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4f80ee38-d390-4d90-bcf4-dddaebd05e2a | deep-koopman-operator-based-model-predictive | 2103.14321 | null | https://arxiv.org/abs/2103.14321v5 | https://arxiv.org/pdf/2103.14321v5.pdf | Online Learning Koopman operator for closed-loop electrical neurostimulation in epilepsy | Electrical neuromodulation as a palliative treatment has been increasingly used in the control of epilepsy. However, current neuromodulations commonly implement predetermined actuation strategies and lack the capability of self-adaptively adjusting stimulation inputs. In this work, rooted in optimal control theory, we ... | ['Quanying Liu', 'Jingwei Qiu', 'Keyin Liu', 'Zixiang Luo', 'Zhichao Liang'] | 2021-03-26 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 4.76117395e-02 3.18592668e-01 -8.47577453e-02 4.88503605e-01
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-3.31195086e-01 9.70847979e-02 -2.70796984e-01 -4.14894342... | [6.5599822998046875, 3.372737407684326] |
76203f42-f646-4077-a348-b22bfd54f116 | using-generalized-additive-models-to | null | null | https://link.springer.com/article/10.1007/s10877-022-00873-7 | https://link.springer.com/content/pdf/10.1007/s10877-022-00873-7.pdf | Using generalized additive models to decompose time series and waveforms, and dissect heart-lung interaction physiology | Common physiological time series and waveforms are composed of repeating cardiac and respiratory cycles. Often, the cardiac effect is the primary interest, but for, e.g., fluid responsiveness prediction, the respiratory effect on arterial blood pressure also convey important information. In either case, it is relevant ... | ['Simon T Vistisen', 'Gavin L Simpson', 'Johannes Enevoldsen'] | 2022-06-13 | null | null | null | journal-of-clinical-monitoring-and-computing | ['medical-waveform-analysis', 'additive-models'] | ['medical', 'methodology'] | [ 2.06387550e-01 -1.48540452e-01 1.34421960e-01 -2.20715478e-01
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-5.71137786e-01 2.08168089e-01 1.96140376e-03 1.37106448... | [13.92120361328125, 2.9482219219207764] |
13768a75-0354-4ef5-a9ff-8ed4126ab7d4 | dual-distribution-alignment-network-for | 2007.13249 | null | https://arxiv.org/abs/2007.13249v1 | https://arxiv.org/pdf/2007.13249v1.pdf | Dual Distribution Alignment Network for Generalizable Person Re-Identification | Domain generalization (DG) serves as a promising solution to handle person Re-Identification (Re-ID), which trains the model using labels from the source domain alone, and then directly adopts the trained model to the target domain without model updating. However, existing DG approaches are usually disturbed by serious... | ['Peixian Chen', 'Jianzhuang Liu', 'Feng Zheng', 'Qi Tian', 'Rongrong Ji', 'Pingyang Dai'] | 2020-07-27 | null | null | null | null | ['generalizable-person-re-identification'] | ['computer-vision'] | [ 2.18824074e-01 -4.31036592e-01 -2.25294217e-01 -5.18194616e-01
-4.98497784e-01 -7.41880417e-01 7.67506599e-01 -9.73583758e-02
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4.89844382e-01 6.01689935e-01 9.27219018e-02 -1.90657198... | [14.71580982208252, 1.0890132188796997] |
688231f7-0c71-4e48-a935-f53218e1a531 | assessing-the-eligibility-of-backtranslated | null | null | https://aclanthology.org/2021.ranlp-main.35 | https://aclanthology.org/2021.ranlp-main.35.pdf | Assessing the Eligibility of Backtranslated Samples Based on Semantic Similarity for the Paraphrase Identification Task | In the domain of natural language augmentation, the eligibility of generated samples remains not well understood. To gather insights around this eligibility issue, we apply a transformer-based similarity calculation within the BET framework based on backtranslation, in the context of automated paraphrase detection. Whi... | ['Hadi Abdi Ghavidel', 'Jean-Philippe Corbeil'] | null | null | https://aclanthology.org/2021.ranlp-1.35 | https://aclanthology.org/2021.ranlp-1.35.pdf | ranlp-2021-9 | ['paraphrase-identification'] | ['natural-language-processing'] | [ 4.40746725e-01 4.41620171e-01 -2.64403254e-01 -1.29929394e-01
-1.03226304e+00 -6.37053430e-01 9.22654867e-01 5.36485314e-01
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2.40877479e-01 5.75704932e-01 1.94244087e-01 -7.17854559... | [11.204184532165527, 9.063054084777832] |
1b4857bf-55ea-43f7-a764-e8d48732f5fb | bieru-bidirectional-emotional-recurrent-unit | 2006.00492 | null | https://arxiv.org/abs/2006.00492v3 | https://arxiv.org/pdf/2006.00492v3.pdf | BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis | Sentiment analysis in conversations has gained increasing attention in recent years for the growing amount of applications it can serve, e.g., sentiment analysis, recommender systems, and human-robot interaction. The main difference between conversational sentiment analysis and single sentence sentiment analysis is the... | ['Wei Shao', 'Shaoxiong Ji', 'Erik Cambria', 'Wei Li'] | 2020-05-31 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 2.00606257e-01 -1.45149782e-01 -1.98171213e-01 -8.28390360e-01
-5.76261044e-01 -5.46783984e-01 6.57909513e-01 2.61902660e-02
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4.99490410e-01 1.36257887e-01 -2.70481914e-01 -6.57272637... | [12.966940879821777, 6.159540176391602] |
7420f263-e0c4-402e-b41a-299d84f67184 | do-neural-language-representations-learn | 1908.02899 | null | https://arxiv.org/abs/1908.02899v1 | https://arxiv.org/pdf/1908.02899v1.pdf | Do Neural Language Representations Learn Physical Commonsense? | Humans understand language based on the rich background knowledge about how the physical world works, which in turn allows us to reason about the physical world through language. In addition to the properties of objects (e.g., boats require fuel) and their affordances, i.e., the actions that are applicable to them (e.g... | ['Ari Holtzman', 'Yejin Choi', 'Maxwell Forbes'] | 2019-08-08 | null | null | null | null | ['physical-commonsense-reasoning'] | ['reasoning'] | [ 6.92566112e-02 2.90998310e-01 -3.86466801e-01 -4.68734443e-01
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1.00398779e-01 2.80830920e-01 1.55209303e-01 -5.01826406... | [9.626205444335938, 7.2393598556518555] |
627567ed-9675-4354-960e-1f19ffbca710 | active-inference-for-autonomous-decision | 2209.09185 | null | https://arxiv.org/abs/2209.09185v2 | https://arxiv.org/pdf/2209.09185v2.pdf | Active Inference for Autonomous Decision-Making with Contextual Multi-Armed Bandits | In autonomous robotic decision-making under uncertainty, the tradeoff between exploitation and exploration of available options must be considered. If secondary information associated with options can be utilized, such decision-making problems can often be formulated as contextual multi-armed bandits (CMABs). In this s... | ['Nisar Ahmed', 'Shohei Wakayama'] | 2022-09-19 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 3.45388174e-01 4.09253150e-01 -5.51877022e-01 -3.01059663e-01
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5.60045019e-02 4.36541885e-01 -3.78462791e-01 3.72980952... | [4.580745220184326, 3.1547744274139404] |
91ea7e91-104f-4e05-acb3-3bc45597a46a | toward-contextual-valence-shifters-in | null | null | https://aclanthology.org/O17-1016 | https://aclanthology.org/O17-1016.pdf | Toward Contextual Valence Shifters in Vietnamese Reviews | null | ['Tuoi Thi Phan', 'Thien Khai Tran'] | 2017-11-01 | toward-contextual-valence-shifters-in-1 | https://aclanthology.org/O17-1016 | https://aclanthology.org/O17-1016.pdf | roclingijclclp-2017-11 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.3006486892700195, 3.709514617919922] |
d89512a4-4538-4fed-aabc-3118faea7db5 | generator-knows-what-discriminator-should | 2207.13320 | null | https://arxiv.org/abs/2207.13320v1 | https://arxiv.org/pdf/2207.13320v1.pdf | Generator Knows What Discriminator Should Learn in Unconditional GANs | Recent methods for conditional image generation benefit from dense supervision such as segmentation label maps to achieve high-fidelity. However, it is rarely explored to employ dense supervision for unconditional image generation. Here we explore the efficacy of dense supervision in unconditional generation and find g... | ['Yunjey Choi', 'Jung-Woo Ha', 'Seonghyeon Kim', 'Junho Kim', 'Hyunsu Kim', 'Gayoung Lee'] | 2022-07-27 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 5.13686776e-01 5.05318582e-01 -3.69258285e-01 -4.03469741e-01
-8.92173886e-01 -4.46733057e-01 8.97488356e-01 -5.48607409e-01
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4.31441545e-01 2.30920970e-01 -1.36250138e-01 -7.26626590... | [11.526966094970703, -0.308260053396225] |
1d98a0d7-e7df-4d5e-bc40-5006ee94aefd | lightweight-and-unobtrusive-privacy | 1912.09859 | null | https://arxiv.org/abs/1912.09859v3 | https://arxiv.org/pdf/1912.09859v3.pdf | Lightweight and Unobtrusive Data Obfuscation at IoT Edge for Remote Inference | Executing deep neural networks for inference on the server-class or cloud backend based on data generated at the edge of Internet of Things is desirable due primarily to the limited compute power of edge devices and the need to protect the confidentiality of the inference neural networks. However, such a remote inferen... | ['Peng Cheng', 'Rui Tan', 'Linshan Jiang', 'Mengyao Zheng', 'Dixing Xu', 'Chaojie Gu'] | 2019-12-20 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-1.31188273e-01 1.91616565e-01 5.33946939e-02 -4.78595465e-01
-4.54695344e-01 -8.95286620e-01 2.09886745e-01 -2.94134289e-01
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2.48618707e-01 3.39097157e-02 5.85816801e-02 4.78909314... | [5.865087032318115, 6.845920562744141] |
7628660c-f869-4680-9e78-190a4429c3c2 | multi-modality-in-music-predicting-emotion-in | 2302.13321 | null | https://arxiv.org/abs/2302.13321v1 | https://arxiv.org/pdf/2302.13321v1.pdf | Multi-Modality in Music: Predicting Emotion in Music from High-Level Audio Features and Lyrics | This paper aims to test whether a multi-modal approach for music emotion recognition (MER) performs better than a uni-modal one on high-level song features and lyrics. We use 11 song features retrieved from the Spotify API, combined lyrics features including sentiment, TF-IDF, and Anew to predict valence and arousal (R... | ['Ninell Oldenburg', 'Yana Nikolova', 'Tibor Krols'] | 2023-02-26 | null | null | null | null | ['music-emotion-recognition'] | ['music'] | [-3.36000890e-01 -4.41651523e-01 -3.07589352e-01 -1.89348027e-01
-1.02147806e+00 -9.39620972e-01 4.56580132e-01 7.06971884e-02
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-2.30963528e-01 -4.14513886e-01 -4.88608703e-02 -3.24155748e-01
-1.37395710e-01 -7.07991049e-02 -4.74848360e-01 -5.42314887... | [15.86853313446045, 5.204485893249512] |
72d3b87a-f639-472c-be32-fcbe213e1a89 | multi-lingual-discourse-segmentation-and | null | null | https://aclanthology.org/2021.disrpt-1.3 | https://aclanthology.org/2021.disrpt-1.3.pdf | Multi-lingual Discourse Segmentation and Connective Identification: MELODI at Disrpt2021 | We present an approach for discourse segmentation and discourse connective identification, both at the sentence and document level, within the Disrpt 2021 shared task, a multi-lingual and multi-formalism evaluation campaign. Building on the most successful architecture from the 2019 similar shared task, we leverage dat... | ['Chloé Braud', 'Philippe Muller', 'Morteza Kamaladdini Ezzabady'] | null | null | null | null | emnlp-disrpt-2021-11 | ['discourse-segmentation', 'discourse-parsing', 'connective-detection'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.23133087e-01 5.56283653e-01 -2.43508324e-01 -2.75949448e-01
-1.47610068e+00 -1.04842889e+00 1.06122041e+00 2.26606414e-01
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1.96915314e-01 -1.62645057e-01 -4.63711500e-01 -2.65438169e-01
-3.76983918e-02 8.04795384e-01 5.02913535e-01 -6.36333704... | [10.846380233764648, 9.471080780029297] |
843c813c-970b-4d55-bebe-ceea75b0f16b | leveraging-schema-labels-to-enhance-dataset | 2001.10112 | null | https://arxiv.org/abs/2001.10112v1 | https://arxiv.org/pdf/2001.10112v1.pdf | Leveraging Schema Labels to Enhance Dataset Search | A search engine's ability to retrieve desirable datasets is important for data sharing and reuse. Existing dataset search engines typically rely on matching queries to dataset descriptions. However, a user may not have enough prior knowledge to write a query using terms that match with description text.We propose a nov... | ['Zhiyu Chen', 'Jeff Heflin', 'Brian D. Davison', 'Haiyan Jia'] | 2020-01-27 | null | null | null | null | ['table-retrieval'] | ['natural-language-processing'] | [ 2.68652886e-01 -4.79473397e-02 -7.03749776e-01 -6.91664815e-01
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2.75188029e-01 7.51879275e-01 4.68893319e-01 -2.46437028... | [9.73427677154541, 7.934049129486084] |
a102c6fa-f2b8-49f1-a43a-b19713b85b54 | multi-scale-contrastive-co-training-for-event | 2209.00568 | null | https://arxiv.org/abs/2209.00568v1 | https://arxiv.org/pdf/2209.00568v1.pdf | Multi-Scale Contrastive Co-Training for Event Temporal Relation Extraction | Extracting temporal relationships between pairs of events in texts is a crucial yet challenging problem for natural language understanding. Depending on the distance between the events, models must learn to differently balance information from local and global contexts surrounding the event pair for temporal relation p... | ['Carolyn Rose', 'Chunxiao Zhou', 'Aakanksha Naik', 'Luke Breitfeller', 'Hao-Ren Yao'] | 2022-09-01 | null | null | null | null | ['temporal-relation-extraction'] | ['natural-language-processing'] | [-3.58792841e-02 1.36499897e-01 -3.09061348e-01 -5.48546553e-01
