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18fbdd78-e33e-4d99-a622-64e69112fbed | deep-anomaly-detection-and-search-via | 2208.14834 | null | https://arxiv.org/abs/2208.14834v2 | https://arxiv.org/pdf/2208.14834v2.pdf | Deep Anomaly Detection and Search via Reinforcement Learning | Semi-supervised Anomaly Detection (AD) is a kind of data mining task which aims at learning features from partially-labeled datasets to help detect outliers. In this paper, we classify existing semi-supervised AD methods into two categories: unsupervised-based and supervised-based, and point out that most of them suffe... | ['Yang Yu', 'Zongzhang Zhang', 'Feng Mao', 'Dawei Wang', 'Chao Chen'] | 2022-08-31 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [-0.1101412 0.07243813 -0.02260375 -0.50242835 -0.6819138 -0.2685731
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0.12230595 0.03135561 0.40430695 0.62741816 -1.708849 -0.01310287
1.2057859 1.346499 -0.58... | [7.613340854644775, 2.426100730895996] |
1e8c10b6-b34b-4d26-8d2e-074d4c770a35 | ggadn-guided-generative-adversarial-dehazing | null | null | https://link.springer.com/article/10.1007/s00500-021-06049-w | https://link.springer.com/content/pdf/10.1007/s00500-021-06049-w.pdf | GGADN: Guided generative adversarial dehazing network | Image dehazing has always been a challenging topic in image processing. The development of deep learning methods,
especially the generative adversarial networks (GAN), provides a new way for image dehazing. In recent years, many deep
learning methods based on GAN have been applied to image dehazing. However, GAN has ... | ['Jian Zhang1 · Qinqin Dong2 · Wanjuan Song3'] | 2021-07-13 | null | null | null | journal-2021-7 | ['image-dehazing'] | ['computer-vision'] | [ 3.75751197e-01 -1.97984260e-02 4.41778153e-01 1.02582119e-01
-1.79363415e-01 -1.24636732e-01 4.72050637e-01 -3.71625006e-01
-2.51290321e-01 7.90871799e-01 2.22669199e-01 1.03164479e-01
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5.28428912e-01 -2.07782000e-01 4.12254125e-01 -4.04359311... | [10.923036575317383, -3.040510416030884] |
f6560cbc-139a-48f3-863c-9a7ce20cc4ba | explainable-automated-coding-of-clinical | 2010.15728 | null | https://arxiv.org/abs/2010.15728v4 | https://arxiv.org/pdf/2010.15728v4.pdf | Explainable Automated Coding of Clinical Notes using Hierarchical Label-wise Attention Networks and Label Embedding Initialisation | Diagnostic or procedural coding of clinical notes aims to derive a coded summary of disease-related information about patients. Such coding is usually done manually in hospitals but could potentially be automated to improve the efficiency and accuracy of medical coding. Recent studies on deep learning for automated med... | ['Honghan Wu', 'William Whiteley', 'Víctor Suárez-Paniagua', 'Hang Dong'] | 2020-10-29 | null | null | null | null | ['medical-code-prediction'] | ['medical'] | [ 3.88454944e-01 6.11286700e-01 -1.88603461e-01 -5.68856955e-01
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-9.72961932e-02 8.52890074e-01 -4.59943950e-01 1.75654650... | [8.012855529785156, 6.793702125549316] |
228fb2be-5420-402e-8335-c1fd70ee9dbf | a-3d-cnn-network-with-bert-for-automatic | 2106.14403 | null | https://arxiv.org/abs/2106.14403v3 | https://arxiv.org/pdf/2106.14403v3.pdf | A 3D CNN Network with BERT For Automatic COVID-19 Diagnosis From CT-Scan Images | We present an automatic COVID1-19 diagnosis framework from lung CT-scan slice images. In this framework, the slice images of a CT-scan volume are first proprocessed using segmentation techniques to filter out images of closed lung, and to remove the useless background. Then a resampling method is used to select one or ... | ['Jingfeng Liu', 'Weijun Tan'] | 2021-06-28 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 4.76240307e-01 1.68292359e-01 -1.65407762e-01 -4.10634369e-01
-8.28300536e-01 -2.01783106e-01 1.31957084e-01 5.13621330e-01
-6.91711009e-01 5.03056765e-01 -3.08673307e-02 -2.20686197e-01
-2.42037266e-01 -8.56776655e-01 -2.72422194e-01 -9.18788612e-01
-2.48941317e-01 5.95081568e-01 7.05630839e-01 5.27289689... | [15.21506118774414, -2.143176555633545] |
a2583b87-14e2-4beb-88e3-f2f443a06ff7 | document-image-classification-with-a-specific | 1601.03295 | null | http://arxiv.org/abs/1601.03295v1 | http://arxiv.org/pdf/1601.03295v1.pdf | Document image classification, with a specific view on applications of patent images | The main focus of this paper is document image classification and retrieval,
where we analyze and compare different parameters for the RunLeght Histogram
(RL) and Fisher Vector (FV) based image representations. We do an exhaustive
experimental study using different document image datasets, including the MARG
benchmarks... | ['Gabriela Csurka'] | 2016-01-13 | null | null | null | null | ['document-image-classification'] | ['computer-vision'] | [ 4.06540513e-01 -5.06818652e-01 -4.76018310e-01 -3.44940603e-01
-8.35595489e-01 -9.44112778e-01 9.45658147e-01 1.74048305e-01
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-7.40874350e-01 -6.07390821e-01 -5.48153400e-01 -6.63327515e-01
2.24752814e-01 5.61105907e-01 8.04093704e-02 3.30362707... | [10.881843566894531, 0.5427860021591187] |
b856125f-c2ce-4254-b72f-e8f4bf7b1467 | speak2label-using-domain-knowledge-for | 2004.05973 | null | https://arxiv.org/abs/2004.05973v4 | https://arxiv.org/pdf/2004.05973v4.pdf | Speak2Label: Using Domain Knowledge for Creating a Large Scale Driver Gaze Zone Estimation Dataset | Labelling of human behavior analysis data is a complex and time consuming task. In this paper, a fully automatic technique for labelling an image based gaze behavior dataset for driver gaze zone estimation is proposed. Domain knowledge is added to the data recording paradigm and later labels are generated in an automat... | ['Sarthak Gupta', 'Shreya Ghosh', 'Abhinav Dhall', 'Nicu Sebe', 'Garima Sharma'] | 2020-04-13 | null | null | null | null | ['eye-tracking'] | ['computer-vision'] | [ 2.06142008e-01 2.73655832e-01 -4.07129079e-02 -7.32391655e-01
-3.26155633e-01 -2.96855986e-01 4.38257813e-01 -3.08549374e-01
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-4.44843136e-02 -1.39577150e-01 -4.02490735e-01 -8.16624880e-01
3.77440304e-01 -3.27000439e-01 1.60096094e-01 -3.25343400... | [14.01655101776123, 0.12011481076478958] |
842d8941-538f-4671-b0b1-9f4e911cfe71 | a-multi-media-approach-to-cross-lingual | null | null | https://aclanthology.org/P16-1006 | https://aclanthology.org/P16-1006.pdf | A Multi-media Approach to Cross-lingual Entity Knowledge Transfer | null | ['Shih-Fu Chang', 'Heng Ji', 'Nima Pourdamghani', 'Kevin Knight', 'Xiaoman Pan', 'Di Lu'] | 2016-08-01 | null | null | null | acl-2016-8 | ['cross-lingual-entity-linking'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.326599597930908, 3.7763895988464355] |
43a1d6ec-37b4-4dce-bfcb-948b21bf8730 | pyvhr-a-python-framework-for-remote | null | null | https://peerj.com/articles/cs-929/ | https://peerj.com/articles/cs-929/ | pyVHR: a Python framework for remote photoplethysmography | Remote photoplethysmography (rPPG) aspires to automatically estimate heart rate (HR) variability from videos in realistic environments. A number of effective methods relying on data-driven, model-based and statistical approaches have emerged in the past two decades. They exhibit increasing ability to estimate the blood... | ['Edoardo Mortara', 'Raffaella Lanzarotti', 'Giuliano Grossi', 'Alessandro D’Amelio\u200b', 'Vittorio Cuculo', 'Donatello Conte', 'Giuseppe Boccignone'] | 2022-04-15 | null | null | null | peerj-computer-science-2022-4 | ['physiological-computing', 'photoplethysmography-ppg-heart-rate', 'photoplethysmography-ppg', 'heart-rate-variability', 'heart-rate-estimation'] | ['computer-vision', 'medical', 'medical', 'medical', 'medical'] | [ 1.77637324e-01 -1.97741807e-01 1.85474604e-01 -3.63389760e-01
-6.34084702e-01 -2.73768067e-01 2.61774927e-01 1.58882305e-01
-4.14381027e-01 6.72384322e-01 -7.97301307e-02 -2.48341933e-01
1.40006423e-01 -4.40621853e-01 -8.39091614e-02 -9.03047025e-01
-1.93824306e-01 9.88535285e-02 1.75995499e-01 1.51924565... | [13.870320320129395, 2.8020215034484863] |
ca213940-193d-43dc-830d-f31d2db09973 | clustering-multilayer-graphs-with-missing | 2103.03235 | null | https://arxiv.org/abs/2103.03235v1 | https://arxiv.org/pdf/2103.03235v1.pdf | Clustering multilayer graphs with missing nodes | Relationship between agents can be conveniently represented by graphs. When these relationships have different modalities, they are better modelled by multilayer graphs where each layer is associated with one modality. Such graphs arise naturally in many contexts including biological and social networks. Clustering is ... | ['Christophe Biernacki', 'Hemant Tyagi', 'Guillaume Braun'] | 2021-03-04 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 2.38283977e-01 2.53072590e-01 -2.33855888e-01 -1.10720195e-01
1.90359280e-01 -5.74264407e-01 8.94724905e-01 6.44874394e-01
-3.31161767e-01 7.07120240e-01 1.70658305e-01 -9.36375640e-04
-6.03155255e-01 -8.76193345e-01 -5.37390053e-01 -1.11637700e+00
-3.72536153e-01 7.12755144e-01 3.23877960e-01 -8.15826431... | [7.042267799377441, 5.249378204345703] |
4dd1d8d7-11b2-41b9-9079-26996e613d0e | interpretable-stochastic-model-predictive | 2205.07150 | null | https://arxiv.org/abs/2205.07150v1 | https://arxiv.org/pdf/2205.07150v1.pdf | Interpretable Stochastic Model Predictive Control using Distributional Reinforced Estimation for Quadrotor Tracking Systems | This paper presents a novel trajectory tracker for autonomous quadrotor navigation in dynamic and complex environments. The proposed framework integrates a distributional Reinforcement Learning (RL) estimator for unknown aerodynamic effects into a Stochastic Model Predictive Controller (SMPC) for trajectory tracking. A... | ['David Boyle', 'Qiuchen Qian', "James O'Keeffe", 'Yanran Wang'] | 2022-05-14 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-3.02340448e-01 -2.48245467e-02 -2.61290431e-01 3.26196879e-01
-6.76950097e-01 -9.27468598e-01 6.47032738e-01 1.95250168e-01
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-6.00872278e-01 -4.83432323e-01 -9.74489212e-01 -1.06376493e+00
-1.14434829e-03 2.20160499e-01 -1.87350184e-01 -4.01710838... | [5.026538848876953, 2.349423408508301] |
cbcd6280-7901-4d13-a4a8-c057fb1b0857 | obeying-the-order-introducing-ordered | 2306.16916 | null | https://arxiv.org/abs/2306.16916v1 | https://arxiv.org/pdf/2306.16916v1.pdf | Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimisation | We introduce ordered transfer hyperparameter optimisation (OTHPO), a version of transfer learning for hyperparameter optimisation (HPO) where the tasks follow a sequential order. Unlike for state-of-the-art transfer HPO, the assumption is that each task is most correlated to those immediately before it. This matches ma... | ['Aaron Klein', 'David Salinas', 'François-Xavier Aubet', 'Huibin Shen', 'Sigrid Passano Hellan'] | 2023-06-29 | null | null | null | null | ['transfer-learning', 'movie-recommendation'] | ['miscellaneous', 'miscellaneous'] | [ 9.94723067e-02 9.43047851e-02 -5.89528024e-01 -5.40001810e-01
-9.63882625e-01 -5.15939474e-01 5.19648194e-01 6.87145516e-02
-7.18132973e-01 9.88421202e-01 4.21607256e-01 -2.25978062e-01
-1.04344106e+00 -6.89678907e-01 -9.29820716e-01 -9.39021051e-01
-5.10028660e-01 1.26636302e+00 1.84672356e-01 -4.39237386... | [9.22208309173584, 4.069727420806885] |
0fa80836-f665-4ad2-adee-1c1d3cd0ec5f | m2-ctts-end-to-end-multi-scale-multi-modal | 2305.02269 | null | https://arxiv.org/abs/2305.02269v1 | https://arxiv.org/pdf/2305.02269v1.pdf | M2-CTTS: End-to-End Multi-scale Multi-modal Conversational Text-to-Speech Synthesis | Conversational text-to-speech (TTS) aims to synthesize speech with proper prosody of reply based on the historical conversation. However, it is still a challenge to comprehensively model the conversation, and a majority of conversational TTS systems only focus on extracting global information and omit local prosody fea... | ['Jiaen Liang', 'Jianqing Sun', 'JianHua Tao', 'Yingming Gao', 'Ya Li', 'Fengping Wang', 'Yayue Deng', 'Jinlong Xue'] | 2023-05-03 | null | null | null | null | ['text-to-speech-synthesis', 'speech-synthesis'] | ['speech', 'speech'] | [-9.69037041e-02 -1.92649230e-01 5.75937033e-02 -6.68113470e-01
-1.09790754e+00 -3.58803302e-01 5.26779711e-01 -3.33558500e-01
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3.07951421e-01 -3.61734331e-01 4.62182797e-02 -6.21506751e-01
3.47678483e-01 2.82694072e-01 1.78342223e-01 -7.36488581... | [14.737101554870605, 6.749542236328125] |
d5b4ff23-c62e-4726-9c65-71fab6975689 | tode-trans-transparent-object-depth | 2209.08455 | null | https://arxiv.org/abs/2209.08455v1 | https://arxiv.org/pdf/2209.08455v1.pdf | TODE-Trans: Transparent Object Depth Estimation with Transformer | Transparent objects are widely used in industrial automation and daily life. However, robust visual recognition and perception of transparent objects have always been a major challenge. Currently, most commercial-grade depth cameras are still not good at sensing the surfaces of transparent objects due to the refraction... | ['Bin Li', 'Zhen Kan', 'Dongxu Li', 'Beihao Xia', 'Shaochen Wang', 'Kang Chen'] | 2022-09-18 | null | null | null | null | ['transparent-objects', 'transparent-object-depth-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.55374372e-01 -7.71505088e-02 1.86709598e-01 -3.96229893e-01
-4.76855040e-01 -2.53485620e-01 3.32119077e-01 -1.56773895e-01
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1.67040512e-01 -8.29856932e-01 -4.92163658e-01 -9.22065198e-01
2.25706786e-01 2.11832318e-02 4.48928803e-01 3.27123702... | [7.472599506378174, -1.9088778495788574] |
