paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1e260fd9-8423-4ba1-8b2d-bc0c0e12ee9b | enhancing-balanced-graph-edge-partition-with | 2012.09451 | null | https://arxiv.org/abs/2012.09451v1 | https://arxiv.org/pdf/2012.09451v1.pdf | Enhancing Balanced Graph Edge Partition with Effective Local Search | Graph partition is a key component to achieve workload balance and reduce job completion time in parallel graph processing systems. Among the various partition strategies, edge partition has demonstrated more promising performance in power-law graphs than vertex partition and thereby has been more widely adopted as the... | ['Kian-Lee Tan', 'Dongxiang Zhang', 'Yi Zhou', 'Mingyu Xiao', 'Zhenyu Guo'] | 2020-12-17 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 1.53295079e-03 -1.82357803e-01 -4.02144790e-01 -1.08713266e-02
-2.24619031e-01 -3.52521926e-01 1.25578530e-02 4.93989617e-01
-2.04132229e-01 7.32795060e-01 -3.64816189e-01 -5.00411153e-01
-6.27393305e-01 -1.07240582e+00 -3.19950432e-01 -7.25526571e-01
-5.39496839e-02 7.43179262e-01 6.08691156e-01 -1.72906712... | [7.071807861328125, 5.1654229164123535] |
b47e48fc-ad3c-4226-9bc5-5aefcd1843a2 | automatic-environmental-sound-recognition | 1607.04589 | null | http://arxiv.org/abs/1607.04589v1 | http://arxiv.org/pdf/1607.04589v1.pdf | Automatic Environmental Sound Recognition: Performance versus Computational Cost | In the context of the Internet of Things (IoT), sound sensing applications
are required to run on embedded platforms where notions of product pricing and
form factor impose hard constraints on the available computing power. Whereas
Automatic Environmental Sound Recognition (AESR) algorithms are most often
developed wit... | ['Mark D. Plumbley', 'Sacha Krstulovic', 'Adam M. Stark', 'Siddharth Sigtia'] | 2016-07-15 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 2.60256648e-01 -3.36403996e-01 1.30702212e-01 -2.35641167e-01
-7.90340602e-01 -4.42880124e-01 3.87276679e-01 -9.48826298e-02
-5.71382403e-01 1.12982780e-01 -2.09091336e-01 -6.05795979e-01
-5.25513947e-01 -8.60163867e-01 -1.30407095e-01 -5.72143555e-01
2.63386250e-01 2.31017619e-02 -1.15647607e-01 2.30252430... | [14.573345184326172, 5.464269638061523] |
c383d237-3f2f-4c15-b0d2-04bdb747e17e | itervm-iterative-vision-modeling-module-for | 2204.0263 | null | https://arxiv.org/abs/2204.02630v1 | https://arxiv.org/pdf/2204.02630v1.pdf | IterVM: Iterative Vision Modeling Module for Scene Text Recognition | Scene text recognition (STR) is a challenging problem due to the imperfect imagery conditions in natural images. State-of-the-art methods utilize both visual cues and linguistic knowledge to tackle this challenging problem. Specifically, they propose iterative language modeling module (IterLM) to repeatedly refine the ... | ['Yongtao Wang', 'Xiaojie Chu'] | 2022-04-06 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 2.56463289e-01 -6.12184465e-01 -1.66465849e-01 -7.49170780e-02
-4.23119456e-01 -2.70835191e-01 7.12253332e-01 -1.18151635e-01
-3.97565216e-01 1.34888273e-02 1.32460594e-01 -2.79247165e-01
3.43447179e-01 -6.58294380e-01 -5.38499415e-01 -5.96045196e-01
9.02499437e-01 4.73842137e-02 4.06326741e-01 1.04326203... | [11.801560401916504, 2.070963144302368] |
f02dffed-3a32-4afe-b044-2c2c4380767b | temporal-action-proposal-generation-with-1 | 2112.07984 | null | https://arxiv.org/abs/2112.07984v1 | https://arxiv.org/pdf/2112.07984v1.pdf | Temporal Action Proposal Generation with Background Constraint | Temporal action proposal generation (TAPG) is a challenging task that aims to locate action instances in untrimmed videos with temporal boundaries. To evaluate the confidence of proposals, the existing works typically predict action score of proposals that are supervised by the temporal Intersection-over-Union (tIoU) b... | ['Hujie Huang', 'Hongxun Yao', 'Boyang xia', 'Sheng Jin', 'Lining Wang', 'Wenhao Wu', 'Haosen Yang'] | 2021-12-15 | null | null | null | null | ['temporal-action-proposal-generation', 'action-localization'] | ['computer-vision', 'computer-vision'] | [ 2.93852329e-01 -1.93441305e-02 -4.28423643e-01 -1.62572518e-01
-6.54293716e-01 -3.92916203e-02 5.99388421e-01 -1.72818869e-01
-3.41358542e-01 6.74389780e-01 3.22067678e-01 1.35305017e-01
2.62674272e-01 -4.72832561e-01 -5.62549591e-01 -8.24309826e-01
2.02294856e-01 -8.71425346e-02 9.33006465e-01 -1.07934652... | [8.503103256225586, 0.55224609375] |
b1bffea5-54d2-4996-b9e2-4db603b9449b | a-comparative-study-of-face-detection | 2305.11077 | null | https://arxiv.org/abs/2305.11077v1 | https://arxiv.org/pdf/2305.11077v1.pdf | A Comparative Study of Face Detection Algorithms for Masked Face Detection | Contemporary face detection algorithms have to deal with many challenges such as variations in pose, illumination, and scale. A subclass of the face detection problem that has recently gained increasing attention is occluded face detection, or more specifically, the detection of masked faces. Three years on since the a... | ['Subhankar Mishra', 'Danush Shekar', 'Sahel Mohammad Iqbal'] | 2023-05-18 | null | null | null | null | ['face-detection', 'occluded-face-detection'] | ['computer-vision', 'computer-vision'] | [ 2.55283713e-01 -2.69751728e-01 6.35879040e-02 -3.87207031e-01
-4.24166441e-01 -5.40681601e-01 5.20860791e-01 -2.35012725e-01
-3.63688082e-01 3.54633421e-01 -3.67963850e-03 1.28098235e-01
2.54562289e-01 -3.16498041e-01 -2.78125674e-01 -7.74154723e-01
-3.49051297e-01 3.45576137e-01 4.09540124e-02 -8.17891434... | [13.32138729095459, 0.7720433473587036] |
cca59710-efe9-4a90-8bd7-2c1135240020 | depth-aware-cnn-for-rgb-d-segmentation | 1803.06791 | null | http://arxiv.org/abs/1803.06791v1 | http://arxiv.org/pdf/1803.06791v1.pdf | Depth-aware CNN for RGB-D Segmentation | Convolutional neural networks (CNN) are limited by the lack of capability to
handle geometric information due to the fixed grid kernel structure. The
availability of depth data enables progress in RGB-D semantic segmentation with
CNNs. State-of-the-art methods either use depth as additional images or process
spatial in... | ['Weiyue Wang', 'Ulrich Neumann'] | 2018-03-19 | depth-aware-cnn-for-rgb-d-segmentation-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Weiyue_Wang_Depth-aware_CNN_for_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Weiyue_Wang_Depth-aware_CNN_for_ECCV_2018_paper.pdf | eccv-2018-9 | ['thermal-image-segmentation'] | ['computer-vision'] | [ 1.03061907e-01 1.87361032e-01 1.43904999e-01 -5.48604012e-01
-3.57844263e-01 -6.81163967e-01 5.96679747e-01 3.29415679e-01
-6.61561847e-01 2.26891711e-01 -1.65078461e-01 -4.00709599e-01
1.85730159e-01 -1.22547698e+00 -6.86246812e-01 -2.94026226e-01
-4.23020124e-02 2.07670882e-01 7.12864578e-01 -1.81362107... | [8.271322250366211, -3.02783203125] |
f24acf82-3765-4b19-ad07-e2c6edfa306e | empirical-study-of-diachronic-word-embeddings | 1909.01863 | null | https://arxiv.org/abs/1909.01863v1 | https://arxiv.org/pdf/1909.01863v1.pdf | Empirical Study of Diachronic Word Embeddings for Scarce Data | Word meaning change can be inferred from drifts of time-varying word embeddings. However, temporal data may be too sparse to build robust word embeddings and to discriminate significant drifts from noise. In this paper, we compare three models to learn diachronic word embeddings on scarce data: incremental updating of ... | ['Alexandre Allauzen', 'Syrielle Montariol'] | 2019-09-04 | empirical-study-of-diachronic-word-embeddings-1 | https://aclanthology.org/R19-1092 | https://aclanthology.org/R19-1092.pdf | ranlp-2019-9 | ['diachronic-word-embeddings'] | ['natural-language-processing'] | [-6.56683370e-02 -2.92504221e-01 -3.21729124e-01 -3.34352821e-01
-2.74561107e-01 -7.29025841e-01 9.39413011e-01 5.45028865e-01
-1.04087341e+00 7.74033248e-01 5.55281401e-01 -3.68579626e-01
-3.30632269e-01 -5.76411545e-01 -4.04854029e-01 -6.49703562e-01
-3.14615488e-01 4.16733474e-01 2.22826198e-01 -2.01728344... | [10.18155574798584, 8.752906799316406] |
d577295f-e12d-44e6-baef-9a9bc1ad5dfe | linguistic-knowledge-in-data-augmentation-for | 2111.14709 | null | https://arxiv.org/abs/2111.14709v3 | https://arxiv.org/pdf/2111.14709v3.pdf | Linguistic Knowledge in Data Augmentation for Natural Language Processing: An Example on Chinese Question Matching | To investigate the role of linguistic knowledge in data augmentation (DA) for Natural Language Processing (NLP), we designed two adapted DA programs and applied them to LCQMC (a Large-scale Chinese Question Matching Corpus) for a binary Chinese question matching classification task. The two DA programs produce augmente... | ['Zhengxiang Wang'] | 2021-11-29 | null | null | null | null | ['text-augmentation', 'question-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.43213761e-01 2.84712076e-01 3.60438138e-01 -2.73527861e-01
-8.40307295e-01 -5.10556161e-01 7.19970942e-01 3.19700003e-01
-1.00504279e+00 4.74605143e-01 2.71438181e-01 -9.10027921e-01
2.13097438e-01 -9.22339380e-01 -7.64846742e-01 -1.64554209e-01
3.44730616e-01 3.90300274e-01 2.31745824e-01 -6.94652677... | [10.988883018493652, 9.440794944763184] |
19788220-5804-4299-a5ed-a581b5c4080f | llvip-a-visible-infrared-paired-dataset-for | 2108.10831 | null | https://arxiv.org/abs/2108.10831v4 | https://arxiv.org/pdf/2108.10831v4.pdf | LLVIP: A Visible-infrared Paired Dataset for Low-light Vision | It is very challenging for various visual tasks such as image fusion, pedestrian detection and image-to-image translation in low light conditions due to the loss of effective target areas. In this case, infrared and visible images can be used together to provide both rich detail information and effective target areas. ... | ['Wenli Zhou', 'ShengJie Liu', 'Wenqi Tang', 'Minzhen Li', 'Chuang Zhu', 'Xinyu Jia'] | 2021-08-24 | null | null | null | null | ['multispectral-object-detection', 'infrared-and-visible-image-fusion', 'low-light-pedestrian-detection', 'thermal-infrared-pedestrian-detection'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.70898336e-01 -9.03043926e-01 -1.08655736e-01 -2.84657925e-01
-4.93744701e-01 -5.71888745e-01 5.96690178e-01 -2.23656833e-01
-5.29444158e-01 6.66393280e-01 -1.09490314e-02 -3.05489570e-01
4.75483924e-01 -8.68100882e-01 -4.62777644e-01 -1.04063141e+00
5.12699544e-01 -2.50384748e-01 3.84035259e-01 -3.94501448... | [10.006142616271973, -1.5978096723556519] |
a39dc226-6e8c-4bcc-a712-47e7e17cee20 | conciseness-an-overlooked-language-task | 2211.04126 | null | https://arxiv.org/abs/2211.04126v1 | https://arxiv.org/pdf/2211.04126v1.pdf | Conciseness: An Overlooked Language Task | We report on novel investigations into training models that make sentences concise. We define the task and show that it is different from related tasks such as summarization and simplification. For evaluation, we release two test sets, consisting of 2000 sentences each, that were annotated by two and five human annotat... | ['Shankar Kumar', 'Chris Alberti', 'Aashish Kumar', 'Felix Stahlberg'] | 2022-11-08 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 4.50485080e-01 5.20530641e-01 -8.58782753e-02 -4.97149616e-01
-1.50401211e+00 -6.15002990e-01 8.42739224e-01 2.47462660e-01
-6.08039737e-01 1.26491904e+00 6.36577129e-01 -3.14286292e-01
2.27775007e-01 -4.21854019e-01 -8.66029084e-01 -1.70948625e-01
2.91273803e-01 9.38554466e-01 -6.79182038e-02 -5.66153765... | [11.73682975769043, 9.238557815551758] |
581c48bb-138c-4d2d-aff4-731b35faa5e3 | aug-ila-more-transferable-intermediate-level | null | null | https://openreview.net/forum?id=zKbMQ2NY1y | https://openreview.net/pdf?id=zKbMQ2NY1y | Aug-ILA: More Transferable Intermediate Level Attacks with Augmented References | An intriguing property of deep neural networks is that adversarial attacks can transfer across different models. Existing methods such as the Intermediate Level Attack (ILA) further improve black-box transferability by fine-tuning a reference adversarial attack, so as to maximize the perturbation on a pre-specified lay... | ['Dit-yan Yeung', 'Chiu Wai Yan'] | 2021-09-29 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 4.81473982e-01 1.52491510e-01 2.06303462e-01 -1.48160994e-01
-8.01378965e-01 -8.48423243e-01 6.77413225e-01 -3.25279355e-01
-5.96077979e-01 5.95593929e-01 -1.77105352e-01 -5.23431063e-01
1.13775894e-01 -8.60004365e-01 -1.25774479e+00 -7.47906327e-01
-3.31034571e-01 -1.06836557e-01 1.47617444e-01 -5.75481474... | [5.578927516937256, 7.910180568695068] |
b88898f4-e8be-4a2f-a473-e06a009a5a5e | approaching-neural-chinese-word-segmentation | 2008.05348 | null | https://arxiv.org/abs/2008.05348v3 | https://arxiv.org/pdf/2008.05348v3.pdf | Approaching Neural Chinese Word Segmentation as a Low-Resource Machine Translation Task | Chinese word segmentation has entered the deep learning era which greatly reduces the hassle of feature engineering. Recently, some researchers attempted to treat it as character-level translation, which further simplified model designing, but there is a performance gap between the translation-based approach and other ... | ['Pin-zhen Chen', 'Kenneth Heafield'] | 2020-08-12 | null | null | null | null | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 2.36428723e-01 -1.43483758e-01 -3.76556724e-01 -4.96621400e-01
-1.13347602e+00 -4.26362395e-01 2.60359585e-01 -2.61793762e-01
-8.05370450e-01 6.95357740e-01 2.06811994e-01 -7.02897906e-01
4.89871591e-01 -5.47750711e-01 -5.46054006e-01 -3.03017467e-01
6.60002172e-01 5.93225658e-01 1.74071699e-01 -1.38680547... | [10.001847267150879, 10.162835121154785] |
f2168ce1-2bc2-4c55-a46c-d6607481258a | lm-cppf-paraphrasing-guided-data-augmentation | 2305.18169 | null | https://arxiv.org/abs/2305.18169v3 | https://arxiv.org/pdf/2305.18169v3.pdf | LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning | In recent years, there has been significant progress in developing pre-trained language models for NLP. However, these models often struggle when fine-tuned on small datasets. To address this issue, researchers have proposed various adaptation approaches. Prompt-based tuning is arguably the most common way, especially ... | ['Yadollah Yaghoobzadeh', 'Sascha Rothe', 'Amirhossein Abaskohi'] | 2023-05-29 | null | null | null | null | ['sentiment-analysis', 'linguistic-acceptability'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.58302987e-01 1.31853908e-01 -6.10163391e-01 -4.15783495e-01
