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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7b50646a-4740-4afe-adfa-58125079cb0b | latent-visual-cues-for-neural-machine | 1811.00357 | null | https://arxiv.org/abs/1811.00357v2 | https://arxiv.org/pdf/1811.00357v2.pdf | Latent Variable Model for Multi-modal Translation | In this work, we propose to model the interaction between visual and textual features for multi-modal neural machine translation (MMT) through a latent variable model. This latent variable can be seen as a multi-modal stochastic embedding of an image and its description in a foreign language. It is used in a target-lan... | ['Iacer Calixto', 'Miguel Rios', 'Wilker Aziz'] | 2018-11-01 | latent-variable-model-for-multi-modal | https://aclanthology.org/P19-1642 | https://aclanthology.org/P19-1642.pdf | acl-2019-7 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 4.77817625e-01 4.30936366e-01 -3.27052385e-01 -1.91550151e-01
-1.05639219e+00 -6.19912386e-01 1.36749184e+00 -1.74261719e-01
-4.46479827e-01 6.21338964e-01 3.38901579e-01 -2.34771192e-01
4.63137627e-01 -5.23189902e-01 -1.29391181e+00 -6.36003911e-01
3.59083027e-01 5.98128140e-01 -1.55773923e-01 1.13221921... | [11.283273696899414, 1.4756442308425903] |
931d8108-a781-4115-91ae-b0bd09e0de98 | learning-features-of-music-from-scratch | 1611.09827 | null | http://arxiv.org/abs/1611.09827v2 | http://arxiv.org/pdf/1611.09827v2.pdf | Learning Features of Music from Scratch | This paper introduces a new large-scale music dataset, MusicNet, to serve as
a source of supervision and evaluation of machine learning methods for music
research. MusicNet consists of hundreds of freely-licensed classical music
recordings by 10 composers, written for 11 instruments, together with
instrument/note annot... | ['Sham Kakade', 'Zaid Harchaoui', 'John Thickstun'] | 2016-11-29 | null | null | null | null | ['music-transcription'] | ['music'] | [ 3.87685210e-01 -4.01189029e-01 -7.98897222e-02 -1.96406797e-01
-1.33060455e+00 -1.04702890e+00 2.41195962e-01 -3.73621017e-01
-1.84171855e-01 4.24317092e-01 5.21815717e-01 4.21871185e-01
-3.33424538e-01 -2.92745680e-01 -4.01262462e-01 -4.20776010e-01
-5.82180262e-01 4.91761595e-01 -1.65961847e-01 6.36668727... | [15.85065746307373, 5.28391170501709] |
fa73a4c4-9251-44b5-944e-900157a03803 | personalized-graph-signal-processing-for | 2302.02113 | null | https://arxiv.org/abs/2302.02113v1 | https://arxiv.org/pdf/2302.02113v1.pdf | Personalized Graph Signal Processing for Collaborative Filtering | The collaborative filtering (CF) problem with only user-item interaction information can be solved by graph signal processing (GSP), which uses low-pass filters to smooth the observed interaction signals on the similarity graph to obtain the prediction signals. However, the interaction signal may not be sufficient to a... | ['Ning Gu', 'Li Shang', 'Peng Zhang', 'Tun Lu', 'Hansu Gu', 'Dongsheng Li', 'Jiahao Liu'] | 2023-02-04 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [ 1.35331526e-01 -6.03070967e-02 2.40035757e-01 -2.53064662e-01
-2.22047135e-01 -2.86923677e-01 1.39982283e-01 3.29382688e-01
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-4.86319065e-01 -1.14334536e+00 -3.77434373e-01 -3.37020934e-01
-5.20020366e-01 -1.88119411e-01 3.89748424e-01 -3.01100999... | [10.143997192382812, 5.607990264892578] |
9b23dfca-f79f-4ce6-9a2f-81a75cfde3c9 | tcts-a-task-consistent-two-stage-framework | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Wang_TCTS_A_Task-Consistent_Two-Stage_Framework_for_Person_Search_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_TCTS_A_Task-Consistent_Two-Stage_Framework_for_Person_Search_CVPR_2020_paper.pdf | TCTS: A Task-Consistent Two-Stage Framework for Person Search | The state of the art person search methods separate person search into detection and re-ID stages, but ignore the consistency between these two stages. The general person detector has no special attention on the query target; The re-ID model is trained on hand-drawn bounding boxes which are not available in person sear... | [' Xilin Chen', ' Shiguang Shan', ' Hong Chang', ' Bingpeng Ma', 'Cheng Wang'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['person-search'] | ['computer-vision'] | [-2.51480550e-01 -2.10321337e-01 -1.03429325e-01 -2.47006327e-01
-9.86312926e-01 -5.41568696e-01 7.04873979e-01 -1.86541677e-01
-6.34155631e-01 4.33408260e-01 1.03225864e-01 1.67641118e-01
2.94091046e-01 -8.56450438e-01 -3.29038590e-01 -5.04066527e-01
3.02754849e-01 1.21608877e+00 9.19184566e-01 -3.60285304... | [14.823991775512695, 0.8215827345848083] |
2d6ccdd5-8ef4-4763-b368-35fcd80d8aa1 | towards-unifying-feature-attribution-and | 2011.04917 | null | https://arxiv.org/abs/2011.04917v3 | https://arxiv.org/pdf/2011.04917v3.pdf | Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End | Feature attributions and counterfactual explanations are popular approaches to explain a ML model. The former assigns an importance score to each input feature, while the latter provides input examples with minimal changes to alter the model's predictions. To unify these approaches, we provide an interpretation based o... | ['Ramaravind Kommiya Mothilal', 'Amit Sharma', 'Chenhao Tan', 'Divyat Mahajan'] | 2020-11-10 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 3.19044679e-01 6.98252738e-01 -5.82735360e-01 -5.76252043e-01
-2.67681897e-01 -4.70784396e-01 8.65643740e-01 3.56271625e-01
-1.97777245e-02 1.23602891e+00 6.29300416e-01 -5.48182726e-01
-5.99987447e-01 -7.56837964e-01 -6.69815242e-01 -4.75423366e-01
1.21975243e-02 3.95917445e-01 -3.24685752e-01 -1.79491758... | [8.722393989562988, 5.619814395904541] |
fbfd7a01-9eda-4107-a27e-93a45d847737 | beyond-imitation-zero-shot-task-transfer-on | 1812.02788 | null | http://arxiv.org/abs/1812.02788v1 | http://arxiv.org/pdf/1812.02788v1.pdf | Beyond imitation: Zero-shot task transfer on robots by learning concepts as cognitive programs | Humans can infer concepts from image pairs and apply those in the physical
world in a completely different setting, enabling tasks like IKEA assembly from
diagrams. If robots could represent and infer high-level concepts, it would
significantly improve their ability to understand our intent and to transfer
tasks betwee... | ['Miguel Lázaro-Gredilla', 'J. Swaroop Guntupalli', 'Dianhuan Lin', 'Dileep George'] | 2018-12-06 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 5.67939579e-01 5.12121856e-01 1.98055610e-01 -2.36348987e-01
3.93958688e-01 -8.06994617e-01 8.48822594e-01 2.74809837e-01
-1.84080720e-01 4.20054108e-01 1.93822011e-01 -6.12970948e-01
-6.94106391e-04 -1.03105354e+00 -7.53444374e-01 -3.26463342e-01
-4.93128411e-02 4.47823495e-01 1.69011876e-01 -4.88415182... | [4.49679708480835, 1.1443039178848267] |
fb881052-c498-4d31-a3bf-cd0a87c6b422 | text-in-context-token-level-error-detection | null | null | https://aclanthology.org/2021.inlg-1.25 | https://aclanthology.org/2021.inlg-1.25.pdf | Text-in-Context: Token-Level Error Detection for Table-to-Text Generation | We present our Charles-UPF submission for the Shared Task on Evaluating Accuracy in Generated Texts at INLG 2021. Our system can detect the errors automatically using a combination of a rule-based natural language generation (NLG) system and pretrained language models (LMs). We first utilize a rule-based NLG system to ... | ['Ondřej Dušek', 'Simon Mille', 'Zdeněk Kasner'] | null | null | null | null | inlg-acl-2021-8 | ['table-to-text-generation'] | ['natural-language-processing'] | [ 3.12496006e-01 6.67377710e-01 7.84232393e-02 -5.10635078e-01
-1.46684122e+00 -5.44446290e-01 6.80225194e-01 4.22363937e-01
-4.88990963e-01 1.30109882e+00 5.34566522e-01 -3.23402971e-01
4.11334485e-01 -1.01272023e+00 -1.07497561e+00 3.54584932e-01
4.37094361e-01 7.78765917e-01 2.69660681e-01 -5.50568402... | [11.485095977783203, 8.869659423828125] |
1003b76e-fe23-432d-838a-e0381548c503 | modeling-label-correlations-for-second-order | 2204.03619 | null | https://arxiv.org/abs/2204.03619v1 | https://arxiv.org/pdf/2204.03619v1.pdf | Modeling Label Correlations for Second-Order Semantic Dependency Parsing with Mean-Field Inference | Second-order semantic parsing with end-to-end mean-field inference has been shown good performance. In this work we aim to improve this method by modeling label correlations between adjacent arcs. However, direct modeling leads to memory explosion because second-order score tensors have sizes of $O(n^3L^2)$ ($n$ is the... | ['Kewei Tu', 'Songlin Yang'] | 2022-04-07 | null | null | null | null | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [ 5.24912812e-02 2.41363540e-01 -2.09493507e-02 -6.34397030e-01
-1.24129927e+00 -8.09484541e-01 2.21307293e-01 1.98119402e-01
-5.94239116e-01 5.47044516e-01 8.80300775e-02 -6.09060466e-01
-2.03982353e-01 -6.40153408e-01 -9.58956420e-01 -4.08441901e-01
-2.44867980e-01 5.02599895e-01 2.01714799e-01 2.02639475... | [10.346654891967773, 9.620504379272461] |
c45b0bf2-c9bc-4b61-b571-b703fcd73a02 | real-time-clustering-and-multi-target | 1807.02851 | null | http://arxiv.org/abs/1807.02851v1 | http://arxiv.org/pdf/1807.02851v1.pdf | Real-time clustering and multi-target tracking using event-based sensors | Clustering is crucial for many computer vision applications such as robust
tracking, object detection and segmentation. This work presents a real-time
clustering technique that takes advantage of the unique properties of
event-based vision sensors. Since event-based sensors trigger events only when
the intensity change... | ['Francisco Barranco', 'Eduardo Ros', 'Cornelia Fermuller'] | 2018-07-08 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 2.12996811e-01 -4.38114464e-01 1.18428811e-01 -2.72353049e-02
-4.52534080e-01 -4.92586553e-01 4.54649866e-01 5.74827850e-01
-7.55895376e-01 3.76622051e-01 -5.08755505e-01 8.90460089e-02
-2.30482936e-01 -3.50997597e-01 -4.77371514e-01 -1.01321232e+00
-2.21435025e-01 4.34716344e-01 9.52566326e-01 3.55004430... | [8.471587181091309, -1.2698675394058228] |
aa1abf1f-f7cc-41ce-a5b7-e83cec2f3457 | assessment-of-reinforcement-learning | 2305.05812 | null | https://arxiv.org/abs/2305.05812v1 | https://arxiv.org/pdf/2305.05812v1.pdf | Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization | The nuclear fuel loading pattern optimization problem has been studied since the dawn of the commercial nuclear energy industry. It is characterized by multiple objectives and constraints, with a very high number of candidate patterns, which makes it impossible to solve explicitly. Stochastic optimization methodologies... | ['Koroush Shirvan', 'Paul Seurin'] | 2023-05-09 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-1.44353092e-01 -6.74876720e-02 -3.14537793e-01 6.16917619e-03
-3.98497015e-01 -5.86329937e-01 5.72762311e-01 9.26865339e-02
-6.71660960e-01 9.78685856e-01 4.06721085e-02 -2.93785036e-01
-6.97717786e-01 -8.21299613e-01 -4.47447479e-01 -9.96689022e-01
-7.16194808e-02 7.60846794e-01 2.25260556e-01 -4.26775932... | [5.04980993270874, 2.5197184085845947] |
3950adef-aee6-46ab-9d66-cc004e544ec4 | fast-low-rank-column-wise-compressive-sensing-1 | 2212.09664 | null | https://arxiv.org/abs/2212.09664v1 | https://arxiv.org/pdf/2212.09664v1.pdf | Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRI | This work develops a novel set of algorithms, alternating Gradient Descent (GD) and minimization for MRI (altGDmin-MRI1 and altGDmin-MRI2), for accelerated dynamic MRI by assuming an approximate low-rank (LR) model on the matrix formed by the vectorized images of the sequence. The LR model itself is well-known in the M... | ['Namrata Vaswani', 'Sajan Goud Lingala', 'Silpa Babu'] | 2022-12-19 | null | null | null | null | ['compressive-sensing'] | ['computer-vision'] | [ 3.11360240e-01 -2.31317297e-01 -9.15513039e-02 -4.47234541e-01
-1.22015667e+00 -3.82367760e-01 3.31169724e-01 -1.73725665e-01
-6.43559635e-01 5.89543402e-01 6.50734425e-01 -3.93488735e-01
-5.25726914e-01 1.08650893e-01 -6.04380727e-01 -8.78092945e-01
-8.50114524e-01 7.21436560e-01 1.15501583e-01 -1.34677038... | [13.419368743896484, -2.389461040496826] |
28e7ced3-7104-4445-8f80-19591bca822f | transfer-learning-for-the-efficient-detection | 2307.02975 | null | https://arxiv.org/abs/2307.02975v1 | https://arxiv.org/pdf/2307.02975v1.pdf | Transfer Learning for the Efficient Detection of COVID-19 from Smartphone Audio Data | Disease detection from smartphone data represents an open research challenge in mobile health (m-health) systems. COVID-19 and its respiratory symptoms are an important case study in this area and their early detection is a potential real instrument to counteract the pandemic situation. The efficacy of this solution ma... | ['Elena Pagani', 'Franca Delmastro', 'Mattia Giovanni Campana'] | 2023-07-06 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [ 3.74251574e-01 8.44169259e-02 -6.72214702e-02 5.86248413e-02
-6.07505083e-01 -2.34483153e-01 6.31765246e-01 4.68945682e-01
-1.05746078e+00 7.89969563e-01 -1.52805150e-01 -4.20212984e-01
-4.51874435e-01 -8.48282576e-01 -6.23350382e-01 -7.21607625e-01
-2.53552407e-01 7.45520711e-01 1.37398064e-01 -1.58756614... | [15.462869644165039, -1.7567425966262817] |
b0557f88-ecff-4f05-b2f3-7b4b7a68fdfb | relation-aware-subgraph-embedding-with-co | 2307.01507 | null | https://arxiv.org/abs/2307.01507v1 | https://arxiv.org/pdf/2307.01507v1.pdf | Relation-aware subgraph embedding with co-contrastive learning for drug-drug interaction prediction | Relation-aware subgraph embedding is promising for predicting multi-relational drug-drug interactions (DDIs). Typically, most existing methods begin by constructing a multi-relational DDI graph and then learning relation-aware subgraph embeddings (RaSEs) of drugs from the DDI graph. However, most existing approaches ar... | ['Weiqiang Jin', 'Yuanchao Su', 'Biao Zhao', 'Guizhong Liu', 'Mengying Jiang'] | 2023-07-04 | null | null | null | null | ['contrastive-learning', 'contrastive-learning'] | ['computer-vision', 'methodology'] | [ 3.29692475e-02 6.42060637e-02 -7.16909945e-01 -2.80351937e-01
