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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
b500474a-e5d4-4b24-923b-dfad1455538e | ravitt-random-vision-transformer-tokens | 2306.10959 | null | https://arxiv.org/abs/2306.10959v1 | https://arxiv.org/pdf/2306.10959v1.pdf | RaViTT: Random Vision Transformer Tokens | Vision Transformers (ViTs) have successfully been applied to image classification problems where large annotated datasets are available. On the other hand, when fewer annotations are available, such as in biomedical applications, image augmentation techniques like introducing image variations or combinations have been ... | ['Mauricio Cerda', 'Cristóbal A. Navarro', 'Violeta Chang', 'Jorge Jara-Wilde', 'Manuel Zamorano', 'Cristian Muñoz', 'Carlos F. Navarro', 'Felipe A. Quezada'] | 2023-06-19 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 4.47002411e-01 3.30760151e-01 -1.57518283e-01 -6.48869872e-02
-7.21300364e-01 -2.79925108e-01 8.43631446e-01 2.06328779e-01
-7.73935974e-01 8.45569253e-01 -8.22378024e-02 -2.53953397e-01
3.09323698e-01 -5.06383181e-01 -7.30656385e-01 -6.49694204e-01
2.80878991e-01 5.59866250e-01 4.96250778e-01 -1.49328202... | [9.561477661132812, 1.3850442171096802] |
27f05b2f-068a-436c-a559-319089b18d76 | from-learning-to-match-to-learning-to | null | null | https://aclanthology.org/2021.ccl-1.90 | https://aclanthology.org/2021.ccl-1.90.pdf | From Learning-to-Match to Learning-to-Discriminate:Global Prototype Learning for Few-shot Relation Classification | “Few-shot relation classification has attracted great attention recently and is regarded as an ef-fective way to tackle the long-tail problem in relation classification. Most previous works onfew-shot relation classification are based on learning-to-match paradigms which focus on learn-ing an effective universal matche... | ['Sun Le', 'Wu Hua', 'Dai Dai', 'Han Xianpei', 'Lin Hongyu', 'Yan Lingyong', 'Xiao Xinyan', 'Liu Fangchao'] | null | null | null | null | ccl-2021-8 | ['few-shot-relation-classification', 'relation-classification', 'few-shot-relation-classification'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 4.83944863e-02 -3.22630033e-02 -9.56485808e-01 -5.48207581e-01
-9.99195337e-01 -1.35769740e-01 6.69724405e-01 4.82516736e-01
-3.27284753e-01 7.09575057e-01 -1.78847358e-01 -6.76746964e-02
-4.00179684e-01 -9.69073355e-01 -4.36163694e-01 -6.21320069e-01
-4.00732458e-02 1.06566203e+00 6.73354387e-01 -4.99197870... | [9.151753425598145, 8.528800010681152] |
d6636eeb-cb9f-4376-8a90-b865b2dbbe51 | knowledge-graph-augmented-language-models-for | 2305.18846 | null | https://arxiv.org/abs/2305.18846v1 | https://arxiv.org/pdf/2305.18846v1.pdf | Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation | Language models have achieved impressive performances on dialogue generation tasks. However, when generating responses for a conversation that requires factual knowledge, they are far from perfect, due to an absence of mechanisms to retrieve, encode, and reflect the knowledge in the generated responses. Some knowledge-... | ['Sung Ju Hwang', 'Jinheon Baek', 'Jin Myung Kwak', 'Minki Kang'] | 2023-05-30 | null | null | null | null | ['knowledge-graphs', 'word-embeddings', 'dialogue-generation', 'dialogue-generation'] | ['knowledge-base', 'methodology', 'natural-language-processing', 'speech'] | [ 2.86281910e-02 8.55345428e-01 -1.77828774e-01 -1.36054888e-01
-9.61375237e-01 -6.30551279e-01 9.63838816e-01 2.66193926e-01
4.64846678e-02 1.08938742e+00 1.05068648e+00 -1.11918956e-01
-8.32564314e-04 -1.21206117e+00 -7.41707742e-01 -2.64465451e-01
3.47676009e-01 6.70917869e-01 1.74892366e-01 -7.59118021... | [12.318758964538574, 8.17639446258545] |
867f88a7-70e6-456c-91ea-66d922c94a52 | krnet-image-denoising-with-kernel-regulation | 1910.08867 | null | https://arxiv.org/abs/1910.08867v1 | https://arxiv.org/pdf/1910.08867v1.pdf | KRNET: Image Denoising with Kernel Regulation Network | One popular strategy for image denoising is to design a generalized regularization term that is capable of exploring the implicit prior underlying data observation. Convolutional neural networks (CNN) have shown the powerful capability to learn image prior information through a stack of layers defined by a combination ... | ['Ruogu Fang', 'Xiaoxiao Zhou', 'Peng Liu', 'Junyiyang Li', 'El Basha Mohammad D'] | 2019-10-20 | null | null | null | null | ['color-image-denoising'] | ['computer-vision'] | [ 1.97297260e-01 -4.66533989e-01 3.41256380e-01 -4.12878126e-01
-7.28391767e-01 3.28298360e-02 2.39774287e-01 -3.53549302e-01
-4.72411782e-01 4.42152917e-01 1.70393601e-01 -4.59332839e-02
-4.58853096e-02 -7.75804043e-01 -7.79428244e-01 -9.43587303e-01
5.47320619e-02 -6.65147185e-01 2.07407679e-02 -2.88714767... | [11.413073539733887, -2.3405513763427734] |
dafa5977-77d9-4611-8c05-0dbb698c34ab | optimal-multi-view-correction-of-local-affine | 1905.00519 | null | http://arxiv.org/abs/1905.00519v1 | http://arxiv.org/pdf/1905.00519v1.pdf | Optimal Multi-view Correction of Local Affine Frames | The technique requires the epipolar geometry to be pre-estimated between each
image pair. It exploits the constraints which the camera movement implies, in
order to apply a closed-form correction to the parameters of the input
affinities. Also, it is shown that the rotations and scales obtained by
partially affine-cova... | ['Ivan Eichhardt', 'Daniel Barath'] | 2019-05-01 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 8.43911767e-02 1.70409345e-04 9.03965756e-02 -1.81513175e-01
-4.50963646e-01 -8.49584758e-01 8.17492247e-01 -7.85975978e-02
-6.29123569e-01 3.00135404e-01 -2.41507784e-01 1.90233439e-01
-8.46031830e-02 -4.39451605e-01 -8.86971235e-01 -5.01314163e-01
1.87157795e-01 5.45638442e-01 4.46417928e-01 -1.25821918... | [7.94128942489624, -2.316100835800171] |
e11194ac-0704-48a0-a52e-528e48963b80 | calibrating-cross-modal-feature-for-text | 2304.02278 | null | https://arxiv.org/abs/2304.02278v2 | https://arxiv.org/pdf/2304.02278v2.pdf | Calibrating Cross-modal Features for Text-Based Person Searching | Text-Based Person Searching (TBPS) aims to identify the images of pedestrian targets from a large-scale gallery with given textual caption. For cross-modal TBPS task, it is critical to obtain well-distributed representation in the common embedding space to reduce the inter-modal gap. Furthermore, it is also essential t... | ['Jing Liu', 'Yang Liu', 'Tong Yang', 'Sipeng Zhang', 'Donglai Wei'] | 2023-04-05 | null | null | null | null | ['person-search'] | ['computer-vision'] | [ 4.97500449e-02 -3.16788673e-01 -2.97041804e-01 -5.02020478e-01
-1.43038404e+00 -5.84107041e-01 7.51130641e-01 -3.07258099e-01
-4.79652375e-01 6.85585260e-01 4.00029302e-01 -8.22105817e-03
1.30385473e-01 -6.00948691e-01 -9.36413586e-01 -6.12364054e-01
2.92942643e-01 4.53303546e-01 3.80136907e-01 -2.24267263... | [14.631145477294922, 0.8378008604049683] |
f705f90a-6b48-4776-ab6a-ab2adbdf2323 | towards-generalized-and-explainable-long | 2210.06282 | null | https://arxiv.org/abs/2210.06282v2 | https://arxiv.org/pdf/2210.06282v2.pdf | Towards Generalized and Explainable Long-Range Context Representation for Dialogue Systems | Long-range context modeling is crucial to both dialogue understanding and generation. The most popular method for dialogue context representation is to concatenate the last-$k$ previous utterances. However, this method may not be ideal for conversations containing long-range dependencies. In this work, we propose Dialo... | ['P. K. Srijith', 'Maunendra Sankar Desarkar', 'Suvodip Dey'] | 2022-10-12 | null | null | null | null | ['dialogue-understanding', 'conversational-response-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.20299301e-02 6.54994607e-01 9.48575735e-02 -8.01617503e-01
-6.54552460e-01 -4.94802296e-01 1.07417500e+00 1.80900633e-01
-2.42538005e-01 1.17727900e+00 1.09841430e+00 -3.95476371e-01
1.40351132e-01 -6.32800400e-01 -2.58922756e-01 -1.22883894e-01
1.74549773e-01 7.52600849e-01 2.96175480e-02 -1.07781363... | [12.697735786437988, 8.060471534729004] |
eb881a06-76ff-4b9d-b7fa-a214dc6c3ee9 | dynamic-deep-multi-task-learning-for | 1911.03341 | null | https://arxiv.org/abs/1911.03341v1 | https://arxiv.org/pdf/1911.03341v1.pdf | Dynamic Deep Multi-task Learning for Caricature-Visual Face Recognition | Rather than the visual images, the face recognition of the caricatures is far from the performance of the visual images. The challenge is the extreme non-rigid distortions of the caricatures introduced by exaggerating the facial features to strengthen the characters. In this paper, we propose dynamic multi-task learnin... | ['Jean-Christophe Burie', 'Zuheng Ming', 'Muhammad Muzzamil Luqman'] | 2019-11-08 | null | null | null | null | ['caricature'] | ['computer-vision'] | [-9.06342417e-02 -2.02703789e-01 2.13620260e-01 -3.78596246e-01
-3.37116003e-01 -4.25360709e-01 6.45635545e-01 -7.35273242e-01
-4.33072686e-01 3.61313373e-01 3.01908469e-04 1.37114912e-01
-2.71738410e-01 -1.77509204e-01 -8.28520477e-01 -1.05070698e+00
2.47878522e-01 5.84463954e-01 1.19896960e-02 -3.15149501... | [13.368551254272461, 0.6845967769622803] |
736b602e-9e38-4e42-8ea1-10d72114258a | forest-an-interactive-multi-tree-synthesizer | 2012.14235 | null | https://arxiv.org/abs/2012.14235v1 | https://arxiv.org/pdf/2012.14235v1.pdf | FOREST: An Interactive Multi-tree Synthesizer for Regular Expressions | Form validators based on regular expressions are often used on digital forms to prevent users from inserting data in the wrong format. However, writing these validators can pose a challenge to some users. We present FOREST, a regular expression synthesizer for digital form validations. FOREST produces a regular express... | ['Ruben Martins', 'Inês Lynce', 'Miguel Ventura', 'Miguel Terra-Neves', 'Margarida Ferreira'] | 2020-12-28 | null | null | null | null | ['enumerative-search'] | ['computer-code'] | [ 7.53493607e-01 2.75215089e-01 -4.86533731e-01 -4.60821301e-01
-8.31848800e-01 -9.09004211e-01 -1.09698482e-01 2.07166508e-01
2.21645102e-01 9.70934272e-01 -4.27027881e-01 -9.57271934e-01
4.89262119e-02 -1.20544744e+00 -8.78966093e-01 1.93832830e-01
9.22998935e-02 5.26565373e-01 2.29144067e-01 -1.55833825... | [8.239447593688965, 7.2476348876953125] |
672205a4-4483-4828-a377-8edd09104605 | uszeged-correction-type-sensitive | null | null | https://aclanthology.org/W15-4318 | https://aclanthology.org/W15-4318.pdf | USZEGED: Correction Type-sensitive Normalization of English Tweets Using Efficiently Indexed n-gram Statistics | null | ["Ervin Tasn{\\'a}di", "G{\\'a}bor Berend"] | 2015-07-01 | null | null | null | ws-2015-7 | ['lexical-normalization'] | ['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... | [-7.311792850494385, 3.6623711585998535] |
8c92d80d-c55a-4c5c-a3d0-0cb6e33faecb | reading-between-the-lanes-text-videoqa-on-the | 2307.03948 | null | https://arxiv.org/abs/2307.03948v1 | https://arxiv.org/pdf/2307.03948v1.pdf | Reading Between the Lanes: Text VideoQA on the Road | Text and signs around roads provide crucial information for drivers, vital for safe navigation and situational awareness. Scene text recognition in motion is a challenging problem, while textual cues typically appear for a short time span, and early detection at a distance is necessary. Systems that exploit such inform... | ['C. V. Jawahar', 'Dimosthenis Karatzas', 'Sergi Garcia', 'Minesh Mathew', 'George Tom'] | 2023-07-08 | null | null | null | null | ['scene-text-recognition', 'video-question-answering', 'question-answering'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 9.15177763e-02 -2.09857374e-01 -4.76324052e-01 -7.20090628e-01
-1.08321083e+00 -7.11786151e-01 7.11982667e-01 1.42853469e-01
-6.43069685e-01 3.24882716e-01 5.16921937e-01 -6.88635468e-01
-6.42267242e-02 -5.39182782e-01 -7.48241484e-01 -4.12042588e-01
2.16853634e-01 5.05781136e-02 5.41289985e-01 -6.09028697... | [7.631689071655273, -0.2462712526321411] |
52386d72-359f-4149-b0ef-188bdc059181 | evars-gpr-event-triggered-augmented-refitting | 2107.02463 | null | https://arxiv.org/abs/2107.02463v1 | https://arxiv.org/pdf/2107.02463v1.pdf | EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data | Time series forecasting is a growing domain with diverse applications. However, changes of the system behavior over time due to internal or external influences are challenging. Therefore, predictions of a previously learned fore-casting model might not be useful anymore. In this paper, we present EVent-triggered Augmen... | ['Dominik G. Grimm', 'Florian Haselbeck'] | 2021-07-06 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 9.54546481e-02 -5.35504341e-01 3.93030763e-01 -2.14799434e-01
-7.44451046e-01 -6.82952106e-01 6.47351503e-01 2.43839562e-01
5.55801243e-02 5.03914654e-01 -3.70990396e-01 -5.21948457e-01
-1.57632336e-01 -7.16669977e-01 -8.28646719e-01 -9.39265430e-01
-3.30271453e-01 4.93826598e-01 3.66111130e-01 -2.12327272... | [6.96909761428833, 3.228712797164917] |
3a94f1f7-885d-42ea-ac1b-50624c24e34f | task-driven-dynamic-fusion-reducing-ambiguity | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Zhang_Task-Driven_Dynamic_Fusion_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Task-Driven_Dynamic_Fusion_CVPR_2017_paper.pdf | Task-Driven Dynamic Fusion: Reducing Ambiguity in Video Description | Integrating complementary features from multiple channels is expected to solve the description ambiguity problem in video captioning, whereas inappropriate fusion strategies often harm rather than help the performance. Existing static fusion methods in video captioning such as concatenation and summation cannot attend ... | ['Xishan Zhang', 'Qi Tian', 'Yongdong Zhang', 'Ke Gao', 'Dongming Zhang', 'Jintao Li'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['video-description'] | ['computer-vision'] | [ 4.17998165e-01 -4.18454379e-01 -2.11998463e-01 -2.96239048e-01
-1.19852281e+00 -5.39377332e-01 8.47525954e-01 -1.41172528e-01
