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e90158d1-2a36-431a-b8e4-ce4f63721534 | domain-mismatch-doesnt-always-prevent-cross | null | null | https://aclanthology.org/2022.lrec-1.94 | https://aclanthology.org/2022.lrec-1.94.pdf | Domain Mismatch Doesn’t Always Prevent Cross-lingual Transfer Learning | Cross-lingual transfer learning without labeled target language data or parallel text has been surprisingly effective in zero-shot cross-lingual classification, question answering, unsupervised machine translation, etc. However, some recent publications have claimed that domain mismatch prevents cross-lingual transfer,... | ['Noah A. Smith', 'Phillip Keung', 'Daniel Edmiston'] | null | null | null | null | lrec-2022-6 | ['word-similarity', 'unsupervised-machine-translation'] | ['natural-language-processing', 'natural-language-processing'] | [-4.38422989e-03 -1.81844726e-01 -5.00920296e-01 -3.54962617e-01
-1.44330239e+00 -9.53718066e-01 8.57831538e-01 2.01479867e-02
-8.03366661e-01 1.16144252e+00 4.25726205e-01 -5.79634190e-01
3.24913830e-01 -4.18265194e-01 -9.40760314e-01 -4.11186337e-01
4.81565267e-01 1.04433978e+00 7.14040473e-02 -4.88275409... | [11.03689956665039, 9.970775604248047] |
bef3be3f-ec28-4d50-a8f5-a2185446e193 | 3d-meta-point-signature-learning-to-learn-3d | 2010.11159 | null | https://arxiv.org/abs/2010.11159v1 | https://arxiv.org/pdf/2010.11159v1.pdf | 3D Meta Point Signature: Learning to Learn 3D Point Signature for 3D Dense Shape Correspondence | Point signature, a representation describing the structural neighborhood of a point in 3D shapes, can be applied to establish correspondences between points in 3D shapes. Conventional methods apply a weight-sharing network, e.g., any kind of graph neural networks, across all neighborhoods to directly generate point sig... | ['Yi Fang', 'Xiang Li', 'Lingjing Wang', 'Hao Huang'] | 2020-10-21 | null | null | null | null | ['3d-dense-shape-correspondence'] | ['computer-vision'] | [ 4.08406258e-02 -8.74714553e-02 -5.32407276e-02 -3.51415694e-01
-7.82502055e-01 -5.58836579e-01 5.69013596e-01 -4.24025841e-02
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-2.66491175e-01 7.86782324e-01 6.40017211e-01 -2.87078083... | [7.945202827453613, -3.563032627105713] |
b546e193-123d-48b6-b858-d7310e3bc933 | a-flexible-schema-guided-dialogue-management | 2207.07276 | null | https://arxiv.org/abs/2207.07276v1 | https://arxiv.org/pdf/2207.07276v1.pdf | A Flexible Schema-Guided Dialogue Management Framework: From Friendly Peer to Virtual Standardized Cancer Patient | A schema-guided approach to dialogue management has been shown in recent work to be effective in creating robust customizable virtual agents capable of acting as friendly peers or task assistants. However, successful applications of these methods in open-ended, mixed-initiative domains remain elusive -- particularly wi... | ['Ehsan Hoque', 'Caleb Wohn', 'Kurtis Haut', 'Lenhart Schubert', 'Catherine Giugno', 'Benjamin Kane'] | 2022-07-15 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [ 7.54891261e-02 1.08026409e+00 2.85372622e-02 -8.00205708e-01
-8.79998028e-01 -5.34787595e-01 5.05783081e-01 4.52448040e-01
-5.41702986e-01 9.82587576e-01 7.90935397e-01 -1.75451785e-01
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9.76272225e-02 1.30990708e+00 -4.29856300e-01 -9.96614456... | [12.75011157989502, 8.203629493713379] |
748b1e02-1882-40d2-8eea-721d0f15855b | towards-using-data-centric-approach-for | 2205.13022 | null | https://arxiv.org/abs/2205.13022v2 | https://arxiv.org/pdf/2205.13022v2.pdf | Towards Using Data-Influence Methods to Detect Noisy Samples in Source Code Corpora | Despite the recent trend of developing and applying neural source code models to software engineering tasks, the quality of such models is insufficient for real-world use. This is because there could be noise in the source code corpora used to train such models. We adapt data-influence methods to detect such noises in ... | ['Nghi D. Q. Bui', 'Anh T. V. Dau', 'Hoang Thanh-Tung', 'Thang Nguyen-Duc'] | 2022-05-25 | null | null | null | null | ['code-classification'] | ['computer-code'] | [ 2.36253850e-02 -6.53262585e-02 -9.86575708e-02 -5.07314086e-01
-7.93230593e-01 -3.52385491e-01 1.40257418e-01 3.69410962e-01
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2.34867170e-01 -5.23370616e-02 2.13055447e-01 -1.71483949... | [7.652684688568115, 7.801513671875] |
51f7c933-007f-4025-ace9-d2215a63e580 | compositional-networks-enable-systematic | 2008.02742 | null | https://arxiv.org/abs/2008.02742v3 | https://arxiv.org/pdf/2008.02742v3.pdf | Compositional Networks Enable Systematic Generalization for Grounded Language Understanding | Humans are remarkably flexible when understanding new sentences that include combinations of concepts they have never encountered before. Recent work has shown that while deep networks can mimic some human language abilities when presented with novel sentences, systematic variation uncovers the limitations in the langu... | ['Yen-Ling Kuo', 'Boris Katz', 'Andrei Barbu'] | 2020-08-06 | null | https://aclanthology.org/2021.findings-emnlp.21 | https://aclanthology.org/2021.findings-emnlp.21.pdf | findings-emnlp-2021-11 | ['systematic-generalization'] | ['reasoning'] | [ 1.77488551e-01 3.40340346e-01 2.84184217e-01 -6.09831095e-01
-8.99338946e-02 -8.73678803e-01 8.65611613e-01 -1.47076845e-01
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-2.99985036e-02 -7.53287613e-01 -9.67688918e-01 -2.12771475e-01
-2.77903885e-01 7.14767992e-01 7.36898482e-02 -8.76233459... | [9.625543594360352, 7.035591125488281] |
d0d9c4a5-9cc0-4b4d-a34c-7c468eca6133 | qiuniu-a-chinese-lyrics-generation-system | null | null | https://aclanthology.org/2022.acl-demo.7 | https://aclanthology.org/2022.acl-demo.7.pdf | QiuNiu: A Chinese Lyrics Generation System with Passage-Level Input | Lyrics generation has been a very popular application of natural language generation. Previous works mainly focused on generating lyrics based on a couple of attributes or keywords, rendering very limited control over the content of the lyrics. In this paper, we demonstrate the QiuNiu, a Chinese lyrics generation syste... | ['Yongzhu Chang', 'Xiaoxi Mao', 'Rongsheng Zhang', 'Le Zhang'] | null | null | null | null | acl-2022-5 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 1.09349191e-01 -1.11431777e-01 -7.46973976e-02 -1.82860643e-01
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7.82200634e-01 6.31819844e-01 -1.90224707e-01 -4.76392746... | [11.791081428527832, 9.017899513244629] |
51635e66-db83-4000-95bb-5c3b53fab04a | low-rank-autoregressive-tensor-completion-for-1 | 2104.14936 | null | https://arxiv.org/abs/2104.14936v1 | https://arxiv.org/pdf/2104.14936v1.pdf | Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation | Spatiotemporal traffic time series (e.g., traffic volume/speed) collected from sensing systems are often incomplete with considerable corruption and large amounts of missing values, preventing users from harnessing the full power of the data. Missing data imputation has been a long-standing research topic and critical ... | ['Lijun Sun', 'Nicolas Saunier', 'MengYing Lei', 'Xinyu Chen'] | 2021-04-30 | null | null | null | null | ['traffic-data-imputation'] | ['time-series'] | [-1.46922573e-01 -7.41856873e-01 -2.63956130e-01 -4.30384070e-01
-7.56591916e-01 -2.59097278e-01 3.29150230e-01 -5.78868151e-01
-5.55297993e-02 6.70401275e-01 3.96481484e-01 -2.13266194e-01
-6.64080739e-01 -6.46827757e-01 -8.73262107e-01 -9.40944791e-01
-2.10895866e-01 1.14536770e-01 -1.63988695e-01 -2.86489576... | [6.586612224578857, 2.1495306491851807] |
840a105d-a3b3-4b0c-a8cd-bc7b4a937bfb | markerless-camera-to-robot-pose-estimation | 2302.14332 | null | https://arxiv.org/abs/2302.14332v2 | https://arxiv.org/pdf/2302.14332v2.pdf | Markerless Camera-to-Robot Pose Estimation via Self-supervised Sim-to-Real Transfer | Solving the camera-to-robot pose is a fundamental requirement for vision-based robot control, and is a process that takes considerable effort and cares to make accurate. Traditional approaches require modification of the robot via markers, and subsequent deep learning approaches enabled markerless feature extraction. M... | ['Michael C. Yip', 'Florian Richter', 'Jingpei Lu'] | 2023-02-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lu_Markerless_Camera-to-Robot_Pose_Estimation_via_Self-Supervised_Sim-to-Real_Transfer_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lu_Markerless_Camera-to-Robot_Pose_Estimation_via_Self-Supervised_Sim-to-Real_Transfer_CVPR_2023_paper.pdf | cvpr-2023-1 | ['foreground-segmentation'] | ['computer-vision'] | [ 2.13171303e-01 2.28944331e-01 -1.15421988e-01 -4.46792811e-01
-8.83806705e-01 -5.98727286e-01 3.94096941e-01 -4.67059344e-01
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2.71985054e-01 8.30920041e-01 3.97993773e-01 -1.33534819... | [7.531944751739502, -2.586904287338257] |
0beacacc-5e73-46b7-8e68-19db52fdfab3 | temporally-layered-architecture-for-adaptive | 2301.00723 | null | https://arxiv.org/abs/2301.00723v2 | https://arxiv.org/pdf/2301.00723v2.pdf | Temporally Layered Architecture for Adaptive, Distributed and Continuous Control | We present temporally layered architecture (TLA), a biologically inspired system for temporally adaptive distributed control. TLA layers a fast and a slow controller together to achieve temporal abstraction that allows each layer to focus on a different time-scale. Our design is biologically inspired and draws on the a... | ['Terrence Sejnowski', 'Hava Siegelmann', 'Tauhidur Rahman', 'Francesca Walsh', 'Joshua Russell', 'Devdhar Patel'] | 2022-12-25 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-4.88591231e-02 -8.27382654e-02 4.43381965e-02 3.56344655e-02
1.38695557e-02 -6.58857107e-01 8.16980243e-01 2.44539119e-02
-4.02679443e-01 9.07715321e-01 2.79834390e-01 -1.75269663e-01
-4.07187343e-01 -6.54141366e-01 -6.77105367e-01 -1.14861298e+00
-7.31425166e-01 6.73585594e-01 6.58806443e-01 -3.33039790... | [4.161577224731445, 1.6920766830444336] |
337a2e33-f408-49c5-b5bf-f246b4d37d17 | can-foundation-models-perform-zero-shot-task | 2204.11134 | null | https://arxiv.org/abs/2204.11134v1 | https://arxiv.org/pdf/2204.11134v1.pdf | Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation? | Task specification is at the core of programming autonomous robots. A low-effort modality for task specification is critical for engagement of non-expert end-users and ultimate adoption of personalized robot agents. A widely studied approach to task specification is through goals, using either compact state vectors or ... | ['Aravind Rajeswaran', 'Vikash Kumar', 'Abhinav Gupta', 'Scott Niekum', 'Yuchen Cui'] | 2022-04-23 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 3.75187725e-01 3.82817715e-01 5.87170795e-02 -4.85554963e-01
-3.65950972e-01 -7.76037872e-01 8.03709865e-01 4.77175750e-02
-3.59294266e-01 5.11088967e-01 6.77012801e-02 -1.72898322e-01
-2.23556146e-01 -3.67397815e-01 -5.11335254e-01 -2.91391551e-01
-1.68313924e-02 8.61897945e-01 1.57929197e-01 -4.28355515... | [4.498474597930908, 0.8354924917221069] |
524295b0-0654-4e8b-bf8c-101ab75a115d | the-interpreter-understands-your-meaning-end | 2305.09652 | null | https://arxiv.org/abs/2305.09652v1 | https://arxiv.org/pdf/2305.09652v1.pdf | The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation | End-to-end spoken language understanding (SLU) remains elusive even with current large pretrained language models on text and speech, especially in multilingual cases. Machine translation has been established as a powerful pretraining objective on text as it enables the model to capture high-level semantics of the inpu... | ['Philip N. Garner', 'Mutian He'] | 2023-05-16 | null | null | null | null | ['abstractive-text-summarization', 'spoken-language-understanding', 'intent-classification', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 2.36888409e-01 2.13469312e-01 -1.91509724e-01 -6.46998227e-01
-1.42965662e+00 -5.29157400e-01 6.03852510e-01 -1.16386972e-01
-5.01884580e-01 6.72455072e-01 8.38413060e-01 -6.13151014e-01
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9.18694884e-02 6.61997259e-01 -3.21390666e-02 -4.07539010... | [14.088396072387695, 7.029539585113525] |
4d2666ed-8e43-43f8-b8a6-f660b456c705 | the-effect-of-spectrogram-reconstruction-on | 2010.09969 | null | https://arxiv.org/abs/2010.09969v1 | https://arxiv.org/pdf/2010.09969v1.pdf | The Effect of Spectrogram Reconstruction on Automatic Music Transcription: An Alternative Approach to Improve Transcription Accuracy | Most of the state-of-the-art automatic music transcription (AMT) models break down the main transcription task into sub-tasks such as onset prediction and offset prediction and train them with onset and offset labels. These predictions are then concatenated together and used as the input to train another model with the... | ['Dorien Herremans', 'Emmanouil Benetos', 'Yin-Jyun Luo', 'Kin Wai Cheuk'] | 2020-10-20 | null | null | null | null | ['music-transcription'] | ['music'] | [ 2.79851526e-01 1.72853574e-01 9.56321973e-03 -1.03388734e-01
-9.36208963e-01 -5.94240606e-01 4.31367964e-01 8.82632360e-02
-2.98009187e-01 4.34156120e-01 3.84917885e-01 -9.45236757e-02
1.17776692e-01 -4.96784419e-01 -8.30867767e-01 -8.22746277e-01
