paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6ffc7ce3-6ef2-408b-bf45-00cf11af7ece | interactive-acquisition-of-fine-grained | 2305.03461 | null | https://arxiv.org/abs/2305.03461v1 | https://arxiv.org/pdf/2305.03461v1.pdf | Interactive Acquisition of Fine-grained Visual Concepts by Exploiting Semantics of Generic Characterizations in Discourse | Interactive Task Learning (ITL) concerns learning about unforeseen domain concepts via natural interactions with human users. The learner faces a number of significant constraints: learning should be online, incremental and few-shot, as it is expected to perform tangible belief updates right after novel words denoting ... | ['Subramanian Ramamoorthy', 'Alex Lascarides', 'Jonghyuk Park'] | 2023-05-05 | null | null | null | null | ['implicatures'] | ['natural-language-processing'] | [ 4.86657470e-01 8.44007075e-01 -1.46855563e-01 -7.64368236e-01
-5.94267488e-01 -7.26015210e-01 5.40962458e-01 8.33743691e-01
-3.80531341e-01 1.06313288e+00 1.51490018e-01 -6.69446111e-01
-2.84729540e-01 -5.75900137e-01 -9.21825588e-01 -3.25249672e-01
-3.77425253e-01 5.37562072e-01 2.07286730e-01 -2.41276696... | [9.932011604309082, 7.60526180267334] |
dccac65b-5eb6-4c1c-871d-8b16ad1ea0fb | demystifying-oversmoothing-in-attention-based | 2305.16102 | null | https://arxiv.org/abs/2305.16102v1 | https://arxiv.org/pdf/2305.16102v1.pdf | Demystifying Oversmoothing in Attention-Based Graph Neural Networks | Oversmoothing in Graph Neural Networks (GNNs) refers to the phenomenon where increasing network depth leads to homogeneous node representations. While previous work has established that Graph Convolutional Networks (GCNs) exponentially lose expressive power, it remains controversial whether the graph attention mechanis... | ['Ali Jadbabaie', 'Zihui Wu', 'Amir Ajorlou', 'Xinyi Wu'] | 2023-05-25 | null | null | null | null | ['graph-attention'] | ['graphs'] | [ 1.89602554e-01 6.70762658e-01 1.66419193e-01 9.49635133e-02
1.54507488e-01 -5.72144806e-01 5.94031394e-01 2.05695629e-01
-8.88351500e-02 6.35112464e-01 5.28712682e-02 -6.87694252e-01
-4.17477310e-01 -7.17287779e-01 -8.03365767e-01 -9.68036056e-01
-6.63591504e-01 -1.51015241e-02 1.08857252e-01 -6.84853077... | [6.832408905029297, 6.025649070739746] |
0166eb9f-0a5e-42d7-8db1-389e07bad192 | xview3-sar-detecting-dark-fishing-activity | 2206.00897 | null | https://arxiv.org/abs/2206.00897v4 | https://arxiv.org/pdf/2206.00897v4.pdf | xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery | Unsustainable fishing practices worldwide pose a major threat to marine resources and ecosystems. Identifying vessels that do not show up in conventional monitoring systems -- known as ``dark vessels'' -- is key to managing and securing the health of marine environments. With the rise of satellite-based synthetic apert... | ['Jared Dunnmon', 'David Kroodsma', 'Daniel Kuster', 'Nirav Patel', 'Bryce Goodman', 'Ritwik Gupta', 'Tsu-ting Tim Lin', 'Fernando Paolo'] | 2022-06-02 | null | null | null | null | ['holdout-set', 'decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['computer-vision', 'medical', 'reasoning'] | [ 2.14986168e-02 -1.65822297e-01 4.01151001e-01 -4.35483485e-01
-9.36941803e-01 -1.12335372e+00 4.21751648e-01 7.84719661e-02
-6.44922316e-01 3.96380246e-01 9.46966633e-02 -3.54612678e-01
-1.39053538e-01 -8.88280869e-01 -5.54036677e-01 -6.77826643e-01
-7.12388635e-01 1.59298465e-01 2.36706108e-01 -3.48515272... | [8.41639518737793, -1.2212144136428833] |
61ee5075-faf5-4660-948f-d1a942138206 | zero-shot-entity-linking-with-less-data | null | null | https://aclanthology.org/2022.findings-naacl.127 | https://aclanthology.org/2022.findings-naacl.127.pdf | Zero-shot Entity Linking with Less Data | Entity Linking (EL) maps an entity mention in a natural language sentence to an entity in a knowledge base (KB). The Zero-shot Entity Linking (ZEL) extends the scope of EL to unseen entities at the test time without requiring new labeled data. BLINK (BERT-based) is one of the SOTA models for ZEL. Interestingly, we disc... | ['L Venkata Subramaniam', 'Alexander Gray', 'Salim Roukos', 'Pavan Kapanipathi', 'Dinesh Garg', 'Saswati Dana', 'Dinesh Khandelwal', 'G P Shrivatsa Bhargav'] | null | null | null | null | findings-naacl-2022-7 | ['type-prediction'] | ['computer-code'] | [-2.69225508e-01 4.98846620e-01 -4.91934180e-01 -1.86011359e-01
-7.91094899e-01 -6.54052436e-01 4.24332440e-01 4.89679277e-01
-7.72132218e-01 1.01126373e+00 -1.45674974e-01 -2.56153286e-01
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3.14666033e-02 6.05914652e-01 5.48954189e-01 -3.80313724... | [9.523014068603516, 8.72318172454834] |
7d646187-44c3-41fb-a5e2-1d93880602be | 3d-point-cloud-pre-training-with-knowledge | 2212.08974 | null | https://arxiv.org/abs/2212.08974v1 | https://arxiv.org/pdf/2212.08974v1.pdf | 3D Point Cloud Pre-training with Knowledge Distillation from 2D Images | The recent success of pre-trained 2D vision models is mostly attributable to learning from large-scale datasets. However, compared with 2D image datasets, the current pre-training data of 3D point cloud is limited. To overcome this limitation, we propose a knowledge distillation method for 3D point cloud pre-trained mo... | ['Xiaoshui Huang', 'Wanli Ouyang', 'Jiebo Luo', 'Zhenfei Yin', 'Yuanhan Zhang', 'Yuan YAO'] | 2022-12-17 | null | null | null | null | ['concept-alignment', 'point-cloud-pre-training'] | ['computer-vision', 'computer-vision'] | [ 1.46088138e-01 3.98673236e-01 -3.11530113e-01 -4.60445344e-01
-6.00469172e-01 -5.20399034e-01 4.46814060e-01 4.17697839e-02
-1.48816854e-01 5.85762784e-02 -3.33541572e-01 -2.85148352e-01
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2.83990920e-01 9.16433752e-01 3.75937223e-01 2.04876199... | [8.103740692138672, -3.2538599967956543] |
17c314d2-35a2-4da3-93fc-4ce106266b91 | score-based-conditional-generation-with-fewer | 2307.04081 | null | https://arxiv.org/abs/2307.04081v1 | https://arxiv.org/pdf/2307.04081v1.pdf | Score-based Conditional Generation with Fewer Labeled Data by Self-calibrating Classifier Guidance | Score-based Generative Models (SGMs) are a popular family of deep generative models that achieves leading image generation quality. Earlier studies have extended SGMs to tackle class-conditional generation by coupling an unconditional SGM with the guidance of a trained classifier. Nevertheless, such classifier-guided S... | ['Hsuan-Tien Lin', 'Si-An Chen', 'Paul Kuo-Ming Huang'] | 2023-07-09 | null | null | null | null | ['image-generation'] | ['computer-vision'] | [ 3.20466727e-01 2.31503800e-01 -1.31731242e-01 -4.14422303e-01
-1.07618058e+00 -4.43072796e-01 7.76445448e-01 -2.45373473e-01
-1.36259928e-01 8.93582046e-01 -1.05703697e-01 2.85313819e-02
1.72265336e-01 -9.14220631e-01 -7.01548398e-01 -1.09764946e+00
4.99792129e-01 3.64079803e-01 7.71733299e-02 5.84723540... | [11.37212085723877, -0.11953268200159073] |
50425178-c505-45c4-b0e5-4d494eb94c43 | spontaneous-facial-micro-expression | 1608.02255 | null | http://arxiv.org/abs/1608.02255v1 | http://arxiv.org/pdf/1608.02255v1.pdf | Spontaneous Facial Micro-Expression Recognition using Discriminative Spatiotemporal Local Binary Pattern with an Improved Integral Projection | Recently, there are increasing interests in inferring mirco-expression from
facial image sequences. Due to subtle facial movement of micro-expressions,
feature extraction has become an important and critical issue for spontaneous
facial micro-expression recognition. Recent works usually used spatiotemporal
local binary... | ['Su-Jing Wang', 'Guoying Zhao', 'Xiaoyi Feng', 'Xiaohua Huang', 'Xin Liu', 'Matti Pietikainen'] | 2016-08-07 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 3.00193071e-01 -8.65048468e-01 -4.45376813e-01 -5.61990023e-01
-4.54125911e-01 -1.01185858e-01 3.37714136e-01 -5.44221282e-01
-3.63533318e-01 4.71402675e-01 -5.33955060e-02 2.85048604e-01
-1.22478805e-01 -6.14892244e-01 -3.85698885e-01 -1.39019549e+00
1.32078335e-01 -4.68551219e-01 -9.47640017e-02 -1.70120195... | [13.640531539916992, 1.7484341859817505] |
d1824562-3587-4697-b541-d61439a09359 | deep-contextualized-pairwise-semantic | 1909.09490 | null | https://arxiv.org/abs/1909.09490v1 | https://arxiv.org/pdf/1909.09490v1.pdf | Deep Contextualized Pairwise Semantic Similarity for Arabic Language Questions | Question semantic similarity is a challenging and active research problem that is very useful in many NLP applications, such as detecting duplicate questions in community question answering platforms such as Quora. Arabic is considered to be an under-resourced language, has many dialects, and rich in morphology. Combin... | ['Hussein T. Al-Natsheh', 'Hesham Al-Bataineh', 'Wael Farhan', 'Ahmad Mustafa', 'Haitham Seelawi'] | 2019-09-19 | null | null | null | null | ['question-similarity'] | ['natural-language-processing'] | [-1.50629237e-01 -3.77864651e-02 2.14085639e-01 -3.55745107e-01
-1.23159826e+00 -8.28164816e-01 7.99439371e-01 5.50792336e-01
-4.87652868e-01 5.08596659e-01 3.73087853e-01 -1.96052343e-01
-1.66333854e-01 -8.70231926e-01 -3.82454842e-01 -3.04012954e-01
2.13967860e-01 8.46902192e-01 6.74375296e-01 -1.23058093... | [11.334060668945312, 8.088408470153809] |
39e13f4a-d126-49b9-ace4-183093ce263f | weakly-supervised-mapping-of-natural-language | 2112.06311 | null | https://arxiv.org/abs/2112.06311v3 | https://arxiv.org/pdf/2112.06311v3.pdf | Weakly Supervised Text-to-SQL Parsing through Question Decomposition | Text-to-SQL parsers are crucial in enabling non-experts to effortlessly query relational data. Training such parsers, by contrast, generally requires expertise in annotating natural language (NL) utterances with corresponding SQL queries. In this work, we propose a weak supervision approach for training text-to-SQL par... | ['Daniel Deutch', 'Jonathan Berant', 'Tomer Wolfson'] | 2021-12-12 | null | https://aclanthology.org/2022.findings-naacl.193 | https://aclanthology.org/2022.findings-naacl.193.pdf | findings-naacl-2022-7 | ['text-to-sql'] | ['computer-code'] | [ 1.54532492e-01 7.70089567e-01 -1.66780710e-01 -8.99533272e-01
-1.32343733e+00 -7.29346335e-01 5.33185601e-01 4.83561367e-01
-2.48563021e-01 2.95851260e-01 3.26948553e-01 -8.37331116e-01
2.80975640e-01 -1.14295495e+00 -1.01605058e+00 4.05605108e-01
4.06403810e-01 8.82686496e-01 5.78922868e-01 -4.16291356... | [10.011574745178223, 7.837662220001221] |
8d03f52d-e037-412b-8956-a487972110d3 | uni-perceiver-moe-learning-sparse-generalist | 2206.04674 | null | https://arxiv.org/abs/2206.04674v2 | https://arxiv.org/pdf/2206.04674v2.pdf | Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs | To build an artificial neural network like the biological intelligence system, recent works have unified numerous tasks into a generalist model, which can process various tasks with shared parameters and do not have any task-specific modules. While generalist models achieve promising results on various benchmarks, they... | ['Jifeng Dai', 'Xiaogang Wang', 'Hongsheng Li', 'Xiaohua Wang', 'Wenhai Wang', 'Xizhou Zhu', 'Jinguo Zhu'] | 2022-06-09 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [ 2.71658659e-01 7.65579846e-03 -8.12240317e-02 -5.12558818e-01
-5.55594444e-01 -3.00374091e-01 6.47965670e-01 -2.75273234e-01
-6.85692430e-01 4.63299870e-01 4.03147079e-02 -6.47699609e-02
-1.60144374e-01 -3.41997743e-01 -7.87146270e-01 -5.86256444e-01
3.30725372e-01 3.10435832e-01 4.53650564e-01 -1.67789832... | [10.276668548583984, 1.6039178371429443] |
222c8bef-6f7b-4801-9d5b-c0d7cd1bae57 | few-shot-fine-grained-action-recognition-via | 2108.06647 | null | https://arxiv.org/abs/2108.06647v1 | https://arxiv.org/pdf/2108.06647v1.pdf | Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning | Fine-grained action recognition is attracting increasing attention due to the emerging demand of specific action understanding in real-world applications, whereas the data of rare fine-grained categories is very limited. Therefore, we propose the few-shot fine-grained action recognition problem, aiming to recognize nov... | ['Annan Li', 'Sheng Liu', 'Yunhong Wang', 'Jiahao Wang'] | 2021-08-15 | null | null | null | null | ['action-understanding', 'fine-grained-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 5.40877104e-01 -4.35831696e-01 -4.65312302e-01 -1.99525312e-01
-8.40827823e-01 -2.36401856e-02 9.01795566e-01 -1.90800205e-01
-3.26893032e-01 7.87010849e-01 8.12362373e-01 4.76051927e-01
-2.48511001e-01 -5.55781245e-01 -6.00915372e-01 -7.97183633e-01
1.28198683e-01 1.01349786e-01 6.48333371e-01 -1.10770576... | [8.428170204162598, 0.7417348623275757] |
0df1af25-207d-4f3d-b0e4-053f53aa49c3 | cross-language-speech-emotion-recognition | 2306.13804 | null | https://arxiv.org/abs/2306.13804v2 | https://arxiv.org/pdf/2306.13804v2.pdf | Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers | Despite the recent progress in speech emotion recognition (SER), state-of-the-art systems are unable to achieve improved performance in cross-language settings. In this paper, we propose a Multimodal Dual Attention Transformer (MDAT) model to improve cross-language SER. Our model utilises pre-trained models for multimo... | ['Junaid Qadir', 'Siddique Latif', 'Syed Aun Muhammad Zaidi'] | 2023-06-23 | null | null | null | null | ['emotion-recognition', 'emotion-classification', 'graph-attention', 'emotion-classification', 'speech-emotion-recognition'] | ['computer-vision', 'computer-vision', 'graphs', 'natural-language-processing', 'speech'] | [ 6.58732802e-02 4.21656445e-02 5.15141338e-02 -5.42663515e-01
-9.65229452e-01 -3.65893006e-01 6.02971733e-01 3.84979397e-01
-5.33381104e-01 4.59521353e-01 4.96733189e-01 1.55486371e-02
2.37158060e-01 -1.97753549e-01 -4.74760920e-01 -2.91531593e-01
-8.45284984e-02 1.34665132e-01 -2.27634564e-01 -4.51475829... | [13.250535011291504, 5.2648115158081055] |
acdd8759-3eea-4c5f-9d53-0890e4e8b122 | neural-headline-generation-with-sentence-wise | 1604.01904 | null | http://arxiv.org/abs/1604.01904v2 | http://arxiv.org/pdf/1604.01904v2.pdf | Neural Headline Generation with Sentence-wise Optimization | Recently, neural models have been proposed for headline generation by
learning to map documents to headlines with recurrent neural networks.