-3.57014120e-01 8.44224513e-01 5.73756278e-01 -1.62687507e-02
7.08322674e-02 -4.98632528e-02 -9.22811508e-01 -6.53515816e-01
-1.16826557e-01 4.31861252e-01 4.04078156e-01 -2.39187509... | [10.556990623474121, 0.8146122097969055] |
0110b1cd-02cb-4fb8-9698-36c4abb32da6 | physics-informed-transfer-learning-strategy | 2206.06817 | null | https://arxiv.org/abs/2206.06817v1 | https://arxiv.org/pdf/2206.06817v1.pdf | Physics-Informed Transfer Learning Strategy to Accelerate Unsteady Fluid Flow Simulations | Since the derivation of the Navier Stokes equations, it has become possible to numerically solve real world viscous flow problems (computational fluid dynamics (CFD)). However, despite the rapid advancements in the performance of central processing units (CPUs), the computational cost of simulating transient flows with... | ['Sung Joong Kim', 'Ricardo Vinuesa', 'Hamidreza Eivazi', 'Juhyeong Lee', 'Joongoo Jeon'] | 2022-06-14 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-3.10780317e-01 -5.87536097e-01 4.51257050e-01 9.24132988e-02
-1.75611123e-01 -3.65789533e-01 5.01199067e-01 1.27381980e-01
-3.43236685e-01 1.04069340e+00 -3.69051248e-01 -7.58462548e-01
-4.25867081e-01 -8.82691324e-01 -5.17063677e-01 -7.84928918e-01
-5.88431180e-01 2.56527513e-01 8.14653859e-02 -2.29083583... | [6.374551296234131, 3.2897212505340576] |
62fcc439-1ae8-4235-aea7-107be68b152d | crowdsourcing-the-perception-of-machine | 2002.01618 | null | https://arxiv.org/abs/2002.01618v1 | https://arxiv.org/pdf/2002.01618v1.pdf | Crowdsourcing the Perception of Machine Teaching | Teachable interfaces can empower end-users to attune machine learning systems to their idiosyncratic characteristics and environment by explicitly providing pertinent training examples. While facilitating control, their effectiveness can be hindered by the lack of expertise or misconceptions. We investigate how users m... | ['Jonggi Hong', 'Hernisa Kacorri', 'Kyungjun Lee', 'June Xu'] | 2020-02-05 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [-1.99816585e-01 5.28535657e-02 -9.72026363e-02 -6.80459678e-01
-4.35053468e-01 -1.29559469e+00 2.09938452e-01 2.28161216e-01
-6.07662082e-01 2.80503839e-01 -1.88384071e-01 -7.88010895e-01
-1.18827187e-01 -2.83489674e-01 -6.33908272e-01 -7.08873272e-02
4.66449499e-01 4.16971356e-01 -1.32705942e-01 1.38390139... | [8.81242847442627, 7.348394870758057] |
f065162f-10ef-4d9d-b328-ec29524586b6 | polyretro-few-shot-polymer-retrosynthesis-via | null | null | https://openreview.net/forum?id=JHx9ZDCQEA | https://openreview.net/pdf?id=JHx9ZDCQEA | PolyRetro: Few-shot Polymer Retrosynthesis via Domain Adaptation | Polymers appear everywhere in our daily lives -- fabrics, plastics, rubbers, etc. -- and we could hardly live without them. To make polymers, chemists develop processes that combine smaller building blocks~(monomers) to form long chains or complex networks~(polymers). These processes are called polymerizations and wil... | ['Le Song', 'Rampi Ramprasad', 'Hanjun Dai', 'Chengtao Li', 'Binghong Chen'] | 2021-01-01 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 6.91556156e-01 3.06066424e-01 -4.35972065e-01 -3.21303532e-02
-3.17023903e-01 -1.02884257e+00 6.90997362e-01 3.55914474e-01
2.61019878e-02 8.77665043e-01 -6.20638207e-02 -5.68005979e-01
2.12017596e-01 -1.07270825e+00 -8.20420861e-01 -8.89606953e-01
1.88545302e-01 8.69181037e-01 4.50400412e-01 -2.17206389... | [4.516083240509033, 6.094794273376465] |
da6210df-4c78-4df5-8d3f-45194db421c6 | hierarchical-decision-transformer | 2209.10447 | null | https://arxiv.org/abs/2209.10447v1 | https://arxiv.org/pdf/2209.10447v1.pdf | Hierarchical Decision Transformer | Sequence models in reinforcement learning require task knowledge to estimate the task policy. This paper presents a hierarchical algorithm for learning a sequence model from demonstrations. The high-level mechanism guides the low-level controller through the task by selecting sub-goals for the latter to reach. This seq... | ['Luís A. Alexandre', 'André Correia'] | 2022-09-21 | null | null | null | null | ['d4rl'] | ['robots'] | [ 1.03300894e-02 3.42186540e-02 -4.58232582e-01 -1.32826477e-01
-7.15017378e-01 -6.71442091e-01 6.99151218e-01 -5.16859181e-02
-8.09877217e-01 1.36139858e+00 1.42969668e-01 -2.42818117e-01
-2.40954116e-01 -1.81696013e-01 -7.54624128e-01 -5.05336463e-01
-4.26269293e-01 5.42109311e-01 5.30870497e-01 -4.48723674... | [4.2647857666015625, 1.3477767705917358] |
cbf6d306-1443-4c5e-8a67-c002548b8929 | anyonenet-synchronized-speech-and-talking | 2108.04325 | null | https://arxiv.org/abs/2108.04325v2 | https://arxiv.org/pdf/2108.04325v2.pdf | AnyoneNet: Synchronized Speech and Talking Head Generation for Arbitrary Person | Automatically generating videos in which synthesized speech is synchronized with lip movements in a talking head has great potential in many human-computer interaction scenarios. In this paper, we present an automatic method to generate synchronized speech and talking-head videos on the basis of text and a single face ... | ['Scharenborg', 'Lei Xie', 'Jihua Zhu', 'Qicong Xie', 'Xinsheng Wang'] | 2021-08-09 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [ 2.17494339e-01 3.50753307e-01 4.10070382e-02 -4.45941150e-01
-7.12923408e-01 -3.23678225e-01 7.87949145e-01 -8.60769391e-01
9.62625742e-02 5.45341134e-01 4.09718156e-01 1.40418485e-01
3.66460979e-01 -3.11242789e-01 -5.09516418e-01 -9.80305672e-01
5.47565758e-01 4.64038908e-01 -1.26085758e-01 -1.69415921... | [13.250200271606445, -0.40312108397483826] |
ba3ae472-d523-43c6-a5ba-08125179fa22 | horizon-free-reinforcement-learning-in-1 | 2305.08359 | null | https://arxiv.org/abs/2305.08359v1 | https://arxiv.org/pdf/2305.08359v1.pdf | Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs | Recent studies have shown that episodic reinforcement learning (RL) is no harder than bandits when the total reward is bounded by $1$, and proved regret bounds that have a polylogarithmic dependence on the planning horizon $H$. However, it remains an open question that if such results can be carried over to adversarial... | ['Quanquan Gu', 'Weitong Zhang', 'Jiafan He', 'Qingyue Zhao', 'Kaixuan Ji'] | 2023-05-15 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 1.02935903e-01 6.08355284e-01 -1.43979445e-01 4.95599844e-02
-1.21389461e+00 -8.12367797e-01 7.23396167e-02 2.39534363e-01
-9.24232781e-01 1.15179598e+00 -2.99396932e-01 -7.06011593e-01
-8.34896266e-01 -9.79642272e-01 -1.13768387e+00 -8.72650862e-01
-6.38558030e-01 5.41041970e-01 5.98667488e-02 -1.47460982... | [4.366111755371094, 2.9043614864349365] |
629d983d-6db2-4cfd-ae80-434f3e10c301 | carb-a-crowdsourced-benchmark-for-open-ie | null | null | https://aclanthology.org/D19-1651 | https://aclanthology.org/D19-1651.pdf | CaRB: A Crowdsourced Benchmark for Open IE | Open Information Extraction (Open IE) systems have been traditionally evaluated via manual annotation. Recently, an automated evaluator with a benchmark dataset (OIE2016) was released {--} it scores Open IE systems automatically by matching system predictions with predictions in the benchmark dataset. Unfortunately, ou... | ['Sangnie Bhardwaj', 'Samarth Aggarwal', 'Mausam Mausam'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['open-information-extraction'] | ['natural-language-processing'] | [-1.80394620e-01 6.01905167e-01 -1.54118776e-01 -2.48468980e-01
-1.22390008e+00 -1.01156902e+00 5.06631851e-01 2.89562315e-01
-2.51904368e-01 7.78287649e-01 3.73910546e-01 -2.14329183e-01
-4.00133282e-01 -4.67086464e-01 -9.49225366e-01 2.48506457e-01
4.61839885e-01 6.70029700e-01 3.28137517e-01 -3.74815673... | [9.514572143554688, 8.658400535583496] |
633a8166-c82c-4057-b4cc-3a055e44bacd | fsa-net-learning-fine-grained-structure | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Yang_FSA-Net_Learning_Fine-Grained_Structure_Aggregation_for_Head_Pose_Estimation_From_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_FSA-Net_Learning_Fine-Grained_Structure_Aggregation_for_Head_Pose_Estimation_From_CVPR_2019_paper.pdf | FSA-Net: Learning Fine-Grained Structure Aggregation for Head Pose Estimation From a Single Image | This paper proposes a method for head pose estimation from a single image. Previous methods often predict head poses through landmark or depth estimation and would require more computation than necessary. Our method is based on regression and feature aggregation. For having a compact model, we employ the soft stagewise... | [' Yung-Yu Chuang', ' Yen-Yu Lin', ' Yi-Ting Chen', 'Tsun-Yi Yang'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['head-pose-estimation'] | ['computer-vision'] | [-1.52233839e-01 1.29409507e-01 -1.88415408e-01 -8.53111207e-01
-1.21431077e+00 -1.93015680e-01 4.39665645e-01 2.42613375e-01
-5.37152529e-01 8.24672222e-01 5.31478465e-01 2.88195401e-01
-1.46705195e-01 -6.27159595e-01 -6.39767528e-01 -8.47444117e-01
-9.32743624e-02 3.41202617e-01 3.37156057e-01 -1.36153638... | [13.652770042419434, 0.26784494519233704] |
a63e6520-b2e4-4b91-8aec-6068982d7169 | weakly-supervised-discourse-segmentation-for | null | null | https://aclanthology.org/2021.emnlp-main.104 | https://aclanthology.org/2021.emnlp-main.104.pdf | Weakly supervised discourse segmentation for multiparty oral conversations | Discourse segmentation, the first step of discourse analysis, has been shown to improve results for text summarization, translation and other NLP tasks. While segmentation models for written text tend to perform well, they are not directly applicable to spontaneous, oral conversation, which has linguistic features fore... | ['Isabelle Ferrané', 'Thomas Pellegrini', 'Philippe Muller', 'Julie Hunter', 'Lila Gravellier'] | null | null | null | null | emnlp-2021-11 | ['discourse-segmentation'] | ['natural-language-processing'] | [ 5.70036113e-01 8.09293687e-01 -3.57389987e-01 -4.04032052e-01
-1.17358899e+00 -7.50956297e-01 9.34327960e-01 5.01107335e-01
-5.17443180e-01 1.13231230e+00 8.47125828e-01 -2.16725156e-01
3.21068317e-01 -1.91962168e-01 -4.37802196e-01 -4.63755220e-01
2.45591193e-01 9.90098417e-01 4.14464563e-01 -3.10921311... | [10.862455368041992, 9.480045318603516] |
fec121ed-3af1-4053-9afe-4925dcc78709 | machine-learning-based-early-detection-of-iot | 2010.11453 | null | https://arxiv.org/abs/2010.11453v1 | https://arxiv.org/pdf/2010.11453v1.pdf | Machine Learning-Based Early Detection of IoT Botnets Using Network-Edge Traffic | In this work, we present a lightweight IoT botnet detection solution, EDIMA, which is designed to be deployed at the edge gateway installed in home networks and targets early detection of botnets prior to the launch of an attack. EDIMA includes a novel two-stage Machine Learning (ML)-based detector developed specifical... | ['Teng Joon Lim', 'Sahithya Swaminathan', 'Mrinalini Shridhar', 'Ayush Kumar'] | 2020-10-22 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [-2.26715982e-01 -3.89506459e-01 -1.87533364e-01 3.68692607e-01
6.95810467e-02 -4.96656030e-01 3.39235902e-01 -7.39023313e-02
-4.49579120e-01 1.72521576e-01 -6.63102269e-01 -1.12675822e+00
2.61499137e-01 -9.80057418e-01 1.83143675e-01 -5.72285891e-01
-1.98169649e-01 7.28281736e-01 1.08376467e+00 6.26551509... | [5.190512657165527, 7.2080488204956055] |
4887e875-d1f9-427e-919b-3ab63d47a2bf | improving-perceptual-quality-intelligibility | 2303.09048 | null | https://arxiv.org/abs/2303.09048v1 | https://arxiv.org/pdf/2303.09048v1.pdf | Improving Perceptual Quality, Intelligibility, and Acoustics on VoIP Platforms | In this paper, we present a method for fine-tuning models trained on the Deep Noise Suppression (DNS) 2020 Challenge to improve their performance on Voice over Internet Protocol (VoIP) applications. Our approach involves adapting the DNS 2020 models to the specific acoustic characteristics of VoIP communications, which... | ['Bhiksha Raj', 'Minjeong Kim', 'Kawon Lee', 'Minseon Gwak', 'Hamza Khalid', 'Xuankai Chang', 'Haohui Liu', 'Amanda Shu', 'Yunyang Zeng', 'Shuo Han', 'Ankit Shah', 'Hojeong Lee', 'Shikhar Agnihotri', 'Ojas Bhargave', 'Joseph Konan'] | 2023-03-16 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [-6.04244508e-02 -4.91493016e-01 -9.74428356e-02 -1.42290041e-01
-1.00426650e+00 -5.45155942e-01 2.22705364e-01 -7.03076482e-01
-1.10299751e-01 2.96589792e-01 7.04013824e-01 -6.12026572e-01
1.79530308e-02 -1.77261740e-01 -1.82475254e-01 -5.49905241e-01
3.63193601e-02 7.29474872e-02 -1.52421355e-01 -3.61128360... | [14.916632652282715, 6.023006439208984] |
f16dcd2f-041f-4ca6-a32c-fd61725bab1e | single-image-deraining-network-with-rain | 2111.03615 | null | https://arxiv.org/abs/2111.03615v1 | https://arxiv.org/pdf/2111.03615v1.pdf | Single Image Deraining Network with Rain Embedding Consistency and Layered LSTM | Single image deraining is typically addressed as residual learning to predict the rain layer from an input rainy image. For this purpose, an encoder-decoder network draws wide attention, where the encoder is required to encode a high-quality rain embedding which determines the performance of the subsequent decoding sta... | ['Masatoshi Okutomi', 'Yusuke Monno', 'Yizhou Li'] | 2021-11-05 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 1.08900875e-01 -7.97686353e-02 1.55296966e-01 -5.30448616e-01
-6.30997002e-01 5.57061955e-02 2.72447884e-01 -4.00490999e-01
-2.96695590e-01 7.72070289e-01 2.05837414e-01 6.73734993e-02
2.38651335e-01 -1.02741110e+00 -9.00862992e-01 -1.28456998e+00
2.13990688e-01 -1.36155188e-01 -1.65202573e-01 -2.66324669... | [10.93331241607666, -3.2424442768096924] |
8ecf12ac-c6f4-4aaf-b99a-45a68e370588 | transfer-learning-for-atomistic-simulations | 2306.01589 | null | https://arxiv.org/abs/2306.01589v3 | https://arxiv.org/pdf/2306.01589v3.pdf | Transfer learning for atomistic simulations using GNNs and kernel mean embeddings | Interatomic potentials learned using machine learning methods have been successfully applied to atomistic simulations. However, deep learning pipelines are notoriously data-hungry, while generating reference calculations is computationally demanding. To overcome this difficulty, we propose a transfer learning algorithm... | ['Michele Parrinello', 'Massimiliano Pontil', 'Pietro Novelli', 'Luigi Bonati', 'John Falk'] | 2023-06-02 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 2.16894463e-01 -4.51525599e-02 -1.92998594e-03 -3.07687938e-01
