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
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6df82d70-3f36-4c51-bde6-11cc07f2d35d | a-new-benchmark-dataset-for-texture-image | 1906.11561 | null | https://arxiv.org/abs/1906.11561v1 | https://arxiv.org/pdf/1906.11561v1.pdf | A New Benchmark Dataset for Texture Image Analysis and Surface Defect Detection | Texture analysis plays an important role in many image processing applications to describe the image content or objects. On the other hand, visual surface defect detection is a highly research field in the computer vision. Surface defect refers to abnormalities in the texture of the surface. So, in this paper a dual pu... | ['Shervan Fekri-Ershad'] | 2019-06-27 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 4.47120577e-01 -3.24018002e-01 1.18895873e-01 -2.46159419e-01
-1.07411735e-01 -1.57835752e-01 6.68985367e-01 4.68691140e-01
1.04075568e-02 3.18162173e-01 -1.53969288e-01 1.21328153e-01
-5.21573126e-01 -1.05341327e+00 -3.35596263e-01 -7.57780910e-01
5.33035249e-02 5.88746428e-01 8.52171600e-01 -3.04303110... | [10.321737289428711, -0.349997341632843] |
ed32b489-c2b9-46ed-83d3-65420d134dbc | inter-database-validation-of-a-deep-learning | 2009.10365 | null | https://arxiv.org/abs/2009.10365v1 | https://arxiv.org/pdf/2009.10365v1.pdf | Inter-database validation of a deep learning approach for automatic sleep scoring | In this work we describe a new deep learning approach for automatic sleep staging, and carry out its validation by addressing its generalization capabilities on a wide range of sleep staging databases. Prediction capabilities are evaluated in the context of independent local and external generalization scenarios. Effec... | ['Diego Alvarez-Estevez', 'Roselyne M. Rijsman'] | 2020-09-22 | null | null | null | null | ['sleep-staging'] | ['medical'] | [-2.58332640e-02 5.80320284e-02 -2.11450756e-01 -5.97503543e-01
-5.50836444e-01 -1.91991851e-01 6.99652135e-01 3.65352929e-01
-8.10605705e-01 8.18460464e-01 1.03951320e-01 -1.02487862e-01
-3.50596756e-01 -5.88886261e-01 3.22974399e-02 -6.72970176e-01
1.55583143e-01 7.30890691e-01 4.12400693e-01 -3.18752378... | [13.50643539428711, 3.5156495571136475] |
a6783449-5472-48ee-9a63-c23e709b2a50 | adversarially-robust-prototypical-few-shot | 2210.03429 | null | https://arxiv.org/abs/2210.03429v1 | https://arxiv.org/pdf/2210.03429v1.pdf | Adversarially Robust Prototypical Few-shot Segmentation with Neural-ODEs | Few-shot Learning (FSL) methods are being adopted in settings where data is not abundantly available. This is especially seen in medical domains where the annotations are expensive to obtain. Deep Neural Networks have been shown to be vulnerable to adversarial attacks. This is even more severe in the case of FSL due to... | ['Brejesh lall', 'Tanuj Sur', 'Mustafa Chasmai', 'Aleti Vardhan', 'Prashant Pandey'] | 2022-10-07 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 4.46609765e-01 3.42116594e-01 3.29531014e-01 -1.80361882e-01
-9.08579469e-01 -9.15462554e-01 4.61212903e-01 2.49554574e-01
-5.54920495e-01 6.01872146e-01 -1.28768921e-01 -2.04168990e-01
-8.89139175e-02 -8.18519771e-01 -7.34396160e-01 -6.86502755e-01
-5.47822528e-02 5.71770608e-01 6.93184197e-01 -4.32460159... | [5.62180233001709, 7.816524505615234] |
68f39507-469a-44b3-a38b-10f196211a1f | sequential-transformer-for-end-to-end-person | 2211.04323 | null | https://arxiv.org/abs/2211.04323v2 | https://arxiv.org/pdf/2211.04323v2.pdf | Sequential Transformer for End-to-End Person Search | Person Search aims to simultaneously localize and recognize a target person from realistic and uncropped gallery images. One major challenge of person search comes from the contradictory goals of the two sub-tasks, i.e., person detection focuses on finding the commonness of all persons so as to distinguish persons from... | ['Jinhua Xu', 'Long Chen'] | 2022-11-06 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-2.06907883e-01 -4.51593965e-01 4.47327234e-02 -1.03198282e-01
-8.67986500e-01 -2.81652331e-01 5.83208978e-01 -2.93609589e-01
-7.15945959e-01 4.55327809e-01 5.10774076e-01 4.51570123e-01
5.65995201e-02 -4.73453879e-01 -3.86615276e-01 -5.04505873e-01
2.76263207e-01 7.50557661e-01 2.09860504e-01 -1.08661577... | [14.825671195983887, 0.8115118741989136] |
aa3b3f97-933e-4c2a-830c-f2fb581e7711 | improving-temporal-relation-extraction-with-a | 1804.06020 | null | http://arxiv.org/abs/1804.06020v1 | http://arxiv.org/pdf/1804.06020v1.pdf | Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource | Extracting temporal relations (before, after, overlapping, etc.) is a key
aspect of understanding events described in natural language. We argue that
this task would gain from the availability of a resource that provides prior
knowledge in the form of the temporal order that events usually follow. This
paper develops s... | ['Qiang Ning', 'Hao Wu', 'Dan Roth', 'Haoruo Peng'] | 2018-04-17 | improving-temporal-relation-extraction-with-a-1 | https://aclanthology.org/N18-1077 | https://aclanthology.org/N18-1077.pdf | naacl-2018-6 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [-1.73671797e-01 2.32912868e-01 -4.81805742e-01 -6.01061106e-01
-7.59650886e-01 -8.60023916e-01 1.07643986e+00 6.81994319e-01
-7.44422138e-01 1.23724341e+00 6.44024551e-01 -1.39706105e-01
-2.84162730e-01 -9.49313819e-01 -5.65243721e-01 -3.49831820e-01
-7.32957482e-01 4.71405417e-01 6.68477476e-01 -2.38009229... | [9.08694839477539, 9.264551162719727] |
da03b5af-cf57-43ac-9ab5-f6e638ef8f66 | on-the-cross-lingual-transferability-of-2 | null | null | https://aclanthology.org/2021.mrl-1.10 | https://aclanthology.org/2021.mrl-1.10.pdf | On the Cross-lingual Transferability of Contextualized Sense Embeddings | In this paper we analyze the extent to which contextualized sense embeddings, i.e., sense embeddings that are computed based on contextualized word embeddings, are transferable across languages.To this end, we compiled a unified cross-lingual benchmark for Word Sense Disambiguation. We then propose two simple strategie... | ['Mohammad Taher Pilehvar', 'Jose Camacho-Collados', 'Daniel Loureiro', 'Kiamehr Rezaee'] | null | null | null | null | emnlp-mrl-2021-11 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-1.35908052e-01 -2.61281058e-02 -5.56988776e-01 -3.34668696e-01
-8.72787654e-01 -1.00057471e+00 8.20130467e-01 4.63954389e-01
-9.15141702e-01 1.02053571e+00 5.70164740e-01 -4.47692275e-01
5.21363020e-02 -7.79983997e-01 -6.76096380e-01 -2.11353093e-01
2.06219360e-01 3.51479948e-01 2.86752805e-02 -7.68575430... | [10.7543306350708, 9.6847562789917] |
fe9e2576-6146-40ad-aa4e-4ea3cc7c1310 | jointly-learning-sentence-embeddings-and | 1705.09189 | null | http://arxiv.org/abs/1705.09189v1 | http://arxiv.org/pdf/1705.09189v1.pdf | Jointly Learning Sentence Embeddings and Syntax with Unsupervised Tree-LSTMs | We introduce a neural network that represents sentences by composing their
words according to induced binary parse trees. We use Tree-LSTM as our
composition function, applied along a tree structure found by a fully
differentiable natural language chart parser. Our model simultaneously
optimises both the composition fu... | ['Stephen Clark', 'Jean Maillard', 'Dani Yogatama'] | 2017-05-25 | jointly-learning-sentence-embeddings-and-1 | https://openreview.net/forum?id=BJMuY-gRW | https://openreview.net/pdf?id=BJMuY-gRW | iclr-2018-1 | ['reverse-dictionary'] | ['natural-language-processing'] | [ 4.92376655e-01 5.83868861e-01 -1.26197666e-01 -6.42111242e-01
-7.20694423e-01 -6.80170238e-01 4.51835334e-01 2.00538605e-01
-5.87100327e-01 4.83923584e-01 3.59325409e-01 -1.10121202e+00
4.19426650e-01 -1.06883538e+00 -8.30685914e-01 -2.56158084e-01
5.38104773e-03 5.62035143e-01 -3.23185027e-01 -1.97841063... | [10.372481346130371, 9.277873039245605] |
4442e09a-7cde-4e53-8792-7fbc333b3bbf | harnessing-label-semantics-to-extract-higher | 2212.01685 | null | https://arxiv.org/abs/2212.01685v1 | https://arxiv.org/pdf/2212.01685v1.pdf | Harnessing label semantics to extract higher performance under noisy label for Company to Industry matching | Assigning appropriate industry tag(s) to a company is a critical task in a financial institution as it impacts various financial machineries. Yet, it remains a complex task. Typically, such industry tags are to be assigned by Subject Matter Experts (SME) after evaluating company business lines against the industry defi... | ['Abhishek Mitra', 'Apoorva Jaiswal'] | 2022-12-03 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 4.23504770e-01 6.98627904e-02 -6.00550957e-02 -4.67444181e-01
-7.23753989e-01 -9.48551893e-01 6.45222962e-01 4.76526290e-01
-4.38140035e-01 4.13128853e-01 -6.68094605e-02 -4.97204065e-01
-3.37548792e-01 -5.41765690e-01 -3.11299264e-01 -4.29152936e-01
4.15295005e-01 1.02587974e+00 1.55401468e-01 -1.74314424... | [9.745451927185059, 6.352908611297607] |
7ffe9c94-80f2-4aa2-8d50-4331ba087814 | ms-a-new-exact-algorithm-for-multi-agent | 2103.09979 | null | https://arxiv.org/abs/2103.09979v1 | https://arxiv.org/pdf/2103.09979v1.pdf | MS*: A New Exact Algorithm for Multi-agent Simultaneous Multi-goal Sequencing and Path Finding | In multi-agent applications such as surveillance and logistics, fleets of mobile agents are often expected to coordinate and safely visit a large number of goal locations as efficiently as possible. The multi-agent planning problem in these applications involves allocating and sequencing goals for each agent while simu... | ['Howie Choset', 'Sivakumar Rathinam', 'Zhongqiang Ren'] | 2021-03-18 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 4.71713543e-02 3.49339873e-01 2.60038488e-02 4.45440076e-02
-5.94897687e-01 -9.75601435e-01 4.47126031e-01 4.26666230e-01
-5.55334032e-01 1.12457347e+00 -2.10055739e-01 -4.21331286e-01
-8.33114207e-01 -9.31708932e-01 -4.26770419e-01 -5.46283841e-01
-7.73425341e-01 1.52091312e+00 2.94850349e-01 -3.97116482... | [4.957412242889404, 1.794334053993225] |
dc2f5f31-0327-46dd-8308-d3609551242a | tinc-tree-structured-implicit-neural | 2211.06689 | null | https://arxiv.org/abs/2211.06689v4 | https://arxiv.org/pdf/2211.06689v4.pdf | TINC: Tree-structured Implicit Neural Compression | Implicit neural representation (INR) can describe the target scenes with high fidelity using a small number of parameters, and is emerging as a promising data compression technique. However, limited spectrum coverage is intrinsic to INR, and it is non-trivial to remove redundancy in diverse complex data effectively. Pr... | ['Qionghai Dai', 'Jinli Suo', 'Yuxiao Cheng', 'Tingxiong Xiao', 'Runzhao Yang'] | 2022-11-12 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_TINC_Tree-Structured_Implicit_Neural_Compression_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_TINC_Tree-Structured_Implicit_Neural_Compression_CVPR_2023_paper.pdf | cvpr-2023-1 | ['data-compression'] | ['time-series'] | [ 3.15380663e-01 -2.32178017e-01 -4.65557724e-01 -1.60300449e-01
-5.45330048e-01 4.10333872e-02 1.91506758e-01 6.01822920e-02
-2.50606924e-01 5.11839986e-01 5.02382636e-01 2.68198308e-02
-5.22619784e-01 -8.49615395e-01 -6.10128939e-01 -8.23796451e-01
-2.86537170e-01 -6.27505779e-02 1.53948590e-01 -5.81869483... | [11.249505996704102, -1.6510545015335083] |
3402048c-2bc0-40ef-b6f4-15098cf49f9e | bi-directional-feature-reconstruction-network | 2211.17161 | null | https://arxiv.org/abs/2211.17161v2 | https://arxiv.org/pdf/2211.17161v2.pdf | Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification | The main challenge for fine-grained few-shot image classification is to learn feature representations with higher inter-class and lower intra-class variations, with a mere few labelled samples. Conventional few-shot learning methods however cannot be naively adopted for this fine-grained setting -- a quick pilot study ... | ['Yi-Zhe Song', 'Jun Guo', 'Jie Cao', 'Zhanyu Ma', 'Xiaoxu Li', 'Aneeshan Sain', 'Dongliang Chang', 'Jijie Wu'] | 2022-11-30 | null | null | null | null | ['few-shot-image-classification', 'fine-grained-image-classification'] | ['computer-vision', 'computer-vision'] | [ 2.21158400e-01 -2.82207310e-01 -4.77538198e-01 -4.99295801e-01
-8.55830550e-01 -4.69130129e-01 5.18371761e-01 -4.47078981e-02
-1.62218958e-01 5.66138327e-01 8.91270563e-02 1.49386510e-01
-3.03754896e-01 -7.83606946e-01 -2.28200614e-01 -7.57827759e-01
1.88401163e-01 2.18167171e-01 4.20811832e-01 -2.07244441... | [9.88793659210205, 2.425708532333374] |
3e964aa0-74a6-451d-a979-e7a95f7133dc | self-generated-defocus-blur-detection-via | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhao_Self-Generated_Defocus_Blur_Detection_via_Dual_Adversarial_Discriminators_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhao_Self-Generated_Defocus_Blur_Detection_via_Dual_Adversarial_Discriminators_CVPR_2021_paper.pdf | Self-Generated Defocus Blur Detection via Dual Adversarial Discriminators | Although existing fully-supervised defocus blur detection (DBD) models significantly improve performance, training such deep models requires abundant pixel-level manual annotation, which is highly time-consuming and error-prone. Addressing this issue, this paper makes an effort to train a deep DBD model without usi... | ['Huchuan Lu', 'Cai Shang', 'Wenda Zhao'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['defocus-blur-detection'] | ['computer-vision'] | [ 4.96081859e-01 -1.19120106e-01 2.16221139e-01 -4.37172264e-01
-5.54818809e-01 -8.12832355e-01 4.12242532e-01 -8.14270854e-01
-2.03882083e-01 1.08325291e+00 5.71237430e-02 -3.44511360e-01
8.21419582e-02 -6.05568647e-01 -7.69613743e-01 -1.06891322e+00
3.19592118e-01 -6.25295117e-02 8.86982083e-02 2.57638901... | [11.37968635559082, -2.720745801925659] |