-6.60858631e-01 -4.75886822e-01 9.71652508e-01 6.93375111e-01
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230a405f-35db-4c19-871e-53d1d111d3d4 | handling-class-imbalance-in-low-resource | 2010.15090 | null | https://arxiv.org/abs/2010.15090v1 | https://arxiv.org/pdf/2010.15090v1.pdf | Handling Class Imbalance in Low-Resource Dialogue Systems by Combining Few-Shot Classification and Interpolation | Utterance classification performance in low-resource dialogue systems is constrained by an inevitably high degree of data imbalance in class labels. We present a new end-to-end pairwise learning framework that is designed specifically to tackle this phenomenon by inducing a few-shot classification capability in the utt... | ['Eric Fosler-Lussier', 'Vishal Sunder'] | 2020-10-28 | null | null | null | null | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 5.08128285e-01 1.00053596e+00 4.40934785e-02 -9.94242430e-01
-1.12956965e+00 -2.29796052e-01 5.39404988e-01 3.31678241e-01
-5.59994996e-01 1.01101017e+00 6.04119658e-01 -1.46944284e-01
5.71629182e-02 -3.25114787e-01 5.73685355e-02 -5.39662957e-01
-1.57088697e-01 9.24456060e-01 -3.98779422e-01 -8.20951819... | [12.808146476745605, 7.8493499755859375] |
e1ea0ccc-50d2-42e7-81ae-6a935a019ca9 | learning-common-rationale-to-improve-self | 2303.01669 | null | https://arxiv.org/abs/2303.01669v1 | https://arxiv.org/pdf/2303.01669v1.pdf | Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems | Self-supervised learning (SSL) strategies have demonstrated remarkable performance in various recognition tasks. However, both our preliminary investigation and recent studies suggest that they may be less effective in learning representations for fine-grained visual recognition (FGVR) since many features helpful for o... | ['Lingqiao Liu', 'Anton Van Den Hengel', 'Yangyang Shu'] | 2023-03-03 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shu_Learning_Common_Rationale_To_Improve_Self-Supervised_Representation_for_Fine-Grained_Visual_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shu_Learning_Common_Rationale_To_Improve_Self-Supervised_Representation_for_Fine-Grained_Visual_CVPR_2023_paper.pdf | cvpr-2023-1 | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 2.93242961e-01 -1.24988012e-01 -2.33484656e-01 -4.62279052e-01
-6.27922833e-01 -4.12564993e-01 5.95968008e-01 2.94906974e-01
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1.09869830e-01 7.19229579e-02 7.76821494e-01 1.52741179... | [9.767681121826172, 1.7504488229751587] |
54fec154-b431-4826-8da9-70337f9a21e4 | improving-human-ai-collaboration-with | 2301.06937 | null | https://arxiv.org/abs/2301.06937v1 | https://arxiv.org/pdf/2301.06937v1.pdf | Improving Human-AI Collaboration With Descriptions of AI Behavior | People work with AI systems to improve their decision making, but often under- or over-rely on AI predictions and perform worse than they would have unassisted. To help people appropriately rely on AI aids, we propose showing them behavior descriptions, details of how AI systems perform on subgroups of instances. We te... | ['Jason I. Hong', 'Adam Perer', 'Ángel Alexander Cabrera'] | 2023-01-06 | null | null | null | null | ['satellite-image-classification'] | ['computer-vision'] | [-1.01419717e-01 3.48760515e-01 -1.46146446e-01 -4.87287939e-01
1.87979341e-01 -3.51526707e-01 4.50140923e-01 3.08750361e-01
-6.21125638e-01 4.94781733e-01 3.19603741e-01 -4.18051690e-01
2.55939126e-01 -7.46635556e-01 3.61487977e-02 -1.92578211e-02
5.20972610e-01 8.52176189e-01 -1.39683947e-01 -5.32014251... | [9.116546630859375, 6.263894557952881] |
2272bc05-0a6a-45af-acca-d1c1ada6b715 | gans-for-semi-supervised-opinion-spam | 1903.08289 | null | https://arxiv.org/abs/1903.08289v2 | https://arxiv.org/pdf/1903.08289v2.pdf | GANs for Semi-Supervised Opinion Spam Detection | Online reviews have become a vital source of information in purchasing a service (product). Opinion spammers manipulate reviews, affecting the overall perception of the service. A key challenge in detecting opinion spam is obtaining ground truth. Though there exists a large set of reviews online, only a few of them hav... | ['Gray Stanton', 'Athirai A. Irissappane'] | 2019-03-19 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 2.94037640e-01 2.68093318e-01 -1.56869233e-01 -5.47610641e-01
-7.01408505e-01 -8.70982051e-01 7.00550854e-01 -2.62419432e-01
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5.55618048e-01 4.92368728e-01 6.90108836e-02 -6.74253941... | [7.8432159423828125, 10.012606620788574] |
096e4c4f-a6cd-48ff-91c0-2de638f60b93 | cogtree-cognition-tree-loss-for-unbiased | 2009.07526 | null | https://arxiv.org/abs/2009.07526v2 | https://arxiv.org/pdf/2009.07526v2.pdf | CogTree: Cognition Tree Loss for Unbiased Scene Graph Generation | Scene graphs are semantic abstraction of images that encourage visual understanding and reasoning. However, the performance of Scene Graph Generation (SGG) is unsatisfactory when faced with biased data in real-world scenarios. Conventional debiasing research mainly studies from the view of balancing data distribution o... | ['Qi Wu', 'Yujing Wang', 'Yuan Chai', 'Yue Hu', 'Jing Yu'] | 2020-09-16 | null | null | null | null | ['unbiased-scene-graph-generation'] | ['computer-vision'] | [ 7.04898909e-02 3.83885801e-01 -9.88095105e-02 -4.92116898e-01
-1.64550915e-01 -3.85944396e-01 6.56249523e-01 1.87692136e-01
9.28723216e-02 4.26716179e-01 5.71477175e-01 -2.15049312e-01
-3.41452777e-01 -9.38654423e-01 -6.40175879e-01 -6.03175879e-01
2.92348385e-01 7.06290722e-01 3.49440962e-01 -2.56056964... | [10.363999366760254, 1.8158448934555054] |
d12eeb77-3463-4ab4-acf3-bf99f3dccf1f | decop-a-multilingual-and-multi-domain-corpus | null | null | https://aclanthology.org/2020.lrec-1.178 | https://aclanthology.org/2020.lrec-1.178.pdf | DecOp: A Multilingual and Multi-domain Corpus For Detecting Deception In Typed Text | In recent years, the increasing interest in the development of automatic approaches for unmasking deception in online sources led to promising results. Nonetheless, among the others, two major issues remain still unsolved: the stability of classifiers performances across different domains and languages. Tackling these ... | ['Carlo Strapparava', 'Giuseppe Sartori', 'Ivano Lauriola', 'Fabio Aiolli', 'Pasquale Capuozzo'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['deception-detection'] | ['miscellaneous'] | [-1.40635163e-01 -2.71394759e-01 -1.75610900e-01 -5.09809375e-01
-1.08815157e+00 -1.04861224e+00 1.03287995e+00 4.05176193e-01
-4.91789103e-01 1.07834780e+00 1.05229877e-01 -1.79381341e-01
6.62088990e-02 -2.75483042e-01 -2.04453558e-01 -4.99208122e-01
3.89605463e-01 6.07021213e-01 5.25108911e-02 -2.05607533... | [8.22893238067627, 10.410019874572754] |
1f864052-f7df-4a53-8ccf-3003ba65503a | a-systematic-study-reveals-unexpected | null | null | https://aclanthology.org/2022.lrec-1.154 | https://aclanthology.org/2022.lrec-1.154.pdf | A Systematic Study Reveals Unexpected Interactions in Pre-Trained Neural Machine Translation | A significant challenge in developing translation systems for the world’s ∼7,000 languages is that very few have sufficient data for state-of-the-art techniques. Transfer learning is a promising direction for low-resource neural machine translation (NMT), but introduces many new variables which are often selected throu... | ['Janet Wiles', 'Ashleigh Richardson'] | null | null | null | null | lrec-2022-6 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 9.58102196e-02 -1.73797175e-01 -4.77457345e-01 -2.44750753e-01
-1.09694529e+00 -7.01880455e-01 9.05921876e-01 -1.26323402e-01
-9.19668376e-01 7.57770360e-01 4.40044343e-01 -9.37934101e-01
1.29502878e-01 -4.58823234e-01 -8.83441269e-01 -3.32366705e-01
3.01356584e-01 5.94184697e-01 -1.97416693e-01 -3.64178628... | [11.561208724975586, 10.270856857299805] |
fdf31892-6279-4da4-9807-ff35a2825829 | fever-basketball-a-complex-flexible-and | 2012.03204 | null | https://arxiv.org/abs/2012.03204v1 | https://arxiv.org/pdf/2012.03204v1.pdf | Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning | The development of deep reinforcement learning (DRL) has benefited from the emergency of a variety type of game environments where new challenging problems are proposed and new algorithms can be tested safely and quickly, such as Board games, RTS, FPS, and MOBA games. However, many existing environments lack complexity... | ['Chongjie Zhang', 'Changjie Fan', 'Tangjie Lv', 'Chunxu Ren', 'Yingfeng Chen', 'Yujing Hu', 'Hangtian Jia'] | 2020-12-06 | null | null | null | null | ['board-games'] | ['playing-games'] | [-5.50353229e-01 -3.46707016e-01 -6.76462948e-02 2.42200106e-01
-2.27244318e-01 -7.48838305e-01 3.82760078e-01 1.03930332e-01
-8.48295271e-01 1.23468411e+00 -2.00649157e-01 -2.88668126e-01
-6.38422489e-01 -8.88100684e-01 -6.13718331e-01 -7.61991560e-01
-6.79293633e-01 8.46332788e-01 7.27770329e-01 -1.07402062... | [3.6954216957092285, 1.7168048620224] |
c0250008-31c7-4fbe-bec1-e1564051a31b | making-small-language-models-better-few-shot | null | null | https://openreview.net/forum?id=ryDLEZuACp | https://openreview.net/pdf?id=ryDLEZuACp | Making Small Language Models Better Few-Shot Learners | Large-scale language models coupled with prompts have shown remarkable performance on few-shot learning. However, through systematic experiments, we find that the few-shot performance of small language models is poor, and using prompts on them brings fewer improvements than on larger ones. In this paper, we propose \te... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['sentence-classification'] | ['natural-language-processing'] | [ 1.20836511e-01 3.35945845e-01 -2.24547878e-01 -5.92620790e-01
-1.33658183e+00 -2.50667185e-01 5.89286566e-01 1.49012610e-01
-8.92160654e-01 7.72513211e-01 6.13027096e-01 -4.90519971e-01
1.51453704e-01 -6.88698530e-01 -5.23094594e-01 -3.04980516e-01
3.01170975e-01 6.28801048e-01 5.27172327e-01 -5.77802360... | [10.85290813446045, 8.117700576782227] |
ffd3ff10-4338-43c9-a5a5-3e619628067d | is-reinforcement-learning-not-for-natural | 2210.01241 | null | https://arxiv.org/abs/2210.01241v3 | https://arxiv.org/pdf/2210.01241v3.pdf | Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization | We tackle the problem of aligning pre-trained large language models (LMs) with human preferences. If we view text generation as a sequential decision-making problem, reinforcement learning (RL) appears to be a natural conceptual framework. However, using RL for LM-based generation faces empirical challenges, including ... | ['Yejin Choi', 'Hannaneh Hajishirzi', 'Christian Bauckhage', 'Rafet Sifa', 'Jack Hessel', 'Kianté Brantley', 'Prithviraj Ammanabrolu', 'Rajkumar Ramamurthy'] | 2022-10-03 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [ 3.77768010e-01 4.94126350e-01 -4.56108928e-01 1.58852562e-02
-1.37129295e+00 -9.45782065e-01 8.74146104e-01 -1.56947985e-01
-3.96554232e-01 1.17637002e+00 5.21082699e-01 -5.96350610e-01
2.85208523e-01 -5.67430556e-01 -6.94526136e-01 -4.65605199e-01
2.47045711e-01 1.02579498e+00 -4.09839243e-01 -5.07100701... | [11.79390811920166, 8.977591514587402] |
41299c26-53a0-4b81-bdd9-4d21a8058a59 | less-than-few-self-shot-video-instance | 2204.08874 | null | https://arxiv.org/abs/2204.08874v1 | https://arxiv.org/pdf/2204.08874v1.pdf | Less than Few: Self-Shot Video Instance Segmentation | The goal of this paper is to bypass the need for labelled examples in few-shot video understanding at run time. While proven effective, in many practical video settings even labelling a few examples appears unrealistic. This is especially true as the level of details in spatio-temporal video understanding and with it, ... | ['Cees G. M. Snoek', 'Pascal Mettes', 'Yuki M. Asano', 'Pengwan Yang'] | 2022-04-19 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 5.36689699e-01 3.07117134e-01 -4.89966929e-01 -3.94591600e-01
-1.33181059e+00 -6.01721048e-01 5.27157724e-01 3.04815378e-02
-4.28781778e-01 5.52821279e-01 1.61670417e-01 2.78419256e-02
-2.81029254e-01 -4.41801906e-01 -1.02829492e+00 -5.54681361e-01
-2.35311657e-01 5.50090909e-01 7.94188321e-01 1.29829794... | [8.782856941223145, 0.7546725273132324] |
efc4d488-1e87-4c0d-9a00-6b011472fdca | lower-bound-on-transmission-using-non-linear | null | null | https://ieeexplore.ieee.org/document/9018379 | https://ieeexplore.ieee.org/document/9018379 | Lower Bound on Transmission Using Non-Linear Bounding Function in Single Image Dehazing | The visibility of an image captured in poor weather (such as haze, fog, mist, smog) degrades due to scattering of light by atmospheric particles. Single image dehazing (SID) methods are used to restore visibility from a single hazy image. The SID is a challenging problem due to its ill-posed nature. Typically, the atmo... | ['Shashikala Tapaswi', 'Suresh Chandra Raikwar'] | 2020-02-28 | null | null | null | ieee-transaction-on-image-processing-2020-2 | ['image-dehazing', 'single-image-haze-removal'] | ['computer-vision', 'computer-vision'] | [ 2.79114306e-01 -5.18743694e-01 7.59672403e-01 -1.32648677e-01
-1.48650721e-01 -3.67465287e-01 3.04807276e-01 -1.69123635e-01
-4.61033940e-01 8.70717585e-01 -1.64544825e-02 -1.43967271e-01
-2.80516773e-01 -8.98928404e-01 -3.55669051e-01 -1.17756212e+00
-5.03380224e-02 -1.41617507e-01 6.26728475e-01 -3.43112946... | [10.838981628417969, -3.153571605682373] |
bb1dce93-d32d-4814-825c-a99686fecdf2 | geometry-based-multiple-camera-head-detection | 1808.00856 | null | http://arxiv.org/abs/1808.00856v1 | http://arxiv.org/pdf/1808.00856v1.pdf | Geometry-Based Multiple Camera Head Detection in Dense Crowds | This paper addresses the problem of head detection in crowded environments.