-9.31011617e-01 -6.88748300e-01 7.98936307e-01 4.18348700e-01
-4.10616249e-01 1.02584755e+00 1.83536410e-01 -6.70421481e-01
-1.07557885e-01 -9.38714325e-01 -9.26561475e-01 -9.14578557e-01
-1.20050289e-01 5.82313120e-01 -2.59192213e-02 -2.78896749... | [5.156952857971191, 5.597355842590332] |
91ecaadc-3965-471a-857a-d8ce4365a158 | modelling-commonsense-properties-using-pre | 2210.02771 | null | https://arxiv.org/abs/2210.02771v1 | https://arxiv.org/pdf/2210.02771v1.pdf | Modelling Commonsense Properties using Pre-Trained Bi-Encoders | Grasping the commonsense properties of everyday concepts is an important prerequisite to language understanding. While contextualised language models are reportedly capable of predicting such commonsense properties with human-level accuracy, we argue that such results have been inflated because of the high similarity b... | ['Steven Schockaert', 'Luis Espinosa-Anke', 'Amit Gajbhiye'] | 2022-10-06 | null | https://aclanthology.org/2022.coling-1.349 | https://aclanthology.org/2022.coling-1.349.pdf | coling-2022-10 | ['hypernym-discovery'] | ['natural-language-processing'] | [ 5.00889301e-01 3.08495104e-01 -1.18641503e-01 -5.87888956e-01
-3.37311059e-01 -6.48201406e-01 7.97690272e-01 5.99296212e-01
-6.88544154e-01 7.59309590e-01 3.81305724e-01 -4.04748231e-01
-2.93592494e-02 -1.07851934e+00 -4.53300267e-01 -1.96928039e-01
4.39379737e-02 7.23060668e-01 1.51685476e-01 -5.06443262... | [10.147475242614746, 8.703987121582031] |
c9e30a80-6743-42b5-bdf5-a5837ea8293a | humangan-a-generative-model-of-humans-images | 2103.06902 | null | https://arxiv.org/abs/2103.06902v1 | https://arxiv.org/pdf/2103.06902v1.pdf | HumanGAN: A Generative Model of Humans Images | Generative adversarial networks achieve great performance in photorealistic image synthesis in various domains, including human images. However, they usually employ latent vectors that encode the sampled outputs globally. This does not allow convenient control of semantically-relevant individual parts of the image, and... | ['Christian Theobalt', 'Vladislav Golyanik', 'Lingjie Liu', 'Kripasindhu Sarkar'] | 2021-03-11 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 5.00003219e-01 8.43892992e-02 2.46014595e-02 -3.70487630e-01
-5.48509359e-01 -7.52887845e-01 6.63806319e-01 -8.30690682e-01
3.14261764e-02 7.69912601e-01 2.57636845e-01 4.71903652e-01
3.54519159e-01 -8.49335015e-01 -1.01629162e+00 -7.86495805e-01
4.92745668e-01 6.25882566e-01 -6.40004054e-02 -4.43714559... | [11.965887069702148, -0.7075623273849487] |
2dc4187c-7ed4-4537-bbe4-6413311f2e9f | conservative-optimistic-policy-optimization | 2103.03307 | null | https://arxiv.org/abs/2103.03307v1 | https://arxiv.org/pdf/2103.03307v1.pdf | Conservative Optimistic Policy Optimization via Multiple Importance Sampling | Reinforcement Learning (RL) has been able to solve hard problems such as playing Atari games or solving the game of Go, with a unified approach. Yet modern deep RL approaches are still not widely used in real-world applications. One reason could be the lack of guarantees on the performance of the intermediate executed ... | ['Othman Gaizi', 'Achraf Azize'] | 2021-03-04 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-1.49199083e-01 3.39631349e-01 -2.02443987e-01 5.30612767e-02
-1.04873979e+00 -6.13257945e-01 2.70145535e-01 7.81021873e-03
-9.74159956e-01 1.43766797e+00 -3.33472878e-01 -7.55959570e-01
-3.52957338e-01 -6.59599662e-01 -8.29640388e-01 -7.94194043e-01
-2.76181906e-01 6.75560951e-01 1.24513023e-01 -2.70150602... | [4.226208209991455, 2.385446071624756] |
0fa42451-8020-4958-8faa-0d1fd00944cf | on-wind-farm-wake-mixing-strategies-using | 2003.11319 | null | http://arxiv.org/abs/2003.11319v1 | http://arxiv.org/pdf/2003.11319v1.pdf | On wind farm wake mixing strategies using dynamic individual pitch control | Dynamic wind farm control is a new strategy that aims to apply time-varying,
often periodic, control signals on upstream wind turbines to increase the wake
mixing behind the turbine. As a result, wake recovery is accelerated, leading
to a higher power production of downstream turbines. As the amount of interest
in dyna... | [] | 2020-03-25 | null | null | null | null | ['pitch-control'] | ['audio'] | [-3.28901231e-01 -3.07419747e-01 9.88110155e-02 5.76200962e-01
6.15965664e-01 -1.00516450e+00 5.34143448e-01 4.85746004e-03
-1.11845778e-02 9.50179815e-01 8.63825902e-02 -1.91187516e-01
-3.49036068e-01 -7.77501285e-01 -1.47205750e-02 -1.19757521e+00
-2.24696472e-01 -3.67923617e-01 1.75869599e-01 -5.75052857... | [5.451642036437988, 2.5054757595062256] |
e3b556c0-00fc-4d19-9b31-eab6f7d0c409 | imperceptible-adversarial-examples-for-fake | 2106.01615 | null | https://arxiv.org/abs/2106.01615v1 | https://arxiv.org/pdf/2106.01615v1.pdf | Imperceptible Adversarial Examples for Fake Image Detection | Fooling people with highly realistic fake images generated with Deepfake or GANs brings a great social disturbance to our society. Many methods have been proposed to detect fake images, but they are vulnerable to adversarial perturbations -- intentionally designed noises that can lead to the wrong prediction. Existing ... | ['Xi Wu', 'Qi Song', 'Youbing Yin', 'Siwei Lyu', 'Bin Zhu', 'Bin Kong', 'Xin Wang', 'Yuezun Li', 'Quanyu Liao'] | 2021-06-03 | null | null | null | null | ['fake-image-detection'] | ['computer-vision'] | [ 2.85418391e-01 1.93871185e-01 2.85238743e-01 -1.80196688e-01
-3.44274372e-01 -7.06843138e-01 4.70443338e-01 -4.90519494e-01
-2.11216167e-01 8.89645576e-01 -1.99995950e-01 -1.29912362e-01
6.81323528e-01 -9.35473979e-01 -8.56501341e-01 -6.40147150e-01
3.50742728e-01 -1.48448171e-02 4.04532701e-01 -4.60533053... | [12.487414360046387, 1.0940731763839722] |
8e9764b9-72ee-416e-bd38-de111ca264d7 | unpwc-svdlo-multi-svd-on-pointpwc-for | 2205.08150 | null | https://arxiv.org/abs/2205.08150v1 | https://arxiv.org/pdf/2205.08150v1.pdf | UnPWC-SVDLO: Multi-SVD on PointPWC for Unsupervised Lidar Odometry | High-precision lidar odomety is an essential part of autonomous driving. In recent years, deep learning methods have been widely used in lidar odomety tasks, but most of the current methods only extract the global features of the point clouds. It is impossible to obtain more detailed point-level features in this way. I... | ['Yiming Tu'] | 2022-05-17 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-5.11681497e-01 -3.77692133e-01 -1.77146420e-01 -4.84696954e-01
-3.59388560e-01 -3.95129085e-01 4.70227748e-01 -3.01917404e-01
-7.92432249e-01 5.65483153e-01 -4.24436063e-01 -1.40429944e-01
-2.22208854e-02 -1.22350848e+00 -7.16780663e-01 -6.23351395e-01
1.26383528e-01 9.14008021e-01 5.27455807e-01 -4.05979544... | [7.811258792877197, -2.5404887199401855] |
abef6cdf-79d1-405c-b8d6-4f065e206031 | detecting-cell-and-protein-concentrations-by | 2102.08335 | null | https://arxiv.org/abs/2102.08335v1 | https://arxiv.org/pdf/2102.08335v1.pdf | Detecting cell and protein concentrations by the use of a thermal based sensor | Biosensors are frequently used nowadays for the sake of their attractive capabilities. Because of their high accuracy and precision, they are more and more used in the medical sector. Natural receptors are mostly used, but their use have some specific drawbacks. Therefore, new read-out methods are being developed where... | ['Ronald Thoelen', 'Thijs Vandenryt', 'Seppe Bormans', 'Gilles Oudebrouckx', 'Juul Goossens'] | 2021-02-16 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 3.97167146e-01 -2.42410794e-01 -3.18970531e-02 9.91972238e-02
2.08613604e-01 -3.45882624e-01 3.52219671e-01 4.76936668e-01
-4.90560174e-01 1.06635010e+00 -4.25871044e-01 2.45240971e-01
2.36879215e-01 -9.75698709e-01 -3.37320805e-01 -1.41479468e+00
9.16898903e-03 -1.39156878e-01 4.65319037e-01 -1.27768457... | [13.72356128692627, -3.024170160293579] |
e1849204-162b-414e-b451-8bf5ba592988 | double-a3c-deep-reinforcement-learning-on | 2303.02271 | null | https://arxiv.org/abs/2303.02271v1 | https://arxiv.org/pdf/2303.02271v1.pdf | Double A3C: Deep Reinforcement Learning on OpenAI Gym Games | Reinforcement Learning (RL) is an area of machine learning figuring out how agents take actions in an unknown environment to maximize its rewards. Unlike classical Markov Decision Process (MDP) in which agent has full knowledge of its state, rewards, and transitional probability, reinforcement learning utilizes explora... | ['Lingjie Kong', 'Jiajie He', 'Yangxin Zhong'] | 2023-03-04 | null | null | null | null | ['atari-games'] | ['playing-games'] | [ 9.87103302e-03 4.40984875e-01 -4.57460791e-01 -4.33119666e-03
-3.59822601e-01 -3.05996835e-01 5.64028621e-01 2.69656360e-01
-8.87700558e-01 1.29751301e+00 -2.00302869e-01 -3.05495411e-01
-1.47951424e-01 -7.75764287e-01 -5.95109880e-01 -7.00173378e-01
-3.88502181e-01 5.58950365e-01 1.17038801e-01 -2.96824962... | [4.050804138183594, 1.9589765071868896] |
fa48a5f8-b8c8-43b6-9d14-7dbc9762bff9 | line-segment-detection-using-transformers | 2101.01909 | null | https://arxiv.org/abs/2101.01909v2 | https://arxiv.org/pdf/2101.01909v2.pdf | Line Segment Detection Using Transformers without Edges | In this paper, we present a joint end-to-end line segment detection algorithm using Transformers that is post-processing and heuristics-guided intermediate processing (edge/junction/region detection) free. Our method, named LinE segment TRansformers (LETR), takes advantages of having integrated tokenized queries, a sel... | ['Zhuowen Tu', 'David Cheung', 'Weijian Xu', 'Yifan Xu'] | 2021-01-06 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Xu_Line_Segment_Detection_Using_Transformers_Without_Edges_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Xu_Line_Segment_Detection_Using_Transformers_Without_Edges_CVPR_2021_paper.pdf | cvpr-2021-1 | ['line-segment-detection'] | ['computer-vision'] | [ 1.02912158e-01 9.11056027e-02 -3.60814601e-01 -2.91055769e-01
-1.33355105e+00 -6.02518976e-01 2.89808452e-01 6.85207725e-01
-3.88631582e-01 2.44433194e-01 -1.47169335e-02 -5.67233026e-01
2.21984774e-01 -1.00303698e+00 -1.05999196e+00 -1.24665968e-01
-3.56419891e-01 4.67802703e-01 8.00840557e-01 -3.44193965... | [8.362205505371094, -1.5552237033843994] |
adf79f8d-6de4-458c-a244-cc28c1bb1a85 | recurrent-coupled-topic-modeling-over | 2106.13732 | null | https://arxiv.org/abs/2106.13732v1 | https://arxiv.org/pdf/2106.13732v1.pdf | Recurrent Coupled Topic Modeling over Sequential Documents | The abundant sequential documents such as online archival, social media and news feeds are streamingly updated, where each chunk of documents is incorporated with smoothly evolving yet dependent topics. Such digital texts have attracted extensive research on dynamic topic modeling to infer hidden evolving topics and th... | ['Zhiguo Gong', 'Longbing Cao', 'Jinjin Guo'] | 2021-06-23 | null | null | null | null | ['dynamic-topic-modeling'] | ['natural-language-processing'] | [-4.36104946e-02 -1.44445851e-01 -3.45900923e-01 -3.54174405e-01
-8.43144596e-01 -3.03801715e-01 1.01706421e+00 2.08222866e-02
-6.90908432e-02 5.65156698e-01 3.42400879e-01 -5.97173832e-02
2.37654503e-02 -7.79637814e-01 -5.05193233e-01 -1.01614130e+00
-1.81655392e-01 7.48502433e-01 3.83821964e-01 6.77202940... | [10.386713027954102, 6.929217338562012] |
1d9354a5-77d3-47c2-a96b-256208fdbe8f | joint-event-and-temporal-relation-extraction | 1909.05360 | null | https://arxiv.org/abs/1909.05360v2 | https://arxiv.org/pdf/1909.05360v2.pdf | Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction | We propose a joint event and temporal relation extraction model with shared representation learning and structured prediction. The proposed method has two advantages over existing work. First, it improves event representation by allowing the event and relation modules to share the same contextualized embeddings and neu... | ['Nanyun Peng', 'Qiang Ning', 'Rujun Han'] | 2019-09-02 | joint-event-and-temporal-relation-extraction-1 | https://aclanthology.org/D19-1041 | https://aclanthology.org/D19-1041.pdf | ijcnlp-2019-11 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 3.84051204e-02 3.34150136e-01 -4.95545268e-01 -5.55966973e-01
-6.50039673e-01 -3.03768784e-01 8.64601195e-01 8.35061371e-01
-5.90235710e-01 6.86676860e-01 4.62892950e-01 4.42929082e-02
-1.52527452e-01 -8.88167799e-01 -4.83046710e-01 -4.11934018e-01
-6.17838740e-01 1.60552651e-01 8.78014684e-01 3.18548262... | [9.067375183105469, 9.136946678161621] |
26b402ee-ce20-4d83-a93a-3e28de4588ad | visual-writing-prompts-character-grounded | 2301.08571 | null | https://arxiv.org/abs/2301.08571v1 | https://arxiv.org/pdf/2301.08571v1.pdf | Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences | Current work on image-based story generation suffers from the fact that the existing image sequence collections do not have coherent plots behind them. We improve visual story generation by producing a new image-grounded dataset, Visual Writing Prompts (VWP). VWP contains almost 2K selected sequences of movie shots, ea... | ['Bernt Schiele', 'Vera Demberg', 'Khushboo Mehra', 'Asad Sayeed', 'Xudong Hong'] | 2023-01-20 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 4.75577533e-01 3.33879828e-01 -3.53734903e-02 -1.98243439e-01
-6.34776294e-01 -6.36382461e-01 1.20665264e+00 -1.33254647e-01
-4.88250107e-02 8.98127973e-01 1.21207309e+00 2.97399163e-01
5.35569370e-01 -8.17417443e-01 -8.17489266e-01 -3.90359789e-01
2.75652528e-01 1.86544761e-01 5.40963173e-01 -5.40705383... | [11.165159225463867, 0.7822919487953186] |