Nevertheless, as traditional neural network utilizes maximum likelihood
estimation for parameter optimization, it essentially constrains the expected
training objective within wor... | ['Yu Zhao', 'Ayana', 'Maosong Sun', 'Zhiyuan Liu', 'Shiqi Shen'] | 2016-04-07 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [ 2.53074318e-01 4.17202324e-01 -5.22879601e-01 -4.35539633e-01
-9.26598012e-01 -1.06255986e-01 5.95789731e-01 6.85640723e-02
-5.52369416e-01 1.14384937e+00 6.79679871e-01 -3.88934225e-01
1.95993632e-01 -8.80673528e-01 -5.47717988e-01 -2.28168592e-01
4.05885190e-01 4.33135986e-01 -7.11789504e-02 -3.08652610... | [12.034600257873535, 9.157788276672363] |
e881f7c7-f752-43c0-9792-f10e1f1b8261 | deep-neural-network-or-dermatologist | 1908.06612 | null | https://arxiv.org/abs/1908.06612v1 | https://arxiv.org/pdf/1908.06612v1.pdf | Deep neural network or dermatologist? | Deep learning techniques have proven high accuracy for identifying melanoma in digitised dermoscopic images. A strength is that these methods are not constrained by features that are pre-defined by human semantics. A down-side is that it is difficult to understand the rationale of the model predictions and to identify ... | ['Sally Shrapnel', 'Becks Simpson', 'Reuben Dutton', 'Kyle Young', 'Gareth Booth'] | 2019-08-19 | null | null | null | null | ['skin-cancer-classification'] | ['medical'] | [ 6.52674019e-01 5.70910633e-01 -3.24936897e-01 -4.05121475e-01
-7.86272883e-01 -5.14854312e-01 5.60537994e-01 3.74654800e-01
-3.91735017e-01 6.98381662e-01 3.12196463e-01 -7.33836591e-01
-7.52687514e-01 -5.57738483e-01 -3.80325019e-01 -6.24531686e-01
1.18826665e-01 4.16963995e-01 1.31621838e-01 -2.41210029... | [15.52209758758545, -2.765129566192627] |
75d366d2-647b-462a-95cc-dae6aacbc3f9 | exploring-example-selection-for-few-shot-text | null | null | https://openreview.net/forum?id=tnHT06ijUPZ | https://openreview.net/pdf?id=tnHT06ijUPZ | Exploring Example Selection for Few-shot Text-to-SQL Semantic Parsing | We study example selection methods for few-shot text-to-SQL tasks with unseen databases. Annotating natural language questions with corresponding SQL queries is expensive, but we can use abundant unlabeled questions to efficiently select examples to annotate and then use them to adapt models. Many previous works only r... | ['Anonymous'] | 2022-01-16 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 4.20442969e-02 -1.31745264e-01 -7.78540611e-01 -8.26523840e-01
-1.20252919e+00 -5.34315705e-01 3.34163308e-01 4.57134604e-01
-5.04912198e-01 6.97966754e-01 2.53938109e-01 -5.89959994e-02
-6.87309802e-02 -9.57581997e-01 -6.63444638e-01 -8.29954147e-02
1.11540131e-01 9.15802956e-01 5.23761153e-01 -4.52944338... | [10.106372833251953, 3.639873504638672] |
7202b0ce-9a15-4913-b6ee-41fba8037546 | fighting-uncertainty-with-gradients-offline | 2306.14079 | null | https://arxiv.org/abs/2306.14079v1 | https://arxiv.org/pdf/2306.14079v1.pdf | Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching | Offline optimization paradigms such as offline Reinforcement Learning (RL) or Imitation Learning (IL) allow policy search algorithms to make use of offline data, but require careful incorporation of uncertainty in order to circumvent the challenges of distribution shift. Gradient-based policy search methods are a promi... | ['Russ Tedrake', 'Abhishek Gupta', 'Lujie Yang', 'Hongkai Dai', 'Glen Chou', 'H. J. Terry Suh'] | 2023-06-24 | null | null | null | null | ['imitation-learning', 'offline-rl'] | ['methodology', 'playing-games'] | [-1.44961298e-01 2.89324045e-01 -4.50805277e-01 -3.71635482e-02
-1.28001595e+00 -9.03889716e-01 6.92885876e-01 2.84104228e-01
-6.96115971e-01 1.12447739e+00 2.60593593e-01 -5.62511742e-01
-4.61540014e-01 -7.37592876e-01 -9.92487192e-01 -6.79344893e-01
-3.18248212e-01 4.97585267e-01 1.18784308e-01 -6.10576756... | [4.163730144500732, 2.3679394721984863] |
17867915-b0f7-4805-9904-d38de380aa3e | spectral-graph-clustering-for-intentional | 2203.06579 | null | https://arxiv.org/abs/2203.06579v1 | https://arxiv.org/pdf/2203.06579v1.pdf | Spectral Graph Clustering for Intentional Islanding Operations in Resilient Hybrid Energy Systems | Establishing cleaner energy generation therefore improving the sustainability of the power system is a crucial task in this century, and one of the key strategies being pursued is to shift the dependence on fossil fuel to renewable technologies such as wind, solar, and nuclear. However, with the increasing number of he... | ['Pingfeng Wang', 'Jie Zhang', 'Sobhan Badakhshan', 'Xin Chen', 'Jiaxin Wu'] | 2022-03-13 | null | null | null | null | ['graph-clustering', 'spectral-graph-clustering'] | ['graphs', 'graphs'] | [-7.77908489e-02 -2.78534710e-01 1.11449910e-02 3.51463348e-01
-6.44102693e-03 -7.35182881e-01 3.68588060e-01 1.44327283e-01
3.40280324e-01 1.12965357e+00 -1.73524275e-01 -8.43488574e-02
-5.98461390e-01 -9.68368649e-01 -7.80865252e-02 -1.33357859e+00
-1.43944934e-01 -1.84442595e-01 -1.78850368e-02 -5.14771163... | [5.7233195304870605, 2.5720808506011963] |
88e8744d-0e74-429e-b033-07466e80293d | reliable-natural-language-understanding-with | 2302.03780 | null | https://arxiv.org/abs/2302.03780v2 | https://arxiv.org/pdf/2302.03780v2.pdf | Reliable Natural Language Understanding with Large Language Models and Answer Set Programming | Humans understand language by extracting information (meaning) from sentences, combining it with existing commonsense knowledge, and then performing reasoning to draw conclusions. While large language models (LLMs) such as GPT-3 and ChatGPT are able to leverage patterns in the text to solve a variety of NLP tasks, they... | ['Gopal Gupta', 'Parth Padalkar', 'Yankai Zeng', 'Abhiramon Rajasekharan'] | 2023-02-07 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 2.75595635e-01 9.26639795e-01 -2.21900821e-01 -3.69349569e-01
-7.87822902e-01 -7.42171228e-01 6.31845772e-01 5.20152390e-01
2.63287693e-01 8.00003111e-01 2.55756140e-01 -9.25130546e-01
-2.34047294e-01 -1.29781544e+00 -8.98971498e-01 2.17267826e-01
2.28041053e-01 6.25954211e-01 2.49479696e-01 -4.32943970... | [9.406176567077637, 7.29893684387207] |
dcbf959b-831b-49be-b32f-8075d90e1c38 | unsupervised-term-extraction-for-highly | 2210.13118 | null | https://arxiv.org/abs/2210.13118v1 | https://arxiv.org/pdf/2210.13118v1.pdf | Unsupervised Term Extraction for Highly Technical Domains | Term extraction is an information extraction task at the root of knowledge discovery platforms. Developing term extractors that are able to generalize across very diverse and potentially highly technical domains is challenging, as annotations for domains requiring in-depth expertise are scarce and expensive to obtain. ... | ['Diego Antognini', 'Peter Staar', 'Francesco Fusco'] | 2022-10-24 | null | null | null | null | ['term-extraction'] | ['natural-language-processing'] | [ 4.82157290e-01 -1.97186563e-02 -3.96569520e-01 -3.56618345e-01
-1.35310960e+00 -1.01989615e+00 4.26233947e-01 6.62084520e-01
-6.13114357e-01 7.49410152e-01 -8.14320743e-02 -6.92875743e-01
1.06352665e-01 -5.66980898e-01 -7.63879299e-01 -5.04577100e-01
-7.48624876e-02 7.17791617e-01 1.38161346e-01 -2.78802104... | [8.703512191772461, 8.749719619750977] |
371b2a22-0dbc-467a-b3ba-2dce0abdd6ff | simplify-the-usage-of-lexicon-in-chinese-ner | 1908.05969 | null | https://arxiv.org/abs/1908.05969v2 | https://arxiv.org/pdf/1908.05969v2.pdf | Simplify the Usage of Lexicon in Chinese NER | Recently, many works have tried to augment the performance of Chinese named entity recognition (NER) using word lexicons. As a representative, Lattice-LSTM (Zhang and Yang, 2018) has achieved new benchmark results on several public Chinese NER datasets. However, Lattice-LSTM has a complex model architecture. This limit... | ['Ruotian Ma', 'Xuanjing Huang', 'Minlong Peng', 'Qi Zhang'] | 2019-08-16 | simplify-the-usage-of-lexicon-in-chinese-ner-1 | https://aclanthology.org/2020.acl-main.528 | https://aclanthology.org/2020.acl-main.528.pdf | acl-2020-6 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-1.43872663e-01 -2.68911391e-01 1.31972313e-01 -3.07076007e-01
-6.43956959e-01 -5.10429740e-01 2.41928488e-01 -7.74666071e-02
-1.04721105e+00 8.17728460e-01 2.36928031e-01 -5.13667405e-01
4.68461037e-01 -9.73835468e-01 -3.27310950e-01 -4.67717409e-01
2.92393506e-01 2.06920817e-01 1.84126928e-01 -2.46715605... | [9.842507362365723, 9.76589584350586] |
8575fb81-6834-4357-98bb-dfe7517fcaf3 | randomized-greedy-learning-for-non-monotone | 2302.01324 | null | https://arxiv.org/abs/2302.01324v1 | https://arxiv.org/pdf/2302.01324v1.pdf | Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback | We investigate the problem of unconstrained combinatorial multi-armed bandits with full-bandit feedback and stochastic rewards for submodular maximization. Previous works investigate the same problem assuming a submodular and monotone reward function. In this work, we study a more general problem, i.e., when the reward... | ['Mohamed-Slim Alouini', 'Christopher John Quinn', 'Vaneet Aggarwal', 'Fares Fourati'] | 2023-02-02 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 2.19095852e-02 4.48382288e-01 -9.97401536e-01 -2.32758000e-01
-1.15766251e+00 -9.37716126e-01 -3.46479505e-01 -1.72571898e-01
-4.26866859e-01 1.50080121e+00 6.73356652e-02 -7.87073314e-01
-9.81366634e-01 -6.94230855e-01 -1.14839935e+00 -9.12908852e-01
-3.59025210e-01 7.04255641e-01 -2.08783433e-01 -4.31529656... | [4.559925556182861, 3.3553757667541504] |
6728e29d-2520-4007-ace0-58edd02a2c63 | uni-em-an-environment-for-deep-neural-network | null | null | https://doi.org/10.1101/607366 | https://www.biorxiv.org/content/biorxiv/early/2019/04/12/607366.full-text.pdf | UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images | Recently, there has been a rapid expansion in the field of micro-connectomics, which targets the three-dimensional (3D) reconstruction of neuronal networks from a stack of two-dimensional (2D) electron microscopic (EM) images. The spatial scale of the 3D reconstruction grows rapidly owing to deep neural networks (DNNs)... | ['Hidetoshi Urakubo', 'Yoshiyuki Kubota', 'Torsten Bullmann', 'Shin Ishii', 'Shigeyuki Oba'] | 2019-04-12 | null | null | null | biorxiv-neuroscience-2019-4 | ['electron-microscopy-image-segmentation'] | ['computer-vision'] | [-2.65012197e-02 -2.66593099e-02 4.77047920e-01 -1.62604034e-01
-1.83774993e-01 -5.57590067e-01 1.15970418e-01 8.62865001e-02
-9.02314484e-01 8.16537499e-01 -5.54614425e-01 -6.38041794e-01
2.21826985e-01 -7.66278148e-01 -7.38969326e-01 -7.16296554e-01
1.04001187e-01 7.18925834e-01 7.13405311e-01 1.33537725... | [14.265244483947754, -3.10966420173645] |
d90cc550-89f8-47aa-ae3c-9996de6286d8 | chatgpt-is-more-likely-to-be-perceived-as | 2305.12564 | null | https://arxiv.org/abs/2305.12564v1 | https://arxiv.org/pdf/2305.12564v1.pdf | ChatGPT Is More Likely to Be Perceived as Male Than Female | We investigate how people perceive ChatGPT, and, in particular, how they assign human-like attributes such as gender to the chatbot. Across five pre-registered studies (N = 1,552), we find that people are more likely to perceive ChatGPT to be male than female. Specifically, people perceive male gender identity (1) foll... | ['Jin Kim', 'Jared Wong'] | 2023-05-21 | null | null | null | null | ['chatbot', 'chatbot'] | ['methodology', 'natural-language-processing'] | [-2.71330714e-01 6.80199802e-01 3.44024897e-02 -4.09386635e-01
-2.75803894e-01 -8.88914645e-01 7.53888607e-01 1.40344635e-01
-3.84541899e-01 5.34440100e-01 5.14978886e-01 -2.55340189e-01
3.25006694e-01 -5.71558177e-01 3.01625639e-01 -5.23525953e-01
7.55250573e-01 5.36302984e-01 -3.86028767e-01 -2.02053756... | [12.470973014831543, 7.773597717285156] |
58cb80f1-6b91-417f-9d20-67e3b449127d | masking-of-quantum-information-into | 1910.00938 | null | https://arxiv.org/abs/1910.00938v4 | https://arxiv.org/pdf/1910.00938v4.pdf | Masking of Quantum Information into Restricted Set of states | Masking of data is a method to protect information by shielding it from a third party, however keeping it usable for further usages like application development, building program extensions to name a few. Whereas it is possible for classical information encoded in composite quantum states to be completely masked from r... | ['Prasanta K. Panigrahi', 'Bikash K. Behera', 'Soumya Sarkar', 'Tamal Ghosh'] | 2019-10-01 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 6.14122391e-01 3.77944469e-01 1.65154472e-01 -1.63068935e-01
