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
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
883114cb-92b0-4faa-a503-832a36a85f04 | prompt-tuning-can-be-much-better-than-fine | 2210.12360 | null | https://arxiv.org/abs/2210.12360v2 | https://arxiv.org/pdf/2210.12360v2.pdf | Prompt-Tuning Can Be Much Better Than Fine-Tuning on Cross-lingual Understanding With Multilingual Language Models | Pre-trained multilingual language models show significant performance gains for zero-shot cross-lingual model transfer on a wide range of natural language understanding (NLU) tasks. Previously, for zero-shot cross-lingual evaluation, pre-trained models are only fine-tuned on English data and tested on a variety of targ... | ['Yingbo Zhou', 'Caiming Xiong', 'Lifu Tu'] | 2022-10-22 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [ 3.70518230e-02 1.32846475e-01 -3.62246782e-01 -6.48234129e-01
-1.46990955e+00 -9.47459340e-01 6.87570095e-01 2.92288929e-01
-7.59696722e-01 8.43583465e-01 3.64948779e-01 -6.50591433e-01
3.64391625e-01 -4.97428775e-01 -9.70354557e-01 -1.80366058e-02
1.42023355e-01 5.01718223e-01 3.57615352e-02 -4.96950358... | [11.006399154663086, 9.68310260772705] |
7eda846f-025c-4f59-b234-0238d05d8e07 | the-npu-elevoc-personalized-speech | 2303.06811 | null | https://arxiv.org/abs/2303.06811v2 | https://arxiv.org/pdf/2303.06811v2.pdf | The NPU-Elevoc Personalized Speech Enhancement System for ICASSP2023 DNS Challenge | This paper describes our NPU-Elevoc personalized speech enhancement system (NAPSE) for the 5th Deep Noise Suppression Challenge at ICASSP 2023. Based on the superior two-stage model TEA-PSE 2.0, our system particularly explores better strategy for speaker embedding fusion, optimizes the model training pipeline, and lev... | ['Lei Xie', 'Liangliang Peng', 'Zhihao Guo', 'Yindi Yang', 'Xiaopeng Yan'] | 2023-03-13 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [-1.21980637e-01 3.06312680e-01 -3.56743038e-02 -2.12236807e-01
-1.23113549e+00 -3.58282238e-01 4.18998480e-01 -2.67857313e-01
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-3.82016078e-02 2.68527661e-02 -4.34489578e-01 -4.96808380e-01
-1.82381243e-01 -1.29076719e-01 1.53015956e-01 -5.37162125... | [14.600485801696777, 6.073037147521973] |
350b3672-35fa-4c78-a194-155f53144e1b | gated-attentive-autoencoder-for-content-aware | 1812.02869 | null | http://arxiv.org/abs/1812.02869v1 | http://arxiv.org/pdf/1812.02869v1.pdf | Gated Attentive-Autoencoder for Content-Aware Recommendation | The rapid growth of Internet services and mobile devices provides an
excellent opportunity to satisfy the strong demand for the personalized item or
product recommendation. However, with the tremendous increase of users and
items, personalized recommender systems still face several challenging
problems: (1) the hardnes... | ['Qinglong Wang', 'Xue Liu', 'Peng Kang', 'Bin Wu', 'Chen Ma'] | 2018-12-07 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 3.21939178e-02 -3.60249877e-01 -5.55065095e-01 -5.36763310e-01
-3.90304476e-01 -1.22305676e-01 1.36699602e-01 9.99602024e-03
-3.54481012e-01 4.65822309e-01 6.46375358e-01 -3.85780483e-02
-3.66426051e-01 -8.55442345e-01 -5.25386989e-01 -6.91651523e-01
-1.38528466e-01 2.76857466e-01 -8.09198059e-03 -5.52931547... | [10.182340621948242, 5.614650726318359] |
21cde201-87b9-4506-891e-e2b988caa7a4 | multi-level-approach-to-accurate-and-scalable | null | null | https://openreview.net/forum?id=a4W0tSTN9Kn | https://openreview.net/pdf?id=a4W0tSTN9Kn | MULTI-LEVEL APPROACH TO ACCURATE AND SCALABLE HYPERGRAPH EMBEDDING | Many problems such as node classification and link prediction in network data
can be solved using graph embeddings, and a number of algorithms are known for constructing
such embeddings. However, it is difficult to use graphs to capture
non-binary relations such as communities of nodes. These kinds of
complex relatio... | ['Keshav Pingali', 'Dennis Wall', 'Donya Saless', 'Sepideh Maleki'] | 2021-09-29 | null | null | null | null | ['hypergraph-embedding'] | ['graphs'] | [-2.98020512e-01 6.13098323e-01 -3.21662873e-01 -1.67429432e-01
-1.03681162e-01 -7.24538565e-01 5.27389765e-01 6.78703785e-01
-4.16769311e-02 5.60100496e-01 1.72783881e-02 -6.13412023e-01
