paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
949fe9b8-bdb1-40a2-8e77-923cc42bf46d | reinforced-iterative-knowledge-distillation | 2106.00241 | null | https://arxiv.org/abs/2106.00241v1 | https://arxiv.org/pdf/2106.00241v1.pdf | Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition | Named entity recognition (NER) is a fundamental component in many applications, such as Web Search and Voice Assistants. Although deep neural networks greatly improve the performance of NER, due to the requirement of large amounts of training data, deep neural networks can hardly scale out to many languages in an indus... | ['Daxin Jiang', 'Xianglin Zuo', 'Wanli Zuo', 'Linjun Shou', 'Jian Pei', 'Ming Gong', 'Shining Liang'] | 2021-06-01 | null | null | null | null | ['cross-lingual-ner'] | ['natural-language-processing'] | [-2.39024788e-01 -1.62696078e-01 -2.89931267e-01 -4.75428969e-01
-9.92026567e-01 -8.54715228e-01 4.17534977e-01 -2.45715097e-01
-8.68800819e-01 8.97497237e-01 1.05957530e-01 -5.53329110e-01
7.98318312e-02 -7.23615170e-01 -6.28107607e-01 -7.34055713e-02
3.31323266e-01 6.13542020e-01 7.74192438e-02 -5.82477689... | [10.001861572265625, 9.671125411987305] |
fc718eb0-62da-49d7-837c-962b9559ff42 | tracking-motion-and-proxemics-using-thermal | 1511.08166 | null | http://arxiv.org/abs/1511.08166v1 | http://arxiv.org/pdf/1511.08166v1.pdf | Tracking Motion and Proxemics using Thermal-sensor Array | Indoor tracking has all-pervasive applications beyond mere surveillance, for
example in education, health monitoring, marketing, energy management and so
on. Image and video based tracking systems are intrusive. Thermal array sensors
on the other hand can provide coarse-grained tracking while preserving privacy
of the ... | ['Chandrayee Basu', 'Anthony Rowe'] | 2015-11-25 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 2.38379821e-01 -5.97507894e-01 2.01210350e-01 -3.93974423e-01
5.42989420e-03 -6.00479662e-01 2.79680640e-01 -1.02650933e-01
-3.83255243e-01 5.32584846e-01 4.23617035e-01 -1.28877580e-01
-1.18336782e-01 -6.94312751e-01 -3.90930086e-01 -7.93461204e-01
-2.63828665e-01 -1.92673177e-01 1.27296150e-01 1.06115364... | [6.9697394371032715, 0.36830049753189087] |
aa82ef1c-9775-4a14-9323-d550272a2043 | saliency-detection-and-quantization-index | 2302.11361 | null | https://arxiv.org/abs/2302.11361v2 | https://arxiv.org/pdf/2302.11361v2.pdf | HDR image watermarking using saliency detection and quantization index modulation | High-dynamic range (HDR) images are circulated rapidly over the internet with risks of being exploited for unauthorized usage. To protect these images, some HDR image based watermarking (HDR-IW) methods were put forward. However, they inherited the same problem faced by conventional IW methods for standard dynamic rang... | ['Vishnu Monn Baskaran', 'KokSheik Wong', 'Minoru Kuribayashi', 'Ahmed Khan'] | 2023-02-22 | null | null | null | null | ['saliency-detection'] | ['computer-vision'] | [ 9.25423801e-01 -1.98601365e-01 -4.58709717e-01 2.50535995e-01
-2.27457911e-01 -4.28338826e-01 3.39202732e-01 -7.56930411e-02
-2.86688179e-01 6.36961877e-01 -4.80860732e-02 -2.23161191e-01
-1.82209194e-01 -6.42079175e-01 -1.48868933e-01 -8.98034751e-01
-2.98470736e-01 -5.87962568e-01 9.13071871e-01 -2.73088038... | [4.363544464111328, 8.013874053955078] |
2fefd10b-4d40-41c7-8541-6a1a38b14076 | spatial-temporal-super-resolution-of | 2106.11485 | null | https://arxiv.org/abs/2106.11485v3 | https://arxiv.org/pdf/2106.11485v3.pdf | Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis | High-resolution satellite imagery has proven useful for a broad range of tasks, including measurement of global human population, local economic livelihoods, and biodiversity, among many others. Unfortunately, high-resolution imagery is both infrequently collected and expensive to purchase, making it hard to efficientl... | ['Stefano Ermon', 'David B. Lobell', 'Marshall Burke', 'Chenlin Meng', 'William Zhang', 'Nicholas Lai', 'Dingjie Wang', 'Yutong He'] | 2021-06-22 | null | http://proceedings.neurips.cc/paper/2021/hash/ead81fe8cfe9fda9e4c2093e17e4d024-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/ead81fe8cfe9fda9e4c2093e17e4d024-Paper.pdf | neurips-2021-12 | ['object-counting'] | ['computer-vision'] | [ 6.07489824e-01 -5.33896923e-01 -3.18158984e-01 -1.97926879e-01
-1.13390338e+00 -4.66452897e-01 7.27727413e-01 1.94701210e-01
-6.43605530e-01 1.04902363e+00 3.22673947e-01 -3.28723013e-01
1.92722633e-01 -1.31565535e+00 -6.90363526e-01 -7.23789930e-01
-1.73658729e-01 2.59572744e-01 -8.40234682e-02 -8.12862813... | [9.438685417175293, -1.3646252155303955] |
f2392ac3-04ec-4d15-b908-ae7140446670 | species-interactions-reproduce-abundance | 2305.19154 | null | https://arxiv.org/abs/2305.19154v4 | https://arxiv.org/pdf/2305.19154v4.pdf | Species interactions reproduce abundance correlation patterns in microbial communities | During the last decades macroecology has identified broad-scale patterns of abundances and diversity of microbial communities and put forward some potential explanations for them. However, these advances are not paralleled by a full understanding of the underlying dynamical processes. In particular, abundance fluctuati... | ['José A. Cuesta', 'Miguel Ángel Muñoz', 'Matteo Sireci', 'Aniello Lampo', 'José Camacho-Mateu'] | 2023-05-30 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 1.46360472e-01 -3.68960321e-01 9.75951701e-02 1.64238378e-01
3.83096695e-01 -5.61256111e-01 1.03799725e+00 6.80424392e-01
-5.23571134e-01 8.91527653e-01 4.68343608e-02 -2.89608926e-01
-6.03824615e-01 -7.34202921e-01 -5.76770961e-01 -1.41941166e+00
-5.40505886e-01 7.47047126e-01 3.63804489e-01 -4.15598005... | [5.837083339691162, 4.288794994354248] |
7a60906c-610c-4967-b4ad-6d377a0ca42e | detecting-can-masquerade-attacks-with-signal | 2201.02665 | null | https://arxiv.org/abs/2201.02665v2 | https://arxiv.org/pdf/2201.02665v2.pdf | Detecting CAN Masquerade Attacks with Signal Clustering Similarity | Vehicular Controller Area Networks (CANs) are susceptible to cyber attacks of different levels of sophistication. Fabrication attacks are the easiest to administer -- an adversary simply sends (extra) frames on a CAN -- but also the easiest to detect because they disrupt frame frequency. To overcome time-based detectio... | ['Michael D. Iannacone', 'Robert A. Bridges', 'Pablo Moriano'] | 2022-01-07 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 2.55348057e-01 -5.47067672e-02 4.36916873e-02 6.53569922e-02
-6.47647560e-01 -1.15700626e+00 7.34622240e-01 4.07454930e-02
-1.31254405e-01 2.77095437e-01 -3.00304681e-01 -8.66497993e-01
1.43567100e-01 -8.14518690e-01 -9.46854770e-01 -5.62951088e-01
-5.01978099e-01 -2.27897111e-02 7.19469547e-01 -2.35368595... | [5.382391929626465, 7.509516716003418] |
ed3f6979-08c3-4ac1-88f8-3a000368f45d | cutpaste-self-supervised-learning-for-anomaly | 2104.04015 | null | https://arxiv.org/abs/2104.04015v1 | https://arxiv.org/pdf/2104.04015v1.pdf | CutPaste: Self-Supervised Learning for Anomaly Detection and Localization | We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data. To this end, we propose a two-stage framework for building anomaly detectors using normal training data only. We first learn self-supervised deep representations and then buil... | ['Tomas Pfister', 'Jinsung Yoon', 'Kihyuk Sohn', 'Chun-Liang Li'] | 2021-04-08 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Li_CutPaste_Self-Supervised_Learning_for_Anomaly_Detection_and_Localization_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Li_CutPaste_Self-Supervised_Learning_for_Anomaly_Detection_and_Localization_CVPR_2021_paper.pdf | cvpr-2021-1 | ['one-class-classifier'] | ['methodology'] | [ 6.01589680e-01 3.99006128e-01 2.26307929e-01 -3.06627870e-01
-8.45018089e-01 -1.01921052e-01 4.25236225e-01 2.74282336e-01
-1.30708605e-01 1.91830657e-02 -2.52947927e-01 -8.06749314e-02
3.96272063e-01 -9.52793837e-01 -1.17443728e+00 -6.28028214e-01
-3.17705572e-01 3.59962881e-01 4.77368683e-01 -1.33825734... | [7.678961753845215, 2.0502963066101074] |
f923fe93-6bf3-481e-9e65-6bcd8a630a63 | chat-or-learn-a-data-driven-robust-question | null | null | https://aclanthology.org/2020.lrec-1.672 | https://aclanthology.org/2020.lrec-1.672.pdf | Chat or Learn: a Data-Driven Robust Question-Answering System | We present a voice-based conversational agent which combines the robustness of chatbots and the utility of question answering (QA) systems. Indeed, while data-driven chatbots are typically user-friendly but not goal-oriented, QA systems tend to perform poorly at chitchat. The proposed chatbot relies on a controller whi... | ['Andrei Popescu-Belis', 'Gabriel Luthier'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['dialogue-act-classification'] | ['natural-language-processing'] | [-1.6022468e-01 6.3929409e-01 5.4199457e-01 -6.0152799e-01
-1.0963888e+00 -8.0407947e-01 6.2790012e-01 -2.4002707e-01
-3.8229439e-01 7.2484857e-01 4.7103471e-01 -2.9384381e-01
-1.6117336e-01 -4.5902279e-01 3.9576672e-02 -4.2215574e-01
2.0447625e-01 1.0814419e+00 4.6426135e-01 -9.7343910e-01
3.2285899e-01... | [12.758782386779785, 7.971164703369141] |
bc21bb62-d4ff-46e4-ae8b-408ec0e6ca6c | unlocking-the-power-of-deep-pico-extraction | 2005.06601 | null | https://arxiv.org/abs/2005.06601v1 | https://arxiv.org/pdf/2005.06601v1.pdf | Unlocking the Power of Deep PICO Extraction: Step-wise Medical NER Identification | The PICO framework (Population, Intervention, Comparison, and Outcome) is usually used to formulate evidence in the medical domain. The major task of PICO extraction is to extract sentences from medical literature and classify them into each class. However, in most circumstances, there will be more than one evidences i... | ['Shaochun Li', 'Zefang Tang', 'Tengteng Zhang', 'Yiqin Yu', 'Jing Mei', 'Xiang Zhang'] | 2020-04-30 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [ 2.84437239e-01 9.61344540e-02 -6.07240021e-01 -2.11699903e-01
-5.72695434e-01 -3.05237353e-01 4.61304843e-01 8.57500672e-01
-2.73436517e-01 1.01593971e+00 5.30402482e-01 -2.36190438e-01
-2.34917447e-01 -9.64526117e-01 -2.97529787e-01 -4.98334736e-01
3.23533237e-01 3.91191006e-01 5.47243841e-02 3.42643470... | [8.494970321655273, 8.721845626831055] |
9e68b7d4-8ce5-4730-97fd-1dbdbd6ebbad | a-deep-content-based-model-for-persian-rumor | null | null | https://doi.org/10.1145/3487289 | https://dl.acm.org/doi/abs/10.1145/3487289 | A Deep Content-Based Model for Persian Rumor Verification | During the development of social media, there has been a transformation in social communication. Despite their positive applications in social interactions and news spread, it also provides an ideal platform for spreading rumors. Rumors can endanger the security of society in normal or critical situations. Therefore, i... | ['Arash Sharifi', 'Mohammad-Reza Feizi-Derakhshi', 'Zoleikha Jahanbakhsh-Nagadeh'] | 2020-11-29 | null | null | null | acm-transactions-on-asian-and-low-resource | ['rumour-detection'] | ['natural-language-processing'] | [-4.22337562e-01 -1.28541499e-01 -3.52000594e-01 -1.95375353e-01
2.58972049e-02 -9.59594175e-02 9.87915814e-01 5.38448334e-01
-3.55505019e-01 4.41697359e-01 6.68229043e-01 -7.46904090e-02
1.97098807e-01 -9.12155688e-01 -4.47846688e-02 -3.67063314e-01
-1.27841860e-01 4.68407601e-01 3.95583689e-01 -8.27824771... | [8.245616912841797, 10.190134048461914] |
dc6f673f-0705-427b-9171-c0bffea96425 | deep-reinforcement-learning-for-interference | 2305.07069 | null | https://arxiv.org/abs/2305.07069v1 | https://arxiv.org/pdf/2305.07069v1.pdf | Deep Reinforcement Learning for Interference Management in UAV-based 3D Networks: Potentials and Challenges | Modern cellular networks are multi-cell and use universal frequency reuse to maximize spectral efficiency. This results in high inter-cell interference. This problem is growing as cellular networks become three-dimensional with the adoption of unmanned aerial vehicles (UAVs). This is because the strength and number of ... | ['H. Vincent Poor', 'Walid Saad', 'Hongliang Zhang', 'Xingqin Lin', 'Mojtaba Vaezi'] | 2023-05-11 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-3.27467993e-02 2.01056451e-01 -1.14191525e-01 6.48485243e-01
-6.18014820e-02 -6.83474243e-01 1.93894818e-01 -2.26437435e-01
-4.17755693e-01 1.60553551e+00 -2.21401200e-01 -4.41779852e-01
-4.46067929e-01 -8.90734196e-01 -4.75109905e-01 -1.00958681e+00
-7.12261319e-01 1.79035529e-01 -1.75999865e-01 -4.87866819... | [5.8680243492126465, 1.6039283275604248] |
80b04517-b085-47f0-981f-41401865cb9f | soccernet-v2-a-dataset-and-benchmarks-for | 2011.13367 | null | https://arxiv.org/abs/2011.13367v3 | https://arxiv.org/pdf/2011.13367v3.pdf | SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos | Understanding broadcast videos is a challenging task in computer vision, as it requires generic reasoning capabilities to appreciate the content offered by the video editing. In this work, we propose SoccerNet-v2, a novel large-scale corpus of manual annotations for the SoccerNet video dataset, along with open challeng... | ['Marc Van Droogenbroeck', 'Thomas B. Moeslund', 'Bernard Ghanem', 'Kamal Nasrollahi', 'Jacob V. Dueholm', 'Meisam J. Seikavandi', 'Silvio Giancola', 'Anthony Cioppa', 'Adrien Deliège'] | 2020-11-26 | null | null | null | null | ['action-spotting', 'camera-shot-boundary-detection', 'camera-shot-segmentation', 'replay-grounding'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.66962850e-01 -5.39912768e-02 -3.56124371e-01 -2.08169341e-01
-7.06014812e-01 -8.50187123e-01 2.28055060e-01 -3.10912997e-01
-3.71853948e-01 5.23485601e-01 4.33072448e-01 1.23029657e-01
1.43354490e-01 -1.11411147e-01 -1.02906644e+00 -4.90832508e-01
-1.11946449e-01 3.10506761e-01 8.80752265e-01 -6.26024187... | [7.937693119049072, 0.20294694602489471] |
