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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7bf95c81-81df-4624-a7f9-60fce2197ffc | linear-convergence-of-a-policy-gradient | 2203.11758 | null | https://arxiv.org/abs/2203.11758v3 | https://arxiv.org/pdf/2203.11758v3.pdf | Linear convergence of a policy gradient method for some finite horizon continuous time control problems | Despite its popularity in the reinforcement learning community, a provably convergent policy gradient method for continuous space-time control problems with nonlinear state dynamics has been elusive. This paper proposes proximal gradient algorithms for feedback controls of finite-time horizon stochastic control problem... | ['Yufei Zhang', 'Wolfgang Stockinger', 'Christoph Reisinger'] | 2022-03-22 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-3.28907251e-01 2.05200240e-01 -5.19407988e-01 2.99470603e-01
-6.96446359e-01 -5.85934460e-01 3.99840236e-01 7.85031617e-02
-7.15087473e-01 1.35352838e+00 1.28412992e-01 -6.02329493e-01
-2.74626046e-01 -4.25337821e-01 -8.37974548e-01 -9.90588546e-01
-1.96143627e-01 1.94123641e-01 -6.24923483e-02 -2.50544488... | [4.39134407043457, 2.608452796936035] |
7489a2e6-8e97-43be-a3ed-c474cde2798f | reweighted-laplace-prior-based-hyperspectral | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Zhang_Reweighted_Laplace_Prior_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Zhang_Reweighted_Laplace_Prior_2015_CVPR_paper.pdf | Reweighted Laplace Prior Based Hyperspectral Compressive Sensing for Unknown Sparsity | Compressive sensing(CS) has been exploited for hypespectral image(HSI) compression in recent years. Though it can greatly reduce the costs of computation and storage, the reconstruction of HSI from a few linear measurements is challenging. The underlying sparsity of HSI is crucial to improve the reconstruction accuracy... | ['Chunna Tian', 'Yanning Zhang', 'Wei Wei', 'Lei Zhang', 'Fei Li'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['noise-estimation'] | ['medical'] | [ 7.45344222e-01 -5.71565926e-01 -7.37633929e-02 -1.58874869e-01
-7.07615316e-01 -2.35915154e-01 1.54818118e-01 -5.50629973e-01
-1.03532579e-02 5.62686145e-01 2.83541560e-01 3.92286070e-02
-4.37492490e-01 -7.22680748e-01 -5.03967106e-01 -1.38653195e+00
3.53911191e-01 -8.53595585e-02 -1.03109717e-01 1.22747652... | [10.29753303527832, -2.089982509613037] |
d44f2b4b-9613-43e9-ad5b-c55831a3ad85 | good-examples-make-a-faster-learner-simple-1 | null | null | https://openreview.net/forum?id=W4ZeajjH2u9 | https://openreview.net/pdf?id=W4ZeajjH2u9 | Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER | Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates.
Similar attempts have been made on named entity recognition (NER) which manually design templates to predict entity types for every text span in a sentence. However, such methods may suffer... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['few-shot-text-classification'] | ['natural-language-processing'] | [-1.88267939e-02 -4.90594395e-02 -3.88014689e-02 -6.18988514e-01
-1.05154133e+00 -6.75222635e-01 5.16389489e-01 2.53457427e-01
-1.01835573e+00 9.32328880e-01 3.70798320e-01 -4.05516177e-01
2.70258579e-02 -4.77055788e-01 -3.27465326e-01 -2.43097454e-01
9.62578654e-02 3.35755259e-01 4.16713119e-01 -1.73452348... | [10.554566383361816, 8.741403579711914] |
fd42c01e-bb3b-4c95-bda5-e0c6c1db32db | towards-automating-codenames-spymasters-with | 2212.14104 | null | https://arxiv.org/abs/2212.14104v1 | https://arxiv.org/pdf/2212.14104v1.pdf | Towards automating Codenames spymasters with deep reinforcement learning | Although most reinforcement learning research has centered on competitive games, little work has been done on applying it to co-operative multiplayer games or text-based games. Codenames is a board game that involves both asymmetric co-operation and natural language processing, which makes it an excellent candidate for... | ['Sherman Siu'] | 2022-12-28 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [-4.12946969e-01 1.17224984e-01 -1.65072978e-01 7.29041472e-02
-5.85414529e-01 -6.87510729e-01 4.21506315e-01 4.76243719e-02
-9.39620316e-01 9.90418613e-01 -5.60759231e-02 -7.60362267e-01
-1.88800007e-01 -8.00376654e-01 -2.14173049e-01 -3.09819937e-01
-3.28794688e-01 8.91796589e-01 2.28112265e-01 -7.68742204... | [3.5588700771331787, 1.5349539518356323] |
fa51e24e-607a-4f5b-8344-bd35a1643fd8 | pointcmc-cross-modal-multi-scale | 2211.12032 | null | https://arxiv.org/abs/2211.12032v2 | https://arxiv.org/pdf/2211.12032v2.pdf | PointCMC: Cross-Modal Multi-Scale Correspondences Learning for Point Cloud Understanding | Some self-supervised cross-modal learning approaches have recently demonstrated the potential of image signals for enhancing point cloud representation. However, it remains a question on how to directly model cross-modal local and global correspondences in a self-supervised fashion. To solve it, we proposed PointCMC, a... | ['Ming Zeng', 'Zizhao Wu', 'Jiawei Mao', 'Xiaogang Peng', 'Honggu Zhou'] | 2022-11-22 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [ 2.99070217e-02 1.92919597e-02 -3.01002532e-01 -4.99390036e-01
-1.52257788e+00 -5.45256138e-01 6.31382525e-01 2.17390522e-01
-2.41229683e-02 1.70018852e-01 -1.52161330e-01 7.43371397e-02
-1.86558887e-01 -8.22475433e-01 -1.09129763e+00 -6.25990868e-01
-5.77019714e-02 4.34072882e-01 3.27601433e-01 -7.98850209... | [8.120146751403809, -3.307295799255371] |
42a56cf9-e02e-40b6-8a42-4d4b58c8d57e | risk-management-via-anomaly-circumvent | 1908.01112 | null | https://arxiv.org/abs/1908.01112v1 | https://arxiv.org/pdf/1908.01112v1.pdf | Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction | Midterm stock price prediction is crucial for value investments in the stock market. However, most deep learning models are essentially short-term and applying them to midterm predictions encounters large cumulative errors because they cannot avoid anomalies. In this paper, we propose a novel deep neural network Mid-LS... | ['Xiao-Yang Liu', 'Xinyi Li', 'Yinchuan Li', 'Christina Dan Wang'] | 2019-08-03 | null | null | null | null | ['stock-price-prediction', 'stock-prediction'] | ['time-series', 'time-series'] | [-6.17357671e-01 -3.06110412e-01 -3.25298727e-01 -2.52216429e-01
-3.68868798e-01 -2.73226529e-01 4.50333089e-01 -4.43801850e-01
-1.41468942e-01 7.11405814e-01 3.03563803e-01 -7.97396064e-01
-9.04253349e-02 -1.07480597e+00 -5.77741981e-01 -6.00725472e-01
-2.96335697e-01 1.39508456e-01 2.54019320e-01 -3.23256761... | [4.436098575592041, 4.2483391761779785] |
e7ff606e-c8d1-4ff0-9019-c46d7622d484 | a-convolutional-neural-network-smartphone-app | null | null | https://ieeexplore.ieee.org/abstract/document/8278160 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8278160 | A Convolutional Neural Network Smartphone App for Real-Time Voice Activity Detection | This paper presents a smartphone app that performs real-time voice activity detection based on convolutional neural network. Real-time implementation issues are discussed showing how the slow inference time associated with convolutional neural networks is addressed. The developed smartphone app is meant to act as a swi... | ['Nasser Kehtarnavaz', 'Abhishek Sehgal'] | 2018-02-01 | null | null | null | ieee-access-2018-2 | ['audio-signal-recognition', 'noise-estimation'] | ['audio', 'medical'] | [ 2.34045550e-01 -2.52935793e-02 5.28717637e-01 -9.73765478e-02
-6.02617741e-01 -2.20966548e-01 2.98806518e-01 -8.47215652e-02
-8.01562846e-01 5.51499784e-01 4.42640692e-01 -5.58080316e-01
-1.71488479e-01 -4.70900536e-01 -1.62084416e-01 -6.71416819e-01
6.92877918e-02 2.34303474e-02 2.31938243e-01 -3.08208838... | [14.853555679321289, 5.720175266265869] |
4fb892ec-c60b-464e-8d85-2c1faf8f4796 | gansynth-adversarial-neural-audio-synthesis | 1902.08710 | null | http://arxiv.org/abs/1902.08710v2 | http://arxiv.org/pdf/1902.08710v2.pdf | GANSynth: Adversarial Neural Audio Synthesis | Efficient audio synthesis is an inherently difficult machine learning task,
as human perception is sensitive to both global structure and fine-scale
waveform coherence. Autoregressive models, such as WaveNet, model local
structure at the expense of global latent structure and slow iterative
sampling, while Generative A... | ['Jesse Engel', 'Ishaan Gulrajani', 'Chris Donahue', 'Adam Roberts', 'Kumar Krishna Agrawal', 'Shuo Chen'] | 2019-02-23 | gansynth-adversarial-neural-audio-synthesis-1 | https://openreview.net/forum?id=H1xQVn09FX | https://openreview.net/pdf?id=H1xQVn09FX | iclr-2019-5 | ['audio-generation'] | ['audio'] | [ 3.02076787e-01 9.13436115e-02 2.19133049e-01 6.54148385e-02
-1.60567534e+00 -8.87605548e-01 5.72423518e-01 -3.40197533e-01
3.71777624e-01 8.07565272e-01 7.61156797e-01 1.83671236e-01
3.25057618e-02 -8.08534205e-01 -6.68083608e-01 -7.06397176e-01
-2.97597677e-01 2.74593264e-01 -4.07591045e-01 -4.23049964... | [15.592939376831055, 5.929234027862549] |
1b396001-a184-412e-b2bf-97ba0657573c | crossclr-cross-modal-contrastive-learning-for | 2109.14910 | null | https://arxiv.org/abs/2109.14910v1 | https://arxiv.org/pdf/2109.14910v1.pdf | CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations | Contrastive learning allows us to flexibly define powerful losses by contrasting positive pairs from sets of negative samples. Recently, the principle has also been used to learn cross-modal embeddings for video and text, yet without exploiting its full potential. In particular, previous losses do not take the intra-mo... | ['Thomas Brox', 'Peter Gehler', 'Yi Zhu', 'Mohammadreza Zolfaghari'] | 2021-09-30 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zolfaghari_CrossCLR_Cross-Modal_Contrastive_Learning_for_Multi-Modal_Video_Representations_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zolfaghari_CrossCLR_Cross-Modal_Contrastive_Learning_for_Multi-Modal_Video_Representations_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-text-retrieval'] | ['computer-vision'] | [-6.65269867e-02 -1.30382761e-01 -4.41930503e-01 -3.25029492e-01
-9.10767496e-01 -8.57061386e-01 9.73389983e-01 6.38346523e-02
-6.93860590e-01 4.80369896e-01 4.92437392e-01 1.22889020e-01
-2.55013227e-01 -5.78583360e-01 -6.11023545e-01 -7.87455320e-01
-3.64996716e-02 2.59513825e-01 2.44525075e-01 -5.91829345... | [10.569217681884766, 1.1777708530426025] |
ab430381-c3df-42fd-a6c8-048027502f6f | zemi-learning-zero-shot-semi-parametric | 2210.00185 | null | https://arxiv.org/abs/2210.00185v2 | https://arxiv.org/pdf/2210.00185v2.pdf | Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks | Although large language models have achieved impressive zero-shot ability, the huge model size generally incurs high cost. Recently, semi-parametric language models, which augment a smaller language model with an external retriever, have demonstrated promising language modeling capabilities. However, it remains unclear... | ['Heng Ji', 'Jianshu Chen', 'Dong Yu', 'Dian Yu', 'Xiaoman Pan', 'Zhenhailong Wang'] | 2022-10-01 | null | null | null | null | ['text-augmentation'] | ['natural-language-processing'] | [ 1.11896142e-01 4.13730666e-02 -1.29558876e-01 -3.30996722e-01
-1.74648046e+00 -3.72328669e-01 4.71142828e-01 -1.45981580e-01
-7.24363446e-01 6.67685091e-01 1.04408711e-01 -3.75577629e-01
-6.52574599e-02 -4.16246027e-01 -9.34934497e-01 -4.67650741e-01
1.87295273e-01 8.31009686e-01 1.87659234e-01 -4.37096357... | [11.016406059265137, 7.971891403198242] |
ccca0717-6d3f-46d3-8559-ad2b00e08a4f | basetransformers-attention-over-base-data | 2210.02476 | null | https://arxiv.org/abs/2210.02476v1 | https://arxiv.org/pdf/2210.02476v1.pdf | BaseTransformers: Attention over base data-points for One Shot Learning | Few shot classification aims to learn to recognize novel categories using only limited samples per category. Most current few shot methods use a base dataset rich in labeled examples to train an encoder that is used for obtaining representations of support instances for novel classes. Since the test instances are from ... | ["Noel O'Connor", 'Kevin McGuinness', 'Mayug Maniparambil'] | 2022-10-05 | null | null | null | null | ['few-shot-image-classification', 'one-shot-learning'] | ['computer-vision', 'methodology'] | [ 4.73940074e-01 1.82511777e-01 -7.26683438e-01 -6.33119285e-01
-9.29815471e-01 -3.43909204e-01 5.49582183e-01 2.86916137e-01
-2.00141087e-01 9.41667497e-01 2.76761591e-01 2.77695268e-01
-9.66964439e-02 -1.07528865e+00 -8.98522973e-01 -4.32514846e-01
-2.95954477e-03 7.31046975e-01 5.65162957e-01 -1.69693947... | [9.937712669372559, 3.0201849937438965] |
1b45f5b2-e1e5-4e82-a000-31bc11db1a83 | unsupervised-deep-clustering-for-source | 1811.01531 | null | http://arxiv.org/abs/1811.01531v2 | http://arxiv.org/pdf/1811.01531v2.pdf | Unsupervised Deep Clustering for Source Separation: Direct Learning from Mixtures using Spatial Information | We present a monophonic source separation system that is trained by only
observing mixtures with no ground truth separation information. We use a deep
clustering approach which trains on multi-channel mixtures and learns to
project spectrogram bins to source clusters that correlate with various spatial
features. We sho... | ['Shrikant Venkataramani', 'Paris Smaragdis', 'Efthymios Tzinis'] | 2018-11-05 | null | null | null | null | ['multi-speaker-source-separation'] | ['speech'] | [ 2.41409428e-02 -2.58292556e-01 2.99623698e-01 -1.60577595e-01
-1.37254429e+00 -8.22957218e-01 5.72486162e-01 -1.62554264e-01
-1.97641864e-01 4.89400715e-01 2.86129355e-01 -2.17773616e-01
-1.72092512e-01 -4.48209852e-01 -5.96482158e-01 -9.65654671e-01
-2.68208086e-01 5.19508064e-01 8.62565935e-02 2.09767282... | [15.370621681213379, 5.544013023376465] |
4826b99d-3afc-43fe-82d2-256cb1799561 | sinogram-constrained-tv-minimization-for | 1404.6691 | null | http://arxiv.org/abs/1404.6691v1 | http://arxiv.org/pdf/1404.6691v1.pdf | Sinogram constrained TV-minimization for metal artifact reduction in CT | A new method for reducing metal artifacts in X-ray computed tomography (CT)
