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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
669b13ee-89ad-476f-95d0-fd189524289a | analyzing-speaker-information-in-self | 2108.00917 | null | https://arxiv.org/abs/2108.00917v1 | https://arxiv.org/pdf/2108.00917v1.pdf | Analyzing Speaker Information in Self-Supervised Models to Improve Zero-Resource Speech Processing | Contrastive predictive coding (CPC) aims to learn representations of speech by distinguishing future observations from a set of negative examples. Previous work has shown that linear classifiers trained on CPC features can accurately predict speaker and phone labels. However, it is unclear how the features actually cap... | ['Herman Kamper', 'Matthew Baas', 'Leanne Nortje', 'Benjamin van Niekerk'] | 2021-08-02 | null | null | null | null | ['acoustic-unit-discovery'] | ['speech'] | [ 5.54755926e-01 2.23513424e-01 -1.45521492e-01 -8.70962620e-01
-1.20436037e+00 -6.88694179e-01 7.31823623e-01 4.27172929e-02
-1.98383018e-01 1.23255752e-01 6.06203973e-01 -4.79277462e-01
1.71359643e-01 -6.04467541e-02 -5.72350860e-01 -8.12081516e-01
-3.81519556e-01 2.01817468e-01 -1.35289431e-01 3.33483219... | [14.42087459564209, 6.373611927032471] |
b1b36b96-8cb4-42e3-93e7-33ef8e4a4a93 | unsupervised-object-segmentation-with | 1911.09228 | null | https://arxiv.org/abs/1911.09228v1 | https://arxiv.org/pdf/1911.09228v1.pdf | Unsupervised Object Segmentation with Explicit Localization Module | In this paper, we propose a novel architecture that iteratively discovers and segments out the objects of a scene based on the image reconstruction quality. Different from other approaches, our model uses an explicit localization module that localizes objects of the scene based on the pixel-level reconstruction qualiti... | ['John D. Owens', 'Weitang Liu', 'James Sharpnack', 'Lifeng Wei'] | 2019-11-21 | null | null | null | null | ['unsupervised-object-segmentation'] | ['computer-vision'] | [ 5.88730276e-02 1.91315517e-01 -6.90817833e-02 -2.01882049e-01
-2.89611578e-01 -5.40944338e-01 1.48971826e-01 2.80085653e-01
-4.24560308e-01 4.32313710e-01 -4.27190036e-01 2.69458480e-02
-1.24231167e-02 -8.05940926e-01 -6.25909984e-01 -5.30792534e-01
-2.09842697e-02 5.88112652e-01 1.10820746e+00 9.97756273... | [9.236400604248047, -0.2590867877006531] |
ce01fae8-aef6-4758-933a-0b2d8dc565b6 | fidelity-weighted-learning | 1711.02799 | null | http://arxiv.org/abs/1711.02799v2 | http://arxiv.org/pdf/1711.02799v2.pdf | Fidelity-Weighted Learning | Training deep neural networks requires many training samples, but in practice
training labels are expensive to obtain and may be of varying quality, as some
may be from trusted expert labelers while others might be from heuristics or
other sources of weak supervision such as crowd-sourcing. This creates a
fundamental q... | ['Bernhard Schölkopf', 'Stephan Gouws', 'Mostafa Dehghani', 'Jaap Kamps', 'Arash Mehrjou'] | 2017-11-08 | fidelity-weighted-learning-1 | https://openreview.net/forum?id=B1X0mzZCW | https://openreview.net/pdf?id=B1X0mzZCW | iclr-2018-1 | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [ 1.01158887e-01 4.56671417e-01 -6.90683603e-01 -9.72558081e-01
-1.15868855e+00 -6.21515334e-01 6.39935493e-01 5.35895765e-01
-1.02942216e+00 9.23381984e-01 9.85804796e-02 -1.05832070e-01
-2.96471208e-01 -8.67592633e-01 -1.01513231e+00 -8.27285230e-01
3.40283036e-01 1.09282708e+00 6.52464852e-02 -5.50147481... | [9.434064865112305, 3.788414478302002] |
f02ebde5-e6da-4692-b9dc-a49f09652784 | learning-towards-selective-data-augmentation | 2303.09719 | null | https://arxiv.org/abs/2303.09719v1 | https://arxiv.org/pdf/2303.09719v1.pdf | Learning towards Selective Data Augmentation for Dialogue Generation | As it is cumbersome and expensive to acquire a huge amount of data for training neural dialog models, data augmentation is proposed to effectively utilize existing training samples. However, current data augmentation techniques on the dialog generation task mostly augment all cases in the training dataset without consi... | ['Rui Yan', 'Xiangliang Zhang', 'Xin Gao', 'Jianwei Cui', 'Chen Wei', 'Xiaoqiang Xia', 'Jiayi Zhang', 'Mingzhe Li', 'Xiuying Chen'] | 2023-03-17 | null | null | null | null | ['dialogue-generation', 'response-generation', 'dialogue-generation'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 2.01734021e-01 3.52618933e-01 -2.03710303e-01 -6.48515642e-01
-7.88911343e-01 -5.95158875e-01 7.77016521e-01 -7.01666102e-02
-4.56709862e-01 1.02028656e+00 3.12968314e-01 -2.35453770e-01
2.52855867e-01 -9.00462568e-01 -3.48882347e-01 -5.80600917e-01
4.85637039e-01 8.83843839e-01 6.55299649e-02 -6.45155489... | [12.660776138305664, 8.178911209106445] |
3c937030-0195-47a2-a4e0-45276e713fb4 | tart-improved-few-shot-text-classification | 2306.02175 | null | https://arxiv.org/abs/2306.02175v1 | https://arxiv.org/pdf/2306.02175v1.pdf | TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation | Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieve state-of-the-art performance. However, the performance of existing approaches heavily depends on the inter-class variance of the support set. As a result, it can perform well on tasks when the semantics of sampled class... | ['Chang-Tien Lu', 'Fanglan Chen', 'Jianfeng He', 'Xuchao Zhang', 'Shuo Lei'] | 2023-06-03 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 1.33100793e-01 -4.12544608e-01 -3.29697102e-01 -6.50110900e-01
-8.09401989e-01 7.83826858e-02 6.24585271e-01 3.19049180e-01
-6.34637535e-01 4.78910744e-01 1.00504331e-01 2.94061691e-01
-3.62037897e-01 -6.30576611e-01 -1.48531631e-01 -5.64008474e-01
3.60559046e-01 5.02269685e-01 6.76442206e-01 -4.63891655... | [10.193339347839355, 3.5264813899993896] |
78424d08-2cbf-4030-8935-2b4392b509a1 | convolutional-pose-machines | 1602.00134 | null | http://arxiv.org/abs/1602.00134v4 | http://arxiv.org/pdf/1602.00134v4.pdf | Convolutional Pose Machines | Pose Machines provide a sequential prediction framework for learning rich
implicit spatial models. In this work we show a systematic design for how
convolutional networks can be incorporated into the pose machine framework for
learning image features and image-dependent spatial models for the task of pose
estimation. T... | ['Shih-En Wei', 'Takeo Kanade', 'Varun Ramakrishna', 'Yaser Sheikh'] | 2016-01-30 | convolutional-pose-machines-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Wei_Convolutional_Pose_Machines_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Wei_Convolutional_Pose_Machines_CVPR_2016_paper.pdf | cvpr-2016-6 | ['car-pose-estimation'] | ['computer-vision'] | [ 1.78254366e-01 5.77249050e-01 -3.12000483e-01 -6.75948918e-01
-7.97327340e-01 -4.06995893e-01 6.05725288e-01 -1.17346719e-01
-4.22165751e-01 6.58959627e-01 2.52566665e-01 -7.78459609e-02
-6.69601411e-02 -5.07580340e-01 -1.33305800e+00 -4.48826283e-01
-3.13271821e-01 8.21165264e-01 2.39293396e-01 3.00494526... | [7.144000053405762, -1.4093047380447388] |
eb9cb842-0854-4589-a751-cb5442abe2ce | natural-language-sentence-generation-from-api | 2206.06868 | null | https://arxiv.org/abs/2206.06868v1 | https://arxiv.org/pdf/2206.06868v1.pdf | Natural Language Sentence Generation from API Specifications | APIs are everywhere; they provide access to automation solutions that could help businesses automate some of their tasks. Unfortunately, they may not be accessible to the business users who need them but are not equipped with the necessary technical skills to leverage them. Wrapping these APIs with chatbot capabilities... | ['Yara Rizk', 'Vinod Muthusamy', 'Vatche Isahagian', 'Jayachandu Bandlamudi', 'Kushal Mukherjee', 'Siyu Huo'] | 2022-06-01 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [-3.40401620e-01 5.20960450e-01 1.46423891e-01 -7.62675107e-01
-2.75333852e-01 -7.39016831e-01 5.86480200e-01 -3.98112118e-01
8.10902193e-03 4.06294018e-01 3.13936472e-01 -8.60293984e-01
2.73952037e-01 -8.80767584e-01 -6.83553591e-02 -1.85137257e-01
4.73329574e-01 4.89779800e-01 1.38781145e-01 -7.45251060... | [12.73116397857666, 7.8353166580200195] |
62bb2f14-c505-40e3-a373-45ca8a42bb40 | disentangled-ontology-embedding-for-zero-shot | 2206.03739 | null | https://arxiv.org/abs/2206.03739v1 | https://arxiv.org/pdf/2206.03739v1.pdf | Disentangled Ontology Embedding for Zero-shot Learning | Knowledge Graph (KG) and its variant of ontology have been widely used for knowledge representation, and have shown to be quite effective in augmenting Zero-shot Learning (ZSL). However, existing ZSL methods that utilize KGs all neglect the intrinsic complexity of inter-class relationships represented in KGs. One typic... | ['Huajun Chen', 'Feiyu Xiong', 'Yufeng Huang', 'Jeff Z. Pan', 'Zhuo Chen', 'Yajing Xu', 'Wen Zhang', 'Jiaoyan Chen', 'Yuxia Geng'] | 2022-06-08 | null | null | null | null | ['ontology-embedding'] | ['knowledge-base'] | [-1.09383591e-01 3.81391019e-01 -4.57510889e-01 -1.76034853e-01
-3.66519362e-01 -1.94560483e-01 5.99069297e-01 2.95398593e-01
-2.57911510e-03 5.78464448e-01 4.45794076e-01 1.26751721e-01
-6.09064519e-01 -1.20716429e+00 -5.50717711e-01 -6.39507055e-01
-9.58095863e-02 4.29877877e-01 1.38076022e-01 -3.25639874... | [8.681775093078613, 7.875489234924316] |
f3b7df1f-b37b-465c-805c-b6994727dc7d | general-purpose-question-answering-with-macaw | 2109.02593 | null | https://arxiv.org/abs/2109.02593v1 | https://arxiv.org/pdf/2109.02593v1.pdf | General-Purpose Question-Answering with Macaw | Despite the successes of pretrained language models, there are still few high-quality, general-purpose QA systems that are freely available. In response, we present Macaw, a versatile, generative question-answering (QA) system that we are making available to the community. Macaw is built on UnifiedQA, itself built on T... | ['Peter Clark', 'Oyvind Tafjord'] | 2021-09-06 | null | null | null | null | ['generative-question-answering'] | ['natural-language-processing'] | [-3.50567512e-02 2.59760648e-01 2.93530673e-01 -4.92743313e-01
-1.90812755e+00 -1.15438294e+00 6.78909779e-01 -9.90784168e-02
-3.50756526e-01 8.54249835e-01 4.36220556e-01 -8.48020196e-01
-7.52074569e-02 -8.39998245e-01 -6.06063664e-01 -2.53950685e-01
4.07904744e-01 1.08772278e+00 2.47657269e-01 -6.78164661... | [11.383686065673828, 8.142871856689453] |
4604e003-c243-429f-ac0f-64626de55f7c | asymreg-robust-symmetric-image-registration | 2303.10211 | null | https://arxiv.org/abs/2303.10211v2 | https://arxiv.org/pdf/2303.10211v2.pdf | SITReg: Multi-resolution architecture for symmetric, inverse consistent, and topology preserving image registration using deformation inversion layers | Deep learning based deformable medical image registration methods have emerged as a strong alternative for classical iterative registration methods. Since image registration is in general an ill-defined problem, the usefulness of inductive biases of symmetricity, inverse consistency and topology preservation has been w... | ['Pekka Marttinen', 'Joel Honkamaa'] | 2023-03-17 | null | null | null | null | ['deformable-medical-image-registration', 'medical-image-registration'] | ['medical', 'medical'] | [ 1.32318333e-01 -1.73508152e-02 -1.03367195e-01 -6.39616013e-01
-7.72843122e-01 -2.89320797e-01 5.89769363e-01 1.32651493e-01
-6.07987404e-01 6.18290842e-01 4.46972288e-02 1.28745764e-01
-4.81989592e-01 -8.01736593e-01 -4.98658180e-01 -7.70397007e-01
-4.64103296e-02 8.02091718e-01 2.72658944e-01 -3.21237355... | [13.974376678466797, -2.562040328979492] |
512bbaf5-9324-48f3-b8a1-499d4362fa30 | pedagogical-rule-extraction-for-learning | 2112.13285 | null | https://arxiv.org/abs/2112.13285v2 | https://arxiv.org/pdf/2112.13285v2.pdf | Pedagogical Rule Extraction to Learn Interpretable Models - an Empirical Study | Machine-learning models are ubiquitous. In some domains, for instance, in medicine, the models' predictions must be interpretable. Decision trees, classification rules, and subgroup discovery are three broad categories of supervised machine-learning models presenting knowledge in the form of interpretable rules. The ac... | ['Klemens Böhm', 'Benjamin Jochum', 'Vadim Arzamasov'] | 2021-12-25 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 1.81169033e-01 7.09451616e-01 -9.09184873e-01 -5.17140090e-01
-3.41135293e-01 -4.61582869e-01 4.07686293e-01 4.67056215e-01
2.11153910e-01 1.32434177e+00 7.99095351e-03 -1.05120659e+00
-7.19913602e-01 -7.25374341e-01 -7.59231687e-01 -5.02256751e-01
4.79298132e-03 5.04996896e-01 4.10399705e-01 7.36003891... | [8.903375625610352, 6.76384162902832] |
8b7d488d-b44f-4dc8-9d62-b4cd40f7bdbd | sequence-to-sequence-pre-training-with | 2305.10448 | null | https://arxiv.org/abs/2305.10448v1 | https://arxiv.org/pdf/2305.10448v1.pdf | Sequence-to-Sequence Pre-training with Unified Modality Masking for Visual Document Understanding | This paper presents GenDoc, a general sequence-to-sequence document understanding model pre-trained with unified masking across three modalities: text, image, and layout. The proposed model utilizes an encoder-decoder architecture, which allows for increased adaptability to a wide range of downstream tasks with diverse... | ['Xiaoran Jin', 'Trung Quoc Luong', 'Zhanming Jie', 'Tianyang Zhan', 'Shuwei Feng'] | 2023-05-16 | null | null | null | null | ['optical-character-recognition', 'text-infilling'] | ['computer-vision', 'natural-language-processing'] | [ 9.46695089e-01 1.27790451e-01 -1.13166407e-01 -3.13270926e-01
-1.16653264e+00 -7.16194510e-01 1.02220750e+00 -2.42953468e-02
-1.08159550e-01 4.38385636e-01 4.90958184e-01 -6.48453593e-01
2.14588553e-01 -3.29732776e-01 -9.79298234e-01 -4.62482780e-01
4.60721523e-01 3.07418048e-01 -3.21497582e-02 -1.10091977... | [11.522623062133789, 2.2159957885742188] |
