paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5e55ca74-9307-41f2-aba6-c56f13e23861 | controllable-user-dialogue-act-augmentation | 2207.12757 | null | https://arxiv.org/abs/2207.12757v1 | https://arxiv.org/pdf/2207.12757v1.pdf | Controllable User Dialogue Act Augmentation for Dialogue State Tracking | Prior work has demonstrated that data augmentation is useful for improving dialogue state tracking. However, there are many types of user utterances, while the prior method only considered the simplest one for augmentation, raising the concern about poor generalization capability. In order to better cover diverse dialo... | ['Yun-Nung Chen', 'Chao-Wei Huang', 'Ming-Hao Hsu', 'Chun-Mao Lai'] | 2022-07-26 | null | https://aclanthology.org/2022.sigdial-1.5 | https://aclanthology.org/2022.sigdial-1.5.pdf | sigdial-acl-2022-9 | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 3.09270024e-02 5.22664070e-01 -3.99919122e-01 -2.76310295e-01
-4.73534346e-01 -4.31102455e-01 9.51341033e-01 -6.06680438e-02
-2.60419190e-01 9.35542166e-01 6.36092186e-01 -3.39652419e-01
4.64767992e-01 -3.58329326e-01 1.97368100e-01 -4.91421223e-01
7.98771381e-02 6.77866459e-01 1.18486114e-01 -1.20591569... | [12.848149299621582, 7.906217098236084] |
33abd778-ba59-47aa-8ab1-2f21cb25774c | a-high-accuracy-unsupervised-person-re | 2205.03124 | null | https://arxiv.org/abs/2205.03124v1 | https://arxiv.org/pdf/2205.03124v1.pdf | A High-Accuracy Unsupervised Person Re-identification Method Using Auxiliary Information Mined from Datasets | Supervised person re-identification methods rely heavily on high-quality cross-camera training label. This significantly hinders the deployment of re-ID models in real-world applications. The unsupervised person re-ID methods can reduce the cost of data annotation, but their performance is still far lower than the supe... | ['Guiguang Ding', 'Yuchen Guo', 'Tao He', 'Hehan Teng'] | 2022-05-06 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-2.30484217e-01 -4.11567122e-01 -2.75202155e-01 -4.58572656e-01
-4.85218108e-01 -5.25531709e-01 7.65811741e-01 -6.39278069e-02
-7.43866265e-01 7.23340392e-01 2.91476756e-01 2.21484512e-01
-2.01474298e-02 -4.01023030e-01 -4.68214154e-01 -6.71786249e-01
8.92890617e-03 2.76425511e-01 2.63945848e-01 9.48499888... | [14.767950057983398, 1.0272873640060425] |
186f380e-2b8b-4682-9a0b-7f1a06483ed3 | a-probabilistic-constrained-clustering-for | 1806.11078 | null | http://arxiv.org/abs/1806.11078v1 | http://arxiv.org/pdf/1806.11078v1.pdf | A probabilistic constrained clustering for transfer learning and image category discovery | Neural network-based clustering has recently gained popularity, and in
particular a constrained clustering formulation has been proposed to perform
transfer learning and image category discovery using deep learning. The core
idea is to formulate a clustering objective with pairwise constraints that can
be used to train... | ['Joel Schlosser', 'Yen-Chang Hsu', 'Zhaoyang Lv', 'Zsolt Kira', 'Phillip Odom'] | 2018-06-28 | null | null | null | null | ['ecg-risk-stratification'] | ['medical'] | [-1.84041589e-01 -1.97975993e-01 1.01360910e-01 -8.46508563e-01
-6.13760114e-01 -4.61332768e-01 2.71917313e-01 2.17987373e-01
-6.99346840e-01 1.19032390e-01 -1.17520697e-01 7.82539397e-02
-4.83264357e-01 -3.86971623e-01 -4.33147818e-01 -8.53553891e-01
-3.32400292e-01 7.89897203e-01 -2.40662411e-01 5.13554037... | [9.175588607788086, 3.2306647300720215] |
4193eb94-d280-4ad8-94d2-d2808c001117 | contextual-dynamic-prompting-for-response | 2301.13268 | null | https://arxiv.org/abs/2301.13268v2 | https://arxiv.org/pdf/2301.13268v2.pdf | Contextual Dynamic Prompting for Response Generation in Task-oriented Dialog Systems | Response generation is one of the critical components in task-oriented dialog systems. Existing studies have shown that large pre-trained language models can be adapted to this task. The typical paradigm of adapting such extremely large language models would be by fine-tuning on the downstream tasks which is not only t... | ['Rashmi Gangadharaiah', 'Chacha Chen', 'Narges Tabari', 'Sandesh Swamy'] | 2023-01-30 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 1.12542398e-01 5.22234857e-01 1.04484744e-01 -7.37343550e-01
-6.88441396e-01 -9.25510406e-01 1.05699217e+00 -7.71275386e-02
-6.78188980e-01 1.09062672e+00 8.62445414e-01 -3.57531637e-01
1.09834678e-01 -6.07627511e-01 7.57681997e-03 -2.22758099e-01
2.22298682e-01 9.39372897e-01 2.85716593e-01 -1.02633548... | [12.804956436157227, 8.045710563659668] |
91d49b38-b6e8-4433-867d-d60cd73e7365 | forward-stagewise-additive-model-for | 1608.01874 | null | http://arxiv.org/abs/1608.01874v1 | http://arxiv.org/pdf/1608.01874v1.pdf | Forward Stagewise Additive Model for Collaborative Multiview Boosting | Multiview assisted learning has gained significant attention in recent years
in supervised learning genre. Availability of high performance computing
devices enables learning algorithms to search simultaneously over multiple
views or feature spaces to obtain an optimum classification performance. The
paper is a pioneer... | ['Prabir Kumar Biswas', 'Avisek Lahiri', 'Biswajit Paria'] | 2016-08-05 | null | null | null | null | ['multiview-learning'] | ['computer-vision'] | [ 1.57171622e-01 -1.47496611e-01 -5.53292155e-01 -7.92515218e-01
-1.06627512e+00 -6.53699040e-01 4.27846998e-01 3.31082731e-01
-2.91233808e-01 7.77868211e-01 -1.31894842e-01 -3.88723582e-01
-3.95434231e-01 -6.61895573e-01 -6.68525696e-01 -9.27224219e-01
2.04906263e-03 1.62177667e-01 -1.05312891e-01 -3.27922046... | [8.436976432800293, 4.437785625457764] |
2b0065dc-74b8-4a94-b81f-6c1c955f4321 | fusionnet-a-deep-fully-residual-convolutional | 1612.05360 | null | http://arxiv.org/abs/1612.05360v2 | http://arxiv.org/pdf/1612.05360v2.pdf | FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics | Electron microscopic connectomics is an ambitious research direction with the
goal of studying comprehensive brain connectivity maps by using
high-throughput, nano-scale microscopy. One of the main challenges in
connectomics research is developing scalable image analysis algorithms that
require minimal user interventio... | ['Won-Ki Jeong', 'Tran Minh Quan', 'David G. C. Hildebrand'] | 2016-12-16 | null | null | null | null | ['brain-image-segmentation'] | ['medical'] | [ 1.19712763e-01 -1.39896646e-01 3.10954094e-01 -3.99853736e-01
-3.49624217e-01 -2.88248122e-01 2.07020164e-01 2.24648416e-01
-1.04929721e+00 7.89862394e-01 -3.53972554e-01 -2.10745111e-01
9.58285555e-02 -5.19252121e-01 -6.17482066e-01 -8.84395182e-01
4.49583009e-02 8.30220282e-01 3.01041305e-01 2.91050728... | [14.31287670135498, -3.107102870941162] |
b67ed874-c63e-46d9-8655-a0667d153ca8 | open-world-semantic-segmentation-via | 2207.08455 | null | https://arxiv.org/abs/2207.08455v3 | https://arxiv.org/pdf/2207.08455v3.pdf | Open-world Semantic Segmentation via Contrasting and Clustering Vision-Language Embedding | To bridge the gap between supervised semantic segmentation and real-world applications that acquires one model to recognize arbitrary new concepts, recent zero-shot segmentation attracts a lot of attention by exploring the relationships between unseen and seen object categories, yet requiring large amounts of densely-a... | ['Xiaodan Liang', 'Hang Xu', 'Chunjing Xu', 'Jianhua Han', 'Youpeng Wen', 'Quande Liu'] | 2022-07-18 | null | null | null | null | ['zero-shot-segmentation', 'online-clustering'] | ['computer-vision', 'computer-vision'] | [ 3.82111847e-01 3.71281445e-01 -2.56273597e-01 -5.45650303e-01
-6.33475602e-01 -7.35461712e-01 6.18699908e-01 1.16561249e-01
-4.67167467e-01 1.38348639e-01 3.54554062e-03 -1.76270291e-01
3.27924699e-01 -8.65398645e-01 -8.86123002e-01 -4.99805599e-01
4.75725055e-01 7.21239448e-01 8.71789157e-01 -1.35631487... | [9.746145248413086, 0.9110327959060669] |
2205bd3f-40f1-4bdd-b2d1-87fc6829be63 | suggestion-mining-from-online-reviews-using | 1904.09076 | null | http://arxiv.org/abs/1904.09076v1 | http://arxiv.org/pdf/1904.09076v1.pdf | Suggestion Mining from Online Reviews using ULMFiT | In this paper we present our approach and the system description for Sub Task
A of SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums.
Given a sentence, the task asks to predict whether the sentence consists of a
suggestion or not. Our model is based on Universal Language Model Fine-tuning
for Text C... | ['Simra Shahid', 'Laiba Mehnaz', 'Debanjan Mahata', 'Rajiv Ratn Shah', 'Karan Uppal', 'Haimin Zhang', 'Yaman Kumar', 'Sarthak Anand', 'Kartik Aggarwal'] | 2019-04-19 | null | null | null | null | ['suggestion-mining'] | ['natural-language-processing'] | [-1.78666249e-01 3.47281694e-01 -3.09853286e-01 -6.63529396e-01
-9.51807320e-01 -4.33751255e-01 8.12683403e-01 5.93286753e-01
-8.05121362e-01 5.41604877e-01 4.71828431e-01 -8.48036468e-01
2.39340335e-01 -2.84664243e-01 -4.10789251e-01 -4.03283797e-02
2.38443702e-01 4.28876370e-01 -2.33756080e-02 -2.80231982... | [10.936917304992676, 7.489565849304199] |
a6e3ce46-a7b1-4414-9854-ca8b701c4a18 | intermediate-deep-feature-compression-the | 1809.06196 | null | http://arxiv.org/abs/1809.06196v1 | http://arxiv.org/pdf/1809.06196v1.pdf | Intermediate Deep Feature Compression: the Next Battlefield of Intelligent Sensing | The recent advances of hardware technology have made the intelligent analysis
equipped at the front-end with deep learning more prevailing and practical. To
better enable the intelligent sensing at the front-end, instead of compressing
and transmitting visual signals or the ultimately utilized top-layer deep
learning f... | ['Ling-Yu Duan', 'Zhuo Chen', 'Shiqi Wang', 'Alex C. Kot', 'Weisi Lin'] | 2018-09-17 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 1.66937828e-01 -8.31023678e-02 1.12484001e-01 -3.59779984e-01
-3.18858385e-01 -1.38298184e-01 3.46315920e-01 -7.19963312e-02
-5.94468057e-01 3.00919682e-01 -4.66136187e-02 -8.98859203e-02
-3.50998998e-01 -1.16089487e+00 -5.67254484e-01 -9.68380451e-01
-4.19798613e-01 6.19315356e-02 -1.19126923e-01 3.81307065... | [8.441634178161621, 2.859452247619629] |
0971e9e0-90a7-4e9a-a2b2-c3361abfa893 | cma-es-for-post-hoc-ensembling-in-automl-a | 2307.00286 | null | https://arxiv.org/abs/2307.00286v1 | https://arxiv.org/pdf/2307.00286v1.pdf | CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure | Many state-of-the-art automated machine learning (AutoML) systems use greedy ensemble selection (GES) by Caruana et al. (2004) to ensemble models found during model selection post hoc. Thereby, boosting predictive performance and likely following Auto-Sklearn 1's insight that alternatives, like stacking or gradient-fre... | ['Joeran Beel', 'Lennart Purucker'] | 2023-07-01 | null | null | null | null | ['model-selection', 'automl'] | ['methodology', 'methodology'] | [-1.28464401e-01 5.94644882e-02 2.32379153e-01 -1.13137983e-01
-7.91792035e-01 -5.71042120e-01 5.61676145e-01 3.12405020e-01
-5.45882761e-01 9.64968979e-01 -2.80841827e-01 -5.20265877e-01
-5.12129724e-01 -6.80562019e-01 -6.11688375e-01 -7.96230912e-01
-1.95915475e-01 5.95030248e-01 3.28256078e-02 -3.83267552... | [8.070235252380371, 4.28082799911499] |
281b4767-56f0-4e06-9420-48260fbfa093 | deepps2-revisiting-photometric-stereo-using | 2207.02025 | null | https://arxiv.org/abs/2207.02025v2 | https://arxiv.org/pdf/2207.02025v2.pdf | DeepPS2: Revisiting Photometric Stereo Using Two Differently Illuminated Images | Photometric stereo, a problem of recovering 3D surface normals using images of an object captured under different lightings, has been of great interest and importance in computer vision research. Despite the success of existing traditional and deep learning-based methods, it is still challenging due to: (i) the require... | ['Shanmuganathan Raman', 'Ashish Tiwari'] | 2022-07-05 | null | null | null | null | ['lighting-estimation', 'image-relighting'] | ['computer-vision', 'computer-vision'] | [ 7.51517415e-01 -2.15471938e-01 5.75382173e-01 -5.63827395e-01
-7.28141248e-01 -4.78151202e-01 6.58575296e-01 -3.35985124e-01
-1.11786939e-01 6.17715478e-01 -1.19326048e-01 -2.45788038e-01
1.15392089e-01 -7.85998046e-01 -9.24009323e-01 -8.09917390e-01
5.57465613e-01 5.90537727e-01 7.63836801e-02 -3.10228258... | [9.842090606689453, -2.9600391387939453] |
af7f9d6c-4180-4bd5-812f-3ff405a4c5b0 | an-acceleration-method-based-on-deep-learning | 2110.08679 | null | https://arxiv.org/abs/2110.08679v1 | https://arxiv.org/pdf/2110.08679v1.pdf | An Acceleration Method Based on Deep Learning and Multilinear Feature Space | Computer vision plays a crucial role in Advanced Assistance Systems. Most computer vision systems are based on Deep Convolutional Neural Networks (deep CNN) architectures. However, the high computational resource to run a CNN algorithm is demanding. Therefore, the methods to speed up computation have become a relevant ... | ['Michel Vinagreiro Edson Kitani Armando Lagana Leopoldo Yoshioka'] | 2021-10-16 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 9.63130221e-02 -2.35885158e-01 -1.00259520e-01 -3.19087982e-01
-1.30431220e-01 -1.71731338e-01 3.36548626e-01 -5.07803321e-01
-9.62267280e-01 5.61871529e-01 -4.39233303e-01 -6.69255674e-01
5.00119515e-02 -1.10543776e+00 -6.32098675e-01 -7.60813832e-01
3.11414182e-01 6.17367998e-02 3.76186073e-01 -3.04920197... | [8.206982612609863, -0.6125100255012512] |
eb7ac7df-5593-4200-aa0d-19d24321ff05 | group-sparse-regularization-for-deep-neural | 1607.00485 | null | http://arxiv.org/abs/1607.00485v1 | http://arxiv.org/pdf/1607.00485v1.pdf | Group Sparse Regularization for Deep Neural Networks | In this paper, we consider the joint task of simultaneously optimizing (i)
the weights of a deep neural network, (ii) the number of neurons for each
hidden layer, and (iii) the subset of active input features (i.e., feature
selection). While these problems are generally dealt with separately, we