images is presented. It bases on the solution of a convex optimization problem
with inequality constraints on the sinogram, and total variation regularization
for the reconstructed image. The Chambolle-Pock algorithm is used to
numerically solv... | ['Kristian Bredies', 'Clemens Schiffer'] | 2014-04-26 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 3.22450638e-01 2.99267054e-01 2.10213497e-01 -2.27886796e-01
-9.10524666e-01 6.81156293e-02 3.77071425e-02 4.15139981e-02
-5.73249519e-01 8.09914529e-01 4.38858382e-02 -2.87043124e-01
-4.70030427e-01 -2.94818729e-01 -3.48133981e-01 -8.28273594e-01
-1.40831769e-02 6.25540435e-01 1.99527055e-01 2.31678694... | [13.182059288024902, -2.6926281452178955] |
9387e2df-bff1-415c-a4e9-d98fcd65e486 | discriminative-sampling-of-proposals-in-self | 2209.09209 | null | https://arxiv.org/abs/2209.09209v2 | https://arxiv.org/pdf/2209.09209v2.pdf | Discriminative Sampling of Proposals in Self-Supervised Transformers for Weakly Supervised Object Localization | Drones are employed in a growing number of visual recognition applications. A recent development in cell tower inspection is drone-based asset surveillance, where the autonomous flight of a drone is guided by localizing objects of interest in successive aerial images. In this paper, we propose a method to train deep we... | ['Eric Granger', 'Aydin Sarraf', 'Marco Pedersoli', 'Soufiane Belharbi', 'Shakeeb Murtaza'] | 2022-09-09 | null | null | null | null | ['weakly-supervised-object-localization'] | ['computer-vision'] | [ 4.40238595e-01 -1.91793650e-01 1.40010277e-02 -3.39527905e-01
-6.95266366e-01 -7.89759874e-01 6.38233900e-01 1.46309054e-02
-3.80873591e-01 6.64491713e-01 -5.61400235e-01 1.12893261e-01
-2.41580841e-04 -8.64299595e-01 -9.77296174e-01 -9.17593241e-01
3.41037735e-02 4.01463956e-01 8.52920651e-01 -2.00256497... | [9.289865493774414, 0.6082954406738281] |
66d414ae-2118-4711-8cc0-6e2f1eea65a5 | unetr-transformers-for-3d-medical-image | 2103.10504 | null | https://arxiv.org/abs/2103.10504v3 | https://arxiv.org/pdf/2103.10504v3.pdf | UNETR: Transformers for 3D Medical Image Segmentation | Fully Convolutional Neural Networks (FCNNs) with contracting and expanding paths have shown prominence for the majority of medical image segmentation applications since the past decade. In FCNNs, the encoder plays an integral role by learning both global and local features and contextual representations which can be ut... | ['Bennett Landman', 'Andriy Myronenko', 'Dong Yang', 'Vishwesh Nath', 'Yucheng Tang', 'Daguang Xu', 'Holger Roth', 'Ali Hatamizadeh'] | 2021-03-18 | null | null | null | null | ['3d-medical-imaging-segmentation'] | ['medical'] | [ 3.48414838e-01 3.47028673e-01 -2.03151003e-01 -6.21640742e-01
-8.06820571e-01 -4.62557495e-01 4.61662233e-01 2.28036195e-02
-5.01421452e-01 4.50908124e-01 1.99306488e-01 -5.16741693e-01
1.73478439e-01 -8.23461413e-01 -8.59814167e-01 -5.91135204e-01
-1.67602494e-01 6.95246994e-01 4.50166017e-01 -2.03360379... | [14.57532787322998, -2.5233983993530273] |
250e6138-55a1-4a5d-9189-7b099ae555fc | rcl-relation-contrastive-learning-for-zero | null | null | https://aclanthology.org/2022.findings-naacl.188 | https://aclanthology.org/2022.findings-naacl.188.pdf | RCL: Relation Contrastive Learning for Zero-Shot Relation Extraction | Zero-shot relation extraction aims to identify novel relations which cannot be observed at the training stage. However, it still faces some challenges since the unseen relations of instances are similar or the input sentences have similar entities, the unseen relation representations from different categories tend to o... | ['Bo Xiao', 'Yanan Wu', 'Yajing Xu', 'Bosen Zhang', 'Shusen Wang'] | null | null | null | null | findings-naacl-2022-7 | ['relation-classification'] | ['natural-language-processing'] | [ 2.43627936e-01 6.30522609e-01 -4.01136845e-01 -4.02105004e-01
-4.80771571e-01 -1.86629802e-01 5.92724025e-01 5.53069949e-01
-2.59992778e-01 8.57734084e-01 6.58089072e-02 1.16985351e-01
-3.33418459e-01 -1.13780022e+00 -5.57855844e-01 -5.16503572e-01
-4.18560170e-02 4.35849458e-01 3.81892383e-01 -3.57635736... | [9.207184791564941, 8.466341018676758] |
99804fc7-5065-4240-92ed-660b45079349 | tablebank-table-benchmark-for-image-based | 1903.01949 | null | https://arxiv.org/abs/1903.01949v2 | https://arxiv.org/pdf/1903.01949v2.pdf | TableBank: A Benchmark Dataset for Table Detection and Recognition | We present TableBank, a new image-based table detection and recognition dataset built with novel weak supervision from Word and Latex documents on the internet. Existing research for image-based table detection and recognition usually fine-tunes pre-trained models on out-of-domain data with a few thousand human-labeled... | ['Ming Zhou', 'Furu Wei', 'Zhoujun Li', 'Shaohan Huang', 'Minghao Li', 'Lei Cui'] | 2019-03-05 | null | null | null | lrec-2020-5 | ['table-detection'] | ['miscellaneous'] | [-3.80657800e-02 -1.18611734e-02 -4.37870771e-01 -5.41612685e-01
-1.41309452e+00 -8.53903651e-01 4.72480267e-01 2.74466038e-01
-3.05312395e-01 8.18410575e-01 2.33006567e-01 -2.46731818e-01
6.48050845e-01 -1.08870864e+00 -1.23207378e+00 -2.54259348e-01
4.11403291e-02 8.69939744e-01 1.21816479e-01 -1.78996399... | [11.696331977844238, 3.0207626819610596] |
77434056-986c-4c65-988c-fdc21a5bf8b0 | adversarial-aware-deep-learning-system-based | 2306.00314 | null | https://arxiv.org/abs/2306.00314v1 | https://arxiv.org/pdf/2306.00314v1.pdf | Adversarial-Aware Deep Learning System based on a Secondary Classical Machine Learning Verification Approach | Deep learning models have been used in creating various effective image classification applications. However, they are vulnerable to adversarial attacks that seek to misguide the models into predicting incorrect classes. Our study of major adversarial attack models shows that they all specifically target and exploit th... | ['Cliff Zou', 'Abdulmajeed Alghamdi', 'Mnassar Alyami', 'Hisham Kholidy', 'Mohammed Alkhowaiter'] | 2023-06-01 | null | null | null | null | ['adversarial-defense', 'adversarial-attack'] | ['adversarial', 'adversarial'] | [ 1.89862758e-01 1.27214402e-01 -4.73989695e-02 -1.93949163e-01
-2.97799498e-01 -1.13361633e+00 7.86549568e-01 -2.20746294e-01
-2.47023270e-01 6.02112710e-01 -2.65119702e-01 -9.50985312e-01
2.57775038e-01 -1.19059980e+00 -9.67253268e-01 -6.92260742e-01
-8.30747262e-02 2.22217426e-01 4.09054220e-01 -4.72370774... | [5.564297199249268, 7.781695365905762] |
d80d617c-359f-4ceb-81d6-aebf5cdb6471 | twitter-sentiment-analysis-system | 1807.07752 | null | http://arxiv.org/abs/1807.07752v1 | http://arxiv.org/pdf/1807.07752v1.pdf | Twitter Sentiment Analysis System | Social media is increasingly used by humans to express their feelings and
opinions in the form of short text messages. Detecting sentiments in the text
has a wide range of applications including identifying anxiety or depression of
individuals and measuring well-being or mood of a community. Sentiments can be
expressed... | ['Shaunak Joshi', 'Deepali Deshpande'] | 2018-07-20 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [ 1.21136442e-01 -1.78912029e-01 -3.85559916e-01 -1.03410697e+00
-3.06877214e-02 -6.72407210e-01 6.81597233e-01 6.59987509e-01
-3.46012384e-01 6.28412426e-01 4.81546104e-01 2.95958251e-01
3.85157317e-01 -7.20076442e-01 4.07108992e-01 -5.29468596e-01
2.58078009e-01 -1.10291094e-01 -1.67630211e-01 -5.93790472... | [11.260656356811523, 6.802211284637451] |
cdd5c62c-a648-491d-9f72-c61e7120c2ae | light-source-guided-single-image-flare | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Qiao_Light_Source_Guided_Single-Image_Flare_Removal_From_Unpaired_Data_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Qiao_Light_Source_Guided_Single-Image_Flare_Removal_From_Unpaired_Data_ICCV_2021_paper.pdf | Light Source Guided Single-Image Flare Removal From Unpaired Data | Causally-taken images often suffer from flare artifacts, due to the unintended reflections and scattering of light inside the camera. However, as flares may appear in a variety of shapes, positions, and colors, detecting and removing them entirely from an image is very challenging. Existing methods rely on predefin... | ['Rynson W.H. Lau', 'Gerhard P. Hancke', 'Xiaotian Qiao'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['flare-removal'] | ['computer-vision'] | [ 4.98039424e-01 -7.54650235e-01 2.44668886e-01 -2.04936936e-01
-5.39281785e-01 -1.19281709e+00 4.29878831e-01 -2.81862766e-01
1.55191123e-01 5.71898937e-01 7.88652301e-02 7.31646121e-02
2.10032351e-02 -6.64037108e-01 -7.94694543e-01 -1.01836598e+00
3.37087452e-01 5.37732663e-03 4.77512717e-01 -1.82731986... | [10.729279518127441, -3.0965073108673096] |
491c8419-72ac-465a-88bb-d36a31ecb032 | manifold-learning-supported-estimation-of | 2110.02189 | null | https://arxiv.org/abs/2110.02189v1 | https://arxiv.org/pdf/2110.02189v1.pdf | Manifold learning-supported estimation of relative transfer functions for spatial filtering | Many spatial filtering algorithms used for voice capture in, e.g., teleconferencing applications, can benefit from or even rely on knowledge of Relative Transfer Functions (RTFs). Accordingly, many RTF estimators have been proposed which, however, suffer from performance degradation under acoustically adverse condition... | ['Walter Kellermann', 'Johannes Zeitler', 'Andreas Brendel'] | 2021-10-05 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 1.14670172e-01 -2.00179860e-01 5.58039486e-01 -2.01633260e-01
-8.52639437e-01 -5.34776509e-01 2.66925216e-01 -1.06260262e-01
-6.64702058e-02 6.05775177e-01 5.89495003e-02 -2.06583440e-01
-3.69299859e-01 -5.74086666e-01 -8.60814512e-01 -1.00418961e+00
1.93038419e-01 -7.95140490e-02 1.41510665e-01 -2.61162128... | [15.018864631652832, 5.858575344085693] |
126eeb2d-6c98-4091-bc2d-90096320a20f | what-is-the-machine-learning | 1709.10106 | null | http://arxiv.org/abs/1709.10106v2 | http://arxiv.org/pdf/1709.10106v2.pdf | What is the Machine Learning? | Applications of machine learning tools to problems of physical interest are
often criticized for producing sensitivity at the expense of transparency. To
address this concern, we explore a data planing procedure for identifying
combinations of variables -- aided by physical intuition -- that can
discriminate signal fro... | ['Timothy Cohen', 'Spencer Chang', 'Bryan Ostdiek'] | 2017-09-28 | null | null | null | null | ['physical-intuition'] | ['reasoning'] | [ 3.80973995e-01 5.58763325e-01 -5.79400957e-01 -5.36027014e-01
-6.94417834e-01 -2.96184182e-01 8.81377995e-01 2.08917931e-01
-4.22076166e-01 8.45468342e-01 1.20368868e-01 -7.28418350e-01
-1.99593946e-01 -4.77758467e-01 -3.93270254e-01 -1.04773927e+00
-1.14364438e-01 4.89548683e-01 1.36098433e-02 -1.17435809... | [15.604850769042969, 2.9455795288085938] |
76c837b3-d87d-4b2a-9221-4d0a2c0751d7 | sparse-skill-coding-learning-behavioral | null | null | https://openreview.net/forum?id=Hygv3xrtDr | https://openreview.net/pdf?id=Hygv3xrtDr | Sparse Skill Coding: Learning Behavioral Hierarchies with Sparse Codes | Many approaches to hierarchical reinforcement learning aim to identify sub-goal structure in tasks. We consider an alternative perspective based on identifying behavioral `motifs'---repeated action sequences that can be compressed to yield a compact code of action trajectories. We present a method for iteratively compr... | ['Thomas Griffiths', 'Sergey Levine', 'Michael Chang', 'Sophia Sanborn'] | 2019-09-25 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 2.53014386e-01 3.52936566e-01 -4.54550683e-01 -1.26140788e-01
-9.19180989e-01 -7.01516747e-01 6.39640868e-01 7.10338503e-02
-5.27198434e-01 9.85815883e-01 7.39848971e-01 -2.69817501e-01
-3.29170793e-01 -5.09035826e-01 -9.59547162e-01 -8.73724520e-01
-9.35577512e-01 4.35397685e-01 5.47095180e-01 -2.03837723... | [4.17252254486084, 1.5810760259628296] |
1d39b624-59d6-4f2f-9e1a-48e301f95fd9 | mlphon-a-multifunctional-grapheme-phoneme | null | null | https://ieeexplore.ieee.org/document/9877808 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9877808 | Mlphon: A Multifunctional Grapheme-Phoneme Conversion Tool Using Finite State Transducers | In this article we present the design and the development of a knowledge based computational linguistic tool, Mlphon for Malayalam language. Mlphon computationally models linguistic rules using finite state transducers and performs multiple functions including grapheme to phoneme (g2p) and phoneme to grapheme (p2g) con... | ['Rajeev Rajan', 'A R jayan', 'Kavya Manohar'] | 2022-09-05 | null | null | null | ieee-access-2022-9 | ['pronunciation-dictionary-creation', 'text-to-speech-synthesis'] | ['natural-language-processing', 'speech'] | [ 3.23439181e-01 6.79150000e-02 3.29303592e-01 -1.78675771e-01
-6.08740270e-01 -7.37805784e-01 5.26703477e-01 1.86175719e-01
-4.78359550e-01 7.31378198e-01 2.74984062e-01 -1.00192976e+00
8.89887810e-02 -7.35467494e-01 -2.92524010e-01 -2.94960320e-01
3.22523057e-01 8.56929064e-01 1.97327092e-01 -4.92315263... | [14.258752822875977, 6.893163204193115] |
0b375805-0d0c-4273-80e2-9e5c4bc8caad | implicit-representation-priors-meet | 2304.08805 | null | https://arxiv.org/abs/2304.08805v2 | https://arxiv.org/pdf/2304.08805v2.pdf | Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping | Robotic grasping in highly noisy environments presents complex challenges, especially with limited prior knowledge about the scene. In particular, identifying good grasping poses with Bayesian inference becomes difficult due to two reasons: i) generating data from uninformative priors proves to be inefficient, and ii) ... | ['Olivier Brüls', 'Gilles Louppe', 'Julien Gustin', 'Norman Marlier'] | 2023-04-18 | null | null | null | null | ['bayesian-inference', 'robotic-grasping'] | ['methodology', 'robots'] | [ 1.51636675e-01 -1.02272525e-01 2.61779040e-01 -2.74986118e-01
-7.40774035e-01 -4.94933635e-01 3.98398221e-01 8.88972953e-02
-3.99331331e-01 7.08750188e-01 -5.95404692e-02 4.61931527e-02
-6.29440427e-01 -6.23100221e-01 -8.38949859e-01 -9.28443015e-01