-9.89498317e-01 -6.86879277e-01 8.91020417e-01 2.37220481e-01
-4.99578089e-01 7.87842572e-01 5.78290582e-01 -4.42532331e-01
3.04511786e-01 -7.43691981e-01 -6.93087161e-01 -3.41922522e-01
6.06377900e-01 1.02472270e+00 -1.16400875e-01 -5.63155115... | [10.818796157836914, 8.280889511108398] |
d9d7e011-ce0b-4bc6-9ad7-82e092c92d94 | data-driven-segmentation-of-post-mortem-iris | 1807.04154 | null | http://arxiv.org/abs/1807.04154v1 | http://arxiv.org/pdf/1807.04154v1.pdf | Data-Driven Segmentation of Post-mortem Iris Images | This paper presents a method for segmenting iris images obtained from the
deceased subjects, by training a deep convolutional neural network (DCNN)
designed for the purpose of semantic segmentation. Post-mortem iris recognition
has recently emerged as an alternative, or additional, method useful in
forensic analysis. A... | ['Mateusz Trokielewicz', 'Adam Czajka'] | 2018-07-11 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 2.87004799e-01 1.65580660e-01 4.79795970e-03 -3.78996849e-01
-6.66019261e-01 -5.08211493e-01 3.12832057e-01 1.88259214e-01
-6.26347959e-01 4.28368628e-01 -1.41402498e-01 -3.90015811e-01
-6.51211083e-01 -4.22894388e-01 -4.18765604e-01 -1.02316999e+00
-7.79533014e-02 9.23671961e-01 -2.54276991e-01 -2.94253640... | [3.7429049015045166, -3.631761312484741] |
e76a19db-e76a-4b77-8b4b-4bff2746838f | lexically-constrained-text-generation-through | 2012.10813 | null | https://arxiv.org/abs/2012.10813v1 | https://arxiv.org/pdf/2012.10813v1.pdf | Lexically-constrained Text Generation through Commonsense Knowledge Extraction and Injection | Conditional text generation has been a challenging task that is yet to see human-level performance from state-of-the-art models. In this work, we specifically focus on the Commongen benchmark, wherein the aim is to generate a plausible sentence for a given set of input concepts. Despite advances in other tasks, large p... | ['Alessandro Oltramari', 'Eric Nyberg', 'Kaixin Ma', 'Jonathan Francis', 'Har Simrat Singh', 'Varsha Kuppur Rajendra', 'Pulkit Goel', 'Yikang Li'] | 2020-12-19 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [ 9.18233514e-01 6.60512447e-01 -1.05291894e-02 -4.22089785e-01
-9.00248885e-01 -5.64328969e-01 8.20098996e-01 1.83628127e-01
-2.54790872e-01 9.33152616e-01 6.44051969e-01 -4.20636147e-01
2.33756661e-01 -9.52516019e-01 -7.31629193e-01 -3.42855044e-02
7.19791174e-01 6.16762280e-01 -1.17796317e-01 -6.66434586... | [11.2650728225708, 8.849175453186035] |
2e68935b-16ea-4853-9bdc-c14562998ea9 | centam-creation-and-validation-of-a-new | null | null | https://aclanthology.org/2020.bucc-1.10 | https://aclanthology.org/2020.bucc-1.10.pdf | cEnTam: Creation and Validation of a New English-Tamil Bilingual Corpus | Natural Language Processing (NLP), is the field of artificial intelligence that gives the computer the ability to interpret, perceive and extract appropriate information from human languages. Contemporary NLP is predominantly a data driven process. It employs machine learning and statistical algorithms to learn languag... | ['Soman Kp', 'Premjith B', 'Sanjanasri JP', 'Vijay Krishna Menon'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [ 1.02891788e-01 -5.48450984e-02 -3.75120103e-01 -3.08976233e-01
-6.84136629e-01 -1.05663598e+00 9.26108956e-01 6.95015132e-01
-6.64460957e-01 1.29269743e+00 3.16923410e-01 -7.65268683e-01
-5.79743795e-02 -4.77847725e-01 -2.33650312e-01 -2.37589896e-01
1.64241150e-01 9.63912010e-01 2.45605726e-02 -5.74261606... | [10.610873222351074, 10.025845527648926] |
723f1334-0d76-40c0-99ad-86f3a5947cf5 | challenges-in-clinical-natural-language | null | null | https://www.sciencedirect.com/science/article/pii/S1532046415001501?via%3Dihub | https://www.sciencedirect.com/science/article/pii/S1532046415001501/pdfft?md5=0f078fadd8924b8ec74b9a861e96863f&pid=1-s2.0-S1532046415001501-main.pdf | Challenges in clinical natural language processing for automated disorder normalization | Background
Identifying key variables such as disorders within the clinical narratives in electronic health records has wide-ranging applications within clinical practice and biomedical research. Previous research has demonstrated reduced performance of disorder named entity recognition (NER) and normalization (or grou... | ['Robert Leaman', 'Zhiyong Lu', 'Ritu Khare'] | 2015-07-14 | null | null | null | journal-of-biomedical-informatics-2015-7 | ['medical-named-entity-recognition'] | ['natural-language-processing'] | [ 1.12379879e-01 2.20633537e-01 -2.36746117e-01 -1.92269936e-01
-1.26146817e+00 -6.83102787e-01 3.29380184e-01 8.37584376e-01
-9.39742923e-01 9.21046436e-01 7.14799047e-01 -2.96448022e-01
-5.13735771e-01 -5.85852146e-01 -3.53591084e-01 -3.77956152e-01
1.36253029e-01 5.12998283e-01 -1.91795796e-01 -2.12981939... | [8.430915832519531, 8.743837356567383] |
e927908f-0a40-42d6-af7a-48fec7c07f19 | cup-a-conservative-update-policy-algorithm-1 | 2202.07565 | null | https://arxiv.org/abs/2202.07565v1 | https://arxiv.org/pdf/2202.07565v1.pdf | CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning | Safe reinforcement learning (RL) is still very challenging since it requires the agent to consider both return maximization and safe exploration. In this paper, we propose CUP, a Conservative Update Policy algorithm with a theoretical safety guarantee. We derive the CUP based on the new proposed performance bounds and ... | ['Gang Pan', 'Pengfei Li', 'Yu Zhang', 'Juntao Dai', 'Jiaming Ji', 'Long Yang'] | 2022-02-15 | cup-a-conservative-update-policy-algorithm | https://openreview.net/forum?id=2wiaitACS_O | https://openreview.net/pdf?id=2wiaitACS_O | null | ['safe-exploration'] | ['robots'] | [-5.48695326e-01 1.86380550e-01 -5.06209850e-01 -2.08022855e-02
-1.02206779e+00 -6.03460550e-01 1.32430390e-01 1.44139886e-01
-6.52639508e-01 1.08100533e+00 9.85179842e-02 -3.48637968e-01
-3.35132569e-01 -6.64217889e-01 -9.93605793e-01 -8.50710094e-01
-5.07275939e-01 5.36840148e-02 2.80249864e-01 -2.76356965... | [4.296194076538086, 2.3286256790161133] |
ba3cf05a-5f05-446c-b01e-e294b8512f12 | entity-tracking-improves-cloze-style-reading | 1810.02891 | null | http://arxiv.org/abs/1810.02891v1 | http://arxiv.org/pdf/1810.02891v1.pdf | Entity Tracking Improves Cloze-style Reading Comprehension | Reading comprehension tasks test the ability of models to process long-term
context and remember salient information. Recent work has shown that relatively
simple neural methods such as the Attention Sum-Reader can perform well on
these tasks; however, these systems still significantly trail human
performance. Analysis... | ['Alexander M. Rush', 'Sam Wiseman', 'Luong Hoang'] | 2018-10-05 | entity-tracking-improves-cloze-style-reading-1 | https://aclanthology.org/D18-1130 | https://aclanthology.org/D18-1130.pdf | emnlp-2018-10 | ['lambada'] | ['natural-language-processing'] | [ 8.64676237e-02 2.35172004e-01 5.31978197e-02 -1.68618232e-01
-7.91141570e-01 -6.18495941e-01 1.04132593e+00 7.15987742e-01
-9.22138631e-01 8.62281144e-01 4.18685019e-01 -2.34628141e-01
-2.79506952e-01 -5.28578460e-01 -7.28509307e-01 -1.82484791e-01
-1.21398382e-01 5.75851500e-01 7.60730863e-01 -3.12731683... | [11.041815757751465, 8.194320678710938] |
4acfb409-f9f3-4ee4-930a-b75382787234 | robust-depth-completion-with-uncertainty | 2112.07895 | null | https://arxiv.org/abs/2112.07895v2 | https://arxiv.org/pdf/2112.07895v2.pdf | Robust Depth Completion with Uncertainty-Driven Loss Functions | Recovering a dense depth image from sparse LiDAR scans is a challenging task. Despite the popularity of color-guided methods for sparse-to-dense depth completion, they treated pixels equally during optimization, ignoring the uneven distribution characteristics in the sparse depth map and the accumulated outliers in the... | ['Guangming Shi', 'Xin Li', 'Jinjian Wu', 'Leida Li', 'Weisheng Dong', 'Yufan Zhu'] | 2021-12-15 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 3.76852036e-01 1.85433120e-01 1.80187821e-01 -5.36908746e-01
-1.28333592e+00 -7.01391920e-02 2.84933537e-01 1.07722253e-01
-1.61829054e-01 7.28065908e-01 3.64900291e-01 4.34063584e-01
-2.53092080e-01 -7.63057053e-01 -6.63656235e-01 -6.61951959e-01
2.23599538e-01 5.88997543e-01 2.12147385e-01 2.79659152... | [8.88848876953125, -2.6954450607299805] |
c0d69dd4-5e28-42d9-bb52-c174e8867a72 | orex-object-reconstruction-from-planner-cross | 2211.12886 | null | https://arxiv.org/abs/2211.12886v3 | https://arxiv.org/pdf/2211.12886v3.pdf | OReX: Object Reconstruction from Planar Cross-sections Using Neural Fields | Reconstructing 3D shapes from planar cross-sections is a challenge inspired by downstream applications like medical imaging and geographic informatics. The input is an in/out indicator function fully defined on a sparse collection of planes in space, and the output is an interpolation of the indicator function to the e... | ['Amit H. Bermano', 'Amir Vaxman', 'Haim Sawdayee'] | 2022-11-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sawdayee_OReX_Object_Reconstruction_From_Planar_Cross-Sections_Using_Neural_Fields_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sawdayee_OReX_Object_Reconstruction_From_Planar_Cross-Sections_Using_Neural_Fields_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-shape-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 3.79203260e-01 3.87509227e-01 4.29416038e-02 -2.66218275e-01
-8.55746746e-01 -4.00202841e-01 4.35346186e-01 1.57927275e-01
-7.89850205e-02 5.81145048e-01 4.57297385e-01 -1.09327868e-01
-2.38368794e-01 -8.41544926e-01 -8.13101470e-01 -4.49090332e-01
-4.13803518e-01 3.73052269e-01 2.66416878e-01 -1.84817277... | [9.220884323120117, -3.2005867958068848] |
c67ed0e0-16e2-4965-bcd6-0349b8194567 | compositional-generalization-without-trees | 2305.16954 | null | https://arxiv.org/abs/2305.16954v1 | https://arxiv.org/pdf/2305.16954v1.pdf | Compositional Generalization without Trees using Multiset Tagging and Latent Permutations | Seq2seq models have been shown to struggle with compositional generalization in semantic parsing, i.e. generalizing to unseen compositions of phenomena that the model handles correctly in isolation. We phrase semantic parsing as a two-step process: we first tag each input token with a multiset of output tokens. Then we... | ['Ivan Titov', 'Alexander Koller', 'Matthias Lindemann'] | 2023-05-26 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 8.69514942e-01 6.36634052e-01 7.34770484e-03 -6.42592430e-01
-9.22133923e-01 -1.15992951e+00 3.62430364e-01 6.65098131e-02
-3.23196173e-01 6.88925624e-01 3.93964559e-01 -6.08052015e-01
1.65843338e-01 -9.56188679e-01 -1.01321304e+00 -4.38986242e-01
2.91829649e-02 8.38392138e-01 2.16844067e-01 -1.90605238... | [10.506134986877441, 9.148557662963867] |
bbdbaf74-3905-4d3b-b594-8fa4c18a6d97 | visualizing-representational-dynamics-with | 1906.09264 | null | https://arxiv.org/abs/1906.09264v2 | https://arxiv.org/pdf/1906.09264v2.pdf | Visualizing Representational Dynamics with Multidimensional Scaling Alignment | Representational similarity analysis (RSA) has been shown to be an effective framework to characterize brain-activity profiles and deep neural network activations as representational geometry by computing the pairwise distances of the response patterns as a representational dissimilarity matrix (RDM). However, how to p... | ['Marieke Mur', 'Tim Kietzmann', 'Nikolaus Kriegeskorte', 'Baihan Lin'] | 2019-06-21 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 3.27873200e-01 -4.91899908e-01 3.66602361e-01 -4.82771963e-01
3.19983102e-02 -1.07266140e+00 9.68773067e-01 3.47314894e-01
-2.29439124e-01 6.05796017e-02 4.53380495e-01 -1.06257200e-01
-6.99850559e-01 -3.01572263e-01 -2.04003885e-01 -6.75993860e-01
-5.67826867e-01 1.03628509e-01 -1.09113425e-01 -2.60639697... | [7.951205730438232, 3.9365410804748535] |
ece31ae3-148e-4c9b-8f46-aab13ed5b07b | a-little-birdie-told-me-inductive-biases-for | null | null | https://aclanthology.org/2020.wnut-1.31 | https://aclanthology.org/2020.wnut-1.31.pdf | “A Little Birdie Told Me ... ” - Inductive Biases for Rumour Stance Detection on Social Media | The rise in the usage of social media has placed it in a central position for news dissemination and consumption. This greatly increases the potential for proliferation of rumours and misinformation. In an effort to mitigate the spread of rumours, we tackle the related task of identifying the stance (Support, Deny, Que... | ['Vidhisha Balachandran', 'Sharanya Chakravarthy', 'Tushar Kanakagiri', 'Karthik Radhakrishnan'] | null | null | null | null | emnlp-wnut-2020-11 | ['rumour-detection'] | ['natural-language-processing'] | [-1.90093666e-01 2.74100661e-01 -5.98264217e-01 -7.58034587e-02
-3.27076018e-01 -6.28797948e-01 1.22435796e+00 8.95187616e-01
-5.14692724e-01 7.79407203e-01 9.76159155e-01 -4.73646194e-01
3.59578699e-01 -8.03180099e-01 -4.35035944e-01 -1.01252295e-01
1.35844080e-02 1.35604233e-01 3.66482735e-01 -5.98739207... | [8.340951919555664, 10.09502124786377] |
8a701d03-fa97-4b8f-aa88-314a9dc23719 | dual-variational-generation-for-low-shot | 1903.10203 | null | https://arxiv.org/abs/1903.10203v3 | https://arxiv.org/pdf/1903.10203v3.pdf | Dual Variational Generation for Low-Shot Heterogeneous Face Recognition | Heterogeneous Face Recognition (HFR) is a challenging issue because of the large domain discrepancy and a lack of heterogeneous data. This paper considers HFR as a dual generation problem, and proposes a novel Dual Variational Generation (DVG) framework. It generates large-scale new paired heterogeneous images with the... | ['Yibo Hu', 'Xiang Wu', 'Huaibo Huang', 'Ran He', 'Chaoyou Fu'] | 2019-03-25 | null | null | null | null | ['heterogeneous-face-recognition'] | ['computer-vision'] | [-4.14783210e-02 -1.08481109e-01 3.20169926e-02 -2.77686626e-01
-1.06962276e+00 -3.41217488e-01 4.63523835e-01 -7.78554380e-01