d84b9b73-dfbe-413b-8d4e-480c0cc9cc01 | explicit-homography-estimation-improves-1 | 2101.04713 | null | https://arxiv.org/abs/2101.04713v1 | https://arxiv.org/pdf/2101.04713v1.pdf | Explicit homography estimation improves contrastive self-supervised learning | The typical contrastive self-supervised algorithm uses a similarity measure in latent space as the supervision signal by contrasting positive and negative images directly or indirectly. Although the utility of self-supervised algorithms has improved recently, there are still bottlenecks hindering their widespread use, ... | ['Richard Klein', 'David Torpey'] | 2021-01-12 | explicit-homography-estimation-improves | https://openreview.net/forum?id=bWqodw-mFi1 | https://openreview.net/pdf?id=bWqodw-mFi1 | null | ['homography-estimation'] | ['computer-vision'] | [ 3.68007541e-01 6.11543097e-02 -3.01803142e-01 -5.10687351e-01
-2.65907258e-01 -5.27190149e-01 9.69849706e-01 2.61848420e-01
-5.63952804e-01 7.39876330e-01 -1.27010554e-01 1.08198896e-01
-2.56208122e-01 -4.88388032e-01 -5.81242502e-01 -9.37979817e-01
7.47984052e-02 3.62487763e-01 3.07051390e-01 -2.19859138... | [9.55462646484375, 2.4302520751953125] |
42042e4f-f64a-45a2-ac90-39a2ffdef576 | span-selective-linear-attention-transformers | 2306.09340 | null | https://arxiv.org/abs/2306.09340v1 | https://arxiv.org/pdf/2306.09340v1.pdf | Span-Selective Linear Attention Transformers for Effective and Robust Schema-Guided Dialogue State Tracking | In schema-guided dialogue state tracking models estimate the current state of a conversation using natural language descriptions of the service schema for generalization to unseen services. Prior generative approaches which decode slot values sequentially do not generalize well to variations in schema, while discrimina... | ['Haejun Lee', 'Björn Bebensee'] | 2023-06-15 | null | null | null | null | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 7.99771957e-03 8.16892743e-01 -4.00060624e-01 -9.63954568e-01
-1.12929189e+00 -7.56096721e-01 9.64958191e-01 -9.01606213e-03
-1.52815700e-01 6.26220107e-01 7.89120495e-01 -3.69682521e-01
2.12295130e-01 -4.29667711e-01 -4.57608521e-01 -9.69811380e-02
-4.57244515e-02 1.31637394e+00 3.68937671e-01 -7.15449274... | [12.789981842041016, 7.878447532653809] |
39f3d690-0046-468d-a98e-6f9976000949 | monocular-direct-sparse-localization-in-a | 2002.09923 | null | https://arxiv.org/abs/2002.09923v1 | https://arxiv.org/pdf/2002.09923v1.pdf | Monocular Direct Sparse Localization in a Prior 3D Surfel Map | In this paper, we introduce an approach to tracking the pose of a monocular camera in a prior surfel map. By rendering vertex and normal maps from the prior surfel map, the global planar information for the sparse tracked points in the image frame is obtained. The tracked points with and without the global planar infor... | ['Huaiyang Huang', 'Haoyang Ye', 'Ming Liu'] | 2020-02-23 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-2.39325896e-01 -5.52238464e-01 -2.56761104e-01 -8.46717134e-02
-2.39350155e-01 -7.76677489e-01 4.00518209e-01 -6.32560909e-01
-1.97184294e-01 6.34767413e-01 -2.94581652e-01 3.05503845e-01
2.34646767e-01 -3.79040658e-01 -8.42970610e-01 -6.20243609e-01
2.46398032e-01 2.63670146e-01 5.42768002e-01 1.12979285... | [7.588642597198486, -2.214197874069214] |
aa56889b-614e-4e85-aca9-852ecbb4c04b | learning-transferable-domain-priors-for-safe | 1909.04307 | null | https://arxiv.org/abs/1909.04307v5 | https://arxiv.org/pdf/1909.04307v5.pdf | Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning | Prior access to domain knowledge could significantly improve the performance of a reinforcement learning agent. In particular, it could help agents avoid potentially catastrophic exploratory actions, which would otherwise have to be experienced during learning. In this work, we identify consistently undesirable actions... | ['Truyen Tran', 'Thommen George Karimpanal', 'Sunil Gupta', 'Svetha Venkatesh', 'Santu Rana'] | 2019-09-10 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 3.06030065e-01 3.29852968e-01 -2.84503531e-02 -4.11389023e-02
-5.59856832e-01 -8.35203886e-01 6.84897065e-01 1.79940179e-01
-9.09162045e-01 1.37095118e+00 6.88053221e-02 -1.42522335e-01
-5.71259737e-01 -6.17110014e-01 -8.50076735e-01 -9.67009246e-01
-6.10131800e-01 6.04584455e-01 3.12392414e-01 -1.77933484... | [4.118465900421143, 1.7774672508239746] |
a49bd9b7-d78f-4d6a-bc91-86d66521170b | artelingo-a-million-emotion-annotations-of | 2211.10780 | null | https://arxiv.org/abs/2211.10780v1 | https://arxiv.org/pdf/2211.10780v1.pdf | ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture | This paper introduces ArtELingo, a new benchmark and dataset, designed to encourage work on diversity across languages and cultures. Following ArtEmis, a collection of 80k artworks from WikiArt with 0.45M emotion labels and English-only captions, ArtELingo adds another 0.79M annotations in Arabic and Chinese, plus 4.8K... | ['Mohamed Elhoseiny', 'Kenneth Ward Church', 'Xiangliang Zhang', 'Feifan Li', 'Shyma Alhuwaider', 'Mohamed Abdelfattah', 'Youssef Mohamed'] | 2022-11-19 | null | null | null | null | ['culture'] | ['speech'] | [-3.27342063e-01 -5.07747009e-02 -3.35792333e-01 -4.06117857e-01
-7.54883409e-01 -1.14807081e+00 7.51789331e-01 -1.65233552e-01
-6.49115264e-01 8.22046936e-01 7.22680509e-01 3.31882894e-01
4.85209614e-01 -2.36480668e-01 -7.39426196e-01 -3.24622810e-01
8.30017701e-02 5.42960048e-01 -6.04418457e-01 -4.49795038... | [11.40301513671875, 9.716544151306152] |
bd796d94-1701-4d52-96fe-8cbaeb3f651c | score-based-diffusion-models-for-bayesian | 2305.16482 | null | https://arxiv.org/abs/2305.16482v1 | https://arxiv.org/pdf/2305.16482v1.pdf | Score-based Diffusion Models for Bayesian Image Reconstruction | This paper explores the use of score-based diffusion models for Bayesian image reconstruction. Diffusion models are an efficient tool for generative modeling. Diffusion models can also be used for solving image reconstruction problems. We present a simple and flexible algorithm for training a diffusion model and using ... | ['Marc L. Klasky', 'Jong Chul Ye', 'Hyungjin Chung', 'Michael T. McCann'] | 2023-05-25 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 2.23532319e-01 9.61957350e-02 -2.47971117e-01 -3.99710655e-01
-1.00338912e+00 -9.76668745e-02 7.74422705e-01 -3.50172728e-01
-5.07778227e-01 6.22949064e-01 4.61359113e-01 -2.75958419e-01
-4.76307690e-01 -7.05968201e-01 -2.46016100e-01 -9.79848742e-01
-1.66054651e-01 7.48227358e-01 5.40843487e-01 2.84786731... | [11.787216186523438, -2.325680732727051] |
2c469464-f972-48e8-b320-0b0707452f04 | self-supervised-multi-view-learning-via-auto-1 | 2103.00787 | null | https://arxiv.org/abs/2103.00787v1 | https://arxiv.org/pdf/2103.00787v1.pdf | Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations | 3D object representation learning is a fundamental challenge in computer vision to infer about the 3D world. Recent advances in deep learning have shown their efficiency in 3D object recognition, among which view-based methods have performed best so far. However, feature learning of multiple views in existing methods i... | ['Guo-Jun Qi', 'Wei Hu', 'Xiang Gao'] | 2021-03-01 | self-supervised-multi-view-learning-via-auto | https://openreview.net/forum?id=0fqoSxXBwI6 | https://openreview.net/pdf?id=0fqoSxXBwI6 | null | ['3d-object-classification', '3d-object-recognition', 'multi-view-learning'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-9.23745893e-03 -3.92236501e-01 -7.15364590e-02 -6.35092616e-01
-8.22085619e-01 -6.77454472e-01 8.82245839e-01 -2.72514462e-01
5.21801375e-02 2.34320052e-02 2.19740853e-01 1.64482176e-01
-9.99797732e-02 -6.63459063e-01 -8.83122385e-01 -7.73363829e-01
3.80050659e-01 8.21966827e-01 -3.33939679e-02 1.36769056... | [8.230120658874512, -3.5648598670959473] |
0c5d6ba9-9df5-4813-93b2-ff92858f407f | learning-open-world-object-proposals-without | 2108.06753 | null | https://arxiv.org/abs/2108.06753v1 | https://arxiv.org/pdf/2108.06753v1.pdf | Learning Open-World Object Proposals without Learning to Classify | Object proposals have become an integral preprocessing steps of many vision pipelines including object detection, weakly supervised detection, object discovery, tracking, etc. Compared to the learning-free methods, learning-based proposals have become popular recently due to the growing interest in object detection. Th... | ['Weicheng Kuo', 'In So Kweon', 'Anelia Angelova', 'Tsung-Yi Lin', 'Dahun Kim'] | 2021-08-15 | null | null | null | null | ['zero-shot-object-detection', 'open-world-object-detection'] | ['computer-vision', 'computer-vision'] | [-1.77005768e-01 -2.17169255e-01 -2.16436639e-01 -5.18394291e-01
-6.04451358e-01 -7.38915741e-01 7.97890127e-01 2.73363292e-01
-5.66438973e-01 4.98813033e-01 -2.46626556e-01 1.71619311e-01
9.70361754e-02 -4.74564433e-01 -9.86173868e-01 -6.27749085e-01
-1.66224986e-02 6.60493016e-01 9.39489782e-01 1.31835416... | [9.436429023742676, 1.3834381103515625] |
38cfd427-565b-4b80-8742-7b51c60cbed7 | a-subsequence-interleaving-model-for | 1602.05012 | null | http://arxiv.org/abs/1602.05012v2 | http://arxiv.org/pdf/1602.05012v2.pdf | A Subsequence Interleaving Model for Sequential Pattern Mining | Recent sequential pattern mining methods have used the minimum description
length (MDL) principle to define an encoding scheme which describes an
algorithm for mining the most compressing patterns in a database. We present a
novel subsequence interleaving model based on a probabilistic model of the
sequence database, w... | ['Jaroslav Fowkes', 'Charles Sutton'] | 2016-02-16 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 6.83811426e-01 3.56409252e-02 -4.45689738e-01 -2.98277020e-01
-8.81723911e-02 -5.30636728e-01 1.28914326e-01 5.40403306e-01
-4.50799108e-01 6.63166285e-01 -1.49286211e-01 -3.26226205e-01
-6.69414937e-01 -1.12552583e+00 -6.54087067e-01 -5.76780260e-01
-4.92816031e-01 7.56265581e-01 5.10156810e-01 6.78657964... | [8.300629615783691, 6.2855024337768555] |
1d739aa6-a4a4-4717-8e77-353f611d61ae | the-hybridization-of-branch-and-bound-with | 2212.04624 | null | https://arxiv.org/abs/2212.04624v1 | https://arxiv.org/pdf/2212.04624v1.pdf | The Hybridization of Branch and Bound with Metaheuristics for Nonconvex Multiobjective Optimization | A hybrid framework combining the branch and bound method with multiobjective evolutionary algorithms is proposed for nonconvex multiobjective optimization. The hybridization exploits the complementary character of the two optimization strategies. A multiobjective evolutionary algorithm is intended for inducing tight lo... | ['Xin-min Yang', 'Wei-tian Wu'] | 2022-12-09 | null | null | null | null | ['multiobjective-optimization'] | ['methodology'] | [-9.72609222e-02 -1.26085982e-01 -1.07286036e-01 -9.54419523e-02
-4.48085487e-01 -5.19917250e-01 -1.67719752e-01 1.10037878e-01
-4.78764474e-01 1.42097700e+00 -2.58742720e-01 -5.51078394e-02
-9.71181989e-01 -8.61549497e-01 -3.70757788e-01 -1.10579395e+00
-2.45687868e-02 5.87231457e-01 -1.52211860e-01 -4.97483104... | [5.711061000823975, 3.5119221210479736] |
a29244bf-5922-44e0-a90a-ec046095db14 | beyond-reward-offline-preference-guided | 2305.16217 | null | https://arxiv.org/abs/2305.16217v2 | https://arxiv.org/pdf/2305.16217v2.pdf | Beyond Reward: Offline Preference-guided Policy Optimization | This study focuses on the topic of offline preference-based reinforcement learning (PbRL), a variant of conventional reinforcement learning that dispenses with the need for online interaction or specification of reward functions. Instead, the agent is provided with fixed offline trajectories and human preferences betwe... | ['Donglin Wang', 'Li He', 'Jinxin Liu', 'Diyuan Shi', 'Yachen Kang'] | 2023-05-25 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.15712994e-01 -3.25116180e-02 -5.19064426e-01 -2.26454258e-01
-1.05342031e+00 -1.02359366e+00 6.75537884e-01 9.65306684e-02
-8.10778499e-01 7.46251881e-01 2.59273976e-01 -6.65206909e-01
-2.33412981e-01 -4.45550650e-01 -6.41343892e-01 -7.26915359e-01
-2.10304663e-01 6.47890270e-01 3.07777412e-02 -1.05436996... | [4.0850510597229, 1.9977936744689941] |
d73c24db-0b4f-4f0b-8666-f84dc722c955 | magic3d-high-resolution-text-to-3d-content | 2211.10440 | null | https://arxiv.org/abs/2211.10440v2 | https://arxiv.org/pdf/2211.10440v2.pdf | Magic3D: High-Resolution Text-to-3D Content Creation | DreamFusion has recently demonstrated the utility of a pre-trained text-to-image diffusion model to optimize Neural Radiance Fields (NeRF), achieving remarkable text-to-3D synthesis results. However, the method has two inherent limitations: (a) extremely slow optimization of NeRF and (b) low-resolution image space supe... | ['Tsung-Yi Lin', 'Ming-Yu Liu', 'Sanja Fidler', 'Karsten Kreis', 'Xun Huang', 'Xiaohui Zeng', 'Towaki Takikawa', 'Luming Tang', 'Jun Gao', 'Chen-Hsuan Lin'] | 2022-11-18 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lin_Magic3D_High-Resolution_Text-to-3D_Content_Creation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lin_Magic3D_High-Resolution_Text-to-3D_Content_Creation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['text-to-3d'] | ['computer-vision'] | [ 5.19143604e-02 2.14962177e-02 1.26605138e-01 -2.30955571e-01
-1.08629322e+00 -5.36352634e-01 7.41783738e-01 -3.83137465e-01
-2.69629151e-01 3.95600468e-01 3.98300588e-01 -1.79560974e-01
2.97231883e-01 -1.05997360e+00 -7.26505101e-01 -7.03104258e-01
1.51808277e-01 5.64147949e-01 -1.63222030e-01 -1.67989671... | [9.384343147277832, -3.246286153793335] |
9d95b0ae-bdf8-4929-9c64-2f0dc2c81c0e | spatiotemporal-cnns-for-pornography-detection | 1810.10519 | null | http://arxiv.org/abs/1810.10519v1 | http://arxiv.org/pdf/1810.10519v1.pdf | Spatiotemporal CNNs for Pornography Detection in Videos | With the increasing use of social networks and mobile devices, the number of
videos posted on the Internet is growing exponentially. Among the inappropriate
contents published on the Internet, pornography is one of the most worrying as
it can be accessed by teens and children. Two spatiotemporal CNNs, VGG-C3D CNN