1.01368390e-01 2.95750290e-01 1.62250146e-01 -4.02889550... | [15.790068626403809, 5.46782922744751] |
8dc3e803-2024-46d0-9e1c-39a709ab3562 | deep-transfer-learning-for-texture | 2004.01614 | null | https://arxiv.org/abs/2004.01614v1 | https://arxiv.org/pdf/2004.01614v1.pdf | Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology | Microscopic examination of tissues or histopathology is one of the diagnostic procedures for detecting colorectal cancer. The pathologist involved in such an examination usually identifies tissue type based on texture analysis, especially focusing on tumour-stroma ratio. In this work, we automate the task of tissue cla... | ['Srinath Jayachandran', 'Ashlin Ghosh'] | 2020-04-03 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 1.36002406e-01 -8.50081630e-03 -1.84332073e-01 -3.09122711e-01
-8.01430404e-01 -4.42953050e-01 1.45600975e-01 4.60656762e-01
-6.57256722e-01 4.34989184e-01 -3.13383371e-01 -1.03355229e+00
1.20326795e-01 -8.96071196e-01 -4.47624356e-01 -1.11636972e+00
-2.37494543e-01 3.43440861e-01 6.80463389e-03 4.35897969... | [15.141414642333984, -2.9891250133514404] |
ab685a6a-d746-4844-adc7-b13f27f3d043 | q-shed-distributed-optimization-at-the-edge | 2305.10852 | null | https://arxiv.org/abs/2305.10852v1 | https://arxiv.org/pdf/2305.10852v1.pdf | Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization | Edge networks call for communication efficient (low overhead) and robust distributed optimization (DO) algorithms. These are, in fact, desirable qualities for DO frameworks, such as federated edge learning techniques, in the presence of data and system heterogeneity, and in scenarios where internode communication is th... | ['Subhrakanti Dey', 'Luca Schenato', 'Michele Rossi', 'Nicolò Dal Fabbro'] | 2023-05-18 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-2.88805604e-01 9.78830084e-02 -4.70678300e-01 8.81327614e-02
-6.35905266e-01 -3.52559537e-01 3.37683707e-01 1.65938482e-01
-2.77370691e-01 9.67176855e-01 1.00812934e-01 -4.90758121e-01
-7.57170022e-01 -6.39546394e-01 -4.14744794e-01 -9.30261672e-01
-3.04553062e-01 2.91271240e-01 -1.34895325e-01 -3.25615443... | [6.186124324798584, 5.065540790557861] |
d80726a6-a488-41d1-9fa2-28812eccb022 | deep-masking-generative-network-a-unified | 2010.04324 | null | https://arxiv.org/abs/2010.04324v2 | https://arxiv.org/pdf/2010.04324v2.pdf | Deep-Masking Generative Network: A Unified Framework for Background Restoration from Superimposed Images | Restoring the clean background from the superimposed images containing a noisy layer is the common crux of a classical category of tasks on image restoration such as image reflection removal, image deraining and image dehazing. These tasks are typically formulated and tackled individually due to the diverse and complic... | ['Fanglin Chen', 'Guangming Lu', 'David Zhang', 'Zihui Jia', 'Wenjie Pei', 'Xin Feng'] | 2020-10-09 | null | null | null | null | ['reflection-removal'] | ['computer-vision'] | [ 9.80995357e-01 -4.20304596e-01 4.64068919e-01 -6.78151771e-02
-6.00817919e-01 -2.29066595e-01 5.92471659e-01 -5.44510663e-01
-1.16291717e-01 6.80020392e-01 1.57287866e-01 -3.15166265e-01
1.60122424e-01 -9.28188384e-01 -7.46590257e-01 -1.42747509e+00
4.25766677e-01 -3.29713047e-01 2.50201523e-01 -4.80909228... | [11.022128105163574, -2.5310730934143066] |
e211a354-e059-48f5-9e74-b672736a218d | signal-processing-on-large-networks-with | 2303.17065 | null | https://arxiv.org/abs/2303.17065v1 | https://arxiv.org/pdf/2303.17065v1.pdf | Signal processing on large networks with group symmetries | Current methods of graph signal processing rely heavily on the specific structure of the underlying network: the shift operator and the graph Fourier transform are both derived directly from a specific graph. In many cases, the network is subject to error or natural changes over time. This motivated a new perspective o... | ['Nauzer Kalyaniwalla', 'Jeannette Janssen', 'Mahya Ghandehari', 'Kathryn Beck'] | 2023-03-29 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 2.74522692e-01 4.53453153e-01 1.25925526e-01 1.58201963e-01
3.13243240e-01 -6.80543184e-01 7.03958929e-01 5.33914268e-02
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-7.71167934e-01 3.01706284e-01 2.59944469e-01 -3.69862169... | [6.971334934234619, 5.176416873931885] |
23192025-26ef-47a0-9a7c-bf01520e6c96 | text-based-person-search-in-full-images-via | 2109.12965 | null | https://arxiv.org/abs/2109.12965v1 | https://arxiv.org/pdf/2109.12965v1.pdf | Text-based Person Search in Full Images via Semantic-Driven Proposal Generation | Finding target persons in full scene images with a query of text description has important practical applications in intelligent video surveillance.However, different from the real-world scenarios where the bounding boxes are not available, existing text-based person retrieval methods mainly focus on the cross modal ma... | ['Yanning Zhang', 'Kai Niu', 'Qian Zhang', 'Liying Gao', 'Yitao Gao', 'Duo Long', 'Shizhou Zhang'] | 2021-09-27 | null | null | null | null | ['nlp-based-person-retrival', 'person-search'] | ['computer-vision', 'computer-vision'] | [-1.67955175e-01 -5.23611069e-01 -7.39900023e-02 -4.96855438e-01
-8.73613894e-01 -3.53311598e-01 7.49113798e-01 -2.06163734e-01
-9.03905272e-01 4.95199561e-01 4.63054627e-01 1.78688779e-01
-3.35258767e-02 -6.20304942e-01 -4.95374978e-01 -6.89666212e-01
3.61092120e-01 4.17543739e-01 5.69488108e-01 1.87357329... | [14.674172401428223, 0.8195810914039612] |
d0ef18c8-7a38-43b1-8856-cc546f5727d2 | physically-plausible-spectral-reconstruction | 2001.00558 | null | https://arxiv.org/abs/2001.00558v1 | https://arxiv.org/pdf/2001.00558v1.pdf | Physically Plausible Spectral Reconstruction from RGB Images | Recently Convolutional Neural Networks (CNN) have been used to reconstruct hyperspectral information from RGB images. Moreover, this spectral reconstruction problem (SR) can often be solved with good (low) error. However, these methods are not physically plausible: that is when the recovered spectra are reintegrated wi... | ['Yi-Tun Lin', 'Graham D. Finlayson'] | 2020-01-02 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 7.88967073e-01 -1.52357727e-01 1.38066754e-01 -6.76429644e-02
-7.99856663e-01 -8.59808266e-01 2.02405334e-01 -7.52123743e-02
-2.13314354e-01 1.04684281e+00 -5.17616905e-02 -2.14534998e-01
-4.33338374e-01 -9.36839044e-01 -1.06254518e+00 -1.00231516e+00
3.32172304e-01 -5.13730347e-02 -3.20645273e-02 -7.59568512... | [10.22118091583252, -2.252643346786499] |
383bcfca-5585-4fb0-8bd2-43781944c147 | efficient-deep-learning-a-survey-on-making | 2106.08962 | null | https://arxiv.org/abs/2106.08962v2 | https://arxiv.org/pdf/2106.08962v2.pdf | Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better | Deep Learning has revolutionized the fields of computer vision, natural language understanding, speech recognition, information retrieval and more. However, with the progressive improvements in deep learning models, their number of parameters, latency, resources required to train, etc. have all have increased significa... | ['Gaurav Menghani'] | 2021-06-16 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [-1.34780973e-01 -1.09665170e-01 -4.43063676e-01 -6.03859663e-01
-6.05776966e-01 -5.48039734e-01 3.14428449e-01 -6.85515702e-02
-6.62241101e-01 1.86571658e-01 -9.38306972e-02 -7.32820809e-01
-8.38796869e-02 -5.33302367e-01 -4.58527625e-01 -4.74655360e-01
-1.64871946e-01 5.51854551e-01 2.11441070e-01 -2.51086596... | [8.69515609741211, 2.934115409851074] |
ea4c264c-724b-4886-a1ad-33edfcf4ac79 | robust-model-predictive-control-with-1 | 2008.04980 | null | https://arxiv.org/abs/2008.04980v4 | https://arxiv.org/pdf/2008.04980v4.pdf | Robust Output Feedback MPC with Reduced Conservatism under Ellipsoidal Uncertainty | Robust design of autonomous systems under uncertainty is an important yet challenging problem. This work proposes a robust controller that consists of a state estimator and a tube based predictive control law. The class of linear systems under ellipsoidal uncertainty is considered. In contrast to existing approaches ba... | ['Katherine Driggs-Campbell', 'Junyi Geng', 'Tianchen Ji'] | 2020-08-11 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 1.32111341e-01 6.03433669e-01 -6.50200248e-02 2.04928681e-01
-3.26059759e-01 -8.11162293e-01 4.99280065e-01 2.36483693e-01
-2.67037928e-01 1.20581031e+00 -4.28764313e-01 -1.15987405e-01
-6.14096880e-01 -6.68867767e-01 -7.34908164e-01 -8.46134782e-01
-2.64594518e-02 3.83875370e-01 1.68399379e-01 -2.82363325... | [5.269514083862305, 2.3273797035217285] |
60af21a1-1008-4dc7-9b67-c2da607fec2d | semantic-single-image-dehazing | 1804.05624 | null | http://arxiv.org/abs/1804.05624v2 | http://arxiv.org/pdf/1804.05624v2.pdf | Semantic Single-Image Dehazing | Single-image haze-removal is challenging due to limited information contained
in one single image. Previous solutions largely rely on handcrafted priors to
compensate for this deficiency. Recent convolutional neural network (CNN)
models have been used to learn haze-related priors but they ultimately work as
advanced im... | ['ShaoDi You', 'Ziang Cheng', 'Viorela Ila', 'Hongdong Li'] | 2018-04-16 | null | null | null | null | ['single-image-haze-removal'] | ['computer-vision'] | [ 5.33032417e-01 -1.30779013e-01 4.97606695e-01 -3.68058175e-01
-6.36033535e-01 -3.46633613e-01 4.41038579e-01 -3.41602087e-01
-2.29753837e-01 7.54700065e-01 2.14288056e-01 -1.63268798e-03
1.33332446e-01 -7.25425363e-01 -8.01331282e-01 -1.14143705e+00
3.65545690e-01 -2.09433720e-01 3.41005147e-01 -3.99373651... | [10.906042098999023, -3.172971248626709] |
b1998cc9-3e91-4203-95c7-c8a11c339db0 | weisfeiler-leman-in-the-bamboo-novel-amr | 2108.11949 | null | https://arxiv.org/abs/2108.11949v1 | https://arxiv.org/pdf/2108.11949v1.pdf | Weisfeiler-Leman in the BAMBOO: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity | Several metrics have been proposed for assessing the similarity of (abstract) meaning representations (AMRs), but little is known about how they relate to human similarity ratings. Moreover, the current metrics have complementary strengths and weaknesses: some emphasize speed, while others make the alignment of graph s... | ['Anette Frank', 'Angel Daza', 'Juri Opitz'] | 2021-08-26 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 6.96309328e-01 5.31899095e-01 -2.36538917e-01 -6.57712579e-01
-4.21203703e-01 -7.56535113e-01 7.35374749e-01 9.56060052e-01
-3.33397925e-01 3.24166089e-01 5.96462309e-01 -3.68285090e-01
-4.18604732e-01 -6.50765955e-01 -9.49881151e-02 -3.62001657e-02
-5.35217822e-02 3.32867950e-01 1.36665091e-01 -6.71078682... | [11.260128021240234, 9.2017240524292] |
26c407e4-2ce9-4a39-9d75-44592597fd7a | improving-open-domain-dialogue-evaluation | 2301.13372 | null | https://arxiv.org/abs/2301.13372v1 | https://arxiv.org/pdf/2301.13372v1.pdf | Improving Open-Domain Dialogue Evaluation with a Causal Inference Model | Effective evaluation methods remain a significant challenge for research on open-domain conversational dialogue systems. Explicit satisfaction ratings can be elicited from users, but users often do not provide ratings when asked, and those they give can be highly subjective. Post-hoc ratings by experts are an alternati... | ['Reza Ghanadan', 'Marilyn Walker', 'Yang Liu', 'Michael Johnston', 'Luke Dai', 'Cat P. Le'] | 2023-01-31 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 3.65999201e-03 6.10820293e-01 -3.42987835e-01 -9.48995352e-01
-8.73229444e-01 -4.64391828e-01 8.00215900e-01 1.97644442e-01
-2.19611496e-01 1.16022742e+00 8.19118321e-01 -3.48749936e-01
1.83661997e-01 -7.58465111e-01 -2.38216430e-01 -3.01925331e-01
-4.19545844e-02 5.42756915e-01 -1.51634514e-01 -6.71864688... | [12.84613037109375, 7.983914852142334] |
a7577f02-efea-4e3e-892b-173b24f4f569 | the-one-where-they-reconstructed-3d-humans | 2207.14279 | null | https://arxiv.org/abs/2207.14279v1 | https://arxiv.org/pdf/2207.14279v1.pdf | The One Where They Reconstructed 3D Humans and Environments in TV Shows | TV shows depict a wide variety of human behaviors and have been studied extensively for their potential to be a rich source of data for many applications. However, the majority of the existing work focuses on 2D recognition tasks. In this paper, we make the observation that there is a certain persistence in TV shows, i... | ['Angjoo Kanazawa', 'Matthew Tancik', 'Ethan Weber', 'Georgios Pavlakos'] | 2022-07-28 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [ 3.77285093e-01 -1.63413361e-01 1.13995269e-01 -4.33696359e-01
7.03814030e-02 -6.12231910e-01 7.75077999e-01 -3.08462214e-02
3.47171314e-02 2.10404381e-01 3.29844087e-01 2.22871363e-01
1.80191547e-01 -4.17742997e-01 -7.91212440e-01 -3.95128280e-01
-1.21785803e-02 4.83742058e-01 3.84855002e-01 -4.83754694... | [7.298220634460449, -0.8949249982833862] |
e33e026d-5ff8-4e9d-b8de-baa18424c51b | transition-based-dependency-parsing-with-1 | null | null | https://aclanthology.org/P13-1104 | https://aclanthology.org/P13-1104.pdf | Transition-based Dependency Parsing with Selectional Branching | null | ['Andrew McCallum', 'Jinho D. Choi'] | 2013-08-01 | null | null | null | acl-2013-8 | ['transition-based-dependency-parsing'] | ['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... | [-7.270509719848633, 3.7760043144226074] |