7.61283785e-02 7.89801300e-01 2.50655711e-01 2.78213203e-01
-1.45947754e-01 -7.68317282e-01 -7.49304235e-01 -1.10220206e+00
6.19662464e-01 5.48680484e-01 -3.09492916e-01 -1.48359194... | [13.102635383605957, 0.26999735832214355] |
9fa664c5-80ad-43e8-897c-99e46c85145e | taming-visually-guided-sound-generation | 2110.08791 | null | https://arxiv.org/abs/2110.08791v1 | https://arxiv.org/pdf/2110.08791v1.pdf | Taming Visually Guided Sound Generation | Recent advances in visually-induced audio generation are based on sampling short, low-fidelity, and one-class sounds. Moreover, sampling 1 second of audio from the state-of-the-art model takes minutes on a high-end GPU. In this work, we propose a single model capable of generating visually relevant, high-fidelity sound... | ['Esa Rahtu', 'Vladimir Iashin'] | 2021-10-17 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 3.53298217e-01 -2.17192873e-01 4.44700181e-01 9.47247222e-02
-1.45122159e+00 -5.90982914e-01 3.98213506e-01 1.13082558e-01
-6.55937269e-02 5.31428158e-01 4.77520823e-01 7.00247660e-02
3.26179266e-01 -8.83242905e-01 -9.35407579e-01 -5.01296699e-01
-1.77952975e-01 -1.51589602e-01 1.71936750e-01 1.27579093... | [15.565764427185059, 5.72912073135376] |
3491aee0-8467-40c3-9e61-27d7208d7920 | alphadesign-a-graph-protein-design-method-and | 2202.01079 | null | https://arxiv.org/abs/2202.01079v2 | https://arxiv.org/pdf/2202.01079v2.pdf | AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB | While DeepMind has tentatively solved protein folding, its inverse problem -- protein design which predicts protein sequences from their 3D structures -- still faces significant challenges. Particularly, the lack of large-scale standardized benchmark and poor accuray hinder the research progress. In order to standardiz... | ['Stan Z. Li', 'Cheng Tan', 'Zhangyang Gao'] | 2022-02-01 | null | null | null | null | ['protein-design'] | ['medical'] | [ 3.09924185e-02 -1.92165300e-02 -3.99367273e-01 -4.96482760e-01
-3.62514734e-01 -3.43623489e-01 -1.74941286e-01 1.32844880e-01
-9.04893428e-02 1.06581008e+00 4.55663316e-02 -6.80292308e-01
3.12026948e-01 -6.92362845e-01 -1.21443808e+00 -7.07817554e-01
4.23042066e-02 4.39890385e-01 2.42203727e-01 -1.56177506... | [4.722708702087402, 5.646328449249268] |
871fb506-8c4f-43fb-ab4e-18faec96c27f | lifespan-age-transformation-synthesis | 2003.09764 | null | https://arxiv.org/abs/2003.09764v2 | https://arxiv.org/pdf/2003.09764v2.pdf | Lifespan Age Transformation Synthesis | We address the problem of single photo age progression and regression-the prediction of how a person might look in the future, or how they looked in the past. Most existing aging methods are limited to changing the texture, overlooking transformations in head shape that occur during the human aging and growth process. ... | ['Ira Kemelmacher-Shlizerman', 'Ohad Fried', 'Roy Or-El', 'Soumyadip Sengupta', 'Eli Shechtman'] | 2020-03-21 | lifespan-age-transformation-synthesis-1 | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/88_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123510732.pdf | eccv-2020-8 | ['image-to-video', 'multimodal-unsupervised-image-to-image', 'face-age-editing', 'human-aging'] | ['computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous'] | [ 2.99611270e-01 2.96633095e-01 -1.72756866e-01 -5.87387800e-01
-4.79206830e-01 -1.84385315e-01 5.14688313e-01 -2.31713519e-01
-4.90879655e-01 7.87538528e-01 5.73060870e-01 1.85597315e-01
6.46989703e-01 -8.59211564e-01 -8.38480175e-01 -5.84984601e-01
1.45685524e-01 5.94777465e-01 -9.57699642e-02 5.12705892... | [13.185416221618652, 0.4367968440055847] |
790fcb8e-c8bf-4bb9-8ee3-b93e0432902c | homography-estimation-from-the-common-self | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Huang_Homography_Estimation_From_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Huang_Homography_Estimation_From_CVPR_2016_paper.pdf | Homography Estimation From the Common Self-Polar Triangle of Separate Ellipses | How to avoid ambiguity is a challenging problem for conic-based homography estimation. In this paper, we address the problem of homography estimation from two separate ellipses. We find that any two ellipses have a unique common self-polar triangle, which can provide three line correspondences. Furthermore, by investig... | ['Yiu-ming Cheung', 'HUI ZHANG', 'Haifei Huang'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['homography-estimation'] | ['computer-vision'] | [-4.44446243e-02 2.51642048e-01 -6.03970401e-02 1.06220908e-01
-5.79285100e-02 -6.94556057e-01 4.06063586e-01 -3.57106626e-01
1.47716984e-01 5.80945969e-01 -2.12948620e-01 -2.15201259e-01
-1.58601761e-01 -6.70812488e-01 -7.79852986e-01 -6.26552939e-01
9.52461064e-02 7.67958760e-01 2.96868622e-01 -2.72076577... | [7.997897624969482, -2.3076441287994385] |
92e72a0e-b742-40a9-a8a2-edeb3d0d20a0 | bsn-boundary-sensitive-network-for-temporal | 1806.02964 | null | http://arxiv.org/abs/1806.02964v3 | http://arxiv.org/pdf/1806.02964v3.pdf | BSN: Boundary Sensitive Network for Temporal Action Proposal Generation | Temporal action proposal generation is an important yet challenging problem,
since temporal proposals with rich action content are indispensable for
analysing real-world videos with long duration and high proportion irrelevant
content. This problem requires methods not only generating proposals with
precise temporal bo... | ['Ming Yang', 'Tianwei Lin', 'Haisheng Su', 'Xu Zhao', 'Chongjing Wang'] | 2018-06-08 | bsn-boundary-sensitive-network-for-temporal-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Tianwei_Lin_BSN_Boundary_Sensitive_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Tianwei_Lin_BSN_Boundary_Sensitive_ECCV_2018_paper.pdf | eccv-2018-9 | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 4.63551342e-01 -6.77311122e-02 -6.04116201e-01 -3.97948548e-02
-8.90833616e-01 -2.34355554e-01 7.37309396e-01 -9.06959176e-02
-5.11435628e-01 8.68564725e-01 5.74529588e-01 2.92904437e-01
2.66257469e-02 -7.15324223e-01 -4.23069507e-01 -6.43475711e-01
-2.55178332e-01 1.78740278e-01 1.41681600e+00 -3.07929981... | [8.315140724182129, 0.4194517731666565] |
d509a3b0-5ba9-41e7-ad71-47fdfbe4b785 | joint-classification-and-prediction-cnn | 1805.06546 | null | http://arxiv.org/abs/1805.06546v3 | http://arxiv.org/pdf/1805.06546v3.pdf | Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification | Correctly identifying sleep stages is important in diagnosing and treating
sleep disorders. This work proposes a joint classification-and-prediction
framework based on CNNs for automatic sleep staging, and, subsequently,
introduces a simple yet efficient CNN architecture to power the framework.
Given a single input epo... | ['Oliver Y. Chén', 'Navin Cooray', 'Fernando Andreotti', 'Huy Phan', 'Maarten De Vos'] | 2018-05-16 | null | null | null | null | ['sleep-stage-detection', 'sleep-staging', 'automatic-sleep-stage-classification'] | ['medical', 'medical', 'medical'] | [ 3.21436852e-01 -6.31507160e-03 -4.48287815e-01 -7.52763867e-01
-7.11613297e-01 -1.04300007e-01 3.84566903e-01 1.02804322e-02
-6.64055407e-01 8.16881597e-01 5.78906238e-02 -3.49634826e-01
-1.03604458e-01 -3.31476271e-01 -1.89987168e-01 -9.30513442e-01
-2.87207048e-02 3.54106814e-01 1.57436460e-01 2.29118422... | [13.513830184936523, 3.5224344730377197] |
07bfa2b1-fc1a-4851-b04b-c76e72cfe63f | actor-and-action-modular-network-for-text | 2011.00786 | null | https://arxiv.org/abs/2011.00786v2 | https://arxiv.org/pdf/2011.00786v2.pdf | Actor and Action Modular Network for Text-based Video Segmentation | Text-based video segmentation aims to segment an actor in video sequences by specifying the actor and its performing action with a textual query. Previous methods fail to explicitly align the video content with the textual query in a fine-grained manner according to the actor and its action, due to the problem of \emph... | ['Zhanyu Ma', 'Linjiang Huang', 'Liang Wang', 'Kai Niu', 'Yan Huang', 'Jianhua Yang'] | 2020-11-02 | null | null | null | null | ['action-understanding', 'referring-expression-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.27370822e-01 4.32445928e-02 -3.29760134e-01 -4.75187510e-01
-8.55255485e-01 -5.45283675e-01 5.95857978e-01 -1.54223129e-01
-4.48934644e-01 2.19246984e-01 1.84106514e-01 8.71405825e-02
1.27429113e-01 -3.80347788e-01 -7.52062261e-01 -7.44623721e-01
1.99953124e-01 5.67013204e-01 8.86959076e-01 5.94937652... | [9.661460876464844, 0.5223012566566467] |
5af6ec5b-06b4-4175-8c29-12dd7383475e | cyclic-generative-adversarial-networks-with | 2211.08424 | null | https://arxiv.org/abs/2211.08424v1 | https://arxiv.org/pdf/2211.08424v1.pdf | Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysis | We present a novel framework for explainable labeling and interpretation of medical images. Medical images require specialized professionals for interpretation, and are explained (typically) via elaborate textual reports. Different from prior methods that focus on medical report generation from images or vice-versa, we... | ['Dwarikanath Mahapatra'] | 2022-11-16 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 1.04357100e+00 1.29621124e+00 -6.94683641e-02 -6.00468636e-01
-1.24815047e+00 -6.65845752e-01 5.03766418e-01 -7.21877366e-02
3.08718592e-01 9.53662395e-01 2.39134222e-01 -7.16947198e-01
1.59661725e-01 -4.82333511e-01 -8.61541271e-01 -4.74254757e-01
1.54459774e-01 6.14727497e-01 -5.35806179e-01 4.51812029... | [14.982868194580078, -1.4365657567977905] |
4c02fed0-18a9-4032-83e8-186616e15390 | crunchgpt-a-chatgpt-assisted-framework-for | 2306.15551 | null | https://arxiv.org/abs/2306.15551v1 | https://arxiv.org/pdf/2306.15551v1.pdf | CrunchGPT: A chatGPT assisted framework for scientific machine learning | Scientific Machine Learning (SciML) has advanced recently across many different areas in computational science and engineering. The objective is to integrate data and physics seamlessly without the need of employing elaborate and computationally taxing data assimilation schemes. However, preprocessing, problem formulat... | ['George Em Karniadakis', 'Khemraj Shukla', 'Adar Kahana', 'Leonard Gleyzer', 'Varun Kumar'] | 2023-06-27 | null | null | null | null | ['code-generation', 'geophysics'] | ['computer-code', 'miscellaneous'] | [-2.72315353e-01 -4.43095148e-01 5.60424685e-01 1.65078461e-01
4.26571630e-02 -6.98840261e-01 5.64510763e-01 4.23465967e-01
5.00952452e-02 7.28252411e-01 -3.23631793e-01 -8.56315374e-01
-4.92244959e-01 -7.74322033e-01 -5.70072711e-01 -6.82857275e-01
-2.45425373e-01 5.31281233e-01 -2.58911431e-01 -2.16679975... | [6.385396480560303, 3.275775909423828] |
d4829767-40d6-48f3-8767-d9159afc1acb | lexicographic-multi-objective-reinforcement | 2212.13769 | null | https://arxiv.org/abs/2212.13769v1 | https://arxiv.org/pdf/2212.13769v1.pdf | Lexicographic Multi-Objective Reinforcement Learning | In this work we introduce reinforcement learning techniques for solving lexicographic multi-objective problems. These are problems that involve multiple reward signals, and where the goal is to learn a policy that maximises the first reward signal, and subject to this constraint also maximises the second reward signal,... | ['Alessandro Abate', 'Charlie Griffin', 'Lewis Hammond', 'Joar Skalse'] | 2022-12-28 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 3.53180975e-01 1.80532277e-01 -5.86713791e-01 -7.52715336e-04
-4.82802540e-01 -6.57747447e-01 5.21810949e-01 1.06522053e-01
-8.91304135e-01 1.33685684e+00 -6.05453961e-02 -4.22459692e-01
-5.90119123e-01 -5.97006381e-01 -5.11467755e-01 -8.07922542e-01
-4.33749765e-01 5.66933692e-01 3.21337402e-01 -4.98368800... | [4.205593585968018, 2.3066813945770264] |
aa318500-b17b-4674-899b-919b192f2a40 | context-dependent-sentiment-analysis-in-user | null | null | https://aclanthology.org/P17-1081 | https://aclanthology.org/P17-1081.pdf | Context-Dependent Sentiment Analysis in User-Generated Videos | Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos. Current research considers utterances as independent entities, i.e., ignores the interdependencies and relations among the utterances of a video. In this paper, we propose a LSTM-based model that e... | ['Louis-Philippe Morency', 'Amir Zadeh', 'Soujanya Poria', 'Navonil Majumder', 'Erik Cambria', 'Devamanyu Hazarika'] | 2017-07-01 | null | null | null | acl-2017-7 | ['multimodal-emotion-recognition', 'emotion-recognition-in-conversation', 'multimodal-emotion-recognition'] | ['computer-vision', 'natural-language-processing', 'speech'] | [ 4.39840667e-02 -2.55586982e-01 -5.71340062e-02 -6.79761827e-01
-2.70233214e-01 -5.19796073e-01 3.11976850e-01 9.46103185e-02
-4.72306550e-01 5.16222537e-01 5.07832646e-01 -2.57320870e-02
3.97288471e-01 -3.59477341e-01 -6.78229094e-01 -7.80203044e-01
1.03883579e-01 -3.33387047e-01 -3.95953432e-02 -3.28484714... | [13.155423164367676, 5.25435733795166] |
2041978e-3477-4abf-a985-e8516e4b5362 | 190600654 | 1906.00654 | null | https://arxiv.org/abs/1906.00654v1 | https://arxiv.org/pdf/1906.00654v1.pdf | Continual Learning of New Sound Classes using Generative Replay | Continual learning consists in incrementally training a model on a sequence of datasets and testing on the union of all datasets. In this paper, we examine continual learning for the problem of sound classification, in which we wish to refine already trained models to learn new sound classes. In practice one does not w... | ['Cem Subakan', 'Zhepei Wang', 'Paris Smaragdis', 'Efthymios Tzinis', 'Laurent Charlin'] | 2019-06-03 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 7.59167969e-01 2.91109294e-01 3.59214395e-01 -2.57682681e-01
-8.57057750e-01 -6.73455715e-01 4.05004740e-01 1.67499930e-01
-5.67833066e-01 1.05947340e+00 -8.18546638e-02 -8.19640085e-02
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-1.17129982e-01 5.81144452e-01 4.74182725e-01 3.29543068... | [9.971027374267578, 3.453063726425171] |
