145e+00 3.82326990e-01 -9.34895501... | [8.076512336730957, -2.7975263595581055] |
4f55e778-3e4f-4077-8894-159c987dc398 | dense-prediction-with-attentive-feature | 2111.00770 | null | https://arxiv.org/abs/2111.00770v3 | https://arxiv.org/pdf/2111.00770v3.pdf | Dense Prediction with Attentive Feature Aggregation | Aggregating information from features across different layers is an essential operation for dense prediction models. Despite its limited expressiveness, feature concatenation dominates the choice of aggregation operations. In this paper, we introduce Attentive Feature Aggregation (AFA) to fuse different network layers ... | ['Peter Kontschieder', 'Samuel Rota Bulò', 'Min Sun', 'Fisher Yu', 'Thomas E. Huang', 'Yung-Hsu Yang'] | 2021-11-01 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 1.46027267e-01 6.42092451e-02 -5.03619201e-02 -5.59688687e-01
-8.75819862e-01 -3.38322073e-01 5.06147623e-01 -4.53552380e-02
-2.61790037e-01 5.57314694e-01 4.41058502e-02 -2.94728518e-01
-3.30643426e-03 -9.03916001e-01 -8.19912374e-01 -4.16149795e-01
-1.71022996e-01 2.93526739e-01 6.20934844e-01 -1.87341839... | [9.513556480407715, 0.057499468326568604] |
315a4388-3a35-41f5-9790-acce8dee2c49 | cuhk-ee-voice-cloning-system-for-icassp-2021 | 2103.04699 | null | https://arxiv.org/abs/2103.04699v5 | https://arxiv.org/pdf/2103.04699v5.pdf | CUHK-EE Voice Cloning System for ICASSP 2021 M2VoC Challenge | This paper presents the CUHK-EE voice cloning system for ICASSP 2021 M2VoC challenge. The challenge provides two Mandarin speech corpora: the AIShell-3 corpus of 218 speakers with noise and reverberation and the MST corpus including high-quality speech of one male and one female speakers. 100 and 5 utterances of 3 targ... | ['Tan Lee', 'Guangyan Zhang', 'Hingpang Huang', 'Daxin Tan'] | 2021-03-08 | null | null | null | null | ['voice-cloning'] | ['speech'] | [-3.07614416e-01 3.14201154e-02 3.29106838e-01 -2.48810381e-01
-1.43838668e+00 -5.62296867e-01 1.58877149e-01 -5.34528196e-01
-1.58632502e-01 2.85698891e-01 4.09393489e-01 -4.95463014e-01
4.29702908e-01 8.28174502e-02 -4.08058614e-01 -6.92284763e-01
1.99068729e-02 3.30555201e-01 -1.59106448e-01 -2.12368235... | [14.879973411560059, 6.49099063873291] |
9d8e712f-631f-43ed-9760-f6cb52761b93 | deep-implicit-coordination-graphs-for-multi | 2006.11438 | null | https://arxiv.org/abs/2006.11438v2 | https://arxiv.org/pdf/2006.11438v2.pdf | Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning | Multi-agent reinforcement learning (MARL) requires coordination to efficiently solve certain tasks. Fully centralized control is often infeasible in such domains due to the size of joint action spaces. Coordination graph based formalization allows reasoning about the joint action based on the structure of interactions.... | ['Mykel J. Kochenderfer', 'Jayesh K. Gupta', 'Peter Morales', 'Sheng Li', 'Ross Allen'] | 2020-06-19 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-5.41593790e-01 3.67589861e-01 -1.31267712e-01 7.90777132e-02
-5.70485234e-01 -7.13237345e-01 8.14968109e-01 2.26302236e-01
-5.60064077e-01 9.83907640e-01 1.44196272e-01 -2.54516363e-01
-2.55713820e-01 -6.05743349e-01 -8.36999476e-01 -6.49097323e-01
-5.99728227e-01 1.06250405e+00 2.31126592e-01 -6.97036803... | [3.758683919906616, 1.934697151184082] |
0c3ad60d-1c5e-42f8-a12d-17d23e4f0497 | embedding-knowledge-graphs-attentive-to | null | null | https://2021.ecmlpkdd.org/wp-content/uploads/2021/07/sub_1096.pdf | https://2021.ecmlpkdd.org/wp-content/uploads/2021/07/sub_1096.pdf | Embedding Knowledge Graphs Attentive to Positional and Centrality Qualities | Knowledge graphs embeddings (KGE) are lately at the center of many artificial intelligence studies due to their applicability for solving downstream tasks, including link prediction and node classification. However, most Knowledge Graph embedding models encode, into the vector space, only the local graph structure of a... | ['Jens Lehmann', 'Damien Graux', 'Diego Collarana', 'Afshin Sadeghi'] | 2021-06-01 | null | null | null | ecml-pkdd-2021-6 | ['link-property-prediction'] | ['graphs'] | [-3.02964449e-01 5.72082341e-01 -5.52585959e-01 -2.29439855e-01
2.42210329e-01 -5.84773421e-01 6.72424197e-01 9.13917482e-01
-2.93977737e-01 5.77032864e-01 4.17881280e-01 -3.19318771e-01
-7.29164243e-01 -1.65036762e+00 -7.62032866e-01 -5.19329429e-01
-6.15031064e-01 6.94219351e-01 1.39061958e-01 -3.32452267... | [8.60036849975586, 7.723392486572266] |
8f6ae798-c09b-4c11-a43b-b2e561bbead9 | predicting-the-effects-of-news-sentiments-on | 1812.04199 | null | http://arxiv.org/abs/1812.04199v1 | http://arxiv.org/pdf/1812.04199v1.pdf | Predicting the Effects of News Sentiments on the Stock Market | Stock market forecasting is very important in the planning of business
activities. Stock price prediction has attracted many researchers in multiple
disciplines including computer science, statistics, economics, finance, and
operations research. Recent studies have shown that the vast amount of online
information in th... | ['Haruna Isah', 'Farhana Zulkernine', 'Dev Shah'] | 2018-12-11 | null | null | null | null | ['stock-market-prediction', 'stock-price-prediction'] | ['time-series', 'time-series'] | [-8.78810048e-01 -3.41544330e-01 -7.70740807e-01 -1.02370963e-01
-1.18782170e-01 -7.04487026e-01 6.82970464e-01 4.61044908e-01
-5.41924417e-01 6.76281214e-01 8.47710729e-01 -7.18989134e-01
2.46955425e-01 -1.16796887e+00 -4.80606228e-01 -2.73188710e-01
4.72609922e-02 -1.15716718e-01 3.80388737e-01 -7.04438448... | [4.470396995544434, 4.377518653869629] |
74f080d2-be01-426a-ac57-752106132835 | vsec-transformer-based-model-for-vietnamese | 2111.00640 | null | https://arxiv.org/abs/2111.00640v2 | https://arxiv.org/pdf/2111.00640v2.pdf | VSEC: Transformer-based Model for Vietnamese Spelling Correction | Spelling error correction is one of topics which have a long history in natural language processing. Although previous studies have achieved remarkable results, challenges still exist. In the Vietnamese language, a state-of-the-art method for the task infers a syllable's context from its adjacent syllables. The method'... | ['Dinh Hieu Vo', 'Thang Ngoc Bui', 'Ha Thanh Nguyen', 'Dinh-Truong Do'] | 2021-11-01 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 3.88170868e-01 -5.07055700e-01 2.56243169e-01 -3.46541882e-01