-6.35253370e-01 -6.90391302e-01 4.43741411e-01 -7.92216435e-02
-4.06698763e-01 9.99545395e-01 -1.60416767e-01 -8.32791030e-01
1.14252470e-01 -1.04554701e+00 -5.27254403e-01 -1.00299418e+00
-2.23298177e-01 1.63710322e-02 2.63331085e-01 -5.17395020... | [5.599791049957275, 4.943629741668701] |
d33b1f4b-e554-4b04-951d-a7b72cdbac0c | leveraging-gpt-4-for-food-effect | 2306.16275 | null | https://arxiv.org/abs/2306.16275v1 | https://arxiv.org/pdf/2306.16275v1.pdf | Leveraging GPT-4 for Food Effect Summarization to Enhance Product-Specific Guidance Development via Iterative Prompting | Food effect summarization from New Drug Application (NDA) is an essential component of product-specific guidance (PSG) development and assessment. However, manual summarization of food effect from extensive drug application review documents is time-consuming, which arouses a need to develop automated methods. Recent ad... | ['Hualou Liang', 'Liang Zhao', 'Meng Hu', 'Yi Zhang', 'Felix Agbavor', 'Taha ValizadehAslani', 'Biao Han', 'Jing Wang', 'Ping Ren', 'Yiwen Shi'] | 2023-06-28 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [ 3.25115919e-01 1.52859211e-01 -7.43625522e-01 -1.19233690e-01
-1.41279364e+00 -1.08833778e+00 4.38985080e-01 1.09088862e+00
-9.84867066e-02 7.31672585e-01 7.42502928e-01 -6.66908145e-01
-4.36632186e-01 -3.01168889e-01 -4.74079400e-01 -2.64934003e-01
2.33792156e-01 2.67841011e-01 -2.02815488e-01 -1.00562789... | [12.241127014160156, 9.640447616577148] |
d8d548ff-d0b8-4df2-9077-b7d1403ec07c | neural-boxer-at-the-iwcs-shared-task-on-drs | null | null | https://aclanthology.org/W19-1204 | https://aclanthology.org/W19-1204.pdf | Neural Boxer at the IWCS Shared Task on DRS Parsing | This paper describes our participation in the shared task of Discourse Representation Structure parsing. It follows the work of Van Noord et al. (2018), who employed a neural sequence-to-sequence model to produce DRSs, also exploiting linguistic information with multiple encoders. We provide a detailed look in the perf... | ['Rik van Noord'] | 2019-05-01 | null | null | null | ws-2019-5 | ['drs-parsing'] | ['natural-language-processing'] | [ 4.25757021e-01 9.40974474e-01 3.81934121e-02 -4.51776683e-01
-1.03273165e+00 -8.90698731e-01 8.45742702e-01 3.19246858e-01
-4.63213772e-01 8.56540501e-01 8.74376774e-01 -5.79908609e-01
1.17005527e-01 -4.60210621e-01 -6.90887213e-01 -9.24893022e-02
-9.94632915e-02 3.43360513e-01 3.43575686e-01 -6.54619157... | [10.74677848815918, 9.304732322692871] |
afab943d-dc8e-4fca-a617-0cbb8116b0e4 | evaluating-the-robustness-of-self-supervised | 2105.06986 | null | https://arxiv.org/abs/2105.06986v1 | https://arxiv.org/pdf/2105.06986v1.pdf | Evaluating the Robustness of Self-Supervised Learning in Medical Imaging | Self-supervision has demonstrated to be an effective learning strategy when training target tasks on small annotated data-sets. While current research focuses on creating novel pretext tasks to learn meaningful and reusable representations for the target task, these efforts obtain marginal performance gains compared to... | ['Bjoern H. Menze', 'Stephanie E. Combs', 'Jan C. Peeken', 'Anjany Sekuboyina', 'Suprosanna Shit', 'Christopher Watanabe', 'Fernando Navarro'] | 2021-05-14 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 6.65853858e-01 5.91804326e-01 -4.26909626e-01 -7.42655277e-01
-8.83580089e-01 -3.07080243e-02 3.09953570e-01 3.05112839e-01
-3.85563731e-01 4.87875164e-01 3.75805080e-01 -1.67250067e-01
-1.65280804e-01 -3.90766472e-01 -6.98410928e-01 -5.90896368e-01
-3.99033725e-01 4.62051451e-01 5.45048751e-02 1.06990188... | [14.878756523132324, -2.2396321296691895] |
d304d07d-de4d-4968-b1e7-9b93c73f24f0 | calibrated-data-dependent-constraints-with | 2301.06195 | null | https://arxiv.org/abs/2301.06195v1 | https://arxiv.org/pdf/2301.06195v1.pdf | Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees | We consider the task of training machine learning models with data-dependent constraints. Such constraints often arise as empirical versions of expected value constraints that enforce fairness or stability goals. We reformulate data-dependent constraints so that they are calibrated: enforcing the reformulated constrain... | ['Mikhail Yurochkin', 'Yuekai Sun', 'Songkai Xue'] | 2023-01-15 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 2.36737952e-01 4.68660980e-01 -8.66169393e-01 -1.11159968e+00
-8.82769823e-01 -5.81097305e-01 4.85832423e-01 8.66269544e-02
-9.61471677e-01 1.29514253e+00 -2.20646486e-01 -4.87039655e-01
-3.44249457e-01 -5.33925712e-01 -3.50989282e-01 -6.61844552e-01
1.22131750e-01 5.28611839e-01 -3.47309887e-01 9.85149965... | [8.841141700744629, 5.309725761413574] |
9fd332e5-4f53-4694-9035-a6aeaa38c69e | learning-to-transfer-role-assignment-across | 2204.12937 | null | https://arxiv.org/abs/2204.12937v1 | https://arxiv.org/pdf/2204.12937v1.pdf | Learning to Transfer Role Assignment Across Team Sizes | Multi-agent reinforcement learning holds the key for solving complex tasks that demand the coordination of learning agents. However, strong coordination often leads to expensive exploration over the exponentially large state-action space. A powerful approach is to decompose team works into roles, which are ideally assi... | ['Truyen Tran', 'Svetha Venkatesh', 'Phuoc Nguyen', 'Dung Nguyen'] | 2022-04-17 | null | null | null | null | ['starcraft-ii'] | ['playing-games'] | [ 7.44423121e-02 6.69236248e-03 9.83354151e-02 8.55522975e-02
-4.03374195e-01 -7.50398457e-01 5.26986361e-01 3.69683713e-01
-9.27486420e-01 1.17931199e+00 -1.88922375e-01 1.10726401e-01
-3.66246372e-01 -7.97068357e-01 -6.10607803e-01 -9.93021011e-01
-5.46126544e-01 1.14691269e+00 5.74122369e-01 -9.57165480... | [3.7699625492095947, 1.9702125787734985] |
e7770a8d-1cd3-448c-87b5-755c7fcb4ab2 | mmdag-multimodal-directed-acyclic-graph | null | null | https://aclanthology.org/2022.lrec-1.733 | https://aclanthology.org/2022.lrec-1.733.pdf | MMDAG: Multimodal Directed Acyclic Graph Network for Emotion Recognition in Conversation | Emotion recognition in conversation is important for an empathetic dialogue system to understand the user’s emotion and then generate appropriate emotional responses. However, most previous researches focus on modeling conversational contexts primarily based on the textual modality or simply utilizing multimodal inform... | ['Hongying Zan', 'Changyong Niu', 'Yuxiang Jia', 'Shuo Xu'] | null | null | null | null | lrec-2022-6 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-1.86291516e-01 -1.01341106e-01 -1.94925129e-01 -4.99909669e-01
-4.20009404e-01 -4.47984934e-01 7.48139143e-01 1.07110523e-01
-2.32628509e-01 7.99377024e-01 9.37763572e-01 2.30989642e-02
1.80264324e-01 -5.03701687e-01 1.59447134e-01 -4.32315379e-01
2.21051559e-01 -8.64422601e-03 -3.21550280e-01 -6.97474122... | [13.057839393615723, 5.881637096405029] |
07ca1187-37c0-45b8-b458-d10f69ed8c0d | simple-yet-effective-synthetic-dataset | 2303.11660 | null | https://arxiv.org/abs/2303.11660v1 | https://arxiv.org/pdf/2303.11660v1.pdf | Simple Yet Effective Synthetic Dataset Construction for Unsupervised Opinion Summarization | Opinion summarization provides an important solution for summarizing opinions expressed among a large number of reviews. However, generating aspect-specific and general summaries is challenging due to the lack of annotated data. In this work, we propose two simple yet effective unsupervised approaches to generate both ... | ['Yassine Benajiba', 'Miguel Ballesteros', 'Kalpit Dixit', 'Yogarshi Vyas', 'Shuai Wang', 'Jie Ma', 'Ming Shen'] | 2023-03-21 | null | null | null | null | ['unsupervised-opinion-summarization'] | ['natural-language-processing'] | [ 2.99756318e-01 4.89136308e-01 -4.23236489e-01 -3.48943740e-01
-1.50019324e+00 -9.21426594e-01 7.73238301e-01 8.82887542e-01
-2.88054168e-01 1.12613285e+00 6.53935134e-01 -1.89228982e-01
6.42491803e-02 -7.98484683e-01 -5.11555314e-01 -3.47202599e-01
3.85736793e-01 6.16867185e-01 1.85753629e-01 -4.15845037... | [12.431170463562012, 9.358367919921875] |
dfa41940-3ce4-4962-9614-1f33a217bd46 | object-proposal-generation-applying-the | 1704.03706 | null | http://arxiv.org/abs/1704.03706v1 | http://arxiv.org/pdf/1704.03706v1.pdf | Object proposal generation applying the distance dependent Chinese restaurant process | In application domains such as robotics, it is useful to represent the
uncertainty related to the robot's belief about the state of its environment.
Algorithms that only yield a single "best guess" as a result are not
sufficient. In this paper, we propose object proposal generation based on
non-parametric Bayesian infe... | ['Mikko Lauri', 'Simone Frintrop'] | 2017-04-12 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [-4.59155366e-02 1.07396245e-01 -1.21454068e-01 -6.09148324e-01
-1.16257441e+00 -5.34381926e-01 6.13720357e-01 3.72841090e-01
-7.17862785e-01 6.81403875e-01 -2.27435037e-01 -4.62769955e-01
-6.24939762e-02 -9.33478773e-01 -9.02711332e-01 -5.42690456e-01
7.16340840e-02 1.11065781e+00 6.18992627e-01 2.58550495... | [7.230166912078857, -1.078290581703186] |
8136ee75-a5f3-4f34-b05e-679f0d0a3493 | unsupervised-few-shot-learning-via | 2004.05805 | null | https://arxiv.org/abs/2004.05805v2 | https://arxiv.org/pdf/2004.05805v2.pdf | Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation | Few-shot learning aims to learn a new concept when only a few training examples are available, which has been extensively explored in recent years. However, most of the current works heavily rely on a large-scale labeled auxiliary set to train their models in an episodic-training paradigm. Such a kind of supervised set... | ['Tiexin Qin', 'Yang Gao', 'Wenbin Li', 'Yinghuan Shi'] | 2020-04-13 | null | null | null | null | ['unsupervised-few-shot-learning', 'unsupervised-few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 1.35963336e-01 -1.99861363e-01 -4.39331114e-01 -2.28469506e-01
-4.77432847e-01 -4.68945056e-02 7.87416875e-01 7.13577569e-02
-5.11031985e-01 5.06674230e-01 1.38642967e-01 1.32457048e-01
-1.41975302e-02 -9.90482569e-01 -5.69562614e-01 -8.98873031e-01
2.50729471e-01 1.73519969e-01 4.82549310e-01 -4.60011870... | [9.99714183807373, 3.0037131309509277] |
1def2b99-4a6f-4d2c-99d2-79bec803ec73 | multi-scale-progressive-fusion-learning-for | 2011.11865 | null | https://arxiv.org/abs/2011.11865v1 | https://arxiv.org/pdf/2011.11865v1.pdf | Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution | Limited by the cost and technology, the resolution of depth map collected by depth camera is often lower than that of its associated RGB camera. Although there have been many researches on RGB image super-resolution (SR), a major problem with depth map super-resolution is that there will be obvious jagged edges and exc... | ['Charlie C. L. Wang', 'Zitian Zhang', 'Kun Qian', 'Chuhua Xian'] | 2020-11-24 | null | null | null | null | ['depth-map-super-resolution', 'single-image-deraining'] | ['computer-vision', 'computer-vision'] | [ 5.10044336e-01 -1.41888455e-01 9.82078090e-02 -3.51685733e-01
-9.36948538e-01 1.08740823e-02 6.58523366e-02 -3.49016368e-01
-3.42593521e-01 9.21236694e-01 1.57001227e-01 2.72964954e-01
-2.47254387e-01 -9.92650032e-01 -4.06158537e-01 -7.70568609e-01
2.58451402e-01 -1.73828825e-01 6.78506076e-01 -2.52818227... | [9.861794471740723, -2.386183738708496] |
374c88ee-6141-4f7a-9575-f07497382425 | sinusoidal-wave-generating-network-based-on | 1901.02050 | null | http://arxiv.org/abs/1901.02050v1 | http://arxiv.org/pdf/1901.02050v1.pdf | Sinusoidal wave generating network based on adversarial learning and its application: synthesizing frog sounds for data augmentation | Simulators that generate observations based on theoretical models can be
important tools for development, prediction, and assessment of signal
processing algorithms. In order to design these simulators, painstaking effort
is required to construct mathematical models according to their application.