af6e292b-388c-4b02-a955-2d87c915ac57 | cae-lo-lidar-odometry-leveraging-fully | 2001.01354 | null | https://arxiv.org/abs/2001.01354v3 | https://arxiv.org/pdf/2001.01354v3.pdf | CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description | As an important technology in 3D mapping, autonomous driving, and robot navigation, LiDAR odometry is still a challenging task. Appropriate data structure and unsupervised deep learning are the keys to achieve an easy adjusted LiDAR odometry solution with high performance. Utilizing compact 2D structured spherical ring... | ['Juha Hyyppä', 'Jyri Maanpää', 'Yunsheng Wang', 'Qian zhang', 'Deyu Yin', 'Xinlian Liang', 'Hao Ma', 'Ruizhi Chen', 'Jingbin Liu'] | 2020-01-06 | null | null | null | null | ['interest-point-detection'] | ['computer-vision'] | [-3.79388213e-01 -3.99531722e-02 -2.13764980e-01 -5.92875063e-01
-6.13634884e-01 4.69000377e-02 4.42415535e-01 -5.51499613e-02
-5.73726714e-01 3.67706835e-01 -1.16896315e-03 6.24221973e-02
-3.06188345e-01 -1.02822530e+00 -9.70370114e-01 -1.74857393e-01
5.85746281e-02 1.12918353e+00 5.61097682e-01 -4.69290227... | [7.513513565063477, -2.3210997581481934] |
d92d7b3e-47d5-4c74-8f20-64f5872c7032 | geometric-based-pruning-rules-for-change | 2306.09555 | null | https://arxiv.org/abs/2306.09555v1 | https://arxiv.org/pdf/2306.09555v1.pdf | Geometric-Based Pruning Rules For Change Point Detection in Multiple Independent Time Series | We consider the problem of detecting multiple changes in multiple independent time series. The search for the best segmentation can be expressed as a minimization problem over a given cost function. We focus on dynamic programming algorithms that solve this problem exactly. When the number of changes is proportional to... | ['Vincent Runge', 'Guillem Rigaill', 'Liudmila Pishchagina'] | 2023-06-15 | null | null | null | null | ['change-point-detection'] | ['time-series'] | [ 1.44442663e-01 -2.95683444e-01 4.68755923e-02 -9.66709182e-02
-4.43581790e-01 -8.61948490e-01 9.04223993e-02 6.04562700e-01
-6.78875566e-01 5.72136402e-01 -4.49701130e-01 -4.01255697e-01
-5.51288545e-01 -8.07693958e-01 -7.13586926e-01 -7.43984282e-01
-7.86308944e-01 7.02057302e-01 6.61522865e-01 -1.94448546... | [7.233951091766357, 3.7865939140319824] |
8abd8802-68a3-4e0e-9007-f5c58af7dfd3 | comma-modeling-relationship-among-motivations | 2209.06470 | null | https://arxiv.org/abs/2209.06470v1 | https://arxiv.org/pdf/2209.06470v1.pdf | COMMA: Modeling Relationship among Motivations, Emotions and Actions in Language-based Human Activities | Motivations, emotions, and actions are inter-related essential factors in human activities. While motivations and emotions have long been considered at the core of exploring how people take actions in human activities, there has been relatively little research supporting analyzing the relationship between human mental ... | ['Luxi Xing', 'Guanqun Bi', 'Wei Peng', 'Yue Hu', 'Yuqiang Xie'] | 2022-09-14 | null | https://aclanthology.org/2022.coling-1.15 | https://aclanthology.org/2022.coling-1.15.pdf | coling-2022-10 | ['action-generation'] | ['computer-vision'] | [ 4.04590309e-01 3.33487779e-01 -4.67298061e-01 -3.44075561e-01
1.48368865e-01 -3.04392457e-01 1.24898386e+00 8.69658515e-02
-1.85232416e-01 5.72599411e-01 1.13752580e+00 1.65157244e-01
-1.00316321e-02 -8.86313200e-01 -2.70976514e-01 -2.70533562e-01
2.25105122e-01 1.38915414e-02 -2.12548912e-01 -3.17961097... | [11.679861068725586, 8.452131271362305] |
686b627a-1317-449d-9841-02ef3aa15387 | complex-word-identification-challenges-in | 1710.04989 | null | http://arxiv.org/abs/1710.04989v1 | http://arxiv.org/pdf/1710.04989v1.pdf | Complex Word Identification: Challenges in Data Annotation and System Performance | This paper revisits the problem of complex word identification (CWI)
following up the SemEval CWI shared task. We use ensemble classifiers to
investigate how well computational methods can discriminate between complex and
non-complex words. Furthermore, we analyze the classification performance to
understand what makes... | ['Lucia Specia', 'Marcos Zampieri', 'Gustavo Paetzold', 'Shervin Malmasi'] | 2017-10-13 | complex-word-identification-challenges-in-1 | https://aclanthology.org/W17-5910 | https://aclanthology.org/W17-5910.pdf | ws-2017-12 | ['complex-word-identification'] | ['natural-language-processing'] | [ 2.61839509e-01 -9.67785716e-02 -1.06389761e-01 -2.74864286e-01
-4.55405086e-01 -8.97297323e-01 6.75747097e-01 3.90392751e-01
-1.08767879e+00 6.92651749e-01 3.32334727e-01 -6.76071644e-01
-6.24500774e-02 -4.89961416e-01 2.99121458e-02 -2.29549050e-01
2.67681092e-01 7.23306000e-01 -9.01807919e-02 -4.33080286... | [10.716788291931152, 10.410490036010742] |
9527e157-bca4-450b-af37-6ebad56a41b9 | dynamics-regulated-kinematic-policy-for | 2106.05969 | null | https://arxiv.org/abs/2106.05969v3 | https://arxiv.org/pdf/2106.05969v3.pdf | Dynamics-Regulated Kinematic Policy for Egocentric Pose Estimation | We propose a method for object-aware 3D egocentric pose estimation that tightly integrates kinematics modeling, dynamics modeling, and scene object information. Unlike prior kinematics or dynamics-based approaches where the two components are used disjointly, we synergize the two approaches via dynamics-regulated train... | ['Kris Kitani', 'Ye Yuan', 'Ryo Hachiuma', 'Zhengyi Luo'] | 2021-06-10 | null | http://proceedings.neurips.cc/paper/2021/hash/d1fe173d08e959397adf34b1d77e88d7-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/d1fe173d08e959397adf34b1d77e88d7-Paper.pdf | neurips-2021-12 | ['egocentric-pose-estimation'] | ['computer-vision'] | [-1.58096761e-01 -7.28847086e-02 1.32650509e-01 -1.50063038e-01
-3.17218125e-01 -6.96371317e-01 5.49908996e-01 -9.31461006e-02
-3.80916417e-01 5.50593197e-01 1.66441366e-01 1.06378086e-01
-1.14275115e-02 -4.48640198e-01 -1.02973688e+00 -3.80845100e-01
-7.17871934e-02 5.95167041e-01 2.41091549e-01 5.13270572... | [6.857093334197998, -0.8830142021179199] |
97f641fc-9f7d-4528-ade2-1c11fd8706ff | multitask-learning-for-mental-health | null | null | https://aclanthology.org/e17-1015 | https://aclanthology.org/e17-1015.pdf | Multitask Learning for Mental Health Conditions with Limited Social Media Data | null | ['Dirk Hovy', 'Margaret Mitchell', 'Adrian Benton'] | 2017-04-01 | multitask-learning-for-mental-health-1 | https://aclanthology.org/E17-1015 | https://aclanthology.org/E17-1015.pdf | eacl-2017-4 | ['gender-prediction'] | ['computer-vision'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.539209008216858, 15.869219779968262] |
fefec4aa-03cc-4bab-ad7d-3fa55f7caf4f | disentangled-image-colorization-via-global | null | null | https://dl.acm.org/doi/abs/10.1145/3550454.3555432 | https://menghanxia.github.io/projects/disco/disco_main.pdf | Disentangled Image Colorization via Global Anchors | Colorization is multimodal by nature and challenges existing frameworks to achieve colorful and structurally consistent results. Even the sophisticated autoregressive model struggles to maintain long-distance color consistency due to the fragility of sequential dependence. To overcome this challenge, we propose a novel... | ['Jue Wang', 'Tien-Tsin Wong', 'WenBo Hu', 'Menghan Xia'] | 2022-11-30 | null | null | null | siggraph-2022-11 | ['colorization'] | ['computer-vision'] | [-1.43935993e-01 -4.33752060e-01 -1.56258449e-01 -1.87370524e-01
-5.96779048e-01 -8.06962192e-01 3.16258520e-01 -4.71941859e-01
1.39135689e-01 3.87877345e-01 -2.37649344e-02 -1.18240684e-01
-9.82319191e-02 -7.64950395e-01 -6.63176715e-01 -1.20662260e+00
4.12577212e-01 7.45365098e-02 -1.15908079e-01 -2.15432733... | [11.367652893066406, -1.0374029874801636] |
fead4844-ff5a-4258-9f5f-6c4ea1ad4309 | patmat-person-aware-tuning-of-mask-aware | 2304.06107 | null | https://arxiv.org/abs/2304.06107v1 | https://arxiv.org/pdf/2304.06107v1.pdf | PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face Inpainting | Generative models such as StyleGAN2 and Stable Diffusion have achieved state-of-the-art performance in computer vision tasks such as image synthesis, inpainting, and de-noising. However, current generative models for face inpainting often fail to preserve fine facial details and the identity of the person, despite crea... | ['Fernando de la Torre', 'Chen Henry Wu', 'Jianjin Xu', 'Saman Motamed'] | 2023-04-12 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 3.01244140e-01 2.87241936e-01 1.55200690e-01 -5.57100892e-01
-6.38375938e-01 -4.18722957e-01 4.59035635e-01 -7.93813348e-01
9.79540050e-02 6.53224468e-01 3.98693532e-01 4.02706683e-01
3.54382962e-01 -6.89655423e-01 -8.93643320e-01 -5.93082726e-01
5.32068849e-01 3.80188048e-01 -3.20315897e-01 -2.88767099... | [12.539721488952637, -0.2396947741508484] |
67246893-cc2f-4c8e-a565-965332267c6c | majorcom-a-dual-function-radar-communication | 1909.04223 | null | http://arxiv.org/abs/1909.04223v1 | http://arxiv.org/pdf/1909.04223v1.pdf | MAJoRCom: A Dual-Function Radar Communication System Using Index Modulation | Dual-function radar communication (DFRC) systems implement both sensing and
communication using the same hardware. Such schemes are often more efficient in
terms of size, power, and cost, over using distinct radar and communication
systems. Since these functionalities share resources such as spectrum, power,
and antenn... | [] | 2019-09-10 | null | null | null | null | ['joint-radar-communication'] | ['robots'] | [ 7.19398975e-01 5.70879467e-02 2.86380202e-01 3.05900164e-03
-8.16209137e-01 -6.95388973e-01 7.56992757e-01 -2.00067371e-01
-4.74306762e-01 7.16261744e-01 -1.39924849e-03 -2.53741980e-01
-6.40344560e-01 -8.42805564e-01 -1.74856573e-01 -1.09838009e+00
-2.96374798e-01 4.63749021e-02 -2.30238572e-01 -1.13447249... | [6.389102935791016, 1.2561306953430176] |
543a8c57-e0cf-4d74-860b-f8dc0f4b0ed5 | visual-question-rewriting-for-increasing | 2106.02257 | null | https://arxiv.org/abs/2106.02257v1 | https://arxiv.org/pdf/2106.02257v1.pdf | Visual Question Rewriting for Increasing Response Rate | When a human asks questions online, or when a conversational virtual agent asks human questions, questions triggering emotions or with details might more likely to get responses or answers. we explore how to automatically rewrite natural language questions to improve the response rate from people. In particular, a new ... | ['Xin Wang', 'Yi Zhang', 'Xilian Li', 'Jiayi Wei'] | 2021-06-04 | null | null | null | null | ['question-rewriting'] | ['natural-language-processing'] | [ 2.39362225e-01 5.48831582e-01 4.41210896e-01 -6.59209192e-01
-6.14628673e-01 -9.11674917e-01 9.55481887e-01 -1.65586188e-01
-6.75972760e-01 5.83651304e-01 3.98990750e-01 -3.19568753e-01
4.97502834e-01 -5.86059451e-01 -5.16120672e-01 1.17564023e-01
6.81177974e-01 5.63420594e-01 4.69926715e-01 -6.07187867... | [10.96357250213623, 1.5452862977981567] |
51740480-9145-42a5-9923-a9d6577abbc8 | skillnet-x-a-multilingual-multitask-model | 2306.16176 | null | https://arxiv.org/abs/2306.16176v1 | https://arxiv.org/pdf/2306.16176v1.pdf | SkillNet-X: A Multilingual Multitask Model with Sparsely Activated Skills | Traditional multitask learning methods basically can only exploit common knowledge in task- or language-wise, which lose either cross-language or cross-task knowledge. This paper proposes a general multilingual multitask model, named SkillNet-X, which enables a single model to tackle many different tasks from different... | ['Shuming Shi', 'Yunbo Cao', 'Bing Qin', 'Shuangzhi Wu', 'Xiaocheng Feng', 'Duyu Tang', 'Fan Zhang', 'Yong Dai', 'Zhangyin Feng'] | 2023-06-28 | null | null | null | null | ['natural-language-understanding'] | ['natural-language-processing'] | [ 2.43885219e-02 -1.36218131e-01 -1.85093299e-01 -3.55199039e-01
-9.13555801e-01 -6.66924953e-01 7.02854216e-01 -2.75455505e-01
-7.23366559e-01 8.62276554e-01 3.94230098e-01 -3.06383193e-01
9.78993997e-03 -3.96456838e-01 -7.80212462e-01 -3.21396798e-01
4.02316839e-01 6.85817063e-01 3.47669274e-01 -5.28354645... | [10.813287734985352, 8.343961715698242] |
fd38ec16-2bb6-409b-be4c-53774fc7f16e | generating-cyber-threat-intelligence-to | 2108.06862 | null | https://arxiv.org/abs/2108.06862v3 | https://arxiv.org/pdf/2108.06862v3.pdf | Generating Cyber Threat Intelligence to Discover Potential Security Threats Using Classification and Topic Modeling | Due to the variety of cyber-attacks or threats, the cybersecurity community enhances the traditional security control mechanisms to an advanced level so that automated tools can encounter potential security threats. Very recently, Cyber Threat Intelligence (CTI) has been presented as one of the proactive and robust mec... | ['Hei', 'Xiali', 'Mohammad Masudur Rahman', 'Eshtiak Ahmed', 'Farzana Anowar', 'Ashraful Islam', 'Md Imran Hossen'] | 2021-08-16 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 3.77002768e-02 -1.99605107e-01 -2.60364920e-01 7.42918327e-02
-5.26516199e-01 -1.04314518e+00 8.62163544e-01 6.79643214e-01
-2.43153617e-01 3.73352051e-01 1.76295400e-01 -8.18804264e-01
-2.51652539e-01 -1.19649816e+00 -1.59417659e-01 -4.43617076e-01
-2.35482678e-02 9.48785171e-02 2.32417345e-01 5.57885766... | [5.356112957000732, 7.234470844268799] |
5e5c6cc1-c852-4b39-bf27-b9b0c759fb98 | unsupervised-semantic-segmentation-by | 2102.06191 | null | https://arxiv.org/abs/2102.06191v3 | https://arxiv.org/pdf/2102.06191v3.pdf | Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals | Being able to learn dense semantic representations of images without supervision is an important problem in computer vision. However, despite its significance, this problem remains rather unexplored, with a few exceptions that considered unsupervised semantic segmentation on small-scale datasets with a narrow visual do... | ['Luc van Gool', 'Stamatios Georgoulis', 'Simon Vandenhende', 'Wouter Van Gansbeke'] | 2021-02-11 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Van_Gansbeke_Unsupervised_Semantic_Segmentation_by_Contrasting_Object_Mask_Proposals_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Van_Gansbeke_Unsupervised_Semantic_Segmentation_by_Contrasting_Object_Mask_Proposals_ICCV_2021_paper.pdf | iccv-2021-1 | ['unsupervised-semantic-segmentation', 'unsupervised-pre-training'] | ['computer-vision', 'methodology'] | [ 4.96594310e-01 4.00343895e-01 -1.17481604e-01 -5.82237780e-01