53dbb001-bb20-4bc6-aee5-caf38c3fe0ce | novelty-controlled-paraphrase-generation-with | 2202.00535 | null | https://arxiv.org/abs/2202.00535v2 | https://arxiv.org/pdf/2202.00535v2.pdf | Novelty Controlled Paraphrase Generation with Retrieval Augmented Conditional Prompt Tuning | Paraphrase generation is a fundamental and long-standing task in natural language processing. In this paper, we concentrate on two contributions to the task: (1) we propose Retrieval Augmented Prompt Tuning (RAPT) as a parameter-efficient method to adapt large pre-trained language models for paraphrase generation; (2) ... | ['Shuyi Wang', 'Yong Zhuang', 'Jishnu Ray Chowdhury'] | 2022-02-01 | null | null | null | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 2.33303368e-01 -9.90613326e-02 -3.22597533e-01 -1.65560931e-01
-1.10441148e+00 -6.53572142e-01 9.54668880e-01 3.24219584e-01
-4.99339491e-01 8.20878267e-01 6.85142159e-01 -8.78045857e-02
-8.49682689e-02 -5.35421550e-01 -7.89394975e-01 -1.50611281e-01
3.83712143e-01 5.11298180e-01 1.27636388e-01 -5.52758336... | [11.796853065490723, 9.285239219665527] |
a0e07ad8-9721-41a3-8aa8-9a8100219950 | towards-dynamic-multi-modal-phenotyping-using | 2111.02710 | null | https://arxiv.org/abs/2111.02710v1 | https://arxiv.org/pdf/2111.02710v1.pdf | Towards dynamic multi-modal phenotyping using chest radiographs and physiological data | The healthcare domain is characterized by heterogeneous data modalities, such as imaging and physiological data. In practice, the variety of medical data assists clinicians in decision-making. However, most of the current state-of-the-art deep learning models solely rely upon carefully curated data of a single modality... | ['Farah E. Shamout', 'Krzysztof J. Geras', 'Nasir Hayat'] | 2021-11-04 | null | null | null | null | ['patient-phenotyping'] | ['medical'] | [ 1.39603794e-01 -1.03925318e-01 -1.30121931e-01 -3.37987572e-01
-1.03289080e+00 -4.29792911e-01 7.57343173e-02 4.19096053e-01
-2.88419038e-01 8.89075398e-01 1.44001335e-01 -6.04213119e-01
-4.31187272e-01 -4.04826522e-01 -3.86600822e-01 -8.73703539e-01
-2.58209914e-01 5.49305201e-01 -3.08946818e-01 3.96601617... | [15.053646087646484, -1.9701387882232666] |
979faaee-dc17-4387-9766-41488866ac09 | improving-answer-selection-and-answer | null | null | https://aclanthology.org/D19-1604 | https://aclanthology.org/D19-1604.pdf | Improving Answer Selection and Answer Triggering using Hard Negatives | In this paper, we establish the effectiveness of using hard negatives, coupled with a siamese network and a suitable loss function, for the tasks of answer selection and answer triggering. We show that the choice of sampling strategy is key for achieving improved performance on these tasks. Evaluating on recent answer ... | ['Nikhil Rasiwasia', 'Shweta Garg', 'Sawan Kumar', 'Kartik Mehta'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['answer-selection'] | ['natural-language-processing'] | [ 2.27750540e-01 1.87704507e-02 -1.63612366e-01 -5.58721185e-01
-1.69705403e+00 -7.64915228e-01 5.24896622e-01 3.96471322e-01
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-1.48795277e-01 -7.84507096e-01 -7.92582393e-01 -3.39384824e-01
1.82719290e-01 8.62450898e-01 4.84986812e-01 -7.10471570... | [11.320474624633789, 8.117355346679688] |
9220d84c-6abb-41f5-970d-48bc989bf534 | simlm-pre-training-with-representation | 2207.02578 | null | https://arxiv.org/abs/2207.02578v2 | https://arxiv.org/pdf/2207.02578v2.pdf | SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval | In this paper, we propose SimLM (Similarity matching with Language Model pre-training), a simple yet effective pre-training method for dense passage retrieval. It employs a simple bottleneck architecture that learns to compress the passage information into a dense vector through self-supervised pre-training. We use a r... | ['Furu Wei', 'Rangan Majumder', 'Daxin Jiang', 'Linjun Yang', 'Binxing Jiao', 'Xiaolong Huang', 'Nan Yang', 'Liang Wang'] | 2022-07-06 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-2.70341575e-01 -5.28387725e-01 -5.42450130e-01 -1.74756750e-01
-1.69681621e+00 -9.07599092e-01 7.70510435e-01 4.71429676e-01
-8.18574488e-01 7.20204771e-01 6.05026841e-01 -5.02095938e-01
8.02042782e-02 -6.66333735e-01 -7.80100524e-01 -3.40451956e-01
1.70049027e-01 8.05892646e-01 2.65471131e-01 -4.54609662... | [11.430153846740723, 7.754085540771484] |
34ec19f6-8418-48ec-995c-b7cd9290c199 | chatgpt-may-pass-the-bar-exam-soon-but-has-a | 2304.12202 | null | https://arxiv.org/abs/2304.12202v1 | https://arxiv.org/pdf/2304.12202v1.pdf | ChatGPT may Pass the Bar Exam soon, but has a Long Way to Go for the LexGLUE benchmark | Following the hype around OpenAI's ChatGPT conversational agent, the last straw in the recent development of Large Language Models (LLMs) that demonstrate emergent unprecedented zero-shot capabilities, we audit the latest OpenAI's GPT-3.5 model, `gpt-3.5-turbo', the first available ChatGPT model, in the LexGLUE benchma... | ['Ilias Chalkidis'] | 2023-03-09 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [-4.14922595e-01 4.53110695e-01 -3.53798181e-01 -1.51472986e-01
-1.24243724e+00 -5.97428799e-01 7.88831413e-01 -1.44127965e-01
-2.05091029e-01 6.51604414e-01 4.56697106e-01 -1.07992661e+00
3.43620777e-01 -2.60432005e-01 -7.34632492e-01 -3.44178855e-01
-3.69227976e-02 7.51352668e-01 1.44498453e-01 -8.76284420... | [11.837773323059082, 8.313521385192871] |
66c64c55-eda0-44f7-b7ec-276f404a6a4f | o-cnn-octree-based-convolutional-neural | 1712.01537 | null | http://arxiv.org/abs/1712.01537v1 | http://arxiv.org/pdf/1712.01537v1.pdf | O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis | We present O-CNN, an Octree-based Convolutional Neural Network (CNN) for 3D
shape analysis. Built upon the octree representation of 3D shapes, our method
takes the average normal vectors of a 3D model sampled in the finest leaf
octants as input and performs 3D CNN operations on the octants occupied by the
3D shape surf... | ['Chun-Yu Sun', 'Yu-Xiao Guo', 'Peng-Shuai Wang', 'Yang Liu', 'Xin Tong'] | 2017-12-05 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [-3.85348052e-01 -7.77009055e-02 1.22473978e-01 -2.83224404e-01
-1.33017659e-01 -5.93788028e-01 9.72766206e-02 2.81064510e-01
-9.03269276e-02 -1.24132648e-01 -2.50362959e-02 -3.69000673e-01
1.42316461e-01 -1.49849534e+00 -5.73948443e-01 -1.90562978e-01
-3.93093109e-01 8.11147273e-01 5.65026164e-01 -1.13112079... | [8.054408073425293, -3.6952645778656006] |
31a1fc97-2e55-4e7c-894c-bc5eaf06db35 | quantum-kernel-mixtures-for-probabilistic | 2305.18204 | null | https://arxiv.org/abs/2305.18204v1 | https://arxiv.org/pdf/2305.18204v1.pdf | Quantum Kernel Mixtures for Probabilistic Deep Learning | This paper presents a novel approach to probabilistic deep learning (PDL), quantum kernel mixtures, derived from the mathematical formalism of quantum density matrices, which provides a simpler yet effective mechanism for representing joint probability distributions of both continuous and discrete random variables. The... | ['Joseph A. Gallego-Mejia', 'Raúl Ramos-Pollán', 'Fabio A. González'] | 2023-05-26 | null | null | null | null | ['probabilistic-deep-learning', 'density-estimation'] | ['computer-vision', 'methodology'] | [ 1.11188136e-01 2.03226238e-01 -1.15177736e-01 -3.09108824e-01
-8.73301864e-01 -5.04134357e-01 1.13176680e+00 -2.04960391e-01
-2.51657665e-01 9.38509285e-01 2.95204632e-02 -2.17106417e-01
-1.65130183e-01 -1.05251491e+00 -7.87374258e-01 -1.07484210e+00
1.10420741e-01 7.22149909e-01 -8.94377157e-02 4.79724765... | [6.977067470550537, 3.942051887512207] |
c87919bd-8faa-465d-b3ea-4c2292c91150 | clac-at-semeval-2016-task-11-exploring | 1709.02843 | null | http://arxiv.org/abs/1709.02843v1 | http://arxiv.org/pdf/1709.02843v1.pdf | CLaC at SemEval-2016 Task 11: Exploring linguistic and psycho-linguistic Features for Complex Word Identification | This paper describes the system deployed by the CLaC-EDLK team to the
"SemEval 2016, Complex Word Identification task". The goal of the task is to
identify if a given word in a given context is "simple" or "complex". Our
system relies on linguistic features and cognitive complexity. We used several
supervised models, h... | ['Leila Kosseim', 'Elnaz Davoodi'] | 2017-09-08 | clac-at-semeval-2016-task-11-exploring-1 | https://aclanthology.org/S16-1151 | https://aclanthology.org/S16-1151.pdf | semeval-2016-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-1.98493421e-01 -2.79839840e-02 1.66183144e-01 -2.39717185e-01
-5.98849416e-01 -8.27184141e-01 9.51280355e-01 4.87561584e-01
-1.04568803e+00 4.98246461e-01 3.72040153e-01 -5.34554839e-01
-1.47355452e-01 -3.71141165e-01 -6.82003275e-02 -1.74568385e-01
2.85468809e-02 8.20065022e-01 7.77027160e-02 -5.05107939... | [10.614543914794922, 10.468265533447266] |
ccb409b6-98cc-4824-9fa8-a1e92b2f4858 | data-dependent-regret-guarantees-against | 2303.06526 | null | https://arxiv.org/abs/2303.06526v1 | https://arxiv.org/pdf/2303.06526v1.pdf | Data Dependent Regret Guarantees Against General Comparators for Full or Bandit Feedback | We study the adversarial online learning problem and create a completely online algorithmic framework that has data dependent regret guarantees in both full expert feedback and bandit feedback settings. We study the expected performance of our algorithm against general comparators, which makes it applicable for a wide ... | ['Hakan Gokcesu', 'Kaan Gokcesu'] | 2023-03-12 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 2.44527295e-01 1.63055539e-01 -6.73243523e-01 -3.90360028e-01
-9.97451544e-01 -1.01930106e+00 1.05040237e-01 3.30453932e-01
-6.54876828e-01 1.08397305e+00 -8.43604654e-02 -5.94965875e-01
-6.30113363e-01 -7.87737131e-01 -1.06454289e+00 -8.41177046e-01
-1.27742603e-01 6.12481117e-01 2.73542076e-01 -3.29395264... | [4.576440811157227, 3.3414766788482666] |
94f78718-f120-448e-a192-3e0415216a0b | a-reliable-self-adaptive-face-identification | 2109.01212 | null | https://arxiv.org/abs/2109.01212v1 | https://arxiv.org/pdf/2109.01212v1.pdf | A Reliable, Self-Adaptive Face Identification Framework via Lyapunov Optimization | Realtime face identification (FID) from a video feed is highly computation-intensive, and may exhaust computation resources if performed on a device with a limited amount of resources (e.g., a mobile device). In general, FID performs better when images are sampled at a higher rate, minimizing false negatives. However, ... | ['Jae young Bang', 'Joongheon Kim', 'Dohyeon Kim'] | 2021-09-02 | null | null | null | null | ['face-identification'] | ['computer-vision'] | [-4.59753862e-03 -5.22857249e-01 -1.59053370e-01 -1.40628278e-01
-5.04755437e-01 -4.02840912e-01 -3.85393091e-02 -4.93506454e-02
-5.09398222e-01 4.92547840e-01 -6.55374050e-01 -5.48799872e-01
2.84949709e-02 -5.89576483e-01 -4.39594656e-01 -6.60886407e-01
-2.27481872e-01 1.38324365e-01 2.30067119e-01 2.48660088... | [8.459314346313477, -0.3199966549873352] |
31418f06-5d8c-4931-964c-88cddc04aacb | real-a-representative-error-driven-approach | 2307.00968 | null | https://arxiv.org/abs/2307.00968v2 | https://arxiv.org/pdf/2307.00968v2.pdf | REAL: A Representative Error-Driven Approach for Active Learning | Given a limited labeling budget, active learning (AL) aims to sample the most informative instances from an unlabeled pool to acquire labels for subsequent model training. To achieve this, AL typically measures the informativeness of unlabeled instances based on uncertainty and diversity. However, it does not consider ... | ['Xiaoyong Du', 'Yueguo Chen', 'Lizi Liao', 'Yong Wang', 'Cheng Chen'] | 2023-07-03 | null | null | null | null | ['active-learning', 'text-classification', 'active-learning'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [-4.11618203e-02 3.91002238e-01 -7.36994982e-01 -7.00725436e-01
-1.42939985e+00 -4.43121135e-01 1.95182085e-01 4.43985820e-01
-3.81632537e-01 9.73095179e-01 -3.67046952e-01 -2.82220066e-01
-2.37714589e-01 -6.34404361e-01 -6.51722312e-01 -7.81309664e-01
2.49571249e-01 7.81452298e-01 -9.16175917e-03 3.94405603... | [9.465177536010742, 3.817034959793091] |
17bd20b4-3f9d-49ee-8b67-b62c5c33c8fd | structure-based-approach-can-identify-driver | 2303.04888 | null | https://arxiv.org/abs/2303.04888v1 | https://arxiv.org/pdf/2303.04888v1.pdf | Structure-based approach can identify driver nodes in ensembles of biologically-inspired Boolean networks | Because the attractors of biological networks reflect stable behaviors (e.g., cell phenotypes), identifying control interventions that can drive a system towards its attractors (attractor control) is of particular relevance when controlling biological systems. Driving a network's feedback vertex set (FVS) by node-state... | ['Réka Albert', 'Jorge Gómez Tejeda Zañudo', 'Eli Newby'] | 2023-03-08 | null | null | null | null | ['feedback-vertex-set-fvs'] | ['graphs'] | [ 4.26112890e-01 3.55387956e-01 -1.64244071e-01 1.18812717e-01
4.07188237e-01 -8.66192877e-01 9.43848908e-01 3.53449434e-01
5.13731502e-02 9.37602282e-01 2.99510080e-02 -3.70874405e-01
-6.54138505e-01 -1.11511981e+00 -5.26205897e-01 -9.02463019e-01
-5.09750545e-01 2.30797514e-01 6.04886651e-01 -7.09884107... | [6.307715892791748, 4.61605978012085] |
6964483f-f1e3-4a86-9257-8d765a5b27b7 | boundary-unlearning-rapid-forgetting-of-deep | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_Boundary_Unlearning_Rapid_Forgetting_of_Deep_Networks_via_Shifting_the_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_Boundary_Unlearning_Rapid_Forgetting_of_Deep_Networks_via_Shifting_the_CVPR_2023_paper.pdf | Boundary Unlearning: Rapid Forgetting of Deep Networks via Shifting the Decision Boundary | The practical needs of the "right to be forgotten" and poisoned data removal call for efficient machine unlearning techniques, which enable machine learning models to unlearn, or to forget a fraction of training data and its lineage. Recent studies on machine unlearning for deep neural networks (DNNs) attempt to de... | ['Chen Wang', 'Kai Peng', 'Gaoyang Liu', 'Weizhuo Gao', 'Min Chen'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['face-recognition'] | ['computer-vision'] | [ 2.41865978e-01 3.10924083e-01 -3.08943748e-01 -3.26207370e-01
-1.71004191e-01 -7.58138180e-01 8.07772875e-02 -2.73549557e-01