Complex models are so... | ['David K. Han', 'Sangwook Park', 'Hanseok Ko'] | 2019-01-07 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 5.29694438e-01 -9.87201259e-02 6.35275781e-01 -2.34052926e-01
-4.83637452e-01 -4.33212548e-01 5.50091088e-01 -2.73511320e-01
-5.09006828e-02 6.66333735e-01 -9.46138501e-02 -1.17631769e-02
1.03403755e-01 -9.30270374e-01 -8.04358184e-01 -6.90514207e-01
-2.43427053e-01 3.00385207e-01 3.18289921e-02 -3.99398059... | [15.528802871704102, 5.962412357330322] |
3c6e0aaa-3a11-4762-b2e0-a02486f9aee6 | big-bird-transformers-for-longer-sequences | 2007.14062 | null | https://arxiv.org/abs/2007.14062v2 | https://arxiv.org/pdf/2007.14062v2.pdf | Big Bird: Transformers for Longer Sequences | Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP. Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse att... | ['Anirudh Ravula', 'Santiago Ontanon', 'Manzil Zaheer', 'Chris Alberti', 'Avinava Dubey', 'Philip Pham', 'Joshua Ainslie', 'Amr Ahmed', 'Qifan Wang', 'Li Yang', 'Guru Guruganesh'] | 2020-07-28 | null | http://proceedings.neurips.cc/paper/2020/hash/c8512d142a2d849725f31a9a7a361ab9-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/c8512d142a2d849725f31a9a7a361ab9-Paper.pdf | neurips-2020-12 | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 2.55932398e-02 1.87826231e-01 -6.33845776e-02 -2.13328153e-01
-8.88005674e-01 -7.20582902e-01 2.47681081e-01 5.81823111e-01
-5.81404150e-01 6.70200706e-01 3.80781054e-01 -5.11165679e-01
1.19659156e-01 -7.20324218e-01 -1.21734738e+00 -5.72595179e-01
-2.55938563e-02 4.93399203e-01 1.34418368e-01 -3.63561869... | [10.838602066040039, 7.473899841308594] |
0e36f99c-f6b5-4bf6-8be6-175d5052534d | embedding-physics-domain-knowledge-into-a | null | null | https://www.nature.com/articles/s41524-020-0277-x | https://www.nature.com/articles/s41524-020-0277-x.pdf | Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics | Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network ... | ['Ian Marius Peters & Tonio Buonassisi', 'Fen Lin', 'Shijing Sun', 'Qianxiao Li', 'Rolf Stangl', 'Christoph J. Brabec', 'Armin G. Aberle', 'Erik Birgersson', 'Thomas Heumueller', 'Mariya Layurova', 'Jose Dario Perea', 'Hansong Xue', 'Yue Wang', 'Siyu I. P. Tian', 'Maung Thway', 'Felipe Oviedo', 'Zekun Ren'] | 2020-01-31 | null | null | null | null | ['physics-informed-machine-learning', 'physical-simulations'] | ['graphs', 'miscellaneous'] | [ 5.66157758e-01 -1.48248255e-01 -1.47296980e-01 2.04482600e-02
-6.90419674e-01 -5.79164207e-01 3.54335994e-01 3.70537251e-01
-1.86513171e-01 1.23283839e+00 -3.46435279e-01 -3.94352019e-01
-2.12514594e-01 -7.32907176e-01 -4.26196575e-01 -1.35001504e+00
6.34135962e-01 6.04725778e-01 -7.23459795e-02 2.06734836... | [5.770608425140381, 4.464186191558838] |
fe625eba-1bf3-42b8-9253-4fd0b91abe5e | non-separable-multi-dimensional-network-flows | 2305.08628 | null | https://arxiv.org/abs/2305.08628v1 | https://arxiv.org/pdf/2305.08628v1.pdf | Non-Separable Multi-Dimensional Network Flows for Visual Computing | Flows in networks (or graphs) play a significant role in numerous computer vision tasks. The scalar-valued edges in these graphs often lead to a loss of information and thereby to limitations in terms of expressiveness. For example, oftentimes high-dimensional data (e.g. feature descriptors) are mapped to a single scal... | ['Florian Bernard', 'Daniel Cremers', 'Viktoria Ehm'] | 2023-05-15 | null | null | null | null | ['multi-object-tracking'] | ['computer-vision'] | [ 1.60996929e-01 -1.12878524e-01 -2.59645909e-01 -2.53622234e-01
-1.60607949e-01 -7.27233410e-01 6.92097664e-01 3.25997740e-01
-5.03136873e-01 6.98907733e-01 1.57707203e-02 2.46818806e-03
-8.84957969e-01 -9.03773367e-01 -2.05890328e-01 -7.97668993e-01
-4.88603085e-01 4.43726540e-01 3.33991647e-01 -8.12717006... | [7.5842604637146, 4.301497936248779] |
41de14d6-1f48-4a99-b30a-fe2fdce7db3a | bias-threat-and-aggression-identification | null | null | https://aclanthology.org/2022.trac-1.4 | https://aclanthology.org/2022.trac-1.4.pdf | Bias, Threat and Aggression Identification Using Machine Learning Techniques on Multilingual Comments | In this paper, we presented our team "IIITRanchi” for the Trolling, Aggression and Cyberbullying (TRAC-3) 2022 shared tasks. Aggression and its different forms on social media and other platforms had tremendous growth on the Internet. In this work we have tried upon different aspects of aggression, aggression intensity... | ['Rajiv Ranjan Suman', 'Shaury Srivastav', 'Kirti Kumari'] | null | null | null | null | trac-coling-2022-10 | ['aggression-identification'] | ['natural-language-processing'] | [-9.93076444e-01 -1.45456284e-01 -1.36686012e-01 -1.62668541e-01
-4.77732897e-01 -1.49209380e-01 5.73487103e-01 2.54381835e-01
-7.47507274e-01 8.70059609e-01 3.57938081e-01 3.03449072e-02
-3.91920954e-01 -3.54677975e-01 1.50578216e-01 -4.50576067e-01
-1.16899729e-01 7.56810665e-01 2.27034390e-01 -7.78669357... | [8.789952278137207, 10.748431205749512] |
c2fe5dcd-eff2-4035-9386-81ef62fb7462 | mining-negative-temporal-contexts-for-false | 2305.18060 | null | https://arxiv.org/abs/2305.18060v1 | https://arxiv.org/pdf/2305.18060v1.pdf | Mining Negative Temporal Contexts For False Positive Suppression In Real-Time Ultrasound Lesion Detection | During ultrasonic scanning processes, real-time lesion detection can assist radiologists in accurate cancer diagnosis. However, this essential task remains challenging and underexplored. General-purpose real-time object detection models can mistakenly report obvious false positives (FPs) when applied to ultrasound vide... | ['LiWei Wang', 'Dong Wang', 'Dengbo Chen', 'Ziwei Zhao', 'Quanlin Wu', 'Youcheng Li', 'Haojun Yu'] | 2023-05-29 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [ 5.43210983e-01 2.06847563e-01 -2.70087183e-01 -2.00466871e-01
-1.06344771e+00 -3.87159556e-01 3.38160455e-01 -1.57830492e-01
-3.54688674e-01 7.05357552e-01 1.26596196e-02 -6.41677380e-01
-7.18386769e-02 -2.95528233e-01 -5.71417451e-01 -6.73978925e-01
-2.23216996e-01 8.10119808e-02 5.47986805e-01 4.52309281... | [9.084856033325195, -0.0822000503540039] |
bbe617a1-a611-427e-9149-cb5283eba252 | lm-vc-zero-shot-voice-conversion-via-speech | 2306.10521 | null | https://arxiv.org/abs/2306.10521v1 | https://arxiv.org/pdf/2306.10521v1.pdf | LM-VC: Zero-shot Voice Conversion via Speech Generation based on Language Models | Language model (LM) based audio generation frameworks, e.g., AudioLM, have recently achieved new state-of-the-art performance in zero-shot audio generation. In this paper, we explore the feasibility of LMs for zero-shot voice conversion. An intuitive approach is to follow AudioLM - Tokenizing speech into semantic and a... | ['Yuping Wang', 'Qiao Tian', 'Lei Xie', 'Yuanzhe Chen', 'Zhichao Wang'] | 2023-06-18 | null | null | null | null | ['voice-conversion', 'audio-generation', 'disentanglement', 'voice-conversion'] | ['audio', 'audio', 'methodology', 'speech'] | [ 2.43394732e-01 2.27526203e-01 -1.43341199e-02 3.49575616e-02
-1.23044825e+00 -2.74731666e-01 2.98577726e-01 -7.89234266e-02
-1.13175847e-02 6.45597041e-01 6.57316506e-01 2.06609797e-02
2.58019298e-01 -6.29978478e-01 -5.50214112e-01 -7.72805572e-01
2.52041072e-01 6.03452474e-02 1.25206262e-01 -9.85653177... | [15.094902992248535, 6.428300857543945] |
97556320-e27c-484d-b9fe-2dd91ee9cbe7 | mega-rst-discourse-treebanks-with-structure | 2011.03017 | null | https://arxiv.org/abs/2011.03017v1 | https://arxiv.org/pdf/2011.03017v1.pdf | MEGA RST Discourse Treebanks with Structure and Nuclearity from Scalable Distant Sentiment Supervision | The lack of large and diverse discourse treebanks hinders the application of data-driven approaches, such as deep-learning, to RST-style discourse parsing. In this work, we present a novel scalable methodology to automatically generate discourse treebanks using distant supervision from sentiment-annotated datasets, cre... | ['Giuseppe Carenini', 'Patrick Huber'] | 2020-11-05 | null | https://aclanthology.org/2020.emnlp-main.603 | https://aclanthology.org/2020.emnlp-main.603.pdf | emnlp-2020-11 | ['discourse-parsing'] | ['natural-language-processing'] | [ 2.48767093e-01 1.03914642e+00 -2.48251185e-01 -4.20820743e-01
-1.46218991e+00 -8.99723291e-01 1.01444256e+00 3.46233457e-01
-2.95745552e-01 1.31018627e+00 1.08536410e+00 -6.13046646e-01
3.65878046e-01 -7.25007057e-01 -5.21147490e-01 -4.10881341e-01
1.84861481e-01 9.11204219e-01 4.72553432e-01 -6.43881619... | [10.847591400146484, 9.419922828674316] |
19d6083d-e5cf-4b77-a9c1-0f66bbefbd47 | event-based-visual-tracking-in-dynamic | 2212.07754 | null | https://arxiv.org/abs/2212.07754v1 | https://arxiv.org/pdf/2212.07754v1.pdf | Event-based Visual Tracking in Dynamic Environments | Visual object tracking under challenging conditions of motion and light can be hindered by the capabilities of conventional cameras, prone to producing images with motion blur. Event cameras are novel sensors suited to robustly perform vision tasks under these conditions. However, due to the nature of their output, app... | ['Carlos Sagues', 'Rodrigo Aldana-Lopez', 'Irene Perez-Salesa'] | 2022-12-15 | null | null | null | null | ['visual-tracking', 'visual-object-tracking'] | ['computer-vision', 'computer-vision'] | [ 2.27502152e-01 -7.35307693e-01 8.65737945e-02 -4.04049568e-02
-2.51355946e-01 -7.65551090e-01 6.45422637e-01 -1.22425914e-01
-5.96853077e-01 4.87446159e-01 -3.08231324e-01 -7.91354775e-02
2.53569514e-01 -3.75550002e-01 -8.75817895e-01 -6.08600855e-01
2.74475306e-01 -3.85195501e-02 8.06897402e-01 2.73834467... | [8.48865795135498, -1.206282615661621] |
dbe71648-245e-49ae-af52-6f007aceba31 | consensus-clustering-with-unsupervised-1 | 2010.01245 | null | https://arxiv.org/abs/2010.01245v2 | https://arxiv.org/pdf/2010.01245v2.pdf | Consensus Clustering With Unsupervised Representation Learning | Recent advances in deep clustering and unsupervised representation learning are based on the idea that different views of an input image (generated through data augmentation techniques) must either be closer in the representation space, or have a similar cluster assignment. Bootstrap Your Own Latent (BYOL) is one such ... | ['Urun Dogan', 'Eren Manavoglu', 'Aniket Anand Deshmukh', 'Jayanth Reddy Regatti'] | 2020-10-03 | consensus-clustering-with-unsupervised | https://openreview.net/forum?id=x9C7Nlwgydy | https://openreview.net/pdf?id=x9C7Nlwgydy | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 1.04205728e-01 -7.73870274e-02 -1.16985522e-01 -8.10821354e-01
-8.15732539e-01 -5.38199306e-01 6.29537702e-01 1.51585698e-01
-1.97316766e-01 2.33185381e-01 2.80840844e-01 2.41927221e-01
-3.81098181e-01 -3.21146220e-01 -7.19521821e-01 -1.09375477e+00
-1.88972384e-01 9.00545597e-01 -1.62081867e-01 4.35677528... | [9.319465637207031, 3.050753593444824] |
85117ebd-6ad2-4a97-bda4-297417c39093 | can-llms-express-their-uncertainty-an | 2306.13063 | null | https://arxiv.org/abs/2306.13063v1 | https://arxiv.org/pdf/2306.13063v1.pdf | Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs | The task of empowering large language models (LLMs) to accurately express their confidence, referred to as confidence elicitation, is essential in ensuring reliable and trustworthy decision-making processes. Previous methods, which primarily rely on model logits, have become less suitable for LLMs and even infeasible w... | ['Bryan Hooi', 'Junxian He', 'Jie Fu', 'Yifei Li', 'Xinyang Lu', 'Zhiyuan Hu', 'Miao Xiong'] | 2023-06-22 | null | null | null | null | ['benchmarking', 'arithmetic-reasoning', 'decision-making', 'benchmarking'] | ['miscellaneous', 'reasoning', 'reasoning', 'robots'] | [-2.09371254e-01 3.32095265e-01 -3.08357477e-01 -6.71441436e-01
-1.14436173e+00 -7.56190598e-01 9.64923799e-01 8.01102996e-01
-6.39463961e-01 8.57000649e-01 -2.40859184e-02 -7.46715009e-01
-3.60964745e-01 -6.79537356e-01 -6.36836469e-01 -1.25031561e-01
2.04252779e-01 5.56582332e-01 4.34636846e-02 1.14609562... | [9.903563499450684, 7.3771257400512695] |
bb19e14c-d522-4ae3-959e-85fc667878c3 | one-class-classification-for-wafer-map-using | 2107.08823 | null | https://arxiv.org/abs/2107.08823v1 | https://arxiv.org/pdf/2107.08823v1.pdf | One-Class Classification for Wafer Map using Adversarial Autoencoder with DSVDD Prior | Recently, semiconductors' demand has exploded in virtual reality, smartphones, wearable devices, the internet of things, robotics, and automobiles. Semiconductor manufacturers want to make semiconductors with high yields. To do this, manufacturers conduct many quality assurance activities. Wafer map pattern classificat... | ['Seong-Whan Lee', 'Ha Young Jo'] | 2021-07-15 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [-1.74385116e-01 -1.53786644e-01 -3.68040078e-03 -6.55383646e-01
-2.43513301e-01 -4.36731964e-01 2.18181804e-01 -1.92484692e-01
4.63759363e-01 5.50127804e-01 -2.65939087e-01 -3.44440848e-01
-2.15420574e-02 -1.24391699e+00 -6.54193759e-01 -7.05423474e-01
6.13078535e-01 5.29365242e-01 2.08714250e-02 -1.85137495... | [7.3827924728393555, 2.015512228012085] |
bcd7d73a-f689-446b-822a-0b56c368c2cf | black-box-variational-inference-converges | 2305.15349 | null | https://arxiv.org/abs/2305.15349v1 | https://arxiv.org/pdf/2305.15349v1.pdf | Black-Box Variational Inference Converges | We provide the first convergence guarantee for full black-box variational inference (BBVI), also known as Monte Carlo variational inference. While preliminary investigations worked on simplified versions of BBVI (e.g., bounded domain, bounded support, only optimizing for the scale, and such), our setup does not need an... | ['Jacob R. Gardner', 'Yian Ma', 'Jisu Oh', 'Kaiwen Wu', 'Kyurae Kim'] | 2023-05-24 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-1.47051916e-01 8.41824338e-02 -1.63427263e-01 -3.74642104e-01
-1.11163962e+00 -6.69628501e-01 6.04249537e-01 -3.32899809e-01
-3.56306374e-01 1.14982033e+00 3.82712670e-02 -5.83152890e-01
-3.39016229e-01 -7.25730062e-01 -9.14678216e-01 -8.75815153e-01
-7.58691356e-02 8.05103362e-01 1.19042076e-01 2.88592458... | [6.942061424255371, 3.9629313945770264] |
e0ef9276-a8b0-43cd-b21a-c587688281ef | masked-face-recognition-with-latent-part | null | null | https://dl.acm.org/doi/10.1145/3394171.3413731 | https://dl.acm.org/doi/pdf/10.1145/3394171.3413731 | Masked Face Recognition with Latent Part Detection | This paper focuses on a novel task named masked faces recognition (MFR), which aims to match masked faces with common faces and is important especially during the global outbreak of COVID-19. It is challenging to identify masked faces for two main reasons. Firstly, there is no large-scale training data and test data wi... | ['Yonghong Tian', 'Mengyue Geng', 'Yangru Huang', 'Peixi Peng', 'Feifei Ding'] | 2020-10-01 | null | null | null | proceedings-of-the-28th-acm-international | ['robust-face-recognition'] | ['computer-vision'] | [ 3.84415507e-01 -2.34811723e-01 3.54776904e-03 -6.36733294e-01