Our detection is based entirely on the geometric consistency across cameras
with overlapping fields of view, and no additional learning process is
required. We propose a fully unsupervised method for inferring scene and camera
geometry, in cont... | ['Sylvie Le Hégarat-Mascle', 'Emanuel Aldea', 'Nicola Pellicanò'] | 2018-08-02 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [ 6.28366461e-03 -5.82883954e-02 4.07494217e-01 -3.28689635e-01
-4.32488650e-01 -4.34801787e-01 5.35020292e-01 2.28811234e-01
-7.74139583e-01 7.21136272e-01 1.40631080e-01 -6.43856125e-03
4.95844930e-01 -7.61553407e-01 -6.19669914e-01 -6.56453311e-01
2.16380149e-01 4.07834947e-01 6.85716510e-01 5.11889048... | [7.322709560394287, -0.984245777130127] |
693742fb-6ddc-4bb0-83ff-a485431d98c5 | learning-to-select-from-multiple-options | 2212.00301 | null | https://arxiv.org/abs/2212.00301v1 | https://arxiv.org/pdf/2212.00301v1.pdf | Learning to Select from Multiple Options | Many NLP tasks can be regarded as a selection problem from a set of options, such as classification tasks, multi-choice question answering, etc. Textual entailment (TE) has been shown as the state-of-the-art (SOTA) approach to dealing with those selection problems. TE treats input texts as premises (P), options as hypo... | ['Philip S. Yu', 'Congying Xia', 'Wenpeng Yin', 'Jiangshu Du'] | 2022-12-01 | null | null | null | null | ['entity-typing', 'intent-detection'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.00998893e-01 -5.71020283e-02 -2.25601479e-01 -4.93275970e-01
-1.15472221e+00 -6.00580275e-01 5.39955258e-01 7.65031874e-02
-7.34838605e-01 9.61903870e-01 1.47336081e-01 -7.28865445e-01
-2.69468725e-01 -7.84438968e-01 -5.93495131e-01 -6.28862083e-01
3.12613785e-01 1.04476285e+00 4.88261640e-01 -2.54671931... | [11.058350563049316, 8.159783363342285] |
b2b57897-fd16-4be4-92b0-e98744b6d014 | mentos-tracklets-association-with-a-space | 2107.07067 | null | https://arxiv.org/abs/2107.07067v1 | https://arxiv.org/pdf/2107.07067v1.pdf | MeNToS: Tracklets Association with a Space-Time Memory Network | We propose a method for multi-object tracking and segmentation (MOTS) that does not require fine-tuning or per benchmark hyperparameter selection. The proposed method addresses particularly the data association problem. Indeed, the recently introduced HOTA metric, that has a better alignment with the human visual asses... | ['Nicolas Saunier', 'Guillaume-Alexandre Bilodeau', 'Mehdi Miah'] | 2021-07-15 | null | null | null | null | ['multi-object-tracking-and-segmentation'] | ['computer-vision'] | [ 5.73238954e-02 -6.51502237e-02 -1.36933789e-01 -9.97543558e-02
-3.17009091e-01 -5.31201184e-01 3.25284451e-01 2.33959571e-01
-5.74735403e-01 5.99829972e-01 -5.06805003e-01 -1.21930260e-02
-2.95676649e-01 -5.08114338e-01 -8.30825806e-01 -3.41423661e-01
-1.12374797e-01 8.56350958e-01 1.07966626e+00 2.47365534... | [6.539021968841553, -1.992986798286438] |
9fc90bdd-b26d-4af9-a1fd-4c6125291ada | cross-modal-subspace-learning-for-fine | 1705.09888 | null | http://arxiv.org/abs/1705.09888v1 | http://arxiv.org/pdf/1705.09888v1.pdf | Cross-modal Subspace Learning for Fine-grained Sketch-based Image Retrieval | Sketch-based image retrieval (SBIR) is challenging due to the inherent
domain-gap between sketch and photo. Compared with pixel-perfect depictions of
photos, sketches are iconic renderings of the real world with highly abstract.
Therefore, matching sketch and photo directly using low-level visual clues are
unsufficient... | ['Yi-Zhe Song', 'Zhanyu Ma', 'Tao Xiang', 'Qiyue Yin', 'Peng Xu', 'Liang Wang', 'Yongye Huang', 'W. Bastiaan Kleijn', 'Jun Guo'] | 2017-05-28 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.88562953e-01 -7.13991404e-01 -4.11975712e-01 -7.78266713e-02
-1.04034698e+00 -8.19822848e-01 1.05035388e+00 -3.24162483e-01
7.62066692e-02 2.32518047e-01 4.90103632e-01 9.61437542e-03
-3.49546999e-01 -4.86288130e-01 -4.22359973e-01 -5.68399787e-01
3.36863369e-01 3.55943561e-01 1.18890844e-01 -2.12592527... | [11.63054370880127, 0.642681896686554] |
9c6d7491-e6a5-48c0-b325-4ae6637a4e74 | track-mix-generation-on-music-streaming | 2307.03045 | null | https://arxiv.org/abs/2307.03045v1 | https://arxiv.org/pdf/2307.03045v1.pdf | Track Mix Generation on Music Streaming Services using Transformers | This paper introduces Track Mix, a personalized playlist generation system released in 2022 on the music streaming service Deezer. Track Mix automatically generates "mix" playlists inspired by initial music tracks, allowing users to discover music similar to their favorite content. To generate these mixes, we consider ... | ['Guillaume Salha-Galvan', 'Thomas Bouabça', 'Thibault Cador', 'Benjamin Chapus', 'Mathieu Morlon', 'Théo Bontempelli', 'Walid Bendada'] | 2023-07-06 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-1.75095022e-01 -1.03252664e-01 -2.42369816e-01 -1.45062460e-02
-9.92302597e-01 -1.02002358e+00 2.42084637e-01 7.55826244e-03
3.02811526e-02 4.38056082e-01 8.70987475e-01 9.74900350e-02
-3.94318998e-01 -1.02611601e+00 -5.91643870e-01 -1.80418521e-01
-1.34262979e-01 7.34799683e-01 7.60159016e-01 -4.22663927... | [15.856563568115234, 5.3938422203063965] |
a726413c-d75a-4df0-be6e-d2a7ae22b8c7 | multisiam-self-supervised-multi-instance | 2108.12178 | null | https://arxiv.org/abs/2108.12178v1 | https://arxiv.org/pdf/2108.12178v1.pdf | MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving | Autonomous driving has attracted much attention over the years but turns out to be harder than expected, probably due to the difficulty of labeled data collection for model training. Self-supervised learning (SSL), which leverages unlabeled data only for representation learning, might be a promising way to improve mode... | ['Dit-yan Yeung', 'Zhenguo Li', 'Hang Xu', 'Lanqing Hong', 'Kai Chen'] | 2021-08-27 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Chen_MultiSiam_Self-Supervised_Multi-Instance_Siamese_Representation_Learning_for_Autonomous_Driving_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Chen_MultiSiam_Self-Supervised_Multi-Instance_Siamese_Representation_Learning_for_Autonomous_Driving_ICCV_2021_paper.pdf | iccv-2021-1 | ['image-clustering'] | ['computer-vision'] | [-3.15812156e-02 -2.18137950e-01 -5.42468727e-01 -5.83839297e-01
-8.15357029e-01 -4.12522942e-01 6.54996455e-01 -1.98874444e-01
-4.50167328e-01 5.09195626e-01 -1.21995710e-01 -2.35662714e-01
-1.55360222e-01 -6.29158258e-01 -9.81427193e-01 -5.75052857e-01
1.14248134e-01 4.05766726e-01 3.62649173e-01 -5.59696555... | [8.1895751953125, -1.8268353939056396] |
76bd0f18-8948-4f3a-90ba-ef9c3745d590 | supplementing-missing-visions-via-dialog-for | 2204.11143 | null | https://arxiv.org/abs/2204.11143v1 | https://arxiv.org/pdf/2204.11143v1.pdf | Supplementing Missing Visions via Dialog for Scene Graph Generations | Most current AI systems rely on the premise that the input visual data are sufficient to achieve competitive performance in various computer vision tasks. However, the classic task setup rarely considers the challenging, yet common practical situations where the complete visual data may be inaccessible due to various r... | ['Yan Yan', 'Zhenghao Zhao', 'Yuzhang Shang', 'Xiaoguang Zhu', 'Ye Zhu'] | 2022-04-23 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 2.54100084e-01 3.42174470e-01 -1.69387851e-02 -3.90684366e-01
-3.38172615e-01 -6.85549021e-01 8.71199191e-01 -3.16805780e-01
-2.72073328e-01 4.91985142e-01 3.02989721e-01 -6.25455081e-01
2.75505662e-01 -4.02688861e-01 -7.29243457e-01 -3.95624578e-01
7.26397276e-01 4.11981851e-01 2.50377089e-01 -4.18167919... | [10.77197551727295, 1.558992624282837] |
6e529266-988b-44b0-adde-e3db917e0a24 | an-empirical-study-of-leading-measures-of | 1505.02214 | null | http://arxiv.org/abs/1505.02214v2 | http://arxiv.org/pdf/1505.02214v2.pdf | An Empirical Study of Leading Measures of Dependence | In exploratory data analysis, we are often interested in identifying
promising pairwise associations for further analysis while filtering out
weaker, less interesting ones. This can be accomplished by computing a measure
of dependence on all variable pairs and examining the highest-scoring pairs,
provided the measure o... | ['Michael M. Mitzenmacher', 'Yakir A. Reshef', 'Pardis C. Sabeti', 'David N. Reshef'] | 2015-05-09 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [-1.17125720e-01 -7.21440092e-02 -1.01468645e-01 -3.22845161e-01
-3.78989518e-01 -8.69560599e-01 4.44847733e-01 6.50317252e-01
-6.31304681e-01 1.01977694e+00 1.86822519e-01 -4.76158679e-01
-8.35285008e-01 -9.20717597e-01 -5.20442307e-01 -6.16612613e-01
-6.77412808e-01 4.67342615e-01 1.95436314e-01 -6.39132708... | [7.621336936950684, 4.770514011383057] |
8ad62202-f3f3-4dcb-b139-1307a267b284 | a-privacy-preserving-unsupervised-domain | 2201.07317 | null | https://arxiv.org/abs/2201.07317v1 | https://arxiv.org/pdf/2201.07317v1.pdf | A Privacy-Preserving Unsupervised Domain Adaptation Framework for Clinical Text Analysis | Unsupervised domain adaptation (UDA) generally aligns the unlabeled target domain data to the distribution of the source domain to mitigate the distribution shift problem. The standard UDA requires sharing the source data with the target, having potential data privacy leaking risks. To protect the source data's privacy... | ['Yingying Zhu', 'Fei Wang', 'Zhiyong Lu', 'Qingyu Chen', 'Hao Zhang', 'Lin Gu', 'Ruijiang Li', 'Qiyuan An'] | 2022-01-18 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 4.60127115e-01 4.26687241e-01 -3.49478394e-01 -7.24165142e-01
-1.05851924e+00 -9.57412779e-01 7.93008953e-02 3.14918280e-01
-4.69221085e-01 9.09496903e-01 1.43924102e-01 -3.84339064e-01
4.74654734e-02 -8.37445676e-01 -6.58231258e-01 -1.05334735e+00
1.19983919e-01 3.68584603e-01 -1.52836904e-01 3.08780044... | [6.0075602531433105, 6.7034807205200195] |
2b1b8da3-c4d4-4105-8545-9af43a497107 | sentence-encoding-with-tree-constrained | 1811.10475 | null | http://arxiv.org/abs/1811.10475v1 | http://arxiv.org/pdf/1811.10475v1.pdf | Sentence Encoding with Tree-constrained Relation Networks | The meaning of a sentence is a function of the relations that hold between
its words. We instantiate this relational view of semantics in a series of
neural models based on variants of relation networks (RNs) which represent a
set of objects (for us, words forming a sentence) in terms of representations
of pairs of obj... | ["Cyprien de Masson d'Autume", 'Lingpeng Kong', 'Wang Ling', 'Lei Yu', 'Chris Dyer', 'Phil Blunsom'] | 2018-11-26 | null | https://openreview.net/forum?id=rJxXDsCqYX | https://openreview.net/pdf?id=rJxXDsCqYX | null | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 5.70267260e-01 5.33906698e-01 -3.27733487e-01 -8.52656126e-01
-7.75570273e-02 -5.99601686e-01 9.81243670e-01 4.81627494e-01
-3.07154655e-01 4.65736389e-01 1.03114951e+00 -6.69061661e-01
-1.81804925e-01 -1.07270277e+00 -5.20584643e-01 -3.29836458e-01
6.14016391e-02 3.65803570e-01 -5.61175458e-02 -7.68596590... | [10.50158405303955, 8.997766494750977] |
c8a5d101-c225-4325-a8d6-aefb7809ddee | divbo-diversity-aware-cash-for-ensemble | 2302.03255 | null | https://arxiv.org/abs/2302.03255v1 | https://arxiv.org/pdf/2302.03255v1.pdf | DivBO: Diversity-aware CASH for Ensemble Learning | The Combined Algorithm Selection and Hyperparameters optimization (CASH) problem is one of the fundamental problems in Automated Machine Learning (AutoML). Motivated by the success of ensemble learning, recent AutoML systems build post-hoc ensembles to output the final predictions instead of using the best single learn... | ['Bin Cui', 'Wentao Zhang', 'Yaofeng Tu', 'Yang Li', 'Yupeng Lu', 'Yu Shen'] | 2023-02-07 | null | null | null | null | ['automl'] | ['methodology'] | [-1.50854170e-01 -4.06814992e-01 -7.12412670e-02 -4.81048048e-01
-7.97883272e-01 -6.16368294e-01 5.23099184e-01 3.91733319e-01
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1.61211699e-01 6.46806121e-01 2.11778600e-02 -1.42069474... | [8.40622329711914, 4.226541042327881] |
a93b5f9b-481b-4e36-bdf0-dc2b0f5b2025 | hdrvideo-gan-deep-generative-hdr-video | 2110.11795 | null | https://arxiv.org/abs/2110.11795v2 | https://arxiv.org/pdf/2110.11795v2.pdf | HDRVideo-GAN: Deep Generative HDR Video Reconstruction | High dynamic range (HDR) videos provide a more visually realistic experience than the standard low dynamic range (LDR) videos. Despite having significant progress in HDR imaging, it is still a challenging task to capture high-quality HDR video with a conventional off-the-shelf camera. Existing approaches rely entirely ... | ['Shanmuganathan Raman', 'Chandan Kumar', 'Nidhin Harilal', 'Mrinal Anand'] | 2021-10-22 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 2.97614127e-01 -3.53821397e-01 2.28585213e-01 -1.62105635e-01
-8.54263604e-01 -5.70257843e-01 3.02579731e-01 -9.11503434e-01
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1.06085062e-01 -2.14432195e-01 3.93566899e-02 -2.08647057... | [10.820395469665527, -2.084124803543091] |
dda4f32b-690d-43a1-bae5-250ad90177ad | deep-multimodal-guidance-for-medical-image | 2203.05683 | null | https://arxiv.org/abs/2203.05683v2 | https://arxiv.org/pdf/2203.05683v2.pdf | Deep Multimodal Guidance for Medical Image Classification | Medical imaging is a cornerstone of therapy and diagnosis in modern medicine. However, the choice of imaging modality for a particular theranostic task typically involves trade-offs between the feasibility of using a particular modality (e.g., short wait times, low cost, fast acquisition, reduced radiation/invasiveness... | ['Ghassan Hamarneh', 'Mayur Mallya'] | 2022-03-10 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 7.74623930e-01 9.04005915e-02 -3.88826162e-01 -2.23838866e-01