926fb142-a711-42f2-9e2c-5a7b6fc4e662 | int-hrl-towards-intention-based-hierarchical | 2306.11483 | null | https://arxiv.org/abs/2306.11483v1 | https://arxiv.org/pdf/2306.11483v1.pdf | Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning | While deep reinforcement learning (RL) agents outperform humans on an increasing number of tasks, training them requires data equivalent to decades of human gameplay. Recent hierarchical RL methods have increased sample efficiency by incorporating information inherent to the structure of the decision problem but at the... | ['Andreas Bulling', 'Stefan Leutenegger', 'Mihai Bâce', 'Florian Strohm', 'Simon Schaefer', 'Anna Penzkofer'] | 2023-06-20 | null | null | null | null | ['hierarchical-reinforcement-learning', 'montezumas-revenge'] | ['methodology', 'playing-games'] | [-1.98595878e-02 6.98011458e-01 1.21845435e-02 -1.81101635e-01
-8.23756456e-01 -5.01403689e-01 7.02368200e-01 -1.21761657e-01
-8.57925296e-01 8.26651752e-01 4.60383624e-01 -2.97456592e-01
-1.09134614e-03 -3.93921047e-01 -2.67056167e-01 -3.09313089e-01
-1.48701876e-01 7.74092197e-01 3.22503895e-01 -5.40782988... | [3.911484479904175, 1.4744102954864502] |
f16429fd-700f-47ad-a248-47b6dc63acd2 | monte-carlo-tree-search-with-sampled | 1704.05963 | null | http://arxiv.org/abs/1704.05963v1 | http://arxiv.org/pdf/1704.05963v1.pdf | Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds | Monte Carlo Tree Search (MCTS), most famously used in game-play artificial
intelligence (e.g., the game of Go), is a well-known strategy for constructing
approximate solutions to sequential decision problems. Its primary innovation
is the use of a heuristic, known as a default policy, to obtain Monte Carlo
estimates of... | ['Warren B. Powell', 'Lina Al-Kanj', 'Daniel R. Jiang'] | 2017-04-20 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-1.50129214e-01 3.03162545e-01 -5.30198991e-01 -8.51583034e-02
-7.01323092e-01 -5.43084919e-01 2.03530297e-01 -3.91751155e-02
-4.97220904e-01 1.01470625e+00 8.57106224e-03 -9.59843040e-01
-4.10110176e-01 -1.07171953e+00 -5.88255525e-01 -7.55111516e-01
4.13756743e-02 9.47819233e-01 4.14406717e-01 -5.08099973... | [4.30506706237793, 2.3433966636657715] |
91c2226a-a3e7-4850-afbd-763b455d30be | unsupervised-data-augmentation-for-aspect | null | null | https://aclanthology.org/2022.coling-1.586 | https://aclanthology.org/2022.coling-1.586.pdf | Unsupervised Data Augmentation for Aspect Based Sentiment Analysis | Recent approaches to Aspect-based Sentiment Analysis (ABSA) take a co-extraction approach to this span-level classification task, performing the subtasks of aspect term extraction (ATE) and aspect sentiment classification (ASC) simultaneously. In this work, we build on recent progress in applying pre-training to this c... | ['Sahil Badyal', 'Adam Faulkner', 'David Z. Chen'] | null | null | null | null | coling-2022-10 | ['term-extraction', 'aspect-based-sentiment-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.94251084e-01 2.04720590e-02 -1.53766572e-01 -6.66115284e-01
-1.23866224e+00 -9.09792483e-01 9.02529836e-01 6.17115140e-01
-4.95066941e-01 3.21944118e-01 3.17521363e-01 -7.49395728e-01
3.51929039e-01 -6.57506406e-01 -6.44045055e-01 -2.96240777e-01
5.34335487e-02 6.80943191e-01 1.14128746e-01 -6.33676350... | [11.379034042358398, 6.8027238845825195] |
a872f570-cba2-4635-8599-05a3a675e3bd | stars-are-all-you-need-a-distantly-supervised | 2305.01710 | null | https://arxiv.org/abs/2305.01710v1 | https://arxiv.org/pdf/2305.01710v1.pdf | Stars Are All You Need: A Distantly Supervised Pyramid Network for Document-Level End-to-End Sentiment Analysis | In this paper, we propose document-level end-to-end sentiment analysis to efficiently understand aspect and review sentiment expressed in online reviews in a unified manner. In particular, we assume that star rating labels are a "coarse-grained synthesis" of aspect ratings across in the review. We propose a Distantly S... | ['John P. Lalor', 'Yixing Chen', 'Wenchang Li'] | 2023-05-02 | null | null | null | null | ['aspect-category-detection'] | ['natural-language-processing'] | [ 7.29579628e-02 2.20228612e-01 -5.47549367e-01 -9.90284503e-01
-1.07085431e+00 -8.85634661e-01 6.28990889e-01 2.81224251e-01
-2.02589005e-01 3.61597203e-02 7.46290624e-01 -4.91996884e-01
3.13120395e-01 -6.43263817e-01 -3.76189530e-01 -7.34670907e-02
5.02204537e-01 3.82296920e-01 -3.09988469e-01 -5.72587788... | [11.435355186462402, 6.689326286315918] |
16da1bd5-a606-465d-acfc-aa19b9add6f9 | improving-target-speaker-extraction-with | 2301.06277 | null | https://arxiv.org/abs/2301.06277v1 | https://arxiv.org/pdf/2301.06277v1.pdf | Improving Target Speaker Extraction with Sparse LDA-transformed Speaker Embeddings | As a practical alternative of speech separation, target speaker extraction (TSE) aims to extract the speech from the desired speaker using additional speaker cue extracted from the speaker. Its main challenge lies in how to properly extract and leverage the speaker cue to benefit the extracted speech quality. The cue e... | ['Huan Zhou', 'Ziqing Du', 'Xucheng Wan', 'Kai Liu'] | 2023-01-16 | null | null | null | null | ['target-speaker-extraction', 'speech-separation', 'speaker-verification'] | ['audio', 'speech', 'speech'] | [ 1.77645072e-01 -4.65341546e-02 -2.23606631e-01 -2.78259188e-01
-1.33351910e+00 -3.23472083e-01 5.49316585e-01 -1.80018857e-01
-1.42925635e-01 3.67841423e-01 4.73200679e-01 -2.56519675e-01
-1.42206624e-01 5.18329628e-02 -2.48336971e-01 -1.08934951e+00
2.99853855e-03 -2.67884210e-02 -1.14047714e-01 -1.48617670... | [14.734637260437012, 5.949735164642334] |
0793ebc1-e454-42ae-9a7e-2e2a4269fc76 | taking-modality-free-human-identification-as | 2010.00975 | null | https://arxiv.org/abs/2010.00975v2 | https://arxiv.org/pdf/2010.00975v2.pdf | Taking Modality-free Human Identification as Zero-shot Learning | Human identification is an important topic in event detection, person tracking, and public security. There have been numerous methods proposed for human identification, such as face identification, person re-identification, and gait identification. Typically, existing methods predominantly classify a queried image to a... | ['Jian Cheng', 'Yao Zhao', 'Shuai Zheng', 'Zhenfeng Zhu', 'Xingxing Zhang', 'Zhizhe Liu'] | 2020-10-02 | null | null | null | null | ['gait-identification'] | ['computer-vision'] | [ 3.68118227e-01 -6.01766109e-01 -3.40793878e-01 -3.15585822e-01
-8.39472234e-01 -5.13490617e-01 7.39023328e-01 -8.37808996e-02
-3.90354931e-01 5.57066798e-01 1.94029659e-01 2.49467716e-01
5.36623299e-02 -5.69834828e-01 -2.99955934e-01 -7.02551961e-01
3.05971503e-01 2.72455394e-01 -1.05426395e-02 4.79299622... | [14.607255935668945, 0.9910005331039429] |
8c01c433-3de0-418b-b7b5-63aa2ca41881 | challenging-environments-for-traffic-sign | 1902.06857 | null | https://arxiv.org/abs/1902.06857v2 | https://arxiv.org/pdf/1902.06857v2.pdf | Challenging Environments for Traffic Sign Detection: Reliability Assessment under Inclement Conditions | State-of-the-art algorithms successfully localize and recognize traffic signs over existing datasets, which are limited in terms of challenging condition type and severity. Therefore, it is not possible to estimate the performance of traffic sign detection algorithms under overlooked challenging conditions. Another sho... | ['Min-Hung Chen', 'Tariq Alshawi', 'Ghassan AlRegib', 'Dogancan Temel'] | 2019-02-19 | null | null | null | null | ['traffic-sign-detection'] | ['computer-vision'] | [-1.92463081e-02 -6.67348385e-01 -1.29443541e-01 -1.60749748e-01
-8.24127793e-01 -5.68160772e-01 4.24184442e-01 -2.88066894e-01
-4.46090162e-01 6.52502716e-01 -1.30725428e-01 -3.23224634e-01
-1.48827925e-01 -3.16452175e-01 -5.43539464e-01 -6.05812728e-01
-3.19370031e-01 -4.00342420e-02 7.01701403e-01 1.01915926... | [8.008650779724121, -0.8316307663917542] |
1cf8d1b3-1fc0-4136-a228-781d81f8047e | residual-expansion-algorithm-fast-and | 1705.09549 | null | http://arxiv.org/abs/1705.09549v1 | http://arxiv.org/pdf/1705.09549v1.pdf | Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems | We propose the residual expansion (RE) algorithm: a global (or near-global)
optimization method for nonconvex least squares problems. Unlike most existing
nonconvex optimization techniques, the RE algorithm is not based on either
stochastic or multi-point searches; therefore, it can achieve fast global
optimization. Mo... | ['Daiki Ikami', 'Toshihiko Yamasaki', 'Kiyoharu Aizawa'] | 2017-05-26 | residual-expansion-algorithm-fast-and-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Ikami_Residual_Expansion_Algorithm_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Ikami_Residual_Expansion_Algorithm_CVPR_2017_paper.pdf | cvpr-2017-7 | ['blind-image-deblurring'] | ['computer-vision'] | [-3.42371285e-01 -5.39308786e-01 -2.78214008e-01 2.69446410e-02
-1.30809796e+00 -2.60491222e-01 1.61984667e-01 -2.36852959e-01
-3.12200367e-01 7.44456351e-01 3.52201909e-01 7.94926435e-02
-4.05273080e-01 -2.91105092e-01 -5.34571648e-01 -9.69738483e-01
1.16940089e-01 4.69426811e-01 -5.45057915e-02 9.61459894... | [11.659388542175293, -2.6389853954315186] |
0140a08e-cba8-43b8-9f58-280dee0ffee9 | synthetic-propaganda-embeddings-to-train-a | null | null | https://aclanthology.org/D19-5023 | https://aclanthology.org/D19-5023.pdf | Synthetic Propaganda Embeddings To Train A Linear Projection | This paper presents a method of detecting fine-grained categories of propaganda in text. Given a sentence, our method aims to identify a span of words and predict the type of propaganda used. To detect propaganda, we explore a method for extracting features of propaganda from contextualized embeddings without fine-tuni... | ['Mehdi Ghanimifard', 'Adam Ek'] | 2019-11-01 | null | null | null | ws-2019-11 | ['propaganda-detection'] | ['natural-language-processing'] | [ 3.42218548e-01 1.34496585e-01 2.00712569e-02 -3.07372004e-01
-9.80337262e-01 -6.78332031e-01 1.24437392e+00 4.51461196e-01
-5.09114206e-01 4.92360741e-01 1.17980254e+00 -4.79317248e-01
8.94569457e-02 -9.02458727e-01 -5.19947350e-01 -4.44475740e-01
-2.32604459e-01 1.74816549e-01 -2.97190994e-01 -4.50062692... | [8.43415355682373, 10.676199913024902] |
1492d654-abc0-4faa-810c-a18129fef4cb | integrating-incremental-speech-recognition | null | null | https://aclanthology.org/W12-1638 | https://aclanthology.org/W12-1638.pdf | Integrating Incremental Speech Recognition and POMDP-Based Dialogue Systems | null | ['Peter A. Heeman', 'Ethan O. Selfridge', 'Jason D. Williams', 'Iker Arizmendi'] | 2012-07-01 | null | null | null | ws-2012-7 | ['dialogue-management'] | ['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... | [-7.275010585784912, 3.617556571960449] |
234fe5aa-0265-4852-a64a-f1001f26751e | separability-and-scatteredness-s-s-ratio | 2305.10219 | null | https://arxiv.org/abs/2305.10219v1 | https://arxiv.org/pdf/2305.10219v1.pdf | Separability and Scatteredness (S&S) Ratio-Based Efficient SVM Regularization Parameter, Kernel, and Kernel Parameter Selection | Support Vector Machine (SVM) is a robust machine learning algorithm with broad applications in classification, regression, and outlier detection. SVM requires tuning the regularization parameter (RP) which controls the model capacity and the generalization performance. Conventionally, the optimum RP is found by compari... | ['Soosan Beheshti', 'Mahdi Shamsi'] | 2023-05-17 | null | null | null | null | ['outlier-detection'] | ['methodology'] | [-3.42204601e-01 -4.05461520e-01 -4.23247665e-01 -2.33134940e-01
-3.09126973e-01 -3.63700926e-01 1.95777357e-01 4.28143591e-01
-3.91334862e-01 6.40111685e-01 -4.54363734e-01 -3.98820639e-01
-4.24333960e-01 -6.62648141e-01 -2.17070878e-01 -1.02106953e+00
1.73176881e-02 1.73528433e-01 3.63369197e-01 -1.53196201... | [8.272941589355469, 4.059072494506836] |
72eb793c-fc74-4922-8470-bab1de6584dc | cross-modal-pattern-propagation-for-rgb-t | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Cross-Modal_Pattern-Propagation_for_RGB-T_Tracking_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Cross-Modal_Pattern-Propagation_for_RGB-T_Tracking_CVPR_2020_paper.pdf | Cross-Modal Pattern-Propagation for RGB-T Tracking | Motivated by our observations on RGB-T data that pattern correlations are high-frequently recurred across modalities also along sequence frames, in this paper, we propose a cross-modal pattern-propagation (CMPP) tracking framework to diffuse instance patterns across RGB-T data on spatial domain as well as temporal doma... | [' Jian Yang', ' Xiaoya Zhang', ' Tong Zhang', ' Ling Zhou', ' Zhen Cui', ' Chunyan Xu', 'Chaoqun Wang'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['rgb-t-tracking'] | ['computer-vision'] | [ 1.79114610e-01 -5.76380193e-01 -2.99199998e-01 -2.46581137e-01
-3.06802511e-01 -6.11306846e-01 6.54886901e-01 -4.42957669e-01
1.35736624e-02 5.05002141e-01 4.34299290e-01 1.26157954e-01
-5.07829189e-01 -5.13309658e-01 -6.84930444e-01 -9.18335855e-01
-8.22233185e-02 -2.35584110e-01 8.02600741e-01 -1.46546081... | [9.364490509033203, 0.17459428310394287] |
3c1cee1a-d44c-4bf6-bd14-f6999eee40fa | aspect-sentiment-triplet-extraction-using | 2108.06107 | null | https://arxiv.org/abs/2108.06107v1 | https://arxiv.org/pdf/2108.06107v1.pdf | Aspect Sentiment Triplet Extraction Using Reinforcement Learning | Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting triplets of aspect terms, their associated sentiments, and the opinion terms that provide evidence for the expressed sentiments. Previous approaches to ASTE usually simultaneously extract all three components or first identify the aspect and opinion t... | ['Soujanya Poria', 'Navonil Majumder', 'Tapas Nayak', 'Samson Yu Bai Jian'] | 2021-08-13 | null | null | null | null | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [-3.37621905e-02 -3.50783542e-02 -3.27066094e-01 -5.97143173e-01
-1.10265017e+00 -8.49152863e-01 3.89455557e-01 4.09100473e-01
-1.05563976e-01 6.24178529e-01 4.48391467e-01 -3.88663739e-01
2.17283770e-01 -7.44439006e-01 -6.24656558e-01 -5.89126229e-01