91fa279d-bf06-468e-909c-212495b796ab | transfer-learning-for-non-intrusive-load | 1902.08835 | null | https://arxiv.org/abs/1902.08835v3 | https://arxiv.org/pdf/1902.08835v3.pdf | Transfer Learning for Non-Intrusive Load Monitoring | Non-intrusive load monitoring (NILM) is a technique to recover source appliances from only the recorded mains in a household. NILM is unidentifiable and thus a challenge problem because the inferred power value of an appliance given only the mains could not be unique. To mitigate the unidentifiable problem, various met... | ['Michele DIncecco', 'Mingjun Zhong', 'Stefano Squartini'] | 2019-02-23 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 1.45508870e-01 1.48064151e-01 -4.11957979e-01 -4.59685534e-01
-7.92647362e-01 -6.66077316e-01 2.68877774e-01 -1.60999656e-01
8.63434300e-02 1.06605196e+00 -2.07322195e-01 -2.86657989e-01
-2.79720873e-01 -8.90820026e-01 -9.95173752e-01 -8.45827818e-01
9.97398719e-02 4.74886417e-01 -1.19535059e-01 -4.04727906... | [16.053791046142578, 7.571435451507568] |
ac4343bc-fa5b-4713-869f-14c4e42e8fe1 | symantoresearch-at-semeval-2019-task-3 | null | null | https://aclanthology.org/S19-2057 | https://aclanthology.org/S19-2057.pdf | SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations | In this paper, we present our participation to the EmoContext shared task on detecting emotions in English textual conversations between a human and a chatbot. We propose four neural systems and combine them to further improve the results. We show that our neural ensemble systems can successfully distinguish three emot... | ['Sanja {\\v{S}}tajner', 'Marc Franco-Salvador', 'Neha Pawar', 'Angelo Basile', 'Mara Chinea Rios', 'Yassine Benajiba'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-2.41687194e-01 1.71333164e-01 3.62704903e-01 -7.47637212e-01
-4.01429325e-01 -2.03601107e-01 5.00617325e-01 -1.96649164e-01
-5.30016005e-01 9.61167276e-01 1.09670423e-01 1.25812292e-01
4.79866505e-01 -2.55211890e-01 1.72287330e-01 -4.37517107e-01
8.04523230e-02 4.89079297e-01 -3.81373376e-01 -7.17075467... | [13.012948989868164, 6.208339214324951] |
353d021a-66d6-4a22-a899-f973de61b5ad | price-graphs-utilizing-the-structural | 2106.02522 | null | https://arxiv.org/abs/2106.02522v5 | https://arxiv.org/pdf/2106.02522v5.pdf | Price graphs: Utilizing the structural information of financial time series for stock prediction | Great research efforts have been devoted to exploiting deep neural networks in stock prediction. While long-range dependencies and chaotic property are still two major issues that lower the performance of state-of-the-art deep learning models in forecasting future price trends. In this study, we propose a novel framewo... | ['Jichang Zhao', 'Shangzhe Li', 'Xueyuan Chen', 'Ke Xu', 'Junran Wu'] | 2021-06-04 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-6.2124830e-01 -2.8557798e-01 -2.5230974e-01 -5.4326329e-02
3.3536232e-01 -6.0609013e-01 6.9062155e-01 1.7387876e-01
-3.2286352e-01 5.1402473e-01 6.8223141e-02 -4.8573482e-01
-4.8979118e-01 -1.2405214e+00 -5.0130254e-01 -5.9197438e-01
-7.0663637e-01 1.4062546e-01 2.6493075e-01 -6.2793630e-01
4.0456620e-01... | [4.373880863189697, 4.292166709899902] |
9e7c9aa8-5ecd-48ac-814d-6880ad73c3d2 | offline-primal-dual-reinforcement-learning | 2305.12944 | null | https://arxiv.org/abs/2305.12944v1 | https://arxiv.org/pdf/2305.12944v1.pdf | Offline Primal-Dual Reinforcement Learning for Linear MDPs | Offline Reinforcement Learning (RL) aims to learn a near-optimal policy from a fixed dataset of transitions collected by another policy. This problem has attracted a lot of attention recently, but most existing methods with strong theoretical guarantees are restricted to finite-horizon or tabular settings. In constrast... | ['Matteo Papini', 'Nneka Okolo', 'Gergely Neu', 'Germano Gabbianelli'] | 2023-05-22 | null | null | null | null | ['stochastic-optimization', 'offline-rl'] | ['methodology', 'playing-games'] | [-6.39704764e-02 1.69453055e-01 -9.35425699e-01 -8.44898745e-02
-1.12742853e+00 -7.10802615e-01 -6.71787607e-03 3.41196328e-01
-6.91720665e-01 1.27210581e+00 -2.21008882e-01 -6.80546463e-01
-5.16855955e-01 -7.85139561e-01 -9.27233040e-01 -7.61447966e-01
-5.67809343e-01 4.78579998e-01 5.42747080e-02 -1.60313457... | [4.3239240646362305, 2.752511739730835] |
04ff4fc3-6481-46c9-b9af-ec602f23fcc2 | egyptian-sign-language-recognition-using-cnn | 2107.13647 | null | https://arxiv.org/abs/2107.13647v1 | https://arxiv.org/pdf/2107.13647v1.pdf | Egyptian Sign Language Recognition Using CNN and LSTM | Sign language is a set of gestures that deaf people use to communicate. Unfortunately, normal people don't understand it, which creates a communication gap that needs to be filled. Because of the variations in (Egyptian Sign Language) ESL from one region to another, ESL provides a challenging research problem. In this ... | ['Rawan Gla', 'Ahmed Elhagry'] | 2021-07-28 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [-1.04718238e-01 -4.90475476e-01 9.24011916e-02 -3.54474843e-01
-3.95116240e-01 -3.68547708e-01 5.41610479e-01 -7.81937122e-01
-7.95434237e-01 6.26933575e-01 6.46621704e-01 -3.20776314e-01
-1.59021512e-01 -5.10842741e-01 -1.36097729e-01 -8.26469660e-01
-1.88804910e-01 3.05371750e-02 1.95357010e-01 -3.40431541... | [9.077654838562012, -6.385664939880371] |
9bec7d26-1d2f-45ff-9e6d-27dcf4f53b09 | multimodal-sentiment-analysis-with-word-level | 1802.00924 | null | http://arxiv.org/abs/1802.00924v1 | http://arxiv.org/pdf/1802.00924v1.pdf | Multimodal Sentiment Analysis with Word-Level Fusion and Reinforcement Learning | With the increasing popularity of video sharing websites such as YouTube and
Facebook, multimodal sentiment analysis has received increasing attention from
the scientific community. Contrary to previous works in multimodal sentiment
analysis which focus on holistic information in speech segments such as bag of
words re... | ['Louis-Philippe Morency', 'Tadas Baltrušaitis', 'Sen Wang', 'Paul Pu Liang', 'Amir Zadeh', 'Minghai Chen'] | 2018-02-03 | null | null | null | null | ['subjectivity-analysis'] | ['natural-language-processing'] | [ 1.73172250e-01 -3.08263972e-02 -6.68497458e-02 -4.17649835e-01
-9.01095390e-01 -2.99100250e-01 5.85486770e-01 1.74722686e-01
-6.43714190e-01 1.66752577e-01 5.80409706e-01 -9.34095904e-02
8.44359584e-03 -4.11216170e-01 -4.06679034e-01 -8.07811141e-01
7.20261876e-03 -2.86963135e-01 -2.71205366e-01 -6.49157524... | [13.242037773132324, 5.221391201019287] |
3841a0fe-500b-451d-80f9-c99981ee0201 | low-light-video-enhancement-with-synthetic | 2208.11014 | null | https://arxiv.org/abs/2208.11014v1 | https://arxiv.org/pdf/2208.11014v1.pdf | Low-Light Video Enhancement with Synthetic Event Guidance | Low-light video enhancement (LLVE) is an important yet challenging task with many applications such as photographing and autonomous driving. Unlike single image low-light enhancement, most LLVE methods utilize temporal information from adjacent frames to restore the color and remove the noise of the target frame. Howev... | ['Qi Tian', 'Yanfeng Wang', 'Houqiang Li', 'Wengang Zhou', 'Xiangyu Chen', 'Shanxin Yuan', 'Jianzhuang Liu', 'Junfeng An', 'Lin Liu'] | 2022-08-23 | null | null | null | null | ['video-enhancement'] | ['computer-vision'] | [ 7.00846732e-01 -7.25982785e-01 2.59689152e-01 -2.70908117e-01
-3.83447468e-01 -2.31531486e-01 5.57472467e-01 -1.45969599e-01
-5.80463767e-01 8.36599350e-01 4.21868935e-02 -1.20995484e-01
2.54219741e-01 -9.34008360e-01 -6.89912736e-01 -8.22854102e-01
4.03753281e-01 -6.60555840e-01 9.20420170e-01 -1.98079541... | [10.854111671447754, -2.1480278968811035] |
a19de779-8529-4b05-beef-60b480dfb648 | controlvc-zero-shot-voice-conversion-with | 2209.11866 | null | https://arxiv.org/abs/2209.11866v4 | https://arxiv.org/pdf/2209.11866v4.pdf | ControlVC: Zero-Shot Voice Conversion with Time-Varying Controls on Pitch and Speed | Recent developments in neural speech synthesis and vocoding have sparked a renewed interest in voice conversion (VC). Beyond timbre transfer, achieving controllability on para-linguistic parameters such as pitch and Speed is critical in deploying VC systems in many application scenarios. Existing studies, however, eith... | ['Zhiyao Duan', 'Meiying Chen'] | 2022-09-23 | null | null | null | null | ['pitch-control'] | ['audio'] | [-3.66787873e-02 -4.26347554e-02 -3.49148005e-01 -2.99435288e-01
-7.89680183e-01 -8.38322520e-01 5.08350551e-01 4.95358333e-02
-1.46952391e-01 3.61910522e-01 5.91213644e-01 -2.83430099e-01
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1.24523342e-01 2.65451875e-02 -1.27915610e-02 -3.88833225... | [15.037619590759277, 6.518710613250732] |
3d4761be-bdcf-4158-9479-cfed3d14086a | clood-cbr-towards-microservices-oriented-case | null | null | https://rgu-repository.worktribe.com/output/895530/clood-cbr-towards-microservices-oriented-case-based-reasoning | https://rgu-repository.worktribe.com/output/895530/clood-cbr-towards-microservices-oriented-case-based-reasoning | Clood CBR: towards microservices oriented case-based reasoning | CBR applications have been deployed in a wide range of sectors, from pharmaceuticals; to defence and aerospace to IoT and transportation, to poetry and music generation; for example. However, a majority of these have been built using monolithic architectures which impose size and complexity constraints. As such these a... | ['David Corsar', 'Juan A. Recio-García', 'Chamath Palihawadana', 'Nirmalie Wiratunga', 'Ikechukwu Nkisi-Orji'] | 2020-10-03 | null | null | null | international-conference-on-case-based | ['music-generation', 'music-generation'] | ['audio', 'music'] | [-6.83312178e-01 -3.56432855e-01 -7.09870178e-03 -1.89852849e-01
-6.45038366e-01 -1.01884389e+00 7.59830058e-01 -8.06034803e-02
-3.69800702e-02 5.06947696e-01 2.09100142e-01 -5.98462462e-01
-3.17248791e-01 -7.91483164e-01 -1.64289847e-01 -3.79711688e-01
1.60643473e-01 6.72704041e-01 5.22963703e-01 -5.00615180... | [8.829940795898438, 7.371221542358398] |
aafcdd78-7422-4f53-9000-e67a92246166 | improving-the-diagnosis-of-breast-cancer | 2207.06560 | null | https://arxiv.org/abs/2207.06560v1 | https://arxiv.org/pdf/2207.06560v1.pdf | Improving the diagnosis of breast cancer based on biophysical ultrasound features utilizing machine learning | The improved diagnostic accuracy of ultrasound breast examinations remains an important goal. In this study, we propose a biophysical feature based machine learning method for breast cancer detection to improve the performance beyond a benchmark deep learning algorithm and to furthermore provide a color overlay visual ... | ['Kevin J. Parker', "Avice M. O'Connell", 'Jihye Baek'] | 2022-07-13 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 1.74251392e-01 7.74064809e-02 -2.19078690e-01 -3.27743471e-01
-7.48896360e-01 -2.84504354e-01 4.13032509e-02 5.02566516e-01
-9.42870453e-02 3.02353799e-01 -2.26876512e-01 -7.37467349e-01
-5.03662527e-01 -7.92769670e-01 -4.42683041e-01 -1.11749315e+00
-5.14762640e-01 2.07797006e-01 1.25139728e-01 3.00333649... | [15.2028169631958, -2.690666913986206] |
575f8729-c74e-4a87-9af2-5855ded6f45f | multi-context-attention-fusion-neural-network | 2104.09225 | null | https://arxiv.org/abs/2104.09225v1 | https://arxiv.org/pdf/2104.09225v1.pdf | Multi-context Attention Fusion Neural Network for Software Vulnerability Identification | Security issues in shipped code can lead to unforeseen device malfunction, system crashes or malicious exploitation by crackers, post-deployment. These vulnerabilities incur a cost of repair and foremost risk the credibility of the company. It is rewarding when these issues are detected and fixed well ahead of time, be... | ['Sriram Ravi', 'Sathish Kumar Chandrasekaran', 'Prasanna Ganesan', 'Krishna Sundaresan', 'Hariharan Manikandan', 'Anshul Tanwar'] | 2021-04-19 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-1.99459475e-02 1.91454232e-01 -1.04688458e-01 -5.83699457e-02
-8.75181735e-01 -8.46243560e-01 -1.13116264e-01 4.37105209e-01
2.39486277e-01 -8.97963941e-02 1.93663299e-01 -7.24921227e-01
-2.03549281e-01 -6.03053033e-01 -7.20541060e-01 -3.12889159e-01
-2.25008309e-01 -3.30437541e-01 7.18127489e-02 -4.07120377... | [7.054438591003418, 7.771771430969238] |
4fe37ac7-d4e9-4bb9-9f8e-3bfc0d1d0a73 | the-impact-of-cross-lingual-adjustment-of-1 | 2204.06457 | null | https://arxiv.org/abs/2204.06457v1 | https://arxiv.org/pdf/2204.06457v1.pdf | The Impact of Cross-Lingual Adjustment of Contextual Word Representations on Zero-Shot Transfer | Large pre-trained multilingual models such as mBERT and XLM-R enabled effective cross-lingual zero-shot transfer in many NLP tasks. A cross-lingual adjustment of these models using a small parallel corpus can potentially further improve results. This is a more data efficient method compared to training a machine-transl... | ['Pavel Braslavski', 'Elena Arslanova', 'Leonid Boytsov', 'Pavel Efimov'] | 2022-04-13 | null | null | null | null | ['xlm-r', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [-2.68320650e-01 -1.56889215e-01 -1.82104543e-01 -3.95807683e-01
-1.43470538e+00 -1.01493442e+00 6.37081683e-01 3.66682410e-01
-1.07212901e+00 7.24781215e-01 4.24868822e-01 -6.37556672e-01
3.31667334e-01 -7.59086668e-01 -9.64715183e-01 -4.09524798e-01
3.56780499e-01 7.30117202e-01 -1.48605788e-02 -5.70681453... | [10.983302116394043, 9.952831268310547] |