-3.94180119e-01 -4.21174109e-01 6.95470870e-01 6.52852595e-01
3.12677622e-01 6.18349910e-01 4.74684358e-01 -2.43521824e-01
-5.00357568e-01 -9.02158856e-01 -7.00266242e-01 -8.74388158e-01
-1.21882655e-01 6.74325764e-01 1.32534519e-01 -4.71518822... | [5.2361931800842285, 5.897984504699707] |
4aea4007-a525-461d-bca5-7d6d4cac51d5 | speech-privacy-leakage-from-shared-gradients | 2302.10441 | null | https://arxiv.org/abs/2302.10441v1 | https://arxiv.org/pdf/2302.10441v1.pdf | Speech Privacy Leakage from Shared Gradients in Distributed Learning | Distributed machine learning paradigms, such as federated learning, have been recently adopted in many privacy-critical applications for speech analysis. However, such frameworks are vulnerable to privacy leakage attacks from shared gradients. Despite extensive efforts in the image domain, the exploration of speech pri... | ['Jian Liu', 'Jiaxin Zhang', 'Zhuohang Li'] | 2023-02-21 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 1.14029616e-01 5.31267673e-02 -1.54656097e-01 -4.85474914e-01
-1.42492878e+00 -9.66125011e-01 4.35748875e-01 -4.75465413e-03
-2.62963027e-01 4.99802411e-01 5.02533972e-01 -4.71322298e-01
1.44721299e-01 -4.48794127e-01 -6.09342396e-01 -9.85397339e-01
-2.90969074e-01 -4.57202941e-01 -3.63615692e-01 2.48245433... | [5.869373798370361, 6.690192699432373] |
467c5e7c-86c3-44ad-92c3-53ce2a00c90c | the-story-in-your-eyes-an-individual | 2106.14183 | null | https://arxiv.org/abs/2106.14183v1 | https://arxiv.org/pdf/2106.14183v1.pdf | The Story in Your Eyes: An Individual-difference-aware Model for Cross-person Gaze Estimation | We propose a novel method on refining cross-person gaze prediction task with eye/face images only by explicitly modelling the person-specific differences. Specifically, we first assume that we can obtain some initial gaze prediction results with existing method, which we refer to as InitNet, and then introduce three mo... | ['Jun Yu', 'Buyu Liu', 'Jun Bao'] | 2021-06-27 | null | null | null | null | ['gaze-estimation', 'eye-tracking'] | ['computer-vision', 'computer-vision'] | [ 1.73735797e-01 9.00337845e-02 -1.65000916e-01 -5.03930807e-01
-4.25995767e-01 -1.91739872e-01 4.00567114e-01 -5.38554072e-01
-4.00631934e-01 7.96575069e-01 2.95065884e-02 1.13778621e-01
-3.20735388e-02 -1.14613496e-01 -7.63005614e-01 -5.70922434e-01
4.17365372e-01 2.01985285e-01 1.53798744e-01 4.05436195... | [14.114008903503418, 0.0430297777056694] |
e50e0cd5-bda3-4be3-8cfd-6d2e79f63e53 | vertibench-advancing-feature-distribution | 2307.02040 | null | https://arxiv.org/abs/2307.02040v1 | https://arxiv.org/pdf/2307.02040v1.pdf | VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks | Vertical Federated Learning (VFL) is a crucial paradigm for training machine learning models on feature-partitioned, distributed data. However, due to privacy restrictions, few public real-world VFL datasets exist for algorithm evaluation, and these represent a limited array of feature distributions. Existing benchmark... | ['Bingsheng He', 'Junyi Hou', 'Zhaomin Wu'] | 2023-07-05 | null | null | null | null | ['feature-importance'] | ['methodology'] | [-2.02152021e-02 -4.93086606e-01 -6.36993527e-01 -4.79347944e-01
-9.78763163e-01 -8.28045249e-01 3.50838035e-01 4.32289928e-01
-1.75988093e-01 9.54791665e-01 1.76585630e-01 -1.55573189e-01
-4.76394564e-01 -8.90066683e-01 -4.38022405e-01 -7.26243615e-01
-2.08602816e-01 1.81701303e-01 -3.01399291e-01 1.87234521... | [6.070060729980469, 6.3784918785095215] |
bac2be6b-3b48-40b0-a750-bd486b80d3d6 | exploring-neural-methods-for-parsing | 1810.12579 | null | http://arxiv.org/abs/1810.12579v1 | http://arxiv.org/pdf/1810.12579v1.pdf | Exploring Neural Methods for Parsing Discourse Representation Structures | Neural methods have had several recent successes in semantic parsing, though
they have yet to face the challenge of producing meaning representations based
on formal semantics. We present a sequence-to-sequence neural semantic parser
that is able to produce Discourse Representation Structures (DRSs) for English
sentenc... | ['Rik van Noord', 'Antonio Toral', 'Johan Bos', 'Lasha Abzianidze'] | 2018-10-30 | exploring-neural-methods-for-parsing-1 | https://aclanthology.org/Q18-1043 | https://aclanthology.org/Q18-1043.pdf | tacl-2018-1 | ['drs-parsing'] | ['natural-language-processing'] | [ 5.62470078e-01 9.07758594e-01 -3.55642140e-02 -7.21652210e-01
-9.18414354e-01 -9.65202570e-01 4.98296469e-01 2.41289809e-01
-2.21149907e-01 9.18526053e-01 6.89051867e-01 -7.88123667e-01
2.87172645e-01 -1.09419525e+00 -7.63382673e-01 -2.41700649e-01
1.14619128e-01 6.01584315e-01 3.25369388e-01 -4.90208656... | [10.461100578308105, 9.262295722961426] |
d6490856-d9e5-446c-916a-b3470c21c295 | data-augmentation-for-compositional-data | 2205.09906 | null | https://arxiv.org/abs/2205.09906v1 | https://arxiv.org/pdf/2205.09906v1.pdf | Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome | Data augmentation plays a key role in modern machine learning pipelines. While numerous augmentation strategies have been studied in the context of computer vision and natural language processing, less is known for other data modalities. Our work extends the success of data augmentation to compositional data, i.e., sim... | ['John P. Cunningham', 'Thomas P. Quinn', 'Elliott Gordon-Rodriguez'] | 2022-05-20 | null | null | null | null | ['disease-prediction'] | ['medical'] | [ 8.35003257e-01 -1.42217636e-01 -3.76970500e-01 -1.97301939e-01
-3.02971721e-01 -5.61700463e-01 8.82794499e-01 8.86851013e-01
-2.43126526e-01 3.93374115e-01 6.57815933e-01 -5.50717652e-01
1.89854622e-01 -9.03470814e-01 -8.38591099e-01 -8.19958210e-01
-4.59852368e-01 4.29527134e-01 -4.05763209e-01 -2.82948136... | [5.696694374084473, 5.682091236114502] |
27a4a8c6-0597-410b-9d61-747ea0fb58a1 | cyclegan-without-checkerboard-artifacts-for | 2012.00287 | null | https://arxiv.org/abs/2012.00287v1 | https://arxiv.org/pdf/2012.00287v1.pdf | CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection | In this paper, we propose a novel CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection. Recent rapid advances in image manipulation tools and deep image synthesis techniques, such as Generative Adversarial Networks (GANs) have easily generated fake images, so detecting manipulated image... | ['Hitoshi Kiya', 'Yuma Kinoshita', 'Miki Tanaka', 'Takayuki Osakabe'] | 2020-12-01 | null | null | null | null | ['fake-image-detection'] | ['computer-vision'] | [ 5.17931223e-01 -1.53134972e-01 2.75127113e-01 3.18777919e-01
-2.81415015e-01 -7.11393952e-01 6.10741556e-01 -1.43792063e-01
-4.42587808e-02 8.79629433e-01 -4.09010559e-01 -5.78025579e-01
5.06874204e-01 -1.02221930e+00 -9.41626132e-01 -7.18987346e-01
2.83868700e-01 -2.22375557e-01 2.65579313e-01 -4.05493855... | [12.528511047363281, 1.1292022466659546] |
2fd5959f-8f81-42fd-bc59-c2393064dd20 | poseaug-a-differentiable-pose-augmentation | 2105.02465 | null | https://arxiv.org/abs/2105.02465v1 | https://arxiv.org/pdf/2105.02465v1.pdf | PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation | Existing 3D human pose estimators suffer poor generalization performance to new datasets, largely due to the limited diversity of 2D-3D pose pairs in the training data. To address this problem, we present PoseAug, a new auto-augmentation framework that learns to augment the available training poses towards a greater di... | ['Jiashi Feng', 'Jianfeng Zhang', 'Kehong Gong'] | 2021-05-06 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Gong_PoseAug_A_Differentiable_Pose_Augmentation_Framework_for_3D_Human_Pose_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Gong_PoseAug_A_Differentiable_Pose_Augmentation_Framework_for_3D_Human_Pose_CVPR_2021_paper.pdf | cvpr-2021-1 | ['monocular-3d-human-pose-estimation', 'weakly-supervised-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-2.43836820e-01 2.70974517e-01 -2.44471520e-01 -3.95386875e-01
-8.32196951e-01 -5.46259582e-01 3.73048842e-01 -2.30429947e-01
-1.85538322e-01 6.77652776e-01 3.80135864e-01 7.74420649e-02
7.03945458e-02 -5.41733921e-01 -1.05216897e+00 -4.92078662e-01
-1.48849770e-01 8.87487173e-01 3.62120429e-03 -4.76710469... | [6.977750778198242, -0.9864155650138855] |
d3bf5fa4-d04a-4ea4-ae9a-672d9197fde5 | informative-bayesian-model-selection-for-rr | 2105.11531 | null | https://arxiv.org/abs/2105.11531v1 | https://arxiv.org/pdf/2105.11531v1.pdf | Informative Bayesian model selection for RR Lyrae star classifiers | Machine learning has achieved an important role in the automatic classification of variable stars, and several classifiers have been proposed over the last decade. These classifiers have achieved impressive performance in several astronomical catalogues. However, some scientific articles have also shown that the traini... | ['D. Mery', 'M. Catelan', 'P. Huijse', 'K. Pichara', 'F. Pérez-Galarce'] | 2021-05-24 | null | null | null | null | ['classification-of-variable-stars'] | ['miscellaneous'] | [ 4.78828419e-03 -2.82777455e-02 -2.55205750e-01 -4.44285184e-01
-6.88962162e-01 -6.58643901e-01 9.62682605e-01 -3.08741387e-02
-2.79740751e-01 1.10309863e+00 -3.46359640e-01 -5.04914045e-01
-4.20867920e-01 -6.91190243e-01 -4.09489185e-01 -1.13725638e+00
2.37151742e-01 7.51853645e-01 5.40050566e-01 8.04822668... | [7.642273426055908, 3.4706814289093018] |
54efc717-2a99-43d3-b47e-1472baa52a42 | disfluency-detection-for-vietnamese | null | null | https://aclanthology.org/2022.wnut-1.21 | https://aclanthology.org/2022.wnut-1.21.pdf | Disfluency Detection for Vietnamese | In this paper, we present the first empirical study for Vietnamese disfluency detection. To conduct this study, we first create a disfluency detection dataset for Vietnamese, with manual annotations over two disfluency types. We then empirically perform experiments using strong baseline models, and find that: automatic... | ['Dat Quoc Nguyen', 'Thinh Hung Truong', 'Mai Dao'] | null | null | null | null | coling-wnut-2022-10 | ['vietnamese-word-segmentation', 'xlm-r'] | ['natural-language-processing', 'natural-language-processing'] | [-6.30410194e-01 -2.81007260e-01 -4.02025491e-01 -7.30870664e-02
-8.38910639e-01 -1.15150642e+00 3.84722799e-01 -2.04185247e-01
-8.22571516e-01 8.43751609e-01 5.40424585e-01 -5.62661052e-01
7.15794861e-01 -1.76589131e-01 -2.55776793e-01 -3.33541363e-01
-1.37047321e-01 5.62272429e-01 1.96685806e-01 -4.39298421... | [10.541409492492676, 9.964916229248047] |
5a013e93-87ae-40a8-9583-fcbc9f464535 | ganglionnet-objectively-assess-the-density | 2007.02367 | null | https://arxiv.org/abs/2007.02367v1 | https://arxiv.org/pdf/2007.02367v1.pdf | GanglionNet: Objectively Assess the Density and Distribution of Ganglion Cells With NABLA-N Network | Hirschsprungs disease (HD) is a birth defect which is diagnosed and managed by multiple medical specialties such as pediatric gastroenterology, surgery, radiology, and pathology. HD is characterized by absence of ganglion cells in the distal intestinal tract with a gradual normalization of ganglion cell numbers in adja... | ['Vijayan K. Asari', 'TJ Browen', 'Raj P. Kapur', 'Md Zahangir Alom'] | 2020-07-05 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 1.20104201e-01 3.83700252e-01 1.21031947e-01 2.84328848e-01
-1.18829221e-01 -5.92373729e-01 3.71741690e-02 7.71760643e-01
-7.18861759e-01 7.03853428e-01 -2.39397570e-01 -2.38573804e-01
2.18157202e-01 -9.42196965e-01 -5.93353987e-01 -9.42205429e-01
-3.37336779e-01 5.74040174e-01 3.24658126e-01 1.99264005... | [14.669384956359863, -2.9980196952819824] |
94328455-5c2d-4e14-9cb5-714eba08f5b9 | compliance-challenges-in-forensic-image | 2203.00469 | null | https://arxiv.org/abs/2203.00469v1 | https://arxiv.org/pdf/2203.00469v1.pdf | Compliance Challenges in Forensic Image Analysis Under the Artificial Intelligence Act | In many applications of forensic image analysis, state-of-the-art results are nowadays achieved with machine learning methods. However, concerns about their reliability and opaqueness raise the question whether such methods can be used in criminal investigations. So far, this question of legal compliance has hardly bee... | ['Christian Riess', 'Nicole Scheler', 'Benedikt Lorch'] | 2022-03-01 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 4.03557479e-01 2.63227820e-01 6.93680942e-02 -3.57100636e-01
-6.35630071e-01 -5.31514227e-01 6.26644433e-01 7.55656511e-02
-6.05723739e-01 5.80541074e-01 -4.14713085e-01 -6.94831312e-01
-1.55317649e-01 -6.67770028e-01 -3.00436139e-01 -6.07274532e-01
3.10930431e-01 3.66100758e-01 1.54214963e-01 1.60598725... | [12.510102272033691, 1.0451914072036743] |
82a0e584-3373-4a14-9d19-aeb6c6b71a1e | sentiment-after-translation-a-case-study-on | null | null | https://aclanthology.info/papers/N15-1078/n15-1078 | https://www.aclweb.org/anthology/N15-1078 | Sentiment after Translation: A Case-Study on Arabic Social Media Posts | null | ['Saif Mohammad', 'Svetlana Kiritchenko', 'Mohammad Salameh'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.539193034172058, 15.869195938110352] |
1434d894-7425-43cb-8197-36edead25ee5 | full-resolution-residual-networks-for | 1611.08323 | null | http://arxiv.org/abs/1611.08323v2 | http://arxiv.org/pdf/1611.08323v2.pdf | Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes | Semantic image segmentation is an essential component of modern autonomous
driving systems, as an accurate understanding of the surrounding scene is
crucial to navigation and action planning. Current state-of-the-art approaches
in semantic image segmentation rely on pre-trained networks that were initially
