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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6c98bc4f-fd08-4a0e-a436-28eef7f4248e | cross-stream-contrastive-learning-for-self | 2305.02324 | null | https://arxiv.org/abs/2305.02324v1 | https://arxiv.org/pdf/2305.02324v1.pdf | Cross-Stream Contrastive Learning for Self-Supervised Skeleton-Based Action Recognition | Self-supervised skeleton-based action recognition enjoys a rapid growth along with the development of contrastive learning. The existing methods rely on imposing invariance to augmentations of 3D skeleton within a single data stream, which merely leverages the easy positive pairs and limits the ability to explore the c... | ['Wensheng Zhang', 'Zhizhong Zhang', 'Yongqiang Tang', 'Ding Li'] | 2023-05-03 | null | null | null | null | ['skeleton-based-action-recognition', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision'] | [ 5.29561937e-01 -4.81479675e-01 -5.60592949e-01 -1.19474038e-01
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-3.47485304e-01 3.90145183e-01 3.90520453e-01 1.65944129e-01
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-1.20495662e-01 -2.48014659e-01 6.43054426e-01 -3.73283207... | [8.203736305236816, 0.627679169178009] |
7fb69470-0412-4481-996c-65f1be0f01e6 | on-imitation-in-mean-field-games | 2306.14799 | null | https://arxiv.org/abs/2306.14799v1 | https://arxiv.org/pdf/2306.14799v1.pdf | On Imitation in Mean-field Games | We explore the problem of imitation learning (IL) in the context of mean-field games (MFGs), where the goal is to imitate the behavior of a population of agents following a Nash equilibrium policy according to some unknown payoff function. IL in MFGs presents new challenges compared to single-agent IL, particularly whe... | ['Matthieu Geist', 'Mathieu Laurière', 'Niao He', 'Olivier Pietquin', 'Pavel Kolev', 'Giorgia Ramponi'] | 2023-06-26 | null | null | null | null | ['imitation-learning'] | ['methodology'] | [ 2.06220984e-01 4.91335332e-01 4.04543690e-02 6.72985196e-01
-5.54920018e-01 -7.25893080e-01 4.89381701e-01 1.02840126e-01
-6.58864975e-01 1.24061024e+00 -2.74147958e-01 -1.64665312e-01
-5.48537731e-01 -6.53688788e-01 -1.05404103e+00 -9.74693239e-01
-3.56082290e-01 2.49079451e-01 -8.92127901e-02 -6.37691379... | [4.200345516204834, 2.5493886470794678] |
5f17853b-3b64-4bce-9ebd-afb68127837b | dynamical-variational-autoencoders-a | 2008.12595 | null | https://arxiv.org/abs/2008.12595v4 | https://arxiv.org/pdf/2008.12595v4.pdf | Dynamical Variational Autoencoders: A Comprehensive Review | Variational autoencoders (VAEs) are powerful deep generative models widely used to represent high-dimensional complex data through a low-dimensional latent space learned in an unsupervised manner. In the original VAE model, the input data vectors are processed independently. Recently, a series of papers have presented ... | ['Xavier Alameda-Pineda', 'Julien Diard', 'Xiaoyu Bie', 'Simon Leglaive', 'Thomas Hueber', 'Laurent Girin'] | 2020-08-28 | null | null | null | null | ['3d-human-dynamics'] | ['computer-vision'] | [-1.73195571e-01 -9.20927687e-06 6.89745992e-02 -3.35203740e-03
-1.66649476e-01 -5.78452945e-01 9.84154642e-01 -5.99738002e-01
-2.08013937e-01 5.37000418e-01 3.98012042e-01 -1.02255352e-01
-2.27537483e-01 -6.49636507e-01 -5.21950126e-01 -1.02749252e+00
-2.47980412e-02 4.25736934e-01 -4.49565984e-02 -2.55538464... | [15.106929779052734, 6.313415050506592] |
f1e699bb-71a5-4443-b217-0c094fb5d275 | learning-under-selective-labels-with | 2306.07566 | null | https://arxiv.org/abs/2306.07566v2 | https://arxiv.org/pdf/2306.07566v2.pdf | Learning under Selective Labels with Data from Heterogeneous Decision-makers: An Instrumental Variable Approach | We study the problem of learning with selectively labeled data, which arises when outcomes are only partially labeled due to historical decision-making. The labeled data distribution may substantially differ from the full population, especially when the historical decisions and the target outcome can be simultaneously ... | ['Xiaojie Mao', 'Zhehao Li', 'Jian Chen'] | 2023-06-13 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 4.80898082e-01 3.58238339e-01 -8.96201909e-01 -3.41895223e-01
-1.09817684e+00 -5.21079242e-01 5.63650131e-01 1.85978472e-01
-4.01694208e-01 1.00871718e+00 1.27885729e-01 -3.30592692e-01
-4.84645188e-01 -6.51741743e-01 -8.87560844e-01 -8.82962525e-01
3.87373492e-02 7.63592720e-01 -5.48924446e-01 5.66756845... | [8.131221771240234, 4.809854984283447] |
432ffe44-a943-43a6-8f4e-1a19c55c244f | applying-standards-to-advance-upstream | 2306.03503 | null | https://arxiv.org/abs/2306.03503v2 | https://arxiv.org/pdf/2306.03503v2.pdf | Applying Standards to Advance Upstream & Downstream Ethics in Large Language Models | This paper explores how AI-owners can develop safeguards for AI-generated content by drawing from established codes of conduct and ethical standards in other content-creation industries. It delves into the current state of ethical awareness on Large Language Models (LLMs). By dissecting the mechanism of content generat... | ['Marybeth Sandell', 'Jose Berengueres'] | 2023-06-06 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [ 4.70721126e-01 1.01539683e+00 -1.79342583e-01 7.04308599e-02
-7.05947459e-01 -1.03876746e+00 8.96920204e-01 3.62881839e-01
-3.33379954e-01 5.31124055e-01 9.19224441e-01 -6.55517578e-01
-4.26433533e-01 -3.75075668e-01 -6.39296353e-01 -2.15327740e-01
4.63167578e-01 -1.00189745e-02 -5.73676050e-01 -2.71928668... | [9.131610870361328, 6.519079685211182] |
f1bd682f-410c-4d75-963b-ff0b07fa57ff | multi-microgrid-collaborative-optimization | 2304.01223 | null | https://arxiv.org/abs/2304.01223v1 | https://arxiv.org/pdf/2304.01223v1.pdf | Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm | The implementation of a multi-microgrid (MMG) system with multiple renewable energy sources enables the facilitation of electricity trading. To tackle the energy management problem of a MMG system, which consists of multiple renewable energy microgrids belonging to different operating entities, this paper proposes a MM... | ['Haibo Wu', 'Bin Wang', 'Yang Li', 'Jiankai Gao'] | 2023-04-01 | null | null | null | null | ['automl', 'energy-management'] | ['methodology', 'time-series'] | [-1.09725654e+00 9.78907943e-02 -1.95465177e-01 2.12610543e-01
-4.36924130e-01 -3.12038124e-01 4.76003557e-01 -8.39054137e-02
-6.31264895e-02 1.37304962e+00 -2.35772565e-01 1.17473258e-02
-3.80502582e-01 -1.11355531e+00 -4.12523270e-01 -1.30384588e+00
-3.66913617e-01 7.99573302e-01 -4.62918252e-01 -2.73611069... | [5.596184730529785, 2.5400009155273438] |
22609bd4-9144-452d-9fef-3263b0c77b2d | hardware-agnostic-computation-for-large-scale | 2207.08544 | null | https://arxiv.org/abs/2207.08544v1 | https://arxiv.org/pdf/2207.08544v1.pdf | Hardware-agnostic Computation for Large-scale Knowledge Graph Embeddings | Knowledge graph embedding research has mainly focused on learning continuous representations of knowledge graphs towards the link prediction problem. Recently developed frameworks can be effectively applied in research related applications. Yet, these frameworks do not fulfill many requirements of real-world applicatio... | ['Axel-Cyrille Ngonga Ngomo', 'Caglar Demir'] | 2022-07-18 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-3.81150603e-01 1.36475056e-01 -4.97020245e-01 4.46500853e-02
-1.03063583e-01 -6.03526950e-01 3.90720397e-01 2.87294835e-01
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-5.09095132e-01 -1.22791600e+00 -5.26859701e-01 -3.92506331e-01
-1.92408115e-01 5.56139827e-01 3.61846268e-01 -3.53418797... | [8.673843383789062, 7.830080509185791] |
a9556ad6-7f88-4e53-8fd4-ae8951ac2032 | cluster-analysis-of-online-mental-health | null | null | https://aclanthology.org/2021.louhi-1.10 | https://aclanthology.org/2021.louhi-1.10.pdf | Cluster Analysis of Online Mental Health Discourse using Topic-Infused Deep Contextualized Representations | With mental health as a problem domain in NLP, the bulk of contemporary literature revolves around building better mental illness prediction models. The research focusing on the identification of discussion clusters in online mental health communities has been relatively limited. Moreover, as the underlying methodologi... | ['Manisha Marathe', 'Pradnya Kulkarni', 'Amey Hengle', 'Atharva Kulkarni'] | null | null | null | null | eacl-louhi-2021-4 | ['text-clustering'] | ['natural-language-processing'] | [ 1.60124257e-01 7.99790502e-01 -5.16586065e-01 -1.26134068e-01
-6.59791648e-01 5.09209782e-02 5.23624361e-01 1.26304638e+00
3.40553857e-02 2.84972161e-01 1.21444094e+00 -1.93842471e-01
-5.74389577e-01 -7.01824367e-01 1.04913831e-01 -7.23516524e-01
-3.70279610e-01 4.69657153e-01 -7.39531875e-01 -2.12764680... | [8.766376495361328, 9.544416427612305] |
5f0da607-7529-4742-ae02-fe5d87ecd2d5 | diffusion-model-based-posterior-sampling-for | 2211.12343 | null | https://arxiv.org/abs/2211.12343v2 | https://arxiv.org/pdf/2211.12343v2.pdf | Diffusion Model Based Posterior Sampling for Noisy Linear Inverse Problems | We consider the ubiquitous linear inverse problems with additive Gaussian noise and propose an unsupervised sampling approach called diffusion model based posterior sampling (DMPS) to reconstruct the unknown signal from noisy linear measurements. Specifically, using one diffusion model (DM) as an implicit prior, the fu... | ['Yoshiyuki Kabashima', 'Xiangming Meng'] | 2022-11-20 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 3.84694129e-01 -1.17663488e-01 2.47093171e-01 -2.19735518e-01
-1.40262508e+00 -2.84855008e-01 6.00808620e-01 -6.38533413e-01
-3.36299449e-01 6.99192345e-01 4.06986356e-01 2.62625087e-02
-9.09558162e-02 -4.32605237e-01 -6.71081424e-01 -9.81026232e-01
2.22568840e-01 4.57451820e-01 1.96255744e-01 2.11173326... | [11.714147567749023, -2.3806238174438477] |
0261d714-ac5c-467e-a9e5-2f00a9724a3b | policy-regularization-with-dataset-constraint | 2306.06569 | null | https://arxiv.org/abs/2306.06569v1 | https://arxiv.org/pdf/2306.06569v1.pdf | Policy Regularization with Dataset Constraint for Offline Reinforcement Learning | We consider the problem of learning the best possible policy from a fixed dataset, known as offline Reinforcement Learning (RL). A common taxonomy of existing offline RL works is policy regularization, which typically constrains the learned policy by distribution or support of the behavior policy. However, distribution... | ['Yang Yu', 'Zongzhang Zhang', 'Fuxiang Zhang', 'Yi-Chen Li', 'Yuhang Ran'] | 2023-06-11 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.36436069e-01 7.67949373e-02 -1.06301999e+00 -4.98431697e-02
-5.36416352e-01 -7.72361159e-01 3.42577487e-01 6.89575868e-03
-8.53185594e-01 1.22224152e+00 2.26457551e-01 -4.44866061e-01
-2.45690674e-01 -6.22556567e-01 -8.12260985e-01 -9.74016309e-01
-6.89612776e-02 3.76609564e-01 1.52356058e-01 -2.39421889... | [4.120899200439453, 2.1730480194091797] |
2fee2e94-3d2f-4865-af03-4f0c40d65bd9 | techniques-to-improve-neural-math-word | 2302.03145 | null | https://arxiv.org/abs/2302.03145v1 | https://arxiv.org/pdf/2302.03145v1.pdf | Techniques to Improve Neural Math Word Problem Solvers | Developing automatic Math Word Problem (MWP) solvers is a challenging task that demands the ability of understanding and mathematical reasoning over the natural language. Recent neural-based approaches mainly encode the problem text using a language model and decode a mathematical expression over quantities and operato... | ['Youyuan Zhang'] | 2023-02-06 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 7.29949027e-02 9.69119277e-03 -2.15173244e-01 -4.90798622e-01
-6.74569368e-01 -8.08362424e-01 3.76971692e-01 2.10027426e-01
-2.45633841e-01 5.36709309e-01 3.57066154e-01 -6.68850124e-01
5.36243953e-02 -1.27882302e+00 -1.03862834e+00 -2.61863559e-01
2.31684029e-01 1.89762831e-01 -2.76611894e-01 -3.82453710... | [9.749075889587402, 7.466348171234131] |
477cd1e9-8dab-4f86-a74f-d20723c204eb | autofits-automatic-feature-engineering-for | 2112.14806 | null | https://arxiv.org/abs/2112.14806v1 | https://arxiv.org/pdf/2112.14806v1.pdf | AutoFITS: Automatic Feature Engineering for Irregular Time Series | A time series represents a set of observations collected over time. Typically, these observations are captured with a uniform sampling frequency (e.g. daily). When data points are observed in uneven time intervals the time series is referred to as irregular or intermittent. In such scenarios, the most common solution i... | ['João Vinagre', 'Vitor Cerqueira', 'Pedro Costa'] | 2021-12-29 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [ 2.75752187e-01 -1.67248741e-01 1.08174756e-01 -1.46750599e-01
-2.56671786e-01 -8.84271204e-01 9.29692864e-01 7.63441324e-01
-1.13873973e-01 5.64053118e-01 1.36626884e-01 -2.85487711e-01
-5.01518488e-01 -1.00150526e+00 -5.19702971e-01 -7.09034562e-01
-3.88531983e-01 2.23574609e-01 2.96448451e-02 -4.26056385... | [7.222724914550781, 3.2871596813201904] |
b19d2234-2dd3-4e2c-b5ff-1e3f8cdd862f | eva-exploring-the-limits-of-masked-visual | 2211.07636 | null | https://arxiv.org/abs/2211.07636v2 | https://arxiv.org/pdf/2211.07636v2.pdf | EVA: Exploring the Limits of Masked Visual Representation Learning at Scale | We launch EVA, a vision-centric foundation model to explore the limits of visual representation at scale using only publicly accessible data. EVA is a vanilla ViT pre-trained to reconstruct the masked out image-text aligned vision features conditioned on visible image patches. Via this pretext task, we can efficiently ... | ['Yue Cao', 'Xinlong Wang', 'Tiejun Huang', 'Xinggang Wang', 'Ledell Wu', 'Quan Sun', 'Binhui Xie', 'Wen Wang', 'Yuxin Fang'] | 2022-11-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Fang_EVA_Exploring_the_Limits_of_Masked_Visual_Representation_Learning_at_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Fang_EVA_Exploring_the_Limits_of_Masked_Visual_Representation_Learning_at_CVPR_2023_paper.pdf | cvpr-2023-1 | ['self-supervised-image-classification', 'action-classification'] | ['computer-vision', 'computer-vision'] | [ 1.28148749e-01 8.47141668e-02 -2.62374967e-01 -2.42308363e-01
-7.91019678e-01 -8.43928635e-01 7.39990950e-01 -3.24712157e-01
-3.97664994e-01 1.98925197e-01 1.34113476e-01 -4.88303244e-01
3.99822384e-01 -4.44420636e-01 -1.23142016e+00 -4.68808323e-01
4.01267767e-01 6.33282125e-01 2.80171752e-01 -7.50217736... | [9.876096725463867, 1.3773658275604248] |