1.87386014e-02 5.76220334e-01 -1.67290822e-01 -5.28176248... | [11.491742134094238, 6.6636433601379395] |
6e3785aa-8c3f-4cde-939a-c54bd497309c | importance-attribution-in-neural-networks-by | 2302.03132 | null | https://arxiv.org/abs/2302.03132v1 | https://arxiv.org/pdf/2302.03132v1.pdf | Importance attribution in neural networks by means of persistence landscapes of time series | We propose and implement a method to analyze time series with a neural network using a matrix of area-normalized persistence landscapes obtained through topological data analysis. We include a gating layer in the network's architecture that is able to identify the most relevant landscape levels for the classification t... | ['Oriol Pujol', 'Carles Casacuberta', 'Aina Ferrà'] | 2023-02-06 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [ 2.20219865e-01 -1.40976191e-01 -8.30664784e-02 -4.19399202e-01
-1.10853766e-03 -5.71568191e-01 5.86977541e-01 7.87170231e-01
-4.26259547e-01 7.33811915e-01 1.14110261e-01 -3.15103948e-01
-8.05653632e-01 -1.13633299e+00 -4.03144240e-01 -6.53240621e-01
-7.70614803e-01 3.26675147e-01 3.87716919e-01 -4.91022289... | [7.501455307006836, 3.741959571838379] |
bc6ba7dd-bad1-423c-8f6f-1bf135a55ceb | reconstruction-attack-on-instance-encoding | null | null | https://aclanthology.org/2021.emnlp-main.154 | https://aclanthology.org/2021.emnlp-main.154.pdf | Reconstruction Attack on Instance Encoding for Language Understanding | A private learning scheme TextHide was recently proposed to protect the private text data during the training phase via so-called instance encoding. We propose a novel reconstruction attack to break TextHide by recovering the private training data, and thus unveil the privacy risks of instance encoding. We have experim... | ['Yuan Hong', 'Shangyu Xie'] | null | null | null | null | emnlp-2021-11 | ['sentence-classification'] | ['natural-language-processing'] | [ 6.56090796e-01 4.35498178e-01 -1.47111028e-01 -6.09182537e-01
-9.09990430e-01 -9.21497941e-01 3.76343489e-01 4.43408370e-01
-5.76919854e-01 7.07059920e-01 1.77859098e-01 -6.71024561e-01
1.50879756e-01 -1.06503379e+00 -8.94747615e-01 -9.71582115e-01
-1.16651721e-01 -7.75382593e-02 -1.50260299e-01 1.89474970... | [5.892279624938965, 7.020198822021484] |
5b8de74a-9c6b-4d34-81c5-0d1e7628d5d6 | security-vulnerability-detection-using-deep | 2105.02388 | null | https://arxiv.org/abs/2105.02388v1 | https://arxiv.org/pdf/2105.02388v1.pdf | Security Vulnerability Detection Using Deep Learning Natural Language Processing | Detecting security vulnerabilities in software before they are exploited has been a challenging problem for decades. Traditional code analysis methods have been proposed, but are often ineffective and inefficient. In this work, we model software vulnerability detection as a natural language processing (NLP) problem wit... | ['Shaoen Wu', 'Noah Ziems'] | 2021-05-06 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-1.44701466e-01 -2.28601560e-01 -2.95395821e-01 -2.66991198e-01
-9.13716018e-01 -9.24699068e-01 1.71261281e-01 4.33977723e-01
-1.64524376e-01 1.88505232e-01 6.18578047e-02 -1.21665108e+00
2.31568813e-01 -8.91129792e-01 -6.80230677e-01 7.16268495e-02
-6.35393620e-01 -3.43910784e-01 2.50246078e-01 -1.43291742... | [7.046045303344727, 7.776185035705566] |
b5e87b6b-38f9-4800-b0ff-565c74be4cb0 | setgen-scalable-and-efficient-template | 2211.05622 | null | https://arxiv.org/abs/2211.05622v1 | https://arxiv.org/pdf/2211.05622v1.pdf | SETGen: Scalable and Efficient Template Generation Framework for Groupwise Medical Image Registration | Template generation is a crucial step of groupwise image registration which deforms a group of subjects into a common space. Existing traditional and deep learning-based methods can generate high-quality template images. However, they suffer from substantial time costs or limited application scenarios like fixed group ... | ['Albert C. S. Chung', 'Ziyi He'] | 2022-11-10 | null | null | null | null | ['medical-image-registration'] | ['medical'] | [ 4.50270951e-01 2.32496262e-01 1.61533728e-01 -5.66493332e-01
-9.36015964e-01 -2.05166548e-01 4.92311835e-01 -5.93431771e-01
-4.09725308e-01 4.37457323e-01 3.42976749e-01 2.73607373e-01
-6.45775869e-02 -5.30567229e-01 -6.08773112e-01 -9.83603776e-01
6.91244453e-02 5.57150483e-01 7.11914599e-02 6.41140789... | [13.985038757324219, -2.5141360759735107] |
9a78d54c-5dae-4feb-b49d-07e9fe3c048d | personality-testing-of-gpt-3-limited-temporal | 2306.04308 | null | https://arxiv.org/abs/2306.04308v1 | https://arxiv.org/pdf/2306.04308v1.pdf | Personality testing of GPT-3: Limited temporal reliability, but highlighted social desirability of GPT-3's personality instruments results | To assess the potential applications and limitations of chatbot GPT-3 Davinci-003, this study explored the temporal reliability of personality questionnaires applied to the chatbot and its personality profile. Psychological questionnaires were administered to the chatbot on two separate occasions, followed by a compari... | ['Ljubisa Bojic', 'Bojana M. Dinic', 'Bojana Bodroza'] | 2023-06-07 | null | null | null | null | ['chatbot', 'chatbot'] | ['methodology', 'natural-language-processing'] | [-6.23425305e-01 6.33421183e-01 9.25805122e-02 -5.01025736e-01
-1.28803074e-01 -4.52036291e-01 3.34537208e-01 4.63729799e-02
-3.78421336e-01 7.39418745e-01 2.57450402e-01 2.74960883e-02
-2.52190411e-01 -3.89653206e-01 4.25649971e-01 -4.15930569e-01
-6.98924204e-03 5.49685419e-01 -6.78671449e-02 -5.84805131... | [12.517544746398926, 7.796350002288818] |
7d32f035-9274-4c61-a187-8a620e0d2144 | remote-sensing-image-classification | 1410.5358 | null | http://arxiv.org/abs/1410.5358v3 | http://arxiv.org/pdf/1410.5358v3.pdf | Remote sensing image classification exploiting multiple kernel learning | We propose a strategy for land use classification which exploits Multiple
Kernel Learning (MKL) to automatically determine a suitable combination of a
set of features without requiring any heuristic knowledge about the
classification task. We present a novel procedure that allows MKL to achieve
good performance in the ... | ['Paolo Napoletano', 'Claudio Cusano', 'Raimondo Schettini'] | 2014-10-20 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [-5.04790153e-03 -4.14022624e-01 -5.56388259e-01 -4.91777092e-01
-8.45402837e-01 -3.27083647e-01 6.81928396e-01 2.93326467e-01
-8.26762140e-01 9.02887702e-01 -3.58887285e-01 -6.79879189e-01
-4.77537245e-01 -1.08178747e+00 -2.28580803e-01 -8.45774829e-01
-3.42205763e-01 -1.20601833e-01 3.94353509e-01 -3.82229835... | [7.97214412689209, 3.939903974533081] |
b0cddb37-76ae-4bd2-8c72-787c87d866d5 | three-dimensional-reconstruction-of-human | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Fieraru_Three-Dimensional_Reconstruction_of_Human_Interactions_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Fieraru_Three-Dimensional_Reconstruction_of_Human_Interactions_CVPR_2020_paper.pdf | Three-Dimensional Reconstruction of Human Interactions | Understanding 3d human interactions is fundamental for fine grained scene analysis and behavioural modeling. However, most of the existing models focus on analyzing a single person in isolation, and those who process several people focus largely on resolving multi-person data association, rather than inferring interact... | [' Cristian Sminchisescu', ' Vlad Olaru', ' Alin-Ionut Popa', ' Elisabeta Oneata', ' Mihai Zanfir', 'Mihai Fieraru'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['contact-detection'] | ['robots'] | [ 4.93292809e-02 -3.13982755e-01 -1.08341217e-01 -3.03827673e-01
-5.99133253e-01 -3.98001075e-01 5.20539403e-01 -1.92650527e-01
-4.48052555e-01 3.69416833e-01 5.86128891e-01 2.03873530e-01
-1.38090536e-01 -4.75430876e-01 -7.43224561e-01 -1.42911196e-01
-3.26321304e-01 8.97876799e-01 2.73028463e-01 -9.40001383... | [7.0147294998168945, -0.9650317430496216] |
0c7ecff7-9397-4db8-8583-a01a3428e884 | causalvlr-a-toolbox-and-benchmark-for-visual | 2306.17462 | null | https://arxiv.org/abs/2306.17462v1 | https://arxiv.org/pdf/2306.17462v1.pdf | CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal Reasoning | We present CausalVLR (Causal Visual-Linguistic Reasoning), an open-source toolbox containing a rich set of state-of-the-art causal relation discovery and causal inference methods for various visual-linguistic reasoning tasks, such as VQA, image/video captioning, medical report generation, model generalization and robus... | ['Liang Lin', 'Guanbin Li', 'Weixing Chen', 'Yang Liu'] | 2023-06-30 | null | null | null | null | ['visual-question-answering', 'video-captioning', 'causal-inference', 'medical-report-generation', 'causal-inference'] | ['computer-vision', 'computer-vision', 'knowledge-base', 'medical', 'miscellaneous'] | [-2.51718342e-01 4.11659092e-01 -5.59227169e-01 -4.13482368e-01
-4.97111589e-01 -5.32836556e-01 7.99835086e-01 2.86671566e-03
2.40592167e-01 7.11894155e-01 9.13828969e-01 -7.42621839e-01
-9.38595384e-02 -5.48045218e-01 -6.27741933e-01 -3.25841963e-01
-7.91881084e-02 4.23046380e-01 1.34770721e-01 -7.91571438... | [10.538769721984863, 1.6316741704940796] |
c267e0d2-ae8a-440d-bde5-ad0ba16921ee | robust-video-object-tracking-using-particle | 1509.08182 | null | http://arxiv.org/abs/1509.08182v1 | http://arxiv.org/pdf/1509.08182v1.pdf | Robust video object tracking using particle filter with likelihood based feature fusion and adaptive template updating | A robust algorithm solution is proposed for tracking an object in complex
video scenes. In this solution, the bootstrap particle filter (PF) is
initialized by an object detector, which models the time-evolving background of
the video signal by an adaptive Gaussian mixture. The motion of the object is
expressed by a Mar... | ['Bin Liu', 'Yi Dai'] | 2015-09-28 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [ 1.80092245e-01 -4.98632997e-01 8.44937377e-03 4.74290438e-02
-1.42684162e-01 -3.49113673e-01 6.78953409e-01 -4.55359787e-01
-5.12133181e-01 6.87537193e-01 -4.66256261e-01 3.76022786e-01
9.52108130e-02 -5.15349209e-01 -3.66556555e-01 -1.09355497e+00
7.00691640e-02 5.35862803e-01 9.46123123e-01 4.05968964... | [6.592889785766602, -2.0042612552642822] |
d8d234a4-1d18-457c-9f2c-f0f68e4b3945 | regret-guarantees-for-adversarial-online | 2302.05765 | null | https://arxiv.org/abs/2302.05765v1 | https://arxiv.org/pdf/2302.05765v1.pdf | Regret Guarantees for Adversarial Online Collaborative Filtering | We investigate the problem of online collaborative filtering under no-repetition constraints, whereby users need to be served content in an online fashion and a given user cannot be recommended the same content item more than once. We design and analyze a fully adaptive algorithm that works under biclustering assumptio... | ['Claudio Gentile', 'Mark Herbster', 'Fabio Vitale', 'Stephen Pasteris'] | 2023-02-11 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [ 1.02060147e-01 -3.07272989e-02 -4.47299927e-02 -1.48432612e-01
-2.62858063e-01 -1.28691888e+00 1.49123156e-02 8.93778875e-02
-6.27540052e-01 5.64893603e-01 3.90920818e-01 -5.60100079e-01
-6.72050536e-01 -9.81999815e-01 -8.98781538e-01 -6.97078884e-01
-4.57309514e-01 6.17132008e-01 1.82613492e-01 -3.88885200... | [4.601436138153076, 3.4043312072753906] |
ce210dde-e54e-442b-b6d2-2756a2c89fba | phocal-a-multi-modal-dataset-for-category | 2205.08811 | null | https://arxiv.org/abs/2205.08811v1 | https://arxiv.org/pdf/2205.08811v1.pdf | PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation with Photometrically Challenging Objects | Object pose estimation is crucial for robotic applications and augmented reality. Beyond instance level 6D object pose estimation methods, estimating category-level pose and shape has become a promising trend. As such, a new research field needs to be supported by well-designed datasets. To provide a benchmark with hig... | ['Benjamin Busam', 'Nassir Navab', 'Sven Meier', 'Lorenzo Garattoni', 'Rahul Parthasarathy Srikanth', 'Siyuan Shen', 'Yitong Li', 'HyunJun Jung', 'Pengyuan Wang'] | 2022-05-18 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_PhoCaL_A_Multi-Modal_Dataset_for_Category-Level_Object_Pose_Estimation_With_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_PhoCaL_A_Multi-Modal_Dataset_for_Category-Level_Object_Pose_Estimation_With_CVPR_2022_paper.pdf | cvpr-2022-1 | ['transparent-objects', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 5.14604934e-02 9.41903964e-02 2.51508415e-01 -4.31351006e-01
-8.75609994e-01 -6.59616470e-01 5.64667821e-01 -6.51494861e-02
-1.35862604e-01 3.92138898e-01 -2.53399581e-01 2.65571058e-01
-1.08570429e-02 -3.54092032e-01 -8.30553949e-01 -6.65598214e-01
1.69840887e-01 1.11337316e+00 5.11518002e-01 1.74386539... | [7.22430419921875, -2.4037723541259766] |
9f1c37ff-467f-4cd0-8a62-95ef14f0f666 | a-baseline-temporal-tagger-for-all-languages | null | null | https://aclanthology.org/D15-1063 | https://aclanthology.org/D15-1063.pdf | A Baseline Temporal Tagger for all Languages | null | ['Jannik Str{\\"o}tgen', 'Michael Gertz'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['timex-normalization', 'temporal-information-extraction'] | ['natural-language-processing', '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... | [-7.197238922119141, 3.749377489089966] |
d89e4816-fff5-4bd3-b26c-a2c909e2c54a | fast-learning-of-multidimensional-hawkes | 2212.06081 | null | https://arxiv.org/abs/2212.06081v1 | https://arxiv.org/pdf/2212.06081v1.pdf | Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe | Hawkes processes have recently risen to the forefront of tools when it comes to modeling and generating sequential events data. Multidimensional Hawkes processes model both the self and cross-excitation between different types of events and have been applied successfully in various domain such as finance, epidemiology ... | ['Manuela Veloso', 'Tucker Balch', 'Vamsi K. Potluru', 'Mohsen Ghassemi', 'Niccolò Dalmasso', 'Renbo Zhao'] | 2022-12-12 | null | null | null | null | ['epidemiology'] | ['medical'] | [-2.05097690e-01 -2.44060144e-01 1.92467317e-01 5.58567885e-03