-6.46815658e-01 -6.41178727e-01 6.68915212e-01 8.81246477e-02
-8.00918996e-01 4.31929171e-01 6.98823780e-02 -1.56193271e-01
-7.46185035e-02 -6.38879716e-01 -9.82625127e-01 -7.92205334e-01
1.64046973e-01 6.17947936e-01 4.70249414e-01 -2.80155651... | [9.535881042480469, 1.0693445205688477] |
66a5e310-371a-4936-9c28-b018be470d4e | hopc-histogram-of-oriented-principal | 1408.3809 | null | http://arxiv.org/abs/1408.3809v4 | http://arxiv.org/pdf/1408.3809v4.pdf | HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition | Existing techniques for 3D action recognition are sensitive to viewpoint
variations because they extract features from depth images which change
significantly with viewpoint. In contrast, we directly process the pointclouds
and propose a new technique for action recognition which is more robust to
noise, action speed a... | ['Du. Q. Huynh', 'Ajmal Mian', 'Arif Mahmood', 'Hossein Rahmani'] | 2014-08-17 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 3.06601584e-01 -7.39357948e-01 -2.11690113e-01 4.25675288e-02
-5.85761607e-01 -4.56580102e-01 8.41776133e-01 2.79043347e-01
-3.77896726e-01 2.46794522e-01 2.68903732e-01 4.59940702e-01
-2.14435473e-01 -5.15135050e-01 -3.69623005e-01 -7.36276805e-01
-2.58109301e-01 1.29252121e-01 1.03229010e+00 -3.06223501... | [7.895317554473877, 0.2595188021659851] |
3efdf31d-2bb4-4cbd-b33f-a1faa7fff273 | langevin-monte-carlo-for-contextual-bandits | 2206.11254 | null | https://arxiv.org/abs/2206.11254v1 | https://arxiv.org/pdf/2206.11254v1.pdf | Langevin Monte Carlo for Contextual Bandits | We study the efficiency of Thompson sampling for contextual bandits. Existing Thompson sampling-based algorithms need to construct a Laplace approximation (i.e., a Gaussian distribution) of the posterior distribution, which is inefficient to sample in high dimensional applications for general covariance matrices. Moreo... | ['Anima Anandkumar', 'Kamyar Azizzadenesheli', 'Eric Mazumdar', 'Hongkai Zheng', 'Pan Xu'] | 2022-06-22 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 5.59098199e-02 -1.59761578e-01 -7.50186324e-01 -1.98222041e-01
-1.49090374e+00 -5.96722543e-01 4.77854848e-01 -1.38730094e-01
-3.59771311e-01 1.23103142e+00 -2.23670788e-02 -9.18984890e-01
-2.48526797e-01 -8.28011096e-01 -1.23820746e+00 -8.91571879e-01
2.87258714e-01 1.00533533e+00 -5.71293756e-02 4.43255544... | [4.613702297210693, 3.274982213973999] |
b2eafbdf-40ba-43e8-b28d-967b3fb93856 | linknet-exploiting-encoder-representations | 1707.03718 | null | http://arxiv.org/abs/1707.03718v1 | http://arxiv.org/pdf/1707.03718v1.pdf | LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation | Pixel-wise semantic segmentation for visual scene understanding not only
needs to be accurate, but also efficient in order to find any use in real-time
application. Existing algorithms even though are accurate but they do not focus
on utilizing the parameters of neural network efficiently. As a result they are
huge in ... | ['Eugenio Culurciello', 'Abhishek Chaurasia'] | 2017-06-14 | null | null | null | null | ['thermal-image-segmentation'] | ['computer-vision'] | [ 4.51649353e-03 -4.21613336e-01 1.60425454e-01 -4.37430352e-01
-1.65821627e-01 -4.39653307e-01 1.37904793e-01 2.28102505e-02
-8.27510774e-01 4.62324619e-01 -4.57068145e-01 -6.68022394e-01
2.41580591e-01 -1.26348376e+00 -7.55250990e-01 -4.27903712e-01
2.47609973e-01 4.15206492e-01 6.97582424e-01 -1.80239350... | [9.112764358520508, -0.5977149605751038] |
4c9bedce-f900-4555-932c-ecc71dd0e846 | investigating-data-memorization-in-3d-latent | 2307.01148 | null | https://arxiv.org/abs/2307.01148v2 | https://arxiv.org/pdf/2307.01148v2.pdf | Investigating Data Memorization in 3D Latent Diffusion Models for Medical Image Synthesis | Generative latent diffusion models have been established as state-of-the-art in data generation. One promising application is generation of realistic synthetic medical imaging data for open data sharing without compromising patient privacy. Despite the promise, the capacity of such models to memorize sensitive patient ... | ['Theano Papavassiliu', 'Sandy Engelhardt', 'Stefan O. Schoenberg', 'Isabelle Ayx', 'Jannik Kahmann', 'Arman Ghanaat', 'Salman Ul Hassan Dar'] | 2023-07-03 | null | null | null | null | ['contrastive-learning', 'image-generation', 'contrastive-learning', 'memorization'] | ['computer-vision', 'computer-vision', 'methodology', 'natural-language-processing'] | [ 5.80208421e-01 8.34078908e-01 -1.27607405e-01 -4.75823611e-01
-1.01590025e+00 -2.07614273e-01 6.51852250e-01 1.60276964e-01
-3.88818383e-01 1.13325524e+00 5.52345693e-01 -2.62167156e-01
-5.62568605e-02 -7.83923090e-01 -4.87294257e-01 -8.96624923e-01
-2.07387730e-01 7.92704344e-01 -2.01367185e-01 2.94878572... | [14.257729530334473, -1.8863835334777832] |
a756ecea-01ab-4487-b686-a12bcd3478ea | fully-dense-neural-network-for-the-automatic | 1912.03449 | null | https://arxiv.org/abs/1912.03449v1 | https://arxiv.org/pdf/1912.03449v1.pdf | Fully Dense Neural Network for the Automatic Modulation Recognition | Nowadays, we mainly use various convolution neural network (CNN) structures to extract features from radio data or spectrogram in AMR. Based on expert experience and spectrograms, they not only increase the difficulty of preprocessing, but also consume a lot of memory. In order to directly use in-phase and quadrature (... | ['Xiao-Feng Gong', 'Chen Wang', 'Miao Du', 'Ruisen Luo', 'Qin Yu', 'Shaomin Fei'] | 2019-12-07 | null | null | null | null | ['automatic-modulation-recognition'] | ['time-series'] | [-4.79043722e-02 -4.57465708e-01 4.42832075e-02 -2.17913672e-01
-8.95011798e-02 -2.84280535e-02 1.16189159e-01 -7.87978590e-01
-4.89681184e-01 4.91562068e-01 1.80552211e-02 -5.88740647e-01
-3.19389701e-01 -9.49459255e-01 -1.26565129e-01 -5.82721114e-01
-1.62812069e-01 -3.20752174e-01 -8.55881721e-02 -4.57056969... | [14.71312427520752, 5.829145908355713] |
ad89eecb-a789-4dbb-83c3-09bb70e9acbb | physics-informed-machine-learning-models-for | 1908.10929 | null | https://arxiv.org/abs/1908.10929v1 | https://arxiv.org/pdf/1908.10929v1.pdf | Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing | This paper presents a physics-informed machine learning (ML) framework to construct reduced-order models (ROMs) for reactive-transport quantities of interest (QoIs) based on high-fidelity numerical simulations. QoIs include species decay, product yield, and degree of mixing. The ROMs for QoIs are applied to quantify an... | ['M. K. Mudunuru', 'S. Karra'] | 2019-08-28 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 9.03031379e-02 -6.61309004e-01 -2.72464514e-01 2.54554778e-01
-3.99972141e-01 -5.45317531e-01 9.37511444e-01 7.80115962e-01
-4.70300406e-01 8.93381596e-01 -2.89870024e-01 -4.89785135e-01
-6.35930717e-01 -1.12133789e+00 -4.26732570e-01 -1.26845133e+00
-3.73286009e-01 3.77735823e-01 2.81502157e-01 -3.56627405... | [6.3325419425964355, 3.6910762786865234] |
2c11dd07-e6ad-4003-8706-4f5a69813112 | deep-supervised-learning-for-hyperspectral | null | null | https://doi.org/10.1109/IGARSS.2015.7326945 | https://doi.org/10.1109/IGARSS.2015.7326945 | Deep supervised learning for hyperspectral data classification through convolutional neural networks | Spectral observations along the spectrum in many narrow spectral bands through hyperspectral imaging provides valuable information towards material and object recognition, which can be consider as a classification task. Most of the existing studies and research efforts are following the conventional pattern recognition... | ['Nikolaos Doulamis', 'Anastasios Doulamis', 'Konstantinos Karantzalos', 'Konstantinos Makantasis'] | 2015-07-26 | null | null | null | 2015-ieee-international-geoscience-and-remote | ['object-recognition', 'few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 8.54733944e-01 -5.27049005e-01 -8.34992528e-02 -4.06070203e-01
-3.58029455e-01 -4.17703509e-01 7.00697243e-01 2.41078675e-01
-2.40705237e-01 6.02630496e-01 -5.46399429e-02 -4.62192744e-01
-6.71092570e-01 -1.12153828e+00 -3.91522259e-01 -1.04861391e+00
-1.74704865e-02 -2.25345381e-02 -1.61043689e-01 -8.75291750... | [9.850811004638672, -1.6446475982666016] |
3d90ced9-88ae-4a3d-a634-9fcaa8abef64 | mri-recovery-with-self-calibrated-denoisers | 2304.12890 | null | https://arxiv.org/abs/2304.12890v1 | https://arxiv.org/pdf/2304.12890v1.pdf | MRI Recovery with Self-Calibrated Denoisers without Fully-Sampled Data | PURPOSE: To present and validate a self-supervised MRI reconstruction method that does not require fully sampled k-space data. METHODS: ReSiDe is inspired by plug-and-play (PnP) methods and employs a denoiser as a regularizer. In contrast to traditional PnP approaches that utilize generic denoisers or train deep learni... | ['Rizwan Ahmad', 'Philip Schniter', 'Sizhuo Liu'] | 2023-04-25 | null | null | null | null | ['image-reconstruction', 'mri-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 4.59559947e-01 -7.60474801e-03 -6.77737966e-02 -4.15280879e-01
-1.08429539e+00 -2.95131266e-01 4.80031133e-01 1.27012134e-01
-5.60216904e-01 6.88125074e-01 2.87397861e-01 -9.14899334e-02
-4.38690007e-01 -5.25963664e-01 -6.64608061e-01 -1.08272696e+00
-2.85449207e-01 3.88005495e-01 3.66528660e-01 -1.39730170... | [13.500967979431152, -2.4262123107910156] |
b035b15b-4707-4ae8-8de4-1cba66352677 | cyclic-learning-bridging-image-level-labels | 2306.02691 | null | https://arxiv.org/abs/2306.02691v1 | https://arxiv.org/pdf/2306.02691v1.pdf | Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation | Nuclei instance segmentation on histopathology images is of great clinical value for disease analysis. Generally, fully-supervised algorithms for this task require pixel-wise manual annotations, which is especially time-consuming and laborious for the high nuclei density. To alleviate the annotation burden, we seek to ... | ['Yan Xu', 'Yubo Fan', 'Jianzhong Shou', 'Maode Lai', 'Bingzheng Wei', 'Zihua Wang', 'Yongjian Wu', 'Yang Zhou'] | 2023-06-05 | null | null | null | null | ['semi-supervised-instance-segmentation'] | ['computer-vision'] | [ 5.44149280e-01 4.49849755e-01 -4.70032007e-01 -3.56544226e-01
-1.19533539e+00 -5.40283024e-01 2.55085140e-01 1.39970005e-01
-5.55407703e-01 7.37371027e-01 -2.46408731e-01 -4.73251611e-01
1.83943510e-01 -6.37844265e-01 -5.81712067e-01 -1.20943093e+00
3.66705388e-01 5.62896609e-01 4.99162465e-01 1.35056540... | [14.860212326049805, -2.7022008895874023] |
bae79f92-ff2f-4e23-91e8-9576f1b06559 | improved-her2-tumor-segmentation-with-subtype | 2211.06150 | null | https://arxiv.org/abs/2211.06150v1 | https://arxiv.org/pdf/2211.06150v1.pdf | Improved HER2 Tumor Segmentation with Subtype Balancing using Deep Generative Networks | Tumor segmentation in histopathology images is often complicated by its composition of different histological subtypes and class imbalance. Oversampling subtypes with low prevalence features is not a satisfactory solution since it eventually leads to overfitting. We propose to create synthetic images with semantically-... | ['Katharina Breininger', 'Ramona Erber', 'Andreas Maier', 'Peter A. Fasching', 'Matthias W. Beckmann', 'Arndt Hartmann', 'Frauke Wilm', 'Jingna Qiu', 'Carol I. Geppert', 'Matthias Rübner', 'Jana Mönius', 'Mathias Öttl'] | 2022-11-11 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 3.06175202e-01 6.26605451e-01 6.85837865e-02 -4.69125271e-01
-1.11228311e+00 -5.26449621e-01 3.87332350e-01 -5.60587235e-02
-4.71940815e-01 1.07637513e+00 9.18253958e-02 -3.39519203e-01
3.29069830e-02 -1.06402791e+00 -5.15554488e-01 -1.13528347e+00
3.21712792e-01 8.83421361e-01 2.36249603e-02 -1.43012732... | [14.770851135253906, -2.5707459449768066] |
f905e32d-ba40-4088-ad58-a51f42efad4d | seizure-detection-and-prediction-by-parallel | 2206.09951 | null | https://arxiv.org/abs/2206.09951v1 | https://arxiv.org/pdf/2206.09951v1.pdf | Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks | During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly. However, despite significant performance improvements, their hardware implementation using conventional technologies, such as Complementary Metal-Oxide-Semiconductor (CMOS), in power and area-constrained settings re... | ['Roman Genov', 'Mostafa Rahimi Azghadi', 'Amirali Amirsoleimani', 'Xuening Dong', 'Corey Lammie', 'Chenqi Li'] | 2022-06-20 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 2.17074409e-01 -3.71439695e-01 1.11501031e-01 -5.92535175e-02
-1.27473012e-01 -8.90303254e-02 -1.14030503e-01 2.24906802e-01
-8.42480123e-01 8.07119906e-01 -4.54192340e-01 -6.71093643e-01
-1.51270807e-01 -6.50652885e-01 -5.19173265e-01 -5.29428065e-01
-2.69012034e-01 -1.63297296e-01 2.52155572e-01 -1.15777150... | [8.290987968444824, 2.5629467964172363] |
f765c014-c91a-4a45-8573-d1e2cd6338fd | learning-graph-embeddings-for-compositional | 2102.01987 | null | https://arxiv.org/abs/2102.01987v3 | https://arxiv.org/pdf/2102.01987v3.pdf | Learning Graph Embeddings for Compositional Zero-shot Learning | In compositional zero-shot learning, the goal is to recognize unseen compositions (e.g. old dog) of observed visual primitives states (e.g. old, cute) and objects (e.g. car, dog) in the training set. This is challenging because the same state can for example alter the visual appearance of a dog drastically differently ... | ['Zeynep Akata', 'Federico Tombari', 'Yongqin Xian', 'Muhammad Ferjad Naeem'] | 2021-02-03 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Naeem_Learning_Graph_Embeddings_for_Compositional_Zero-Shot_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Naeem_Learning_Graph_Embeddings_for_Compositional_Zero-Shot_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 2.90887505e-01 3.11037242e-01 -1.15572125e-01 -2.12583527e-01
-3.57564598e-01 -5.91845751e-01 9.31380928e-01 1.14643492e-01