-8.25300336e-01 -2.43742049e-01 2.72494048e-01 2.71836579e-01
-8.40610385e-01 9.40500200e-01 1.57273456e-01 -2.67983645e-01
4.51886296e-01 -6.95792317e-01 -8.86096001e-01 -6.67176306e-01
2.23916411e-01 1.88952237e-01 4.63735461e-01 -3.29544842... | [10.954957008361816, 10.781373977661133] |
fa746870-a77c-418f-ab49-e546f73f8419 | multiple-instance-learning-for-content | null | null | https://aclanthology.org/2020.bea-1.3 | https://aclanthology.org/2020.bea-1.3.pdf | Multiple Instance Learning for Content Feedback Localization without Annotation | Automated Essay Scoring (AES) can be used to automatically generate holistic scores with reliability comparable to human scoring. In addition, AES systems can provide formative feedback to learners, typically at the essay level. In contrast, we are interested in providing feedback specialized to the content of the essa... | ['Marcia Derr', 'Peter Foltz', 'Mark Rosenstein', 'William Murray', 'Lee Becker', 'Scott Hellman', 'Adam Wiemerslage'] | 2020-07-01 | null | null | null | ws-2020-7 | ['automated-essay-scoring'] | ['natural-language-processing'] | [ 3.29717785e-01 2.19720855e-01 -2.70042956e-01 -5.44690967e-01
-1.28693664e+00 -9.54656184e-01 2.32297286e-01 6.19086564e-01
-3.69222909e-01 7.83030987e-01 4.46605712e-01 -3.81313890e-01
-8.87788683e-02 -7.13352323e-01 -4.40668374e-01 -1.25877216e-01
7.07953870e-01 4.96546328e-01 1.23171657e-01 -3.32782924... | [11.330339431762695, 9.232720375061035] |
aa382d32-fe7e-4cba-b062-728065fab11b | robust-low-rank-training-via-approximate | 2306.01485 | null | https://arxiv.org/abs/2306.01485v1 | https://arxiv.org/pdf/2306.01485v1.pdf | Robust low-rank training via approximate orthonormal constraints | With the growth of model and data sizes, a broad effort has been made to design pruning techniques that reduce the resource demand of deep learning pipelines, while retaining model performance. In order to reduce both inference and training costs, a prominent line of work uses low-rank matrix factorizations to represen... | ['Francesco Tudisco', 'Gianluca Ceruti', 'Emanuele Zangrando', 'Dayana Savostianova'] | 2023-06-02 | null | null | null | null | ['adversarial-robustness'] | ['adversarial'] | [-6.68253824e-02 3.72339338e-01 1.20070940e-02 3.87233198e-02
-3.98983538e-01 -7.76020944e-01 4.75138366e-01 -3.03515732e-01
-5.04938304e-01 4.73647118e-01 1.67829506e-02 -4.37082201e-01
-3.42257291e-01 -4.40290153e-01 -9.54136968e-01 -5.52268505e-01
-9.88771468e-02 2.76889771e-01 1.53688505e-01 -4.99533862... | [8.371453285217285, 3.4499330520629883] |
0e92a9a5-54af-46e0-85fd-a2494db088c1 | orthonormal-convolutions-for-the-rotation | 2206.03860 | null | https://arxiv.org/abs/2206.03860v1 | https://arxiv.org/pdf/2206.03860v1.pdf | Orthonormal Convolutions for the Rotation Based Iterative Gaussianization | In this paper we elaborate an extension of rotation-based iterative Gaussianization, RBIG, which makes image Gaussianization possible. Although RBIG has been successfully applied to many tasks, it is limited to medium dimensionality data (on the order of a thousand dimensions). In images its application has been restri... | ['Jesús Malo', 'J. Emmanuel Johnson', 'Alexander Hepburn', 'Valero Laparra'] | 2022-06-08 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 2.87114590e-01 3.17665815e-01 2.39188090e-01 -3.77848178e-01
-1.63501844e-01 -4.10770595e-01 6.13213181e-01 -4.22489822e-01
-6.70055211e-01 5.08302748e-01 2.34165221e-01 -1.91392407e-01
-3.49558204e-01 -8.13849568e-01 -7.92344689e-01 -1.12828159e+00
-3.09492624e-03 1.82758212e-01 -2.10689068e-01 1.42531693... | [9.120240211486816, 2.3254644870758057] |
8c776ef9-61bf-4550-9c46-5a2d30aca8fa | semantic-aware-representation-learning-via-1 | 2111.06021 | null | https://arxiv.org/abs/2111.06021v4 | https://arxiv.org/pdf/2111.06021v4.pdf | Probabilistic Contrastive Learning for Domain Adaptation | The standard contrastive learning acts on the extracted features with $\ell_{2}$ normalization. For domain adaptation tasks, however, we find that contrastive learning with the standard paradigm does not perform well. The reason is mainly that the class weights (weights of the final fully connected layer) are not invol... | ['Keyu Tu', 'Zilei Wang', 'Yixin Zhang', 'Junjie Li'] | 2021-11-11 | semantic-aware-representation-learning-via | https://openreview.net/forum?id=XizHAfgfd3J | https://openreview.net/pdf?id=XizHAfgfd3J | null | ['semi-supervised-image-classification'] | ['computer-vision'] | [ 9.35273170e-02 -7.39438683e-02 -3.21426719e-01 -4.74971294e-01
-6.93274319e-01 -4.93787348e-01 5.71822464e-01 2.67563403e-01
-7.46842027e-01 6.08839691e-01 -1.40847161e-01 -2.10615128e-01
-2.65209377e-01 -7.34104216e-01 -6.70487702e-01 -8.88172626e-01
1.88694462e-01 3.54959309e-01 5.29075682e-01 -6.34812191... | [9.588699340820312, 1.5976455211639404] |
40ec4134-a6b8-4e7b-92dc-54d3951f5a48 | objective-probabilistic-and-generalized-noise | 2009.08539 | null | https://arxiv.org/abs/2009.08539v2 | https://arxiv.org/pdf/2009.08539v2.pdf | Objective, Probabilistic, and Generalized Noise Level Dependent Classifications of sets of more or less 2D Periodic Images into Plane Symmetry Groups | Crystallographic symmetry classifications from real-world images with periodicities in two dimensions (2D) are of interest to crystallographers and practitioners of computer vision studies alike. Currently, these classifications are typically made by both communities in a subjective manner that relies on arbitrary thre... | ['Peter Moeck', 'Andrew Dempsey'] | 2020-09-17 | null | null | null | null | ['symmetry-detection'] | ['computer-vision'] | [ 7.06124723e-01 -2.59983391e-01 6.26893863e-02 -3.10371280e-01
-8.21253419e-01 -5.97565711e-01 7.84626901e-01 -2.16645658e-01
-4.54790741e-01 6.04407251e-01 1.56628668e-01 -3.34123105e-01
-3.95034403e-01 -6.58797443e-01 -2.87670344e-01 -1.08392167e+00
1.71818256e-01 7.30532050e-01 1.50880277e-01 -1.31445583... | [8.505260467529297, -2.3243463039398193] |