-9.59976554e-01 -4.21175659e-01 5.74919283e-01 1.21459544e-01
-6.32055759e-01 5.72631478e-01 -2.28358079e-02 -7.24087059e-01
-3.54398817e-01 -5.53174675e-01 -4.63139951e-01 -1.07669699e+00
1.56466976e-01 5.77575684e-01 -1.53996900e-01 4.34011519... | [14.709633827209473, -2.1917550563812256] |
67db10f2-dab0-4db9-bb52-e037b77ab2ab | low-light-image-enhancement-based-on | null | null | https://www.sciencedirect.com/science/article/pii/S1051200423001495 | https://www.sciencedirect.com/science/article/pii/S1051200423001495 | Low-light image enhancement based on sharpening-smoothing image filter | Low-light images suffer from poor visibility, severe noise, low contrast, and low brightness. To overcome these issues, many image enhancement methods have been proposed. Few techniques solve these problems simultaneously. This paper presents a low-light image enhancement method. The proposed method first applies the H... | ['N.H. Kaplan', 'Y. Demir'] | 2023-08-30 | null | null | null | https-www-sciencedirect-com-science-article | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 5.48362255e-01 -7.45081902e-01 2.09787324e-01 -6.96650967e-02
-2.22938031e-01 -1.99884892e-01 1.93466097e-01 -1.21013690e-02
-4.98164862e-01 7.45827138e-01 -8.96861255e-02 -9.46010055e-04
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3.16641659e-01 -6.90803766e-01 6.60329223e-01 -2.45503515... | [10.935510635375977, -2.473680257797241] |
fb4cce09-16c8-4f7b-bd8f-4ad5cf828ce6 | visual-prompting-via-image-inpainting | 2209.00647 | null | https://arxiv.org/abs/2209.00647v1 | https://arxiv.org/pdf/2209.00647v1.pdf | Visual Prompting via Image Inpainting | How does one adapt a pre-trained visual model to novel downstream tasks without task-specific finetuning or any model modification? Inspired by prompting in NLP, this paper investigates visual prompting: given input-output image example(s) of a new task at test time and a new input image, the goal is to automatically p... | ['Alexei A. Efros', 'Amir Globerson', 'Trevor Darrell', 'Yossi Gandelsman', 'Amir Bar'] | 2022-09-01 | null | null | null | null | ['colorization', 'visual-prompting', 'personalized-segmentation', 'edge-detection', 'foreground-segmentation', 'image-inpainting'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 9.47295845e-01 4.21986818e-01 1.07963420e-01 -6.07005894e-01
-8.12755346e-01 -9.73331511e-01 6.65402770e-01 4.68651354e-02
-4.44586396e-01 6.92514956e-01 -6.46170825e-02 -6.43340886e-01
2.93952644e-01 -3.39112490e-01 -1.46565890e+00 -5.17839909e-01
3.47804785e-01 4.63991344e-01 1.64356351e-01 2.01144800... | [10.512249946594238, 1.6727839708328247] |
a64eb0ad-9b4c-4fb7-9ac4-7569876d9b91 | classical-to-quantum-transfer-learning-for | 2110.08689 | null | https://arxiv.org/abs/2110.08689v1 | https://arxiv.org/pdf/2110.08689v1.pdf | Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks | This work investigates an extension of transfer learning applied in machine learning algorithms to the emerging hybrid end-to-end quantum neural network (QNN) for spoken command recognition (SCR). Our QNN-based SCR system is composed of classical and quantum components: (1) the classical part mainly relies on a 1D conv... | ['Javier Tejedor', 'Jun Qi'] | 2021-10-17 | null | null | null | null | ['spoken-command-recognition'] | ['speech'] | [ 3.68084431e-01 2.19925478e-01 2.94370443e-01 -2.21406221e-01
-1.45123804e+00 -4.35576499e-01 7.60515690e-01 -1.87136918e-01
-6.42566502e-01 4.87028241e-01 -1.36869445e-01 -5.75685322e-01
7.12659070e-03 -8.91072571e-01 -1.02601695e+00 -8.95677924e-01
-1.48310245e-03 6.56998336e-01 2.72818238e-01 -1.00027502... | [5.563143253326416, 4.969183444976807] |
6a93f328-1590-47eb-8c67-86e99d4d3f58 | compressive-shack-hartmann-wavefront-sensing | 2011.10241 | null | https://arxiv.org/abs/2011.10241v2 | https://arxiv.org/pdf/2011.10241v2.pdf | Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks | The Shack-Hartmann wavefront sensor is widely used to measure aberrations induced by atmospheric turbulence in adaptive optics systems. However if there exists strong atmospheric turbulence or the brightness of guide stars is low, the accuracy of wavefront measurements will be affected. In this paper, we propose a comp... | ['Can Li', 'Juanjuan Li', 'Weihua Wang', 'Dongmei Cai', 'Mingyang Ma', 'Peng Jia'] | 2020-11-20 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 4.09237385e-01 -5.08923352e-01 8.84884238e-01 -2.46066272e-01
-2.60700375e-01 -3.05825621e-01 2.47565396e-02 -1.08907378e+00
-3.29665184e-01 5.19165337e-01 1.20072350e-01 -3.17369550e-01
-3.27024817e-01 -7.46409118e-01 -7.01729119e-01 -8.70610952e-01
3.17391187e-01 1.13969900e-01 5.22205047e-02 -1.90396652... | [10.932690620422363, -2.531053066253662] |
ae9d2c39-a019-4263-9056-cc80f4046908 | perspective-corrected-spatial-referring | 2104.01558 | null | https://arxiv.org/abs/2104.01558v3 | https://arxiv.org/pdf/2104.01558v3.pdf | Perspective-corrected Spatial Referring Expression Generation for Human-Robot Interaction | Intelligent robots designed to interact with humans in real scenarios need to be able to refer to entities actively by natural language. In spatial referring expression generation, the ambiguity is unavoidable due to the diversity of reference frames, which will lead to an understanding gap between humans and robots. T... | ['Chunlin Chen', 'Chengli Xiao', 'Mingjiang Liu'] | 2021-04-04 | null | null | null | null | ['referring-expression-generation'] | ['computer-vision'] | [ 1.33820370e-01 3.27063382e-01 7.27959499e-02 -3.95657748e-01
-3.87085617e-01 -4.43632662e-01 5.82783341e-01 -1.08123064e-01
-3.93249482e-01 9.00449872e-01 3.57729018e-01 7.48315975e-02
-3.44292730e-01 -8.94642711e-01 -4.35768157e-01 -4.09585506e-01
2.93288082e-01 5.00476658e-01 4.05455589e-01 -5.30486643... | [4.860467910766602, 0.6328381896018982] |
45804ccf-30ef-4189-96b8-6a6b84db0833 | multi-label-ecg-classification-using-temporal | 2306.03844 | null | https://arxiv.org/abs/2306.03844v1 | https://arxiv.org/pdf/2306.03844v1.pdf | Multi-Label ECG Classification using Temporal Convolutional Neural Network | Automated analysis of 12-lead electrocardiogram (ECG) plays a crucial role in the early screening and management of cardiovascular diseases (CVDs). In practice, it is common to see multiple co-occurring cardiac disorders, i.e., multi-label or multimorbidity in patients with CVDs, which increases the risk for mortality.... | ['Samarendra Dandapt', 'Eedara Prabhakararao'] | 2023-06-06 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 3.61830324e-01 -4.38443094e-01 7.64406696e-02 -4.41901654e-01
-5.31171799e-01 -3.79118711e-01 -1.93641305e-01 4.27834868e-01
-4.38469164e-02 4.32952940e-01 -1.84661046e-01 -5.33649087e-01
-6.19708419e-01 -5.43127716e-01 3.40859890e-02 -7.38712251e-01
-4.75976646e-01 6.76667690e-01 -2.56821543e-01 3.25708419... | [14.269818305969238, 3.247087001800537] |
0aff88d9-2b1f-44d8-8afd-76ef128903e2 | counterfactual-explanation-algorithms-for | 1912.01819 | null | https://arxiv.org/abs/1912.01819v1 | https://arxiv.org/pdf/1912.01819v1.pdf | Counterfactual Explanation Algorithms for Behavioral and Textual Data | We study the interpretability of predictive systems that use high-dimensonal behavioral and textual data. Examples include predicting product interest based on online browsing data and detecting spam emails or objectionable web content. Recently, counterfactual explanations have been proposed for generating insight int... | ['Theodoros Evgeniou', 'Yanou Ramon', 'David Martens', 'Foster Provost'] | 2019-12-04 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 1.06915690e-01 4.91121501e-01 -7.17008233e-01 -4.24681067e-01
-3.33716303e-01 -5.75178325e-01 7.42565632e-01 -2.51964349e-02
-1.79915786e-01 9.87718165e-01 3.13949913e-01 -1.08600640e+00
-7.48955190e-01 -7.09308743e-01 -6.95437372e-01 -2.64686435e-01
-1.31066456e-01 5.54449201e-01 -1.83062047e-01 8.10154155... | [8.716164588928223, 5.630002975463867] |
36e64028-f7dc-431b-9061-a0cbcdb67cea | construction-of-unbiased-dental-template-and | 2304.03556 | null | https://arxiv.org/abs/2304.03556v1 | https://arxiv.org/pdf/2304.03556v1.pdf | Construction of unbiased dental template and parametric dental model for precision digital dentistry | Dental template and parametric dental models are important tools for various applications in digital dentistry. However, constructing an unbiased dental template and accurate parametric dental models remains a challenging task due to the complex anatomical and morphological dental structures and also low volume ratio o... | ['Dinggang Shen', 'Zhongxiang Ding', 'Min Zhu', 'Yue Zhao', 'Minhui Tang', 'Yu Fang', 'Zhiming Cui', 'Peng Xue', 'Ke Deng', 'Jingyang Zhang', 'Lei Ma'] | 2023-04-07 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 3.13199133e-01 3.76061887e-01 -4.60963137e-02 -5.04913747e-01
-1.01264036e+00 -1.87323079e-01 4.79036830e-02 -2.47765005e-01
-7.64821395e-02 3.27445179e-01 1.70936540e-01 -3.51198055e-02
-6.22276179e-02 -6.64690793e-01 -3.34145606e-01 -8.94130170e-01
3.18296194e-01 8.31142426e-01 2.94201612e-01 1.47313580... | [13.730916976928711, -2.2028801441192627] |
c4cfe0cf-ec6e-4334-b6dc-40eb7443687d | blind-image-deblurring-based-on-kernel | 2101.06241 | null | https://arxiv.org/abs/2101.06241v1 | https://arxiv.org/pdf/2101.06241v1.pdf | Blind Image Deblurring based on Kernel Mixture | Blind Image deblurring tries to estimate blurriness and a latent image out of a blurred image. This estimation, as being an ill-posed problem, requires imposing restrictions on the latent image or a blur kernel that represents blurriness. Different from recent studies that impose some priors on the latent image, this p... | ['Hoon Hwangbo', 'Sajjad Amrollahi Biyouki'] | 2021-01-15 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 1.03417814e-01 -2.14542523e-01 3.23045373e-01 -2.10686564e-01
-2.30748609e-01 -7.47107148e-01 6.85611367e-01 -6.70100093e-01
-3.69790137e-01 8.19421649e-01 4.74211425e-01 -1.80034712e-01
-5.51832616e-01 -3.69179785e-01 -5.12528002e-01 -1.13676572e+00
5.32214008e-02 4.12048437e-02 9.86334682e-02 2.01875255... | [11.623398780822754, -2.7597250938415527] |
0006cf0f-a724-4e9e-b3dc-cc182601b8d6 | on-sampling-determinantal-and-pfaffian-point | 2305.15851 | null | https://arxiv.org/abs/2305.15851v1 | https://arxiv.org/pdf/2305.15851v1.pdf | On sampling determinantal and Pfaffian point processes on a quantum computer | DPPs were introduced by Macchi as a model in quantum optics the 1970s. Since then, they have been widely used as models and subsampling tools in statistics and computer science. Most applications require sampling from a DPP, and given their quantum origin, it is natural to wonder whether sampling a DPP on a quantum com... | ['Alexandre Feller', 'Michaël Fanuel', 'Rémi Bardenet'] | 2023-05-25 | null | null | null | null | ['point-processes'] | ['methodology'] | [ 3.30398083e-01 1.25747576e-01 1.54865056e-01 -1.50145561e-01
-9.59034443e-01 -6.52006865e-01 3.40418339e-01 7.28644952e-02
-6.29736662e-01 8.44825864e-01 -4.86271054e-01 -6.57542408e-01
-1.20346723e-02 -1.45667112e+00 -8.80967081e-01 -9.52299416e-01
-4.15029019e-01 8.52255821e-01 2.88765252e-01 -4.68676776... | [5.5863938331604, 4.93954610824585] |
7d41d643-3941-4548-9d2e-44c39e05e302 | semantic-code-classification-for-automated | 2201.11252 | null | https://arxiv.org/abs/2201.11252v1 | https://arxiv.org/pdf/2201.11252v1.pdf | Semantic Code Classification for Automated Machine Learning | A range of applications for automatic machine learning need the generation process to be controllable. In this work, we propose a way to control the output via a sequence of simple actions, that are called semantic code classes. Finally, we present a semantic code classification task and discuss methods for solving thi... | ['Andrey Ustuzhanin', 'Anna Scherbakova', 'Ivan Pyaternev', 'Daria Sapozhnikova', 'Natalia Denisenko', 'Anastasia Drozdova', 'Polina Guseva'] | 2022-01-25 | null | null | null | null | ['code-classification'] | ['computer-code'] | [ 4.14015442e-01 6.59231067e-01 -2.31927916e-01 -7.71708906e-01
-2.57269561e-01 -8.18071067e-01 9.14920092e-01 3.62547934e-02
2.49100447e-01 7.11174190e-01 8.37250322e-04 -6.26323819e-01
1.58203691e-01 -1.12154281e+00 -6.70345187e-01 -5.17801456e-02
2.36087278e-01 3.48533928e-01 2.69227773e-01 -2.84504771... | [7.917652130126953, 7.700723171234131] |
abdc1fef-c356-4727-b31a-c8cfd2f6becc | masked-student-dataset-of-expressions | 2304.03867 | null | https://arxiv.org/abs/2304.03867v1 | https://arxiv.org/pdf/2304.03867v1.pdf | Masked Student Dataset of Expressions | Facial expression recognition (FER) algorithms work well in constrained environments with little or no occlusion of the face. However, real-world face occlusion is prevalent, most notably with the need to use a face mask in the current Covid-19 scenario. While there are works on the problem of occlusion in FER, little ... | ['Darshan Gera', 'Sridhar Sola'] | 2023-04-07 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 4.62705702e-01 1.72473326e-01 1.95453852e-01 -6.83193088e-01
-4.10037994e-01 -2.30731472e-01 6.15656197e-01 -7.61613309e-01
-3.90102684e-01 9.11355138e-01 2.08441421e-01 4.29435298e-02
1.12529077e-01 -3.40051711e-01 -5.47435701e-01 -6.39131367e-01
-3.78406852e-01 9.43343565e-02 -2.87799805e-01 -5.43953538... | [13.330445289611816, 1.196030616760254] |