c874aae2-638d-406c-ab37-8bd55c464ecd | sdst-successive-decoding-for-speech-to-text | 2009.09737 | null | https://arxiv.org/abs/2009.09737v4 | https://arxiv.org/pdf/2009.09737v4.pdf | Consecutive Decoding for Speech-to-text Translation | Speech-to-text translation (ST), which directly translates the source language speech to the target language text, has attracted intensive attention recently. However, the combination of speech recognition and machine translation in a single model poses a heavy burden on the direct cross-modal cross-lingual mapping. To... | ['Lei LI', 'Bo Xu', 'Hao Zhou', 'Qianqian Dong', 'Shuang Xu', 'Mingxuan Wang'] | 2020-09-21 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 3.17933768e-01 -5.89786321e-02 -2.74581730e-01 -3.44190270e-01
-1.78587496e+00 -6.55297816e-01 7.03217447e-01 -5.64281166e-01
-1.89100400e-01 7.59582579e-01 4.16422725e-01 -7.24702895e-01
5.44399202e-01 -1.89085573e-01 -7.15837181e-01 -5.29349267e-01
8.10479224e-01 6.16055608e-01 6.98516518e-02 -2.58855641... | [14.491615295410156, 7.225666046142578] |
55e27da9-aae3-4fb5-9ef3-fe7576aec840 | mofi-learning-image-representations-from | 2306.07952 | null | https://arxiv.org/abs/2306.07952v2 | https://arxiv.org/pdf/2306.07952v2.pdf | MOFI: Learning Image Representations from Noisy Entity Annotated Images | We present MOFI, a new vision foundation model designed to learn image representations from noisy entity annotated images. MOFI differs from previous work in two key aspects: ($i$) pre-training data, and ($ii$) training recipe. Regarding data, we introduce a new approach to automatically assign entity labels to images ... | ['Yinfei Yang', 'Zhe Gan', 'Xianzhi Du', 'Jon Shlens', 'Yantao Zheng', 'Shuangning Liu', 'Kun Duan', 'BoWen Zhang', 'Chen Chen', 'Aleksei Timofeev', 'Wentao Wu'] | 2023-06-13 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [ 2.09702730e-01 -2.57734694e-02 -4.66082804e-02 -5.08994818e-01
-1.28375065e+00 -6.56166375e-01 5.09906292e-01 -3.31645533e-02
-7.73198843e-01 4.91475850e-01 2.39936978e-01 6.97353557e-02
1.00567833e-01 -6.58883631e-01 -9.91096318e-01 -5.29653132e-01
1.29786566e-01 5.92854261e-01 2.63777971e-01 2.84768827... | [10.111721992492676, 1.6540344953536987] |
e0a12eab-2859-43f7-9c96-50a56bedfb10 | learning-robust-deep-equilibrium-models | 2304.12707 | null | https://arxiv.org/abs/2304.12707v2 | https://arxiv.org/pdf/2304.12707v2.pdf | Learning Robust Deep Equilibrium Models | Deep equilibrium (DEQ) models have emerged as a promising class of implicit layer models in deep learning, which abandon traditional depth by solving for the fixed points of a single nonlinear layer. Despite their success, the stability of the fixed points for these models remains poorly understood. Recently, Lyapunov ... | ['Yao Zhao', 'Ting Liu', 'Shikui Wei', 'Haoyu Chu'] | 2023-04-25 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [-5.94862819e-01 2.33222663e-01 2.36062482e-02 2.78472900e-01
-3.43402714e-01 -1.10137415e+00 4.08019900e-01 -3.31992567e-01
-2.31271192e-01 6.87266350e-01 -2.07448050e-01 -5.42036951e-01
-2.94275314e-01 -4.94287163e-01 -9.44617569e-01 -9.96431589e-01
-3.19767267e-01 -2.94597685e-01 2.54084855e-01 -7.57259905... | [5.642575740814209, 7.856022834777832] |
7b54ffe8-3d36-4524-aeff-938de66f0b3b | netflix-and-forget-efficient-and-exact | 2302.06676 | null | https://arxiv.org/abs/2302.06676v1 | https://arxiv.org/pdf/2302.06676v1.pdf | Netflix and Forget: Efficient and Exact Machine Unlearning from Bi-linear Recommendations | People break up, miscarry, and lose loved ones. Their online streaming and shopping recommendations, however, do not necessarily update, and may serve as unhappy reminders of their loss. When users want to renege on their past actions, they expect the recommender platforms to erase selective data at the model level. Id... | ['Chong Wang', 'Kevin Yao', 'Xin Yang', 'Jiankai Sun', 'Mimee Xu'] | 2023-02-13 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 3.16037238e-01 1.35007754e-01 -3.11120391e-01 -4.84421104e-01
-3.69625211e-01 -4.91596252e-01 1.36724621e-01 -3.55760939e-02
-4.83306378e-01 7.31152117e-01 3.06197494e-01 -3.49539220e-01
-4.30881619e-01 -4.39676553e-01 -9.01258171e-01 -6.62993133e-01
-1.62523240e-01 6.19545579e-01 -2.64692128e-01 -3.85766655... | [10.009511947631836, 5.603546142578125] |
caf82247-68b3-4e69-a1a8-1d77df8ac2e9 | constructing-datasets-for-multi-hop-reading | 1710.06481 | null | http://arxiv.org/abs/1710.06481v2 | http://arxiv.org/pdf/1710.06481v2.pdf | Constructing Datasets for Multi-hop Reading Comprehension Across Documents | Most Reading Comprehension methods limit themselves to queries which can be
answered using a single sentence, paragraph, or document. Enabling models to
combine disjoint pieces of textual evidence would extend the scope of machine
comprehension methods, but currently there exist no resources to train and test
this capa... | ['Sebastian Riedel', 'Johannes Welbl', 'Pontus Stenetorp'] | 2017-10-17 | constructing-datasets-for-multi-hop-reading-1 | https://aclanthology.org/Q18-1021 | https://aclanthology.org/Q18-1021.pdf | tacl-2018-1 | ['multi-hop-reading-comprehension'] | ['natural-language-processing'] | [ 5.78525424e-01 4.14437234e-01 -2.63819277e-01 -6.18832290e-01
-1.64701080e+00 -8.52142394e-01 8.65672767e-01 7.81316698e-01
-6.27750933e-01 8.32192302e-01 5.55805266e-01 -6.65658534e-01
-2.79195398e-01 -6.14113510e-01 -7.23746955e-01 -5.52633293e-02
2.52467364e-01 7.88232207e-01 6.33518040e-01 -3.92270476... | [11.234124183654785, 8.066829681396484] |
d40df6d9-d7e3-4561-a5fb-a1bef49fff21 | sequence-to-sequence-models-can-directly | 1703.08581 | null | http://arxiv.org/abs/1703.08581v2 | http://arxiv.org/pdf/1703.08581v2.pdf | Sequence-to-Sequence Models Can Directly Translate Foreign Speech | We present a recurrent encoder-decoder deep neural network architecture that
directly translates speech in one language into text in another. The model does
not explicitly transcribe the speech into text in the source language, nor does
it require supervision from the ground truth source language transcription
during t... | ['Ron J. Weiss', 'Navdeep Jaitly', 'Jan Chorowski', 'Yonghui Wu', 'Zhifeng Chen'] | 2017-03-24 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 6.34741068e-01 3.67334217e-01 -1.19657062e-01 -5.90283155e-01
-1.72332275e+00 -6.84013426e-01 7.01829433e-01 -4.58487868e-01
-4.47827697e-01 7.53281653e-01 4.13710684e-01 -9.65068102e-01
7.33844995e-01 -2.41312712e-01 -1.10905147e+00 -5.71163714e-01
4.15431082e-01 9.05516267e-01 -1.75512731e-01 -3.01015228... | [14.495596885681152, 7.187502861022949] |
eceb11af-94f0-488a-aebf-652376f096a0 | beyond-ood-state-actions-supported-cross | 2306.12755 | null | https://arxiv.org/abs/2306.12755v1 | https://arxiv.org/pdf/2306.12755v1.pdf | Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement Learning | Offline reinforcement learning (RL) aims to learn a policy using only pre-collected and fixed data. Although avoiding the time-consuming online interactions in RL, it poses challenges for out-of-distribution (OOD) state actions and often suffers from data inefficiency for training. Despite many efforts being devoted to... | ['Donglin Wang', 'Sibo Gai', 'Yachen Kang', 'Zifeng Zhuang', 'Zhenyu Wei', 'Ziqi Zhang', 'Jinxin Liu'] | 2023-06-22 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.57147640e-01 6.65754303e-02 -6.61531508e-01 -5.25493063e-02
-8.49744618e-01 -7.32129991e-01 6.45351350e-01 -4.44477610e-02
-6.46663904e-01 1.00966358e+00 1.83841959e-01 -7.23382056e-01
-1.53994024e-01 -5.25262177e-01 -8.22685897e-01 -5.19085348e-01
-4.49552506e-01 7.39768863e-01 -6.08621305e-03 -3.40409249... | [4.069471836090088, 2.157606601715088] |
12a1561b-595a-4433-95f5-0b85a59e712e | overcoming-barriers-to-data-sharing-with | 2012.03769 | null | https://arxiv.org/abs/2012.03769v3 | https://arxiv.org/pdf/2012.03769v3.pdf | Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluation | Privacy concerns around sharing personally identifiable information are a major practical barrier to data sharing in medical research. However, in many cases, researchers have no interest in a particular individual's information but rather aim to derive insights at the level of cohorts. Here, we utilize Generative Adve... | ['Patrick Schwab', 'Stefan Bauer', 'Benedikt Dietz', 'Tobias Hepp', 'Sergios Gatidis', 'Jürgen Hetzel', 'August DuMont Schütte'] | 2020-11-29 | null | null | null | null | ['medical-image-generation'] | ['medical'] | [ 6.34225249e-01 6.74325883e-01 -7.33887553e-02 -4.96126711e-01
-1.33207786e+00 -6.27221167e-01 3.79448533e-01 2.16896623e-01
-6.71637893e-01 1.01515317e+00 3.85083139e-01 -3.24795723e-01
1.57184586e-01 -7.75761783e-01 -6.99516118e-01 -7.90032804e-01
1.68071225e-01 4.35997814e-01 -3.30844730e-01 2.81595826... | [14.15540885925293, -1.8616517782211304] |
15e96c93-1ba3-46b6-8d93-18355611ea73 | sub-optimal-multi-phase-path-planning-a | 1601.05744 | null | http://arxiv.org/abs/1601.05744v1 | http://arxiv.org/pdf/1601.05744v1.pdf | Sub-Optimal Multi-Phase Path Planning: A Method for Solving Rubik's Revenge | Rubik's Revenge, a 4x4x4 variant of the Rubik's puzzles, remains to date as
an unsolved puzzle. That is to say, we do not have a method or successful
categorization to optimally solve every one of its approximately $7.401 \times
10^{45}$ possible configurations. Rubik's Cube, Rubik's Revenge's predecessor
(3x3x3), with... | ['Jared Weed'] | 2016-01-20 | null | null | null | null | ['rubik-s-cube'] | ['graphs'] | [ 4.12999094e-02 9.93548036e-02 1.07503332e-01 3.42576474e-01
-7.21579909e-01 -1.17270029e+00 -2.28117526e-01 -3.79851967e-01
-2.09502310e-01 1.27097273e+00 -4.12448168e-01 -8.41724217e-01
-1.19285154e+00 -7.76222050e-01 -5.38209915e-01 -9.70954716e-01
-4.73033935e-01 8.71173561e-01 7.16107339e-02 -4.99394357... | [4.840744495391846, 1.8322033882141113] |
0baf844b-65e3-4556-bcec-fcfa1a67b34d | mcmlsd-a-probabilistic-algorithm-and | 2001.01788 | null | https://arxiv.org/abs/2001.01788v1 | https://arxiv.org/pdf/2001.01788v1.pdf | MCMLSD: A Probabilistic Algorithm and Evaluation Framework for Line Segment Detection | Traditional approaches to line segment detection typically involve perceptual grouping in the image domain and/or global accumulation in the Hough domain. Here we propose a probabilistic algorithm that merges the advantages of both approaches. In a first stage lines are detected using a global probabilistic Hough appro... | ['Emilio J. Almazàn', 'Yiming Qian', 'Ron Tal', 'James H. Elder'] | 2020-01-06 | null | null | null | null | ['line-segment-detection'] | ['computer-vision'] | [ 1.24175310e-01 2.26891667e-01 -1.85946032e-01 -2.68491089e-01
-9.52269554e-01 -5.66267252e-01 5.58698714e-01 6.20116115e-01
-5.98369062e-01 4.30291653e-01 -3.82512003e-01 -1.43110454e-01
-4.58078738e-03 -7.29628325e-01 -8.06749463e-01 -6.94902837e-01
-8.47201273e-02 9.25099492e-01 1.08204246e+00 3.79750669... | [8.30703353881836, -1.40459144115448] |
b9ce67b1-da3b-4f5c-b269-bca934532467 | can-cognate-prediction-be-modelled-as-a-low | null | null | https://aclanthology.org/2021.findings-acl.75 | https://aclanthology.org/2021.findings-acl.75.pdf | Can Cognate Prediction Be Modelled as a Low-Resource Machine Translation Task? | null | ['Benoît Sagot', 'Rachel Bawden', 'Clémentine Fourrier'] | null | null | null | null | findings-acl-2021-8 | ['cognate-prediction'] | ['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... | [-7.368314266204834, 3.712765693664551] |
0fe4ae8d-2308-4596-b412-6eb21d7e3a1d | cardiac-mri-segmentation-with-strong | 1907.02865 | null | https://arxiv.org/abs/1907.02865v2 | https://arxiv.org/pdf/1907.02865v2.pdf | Cardiac MRI Segmentation with Strong Anatomical Guarantees | Recent publications have shown that the segmentation accuracy of modern-day convolutional neural networks (CNN) applied on cardiac MRI can reach the inter-expert variability, a great achievement in this area of research. However, despite these successes, CNNs still produce anatomically inaccurate segmentations as they ... | ['Pierre-Marc Jodoin', 'Thierry Judge', 'Olivier Bernard', 'Youssef Skandarani', 'Nathan Painchaud', 'Alain Lalande'] | 2019-07-05 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 8.75092596e-02 6.51411176e-01 2.59298444e-01 -3.64884734e-01
-7.67727315e-01 -8.05787146e-01 4.12664533e-01 -1.50200352e-01
-3.32734376e-01 6.39070094e-01 -5.68825006e-02 -4.92085665e-01
-4.41671424e-02 -7.34347999e-01 -7.10291624e-01 -7.85320342e-01
1.15383536e-01 6.36986375e-01 1.99587375e-01 -9.66651961... | [14.184260368347168, -2.2490007877349854] |
12009b12-a12a-4254-8950-2e73caaa6631 | optimizing-bi-encoder-for-named-entity | 2208.14565 | null | https://arxiv.org/abs/2208.14565v2 | https://arxiv.org/pdf/2208.14565v2.pdf | Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning | We present a bi-encoder framework for named entity recognition (NER), which applies contrastive learning to map candidate text spans and entity types into the same vector representation space. Prior work predominantly approaches NER as sequence labeling or span classification. We instead frame NER as a representation l... | ['Hoifung Poon', 'Jianfeng Gao', 'Hao Cheng', 'Sheng Zhang'] | 2022-08-30 | null | null | null | null | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [ 1.42766073e-01 1.79067820e-01 -2.67123699e-01 -3.77473295e-01