and R... | ['Aparecido Nilceu Marana', 'Murilo Varges da Silva'] | 2018-10-24 | null | null | null | null | ['pornography-detection'] | ['computer-vision'] | [-4.73051310e-01 -1.37979805e-01 -5.69550157e-01 1.93770900e-01
-3.27708542e-01 -5.66917360e-01 4.08584118e-01 3.08208704e-01
-2.70423204e-01 3.55334371e-01 4.34042364e-01 9.67281833e-02
3.74485888e-02 -1.00552058e+00 -7.28725672e-01 -3.47280979e-01
-1.66355580e-01 -3.23629171e-01 7.07341969e-01 -6.95853531... | [12.41281795501709, 1.173715591430664] |
22ae7fd2-453e-4168-aaed-dd3647c5f0c2 | generalized-radio-environment-monitoring-for | 2008.06203 | null | https://arxiv.org/abs/2008.06203v2 | https://arxiv.org/pdf/2008.06203v2.pdf | Generalized Radio Environment Monitoring for Next Generation Wireless Networks | Enabling technologies of 5G and beyond wireless communication networks, such as millimeter-wave communication, beamforming, and multiple-input multiple-output (MIMO) antenna systems, are becoming increasingly dependent on accurate information of the physical environment for optimized communication performance. The acqu... | ['Huseyin Arslan', 'Haji M. Furqan', 'Muhammad Sohaib J. Solaija', 'Halise Turkmen'] | 2020-08-14 | null | null | null | null | ['intelligent-communication'] | ['time-series'] | [ 4.98509586e-01 -2.36451887e-02 -1.19038790e-01 -1.50946543e-01
-1.65057257e-01 -5.25447011e-01 4.01415467e-01 -2.53272623e-01
-2.25626022e-01 1.02093077e+00 6.43765703e-02 -4.63029563e-01
-6.39619589e-01 -1.19703937e+00 2.65702933e-01 -7.98354805e-01
-4.12552267e-01 -2.47104019e-02 -2.82256812e-01 -3.35710496... | [6.313308238983154, 1.1945964097976685] |
162f1749-aacf-4dcf-9a6b-b4337e0dc531 | self-supervised-learning-for-large-scale | 2008.10312 | null | https://arxiv.org/abs/2008.10312v2 | https://arxiv.org/pdf/2008.10312v2.pdf | Self-Supervised Learning for Large-Scale Unsupervised Image Clustering | Unsupervised learning has always been appealing to machine learning researchers and practitioners, allowing them to avoid an expensive and complicated process of labeling the data. However, unsupervised learning of complex data is challenging, and even the best approaches show much weaker performance than their supervi... | ['Alex M. Bronstein', 'Evgenii Zheltonozhskii', 'Chaim Baskin', 'Avi Mendelson'] | 2020-08-24 | null | null | null | null | ['image-clustering', 'unsupervised-image-classification'] | ['computer-vision', 'computer-vision'] | [ 1.08907379e-01 1.42674893e-01 -4.83344615e-01 -7.55708933e-01
-3.18056047e-01 -4.03749734e-01 6.64865255e-01 3.43737334e-01
-6.50823772e-01 5.62517941e-01 1.10958591e-01 -7.13212714e-02
1.05876625e-01 -6.84782684e-01 -5.34268498e-01 -7.46698380e-01
-7.41507392e-04 6.83765888e-01 1.72511473e-01 1.51181534... | [9.435525894165039, 2.6181747913360596] |
56846e59-48ca-417b-a992-3a05e0b56932 | comparative-study-on-supervised-learning | 1701.06421 | null | http://arxiv.org/abs/1701.06421v1 | http://arxiv.org/pdf/1701.06421v1.pdf | Comparative study on supervised learning methods for identifying phytoplankton species | Phytoplankton plays an important role in marine ecosystem. It is defined as a
biological factor to assess marine quality. The identification of phytoplankton
species has a high potential for monitoring environmental, climate changes and
for evaluating water quality. However, phytoplankton species identification is
not ... | ['André Bigand', 'Thi-Thu-Hong Phan', 'Emilie Poisson Caillault'] | 2017-01-23 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [-6.17671311e-02 -7.26918161e-01 7.76046455e-01 -9.06135961e-02
-8.23683888e-02 -6.78499997e-01 6.71678185e-01 5.49576163e-01
-7.21306086e-01 8.80261600e-01 8.39277953e-02 1.16123378e-01
-2.16854230e-01 -7.92588472e-01 -1.35960817e-01 -1.24107730e+00
-2.24268630e-01 2.09028840e-01 2.42027149e-01 -5.49048968... | [8.701903343200684, -1.2517521381378174] |
e53802e4-b1a6-4cf9-8674-1d5d2761e621 | distributed-optimization-for-quadratic-cost | 2304.00596 | null | https://arxiv.org/abs/2304.00596v1 | https://arxiv.org/pdf/2304.00596v1.pdf | Distributed Optimization for Quadratic Cost Functions over Large-Scale Networks with Quantized Communication and Finite-Time Convergence | We propose two distributed iterative algorithms that can be used to solve, in finite time, the distributed optimization problem over quadratic local cost functions in large-scale networks. The first algorithm exhibits synchronous operation whereas the second one exhibits asynchronous operation. Both algorithms share sa... | ['Karl H. Johansson', 'Themistoklis Charalambous', 'Christoforos N. Hadjicostis', 'Evangelia Kalyvianaki', 'Andreas Grammenos', 'Apostolos I. Rikos'] | 2023-04-02 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-5.75382523e-02 -1.02290899e-01 -1.44793198e-01 -2.38532290e-01
-5.39334774e-01 -4.86878157e-01 2.55967230e-02 6.84047818e-01
-4.87121463e-01 9.33135748e-01 -3.35489422e-01 -1.22800712e-02
-6.50784075e-01 -7.21130490e-01 -6.30874217e-01 -1.10068750e+00
-6.66299462e-01 5.78705490e-01 5.83810685e-03 1.87117130... | [6.174696445465088, 4.992365837097168] |
0812321e-3d00-4b5a-b26e-f7b0c4899fbf | sam-gcnn-a-gated-convolutional-neural-network | 1810.03986 | null | http://arxiv.org/abs/1810.03986v2 | http://arxiv.org/pdf/1810.03986v2.pdf | SAM-GCNN: A Gated Convolutional Neural Network with Segment-Level Attention Mechanism for Home Activity Monitoring | In this paper, we propose a method for home activity monitoring. We
demonstrate our model on dataset of Detection and Classification of Acoustic
Scenes and Events (DCASE) 2018 Challenge Task 5. This task aims to classify
multi-channel audios into one of the provided pre-defined classes. All of these
classes are daily a... | ['Wei-Qiang Zhang', 'Ke-Xin He', 'Yu-Han Shen'] | 2018-10-03 | null | null | null | null | ['home-activity-monitoring'] | ['miscellaneous'] | [ 3.92025083e-01 -7.73083791e-02 4.51876163e-01 -3.31791312e-01
-1.24236166e+00 -2.17178866e-01 2.87033439e-01 -1.20129697e-02
-6.13665342e-01 5.02268434e-01 5.11873424e-01 1.02376446e-01
2.26634711e-01 -6.49790108e-01 -7.84363806e-01 -6.05707288e-01
-3.51777315e-01 -1.40266314e-01 2.00501248e-01 7.11618811... | [15.187185287475586, 5.186405658721924] |
b5a58841-4972-40bf-a114-344e7ac195c0 | deep-reinforcement-learning-for-modeling-chit | 1907.02848 | null | https://arxiv.org/abs/1907.02848v2 | https://arxiv.org/pdf/1907.02848v2.pdf | Deep Reinforcement Learning For Modeling Chit-Chat Dialog With Discrete Attributes | Open domain dialog systems face the challenge of being repetitive and producing generic responses. In this paper, we demonstrate that by conditioning the response generation on interpretable discrete dialog attributes and composed attributes, it helps improve the model perplexity and results in diverse and interesting ... | ['Chinnadhurai Sankar', 'Sujith Ravi'] | 2019-07-05 | deep-reinforcement-learning-for-modeling-chit-1 | https://aclanthology.org/W19-5901 | https://aclanthology.org/W19-5901.pdf | ws-2019-9 | ['open-domain-dialog'] | ['natural-language-processing'] | [ 1.29269525e-01 7.16464162e-01 -1.84993863e-01 -8.13206017e-01
-9.78496015e-01 -7.81184077e-01 8.23068321e-01 -8.92071351e-02
-5.91917753e-01 1.20419157e+00 6.77719772e-01 -3.63348812e-01
8.25413093e-02 -6.17794514e-01 -2.50085503e-01 -3.22575152e-01
2.61664748e-01 1.02030122e+00 -2.48993278e-01 -4.76978421... | [12.905674934387207, 8.077505111694336] |
5154ec11-6df2-434d-ba08-9134e41348a9 | csi-based-data-driven-localization | 2304.11455 | null | https://arxiv.org/abs/2304.11455v1 | https://arxiv.org/pdf/2304.11455v1.pdf | CSI-Based Data-driven Localization Frameworking using Small-scale Training Datasets in Single-site MIMO Systems | This work presents a date-driven user localization framework for single-site massive Multiple-Input-Multiple-Output (MIMO) systems. The framework is trained on a geo-tagged Channel State Information (CSI) dataset. Unlike the state-of-the-art Convolutional Neural Network (CNN) models, which require large training datase... | ['Nazanin Rahnavard', 'Farzam Hejazi', 'Katarina Vuckovic'] | 2023-04-22 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 8.65695104e-02 4.03027013e-02 1.52878329e-01 -2.03909412e-01
-1.10833728e+00 -7.03341961e-02 2.44265035e-01 -1.97969049e-01
-5.67351997e-01 9.27560091e-01 -1.77673250e-01 -7.32977867e-01
1.32435998e-02 -8.82533669e-01 -1.07526672e+00 -6.64548457e-01
-4.31013703e-01 1.37818590e-01 3.91453467e-02 7.36876726... | [6.429171562194824, 1.0128676891326904] |
ae9b2088-232c-4774-a8d7-d3d3ffa21ada | 3rd-place-solution-to-meta-ai-video | 2304.11964 | null | https://arxiv.org/abs/2304.11964v2 | https://arxiv.org/pdf/2304.11964v2.pdf | 3rd Place Solution to Meta AI Video Similarity Challenge | This paper presents our 3rd place solution in both Descriptor Track and Matching Track of the Meta AI Video Similarity Challenge (VSC2022), a competition aimed at detecting video copies. Our approach builds upon existing image copy detection techniques and incorporates several strategies to exploit on the properties of... | ['Rintaro Hasegawa', 'Junki Ishikawa', 'Peifei Zhu', 'Shuhei Yokoo'] | 2023-04-24 | null | null | null | null | ['video-similarity'] | ['computer-vision'] | [ 2.14933261e-01 -6.23832643e-01 -2.38669261e-01 1.13403179e-01
-1.15202248e+00 -7.79113114e-01 9.19661582e-01 -1.76360756e-02
-2.47313142e-01 -4.48147915e-02 3.42067927e-01 5.66718355e-02
1.41897090e-02 -3.58734459e-01 -7.88550079e-01 -3.26453835e-01
-4.40764755e-01 1.22200288e-01 7.65973747e-01 -2.21823677... | [10.258861541748047, 0.644733726978302] |
16a0b633-547f-42c6-bc0e-8ef32712b747 | idql-implicit-q-learning-as-an-actor-critic | 2304.10573 | null | https://arxiv.org/abs/2304.10573v2 | https://arxiv.org/pdf/2304.10573v2.pdf | IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies | Effective offline RL methods require properly handling out-of-distribution actions. Implicit Q-learning (IQL) addresses this by training a Q-function using only dataset actions through a modified Bellman backup. However, it is unclear which policy actually attains the values represented by this implicitly trained Q-fun... | ['Sergey Levine', 'Jakub Grudzien Kuba', 'Michael Janner', 'Ilya Kostrikov', 'Philippe Hansen-Estruch'] | 2023-04-20 | null | null | null | null | ['q-learning', 'offline-rl'] | ['methodology', 'playing-games'] | [-2.35790938e-01 3.62868607e-01 -7.26976514e-01 -1.37645543e-01
-1.07093287e+00 -7.52098799e-01 6.83159828e-01 2.15371326e-03
-7.08863378e-01 1.04590976e+00 3.89391959e-01 -4.40580249e-01
-3.28329414e-01 -4.35874820e-01 -5.99478185e-01 -9.31554258e-01
-2.64869258e-02 6.24000311e-01 -8.56267363e-02 -6.17052466... | [4.091554164886475, 2.3086323738098145] |
294ce550-b692-4be8-b34b-7decbfb701a7 | heteroskedastic-geospatial-tracking-with | 2306.02407 | null | https://arxiv.org/abs/2306.02407v1 | https://arxiv.org/pdf/2306.02407v1.pdf | Heteroskedastic Geospatial Tracking with Distributed Camera Networks | Visual object tracking has seen significant progress in recent years. However, the vast majority of this work focuses on tracking objects within the image plane of a single camera and ignores the uncertainty associated with predicted object locations. In this work, we focus on the geospatial object tracking problem usi... | ['Benjamin M. Marlin', 'Mani Srivastava', 'Deepak Ganesan', 'Ziqi Wang', 'Shiwei Fang', 'Colin Samplawski'] | 2023-06-04 | null | null | null | null | ['object-tracking', 'visual-object-tracking'] | ['computer-vision', 'computer-vision'] | [-1.78722978e-01 -3.04750204e-01 -2.59867579e-01 -3.31421047e-01
-5.85216403e-01 -8.83610189e-01 4.29497510e-01 7.85912946e-02
-4.39359546e-01 6.27753556e-01 -2.12753624e-01 -1.28928155e-01
-2.20426336e-01 -4.63858366e-01 -1.09989870e+00 -4.26899046e-01
-2.93335289e-01 5.16179204e-01 5.81840873e-01 6.86555445... | [6.406667232513428, -2.0686230659484863] |
adb49536-557b-4ee6-a74c-9d5fa544afc8 | towards-a-modeling-optimization-and | 2302.02177 | null | https://arxiv.org/abs/2302.02177v1 | https://arxiv.org/pdf/2302.02177v1.pdf | Towards a modeling, optimization and predictive control framework for fed-batch metabolic cybergenetics | Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes \textit{extracellular} process elements such as the bioreactor design and mode of operation, medium formulation, culture conditions, feeding rates, etc. However, these elements a... | ['Rolf Findeisen', 'Steffen Klamt', 'Katja Bettenbrock', 'Johannes Pohlodek', 'Bruno Morabito', 'Sebastián Espinel-Ríos'] | 2023-02-04 | null | null | null | null | ['culture'] | ['speech'] | [ 3.64711463e-01 -1.06506817e-01 9.93834343e-03 3.17750454e-01
6.28552318e-01 -9.03865874e-01 2.42283419e-01 4.82076585e-01
-2.34164745e-01 1.02198458e+00 -4.58540618e-01 -2.18451068e-01
-4.37532485e-01 -7.34040022e-01 -8.62436771e-01 -1.08836508e+00
3.38439941e-01 1.10640876e-01 -2.70447671e-01 -1.90229312... | [5.848296165466309, 4.289592266082764] |
9a7eeeec-17de-4e0c-8bdd-6e4064be94f0 | accurate-image-restoration-with-attention | 2210.01427 | null | https://arxiv.org/abs/2210.01427v4 | https://arxiv.org/pdf/2210.01427v4.pdf | Accurate Image Restoration with Attention Retractable Transformer | Recently, Transformer-based image restoration networks have achieved promising improvements over convolutional neural networks due to parameter-independent global interactions. To lower computational cost, existing works generally limit self-attention computation within non-overlapping windows. However, each group of t... | ['Xin Yuan', 'Linghe Kong', 'Yongbing Zhang', 'Jinjin Gu', 'Yulun Zhang', 'Jiale Zhang'] | 2022-10-04 | null | null | null | null | ['jpeg-compression-artifact-reduction'] | ['computer-vision'] | [ 1.92228213e-01 -1.93957806e-01 1.87724251e-02 -3.65007035e-02
-5.99038005e-01 6.91982806e-02 2.04649925e-01 -3.22965920e-01
-6.16341010e-02 4.84334469e-01 4.98123944e-01 1.89453900e-01
-1.43053710e-01 -7.75547326e-01 -7.71592438e-01 -1.02565491e+00
1.46078676e-01 -2.19587773e-01 2.68302262e-01 -2.59580225... | [11.045533180236816, -2.0145187377929688] |