-8.14481676e-01 1.25751722e+00 -1.67555928e-01 -6.29637599e-01
-4.85563837e-02 -6.17962778e-01 -8.67877007e-01 -1.06341410e+00
4.01003540e-01 5.42207301e-01 -1.57307863e-01 4.27276105... | [9.547991752624512, 3.664921760559082] |
e75cb865-718c-4e6d-b0e3-e51fb33fea76 | on-field-player-workload-exposure-and-knee | 1809.08016 | null | https://arxiv.org/abs/1809.08016v3 | https://arxiv.org/pdf/1809.08016v3.pdf | On-field player workload exposure and knee injury risk monitoring via deep learning | In sports analytics, an understanding of accurate on-field 3D knee joint moments (KJM) could provide an early warning system for athlete workload exposure and knee injury risk. Traditionally, this analysis has relied on captive laboratory force plates and associated downstream biomechanical modeling, and many researche... | ['Ajmal Mian', 'William R. Johnson', 'Jacqueline A. Alderson', 'David G. Lloyd'] | 2018-09-21 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [-5.80687404e-01 -3.39810401e-01 -6.23165369e-01 2.07755610e-01
-8.50279093e-01 -1.17424922e-02 1.72287188e-02 2.91642904e-01
-8.07930410e-01 4.55148041e-01 4.47365403e-01 -2.28836760e-01
-2.64178574e-01 -5.07664800e-01 -9.66041327e-01 8.52442458e-02
-8.59187603e-01 3.78164023e-01 1.72106415e-01 -5.43955207... | [6.980124473571777, -0.025964412838220596] |
69886ae4-dddd-45d8-a0ab-4a4f12e03c6b | an-analysis-of-classification-approaches-for | 2301.13507 | null | https://arxiv.org/abs/2301.13507v1 | https://arxiv.org/pdf/2301.13507v1.pdf | An Analysis of Classification Approaches for Hit Song Prediction using Engineered Metadata Features with Lyrics and Audio Features | Hit song prediction, one of the emerging fields in music information retrieval (MIR), remains a considerable challenge. Being able to understand what makes a given song a hit is clearly beneficial to the whole music industry. Previous approaches to hit song prediction have focused on using audio features of a record. T... | ['Valerie J. Gillet', 'Frank Hopfgartner', 'David Cameron', 'Morgan Harvey', 'Mengyisong Zhao'] | 2023-01-31 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [ 9.72622707e-02 -5.70984304e-01 -5.95105469e-01 -1.24166653e-01
-9.79125857e-01 -7.43958652e-01 4.45995301e-01 2.90702969e-01
-3.92457128e-01 7.24452555e-01 6.65747583e-01 2.38852262e-01
-8.10879827e-01 -6.80692255e-01 -3.16900134e-01 -4.46665943e-01
-3.61612290e-01 4.35734600e-01 1.91448689e-01 1.00258783... | [15.929658889770508, 5.187254428863525] |
09b0061e-965e-4d67-ae27-26efa2baab9a | sample-based-uncertainty-quantification-with | 2209.08418 | null | https://arxiv.org/abs/2209.08418v2 | https://arxiv.org/pdf/2209.08418v2.pdf | Sample-based Uncertainty Quantification with a Single Deterministic Neural Network | Development of an accurate, flexible, and numerically efficient uncertainty quantification (UQ) method is one of fundamental challenges in machine learning. Previously, a UQ method called DISCO Nets has been proposed (Bouchacourt et al., 2016), which trains a neural network by minimizing the energy score. In this metho... | ['Chetan Gupta', 'Takuya Kanazawa'] | 2022-09-17 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [ 5.57019003e-02 1.21119745e-01 -3.33511233e-02 -3.37802380e-01
-9.22995269e-01 -6.01352632e-01 4.74340200e-01 1.44033328e-01
-3.54637206e-01 1.07431710e+00 -1.90713242e-01 -3.01401436e-01
-5.63042760e-01 -7.73802876e-01 -9.90639150e-01 -9.93146837e-01
5.66014014e-02 8.77697289e-01 -5.59041984e-02 -1.58230484... | [7.848121643066406, 3.8958232402801514] |
8d2df339-1be6-4090-8037-923387146923 | categorical-feature-compression-via | 1904.13389 | null | http://arxiv.org/abs/1904.13389v1 | http://arxiv.org/pdf/1904.13389v1.pdf | Categorical Feature Compression via Submodular Optimization | In the era of big data, learning from categorical features with very large
vocabularies (e.g., 28 million for the Criteo click prediction dataset) has
become a practical challenge for machine learning researchers and
practitioners. We design a highly-scalable vocabulary compression algorithm
that seeks to maximize the ... | ['Mohammadhossein Bateni', 'Afshin Rostamizadeh', 'Vahab S. Mirrokni', 'Hossein Esfandiari', 'Lin Chen', 'Thomas Fu'] | 2019-04-30 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [-7.15022348e-03 1.15252055e-01 -6.38414741e-01 -5.08449495e-01
-1.24857759e+00 -6.80544972e-01 -3.07336479e-01 5.53224027e-01
-7.54748404e-01 5.60914993e-01 -1.72614843e-01 -4.13192332e-01
-4.49051946e-01 -9.12132800e-01 -1.06550109e+00 -6.48104012e-01
-4.87816662e-01 9.53216851e-01 3.14907879e-02 6.67020380... | [6.780900955200195, 4.815514087677002] |
629d25f3-fd03-4b15-8306-89b279afec36 | deep-learning-for-background-replacement-in | null | null | https://www.sciltp.com/journals/ijndi/article/view/256 | https://www.sciltp.com/journals/ijndi/article/view/256/128 | Deep learning for Background Replacement in Video Conferencing | Background replacement is one of the most used features in video conferencing applications by many people, perhaps mainly for privacy protection, but also for other purposes such as branding, marketing and promoting professionalism. However, the existing applications in video conference tools have serious limitations. ... | ['Yongmin Li', 'Kiran Shahi'] | 2023-06-12 | null | null | null | international-journal-of-network-dynamics-and | ['video-background-subtraction', 'marketing'] | ['computer-vision', 'miscellaneous'] | [ 5.14317393e-01 -5.55446520e-02 -2.80529279e-02 -1.07481763e-01
-2.59131312e-01 -2.50126183e-01 4.03720021e-01 -3.55447203e-01
-4.74305838e-01 7.74879932e-01 -1.08759247e-01 -4.86891568e-01
3.46174210e-01 -5.51924348e-01 -7.13298261e-01 -8.79649460e-01
2.11012706e-01 -2.14160204e-01 7.34980524e-01 6.49726167... | [9.197328567504883, -0.5853133201599121] |
2cffb199-510f-4db3-8e62-0ab40f609183 | r3det-refined-single-stage-detector-with | 1908.05612 | null | https://arxiv.org/abs/1908.05612v6 | https://arxiv.org/pdf/1908.05612v6.pdf | R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object | Rotation detection is a challenging task due to the difficulties of locating the multi-angle objects and separating them effectively from the background. Though considerable progress has been made, for practical settings, there still exist challenges for rotating objects with large aspect ratio, dense distribution and ... | ['Tao He', 'Ziming Feng', 'Xue Yang', 'Junchi Yan'] | 2019-08-15 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-3.27038944e-01 -5.69218516e-01 8.80947039e-02 -2.59007722e-01
-7.37142205e-01 -3.18386972e-01 2.72653729e-01 -2.28294283e-01
-3.09573054e-01 1.80755660e-01 1.28631681e-01 -2.85438061e-01
-1.39930546e-01 -7.33480036e-01 -4.37090933e-01 -9.45338428e-01
-4.42279764e-02 2.14115173e-01 1.11878783e-01 -7.58576989... | [8.792088508605957, -0.7884263396263123] |
b29d5260-c9bb-41ae-947c-f0d005171672 | one2set-generating-diverse-keyphrases-as-a | 2105.11134 | null | https://arxiv.org/abs/2105.11134v1 | https://arxiv.org/pdf/2105.11134v1.pdf | One2Set: Generating Diverse Keyphrases as a Set | Recently, the sequence-to-sequence models have made remarkable progress on the task of keyphrase generation (KG) by concatenating multiple keyphrases in a predefined order as a target sequence during training. However, the keyphrases are inherently an unordered set rather than an ordered sequence. Imposing a predefined... | ['Qi Zhang', 'Yige Xu', 'Yichao Luo', 'Tao Gui', 'Jiacheng Ye'] | 2021-05-24 | null | https://aclanthology.org/2021.acl-long.354 | https://aclanthology.org/2021.acl-long.354.pdf | acl-2021-5 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 5.69706202e-01 -3.60943705e-01 -4.52556223e-01 -7.96029121e-02
-5.80543280e-01 -8.68557215e-01 4.54835862e-01 2.25522608e-01
-4.79646176e-01 9.65762675e-01 1.65736660e-01 -4.47413534e-01
-2.70163547e-02 -9.77324903e-01 -9.56601381e-01 -6.48451746e-01
2.18730256e-01 3.38703245e-01 4.79718387e-01 -4.67634201... | [12.298727989196777, 8.906655311584473] |
39e140fe-0efb-4bbf-9d4f-f238e9b56fff | online-bag-of-visual-words-generation-for | 2012.11552 | null | https://arxiv.org/abs/2012.11552v2 | https://arxiv.org/pdf/2012.11552v2.pdf | OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning | Learning image representations without human supervision is an important and active research field. Several recent approaches have successfully leveraged the idea of making such a representation invariant under different types of perturbations, especially via contrastive-based instance discrimination training. Although... | ['Patrick Pérez', 'Matthieu Cord', 'Nikos Komodakis', 'Gilles Puy', 'Andrei Bursuc', 'Spyros Gidaris'] | 2020-12-21 | obow-online-bag-of-visual-words-generation | http://openaccess.thecvf.com//content/CVPR2021/html/Gidaris_OBoW_Online_Bag-of-Visual-Words_Generation_for_Self-Supervised_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Gidaris_OBoW_Online_Bag-of-Visual-Words_Generation_for_Self-Supervised_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 3.95549834e-01 1.80655748e-01 -6.97833523e-02 -4.16809678e-01
-5.84991872e-01 -5.21283388e-01 8.43932927e-01 4.70934987e-01
-5.10747313e-01 3.99345011e-01 5.04198037e-02 -3.79240483e-01
-8.70110560e-03 -9.58948672e-01 -1.13891792e+00 -9.52942073e-01
2.37102322e-02 4.01286781e-01 4.42031085e-01 -4.49796438... | [9.724201202392578, 2.223015546798706] |
5fde8627-3785-4deb-aad7-9fd97ff91bea | s-clip-semi-supervised-vision-language-pre | 2305.14095 | null | https://arxiv.org/abs/2305.14095v1 | https://arxiv.org/pdf/2305.14095v1.pdf | S-CLIP: Semi-supervised Vision-Language Pre-training using Few Specialist Captions | Vision-language models, such as contrastive language-image pre-training (CLIP), have demonstrated impressive results in natural image domains. However, these models often struggle when applied to specialized domains like remote sensing, and adapting to such domains is challenging due to the limited number of image-text... | ['Jinwoo Shin', 'Kyungmin Lee', 'Minkyu Kim', 'Sangwoo Mo'] | 2023-05-23 | null | null | null | null | ['partial-label-learning', 'pseudo-label'] | ['methodology', 'miscellaneous'] | [ 7.92626381e-01 -1.26083001e-01 -3.02630037e-01 -4.33766127e-01
-1.31099939e+00 -6.84859216e-01 6.78620279e-01 2.56136596e-01
-6.43191099e-01 5.83027542e-01 -1.80563852e-01 -3.84699374e-01
5.05569205e-02 -4.38176692e-01 -1.03012311e+00 -5.66005290e-01
1.49219364e-01 4.25259441e-01 -9.73293558e-02 1.65650458... | [10.741477966308594, 1.3167554140090942] |
8eb341ab-d2b4-4532-951c-07c69ea5d0a2 | exploring-modality-agnostic-representations | 2106.01149 | null | https://arxiv.org/abs/2106.01149v1 | https://arxiv.org/pdf/2106.01149v1.pdf | Exploring modality-agnostic representations for music classification | Music information is often conveyed or recorded across multiple data modalities including but not limited to audio, images, text and scores. However, music information retrieval research has almost exclusively focused on single modality recognition, requiring development of separate models for each modality. Some multi... | ['Juan P. Bello', 'Magdalena Fuentes', 'Ho-Hsiang Wu'] | 2021-06-02 | null | null | null | null | ['music-classification', 'music-information-retrieval'] | ['music', 'music'] | [ 6.54603302e-01 -4.81629163e-01 -2.75299609e-01 -1.40672937e-01
-1.16808093e+00 -1.06205821e+00 8.07155967e-01 8.53980705e-02
-5.60793400e-01 4.05155331e-01 2.97631979e-01 3.47008556e-02
-3.76737386e-01 -4.65049356e-01 -4.64533091e-01 -4.85104948e-01
1.52291059e-01 3.32662046e-01 2.08826184e-01 -7.66870603... | [15.498393058776855, 5.097670555114746] |
b4cd2cf5-2679-4b23-89e9-12e6a4d107ea | voice-command-generation-using-progressive | 1903.07395 | null | http://arxiv.org/abs/1903.07395v1 | http://arxiv.org/pdf/1903.07395v1.pdf | Voice command generation using Progressive Wavegans | Generative Adversarial Networks (GANs) have become exceedingly popular in a
wide range of data-driven research fields, due in part to their success in
image generation. Their ability to generate new samples, often from only a
small amount of input data, makes them an exciting research tool in areas with
limited data re... | ['Björn Schuller', 'NIcholas Cummins', 'Thomas Wiest', 'Simone Hantke', 'Alice Baird', 'Judith Dineley'] | 2019-03-13 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 4.62349504e-01 4.10569817e-01 2.61735171e-01 -1.46342874e-01
-8.73741984e-01 -4.11565244e-01 7.95304120e-01 -4.56295073e-01
-1.12070227e-02 1.16697001e+00 5.77376604e-01 1.95973497e-02
3.10052246e-01 -8.48787129e-01 -5.90617180e-01 -7.57771432e-01
1.69153184e-01 2.09920615e-01 7.13780820e-02 -2.63584822... | [15.495232582092285, 5.999670505523682] |
ddc450da-ce1e-4d86-96b8-1551a09595b8 | reducing-labelled-data-requirement-for | 2102.12764 | null | https://arxiv.org/abs/2102.12764v1 | https://arxiv.org/pdf/2102.12764v1.pdf | Reducing Labelled Data Requirement for Pneumonia Segmentation using Image Augmentations | Deep learning semantic segmentation algorithms can localise abnormalities or opacities from chest radiographs. However, the task of collecting and annotating training data is expensive and requires expertise which remains a bottleneck for algorithm performance. We investigate the effect of image augmentations on reduci... | ['Amit Kharat', 'Aniruddha Pant', 'Viraj Kulkarni', 'Rohit Lokwani', 'Jitesh Seth'] | 2021-02-25 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 6.13510668e-01 1.49553061e-01 -2.04225853e-02 -5.17822683e-01
-1.08725214e+00 -8.44174147e-01 1.99820518e-01 3.67809057e-01
-8.12708437e-01 3.29433829e-01 4.19695042e-02 -6.75597847e-01
1.60161316e-01 -4.67266530e-01 -8.31944823e-01 -6.02884769e-01
2.51979113e-01 7.15144277e-01 5.41656017e-01 3.39530617... | [15.052009582519531, -2.0510826110839844] |