3.67844850e-02 1.81297556e-01 4.23605680e-01 -6.99964836e-02
2.19542757e-01 -8.66775155e-01 -1.25445700e+00 -7.64013886e-01
1.66480005e-01 5.51534891e-01 3.75229061e-01 -1.43515751... | [10.280181884765625, 2.2103545665740967] |
320506ed-3dd4-4604-b907-c69e43bdb58b | progressive-learning-of-3d-reconstruction | 2305.11102 | null | https://arxiv.org/abs/2305.11102v1 | https://arxiv.org/pdf/2305.11102v1.pdf | Progressive Learning of 3D Reconstruction Network from 2D GAN Data | This paper presents a method to reconstruct high-quality textured 3D models from single images. Current methods rely on datasets with expensive annotations; multi-view images and their camera parameters. Our method relies on GAN generated multi-view image datasets which have a negligible annotation cost. However, they ... | ['Bryan Catanzaro', 'Andrew Tao', 'Jun Gao', 'Aysegul Dundar'] | 2023-05-18 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [ 2.29997367e-01 1.56843454e-01 7.52421841e-02 -3.07116538e-01
-1.21258235e+00 -6.50537550e-01 5.01515388e-01 -9.33187723e-01
9.43725035e-02 6.05241299e-01 8.05766508e-02 -1.36350468e-01
5.86992443e-01 -7.37623215e-01 -1.05081463e+00 -5.13861954e-01
5.23041725e-01 6.75871193e-01 2.85153478e-01 -2.70278782... | [9.27061653137207, -3.0904335975646973] |
095d1e54-216f-435d-90a4-a8e1b4bea7c6 | fedaux-leveraging-unlabeled-auxiliary-data-in | 2102.02514 | null | https://arxiv.org/abs/2102.02514v1 | https://arxiv.org/pdf/2102.02514v1.pdf | FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning | Federated Distillation (FD) is a popular novel algorithmic paradigm for Federated Learning, which achieves training performance competitive to prior parameter averaging based methods, while additionally allowing the clients to train different model architectures, by distilling the client predictions on an unlabeled aux... | ['Wojciech Samek', 'Roman Rischke', 'Tim Korjakow', 'Felix Sattler'] | 2021-02-04 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [-4.77117598e-02 4.70236629e-01 -3.95852596e-01 -6.49802625e-01
-1.13240528e+00 -6.92930937e-01 7.18105912e-01 -1.66775405e-01
-2.65103191e-01 8.64401817e-01 1.32503897e-01 -5.51650703e-01
-1.97461378e-02 -6.90606475e-01 -8.14915240e-01 -9.25164044e-01
1.18599005e-01 8.30040336e-01 -1.04851216e-01 1.07584216... | [5.865743637084961, 6.277635097503662] |
6d88090d-dd65-4247-92aa-e12db76067c7 | exploring-the-impact-of-tunable-agents-in | 2101.11967 | null | https://arxiv.org/abs/2101.11967v1 | https://arxiv.org/pdf/2101.11967v1.pdf | Exploring the Impact of Tunable Agents in Sequential Social Dilemmas | When developing reinforcement learning agents, the standard approach is to train an agent to converge to a fixed policy that is as close to optimal as possible for a single fixed reward function. If different agent behaviour is required in the future, an agent trained in this way must normally be either fully or partia... | ['Patrick Mannion', "David O'Callaghan"] | 2021-01-28 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 8.56343582e-02 2.80103981e-01 -8.53122100e-02 -1.26553223e-01
-2.51946628e-01 -7.96427488e-01 4.68594223e-01 -4.13235389e-02
-9.76266623e-01 1.17688632e+00 -3.06240022e-01 -1.89069763e-01
-4.35000598e-01 -6.76406562e-01 -4.04003561e-01 -8.93153906e-01
-8.90062228e-02 7.73658693e-01 3.23353797e-01 -6.57262743... | [3.7984461784362793, 2.059037685394287] |
ad8f289a-b433-46b2-9794-ecd0e7ddc90c | efficient-single-image-depth-estimation-on | 2211.04470 | null | https://arxiv.org/abs/2211.04470v1 | https://arxiv.org/pdf/2211.04470v1.pdf | Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report | Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus, it is very crucial to have efficient and accurate depth estimation models that can run fast on low-power mobile chipsets. In this Mobile A... | ['Se Young Chun', 'Seunggyu Lee', 'Joonhee Lee', 'Seongmin Hong', 'Dongwon Park', 'Byeong Hyun Lee', 'Denis Sapozhnikov', 'Marcos V. Conde', 'Zhiguo Cao', 'Zihao Huang', 'Yiran Wang', 'Jiaqi Li', 'Bin Fu', 'Gang Yu', 'Guozhong Luo', 'Zilong Huang', 'Yicheng Wang', 'Ziyu Zhang', 'Lei Lei', 'Xiaotao Wang', 'Yanan Li', 'D... | 2022-11-07 | null | null | null | null | ['bokeh-effect-rendering'] | ['computer-vision'] | [ 1.44737959e-01 -1.06373370e-01 3.93986970e-01 -2.31138900e-01
-5.33450782e-01 -2.13655517e-01 2.93116271e-01 -6.76198378e-02
-5.26201904e-01 5.00199735e-01 -6.05122924e-01 -2.16915071e-01
8.23530555e-02 -1.12414324e+00 -4.61074114e-01 -4.93696988e-01
-1.22148305e-01 8.38223517e-01 7.13799596e-01 9.69771668... | [8.93791675567627, -2.317431688308716] |
f90ec7e2-56a0-4fce-92e4-3887102d5ff3 | bladder-segmentation-based-on-deep-learning | 2101.06498 | null | https://arxiv.org/abs/2101.06498v1 | https://arxiv.org/pdf/2101.06498v1.pdf | Bladder segmentation based on deep learning approaches: current limitations and lessons | Precise determination and assessment of bladder cancer (BC) extent of muscle invasion involvement guides proper risk stratification and personalized therapy selection. In this context, segmentation of both bladder walls and cancer are of pivotal importance, as it provides invaluable information to stage the primary tum... | ['Jose Dolz', 'K. C. Balaji', 'Chandana Lall', 'Dheeraj R Gopireddy', 'Mark G. Bandyk'] | 2021-01-16 | null | null | null | null | ['bladder-segmentation'] | ['medical'] | [ 5.37262380e-01 3.67793053e-01 -8.98113132e-01 3.17244492e-02
-7.72009015e-01 -5.48692763e-01 1.67308927e-01 5.54039538e-01
-6.93642139e-01 6.09586835e-01 2.72084236e-01 -7.83839226e-01
-1.97742999e-01 -6.65694714e-01 -2.18557537e-01 -7.67629087e-01
-1.40775248e-01 7.72800982e-01 -6.65805563e-02 -2.75599539... | [14.724312782287598, -2.6370041370391846] |
0e1eef72-62f3-46a8-9cec-2899dbd6565d | a-generative-appearance-model-for-end-to-end | 1811.11611 | null | http://arxiv.org/abs/1811.11611v2 | http://arxiv.org/pdf/1811.11611v2.pdf | A Generative Appearance Model for End-to-end Video Object Segmentation | One of the fundamental challenges in video object segmentation is to find an
effective representation of the target and background appearance. The best
performing approaches resort to extensive fine-tuning of a convolutional neural
network for this purpose. Besides being prohibitively expensive, this strategy
cannot be... | ['Emil Brissman', 'Fahad Shahbaz Khan', 'Martin Danelljan', 'Michael Felsberg', 'Joakim Johnander'] | 2018-11-28 | a-generative-appearance-model-for-end-to-end-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Johnander_A_Generative_Appearance_Model_for_End-To-End_Video_Object_Segmentation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Johnander_A_Generative_Appearance_Model_for_End-To-End_Video_Object_Segmentation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 2.26183385e-01 -1.58151388e-01 -1.99147597e-01 -5.11120498e-01
-6.52324200e-01 -6.09942079e-01 2.03379363e-01 -2.38781143e-02
-6.50879562e-01 3.29438537e-01 -5.23109794e-01 -2.84635186e-01
3.45023632e-01 -7.27112830e-01 -1.05382490e+00 -6.54231787e-01
1.80396512e-01 5.26297987e-01 6.74987912e-01 3.03062052... | [9.264240264892578, -0.0997573658823967] |
2852115a-d3b5-4b90-ace5-fe592914724e | large-scale-historical-watermark-recognition | 1908.10254 | null | https://arxiv.org/abs/1908.10254v1 | https://arxiv.org/pdf/1908.10254v1.pdf | Large-Scale Historical Watermark Recognition: dataset and a new consistency-based approach | Historical watermark recognition is a highly practical, yet unsolved challenge for archivists and historians. With a large number of well-defined classes, cluttered and noisy samples, different types of representations, both subtle differences between classes and high intra-class variation, historical watermarks are al... | ['Oumayma Bounou', 'Marc Smith', 'Ilaria Pastrolin', 'Mathieu Aubry', 'Xi Shen', 'Spyros Gidaris', 'Olivier Poncet'] | 2019-08-27 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.44417951e-01 -5.68156123e-01 -5.52506030e-01 -3.30774002e-02
-1.50303113e+00 -9.44775999e-01 1.02913523e+00 1.14227578e-01
-4.42043185e-01 6.26594722e-01 2.81765193e-01 5.70457336e-03
-3.25760305e-01 -7.58837521e-01 -6.63122177e-01 -6.26744807e-01
-2.56402999e-01 2.74945199e-01 3.35871279e-01 -4.82801609... | [11.662030220031738, 0.5692589282989502] |
1034b924-347d-45e0-8544-9e8f5fc61dc9 | using-descriptive-video-services-to-create-a | 1503.01070 | null | http://arxiv.org/abs/1503.01070v1 | http://arxiv.org/pdf/1503.01070v1.pdf | Using Descriptive Video Services to Create a Large Data Source for Video Annotation Research | In this work, we introduce a dataset of video annotated with high quality
natural language phrases describing the visual content in a given segment of
time. Our dataset is based on the Descriptive Video Service (DVS) that is now
encoded on many digital media products such as DVDs. DVS is an audio narration
describing t... | ['Aaron Courville', 'Hugo Larochelle', 'Christopher Pal', 'Atousa Torabi'] | 2015-03-03 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 5.00423610e-01 -2.78666198e-01 -2.45653167e-01 -4.47221726e-01
-8.30353320e-01 -7.46907949e-01 4.51343268e-01 5.40535748e-01
-9.43409428e-02 3.47969413e-01 8.91021729e-01 1.77091107e-01
3.98642085e-02 -3.32535148e-01 -5.74992597e-01 -2.05960125e-01
-7.35246688e-02 4.11114246e-02 6.08161986e-01 -4.52465303... | [10.520585060119629, 0.7413102388381958] |
20aac081-baa7-4c5d-89f6-301aa54d837a | understanding-contrastive-learning-through | 2306.11526 | null | https://arxiv.org/abs/2306.11526v1 | https://arxiv.org/pdf/2306.11526v1.pdf | Understanding Contrastive Learning Through the Lens of Margins | Self-supervised learning, or SSL, holds the key to expanding the usage of machine learning in real-world tasks by alleviating heavy human supervision. Contrastive learning and its varieties have been SSL strategies in various fields. We use margins as a stepping stone for understanding how contrastive learning works at... | ['JaeHan Park', 'JaeHyun Park', 'Sooill Park', 'Taesoo Kim', 'Daniel Rho'] | 2023-06-20 | null | null | null | null | ['contrastive-learning', 'self-supervised-learning', 'contrastive-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 3.07079464e-01 5.48137836e-02 -9.41896975e-01 -5.33773065e-01
-5.40035844e-01 -4.60770547e-01 6.40334189e-01 2.11830422e-01
-3.15755308e-01 6.38647377e-01 3.87779534e-01 -1.91338807e-01
-1.32701457e-01 -5.75850010e-01 -5.83078504e-01 -6.95509374e-01
-1.85938254e-01 5.02141193e-02 4.24918711e-01 -2.92631924... | [9.393194198608398, 2.97517728805542] |
dd7965e9-4965-4026-bdb0-0121638dc0ca | deep-learning-method-for-object-tracking | 2306.06126 | null | https://arxiv.org/abs/2306.06126v2 | https://arxiv.org/pdf/2306.06126v2.pdf | Deep Learning Method for Cell-Wise Object Tracking, Velocity Estimation and Projection of Sensor Data over Time | Current Deep Learning methods for environment segmentation and velocity estimation rely on Convolutional Recurrent Neural Networks to exploit spatio-temporal relationships within obtained sensor data. These approaches derive scene dynamics implicitly by correlating novel input and memorized data utilizing ConvNets. We ... | ['Anton Kummert', 'Kevin Kollek', 'Dominic Spata', 'Mirko Meuter', 'Moritz Luszek', 'Marco Braun'] | 2023-06-08 | null | null | null | null | ['object-tracking'] | ['computer-vision'] | [ 4.71858323e-01 -5.29455483e-01 -6.65724352e-02 -1.49571314e-01
-3.88580412e-01 -6.21170700e-01 4.31876272e-01 -5.92813222e-03
-7.48018503e-01 5.96351624e-01 8.06287751e-02 1.21188406e-02
-2.64215618e-01 -8.47555935e-01 -8.37781966e-01 -6.09510541e-01
-3.63920659e-01 -1.72417194e-01 5.29338837e-01 1.60812326... | [8.819438934326172, -0.3180311322212219] |
30d539f5-9e21-4278-8e85-b0b4130fe7ff | deep-reinforcement-learning-for-complex-1 | 2001.03877 | null | https://arxiv.org/abs/2001.03877v1 | https://arxiv.org/pdf/2001.03877v1.pdf | Deep Reinforcement Learning for Complex Manipulation Tasks with Sparse Feedback | Learning optimal policies from sparse feedback is a known challenge in reinforcement learning. Hindsight Experience Replay (HER) is a multi-goal reinforcement learning algorithm that comes to solve such tasks. The algorithm treats every failure as a success for an alternative (virtual) goal that has been achieved in th... | ['Binyamin Manela'] | 2020-01-12 | null | null | null | null | ['multi-goal-reinforcement-learning'] | ['methodology'] | [ 5.22798672e-02 1.57277167e-01 -6.94290847e-02 -1.08025402e-01
-6.92553461e-01 -5.57465196e-01 5.53662658e-01 7.50966594e-02
-7.18865275e-01 1.47758269e+00 1.62474990e-01 -1.75586954e-01
-6.23603821e-01 -6.43049300e-01 -7.19843805e-01 -7.39230633e-01
-3.70197982e-01 5.46469808e-01 1.76956058e-01 -5.98270595... | [4.000482559204102, 1.6427114009857178] |
0a5195a3-9212-4629-b433-e147bb5e316a | softtreemax-policy-gradient-with-tree-search | 2209.13966 | null | https://arxiv.org/abs/2209.13966v1 | https://arxiv.org/pdf/2209.13966v1.pdf | SoftTreeMax: Policy Gradient with Tree Search | Policy-gradient methods are widely used for learning control policies. They can be easily distributed to multiple workers and reach state-of-the-art results in many domains. Unfortunately, they exhibit large variance and subsequently suffer from high-sample complexity since they aggregate gradients over entire trajecto... | ['Gal Chechik', 'Shie Mannor', 'Assaf Hallak', 'Gal Dalal'] | 2022-09-28 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-5.99703416e-02 -6.23451620e-02 -1.02162850e+00 -7.52153844e-02
-1.13564682e+00 -7.58998871e-01 8.37945580e-01 2.37636268e-01
-7.16643453e-01 1.00454307e+00 3.48363072e-01 -7.43733943e-01
-1.54976904e-01 -5.90982378e-01 -6.89734459e-01 -7.60461032e-01
-3.20780396e-01 4.25895602e-01 6.27220929e-01 4.02509887... | [3.9974238872528076, 2.124027967453003] |