67cbb04f-b405-412f-808d-a879d5257cee | blind-image-deconvolution-using-deep | 1802.04073 | null | http://arxiv.org/abs/1802.04073v4 | http://arxiv.org/pdf/1802.04073v4.pdf | Blind Image Deconvolution using Deep Generative Priors | This paper proposes a novel approach to regularize the \textit{ill-posed} and
\textit{non-linear} blind image deconvolution (blind deblurring) using deep
generative networks as priors. We employ two separate generative models --- one
trained to produce sharp images while the other trained to generate blur
kernels from ... | ['Muhammad Asim', 'Fahad Shamshad', 'Ali Ahmed'] | 2018-02-12 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 3.41000974e-01 -5.15066423e-02 4.34632331e-01 -1.01907760e-01
-6.55637383e-01 -7.92303026e-01 7.77064979e-01 -1.01682854e+00
-1.99046358e-01 7.74987876e-01 6.64589703e-01 -1.55903175e-01
-1.76095609e-02 -3.37420195e-01 -8.57080400e-01 -1.06022930e+00
2.93682069e-01 2.41779432e-01 -6.48405179e-02 8.86260718... | [11.675841331481934, -2.6393215656280518] |
c5d2a6b8-e7a5-4ffb-8f62-2d2a03bd7475 | a-survey-on-dragonfly-algorithm-and-its | 2002.12126 | null | https://arxiv.org/abs/2002.12126v3 | https://arxiv.org/pdf/2002.12126v3.pdf | A survey on dragonfly algorithm and its applications in engineering | The dragonfly algorithm was developed in 2016. It is one of the algorithms used by researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties w... | ['Seyedali Mirjalili', 'Polla Fattah', 'Tarik A. Rashid', 'Nebojsa Bacanin', 'Abeer Alsadoon', 'Chnoor M. Rahman'] | 2020-02-19 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [-7.58302957e-02 -5.49951732e-01 1.03203788e-01 9.61429551e-02
4.19416070e-01 -4.82106626e-01 6.11593649e-02 2.11666673e-01
-5.32601476e-01 1.20984805e+00 -5.22045612e-01 -7.70646930e-02
-8.77964914e-01 -1.00685811e+00 -2.42325872e-01 -1.00825226e+00
-2.79018849e-01 5.67201316e-01 5.58415279e-02 -8.39495897... | [5.646997451782227, 3.496082305908203] |
b77228d6-9008-4f59-a347-4e29f29a08a4 | vimq-a-vietnamese-medical-question-dataset | 2304.14405 | null | https://arxiv.org/abs/2304.14405v1 | https://arxiv.org/pdf/2304.14405v1.pdf | ViMQ: A Vietnamese Medical Question Dataset for Healthcare Dialogue System Development | Existing medical text datasets usually take the form of ques- tion and answer pairs that support the task of natural language gener- ation, but lacking the composite annotations of the medical terms. In this study, we publish a Vietnamese dataset of medical questions from patients with sentence-level and entity-level a... | ['Steven Q. H. Truong', 'Trung H. Bui', 'Nguyen Phan', 'Nguyen Phuc Minh', 'Tran Hoang Vu', 'Nguyen Anh Tu', 'Ta Duc Huy'] | 2023-04-27 | null | null | null | null | ['named-entity-recognition-ner', 'intent-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.67324497e-02 5.22990227e-01 -2.32313976e-01 -6.90781474e-01
-1.39625669e+00 -4.00524557e-01 9.09785032e-02 6.90792382e-01
-7.56057918e-01 8.19923341e-01 7.14316607e-01 -6.35679841e-01
-4.81992848e-02 -5.63644767e-01 -2.68643945e-01 -1.62011892e-01
6.11438751e-02 7.36322165e-01 6.67442977e-02 -4.54532266... | [8.736634254455566, 8.733993530273438] |
7858e52a-587e-44f4-a79b-2ca308499888 | max-min-diversification-with-fairness | 2301.02053 | null | https://arxiv.org/abs/2301.02053v1 | https://arxiv.org/pdf/2301.02053v1.pdf | Max-Min Diversification with Fairness Constraints: Exact and Approximation Algorithms | Diversity maximization aims to select a diverse and representative subset of items from a large dataset. It is a fundamental optimization task that finds applications in data summarization, feature selection, web search, recommender systems, and elsewhere. However, in a setting where data items are associated with diff... | ['Francesco Fabbri', 'Jia Li', 'Michael Mathioudakis', 'Yanhao Wang'] | 2023-01-05 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 2.59995222e-01 -3.76170548e-03 -5.60600996e-01 -5.10752201e-01
-4.31841195e-01 -3.87691230e-01 -2.15260684e-01 7.85773098e-01
-5.19960463e-01 9.85821068e-01 2.53214631e-02 -5.24039306e-02
-7.63937473e-01 -9.91710722e-01 -2.19056711e-01 -7.98268735e-01
-3.11123461e-01 6.08894646e-01 -3.10081095e-02 -1.20253958... | [6.591451168060303, 4.8863606452941895] |
590dd3ea-6d30-496e-aea9-a6d64596d6a5 | displacing-objects-improving-dynamic-vehicle | 2306.17536 | null | https://arxiv.org/abs/2306.17536v1 | https://arxiv.org/pdf/2306.17536v1.pdf | DisPlacing Objects: Improving Dynamic Vehicle Detection via Visual Place Recognition under Adverse Conditions | Can knowing where you are assist in perceiving objects in your surroundings, especially under adverse weather and lighting conditions? In this work we investigate whether a prior map can be leveraged to aid in the detection of dynamic objects in a scene without the need for a 3D map or pixel-level map-query corresponde... | ['Michael Milford', 'Ankit Vora', 'Shubham Shrivastava', 'Punarjay Chakravarty', 'Sourav Garg', 'Stephen Hausler'] | 2023-06-30 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 1.39787853e-01 -1.55607179e-01 4.89548482e-02 -5.53017616e-01
-1.22131574e+00 -9.16345954e-01 7.84184396e-01 5.14233172e-01
-6.39639318e-01 3.99862319e-01 -2.68483549e-01 -5.40586054e-01
3.42657343e-02 -7.21896231e-01 -9.59835529e-01 -2.57928789e-01
-1.84760720e-01 5.70297062e-01 1.00368750e+00 -2.45202899... | [7.633791446685791, -1.9304096698760986] |
c9a35cda-a2c3-4ec2-a62e-ad776ebfd218 | towards-unified-dialogue-system-evaluation-a | 2006.06110 | null | https://arxiv.org/abs/2006.06110v1 | https://arxiv.org/pdf/2006.06110v1.pdf | Towards Unified Dialogue System Evaluation: A Comprehensive Analysis of Current Evaluation Protocols | As conversational AI-based dialogue management has increasingly become a trending topic, the need for a standardized and reliable evaluation procedure grows even more pressing. The current state of affairs suggests various evaluation protocols to assess chat-oriented dialogue management systems, rendering it difficult ... | ['Sarah E. Finch', 'Jinho D. Choi'] | 2020-06-10 | null | https://aclanthology.org/2020.sigdial-1.29 | https://aclanthology.org/2020.sigdial-1.29.pdf | sigdial-acl-2020-7 | ['dialogue-management'] | ['natural-language-processing'] | [-7.22772479e-02 3.65794927e-01 -3.27308252e-02 -5.09948552e-01