3c3d2dbe-dc6f-48c8-ab9d-e507e4874979 | the-contribution-of-local-variations-in-hue | 2108.04730 | null | https://arxiv.org/abs/2108.04730v2 | https://arxiv.org/pdf/2108.04730v2.pdf | The contribution of local variations in hue or contrast to symmetry of things in a thing | Symmetry contributes to processes of perceptual organization in biological vision and influences the quality and time of goal directed decision making in animals and humans, as discussed in recent work on the examples of symmetry of things in a thing and bilateral shape symmetry. The present study was designed to show ... | ['Birgitta Dresp-Langley'] | 2021-08-10 | null | null | null | null | ['symmetry-detection'] | ['computer-vision'] | [ 5.01568377e-01 -2.96777695e-01 2.52637774e-01 -4.37998861e-01
4.09462035e-01 -8.40087414e-01 5.73169589e-01 -6.32698610e-02
-8.01190794e-01 4.52891469e-01 2.62890279e-01 -2.36753300e-01
-4.33896422e-01 -4.55685347e-01 -2.89875299e-01 -6.40699387e-01
2.76526690e-01 1.60687268e-01 3.04042786e-01 -2.46967584... | [10.114282608032227, 2.0806307792663574] |
2d7aaeab-9673-4cb9-ac5d-a2e2bc13695b | discovering-representation-sprachbund-for | 2109.00271 | null | https://arxiv.org/abs/2109.00271v1 | https://arxiv.org/pdf/2109.00271v1.pdf | Discovering Representation Sprachbund For Multilingual Pre-Training | Multilingual pre-trained models have demonstrated their effectiveness in many multilingual NLP tasks and enabled zero-shot or few-shot transfer from high-resource languages to low resource ones. However, due to significant typological differences and contradictions between some languages, such models usually perform po... | ['Nan Duan', 'Ming Zhou', 'Houqiang Li', 'Hany Hassan', 'Alexandre Muzio', 'Yaobo Liang', 'Yimin Fan'] | 2021-09-01 | null | https://aclanthology.org/2021.findings-emnlp.75 | https://aclanthology.org/2021.findings-emnlp.75.pdf | findings-emnlp-2021-11 | ['multilingual-nlp'] | ['natural-language-processing'] | [-2.65961587e-01 -2.69686401e-01 -5.69749236e-01 -4.67066705e-01
-1.11488152e+00 -7.42531002e-01 7.71839619e-01 1.54207885e-01
-6.39295578e-01 8.73757422e-01 6.40189588e-01 -2.58574367e-01
2.77416825e-01 -7.06526935e-01 -6.84403539e-01 -3.51835370e-01
3.07236731e-01 7.75781214e-01 -8.08654726e-02 -7.03441441... | [10.9260892868042, 9.945755958557129] |
176874c5-4c3b-417e-ae4b-10cdf2cf9795 | a-fast-and-robust-bert-based-dialogue-state | 2008.12335 | null | https://arxiv.org/abs/2008.12335v1 | https://arxiv.org/pdf/2008.12335v1.pdf | A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset | Dialog State Tracking (DST) is one of the most crucial modules for goal-oriented dialogue systems. In this paper, we introduce FastSGT (Fast Schema Guided Tracker), a fast and robust BERT-based model for state tracking in goal-oriented dialogue systems. The proposed model is designed for the Schema-Guided Dialogue (SGD... | ['Yang Zhang', 'Evelina Bakhturina', 'Vahid Noroozi', 'Tomasz Kornuta'] | 2020-08-27 | null | null | null | null | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [-7.73087218e-02 8.60572338e-01 1.36314169e-01 -6.24657929e-01
-5.64221323e-01 -5.34561515e-01 1.00162101e+00 1.79955751e-01
-6.52089298e-01 6.39441192e-01 6.96444333e-01 -2.34534577e-01
1.50549918e-01 -4.19818938e-01 -9.35201496e-02 -3.08572233e-01
8.26963410e-02 9.62124884e-01 4.67361391e-01 -8.06940854... | [12.845308303833008, 7.907772541046143] |
e35ffb4b-c6fc-43b1-97ed-0d83ba55ef12 | maza-at-semeval-2016-task-11-detecting | null | null | https://aclanthology.org/S16-1153 | https://aclanthology.org/S16-1153.pdf | MAZA at SemEval-2016 Task 11: Detecting Lexical Complexity Using a Decision Stump Meta-Classifier | null | ['Marcos Zampieri', 'Shervin Malmasi'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.251986980438232, 3.6041040420532227] |
5397bd81-7854-48ff-b9c1-4b1a89191847 | locate-then-ask-interpretable-stepwise | 2208.10297 | null | https://arxiv.org/abs/2208.10297v1 | https://arxiv.org/pdf/2208.10297v1.pdf | Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering | Multi-hop reasoning requires aggregating multiple documents to answer a complex question. Existing methods usually decompose the multi-hop question into simpler single-hop questions to solve the problem for illustrating the explainable reasoning process. However, they ignore grounding on the supporting facts of each re... | ['Xuanjing Huang', 'Qi Zhang', 'Zhihao Fan', 'Zhongyu Wei', 'Siyuan Wang'] | 2022-08-22 | null | https://aclanthology.org/2022.coling-1.142 | https://aclanthology.org/2022.coling-1.142.pdf | coling-2022-10 | ['multi-hop-question-answering', 'question-generation'] | ['knowledge-base', 'natural-language-processing'] | [-1.13850914e-01 7.58786142e-01 -1.88765541e-01 -6.22335970e-01
-1.41787851e+00 -7.61289775e-01 3.71135354e-01 3.26687783e-01
7.08584264e-02 8.94492924e-01 6.79146647e-01 -8.27511728e-01
-4.43942487e-01 -7.90622652e-01 -6.66063011e-01 -1.29530773e-01
6.34331465e-01 7.18135893e-01 3.76446575e-01 -3.01483363... | [10.929278373718262, 7.8828606605529785] |
57e78420-ba04-4a68-9b1d-879b4d82c0da | matrix-variate-rbm-and-its-applications | 1601.00722 | null | http://arxiv.org/abs/1601.00722v1 | http://arxiv.org/pdf/1601.00722v1.pdf | Matrix Variate RBM and Its Applications | Restricted Boltzmann Machine (RBM) is an importan- t generative model
modeling vectorial data. While applying an RBM in practice to images, the data
have to be vec- torized. This results in high-dimensional data and valu- able
spatial information has got lost in vectorization. In this paper, a
Matrix-Variate Restricted... | ['Yongli Hu', 'Jinghua Li', 'Yanfeng Sun', 'Guanglei Qi', 'Junbin Gao'] | 2016-01-05 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 1.88046545e-01 -4.94062267e-02 -8.31194445e-02 -3.72768193e-01
-5.74102402e-01 -2.14610234e-01 9.33883071e-01 -5.02592027e-01
-6.70665383e-01 7.62130976e-01 4.98004742e-02 -3.30005020e-01
3.96124981e-02 -9.10804451e-01 -7.03527451e-01 -1.12534630e+00
1.52632460e-01 7.01760828e-01 2.86826175e-02 -1.41927689... | [9.184768676757812, 2.469943046569824] |
7ee772ba-6c79-48b3-8fbf-804d244b9235 | improved-semantic-representation-for-domain | null | null | https://aclanthology.org/W16-2902 | https://aclanthology.org/W16-2902.pdf | Improved Semantic Representation for Domain-Specific Entities | null | ['Nigel Collier', 'Mohammad Taher Pilehvar'] | 2016-08-01 | null | null | null | ws-2016-8 | ['learning-semantic-representations'] | ['methodology'] | [-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... | [-7.2740349769592285, 3.808521270751953] |
30205812-fd16-4b8f-a7bd-2aead1d06766 | bioflair-pretrained-pooled-contextualized | 1908.05760 | null | https://arxiv.org/abs/1908.05760v1 | https://arxiv.org/pdf/1908.05760v1.pdf | BioFLAIR: Pretrained Pooled Contextualized Embeddings for Biomedical Sequence Labeling Tasks | Biomedical Named Entity Recognition (NER) is a challenging problem in biomedical information processing due to the widespread ambiguity of out of context terms and extensive lexical variations. Performance on bioNER benchmarks continues to improve due to advances like BERT, GPT, and XLNet. FLAIR (1) is an alternative e... | ['Ron Daniel Jr', 'Shreyas Sharma'] | 2019-08-13 | null | null | null | null | ['medical-named-entity-recognition'] | ['natural-language-processing'] | [-1.72159802e-02 1.21657558e-01 -9.08002034e-02 -2.49361038e-01
-7.18578756e-01 -4.41287369e-01 6.39906943e-01 7.94278800e-01
-1.13642669e+00 9.98401701e-01 7.52050877e-01 -2.39019558e-01
-1.83029503e-01 -5.24647117e-01 -4.47359025e-01 -5.93126476e-01
-2.85621077e-01 5.25586188e-01 6.32503629e-02 -1.84691250... | [8.505561828613281, 8.753168106079102] |
4061bbb9-4b68-431f-9caf-f8791d1bbe5b | probabilistic-inference-for-camera | 1910.13740 | null | https://arxiv.org/abs/1910.13740v1 | https://arxiv.org/pdf/1910.13740v1.pdf | Probabilistic Inference for Camera Calibration in Light Microscopy under Circular Motion | Robust and accurate camera calibration is essential for 3D reconstruction in light microscopy under circular motion. Conventional methods require either accurate key point matching or precise segmentation of the axial-view images. Both remain challenging because specimens often exhibit transparency/translucency in a li... | ['Ge Yang', 'Yuanhao Guo', 'Fons J. Verbeek'] | 2019-10-30 | null | null | null | null | ['key-point-matching'] | ['natural-language-processing'] | [ 3.86069924e-01 -3.03032964e-01 2.74975508e-01 -2.13147894e-01
-6.61676228e-01 -5.30509949e-01 1.90052792e-01 -2.92373523e-02
-5.08732736e-01 4.82724369e-01 -4.64397669e-01 -2.43376151e-01
6.23918474e-02 -5.66910267e-01 -5.94776511e-01 -9.62621093e-01
6.13615751e-01 8.42178941e-01 5.03550351e-01 7.96375573... | [9.642438888549805, -2.807058572769165] |
86cbcf8a-88cb-480c-bb5a-fb2b86103544 | hub-at-semeval-2021-task-1-fusion-of-sentence | null | null | https://aclanthology.org/2021.semeval-1.75 | https://aclanthology.org/2021.semeval-1.75.pdf | hub at SemEval-2021 Task 1: Fusion of Sentence and Word Frequency to Predict Lexical Complexity | In this paper, we propose a method of fusing sentence information and word frequency information for the SemEval 2021 Task 1-Lexical Complexity Prediction (LCP) shared task. In our system, the sentence information comes from the RoBERTa model, and the word frequency information comes from the Tf-Idf algorithm. Use Ince... | ['Xiaobing Zhou', 'Yang Bai', 'Bo Huang'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-2.48171091e-01 -2.53982395e-01 -1.92718908e-01 -5.43035626e-01
-8.98728669e-01 -3.52650762e-01 4.61897999e-01 1.19236588e-01
-9.57551897e-01 8.85661840e-01 2.82676756e-01 -3.63782197e-01
7.57002532e-02 -6.85677767e-01 -1.78258836e-01 -4.59998339e-01
-1.05350032e-01 1.55256480e-01 3.39455247e-01 -4.32209074... | [10.643777847290039, 10.513359069824219] |
501732c1-2c3e-4231-8474-56e9ae0fafd5 | beyond-white-ground-truth-colors-for-color | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Cheng_Beyond_White_Ground_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Cheng_Beyond_White_Ground_ICCV_2015_paper.pdf | Beyond White: Ground Truth Colors for Color Constancy Correction | A limitation in color constancy research is the inability to establish ground truth colors for evaluating corrected images. Many existing datasets contain images of scenes with a color chart included; however, only the chart's neutral colors (grayscale patches) are used to provide the ground truth for illumination esti... | ['Brian Price', 'Scott Cohen', 'Michael S. Brown', 'Dongliang Cheng'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['color-constancy'] | ['computer-vision'] | [ 1.35013804e-01 -5.58835864e-01 6.04511313e-02 -2.46344894e-01
-4.91074026e-02 -9.73581016e-01 -1.11981221e-01 -2.64445156e-01
-1.81019649e-01 6.25031114e-01 -2.72670984e-01 -2.79990971e-01
2.92638302e-01 -7.59251475e-01 -6.46629155e-01 -8.79900217e-01
3.74184221e-01 -1.38906047e-01 1.96736440e-01 -4.52997863... | [10.461609840393066, -2.5770795345306396] |
78828cc7-c960-416a-8168-e93b64d5dcdb | dialogue-response-ranking-training-with-large | 2009.06978 | null | https://arxiv.org/abs/2009.06978v1 | https://arxiv.org/pdf/2009.06978v1.pdf | Dialogue Response Ranking Training with Large-Scale Human Feedback Data | Existing open-domain dialog models are generally trained to minimize the perplexity of target human responses. However, some human replies are more engaging than others, spawning more followup interactions. Current conversational models are increasingly capable of producing turns that are context-relevant, but in order... | ['Xiang Gao', 'Chris Brockett', 'Yizhe Zhang', 'Michel Galley', 'Bill Dolan'] | 2020-09-15 | null | https://aclanthology.org/2020.emnlp-main.28 | https://aclanthology.org/2020.emnlp-main.28.pdf | emnlp-2020-11 | ['conversational-response-selection', 'open-domain-dialog'] | ['natural-language-processing', 'natural-language-processing'] | [-8.03629830e-02 4.91918802e-01 -2.15340763e-01 -9.54292357e-01
-1.00820518e+00 -9.51139450e-01 1.01357424e+00 -2.52250880e-01
-4.09562439e-01 9.89956677e-01 1.10314631e+00 -3.12528461e-01
4.68026578e-01 -4.20275390e-01 -5.44612408e-02 -3.25831361e-02
3.57452780e-02 1.05598879e+00 2.06043646e-01 -8.87891173... | [12.76606273651123, 8.13489818572998] |
a1f9303c-0d23-4350-9ce1-f98b7926ac0d | smoothed-separable-nonnegative-matrix | 2110.05528 | null | https://arxiv.org/abs/2110.05528v2 | https://arxiv.org/pdf/2110.05528v2.pdf | Smoothed Separable Nonnegative Matrix Factorization | Given a set of data points belonging to the convex hull of a set of vertices, a key problem in linear algebra, signal processing, data analysis and machine learning is to estimate these vertices in the presence of noise. Many algorithms have been developed under the assumption that there is at least one nearby data poi... | ['Christophe Kervazo', 'Nicolas Gillis', 'Nicolas Nadisic'] | 2021-10-11 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 5.83594978e-01 -2.83567220e-01 8.91582072e-02 5.68487011e-02
-4.07170504e-01 -4.98026907e-01 5.11405110e-01 1.65091828e-01
-5.38642555e-02 5.47135949e-01 2.27548666e-02 -1.67814746e-01
-6.86555207e-01 -6.93857491e-01 -6.57848120e-01 -1.18273604e+00
-2.89994068e-02 4.86670911e-01 -2.37794518e-01 -8.50145295... | [10.074056625366211, -1.9937150478363037] |
998ac095-3cb3-431b-b241-1e2d0010acec | enhanced-word-representations-for-bridging | 1803.04790 | null | http://arxiv.org/abs/1803.04790v2 | http://arxiv.org/pdf/1803.04790v2.pdf | Enhanced Word Representations for Bridging Anaphora Resolution | Most current models of word representations(e.g.,GloVe) have successfully
captured fine-grained semantics. However, semantic similarity exhibited in
these word embeddings is not suitable for resolving bridging anaphora, which
requires the knowledge of associative similarity (i.e., relatedness) instead of