developed fo... | ['Alexander Hermans', 'Tobias Pohlen', 'Bastian Leibe', 'Markus Mathias'] | 2016-11-24 | full-resolution-residual-networks-for-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Pohlen_Full-Resolution_Residual_Networks_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Pohlen_Full-Resolution_Residual_Networks_CVPR_2017_paper.pdf | cvpr-2017-7 | ['thermal-image-segmentation'] | ['computer-vision'] | [ 7.05902100e-01 -3.16667333e-02 -2.21892167e-02 -6.28984749e-01
-7.42126226e-01 -5.05069137e-01 5.28828502e-01 1.47363096e-01
-8.93574715e-01 4.42220271e-01 -2.95658410e-01 -2.61600733e-01
1.48499921e-01 -1.02564013e+00 -8.63965452e-01 -6.71955943e-01
2.68120229e-01 2.64459819e-01 9.05905604e-01 -3.81319076... | [9.012113571166992, -1.1556973457336426] |
2306c7aa-a994-4548-aa51-57dfdf2f7697 | vehicle-and-license-plate-recognition-with | 2202.05631 | null | https://arxiv.org/abs/2202.05631v2 | https://arxiv.org/pdf/2202.05631v2.pdf | Vehicle and License Plate Recognition with Novel Dataset for Toll Collection | We propose an automatic framework for toll collection, consisting of three steps: vehicle type recognition, license plate localization, and reading. However, each of the three steps becomes non-trivial due to image variations caused by several factors. The traditional vehicle decorations on the front cause variations a... | ['Saeed Anwar', 'Abbas Anwar', 'Hafeez Anwar', 'Muhammad Usama'] | 2022-02-11 | null | null | null | null | ['license-plate-recognition', 'license-plate-detection'] | ['computer-vision', 'computer-vision'] | [-3.18413675e-01 -7.43082345e-01 -1.23061180e-01 -3.45229298e-01
-8.32745016e-01 -1.05458450e+00 4.13550615e-01 -5.05096674e-01
-1.88552663e-01 5.70304096e-01 -5.83029568e-01 -4.08907950e-01
4.26301450e-01 -7.89619207e-01 -1.08000839e+00 -7.29678154e-01
5.14828444e-01 3.68502945e-01 3.60553086e-01 2.79652067... | [9.841649055480957, -4.916127681732178] |
f29f3e19-99db-445d-aafa-fd7e7b155143 | dimensionality-expansion-and-transfer | 2204.02802 | null | https://arxiv.org/abs/2204.02802v4 | https://arxiv.org/pdf/2204.02802v4.pdf | Dimensionality Expansion of Load Monitoring Time Series and Transfer Learning for EMS | Energy management systems (EMS) rely on (non)-intrusive load monitoring (N)ILM to monitor and manage appliances and help residents be more energy efficient and thus more frugal. The robustness as well as the transfer potential of the most promising machine learning solutions for (N)ILM is not yet fully understood as th... | ['Carolina Fortuna', 'Jakob Jenko', 'Blaž Bertalanič'] | 2022-04-06 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-4.67378311e-02 -3.37944031e-01 -2.73703665e-01 -1.20997258e-01
-1.04985392e+00 -4.34603304e-01 3.37850392e-01 -9.21026915e-02
-2.63194412e-01 6.49169028e-01 -8.74392986e-02 -2.51402378e-01
-4.95092481e-01 -8.41176391e-01 -7.65500128e-01 -8.42593849e-01
-2.59089619e-01 3.19140315e-01 -1.76378131e-01 -5.99796027... | [16.05760955810547, 7.575520992279053] |
b6d5baa4-fb65-41b4-96cf-b9bdd099577b | real-valued-syntactic-word-vectors-rsv-for | null | null | https://aclanthology.org/W17-0203 | https://aclanthology.org/W17-0203.pdf | Real-valued Syntactic Word Vectors (RSV) for Greedy Neural Dependency Parsing | null | ['Ali Basirat', 'Joakim Nivre'] | 2017-05-01 | null | null | null | ws-2017-5 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.480249881744385, 3.574787139892578] |
b1169544-d7c4-49eb-97a8-c83591304670 | unsupervised-semantic-scene-labeling-for | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Wigness_Unsupervised_Semantic_Scene_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Wigness_Unsupervised_Semantic_Scene_CVPR_2017_paper.pdf | Unsupervised Semantic Scene Labeling for Streaming Data | We introduce an unsupervised semantic scene labeling approach that continuously learns and adapts semantic models discovered within a data stream. While closely related to unsupervised video segmentation, our algorithm is not designed to be an early video processing strategy that produces coherent over-segmentations, b... | ['Maggie Wigness', 'John G. Rogers III'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['scene-labeling'] | ['computer-vision'] | [ 6.11432850e-01 2.06883967e-01 -4.88849461e-01 -7.31386364e-01
-6.34687603e-01 -6.68408990e-01 3.16346854e-01 5.62147081e-01
-3.37389916e-01 5.69707938e-02 1.65189758e-01 2.44897325e-02
-4.22164872e-02 -6.29800975e-01 -6.18852556e-01 -4.40280586e-01
-2.95908093e-01 5.97216010e-01 7.74601161e-01 3.64973634... | [9.00516128540039, 0.008007034659385681] |
1a88641f-658b-49ac-b3ef-f445ad3db2c5 | an-evolution-kernel-method-for-graph | 2306.14688 | null | https://arxiv.org/abs/2306.14688v1 | https://arxiv.org/pdf/2306.14688v1.pdf | An Evolution Kernel Method for Graph Classification through Heat Diffusion Dynamics | Autonomous individuals establish a structural complex system through pairwise connections and interactions. Notably, the evolution reflects the dynamic nature of each complex system since it recodes a series of temporal changes from the past, the present into the future. Different systems follow distinct evolutionary t... | ['Zhiming Zheng', 'Wei Wei', 'Dan Sun', 'Xue Liu'] | 2023-06-26 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 1.64718270e-01 -1.89598814e-01 2.01272771e-01 1.13563143e-01
3.25220436e-01 -7.62374163e-01 9.38741982e-01 5.24027288e-01
2.25675609e-02 6.17619812e-01 -2.31301114e-01 -1.65668547e-01
-4.30984557e-01 -1.13412893e+00 -4.25699323e-01 -1.07768738e+00
-5.08347273e-01 4.92503971e-01 2.82971591e-01 -6.11870527... | [7.1616010665893555, 5.862022876739502] |
ca3066b6-464d-4cc3-b399-95850c50b7dd | deep-learning-based-segmentation-free-license | 1912.02441 | null | https://arxiv.org/abs/1912.02441v1 | https://arxiv.org/pdf/1912.02441v1.pdf | Deep Learning Based Segmentation Free License Plate Recognition Using Roadway Surveillance Camera Images | Smart automated traffic enforcement solutions have been gaining popularity in recent years. These solutions are ubiquitously used for seat-belt violation detection, red-light violation detection and speed violation detection purposes. Highly accurate license plate recognition is an indispensable part of these systems. ... | ['Yusuf Artan', 'Bensu Alkan', 'Alperen Elihos', 'Burak Balci'] | 2019-12-05 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 2.41110861e-01 -8.49584818e-01 -4.24327582e-01 -2.30811387e-01
-5.87549150e-01 -6.84507906e-01 4.47561771e-01 -4.54502732e-01
-6.06939733e-01 6.83436632e-01 -4.74770576e-01 -4.27982330e-01
2.18391791e-01 -7.54092872e-01 -3.11100096e-01 -5.36040962e-01
9.46880162e-01 2.45701775e-01 7.36564755e-01 -4.66458611... | [9.825849533081055, -4.964443206787109] |
d1feafef-067b-47bf-91b6-2c184c020a17 | electricity-price-forecasting-the-dawn-of | 2204.00883 | null | https://arxiv.org/abs/2204.00883v1 | https://arxiv.org/pdf/2204.00883v1.pdf | Electricity Price Forecasting: The Dawn of Machine Learning | Electricity price forecasting (EPF) is a branch of forecasting on the interface of electrical engineering, statistics, computer science, and finance, which focuses on predicting prices in wholesale electricity markets for a whole spectrum of horizons. These range from a few minutes (real-time/intraday auctions and cont... | ['Rafał Weron', 'Grzegorz Marcjasz', 'Jesus Lago', 'Arkadiusz Jędrzejewski'] | 2022-04-02 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [-4.81550872e-01 -1.79759726e-01 -1.29187673e-01 -2.88681030e-01
-3.69481474e-01 -9.26175058e-01 8.64190936e-01 3.22750479e-01
-1.01708323e-01 1.11413991e+00 -6.33199960e-02 -7.30220914e-01
-3.85513246e-01 -1.04830897e+00 -1.13068469e-01 -5.48255086e-01
-6.65768206e-01 5.01713514e-01 -1.97525054e-01 -5.02454996... | [4.682650089263916, 4.0253472328186035] |
24320d10-97d3-4c2c-8dfb-cae8ac12eb34 | iterative-soft-shrinkage-learning-for | 2303.09650 | null | https://arxiv.org/abs/2303.09650v1 | https://arxiv.org/pdf/2303.09650v1.pdf | Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution | The field of image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures. However, prevailing SR models suffer from prohibitive memory footprint and intensive computations, which limits further deployment on computational-constrained platforms. In this work, we inves... | ['Zhiqiang Tao', 'Yun Fu', 'Yulun Zhang', 'Huan Wang', 'Jiamian Wang'] | 2023-03-16 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 5.97642958e-01 3.89237218e-02 -1.98519915e-01 -2.96010911e-01
-4.48714346e-01 -5.51479794e-02 1.94286957e-01 -2.18178630e-01
-3.48531514e-01 7.12901294e-01 9.37523618e-02 -1.43379241e-01
-2.62835801e-01 -9.07593906e-01 -7.33590424e-01 -5.23408771e-01
-2.51089931e-02 4.90786172e-02 4.85934138e-01 -2.54038572... | [10.898613929748535, -1.6566725969314575] |
643782cc-a28b-4725-a633-02facd88e25d | polarimetric-pose-prediction | 2112.03810 | null | https://arxiv.org/abs/2112.03810v2 | https://arxiv.org/pdf/2112.03810v2.pdf | Polarimetric Pose Prediction | Light has many properties that vision sensors can passively measure. Colour-band separated wavelength and intensity are arguably the most commonly used for monocular 6D object pose estimation. This paper explores how complementary polarisation information, i.e. the orientation of light wave oscillations, influences the... | ['Benjamin Busam', 'Arturo Guridi', 'Pengyuan Wang', 'HyunJun Jung', 'Magdalena Wysock', 'Iuliia Skobleva', 'Patrick Ruhkamp', 'Yitong Li', 'Daoyi Gao'] | 2021-12-07 | null | null | null | null | ['transparent-objects', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.97060996e-01 -1.31661654e-01 1.06635556e-01 -4.10599172e-01
-7.48864114e-01 -7.33688354e-01 8.01430166e-01 -4.15115327e-01
-3.53136271e-01 2.91520417e-01 -9.95424092e-02 3.07438105e-01
-2.78637171e-01 -4.02083546e-01 -7.75684476e-01 -9.58882630e-01
1.88833043e-01 8.87157679e-01 5.26855469e-01 1.81571737... | [7.205202579498291, -2.365288019180298] |
35269bb0-75a4-44a6-bc73-e20bc05ccdd2 | rethinking-of-pedestrian-attribute-1 | 2107.03576 | null | https://arxiv.org/abs/2107.03576v2 | https://arxiv.org/pdf/2107.03576v2.pdf | Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting | Pedestrian attribute recognition aims to assign multiple attributes to one pedestrian image captured by a video surveillance camera. Although numerous methods are proposed and make tremendous progress, we argue that it is time to step back and analyze the status quo of the area. We review and rethink the recent progres... | ['Kaiqi Huang', 'Xiaotang Chen', 'Houjing Huang', 'Jian Jia'] | 2021-07-08 | null | null | null | null | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [ 4.16944355e-01 -3.74768406e-01 -1.75402626e-01 -9.62962925e-01
-4.95757520e-01 -4.61850196e-01 8.05593431e-01 1.17153101e-01
-4.31559563e-01 9.31528270e-01 1.44108906e-01 -1.05070725e-01
7.74616152e-02 -8.18438530e-01 -4.24997061e-01 -7.24822998e-01
2.48399805e-02 3.55042964e-01 7.40024596e-02 -8.26362222... | [14.519291877746582, 0.9557169079780579] |
74cc0d7c-8644-4f37-9386-a30d6b4f0873 | sparse-coding-for-alpha-matting | 1604.02898 | null | http://arxiv.org/abs/1604.02898v1 | http://arxiv.org/pdf/1604.02898v1.pdf | Sparse Coding for Alpha Matting | Existing color sampling based alpha matting methods use the compositing
equation to estimate alpha at a pixel from pairs of foreground (F) and
background (B) samples. The quality of the matte depends on the selected (F,B)
pairs. In this paper, the matting problem is reinterpreted as a sparse coding
of pixel features, w... | ['Hisham Cholakkal', 'Ehsan Shahrian Varnousfaderani', 'Jubin Johnson', 'Deepu Rajan'] | 2016-04-11 | null | null | null | null | ['video-matting'] | ['computer-vision'] | [ 5.25907815e-01 -1.93746641e-01 -1.90244377e-01 -2.29847655e-01
-6.58155560e-01 -2.37342626e-01 4.28372800e-01 4.95559245e-04
-1.88221321e-01 7.03677356e-01 -6.91910684e-02 2.56461442e-01
1.54927105e-01 -8.61056268e-01 -7.95773625e-01 -1.15356445e+00
1.94647759e-01 3.14886868e-01 2.20758885e-01 2.47002721... | [10.791590690612793, -1.6867763996124268] |
5d09561c-af38-4f58-932a-45258b17d61c | spatio-temporal-prediction-in-video-coding-by-2 | 2207.09727 | null | https://arxiv.org/abs/2207.09727v1 | https://arxiv.org/pdf/2207.09727v1.pdf | Spatio-temporal prediction in video coding by best approximation | Within the scope of this contribution we propose a novel efficient spatio-temporal prediction algorithm for video coding. The algorithm operates in two stages. First, motion compensation is performed on the block to be predicted in order to exploit temporal correlations. Afterwards, in order to exploit spatial correlat... | ['André Kaup', 'Haricharan Lakshman', 'Jürgen Seiler'] | 2022-07-20 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 5.61364710e-01 1.86739698e-01 3.03705446e-02 -1.18437164e-01
-2.48359576e-01 -4.85606417e-02 4.24024254e-01 4.92151201e-01
-6.08487666e-01 7.73840249e-01 1.63142085e-01 -1.90787047e-01
1.51987355e-02 -5.31643271e-01 -3.13846439e-01 -8.15866351e-01
-3.09135258e-01 7.32690736e-04 9.74240065e-01 1.98626310... | [11.324528694152832, -2.0726277828216553] |
9f657809-b346-4186-90b8-bc566f7e2947 | a-unifying-partially-interpretable-framework | 2208.07581 | null | https://arxiv.org/abs/2208.07581v3 | https://arxiv.org/pdf/2208.07581v3.pdf | Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks | Risk management in many environmental settings requires an understanding of the mechanisms that drive extreme events. Useful metrics for quantifying such risk are extreme quantiles of response variables conditioned on predictor variables that describe, e.g., climate, biosphere and environmental states. Typically these ... | ['Raphaël Huser', 'Jordan Richards'] | 2022-08-16 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 2.87563503e-01 -1.29256487e-01 -1.70064869e-03 -5.10800481e-01
-3.69974554e-01 -5.88311136e-01 6.92089140e-01 4.10630405e-01
-4.46547508e-01 1.01310849e+00 3.01233590e-01 -6.61658645e-01