d4bb122a-c859-4fd2-af9e-035dbc48c0c4 | learning-deep-bilinear-transformation-for | 1911.03621 | null | https://arxiv.org/abs/1911.03621v1 | https://arxiv.org/pdf/1911.03621v1.pdf | Learning Deep Bilinear Transformation for Fine-grained Image Representation | Bilinear feature transformation has shown the state-of-the-art performance in learning fine-grained image representations. However, the computational cost to learn pairwise interactions between deep feature channels is prohibitively expensive, which restricts this powerful transformation to be used in deep neural netwo... | ['Zheng-Jun Zha', 'Heliang Zheng', 'Jiebo Luo', 'Jianlong Fu'] | 2019-11-09 | learning-deep-bilinear-transformation-for-1 | http://papers.nips.cc/paper/8680-learning-deep-bilinear-transformation-for-fine-grained-image-representation | http://papers.nips.cc/paper/8680-learning-deep-bilinear-transformation-for-fine-grained-image-representation.pdf | neurips-2019-12 | ['fine-grained-image-recognition'] | ['computer-vision'] | [-5.62282205e-02 -2.97908455e-01 1.23128414e-01 -6.73822582e-01
-7.43043482e-01 -6.49896204e-01 5.15529215e-01 -1.75202608e-01
-3.22379500e-01 5.31712234e-01 2.48084068e-01 -1.47232682e-01
-1.65590897e-01 -1.03514981e+00 -1.06515408e+00 -8.62965345e-01
7.96993300e-02 -6.25888780e-02 1.08722053e-01 -1.66109130... | [9.563851356506348, 2.0379958152770996] |
539135a8-f185-4a9d-b5db-46dd21b6b102 | advancements-in-noncontact-multiparameter | null | null | https://ieeexplore.ieee.org/document/5599853 | https://affect.media.mit.edu/pdfs/11.Poh-etal-TBME.pdf | Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam | We present a simple, low-cost method for measuring multiple physiological parameters using a basic webcam. By applying independent component analysis on the color channels in video recordings, we extracted the blood volume pulse from the facial regions. Heart rate (HR), respiratory rate, and HR variability (HRV, an ind... | ['Rosalind W. Picard', 'Daniel J. McDuff', 'Ming-Zher Poh'] | 2010-10-14 | null | null | null | ieee-transactions-on-biomedical-engineering-4 | ['photoplethysmography-ppg-heart-rate'] | ['medical'] | [ 1.41276687e-01 -2.98506826e-01 -1.62898302e-01 -4.04684663e-01
-2.87690014e-02 -4.22731996e-01 -3.32734197e-01 2.57630795e-02
-3.91946375e-01 8.06981623e-01 7.53800943e-02 2.23593995e-01
3.53108048e-01 -2.92583972e-01 2.99145937e-01 -6.84466481e-01
-3.52657109e-01 -3.95943701e-01 -4.74225551e-01 1.95562705... | [13.907142639160156, 2.8982560634613037] |
7e05c80e-5940-46b9-a794-217fd223233c | asm-adaptive-skinning-model-for-high-quality | 2304.09423 | null | https://arxiv.org/abs/2304.09423v1 | https://arxiv.org/pdf/2304.09423v1.pdf | ASM: Adaptive Skinning Model for High-Quality 3D Face Modeling | The research fields of parametric face models and 3D face reconstruction have been extensively studied. However, a critical question remains unanswered: how to tailor the face model for specific reconstruction settings. We argue that reconstruction with multi-view uncalibrated images demands a new model with stronger c... | ['Wei Yang', 'Zhongqian Sun', 'Jingkai Zhou', 'Xinghan Chen', 'Tianyang Shi', 'Hong Shang', 'Kai Yang'] | 2023-04-19 | null | null | null | null | ['3d-face-reconstruction', 'face-model', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.97558084e-02 -2.59992871e-02 8.41400027e-02 -2.22748250e-01
-5.69429874e-01 -4.82521325e-01 3.52044255e-01 -8.41426313e-01
1.01178311e-01 2.83919156e-01 1.14846043e-01 -3.35174077e-03
-9.81326476e-02 -7.34716713e-01 -7.51936972e-01 -6.14397943e-01
2.11365104e-01 6.94172561e-01 1.33162960e-01 -4.30559516... | [13.122142791748047, -0.04140983521938324] |
3c76810a-117e-4f2a-82f7-97ac4ad42cf4 | id-free-person-similarity-learning | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Shuai_Id-Free_Person_Similarity_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Shuai_Id-Free_Person_Similarity_Learning_CVPR_2022_paper.pdf | Id-Free Person Similarity Learning | Learning a unified person detection and re-identification model is a key component of modern trackers. However, training such models usually relies on the availability of training images / videos that are manually labeled with both person boxes and their identities. In this work, we explore training such a model by... | ['Joseph Tighe', 'Kaustav Kundu', 'Xinyu Li', 'Bing Shuai'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['person-search'] | ['computer-vision'] | [ 1.76788852e-01 -2.58958280e-01 -7.03569353e-02 -4.68479604e-01
-5.71564674e-01 -7.68406630e-01 6.85041726e-01 8.26469585e-02
-7.56697297e-01 5.73476493e-01 -2.23361757e-02 2.39300743e-01
3.89977187e-01 -4.03347880e-01 -9.27770138e-01 -3.34186345e-01
1.68309987e-01 5.90914607e-01 5.14361747e-02 1.14378236... | [14.749338150024414, 0.9558098912239075] |
9446d35b-5e84-41cc-94ce-7b503f0fb9a8 | meta-distribution-alignment-for-generalizable | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ni_Meta_Distribution_Alignment_for_Generalizable_Person_Re-Identification_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ni_Meta_Distribution_Alignment_for_Generalizable_Person_Re-Identification_CVPR_2022_paper.pdf | Meta Distribution Alignment for Generalizable Person Re-Identification | Domain Generalizable (DG) person ReID is a challenging task which trains a model on source domains yet generalizes well on target domains. Existing methods use source domains to learn domain-invariant features, and assume those features are also irrelevant with target domains. However, they do not consider the targ... | ['Heng Tao Shen', 'Wen Li', 'Feng Zheng', 'Xiaopeng Luo', 'Jingkuan Song', 'Hao Ni'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['generalizable-person-re-identification'] | ['computer-vision'] | [-5.33420704e-02 -3.71086448e-01 -4.14987803e-01 -8.35820436e-01
-7.27517128e-01 -6.96445882e-01 5.10590672e-01 -2.96027303e-01
-4.32790011e-01 1.16192222e+00 1.58131912e-01 2.43057951e-01
-2.60997236e-01 -7.59930670e-01 -7.67496347e-01 -6.44883811e-01
2.16080606e-01 8.56658936e-01 3.69946927e-01 -2.59229958... | [14.733835220336914, 1.1135196685791016] |
008f0bbc-8339-48bb-996c-a094315b5d10 | eco-driving-trajectory-planning-of-a | 2205.09618 | null | https://arxiv.org/abs/2205.09618v1 | https://arxiv.org/pdf/2205.09618v1.pdf | Eco-driving Trajectory Planning of a Heterogeneous Platoon in Urban Environments | Given the increasing popularity and demand for connected and autonomous vehicles (CAVs), Eco-driving and platooning in highways and urban areas to increase the efficiency of the traffic system is becoming a possibility. This paper presents Eco-driving trajectory planning for a platoon of heterogeneous electric vehicles... | ['Javad Mohammadpour Velni', 'Jidong J. Yang', 'Sahand Mosharafian', 'Hao Zhen'] | 2022-05-19 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-4.05764282e-01 6.75392985e-01 -3.11022133e-01 -2.16179773e-01
-6.73636943e-02 -5.01988590e-01 3.15084994e-01 5.94753958e-02
-4.65975136e-01 1.04669166e+00 -6.98654830e-01 -7.66592920e-01
-5.09193122e-01 -1.19731307e+00 -3.78295839e-01 -1.16653240e+00
-2.17586473e-01 4.31269199e-01 5.43972969e-01 -1.96547791... | [5.542645454406738, 1.6505099534988403] |
77ee6d07-c217-4e5e-9d83-91f852f3a28c | counterfactual-debiasing-inference-for | null | null | https://dl.acm.org/doi/abs/10.1145/3474085.3475472 | https://dl.acm.org/doi/pdf/10.1145/3474085.3475472?casa_token=vpmtrdT6DSMAAAAA:E97KG5JVQqGGGmptKQpIIxOrOpAJD6wkStOHKsmh4sDJ6qVB7DVxxkOKXrG-WgCb3CtmEz_nl9dXlg | Counterfactual Debiasing Inference for Compositional Action Recognition | Compositional action recognition is a novel challenge in the computer vision community and focuses on revealing the different combinations of verbs and nouns instead of treating subject-object interactions in videos as individual instances only. Existing methods tackle this challenging task by simply ignoring appearanc... | ['Chuang Gan', 'Lixin Duan', 'Wen Li', 'Xunsong Li', 'Bo Wu', 'Pengzhan Sun'] | 2021-10-17 | null | null | null | acm-international-conference-on-multimedia-2 | ['counterfactual-inference'] | ['miscellaneous'] | [ 5.34464717e-01 1.17398426e-01 -3.56810331e-01 -1.79887116e-01
-8.56299102e-02 -4.67597425e-01 9.38796759e-01 -4.75776881e-01
-1.65202603e-01 7.51194656e-01 5.81709921e-01 1.38770686e-02
-7.02018440e-02 -6.31856799e-01 -1.18705881e+00 -9.20975387e-01
1.91415370e-01 -3.53243016e-02 2.48046309e-01 2.36321837... | [8.656194686889648, 0.7551302313804626] |
04a4fb03-d348-499b-8a1b-c0138221f8b2 | image-quality-assessment-for-omnidirectional | 1904.04960 | null | http://arxiv.org/abs/1904.04960v2 | http://arxiv.org/pdf/1904.04960v2.pdf | Image Quality Assessment for Omnidirectional Cross-reference Stitching | Along with the development of virtual reality (VR), omnidirectional images
play an important role in producing multimedia content with immersive
experience. However, despite various existing approaches for omnidirectional
image stitching, how to quantitatively assess the quality of stitched images is
still insufficient... | ['Yu Zhang', 'Yifan Zhao', 'Long Xu', 'Kaiwen Yu', 'Jia Li'] | 2019-04-10 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 2.12006211e-01 -3.31867397e-01 2.94261694e-01 6.59741415e-03
-2.39109918e-01 -7.38845825e-01 5.26634574e-01 -2.58472979e-01
-3.47600758e-01 4.46208060e-01 -5.74768335e-02 -5.02964020e-01
-6.62182048e-02 -7.79751956e-01 -6.83491886e-01 -6.17632926e-01
-2.25817651e-01 -1.34422824e-01 2.07019731e-01 -5.54881930... | [9.592329978942871, -2.344963312149048] |
59b56b0c-9e94-4407-9c81-b3e4853859e5 | x-reid-cross-instance-transformer-for | 2302.02075 | null | https://arxiv.org/abs/2302.02075v1 | https://arxiv.org/pdf/2302.02075v1.pdf | X-ReID: Cross-Instance Transformer for Identity-Level Person Re-Identification | Currently, most existing person re-identification methods use Instance-Level features, which are extracted only from a single image. However, these Instance-Level features can easily ignore the discriminative information due to the appearance of each identity varies greatly in different images. Thus, it is necessary to... | ['Guiguang Ding', 'Yuchen Guo', 'Tao He', 'Leqi Shen'] | 2023-02-04 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 7.71200284e-02 -5.13370275e-01 -1.39293931e-02 -6.99014068e-01
-5.04809916e-01 -4.27143455e-01 5.32525539e-01 7.26426160e-03
-5.86146712e-01 6.85855448e-01 8.36639702e-02 3.56277436e-01
3.86539288e-02 -7.34343052e-01 -8.33929420e-01 -8.23589027e-01
3.24320912e-01 1.93733886e-01 6.47040680e-02 2.38305493... | [14.686485290527344, 0.9470586776733398] |
7138edd5-2026-4b09-b53f-8f4a95c0e419 | controlling-high-dimensional-data-with-sparse | 2303.09446 | null | https://arxiv.org/abs/2303.09446v1 | https://arxiv.org/pdf/2303.09446v1.pdf | Controlling High-Dimensional Data With Sparse Input | We address the problem of human-in-the-loop control for generating highly-structured data. This task is challenging because existing generative models lack an efficient interface through which users can modify the output. Users have the option to either manually explore a non-interpretable latent space, or to laborious... | ['Zack Hodari', 'Tian Huey Teh', 'Devang Savita Ram Mohan', 'Dan Andrei Iliescu'] | 2023-03-14 | null | null | null | null | ['text-to-speech-synthesis', 'speech-synthesis'] | ['speech', 'speech'] | [ 3.37018043e-01 4.84766066e-01 6.68499665e-03 -4.50174958e-01
-9.40494895e-01 -9.47090149e-01 4.41092610e-01 -3.62785339e-01
1.22780561e-01 5.28164446e-01 4.96674746e-01 -1.33116588e-01
2.02470079e-01 -6.22750938e-01 -5.71389675e-01 -4.42641854e-01
2.55584657e-01 5.74800909e-01 -2.47441649e-01 -2.34964743... | [15.417756080627441, 6.320740222930908] |
f0457019-b943-4f96-a788-e1aa4bb66a30 | exploring-graph-structured-passage | 1809.02040 | null | http://arxiv.org/abs/1809.02040v1 | http://arxiv.org/pdf/1809.02040v1.pdf | Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks | Multi-hop reading comprehension focuses on one type of factoid question,
where a system needs to properly integrate multiple pieces of evidence to
correctly answer a question. Previous work approximates global evidence with
local coreference information, encoding coreference chains with DAG-styled GRU
layers within a g... | ['Yue Zhang', 'Mo Yu', 'Zhiguo Wang', 'Radu Florian', 'Linfeng Song', 'Daniel Gildea'] | 2018-09-06 | null | null | null | null | ['multi-hop-reading-comprehension'] | ['natural-language-processing'] | [ 8.62025172e-02 8.42077255e-01 -5.18870711e-01 -2.95433521e-01
-6.59844637e-01 -5.80408096e-01 6.19638741e-01 8.68876874e-01
-1.51767045e-01 6.77547991e-01 9.22248185e-01 -8.08171928e-01
-3.45035732e-01 -1.28566742e+00 -8.73280883e-01 4.83633205e-02
-6.72211647e-02 8.28284383e-01 7.49891222e-01 -6.33891165... | [10.778260231018066, 7.933235168457031] |
9549f963-f1b0-4377-ac3f-09a1a5ab8bfc | papooling-graph-based-position-adaptive | 2111.14067 | null | https://arxiv.org/abs/2111.14067v1 | https://arxiv.org/pdf/2111.14067v1.pdf | PAPooling: Graph-based Position Adaptive Aggregation of Local Geometry in Point Clouds | Fine-grained geometry, captured by aggregation of point features in local regions, is crucial for object recognition and scene understanding in point clouds. Nevertheless, existing preeminent point cloud backbones usually incorporate max/average pooling for local feature aggregation, which largely ignores points' posit... | ['Tingfa Xu', 'Ying Wang', 'Lihe Ding', 'Jianan Li', 'Jie Wang'] | 2021-11-28 | null | null | null | null | ['3d-shape-retrieval', 'scene-segmentation'] | ['computer-vision', 'computer-vision'] | [-6.73904270e-02 -1.40996128e-01 4.08787616e-02 -4.74789590e-01
-4.42192882e-01 -5.88831186e-01 4.42985386e-01 6.22773767e-01
-1.43079087e-01 3.23889524e-01 -3.67978871e-01 -2.30895609e-01
-3.13875139e-01 -1.39916265e+00 -8.09011102e-01 -6.15341783e-01
-4.47246373e-01 5.01952648e-01 7.72801638e-01 -4.06892300... | [7.9206862449646, -3.5113942623138428] |
def27bac-6ea4-42a8-9566-b6f65a94bbd9 | emfet-e-mail-features-extraction-tool | 1711.08521 | null | http://arxiv.org/abs/1711.08521v1 | http://arxiv.org/pdf/1711.08521v1.pdf | EMFET: E-mail Features Extraction Tool | EMFET is an open source and flexible tool that can be used to extract a large