-4.03846353e-01 7.99590707e-01 5.62418327e-02 9.38413441... | [5.722733497619629, -0.7108643054962158] |
22b60509-9832-4561-b844-8c1672a6a868 | learning-model-blind-temporal-denoisers | 2007.03241 | null | https://arxiv.org/abs/2007.03241v2 | https://arxiv.org/pdf/2007.03241v2.pdf | Learning Model-Blind Temporal Denoisers without Ground Truths | Denoisers trained with synthetic data often fail to cope with the diversity of unknown noises, giving way to methods that can adapt to existing noise without knowing its ground truth. Previous image-based method leads to noise overfitting if directly applied to video denoisers, and has inadequate temporal information m... | ['Yuxing Han', 'Shan Liu', 'Zhen Xia', 'Yanghao Li', 'Bichuan Guo', 'Jiangtao Wen'] | 2020-07-07 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 3.62373143e-01 -5.30359089e-01 2.53051788e-01 -6.37088567e-02
-7.05992699e-01 -4.80258197e-01 4.22388703e-01 -5.33046544e-01
-3.74223739e-01 6.68275952e-01 5.23739159e-01 2.99831238e-02
-1.94059219e-02 -5.70054471e-01 -7.24698901e-01 -8.79729092e-01
2.59714961e-01 -3.28202248e-01 1.38255581e-01 -3.59694213... | [11.40047550201416, -2.1967849731445312] |
76a7576b-49dc-4488-8fab-8c956218db91 | corediff-contextual-error-modulated | 2304.01814 | null | https://arxiv.org/abs/2304.01814v1 | https://arxiv.org/pdf/2304.01814v1.pdf | CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization | Low-dose computed tomography (CT) images suffer from noise and artifacts due to photon starvation and electronic noise. Recently, some works have attempted to use diffusion models to address the over-smoothness and training instability encountered by previous deep-learning-based denoising models. However, diffusion mod... | ['Hongming Shan', 'Yi Zhang', 'Junping Zhang', 'Zilong Li', 'Qi Gao'] | 2023-04-04 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 2.90050447e-01 -7.96533450e-02 5.58928028e-02 -3.22270870e-01
-8.05576503e-01 -3.89644988e-02 5.85822701e-01 -1.81553909e-03
-5.38297594e-01 6.34768128e-01 4.46832418e-01 7.54386932e-02
-3.64617437e-01 -8.69073510e-01 -4.65665519e-01 -1.27158070e+00
9.99585837e-02 2.75176138e-01 3.81373644e-01 1.10241517... | [13.395915985107422, -2.480226755142212] |
37f829e1-fdfe-4177-a586-b9d9eef016d1 | combinatorial-optimization-enriched-machine | 2304.00789 | null | https://arxiv.org/abs/2304.00789v1 | https://arxiv.org/pdf/2304.00789v1.pdf | Combinatorial Optimization enriched Machine Learning to solve the Dynamic Vehicle Routing Problem with Time Windows | With the rise of e-commerce and increasing customer requirements, logistics service providers face a new complexity in their daily planning, mainly due to efficiently handling same day deliveries. Existing multi-stage stochastic optimization approaches that allow to solve the underlying dynamic vehicle routing problem ... | ['Maximilian Schiffer', 'Axel Parmentier', 'Patrick S. Klein', 'Kai Jungel', 'Léo Baty'] | 2023-04-03 | null | null | null | null | ['combinatorial-optimization', 'stochastic-optimization'] | ['methodology', 'methodology'] | [-6.93726866e-03 1.22685730e-01 -2.41936520e-01 -4.57030773e-01
-1.10964870e+00 -7.64831424e-01 3.63491088e-01 2.29182810e-01
-5.79961598e-01 8.49047303e-01 -7.56052732e-02 -5.86374342e-01
-6.29586816e-01 -7.23927677e-01 -9.30474102e-01 -5.16476512e-01
-4.45681602e-01 1.15200281e+00 -1.86093256e-01 -6.00473702... | [5.1087646484375, 2.8122241497039795] |
56c735e2-d6d4-4eb9-9177-046efca20383 | open-world-object-detection-via | 2302.11757 | null | https://arxiv.org/abs/2302.11757v1 | https://arxiv.org/pdf/2302.11757v1.pdf | Open-World Object Detection via Discriminative Class Prototype Learning | Open-world object detection (OWOD) is a challenging problem that combines object detection with incremental learning and open-set learning. Compared to standard object detection, the OWOD setting is task to: 1) detect objects seen during training while identifying unseen classes, and 2) incrementally learn the knowledg... | ['Shaorong Xie', 'Yan Peng', 'Zhenglin Li', 'Liyan Ma', 'Jinan Yu'] | 2023-02-23 | null | null | null | null | ['open-world-object-detection', 'open-set-learning'] | ['computer-vision', 'miscellaneous'] | [ 7.66208544e-02 -1.14517258e-02 -1.17942885e-01 -2.19743729e-01
-6.90516591e-01 -5.60929060e-01 4.14769918e-01 4.04916465e-01
-5.24601221e-01 3.68968487e-01 -1.02992304e-01 1.91428572e-01
-2.15943128e-01 -5.29886425e-01 -6.06128454e-01 -8.01903009e-01
-1.80818483e-01 5.38083494e-01 8.06511343e-01 4.74812180... | [9.295890808105469, 1.482654333114624] |
24fbe57e-e0ce-4582-b565-d1bfd210d06c | importance-tempering-group-robustness-for | 2209.08745 | null | https://arxiv.org/abs/2209.08745v2 | https://arxiv.org/pdf/2209.08745v2.pdf | Importance Tempering: Group Robustness for Overparameterized Models | Although overparameterized models have shown their success on many machine learning tasks, the accuracy could drop on the testing distribution that is different from the training one. This accuracy drop still limits applying machine learning in the wild. At the same time, importance weighting, a traditional technique t... | ['Lexing Ying', 'Zachary Izzo', 'Wenlong Ji', 'Yiping Lu'] | 2022-09-19 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 2.28511766e-01 -3.61795984e-02 -4.70158607e-01 -4.61416364e-01
-7.22539842e-01 -3.69694650e-01 2.97460347e-01 4.69030589e-01
-4.97772574e-01 1.02506244e+00 -3.74256223e-01 -2.43334532e-01
-3.85828406e-01 -5.47279537e-01 -4.62196261e-01 -1.20973742e+00
1.33145526e-01 7.45727539e-01 2.40764111e-01 -1.29686713... | [8.685840606689453, 4.350001335144043] |
25ab19c5-72bf-43f2-92f4-6dc73caf5155 | how-can-cross-lingual-knowledge-contribute | null | null | https://aclanthology.org/2022.findings-acl.243 | https://aclanthology.org/2022.findings-acl.243.pdf | How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing? | Cross-lingual Entity Typing (CLET) aims at improving the quality of entity type prediction by transferring semantic knowledge learned from rich-resourced languages to low-resourced languages. In this paper, by utilizing multilingual transfer learning via the mixture-of-experts approach, our model dynamically capture th... | ['Qu Yincen', 'Zelin Dai', 'Hui Chen', 'Juanzi Li', 'Lei Hou', 'Tiansi Dong', 'Hailong Jin'] | null | null | null | null | findings-acl-2022-5 | ['type-prediction', 'entity-typing'] | ['computer-code', 'natural-language-processing'] | [-3.20153803e-01 -7.35153556e-02 -6.60911143e-01 -4.89124954e-01
-7.02532768e-01 -8.25131357e-01 6.67344332e-01 1.16723977e-01
-9.13784385e-01 1.12996602e+00 2.24843755e-01 -4.98277396e-01
3.77437323e-01 -8.22665274e-01 -1.17083848e+00 -2.26309802e-02
6.04583658e-02 7.57458687e-01 1.06314085e-01 -4.39258277... | [10.739229202270508, 9.792908668518066] |
0af5d748-0e9a-45a9-9083-af3e3889c278 | audio-visual-scene-aware-dialog-avsd | 1806.00525 | null | http://arxiv.org/abs/1806.00525v1 | http://arxiv.org/pdf/1806.00525v1.pdf | Audio Visual Scene-Aware Dialog (AVSD) Challenge at DSTC7 | Scene-aware dialog systems will be able to have conversations with users
about the objects and events around them. Progress on such systems can be made
by integrating state-of-the-art technologies from multiple research areas
including end-to-end dialog systems visual dialog, and video description. We
introduce the Aud... | ['Chiori Hori', 'Tim K. Marks', 'Raphael Gontijo Lopes', 'Vincent Cartillier', 'Huda Alamri', 'Jue Wang', 'Dhruv Batra', 'Irfan Essa', 'Devi Parikh', 'Anoop Cherian', 'Abhishek Das'] | 2018-06-01 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 5.51189668e-02 1.82046846e-01 2.95457482e-01 -9.52585578e-01
-7.02966332e-01 -9.21759367e-01 1.03938663e+00 -6.98565468e-02
-1.46467566e-01 5.00261188e-01 1.07956004e+00 -1.96405724e-02
5.36986470e-01 -2.44337529e-01 9.83514488e-02 3.33491480e-03
4.89897281e-01 9.77864444e-01 7.06041694e-01 -6.70328617... | [10.87518310546875, 1.1987519264221191] |
281b91ac-9370-4122-b735-6fa53bbbd0ca | on-provably-robust-meta-bayesian-optimization | 2206.06872 | null | https://arxiv.org/abs/2206.06872v2 | https://arxiv.org/pdf/2206.06872v2.pdf | On Provably Robust Meta-Bayesian Optimization | Bayesian optimization (BO) has become popular for sequential optimization of black-box functions. When BO is used to optimize a target function, we often have access to previous evaluations of potentially related functions. This begs the question as to whether we can leverage these previous experiences to accelerate th... | ['Patrick Jaillet', 'Bryan Kian Hsiang Low', 'Haibin Yu', 'Yizhou Chen', 'Zhongxiang Dai'] | 2022-06-14 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 8.07842389e-02 -1.49034992e-01 7.56616890e-02 8.13210458e-02
-1.42614472e+00 -4.63324130e-01 3.65170985e-01 7.12460130e-02
-5.19101083e-01 1.02344143e+00 -5.25348857e-02 -3.54772776e-01
-7.14388430e-01 -3.45045716e-01 -1.04481900e+00 -9.53560114e-01
-2.88197398e-01 5.99580824e-01 1.16549529e-01 1.38404891... | [6.446900367736816, 3.87583065032959] |
9c146954-606e-4336-9586-04e7ef609b56 | robust-unsupervised-cross-lingual-word | 2210.03319 | null | https://arxiv.org/abs/2210.03319v1 | https://arxiv.org/pdf/2210.03319v1.pdf | Robust Unsupervised Cross-Lingual Word Embedding using Domain Flow Interpolation | This paper investigates an unsupervised approach towards deriving a universal, cross-lingual word embedding space, where words with similar semantics from different languages are close to one another. Previous adversarial approaches have shown promising results in inducing cross-lingual word embedding without parallel ... | ['Helen Meng', 'ZhiQuan Luo', 'Zhen Li', 'Liping Tang'] | 2022-10-07 | null | null | null | null | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [-6.32985830e-02 1.82873264e-01 -2.59173751e-01 -4.34942961e-01
-1.05474377e+00 -9.24322605e-01 9.48107481e-01 -5.07510938e-02
-7.11530745e-01 7.39007235e-01 2.59069592e-01 -5.90286851e-01
4.02228236e-01 -6.86245263e-01 -9.46096122e-01 -5.93411624e-01
3.09737712e-01 5.53999364e-01 -3.17299995e-03 -5.43761611... | [11.112407684326172, 10.005834579467773] |
433831e8-e575-4810-99e9-a3d9d3f07ec8 | causal-inference-via-predictive-coding | 2306.15479 | null | https://arxiv.org/abs/2306.15479v1 | https://arxiv.org/pdf/2306.15479v1.pdf | Causal Inference via Predictive Coding | Bayesian and causal inference are fundamental processes for intelligence. Bayesian inference models observations: what can be inferred about y if we observe a related variable x? Causal inference models interventions: if we directly change x, how will y change? Predictive coding is a neuroscience-inspired method for pe... | ['Thomas Lukasiewicz', 'Beren Millidge', "Amine M'Charrak", 'Luca Pinchetti', 'Tommaso Salvatori'] | 2023-06-27 | null | null | null | null | ['causal-inference', 'causal-discovery', 'bayesian-inference', 'counterfactual-inference', 'causal-inference'] | ['knowledge-base', 'knowledge-base', 'methodology', 'miscellaneous', 'miscellaneous'] | [ 7.33529329e-01 6.98033810e-01 -7.69791722e-01 -3.65674466e-01
-1.51610941e-01 -6.31064773e-01 8.98524702e-01 8.50463435e-02
-1.85422719e-01 1.16471899e+00 7.08649993e-01 -8.27467799e-01
-6.60805523e-01 -8.62513065e-01 -1.08038235e+00 -6.07930720e-01
-5.65766692e-01 4.66791362e-01 -2.76160210e-01 3.75425160... | [8.003249168395996, 5.403443336486816] |
45cc6c0d-b532-4647-ad15-8c984bf085da | semi-supervised-teacher-student-architecture | null | null | https://aclanthology.org/W19-1505 | https://aclanthology.org/W19-1505.pdf | Semi-Supervised Teacher-Student Architecture for Relation Extraction | Generating a large amount of training data for information extraction (IE) is either costly (if annotations are created manually), or runs the risk of introducing noisy instances (if distant supervision is used). On the other hand, semi-supervised learning (SSL) is a cost-efficient solution to combat lack of training d... | ['Mihai Surdeanu', 'Rebecca Sharp', 'Ajay Nagesh', 'Fan Luo'] | 2019-06-01 | null | null | null | ws-2019-6 | ['binary-relation-extraction'] | ['natural-language-processing'] | [ 2.97757715e-01 7.23233759e-01 -4.26123083e-01 -6.84167206e-01
-9.83935058e-01 -5.80478728e-01 6.20945454e-01 4.02207702e-01
-6.63469017e-01 9.01035428e-01 3.26035172e-01 -5.80250144e-01
-2.22375929e-01 -7.87001550e-01 -7.53853559e-01 -2.84138203e-01
8.37302804e-02 4.93412524e-01 3.51510137e-01 -4.38093424... | [9.551674842834473, 8.832393646240234] |
7a55ae0b-3f15-46cc-9a87-a475f78c40ae | offline-rl-with-resource-constrained-online | 2110.03165 | null | https://arxiv.org/abs/2110.03165v2 | https://arxiv.org/pdf/2110.03165v2.pdf | Offline RL With Resource Constrained Online Deployment | Offline reinforcement learning is used to train policies in scenarios where real-time access to the environment is expensive or impossible. As a natural consequence of these harsh conditions, an agent may lack the resources to fully observe the online environment before taking an action. We dub this situation the resou... | ['Urun Dogan', 'Abhishek Gupta', 'Young Hun Jung', 'Frank Cheng', 'Aniket Anand Deshmukh', 'Jayanth Reddy Regatti'] | 2021-10-07 | null | null | null | null | ['d4rl'] | ['robots'] | [ 7.32959434e-02 -3.77076603e-02 -3.51624817e-01 -2.16683537e-01
-7.79275000e-01 -9.19916034e-01 7.28196979e-01 1.17756635e-01
-9.36712086e-01 9.58284020e-01 -2.28450336e-02 -5.24505496e-01
1.12915806e-01 -7.11104393e-01 -9.62457716e-01 -7.49371052e-01
-1.70115933e-01 5.66463888e-01 8.27801898e-02 -3.26637089... | [4.106484889984131, 1.7727110385894775] |
d5e609ca-0345-4c33-9274-60faca04541a | deepblueai-at-semeval-2021-task-1-lexical | null | null | https://aclanthology.org/2021.semeval-1.72 | https://aclanthology.org/2021.semeval-1.72.pdf | DeepBlueAI at SemEval-2021 Task 1: Lexical Complexity Prediction with A Deep Ensemble Approach | Lexical complexity plays an important role in reading comprehension. lexical complexity prediction (LCP) can not only be used as a part of Lexical Simplification systems, but also as a stand-alone application to help people better reading. This paper presents the winning system we submitted to the LCP Shared Task of Se... | ['Zhipeng Luo', 'Shengguang Wang', 'Bingyan Song', 'Chunguang Pan'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-simplification', 'lexical-complexity-prediction'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.27424794e-01 3.56857985e-01 -8.69414806e-02 -4.26978230e-01