300d80d9-98a8-418e-9142-c2bf2aee462c | modelling-multi-agent-epistemic-planning-in | 2008.03007 | null | https://arxiv.org/abs/2008.03007v1 | https://arxiv.org/pdf/2008.03007v1.pdf | Modelling Multi-Agent Epistemic Planning in ASP. Theory and Practice of Logic Programming | Designing agents that reason and act upon the world has always been one of the main objectives of the Artificial Intelligence community. While for planning in "simple" domains the agents can solely rely on facts about the world, in several contexts, e.g., economy, security, justice and politics, the mere knowledge of t... | ['Enrico Pontelli', 'Francesco Fabiano', 'Alessandro Burigana', 'Agostino Dovier'] | 2020-08-07 | null | null | null | null | ['epistemic-reasoning'] | ['miscellaneous'] | [-1.32995591e-01 8.79036069e-01 -6.20661937e-02 -2.64620662e-01
-3.74687940e-01 -6.30375445e-01 8.11538100e-01 4.52258378e-01
-4.33611989e-01 1.07448518e+00 1.66220382e-01 -5.14697790e-01
-3.28584552e-01 -1.34928763e+00 -5.85219443e-01 -4.99582410e-01
-8.91568791e-03 9.46921945e-01 6.91049099e-01 -5.32270730... | [8.63351821899414, 6.7196526527404785] |
4bcd5374-8665-4a13-8fb0-99c43f29998c | dependency-decomposition-and-a-reject-option | 2012.06523 | null | https://arxiv.org/abs/2012.06523v1 | https://arxiv.org/pdf/2012.06523v1.pdf | Dependency Decomposition and a Reject Option for Explainable Models | Deploying machine learning models in safety-related do-mains (e.g. autonomous driving, medical diagnosis) demands for approaches that are explainable, robust against adversarial attacks and aware of the model uncertainty. Recent deep learning models perform extremely well in various inference tasks, but the black-box n... | ['Anselm Haselhoff', 'Jan Kronenberger'] | 2020-12-11 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 2.67332792e-01 8.70333552e-01 -3.91869023e-02 -6.98505700e-01
-1.93255112e-01 -7.17982352e-01 8.37449551e-01 1.85535163e-01
1.37245670e-01 8.51295292e-01 1.43050492e-01 -9.50709164e-01
-4.35854346e-01 -7.75637746e-01 -9.60911512e-01 -7.82306075e-01
1.61178131e-03 4.60058808e-01 -1.95816979e-02 5.85790426... | [8.788185119628906, 5.694888591766357] |
7ed3cfd5-7f93-472e-aca3-3e5583ed5fdb | deep-survival-machines-fully-parametric | 2003.01176 | null | https://arxiv.org/abs/2003.01176v3 | https://arxiv.org/pdf/2003.01176v3.pdf | Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks | We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner. Our approach does not require making strong assumptions of constant proportional hazard of the underlying survival distribution, as required by the Cox-proportional hazard model.... | ['Xinyu Rachel Li', 'Chirag Nagpal', 'Artur Dubrawski'] | 2020-03-02 | null | null | null | null | ['time-to-event-prediction'] | ['time-series'] | [-8.92056450e-02 9.55727976e-03 -5.45126855e-01 -8.68520916e-01
-1.16482937e+00 -2.19295442e-01 4.42289114e-01 2.83276916e-01
-4.28877413e-01 8.71113181e-01 5.13424397e-01 -8.40875745e-01
-3.50735337e-01 -7.29686320e-01 -6.56054020e-01 -3.19641888e-01
-8.07571232e-01 7.97681570e-01 -3.56639564e-01 7.35463947... | [7.818237781524658, 5.5562520027160645] |
6d295c18-9a9e-41ef-9796-d08637f66c79 | leveraging-synthetic-data-to-learn-video | 2208.12763 | null | https://arxiv.org/abs/2208.12763v1 | https://arxiv.org/pdf/2208.12763v1.pdf | Leveraging Synthetic Data to Learn Video Stabilization Under Adverse Conditions | Video stabilization plays a central role to improve videos quality. However, despite the substantial progress made by these methods, they were, mainly, tested under standard weather and lighting conditions, and may perform poorly under adverse conditions. In this paper, we propose a synthetic-aware adverse weather robu... | ['Richard Jiang', 'Erickson R. Nascimento', 'Leandro Soriano Marcolino', 'Washington L. S. Ramos', 'Abdulrahman Kerim'] | 2022-08-26 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [ 1.65338278e-01 -5.73798776e-01 2.00324617e-02 1.93166919e-02
-6.12395763e-01 -7.20616341e-01 5.06208003e-01 -1.00872979e-01
-1.29904523e-01 7.46896982e-01 6.00508451e-02 -1.78423971e-01
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-4.31149639e-02 -1.81810945e-01 5.45293093e-01 -5.64546645... | [10.666339874267578, -1.3503116369247437] |
4fd8af12-10dd-459a-953a-a874ce78a20b | ocbev-object-centric-bev-transformer-for | 2306.01738 | null | https://arxiv.org/abs/2306.01738v1 | https://arxiv.org/pdf/2306.01738v1.pdf | OCBEV: Object-Centric BEV Transformer for Multi-View 3D Object Detection | Multi-view 3D object detection is becoming popular in autonomous driving due to its high effectiveness and low cost. Most of the current state-of-the-art detectors follow the query-based bird's-eye-view (BEV) paradigm, which benefits from both BEV's strong perception power and end-to-end pipeline. Despite achieving sub... | ['Hengshuang Zhao', 'Xiaoyang Wu', 'Jiaqi Wang', 'Zhangyang Qi'] | 2023-06-02 | null | null | null | null | ['3d-object-detection'] | ['computer-vision'] | [-9.04394388e-02 -4.00575846e-01 -2.55268067e-01 -5.96957743e-01
-8.55509818e-01 -4.80404466e-01 7.00834155e-01 -9.59371254e-02
-6.99204624e-01 1.85033277e-01 -4.60805297e-02 1.67247504e-02
6.64910525e-02 -5.79564750e-01 -9.62942660e-01 -6.46750450e-01
8.57496113e-02 3.66798341e-01 1.28238583e+00 -3.74577463... | [7.87435245513916, -2.1656367778778076] |
80585d4c-4495-46a7-b37f-d23fe04e9aa2 | 190807919 | 1908.07919 | null | https://arxiv.org/abs/1908.07919v2 | https://arxiv.org/pdf/1908.07919v2.pdf | Deep High-Resolution Representation Learning for Visual Recognition | High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection. Existing state-of-the-art frameworks first encode the input image as a low-resolution representation through a subnetwork that is formed by connecting high-to... | ['Bin Xiao', 'Chaorui Deng', 'Ke Sun', 'Wenyu Liu', 'Yadong Mu', 'Xinggang Wang', 'Tianheng Cheng', 'Mingkui Tan', 'Jingdong Wang', 'Yang Zhao', 'Dong Liu', 'Borui Jiang'] | 2019-08-20 | deep-high-resolution-representation-learning-2 | null | null | null | ['thermal-image-segmentation', 'dichotomous-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.36474380e-01 2.61071716e-02 -3.99625711e-02 -2.82291651e-01
-5.95975757e-01 -1.26265094e-01 2.52106577e-01 -1.57683641e-01
-5.81858873e-01 5.83439171e-01 1.63580775e-01 3.17014992e-01
-1.04719564e-01 -1.01500034e+00 -8.69273901e-01 -4.78699088e-01
9.81685817e-02 1.82372689e-01 7.08118200e-01 -3.74386042... | [9.614105224609375, -0.5760602355003357] |
e609fda6-b44b-465f-8579-d4be26189f17 | learning-to-mine-aligned-code-and-natural | 1805.08949 | null | http://arxiv.org/abs/1805.08949v1 | http://arxiv.org/pdf/1805.08949v1.pdf | Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow | For tasks like code synthesis from natural language, code retrieval, and code
summarization, data-driven models have shown great promise. However, creating
these models require parallel data between natural language (NL) and code with
fine-grained alignments. Stack Overflow (SO) is a promising source to create
such a d... | ['Pengcheng Yin', 'Bogdan Vasilescu', 'Edgar Chen', 'Bowen Deng', 'Graham Neubig'] | 2018-05-23 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 8.81223977e-02 -6.21542260e-02 -4.81633395e-01 -4.25024658e-01
-1.18956137e+00 -6.89889491e-01 3.42928410e-01 5.19576788e-01
-8.05842727e-02 5.38231969e-01 3.11169714e-01 -4.83242303e-01
-1.48552850e-01 -7.87104487e-01 -6.89507246e-01 -1.41078085e-01
-2.63091959e-02 2.12836564e-01 3.06332380e-01 -6.21449621... | [7.684010028839111, 7.864686965942383] |
cf7a819f-0eff-4b9b-89da-f354efcc9bf6 | learning-a-deep-embedding-model-for-zero-shot | 1611.05088 | null | https://arxiv.org/abs/1611.05088v4 | https://arxiv.org/pdf/1611.05088v4.pdf | Learning a Deep Embedding Model for Zero-Shot Learning | Zero-shot learning (ZSL) models rely on learning a joint embedding space where both textual/semantic description of object classes and visual representation of object images can be projected to for nearest neighbour search. Despite the success of deep neural networks that learn an end-to-end model between text and imag... | ['Li Zhang', 'Tao Xiang', 'Shaogang Gong'] | 2016-11-15 | learning-a-deep-embedding-model-for-zero-shot-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Zhang_Learning_a_Deep_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Learning_a_Deep_CVPR_2017_paper.pdf | cvpr-2017-7 | ['zero-shot-action-recognition'] | ['computer-vision'] | [-6.41978113e-03 3.42202842e-01 -2.77458042e-01 -6.37024939e-01
-8.28415573e-01 -3.82795066e-01 9.41866219e-01 1.62478566e-01
-5.02655685e-01 2.62308538e-01 4.44904208e-01 -5.49347885e-02
-1.98485285e-01 -7.01688528e-01 -7.82819033e-01 -5.37517488e-01
1.73918143e-01 5.45044541e-01 1.43557116e-01 -1.27504617... | [10.450786590576172, 1.8924065828323364] |
eef26543-b3c4-4275-88b2-36d9b35d85d7 | semi-on-policy-training-for-sample-efficient | 2104.13446 | null | https://arxiv.org/abs/2104.13446v2 | https://arxiv.org/pdf/2104.13446v2.pdf | Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients | Policy gradient methods are an attractive approach to multi-agent reinforcement learning problems due to their convergence properties and robustness in partially observable scenarios. However, there is a significant performance gap between state-of-the-art policy gradient and value-based methods on the popular StarCraf... | ['Shimon Whiteson', 'Bei Peng', 'Tarun Gupta', 'Bozhidar Vasilev'] | 2021-04-27 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-4.58766490e-01 -3.51856321e-01 -8.86138141e-01 1.94682404e-01
-9.18333411e-01 -4.56921726e-01 9.15239275e-01 6.04374930e-02
-9.25495803e-01 1.45639634e+00 3.41711015e-01 -6.50665164e-01
-1.78538516e-01 -3.53305072e-01 -7.37152934e-01 -5.82146943e-01
-3.60385895e-01 7.20817268e-01 4.03860062e-01 -6.22891068... | [4.01867151260376, 2.1535942554473877] |
97643acf-3e5d-4416-af4f-b5c173149b8f | flex-convolution-million-scale-point-cloud | 1803.07289 | null | https://arxiv.org/abs/1803.07289v4 | https://arxiv.org/pdf/1803.07289v4.pdf | Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds) | Traditional convolution layers are specifically designed to exploit the natural data representation of images -- a fixed and regular grid. However, unstructured data like 3D point clouds containing irregular neighborhoods constantly breaks the grid-based data assumption. Therefore applying best-practices and design cho... | ['Hendrik P. A. Lensch', 'Patrick Wieschollek', 'Fabian Groh'] | 2018-03-20 | null | null | null | null | ['classify-3d-point-clouds'] | ['computer-vision'] | [-1.74540326e-01 -3.59409243e-01 7.72797763e-02 -2.94060498e-01
-3.76193643e-01 -4.37524557e-01 6.05761230e-01 1.35263324e-01
-5.77233613e-01 4.21768159e-01 -2.66029835e-01 -5.19164145e-01
9.31551307e-02 -1.25545371e+00 -9.51786995e-01 -4.98393804e-01
-4.12394851e-01 4.51156825e-01 2.03301728e-01 -1.87337145... | [8.021674156188965, -3.702613353729248] |
50e42922-cb7f-4e45-81c1-5fe88f71f156 | towards-realistic-generative-3d-face-models | 2304.12483 | null | https://arxiv.org/abs/2304.12483v1 | https://arxiv.org/pdf/2304.12483v1.pdf | Towards Realistic Generative 3D Face Models | In recent years, there has been significant progress in 2D generative face models fueled by applications such as animation, synthetic data generation, and digital avatars. However, due to the absence of 3D information, these 2D models often struggle to accurately disentangle facial attributes like pose, expression, and... | ['Fernando de la Torre', 'Aayush Prakash', 'Daeil Kim', 'Amaury Aubel', 'Shingo Jason Takagi', 'Francisco Vicente Carrasco', 'Ayush Pandey', 'Hiresh Gupta', 'Aashish Rai'] | 2023-04-24 | null | null | null | null | ['3d-face-reconstruction', 'face-model', 'synthetic-data-generation', 'synthetic-data-generation'] | ['computer-vision', 'computer-vision', 'medical', 'miscellaneous'] | [ 1.95696950e-01 3.47583681e-01 4.54546958e-01 -5.32035053e-01
-4.33147490e-01 -5.27728140e-01 8.90960097e-01 -7.92447090e-01
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2.05223083e-01 -8.42333972e-01 -7.00283647e-01 -6.67713404e-01
2.05731362e-01 5.79738736e-01 -5.23393154e-01 -3.29267323... | [12.70369815826416, -0.3321238160133362] |
4dd296a4-be3d-4013-a42e-fed70ea88104 | separable-batch-normalization-for-robust | 2101.06663 | null | https://arxiv.org/abs/2101.06663v1 | https://arxiv.org/pdf/2101.06663v1.pdf | Separable Batch Normalization for Robust Facial Landmark Localization with Cross-protocol Network Training | A big, diverse and balanced training data is the key to the success of deep neural network training. However, existing publicly available datasets used in facial landmark localization are usually much smaller than those for other computer vision tasks. A small dataset without diverse and balanced training samples canno... | ['Josef Kittler', 'Wankou Yang', 'ZhenHua Feng', 'Shuangping Jin'] | 2021-01-17 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [ 1.65585503e-01 -2.03647092e-01 -3.30039382e-01 -7.90978611e-01
-4.71795022e-01 -6.04497306e-02 3.40445817e-01 -1.21061347e-01
-6.85716510e-01 6.31503046e-01 -1.73435524e-01 7.66843781e-02
-2.22710863e-01 -7.41539598e-01 -4.77812648e-01 -1.16842544e+00
3.66270930e-01 4.64293450e-01 2.02966556e-01 -6.01455085... | [13.48937702178955, 0.7014901638031006] |
c4423501-3bed-4529-8c2a-dfe51dbadb89 | mitigating-severe-over-parameterization-in | 2106.14190 | null | https://arxiv.org/abs/2106.14190v1 | https://arxiv.org/pdf/2106.14190v1.pdf | Mitigating severe over-parameterization in deep convolutional neural networks through forced feature abstraction and compression with an entropy-based heuristic | Convolutional Neural Networks (CNNs) such as ResNet-50, DenseNet-40 and ResNeXt-56 are severely over-parameterized, necessitating a consequent increase in the computational resources required for model training which scales exponentially for increments in model depth. In this paper, we propose an Entropy-Based Convolut... | ['Wei Qi Yan', 'Stephen MacDonell', 'Roopak Sinha', 'Nidhi Gowdra'] | 2021-06-27 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 6.03591725e-02 3.79446387e-01 -1.58908933e-01 -2.16677636e-01