semantic simil... | ['Yufang Hou'] | 2018-03-13 | enhanced-word-representations-for-bridging-1 | https://aclanthology.org/N18-2001 | https://aclanthology.org/N18-2001.pdf | naacl-2018-6 | ['bridging-anaphora-resolution'] | ['natural-language-processing'] | [-2.26862758e-01 3.32234800e-01 -5.85192740e-01 -2.52511829e-01
-6.51012719e-01 -4.66843873e-01 5.56720197e-01 6.09783888e-01
-7.01819777e-01 6.65957451e-01 9.60005581e-01 7.09828921e-03
-3.71835172e-01 -9.10824776e-01 -2.50235587e-01 -1.45611599e-01
6.35765716e-02 7.85784543e-01 1.60250083e-01 -7.14730501... | [9.95705509185791, 9.016329765319824] |
88fca47d-76fd-481f-be83-1f48d2e7d750 | stack-operation-of-tensor-networks | 2203.16338 | null | https://arxiv.org/abs/2203.16338v2 | https://arxiv.org/pdf/2203.16338v2.pdf | Stack operation of tensor networks | The tensor network, as a facterization of tensors, aims at performing the operations that are common for normal tensors, such as addition, contraction and stacking. However, due to its non-unique network structure, only the tensor network contraction is so far well defined. In this paper, we propose a mathematically ri... | ['Tianqi Chen', 'Erping Li', 'Bo Yang', 'L. K. Ang', 'Tianning Zhang'] | 2022-03-28 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 1.40155673e-01 -1.02136001e-01 8.67732912e-02 -1.79616079e-01
2.71870315e-01 -8.43255341e-01 4.75835502e-01 6.79378584e-02
-1.90486088e-01 3.22209388e-01 2.08596677e-01 -6.66999757e-01
-4.21122432e-01 -5.45998812e-01 -6.11103415e-01 -7.29512334e-01
-6.34020865e-01 5.32295942e-01 2.56411970e-01 -3.30442488... | [6.207220554351807, 5.006288528442383] |
71551d16-f1da-423a-a023-4f64edb6088e | pypots-a-python-toolbox-for-data-mining-on | 2305.18811 | null | https://arxiv.org/abs/2305.18811v1 | https://arxiv.org/pdf/2305.18811v1.pdf | PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series | PyPOTS is an open-source Python library dedicated to data mining and analysis on multivariate partially-observed time series, i.e. incomplete time series with missing values, A.K.A. irregularlysampled time series. Particularly, it provides easy access to diverse algorithms categorized into four tasks: imputation, class... | ['Wenjie Du'] | 2023-05-30 | null | null | null | null | ['imputation', 'imputation', 'philosophy', 'multivariate-time-series-imputation', 'time-series-clustering', 'irregular-time-series', 'imputation', 'classification-on-time-series-with-missing', 'traffic-data-imputation'] | ['computer-vision', 'miscellaneous', 'miscellaneous', 'time-series', 'time-series', 'time-series', 'time-series', 'time-series', 'time-series'] | [-3.05515766e-01 -5.02323091e-01 -1.86480522e-01 -3.45153600e-01
-7.42106020e-01 -6.73083246e-01 1.65432274e-01 8.75482485e-02
5.26842624e-02 7.08584607e-01 -6.06534258e-02 -5.51090956e-01
-3.71799767e-01 -6.71713412e-01 -5.85334539e-01 -8.58012319e-01
-4.12831843e-01 5.42092383e-01 -3.87533039e-01 1.98579177... | [7.209350109100342, 3.5607569217681885] |
4a02546e-3858-4a70-895c-0fc0fcb8c9c6 | dependency-language-models-for-transition | 1607.04982 | null | http://arxiv.org/abs/1607.04982v2 | http://arxiv.org/pdf/1607.04982v2.pdf | Dependency Language Models for Transition-based Dependency Parsing | In this paper, we present an approach to improve the accuracy of a strong
transition-based dependency parser by exploiting dependency language models
that are extracted from a large parsed corpus. We integrated a small number of
features based on the dependency language models into the parser. To
demonstrate the effect... | ['Juntao Yu', 'Bernd Bohnet'] | 2016-07-18 | dependency-language-models-for-transition-1 | https://aclanthology.org/W17-6302 | https://aclanthology.org/W17-6302.pdf | ws-2017-9 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-3.61776084e-01 3.39781702e-01 -2.28175521e-01 -7.25169599e-01
-1.39077687e+00 -6.12998188e-01 3.01441818e-01 1.85002878e-01
-7.07067907e-01 7.86468148e-01 3.07484120e-01 -5.79966009e-01
3.73959064e-01 -6.83879495e-01 -4.53955531e-01 -2.38426074e-01
-1.70904920e-01 2.41647616e-01 6.44785285e-01 -3.91755551... | [10.343497276306152, 9.821457862854004] |
a2efee67-47bb-4013-8742-c76f60339b20 | jdcfc-a-japanese-dialogue-corpus-with-feature | null | null | https://aclanthology.org/L18-1461 | https://aclanthology.org/L18-1461.pdf | JDCFC: A Japanese Dialogue Corpus with Feature Changes | null | ['Daisuke Kawahara', 'Tetsuaki Nakamura'] | 2018-05-01 | jdcfc-a-japanese-dialogue-corpus-with-feature-1 | https://aclanthology.org/L18-1461 | https://aclanthology.org/L18-1461.pdf | lrec-2018-5 | ['dialogue-understanding'] | ['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... | [-7.270104885101318, 3.7413828372955322] |
90434c04-4a8b-4a5f-be31-842193a8cd7f | few-shot-text-classification-with-dual | 2209.15069 | null | https://arxiv.org/abs/2209.15069v1 | https://arxiv.org/pdf/2209.15069v1.pdf | Few-shot Text Classification with Dual Contrastive Consistency | In this paper, we explore how to utilize pre-trained language model to perform few-shot text classification where only a few annotated examples are given for each class. Since using traditional cross-entropy loss to fine-tune language model under this scenario causes serious overfitting and leads to sub-optimal general... | ['Jiawei Han', 'Liwen Sun'] | 2022-09-29 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 1.41880304e-01 -1.86380893e-01 -4.62336361e-01 -6.42496586e-01
-1.02330887e+00 -2.53863633e-01 6.03578269e-01 3.11890453e-01
-7.23631561e-01 9.50463533e-01 1.75239727e-01 -1.39137402e-01
2.04176113e-01 -4.75775212e-01 -1.66283637e-01 -3.36732835e-01
3.51261586e-01 2.19559640e-01 2.04670966e-01 -2.33474359... | [10.721917152404785, 7.461348056793213] |
19cc9dc8-0fe7-4e25-a9fa-4ee10ccd94dd | road-extraction-by-deep-residual-u-net | 1711.10684 | null | http://arxiv.org/abs/1711.10684v1 | http://arxiv.org/pdf/1711.10684v1.pdf | Road Extraction by Deep Residual U-Net | Road extraction from aerial images has been a hot research topic in the field
of remote sensing image analysis. In this letter, a semantic segmentation
neural network which combines the strengths of residual learning and U-Net is
proposed for road area extraction. The network is built with residual units and
has simila... | ['Qingjie Liu', 'Zhengxin Zhang', 'Yunhong Wang'] | 2017-11-29 | null | null | null | null | ['skin-cancer-segmentation', 'lung-nodule-segmentation'] | ['medical', 'medical'] | [ 4.09851968e-01 2.14931831e-01 -1.79947823e-01 -3.95159602e-01
-7.98821449e-02 -2.42841169e-01 3.68393660e-01 -4.58540976e-01
-5.00942707e-01 8.54532778e-01 1.07303159e-02 -4.80601013e-01
-8.52882117e-02 -1.61349893e+00 -6.12827957e-01 -5.07489562e-01
-1.19629819e-02 -1.55571491e-01 5.86266756e-01 -1.71537191... | [9.22656536102295, -1.3573840856552124] |
0e39e903-7c2f-4ece-bb93-26415b6eecb4 | conflict-based-search-for-explainable-multi | 2202.09930 | null | https://arxiv.org/abs/2202.09930v2 | https://arxiv.org/pdf/2202.09930v2.pdf | Conflict-Based Search for Explainable Multi-Agent Path Finding | In the Multi-Agent Path Finding (MAPF) problem, the goal is to find non-colliding paths for agents in an environment, such that each agent reaches its goal from its initial location. In safety-critical applications, a human supervisor may want to verify that the plan is indeed collision-free. To this end, a recent work... | ['Morteza Lahijanian', 'Shaull Almagor', 'Justin Kottinger'] | 2022-02-20 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 1.32823616e-01 7.44432211e-01 -5.62465154e-02 -9.94984582e-02
-3.53207499e-01 -9.12411034e-01 4.50025439e-01 5.90285838e-01
-1.32959159e-02 9.54011798e-01 -2.38042861e-01 -5.60330927e-01
-8.56241643e-01 -9.59187746e-01 -7.24347711e-01 -4.78411287e-01
-5.38285196e-01 1.03998411e+00 5.92049837e-01 -3.32616299... | [4.892002582550049, 1.7628222703933716] |
366ef9f3-c20e-4cfa-aa4a-a6daf2606940 | chinese-named-entity-recognition-with-graph | null | null | https://aclanthology.org/W15-3103 | https://aclanthology.org/W15-3103.pdf | Chinese Named Entity Recognition with Graph-based Semi-supervised Learning Model | null | ['Aaron Li-Feng Han', 'Lidia S. Chao', 'Xiaodong Zeng', 'Derek F. Wong'] | 2015-07-01 | null | null | null | ws-2015-7 | ['chinese-named-entity-recognition'] | ['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... | [-7.4049072265625, 3.7947235107421875] |
f15423be-9033-469f-b775-cb8ee7d7d96b | chinese-zero-pronoun-resolution-with-deep-1 | null | null | https://aclanthology.org/P16-1074 | https://aclanthology.org/P16-1074.pdf | Chinese Zero Pronoun Resolution with Deep Neural Networks | null | ['Vincent Ng', 'Chen Chen'] | 2016-08-01 | null | null | null | acl-2016-8 | ['chinese-zero-pronoun-resolution'] | ['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... | [-7.365434169769287, 3.696352958679199] |
cb04c335-8eb4-4e4a-ab56-a086d3fe7a6a | breast-cancer-detection-using-convolutional | 2003.07911 | null | https://arxiv.org/abs/2003.07911v3 | https://arxiv.org/pdf/2003.07911v3.pdf | Breast Cancer Detection Using Convolutional Neural Networks | Breast cancer is prevalent in Ethiopia that accounts 34% among women cancer patients. The diagnosis technique in Ethiopia is manual which was proven to be tedious, subjective, and challenging. Deep learning techniques are revolutionizing the field of medical image analysis and hence in this study, we proposed Convoluti... | ['Yaecob Girmay', 'Simon Hadush', 'Gebrekirstos Hagos', 'Abiot Sinamo'] | 2020-03-17 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 1.76707402e-01 4.60121781e-01 -1.93271950e-01 -3.73333365e-01
-3.19797963e-01 -1.49965107e-01 4.14946824e-02 5.78550637e-01
-4.58107620e-01 3.18670154e-01 -3.97608317e-02 -7.49469101e-01
-6.55942261e-02 -1.11772442e+00 -3.67289752e-01 -8.21724057e-01
-2.22863659e-01 3.12550664e-01 2.04019979e-01 1.77086070... | [15.28353500366211, -2.5559744834899902] |
7033488d-90f0-45db-a4e3-fd287580ff2b | lifelong-3d-object-recognition-and-grasp | 2109.11544 | null | https://arxiv.org/abs/2109.11544v2 | https://arxiv.org/pdf/2109.11544v2.pdf | Lifelong 3D Object Recognition and Grasp Synthesis Using Dual Memory Recurrent Self-Organization Networks | Humans learn to recognize and manipulate new objects in lifelong settings without forgetting the previously gained knowledge under non-stationary and sequential conditions. In autonomous systems, the agents also need to mitigate similar behavior to continually learn the new object categories and adapt to new environmen... | ['Hamidreza Kasaei', 'Krishnakumar Santhakumar'] | 2021-09-23 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [ 2.76669741e-01 -9.37396213e-02 2.35604912e-01 4.78381030e-02
2.74656326e-01 -3.33992839e-01 4.50177491e-01 2.02721823e-02
-3.67436230e-01 8.00923765e-01 -4.21514630e-01 2.78822273e-01
-1.42879859e-01 -1.03730905e+00 -1.23353708e+00 -1.11063218e+00
-2.43797585e-01 6.44219100e-01 4.57663924e-01 -1.26367986... | [9.826327323913574, 3.4024224281311035] |
3968273a-cb38-4574-8948-c18927d97b01 | few-shot-speaker-identification-using-1 | 2305.19541 | null | https://arxiv.org/abs/2305.19541v1 | https://arxiv.org/pdf/2305.19541v1.pdf | Few-Shot Speaker Identification Using Lightweight Prototypical Network with Feature Grouping and Interaction | Existing methods for few-shot speaker identification (FSSI) obtain high accuracy, but their computational complexities and model sizes need to be reduced for lightweight applications. In this work, we propose a FSSI method using a lightweight prototypical network with the final goal to implement the FSSI on intelligent... | ['Qianhua He', 'Qisheng Huang', 'Wenchang Cao', 'Hao Chen', 'Yanxiong Li'] | 2023-05-31 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 6.72330335e-02 -2.17806384e-01 1.65329620e-01 -6.70175433e-01
-7.37465143e-01 -1.09883212e-01 3.59014302e-01 -2.52930254e-01
-5.23259163e-01 2.31579185e-01 2.93200642e-01 -1.66588724e-01
5.60478233e-02 -5.17997324e-01 -2.28054106e-01 -9.19349313e-01
-1.04387969e-01 -1.64880291e-01 3.26846927e-01 -1.63691148... | [14.358156204223633, 5.985490798950195] |
1dbe0431-138b-4ec1-9dc4-d52a62ec1cf0 | efficient-and-interpretable-neural-models-for | 2208.14252 | null | https://arxiv.org/abs/2208.14252v1 | https://arxiv.org/pdf/2208.14252v1.pdf | Efficient and Interpretable Neural Models for Entity Tracking | What would it take for a natural language model to understand a novel, such as The Lord of the Rings? Among other things, such a model must be able to: (a) identify and record new characters (entities) and their attributes as they are introduced in the text, and (b) identify subsequent references to the characters prev... | ['Shubham Toshniwal'] | 2022-08-30 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 1.58060759e-01 2.24527985e-01 -2.24726677e-01 -1.93640113e-01
-6.81035042e-01 -9.74134266e-01 8.39895785e-01 9.74061370e-01
-7.12298453e-01 8.86064053e-01 4.84220773e-01 -4.91656423e-01
-3.47804099e-01 -7.43989289e-01 -6.58444047e-01 -7.47608766e-02
-2.44120136e-01 7.86061108e-01 2.53379613e-01 -8.55041817... | [9.490523338317871, 9.045074462890625] |
d2927675-2600-40d9-aaff-0b68c2e7ceed | retrieval-enhanced-visual-prompt-learning-for | 2306.02243 | null | https://arxiv.org/abs/2306.02243v1 | https://arxiv.org/pdf/2306.02243v1.pdf | Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification | Prompt learning has become a popular approach for adapting large vision-language models, such as CLIP, to downstream tasks. Typically, prompt learning relies on a fixed prompt token or an input-conditional token to fit a small amount of data under full supervision. While this paradigm can generalize to a certain range ... | ['Yifan Liu', 'Xinyi Yu', 'Linlin Ou', 'Tianxiao Chen', 'Hao Chen', 'Jintao Rong'] | 2023-06-04 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [ 2.27958843e-01 -3.72074842e-01 -5.57484627e-01 -6.35408401e-01