-6.07127190e-01 -4.13254261e-01 1.20819116e+00 5.78759909e-01
-1.93488061e-01 6.22504711e-01 4.61163610e-01 -2.74211556e-01
-4.75197971e-01 -9.32905078e-01 -3.88850063e-01 -9.91495252e-01
-1.37039736e-01 8.23287368e-01 3.93228948e-01 -1.41898468... | [6.919283866882324, 3.462026596069336] |
80185037-c7ea-4a63-ba5e-b94d3977d67d | are-quantum-computers-practical-yet-a-case | 2205.04490 | null | https://arxiv.org/abs/2205.04490v2 | https://arxiv.org/pdf/2205.04490v2.pdf | Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks | Collaborative filtering models generally perform better than content-based filtering models and do not require careful feature engineering. However, in the cold-start scenario collaborative information may be scarce or even unavailable, whereas the content information may be abundant, but also noisy and expensive to ac... | ['Evgeny Frolov', 'Ivan Oseledets', 'Rafael Ballester-Ripoll', 'Andrei Chertkov', 'Artyom Nikitin'] | 2022-05-09 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-1.23061366e-01 -2.52587050e-01 2.21142266e-03 -2.98503280e-01
-6.58063293e-01 -7.69028068e-01 4.37493324e-01 4.33649182e-01
-6.43013835e-01 6.75406873e-01 -1.01770572e-01 -2.54528731e-01
-8.38502765e-01 -9.90314245e-01 -4.28057164e-01 -8.53776515e-01
-1.36489823e-01 7.29404807e-01 -1.87565297e-01 -6.16165996... | [5.914766311645508, 4.830348491668701] |
ef6a596a-22fd-4d8d-ba09-faf6761a2126 | targeted-syntactic-evaluation-of-language | 1808.09031 | null | http://arxiv.org/abs/1808.09031v1 | http://arxiv.org/pdf/1808.09031v1.pdf | Targeted Syntactic Evaluation of Language Models | We present a dataset for evaluating the grammaticality of the predictions of
a language model. We automatically construct a large number of minimally
different pairs of English sentences, each consisting of a grammatical and an
ungrammatical sentence. The sentence pairs represent different variations of
structure-sensi... | ['Tal Linzen', 'Rebecca Marvin'] | 2018-08-27 | targeted-syntactic-evaluation-of-language-1 | https://aclanthology.org/D18-1151 | https://aclanthology.org/D18-1151.pdf | emnlp-2018-10 | ['ccg-supertagging'] | ['natural-language-processing'] | [ 5.21175936e-02 5.81803620e-01 8.72878656e-02 -8.66094291e-01
-9.67007697e-01 -6.23760641e-01 5.00455618e-01 2.15833977e-01
-6.54958248e-01 6.70443714e-01 5.42954564e-01 -6.52880609e-01
2.39877596e-01 -8.48153651e-01 -7.75050879e-01 -3.19694519e-01
-5.92633560e-02 8.11487734e-01 -2.47061476e-02 -2.76246399... | [10.617230415344238, 9.183589935302734] |
91dd9f2f-3b49-48c0-a523-01e6277fc60e | machine-learning-diffusion-monte-carlo-energy | 2205.04547 | null | https://arxiv.org/abs/2205.04547v2 | https://arxiv.org/pdf/2205.04547v2.pdf | Machine Learning Diffusion Monte Carlo Energies | We present two machine learning methodologies that are capable of predicting diffusion Monte Carlo (DMC) energies with small datasets (~60 DMC calculations in total). The first uses voxel deep neural networks (VDNNs) to predict DMC energy densities using Kohn-Sham density functional theory (DFT) electron densities as i... | ['Isaac Tamblyn', 'Jaron T. Krogel', 'Kevin Ryczko'] | 2022-05-09 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 7.96649307e-02 -3.58184576e-01 -2.20536664e-01 -2.96370517e-02
-8.17076325e-01 7.34586045e-02 8.10003936e-01 3.59315306e-01
-7.46054292e-01 1.29874897e+00 3.35235178e-01 -6.49412692e-01
-7.62551799e-02 -1.21458793e+00 -6.73089981e-01 -1.27460349e+00
-3.60742629e-01 5.43318450e-01 1.41869664e-01 -2.20750272... | [5.261703014373779, 5.324174880981445] |
e87d0861-0d16-4724-a209-2dae9fc55549 | deep-learning-based-dereverberation-of | 2008.03339 | null | https://arxiv.org/abs/2008.03339v1 | https://arxiv.org/pdf/2008.03339v1.pdf | Deep Learning Based Dereverberation of Temporal Envelopesfor Robust Speech Recognition | Automatic speech recognition in reverberant conditions is a challenging task as the long-term envelopes of the reverberant speech are temporally smeared. In this paper, we propose a neural model for enhancement of sub-band temporal envelopes for dereverberation of speech. The temporal envelopes are derived using the au... | ['Sriram Ganapathy', 'Rohit Kumar', 'Anirudh Sreeram', 'Anurenjan Purushothaman'] | 2020-08-07 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 2.36551881e-01 -1.36047333e-01 7.16418147e-01 -4.50984508e-01
-1.32847857e+00 -3.66130412e-01 1.81815892e-01 -3.32483739e-01
-4.07322764e-01 5.65185308e-01 6.51112139e-01 -5.44635773e-01
4.38427106e-02 -2.53899395e-01 -6.41381741e-01 -8.02129865e-01
-2.56157339e-01 -5.45496702e-01 -1.80268645e-01 -4.82141644... | [14.998860359191895, 5.994904041290283] |
5d0025dc-d9f9-4ae0-8549-11a0719a1cc8 | a-comparison-study-the-impact-of-age-and | null | null | https://dl.acm.org/doi/10.1145/3469877.3490576 | https://dl.acm.org/doi/10.1145/3469877.3490576 | A comparison study: the impact of age and gender distribution on age estimation | Age estimation from a single facial image is a challenging and attractive research area in the computer vision community. Several facial datasets annotated with age and gender attributes became available in the literature. However, one major drawback is that these datasets do not consider the label distribution during ... | ['Guoliang Chen', 'Qiuming Luo', 'Chang Kong'] | 2022-01-10 | null | null | null | mmasia-2022-1 | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-4.41917002e-01 6.26319125e-02 -1.32027432e-01 -7.98147798e-01
2.00656548e-01 -2.76264455e-02 6.34825945e-01 1.25513181e-01
-7.10509777e-01 7.02192545e-01 -1.03766344e-01 2.43213907e-01
2.39319950e-01 -8.54611158e-01 -1.92748979e-01 -8.64825785e-01
-3.40367071e-02 5.05728602e-01 -2.51998454e-01 2.88040161... | [13.558060646057129, 0.979480504989624] |
edb864ed-6f01-44fd-93e0-40519562cc08 | on-advances-in-text-generation-from-images | 2205.11686 | null | https://arxiv.org/abs/2205.11686v2 | https://arxiv.org/pdf/2205.11686v2.pdf | On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization | Combining the visual modality with pretrained language models has been surprisingly effective for simple descriptive tasks such as image captioning. More general text generation however remains elusive. We take a step back and ask: How do these models work for more complex generative tasks, i.e. conditioning on both te... | ['Ana Marasović', 'Alan W Black', 'Florian Metze', 'Yonatan Bisk', 'Akshita Bhagia', 'Shruti Palaskar'] | 2022-05-24 | null | null | null | null | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 4.63721454e-01 5.38567305e-01 -7.72817507e-02 -5.44688702e-01
-9.03429985e-01 -8.36486042e-01 1.11901462e+00 -8.75210837e-02
-3.71397883e-01 7.68160880e-01 6.40509665e-01 -6.70183241e-01
2.51750082e-01 -4.16760564e-01 -1.03100812e+00 -4.60705429e-01
6.90437436e-01 7.69665301e-01 -1.72496915e-01 -2.80558020... | [10.851419448852539, 1.7017357349395752] |
9895030e-348c-4750-8e4f-d4d6f6b12c02 | client-recruitment-for-federated-learning-in | 2304.14663 | null | https://arxiv.org/abs/2304.14663v1 | https://arxiv.org/pdf/2304.14663v1.pdf | Client Recruitment for Federated Learning in ICU Length of Stay Prediction | Machine and deep learning methods for medical and healthcare applications have shown significant progress and performance improvement in recent years. These methods require vast amounts of training data which are available in the medical sector, albeit decentralized. Medical institutions generate vast amounts of data f... | ['Bart De Moor', 'Wouter Verbeke', 'Lyse Naomi Wamba Momo', 'Vincent Scheltjens'] | 2023-04-28 | null | null | null | null | ['length-of-stay-prediction'] | ['medical'] | [-9.44944248e-02 3.12151730e-01 -1.38627827e-01 -5.02541959e-01
-6.77926302e-01 -4.02845472e-01 1.28530964e-01 3.39899808e-01
-6.64515197e-01 9.26954389e-01 -6.30470663e-02 -5.91701686e-01
-6.80379093e-01 -8.60141456e-01 -4.54942048e-01 -7.70550609e-01
-2.04759181e-01 1.06783926e+00 -5.06506741e-01 3.19116771... | [6.151136875152588, 6.457866191864014] |
8fdf7abc-73a7-413c-b92b-5768c047a57d | tap-a-comprehensive-data-repository-for | 2304.08640 | null | https://arxiv.org/abs/2304.08640v1 | https://arxiv.org/pdf/2304.08640v1.pdf | TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networks | Road safety is a major global public health concern. Effective traffic crash prediction can play a critical role in reducing road traffic accidents. However, Existing machine learning approaches tend to focus on predicting traffic accidents in isolation, without considering the potential relationships between different... | ['Kai Shu', 'Bryan Hooi', 'Baixiang Huang'] | 2023-04-17 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [ 2.57137641e-02 1.34745032e-01 -6.53808057e-01 -1.43777624e-01
-5.76980472e-01 1.71902087e-02 3.01407725e-01 3.61200541e-01
-1.94067851e-01 6.80606604e-01 4.17284191e-01 -9.14804220e-01
-6.56719565e-01 -1.39445126e+00 -6.49259448e-01 -3.90202403e-01
-1.57631233e-01 5.07336020e-01 4.99358416e-01 -5.85831881... | [6.473294258117676, 2.0650875568389893] |
f060111b-c648-45a1-a1c1-e5e5b4b3c1ef | provable-subspace-identification-under-post | 2210.07532 | null | https://arxiv.org/abs/2210.07532v1 | https://arxiv.org/pdf/2210.07532v1.pdf | Provable Subspace Identification Under Post-Nonlinear Mixtures | Unsupervised mixture learning (UML) aims at identifying linearly or nonlinearly mixed latent components in a blind manner. UML is known to be challenging: Even learning linear mixtures requires highly nontrivial analytical tools, e.g., independent component analysis or nonnegative matrix factorization. In this work, th... | ['Xiao Fu', 'Qi Lyu'] | 2022-10-14 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 3.57563049e-01 -9.51492563e-02 -3.31406116e-01 3.71881872e-02
-5.51691234e-01 -6.34809852e-01 4.52478230e-01 -3.37200344e-01
-2.61691324e-02 7.71980286e-01 1.54682219e-01 -5.63870430e-01
-7.12535143e-01 -4.13609520e-02 -5.55538297e-01 -1.43969381e+00
-1.29617199e-01 2.70071447e-01 -5.03185868e-01 1.47137269... | [7.804302215576172, 4.2917938232421875] |
848bb017-efd2-408b-a599-325a0d4c473c | time-series-anomaly-detection-detection-of | 1708.03665 | null | http://arxiv.org/abs/1708.03665v1 | http://arxiv.org/pdf/1708.03665v1.pdf | Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data | Google uses continuous streams of data from industry partners in order to
deliver accurate results to users. Unexpected drops in traffic can be an
indication of an underlying issue and may be an early warning that remedial
action may be necessary. Detecting such drops is non-trivial because streams
are variable and noi... | ['Stephen T. Edwards', 'Paolo M. Piselli', 'Jason M. Gurevitch', 'Dominique T. Shipmon'] | 2017-08-11 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [ 3.39998864e-02 -4.35599893e-01 6.85284883e-02 -3.20450693e-01
-5.45729935e-01 -6.18489087e-01 5.36790133e-01 4.98198032e-01
-1.60900921e-01 6.05928838e-01 -4.91631702e-02 -9.08900559e-01
-1.33865267e-01 -8.06629479e-01 -5.41152418e-01 -3.51611167e-01
-4.88561898e-01 3.53171319e-01 6.14385724e-01 -2.33303621... | [7.387280464172363, 2.657933473587036] |
d1a1583b-d2c4-4f87-a884-a7bb2e26fb3e | meta-learning-based-knowledge-extrapolation-1 | 2302.05640 | null | https://arxiv.org/abs/2302.05640v1 | https://arxiv.org/pdf/2302.05640v1.pdf | Meta-Learning Based Knowledge Extrapolation for Temporal Knowledge Graph | In the last few years, the solution to Knowledge Graph (KG) completion via learning embeddings of entities and relations has attracted a surge of interest. Temporal KGs(TKGs) extend traditional Knowledge Graphs (KGs) by associating static triples with timestamps forming quadruples. Different from KGs and TKGs in the tr... | ['You Dou', 'Zhen Huang', 'Fenglong Su', 'Chengjin Xu', 'Zhongwu Chen'] | 2023-02-11 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-4.32411224e-01 7.00782955e-01 -7.24402785e-01 -3.14897858e-02
-2.27488019e-02 -4.23998833e-01 7.28138328e-01 3.85410815e-01
-5.20075262e-02 8.83184850e-01 3.32215935e-01 -4.35628563e-01
-4.93179709e-01 -1.33897054e+00 -1.22460139e+00 -3.05158079e-01
-5.72433650e-01 6.44136965e-01 4.32587385e-01 -3.89871478... | [8.667867660522461, 7.956736087799072] |
9ebef11b-64ca-49a4-9d1a-22393ddca78b | hyperspectral-unmixing-based-on-clustered | 1812.10788 | null | http://arxiv.org/abs/1812.10788v1 | http://arxiv.org/pdf/1812.10788v1.pdf | Hyperspectral Unmixing Based on Clustered Multitask Networks | Hyperspectral remote sensing is a prominent research topic in data
processing. Most of the spectral unmixing algorithms are developed by adopting
the linear mixing models. Nonnegative matrix factorization (NMF) and its
developments are used widely for estimation of signatures and fractional
abundances in the SU problem... | ['Sara Khoshsokhan', 'Roozbeh Rajabi', 'Hadi Zayyani'] | 2018-12-27 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 6.54421329e-01 -7.28742123e-01 -5.41894957e-02 3.63652036e-02
6.18099496e-02 -6.16354346e-01 9.28592905e-02 -1.82631105e-01
-3.71974438e-01 6.57156467e-01 -9.12883580e-02 -1.66312382e-01
-7.77084649e-01 -7.58276880e-01 1.58971269e-02 -1.34795320e+00
-1.63526893e-01 2.73762077e-01 -4.69693810e-01 -1.62036698... | [10.070609092712402, -2.0452301502227783] |
f9ac094d-b6b8-4b88-b7d8-f8a029cef841 | nested-grassmanns-for-dimensionality | 2010.14589 | null | https://arxiv.org/abs/2010.14589v3 | https://arxiv.org/pdf/2010.14589v3.pdf | Nested Grassmannians for Dimensionality Reduction with Applications | In the recent past, nested structures in Riemannian manifolds has been studied in the context of dimensionality reduction as an alternative to the popular principal geodesic analysis (PGA) technique, for example, the principal nested spheres. In this paper, we propose a novel framework for constructing a nested sequenc... | ['Baba C. Vemuri', 'Chun-Hao Yang'] | 2020-10-27 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [-1.49336979e-01 6.85378760e-02 4.98399943e-01 -1.26891181e-01