number of features from any email corpus with emails saved in EML format. The
extracted features can be categorized into three main groups: header features,
payload (body) features, and attachment features. The purpose of the tool is to
help ... | ["Ala' M. Al-Zoubi", 'Ibrahim Aljarah', "Ja'far Alqatawna", 'Hossam Faris', "Wadi' Hijawi", 'Maria Habib'] | 2017-11-22 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-3.95922154e-01 -4.45259362e-01 -1.35140330e-01 -4.27810341e-01
-5.57977498e-01 -6.84512615e-01 6.16186261e-01 2.69772977e-01
-3.34546357e-01 5.79029262e-01 2.08324656e-01 -5.38517714e-01
-5.25128804e-02 -7.11650550e-01 3.51035967e-02 -2.85845309e-01
3.21445495e-01 3.27643096e-01 3.41293752e-01 -3.66054207... | [7.940494537353516, 9.982787132263184] |
6d43d10a-0314-4b92-80ed-c77f1141aff6 | disaggregating-hops-can-we-guide-a-multi-hop | null | null | https://openreview.net/forum?id=RxOWqx2hwgz | https://openreview.net/pdf?id=RxOWqx2hwgz | Disaggregating Hops: Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at each Hop? | Despite the success of state-of-the-art pre-trained language models (PLMs) on a series of multi-hop reasoning tasks, they still suffer from their limited abilities to transfer learning from simple to complex tasks and vice-versa. We argue that one step forward to overcome this limitation is to better understand the beh... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['multiple-choice-qa'] | ['natural-language-processing'] | [ 2.39527360e-01 4.99392211e-01 1.71973761e-02 -3.89567733e-01
-8.49318027e-01 -6.09046638e-01 5.17022848e-01 4.45959747e-01
-5.47901630e-01 8.28133225e-01 1.59531713e-01 -9.28604722e-01
-2.71590918e-01 -9.64022040e-01 -8.98383498e-01 -6.49435371e-02
1.50460705e-01 7.76110113e-01 4.89109516e-01 -5.39368808... | [9.855378150939941, 7.507110118865967] |
f3d51a1d-21ec-491f-991d-eef4ef692f4c | blind-image-super-resolution-with-semantic | 2202.13142 | null | https://arxiv.org/abs/2202.13142v2 | https://arxiv.org/pdf/2202.13142v2.pdf | Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors | A key challenge of real-world image super-resolution (SR) is to recover the missing details in low-resolution (LR) images with complex unknown degradations (e.g., downsampling, noise and compression). Most previous works restore such missing details in the image space. To cope with the high diversity of natural images,... | ['Shihui Guo', 'Tao Yang', 'Xiaoguang Han', 'Xiaoming Li', 'Yipeng Qin', 'Xinyu Shi', 'Chaofeng Chen'] | 2022-02-26 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 6.24353230e-01 7.60861486e-02 -7.93312211e-03 -9.58561823e-02
-1.23169708e+00 -3.89221191e-01 2.45042086e-01 -7.90126979e-01
8.74590501e-02 7.92101502e-01 4.91418481e-01 8.33500549e-02
-6.32289797e-02 -9.81796384e-01 -9.28997695e-01 -8.08090150e-01
4.32121933e-01 -8.65650624e-02 -1.74948588e-01 -4.36212957... | [11.07148265838623, -2.006082534790039] |
5d7e702d-07db-4391-9cb8-248daba71d64 | percol0-un-systeme-multimodal-de-detection-de | null | null | https://aclanthology.org/F12-1070 | https://aclanthology.org/F12-1070.pdf | Percol0 - un syst\`eme multimodal de d\'etection de personnes dans des documents vid\'eo (Percol0 - A multimodal person detection system in video documents) [in French] | null | ['Stephane Ayache', 'Remi Auguste', 'Frederic Bechet', 'Delphine Charlet', 'Corinne Fredouille', 'Christophe Levy', 'Georges Linares', 'Benoit Favre', 'Jean Martinet', 'Geraldine Damnati'] | 2012-06-01 | percol0-un-systeme-multimodal-de-detection-de-1 | https://aclanthology.org/F12-1070 | https://aclanthology.org/F12-1070.pdf | jeptalnrecital-2012-6 | ['person-recognition'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.214419364929199, 3.7613120079040527] |
329d6766-412b-4f75-8433-ce2894e8086c | few-shot-text-classification-with | 1908.06039 | null | https://arxiv.org/abs/1908.06039v3 | https://arxiv.org/pdf/1908.06039v3.pdf | Few-shot Text Classification with Distributional Signatures | In this paper, we explore meta-learning for few-shot text classification. Meta-learning has shown strong performance in computer vision, where low-level patterns are transferable across learning tasks. However, directly applying this approach to text is challenging--lexical features highly informative for one task may ... | ['Menghua Wu', 'Shiyu Chang', 'Yujia Bao', 'Regina Barzilay'] | 2019-08-16 | null | https://openreview.net/forum?id=H1emfT4twB | https://openreview.net/pdf?id=H1emfT4twB | iclr-2020-1 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 5.23990512e-01 3.49304937e-02 -6.87763989e-01 -3.64668429e-01
-7.32367992e-01 -4.75064665e-02 1.06308305e+00 6.77875161e-01
-6.61796689e-01 6.63488269e-01 5.72369397e-01 -2.14273527e-01
3.64709347e-02 -7.98403263e-01 -4.19673711e-01 -3.82665485e-01
3.04581374e-01 2.55200535e-01 2.69129481e-02 -4.69238460... | [10.685150146484375, 7.747781753540039] |
249b5d4f-0dd7-4b83-9fec-6787bcc5a641 | deep-embedding-using-bayesian-risk | 1812.02466 | null | http://arxiv.org/abs/1812.02466v1 | http://arxiv.org/pdf/1812.02466v1.pdf | Deep Embedding using Bayesian Risk Minimization with Application to Sketch Recognition | In this paper, we address the problem of hand-drawn sketch recognition.
Inspired by the Bayesian decision theory, we present a deep metric learning
loss with the objective to minimize the Bayesian risk of misclassification. We
estimate this risk for every mini-batch during training, and learn robust deep
embeddings by ... | ['Ajeet Kumar Singh', 'Anand Mishra'] | 2018-12-06 | null | null | null | null | ['sketch-recognition'] | ['computer-vision'] | [-1.24879695e-01 -7.06894398e-02 -2.78177857e-01 -8.25017869e-01
-9.75355506e-01 -3.53649735e-01 6.68337047e-01 -2.53409743e-01
-5.48010409e-01 5.93447983e-01 -1.27848357e-01 -1.65358171e-01
-3.17183822e-01 -7.90579379e-01 -8.27101469e-01 -1.66869104e-01
-5.03100492e-02 5.78425467e-01 -1.08455427e-01 3.78741533... | [11.655019760131836, 0.5384122133255005] |
c9f1ad43-2434-41e1-b0a6-216817eb84ec | counting-with-adaptive-auxiliary-learning | 2203.04061 | null | https://arxiv.org/abs/2203.04061v1 | https://arxiv.org/pdf/2203.04061v1.pdf | Counting with Adaptive Auxiliary Learning | This paper proposes an adaptive auxiliary task learning based approach for object counting problems. Unlike existing auxiliary task learning based methods, we develop an attention-enhanced adaptively shared backbone network to enable both task-shared and task-tailored features learning in an end-to-end manner. The netw... | ['Yalin Zheng', 'Xiaowei Huang', 'Xiaoyun Yang', 'Yihong Qiao', 'Yitian Zhao', 'Meng Wei', 'Joshua Bridge', 'Yanda Meng'] | 2022-03-08 | null | null | null | null | ['object-counting', 'auxiliary-learning'] | ['computer-vision', 'methodology'] | [ 1.65158689e-01 -2.33094066e-01 -1.46906272e-01 -5.92168808e-01
-5.91504812e-01 -2.84158200e-01 7.30514824e-01 1.43138126e-01
-9.39873219e-01 9.33688164e-01 1.39226332e-01 -3.69422808e-02
-5.65606840e-02 -7.84366488e-01 -7.77620912e-01 -7.78832555e-01
1.50360405e-01 7.09816873e-01 3.54649544e-01 1.79552138... | [8.951532363891602, 0.2501373887062073] |
a2f4e19d-6c19-41cb-b078-919ca3754103 | vs-transgru-a-novel-transformer-gru-based | 2307.03918 | null | https://arxiv.org/abs/2307.03918v1 | https://arxiv.org/pdf/2307.03918v1.pdf | VS-TransGRU: A Novel Transformer-GRU-based Framework Enhanced by Visual-Semantic Fusion for Egocentric Action Anticipation | Egocentric action anticipation is a challenging task that aims to make advanced predictions of future actions from current and historical observations in the first-person view. Most existing methods focus on improving the model architecture and loss function based on the visual input and recurrent neural network to boo... | ['Yanning Zhang', 'Lingtong Min', 'Qinyi Lv', 'Ze Sun', 'Congqi Cao'] | 2023-07-08 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 1.70299158e-01 -1.15067005e-01 -2.32628003e-01 -6.20421410e-01
-4.26342189e-01 -6.32354524e-03 7.26801634e-01 -3.42473179e-01
-3.39430571e-01 4.55300838e-01 7.83742249e-01 2.76597857e-01
8.24825466e-02 -4.82627183e-01 -5.93200147e-01 -4.40746784e-01
2.76473939e-01 5.52354865e-02 5.70728146e-02 -2.33541191... | [8.24383544921875, 0.4918098449707031] |
e74437a9-1dc9-40b4-843b-534524ab0f92 | ahead-a-triple-attention-based-heterogeneous | 2208.08200 | null | https://arxiv.org/abs/2208.08200v1 | https://arxiv.org/pdf/2208.08200v1.pdf | AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approach | Graph anomaly detection on attributed networks has become a prevalent research topic due to its broad applications in many influential domains. In real-world scenarios, nodes and edges in attributed networks usually display distinct heterogeneity, i.e. attributes of different types of nodes show great variety, differen... | ['Minnan Luo', 'Jun Zhou', 'Qinghua Zheng', 'Zhaoxuan Tan', 'Shangbin Feng', 'Binchi Zhang', 'Shujie Yang'] | 2022-08-17 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [ 2.34154817e-02 2.52346426e-01 -1.50029525e-01 -3.17832559e-01
1.58781826e-01 -2.89167970e-01 4.79531139e-01 5.96212447e-01
6.67982325e-02 3.52909446e-01 2.43512258e-01 -4.64057289e-02
-1.70658335e-01 -9.43326056e-01 -6.12329006e-01 -5.70274770e-01
-1.93158224e-01 4.39970821e-01 2.86550254e-01 -1.82935178... | [6.7357635498046875, 5.872530937194824] |
955731dd-fac8-4d66-bcea-ec9c89b8cf99 | multi-grained-spatio-temporal-modeling-for | 1908.11618 | null | https://arxiv.org/abs/1908.11618v2 | https://arxiv.org/pdf/1908.11618v2.pdf | Multi-Grained Spatio-temporal Modeling for Lip-reading | Lip-reading aims to recognize speech content from videos via visual analysis of speakers' lip movements. This is a challenging task due to the existence of homophemes-words which involve identical or highly similar lip movements, as well as diverse lip appearances and motion patterns among the speakers. To address thes... | ['Chenhao Wang'] | 2019-08-30 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [-6.47934899e-02 -7.59486914e-01 -3.93906653e-01 -1.49034306e-01
-8.89647782e-01 -4.31668520e-01 5.95412254e-01 -2.66006202e-01
-7.17992783e-02 3.95233333e-01 7.35515773e-01 3.47288921e-02
2.82623798e-01 -1.48645937e-01 -6.38892949e-01 -8.80921423e-01
2.63664126e-01 -2.65326142e-01 2.83807129e-01 1.39682472... | [14.30845832824707, 4.972666263580322] |
d8f7c195-04bb-40dd-893c-4b9096442c17 | simple-and-effective-augmentation-methods-for | 2211.10790 | null | https://arxiv.org/abs/2211.10790v2 | https://arxiv.org/pdf/2211.10790v2.pdf | Simple and Effective Augmentation Methods for CSI Based Indoor Localization | Indoor localization is a challenging task. Compared to outdoor environments where GPS is dominant, there is no robust and almost-universal approach. Recently, machine learning (ML) has emerged as the most promising approach for achieving accurate indoor localization. Nevertheless, its main challenge is requiring large ... | ['Andreas F. Molisch', 'Daoud Burghal', 'Ju-Hyung Lee', 'Omer Gokalp Serbetci'] | 2022-11-19 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 1.54580712e-01 -2.96995938e-01 -1.45065576e-01 -5.26661277e-01
-9.51232493e-01 -5.38861215e-01 2.97996879e-01 2.42130756e-01
-5.79734504e-01 1.31849480e+00 -1.85247958e-01 -6.62746429e-01
-2.60997564e-01 -8.46602380e-01 -8.18710804e-01 -9.38090205e-01
-1.96300477e-01 1.54160196e-02 2.28092838e-02 1.02055304... | [6.415460109710693, 0.9722769260406494] |
2d42b481-2ddd-4d53-a0f7-9d86ed10e32d | wearable-based-human-activity-recognition | 2212.02233 | null | https://arxiv.org/abs/2212.02233v1 | https://arxiv.org/pdf/2212.02233v1.pdf | Wearable-based Human Activity Recognition with Spatio-Temporal Spiking Neural Networks | We study the Human Activity Recognition (HAR) task, which predicts user daily activity based on time series data from wearable sensors. Recently, researchers use end-to-end Artificial Neural Networks (ANNs) to extract the features and perform classification in HAR. However, ANNs pose a huge computation burden on wearab... | ['Priyadarshini Panda', 'Youngeun Kim', 'Hyoungseob Park', 'Ruokai Yin', 'Yuhang Li'] | 2022-11-14 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 2.63528854e-01 -4.99765426e-01 -4.53446694e-02 -2.51665056e-01
-2.37876981e-01 -4.45740581e-01 1.11497208e-01 2.54231114e-02
-5.22339821e-01 9.50531781e-01 3.64142470e-02 -2.01731529e-02
5.25740311e-02 -8.31412435e-01 -7.19704509e-01 -6.92342520e-01
-2.69571364e-01 -3.72983634e-01 -1.86601076e-02 2.25458711... | [8.254141807556152, 2.3837924003601074] |
46d01c7b-d614-42a8-824f-f7f7180f3738 | multi-view-subspace-clustering-networks-with | 2010.09323 | null | https://arxiv.org/abs/2010.09323v3 | https://arxiv.org/pdf/2010.09323v3.pdf | Multi-view Subspace Clustering Networks with Local and Global Graph Information | This study investigates the problem of multi-view subspace clustering, the goal of which is to explore the underlying grouping structure of data collected from different fields or measurements. Since data do not always comply with the linear subspace models in many real-world applications, most existing multi-view subs... | ['Zhiqiang Tian', 'Zhongyu Li', 'Yuanyuan Ma', 'Jihua Zhu', 'Qinghai Zheng'] | 2020-10-19 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-2.87349761e-01 -3.94026995e-01 -9.92064923e-02 -2.08826452e-01
-2.93303370e-01 -4.94873226e-01 3.79538953e-01 -5.07764697e-01
1.31947502e-01 1.02864012e-01 5.03719926e-01 1.07539259e-01
-5.16464353e-01 -4.35904711e-01 -3.27931434e-01 -1.15606725e+00
3.94574970e-01 2.39514008e-01 -1.91802531e-01 1.76759064... | [8.278345108032227, 4.598697662353516] |
658a5af0-79b6-499a-afe5-08e151a917b6 | lrw-1000-a-naturally-distributed-large-scale | 1810.06990 | null | http://arxiv.org/abs/1810.06990v6 | http://arxiv.org/pdf/1810.06990v6.pdf | LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild | Large-scale datasets have successively proven their fundamental importance in
several research fields, especially for early progress in some emerging topics.