-6.47319853e-01 -1.09345424e+00 -6.87748194e-01 -7.71352232e-01
-4.65065569e-01 3.35428387e-01 -2.91119456e-01 -3.93100560... | [7.034133434295654, 3.6631884574890137] |
0912b7d7-412f-4e6e-b8ff-82fee4a0fe5d | improving-generalizability-of-graph-anomaly | 2209.10168 | null | https://arxiv.org/abs/2209.10168v1 | https://arxiv.org/pdf/2209.10168v1.pdf | Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation | Graph anomaly detection (GAD) is a vital task since even a few anomalies can pose huge threats to benign users. Recent semi-supervised GAD methods, which can effectively leverage the available labels as prior knowledge, have achieved superior performances than unsupervised methods. In practice, people usually need to i... | ['Long-Kai Huang', 'Fu-Lai Chung', 'Ninghao Liu', 'Xiao Huang', 'Shuang Zhou'] | 2022-09-21 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [ 2.01437756e-01 1.12497933e-01 -1.94702744e-01 -2.00957075e-01
-2.56201237e-01 -6.07878506e-01 4.51969951e-01 4.60455149e-01
1.33862086e-02 5.58336973e-01 -4.02757883e-01 -5.70949733e-01
-1.47159351e-02 -1.07256877e+00 -6.39440656e-01 -6.37265384e-01
-2.03841925e-03 2.66016096e-01 4.68311101e-01 -2.68530399... | [6.63387393951416, 5.774518013000488] |
4546730e-24ca-4413-b35c-97844c49b2e3 | palm-pre-training-an-autoencoding | 2004.07159 | null | https://arxiv.org/abs/2004.07159v2 | https://arxiv.org/pdf/2004.07159v2.pdf | PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation | Self-supervised pre-training, such as BERT, MASS and BART, has emerged as a powerful technique for natural language understanding and generation. Existing pre-training techniques employ autoencoding and/or autoregressive objectives to train Transformer-based models by recovering original word tokens from corrupted text... | ['Chenliang Li', 'Ming Yan', 'Fei Huang', 'Bin Bi', 'Luo Si', 'Chen Wu', 'Wei Wang', 'Songfang Huang'] | 2020-04-14 | null | null | null | null | ['generative-question-answering', 'conversational-response-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.56415737e-01 6.47843003e-01 2.52120495e-01 -5.15659332e-01
-1.49661040e+00 -5.44911504e-01 1.05116868e+00 2.02303808e-02
-2.64263839e-01 1.11311555e+00 9.73568320e-01 -2.69633561e-01
2.20734000e-01 -8.90752792e-01 -7.62750268e-01 -4.63263661e-01
4.08544511e-01 1.01132941e+00 -3.13283086e-01 -8.11268687... | [11.934329986572266, 8.935415267944336] |
8b283d9d-3aa7-42c0-9c4c-a13bbe85a752 | zero-shot-action-recognition-in-videos-a | 1909.06423 | null | https://arxiv.org/abs/1909.06423v2 | https://arxiv.org/pdf/1909.06423v2.pdf | Zero-Shot Action Recognition in Videos: A Survey | Zero-Shot Action Recognition has attracted attention in the last years and many approaches have been proposed for recognition of objects, events and actions in images and videos. There is a demand for methods that can classify instances from classes that are not present in the training of models, especially in the comp... | ['Valter Estevam', 'Helio Pedrini', 'David Menotti'] | 2019-09-13 | null | null | null | null | ['zero-shot-action-recognition', 'action-recognition-in-still-images'] | ['computer-vision', 'computer-vision'] | [ 6.07136965e-01 -3.18180978e-01 -3.90412033e-01 -4.49593753e-01
-3.17090213e-01 -3.71483415e-01 5.61312497e-01 -1.10761523e-01
-3.40721160e-01 5.21664798e-01 2.06211686e-01 2.89496005e-01
-2.32985944e-01 -4.78833675e-01 -4.44813937e-01 -8.43394637e-01
-1.87610537e-01 7.49797076e-02 6.34991765e-01 1.70624822... | [8.219636917114258, 0.48429521918296814] |
7402385b-e7da-4c7a-b3ab-de085703670d | tea-program-repair-using-neural-network-based | 2107.08262 | null | https://arxiv.org/abs/2107.08262v1 | https://arxiv.org/pdf/2107.08262v1.pdf | Tea: Program Repair Using Neural Network Based on Program Information Attention Matrix | The advance in machine learning (ML)-driven natural language process (NLP) points a promising direction for automatic bug fixing for software programs, as fixing a buggy program can be transformed to a translation task. While software programs contain much richer information than one-dimensional natural language docume... | ['Yang Zhang', 'Liang Cheng', 'Chen Wu', 'Wenshuo Wang'] | 2021-07-17 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 3.46489362e-02 3.39702368e-01 -5.61781168e-01 -3.40778440e-01
-7.91264713e-01 -7.49326050e-01 1.12272300e-01 4.45798665e-01
3.56644213e-01 4.68746632e-01 2.62376934e-01 -8.18721354e-01
3.13937031e-02 -9.64903593e-01 -9.35856640e-01 1.70535043e-01
-1.58047751e-01 8.47674236e-02 2.23180026e-01 -1.09767981... | [7.692233085632324, 7.700230598449707] |
d8f765ef-2de3-490b-ab09-96f6acb4fb6d | an-experimental-study-of-the-effects-of | 2007.15066 | null | https://arxiv.org/abs/2007.15066v1 | https://arxiv.org/pdf/2007.15066v1.pdf | An Experimental Study of The Effects of Position Bias on Emotion CauseExtraction | Emotion Cause Extraction (ECE) aims to identify emotion causes from a document after annotating the emotion keywords. Some baselines have been proposed to address this problem, such as rule-based, commonsense based and machine learning methods. We show, however, that a simple random selection approach toward ECE that d... | ['Jiayuan Ding', 'Mayank Kejriwal'] | 2020-07-16 | null | null | null | null | ['emotion-cause-extraction'] | ['natural-language-processing'] | [ 1.19287163e-01 3.22331876e-01 -2.53290564e-01 -3.81651044e-01
-6.79640234e-01 -8.51976871e-01 8.95374060e-01 2.42183000e-01
-4.42165554e-01 8.41423273e-01 7.05886185e-01 -2.88205773e-01
-3.57823759e-01 -5.52429676e-01 -7.94531286e-01 -6.11408710e-01
5.76737560e-02 5.80769628e-02 -4.45726097e-01 -4.19791877... | [12.621248245239258, 6.224817276000977] |
ad7e6d5f-c83c-41bf-9ba6-f8a6003e87ee | code-prediction-by-feeding-trees-to | 2003.13848 | null | https://arxiv.org/abs/2003.13848v1 | https://arxiv.org/pdf/2003.13848v1.pdf | Code Prediction by Feeding Trees to Transformers | In this paper, we describe how to leverage \emph{Transformer}, a recent neural architecture for learning from sequential data (such as text), for code completion. As in the realm of natural language processing, Transformers surpass the prediction accuracy achievable by RNNs; we provide an experimental confirmation of t... | ['Satish Chandra', 'Jinman Zhao', 'Yuchi Tian', 'Seohyun Kim'] | 2020-03-30 | null | null | null | null | ['type-prediction', 'value-prediction'] | ['computer-code', 'computer-code'] | [ 3.46316904e-01 4.12819475e-01 3.64056341e-02 -3.86541069e-01
-5.11878371e-01 -7.87186384e-01 3.13369185e-01 3.96238834e-01
-4.89144713e-01 2.39722759e-01 7.80264974e-01 -7.16789544e-01
2.17474416e-01 -7.99992144e-01 -1.08059919e+00 -1.78399757e-01
-2.02427860e-02 2.47795209e-02 -1.26722768e-01 -1.02661364... | [9.947339057922363, 8.502829551696777] |
1ebe8bf6-61e2-4124-949b-e896753ca80e | universal-lesion-detection-by-learning-from | 2005.13753 | null | https://arxiv.org/abs/2005.13753v1 | https://arxiv.org/pdf/2005.13753v1.pdf | Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled Datasets | Lesion detection is an important problem within medical imaging analysis. Most previous work focuses on detecting and segmenting a specialized category of lesions (e.g., lung nodules). However, in clinical practice, radiologists are responsible for finding all possible types of anomalies. The task of universal lesion d... | ['Dakai Jin', 'Jinzheng Cai', 'Ke Yan', 'Le Lu', 'Jing Xiao', 'Adam P. Harrison'] | 2020-05-28 | null | null | null | null | ['medical-object-detection'] | ['computer-vision'] | [ 3.91325280e-02 2.86964297e-01 -6.76008344e-01 -1.48897067e-01
-1.44368005e+00 -6.52730048e-01 3.66936594e-01 2.16415361e-01
-1.90466076e-01 3.96086633e-01 3.39036793e-01 -3.35201979e-01
1.05949841e-01 -6.42331481e-01 -6.63072586e-01 -7.54001439e-01
1.30437650e-02 7.41014004e-01 6.93629682e-01 4.38663065... | [15.234066009521484, -2.2379093170166016] |
b79cf410-5b41-4cf7-ada9-d78e4e929393 | ontoprotein-protein-pretraining-with-gene-1 | 2201.11147 | null | https://arxiv.org/abs/2201.11147v6 | https://arxiv.org/pdf/2201.11147v6.pdf | OntoProtein: Protein Pretraining With Gene Ontology Embedding | Self-supervised protein language models have proved their effectiveness in learning the proteins representations. With the increasing computational power, current protein language models pre-trained with millions of diverse sequences can advance the parameter scale from million-level to billion-level and achieve remark... | ['Huajun Chen', 'Qiang Zhang', 'Jiazhang Lian', 'Shumin Deng', 'Haosen Hong', 'Siyuan Cheng', 'Xiaozhuan Liang', 'Zhen Bi', 'Ningyu Zhang'] | 2022-01-23 | ontoprotein-protein-pretraining-with-gene | https://openreview.net/forum?id=yfe1VMYAXa4 | https://openreview.net/pdf?id=yfe1VMYAXa4 | iclr-2022-4 | ['ontology-embedding', 'protein-function-prediction'] | ['knowledge-base', 'medical'] | [ 1.86188549e-01 3.70409191e-01 -6.10384285e-01 -4.15678889e-01
-4.94392335e-01 -5.39413750e-01 8.48336071e-02 5.15549481e-01
-2.15471461e-01 1.30732012e+00 2.50191480e-01 -3.16285551e-01
1.14207894e-01 -7.47328222e-01 -1.37054598e+00 -9.21907246e-01
-3.21032964e-02 6.50043786e-01 2.34265372e-01 -1.96817562... | [4.898274898529053, 5.759952545166016] |
afe344f8-d255-47c8-91ac-a1c40975a592 | merge-and-label-a-novel-neural-network | 1907.00464 | null | https://arxiv.org/abs/1907.00464v1 | https://arxiv.org/pdf/1907.00464v1.pdf | Merge and Label: A novel neural network architecture for nested NER | Named entity recognition (NER) is one of the best studied tasks in natural language processing. However, most approaches are not capable of handling nested structures which are common in many applications. In this paper we introduce a novel neural network architecture that first merges tokens and/or entities into entit... | ['Andreas Vlachos', 'Joseph Fisher'] | 2019-06-30 | merge-and-label-a-novel-neural-network-1 | https://aclanthology.org/P19-1585 | https://aclanthology.org/P19-1585.pdf | acl-2019-7 | ['nested-named-entity-recognition', 'nested-mention-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-1.61374232e-03 3.44248772e-01 -1.31584421e-01 -4.36598927e-01
-7.19527245e-01 -8.94225121e-01 6.47045910e-01 7.28008807e-01
-1.09951115e+00 7.20264018e-01 3.33047479e-01 -4.67373788e-01
1.53507248e-01 -8.45407426e-01 -6.09755099e-01 -3.25565249e-01
-2.15924814e-01 5.22974908e-01 3.45914781e-01 7.75091127... | [9.800224304199219, 9.664308547973633] |
954f763c-a458-40a1-abae-6c37dd790437 | a-corpus-for-multilingual-document | 1805.09821 | null | http://arxiv.org/abs/1805.09821v1 | http://arxiv.org/pdf/1805.09821v1.pdf | A Corpus for Multilingual Document Classification in Eight Languages | Cross-lingual document classification aims at training a document classifier
on resources in one language and transferring it to a different language
without any additional resources. Several approaches have been proposed in the
literature and the current best practice is to evaluate them on a subset of the
Reuters Cor... | ['Xi-An Li', 'Holger Schwenk'] | 2018-05-24 | a-corpus-for-multilingual-document-1 | https://aclanthology.org/L18-1560 | https://aclanthology.org/L18-1560.pdf | lrec-2018-5 | ['cross-lingual-document-classification'] | ['natural-language-processing'] | [-1.13655843e-01 -2.98157543e-01 -4.01354551e-01 -3.93245399e-01
-7.91405976e-01 -1.00601876e+00 1.06475210e+00 4.29534405e-01
-9.18019652e-01 9.75984275e-01 3.36855710e-01 -3.96629602e-01
2.41388217e-01 -6.99033499e-01 -5.22867382e-01 -4.42372799e-01
1.92893878e-01 6.68231487e-01 1.20669022e-01 -3.88880372... | [10.943464279174805, 9.973381042480469] |
a5d1bbfb-7fee-4d17-949c-cc7756d6d292 | joint-task-self-supervised-learning-for | 1909.11895 | null | https://arxiv.org/abs/1909.11895v1 | https://arxiv.org/pdf/1909.11895v1.pdf | Joint-task Self-supervised Learning for Temporal Correspondence | This paper proposes to learn reliable dense correspondence from videos in a self-supervised manner. Our learning process integrates two highly related tasks: tracking large image regions \emph{and} establishing fine-grained pixel-level associations between consecutive video frames. We exploit the synergy between both t... | ['Ming-Hsuan Yang', 'Xiaolong Wang', 'Sifei Liu', 'Jan Kautz', 'Shalini De Mello', 'Xueting Li'] | 2019-09-26 | joint-task-self-supervised-learning-for-1 | http://papers.nips.cc/paper/8324-joint-task-self-supervised-learning-for-temporal-correspondence | http://papers.nips.cc/paper/8324-joint-task-self-supervised-learning-for-temporal-correspondence.pdf | neurips-2019-12 | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [-5.69466352e-02 -9.82402563e-02 -5.92976928e-01 -4.94501889e-01
-9.67241764e-01 -5.54212570e-01 4.87332374e-01 3.25633258e-01
-5.55374861e-01 4.68428850e-01 2.78328449e-01 4.56979543e-01
2.49655228e-02 -7.02314377e-01 -1.18300116e+00 -4.14640963e-01
-9.39282849e-02 4.33474749e-01 9.82913315e-01 3.22179385... | [8.95966911315918, -0.32564806938171387] |
fba4dff7-e140-4fab-8280-192e8864f1bf | multitask-vocal-burst-modeling-with-resnets | 2206.12494 | null | https://arxiv.org/abs/2206.12494v1 | https://arxiv.org/pdf/2206.12494v1.pdf | Multitask vocal burst modeling with ResNets and pre-trained paralinguistic Conformers | This technical report presents the modeling approaches used in our submission to the ICML Expressive Vocalizations Workshop & Competition multitask track (ExVo-MultiTask). We first applied image classification models of various sizes on mel-spectrogram representations of the vocal bursts, as is standard in sound event ... | ['Brendan Jou', 'Brian Eoff', 'Krishna Somandepalli', 'Josh Belanich'] | 2022-06-24 | null | null | null | null | ['sound-event-detection'] | ['audio'] | [ 1.01310713e-02 -2.46426184e-02 1.91138074e-01 -3.91860962e-01
-1.17033505e+00 -4.78298843e-01 6.74622774e-01 -1.03357531e-01
-6.36390328e-01 3.01057726e-01 4.28203404e-01 8.92994180e-02
-5.74123785e-02 2.08956882e-01 -4.67148781e-01 -3.46757948e-01