67c86202-455b-4afa-a444-c024bb3edb1a | a-transition-based-system-for-universal | null | null | https://aclanthology.org/K17-3020 | https://aclanthology.org/K17-3020.pdf | A Transition-based System for Universal Dependency Parsing | This paper describes the system for our participation in the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. In this work, we design a system based on UDPipe1 for universal dependency parsing, where multilingual transition-based models are trained for different treebanks. Our syste... | ['Hao Wang', 'Zhisong Zhang', 'Hai Zhao'] | 2017-08-01 | null | null | null | conll-2017-8 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-2.35163733e-01 9.15662050e-02 -2.05190629e-01 -6.52833164e-01
-1.52637446e+00 -8.16013098e-01 3.76244009e-01 1.25420123e-01
-7.09801972e-01 1.08976698e+00 3.30705911e-01 -8.38543177e-01
6.72524393e-01 -4.99184668e-01 -7.41745234e-01 -2.64849961e-01
-2.78650187e-02 6.19068384e-01 3.60455334e-01 -3.06779951... | [10.477179527282715, 9.928075790405273] |
9474ffb7-4556-4bfc-af86-f05eac618905 | single-sequence-prediction-over-reasoning | 2307.00335 | null | https://arxiv.org/abs/2307.00335v1 | https://arxiv.org/pdf/2307.00335v1.pdf | Single Sequence Prediction over Reasoning Graphs for Multi-hop QA | Recent generative approaches for multi-hop question answering (QA) utilize the fusion-in-decoder method~\cite{izacard-grave-2021-leveraging} to generate a single sequence output which includes both a final answer and a reasoning path taken to arrive at that answer, such as passage titles and key facts from those passag... | ['Junjie Hu', 'Makesh Sreedhar', 'Gowtham Ramesh'] | 2023-07-01 | null | null | null | null | ['multi-hop-question-answering', 'question-answering'] | ['knowledge-base', 'natural-language-processing'] | [ 1.82148427e-01 5.79211533e-01 6.48955777e-02 -4.70497251e-01
-1.53211093e+00 -8.54827404e-01 4.29892510e-01 5.95373154e-01
-1.27006575e-01 7.83676326e-01 6.68145537e-01 -5.65644741e-01
1.73674617e-02 -1.12361610e+00 -1.10242748e+00 7.08541125e-02
5.79405427e-01 9.27143455e-01 5.11618495e-01 -7.66378105... | [11.0382661819458, 7.969152927398682] |
768de6e3-6b38-4807-bb40-cd85928a4937 | deep-speaker-vectors-for-semi-text | 1505.06427 | null | http://arxiv.org/abs/1505.06427v1 | http://arxiv.org/pdf/1505.06427v1.pdf | Deep Speaker Vectors for Semi Text-independent Speaker Verification | Recent research shows that deep neural networks (DNNs) can be used to extract
deep speaker vectors (d-vectors) that preserve speaker characteristics and can
be used in speaker verification. This new method has been tested on
text-dependent speaker verification tasks, and improvement was reported when
combined with the ... | ['Thomas Fang Zheng', 'Lantian Li', 'Zhiyong Zhang', 'Dong Wang'] | 2015-05-24 | null | null | null | null | ['text-independent-speaker-recognition', 'text-independent-speaker-verification', 'text-dependent-speaker-verification'] | ['speech', 'speech', 'speech'] | [ 1.69976249e-01 -2.61840552e-01 -3.01753134e-01 -8.81014884e-01
-7.16836631e-01 -6.12498999e-01 5.65717697e-01 -4.37208802e-01
-3.87771755e-01 5.14704466e-01 6.64414346e-01 -5.93037844e-01
2.08502546e-01 -1.83865100e-01 -3.59620363e-01 -1.02094233e+00
1.10919122e-02 2.47410297e-01 -2.71750957e-01 -3.12490612... | [14.338537216186523, 6.089420795440674] |
f9903160-bcaf-4202-90b5-0fa7f346d8b0 | elastic-weight-removal-for-faithful-and | 2303.17574 | null | https://arxiv.org/abs/2303.17574v1 | https://arxiv.org/pdf/2303.17574v1.pdf | Elastic Weight Removal for Faithful and Abstractive Dialogue Generation | Ideally, dialogue systems should generate responses that are faithful to the knowledge contained in relevant documents. However, many models generate hallucinated responses instead that contradict it or contain unverifiable information. To mitigate such undesirable behaviour, it has been proposed to fine-tune a `negati... | ['Edoardo M. Ponti', 'Iryna Gurevych', 'Mrinmaya Sachan', 'Nouha Dziri', 'Nico Daheim'] | 2023-03-30 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [ 2.21711904e-01 6.82267368e-01 1.03973448e-01 -2.83122689e-01
-8.23413730e-01 -6.60416186e-01 8.76570463e-01 1.88755646e-01
-5.46612620e-01 9.72626567e-01 9.41043198e-01 -1.48542494e-01
-9.31320339e-02 -5.45815527e-01 -2.53265709e-01 -6.16363347e-01
3.38971943e-01 6.94312513e-01 8.84006023e-02 -7.90130973... | [12.480759620666504, 8.597088813781738] |
dda4dd60-dfdb-4132-9221-eb305f6f942b | data-driven-approach-for-formality-sensitive | 2306.14514 | null | https://arxiv.org/abs/2306.14514v2 | https://arxiv.org/pdf/2306.14514v2.pdf | Data-Driven Approach for Formality-Sensitive Machine Translation: Language-Specific Handling and Synthetic Data Generation | In this paper, we introduce a data-driven approach for Formality-Sensitive Machine Translation (FSMT) that caters to the unique linguistic properties of four target languages. Our methodology centers on two core strategies: 1) language-specific data handling, and 2) synthetic data generation using large-scale language ... | ['Heuiseok Lim', 'Chanjun Park', 'Hyeonseok Moon', 'Seugnjun Lee'] | 2023-06-26 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation', 'machine-translation', 'prompt-engineering'] | ['medical', 'miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [ 3.91219884e-01 3.78197022e-02 -7.56507158e-01 -3.05104136e-01
-1.59123528e+00 -8.81242335e-01 1.34314561e+00 5.02771959e-02
-1.93190426e-01 1.11253667e+00 3.53394449e-01 -9.98658836e-01
2.07733095e-01 -4.41105098e-01 -6.94887698e-01 5.66968955e-02
3.09344292e-01 9.47848380e-01 1.41096860e-01 -6.52418494... | [11.57086181640625, 10.289828300476074] |
e600d9cf-17c8-4684-a3bd-daf868560d7b | structured-face-hallucination | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Yang_Structured_Face_Hallucination_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Yang_Structured_Face_Hallucination_2013_CVPR_paper.pdf | Structured Face Hallucination | The goal of face hallucination is to generate highresolution images with fidelity from low-resolution ones. In contrast to existing methods based on patch similarity or holistic constraints in the image space, we propose to exploit local image structures for face hallucination. Each face image is represented in terms o... | ['Ming-Hsuan Yang', 'Chih-Yuan Yang', 'Sifei Liu'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['face-hallucination', 'patch-matching'] | ['computer-vision', 'computer-vision'] | [ 4.24246699e-01 3.15868407e-01 -1.62171796e-01 -3.43836635e-01
-6.48256481e-01 1.61199681e-02 4.95261610e-01 -4.89986658e-01
2.30075687e-01 7.36291766e-01 5.60742736e-01 7.59178638e-01
-1.04911327e-01 -1.00629544e+00 -6.48696244e-01 -8.03148150e-01
1.50395483e-01 -2.17440605e-01 -6.97894916e-02 -1.58343270... | [12.810325622558594, -0.07678718864917755] |
1799463b-5019-4413-91ed-2e7482e9d011 | action-spotting-using-dense-detection-anchors | 2206.07846 | null | https://arxiv.org/abs/2206.07846v2 | https://arxiv.org/pdf/2206.07846v2.pdf | Action Spotting using Dense Detection Anchors Revisited: Submission to the SoccerNet Challenge 2022 | This brief technical report describes our submission to the Action Spotting SoccerNet Challenge 2022. The challenge was part of the CVPR 2022 ActivityNet Workshop. Our submission was based on a recently proposed method which focuses on increasing temporal precision via a densely sampled set of detection anchors. Due to... | ['Avijit Shah', 'João V. B. Soares'] | 2022-06-15 | null | null | null | null | ['action-spotting'] | ['computer-vision'] | [-2.75810678e-02 -2.68676013e-01 -1.41826287e-01 -3.62147212e-01
-8.95827293e-01 -3.59028876e-01 5.66658676e-01 1.65104568e-01
-1.04979324e+00 9.07521963e-01 2.04953834e-01 4.10044938e-01
-2.80280650e-01 -4.49569553e-01 -4.91318911e-01 -4.08720195e-01
-7.12465465e-01 5.42869449e-01 1.15326881e+00 -7.04396844... | [8.039730072021484, 0.1878150999546051] |
5576b60b-460b-4a6d-9f23-f4bc82f0601b | salsanext-fast-semantic-segmentation-of-lidar | 2003.03653 | null | https://arxiv.org/abs/2003.03653v3 | https://arxiv.org/pdf/2003.03653v3.pdf | SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving | In this paper, we introduce SalsaNext for the uncertainty-aware semantic segmentation of a full 3D LiDAR point cloud in real-time. SalsaNext is the next version of SalsaNet [1] which has an encoder-decoder architecture where the encoder unit has a set of ResNet blocks and the decoder part combines upsampled features fr... | ['George Tzelepis', 'Eren Erdal Aksoy', 'Tiago Cortinhal'] | 2020-03-07 | null | null | null | null | ['robust-3d-semantic-segmentation'] | ['computer-vision'] | [ 2.01795787e-01 3.18948925e-01 1.18740931e-01 -7.51796722e-01
-9.01437998e-01 -3.30393940e-01 4.81725812e-01 -2.08750248e-01
-5.51241755e-01 7.16301799e-01 -2.73134500e-01 6.12068959e-02
-1.52539760e-01 -8.93694937e-01 -1.12316203e+00 -5.52122056e-01
6.63632378e-02 7.80158818e-01 5.80905497e-01 1.36234090... | [8.243489265441895, -2.6819064617156982] |
09c54ed6-39a4-407e-9f84-0c0c3973f037 | cross-lingual-adaptation-for-type-inference | 2107.00157 | null | https://arxiv.org/abs/2107.00157v5 | https://arxiv.org/pdf/2107.00157v5.pdf | Cross-Lingual Transfer Learning for Statistical Type Inference | Hitherto statistical type inference systems rely thoroughly on supervised learning approaches, which require laborious manual effort to collect and label large amounts of data. Most Turing-complete imperative languages share similar control- and data-flow structures, which make it possible to transfer knowledge learned... | ['Yang Liu', 'Yi Li', 'Zhengzi Xu', 'Haoliang Li', 'Xiaofei Xie', 'Zhiming Li'] | 2021-07-01 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [-2.29582153e-02 3.07651460e-02 -6.28643036e-01 -4.81366277e-01
-8.12454164e-01 -9.43975329e-01 6.90718532e-01 2.18199589e-03
-6.15065575e-01 7.27896512e-01 -3.07119470e-02 -5.53286254e-01
5.64494193e-01 -9.60229933e-01 -1.23454773e+00 -5.14657199e-01
1.91181257e-01 3.49439949e-01 2.62037754e-01 -4.43948470... | [10.986761093139648, 9.856405258178711] |
6474ae08-5810-46b1-8fe9-5f0d1212d019 | towards-automated-imbalanced-learning-with | 2208.12433 | null | https://arxiv.org/abs/2208.12433v1 | https://arxiv.org/pdf/2208.12433v1.pdf | Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning | Imbalanced learning is a fundamental challenge in data mining, where there is a disproportionate ratio of training samples in each class. Over-sampling is an effective technique to tackle imbalanced learning through generating synthetic samples for the minority class. While numerous over-sampling algorithms have been p... | ['Xia Hu', 'Na Zou', 'Sirui Ding', 'Qiaoyu Tan', 'Kwei-Herng Lai', 'Daochen Zha'] | 2022-08-26 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-8.67349878e-02 -1.04358926e-01 -4.67350274e-01 -2.86000401e-01
-9.60220993e-01 -1.18393570e-01 1.74343243e-01 1.57583937e-01
-2.43682444e-01 1.00504804e+00 -9.35706049e-02 -3.08540285e-01
-3.28149758e-02 -1.07492840e+00 -7.74066985e-01 -7.46483386e-01
1.86696067e-01 7.46767223e-01 2.64580268e-02 -1.61895782... | [8.991134643554688, 4.033498764038086] |
12ce1907-c614-412a-b84b-f5f6121d6f49 | a-kolmogorov-complexity-approach-to | null | null | https://openreview.net/forum?id=Bke7MANKvS | https://openreview.net/pdf?id=Bke7MANKvS | A Kolmogorov Complexity Approach to Generalization in Deep Learning | Deep artificial neural networks can achieve an extremely small difference between training and test accuracies on identically distributed training and test sets, which is a standard measure of generalization. However, the training and test sets may not be sufficiently representative of the empirical sample set, which c... | ['Brian Kingsbury', 'Kush R. Varshney', 'Hazar Yueksel'] | 2019-09-25 | null | null | null | null | ['classification'] | ['methodology'] | [ 6.41955078e-01 -9.42695048e-03 9.61329639e-02 -3.96966875e-01
-6.87435746e-01 -7.56506860e-01 4.29042816e-01 1.20848298e-01
-5.59209287e-01 8.94259930e-01 -2.65381038e-01 -2.71469116e-01
-2.03486681e-01 -1.12457299e+00 -1.07341409e+00 -9.40184832e-01
-3.44862118e-02 8.98539945e-02 -5.13353609e-02 1.89746767... | [5.718148708343506, 7.708550453186035] |
04c3a8e6-ec64-4b98-8cdf-535a79a9218b | few-shot-text-independent-speaker | 2008.11088 | null | https://arxiv.org/abs/2008.11088v1 | https://arxiv.org/pdf/2008.11088v1.pdf | Few Shot Text-Independent speaker verification using 3D-CNN | Facial recognition system is one of the major successes of Artificial intelligence and has been used a lot over the last years. But, images are not the only biometric present: audio is another possible biometric that can be used as an alternative to the existing recognition systems. However, the text-independent audio ... | ['Prateek Mishra'] | 2020-08-25 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 1.04989350e-01 -1.57429531e-01 1.96464099e-02 -6.75538063e-01
-6.12557530e-01 -1.33743942e-01 5.14230430e-01 -1.51570201e-01
-4.41359997e-01 7.56964087e-01 6.05957806e-02 2.49602832e-02
-1.35263875e-01 -2.14905411e-01 -4.73031431e-01 -9.78523850e-01
2.62224853e-01 6.01323307e-01 1.16258226e-02 -1.58220738... | [13.304975509643555, 1.1581902503967285] |
367ccfbd-e21f-4f31-a098-21585051ee79 | semi-weakly-supervised-learning-of-complex | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Shen_Semi-Weakly-Supervised_Learning_of_Complex_Actions_From_Instructional_Task_Videos_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Shen_Semi-Weakly-Supervised_Learning_of_Complex_Actions_From_Instructional_Task_Videos_CVPR_2022_paper.pdf | Semi-Weakly-Supervised Learning of Complex Actions From Instructional Task Videos | We address the problem of action segmentation in instructional task videos with a small number of weakly-labeled training videos and a large number of unlabeled videos, which we refer to as Semi-Weakly-Supervised Learning (SWSL) of actions. We propose a general SWSL framework that can efficiently learn from both ty... | ['Ehsan Elhamifar', 'YuHan Shen'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['action-segmentation'] | ['computer-vision'] | [ 6.42354071e-01 1.00920543e-01 -5.61634123e-01 -4.86098588e-01
-1.08330142e+00 -8.37331295e-01 2.08583280e-01 -4.52639937e-01
-2.34772518e-01 6.13337696e-01 2.18239322e-01 -2.32919946e-01