In this paper, we focus on the problem of visual speech recognition, also known
as lipreading, which has received increasing interest in recent years. We
present ... | ['Yuan-Hang Zhang', 'Jing-Yun Xiao', 'Keyu Long', 'Xilin Chen', 'Mingmin Yang', 'Dalu Feng', 'Shuang Yang', 'Shiguang Shan', 'Chenhao Wang'] | 2018-10-16 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 1.97462782e-01 -2.89361119e-01 -5.60873091e-01 -2.31132314e-01
-1.10948241e+00 -3.20196390e-01 7.20121503e-01 -2.23532543e-01
-3.60321760e-01 6.30240977e-01 4.04093534e-01 -2.38050386e-01
4.28785950e-01 1.70001201e-02 -5.21267831e-01 -7.91556895e-01
2.50483572e-01 2.16282889e-01 1.40153080e-01 5.91905303... | [14.315237045288086, 4.988456726074219] |
6afd27eb-7d9d-40d4-adfd-6370d0794788 | bandit-algorithms-for-tree-search | 1408.2028 | null | http://arxiv.org/abs/1408.2028v1 | http://arxiv.org/pdf/1408.2028v1.pdf | Bandit Algorithms for Tree Search | Bandit based methods for tree search have recently gained popularity when
applied to huge trees, e.g. in the game of go [6]. Their efficient exploration
of the tree enables to re- turn rapidly a good value, and improve preci- sion
if more time is provided. The UCT algo- rithm [8], a tree search method based
on Up- per ... | ['Pierre-Arnuad Coquelin', 'Remi Munos'] | 2014-08-09 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [ 1.26311198e-01 6.18210971e-01 -6.59349501e-01 -9.47934017e-02
-1.34972501e+00 -7.05547869e-01 5.54180220e-02 2.11048990e-01
-4.12142903e-01 1.24269950e+00 -6.59773424e-02 -6.51865065e-01
-6.25867784e-01 -9.58214521e-01 -8.94598067e-01 -7.90934384e-01
-2.90611118e-01 7.80845642e-01 1.64739430e-01 -3.50251421... | [4.517509460449219, 3.2748262882232666] |
8a0e7f2e-3e98-42db-9ea8-fb27a2c0dd2e | upar-unified-pedestrian-attribute-recognition | 2209.02522 | null | https://arxiv.org/abs/2209.02522v1 | https://arxiv.org/pdf/2209.02522v1.pdf | UPAR: Unified Pedestrian Attribute Recognition and Person Retrieval | Recognizing soft-biometric pedestrian attributes is essential in video surveillance and fashion retrieval. Recent works show promising results on single datasets. Nevertheless, the generalization ability of these methods under different attribute distributions, viewpoints, varying illumination, and low resolutions rema... | ['Jürgen Beyerer', 'Mickael Cormier', 'Andreas Specker'] | 2022-09-06 | null | null | null | null | ['pedestrian-attribute-recognition', 'person-retrieval'] | ['computer-vision', 'computer-vision'] | [-3.34866196e-02 -6.49347723e-01 -2.85438627e-01 -7.76529431e-01
-8.70677114e-01 -6.60611331e-01 7.40127981e-01 1.05779216e-01
-2.56534636e-01 6.62281454e-01 3.17757905e-01 3.53662223e-01
-1.10973820e-01 -6.70626760e-01 -4.82763618e-01 -8.13655615e-01
1.06065586e-01 8.10639679e-01 -1.16263188e-01 -1.09685864... | [14.530478477478027, 0.9476910829544067] |
cfd7f9ec-ee80-47f7-9dc9-bc2a750a985f | robust-and-efficient-post-processing-for | null | null | https://arxiv.org/abs/2009.11050 | https://arxiv.org/pdf/2009.11050.pdf | Robust and Efficient Post-Processing for Video Object Detection (REPP) | Object recognition in video is an important task for plenty of applications, including autonomous driving perception, surveillance tasks, wearable devices or IoT networks. Object recognition using video data is more challenging than using still images due to blur, occlusions or rare object poses. Specific video detecto... | ['Luis Montesano', 'Alberto Sabater', 'Ana C. Murillo'] | 2020-10-01 | null | null | null | null | ['dense-object-detection'] | ['computer-vision'] | [ 2.85645455e-01 -6.24909341e-01 -4.70927842e-02 -2.10259154e-01
-4.57785636e-01 -3.62203687e-01 5.04622102e-01 2.11959258e-01
-8.89208078e-01 3.17320585e-01 -3.23320836e-01 3.25444750e-02
1.38851255e-01 -4.12556976e-01 -6.22907221e-01 -5.28714418e-01
-1.53503552e-01 6.98185414e-02 1.25519824e+00 -7.62437209... | [8.5133056640625, -0.7111911177635193] |
4f2c547d-432d-46ed-8209-fcd337ad018a | interpretable-convolutional-filters-with | 1811.09725 | null | https://arxiv.org/abs/1811.09725v2 | https://arxiv.org/pdf/1811.09725v2.pdf | Interpretable Convolutional Filters with SincNet | Deep learning is currently playing a crucial role toward higher levels of artificial intelligence. This paradigm allows neural networks to learn complex and abstract representations, that are progressively obtained by combining simpler ones. Nevertheless, the internal "black-box" representations automatically discovere... | ['Mirco Ravanelli', 'Yoshua Bengio'] | 2018-11-23 | null | null | null | null | ['distant-speech-recognition'] | ['speech'] | [ 1.37122259e-01 5.94086230e-01 -7.03663304e-02 -6.76716685e-01
-1.26855001e-01 -4.81064111e-01 5.62873125e-01 -7.08358884e-02
1.98457055e-02 5.70222914e-01 3.04738790e-01 -6.15267932e-01
-2.49832585e-01 -5.45435429e-01 -8.27049732e-01 -6.63539350e-01
-1.27253056e-01 4.50845063e-02 -1.53040364e-01 -3.88400614... | [15.295611381530762, 5.463650703430176] |
40b46a3e-698b-4949-9880-495e3ace347c | iwa-integrated-gradient-based-white-box | 2102.02128 | null | https://arxiv.org/abs/2102.02128v1 | https://arxiv.org/pdf/2102.02128v1.pdf | IWA: Integrated Gradient based White-box Attacks for Fooling Deep Neural Networks | The widespread application of deep neural network (DNN) techniques is being challenged by adversarial examples, the legitimate input added with imperceptible and well-designed perturbations that can fool DNNs easily in the DNN testing/deploying stage. Previous adversarial example generation algorithms for adversarial w... | ['Vojislav B. Mišić', 'Jelena Mišić', 'Xiaolin Chang', 'Jiqiang Liu', 'Yixiang Wang'] | 2021-02-03 | null | null | null | null | ['dnn-testing'] | ['adversarial'] | [ 2.87527919e-01 9.29288790e-02 1.56506598e-01 2.86666155e-02
-4.47872519e-01 -9.86002445e-01 5.89696646e-01 -5.18018723e-01
-5.31758010e-01 1.02778625e+00 -1.15061566e-01 -5.53309023e-01
-1.71131194e-01 -1.07691455e+00 -8.94716322e-01 -7.75295973e-01
-1.02123119e-01 5.58951013e-02 1.76793233e-01 -5.44034660... | [5.534627914428711, 7.936845779418945] |
08d1a508-ba53-495a-bdf3-ebab3f0685f1 | a-comparison-of-deep-learning-architectures | 2111.04353 | null | https://arxiv.org/abs/2111.04353v1 | https://arxiv.org/pdf/2111.04353v1.pdf | A Comparison of Deep Learning Architectures for Optical Galaxy Morphology Classification | The classification of galaxy morphology plays a crucial role in understanding galaxy formation and evolution. Traditionally, this process is done manually. The emergence of deep learning techniques has given room for the automation of this process. As such, this paper offers a comparison of deep learning architectures ... | ['Mattia Vaccari', 'Clement N. Nyirenda', 'Ezra Fielding'] | 2021-11-08 | null | null | null | null | ['morphology-classification'] | ['computer-vision'] | [-4.32505727e-01 -1.92331038e-02 4.98835891e-01 -2.58959800e-01
3.19868326e-02 -7.14795172e-01 9.03105736e-01 3.66948582e-02
-3.78520519e-01 4.38292354e-01 8.04063156e-02 -7.12535143e-01
-1.93349361e-01 -1.06129146e+00 -9.47794318e-02 -6.16232753e-01
8.34363848e-02 7.78052568e-01 5.27427614e-01 -1.60566103... | [7.9313883781433105, 2.9625141620635986] |
d8a2924d-6b9a-4643-90fb-f1f809f7b197 | styletalk-one-shot-talking-head-generation | 2301.01081 | null | https://arxiv.org/abs/2301.01081v2 | https://arxiv.org/pdf/2301.01081v2.pdf | StyleTalk: One-shot Talking Head Generation with Controllable Speaking Styles | Different people speak with diverse personalized speaking styles. Although existing one-shot talking head methods have made significant progress in lip sync, natural facial expressions, and stable head motions, they still cannot generate diverse speaking styles in the final talking head videos. To tackle this problem, ... | ['Xin Yu', 'Zhidong Deng', 'Yu Ding', 'Tangjie Lv', 'Changjie Fan', 'Zhipeng Hu', 'Suzhen Wang', 'Yifeng Ma'] | 2023-01-03 | null | null | null | null | ['talking-head-generation', 'talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.65284351e-01 -2.48061530e-02 -1.12605795e-01 -7.03834951e-01
-5.62115610e-01 -6.12432897e-01 5.30716598e-01 -1.13038814e+00
1.90431505e-01 4.52537864e-01 5.33365667e-01 2.94347495e-01
4.98564810e-01 -5.51486611e-01 -7.15631247e-01 -7.55135953e-01
6.52438998e-01 2.93815825e-02 -2.35779479e-01 -1.77255154... | [13.177865028381348, -0.4083639681339264] |
97fada87-7702-40b7-a218-0af3bc9aa8c1 | translate-reverberated-speech-to-anechoic | 2007.08052 | null | https://arxiv.org/abs/2007.08052v1 | https://arxiv.org/pdf/2007.08052v1.pdf | Translate Reverberated Speech to Anechoic Ones: Speech Dereverberation with BERT | Single channel speech dereverberation is considered in this work. Inspired by the recent success of Bidirectional Encoder Representations from Transformers (BERT) model in the domain of Natural Language Processing (NLP), we investigate its applicability as backbone sequence model to enhance reverberated speech signal. ... | ['Yang Jiao'] | 2020-07-16 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 4.87442106e-01 -5.56310313e-03 3.33262533e-01 5.15547208e-02
-9.10641789e-01 -4.03116584e-01 6.13244832e-01 -1.55465603e-01
-6.77996337e-01 7.90679455e-01 7.65822470e-01 -5.89301229e-01
2.81021982e-01 -3.25417757e-01 -7.46096790e-01 -7.38215566e-01
-9.60503146e-03 -1.25474393e-01 3.75785917e-01 -4.62056130... | [14.958824157714844, 6.028085708618164] |
2f5a891e-c185-4dcd-8320-8ff85efa7263 | integrated-community-occupancy-models-a | 2109.01894 | null | https://arxiv.org/abs/2109.01894v1 | https://arxiv.org/pdf/2109.01894v1.pdf | Integrated community occupancy models: A framework to assess occurrence and biodiversity dynamics using multiple data sources | The occurrence and distributions of wildlife populations and communities are shifting as a result of global changes. To evaluate whether these shifts are negatively impacting biodiversity processes, it is critical to monitor the status, trends, and effects of environmental variables on entire communities. However, mode... | ['Elise F. Zipkin', 'Michael T. Hallworth', 'T. Scott Sillett', 'Wendy Leuenberger', 'Jeffrey W. Doser'] | 2021-09-04 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 5.64984158e-02 -7.53907561e-01 -2.02226818e-01 2.79605865e-01
2.05913991e-01 -6.94574773e-01 6.48116946e-01 6.71895146e-01
-8.09727967e-01 9.14680064e-01 6.20027542e-01 -5.93412161e-01
-2.80192375e-01 -9.77294564e-01 -5.17227113e-01 -4.89377886e-01
-8.08321774e-01 1.31836727e-01 3.61531198e-01 -1.71256796... | [9.356217384338379, -1.4632439613342285] |
c25017e4-ec7c-4ced-99a5-260a2d3eeeae | iw-net-an-automatic-and-minimalistic | 1811.12789 | null | http://arxiv.org/abs/1811.12789v1 | http://arxiv.org/pdf/1811.12789v1.pdf | iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network | We propose iW-Net, a deep learning model that allows for both automatic and
interactive segmentation of lung nodules in computed tomography images. iW-Net
is composed of two blocks: the first one provides an automatic segmentation and
the second one allows to correct it by analyzing 2 points introduced by the
user in t... | ['Aurélio Campilho', 'António Cunha', 'Teresa Araújo', 'Isabel Ramos', 'Bram van Ginneken', 'Guilherme Aresta', 'Colin Jacobs'] | 2018-11-30 | null | null | null | null | ['lung-nodule-segmentation'] | ['medical'] | [-6.27833009e-02 5.80013037e-01 -4.83177677e-02 -2.58699119e-01
-9.11015451e-01 -2.76634932e-01 2.87345350e-01 3.30684274e-01
-6.06525064e-01 3.50293517e-01 -1.63986087e-01 -4.77958500e-01
-1.48460403e-01 -7.71036685e-01 -6.12415493e-01 -9.21220422e-01
1.76463619e-01 1.11085808e+00 9.35246229e-01 1.44881025... | [15.372638702392578, -2.1379261016845703] |
bcfab388-f6f1-4593-9933-5991daf6e337 | synthref-generation-of-synthetic-referring | 2106.04403 | null | https://arxiv.org/abs/2106.04403v2 | https://arxiv.org/pdf/2106.04403v2.pdf | SynthRef: Generation of Synthetic Referring Expressions for Object Segmentation | Recent advances in deep learning have brought significant progress in visual grounding tasks such as language-guided video object segmentation. However, collecting large datasets for these tasks is expensive in terms of annotation time, which represents a bottleneck. To this end, we propose a novel method, namely Synth... | ['Xavier Giro-i-Nieto', 'Carina Silberer', 'Miriam Bellver', 'Carles Ventura', 'Ioannis Kazakos'] | 2021-06-08 | null | null | null | null | ['referring-expression-segmentation'] | ['computer-vision'] | [ 4.49147731e-01 1.38530061e-01 -2.95978993e-01 -3.80816340e-01
-9.35972631e-01 -6.98951781e-01 3.91342074e-01 4.13762219e-03
-2.79999435e-01 7.17036784e-01 -1.19271770e-01 -2.95228988e-01
2.57311910e-01 -6.62583232e-01 -1.08469701e+00 -2.92474359e-01
2.93376625e-01 3.38427871e-01 4.76787806e-01 -3.30840237... | [9.455619812011719, 0.5571905970573425] |
6fd79999-b694-4ce9-9593-7ead83406db3 | on-sir-type-epidemiological-models-and | 2210.11342 | null | https://arxiv.org/abs/2210.11342v1 | https://arxiv.org/pdf/2210.11342v1.pdf | On SIR-type epidemiological models and population heterogeneity effects | In this paper we elaborate on homogeneous and heterogeneous SIR-type epidemiological models. We find an unexpected correspondence between the epidemic trajectory of a transmissible disease in a homogeneous SIR-type model and radial null geodesics in the Schwarzschild spacetime. We also discuss modeling of population he... | ['Lucrezia Ravera', 'Silke Klemm'] | 2022-10-20 | null | null | null | null | ['type'] | ['speech'] | [-2.07238778e-01 2.03201979e-01 2.20607355e-01 -2.80441254e-01
2.78788656e-01 -2.83369511e-01 6.99894369e-01 -5.25936075e-02
-5.21579862e-01 8.97847295e-01 1.52380750e-01 -4.61071730e-01
-7.94690728e-01 -8.59165609e-01 -3.30771983e-01 -1.22914934e+00