-6.65439248e-01 -2.13018879e-01 6.72503293e-01 6.04688108e-01
-9.45955098e-01 7.33939230e-01 5.54732203e-01 -4.45534527e-01
-2.43207570e-02 -6.56388044e-01 -6.12654507e-01 -2.08691638e-02
4.11577255e-01 8.14168811e-01 5.56549191e-01 -6.54138446... | [10.943852424621582, 10.36070728302002] |
f7f4b61b-78b3-4337-bde6-605b31f0e8ee | unsupervised-3d-pose-transfer-with-cross | 2211.10278 | null | https://arxiv.org/abs/2211.10278v3 | https://arxiv.org/pdf/2211.10278v3.pdf | Unsupervised 3D Pose Transfer with Cross Consistency and Dual Reconstruction | The goal of 3D pose transfer is to transfer the pose from the source mesh to the target mesh while preserving the identity information (e.g., face, body shape) of the target mesh. Deep learning-based methods improved the efficiency and performance of 3D pose transfer. However, most of them are trained under the supervi... | ['Guosheng Lin', 'Fayao Liu', 'Ruibo Li', 'Jiacheng Wei', 'Chaoyue Song'] | 2022-11-18 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [-8.53153598e-03 2.27351904e-01 5.47729284e-02 -4.57077563e-01
-7.86895752e-01 -3.74747813e-01 3.50347996e-01 -3.03494394e-01
-2.31360018e-01 5.25787175e-01 -1.99430689e-01 2.36116499e-01
1.40732944e-01 -1.08850014e+00 -1.28521645e+00 -6.89022481e-01
1.64822474e-01 8.98027241e-01 3.81819785e-01 -3.61002058... | [7.274942874908447, -1.4855661392211914] |
9f610d30-f26d-493b-8c6b-eddbcd85d7f5 | a-hypergraph-neural-network-framework-for | 2212.14077 | null | https://arxiv.org/abs/2212.14077v1 | https://arxiv.org/pdf/2212.14077v1.pdf | A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddings | In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph. HNN derives multiple embeddings per node in the hypergraph where each embedding f... | ['Nesreen Ahmed', 'Eunyee Koh', 'Gromit Chan', 'Chang Xiao', 'Nedim Lipka', 'Jane Hoffswell', 'Shunan Guo', 'Ryan A. Rossi', 'Ryan Aponte'] | 2022-12-28 | null | null | null | null | ['hyperedge-prediction'] | ['graphs'] | [-1.29112393e-01 7.96376467e-01 -6.29424393e-01 -2.10632801e-01
4.69983229e-03 -5.82766235e-01 4.58570778e-01 1.86323375e-01
1.78031668e-01 4.32483256e-01 3.33515078e-01 -5.30374408e-01
-3.91779006e-01 -1.52142656e+00 -3.78306210e-01 -5.91712415e-01
-4.27971929e-01 7.59170890e-01 7.68388435e-02 -7.43216127... | [7.198300838470459, 6.351806640625] |
7138301f-65d3-4e56-a1e4-df7cb5d142ff | image-colorization-using-u-net-with-skip | 2205.12867 | null | https://arxiv.org/abs/2205.12867v1 | https://arxiv.org/pdf/2205.12867v1.pdf | Image Colorization using U-Net with Skip Connections and Fusion Layer on Landscape Images | We present a novel technique to automatically colorize grayscale images that combine the U-Net model and Fusion Layer features. This approach allows the model to learn the colorization of images from pre-trained U-Net. Moreover, the Fusion layer is applied to merge local information results dependent on small image pat... | ['Randy Cahya Wihandika', 'Novanto Yudistira', 'Muhammad Hisyam Zayd'] | 2022-05-25 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 1.23499148e-01 1.68923978e-02 9.78204757e-02 -3.21717858e-01
-4.54513341e-01 -5.48598349e-01 4.97710139e-01 1.15288235e-01
-6.26920760e-01 5.31819463e-01 -2.16417506e-01 -6.16576523e-02
3.24584693e-01 -9.76313710e-01 -9.25251245e-01 -4.55687076e-01
7.68401176e-02 -1.08048238e-01 5.01081705e-01 -1.59498289... | [11.249242782592773, -1.2805768251419067] |
121a21e7-5490-42bd-8b2f-531b2449366e | pseudo-lidar-accurate-depth-for-3d-object | 1906.06310 | null | https://arxiv.org/abs/1906.06310v3 | https://arxiv.org/pdf/1906.06310v3.pdf | Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving | Detecting objects such as cars and pedestrians in 3D plays an indispensable role in autonomous driving. Existing approaches largely rely on expensive LiDAR sensors for accurate depth information. While recently pseudo-LiDAR has been introduced as a promising alternative, at a much lower cost based solely on stereo imag... | ['Mark Campbell', 'Wei-Lun Chao', 'Divyansh Garg', 'Kilian Q. Weinberger', 'Yurong You', 'Yan Wang', 'Geoff Pleiss', 'Bharath Hariharan'] | 2019-06-14 | null | https://openreview.net/forum?id=BJedHRVtPB | https://openreview.net/pdf?id=BJedHRVtPB | iclr-2020-1 | ['stereo-depth-estimation', '3d-object-detection-from-stereo-images'] | ['computer-vision', 'computer-vision'] | [ 3.07476558e-02 -8.61497372e-02 -9.36857164e-02 -4.71506178e-01
-1.00434875e+00 -5.15138388e-01 5.79709768e-01 1.25997618e-01
-5.42187452e-01 4.76357758e-01 -1.81008384e-01 -4.19372141e-01
3.15523535e-01 -9.51315820e-01 -6.78314745e-01 -4.90439147e-01
1.16297476e-01 6.67952001e-01 7.49044240e-01 -2.78204769... | [7.796609878540039, -2.5858423709869385] |
7d0a0996-c393-4a39-b9b3-6c3deedc8998 | depth-estimation-by-combining-binocular | 2203.10493 | null | https://arxiv.org/abs/2203.10493v1 | https://arxiv.org/pdf/2203.10493v1.pdf | Depth Estimation by Combining Binocular Stereo and Monocular Structured-Light | It is well known that the passive stereo system cannot adapt well to weak texture objects, e.g., white walls. However, these weak texture targets are very common in indoor environments. In this paper, we present a novel stereo system, which consists of two cameras (an RGB camera and an IR camera) and an IR speckle proj... | ['Yulan Guo', 'Zhaobi Chu', 'Wei Jia', 'Yushan Yu', 'Xiaoli Yang', 'Yuhua Xu'] | 2022-03-20 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Xu_Depth_Estimation_by_Combining_Binocular_Stereo_and_Monocular_Structured-Light_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xu_Depth_Estimation_by_Combining_Binocular_Stereo_and_Monocular_Structured-Light_CVPR_2022_paper.pdf | cvpr-2022-1 | ['stereo-matching-1'] | ['computer-vision'] | [ 2.25623906e-01 -2.43818209e-01 2.37318844e-01 -3.66079718e-01
7.85209015e-02 -1.96504816e-01 2.35660300e-01 -9.85601366e-01
-3.83090228e-01 4.45132464e-01 -1.35785297e-01 -8.37179646e-02
4.95917231e-01 -1.04081106e+00 -5.56023121e-01 -1.04650199e+00
9.20658231e-01 1.12524241e-01 6.63628459e-01 2.09972598... | [9.176379203796387, -2.51713490486145] |
909a0273-c749-46c7-be58-73b1b0ae2196 | semi-supervised-text-simplification-with-back | 2004.14693 | null | https://arxiv.org/abs/2004.14693v1 | https://arxiv.org/pdf/2004.14693v1.pdf | Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising Autoencoders | Text simplification (TS) rephrases long sentences into simplified variants while preserving inherent semantics. Traditional sequence-to-sequence models heavily rely on the quantity and quality of parallel sentences, which limits their applicability in different languages and domains. This work investigates how to lever... | ['Zhi Chen', 'Yanbin Zhao', 'Kai Yu', 'Lu Chen'] | 2020-04-30 | null | null | null | null | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 3.32441926e-01 -8.73303935e-02 7.37772584e-02 -3.96393508e-01
-8.28919649e-01 -5.27154267e-01 5.09800732e-01 -1.76197320e-01
-5.12584805e-01 7.51768351e-01 4.39433008e-01 -3.44632894e-01
5.08286417e-01 -7.17690647e-01 -7.83453286e-01 -4.64982271e-01
5.94319582e-01 7.54806578e-01 -1.87842563e-01 -7.30796576... | [11.626862525939941, 10.061519622802734] |
bf391331-97ef-48bf-89ac-350cf8b6b0a2 | decision-making-and-control-with-metasurface | 2212.11278 | null | https://arxiv.org/abs/2212.11278v2 | https://arxiv.org/pdf/2212.11278v2.pdf | Decision-making and control with metasurface-based diffractive neural networks | The ultimate goal of artificial intelligence is to mimic the human brain to perform decision-making and control directly from high-dimensional sensory input. All-optical diffractive neural networks provide a promising solution for implementing artificial intelligence with high-speed and low-power consumption. To date, ... | ['Shuyuan Xiao', 'Andrey Miroshnichenko', 'Lujun Huang', 'Tianbao Yu', 'Jumin Qiu'] | 2022-12-21 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [ 9.73678231e-02 3.83882731e-01 2.51435757e-01 4.96889167e-02
1.35519013e-01 -8.55975077e-02 5.60538530e-01 -4.13165390e-01
-4.68185931e-01 6.30437970e-01 -5.78698277e-01 -5.24410047e-02
-2.67210126e-01 -1.15517843e+00 -7.42435873e-01 -1.28976297e+00
4.16971669e-02 6.57103360e-01 3.36843789e-01 -6.52955949... | [8.175936698913574, 2.4633679389953613] |
62ef05bb-8a34-4229-9583-658528610543 | motrv2-bootstrapping-end-to-end-multi-object | 2211.09791 | null | https://arxiv.org/abs/2211.09791v2 | https://arxiv.org/pdf/2211.09791v2.pdf | MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors | In this paper, we propose MOTRv2, a simple yet effective pipeline to bootstrap end-to-end multi-object tracking with a pretrained object detector. Existing end-to-end methods, MOTR and TrackFormer are inferior to their tracking-by-detection counterparts mainly due to their poor detection performance. We aim to improve ... | ['Xiangyu Zhang', 'Tiancai Wang', 'Yuang Zhang'] | 2022-11-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_MOTRv2_Bootstrapping_End-to-End_Multi-Object_Tracking_by_Pretrained_Object_Detectors_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_MOTRv2_Bootstrapping_End-to-End_Multi-Object_Tracking_by_Pretrained_Object_Detectors_CVPR_2023_paper.pdf | cvpr-2023-1 | ['multiple-people-tracking'] | ['computer-vision'] | [-3.28286618e-01 -1.96062699e-01 -2.06338868e-01 -1.41925916e-01
-1.14693832e+00 -6.04594350e-01 4.09872830e-01 -1.39815882e-01
-7.60247290e-01 5.41206539e-01 1.62064761e-01 1.99386775e-01
2.02573031e-01 -3.26875120e-01 -7.16981053e-01 -3.37997019e-01
-4.52659987e-02 9.28747952e-01 1.04905117e+00 -1.54357418... | [6.3171586990356445, -2.0530576705932617] |
9357c4f7-9be9-4892-899f-90aced0d485d | text-guided-mask-free-local-image-retouching | 2212.07603 | null | https://arxiv.org/abs/2212.07603v2 | https://arxiv.org/pdf/2212.07603v2.pdf | Text-Guided Mask-free Local Image Retouching | In the realm of multi-modality, text-guided image retouching techniques emerged with the advent of deep learning. Most currently available text-guided methods, however, rely on object-level supervision to constrain the region that may be modified. This not only makes it more challenging to develop these algorithms, but... | ['Lechao Cheng', 'Zhangye Wang', 'Jin Wang', 'Jingxuan He', 'Fan Zhang', 'Zerun Liu'] | 2022-12-15 | null | null | null | null | ['image-retouching'] | ['computer-vision'] | [ 5.89883924e-01 2.91072845e-01 -4.42316644e-02 -7.59986416e-02
-7.73587525e-01 -4.32669699e-01 6.63678288e-01 -2.33902976e-01
-4.92440671e-01 6.08664215e-01 1.84218988e-01 -2.36998469e-01
2.82021195e-01 -3.12919736e-01 -8.28886509e-01 -6.57308340e-01
4.65735108e-01 9.26738828e-02 4.91078615e-01 -1.82891771... | [11.331307411193848, -0.4347083866596222] |
9e092429-2bf0-47a7-874a-3f872af2ec31 | comparing-the-accuracy-of-deep-neural | 2111.11063 | null | https://arxiv.org/abs/2111.11063v1 | https://arxiv.org/pdf/2111.11063v1.pdf | Comparing the Accuracy of Deep Neural Networks (DNN) and Convolutional Neural Network (CNN) in Music Genre Recognition (MGR): Experiments on Kurdish Music | Musicologists use various labels to classify similar music styles under a shared title. But, non-specialists may categorize music differently. That could be through finding patterns in harmony, instruments, and form of the music. People usually identify a music genre solely by listening, but now computers and Artificia... | ['Hossein Hassani', 'Aza Zuhair'] | 2021-11-22 | null | null | null | null | ['music-genre-recognition'] | ['music'] | [-5.34178503e-02 -5.03890038e-01 -1.57815412e-01 -9.16283652e-02
-1.43392488e-01 -1.04852617e+00 2.61040926e-01 -2.78482735e-01
-2.28023887e-01 2.58299023e-01 3.41608524e-01 -1.65884048e-01
-4.48033929e-01 -8.43664944e-01 -6.48017898e-02 -5.04110336e-01
1.49318531e-01 6.57707751e-01 -3.19393635e-01 -2.06771508... | [15.916375160217285, 5.226016998291016] |
72e18ff7-a3ef-4ae6-8d40-99a6774873a4 | automating-staged-rollout-with-reinforcement | 2204.02189 | null | https://arxiv.org/abs/2204.02189v2 | https://arxiv.org/pdf/2204.02189v2.pdf | Automating Staged Rollout with Reinforcement Learning | Staged rollout is a strategy of incrementally releasing software updates to portions of the user population in order to accelerate defect discovery without incurring catastrophic outcomes such as system wide outages. Some past studies have examined how to quantify and automate staged rollout, but stop short of simultan... | ['Lance Fiondella', 'Vidhyashree Nagaraju', 'Shadow Pritchard'] | 2022-04-01 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-1.24692708e-01 1.95429832e-01 1.01062423e-02 -2.18610719e-01
-5.82662761e-01 -3.90864372e-01 -2.37602711e-01 5.73141098e-01
1.55558795e-01 7.17485428e-01 -1.63858116e-01 -6.67566359e-01
-4.53766257e-01 -6.87445819e-01 -4.01576996e-01 -1.06566437e-01
-3.33159983e-01 6.12850368e-01 -1.70974210e-01 -1.89111859... | [6.402028560638428, 2.5783252716064453] |
dc1f9e8e-e6b9-4cf3-9000-18ba07c975dd | improving-speaker-identification-for-shared | 2109.02576 | null | https://arxiv.org/abs/2109.02576v1 | https://arxiv.org/pdf/2109.02576v1.pdf | Improving Speaker Identification for Shared Devices by Adapting Embeddings to Speaker Subsets | Speaker identification typically involves three stages. First, a front-end speaker embedding model is trained to embed utterance and speaker profiles. Second, a scoring function is applied between a runtime utterance and each speaker profile. Finally, the speaker is identified using nearest neighbor according to the sc... | ['Andreas Stolcke', 'Eunjung Han', 'Yuguang Yang', 'Zhenning Tan'] | 2021-09-06 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 7.03046992e-02 2.53878772e-01 1.15241803e-01 -9.05696213e-01