-1.03812122e+00 -6.46593690e-01 7.64784217e-01 1.15113877e-01
-6.41468704e-01 6.37622237e-01 6.32622689e-02 -3.71036679e-02
-1.69558004e-02 -7.05660343e-01 -7.54259467e-01 -5.93344808e-01
1.68366343e-01 4.67225730e-01 8.20446372e-01 -1.42326683... | [10.121963500976562, 2.4556355476379395] |
4bb2298b-165e-48b0-b3bb-3a9c8e702c24 | recent-advance-in-content-based-image | 1706.06064 | null | https://arxiv.org/abs/1706.06064v2 | https://arxiv.org/pdf/1706.06064v2.pdf | Recent Advance in Content-based Image Retrieval: A Literature Survey | The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and... | ['Qi Tian', 'Houqiang Li', 'Wengang Zhou'] | 2017-06-19 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 1.77791193e-01 -4.93073374e-01 -4.53904539e-01 -9.32910815e-02
-7.33583689e-01 -6.02841496e-01 8.23351085e-01 5.85986078e-01
-5.00011921e-01 3.37633789e-01 3.53830487e-01 -2.57181495e-01
-3.07453334e-01 -6.26522899e-01 -9.52121839e-02 -3.96657377e-01
2.58875132e-01 -1.18794337e-01 5.04323781e-01 -2.77835459... | [10.868904113769531, 0.31133797764778137] |
ae35f0be-5b85-4233-94e0-8ec068ccdbc8 | evaluating-dense-passage-retrieval-using | 2208.06959 | null | https://arxiv.org/abs/2208.06959v1 | https://arxiv.org/pdf/2208.06959v1.pdf | Evaluating Dense Passage Retrieval using Transformers | Although representational retrieval models based on Transformers have been able to make major advances in the past few years, and despite the widely accepted conventions and best-practices for testing such models, a $\textit{standardized}$ evaluation framework for testing them has not been developed. In this work, we f... | ['Nima Sadri'] | 2022-08-15 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [ 1.00153960e-01 -4.25392181e-01 -8.80024489e-03 -3.89853686e-01
-1.06539309e+00 -6.67033851e-01 1.05633318e+00 2.75082916e-01
-7.46795416e-01 3.27444404e-01 8.05988163e-02 -4.08069015e-01
-5.59382498e-01 -7.26693392e-01 -4.21924829e-01 -4.27376539e-01
-2.40367770e-01 5.36392093e-01 4.06518310e-01 -3.73916805... | [11.414566993713379, 7.609040260314941] |
4b742f1f-6dfd-4221-8155-46aa093bf267 | pointclip-point-cloud-understanding-by-clip | 2112.02413 | null | https://arxiv.org/abs/2112.02413v1 | https://arxiv.org/pdf/2112.02413v1.pdf | PointCLIP: Point Cloud Understanding by CLIP | Recently, zero-shot and few-shot learning via Contrastive Vision-Language Pre-training (CLIP) have shown inspirational performance on 2D visual recognition, which learns to match images with their corresponding texts in open-vocabulary settings. However, it remains under explored that whether CLIP, pre-trained by large... | ['Hongsheng Li', 'Peng Gao', 'Yu Qiao', 'Bin Cui', 'Xupeng Miao', 'Kunchang Li', 'Wei zhang', 'Ziyu Guo', 'Renrui Zhang'] | 2021-12-04 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhang_PointCLIP_Point_Cloud_Understanding_by_CLIP_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhang_PointCLIP_Point_Cloud_Understanding_by_CLIP_CVPR_2022_paper.pdf | cvpr-2022-1 | ['training-free-3d-point-cloud-classification', 'zero-shot-transfer-3d-point-cloud', 'training-free-3d-part-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-5.06288484e-02 -1.72371134e-01 -3.14037919e-01 -4.32237536e-01
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4.29338366e-01 5.61602175e-01 1.89120203e-01 -5.69458976... | [8.132299423217773, -3.31589674949646] |
5f908701-b8ba-4215-aba1-b0cb7bb83331 | energy-dissipative-evolutionary-deep-operator | 2306.06281 | null | https://arxiv.org/abs/2306.06281v1 | https://arxiv.org/pdf/2306.06281v1.pdf | Energy-Dissipative Evolutionary Deep Operator Neural Networks | Energy-Dissipative Evolutionary Deep Operator Neural Network is an operator learning neural network. It is designed to seed numerical solutions for a class of partial differential equations instead of a single partial differential equation, such as partial differential equations with different parameters or different i... | ['Guang Lin', 'Jie Shen', 'Shiheng Zhang', 'Jiahao Zhang'] | 2023-06-09 | null | null | null | null | ['operator-learning'] | ['miscellaneous'] | [-1.51457610e-02 1.57242343e-01 4.22635585e-01 1.89388260e-01
-9.02721360e-02 -4.14544374e-01 -5.82715683e-02 -1.35551644e-02
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-7.94347823e-02 -1.11404300e+00 -7.48830974e-01 -1.04269767e+00
-1.76800266e-01 1.50366826e-02 -4.43015136e-02 -3.16774875... | [6.504624843597412, 3.431114912033081] |
073d4e5a-7969-408a-bc6e-f49e4e61d82a | paxqa-generating-cross-lingual-question | 2304.12206 | null | https://arxiv.org/abs/2304.12206v1 | https://arxiv.org/pdf/2304.12206v1.pdf | PAXQA: Generating Cross-lingual Question Answering Examples at Training Scale | Existing question answering (QA) systems owe much of their success to large, high-quality training data. Such annotation efforts are costly, and the difficulty compounds in the cross-lingual setting. Therefore, prior cross-lingual QA work has focused on releasing evaluation datasets, and then applying zero-shot methods... | ['Chris Callison-Burch', 'Bryan Li'] | 2023-04-24 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation', 'cross-lingual-question-answering', 'question-generation'] | ['medical', 'miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [ 1.49595216e-01 2.67595619e-01 8.07575230e-03 -4.85656947e-01
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-3.73550415e-01 9.58755672e-01 5.58234811e-01 -6.28183007e-01
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6.72746599e-01 9.51121807e-01 1.38108894e-01 -7.37734914... | [11.350125312805176, 8.375720024108887] |
4abf1100-169b-4bbb-a7a7-60d3748488f4 | explainability-of-text-processing-and | 2212.07126 | null | https://arxiv.org/abs/2212.07126v1 | https://arxiv.org/pdf/2212.07126v1.pdf | Explainability of Text Processing and Retrieval Methods: A Critical Survey | Deep Learning and Machine Learning based models have become extremely popular in text processing and information retrieval. However, the non-linear structures present inside the networks make these models largely inscrutable. A significant body of research has focused on increasing the transparency of these models. Thi... | ['Mandar Mitra', 'Debapriyo Majumdar', 'Sourav Saha'] | 2022-12-14 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [ 7.66739622e-03 4.29444313e-01 -5.25593162e-01 -5.53918123e-01
-1.82384238e-01 -5.22843599e-01 1.01877892e+00 5.82957923e-01
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-1.36967555e-01 4.81141150e-01 -3.79109591e-01 -1.40202060... | [10.858099937438965, 8.109405517578125] |
ad691664-2799-43ea-98f5-abce0aad7ca1 | corri2p-deep-image-to-point-cloud | 2207.05483 | null | https://arxiv.org/abs/2207.05483v3 | https://arxiv.org/pdf/2207.05483v3.pdf | CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence | Motivated by the intuition that the critical step of localizing a 2D image in the corresponding 3D point cloud is establishing 2D-3D correspondence between them, we propose the first feature-based dense correspondence framework for addressing the image-to-point cloud registration problem, dubbed CorrI2P, which consists... | ['Xiaodong Chen', 'Junhui Hou', 'Yiming Zeng', 'Siyu Ren'] | 2022-07-12 | null | null | null | null | ['point-cloud-registration', 'image-to-point-cloud-registration'] | ['computer-vision', 'computer-vision'] | [-3.75505909e-02 -4.23001796e-01 9.57500339e-02 -2.63509154e-01
-8.68884087e-01 -7.98415661e-01 6.82238102e-01 -1.28107131e-01
-1.75026432e-01 -1.64077953e-01 -1.61617905e-01 3.78994793e-02
9.75196362e-02 -6.61787868e-01 -7.43140399e-01 -5.26796758e-01
1.50830418e-01 7.19489694e-01 4.32211429e-01 4.28217053... | [7.65981388092041, -2.840498447418213] |
11cc611f-6ef0-4634-834b-9fe38004b0df | combining-verbal-and-nonverbal-features-to | null | null | https://aclanthology.org/W12-1634 | https://aclanthology.org/W12-1634.pdf | Combining Verbal and Nonverbal Features to Overcome the ``Information Gap'' in Task-Oriented Dialogue | null | ['Christopher Mitchell', 'Kristy Elizabeth Boyer', 'Joseph F. Grafsgaard', 'James C. Lester', 'Eun Young Ha'] | 2012-07-01 | null | null | null | ws-2012-7 | ['dialogue-act-classification'] | ['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... | [-7.288228511810303, 3.7071192264556885] |
14e0359b-e145-483c-a71d-f858b2562f26 | a-corpus-for-detecting-high-context-medical | 2003.03044 | null | https://arxiv.org/abs/2003.03044v1 | https://arxiv.org/pdf/2003.03044v1.pdf | A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients | A crucial step within secondary analysis of electronic health records (EHRs) is to identify the patient cohort under investigation. While EHRs contain medical billing codes that aim to represent the conditions and treatments patients may have, much of the information is only present in the patient notes. Therefore, it ... | ['Jonathan Welt', 'Joy T. Wu', 'Franck Dernoncourt', 'David W. Grant', 'Leo Anthony Celi', 'John Foote', 'Edward T. Moseley', 'Eric T. Carlson', 'Sebastian Gehrmann', 'Patrick D. Tyler'] | 2020-03-06 | a-corpus-for-detecting-high-context-medical-1 | https://aclanthology.org/2020.lrec-1.170 | https://aclanthology.org/2020.lrec-1.170.pdf | lrec-2020-5 | ['patient-phenotyping'] | ['medical'] | [ 4.98536974e-01 3.06105852e-01 -6.07210040e-01 -4.74233061e-01
-9.31021929e-01 -5.24098456e-01 -1.79297268e-01 1.38622153e+00
-2.07028180e-01 9.22056794e-01 8.60930324e-01 -4.63497579e-01
-4.00244385e-01 -5.88163733e-01 -1.28527477e-01 -4.28012818e-01
4.21996787e-02 8.74940813e-01 -8.10128093e-01 5.94518006... | [8.411185264587402, 8.512163162231445] |
b20a985c-432d-473a-af6b-971228cc9f9f | a-two-stage-approach-towards-generalization | 2111.05825 | null | https://arxiv.org/abs/2111.05825v2 | https://arxiv.org/pdf/2111.05825v2.pdf | A Two-Stage Approach towards Generalization in Knowledge Base Question Answering | Most existing approaches for Knowledge Base Question Answering (KBQA) focus on a specific underlying knowledge base either because of inherent assumptions in the approach, or because evaluating it on a different knowledge base requires non-trivial changes. However, many popular knowledge bases share similarities in the... | ['Gaetano Rossiello', 'Achille Fokoue', 'Pavan Kapanipathi', 'Tahira Naseem', 'Nandana Mihidukulasooriya', 'Ibrahim Abdelaziz', 'June Thai', 'Srinivas Ravishankar'] | 2021-11-10 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-2.70498872e-01 6.08254433e-01 -3.61436546e-01 -6.06407225e-01
-1.12438428e+00 -9.66079652e-01 3.44427407e-01 4.92351055e-01
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-4.94899422e-01 -1.43541920e+00 -1.27951181e+00 1.44340396e-01
5.34887984e-02 9.85641062e-01 8.97365868e-01 -7.89671183... | [10.191878318786621, 7.872884750366211] |
ea94c80c-fe66-4700-9eb0-b3886072d0d6 | identifying-electrocardiogram-abnormalities | 2206.10592 | null | https://arxiv.org/abs/2206.10592v1 | https://arxiv.org/pdf/2206.10592v1.pdf | Identifying Electrocardiogram Abnormalities Using a Handcrafted-Rule-Enhanced Neural Network | A large number of people suffer from life-threatening cardiac abnormalities, and electrocardiogram (ECG) analysis is beneficial to determining whether an individual is at risk of such abnormalities. Automatic ECG classification methods, especially the deep learning based ones, have been proposed to detect cardiac abnor... | ['Jian Wu', 'Danny Z. Chen', 'Xiaoxian Yang', 'Xiaojun Chen', 'Jintai Chen', 'Yuexin Bian'] | 2022-06-16 | null | null | null | null | ['ecg-classification', 'clinical-knowledge'] | ['medical', 'miscellaneous'] | [ 2.24401817e-01 -2.11744353e-01 5.77359693e-03 -5.91813505e-01
-5.60408592e-01 -2.00317904e-01 -3.82548898e-01 4.48722750e-01
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-2.73141742e-01 -9.57136869e-01 -2.44442910e-01 -5.63301563e-01
-2.52444237e-01 4.75936979e-01 -1.56107873e-01 7.69741684... | [14.285202980041504, 3.249361276626587] |
4f845ab5-bab3-49d7-b94d-ef8c488056bf | multilevel-profiling-of-situation-and | 2109.06488 | null | https://arxiv.org/abs/2109.06488v1 | https://arxiv.org/pdf/2109.06488v1.pdf | Multilevel profiling of situation and dialogue-based deep networks for movie genre classification using movie trailers | Automated movie genre classification has emerged as an active and essential area of research and exploration. Short duration movie trailers provide useful insights about the movie as video content consists of the cognitive and the affective level features. Previous approaches were focused upon either cognitive or affec... | ['Aditya Sharma', 'Ayush Mittal', 'Mayank Jindal', 'Dinesh Kumar Vishwakarma'] | 2021-09-14 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [-1.56163583e-02 -4.67114478e-01 -1.35156035e-01 -3.76384646e-01
-6.85908973e-01 -7.13756859e-01 8.72759759e-01 7.13256896e-01
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-2.99813598e-01 -4.64878500e-01 8.29721838e-02 -6.15758121e-01
-1.74649999e-01 -2.94547677e-01 6.29647374e-02 -4.21371043... | [15.231179237365723, 4.871703624725342] |
bc7df4d6-33c0-4303-9b6e-f82698b56faa | human-like-controllable-image-captioning-with | 2103.12204 | null | https://arxiv.org/abs/2103.12204v1 | https://arxiv.org/pdf/2103.12204v1.pdf | Human-like Controllable Image Captioning with Verb-specific Semantic Roles | Controllable Image Captioning (CIC) -- generating image descriptions following designated control signals -- has received unprecedented attention over the last few years. To emulate the human ability in controlling caption generation, current CIC studies focus exclusively on control signals concerning objective propert... | ['Wei Liu', 'Jun Xiao', 'Zhihong Jiang', 'Long Chen'] | 2021-03-22 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Chen_Human-Like_Controllable_Image_Captioning_With_Verb-Specific_Semantic_Roles_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Chen_Human-Like_Controllable_Image_Captioning_With_Verb-Specific_Semantic_Roles_CVPR_2021_paper.pdf | cvpr-2021-1 | ['controllable-image-captioning'] | ['computer-vision'] | [ 6.47399724e-01 4.38242912e-01 -3.22164059e-01 -5.64101577e-01