be62439c-d7a1-4035-a0d4-3b735210317b | transformer-based-source-free-domain | 2105.14138 | null | https://arxiv.org/abs/2105.14138v1 | https://arxiv.org/pdf/2105.14138v1.pdf | Transformer-Based Source-Free Domain Adaptation | In this paper, we study the task of source-free domain adaptation (SFDA), where the source data are not available during target adaptation. Previous works on SFDA mainly focus on aligning the cross-domain distributions. However, they ignore the generalization ability of the pretrained source model, which largely influe... | ['Elisa Ricci', 'Nicu Sebe', 'Ling Shao', 'Mingli Ding', 'Zhun Zhong', 'Hao Tang', 'Guanglei Yang'] | 2021-05-28 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [-8.51676147e-03 -1.80344671e-01 -2.64727652e-01 -5.22336304e-01
-4.26678449e-01 -5.11084557e-01 4.77460533e-01 -2.89231598e-01
-3.62706810e-01 5.74081481e-01 8.63661095e-02 2.55154222e-02
1.79003686e-01 -8.00023615e-01 -6.77530229e-01 -8.03573668e-01
5.47140062e-01 4.63055491e-01 4.06202048e-01 -2.73745865... | [10.332412719726562, 3.0490522384643555] |
26d5b7a5-df10-43e0-b25f-09177d41b3d5 | clustering-friendly-representation-learning-1 | 2106.00131 | null | https://arxiv.org/abs/2106.00131v1 | https://arxiv.org/pdf/2106.00131v1.pdf | Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation | Clustering is one of the most fundamental tasks in machine learning. Recently, deep clustering has become a major trend in clustering techniques. Representation learning often plays an important role in the effectiveness of deep clustering, and thus can be a principal cause of performance degradation. In this paper, we... | ['Kouta Nakata', 'Kentaro Takagi', 'Yaling Tao'] | 2021-05-31 | clustering-friendly-representation-learning | https://openreview.net/forum?id=e12NDM7wkEY | https://openreview.net/pdf?id=e12NDM7wkEY | iclr-2021-1 | ['image-clustering'] | ['computer-vision'] | [ 1.65758103e-01 -2.09109202e-01 -1.06121980e-01 -7.78420329e-01
-7.76338339e-01 -3.28288019e-01 4.88198519e-01 -7.27974698e-02
-4.67622131e-01 3.25235575e-01 5.98778985e-02 8.33751783e-02
-5.93259215e-01 -4.82304305e-01 -4.47362036e-01 -9.77138042e-01
-1.57656431e-01 5.02366304e-01 -2.44058713e-01 3.31796318... | [9.214543342590332, 3.2075345516204834] |
00acb101-278c-465a-840b-502be5bc7987 | utnlp-at-semeval-2021-task-5-a-comparative | 2104.04770 | null | https://arxiv.org/abs/2104.04770v1 | https://arxiv.org/pdf/2104.04770v1.pdf | UTNLP at SemEval-2021 Task 5: A Comparative Analysis of Toxic Span Detection using Attention-based, Named Entity Recognition, and Ensemble Models | Detecting which parts of a sentence contribute to that sentence's toxicity -- rather than providing a sentence-level verdict of hatefulness -- would increase the interpretability of models and allow human moderators to better understand the outputs of the system. This paper presents our team's, UTNLP, methodology and r... | ['Azadeh Shakery', 'Behnam Bahrak', 'Emad Kebriaei', 'Nazanin Sabri', 'Alireza Salemi'] | 2021-04-10 | null | https://aclanthology.org/2021.semeval-1.136 | https://aclanthology.org/2021.semeval-1.136.pdf | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [-6.04791008e-02 1.83241487e-01 -8.74322578e-02 -3.22537124e-01
-7.89768636e-01 -7.07541883e-01 6.79534793e-01 8.16314340e-01
-5.04216254e-01 7.48945594e-01 9.61226702e-01 -5.29865563e-01
2.37756353e-02 -3.46273154e-01 -3.62224698e-01 -4.80816245e-01
2.98099946e-02 -1.79258753e-02 -9.92763489e-02 -3.43203336... | [8.915112495422363, 10.640345573425293] |
28b27bdd-50c8-458a-80a6-7ade1cfc8d0c | document-grounded-goal-oriented-dialogue | null | null | https://aclanthology.org/2021.dialdoc-1.12 | https://aclanthology.org/2021.dialdoc-1.12.pdf | Document-Grounded Goal-Oriented Dialogue Systems on Pre-Trained Language Model with Diverse Input Representation | Document-grounded goal-oriented dialog system understands users’ utterances, and generates proper responses by using information obtained from documents. The Dialdoc21 shared task consists of two subtasks; subtask1, finding text spans associated with users’ utterances from documents, and subtask2, generating responses ... | ['Harksoo Kim', 'Oh-Woog Kwon', 'Jin-Xia Huang', 'Yejin Lee', 'Sihyung Kim', 'Dohaeng Lee', 'Boeun Kim'] | null | null | null | null | acl-dialdoc-2021-8 | ['goal-oriented-dialog', 'goal-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [ 9.83127132e-02 4.52748954e-01 1.13552772e-01 -5.29964685e-01
-9.61535156e-01 -7.24812746e-01 6.08976185e-01 1.74659178e-01
-3.61343235e-01 9.04835939e-01 7.64220655e-01 -2.63076097e-01
-3.43444943e-02 -6.20278358e-01 -1.88689694e-01 -1.13762803e-01
1.83680594e-01 6.50534153e-01 3.81210804e-01 -6.23378158... | [12.715385437011719, 8.079568862915039] |
8acc148b-9547-4235-9d83-7b12f036b4a5 | rainbow-combining-improvements-in-deep | 1710.02298 | null | http://arxiv.org/abs/1710.02298v1 | http://arxiv.org/pdf/1710.02298v1.pdf | Rainbow: Combining Improvements in Deep Reinforcement Learning | The deep reinforcement learning community has made several independent
improvements to the DQN algorithm. However, it is unclear which of these
extensions are complementary and can be fruitfully combined. This paper
examines six extensions to the DQN algorithm and empirically studies their
combination. Our experiments ... | ['Dan Horgan', 'Joseph Modayil', 'Hado van Hasselt', 'Matteo Hessel', 'David Silver', 'Will Dabney', 'Tom Schaul', 'Mohammad Azar', 'Georg Ostrovski', 'Bilal Piot'] | 2017-10-06 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-4.12856042e-01 -2.98887193e-01 -5.10828674e-01 -1.30848423e-01
-7.87505448e-01 -8.01356673e-01 7.34747052e-01 -1.78238414e-02
-7.37853706e-01 9.30085301e-01 5.48867844e-02 -5.96046746e-01
-4.24242795e-01 -6.65121615e-01 -4.59946990e-01 -6.66350245e-01
-3.40185195e-01 6.39906526e-01 3.99377197e-01 -5.76456726... | [3.994795560836792, 1.7203022241592407] |
2dba3319-93c4-44f0-8390-ba4809775fb8 | graph-toolformer-to-empower-llms-with-graph | null | null | https://github.com/jwzhanggy/Graph_Toolformer/blob/main/figures/README.md | http://www.ifmlab.org/files/paper/graph_toolformer.pdf | Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Dataset Augmented by ChatGPT | In this paper, we aim to develop a large language model (LLM) with the reasoning ability on complex graph data. Currently, LLMs have achieved very impressive performance on various natural language learning tasks, extensions of which have also been applied to study the vision tasks with multi-modal data. However, when ... | ['Jiawei Zhang'] | 2023-04-10 | null | null | null | preprint-2023-4 | ['graph-classification', 'community-detection'] | ['graphs', 'graphs'] | [-7.19883293e-02 4.89944518e-01 -2.02843212e-02 -2.76498795e-01
2.03643218e-01 -6.02818370e-01 5.95833898e-01 3.56458813e-01
9.06422287e-02 2.16336533e-01 -8.35709572e-02 -9.83907282e-01
-5.51757932e-01 -1.30408192e+00 -6.07148230e-01 -1.04426429e-01
-3.26460600e-01 7.64964223e-01 4.57626045e-01 -4.63389158... | [8.896656036376953, 7.524499893188477] |
00399539-9331-499d-8f01-10013f08a383 | unifying-topic-sentiment-preference-in-an-hdp | 1812.07805 | null | http://arxiv.org/abs/1812.07805v1 | http://arxiv.org/pdf/1812.07805v1.pdf | Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews | This paper proposes a new HDP based online review rating regression model
named Topic-Sentiment-Preference Regression Analysis (TSPRA). TSPRA combines
topics (i.e. product aspects), word sentiment and user preference as regression
factors, and is able to perform topic clustering, review rating prediction,
sentiment ana... | ['Yue Shang', 'Yong Zhang', 'Xiaohua Hu', 'Zheng Chen'] | 2018-12-19 | null | null | null | null | ['online-review-rating'] | ['miscellaneous'] | [-3.74637604e-01 2.37745587e-02 -5.05818665e-01 -5.02610445e-01
-4.86255497e-01 -5.69577515e-01 7.50782728e-01 7.61157453e-01
-6.52937889e-01 4.14291233e-01 4.62349147e-01 -2.60416299e-01
-3.61804217e-01 -9.18735802e-01 -7.61526525e-02 -4.57491130e-01
5.62955320e-01 3.63846093e-01 -5.86665459e-02 -8.94597232... | [11.147998809814453, 6.862685203552246] |
8f5655bb-84b8-4586-8508-f731d98efa8c | real-time-user-guided-image-colorization-with | 1705.02999 | null | http://arxiv.org/abs/1705.02999v1 | http://arxiv.org/pdf/1705.02999v1.pdf | Real-Time User-Guided Image Colorization with Learned Deep Priors | We propose a deep learning approach for user-guided image colorization. The
system directly maps a grayscale image, along with sparse, local user "hints"
to an output colorization with a Convolutional Neural Network (CNN). Rather
than using hand-defined rules, the network propagates user edits by fusing
low-level cues ... | ['Xinyang Geng', 'Jun-Yan Zhu', 'Alexei A. Efros', 'Tianhe Yu', 'Richard Zhang', 'Angela S. Lin', 'Phillip Isola'] | 2017-05-08 | null | null | null | null | ['point-interactive-image-colorization'] | ['computer-vision'] | [ 2.09096938e-01 -1.69619784e-01 -2.15380676e-02 -5.68196058e-01
-6.18824720e-01 -8.85485947e-01 2.86391348e-01 1.80942506e-01
-6.22163177e-01 3.59305203e-01 1.07030146e-01 -3.76607567e-01
4.14072067e-01 -8.25593233e-01 -9.25837159e-01 -2.13082150e-01
2.57660151e-01 1.40667751e-01 3.19505250e-03 -1.68662861... | [11.435018539428711, -0.9080434441566467] |
1650a676-e30a-415e-822f-e438d9577421 | latent-structure-blockmodels-for-bayesian | 2107.01734 | null | https://arxiv.org/abs/2107.01734v2 | https://arxiv.org/pdf/2107.01734v2.pdf | Latent structure blockmodels for Bayesian spectral graph clustering | Spectral embedding of network adjacency matrices often produces node representations living approximately around low-dimensional submanifold structures. In particular, hidden substructure is expected to arise when the graph is generated from a latent position model. Furthermore, the presence of communities within the n... | ['Nicholas A. Heard', 'Francesco Sanna Passino'] | 2021-07-04 | null | null | null | null | ['spectral-graph-clustering'] | ['graphs'] | [ 5.86167350e-02 5.89754105e-01 -6.64734915e-02 7.75935724e-02
2.21954957e-01 -5.58764040e-01 8.75376165e-01 -1.10604756e-01
4.15130645e-01 2.67529458e-01 3.40437442e-01 -2.32458755e-01
-4.81068343e-01 -9.96771097e-01 -6.31826878e-01 -1.12513041e+00
-4.27107006e-01 8.50038707e-01 8.78197104e-02 8.60164165... | [7.014162540435791, 5.212822437286377] |
542a18e9-247b-4e86-80e5-6009e4eb1732 | ibbt-informed-batch-belief-trees-for-motion | 2304.10984 | null | https://arxiv.org/abs/2304.10984v1 | https://arxiv.org/pdf/2304.10984v1.pdf | IBBT: Informed Batch Belief Trees for Motion Planning Under Uncertainty | In this work, we propose the Informed Batch Belief Trees (IBBT) algorithm for motion planning under motion and sensing uncertainties. The original stochastic motion planning problem is divided into a deterministic motion planning problem and a graph search problem. We solve the deterministic planning problem using samp... | ['Panagiotis Tsiotras', 'Dongliang Zheng'] | 2023-04-21 | null | null | null | null | ['graph-construction', 'motion-planning'] | ['graphs', 'robots'] | [ 1.80791214e-01 4.99648601e-01 -2.62833089e-01 4.11753282e-02
-8.00020814e-01 -4.19761211e-01 6.44160271e-01 1.54455289e-01
-3.04840833e-01 8.70025814e-01 1.75603732e-01 -6.58614218e-01
-4.53676164e-01 -1.13903666e+00 -5.50578594e-01 -6.86594665e-01
-5.53492725e-01 9.77079868e-01 1.00581539e+00 -9.00833011... | [4.903086185455322, 1.623190999031067] |
a85dac7f-155d-4d99-9763-d69f62bc524a | blulab-temporal-information-extraction-for | null | null | https://aclanthology.org/S15-2137 | https://aclanthology.org/S15-2137.pdf | BluLab: Temporal Information Extraction for the 2015 Clinical TempEval Challenge | null | ['Danielle L. Mowery', 'Samir AbdelRahman', 'Sumithra Velupillai', 'Lee Christensen', 'Wendy Chapman'] | 2015-06-01 | null | null | null | semeval-2015-6 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.331686496734619, 3.7124691009521484] |
fdffe57d-792d-4fd9-af13-bb76a038a92c | sar-image-despeckling-by-deep-neural-networks | 2006.15559 | null | https://arxiv.org/abs/2006.15559v4 | https://arxiv.org/pdf/2006.15559v4.pdf | SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy | Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) images. Many different schemes have been proposed for the restoration of intensity SAR images. Among the different possible approaches, methods based on convolutional neural networks (CNNs) have recently shown to reach state-of-the-art performa... | ['Loïc Denis', 'Florence Tupin', 'Xiangli Yang', 'Wen Yang', 'Emanuele Dalsasso'] | 2020-06-28 | null | null | null | null | ['sar-image-despeckling'] | ['computer-vision'] | [ 5.62550068e-01 -3.17431897e-01 6.75299823e-01 -3.02437067e-01
-7.66860783e-01 -1.52744710e-01 3.54663581e-01 -5.34972787e-01
-8.28245401e-01 7.99723625e-01 4.47616465e-02 -4.56128828e-02
-4.91158277e-01 -9.37661588e-01 -2.86953598e-01 -1.20756865e+00
6.48712218e-02 2.38926083e-01 1.70939878e-01 -5.10366142... | [10.48383617401123, -2.2175261974334717] |
c5da8f16-c7e5-4555-9240-6de62a117e1c | unifying-count-based-exploration-and | 1606.01868 | null | http://arxiv.org/abs/1606.01868v2 | http://arxiv.org/pdf/1606.01868v2.pdf | Unifying Count-Based Exploration and Intrinsic Motivation | We consider an agent's uncertainty about its environment and the problem of
generalizing this uncertainty across observations. Specifically, we focus on
the problem of exploration in non-tabular reinforcement learning. Drawing
inspiration from the intrinsic motivation literature, we use density models to
measure uncert... | ['David Saxton', 'Sriram Srinivasan', 'Tom Schaul', 'Remi Munos', 'Marc G. Bellemare', 'Georg Ostrovski'] | 2016-06-06 | unifying-count-based-exploration-and-1 | http://papers.nips.cc/paper/6383-unifying-count-based-exploration-and-intrinsic-motivation | http://papers.nips.cc/paper/6383-unifying-count-based-exploration-and-intrinsic-motivation.pdf | neurips-2016-12 | ['montezumas-revenge'] | ['playing-games'] | [ 6.99571669e-02 2.48778626e-01 -3.31196517e-01 -2.54476607e-01
-1.19593668e+00 -7.51117170e-01 5.19194365e-01 -1.02265075e-01
-9.08143520e-01 1.50198078e+00 2.82159038e-02 -3.89643192e-01
-5.16128719e-01 -8.86012435e-01 -8.45534027e-01 -8.15835059e-01
-5.29878020e-01 8.66436958e-01 -1.86397508e-01 -2.10416555... | [4.040951728820801, 2.1078813076019287] |