present a simple regula... | ['Simone Scardapane', 'Amir Hussain', 'Danilo Comminiello', 'Aurelio Uncini'] | 2016-07-02 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 3.31852198e-01 1.47593185e-01 -5.51523976e-02 -2.81495750e-01
-2.14464039e-01 -2.01464847e-01 3.51725847e-01 4.10737097e-01
-7.69940078e-01 6.92676008e-01 -2.74873376e-01 -1.46851480e-01
-3.27110976e-01 -6.84469044e-01 -7.10915506e-01 -1.01842713e+00
-1.45613536e-01 4.63776082e-01 -6.96065575e-02 2.40317687... | [8.520243644714355, 3.4809741973876953] |
32bf93c7-d84f-4a17-9333-3b5661f8d3b0 | clustering-label-inference-attack-against | 2203.05222 | null | https://arxiv.org/abs/2203.05222v1 | https://arxiv.org/pdf/2203.05222v1.pdf | Clustering Label Inference Attack against Practical Split Learning | Split learning is deemed as a promising paradigm for privacy-preserving distributed learning, where the learning model can be cut into multiple portions to be trained at the participants collaboratively. The participants only exchange the intermediate learning results at the cut layer, including smashed data via forwar... | ['Xinchen Lyu', 'Junlin Liu'] | 2022-03-10 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 2.71567941e-01 -6.69267103e-02 -3.25769633e-01 -7.95120239e-01
-1.23158908e+00 -1.42390454e+00 2.73727477e-01 4.20711070e-01
-6.74860716e-01 4.07670051e-01 -3.23225528e-01 -3.88660580e-01
-1.61594272e-01 -6.11293197e-01 -7.54364133e-01 -1.38416612e+00
-2.79947519e-01 2.90067941e-01 5.57535328e-02 5.24531424... | [5.812312602996826, 6.752541542053223] |
5ee42830-8d90-48b5-93db-48a182d2fb09 | compositional-learning-of-image-text-query | 2006.11149 | null | https://arxiv.org/abs/2006.11149v3 | https://arxiv.org/pdf/2006.11149v3.pdf | Compositional Learning of Image-Text Query for Image Retrieval | In this paper, we investigate the problem of retrieving images from a database based on a multi-modal (image-text) query. Specifically, the query text prompts some modification in the query image and the task is to retrieve images with the desired modifications. For instance, a user of an E-Commerce platform is interes... | ['Martin Kleinsteuber', 'Egor Labintcev', 'Muhammad Umer Anwaar'] | 2020-06-19 | null | null | null | null | ['multi-modal'] | ['miscellaneous'] | [ 1.40926301e-01 -3.57705206e-01 9.27696079e-02 -6.12554967e-01
-7.56472707e-01 -6.57577515e-01 5.88903546e-01 3.54592353e-02
-5.03900051e-01 1.50789067e-01 1.19101271e-01 1.21811561e-01
-1.83788180e-01 -7.99266577e-01 -1.06754398e+00 -5.67199469e-01
5.00472009e-01 3.70568633e-01 -1.24368899e-01 -3.48193616... | [10.78461742401123, 1.1444071531295776] |
74a19d10-a4d2-4a7a-bbd7-3eeb1ab92669 | tormentor-deterministic-dynamic-path-data | 2204.03776 | null | https://arxiv.org/abs/2204.03776v1 | https://arxiv.org/pdf/2204.03776v1.pdf | TorMentor: Deterministic dynamic-path, data augmentations with fractals | We propose the use of fractals as a means of efficient data augmentation. Specifically, we employ plasma fractals for adapting global image augmentation transformations into continuous local transforms. We formulate the diamond square algorithm as a cascade of simple convolution operations allowing efficient computatio... | ['Mathias Seuret', 'Georg Vogeler', 'Jian Shi', 'Edgar Riba', 'Vincent Christlein', 'Anguelos Nicolaou'] | 2022-04-07 | null | null | null | null | ['activity-recognition-in-videos', 'image-augmentation'] | ['computer-vision', 'computer-vision'] | [ 7.67702341e-01 1.36464834e-01 1.20473817e-01 -2.61202604e-01
-2.73343116e-01 -6.62516475e-01 1.18241453e+00 -4.44241166e-02
-4.74163592e-01 4.79288399e-01 -1.52333707e-01 -6.86349928e-01
3.65043730e-01 -1.01526940e+00 -9.62956250e-01 -8.57231736e-01
-7.39831254e-02 6.96070313e-01 2.04902202e-01 -3.35263908... | [9.671895980834961, 0.08167777955532074] |
5d3fbfe2-d738-4578-af17-d308eb761a2b | 2d-reconstruction-of-small-intestines | 1803.05817 | null | http://arxiv.org/abs/1803.05817v1 | http://arxiv.org/pdf/1803.05817v1.pdf | 2D Reconstruction of Small Intestine's Interior Wall | Examining and interpreting of a large number of wireless endoscopic images
from the gastrointestinal tract is a tiresome task for physicians. A practical
solution is to automatically construct a two dimensional representation of the
gastrointestinal tract for easy inspection. However, little has been done on
wireless e... | ['Xiang Xie', 'Rahman Attar', 'Zhihua Wang', 'Shigang Yue'] | 2018-03-15 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 2.33503059e-01 -8.70785788e-02 2.64657110e-01 2.02367708e-01
-5.18593490e-01 -4.48009908e-01 1.47333398e-01 2.59361595e-01
-7.23088801e-01 5.78964129e-02 -1.79332122e-02 -2.54320838e-02
-5.09846210e-01 -2.45982513e-01 -4.53560919e-01 -9.00799394e-01
-2.72195011e-01 1.07206441e-01 1.88780248e-01 -5.55445366... | [13.904006958007812, -3.15810489654541] |
72e55249-eb12-4a2c-9dc7-fa8c8a4b6d44 | a-relational-learning-perspective-to-multi | 2103.06220 | null | https://arxiv.org/abs/2103.06220v1 | https://arxiv.org/pdf/2103.06220v1.pdf | A Relational-learning Perspective to Multi-label Chest X-ray Classification | Multi-label classification of chest X-ray images is frequently performed using discriminative approaches, i.e. learning to map an image directly to its binary labels. Such approaches make it challenging to incorporate auxiliary information such as annotation uncertainty or a dependency among the labels. Building toward... | ['Brandon Malone', 'Jens Kleesiek', 'Daniel Oñoro-Rubio', 'Anjany Sekuboyina'] | 2021-03-10 | null | null | null | null | ['multi-modal-knowledge-graph'] | ['knowledge-base'] | [ 6.26819611e-01 5.38433850e-01 -6.43594444e-01 -6.79549813e-01
-1.34114599e+00 -6.88135505e-01 4.70724046e-01 7.67031252e-01
-2.64802992e-01 7.34348416e-01 9.41689163e-02 -2.93152601e-01
-4.59181488e-01 -6.68820262e-01 -7.13727355e-01 -6.95909441e-01
1.35850444e-01 8.37624907e-01 3.91307801e-01 2.34506413... | [9.501699447631836, 4.129260063171387] |
1702d621-2e5a-49ca-a881-096d6ad59636 | publicly-available-datasets-of-breast | 2306.01546 | null | https://arxiv.org/abs/2306.01546v1 | https://arxiv.org/pdf/2306.01546v1.pdf | Publicly available datasets of breast histopathology H&E whole-slide images: A systematic review | Advancements in digital pathology and computing resources have made a significant impact in the field of computational pathology for breast cancer diagnosis and treatment. However, access to high-quality labeled histopathological images of breast cancer is a big challenge that limits the development of accurate and rob... | ['Kajsa Møllersen', 'Lill-Tove Rasmussen Busund', 'Nikita Shvetsov', 'Lars Ailo Bongo', 'Masoud Tafavvoghi'] | 2023-06-02 | null | null | null | null | ['whole-slide-images', 'selection-bias'] | ['computer-vision', 'natural-language-processing'] | [ 3.27851065e-02 -6.16221614e-02 -8.06733012e-01 -2.30246276e-01
-1.28250301e+00 -4.11014885e-01 9.66043994e-02 6.92286849e-01
-6.06188476e-01 6.20288670e-01 1.72989070e-01 -6.84388340e-01
-2.85632163e-01 -8.63759398e-01 -7.64221489e-01 -1.16440380e+00
2.25511685e-01 3.24908495e-01 -6.85777068e-02 4.44560170... | [15.143091201782227, -2.9933855533599854] |
abe6c22a-f3d9-496f-8915-aeb915b37f4c | an-evidential-real-time-multi-mode-fault | 2305.00169 | null | https://arxiv.org/abs/2305.00169v2 | https://arxiv.org/pdf/2305.00169v2.pdf | An Evidential Real-Time Multi-Mode Fault Diagnosis Approach Based on Broad Learning System | Fault diagnosis is a crucial area of research in industry. Industrial processes exhibit diverse operating conditions, where data often have non-Gaussian, multi-mode, and center-drift characteristics. Data-driven approaches are currently the main focus in the field, but continuous fault classification and parameter upda... | ['Xiao He', 'Minyue Li', 'LiMin Wang', 'Zeyi Liu', 'Chen Li'] | 2023-04-29 | null | null | null | null | ['pseudo-label'] | ['miscellaneous'] | [ 2.47101575e-01 -5.59893131e-01 -2.51253396e-01 -3.84249300e-01
-6.68672621e-01 -3.56492728e-01 3.48031931e-02 1.93055689e-01
4.72446889e-01 7.32486665e-01 -8.20629120e-01 -4.49023485e-01
-8.16687107e-01 -6.08178020e-01 -2.97821730e-01 -9.11553502e-01
5.69470506e-03 7.42733061e-01 2.08754957e-01 2.92448640... | [6.882375717163086, 2.398475170135498] |
93a8996f-b0eb-4b82-8e53-389e62645626 | an-acne-grading-framework-on-face-images-via | null | null | https://ieeexplore.ieee.org/document/9669431/ | https://ieeexplore.ieee.org/document/9669431 | An Acne Grading Framework on Face Images via Skin Attention and SFNet | Severity level grading is a vitally important step to make correct diagnoses and personalized treatment schemes for acne, which is mainly carried out in two ways: criterion-based lesion counting and experience-based global estimation. In this paper, the global estimation of acne severity grading is studied by Convoluti... | ['Jingchi Jiang', 'Xue Cheng', 'Haiyan You', 'Zhaoyang Ma', 'Yi Guan', 'Yi Lin'] | 2022-01-14 | null | null | null | ieee-international-conference-on-5 | ['acne-severity-grading'] | ['medical'] | [ 2.22811550e-01 -4.72759426e-01 -1.81957528e-01 -1.73903942e-01
-6.38665199e-01 -3.24254990e-01 2.04868987e-01 2.71150798e-01
-3.56229514e-01 5.19705951e-01 2.05484666e-02 1.77020460e-01
-3.39875042e-01 -9.38087821e-01 2.62031138e-01 -9.21958089e-01
3.19656909e-01 -1.15859985e-01 1.08586289e-01 -3.67897265... | [15.695219039916992, -3.0297603607177734] |
974c4319-0b58-4c5e-a68d-6f3153461f63 | leveraging-context-to-support-automated-food | 1510.02078 | null | http://arxiv.org/abs/1510.02078v1 | http://arxiv.org/pdf/1510.02078v1.pdf | Leveraging Context to Support Automated Food Recognition in Restaurants | The pervasiveness of mobile cameras has resulted in a dramatic increase in
food photos, which are pictures reflecting what people eat. In this paper, we
study how taking pictures of what we eat in restaurants can be used for the
purpose of automating food journaling. We propose to leverage the context of
where the pict... | ['Gregory Abowd', 'Vinay Bettadapura', 'Irfan Essa', 'Edison Thomaz', 'Aman Parnami'] | 2015-10-07 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [ 4.07374114e-01 -5.86514831e-01 -3.04736942e-01 -5.54886580e-01
-6.69231474e-01 -9.96521354e-01 1.36003464e-01 7.70805478e-01
-4.09850627e-01 -4.71374057e-02 5.51178336e-01 -8.01252052e-02
6.06050134e-01 -9.90842044e-01 -1.08260441e+00 -3.92484337e-01
-1.67618934e-02 -2.86135197e-01 -1.75709248e-01 -8.46687704... | [11.562614440917969, 4.414370059967041] |
871a3f01-07e6-4808-9938-82e8f9113679 | laser-neuro-symbolic-learning-of-semantic | 2304.07647 | null | https://arxiv.org/abs/2304.07647v1 | https://arxiv.org/pdf/2304.07647v1.pdf | LASER: Neuro-Symbolic Learning of Semantic Video Representations | Modern AI applications involving video, such as video-text alignment, video search, and video captioning, benefit from a fine-grained understanding of video semantics. Existing approaches for video understanding are either data-hungry and need low-level annotation, or are based on general embeddings that are uninterpre... | ['Ser-Nam Lim', 'Mayur Naik', 'David Jacobs', 'Ziyang Li', 'Jiani Huang'] | 2023-04-15 | null | null | null | null | ['video-captioning', 'video-retrieval', 'video-understanding'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.62780994e-01 -1.89630941e-01 -6.37880206e-01 -7.66825736e-01
-7.36415446e-01 -7.24970579e-01 6.47923827e-01 -3.25771905e-02
-2.11270988e-01 2.64123827e-01 8.38655710e-01 -1.03569277e-01
9.45473537e-02 -3.93541604e-01 -1.34483480e+00 -9.09042805e-02
-1.71401888e-01 4.19131935e-01 2.58038312e-01 -1.36415418... | [9.967022895812988, 0.8584257960319519] |
9bb265ca-5994-4dec-9036-f032ba8d982c | bilingual-gan-a-step-towards-parallel-text | 1904.04742 | null | https://arxiv.org/abs/1904.04742v2 | https://arxiv.org/pdf/1904.04742v2.pdf | Bilingual-GAN: A Step Towards Parallel Text Generation | Latent space based GAN methods and attention based sequence to sequence models have achieved impressive results in text generation and unsupervised machine translation respectively. Leveraging the two domains, we propose an adversarial latent space based model capable of generating parallel sentences in two languages c... | ['Mehdi Rezagholizadeh', 'Alan Do-Omri', 'Qun Liu', 'Ahmad Rashid', 'Md. Akmal Haidar'] | 2019-04-09 | bilingual-gan-a-step-towards-parallel-text-1 | https://aclanthology.org/W19-2307 | https://aclanthology.org/W19-2307.pdf | ws-2019-6 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 5.56655049e-01 4.33928847e-01 -2.76021183e-01 -2.08103180e-01
-1.22500563e+00 -8.51789176e-01 1.24967217e+00 -7.83822894e-01
1.44881696e-01 1.24016225e+00 7.21392453e-01 -4.86746401e-01
7.27008700e-01 -8.53357196e-01 -9.58598018e-01 -6.55476511e-01
6.05855107e-01 9.42401648e-01 -7.20534086e-01 -2.47615099... | [11.69420051574707, 9.904844284057617] |
71467a3e-1768-4dc6-818c-9e222308d76e | negbert-a-transfer-learning-approach-for | 1911.04211 | null | https://arxiv.org/abs/1911.04211v4 | https://arxiv.org/pdf/1911.04211v4.pdf | NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution | Negation is an important characteristic of language, and a major component of information extraction from text. This subtask is of considerable importance to the biomedical domain. Over the years, multiple approaches have been explored to address this problem: Rule-based systems, Machine Learning classifiers, Condition... | ['Suraj Sawant', 'Aditya Khandelwal'] | 2019-11-11 | negbert-a-transfer-learning-approach-for-1 | https://aclanthology.org/2020.lrec-1.704 | https://aclanthology.org/2020.lrec-1.704.pdf | lrec-2020-5 | ['negation-and-speculation-cue-detection', 'negation-detection', 'negation-scope-resolution'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.07973117e-01 1.59808099e-01 -7.69405246e-01 -2.71182090e-01
-1.28866374e+00 -3.86898071e-01 5.11076391e-01 4.31873858e-01
-9.51456368e-01 1.31816006e+00 9.41678360e-02 -5.23385525e-01
-3.94278355e-02 -5.68826616e-01 -7.40952075e-01 -3.26702923e-01