-1.08423400e+00 -9.00944352e-01 1.15329236e-01 6.55650377e-01
-8.80969584e-01 9.49961245e-01 5.09098113e-01 -2.72746205e-01
6.17264658e-02 -6.86230004e-01 -7.20388353e-01 -5.05405188e-01
8.79477337e-02 4.25169110e-01 -2.22588897e-01 5.95292496... | [8.811552047729492, 8.895841598510742] |
c6c44e5e-ec19-4686-b228-2909fac1520c | 190409406 | 1904.09406 | null | https://arxiv.org/abs/1904.09406v3 | https://arxiv.org/pdf/1904.09406v3.pdf | DeepMoD: Deep learning for Model Discovery in noisy data | We introduce DeepMoD, a Deep learning based Model Discovery algorithm. DeepMoD discovers the partial differential equation underlying a spatio-temporal data set using sparse regression on a library of possible functions and their derivatives. A neural network approximates the data and constructs the function library, b... | ['Pierre Sens', 'Gert-Jan Both', 'Subham Choudhury', 'Remy Kusters'] | 2019-04-20 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [-2.05935374e-01 -2.70493656e-01 2.23665565e-01 1.03888707e-03
-4.61093873e-01 -3.17645222e-01 4.10814404e-01 2.74314731e-01
-5.69064260e-01 1.00303805e+00 -3.98714781e-01 -3.06383789e-01
-4.14780766e-01 -6.91969633e-01 -7.08490968e-01 -1.02618730e+00
-7.71135211e-01 6.82174087e-01 1.34583026e-01 -2.19970316... | [6.605240345001221, 3.53838849067688] |
9d400093-ed25-48e6-a3c4-56bad21f21df | multi-modal-facial-action-unit-detection-with | 2303.10590 | null | https://arxiv.org/abs/2303.10590v3 | https://arxiv.org/pdf/2303.10590v3.pdf | Multi-modal Facial Action Unit Detection with Large Pre-trained Models for the 5th Competition on Affective Behavior Analysis in-the-wild | Facial action unit detection has emerged as an important task within facial expression analysis, aimed at detecting specific pre-defined, objective facial expressions, such as lip tightening and cheek raising. This paper presents our submission to the Affective Behavior Analysis in-the-wild (ABAW) 2023 Competition for ... | ['Mohammad Soleymani', 'Xinrui Wang', 'Di Chang', 'Minh Tran', 'Yufeng Yin'] | 2023-03-19 | null | null | null | null | ['face-alignment', 'action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.95204073e-01 6.20080009e-02 -1.98549509e-01 -6.82189822e-01
-1.23357046e+00 -4.60339099e-01 4.87316281e-01 -2.24964395e-01
-5.27236700e-01 2.28592277e-01 6.27240062e-01 8.08692932e-01
4.98990178e-01 4.52565849e-02 -2.93993056e-01 -7.15344727e-01
-2.89532691e-01 -4.96077165e-02 -2.16685936e-01 -3.35251898... | [13.55714225769043, 1.9141325950622559] |
33381ce4-1fd7-46a2-931f-983ec7bdeb33 | reconstruct-from-top-view-a-3d-lane-detection-1 | null | null | https://openaccess.thecvf.com/content/CVPR2022W/WAD/papers/Li_Reconstruct_From_Top_View_A_3D_Lane_Detection_Approach_Based_CVPRW_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022W/WAD/papers/Li_Reconstruct_From_Top_View_A_3D_Lane_Detection_Approach_Based_CVPRW_2022_paper.pdf | Reconstruct from Top View: A 3D Lane Detection Approach based on Geometry Structure Prior | In this paper, we propose an advanced approach in targeting the problem of monocular 3D lane detection by leveraging geometry structure underneath the process of 2D to 3D lane reconstruction. Inspired by previous methods, we first analyze the geometry heuristic between the 3D lane and its 2D representation on the groun... | ['Guangliang Cheng', 'Ya Wang', 'Jia Shi', 'Chenguang Li'] | 2022-06-20 | null | null | null | cvpr-2022-6 | ['3d-lane-detection', 'lane-detection'] | ['computer-vision', 'computer-vision'] | [ 9.21326503e-02 1.70944512e-01 -2.79227830e-03 -4.01555628e-01
-5.48587143e-01 -7.50233769e-01 4.12652522e-01 -2.13164508e-01
-3.63577873e-01 1.80942848e-01 -2.51968652e-01 -6.49295986e-01
4.14382458e-01 -6.69835031e-01 -8.83430362e-01 -4.30583060e-01
2.70385534e-01 3.76344353e-01 9.45818782e-01 -3.00092906... | [7.990467548370361, -1.6969341039657593] |
86b6ee18-6bb8-4c32-9129-66eef3daf6e5 | simpleview-neighborhood-views-for-point-cloud | null | null | https://ieeexplore.ieee.org/document/9874679 | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9874679 | SimpleView++: Neighborhood Views for Point Cloud Classification | Existing multi-view-based point cloud classification methods only utilize multiple views of point clouds and discard the point clouds from further processing. Among these methods, the Simple View model demonstrates that features from six orthogonal perspective projections of a point cloud achieved comparable 3D classif... | ['Chandra Kambhamettu', 'Shivanand Venkanna Sheshappanavar'] | 2022-09-08 | null | null | null | ieee-5th-international-conference-on | ['3d-point-cloud-classification', '3d-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.89441964e-01 -3.99482727e-01 -2.33395964e-01 -5.51771164e-01
-5.66763341e-01 -8.86189401e-01 8.16735268e-01 -6.68211747e-03
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-2.17974380e-01 -9.65224624e-01 -6.51764572e-01 -5.30375838e-01
1.60108685e-01 7.71262348e-01 6.56574547e-01 -1.65659830... | [8.149969100952148, -3.345304250717163] |
f570d4a3-d575-4382-b6c6-4440a694d246 | continuous-diagnosis-and-prognosis-by | 2210.02719 | null | https://arxiv.org/abs/2210.02719v1 | https://arxiv.org/pdf/2210.02719v1.pdf | Continuous Diagnosis and Prognosis by Controlling the Update Process of Deep Neural Networks | Continuous diagnosis and prognosis are essential for intensive care patients. It can provide more opportunities for timely treatment and rational resource allocation, especially for sepsis, a main cause of death in ICU, and COVID-19, a new worldwide epidemic. Although deep learning methods have shown their great superi... | ['Shenda Hong', 'Baofeng Zhang', 'Derun Cai', 'Moxian Song', 'Hongyan Li', 'Chenxi Sun'] | 2022-10-06 | null | null | null | null | ['mortality-prediction'] | ['medical'] | [-2.12306097e-01 -5.28965533e-01 -1.81380421e-01 -1.56065673e-01
3.83008318e-03 7.34338686e-02 1.61323890e-01 3.21283728e-01
-3.67598355e-01 8.69538724e-01 7.43441433e-02 -6.32622659e-01
-5.02087891e-01 -6.19937181e-01 -3.68829444e-02 -8.03179801e-01
-5.29387712e-01 9.13770914e-01 -1.55490220e-01 -8.81035626... | [7.957047462463379, 6.179295063018799] |
fa8029d3-cdbd-4b91-a13d-b835047dea5d | a-mixed-quantization-network-for | 2203.06504 | null | https://arxiv.org/abs/2203.06504v1 | https://arxiv.org/pdf/2203.06504v1.pdf | A Mixed Quantization Network for Computationally Efficient Mobile Inverse Tone Mapping | Recovering a high dynamic range (HDR) image from a single low dynamic range (LDR) image, namely inverse tone mapping (ITM), is challenging due to the lack of information in over- and under-exposed regions. Current methods focus exclusively on training high-performing but computationally inefficient ITM models, which in... | ['Paul Wisbey', 'Frederik Laboyrie', 'Mete Ozay', 'Juan Borrego-Carazo'] | 2022-03-12 | null | null | null | null | ['tone-mapping', 'inverse-tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 5.84921360e-01 -1.94505110e-01 -2.44577661e-01 -2.43855521e-01
-1.02650523e+00 -2.01977894e-01 4.94277179e-01 -5.78618884e-01
-4.84213412e-01 2.96810955e-01 2.70642012e-01 -3.53210866e-01
-1.82386823e-02 -8.18610072e-01 -1.01367104e+00 -2.80309469e-01
1.07713595e-01 1.49357557e-01 2.24373385e-01 -2.50110537... | [10.9269380569458, -2.117189407348633] |
781c2496-09b2-489a-a143-3e263a2139dc | ask-me-anything-in-your-native-language | null | null | https://openreview.net/forum?id=OJYPlFa4rmX | https://openreview.net/pdf?id=OJYPlFa4rmX | Ask Me Anything in Your Native Language | Cross-lingual question answering is a thriving field in the modern world, helping people to search information on the web more efficiently. One of the important scenarios is to give an answer even there is no answer in the language a person asks a question with. We present a novel approach to achieving a new state of t... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['cross-lingual-question-answering'] | ['natural-language-processing'] | [-3.46601725e-01 -2.65626818e-01 -1.31600529e-01 -2.80187786e-01
-1.68950522e+00 -8.84539068e-01 5.33334732e-01 4.01668817e-01
-8.41203749e-01 7.78220594e-01 3.36015999e-01 -3.47471118e-01
-2.97508389e-01 -7.94770896e-01 -3.19408745e-01 1.84901163e-01
2.52783298e-01 1.07316959e+00 8.25289011e-01 -1.00087893... | [11.427189826965332, 7.931922435760498] |
b1cc2b3e-a9e8-4e03-8f6c-5b681c5b26c5 | primary-object-segmentation-in-videos-via | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Jang_Primary_Object_Segmentation_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Jang_Primary_Object_Segmentation_CVPR_2016_paper.pdf | Primary Object Segmentation in Videos via Alternate Convex Optimization of Foreground and Background Distributions | An unsupervised video object segmentation algorithm, which discovers a primary object in a video sequence automatically, is proposed in this work. We introduce three energies in terms of foreground and background probability distributions: Markov, spatiotemporal, and antagonistic energies. Then, we minimize a hybrid of... | ['Chang-Su Kim', 'Won-Dong Jang', 'Chulwoo Lee'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 2.35932037e-01 -2.21657947e-01 -1.86113641e-01 -8.60076919e-02
-4.88973141e-01 -4.13509041e-01 1.99840993e-01 -1.96268618e-01
-4.67625052e-01 6.03800476e-01 -2.60025561e-01 -1.25323147e-01
-6.40647486e-02 -3.75371009e-01 -6.56255186e-01 -1.23188472e+00
1.45052418e-01 3.59491892e-02 6.09890521e-01 3.46855551... | [9.129173278808594, -0.4710143804550171] |
99ebfbd5-95cc-4a05-ae49-ecd9ea6ea2ac | holistic-3d-human-and-scene-mesh-estimation | 2012.01591 | null | https://arxiv.org/abs/2012.01591v2 | https://arxiv.org/pdf/2012.01591v2.pdf | Holistic 3D Human and Scene Mesh Estimation from Single View Images | The 3D world limits the human body pose and the human body pose conveys information about the surrounding objects. Indeed, from a single image of a person placed in an indoor scene, we as humans are adept at resolving ambiguities of the human pose and room layout through our knowledge of the physical laws and prior per... | ['Serena Yeung', 'Zhenzhen Weng'] | 2020-12-02 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Weng_Holistic_3D_Human_and_Scene_Mesh_Estimation_From_Single_View_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Weng_Holistic_3D_Human_and_Scene_Mesh_Estimation_From_Single_View_CVPR_2021_paper.pdf | cvpr-2021-1 | ['indoor-scene-reconstruction'] | ['computer-vision'] | [ 1.14783615e-01 4.82902527e-01 2.70591617e-01 -3.08673322e-01
-3.50969583e-01 -3.72181356e-01 3.67810220e-01 -6.56403005e-02
-2.41435125e-01 2.25787818e-01 1.99194983e-01 2.21680254e-01
1.53908566e-01 -5.30895174e-01 -1.14221573e+00 -2.46464297e-01
2.90044218e-01 1.06805766e+00 2.47121260e-01 -7.90422261... | [7.036870956420898, -1.1905986070632935] |
0561bcba-6f89-42bd-aadf-1b65a9c413d5 | pushing-the-envelope-from-discrete-to | 2004.13477 | null | https://arxiv.org/abs/2004.13477v1 | https://arxiv.org/pdf/2004.13477v1.pdf | Pushing the Envelope: From Discrete to Continuous Movements in Multi-Agent Path Finding via Lazy Encodings | Multi-agent path finding in continuous space and time with geometric agents MAPF$^\mathcal{R}$ is addressed in this paper. The task is to navigate agents that move smoothly between predefined positions to their individual goals so that they do not collide. We introduce a novel solving approach for obtaining makespan op... | ['Pavel Surynek'] | 2020-04-25 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 3.45173687e-01 3.54662120e-01 1.05753034e-01 6.19560182e-02
-7.68189907e-01 -8.06297362e-01 6.29404634e-02 4.43028897e-01
-5.86673200e-01 1.52379417e+00 -7.08579242e-01 -6.66435838e-01
-1.18566275e+00 -1.30741370e+00 -5.93125641e-01 -6.49382889e-01
-6.48628831e-01 1.24330795e+00 5.59602499e-01 -6.11536980... | [4.979506015777588, 1.847761869430542] |
ff259b90-cd9e-4ab1-b17e-3629dffd10b8 | relative-and-incomplete-time-expression | null | null | https://aclanthology.org/2020.clinicalnlp-1.14 | https://aclanthology.org/2020.clinicalnlp-1.14.pdf | Relative and Incomplete Time Expression Anchoring for Clinical Text | Extracting and modeling temporal information in clinical text is an important element for developing timelines and disease trajectories. Time information in written text varies in preciseness and explicitness, posing challenges for NLP approaches that aim to accurately anchor temporal information on a timeline. Relativ... | ['Sumithra Velupillai', 'Hegler Tissot', 'Nicol Bergou', 'Louise Dupuis'] | null | null | null | null | emnlp-clinicalnlp-2020-11 | ['relation-classification'] | ['natural-language-processing'] | [ 2.05230191e-01 3.38394940e-02 -9.13698018e-01 -6.81936443e-01
-4.74304438e-01 -7.30380476e-01 5.75474918e-01 1.28743255e+00
-4.90863681e-01 9.99620676e-01 5.72922111e-01 -5.43136954e-01
-8.62953126e-01 -5.22378981e-01 -1.36362268e-02 -2.66261727e-01
-6.12330675e-01 6.43344522e-01 -1.07803345e-01 6.26356900... | [8.663333892822266, 9.076717376708984] |
87113742-6785-4549-9668-48fc58d4b1e4 | point2sequence-learning-the-shape | 1811.02565 | null | http://arxiv.org/abs/1811.02565v2 | http://arxiv.org/pdf/1811.02565v2.pdf | Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network | Exploring contextual information in the local region is important for shape
understanding and analysis. Existing studies often employ hand-crafted or