-1.18255980e-01 -6.35860384e-01 4.03684616e-01 -4.46037948e-01
-2.02734515e-01 4.20399100e-01 2.42751706e-02 -2.53213972e-01
-6.44921184e-01 -6.14343047e-01 -2.90810615e-01 -9.93473768e-01
-2.82188654e-01 6.36273203e-03 1.22261085e-02 -2.83761472... | [7.7716875076293945, 4.1582770347595215] |
7c751fdb-6f35-4f42-9f3a-c3beefc798f2 | indudonet-an-interpretable-dual-domain | 2109.05298 | null | https://arxiv.org/abs/2109.05298v1 | https://arxiv.org/pdf/2109.05298v1.pdf | InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction | For the task of metal artifact reduction (MAR), although deep learning (DL)-based methods have achieved promising performances, most of them suffer from two problems: 1) the CT imaging geometry constraint is not fully embedded into the network during training, leaving room for further performance improvement; 2) the mo... | ['Yefeng Zheng', 'Deyu Meng', 'Kai Ma', 'Jiawei Chen', 'Haimiao Zhang', 'Yuexiang Li', 'Hong Wang'] | 2021-09-11 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 8.60652775e-02 3.13232869e-01 -1.18851140e-01 -4.41371143e-01
-8.42418253e-01 -6.96273372e-02 8.82674977e-02 -1.06506862e-01
-2.44295686e-01 6.50847912e-01 1.98529810e-01 -5.79397261e-01
-4.03173387e-01 -7.86899745e-01 -7.26805687e-01 -6.35765970e-01
1.60446674e-01 4.95010704e-01 1.57310888e-01 -1.37742490... | [13.633004188537598, -2.5477066040039062] |
3b0c1833-8a1c-4e08-a5ea-0bdf74087f01 | towards-realistic-symmetry-based-completion | 2201.01858 | null | https://arxiv.org/abs/2201.01858v1 | https://arxiv.org/pdf/2201.01858v1.pdf | Towards realistic symmetry-based completion of previously unseen point clouds | 3D scanning is a complex multistage process that generates a point cloud of an object typically containing damaged parts due to occlusions, reflections, shadows, scanner motion, specific properties of the object surface, imperfect reconstruction algorithms, etc. Point cloud completion is specifically designed to fill i... | ['Mykola Maksymenko', 'Volodymyr Karpiv', 'Vladyslav Selotkin', 'Rostyslav Hryniv', 'Oles Dobosevych', 'Taras Rumezhak'] | 2022-01-05 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [ 2.94345349e-01 -1.82507262e-01 5.27159035e-01 -4.09143388e-01
-7.54820049e-01 -4.15548682e-01 1.85054809e-01 6.06819652e-02
5.94928209e-03 1.33353204e-01 -4.72574592e-01 -9.84134153e-02
-1.53835848e-01 -8.08720231e-01 -9.53803897e-01 -5.15823245e-01
9.49085429e-02 1.26231503e+00 5.42335093e-01 -5.79937249... | [8.350464820861816, -3.1627163887023926] |
ec5bf21b-16d0-42ac-a6db-bfc96d82ff93 | creating-unbiased-public-benchmark-datasets | 2107.01905 | null | https://arxiv.org/abs/2107.01905v1 | https://arxiv.org/pdf/2107.01905v1.pdf | Creating Unbiased Public Benchmark Datasets with Data Leakage Prevention for Predictive Process Monitoring | Advances in AI, and especially machine learning, are increasingly drawing research interest and efforts towards predictive process monitoring, the subfield of process mining (PM) that concerns predicting next events, process outcomes and remaining execution times. Unfortunately, researchers use a variety of datasets an... | ['Jochen De Weerdt', 'Hans Weytjens'] | 2021-07-05 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 0.5361042 -0.10617581 -0.15949924 -0.1268553 -0.58109355 -0.6818739
0.9207862 0.70629627 -0.16933696 0.6760449 -0.01220533 -0.39388826
-0.41790187 -0.97370553 -0.32986903 -0.64424735 0.09005237 0.7902962
-0.05720818 0.43261412 0.6321238 0.4358001 -1.6562351 0.2243367
0.70037824 0.8610642 -0.165... | [8.653228759765625, 6.049703121185303] |
2ca97b8e-f288-4c69-9fde-857d6ccb8461 | texttovec-deep-contextualized-neural | 1810.03947 | null | http://arxiv.org/abs/1810.03947v4 | http://arxiv.org/pdf/1810.03947v4.pdf | textTOvec: Deep Contextualized Neural Autoregressive Topic Models of Language with Distributed Compositional Prior | We address two challenges of probabilistic topic modelling in order to better
estimate the probability of a word in a given context, i.e., P(word|context):
(1) No Language Structure in Context: Probabilistic topic models ignore word
order by summarizing a given context as a "bag-of-word" and consequently the
semantics ... | ['Hinrich Schütze', 'Yatin Chaudhary', 'Florian Buettner', 'Pankaj Gupta'] | 2018-10-09 | texttovec-deep-contextualized-neural-1 | https://openreview.net/forum?id=rkgoyn09KQ | https://openreview.net/pdf?id=rkgoyn09KQ | iclr-2019-5 | ['information-extraction'] | ['natural-language-processing'] | [ 2.48458058e-01 2.59737939e-01 -3.21987033e-01 -3.82712245e-01
-6.90833449e-01 -3.85638744e-01 1.00131214e+00 1.18276052e-01
-5.21079957e-01 4.81118262e-01 5.54720879e-01 -3.22545975e-01
-2.86296278e-01 -8.98874283e-01 -7.41933346e-01 -9.23447728e-01
1.41894117e-01 7.91528821e-01 1.70737244e-02 2.59950086... | [10.442458152770996, 6.952220916748047] |
b983090a-3bff-4717-8ad9-609cb0deeb54 | cgpart-a-part-segmentation-dataset-based-on | 2103.14098 | null | https://arxiv.org/abs/2103.14098v2 | https://arxiv.org/pdf/2103.14098v2.pdf | Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehicles | Part segmentations provide a rich and detailed part-level description of objects. However, their annotation requires an enormous amount of work, which makes it difficult to apply standard deep learning methods. In this paper, we propose the idea of learning part segmentation through unsupervised domain adaptation (UDA)... | ['Alan Yuille', 'Weichao Qiu', 'Jiteng Mu', 'Xiaoding Yuan', 'Qihao Liu', 'Mengqi Guo', 'Zizhang Li', 'Zhishuai Zhang', 'Adam Kortylewski', 'Qing Liu'] | 2021-03-25 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_Learning_Part_Segmentation_Through_Unsupervised_Domain_Adaptation_From_Synthetic_Vehicles_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_Learning_Part_Segmentation_Through_Unsupervised_Domain_Adaptation_From_Synthetic_Vehicles_CVPR_2022_paper.pdf | cvpr-2022-1 | ['geometric-matching'] | ['computer-vision'] | [ 6.19753897e-02 1.50726840e-01 -3.18286389e-01 -5.45429766e-01
-6.87419593e-01 -7.97733843e-01 5.39765596e-01 -4.60644096e-01
5.60719147e-03 5.02994061e-01 -1.96388975e-01 -1.78464383e-01
4.77357388e-01 -8.40604007e-01 -1.16925263e+00 -4.39492971e-01
2.98754960e-01 7.75782645e-01 7.55596638e-01 -1.69927537... | [9.245656967163086, 0.5163228511810303] |
0317fec0-f52d-4ea3-8d4c-aba2761f5a9b | da-gan-instance-level-image-translation-by | 1802.06454 | null | http://arxiv.org/abs/1802.06454v1 | http://arxiv.org/pdf/1802.06454v1.pdf | DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks (with Supplementary Materials) | Unsupervised image translation, which aims in translating two independent
sets of images, is challenging in discovering the correct correspondences
without paired data. Existing works build upon Generative Adversarial Network
(GAN) such that the distribution of the translated images are indistinguishable
from the distr... | ['Jianlong Fu', 'Chang Wen Chen', 'Shuang Ma', 'Tao Mei'] | 2018-02-18 | null | null | null | cvpr-2018 | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 5.50038993e-01 3.11180294e-01 -6.10893182e-02 -2.96934694e-01
-1.03414011e+00 -7.80649602e-01 7.73268580e-01 -6.60334349e-01
1.57839321e-02 8.46848845e-01 -2.23242883e-02 -3.78280580e-02
4.58024703e-02 -9.39617455e-01 -1.24211514e+00 -9.08959210e-01
5.51258683e-01 7.27448404e-01 -2.31715724e-01 -1.18203789... | [11.68026351928711, -0.37211790680885315] |
4aaba07b-91ca-4b2b-8eb5-f6260f92896e | on-text-style-transfer-via-style-masked | 2210.06394 | null | https://arxiv.org/abs/2210.06394v1 | https://arxiv.org/pdf/2210.06394v1.pdf | On Text Style Transfer via Style Masked Language Models | Text Style Transfer (TST) is performable through approaches such as latent space disentanglement, cycle-consistency losses, prototype editing etc. The prototype editing approach, which is known to be quite successful in TST, involves two key phases a) Masking of source style-associated tokens and b) Reconstruction of t... | ['Maunendra Sankar Desarkar', 'Suvodip Dey', 'Pooja Shekar', 'Sharan Narasimhan'] | 2022-10-12 | null | null | null | null | ['text-style-transfoer'] | ['natural-language-processing'] | [ 7.60903478e-01 4.24643338e-01 3.89274210e-02 -4.85184938e-01
-1.04641521e+00 -8.70785713e-01 1.19370282e+00 -2.55171955e-01
-2.89115161e-01 8.52462292e-01 4.74663883e-01 -3.81451875e-01
1.54234797e-01 -4.01233524e-01 -8.38039577e-01 -5.67932963e-01
4.87645686e-01 9.84615564e-01 -6.27472103e-02 -4.42956239... | [11.5960693359375, 9.54311466217041] |
8b055f45-0011-460c-ba7f-95eca659fbff | machine-learning-with-tree-tensor-networks-cp | 2305.19440 | null | https://arxiv.org/abs/2305.19440v1 | https://arxiv.org/pdf/2305.19440v1.pdf | Machine learning with tree tensor networks, CP rank constraints, and tensor dropout | Tensor networks approximate order-$N$ tensors with a reduced number of degrees of freedom that is only polynomial in $N$ and arranged as a network of partially contracted smaller tensors. As suggested in [arXiv:2205.15296] in the context of quantum many-body physics, computation costs can be further substantially reduc... | ['Thomas Barthel', 'Hao Chen'] | 2023-05-30 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 6.99980035e-02 2.61868209e-01 -3.97217095e-01 -4.48141098e-01
-3.65743965e-01 -5.17982006e-01 4.27598804e-01 -1.57022536e-01
-3.75531614e-01 5.14305770e-01 -1.76069438e-01 -4.58823055e-01
-5.09913683e-01 -7.53000557e-01 -6.65105104e-01 -8.66768360e-01
-5.87490559e-01 5.33758461e-01 1.99650899e-01 -3.39135647... | [5.9344024658203125, 4.986341953277588] |
98580531-9af6-4838-b7d5-2d31e151acd5 | a-comparative-analysis-on-bangla-handwritten | null | null | https://scholar.google.com/citations?view_op=view_citation&hl=en&user=zQKHA64AAAAJ&citation_for_view=zQKHA64AAAAJ:d1gkVwhDpl0C | https://ieeexplore.ieee.org/abstract/document/9152905/ | A Comparative Analysis on Bangla Handwritten Digit Recognition with Data Augmentation and Non-Augmentation Process | Determination of Bangla handwritten digit is a
momentous image classification task. Though object recognition
technology is getting smarter day by day, still Bangla handwritten
digit recognition remains inconclusive. Researchers are becoming
more concerned about handwritten digit recognition for it’s
educational a... | ['Atiqul Islam Chowdhury', 'Mahim Anzum Haque Pantho', 'Refat E Ferdous', 'MD Abdullah Al Nasim'] | 2020-06-26 | null | null | null | international-congress-on-human-computer | ['handwritten-digit-recognition'] | ['computer-vision'] | [-3.97916585e-01 -3.88886392e-01 1.19875848e-01 -8.29995453e-01
1.16066508e-01 -6.88101470e-01 7.00344801e-01 -3.67868751e-01
-5.97045660e-01 5.36272526e-01 2.17456087e-01 -7.53735483e-01
1.40758991e-01 -9.44149494e-01 -1.95434526e-01 -6.56191587e-01
4.90576208e-01 4.00980026e-01 -4.12271842e-02 -2.42088825... | [11.828791618347168, 2.6816489696502686] |
0138ecf5-b01c-4f55-9887-73df2eb90ff5 | unsupervised-vision-language-grammar | null | null | https://openreview.net/forum?id=N0n_QyQ5lBF | https://openreview.net/pdf?id=N0n_QyQ5lBF | Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling | We introduce a new task, unsupervised vision-language (VL) grammar induction. Given an image-caption pair, the goal is to extract a shared hierarchical structure for both image and language simultaneously. We argue that such structured output, grounded in both modalities, is a clear step towards the high-level underst... | ['Tinne Tuytelaars', 'Zilong Zheng', 'Wenjuan Han', 'Bo Wan'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['phrase-grounding'] | ['natural-language-processing'] | [ 5.58557630e-01 3.65601599e-01 -9.83342081e-02 -5.07908344e-01
-1.42374933e+00 -7.91081429e-01 6.10399961e-01 1.85636163e-01
-2.34391421e-01 3.43175530e-01 5.10898232e-01 -3.53755176e-01
3.85306716e-01 -7.32148409e-01 -1.05701935e+00 -6.89453006e-01
1.55592725e-01 5.00407040e-01 2.31325582e-01 -3.64358872... | [10.570443153381348, 1.489418864250183] |
e21111c5-5645-4925-99b7-b63fe2c85666 | sloper4d-a-scene-aware-dataset-for-global-4d | 2303.09095 | null | https://arxiv.org/abs/2303.09095v2 | https://arxiv.org/pdf/2303.09095v2.pdf | SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Estimation in Urban Environments | We present SLOPER4D, a novel scene-aware dataset collected in large urban environments to facilitate the research of global human pose estimation (GHPE) with human-scene interaction in the wild. Employing a head-mounted device integrated with a LiDAR and camera, we record 12 human subjects' activities over 10 diverse u... | ['Cheng Wang', 'Yuexin Ma', 'Siqi Shen', 'Hongwei Yi', 'Lan Xu', 'Chenglu Wen', 'Xiping Lin', 'Yitai Lin', 'Yudi Dai'] | 2023-03-16 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Dai_SLOPER4D_A_Scene-Aware_Dataset_for_Global_4D_Human_Pose_Estimation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Dai_SLOPER4D_A_Scene-Aware_Dataset_for_Global_4D_Human_Pose_Estimation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['camera-calibration', '3d-human-pose-estimation', 'human-scene-contact-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-3.72588247e-01 -3.05179864e-01 6.75123855e-02 -3.04900289e-01
-7.74388552e-01 -2.78351247e-01 2.31765926e-01 -5.44107676e-01
-5.26762903e-01 4.79798257e-01 2.55090177e-01 1.06794111e-01
1.89369082e-01 -6.44900322e-01 -6.89536572e-01 -2.84048259e-01
-1.72414541e-01 8.65790367e-01 3.52195084e-01 -3.67471069... | [7.050492286682129, -0.9239473342895508] |