-5.46198964e-01 -5.29654801e-01 5.05119085e-01 -7.59670794e-01
-4.25261185e-02 6.46250308e-01 5.11265211e-02 2.80317087e-02
1.71615407e-01 -5.26478171e-01 -7.02944696e-01 -7.91766405e-01
-1.52312443e-01 3.14142346e-01 -2.65043348e-01 -1.00782223... | [13.108011245727539, 0.5103166699409485] |
8d96aa38-c24a-41de-9daf-550758cfc2d2 | supervised-speech-separation-based-on-deep | 1708.07524 | null | http://arxiv.org/abs/1708.07524v2 | http://arxiv.org/pdf/1708.07524v2.pdf | Supervised Speech Separation Based on Deep Learning: An Overview | Speech separation is the task of separating target speech from background
interference. Traditionally, speech separation is studied as a signal
processing problem. A more recent approach formulates speech separation as a
supervised learning problem, where the discriminative patterns of speech,
speakers, and background ... | ['Jitong Chen', 'DeLiang Wang'] | 2017-08-24 | null | null | null | null | ['speaker-separation', 'speech-dereverberation'] | ['speech', 'speech'] | [ 3.01669300e-01 -2.84343064e-01 -3.53138172e-03 -5.23686290e-01
-1.32205570e+00 -4.18803662e-01 3.54448497e-01 -1.57992974e-01
-8.18571821e-02 5.39748549e-01 5.29915273e-01 -1.42236039e-01
-1.37007236e-01 5.37046641e-02 -3.47175777e-01 -1.30238581e+00
1.26221171e-02 6.09566793e-02 -3.76572877e-01 -1.48281544... | [14.822742462158203, 5.84196662902832] |
bca8cb5a-c44c-4d29-af84-3262c82024bc | a-tube-and-droplet-based-approach-for | 1609.03058 | null | http://arxiv.org/abs/1609.03058v2 | http://arxiv.org/pdf/1609.03058v2.pdf | A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories | Trajectory analysis is essential in many applications. In this paper, we
address the problem of representing motion trajectories in a highly informative
way, and consequently utilize it for analyzing trajectories. Our approach first
leverages the complete information from given trajectories to construct a
thermal trans... | ['Mingliang Xu', 'Yang Zhou', 'Zicheng Liu', 'Junchi Yan', 'Weiyao Lin', 'Jianxin Wu', 'Hongteng Xu'] | 2016-09-10 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [-3.91469970e-02 -7.86657810e-01 -2.53034145e-01 6.81603793e-03
-5.10898113e-01 -8.88514042e-01 5.99922836e-01 2.90552586e-01
8.65716636e-02 1.14823073e-01 5.01739323e-01 -2.81784534e-01
-7.93754160e-02 -7.71810830e-01 -4.88953769e-01 -1.01336849e+00
1.99063960e-02 -2.56663840e-02 5.15794158e-01 1.58008859... | [8.107708930969238, 0.2822382152080536] |
0163cde5-7e9a-4aa8-a702-15ab50a8974f | identifying-adversarial-attacks-on-text | 2201.08555 | null | https://arxiv.org/abs/2201.08555v1 | https://arxiv.org/pdf/2201.08555v1.pdf | Identifying Adversarial Attacks on Text Classifiers | The landscape of adversarial attacks against text classifiers continues to grow, with new attacks developed every year and many of them available in standard toolkits, such as TextAttack and OpenAttack. In response, there is a growing body of work on robust learning, which reduces vulnerability to these attacks, though... | ['Daniel Lowd', 'Sameer Singh', 'Sabrina Reis', 'Carter Perkins', 'Kalyani Asthana', 'Wencong You', 'Adam Noack', 'Jonathan Brophy', 'Zhouhang Xie'] | 2022-01-21 | null | null | null | null | ['adversarial-text', 'abuse-detection'] | ['adversarial', 'natural-language-processing'] | [ 4.26382959e-01 -2.15218097e-01 -1.75813392e-01 -2.84168720e-01
-8.69916499e-01 -1.48597991e+00 9.57600057e-01 5.67500472e-01
-2.22805932e-01 3.89994770e-01 2.34114587e-01 -8.02598894e-01
1.64183140e-01 -8.98576081e-01 -5.34783840e-01 -5.04463017e-01
-4.86937240e-02 3.51708084e-01 1.05997689e-01 -3.69237959... | [5.988375663757324, 8.011249542236328] |
4da3a484-4c71-4fb6-ba9f-693a87ab4450 | nasnet-a-neuron-attention-stage-by-stage-net | 1912.03151 | null | https://arxiv.org/abs/1912.03151v2 | https://arxiv.org/pdf/1912.03151v2.pdf | NASNet: A Neuron Attention Stage-by-Stage Net for Single Image Deraining | Images captured under complicated rain conditions often suffer from noticeable degradation of visibility. The rain models generally introduce diversity visibility degradation, which includes rain streak, rain drop as well as rain mist. Numerous existing single image deraining methods focus on the only one type rain mod... | ['Zhilin Wang', 'Xu Qin'] | 2019-12-06 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [-2.09621191e-01 -5.75506508e-01 5.31032681e-01 -7.24587142e-01
-1.25109777e-01 -1.64236054e-01 1.55005381e-01 -4.44542110e-01
-5.20445883e-01 7.51330853e-01 2.41964743e-01 -9.27492231e-02
5.71884438e-02 -7.38382995e-01 -7.13775396e-01 -1.13153934e+00
2.41066958e-03 3.00182477e-02 3.13948005e-01 -5.10433614... | [10.92706298828125, -3.2225069999694824] |
2a5ca32b-4965-4cf0-a8d2-395e1ae0cce3 | ruccmu-system-report-for-dense-captioning | 1806.08854 | null | http://arxiv.org/abs/1806.08854v1 | http://arxiv.org/pdf/1806.08854v1.pdf | RUC+CMU: System Report for Dense Captioning Events in Videos | This notebook paper presents our system in the ActivityNet Dense Captioning
in Video task (task 3). Temporal proposal generation and caption generation are
both important to the dense captioning task. Therefore, we propose a proposal
ranking model to employ a set of effective feature representations for proposal
genera... | ['Alexander Hauptmann', 'Shizhe Chen', 'Yuqing Song', 'Yida Zhao', 'Qin Jin', 'Jiarong Qiu'] | 2018-06-22 | null | null | null | null | ['dense-captioning', 'dense-video-captioning'] | ['computer-vision', 'computer-vision'] | [ 2.36311764e-01 2.55495548e-01 -2.71042079e-01 -3.62507343e-01
-1.26421320e+00 -2.96765029e-01 9.65753734e-01 -5.49113929e-01
-1.70082510e-01 1.05779183e+00 1.05993640e+00 2.29833573e-01
5.97651601e-01 -2.70924360e-01 -1.02592516e+00 -3.97068769e-01
-2.01992974e-01 6.45603895e-01 5.20123005e-01 -1.00283302... | [10.458306312561035, 0.6539652943611145] |
5b5ad782-45c7-4a91-b1f7-1cb4e9d15c4c | wav2code-restore-clean-speech-representations | 2304.04974 | null | https://arxiv.org/abs/2304.04974v2 | https://arxiv.org/pdf/2304.04974v2.pdf | Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASR | Automatic speech recognition (ASR) has gained a remarkable success thanks to recent advances of deep learning, but it usually degrades significantly under real-world noisy conditions. Recent works introduce speech enhancement (SE) as front-end to improve speech quality, which is proved effective but may not be optimal ... | ['Eng Siong Chng', 'Qiushi Zhu', 'Chen Chen', 'Yuchen Hu'] | 2023-04-11 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 1.89045608e-01 -5.48853338e-01 1.30118176e-01 -2.89385885e-01
-9.28975821e-01 -2.26182014e-01 2.94846773e-01 -7.52119496e-02
-3.97640131e-02 3.71998906e-01 6.62348807e-01 -2.23328426e-01
-1.45624459e-01 -5.66156507e-01 -4.52543199e-01 -8.95067871e-01
1.95606664e-01 -3.58783424e-01 4.01086174e-02 -5.12310147... | [14.905423164367676, 5.985017776489258] |
dd5e2e37-0600-411d-945f-9f745afdd789 | cyber-attack-detection-in-discrete-nonlinear | 2102.01166 | null | https://arxiv.org/abs/2102.01166v1 | https://arxiv.org/pdf/2102.01166v1.pdf | Cyber-Attack Detection in Discrete Nonlinear Multi-Agent Systems Using Neural Networks | This paper proposes a distributed cyber-attack detection method in communication channels for a class of discrete, nonlinear, heterogeneous, multi-agent systems that are controlled by our proposed formation-based controller. A residual-based detection system, exploiting a neural network (NN)-based observer, is develope... | ['Rastko R. Selmic', 'Kiarash Aryankia', 'Amirreza Mousavi'] | 2021-02-01 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [-1.66942149e-01 2.40482464e-01 -2.06186138e-02 5.54855347e-01
-2.07993761e-01 -6.89166903e-01 5.49345791e-01 3.93902719e-01
-1.37877658e-01 7.97480524e-01 -4.87221986e-01 -3.02692860e-01
-3.64650220e-01 -6.92210257e-01 -3.01506162e-01 -9.72418487e-01
-6.02975368e-01 -1.98866889e-01 3.32014084e-01 -2.93483466... | [5.284797668457031, 2.5780701637268066] |
eea05c1d-bf21-4722-a8cf-df5f964ba845 | transform-retrieve-generate-natural-language | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Gao_Transform-Retrieve-Generate_Natural_Language-Centric_Outside-Knowledge_Visual_Question_Answering_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Gao_Transform-Retrieve-Generate_Natural_Language-Centric_Outside-Knowledge_Visual_Question_Answering_CVPR_2022_paper.pdf | Transform-Retrieve-Generate: Natural Language-Centric Outside-Knowledge Visual Question Answering | Outside-knowledge visual question answering (OK-VQA) requires the agent to comprehend the image, make use of relevant knowledge from the entire web, and digest all the information to answer the question. Most previous works address the problem by first fusing the image and question in the multi-modal space, which i... | ['Prem Natarajan', 'Ying Nian Wu', 'Aishwarya Reganti', 'Govind Thattai', 'Qing Ping', 'Feng Gao'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['generative-question-answering', 'passage-retrieval'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.41339734e-01 3.79182130e-01 -1.17412619e-02 -1.04400188e-01
-1.28331232e+00 -9.40535784e-01 8.86889815e-01 -1.13861568e-01
-3.26512307e-01 6.95199072e-01 2.10300148e-01 -4.79492515e-01
2.05577329e-01 -9.87727940e-01 -8.97052944e-01 -5.44107080e-01
7.26683557e-01 5.78508556e-01 4.37948525e-01 -3.07701319... | [10.895867347717285, 1.5566951036453247] |
4b25282e-154d-4bcd-89b5-445cd2dc3bdc | harnessing-expressive-capacity-of-machine | 2111.14998 | null | https://arxiv.org/abs/2111.14998v1 | https://arxiv.org/pdf/2111.14998v1.pdf | Harnessing expressive capacity of Machine Learning modeling to represent complex coupling of Earth's auroral space weather regimes | We develop multiple Deep Learning (DL) models that advance the state-of-the-art predictions of the global auroral particle precipitation. We use observations from low Earth orbiting spacecraft of the electron energy flux to develop a model that improves global nowcasts (predictions at the time of observation) of the ac... | ['Ryan M. McGranaghan', 'Jack Ziegler'] | 2021-11-29 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-3.46754372e-01 -3.48692715e-01 -1.15371041e-01 -6.77844286e-01
-1.01453018e+00 -3.88051063e-01 8.96095693e-01 9.18195322e-02
-2.82724321e-01 1.17171419e+00 2.60611802e-01 -8.76976490e-01
-2.16892511e-01 -6.77271485e-01 -7.74119675e-01 -9.22318339e-01
-6.95254147e-01 6.64876521e-01 -5.02377450e-01 -4.81438041... | [6.589149475097656, 2.9500465393066406] |
307cabdd-173c-451d-ba20-119b7468542c | estimating-parkinsonism-severity-in-natural | 2105.03464 | null | https://arxiv.org/abs/2105.03464v3 | https://arxiv.org/pdf/2105.03464v3.pdf | Estimating Parkinsonism Severity in Natural Gait Videos of Older Adults with Dementia | Drug-induced parkinsonism affects many older adults with dementia, often causing gait disturbances. New advances in vision-based human pose-estimation have opened possibilities for frequent and unobtrusive analysis of gait in residential settings. This work leverages novel spatial-temporal graph convolutional network (... | ['Babak Taati', 'Andrea Iaboni', 'Sina Mehdizadeh', 'Andrea Sabo'] | 2021-05-07 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-2.33458921e-01 -3.95983793e-02 -3.41459751e-01 -2.64192134e-01
-7.78276145e-01 -1.00209396e-02 8.14907476e-02 -3.62670541e-01
-9.35329854e-01 1.06915820e+00 5.87994635e-01 -3.76642980e-02
-1.21932238e-01 -6.17131174e-01 -2.85925359e-01 -3.47914666e-01
-7.81060874e-01 8.01610231e-01 4.41965818e-01 -2.87554741... | [7.103670597076416, 0.3224536180496216] |
f7f53dc4-7a6d-4fca-9765-7f8a1b766720 | actively-learning-a-bayesian-matrix-fusion | 2306.05331 | null | https://arxiv.org/abs/2306.05331v1 | https://arxiv.org/pdf/2306.05331v1.pdf | Actively learning a Bayesian matrix fusion model with deep side information | High-dimensional deep neural network representations of images and concepts can be aligned to predict human annotations of diverse stimuli. However, such alignment requires the costly collection of behavioral responses, such that, in practice, the deep-feature spaces are only ever sparsely sampled. Here, we propose an ... | ['Jordan W. Suchow', 'Yangyang Yu'] | 2023-06-08 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 1.82686344e-01 -2.15315685e-01 -3.33608299e-01 -7.53376186e-01
-8.79299045e-01 -6.47243917e-01 3.70509148e-01 7.15360194e-02
-9.87482607e-01 6.85706615e-01 3.37098986e-01 3.19446534e-01
1.36067076e-02 -2.99189985e-01 -9.15749490e-01 -4.85632032e-01
1.73062339e-01 6.80096149e-01 -1.48000851e-01 4.64207917... | [9.97717571258545, 2.3417396545410156] |
11f80348-fc61-48d1-9b5f-c2a67e68a591 | ym2413-mdb-a-multi-instrumental-fm-video-game | 2211.07131 | null | https://arxiv.org/abs/2211.07131v1 | https://arxiv.org/pdf/2211.07131v1.pdf | YM2413-MDB: A Multi-Instrumental FM Video Game Music Dataset with Emotion Annotations | Existing multi-instrumental datasets tend to be biased toward pop and classical music. In addition, they generally lack high-level annotations such as emotion tags. In this paper, we propose YM2413-MDB, an 80s FM video game music dataset with multi-label emotion annotations. It includes 669 audio and MIDI files of musi... | ['Juhan Nam', 'Taegyun Kwon', 'JongIk Jeon', 'Seolhee Lee', 'Yoonjin Chung', 'Eunjin Choi'] | 2022-11-14 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 1.56626254e-01 -2.51511514e-01 1.53642260e-02 -5.99422567e-02
-1.09845138e+00 -1.14183772e+00 -4.47416957e-03 -3.45636815e-01
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-3.14157337e-01 -3.11675668e-01 -3.25039476e-01 -5.10053337e-01
-8.30109790e-02 5.87783992e-01 -1.60488546e-01 -2.02444300... | [15.927909851074219, 5.307121753692627] |
d30fb973-136b-42a5-9725-87589c976cc7 | grown-up-a-graph-representation-of-a-webpage | 2208.02252 | null | https://arxiv.org/abs/2208.02252v2 | https://arxiv.org/pdf/2208.02252v2.pdf | GROWN+UP: A Graph Representation Of a Webpage Network Utilizing Pre-training | Large pre-trained neural networks are ubiquitous and critical to the success of many downstream tasks in natural language processing and computer vision. However, within the field of web information retrieval, there is a stark contrast in the lack of similarly flexible and powerful pre-trained models that can properly ... | ['Huijuan Wang', 'Benedict Yeoh'] | 2022-08-03 | null | null | null | null | ['webpage-object-detection', 'genre-classification'] | ['computer-vision', 'computer-vision'] | [ 3.87572914e-01 1.29337072e-01 -3.05470169e-01 -2.24261090e-01