7095fdab-cde5-4a8c-950b-7428ab3873f7 | physics-informed-neural-networks-for-solving | 2002.08235 | null | https://arxiv.org/abs/2002.08235v1 | https://arxiv.org/pdf/2002.08235v1.pdf | Physics-informed Neural Networks for Solving Nonlinear Diffusivity and Biot's equations | This paper presents the potential of applying physics-informed neural networks for solving nonlinear multiphysics problems, which are essential to many fields such as biomedical engineering, earthquake prediction, and underground energy harvesting. Specifically, we investigate how to extend the methodology of physics-i... | ['Teeratorn Kadeethum', 'Hamidreza M Nick', 'Thomas M Jorgensen'] | 2020-02-19 | null | null | null | null | ['earthquake-prediction'] | ['computer-vision'] | [ 3.56088579e-01 5.44428006e-02 3.13381612e-01 1.08549967e-01
-2.62909025e-01 -1.86240047e-01 5.77404618e-01 -1.34182498e-02
-7.54578590e-01 1.20856631e+00 1.70066983e-01 -2.43606925e-01
-7.63297021e-01 -8.69952142e-01 -7.21826553e-01 -1.27427089e+00
-2.58797437e-01 4.44301873e-01 -1.97322872e-02 -3.64603430... | [6.348864555358887, 3.3831090927124023] |
bf92ae91-3340-429c-a022-8cfc9004c030 | human-detection-of-machine-manipulated-media | 1907.05276 | null | https://arxiv.org/abs/1907.05276v2 | https://arxiv.org/pdf/1907.05276v2.pdf | Human detection of machine manipulated media | Recent advances in neural networks for content generation enable artificial intelligence (AI) models to generate high-quality media manipulations. Here we report on a randomized experiment designed to study the effect of exposure to media manipulations on over 15,000 individuals' ability to discern machine-manipulated ... | ['Nick Obradovich', 'Ziv Epstein', 'Matthew Groh', 'Manuel Cebrian', 'Iyad Rahwan'] | 2019-07-06 | null | null | null | null | ['causal-identification'] | ['reasoning'] | [ 4.52673167e-01 1.56132162e-01 4.94501814e-02 -6.42258376e-02
-3.55762839e-01 -7.11413026e-01 6.66650474e-01 2.34921291e-01
-7.18815506e-01 5.66441655e-01 1.01370774e-01 -2.87436813e-01
4.42059934e-01 -8.59146893e-01 -1.01292324e+00 3.70013304e-02
1.09125480e-01 2.47984882e-02 -1.85737908e-01 -1.68851301... | [12.460607528686523, 1.1787183284759521] |
8f7566cb-a270-4a66-b311-624b37b68073 | prior-enhanced-temporal-action-localization | 2211.05299 | null | https://arxiv.org/abs/2211.05299v1 | https://arxiv.org/pdf/2211.05299v1.pdf | Prior-enhanced Temporal Action Localization using Subject-aware Spatial Attention | Temporal action localization (TAL) aims to detect the boundary and identify the class of each action instance in a long untrimmed video. Current approaches treat video frames homogeneously, and tend to give background and key objects excessive attention. This limits their sensitivity to localize action boundaries. To t... | ['Haoqian Wang', 'Ruei-Sung Lin', 'Ning Zhang', 'YouBao Tang', 'Yifan Liu'] | 2022-11-10 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 3.30187738e-01 -1.34370431e-01 -4.39289212e-01 -1.70434892e-01
-5.30135155e-01 -3.43926340e-01 7.09226847e-01 -2.73755938e-01
-6.45263970e-01 5.35042524e-01 4.24317569e-01 1.81940705e-01
8.86290595e-02 -4.09254313e-01 -4.39408660e-01 -7.39819586e-01
-2.08145559e-01 -8.52819011e-02 7.37688124e-01 9.00625885... | [8.342390060424805, 0.5097836852073669] |
fe6ac83c-333d-4e28-930c-de65608a87b5 | enhancing-medical-named-entity-recognition | null | null | https://aclanthology.org/E14-3003 | https://aclanthology.org/E14-3003.pdf | Enhancing Medical Named Entity Recognition with Features Derived from Unsupervised Methods | null | ['Maria Skeppstedt'] | 2014-04-01 | null | null | null | eacl-2014-4 | ['medical-named-entity-recognition'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.284157752990723, 3.8179306983947754] |
c1a6beeb-61c8-4ec8-bc4a-5e388a635792 | trajectory-oriented-optimization-of | 2305.03926 | null | https://arxiv.org/abs/2305.03926v1 | https://arxiv.org/pdf/2305.03926v1.pdf | Trajectory-oriented optimization of stochastic epidemiological models | Epidemiological models must be calibrated to ground truth for downstream tasks such as producing forward projections or running what-if scenarios. The meaning of calibration changes in case of a stochastic model since output from such a model is generally described via an ensemble or a distribution. Each member of the ... | ['Jonathan Ozik', 'Kok Ben Toh', 'Abby Stevens', 'Nicholson Collier', 'Mickael Binois', 'Arindam Fadikar'] | 2023-05-06 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 4.08825934e-01 2.41393015e-01 -2.03788191e-01 -3.38510305e-01
-8.37167919e-01 -6.90813839e-01 9.93427873e-01 4.29246336e-01
-2.83127189e-01 1.04066420e+00 1.97016612e-01 -5.48396349e-01
-3.44806999e-01 -9.66257513e-01 -8.43936622e-01 -9.22111928e-01
-1.00994229e-01 1.16619015e+00 1.66444346e-01 1.19123876... | [6.870138645172119, 4.016742706298828] |
ab929e59-30b5-49e7-a78f-5e1f2fffa136 | character-time-series-matching-for-robust | null | null | https://ieeexplore.ieee.org/document/9924897 | https://ieeexplore.ieee.org/document/9924897 | Character Time-series Matching For Robust License Plate Recognition | Automatic License Plate Recognition (ALPR) is becoming a popular study area and is applied in many fields such as transportation or smart city. However, there are still several limitations when applying many current methods to practical problems due to the variation in real-world situations such as light changes, uncle... | ['Cuong Truong Van', 'Tung Do Thanh', 'Huy Che Quang'] | 2022-10-25 | null | null | null | international-conference-on-multimedia | ['license-plate-recognition', 'license-plate-detection'] | ['computer-vision', 'computer-vision'] | [ 6.36856705e-02 -1.27892351e+00 9.51326080e-03 -2.03676745e-02
-8.44453514e-01 -8.69544685e-01 2.23565996e-01 -5.63916624e-01
-4.31464106e-01 3.88910919e-01 -4.03880924e-01 -3.28319520e-01
3.26506019e-01 -7.69616544e-01 -5.55664837e-01 -5.46658218e-01
5.47467053e-01 3.43911976e-01 7.87459314e-01 -2.07638547... | [9.827791213989258, -4.9620161056518555] |
d20a7226-77e4-4333-b203-875afe992329 | reading-and-writing-discriminative-and | 2207.00193 | null | https://arxiv.org/abs/2207.00193v2 | https://arxiv.org/pdf/2207.00193v2.pdf | Reading and Writing: Discriminative and Generative Modeling for Self-Supervised Text Recognition | Existing text recognition methods usually need large-scale training data. Most of them rely on synthetic training data due to the lack of annotated real images. However, there is a domain gap between the synthetic data and real data, which limits the performance of the text recognition models. Recent self-supervised te... | ['Xiang Bai', 'Qi Tian', 'Hualin Luo', 'Shenggao Zhu', 'Jing Wang', 'Pu Lu', 'Minghui Liao', 'Mingkun Yang'] | 2022-07-01 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 6.64074481e-01 -4.92435217e-01 -1.44546986e-01 -5.68685830e-01
-5.19321322e-01 -3.01100820e-01 9.52072918e-01 -1.92168772e-01
-4.10551965e-01 3.30795377e-01 -9.15409923e-02 -1.80319458e-01
4.27317262e-01 -5.96326888e-01 -7.15233922e-01 -8.46080899e-01
9.24277723e-01 4.93630886e-01 2.32557133e-01 -1.33110240... | [11.857489585876465, 2.178067207336426] |
99bd6a4f-87cd-4864-8ab7-f93513276bf1 | michelangelo-conditional-3d-shape-generation | 2306.17115 | null | https://arxiv.org/abs/2306.17115v2 | https://arxiv.org/pdf/2306.17115v2.pdf | Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation | We present a novel alignment-before-generation approach to tackle the challenging task of generating general 3D shapes based on 2D images or texts. Directly learning a conditional generative model from images or texts to 3D shapes is prone to producing inconsistent results with the conditions because 3D shapes have an ... | ['Shenghua Gao', 'Gang Yu', 'Tao Chen', 'Bin Fu', 'Pei Cheng', 'Rui Wang', 'Xianfang Zeng', 'Xin Chen', 'Wen Liu', 'Zibo Zhao'] | 2023-06-29 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [ 4.1989794e-01 3.2050121e-01 7.7689812e-02 -3.6888969e-01
-9.0309203e-01 -6.9530594e-01 1.0802439e+00 -6.9996184e-01
2.3359209e-01 2.2592074e-01 4.8855734e-01 -1.9719712e-01
2.0549934e-01 -1.0339837e+00 -1.0224376e+00 -9.4478333e-01
7.7418637e-01 9.1390735e-01 -9.7876891e-02 6.1430234e-02
-1.3719365e-03... | [9.050233840942383, -3.5207295417785645] |
7394e9ab-1900-4790-8bcd-018eb4c33d2b | an-empirical-study-on-google-research | 2305.09458 | null | https://arxiv.org/abs/2305.09458v1 | https://arxiv.org/pdf/2305.09458v1.pdf | An Empirical Study on Google Research Football Multi-agent Scenarios | Few multi-agent reinforcement learning (MARL) research on Google Research Football (GRF) focus on the 11v11 multi-agent full-game scenario and to the best of our knowledge, no open benchmark on this scenario has been released to the public. In this work, we fill the gap by providing a population-based MARL training pip... | ['Jun Wang', 'Weinan Zhang', 'Zonghong Dai', 'Jiangcheng Zhu', 'Yingping Zhang', 'Haifeng Zhang', 'Zheng Tian', 'He Jiang', 'Yan Song'] | 2023-05-16 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-7.11255968e-01 -3.75477880e-01 -5.02217293e-01 1.90842673e-01
-8.75179529e-01 -6.01899505e-01 3.18602145e-01 -1.42809138e-01
-9.55292165e-01 1.35338116e+00 -1.50737599e-01 -3.75223070e-01
-2.28689522e-01 -7.61104941e-01 -1.13189328e+00 -8.35943341e-01
-3.40940148e-01 1.16664624e+00 2.52584428e-01 -9.99817431... | [3.7430191040039062, 1.6375354528427124] |
68283045-7ea0-44ea-9689-b5d7ff0b9d23 | deep-multi-modality-soft-decoding-of-very-low | 2008.01652 | null | https://arxiv.org/abs/2008.01652v1 | https://arxiv.org/pdf/2008.01652v1.pdf | Deep Multi-modality Soft-decoding of Very Low Bit-rate Face Videos | We propose a novel deep multi-modality neural network for restoring very low bit rate videos of talking heads. Such video contents are very common in social media, teleconferencing, distance education, tele-medicine, etc., and often need to be transmitted with limited bandwidth. The proposed CNN method exploits the cor... | ['Yanhui Guo', 'Xi Zhang', 'Xiaolin Wu'] | 2020-08-02 | null | null | null | null | ['video-restoration'] | ['computer-vision'] | [ 2.47251287e-01 -8.85324478e-02 -7.04205260e-02 -1.34432733e-01
-7.49847531e-01 -6.48677796e-02 3.11824769e-01 -1.23056106e-01
-2.12870345e-01 5.61835825e-01 6.16950989e-01 -2.01975659e-01
-3.32003146e-01 -3.29650611e-01 -6.92395627e-01 -7.89859831e-01
-8.12511593e-02 -1.16852090e-01 -6.67147860e-02 -3.60472292... | [11.370434761047363, -1.7600518465042114] |
80bcdf4c-bc12-490e-b400-39c75b98a84a | automatic-discourse-segmentation-review-and | 2005.00468 | null | https://arxiv.org/abs/2005.00468v1 | https://arxiv.org/pdf/2005.00468v1.pdf | Automatic Discourse Segmentation: Review and Perspectives | Multilingual discourse parsing is a very prominent research topic. The first stage for discourse parsing is discourse segmentation. The study reported in this article addresses a review of two on-line available discourse segmenters (for English and Portuguese). We evaluate the possibility of developing similar discours... | ['Juan-Manuel Torres-Moreno', 'Iria da Cunha'] | 2020-05-01 | null | null | null | null | ['discourse-segmentation'] | ['natural-language-processing'] | [ 1.43196568e-01 9.12714183e-01 -5.37476301e-01 -8.98712277e-02
-1.02266645e+00 -9.92057323e-01 8.82686317e-01 7.23179698e-01
-5.47749519e-01 1.34158587e+00 8.63503456e-01 -9.12048161e-01
3.91895205e-01 -4.96717036e-01 -4.13516700e-01 -1.69026807e-01
9.31284800e-02 5.56157351e-01 7.78707564e-01 -4.29497004... | [10.759015083312988, 9.466187477111816] |
4d538d24-fb59-4acf-b80a-39e3148343aa | sdfusion-multimodal-3d-shape-completion | 2212.04493 | null | https://arxiv.org/abs/2212.04493v2 | https://arxiv.org/pdf/2212.04493v2.pdf | SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generation | In this work, we present a novel framework built to simplify 3D asset generation for amateur users. To enable interactive generation, our method supports a variety of input modalities that can be easily provided by a human, including images, text, partially observed shapes and combinations of these, further allowing to... | ['LiangYan Gui', 'Alexander Schwing', 'Sergey Tulyakov', 'Hsin-Ying Lee', 'Yen-Chi Cheng'] | 2022-12-08 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cheng_SDFusion_Multimodal_3D_Shape_Completion_Reconstruction_and_Generation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cheng_SDFusion_Multimodal_3D_Shape_Completion_Reconstruction_and_Generation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-shape-generation', 'text-to-shape-generation', 'text-to-3d'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.79289097e-01 4.91978496e-01 2.34142005e-01 -1.48723960e-01
-7.35053062e-01 -8.71821463e-01 8.85221541e-01 -1.20264895e-01
-1.45640135e-01 3.59539568e-01 5.23940980e-01 -3.65233928e-01
2.08258688e-01 -9.39689100e-01 -8.61567795e-01 -2.48449221e-01
2.89712518e-01 7.44040966e-01 -5.29547855e-02 -2.45473057... | [9.103944778442383, -3.513871431350708] |
0754f922-98dc-4c1e-a181-8b3620e95f00 | web-photo-source-identification-based-on | 2302.09228 | null | https://arxiv.org/abs/2302.09228v1 | https://arxiv.org/pdf/2302.09228v1.pdf | Web Photo Source Identification based on Neural Enhanced Camera Fingerprint | With the growing popularity of smartphone photography in recent years, web photos play an increasingly important role in all walks of life. Source camera identification of web photos aims to establish a reliable linkage from the captured images to their source cameras, and has a broad range of applications, such as ima... | ['Lei Yang', 'Xiaobo Zhang', 'Huanyu Ma', 'Honghao Huang', 'Sifeng He', 'Feng Qian'] | 2023-02-18 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 4.86593574e-01 -6.96444511e-01 -7.58020461e-01 -4.89332438e-01
-6.28506541e-01 -8.95127118e-01 3.04829240e-01 -2.03346565e-01
-2.42454007e-01 4.85903323e-01 -2.54514098e-01 -3.80138427e-01
-2.41309687e-01 -9.70374644e-01 -1.01369822e+00 -5.21644413e-01
1.37500048e-01 -4.23784852e-01 3.17958653e-01 3.44663531... | [12.493352890014648, 0.9401065707206726] |
bfe5c0d6-941b-4f97-9e6d-6e05aba33df7 | partnet-a-recursive-part-decomposition | 1903.00709 | null | https://arxiv.org/abs/1903.00709v5 | https://arxiv.org/pdf/1903.00709v5.pdf | PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation | Deep learning approaches to 3D shape segmentation are typically formulated as a multi-class labeling problem. Existing models are trained for a fixed set of labels, which greatly limits their flexibility and adaptivity. We opt for top-down recursive decomposition and develop the first deep learning model for hierarchic... | ['Kun Liu', 'Fenggen Yu', 'Yan Zhang', 'Kai Xu', 'Chenyang Zhu'] | 2019-03-02 | partnet-a-recursive-part-decomposition-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Yu_PartNet_A_Recursive_Part_Decomposition_Network_for_Fine-Grained_and_Hierarchical_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yu_PartNet_A_Recursive_Part_Decomposition_Network_for_Fine-Grained_and_Hierarchical_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-instance-segmentation-1', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.24566779e-01 3.19852710e-01 -2.98246175e-01 -3.60335916e-01