-9.04765129e-01 -7.67388105e-01 9.09313560e-01 -1.39459074e-01
-2.01293543e-01 8.31860423e-01 5.96087098e-01 4.06142846e-02
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4.12706316e-01 4.43234235e-01 1.97603256e-01 -3.12905937... | [10.843063354492188, 0.9699905514717102] |
79d8014d-9032-4ddb-bf4e-02e0b3f48d38 | vimi-vehicle-infrastructure-multi-view | 2303.10975 | null | https://arxiv.org/abs/2303.10975v1 | https://arxiv.org/pdf/2303.10975v1.pdf | VIMI: Vehicle-Infrastructure Multi-view Intermediate Fusion for Camera-based 3D Object Detection | In autonomous driving, Vehicle-Infrastructure Cooperative 3D Object Detection (VIC3D) makes use of multi-view cameras from both vehicles and traffic infrastructure, providing a global vantage point with rich semantic context of road conditions beyond a single vehicle viewpoint. Two major challenges prevail in VIC3D: 1)... | ['Ya-Qin Zhang', 'Yilun Chen', 'Jingjing Liu', 'Yan Wang', 'Tongda Xu', 'Xiaoliang Huo', 'Siqi Fan', 'Zhe Wang'] | 2023-03-20 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 1.82574242e-02 -4.29965019e-01 -1.86856121e-01 -2.71882594e-01
-9.50929761e-01 -6.50397599e-01 6.55171037e-01 -3.64688188e-01
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3.23882788e-01 -7.28378356e-01 -9.77818370e-01 -6.67228222e-01
5.78782037e-02 -1.79794729e-01 5.96100807e-01 -1.27186045... | [7.9682416915893555, -1.7866265773773193] |
cb4b894f-993d-4d5a-b424-bafef8c18822 | fast-template-matching-and-update-for-video | 2004.07538 | null | https://arxiv.org/abs/2004.07538v1 | https://arxiv.org/pdf/2004.07538v1.pdf | Fast Template Matching and Update for Video Object Tracking and Segmentation | In this paper, the main task we aim to tackle is the multi-instance semi-supervised video object segmentation across a sequence of frames where only the first-frame box-level ground-truth is provided. Detection-based algorithms are widely adopted to handle this task, and the challenges lie in the selection of the match... | ['Jimin Xiao', 'Mingjie Sun', 'Yao Zhao', 'Eng Gee Lim', 'Bingfeng Zhang'] | 2020-04-16 | fast-template-matching-and-update-for-video-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Sun_Fast_Template_Matching_and_Update_for_Video_Object_Tracking_and_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Sun_Fast_Template_Matching_and_Update_for_Video_Object_Tracking_and_CVPR_2020_paper.pdf | cvpr-2020-6 | ['template-matching', 'video-object-tracking'] | ['computer-vision', 'computer-vision'] | [ 3.92251164e-01 -2.06894338e-01 -2.15608507e-01 -2.90952176e-01
-7.58022070e-01 -5.51343918e-01 4.48990703e-01 2.97143787e-01
-8.50083888e-01 6.98698163e-01 -4.15741324e-01 -4.91330214e-02
1.64336920e-01 -8.80394459e-01 -7.05092072e-01 -9.11259294e-01
2.75806278e-01 7.63310432e-01 9.54665959e-01 5.10914139... | [9.01196575164795, -0.17457488179206848] |
cca8910d-2fa9-4feb-933c-c2a84da0098a | cgodial-a-large-scale-benchmark-for-chinese | 2211.11617 | null | https://arxiv.org/abs/2211.11617v1 | https://arxiv.org/pdf/2211.11617v1.pdf | CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation | Practical dialog systems need to deal with various knowledge sources, noisy user expressions, and the shortage of annotated data. To better solve the above problems, we propose CGoDial, new challenging and comprehensive Chinese benchmark for multi-domain Goal-oriented Dialog evaluation. It contains 96,763 dialog sessio... | ['Yongbin Li', 'Jian Sun', 'Zhongqi An', 'Zheng Cao', 'Yuchuan Wu', 'Bowen Li', 'Wanwei He', 'Yinpei Dai'] | 2022-11-21 | null | null | null | null | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-4.08608347e-01 2.39400379e-02 3.24710719e-02 -5.71525633e-01
-6.60996199e-01 -7.64031172e-01 6.37932301e-01 -1.90634459e-01
-3.24478596e-01 1.24732924e+00 6.30333066e-01 -2.67425865e-01
-8.10413214e-04 -5.74491918e-01 2.96499282e-01 -4.47816938e-01
3.14083129e-01 1.15147042e+00 5.12243986e-01 -9.66065586... | [12.810440063476562, 7.965880870819092] |
3d5421de-11c1-456f-bb78-d4752ed003db | high-quality-rgb-d-reconstruction-via-multi | 2210.12202 | null | https://arxiv.org/abs/2210.12202v1 | https://arxiv.org/pdf/2210.12202v1.pdf | High-Quality RGB-D Reconstruction via Multi-View Uncalibrated Photometric Stereo and Gradient-SDF | Fine-detailed reconstructions are in high demand in many applications. However, most of the existing RGB-D reconstruction methods rely on pre-calculated accurate camera poses to recover the detailed surface geometry, where the representation of a surface needs to be adapted when optimizing different quantities. In this... | ['Daniel Cremers', 'Xingxing Zuo', 'Bjoern Haefner', 'Lu Sang'] | 2022-10-21 | null | null | null | null | ['rgb-d-reconstruction'] | ['computer-vision'] | [ 3.51188719e-01 -2.95264840e-01 4.97024506e-01 -3.75926167e-01
-5.39920509e-01 -3.09101611e-01 3.91571462e-01 -8.55762139e-03
-1.13597028e-01 4.33448255e-01 -1.53051674e-01 2.23487407e-01
9.52308178e-02 -9.97881711e-01 -6.72394216e-01 -5.44840753e-01
5.24469376e-01 5.73694289e-01 4.64348316e-01 -3.12648863... | [9.319488525390625, -2.918010711669922] |
800f50c8-2106-4e11-8910-59f8af7d0670 | towards-multimodal-multitask-scene | 2209.13156 | null | https://arxiv.org/abs/2209.13156v1 | https://arxiv.org/pdf/2209.13156v1.pdf | Towards Multimodal Multitask Scene Understanding Models for Indoor Mobile Agents | The perception system in personalized mobile agents requires developing indoor scene understanding models, which can understand 3D geometries, capture objectiveness, analyze human behaviors, etc. Nonetheless, this direction has not been well-explored in comparison with models for outdoor environments (e.g., the autonom... | ['Jian Zhang', 'Ali Farhadi', 'Hanlin Goh', 'Yao-Hung Hubert Tsai'] | 2022-09-27 | null | null | null | null | ['traffic-sign-recognition', 'depth-completion'] | ['computer-vision', 'computer-vision'] | [-4.37438153e-02 -1.53890759e-01 1.01512820e-01 -5.51280618e-01
-8.02179217e-01 -5.64552844e-01 4.49727714e-01 -4.33137976e-02
-5.24246573e-01 4.20436293e-01 -1.32667109e-01 -3.79675299e-01
2.63235658e-01 -7.79453993e-01 -1.00717592e+00 -6.69236958e-01
3.39229703e-01 9.22343016e-01 5.34124792e-01 -1.50853753... | [8.016133308410645, -2.331118583679199] |
36c64352-c149-4de9-a61e-f59585c70eb4 | deep-learning-for-short-latency-epileptic | 2301.03465 | null | https://arxiv.org/abs/2301.03465v2 | https://arxiv.org/pdf/2301.03465v2.pdf | Shorter Latency of Real-time Epileptic Seizure Detection via Probabilistic Prediction | Although recent studies have proposed seizure detection algorithms with good sensitivity performance, there is a remained challenge that they were hard to achieve significantly short detection latency in real-time scenarios. In this manuscript, we propose a novel deep learning framework intended for shortening epilepti... | ['Mohamad Sawan', 'Shuang Wang', 'Wenjie Ming', 'Jie Yang', 'Yankun Xu'] | 2023-01-04 | null | null | null | null | ['seizure-detection'] | ['medical'] | [ 2.65807807e-01 -2.00027466e-01 2.97224253e-01 -3.19075525e-01
-1.13294411e+00 -3.18194002e-01 2.58507282e-01 3.53863120e-01
-6.70830667e-01 9.64326024e-01 -2.55741030e-01 -1.08556166e-01
-6.62615776e-01 -4.19099897e-01 -4.63105232e-01 -7.20340014e-01
-6.40721083e-01 3.74159366e-02 3.13052088e-01 2.86236674... | [13.224255561828613, 3.5161561965942383] |
26765b21-7a4e-4918-83a0-1883555fd740 | graph-constrained-reinforcement-learning-for-1 | 2001.08837 | null | https://arxiv.org/abs/2001.08837v1 | https://arxiv.org/pdf/2001.08837v1.pdf | Graph Constrained Reinforcement Learning for Natural Language Action Spaces | Interactive Fiction games are text-based simulations in which an agent interacts with the world purely through natural language. They are ideal environments for studying how to extend reinforcement learning agents to meet the challenges of natural language understanding, partial observability, and action generation in ... | ['Prithviraj Ammanabrolu', 'Matthew Hausknecht'] | 2020-01-23 | null | https://openreview.net/forum?id=B1x6w0EtwH | https://openreview.net/pdf?id=B1x6w0EtwH | iclr-2020-1 | ['action-generation'] | ['computer-vision'] | [-7.89433438e-03 6.23965263e-01 -2.16353297e-01 3.43643576e-01
-4.09608632e-01 -1.05025303e+00 1.03829372e+00 -3.95887703e-01
-5.08282661e-01 1.09401000e+00 2.67430484e-01 -7.61635125e-01
-1.64447621e-01 -1.19794047e+00 -2.91197419e-01 -6.55922741e-02
-4.79069442e-01 7.93073475e-01 4.78180140e-01 -8.26615930... | [3.7996842861175537, 1.4434049129486084] |
cac5fa75-a3a8-4b43-b620-5442475597c5 | identifying-individual-differences-in-gender | null | null | https://aclanthology.org/W16-0806 | https://aclanthology.org/W16-0806.pdf | Identifying Individual Differences in Gender, Ethnicity, and Personality from Dialogue for Deception Detection | null | ['Yocheved Levitan', 'Michelle Levine', 'Sarah Ita Levitan', 'Rivka Levitan', 'Andrew Rosenberg', 'Guozhen An', 'Julia Hirschberg'] | 2016-06-01 | null | null | null | ws-2016-6 | ['deception-detection'] | ['miscellaneous'] | [-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... | [-7.23374605178833, 3.792767286300659] |
8531a677-35bc-4b53-975b-96fda1c21b82 | learning-ordinal-relationships-for-mid-level | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Zoran_Learning_Ordinal_Relationships_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Zoran_Learning_Ordinal_Relationships_ICCV_2015_paper.pdf | Learning Ordinal Relationships for Mid-Level Vision | We propose a framework that infers mid-level visual properties of an image by learning about ordinal relation- ships. Instead of estimating metric quantities directly, the system proposes pairwise relationship estimates for points in the input image. These sparse probabilistic ordinal mea- surements are globalized to c... | ['Daniel Zoran', 'William T. Freeman', 'Phillip Isola', 'Dilip Krishnan'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 1.64397553e-01 2.82192491e-02 -2.18252599e-01 -8.61282527e-01
-8.38734031e-01 -5.21473646e-01 5.70354998e-01 1.68475419e-01
-7.07316995e-01 4.96255994e-01 3.43969494e-01 1.92702189e-01
-1.82886884e-01 -7.01839507e-01 -6.71569884e-01 -6.46976769e-01
-2.07819715e-01 7.51983404e-01 1.03921227e-01 4.97672744... | [8.42219066619873, -2.4956612586975098] |
8b31a9b9-1bb1-4e43-a908-cef2f41216fb | universal-denoising-networks-a-novel-cnn | 1711.07807 | null | http://arxiv.org/abs/1711.07807v2 | http://arxiv.org/pdf/1711.07807v2.pdf | Universal Denoising Networks : A Novel CNN Architecture for Image Denoising | We design a novel network architecture for learning discriminative image
models that are employed to efficiently tackle the problem of grayscale and
color image denoising. Based on the proposed architecture, we introduce two
different variants. The first network involves convolutional layers as a core
component, while ... | ['Stamatios Lefkimmiatis'] | 2017-11-21 | universal-denoising-networks-a-novel-cnn-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Lefkimmiatis_Universal_Denoising_Networks_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Lefkimmiatis_Universal_Denoising_Networks_CVPR_2018_paper.pdf | cvpr-2018-6 | ['color-image-denoising'] | ['computer-vision'] | [ 3.31750363e-01 -2.79688597e-01 3.39698702e-01 -2.13433966e-01
-5.95396638e-01 -2.14842975e-01 7.19911635e-01 -3.44196521e-02
-7.29844987e-01 6.07072175e-01 5.34806512e-02 8.31560120e-02
-4.56325769e-01 -7.16099977e-01 -6.46987200e-01 -1.13467181e+00
-4.07310165e-02 3.73849332e-01 2.08256885e-01 -5.19195318... | [11.494464874267578, -2.3639068603515625] |
5efb4ef2-4c62-4f7a-8763-00338ec37fd6 | anise-assembly-based-neural-implicit-surface | 2205.13682 | null | https://arxiv.org/abs/2205.13682v2 | https://arxiv.org/pdf/2205.13682v2.pdf | ANISE: Assembly-based Neural Implicit Surface rEconstruction | We present ANISE, a method that reconstructs a 3D~shape from partial observations (images or sparse point clouds) using a part-aware neural implicit shape representation. The shape is formulated as an assembly of neural implicit functions, each representing a different part instance. In contrast to previous approaches,... | ['Evangelos Kalogerakis', 'Radomir Mech', 'Matheus Gadelha', 'Dmitry Petrov'] | 2022-05-27 | null | null | null | null | ['point-cloud-reconstruction'] | ['computer-vision'] | [ 3.32217485e-01 1.94024965e-01 5.46981115e-03 -3.35210830e-01
-1.03453267e+00 -8.79243135e-01 7.05081105e-01 -3.25201303e-02
4.38983291e-01 1.92485258e-01 3.87600332e-01 1.19725451e-01
-1.18400333e-02 -1.18867052e+00 -1.52134907e+00 -5.30514061e-01
3.13615799e-01 1.43380618e+00 1.50417805e-01 -3.01016085... | [8.65503978729248, -3.6074912548065186] |
c24b42f1-f6c5-442f-a49d-004ba6af6771 | predicting-soil-properties-from-hyperspectral | null | null | https://ieeexplore.ieee.org/abstract/document/9897254 | https://github.com/ridvansalihkuzu/hyperview_eagleeyes/blob/master/challenge_submission_eagleeyes/hyperview_for_ICIP_camera_ready_eagleeyes.pdf | Predicting Soil Properties from Hyperspectral Satellite Images | The AI4EO HYPERVIEW challenge seeks machine learning
methods that predict agriculturally relevant soil parameters
(K, Mg, P2O5, pH) from airborne hyperspectral images.