ab7a1d80-7446-45b9-99ce-22b8c5d6e908 | gp-net-grasp-proposal-for-mobile-manipulators | 2209.10404 | null | https://arxiv.org/abs/2209.10404v2 | https://arxiv.org/pdf/2209.10404v2.pdf | GP-net: Flexible Viewpoint Grasp Proposal | We present the Grasp Proposal Network (GP-net), a Convolutional Neural Network model which can generate 6-DOF grasps from flexible viewpoints, e.g. as experienced by mobile manipulators. To train GP-net, we synthetically generate a dataset containing depth-images and ground-truth grasp information. In real-world experi... | ['Rudi Villing', 'John McDonald', 'Anna Konrad'] | 2022-09-21 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [-3.00677240e-01 2.13103756e-01 2.47784913e-01 -1.90793261e-01
-4.38572407e-01 -9.72355247e-01 1.11339308e-01 -3.26274931e-01
-7.32198730e-02 2.64013290e-01 -3.81473064e-01 -3.26018304e-01
-2.71946222e-01 -9.35407341e-01 -1.35444164e+00 -6.95559442e-01
-5.66400230e-01 8.15882504e-01 2.73718797e-02 -2.16201499... | [5.7162909507751465, -0.80501389503479] |
6b699c29-f26e-4c41-9c23-98361dfee152 | geometric-estimation-via-robust-subspace | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4101_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123670460.pdf | Geometric Estimation via Robust Subspace Recovery | Geometric estimation from image point correspondences is the core procedure of many 3D vision problems, which is prevalently accomplished by random sampling techniques. In this paper, we consider the problem from an optimization perspective, to exploit the intrinsic linear structure of point correspondences to assist e... | ['Junjun Jiang', 'Jiayi Ma', 'Yang Wang', 'Aoxiang Fan', 'Xingyu Jiang'] | null | null | null | null | eccv-2020-8 | ['homography-estimation'] | ['computer-vision'] | [ 1.76661715e-01 -3.31169814e-01 -1.45760402e-01 -1.50278313e-02
-6.57179892e-01 -5.11216462e-01 6.51088357e-01 -4.11753058e-01
-1.58649027e-01 4.11541790e-01 -4.01109383e-02 4.49816277e-03
-1.85688719e-01 -3.52471590e-01 -5.57672739e-01 -6.45196438e-01
4.59500611e-01 3.99397701e-01 -2.97836354e-03 -4.43246542... | [7.933624744415283, -2.3400654792785645] |
ee40a985-41fe-4ce4-91dd-eb72212e5057 | neural-driven-multi-criteria-tree-search-for | null | null | https://openreview.net/forum?id=vjea2ynL7MW | https://openreview.net/pdf?id=vjea2ynL7MW | Neural-Driven Multi-criteria Tree Search for Paraphrase Generation | A good paraphrase is semantically similar to the original sentence but it must be also well formed, and syntactically different to ensure diversity. To deal with this trade-off, we propose to cast the paraphrase generation task as a multi-objectives search problem on the lattice of text transformations. We use BERT and... | ['Damien Lolive', 'Jonathan Chevelu', 'Tanguy Urvoy', 'Betty Fabre'] | 2020-10-17 | null | null | null | neurips-workshop-lmca-2020-12 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 3.34451973e-01 2.47322679e-01 -1.06819473e-01 -2.46943250e-01
-1.16254878e+00 -8.12516272e-01 8.99393916e-01 1.87692940e-01
-2.77325481e-01 1.12011695e+00 5.99856973e-01 -3.85484397e-01
-1.81548268e-01 -8.79669905e-01 -7.76774168e-01 -1.92964792e-01
6.26383841e-01 9.34160650e-01 4.12562430e-01 -4.95208263... | [11.690237998962402, 9.146919250488281] |
54c5c792-6b28-4d41-a92a-06f88c5ef0ab | sit-back-and-relax-learning-to-drive | 2305.18953 | null | https://arxiv.org/abs/2305.18953v1 | https://arxiv.org/pdf/2305.18953v1.pdf | Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions | In autonomous driving scenarios, current object detection models show strong performance when tested in clear weather. However, their performance deteriorates significantly when tested in degrading weather conditions. In addition, even when adapted to perform robustly in a sequence of different weather conditions, they... | ['Horst Bischof', 'Horst Possegger', 'Mateusz Kozinski', 'Marc Masana', 'Jakub Micorek', 'Wei Lin', 'M. Jehanzeb Mirza', 'Stefan Leitner'] | 2023-05-30 | null | null | null | null | ['incremental-learning'] | ['methodology'] | [ 1.14289485e-01 -1.93398476e-01 1.40529916e-01 -6.66338980e-01
-3.17133933e-01 -7.50824988e-01 6.39073312e-01 1.28185928e-01
-8.15497398e-01 6.64812207e-01 -3.29500943e-01 -3.10684621e-01
1.27604380e-01 -6.59235537e-01 -9.48361099e-01 -6.33584678e-01
8.70205909e-02 4.66205090e-01 7.11380959e-01 -1.68940231... | [8.04316234588623, -1.394639015197754] |
97fa2a9b-26d7-472f-8121-6bc989b23cf5 | graph-neural-networks-for-the-prediction-of-1 | 2208.04852 | null | https://arxiv.org/abs/2208.04852v1 | https://arxiv.org/pdf/2208.04852v1.pdf | Graph neural networks for the prediction of molecular structure-property relationships | Molecular property prediction is of crucial importance in many disciplines such as drug discovery, molecular biology, or material and process design. The frequently employed quantitative structure-property/activity relationships (QSPRs/QSARs) characterize molecules by descriptors which are then mapped to the properties... | ['Artur M. Schweidtmann', 'Alexander Mitsos', 'Manuel Dahmen', 'Qinghe Gao', 'Jan G. Rittig'] | 2022-07-25 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 6.17972851e-01 1.57681614e-01 -8.43382955e-01 -1.52178347e-01
-2.43391737e-01 -5.06520569e-01 5.46251893e-01 1.06032193e+00
-9.36376154e-02 1.29243708e+00 -4.17921543e-02 -4.19881612e-01
-6.53093696e-01 -1.17099226e+00 -7.80912399e-01 -8.34574103e-01
-5.39273560e-01 4.58614528e-01 5.67091703e-02 -2.22347826... | [5.139320373535156, 5.830239772796631] |
6f8b15b0-6d38-4e6e-97ac-52dc92e5e513 | satvsr-scenario-adaptive-transformer-for | 2211.08703 | null | https://arxiv.org/abs/2211.08703v1 | https://arxiv.org/pdf/2211.08703v1.pdf | SATVSR: Scenario Adaptive Transformer for Cross Scenarios Video Super-Resolution | Video Super-Resolution (VSR) aims to recover sequences of high-resolution (HR) frames from low-resolution (LR) frames. Previous methods mainly utilize temporally adjacent frames to assist the reconstruction of target frames. However, in the real world, there is a lot of irrelevant information in adjacent frames of vide... | ['Tieru Wu', 'Yongjie Chen'] | 2022-11-16 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 3.50202143e-01 -5.27938128e-01 -2.49779761e-01 -1.62187099e-01
-6.82062924e-01 -2.82715917e-01 6.09103292e-02 -3.47774506e-01
-2.35223293e-01 7.94296384e-01 4.90152389e-01 4.07618940e-01
-2.13447034e-01 -7.18791187e-01 -3.63494605e-01 -8.47053528e-01
1.73030138e-01 -3.77011478e-01 9.03909385e-01 -2.60728121... | [11.0180082321167, -1.9045499563217163] |
d92d1f42-0fd4-484b-9ff2-9a5afc029ece | robust-tensor-decomposition-for-image | 2005.04605 | null | https://arxiv.org/abs/2005.04605v1 | https://arxiv.org/pdf/2005.04605v1.pdf | Robust Tensor Decomposition for Image Representation Based on Generalized Correntropy | Traditional tensor decomposition methods, e.g., two dimensional principal component analysis and two dimensional singular value decomposition, that minimize mean square errors, are sensitive to outliers. To overcome this problem, in this paper we propose a new robust tensor decomposition method using generalized corren... | ['Yongsheng Gao', 'Miaohua Zhang', 'Michael Blumenstein', 'Changming Sun'] | 2020-05-10 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-4.28937465e-01 -6.78168058e-01 2.75429696e-01 -1.13785811e-01
-5.47451019e-01 -3.08619261e-01 -1.31027147e-01 -2.95015335e-01
-1.42386511e-01 2.65760809e-01 7.07722232e-02 -9.28182006e-02
-5.60228705e-01 -1.67710096e-01 -1.94527373e-01 -9.73538160e-01
-7.71163180e-02 -1.59187123e-01 -1.12951934e-01 1.26909688... | [7.539254188537598, 4.407192230224609] |
2b2bf0b0-5508-4c19-81c4-0b574ee8b5e9 | swarm-intelligence-algorithms-applied-to | 2111.02212 | null | https://arxiv.org/abs/2111.02212v5 | https://arxiv.org/pdf/2111.02212v5.pdf | A swarm intelligence-based robust solution for Virtual Reference Feedback Tuning | This work proposes the inclusion of an $\mathcal{H}_{\infty}$ robustness constraint to the Virtual Reference Feedback Tuning (VRFT) cost function, which is solved by metaheuristic optimization with only a single batch of data (one-shot). The $\mathcal{H}_{\infty}$ norm of the sensitivity transfer function is estimated ... | ['Y. R. de Novaes', 'C. L. Remes', 'L. V. Fiorio'] | 2021-11-03 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [ 2.69213110e-01 1.15777798e-01 2.65596062e-01 2.31685806e-02
-3.51017147e-01 -3.39881659e-01 2.25042462e-01 2.39774048e-01
-6.89788103e-01 1.24428165e+00 -4.16746497e-01 -5.50115779e-02
-8.09192061e-01 -5.12653828e-01 -3.91207069e-01 -1.08732283e+00
-3.39125752e-01 1.68691173e-01 6.00536615e-02 -4.71664160... | [5.581561088562012, 3.367286205291748] |
cbd75eb5-a908-4e77-b148-5c641177423e | vector-space-models-for-scientific-document | null | null | https://aclanthology.org/W15-1525 | https://aclanthology.org/W15-1525.pdf | Vector Space Models for Scientific Document Summarization | null | ['Sashka Davis', 'John Conroy'] | 2015-06-01 | null | null | null | ws-2015-6 | ['scientific-article-summarization'] | ['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... | [-7.4018378257751465, 3.8565564155578613] |
1e9b1c2a-eadf-4fd4-a184-893bf8eb42bc | deep-lighting-environment-map-estimation-from | 2005.08000 | null | https://arxiv.org/abs/2005.08000v1 | https://arxiv.org/pdf/2005.08000v1.pdf | Deep Lighting Environment Map Estimation from Spherical Panoramas | Estimating a scene's lighting is a very important task when compositing synthetic content within real environments, with applications in mixed reality and post-production. In this work we present a data-driven model that estimates an HDR lighting environment map from a single LDR monocular spherical panorama. In additi... | ['Petros Daras', 'Federico Alvarez', 'Dimitrios Zarpalas', 'Vasileios Gkitsas', 'Nikolaos Zioulis'] | 2020-05-16 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 4.14677531e-01 2.06124736e-03 3.49460721e-01 -3.98013055e-01
-7.22996712e-01 -6.98232412e-01 7.13777125e-01 -2.76739091e-01
-5.81007078e-02 7.47401357e-01 1.99666619e-01 -6.60208464e-02
2.68799096e-01 -7.50349343e-01 -1.22999799e+00 -7.09941030e-01
4.30241346e-01 3.27012837e-01 2.58227717e-02 -2.17793256... | [9.74358081817627, -2.9914190769195557] |
b5097d90-feac-46e3-ba53-9e6ade5a9b3a | pulsenet-deep-learning-ecg-signal | 2305.15424 | null | https://arxiv.org/abs/2305.15424v2 | https://arxiv.org/pdf/2305.15424v2.pdf | PulseNet: Deep Learning ECG-signal classification using random augmentation policy and continous wavelet transform for canines | Evaluating canine electrocardiograms (ECG) require skilled veterinarians, but current availability of veterinary cardiologists for ECG interpretation and diagnostic support is limited. Developing tools for automated assessment of ECG sequences can improve veterinary care by providing clinicians real-time results and de... | ['Mark Parkinson', 'Xiaoli Qiao', 'Emil Walleser', 'Norbert Sithirangathan', 'Michael Fitzke', 'Fernando Junior', 'Oliver Roman Stiel', 'Jennifer Schneiderman', 'Federica Marchesotti', 'Roberto Santilli', 'Andre Dourson'] | 2023-05-17 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 4.33692098e-01 -1.04760081e-01 4.60981131e-02 -6.65680468e-01
-4.88288522e-01 -5.87985635e-01 -4.62694198e-01 7.01093197e-01
-5.39235413e-01 5.42344272e-01 -5.93211710e-01 -9.94049072e-01
-2.57009417e-02 -6.52871907e-01 -3.42110336e-01 -3.64207119e-01
-8.59698534e-01 7.66992509e-01 -3.57393891e-01 1.78234056... | [14.369644165039062, 3.3089137077331543] |
7970aa88-96e7-402c-8485-0392f2e9ecfd | gaussian-smoothed-imbalance-data-improves | 2302.08650 | null | https://arxiv.org/abs/2302.08650v1 | https://arxiv.org/pdf/2302.08650v1.pdf | Gaussian-smoothed Imbalance Data Improves Speech Emotion Recognition | In speech emotion recognition tasks, models learn emotional representations from datasets. We find the data distribution in the IEMOCAP dataset is very imbalanced, which may harm models to learn a better representation. To address this issue, we propose a novel Pairwise-emotion Data Distribution Smoothing (PDDS) method... | ['Ying Zhou', 'Wenxin Xu', 'Hexin Jiang', 'Xuefeng Liang'] | 2023-02-17 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [-1.36114076e-01 3.64972174e-01 -1.43039525e-01 -6.32903576e-01
-8.05338204e-01 -1.83089495e-01 3.36617410e-01 -3.79455909e-02
-2.62184471e-01 7.62585461e-01 4.47728455e-01 5.57518788e-02
7.11827427e-02 -5.76564729e-01 -4.35943693e-01 -6.56810522e-01
7.48562813e-02 7.29077756e-02 -3.50624740e-01 -2.76535928... | [13.631929397583008, 5.8661370277404785] |
f141f86f-2824-4a3a-8de6-1bcfc2de512f | stock-price-prediction-using-temporal-graph | 2303.09406 | null | https://arxiv.org/abs/2303.09406v1 | https://arxiv.org/pdf/2303.09406v1.pdf | Stock Price Prediction Using Temporal Graph Model with Value Chain Data | Stock price prediction is a crucial element in financial trading as it allows traders to make informed decisions about buying, selling, and holding stocks. Accurate predictions of future stock prices can help traders optimize their trading strategies and maximize their profits. In this paper, we introduce a neural netw... | ['Sandra Paterlini', 'Chang Liu'] | 2023-03-07 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-6.48626149e-01 -1.75674930e-01 -3.64844292e-01 -1.85838997e-01
6.99169338e-02 -7.35327125e-01 6.54985726e-01 -1.12092905e-02
-1.72962859e-01 5.03834248e-01 5.20178854e-01 -6.89292729e-01
3.19756046e-02 -1.55106568e+00 -7.84660280e-01 -1.68507129e-01
-7.10033476e-01 6.10859990e-01 3.48574191e-01 -5.01250684... | [4.3574395179748535, 4.306427955627441] |