1.43236622e-01 1.98667571e-01 2.97708094e-01 -4.24958020... | [8.560348510742188, 8.75224494934082] |
8df82a06-717b-4138-9870-7816f4ddc8d2 | multiple-combined-constraints-for-image | 1809.06706 | null | http://arxiv.org/abs/1809.06706v1 | http://arxiv.org/pdf/1809.06706v1.pdf | Multiple Combined Constraints for Image Stitching | Several approaches to image stitching use different constraints to estimate
the motion model between image pairs. These constraints can be roughly divided
into two categories: geometric constraints and photometric constraints. In this
paper, geometric and photometric constraints are combined to improve the
alignment qu... | ['Li Li', 'Kai Chen', 'Jian Yao', 'Jingmin Tu', 'Binbin Xiang'] | 2018-09-18 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 3.50278616e-01 -4.29171681e-01 -2.20028639e-01 -6.17827401e-02
-1.31549478e-01 -5.23950040e-01 5.52966952e-01 -3.46522033e-02
-3.69074196e-01 4.86722410e-01 -5.10014556e-02 1.34419113e-01
-2.60357589e-01 -5.38263738e-01 -4.23434407e-01 -1.04665160e+00
5.13510287e-01 3.84062976e-01 7.14834571e-01 -3.66816461... | [9.289320945739746, -2.371074676513672] |
213bf32e-54ed-44f6-9ae8-b94311266a83 | seqtrack-sequence-to-sequence-learning-for | 2304.14394 | null | https://arxiv.org/abs/2304.14394v1 | https://arxiv.org/pdf/2304.14394v1.pdf | SeqTrack: Sequence to Sequence Learning for Visual Object Tracking | In this paper, we present a new sequence-to-sequence learning framework for visual tracking, dubbed SeqTrack. It casts visual tracking as a sequence generation problem, which predicts object bounding boxes in an autoregressive fashion. This is different from prior Siamese trackers and transformer trackers, which rely o... | ['Han Hu', 'Huchuan Lu', 'Dong Wang', 'Houwen Peng', 'Xin Chen'] | 2023-04-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_SeqTrack_Sequence_to_Sequence_Learning_for_Visual_Object_Tracking_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_SeqTrack_Sequence_to_Sequence_Learning_for_Visual_Object_Tracking_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-tracking', 'visual-object-tracking'] | ['computer-vision', 'computer-vision'] | [ 1.10326663e-01 5.07710017e-02 -4.04933095e-01 -2.74935126e-01
-8.31691682e-01 -7.13790357e-01 9.25944507e-01 -5.81090569e-01
-2.32660294e-01 6.45711780e-01 1.58054799e-01 -2.73121059e-01
6.63719296e-01 -3.16277266e-01 -1.21049356e+00 -7.51557648e-01
-2.90212892e-02 3.36281687e-01 6.16356730e-01 1.04098767... | [6.289892673492432, -2.100856304168701] |
b4a6e091-0a30-473d-83c1-fdc639e3df8d | neuromorphic-bayesian-optimization-in-lava | 2305.11060 | null | https://arxiv.org/abs/2305.11060v1 | https://arxiv.org/pdf/2305.11060v1.pdf | Neuromorphic Bayesian Optimization in Lava | The ever-increasing demands of computationally expensive and high-dimensional problems require novel optimization methods to find near-optimal solutions in a reasonable amount of time. Bayesian Optimization (BO) stands as one of the best methodologies for learning the underlying relationships within multi-variate probl... | ['Maryam Parsa', 'Sumedh R. Risbud', 'Shay Snyder'] | 2023-05-18 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [-1.38622686e-01 -4.63017195e-01 3.16414118e-01 -2.50908524e-01
-5.34113526e-01 -4.04211104e-01 2.24026024e-01 2.10833699e-02
-9.94245946e-01 1.10397148e+00 -6.01301193e-01 -4.90352139e-02
-4.39437270e-01 -8.41086328e-01 -7.85585105e-01 -9.96379077e-01
-2.35173032e-01 9.11939621e-01 5.00204027e-01 8.81003402... | [7.871139049530029, 3.109462022781372] |
8c07f702-32bc-4dbf-91b4-40c81524c1ef | similarity-preserving-unsupervised-feature | null | null | https://ieeexplore.ieee.org/abstract/document/9345884 | https://ieeexplore.ieee.org/abstract/document/9345884 | Similarity Preserving Unsupervised Feature Selection based on Sparse Learning | Various feature selection methods have been recently proposed on different applications to reduce the computational burden of machine learning algorithms as well as the complexity of learned models. Preserving sample similarities and selecting discriminative features are two major factors should be satisfied, especiall... | ['Sasan H. Alizadeh', 'Mehdi Ghatee', 'Mohsen Ghassemi Parsa', 'Hadi Zare'] | 2020-12-15 | null | null | null | 10th-international-symposium-on | ['sparse-learning'] | ['methodology'] | [ 2.66424805e-01 -4.54380512e-01 -5.89128435e-02 -6.28584683e-01
-4.11510170e-01 -1.44386664e-02 4.06658977e-01 3.20412368e-01
-6.44675195e-01 7.51984477e-01 9.88370255e-02 5.23502171e-01
-7.21502900e-01 -6.31292939e-01 1.21297516e-01 -9.20232952e-01
-1.07767425e-01 2.13334233e-01 1.76720828e-01 1.04051217... | [8.210809707641602, 4.1976494789123535] |
57cb3c06-489a-4cba-ad14-c83c8f8bcad7 | hierarchical-aggregation-for-3d-instance | 2108.02350 | null | https://arxiv.org/abs/2108.02350v1 | https://arxiv.org/pdf/2108.02350v1.pdf | Hierarchical Aggregation for 3D Instance Segmentation | Instance segmentation on point clouds is a fundamental task in 3D scene perception. In this work, we propose a concise clustering-based framework named HAIS, which makes full use of spatial relation of points and point sets. Considering clustering-based methods may result in over-segmentation or under-segmentation, we ... | ['Xinggang Wang', 'Wenyu Liu', 'Qian Zhang', 'Jiemin Fang', 'Shaoyu Chen'] | 2021-08-05 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Chen_Hierarchical_Aggregation_for_3D_Instance_Segmentation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Chen_Hierarchical_Aggregation_for_3D_Instance_Segmentation_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 1.09290443e-01 3.28083187e-02 6.87213540e-02 -4.39190984e-01
-8.36386919e-01 -4.52558368e-01 5.02008915e-01 2.55161107e-01
-2.60159880e-01 2.93256611e-01 -4.48158145e-01 -1.64754272e-01
-1.57482460e-01 -8.44263196e-01 -8.49412560e-01 -6.51276648e-01
-2.22846419e-02 7.50423789e-01 7.76953578e-01 5.13694957... | [8.051158905029297, -3.047004461288452] |
efd71191-cac2-4309-b6d5-30129666b655 | dynamite-dynamic-query-bootstrapping-for | 2304.06668 | null | https://arxiv.org/abs/2304.06668v1 | https://arxiv.org/pdf/2304.06668v1.pdf | DynaMITe: Dynamic Query Bootstrapping for Multi-object Interactive Segmentation Transformer | Most state-of-the-art instance segmentation methods rely on large amounts of pixel-precise ground-truth annotations for training, which are expensive to create. Interactive segmentation networks help generate such annotations based on an image and the corresponding user interactions such as clicks. Existing methods for... | ['Bastian Leibe', 'Alexander Hermans', 'Sabarinath Mahadevan', 'Amit Kumar Rana'] | 2023-04-13 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [ 5.18544197e-01 3.49066943e-01 -1.59812361e-01 -6.16335213e-01
-1.15251839e+00 -7.13882446e-01 4.18626070e-01 1.60167113e-01
-7.16926396e-01 5.80384791e-01 -3.45249921e-01 -2.35610127e-01
2.85089582e-01 -8.76238048e-01 -1.07231092e+00 -2.68540591e-01
1.68142006e-01 1.03411317e+00 1.08357799e+00 1.57254651... | [9.428855895996094, 0.09840928018093109] |
01a6263f-8465-45a2-8752-a7c0ecbb66cd | lvp-m3-language-aware-visual-prompt-for | 2210.15461 | null | https://arxiv.org/abs/2210.15461v2 | https://arxiv.org/pdf/2210.15461v2.pdf | LVP-M3: Language-aware Visual Prompt for Multilingual Multimodal Machine Translation | Multimodal Machine Translation (MMT) focuses on enhancing text-only translation with visual features, which has attracted considerable attention from both natural language processing and computer vision communities. Recent advances still struggle to train a separate model for each language pair, which is costly and una... | ['Zheng Cui', 'Furu Wei', 'Dongdong Zhang', 'Zhoujun Li', 'Jian Yang', 'Haoyang Huang', 'Jiaheng Liu', 'Hongcheng Guo'] | 2022-10-19 | null | null | null | null | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 9.39583108e-02 -4.13758159e-01 -2.31445625e-01 -1.66030422e-01
-1.06565166e+00 -5.55244565e-01 8.11986685e-01 -6.37470633e-02
-3.97527337e-01 6.97168589e-01 1.26434401e-01 -5.70510745e-01
4.97249871e-01 -4.31108087e-01 -6.88061714e-01 -5.63341320e-01
6.95591748e-01 4.02772278e-01 -1.34291381e-01 -2.48671070... | [11.460238456726074, 1.5413928031921387] |
93ea00fb-73d0-441c-8a9d-f416072cf9c4 | pefll-a-lifelong-learning-approach-to | 2306.05515 | null | https://arxiv.org/abs/2306.05515v1 | https://arxiv.org/pdf/2306.05515v1.pdf | PeFLL: A Lifelong Learning Approach to Personalized Federated Learning | Personalized federated learning (pFL) has emerged as a popular approach to dealing with the challenge of statistical heterogeneity between the data distributions of the participating clients. Instead of learning a single global model, pFL aims to learn an individual model for each client while still making use of the d... | ['Christoph H. Lampert', 'Hossein Zakerinia', 'Jonathan Scott'] | 2023-06-08 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-4.30263370e-01 -1.01169292e-02 -4.27944511e-01 -5.33776999e-01
-9.78053629e-01 -4.65614200e-01 5.46599150e-01 -9.06439647e-02
-1.58524379e-01 4.53626841e-01 1.36334136e-01 1.12665638e-01
-4.44660932e-01 -8.11627567e-01 -7.48976707e-01 -9.53564286e-01
-2.39480972e-01 1.28581595e+00 1.81480959e-01 3.74842703... | [5.779453754425049, 6.280388832092285] |
fc0a0d78-eb70-4a9c-911c-35cdf5cbc965 | self-supervised-learning-of-a-biologically | 2006.16976 | null | https://arxiv.org/abs/2006.16976v1 | https://arxiv.org/pdf/2006.16976v1.pdf | Self-Supervised Learning of a Biologically-Inspired Visual Texture Model | We develop a model for representing visual texture in a low-dimensional feature space, along with a novel self-supervised learning objective that is used to train it on an unlabeled database of texture images. Inspired by the architecture of primate visual cortex, the model uses a first stage of oriented linear filters... | ['Nikhil Parthasarathy', 'Eero P. Simoncelli'] | 2020-06-30 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 5.88723958e-01 3.13307405e-01 1.27735496e-01 -6.09350860e-01
-6.13164127e-01 -3.48463207e-01 7.71978140e-01 -1.58378020e-01
-4.36466157e-01 4.18950915e-01 2.63196468e-01 3.12033564e-01
6.14950806e-02 -8.75856161e-01 -8.11414421e-01 -1.02216113e+00
-7.85192624e-02 2.53900766e-01 4.51386631e-01 -1.72403425... | [9.554303169250488, 2.3968663215637207] |
ee48c168-f625-4782-8720-f1695ca2b004 | line-as-a-visual-sentence-context-aware-line | 2109.04753 | null | https://arxiv.org/abs/2109.04753v1 | https://arxiv.org/pdf/2109.04753v1.pdf | Line as a Visual Sentence: Context-aware Line Descriptor for Visual Localization | Along with feature points for image matching, line features provide additional constraints to solve visual geometric problems in robotics and computer vision (CV). Although recent convolutional neural network (CNN)-based line descriptors are promising for viewpoint changes or dynamic environments, we claim that the CNN... | ['Ayoung Kim', 'Sungho Yoon'] | 2021-09-10 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [-1.71649307e-01 -2.05497637e-01 -3.25429916e-01 -5.95908523e-01
-2.64446437e-01 -7.25574017e-01 7.79306293e-01 3.40643525e-01
-4.15267020e-01 3.02926689e-01 -2.89268084e-02 -4.53549586e-02
-1.65676340e-01 -1.06449914e+00 -1.00485456e+00 -1.04134277e-01
-4.07966487e-02 1.96977943e-01 1.16728783e-01 -5.45158446... | [8.160331726074219, -2.036993980407715] |
3fbeceb6-4427-439a-a460-b2429ff00001 | leveraging-uni-modal-self-supervised-learning | null | null | https://openreview.net/forum?id=sFZPi0Dy6XV | https://openreview.net/pdf?id=sFZPi0Dy6XV | Leveraging Uni-Modal Self-Supervised Learning for Multimodal Audio-visual Speech Recognition | Training Transformer-based models demands a large amount of data, while obtaining parallel aligned and labelled data in multimodality is rather cost-demanding, especially for audio-visual speech recognition (AVSR). Thus it makes a lot of sense to make use of unlabelled uni-modal data. On the other side, although the ef... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['audio-visual-speech-recognition'] | ['speech'] | [ 5.54299235e-01 1.88958675e-01 -3.40193987e-01 -3.34255069e-01
-1.66701722e+00 -4.36263174e-01 5.55989206e-01 -1.88727319e-01
-4.49855953e-01 6.16081059e-01 4.49673921e-01 -2.77495503e-01
3.79411697e-01 -4.49323654e-01 -9.58441675e-01 -8.51739764e-01
4.35829163e-01 3.56668293e-01 1.30528390e-01 -2.21330360... | [14.206060409545898, 5.145693302154541] |
99595eb0-7f50-4509-987d-710938a5e9cf | a-unified-semantic-embedding-relating | 1411.5879 | null | http://arxiv.org/abs/1411.5879v2 | http://arxiv.org/pdf/1411.5879v2.pdf | A Unified Semantic Embedding: Relating Taxonomies and Attributes | We propose a method that learns a discriminative yet semantic space for
object categorization, where we also embed auxiliary semantic entities such as
supercategories and attributes. Contrary to prior work which only utilized them
as side information, we explicitly embed the semantic entities into the same
space where ... | ['Sung Ju Hwang', 'Leonid Sigal'] | 2014-11-18 | a-unified-semantic-embedding-relating-1 | http://papers.nips.cc/paper/5289-a-unified-semantic-embedding-relating-taxonomies-and-attributes | http://papers.nips.cc/paper/5289-a-unified-semantic-embedding-relating-taxonomies-and-attributes.pdf | neurips-2014-12 | ['object-categorization'] | ['computer-vision'] | [ 8.08103681e-02 4.60670531e-01 -4.44358677e-01 -8.68307948e-01
-9.65800211e-02 -8.58354390e-01 7.83021808e-01 1.26813084e-01
-2.46716097e-01 3.53260577e-01 6.95381045e-01 2.10514084e-01
4.95017506e-02 -9.11860585e-01 -7.15871871e-01 -6.02382898e-01
2.06652984e-01 2.61212498e-01 -1.28033757e-01 5.27231097... | [10.084138870239258, 2.2501728534698486] |
b1170f1e-7cd5-48c0-aba4-5832c102788b | instance-embedding-transfer-to-unsupervised | 1801.00908 | null | http://arxiv.org/abs/1801.00908v2 | http://arxiv.org/pdf/1801.00908v2.pdf | Instance Embedding Transfer to Unsupervised Video Object Segmentation | We propose a method for unsupervised video object segmentation by