-5.51434875e-01 -8.20422649e-01 3.64229709e-01 3.40904385e-01
-4.91822995e-02 7.84567446e-02 -9.41634402e-02 -2.04003632e-01
-8.92318040e-02 -1.17914319e+00 -5.74167728e-01 -7.37735748e-01
6.52765259e-02 1.00087333e+00 5.53045392e-01 1.65862087... | [7.985280990600586, -3.47749662399292] |
02c10805-1f82-4841-a9ab-7ba59c520bb5 | structural-adapters-in-pretrained-language | 2103.09120 | null | https://arxiv.org/abs/2103.09120v2 | https://arxiv.org/pdf/2103.09120v2.pdf | Structural Adapters in Pretrained Language Models for AMR-to-text Generation | Pretrained language models (PLM) have recently advanced graph-to-text generation, where the input graph is linearized into a sequence and fed into the PLM to obtain its representation. However, efficiently encoding the graph structure in PLMs is challenging because such models were pretrained on natural language, and m... | ['Iryna Gurevych', 'Yue Zhang', 'Leonardo F. R. Ribeiro'] | 2021-03-16 | null | https://aclanthology.org/2021.emnlp-main.351 | https://aclanthology.org/2021.emnlp-main.351.pdf | emnlp-2021-11 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 3.60728651e-01 7.22549260e-01 -7.96752274e-02 -1.80587053e-01
-5.30299246e-01 -8.41444016e-01 6.34222209e-01 3.82927418e-01
-1.51842356e-01 5.25899231e-01 5.29698789e-01 -6.09611511e-01
2.63459325e-01 -1.27007520e+00 -1.10346782e+00 -1.93492994e-01
-3.53507288e-02 9.39585209e-01 -2.09168389e-01 -2.56551743... | [10.231748580932617, 8.267812728881836] |
1ceefccd-9017-4a88-8780-379a148af1b9 | medical-image-deidentification-cleaning-and | 2304.12322 | null | https://arxiv.org/abs/2304.12322v5 | https://arxiv.org/pdf/2304.12322v5.pdf | Medical Image Deidentification, Cleaning and Compression Using Pylogik | Leveraging medical record information in the era of big data and machine learning comes with the caveat that data must be cleaned and de-identified. Facilitating data sharing and harmonization for multi-center collaborations are particularly difficult when protected health information (PHI) is contained or embedded in ... | ['Sanjiv Shah', 'Yuan Luo', 'Vinesh Appadurai', 'Adrienne Kline'] | 2023-04-20 | null | null | null | null | ['de-identification'] | ['natural-language-processing'] | [ 1.57227457e-01 -1.02514267e-01 3.77400100e-01 -3.66246998e-01
-9.57380831e-01 -5.74253559e-01 -7.93226659e-02 9.79623258e-01
-5.70436835e-01 3.17246884e-01 1.91088170e-01 -4.65439141e-01
-1.73831522e-01 -7.13908315e-01 -5.15983284e-01 -8.72277796e-01
-1.49780586e-01 4.68138665e-01 -6.70416355e-02 4.64785933... | [14.328330039978027, -2.391120433807373] |
152b3138-34ba-4197-9f9f-d3c59a4e3753 | incremental-speech-synthesis-for-speech-to | 2110.08214 | null | https://arxiv.org/abs/2110.08214v3 | https://arxiv.org/pdf/2110.08214v3.pdf | From Start to Finish: Latency Reduction Strategies for Incremental Speech Synthesis in Simultaneous Speech-to-Speech Translation | Speech-to-speech translation (S2ST) converts input speech to speech in another language. A challenge of delivering S2ST in real time is the accumulated delay between the translation and speech synthesis modules. While recently incremental text-to-speech (iTTS) models have shown large quality improvements, they typicall... | ['Juan Pino', 'Yun Tang', 'Xutai Ma', 'Hongyu Gong', 'Changhan Wang', 'Danni Liu'] | 2021-10-15 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [ 4.40961570e-01 7.13964775e-02 -3.57057810e-01 -2.92026639e-01
-1.31166947e+00 -9.84933496e-01 6.09210372e-01 6.43826798e-02
-2.02376127e-01 4.30750817e-01 3.50411028e-01 -1.00814092e+00
4.79873687e-01 -3.21851999e-01 -6.14858568e-01 -1.41142085e-01
1.24901064e-01 2.64289379e-01 6.33054137e-01 -1.82068974... | [14.530617713928223, 7.084510326385498] |
7d39c310-25a6-4a44-929c-eaa2e1cb1287 | unsupervised-discontinuous-constituency | 2212.09140 | null | https://arxiv.org/abs/2212.09140v2 | https://arxiv.org/pdf/2212.09140v2.pdf | Unsupervised Discontinuous Constituency Parsing with Mildly Context-Sensitive Grammars | We study grammar induction with mildly context-sensitive grammars for unsupervised discontinuous parsing. Using the probabilistic linear context-free rewriting system (LCFRS) formalism, our approach fixes the rule structure in advance and focuses on parameter learning with maximum likelihood. To reduce the computationa... | ['Yoon Kim', 'Roger P. Levy', 'Songlin Yang'] | 2022-12-18 | null | null | null | null | ['constituency-parsing'] | ['natural-language-processing'] | [ 4.97197241e-01 3.68746907e-01 -4.98535819e-02 -4.76899892e-01
-9.79276776e-01 -1.00082374e+00 4.46183980e-01 2.85260022e-01
-4.02495831e-01 4.86892164e-01 1.72189236e-01 -1.08676553e+00
-1.32952437e-01 -9.60745990e-01 -5.03413618e-01 -6.85605705e-01
-4.21660870e-01 8.15071404e-01 4.59018677e-01 -3.30475003... | [10.372702598571777, 9.595609664916992] |
626b1d45-e784-4b4f-b896-3d5ffaea7eb4 | dyernie-dynamic-evolution-of-riemannian | 2011.03984 | null | https://arxiv.org/abs/2011.03984v2 | https://arxiv.org/pdf/2011.03984v2.pdf | DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion | There has recently been increasing interest in learning representations of temporal knowledge graphs (KGs), which record the dynamic relationships between entities over time. Temporal KGs often exhibit multiple simultaneous non-Euclidean structures, such as hierarchical and cyclic structures. However, existing embeddin... | ['Volker Tresp', 'Yunpu Ma', 'Peng Chen', 'Zhen Han'] | 2020-11-08 | null | https://aclanthology.org/2020.emnlp-main.593 | https://aclanthology.org/2020.emnlp-main.593.pdf | emnlp-2020-11 | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-6.61999881e-01 -8.22141021e-02 9.23843384e-02 -2.08449394e-01
1.66075319e-01 -7.51958907e-01 6.35690868e-01 3.17881465e-01
-9.44523811e-02 1.30998537e-01 3.08577240e-01 -7.99836963e-02
-7.25144386e-01 -1.07084143e+00 -6.64986074e-01 -7.44322479e-01
-9.11662936e-01 2.11105153e-01 1.15480542e-01 -5.38205922... | [8.533567428588867, 7.756981372833252] |
00e4c461-99ec-4d2d-879a-ddbcb7687325 | a-robust-and-efficient-framework-for-sports | null | null | https://assets.amazon.science/2c/75/0f3bbae44ac7aed02c700bed0694/a-robust-and-efficient-framework-for-sports-field-registration.pdf | https://assets.amazon.science/2c/75/0f3bbae44ac7aed02c700bed0694/a-robust-and-efficient-framework-for-sports-field-registration.pdf | A Robust and Efficient Framework for Sports-Field Registration | We propose a novel framework to register sports-fields as
they appear in broadcast sports videos. Unlike previous
approaches, we particularly address the challenge of fieldregistration when: (a) there are not enough distinguishable
features on the field, and (b) no prior knowledge is available about the camera. To t... | ['and Raffay Hamid', 'Shixing Chen', 'Xiaohan Nie'] | 2021-01-01 | null | null | null | ieee-winter-conference-on-applications-of-7 | ['homography-estimation'] | ['computer-vision'] | [ 1.00186907e-01 -5.04313648e-01 -2.68132448e-01 -2.46817306e-01
-1.04965043e+00 -7.92803228e-01 4.23909605e-01 3.07609200e-01
-5.21181643e-01 3.49574387e-01 1.82939023e-01 5.17339647e-01
-1.07341841e-01 -8.59773397e-01 -1.14514160e+00 -2.98788369e-01
-4.66096073e-01 4.06190813e-01 6.76883042e-01 -3.74413222... | [7.928438186645508, -1.5617941617965698] |
f8ab82ca-2cc9-4760-a8ec-be09afb2c39c | using-natural-language-for-reward-shaping-in | 1903.02020 | null | https://arxiv.org/abs/1903.02020v2 | https://arxiv.org/pdf/1903.02020v2.pdf | Using Natural Language for Reward Shaping in Reinforcement Learning | Recent reinforcement learning (RL) approaches have shown strong performance in complex domains such as Atari games, but are often highly sample inefficient. A common approach to reduce interaction time with the environment is to use reward shaping, which involves carefully designing reward functions that provide the ag... | ['Scott Niekum', 'Prasoon Goyal', 'Raymond J. Mooney'] | 2019-03-05 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-3.65256667e-02 1.02921855e-02 -2.36659288e-01 -1.27274022e-01
-8.72058332e-01 -6.57775044e-01 7.55557835e-01 1.39081568e-01
-9.16382372e-01 1.10326314e+00 1.52045265e-01 -3.33552420e-01
-1.34312302e-01 -6.89830422e-01 -6.90579832e-01 -5.63199759e-01
-3.06029409e-01 5.85220337e-01 8.25359374e-02 -6.48889303... | [3.9490857124328613, 1.5670779943466187] |
70ebeea9-5e35-4ba2-aadd-b61c257f0462 | unsupervised-pansharpening-based-on-self | 2006.09303 | null | https://arxiv.org/abs/2006.09303v3 | https://arxiv.org/pdf/2006.09303v3.pdf | Unsupervised Pansharpening Based on Self-Attention Mechanism | Pansharpening is to fuse a multispectral image (MSI) of low-spatial-resolution (LR) but rich spectral characteristics with a panchromatic image (PAN) of high-spatial-resolution (HR) but poor spectral characteristics. Traditional methods usually inject the extracted high-frequency details from PAN into the up-sampled MS... | ['Razieh Kaviani Baghbaderani', 'Ying Qu', 'Hairong Qi', 'Chiman Kwan'] | 2020-06-16 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 6.47656262e-01 -1.59114957e-01 -8.30061641e-03 -4.76448946e-02
-8.83002460e-01 -3.60972404e-01 2.96431065e-01 -2.76733220e-01
-4.22379673e-02 8.16986263e-01 1.04289033e-01 -5.43520041e-02
-4.43667352e-01 -1.10164285e+00 -7.41091669e-01 -1.13928890e+00
6.60184473e-02 -1.88831255e-01 -7.89825618e-02 -5.65667093... | [10.203606605529785, -1.8865585327148438] |
59a22247-45d6-4dfb-90ed-29cc938130aa | guarded-policy-optimization-with-imperfect | 2303.01728 | null | https://arxiv.org/abs/2303.01728v2 | https://arxiv.org/pdf/2303.01728v2.pdf | Guarded Policy Optimization with Imperfect Online Demonstrations | The Teacher-Student Framework (TSF) is a reinforcement learning setting where a teacher agent guards the training of a student agent by intervening and providing online demonstrations. Assuming optimal, the teacher policy has the perfect timing and capability to intervene in the learning process of the student agent, p... | ['Bolei Zhou', 'Zhihan Liu', 'Quanyi Li', 'Zhenghao Peng', 'Zhenghai Xue'] | 2023-03-03 | null | null | null | null | ['efficient-exploration', 'continuous-control'] | ['methodology', 'playing-games'] | [-4.53796536e-02 3.94780487e-01 -4.48147744e-01 7.48269334e-02
-6.76988244e-01 -7.81100631e-01 4.67750311e-01 8.55106115e-02
-5.22428036e-01 1.06325805e+00 -6.11633956e-01 -7.98926651e-01
-3.71405214e-01 -7.17514515e-01 -1.00261915e+00 -1.02993441e+00
-2.75713116e-01 3.37772459e-01 2.72591591e-01 -8.56435597... | [4.331432819366455, 2.154660701751709] |
fb209fac-041c-4184-a337-5933bfcc3a98 | thompson-sampling-for-robust-transfer-in | 2206.08556 | null | https://arxiv.org/abs/2206.08556v1 | https://arxiv.org/pdf/2206.08556v1.pdf | Thompson Sampling for Robust Transfer in Multi-Task Bandits | We study the problem of online multi-task learning where the tasks are performed within similar but not necessarily identical multi-armed bandit environments. In particular, we study how a learner can improve its overall performance across multiple related tasks through robust transfer of knowledge. While an upper conf... | ['Kamalika Chaudhuri', 'Chicheng Zhang', 'Zhi Wang'] | 2022-06-17 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 2.25297838e-01 -6.59368336e-02 -4.50056702e-01 -1.17774233e-01
-1.71237457e+00 -8.15668404e-01 2.59007663e-01 3.15616369e-01
-7.70631850e-01 1.25821459e+00 -1.32314891e-01 -4.82078373e-01
-9.00335908e-01 -1.82047680e-01 -1.34686589e+00 -9.21099782e-01
-1.21533848e-01 9.02508020e-01 2.19931230e-01 3.38024884... | [4.677624702453613, 3.229003667831421] |
1a41cba4-0e57-47d6-855a-0c2ccccb64df | using-vaes-and-normalizing-flows-for-one-shot | 1911.12760 | null | https://arxiv.org/abs/1911.12760v2 | https://arxiv.org/pdf/1911.12760v2.pdf | Using VAEs and Normalizing Flows for One-shot Text-To-Speech Synthesis of Expressive Speech | We propose a Text-to-Speech method to create an unseen expressive style using one utterance of expressive speech of around one second. Specifically, we enhance the disentanglement capabilities of a state-of-the-art sequence-to-sequence based system with a Variational AutoEncoder (VAE) and a Householder Flow. The propos... | ['Roberto Barra-Chicote', 'Jaime Lorenzo-Trueba', 'Marius Cotescu', 'Nishant Prateek', 'Vatsal Aggarwal'] | 2019-11-28 | null | null | null | null | ['expressive-speech-synthesis'] | ['speech'] | [ 3.97479147e-01 4.48904812e-01 2.20273614e-01 -2.86696762e-01
-7.82489657e-01 -5.71277320e-01 7.45472491e-01 -5.38583755e-01
-9.67267826e-02 7.29932964e-01 5.50595760e-01 -2.16034621e-01
4.24220026e-01 -6.05507731e-01 -5.88167071e-01 -7.06561148e-01
4.04417038e-01 2.54988521e-01 -2.25082040e-01 -6.38296902... | [15.068731307983398, 6.482466220855713] |
03dd9691-9204-4514-9dbf-9b03a6b3f52b | gem-2-next-generation-molecular-property | 2208.05863 | null | https://arxiv.org/abs/2208.05863v4 | https://arxiv.org/pdf/2208.05863v4.pdf | GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactions | Molecular property prediction is a fundamental task in the drug and material industries. Physically, the properties of a molecule are determined by its own electronic structure, which is a quantum many-body system and can be exactly described by the Schr"odinger equation. Full-range many-body interactions between elect... | ['Hua Wu', 'Jingzhou He', 'Fan Wang', 'Shanzhuo Zhang', 'Xiaomin Fang', 'Donglong He', 'Lihang Liu'] | 2022-08-11 | null | null | null | null | ['graph-regression', 'molecular-property-prediction'] | ['graphs', 'miscellaneous'] | [-1.43638372e-01 -4.15127933e-01 -5.15820682e-01 -3.86831045e-01
-6.16232276e-01 -1.47586733e-01 4.54731405e-01 1.08819507e-01
-1.32426515e-01 1.16236377e+00 -7.28301331e-02 -4.43016142e-01
-1.87049732e-01 -8.38285565e-01 -1.06129837e+00 -1.16173923e+00
-1.36580288e-01 6.42963886e-01 -1.08836189e-01 -3.34475100... | [5.100106716156006, 5.711004257202148] |