explicit ways to encode contextual information of local regions. However, it is
hard to capture fine-grained contextual information in hand-crafted or explicit
manners, s... | ['Yu-Shen Liu', 'Xinhai Liu', 'Matthias Zwicker', 'Zhizhong Han'] | 2018-11-06 | null | null | null | null | ['3d-part-segmentation', 'shape-representation-of-3d-point-clouds'] | ['computer-vision', 'computer-vision'] | [ 4.16439101e-02 -2.46178955e-01 -9.58000869e-02 -6.02599084e-01
-7.66802311e-01 -4.03441399e-01 4.95042384e-01 2.55906552e-01
-1.98200196e-01 1.78602487e-01 3.30350846e-01 -9.15665403e-02
-6.42594397e-02 -1.02508688e+00 -8.76414955e-01 -8.03183377e-01
1.44732058e-01 4.08487171e-01 3.19333255e-01 -1.27399176... | [8.027195930480957, -3.5167465209960938] |
c551ce3d-c0ab-4c21-8b19-410740a37740 | deep-amortized-variational-inference-for | null | null | https://openreview.net/forum?id=H1xXYy3VKr | https://openreview.net/pdf?id=H1xXYy3VKr | Deep Amortized Variational Inference for Multivariate Time Series Imputation with Latent Gaussian Process Models | Multivariate time series with missing values are common in areas such as healthcare and finance, and have grown in number and complexity over the years. This raises the question whether deep learning methodologies can outperform classical data imputation methods in this domain. However, naive applications of deep learn... | ['Stephan Mandt', 'Gunnar Rätsch', 'Dmitry Baranchuk', 'Vincent Fortuin'] | 2019-10-16 | null | null | null | pproximateinference-aabi-symposium-2019-12 | ['multivariate-time-series-imputation'] | ['time-series'] | [ 9.98777524e-02 2.20598504e-01 -1.04870729e-01 -6.54840648e-01
-1.00463986e+00 -1.36188507e-01 5.61909854e-01 4.81281988e-02
-2.46486604e-01 1.08423197e+00 5.33493698e-01 -1.25647232e-01
-5.39737403e-01 -6.33354127e-01 -8.95341218e-01 -8.95697653e-01
1.07065403e-04 8.73687327e-01 -8.49407732e-01 2.56957293... | [7.192180633544922, 3.592771053314209] |
a3874947-373f-42e6-9c18-18f32ec0e779 | evaluating-open-question-answering-evaluation | 2305.12421 | null | https://arxiv.org/abs/2305.12421v1 | https://arxiv.org/pdf/2305.12421v1.pdf | Evaluating Open Question Answering Evaluation | This study focuses on the evaluation of Open Question Answering (Open-QA) tasks, which have become vital in the realm of artificial intelligence. Current automatic evaluation methods have shown limitations, indicating that human evaluation still remains the most reliable approach. We introduce a new task, QA Evaluation... | ['Yue Zhang', 'Yidong Wang', 'Bowen Ding', 'Zhikun Xu', 'Sirui Cheng', 'Cunxiang Wang'] | 2023-05-21 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 2.11159229e-01 3.89359862e-01 2.93433994e-01 -6.33325577e-01
-1.54586935e+00 -9.64524746e-01 5.54613829e-01 4.51416701e-01
-5.45054555e-01 9.49592233e-01 4.38623220e-01 -4.92144793e-01
-1.39009759e-01 -6.92952454e-01 -2.74121463e-01 -9.23672616e-02
3.07030857e-01 8.93569767e-01 4.08076465e-01 -5.79863966... | [11.416780471801758, 8.171406745910645] |
3524f274-d816-4249-ac5c-7dd867d9ac95 | near-optimal-high-probability-convergence-for | 2302.06032 | null | https://arxiv.org/abs/2302.06032v1 | https://arxiv.org/pdf/2302.06032v1.pdf | Near-Optimal High-Probability Convergence for Non-Convex Stochastic Optimization with Variance Reduction | Traditional analyses for non-convex stochastic optimization problems characterize convergence bounds in expectation, which is inadequate as it does not supply a useful performance guarantee on a single run. Motivated by its importance, an emerging line of literature has recently studied the high-probability convergence... | ['Zhengyuan Zhou', 'Srikanth Jagabathula', 'Perry Dong', 'Zijian Liu'] | 2023-02-13 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 1.18383961e-02 -7.95881078e-02 -9.60207134e-02 -2.02685699e-01
-1.13900554e+00 -5.84376395e-01 -3.22235003e-02 3.09266210e-01
-7.08272398e-01 9.16074574e-01 -1.67830274e-01 -6.68569744e-01
-4.60601419e-01 -4.76191461e-01 -8.79099250e-01 -1.12272012e+00
-1.75291911e-01 4.06122059e-01 2.21862830e-02 -1.73132896... | [6.639564037322998, 4.442726135253906] |
c648e18c-bdd0-421b-a3b4-6fb98f0a2543 | a-multi-class-structured-dictionary-learning | 1812.01389 | null | http://arxiv.org/abs/1812.01389v1 | http://arxiv.org/pdf/1812.01389v1.pdf | A multi-class structured dictionary learning method using discriminant atom selection | In the last decade, traditional dictionary learning methods have been
successfully applied to various pattern classification tasks. Although these
methods produce sparse representations of signals which are robust against
distortions and missing data, such representations quite often turn out to be
unsuitable if the fi... | ['H. L. Rufiner', 'R. E. Rolón', 'L. E. Di Persia', 'R. D. Spies'] | 2018-12-04 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 3.38047296e-01 -3.63167137e-01 -1.31792486e-01 -1.87867448e-01
-5.18155158e-01 -3.08894128e-01 6.25253439e-01 6.63641393e-01
-4.38717365e-01 6.53073072e-01 7.91186932e-03 1.09193787e-01
-3.35421830e-01 -9.51347470e-01 -2.91353106e-01 -1.08572519e+00
1.39391735e-01 4.25742507e-01 8.10275003e-02 -2.51127809... | [12.248486518859863, 0.5902098417282104] |
74cd9409-bd38-40e8-971d-6e2b6965dd85 | depth-induced-multi-scale-recurrent-attention | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Piao_Depth-Induced_Multi-Scale_Recurrent_Attention_Network_for_Saliency_Detection_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Piao_Depth-Induced_Multi-Scale_Recurrent_Attention_Network_for_Saliency_Detection_ICCV_2019_paper.pdf | Depth-Induced Multi-Scale Recurrent Attention Network for Saliency Detection | In this work, we propose a novel depth-induced multi-scale recurrent attention network for saliency detection. It achieves dramatic performance especially in complex scenarios. There are three main contributions of our network that are experimentally demonstrated to have significant practical merits. First, we design a... | [' Huchuan Lu', ' Miao Zhang', ' Jingjing Li', ' Wei Ji', 'Yongri Piao'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 2.75517553e-01 -1.06972501e-01 -1.96478754e-01 -4.64856595e-01
-8.15872192e-01 5.21457605e-02 2.72261322e-01 -8.72867629e-02
-1.50621876e-01 3.82259458e-01 6.82535827e-01 1.10700183e-01
5.60446642e-02 -6.88342035e-01 -6.34179592e-01 -5.96114576e-01
1.28475279e-01 -2.93723762e-01 8.21378589e-01 -4.64151233... | [9.73343563079834, -0.6393444538116455] |
4384fcb4-2039-4353-b2b0-3b0988751e57 | ecg-qa-a-comprehensive-question-answering | 2306.15681 | null | https://arxiv.org/abs/2306.15681v1 | https://arxiv.org/pdf/2306.15681v1.pdf | ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram | Question answering (QA) in the field of healthcare has received much attention due to significant advancements in natural language processing. However, existing healthcare QA datasets primarily focus on medical images, clinical notes, or structured electronic health record tables. This leaves the vast potential of comb... | ['Edward Choi', 'Joon-Myoung Kwon', 'Gyubok Lee', 'Seongsu Bae', 'JungWoo Oh'] | 2023-06-21 | null | null | null | null | ['question-answering'] | ['natural-language-processing'] | [ 3.58090311e-01 1.84167847e-01 1.75763398e-01 -7.67997921e-01
-1.24256933e+00 -5.78486085e-01 -1.64479062e-01 7.91165650e-01
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-3.61516893e-01 -7.76197970e-01 -6.07417338e-02 -2.38903478e-01
-9.68036279e-02 6.01245522e-01 -1.12640433e-01 7.34018674... | [8.721908569335938, 8.533791542053223] |
b7f1beb0-25b3-49a1-9475-86ac788df0e5 | a-correct-and-certify-approach-to-self | 2302.06019 | null | https://arxiv.org/abs/2302.06019v2 | https://arxiv.org/pdf/2302.06019v2.pdf | A Correct-and-Certify Approach to Self-Supervise Object Pose Estimators via Ensemble Self-Training | Real-world robotics applications demand object pose estimation methods that work reliably across a variety of scenarios. Modern learning-based approaches require large labeled datasets and tend to perform poorly outside the training domain. Our first contribution is to develop a robust corrector module that corrects po... | ['Luca Carlone', 'Dominic Maggio', 'Rajat Talak', 'Jingnan Shi'] | 2023-02-12 | null | null | null | null | ['keypoint-detection'] | ['computer-vision'] | [ 1.12993713e-03 1.20263971e-01 -1.31482139e-01 -4.07930762e-01
-1.14090705e+00 -7.12551355e-01 5.25403857e-01 1.77525952e-01
-3.93416524e-01 3.91668886e-01 -2.62815356e-01 2.19751433e-01
-2.67237961e-01 -5.85432410e-01 -1.27635407e+00 -6.88585281e-01
-6.63553476e-02 7.29051590e-01 7.95426726e-01 -1.39705855... | [7.51814079284668, -2.624617338180542] |
bce8cb0c-e8af-4e92-aed4-a2fb0d430a29 | uriel-and-lang2vec-representing-languages-as | null | null | https://aclanthology.org/E17-2002 | https://aclanthology.org/E17-2002.pdf | URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors | We introduce the URIEL knowledge base for massively multilingual NLP and the lang2vec utility, which provides information-rich vector identifications of languages drawn from typological, geographical, and phylogenetic databases and normalized to have straightforward and consistent formats, naming, and semantics. The go... | ['David R. Mortensen', 'Patrick Littell', 'Lori Levin', 'Ke Lin', 'Katherine Kairis', 'Carlisle Turner'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['multilingual-nlp'] | ['natural-language-processing'] | [-5.73256731e-01 -3.69404852e-01 -4.58367795e-01 2.46598776e-02
-4.23720747e-01 -1.16104400e+00 7.86905885e-01 4.33192521e-01
-7.52507567e-01 1.05767429e+00 7.86116302e-01 -6.27557933e-01
1.95033342e-01 -5.16065180e-01 -3.79668564e-01 -1.62741229e-01
-5.24189249e-02 8.00581396e-01 -3.48973840e-01 -3.46005887... | [10.862445831298828, 9.86462116241455] |
7df98c64-6402-409b-9fea-e4a57c6d5eeb | towards-end-to-end-semi-supervised-learning | 2302.11299 | null | https://arxiv.org/abs/2302.11299v1 | https://arxiv.org/pdf/2302.11299v1.pdf | Towards End-to-end Semi-supervised Learning for One-stage Object Detection | Semi-supervised object detection (SSOD) is a research hot spot in computer vision, which can greatly reduce the requirement for expensive bounding-box annotations. Despite great success, existing progress mainly focuses on two-stage detection networks like FasterRCNN, while the research on one-stage detectors is often ... | ['Rongrong Ji', 'Xiaoshuai Sun', 'Lei Jin', 'Yiyi Zhou', 'Gen Luo'] | 2023-02-22 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [-1.67295858e-01 -9.38856900e-02 -2.94752359e-01 -3.59794408e-01
-9.03423429e-01 -2.96133518e-01 2.44354367e-01 -3.77779067e-01
-6.00558519e-01 3.12116295e-01 -1.84090644e-01 -6.98396489e-02
3.31763625e-01 -4.13218915e-01 -5.56433022e-01 -8.88001859e-01
5.21306872e-01 2.53483891e-01 7.13184118e-01 -1.02131777... | [9.21894645690918, 1.337202548980713] |
f55d155f-f735-405e-a9e9-0eaca93c3867 | gptips-2-an-open-source-software-platform-for | 1412.4690 | null | http://arxiv.org/abs/1412.4690v2 | http://arxiv.org/pdf/1412.4690v2.pdf | GPTIPS 2: an open-source software platform for symbolic data mining | GPTIPS is a free, open source MATLAB based software platform for symbolic
data mining (SDM). It uses a multigene variant of the biologically inspired
machine learning method of genetic programming (MGGP) as the engine that drives
the automatic model discovery process. Symbolic data mining is the process of
extracting h... | ['Dominic P. Searson'] | 2014-12-15 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 1.59243226e-01 1.05616651e-01 1.57369122e-01 1.01002455e-01
9.79170576e-03 -4.61218596e-01 4.13572103e-01 5.43217719e-01
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-8.29580963e-01 -6.93017960e-01 -4.07106847e-01 -8.74452949e-01
-6.99244797e-01 4.26249653e-01 5.37607931e-02 -4.48747844... | [6.969601631164551, 4.218203067779541] |
564a3f14-b21d-42a1-834a-bd9043d40c8f | earthformer-exploring-space-time-transformers | 2207.05833 | null | https://arxiv.org/abs/2207.05833v2 | https://arxiv.org/pdf/2207.05833v2.pdf | Earthformer: Exploring Space-Time Transformers for Earth System Forecasting | Conventionally, Earth system (e.g., weather and climate) forecasting relies on numerical simulation with complex physical models and are hence both expensive in computation and demanding on domain expertise. With the explosive growth of the spatiotemporal Earth observation data in the past decade, data-driven models th... | ['Dit-yan Yeung', 'Mu Li', 'Yuyang Wang', 'Yi Zhu', 'Hao Wang', 'Xingjian Shi', 'Zhihan Gao'] | 2022-07-12 | null | null | null | null | ['weather-forecasting', 'earth-surface-forecasting'] | ['miscellaneous', 'time-series'] | [-6.58082306e-01 -5.10052741e-01 1.36333510e-01 -3.59402955e-01
-2.01666474e-01 -4.53547210e-01 8.90160084e-01 -1.25927553e-01
-9.00094807e-02 4.96057540e-01 1.79117054e-01 -6.55315399e-01
-9.51733589e-02 -8.18773508e-01 -5.61266720e-01 -9.73895669e-01
-2.94241488e-01 4.49107826e-01 -8.07139874e-02 -5.71338892... | [6.613004207611084, 2.8383259773254395] |
32433187-d1b8-4c4b-9461-4d7d095dd6fc | dropping-convexity-for-more-efficient-and | 1702.08134 | null | http://arxiv.org/abs/1702.08134v9 | http://arxiv.org/pdf/1702.08134v9.pdf | Dropping Convexity for More Efficient and Scalable Online Multiview Learning | Multiview representation learning is very popular for latent factor analysis.