-9.70088243e-02 3.87558222e-01 -2.13471293e-01 1.00147925... | [13.592513084411621, 5.7543134689331055] |
fd1a42c9-0649-4c74-b04c-231a6c49b4b5 | eco-amlp-a-decision-support-system-using-an | 1706.07679 | null | http://arxiv.org/abs/1706.07679v1 | http://arxiv.org/pdf/1706.07679v1.pdf | ECO-AMLP: A Decision Support System using an Enhanced Class Outlier with Automatic Multilayer Perceptron for Diabetes Prediction | With advanced data analytical techniques, efforts for more accurate decision
support systems for disease prediction are on rise. Surveys by World Health
Organization (WHO) indicate a great increase in number of diabetic patients and
related deaths each year. Early diagnosis of diabetes is a major concern among
research... | ['Hammad Afzal', 'Raheel Nawaz', 'Khawar Khurshid', 'Mehreen Ahmed', 'Maham Jahangir'] | 2017-06-23 | null | null | null | null | ['diabetes-prediction'] | ['medical'] | [-5.07768616e-02 -2.59715080e-01 5.38110584e-02 -7.32258201e-01
-2.63666630e-01 2.08255515e-01 2.19359696e-01 1.39520121e+00
-4.51644570e-01 9.55728650e-01 1.02500431e-01 -1.14914812e-01
-3.93996686e-01 -5.45731843e-01 -2.42991596e-01 -5.38169444e-01
-2.27570608e-01 1.00798821e+00 -5.30721620e-02 1.19086832... | [8.389045715332031, 4.933208465576172] |
daa1a7e8-b5e3-45f6-a6cd-79599fb0d56c | tunet-a-block-online-bandwidth-extension | 2110.13492 | null | https://arxiv.org/abs/2110.13492v5 | https://arxiv.org/pdf/2110.13492v5.pdf | TUNet: A Block-online Bandwidth Extension Model based on Transformers and Self-supervised Pretraining | We introduce a block-online variant of the temporal feature-wise linear modulation (TFiLM) model to achieve bandwidth extension. The proposed architecture simplifies the UNet backbone of the TFiLM to reduce inference time and employs an efficient transformer at the bottleneck to alleviate performance degradation. We al... | ['Andy W. H. Khong', 'Anh H. T. Nguyen', 'Viet-Anh Nguyen'] | 2021-10-26 | null | null | null | null | ['bandwidth-extension', 'audio-super-resolution', 'audio-super-resolution', 'bandwidth-extension'] | ['audio', 'audio', 'music', 'speech'] | [ 2.35405907e-01 -5.82987480e-02 -6.04936004e-01 -4.40414816e-01
-8.51375699e-01 -1.84924796e-01 7.61275768e-01 -3.77032250e-01
-4.41291779e-01 6.52007759e-01 2.40305841e-01 -5.22144377e-01
-2.47393981e-01 -4.52365041e-01 -5.16878843e-01 -4.84879076e-01
-2.78228998e-01 -2.07813337e-01 3.38282764e-01 -5.49114533... | [14.556925773620605, 6.064140796661377] |
95c9cbfc-5a1a-4866-be0a-80d35e9f35e0 | continual-active-learning-for-efficient | 2106.03351 | null | https://arxiv.org/abs/2106.03351v1 | https://arxiv.org/pdf/2106.03351v1.pdf | Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition | Imaging in clinical routine is subject to changing scanner protocols, hardware, or policies in a typically heterogeneous set of acquisition hardware. Accuracy and reliability of deep learning models suffer from those changes as data and targets become inconsistent with their initial static training set. Continual learn... | ['Georg Langs', 'Johannes Hofmanninger', 'Matthias Perkonigg'] | 2021-06-07 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [ 6.83704674e-01 2.62564570e-01 -4.94122952e-01 -8.16134155e-01
-9.57762122e-01 -2.77731687e-01 3.72436672e-01 4.38472748e-01
-1.04234755e+00 7.14639604e-01 -8.28229189e-02 -1.13144696e-01
-3.21326554e-01 -2.36317724e-01 -6.06039703e-01 -7.22187519e-01
-7.15276599e-01 1.16177404e+00 5.77135980e-01 4.21850473... | [14.698555946350098, -2.1386606693267822] |
fda8c548-bcbd-43bc-966e-9d6697ce037f | evaluating-active-learning-heuristics-for | 1807.03083 | null | https://arxiv.org/abs/1807.03083v2 | https://arxiv.org/pdf/1807.03083v2.pdf | Evaluating Active Learning Heuristics for Sequential Diagnosis | Given a malfunctioning system, sequential diagnosis aims at identifying the root cause of the failure in terms of abnormally behaving system components. As initial system observations usually do not suffice to deterministically pin down just one explanation of the system's misbehavior, additional system measurements ca... | ['Wolfgang Schmid', 'Patrick Rodler'] | 2018-07-09 | null | null | null | null | ['sequential-diagnosis'] | ['medical'] | [ 1.89751893e-01 2.05341622e-01 -2.09643140e-01 -4.02784497e-01
-5.85977912e-01 -4.11927074e-01 4.68368292e-01 5.15898049e-01
8.60157385e-02 7.20997274e-01 -2.01721117e-01 -8.40529263e-01
-5.53530931e-01 -3.51742476e-01 -5.49250059e-02 -7.81591117e-01
-3.12869877e-01 8.02043974e-01 5.05132079e-01 4.82898168... | [5.423994541168213, 2.699327230453491] |
1bf3d4c9-faf2-446b-9c37-cb8061d81899 | episodic-policy-gradient-training | 2112.01853 | null | https://arxiv.org/abs/2112.01853v1 | https://arxiv.org/pdf/2112.01853v1.pdf | Episodic Policy Gradient Training | We introduce a novel training procedure for policy gradient methods wherein episodic memory is used to optimize the hyperparameters of reinforcement learning algorithms on-the-fly. Unlike other hyperparameter searches, we formulate hyperparameter scheduling as a standard Markov Decision Process and use episodic memory ... | ['Svetha Venkatesh', 'Dung Nguyen', 'Kien Do', 'Thommen K. George', 'Majid Abdolshah', 'Hung Le'] | 2021-12-03 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-2.00001210e-01 -1.40805349e-01 -8.48758042e-01 -7.98831284e-02
-7.52152056e-02 -4.99410838e-01 7.72234976e-01 -1.20695621e-01
-8.39336157e-01 1.40653801e+00 4.00331020e-02 -4.44743186e-01
-2.74768442e-01 -9.15052891e-01 -4.48570758e-01 -1.08104050e+00
-1.93097502e-01 5.31535864e-01 1.83352038e-01 -1.24988556... | [4.112760066986084, 2.160810947418213] |
8e0170ed-dde1-4130-8edf-b418d804649e | attention-based-depth-distillation-with-3d | 2211.16779 | null | https://arxiv.org/abs/2211.16779v2 | https://arxiv.org/pdf/2211.16779v2.pdf | Attention-Based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection | Monocular 3D object detection is a low-cost but challenging task, as it requires generating accurate 3D localization solely from a single image input. Recent developed depth-assisted methods show promising results by using explicit depth maps as intermediate features, which are either precomputed by monocular depth est... | ['Xiaoquan Wang', 'Xianzhi Li', 'Jian Pu', 'Yunzhe Wu', 'Zizhang Wu'] | 2022-11-30 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [-0.08611285 0.1858308 -0.17693138 -0.62372804 -1.0700856 -0.76883
0.5620788 -0.14460602 -0.56656843 0.31674874 0.07132462 -0.35935012
0.1780152 -0.66670436 -1.195433 -0.57482684 0.5677547 0.533717
0.62771195 0.24729475 0.10578456 0.49435365 -1.7781434 0.33029404
0.94804263 1.0793831 0.55585... | [7.900338172912598, -2.642502784729004] |
f40a486a-9f10-4098-acd3-f098d8a5160b | depression-diagnosis-and-drug-response | 2303.06033 | null | https://arxiv.org/abs/2303.06033v1 | https://arxiv.org/pdf/2303.06033v1.pdf | Depression Diagnosis and Drug Response Prediction via Recurrent Neural Networks and Transformers Utilizing EEG Signals | The Early diagnosis and treatment of depression is essential for effective treatment. Depression, while being one of the most common mental illnesses, is still poorly understood in both research and clinical practice. Among different treatments, drug prescription is widely used, however the drug treatment is not effect... | ['Fereidoun Nowshiravan Rahatabad', 'Arash Maghsoudi', 'Abdolkarim Saeedi'] | 2023-03-09 | null | null | null | null | ['drug-response-prediction', 'eeg', 'eeg'] | ['medical', 'methodology', 'time-series'] | [-7.74851292e-02 -3.27548444e-01 -8.33620727e-02 -4.92707729e-01
-3.66502315e-01 -1.38685197e-01 2.33943939e-01 2.08749264e-01
-2.81051099e-01 8.38272095e-01 -1.61971390e-01 -3.20445031e-01
-4.22163010e-01 -7.89712548e-01 9.30423141e-02 -6.77500904e-01
-1.67572632e-01 5.47838688e-01 -1.83068201e-01 -1.31495193... | [13.267805099487305, 3.4744272232055664] |
68f4c701-c79b-487a-9216-179633404bfd | learning-the-sequential-temporal-information | 1807.02857 | null | http://arxiv.org/abs/1807.02857v1 | http://arxiv.org/pdf/1807.02857v1.pdf | Learning The Sequential Temporal Information with Recurrent Neural Networks | Recurrent Networks are one of the most powerful and promising artificial
neural network algorithms to processing the sequential data such as natural
languages, sound, time series data. Unlike traditional feed-forward network,
Recurrent Network has a inherent feed back loop that allows to store the
temporal context info... | ['Pushparaja Murugan'] | 2018-07-08 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [ 4.00204770e-02 -4.67577785e-01 -2.58120000e-01 -1.14510886e-01
1.68071240e-01 -2.26333901e-01 7.89515018e-01 -2.49935269e-01
-3.80418956e-01 5.91200829e-01 1.95048138e-01 -5.45296848e-01
1.34481534e-01 -5.40192306e-01 -4.38138574e-01 -4.72131222e-01
-1.98219776e-01 1.33912608e-01 4.05972719e-01 -1.94800347... | [10.83125114440918, 6.249029159545898] |
8b690070-e285-445a-8e1c-28cabb11e506 | text-perceptron-towards-end-to-end-arbitrary | 2002.06820 | null | https://arxiv.org/abs/2002.06820v2 | https://arxiv.org/pdf/2002.06820v2.pdf | Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting | Many approaches have recently been proposed to detect irregular scene text and achieved promising results. However, their localization results may not well satisfy the following text recognition part mainly because of two reasons: 1) recognizing arbitrary shaped text is still a challenging task, and 2) prevalent non-tr... | ['ShiLiang Pu', 'Zhanzhan Cheng', 'Yunlu Xu', 'Liang Qiao', 'Yi Niu', 'Sanli Tang', 'Fei Wu'] | 2020-02-17 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 5.26006401e-01 -2.78434515e-01 5.98547570e-02 -2.99675226e-01
-7.91829526e-01 -3.57884109e-01 6.17322326e-01 8.84181932e-02
-3.88764709e-01 5.68065755e-02 -9.17101838e-03 -2.74689674e-01
2.08048016e-01 -7.20949054e-01 -6.23052120e-01 -7.20620453e-01
8.16837311e-01 9.78357017e-01 6.93226576e-01 9.53025445... | [12.020853042602539, 2.281815528869629] |
1d0f2851-7e7c-4f2a-bc28-a7def2ca7f1c | integrating-visuospatial-linguistic-and | 2110.10834 | null | https://arxiv.org/abs/2110.10834v1 | https://arxiv.org/pdf/2110.10834v1.pdf | Integrating Visuospatial, Linguistic and Commonsense Structure into Story Visualization | While much research has been done in text-to-image synthesis, little work has been done to explore the usage of linguistic structure of the input text. Such information is even more important for story visualization since its inputs have an explicit narrative structure that needs to be translated into an image sequence... | ['Mohit Bansal', 'Adyasha Maharana'] | 2021-10-21 | null | null | null | null | ['dense-captioning', 'story-visualization'] | ['computer-vision', 'computer-vision'] | [ 5.31200230e-01 3.58795196e-01 8.61161500e-02 -3.58146071e-01
-8.41696382e-01 -7.42998719e-01 8.73723805e-01 -1.84028372e-01
-6.21621422e-02 6.77559495e-01 8.35519254e-01 -2.47620553e-01
5.35781205e-01 -7.66412795e-01 -1.06919527e+00 -4.99473304e-01
3.65376234e-01 2.80754298e-01 1.42598376e-01 -4.20551389... | [11.167121887207031, 0.7110996246337891] |
8a4160ed-739c-48e7-8664-a4c4d4849bbf | the-use-of-mutual-coherence-to-prove-ell1ell0 | 1901.02783 | null | http://arxiv.org/abs/1901.02783v1 | http://arxiv.org/pdf/1901.02783v1.pdf | The Use of Mutual Coherence to Prove $\ell^1/\ell^0$-Equivalence in Classification Problems | We consider the decomposition of a signal over an overcomplete set of
vectors. Minimization of the $\ell^1$-norm of the coefficient vector can often
retrieve the sparsest solution (so-called "$\ell^1/\ell^0$-equivalence"), a
generally NP-hard task, and this fact has powered the field of compressed
sensing. Wright et al... | ['Chelsea Weaver', 'Naoki Saito'] | 2019-01-09 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 5.64745367e-01 1.90321475e-01 -4.03314292e-01 -1.54796988e-01
-1.04881930e+00 -5.00231922e-01 4.08850200e-02 -1.72083825e-01
4.98485379e-02 7.99429357e-01 2.01763347e-01 -2.90202737e-01
-5.84288895e-01 -6.78228498e-01 -7.27203906e-01 -9.87528622e-01
-5.49444258e-01 4.44246344e-02 -5.66397250e-01 -2.11666271... | [7.119048118591309, 4.47360372543335] |
0c2bf049-9232-4f22-b79f-d076e788819a | structure-aware-slam-using-quadrics-and | 1804.09111 | null | http://arxiv.org/abs/1804.09111v3 | http://arxiv.org/pdf/1804.09111v3.pdf | Structure Aware SLAM using Quadrics and Planes | Simultaneous Localization And Mapping (SLAM) is a fundamental problem in
mobile robotics. While point-based SLAM methods provide accurate camera
localization, the generated maps lack semantic information. On the other hand,
state of the art object detection methods provide rich information about
entities present in the... | ['Mehdi Hosseinzadeh', 'Niko Suenderhauf', 'Trung Pham', 'Yasir Latif', 'Ian Reid'] | 2018-04-24 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-9.86325666e-02 -1.50424480e-01 -2.38338470e-01 -5.09590209e-01
-5.64280570e-01 -7.43848383e-01 6.07529819e-01 1.34050772e-01
-2.84498364e-01 5.80511332e-01 -1.93081617e-01 -1.68544933e-01
-1.43177003e-01 -8.41838300e-01 -8.54271293e-01 -1.87133834e-01
1.97968423e-01 8.26584995e-01 6.71446502e-01 -1.80000022... | [7.327858924865723, -2.2907612323760986] |
ac5409c6-8f95-4073-968f-17cb5af5e15c | singgan-generative-adversarial-network-for | 2110.07468 | null | https://arxiv.org/abs/2110.07468v4 | https://arxiv.org/pdf/2110.07468v4.pdf | SingGAN: Generative Adversarial Network For High-Fidelity Singing Voice Generation | Deep generative models have achieved significant progress in speech synthesis to date, while high-fidelity singing voice synthesis is still an open problem for its long continuous pronunciation, rich high-frequency parts, and strong expressiveness. Existing neural vocoders designed for text-to-speech cannot directly be... | ['Zhefeng Wang', 'Baoxing Huai', 'Zhou Zhao', 'Jinglin Liu', 'Yi Ren', 'Chenye Cui', 'Rongjie Huang', 'Feiyang Chen'] | 2021-10-14 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-2.18553673e-02 -1.15353204e-01 -3.84357851e-03 1.34058028e-01