39e4922b-691c-4a33-a4db-73906c56756b | hindi-visual-genome-a-dataset-for-multimodal | 1907.08948 | null | https://arxiv.org/abs/1907.08948v1 | https://arxiv.org/pdf/1907.08948v1.pdf | Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation | Visual Genome is a dataset connecting structured image information with English language. We present ``Hindi Visual Genome'', a multimodal dataset consisting of text and images suitable for English-Hindi multimodal machine translation task and multimodal research. We have selected short English segments (captions) from... | ['Satya Ranjan Dash', 'Ondřej Bojar', 'Shantipriya Parida'] | 2019-07-21 | null | null | null | null | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 6.25821173e-01 1.84530169e-01 -2.68576536e-02 -4.90024388e-01
-1.42788947e+00 -1.09878588e+00 6.69996738e-01 -5.50511144e-02
-6.87104762e-01 9.32294786e-01 1.97032198e-01 -2.53363967e-01
4.05979604e-01 -2.36596301e-01 -9.52575505e-01 -5.20047367e-01
4.18605626e-01 1.13912058e+00 -1.30624115e-01 -1.23476468... | [11.37801456451416, 1.5135071277618408] |
e5da3fdb-0314-4028-9388-955128c3692d | dilated-deep-residual-network-for-image | 1708.05473 | null | http://arxiv.org/abs/1708.05473v3 | http://arxiv.org/pdf/1708.05473v3.pdf | Dilated Deep Residual Network for Image Denoising | Variations of deep neural networks such as convolutional neural network (CNN)
have been successfully applied to image denoising. The goal is to automatically
learn a mapping from a noisy image to a clean image given training data
consisting of pairs of noisy and clean images. Most existing CNN models for
image denoisin... | ['Mingxuan Sun', 'Kaoning Hu', 'Tianyang Wang'] | 2017-08-18 | null | null | null | null | ['color-image-denoising'] | ['computer-vision'] | [ 2.37282485e-01 -3.39273334e-01 4.68650162e-01 -5.43356180e-01
-5.44745624e-01 -3.31906199e-01 3.77619416e-01 -2.21508861e-01
-6.63969457e-01 4.25999105e-01 1.39576988e-02 -1.50063589e-01
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1.45234644e-01 -6.84851289e-01 -2.85332110e-02 -3.79592836... | [11.418506622314453, -2.3465166091918945] |
73e28940-1f72-4f7e-a433-50ee5f5378bb | penalized-deep-partially-linear-cox-models | 2303.05341 | null | https://arxiv.org/abs/2303.05341v1 | https://arxiv.org/pdf/2303.05341v1.pdf | Penalized Deep Partially Linear Cox Models with Application to CT Scans of Lung Cancer Patients | Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective patient-centered therapies. The National Lung Screening Trial (NLST) was a nationwide study aimed at investigating risk factors for lung cancer. The study employed computed t... | ['Yi Li', 'David C. Christiani', 'Chi-Fu Jeffrey Yang', 'Alexandra L. Potter', 'Nicholas R. Mayne', 'Chinmay Haridas', 'Jian Kang', 'Yuming Sun'] | 2023-03-09 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [-6.26117215e-02 -5.03636301e-01 -8.93055558e-01 -2.13639185e-01
-1.16076481e+00 -1.49532393e-01 2.88100481e-01 4.45052415e-01
-3.88312310e-01 5.15541613e-01 4.32911813e-01 -5.73890388e-01
-4.50427324e-01 -7.92645276e-01 -4.57890511e-01 -1.00638354e+00
-3.80123585e-01 7.54886687e-01 3.88402008e-02 4.59462583... | [15.283697128295898, -2.2776267528533936] |
9b6fecf3-a301-48dc-a56d-5d4f56cd2eaf | freebaseqa-a-new-factoid-qa-data-set-matching | null | null | https://aclanthology.org/N19-1028 | https://aclanthology.org/N19-1028.pdf | FreebaseQA: A New Factoid QA Data Set Matching Trivia-Style Question-Answer Pairs with Freebase | In this paper, we present a new data set, named FreebaseQA, for open-domain factoid question answering (QA) tasks over structured knowledge bases, like Freebase. The data set is generated by matching trivia-type question-answer pairs with subject-predicate-object triples in Freebase. For each collected question-answer ... | ['Hui Jiang', 'Dekun Wu', 'Kelvin Jiang'] | 2019-06-01 | null | null | null | naacl-2019-6 | ['set-matching'] | ['computer-vision'] | [-3.58449399e-01 8.77916098e-01 -7.27144629e-02 -4.62307811e-01
-1.70218539e+00 -1.06142640e+00 4.47216868e-01 6.54390872e-01
-4.88418937e-01 1.37234926e+00 3.47633123e-01 -3.14069510e-01
-2.06861481e-01 -1.29194224e+00 -9.06782568e-01 3.90175991e-02
1.21173605e-01 1.22407138e+00 9.57794905e-01 -8.94802153... | [10.543206214904785, 7.906019687652588] |
0e657964-70fd-4d69-9cbf-00c826cc0ac5 | coresets-for-relational-data-and-the | 2210.04249 | null | https://arxiv.org/abs/2210.04249v1 | https://arxiv.org/pdf/2210.04249v1.pdf | Coresets for Relational Data and The Applications | A coreset is a small set that can approximately preserve the structure of the original input data set. Therefore we can run our algorithm on a coreset so as to reduce the total computational complexity. Conventional coreset techniques assume that the input data set is available to process explicitly. However, this assu... | ['Hu Ding', 'Ruomin Huang', 'Qingyuan Yang', 'Jiaxiang Chen'] | 2022-10-09 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 5.16400672e-02 2.14032918e-01 -1.98315606e-01 -3.44386339e-01
-6.22735381e-01 -5.44990301e-01 1.49370015e-01 6.04176044e-01
-1.84201330e-01 7.19970405e-01 -2.21899197e-01 -4.06373382e-01
-3.91538262e-01 -1.35459375e+00 -1.06269109e+00 -8.37076366e-01
-4.19439189e-02 1.02755439e+00 2.35070854e-01 7.69845918... | [7.850734233856201, 4.8835530281066895] |
4eb50db7-0eea-49a7-85d3-ce60483003b3 | f-vlm-open-vocabulary-object-detection-upon | 2209.15639 | null | https://arxiv.org/abs/2209.15639v2 | https://arxiv.org/pdf/2209.15639v2.pdf | F-VLM: Open-Vocabulary Object Detection upon Frozen Vision and Language Models | We present F-VLM, a simple open-vocabulary object detection method built upon Frozen Vision and Language Models. F-VLM simplifies the current multi-stage training pipeline by eliminating the need for knowledge distillation or detection-tailored pretraining. Surprisingly, we observe that a frozen VLM: 1) retains the loc... | ['Anelia Angelova', 'AJ Piergiovanni', 'Xiuye Gu', 'Yin Cui', 'Weicheng Kuo'] | 2022-09-30 | null | null | null | null | ['open-vocabulary-object-detection'] | ['computer-vision'] | [-2.02801749e-01 -1.30558133e-01 -3.24218839e-01 -2.03790531e-01
-1.29686141e+00 -9.40803170e-01 6.43629491e-01 1.02344222e-01
-4.53430295e-01 2.64884710e-01 -2.83969636e-03 -3.32787305e-01
5.52171528e-01 -3.31983685e-01 -8.82703900e-01 -4.84414667e-01
5.43976389e-02 6.36558771e-01 6.83271229e-01 5.32633811... | [9.508271217346191, 1.4361363649368286] |
528b1555-eacf-4b33-af42-0af08ad7540b | lda2net-digging-under-the-surface-of-covid-19 | 2112.01181 | null | https://arxiv.org/abs/2112.01181v2 | https://arxiv.org/pdf/2112.01181v2.pdf | LDA2Net: Digging under the surface of COVID-19 topics in scientific literature | During the COVID-19 pandemic, the scientific literature related to SARS-COV-2 has been growing dramatically, both in terms of the number of publications and of its impact on people's life. This literature encompasses a varied set of sensible topics, ranging from vaccination, to protective equipment efficacy, to lockdow... | ['Massimo Warglien', 'Carlo R. M. A. Santagiustina', 'Giorgia Minello'] | 2021-12-02 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-8.33511204e-02 -1.04770578e-01 -3.61663699e-01 1.62871480e-01
-1.61064684e-01 -6.43698692e-01 9.35626149e-01 9.05913353e-01
-2.12985530e-01 6.02731645e-01 6.41056836e-01 -5.08554578e-01
-3.35988075e-01 -1.12498164e+00 -1.44993573e-01 -5.21196127e-01
-4.09093380e-01 6.41105771e-01 3.72336596e-01 -2.52165794... | [9.86160945892334, 7.721771717071533] |
0ebb5123-8c49-4c95-bbc0-4d696cccb5e2 | molecule-property-prediction-based-on-spatial | null | null | https://doi.org/10.1021/acs.jcim.9b00410 | https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.9b00410?rand=oin4mnup | Molecule Property Prediction Based on Spatial Graph Embedding | Accurate prediction of molecular properties is important for new compound design, which is a crucial step in drug discovery. In this paper, molecular graph data is utilized for property prediction based on graph convolution neural networks. In addition, a convolution spatial graph embedding layer (C-SGEL) is introduced... | ['Xiao-Feng Wang', 'Zhiqiang Wei', 'Shugang Zhang', 'Shuang Wang', 'Mingjian Jiang', 'Zhen Li'] | 2019-08-22 | null | null | null | journal-of-chemical-information-and-modeling | ['graph-regression'] | ['graphs'] | [ 3.02646589e-02 -3.10264915e-01 -3.13556284e-01 -2.73290575e-01
5.99283949e-02 -2.52347112e-01 2.23883972e-01 6.10254407e-01
-5.93128651e-02 7.58616447e-01 7.04960898e-02 -6.70489609e-01
-1.92135975e-01 -1.08757627e+00 -8.07433188e-01 -7.16306329e-01
-2.70922035e-01 -3.87224734e-01 1.92878976e-01 2.52135042... | [5.139902591705322, 5.898253917694092] |
a725eb37-26da-4666-9542-7fe82dd6425e | retcl-a-selection-based-approach-for-1 | 2105.00795 | null | https://arxiv.org/abs/2105.00795v2 | https://arxiv.org/pdf/2105.00795v2.pdf | RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning | Retrosynthesis, of which the goal is to find a set of reactants for synthesizing a target product, is an emerging research area of deep learning. While the existing approaches have shown promising results, they currently lack the ability to consider availability (e.g., stability or purchasability) of the reactants or g... | ['Jinwoo Shin', 'Eunho Yang', 'Sung-Ju Hwang', 'You Young Song', 'Seung-Woo Seo', 'Sungsoo Ahn', 'Hankook Lee'] | 2021-05-03 | retcl-a-selection-based-approach-for | https://openreview.net/forum?id=3u3ny6UYmjy | https://openreview.net/pdf?id=3u3ny6UYmjy | null | ['retrosynthesis'] | ['medical'] | [ 3.87747496e-01 -2.72991657e-01 -5.65787971e-01 -1.34521589e-01
-7.03419447e-01 -8.21842253e-01 5.05369008e-01 6.08680010e-01
-3.33012491e-01 8.08432519e-01 -2.17161253e-01 -4.11130100e-01
-1.50227323e-01 -1.07462549e+00 -8.94828677e-01 -8.99186254e-01
1.15786858e-01 3.35102260e-01 1.08784780e-01 -2.95483321... | [4.506865501403809, 6.099552631378174] |
ddc8182d-3c61-4ccf-9071-dff18f9f66c5 | multi-label-music-genre-classification-from | 1707.04916 | null | http://arxiv.org/abs/1707.04916v1 | http://arxiv.org/pdf/1707.04916v1.pdf | Multi-label Music Genre Classification from Audio, Text, and Images Using Deep Features | Music genres allow to categorize musical items that share common
characteristics. Although these categories are not mutually exclusive, most
related research is traditionally focused on classifying tracks into a single
class. Furthermore, these categories (e.g., Pop, Rock) tend to be too broad for
certain applications.... | ['Serra Xavier', 'Barbieri Francesco', 'Nieto Oriol', 'Oramas Sergio'] | 2017-07-16 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 1.88307703e-01 -5.27959406e-01 -3.00434083e-01 -1.20269164e-01
-1.05836082e+00 -9.94096637e-01 6.65371835e-01 3.87184978e-01
-2.70337373e-01 4.19105828e-01 4.75145847e-01 3.59723598e-01
-8.46014619e-02 -6.56976700e-01 -4.95904267e-01 -5.70278108e-01
1.20591134e-01 2.37100884e-01 -1.07747279e-01 -1.89681705... | [15.62899112701416, 5.121687889099121] |
ef987f72-d42b-4667-b1db-bfd00e21cf25 | adaptive-graph-contrastive-learning-for | 2305.10837 | null | https://arxiv.org/abs/2305.10837v2 | https://arxiv.org/pdf/2305.10837v2.pdf | Adaptive Graph Contrastive Learning for Recommendation | Graph neural networks (GNNs) have recently emerged as an effective collaborative filtering (CF) approaches for recommender systems. The key idea of GNN-based recommender systems is to recursively perform message passing along user-item interaction edges to refine encoded embeddings, relying on sufficient and high-quali... | ['Lianghao Xia', 'Chao Huang', 'Yangqin Jiang'] | 2023-05-18 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-9.58201289e-02 -1.43917486e-01 -3.45563203e-01 -3.93702716e-01
-1.20799832e-01 -4.55063611e-01 6.15247011e-01 -1.12400770e-01
1.30145952e-01 2.07946748e-01 6.27332389e-01 -3.93730849e-01
-3.12673360e-01 -1.11622834e+00 -4.36866283e-01 -5.19931138e-01
1.06593534e-01 2.08945364e-01 -1.38016373e-01 -5.61145961... | [10.187045097351074, 5.5976057052612305] |
69db42f7-1563-4846-a2ba-b819248d32de | enhancing-targeted-minority-class-prediction | null | null | https://www.mdpi.com/1424-8220/22/13/4911 | https://www.mdpi.com/1424-8220/22/13/4911/pdf?version=1656502842 | Enhancing Targeted Minority Class Prediction in Sentence-Level Relation Extraction | Sentence-level relation extraction (RE) has a highly imbalanced data distribution that about 80% of data are labeled as negative, i.e., no relation; and there exist minority classes (MC) among positive labels; furthermore, some of MC instances have an incorrect label. Due to those challenges, i.e., label noise and low ... | ['Yong-Suk Choi', 'Hyeong-Ryeol Baek'] | 2022-06-29 | null | null | null | sensors-2022 | ['relation-classification'] | ['natural-language-processing'] | [ 2.88235664e-01 5.56324899e-01 -7.85225868e-01 -4.63149458e-01
-9.62638736e-01 -9.98282209e-02 4.12087440e-01 3.86703342e-01
-2.37629101e-01 1.14790154e+00 6.31588027e-02 -2.08936557e-01
2.89199412e-01 -9.31157231e-01 -6.46318376e-01 -5.90411246e-01
3.21851283e-01 6.63851976e-01 2.76137918e-01 -2.09429562... | [9.155535697937012, 8.591146469116211] |
6278100f-84da-4b60-97eb-b0e2ffe0d870 | autotrigger-named-entity-recognition-with-1 | null | null | https://openreview.net/forum?id=8LQwB0FkR0X | https://openreview.net/pdf?id=8LQwB0FkR0X | AutoTriggER: Named Entity Recognition with Auxiliary Trigger Extraction | Deep neural models for low-resource named entity recognition (NER) have shown impressive results by leveraging distant super-vision or other meta-level information (e.g. explanation). However, the costs of acquiring such additional information are generally prohibitive, especially in domains where existing resources (e... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [-1.62776604e-01 3.92206967e-01 -2.11520776e-01 -6.26025259e-01
-1.09018457e+00 -8.24549973e-01 8.06438088e-01 3.55017930e-01
-9.91668999e-01 8.40324044e-01 5.37552416e-01 -2.14081109e-01
2.53623664e-01 -7.06142187e-01 -8.47047091e-01 -1.68049753e-01
2.00219914e-01 6.18353724e-01 3.29022110e-02 -9.34697911... | [9.676393508911133, 9.43397331237793] |