4.04146105e-01 -1.55018255e-01 -1.16462290e+00 -8.76514494e-01
3.27583961e-02 1.27788782e-01 2.89108038e-01 4.59712327... | [8.603788375854492, 0.6318374872207642] |
16baf432-aa1d-4bc7-9a48-50133c7940a3 | self-supervised-learning-for-fine-grained | 2105.08788 | null | https://arxiv.org/abs/2105.08788v1 | https://arxiv.org/pdf/2105.08788v1.pdf | Self-Supervised Learning for Fine-Grained Visual Categorization | Recent research in self-supervised learning (SSL) has shown its capability in learning useful semantic representations from images for classification tasks. Through our work, we study the usefulness of SSL for Fine-Grained Visual Categorization (FGVC). FGVC aims to distinguish objects of visually similar sub categories... | ['Dhanalaxmi Gaddam', 'Hanoona Abdul Rasheed', 'Muhammad Maaz'] | 2021-05-18 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 3.02854359e-01 -1.41571641e-01 -4.59312052e-01 -3.89892757e-01
-8.13637674e-01 -8.16643059e-01 6.66901350e-01 1.48637965e-01
-6.29438236e-02 3.39958072e-01 2.10219204e-01 -3.24878693e-01
8.39922056e-02 -6.18201852e-01 -8.70709598e-01 -7.46156156e-01
1.66950785e-02 -6.53563738e-02 2.71753371e-01 1.26491822... | [9.607216835021973, 2.0105345249176025] |
90e26be0-a44c-4ddf-8c35-a381c4e40240 | x-llm-bootstrapping-advanced-large-language | 2305.0416 | null | https://arxiv.org/abs/2305.04160v3 | https://arxiv.org/pdf/2305.04160v3.pdf | X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages | Large language models (LLMs) have demonstrated remarkable language abilities. GPT-4, based on advanced LLMs, exhibits extraordinary multimodal capabilities beyond previous visual language models. We attribute this to the use of more advanced LLMs compared with previous multimodal models. Unfortunately, the model archit... | ['Bo Xu', 'Shuang Xu', 'Jing Shi', 'Qingyang Zhang', 'Haozhi Zhao', 'Minglun Han', 'Feilong Chen'] | 2023-05-07 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 2.14239150e-01 2.47248366e-01 -3.56664121e-01 -2.20737115e-01
-1.26535094e+00 -6.40727639e-01 6.53380632e-01 -4.62922633e-01
-3.98659259e-01 4.14447784e-01 1.56585351e-01 -6.91683471e-01
5.33131003e-01 -4.31494623e-01 -1.24145675e+00 -4.77512211e-01
8.86997730e-02 5.58873177e-01 -1.33828282e-01 -2.30834022... | [10.986824035644531, 1.5183112621307373] |
71ec62ad-167d-4c02-88d2-a8aebb2e3503 | enhancement-of-underwater-images-with | 1906.08673 | null | http://arxiv.org/abs/1906.08673v1 | http://arxiv.org/pdf/1906.08673v1.pdf | Enhancement of Underwater Images with Statistical Model of Background Light and Optimization of Transmission Map | Underwater images often have severe quality degradation and distortion due to
light absorption and scattering in the water medium. A hazed image formation
model is widely used to restore the image quality. It depends on two optical
parameters: the background light and the transmission map. Underwater images
can also be... | [] | 2019-06-19 | null | null | null | null | ['underwater-image-restoration'] | ['computer-vision'] | [ 3.86941761e-01 -4.44496989e-01 8.57742965e-01 -3.33104312e-01
-2.72978246e-01 -1.20937623e-01 -2.77640019e-02 -3.56862657e-02
-9.01381135e-01 6.71912909e-01 1.71693772e-01 8.01474601e-02
-5.22240140e-02 -9.60487306e-01 -3.62700015e-01 -1.32143736e+00
3.79288793e-02 -4.54532892e-01 4.94254470e-01 -4.78094399... | [10.704217910766602, -3.4148592948913574] |
e1f4816f-d740-41ba-bf36-3c334eef5e9c | greener-yet-powerful-taming-large-code | 2303.05378 | null | https://arxiv.org/abs/2303.05378v1 | https://arxiv.org/pdf/2303.05378v1.pdf | Greener yet Powerful: Taming Large Code Generation Models with Quantization | ML-powered code generation aims to assist developers to write code in a more productive manner, by intelligently generating code blocks based on natural language prompts. Recently, large pretrained deep learning models have substantially pushed the boundary of code generation and achieved impressive performance. Despit... | ['Bing Xiang', 'Parminder Bhatia', 'Murali Krishna Ramanathan', 'Mingyue Shang', 'Ben Athiwaratkun', 'Qing Sun', 'Yuchen Tian', 'Zijian Wang', 'Varun Kumar', 'Xiaopeng Li', 'Haifeng Qian', 'Baishakhi Ray', 'Shiqi Wang', 'Wasi Ahmad', 'Sujan Gonugondla', 'Xiaokai Wei'] | 2023-03-09 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 3.57120663e-01 1.20723464e-01 -3.66279751e-01 -7.72526935e-02
-8.15726280e-01 -5.77507615e-01 3.49483341e-01 1.21657118e-01
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1.05494373e-01 -1.00947249e+00 -1.05442035e+00 -3.50280702e-01
1.41406074e-01 -1.27030522e-01 -2.50061929e-01 -7.07308725... | [7.880749225616455, 7.6932454109191895] |
7aa942f9-83a3-452d-91b5-bb9be20be2ed | assurance-monitoring-of-cyber-physical | 2001.05014 | null | https://arxiv.org/abs/2001.05014v2 | https://arxiv.org/pdf/2001.05014v2.pdf | Assurance Monitoring of Cyber-Physical Systems with Machine Learning Components | Machine learning components such as deep neural networks are used extensively in Cyber-Physical Systems (CPS). However, they may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, t... | ['Xenofon Koutsoukos', 'Dimitrios Boursinos'] | 2020-01-14 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [-1.38917133e-01 4.80910182e-01 -1.59944482e-02 -4.24114227e-01
-4.79293644e-01 -3.43096614e-01 5.02338707e-01 4.61232476e-02
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-3.21910888e-01 -1.04581964e+00 -9.64979768e-01 -7.64919400e-01
-2.10804701e-01 3.38481188e-01 4.61119741e-01 -4.83758859... | [5.505715847015381, 7.337414741516113] |
f5ef0979-ac3a-40f7-93c3-7896de25d4cd | benchmarking-common-uncertainty-estimation | 2301.01054 | null | https://arxiv.org/abs/2301.01054v2 | https://arxiv.org/pdf/2301.01054v2.pdf | Benchmarking common uncertainty estimation methods with histopathological images under domain shift and label noise | In the past years, deep learning has seen an increase in usage in the domain of histopathological applications. However, while these approaches have shown great potential, in high-risk environments deep learning models need to be able to judge their uncertainty and be able to reject inputs when there is a significant c... | ['Titus J. Brinker', 'Tabea-Clara Bucher', 'Alexander Kurz', 'Hendrik A. Mehrtens'] | 2023-01-03 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 3.77319276e-01 1.71928346e-01 2.96545289e-02 -4.65413541e-01
-1.37418211e+00 -4.56576139e-01 6.44655347e-01 4.70274925e-01
-6.19587541e-01 1.15433979e+00 -3.74963023e-02 -3.53681713e-01
-3.49455178e-01 -7.44779825e-01 -7.00196445e-01 -1.21931458e+00
9.47341621e-02 8.29039335e-01 3.22224021e-01 3.26485336... | [14.801787376403809, -2.623075008392334] |
a7b47a80-29ae-4f66-a5d9-ecfc245fda39 | modelling-aspects-of-planar-multi-mode | 1807.02077 | null | http://arxiv.org/abs/1807.02077v2 | http://arxiv.org/pdf/1807.02077v2.pdf | Modelling Aspects of Planar Multi-Mode Antennas for Direction-of-Arrival Estimation | Multi-mode antennas are an alternative to classical antenna arrays, and hence
a promising emerging sensor technology for a vast variety of applications in
the areas of array signal processing and digital communications. An unsolved
problem is to describe the radiation pattern of multi-mode antennas in closed
analytic f... | [] | 2019-05-29 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 2.63438344e-01 -2.38701090e-01 4.90422696e-01 -1.45403385e-01
-6.94181025e-01 -3.87270242e-01 3.98000777e-01 1.72850206e-01
-2.61021614e-01 5.71612000e-01 -2.87396815e-02 -2.42731199e-01
-8.06402743e-01 -9.17854726e-01 -4.49312270e-01 -1.07251060e+00
-2.61742234e-01 3.04453343e-01 -1.61585242e-01 3.26688699... | [6.497955799102783, 1.3432073593139648] |
2f41baf5-8a98-4431-ab1b-ff71f9024265 | medsegdiff-medical-image-segmentation-with | 2211.00611 | null | https://arxiv.org/abs/2211.00611v5 | https://arxiv.org/pdf/2211.00611v5.pdf | MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model | Diffusion probabilistic model (DPM) recently becomes one of the hottest topic in computer vision. Its image generation application such as Imagen, Latent Diffusion Models and Stable Diffusion have shown impressive generation capabilities, which aroused extensive discussion in the community. Many recent studies also fou... | ['Huiying Liu', 'Haoyi Xiong', 'Yu Zhang', 'Huihui Fang', 'Rao Fu', 'Yanwu Xu', 'Yehui Yang', 'Junde Wu'] | 2022-11-01 | null | null | null | null | ['tumor-segmentation', 'optic-cup-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical', 'medical'] | [ 3.30077261e-01 3.05036247e-01 -7.45197833e-02 -2.19968930e-01
-7.19295681e-01 -4.41155247e-02 7.62321055e-01 -1.55922115e-01
-4.33543861e-01 2.03913093e-01 6.23450816e-01 -1.28927812e-01
-1.51519716e-01 -4.56257641e-01 -2.37295389e-01 -1.06189466e+00
5.02646118e-02 2.16212809e-01 7.59757996e-01 2.02340424... | [14.549259185791016, -2.2755320072174072] |
3f9539bc-9b04-4760-927d-6b45182f7ee3 | clip-vip-adapting-pre-trained-image-text | 2209.0643 | null | https://arxiv.org/abs/2209.06430v4 | https://arxiv.org/pdf/2209.06430v4.pdf | CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Representation Alignment | The pre-trained image-text models, like CLIP, have demonstrated the strong power of vision-language representation learned from a large scale of web-collected image-text data. In light of the well-learned visual features, some existing works transfer image representation to video domain and achieve good results. Howeve... | ['Jiebo Luo', 'Houqiang Li', 'Ruihua Song', 'Jianlong Fu', 'Bei Liu', 'Yuchong Sun', 'Hongwei Xue'] | 2022-09-14 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [-7.50812739e-02 -7.41729438e-01 -5.53250968e-01 -2.37749889e-01
-8.55813622e-01 -4.67037708e-01 7.42663383e-01 -4.40467149e-01
-6.08803034e-01 3.90690535e-01 4.61116344e-01 -1.51076302e-01
1.87419131e-01 -4.86248523e-01 -9.90875840e-01 -4.58726078e-01
2.30568215e-01 1.66534372e-02 3.02007645e-01 -2.07610324... | [10.319777488708496, 0.9855776429176331] |
c15e4e83-123c-4428-9ffe-e064cf22ed76 | volatility-inspired-s-lstm-cell | 2205.07022 | null | https://arxiv.org/abs/2205.07022v1 | https://arxiv.org/pdf/2205.07022v1.pdf | Volatility-inspired $σ$-LSTM cell | Volatility models of price fluctuations are well studied in the econometrics literature, with more than 50 years of theoretical and empirical findings. The recent advancements in neural networks (NN) in the deep learning field have naturally offered novel econometric modeling tools. However, there is still a lack of ex... | ['Nino Antulov-Fantulin', 'German Rodikov'] | 2022-05-14 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-4.71826524e-01 -1.44780055e-01 -1.18381344e-01 -4.44094688e-01
-4.10877280e-02 -2.74492055e-01 8.88583362e-01 -1.56640068e-01
-1.78313389e-01 6.85256600e-01 2.15547964e-01 -7.07203567e-01
-1.70888096e-01 -1.00922775e+00 -6.54974401e-01 -7.84202933e-01
-1.46912649e-01 4.16455835e-01 -2.37138793e-01 -2.62322098... | [4.584366321563721, 4.156107425689697] |
28b52fde-bf93-4f5b-925e-b10f24471886 | explainable-artificial-intelligence-toward | 2302.06613 | null | https://arxiv.org/abs/2302.06613v1 | https://arxiv.org/pdf/2302.06613v1.pdf | Explainable artificial intelligence toward usable and trustworthy computer-aided early diagnosis of multiple sclerosis from Optical Coherence Tomography | Background: Several studies indicate that the anterior visual pathway provides information about the dynamics of axonal degeneration in Multiple Sclerosis (MS). Current research in the field is focused on the quest for the most discriminative features among patients and controls and the development of machine learning ... | ['Elena Garcia-Martin', 'Elvira Mayordomo', 'Beatriz Cordon', 'Elisa Vilades', 'Ubaldo Ramon-Julvez', 'Monica Hernandez'] | 2023-02-13 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [-3.82230873e-03 -1.45057470e-01 -4.62884635e-01 -2.95275122e-01
-2.31321320e-01 -2.06601679e-01 1.99731678e-01 -5.43214455e-02
-5.13489962e-01 1.09439754e+00 2.42469355e-01 -5.56358397e-01
-4.55378324e-01 -3.52527112e-01 -3.08885247e-01 -7.67493665e-01
-2.70555794e-01 7.31642544e-01 -2.58220732e-02 1.88380294... | [15.801244735717773, -3.9593403339385986] |
78f1a42f-bd9f-44c5-aedc-77b33a4ba4b3 | modelling-stance-detection-as-textual | 2212.06543 | null | https://arxiv.org/abs/2212.06543v1 | https://arxiv.org/pdf/2212.06543v1.pdf | Modelling Stance Detection as Textual Entailment Recognition and Leveraging Measurement Knowledge from Social Sciences | Stance detection (SD) can be considered a special case of textual entailment recognition (TER), a generic natural language task. Modelling SD as TER may offer benefits like more training data and a more general learning scheme. In this paper, we present an initial empirical analysis of this approach. We apply it to a d... | ['Ayoub Bagheri', 'Anastasia Giachanou', 'Qixiang Fang'] | 2022-12-13 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 4.41625237e-01 6.32356465e-01 -6.92340016e-01 -5.64243972e-01
-1.06298125e+00 -5.57521641e-01 9.96290267e-01 4.66513515e-01
-6.88908458e-01 9.13846016e-01 7.04821229e-01 -8.04080427e-01
9.82786529e-03 -5.60767114e-01 -5.50960183e-01 -1.97544456e-01
1.07880034e-01 4.74179864e-01 1.38482690e-01 -2.26593032... | [9.16277027130127, 9.8939847946167] |
36e88279-76c2-4068-bfd8-3691fdeccd78 | diaasq-a-benchmark-of-conversational-aspect | 2211.05705 | null | https://arxiv.org/abs/2211.05705v4 | https://arxiv.org/pdf/2211.05705v4.pdf | DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis | The rapid development of aspect-based sentiment analysis (ABSA) within recent decades shows great potential for real-world society. The current ABSA works, however, are mostly limited to the scenario of a single text piece, leaving the study in dialogue contexts unexplored. To bridge the gap between fine-grained sentim... | ['Shengqiong Wu', 'Jinsong Zhang', 'Donghong Ji', 'Fei Li', 'Tat-Seng Chua', 'Lizi Liao', 'Yijiang Liu', 'Jingye Li', 'Yuhan Wu', 'Hao Fei', 'Bobo Li'] | 2022-11-10 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 2.84266621e-01 2.08381310e-01 -9.97055694e-02 -6.93055868e-01