transferring the knowledge encapsulated in image-based instance embedding
networks. The instance embedding network produces an embedding vector for each
pixel that enables identifying all pixels belonging to the same object. Though
trained on static imag... | ['C. -C. Jay Kuo', 'Alireza Fathi', 'Siyang Li', 'Qin Huang', 'Bryan Seybold', 'Alexey Vorobyov'] | 2018-01-03 | instance-embedding-transfer-to-unsupervised-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Instance_Embedding_Transfer_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Instance_Embedding_Transfer_CVPR_2018_paper.pdf | cvpr-2018-6 | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 1.47284836e-01 1.92019522e-01 -5.94182432e-01 -3.45658481e-01
-1.74539238e-01 -5.70728362e-01 3.56782049e-01 -8.98893643e-03
-6.76673591e-01 3.79374802e-01 -2.06711069e-01 2.07968429e-01
-5.36781885e-02 -9.31335449e-01 -1.02756357e+00 -6.68593824e-01
-3.09474468e-01 3.73296052e-01 7.72929192e-01 4.16817516... | [9.153938293457031, -0.19848661124706268] |
a4832aeb-f4c2-479c-9a5f-03a22f73baef | deep-attentive-sentence-ordering-network | null | null | https://aclanthology.org/D18-1465 | https://aclanthology.org/D18-1465.pdf | Deep Attentive Sentence Ordering Network | In this paper, we propose a novel deep attentive sentence ordering network (referred as ATTOrderNet) which integrates self-attention mechanism with LSTMs in the encoding of input sentences. It enables us to capture global dependencies among sentences regardless of their input order and obtains a reliable representation... | ['Baiyun Cui', 'Zhongfei Zhang', 'Yingming Li', 'Ming Chen'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['sentence-ordering', 'concept-to-text-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.57145160e-01 -1.33350134e-01 -3.54281291e-02 -7.23865509e-01
-1.08456142e-01 -2.09793001e-01 3.51466209e-01 1.78323716e-01
-5.82072675e-01 7.17891276e-01 5.58445573e-01 -4.02252704e-01
-5.18642087e-03 -8.02632034e-01 -5.28457463e-01 -2.91296422e-01
-7.50268102e-02 3.79153520e-01 3.96490425e-01 -4.78065819... | [11.167977333068848, 8.596162796020508] |
07682c18-c8e4-4f95-8aaf-e9e71c30380e | deepbillboard-systematic-physical-world | 1812.10812 | null | http://arxiv.org/abs/1812.10812v1 | http://arxiv.org/pdf/1812.10812v1.pdf | DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems | Deep Neural Networks (DNNs) have been widely applied in many autonomous
systems such as autonomous driving. Recently, DNN testing has been intensively
studied to automatically generate adversarial examples, which inject
small-magnitude perturbations into inputs to test DNNs under extreme
situations. While existing test... | ['Cong Liu', 'Yuankun Zhu', 'Yuqun Zhang', 'Lingming Zhang', 'Husheng Zhou', 'Wei Li', 'Bei Yu'] | 2018-12-27 | null | null | null | null | ['dnn-testing'] | ['adversarial'] | [ 4.60982285e-02 2.04924028e-02 3.19797635e-01 -2.62207121e-01
-2.89214939e-01 -8.60133708e-01 5.60307205e-01 -5.91361344e-01
-1.76737204e-01 8.94227266e-01 -6.19738996e-01 -7.23818004e-01
6.45465478e-02 -1.05672526e+00 -1.33321965e+00 -5.90408862e-01
-1.89058244e-01 2.01975971e-01 4.73319948e-01 -6.03714466... | [5.352816581726074, 7.820037364959717] |
d81eb498-d40c-46a0-aa1d-55c004db6096 | multi-task-end-to-end-training-improves | null | null | https://openreview.net/forum?id=D5u046Zw_2F | https://openreview.net/pdf?id=D5u046Zw_2F | Multi-Task End-to-End Training Improves Conversational Recommendation | In this paper, we analyze the performance of a multitask end-to-end transformer model on the task of conversational recommendations, which aim to provide recommendations based on a user’s explicit preferences expressed in dialogue. While previous works in this area adopt complex multi-component approaches where the dia... | ['Anonymous'] | 2021-10-16 | null | null | null | acl-arr-october-2021-10 | ['movie-recommendation'] | ['miscellaneous'] | [ 3.70882720e-01 2.81549394e-01 1.77857019e-02 -8.00946295e-01
-1.02650332e+00 -7.26019859e-01 7.02452958e-01 -1.50409713e-01
-3.17960948e-01 6.61996245e-01 8.18716168e-01 -3.32026809e-01
-1.36643514e-01 -5.42919099e-01 -3.80648494e-01 -3.80389154e-01
1.29721895e-01 9.81661618e-01 9.72013399e-02 -5.71949303... | [12.401700019836426, 7.569725036621094] |
32c954f3-685a-4320-9a14-b672a22ce000 | robust-learning-protocol-for-federated-tumor | 2212.08290 | null | https://arxiv.org/abs/2212.08290v1 | https://arxiv.org/pdf/2212.08290v1.pdf | Robust Learning Protocol for Federated Tumor Segmentation Challenge | In this work, we devise robust and efficient learning protocols for orchestrating a Federated Learning (FL) process for the Federated Tumor Segmentation Challenge (FeTS 2022). Enabling FL for FeTS setup is challenging mainly due to data heterogeneity among collaborators and communication cost of training. To tackle the... | ['Stefano Braghin', 'Jonathan P. Epperlein', 'Swanand Kadhe', 'Giulio Zizzo', 'Ambrish Rawat'] | 2022-12-16 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 2.28511482e-01 2.59077605e-02 8.35667625e-02 -2.47858018e-01
-1.17722058e+00 -6.26920164e-01 7.17111290e-01 4.92681623e-01
-7.09063649e-01 6.44882143e-01 1.31199628e-01 -6.90657139e-01
-2.95416862e-01 -6.16020024e-01 -4.49898094e-01 -1.15347171e+00
-1.55776367e-01 7.19536602e-01 3.79997879e-01 1.63922414... | [6.071413040161133, 6.401844501495361] |
c9a26ff6-05a1-4e4c-88fa-ecb3c2adad0b | global-attention-decoder-for-chinese-spelling | null | null | https://aclanthology.org/2021.findings-acl.122 | https://aclanthology.org/2021.findings-acl.122.pdf | Global Attention Decoder for Chinese Spelling Error Correction | null | ['Guotong Xie', 'Wei Zhu', 'Keqiang Wang', 'Yuan Ni', 'Zhao Guo'] | null | null | null | null | findings-acl-2021-8 | ['csc'] | ['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... | [-7.444065570831299, 3.794548273086548] |
d5e5b2a4-9e3a-4d33-977b-74159a7066f2 | causal-effect-estimation-with-global | 2209.08885 | null | https://arxiv.org/abs/2209.08885v2 | https://arxiv.org/pdf/2209.08885v2.pdf | Causal Effect Estimation with Global Probabilistic Forecasting: A Case Study of the Impact of Covid-19 Lockdowns on Energy Demand | The electricity industry is heavily implementing smart grid technologies to improve reliability, availability, security, and efficiency. This implementation needs technological advancements, the development of standards and regulations, as well as testing and planning. Smart grid load forecasting and management are cri... | ['Christoph Bergmeir', 'Angela Dieyu Weng', 'Priscila Grecov', 'Ankitha Nandipura Prasanna'] | 2022-09-19 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-1.55008659e-01 -2.69218653e-01 3.74078751e-02 -3.24017256e-02
-2.24751770e-01 -7.57799208e-01 8.40977490e-01 4.01994765e-01
4.90059927e-02 9.38302100e-01 5.60794115e-01 -6.46393359e-01
-4.56760675e-01 -1.18452191e+00 -1.50745198e-01 -1.08487463e+00
-2.38180712e-01 6.94194496e-01 -3.95489126e-01 4.03286517... | [6.089128494262695, 2.8376734256744385] |
85b780c6-41f7-40cc-ac87-8ba4e4bb5421 | loop-closure-detection-with-rgb-d-feature | 1811.09938 | null | http://arxiv.org/abs/1811.09938v1 | http://arxiv.org/pdf/1811.09938v1.pdf | Loop Closure Detection with RGB-D Feature Pyramid Siamese Networks | In visual Simultaneous Localization And Mapping (SLAM), detecting loop
closures has been an important but difficult task. Currently, most solutions
are based on the bag-of-words approach. Yet the possibility of deep neural
network application to this task has not been fully explored due to the lack of
appropriate archi... | ['Alexander Mai', 'Joseph Menke', 'Zhang Qianhao', 'Allen Yang'] | 2018-11-25 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [ 2.12898150e-01 -2.96919733e-01 5.79610802e-02 -4.59876329e-01
-8.99025917e-01 -6.20803893e-01 6.87332511e-01 4.42850530e-01
-7.60246396e-01 3.82555753e-01 -1.11988530e-01 -5.53076506e-01
4.20254916e-02 -5.39990902e-01 -8.83363366e-01 -1.26959279e-01
-4.15321797e-01 5.69623232e-01 2.36779362e-01 -2.71801293... | [7.563068866729736, -2.085418462753296] |
577c12ec-4c72-4ae4-9b14-c8795188fd05 | augmenting-autotelic-agents-with-large | 2305.12487 | null | https://arxiv.org/abs/2305.12487v1 | https://arxiv.org/pdf/2305.12487v1.pdf | Augmenting Autotelic Agents with Large Language Models | Humans learn to master open-ended repertoires of skills by imagining and practicing their own goals. This autotelic learning process, literally the pursuit of self-generated (auto) goals (telos), becomes more and more open-ended as the goals become more diverse, abstract and creative. The resulting exploration of the s... | ['Marc-Alexandre Côté', 'Xingdi Yuan', 'Pierre-Yves Oudeyer', 'Laetitia Teodorescu', 'Cédric Colas'] | 2023-05-21 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 8.83644521e-02 3.83383274e-01 4.02015030e-01 -5.94953075e-03
-1.34001166e-01 -7.60429978e-01 9.74174500e-01 8.71373340e-02
-2.34163314e-01 8.08313251e-01 3.62611920e-01 -1.19856857e-01
-3.27081949e-01 -9.05954540e-01 -6.00017250e-01 -4.04746950e-01
-2.37885639e-01 8.16240609e-01 -3.20759654e-01 -8.64026487... | [4.20241117477417, 1.3791775703430176] |
fe158c80-f377-40e7-bab6-5347a5792458 | rebooting-acgan-auxiliary-classifier-gans | 2111.01118 | null | https://arxiv.org/abs/2111.01118v1 | https://arxiv.org/pdf/2111.01118v1.pdf | Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training | Conditional Generative Adversarial Networks (cGAN) generate realistic images by incorporating class information into GAN. While one of the most popular cGANs is an auxiliary classifier GAN with softmax cross-entropy loss (ACGAN), it is widely known that training ACGAN is challenging as the number of classes in the data... | ['Jaesik Park', 'Minsu Cho', 'Woohyeon Shim', 'Minguk Kang'] | 2021-11-01 | null | http://proceedings.neurips.cc/paper/2021/hash/c5ab6cebaca97f7171139e4d414ff5a6-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/c5ab6cebaca97f7171139e4d414ff5a6-Paper.pdf | neurips-2021-12 | ['conditional-image-generation'] | ['computer-vision'] | [ 1.16004199e-01 3.61706376e-01 1.02688223e-01 -2.28643164e-01
-6.12265229e-01 -5.15788019e-01 6.89458251e-01 -8.41655552e-01
-8.08421001e-02 9.83023763e-01 8.65447521e-02 -1.81083545e-01
2.38520786e-01 -1.10953736e+00 -9.08124983e-01 -8.71346533e-01
2.42154121e-01 3.26808512e-01 -2.93794960e-01 -3.24514478... | [11.633837699890137, -0.27682408690452576] |
6aa14bfc-e9e6-4516-bc61-d6b42bcc5353 | refocusing-is-key-to-transfer-learning | 2305.15542 | null | https://arxiv.org/abs/2305.15542v2 | https://arxiv.org/pdf/2305.15542v2.pdf | TOAST: Transfer Learning via Attention Steering | Transfer learning involves adapting a pre-trained model to novel downstream tasks. However, we observe that current transfer learning methods often fail to focus on task-relevant features. In this work, we explore refocusing model attention for transfer learning. We introduce Top-Down Attention Steering (TOAST), a nove... | ['Xin Wang', 'Trevor Darrell', 'Siyu Gai', 'Baifeng Shi'] | 2023-05-24 | null | null | null | null | ['fine-grained-image-classification', 'instruction-following'] | ['computer-vision', 'natural-language-processing'] | [ 1.60254419e-01 -2.77753383e-01 -4.88897592e-01 -5.39246500e-01
-8.72299373e-01 -7.06818938e-01 6.87425792e-01 -1.61927149e-01
-3.72398138e-01 8.65260065e-01 3.42513293e-01 -5.40991127e-01
1.66090220e-01 -7.00389266e-01 -1.04859757e+00 -3.35871458e-01
2.28459999e-01 2.88190216e-01 4.16672140e-01 -4.54718083... | [10.673981666564941, 8.222457885742188] |
fdd619af-cc61-463e-9508-767da2d706bb | drugehrqa-a-question-answering-dataset-on | null | null | https://openreview.net/forum?id=HBYJawngw5 | https://openreview.net/pdf?id=HBYJawngw5 | DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries | This paper develops the first question answering dataset (DrugEHRQA) containing question-answer pairs from both structured tables and unstructured notes from a publicly available Electronic Health Record (EHR). EHRs contain patient records, stored in structured tables as well as unstructured clinical notes. The informa... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['text-to-sql'] | ['computer-code'] | [ 1.12071998e-01 6.00713789e-01 -9.76922289e-02 -5.32840073e-01
-1.44399512e+00 -8.00176561e-01 1.31109670e-01 1.29215682e+00
-3.94700281e-02 9.01151180e-01 6.59695745e-01 -6.04666293e-01
-7.07363665e-01 -1.35354531e+00 -5.17585814e-01 2.11931124e-01
4.31319624e-02 1.03269577e+00 4.63998765e-01 -5.59482098... | [8.753884315490723, 8.50833511352539] |
23fcb4de-7b82-4c76-8884-51bb260ed077 | quantum-neural-network-for-quantum-neural | 2305.08544 | null | https://arxiv.org/abs/2305.08544v1 | https://arxiv.org/pdf/2305.08544v1.pdf | Quantum Neural Network for Quantum Neural Computing | Neural networks have achieved impressive breakthroughs in both industry and academia. How to effectively develop neural networks on quantum computing devices is a challenging open problem. Here, we propose a new quantum neural network model for quantum neural computing using (classically-controlled) single-qubit operat... | ['Zeng-Bing Chen', 'Tong-Kai Xu', 'Chen-Long Li', 'Hua-Lei Yin', 'Zhi-Ping Liu', 'Min-Gang Zhou'] | 2023-05-15 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 2.82732874e-01 -3.79933953e-01 2.97264792e-02 -2.53185660e-01
-2.52913594e-01 -4.30710346e-01 4.67312962e-01 -1.56544104e-01
-7.89742291e-01 7.55513608e-01 -4.59575742e-01 -5.29337287e-01
-1.93797182e-02 -1.29486501e+00 -6.45058334e-01 -1.15907538e+00
1.08788818e-01 1.82923153e-01 1.57164350e-01 -4.83510107... | [5.564149379730225, 4.947953701019287] |