c07db852-4be0-4b22-b6f9-eb31ec6881d2 | multi-modal-text-recognition-networks | 2111.15263 | null | https://arxiv.org/abs/2111.15263v3 | https://arxiv.org/pdf/2111.15263v3.pdf | Multi-modal Text Recognition Networks: Interactive Enhancements between Visual and Semantic Features | Linguistic knowledge has brought great benefits to scene text recognition by providing semantics to refine character sequences. However, since linguistic knowledge has been applied individually on the output sequence, previous methods have not fully utilized the semantics to understand visual clues for text recognition... | ['Sungrae Park', 'Yoonsik Kim', 'Byeonghu Na'] | 2021-11-30 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 2.66488582e-01 -4.76165563e-01 -2.56966680e-01 -3.22797954e-01
-5.95634699e-01 -6.28668427e-01 8.02192390e-01 1.07562795e-01
-3.87808084e-01 3.05600673e-01 4.72896785e-01 -1.15186147e-01
1.28892928e-01 -6.61797643e-01 -5.08039474e-01 -6.64654016e-01
5.16611278e-01 1.80833399e-01 4.00684655e-01 -2.19028458... | [11.674229621887207, 2.1278977394104004] |
f10e5ccb-55b0-4d3a-a311-a6d75a9cd9fa | resonant-machine-learning-based-on-complex | 1908.05377 | null | https://arxiv.org/abs/1908.05377v3 | https://arxiv.org/pdf/1908.05377v3.pdf | Resonant Machine Learning Based on Complex Growth Transform Dynamical Systems | Traditional energy-based learning models associate a single energy metric to each configuration of variables involved in the underlying optimization process. Such models associate the lowest energy state to the optimal configuration of variables under consideration, and are thus inherently dissipative. In this paper we... | ['Shantanu Chakrabartty', 'Oindrila Chatterjee'] | 2019-08-15 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 2.46623218e-01 3.09442729e-01 -4.90455776e-01 1.24530837e-01
9.95602757e-02 -3.92251968e-01 3.66100311e-01 3.90402585e-01
-3.49962205e-01 7.67725289e-01 -6.67190254e-01 -8.21698755e-02
-6.14855230e-01 -1.03263104e+00 -6.36265397e-01 -1.26565909e+00
-1.63398400e-01 2.19155755e-02 -8.31432119e-02 -2.72669286... | [6.064100742340088, 3.101614475250244] |
270140db-981f-42f1-8f5c-47a714ba6dda | generative-image-inpainting-with-segmentation | 2303.13133 | null | https://arxiv.org/abs/2303.13133v1 | https://arxiv.org/pdf/2303.13133v1.pdf | Generative Image Inpainting with Segmentation Confusion Adversarial Training and Contrastive Learning | This paper presents a new adversarial training framework for image inpainting with segmentation confusion adversarial training (SCAT) and contrastive learning. SCAT plays an adversarial game between an inpainting generator and a segmentation network, which provides pixel-level local training signals and can adapt to im... | ['Dongming Lu', 'Wei Xing', 'Jiafu Chen', 'Zhanjie Zhang', 'Zhizhong Wang', 'Ailin Li', 'Lei Zhao', 'Zhiwen Zuo'] | 2023-03-23 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 3.34651351e-01 2.50162929e-01 -8.88285264e-02 3.42040136e-02
-8.76224816e-01 -4.10934001e-01 1.16268367e-01 -4.50289607e-01
-1.81693628e-01 8.17508459e-01 -9.50175617e-03 1.31640032e-01
1.81340829e-01 -9.41365123e-01 -1.16175878e+00 -1.00015724e+00
1.81356966e-01 1.88219145e-01 1.79795101e-01 -2.56569535... | [11.433281898498535, -1.1776854991912842] |
55ecdf3e-17c0-490b-924d-bf7bff9dc023 | point-cloud-color-constancy | 2111.11280 | null | https://arxiv.org/abs/2111.11280v1 | https://arxiv.org/pdf/2111.11280v1.pdf | Point Cloud Color Constancy | In this paper, we present Point Cloud Color Constancy, in short PCCC, an illumination chromaticity estimation algorithm exploiting a point cloud. We leverage the depth information captured by the time-of-flight (ToF) sensor mounted rigidly with the RGB sensor, and form a 6D cloud where each point contains the coordinat... | ['Jiri Matas', 'Yuhan Dong', 'Sibo Feng', 'Yanlin Qian', 'Xiaoyan Xing'] | 2021-11-22 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Xing_Point_Cloud_Color_Constancy_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xing_Point_Cloud_Color_Constancy_CVPR_2022_paper.pdf | cvpr-2022-1 | ['color-constancy'] | ['computer-vision'] | [ 2.73606535e-02 -3.72793913e-01 2.53074855e-01 -2.41086423e-01
-2.86723763e-01 -9.57021415e-01 3.73539060e-01 -1.81921259e-01
-4.36760604e-01 4.03038353e-01 -3.88331145e-01 -2.59400934e-01
1.58477455e-01 -7.11951315e-01 -8.46856833e-01 -7.44391739e-01
2.63062119e-01 4.72665220e-01 2.65081525e-01 -6.40916154... | [7.789716720581055, -2.644195079803467] |
cd48f16d-856e-4ec0-bcc4-2c7c5252abca | convergence-bounds-for-nonlinear-least-1 | 2208.10954 | null | https://arxiv.org/abs/2208.10954v2 | https://arxiv.org/pdf/2208.10954v2.pdf | Convergence bounds for local least squares approximation | We consider the problem of approximating a function in a general nonlinear subset of $L^2$, when only a weighted Monte Carlo estimate of the $L^2$-norm can be computed. Of particular interest in this setting is the concept of sample complexity, the number of sample points that are necessary to achieve a prescribed erro... | ['Philipp Trunschke'] | 2022-08-23 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-1.76522657e-01 4.69445795e-01 -5.34275733e-03 -1.37604043e-01
-9.44443822e-01 -3.51787508e-01 3.95776540e-01 3.07027936e-01
-6.46486104e-01 8.42829227e-01 -4.25697714e-01 -1.90521762e-01
-3.91153961e-01 -7.98217416e-01 -9.84185159e-01 -8.92580152e-01
-6.25517070e-01 6.10172868e-01 3.18989940e-02 1.22573726... | [6.938559532165527, 4.21763801574707] |
bd8552da-5972-49bc-8b30-80ba70cbc5a0 | on-the-interaction-of-regularization-factors | null | null | https://aclanthology.org/2022.eamt-1.14 | https://aclanthology.org/2022.eamt-1.14.pdf | On the Interaction of Regularization Factors in Low-resource Neural Machine Translation | We explore the roles and interactions of the hyper-parameters governing regularization, and propose a range of values applicable to low-resource neural machine translation. We demonstrate that default or recommended values for high-resource settings are not optimal for low-resource ones, and that more aggressive regula... | ['Andrei Popescu-Belis', 'None Àlex R. Atrio'] | null | null | null | null | eamt-2022-6 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 1.26893073e-01 -3.44179757e-03 -6.16804659e-01 -2.69979179e-01
-8.26604962e-01 -5.97127080e-01 4.87700313e-01 8.81641731e-02
-1.05372858e+00 9.40533876e-01 5.11381149e-01 -5.73161006e-01
-1.45524159e-01 -3.30877393e-01 -6.67483926e-01 -4.41424966e-01
2.37129033e-01 2.29459688e-01 -2.99699921e-02 -2.27078944... | [10.746894836425781, 8.280795097351074] |
1e68af2d-cae4-42ba-b504-03dae2a9ddd6 | colde-a-depth-estimation-framework-for | 2111.10371 | null | https://arxiv.org/abs/2111.10371v1 | https://arxiv.org/pdf/2111.10371v1.pdf | ColDE: A Depth Estimation Framework for Colonoscopy Reconstruction | One of the key elements of reconstructing a 3D mesh from a monocular video is generating every frame's depth map. However, in the application of colonoscopy video reconstruction, producing good-quality depth estimation is challenging. Neural networks can be easily fooled by photometric distractions or fail to capture t... | ['Stephen M. Pizer', 'Shuxian Wang', 'Julian G. Rosenman', 'Sarah K. McGill', 'Samuel Ehrenstein', 'Jan-Michael Frahm', 'Yubo Zhang'] | 2021-11-19 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 3.03800315e-01 3.83351177e-01 1.19739324e-01 -2.66002208e-01
-4.58206862e-01 -1.79353774e-01 1.27005026e-01 2.51861751e-01
-2.88670421e-01 5.66537023e-01 -4.46703881e-02 -7.42147192e-02
-1.14256933e-01 -1.08338642e+00 -1.00508165e+00 -5.65925002e-01
-8.39407742e-02 2.22022846e-01 1.51215076e-01 -1.01361513... | [13.866047859191895, -3.071892261505127] |
412a1a52-8bde-4de3-b6a1-c4712fd35c82 | quantum-neural-network-compression | 2207.01578 | null | https://arxiv.org/abs/2207.01578v2 | https://arxiv.org/pdf/2207.01578v2.pdf | Quantum Neural Network Compression | Model compression, such as pruning and quantization, has been widely applied to optimize neural networks on resource-limited classical devices. Recently, there are growing interest in variational quantum circuits (VQC), that is, a type of neural network on quantum computers (a.k.a., quantum neural networks). It is well... | ['Weiwen Jiang', 'Yanzhi Wang', 'Youzuo Lin', 'Zhepeng Wang', 'Peiyan Dong', 'Zhirui Hu'] | 2022-07-04 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 5.48342764e-01 8.70949328e-02 -2.96607916e-03 -3.85654159e-02
-4.85723555e-01 -3.13387483e-01 3.06351900e-01 -4.20507230e-02
-7.04825878e-01 7.22916842e-01 -3.22991997e-01 -4.56412494e-01
-3.47521305e-01 -1.24545944e+00 -1.16072166e+00 -1.09248221e+00
2.96971291e-01 3.31897914e-01 1.79504901e-01 -5.84609330... | [5.581416606903076, 4.976219177246094] |
02bf8ee9-1570-4ddf-a008-a46ee195d1ab | modeling-electronic-health-record-data-using | 2206.01436 | null | https://arxiv.org/abs/2206.01436v1 | https://arxiv.org/pdf/2206.01436v1.pdf | Modeling electronic health record data using a knowledge-graph-embedded topic model | The rapid growth of electronic health record (EHR) datasets opens up promising opportunities to understand human diseases in a systematic way. However, effective extraction of clinical knowledge from the EHR data has been hindered by its sparsity and noisy information. We present KG-ETM, an end-to-end knowledge graph-b... | ['Yue Li', 'David Buckeridge', 'Aman Verma', 'Ahmad Pesaranghader', 'Yuesong Zou'] | 2022-06-03 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 8.58904719e-02 7.07736850e-01 -4.30043280e-01 -5.74162900e-01
-9.27144170e-01 -2.30095744e-01 -2.35635340e-02 7.09847927e-01
2.04960898e-01 6.07426584e-01 9.83042896e-01 -1.74852952e-01
-5.66862524e-01 -8.22495461e-01 -5.09917080e-01 -4.29215848e-01
-4.55477476e-01 8.97488534e-01 -6.36664152e-01 3.17577064... | [7.683936595916748, 6.514804363250732] |
2f2741cd-1946-498e-8657-1dc42d5d8f26 | differentially-private-community-detection | 2202.00636 | null | https://arxiv.org/abs/2202.00636v1 | https://arxiv.org/pdf/2202.00636v1.pdf | Differentially Private Community Detection for Stochastic Block Models | The goal of community detection over graphs is to recover underlying labels/attributes of users (e.g., political affiliation) given the connectivity between users (represented by adjacency matrix of a graph). There has been significant recent progress on understanding the fundamental limits of community detection when ... | ['Ravi Tandon', 'Anil Vullikanti', 'Dung Nguyen', 'Mohamed Seif'] | 2022-01-31 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 3.19503576e-01 2.60275364e-01 -4.98785861e-02 7.47552738e-02
-5.84689915e-01 -9.87350106e-01 8.50828439e-02 4.93488550e-01
-4.54463035e-01 6.16391957e-01 -3.81413728e-01 -4.34440911e-01
-3.84549916e-01 -1.10678566e+00 -5.14689207e-01 -7.99018264e-01
-7.46570528e-01 2.60421932e-01 -7.63854338e-03 6.00708760... | [6.687252998352051, 5.236156463623047] |
d7dddd1d-0ed0-4d08-99c8-8c2739cdec0f | hop-union-generate-explainable-multi-hop | 2305.14237 | null | https://arxiv.org/abs/2305.14237v1 | https://arxiv.org/pdf/2305.14237v1.pdf | HOP, UNION, GENERATE: Explainable Multi-hop Reasoning without Rationale Supervision | Explainable multi-hop question answering (QA) not only predicts answers but also identifies rationales, i. e. subsets of input sentences used to derive the answers. This problem has been extensively studied under the supervised setting, where both answer and rationale annotations are given. Because rationale annotation... | ['Alexander M. Rush', 'Claire Cardie', 'Justin T. Chiu', 'Wenting Zhao'] | 2023-05-23 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 3.39396596e-01 8.17676008e-01 -1.69372812e-01 -8.02159965e-01
-1.31877041e+00 -6.03577673e-01 6.18492603e-01 4.68459576e-01
1.56930536e-01 9.88471389e-01 4.85153377e-01 -4.79233652e-01
-2.60853946e-01 -7.81188130e-01 -7.69127190e-01 -1.84997514e-01
4.22335863e-01 1.06051421e+00 5.29257298e-01 -2.18598038... | [10.999000549316406, 7.901731491088867] |
68f0f45d-d24e-4c64-ad56-dcf0ab6e482c | evaluating-deception-detection-model | 2104.11729 | null | https://arxiv.org/abs/2104.11729v1 | https://arxiv.org/pdf/2104.11729v1.pdf | Evaluating Deception Detection Model Robustness To Linguistic Variation | With the increasing use of machine-learning driven algorithmic judgements, it is critical to develop models that are robust to evolving or manipulated inputs. We propose an extensive analysis of model robustness against linguistic variation in the setting of deceptive news detection, an important task in the context of... | ['Svitlana Volkova', 'Dustin Arendt', 'Robin Cosbey', 'Ellyn Ayton', 'Maria Glenski'] | 2021-04-23 | null | https://aclanthology.org/2021.socialnlp-1.6 | https://aclanthology.org/2021.socialnlp-1.6.pdf | naacl-socialnlp-2021-6 | ['deception-detection'] | ['miscellaneous'] | [ 8.69641975e-02 -4.02756393e-01 -1.55535713e-01 -9.09665152e-02
-7.41481125e-01 -1.02731824e+00 1.22668493e+00 6.12351537e-01
-5.33706665e-01 4.22887921e-01 5.50472140e-01 -6.50181115e-01
-2.97873676e-01 -6.50724590e-01 -5.26091576e-01 -3.81154954e-01
-1.55478328e-01 4.37353909e-01 -1.33115668e-02 -6.60161912... | [6.164788246154785, 8.20001220703125] |
36d6503d-188f-49ae-a7b0-46b885834070 | the-dirha-simulated-corpus | null | null | https://aclanthology.org/L14-1516 | https://aclanthology.org/L14-1516.pdf | The DIRHA simulated corpus | This paper describes a multi-microphone multi-language acoustic corpus being developed under the EC project Distant-speech Interaction for Robust Home Applications (DIRHA). The corpus is composed of several sequences obtained by convolution of dry acoustic events with more than 9000 impulse responses measured in a real... | ['ro', 'Maurizio Omologo', 'Mirco Ravanelli', 'Luca Cristoforetti', 'Aless Sosi', 'Alberto Abad', 'Petros Maragos', 'Martin Hagmueller'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['dialogue-management', 'distant-speech-recognition'] | ['natural-language-processing', 'speech'] | [ 6.86688125e-02 -4.01888400e-01 1.01640439e+00 -3.92986864e-01
-9.30822372e-01 -3.97132307e-01 4.74231243e-01 -1.18065238e-01
-5.37675798e-01 5.65249503e-01 6.08288169e-01 9.02125239e-02
1.12899147e-01 -5.29187799e-01 -3.75641495e-01 -7.45393157e-01