76e1bc27-245a-4a29-bf20-6f1b50ec0a77 | sdc-stacked-dilated-convolution-a-unified-1 | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Schuster_SDC_-_Stacked_Dilated_Convolution_A_Unified_Descriptor_Network_for_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Schuster_SDC_-_Stacked_Dilated_Convolution_A_Unified_Descriptor_Network_for_CVPR_2019_paper.pdf | SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks | Dense pixel matching is important for many computer vision tasks such as disparity and flow estimation. We present a robust, unified descriptor network that considers a large context region with high spatial variance. Our network has a very large receptive field and avoids striding layers to maintain spatial resolutio... | [' Didier Stricker', ' Christian Unger', ' Oliver Wasenmuller', 'Rene Schuster'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['stereo-matching'] | ['computer-vision'] | [ 7.13646784e-02 -9.52755153e-01 -2.26288944e-01 -3.55856925e-01
-2.07829848e-01 -2.20770687e-01 7.18629837e-01 -2.71004528e-01
-5.12851357e-01 5.25602520e-01 3.92194629e-01 3.23423184e-02
-2.30729277e-03 -9.32318628e-01 -5.47396302e-01 -6.80544674e-01
-1.30398944e-01 -1.73772112e-01 6.10981584e-01 -2.71156609... | [8.868021965026855, -2.08463716506958] |
ec09579f-4d13-4fbb-a612-aa3d4f3d2b75 | super-fan-integrated-facial-landmark | 1712.02765 | null | http://arxiv.org/abs/1712.02765v2 | http://arxiv.org/pdf/1712.02765v2.pdf | Super-FAN: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs | This paper addresses 2 challenging tasks: improving the quality of low
resolution facial images and accurately locating the facial landmarks on such
poor resolution images. To this end, we make the following 5 contributions: (a)
we propose Super-FAN: the very first end-to-end system that addresses both
tasks simultaneo... | ['Georgios Tzimiropoulos', 'Adrian Bulat'] | 2017-12-07 | super-fan-integrated-facial-landmark-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Bulat_Super-FAN_Integrated_Facial_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Bulat_Super-FAN_Integrated_Facial_CVPR_2018_paper.pdf | cvpr-2018-6 | ['face-hallucination'] | ['computer-vision'] | [ 2.83577174e-01 2.81277090e-01 1.85420215e-01 -5.20776808e-01
-1.30723298e+00 -2.10148856e-01 4.41496253e-01 -8.57003033e-01
-9.28898901e-02 5.24851382e-01 5.18387079e-01 5.63638568e-01
4.75434447e-03 -5.69776356e-01 -7.25479901e-01 -2.99632996e-01
-1.02242909e-01 4.82265472e-01 -1.34856906e-02 -3.98461133... | [12.801054954528809, -0.1183665469288826] |
08d1844c-3c91-4ef8-a6ad-154f7e02e294 | subspace-hybrid-beamforming-for-head-worn | 2303.08967 | null | https://arxiv.org/abs/2303.08967v1 | https://arxiv.org/pdf/2303.08967v1.pdf | Subspace Hybrid Beamforming for Head-worn Microphone Arrays | A two-stage multi-channel speech enhancement method is proposed which consists of a novel adaptive beamformer, Hybrid Minimum Variance Distortionless Response (MVDR), Isotropic-MVDR (Iso), and a novel multi-channel spectral Principal Components Analysis (PCA) denoising. In the first stage, the Hybrid-MVDR performs mult... | ['Thomas Lunner', 'Vladimir Tourbabin', 'Jacob Donley', 'Patrick A. Naylor', 'Pierre Guiraud', 'Alastair H. Moore', 'Sina Hafezi'] | 2023-03-15 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 4.82533902e-01 -3.90698880e-01 6.56379580e-01 -1.73865799e-02
-1.26596236e+00 -3.88460219e-01 3.48286688e-01 -2.32450128e-01
-4.78543043e-01 2.74576902e-01 1.16247642e+00 -1.13606356e-01
-5.50442159e-01 -9.54813957e-02 -1.60657391e-01 -1.36970508e+00
9.01138708e-02 -5.59768319e-01 -6.76437393e-02 -3.12174231... | [14.990835189819336, 5.8549957275390625] |
71da79a9-dc81-4b54-849e-0979166dae95 | progress-measures-for-grokking-via | 2301.05217 | null | https://arxiv.org/abs/2301.05217v2 | https://arxiv.org/pdf/2301.05217v2.pdf | Progress measures for grokking via mechanistic interpretability | Neural networks often exhibit emergent behavior, where qualitatively new capabilities arise from scaling up the amount of parameters, training data, or training steps. One approach to understanding emergence is to find continuous \textit{progress measures} that underlie the seemingly discontinuous qualitative changes. ... | ['Tom Lieberum', 'Jacob Steinhardt', 'Jess Smith', 'Lawrence Chan', 'Neel Nanda'] | 2023-01-12 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 5.67000270e-01 2.32242405e-01 3.41437072e-01 -5.20332903e-03
2.08370328e-01 -7.69758582e-01 1.02391636e+00 2.24197358e-01
-4.15403247e-01 6.09734833e-01 2.41743103e-01 -2.99683422e-01
-4.03432995e-01 -6.51131272e-01 -1.02678835e+00 -8.57904315e-01
-3.21548790e-01 1.27646923e-01 1.65388193e-02 -4.87827957... | [8.195107460021973, 3.3445687294006348] |
256bc3fc-d1b3-4ae1-96a3-017e7eefe0b6 | user-generated-text-corpus-for-evaluating | 2104.03523 | null | https://arxiv.org/abs/2104.03523v1 | https://arxiv.org/pdf/2104.03523v1.pdf | User-Generated Text Corpus for Evaluating Japanese Morphological Analysis and Lexical Normalization | Morphological analysis (MA) and lexical normalization (LN) are both important tasks for Japanese user-generated text (UGT). To evaluate and compare different MA/LN systems, we have constructed a publicly available Japanese UGT corpus. Our corpus comprises 929 sentences annotated with morphological and normalization inf... | ['Eiichiro Sumita', 'Taro Watanabe', 'Masao Utiyama', 'Shohei Higashiyama'] | 2021-04-08 | null | https://aclanthology.org/2021.naacl-main.438 | https://aclanthology.org/2021.naacl-main.438.pdf | naacl-2021-4 | ['lexical-normalization'] | ['natural-language-processing'] | [ 1.78240985e-01 -7.14495704e-02 4.47853096e-02 -2.38208771e-01
-8.69211674e-01 -7.51984954e-01 4.99004006e-01 3.41371775e-01
-6.22976065e-01 8.84614885e-01 4.23866928e-01 -5.34713984e-01
5.06549954e-01 -6.71890736e-01 -1.22586131e-01 -4.98182327e-01
3.03670824e-01 4.81501520e-01 2.54473805e-01 -4.51062590... | [10.584929466247559, 10.270169258117676] |
18baaaeb-3d8d-4e82-b5a3-c8fc90c06823 | optimal-and-robust-category-level-perception | 2206.12498 | null | https://arxiv.org/abs/2206.12498v2 | https://arxiv.org/pdf/2206.12498v2.pdf | Optimal and Robust Category-level Perception: Object Pose and Shape Estimation from 2D and 3D Semantic Keypoints | We consider a category-level perception problem, where one is given 2D or 3D sensor data picturing an object of a given category (e.g., a car), and has to reconstruct the 3D pose and shape of the object despite intra-class variability (i.e., different car models have different shapes). We consider an active shape model... | ['Luca Carlone', 'Heng Yang', 'Jingnan Shi'] | 2022-06-24 | null | null | null | null | ['vehicle-pose-estimation'] | ['computer-vision'] | [-1.30442232e-01 3.37761194e-01 1.63686693e-01 -1.52338907e-01
-9.89747345e-01 -8.54198635e-01 3.13206315e-01 1.13997437e-01
-4.77042533e-02 -5.45994518e-03 -2.47700959e-01 -2.40447782e-02
-1.70234695e-01 -4.72373277e-01 -1.42343724e+00 -4.45137829e-01
-2.47148752e-01 8.49554598e-01 1.96719438e-01 -1.40356183... | [7.602764129638672, -2.6636240482330322] |
d809706d-75ce-4414-9299-98e958fe78b2 | task-adaptive-network-for-image-restoration | null | null | https://openaccess.thecvf.com/content/WACV2022W/VAQ/html/Zhou_Task_Adaptive_Network_for_Image_Restoration_With_Combined_Degradation_Factors_WACVW_2022_paper.html | https://openaccess.thecvf.com/content/WACV2022W/VAQ/papers/Zhou_Task_Adaptive_Network_for_Image_Restoration_With_Combined_Degradation_Factors_WACVW_2022_paper.pdf | Task Adaptive Network for Image Restoration With Combined Degradation Factors | Existing methods have achieved excellent performance on image restoration, but most of them are designed for one type of degradation. However, the weather is complex in the real world. So networks designed for single tasks are usually difficult to apply. Therefore, we propose a task-adaptive attention module to enable ... | ['Congduan Li', 'Wantong Liao', 'Minyi Lin', 'Chaktou Leong', 'Jingyuan Zhou'] | 2022-02-15 | null | null | null | ieee-cvf-winter-conference-on-applications-of-3 | ['single-image-haze-removal', 'single-image-deraining'] | ['computer-vision', 'computer-vision'] | [ 7.18599232e-03 -6.40714824e-01 2.62664229e-01 -4.31309044e-01
-4.01584446e-01 1.42429203e-01 2.66152769e-01 -6.27958238e-01
-2.05091417e-01 6.22712195e-01 5.38618326e-01 -3.07466984e-01
1.18743010e-01 -4.92409468e-01 -5.28613925e-01 -9.67554867e-01
1.41330808e-01 -4.50810827e-02 3.11376691e-01 -3.69817168... | [11.04465389251709, -2.9183356761932373] |
56ce3fe1-b24f-476b-a6f0-2ac03d81a195 | a-self-supervised-miniature-one-shot-texture | 2306.08814 | null | https://arxiv.org/abs/2306.08814v1 | https://arxiv.org/pdf/2306.08814v1.pdf | A Self-Supervised Miniature One-Shot Texture Segmentation (MOSTS) Model for Real-Time Robot Navigation and Embedded Applications | Determining the drivable area, or free space segmentation, is critical for mobile robots to navigate indoor environments safely. However, the lack of coherent markings and structures (e.g., lanes, curbs, etc.) in indoor spaces places the burden of traversability estimation heavily on the mobile robot. This paper explor... | ['William R. Norris', 'Zheyu Zhou', 'Chirag Rastogi', 'Yu Chen'] | 2023-06-15 | null | null | null | null | ['navigate', 'robot-navigation'] | ['reasoning', 'robots'] | [ 2.76274651e-01 1.08466573e-01 2.98749715e-01 -5.61732829e-01
-4.74432826e-01 -4.99570638e-01 4.98962879e-01 -4.25701439e-02
-4.62181985e-01 7.39644408e-01 -6.61479115e-01 -6.27916992e-01
-2.22438842e-01 -1.23458505e+00 -6.86332345e-01 -5.38400769e-01
4.44589406e-02 8.40108752e-01 7.84435570e-01 -4.57849264... | [8.399362564086914, -2.207371473312378] |
2ee6659a-3981-4b4a-a6bb-fbd4ccf551fd | supporting-search-engines-with-knowledge-and | 2102.06762 | null | https://arxiv.org/abs/2102.06762v1 | https://arxiv.org/pdf/2102.06762v1.pdf | Supporting search engines with knowledge and context | Search engines leverage knowledge to improve information access. In order to effectively leverage knowledge, search engines should account for context, i.e., information about the user and query. In this thesis, we aim to support search engines in leveraging knowledge while accounting for context. In the first part of ... | ['Nikos Voskarides'] | 2021-02-12 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 2.34547868e-01 4.57086533e-01 -4.76373583e-01 1.19496293e-01
-1.01353014e+00 -8.74063492e-01 9.04281676e-01 4.96762902e-01
-4.68505949e-01 1.06315827e+00 7.58884728e-01 -2.11794451e-01
-6.51175976e-01 -8.41005147e-01 -5.00909388e-01 9.73951668e-02
4.59751308e-01 7.10922360e-01 5.15811145e-01 -6.16113424... | [12.106457710266113, 7.920120716094971] |
c58ba8e3-92e9-4694-84a4-a609e0af2995 | spectral-processing-and-optimization-of | 2107.07379 | null | https://arxiv.org/abs/2107.07379v1 | https://arxiv.org/pdf/2107.07379v1.pdf | Spectral Processing and Optimization of Static and Dynamic 3D Geometries | Geometry processing of 3D objects is of primary interest in many areas of computer vision and graphics, including robot navigation, 3D object recognition, classification, feature extraction, etc. The recent introduction of cheap range sensors has created a great interest in many new areas, driving the need for developi... | ['Gerasimos Arvanitis'] | 2021-07-15 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [ 3.62595439e-01 -5.22159994e-01 3.46365154e-01 -3.28642845e-01
-1.84792250e-01 -4.34236228e-01 3.85289520e-01 3.14878702e-01
-4.66204822e-01 1.37850955e-01 -5.21293521e-01 -5.25780991e-02
-3.77107173e-01 -7.92949200e-01 -4.07487482e-01 -5.96954525e-01
-1.13698184e-01 7.67286420e-01 4.43443209e-01 -2.25921702... | [8.125543594360352, -2.781708240509033] |
9e5ff328-3600-4614-a631-8978f2a72736 | potential-based-reward-shaping-for-learning | 2302.10720 | null | https://arxiv.org/abs/2302.10720v2 | https://arxiv.org/pdf/2302.10720v2.pdf | Learning to Play Text-based Adventure Games with Maximum Entropy Reinforcement Learning | Text-based games are a popular testbed for language-based reinforcement learning (RL). In previous work, deep Q-learning is commonly used as the learning agent. Q-learning algorithms are challenging to apply to complex real-world domains due to, for example, their instability in training. Therefore, in this paper, we a... | ['Sophie Fellenz', 'Rati Devidze', 'Weichen Li'] | 2023-02-21 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [-2.32806981e-01 1.09743483e-01 -1.38506949e-01 1.47400603e-01
-7.80054212e-01 -3.99729520e-01 4.85928595e-01 2.38274917e-01
-1.00249815e+00 1.12854195e+00 -6.23182580e-02 -2.00119451e-01
-1.46137536e-01 -7.79984474e-01 -7.57355988e-01 -8.39145958e-01
-1.57695860e-01 6.67752862e-01 3.66404146e-01 -8.42615604... | [3.9415717124938965, 1.8335745334625244] |
9261c057-6e3a-41c3-80fd-37a1a29fba99 | neural-surface-reconstruction-of-dynamic | 2206.15258 | null | https://arxiv.org/abs/2206.15258v2 | https://arxiv.org/pdf/2206.15258v2.pdf | Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera | We propose Neural-DynamicReconstruction (NDR), a template-free method to recover high-fidelity geometry and motions of a dynamic scene from a monocular RGB-D camera. In NDR, we adopt the neural implicit function for surface representation and rendering such that the captured color and depth can be fully utilized to joi... | ['Juyong Zhang', 'Yan Wang', 'Xuetao Feng', 'Wanquan Feng', 'Hongrui Cai'] | 2022-06-30 | null | null | null | null | ['rgb-d-reconstruction'] | ['computer-vision'] | [ 1.31335825e-01 -1.94718674e-01 5.52921891e-02 -4.37979996e-01
-4.15333152e-01 -8.41491759e-01 3.22418571e-01 -9.43709612e-01
-1.21409610e-01 4.89388764e-01 1.58070043e-01 4.53854166e-02
-2.74582505e-02 -7.96029031e-01 -1.01654065e+00 -6.49729192e-01
3.90988678e-01 2.15638950e-01 2.25820974e-01 -2.61258427... | [8.965178489685059, -2.768359422683716] |