a269aef1-cdb2-4762-b90f-3c3222517fc6 | two-staged-acoustic-modeling-adaption-for | 1908.06709 | null | https://arxiv.org/abs/1908.06709v1 | https://arxiv.org/pdf/1908.06709v1.pdf | Two-Staged Acoustic Modeling Adaption for Robust Speech Recognition by the Example of German Oral History Interviews | In automatic speech recognition, often little training data is available for specific challenging tasks, but training of state-of-the-art automatic speech recognition systems requires large amounts of annotated speech. To address this issue, we propose a two-staged approach to acoustic modeling that combines noise and ... | ['Joachim köhler', 'Sven Behnke', 'Christoph Schmidt', 'Michael Gref'] | 2019-08-19 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 3.11087221e-01 3.71977031e-01 4.09360021e-01 -7.11288750e-01
-1.46192479e+00 -1.36101916e-01 3.44451934e-01 -2.04109903e-02
-6.57223821e-01 7.02338278e-01 5.88768840e-01 -5.97432613e-01
4.40082431e-01 -1.66806579e-01 -4.00434732e-01 -2.95466989e-01
6.83004260e-02 3.89035910e-01 5.86494477e-03 -3.95725578... | [14.499161720275879, 6.449317932128906] |
832fa60b-9aa6-49bc-a9bf-4412d6935a18 | improving-deep-image-matting-via-local | 2112.13809 | null | https://arxiv.org/abs/2112.13809v2 | https://arxiv.org/pdf/2112.13809v2.pdf | Improving Deep Image Matting via Local Smoothness Assumption | Natural image matting is a fundamental and challenging computer vision task. Conventionally, the problem is formulated as an underconstrained problem. Since the problem is ill-posed, further assumptions on the data distribution are required to make the problem well-posed. For classical matting methods, a commonly adopt... | ['Dezhen Qi', 'Jiacheng Han', 'Jun Xie', 'Rui Wang'] | 2021-12-27 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 3.51443172e-01 -1.84244409e-01 -1.36394605e-01 -3.03295076e-01
-3.64061475e-01 -2.32545529e-02 3.79310936e-01 -4.06226248e-01
-2.42488325e-01 6.23905241e-01 -6.19025230e-02 -3.24843079e-01
1.83782727e-01 -5.91876805e-01 -7.34908581e-01 -9.48483527e-01
5.49736917e-01 1.54652715e-01 1.25125438e-01 1.00485139... | [10.654693603515625, -0.9530157446861267] |
aa71d913-c0ea-4065-a1ef-5e00b1a5dd49 | a-pragmatic-machine-learning-approach-to | 2202.06590 | null | https://arxiv.org/abs/2202.06590v1 | https://arxiv.org/pdf/2202.06590v1.pdf | A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images | Increased levels of tumor infiltrating lymphocytes (TILs) in cancer tissue indicate favourable outcomes in many types of cancer. Manual quantification of immune cells is inaccurate and time consuming for pathologists. Our aim is to leverage a computational solution to automatically quantify TILs in whole slide images (... | ['Thomas K. Kilvaer', 'Lars Ailo Bongo', 'Ruth Schwienbacher', 'Lill-Tove Rasmussen Busund', 'Kajsa Møllersen', 'Edvard Pedersen', 'Morten Grønnesby', 'Nikita Shvetsov'] | 2022-02-14 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 6.74662665e-02 2.21706316e-01 -3.83287400e-01 1.63376063e-01
-1.26779616e+00 -7.35870063e-01 3.70803595e-01 6.53966248e-01
-8.25994074e-01 6.61807835e-01 -9.14979950e-02 -8.65489960e-01
3.78535032e-01 -8.72065008e-01 -1.42737344e-01 -1.19207132e+00
2.72351533e-01 1.06483388e+00 1.79504365e-01 1.66009814... | [15.092957496643066, -3.1036362648010254] |
7cf922f6-5358-4d12-a5d0-834b418cdb34 | better-computer-go-player-with-neural-network | 1511.06410 | null | http://arxiv.org/abs/1511.06410v3 | http://arxiv.org/pdf/1511.06410v3.pdf | Better Computer Go Player with Neural Network and Long-term Prediction | Competing with top human players in the ancient game of Go has been a
long-term goal of artificial intelligence. Go's high branching factor makes
traditional search techniques ineffective, even on leading-edge hardware, and
Go's evaluation function could change drastically with one stone change. Recent
works [Maddison ... | ['Yuandong Tian', 'Yan Zhu'] | 2015-11-19 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-4.59856689e-01 1.94986556e-02 -3.38070810e-01 2.94679523e-01
-5.94615936e-01 -7.97536910e-01 5.35356641e-01 -3.78491610e-01
-7.34837830e-01 5.59585273e-01 -2.24314809e-01 -8.27582240e-01
-3.82377356e-01 -1.13529313e+00 -8.39361608e-01 -3.22692364e-01
-3.04244906e-01 9.82179463e-01 1.04188573e+00 -8.84647250... | [3.4663331508636475, 1.4182074069976807] |
efa92187-675d-4540-9b8b-71adb6dda85d | octet-object-aware-counterfactual | 2211.12380 | null | https://arxiv.org/abs/2211.12380v2 | https://arxiv.org/pdf/2211.12380v2.pdf | OCTET: Object-aware Counterfactual Explanations | Nowadays, deep vision models are being widely deployed in safety-critical applications, e.g., autonomous driving, and explainability of such models is becoming a pressing concern. Among explanation methods, counterfactual explanations aim to find minimal and interpretable changes to the input image that would also chan... | ['Matthieu Cord', 'Patrick Pérez', 'Hédi Ben-Younes', 'Éloi Zablocki', 'Mickaël Chen', 'Mehdi Zemni'] | 2022-11-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zemni_OCTET_Object-Aware_Counterfactual_Explanations_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zemni_OCTET_Object-Aware_Counterfactual_Explanations_CVPR_2023_paper.pdf | cvpr-2023-1 | ['counterfactual-explanation', 'explanation-generation'] | ['miscellaneous', 'natural-language-processing'] | [ 3.97968113e-01 4.61602271e-01 -1.22650199e-01 -5.25123358e-01
-1.81255102e-01 -6.32891238e-01 8.39333057e-01 -2.84562707e-02
-1.18373185e-01 5.64913750e-01 1.57356650e-01 -7.85642505e-01
-5.20592667e-02 -7.86851108e-01 -1.08307803e+00 -3.92156303e-01
3.74987751e-01 5.39240718e-01 6.08769618e-03 -1.13774225... | [8.930286407470703, 5.3797125816345215] |
ed2d7469-f900-43e4-afbd-ff51ef7acc03 | dvhn-a-deep-hashing-framework-for-large-scale | 2112.04937 | null | https://arxiv.org/abs/2112.04937v1 | https://arxiv.org/pdf/2112.04937v1.pdf | DVHN: A Deep Hashing Framework for Large-scale Vehicle Re-identification | In this paper, we make the very first attempt to investigate the integration of deep hash learning with vehicle re-identification. We propose a deep hash-based vehicle re-identification framework, dubbed DVHN, which substantially reduces memory usage and promotes retrieval efficiency while reserving nearest neighbor se... | ['Zhengwei Qi', 'Kaicheng Guo', 'Chenggang Wu', 'Fangxin Liu', 'Sheng Zhang', 'Yongbiao Chen'] | 2021-12-09 | null | null | null | null | ['2048'] | ['playing-games'] | [-3.70096833e-01 -1.55146420e-01 -5.85055172e-01 -6.42552257e-01
-1.04084289e+00 -4.91030127e-01 3.24721396e-01 2.26985529e-01
-7.16895401e-01 3.97673309e-01 -3.82090330e-01 -4.68042165e-01
-4.00239136e-03 -1.02846301e+00 -1.12385249e+00 -7.19734192e-01
-2.99295813e-01 4.49631423e-01 -9.51700751e-03 -8.89343321... | [11.40276050567627, 0.9134751558303833] |
904a0b91-4d3d-4b4b-93c9-e70ef37d9542 | dae-talker-high-fidelity-speech-driven | 2303.17550 | null | https://arxiv.org/abs/2303.17550v2 | https://arxiv.org/pdf/2303.17550v2.pdf | DAE-Talker: High Fidelity Speech-Driven Talking Face Generation with Diffusion Autoencoder | While recent research has made significant progress in speech-driven talking face generation, the quality of the generated video still lags behind that of real recordings. One reason for this is the use of handcrafted intermediate representations like facial landmarks and 3DMM coefficients, which are designed based on ... | ['Jiang Bian', 'Sheng Zhao', 'Kai Yu', 'Xie Chen', 'Xu Tan', 'Tianyu He', 'Qi Chen', 'Chenpng Du'] | 2023-03-30 | null | null | null | null | ['talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision'] | [ 1.33879617e-01 4.08333719e-01 -1.63668245e-01 -4.25030142e-01
-8.37751865e-01 -4.02557194e-01 6.72224045e-01 -9.05842066e-01
5.81953563e-02 4.27897602e-01 5.97951055e-01 7.51598999e-02
3.41885209e-01 -4.24301744e-01 -9.80143428e-01 -7.65065968e-01
2.41557792e-01 1.35601878e-01 -1.12980701e-01 -6.92704367... | [13.246081352233887, -0.41128629446029663] |
af184747-610e-4038-acd2-791ec6cb124c | is-the-computation-of-abstract-sameness | 2205.06149 | null | https://arxiv.org/abs/2205.06149v1 | https://arxiv.org/pdf/2205.06149v1.pdf | Is the Computation of Abstract Sameness Relations Human-Like in Neural Language Models? | In recent years, deep neural language models have made strong progress in various NLP tasks. This work explores one facet of the question whether state-of-the-art NLP models exhibit elementary mechanisms known from human cognition. The exploration is focused on a relatively primitive mechanism for which there is a lot ... | ['Benjamin Roth', 'Lukas Thoma'] | 2022-05-12 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [-1.32378891e-01 4.78025883e-01 1.58674106e-01 -4.74302262e-01
4.67634678e-01 -3.82627547e-01 7.85775840e-01 5.46977520e-01
-6.23531520e-01 2.14220256e-01 9.85215008e-02 -5.60910642e-01
-2.26802900e-01 -9.34263289e-01 -9.52570140e-01 -3.60960960e-01
7.71434978e-03 4.83226359e-01 2.70756602e-01 -4.32770520... | [10.265765190124512, 8.811880111694336] |
17f2ef62-959f-4185-849e-8bdd6ffdad66 | temporal-superimposed-crossover-module-for | 2211.03387 | null | https://arxiv.org/abs/2211.03387v3 | https://arxiv.org/pdf/2211.03387v3.pdf | Temporal superimposed crossover module for effective continuous sign language | The ultimate goal of continuous sign language recognition(CSLR) is to facilitate the communication between special people and normal people, which requires a certain degree of real-time and deploy-ability of the model. However, in the previous research on CSLR, little attention has been paid to the real-time and deploy... | ['Quan Gan', 'Fei Yuan', 'Jing Li', 'Qidan Zhu'] | 2022-11-07 | null | null | null | null | ['sign-language-recognition', 'video-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.31322041e-01 -5.08224070e-01 1.23905338e-01 -3.03255558e-01
-5.17701328e-01 -1.51840985e-01 3.78181368e-01 -1.15051806e+00
-8.65776718e-01 2.81212121e-01 1.70227766e-01 -4.87059176e-01
1.11886352e-01 -5.39464056e-01 -4.45846766e-01 -7.31300235e-01
1.53769627e-01 -2.70313680e-01 5.31798899e-01 -9.48598310... | [9.246593475341797, -6.5054731369018555] |
21d825be-c76a-4382-9bed-e26716a881b7 | unsupervised-domain-agnostic-fake-news | 2305.11349 | null | https://arxiv.org/abs/2305.11349v1 | https://arxiv.org/pdf/2305.11349v1.pdf | Unsupervised Domain-agnostic Fake News Detection using Multi-modal Weak Signals | The emergence of social media as one of the main platforms for people to access news has enabled the wide dissemination of fake news. This has motivated numerous studies on automating fake news detection. Although there have been limited attempts at unsupervised fake news detection, their performance suffers due to not... | ['Christopher Leckie', 'Shanika Karunasekera', 'Ling Luo', 'Amila Silva'] | 2023-05-18 | null | null | null | null | ['fake-news-detection'] | ['natural-language-processing'] | [-6.87743947e-02 3.15631106e-02 -5.50586998e-01 -8.72645676e-02
-8.02040100e-01 -4.45270211e-01 1.14496529e+00 3.33748907e-01
-4.36211169e-01 6.44445240e-01 7.70424545e-01 1.26151368e-01
2.30784789e-01 -7.86039412e-01 -7.19494939e-01 -3.37668538e-01
3.88553113e-01 1.92509204e-01 1.99012488e-01 -3.82428229... | [8.17074966430664, 10.300657272338867] |
26facaae-c461-40eb-8c38-cec1efd7d1ab | watch-the-neighbors-a-unified-k-nearest | 2210.08909 | null | https://arxiv.org/abs/2210.08909v1 | https://arxiv.org/pdf/2210.08909v1.pdf | Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent Discovery | Discovering out-of-domain (OOD) intent is important for developing new skills in task-oriented dialogue systems. The key challenges lie in how to transfer prior in-domain (IND) knowledge to OOD clustering, as well as jointly learn OOD representations and cluster assignments. Previous methods suffer from in-domain overf... | ['Weiran Xu', 'Wei Wu', 'Jingang Wang', 'Yanan Wu', 'Pei Wang', 'Keqing He', 'Yutao Mou'] | 2022-10-17 | null | null | null | null | ['intent-discovery', 'task-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.00724110e-01 2.10446611e-01 -4.96117771e-01 -7.29380667e-01
-7.73553848e-01 -5.35004258e-01 4.23198164e-01 1.64391503e-01
-2.23302498e-01 3.05536330e-01 5.15816689e-01 -1.50346100e-01
-2.82523632e-01 -2.83940852e-01 -7.83587694e-02 -4.93803531e-01
1.84518829e-01 8.41335952e-01 1.20981544e-01 -1.58498645... | [12.42894172668457, 7.53891658782959] |
3b4d2c85-52f4-4d09-aee3-273780591558 | upsampling-layers-for-music-source-separation | 2111.11773 | null | https://arxiv.org/abs/2111.11773v1 | https://arxiv.org/pdf/2111.11773v1.pdf | Upsampling layers for music source separation | Upsampling artifacts are caused by problematic upsampling layers and due to spectral replicas that emerge while upsampling. Also, depending on the used upsampling layer, such artifacts can either be tonal artifacts (additive high-frequency noise) or filtering artifacts (substractive, attenuating some bands). In this wo... | ['Davide Scaini', 'Daniel Arteaga', 'Giulio Cengarle', 'Santiago Pascual', 'Joan Serrà', 'Jordi Pons'] | 2021-11-23 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 3.04994822e-01 -1.71917617e-01 1.58093408e-01 1.46930501e-01
-8.33709896e-01 -5.75550854e-01 4.17526335e-01 -1.07644238e-01
-1.90631568e-01 7.29890168e-01 6.28131807e-01 5.48576936e-02
-2.81702161e-01 -5.60561478e-01 -8.09899449e-01 -5.60935438e-01
-2.88396925e-01 -2.31935516e-01 2.52288371e-01 -1.25795707... | [15.470831871032715, 5.801798343658447] |
10371563-ba38-4c58-8726-c56d99c5e463 | infusing-future-information-into-monotonic | 2109.03121 | null | https://arxiv.org/abs/2109.03121v1 | https://arxiv.org/pdf/2109.03121v1.pdf | Infusing Future Information into Monotonic Attention Through Language Models | Simultaneous neural machine translation(SNMT) models start emitting the target sequence before they have processed the source sequence. The recent adaptive policies for SNMT use monotonic attention to perform read/write decisions based on the partial source and target sequences. The lack of sufficient information might... | ['Sangha Kim', 'Nikhil Kumar Lakumarapu', 'Beomseok Lee', 'Sathish Indurthi', 'Mohd Abbas Zaidi'] | 2021-09-07 | infusing-future-information-into-monotonic-1 | https://openreview.net/forum?id=lgGKToqwtwG | https://openreview.net/pdf?id=lgGKToqwtwG | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 3.05288702e-01 -2.76091937e-02 -4.64012772e-01 -4.45715874e-01