-1.19355786e+00 -5.44097364e-01 1.75693169e-01 -7.73280561e-01
2.88023919e-01 5.61108351e-01 5.77999592e-01 -9.12074745e-02
3.29655379e-01 -5.94853163e-01 -7.04305112e-01 -6.44183695e-01
1.08266048e-01 1.53454781e-01 -1.87145531e-01 -3.31093132... | [15.49459171295166, 6.151684761047363] |
fd67a336-dccb-4f76-8412-b9201fdd70d3 | spatiotemporal-self-attention-modeling-with | 2207.13259 | null | https://arxiv.org/abs/2207.13259v1 | https://arxiv.org/pdf/2207.13259v1.pdf | Spatiotemporal Self-attention Modeling with Temporal Patch Shift for Action Recognition | Transformer-based methods have recently achieved great advancement on 2D image-based vision tasks. For 3D video-based tasks such as action recognition, however, directly applying spatiotemporal transformers on video data will bring heavy computation and memory burdens due to the largely increased number of patches and ... | ['Lei Zhang', 'Xian-Sheng Hua', 'Xihan Wei', 'Biao Wang', 'Chao Li', 'Wangmeng Xiang'] | 2022-07-27 | null | null | null | null | ['action-classification'] | ['computer-vision'] | [ 3.04622501e-02 -4.38752800e-01 -6.59805909e-02 1.81016158e-02
-6.33776069e-01 -1.82073653e-01 4.48519468e-01 -2.37301022e-01
-3.25986356e-01 1.14303686e-01 1.34289384e-01 -1.51834488e-01
1.66263565e-01 -5.48312485e-01 -8.05297375e-01 -7.87773132e-01
1.08473919e-01 1.46960586e-01 6.36862218e-01 -1.51281565... | [8.68067455291748, 0.3186199963092804] |
4f16fb02-443b-4cb9-8f74-9600f7347b06 | anything-3d-towards-single-view-anything | 2304.10261 | null | https://arxiv.org/abs/2304.10261v1 | https://arxiv.org/pdf/2304.10261v1.pdf | Anything-3D: Towards Single-view Anything Reconstruction in the Wild | 3D reconstruction from a single-RGB image in unconstrained real-world scenarios presents numerous challenges due to the inherent diversity and complexity of objects and environments. In this paper, we introduce Anything-3D, a methodical framework that ingeniously combines a series of visual-language models and the Segm... | ['Xinchao Wang', 'Xingyi Yang', 'Qiuhong Shen'] | 2023-04-19 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [ 2.58107811e-01 -5.49190417e-02 1.51625782e-01 -3.05731893e-01
-8.37449014e-01 -6.05069816e-01 5.58790684e-01 -2.04273850e-01
7.94036463e-02 2.98102319e-01 7.23465383e-02 -4.17620331e-01
-1.24876708e-01 -6.50082827e-01 -5.81312954e-01 -6.03764474e-01
7.70539865e-02 3.76179665e-01 2.11163431e-01 -2.77376771... | [9.016722679138184, -2.9570748805999756] |
626a6d33-06c5-4714-ab91-d5d6af6e87a3 | re2g-retrieve-rerank-generate | null | null | https://openreview.net/forum?id=_R7UMusdRsc | https://openreview.net/pdf?id=_R7UMusdRsc | Re2G: Retrieve, Rerank, Generate | As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger. However, for tasks that require a large amount of knowledge, non-parametric memory allows models to grow dramatically with a sub-linear increase in computational cost and GPU memory requirements. Recent mode... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['zero-shot-slot-filling', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.02496320e-01 3.17751318e-01 -2.33058184e-01 -6.01670444e-02
-1.72724223e+00 -7.93420315e-01 7.15298772e-01 3.43388975e-01
-4.97891396e-01 9.70363855e-01 5.03790140e-01 -4.36432183e-01
-8.79651122e-03 -8.68716538e-01 -7.87469089e-01 8.90371948e-02
1.01340592e-01 1.20339894e+00 5.30762076e-01 -5.81442654... | [11.453539848327637, 8.029667854309082] |
3ae86604-c353-4289-a5b5-331c218ec6c7 | generate-segment-and-replace-towards-generic | 1811.09729 | null | https://arxiv.org/abs/1811.09729v3 | https://arxiv.org/pdf/1811.09729v3.pdf | Generate, Segment and Refine: Towards Generic Manipulation Segmentation | Detecting manipulated images has become a significant emerging challenge. The advent of image sharing platforms and the easy availability of advanced photo editing software have resulted in a large quantities of manipulated images being shared on the internet. While the intent behind such manipulations varies widely, c... | ['Mahyar Najibi', 'Bor-Chun Chen', 'Peng Zhou', 'Ser Nam Lim', 'Xintong Han', 'Larry S. Davis', 'Abhinav Shrivastava'] | 2018-11-24 | null | null | null | null | ['image-manipulation-detection', 'detecting-image-manipulation'] | ['computer-vision', 'computer-vision'] | [ 7.58914173e-01 9.80249569e-02 -1.97024912e-01 -1.79004848e-01
-6.48325443e-01 -7.41000295e-01 7.67830789e-01 1.33835211e-01
-1.87843055e-01 6.48899674e-01 1.71496589e-02 -1.87427551e-01
3.59394699e-01 -5.85684955e-01 -8.78635108e-01 -2.42973998e-01
1.77362978e-01 2.36971416e-02 3.50218803e-01 -1.59171000... | [12.394346237182617, 1.0448423624038696] |
94fff1c7-5d5a-421e-9326-776d6934bbce | a-glimpse-of-the-whole-path-optimization | 2010.13285 | null | https://arxiv.org/abs/2010.13285v4 | https://arxiv.org/pdf/2010.13285v4.pdf | Malicious Requests Detection with Improved Bidirectional Long Short-term Memory Neural Networks | Detecting and intercepting malicious requests are one of the most widely used ways against attacks in the network security. Most existing detecting approaches, including matching blacklist characters and machine learning algorithms have all shown to be vulnerable to sophisticated attacks. To address the above issues, a... | ['Jiajie Zhang', 'Bincheng Zhang', 'Wenhao Li'] | 2020-10-26 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 1.36761926e-02 -8.87818098e-01 -2.33514994e-01 -2.94676483e-01
-4.62436497e-01 -5.99106967e-01 7.43223965e-01 -1.85735270e-01
-4.36493844e-01 7.78730437e-02 -1.44654319e-01 -9.23497856e-01
2.88394928e-01 -6.34226978e-01 -5.07336438e-01 -6.48514628e-01
-3.55548799e-01 -5.56858769e-03 8.47818375e-01 -4.56540763... | [5.170575141906738, 7.293119430541992] |
886b6eb8-bc5e-4e1d-ac56-46fae3d36928 | pixel-level-reconstruction-and-classification | 1806.08037 | null | http://arxiv.org/abs/1806.08037v1 | http://arxiv.org/pdf/1806.08037v1.pdf | Pixel-level Reconstruction and Classification for Noisy Handwritten Bangla Characters | Classification techniques for images of handwritten characters are
susceptible to noise. Quadtrees can be an efficient representation for learning
from sparse features. In this paper, we improve the effectiveness of
probabilistic quadtrees by using a pixel level classifier to extract the
character pixels and remove noi... | ['Supratik Mukhopadhyay', 'Qun Liu', 'Manohar Karki', 'Saikat Basu', 'Robert DiBiano'] | 2018-06-21 | null | null | null | null | ['document-image-classification'] | ['computer-vision'] | [ 4.33475971e-01 -2.17734620e-01 2.39751577e-01 -5.05036533e-01
-6.88980103e-01 -4.97001767e-01 3.01714152e-01 -1.62443966e-01
-4.24295038e-01 7.92888880e-01 2.25081056e-01 -5.66470511e-02
-2.24955425e-01 -1.14107931e+00 -7.92911232e-01 -1.00036752e+00
3.34554195e-01 2.31358692e-01 4.47480977e-01 -3.68656428... | [11.785651206970215, 2.6096296310424805] |
5aafd17b-8164-447a-bfb0-dd86213f89b2 | predicting-crime-using-spatial-features | 1803.04474 | null | http://arxiv.org/abs/1803.04474v1 | http://arxiv.org/pdf/1803.04474v1.pdf | Predicting Crime Using Spatial Features | Our study aims to build a machine learning model for crime prediction using
geospatial features for different categories of crime. The reverse geocoding
technique is applied to retrieve open street map (OSM) spatial data. This study
also proposes finding hotpoints extracted from crime hotspots area found by
Hierarchica... | ['Stan Matwin', 'Amilcar Soares Junior', 'Fateha Khanam Bappee'] | 2018-03-12 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [-1.21045753e-01 -3.68852526e-01 1.68260798e-01 -4.37110722e-01
-7.61860490e-01 -8.77399817e-02 6.71722651e-01 9.75364208e-01
-7.88631797e-01 7.19703615e-01 7.92961359e-01 -5.82114875e-01
-6.48763001e-01 -1.53603864e+00 -3.67253482e-01 -2.70418763e-01
-7.36634672e-01 2.19426870e-01 2.30798349e-01 -3.53813842... | [6.761672019958496, 1.9470819234848022] |
315d6bf6-0b80-4814-af9b-dcb4fb874884 | bert-based-chinese-text-classification-for | 2104.04197 | null | https://arxiv.org/abs/2104.04197v1 | https://arxiv.org/pdf/2104.04197v1.pdf | BERT-based Chinese Text Classification for Emergency Domain with a Novel Loss Function | This paper proposes an automatic Chinese text categorization method for solving the emergency event report classification problem. Since bidirectional encoder representations from transformers (BERT) has achieved great success in natural language processing domain, it is employed to derive emergency text features in th... | ['Xiong Luo', 'Chao Huang', 'Long Wang', 'Zhongju Wang'] | 2021-04-09 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [-3.90839810e-03 -1.20652318e-01 4.47151251e-02 -4.64233518e-01
-7.88999438e-01 6.87243268e-02 5.28400242e-01 4.78008002e-01
-6.83128178e-01 7.64516354e-01 3.94255340e-01 -3.42838407e-01
-3.73186678e-01 -8.31495941e-01 -2.02672735e-01 -8.08001876e-01
-2.27262340e-02 2.40161106e-01 -1.83650225e-01 -2.49734268... | [9.073366165161133, 3.966703176498413] |
a558b6f8-a60c-4dbe-8030-3a8e612322d3 | iitransformer-a-unified-approach-to | null | null | https://bmvc2022.mpi-inf.mpg.de/377/ | https://bmvc2022.mpi-inf.mpg.de/0377.pdf | iiTransformer: A Unified Approach to Exploiting Local and Non-Local Information for Image Restoration | The goal of image restoration is to recover a high-quality image from its degraded input. While impressive results on various image restoration tasks have been achieved using CNNs, the convolution operation has limited its ability to utilize information outside of its receptive field. Transformers, which use the self-a... | ['Tammy Lee', 'Hanul Shin', 'Youngchan Song', 'Soo Min Kang'] | 2022-11-21 | null | null | null | bmvc-2022-11 | ['color-image-denoising', 'jpeg-compression-artifact-reduction'] | ['computer-vision', 'computer-vision'] | [ 4.23268586e-01 -2.07716957e-01 2.58421093e-01 -3.40833098e-01
-5.93369007e-01 -4.20127772e-02 5.29059708e-01 -3.98427814e-01
-1.71193719e-01 5.79769909e-01 5.97423494e-01 -2.23646134e-01
-8.17618594e-02 -6.73012614e-01 -8.85870457e-01 -8.21587443e-01
3.32650580e-02 -2.95955032e-01 1.95029154e-01 -3.09263170... | [11.169465065002441, -2.1930179595947266] |
95c9f7d4-a17c-4e10-bf0c-c7d92f32fc33 | retrieve-caption-generate-visual-grounding | 2109.03892 | null | https://arxiv.org/abs/2109.03892v3 | https://arxiv.org/pdf/2109.03892v3.pdf | Retrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models | We investigate the use of multimodal information contained in images as an effective method for enhancing the commonsense of Transformer models for text generation. We perform experiments using BART and T5 on concept-to-text generation, specifically the task of generative commonsense reasoning, or CommonGen. We call ou... | ['Varun Gangal', 'Eduard Hovy', 'Teruko Mitamura', 'Malihe Alikhani', 'Zhuofu Tao', 'Kevin Lu', 'Steven Y. Feng'] | 2021-09-08 | null | https://openreview.net/forum?id=MvFv92fUFof | https://openreview.net/pdf?id=MvFv92fUFof | akbc-workshop-cskb-2021-10 | ['concept-to-text-generation'] | ['natural-language-processing'] | [ 6.48062766e-01 5.88567555e-01 2.60674894e-01 -1.44381836e-01
-7.33399153e-01 -5.93928337e-01 1.28800154e+00 -2.11230084e-01
1.47101702e-02 9.10104156e-01 9.14008439e-01 -3.03609729e-01
4.75922137e-01 -7.26397097e-01 -7.17191517e-01 -2.11945370e-01
6.05676234e-01 8.35973918e-01 -3.64769101e-01 -6.11007869... | [11.178927421569824, 0.9095774292945862] |
6d9c0d8b-1574-4dff-add7-5c7b5e88a1fe | harnessing-pre-trained-neural-networks-with | null | null | https://aclanthology.org/D19-1365 | https://aclanthology.org/D19-1365.pdf | Harnessing Pre-Trained Neural Networks with Rules for Formality Style Transfer | Formality text style transfer plays an important role in various NLP applications, such as non-native speaker assistants and child education. Early studies normalize informal sentences with rules, before statistical and neural models become a prevailing method in the field. While a rule-based system is still a common p... | ['WenHan Chao', 'Yunli Wang', 'Lili Mou', 'Zhoujun Li', 'Yu Wu'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['formality-style-transfer'] | ['natural-language-processing'] | [ 5.14820397e-01 4.41296279e-01 -2.49108046e-01 -7.56821394e-01
-1.86668023e-01 -5.54007828e-01 6.04566574e-01 3.96010578e-02
-7.71970570e-01 9.58207428e-01 3.10105920e-01 -6.47484362e-01
3.72832865e-02 -9.32376027e-01 -8.54999304e-01 -1.72963142e-01
4.31092590e-01 4.98029381e-01 3.93533707e-01 -6.53811693... | [10.928632736206055, 9.069101333618164] |
a6c79db8-2c76-425b-a19a-83da91b3a26d | findings-of-the-iwslt-2022-evaluation | null | null | https://aclanthology.org/2022.iwslt-1.10 | https://aclanthology.org/2022.iwslt-1.10.pdf | Findings of the IWSLT 2022 Evaluation Campaign | The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speec... | ['Shinji Watanabe', 'Changhan Wang', 'Alexander Waibel', 'Yogesh Virkar', 'Marco Turchi', 'Katsuhito Sudoh', 'Sebastian Stüker', 'Matthias Sperber', 'Jiatong Shi', 'Elizabeth Salesky', 'Juan Pino', 'John Ortega', 'Xing Niu', 'Jan Niehues', 'Matteo Negri', 'Satoshi Nakamura', 'Maria Nǎdejde', 'Kenton Murray', 'Paul McNa... | null | null | null | null | iwslt-acl-2022-5 | ['speech-to-speech-translation'] | ['speech'] | [ 2.48660743e-01 1.68736994e-01 -1.06802642e-01 -5.18712997e-01
-1.77489710e+00 -8.07178140e-01 1.30780029e+00 7.12061897e-02
-4.09909576e-01 1.00410938e+00 6.22526467e-01 -8.61102045e-01
1.58186138e-01 1.44358014e-03 -6.58069015e-01 -4.40408438e-01
1.94950715e-01 1.03928339e+00 2.35281549e-02 -5.62363803... | [14.341968536376953, 7.268989086151123] |