-1.03043568e+00 -1.02050102e+00 7.35051751e-01 4.33385998e-01
-4.82832372e-01 2.39197597e-01 3.62143040e-01 -7.42943525e-01
-1.66677102e-01 -8.61676872e-01 -8.87552142e-01 -2.46825412e-01
-3.70104313e-01 2.51382947e-01 1.97597519e-01 -3.82161766... | [9.924803733825684, 7.8775954246521] |
400da096-5923-4b6e-9014-ab389e197bbc | a-novel-approach-for-neuromorphic-vision-data | 2210.15362 | null | https://arxiv.org/abs/2210.15362v1 | https://arxiv.org/pdf/2210.15362v1.pdf | A Novel Approach for Neuromorphic Vision Data Compression based on Deep Belief Network | A neuromorphic camera is an image sensor that emulates the human eyes capturing only changes in local brightness levels. They are widely known as event cameras, silicon retinas or dynamic vision sensors (DVS). DVS records asynchronous per-pixel brightness changes, resulting in a stream of events that encode the brightn... | ['Abhipraay Nevatia', 'Mansi Sharma', 'Sally Khaidem'] | 2022-10-27 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 6.45323992e-01 -4.74882245e-01 -8.68979916e-02 -2.98447222e-01
-2.75133908e-01 -1.95560575e-01 5.79587579e-01 2.38363832e-01
-6.82224691e-01 7.02875793e-01 5.92117831e-02 2.40498856e-01
1.79359615e-01 -9.03680265e-01 -9.48624134e-01 -8.53281796e-01
4.79422044e-03 -2.77839899e-01 6.55215144e-01 5.45140505... | [8.740516662597656, -1.1224652528762817] |
68842705-e7f2-490b-b616-0b0035871219 | unification-based-reconstruction-of | 2004.00061 | null | https://arxiv.org/abs/2004.00061v2 | https://arxiv.org/pdf/2004.00061v2.pdf | Unification-based Reconstruction of Multi-hop Explanations for Science Questions | This paper presents a novel framework for reconstructing multi-hop explanations in science Question Answering (QA). While existing approaches for multi-hop reasoning build explanations considering each question in isolation, we propose a method to leverage explanatory patterns emerging in a corpus of scientific explana... | ['André Freitas', 'Mokanarangan Thayaparan', 'Marco Valentino'] | 2020-03-31 | null | https://aclanthology.org/2021.eacl-main.15 | https://aclanthology.org/2021.eacl-main.15.pdf | eacl-2021-2 | ['science-question-answering'] | ['miscellaneous'] | [ 3.78241912e-02 1.13856983e+00 -3.70206356e-01 -3.52251351e-01
-1.15955472e+00 -7.55925179e-01 1.00184035e+00 6.99105144e-01
-5.28686568e-02 9.59504187e-01 8.10606122e-01 -8.53332520e-01
-1.03750312e+00 -7.07409263e-01 -9.23174143e-01 -3.52476984e-01
2.64285564e-01 1.13753235e+00 5.21604419e-01 -3.07793736... | [10.919465065002441, 7.878127574920654] |
fe64d4ea-c3fa-4beb-9068-e6c32615c0ee | clinical-temporal-relation-extraction-with | 2012.08790 | null | https://arxiv.org/abs/2012.08790v1 | https://arxiv.org/pdf/2012.08790v1.pdf | Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference | There has been a steady need in the medical community to precisely extract the temporal relations between clinical events. In particular, temporal information can facilitate a variety of downstream applications such as case report retrieval and medical question answering. Existing methods either require expensive featu... | ['Wei Wang', 'Peipei Ping', 'Yizhou Sun', 'Kai-Wei Chang', 'J. Harry Caufield', 'Rujun Han', 'Yu Yan', 'Yichao Zhou'] | 2020-12-16 | null | null | null | null | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 4.21334803e-02 -2.17195172e-02 -7.02242732e-01 -5.67903936e-01
-1.14955521e+00 -3.09731811e-01 5.50684035e-01 8.92059982e-01
-1.02284372e-01 9.48016763e-01 5.01893520e-01 -5.53888500e-01
-7.41812289e-01 -6.19734585e-01 -3.95400107e-01 -5.25821269e-01
-5.16621649e-01 5.13911545e-01 2.54585445e-01 1.16783284... | [8.649724006652832, 8.929250717163086] |
0606ff36-0363-41e2-8a79-ff21f00fff21 | automating-feature-engineering | null | null | http://workshops.inf.ed.ac.uk/nips2016-ai4datasci/papers/NIPS2016-AI4DataSci_paper_13.pdf | http://workshops.inf.ed.ac.uk/nips2016-ai4datasci/papers/NIPS2016-AI4DataSci_paper_13.pdf | Automating Feature Engineering | Feature Engineering is the task of transforming the feature space in a given learning problem to improve the performance of a trained model. It is a crucial but time intensive and skillful process, involving a data scientist or a domain expert. It is often the key determinant of the time and cost required to build an e... | ['Horst Samulowitz', 'Deepak Turaga', 'Elias Khalil', 'Udayan Khurana', 'Fatemeh Nargesian'] | 2016-01-01 | automating-feature-engineering-1 | https://pdfs.semanticscholar.org/959e/dbe6a4720d49d87d9b64440cd394f934815d.pdf | https://pdfs.semanticscholar.org/959e/dbe6a4720d49d87d9b64440cd394f934815d.pdf | nips-2016-2016-1 | ['automated-feature-engineering'] | ['methodology'] | [ 1.95028752e-01 1.16351554e-02 8.87654126e-02 -7.24427164e-01
-7.58245051e-01 -9.01024103e-01 5.76652169e-01 5.25219560e-01
-2.74981022e-01 5.57787299e-01 3.38920504e-02 -5.79867482e-01
-4.59278405e-01 -6.39508605e-01 -8.94131958e-01 -3.00838500e-01
2.45546132e-01 5.12232304e-01 3.50393471e-03 -1.64954662... | [8.367745399475098, 4.674412250518799] |
65f80190-c0a8-4d36-8f3d-ba85374c201c | vox-populi-vox-diy-benchmark-dataset-for | 2107.01091 | null | https://arxiv.org/abs/2107.01091v2 | https://arxiv.org/pdf/2107.01091v2.pdf | CrowdSpeech and VoxDIY: Benchmark Datasets for Crowdsourced Audio Transcription | Domain-specific data is the crux of the successful transfer of machine learning systems from benchmarks to real life. In simple problems such as image classification, crowdsourcing has become one of the standard tools for cheap and time-efficient data collection: thanks in large part to advances in research on aggregat... | ['Dmitry Ustalov', 'Ivan Stelmakh', 'Nikita Pavlichenko'] | 2021-07-02 | null | null | null | null | ['crowdsourced-text-aggregation'] | ['natural-language-processing'] | [-1.61443055e-02 1.57379471e-02 2.11952522e-01 -5.04145622e-01
-1.48804760e+00 -9.08749878e-01 6.44117951e-01 1.70387670e-01
-5.88735521e-01 8.23571563e-01 5.67109644e-01 -1.50517821e-01
2.46686935e-01 -3.84017080e-01 -6.63095653e-01 -5.54135859e-01
2.71402776e-01 7.25672722e-01 3.22810829e-01 -5.07859588... | [9.832755088806152, 4.915410995483398] |
97ef07f8-d409-46ee-b6a1-1e8196ac6baf | moviemat-context-aware-movie-recommendation | 2204.13003 | null | https://arxiv.org/abs/2204.13003v1 | https://arxiv.org/pdf/2204.13003v1.pdf | MovieMat: Context-aware Movie Recommendation with Matrix Factorization by Matrix Fitting | Movie Recommender System is widely applied in commercial environments such as NetFlix and Tubi. Classic recommender models utilize technologies such as collaborative filtering, learning to rank, matrix factorization and deep learning models to achieve lower marketing expenses and higher revenues. However, audience of m... | ['Hao Wang'] | 2022-04-27 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-0.7365543 -0.731469 -0.22899254 -0.6104998 -0.01350747 -0.72249717
0.14971216 0.11632806 -0.18079877 0.41910937 0.5526306 -0.48951477
-0.4528572 -0.81788987 -0.22220637 -0.27221477 -0.05297009 0.06852005
-0.05517368 -0.7475377 0.361671 0.3325703 -1.2994012 0.78090125
0.6981041 1.0013363 0.... | [10.056098937988281, 5.761201858520508] |
c5d7af01-eed3-4365-b7f3-ce418e294598 | on-the-validation-of-pansharpening-methods | 2111.07625 | null | https://arxiv.org/abs/2111.07625v1 | https://arxiv.org/pdf/2111.07625v1.pdf | On the validation of pansharpening methods | Validation of the quality of pansharpening methods is a difficult task because the reference is not directly available. In the meantime, two main approaches have been established: validation in reduced resolution and original resolution. In the former approach it is still not clear how the data are to be processed to a... | ['Gintautas Palubinskas'] | 2021-11-15 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 4.61218894e-01 -4.18241322e-01 3.39836061e-01 -1.02583557e-01
-4.63221848e-01 -3.87106419e-01 4.54200476e-01 6.07621133e-01
-5.88119209e-01 9.85368490e-01 -2.87550509e-01 -2.70480543e-01
-6.92514539e-01 -1.17516947e+00 -2.07615837e-01 -1.06949449e+00
2.08110839e-01 2.54242092e-01 6.39935315e-01 -5.27879894... | [10.008824348449707, -2.0457656383514404] |
e188b92e-de74-499f-827d-bc08a63470ce | camliflow-bidirectional-camera-lidar-fusion | 2111.10502 | null | https://arxiv.org/abs/2111.10502v4 | https://arxiv.org/pdf/2111.10502v4.pdf | CamLiFlow: Bidirectional Camera-LiDAR Fusion for Joint Optical Flow and Scene Flow Estimation | In this paper, we study the problem of jointly estimating the optical flow and scene flow from synchronized 2D and 3D data. Previous methods either employ a complex pipeline that splits the joint task into independent stages, or fuse 2D and 3D information in an "early-fusion" or "late-fusion" manner. Such one-size-fits... | ['Lijun Chen', 'Wenjie Li', 'Jia Liu', 'Yihui Xu', 'Tao Lu', 'Haisong Liu'] | 2021-11-20 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_CamLiFlow_Bidirectional_Camera-LiDAR_Fusion_for_Joint_Optical_Flow_and_Scene_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_CamLiFlow_Bidirectional_Camera-LiDAR_Fusion_for_Joint_Optical_Flow_and_Scene_CVPR_2022_paper.pdf | cvpr-2022-1 | ['scene-flow-estimation'] | ['computer-vision'] | [-2.28211284e-01 -5.18098533e-01 -2.71213233e-01 -3.47957104e-01
-6.00956380e-01 -6.00545526e-01 4.72825587e-01 -2.06929550e-01
-2.84878492e-01 4.74289924e-01 3.75368804e-01 -1.37035355e-01
-5.77561446e-02 -4.65754807e-01 -4.69416410e-01 -5.06128728e-01
-2.29976885e-02 1.98338076e-01 3.93126249e-01 4.70103770... | [8.664029121398926, -1.960135579109192] |
bb415987-801e-4956-a7bd-27f388ba6875 | ecg-classification-system-for-arrhythmia | 2303.03660 | null | https://arxiv.org/abs/2303.03660v1 | https://arxiv.org/pdf/2303.03660v1.pdf | ECG Classification System for Arrhythmia Detection Using Convolutional Neural Networks | Arrhythmia is just one of the many cardiovascular illnesses that have been extensively studied throughout the years. Using a multi-lead ECG data, this research describes a deep learning (DL) technique based on a convolutional neural network (CNN) algorithm to detect cardiovascular arrhythmia in patients. The suggested ... | ['Jaskaran Singh Walia', 'Aryan odugoudar'] | 2023-03-07 | null | null | null | null | ['arrhythmia-detection', 'ecg-classification'] | ['medical', 'medical'] | [-9.92323160e-02 -1.12093017e-01 1.51874796e-01 -1.14047222e-01
-2.08481066e-02 -4.79288548e-01 -1.83120683e-01 8.48412588e-02
-2.66865909e-01 9.26399827e-01 -8.73241350e-02 -7.30101407e-01
-4.79063690e-02 -6.10905766e-01 -7.34514818e-02 -5.64369261e-01
-3.62037361e-01 3.59792076e-02 -2.94413745e-01 2.69800723... | [14.329151153564453, 3.2804625034332275] |
42df1ec3-2487-4e19-8d34-deb46c0247a8 | modeling-the-graphotactics-of-low-resource | 2210.14409 | null | https://arxiv.org/abs/2210.14409v1 | https://arxiv.org/pdf/2210.14409v1.pdf | Modeling the Graphotactics of Low-Resource Languages Using Sequential GANs | Generative Adversarial Networks (GANs) have been shown to aid in the creation of artificial data in situations where large amounts of real data are difficult to come by. This issue is especially salient in the computational linguistics space, where researchers are often tasked with modeling the complex morphologic and ... | ['Isaac Wasserman'] | 2022-10-26 | null | null | null | null | ['morphological-inflection'] | ['natural-language-processing'] | [ 2.39470258e-01 3.81781042e-01 3.10129672e-01 -4.76438671e-01
-5.52208006e-01 -7.30903864e-01 5.47505498e-01 2.94088037e-03
-5.70931613e-01 7.30758727e-01 4.09636736e-01 -6.82551026e-01
3.87921453e-01 -8.46718788e-01 -6.97204411e-01 -2.72435993e-01
2.50015974e-01 1.03480434e+00 -5.94515204e-01 -5.50887942... | [10.777045249938965, 9.721062660217285] |
59043c23-96d2-4801-858f-b02ed16a0806 | nubo-a-transparent-python-package-for | 2305.06709 | null | https://arxiv.org/abs/2305.06709v1 | https://arxiv.org/pdf/2305.06709v1.pdf | NUBO: A Transparent Python Package for Bayesian Optimisation | NUBO, short for Newcastle University Bayesian Optimisation, is a Bayesian optimisation framework for the optimisation of expensive-to-evaluate black-box functions, such as physical experiments and computer simulators. Bayesian optimisation is a cost-efficient optimisation strategy that uses surrogate modelling via Gaus... | ['Richard D. Whalley', 'Kevin Wilson', 'Mike Diessner'] | 2023-05-11 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [-1.70263007e-01 -2.07580268e-01 2.05921590e-01 -1.65074855e-01
-7.34389484e-01 -4.97739494e-01 6.05472088e-01 2.44812250e-01
-4.06727850e-01 7.39639878e-01 -1.75733343e-01 -7.17982411e-01
-7.23837197e-01 -5.73315859e-01 -3.63793522e-01 -9.52979505e-01
-1.01324223e-01 6.88554227e-01 1.75800413e-01 6.71533123... | [6.251134395599365, 3.760422945022583] |
b0dae793-7cdf-46d0-b22d-2092554bbcdb | zmbart-an-unsupervised-cross-lingual-transfer | 2106.01597 | null | https://arxiv.org/abs/2106.01597v1 | https://arxiv.org/pdf/2106.01597v1.pdf | ZmBART: An Unsupervised Cross-lingual Transfer Framework for Language Generation | Despite the recent advancement in NLP research, cross-lingual transfer for natural language generation is relatively understudied. In this work, we transfer supervision from high resource language (HRL) to multiple low-resource languages (LRLs) for natural language generation (NLG). We consider four NLG tasks (text sum... | ['Kumari Deepshikha', 'Yoshinobu Kano', 'Maunendra Sankar Desarkar', 'Kaushal Kumar Maurya'] | 2021-06-03 | null | https://aclanthology.org/2021.findings-acl.248 | https://aclanthology.org/2021.findings-acl.248.pdf | findings-acl-2021-8 | ['headline-generation', 'distractor-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.02344641e-01 1.72200024e-01 -2.46247500e-02 -2.12749317e-01
-1.50657892e+00 -4.63299990e-01 9.00549352e-01 -2.20416173e-01
-5.16336858e-01 1.35217953e+00 5.87500215e-01 -2.38717824e-01
4.28274810e-01 -6.82016611e-01 -7.99585104e-01 -5.89564145e-01
2.40235761e-01 6.51825190e-01 -8.63254145e-02 -5.72547019... | [11.911966323852539, 9.291004180908203] |
f6fddc64-dbdd-4966-bc7c-95ef32759bee | multi-task-self-supervised-time-series | 2303.01034 | null | https://arxiv.org/abs/2303.01034v1 | https://arxiv.org/pdf/2303.01034v1.pdf | Multi-Task Self-Supervised Time-Series Representation Learning | Time-series representation learning can extract representations from data with temporal dynamics and sparse labels. When labeled data are sparse but unlabeled data are abundant, contrastive learning, i.e., a framework to learn a latent space where similar samples are close to each other while dissimilar ones are far fr... | ['Pilsung Kang', 'Heejeong Choi'] | 2023-03-02 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 2.16691360e-01 -3.56934249e-01 -2.71057129e-01 -6.43800318e-01