We present a hybrid model fusing Random Forest and K-
nearest neighbor regressors that exploit the average spectral
reflectance, as well as derived... | ['Roshni Kamath', 'Caroline Arnold', 'Frauke Albrecht', 'Rıdvan Salih Kuzu'] | 2022-10-18 | null | null | null | conference-2022-10 | ['seeing-beyond-the-visible'] | ['computer-vision'] | [ 6.94632113e-01 -9.70689654e-02 -4.45391834e-01 -4.33307052e-01
-5.21906376e-01 -6.91619039e-01 3.44461709e-01 1.09155692e-01
-9.89821106e-02 1.11122370e+00 1.48634464e-01 -7.23207593e-01
-5.75142086e-01 -1.24434757e+00 -6.65182173e-01 -8.46758425e-01
-3.93659055e-01 -3.21853943e-02 -1.31460249e-01 -5.11363685... | [9.465338706970215, -1.545013666152954] |
2b810d7e-c8ae-49f7-8c99-491dd2a81e13 | cqare-contrastive-question-answering-for-few | null | null | https://openreview.net/forum?id=FEg_0BrW4Ks | https://openreview.net/pdf?id=FEg_0BrW4Ks | CQARE: Contrastive Question-Answering for Few-shot Relation Extraction with Prompt Tuning | Prompt tuning with pre-trained language models (PLM) has exhibited outstanding performance by closing the gap between pre-training tasks and various downstream applications, without the need for uninitialized parameters to be introduced. However, prompt tuning requires vast amounts of prompt engineering and predefined ... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['cross-domain-few-shot'] | ['computer-vision'] | [ 2.84525692e-01 1.98480785e-01 -6.81572454e-03 -4.71560836e-01
-9.75563586e-01 -4.40273046e-01 6.95895493e-01 3.32773119e-01
-7.91415691e-01 4.93776053e-01 1.40471414e-01 -2.67074168e-01
-3.60190928e-01 -8.54794323e-01 -3.56337011e-01 -3.49417478e-01
4.52212602e-01 6.56592906e-01 5.08639574e-01 -7.18497336... | [10.580045700073242, 8.128897666931152] |
1149e2c0-40ff-49a7-8ea3-4f6ce457fc2d | efficient-model-monitoring-for-quality | 2104.05533 | null | https://arxiv.org/abs/2104.05533v1 | https://arxiv.org/pdf/2104.05533v1.pdf | Efficient Model Monitoring for Quality Control in Cardiac Image Segmentation | Deep learning methods have reached state-of-the-art performance in cardiac image segmentation. Currently, the main bottleneck towards their effective translation into clinics requires assuring continuous high model performance and segmentation results. In this work, we present a novel learning framework to monitor the ... | ['Maria A. Zuluaga', 'Francesco Galati'] | 2021-04-12 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 2.77038634e-01 9.28631797e-02 -3.42809260e-01 -4.60557610e-01
-1.27228510e+00 -5.39335608e-01 2.54609138e-01 8.01442802e-01
-6.24992549e-01 6.52188897e-01 -7.19351545e-02 -3.11996400e-01
-1.96385950e-01 -6.44529104e-01 -4.29289937e-01 -6.05143428e-01
-9.95755121e-02 8.28178346e-01 2.87799239e-01 3.13784778... | [14.302199363708496, -2.4328978061676025] |
fdd8054d-968f-442e-8476-6326deb5f4cc | manydg-many-domain-generalization-for | 2301.08834 | null | https://arxiv.org/abs/2301.08834v2 | https://arxiv.org/pdf/2301.08834v2.pdf | ManyDG: Many-domain Generalization for Healthcare Applications | The vast amount of health data has been continuously collected for each patient, providing opportunities to support diverse healthcare predictive tasks such as seizure detection and hospitalization prediction. Existing models are mostly trained on other patients data and evaluated on new patients. Many of them might su... | ['M. Brandon Westover', 'Jimeng Sun', 'Chaoqi Yang'] | 2023-01-21 | null | null | null | null | ['seizure-detection'] | ['medical'] | [ 3.31795633e-01 -4.24788967e-02 -5.10732949e-01 -5.37985682e-01
-5.70736110e-01 -3.62590939e-01 2.72449523e-01 2.33951569e-01
-1.68035269e-01 9.68642712e-01 3.96929055e-01 -1.48674533e-01
-3.86194944e-01 -4.84451473e-01 -6.04689658e-01 -8.55865955e-01
-3.16721834e-02 8.11259687e-01 -1.58542544e-01 -9.67234671... | [10.346600532531738, 3.276689291000366] |
b4a1e374-3ce4-463e-86d4-d307d901e222 | multi-task-deep-cnn-model-for-no-reference | 2008.11961 | null | https://arxiv.org/abs/2008.11961v1 | https://arxiv.org/pdf/2008.11961v1.pdf | Multi-task deep CNN model for no-reference image quality assessment on smartphone camera photos | Smartphone is the most successful consumer electronic product in today's mobile social network era. The smartphone camera quality and its image post-processing capability is the dominant factor that impacts consumer's buying decision. However, the quality evaluation of photos taken from smartphones remains a labor-inte... | ['Ja-Ling Wu', 'Chen-Hsiu Huang'] | 2020-08-27 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 2.28959456e-01 -6.59597397e-01 -1.32757425e-02 -5.36160290e-01
-8.21648896e-01 -3.67402107e-01 8.00851285e-02 -3.85148555e-01
-5.46048760e-01 3.06746423e-01 5.41457394e-03 -4.16332185e-01
1.54938325e-01 -8.35810304e-01 -6.71726704e-01 -7.12867737e-01
5.32128572e-01 -2.91377813e-01 1.77974403e-01 -2.79402226... | [11.642634391784668, -1.9256056547164917] |
c81a4d6f-0fc0-403d-87c9-e238bef66ec4 | foit-fast-online-instance-transfer-for | null | null | https://www.researchgate.net/profile/Jinpeng-Li-3/publication/348932155_FOIT_Fast_Online_Instance_Transfer_for_Improved_EEG_Emotion_Recognition/links/6017f2a692851c2d4d0b0b69/FOIT-Fast-Online-Instance-Transfer-for-Improved-EEG-Emotion-Recognition.pdf | https://www.researchgate.net/profile/Jinpeng-Li-3/publication/348932155_FOIT_Fast_Online_Instance_Transfer_for_Improved_EEG_Emotion_Recognition/links/6017f2a692851c2d4d0b0b69/FOIT-Fast-Online-Instance-Transfer-for-Improved-EEG-Emotion-Recognition.pdf | FOIT: Fast Online Instance Transfer for Improved EEG Emotion Recognition | The Electroencephalogram (EEG)-based emotion recognition is promising yet limited by the requirement of a large number of training data. Collecting substantial labeled samples in the training trails is the key to the generalization on the test trails. This process is time-consuming and laborious. In recent years, sever... | ['Ting Cai', 'Hao Chen', 'Jinpeng Li'] | 2021-02-01 | null | null | null | 2020-ieee-international-conference-on-3 | ['eeg-emotion-recognition'] | ['miscellaneous'] | [ 3.13779145e-01 -1.57999724e-01 -1.54251426e-01 -7.55104899e-01
-8.29792261e-01 -2.84704328e-01 3.25458422e-02 3.32139693e-02
-6.55189931e-01 1.06006718e+00 -4.63710368e-01 -1.10124074e-01
-2.47215673e-01 -4.50005323e-01 -4.00221616e-01 -8.43307674e-01
-2.91373134e-01 2.90263712e-01 1.62065879e-01 -1.26525104... | [13.128241539001465, 3.4134652614593506] |
ea9b4201-76a9-4c34-aa38-2d9ce88fb608 | care-coherent-actionable-recourse-based-on | 2108.08197 | null | https://arxiv.org/abs/2108.08197v1 | https://arxiv.org/pdf/2108.08197v1.pdf | CARE: Coherent Actionable Recourse based on Sound Counterfactual Explanations | Counterfactual explanation methods interpret the outputs of a machine learning model in the form of "what-if scenarios" without compromising the fidelity-interpretability trade-off. They explain how to obtain a desired prediction from the model by recommending small changes to the input features, aka recourse. We belie... | ['Ingrid Chieh Yu', 'Peyman Rasouli'] | 2021-08-18 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 3.85206848e-01 7.03447521e-01 -6.19045615e-01 -7.11725771e-01
-6.24464989e-01 -5.37961185e-01 6.84936166e-01 2.43123192e-02
-1.61047339e-01 1.24136484e+00 4.27207768e-01 -6.13858163e-01
-8.38185728e-01 -6.96312129e-01 -7.23312199e-01 -6.38955712e-01
1.13345027e-01 6.19254053e-01 -4.97288644e-01 -7.49539807... | [8.729100227355957, 5.640194892883301] |
3ad5bca9-9349-40ea-9d88-24905becd68b | constrained-sampling-for-class-agnostic | 2209.09195 | null | https://arxiv.org/abs/2209.09195v1 | https://arxiv.org/pdf/2209.09195v1.pdf | Constrained Sampling for Class-Agnostic Weakly Supervised Object Localization | Self-supervised vision transformers can generate accurate localization maps of the objects in an image. However, since they decompose the scene into multiple maps containing various objects, and they do not rely on any explicit supervisory signal, they cannot distinguish between the object of interest from other object... | ['Eric Granger', 'Aydin Sarraf', 'Marco Pedersoli', 'Soufiane Belharbi', 'Shakeeb Murtaza'] | 2022-09-09 | null | null | null | null | ['weakly-supervised-object-localization'] | ['computer-vision'] | [ 5.42026043e-01 1.50869176e-01 -2.59312183e-01 -4.77972120e-01
-9.54180419e-01 -5.55531919e-01 6.64271355e-01 1.10288054e-01
-2.54724860e-01 6.32667184e-01 -3.36863339e-01 2.37024620e-01
2.23087549e-01 -8.88322949e-01 -1.00695622e+00 -1.02907670e+00
3.60629737e-01 6.39864683e-01 9.24714029e-01 3.22824687... | [9.496408462524414, 0.8478989005088806] |
1d72f21a-1c7c-425c-bacb-05ae00a065a5 | approximate-adapt-anonymize-3a-a-framework | 2307.01875 | null | https://arxiv.org/abs/2307.01875v1 | https://arxiv.org/pdf/2307.01875v1.pdf | Approximate, Adapt, Anonymize (3A): a Framework for Privacy Preserving Training Data Release for Machine Learning | The availability of large amounts of informative data is crucial for successful machine learning. However, in domains with sensitive information, the release of high-utility data which protects the privacy of individuals has proven challenging. Despite progress in differential privacy and generative modeling for privac... | ['Matthew Howard', 'Olivia Choudhury', 'Weijie Xu', 'Tamas Madl'] | 2023-07-04 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 2.25865752e-01 4.24388051e-01 -2.31128529e-01 -6.05649531e-01
-1.01034999e+00 -1.04380810e+00 6.44567609e-01 3.14655751e-01
-4.80838925e-01 1.10141206e+00 1.65687680e-01 -1.23327419e-01
-1.35749847e-01 -1.04784107e+00 -9.06689942e-01 -7.89608777e-01
-1.21540383e-01 4.67955351e-01 -3.77162725e-01 9.74171758... | [6.02170991897583, 6.908913612365723] |
1f38fa04-dd54-4a08-aebc-051bc660821e | a-machine-learning-pressure-emulator-for | 2306.13116 | null | https://arxiv.org/abs/2306.13116v1 | https://arxiv.org/pdf/2306.13116v1.pdf | A Machine Learning Pressure Emulator for Hydrogen Embrittlement | A recent alternative for hydrogen transportation as a mixture with natural gas is blending it into natural gas pipelines. However, hydrogen embrittlement of material is a major concern for scientists and gas installation designers to avoid process failures. In this paper, we propose a physics-informed machine learning ... | ['Alberto Costa Nogueira Junior', 'Elie Alhajjar', 'João Lucas de Sousa Almeida', 'Minh Triet Chau'] | 2023-06-22 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-4.00249988e-01 1.43206030e-01 2.00220987e-01 6.08806349e-02
-1.40280724e-01 -4.40923929e-01 5.34845412e-01 1.64626881e-01
1.74057763e-02 6.17350459e-01 -3.68449599e-01 -7.59232342e-01
4.12336551e-02 -1.20521438e+00 -8.26800287e-01 -1.02553093e+00
-2.17894554e-01 4.45525378e-01 5.29943287e-01 -1.43598437... | [6.369542121887207, 3.32501482963562] |
c061215e-757e-4f47-a527-a3c158f0b458 | multiple-reflection-symmetry-detection-via | 1704.06392 | null | http://arxiv.org/abs/1704.06392v1 | http://arxiv.org/pdf/1704.06392v1.pdf | Multiple Reflection Symmetry Detection via Linear-Directional Kernel Density Estimation | Symmetry is an important composition feature by investigating similar sides
inside an image plane. It has a crucial effect to recognize man-made or nature
objects within the universe. Recent symmetry detection approaches used a
smoothing kernel over different voting maps in the polar coordinate system to
detect symmetr... | ['Christophe Ducottet', 'Philippe Colantoni', 'Olivier Alata', 'Cecile Barat', 'Mohamed Elawady'] | 2017-04-21 | null | null | null | null | ['symmetry-detection'] | ['computer-vision'] | [ 7.51319081e-02 -9.55526307e-02 -1.31277218e-01 -3.95066082e-01
-3.95782471e-01 -6.93494558e-01 1.13437188e+00 -4.49012190e-01
-9.38863978e-02 2.87679523e-01 3.70778114e-01 -6.10284023e-02
-4.50472891e-01 -9.23127234e-01 -2.14385018e-01 -7.67240942e-01
-6.76082820e-02 7.85975158e-01 7.17275798e-01 2.57806629... | [8.934125900268555, -2.0412662029266357] |
59f59af3-c756-4984-897e-7a35fd7837f3 | an-ai-ready-multiplex-staining-dataset-for | 2305.16465 | null | https://arxiv.org/abs/2305.16465v1 | https://arxiv.org/pdf/2305.16465v1.pdf | An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate Characterization of Tumor Immune Microenvironment | We introduce a new AI-ready computational pathology dataset containing restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients. Specifically, the same tumor sections were stained with the expensive multiplex immunofluorescence (mIF) assay first and then restained with chea... | ['Saad Nadeem', 'Christine H. Chung', 'Robbert JC Slebos', 'Janis V. de la Iglesia', 'Juan Hernandez-Prera', 'Joseph Marino', 'Parmida Ghahremani'] | 2023-05-25 | null | null | null | null | ['style-transfer'] | ['computer-vision'] | [-5.41018695e-02 2.14752229e-03 -4.81210113e-01 -2.13572998e-02
-1.28398144e+00 -7.30694830e-01 2.18899637e-01 3.18810284e-01
-6.64508879e-01 9.68710482e-01 1.35892332e-02 -6.76281631e-01
3.52425545e-01 -6.46271646e-01 -1.84848323e-01 -1.19379020e+00
2.29478016e-01 1.03784263e+00 7.93037564e-02 -1.21008284... | [15.059233665466309, -3.0642971992492676] |