-6.84203744e-01 -8.88979852e-01 1.13870156e+00 1.71442047e-01
-5.06758213e-01 1.05221868e+00 4.68714952e-01 -4.75597262e-01
-1.80020168e-01 -3.28247994e-01 4.57987458e-01 -2.90035695e-01
5.51292971e-02 8.22684705e-01 2.37291664e-01 -8.30795348... | [12.935574531555176, 7.969776630401611] |
139fd8dd-308c-4fcc-8da4-7100e12f82e9 | patient-specific-seizure-prediction-using | 2011.08982 | null | https://arxiv.org/abs/2011.08982v1 | https://arxiv.org/pdf/2011.08982v1.pdf | Patient-Specific Seizure Prediction Using Single Seizure Electroencephalography Recording | Electroencephalogram (EEG) is a prominent way to measure the brain activity for studying epilepsy, thereby helping in predicting seizures. Seizure prediction is an active research area with many deep learning based approaches dominating the recent literature for solving this problem. But these models require a consider... | ['Bülent Yener', 'Hui Su', 'Lara Marcuse', 'Arun Iyengar', 'Zaid Bin Tariq'] | 2020-11-14 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 1.89511888e-02 -3.19015115e-01 4.69578564e-01 -3.46521914e-01
-5.84446728e-01 -3.34510267e-01 2.60557860e-01 4.50216308e-02
-3.94710779e-01 6.56897366e-01 6.13142736e-02 -1.66911229e-01
-3.48591506e-01 -5.28209627e-01 -2.73813009e-01 -7.62070537e-01
-6.55233264e-01 3.43874097e-02 5.88579029e-02 -1.55179977... | [13.208760261535645, 3.513373613357544] |
828a3b3e-7c78-4ad6-a6c1-694ca3c63580 | conceptnet-at-semeval-2017-task-2-extending | 1704.03560 | null | http://arxiv.org/abs/1704.03560v2 | http://arxiv.org/pdf/1704.03560v2.pdf | ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge | This paper describes Luminoso's participation in SemEval 2017 Task 2,
"Multilingual and Cross-lingual Semantic Word Similarity", with a system based
on ConceptNet. ConceptNet is an open, multilingual knowledge graph that focuses
on general knowledge that relates the meanings of words and phrases. Our
submission to SemE... | ['Joanna Lowry-Duda', 'Robyn Speer'] | 2017-04-11 | conceptnet-at-semeval-2017-task-2-extending-1 | https://aclanthology.org/S17-2008 | https://aclanthology.org/S17-2008.pdf | semeval-2017-8 | ['multilingual-word-embeddings'] | ['methodology'] | [-6.67066753e-01 -9.93774012e-02 -3.97478938e-01 -2.31173441e-01
-6.27636194e-01 -9.20375586e-01 9.21707809e-01 9.04029608e-01
-9.74166870e-01 6.52999103e-01 8.58855307e-01 -3.66688162e-01
-1.89100042e-01 -5.74716866e-01 -2.66527742e-01 1.15653398e-02
1.12599850e-01 8.12999845e-01 1.08690798e-01 -8.86835456... | [10.760143280029297, 9.69489860534668] |
b707e5b7-9e67-438e-a57e-d840b43d5fe1 | wenlan-2-0-make-ai-imagine-via-a-multimodal | 2110.14378 | null | https://arxiv.org/abs/2110.14378v2 | https://arxiv.org/pdf/2110.14378v2.pdf | Towards artificial general intelligence via a multimodal foundation model | The fundamental goal of artificial intelligence (AI) is to mimic the core cognitive activities of human. Despite tremendous success in the AI research, most of existing methods have only single-cognitive ability. To overcome this limitation and take a solid step towards artificial general intelligence (AGI), we develop... | ['Ji-Rong Wen', 'Hao Sun', 'Tao Xiang', 'Xin Gao', 'Ruihua Song', 'Haoyu Lu', 'Jingyuan Wen', 'Yuqi Huo', 'Guoxing Yang', 'Yizhao Gao', 'Zhiwu Lu', 'Nanyi Fei'] | 2021-10-27 | null | null | null | null | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 3.11603874e-01 4.80048597e-01 1.41378984e-01 -4.04006064e-01
4.98929136e-02 -4.57328916e-01 8.32569122e-01 -2.15685606e-01
-2.31456712e-01 6.23727202e-01 1.55356348e-01 -1.70355707e-01
-6.08200371e-01 -9.06792223e-01 -4.59874660e-01 -2.89731175e-01
1.45427123e-01 9.17105377e-01 3.49377990e-02 -7.68689334... | [9.163100242614746, 6.494417667388916] |
ced402b5-8232-4e44-b3d6-dc918feb8a9b | decoding-anagrammed-texts-written-in-an | null | null | https://aclanthology.org/Q16-1006 | https://aclanthology.org/Q16-1006.pdf | Decoding Anagrammed Texts Written in an Unknown Language and Script | Algorithmic decipherment is a prime example of a truly unsupervised problem. The first step in the decipherment process is the identification of the encrypted language. We propose three methods for determining the source language of a document enciphered with a monoalphabetic substitution cipher. The best method achiev... | ['Grzegorz Kondrak', 'Bradley Hauer'] | 2016-01-01 | null | null | null | tacl-2016-1 | ['decipherment'] | ['natural-language-processing'] | [ 4.98503506e-01 -3.70870620e-01 5.88833466e-02 -7.11555928e-02
-6.50357664e-01 -1.11941671e+00 1.03635859e+00 -7.37796500e-02
-9.95816588e-01 1.02776015e+00 1.72794238e-01 -9.88758445e-01
2.40635619e-01 -6.36231244e-01 -6.19070590e-01 -8.59871030e-01
2.89423317e-02 4.74185795e-01 -4.83011276e-01 -4.89633352... | [10.23220443725586, 10.515986442565918] |
ccf42a9b-6df1-4f32-8c0c-573ae06ed0e5 | deep-iterative-frame-interpolation-for-full | 1909.02641 | null | https://arxiv.org/abs/1909.02641v1 | https://arxiv.org/pdf/1909.02641v1.pdf | Deep Iterative Frame Interpolation for Full-frame Video Stabilization | Video stabilization is a fundamental and important technique for higher quality videos. Prior works have extensively explored video stabilization, but most of them involve cropping of the frame boundaries and introduce moderate levels of distortion. We present a novel deep approach to video stabilization which can gene... | ['Jinsoo Choi', 'In So Kweon'] | 2019-09-05 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [ 1.18202396e-01 8.59299153e-02 -7.15099722e-02 -1.18855521e-01
-5.61283946e-01 -4.86801535e-01 4.21010464e-01 9.07292962e-03
-3.19276929e-01 7.15165675e-01 2.17444211e-01 -9.96381491e-02
4.10165966e-01 -4.97742474e-01 -1.04593956e+00 -4.84953582e-01
-6.58617318e-02 -2.62641162e-01 5.55233121e-01 -2.84210920... | [10.660002708435059, -1.3691519498825073] |