-9.03553903e-01 -4.69180733e-01 6.32772505e-01 -2.29851276e-01
-6.12275481e-01 8.42205942e-01 3.92493546e-01 -7.59295344e-01
5.33114672e-01 -4.54871088e-01 -7.79903233e-01 -5.14444232e-01
6.54187083e-01 6.32472396e-01 5.18937521e-02 -1.92228243... | [11.823163986206055, 9.984027862548828] |
4d8a3e1d-2ad5-4071-b6b6-2f40cfefef72 | speech-text-dialog-pre-training-for-spoken | 2305.11579 | null | https://arxiv.org/abs/2305.11579v2 | https://arxiv.org/pdf/2305.11579v2.pdf | Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment | Recently, speech-text pre-training methods have shown remarkable success in many speech and natural language processing tasks. However, most previous pre-trained models are usually tailored for one or two specific tasks, but fail to conquer a wide range of speech-text tasks. In addition, existing speech-text pre-traini... | ['Yongbin Li', 'Fei Huang', 'Chao Wang', 'Wentao Ma', 'Yuchuan Wu', 'Min Yang', 'Ting-En Lin', 'Haoyu Gao', 'Tianshu Yu'] | 2023-05-19 | null | null | null | null | ['multimodal-sentiment-analysis', 'multimodal-intent-recognition', 'emotion-recognition-in-conversation', 'multimodal-sentiment-analysis'] | ['computer-vision', 'miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [ 5.41193426e-01 2.63224989e-01 -2.31947780e-01 -9.49285448e-01
-6.07863367e-01 -4.32068080e-01 7.85212994e-01 1.70372918e-01
-2.26573452e-01 4.64913636e-01 7.71714866e-01 -7.30675340e-01
3.08124218e-02 -1.75858229e-01 -1.08416900e-01 -2.92683899e-01
2.65441984e-01 7.35195875e-01 1.51376233e-01 -7.15243697... | [12.784989356994629, 7.76558780670166] |
58cc8166-ae91-4207-9133-89f5a092d839 | multi-input-architecture-and-disentangled | 2111.01710 | null | https://arxiv.org/abs/2111.01710v1 | https://arxiv.org/pdf/2111.01710v1.pdf | Multi-input Architecture and Disentangled Representation Learning for Multi-dimensional Modeling of Music Similarity | In the context of music information retrieval, similarity-based approaches are useful for a variety of tasks that benefit from a query-by-example scenario. Music however, naturally decomposes into a set of semantically meaningful factors of variation. Current representation learning strategies pursue the disentanglemen... | ['Hanna Lukashevich', 'Jakob Abeßer', 'Sebastian Ribecky'] | 2021-11-02 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [ 2.84955978e-01 -3.27230096e-01 -5.64442724e-02 -1.43381327e-01
-8.44680667e-01 -7.56488383e-01 8.61713171e-01 3.50138307e-01
-2.59491056e-01 -4.45144325e-02 7.61794388e-01 1.50718525e-01
-7.58794606e-01 -5.04599512e-01 -2.00140193e-01 -5.25997996e-01
9.65455994e-02 3.10208082e-01 -4.19636816e-01 -2.91065216... | [15.873465538024902, 5.3262529373168945] |
edd582b8-fe00-4998-827a-8b86b0a6ef3e | masked-autoencoders-for-egocentric-video | 2211.15286 | null | https://arxiv.org/abs/2211.15286v1 | https://arxiv.org/pdf/2211.15286v1.pdf | Masked Autoencoders for Egocentric Video Understanding @ Ego4D Challenge 2022 | In this report, we present our approach and empirical results of applying masked autoencoders in two egocentric video understanding tasks, namely, Object State Change Classification and PNR Temporal Localization, of Ego4D Challenge 2022. As team TheSSVL, we ranked 2nd place in both tasks. Our code will be made availabl... | ['Kui Ren', 'Ashish Kapoor', 'Sai Vemprala', 'Zhongjie Ba', 'Shuang Ma', 'Jiachen Lei'] | 2022-11-18 | null | null | null | null | ['video-understanding'] | ['computer-vision'] | [-3.97656143e-01 -1.98329277e-02 -2.21932366e-01 -3.28461677e-01
4.71713245e-02 -5.20194292e-01 8.83637667e-01 -5.90552926e-01
-4.42052096e-01 6.25857055e-01 6.44507229e-01 1.17160656e-01
2.11293310e-01 -1.45809487e-01 -9.78108525e-01 -1.86446235e-01
-6.37419522e-01 1.70781404e-01 1.34273618e-01 6.84552416... | [8.372199058532715, 0.554556131362915] |
5c7ddf51-c452-428a-90f2-82d1cf4e95b5 | mobile-mapping-mesh-change-detection-and | 2303.07182 | null | https://arxiv.org/abs/2303.07182v1 | https://arxiv.org/pdf/2303.07182v1.pdf | Mobile Mapping Mesh Change Detection and Update | Mobile mapping, in particular, Mobile Lidar Scanning (MLS) is increasingly widespread to monitor and map urban scenes at city scale with unprecedented resolution and accuracy. The resulting point cloud sampling of the scene geometry can be meshed in order to create a continuous representation for different applications... | ['Cédric Demonceaux', 'Bruno Vallet', 'Teng Wu'] | 2023-03-13 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 3.39725375e-01 -3.88235271e-01 4.44216996e-01 -3.34804803e-01
-3.23756248e-01 -5.57616591e-01 7.42963970e-01 5.32754004e-01
-4.13533807e-01 1.03239238e+00 -4.07934427e-01 -2.14347303e-01
-2.76413560e-01 -1.36567044e+00 -5.68291545e-01 -4.69570339e-01
2.57385820e-02 1.05014241e+00 8.00747037e-01 -5.45489490... | [8.370725631713867, -2.6152114868164062] |
594f4953-45cc-4a06-816c-bd8c53c59cb5 | generalization-in-transfer-learning | 1909.01331 | null | https://arxiv.org/abs/1909.01331v2 | https://arxiv.org/pdf/1909.01331v2.pdf | Generalization in Transfer Learning | Agents trained with deep reinforcement learning algorithms are capable of performing highly complex tasks including locomotion in continuous environments. We investigate transferring the learning acquired in one task to a set of previously unseen tasks. Generalization and overfitting in deep reinforcement learning are ... | ['Suzan Ece Ada', 'Emre Ugur', 'H. Levent Akin'] | 2019-09-03 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [ 4.53394443e-01 2.58020103e-01 1.55495107e-01 1.94978192e-01
-5.63869655e-01 -5.31187713e-01 6.16967976e-01 1.32060468e-01
-9.11392987e-01 1.27930522e+00 -1.67527169e-01 -8.13714862e-02
-2.23432511e-01 -7.05192566e-01 -1.11472058e+00 -9.04882133e-01
-1.20440431e-01 3.60006034e-01 1.30103588e-01 -4.81946141... | [4.297308921813965, 1.88939368724823] |
38e96b5c-aa6d-4d6d-be81-bf05e2db38b7 | towards-responsible-ai-for-financial | 2206.02419 | null | https://arxiv.org/abs/2206.02419v1 | https://arxiv.org/pdf/2206.02419v1.pdf | Towards Responsible AI for Financial Transactions | The application of AI in finance is increasingly dependent on the principles of responsible AI. These principles - explainability, fairness, privacy, accountability, transparency and soundness form the basis for trust in future AI systems. In this study, we address the first principle by providing an explanation for a ... | ['Christian W. Omlin', 'Jan Erik Modal', 'Charl Maree'] | 2022-06-06 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [-2.86737308e-02 9.45453227e-01 -2.01785371e-01 -6.43441677e-01
-5.22495061e-02 -5.60379744e-01 9.10574138e-01 2.99614906e-01
-9.49642807e-02 7.44010866e-01 2.91941077e-01 -7.44440436e-01
-2.89430618e-01 -5.63515902e-01 -7.34334648e-01 -3.87295932e-01
-6.16567880e-02 3.66060048e-01 -5.56236029e-01 7.49854976... | [8.751476287841797, 5.7056474685668945] |
2880d717-606c-42f4-8fac-cc03fa8dd3c3 | learning-hierarchical-metrical-structure | 2209.10259 | null | https://arxiv.org/abs/2209.10259v1 | https://arxiv.org/pdf/2209.10259v1.pdf | Learning Hierarchical Metrical Structure Beyond Measures | Music contains hierarchical structures beyond beats and measures. While hierarchical structure annotations are helpful for music information retrieval and computer musicology, such annotations are scarce in current digital music databases. In this paper, we explore a data-driven approach to automatically extract hierar... | ['Gus Xia', 'Yixiao Zhang', 'Daniel Chin', 'Junyan Jiang'] | 2022-09-21 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [ 2.05568761e-01 -4.80043665e-02 -3.08031023e-01 -3.32089126e-01
-9.36980426e-01 -8.63710165e-01 3.65723431e-01 -1.71025753e-01
4.35176156e-02 4.91539776e-01 7.61755407e-01 1.02732316e-01
-6.36878550e-01 -5.08394122e-01 -3.92838538e-01 -2.58061051e-01
-5.86152196e-01 6.54961467e-01 1.51009336e-01 2.22295616... | [15.916718482971191, 5.358638286590576] |
96937517-a7cc-4993-b9e2-864664f9c66a | hq-50k-a-large-scale-high-quality-dataset-for | 2306.05390 | null | https://arxiv.org/abs/2306.05390v1 | https://arxiv.org/pdf/2306.05390v1.pdf | HQ-50K: A Large-scale, High-quality Dataset for Image Restoration | This paper introduces a new large-scale image restoration dataset, called HQ-50K, which contains 50,000 high-quality images with rich texture details and semantic diversity. We analyze existing image restoration datasets from five different perspectives, including data scale, resolution, compression rates, texture deta... | ['Nenghai Yu', 'Gang Hua', 'Lu Yuan', 'Jianmin Bao', 'Qi Chu', 'Qiankun Liu', 'Zhentao Tan', 'Dongdong Chen', 'Qinhong Yang'] | 2023-06-08 | null | null | null | null | ['super-resolution', 'image-restoration'] | ['computer-vision', 'computer-vision'] | [ 4.42846268e-02 -5.27903497e-01 -8.64232555e-02 -2.02687711e-01
-1.30217445e+00 -9.48114172e-02 1.54976681e-01 -2.47347057e-01
5.96418343e-02 7.22075284e-01 6.05789542e-01 1.19336545e-01
-2.25819752e-01 -7.52382576e-01 -5.03893375e-01 -9.25352037e-01
9.28583071e-02 -1.61449283e-01 3.93406451e-01 -3.34172368... | [11.208003997802734, -2.2024459838867188] |
dc578e38-1926-416b-b3f0-37f37d099dfc | ps-arm-an-end-to-end-attention-aware-relation | 2210.03433 | null | https://arxiv.org/abs/2210.03433v1 | https://arxiv.org/pdf/2210.03433v1.pdf | PS-ARM: An End-to-End Attention-aware Relation Mixer Network for Person Search | Person search is a challenging problem with various real-world applications, that aims at joint person detection and re-identification of a query person from uncropped gallery images. Although, the previous study focuses on rich feature information learning, it is still hard to retrieve the query person due to the occu... | ['Fahad Shahbaz Khan', 'Rao Muhammad Anwer', 'Sanath Narayan', 'Hisham Cholakkal', 'Mustansar Fiaz'] | 2022-10-07 | null | null | null | null | ['person-search'] | ['computer-vision'] | [ 9.62508842e-02 -3.71268958e-01 6.56542554e-02 -2.30439052e-01
-7.54006386e-01 -3.52306187e-01 8.50752532e-01 -6.76385537e-02
-7.68854320e-01 4.37748522e-01 2.77320653e-01 1.65720895e-01
-1.33762673e-01 -5.80553949e-01 -4.88901138e-01 -7.69872725e-01
1.07617453e-01 5.13544142e-01 3.48784149e-01 -1.20028391... | [14.793800354003906, 0.824307918548584] |
100a0773-441e-427f-9b5e-fc3b5408ceee | a-perceptual-measure-for-evaluating-the | 2202.12257 | null | https://arxiv.org/abs/2202.12257v2 | https://arxiv.org/pdf/2202.12257v2.pdf | A Perceptual Measure for Evaluating the Resynthesis of Automatic Music Transcriptions | This study focuses on the perception of music performances when contextual factors, such as room acoustics and instrument, change. We propose to distinguish the concept of "performance" from the one of "interpretation", which expresses the "artistic intention". Towards assessing this distinction, we carried out an expe... | ['Stavros Ntalampiras', 'Federico Avanzini', 'Federico Simonetta'] | 2022-02-24 | null | null | null | null | ['music-transcription'] | ['music'] | [ 3.63342762e-01 -7.21862763e-02 2.71664113e-01 -3.42894763e-01
-1.01344395e+00 -6.27978563e-01 4.19307888e-01 1.63040698e-01
-3.43414515e-01 5.35977781e-01 4.15779918e-01 3.10095400e-01
-3.85834754e-01 -3.99001211e-01 -5.30084789e-01 -6.89655542e-01
1.20406479e-01 2.72695124e-01 -1.99471503e-01 -1.59090593... | [15.725272178649902, 5.410780429840088] |
2d8a1e0f-970e-47e0-a86c-df63352bad28 | a-warm-start-and-a-clean-crawled-corpus-a | 2201.05601 | null | https://arxiv.org/abs/2201.05601v2 | https://arxiv.org/pdf/2201.05601v2.pdf | A Warm Start and a Clean Crawled Corpus -- A Recipe for Good Language Models | We train several language models for Icelandic, including IceBERT, that achieve state-of-the-art performance in a variety of downstream tasks, including part-of-speech tagging, named entity recognition, grammatical error detection and constituency parsing. To train the models we introduce a new corpus of Icelandic text... | ['Svanhvít Lilja Ingólfsdóttir', 'Hafsteinn Einarsson', 'Vilhjálmur Þorsteinsson', 'Haukur Páll Jónsson', 'Pétur Orri Ragnarsson', 'Haukur Barri Símonarson', 'Vésteinn Snæbjarnarson'] | 2022-01-14 | null | null | null | null | ['grammatical-error-detection', 'constituency-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [-3.07298779e-01 1.82961524e-01 -2.82832831e-02 -3.69976163e-01
-1.78771448e+00 -1.07378411e+00 6.19305134e-01 3.57246757e-01
-7.63527572e-01 8.19805026e-01 6.37428880e-01 -5.76152861e-01
2.79772878e-01 -4.95514125e-01 -8.68016601e-01 -8.68949071e-02
-1.35174453e-01 9.55076039e-01 1.25626281e-01 -4.15425301... | [10.408175468444824, 9.840657234191895] |
ab86937f-09e6-465e-8a6e-20127d1f3de5 | rgcl-wlv-at-semeval-2019-task-12-toponym | null | null | https://aclanthology.org/S19-2228 | https://aclanthology.org/S19-2228.pdf | RGCL-WLV at SemEval-2019 Task 12: Toponym Detection | This article describes the system submitted by the RGCL-WLV team to the SemEval 2019 Task 12: Toponym resolution in scientific papers. The system detects toponyms using a bootstrapped machine learning (ML) approach which classifies names identified using gazetteers extracted from the GeoNames geographical database. The... | ['Constantin Or{\\u{a}}san', 'Tharindu Ranasinghe', 'Pablo Calleja', 'Alistair Plum', 'Ruslan Mitkov'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['toponym-resolution'] | ['natural-language-processing'] | [-3.41311663e-01 1.53134137e-01 -4.17268813e-01 -6.35993257e-02
-7.42375672e-01 -9.39363480e-01 1.17265654e+00 5.65811634e-01
-6.91174746e-01 1.19305158e+00 -8.88936073e-02 -2.80139714e-01
-4.94270205e-01 -6.21607661e-01 -4.46921587e-01 -3.23954731e-01
2.23543465e-01 9.04655516e-01 1.09127909e-02 -4.67139557... | [9.189214706420898, 8.951967239379883] |