It naturally arises in many data analysis, machine learning, and information
retrieval applications to model dependent structures among multiple data
sources. For computational convenience, existing approaches usually formulate
the multiview ... | ['Chris J. Li', 'Tuo Zhao', 'Lin F. Yang', 'Zhehui Chen'] | 2017-02-27 | null | null | null | null | ['multiview-learning'] | ['computer-vision'] | [-1.32747665e-01 -8.17823783e-02 -7.02908516e-01 -3.11930954e-01
-1.20852077e+00 -5.53437352e-01 3.88316661e-01 6.10589683e-02
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-4.49914485e-01 -4.31047618e-01 -7.04207122e-01 -9.24077272e-01
4.41374071e-02 2.94663697e-01 -5.64382851e-01 1.99240949... | [7.179752826690674, 4.476853847503662] |
541e2810-01c3-47a9-bf63-0d37d3f15eb0 | advances-in-collaborative-filtering-and | 2002.12312 | null | https://arxiv.org/abs/2002.12312v1 | https://arxiv.org/pdf/2002.12312v1.pdf | Advances in Collaborative Filtering and Ranking | In this dissertation, we cover some recent advances in collaborative filtering and ranking. In chapter 1, we give a brief introduction of the history and the current landscape of collaborative filtering and ranking; chapter 2 we first talk about pointwise collaborative filtering problem with graph information, and how ... | ['Liwei Wu'] | 2020-02-27 | null | null | null | null | ['collaborative-ranking'] | ['graphs'] | [-1.91510260e-01 4.07919427e-03 -3.46840546e-02 -4.99324977e-01
-4.31806535e-01 -5.02259195e-01 3.22182745e-01 2.85812169e-01
-2.25651398e-01 3.70979011e-01 9.67404366e-01 -3.12963068e-01
-1.14794672e+00 -1.17999804e+00 -3.69624555e-01 -4.78897870e-01
-6.48873389e-01 5.11087298e-01 8.88474956e-02 -6.85318708... | [10.1134672164917, 5.657388210296631] |
c7dd95a6-5741-44a8-99f1-bdc618564f84 | bert-based-arabic-social-media | 1909.04181 | null | https://arxiv.org/abs/1909.04181v3 | https://arxiv.org/pdf/1909.04181v3.pdf | BERT-Based Arabic Social Media Author Profiling | We report our models for detecting age, language variety, and gender from social media data in the context of the Arabic author profiling and deception detection shared task (APDA). We build simple models based on pre-trained bidirectional encoders from transformers (BERT). We first fine-tune the pre-trained BERT model... | ['Muhammad Abdul-Mageed', 'Chiyu Zhang'] | 2019-09-09 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [-4.27065104e-01 1.42366469e-01 6.94601284e-03 -7.27029145e-01
-9.74857509e-01 -7.23392546e-01 9.62824643e-01 -1.40558124e-01
-8.07635307e-01 6.89352334e-01 2.57914573e-01 -1.53347388e-01
3.80691051e-01 -4.72231120e-01 -4.41110015e-01 -3.57050836e-01
-1.08774588e-01 6.59859598e-01 -3.37054551e-01 -2.77930975... | [9.318061828613281, 10.499059677124023] |
e4e4d1ac-d38e-4350-9c43-ec5026b3b327 | legal-case-document-summarization-extractive | 2210.07544 | null | https://arxiv.org/abs/2210.07544v1 | https://arxiv.org/pdf/2210.07544v1.pdf | Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation | Summarization of legal case judgement documents is a challenging problem in Legal NLP. However, not much analyses exist on how different families of summarization models (e.g., extractive vs. abstractive) perform when applied to legal case documents. This question is particularly important since many recent transformer... | ['Saptarshi Ghosh', 'Pawan Goyal', 'Kripabandhu Ghosh', 'Rajdeep Mukherjee', 'Soham Poddar', 'Paheli Bhattacharya', 'Abhay Shukla'] | 2022-10-14 | null | null | null | null | ['abstractive-text-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.26549339e-01 2.75066704e-01 -6.61226749e-01 -1.95765078e-01
-1.18748939e+00 -9.70586479e-01 6.91743970e-01 7.13345647e-01
-3.02204162e-01 1.24819291e+00 1.02816474e+00 -5.47842741e-01
-5.88065922e-01 -4.96049762e-01 -2.07598671e-01 -2.64016896e-01
1.82444349e-01 7.34251678e-01 2.11521521e-01 -3.67605835... | [12.125465393066406, 9.586036682128906] |
f028722f-f60f-441b-b673-9f893910c223 | using-known-information-to-accelerate | 1811.03322 | null | https://arxiv.org/abs/1811.03322v2 | https://arxiv.org/pdf/1811.03322v2.pdf | Using Known Information to Accelerate HyperParameters Optimization Based on SMBO | Automl is the key technology for machine learning problem. Current state of art hyperparameter optimization methods are based on traditional black-box optimization methods like SMBO (SMAC, TPE). The objective function of black-box optimization is non-smooth, or time-consuming to evaluate, or in some way noisy. Recent y... | ['Zhang Yunquan', 'Xia Fen', 'Li Shigang', 'Cheng Daning', 'Zhang Hanping'] | 2018-11-08 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-5.98744571e-01 -3.77725601e-01 -4.47422326e-01 -7.91586936e-02
-6.92454278e-01 -3.45096022e-01 2.01641902e-01 1.47606153e-02
-5.58636844e-01 1.01592982e+00 -1.39762670e-01 -2.59840310e-01
-4.02382791e-01 -5.21186650e-01 -6.66359365e-01 -1.03807223e+00
1.87880158e-01 7.33161986e-01 6.91105872e-02 -4.53796834... | [6.5842390060424805, 4.104918956756592] |
87706498-f3db-4d22-a6ac-b0d0ecf4e64f | boltzman-tuning-of-generative-models | null | null | https://openreview.net/forum?id=hP-pn8bKffe | https://openreview.net/pdf?id=hP-pn8bKffe | Boltzman Tuning of Generative Models | The paper focuses on the a posteriori tuning of a generative model in order to favor the generation of good instances in the sense of some external differentiable criterion. The proposed approach, called Boltzmann Tuning of Generative Models (BTGM) applies to a wide range of applications. It covers conditional genera... | ['Michele Sebag', 'Victor Berger'] | 2021-01-01 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 3.45886916e-01 4.18663710e-01 -4.64450344e-02 -5.92732653e-02
-9.40853834e-01 -8.50513577e-02 8.51408362e-01 2.33376492e-02
-5.95810235e-01 9.46034014e-01 -1.33646697e-01 1.68635882e-02
-7.09031105e-01 -9.25598443e-01 -4.86061186e-01 -1.24764347e+00
1.92753285e-01 8.57729256e-01 -2.33369231e-01 -2.11482689... | [6.091376304626465, 3.6492083072662354] |
b6495759-bd9c-45c1-a1c9-4cbc24d28494 | equal-confusion-fairness-measuring-group | 2307.00472 | null | https://arxiv.org/abs/2307.00472v1 | https://arxiv.org/pdf/2307.00472v1.pdf | Equal Confusion Fairness: Measuring Group-Based Disparities in Automated Decision Systems | As artificial intelligence plays an increasingly substantial role in decisions affecting humans and society, the accountability of automated decision systems has been receiving increasing attention from researchers and practitioners. Fairness, which is concerned with eliminating unjust treatment and discrimination agai... | ['Ioannis A. Kakadiaris', 'Furkan Gursoy'] | 2023-07-02 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [ 1.38441294e-01 1.21496230e-01 -1.39429763e-01 -6.62694812e-01
-1.61566690e-01 -6.02015853e-01 5.52345037e-01 7.77203083e-01
-6.90858603e-01 7.59796619e-01 2.47639969e-01 -6.54435992e-01
-5.67281544e-01 -5.38717866e-01 3.36760193e-01 -3.73265475e-01
4.46698606e-01 3.49441111e-01 -4.93197322e-01 -1.09564245... | [8.880069732666016, 5.475654602050781] |
0f0bef1f-f827-4ee0-b886-09c71fb027a2 | acm-net-action-context-modeling-network-for | 2104.02967 | null | https://arxiv.org/abs/2104.02967v1 | https://arxiv.org/pdf/2104.02967v1.pdf | ACM-Net: Action Context Modeling Network for Weakly-Supervised Temporal Action Localization | Weakly-supervised temporal action localization aims to localize action instances temporal boundary and identify the corresponding action category with only video-level labels. Traditional methods mainly focus on foreground and background frames separation with only a single attention branch and class activation sequenc... | ['Alois Knoll', 'Fan Lu', 'Lijun Zhang', 'Zhijun Li', 'Guang Chen', 'Sanqing Qu'] | 2021-04-07 | null | null | null | null | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 5.64853370e-01 -1.70821637e-01 -5.57871521e-01 -2.13046893e-01
-6.50825143e-01 -4.03370857e-01 5.59467137e-01 -3.99530381e-02
-3.29867780e-01 6.02111161e-01 2.87661403e-01 -9.77954939e-02
2.04382613e-01 -3.51661682e-01 -5.46774209e-01 -8.82719696e-01
8.32888186e-02 -1.13340542e-01 8.39429259e-01 3.55761379... | [8.543376922607422, 0.6376709342002869] |
b88f12a5-27c5-4f21-b691-83d5135aaa73 | deep-hr-fast-heart-rate-estimation-from-face | 2002.04821 | null | https://arxiv.org/abs/2002.04821v1 | https://arxiv.org/pdf/2002.04821v1.pdf | Deep-HR: Fast Heart Rate Estimation from Face Video Under Realistic Conditions | This paper presents a novel method for remote heart rate (HR) estimation. Recent studies have proved that blood pumping by the heart is highly correlated to the intense color of face pixels, and surprisingly can be utilized for remote HR estimation. Researchers successfully proposed several methods for this task, but m... | ['Xiaobai Li', 'Mahmood Fathy', 'Mohammad Sabokrou', 'Masoud Pourreza', 'Guoying Zhao'] | 2020-02-12 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 3.31166759e-02 5.05583212e-02 -4.89514433e-02 -4.75810409e-01
-4.00383979e-01 2.67863005e-01 1.32890761e-01 -5.68687260e-01
-2.71106362e-01 7.88594186e-01 -1.45942152e-01 1.51961550e-01
3.18992108e-01 -7.19253123e-01 -5.32458603e-01 -8.26885402e-01
4.70832810e-02 -1.59304515e-01 -8.22922811e-02 -6.88498169... | [13.890766143798828, 2.694209098815918] |
4a31e987-34d4-4c95-84ee-6c6be365bba3 | topic-modeling-based-sentiment-analysis-on | null | null | https://aclanthology.org/P15-1131 | https://aclanthology.org/P15-1131.pdf | Topic Modeling based Sentiment Analysis on Social Media for Stock Market Prediction | null | ['Thien Hai Nguyen', 'Kiyoaki Shirai'] | 2015-07-01 | topic-modeling-based-sentiment-analysis-on-1 | https://aclanthology.org/P15-1131 | https://aclanthology.org/P15-1131.pdf | ijcnlp-2015-7 | ['stock-market-prediction', 'stock-prediction'] | ['time-series', 'time-series'] | [-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... | [-7.359321117401123, 3.7348501682281494] |
687c4bee-82e4-47ce-a165-f8ba42b222bd | g2netpl-generic-game-theoretic-network-for | 2210.11469 | null | https://arxiv.org/abs/2210.11469v1 | https://arxiv.org/pdf/2210.11469v1.pdf | G2NetPL: Generic Game-Theoretic Network for Partial-Label Image Classification | Multi-label image classification aims to predict all possible labels in an image. It is usually formulated as a partial-label learning problem, since it could be expensive in practice to annotate all the labels in every training image. Existing works on partial-label learning focus on the case where each training image... | ['Song Wang', 'XiaoFeng Wang', 'Mostafa M. Fouda', 'Xin Zhang', 'Rabab Abdelfattah'] | 2022-10-20 | null | null | null | null | ['multi-label-image-classification', 'partial-label-learning'] | ['computer-vision', 'methodology'] | [ 5.02097726e-01 4.50523227e-01 -6.40600801e-01 -4.78209764e-01
-8.92503023e-01 -5.69570422e-01 7.49219134e-02 5.87929003e-02
-4.38730955e-01 7.47537553e-01 -7.56189167e-01 -1.31829187e-01
-1.44689083e-01 -5.40883601e-01 -4.75257427e-01 -1.12236977e+00
4.64209393e-02 7.64991879e-01 -1.55937998e-02 2.48626113... | [9.50788688659668, 4.115539073944092] |
3a9a6285-4d93-4b2a-aa5d-f02baa509da0 | diachronic-sense-modeling-with-deep | null | null | https://aclanthology.org/P19-1379 | https://aclanthology.org/P19-1379.pdf | Diachronic Sense Modeling with Deep Contextualized Word Embeddings: An Ecological View | Diachronic word embeddings have been widely used in detecting temporal changes. However, existing methods face the meaning conflation deficiency by representing a word as a single vector at each time period. To address this issue, this paper proposes a sense representation and tracking framework based on deep contextua... | ['Renfen Hu', 'Shichen Liang', 'Shen Li'] | 2019-07-01 | null | null | null | acl-2019-7 | ['diachronic-word-embeddings'] | ['natural-language-processing'] | [ 8.27973112e-02 -7.26642609e-01 -1.86337471e-01 -9.72817391e-02
2.13143677e-01 -7.01076388e-01 7.03430593e-01 7.01264858e-01
-6.00112021e-01 3.98753226e-01 6.65647924e-01 -1.32622331e-01
1.90342851e-02 -8.90436769e-01 -1.11386076e-01 -6.92299902e-01
2.23589957e-01 -7.83051029e-02 2.63335168e-01 -8.07942867... | [10.319664001464844, 8.902417182922363] |
78ea34b8-1faa-4d80-958b-4c4d527b214e | the-ntt-dcase2020-challenge-task-6-system | 2007.00225 | null | https://arxiv.org/abs/2007.00225v1 | https://arxiv.org/pdf/2007.00225v1.pdf | The NTT DCASE2020 Challenge Task 6 system: Automated Audio Captioning with Keywords and Sentence Length Estimation | This technical report describes the system participating to the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Challenge, Task 6: automated audio captioning. Our submission focuses on solving two indeterminacy problems in automated audio captioning: word selection indeterminacy and sentence len... | ['Yasunori Ohishi', 'Kunio Kashino', 'Yuma Koizumi', 'Daiki Takeuchi', 'Noboru Harada'] | 2020-07-01 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 4.18552816e-01 -3.36024836e-02 3.42225879e-01 -2.09961399e-01
-2.01330757e+00 -8.35917950e-01 9.81234014e-02 -5.31754754e-02
-2.63166100e-01 8.56754065e-01 4.94464010e-01 -2.08552480e-01
6.12675920e-02 9.53002796e-02 -7.25703001e-01 -2.25984350e-01
-1.22302569e-01 6.91985250e-01 5.11709303e-02 -1.90836206... | [15.276212692260742, 4.893064498901367] |
28ecbecc-05ec-4672-8aae-c8c56e3e296f | perturbation-based-two-stage-multi-domain | 2306.10700 | null | https://arxiv.org/abs/2306.10700v1 | https://arxiv.org/pdf/2306.10700v1.pdf | Perturbation-Based Two-Stage Multi-Domain Active Learning | In multi-domain learning (MDL) scenarios, high labeling effort is required due to the complexity of collecting data from various domains. Active Learning (AL) presents an encouraging solution to this issue by annotating a smaller number of highly informative instances, thereby reducing the labeling effort. Previous res... | ['Ke Tang', 'Shan He', 'Zeyu Dai', 'Rui He'] | 2023-06-19 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 3.91190916e-01 2.92814404e-01 -5.46838582e-01 -2.77569592e-01