ff7f3eca-84b5-4fb4-bee8-9f62b4d7c7ad | early-heart-disease-prediction-using-hybrid | 2208.08882 | null | https://arxiv.org/abs/2208.08882v2 | https://arxiv.org/pdf/2208.08882v2.pdf | Early heart disease prediction using hybrid quantum classification | The rate of heart morbidity and heart mortality increases significantly which affect the global public health and world economy. Early prediction of heart disease is crucial for reducing heart morbidity and mortality. This paper proposes two quantum machine learning methods i.e. hybrid quantum neural network and hybrid... | ['Gerhard Hellstern', 'Hanif Heidari'] | 2022-08-17 | null | null | null | null | ['disease-prediction'] | ['medical'] | [-2.76231080e-01 8.85376483e-02 -2.87809223e-01 -1.98543981e-01
-6.95963979e-01 1.64458528e-01 2.13493668e-02 5.88902771e-01
-2.63884366e-01 9.84155476e-01 -1.77947491e-01 -2.16097906e-01
-2.24721268e-01 -1.30717123e+00 5.46582639e-02 -6.36821628e-01
-1.21482305e-01 8.41344476e-01 1.77103803e-01 9.93549228... | [14.11827564239502, 3.3437511920928955] |
62d8146b-fd27-4e11-9703-10479c89a96c | on-the-audio-visual-synchronization-for-lip | 2303.00502 | null | https://arxiv.org/abs/2303.00502v1 | https://arxiv.org/pdf/2303.00502v1.pdf | On the Audio-visual Synchronization for Lip-to-Speech Synthesis | Most lip-to-speech (LTS) synthesis models are trained and evaluated under the assumption that the audio-video pairs in the dataset are perfectly synchronized. In this work, we show that the commonly used audio-visual datasets, such as GRID, TCD-TIMIT, and Lip2Wav, can have data asynchrony issues. Training lip-to-speech... | ['Brian Mak', 'Zhe Niu'] | 2023-03-01 | null | null | null | null | ['audio-visual-synchronization', 'audio-visual-synchronization', 'lip-to-speech-synthesis', 'speech-synthesis'] | ['audio', 'computer-vision', 'computer-vision', 'speech'] | [-1.29837602e-01 -2.03190446e-01 -2.34158561e-01 -2.34632179e-01
-1.05514252e+00 -5.07160485e-01 7.34886646e-01 1.62418857e-02
-2.27855109e-02 3.83968145e-01 1.93665355e-01 -1.61280751e-01
2.46941775e-01 7.35446736e-02 -7.19545662e-01 -5.88453531e-01
-1.39624089e-01 3.75717103e-01 5.18641353e-01 2.47371614... | [14.51187801361084, 5.200313568115234] |
6320dea6-bde8-46c2-940f-027c3435ddb5 | efficient-human-pose-estimation-via-3d-event | 2206.04511 | null | https://arxiv.org/abs/2206.04511v2 | https://arxiv.org/pdf/2206.04511v2.pdf | Efficient Human Pose Estimation via 3D Event Point Cloud | Human Pose Estimation (HPE) based on RGB images has experienced a rapid development benefiting from deep learning. However, event-based HPE has not been fully studied, which remains great potential for applications in extreme scenes and efficiency-critical conditions. In this paper, we are the first to estimate 2D huma... | ['Kaiwei Wang', 'Lei Sun', 'Kailun Yang', 'Yaozu Ye', 'Hao Shi', 'Jiaan Chen'] | 2022-06-09 | null | null | null | null | ['2048'] | ['playing-games'] | [-2.46068001e-01 -4.70766336e-01 9.08522829e-02 -8.25917497e-02
-6.81071818e-01 -1.45749420e-01 2.50249952e-01 1.64827660e-01
-7.89569199e-01 4.30610359e-01 -3.41187753e-02 1.31021842e-01
2.19306901e-01 -7.67938018e-01 -9.35756803e-01 -2.72365749e-01
-4.03049707e-01 5.77184260e-01 5.68111956e-01 -1.07836187... | [7.216372489929199, -0.9016264081001282] |
7c77e79b-8ff6-47c1-b42c-5c36252537b5 | face-from-depth-for-head-pose-estimation-on | 1712.05277 | null | http://arxiv.org/abs/1712.05277v2 | http://arxiv.org/pdf/1712.05277v2.pdf | Face-from-Depth for Head Pose Estimation on Depth Images | Depth cameras allow to set up reliable solutions for people monitoring and
behavior understanding, especially when unstable or poor illumination
conditions make unusable common RGB sensors. Therefore, we propose a complete
framework for the estimation of the head and shoulder pose based on depth
images only. A head det... | ['Rita Cucchiara', 'Roberto Vezzani', 'Matteo Fabbri', 'Guido Borghi', 'Simone Calderara'] | 2017-12-12 | null | null | null | null | ['head-detection', 'head-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.00603402e-01 1.64603084e-01 3.02081615e-01 -7.24821866e-01
-6.26231611e-01 -2.34047756e-01 4.56902921e-01 -5.22042215e-01
-7.53367603e-01 4.71357733e-01 -2.04643477e-02 2.20652580e-01
4.37480479e-01 -8.43074322e-01 -7.92162418e-01 -7.37573624e-01
2.47104079e-01 5.77750742e-01 -1.30748870e-02 -1.14475880... | [13.654485702514648, 0.2764734923839569] |
17cc45b4-0040-4d59-9538-15f2c5e9b697 | discourse-segmentation-for-building-a-rst | null | null | https://aclanthology.org/W17-3610 | https://aclanthology.org/W17-3610.pdf | Discourse Segmentation for Building a RST Chinese Treebank | null | ['Mikel Iruskieta', 'Shuyuan Cao', 'Iria da Cunha', 'Chuan Wang', 'Nianwen Xue'] | 2017-09-01 | null | null | null | ws-2017-9 | ['discourse-segmentation'] | ['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... | [-7.210562229156494, 3.7606616020202637] |
e3bf34a2-b180-4e9c-9dbe-4e9a75c613ad | multimodal-dialogue-state-tracking-1 | 2206.07898 | null | https://arxiv.org/abs/2206.07898v1 | https://arxiv.org/pdf/2206.07898v1.pdf | Multimodal Dialogue State Tracking | Designed for tracking user goals in dialogues, a dialogue state tracker is an essential component in a dialogue system. However, the research of dialogue state tracking has largely been limited to unimodality, in which slots and slot values are limited by knowledge domains (e.g. restaurant domain with slots of restaura... | ['Steven C. H. Hoi', 'Nancy F. Chen', 'Hung Le'] | 2022-06-16 | multimodal-dialogue-state-tracking | https://aclanthology.org/2022.naacl-main.248 | https://aclanthology.org/2022.naacl-main.248.pdf | naacl-2022-7 | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 2.99599230e-01 2.10337773e-01 -2.62209922e-01 -4.64337826e-01
-6.25842273e-01 -8.37556779e-01 1.01991892e+00 -3.78583781e-02
-3.88325810e-01 7.68374979e-01 6.15799367e-01 4.68600392e-02
4.64786142e-01 -5.56327045e-01 -5.70179760e-01 -5.49533010e-01
1.22505881e-01 5.19914567e-01 4.06748831e-01 -6.42222285... | [10.90459156036377, 1.2040150165557861] |
091567e9-0d9f-4300-b88a-054facc27132 | routing-by-spontaneous-synchronization | 2305.13914 | null | https://arxiv.org/abs/2305.13914v1 | https://arxiv.org/pdf/2305.13914v1.pdf | Routing by spontaneous synchronization | Selective attention allows to process stimuli which are behaviorally relevant, while attenuating distracting information. However, it is an open question what mechanisms implement selective routing, and how they are engaged in dependence on behavioral need. Here we introduce a novel framework for selective processing b... | ['Udo Ernst', 'Maik Schünemann'] | 2023-05-23 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 6.33622408e-01 -2.40827620e-01 8.45546350e-02 1.16874970e-01
-4.74084318e-02 -8.72904956e-01 6.74715340e-01 2.61203557e-01
-6.75302505e-01 9.98837352e-01 4.64198798e-01 1.57417044e-01
-2.13024244e-01 -5.35591960e-01 -6.60827219e-01 -1.23143446e+00
-9.34868604e-02 -2.83300653e-02 5.90557039e-01 -3.58762056... | [8.156854629516602, 3.08337664604187] |
88824a5a-3e47-4454-98d0-ce4c00cbca0b | end-to-end-audiovisual-speech-recognition | 1802.06424 | null | http://arxiv.org/abs/1802.06424v2 | http://arxiv.org/pdf/1802.06424v2.pdf | End-to-end Audiovisual Speech Recognition | Several end-to-end deep learning approaches have been recently presented
which extract either audio or visual features from the input images or audio
signals and perform speech recognition. However, research on end-to-end
audiovisual models is very limited. In this work, we present an end-to-end
audiovisual model based... | ['Pingchuan Ma', 'Maja Pantic', 'Stavros Petridis', 'Georgios Tzimiropoulos', 'Feipeng Cai', 'Themos Stafylakis'] | 2018-02-18 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 2.32866943e-01 -2.15534985e-01 1.13207251e-01 -3.02956134e-01
-1.57743037e+00 -3.70112479e-01 7.14513361e-01 -2.84025278e-02
-4.21176493e-01 4.25359249e-01 4.31961864e-01 -1.74275503e-01
3.45103562e-01 -1.47064880e-01 -8.05948496e-01 -7.30005980e-01
-1.17559835e-01 -3.54947627e-01 7.92792216e-02 1.27393141... | [14.378901481628418, 5.157271385192871] |
aec68f01-80b7-4cbe-ace3-d3ff04114cd9 | challenging-deep-image-descriptors-for | 1909.08866 | null | https://arxiv.org/abs/1909.08866v1 | https://arxiv.org/pdf/1909.08866v1.pdf | Challenging deep image descriptors for retrieval in heterogeneous iconographic collections | This article proposes to study the behavior of recent and efficient state-of-the-art deep-learning based image descriptors for content-based image retrieval, facing a panel of complex variations appearing in heterogeneous image datasets, in particular in cultural collections that may involve multi-source, multi-date an... | ['Valérie Gouet-Brunet', 'Dimitri Gominski', 'Liming Chen', 'Martyna Poreba'] | 2019-09-19 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [-1.92648560e-01 -9.26164269e-01 -3.50602329e-01 -3.44893456e-01
-7.80228019e-01 -6.52230382e-01 8.51134479e-01 5.83259225e-01
-8.26964974e-01 2.18211398e-01 2.21885473e-01 2.00124383e-01
-6.90820694e-01 -6.22725606e-01 -2.14951798e-01 -6.21946156e-01
-2.65485257e-01 3.40035617e-01 -1.12982169e-01 -6.16487265... | [10.701857566833496, 0.4902758002281189] |
e3ddf6e6-2729-4975-a167-e68668f9bb6e | net2vec-deep-learning-for-the-network | 1705.03881 | null | http://arxiv.org/abs/1705.03881v1 | http://arxiv.org/pdf/1705.03881v1.pdf | Net2Vec: Deep Learning for the Network | We present Net2Vec, a flexible high-performance platform that allows the
execution of deep learning algorithms in the communication network. Net2Vec is
able to capture data from the network at more than 60Gbps, transform it into
meaningful tuples and apply predictions over the tuples in real time. This
platform can be ... | ['Alberto Garcia-Duran', 'Saverio Niccolini', 'Felipe Huici', 'Jose Mendes', 'Filipe Manco', 'Roberto Gonzalez', 'Mathias Niepert'] | 2017-05-10 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [-6.31816804e-01 -1.36769384e-01 -1.39809266e-01 -5.42769372e-01
-1.46836445e-01 -3.24091762e-01 5.63275933e-01 1.88178062e-01
-2.88774729e-01 7.28584528e-01 -1.27326250e-01 -8.91380727e-01
-2.07205206e-01 -1.31263638e+00 -5.08745193e-01 -2.18904838e-01
-5.93559504e-01 1.29735732e+00 5.22663176e-01 -4.51540917... | [5.07667875289917, 7.230299472808838] |
7da6b93a-5dee-447b-9442-079e204ffa00 | unconstrained-scene-generation-with-locally | 2104.00670 | null | https://arxiv.org/abs/2104.00670v1 | https://arxiv.org/pdf/2104.00670v1.pdf | Unconstrained Scene Generation with Locally Conditioned Radiance Fields | We tackle the challenge of learning a distribution over complex, realistic, indoor scenes. In this paper, we introduce Generative Scene Networks (GSN), which learns to decompose scenes into a collection of many local radiance fields that can be rendered from a free moving camera. Our model can be used as a prior to gen... | ['Joshua M. Susskind', 'Graham W. Taylor', 'Nitish Srivastava', 'Miguel Angel Bautista', 'Terrance DeVries'] | 2021-04-01 | null | http://openaccess.thecvf.com//content/ICCV2021/html/DeVries_Unconstrained_Scene_Generation_With_Locally_Conditioned_Radiance_Fields_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/DeVries_Unconstrained_Scene_Generation_With_Locally_Conditioned_Radiance_Fields_ICCV_2021_paper.pdf | iccv-2021-1 | ['scene-generation'] | ['computer-vision'] | [ 3.50643754e-01 -3.36952478e-01 3.98240298e-01 -6.78483546e-01
-5.87000191e-01 -6.98936701e-01 7.00053334e-01 -4.47275072e-01
3.25836062e-01 6.52294934e-01 4.05062914e-01 -4.84055914e-02
-1.37945055e-03 -9.44463909e-01 -8.14153969e-01 -8.28849733e-01
2.92707175e-01 4.35035825e-01 6.54683039e-02 -1.48886368... | [9.316012382507324, -3.082720994949341] |
df3f84ee-c6df-4b5f-9fa0-b83efb1b8620 | minimax-bayes-reinforcement-learning | 2302.10831 | null | https://arxiv.org/abs/2302.10831v1 | https://arxiv.org/pdf/2302.10831v1.pdf | Minimax-Bayes Reinforcement Learning | While the Bayesian decision-theoretic framework offers an elegant solution to the problem of decision making under uncertainty, one question is how to appropriately select the prior distribution. One idea is to employ a worst-case prior. However, this is not as easy to specify in sequential decision making as in simple... | ['Emilio Jorge', 'Divya Grover', 'Hannes Eriksson', 'Christos Dimitrakakis', 'Thomas Kleine Buening'] | 2023-02-21 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 3.45049679e-01 1.69341803e-01 -4.71741468e-01 -5.58433533e-01
-7.56199956e-01 -5.78020632e-01 4.95672971e-01 3.23162466e-01
-9.02684391e-01 9.90127683e-01 -1.13430284e-01 -7.30300903e-01
-7.33577311e-01 -7.66647041e-01 -4.64623183e-01 -8.51630390e-01
2.23055750e-01 5.55121839e-01 1.14606030e-01 -1.60376102... | [4.487454414367676, 3.0001368522644043] |
8621164b-7062-441f-ac6b-44b2e598362b | msinet-twins-contrastive-search-of-multi | 2303.07065 | null | https://arxiv.org/abs/2303.07065v1 | https://arxiv.org/pdf/2303.07065v1.pdf | MSINet: Twins Contrastive Search of Multi-Scale Interaction for Object ReID | Neural Architecture Search (NAS) has been increasingly appealing to the society of object Re-Identification (ReID), for that task-specific architectures significantly improve the retrieval performance. Previous works explore new optimizing targets and search spaces for NAS ReID, yet they neglect the difference of train... | ['Jian Zhao', 'Yang You', 'Shanghang Zhang', 'Yuqiang Fang', 'Wei Jiang', 'Chen Chen', 'Hao Luo', 'Kai Wang', 'Jianyang Gu'] | 2023-03-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Gu_MSINet_Twins_Contrastive_Search_of_Multi-Scale_Interaction_for_Object_ReID_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gu_MSINet_Twins_Contrastive_Search_of_Multi-Scale_Interaction_for_Object_ReID_CVPR_2023_paper.pdf | cvpr-2023-1 | ['person-re-identification', 'vehicle-re-identification', 'architecture-search'] | ['computer-vision', 'computer-vision', 'methodology'] | [-2.94480562e-01 -5.56697905e-01 -1.10741369e-01 -5.32178998e-01
-5.54890156e-01 -4.43546057e-01 7.14849234e-01 -3.71527344e-01
-5.58062673e-01 2.80957341e-01 1.62309274e-01 -1.94519073e-01
-3.73904496e-01 -4.92723733e-01 -6.30221665e-01 -6.71762168e-01