eb8f1479-532b-4838-b429-d11406b14f6c | encoding-clinical-priori-in-3d-convolutional | 2011.00263 | null | https://arxiv.org/abs/2011.00263v4 | https://arxiv.org/pdf/2011.00263v4.pdf | Encoding Clinical Priori in 3D Convolutional Neural Networks for Prostate Cancer Detection in bpMRI | We hypothesize that anatomical priors can be viable mediums to infuse domain-specific clinical knowledge into state-of-the-art convolutional neural networks (CNN) based on the U-Net architecture. We introduce a probabilistic population prior which captures the spatial prevalence and zonal distinction of clinically sign... | ['Henkjan Huisman', 'Matin Hosseinzadeh', 'Anindo Saha'] | 2020-10-31 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 5.65652013e-01 7.45995939e-01 -3.68059307e-01 -4.60912585e-01
-1.49815702e+00 -4.78586525e-01 7.32426703e-01 3.77011150e-01
-5.72885811e-01 9.99838352e-01 3.10744643e-01 -5.65291464e-01
-3.52134675e-01 -7.53736854e-01 -8.78078282e-01 -6.16507709e-01
-7.83556759e-01 8.27362895e-01 1.75384611e-01 3.28790784... | [14.787736892700195, -2.4693758487701416] |
84c2e6c7-ee6a-43c3-8f97-b878dcfeb697 | low-resource-neural-machine-translation-with | 2210.06716 | null | https://arxiv.org/abs/2210.06716v1 | https://arxiv.org/pdf/2210.06716v1.pdf | Low-resource Neural Machine Translation with Cross-modal Alignment | How to achieve neural machine translation with limited parallel data? Existing techniques often rely on large-scale monolingual corpora, which is impractical for some low-resource languages. In this paper, we turn to connect several low-resource languages to a particular high-resource one by additional visual modality.... | ['Yang Feng', 'Qingkai Fang', 'Zhe Yang'] | 2022-10-13 | null | null | null | null | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 6.35945052e-02 -6.79823756e-01 -4.21261787e-01 -3.20154071e-01
-1.56847370e+00 -4.42978203e-01 8.69783640e-01 -3.51207912e-01
-6.78960502e-01 7.99464941e-01 3.45397949e-01 -2.86106199e-01
5.72279990e-01 -4.96812284e-01 -8.16440165e-01 -5.82158625e-01
5.25551975e-01 5.75449049e-01 -6.61374629e-02 -2.84020454... | [11.255960464477539, 1.6405242681503296] |
e75e2c15-eafe-4dcb-b5eb-ee684574cc7e | self-supervised-assisted-active-learning-for | 2205.07021 | null | https://arxiv.org/abs/2205.07021v1 | https://arxiv.org/pdf/2205.07021v1.pdf | Self-supervised Assisted Active Learning for Skin Lesion Segmentation | Label scarcity has been a long-standing issue for biomedical image segmentation, due to high annotation costs and professional requirements. Recently, active learning (AL) strategies strive to reduce annotation costs by querying a small portion of data for annotation, receiving much traction in the field of medical ima... | ['Cuntai Guan', 'Bharadwaj Veeravalli', 'Kaixin Xu', 'Zeng Zeng', 'Wenjing Lu', 'Ziyuan Zhao'] | 2022-05-14 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 7.46992648e-01 4.50082868e-01 -7.64476776e-01 -6.80274308e-01
-1.31668425e+00 -3.32993388e-01 2.90050417e-01 5.31448126e-01
-7.02074111e-01 6.53911352e-01 4.20248173e-02 2.25360766e-02
-8.02527815e-02 -3.34372044e-01 -2.80190676e-01 -1.04778552e+00
3.04941326e-01 7.30323017e-01 3.65243435e-01 4.69938159... | [14.774646759033203, -2.126020908355713] |
4411ab94-41cb-4e40-8583-f673dd44078f | the-performance-impact-of-combining-agent | null | null | https://dl.acm.org/doi/abs/10.1145/3549737.3549773 | https://dl.acm.org/doi/abs/10.1145/3549737.3549773 | The Performance Impact of Combining Agent Factorization with Different Learning Algorithms for Multiagent Coordination | Factorizing a multiagent system refers to partitioning the state-
action space to individual agents and defining the interactions be-
tween those agents. This so-called agent factorization is of much im-
portance in real-world industrial settings, and is a process that can
have significant performance implications.... | ['Georgios Chalkiadakis', 'Stavros Orfanoudakis', 'Andreas Kallinteris'] | 2022-09-09 | null | null | null | setn-2022-9 | ['policy-gradient-methods'] | ['methodology'] | [-8.38891789e-02 -1.74694974e-02 -8.96968618e-02 3.74687314e-01
-2.62190014e-01 -7.26455152e-01 7.07011223e-01 4.02243555e-01
-6.35747850e-01 1.16295266e+00 -1.26396894e-01 -4.52530205e-01
-8.14487338e-01 -7.43543088e-01 -5.40886939e-01 -1.00095356e+00
-6.13293946e-01 1.05101919e+00 6.45580962e-02 -7.23887742... | [3.774029493331909, 2.0399718284606934] |
098d9e8f-b4e8-4f2f-9092-0c68d0852479 | moms-with-events-multi-object-motion | 2006.06158 | null | https://arxiv.org/abs/2006.06158v2 | https://arxiv.org/pdf/2006.06158v2.pdf | 0-MMS: Zero-Shot Multi-Motion Segmentation With A Monocular Event Camera | Segmentation of moving objects in dynamic scenes is a key process in scene understanding for navigation tasks. Classical cameras suffer from motion blur in such scenarios rendering them effete. On the contrary, event cameras, because of their high temporal resolution and lack of motion blur, are tailor-made for this pr... | ['Cornelia Fermüller', 'Chahat Deep Singh', 'Nitin J. Sanket', 'Yiannis Aloimonos', 'Chethan M. Parameshwara'] | 2020-06-11 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 1.13954358e-01 -7.22224474e-01 1.57171801e-01 -1.11955270e-01
-4.69018370e-01 -8.40892196e-01 6.02011502e-01 -1.36635795e-01
-8.72670591e-01 4.18252259e-01 -2.18463734e-01 -1.32912517e-01
1.03918366e-01 -5.14429867e-01 -6.09246731e-01 -7.41166294e-01
6.59018978e-02 2.72525221e-01 1.16987228e+00 5.43268993... | [8.64375114440918, -1.1160309314727783] |
a9cb774c-143c-4ac7-9e5c-b5dfbd76ab9b | complex-and-precise-movie-and-book | null | null | https://aclanthology.org/L18-1419 | https://aclanthology.org/L18-1419.pdf | Complex and Precise Movie and Book Annotations in French Language for Aspect Based Sentiment Analysis | null | ['Stefania Pecore', 'Jeanne Villaneau'] | 2018-05-01 | complex-and-precise-movie-and-book-1 | https://aclanthology.org/L18-1419 | https://aclanthology.org/L18-1419.pdf | lrec-2018-5 | ['subjectivity-analysis'] | ['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... | [-7.204014778137207, 3.607712507247925] |
7f964f07-30d8-446d-971a-25a1315b8975 | flow-adapter-architecture-for-unsupervised | 2204.12225 | null | https://arxiv.org/abs/2204.12225v1 | https://arxiv.org/pdf/2204.12225v1.pdf | Flow-Adapter Architecture for Unsupervised Machine Translation | In this work, we propose a flow-adapter architecture for unsupervised NMT. It leverages normalizing flows to explicitly model the distributions of sentence-level latent representations, which are subsequently used in conjunction with the attention mechanism for the translation task. The primary novelties of our model a... | ['Hinrich Schütze', 'Haris Jabbar', 'Yihong Liu'] | 2022-04-26 | null | https://aclanthology.org/2022.acl-long.89 | https://aclanthology.org/2022.acl-long.89.pdf | acl-2022-5 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 4.36572045e-01 2.57838696e-01 -7.19220042e-01 -4.94105458e-01
-8.93783450e-01 -6.88963234e-01 1.00336564e+00 -3.04636862e-02
-2.18683615e-01 6.17186368e-01 6.97893083e-01 -7.30248988e-01
2.83206373e-01 -6.54196143e-01 -7.20989287e-01 -3.76374573e-01
3.32367748e-01 8.59598398e-01 -3.88210118e-01 -2.48865664... | [11.596756935119629, 9.677367210388184] |
eb5c9dd7-10d0-4305-8f82-804463259524 | acceleration-of-subspace-learning-machine-via | 2208.07023 | null | https://arxiv.org/abs/2208.07023v1 | https://arxiv.org/pdf/2208.07023v1.pdf | Acceleration of Subspace Learning Machine via Particle Swarm Optimization and Parallel Processing | Built upon the decision tree (DT) classification and regression idea, the subspace learning machine (SLM) has been recently proposed to offer higher performance in general classification and regression tasks. Its performance improvement is reached at the expense of higher computational complexity. In this work, we inve... | ['C. -C. Jay Kuo', 'Vinod K. Mishra', 'Ethan Harrison', 'Joseph Lin', 'Yuhuai Liu', 'Yijing Yang', 'Hongyu Fu'] | 2022-08-15 | null | null | null | null | ['classification'] | ['methodology'] | [ 1.49375007e-01 -4.63707268e-01 -3.39057952e-01 -8.34206939e-02
-6.52420580e-01 -9.50357690e-02 5.75117886e-01 -4.47682552e-02
-3.88998419e-01 6.78621650e-01 -5.35773160e-03 -5.25928795e-01
-3.37737501e-01 -6.20997787e-01 1.16241485e-01 -9.81221735e-01
-2.29642633e-03 4.67159599e-01 1.87393680e-01 4.40219864... | [7.871343612670898, 4.145880699157715] |
6c51b266-5271-497c-b911-05894aa18c93 | causality-analysis-of-twitter-sentiments-and | null | null | https://aclanthology.org/W18-3102 | https://aclanthology.org/W18-3102.pdf | Causality Analysis of Twitter Sentiments and Stock Market Returns | Sentiment analysis is the process of identifying the opinion expressed in text. Recently, it has been used to study behavioral finance, and in particular the effect of opinions and emotions on economic or financial decisions. In this paper, we use a public dataset of labeled tweets that has been labeled by Amazon Mecha... | ['Wlodek Zadrozny', 'Bhanu Praneeth', 'Narges Tabari', 'Mirsad Hadzikadic', 'Armin Seyeditabari', 'Piyusha Biswas'] | 2018-07-01 | null | null | null | ws-2018-7 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-7.45056272e-01 -3.01383197e-01 -6.64996088e-01 -3.82317662e-01
8.02245438e-02 -9.23416018e-01 1.14996743e+00 4.88193840e-01
-4.87757474e-01 7.94283032e-01 6.32770240e-01 -4.85431045e-01
3.13172042e-01 -1.17616606e+00 -4.81985927e-01 -4.33562130e-01
-4.89667766e-02 3.33733819e-02 2.99966127e-01 -5.94319463... | [4.517208099365234, 4.415956020355225] |
0689ce85-6691-4120-91d4-27887539b1fb | two-for-one-diffusion-models-and-force-fields | 2302.00600 | null | https://arxiv.org/abs/2302.00600v1 | https://arxiv.org/pdf/2302.00600v1.pdf | Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics | Coarse-grained (CG) molecular dynamics enables the study of biological processes at temporal and spatial scales that would be intractable at an atomistic resolution. However, accurately learning a CG force field remains a challenge. In this work, we leverage connections between score-based generative models, force fiel... | ['Rianne van den Berg', 'Robert Pinsler', 'Frank Noé', 'Cecilia Clementi', 'Marco Federici', 'Daniel Zuegner', 'Chin-wei Huang', 'Victor Garcia Satorras', 'Marloes Arts'] | 2023-02-01 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [ 1.81798056e-01 -2.01696083e-01 1.37133688e-01 -2.16278628e-01
-6.72876239e-01 -7.58274555e-01 7.20501125e-01 7.40515366e-02
-4.85575914e-01 1.14594710e+00 7.68711716e-02 -6.38470888e-01
4.18125466e-02 -8.34720969e-01 -9.55259502e-01 -1.05701888e+00
-2.08742544e-01 8.02336931e-01 3.13196868e-01 -1.87859088... | [4.966746807098389, 5.3663763999938965] |
b2bec621-09b0-42a9-85b6-ec29084cc376 | adversarial-attacks-on-graph-classification | 2111.02842 | null | https://arxiv.org/abs/2111.02842v1 | https://arxiv.org/pdf/2111.02842v1.pdf | Adversarial Attacks on Graph Classification via Bayesian Optimisation | Graph neural networks, a popular class of models effective in a wide range of graph-based learning tasks, have been shown to be vulnerable to adversarial attacks. While the majority of the literature focuses on such vulnerability in node-level classification tasks, little effort has been dedicated to analysing adversar... | ['Xiaowen Dong', 'Michael A. Osborne', 'Arno Blaas', 'Binxin Ru', 'Henry Kenlay', 'Xingchen Wan'] | 2021-11-04 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 6.66250050e-01 1.80422977e-01 -3.87980863e-02 1.15763426e-01
-3.63315821e-01 -9.78684723e-01 7.49785304e-01 6.02060676e-01
-1.25627279e-01 7.83542514e-01 -3.33053201e-01 -7.77892053e-01
-5.42922616e-01 -9.49145973e-01 -6.72205210e-01 -8.88889194e-01
-5.87747812e-01 5.66178083e-01 4.99473602e-01 -3.53340805... | [6.01223087310791, 7.426535606384277] |
cf16cc47-e66d-487d-a5a9-0c385f1078bd | deep-learning-and-its-applications-to-wifi | 2207.07859 | null | https://arxiv.org/abs/2207.07859v3 | https://arxiv.org/pdf/2207.07859v3.pdf | SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing | WiFi sensing has been evolving rapidly in recent years. Empowered by propagation models and deep learning methods, many challenging applications are realized such as WiFi-based human activity recognition and gesture recognition. However, in contrast to deep learning for visual recognition and natural language processin... | ['Lihua Xie', 'Sumei Sun', 'Chris Xiaoxuan Lu', 'Han Zou', 'Dazhuo Wang', 'Xinyan Chen', 'Jianfei Yang'] | 2022-07-16 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 5.26351094e-01 -3.90610188e-01 -3.89727950e-01 -4.08331543e-01
-9.84263957e-01 -5.34251869e-01 3.44869167e-01 -7.26589262e-01
-2.03355804e-01 6.06190860e-01 2.48315692e-01 -2.99889952e-01
-2.62129873e-01 -7.13266611e-01 -6.04233503e-01 -7.46416986e-01
-2.68764019e-01 -1.05457567e-01 -1.19151644e-01 2.47525334... | [6.665046691894531, 0.7131935954093933] |
cd0e9cd5-fb12-4a1e-99af-8bc0f3431623 | robust-template-matching-via-hierarchical | 2007.15817 | null | https://arxiv.org/abs/2007.15817v3 | https://arxiv.org/pdf/2007.15817v3.pdf | Robust Template Matching via Hierarchical Convolutional Features from a Shape Biased CNN | Finding a template in a search image is an important task underlying many computer vision applications. Recent approaches perform template matching in a deep feature-space, produced by a convolutional neural network (CNN), which is found to provide more tolerance to changes in appearance. In this article we investigate... | ['M. W. Spratling', 'Bo Gao'] | 2020-07-31 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 3.57490212e-01 -3.45737189e-01 -2.25504357e-02 -4.47329491e-01
-3.41918111e-01 -5.86348593e-01 7.45461345e-01 -2.75316536e-01
-3.36577982e-01 2.24194333e-01 -3.65997292e-02 1.10412598e-01
-1.01892531e-01 -7.90305972e-01 -6.70958102e-01 -5.15698075e-01
1.83841348e-01 1.18399568e-01 5.21292925e-01 -1.44922897... | [10.49347972869873, 0.20223908126354218] |
32876027-4057-40a7-b5ac-43b387cc859b | trainable-referring-expression-generation | 1704.03693 | null | http://arxiv.org/abs/1704.03693v1 | http://arxiv.org/pdf/1704.03693v1.pdf | Trainable Referring Expression Generation using Overspecification Preferences | Referring expression generation (REG) models that use speaker-dependent