-1.19346297e+00 -5.05207717e-01 9.18462425e-02 1.66017875e-01
-1.43156841e-01 2.23950997e-01 5.18293619e-01 -1.38296723e-01
2.34760106e-01 -3.89559895e-01 -5.06729364e-01 -6.81842923e-01
-2.83785939e-01 2.15909079e-01 -2.35973030e-01 3.12476724... | [14.318214416503906, 6.100318908691406] |
967e2235-50a7-4d94-bc23-f6d267a95f83 | statistical-reproducibility-of-meta-analysis | 2301.09189 | null | https://arxiv.org/abs/2301.09189v1 | https://arxiv.org/pdf/2301.09189v1.pdf | Statistical reproducibility of meta-analysis research claims for medical mask use in community settings to prevent COVID infection | The coronavirus pandemic (COVID) has been an exceptional test of current scientific evidence that inform and shape policy. Many US states, cities, and counties implemented public orders for mask use on the notion that this intervention would delay and flatten the epidemic peak and largely benefit public health outcomes... | ['Warren B. Kindzierski', 'S. Stanley Young'] | 2023-01-22 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [ 3.81622970e-01 -1.22054935e-01 -8.55342746e-01 1.94166452e-01
-3.77540380e-01 -6.29824340e-01 4.62555349e-01 5.65838754e-01
-6.76334441e-01 7.69862533e-01 5.12604535e-01 -1.31047440e+00
-3.46344531e-01 -5.47034383e-01 -6.71094239e-01 -4.35459316e-01
-3.68678778e-01 1.85830101e-01 6.40359521e-02 4.95185107... | [5.994863986968994, 4.467971324920654] |
c24b720a-226f-44b0-ad3f-66d4a14e2d90 | referring-image-segmentation-via-recurrent | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Referring_Image_Segmentation_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Referring_Image_Segmentation_CVPR_2018_paper.pdf | Referring Image Segmentation via Recurrent Refinement Networks | We address the problem of image segmentation from natural language descriptions. Existing deep learning-based methods encode image representations based on the output of the last convolutional layer. One general issue is that the resulting image representation lacks multi-scale semantics, which are key components in ad... | ['Yi-Chun Kuo', 'Xiaojuan Qi', 'Ruiyu Li', 'Xiaoyong Shen', 'Jiaya Jia', 'Kaican Li', 'Michelle Shu'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['referring-expression-segmentation'] | ['computer-vision'] | [ 4.30174708e-01 1.83616772e-01 -4.46040332e-01 -4.99565631e-01
-4.16962445e-01 -3.95870239e-01 2.76166052e-01 9.32552367e-02
-4.51859772e-01 2.66512483e-01 4.11395401e-01 8.89498964e-02
1.94273874e-01 -9.28459942e-01 -5.81782162e-01 -2.79949903e-01
1.78341463e-01 -4.28233072e-02 7.65536904e-01 -2.73591548... | [9.565842628479004, 0.4556925892829895] |
0494faeb-4c23-4320-9834-5c4d24f75c87 | learning-to-detect-objects-with-a-1-megapixel | 2009.13436 | null | https://arxiv.org/abs/2009.13436v2 | https://arxiv.org/pdf/2009.13436v2.pdf | Learning to Detect Objects with a 1 Megapixel Event Camera | Event cameras encode visual information with high temporal precision, low data-rate, and high-dynamic range. Thanks to these characteristics, event cameras are particularly suited for scenarios with high motion, challenging lighting conditions and requiring low latency. However, due to the novelty of the field, the per... | ['Davide Nitti', 'Pierre de Tournemire', 'Jonathan Masci', 'Etienne Perot', 'Amos Sironi'] | 2020-09-28 | null | http://proceedings.neurips.cc/paper/2020/hash/c213877427b46fa96cff6c39e837ccee-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/c213877427b46fa96cff6c39e837ccee-Paper.pdf | neurips-2020-12 | ['event-based-vision'] | ['computer-vision'] | [ 2.95160145e-01 -4.88809049e-01 1.14887573e-01 -3.52007747e-01
-6.93114579e-01 -2.03033298e-01 7.93936610e-01 2.73275286e-01
-7.59826422e-01 5.26863158e-01 -1.36479035e-01 3.57327573e-02
6.43911734e-02 -7.62737513e-01 -8.87258112e-01 -6.35197937e-01
-1.49827957e-01 1.23557515e-01 9.78545010e-01 1.65320978... | [8.463912963867188, -1.0708521604537964] |
1178d811-f23c-4a8c-b4ab-14023dd5012b | frame-level-prediction-of-facial-expressions | 2203.13436 | null | https://arxiv.org/abs/2203.13436v2 | https://arxiv.org/pdf/2203.13436v2.pdf | Frame-level Prediction of Facial Expressions, Valence, Arousal and Action Units for Mobile Devices | In this paper, we consider the problem of real-time video-based facial emotion analytics, namely, facial expression recognition, prediction of valence and arousal and detection of action unit points. We propose the novel frame-level emotion recognition algorithm by extracting facial features with the single EfficientNe... | ['Andrey V. Savchenko'] | 2022-03-25 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 1.91790655e-01 2.95920130e-02 -5.20582348e-02 -8.03265691e-01
-5.89762688e-01 -3.73840660e-01 5.22717237e-01 -3.53637487e-02
-6.21006489e-01 5.78087628e-01 -4.87284027e-02 3.94316286e-01
3.46872658e-01 -1.92657515e-01 -3.04650038e-01 -7.28313684e-01
-4.76014972e-01 -2.84548439e-02 -4.37574238e-01 -4.37705755... | [13.58090877532959, 1.9489903450012207] |
31d7dfc7-95eb-4189-936e-143ded5a8fbf | the-battle-of-information-representations | 2303.14221 | null | https://arxiv.org/abs/2303.14221v1 | https://arxiv.org/pdf/2303.14221v1.pdf | The Battle of Information Representations: Comparing Sentiment and Semantic Features for Forecasting Market Trends | The study of the stock market with the attraction of machine learning approaches is a major direction for revealing hidden market regularities. This knowledge contributes to a profound understanding of financial market dynamics and getting behavioural insights, which could hardly be discovered with traditional analytic... | ['Semen Budennyy', 'Elizaveta Kovtun', 'Aleksei Kazakov', 'Andrei Zaichenko'] | 2023-03-24 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-4.85390157e-01 -1.15435813e-02 -4.25529093e-01 -2.15970036e-02
1.07762754e-01 -6.52533829e-01 1.20696497e+00 4.52963829e-01
-5.76546192e-01 6.41743839e-01 5.27491570e-01 -3.97248149e-01
-1.86929628e-01 -1.25352383e+00 -4.97322977e-01 -5.44252753e-01
-1.46878392e-01 1.98124304e-01 3.60081196e-02 -6.87485754... | [4.47824239730835, 4.35312557220459] |
cd70ba1a-9b67-408a-a871-647e3fa7d7a1 | crvi-convex-relaxation-for-variational | null | null | https://icml.cc/Conferences/2018/Schedule?showEvent=2175 | http://proceedings.mlr.press/v80/fazelnia18a/fazelnia18a.pdf | CRVI: Convex Relaxation for Variational Inference |
We present a new technique for solving non-convex variational inference optimization problems. Variational inference is a widely used method for posterior approximation in which the inference problem is transformed into an optimization problem. For most models, this optimization is highly non-convex and so hard to... | ['Ghazal Fazelnia', 'John Paisley'] | 2018-07-01 | null | null | null | icml-2018-7 | ['inference-optimization'] | ['audio'] | [-1.26570582e-01 1.56659424e-01 -5.03753424e-01 -4.68174756e-01
-1.31353486e+00 -5.12747288e-01 2.84845650e-01 -2.78232872e-01
-6.04674928e-02 1.01944554e+00 3.76520932e-01 -3.41269553e-01
2.18628254e-02 -4.28823650e-01 -9.30243790e-01 -7.65535712e-01
2.89733768e-01 9.91326094e-01 -2.99024165e-01 -3.96031775... | [7.002007484436035, 4.081900596618652] |
1d1efd5e-e5f7-430a-bfda-7c1f7f6c32d5 | unbiased-mean-teacher-for-cross-domain-object | 2003.00707 | null | https://arxiv.org/abs/2003.00707v2 | https://arxiv.org/pdf/2003.00707v2.pdf | Unbiased Mean Teacher for Cross-domain Object Detection | Cross-domain object detection is challenging, because object detection model is often vulnerable to data variance, especially to the considerable domain shift between two distinctive domains. In this paper, we propose a new Unbiased Mean Teacher (UMT) model for cross-domain object detection. We reveal that there often ... | ['Yu-Hua Chen', 'Lixin Duan', 'Wen Li', 'Jinhong Deng'] | 2020-03-02 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Deng_Unbiased_Mean_Teacher_for_Cross-Domain_Object_Detection_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Deng_Unbiased_Mean_Teacher_for_Cross-Domain_Object_Detection_CVPR_2021_paper.pdf | cvpr-2021-1 | ['small-data'] | ['computer-vision'] | [ 5.62690720e-02 -1.81510225e-01 -1.53217629e-01 -1.11579113e-01
-1.10728824e+00 -6.30160570e-01 5.64604163e-01 -2.32846573e-01
-4.01913911e-01 7.54559100e-01 -2.81216592e-01 -2.77506202e-01
-6.71617780e-03 -5.41911960e-01 -8.11365902e-01 -9.43389654e-01
4.63250399e-01 2.92178482e-01 7.94647753e-01 -4.23917174... | [9.355879783630371, 1.4716541767120361] |
a2ac0c77-d18e-4c42-8be2-41dfb993195a | object-centric-auto-encoders-and-dummy | 1812.04960 | null | http://arxiv.org/abs/1812.04960v2 | http://arxiv.org/pdf/1812.04960v2.pdf | Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video | Abnormal event detection in video is a challenging vision problem. Most
existing approaches formulate abnormal event detection as an outlier detection
task, due to the scarcity of anomalous data during training. Because of the
lack of prior information regarding abnormal events, these methods are not
fully-equipped to ... | ['Mariana-Iuliana Georgescu', 'Fahad Shahbaz Khan', 'Radu Tudor Ionescu', 'Ling Shao'] | 2018-12-11 | object-centric-auto-encoders-and-dummy-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Ionescu_Object-Centric_Auto-Encoders_and_Dummy_Anomalies_for_Abnormal_Event_Detection_in_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ionescu_Object-Centric_Auto-Encoders_and_Dummy_Anomalies_for_Abnormal_Event_Detection_in_CVPR_2019_paper.pdf | cvpr-2019-6 | ['abnormal-event-detection-in-video', 'abnormal-event-detection-in-video'] | ['computer-vision', 'methodology'] | [ 2.46226653e-01 -1.71297863e-01 -4.47315313e-02 -3.57600927e-01
-5.62128425e-01 -1.71528399e-01 5.41622162e-01 3.01272422e-01
-2.69088358e-01 4.33244616e-01 -6.93759695e-02 -8.34722891e-02
2.73629934e-01 -4.69139069e-01 -7.53796875e-01 -6.62032247e-01
-2.32018471e-01 3.52875181e-02 4.38801348e-01 1.96442723... | [7.814369201660156, 1.6278613805770874] |
36bfac1c-7d54-4eb4-8f44-ac34a0f3e636 | source-free-domain-adaptation-for-rgb-d | 2305.14269 | null | https://arxiv.org/abs/2305.14269v1 | https://arxiv.org/pdf/2305.14269v1.pdf | Source-Free Domain Adaptation for RGB-D Semantic Segmentation with Vision Transformers | With the increasing availability of depth sensors, multimodal frameworks that combine color information with depth data are attracting increasing interest. In the challenging task of semantic segmentation, depth maps allow to distinguish between similarly colored objects at different depths and provide useful geometric... | ['Pietro Zanuttigh', 'Donald Shenaj', 'Giulia Rizzoli'] | 2023-05-23 | null | null | null | null | ['style-transfer', 'source-free-domain-adaptation'] | ['computer-vision', 'computer-vision'] | [ 5.43142140e-01 -1.62784616e-03 -9.85589400e-02 -4.62997705e-01
-8.30643356e-01 -7.70059884e-01 5.14334798e-01 3.24629992e-01
-4.71445054e-01 2.79265821e-01 -3.36640142e-02 1.40689701e-01
6.61465980e-04 -7.80098975e-01 -4.96179551e-01 -7.48848975e-01
5.80021679e-01 4.95093346e-01 4.80772167e-01 -1.80821776... | [9.543988227844238, -0.9285712838172913] |
f73b6674-d1f1-4071-b677-1d7dd3a82675 | attention-based-graph-resnet-for-motor-intent | 2007.13484 | null | https://arxiv.org/abs/2007.13484v1 | https://arxiv.org/pdf/2007.13484v1.pdf | Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals | In previous studies, decoding electroencephalography (EEG) signals has not considered the topological relationship of EEG electrodes. However, the latest neuroscience has suggested brain network connectivity. Thus, the exhibited interaction between EEG channels might not be appropriately measured via Euclidean distance... | ['Yang Li', 'Yimin Hou', 'Yan Shi', 'Shuyue Jia'] | 2020-06-25 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 1.68511733e-01 3.45339894e-01 6.15873635e-01 -1.23984925e-01
-6.95229787e-03 1.03010103e-01 1.31082639e-01 -2.90134966e-01
-1.69711277e-01 8.38628352e-01 5.08114100e-02 -2.41742894e-01
-6.16428971e-01 -6.45016849e-01 -7.43194342e-01 -6.90620542e-01
-8.13241899e-01 -5.20649785e-03 -2.26857305e-01 -2.11672157... | [12.99120044708252, 3.505115509033203] |
92ef8525-6ac8-44be-aa6a-688e7c1e72c9 | benchmarking-bayesian-deep-learning-on | 2211.12717 | null | https://arxiv.org/abs/2211.12717v1 | https://arxiv.org/pdf/2211.12717v1.pdf | Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks | Bayesian deep learning seeks to equip deep neural networks with the ability to precisely quantify their predictive uncertainty, and has promised to make deep learning more reliable for safety-critical real-world applications. Yet, existing Bayesian deep learning methods fall short of this promise; new methods continue ... | ['Yarin Gal', 'Dustin Tran', 'Ghassen Jerfel', 'Michael W. Dusenberry', 'Zachary Nado', 'Angelos Filos', 'Qixuan Feng', 'Tim G. J. Rudner', 'Neil Band'] | 2022-11-23 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [-1.27925411e-01 -1.26343995e-01 1.09158158e-01 -7.07710505e-01
-1.28521478e+00 -3.89735579e-01 7.26898313e-01 3.30789462e-02
-5.20381033e-01 8.84938478e-01 2.10076496e-01 -4.50012833e-01
-5.51545382e-01 -4.28791553e-01 -6.71332896e-01 -7.11869121e-01
-3.55671734e-01 6.39132857e-01 2.43003219e-01 4.07311678... | [14.237930297851562, -2.0365378856658936] |
5e802531-3552-4af1-b9a5-dbcce7e07495 | temporal-tagging-on-different-domains | null | null | https://aclanthology.org/L12-1219 | https://aclanthology.org/L12-1219.pdf | Temporal Tagging on Different Domains: Challenges, Strategies, and Gold Standards | In the last years, temporal tagging has received increasing attention in the area of natural language processing. However, most of the research so far concentrated on processing news documents. Only recently, two temporal annotated corpora of narrative-style documents were developed, and it was shown that a domain shif... | ['Jannik Str{\\"o}tgen', 'Michael Gertz'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['temporal-information-extraction', 'temporal-tagging'] | ['natural-language-processing', 'natural-language-processing'] | [-1.07083388e-01 -3.27884518e-02 -5.82687676e-01 -5.85519910e-01