-2.08466887e-01 1.14801019e-01 1.03753172e-01 -4.03699905... | [14.884452819824219, 6.086986541748047] |
7d43093d-e237-4fda-bb51-cd2d703fd594 | unsupervised-speech-domain-adaptation-based | 1904.06086 | null | http://arxiv.org/abs/1904.06086v1 | http://arxiv.org/pdf/1904.06086v1.pdf | Unsupervised Speech Domain Adaptation Based on Disentangled Representation Learning for Robust Speech Recognition | In general, the performance of automatic speech recognition (ASR) systems is
significantly degraded due to the mismatch between training and test
environments. Recently, a deep-learning-based image-to-image translation
technique to translate an image from a source domain to a desired domain was
presented, and cycle-con... | ['Hyung-Min Park', 'Jong-Hyeon Park', 'Myungwoo Oh'] | 2019-04-12 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 6.27069056e-01 1.58268467e-01 1.72106504e-01 -4.86954361e-01
-1.00012708e+00 -6.28166795e-01 7.83048511e-01 -6.47353232e-01
-2.61251688e-01 7.81948447e-01 3.60761210e-03 -2.35615134e-01
5.67783833e-01 -6.85106933e-01 -8.33525062e-01 -1.01260209e+00
6.55279875e-01 3.42285633e-01 -3.38579714e-01 -3.50400746... | [14.675856590270996, 6.506832122802734] |
79973f60-82b9-4501-b73b-1350d423106f | tradeoffs-in-resampling-and-filtering-for | 2209.00127 | null | https://arxiv.org/abs/2209.00127v1 | https://arxiv.org/pdf/2209.00127v1.pdf | Tradeoffs in Resampling and Filtering for Imbalanced Classification | Imbalanced classification problems are extremely common in natural language processing and are solved using a variety of resampling and filtering techniques, which often involve making decisions on how to select training data or decide which test examples should be labeled by the model. We examine the tradeoffs in mode... | ['David Smith', 'Ryan Muther'] | 2022-08-31 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 3.04169655e-01 -2.34558046e-01 -4.86024320e-01 -6.17578983e-01
-8.33038390e-01 -3.91702533e-01 5.55808187e-01 9.01858926e-01
-9.54791963e-01 7.59101629e-01 4.68233973e-01 -4.23001528e-01
-1.07588224e-01 -9.45324719e-01 -3.86674851e-01 -4.98736888e-01
1.88290536e-01 7.44109333e-01 2.35177472e-01 -1.56985119... | [8.976536750793457, 4.6405439376831055] |
97599ab2-f316-405d-8dd2-97a67dd9e395 | structured-sparse-method-for-hyperspectral | 1403.4682 | null | http://arxiv.org/abs/1403.4682v1 | http://arxiv.org/pdf/1403.4682v1.pdf | Structured Sparse Method for Hyperspectral Unmixing | Hyperspectral Unmixing (HU) has received increasing attention in the past
decades due to its ability of unveiling information latent in hyperspectral
data. Unfortunately, most existing methods fail to take advantage of the
spatial information in data. To overcome this limitation, we propose a
Structured Sparse regulari... | ['Chunhong Pan', 'Ying Wang', 'Feiyun Zhu', 'Shiming Xiang', 'Bin Fan'] | 2014-03-19 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 4.75940347e-01 -3.12831730e-01 -2.05426678e-01 -1.69788361e-01
-3.05950671e-01 -4.05995369e-01 1.84585467e-01 -2.18315929e-01
1.27596557e-01 5.43304145e-01 4.86377358e-01 1.61272019e-01
-4.76591259e-01 -6.88465238e-01 -3.59402150e-01 -1.24836791e+00
1.36160150e-01 -1.00106649e-01 -3.38909686e-01 -6.57441048... | [10.106929779052734, -2.000074863433838] |
e0bbd597-6497-4c0b-badd-72d47a56c684 | poe-a-panel-of-experts-for-generalized | 2212.08992 | null | https://arxiv.org/abs/2212.08992v1 | https://arxiv.org/pdf/2212.08992v1.pdf | PoE: a Panel of Experts for Generalized Automatic Dialogue Assessment | Chatbots are expected to be knowledgeable across multiple domains, e.g. for daily chit-chat, exchange of information, and grounding in emotional situations. To effectively measure the quality of such conversational agents, a model-based automatic dialogue evaluation metric (ADEM) is expected to perform well across mult... | ['Haizhou Li', 'Thomas Friedrichs', 'Qiquan Zhang', "Luis Fernando D'Haro", 'Chen Zhang'] | 2022-12-18 | null | null | null | null | ['general-knowledge', 'dialogue-evaluation'] | ['miscellaneous', 'natural-language-processing'] | [ 3.37620489e-02 3.84782016e-01 -4.92409281e-02 -6.09673858e-01
-6.96172416e-01 -7.23017931e-01 9.15230155e-01 -1.07324962e-03
-3.50829095e-01 1.11125088e+00 2.67783016e-01 9.67923477e-02
-3.18349414e-02 -7.21987426e-01 -1.09527580e-01 -2.48408660e-01
8.72757137e-02 1.17640293e+00 3.20423692e-01 -9.36015427... | [12.786462783813477, 8.013360023498535] |
f6e6e8a6-53b7-4461-af41-91deb8813756 | transformers-in-speech-processing-a-survey | 2303.11607 | null | https://arxiv.org/abs/2303.11607v1 | https://arxiv.org/pdf/2303.11607v1.pdf | Transformers in Speech Processing: A Survey | The remarkable success of transformers in the field of natural language processing has sparked the interest of the speech-processing community, leading to an exploration of their potential for modeling long-range dependencies within speech sequences. Recently, transformers have gained prominence across various speech-r... | ['Junaid Qadir', 'Moazzam Shoukat', 'Fahad Shamshad', 'Heriberto Cuayahuitl', 'Aun Zaidi', 'Siddique Latif'] | 2023-03-21 | null | null | null | null | ['spoken-dialogue-systems', 'speech-enhancement', 'speech-synthesis'] | ['speech', 'speech', 'speech'] | [ 5.61361969e-01 2.25574866e-01 -1.73520684e-01 -5.88895142e-01
-8.25422704e-01 -7.44968653e-01 6.71120048e-01 1.91396579e-01
-2.08153576e-01 3.29355061e-01 6.75771892e-01 -9.27895606e-01
3.00570205e-02 -3.35183591e-01 -2.41379533e-02 -1.93200976e-01
-1.91752523e-01 1.42130151e-01 7.65679479e-02 -4.26104754... | [14.370370864868164, 6.9083685874938965] |
96ff5762-89c0-4e8d-a413-fcdc28c99086 | routing-enforced-generative-model-for-recipe | null | null | https://aclanthology.org/2020.emnlp-main.311 | https://aclanthology.org/2020.emnlp-main.311.pdf | Routing Enforced Generative Model for Recipe Generation | One of the most challenging part of recipe generation is to deal with the complex restrictions among the input ingredients. Previous researches simplify the problem by treating the inputs independently and generating recipes containing as much information as possible. In this work, we propose a routing method to dive i... | ['Xiaojun Wan', 'Hongyu Zang', 'Zhiwei Yu'] | null | null | null | null | emnlp-2020-11 | ['recipe-generation'] | ['miscellaneous'] | [ 1.30157143e-01 -2.51321308e-02 -2.69617051e-01 -3.98627222e-01
-3.74731213e-01 -9.02143061e-01 3.15697819e-01 1.69563994e-01
-1.88335121e-01 5.14539838e-01 6.14564121e-01 -2.67374776e-02
1.46088898e-01 -1.23802578e+00 -6.06841087e-01 -3.55465263e-01
5.22842050e-01 4.16224629e-01 -1.71361789e-01 -5.84843993... | [11.5195894241333, 4.537475109100342] |
3f935501-6756-48bf-bdce-85b6d8a99b1b | rdd2022-a-multi-national-image-dataset-for | 2209.08538 | null | https://arxiv.org/abs/2209.08538v1 | https://arxiv.org/pdf/2209.08538v1.pdf | RDD2022: A multi-national image dataset for automatic Road Damage Detection | The data article describes the Road Damage Dataset, RDD2022, which comprises 47,420 road images from six countries, Japan, India, the Czech Republic, Norway, the United States, and China. The images have been annotated with more than 55,000 instances of road damage. Four types of road damage, namely longitudinal cracks... | ['Yoshihide Sekimoto', 'Durga Toshniwal', 'Sanjay Kumar Ghosh', 'Hiroya Maeda', 'Deeksha Arya'] | 2022-09-18 | null | null | null | null | ['road-damage-detection'] | ['computer-vision'] | [-1.74128041e-01 1.38647184e-01 1.44367367e-01 3.07711177e-02
-7.49170184e-01 -2.60111392e-01 6.28050148e-01 8.30382183e-02
-1.82405397e-01 4.96768028e-01 6.00346506e-01 -4.23557669e-01
2.61007875e-01 -1.54453063e+00 -3.10293078e-01 -5.79197168e-01
5.72068654e-02 1.91764936e-01 6.20192528e-01 -1.86435476... | [7.3896894454956055, 1.158888816833496] |
c2abf829-3838-4007-b568-39a88f0aa183 | unsupervised-learning-based-depth-estimation | 1901.07288 | null | http://arxiv.org/abs/1901.07288v1 | http://arxiv.org/pdf/1901.07288v1.pdf | Unsupervised Learning-based Depth Estimation aided Visual SLAM Approach | The RGB-D camera maintains a limited range for working and is hard to
accurately measure the depth information in a far distance. Besides, the RGB-D
camera will easily be influenced by strong lighting and other external factors,
which will lead to a poor accuracy on the acquired environmental depth
information. Recentl... | ['Huaimin Wang', 'Bo Ding', 'Suning Shang', 'Pengfei Zhang', 'Lei Zhang', 'Mingyang Geng'] | 2019-01-22 | null | null | null | null | ['depth-and-camera-motion'] | ['computer-vision'] | [-7.73435533e-02 -3.77227634e-01 -3.67109060e-01 -5.65210640e-01
-4.10352290e-01 -1.44281521e-01 4.34268415e-01 -3.64217490e-01
-4.94537920e-01 5.57570577e-01 -5.80010042e-02 -5.32493219e-02
-7.01751036e-04 -9.60061789e-01 -6.81481004e-01 -8.16591799e-01
1.45466283e-01 4.55445886e-01 2.33366966e-01 -1.76599309... | [8.007560729980469, -2.232306480407715] |
d4148412-661c-4e2a-9980-191b3d7becb7 | humman-multi-modal-4d-human-dataset-for | 2204.13686 | null | https://arxiv.org/abs/2204.13686v2 | https://arxiv.org/pdf/2204.13686v2.pdf | HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling | 4D human sensing and modeling are fundamental tasks in vision and graphics with numerous applications. With the advances of new sensors and algorithms, there is an increasing demand for more versatile datasets. In this work, we contribute HuMMan, a large-scale multi-modal 4D human dataset with 1000 human subjects, 400k... | ['Ziwei Liu', 'Lei Yang', 'Chen Change Loy', 'Mingyuan Zhang', 'Fangzhou Hong', 'Liang Pan', 'Yifan Yu', 'Yang Gao', 'Xiangyu Fan', 'Wenjia Wang', 'Tao Yu', 'Zhengyu Lin', 'Ailing Zeng', 'Daxuan Ren', 'Zhongang Cai'] | 2022-04-28 | null | null | null | null | ['fine-grained-action-recognition'] | ['computer-vision'] | [ 1.72112927e-01 -4.96412188e-01 2.37318408e-03 -5.57107590e-02
-7.45174408e-01 -6.44309074e-02 4.27713633e-01 -2.78411478e-01
-1.77454814e-01 2.32084855e-01 3.73855680e-01 6.72326148e-01
1.00074902e-01 -2.67766923e-01 -5.25845230e-01 -2.78932899e-01
-7.14442208e-02 1.11087310e+00 6.37369871e-01 -2.78461874... | [7.047723293304443, -0.966816246509552] |
4229bfd1-8c6a-423c-800c-089596383b3a | generative-job-recommendations-with-large | 2307.02157 | null | https://arxiv.org/abs/2307.02157v1 | https://arxiv.org/pdf/2307.02157v1.pdf | Generative Job Recommendations with Large Language Model | The rapid development of online recruitment services has encouraged the utilization of recommender systems to streamline the job seeking process. Predominantly, current job recommendations deploy either collaborative filtering or person-job matching strategies. However, these models tend to operate as "black-box" syste... | ['Hui Xiong', 'HengShu Zhu', 'Likang Wu', 'Xiao Hu', 'Zhaopeng Qiu', 'Zhi Zheng'] | 2023-07-05 | null | null | null | null | ['reinforcement-learning-1', 'collaborative-filtering'] | ['methodology', 'miscellaneous'] | [ 1.13237716e-01 4.11416382e-01 -7.19348550e-01 -6.07162893e-01
-5.51174521e-01 -3.54278117e-01 7.12938011e-01 -1.26961991e-02
-4.50543255e-01 4.36021328e-01 4.75532234e-01 -6.27082169e-01
-6.40674770e-01 -9.38233197e-01 -2.32933804e-01 -3.69867086e-01
4.08523470e-01 1.04726362e+00 -2.07674682e-01 -6.87718511... | [10.230833053588867, 5.8176374435424805] |
d2480161-86ba-4b83-ade1-92ac4e532f7c | suppress-and-balance-a-simple-gated-network | 2007.08074 | null | https://arxiv.org/abs/2007.08074v3 | https://arxiv.org/pdf/2007.08074v3.pdf | Suppress and Balance: A Simple Gated Network for Salient Object Detection | Most salient object detection approaches use U-Net or feature pyramid networks (FPN) as their basic structures. These methods ignore two key problems when the encoder exchanges information with the decoder: one is the lack of interference control between them, the other is without considering the disparity of the contr... | ['Youwei Pang', 'Lihe Zhang', 'Lei Zhang', 'Huchuan Lu', 'Xiaoqi Zhao'] | 2020-07-16 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2852_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123470035.pdf | eccv-2020-8 | ['dichotomous-image-segmentation'] | ['computer-vision'] | [ 1.57200083e-01 -6.91480096e-03 -4.77968715e-02 -1.41083658e-01
-2.16924459e-01 -1.02730311e-01 2.45233148e-01 5.35166636e-02
-2.38109022e-01 6.75602674e-01 3.98821533e-01 2.26215005e-01
9.15649068e-03 -9.24922943e-01 -7.42774129e-01 -7.75274813e-01
1.04998603e-01 -5.86232483e-01 1.12265956e+00 -3.66860390... | [9.696982383728027, -0.48734596371650696] |
7d22d115-67f0-4cb0-b916-2dc948ab165c | on-bonus-based-exploration-methods-in-the | null | null | https://openreview.net/forum?id=BJewlyStDr | https://openreview.net/pdf?id=BJewlyStDr | On Bonus Based Exploration Methods In The Arcade Learning Environment | Research on exploration in reinforcement learning, as applied to Atari 2600 game-playing, has emphasized tackling difficult exploration problems such as Montezuma's Revenge (Bellemare et al., 2016). Recently, bonus-based exploration methods, which explore by augmenting the environment reward, have reached above-human a... | ['Aaron Courville', 'William Fedus', 'Marc G. Bellemare', 'Adrien Ali Taiga', 'Marlos C. Machado'] | 2020-01-01 | null | null | null | iclr-2020-1 | ['montezumas-revenge'] | ['playing-games'] | [-4.88501638e-01 1.19107075e-01 -1.45592138e-01 2.03883186e-01
-7.96383500e-01 -8.33250225e-01 5.97774386e-01 -2.33950540e-01
-9.60518241e-01 1.02399385e+00 3.32280427e-01 -6.29018068e-01
-4.14890230e-01 -7.31949151e-01 -7.55804777e-01 -5.82383811e-01
-8.06355536e-01 5.22303045e-01 -6.48231283e-02 -8.48864555... | [3.8668572902679443, 1.689868688583374] |