e898b0a3-8b31-486d-80e2-1536035df3d5 | cnn-aided-factor-graphs-with-estimated-mutual | 2203.05950 | null | https://arxiv.org/abs/2203.05950v1 | https://arxiv.org/pdf/2203.05950v1.pdf | CNN-Aided Factor Graphs with Estimated Mutual Information Features for Seizure Detection | We propose a convolutional neural network (CNN) aided factor graphs assisted by mutual information features estimated by a neural network for seizure detection. Specifically, we use neural mutual information estimation to evaluate the correlation between different electroencephalogram (EEG) channels as features. We the... | ['Nariman Farsad', 'Sandrine de Ribaupierre', 'Nir Shlezinger', 'Eyal Fishel Ben-Knaan', 'Bahareh Salafian'] | 2022-03-11 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [-1.75667211e-01 -1.93024203e-01 -1.85741559e-02 -6.84245527e-01
-6.38455987e-01 -7.85388350e-02 1.57390639e-01 -2.16266923e-02
-6.14471138e-01 6.81455612e-01 2.28189066e-01 -6.14116602e-02
-5.49368680e-01 -4.41298574e-01 -4.36733514e-01 -5.62789500e-01
-9.86726046e-01 -2.02244073e-01 -2.28389889e-01 1.18565574... | [13.1997652053833, 3.5113449096679688] |
5f5652b4-3e93-4a18-be0b-7b4b46a26d7b | korean-drama-scene-transcript-dataset-for | null | null | https://ieeexplore.ieee.org/document/9946853 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9946853 | Korean Drama Scene Transcript Dataset for Emotion Recognition in Conversations | Understanding emotions in conversation is a challenging task, as the sentences often have an implied meaning that is not generally understood in isolation. Efficient use of contextual information is essential for emotion recognition in conversations. Many published datasets provide contextual information for situations... | ['Hyerim Jang', 'Young-Shin Kang', 'Soo-Hyung Kim', 'Guee-Sang Lee', 'Hyung-Jeong Yang', 'Eunchae Lim', 'Sudarshan Pant'] | 2022-11-11 | null | null | null | ieee-access-2022-11 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 1.44780648e-03 -5.15320241e-01 2.81207263e-01 -9.63203728e-01
-5.32440543e-01 -5.25107920e-01 6.29498541e-01 7.79720470e-02
-2.43113101e-01 5.60283542e-01 6.56289339e-01 1.90342575e-01
3.62774193e-01 -5.00341892e-01 -1.92386210e-01 -7.51328230e-01
1.36719674e-01 -4.61315028e-02 -3.69312435e-01 -6.07869983... | [13.084771156311035, 6.013156890869141] |
1c84270b-e62b-4de8-b971-9bee397b68ce | representation-learning-with-fine-grained | 2005.09681 | null | https://arxiv.org/abs/2005.09681v3 | https://arxiv.org/pdf/2005.09681v3.pdf | Weakly Supervised Representation Learning with Coarse Labels | With the development of computational power and techniques for data collection, deep learning demonstrates a superior performance over most existing algorithms on visual benchmark data sets. Many efforts have been devoted to studying the mechanism of deep learning. One important observation is that deep learning can le... | ['Qi Qian', 'Juhua Hu', 'Yuanhong Xu', 'Rong Jin', 'Hao Li'] | 2020-05-19 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Xu_Weakly_Supervised_Representation_Learning_With_Coarse_Labels_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Xu_Weakly_Supervised_Representation_Learning_With_Coarse_Labels_ICCV_2021_paper.pdf | iccv-2021-1 | ['learning-with-coarse-labels'] | ['computer-vision'] | [-1.33627821e-02 -4.08764482e-01 -5.85663557e-01 -5.09950399e-01
-6.73655868e-01 -5.81744015e-01 4.56226170e-01 9.68900099e-02
-2.86528379e-01 5.40966928e-01 -1.56869769e-01 -3.05143893e-01
-1.78814426e-01 -7.66613126e-01 -8.59942496e-01 -8.78918290e-01
9.84199122e-02 3.59534681e-01 2.73855254e-02 3.48124467... | [9.65853214263916, 2.2060134410858154] |
4ef05398-d01e-4f15-ab3d-9fd0cbec7877 | intelligent-problem-solving-as-integrated | 2208.08731 | null | https://arxiv.org/abs/2208.08731v1 | https://arxiv.org/pdf/2208.08731v1.pdf | Intelligent problem-solving as integrated hierarchical reinforcement learning | According to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biological agents depends on hierarchical cognitive mechanisms. Hierarchical reinforcement learning is a promising computational approach that may eventually yield comparable problem-solving behaviour in a... | ['Stefan Wermter', 'Martin V. Butz', 'Phuong D. H. Nguyen', 'Matthias Kerzel', 'Christian Gumbsch', 'Manfred Eppe'] | 2022-08-18 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 3.54009897e-01 5.12152135e-01 3.96422833e-01 6.25562966e-02
2.08749235e-01 -4.78408873e-01 6.35287702e-01 4.60376054e-01
-3.82089704e-01 8.52010489e-01 -1.12095363e-01 -7.97841996e-02
-6.36891425e-01 -8.04123223e-01 -3.84659052e-01 -6.33980393e-01
-2.56538332e-01 5.38477838e-01 3.41674149e-01 -6.70799851... | [4.295072078704834, 1.4249848127365112] |
aeffdc2d-118b-4534-a5f1-c027bae9dfa1 | mvtrans-multi-view-perception-of-transparent | 2302.11683 | null | https://arxiv.org/abs/2302.11683v1 | https://arxiv.org/pdf/2302.11683v1.pdf | MVTrans: Multi-View Perception of Transparent Objects | Transparent object perception is a crucial skill for applications such as robot manipulation in household and laboratory settings. Existing methods utilize RGB-D or stereo inputs to handle a subset of perception tasks including depth and pose estimation. However, transparent object perception remains to be an open prob... | ['Animesh Garg', 'Florian Shkurti', 'Alan Aspuru-Guzik', 'Saggi Eppel', 'Haoping Xu', 'Yuchi Zhao', 'Yi Ru Wang'] | 2023-02-22 | null | null | null | null | ['transparent-objects', 'transparent-object-detection', 'robot-manipulation'] | ['computer-vision', 'computer-vision', 'robots'] | [ 2.71477163e-01 3.73542905e-02 2.43351325e-01 -5.76270998e-01
-4.62980509e-01 -6.38422906e-01 3.56314108e-02 -2.45409623e-01
-1.84669480e-01 3.28050166e-01 -2.14672923e-01 -2.50244945e-01
3.18751365e-01 -8.46516311e-01 -8.23800743e-01 -3.92090380e-01
5.91875613e-01 4.07702267e-01 6.73767924e-01 6.49872646... | [7.113190650939941, -1.978243112564087] |
24c99074-245f-4c3a-90e8-5382bb5463e7 | query-focused-abstractive-summarization | 1801.07704 | null | http://arxiv.org/abs/1801.07704v2 | http://arxiv.org/pdf/1801.07704v2.pdf | Query Focused Abstractive Summarization: Incorporating Query Relevance, Multi-Document Coverage, and Summary Length Constraints into seq2seq Models | Query Focused Summarization (QFS) has been addressed mostly using extractive
methods. Such methods, however, produce text which suffers from low coherence.