-6.98514700e-01 -1.26295114e+00 1.27491772e+00 6.59213126e-01
-7.08074868e-01 7.80556440e-01 5.86112618e-01 -4.56526242e-02
-2.63831526e-01 -4.43878591e-01 -5.70279025e-02 -5.45198619e-01
-3.88325661e-01 6.33884311e-01 8.63902867e-01 -2.30900884... | [9.119139671325684, 9.286478042602539] |
975f47c0-a2f0-4bf8-b7be-0e0c31bc18c5 | text-driven-toponym-resolution-using-indirect | null | null | https://aclanthology.org/P13-1144 | https://aclanthology.org/P13-1144.pdf | Text-Driven Toponym Resolution using Indirect Supervision | null | ['Michael Speriosu', 'Jason Baldridge'] | 2013-08-01 | null | null | null | acl-2013-8 | ['toponym-resolution'] | ['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... | [-7.316018581390381, 3.815227746963501] |
ac461542-190f-470a-ba75-0cc891b74774 | chainnet-neural-network-based-successive | 2105.03742 | null | https://arxiv.org/abs/2105.03742v3 | https://arxiv.org/pdf/2105.03742v3.pdf | ChainNet: Neural Network-Based Successive Spectral Analysis | We discuss a new neural network-based direction of arrival estimation scheme that tackles the estimation task as a multidimensional classification problem. The proposed estimator uses a classification chain with as many stages as the number of sources. Each stage is a multiclass classification network that estimates th... | ['Wolfgang Utschick', 'Andreas Barthelme'] | 2021-05-08 | null | null | null | null | ['direction-of-arrival-estimation', 'classification'] | ['audio', 'methodology'] | [ 2.76752412e-01 -8.67714658e-02 -1.14298031e-01 -4.82617915e-01
-6.47757351e-01 -3.80044043e-01 7.06947327e-01 2.84184963e-01
-3.92413497e-01 7.89639354e-01 -1.02246128e-01 -6.05225921e-01
-6.17218792e-01 -6.69035614e-01 -3.94273460e-01 -8.76253903e-01
-3.80175471e-01 4.62931305e-01 4.69221510e-02 6.25533015... | [6.589829921722412, 1.3490451574325562] |
fa61dd9e-acd3-4297-ae25-6f26cbb1e69d | efficient-2d-to-3d-correspondence-filtering | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Hao_Efficient_2D-to-3D_Correspondence_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Hao_Efficient_2D-to-3D_Correspondence_2013_CVPR_paper.pdf | Efficient 2D-to-3D Correspondence Filtering for Scalable 3D Object Recognition | 3D model-based object recognition has been a noticeable research trend in recent years. Common methods find 2D-to-3D correspondences and make recognition decisions by pose estimation, whose efficiency usually suffers from noisy correspondences caused by the increasing number of target objects. To overcome this scalabil... | ['Zhiwei Li', 'Yong Rui', 'Rui Cai', 'Lei Zhang', 'Feng Wu', 'Yanwei Pang', 'Qiang Hao'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['3d-object-recognition'] | ['computer-vision'] | [-3.94441048e-03 -6.07722819e-01 -6.29319027e-02 -3.53899449e-01
-8.87095749e-01 -4.83862907e-01 5.89520216e-01 7.08711222e-02
-2.96942949e-01 2.09507227e-01 1.76496089e-01 -1.66770831e-01
1.10866986e-01 -6.80716872e-01 -5.05499482e-01 -4.13606286e-01
1.02599852e-01 8.07292163e-01 7.02826619e-01 1.20136768... | [7.709705829620361, -2.5853893756866455] |
e1dc56e9-5585-49c9-b408-9777c6703b09 | bayesian-optimisation-of-functions-on-graphs | 2306.05304 | null | https://arxiv.org/abs/2306.05304v1 | https://arxiv.org/pdf/2306.05304v1.pdf | Bayesian Optimisation of Functions on Graphs | The increasing availability of graph-structured data motivates the task of optimising over functions defined on the node set of graphs. Traditional graph search algorithms can be applied in this case, but they may be sample-inefficient and do not make use of information about the function values; on the other hand, Bay... | ['Xiaowen Dong', 'Michael A. Osborne', 'Binxin Ru', 'Henry Kenlay', 'Pierre Osselin', 'Xingchen Wan'] | 2023-06-08 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 2.66917139e-01 3.82515907e-01 1.71383936e-02 -1.16983540e-01
-3.63080144e-01 -4.75519627e-01 7.25800872e-01 4.70275760e-01
-2.08417282e-01 7.68789649e-01 -3.26708913e-01 -2.86721200e-01
-7.88699925e-01 -1.02773130e+00 -5.98898828e-01 -9.64209735e-01
-3.50695491e-01 6.21440470e-01 3.80999207e-01 -7.24210218... | [6.929332256317139, 5.219577312469482] |
1d09b948-e3e9-496f-953f-f80d940ade5d | mt-multi-perspective-feature-learning-network | 2105.05455 | null | https://arxiv.org/abs/2105.05455v1 | https://arxiv.org/pdf/2105.05455v1.pdf | MT: Multi-Perspective Feature Learning Network for Scene Text Detection | Text detection, the key technology for understanding scene text, has become an attractive research topic. For detecting various scene texts, researchers propose plenty of detectors with different advantages: detection-based models enjoy fast detection speed, and segmentation-based algorithms are not limited by text sha... | ['Qi Wang', 'Yuan Yuan', 'Mulin Chen', 'Chuang Yang'] | 2021-05-12 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 2.02675983e-01 -6.51943266e-01 -6.10336661e-02 -2.48646840e-01
-6.62823975e-01 -2.85097212e-01 6.56540692e-01 1.01363741e-01
-4.65039581e-01 -1.73985675e-01 -1.86148316e-01 -8.84767547e-02
3.81819218e-01 -6.75530314e-01 -3.48367840e-01 -5.74280202e-01
7.26704180e-01 5.07290304e-01 8.81189644e-01 3.54845338... | [12.06947135925293, 2.2747042179107666] |
23466fa7-780a-4ebd-91bc-eac88d18321e | sr-forest-a-genetic-programming-based | null | null | https://ieeexplore.ieee.org/abstract/document/10040601 | https://ieeexplore.ieee.org/abstract/document/10040601 | SR-Forest: A Genetic Programming based Heterogeneous Ensemble Learning Method | Ensemble learning methods have been widely used in machine learning in recent years due to their high predictive performance. With the development of genetic programming-based symbolic regression methods, many papers begin to choose a popular ensemble learning method, random forests, as the baseline competitor. Instead... | ['Mengjie Zhang', 'Bing Xue', 'Qi Chen', 'Aimin Zhou', 'Hengzhe Zhang'] | 2023-02-07 | null | null | null | ieee-transactions-on-evolutionary-computation-1 | ['penn-machine-learning-benchmark'] | ['miscellaneous'] | [ 2.46696666e-01 -3.80301893e-01 -3.15069854e-01 -4.75731432e-01
-4.67211574e-01 -1.32263871e-02 3.98516983e-01 -6.57508746e-02
-7.24662915e-02 1.10509789e+00 -3.44477057e-01 -2.86526531e-01
-5.32892585e-01 -1.09116864e+00 -2.54351705e-01 -1.23305714e+00
1.46045119e-01 3.83984149e-01 -2.77529750e-02 -4.66487050... | [8.318327903747559, 4.139974117279053] |
58d92820-7b06-405d-91bf-7ec351f75837 | unifying-vision-language-representation-space | 2211.11153 | null | https://arxiv.org/abs/2211.11153v1 | https://arxiv.org/pdf/2211.11153v1.pdf | Unifying Vision-Language Representation Space with Single-tower Transformer | Contrastive learning is a form of distance learning that aims to learn invariant features from two related representations. In this paper, we explore the bold hypothesis that an image and its caption can be simply regarded as two different views of the underlying mutual information, and train a model to learn a unified... | ['Nojun Kwak', 'Seonhoon Kim', 'Donghyeon Jeon', 'Chaerin Kong', 'Jiho Jang'] | 2022-11-21 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 4.04909223e-01 -1.52481854e-01 -5.36666691e-01 -4.97194022e-01
-1.19204915e+00 -8.00418794e-01 1.23038030e+00 2.36844961e-02
-3.79345566e-01 2.24807128e-01 5.32657564e-01 -3.42929244e-01
-4.30393249e-01 -4.78284329e-01 -7.28641033e-01 -5.95475793e-01
1.84103757e-01 5.52517712e-01 -1.00100964e-01 -2.20552579... | [10.509791374206543, 1.6620858907699585] |
0b6fa30b-b11e-4771-aa1e-46698abad83a | sentence-ordering-in-electronic-navigational | null | null | https://aclanthology.org/W15-4710 | https://aclanthology.org/W15-4710.pdf | Sentence Ordering in Electronic Navigational Chart Companion Text Generation | null | ['Julie Sauvage-Vincent', 'John Puentes', 'Yannis Haralambous'] | 2015-09-01 | null | null | null | ws-2015-9 | ['sentence-ordering'] | ['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... | [-7.393418788909912, 3.7569267749786377] |
f2292da3-80d4-4a20-856c-42e95e1f0ccc | pcpl-predicate-correlation-perception | 2009.00893 | null | https://arxiv.org/abs/2009.00893v1 | https://arxiv.org/pdf/2009.00893v1.pdf | PCPL: Predicate-Correlation Perception Learning for Unbiased Scene Graph Generation | Today, scene graph generation(SGG) task is largely limited in realistic scenarios, mainly due to the extremely long-tailed bias of predicate annotation distribution. Thus, tackling the class imbalance trouble of SGG is critical and challenging. In this paper, we first discover that when predicate labels have strong cor... | ['Xian-Sheng Hua', 'Rongxin Jiang', 'Chen Shen', 'Zhongming Jin', 'Yaowu Chen', 'Jianqiang Huang', 'Shaotian Yan'] | 2020-09-02 | null | null | null | null | ['unbiased-scene-graph-generation'] | ['computer-vision'] | [ 5.36259532e-01 7.97169134e-02 -4.82137799e-01 -3.94462675e-01
-5.04090071e-01 -3.28583926e-01 4.13828582e-01 3.42519194e-01
-1.27882689e-01 7.06609368e-01 2.75940806e-01 -3.08902383e-01
-3.35301250e-01 -1.01917458e+00 -7.63375342e-01 -9.54555392e-01
6.66159093e-02 5.83795667e-01 4.55172926e-01 -2.26837829... | [10.256814002990723, 1.7750835418701172] |
8cef2a39-a570-4ff4-b56d-4c1863ec468a | are-large-language-models-robust-zero-shot | 2305.14489 | null | https://arxiv.org/abs/2305.14489v1 | https://arxiv.org/pdf/2305.14489v1.pdf | Are Large Language Models Robust Zero-shot Coreference Resolvers? | Recent progress in domain adaptation for coreference resolution relies on continued training using annotated data from target domains. At the same time, pre-trained large language models (LMs) have exhibited strong zero- and few-shot learning abilities across a wide range of NLP tasks including pronoun resolution. Whil... | ['Alan Ritter', 'Nghia T. Le'] | 2023-05-23 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 1.94697306e-01 2.27768302e-01 -7.49475837e-01 -2.54451901e-01
-1.45647049e+00 -7.45129406e-01 9.48206484e-01 2.58884221e-01
-8.04238856e-01 9.06699240e-01 7.55150259e-01 -2.05753148e-01
-3.23921204e-01 -4.13667589e-01 -4.29281384e-01 -1.78595141e-01
-6.65492937e-02 1.12668693e+00 4.27122295e-01 -8.49577606... | [9.290755271911621, 9.54389476776123] |
e42c2b6e-5e53-4e21-9785-96d06495f2b0 | continuous-expressive-speaking-styles | null | null | https://aclanthology.org/C16-1036 | https://aclanthology.org/C16-1036.pdf | Continuous Expressive Speaking Styles Synthesis based on CVSM and MR-HMM | This paper introduces a continuous system capable of automatically producing the most adequate speaking style to synthesize a desired target text. This is done thanks to a joint modeling of the acoustic and lexical parameters of the speaker models by adapting the CVSM projection of the training texts using MR-HMM techn... | ['Junichi Yamagishi', 'Ascension Gallardo-Antolin', 'Roberto Barra-Chicote', 'Jaime Lorenzo-Trueba', 'Juan M. Montero'] | 2016-12-01 | continuous-expressive-speaking-styles-1 | https://aclanthology.org/C16-1036 | https://aclanthology.org/C16-1036.pdf | coling-2016-12 | ['expressive-speech-synthesis'] | ['speech'] | [ 2.78746516e-01 3.33836555e-01 3.15908939e-01 -4.86960113e-01
-8.24523866e-01 -5.89897931e-01 1.00747812e+00 5.04682697e-02
-4.34869200e-01 6.75691545e-01 3.47629964e-01 -3.38871211e-01
2.70144921e-02 -4.33410585e-01 -4.03929502e-01 -6.57611966e-01
3.83038193e-01 8.34524930e-01 1.52253285e-01 -5.94090521... | [14.724780082702637, 6.577938079833984] |
09554446-2747-4361-95bc-6303ea72003b | bowfire-detection-of-fire-in-still-images-by | 1506.03495 | null | http://arxiv.org/abs/1506.03495v1 | http://arxiv.org/pdf/1506.03495v1.pdf | BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis | Emergency events involving fire are potentially harmful, demanding a fast and
precise decision making. The use of crowdsourcing image and videos on crisis
management systems can aid in these situations by providing more information
than verbal/textual descriptions. Due to the usual high volume of data,
automatic soluti... | ['Jose F. Rodrigues Jr.', 'Agma J. M. Traina', 'Letricia P. S. Avalhais', 'Daniel Y. T. Chino'] | 2015-06-10 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 3.88652563e-01 -4.77044612e-01 3.61231297e-01 -5.74351400e-02
-4.87644225e-01 -6.72769785e-01 5.70796907e-01 5.85997999e-01
-1.07997465e+00 9.18740809e-01 2.68203299e-02 -1.25051020e-02
-2.33891696e-01 -1.11727226e+00 -2.02712595e-01 -8.69710386e-01
-4.08838838e-02 3.75538915e-01 9.42565501e-01 -3.23595792... | [9.08218002319336, -1.0921251773834229] |
01307380-65a6-4ea6-a782-46a08848241c | combining-multi-sequence-and-synthetic-images | 1909.01182 | null | https://arxiv.org/abs/1909.01182v2 | https://arxiv.org/pdf/1909.01182v2.pdf | Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI | Accurate segmentation of the cardiac boundaries in late gadolinium enhancement magnetic resonance images (LGE-MRI) is a fundamental step for accurate quantification of scar tissue. However, while there are many solutions for automatic cardiac segmentation of cine images, the presence of scar tissue can make the correct... | ['Miguel A. González Ballester', 'Cristian Izquierdo', 'Carlos Martín-Isla', 'Víctor M. Campello', 'Steffen E. Petersen', 'Karim Lekadir'] | 2019-09-03 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 7.48053968e-01 2.16145232e-01 4.46197510e-01 -2.96480417e-01
-1.12046564e+00 -6.87683284e-01 1.64334550e-01 -3.51184934e-01
-4.30245638e-01 7.13799357e-01 -2.76145246e-02 -3.85367364e-01
2.15001404e-01 -4.52338487e-01 -5.62442660e-01 -8.22611690e-01
-3.41010451e-01 7.55775332e-01 4.03591186e-01 -5.17408662... | [14.092446327209473, -2.3265018463134766] |