85da096b-4900-4875-bc07-b9e7f0a3a476 | cdn-medal-two-stage-density-and-difference | 2106.03776 | null | https://arxiv.org/abs/2106.03776v4 | https://arxiv.org/pdf/2106.03776v4.pdf | CDN-MEDAL: Two-stage Density and Difference Approximation Framework for Motion Analysis | Background modeling and subtraction is a promising research area with a variety of applications for video surveillance. Recent years have witnessed a proliferation of effective learning-based deep neural networks in this area. However, the techniques have only provided limited descriptions of scenes' properties while r... | ['Cuong Tien Nguyen', 'Phuong Hoai Ha', 'Hung Ngoc Phan', 'Nhat Minh Chung', 'Synh Viet-Uyen Ha'] | 2021-06-07 | null | null | null | null | ['video-background-subtraction'] | ['computer-vision'] | [ 4.76501167e-01 -4.17806566e-01 1.10909477e-01 -2.01057047e-01
-3.45061630e-01 -1.88350409e-01 8.15317452e-01 -3.83414209e-01
-4.20851052e-01 4.32482302e-01 -1.78483635e-01 -3.50433797e-01
4.30467457e-01 -6.32215202e-01 -6.52223289e-01 -1.03742480e+00
-9.71392468e-02 -9.44370925e-02 9.60033298e-01 -1.28965810... | [8.962491989135742, -0.6460795402526855] |
d651fd73-9494-4218-8f80-533f72af3fef | large-scale-weakly-supervised-pre-training | 1905.00561 | null | http://arxiv.org/abs/1905.00561v1 | http://arxiv.org/pdf/1905.00561v1.pdf | Large-scale weakly-supervised pre-training for video action recognition | Current fully-supervised video datasets consist of only a few hundred
thousand videos and fewer than a thousand domain-specific labels. This hinders
the progress towards advanced video architectures. This paper presents an
in-depth study of using large volumes of web videos for pre-training video
models for the task of... | ['Xueting Yan', 'Heng Wang', 'Du Tran', 'Dhruv Mahajan', 'Matt Feiszli', 'Deepti Ghadiyaram'] | 2019-05-02 | large-scale-weakly-supervised-pre-training-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Ghadiyaram_Large-Scale_Weakly-Supervised_Pre-Training_for_Video_Action_Recognition_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ghadiyaram_Large-Scale_Weakly-Supervised_Pre-Training_for_Video_Action_Recognition_CVPR_2019_paper.pdf | cvpr-2019-6 | ['activity-recognition-in-videos', 'egocentric-activity-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.56637949e-01 3.91514599e-02 -5.80047369e-01 -4.73559022e-01
-8.87634397e-01 -5.84496558e-01 6.31195188e-01 -3.67330253e-01
-6.49064124e-01 6.62479162e-01 6.67821407e-01 2.34204177e-02
1.42147988e-01 -3.82714242e-01 -1.08079135e+00 -5.90883315e-01
-5.43850422e-01 3.68641317e-01 5.05012453e-01 1.03267923... | [8.536910057067871, 0.6766646504402161] |
31e4b1fe-e504-4ef1-817e-e0f242b18766 | gapartnet-cross-category-domain-generalizable | 2211.05272 | null | https://arxiv.org/abs/2211.05272v2 | https://arxiv.org/pdf/2211.05272v2.pdf | GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts | For years, researchers have been devoted to generalizable object perception and manipulation, where cross-category generalizability is highly desired yet underexplored. In this work, we propose to learn such cross-category skills via Generalizable and Actionable Parts (GAParts). By identifying and defining 9 GAPart cla... | ['He Wang', 'Siyuan Huang', 'Li Yi', 'Chao Xu', 'Chengyang Zhao', 'Helin Xu', 'Haoran Geng'] | 2022-11-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Geng_GAPartNet_Cross-Category_Domain-Generalizable_Object_Perception_and_Manipulation_via_Generalizable_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Geng_GAPartNet_Cross-Category_Domain-Generalizable_Object_Perception_and_Manipulation_via_Generalizable_and_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 1.43573955e-01 2.20248699e-01 -5.55108450e-02 -3.10677707e-01
-7.23345518e-01 -1.07287335e+00 3.46442819e-01 -1.11958787e-01
6.18361682e-02 4.55823749e-01 -2.64684409e-02 1.10868186e-01
-8.51324499e-02 -4.74351555e-01 -1.35769665e+00 -3.38539213e-01
-1.44016733e-02 7.95163691e-01 6.33568048e-01 -1.92670673... | [6.748828411102295, -1.6501259803771973] |
7d0812b8-1c89-4d34-ac1c-572c999edf33 | speech-driven-facial-reenactment-using | 1803.07461 | null | http://arxiv.org/abs/1803.07461v1 | http://arxiv.org/pdf/1803.07461v1.pdf | Speech-Driven Facial Reenactment Using Conditional Generative Adversarial Networks | We present a novel approach to generating photo-realistic images of a face
with accurate lip sync, given an audio input. By using a recurrent neural
network, we achieved mouth landmarks based on audio features. We exploited the
power of conditional generative adversarial networks to produce
highly-realistic face condit... | ['Hamid Aghajan', 'Hosein Hasani', 'Seyed Ali Jalalifar'] | 2018-03-20 | null | null | null | null | ['lip-sync-1'] | ['computer-vision'] | [ 5.91537297e-01 6.79419458e-01 3.03145260e-01 -2.74504542e-01
-1.11597931e+00 -5.53894997e-01 6.20891750e-01 -8.89749467e-01
7.22684935e-02 7.51718342e-01 2.44083256e-01 2.27550089e-01
4.21581626e-01 -7.14939117e-01 -1.03658664e+00 -5.60808420e-01
5.85324364e-03 2.08486930e-01 -4.02156740e-01 -1.17678650... | [13.23735237121582, -0.44524404406547546] |
8e2eec36-2e5c-44fc-8a70-2b22b77b82d2 | estimating-the-probabilities-of-causation-via | 2109.01904 | null | https://arxiv.org/abs/2109.01904v6 | https://arxiv.org/pdf/2109.01904v6.pdf | Estimating Categorical Counterfactuals via Deep Twin Networks | Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. To perform counterfactual inference, one requires knowledge of the underlying causal mechanisms. However, causal mechanisms cannot be uniquely determined from observations and interventions alone. This raises t... | ['Ciaran M. Gilligan-Lee', 'Bernhard Kainz', 'Athanasios Vlontzos'] | 2021-09-04 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 5.03071129e-01 5.59103549e-01 -7.64414728e-01 -1.97224557e-01
-1.82974666e-01 -4.92225409e-01 9.69632566e-01 -8.55656713e-02
-2.89484292e-01 1.37657499e+00 6.50943995e-01 -1.10747898e+00
-8.08014274e-01 -1.04026759e+00 -9.83677208e-01 -5.62736928e-01
-6.32720113e-01 5.55780470e-01 -5.18323660e-01 5.98464943... | [8.202164649963379, 5.462851524353027] |
0784daca-be8c-4fa1-80e4-d41a6619bda6 | improving-heterogeneous-model-reuse-by | 2305.13871 | null | https://arxiv.org/abs/2305.13871v1 | https://arxiv.org/pdf/2305.13871v1.pdf | Improving Heterogeneous Model Reuse by Density Estimation | This paper studies multiparty learning, aiming to learn a model using the private data of different participants. Model reuse is a promising solution for multiparty learning, assuming that a local model has been trained for each party. Considering the potential sample selection bias among different parties, some hetero... | ['DaCheng Tao', 'Yixin Chen', 'Bo Du', 'Kehua Su', 'Fengxiang He', 'Han Hu', 'Yong Luo', 'Anke Tang'] | 2023-05-23 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [-7.06238002e-02 5.51937111e-02 -5.55637896e-01 -5.61432779e-01
-1.50368953e+00 -5.33353925e-01 6.17182732e-01 2.21607253e-01
-2.94172227e-01 1.06091511e+00 8.48068073e-02 1.29277095e-01
1.11768670e-01 -7.98470318e-01 -7.90829539e-01 -1.00312495e+00
1.29013568e-01 6.03994846e-01 -1.67177051e-01 3.97370368... | [10.322357177734375, 3.2423222064971924] |
85796fd3-33ab-46c9-b215-9476bd596675 | online-human-action-detection-using-joint | 1604.05633 | null | http://arxiv.org/abs/1604.05633v2 | http://arxiv.org/pdf/1604.05633v2.pdf | Online Human Action Detection using Joint Classification-Regression Recurrent Neural Networks | Human action recognition from well-segmented 3D skeleton data has been
intensively studied and has been attracting an increasing attention. Online
action detection goes one step further and is more challenging, which
identifies the action type and localizes the action positions on the fly from
the untrimmed stream data... | ['Wen-Jun Zeng', 'Junliang Xing', 'Jiaying Liu', 'Yanghao Li', 'Chunfeng Yuan', 'Cuiling Lan'] | 2016-04-19 | null | null | null | null | ['online-action-detection'] | ['computer-vision'] | [ 4.95346189e-01 -2.02855185e-01 -5.07035434e-01 -2.06903607e-01
-8.45418632e-01 -4.33156872e-03 3.67674321e-01 -8.54815990e-02
-5.89215040e-01 2.69379050e-01 3.73666853e-01 1.08259752e-01
-1.71222147e-02 -3.46547127e-01 -5.27836144e-01 -8.99351537e-01
-3.53396297e-01 9.72436517e-02 4.27410334e-01 2.27029368... | [8.317662239074707, 0.5437058210372925] |
82cba57a-3f2c-42a5-ae9a-6fb35d2773f7 | bi-sampling-approach-to-classify-music-mood | 2203.06583 | null | https://arxiv.org/abs/2203.06583v1 | https://arxiv.org/pdf/2203.06583v1.pdf | Bi-Sampling Approach to Classify Music Mood leveraging Raga-Rasa Association in Indian Classical Music | The impact of Music on the mood or emotion of the listener is a well-researched area in human psychology and behavioral science. In Indian classical music, ragas are the melodic structure that defines the various styles and forms of the music. Each raga has been found to evoke a specific emotion in the listener. With t... | ['Narayana Darapaneni', 'Sudha G', 'Pathi Mohan Rao', 'Ullas M S', 'Gayathri Ramesh K K', 'Sushmitha M N', 'Harsha M N', 'Vinayak Arkachaari', 'Mohan Rao B C'] | 2022-03-13 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 7.81554654e-02 -6.92374706e-01 -4.19939160e-01 -1.86919525e-01
-2.56468058e-01 -7.48496473e-01 9.51300338e-02 3.52088869e-01
-2.15387102e-02 1.47285103e-03 6.75314724e-01 1.56826168e-01
-6.72591031e-01 -6.08436882e-01 2.94510216e-01 -5.45207202e-01
5.80396727e-02 3.96204107e-02 -2.52730578e-01 -5.61540365... | [15.88902473449707, 5.2180657386779785] |
5429593b-2689-4246-9194-fd6d8db0aaf0 | global-local-face-upsampling-network | 1603.07235 | null | http://arxiv.org/abs/1603.07235v2 | http://arxiv.org/pdf/1603.07235v2.pdf | Global-Local Face Upsampling Network | Face hallucination, which is the task of generating a high-resolution face
image from a low-resolution input image, is a well-studied problem that is
useful in widespread application areas. Face hallucination is particularly
challenging when the input face resolution is very low (e.g., 10 x 12 pixels)
and/or the image ... | ['Oncel Tuzel', 'John R. Hershey', 'Yuichi Taguchi'] | 2016-03-23 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 4.53109056e-01 2.65119761e-01 1.35956302e-01 -3.66073847e-01
-7.76323199e-01 -1.98052853e-01 3.95213038e-01 -6.12786770e-01
-5.96467294e-02 7.78852701e-01 1.82190493e-01 3.53980899e-01
-6.63526803e-02 -8.63309324e-01 -9.64967191e-01 -6.55521989e-01
1.72464305e-03 2.60381341e-01 -2.29709551e-01 -1.89830422... | [12.790641784667969, -0.10338622331619263] |
74a31fdf-fd88-4163-bcd9-885aa77acf1e | semantic-table-retrieval-using-keyword-and | 2105.06365 | null | https://arxiv.org/abs/2105.06365v1 | https://arxiv.org/pdf/2105.06365v1.pdf | Semantic Table Retrieval using Keyword and Table Queries | Tables on the Web contain a vast amount of knowledge in a structured form. To tap into this valuable resource, we address the problem of table retrieval: answering an information need with a ranked list of tables. We investigate this problem in two different variants, based on how the information need is expressed: as ... | ['Krisztian Balog', 'Shuo Zhang'] | 2021-05-13 | null | null | null | null | ['table-retrieval'] | ['natural-language-processing'] | [ 2.22996905e-01 1.79466203e-01 -5.13928592e-01 -4.95981783e-01
-1.63093507e+00 -9.80528235e-01 8.48119795e-01 9.69946921e-01
-2.44716797e-02 6.31139100e-01 8.96567941e-01 -3.97700854e-02
-3.99568111e-01 -1.11277461e+00 -8.85658443e-01 1.09212570e-01
2.15916932e-01 1.01734757e+00 4.40085530e-01 -6.57656312... | [9.740921974182129, 7.9280877113342285] |
571c0261-bccc-454a-8842-29913cb4dfae | segloc-learning-segmentation-based | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Pietrantoni_SegLoc_Learning_Segmentation-Based_Representations_for_Privacy-Preserving_Visual_Localization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Pietrantoni_SegLoc_Learning_Segmentation-Based_Representations_for_Privacy-Preserving_Visual_Localization_CVPR_2023_paper.pdf | SegLoc: Learning Segmentation-Based Representations for Privacy-Preserving Visual Localization | Inspired by properties of semantic segmentation, in this paper we investigate how to leverage robust image segmentation in the context of privacy-preserving visual localization. We propose a new localization framework, SegLoc, that leverages image segmentation to create robust, compact, and privacy-preserving scene... | ['Gabriela Csurka', 'Torsten Sattler', 'Martin Humenberger', 'Maxime Pietrantoni'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['visual-localization'] | ['computer-vision'] | [ 2.55021900e-01 3.01654518e-01 -2.19775572e-01 -4.50432986e-01
-1.27004433e+00 -1.05285203e+00 4.14186627e-01 2.78765261e-01
-5.01889527e-01 3.74395758e-01 -4.30264845e-02 -1.73197854e-02
-1.41159549e-01 -5.12089014e-01 -1.15564632e+00 -6.25189304e-01
1.18705258e-01 5.38648963e-01 3.06846678e-01 3.83317500... | [7.667574882507324, -2.2105841636657715] |
ca9b9fd4-34b3-4a18-a73b-3ed20a9686e9 | revisiting-resnets-improved-training-and | 2103.07579 | null | https://arxiv.org/abs/2103.07579v1 | https://arxiv.org/pdf/2103.07579v1.pdf | Revisiting ResNets: Improved Training and Scaling Strategies | Novel computer vision architectures monopolize the spotlight, but the impact of the model architecture is often conflated with simultaneous changes to training methodology and scaling strategies. Our work revisits the canonical ResNet (He et al., 2015) and studies these three aspects in an effort to disentangle them. P... | ['Barret Zoph', 'Jonathon Shlens', 'Tsung-Yi Lin', 'Aravind Srinivas', 'Ekin D. Cubuk', 'Xianzhi Du', 'William Fedus', 'Irwan Bello'] | 2021-03-13 | null | http://proceedings.neurips.cc/paper/2021/hash/bef4d169d8bddd17d68303877a3ea945-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/bef4d169d8bddd17d68303877a3ea945-Paper.pdf | neurips-2021-12 | ['document-image-classification'] | ['computer-vision'] | [ 1.96856156e-01 -4.51506302e-02 -2.47733891e-01 -1.74229339e-01
-4.51560855e-01 -5.72369456e-01 8.01278234e-01 -3.04440349e-01
-9.67747748e-01 4.91270453e-01 1.77878946e-01 -3.92850757e-01
-1.15183070e-01 -5.66204190e-01 -8.04200530e-01 -4.73825693e-01
4.68107052e-02 3.65986228e-01 6.10815346e-01 -4.27855045... | [9.191293716430664, 2.152869939804077] |
62202db6-a9ca-4167-8f8c-c247902a6485 | exploring-deep-spiking-neural-networks-for | 1903.02080 | null | http://arxiv.org/abs/1903.02080v1 | http://arxiv.org/pdf/1903.02080v1.pdf | Exploring Deep Spiking Neural Networks for Automated Driving Applications | Neural networks have become the standard model for various computer vision
tasks in automated driving including semantic segmentation, moving object
detection, depth estimation, visual odometry, etc. The main flavors of neural
networks which are used commonly are convolutional (CNN) and recurrent (RNN).