5cfed957-ffda-4af2-883d-1dad47816db9 | vista-vision-transformer-enhanced-by-u-net | 2204.11024 | null | https://arxiv.org/abs/2204.11024v1 | https://arxiv.org/pdf/2204.11024v1.pdf | VISTA: Vision Transformer enhanced by U-Net and Image Colorfulness Frame Filtration for Automatic Retail Checkout | Multi-class product counting and recognition identifies product items from images or videos for automated retail checkout. The task is challenging due to the real-world scenario of occlusions where product items overlap, fast movement in the conveyor belt, large similarity in overall appearance of the items being scann... | ['Nabeel Mohammed', 'Labiba Kanij Rupty', 'Hasib Zunair', 'Nazia Tasnim', 'Md. Istiak Hossain Shihab'] | 2022-04-23 | null | null | null | null | ['hand-segmentation'] | ['computer-vision'] | [ 4.75254536e-01 -4.17579025e-01 -2.74029136e-01 -3.83114427e-01
-4.58204329e-01 -7.67178237e-01 3.50081712e-01 -3.14841010e-02
-1.73045665e-01 3.88360143e-01 -1.41419932e-01 2.24159248e-02
8.56918842e-02 -4.34789836e-01 -7.92578459e-01 -5.96094191e-01
1.72256187e-01 3.26957285e-01 3.81719232e-01 -1.11722564... | [8.973636627197266, -0.3721483647823334] |
d660b72b-0fe0-4adb-b8f2-ba7ac37c74b1 | simultaneous-adversarial-attacks-on-multiple | 2304.05048 | null | https://arxiv.org/abs/2304.05048v1 | https://arxiv.org/pdf/2304.05048v1.pdf | Simultaneous Adversarial Attacks On Multiple Face Recognition System Components | In this work, we investigate the potential threat of adversarial examples to the security of face recognition systems. Although previous research has explored the adversarial risk to individual components of FRSs, our study presents an initial exploration of an adversary simultaneously fooling multiple components: the ... | ['Toshinori Araki', 'Kazuya Kakizaki', 'Inderjeet Singh'] | 2023-04-11 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 4.96178478e-01 3.96617740e-01 5.29145598e-01 -3.22450608e-01
-6.75368369e-01 -1.22130251e+00 6.40038013e-01 -4.36242938e-01
-1.56272531e-01 1.54219314e-01 -3.45401019e-01 -6.12571597e-01
1.19981363e-01 -5.52105963e-01 -9.12725031e-01 -6.41780496e-01
-5.04443169e-01 -1.19039156e-01 4.08629835e-01 -2.00760782... | [5.661550045013428, 7.707456111907959] |
85e295e2-edb9-4cb2-b7b9-f9878e718c32 | deep-learning-for-video-game-playing | 1708.07902 | null | http://arxiv.org/abs/1708.07902v3 | http://arxiv.org/pdf/1708.07902v3.pdf | Deep Learning for Video Game Playing | In this article, we review recent Deep Learning advances in the context of
how they have been applied to play different types of video games such as
first-person shooters, arcade games, and real-time strategy games. We analyze
the unique requirements that different game genres pose to a deep learning
system and highlig... | ['Sebastian Risi', 'Niels Justesen', 'Julian Togelius', 'Philip Bontrager'] | 2017-08-25 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [-2.37715185e-01 -1.58584654e-01 -1.08272962e-01 8.18636045e-02
-3.99406344e-01 -5.46497285e-01 1.40540078e-01 -2.50545710e-01
-8.45931709e-01 7.52845228e-01 -1.85883105e-01 -5.15995324e-01
-4.52888012e-01 -9.80770648e-01 -3.16779017e-01 -3.44653338e-01
-3.96728367e-01 5.89732826e-01 3.96736920e-01 -1.08611178... | [3.569275379180908, 1.484670639038086] |
0d4afba7-be39-43ab-a167-e25d8351416d | application-of-graph-based-features-in | 2205.08467 | null | https://arxiv.org/abs/2205.08467v1 | https://arxiv.org/pdf/2205.08467v1.pdf | Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer | The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagnosis. In this paper, based on the study of computer aided diagnosis system, graph based features are applied to gastric cancer histopathology m... | ['Marcin Grzegorzek', 'Xinyu Huang', 'Hongzan Sun', 'Xiaoyan Li', 'Yixin Li', 'Yuchao Zheng', 'HaoYuan Chen', 'Shiliang Ai', 'Chen Li', 'Haiqing Zhang'] | 2022-05-17 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [ 1.08220756e-01 -1.37934670e-01 -3.75458121e-01 1.43276989e-01
1.59022808e-02 -3.34443867e-01 -1.24805942e-04 7.49243319e-01
-5.40310264e-01 4.74115819e-01 -9.12612975e-02 -5.29909015e-01
-1.94205657e-01 -1.23951912e+00 2.86717653e-01 -1.25736260e+00
-5.33963323e-01 1.66930676e-01 7.09360301e-01 -2.27191254... | [15.237146377563477, -2.8449363708496094] |
ab99e497-877c-42a2-ba52-314cc720d996 | domain-enhanced-arbitrary-image-style | 2205.09542 | null | https://arxiv.org/abs/2205.09542v2 | https://arxiv.org/pdf/2205.09542v2.pdf | Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning | In this work, we tackle the challenging problem of arbitrary image style transfer using a novel style feature representation learning method. A suitable style representation, as a key component in image stylization tasks, is essential to achieve satisfactory results. Existing deep neural network based approaches achiev... | ['Changsheng Xu', 'Tong-Yee Lee', 'Chongyang Ma', 'Haibin Huang', 'WeiMing Dong', 'Fan Tang', 'Yuxin Zhang'] | 2022-05-19 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 2.90730417e-01 -5.24703681e-01 -5.05465316e-03 -4.18854237e-01
-6.08538330e-01 -6.50156617e-01 7.04207063e-01 -3.29386771e-01
-1.71379820e-01 6.41215980e-01 1.88861772e-01 -1.55297831e-01
2.51546830e-01 -7.99328327e-01 -6.98171675e-01 -7.09026873e-01
6.65262938e-01 1.34101808e-01 3.20322171e-04 -3.47130358... | [11.578974723815918, -0.7228875756263733] |
7857715e-b2e4-4509-bebd-3454e5d2c5f4 | hierarchical-document-encoder-for-parallel | 1906.08401 | null | https://arxiv.org/abs/1906.08401v2 | https://arxiv.org/pdf/1906.08401v2.pdf | Hierarchical Document Encoder for Parallel Corpus Mining | We explore using multilingual document embeddings for nearest neighbor mining of parallel data. Three document-level representations are investigated: (i) document embeddings generated by simply averaging multilingual sentence embeddings; (ii) a neural bag-of-words (BoW) document encoding model; (iii) a hierarchical mu... | ['Yun-Hsuan Sung', 'Yinfei Yang', 'Brian Strope', 'Daniel Cer', 'Mandy Guo', 'Heming Ge', 'Ray Kurzweil', 'Keith Stevens'] | 2019-06-20 | hierarchical-document-encoder-for-parallel-1 | https://aclanthology.org/W19-5207 | https://aclanthology.org/W19-5207.pdf | ws-2019-8 | ['parallel-corpus-mining'] | ['natural-language-processing'] | [-1.61451846e-01 -4.36769314e-02 -2.96839237e-01 -3.78242671e-01
-1.55149531e+00 -3.90070379e-01 9.11012590e-01 8.53215337e-01
-8.40040565e-01 4.89579648e-01 1.13423657e+00 -5.87380111e-01
-1.68487608e-01 -8.56734216e-01 -7.87298560e-01 -3.59373301e-01
-4.69419777e-01 3.87109220e-01 -3.53083879e-01 -4.57217485... | [10.853922843933105, 9.103991508483887] |
935e34ba-1c51-4b09-8ea6-72e12ab94a17 | polarimetric-inverse-rendering-for | 2208.11836 | null | https://arxiv.org/abs/2208.11836v1 | https://arxiv.org/pdf/2208.11836v1.pdf | Polarimetric Inverse Rendering for Transparent Shapes Reconstruction | In this work, we propose a novel method for the detailed reconstruction of transparent objects by exploiting polarimetric cues. Most of the existing methods usually lack sufficient constraints and suffer from the over-smooth problem. Hence, we introduce polarization information as a complementary cue. We implicitly rep... | ['Xueqian Wang', 'Dongxu Duan', 'Chongkun Xia', 'Mingqi Shao'] | 2022-08-25 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 1.36910975e-01 -1.34418726e-01 2.05242589e-01 -3.10844928e-01
-4.42000180e-01 -2.73401737e-01 2.77729928e-01 -4.52362001e-01
6.36957064e-02 5.83369315e-01 1.06895596e-01 -9.50295292e-03
7.27938116e-02 -1.18074095e+00 -6.10176027e-01 -1.14162612e+00
3.36319864e-01 3.43063563e-01 2.98493743e-01 -7.47059584... | [9.879579544067383, -2.9508135318756104] |
3c0ab466-a98e-4925-b2d5-cda9d48bf2e5 | modeling-label-semantics-improves-activity | 2301.03462 | null | https://arxiv.org/abs/2301.03462v1 | https://arxiv.org/pdf/2301.03462v1.pdf | Modeling Label Semantics Improves Activity Recognition | Human activity recognition (HAR) aims to classify sensory time series into different activities, with wide applications in activity tracking, healthcare, human computer interaction, etc. Existing HAR works improve recognition performance by designing more complicated feature extraction methods, but they neglect the lab... | ['Jingbo Shang', 'Rajesh K. Gupta', 'Dezhi Hong', 'Ranak Roy Chowdhury', 'Xiyuan Zhang'] | 2023-01-01 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 3.05712730e-01 -4.37552243e-01 -5.24568260e-01 -4.91947949e-01
-6.07076108e-01 -6.39106333e-01 5.74819148e-01 -9.33477134e-02
-3.18181634e-01 9.38921034e-01 5.44964075e-01 -1.09103940e-01
-1.99580118e-01 -6.77512467e-01 -6.91379070e-01 -8.80700827e-01
-2.70096868e-01 2.21874967e-01 3.01681072e-01 5.60293458... | [7.955705165863037, 0.9830049872398376] |
30885e41-2001-4d76-bce9-fbad23459fba | towards-a-design-framework-for-tnn-based | 2205.14248 | null | https://arxiv.org/abs/2205.14248v1 | https://arxiv.org/pdf/2205.14248v1.pdf | Towards a Design Framework for TNN-Based Neuromorphic Sensory Processing Units | Temporal Neural Networks (TNNs) are spiking neural networks that exhibit brain-like sensory processing with high energy efficiency. This work presents the ongoing research towards developing a custom design framework for designing efficient application-specific TNN-based Neuromorphic Sensory Processing Units (NSPUs). T... | ['John Paul Shen', 'Prabhu Vellaisamy'] | 2022-05-27 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 5.81914008e-01 -5.52461743e-01 6.11542724e-02 -5.04633367e-01
1.01683594e-01 -3.69119048e-01 1.89139262e-01 -2.42524087e-01
-5.75114250e-01 5.06234586e-01 -3.53027821e-01 -4.43037540e-01
-1.87251538e-01 -7.05639899e-01 -4.60690856e-01 -6.90485537e-01
-1.45217121e-01 -1.69449285e-01 7.26659060e-01 -6.82545602... | [8.205994606018066, 2.454793691635132] |
b2088864-d283-4eba-a543-af668798af97 | identification-and-validation-of | 2107.02905 | null | https://arxiv.org/abs/2107.02905v2 | https://arxiv.org/pdf/2107.02905v2.pdf | An in silico drug repurposing pipeline to identify drugs with the potential to inhibit SARS-CoV-2 replication | Drug repurposing provides an opportunity to redeploy drugs, which ideally are already approved for use in humans, for the treatment of other diseases. For example, the repurposing of dexamethasone and baricitinib has played a crucial role in saving patient lives during the ongoing SARS-CoV-2 pandemic. There remains a n... | ['Namshik Han', 'Vasanthi Priyadarshini Gaddi', 'Paul Bilokon', 'Mukunthan Tharmakulasingam', 'Justin Barton', 'Alexandre Abraham', 'Eoghan MacMahon', 'Soorin Yim', 'Woochang Hwang', 'Méabh MacMahon'] | 2021-07-05 | null | null | null | null | ['action-analysis'] | ['computer-vision'] | [ 3.95815015e-01 -4.13024366e-01 9.48778242e-02 5.52440016e-03
-3.05276185e-01 -8.10776055e-01 1.64903089e-01 7.63949990e-01
-4.05756354e-01 1.10655248e+00 6.00776412e-02 -8.95718634e-01
-1.21609427e-01 -3.80650997e-01 -4.72821772e-01 -6.53556049e-01
-3.50227803e-01 7.80954897e-01 -7.10925162e-02 -1.86959967... | [4.723056793212891, 5.082431316375732] |
fce976fb-12c3-405c-95e7-33203afa4bea | a-bert-based-distractor-generation-scheme | 2010.05384 | null | https://arxiv.org/abs/2010.05384v1 | https://arxiv.org/pdf/2010.05384v1.pdf | A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies | In this paper, we investigate the following two limitations for the existing distractor generation (DG) methods. First, the quality of the existing DG methods are still far from practical use. There is still room for DG quality improvement. Second, the existing DG designs are mainly for single distractor generation. Ho... | ['Yao-Chung Fan', 'Ying-Hong Chan', 'Ho-Lam Chung'] | 2020-10-12 | null | null | null | null | ['distractor-generation'] | ['natural-language-processing'] | [-2.38253787e-01 -1.45325214e-01 -2.61324972e-01 1.23441607e-01
-1.40206921e+00 -7.18472838e-01 5.02176821e-01 -1.93591475e-01
-1.54397264e-01 1.25752783e+00 3.39032590e-01 -5.87593019e-01
1.23238936e-01 -4.09504503e-01 -3.36564004e-01 -6.15262866e-01
6.55535400e-01 5.98102391e-01 5.10490298e-01 -7.01035857... | [11.587605476379395, 8.308871269226074] |
6cde0dcc-d2d5-46cc-a7cb-5c5e3199e942 | 2305-15017 | 2305.15017 | null | https://arxiv.org/abs/2305.15017v1 | https://arxiv.org/pdf/2305.15017v1.pdf | Calc-X: Enriching Arithmetical Chain-of-Thoughts Datasets by Interaction with Symbolic Systems | This report overviews our ongoing work in enriching chain-of-thoughts datasets requiring arithmetical reasoning with the integration of non-parametric components, such as a calculator. We conduct an analysis of prominent relevant datasets such as GSM8K, Ape210K, AQuA-RAT, and MathQA and propose a machine-processable HT... | ['Michal Štefánik', 'Marek Kadlčík'] | 2023-05-24 | null | null | null | null | ['gsm8k'] | ['natural-language-processing'] | [-6.04434252e-01 4.04605567e-01 -8.32034573e-02 -6.58823013e-01
-3.53252172e-01 -9.39386547e-01 7.23560929e-01 3.35880578e-01
-4.86894399e-02 6.18698776e-01 3.28652531e-01 -1.03552723e+00
-4.48509216e-01 -1.30460954e+00 -6.70162380e-01 2.57192224e-01
-1.75995857e-01 9.86012757e-01 -1.34856656e-01 -5.95363140... | [9.54287052154541, 7.288719654083252] |
f207eddd-a34f-4793-b0a4-5054b8308906 | multiple-instance-dictionary-learning-for | 1706.03373 | null | http://arxiv.org/abs/1706.03373v2 | http://arxiv.org/pdf/1706.03373v2.pdf | Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms | A multiple instance dictionary learning approach, Dictionary Learning using
Functions of Multiple Instances (DL-FUMI), is used to perform beat-to-beat
heart rate estimation and to characterize heartbeat signatures from
ballistocardiogram (BCG) signals collected with a hydraulic bed sensor. DL-FUMI
estimates a "heartbea... | ['Alina Zare', 'Princess Lyons', 'Bo-Yu Su', 'Changzhe Jiao', 'Marjorie Skubic', 'K. C. Ho'] | 2017-06-11 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 3.49492878e-01 7.02092871e-02 -3.83557081e-01 -1.84033215e-01
-6.76639676e-01 -2.65173554e-01 -4.57626842e-02 2.98731059e-01
-3.38489190e-02 7.37370849e-01 1.72243044e-02 -3.40042487e-02
-4.36248690e-01 -3.96733135e-01 -1.10063285e-01 -6.47495091e-01
-1.11209176e-01 8.07395160e-01 -7.57883072e-01 2.55800009... | [14.218935012817383, 3.2100770473480225] |