-2.24449903e-01 -5.45663238e-01 4.96538430e-01 -3.19964200e-01
-1.00558865e+00 8.45379412e-01 -6.50298148e-02 -6.30948067e-01
-3.10464412e-01 -4.85817075e-01 -5.89758217e-01 -5.96034765e-01
-8.30403343e-02 -8.70651752e-02 1.72024027e-01 -3.27877812... | [8.752554893493652, 2.9711647033691406] |
981ef9de-7ef2-4dee-985f-37180ae26446 | teaching-machines-to-understand-baseball | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Minho_Shim_Teaching_Machines_to_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Minho_Shim_Teaching_Machines_to_ECCV_2018_paper.pdf | Teaching Machines to Understand Baseball Games: Large-Scale Baseball Video Database for Multiple Video Understanding Tasks | A major obstacle in teaching machines to understand videos is the lack of training data, as creating temporal annotations for long videos requires a huge amount of human effort. To this end, we introduce a new large-scale baseball video dataset called the BBDB, which is produced semi-automatically by using play-by-pla... | ['Kyung-Min Kim', 'Young Hwi Kim', 'Minho Shim', 'Seon Joo Kim'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['video-alignment'] | ['computer-vision'] | [ 6.86047971e-02 -4.03697580e-01 -5.76520562e-01 -2.44579822e-01
-6.58457577e-01 -7.55039096e-01 1.55184403e-01 -8.97847041e-02
-9.24793631e-02 4.60756838e-01 2.35139161e-01 -1.15823440e-01
1.40035883e-01 -3.87993038e-01 -9.85361278e-01 -4.07000303e-01
-1.42360225e-01 1.12676784e-01 7.16033340e-01 -1.48294186... | [9.421365737915039, 0.6516731977462769] |
97c28323-777f-425d-834a-b1aed64d328e | automated-3d-recovery-from-very-high | 1905.07475 | null | https://arxiv.org/abs/1905.07475v2 | https://arxiv.org/pdf/1905.07475v2.pdf | Automated 3D recovery from very high resolution multi-view satellite images | This paper presents an automated pipeline for processing multi-view satellite images to 3D digital surface models (DSM). The proposed pipeline performs automated geo-referencing and generates high-quality densely matched point clouds. In particular, a novel approach is developed that fuses multiple depth maps derived b... | ['Rongjun Qin'] | 2019-05-17 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [ 3.90536845e-01 -3.37091506e-01 4.52477008e-01 -5.16071200e-01
-1.29826868e+00 -6.76278532e-01 8.26031685e-01 3.41246575e-01
-2.71594048e-01 5.86513400e-01 -1.78715251e-02 -7.54190888e-03
-3.63243133e-01 -1.19765913e+00 -4.81977493e-01 -5.78904748e-01
-1.53640166e-01 8.20064247e-01 4.11214709e-01 -2.55379081... | [8.44258975982666, -2.537508249282837] |
c6ac4301-52d0-49ca-88a0-5f4e3e83c38d | collision-avoidance-detour-for-multi-agent | 2306.11638 | null | https://arxiv.org/abs/2306.11638v1 | https://arxiv.org/pdf/2306.11638v1.pdf | Collision Avoidance Detour for Multi-Agent Trajectory Forecasting | We present our approach, Collision Avoidance Detour (CAD), which won the 3rd place award in the 2023 Waymo Open Dataset Challenge - Sim Agents, held at the 2023 CVPR Workshop on Autonomous Driving. To satisfy the motion prediction factorization requirement, we partition all the valid objects into three mutually exclusi... | ['Stephen F. Smith', 'Hsu-kuang Chiu'] | 2023-06-20 | null | null | null | null | ['motion-prediction', 'trajectory-forecasting'] | ['computer-vision', 'computer-vision'] | [-7.28963792e-01 2.22495481e-01 -1.73183441e-01 -3.83293033e-01
-3.26704830e-01 -5.86284697e-01 8.48512948e-01 -1.53063118e-01
-6.24742270e-01 9.50683355e-01 6.34783506e-02 -4.11252946e-01
-1.04040161e-01 -8.55534673e-01 -8.58737409e-01 -3.30090195e-01
-5.99807501e-01 8.52036953e-01 7.94387341e-01 -7.88874447... | [5.789041996002197, 0.8834385871887207] |
d62c65e0-eb7f-4bd5-95ce-c88fd0de4fbd | towards-interactive-language-modeling | 2112.11911 | null | https://arxiv.org/abs/2112.11911v2 | https://arxiv.org/pdf/2112.11911v2.pdf | Towards Interactive Language Modeling | Interaction between caregivers and children plays a critical role in human language acquisition and development. Given this observation, it is remarkable that explicit interaction plays little to no role in artificial language modeling -- which also targets the acquisition of human language, yet by artificial models. M... | ['Emmanuel Dupoux', 'Dieuwke Hupkes', 'Evgeny Kharitonov', 'Maartje ter Hoeve'] | 2021-12-14 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 9.08903405e-02 1.07951379e+00 -4.07083593e-02 -5.44908047e-01
-1.24425665e-01 -3.79239351e-01 7.28255093e-01 4.29088652e-01
-3.03450376e-01 4.82255578e-01 2.97950029e-01 -6.90987229e-01
5.76960035e-02 -7.90499508e-01 -4.86975074e-01 4.73991297e-02
-8.68429914e-02 5.64995706e-01 1.71387449e-01 -3.06058317... | [10.39538288116455, 8.714683532714844] |
fbd1cfcb-e584-4f07-8fb4-eb7424f0495e | densely-connected-convolutional-networks | 1608.06993 | null | http://arxiv.org/abs/1608.06993v5 | http://arxiv.org/pdf/1608.06993v5.pdf | Densely Connected Convolutional Networks | Recent work has shown that convolutional networks can be substantially
deeper, more accurate, and efficient to train if they contain shorter
connections between layers close to the input and those close to the output. In
this paper, we embrace this observation and introduce the Dense Convolutional
Network (DenseNet), w... | ['Zhuang Liu', 'Kilian Q. Weinberger', 'Gao Huang', 'Laurens van der Maaten'] | 2016-08-25 | densely-connected-convolutional-networks-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Huang_Densely_Connected_Convolutional_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Huang_Densely_Connected_Convolutional_CVPR_2017_paper.pdf | cvpr-2017-7 | ['pedestrian-attribute-recognition', 'speaker-specific-lip-to-speech-synthesis', 'breast-tumour-classification'] | ['computer-vision', 'computer-vision', 'medical'] | [-1.70031920e-01 -1.06625058e-01 -2.03545559e-02 -5.46848893e-01
-8.31847265e-02 -4.02341604e-01 4.35173184e-01 1.00134062e-02
-6.67473435e-01 5.66009760e-01 9.04016718e-02 -2.90480912e-01
1.51215941e-01 -9.40004706e-01 -7.72363305e-01 -5.73414385e-01
-1.93358809e-01 -1.02587178e-01 5.53592861e-01 -3.27545851... | [9.006479263305664, 2.3510706424713135] |
e0ed58ac-0c32-4bcd-9a0a-968a3909ff55 | mpc-protocol-for-g-module-and-its-application | 2007.03975 | null | https://arxiv.org/abs/2007.03975v3 | https://arxiv.org/pdf/2007.03975v3.pdf | MPC Protocol for G-module and its Application in Secure Compare and ReLU | Secure comparison and secure selection are two fundamental MPC (secure Multi-Party Computation) protocols. One important application of these protocols is the secure ReLU and DReLU computation in privacy preserving deep learning. In this paper, we introduce G-module, a mathematics tool, to re-design such protocols. In ... | ['Lichun Li', 'Qizhi Zhang', 'Juanjuan Sun', 'Shan Yin'] | 2020-07-08 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [-7.60520622e-02 4.71765064e-02 2.26496682e-01 -3.81488949e-01
-8.46167922e-01 -1.12474310e+00 1.02539611e+00 1.99679658e-01
-4.81054783e-01 7.13630617e-01 1.26019135e-01 -6.90992117e-01
-9.18906927e-03 -1.39804125e+00 -8.82513821e-01 -1.24767756e+00
-6.31610572e-01 -1.46217704e-01 5.88336363e-02 -4.64081556... | [5.848204612731934, 6.797857284545898] |
fd973f6d-0d88-409b-97d3-36a1bbb2726a | learned-distributed-image-compression-with | 2209.02514 | null | https://arxiv.org/abs/2209.02514v2 | https://arxiv.org/pdf/2209.02514v2.pdf | Learned Distributed Image Compression with Multi-Scale Patch Matching in Feature Domain | Beyond achieving higher compression efficiency over classical image compression codecs, deep image compression is expected to be improved with additional side information, e.g., another image from a different perspective of the same scene. To better utilize the side information under the distributed compression scenari... | ['Shu-Tao Xia', 'Tao Dai', 'YaoWei Wang', 'Jiawei Li', 'Shiyu Qin', 'Bin Chen', 'Yujun Huang'] | 2022-09-06 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 1.95341066e-01 -2.68491089e-01 -4.74782735e-02 -1.21076465e-01
-7.06985176e-01 -2.13452026e-01 2.24204391e-01 -1.56651869e-01
-1.34957269e-01 8.35112259e-02 2.27113068e-01 1.57258362e-01
-2.23400727e-01 -1.18191159e+00 -7.38552094e-01 -8.64580452e-01
1.31071076e-01 -7.49489367e-02 4.27183032e-01 -3.02287042... | [11.131038665771484, -1.7457941770553589] |
938bb2cf-442d-4c7e-877a-930cadb9b4a5 | aerial-image-object-detection-with-vision | 2301.12058 | null | https://arxiv.org/abs/2301.12058v2 | https://arxiv.org/pdf/2301.12058v2.pdf | Aerial Image Object Detection With Vision Transformer Detector (ViTDet) | The past few years have seen an increased interest in aerial image object detection due to its critical value to large-scale geo-scientific research like environmental studies, urban planning, and intelligence monitoring. However, the task is very challenging due to the birds-eye view perspective, complex backgrounds, ... | ['Alex Tien', 'Liya Wang'] | 2023-01-28 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 2.40262076e-01 -5.00350237e-01 6.37896881e-02 -1.68943718e-01
-3.55995744e-01 -5.48484325e-01 5.27311981e-01 -1.02011077e-01
-4.62185144e-01 3.09805810e-01 -2.90064901e-01 -4.85933036e-01
-4.31744047e-02 -7.14874983e-01 -4.94085312e-01 -4.92892146e-01
-4.41513509e-01 -1.17991753e-01 7.90965855e-01 -4.51549262... | [8.664846420288086, -0.8242216110229492] |
8504865d-8e0d-47a8-b53b-69abe2511baf | tasked-transformer-based-adversarial-learning | 2209.09092 | null | https://arxiv.org/abs/2209.09092v2 | https://arxiv.org/pdf/2209.09092v2.pdf | TASKED: Transformer-based Adversarial learning for human activity recognition using wearable sensors via Self-KnowledgE Distillation | Wearable sensor-based human activity recognition (HAR) has emerged as a principal research area and is utilized in a variety of applications. Recently, deep learning-based methods have achieved significant improvement in the HAR field with the development of human-computer interaction applications. However, they are li... | ['Paul Lukowicz', 'Vitor Fortes Rey', 'Sungho Suh'] | 2022-09-14 | null | null | null | null | ['self-knowledge-distillation'] | ['computer-vision'] | [ 3.38123918e-01 -3.91643882e-01 -7.24583045e-02 -3.65125388e-01
-5.83575130e-01 -2.45017171e-01 2.42798433e-01 4.94421199e-02
-5.53767383e-01 7.59182632e-01 1.75999716e-01 3.00123900e-01
-2.87516683e-01 -6.16941929e-01 -7.24248707e-01 -9.14298713e-01
-5.90725280e-02 5.12208492e-02 8.26165825e-02 3.31309550... | [7.722177028656006, 0.9535608887672424] |
27410ece-2221-452a-84c2-849d4fdb2841 | interactive-audio-text-representation-for | 2203.15526 | null | https://arxiv.org/abs/2203.15526v2 | https://arxiv.org/pdf/2203.15526v2.pdf | Interactive Audio-text Representation for Automated Audio Captioning with Contrastive Learning | Automated Audio captioning (AAC) is a cross-modal task that generates natural language to describe the content of input audio. Most prior works usually extract single-modality acoustic features and are therefore sub-optimal for the cross-modal decoding task. In this work, we propose a novel AAC system called CLIP-AAC t... | ['Eng Siong Chng', 'Xiaofeng Qi', 'Heqing Zou', 'Yuchen Hu', 'Nana Hou', 'Chen Chen'] | 2022-03-29 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 5.41801274e-01 -9.38126724e-03 1.37282148e-01 -2.85144269e-01
-1.68140650e+00 -6.52212620e-01 6.77305162e-01 -4.21490930e-02
-1.37788847e-01 4.69948560e-01 7.42163658e-01 -1.59798320e-02
3.33025426e-01 -8.71010795e-02 -1.08580101e+00 -4.75844085e-01
9.39593092e-02 2.08942369e-01 -1.03774436e-01 -2.76738871... | [15.2571439743042, 4.956027507781982] |
5144f47f-9136-43e7-ba2a-ba46ca222e92 | adatriplet-adaptive-gradient-triplet-loss | 2205.02849 | null | https://arxiv.org/abs/2205.02849v2 | https://arxiv.org/pdf/2205.02849v2.pdf | AdaTriplet: Adaptive Gradient Triplet Loss with Automatic Margin Learning for Forensic Medical Image Matching | This paper tackles the challenge of forensic medical image matching (FMIM) using deep neural networks (DNNs). FMIM is a particular case of content-based image retrieval (CBIR). The main challenge in FMIM compared to the general case of CBIR, is that the subject to whom a query image belongs may be affected by aging and... | ['Aleksei Tiulpin', 'Huy Hoang Nguyen', 'Khanh Nguyen'] | 2022-05-05 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 2.65447289e-01 -3.25012296e-01 -2.68260926e-01 -3.44967097e-01
-1.17116916e+00 -3.06824118e-01 2.57507265e-01 3.68134290e-01
-7.88118422e-01 5.79680026e-01 2.50047147e-01 -2.18953982e-01
-4.27552253e-01 -6.57733858e-01 -5.53054571e-01 -6.09996259e-01
-1.45903304e-01 4.18795198e-01 9.60115716e-02 -2.96171784... | [14.271723747253418, -1.4797394275665283] |
d21dd9e5-07f2-403b-8c92-60f3e2aebc53 | rgb-d-salient-object-detection-based-on | 1703.00122 | null | http://arxiv.org/abs/1703.00122v2 | http://arxiv.org/pdf/1703.00122v2.pdf | RGB-D Salient Object Detection Based on Discriminative Cross-modal Transfer Learning | In this work, we propose to utilize Convolutional Neural Networks to boost
the performance of depth-induced salient object detection by capturing the
high-level representative features for depth modality. We formulate the
depth-induced saliency detection as a CNN-based cross-modal transfer problem to
bridge the gap bet... | ['Hao Chen', 'Dan Su', 'Y. F. Li'] | 2017-03-01 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 4.24511999e-01 2.05710441e-01 -2.31347620e-01 -4.45794612e-01