We investigate how abstractive methods can be applied to QFS, to overcome such
limitations. Recent developments in neural-attention based sequence-to-sequence
model... | ['Tal Baumel', 'Matan Eyal', 'Michael Elhadad'] | 2018-01-23 | null | null | null | null | ['query-based-extractive-summarization'] | ['natural-language-processing'] | [ 6.26626134e-01 2.17536911e-01 -1.77089378e-01 -3.97584476e-02
-1.41146469e+00 -5.56980908e-01 6.76340461e-01 3.00271034e-01
-5.85398555e-01 8.96425128e-01 9.59045172e-01 -2.18716115e-02
2.58363318e-02 -6.55763865e-01 -7.88631856e-01 -2.33385712e-01
1.89092278e-01 6.84167504e-01 4.51206475e-01 -7.35837042... | [12.32159423828125, 9.331268310546875] |
a50a2ad7-e20f-42a5-b4b8-4b10694ba0f9 | cross-corpora-spoken-language-identification | 2302.05110 | null | https://arxiv.org/abs/2302.05110v1 | https://arxiv.org/pdf/2302.05110v1.pdf | Cross-Corpora Spoken Language Identification with Domain Diversification and Generalization | This work addresses the cross-corpora generalization issue for the low-resourced spoken language identification (LID) problem. We have conducted the experiments in the context of Indian LID and identified strikingly poor cross-corpora generalization due to corpora-dependent non-lingual biases. Our contribution to this ... | ['Goutam Saha', 'Md Sahidullah', 'Spandan Dey'] | 2023-02-10 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [-8.34475011e-02 -2.39929691e-01 -2.24472642e-01 -2.87093759e-01
-1.22563696e+00 -6.88322961e-01 5.66919446e-01 -3.58146012e-01
-6.19737864e-01 8.54693592e-01 4.99344230e-01 -2.67358035e-01
2.31864937e-02 -6.74619228e-02 -4.19797212e-01 -8.23682666e-01
3.45055461e-02 5.38934886e-01 -4.00552228e-02 -5.24934590... | [14.254858016967773, 6.401511192321777] |
c7cf795b-3954-4bdb-8bab-08607c3ba68e | police-text-analysis-topic-modeling-and | 2202.04176 | null | https://arxiv.org/abs/2202.04176v2 | https://arxiv.org/pdf/2202.04176v2.pdf | Police Text Analysis: Topic Modeling and Spatial Relative Density Estimation | We analyze a large corpus of police incident narrative documents in understanding the spatial distribution of the topics. The motivation for doing this is that police narratives in each incident report contains very fine-grained information that is richer than the category that is manually assigned by the police. Our a... | ['Yao Xie', 'Xiuyuan Cheng', 'Sarah Huestis-Mitchell'] | 2022-02-08 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [-3.08695167e-01 3.63375306e-01 -4.15712565e-01 -3.28128517e-01
-1.07792330e+00 -6.84570074e-01 8.35948288e-01 6.90556884e-01
-3.20532739e-01 5.72433293e-01 1.38019645e+00 -5.27693808e-01
-4.59520161e-01 -9.29967463e-01 -2.89136648e-01 -6.60464525e-01
-2.48318717e-01 8.66836131e-01 1.75117731e-01 2.12526575... | [10.347222328186035, 7.0734686851501465] |
4f399990-c658-4c5c-b828-092fae0e1cba | learning-deep-multi-level-similarity-for | 1906.03568 | null | https://arxiv.org/abs/1906.03568v1 | https://arxiv.org/pdf/1906.03568v1.pdf | Learning Deep Multi-Level Similarity for Thermal Infrared Object Tracking | Existing deep Thermal InfraRed (TIR) trackers only use semantic features to describe the TIR object, which lack the sufficient discriminative capacity for handling distractors. This becomes worse when the feature extraction network is only trained on RGB images.To address this issue, we propose a multi-level similarity... | ['Hongpeng Wang', 'Nana Fan', 'Qiao Liu', 'Zhenyu He', 'Xin Li', 'Di Yuan'] | 2019-06-09 | null | null | null | null | ['thermal-infrared-object-tracking'] | ['computer-vision'] | [ 5.39635867e-02 -6.62862957e-01 -5.34133613e-02 -2.48442769e-01
-3.59250844e-01 -2.47631803e-01 4.58249480e-01 -7.57992208e-01
-5.74150681e-01 2.58908391e-01 -6.85917139e-02 1.33757278e-01
-3.76553349e-02 -1.18446440e-01 -4.16480482e-01 -1.22237170e+00
5.88038981e-01 -1.25318483e-01 5.41152000e-01 -1.94083545... | [6.3203277587890625, -2.213862180709839] |
47ce01fa-543a-4ea2-bff5-a61ddfcf7115 | dinosr-self-distillation-and-online | 2305.10005 | null | https://arxiv.org/abs/2305.10005v1 | https://arxiv.org/pdf/2305.10005v1.pdf | DinoSR: Self-Distillation and Online Clustering for Self-supervised Speech Representation Learning | In this paper, we introduce self-distillation and online clustering for self-supervised speech representation learning (DinoSR) which combines masked language modeling, self-distillation, and online clustering. We show that these concepts complement each other and result in a strong representation learning model for sp... | ['James R. Glass', 'Wei-Ning Hsu', 'Michael Auli', 'Heng-Jui Chang', 'Alexander H. Liu'] | 2023-05-17 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [-6.06307462e-02 5.58223128e-01 -3.05578589e-01 -5.29759765e-01
-1.05210543e+00 -8.31147969e-01 5.25665283e-01 2.63184130e-01
-2.81594992e-01 4.04141277e-01 6.69240892e-01 -6.33608282e-01
1.63362712e-01 -4.27489430e-01 -5.13653278e-01 -4.40361500e-01
-2.00889930e-01 7.14708805e-01 1.27534373e-02 2.68844813... | [14.421330451965332, 6.6013898849487305] |
4452e2a4-5dea-4f82-8826-f2bc0b06adde | shift-invariant-waveform-learning-on | 2108.03177 | null | https://arxiv.org/abs/2108.03177v2 | https://arxiv.org/pdf/2108.03177v2.pdf | Shift-invariant waveform learning on epileptic ECoG | Seizure detection algorithms must discriminate abnormal neuronal activity associated with a seizure from normal neural activity in a variety of conditions. Our approach is to seek spatiotemporal waveforms with distinct morphology in electrocorticographic (ECoG) recordings of epileptic patients that are indicative of a ... | ['Austin J. Brockmeier', 'Carlos H. Mendoza-Cardenas'] | 2021-08-06 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 2.95334011e-01 -3.21858585e-01 2.93401450e-01 -3.31721097e-01