-8.72579396e-01 -4.05971378e-01 7.24794745e-01 2.03095585e-01
-1.79159299e-01 5.63110590e-01 -1.41200930e-01 1.69470552e-02
-3.68838906e-01 -4.60291356e-01 -5.09642005e-01 -9.83231962e-01
-5.26261628e-01 4.25440431e-01 -6.17116019e-02 1.82328485... | [7.373469829559326, 2.9037482738494873] |
1b915ec3-deb9-4d31-a0b5-605d2ed3a834 | graph-to-sequence-neural-machine-translation | 2009.07489 | null | https://arxiv.org/abs/2009.07489v1 | https://arxiv.org/pdf/2009.07489v1.pdf | Graph-to-Sequence Neural Machine Translation | Neural machine translation (NMT) usually works in a seq2seq learning way by viewing either source or target sentence as a linear sequence of words, which can be regarded as a special case of graph, taking words in the sequence as nodes and relationships between words as edges. In the light of the current NMT models mor... | ['Hai Zhao', 'Sufeng Duan', 'Rui Wang'] | 2020-09-16 | null | null | null | null | ['graph-to-sequence'] | ['natural-language-processing'] | [ 5.10323882e-01 2.35357881e-01 -4.72865254e-01 -3.29982370e-01
-4.39495236e-01 -6.91557646e-01 6.50475800e-01 7.50656277e-02
-3.41461718e-01 7.54041672e-01 6.04845464e-01 -7.00521946e-01
1.29472777e-01 -9.27694619e-01 -9.39319551e-01 -3.99628133e-01
2.40624286e-02 5.91643333e-01 -2.97037531e-02 -5.56362510... | [10.288275718688965, 8.35886001586914] |
11ac6698-fa79-4157-904f-3739af31b44a | jukebox-a-multilingual-singer-recognition | 2008.03507 | null | https://arxiv.org/abs/2008.03507v1 | https://arxiv.org/pdf/2008.03507v1.pdf | JukeBox: A Multilingual Singer Recognition Dataset | A text-independent speaker recognition system relies on successfully encoding speech factors such as vocal pitch, intensity, and timbre to achieve good performance. A majority of such systems are trained and evaluated using spoken voice or everyday conversational voice data. Spoken voice, however, exhibits a limited ra... | ['Arun Ross', 'Anurag Chowdhury', 'Austin Cozzo'] | 2020-08-08 | null | null | null | null | ['text-independent-speaker-recognition'] | ['speech'] | [ 2.23439299e-02 -3.42430443e-01 5.62769547e-03 -6.54295564e-01
-1.17214322e+00 -8.84591520e-01 5.26728213e-01 -4.05910611e-01
-1.42509788e-01 3.12644780e-01 3.05186093e-01 -3.04250747e-01
-4.30187723e-03 -1.10030279e-01 -3.95643324e-01 -7.28434265e-01
1.06207415e-01 1.94258496e-01 -1.40376896e-01 -2.32760042... | [14.67196273803711, 6.248265743255615] |
b29c256f-a440-446a-b14a-a872cc0d980f | word-segmentation-by-separation-inference-for | null | null | https://aclanthology.org/2022.findings-acl.309 | https://aclanthology.org/2022.findings-acl.309.pdf | Word Segmentation by Separation Inference for East Asian Languages | Chinese Word Segmentation (CWS) intends to divide a raw sentence into words through sequence labeling. Thinking in reverse, CWS can also be viewed as a process of grouping a sequence of characters into a sequence of words. In such a way, CWS is reformed as a separation inference task in every adjacent character pair. S... | ['Guokai Zheng', 'Ge Chen', 'Jizhe Zhou', 'Jingzhi Guo', 'Yu tong'] | null | null | null | null | findings-acl-2022-5 | ['chinese-word-segmentation'] | ['natural-language-processing'] | [ 2.89447159e-01 -7.02334419e-02 -4.67075437e-01 -4.48251247e-01
-4.99584436e-01 -7.58048713e-01 4.07152802e-01 2.00250968e-01
-7.10079253e-01 5.18650055e-01 2.22551927e-01 -1.02183664e+00
6.66025400e-01 -6.69235528e-01 -3.63678277e-01 -7.94023395e-01
3.12717766e-01 5.08527696e-01 5.95656455e-01 -9.27176774... | [10.073678970336914, 10.089434623718262] |
8a4759ba-844e-42f0-986b-43ed850839ac | reflectance-capture-using-univariate-sampling | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Hui_Reflectance_Capture_Using_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Hui_Reflectance_Capture_Using_ICCV_2017_paper.pdf | Reflectance Capture Using Univariate Sampling of BRDFs | We propose the use of a light-weight setup consisting of a collocated camera and light source --- commonly found on mobile devices --- to reconstruct surface normals and spatially-varying BRDFs of near-planar material samples. A collocated setup provides only a 1-D "univariate" sampling of the 4-D BRDF. We show that a ... | ['Sunil Hadap', 'Joon-Young Lee', 'Jian Wang', 'Zhuo Hui', 'Kalyan Sunkavalli', 'Aswin C. Sankaranarayanan'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['brdf-estimation'] | ['computer-vision'] | [ 7.63663232e-01 -3.23519796e-01 5.45203984e-01 -2.80511945e-01
-6.12757742e-01 -3.63768578e-01 2.69705415e-01 -2.25741267e-01
8.44370201e-02 5.53398669e-01 -3.11245918e-01 -2.52271950e-01
8.88049826e-02 -8.09799850e-01 -7.13350892e-01 -7.61319280e-01
3.43968332e-01 3.99068952e-01 3.00139636e-01 -2.84531284... | [9.790326118469238, -2.975803852081299] |
8a4fd56d-2274-48e8-9c4f-2e712b6d9c36 | sabia-portuguese-large-language-models | 2304.07880 | null | https://arxiv.org/abs/2304.07880v2 | https://arxiv.org/pdf/2304.07880v2.pdf | Sabiá: Portuguese Large Language Models | As the capabilities of language models continue to advance, it is conceivable that "one-size-fits-all" model will remain as the main paradigm. For instance, given the vast number of languages worldwide, many of which are low-resource, the prevalent practice is to pretrain a single model on multiple languages. In this p... | ['Thales Sales Almeida', 'Rodrigo Nogueira', 'Hugo Abonizio', 'Ramon Pires'] | 2023-04-16 | null | null | null | null | ['culture'] | ['speech'] | [-9.31091979e-02 5.01689203e-02 -5.41013241e-01 -2.69160777e-01
-9.14079309e-01 -8.35452795e-01 7.58086205e-01 1.42020673e-01
-8.38419199e-01 9.23847377e-01 4.38045561e-01 -6.46180093e-01
1.15648434e-01 -4.29831684e-01 -8.78416598e-01 -7.76867718e-02
3.01012576e-01 8.59902620e-01 -9.06386822e-02 -7.28988588... | [10.916749954223633, 9.917023658752441] |
5435dc9b-b9e3-48a1-89c1-d13c3debe7a8 | ufo-unified-feature-optimization | 2207.10341 | null | https://arxiv.org/abs/2207.10341v1 | https://arxiv.org/pdf/2207.10341v1.pdf | UFO: Unified Feature Optimization | This paper proposes a novel Unified Feature Optimization (UFO) paradigm for training and deploying deep models under real-world and large-scale scenarios, which requires a collection of multiple AI functions. UFO aims to benefit each single task with a large-scale pretraining on all tasks. Compared with the well known ... | ['Jingdong Wang', 'Errui Ding', 'Jingtuo Liu', 'Junyu Han', 'Haocheng Feng', 'Lufei Liu', 'Jian Wang', 'Jinwen Chen', 'Zhigang Wang', 'Xinyu Zhang', 'Gang Zhang', 'Nan Peng', 'Bi Li', 'Deli Yu', 'Yifan Sun', 'Teng Xi'] | 2022-07-21 | null | null | null | null | ['vehicle-re-identification'] | ['computer-vision'] | [-1.35770589e-02 -1.07437097e-01 -1.55553982e-01 -3.47559720e-01
-4.28915381e-01 -1.50330722e-01 3.22547436e-01 -3.21677893e-01
-2.84246176e-01 4.25858468e-01 -3.06082904e-01 -1.03542162e-02
-4.68107224e-01 -7.75168002e-01 -6.85629845e-01 -6.67748153e-01
-4.41651195e-02 7.21989155e-01 -2.74183638e-02 -3.00587773... | [9.379958152770996, 3.245589256286621] |
0fc2e3a5-cb46-494d-9c63-6f71de88143d | robust-person-identification-a-wifi-vision | 2210.00127 | null | https://arxiv.org/abs/2210.00127v1 | https://arxiv.org/pdf/2210.00127v1.pdf | Robust Person Identification: A WiFi Vision-based Approach | Person re-identification (Re-ID) has become increasingly important as it supports a wide range of security applications. Traditional person Re-ID mainly relies on optical camera-based systems, which incur several limitations due to the changes in the appearance of people, occlusions, and human poses. In this work, we p... | ['Jie Yang', 'Yili Ren'] | 2022-09-30 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [-1.06757790e-01 -6.34620786e-01 1.74949169e-01 -1.01736479e-01
-3.80654693e-01 -5.60021162e-01 3.30122173e-01 -3.07798147e-01
-1.26368165e-01 2.31994122e-01 5.17877400e-01 1.05228096e-01
1.66711569e-01 -7.98384488e-01 -4.93112296e-01 -4.12234306e-01
-1.33108914e-01 1.55397311e-01 -1.23902507e-01 1.14933237... | [6.827883243560791, 0.5062359571456909] |
0944b745-57a4-4b18-a697-bfa335df868d | what-do-you-meme-generating-explanations-for | 2212.00715 | null | https://arxiv.org/abs/2212.00715v2 | https://arxiv.org/pdf/2212.00715v2.pdf | What do you MEME? Generating Explanations for Visual Semantic Role Labelling in Memes | Memes are powerful means for effective communication on social media. Their effortless amalgamation of viral visuals and compelling messages can have far-reaching implications with proper marketing. Previous research on memes has primarily focused on characterizing their affective spectrum and detecting whether the mem... | ['Tanmoy Chakraborty', 'Md. Shad Akhtar', 'Preslav Nakov', 'Tharun Suresh', 'Siddhant Agarwal', 'Shivam Sharma'] | 2022-12-01 | null | null | null | null | ['marketing', 'semantic-role-labeling'] | ['miscellaneous', 'natural-language-processing'] | [ 3.86762768e-01 3.32725734e-01 -3.05694431e-01 -1.78775787e-01
-5.56497812e-01 -8.00161719e-01 1.28566480e+00 5.04950821e-01
3.61720733e-02 9.25555885e-01 8.90697956e-01 -1.63330734e-01
2.21185118e-01 -7.39471674e-01 -5.42967856e-01 -4.17800277e-01
4.17339504e-01 2.86695123e-01 -2.89426506e-01 -7.12155581... | [8.517391204833984, 10.659186363220215] |
71a8501f-6167-4e4b-a6b2-98544a2c7a86 | semi-supervised-object-detection-based-on | 2210.10998 | null | https://arxiv.org/abs/2210.10998v1 | https://arxiv.org/pdf/2210.10998v1.pdf | Semi-supervised object detection based on single-stage detector for thighbone fracture localization | The thighbone is the largest bone supporting the lower body. If the thighbone fracture is not treated in time, it will lead to lifelong inability to walk. Correct diagnosis of thighbone disease is very important in orthopedic medicine. Deep learning is promoting the development of fracture detection technology. However... | ['Shaoquan Wang', 'Yueming Zhang', 'Bin Guan', 'Guoshan Zhanga', 'Jinkun Yao', 'Jinman Wei'] | 2022-10-20 | null | null | null | null | ['semi-supervised-object-detection', 'image-augmentation'] | ['computer-vision', 'computer-vision'] | [ 4.74219210e-03 1.75138086e-01 -4.65891242e-01 -2.06517592e-01
-9.07618761e-01 3.83118987e-01 -2.31770352e-01 -1.62307113e-01
-6.26136005e-01 6.07967556e-01 -1.28131593e-02 3.84170637e-02
5.83157875e-02 -8.94197762e-01 -5.32065570e-01 -7.79850483e-01
-5.46246171e-02 7.09020853e-01 7.45864749e-01 -6.94389939... | [14.926115036010742, -2.241628885269165] |
1fb86b69-b7dc-4ce9-8f20-7de76e212ee6 | cross-lingual-character-level-neural | 1708.09157 | null | https://arxiv.org/abs/1708.09157v3 | https://arxiv.org/pdf/1708.09157v3.pdf | Cross-lingual, Character-Level Neural Morphological Tagging | Even for common NLP tasks, sufficient supervision is not available in many languages -- morphological tagging is no exception. In the work presented here, we explore a transfer learning scheme, whereby we train character-level recurrent neural taggers to predict morphological taggings for high-resource languages and lo... | ['Ryan Cotterell', 'Georg Heigold'] | 2017-08-30 | null | null | null | null | ['morphological-tagging'] | ['natural-language-processing'] | [ 1.08586468e-01 8.81944671e-02 -4.84680772e-01 -8.82496014e-02
-1.02898037e+00 -7.24759817e-01 1.41107559e-01 3.63292962e-01
-8.97927105e-01 1.00451839e+00 3.66230339e-01 -5.49872220e-01
2.26840615e-01 -7.05965996e-01 -5.06923735e-01 -3.12475294e-01
4.56866175e-02 6.22596204e-01 4.43279535e-01 -1.73201039... | [10.476880073547363, 9.930620193481445] |
637c564e-cc7c-4e25-87e4-8183e87dc6f9 | high-resolution-face-age-editing | 2005.04410 | null | https://arxiv.org/abs/2005.04410v1 | https://arxiv.org/pdf/2005.04410v1.pdf | High Resolution Face Age Editing | Face age editing has become a crucial task in film post-production, and is also becoming popular for general purpose photography. Recently, adversarial training has produced some of the most visually impressive results for image manipulation, including the face aging/de-aging task. In spite of considerable progress, cu... | ['Gilles Puy', 'Yann Gousseau', 'Alasdair Newson', 'Xu Yao', 'Pierre Hellier'] | 2020-05-09 | null | null | null | null | ['face-age-editing'] | ['computer-vision'] | [ 6.59476876e-01 5.29876389e-02 2.23331392e-01 -3.99442762e-01
-2.60267466e-01 -2.50120252e-01 6.31312609e-01 -5.15925646e-01
-3.43140692e-01 6.90041959e-01 1.29411340e-01 2.28226796e-01
3.42712104e-01 -6.37977660e-01 -6.53814852e-01 -7.05773652e-01
1.33712992e-01 8.63025561e-02 -2.25133710e-02 -3.11765121... | [12.61470890045166, -0.26702800393104553] |
6d0c3c01-b7d5-422d-8e3b-87d87cfe328b | experimental-design-for-any-p-norm | 2305.01942 | null | https://arxiv.org/abs/2305.01942v1 | https://arxiv.org/pdf/2305.01942v1.pdf | Experimental Design for Any $p$-Norm | We consider a general $p$-norm objective for experimental design problems that captures some well-studied objectives (D/A/E-design) as special cases. We prove that a randomized local search approach provides a unified algorithm to solve this problem for all $p$. This provides the first approximation algorithm for the g... | ['Hong Zhou', 'Robert Wang', 'Lap Chi Lau'] | 2023-05-03 | null | null | null | null | ['experimental-design'] | ['methodology'] | [ 6.70137554e-02 -9.00431871e-02 -7.66713142e-01 -1.81134284e-01
-8.14227104e-01 -4.28630322e-01 -2.35709608e-01 -2.22623169e-01
-4.25563723e-01 9.99221623e-01 -2.81474441e-01 -3.84840965e-01
-7.72814989e-01 -3.52663040e-01 -9.32916641e-01 -9.21352208e-01
-5.13379514e-01 3.95660520e-01 -2.08221093e-01 -2.20076069... | [6.54437255859375, 4.317537784576416] |
4205d148-d015-470c-9c29-ce4df8c1a2dd | separable-hovernet-and-instance-yolo-for | 2203.00262 | null | https://arxiv.org/abs/2203.00262v1 | https://arxiv.org/pdf/2203.00262v1.pdf | Separable-HoverNet and Instance-YOLO for Colon Nuclei Identification and Counting | Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models in computational pathology (CPath). However, automatic recognition of different nuclei is faced with ... | ['Dong Hu', 'Min Wu', 'Lijian Mao', 'Liukun Zhang', 'Chunhui Lin'] | 2022-03-01 | null | null | null | null | ['explainable-models', 'nuclear-segmentation'] | ['computer-vision', 'medical'] | [ 1.42244086e-01 -4.91873268e-03 -2.28928402e-01 -1.32329121e-01
-9.09853935e-01 -7.94429243e-01 2.86912650e-01 6.99616134e-01
-5.02655029e-01 1.04564512e+00 -2.06068326e-02 -2.64776319e-01
-3.77219588e-01 -5.91173172e-01 1.14702638e-02 -1.26899052e+00