63abadf3-02bc-40f3-b752-ee1c36f00af4 | enhancing-deep-neural-networks-testing-by | 2112.01956 | null | https://arxiv.org/abs/2112.01956v1 | https://arxiv.org/pdf/2112.01956v1.pdf | Enhancing Deep Neural Networks Testing by Traversing Data Manifold | We develop DEEPTRAVERSAL, a feedback-driven framework to test DNNs. DEEPTRAVERSAL first launches an offline phase to map media data of various forms to manifolds. Then, in its online testing phase, DEEPTRAVERSAL traverses the prepared manifold space to maximize DNN coverage criteria and trigger prediction errors. In ou... | ['Shuai Wang', 'Qi Pang', 'Yuanyuan Yuan'] | 2021-12-03 | null | null | null | null | ['dnn-testing'] | ['adversarial'] | [-1.52984455e-01 1.04703298e-02 -2.88518190e-01 -1.87810183e-01
-8.22531760e-01 -7.97313809e-01 5.04711688e-01 -1.61509305e-01
-2.97255397e-01 8.63864005e-01 -1.30025759e-01 -5.53485155e-01
-1.83823988e-01 -8.62793088e-01 -1.09322715e+00 -5.01356684e-02
1.39873400e-01 8.31277549e-01 5.03477216e-01 3.56047861... | [8.899528503417969, 3.4024550914764404] |
cb44c868-fa3f-4e46-b51e-50028231abc4 | syntf-synthetic-and-differentially-private | 1805.00904 | null | http://arxiv.org/abs/1805.00904v1 | http://arxiv.org/pdf/1805.00904v1.pdf | SynTF: Synthetic and Differentially Private Term Frequency Vectors for Privacy-Preserving Text Mining | Text mining and information retrieval techniques have been developed to
assist us with analyzing, organizing and retrieving documents with the help of
computers. In many cases, it is desirable that the authors of such documents
remain anonymous: Search logs can reveal sensitive details about a user,
critical articles o... | ['Florian Kerschbaum', 'Benjamin Weggenmann'] | 2018-05-02 | null | null | null | null | ['text-anonymization'] | ['natural-language-processing'] | [ 4.59256351e-01 2.08512932e-01 -2.96543330e-01 -8.65538046e-02
-5.22751212e-01 -1.08985877e+00 8.50965619e-01 7.02938616e-01
-5.80964744e-01 7.06081510e-01 7.03718662e-02 -5.66530406e-01
-2.50235140e-01 -5.59715033e-01 -5.06559730e-01 -5.69504440e-01
3.32722962e-01 5.84304571e-01 -1.59804255e-01 -9.65163019... | [6.14246940612793, 7.005415439605713] |
ccc57730-e0d8-4653-b67d-4fd718324cd7 | palm-2-technical-report | null | null | https://ai.google/static/documents/palm2techreport.pdf | https://ai.google/static/documents/palm2techreport.pdf | PaLM 2 Technical Report | We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities
and is more compute-efficient than its predecessor PaLM (Chowdhery et al., 2022). PaLM 2 is a Transformer-based
model trained using a mixture of objectives similar to UL2 (Tay et al., 2023). Through exte... | ['Google'] | 2023-05-10 | null | null | null | null | ['math-word-problem-solving', 'multi-task-language-understanding', 'movie-recommendation', 'sports-understanding', 'word-sense-disambiguation', 'multiple-choice-qa', 'sarcasm-detection', 'toxic-comment-classification', 'cross-lingual-question-answering', 'text-summarization', 'coreference-resolution', 'cross-lingual-tr... | ['knowledge-base', 'methodology', 'miscellaneous', 'miscellaneous', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'nat... | [-3.99293095e-01 1.29679710e-01 -2.45092645e-01 -2.42460504e-01
-1.23294854e+00 -7.32487440e-01 9.05532837e-01 -1.07566059e-01
-3.39789003e-01 6.55567050e-01 3.06147695e-01 -6.90829098e-01
-1.50685206e-01 -6.94299698e-01 -6.41305447e-01 8.82339943e-03
-1.22755013e-01 1.14687145e+00 6.11885041e-02 -5.65449119... | [9.73069953918457, 7.479613304138184] |
d8d2abf1-49dc-4c14-89d2-5bfc83762e73 | semeval-2021-task-10-source-free-domain | null | null | https://aclanthology.org/2021.semeval-1.42 | https://aclanthology.org/2021.semeval-1.42.pdf | SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing | This paper presents the Source-Free Domain Adaptation shared task held within SemEval-2021. The aim of the task was to explore adaptation of machine-learning models in the face of data sharing constraints. Specifically, we consider the scenario where annotations exist for a domain but cannot be shared. Instead, partici... | ['Steven Bethard', 'Timothy Miller', '{\\"O}zlem Uzuner', 'Yiyun Zhao', 'Xin Su', 'Egoitz Laparra'] | 2021-08-01 | null | null | null | semeval-2021 | ['source-free-domain-adaptation', 'negation-detection'] | ['computer-vision', 'natural-language-processing'] | [ 6.21549904e-01 4.89451647e-01 -2.52736032e-01 -1.16807795e+00
-3.98417383e-01 -6.53410792e-01 6.90812171e-01 5.03519237e-01
-9.50879574e-01 1.12981391e+00 -1.31051034e-01 -5.13224788e-02
2.01443017e-01 -4.14584965e-01 -3.98998111e-01 -6.92723021e-02
2.39687189e-01 7.65015900e-01 1.50030896e-01 -2.62626439... | [10.626606941223145, 7.990034103393555] |
3c7d14fe-b977-4d3c-945c-7cec56d2b441 | offline-reinforcement-learning-via-high | 2209.14548 | null | https://arxiv.org/abs/2209.14548v2 | https://arxiv.org/pdf/2209.14548v2.pdf | Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling | In offline reinforcement learning, weighted regression is a common method to ensure the learned policy stays close to the behavior policy and to prevent selecting out-of-sample actions. In this work, we show that due to the limited distributional expressivity of policy models, previous methods might still select unseen... | ['Jun Zhu', 'Hang Su', 'Chengyang Ying', 'Cheng Lu', 'Huayu Chen'] | 2022-09-29 | null | null | null | null | ['d4rl'] | ['robots'] | [-4.51015495e-03 1.52330801e-01 -6.82556629e-01 -1.23971090e-01
-8.36509883e-01 -7.36970663e-01 7.48617172e-01 -2.78254241e-01
-5.05975604e-01 9.60445344e-01 3.32452357e-01 -3.83062750e-01
-7.11060762e-02 -6.42404318e-01 -7.45998204e-01 -1.01016057e+00
-1.83410626e-02 7.18904912e-01 1.76207557e-01 -2.40167364... | [4.079798221588135, 2.027352809906006] |
e4b7df2e-d5e7-4da0-99a7-f92b84c057a2 | a-comment-on-the-paper-prediction-of-kidney | 1707.09869 | null | http://arxiv.org/abs/1707.09869v1 | http://arxiv.org/pdf/1707.09869v1.pdf | A comment on the paper Prediction of Kidney Function from Biopsy Images using Convolutional Neural Networks | This letter presente a comment on the paper Prediction of Kidney Function
from Biopsy Images using Convolutional Neural Networks by Ledbetter et al.
(2017) | ['Washington LC dos-Santos', 'Luiz AR de Freitas', 'Angelo A Duarte'] | 2017-07-23 | null | null | null | null | ['kidney-function'] | ['medical'] | [ 1.27503604e-01 5.84076643e-01 -1.89660653e-01 -8.83463204e-01
-4.66835164e-02 -1.69398069e-01 1.80356890e-01 2.15527058e-01
-5.53965807e-01 1.19790328e+00 2.69881755e-01 -5.78297794e-01
-2.68430024e-01 -9.00581300e-01 -6.60504878e-01 -5.45251608e-01
-4.59679306e-01 5.84995866e-01 -3.05290282e-01 3.31624210... | [14.2943754196167, -2.482269763946533] |
8589d6db-226a-4f7d-bd8f-61883f3e89e6 | fast-blind-audio-copy-move-detection-and | 2302.07584 | null | https://arxiv.org/abs/2302.07584v1 | https://arxiv.org/pdf/2302.07584v1.pdf | Fast Blind Audio Copy-Move Detection and Localization Using Local Feature Tensors in Noise | The increasing availability of audio editing software altering digital audios and their ease of use allows create forgeries at low cost. A copy-move forgery (CMF) is one of easiest and popular audio forgeries, which created by copying and pasting audio segments within the same audio, and potentially post-processing it.... | ['Muyong Cao', 'Mingle Liu', 'Dong Yang'] | 2023-02-15 | null | null | null | null | ['dynamic-time-warping'] | ['time-series'] | [ 5.14257073e-01 -6.62595987e-01 4.45058823e-01 3.56714696e-01
-1.21244395e+00 -8.39958310e-01 2.35916659e-01 4.08259064e-01
-2.23480687e-01 4.36416626e-01 1.22766078e-01 1.57615036e-01
-4.04476285e-01 -4.93526042e-01 -4.49018270e-01 -8.06920350e-01
-5.05030453e-01 -2.60000497e-01 6.58727288e-01 8.77755880... | [12.357894897460938, 0.9749763011932373] |
7708bad2-cfbf-4abf-853d-d5d5d0d337d6 | lifelong-multi-agent-path-finding-in-large | 2005.07371 | null | https://arxiv.org/abs/2005.07371v2 | https://arxiv.org/pdf/2005.07371v2.pdf | Lifelong Multi-Agent Path Finding in Large-Scale Warehouses | Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to their goal locations without collisions. In this paper, we study the lifelong variant of MAPF, where agents are constantly engaged with new goal locations, such as in large-scale automated warehouses. We propose a new framework Rolling-Horizon... | ['Sven Koenig', 'Andrew Tinka', 'T. K. Satish Kumar', 'Scott Kiesel', 'Joseph W. Durham', 'Jiaoyang Li'] | 2020-05-15 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 4.63492982e-02 4.53476667e-01 2.78123140e-01 1.88390203e-02
-8.71508360e-01 -1.09144187e+00 3.41998696e-01 5.10731518e-01
-4.23876673e-01 1.32582581e+00 -6.78701177e-02 -2.25672871e-01
-8.63748908e-01 -1.14657545e+00 -7.82237411e-01 -5.09618580e-01
-8.41053784e-01 1.50934112e+00 5.89587927e-01 -7.32708275... | [4.962496757507324, 1.7456916570663452] |
3c1f2991-1594-45b8-a487-eb3d36fe6495 | skip-clip-self-supervised-spatiotemporal | 1910.12770 | null | https://arxiv.org/abs/1910.12770v1 | https://arxiv.org/pdf/1910.12770v1.pdf | Skip-Clip: Self-Supervised Spatiotemporal Representation Learning by Future Clip Order Ranking | Deep neural networks require collecting and annotating large amounts of data to train successfully. In order to alleviate the annotation bottleneck, we propose a novel self-supervised representation learning approach for spatiotemporal features extracted from videos. We introduce Skip-Clip, a method that utilizes tempo... | ['Graham W. Taylor', 'Alaaeldin El-Nouby', 'Joshua M. Susskind', 'Shuangfei Zhai'] | 2019-10-28 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 3.51806939e-01 7.60517716e-02 -6.50822341e-01 -8.07540417e-01
-8.57446432e-01 -4.48799491e-01 5.47207057e-01 -2.38756880e-01
-6.35505736e-01 6.42562807e-01 9.20175731e-01 3.60466957e-01
1.43690497e-01 -1.77349553e-01 -1.11786497e+00 -3.16656947e-01
-4.92745310e-01 1.76636800e-01 3.39121580e-01 2.88188040... | [8.45142936706543, 0.6389753818511963] |
33ed38cf-c875-4843-b7ac-d001795686b2 | the-theory-behind-controllable-expressive | 1910.06234 | null | https://arxiv.org/abs/1910.06234v1 | https://arxiv.org/pdf/1910.06234v1.pdf | The Theory behind Controllable Expressive Speech Synthesis: a Cross-disciplinary Approach | As part of the Human-Computer Interaction field, Expressive speech synthesis is a very rich domain as it requires knowledge in areas such as machine learning, signal processing, sociology, psychology. In this Chapter, we will focus mostly on the technical side. From the recording of expressive speech to its modeling, t... | ['Thierry Dutoit', 'Noé Tits', 'Kevin El Haddad'] | 2019-10-14 | null | null | null | null | ['expressive-speech-synthesis'] | ['speech'] | [ 4.35820371e-01 4.39204782e-01 -2.38962501e-01 -1.27501339e-01
-6.42007113e-01 -4.00320798e-01 8.27839315e-01 -3.82499307e-01
2.60001346e-02 7.01974988e-01 6.57618523e-01 -2.36548916e-01
4.43638302e-02 -4.39859271e-01 -5.28041422e-01 -6.26551867e-01
-9.62179061e-03 2.09638864e-01 -9.96351466e-02 -5.67471087... | [14.807310104370117, 6.600111484527588] |
45b93104-f275-46be-91f2-a973735b9f8a | 3d-hierarchical-refinement-and-augmentation | 2112.03045 | null | https://arxiv.org/abs/2112.03045v2 | https://arxiv.org/pdf/2112.03045v2.pdf | 3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose from Monocular Video | Depth and ego-motion estimations are essential for the localization and navigation of autonomous robots and autonomous driving. Recent studies make it possible to learn the per-pixel depth and ego-motion from the unlabeled monocular video. A novel unsupervised training framework is proposed with 3D hierarchical refinem... | ['Hesheng Wang', 'Zhe Liu', 'Wenhua Wu', 'Shijie Zhao', 'Jiquan Zhong', 'Guangming Wang'] | 2021-12-06 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 1.21100329e-01 1.65828526e-01 -2.97689825e-01 -4.71119523e-01
-3.83408606e-01 -3.39699686e-01 4.88373190e-01 -5.98664105e-01
-6.01505637e-01 7.71519840e-01 -8.96414518e-02 2.98004821e-02
3.07983696e-01 -7.78433740e-01 -8.85083854e-01 -9.39701617e-01
4.64303911e-01 6.51202857e-01 3.49732161e-01 -1.09412819... | [8.09899616241455, -2.2465481758117676] |
4bf225cc-c5ec-490b-b8bc-a00620f0fc0c | networks-of-piecewise-linear-neural-mass | 1801.08366 | null | http://arxiv.org/abs/1801.08366v1 | http://arxiv.org/pdf/1801.08366v1.pdf | Networks of piecewise linear neural mass models | Neural mass models are ubiquitous in large scale brain modelling. At the node
level they are written in terms of a set of ODEs with a nonlinearity that is
typically a sigmoidal shape. Using structural data from brain atlases they may
be connected into a network to investigate the emergence of functional dynamic
states,... | [] | 2018-01-25 | null | null | null | null | ['caricature'] | ['computer-vision'] | [-3.35130584e-03 3.74150783e-01 3.79721940e-01 4.07094002e-01
4.02916908e-01 -5.59729397e-01 8.52776825e-01 -1.17465835e-02
-2.97595173e-01 5.28859198e-01 -1.18074469e-01 -1.79527193e-01
-4.17890608e-01 -4.47840333e-01 -7.93332279e-01 -1.37597311e+00
-7.28080809e-01 3.10208917e-01 3.90108645e-01 -7.71074414... | [6.261447906494141, 4.097131252288818] |
22dab665-b10d-4db0-814d-27e3686598d7 | using-kalman-filter-the-right-way-noise | 2104.02372 | null | https://arxiv.org/abs/2104.02372v4 | https://arxiv.org/pdf/2104.02372v4.pdf | The Fragility of Noise Estimation in Kalman Filter: Optimization Can Handle Model-Misspecification | The Kalman Filter (KF) parameters are traditionally determined by noise estimation, since under the KF assumptions, the state prediction errors are minimized when the parameters correspond to the noise covariance. However, noise estimation remains the gold-standard regardless of the assumptions - even when it is not eq... | ['Netanel Yannay', 'Shie Mannor', 'Ido Greenberg'] | 2021-04-06 | null | null | null | null | ['noise-estimation'] | ['medical'] | [-1.03020512e-01 -1.05526365e-01 6.54120147e-02 -2.24289268e-01
-9.56674755e-01 -6.77860320e-01 6.42819345e-01 -4.95984644e-01