-1.05302155e+00 -6.55270815e-01 1.07726908e+00 3.10224324e-01
-1.82391673e-01 5.97022533e-01 9.03814852e-01 -3.98500234e-01
3.89155984e-01 -7.46795475e-01 -2.60370970e-01 -3.90109658e-01
1.49344683e-01 5.82118213e-01 -4.30820026e-02 -1.05975068... | [11.456389427185059, 6.965762138366699] |
1922bdab-088b-47d1-818f-68e049417977 | gn-transformer-fusing-ast-and-source-code | null | null | https://openreview.net/forum?id=XavM6v_q59q | https://openreview.net/pdf?id=XavM6v_q59q | GN-Transformer: Fusing AST and Source Code information in Graph Networks | As opposed to natural languages, source code understanding is influenced by grammar relations between tokens regardless of their identifier name. Considering graph representation of source code such as Abstract Syntax Tree (AST) and Control Flow Graph (CFG), can capture a token’s grammatical relationships that are not ... | ['Barry Boehm', 'Iordanis Fostiropoulos', 'Junyan Cheng'] | 2021-01-01 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 3.16539109e-01 6.90571308e-01 -2.05193400e-01 -2.87527204e-01
-4.84548539e-01 -6.37599409e-01 6.54191971e-01 7.13761628e-01
2.72912145e-01 1.29671782e-01 8.35503340e-01 -6.20735943e-01
-6.41600266e-02 -6.84642136e-01 -8.90009403e-01 -1.02782600e-01
-2.88463086e-01 -3.81896079e-01 -1.12303942e-02 -2.03778699... | [7.540642738342285, 7.918572425842285] |
53c04fc4-a5c5-4523-a67f-be7b01c02a46 | what-is-wrong-with-scene-text-recognition | 1904.01906 | null | https://arxiv.org/abs/1904.01906v4 | https://arxiv.org/pdf/1904.01906v4.pdf | What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis | Many new proposals for scene text recognition (STR) models have been introduced in recent years. While each claim to have pushed the boundary of the technology, a holistic and fair comparison has been largely missing in the field due to the inconsistent choices of training and evaluation datasets. This paper addresses ... | ['Seong Joon Oh', 'Sangdoo Yun', 'Junyeop Lee', 'Hwalsuk Lee', 'Jeonghun Baek', 'Geewook Kim', 'Dongyoon Han', 'Sungrae Park'] | 2019-04-03 | what-is-wrong-with-scene-text-recognition-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Baek_What_Is_Wrong_With_Scene_Text_Recognition_Model_Comparisons_Dataset_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Baek_What_Is_Wrong_With_Scene_Text_Recognition_Model_Comparisons_Dataset_ICCV_2019_paper.pdf | iccv-2019-10 | ['image-matching'] | ['computer-vision'] | [ 3.45582932e-01 -3.33019763e-01 -6.83179945e-02 -4.48742837e-01
-7.68472314e-01 -6.48727655e-01 8.20224822e-01 1.29497662e-01
-2.42116362e-01 2.70313919e-01 1.84610069e-01 -3.02181363e-01
-4.16952521e-01 -4.50712740e-01 -2.42605448e-01 -4.22155887e-01
2.74961948e-01 3.32376748e-01 3.60778719e-01 -3.04757338... | [11.757062911987305, 2.4683685302734375] |
a13ff1b9-2c8d-43cb-9f63-d8c8414d8292 | high-fidelity-image-compression-with-score | 2305.18231 | null | https://arxiv.org/abs/2305.18231v1 | https://arxiv.org/pdf/2305.18231v1.pdf | High-Fidelity Image Compression with Score-based Generative Models | Despite the tremendous success of diffusion generative models in text-to-image generation, replicating this success in the domain of image compression has proven difficult. In this paper, we demonstrate that diffusion can significantly improve perceptual quality at a given bit-rate, outperforming state-of-the-art appro... | ['Lucas Theis', 'George Toderici', 'Luca Versari', 'Fabian Mentzer', 'Eirikur Agustsson', 'Emiel Hoogeboom'] | 2023-05-26 | null | null | null | null | ['image-compression'] | ['computer-vision'] | [ 7.91500032e-01 2.49014050e-01 7.08528981e-02 -6.66345209e-02
-7.58884490e-01 -2.21941322e-01 7.91190386e-01 -2.05131978e-01
-1.76539317e-01 6.76544309e-01 3.46549779e-01 -5.05082428e-01
-1.90467075e-01 -6.19602680e-01 -6.20352566e-01 -8.33817899e-01
-1.29819751e-01 1.63786173e-01 7.24292323e-02 -7.90965632... | [11.293355941772461, -0.9024261236190796] |
5724074f-7b66-4ecf-8e60-8d472409e766 | unsupervised-counselor-dialogue-clustering | null | null | https://aclanthology.org/W18-5017 | https://aclanthology.org/W18-5017.pdf | Unsupervised Counselor Dialogue Clustering for Positive Emotion Elicitation in Neural Dialogue System | Positive emotion elicitation seeks to improve user{'}s emotional state through dialogue system interaction, where a chat-based scenario is layered with an implicit goal to address user{'}s emotional needs. Standard neural dialogue system approaches still fall short in this situation as they tend to generate only short,... | ['Koichiro Yoshino', 'Satoshi Nakamura', 'Sakriani Sakti', 'Nurul Lubis'] | 2018-07-01 | null | null | null | ws-2018-7 | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 5.97625136e-01 7.51542628e-01 1.45485491e-01 -8.23355317e-01
-6.88627958e-01 -4.66569185e-01 4.88327026e-01 1.26380980e-01
-5.11759758e-01 9.81361151e-01 4.40722883e-01 -1.59174368e-01
6.67161271e-02 -4.91608441e-01 3.43552977e-01 -4.78635103e-01
3.52815151e-01 7.33964086e-01 -3.03764880e-01 -6.49324596... | [13.104079246520996, 7.712787628173828] |
0ed119cf-31a0-4f5e-9256-860a413ffc1f | bayesian-eye-tracking | 2106.13387 | null | https://arxiv.org/abs/2106.13387v1 | https://arxiv.org/pdf/2106.13387v1.pdf | Bayesian Eye Tracking | Model-based eye tracking has been a dominant approach for eye gaze tracking because of its ability to generalize to different subjects, without the need of any training data and eye gaze annotations. Model-based eye tracking, however, is susceptible to eye feature detection errors, in particular for eye tracking in the... | ['Kang Wang', 'Qiang Ji'] | 2021-06-25 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-3.04452032e-01 -1.72076553e-01 1.12872552e-02 -5.00426292e-01
-1.73044443e-01 -1.71954557e-01 1.71127200e-01 -3.77287954e-01
-4.06872779e-01 4.82050776e-01 -2.83671945e-01 -1.39476791e-01
-1.06831439e-01 -2.18720689e-01 -8.44880879e-01 -5.68552434e-01
2.60778636e-01 7.55794719e-02 3.54884803e-01 1.52796730... | [14.136371612548828, 0.048909977078437805] |
a9b3089f-16af-4cc4-9533-ef30e0bfac05 | attention-based-occlusion-removal-for-hybrid | 2112.01098 | null | https://arxiv.org/abs/2112.01098v1 | https://arxiv.org/pdf/2112.01098v1.pdf | Attention based Occlusion Removal for Hybrid Telepresence Systems | Traditionally, video conferencing is a widely adopted solution for telecommunication, but a lack of immersiveness comes inherently due to the 2D nature of facial representation. The integration of Virtual Reality (VR) in a communication/telepresence system through Head Mounted Displays (HMDs) promises to provide users ... | ['Avinash Sharma', 'Ashwath Shetty', 'Surabhi Gupta'] | 2021-12-02 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 1.69417396e-01 4.31788772e-01 4.25299346e-01 -4.44716871e-01
-6.87441826e-01 -3.11984122e-01 4.97468770e-01 -9.08233583e-01
-3.80577222e-02 5.02292752e-01 3.33324164e-01 4.10795212e-02
4.17921275e-01 -9.99185517e-02 -5.69048285e-01 -1.77634284e-01
-9.64346454e-02 1.85538217e-01 -1.17106795e-01 -4.46679264... | [12.919301986694336, -0.34482306241989136] |
76191a67-85fc-4af3-a7d8-293c2b2167be | unsupervised-chinese-word-segmentation-with-1 | null | null | https://aclanthology.org/2022.findings-acl.310 | https://aclanthology.org/2022.findings-acl.310.pdf | Unsupervised Chinese Word Segmentation with BERT Oriented Probing and Transformation | Word Segmentation is a fundamental step for understanding Chinese language. Previous neural approaches for unsupervised Chinese Word Segmentation (CWS) only exploits shallow semantic information, which can miss important context. Large scale Pre-trained language models (PLM) have achieved great success in many areas be... | ['Yanqiu Shao', 'Qi Su', 'Yuhan Song', 'Wei Li'] | null | null | null | null | findings-acl-2022-5 | ['chinese-word-segmentation'] | ['natural-language-processing'] | [ 2.76158482e-01 1.08286403e-01 -4.47788745e-01 -5.90058923e-01
-5.79651654e-01 -4.15840000e-01 2.12765068e-01 1.36285797e-01
-7.11342335e-01 4.19493884e-01 3.65970820e-01 -5.25539160e-01
4.98308718e-01 -7.34002948e-01 -4.37798440e-01 -3.63974184e-01
3.69876385e-01 3.91670495e-01 6.54670954e-01 -1.52477860... | [9.955297470092773, 10.076038360595703] |
843791fc-09ee-4203-b93a-e207b0aa79a1 | what-makes-entities-similar-a-similarity | 2306.02622 | null | https://arxiv.org/abs/2306.02622v1 | https://arxiv.org/pdf/2306.02622v1.pdf | What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings | Joint representation learning over multi-sourced knowledge graphs (KGs) yields transferable and expressive embeddings that improve downstream tasks. Entity alignment (EA) is a critical step in this process. Despite recent considerable research progress in embedding-based EA, how it works remains to be explored. In this... | ['Wei Hu', 'Weijun Ren', 'Qijin Chen', 'Xiaozhou Xu', 'Jiacheng Huang', 'Zequn Sun'] | 2023-06-05 | null | null | null | null | ['knowledge-graph-embeddings', 'entity-alignment', 'knowledge-graphs', 'knowledge-graph-embeddings', 'entity-alignment'] | ['graphs', 'knowledge-base', 'knowledge-base', 'methodology', 'natural-language-processing'] | [-1.34294713e-02 2.38867074e-01 -6.10936165e-01 -2.13173106e-01
-6.64883316e-01 -7.20599115e-01 6.82916164e-01 6.17181599e-01
-3.70090365e-01 4.30545330e-01 6.38517737e-01 -6.23440444e-01
-5.96019208e-01 -1.17968500e+00 -6.81986392e-01 -3.58815968e-01
-5.59288681e-01 4.47364926e-01 3.93095911e-01 -4.37320381... | [8.759212493896484, 7.875870704650879] |
3d76edcb-b7cf-4786-a259-bf6e6e9e1f42 | do-saliency-models-detect-odd-one-out-targets | 2005.06583 | null | https://arxiv.org/abs/2005.06583v2 | https://arxiv.org/pdf/2005.06583v2.pdf | Do Saliency Models Detect Odd-One-Out Targets? New Datasets and Evaluations | Recent advances in the field of saliency have concentrated on fixation prediction, with benchmarks reaching saturation. However, there is an extensive body of works in psychology and neuroscience that describe aspects of human visual attention that might not be adequately captured by current approaches. Here, we invest... | ['Iuliia Kotseruba', 'John K. Tsotsos', 'Amir Rasouli', 'Calden Wloka'] | 2020-05-13 | null | null | null | null | ['odd-one-out'] | ['reasoning'] | [ 5.01042485e-01 -1.84621066e-01 -7.16888830e-02 -8.91739279e-02
-3.33928406e-01 -2.26318553e-01 5.49531460e-01 2.75396317e-01
-4.80095714e-01 6.79693878e-01 5.20270057e-02 -2.96002239e-01
1.87867269e-01 -3.17627907e-01 -7.24111617e-01 -3.85496616e-01
-5.51035143e-02 -5.36705926e-02 8.23654175e-01 -3.44340652... | [10.04655647277832, 1.6622695922851562] |
110b2150-da71-4ad5-a989-5ccff4f0571a | unsupervised-learning-of-object-landmarks-by | 1705.02193 | null | http://arxiv.org/abs/1705.02193v2 | http://arxiv.org/pdf/1705.02193v2.pdf | Unsupervised learning of object landmarks by factorized spatial embeddings | Learning automatically the structure of object categories remains an
important open problem in computer vision. In this paper, we propose a novel
unsupervised approach that can discover and learn landmarks in object
categories, thus characterizing their structure. Our approach is based on
factorizing image deformations... | ['Hakan Bilen', 'Andrea Vedaldi', 'James Thewlis'] | 2017-05-05 | unsupervised-learning-of-object-landmarks-by-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Thewlis_Unsupervised_Learning_of_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Thewlis_Unsupervised_Learning_of_ICCV_2017_paper.pdf | iccv-2017-10 | ['unsupervised-facial-landmark-detection'] | ['computer-vision'] | [ 3.00426246e-03 2.19608799e-01 -7.81302005e-02 -7.30641484e-01
-5.09501100e-01 -8.17857504e-01 9.37667966e-01 2.14635625e-01
-3.53922665e-01 2.32888147e-01 2.61082828e-01 2.52677917e-01
-1.47495344e-01 -6.13265514e-01 -1.01862407e+00 -6.79455578e-01
-4.29545864e-02 6.05272233e-01 2.14535952e-01 8.86435211... | [9.109687805175781, 2.414032220840454] |
9b18d0a7-3947-43d0-8d5e-9b581c85ab3f | learning-agent-representations-for-ice-hockey | null | null | http://proceedings.neurips.cc/paper/2020/hash/d90e5b6628b4291225cba0bdc643c295-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/d90e5b6628b4291225cba0bdc643c295-Paper.pdf | Learning Agent Representations for Ice Hockey | Team sports is a new application domain for agent modeling with high real-world impact. A fundamental challenge for modeling professional players is their large number (over 1K), which includes many bench players with sparse participation in a game season. The diversity and sparsity of player observations make it diffi... | ['Mehrsan Javan', 'Mike Rudd', 'Pascal Poupart', 'Oliver Schulte', 'Guiliang Liu'] | 2020-12-01 | null | null | null | neurips-2020-12 | ['sports-analytics'] | ['computer-vision'] | [-7.75554404e-02 3.01481932e-01 -5.49444675e-01 -1.75010059e-02
-9.81891155e-01 -3.29869062e-01 7.51013279e-01 -5.10434471e-02
-2.71210104e-01 5.06706715e-01 8.13971221e-01 3.61169845e-01
-1.71668917e-01 -1.05510485e+00 -8.19001615e-01 -5.54727137e-01
-3.37512612e-01 1.11197495e+00 2.76363790e-01 -6.77335620... | [6.661679744720459, 0.351650208234787] |
c98b0fd1-677d-488c-815e-d9a543208cfb | generalization-bounds-for-set-to-set-matching | 2302.12991 | null | https://arxiv.org/abs/2302.12991v1 | https://arxiv.org/pdf/2302.12991v1.pdf | Generalization Bounds for Set-to-Set Matching with Negative Sampling | The problem of matching two sets of multiple elements, namely set-to-set matching, has received a great deal of attention in recent years. In particular, it has been reported that good experimental results can be obtained by preparing a neural network as a matching function, especially in complex cases where, for examp... | ['Masanari Kimura'] | 2023-02-25 | null | null | null | null | ['set-matching'] | ['computer-vision'] | [ 6.97898090e-01 -1.24575049e-01 1.31770819e-01 -7.70602226e-01
-4.87910002e-01 -4.83553529e-01 3.96918356e-01 1.97620392e-01
-4.16637301e-01 4.84468341e-01 -4.84662235e-01 -9.72736180e-02
-5.53813696e-01 -8.81328940e-01 -7.90779769e-01 -5.86155593e-01
3.63951661e-02 6.30296826e-01 6.58072606e-02 -3.94424558... | [9.730236053466797, 3.1024017333984375] |