-1.33296168e+00 -4.07488734e-01 5.67750752e-01 3.75745922e-01
-3.28218102e-01 9.34743226e-01 1.86526462e-01 1.01399511e-01
-4.62877810e-01 -4.15975541e-01 -4.16040689e-01 -1.00924158e+00
-6.71398714e-02 6.42824590e-01 3.10208440e-01 1.71635225... | [10.145723342895508, 3.469902992248535] |
20628905-cad7-4d57-bf38-8256523f1344 | on-using-context-for-automatic-correction-of | null | null | https://aclanthology.org/W12-2012 | https://aclanthology.org/W12-2012.pdf | On using context for automatic correction of non-word misspellings in student essays | null | ['Yoko Futagi', 'Michael Flor'] | 2012-06-01 | null | null | null | ws-2012-6 | ['lexical-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.318655490875244, 3.686575174331665] |
f0e86f2d-11cf-4431-8316-8ed735b4109d | mass-producing-failures-of-multimodal-systems | 2306.12105 | null | https://arxiv.org/abs/2306.12105v1 | https://arxiv.org/pdf/2306.12105v1.pdf | Mass-Producing Failures of Multimodal Systems with Language Models | Deployed multimodal systems can fail in ways that evaluators did not anticipate. In order to find these failures before deployment, we introduce MultiMon, a system that automatically identifies systematic failures -- generalizable, natural-language descriptions of patterns of model failures. To uncover systematic failu... | ['Jacob Steinhardt', 'Erik Jones', 'Shengbang Tong'] | 2023-06-21 | null | null | null | null | ['self-driving-cars'] | ['computer-vision'] | [-3.66429448e-01 5.63921146e-02 -1.14631809e-01 -5.97857893e-01
-1.08372927e+00 -9.04521286e-01 5.07423341e-01 6.26450032e-02
1.25807256e-01 4.79309410e-01 2.96949595e-01 -7.07314372e-01
2.25442559e-01 -2.56275177e-01 -9.38358486e-01 -8.14370587e-02
8.04462060e-02 4.33424503e-01 1.03915662e-01 -5.28065026... | [8.314888954162598, 7.793886184692383] |
d70044a5-ffaa-4f48-b03c-032d5ff826e7 | deep-learning-based-super-resolution-for | 2210.08080 | null | https://arxiv.org/abs/2210.08080v1 | https://arxiv.org/pdf/2210.08080v1.pdf | Deep Learning based Super-Resolution for Medical Volume Visualization with Direct Volume Rendering | Modern-day display systems demand high-quality rendering. However, rendering at higher resolution requires a large number of data samples and is computationally expensive. Recent advances in deep learning-based image and video super-resolution techniques motivate us to investigate such networks for high-fidelity upscal... | ['Sumanta Pattanaik', 'Sudarshan Devkota'] | 2022-10-14 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 5.08256018e-01 -3.64335686e-01 3.34482074e-01 -1.87086508e-01
-8.81929398e-01 -5.11897653e-02 4.23405081e-01 -3.09104808e-02
-1.99119657e-01 1.00539279e+00 -7.93040916e-02 -2.51154184e-01
5.98554313e-02 -1.18503225e+00 -4.62458491e-01 -6.67124391e-01
-4.09355402e-01 3.39885384e-01 3.59912992e-01 -1.62594169... | [9.446389198303223, -3.1085453033447266] |
c2b13a3d-1e85-4e5b-8f6f-b23c6304a4f7 | robust-face-recognition-via-adaptive-sparse | 1404.4780 | null | https://arxiv.org/abs/1404.4780v1 | https://arxiv.org/pdf/1404.4780v1.pdf | Robust Face Recognition via Adaptive Sparse Representation | Sparse Representation (or coding) based Classification (SRC) has gained great success in face recognition in recent years. However, SRC emphasizes the sparsity too much and overlooks the correlation information which has been demonstrated to be critical in real-world face recognition problems. Besides, some work consid... | ['Can-Yi Lu', 'Pei-Pei Li', 'Jing Wang', 'Meng Wang', 'Xuegang Hu', 'Shuicheng Yan'] | 2014-04-18 | null | null | null | null | ['robust-face-recognition', 'sparse-representation-based-classification'] | ['computer-vision', 'computer-vision'] | [ 2.49438137e-01 -4.07623917e-01 -3.29196334e-01 -3.82032067e-01
-4.45543051e-01 2.49152303e-01 2.30540484e-01 -3.01602423e-01
-8.46355036e-03 5.05821347e-01 3.27803552e-01 2.40329459e-01
-4.36826885e-01 -6.95571423e-01 -1.54436588e-01 -1.04118228e+00
-8.08166116e-02 -9.08488333e-02 -2.37207770e-01 -7.39175975... | [12.478398323059082, 0.426063597202301] |
53810929-3e14-455a-9dd7-7c21f6f7acc7 | bigearthnet-mm-a-large-scale-multi-modal | 2105.07921 | null | https://arxiv.org/abs/2105.07921v2 | https://arxiv.org/pdf/2105.07921v2.pdf | BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval | This paper presents the multi-modal BigEarthNet (BigEarthNet-MM) benchmark archive made up of 590,326 pairs of Sentinel-1 and Sentinel-2 image patches to support the deep learning (DL) studies in multi-modal multi-label remote sensing (RS) image retrieval and classification. Each pair of patches in BigEarthNet-MM is an... | ['Volker Markl', 'Begüm Demir', 'Mário Caetano', 'Pedro Benevides', 'Hugo Costa', 'Filipe Marcelino', 'Tristan Kreuziger', 'Arne de Wall', 'Gencer Sumbul'] | 2021-05-17 | null | null | null | null | ['multi-label-image-retrieval', 'remote-sensing-image-classification'] | ['computer-vision', 'miscellaneous'] | [ 1.66468874e-01 -3.75791699e-01 -2.42858931e-01 -4.74905521e-01
-9.96993482e-01 -6.91527128e-01 8.06696832e-01 4.73472983e-01
-5.44560730e-01 4.90650594e-01 -1.74297363e-01 -4.29185480e-01
-2.31969312e-01 -1.22089684e+00 -5.85053444e-01 -8.13802242e-01
-2.48795152e-01 4.88402963e-01 -1.57029852e-01 -4.44934636... | [9.599770545959473, -1.46067214012146] |
a730938d-b2ac-4d03-a81c-bdf01776d429 | region-refinement-network-for-salient-object | 1906.11443 | null | https://arxiv.org/abs/1906.11443v2 | https://arxiv.org/pdf/1906.11443v2.pdf | Region Refinement Network for Salient Object Detection | Albeit intensively studied, false prediction and unclear boundaries are still major issues of salient object detection. In this paper, we propose a Region Refinement Network (RRN), which recurrently filters redundant information and explicitly models boundary information for saliency detection. Different from existing ... | ['Ruiyu Li', 'Hengshuang Zhao', 'Xiaoyong Shen', 'Jiaya Jia', 'Zhuotao Tian', 'Jiaze Wang', 'Michelle Shu'] | 2019-06-27 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [ 5.77161551e-01 4.88085836e-01 -3.96428078e-01 -4.22253728e-01
-6.15551054e-01 -1.47594109e-01 3.96653771e-01 1.40440375e-01
-1.51672199e-01 6.30185187e-01 2.29317829e-01 -9.37249139e-02
3.88782263e-01 -5.61826408e-01 -8.46006095e-01 -4.07196492e-01
1.95269659e-01 4.51904014e-02 1.08575392e+00 -2.51349688... | [9.765791893005371, -0.2849918007850647] |
3c86efb3-f955-4148-ba24-11e1cbe49f63 | what-would-harry-say-building-dialogue-agents | 2211.06869 | null | https://arxiv.org/abs/2211.06869v3 | https://arxiv.org/pdf/2211.06869v3.pdf | What would Harry say? Building Dialogue Agents for Characters in a Story | We have a Christmas gift for Harry Potter fans all over the world. In this paper, we present Harry Potter Dialogue (HPD), a dataset that helps train Harry Potter-like dialogue agents. Such a task is typically viewed as a variant of personalized dialogue agents, but they differ significantly in three respects: 1) Harry ... | ['Longyue Wang', 'Jia Li', 'Ziyang Chen', 'Deng Cai', 'Haiyun Jiang', 'Yan Wang', 'Nuo Chen'] | 2022-11-13 | null | null | null | null | ['pesona-dialogue-in-story'] | ['natural-language-processing'] | [-2.64521211e-01 4.15536761e-01 2.82676965e-01 -4.71813440e-01
-7.59811282e-01 -8.36152852e-01 1.06856561e+00 -3.42620797e-02
-1.14386939e-01 1.02989292e+00 8.80991220e-01 -4.16081445e-03
3.60024810e-01 -8.01948249e-01 -1.99304163e-01 -3.67819697e-01
2.01983348e-01 1.04617643e+00 2.69143224e-01 -1.08640587... | [12.725726127624512, 8.042787551879883] |
b1200ca2-6b06-4a1a-9731-6d10c5006776 | parallel-vertex-diffusion-for-unified-visual | 2303.07216 | null | https://arxiv.org/abs/2303.07216v2 | https://arxiv.org/pdf/2303.07216v2.pdf | Parallel Vertex Diffusion for Unified Visual Grounding | Unified visual grounding pursues a simple and generic technical route to leverage multi-task data with less task-specific design. The most advanced methods typically present boxes and masks as vertex sequences to model referring detection and segmentation as an autoregressive sequential vertex generation paradigm. Howe... | ['Jie Chen', 'Chang Liu', 'Li Yuan', 'Xiangyang Ji', 'Peng Jin', 'Kehan Li', 'Zesen Cheng'] | 2023-03-13 | null | null | null | null | ['visual-grounding'] | ['computer-vision'] | [ 5.68474904e-02 3.01004171e-01 -6.62651658e-02 -6.83420822e-02
-9.02487814e-01 -5.61710060e-01 5.00233173e-01 -8.82569999e-02
-1.05562054e-01 3.37550849e-01 -7.35341534e-02 -2.91212440e-01
4.22536619e-02 -8.12718570e-01 -8.24004769e-01 -6.22223735e-01
2.08582297e-01 5.56378543e-01 6.47325814e-01 -2.39546955... | [8.11263656616211, -3.2543728351593018] |
5483abce-a9fa-4634-9e4a-1eff8746c7dd | conformal-prediction-with-temporal-quantile | 2205.09940 | null | https://arxiv.org/abs/2205.09940v2 | https://arxiv.org/pdf/2205.09940v2.pdf | Conformal Prediction with Temporal Quantile Adjustments | We develop Temporal Quantile Adjustment (TQA), a general method to construct efficient and valid prediction intervals (PIs) for regression on cross-sectional time series data. Such data is common in many domains, including econometrics and healthcare. A canonical example in healthcare is predicting patient outcomes usi... | ['Jimeng Sun', 'Shubhendu Trivedi', 'Zhen Lin'] | 2022-05-20 | null | null | null | null | ['predicting-patient-outcomes', 'econometrics', 'prediction-intervals', 'time-series-regression'] | ['medical', 'miscellaneous', 'miscellaneous', 'time-series'] | [ 1.41713127e-01 9.63952485e-03 -5.43696642e-01 -5.87284565e-01
-1.29039800e+00 -5.84784150e-01 2.33747497e-01 5.95151663e-01
-1.07935801e-01 7.92562723e-01 4.39114332e-01 -5.00514507e-01
-7.05334961e-01 -8.54182899e-01 -8.41134906e-01 -6.39195561e-01
-6.37549639e-01 4.17423040e-01 6.35549054e-02 4.01929840... | [7.792013168334961, 4.791163444519043] |
41f79d20-ae5f-4dfb-a944-21ccecc32f58 | formal-language-recognition-by-hard-attention | 2204.06618 | null | https://arxiv.org/abs/2204.06618v1 | https://arxiv.org/pdf/2204.06618v1.pdf | Formal Language Recognition by Hard Attention Transformers: Perspectives from Circuit Complexity | This paper analyzes three formal models of Transformer encoders that differ in the form of their self-attention mechanism: unique hard attention (UHAT); generalized unique hard attention (GUHAT), which generalizes UHAT; and averaging hard attention (AHAT). We show that UHAT and GUHAT Transformers, viewed as string acce... | ['Robert Frank', 'Dana Angluin', 'Yiding Hao'] | 2022-04-13 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 2.04477429e-01 8.98269951e-01 1.99704729e-02 -3.77727523e-02
-6.51793480e-01 -1.04586875e+00 4.08947587e-01 2.01626904e-02
-9.23256427e-02 8.11101377e-01 -4.47792234e-03 -9.13299382e-01
-4.96010818e-02 -1.19696987e+00 -1.00940824e+00 -6.30460978e-01
-1.96601793e-01 6.98224247e-01 3.00185323e-01 -2.96618313... | [9.455772399902344, 7.069998741149902] |
58cb6605-a4c1-4bfc-9dbc-dc30e2efb1bf | fast-and-robust-template-matching-with | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0031320319303322 | https://www.sciencedirect.com/science/article/abs/pii/S0031320319303322 | Fast and robust template matching with majority neighbour similarity and annulus projection transformation | In the paper, a novel fast and robust template matching method named A-MNS based on Majority Neighbour Similarity (MNS) and the annulus projection transformation (APT) is proposed. Its essence is the MNS, a useful, rotation-invariant, low computational cost and robust similarity measurement. The proposed method is theo... | ['Zhou Jinyun', 'Yang Ruan', 'Runming Yan', 'Kaiyuan Deng', 'Liang Lei', 'Jinxiang Lai'] | 2020-02-10 | null | null | null | pattern-recognition-2020-2 | ['template-matching'] | ['computer-vision'] | [ 1.45754397e-01 -6.00397110e-01 7.15692118e-02 -6.97208717e-02
-5.66119015e-01 -5.71157515e-01 7.84846783e-01 -1.67514876e-01
-3.13587189e-01 2.07978517e-01 -5.88658266e-02 2.44914796e-02
-2.66812414e-01 -4.90439415e-01 -4.04878110e-01 -6.12749815e-01
1.81160927e-01 6.38879240e-01 6.12952352e-01 -1.41351476... | [8.16873550415039, -2.308897018432617] |
d782f6b5-d4fa-41a3-a7ff-5862cccee069 | exploring-navigation-maps-for-learning-based | 2302.06195 | null | https://arxiv.org/abs/2302.06195v1 | https://arxiv.org/pdf/2302.06195v1.pdf | Exploring Navigation Maps for Learning-Based Motion Prediction | The prediction of surrounding agents' motion is a key for safe autonomous driving. In this paper, we explore navigation maps as an alternative to the predominant High Definition (HD) maps for learning-based motion prediction. Navigation maps provide topological and geometrical information on road-level, HD maps additio... | ['Klaus Dietmayer', 'Thomas Monninger', 'Franz Gritschneder', 'Julian Jordan', 'Julian Schmidt'] | 2023-02-13 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [-3.68751973e-01 3.83490682e-01 -3.59874576e-01 -4.39140975e-01
-8.53722811e-01 -5.89184344e-01 8.76363397e-01 4.20548469e-02
-6.87422395e-01 9.53798890e-01 2.12084576e-01 -6.96221292e-01
-1.25647083e-01 -1.48995960e+00 -8.99188936e-01 -3.49453002e-01
-2.50744641e-01 6.98394895e-01 9.85564053e-01 -5.70993364... | [5.705243110656738, 0.6820877194404602] |
0b41348e-d03f-4532-9719-4623647c6d97 | faster-bounding-box-annotation-for-object | 1807.03142 | null | http://arxiv.org/abs/1807.03142v1 | http://arxiv.org/pdf/1807.03142v1.pdf | Faster Bounding Box Annotation for Object Detection in Indoor Scenes | This paper proposes an approach for rapid bounding box annotation for object
detection datasets. The procedure consists of two stages: The first step is to