-8.01555574e-01 6.58810139e-01 3.93902838e-01 -5.38542330... | [5.94244384765625, 4.390457630157471] |
b6ab1aea-c088-4093-9432-34439ff6778f | combination-of-hidden-markov-random-field-and | 1705.04823 | null | http://arxiv.org/abs/1705.04823v4 | http://arxiv.org/pdf/1705.04823v4.pdf | Combination of Hidden Markov Random Field and Conjugate Gradient for Brain Image Segmentation | Image segmentation is the process of partitioning the image into significant
regions easier to analyze. Nowadays, segmentation has become a necessity in
many practical medical imaging methods as locating tumors and diseases. Hidden
Markov Random Field model is one of several techniques used in image
segmentation. It pr... | ['Samy Ait-Aoudia', 'EL-Hachemi Guerrout', 'Ramdane Mahiou', 'Dominique Michelucci'] | 2017-05-13 | null | null | null | null | ['brain-image-segmentation'] | ['medical'] | [ 3.29303801e-01 9.22835469e-02 -1.89449042e-01 -4.08992767e-01
-7.02938735e-01 -3.03691894e-01 4.81112599e-01 3.80614221e-01
-7.93738008e-01 7.59067416e-01 -2.10114151e-01 -2.53011018e-01
2.21992228e-02 -6.05843961e-01 -1.49526700e-01 -9.43202317e-01
8.05569664e-02 5.62394142e-01 4.87196892e-01 2.43770301... | [14.394984245300293, -2.7938761711120605] |
3819b7eb-9409-4465-a709-bbea7cea2bff | a-survey-of-noma-state-of-the-art-key | 2306.06664 | null | https://arxiv.org/abs/2306.06664v1 | https://arxiv.org/pdf/2306.06664v1.pdf | A Survey of NOMA: State of the Art, Key Techniques, Open Challenges, Security Issues and Future Trends | Non-orthogonal multiple access (NOMA) systems can serve multiple users in contrast to orthogonal multiple-access (OMA), which makes use of the limited time or frequency domain resources. It can help to address the unprecedented technological advancements of the sixth generation (6G) network, which include high spectral... | ['Yanlong Li', 'Syed Agha Hassnain Mohsan'] | 2023-06-11 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-4.02138988e-03 -5.40643409e-02 -2.20222965e-01 4.83314157e-01
1.73822239e-01 -4.55939800e-01 3.47842813e-01 -5.48169196e-01
-2.18378887e-01 1.33582413e+00 -4.36414331e-01 -1.03413260e+00
-6.31828725e-01 -8.47496510e-01 3.41955245e-01 -1.33908713e+00
-7.79704392e-01 -2.43349262e-02 -4.01088178e-01 -3.54137391... | [6.19819974899292, 1.3782732486724854] |
4f162081-7c93-412d-ab5a-02d0e99cd871 | an-incremental-iterated-response-model-of | 1810.00367 | null | http://arxiv.org/abs/1810.00367v2 | http://arxiv.org/pdf/1810.00367v2.pdf | An Incremental Iterated Response Model of Pragmatics | Recent Iterated Response (IR) models of pragmatics conceptualize language use
as a recursive process in which agents reason about each other to increase
communicative efficiency. These models are generally defined over complete
utterances. However, there is substantial evidence that pragmatic reasoning
takes place incr... | ['Christopher Potts', 'Reuben Cohn-Gordon', 'Noah D. Goodman'] | 2018-09-30 | an-incremental-iterated-response-model-of-1 | https://aclanthology.org/W19-0109 | https://aclanthology.org/W19-0109.pdf | ws-2019-1 | ['referring-expression-generation'] | ['computer-vision'] | [ 2.73970544e-01 1.05524707e+00 -9.83660817e-02 -4.74848062e-01
-8.40880930e-01 -7.69768834e-01 1.04681361e+00 -1.62447188e-02
-3.55152994e-01 6.03661001e-01 1.11100233e+00 -5.12449563e-01
-3.04063708e-01 -5.09189963e-01 -1.87607065e-01 -2.73655176e-01
-8.33273120e-03 6.83864117e-01 1.08664200e-01 -8.51058185... | [10.743995666503906, 8.443174362182617] |
649d0c09-e970-48db-b294-8b52dd4cee13 | modeling-dynamic-attributes-for-next-basket | 2109.11654 | null | https://arxiv.org/abs/2109.11654v1 | https://arxiv.org/pdf/2109.11654v1.pdf | Modeling Dynamic Attributes for Next Basket Recommendation | Traditional approaches to next item and next basket recommendation typically extract users' interests based on their past interactions and associated static contextual information (e.g. a user id or item category). However, extracted interests can be inaccurate and become obsolete. Dynamic attributes, such as user inco... | ['Caiming Xiong', 'Julian McAuley', 'Markus Anderle', 'Chenxi Li', 'Chenghao Liu', 'Jia Li', 'Yongjun Chen'] | 2021-09-23 | null | null | null | null | ['next-basket-recommendation'] | ['miscellaneous'] | [-1.94395721e-01 -6.30644917e-01 -8.38367224e-01 -7.34243393e-01
-2.24753767e-01 -6.05238378e-01 3.05042237e-01 2.31134862e-01
-2.23274916e-01 9.19672251e-01 7.34233320e-01 4.38438915e-02
-3.62667948e-01 -1.03042829e+00 -8.71574223e-01 -2.67700136e-01
-4.82874751e-01 4.99429524e-01 7.23771229e-02 -4.39376652... | [10.09586238861084, 5.588942527770996] |
10cff777-da23-497e-8cb8-b49bba33e01f | efficient-novelty-detection-methods-for-early | 2208.04732 | null | https://arxiv.org/abs/2208.04732v1 | https://arxiv.org/pdf/2208.04732v1.pdf | Efficient Novelty Detection Methods for Early Warning of Potential Fatal Diseases | Fatal diseases, as Critical Health Episodes (CHEs), represent real dangers for patients hospitalized in Intensive Care Units. These episodes can lead to irreversible organ damage and death. Nevertheless, diagnosing them in time would greatly reduce their inconvenience. This study therefore focused on building a highly ... | ['Ernest Fokoué', 'Sèdjro Salomon Hotegni'] | 2022-08-06 | null | null | null | null | ['episode-classification'] | ['time-series'] | [ 1.65907994e-01 -1.90640818e-02 8.35183635e-02 -2.16291457e-01
-5.54154336e-01 -6.63381293e-02 4.72463518e-01 9.36517715e-01
-4.99851495e-01 9.37747359e-01 -5.01646101e-02 -4.57521081e-01
-6.79122508e-01 -7.08297431e-01 -3.62373628e-02 -7.73011923e-01
-5.24551451e-01 4.95535791e-01 1.22004963e-01 2.97962129... | [8.446102142333984, 4.866647243499756] |
c999edf9-a3e3-4592-95e8-76f6b55bd90d | deep-clustering-for-unsupervised-learning-of | 1807.05520 | null | http://arxiv.org/abs/1807.05520v2 | http://arxiv.org/pdf/1807.05520v2.pdf | Deep Clustering for Unsupervised Learning of Visual Features | Clustering is a class of unsupervised learning methods that has been
extensively applied and studied in computer vision. Little work has been done
to adapt it to the end-to-end training of visual features on large scale
datasets. In this work, we present DeepCluster, a clustering method that
jointly learns the paramete... | ['Matthijs Douze', 'Armand Joulin', 'Piotr Bojanowski', 'Mathilde Caron'] | 2018-07-15 | deep-clustering-for-unsupervised-learning-of-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Mathilde_Caron_Deep_Clustering_for_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Mathilde_Caron_Deep_Clustering_for_ECCV_2018_paper.pdf | eccv-2018-9 | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [-2.43007958e-01 -8.17628577e-02 -7.84204081e-02 -8.41149390e-01
-1.41341090e-01 -4.59052473e-01 8.38189840e-01 6.88027143e-02
-8.39900613e-01 -7.20743313e-02 1.27860293e-01 -2.39543654e-02
-8.48079175e-02 -2.72085339e-01 -5.39445758e-01 -8.68859112e-01
-2.42247909e-01 7.33496785e-01 3.99308801e-01 3.38765621... | [9.20806884765625, 3.06827974319458] |
9747c8cc-3cc3-4888-a38c-16dc166c6a55 | spatial-temporal-prompt-learning-for | 2305.14244 | null | https://arxiv.org/abs/2305.14244v1 | https://arxiv.org/pdf/2305.14244v1.pdf | Spatial-temporal Prompt Learning for Federated Weather Forecasting | Federated weather forecasting is a promising collaborative learning framework for analyzing meteorological data across participants from different countries and regions, thus embodying a global-scale real-time weather data predictive analytics platform to tackle climate change. This paper is to model the meteorological... | ['Jing Jiang', 'Tianyi Zhou', 'Tao Shen', 'Guodong Long', 'Shengchao Chen'] | 2023-05-23 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-6.31753862e-01 -3.17920834e-01 1.09003283e-01 -5.97207248e-01
-4.06823725e-01 -7.52469540e-01 6.14431322e-01 5.22423565e-01
8.97570280e-04 7.25123823e-01 5.47561109e-01 -4.89657283e-01
-4.70099032e-01 -1.22919583e+00 -4.54518497e-01 -8.87784243e-01
-9.50749099e-01 2.70365834e-01 6.66640997e-02 -2.91011520... | [6.652695655822754, 2.7524795532226562] |
b51db5a6-3ab2-4ef1-be4c-49f784acc444 | lstc-boosting-atomic-action-detection-with | 2110.09819 | null | https://arxiv.org/abs/2110.09819v1 | https://arxiv.org/pdf/2110.09819v1.pdf | LSTC: Boosting Atomic Action Detection with Long-Short-Term Context | In this paper, we place the atomic action detection problem into a Long-Short Term Context (LSTC) to analyze how the temporal reliance among video signals affect the action detection results. To do this, we decompose the action recognition pipeline into short-term and long-term reliance, in terms of the hypothesis that... | ['Feiyue Huang', 'Jilin Li', 'Chengjie Wang', 'Weiyao Lin', 'Yabiao Wang', 'Jian Li', 'Boshen Zhang', 'Yuxi Li'] | 2021-10-19 | null | null | null | null | ['atomic-action-recognition'] | ['computer-vision'] | [ 4.32054490e-01 4.02202196e-02 -3.45761329e-01 -5.61378360e-01
-8.29715908e-01 -3.16089928e-01 8.37653399e-01 -8.63487273e-02
-2.70091355e-01 4.91955370e-01 6.55264914e-01 2.16247812e-02
6.37046024e-02 -4.56468821e-01 -6.60590649e-01 -5.58008790e-01
-2.17704743e-01 1.53835669e-01 6.23167217e-01 1.36626035... | [8.339824676513672, 0.5771890878677368] |
93be1b6f-b419-49c5-9455-b8f6a00ec4cb | towards-autoformalization-of-mathematics-and | 2301.02195 | null | https://arxiv.org/abs/2301.02195v1 | https://arxiv.org/pdf/2301.02195v1.pdf | Towards Autoformalization of Mathematics and Code Correctness: Experiments with Elementary Proofs | The ever-growing complexity of mathematical proofs makes their manual verification by mathematicians very cognitively demanding. Autoformalization seeks to address this by translating proofs written in natural language into a formal representation that is computer-verifiable via interactive theorem provers. In this pap... | ['David Juedes', 'Razvan C. Bunescu', 'Garett Cunningham'] | 2023-01-05 | null | null | null | null | ['mathematical-proofs', 'semantic-parsing'] | ['miscellaneous', 'natural-language-processing'] | [ 2.00668737e-01 7.03641593e-01 1.73948094e-01 -3.13895524e-01
-7.62896895e-01 -1.37813890e+00 6.62270606e-01 1.12278536e-01
2.28329629e-01 8.86716127e-01 -3.81494880e-01 -1.59943044e+00
-2.02828526e-01 -1.12648976e+00 -1.19919670e+00 1.54155686e-01
-3.39900017e-01 4.66314614e-01 3.74296635e-01 -1.70281827... | [8.940459251403809, 7.046481609344482] |
ba1dd0f2-11ce-4847-9948-d7cd6a12b1cb | spotlights-probing-shapes-from-spherical | 2205.12564 | null | https://arxiv.org/abs/2205.12564v3 | https://arxiv.org/pdf/2205.12564v3.pdf | Spotlights: Probing Shapes from Spherical Viewpoints | Recent years have witnessed the surge of learned representations that directly build upon point clouds. Though becoming increasingly expressive, most existing representations still struggle to generate ordered point sets. Inspired by spherical multi-view scanners, we propose a novel sampling model called Spotlights to ... | ['Laurent Kneip', 'Soren Schwertfeger', 'Tao Sun', 'Xinyu Jiang', 'Minghao Xu', 'Wenqing Jiang', 'Ran Cheng', 'Lige Liu', 'Jiaxin Wei'] | 2022-05-25 | null | null | null | null | ['point-cloud-completion', 'point-cloud-registration'] | ['computer-vision', 'computer-vision'] | [ 6.60244226e-02 2.11859688e-01 2.55273879e-01 -1.74603745e-01
-8.46403062e-01 -7.86294520e-01 9.38679993e-01 -9.18787941e-02
-5.56641594e-02 1.11474909e-01 7.34664723e-02 -1.76294714e-01
1.26877785e-01 -8.00004721e-01 -1.10486257e+00 -4.40895557e-01
1.27340958e-01 1.11132348e+00 1.18224330e-01 2.33212635... | [8.416385650634766, -3.298004388809204] |
ed416320-9ac8-4b37-8a5c-8e3d124699c9 | corrnet3d-unsupervised-end-to-end-learning-of | 2012.15638 | null | https://arxiv.org/abs/2012.15638v2 | https://arxiv.org/pdf/2012.15638v2.pdf | CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Clouds | Motivated by the intuition that one can transform two aligned point clouds to each other more easily and meaningfully than a misaligned pair, we propose CorrNet3D -- the first unsupervised and end-to-end deep learning-based framework -- to drive the learning of dense correspondence between 3D shapes by means of deforma... | ['Ying He', 'Hui Yuan', 'Junhui Hou', 'Zhiyu Zhu', 'Yue Qian', 'Yiming Zeng'] | 2020-12-31 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zeng_CorrNet3D_Unsupervised_End-to-End_Learning_of_Dense_Correspondence_for_3D_Point_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zeng_CorrNet3D_Unsupervised_End-to-End_Learning_of_Dense_Correspondence_for_3D_Point_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-dense-shape-correspondence'] | ['computer-vision'] | [-0.11587847 0.20773318 0.1501286 -0.53207564 -0.80505306 -0.6342581
0.5780835 -0.18876946 0.01973961 0.2567884 0.10867994 -0.17093429
-0.09390073 -0.9424478 -1.3113217 -0.5810451 -0.09621061 1.0601618
0.11068848 -0.14852276 0.1268735 0.7726631 -1.2876692 0.03648499
0.7282014 0.73862 0.21... | [8.354134559631348, -3.350670337677002] |
0d2dde20-6186-43d3-9ddc-c11f16bc6318 | vizdoom-a-doom-based-ai-research-platform-for | 1605.02097 | null | http://arxiv.org/abs/1605.02097v2 | http://arxiv.org/pdf/1605.02097v2.pdf | ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning | The recent advances in deep neural networks have led to effective
vision-based reinforcement learning methods that have been employed to obtain
human-level controllers in Atari 2600 games from pixel data. Atari 2600 games,
however, do not resemble real-world tasks since they involve non-realistic 2D
environments and th... | ['Wojciech Jaśkowski', 'Michał Kempka', 'Marek Wydmuch', 'Jakub Toczek', 'Grzegorz Runc'] | 2016-05-06 | null | null | null | null | ['game-of-doom', 'fps-games'] | ['playing-games', 'playing-games'] | [-5.22394955e-01 -2.76572317e-01 2.98371583e-01 4.80455101e-01