04fc2370-cac6-4328-81f0-5ee1b8d9d1aa | knowledge-interactive-network-with-sentiment | null | null | https://aclanthology.org/2021.findings-emnlp.245 | https://aclanthology.org/2021.findings-emnlp.245.pdf | Knowledge-Interactive Network with Sentiment Polarity Intensity-Aware Multi-Task Learning for Emotion Recognition in Conversations | Emotion Recognition in Conversation (ERC) has gained much attention from the NLP community recently. Some models concentrate on leveraging commonsense knowledge or multi-task learning to help complicated emotional reasoning. However, these models neglect direct utterance-knowledge interaction. In addition, these models... | ['Zhenzhou Ji', 'Bingquan Liu', 'Chengjie Sun', 'Kailai Yang', 'Yunhe Xie'] | null | null | null | null | findings-emnlp-2021-11 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-9.91290808e-03 2.76254147e-01 -3.41011286e-01 -7.16336250e-01
-5.18043756e-01 -2.59252459e-01 2.30631351e-01 1.19382497e-02
-2.85904706e-01 6.26541972e-01 4.28365886e-01 2.42205113e-01
1.73870102e-01 -5.37009835e-01 -3.75884235e-01 -3.95691067e-01
4.51173365e-01 1.67114362e-01 -1.32355496e-01 -6.17647231... | [12.874297142028809, 6.167893886566162] |
ee498df1-87cb-48ae-b9bf-077ea20c5e62 | unsupervised-recycled-fpga-detection-using | 2303.01807 | null | https://arxiv.org/abs/2303.01807v1 | https://arxiv.org/pdf/2303.01807v1.pdf | Unsupervised Recycled FPGA Detection Using Symmetry Analysis | Recently, recycled field-programmable gate arrays (FPGAs) pose a significant hardware security problem due to the proliferation of the semiconductor supply chain. Ring oscillator (RO) based frequency analyzing technique is one of the popular methods, where most studies used the known fresh FPGAs (KFFs) in machine learn... | ['Liakot Ali', 'Maksim Jenihhin', 'Foisal Ahmed', 'Tanvir Ahmad Tarique'] | 2023-03-03 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 7.49602988e-02 -4.79491204e-01 -1.40741035e-01 3.90572287e-02
-9.83669013e-02 -7.22997844e-01 2.02930927e-01 5.14340818e-01
-8.90617371e-02 6.91610157e-01 -3.80022258e-01 -4.05363917e-01
-4.99786377e-01 -8.30756962e-01 -5.68987668e-01 -6.67757094e-01
-1.18034594e-01 -1.83405280e-01 3.25587928e-01 -1.98490575... | [7.684655666351318, 2.7483022212982178] |
93673870-d4f9-476f-b6c6-67b41d565cfd | analysis-of-arrhythmia-classification-on-ecg | 2301.10174 | null | https://arxiv.org/abs/2301.10174v1 | https://arxiv.org/pdf/2301.10174v1.pdf | Analysis of Arrhythmia Classification on ECG Dataset | The heart is one of the most vital organs in the human body. It supplies blood and nutrients in other parts of the body. Therefore, maintaining a healthy heart is essential. As a heart disorder, arrhythmia is a condition in which the heart's pumping mechanism becomes aberrant. The Electrocardiogram is used to analyze t... | ['Nazmul Islam Khan', 'Tanzim Ahmed', 'Arindom Kundu', 'Taminul Islam'] | 2023-01-10 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [-5.77956513e-02 -4.06242073e-01 5.77280074e-02 3.82389463e-02
2.70664752e-01 -3.10662925e-01 -4.97389466e-01 1.90713301e-01
-3.05867866e-02 7.29758501e-01 -9.28596556e-02 -2.93330073e-01
5.94158396e-02 -7.04777122e-01 2.40043625e-01 -8.27360034e-01
-2.32169092e-01 -1.61987156e-01 -2.46407941e-01 -1.61927134... | [14.271653175354004, 3.2699482440948486] |
30a5c522-c844-4508-9fae-091536a6adcc | memegraphs-linking-memes-to-knowledge-graphs | 2305.18391 | null | https://arxiv.org/abs/2305.18391v2 | https://arxiv.org/pdf/2305.18391v2.pdf | MemeGraphs: Linking Memes to Knowledge Graphs | Memes are a popular form of communicating trends and ideas in social media and on the internet in general, combining the modalities of images and text. They can express humor and sarcasm but can also have offensive content. Analyzing and classifying memes automatically is challenging since their interpretation relies o... | ['Benjamin Roth', 'Sahand Sharifzadeh', 'Sina Moayed Baharlou', 'Erion Çano', 'Thomas Kirchmair', 'Simon Fetzel', 'Vasiliki Kougia'] | 2023-05-28 | null | null | null | null | ['knowledge-graphs', 'meme-classification', 'entity-linking'] | ['knowledge-base', 'natural-language-processing', 'natural-language-processing'] | [-1.28939986e-01 2.52325624e-01 1.14867449e-01 8.43428150e-02
6.88786730e-02 -8.44945073e-01 1.19008362e+00 9.25268829e-01
-2.32543036e-01 3.26482862e-01 6.84408069e-01 -3.09199784e-02
2.31057182e-01 -9.97070432e-01 -4.04976696e-01 -2.73147792e-01
3.62891495e-01 3.91849905e-01 4.16560620e-01 -6.92765892... | [8.494016647338867, 10.690481185913086] |
e59b45e4-5927-45d3-b8b4-a8ddbbdd9987 | hnerv-a-hybrid-neural-representation-for | 2304.02633 | null | https://arxiv.org/abs/2304.02633v1 | https://arxiv.org/pdf/2304.02633v1.pdf | HNeRV: A Hybrid Neural Representation for Videos | Implicit neural representations store videos as neural networks and have performed well for various vision tasks such as video compression and denoising. With frame index or positional index as input, implicit representations (NeRV, E-NeRV, \etc) reconstruct video from fixed and content-agnostic embeddings. Such embedd... | ['Abhinav Shrivastava', 'Ser-Nam Lim', 'Matt Gwilliam', 'Hao Chen'] | 2023-04-05 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_HNeRV_A_Hybrid_Neural_Representation_for_Videos_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_HNeRV_A_Hybrid_Neural_Representation_for_Videos_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-inpainting'] | ['computer-vision'] | [-2.27871001e-01 4.52443324e-02 -3.80881935e-01 -2.28207856e-01
-4.55132723e-01 1.08030364e-01 3.21999155e-02 -4.68782693e-01
-3.90519142e-01 5.09372950e-01 5.59774339e-01 -2.48780146e-01
2.72598267e-01 -7.88831651e-01 -1.09908020e+00 -6.14157081e-01
-2.01775849e-01 -2.34060958e-01 -9.13461372e-02 -3.17480117... | [11.239336013793945, -1.5293508768081665] |
57b17ca5-0903-4152-902e-54dc0466d394 | towards-unifying-medical-vision-and-language | 2302.08958 | null | https://arxiv.org/abs/2302.08958v1 | https://arxiv.org/pdf/2302.08958v1.pdf | Towards Unifying Medical Vision-and-Language Pre-training via Soft Prompts | Medical vision-and-language pre-training (Med-VLP) has shown promising improvements on many downstream medical tasks owing to its applicability to extracting generic representations from medical images and texts. Practically, there exist two typical types, \textit{i.e.}, the fusion-encoder type and the dual-encoder typ... | ['Xiang Wan', 'Guanbin Li', 'Benyou Wang', 'Shizhe Diao', 'Zhihong Chen'] | 2023-02-17 | null | null | null | null | ['image-text-classification'] | ['miscellaneous'] | [ 5.62219739e-01 6.94715828e-02 -1.57820120e-01 -1.59061238e-01
-1.30800593e+00 -4.59996969e-01 8.75814557e-01 2.44728744e-01
-4.85632718e-01 5.19525349e-01 3.40475619e-01 -4.14921284e-01
-1.08657606e-01 -6.01418495e-01 -6.28959179e-01 -8.61912310e-01
3.96553248e-01 4.32952732e-01 2.28136539e-01 -9.04163420... | [10.742293357849121, 1.4225934743881226] |
c300b2ac-8e02-41c2-8d32-0637ee882cbd | application-of-machine-learning-in-rock | 1808.09856 | null | http://arxiv.org/abs/1808.09856v1 | http://arxiv.org/pdf/1808.09856v1.pdf | Application of Machine Learning in Rock Facies Classification with Physics-Motivated Feature Augmentation | With recent progress in algorithms and the availability of massive amounts of
computation power, application of machine learning techniques is becoming a hot
topic in the oil and gas industry. One of the most promising aspects to apply
machine learning to the upstream field is the rock facies classification in
reservoi... | ['Jie Chen', 'Yu Zeng'] | 2018-08-29 | null | null | null | null | ['facies-classification'] | ['miscellaneous'] | [-1.47921830e-01 -1.90241057e-02 -1.52016252e-01 -3.20346415e-01
-7.75808930e-01 -3.27119857e-01 7.12656796e-01 4.00904536e-01
-4.53994989e-01 7.74644375e-01 4.34706099e-02 -3.86650324e-01
-5.06054819e-01 -1.07266283e+00 -6.07117474e-01 -8.35142612e-01
-6.51472867e-01 7.72035003e-01 3.51940244e-01 -5.91445923... | [7.05145263671875, 2.3213324546813965] |
b7859e46-f113-4f83-86eb-8a8a9444ef39 | mast-multimodal-abstractive-summarization | 2010.08021 | null | https://arxiv.org/abs/2010.08021v1 | https://arxiv.org/pdf/2010.08021v1.pdf | MAST: Multimodal Abstractive Summarization with Trimodal Hierarchical Attention | This paper presents MAST, a new model for Multimodal Abstractive Text Summarization that utilizes information from all three modalities -- text, audio and video -- in a multimodal video. Prior work on multimodal abstractive text summarization only utilized information from the text and video modalities. We examine the ... | ['Udit Arora', 'Aman Khullar'] | 2020-10-15 | null | https://aclanthology.org/2020.nlpbt-1.7 | https://aclanthology.org/2020.nlpbt-1.7.pdf | emnlp-nlpbt-2020-11 | ['multimodal-abstractive-text-summarization'] | ['natural-language-processing'] | [ 5.25578856e-01 9.19349566e-02 -9.06744823e-02 -1.43084645e-01
-1.57304871e+00 -6.51251853e-01 6.97661459e-01 2.91268051e-01
-5.64713657e-01 7.05467105e-01 1.13621962e+00 -4.09110673e-02
3.40134740e-01 1.40267506e-01 -7.17434406e-01 -3.94083232e-01
2.77325749e-01 3.40570122e-01 5.51102981e-02 -3.21665883... | [10.603431701660156, 0.6992446184158325] |
80af1fc0-4982-4ff1-b3a2-cebdc908f54e | state-regularized-recurrent-neural-networks-1 | 2212.05178 | null | https://arxiv.org/abs/2212.05178v1 | https://arxiv.org/pdf/2212.05178v1.pdf | State-Regularized Recurrent Neural Networks to Extract Automata and Explain Predictions | Recurrent neural networks are a widely used class of neural architectures. They have, however, two shortcomings. First, they are often treated as black-box models and as such it is difficult to understand what exactly they learn as well as how they arrive at a particular prediction. Second, they tend to work poorly on ... | ['Mathias Niepert', 'Carolin Lawrence', 'Cheng Wang'] | 2022-12-10 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 6.31090403e-01 1.90982774e-01 -3.34490240e-01 -1.40480489e-01
-3.85748804e-01 -6.90063119e-01 7.23971844e-01 8.69781077e-02
-3.54942024e-01 6.70751870e-01 2.36466840e-01 -9.75923419e-01
2.27059245e-01 -7.55080700e-01 -8.30177665e-01 -7.75304675e-01
4.68721353e-02 5.07879853e-01 2.70260781e-01 -2.79107958... | [10.748822212219238, 6.865193843841553] |
c6bbd246-9bd0-4eae-8b5c-cceb86c3aec6 | cloze-evaluation-for-deeper-understanding-of | null | null | https://openreview.net/forum?id=zPFbyqOX8Cx | https://openreview.net/pdf?id=zPFbyqOX8Cx | Cloze Evaluation for Deeper Understanding of Commonsense Stories in Indonesian | Story comprehension that involves complex causal and temporal relations is imperative in NLP, but previous studies have focused on English, leaving open the question of how the findings generalize to other languages, such as Indonesian. In this paper, we follow the Story Cloze Test framework of Mostafazadeh et al. (201... | ['Anonymous'] | 2021-12-17 | null | null | null | acl-arr-december-2022-12 | ['cloze-test'] | ['natural-language-processing'] | [ 5.96465707e-01 1.62992656e-01 6.46426678e-02 -2.70953268e-01
-8.67037594e-01 -7.70771801e-01 9.84701395e-01 2.61414140e-01
-3.64033490e-01 9.62707818e-01 8.49546134e-01 -7.28353918e-01
3.03378748e-03 -7.78908610e-01 -8.82156909e-01 -2.12096944e-01
8.02584067e-02 4.23036695e-01 -5.87330423e-02 -2.97028154... | [11.2025146484375, 8.874510765075684] |
98cc5be7-a64e-474a-9561-3d27b239024a | cascaded-neural-networks-with-selective | 1611.07136 | null | http://arxiv.org/abs/1611.07136v1 | http://arxiv.org/pdf/1611.07136v1.pdf | Cascaded Neural Networks with Selective Classifiers and its evaluation using Lung X-ray CT Images | Lung nodule detection is a class imbalanced problem because nodules are found
with much lower frequency than non-nodules. In the class imbalanced problem,
conventional classifiers tend to be overwhelmed by the majority class and
ignore the minority class. We therefore propose cascaded convolutional neural
networks to c... | ['Hiroki Nakano', 'Masaharu Sakamoto'] | 2016-11-22 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 2.99705416e-01 4.71302509e-01 -5.91223657e-01 -2.40319431e-01
-4.24489558e-01 -1.00558899e-01 4.22560610e-02 2.28027001e-01
-3.29916120e-01 6.39973164e-01 -1.19641908e-01 -4.39090908e-01
-1.02904029e-01 -1.03262460e+00 -3.36454660e-01 -5.66640198e-01
1.57563522e-01 4.27076489e-01 6.45569444e-01 2.48252958... | [15.405499458312988, -2.1749560832977295] |
d192b64a-14b4-4de4-a3ee-5e3fa39c65f7 | tone-mapping-based-on-multi-scale-histogram | 2102.00408 | null | https://arxiv.org/abs/2102.00408v1 | https://arxiv.org/pdf/2102.00408v1.pdf | Tone Mapping Based on Multi-scale Histogram Synthesis | In this paper, we present a novel tone mapping algorithm that can be used for displaying wide dynamic range (WDR) images on low dynamic range (LDR) devices. The proposed algorithm is mainly motivated by the logarithmic response and local adaptation features of the human visual system (HVS). HVS perceives luminance diff... | ['Orly Yadid-Pecht', 'Ulian Shahnovich', 'Ziyi Liu', 'Jie Yang'] | 2021-01-31 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 3.58343542e-01 -7.15542316e-01 2.43263673e-02 -2.60014504e-01
-4.33132827e-01 -4.45250452e-01 1.47047773e-01 -2.69038171e-01
-2.01039940e-01 6.77192986e-01 2.85432022e-02 -4.87977266e-02
6.25986755e-02 -9.86144304e-01 -3.51762027e-01 -8.03780317e-01
2.70547479e-01 -5.69348097e-01 9.37873662e-01 -5.32573760... | [10.925935745239258, -2.4140310287475586] |
82026f2c-f963-445f-a884-1e99d3c706f0 | adversarial-attacks-against-a-satellite-borne | 2112.01723 | null | https://arxiv.org/abs/2112.01723v1 | https://arxiv.org/pdf/2112.01723v1.pdf | Adversarial Attacks against a Satellite-borne Multispectral Cloud Detector | Data collected by Earth-observing (EO) satellites are often afflicted by cloud cover. Detecting the presence of clouds -- which is increasingly done using deep learning -- is crucial preprocessing in EO applications. In fact, advanced EO satellites perform deep learning-based cloud detection on board the satellites and... | ['Tat-Jun Chin', 'Michael Brown', 'Ken Clarke', 'Bo Chen', 'Michele Sasdelli', 'Yee Wei Law', 'Andrew Du'] | 2021-12-03 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 1.83565766e-01 -3.33606660e-01 5.75176418e-01 6.47268370e-02
-5.07889450e-01 -1.30690873e+00 3.72775912e-01 -3.18008959e-01