In spite of rap... | ['Sambit Mohapatra', 'Senthil Yogamani', 'Raoul Zollner', 'Stefan Milz', 'Heinrich Gotzig'] | 2019-01-11 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 2.29158774e-01 -2.59999305e-01 -5.97899631e-02 -1.91786617e-01
1.33601993e-01 -3.11517447e-01 7.51736343e-01 -2.24722072e-01
-7.95125604e-01 6.15099788e-01 -2.75345325e-01 -2.25462690e-01
2.13340074e-01 -8.36090982e-01 -7.55347788e-01 -8.16222191e-01
7.76761994e-02 1.87384158e-01 9.66283321e-01 -3.19745570... | [8.218421936035156, 2.4112329483032227] |
8350b92b-0b1c-4ffb-b1c2-8ff14ffd2dc4 | distributive-pre-training-of-generative | 2306.14787 | null | https://arxiv.org/abs/2306.14787v1 | https://arxiv.org/pdf/2306.14787v1.pdf | Distributive Pre-Training of Generative Modeling Using Matrix-Product States | Tensor networks have recently found applications in machine learning for both supervised learning and unsupervised learning. The most common approaches for training these models are gradient descent methods. In this work, we consider an alternative training scheme utilizing basic tensor network operations, e.g., summat... | ['Frank Pollmann', 'Sebastian Peterhansl', 'Olivier Kuijpers', 'Sheng-Hsuan Lin'] | 2023-06-26 | null | null | null | null | ['tensor-networks', 'density-estimation'] | ['methodology', 'methodology'] | [ 5.91823578e-01 -4.01095673e-02 -2.53292710e-01 -4.45629090e-01
-4.50486332e-01 -2.42676616e-01 6.61957085e-01 9.06503424e-02
-8.76133204e-01 6.76503897e-01 -3.08404922e-01 -7.04225063e-01
3.05146445e-03 -1.04660916e+00 -6.77769065e-01 -1.12477207e+00
-7.25212023e-02 4.19936717e-01 -1.37207329e-01 -7.22218752... | [5.658999919891357, 4.984944820404053] |
71ba565d-8eb1-4c3e-8b26-25766fc1e6c2 | pca-semi-supervised-segmentation-with-patch | 2207.11683 | null | https://arxiv.org/abs/2207.11683v1 | https://arxiv.org/pdf/2207.11683v1.pdf | PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training | Deep learning based semi-supervised learning (SSL) methods have achieved strong performance in medical image segmentation, which can alleviate doctors' expensive annotation by utilizing a large amount of unlabeled data. Unlike most existing semi-supervised learning methods, adversarial training based methods distinguis... | ['Thomas Lukasiewicz', 'Shuo Zhang', 'Zhenghua Xu', 'Zihang Xu'] | 2022-07-24 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 5.63095748e-01 7.06591845e-01 -4.76797402e-01 -5.46814322e-01
-1.25019133e+00 -4.97116506e-01 4.59968820e-02 1.55788809e-01
-3.68887246e-01 6.79430127e-01 -1.33839324e-01 -3.56616586e-01
3.85227174e-01 -5.73078632e-01 -8.00270259e-01 -9.35768664e-01
1.06537327e-01 8.23402107e-01 2.98409581e-01 1.81033522... | [14.574381828308105, -2.1297781467437744] |
6256ddd7-4374-4c84-83df-a9ed0f022f00 | aesthetics-of-neural-network-art | 1903.05696 | null | http://arxiv.org/abs/1903.05696v2 | http://arxiv.org/pdf/1903.05696v2.pdf | Aesthetics of Neural Network Art | This paper proposes a way to understand neural network artworks as
juxtapositions of natural image cues. It is hypothesized that images with
unusual combinations of realistic visual cues are interesting, and, neural
models trained to model natural images are well-suited to creating interesting
images. Art using neural ... | ['Aaron Hertzmann'] | 2019-03-13 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 4.24644798e-01 6.84302866e-01 -2.02176534e-02 -1.70078173e-01
2.38484159e-01 -4.41995800e-01 1.16682434e+00 -6.33556485e-01
2.94504929e-02 9.96583998e-01 4.88136023e-01 3.72376591e-02
1.46478310e-01 -8.95193517e-01 -1.26597834e+00 -3.67264241e-01
9.92954150e-02 2.01503575e-01 -4.28396493e-01 -5.00977099... | [11.656170845031738, -0.2511152923107147] |
697ad28b-9578-49d3-84d1-d7978ca771c3 | exploiting-relationship-for-complex-scene | 2104.00356 | null | https://arxiv.org/abs/2104.00356v1 | https://arxiv.org/pdf/2104.00356v1.pdf | Exploiting Relationship for Complex-scene Image Generation | The significant progress on Generative Adversarial Networks (GANs) has facilitated realistic single-object image generation based on language input. However, complex-scene generation (with various interactions among multiple objects) still suffers from messy layouts and object distortions, due to diverse configurations... | ['Tao Mei', 'Xiao-Ping Zhang', 'Wei zhang', 'Yalong Bai', 'Hongdong Zheng', 'Tianyu Hua'] | 2021-04-01 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 4.19645548e-01 -5.42575307e-02 2.41114721e-01 -3.90405476e-01
-2.39869744e-01 -7.63218284e-01 8.65094900e-01 -2.34228447e-01
1.33502051e-01 5.61098993e-01 1.61051780e-01 2.19201278e-02
-2.26379916e-01 -1.03097153e+00 -1.16241682e+00 -6.06275797e-01
2.04352438e-01 2.91158170e-01 2.29752824e-01 -3.80248666... | [11.401274681091309, -0.4422183930873871] |
6eee81c4-844c-46e7-a95f-7da56d6198b1 | on-the-limits-of-learning-to-actively-learn | 1910.02228 | null | https://arxiv.org/abs/1910.02228v1 | https://arxiv.org/pdf/1910.02228v1.pdf | On the Limits of Learning to Actively Learn Semantic Representations | One of the goals of natural language understanding is to develop models that map sentences into meaning representations. However, training such models requires expensive annotation of complex structures, which hinders their adoption. Learning to actively-learn (LTAL) is a recent paradigm for reducing the amount of labe... | ['Jonathan Berant', 'Gabriel Stanovsky', 'Yichu Zhou', 'Vivek Srikumar', 'Omri Koshorek'] | 2019-10-05 | on-the-limits-of-learning-to-actively-learn-1 | https://aclanthology.org/K19-1042 | https://aclanthology.org/K19-1042.pdf | conll-2019-11 | ['learning-semantic-representations'] | ['methodology'] | [ 5.68651140e-01 5.46838701e-01 -4.05797809e-01 -6.98311806e-01
-1.06422007e+00 -7.99836636e-01 6.65758908e-01 3.23890895e-01
-5.62211454e-01 8.29662979e-01 2.01858953e-01 -4.72049952e-01
-8.88204500e-02 -9.16147649e-01 -1.00656605e+00 -6.78977489e-01
3.21498692e-01 7.52892137e-01 -5.98824397e-03 5.07308841... | [10.327752113342285, 7.645910263061523] |
4975adda-81d9-4277-943b-c0b68e2377e5 | graph-laplacians-on-shared-nearest-neighbor | 2302.12399 | null | https://arxiv.org/abs/2302.12399v2 | https://arxiv.org/pdf/2302.12399v2.pdf | Graph Laplacians on Shared Nearest Neighbor graphs and graph Laplacians on $k$-Nearest Neighbor graphs having the same limit | A Shared Nearest Neighbor (SNN) graph is a type of graph construction using shared nearest neighbor information, which is a secondary similarity measure based on the rankings induced by a primary $k$-nearest neighbor ($k$-NN) measure. SNN measures have been touted as being less prone to the curse of dimensionality than... | ['A. Martina Neuman'] | 2023-02-24 | null | null | null | null | ['graph-construction'] | ['graphs'] | [-4.99552898e-02 1.62937999e-01 -7.73943067e-02 -3.34329307e-01
-3.98004115e-01 -6.56933784e-01 2.55133182e-01 3.89415979e-01
-2.91239619e-01 4.62237895e-01 2.30681479e-01 -2.62027442e-01
-9.27396595e-01 -9.95178938e-01 -4.96762663e-01 -8.70446265e-01
-6.86753094e-01 3.02925259e-01 2.58696645e-01 -2.73811728... | [7.1160430908203125, 5.4726152420043945] |
7066c99a-31af-4593-b351-bab691124fae | hdr-video-reconstruction-with-tri-exposure | 2103.10982 | null | https://arxiv.org/abs/2103.10982v1 | https://arxiv.org/pdf/2103.10982v1.pdf | HDR Video Reconstruction with Tri-Exposure Quad-Bayer Sensors | We propose a novel high dynamic range (HDR) video reconstruction method with new tri-exposure quad-bayer sensors. Thanks to the larger number of exposure sets and their spatially uniform deployment over a frame, they are more robust to noise and spatial artifacts than previous spatially varying exposure (SVE) HDR video... | ['Jinwei Gu', 'Jun Jiang', 'Inchang Choi', 'Yitong Jiang'] | 2021-03-19 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 3.81157458e-01 -6.20957255e-01 3.19109827e-01 -1.02406338e-01
-2.85377592e-01 -3.84051055e-01 1.53323427e-01 -6.12078846e-01
-3.24431390e-01 8.20265114e-01 3.36307973e-01 1.37449339e-01
-2.44370416e-01 -7.18009889e-01 -5.25171578e-01 -7.45569706e-01
-2.46991158e-01 -4.59411204e-01 8.18752646e-01 -2.42084503... | [10.862834930419922, -2.0572521686553955] |
a20abccf-4ab8-4ba7-a796-66ebd87225fe | contextual-sentence-classification-detecting | 2110.03727 | null | https://arxiv.org/abs/2110.03727v2 | https://arxiv.org/pdf/2110.03727v2.pdf | Contextual Sentence Classification: Detecting Sustainability Initiatives in Company Reports | We introduce the novel task of detecting sustainability initiatives in company reports. Given a full report, the aim is to automatically identify mentions of practical activities that a company has performed in order to tackle specific societal issues. New methods for identifying continuous sentence spans need to be de... | ['Marek Rei', 'Maurizio Zollo', 'Christopher Bryant', 'Dan Hirlea'] | 2021-10-07 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [ 6.64556563e-01 1.95460349e-01 -3.35873216e-01 -2.01197192e-01
-1.20096874e+00 -9.25293207e-01 8.34989369e-01 1.04851389e+00
-3.56742561e-01 7.86302149e-01 6.37238264e-01 -3.42953086e-01
-7.21930861e-02 -9.32073891e-01 -3.88742536e-01 -1.71779066e-01
4.07919884e-01 9.65586025e-03 1.69244289e-01 -4.24290374... | [12.395317077636719, 9.526910781860352] |
1b76019a-1b92-416b-889b-39032fe54af0 | carma-context-aware-runtime-reconfiguration | 2306.15748 | null | https://arxiv.org/abs/2306.15748v1 | https://arxiv.org/pdf/2306.15748v1.pdf | CARMA: Context-Aware Runtime Reconfiguration for Energy-Efficient Sensor Fusion | Autonomous systems (AS) are systems that can adapt and change their behavior in response to unanticipated events and include systems such as aerial drones, autonomous vehicles, and ground/aquatic robots. AS require a wide array of sensors, deep-learning models, and powerful hardware platforms to perceive and safely ope... | ['Sitao Huang', 'Mohammad Abdullah Al Faruque', 'DongHwan Seong', 'Yuhui Li', 'Xiaofang Zhang', 'Arnav Vaibhav Malawade', 'Yifan Zhang'] | 2023-06-27 | null | null | null | null | ['autonomous-vehicles'] | ['computer-vision'] | [ 5.33916771e-01 -3.83692682e-01 -1.81353778e-01 -2.28559658e-01
-2.79065788e-01 -8.99985015e-01 3.48158389e-01 3.06501985e-01
-3.16846222e-01 3.06299388e-01 -4.01703864e-01 -2.45409474e-01
-3.85695621e-02 -9.25487041e-01 -9.00095999e-01 -5.23826838e-01
-2.62696624e-01 5.45672290e-02 5.53339481e-01 -2.33006626... | [8.120922088623047, 2.266244411468506] |
bf8f2082-1cb1-411e-b22f-bb9103317470 | language-guided-face-animation-by-recurrent | 2208.05617 | null | https://arxiv.org/abs/2208.05617v1 | https://arxiv.org/pdf/2208.05617v1.pdf | Language-Guided Face Animation by Recurrent StyleGAN-based Generator | Recent works on language-guided image manipulation have shown great power of language in providing rich semantics, especially for face images. However, the other natural information, motions, in language is less explored. In this paper, we leverage the motion information and study a novel task, language-guided face ani... | ['Baining Guo', 'Xin Geng', 'Jianlong Fu', 'Bei Liu', 'Huan Yang', 'Tiankai Hang'] | 2022-08-11 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 1.28193021e-01 7.49178091e-03 -1.90795034e-01 -4.25326526e-01
-5.59876859e-01 -4.37185168e-01 6.81168020e-01 -8.62913013e-01
-9.79466513e-02 5.10912716e-01 3.08974385e-01 -1.11373790e-01
3.36909026e-01 -5.68921983e-01 -7.71996140e-01 -7.80275881e-01
1.46110550e-01 -6.17302470e-02 -1.96412250e-01 -2.29036108... | [11.154478073120117, -0.6778444647789001] |
3f6ebe8b-e524-4bb6-a70d-3652c4fbfe34 | integrated-parameter-efficient-tuning-for | 2211.02227 | null | https://arxiv.org/abs/2211.02227v2 | https://arxiv.org/pdf/2211.02227v2.pdf | Integrated Parameter-Efficient Tuning for General-Purpose Audio Models | The advent of hyper-scale and general-purpose pre-trained models is shifting the paradigm of building task-specific models for target tasks. In the field of audio research, task-agnostic pre-trained models with high transferability and adaptability have achieved state-of-the-art performances through fine-tuning for dow... | ['Ha-Jin Yu', 'Chan-yeong Lim', 'Hyun-seo Shin', 'Jungwoo Heo', 'Ju-ho Kim'] | 2022-11-04 | null | null | null | null | ['genre-classification', 'keyword-spotting', 'speaker-verification'] | ['computer-vision', 'speech', 'speech'] | [ 3.16117108e-01 -3.55304867e-01 1.41017541e-01 -2.76466191e-01
-1.13581395e+00 -7.26751566e-01 2.85237134e-01 -7.55594373e-02
-5.65318406e-01 4.63048369e-01 2.04671443e-01 -1.98056430e-01
-4.45142001e-01 -5.04393458e-01 -5.79325557e-01 -6.76141739e-01