6f587b6e-2310-403b-89c1-d9fde22a5f3d | dgpose-disentangled-semi-supervised-deep | 1804.06364 | null | https://arxiv.org/abs/1804.06364v2 | https://arxiv.org/pdf/1804.06364v2.pdf | DGPose: Deep Generative Models for Human Body Analysis | Deep generative modelling for human body analysis is an emerging problem with many interesting applications. However, the latent space learned by such approaches is typically not interpretable, resulting in less flexibility. In this work, we present deep generative models for human body analysis in which the body pose ... | ['Adnane Boukhayma', 'N. Siddharth', 'Arnab Ghosh', 'Thalaiyasingam Ajanthan', 'Rodrigo de Bem', 'Philip Torr', 'Ondrej Miksik'] | 2018-04-17 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 2.94283628e-01 4.88696784e-01 8.60919729e-02 -3.62672389e-01
-1.62980929e-01 -5.20242155e-01 7.49988556e-01 -3.90077800e-01
-2.65184879e-01 6.00686610e-01 3.16536486e-01 3.38043213e-01
2.32062079e-02 -7.09419191e-01 -8.49873543e-01 -9.44414735e-01
1.78024054e-01 9.33977723e-01 3.45398411e-02 -2.24169135... | [7.231204986572266, -0.9237357378005981] |
ce2e65d6-edb8-434a-8620-226b7bc62c59 | fine-tuning-of-explainable-cnns-for-skin | 2304.01399 | null | https://arxiv.org/abs/2304.01399v1 | https://arxiv.org/pdf/2304.01399v1.pdf | Fine-tuning of explainable CNNs for skin lesion classification based on dermatologists' feedback towards increasing trust | In this paper, we propose a CNN fine-tuning method which enables users to give simultaneous feedback on two outputs: the classification itself and the visual explanation for the classification. We present the effect of this feedback strategy in a skin lesion classification task and measure how CNNs react to the two typ... | ['Daniel Sonntag', 'Fabrizio Nunnari', 'Md Abdul Kadir'] | 2023-04-03 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 4.16604914e-02 7.27345943e-01 -9.37067866e-02 -5.02134323e-01
-6.51184190e-03 -5.32193124e-01 2.09019884e-01 3.00785780e-01
-4.18503374e-01 3.39129567e-01 1.76975220e-01 -5.27122974e-01
2.45988786e-01 -6.04871392e-01 -4.25551504e-01 -2.67184138e-01
4.23452407e-01 -8.52149725e-02 2.26491496e-01 -2.62851864... | [8.905305862426758, 5.579439640045166] |
2a9b99bc-792f-43e8-9dc8-3f06354da354 | discovering-customer-service-dialog-system | 2212.12363 | null | https://arxiv.org/abs/2212.12363v1 | https://arxiv.org/pdf/2212.12363v1.pdf | Discovering Customer-Service Dialog System with Semi-Supervised Learning and Coarse-to-Fine Intent Detection | Task-oriented dialog(TOD) aims to assist users in achieving specific goals through multi-turn conversation. Recently, good results have been obtained based on large pre-trained models. However, the labeled-data scarcity hinders the efficient development of TOD systems at scale. In this work, we constructed a weakly sup... | ['Zheyu Zhang', 'Anqi Liu', 'Xing Ma', 'Zhitong Yang'] | 2022-12-23 | null | null | null | null | ['intent-detection'] | ['natural-language-processing'] | [-1.87599093e-01 5.33794403e-01 -8.08965489e-02 -7.97891438e-01
-8.62420261e-01 -5.11272490e-01 8.10866475e-01 -1.65422007e-01
-1.58313364e-01 1.08705854e+00 7.32361913e-01 -2.87406802e-01
2.91858107e-01 -4.90953207e-01 2.82962799e-01 -2.60198146e-01
4.73101825e-01 8.16921353e-01 1.11914262e-01 -8.14044237... | [12.834452629089355, 7.983392238616943] |
91349a52-2459-4c3d-8ca8-75321523f52b | can-bert-eat-rucola-topological-data-analysis | 2304.01680 | null | https://arxiv.org/abs/2304.01680v1 | https://arxiv.org/pdf/2304.01680v1.pdf | Can BERT eat RuCoLA? Topological Data Analysis to Explain | This paper investigates how Transformer language models (LMs) fine-tuned for acceptability classification capture linguistic features. Our approach uses the best practices of topological data analysis (TDA) in NLP: we construct directed attention graphs from attention matrices, derive topological features from them, an... | ['Ekaterina Artemova', 'Irina Piontkovskaya', 'Irina Proskurina'] | 2023-04-04 | null | null | null | null | ['topological-data-analysis', 'linguistic-acceptability'] | ['graphs', 'natural-language-processing'] | [-1.78257167e-01 2.92454541e-01 -1.96849838e-01 -4.40942228e-01
-8.66248965e-01 -1.01310146e+00 5.84120512e-01 5.88576436e-01
-2.35681295e-01 2.83580720e-01 5.73539615e-01 -6.72552228e-01
-4.45375413e-01 -7.27016926e-01 -7.98173487e-01 -3.93971473e-01
-3.60388011e-01 7.53556848e-01 1.93514097e-02 -4.86848533... | [10.812097549438477, 9.584216117858887] |
266e4510-ad9b-4096-b797-7907c1aed85b | h2o-two-hands-manipulating-objects-for-first | 2104.11181 | null | https://arxiv.org/abs/2104.11181v2 | https://arxiv.org/pdf/2104.11181v2.pdf | H2O: Two Hands Manipulating Objects for First Person Interaction Recognition | We present a comprehensive framework for egocentric interaction recognition using markerless 3D annotations of two hands manipulating objects. To this end, we propose a method to create a unified dataset for egocentric 3D interaction recognition. Our method produces annotations of the 3D pose of two hands and the 6D po... | ['Marc Pollefeys', 'Federica Bogo', 'Jan Stuhmer', 'Bugra Tekin', 'Taein Kwon'] | 2021-04-22 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Kwon_H2O_Two_Hands_Manipulating_Objects_for_First_Person_Interaction_Recognition_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Kwon_H2O_Two_Hands_Manipulating_Objects_for_First_Person_Interaction_Recognition_ICCV_2021_paper.pdf | iccv-2021-1 | ['hand-object-pose'] | ['computer-vision'] | [-7.75463656e-02 -4.96133715e-02 6.01276346e-02 -3.21093261e-01
-2.71149695e-01 -7.13063896e-01 7.73374498e-01 -4.67632502e-01
-1.67758986e-01 1.90470349e-02 4.30872679e-01 3.24972004e-01
-1.13547429e-01 -1.36395782e-01 -7.09813714e-01 -2.86001861e-01
-1.00509338e-01 1.22378552e+00 1.35371417e-01 -2.06010640... | [6.618928909301758, -0.9094635248184204] |
bf2636f5-a2a5-4a37-906a-eaa1d335ee48 | knowledge-base-question-answering-by-case | 2202.10610 | null | https://arxiv.org/abs/2202.10610v2 | https://arxiv.org/pdf/2202.10610v2.pdf | Knowledge Base Question Answering by Case-based Reasoning over Subgraphs | Question answering (QA) over knowledge bases (KBs) is challenging because of the diverse, essentially unbounded, types of reasoning patterns needed. However, we hypothesize in a large KB, reasoning patterns required to answer a query type reoccur for various entities in their respective subgraph neighborhoods. Leveragi... | ['Andrew McCallum', 'Hannaneh Hajishirzi', 'Manzil Zaheer', 'Robin Jia', 'Elliot Tower', 'Ankita Naik', 'Ameya Godbole', 'Rajarshi Das'] | 2022-02-22 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-4.82351780e-01 6.15615070e-01 -4.29831326e-01 -4.32073414e-01
-1.38764644e+00 -9.33948457e-01 8.90274271e-02 3.92327964e-01
3.12052201e-02 1.00930977e+00 4.70457494e-01 -5.01801074e-01
-4.70413625e-01 -1.36672652e+00 -1.13999176e+00 -2.01546803e-01
-6.88463524e-02 1.01399279e+00 9.19357717e-01 -4.25106674... | [10.415923118591309, 7.84948205947876] |
3435118f-9ad4-4fb0-b049-02de90fdaa37 | collision-free-motion-planning-for-mobile | 2306.17445 | null | https://arxiv.org/abs/2306.17445v1 | https://arxiv.org/pdf/2306.17445v1.pdf | Collision-free Motion Planning for Mobile Robots by Zero-order Robust Optimization-based MPC | This paper presents an implementation of robust model predictive control (MPC) for collision-free reference trajectory tracking for mobile robots. The presented approach considers the robot motion to be subject to process noise bounded by ellipsoidal sets. In order to efficiently handle the evolution of the disturbance... | ['Moritz Diehl', 'Niels van Duijkeren', 'Jonathan Frey', 'Florian Messerer', 'Yunfan Gao'] | 2023-06-30 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 9.56282914e-02 5.27209997e-01 -2.07420856e-01 5.05081296e-01
-1.08569421e-01 -4.01887357e-01 5.88579118e-01 -1.40371382e-01
-4.56340760e-01 9.82737780e-01 -6.51277184e-01 -4.48193252e-01
-4.58288103e-01 -4.19892132e-01 -5.68597317e-01 -1.00841784e+00
-6.57313913e-02 6.65445864e-01 3.29297096e-01 -4.07348365... | [5.214152812957764, 2.2320449352264404] |
c4d29f79-85c4-45a6-a6d0-45c7f739bd11 | arabic-dialect-identification-using-bert-fine | null | null | https://aclanthology.org/2020.wanlp-1.33 | https://aclanthology.org/2020.wanlp-1.33.pdf | Arabic Dialect Identification Using BERT Fine-Tuning | In the last few years, deep learning has proved to be a very effective paradigm to discover patterns in large data sets. Unfortunately, deep learning training on small data sets is not the best option because most of the time traditional machine learning algorithms could get better scores. Now, we can train the neural ... | ['Marwan Torki', 'Zeyad Ezzat', 'Moustafa Tohamy', 'Moataz Mansour'] | null | null | null | null | coling-wanlp-2020-12 | ['dialect-identification'] | ['natural-language-processing'] | [-5.2365750e-01 -3.2605022e-01 -1.1074528e-01 -6.6835368e-01
-7.6705950e-01 -6.5797526e-01 4.8013061e-01 -7.2920278e-02
-5.4811561e-01 9.5031160e-01 1.8545045e-01 -3.5385126e-01
-3.8740760e-01 -1.0054462e+00 -4.8345914e-01 -4.4856757e-01
-1.3263641e-01 1.1565299e+00 8.7449603e-02 -7.3947006e-01
2.2048756e-01... | [10.178744316101074, 10.687644004821777] |
25a27777-0fc1-4940-8041-7dfc25d4cda9 | neural-transition-system-for-end-to-end | 2110.02001 | null | https://arxiv.org/abs/2110.02001v2 | https://arxiv.org/pdf/2110.02001v2.pdf | Mastering the Explicit Opinion-role Interaction: Syntax-aided Neural Transition System for Unified Opinion Role Labeling | Unified opinion role labeling (ORL) aims to detect all possible opinion structures of 'opinion-holder-target' in one shot, given a text. The existing transition-based unified method, unfortunately, is subject to longer opinion terms and fails to solve the term overlap issue. Current top performance has been achieved by... | ['Chong Teng', 'Yijiang Liu', 'Meishan Zhang', 'Donghong Ji', 'Fei Li', 'Hao Fei', 'Shengqiong Wu'] | 2021-10-05 | null | null | null | null | ['fine-grained-opinion-analysis'] | ['natural-language-processing'] | [ 2.56919354e-01 3.23741019e-01 -3.32464129e-01 -4.69774336e-01
-7.97068715e-01 -6.95847154e-01 4.90470797e-01 4.67509478e-01
5.38446978e-02 4.63037014e-01 5.80017984e-01 -7.55964875e-01
-2.11814299e-01 -9.26920235e-01 -3.79148096e-01 -5.72248280e-01
1.18984058e-01 5.41245878e-01 4.27674741e-01 -8.59426975... | [11.457380294799805, 6.7039690017700195] |
ebcdec32-8ee8-4043-ab45-312f6c4f0476 | zhixiaobao-at-semeval-2022-task-10 | null | null | https://aclanthology.org/2022.semeval-1.187 | https://aclanthology.org/2022.semeval-1.187.pdf | ZHIXIAOBAO at SemEval-2022 Task 10: Apporoaching Structured Sentiment with Graph Parsing | This paper presents our submission to task 10, Structured Sentiment Analysis of the SemEval 2022 competition. The task aims to extract all elements of the fine-grained sentiment in a text. We cast structured sentiment analysis to the prediction of the sentiment graphs following (Barnes et al., 2021), where nodes are sp... | ['Yongliang Wang', 'Chong Yang', 'Jing Xu', 'Chen Liang', 'Yangkun Lin'] | null | null | null | null | semeval-naacl-2022-7 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [ 0.37650552 0.52611005 -0.27483326 -0.8275967 -0.7983042 -1.1254382
0.7286422 0.42257422 -0.50991863 0.7517616 0.66821676 -0.4715861
0.4507364 -0.48582068 -0.8972672 -0.20391372 -0.10518327 0.19505891
0.12565264 -0.67680985 0.3497858 -0.08656322 -0.8291558 0.7168327
0.5278644 1.05102 -0.037... | [11.377893447875977, 6.849254131317139] |
1a20ba2c-df4b-4d7b-894c-b6b54ca33a0d | eyebag-accurate-control-of-eye-blink-and-gaze | 2306.17391 | null | https://arxiv.org/abs/2306.17391v1 | https://arxiv.org/pdf/2306.17391v1.pdf | EyeBAG: Accurate Control of Eye Blink and Gaze Based on Data Augmentation Leveraging Style Mixing | Recent developments in generative models have enabled the generation of photo-realistic human face images, and downstream tasks utilizing face generation technology have advanced accordingly. However, models for downstream tasks are yet substandard at eye control (e.g. eye blink, gaze redirection). To overcome such eye... | ['Wonjong Ryu', 'Jeong Young Jeong', 'Bryan S. Kim'] | 2023-06-30 | null | null | null | null | ['gaze-redirection', 'face-generation'] | ['computer-vision', 'computer-vision'] | [ 4.44455534e-01 2.34911174e-01 4.02020723e-01 -4.19703156e-01
-2.74624169e-01 -4.38544422e-01 6.83267534e-01 -7.76786029e-01
9.46466997e-02 7.04383969e-01 9.72889960e-02 -2.34531105e-01
2.05228344e-01 -4.64476138e-01 -6.46835625e-01 -7.10099757e-01
4.84066159e-01 -5.08174486e-02 -2.67974198e-01 -2.36726046... | [13.958250045776367, -0.009716873057186604] |
ee6bf209-43f0-41a8-871a-eed9d3710edf | source-aware-embedding-training-on | 2307.04336 | null | https://arxiv.org/abs/2307.04336v1 | https://arxiv.org/pdf/2307.04336v1.pdf | Source-Aware Embedding Training on Heterogeneous Information Networks | Heterogeneous information networks (HINs) have been extensively applied to real-world tasks, such as recommendation systems, social networks, and citation networks. While existing HIN representation learning methods can effectively learn the semantic and structural features in the network, little awareness was given to... | ['Guosheng Yin', 'Jiajun Shen', 'Chi Ho Wong', 'Tsai Hor Chan'] | 2023-07-10 | null | null | null | null | ['graph-embedding', 'representation-learning', 'network-embedding', 'recommendation-systems'] | ['graphs', 'methodology', 'methodology', 'miscellaneous'] | [-5.16922362e-02 6.02542698e-01 -7.98826516e-01 -1.19113527e-01
-1.31090760e-01 -5.90617359e-01 6.86978400e-01 5.42794228e-01
2.36855745e-01 4.56006825e-01 7.14922667e-01 -3.61485958e-01
-6.29617572e-01 -1.26872480e+00 -2.39906356e-01 -4.47845221e-01
-2.40299612e-01 5.76941013e-01 3.06687564e-01 -3.04673463... | [7.183073043823242, 6.239923477172852] |
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