12798034-4a84-4909-8b03-366ef5c2d591 | automatic-model-selection-with-large-language | 2305.14333 | null | https://arxiv.org/abs/2305.14333v1 | https://arxiv.org/pdf/2305.14333v1.pdf | Automatic Model Selection with Large Language Models for Reasoning | Chain-of-Thought and Program-Aided Language Models represent two distinct reasoning methods, each with its own strengths and weaknesses. We demonstrate that it is possible to combine the best of both worlds by using different models for different problems, employing a large language model (LLM) to perform model selecti... | ['Qizhe Xie', 'Junxian He', 'Kenji Kawaguchi', 'Yuxi Xie', 'Xu Zhao'] | 2023-05-23 | null | null | null | null | ['math-word-problem-solving', 'gsm8k', 'arithmetic-reasoning', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'natural-language-processing', 'reasoning', 'reasoning', 'time-series'] | [-1.13622129e-01 -6.37566075e-02 -3.79974842e-01 -2.88214594e-01
-1.11819589e+00 -4.84934241e-01 5.73586881e-01 7.28361532e-02
-1.52199209e-01 3.83820117e-01 1.00008950e-01 -8.83065104e-01
-9.80326608e-02 -6.05333149e-01 -6.00075066e-01 -4.56415236e-01
1.01056039e-01 4.39754426e-01 1.80735111e-01 -4.27044272... | [9.612101554870605, 7.428637981414795] |
b802dda3-9bcf-4857-a8cb-59f082db7e32 | sad-semi-supervised-anomaly-detection-on | 2305.13573 | null | https://arxiv.org/abs/2305.13573v1 | https://arxiv.org/pdf/2305.13573v1.pdf | SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs | Anomaly detection aims to distinguish abnormal instances that deviate significantly from the majority of benign ones. As instances that appear in the real world are naturally connected and can be represented with graphs, graph neural networks become increasingly popular in tackling the anomaly detection problem. Despit... | ['Liang Chen', 'Tianyi Zhang', 'Changhua Meng', 'Bowen Song', 'Baokun Wang', 'Xiaolong Xu', 'Wenlong Zhao', 'Jintang Li', 'Jihai Dong', 'Sheng Tian'] | 2023-05-23 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection', 'pseudo-label'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 2.96357721e-01 8.50184560e-02 -9.73231122e-02 -2.22277895e-01
-1.26267955e-01 -4.24397528e-01 6.24491811e-01 8.39217901e-01
-5.09938821e-02 4.16536838e-01 -3.40096414e-01 -3.13598245e-01
-1.15823187e-01 -7.86381423e-01 -4.46526229e-01 -5.39775431e-01
-7.62109756e-01 7.27427423e-01 3.31454426e-01 -1.08475551... | [6.623664379119873, 5.758234024047852] |
5710b814-d439-44a0-9024-e82b86482b63 | enhancing-keyphrase-extraction-from-long | 2305.09316 | null | https://arxiv.org/abs/2305.09316v1 | https://arxiv.org/pdf/2305.09316v1.pdf | Enhancing Keyphrase Extraction from Long Scientific Documents using Graph Embeddings | In this study, we investigate using graph neural network (GNN) representations to enhance contextualized representations of pre-trained language models (PLMs) for keyphrase extraction from lengthy documents. We show that augmenting a PLM with graph embeddings provides a more comprehensive semantic understanding of word... | ['José Portela', 'Alvaro J. López-López', 'Debanjan Mahata', 'Roberto Martínez-Cruz'] | 2023-05-16 | null | null | null | null | ['keyphrase-extraction'] | ['natural-language-processing'] | [ 2.46967778e-01 4.60029632e-01 -5.73969543e-01 2.82261610e-01
-5.75625598e-01 -8.54937851e-01 1.09741127e+00 8.67470086e-01
-4.22076672e-01 1.91470683e-01 7.48086572e-01 -7.38309681e-01
4.51748744e-02 -1.09036970e+00 -7.63624907e-01 -1.79072246e-01
-3.59676331e-01 2.53163278e-01 1.33494034e-01 -3.84687871... | [10.169463157653809, 8.238973617553711] |
a7cca79d-f4d7-4f1c-901c-b7b0d8739fb6 | prefix-tuning-for-automated-audio-captioning | 2303.17489 | null | https://arxiv.org/abs/2303.17489v2 | https://arxiv.org/pdf/2303.17489v2.pdf | Prefix tuning for automated audio captioning | Audio captioning aims to generate text descriptions from environmental sounds. One challenge of audio captioning is the difficulty of the generalization due to the lack of audio-text paired training data. In this work, we propose a simple yet effective method of dealing with small-scaled datasets by leveraging a pre-tr... | ['Kim Sung-Bin', 'Tae-Hyun Oh', 'Minkyu Kim'] | 2023-03-30 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 4.79166746e-01 1.76537350e-01 8.07428136e-02 -2.19889715e-01
-1.20357788e+00 -6.18918836e-01 5.13107955e-01 -1.44877598e-01
-2.27975734e-02 6.05255365e-01 6.95935667e-01 1.99309796e-01
1.71536237e-01 -5.43759763e-01 -9.52096462e-01 -2.83163488e-01
-2.48403568e-02 2.33439162e-01 -6.04921207e-02 -2.32967615... | [15.281161308288574, 4.998706817626953] |
3a47af48-9fca-4441-81d6-7e9698e934a5 | graphon-estimation-in-bipartite-graphs-with | 2304.03590 | null | https://arxiv.org/abs/2304.03590v1 | https://arxiv.org/pdf/2304.03590v1.pdf | Graphon Estimation in bipartite graphs with observable edge labels and unobservable node labels | Many real-world data sets can be presented in the form of a matrix whose entries correspond to the interaction between two entities of different natures (number of times a web user visits a web page, a student's grade in a subject, a patient's rating of a doctor, etc.). We assume in this paper that the mentioned intera... | ["Xavier D'Haultfoeuille", 'Philippe Choné', 'Francis Kramarz', 'Arnak S. Dalalyan', 'Etienne Donier-Meroz'] | 2023-04-07 | null | null | null | null | ['graphon-estimation'] | ['graphs'] | [-1.05008714e-01 3.32915694e-01 -1.95396647e-01 -3.07203948e-01
-7.10591316e-01 -3.97532731e-01 3.19294900e-01 3.81792247e-01
-3.82093668e-01 8.30082297e-01 -1.62155703e-02 -2.79827267e-01
-4.45224226e-01 -8.78515005e-01 -1.03750181e+00 -6.34730160e-01
-3.77764791e-01 5.69415510e-01 -1.16357058e-01 2.08951876... | [7.1439528465271, 4.845762252807617] |
7da36d7d-dffa-4df0-9f6e-54b5291fbd20 | constructing-a-dataset-of-support-and-attack | null | null | https://aclanthology.org/2022.lrec-1.51 | https://aclanthology.org/2022.lrec-1.51.pdf | Constructing A Dataset of Support and Attack Relations in Legal Arguments in Court Judgements using Linguistic Rules | Argumentation mining is a growing area of research and has several interesting practical applications of mining legal arguments. Support and Attack relations are the backbone of any legal argument. However, there is no publicly available dataset of these relations in the context of legal arguments expressed in court ju... | ['Rituraj Singh', 'Girish Palshikar', 'Sachin Pawar', 'Basit Ali'] | null | null | null | null | lrec-2022-6 | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 2.71806121e-01 7.19262779e-01 -4.18617219e-01 -8.17360759e-01
-4.79541540e-01 -6.04585886e-01 6.99406207e-01 7.47572064e-01
-2.53026068e-01 1.02733684e+00 6.35299981e-01 -1.07882631e+00
-7.70774484e-01 -9.85713899e-01 -4.40154761e-01 -1.84486926e-01
1.75418153e-01 5.10523438e-01 5.77809095e-01 -7.83105612... | [9.528480529785156, 9.555054664611816] |
6387a486-0810-4b75-ba0c-ce7f42cc21e7 | variational-inference-using-implicit | 1702.08235 | null | http://arxiv.org/abs/1702.08235v1 | http://arxiv.org/pdf/1702.08235v1.pdf | Variational Inference using Implicit Distributions | Generative adversarial networks (GANs) have given us a great tool to fit
implicit generative models to data. Implicit distributions are ones we can
sample from easily, and take derivatives of samples with respect to model
parameters. These models are highly expressive and we argue they can prove just
as useful for vari... | ['Ferenc Huszár'] | 2017-02-27 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 1.43258944e-01 4.71654207e-01 -1.46127271e-03 -1.34855926e-01
-6.16873384e-01 -5.42295456e-01 9.12770450e-01 -8.80779207e-01
6.09213114e-02 1.10465407e+00 1.66610971e-01 -3.20627302e-01
-2.53062785e-01 -1.18282807e+00 -8.84964466e-01 -1.09713352e+00
2.69469440e-01 8.97578537e-01 -3.41570556e-01 -2.76868999... | [11.591882705688477, -0.01844339445233345] |
4d3a0a30-38e0-429e-92e7-6d8a6641b4d5 | collaborative-transformers-for-grounded | 2203.16518 | null | https://arxiv.org/abs/2203.16518v1 | https://arxiv.org/pdf/2203.16518v1.pdf | Collaborative Transformers for Grounded Situation Recognition | Grounded situation recognition is the task of predicting the main activity, entities playing certain roles within the activity, and bounding-box groundings of the entities in the given image. To effectively deal with this challenging task, we introduce a novel approach where the two processes for activity classificatio... | ['Suha Kwak', 'Youngseok Yoon', 'Junhyeong Cho'] | 2022-03-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Cho_Collaborative_Transformers_for_Grounded_Situation_Recognition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Cho_Collaborative_Transformers_for_Grounded_Situation_Recognition_CVPR_2022_paper.pdf | cvpr-2022-1 | ['grounded-situation-recognition', 'situation-recognition'] | ['computer-vision', 'computer-vision'] | [ 7.79861510e-02 3.61432314e-01 4.08669598e-02 -2.44923815e-01
-3.92661273e-01 -4.76273894e-01 8.20372641e-01 3.65985721e-01
-2.39676163e-01 2.88599938e-01 5.67213118e-01 -4.00737021e-03
-1.13323301e-01 -6.74915135e-01 -4.07950908e-01 -5.58595955e-01
-8.87113586e-02 2.64741123e-01 4.55518633e-01 1.06608897... | [8.455126762390137, 0.6906477808952332] |
0c662634-c8ec-4f9c-ac04-f7d830ccceef | xbound-former-toward-cross-scale-boundary | 2206.00806 | null | https://arxiv.org/abs/2206.00806v1 | https://arxiv.org/pdf/2206.00806v1.pdf | XBound-Former: Toward Cross-scale Boundary Modeling in Transformers | Skin lesion segmentation from dermoscopy images is of great significance in the quantitative analysis of skin cancers, which is yet challenging even for dermatologists due to the inherent issues, i.e., considerable size, shape and color variation, and ambiguous boundaries. Recent vision transformers have shown promisin... | ['Jing Qin', 'Qichao Zhou', 'Xiangdong Tang', 'Jianwei Shuai', 'Zhaodong Fei', 'Liansheng Wang', 'Yuxi Ma', 'Fei Chen', 'Jiacheng Wang'] | 2022-06-02 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 5.57678640e-01 -1.15508780e-01 -3.08010906e-01 -2.82555521e-01
-8.53617728e-01 -4.38017696e-01 1.11630239e-01 -9.30671096e-02
-2.97591120e-01 5.33881605e-01 -8.81065652e-02 -4.58591700e-01
-1.61865190e-01 -4.71263975e-01 -4.71783370e-01 -8.61935735e-01
3.14071238e-01 -6.16763681e-02 5.66170394e-01 -5.95051050... | [15.638025283813477, -2.8937132358551025] |
85f3a8b3-9749-496b-a219-46cdd35c69b3 | boosting-performance-of-a-baseline-visual | 2210.07509 | null | https://arxiv.org/abs/2210.07509v1 | https://arxiv.org/pdf/2210.07509v1.pdf | Boosting Performance of a Baseline Visual Place Recognition Technique by Predicting the Maximally Complementary Technique | One recent promising approach to the Visual Place Recognition (VPR) problem has been to fuse the place recognition estimates of multiple complementary VPR techniques using methods such as SRAL and multi-process fusion. These approaches come with a substantial practical limitation: they require all potential VPR methods... | ['Michael Milford', 'Tobias Fischer', 'Stephen Hausler', 'Connor Malone'] | 2022-10-14 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 6.03479028e-01 -4.40397680e-01 -2.70221144e-01 -2.24689096e-01
-1.57076979e+00 -7.54019797e-01 1.14982367e+00 1.72854796e-01
-5.11248529e-01 6.10834181e-01 9.25336555e-02 -2.60410815e-01
-3.35761964e-01 -5.77521026e-01 -7.96716332e-01 -9.40839231e-01
3.40612568e-02 5.30004144e-01 4.37625051e-01 -3.89334142... | [7.526900291442871, -1.8700329065322876] |
91556073-fca3-4ac6-982e-3cb2e2b5bb2f | a-machine-learning-approach-for-non-blind | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Schuler_A_Machine_Learning_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Schuler_A_Machine_Learning_2013_CVPR_paper.pdf | A Machine Learning Approach for Non-blind Image Deconvolution | Image deconvolution is the ill-posed problem of recovering a sharp image, given a blurry one generated by a convolution. In this work, we deal with space-invariant nonblind deconvolution. Currently, the most successful methods involve a regularized inversion of the blur in Fourier domain as a first step. This step ampl... | ['Stefan Harmeling', 'Harold Christopher Burger', 'Christian J. Schuler', 'Bernhard Scholkopf'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['image-deconvolution'] | ['computer-vision'] | [ 6.16063774e-01 -2.42672861e-01 6.62239194e-01 -1.69612706e-01
-6.23129129e-01 -6.04999840e-01 5.56518257e-01 -6.49907231e-01
-4.46566015e-01 1.07805729e+00 1.56009927e-01 -3.02270770e-01
-1.92459717e-01 -2.09553480e-01 -7.58612335e-01 -8.08127344e-01
1.91212386e-01 1.49955079e-01 5.15025891e-02 -6.51622713... | [11.610445022583008, -2.6678693294525146] |
bcf60e77-fc61-4efc-a6d7-17af60d54ca0 | advcodec-towards-a-unified-framework-for-1 | 1912.10375 | null | https://arxiv.org/abs/1912.10375v2 | https://arxiv.org/pdf/1912.10375v2.pdf | T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack | Adversarial attacks against natural language processing systems, which perform seemingly innocuous modifications to inputs, can induce arbitrary mistakes to the target models. Though raised great concerns, such adversarial attacks can be leveraged to estimate the robustness of NLP models. Compared with the adversarial ... | ['Shuohang Wang', 'Qian Chen', 'Boyuan Pan', 'Bo Li', 'Hengzhi Pei', 'Boxin Wang'] | 2019-12-22 | null | https://aclanthology.org/2020.emnlp-main.495 | https://aclanthology.org/2020.emnlp-main.495.pdf | emnlp-2020-11 | ['adversarial-text'] | ['adversarial'] | [ 6.77977324e-01 4.22194093e-01 3.57164294e-01 -3.06326479e-01
-1.23032773e+00 -1.32341218e+00 6.08454347e-01 -1.65552348e-01
-1.61433399e-01 5.14874220e-01 7.53227249e-02 -6.49911463e-01
4.07599032e-01 -1.02485061e+00 -1.28000736e+00 -7.38882303e-01
2.55146027e-01 6.54337779e-02 -9.19121355e-02 -4.17858630... | [6.021524429321289, 8.119564056396484] |
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