-4.34858538e-02 2.49594763e-01 2.20136404e-01 -3.68189871... | [15.325624465942383, 5.201179504394531] |
060bc08c-b5df-4d28-b34b-5f13afda3e03 | enabling-country-scale-land-cover-mapping | 2209.00727 | null | https://arxiv.org/abs/2209.00727v2 | https://arxiv.org/pdf/2209.00727v2.pdf | Enabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite Imagery | High-resolution satellite images can provide abundant, detailed spatial information for land cover classification, which is particularly important for studying the complicated built environment. However, due to the complex land cover patterns, the costly training sample collections, and the severe distribution shifts o... | ['Xiao Xiang Zhu', 'Gui-Song Xia', 'Xin-Yi Tong'] | 2022-09-01 | null | null | null | null | ['segmentation-of-remote-sensing-imagery', 'the-semantic-segmentation-of-remote-sensing'] | ['miscellaneous', 'miscellaneous'] | [ 1.65641397e-01 -2.76155442e-01 -4.82699275e-01 -4.75697845e-01
-9.53619838e-01 -4.66453820e-01 4.70511645e-01 -2.46700257e-01
-6.13064826e-01 1.07747662e+00 3.75336707e-02 -4.52312648e-01
-8.43001008e-02 -1.44422030e+00 -7.38347471e-01 -8.83300543e-01
-4.88649249e-01 5.00650942e-01 -8.05116445e-02 -3.92375827... | [9.533075332641602, -1.4303067922592163] |
4ce3a00f-5eb7-45c5-be1e-4f466ac35197 | signed-network-embedding-with-application-to | 2207.09324 | null | https://arxiv.org/abs/2207.09324v2 | https://arxiv.org/pdf/2207.09324v2.pdf | Signed Network Embedding with Application to Simultaneous Detection of Communities and Anomalies | Signed networks are frequently observed in real life with additional sign information associated with each edge, yet such information has been largely ignored in existing network models. This paper develops a unified embedding model for signed networks to disentangle the intertwined balance structure and anomaly effect... | ['Junhui Wang', 'Haoran Zhang'] | 2022-07-08 | null | null | null | null | ['network-embedding'] | ['methodology'] | [ 5.90545572e-02 1.02360100e-01 -4.34429288e-01 -9.58589464e-02
9.96952578e-02 -6.31165922e-01 4.92410809e-01 3.63810807e-01
6.28283247e-02 6.34016871e-01 1.15070194e-01 -2.29727581e-01
-8.70019972e-01 -7.68601358e-01 -2.20253214e-01 -6.35057211e-01
-8.40039849e-01 2.94404805e-01 -1.07281476e-01 -9.53228176... | [7.235045909881592, 6.073638439178467] |
6660ded1-9ad5-4236-a449-4603bf1f123d | gamesh-guided-and-augmented-meshing-for-deep | 2010.09774 | null | https://arxiv.org/abs/2010.09774v1 | https://arxiv.org/pdf/2010.09774v1.pdf | GAMesh: Guided and Augmented Meshing for Deep Point Networks | We present a new meshing algorithm called guided and augmented meshing, GAMesh, which uses a mesh prior to generate a surface for the output points of a point network. By projecting the output points onto this prior and simplifying the resulting mesh, GAMesh ensures a surface with the same topology as the mesh prior bu... | ['M Gopi', 'Nitin Agarwal'] | 2020-10-19 | null | null | null | null | ['3d-surface-generation', 'single-view-3d-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 3.24921221e-01 8.66747916e-01 2.77373433e-01 -1.45236298e-01
-4.82408553e-01 -4.41613704e-01 5.28291225e-01 -2.06182450e-01
1.98528379e-01 5.42967618e-01 -3.27405393e-01 -1.03721479e-02
-1.67232845e-02 -1.36773145e+00 -1.32356679e+00 -4.74266201e-01
1.77762046e-01 1.39653313e+00 3.68918002e-01 -1.37092173... | [8.713751792907715, -3.655153512954712] |
a04a8cf6-b5b2-4514-97bd-631815f6e837 | increasing-the-scope-as-you-learn-adaptive | 2304.11468 | null | https://arxiv.org/abs/2304.11468v1 | https://arxiv.org/pdf/2304.11468v1.pdf | Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces | Recent advances have extended the scope of Bayesian optimization (BO) to expensive-to-evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful applications, for example, in the life sciences, neural architecture search, and robotics. However, a closer examination reveals that the state-of-th... | ['Matthias Poloczek', 'Luigi Nardi', 'Leonard Papenmeier'] | 2023-04-22 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-1.56652063e-01 -9.27507356e-02 -1.95751652e-01 -1.88598335e-01
-7.94342577e-01 -4.47753906e-01 4.72324848e-01 -3.20836246e-01
-4.43758190e-01 9.43425000e-01 8.00174400e-02 -3.25539798e-01
-7.68064439e-01 -4.22412276e-01 -6.84758604e-01 -9.08847213e-01
-2.35665947e-01 6.52782381e-01 1.01225145e-01 -1.68830119... | [6.670287132263184, 4.033710956573486] |
2f573719-3bb9-446c-a875-847f7cbb654f | contrastive-embedding-distribution-refinement | 2201.11388 | null | https://arxiv.org/abs/2201.11388v1 | https://arxiv.org/pdf/2201.11388v1.pdf | Contrastive Embedding Distribution Refinement and Entropy-Aware Attention for 3D Point Cloud Classification | Learning a powerful representation from point clouds is a fundamental and challenging problem in the field of computer vision. Different from images where RGB pixels are stored in the regular grid, for point clouds, the underlying semantic and structural information of point clouds is the spatial layout of the points. ... | ['Weigong Zhang', 'Xuanpeng Li', 'Shuai Jin', 'Qifan Xue', 'Yichao Cao', 'Feng Yang'] | 2022-01-27 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [ 9.79845971e-03 -2.24265829e-02 -2.19879858e-02 -3.45483094e-01
-5.20189762e-01 -2.59860754e-01 3.22001606e-01 2.16412619e-01
-2.53813684e-01 2.92889953e-01 -3.60783547e-01 -3.99533100e-02
-6.06637657e-01 -9.42518890e-01 -7.92831779e-01 -9.27513659e-01
-2.78778136e-01 4.25508857e-01 2.03385398e-01 1.02605537... | [7.9045491218566895, -3.364353895187378] |
d3da0e45-fbbe-4e1c-b128-b135d04cdf8d | a-3d-model-based-approach-for-fitting-masks | 2103.00803 | null | https://arxiv.org/abs/2103.00803v2 | https://arxiv.org/pdf/2103.00803v2.pdf | A 3D model-based approach for fitting masks to faces in the wild | Face recognition now requires a large number of labelled masked face images in the era of this unprecedented COVID-19 pandemic. Unfortunately, the rapid spread of the virus has left us little time to prepare for such dataset in the wild. To circumvent this issue, we present a 3D model-based approach called WearMask3D f... | ['Ig-Jae Kim', 'Hyeong-Seok Ko', 'Junghyun Cho', 'Gi Pyo Nam', 'Minsoo Kim', 'Hanjo Kim', 'Je Hyeong Hong'] | 2021-03-01 | null | null | null | null | ['face-model'] | ['computer-vision'] | [ 6.58073723e-01 4.44978446e-01 2.67389715e-01 -5.36540985e-01
-4.49308783e-01 -4.60432649e-01 6.45589173e-01 -9.54104424e-01
-6.21284805e-02 3.95670295e-01 2.70758625e-02 -1.93237644e-02
5.37766278e-01 -4.87726778e-01 -6.84645772e-01 -7.77097166e-01
4.87207621e-03 6.67915404e-01 -1.94114164e-01 -1.61568031... | [12.974705696105957, 0.22503578662872314] |
fe5fb1b6-87a3-4cae-97dd-85b9f8c9b85a | learning-from-synthetic-data-facial | 2207.10025 | null | https://arxiv.org/abs/2207.10025v2 | https://arxiv.org/pdf/2207.10025v2.pdf | Learning from Synthetic Data: Facial Expression Classification based on Ensemble of Multi-task Networks | Facial expression in-the-wild is essential for various interactive computing domains. Especially, "Learning from Synthetic Data" (LSD) is an important topic in the facial expression recognition task. In this paper, we propose a multi-task learning-based facial expression recognition approach which consists of emotion a... | ['Yuchul Jung', 'Jin-Woo Jeong', 'Sumin Hong', 'JiYeon Oh', 'Yeong-Gi Hong', 'Jae-Yeop Jeong'] | 2022-07-20 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [-9.54078417e-03 -2.93357909e-01 1.17046304e-01 -9.81216133e-01
-8.25347185e-01 -6.46666288e-02 2.92975187e-01 -4.72996235e-01
-4.17705357e-01 6.44148707e-01 -2.28521749e-01 4.23731893e-01
3.78385335e-01 -8.37101787e-02 -6.39248848e-01 -8.45205307e-01
-2.58853853e-01 1.96324944e-01 -4.51746136e-01 -5.77432334... | [13.587160110473633, 1.8473315238952637] |
6ecedcae-977c-4c4c-8c34-55c74878e946 | towards-metamerism-via-foveated-style | 1705.10041 | null | http://arxiv.org/abs/1705.10041v3 | http://arxiv.org/pdf/1705.10041v3.pdf | Towards Metamerism via Foveated Style Transfer | The problem of $\textit{visual metamerism}$ is defined as finding a family of
perceptually indistinguishable, yet physically different images. In this paper,
we propose our NeuroFovea metamer model, a foveated generative model that is
based on a mixture of peripheral representations and style transfer
forward-pass algo... | ['Miguel Eckstein', 'Aditya Jonnalagadda', 'Arturo Deza'] | 2017-05-29 | towards-metamerism-via-foveated-style-1 | https://openreview.net/forum?id=BJzbG20cFQ | https://openreview.net/pdf?id=BJzbG20cFQ | iclr-2019-5 | ['metamerism'] | ['computer-vision'] | [ 4.02605146e-01 2.57555485e-01 6.68764710e-01 -2.18586624e-01
-4.98486638e-01 -6.23856008e-01 8.60053003e-01 -3.47999305e-01
-4.28128600e-01 3.54230672e-01 2.17829853e-01 -2.28226304e-01
-3.15253496e-01 -7.10670233e-01 -1.12473190e+00 -9.09180701e-01
-3.85300517e-02 -1.10719398e-01 3.36103104e-02 -4.43419904... | [10.192951202392578, 2.198579788208008] |
ce67cd3f-4ddc-4321-936b-98e30773283e | efficient-semantic-scene-completion-network-1 | 1907.05091 | null | https://arxiv.org/abs/1907.05091v1 | https://arxiv.org/pdf/1907.05091v1.pdf | Efficient Semantic Scene Completion Network with Spatial Group Convolution | We introduce Spatial Group Convolution (SGC) for accelerating the computation of 3D dense prediction tasks. SGC is orthogonal to group convolution, which works on spatial dimensions rather than feature channel dimension. It divides input voxels into different groups, then conducts 3D sparse convolution on these separat... | ['Yurong Chen', 'Li Zhang', 'Anbang Yao', 'Hongen Liao', 'Hao Zhao', 'Jiahui Zhang'] | 2019-07-11 | efficient-semantic-scene-completion-network | http://openaccess.thecvf.com/content_ECCV_2018/html/Jiahui_Zhang_Efficient_Semantic_Scene_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Jiahui_Zhang_Efficient_Semantic_Scene_ECCV_2018_paper.pdf | eccv-2018-9 | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 7.03955665e-02 7.20365494e-02 1.51083723e-01 -4.88589227e-01
-6.37674212e-01 -5.48364744e-02 4.71805453e-01 1.20713435e-01
-2.84868568e-01 2.16004789e-01 3.12964499e-01 -2.35177085e-01
1.75985068e-01 -1.03209579e+00 -7.83628583e-01 -4.90356773e-01
-2.19465077e-01 3.54650557e-01 3.59640568e-01 4.14813101... | [8.330948829650879, -3.2233269214630127] |
66cc39d7-a347-4419-ab3f-5106bde12c30 | cooperative-perception-for-safe-control-of | 2302.07341 | null | https://arxiv.org/abs/2302.07341v1 | https://arxiv.org/pdf/2302.07341v1.pdf | Cooperative Perception for Safe Control of Autonomous Vehicles under LiDAR Spoofing Attacks | Autonomous vehicles rely on LiDAR sensors to detect obstacles such as pedestrians, other vehicles, and fixed infrastructures. LiDAR spoofing attacks have been demonstrated that either create erroneous obstacles or prevent detection of real obstacles, resulting in unsafe driving behaviors. In this paper, we propose an a... | ['Andrew Clark', 'Shiyu Cheng', 'Zhouchi Li', 'Hongchao Zhang'] | 2023-02-14 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 2.73485124e-01 9.72065888e-03 -4.97315787e-02 3.07966005e-02
-5.70325375e-01 -7.90816009e-01 4.50771272e-01 3.16893578e-01
-4.82336074e-01 7.03385532e-01 -6.65774882e-01 -6.63409293e-01
1.64839253e-01 -1.21571779e+00 -9.75882769e-01 -4.29403245e-01
-2.37539321e-01 2.03088164e-01 1.08694065e+00 1.56465873... | [5.323195457458496, 7.41552209854126] |
b9516821-e196-411f-86c0-21aa659176da | scalable-low-rank-autoregressive-tensor | 2008.03194 | null | https://arxiv.org/abs/2008.03194v3 | https://arxiv.org/pdf/2008.03194v3.pdf | Scalable Low-Rank Tensor Learning for Spatiotemporal Traffic Data Imputation | Missing value problem in spatiotemporal traffic data has long been a challenging topic, in particular for large-scale and high-dimensional data with complex missing mechanisms and diverse degrees of missingness. Recent studies based on tensor nuclear norm have demonstrated the superiority of tensor learning in imputati... | ['Nicolas Saunier', 'Xinyu Chen', 'Lijun Sun', 'Yixian Chen'] | 2020-08-07 | null | null | null | null | ['traffic-data-imputation'] | ['time-series'] | [-2.06115380e-01 -7.51294017e-01 -3.02877814e-01 -2.72595644e-01
-8.57096434e-01 -1.17859878e-01 2.22804919e-01 -5.47141552e-01
-1.25755385e-01 6.11667693e-01 5.63469887e-01 -4.27997023e-01
-4.73932087e-01 -4.57375169e-01 -6.73331141e-01 -7.40733147e-01
-1.30600825e-01 3.60646546e-01 -7.04191206e-03 -4.76686507... | [6.588647842407227, 2.1368134021759033] |
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