624db4ad-3789-4766-a4b0-0b1c810f7ad8 | end-to-end-optimization-of-scene-layout-1 | 2007.11744 | null | https://arxiv.org/abs/2007.11744v1 | https://arxiv.org/pdf/2007.11744v1.pdf | End-to-End Optimization of Scene Layout | We propose an end-to-end variational generative model for scene layout synthesis conditioned on scene graphs. Unlike unconditional scene layout generation, we use scene graphs as an abstract but general representation to guide the synthesis of diverse scene layouts that satisfy relationships included in the scene graph... | ['Joshua B. Tenenbaum', 'Andrew Luo', 'Zhoutong Zhang', 'Jiajun Wu'] | 2020-07-23 | end-to-end-optimization-of-scene-layout | http://openaccess.thecvf.com/content_CVPR_2020/html/Luo_End-to-End_Optimization_of_Scene_Layout_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Luo_End-to-End_Optimization_of_Scene_Layout_CVPR_2020_paper.pdf | cvpr-2020-6 | ['scene-generation', 'indoor-scene-reconstruction', 'indoor-scene-synthesis'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.11804438e-01 2.54210800e-01 3.18437368e-01 -6.43019795e-01
-6.72977865e-01 -9.31000590e-01 7.61526108e-01 -3.96587364e-02
1.63307115e-01 5.14649808e-01 3.87835592e-01 -3.27823192e-01
2.00545732e-02 -1.10787439e+00 -7.77215540e-01 -3.58842403e-01
3.92466724e-01 4.09479648e-01 -5.19266948e-02 -1.01409398... | [11.1659574508667, -0.32497742772102356] |
004f9660-b0a6-43c8-84ec-c4d4eb308771 | using-causal-analysis-for-conceptual-deep | 2107.06098 | null | https://arxiv.org/abs/2107.06098v1 | https://arxiv.org/pdf/2107.06098v1.pdf | Using Causal Analysis for Conceptual Deep Learning Explanation | Model explainability is essential for the creation of trustworthy Machine Learning models in healthcare. An ideal explanation resembles the decision-making process of a domain expert and is expressed using concepts or terminology that is meaningful to the clinicians. To provide such an explanation, we first associate t... | ['Kayhan Batmanghelich', 'Sofia Triantafillou', 'Stephen Wallace', 'Sumedha Singla'] | 2021-07-10 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 6.65601015e-01 9.08288538e-01 -9.25079107e-01 -5.32894254e-01
-5.25096595e-01 -2.25426272e-01 3.46448481e-01 4.84478056e-01
4.77516614e-02 1.06651950e+00 8.02698493e-01 -1.00943494e+00
-4.99124736e-01 -5.89169323e-01 -8.39411855e-01 -4.23663229e-01
-5.38453385e-02 5.14410436e-01 -6.31235600e-01 4.08646941... | [8.48068618774414, 5.676583290100098] |
c1fb4f31-35f9-4803-84f2-d1a0417c220d | weakly-supervised-object-localization-via | 2207.10447 | null | https://arxiv.org/abs/2207.10447v2 | https://arxiv.org/pdf/2207.10447v2.pdf | Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration | Weakly Supervised Object Localization (WSOL), which aims to localize objects by only using image-level labels, has attracted much attention because of its low annotation cost in real applications. Recent studies leverage the advantage of self-attention in visual Transformer for long-range dependency to re-active semant... | ['Xiang Wan', 'Jiong Wang', 'Ruimao Zhang', 'Haotian Bai'] | 2022-07-21 | null | null | null | null | ['weakly-supervised-object-localization', 'long-range-modeling'] | ['computer-vision', 'natural-language-processing'] | [ 3.22010741e-02 7.25616440e-02 -3.35070401e-01 -4.56320614e-01
-6.75817788e-01 -3.74555379e-01 4.83881235e-01 -2.53708544e-03
-3.89939934e-01 4.49842572e-01 4.96943966e-02 8.74941051e-02
-1.01999104e-01 -6.39134288e-01 -9.84439433e-01 -9.89582241e-01
1.85653090e-01 1.34145498e-01 7.38734961e-01 -2.19030324... | [9.603755950927734, 0.821819543838501] |
fdb6e2a8-4bbe-47e8-ab9e-f226dd5ecd7f | multi-task-pre-training-for-plug-and-play | 2109.14739 | null | https://arxiv.org/abs/2109.14739v2 | https://arxiv.org/pdf/2109.14739v2.pdf | Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System | Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems. Despite their success, existing methods often formulate this task as a cascaded generation problem which can lead to error accumulation across different sub-tasks and greater data annotation overhead. In this study, we... | ['Yi Zhang', 'Yi-An Lai', 'Deng Cai', 'Arshit Gupta', 'Elman Mansimov', 'Lei Shu', 'Yixuan Su'] | 2021-09-29 | null | https://aclanthology.org/2022.acl-long.319 | https://aclanthology.org/2022.acl-long.319.pdf | acl-2022-5 | ['end-to-end-dialogue-modelling'] | ['natural-language-processing'] | [-1.93645041e-02 5.58311641e-01 6.63457513e-02 -6.20223820e-01
-1.06473625e+00 -7.81190813e-01 1.11118889e+00 -2.18787733e-02
-5.38547218e-01 9.46459413e-01 8.46700370e-01 -1.17406659e-01
6.58843994e-01 -2.02379286e-01 1.73407242e-01 -1.00010835e-01
4.26491708e-01 1.32185400e+00 1.84387416e-01 -7.80995727... | [12.713942527770996, 8.123894691467285] |
8672bd59-1488-4398-8522-fa3f4714d7ae | knowledge-distillation-transfer-sets-and | 2210.04834 | null | https://arxiv.org/abs/2210.04834v3 | https://arxiv.org/pdf/2210.04834v3.pdf | Knowledge Distillation Transfer Sets and their Impact on Downstream NLU Tasks | Teacher-student knowledge distillation is a popular technique for compressing today's prevailing large language models into manageable sizes that fit low-latency downstream applications. Both the teacher and the choice of transfer set used for distillation are crucial ingredients in creating a high quality student. Yet... | ['Pan Wei', 'Gokmen Oz', 'Turan Gojayev', 'Thomas Gueudre', 'Lizhen Tan', 'Charith Peris'] | 2022-10-10 | null | null | null | null | ['intent-classification'] | ['natural-language-processing'] | [ 1.71478629e-01 4.34317179e-02 -3.36584657e-01 -4.90439028e-01
-1.05038369e+00 -1.08491707e+00 4.22030836e-01 2.27490827e-01
-9.13309038e-01 9.07384753e-01 3.21458697e-01 -6.91170752e-01
4.46873754e-02 -5.35638750e-01 -6.46249413e-01 -4.48854268e-01
3.76840413e-01 8.74057710e-01 4.14091527e-01 -3.56287718... | [10.800707817077637, 8.474559783935547] |
3ec4b88a-6e55-4ef2-b7f7-52bf087a6b0d | epic-fusion-audio-visual-temporal-binding-for | 1908.08498 | null | https://arxiv.org/abs/1908.08498v1 | https://arxiv.org/pdf/1908.08498v1.pdf | EPIC-Fusion: Audio-Visual Temporal Binding for Egocentric Action Recognition | We focus on multi-modal fusion for egocentric action recognition, and propose a novel architecture for multi-modal temporal-binding, i.e. the combination of modalities within a range of temporal offsets. We train the architecture with three modalities -- RGB, Flow and Audio -- and combine them with mid-level fusion alo... | ['Evangelos Kazakos', 'Arsha Nagrani', 'Andrew Zisserman', 'Dima Damen'] | 2019-08-22 | epic-fusion-audio-visual-temporal-binding-for-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Kazakos_EPIC-Fusion_Audio-Visual_Temporal_Binding_for_Egocentric_Action_Recognition_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Kazakos_EPIC-Fusion_Audio-Visual_Temporal_Binding_for_Egocentric_Action_Recognition_ICCV_2019_paper.pdf | iccv-2019-10 | ['egocentric-activity-recognition'] | ['computer-vision'] | [ 3.30083698e-01 -3.58239055e-01 8.13393742e-02 -2.71011263e-01
-1.04932535e+00 -5.10433614e-01 9.38935220e-01 -4.25583452e-01
-4.76975799e-01 4.97741818e-01 1.09350967e+00 6.67419016e-01
-2.30128467e-01 -2.97841221e-01 -5.91225326e-01 -6.78851068e-01
-2.04411492e-01 1.05040953e-01 1.40450612e-01 -1.66718394... | [8.299202919006348, 0.6285319328308105] |
0da4db7e-99a2-46a6-be34-b27bab54cf16 | fd-on-understanding-the-role-of-deep-feature | 2305.20048 | null | https://arxiv.org/abs/2305.20048v2 | https://arxiv.org/pdf/2305.20048v2.pdf | F?D: On understanding the role of deep feature spaces on face generation evaluation | Perceptual metrics, like the Fr\'echet Inception Distance (FID), are widely used to assess the similarity between synthetically generated and ground truth (real) images. The key idea behind these metrics is to compute errors in a deep feature space that captures perceptually and semantically rich image features. Despit... | ['Guha Balakrishnan', 'Krish Kabra'] | 2023-05-31 | null | null | null | null | ['face-generation'] | ['computer-vision'] | [ 9.56231505e-02 2.52281576e-02 6.58198893e-02 -5.76124310e-01
-3.72457206e-01 -7.51177251e-01 1.19064569e+00 -2.64096290e-01
-2.91660815e-01 5.80598950e-01 5.55063248e-01 1.16690129e-01
-2.34367803e-01 -9.76586521e-01 -6.42338932e-01 -6.19530976e-01
8.45343322e-02 3.74977775e-02 -5.77182055e-01 -2.26069212... | [12.807619094848633, 0.9665404558181763] |
82d52895-89d8-4386-be1d-64ab6ced40e0 | analysis-of-numerical-integration-in-rnn | 2305.0467 | null | https://arxiv.org/abs/2305.04670v1 | https://arxiv.org/pdf/2305.04670v1.pdf | Analysis of Numerical Integration in RNN-Based Residuals for Fault Diagnosis of Dynamic Systems | Data-driven modeling and machine learning are widely used to model the behavior of dynamic systems. One application is the residual evaluation of technical systems where model predictions are compared with measurement data to create residuals for fault diagnosis applications. While recurrent neural network models have ... | ['Mattias Krysander', 'Daniel Jung', 'Theodor Westny', 'Arman Mohammadi'] | 2023-05-08 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [ 1.21500358e-01 -8.55531916e-02 1.50711671e-01 -6.90632686e-02
-3.90622258e-01 3.02020274e-02 9.36876386e-02 -2.46328451e-02
5.48326857e-02 5.37890196e-01 -2.46127442e-01 -7.87327230e-01
-7.34633148e-01 -3.77580911e-01 -2.11731523e-01 -8.26120973e-01
-1.61792189e-01 6.54239476e-01 -1.81243539e-01 -3.76783371... | [6.566550254821777, 2.685302972793579] |
0ec1b063-d7c2-4114-8027-e7ad25ad9364 | generative-steganography-network | 2207.13867 | null | https://arxiv.org/abs/2207.13867v3 | https://arxiv.org/pdf/2207.13867v3.pdf | Generative Steganography Network | Steganography usually modifies cover media to embed secret data. A new steganographic approach called generative steganography (GS) has emerged recently, in which stego images (images containing secret data) are generated from secret data directly without cover media. However, existing GS schemes are often criticized f... | ['Qing Zhou', 'Zhenxing Qian', 'Ge Luo', 'Xinpeng Zhang', 'Sheng Li', 'Ping Wei'] | 2022-07-28 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 9.37119305e-01 3.81679207e-01 3.12795609e-01 7.18633085e-02
-1.59341052e-01 -4.03993219e-01 4.87903386e-01 -9.22940612e-01
-5.83037660e-02 5.54151058e-01 -1.52137995e-01 -2.81792521e-01
5.11890411e-01 -1.35615361e+00 -6.68485463e-01 -1.12684786e+00
-2.50911146e-01 -3.80497456e-01 1.34043202e-01 -4.77595627... | [4.309024810791016, 8.052433967590332] |
62168585-4205-4176-b4cf-721c4c69a02a | simple-unsupervised-similarity-based-aspect | 2008.1082 | null | https://arxiv.org/abs/2008.10820v1 | https://arxiv.org/pdf/2008.10820v1.pdf | Simple Unsupervised Similarity-Based Aspect Extraction | In the context of sentiment analysis, there has been growing interest in performing a finer granularity analysis focusing on the specific aspects of the entities being evaluated. This is the goal of Aspect-Based Sentiment Analysis (ABSA) which basically involves two tasks: aspect extraction and polarity detection. The ... | ['Danny Suarez Vargas', 'Viviane Pereira Moreira', 'Lucas R. C. Pessutto'] | 2020-08-25 | null | null | null | null | ['aspect-extraction'] | ['natural-language-processing'] | [ 4.96778004e-02 1.31014615e-01 -3.09462011e-01 -3.63010556e-01
-5.61959028e-01 -5.37801445e-01 9.59530652e-01 8.06622088e-01
-4.23720807e-01 4.01340276e-01 3.46490592e-01 -3.83060515e-01
7.04254676e-03 -9.19449747e-01 -2.97069669e-01 -5.35925865e-01
3.31719130e-01 4.36570704e-01 1.15940869e-02 -5.68663180... | [11.345892906188965, 6.750380516052246] |
d5d151da-4ad7-4aae-8239-11e88069b41e | uniform-pac-guarantees-for-model-based-rl | 2305.0835 | null | https://arxiv.org/abs/2305.08350v1 | https://arxiv.org/pdf/2305.08350v1.pdf | Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension | Recently, there has been remarkable progress in reinforcement learning (RL) with general function approximation. However, all these works only provide regret or sample complexity guarantees. It is still an open question if one can achieve stronger performance guarantees, i.e., the uniform probably approximate correctne... | ['Quanquan Gu', 'Jiafan He', 'Yue Wu'] | 2023-05-15 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [-3.93303344e-03 4.19050992e-01 -6.93803132e-01 -2.36827046e-01
-1.15893781e+00 -6.80652678e-01 6.82248026e-02 3.53359401e-01
-4.82645929e-01 1.43044829e+00 -1.88633502e-01 -4.84163612e-01
-6.86337471e-01 -8.99143279e-01 -1.09624457e+00 -9.89015460e-01
-1.46612868e-01 7.70127535e-01 2.02469438e-01 -3.48604210... | [4.512645244598389, 3.309234619140625] |
05bea9f3-0e41-41a8-89fa-9f7273a81c48 | a-robotic-visual-grasping-design-rethinking | 2209.07459 | null | https://arxiv.org/abs/2209.07459v2 | https://arxiv.org/pdf/2209.07459v2.pdf | A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions | High-resolution representations are important for vision-based robotic grasping problems. Existing works generally encode the input images into low-resolution representations via sub-networks and then recover high-resolution representations. This will lose spatial information, and errors introduced by the decoder will ... | ['Zhen Kan', 'Mingyu Cai', 'Ziyang Chen', 'Shaochen Wang', 'Zhangli Zhou'] | 2022-09-15 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [ 1.72965571e-01 -1.60204321e-01 -2.43852600e-01 -2.50017881e-01
-2.59099990e-01 -3.31345618e-01 1.35868236e-01 -3.16586256e-01
-7.27238879e-02 4.77946520e-01 1.17674820e-01 1.03612252e-01
-2.04826772e-01 -9.20301497e-01 -1.25631988e+00 -6.32773101e-01
-6.98249638e-02 -1.09951839e-01 3.20958048e-01 -3.22054207... | [5.77485466003418, -0.8965104222297668] |
6c45bb75-cbce-480e-a859-a6ca69d6f159 | convolutional-sequence-to-sequence-model-for | 1805.00655 | null | http://arxiv.org/abs/1805.00655v1 | http://arxiv.org/pdf/1805.00655v1.pdf | Convolutional Sequence to Sequence Model for Human Dynamics | Human motion modeling is a classic problem in computer vision and graphics.