annotate a part of the dataset manually, and the second step proposes
annotations for the remaining samples using a model trained with the first
stage annotations. ... | ['Jukka Peltomäki', 'Jussi Puura', 'Heikki Huttunen', 'Bishwo Adhikari'] | 2018-07-03 | null | null | null | null | ['object-detection-in-indoor-scenes'] | ['computer-vision'] | [ 2.48811170e-01 -4.43828963e-02 3.37544046e-02 -6.61323726e-01
-5.24522007e-01 -6.12558603e-01 1.97419569e-01 2.77921855e-01
-7.43243515e-01 6.64051473e-01 -2.50877798e-01 -1.10116780e-01
4.63022202e-01 -6.03990734e-01 -6.86591446e-01 -4.04971749e-01
3.42153125e-02 6.36461079e-01 1.04932666e+00 3.50733221... | [9.047628402709961, 0.41503024101257324] |
69aae883-82a6-47d8-9ed4-4793dd2d299c | predicting-solar-flares-using-a-long-short | 1905.07095 | null | https://arxiv.org/abs/1905.07095v1 | https://arxiv.org/pdf/1905.07095v1.pdf | Predicting Solar Flares Using a Long Short-Term Memory Network | We present a long short-term memory (LSTM) network for predicting whether an active region (AR) would produce a gamma-class flare within the next 24 hours. We consider three gamma classes, namely >=M5.0 class, >=M class, and >=C class, and build three LSTM models separately, each corresponding to a gamma class. Each LS... | ['Hao Liu', 'Chang Liu', 'Jason T. L. Wang', 'Haimin Wang'] | 2019-05-17 | null | null | null | null | ['solar-flare-prediction'] | ['time-series'] | [ 1.75513074e-01 -5.44278145e-01 -3.97658885e-01 -4.91431564e-01
-8.99874389e-01 -5.63702881e-01 5.68966269e-01 2.27513593e-02
-7.32781217e-02 8.63081813e-01 1.06081307e-01 -3.40119809e-01
-2.88955152e-01 -1.17039120e+00 -7.44517565e-01 -7.50133872e-01
-3.94596517e-01 4.30272430e-01 -1.19451033e-02 -3.31740677... | [6.602529525756836, 2.7492852210998535] |
2add17f4-7ca3-46ba-a8c9-abbb7f7b1c36 | nudgeseg-zero-shot-object-segmentation-by | 2109.13859 | null | https://arxiv.org/abs/2109.13859v1 | https://arxiv.org/pdf/2109.13859v1.pdf | NudgeSeg: Zero-Shot Object Segmentation by Repeated Physical Interaction | Recent advances in object segmentation have demonstrated that deep neural networks excel at object segmentation for specific classes in color and depth images. However, their performance is dictated by the number of classes and objects used for training, thereby hindering generalization to never seen objects or zero-sh... | ['Yiannis Aloimonos', 'Cornelia Fermüller', 'Chethan M. Parameshwara', 'Nitin J. Sanket', 'Chahat Deep Singh'] | 2021-09-22 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 6.50636196e-01 -8.80318135e-02 -2.56063677e-02 -4.39548820e-01
-4.08965886e-01 -7.84674525e-01 2.33672485e-01 -1.81580663e-01
-7.79388011e-01 4.83723313e-01 -7.01393664e-01 -4.30409983e-02
8.74286890e-02 -5.37104309e-01 -7.32563436e-01 -8.32265496e-01
-2.81619430e-02 5.70696712e-01 7.84375072e-01 1.27400666... | [8.974493026733398, -0.45785605907440186] |
a22b2647-9fe7-4a31-9f86-dda75122bffa | leveraging-tripartite-interaction-information | 2106.03415 | null | https://arxiv.org/abs/2106.03415v1 | https://arxiv.org/pdf/2106.03415v1.pdf | Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation | Recently, a new form of online shopping becomes more and more popular, which combines live streaming with E-Commerce activity. The streamers introduce products and interact with their audiences, and hence greatly improve the performance of selling products. Despite of the successful applications in industries, the live... | ['JinFeng Yi', 'Qi Liu', 'Dongsheng Li', 'Shanshan Feng', 'Dong-Dong Chen', 'Zhuoxuan Jiang', 'Sanshi Yu'] | 2021-06-07 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-6.28823936e-02 -3.29134315e-01 -5.64103067e-01 -5.18479824e-01
-7.67027587e-03 -4.25380021e-01 2.46587649e-01 3.81653130e-01
-2.68769432e-02 -5.76377846e-02 5.01287401e-01 -1.80816889e-01
-3.64443481e-01 -1.36750090e+00 -8.85761499e-01 -5.78400671e-01
-3.63616377e-01 4.02874231e-01 2.00609207e-01 -7.83143401... | [10.17721939086914, 5.632208824157715] |
d9de235a-ed88-4caf-85f0-11b20f35c006 | videoretalking-audio-based-lip | 2211.14758 | null | https://arxiv.org/abs/2211.14758v1 | https://arxiv.org/pdf/2211.14758v1.pdf | VideoReTalking: Audio-based Lip Synchronization for Talking Head Video Editing In the Wild | We present VideoReTalking, a new system to edit the faces of a real-world talking head video according to input audio, producing a high-quality and lip-syncing output video even with a different emotion. Our system disentangles this objective into three sequential tasks: (1) face video generation with a canonical expre... | ['Nannan Wang', 'Jue Wang', 'Xuan Wang', 'Mingrui Zhu', 'Fei Yin', 'Menghan Xia', 'Yong Zhang', 'Xiaodong Cun', 'Kun Cheng'] | 2022-11-27 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [ 5.03534317e-01 -5.28420806e-02 2.26104781e-01 -5.13579786e-01
-7.43948281e-01 -5.23701549e-01 4.11051542e-01 -4.06521827e-01
-2.31127903e-01 4.38584507e-01 1.80395797e-01 1.78982750e-01
4.40712959e-01 -4.33201313e-01 -7.51601994e-01 -6.47641063e-01
4.31732178e-01 2.27430165e-02 4.24557999e-02 -4.80368249... | [13.217886924743652, -0.43536219000816345] |
adca0a64-263e-4d1c-98b9-d9b8d6333fde | a-new-local-radon-descriptor-for-content | 2007.15523 | null | https://arxiv.org/abs/2007.15523v1 | https://arxiv.org/pdf/2007.15523v1.pdf | A new Local Radon Descriptor for Content-Based Image Search | Content-based image retrieval (CBIR) is an essential part of computer vision research, especially in medical expert systems. Having a discriminative image descriptor with the least number of parameters for tuning is desirable in CBIR systems. In this paper, we introduce a new simple descriptor based on the histogram of... | ['Morteza Babaie', 'Meghana D. Kumar', 'Hany Kashani', 'Hamid. R. Tizhoosh'] | 2020-07-30 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [-2.86811893e-03 -4.56901550e-01 1.24979526e-01 -4.33221281e-01
-1.14140892e+00 1.87921431e-02 3.04091007e-01 4.39929008e-01
-9.83880341e-01 5.31623542e-01 1.98460206e-01 -1.93010986e-01
-3.98193032e-01 -9.43512321e-01 -1.92134142e-01 -8.92313361e-01
-6.17386252e-02 2.92209268e-01 7.28696227e-01 -6.50535971... | [14.281717300415039, -1.4742181301116943] |
ae52fee2-8c32-4827-918f-eb66457a6395 | sc2-pcr-a-second-order-spatial-compatibility | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_SC2-PCR_A_Second_Order_Spatial_Compatibility_for_Efficient_and_Robust_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_SC2-PCR_A_Second_Order_Spatial_Compatibility_for_Efficient_and_Robust_CVPR_2022_paper.pdf | SC2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration | In this paper, we present a second order spatial compatibility (SC^2) measure based method for efficient and robust point cloud registration (PCR), called SC^2-PCR. Firstly, we propose a second order spatial compatibility (SC^2) measure to compute the similarity between correspondences. It considers the global comp... | ['Wenbing Tao', 'Fan Yang', 'Kun Sun', 'Zhi Chen'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['point-cloud-registration'] | ['computer-vision'] | [-1.34820849e-01 -3.86209369e-01 1.55790344e-01 -2.39781782e-01
-8.77608716e-01 -2.82004267e-01 6.04317188e-01 2.46289372e-01
-2.54198551e-01 1.53531879e-01 3.94669138e-02 2.05444321e-01
-3.00955623e-01 -7.40787327e-01 -5.96727133e-01 -7.18635917e-01
-8.57406110e-02 5.07458389e-01 4.77711439e-01 -7.30478242... | [7.722474575042725, -2.935042142868042] |
d45b4e8c-6735-44a4-ab09-5cc3b0002f25 | efficient-traffic-sign-recognition-with-scale | 1805.12289 | null | http://arxiv.org/abs/1805.12289v1 | http://arxiv.org/pdf/1805.12289v1.pdf | Efficient Traffic-Sign Recognition with Scale-aware CNN | The paper presents a Traffic Sign Recognition (TSR) system, which can fast
and accurately recognize traffic signs of different sizes in images. The system
consists of two well-designed Convolutional Neural Networks (CNNs), one for
region proposals of traffic signs and one for classification of each region. In
the propo... | ['Zheng Liu', 'Yuchen Yang', 'Shuo Liu', 'Qiuyuan Wang', 'Wei Ma'] | 2018-05-31 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 9.01998207e-02 -3.17668885e-01 -3.85913104e-01 -5.20538390e-01
-4.94116485e-01 1.28120303e-01 3.55294138e-01 -6.52272522e-01
-5.55266023e-01 5.00372350e-01 -4.21428084e-01 -7.44924784e-01
-2.60990039e-02 -7.36122668e-01 -6.23111963e-01 -5.43486357e-01
1.76269725e-01 3.24653983e-01 8.88163447e-01 -2.26720437... | [7.984241008758545, -0.8076330423355103] |
dd624ff6-dbf3-4f1c-a49c-86e4eb862ec5 | image-feature-information-extraction-for | 2106.07929 | null | https://arxiv.org/abs/2106.07929v5 | https://arxiv.org/pdf/2106.07929v5.pdf | Image Feature Information Extraction for Interest Point Detection: A Review | Interest point detection is one of the most fundamental and critical problems in computer vision and image processing. In this paper, we carry out a comprehensive review on image feature information (IFI) extraction techniques for interest point detection. To systematically introduce how the existing interest point det... | ['Changming Sun', 'Yongsheng Gao', 'Weichuan Zhang', 'Tian Gao', 'Junfeng Jing'] | 2021-06-15 | null | null | null | null | ['interest-point-detection'] | ['computer-vision'] | [ 1.45543233e-01 -3.82604927e-01 -4.85856265e-01 1.14004351e-01
-5.67047775e-01 -3.34695935e-01 5.99766135e-01 -1.17922418e-01
-1.01203747e-01 2.54791349e-01 -2.60179311e-01 1.18980847e-01
-2.50782698e-01 -6.49224937e-01 -1.99976042e-01 -4.76783991e-01
-3.89895111e-01 4.06535976e-02 6.61442995e-01 -7.08241463... | [8.791410446166992, -1.479999303817749] |
ae1b4b92-fee4-415c-a0ea-276bd3187408 | nuclei-glands-instance-segmentation-in | 2208.12460 | null | https://arxiv.org/abs/2208.12460v1 | https://arxiv.org/pdf/2208.12460v1.pdf | Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review | Instance segmentation of nuclei and glands in the histology images is an important step in computational pathology workflow for cancer diagnosis, treatment planning and survival analysis. With the advent of modern hardware, the recent availability of large-scale quality public datasets and the community organized grand... | ['Muhammad Moazam Fraz', 'Arshi Perviaz', 'Esha Sadia Nasir'] | 2022-08-26 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 3.64015907e-01 3.55367452e-01 -4.48454857e-01 -2.37410083e-01
-1.24164522e+00 -4.31471020e-01 2.62915611e-01 6.80648804e-01
-3.20296228e-01 7.50384569e-01 1.37359604e-01 -1.80319443e-01
-2.86370337e-01 -4.90913957e-01 9.74207297e-02 -1.35034597e+00
-1.52713478e-01 5.95630884e-01 2.14883342e-01 -7.89310187... | [15.030198097229004, -3.051969051361084] |
10817c12-c97e-4d68-a986-a763a92463fb | computer-aided-road-inspection-systems-and | 2203.02355 | null | https://arxiv.org/abs/2203.02355v1 | https://arxiv.org/pdf/2203.02355v1.pdf | Computer-Aided Road Inspection: Systems and Algorithms | Road damage is an inconvenience and a safety hazard, severely affecting vehicle condition, driving comfort, and traffic safety. The traditional manual visual road inspection process is pricey, dangerous, exhausting, and cumbersome. Also, manual road inspection results are qualitative and subjective, as they depend enti... | ['Mohammud Junaid Bocus', 'Li Wang', 'Sicen Guo', 'Rui Fan'] | 2022-03-04 | null | null | null | null | ['road-damage-detection'] | ['computer-vision'] | [-7.60952979e-02 -7.61106461e-02 -6.70951456e-02 5.07963672e-02
-2.33605281e-01 -3.13946545e-01 1.44772470e-01 1.57473102e-01
-2.70721287e-01 6.62321508e-01 -3.47279370e-01 -6.41771019e-01
-1.86753109e-01 -9.06952083e-01 -6.28929809e-02 -7.99088061e-01
2.79761493e-01 8.17108825e-02 5.50649822e-01 -2.71143258... | [7.417540073394775, 1.0203731060028076] |
c9b83210-9f9e-4643-8020-bec52f1709cc | keyphrase-prediction-with-pre-trained | 2004.10462 | null | https://arxiv.org/abs/2004.10462v1 | https://arxiv.org/pdf/2004.10462v1.pdf | Keyphrase Prediction With Pre-trained Language Model | Recently, generative methods have been widely used in keyphrase prediction, thanks to their capability to produce both present keyphrases that appear in the source text and absent keyphrases that do not match any source text. However, the absent keyphrases are generated at the cost of the performance on present keyphra... | ['Zheng Lin', 'Rui Liu', 'Weiping Wang'] | 2020-04-22 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 2.22389638e-01 -1.77817777e-01 -3.63773227e-01 2.33406171e-01
-8.52449894e-01 -7.73035765e-01 1.13124096e+00 3.67257893e-01
-4.45568264e-01 7.61089802e-01 4.21715260e-01 -4.17864352e-01
1.14737280e-01 -1.02843583e+00 -7.44343340e-01 -6.98528945e-01
2.36066848e-01 2.90561408e-01 2.79170990e-01 -3.21743786... | [12.315957069396973, 8.8900146484375] |
3e20234b-9bb6-41ab-a567-09e1aff14fd6 | deep-echo-state-networks-with-uncertainty | 1806.10728 | null | http://arxiv.org/abs/1806.10728v2 | http://arxiv.org/pdf/1806.10728v2.pdf | Deep Echo State Networks with Uncertainty Quantification for Spatio-Temporal Forecasting | Long-lead forecasting for spatio-temporal systems can often entail complex
nonlinear dynamics that are difficult to specify it a priori. Current
statistical methodologies for modeling these processes are often highly
parameterized and thus, challenging to implement from a computational
perspective. One potential parsim... | ['Christopher K. Wikle', 'Patrick L. McDermott'] | 2018-06-28 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-1.09746486e-01 -4.18233782e-01 5.24765909e-01 -5.73476590e-02
-4.36143219e-01 -5.82924306e-01 1.12264609e+00 -5.04782125e-02
-8.34496096e-02 1.08608294e+00 8.59564394e-02 -7.45490670e-01
-5.06402373e-01 -9.31813180e-01 -3.92375410e-01 -9.84792888e-01
-4.60958689e-01 4.63541329e-01 1.19674332e-01 -4.28193450... | [6.56524658203125, 3.309788227081299] |
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