2.87087888e-01 4.91313010e-01 -6.43946826e-02 -2.43215322... | [14.782042503356934, 0.9354726076126099] |
ef4dc84d-5ece-4890-8b42-1cbe062e56a2 | csdn-cross-modal-shape-transfer-dual | 2208.00751 | null | https://arxiv.org/abs/2208.00751v2 | https://arxiv.org/pdf/2208.00751v2.pdf | CSDN: Cross-modal Shape-transfer Dual-refinement Network for Point Cloud Completion | How will you repair a physical object with some missings? You may imagine its original shape from previously captured images, recover its overall (global) but coarse shape first, and then refine its local details. We are motivated to imitate the physical repair procedure to address point cloud completion. To this end, ... | ['Jing Qin', 'Jun Wang', 'Mingqiang Wei', 'Honghua Chen', 'Haoran Xie', 'Liangliang Nan', 'Zhe Zhu'] | 2022-08-01 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [ 8.81856605e-02 1.59015819e-01 3.90482008e-01 -8.14805622e-04
-1.06681490e+00 -5.90110004e-01 4.60023701e-01 -9.59189609e-02
1.05694816e-01 3.03989738e-01 -1.07790604e-01 1.38641730e-01
-2.02913538e-01 -9.90803957e-01 -1.08511901e+00 -7.22016513e-01
4.02039737e-01 7.04183280e-01 2.69235522e-01 -4.09082025... | [8.39142894744873, -3.624077081680298] |
6b8cb772-c72f-4b5e-af05-1905287a15e7 | improved-static-hand-gesture-classification | 2305.02039 | null | https://arxiv.org/abs/2305.02039v1 | https://arxiv.org/pdf/2305.02039v1.pdf | Improved Static Hand Gesture Classification on Deep Convolutional Neural Networks using Novel Sterile Training Technique | In this paper, we investigate novel data collection and training techniques towards improving classification accuracy of non-moving (static) hand gestures using a convolutional neural network (CNN) and frequency-modulated-continuous-wave (FMCW) millimeter-wave (mmWave) radars. Recently, non-contact hand pose and static... | ['Murat Torlak', 'Yiorgos Makris', 'Richard Willis', 'Shiva Thiagarajan', 'Josiah Smith'] | 2023-05-03 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 4.80101675e-01 -4.23618495e-01 -1.07924473e-02 -4.59442705e-01
-5.00787079e-01 -4.63708460e-01 4.74904090e-01 -5.79451442e-01
-8.03359926e-01 6.27803206e-01 -2.69103289e-01 -2.97432661e-01
-6.02723598e-01 -7.86720276e-01 -9.55446288e-02 -1.14603531e+00
-3.20143312e-01 1.80522665e-01 -2.17133567e-01 -1.58084631... | [6.710053443908691, 0.25581711530685425] |
d48ec71c-33e3-46dc-997c-2c29da677907 | hyperspectral-image-super-resolution-via-deep-1 | 2006.10300 | null | https://arxiv.org/abs/2006.10300v2 | https://arxiv.org/pdf/2006.10300v2.pdf | Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning | This paper explores the problem of hyperspectral image (HSI) super-resolution that merges a low resolution HSI (LR-HSI) and a high resolution multispectral image (HR-MSI). The cross-modality distribution of the spatial and spectral information makes the problem challenging. Inspired by the classic wavelet decomposition... | ['Jie Chen', 'Huanqiang Zeng', 'Zhiyu Zhu', 'Junhui Hou', 'Jiantao Zhou'] | 2020-06-18 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 6.17457449e-01 -4.93200421e-01 1.93143904e-01 -2.54139662e-01
-1.12495208e+00 -3.10160398e-01 1.30479112e-01 -5.70848763e-01
-2.46087134e-01 7.79355466e-01 8.09518844e-02 5.94447963e-02
-5.71847856e-01 -1.15175080e+00 -7.45616019e-01 -1.13673246e+00
4.48143892e-02 -4.87783581e-01 -1.03900269e-01 -3.75773311... | [10.27596378326416, -1.955743670463562] |
6c9caf4b-4c39-42b6-95b9-cdb079263593 | lenet-lightweight-and-efficient-lidar | 2301.04275 | null | https://arxiv.org/abs/2301.04275v3 | https://arxiv.org/pdf/2301.04275v3.pdf | LENet: Lightweight And Efficient LiDAR Semantic Segmentation Using Multi-Scale Convolution Attention | LiDAR-based semantic segmentation is critical in the fields of robotics and autonomous driving as it provides a comprehensive understanding of the scene. This paper proposes a lightweight and efficient projection-based semantic segmentation network called LENet with an encoder-decoder structure for LiDAR-based semantic... | ['Ben Ding'] | 2023-01-11 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 1.29757747e-01 1.16906367e-01 -2.96526104e-02 -7.89189577e-01
-6.95666254e-01 -2.95513511e-01 4.44993734e-01 -1.78682148e-01
-6.53713524e-01 4.21469182e-01 -4.52433191e-02 -2.77444899e-01
1.96261272e-01 -1.10882533e+00 -1.00327337e+00 -3.39910328e-01
3.31065238e-01 4.49937433e-01 7.86354184e-01 -2.56575018... | [8.194713592529297, -2.5667667388916016] |
28a02af3-7771-4f60-b87e-741988ec84a6 | lifetime-achievement-award-translating-today | null | null | https://aclanthology.org/J15-4007 | https://aclanthology.org/J15-4007.pdf | Lifetime Achievement Award: Translating Today into Tomorrow | null | ['Sheng Li'] | 2015-12-01 | null | null | null | cl-2015-12 | ['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... | [-7.41171407699585, 3.5683469772338867] |
c916948b-98ea-4064-b365-78ae42e8d27e | deep-learning-based-stereo-camera-multi-video | 2303.12916 | null | https://arxiv.org/abs/2303.12916v1 | https://arxiv.org/pdf/2303.12916v1.pdf | Deep learning-based stereo camera multi-video synchronization | Stereo vision is essential for many applications. Currently, the synchronization of the streams coming from two cameras is done using mostly hardware. A software-based synchronization method would reduce the cost, weight and size of the entire system and allow for more flexibility when building such systems. With this ... | ['Thierry Dutoit', 'François Cresson', 'Thierry Ravet', 'Kevin El Haddad', 'Nicolas Boizard'] | 2023-03-22 | null | null | null | null | ['video-synchronization'] | ['computer-vision'] | [-2.99223930e-01 -3.26966822e-01 2.09600881e-01 -3.90523195e-01
-6.82011396e-02 -2.86159903e-01 5.70478976e-01 -1.34247895e-02
-6.91865444e-01 4.32762027e-01 -3.75015110e-01 -2.55741328e-01
3.30502182e-01 -6.81675792e-01 -5.39045691e-01 -6.39330685e-01
9.26867351e-02 3.09190452e-01 8.01943898e-01 -1.46742091... | [8.75910758972168, -1.5925190448760986] |
58383595-b6de-4201-9eb8-7c8266bb940b | multi-class-classification-of-vulnerabilities | 2004.00362 | null | https://arxiv.org/abs/2004.00362v1 | https://arxiv.org/pdf/2004.00362v1.pdf | Multi-Class classification of vulnerabilities in Smart Contracts using AWD-LSTM, with pre-trained encoder inspired from natural language processing | Vulnerability detection and safety of smart contracts are of paramount importance because of their immutable nature. Symbolic tools like OYENTE and MAIAN are typically used for vulnerability prediction in smart contracts. As these tools are computationally expensive, they are typically used to detect vulnerabilities un... | ['Raj Kishore', 'S. Swayamjyoti', 'Devadatta Sahoo', 'Kisor K. Sahu', 'Ajay K. Gogineni'] | 2020-03-21 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-1.28207728e-01 -1.23155840e-01 -4.76946503e-01 -2.65321672e-01
-5.57906687e-01 -8.56328905e-01 4.39547151e-01 3.84970009e-02
-2.21598446e-01 2.78917670e-01 -2.44280532e-01 -9.80180681e-01
9.00992751e-02 -9.84517574e-01 -3.46089363e-01 -6.43674016e-01
-4.09667760e-01 4.18972999e-01 3.58888209e-01 -1.74515605... | [6.823615550994873, 7.379506587982178] |
d6ed21ed-611d-4991-a6ef-90df47cf2cda | graph-based-collaborative-ranking | 1604.03147 | null | http://arxiv.org/abs/1604.03147v3 | http://arxiv.org/pdf/1604.03147v3.pdf | Graph-based Collaborative Ranking | Data sparsity, that is a common problem in neighbor-based collaborative
filtering domain, usually complicates the process of item recommendation. This
problem is more serious in collaborative ranking domain, in which calculating
the users similarities and recommending items are based on ranking data. Some
graph-based a... | ['Haratizadeh Saman', 'Shams Bita'] | 2017-01-31 | null | null | null | null | ['collaborative-ranking'] | ['graphs'] | [-1.43018350e-01 -4.18076813e-01 -3.90332162e-01 -4.72774267e-01
-5.26548885e-02 -4.48031247e-01 1.28085881e-01 5.54749906e-01
-5.25256433e-02 4.94767219e-01 7.19133258e-01 -3.17809522e-01
-1.01642787e+00 -1.31640327e+00 -1.14548720e-01 -3.82327795e-01
-1.46477520e-01 6.33833230e-01 3.81488323e-01 -6.83140695... | [10.083142280578613, 5.7137370109558105] |
e715bcf3-9d1d-4ead-8d5e-fc99b4aad399 | the-surprising-creativity-of-digital | 1803.03453 | null | https://arxiv.org/abs/1803.03453v4 | https://arxiv.org/pdf/1803.03453v4.pdf | The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities | Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. However, because evolution is an algorithmic process that transcends the substrate in which it occurs, evolution's creativity is not limited to nature. Indeed, many researchers in the fie... | ['Westley Weimer', 'François Taddei', 'Anh Nguyen', 'Jean-Baptiste Mouret', 'David E. Moriarty', 'Sara Mitri', 'Risto Miikkulainen', 'Carlos Maestre', 'Hod Lipson', 'Richard E. Lenski', 'Laurent Keller', 'Christian Gagné', 'Antoine Frénoy', 'Stephanie Forrest', 'Kai Olav Ellefsen', 'Stephane Doncieux', 'Samuel Bernard'... | 2018-03-09 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 2.57403702e-01 1.55946359e-01 2.32341126e-01 2.51348644e-01
2.15224504e-01 -9.35472250e-01 7.69665956e-01 1.71084628e-01
-2.36042783e-01 9.51422453e-01 3.13818038e-01 -3.65734696e-01
-3.52527834e-02 -6.86682880e-01 -7.20008790e-01 -7.12347746e-01
-8.35060477e-02 2.94903189e-01 7.17810392e-02 -6.70669973... | [5.571763515472412, 4.202932357788086] |
160d17ae-865a-4705-b9af-45039c60a40c | improving-image-recognition-by-retrieving | 2304.05173 | null | https://arxiv.org/abs/2304.05173v1 | https://arxiv.org/pdf/2304.05173v1.pdf | Improving Image Recognition by Retrieving from Web-Scale Image-Text Data | Retrieval augmented models are becoming increasingly popular for computer vision tasks after their recent success in NLP problems. The goal is to enhance the recognition capabilities of the model by retrieving similar examples for the visual input from an external memory set. In this work, we introduce an attention-bas... | ['Cordelia Schmid', 'Alireza Fathi', 'Ahmet Iscen'] | 2023-04-11 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Iscen_Improving_Image_Recognition_by_Retrieving_From_Web-Scale_Image-Text_Data_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Iscen_Improving_Image_Recognition_by_Retrieving_From_Web-Scale_Image-Text_Data_CVPR_2023_paper.pdf | cvpr-2023-1 | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 3.16139698e-01 -2.71750093e-01 -3.39224339e-01 -3.81851673e-01
-1.00509977e+00 -1.79855600e-01 8.29225898e-01 6.71974719e-02
-7.70695806e-01 5.81842780e-01 3.05782706e-01 7.76675493e-02
-9.91040990e-02 -5.71603358e-01 -8.63451898e-01 -7.28637636e-01
3.35472226e-01 7.44530916e-01 4.30428505e-01 1.30808726... | [10.234721183776855, 1.784932255744934] |
7cd5675d-e0e3-4954-9118-7077b8329b01 | machine-learning-for-subgroup-discovery-under | 1902.10327 | null | http://arxiv.org/abs/1902.10327v1 | http://arxiv.org/pdf/1902.10327v1.pdf | Machine learning for subgroup discovery under treatment effect | In many practical tasks it is needed to estimate an effect of treatment on
individual level. For example, in medicine it is essential to determine the
patients that would benefit from a certain medicament. In marketing, knowing
the persons that are likely to buy a new product would reduce the amount of
spam. In this ch... | ['Aleksey Buzmakov'] | 2019-02-27 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 2.74687290e-01 3.56610566e-01 -6.80312514e-01 -5.21221817e-01
-1.96762860e-01 -4.31418568e-01 3.32513869e-01 5.82646906e-01
-6.91722214e-01 1.10055637e+00 -2.54759956e-02 -3.98317724e-01
-2.99838245e-01 -1.00799274e+00 -1.00941360e+00 -7.19141185e-01
6.90556364e-03 8.08336973e-01 -1.08764045e-01 -1.34278107... | [8.174763679504395, 5.282687664031982] |
def28e47-700c-42c9-94cf-b24aa04657d6 | accurate-gaze-estimation-using-an-active-gaze | 2301.13186 | null | https://arxiv.org/abs/2301.13186v1 | https://arxiv.org/pdf/2301.13186v1.pdf | Accurate Gaze Estimation using an Active-gaze Morphable Model | Rather than regressing gaze direction directly from images, we show that adding a 3D shape model can: i) improve gaze estimation accuracy, ii) perform well with lower resolution inputs and iii) provide a richer understanding of the eye-region and its constituent gaze system. Specifically, we use an `eyes and nose' 3D m... | ['Nick Pears', 'Hao Sun'] | 2023-01-30 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [ 1.92232635e-02 3.77199918e-01 -1.29533574e-01 -3.64480436e-01
-2.57575989e-01 -6.38363481e-01 6.08971834e-01 -6.07870817e-01
-2.50238270e-01 2.19468355e-01 6.38261735e-02 -1.93114325e-01
1.42131783e-02 -1.58896089e-01 -7.91774273e-01 -7.18215168e-01
1.51059434e-01 3.14798743e-01 1.70239672e-01 -1.91267133... | [14.115095138549805, 0.06408105790615082] |
43aef080-a26f-43c1-9a24-07d0111b3208 | unsupervised-labeled-parsing-with-deep-inside | null | null | https://aclanthology.org/D19-1161 | https://aclanthology.org/D19-1161.pdf | Unsupervised Labeled Parsing with Deep Inside-Outside Recursive Autoencoders | Understanding text often requires identifying meaningful constituent spans such as noun phrases and verb phrases. In this work, we show that we can effectively recover these types of labels using the learned phrase vectors from deep inside-outside recursive autoencoders (DIORA). Specifically, we cluster span representa... | ['Yi-Pei Chen', 'Patrick Verga', 'Andrew McCallum', 'Andrew Drozdov', 'Mohit Iyyer'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['constituency-parsing'] | ['natural-language-processing'] | [ 2.78301954e-01 5.17447412e-01 -3.67604017e-01 -4.73767012e-01
-1.44896495e+00 -9.23158348e-01 2.19832748e-01 4.03872669e-01
-4.16260302e-01 7.83216238e-01 6.79083884e-01 -3.23281139e-01
2.93117940e-01 -6.66005552e-01 -9.71650779e-01 -1.00891672e-01
-1.18207345e-02 5.91615021e-01 -6.42210543e-02 -3.37665565... | [10.398979187011719, 9.573623657226562] |
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