4.47253361e-02 8.71243060e-01 1.66781038e-01 8.75796005... | [15.098689079284668, -3.170558214187622] |
cd0d09f0-fda4-4c7e-8afd-6ea02ad0ac05 | neural-progressive-hedging-enforcing | 2202.13436 | null | https://arxiv.org/abs/2202.13436v1 | https://arxiv.org/pdf/2202.13436v1.pdf | Neural-Progressive Hedging: Enforcing Constraints in Reinforcement Learning with Stochastic Programming | We propose a framework, called neural-progressive hedging (NP), that leverages stochastic programming during the online phase of executing a reinforcement learning (RL) policy. The goal is to ensure feasibility with respect to constraints and risk-based objectives such as conditional value-at-risk (CVaR) during the exe... | ['Duc Thien Nguyen', 'Shiau Hong Lim', 'Laura Wynter', 'Supriyo Ghosh'] | 2022-02-27 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-2.45654643e-01 2.89328575e-01 -7.06981659e-01 -8.95273536e-02
-7.97813535e-01 -5.80537975e-01 5.39287567e-01 -6.34094998e-02
-4.72920150e-01 9.94293034e-01 2.78835267e-01 -8.95070374e-01
-6.57841086e-01 -7.60685265e-01 -5.55734336e-01 -6.41388953e-01
-4.07139927e-01 8.07995558e-01 -1.02839500e-01 -1.56530097... | [4.262150764465332, 2.5767571926116943] |
a3ed8adb-8029-47cf-98b8-5bfcd20372d3 | how-object-information-improves-skeleton | 2306.05844 | null | https://arxiv.org/abs/2306.05844v1 | https://arxiv.org/pdf/2306.05844v1.pdf | How Object Information Improves Skeleton-based Human Action Recognition in Assembly Tasks | As the use of collaborative robots (cobots) in industrial manufacturing continues to grow, human action recognition for effective human-robot collaboration becomes increasingly important. This ability is crucial for cobots to act autonomously and assist in assembly tasks. Recently, skeleton-based approaches are often u... | ['Horst-Michael Gross', 'Markus Eisenbach', 'Sebastian Baake', 'Mona Köhler', 'Dustin Aganian'] | 2023-06-09 | null | null | null | null | ['skeleton-based-action-recognition', 'action-classification', 'action-recognition-in-videos', 'action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.32973179e-01 1.28254980e-01 -5.08991443e-02 -1.68298125e-01
-2.99167693e-01 -1.93607569e-01 4.31749254e-01 -3.53575833e-02
-4.10092056e-01 4.10238445e-01 -1.71852335e-01 2.69974023e-01
-1.49222121e-01 -5.57724237e-01 -5.51402390e-01 -6.61511660e-01
1.17149577e-01 8.40071023e-01 7.70979226e-01 -2.88905442... | [7.4779486656188965, 0.13686013221740723] |
4c538e64-eb6c-4534-a708-19e1bf87088a | summary-level-training-of-sentence-rewriting | 1909.08752 | null | https://arxiv.org/abs/1909.08752v3 | https://arxiv.org/pdf/1909.08752v3.pdf | Summary Level Training of Sentence Rewriting for Abstractive Summarization | As an attempt to combine extractive and abstractive summarization, Sentence Rewriting models adopt the strategy of extracting salient sentences from a document first and then paraphrasing the selected ones to generate a summary. However, the existing models in this framework mostly rely on sentence-level rewards or sub... | ['Sang-goo Lee', 'Sanghwan Bae', 'Taeuk Kim', 'Jihoon Kim'] | 2019-09-19 | summary-level-training-of-sentence-rewriting-1 | https://aclanthology.org/D19-5402 | https://aclanthology.org/D19-5402.pdf | ws-2019-11 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 3.56248915e-01 1.93205476e-01 -4.60580319e-01 -5.29989779e-01
-1.17073286e+00 -5.85056722e-01 7.73641348e-01 2.18927145e-01
-5.92274249e-01 1.03326690e+00 7.26709366e-01 -2.03317806e-01
2.43874416e-01 -6.24200284e-01 -6.34499788e-01 -2.41695330e-01
3.79407942e-01 1.20626271e-01 4.99643981e-02 -4.79962587... | [12.220596313476562, 9.303250312805176] |
6b6d10fd-b3c5-4702-af72-151810df2718 | mitigating-molecular-aggregation-in-drug | 2306.02206 | null | https://arxiv.org/abs/2306.02206v1 | https://arxiv.org/pdf/2306.02206v1.pdf | Mitigating Molecular Aggregation in Drug Discovery with Predictive Insights from Explainable AI | As the importance of high-throughput screening (HTS) continues to grow due to its value in early stage drug discovery and data generation for training machine learning models, there is a growing need for robust methods for pre-screening compounds to identify and prevent false-positive hits. Small, colloidally aggregati... | ['Rebecca L. Davis', 'Pascal Friederich', 'Kaitlin A. Isfeld', 'Jonas Teufel', 'Hunter Sturm'] | 2023-06-03 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 5.22018969e-01 -1.71903893e-01 -1.83249250e-01 -4.89424542e-02
-5.85411012e-01 -8.15123439e-01 3.92154664e-01 9.81508791e-01
-1.89069360e-01 1.41857839e+00 7.37360725e-03 -6.59658015e-01
-6.56398907e-02 -8.03626359e-01 -5.43013215e-01 -7.23538041e-01
-3.52387249e-01 6.50532663e-01 2.26051167e-01 -8.51010613... | [5.049355506896973, 5.656099796295166] |
f5ab05ac-c48e-4ac0-bdb8-1c6d50fd3f4e | sharpy-shape-reconstruction-and-hand-pose | 2303.10042 | null | https://arxiv.org/abs/2303.10042v1 | https://arxiv.org/pdf/2303.10042v1.pdf | ShaRPy: Shape Reconstruction and Hand Pose Estimation from RGB-D with Uncertainty | Despite their potential, markerless hand tracking technologies are not yet applied in practice to the diagnosis or monitoring of the activity in inflammatory musculoskeletal diseases. One reason is that the focus of most methods lies in the reconstruction of coarse, plausible poses for gesture recognition or AR/VR appl... | ['Marc Stamminger', 'Bernhard Egger', 'Martin Vossiek', 'Georg Schett', 'Arnd Kleyer', 'Simon Heinrich', 'Johanna Bräunig', 'Birte Coppers', 'Anna-Maria Liphardt', 'Vanessa Wirth'] | 2023-03-17 | null | null | null | null | ['pose-tracking', 'keypoint-detection', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.61805773e-01 -3.36230942e-03 -1.24326564e-01 4.03761938e-02
-1.04250944e+00 -3.80348325e-01 1.95784837e-01 2.60391623e-01
-5.54612994e-01 4.23257291e-01 2.11320609e-01 -1.41685247e-01
-3.75603676e-01 -1.10141166e-01 -3.42682034e-01 -6.33319080e-01
6.62397891e-02 9.84277844e-01 2.66496003e-01 -6.92947581... | [6.736443042755127, -0.806222677230835] |
b0f4777d-f50d-4b37-86c6-7fd70fe66919 | clue-me-in-semi-supervised-fgvc-with-out-of | 2112.02825 | null | https://arxiv.org/abs/2112.02825v1 | https://arxiv.org/pdf/2112.02825v1.pdf | Clue Me In: Semi-Supervised FGVC with Out-of-Distribution Data | Despite great strides made on fine-grained visual classification (FGVC), current methods are still heavily reliant on fully-supervised paradigms where ample expert labels are called for. Semi-supervised learning (SSL) techniques, acquiring knowledge from unlabeled data, provide a considerable means forward and have sho... | ['Jun Guo', 'Yi-Zhe Song', 'Zhanyu Ma', 'Dongliang Chang', 'Ruoyi Du'] | 2021-12-06 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [-4.86770831e-02 -1.13738864e-03 -4.43765283e-01 -5.99704027e-01
-3.59455884e-01 -7.74640024e-01 7.90668786e-01 -3.00172642e-02
-2.86143541e-01 7.80886889e-01 -6.63666278e-02 -3.16228807e-01
-8.42199922e-02 -5.51008999e-01 -7.30141819e-01 -8.04944456e-01
1.32240551e-02 4.62415963e-01 1.66520089e-01 1.50569350... | [9.59261417388916, 2.6681599617004395] |
1d1bcdbf-f8a2-4fdf-aed7-7bc363abaaa7 | the-case-for-fully-bayesian-optimisation-in | 2208.13960 | null | https://arxiv.org/abs/2208.13960v1 | https://arxiv.org/pdf/2208.13960v1.pdf | The case for fully Bayesian optimisation in small-sample trials | While sample efficiency is the main motive for use of Bayesian optimisation when black-box functions are expensive to evaluate, the standard approach based on type II maximum likelihood (ML-II) may fail and result in disappointing performance in small-sample trials. The paper provides three compelling reasons to adopt ... | ['Yuji Saikai'] | 2022-08-30 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 1.23985112e-01 2.13573217e-01 -2.36239716e-01 -5.01260817e-01
-7.23530412e-01 -4.05029297e-01 6.26900077e-01 2.10196212e-01
-9.14121866e-01 8.74050677e-01 1.21383905e-01 -8.88582230e-01
-5.37748814e-01 -3.88962030e-01 -6.02804661e-01 -6.62782967e-01
1.45771444e-01 2.15147346e-01 -2.98952349e-02 2.64047146... | [7.795362949371338, 5.096977710723877] |
a1fa9427-70b8-4cbe-bed0-bc770c1aba75 | targeting-underrepresented-populations-in | 2108.12112 | null | https://arxiv.org/abs/2108.12112v1 | https://arxiv.org/pdf/2108.12112v1.pdf | Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approach | The limited representation of minorities and disadvantaged populations in large-scale clinical and genomics research has become a barrier to translating precision medicine research into practice. Due to heterogeneity across populations, risk prediction models are often found to be underperformed in these underrepresent... | ['Rui Duan', 'Tianxi Cai', 'Sai Li'] | 2021-08-27 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 2.20490992e-01 3.17234129e-01 -7.52552569e-01 -4.90750968e-01
-1.11036694e+00 -6.27686828e-02 -6.57497644e-02 4.88498777e-01
-4.18559819e-01 1.01542997e+00 4.53958899e-01 -4.91301894e-01
-2.57641971e-01 -9.11053360e-01 -6.53217971e-01 -5.27616143e-01
-2.70539522e-01 4.79527026e-01 -6.15749300e-01 2.70670623... | [6.297177314758301, 6.404817581176758] |
348e3610-7b58-4644-8bca-c95acb971bb8 | guiding-computational-stance-detection-with | 2305.19845 | null | https://arxiv.org/abs/2305.19845v1 | https://arxiv.org/pdf/2305.19845v1.pdf | Guiding Computational Stance Detection with Expanded Stance Triangle Framework | Stance detection determines whether the author of a piece of text is in favor of, against, or neutral towards a specified target, and can be used to gain valuable insights into social media. The ubiquitous indirect referral of targets makes this task challenging, as it requires computational solutions to model semantic... | ['Nancy F. Chen', 'Hai Leong Chieu', 'Yong Keong Yap', 'Zhengyuan Liu'] | 2023-05-31 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 2.46841714e-01 2.67112434e-01 -8.18534017e-01 -6.33910060e-01
-5.34227967e-01 -7.78586090e-01 1.14417434e+00 5.19355834e-01
-4.76123542e-01 7.80526042e-01 5.74881554e-01 -2.18975723e-01
5.48296049e-02 -8.04098189e-01 -2.40846112e-01 -3.90858382e-01
4.53917027e-01 5.92966914e-01 4.54246581e-01 -5.00366032... | [8.928295135498047, 10.038768768310547] |
240d4e80-9104-4861-8bba-bc9832edd8bd | distilling-facial-knowledge-with-teacher | 2209.01115 | null | https://arxiv.org/abs/2209.01115v1 | https://arxiv.org/pdf/2209.01115v1.pdf | Distilling Facial Knowledge With Teacher-Tasks: Semantic-Segmentation-Features For Pose-Invariant Face-Recognition | This paper demonstrates a novel approach to improve face-recognition pose-invariance using semantic-segmentation features. The proposed Seg-Distilled-ID network jointly learns identification and semantic-segmentation tasks, where the segmentation task is then "distilled" (MobileNet encoder). Performance is benchmarked ... | ['Hafiz Malik', 'Rafi Ud Duala Refat', 'Zaid El Shair', 'Ali Hassani'] | 2022-09-02 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 2.32135206e-01 4.27021384e-01 -5.04626811e-01 -9.70449984e-01
-1.03870916e+00 -1.95411339e-01 3.80046844e-01 -6.52475893e-01
-6.57916307e-01 4.66715306e-01 3.75534706e-02 6.85972795e-02
1.01198480e-01 -4.08037454e-01 -1.05178750e+00 -5.61648786e-01
-2.26931617e-01 5.50505102e-01 -7.88070187e-02 -6.77822670... | [13.384145736694336, 0.6395379304885864] |
299b79be-4667-4621-bfd7-602b9c538c73 | schrodinger-spectrum-based-continuous-cuff | 2301.08439 | null | https://arxiv.org/abs/2301.08439v1 | https://arxiv.org/pdf/2301.08439v1.pdf | Schrödinger Spectrum based Continuous Cuff-less Blood Pressure Estimation using Clinically Relevant Features from PPG Signal and its Second Derivative | The presented study aims to estimate blood pressure (BP) using photoplethysmogram (PPG) signals while employing multiple machine learning models. The study proposes a novel algorithm for signal reconstruction, which utilizes the semi-classical signal analysis (SCSA) technique. The proposed algorithm optimises the semi-... | ['Jayant Kalra', 'Sayan Sarkar', 'Aayushman Ghosh'] | 2023-01-20 | null | null | null | null | ['blood-pressure-estimation'] | ['medical'] | [ 4.28877026e-01 5.91274239e-02 4.25782919e-01 -2.22030923e-01
-6.38827443e-01 -3.00143152e-01 5.25254942e-02 1.52217373e-01
-5.34008861e-01 1.04292071e+00 4.84325364e-02 -3.04380625e-01
-5.30883312e-01 -4.99141783e-01 -1.90043867e-01 -8.01410198e-01
-5.22371233e-01 3.22530419e-01 -3.03062275e-02 4.79339669... | [14.05484676361084, 2.9198548793792725] |
b3b8c179-70d9-4819-9af2-7124a6c3eef2 | deep-learning-for-skin-lesion-classification | 1703.04364 | null | http://arxiv.org/abs/1703.04364v1 | http://arxiv.org/pdf/1703.04364v1.pdf | Deep Learning for Skin Lesion Classification | Melanoma, a malignant form of skin cancer is very threatening to life.
Diagnosis of melanoma at an earlier stage is highly needed as it has a very
high cure rate. Benign and malignant forms of skin cancer can be detected by
analyzing the lesions present on the surface of the skin using dermoscopic
images. In this work,... | ['P. Mirunalini', 'Aravindan Chandrabose', 'S. M. Jaisakthi', 'Vignesh Gokul'] | 2017-03-13 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 6.86040878e-01 1.71059012e-01 -2.79803902e-01 -1.32611558e-01
-4.30418938e-01 -3.01442623e-01 5.68357110e-01 2.09451348e-01
-5.03771901e-01 4.70599055e-01 -1.06092110e-01 -3.69479477e-01
3.04757953e-01 -8.87952805e-01 -2.90703207e-01 -7.18307912e-01
2.39865348e-01 -5.43028265e-02 1.87843040e-01 -1.43327981... | [15.669272422790527, -3.0046756267547607] |
41cb829b-fb7a-4669-aea3-822a316044e7 | gender-bias-in-transformer-models-a | 2306.10530 | null | https://arxiv.org/abs/2306.10530v1 | https://arxiv.org/pdf/2306.10530v1.pdf | Gender Bias in Transformer Models: A comprehensive survey | Gender bias in artificial intelligence (AI) has emerged as a pressing concern with profound implications for individuals' lives. This paper presents a comprehensive survey that explores gender bias in Transformer models from a linguistic perspective. While the existence of gender bias in language models has been acknow... | ['Farhana Ferdousi Liza', 'Palla Vijay', 'Yericherla Deepak Joel', 'Praneeth Nemani'] | 2023-06-18 | null | null | null | null | ['machine-translation'] | ['natural-language-processing'] | [ 5.15702739e-02 4.20856446e-01 -7.32692063e-01 -7.08016336e-01
-3.79238814e-01 -7.66505241e-01 8.51194203e-01 3.14762235e-01
-6.87705100e-01 6.92154706e-01 5.35141647e-01 -6.45787239e-01
-1.31445065e-01 -4.68599409e-01 -2.09395289e-02 -3.17924887e-01
3.47766608e-01 7.14461088e-01 -7.67810345e-01 -3.40125263... | [9.379997253417969, 10.131121635437012] |
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