-7.73008049e-01 -1.24089971e-01 6.15810335e-01 7.40712360e-02
-3.08677852e-01 5.02664030e-01 3.94569308e-01 8.84342864e-02
-6.54926477e-03 -6.42161429e-01 -7.54037797e-01 -7.76855350e-01
4.64235842e-01 -1.51311725e-01 6.06345057e-01 -2.25015000... | [9.74781608581543, -0.6773635745048523] |
699b97c2-d20d-46d6-b69c-60a98fdd167f | a-competitive-analysis-of-online-multi-agent | 2106.11454 | null | https://arxiv.org/abs/2106.11454v1 | https://arxiv.org/pdf/2106.11454v1.pdf | A Competitive Analysis of Online Multi-Agent Path Finding | We study online Multi-Agent Path Finding (MAPF), where new agents are constantly revealed over time and all agents must find collision-free paths to their given goal locations. We generalize existing complexity results of (offline) MAPF to online MAPF. We classify online MAPF algorithms into different categories based ... | ['Hang Ma'] | 2021-06-22 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-3.04492265e-01 5.18966973e-01 -2.25701109e-01 3.61569338e-02
-5.08377016e-01 -1.40083337e+00 1.48364678e-01 6.75457656e-01
-5.99963725e-01 9.07760262e-01 -2.55466402e-01 -3.19272488e-01
-9.38541114e-01 -1.30684996e+00 -9.01111484e-01 -6.41287088e-01
-1.08789062e+00 1.18647063e+00 7.30933011e-01 -3.96443933... | [4.978452682495117, 1.8246768712997437] |
6c4b3027-9cfc-430e-8120-f159a16f0740 | motif-guided-time-series-counterfactual | 2211.04411 | null | https://arxiv.org/abs/2211.04411v2 | https://arxiv.org/pdf/2211.04411v2.pdf | Motif-guided Time Series Counterfactual Explanations | With the rising need of interpretable machine learning methods, there is a necessity for a rise in human effort to provide diverse explanations of the influencing factors of the model decisions. To improve the trust and transparency of AI-based systems, the EXplainable Artificial Intelligence (XAI) field has emerged. T... | ['Shah Muhammad Hamdi', 'Soukaina Filali Boubrahimi', 'Peiyu Li'] | 2022-11-08 | null | null | null | null | ['interpretable-machine-learning', 'counterfactual-explanation', 'explanation-generation'] | ['methodology', 'miscellaneous', 'natural-language-processing'] | [ 4.61980373e-01 7.27150023e-01 -5.02269447e-01 -5.74628830e-01
-2.15148218e-02 -3.26617509e-01 1.09152246e+00 1.89904571e-01
2.83747733e-01 8.88711214e-01 7.88751125e-01 -9.14036572e-01
-4.20777023e-01 -4.96581525e-01 -8.09816003e-01 -1.56316772e-01
-1.49434090e-01 2.92040616e-01 -5.17066777e-01 -1.53676078... | [8.73490047454834, 5.659236907958984] |
2a69c19a-8799-4074-9332-29829b5e3aea | learning-data-driven-vector-quantized | 2303.09826 | null | https://arxiv.org/abs/2303.09826v1 | https://arxiv.org/pdf/2303.09826v1.pdf | Learning Data-Driven Vector-Quantized Degradation Model for Animation Video Super-Resolution | Existing real-world video super-resolution (VSR) methods focus on designing a general degradation pipeline for open-domain videos while ignoring data intrinsic characteristics which strongly limit their performance when applying to some specific domains (e.g. animation videos). In this paper, we thoroughly explore the ... | ['Xueming Qian', 'Yujie Dun', 'Jianlong Fu', 'Huan Yang', 'Zixi Tuo'] | 2023-03-17 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 3.26957226e-01 -3.80914003e-01 -2.94734895e-01 1.18933767e-01
-1.03390527e+00 -2.71364450e-01 4.12222475e-01 -6.55512035e-01
1.27440780e-01 6.53696239e-01 7.08960354e-01 2.46922597e-01
9.32722241e-02 -4.72723424e-01 -6.97040975e-01 -7.76681423e-01
-2.20057771e-01 -1.31436318e-01 5.23998618e-01 -4.86548841... | [11.077879905700684, -1.9265483617782593] |
d915ceda-1ee6-477f-b3a9-697a72f4516e | robust-online-video-instance-segmentation | 2211.09108 | null | https://arxiv.org/abs/2211.09108v1 | https://arxiv.org/pdf/2211.09108v1.pdf | Robust Online Video Instance Segmentation with Track Queries | Recently, transformer-based methods have achieved impressive results on Video Instance Segmentation (VIS). However, most of these top-performing methods run in an offline manner by processing the entire video clip at once to predict instance mask volumes. This makes them incapable of handling the long videos that appea... | ['Svetlana Lazebnik', 'Daniel McKee', 'Zitong Zhan'] | 2022-11-16 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 1.42319903e-01 -2.36798137e-01 -5.63576102e-01 -1.42709374e-01
-1.11037803e+00 -1.02130234e+00 5.06365359e-01 -9.24709812e-02
-4.26812232e-01 5.14498413e-01 -3.70857149e-01 -2.14593083e-01
-6.70830533e-03 -3.05106044e-01 -1.07697427e+00 -3.43536586e-01
-2.57006496e-01 7.17248976e-01 9.18218732e-01 3.36796284... | [9.148213386535645, -0.07815365493297577] |
58bd2ec9-3519-4598-ab2c-1f9a64485922 | evaluating-the-impact-of-source-code-parsers | 2206.08713 | null | https://arxiv.org/abs/2206.08713v1 | https://arxiv.org/pdf/2206.08713v1.pdf | Evaluating the Impact of Source Code Parsers on ML4SE Models | As researchers and practitioners apply Machine Learning to increasingly more software engineering problems, the approaches they use become more sophisticated. A lot of modern approaches utilize internal code structure in the form of an abstract syntax tree (AST) or its extensions: path-based representation, complex gra... | ['Timofey Bryksin', 'Egor Bogomolov', 'Egor Spirin', 'Ilya Utkin'] | 2022-06-17 | null | null | null | null | ['method-name-prediction'] | ['natural-language-processing'] | [-1.05478473e-01 3.58020850e-02 -1.86171979e-01 -4.42171246e-01
-7.56720483e-01 -7.68572927e-01 2.56617159e-01 2.02802762e-01
-9.60636735e-02 1.14329912e-01 2.31361076e-01 -9.50880170e-01
2.25921478e-02 -7.39755332e-01 -6.40014827e-01 -1.39662176e-01
4.34759585e-03 1.50530651e-01 3.82135510e-01 -5.56017049... | [7.822881698608398, 7.839896202087402] |
4c674197-9265-4472-b617-92428ba910f9 | an-embarrassingly-simple-consistency | 2202.00677 | null | https://arxiv.org/abs/2202.00677v2 | https://arxiv.org/pdf/2202.00677v2.pdf | An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical Image Segmentation | The scarcity of pixel-level annotation is a prevalent problem in medical image segmentation tasks. In this paper, we introduce a novel regularization strategy involving interpolation-based mixing for semi-supervised medical image segmentation. The proposed method is a new consistency regularization strategy that encour... | ['Agniv Chatterjee', 'Rukhshanda Hussain', 'Rajarshi Bhattacharya', 'Hritam Basak'] | 2022-02-01 | null | null | null | null | ['semi-supervised-medical-image-segmentation', '3d-medical-imaging-segmentation'] | ['computer-vision', 'medical'] | [ 4.62512612e-01 5.40313780e-01 -1.20114677e-01 -5.58303535e-01
-1.24018204e+00 -1.91078141e-01 3.60144228e-01 1.08552657e-01
-6.14728391e-01 1.05200350e+00 -2.64800955e-02 -2.04176843e-01
1.96691647e-01 -4.35462654e-01 -7.61493981e-01 -9.29189622e-01
3.32467109e-01 5.13433814e-01 1.93284556e-01 1.56196654... | [14.532055854797363, -2.1734018325805664] |
8cd9fb5d-480d-40a7-a0f7-b92d4131f531 | from-product-recommendation-to-cyber-attack | 1804.10276 | null | http://arxiv.org/abs/1804.10276v1 | http://arxiv.org/pdf/1804.10276v1.pdf | From product recommendation to cyber-attack prediction: Generating attack graphs and predicting future attacks | Modern information society depends on reliable functionality of information
systems infrastructure, while at the same time the number of cyber-attacks has
been increasing over the years and damages have been caused. Furthermore,
graphs can be used to show paths than can be exploited by attackers to intrude
into systems... | ['Mouratidis Haralambos', 'Papastergiou Spyridon', 'Pavlidis Michalis', 'Pimenidis Elias', 'Polatidis Nikolaos'] | 2018-04-26 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-1.03121735e-01 1.62335768e-01 -1.26726991e-02 9.47162684e-04
1.46659538e-01 -1.02475369e+00 6.06142938e-01 5.05976498e-01
6.90515563e-02 4.11218554e-01 4.01638895e-02 -1.02425182e+00
-6.80929422e-01 -1.37137699e+00 -7.23203048e-02 -2.17291206e-01
-6.13706470e-01 8.16458538e-02 5.68185270e-01 -6.11008346... | [5.2915472984313965, 7.19755744934082] |
f9895674-0c29-435e-b5d9-ee50a2c77d7d | heat-holistic-edge-attention-transformer-for | 2111.15143 | null | https://arxiv.org/abs/2111.15143v3 | https://arxiv.org/pdf/2111.15143v3.pdf | HEAT: Holistic Edge Attention Transformer for Structured Reconstruction | This paper presents a novel attention-based neural network for structured reconstruction, which takes a 2D raster image as an input and reconstructs a planar graph depicting an underlying geometric structure. The approach detects corners and classifies edge candidates between corners in an end-to-end manner. Our contri... | ['Yasutaka Furukawa', 'Yiming Qian', 'Jiacheng Chen'] | 2021-11-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_HEAT_Holistic_Edge_Attention_Transformer_for_Structured_Reconstruction_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_HEAT_Holistic_Edge_Attention_Transformer_for_Structured_Reconstruction_CVPR_2022_paper.pdf | cvpr-2022-1 | ['graph-reconstruction', 'extracting-buildings-in-remote-sensing-images'] | ['graphs', 'miscellaneous'] | [ 5.06612301e-01 4.47710931e-01 -3.57923582e-02 -3.92481387e-01
-7.41888642e-01 -2.20429435e-01 4.14054692e-01 2.13660449e-01
7.92660192e-02 9.37085971e-02 4.77105945e-01 -4.22015250e-01
1.67632133e-01 -1.20574927e+00 -1.20474946e+00 -3.26626450e-01
-3.01534802e-01 6.01584136e-01 1.19900711e-01 -2.47652680... | [8.197868347167969, -3.1050102710723877] |
9d7b26fb-85a0-4411-bacc-b5198a8378c3 | algorithms-of-real-time-navigation-and | 2208.10172 | null | https://arxiv.org/abs/2208.10172v1 | https://arxiv.org/pdf/2208.10172v1.pdf | Algorithms of Real-Time Navigation and Control of Autonomous Unmanned Vehicles | The rapid development of robotics has benefited by more and more people putting their attention to it. With the demand for robots is growing for the purpose of fulfilling tasks instead of humans, how to control the robot better is becoming a hot topic. For obstacle avoidance, we proposed algorithms for both 2D planar e... | ['Yang Zhang'] | 2022-08-22 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [ 5.36404885e-02 1.22233197e-01 1.90025359e-01 -4.79029715e-01
-5.40442467e-02 -4.31359023e-01 3.74425977e-01 -7.89777189e-02
-7.32772946e-01 7.66181529e-01 -3.78870696e-01 -5.78469753e-01
-5.22762716e-01 -1.00629103e+00 -3.00440133e-01 -7.38291681e-01
-1.93658575e-01 5.59544146e-01 8.06431353e-01 -9.76820350... | [4.97337532043457, 1.4359711408615112] |
6fdb3985-c329-44b7-b78b-91f4b9c32933 | referring-image-segmentation-via-cross-modal-1 | 2010.00514 | null | https://arxiv.org/abs/2010.00514v1 | https://arxiv.org/pdf/2010.00514v1.pdf | Referring Image Segmentation via Cross-Modal Progressive Comprehension | Referring image segmentation aims at segmenting the foreground masks of the entities that can well match the description given in the natural language expression. Previous approaches tackle this problem using implicit feature interaction and fusion between visual and linguistic modalities, but usually fail to explore i... | ['Guanbin Li', 'Si Liu', 'Shaofei Huang', 'Luoqi Liu', 'Bo Li', 'Yunchao Wei', 'Tianrui Hui', 'Jizhong Han'] | 2020-10-01 | referring-image-segmentation-via-cross-modal | http://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Referring_Image_Segmentation_via_Cross-Modal_Progressive_Comprehension_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Huang_Referring_Image_Segmentation_via_Cross-Modal_Progressive_Comprehension_CVPR_2020_paper.pdf | cvpr-2020-6 | ['referring-expression-segmentation'] | ['computer-vision'] | [ 3.51383448e-01 2.66819239e-01 -1.28819287e-01 -4.54336435e-01
-9.12069678e-01 -5.55224180e-01 7.21255124e-01 5.10988653e-01
-4.29126501e-01 3.27130020e-01 3.38182420e-01 9.75315869e-02
4.29887213e-02 -5.72520256e-01 -4.45085406e-01 -6.23480797e-01
4.79312360e-01 2.95017004e-01 4.23214555e-01 -3.41791183... | [10.42684555053711, 1.290026307106018] |
e51eb5a7-4b6b-41b1-8ccd-e690f970a841 | privacy-against-inference-attacks-in-vertical | 2207.11788 | null | https://arxiv.org/abs/2207.11788v3 | https://arxiv.org/pdf/2207.11788v3.pdf | Privacy Against Inference Attacks in Vertical Federated Learning | Vertical federated learning is considered, where an active party, having access to true class labels, wishes to build a classification model by utilizing more features from a passive party, which has no access to the labels, to improve the model accuracy. In the prediction phase, with logistic regression as the classif... | ['Deniz Gunduz', 'Morteza Varasteh', 'Borzoo Rassouli'] | 2022-07-24 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 2.00204909e-01 3.15638542e-01 -2.57595479e-01 -2.51786172e-01
-8.92531812e-01 -1.12826109e+00 1.50694260e-02 4.57178026e-01
-3.75866055e-01 7.08569765e-01 -3.86630625e-01 -4.31041896e-01
-1.52417853e-01 -1.22322822e+00 -8.26815963e-01 -1.35616517e+00
4.73101698e-02 1.38811260e-01 -8.26599449e-03 9.52182412... | [5.827385425567627, 6.79453706741333] |
8dd74582-d6d9-439d-b423-8217fb2741b8 | reproducing-personalised-session-search-over | 2201.08622 | null | https://arxiv.org/abs/2201.08622v1 | https://arxiv.org/pdf/2201.08622v1.pdf | Reproducing Personalised Session Search over the AOL Query Log | Despite its troubled past, the AOL Query Log continues to be an important resource to the research community -- particularly for tasks like search personalisation. When using the query log these ranking experiments, little attention is usually paid to the document corpus. Recent work typically uses a corpus containing ... | ['Iadh Ounis', 'Craig Macdonald', 'Sean MacAvaney'] | 2022-01-21 | null | null | null | null | ['session-search'] | ['natural-language-processing'] | [ 5.21886759e-02 -1.28262252e-01 -3.00255597e-01 -1.55249655e-01