-7.87926137e-01 -7.28477418e-01 5.14984846e-01 3.52807373e-01
7.27037564e-02 6.29803300e-01 5.51460207e-01 -1.60860389e-01
-4.32807326e-01 -1.06282435e-01 -1.70595169e-01 -1.01185608e+00
-7.54217386e-01 2.01170027e-01 1.05563447e-01 2.21900180... | [13.199695587158203, 3.5357720851898193] |
db6619c7-123f-4071-8c99-e96fa8240796 | simulation-based-frequentist-inference-with | 2306.07769 | null | https://arxiv.org/abs/2306.07769v1 | https://arxiv.org/pdf/2306.07769v1.pdf | Simulation-Based Frequentist Inference with Tractable and Intractable Likelihoods | High-fidelity simulators that connect theoretical models with observations are indispensable tools in many sciences. When coupled with machine learning, a simulator makes it possible to infer the parameters of a theoretical model directly from real and simulated observations without explicit use of the likelihood funct... | ['Olivia F. Prosper', 'Harrison B. Prosper', 'Ali Al Kadhim'] | 2023-06-13 | null | null | null | null | ['epidemiology', 'astronomy'] | ['medical', 'miscellaneous'] | [ 1.57984570e-01 1.31213292e-01 -2.72572875e-01 5.26309423e-02
-5.03138244e-01 -4.93735999e-01 9.96350288e-01 2.35820606e-01
-5.66488981e-01 1.29806638e+00 -3.51669431e-01 -8.97822261e-01
-6.83851361e-01 -7.81195164e-01 -8.80922675e-01 -7.87327945e-01
-1.60032332e-01 5.96281707e-01 3.20180386e-01 -1.81593057... | [6.6506547927856445, 3.9624979496002197] |
b795c1dd-b39b-4fd0-8321-56733566555a | mathbert-a-pre-trained-model-for-mathematical | 2105.00377 | null | https://arxiv.org/abs/2105.00377v1 | https://arxiv.org/pdf/2105.00377v1.pdf | MathBERT: A Pre-Trained Model for Mathematical Formula Understanding | Large-scale pre-trained models like BERT, have obtained a great success in various Natural Language Processing (NLP) tasks, while it is still a challenge to adapt them to the math-related tasks. Current pre-trained models neglect the structural features and the semantic correspondence between formula and its context. T... | ['Zhi Tang', 'Liangcai Gao', 'Ke Yuan', 'Shuai Peng'] | 2021-05-02 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [ 4.62756604e-01 1.70657441e-01 -3.20319593e-01 -8.02136719e-01
-7.22743750e-01 -2.81553775e-01 3.65492314e-01 3.90195638e-01
2.49053434e-01 3.80246878e-01 1.87392101e-01 -4.47024435e-01
-1.37401238e-01 -1.28154624e+00 -8.75572979e-01 -1.02617882e-01
3.44825864e-01 6.79023087e-01 2.86939353e-01 -3.76690596... | [9.600214004516602, 7.522762298583984] |
de82a6e0-b589-4438-adeb-c9d00baa1782 | conjr-conjunctive-sentence-splitter-without | null | null | https://openreview.net/forum?id=nUcR4c0hg5q | https://openreview.net/pdf?id=nUcR4c0hg5q | CONJR: Conjunctive Sentence Splitter without Parsing | In this paper, we observe and address the challenges of splitting conjunctive sentences around each group of conjuncts. Most existing methods rely on parsers to identify the conjuncts in a sentence and detect the coordination boundaries. However, state-of-the-art syntactic parsers are slow and suffer from errors, espec... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['boundary-detection'] | ['computer-vision'] | [-6.72782362e-02 6.36038482e-02 -2.31069386e-01 -4.95166630e-01
-7.84413457e-01 -6.13842130e-01 5.33275492e-02 3.69386077e-01
-2.06333786e-01 5.38773298e-01 2.91901737e-01 -3.91994834e-01
1.53480768e-01 -5.77093542e-01 -5.55569589e-01 -2.92036176e-01
-5.31441253e-03 2.29212254e-01 5.90612710e-01 -3.23942870... | [10.341859817504883, 9.51630973815918] |
b15b3d19-537d-4087-b921-72c467b4bd8b | incomplete-utterance-rewriting-as-semantic | 2009.13166 | null | https://arxiv.org/abs/2009.13166v1 | https://arxiv.org/pdf/2009.13166v1.pdf | Incomplete Utterance Rewriting as Semantic Segmentation | Recent years the task of incomplete utterance rewriting has raised a large attention. Previous works usually shape it as a machine translation task and employ sequence to sequence based architecture with copy mechanism. In this paper, we present a novel and extensive approach, which formulates it as a semantic segmenta... | ['Jian-Guang Lou', 'Bei Chen', 'Qian Liu', 'Dongmei Zhang', 'Bin Zhou'] | 2020-09-28 | null | https://aclanthology.org/2020.emnlp-main.227 | https://aclanthology.org/2020.emnlp-main.227.pdf | emnlp-2020-11 | ['context-query-reformulation', 'dialogue-rewriting'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.63281286e-01 4.47589666e-01 -2.70806402e-02 -6.52003706e-01
-1.10669446e+00 -6.60216928e-01 8.99470389e-01 -4.38794121e-02
-4.14819628e-01 8.66120219e-01 2.60850877e-01 -5.03130674e-01
4.79452670e-01 -6.44062817e-01 -1.01380646e+00 -1.96311787e-01
4.21061486e-01 7.22342551e-01 2.05207765e-01 -6.21664941... | [11.331196784973145, 9.37177848815918] |
b937e453-2da1-4086-9484-02db218ace44 | unsupervised-wasserstein-distance-guided | 2009.02831 | null | https://arxiv.org/abs/2009.02831v1 | https://arxiv.org/pdf/2009.02831v1.pdf | Unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-Domain Liver Segmentation | Deep neural networks have shown exceptional learning capability and generalizability in the source domain when massive labeled data is provided. However, the well-trained models often fail in the target domain due to the domain shift. Unsupervised domain adaptation aims to improve network performance when applying robu... | ['Junlin Yang', 'James S. Duncan', 'Chenyu You', 'Julius Chapiro'] | 2020-09-06 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 3.81088763e-01 5.05793765e-02 -4.36692029e-01 -4.74015862e-01
-1.11564004e+00 -3.59443098e-01 4.02575701e-01 5.29443622e-02
-1.91470593e-01 6.65558934e-01 4.94163662e-01 -8.44296534e-04
-3.84793788e-01 -4.99051839e-01 -3.15878749e-01 -1.02338552e+00