f481c349-52b8-4b7b-8c89-423e749509d0 | learning-transferrable-knowledge-for-semantic | 1512.07928 | null | http://arxiv.org/abs/1512.07928v1 | http://arxiv.org/pdf/1512.07928v1.pdf | Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network | We propose a novel weakly-supervised semantic segmentation algorithm based on
Deep Convolutional Neural Network (DCNN). Contrary to existing
weakly-supervised approaches, our algorithm exploits auxiliary segmentation
annotations available for different categories to guide segmentations on images
with only image-level c... | ['Junhyuk Oh', 'Honglak Lee', 'Bohyung Han', 'Seunghoon Hong'] | 2015-12-24 | learning-transferrable-knowledge-for-semantic-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Hong_Learning_Transferrable_Knowledge_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Hong_Learning_Transferrable_Knowledge_CVPR_2016_paper.pdf | cvpr-2016-6 | ['foreground-segmentation'] | ['computer-vision'] | [ 6.62060261e-01 5.93195856e-01 -2.37967983e-01 -6.07409298e-01
-7.58744299e-01 -9.32863474e-01 4.12432790e-01 -2.53006846e-01
-4.27775919e-01 4.34165835e-01 -1.85992729e-04 -2.24604398e-01
5.59769750e-01 -6.80142760e-01 -1.20184708e+00 -6.76366866e-01
4.83486056e-01 5.74789405e-01 7.70200312e-01 2.45825648... | [9.588547706604004, 0.5639662742614746] |
07cf7a6b-cdd8-4ebc-b7ec-66aaca856c81 | unraveling-the-arc-puzzle-mimicking-human | 2306.08204 | null | https://arxiv.org/abs/2306.08204v1 | https://arxiv.org/pdf/2306.08204v1.pdf | Unraveling the ARC Puzzle: Mimicking Human Solutions with Object-Centric Decision Transformer | In the pursuit of artificial general intelligence (AGI), we tackle Abstraction and Reasoning Corpus (ARC) tasks using a novel two-pronged approach. We employ the Decision Transformer in an imitation learning paradigm to model human problem-solving, and introduce an object detection algorithm, the Push and Pull clusteri... | ['Sundong Kim', 'Sejin Kim', 'Sabina Ualibekova', 'Mintaek Lim', 'Sanha Hwang', 'Jaegyun Im', 'JaeHyun Park'] | 2023-06-14 | null | null | null | null | ['clustering', 'imitation-learning'] | ['methodology', 'methodology'] | [ 1.31235585e-01 4.97302324e-01 -1.22858398e-01 1.94292501e-01
-1.99655816e-01 -4.96747136e-01 7.97177315e-01 8.98638070e-02
-3.08944643e-01 4.76171285e-01 -7.31278124e-05 -2.81997651e-01
-8.48337948e-01 -5.49120426e-01 -3.50721806e-01 -4.26577091e-01
-5.47905490e-02 1.20555294e+00 -1.31033227e-01 -4.40063536... | [4.214466094970703, 1.548784613609314] |
448629ed-b1bd-4d5e-befc-dc9ce22e46b5 | survey-of-deep-learning-for-autonomous | 2210.08487 | null | https://arxiv.org/abs/2210.08487v3 | https://arxiv.org/pdf/2210.08487v3.pdf | Survey of Deep Learning for Autonomous Surface Vehicles in the Marine Environment | Within the next several years, there will be a high level of autonomous technology that will be available for widespread use, which will reduce labor costs, increase safety, save energy, enable difficult unmanned tasks in harsh environments, and eliminate human error. Compared to software development for other autonomo... | ['Carlo Ratti', 'Jie Yang', 'Fábio Duarte', 'Wei Wang', 'Jiaxin Yin', 'Yuanyuan Qiao'] | 2022-10-16 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [-3.70591879e-02 1.23431414e-01 3.45558091e-03 -3.52614582e-01
-2.72198543e-02 -4.91455644e-01 7.33420014e-01 -1.55316144e-01
-7.34816194e-01 7.69155383e-01 -2.68342912e-01 -3.77480149e-01
-3.90505582e-01 -8.80547881e-01 -3.33536536e-01 -8.21727693e-01
-5.69552541e-01 2.61681050e-01 1.20196521e-01 -9.60403740... | [7.504782199859619, -1.5401514768600464] |
6cd0ca9d-8eae-4fa1-9eaf-4901e8edb7f0 | distribution-shift-inversion-for-out-of-1 | 2306.08328 | null | https://arxiv.org/abs/2306.08328v1 | https://arxiv.org/pdf/2306.08328v1.pdf | Distribution Shift Inversion for Out-of-Distribution Prediction | Machine learning society has witnessed the emergence of a myriad of Out-of-Distribution (OoD) algorithms, which address the distribution shift between the training and the testing distribution by searching for a unified predictor or invariant feature representation. However, the task of directly mitigating the distribu... | ['Xinchao Wang', 'Xingyi Yang', 'Songhua Liu', 'Runpeng Yu'] | 2023-06-14 | distribution-shift-inversion-for-out-of | http://openaccess.thecvf.com//content/CVPR2023/html/Yu_Distribution_Shift_Inversion_for_Out-of-Distribution_Prediction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yu_Distribution_Shift_Inversion_for_Out-of-Distribution_Prediction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['domain-generalization'] | ['methodology'] | [ 3.80677313e-01 -9.94522776e-03 -2.25855261e-01 -3.46662462e-01
-4.24961507e-01 -6.45773768e-01 5.31237245e-01 4.26341817e-02
8.64648893e-02 6.52570605e-01 -3.94170254e-01 -5.24534881e-01
-2.86657751e-01 -8.22027564e-01 -6.50774956e-01 -1.03311133e+00
2.17244968e-01 9.04043972e-01 3.43213320e-01 4.76930439... | [10.218761444091797, 3.203514814376831] |
d2d198b8-8560-4bed-92ec-800d78f2c184 | reducing-ann-snn-conversion-error-through | 2302.02091 | null | https://arxiv.org/abs/2302.02091v1 | https://arxiv.org/pdf/2302.02091v1.pdf | Reducing ANN-SNN Conversion Error through Residual Membrane Potential | Spiking Neural Networks (SNNs) have received extensive academic attention due to the unique properties of low power consumption and high-speed computing on neuromorphic chips. Among various training methods of SNNs, ANN-SNN conversion has shown the equivalent level of performance as ANNs on large-scale datasets. Howeve... | ['Zhaofei Yu', 'Tiejun Huang', 'Jianhao Ding', 'Tong Bu', 'Zecheng Hao'] | 2023-02-04 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [ 2.63993025e-01 -5.09499967e-01 2.70719737e-01 -1.68241799e-01
-1.51352778e-01 -1.71025082e-01 1.83381826e-01 -1.32075056e-01
-6.98518991e-01 9.68708396e-01 -4.93137687e-01 -2.51975954e-01
-4.80990522e-02 -6.57332957e-01 -8.76209378e-01 -9.53542054e-01
2.47320414e-01 1.17509775e-02 4.93878037e-01 -6.37518913... | [8.242831230163574, 2.5207738876342773] |
33cf9278-71eb-41b2-9661-55f518880bf0 | ams-net-adaptive-multiscale-sparse-neural | 2207.11735 | null | https://arxiv.org/abs/2207.11735v1 | https://arxiv.org/pdf/2207.11735v1.pdf | AMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problems | In this work, we propose an adaptive sparse learning algorithm that can be applied to learn the physical processes and obtain a sparse representation of the solution given a large snapshot space. Assume that there is a rich class of precomputed basis functions that can be used to approximate the quantity of interest. W... | ['Guang Lin', 'Wing Tat Leung', 'Yating Wang'] | 2022-07-24 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 6.67348853e-04 -1.27745584e-01 -2.70489007e-02 2.32218765e-02
-1.78645030e-01 -5.67554273e-02 3.29075269e-02 -5.57504259e-02
-7.41158202e-02 9.35913205e-01 -5.36294505e-02 1.50023699e-01
-5.89865148e-01 -7.16982007e-01 -5.91801584e-01 -1.11920774e+00
-1.92284763e-01 9.83244628e-02 -1.75732777e-01 -1.74030125... | [6.532783508300781, 3.4596073627471924] |
ac15bd23-607a-425a-9511-40716e30ff28 | data-augmentation-for-seizure-prediction-with | 2306.08256 | null | https://arxiv.org/abs/2306.08256v1 | https://arxiv.org/pdf/2306.08256v1.pdf | Data Augmentation for Seizure Prediction with Generative Diffusion Model | Objective: Seizure prediction is of great importance to improve the life of patients. The focal point is to distinguish preictal states from interictal ones. With the development of machine learning, seizure prediction methods have achieved significant progress. However, the severe imbalance problem between preictal an... | ['Xun Chen', 'Ruobing Qian', 'Aiping Liu', 'Le Wu', 'Yuchang Zhao', 'Kai Shu'] | 2023-06-14 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 2.32306737e-02 -2.48743996e-01 4.23080735e-02 -2.32985213e-01
-2.19371274e-01 -6.77849278e-02 4.39872861e-01 -1.84928596e-01
-1.23265542e-01 8.21466148e-01 3.97763491e-01 2.60550410e-01
-1.93678126e-01 -8.92738342e-01 -9.49284285e-02 -1.11340368e+00
-7.85221308e-02 3.73874247e-01 1.02667063e-01 -2.76121140... | [13.123577117919922, 3.4492175579071045] |
676f148b-95d1-46a6-9ce6-5d7f10fdeae1 | contextual-visual-similarity | 1612.02534 | null | http://arxiv.org/abs/1612.02534v1 | http://arxiv.org/pdf/1612.02534v1.pdf | Contextual Visual Similarity | Measuring visual similarity is critical for image understanding. But what
makes two images similar? Most existing work on visual similarity assumes that
images are similar because they contain the same object instance or category.
However, the reason why images are similar is much more complex. For example,
from the pe... | ['Xiaofang Wang', 'Martial Hebert', 'Kris M. Kitani'] | 2016-12-08 | null | null | null | null | ['image-similarity-search'] | ['computer-vision'] | [ 5.30605614e-01 -1.38457820e-01 -2.42995724e-01 -6.67603433e-01
-3.87282521e-01 -7.66163647e-01 6.67936444e-01 6.97898090e-01
-4.42152470e-01 1.39606133e-01 -1.06966663e-02 -2.25706607e-01
-1.59874365e-01 -6.77823544e-01 -5.83943665e-01 -5.51917255e-01
2.50372708e-01 1.97822481e-01 3.02636743e-01 -1.17838979... | [10.625020027160645, 1.3396114110946655] |
2d703c1a-5e4e-4feb-a578-57ca3097a04e | grid-partitioned-attention-efficient | 2107.03742 | null | https://arxiv.org/abs/2107.03742v1 | https://arxiv.org/pdf/2107.03742v1.pdf | Grid Partitioned Attention: Efficient TransformerApproximation with Inductive Bias for High Resolution Detail Generation | Attention is a general reasoning mechanism than can flexibly deal with image information, but its memory requirements had made it so far impractical for high resolution image generation. We present Grid Partitioned Attention (GPA), a new approximate attention algorithm that leverages a sparse inductive bias for higher ... | ['Roland Vollgraf', 'Christian Bracher', 'Gökhan Yildirim', 'Nikolay Jetchev'] | 2021-07-08 | null | null | null | null | ['deep-attention', 'conditional-image-generation', 'deep-attention'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 4.19675976e-01 5.82588017e-01 -1.12979330e-01 -1.89398915e-01
-1.13645601e+00 -2.57685125e-01 6.06525779e-01 -3.47914129e-01
-2.24438652e-01 7.73590028e-01 4.28644359e-01 -1.20224208e-01
-7.84067810e-02 -1.10572875e+00 -1.16716337e+00 -4.12059516e-01
-1.23323880e-01 9.26845491e-01 2.43718520e-01 -3.31304133... | [11.330978393554688, -0.33600690960884094] |
2ab03902-bf2d-49b9-874b-99130bbafc0d | multi-channel-target-speaker-extraction-with | 2302.07928 | null | https://arxiv.org/abs/2302.07928v1 | https://arxiv.org/pdf/2302.07928v1.pdf | Multi-Channel Target Speaker Extraction with Refinement: The WavLab Submission to the Second Clarity Enhancement Challenge | This paper describes our submission to the Second Clarity Enhancement Challenge (CEC2), which consists of target speech enhancement for hearing-aid (HA) devices in noisy-reverberant environments with multiple interferers such as music and competing speakers. Our approach builds upon the powerful iterative neural/beamfo... | ['Nobutaka Ono', 'Manuel Pariente', 'Shinji Watanabe', 'Yoshiki Masuyama', 'Zhong-Qiu Wang', 'Samuele Cornell'] | 2023-02-15 | null | null | null | null | ['target-speaker-extraction', 'speech-enhancement', 'speaker-separation'] | ['audio', 'speech', 'speech'] | [ 3.50481242e-01 -7.61183947e-02 5.07739127e-01 4.81280386e-02
-1.62442434e+00 -3.05399328e-01 1.60038143e-01 -2.17389241e-01
-4.74276811e-01 5.88704050e-01 7.11138666e-01 -4.48748767e-01
-2.75310189e-01 -7.37973303e-02 -3.64782721e-01 -7.85992026e-01
-3.27258527e-01 -1.49852738e-01 2.57099956e-01 -4.24059093... | [15.089743614196777, 5.836637496948242] |
0e8cce1f-3bb6-4454-88e9-20f52a721c56 | multiple-angles-of-arrival-estimation-using | 2002.00541 | null | https://arxiv.org/abs/2002.00541v1 | https://arxiv.org/pdf/2002.00541v1.pdf | Multiple Angles of Arrival Estimation using Neural Networks | MUltiple SIgnal Classification (MUSIC) and Estimation of signal parameters via rotational via rotational invariance (ESPRIT) has been widely used in super resolution direction of arrival estimation (DoA) in both Uniform Linear Arrays (ULA) or Uniform Circular Arrays (UCA). However, problems become challenging when the ... | ['Jianyuan Yu'] | 2020-02-03 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 2.49197334e-01 -9.34637725e-01 1.85576126e-01 -6.52516335e-02
-7.21874714e-01 -6.46338403e-01 -1.87926670e-03 -4.42052275e-01
-1.38080195e-01 6.75965369e-01 3.60407591e-01 -6.55474439e-02
-8.05328488e-01 -5.40464759e-01 -3.42198789e-01 -1.00571263e+00
-4.82231438e-01 -2.53283679e-01 -4.23793703e-01 6.09305501... | [6.523139476776123, 1.2866038084030151] |
da350003-e487-4771-9cd2-ab6adf4247ef | geometric-laplacian-eigenmap-embedding | 1905.09763 | null | https://arxiv.org/abs/1905.09763v2 | https://arxiv.org/pdf/1905.09763v2.pdf | GLEE: Geometric Laplacian Eigenmap Embedding | Graph embedding seeks to build a low-dimensional representation of a graph G. This low-dimensional representation is then used for various downstream tasks. One popular approach is Laplacian Eigenmaps, which constructs a graph embedding based on the spectral properties of the Laplacian matrix of G. The intuition behind... | ['Kevin S. Chan', 'Tina Eliassi-Rad', 'Leo Torres'] | 2019-05-23 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [-2.49004453e-01 5.29523551e-01 -2.05243781e-01 7.90066179e-03
-6.36374354e-02 -7.39589989e-01 4.15375113e-01 3.36144626e-01
1.35300428e-01 1.30563676e-01 4.94171947e-01 -5.25696576e-01
-3.26753497e-01 -9.81483400e-01 -3.95259202e-01 -5.55375874e-01
-5.43333173e-01 3.68845820e-01 -2.28387956e-02 -2.52877921... | [7.129220962524414, 5.837766170501709] |
1ffbe726-dd2f-4925-97f0-669bf141d191 | a-tensor-based-sub-mode-coordinate-algorithm | 1805.07979 | null | http://arxiv.org/abs/1805.07979v1 | http://arxiv.org/pdf/1805.07979v1.pdf | A Tensor-Based Sub-Mode Coordinate Algorithm for Stock Prediction | The investment on the stock market is prone to be affected by the Internet.