information require a considerable amount of training data produced by every
individual speaker, or may otherwise perform poorly. In this work we present a
simple REG experiment that allows the use of larger training data sets by
grouping speakers ... | ['Thiago castro Ferreira', 'Ivandre Paraboni'] | 2017-04-12 | null | null | null | null | ['referring-expression-generation'] | ['computer-vision'] | [ 2.08584871e-02 3.84240538e-01 1.25211719e-02 -8.68413568e-01
-1.23279262e+00 -6.56019092e-01 8.27786565e-01 -9.08284560e-02
-4.65103954e-01 9.12536740e-01 6.29225731e-01 -1.65257141e-01
1.90835446e-01 -4.45682466e-01 -1.58470705e-01 -3.51153046e-01
-4.24187407e-02 6.55883431e-01 2.59579360e-01 -7.18560100... | [10.485576629638672, 9.09239673614502] |
0e1ef304-e8a7-4dfe-a535-eb826d1c39c7 | decentralised-sparse-multi-task-regression | 1912.01417 | null | https://arxiv.org/abs/1912.01417v2 | https://arxiv.org/pdf/1912.01417v2.pdf | Distributed Machine Learning with Sparse Heterogeneous Data | Motivated by distributed machine learning settings such as Federated Learning, we consider the problem of fitting a statistical model across a distributed collection of heterogeneous data sets whose similarity structure is encoded by a graph topology. Precisely, we analyse the case where each node is associated with fi... | ['Patrick Rebeschini', 'Dominic Richards', 'Sahand N. Negahban'] | 2019-12-03 | distributed-machine-learning-with-sparse | http://proceedings.neurips.cc/paper/2021/hash/959776b99b006e5785c3a3364949ce47-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/959776b99b006e5785c3a3364949ce47-Paper.pdf | neurips-2021-12 | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 2.17127785e-01 2.90674657e-01 1.03908859e-03 -2.83686426e-02
-9.85813200e-01 -5.10814607e-01 1.60061926e-01 1.32542551e-01
-8.93980339e-02 6.44775093e-01 3.63402426e-01 4.03190814e-02
-6.17087781e-01 -6.94229484e-01 -7.94847727e-01 -1.13442826e+00
-3.78265828e-01 6.16360247e-01 -6.53370500e-01 1.31226569... | [6.459798812866211, 4.9517903327941895] |
d5840607-7c7c-4497-844b-8c9a17727cdb | incorporating-ultrasound-tongue-images-for | 2305.14933 | null | https://arxiv.org/abs/2305.14933v1 | https://arxiv.org/pdf/2305.14933v1.pdf | Incorporating Ultrasound Tongue Images for Audio-Visual Speech Enhancement through Knowledge Distillation | Audio-visual speech enhancement (AV-SE) aims to enhance degraded speech along with extra visual information such as lip videos, and has been shown to be more effective than audio-only speech enhancement. This paper proposes further incorporating ultrasound tongue images to improve lip-based AV-SE systems' performance. ... | ['Zhen-Hua Ling', 'Yang Ai', 'Rui-Chen Zheng'] | 2023-05-24 | null | null | null | null | ['automatic-speech-recognition', 'speech-enhancement'] | ['speech', 'speech'] | [ 3.57275933e-01 4.15288538e-01 -3.02751243e-01 -1.47142097e-01
-1.48241377e+00 -1.49282292e-01 3.07853103e-01 -2.42220819e-01
-3.55834961e-01 4.36308950e-01 8.77371907e-01 -4.33927029e-01
3.05193543e-01 1.30390339e-02 -6.92126930e-01 -7.68442810e-01
3.67817521e-01 -2.37483919e-01 -9.47392806e-02 -1.23952568... | [14.417313575744629, 5.152610778808594] |
efdbc9aa-7d45-4d9a-ba9f-68681f95c7bb | dominant-set-clustering-and-pooling-for-multi | 1906.01592 | null | https://arxiv.org/abs/1906.01592v1 | https://arxiv.org/pdf/1906.01592v1.pdf | Dominant Set Clustering and Pooling for Multi-View 3D Object Recognition | View based strategies for 3D object recognition have proven to be very successful. The state-of-the-art methods now achieve over 90% correct category level recognition performance on appearance images. We improve upon these methods by introducing a view clustering and pooling layer based on dominant sets. The key idea ... | ['Kaleem Siddiqi', 'Chu Wang', 'Marcello Pelillo'] | 2019-06-04 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-1.15799680e-01 -2.69366026e-01 -1.06407270e-01 -5.70456147e-01
-7.70008504e-01 -5.33126116e-01 5.37129581e-01 -2.41886601e-01
-2.93108702e-01 -8.63673091e-02 -7.91394431e-03 -3.75951715e-02
3.68785918e-01 -4.49925363e-01 -6.51593089e-01 -6.65809095e-01
1.19663984e-01 2.23597929e-01 2.45557055e-01 2.50863224... | [8.243128776550293, -3.618553400039673] |
832b457f-d8f9-48df-98fd-c1ab7f5c3add | continual-pre-training-mitigates-forgetting | 2205.09357 | null | https://arxiv.org/abs/2205.09357v1 | https://arxiv.org/pdf/2205.09357v1.pdf | Continual Pre-Training Mitigates Forgetting in Language and Vision | Pre-trained models are nowadays a fundamental component of machine learning research. In continual learning, they are commonly used to initialize the model before training on the stream of non-stationary data. However, pre-training is rarely applied during continual learning. We formalize and investigate the characteri... | ['Davide Bacciu', 'Vincenzo Lomonaco', 'Lucia Passaro', 'Antonio Carta', 'Tinne Tuytelaars', 'Andrea Cossu'] | 2022-05-19 | null | null | null | null | ['continual-pretraining'] | ['methodology'] | [ 3.71311307e-01 -1.36965767e-01 -7.90951326e-02 -3.45506638e-01
-4.13341373e-01 -5.43983757e-01 9.46190894e-01 5.01664102e-01
-9.08673942e-01 6.62768722e-01 8.72626528e-02 -3.79313499e-01
6.44703768e-03 -4.76535916e-01 -1.14182389e+00 -5.34326255e-01
9.44860354e-02 6.18863404e-01 3.68705750e-01 2.04020012... | [9.864861488342285, 3.343271493911743] |
952929f4-337d-4c21-adbc-924029fed4c0 | disentangling-and-unifying-graph-convolutions | 2003.14111 | null | https://arxiv.org/abs/2003.14111v2 | https://arxiv.org/pdf/2003.14111v2.pdf | Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition | Spatial-temporal graphs have been widely used by skeleton-based action recognition algorithms to model human action dynamics. To capture robust movement patterns from these graphs, long-range and multi-scale context aggregation and spatial-temporal dependency modeling are critical aspects of a powerful feature extracto... | ['Zhiyong Wang', 'Zhenghao Chen', 'Wanli Ouyang', 'Ziyu Liu', 'Hongwen Zhang'] | 2020-03-31 | disentangling-and-unifying-graph-convolutions-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Disentangling_and_Unifying_Graph_Convolutions_for_Skeleton-Based_Action_Recognition_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_Disentangling_and_Unifying_Graph_Convolutions_for_Skeleton-Based_Action_Recognition_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-human-action-recognition', 'long-range-modeling'] | ['computer-vision', 'natural-language-processing'] | [ 1.30170330e-01 -3.31260383e-01 -4.68215942e-01 4.97910790e-02
-2.66620547e-01 -4.57109421e-01 7.73681104e-01 1.55170366e-01
-5.08569300e-01 4.66587067e-01 6.80051267e-01 -3.01413715e-01
-5.38664043e-01 -8.74078989e-01 -5.67589998e-01 -3.62656057e-01
-5.74034870e-01 -2.14830674e-02 6.49091423e-01 -3.25376958... | [7.706001281738281, 0.1951930820941925] |
fe936e20-88d4-4cb3-a3ba-08f4a38d2a0c | partial-discharge-direction-of-arrival | 2010.08309 | null | https://arxiv.org/abs/2010.08309v1 | https://arxiv.org/pdf/2010.08309v1.pdf | Partial Discharge Direction of Arrival Estimation in Air-insulated Substation by UHF Wireless Array and RSSI Maximum Likelihood Estimator | The quick detection and localization of partial discharge (PD) in an air-insulated substation (AIS) based on ultrahigh-frequency (UHF) sensor arrays are efficient for power equipment monitoring. The adopted UHF PD time difference of arrival (TDOA) methods mainly use the time difference of electromagnetic wave signals. ... | ['Xiuchen Jiang', 'Gehao Sheng', 'Lingen Luo', 'Bei Han'] | 2020-10-16 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [-3.48646075e-01 -4.80205476e-01 4.28901076e-01 -1.33947447e-01
-7.31005371e-01 -5.54466367e-01 4.11405303e-02 1.10088162e-01
1.37010396e-01 9.87567484e-01 -1.57527462e-01 -5.95459566e-02
-7.23655879e-01 -9.99759853e-01 -5.11741862e-02 -1.46853554e+00
-4.85359222e-01 4.79546897e-02 -1.79676905e-01 3.39263707... | [6.571604251861572, 1.6582679748535156] |
54498f01-029e-4e17-a5ae-87128671de6a | omninerf-hybriding-omnidirectional-distance | 2209.13433 | null | https://arxiv.org/abs/2209.13433v1 | https://arxiv.org/pdf/2209.13433v1.pdf | OmniNeRF: Hybriding Omnidirectional Distance and Radiance fields for Neural Surface Reconstruction | 3D reconstruction from images has wide applications in Virtual Reality and Automatic Driving, where the precision requirement is very high. Ground-breaking research in the neural radiance field (NeRF) by utilizing Multi-Layer Perceptions has dramatically improved the representation quality of 3D objects. Some later stu... | ['Yi Xu', 'Zirui Wu', 'Bolin Song', 'Jiaming Shen'] | 2022-09-27 | null | null | null | null | ['3d-scene-reconstruction', '3d-shape-representation'] | ['computer-vision', 'computer-vision'] | [ 3.21819305e-01 5.93792945e-02 2.31090993e-01 -5.04941761e-01
-3.13059986e-01 -9.11315531e-02 5.60675621e-01 -4.45515841e-01
-2.80888826e-01 5.63271582e-01 9.48826224e-02 -2.78342724e-01
-3.87418509e-01 -1.07357073e+00 -7.88252771e-01 -6.65910423e-01
2.28784516e-01 1.47856832e-01 1.21339701e-01 -4.50497180... | [8.7694091796875, -2.7949726581573486] |
98ee04b3-5f5d-4a4d-8620-f0779d919311 | fast-accuracy-estimation-of-deep-learning | 2010.09453 | null | https://arxiv.org/abs/2010.09453v3 | https://arxiv.org/pdf/2010.09453v3.pdf | Fast accuracy estimation of deep learning based multi-class musical source separation | Music source separation represents the task of extracting all the instruments from a given song. Recent breakthroughs on this challenge have gravitated around a single dataset, MUSDB, only limited to four instrument classes. Larger datasets and more instruments are costly and time-consuming in collecting data and train... | ['Milos Cernak', 'Benjamin Ricaud', 'Alexandru Mocanu'] | 2020-10-19 | null | null | null | null | ['audio-source-separation', 'music-source-separation'] | ['audio', 'music'] | [ 1.73235252e-01 -4.47700411e-01 -1.23296522e-01 9.83439386e-02
-1.03272104e+00 -8.72216940e-01 3.14129815e-02 -1.41226575e-01
-1.23879105e-01 4.20248687e-01 7.96200484e-02 1.43466122e-03
-5.17554581e-01 -4.65629458e-01 -5.07426441e-01 -7.92998493e-01
-3.30918580e-01 2.99884766e-01 -1.33228302e-01 -2.24243671... | [15.461929321289062, 5.5297651290893555] |
bd18fc52-7a13-4068-b931-c94e256f7bfa | action-knowledge-for-video-captioning-with | null | null | https://www.sciencedirect.com/science/article/pii/S1319157823000666 | https://www.sciencedirect.com/science/article/pii/S1319157823000666/pdf | Action knowledge for video captioning with graph neural networks | Many existing video captioning methods capture action information in the video by exploiting features extracted from an action recognition model. However, directly using the action features without object-specific representation may not well capture the object interactions. Consequently, the generated captions may not ... | ['Cheol Jeong', 'Fikriansyah Adzaka', 'Bahy Helmi Hartoyo Putra', 'Vania Velda', 'Willy Fitra Hendria'] | 2023-03-16 | null | null | null | journal-of-king-saud-university-computer-and-3 | ['video-captioning', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision'] | [ 2.58499950e-01 1.36012822e-01 -3.76801580e-01 -2.30742842e-01
-4.05172974e-01 -3.43145460e-01 3.92735511e-01 1.31319791e-01
-8.51787720e-03 7.73745358e-01 4.52263504e-01 1.70439661e-01
9.82797816e-02 -7.75234938e-01 -1.15705824e+00 -5.86544991e-01
3.95545252e-02 2.26547956e-01 3.55215341e-01 7.37919137... | [9.838772773742676, 0.7900264263153076] |
d3247487-d041-4762-aa76-02d3b645db14 | pnp-adanet-plug-and-play-adversarial-domain | 1812.07907 | null | http://arxiv.org/abs/1812.07907v1 | http://arxiv.org/pdf/1812.07907v1.pdf | PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation | Deep convolutional networks have demonstrated the state-of-the-art
performance on various medical image computing tasks. Leveraging images from
different modalities for the same analysis task holds clinical benefits.
However, the generalization capability of deep models on test data with
different distributions remain ... | ['Pheng-Ann Heng', 'Cheng Ouyang', 'Xiahai Zhuang', 'Qi Dou', 'Hao Chen', 'Cheng Chen', 'Ben Glocker'] | 2018-12-19 | null | null | null | null | ['cardiac-segmentation', 'medical-image-generation'] | ['medical', 'medical'] | [ 5.72433531e-01 5.92105277e-02 -3.72489989e-02 -5.52473843e-01
-9.12975609e-01 -7.94666708e-01 2.76501805e-01 -1.05006523e-01
-6.49137974e-01 8.36972117e-01 -1.98743314e-01 -2.25466952e-01
5.09551652e-02 -6.62416816e-01 -6.83068037e-01 -1.02796650e+00
-5.95960543e-02 5.76505482e-01 3.90100032e-01 -1.15304478... | [14.573680877685547, -2.0399627685546875] |
c7b948ac-2aaa-4bef-97d8-5b019e664c20 | practical-and-scalable-simulations-of-non | 2212.05059 | null | https://arxiv.org/abs/2212.05059v3 | https://arxiv.org/pdf/2212.05059v3.pdf | Practical and scalable simulations of non-Markovian stochastic processes | Discrete stochastic processes are widespread in natural systems with many applications across physics, biochemistry, epidemiology, sociology, and finance. While analytic solutions often cannot be derived, existing simulation frameworks can generate stochastic trajectories compatible with the dynamical laws underlying t... | ['Maria Rodriguez Martinez', 'Niko Beerenwinkel', 'Miroslav Phan', 'Aurelien Pelissier'] | 2022-12-09 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 1.85799852e-01 -4.93806332e-01 1.44407079e-01 4.97956127e-01
-1.91299692e-01 -9.44867194e-01 7.26319969e-01 2.68181622e-01
-3.60848576e-01 1.14844239e+00 -3.79898101e-01 -7.83052921e-01
-3.10362369e-01 -9.51858699e-01 -4.22408223e-01 -1.01863742e+00
-1.53973773e-01 6.92262769e-01 2.11493179e-01 -9.60825309... | [6.106252670288086, 4.218613147735596] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.