-9.17055905e-02 5.42209744e-01 -3.51454318e-01 9.74884257... | [14.61380386352539, -2.0127675533294678] |
7a8af637-84d3-468a-b2e7-74a99cc98187 | graph-gpa-2-0-a-graphical-model-for-multi | 2204.06714 | null | https://arxiv.org/abs/2204.06714v1 | https://arxiv.org/pdf/2204.06714v1.pdf | graph-GPA 2.0: A Graphical Model for Multi-disease Analysis of GWAS Results with Integration of Functional Annotation Data | Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to fully understand functional mechanisms underlying many associated variants. This is especially the case when we are interested in variants ... | ['Dongjun Chung', 'Hang J. Kim', 'Lang Li', 'Maciej Pietrzak', 'Won Chang', 'Ayse Selen Yilmaz', 'Jin Hyun Nam', 'Qiaolan Deng'] | 2022-04-14 | null | null | null | null | ['literature-mining'] | ['natural-language-processing'] | [ 3.86531875e-02 -2.31777653e-01 9.37752724e-02 -2.29761094e-01
-4.75822031e-01 -4.53466505e-01 -9.67062265e-02 3.92460078e-01
1.81580871e-01 1.03238761e+00 -5.97485714e-03 -1.52776912e-01
-6.16358757e-01 -7.77477086e-01 -4.02636617e-01 -6.63151622e-01
-6.04268551e-01 5.45001805e-01 -1.09511301e-01 2.01422453... | [6.492081165313721, 5.546047210693359] |
900c7366-74a8-40b4-8e21-29fdc750fa5f | energy-based-sliced-wasserstein-distance | 2304.13586 | null | https://arxiv.org/abs/2304.13586v1 | https://arxiv.org/pdf/2304.13586v1.pdf | Energy-Based Sliced Wasserstein Distance | The sliced Wasserstein (SW) distance has been widely recognized as a statistically effective and computationally efficient metric between two probability measures. A key component of the SW distance is the slicing distribution. There are two existing approaches for choosing this distribution. The first approach is usin... | ['Nhat Ho', 'Khai Nguyen'] | 2023-04-26 | null | null | null | null | ['point-cloud-reconstruction'] | ['computer-vision'] | [-9.67149362e-02 -4.14345354e-01 -7.78750405e-02 -2.23227188e-01
-6.06571376e-01 -3.73993099e-01 3.01595151e-01 -2.47701127e-02
-3.90401393e-01 6.86719000e-01 -1.11139454e-01 -2.81404704e-01
-5.43783665e-01 -9.74558890e-01 -3.37801874e-01 -9.14356768e-01
9.07654539e-02 4.01678383e-01 5.74911118e-01 1.28630072... | [7.413590908050537, 3.873350143432617] |
d09db522-f1ec-40e9-94f9-41355511d51d | multi-task-cross-attention-network-in-facial | 2207.10293 | null | https://arxiv.org/abs/2207.10293v2 | https://arxiv.org/pdf/2207.10293v2.pdf | Affective Behavior Analysis using Action Unit Relation Graph and Multi-task Cross Attention | Facial behavior analysis is a broad topic with various categories such as facial emotion recognition, age, and gender recognition. Many studies focus on individual tasks while the multi-task learning approach is still an open research issue and requires more research. In this paper, we present our solution and experime... | ['Hyung-Jeong Yang', 'Soo-Huyng Kim', 'Guee-Sang Lee', 'Ngoc-Huynh Ho', 'Sudarshan Pant', 'Dang-Khanh Nguyen'] | 2022-07-21 | null | null | null | null | ['facial-emotion-recognition', 'facial-expression-recognition', 'action-unit-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.52305511e-02 -1.64520293e-01 -3.86385769e-01 -7.77494192e-01
-8.40477824e-01 -1.57977477e-01 3.12368959e-01 -1.53002888e-01
-4.03188854e-01 4.25071388e-01 8.72320235e-02 6.05570436e-01
4.07151520e-01 -1.60391107e-01 -3.42411786e-01 -8.17510366e-01
-2.00912744e-01 -9.17368159e-02 -2.00307682e-01 5.27664945... | [13.601109504699707, 1.8698492050170898] |
7af29229-ec55-4491-ae9b-5ceb8d09da01 | pay-more-attention-to-relation-exploration | 2305.02118 | null | https://arxiv.org/abs/2305.02118v2 | https://arxiv.org/pdf/2305.02118v2.pdf | Pay More Attention to Relation Exploration for Knowledge Base Question Answering | Knowledge base question answering (KBQA) is a challenging task that aims to retrieve correct answers from large-scale knowledge bases. Existing attempts primarily focus on entity representation and final answer reasoning, which results in limited supervision for this task. Moreover, the relations, which empirically det... | ['Daniel Hershcovich', 'Min Chen', 'Bin Wang', 'Shuai Chen', 'Wen Dai', 'Huiwen Liu', 'Xianzhi Li', 'Yong Cao'] | 2023-05-03 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [ 7.52643347e-02 4.44597304e-01 -4.11330521e-01 -2.42604032e-01
-9.70068038e-01 -4.29871649e-01 4.03917342e-01 4.53491509e-01
-4.25823063e-01 9.46387231e-01 3.85987490e-01 -4.25714105e-01
-4.87932414e-01 -1.13178039e+00 -8.03699911e-01 -3.13206196e-01
1.21095106e-01 7.80609906e-01 7.38789916e-01 -5.79479694... | [10.437469482421875, 7.976081371307373] |
2b5fb4ce-bbdd-48f1-848e-17b482ca5f91 | learning-lie-group-symmetry-transformations | 2307.01583 | null | https://arxiv.org/abs/2307.01583v1 | https://arxiv.org/pdf/2307.01583v1.pdf | Learning Lie Group Symmetry Transformations with Neural Networks | The problem of detecting and quantifying the presence of symmetries in datasets is useful for model selection, generative modeling, and data analysis, amongst others. While existing methods for hard-coding transformations in neural networks require prior knowledge of the symmetries of the task at hand, this work focuse... | ['Efstratios Gavves', 'Rick Quax', 'Kevin Webster', 'Jeroen S. W. Lamb', 'Riccardo Valperga', 'Victoria Klein', 'Alex Gabel'] | 2023-07-04 | null | null | null | null | ['model-selection'] | ['methodology'] | [ 4.85329390e-01 3.57876942e-02 -2.64340490e-01 -9.44386497e-02
-2.62330979e-01 -8.19357753e-01 1.28483605e+00 -1.17922224e-01
-1.58420056e-01 5.59726655e-01 3.56101751e-01 -6.28631935e-02
-4.98683453e-01 -4.53364283e-01 -8.39784443e-01 -8.64414632e-01
7.05969706e-02 6.84458435e-01 -1.41256317e-01 -6.08367659... | [9.052184104919434, 2.4678966999053955] |
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