-2.51576066e-01 4.82525527e-01 -3.82729411e-01 -5.68719566e-01
2.68591851e-01 -1.09119713e+00 -8.22463274e-01 -1.21459436e+00
-6.72353566e-01 -3.93646061e-02 -8.73582661e-02 -2.74842441... | [9.864017486572266, -1.775280237197876] |
a2c24993-7dfc-445d-b575-1ca6d98c884a | semi-supervised-semantic-segmentation-via-4 | 2301.07340 | null | https://arxiv.org/abs/2301.07340v1 | https://arxiv.org/pdf/2301.07340v1.pdf | Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant | Semi-Supervised Semantic Segmentation aims at training the segmentation model with limited labeled data and a large amount of unlabeled data. To effectively leverage the unlabeled data, pseudo labeling, along with the teacher-student framework, is widely adopted in semi-supervised semantic segmentation. Though proved t... | ['Dahua Lin', 'Jiaqi Wang', 'Ying Jin'] | 2023-01-18 | semi-supervised-semantic-segmentation-via-3 | https://openreview.net/forum?id=r70ZpWKiCW | https://openreview.net/pdf?id=r70ZpWKiCW | nips-2022-11 | ['semi-supervised-semantic-segmentation'] | ['computer-vision'] | [ 3.89360756e-01 5.38509190e-01 -3.40701997e-01 -6.89360738e-01
-8.58842850e-01 -6.32084906e-01 4.19913948e-01 -1.46203861e-01
-3.03097099e-01 6.00055814e-01 -2.22446784e-01 -2.90850163e-01
4.70397025e-02 -7.04860687e-01 -8.00796449e-01 -9.45094883e-01
4.93335217e-01 4.04662699e-01 1.33269593e-01 1.07581906... | [9.250930786132812, 1.8205132484436035] |
c40c072c-215b-40b6-bc36-5470cb73243d | grounding-graph-network-simulators-using | 2302.11864 | null | https://arxiv.org/abs/2302.11864v2 | https://arxiv.org/pdf/2302.11864v2.pdf | Grounding Graph Network Simulators using Physical Sensor Observations | Physical simulations that accurately model reality are crucial for many engineering disciplines such as mechanical engineering and robotic motion planning. In recent years, learned Graph Network Simulators produced accurate mesh-based simulations while requiring only a fraction of the computational cost of traditional ... | ['Gerhard Neumann', 'Franziska Mathis-Ullrich', 'Paul Maria Scheikl', 'Niklas Freymuth', 'Jonas Linkerhägner'] | 2023-02-23 | null | null | null | null | ['physical-simulations', 'motion-planning'] | ['miscellaneous', 'robots'] | [-7.04833120e-03 1.51948899e-01 -9.47855189e-02 1.30068287e-01
-2.99427152e-01 -5.29917479e-01 5.14180720e-01 6.05500281e-01
-8.92327353e-02 1.07926750e+00 -5.01659989e-01 -1.57390013e-01
-2.65982151e-01 -1.16361058e+00 -1.28535652e+00 -4.44899291e-01
-2.79607922e-01 1.15655935e+00 4.28520113e-01 -5.27380884... | [5.410703659057617, -0.07748638838529587] |
179b7938-e892-484e-af15-ff51dfc38161 | an-evaluation-and-ranking-of-different-voting | 2305.05705 | null | https://arxiv.org/abs/2305.05705v1 | https://arxiv.org/pdf/2305.05705v1.pdf | An Evaluation and Ranking of Different Voting Schemes for Improved Visual Place Recognition | Visual Place Recognition has recently seen a surge of endeavours utilizing different ensemble approaches to improve VPR performance. Ideas like multi-process fusion or switching involve combining different VPR techniques together, utilizing different strategies. One major aspect often common to many of these strategies... | ['Shoaib Ehsan', 'Klaus McDonald-Maier', 'Xiaojun Zhai', 'Michael Milford', 'Maria Waheed'] | 2023-05-09 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 1.94681715e-02 -4.41472411e-01 8.19448456e-02 -1.42006740e-01
-4.90578622e-01 -8.74742925e-01 1.17323303e+00 2.43806839e-01
-5.82558990e-01 7.82959819e-01 4.45792794e-01 -5.37750602e-01
-4.15466070e-01 -9.12169337e-01 2.64355689e-02 -9.07971442e-01
3.36899430e-01 4.15101528e-01 3.36483568e-01 -6.84735775... | [7.7445831298828125, 4.5842671394348145] |
f51903c4-f581-4201-8943-670af7c85433 | quantitative-analysis-of-primary-attribution | 2306.04037 | null | https://arxiv.org/abs/2306.04037v1 | https://arxiv.org/pdf/2306.04037v1.pdf | Quantitative Analysis of Primary Attribution Explainable Artificial Intelligence Methods for Remote Sensing Image Classification | We present a comprehensive analysis of quantitatively evaluating explainable artificial intelligence (XAI) techniques for remote sensing image classification. Our approach leverages state-of-the-art machine learning approaches to perform remote sensing image classification across multiple modalities. We investigate the... | ['Joshua Peeples', 'Akshatha Mohan'] | 2023-06-06 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 6.30400598e-01 4.30116542e-02 -5.47634065e-01 -6.46049678e-01
-6.94139957e-01 -5.18778980e-01 8.36292207e-01 -9.21847299e-03
2.23508120e-01 5.28211296e-01 1.96268916e-01 -1.01948786e+00
-8.05111229e-01 -9.22941983e-01 -4.50290203e-01 -7.14299083e-01
-1.92429855e-01 3.35962355e-01 -6.62134826e-01 -1.00138374... | [9.578529357910156, -1.4451253414154053] |
4dc601a5-5f43-48ca-92fb-b59c691cf546 | a-novel-facial-emotion-recognition-model | null | null | https://link.springer.com/article/10.1007/s41870-023-01184-z | https://link.springer.com/article/10.1007/s41870-023-01184-z | A novel facial emotion recognition model using segmentation VGG-19 architecture | Facial Emotion Recognition (FER) has gained popularity in recent years due to its many applications, including biometrics, detection of mental illness, understanding of human behavior, and psychological profiling. However, developing an accurate and robust FER pipeline is still challenging because multiple factors make... | ['Rajeswari Sridhar', 'M. Sridevi', 'M. Savithadevi', 'S. Vignesh'] | 2023-03-24 | null | null | null | international-journal-of-information | ['facial-emotion-recognition', 'facial-expression-recognition'] | ['computer-vision', 'computer-vision'] | [-2.44754236e-02 -3.59408885e-01 -3.90365310e-02 -7.02412903e-01
-9.47229192e-02 6.14615232e-02 -3.03574931e-02 -1.77045792e-01
-4.66935158e-01 5.18775761e-01 -1.51421562e-01 3.75452250e-01
1.25893414e-01 -7.88278282e-01 -3.80722404e-01 -4.46547031e-01
-1.57600343e-01 -1.74390778e-01 2.13400379e-01 -3.43050838... | [13.524002075195312, 1.699205756187439] |
3b18bf19-7db1-4bd2-8048-7b6bf4b10b50 | unsupervised-3d-out-of-distribution-detection | 2307.03777 | null | https://arxiv.org/abs/2307.03777v1 | https://arxiv.org/pdf/2307.03777v1.pdf | Unsupervised 3D out-of-distribution detection with latent diffusion models | Methods for out-of-distribution (OOD) detection that scale to 3D data are crucial components of any real-world clinical deep learning system. Classic denoising diffusion probabilistic models (DDPMs) have been recently proposed as a robust way to perform reconstruction-based OOD detection on 2D datasets, but do not triv... | ['M. Jorge Cardoso', 'Sebastien Ourselin', 'Parashkev Nachev', 'David Werring', 'H. Rolf Jäger', 'James T. Teo', 'Yee H. Mah', 'Petru-Daniel Tudosiu', 'Paul Wright', 'Walter Hugo Lopez Pinaya', 'Mark S. Graham'] | 2023-07-07 | null | null | null | null | ['out-of-distribution-detection', 'denoising', 'ood-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-7.52196237e-02 6.81953728e-02 1.19428717e-01 -1.57019988e-01
-1.02049720e+00 -1.77779973e-01 8.59881461e-01 5.30507803e-01
-3.34282219e-01 4.12298024e-01 5.32697439e-01 -2.32640415e-01
-4.57405657e-01 -7.16037273e-01 -2.03465313e-01 -1.01338506e+00
-3.34219813e-01 7.87899733e-01 7.43447185e-01 1.27931535... | [14.08277702331543, -2.2271831035614014] |
7322d715-94b5-4f29-ae3a-05d7841bf414 | physically-based-editing-of-indoor-scene | 2205.09343 | null | https://arxiv.org/abs/2205.09343v2 | https://arxiv.org/pdf/2205.09343v2.pdf | Physically-Based Editing of Indoor Scene Lighting from a Single Image | We present a method to edit complex indoor lighting from a single image with its predicted depth and light source segmentation masks. This is an extremely challenging problem that requires modeling complex light transport, and disentangling HDR lighting from material and geometry with only a partial LDR observation of ... | ['Manmohan Chandraker', 'Ravi Ramamoorthi', 'Zexiang Xu', 'Miloš Hašan', 'Kalyan Sunkavalli', 'Rui Zhu', 'Sai Bi', 'Jia Shi', 'Zhengqin Li'] | 2022-05-19 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 8.03262413e-01 -1.91501692e-01 6.44478738e-01 -4.94503975e-01
-2.92891234e-01 -6.15032256e-01 5.71104169e-01 -4.83531386e-01
2.31425449e-01 6.96722209e-01 2.01914862e-01 -2.77222723e-01
4.07649666e-01 -9.82518613e-01 -8.88658404e-01 -4.56648380e-01
5.61881244e-01 4.78516668e-01 3.27604562e-02 -2.62441039... | [9.66161060333252, -3.110847234725952] |
379b07a1-ff6c-4200-802a-527f1242a65a | faithful-chain-of-thought-reasoning | 2301.13379 | null | https://arxiv.org/abs/2301.13379v2 | https://arxiv.org/pdf/2301.13379v2.pdf | Faithful Chain-of-Thought Reasoning | While Chain-of-Thought (CoT) prompting boosts Language Models' (LM) performance on a gamut of complex reasoning tasks, the generated reasoning chain does not necessarily reflect how the model arrives at the answer (aka. faithfulness). We propose Faithful CoT, a faithful-by-construction framework that decomposes a reaso... | ['Chris Callison-Burch', 'Marianna Apidianaki', 'Eric Wong', 'Delip Rao', 'Li Zhang', 'Adam Stein', 'Shreya Havaldar', 'Qing Lyu'] | 2023-01-31 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 6.88385218e-02 6.87312782e-01 -1.60857886e-01 -5.36554813e-01
-1.30830574e+00 -6.06352568e-01 6.13735020e-01 2.80116707e-01
-6.92917928e-02 7.41946161e-01 3.60665709e-01 -7.24661291e-01
-2.76936740e-01 -1.20609975e+00 -1.04769909e+00 -3.90248522e-02
4.05705005e-01 1.01809645e+00 3.19716156e-01 -6.29527152... | [9.733342170715332, 7.415586948394775] |
96efcc3e-fcf6-409e-93ed-cee16cf8e5d2 | universal-spam-detection-using-transfer | 2202.03480 | null | https://arxiv.org/abs/2202.03480v1 | https://arxiv.org/pdf/2202.03480v1.pdf | Universal Spam Detection using Transfer Learning of BERT Model | Deep learning transformer models become important by training on text data based on self-attention mechanisms. This manuscript demonstrated a novel universal spam detection model using pre-trained Google's Bidirectional Encoder Representations from Transformers (BERT) base uncased models with four datasets by efficient... | ['Sonya Hsu', 'Vijay Srinivas Tida'] | 2022-02-07 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-1.37813732e-01 1.02101468e-01 -9.87397283e-02 -3.21598023e-01
-7.83282757e-01 -3.01276654e-01 9.30497706e-01 -2.96003848e-01
-1.08967140e-01 6.57325447e-01 2.28994519e-01 -7.37732887e-01
-8.94087777e-02 -7.80703187e-01 -4.89557743e-01 -4.38391596e-01
1.98103964e-01 5.46118557e-01 5.03250957e-01 -4.16943133... | [7.850558757781982, 9.978206634521484] |
978f0caa-b22a-4bd5-af3b-79acd6a68354 | faster-exact-decoding-and-global-training-for | 1708.09403 | null | http://arxiv.org/abs/1708.09403v1 | http://arxiv.org/pdf/1708.09403v1.pdf | Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set | We first present a minimal feature set for transition-based dependency
parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a)
and Cross and Huang (2016a) of using bi-directional LSTM features. We plug our
minimal feature set into the dynamic-programming framework of Huang and Sagae
(2010) and Ku... | ['Tianze Shi', 'Liang Huang', 'Lillian Lee'] | 2017-08-30 | faster-exact-decoding-and-global-training-for-1 | https://aclanthology.org/D17-1002 | https://aclanthology.org/D17-1002.pdf | emnlp-2017-9 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [ 2.66065355e-02 7.56758571e-01 -7.33317360e-02 -5.84809124e-01
-1.50341451e+00 -8.04923296e-01 3.27631950e-01 2.32000083e-01
-5.95015585e-01 1.07735133e+00 2.48251900e-01 -8.63585532e-01
7.28199929e-02 -5.55119038e-01 -6.00305915e-01 -3.79381388e-01
-4.49230939e-01 4.99618739e-01 3.29583257e-01 -2.06686825... | [10.37149429321289, 9.749075889587402] |
d9ab6c57-23b6-4dde-a74c-a8a6f8a24f28 | auxiliary-learning-for-self-supervised-video | 2112.04011 | null | https://arxiv.org/abs/2112.04011v3 | https://arxiv.org/pdf/2112.04011v3.pdf | Auxiliary Learning for Self-Supervised Video Representation via Similarity-based Knowledge Distillation | Despite the outstanding success of self-supervised pretraining methods for video representation learning, they generalise poorly when the unlabeled dataset for pretraining is small or the domain difference between unlabelled data in source task (pretraining) and labeled data in target task (finetuning) is significant. ... | ['Majid Mirmehdi', 'Alan Whone', 'Amirhossein Dadashzadeh'] | 2021-12-07 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 4.09196705e-01 1.26932142e-02 -6.93996668e-01 -4.28313196e-01
-6.92191243e-01 -6.64747655e-01 4.67780888e-01 -3.25802192e-02
-5.52840531e-01 6.55591369e-01 -1.50615189e-04 -2.37783566e-01
-2.83013936e-02 -4.93679285e-01 -1.18781197e+00 -5.97905457e-01
2.52085794e-02 5.17694414e-01 5.13213992e-01 -9.28429663... | [9.126602172851562, 1.1506763696670532] |
fcb61ce4-ac65-47aa-8f2f-de2774f10173 | ask-question-with-double-hints-visual | null | null | https://openreview.net/forum?id=-WwaX9vKKt | https://openreview.net/pdf?id=-WwaX9vKKt | Ask Question with Double Hints: Visual Question Generation with Answer-awareness and Region-reference | The task of visual question generation~(VQG) aims to generate human-like questions from an image and potentially other side information (e.g. answer type or the answer itself). Despite promising results have been achieved, previous works on VQG either i) suffer from one image to many questions mapping problem rendering... | ['Yueting Zhuang', 'Yu Qiang', 'Zhu Zhang', 'Fangli Xu', 'Siliang Tang', 'Lingfei Wu', 'Shen Kai'] | 2021-01-01 | null | null | null | null | ['graph-to-sequence'] | ['natural-language-processing'] | [ 2.70100713e-01 4.16645885e-01 7.72889033e-02 -3.72388303e-01
-8.98907244e-01 -8.56856525e-01 8.25143874e-01 -2.28357539e-02
2.94807870e-02 5.34979105e-01 3.62558663e-01 -4.94593292e-01
1.13635220e-01 -9.13721204e-01 -9.39000964e-01 -2.22906798e-01
4.54969168e-01 6.73721790e-01 6.21945202e-01 -5.36789536... | [10.90198802947998, 1.5999456644058228] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.