-5.91147602e-01 9.16456223e-01 2.91534096e-01 -5.31066895e-01
-1.89449698e-01 -2.64375687e-01 -8.48025978e-01 -8.49315286e-01
7.49449879e-02 1.68381661e-01 2.82631852e-02 -8.02853424... | [6.6999101638793945, 3.7128689289093018] |
fe9b2153-3b8f-48b9-a33f-05a39b34a6de | person-search-new-paradigm-of-person-re | null | null | https://www.sciencedirect.com/science/article/pii/S0262885620301025 | https://reader.elsevier.com/reader/sd/pii/S0262885620301025?token=F42274C35788A55FD4B3B3605EAC08CE31ADEA7600758EF9217B69A4EBFAF97C56F5B0BEDEB837CCF0AEDC4C4D2DB249 | Person search: New paradigm of person re-identification: A survey and outlook of recent works | Person Search (PS) has become a major field because of its need in community and in the field of research among researchers. This task aims to find a probe person from whole scene which shows great significance in video surveillance field to track lost people, re-identification, and verification of person. In last few ... | ['Khawar Islam'] | 2020-06-30 | null | null | null | image-and-vision-computing-2020-6 | ['person-search'] | ['computer-vision'] | [-1.31304294e-01 -7.51780987e-01 1.76222101e-01 -5.71914136e-01
-5.35801589e-01 -5.42134941e-01 5.15072942e-01 6.33560307e-03
-5.68986356e-01 6.72980011e-01 2.98444688e-01 3.78552645e-01
-1.89468399e-01 -6.48324907e-01 -2.21881539e-01 -5.73517263e-01
-6.35121465e-02 3.37931246e-01 1.43931076e-01 -1.45894140... | [14.706201553344727, 0.9139581322669983] |
8b72549e-230e-4fa9-af46-54a346ece7cb | control-theoretically-explainable-application | 2208.01291 | null | https://arxiv.org/abs/2208.01291v2 | https://arxiv.org/pdf/2208.01291v2.pdf | Control theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems | This paper is dedicated to control theoretically explainable application of autoencoders to optimal fault detection in nonlinear dynamic systems. Autoencoder-based learning is a standard machine learning method and widely applied for fault (anomaly) detection and classification. In the context of representation learnin... | ['Ting Xue', 'Zhiwen Chen', 'Ketian Liang', 'Steven X. Ding', 'Linlin Li'] | 2022-08-02 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-1.08471839e-03 1.35146916e-01 1.64809406e-01 2.48097658e-01
1.87018245e-01 8.27698410e-02 1.78524926e-01 -7.62550160e-02
2.43776113e-01 4.55434948e-01 -2.86411881e-01 -1.84516534e-01
-7.97986507e-01 -6.73127592e-01 -7.54833937e-01 -9.77534354e-01
-1.48845434e-01 3.05534333e-01 -3.37892383e-01 -3.17167222... | [6.709981918334961, 2.4166641235351562] |
0970fd9b-9394-4ca1-a8d7-a8f4940f70ca | hitea-hierarchical-temporal-aware-video | 2212.14546 | null | https://arxiv.org/abs/2212.14546v1 | https://arxiv.org/pdf/2212.14546v1.pdf | HiTeA: Hierarchical Temporal-Aware Video-Language Pre-training | Video-language pre-training has advanced the performance of various downstream video-language tasks. However, most previous methods directly inherit or adapt typical image-language pre-training paradigms to video-language pre-training, thus not fully exploiting the unique characteristic of video, i.e., temporal. In thi... | ['Fei Huang', 'Ji Zhang', 'Qi Qian', 'Haiyang Xu', 'Ming Yan', 'Guohai Xu', 'Qinghao Ye'] | 2022-12-30 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [ 1.48971871e-01 -5.08110344e-01 -2.20544651e-01 -3.45296919e-01
-8.65452051e-01 -5.33885181e-01 9.13610220e-01 -1.47238851e-01
-4.36053962e-01 3.97097439e-01 3.93674344e-01 -2.66469359e-01
-5.98962605e-02 -4.67298537e-01 -8.76728892e-01 -4.74385887e-01
-2.73798078e-01 1.77654698e-01 2.91773319e-01 -3.07521850... | [10.19348430633545, 0.8932616114616394] |
cf826164-face-475e-8e01-df0d1892b3e7 | alphadda-game-artificial-intelligence-with | 2111.06266 | null | https://arxiv.org/abs/2111.06266v4 | https://arxiv.org/pdf/2111.06266v4.pdf | AlphaDDA: Strategies for Adjusting the Playing Strength of a Fully Trained AlphaZero System to a Suitable Human Training Partner | Artificial intelligence (AI) has achieved superhuman performance in board games such as Go, chess, and Othello (Reversi). In other words, the AI system surpasses the level of a strong human expert player in such games. In this context, it is difficult for a human player to enjoy playing the games with the AI. To keep h... | ['Kazuhisa Fujita'] | 2021-11-11 | null | null | null | null | ['board-games'] | ['playing-games'] | [-4.94436681e-01 2.53459722e-01 4.95773405e-02 3.08845013e-01
-7.48819336e-02 -8.01916718e-01 3.15325037e-02 -2.81969339e-01
-9.15364563e-01 7.84309566e-01 -6.27857327e-01 -2.00610861e-01
-3.56484830e-01 -1.16606474e+00 -4.55887705e-01 -9.02209342e-01
3.55901830e-02 1.14815640e+00 6.11627221e-01 -6.59225762... | [3.5193121433258057, 1.5423507690429688] |
f11220e4-b97c-41ab-b132-ddbbcf2c10e9 | privacy-preserving-data-filtering-in | 2205.11518 | null | https://arxiv.org/abs/2205.11518v2 | https://arxiv.org/pdf/2205.11518v2.pdf | A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning | Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present a novel technique for filtering, and scoring data based on a pra... | ['Boi Faltings', 'Panayiotis Danassis', 'Ljubomir Rokvic'] | 2022-05-23 | null | null | null | null | ['influence-approximation'] | ['methodology'] | [-3.76367345e-02 1.12472594e-01 -1.39974281e-01 -4.00860697e-01
-9.90958989e-01 -1.04835665e+00 2.43059576e-01 4.55451578e-01
-7.13139474e-01 1.07283115e+00 -3.85423563e-02 -2.27427647e-01
-1.32778749e-01 -8.49349916e-01 -1.05458021e+00 -8.80369127e-01
-5.11340320e-01 2.11136654e-01 8.53447467e-02 9.80062708... | [5.875688076019287, 6.675232887268066] |
fb49cee3-0e77-4020-bd2d-1d92991a3dd3 | towards-expert-level-medical-question | 2305.09617 | null | https://arxiv.org/abs/2305.09617v1 | https://arxiv.org/pdf/2305.09617v1.pdf | Towards Expert-Level Medical Question Answering with Large Language Models | Recent artificial intelligence (AI) systems have reached milestones in "grand challenges" ranging from Go to protein-folding. The capability to retrieve medical knowledge, reason over it, and answer medical questions comparably to physicians has long been viewed as one such grand challenge. Large language models (LLMs)... | ['Vivek Natarajan', 'Alan Karthikesalingam', 'Shekoofeh Azizi', 'Yossi Matias', 'Greg S. Corrado', 'Dale Webster', 'Joelle Barral', 'S. Sara Mahdavi', 'Christopher Semturs', 'Renee Wong', 'Yun Liu', 'Nenad Tomasev', 'Blaise Aguera y Arcas', 'Ewa Dominowska', 'Bradley Green', 'Sushant Prakash', 'Philip Mansfield', 'Sami... | 2023-05-16 | null | null | null | null | ['multiple-choice-qa', 'protein-folding'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.20274618e-01 3.22129905e-01 -1.31494954e-01 -4.09255028e-01
-1.72893524e+00 -7.86459088e-01 3.31772596e-01 6.21690452e-01
-6.17279112e-01 6.54787242e-01 4.66680944e-01 -8.33867908e-01
-7.04054534e-01 -4.78467762e-01 -5.26004374e-01 -1.79485474e-02
2.69600004e-01 1.02510297e+00 -2.57479587e-05 -5.15022933... | [8.78553295135498, 8.549151420593262] |
9ac23656-43f4-41a9-be9e-abc2a1249f96 | stpls3d-a-large-scale-synthetic-and-real | 2203.09065 | null | https://arxiv.org/abs/2203.09065v3 | https://arxiv.org/pdf/2203.09065v3.pdf | STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset | Although various 3D datasets with different functions and scales have been proposed recently, it remains challenging for individuals to complete the whole pipeline of large-scale data collection, sanitization, and annotation. Moreover, the created datasets usually suffer from extremely imbalanced class distribution or ... | ['Qingyong Hu', 'Fengbo Ren', 'Kyle McCullough', 'Hugues Thomas', 'Zifan Yu', 'Lucio Soibelman', 'Yu Hou', 'Andrew Feng', 'Meida Chen'] | 2022-03-17 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 2.45318115e-02 -5.29808328e-02 5.91245651e-01 -3.03467065e-01
-4.13076103e-01 -7.99434364e-01 5.17947018e-01 2.16652945e-01
1.26622079e-04 7.81918049e-01 -3.92193258e-01 -2.19106749e-02
-2.06252009e-01 -1.60827971e+00 -7.70197272e-01 -3.72729778e-01
-9.60119814e-02 7.66169310e-01 3.44848722e-01 -4.10584182... | [8.411576271057129, -2.6018614768981934] |
3ccc7918-4eff-4f7d-9722-363d5a725fa8 | user-modeling-for-task-oriented-dialogues | 1811.04369 | null | http://arxiv.org/abs/1811.04369v1 | http://arxiv.org/pdf/1811.04369v1.pdf | User Modeling for Task Oriented Dialogues | We introduce end-to-end neural network based models for simulating users of
task-oriented dialogue systems. User simulation in dialogue systems is crucial
from two different perspectives: (i) automatic evaluation of different dialogue
models, and (ii) training task-oriented dialogue systems. We design a
hierarchical se... | ['Dilek Hakkani-Tur', 'Izzeddin Gur', 'Pararth Shah', 'Gokhan Tur'] | 2018-11-11 | null | null | null | null | ['user-simulation'] | ['natural-language-processing'] | [ 2.77445585e-01 5.34127474e-01 2.14959294e-01 -4.51176882e-01
-5.91576934e-01 -7.18671560e-01 8.23920131e-01 -2.64054894e-01
-5.02272606e-01 7.98296213e-01 4.93029028e-01 -3.75129193e-01
2.52303302e-01 -5.81846297e-01 -1.65296152e-01 -3.97562504e-01
1.35250255e-01 9.00574267e-01 1.38173968e-01 -1.00023508... | [12.949019432067871, 8.010336875915527] |
4442c759-a521-4925-9bee-2be8ac753702 | automatic-image-cropping-a-computational | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Chen_Automatic_Image_Cropping_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Chen_Automatic_Image_Cropping_CVPR_2016_paper.pdf | Automatic Image Cropping : A Computational Complexity Study | Attention based automatic image cropping aims at preserving the most visually important region in an image. A common task in this kind of method is to search for the smallest rectangle inside which the summed attention is maximized. We demonstrate that under appropriate formulations, this task can be achieved using eff... | ['Gaocheng Bai', 'Jiansheng Chen', 'Zhengqin Li', 'Shaoheng Liang'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['image-cropping'] | ['computer-vision'] | [ 6.83045924e-01 4.13592905e-01 2.55084932e-01 2.64941335e-01
-6.67398930e-01 -5.01964748e-01 1.52274936e-01 2.37504318e-01
-6.64520502e-01 5.18301606e-01 -2.83888876e-01 -1.48114905e-01
-3.98358069e-02 -7.25389123e-01 -8.37476194e-01 -7.88876891e-01
3.42171490e-01 5.75993396e-02 7.53101557e-02 -2.76725501... | [11.002233505249023, -1.0600452423095703] |
36e59bad-3d78-4fdd-bf52-572cfc735ae7 | shape-from-projections-via-differentiable | 2006.16120 | null | https://arxiv.org/abs/2006.16120v4 | https://arxiv.org/pdf/2006.16120v4.pdf | Shape from Projections via Differentiable Forward Projector for Computed Tomography | In computed tomography, the reconstruction is typically obtained on a voxel grid. In this work, however, we propose a mesh-based reconstruction method. For tomographic problems, 3D meshes have mostly been studied to simulate data acquisition, but not for reconstruction, for which a 3D mesh means the inverse process of ... | ['Sara Bals', 'Ja-Keoung Koo', 'J. Andreas Bærentzen', 'Anders B. Dahl', 'Vedrana A. Dahl', 'Qiongyang Chen'] | 2020-06-29 | null | null | null | null | ['electron-tomography'] | ['medical'] | [ 5.60437083e-01 6.37657717e-02 5.84565461e-01 -2.47079462e-01
-5.90382755e-01 -4.82929274e-02 5.53228498e-01 -2.02937320e-01
-2.82436013e-01 6.65184975e-01 -1.01589836e-01 -4.39029843e-01
4.86517698e-02 -1.19227004e+00 -8.71067286e-01 -6.49353147e-01
2.68335491e-01 8.67978454e-01 2.61272281e-01 1.85168535... | [9.402567863464355, -3.136657238006592] |
9973f84d-5e0f-44d3-8cee-fdf201dd1679 | querydet-cascaded-sparse-query-for | 2103.09136 | null | https://arxiv.org/abs/2103.09136v2 | https://arxiv.org/pdf/2103.09136v2.pdf | QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection | While general object detection with deep learning has achieved great success in the past few years, the performance and efficiency of detecting small objects are far from satisfactory. The most common and effective way to promote small object detection is to use high-resolution images or feature maps. However, both app... | ['Naiyan Wang', 'Zehao Huang', 'Chenhongyi Yang'] | 2021-03-16 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_QueryDet_Cascaded_Sparse_Query_for_Accelerating_High-Resolution_Small_Object_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_QueryDet_Cascaded_Sparse_Query_for_Accelerating_High-Resolution_Small_Object_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['small-object-detection'] | ['computer-vision'] | [-2.29102582e-01 -5.13175189e-01 -7.39657432e-02 -1.61438867e-01
-8.93705308e-01 -1.54256836e-01 4.84115392e-01 9.40657184e-02
-5.82209349e-01 2.85794258e-01 -1.88742414e-01 1.23540573e-01
1.18013337e-01 -1.21734869e+00 -7.34785914e-01 -7.02867150e-01
3.10533307e-02 2.63441503e-01 1.13537371e+00 -3.60513441... | [8.743677139282227, -0.5059682130813599] |
d87570c8-7126-467b-9be9-8a40aa711b47 | cotype-joint-extraction-of-typed-entities-and | 1610.08763 | null | http://arxiv.org/abs/1610.08763v2 | http://arxiv.org/pdf/1610.08763v2.pdf | CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases | Extracting entities and relations for types of interest from text is
important for understanding massive text corpora. Traditionally, systems of
entity relation extraction have relied on human-annotated corpora for training
and adopted an incremental pipeline. Such systems require additional human
expertise to be porte... | ['Xiang Ren', 'Zeqiu Wu', 'Meng Qu', 'Heng Ji', 'Jiawei Han', 'Clare R. Voss', 'Wenqi He', 'Tarek F. Abdelzaher'] | 2016-10-27 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-6.41227588e-02 5.83794057e-01 -4.85802621e-01 -5.80912709e-01
-8.18375766e-01 -8.90206993e-01 4.54334944e-01 7.20285594e-01
-6.37879014e-01 8.73945534e-01 1.35087714e-01 -2.58567184e-01
-1.03905633e-01 -9.34759676e-01 -8.52062225e-01 -3.96091640e-01
6.37274161e-02 1.01794302e+00 1.45477816e-01 4.05002758... | [9.456271171569824, 8.765880584716797] |
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