1.10746667e-01 -5.20793200e-01 5.52411675e-01 -7.23036706e-01
-8.94940138e-01 8.33527625e-01 -4.67111945e-01 -4.10781592e-01
-1.46481425e-01 -7.43193328e-01 -6.60179913e-01 -5.39076805e-01
-2.44411379e-01 5.43189168e-01 6.14418149e-01 -1.02669370... | [3.752293825149536, 1.4696509838104248] |
377be5ac-c6e3-4622-9a9b-0bb74cad17f8 | toolformer-language-models-can-teach | 2302.04761 | null | https://arxiv.org/abs/2302.04761v1 | https://arxiv.org/pdf/2302.04761v1.pdf | Toolformer: Language Models Can Teach Themselves to Use Tools | Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also, paradoxically, struggle with basic functionality, such as arithmetic or factual lookup, where much simpler and smaller models excel. In this paper, we show that LMs can... | ['Thomas Scialom', 'Nicola Cancedda', 'Luke Zettlemoyer', 'Maria Lomeli', 'Roberta Raileanu', 'Roberto Dessì', 'Jane Dwivedi-Yu', 'Timo Schick'] | 2023-02-09 | null | null | null | null | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [-1.05595209e-01 6.77768067e-02 -3.80030185e-01 -3.06246459e-01
-8.85649025e-01 -9.81289029e-01 7.85325050e-01 1.52143538e-01
-4.18456912e-01 4.83884424e-01 -1.76328391e-01 -9.63696122e-01
2.43359715e-01 -7.26350367e-01 -7.13945329e-01 4.90972921e-02
8.01431984e-02 6.14245117e-01 2.55243897e-01 -4.65490699... | [8.426835060119629, 7.589385509490967] |
54885383-184a-4c1e-85a9-07e0b08c7de6 | rate-splitting-multiple-access-for-multi | 2102.08738 | null | https://arxiv.org/abs/2102.08738v1 | https://arxiv.org/pdf/2102.08738v1.pdf | Rate-Splitting Multiple Access for Multi-Antenna Broadcast Channel with Imperfect CSIT and CSIR | Rate-splitting multiple access (RSMA) has appeared as a powerful transmission and multiple access strategy for multi-user multi-antenna communications. Uniquely, this paper studies the optimization of the sum-rate of RSMA with imperfect channel state information (CSI) at the transmitter (CSIT) and the receivers (CSIR).... | ['Wonjae Shin', 'Bruno Clerckxz', 'Onur Dizdarz', 'Jihye An'] | 2021-02-17 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 2.02019915e-01 9.51122046e-02 -3.95980060e-01 1.38416858e-02
-7.12316036e-01 -3.28383803e-01 1.66729748e-01 -2.53729850e-01
-2.61220306e-01 1.08326566e+00 3.77153531e-02 -7.20525801e-01
-6.08360350e-01 -5.59200048e-01 -2.38567695e-01 -1.03385627e+00
-5.36886930e-01 -1.25261545e-01 -3.73556972e-01 -4.20313239... | [6.127575397491455, 1.4756481647491455] |
538331c5-4cb1-4ed1-97ef-d3106a79d405 | public-wisdom-matters-discourse-aware | 2209.13017 | null | https://arxiv.org/abs/2209.13017v2 | https://arxiv.org/pdf/2209.13017v2.pdf | Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text Classification | Social media has become the fulcrum of all forms of communication. Classifying social texts such as fake news, rumour, sarcasm, etc. has gained significant attention. The surface-level signals expressed by a social-text itself may not be adequate for such tasks; therefore, recent methods attempted to incorporate other ... | ['Tanmoy Chakraborty', 'Md. Shad Akhtar', 'S. M. Phaneendra Angara', 'Karish Grover'] | 2022-09-15 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [ 1.25058249e-01 7.76423812e-01 -1.31051198e-01 -1.62889659e-01
-4.84205276e-01 -2.47312844e-01 1.11122143e+00 5.71322024e-01
1.93408340e-01 3.13198835e-01 8.49009693e-01 -3.76918465e-01
3.47319305e-01 -7.80673444e-01 -7.28996813e-01 -5.59958875e-01
-2.69304756e-02 2.33895034e-01 3.29268813e-01 -8.33311141... | [8.188837051391602, 10.277271270751953] |
dd6b1dda-eee3-4210-b4b6-3a806f0f5f6d | data-driven-meta-set-based-fine-grained | 2008.02438 | null | https://arxiv.org/abs/2008.02438v1 | https://arxiv.org/pdf/2008.02438v1.pdf | Data-driven Meta-set Based Fine-Grained Visual Classification | Constructing fine-grained image datasets typically requires domain-specific expert knowledge, which is not always available for crowd-sourcing platform annotators. Accordingly, learning directly from web images becomes an alternative method for fine-grained visual recognition. However, label noise in the web training s... | ['Zechao Li', 'Qi Wu', 'Yazhou Yao', 'Chuanyi Zhang', 'Zhenmin Tang', 'Xiangbo Shu'] | 2020-08-06 | null | null | null | null | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 1.69523172e-02 -4.76086617e-01 -6.76905811e-02 -5.35465658e-01
-1.32515967e+00 -8.13694894e-01 3.51254284e-01 -3.99722531e-02
-3.26128811e-01 6.45024776e-01 1.37024477e-01 2.39552483e-01
-3.32949385e-02 -6.95784569e-01 -9.49554682e-01 -8.91382098e-01
7.35453188e-01 2.27977440e-01 2.20538914e-01 -4.68955189... | [9.583373069763184, 2.623800277709961] |
821c6ed4-0f40-4b85-a75f-83cbc43b1efe | gan2x-non-lambertian-inverse-rendering-of | 2206.09244 | null | https://arxiv.org/abs/2206.09244v4 | https://arxiv.org/pdf/2206.09244v4.pdf | GAN2X: Non-Lambertian Inverse Rendering of Image GANs | 2D images are observations of the 3D physical world depicted with the geometry, material, and illumination components. Recovering these underlying intrinsic components from 2D images, also known as inverse rendering, usually requires a supervised setting with paired images collected from multiple viewpoints and lightin... | ['Christian Theobalt', 'Lingjie Liu', 'Ayush Tewari', 'Xingang Pan'] | 2022-06-18 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 6.62975848e-01 2.35162094e-01 5.12507141e-01 -5.51554680e-01
-7.02028036e-01 -5.20763338e-01 6.19388700e-01 -7.01581717e-01
3.94587994e-01 5.01856804e-01 2.06371620e-01 -5.22855259e-02
3.05839241e-01 -9.23203051e-01 -7.58723676e-01 -9.09946799e-01
6.32151723e-01 7.91584253e-01 -3.28836858e-01 -3.61336738... | [12.648963928222656, -0.4665989875793457] |
08463077-762e-4fce-b3ad-74263132bdb5 | improved-beam-search-for-hallucination | 2212.02712 | null | https://arxiv.org/abs/2212.02712v1 | https://arxiv.org/pdf/2212.02712v1.pdf | Improved Beam Search for Hallucination Mitigation in Abstractive Summarization | Advancement in large pretrained language models has significantly improved their performance for conditional language generation tasks including summarization albeit with hallucinations. To reduce hallucinations, conventional methods proposed improving beam search or using a fact checker as a postprocessing step. In th... | ['Erik Visser', 'Arvind Krishna Sridhar'] | 2022-12-06 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 6.00195646e-01 5.32538772e-01 -2.75789708e-01 -2.66663194e-01
-1.08012021e+00 -9.33822021e-02 8.73951495e-01 5.02287626e-01
-5.52641690e-01 1.16786897e+00 1.07421637e+00 -3.13752294e-01
-1.56559888e-02 -7.71862090e-01 -5.94297826e-01 -2.05395177e-01
2.24351704e-01 3.83644044e-01 6.43566251e-02 -1.71804130... | [12.243635177612305, 9.283980369567871] |
f7a80a5c-ee54-4afa-aa01-462b15dc212c | laplace-approximated-neural-additive-models | 2305.16905 | null | https://arxiv.org/abs/2305.16905v1 | https://arxiv.org/pdf/2305.16905v1.pdf | Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference | Deep neural networks (DNNs) have found successful applications in many fields, but their black-box nature hinders interpretability. This is addressed by the neural additive model (NAM), in which the network is divided into additive sub-networks, thus making apparent the interaction between input features and prediction... | ['Vincent Fortuin', 'Gunnar Rätsch', 'Hugo Yèche', 'Alexander Immer', 'Kouroche Bouchiat'] | 2023-05-26 | null | null | null | null | ['bayesian-inference', 'additive-models'] | ['methodology', 'methodology'] | [ 3.82859141e-01 5.76292574e-01 -2.07725167e-01 -7.13917911e-01
-7.22265065e-01 -3.57250541e-01 5.39614856e-01 3.45425189e-01
-2.28421718e-01 1.02232170e+00 2.50838459e-01 -4.77876276e-01
-7.35617399e-01 -5.23315847e-01 -9.66141105e-01 -7.26623893e-01
-3.56807619e-01 5.86614370e-01 -7.32990429e-02 9.33434516... | [8.485803604125977, 5.4185051918029785] |
f84c145d-af29-4bec-bb25-0d8affbac055 | chatgpt-powered-conversational-drug-editing | 2305.18090 | null | https://arxiv.org/abs/2305.18090v1 | https://arxiv.org/pdf/2305.18090v1.pdf | ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback | Recent advancements in conversational large language models (LLMs), such as ChatGPT, have demonstrated remarkable promise in various domains, including drug discovery. However, existing works mainly focus on investigating the capabilities of conversational LLMs on chemical reaction and retrosynthesis. While drug editin... | ['Chaowei Xiao', 'Hongyu Guo', 'Ling Liu', 'Chengpeng Wang', 'Yijin Yang', 'Jiongxiao Wang', 'Shengchao Liu'] | 2023-05-29 | null | null | null | null | ['drug-discovery', 'retrosynthesis'] | ['medical', 'medical'] | [ 4.30210799e-01 1.66063905e-01 -5.43731987e-01 -6.18118495e-02
-8.53785634e-01 -9.29800630e-01 5.18911541e-01 6.58599794e-01
9.27524865e-02 9.69782650e-01 4.90859449e-01 -8.54131699e-01
4.32887264e-02 -4.49880898e-01 -7.13324189e-01 -6.96767330e-01
1.47440732e-01 3.90763700e-01 -3.29541653e-01 -1.89110830... | [4.962607383728027, 5.858999252319336] |
a0654eee-aed5-4d72-b18f-ecf968e5e03b | hardvs-revisiting-human-activity-recognition | 2211.09648 | null | https://arxiv.org/abs/2211.09648v1 | https://arxiv.org/pdf/2211.09648v1.pdf | HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors | The main streams of human activity recognition (HAR) algorithms are developed based on RGB cameras which are suffered from illumination, fast motion, privacy-preserving, and large energy consumption. Meanwhile, the biologically inspired event cameras attracted great interest due to their unique features, such as high d... | ['Yonghong Tian', 'YaoWei Wang', 'Guoqi Li', 'Lin Zhu', 'Zhimin Bao', 'Bo Jiang', 'Zongzhen Wu', 'Xiao Wang'] | 2022-11-17 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 8.49651322e-02 -5.39566100e-01 -1.25792980e-01 -2.29447514e-01
-4.05818880e-01 -3.06741863e-01 6.29244626e-01 3.52603570e-02
-4.49732125e-01 7.92561531e-01 7.00428963e-01 3.98620039e-01
-1.14959002e-01 -6.49118066e-01 -6.42982841e-01 -8.64403546e-01
-7.20254406e-02 -2.66712606e-01 3.41480315e-01 1.44424528... | [7.994204521179199, 0.7675341963768005] |
299917e0-f951-49c8-b345-5241c9964cd5 | uncertainty-inspired-underwater-image | 2207.09689 | null | https://arxiv.org/abs/2207.09689v1 | https://arxiv.org/pdf/2207.09689v1.pdf | Uncertainty Inspired Underwater Image Enhancement | A main challenge faced in the deep learning-based Underwater Image Enhancement (UIE) is that the ground truth high-quality image is unavailable. Most of the existing methods first generate approximate reference maps and then train an enhancement network with certainty. This kind of method fails to handle the ambiguity ... | ['Kai-Kuang Ma', 'Xinghao Ding', 'Yue Huang', 'Wu Wang', 'Zhenqi Fu'] | 2022-07-20 | null | null | null | null | ['uie'] | ['computer-vision'] | [ 1.18899181e-01 -2.93581896e-02 6.41156852e-01 -5.16100943e-01
-1.07960856e+00 -2.55003780e-01 1.91183150e-01 -2.54290223e-01
-7.40586340e-01 8.06539714e-01 1.22476645e-01 2.45000228e-01
-2.67655492e-01 -1.02702737e+00 -8.74402761e-01 -1.12817264e+00
1.81788474e-01 1.01144530e-01 1.13713540e-01 -2.30255887... | [10.707134246826172, -3.523740768432617] |
493b78fd-ca58-4702-9059-d9acb8dbce2d | butterfly-robust-one-step-approach-towards | 1905.07720 | null | https://arxiv.org/abs/1905.07720v3 | https://arxiv.org/pdf/1905.07720v3.pdf | Butterfly: One-step Approach towards Wildly Unsupervised Domain Adaptation | In unsupervised domain adaptation (UDA), classifiers for the target domain (TD) are trained with clean labeled data from the source domain (SD) and unlabeled data from TD. However, in the wild, it is difficult to acquire a large amount of perfectly clean labeled data in SD given limited budget. Hence, we consider a new... | ['Bo Han', 'Masashi Sugiyama', 'Jie Lu', 'Feng Liu', 'Guangquan Zhang', 'Gang Niu'] | 2019-05-19 | butterfly-one-step-approach-towards-wildly | http://128.84.4.34/abs/1905.07720 | http://128.84.4.34/pdf/1905.07720 | null | ['wildly-unsupervised-domain-adaptation'] | ['computer-vision'] | [ 2.84320824e-02 4.45084386e-02 -1.98545661e-02 -5.28610945e-01
-1.03652692e+00 -7.46876240e-01 3.41257185e-01 -2.93312341e-01
-3.00369322e-01 9.81331825e-01 -6.70257583e-02 -2.76054770e-01
1.65154412e-01 -6.61626995e-01 -7.95966327e-01 -9.58854973e-01
3.55343848e-01 7.57861197e-01 -8.42484236e-02 -4.10948209... | [10.442687034606934, 3.090784788131714] |
5f070ff5-8e61-436a-bd5f-0dcd5a473175 | universal-transformers | 1807.03819 | null | http://arxiv.org/abs/1807.03819v3 | http://arxiv.org/pdf/1807.03819v3.pdf | Universal Transformers | Recurrent neural networks (RNNs) sequentially process data by updating their
state with each new data point, and have long been the de facto choice for
sequence modeling tasks. However, their inherently sequential computation makes
them slow to train. Feed-forward and convolutional architectures have recently
been show... | ['Łukasz Kaiser', 'Stephan Gouws', 'Mostafa Dehghani', 'Jakob Uszkoreit', 'Oriol Vinyals'] | 2018-07-10 | universal-transformers-1 | https://openreview.net/forum?id=HyzdRiR9Y7 | https://openreview.net/pdf?id=HyzdRiR9Y7 | iclr-2019-5 | ['learning-to-execute', 'lambada'] | ['computer-code', 'natural-language-processing'] | [ 6.19703829e-01 -7.45656118e-02 -3.11849684e-01 -2.27213651e-01
-6.00264251e-01 -7.52090216e-01 8.04215074e-01 -1.56496286e-01
-5.35808504e-01 6.34102762e-01 1.89586848e-01 -1.07326531e+00
3.88537765e-01 -7.49174654e-01 -1.15497243e+00 -4.94469553e-01
2.74648696e-01 6.98105276e-01 9.56594050e-02 -4.11822051... | [10.808798789978027, 7.078009128570557] |
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