For the purpose of improving the prediction accuracy, we propose a multi-task
stock prediction model that not only considers the stock correlations but also
supports multi-source data fusion. Our proposed model first utilizes tensor to
integrat... | ['Jieyun Huang', 'Jialai Zhang', 'Yunjia Zhang', 'Xi Zhang'] | 2018-05-21 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-9.14225340e-01 -8.50171804e-01 -1.32832453e-01 -1.23669855e-01
-1.77416161e-01 -2.62698025e-01 3.60039592e-01 -2.11931154e-01
-2.47579604e-01 4.36388046e-01 7.71351337e-01 2.44572327e-01
-3.51914674e-01 -1.03671885e+00 -2.65190721e-01 -7.83952415e-01
-1.98333729e-02 -1.28735319e-01 2.72992462e-01 -5.47149360... | [4.538118839263916, 4.181317329406738] |
85743fc2-7625-4eb4-9fc5-1427c6c07590 | a-deep-convolutional-neural-network-using | 1610.09736 | null | http://arxiv.org/abs/1610.09736v3 | http://arxiv.org/pdf/1610.09736v3.pdf | A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction | Due to the potential risk of inducing cancers, radiation dose of X-ray CT
should be reduced for routine patient scanning. However, in low-dose X-ray CT,
severe artifacts usually occur due to photon starvation, beamhardening, etc,
which decrease the reliability of diagnosis. Thus, high quality reconstruction
from low-do... | ['junhong Min', 'Jong Chul Ye', 'Eunhee Kang'] | 2016-10-31 | null | null | null | null | ['low-dose-x-ray-ct-reconstruction'] | ['medical'] | [ 2.47089893e-01 -2.14484259e-01 2.24908274e-02 -3.61167729e-01
-9.24774051e-01 8.72175917e-02 4.55769300e-02 1.27401158e-01
-5.72311044e-01 3.93387824e-01 4.37726378e-01 -2.26397008e-01
-3.87287289e-01 -1.01680470e+00 -6.40524328e-01 -1.01824117e+00
6.27754927e-02 9.87309217e-02 1.74099773e-01 -3.31239879... | [13.457512855529785, -2.5393857955932617] |
f07e9216-55dd-42ef-b56c-fb3abae35d80 | image-restoration-from-parametric | 2005.14036 | null | https://arxiv.org/abs/2005.14036v2 | https://arxiv.org/pdf/2005.14036v2.pdf | Image Restoration from Parametric Transformations using Generative Models | When images are statistically described by a generative model we can use this information to develop optimum techniques for various image restoration problems as inpainting, super-resolution, image coloring, generative model inversion, etc. With the help of the generative model it is possible to formulate, in a natural... | ['Kalliopi Basioti', 'George V. Moustakides'] | 2020-05-27 | null | null | null | null | ['transparency-separation', 'blind-image-deblurring'] | ['computer-vision', 'computer-vision'] | [ 6.36112452e-01 6.02807365e-02 2.45935544e-01 -1.35754913e-01
-7.22467721e-01 -3.66787702e-01 7.83813119e-01 -2.94193476e-01
-4.24623221e-01 9.16496038e-01 1.18914284e-01 1.39318228e-01
-2.79701054e-01 -7.88106620e-01 -6.25557125e-01 -1.07816410e+00
3.94184291e-01 7.83526003e-01 1.69817284e-01 -2.82985955... | [11.511175155639648, -2.46928334236145] |
bb60d39d-f3e5-4146-8549-ca0efc8091eb | robust-learning-at-noisy-labeled-medical | 1901.07759 | null | http://arxiv.org/abs/1901.07759v2 | http://arxiv.org/pdf/1901.07759v2.pdf | Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification | Deep neural networks (DNNs) have achieved great success in a wide variety of
medical image analysis tasks. However, these achievements indispensably rely on
the accurately-annotated datasets. If with the noisy-labeled images, the
training procedure will immediately encounter difficulties, leading to a
suboptimal classi... | ['Cheng Xue', 'Qi Dou', 'Hao Chen', 'Pheng Ann Heng', 'Xueying Shi'] | 2019-01-23 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 3.79471272e-01 3.33018929e-01 -2.29013696e-01 -5.47089398e-01
-8.91457498e-01 1.06541999e-01 8.75876546e-02 2.70761162e-01
-5.84484279e-01 9.26931679e-01 -9.45230499e-02 -7.67805949e-02
-5.58797777e-01 -7.21170008e-01 -2.45799333e-01 -1.08016264e+00
2.68126458e-01 2.92609841e-01 -1.11264512e-01 2.77636260... | [14.738873481750488, -2.2300031185150146] |
537d1927-576c-4f91-a3d3-7fbf96d4fed8 | learning-large-neighborhood-search-for | 2302.13797 | null | https://arxiv.org/abs/2302.13797v1 | https://arxiv.org/pdf/2302.13797v1.pdf | Learning Large Neighborhood Search for Vehicle Routing in Airport Ground Handling | Dispatching vehicle fleets to serve flights is a key task in airport ground handling (AGH). Due to the notable growth of flights, it is challenging to simultaneously schedule multiple types of operations (services) for a large number of flights, where each type of operation is performed by one specific vehicle fleet. T... | ['Zhenghua Chen', 'Jie Zhang', 'Wen Song', 'Zhiguang Cao', 'Yaoxin Wu', 'Jianan Zhou'] | 2023-02-27 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [ 3.35544534e-02 1.84255153e-01 -2.82345235e-01 -7.23278448e-02
-6.28975570e-01 -8.32195997e-01 -7.55788237e-02 1.90519080e-01
-2.70038366e-01 8.47700775e-01 -4.06121016e-01 -6.06589377e-01
-7.94042885e-01 -1.01433611e+00 -9.32453096e-01 -5.83548605e-01
-5.54228127e-01 1.13184106e+00 2.50379741e-02 -6.61594272... | [5.15142297744751, 2.7994680404663086] |
962499f2-47d6-4423-9e9e-098594c45851 | segment-mask-and-predict-augmenting-chinese | null | null | https://aclanthology.org/2021.emnlp-main.158 | https://aclanthology.org/2021.emnlp-main.158.pdf | Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision | Recent state-of-the-art (SOTA) effective neural network methods and fine-tuning methods based on pre-trained models (PTM) have been used in Chinese word segmentation (CWS), and they achieve great results. However, previous works focus on training the models with the fixed corpus at every iteration. The intermediate gen... | ['Maosong Sun', 'Huanbo Luan', 'Ji Zhang', 'Kaiyu Huang', 'Gang Chen', 'Yuanhang Zheng', 'Yang Liu', 'Mieradilijiang Maimaiti'] | null | null | null | null | emnlp-2021-11 | ['chinese-word-segmentation'] | ['natural-language-processing'] | [ 4.39346313e-01 5.15659451e-02 -2.41059065e-01 -5.90518892e-01
-1.01409864e+00 -2.61323482e-01 1.25041068e-01 -1.36077553e-01
-8.65301013e-01 5.16986609e-01 1.48629509e-02 -5.42844236e-01
3.85909259e-01 -6.86699510e-01 -4.68512505e-01 -5.40801823e-01
5.00724614e-01 5.66954672e-01 7.37196326e-01 -2.35678524... | [10.02922534942627, 10.113456726074219] |
6d748cf7-9180-4f50-b93a-d2f0a657e2f9 | human-and-machine-practicable-mechanisms-for | 2302.13937 | null | https://arxiv.org/abs/2302.13937v2 | https://arxiv.org/pdf/2302.13937v2.pdf | Human and Machine Intelligence in n-Person Games with Partial Knowledge | In this note, I introduce a new framework called n-person games with partial knowledge, in which players have only limited knowledge about the aspects of the game -- including actions, outcomes, and other players. For example, playing an actual game of chess is a game of partial knowledge. To analyze these games, I int... | ['Mehmet S. Ismail'] | 2023-02-27 | null | null | null | null | ['game-of-chess'] | ['playing-games'] | [-6.92843422e-02 2.66079694e-01 2.64873654e-01 2.55518943e-01
5.24228290e-02 -8.90777469e-01 3.43071014e-01 -2.52575781e-02
-6.56110108e-01 7.20456839e-01 -3.26033503e-01 -3.10161561e-01
-6.63176179e-01 -1.28551483e+00 -8.80021155e-02 -2.95438051e-01
1.12340907e-02 4.46973324e-01 4.42023814e-01 -9.50323164... | [3.4410412311553955, 1.4797251224517822] |
4855306e-bfc5-4033-944f-463413b4bcae | any-motion-detector-learning-class-agnostic | 2004.11647 | null | https://arxiv.org/abs/2004.11647v1 | https://arxiv.org/pdf/2004.11647v1.pdf | Any Motion Detector: Learning Class-agnostic Scene Dynamics from a Sequence of LiDAR Point Clouds | Object detection and motion parameters estimation are crucial tasks for self-driving vehicle safe navigation in a complex urban environment. In this work we propose a novel real-time approach of temporal context aggregation for motion detection and motion parameters estimation based on 3D point cloud sequence. We intro... | ['Viacheslav Murashkin', 'Artem Filatov', 'Andrey Rykov'] | 2020-04-24 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [-2.52068967e-01 -3.65946978e-01 1.91063151e-01 -4.50363249e-01
-3.01981360e-01 -4.83788610e-01 8.05882335e-01 -3.61136526e-01
-8.05953085e-01 4.66267437e-01 1.01063594e-01 -5.16226351e-01
2.56854534e-01 -7.96230376e-01 -6.95191801e-01 -6.71902418e-01
-2.17124417e-01 5.24481356e-01 8.40904534e-01 -3.07903439... | [8.20074462890625, -1.5871716737747192] |
69900121-95ba-4691-ab60-75ed5f77334a | counterfactual-data-augmentation-via | 2210.16838 | null | https://arxiv.org/abs/2210.16838v1 | https://arxiv.org/pdf/2210.16838v1.pdf | Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues | The construction of open-domain dialogue systems requires high-quality dialogue datasets. The dialogue data admits a wide variety of responses for a given dialogue history, especially responses with different semantics. However, collecting high-quality such a dataset in most scenarios is labor-intensive and time-consum... | ['Jie zhou', 'Yang Feng', 'Jinchao Zhang', 'Jiao Ou'] | 2022-10-30 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 3.40551525e-01 6.25698924e-01 -2.18215548e-02 -7.29217708e-01
-1.04706979e+00 -7.55123138e-01 6.86309159e-01 4.47725914e-02
-4.29003537e-01 1.20313907e+00 1.09490705e+00 -1.57210708e-01
4.59866673e-01 -8.57146740e-01 -1.63101584e-01 -1.52010769e-01
4.54832643e-01 8.50653648e-01 -1.49584934e-02 -8.03035915... | [12.695381164550781, 8.155467987060547] |
a7ceddef-5569-44a6-8b62-6da137ed6876 | a-survey-on-multimodal-large-language-models | 2306.13549 | null | https://arxiv.org/abs/2306.13549v1 | https://arxiv.org/pdf/2306.13549v1.pdf | A Survey on Multimodal Large Language Models | Multimodal Large Language Model (MLLM) recently has been a new rising research hotspot, which uses powerful Large Language Models (LLMs) as a brain to perform multimodal tasks. The surprising emergent capabilities of MLLM, such as writing stories based on images and OCR-free math reasoning, are rare in traditional meth... | ['Enhong Chen', 'Tong Xu', 'Xing Sun', 'Ke Li', 'Sirui Zhao', 'Chaoyou Fu', 'Shukang Yin'] | 2023-06-23 | null | null | null | null | ['optical-character-recognition', 'visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'computer-vision', 'reasoning'] | [-5.96845895e-02 9.67674404e-02 -2.92606384e-01 6.36399314e-02
-6.89093471e-01 -6.55045629e-01 7.77792811e-01 2.59629130e-01
-2.21057430e-01 5.74449003e-01 3.75025123e-01 -5.89565992e-01
-4.01867144e-02 -6.64829016e-01 -9.15711403e-01 -5.01126289e-01
8.37390870e-02 4.20387506e-01 -1.52082428e-01 -3.75313491... | [10.940729141235352, 1.7333155870437622] |
e4d77c9e-d90c-4a29-a63a-d3ac39ba94d3 | to-aggregate-or-not-learning-with-separate | 2206.07181 | null | https://arxiv.org/abs/2206.07181v2 | https://arxiv.org/pdf/2206.07181v2.pdf | To Aggregate or Not? Learning with Separate Noisy Labels | The rawly collected training data often comes with separate noisy labels collected from multiple imperfect annotators (e.g., via crowdsourcing). A typical way of using these separate labels is to first aggregate them into one and apply standard training methods. The literature has also studied extensively on effective ... | ['Yang Liu', 'Abhishek Kumar', 'Ehsan Amid', 'Tianyi Luo', 'Zhaowei Zhu', 'Jiaheng Wei'] | 2022-06-14 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 2.61030883e-01 2.95630962e-01 -1.96603283e-01 -5.15331805e-01
-1.42628193e+00 -8.68830740e-01 2.81169057e-01 4.38214958e-01
-7.15333223e-01 1.09290504e+00 -2.50893176e-01 -1.82148710e-01
1.30097181e-01 -3.72293860e-01 -4.94460255e-01 -9.91554320e-01
4.03874546e-01 6.86763644e-01 1.30451927e-02 2.41189584... | [9.579669952392578, 4.563762664794922] |
024d81aa-bf20-4926-b860-d26043fe608a | stereo-r-cnn-based-3d-object-detection-for | 1902.09738 | null | http://arxiv.org/abs/1902.09738v2 | http://arxiv.org/pdf/1902.09738v2.pdf | Stereo R-CNN based 3D Object Detection for Autonomous Driving | We propose a 3D object detection method for autonomous driving by fully
exploiting the sparse and dense, semantic and geometry information in stereo
imagery. Our method, called Stereo R-CNN, extends Faster R-CNN for stereo
inputs to simultaneously detect and associate object in left and right images.
We add extra branc... | ['Shaojie Shen', 'Xiaozhi Chen', 'Peiliang Li'] | 2019-02-26 | stereo-r-cnn-based-3d-object-detection-for-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Li_Stereo_R-CNN_Based_3D_Object_Detection_for_Autonomous_Driving_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Stereo_R-CNN_Based_3D_Object_Detection_for_Autonomous_Driving_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-object-detection-from-stereo-images'] | ['computer-vision'] | [-1.36594459e-01 7.01423213e-02 -6.13542609e-02 -4.56393182e-01
-6.17484212e-01 -7.86634445e-01 5.47438085e-01 -3.03063393e-01
-4.99504447e-01 2.42560819e-01 -3.66329215e-02 -1.71754614e-01
3.21249962e-01 -6.12849057e-01 -1.19277489e+00 -3.19691896e-01
3.28905731e-01 6.57940567e-01 7.44328916e-01 -2.85106242... | [7.821316242218018, -2.5995378494262695] |
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