-1.03814387e+00 -1.11924028e+00 1.27963305e+00 5.93445063e-01
-9.49835420e-01 6.55440509e-01 7.09196270e-01 -6.24802470e-01
-5.33013225e-01 -5.28335571e-01 -6.94725931e-01 -2.32752681e-01
-4.60070893e-02 8.23183239e-01 7.17037201e-01 -5.81039011... | [11.434247970581055, 7.652737140655518] |
414c938f-a607-4ed3-8e5c-436ce8b17ee6 | self-explainable-graph-neural-networks-for | 2305.12578 | null | https://arxiv.org/abs/2305.12578v1 | https://arxiv.org/pdf/2305.12578v1.pdf | Self-Explainable Graph Neural Networks for Link Prediction | Graph Neural Networks (GNNs) have achieved state-of-the-art performance for link prediction. However, GNNs suffer from poor interpretability, which limits their adoptions in critical scenarios that require knowing why certain links are predicted. Despite various methods proposed for the explainability of GNNs, most of ... | ['Suhang Wang', 'Hui Liu', 'Junjie Xu', 'Xianfeng Tang', 'Dongsheng Luo', 'Huaisheng Zhu'] | 2023-05-21 | null | null | null | null | ['link-prediction'] | ['graphs'] | [ 4.67625931e-02 8.78301084e-01 -6.24021471e-01 -4.51288015e-01
1.93686783e-01 -1.70299590e-01 2.92965472e-01 4.41407084e-01
6.23439312e-01 8.11812878e-01 -5.99406697e-02 -6.10809624e-01
-6.95037723e-01 -1.14198470e+00 -8.05999458e-01 -2.39315152e-01
-4.33689952e-01 7.72482395e-01 1.47293448e-01 -3.21437091... | [7.459128379821777, 6.3078460693359375] |
0b1e9764-921b-46ac-99de-273e4f7eaf14 | towards-modeling-human-attention-from-eye | 2305.09773 | null | https://arxiv.org/abs/2305.09773v1 | https://arxiv.org/pdf/2305.09773v1.pdf | Towards Modeling Human Attention from Eye Movements for Neural Source Code Summarization | Neural source code summarization is the task of generating natural language descriptions of source code behavior using neural networks. A fundamental component of most neural models is an attention mechanism. The attention mechanism learns to connect features in source code to specific words to use when generating natu... | ['Collin McMillan', 'Bonita Sharif', 'Aakash Bansal'] | 2023-05-16 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 3.01797658e-01 6.61355734e-01 -1.18525565e-01 -2.21311644e-01
-3.68069619e-01 -2.73139924e-01 6.26545608e-01 6.32161081e-01
6.30939603e-02 3.74071717e-01 9.21360791e-01 -3.33584756e-01
2.77801067e-01 -4.78381634e-01 -7.55786717e-01 2.35585356e-03
8.41488782e-03 -1.21565618e-01 -5.91006018e-02 -4.57235187... | [7.6651082038879395, 7.9118971824646] |
e6598323-6d7a-4349-b2ab-27be175f6d59 | msgdd-cgan-multi-scale-gradients-dual | 2109.05614 | null | https://arxiv.org/abs/2109.05614v1 | https://arxiv.org/pdf/2109.05614v1.pdf | MSGDD-cGAN: Multi-Scale Gradients Dual Discriminator Conditional Generative Adversarial Network | Conditional Generative Adversarial Networks (cGANs) have been used in many image processing tasks. However, they still have serious problems maintaining the balance between conditioning the output on the input and creating the output with the desired distribution based on the corresponding ground truth. The traditional... | ['Shadrokh Samavi', 'Shahram Shirani', 'Nader Karimi', 'Zahra Nabizadeh', 'Mohammadreza Naderi'] | 2021-09-12 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 3.76360148e-01 3.76928866e-01 3.82195234e-01 -1.70388460e-01
-4.30111617e-01 -4.79194134e-01 3.82020891e-01 1.95235424e-02
-2.28144273e-01 9.12147284e-01 -1.21562220e-01 2.09290860e-03
1.73370793e-01 -1.15039134e+00 -7.95703709e-01 -1.20361030e+00
3.51348430e-01 3.09550494e-01 3.28234494e-01 -5.10364398... | [11.712778091430664, -0.20859798789024353] |
d5333a42-1499-4973-8a3a-90be40fa7fc5 | data-driven-chance-constrained-multiple | 2306.14690 | null | https://arxiv.org/abs/2306.14690v1 | https://arxiv.org/pdf/2306.14690v1.pdf | Data-Driven Chance-Constrained Multiple-Choice Knapsack Problem: Model, Algorithms, and Applications | The multiple-choice knapsack problem (MCKP) is a classic NP-hard combinatorial optimization problem. Motivated by several significant practical applications, this work investigates a novel variant of MCKP called data-driven chance-constrained multiple-choice knapsack problem (DDCCMCKP), where the item weight is a rando... | ['Ke Tang', 'Yew-Soon Ong', 'Xiao Chen', 'Jin Wang', 'Shengcai Liu', 'Xuanfeng Li'] | 2023-06-26 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 6.25904575e-02 -3.85996103e-01 -6.05999768e-01 -1.45463124e-01
-6.91199481e-01 -5.69052398e-01 9.83683914e-02 2.20615650e-03
-3.11242968e-01 1.35954034e+00 -2.88705617e-01 -3.99631500e-01
-8.36915851e-01 -7.42359459e-01 -6.92701280e-01 -1.24521673e+00
-3.04284990e-01 6.72082663e-01 2.64943331e-01 -3.97576205... | [5.222994327545166, 3.104556083679199] |
1bc43f87-a2c3-4c8a-9e4c-3ac892b4639a | nuclei-detection-using-mixture-density | 1808.08279 | null | http://arxiv.org/abs/1808.08279v1 | http://arxiv.org/pdf/1808.08279v1.pdf | Nuclei Detection Using Mixture Density Networks | Nuclei detection is an important task in the histology domain as it is a main
step toward further analysis such as cell counting, cell segmentation, study of
cell connections, etc. This is a challenging task due to the complex texture of
histology image, variation in shape, and touching cells. To tackle these
hurdles, ... | ['Ali Gooya', 'Navid Alemi Koohababni', 'Mostafa Jahanifar', 'Nasir Rajpoot'] | 2018-08-22 | null | null | null | null | ['image-variation'] | ['computer-vision'] | [ 2.65063763e-01 -4.39649150e-02 5.01616448e-02 -1.32249296e-01
-6.66867912e-01 -2.56486475e-01 5.53310215e-01 4.79087919e-01
-7.71096885e-01 8.31237495e-01 -2.00463742e-01 -1.24207869e-01
-4.67465119e-03 -8.01978230e-01 -5.06413758e-01 -1.32899857e+00
1.79997265e-01 5.32879531e-01 6.85662329e-01 1.60668746... | [14.884443283081055, -3.0913949012756348] |
64417ddf-88ec-4fc6-8d14-b2a96aa8a887 | unsupervised-person-re-identification-by | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Wu_Unsupervised_Person_Re-Identification_by_Camera-Aware_Similarity_Consistency_Learning_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wu_Unsupervised_Person_Re-Identification_by_Camera-Aware_Similarity_Consistency_Learning_ICCV_2019_paper.pdf | Unsupervised Person Re-Identification by Camera-Aware Similarity Consistency Learning | For matching pedestrians across disjoint camera views in surveillance, person re-identification (Re-ID) has made great progress in supervised learning. However, it is infeasible to label data in a number of new scenes when extending a Re-ID system. Thus, studying unsupervised learning for Re-ID is important for saving ... | [' Jian-Huang Lai', ' Wei-Shi Zheng', 'Ancong Wu'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-1.96040481e-01 -5.61775029e-01 -9.16847810e-02 -7.16349125e-01
-7.20422924e-01 -5.70393682e-01 4.54915106e-01 -2.11270284e-02
-4.14269328e-01 5.28961122e-01 2.59026617e-01 3.58952045e-01
-7.25043472e-04 -4.70018059e-01 -7.78542697e-01 -6.04687750e-01
3.57436776e-01 4.15620953e-01 4.58254993e-01 2.29064569... | [14.73985481262207, 1.0344021320343018] |
790b3af3-929c-48c4-a1e5-e4403faaa604 | age-and-gender-prediction-from-face-images | 2010.03791 | null | https://arxiv.org/abs/2010.03791v2 | https://arxiv.org/pdf/2010.03791v2.pdf | Age and Gender Prediction From Face Images Using Attentional Convolutional Network | Automatic prediction of age and gender from face images has drawn a lot of attention recently, due it is wide applications in various facial analysis problems. However, due to the large intra-class variation of face images (such as variation in lighting, pose, scale, occlusion), the existing models are still behind the... | ['Shervin Minaee', 'Elham Azimi', 'Mehdi Minaei', 'Amirali Abdolrashidi'] | 2020-10-08 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [-1.16336912e-01 1.44884303e-01 -4.80502658e-02 -7.58911550e-01
-8.05317834e-02 1.38684493e-02 3.76171261e-01 -1.88346252e-01
-9.70415473e-02 3.68081361e-01 3.49379122e-01 2.65090823e-01
1.14923649e-01 -6.94086909e-01 -3.97293538e-01 -8.24784636e-01
-1.78448539e-02 1.95039332e-01 -1.03574954e-01 -1.02055073... | [13.505097389221191, 0.9020289778709412] |
af026a78-d213-4d6e-9ccc-3e3f88001e3b | compression-of-dynamic-medical-ct-data-using | 2302.01014 | null | https://arxiv.org/abs/2302.01014v1 | https://arxiv.org/pdf/2302.01014v1.pdf | Compression of Dynamic Medical CT Data Using Motion Compensated Wavelet Lifting with Denoised Update | For the lossless compression of dynamic 3-D+t volumes as produced by medical devices like Computed Tomography, various coding schemes can be applied. This paper shows that 3-D subband coding outperforms lossless HEVC coding and additionally provides a scalable representation, which is often required in telemedicine app... | ['André Kaup', 'Karina Jaskolka', 'Jürgen Seiler', 'Daniela Lanz'] | 2023-02-02 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 5.54094374e-01 1.15199545e-02 6.62938431e-02 7.35798199e-03
-7.57409692e-01 -8.62866640e-02 1.95027649e-01 4.98588949e-01
-5.84365726e-01 8.51493776e-01 3.36183608e-01 -8.78276080e-02
-8.77541155e-02 -8.80398870e-01 -4.76766646e-01 -8.35365891e-01
-6.67308047e-02 1.22673832e-01 4.43573356e-01 -1.84032515... | [11.463277816772461, -2.300748348236084] |
73bdb342-4e81-492d-aaf6-7f608a96797b | accessing-higher-dimensions-for-unsupervised | 2305.14200 | null | https://arxiv.org/abs/2305.14200v1 | https://arxiv.org/pdf/2305.14200v1.pdf | Accessing Higher Dimensions for Unsupervised Word Translation | The striking ability of unsupervised word translation has been demonstrated with the help of word vectors / pretraining; however, they require large amounts of data and usually fails if the data come from different domains. We propose coocmap, a method that can use either high-dimensional co-occurrence counts or their ... | ['Sida I. Wang'] | 2023-05-23 | null | null | null | null | ['word-translation'] | ['natural-language-processing'] | [ 3.79767679e-02 -1.40332314e-03 -3.69540095e-01 -1.61042079e-01
-1.05668557e+00 -7.57862628e-01 1.06024361e+00 -1.23379361e-02
-9.09156680e-01 1.01060379e+00 6.99213445e-01 -6.18262053e-01
-5.35478592e-02 -4.60483313e-01 -5.46513259e-01 -5.91993988e-01
1.93795919e-01 8.83989573e-01 -1.70023948e-01 -3.73320937... | [11.371798515319824, 10.207185745239258] |
b5d4b308-cafb-47bc-94d2-9671866f08f2 | provably-powerful-graph-networks | 1905.11136 | null | https://arxiv.org/abs/1905.11136v4 | https://arxiv.org/pdf/1905.11136v4.pdf | Provably Powerful Graph Networks | Recently, the Weisfeiler-Lehman (WL) graph isomorphism test was used to measure the expressive power of graph neural networks (GNN). It was shown that the popular message passing GNN cannot distinguish between graphs that are indistinguishable by the 1-WL test (Morris et al. 2018; Xu et al. 2019). Unfortunately, many s... | ['Heli Ben-Hamu', 'Hadar Serviansky', 'Yaron Lipman', 'Haggai Maron'] | 2019-05-27 | provably-powerful-graph-networks-1 | http://papers.nips.cc/paper/8488-provably-powerful-graph-networks | http://papers.nips.cc/paper/8488-provably-powerful-graph-networks.pdf | neurips-2019-12 | ['graph-regression'] | ['graphs'] | [ 2.18510568e-01 4.53288853e-01 -9.91163701e-02 -1.12192772e-01
-2.23242462e-01 -8.20106804e-01 6.78135097e-01 3.31754893e-01
-5.31249285e-01 5.80737233e-01 -2.42216542e-01 -8.25394273e-01
-6.31732464e-01 -8.37783933e-01 -1.11955750e+00 -7.15111256e-01
-9.61646438e-01 4.23332185e-01 1.65250629e-01 -3.97911042... | [6.880330562591553, 6.205511093139648] |
ff7a9700-fb35-47bf-9cb6-4fc37d74f248 | spherical-formulation-of-moving-object | 2003.03262 | null | https://arxiv.org/abs/2003.03262v1 | https://arxiv.org/pdf/2003.03262v1.pdf | Spherical formulation of moving object geometric constraints for monocular fisheye cameras | In this paper, we introduce a moving object detection algorithm for fisheye cameras used in autonomous driving. We reformulate the three commonly used constraints in rectilinear images (epipolar, positive depth and positive height constraints) to spherical coordinates which is invariant to specific camera configuration... | ['Ciaran Hughes', 'Letizia Mariotti'] | 2020-03-06 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 1.81328747e-02 -1.43407704e-02 -1.87935364e-02 -3.36535424e-01
1.65347219e-01 -8.38484108e-01 6.11779094e-01 -5.43960810e-01
-7.21784711e-01 4.45185691e-01 -5.35276651e-01 -2.78460175e-01
-7.28206933e-02 -5.10134280e-01 -7.37322748e-01 -6.31221652e-01
3.21302116e-01 1.58627108e-01 8.85157108e-01 -4.64854509... | [8.100048065185547, -2.18967604637146] |
e48e6649-d2f1-4786-8409-ead481a9bb23 | clipup-a-simple-and-powerful-optimizer-for | 2008.02387 | null | https://arxiv.org/abs/2008.02387v3 | https://arxiv.org/pdf/2008.02387v3.pdf | ClipUp: A Simple and Powerful Optimizer for Distribution-based Policy Evolution | Distribution-based search algorithms are an effective approach for evolutionary reinforcement learning of neural network controllers. In these algorithms, gradients of the total reward with respect to the policy parameters are estimated using a population of solutions drawn from a search distribution, and then used for... | ['Rupesh Kumar Srivastava', 'Paweł Liskowski', 'Nihat Engin Toklu'] | 2020-08-05 | null | null | null | null | ['humanoid-control'] | ['robots'] | [-1.92811191e-01 -2.88393468e-01 -4.15367067e-01 3.71007062e-02
-2.63253808e-01 -5.11843264e-01 4.72445101e-01 9.37761292e-02
-1.03477907e+00 1.19748890e+00 -2.27703720e-01 -1.70399159e-01
-2.98816383e-01 -5.08397996e-01 -7.17194796e-01 -1.14416146e+00
-6.72625527e-02 6.83095932e-01 3.57036710e-01 -6.65605843... | [4.265748023986816, 2.2496871948242188] |
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