Challenges in modeling human motion include high dimensional prediction as well
as extremely complicated dynamics.We present a novel approach to human motion
modeling based on convolutional neural networks (CNN). The hierarchical
structure of C... | ['Zhen Zhang', 'Chen Li', 'Wee Sun Lee', 'Gim Hee Lee'] | 2018-05-02 | convolutional-sequence-to-sequence-model-for-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Convolutional_Sequence_to_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Convolutional_Sequence_to_CVPR_2018_paper.pdf | cvpr-2018-6 | ['human-pose-forecasting', 'human-dynamics'] | ['computer-vision', 'computer-vision'] | [-9.80461612e-02 -1.75383970e-01 -4.36007053e-01 -8.06360170e-02
-1.12389596e-02 -5.93992062e-02 5.75900733e-01 -5.77107072e-01
-3.16654563e-01 4.79610592e-01 5.35085380e-01 -5.33194803e-02
6.16162956e-01 -7.48541534e-01 -7.51424611e-01 -6.41481757e-01
-2.53862560e-01 2.54418194e-01 6.43042147e-01 -6.46906197... | [7.32023811340332, -0.16830916702747345] |
1e51abf8-ed76-4df8-a1d5-45dfb96586b6 | towards-a-music-language-mapping | null | null | https://aclanthology.org/L18-1482 | https://aclanthology.org/L18-1482.pdf | Towards a music-language mapping | null | ['Francesca Bonin', 'Michele Berlingerio'] | 2018-05-01 | towards-a-music-language-mapping-1 | https://aclanthology.org/L18-1482 | https://aclanthology.org/L18-1482.pdf | lrec-2018-5 | ['lexical-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.519640922546387, 3.5545969009399414] |
f27cf850-dd21-4a3f-bd65-7ed5e28e5598 | vanillanet-the-power-of-minimalism-in-deep | 2305.12972 | null | https://arxiv.org/abs/2305.12972v2 | https://arxiv.org/pdf/2305.12972v2.pdf | VanillaNet: the Power of Minimalism in Deep Learning | At the heart of foundation models is the philosophy of "more is different", exemplified by the astonishing success in computer vision and natural language processing. However, the challenges of optimization and inherent complexity of transformer models call for a paradigm shift towards simplicity. In this study, we int... | ['DaCheng Tao', 'Jianyuan Guo', 'Yunhe Wang', 'Hanting Chen'] | 2023-05-22 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [-1.37283355e-01 2.41108820e-01 -1.61467746e-01 -2.46103197e-01
-8.62853676e-02 -4.31353450e-01 7.43699372e-01 -3.53005707e-01
-4.56430227e-01 3.31321895e-01 2.66517907e-01 -6.72144175e-01
-1.74210686e-02 -5.28376579e-01 -5.82937360e-01 -5.01137614e-01
2.28194427e-02 3.82087231e-02 -1.17682494e-01 -5.29902399... | [8.90909194946289, 2.6294686794281006] |
d06b27a3-68b8-45e6-8750-59e0b11169ef | region-proposal-networks-with-contextual | 1812.1033 | null | http://arxiv.org/abs/1812.10330v1 | http://arxiv.org/pdf/1812.10330v1.pdf | Region Proposal Networks with Contextual Selective Attention for Real-Time Organ Detection | State-of-the-art methods for object detection use region proposal networks
(RPN) to hypothesize object location. These networks simultaneously predicts
object bounding boxes and \emph{objectness} scores at each location in the
image. Unlike natural images for which RPN algorithms were originally designed,
most medical ... | ['Antonio R. Porras', 'Awais Mansoor', 'Marius George Linguraru'] | 2018-12-26 | null | null | null | null | ['organ-detection'] | ['medical'] | [ 2.02917919e-01 5.76512180e-02 -1.94829658e-01 -2.53601819e-01
-7.06602573e-01 -4.01496649e-01 3.03329557e-01 6.38981700e-01
-5.37039042e-01 3.46180707e-01 -5.86269833e-02 -2.03593954e-01
-2.82661468e-01 -6.96368158e-01 -5.73977649e-01 -7.25784004e-01
-5.99620454e-02 3.89422417e-01 5.48817873e-01 3.06071669... | [15.153302192687988, -2.308587074279785] |
e38d96ee-3134-4f42-b98e-f5789bcf2c27 | vidosat-high-dimensional-sparsifying | 1710.00947 | null | http://arxiv.org/abs/1710.00947v1 | http://arxiv.org/pdf/1710.00947v1.pdf | VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising | Techniques exploiting the sparsity of images in a transform domain have been
effective for various applications in image and video processing. Transform
learning methods involve cheap computations and have been demonstrated to
perform well in applications such as image denoising and medical image
reconstruction. Recent... | ['Saiprasad Ravishankar', 'Bihan Wen', 'Yoram Bresler'] | 2017-10-03 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 4.04528320e-01 -3.83636087e-01 -8.14648867e-02 -2.23677337e-01
-1.05607188e+00 -6.33601705e-03 1.79964349e-01 5.05579561e-02
-3.77093703e-01 3.23366195e-01 1.40966535e-01 -7.11703226e-02
-2.03822583e-01 -5.33604622e-01 -8.40109468e-01 -1.09092033e+00
-2.78304666e-01 1.34871051e-01 3.43796164e-01 -1.59575865... | [11.567971229553223, -2.1513333320617676] |
d7bdecd6-b487-4373-9cdf-ec7f115f4278 | a-diffusion-probabilistic-prior-for-low-dose | 2305.15887 | null | https://arxiv.org/abs/2305.15887v1 | https://arxiv.org/pdf/2305.15887v1.pdf | A Diffusion Probabilistic Prior for Low-Dose CT Image Denoising | Low-dose computed tomography (CT) image denoising is crucial in medical image computing. Recent years have been remarkable improvement in deep learning-based methods for this task. However, training deep denoising neural networks requires low-dose and normal-dose CT image pairs, which are difficult to obtain in the cli... | ['Xiaokun Liang', 'Shan Tan', 'Songhui Diao', 'Yaoqin Xie', 'Xuan Liu'] | 2023-05-25 | null | null | null | null | ['computed-tomography-ct'] | ['methodology'] | [ 4.48755383e-01 9.18709785e-02 4.19951975e-01 -5.43376923e-01
-1.43685710e+00 -1.18977241e-01 4.08313245e-01 1.84695572e-01
-7.09498644e-01 3.68674487e-01 4.81062382e-01 1.67272910e-02
-1.88058570e-01 -1.11482835e+00 -5.61053276e-01 -1.34245598e+00
6.22795336e-02 6.60391867e-01 2.60262638e-01 -1.37603413... | [13.461468696594238, -2.519710063934326] |
83d4c8d3-9649-45ee-ae83-aec6ae99d45d | surrogate-based-black-box-optimization-method | 2110.03522 | null | https://arxiv.org/abs/2110.03522v1 | https://arxiv.org/pdf/2110.03522v1.pdf | Surrogate-Based Black-Box Optimization Method for Costly Molecular Properties | AI-assisted molecular optimization is a very active research field as it is expected to provide the next-generation drugs and molecular materials. An important difficulty is that the properties to be optimized rely on costly evaluations. Machine learning methods are investigated with success to predict these properties... | ['Benoit Da Mota', 'Beatrice Duval', 'Thomas Cauchy', 'Jules Leguy'] | 2021-10-01 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 6.65004075e-01 9.44915712e-02 -2.55216628e-01 -1.18027434e-01
-7.55959511e-01 -3.58145088e-01 4.32052940e-01 6.95448399e-01
-5.37476301e-01 1.24984026e+00 -3.27514827e-01 -1.49807855e-01
-5.47122478e-01 -9.21110570e-01 -8.00879538e-01 -1.20183039e+00
-8.78566578e-02 8.68388236e-01 1.26787489e-02 -3.10932606... | [5.1240458488464355, 5.308169364929199] |
398468a0-1a0d-4518-90c9-4db70c2e75ec | binaural-signal-representations-for-joint | 2209.059 | null | https://arxiv.org/abs/2209.05900v1 | https://arxiv.org/pdf/2209.05900v1.pdf | Binaural Signal Representations for Joint Sound Event Detection and Acoustic Scene Classification | Sound event detection (SED) and Acoustic scene classification (ASC) are two widely researched audio tasks that constitute an important part of research on acoustic scene analysis. Considering shared information between sound events and acoustic scenes, performing both tasks jointly is a natural part of a complex machin... | ['Annamaria Mesaros', 'Daniel Aleksander Krause'] | 2022-09-13 | null | null | null | null | ['sound-event-detection', 'scene-classification'] | ['audio', 'computer-vision'] | [ 3.58130276e-01 -6.06598556e-01 8.57613146e-01 -5.24904490e-01
-8.78456831e-01 -5.42754352e-01 8.77051830e-01 5.10164976e-01
-7.52853453e-01 3.73732358e-01 4.05554354e-01 -4.34246734e-02
-2.98740298e-01 -3.67865235e-01 -5.03269315e-01 -7.72400796e-01
-3.65487814e-01 -4.20121802e-03 4.49789792e-01 -1.40805215... | [15.19804573059082, 5.4057135581970215] |
aa7d701f-8882-4e24-b9aa-73ccc0aa210d | weakly-supervised-facial-action-unit | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Peng_Weakly_Supervised_Facial_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Peng_Weakly_Supervised_Facial_CVPR_2018_paper.pdf | Weakly Supervised Facial Action Unit Recognition Through Adversarial Training | Current works on facial action unit (AU) recognition typically require fully AU-annotated facial images for supervised AU classifier training. AU annotation is a time-consuming, expensive, and error-prone process. While AUs are hard to annotate, facial expression is relatively easy to label. Furthermore, there exist st... | ['Shangfei Wang', 'Guozhu Peng'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['facial-action-unit-detection'] | ['computer-vision'] | [ 5.79185486e-01 4.70504016e-01 -2.34588474e-01 -6.37373030e-01
-9.87332582e-01 -5.62777758e-01 4.33149338e-01 -5.11743128e-01
-5.88995442e-02 7.36364484e-01 -1.07358478e-01 3.15413684e-01
5.40830195e-01 -9.46123064e-01 -9.18498874e-01 -1.08479810e+00
3.23423505e-01 3.13562363e-01 -2.97663450e-01 -5.95485754... | [13.634804725646973, 1.5684739351272583] |
3a06a97a-f88c-42ca-853b-3a2a9088b8d2 | deep-representation-of-facial-geometric-and | 1511.03015 | null | http://arxiv.org/abs/1511.03015v1 | http://arxiv.org/pdf/1511.03015v1.pdf | Deep Representation of Facial Geometric and Photometric Attributes for Automatic 3D Facial Expression Recognition | In this paper, we present a novel approach to automatic 3D Facial Expression
Recognition (FER) based on deep representation of facial 3D geometric and 2D
photometric attributes. A 3D face is firstly represented by its geometric and
photometric attributes, including the geometry map, normal maps, normalized
curvature ma... | ['Zongben Xu', 'Liming Chen', 'Huibin Li', 'Jian Sun', 'Dong Wang'] | 2015-11-10 | null | null | null | null | ['3d-facial-expression-recognition'] | ['computer-vision'] | [-5.67238405e-02 -1.62820801e-01 -1.99988373e-02 -9.11122382e-01
-3.81590098e-01 -2.44074374e-01 6.48454249e-01 -2.46777579e-01
-2.72021797e-02 3.28555167e-01 -9.43089128e-02 4.72384356e-02
1.98116362e-01 -8.04922223e-01 -5.39253056e-01 -8.50258589e-01
-3.05459321e-01 2.86162168e-01 -2.91097373e-01 -3.67676169... | [13.508399963378906, 1.2746680974960327] |
8ff9e82e-6356-431b-b154-c21b1d648339 | low-resource-unsupervised-nmt-diagnosing-the | null | null | https://aclanthology.org/2020.eamt-1.10 | https://aclanthology.org/2020.eamt-1.10.pdf | Low-Resource Unsupervised NMT: Diagnosing the Problem and Providing a Linguistically Motivated Solution | Unsupervised Machine Translation has been advancing our ability to translate without parallel data, but state-of-the-art methods assume an abundance of monolingual data. This paper investigates the scenario where monolingual data is limited as well, finding that current unsupervised methods suffer in performance under ... | ['Gertjan van Noord', 'Antonio Toral', 'Lukas Edman'] | null | null | null | null | eamt-2020-11 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [-1.65948018e-01 -1.25550985e-01 -6.43946767e-01 -8.71962085e-02
-1.23456347e+00 -8.89623821e-01 1.04043102e+00 2.48793468e-01
-7.90342689e-01 8.48119140e-01 8.59769762e-01 -8.90524387e-01
3.18861306e-01 -3.35415751e-01 -5.81902385e-01 -4.31463242e-01
2.58166075e-01 6.45893216e-01 -2.44686276e-01 -7.35830307... | [11.338127136230469, 10.219405174255371] |
b98f2980-3f89-4130-a6d6-07fb484a3dda | med7-a-transferable-clinical-natural-language | 2003.01271 | null | https://arxiv.org/abs/2003.01271v2 | https://arxiv.org/pdf/2003.01271v2.pdf | Med7: a transferable clinical natural language processing model for electronic health records | The field of clinical natural language processing has been advanced significantly since the introduction of deep learning models. The self-supervised representation learning and the transfer learning paradigm became the methods of choice in many natural language processing application, in particular in the settings wit... | ['Alejo Nevado-Holgado', 'Nemanja Vaci', 'Qiang Liu', 'Andrey Kormilitzin'] | 2020-03-03 | null | null | null | null | ['medical-named-entity-recognition'] | ['natural-language-processing'] | [ 3.96183729e-01 3.76532108e-01 2.59130783e-02 -4.85212743e-01
-1.15122378e+00 -6.34450197e-01 3.83706301e-01 9.52066422e-01
-1.09876132e+00 8.50208580e-01 4.80948091e-01 -5.97372830e-01
-3.67401540e-01 -6.65588319e-01 -3.20529073e-01 -2.98494011e-01
-2.00678393e-01 6.20186150e-01 -2.62106091e-01 6.78354353... | [8.428345680236816, 8.734039306640625] |
3fb2759a-a398-4adf-a6bb-4e8a16b092cf | unsupervised-language-agnostic-wer | 2303.05046 | null | https://arxiv.org/abs/2303.05046v1 | https://arxiv.org/pdf/2303.05046v1.pdf | Unsupervised Language agnostic WER Standardization | Word error rate (WER) is a standard metric for the evaluation of Automated Speech Recognition (ASR) systems. However, WER fails to provide a fair evaluation of human perceived quality in presence of spelling variations, abbreviations, or compound words arising out of agglutination. Multiple spelling variations might be... | ['Rupeshkumar Mehta', 'Manish Gupta', 'Ankur Gupta', 'Rahul Ambavat', 'Satarupa Guha'] | 2023-03-09 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [ 3.19087356e-01 -2.67877817e-01 4.03009415e-01 -5.18682301e-01
-8.15384865e-01 -8.41221333e-01 4.60376799e-01 4.38455909e-01
-7.33232796e-01 7.20519125e-01 2.68568635e-01 -6.74545944e-01
9.56905335e-02 -3.86963814e-01 -3.34590942e-01 -4.68814760e-01
5.14694750e-01 4.92481977e-01 2.48546094e-01 -4.37191129... | [14.230024337768555, 7.000101089477539] |
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