-3.65989089e-01 -1.46481991e+00 -2.72029996e-01 -2.68827289e-01
-7.33240128e-01 9.48606551e-01 3.74927431e-01 -3.13212365... | [7.073575019836426, 6.2044267654418945] |
2c610bfb-e11f-45c7-b232-0a83cb223b18 | single-stage-multi-pose-virtual-try-on | 2211.10715 | null | https://arxiv.org/abs/2211.10715v1 | https://arxiv.org/pdf/2211.10715v1.pdf | Single Stage Multi-Pose Virtual Try-On | Multi-pose virtual try-on (MPVTON) aims to fit a target garment onto a person at a target pose. Compared to traditional virtual try-on (VTON) that fits the garment but keeps the pose unchanged, MPVTON provides a better try-on experience, but is also more challenging due to the dual garment and pose editing objectives. ... | ['Tao Xiang', 'Yi-Zhe Song', 'Sen He'] | 2022-11-19 | null | null | null | null | ['pose-transfer', 'virtual-try-on'] | ['computer-vision', 'computer-vision'] | [ 4.33264673e-01 1.91978082e-01 4.31242064e-02 -1.46112502e-01
-6.95745111e-01 -4.25029218e-01 5.92718184e-01 -5.52954793e-01
-1.09345540e-02 5.22416115e-01 4.95562889e-02 -3.97524424e-02
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3.00031006e-01 8.17330718e-01 2.42970124e-01 -3.17335457... | [11.914639472961426, -0.8636422157287598] |
40020133-7abf-4298-83ce-f7dc02cf8897 | count-based-exploration-with-neural-density | 1703.01310 | null | http://arxiv.org/abs/1703.01310v2 | http://arxiv.org/pdf/1703.01310v2.pdf | Count-Based Exploration with Neural Density Models | Bellemare et al. (2016) introduced the notion of a pseudo-count, derived from
a density model, to generalize count-based exploration to non-tabular
reinforcement learning. This pseudo-count was used to generate an exploration
bonus for a DQN agent and combined with a mixed Monte Carlo update was
sufficient to achieve s... | ['Remi Munos', 'Marc G. Bellemare', 'Georg Ostrovski', 'Aaron van den Oord'] | 2017-03-03 | count-based-exploration-with-neural-density-1 | https://icml.cc/Conferences/2017/Schedule?showEvent=839 | http://proceedings.mlr.press/v70/ostrovski17a/ostrovski17a.pdf | icml-2017-8 | ['montezumas-revenge'] | ['playing-games'] | [-1.76941916e-01 4.11116555e-02 7.45945377e-04 7.66393691e-02
-6.81994140e-01 -6.37881041e-01 8.23199689e-01 -1.80828199e-01
-9.05124068e-01 1.18979704e+00 1.93230603e-02 -4.92125988e-01
-5.30896723e-01 -8.78731906e-01 -8.61394644e-01 -9.09492910e-01
-4.59870517e-01 1.06411803e+00 5.60795106e-02 -6.64596915... | [3.91438627243042, 1.7900830507278442] |
3961d2d4-a073-41c7-a4f1-34211305011c | ossid-online-self-supervised-instance | 2201.07309 | null | https://arxiv.org/abs/2201.07309v2 | https://arxiv.org/pdf/2201.07309v2.pdf | OSSID: Online Self-Supervised Instance Detection by (and for) Pose Estimation | Real-time object pose estimation is necessary for many robot manipulation algorithms. However, state-of-the-art methods for object pose estimation are trained for a specific set of objects; these methods thus need to be retrained to estimate the pose of each new object, often requiring tens of GPU-days of training for ... | ['David Held', 'Brian Okorn', 'Qiao Gu'] | 2022-01-18 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 1.24121524e-01 3.72695327e-02 -1.24185167e-01 -7.69200772e-02
-7.43148029e-01 -5.03006101e-01 3.46877635e-01 3.65403414e-01
-7.70846128e-01 2.55379051e-01 -4.24812227e-01 5.63407242e-02
1.69594437e-01 -4.66503441e-01 -1.12041235e+00 -4.16427433e-01
-6.18057027e-02 1.06907725e+00 9.37330782e-01 6.69282079... | [7.488068580627441, -2.5894548892974854] |
f054d9ae-d668-4ceb-8eef-b9bc14202eff | quality-constant-per-shot-encoding-by-two | 2208.10739 | null | https://arxiv.org/abs/2208.10739v1 | https://arxiv.org/pdf/2208.10739v1.pdf | Quality-Constant Per-Shot Encoding by Two-Pass Learning-based Rate Factor Prediction | Providing quality-constant streams can simultaneously guarantee user experience and prevent wasting bit-rate. In this paper, we propose a novel deep learning based two-pass encoder parameter prediction framework to decide rate factor (RF), with which encoder can output streams with constant quality. For each one-shot s... | ['Tianxiao Ye', 'Xiaobo Li', 'Yi Wang', 'Chunlei Cai'] | 2022-08-23 | null | null | null | null | ['parameter-prediction'] | ['miscellaneous'] | [ 2.94833720e-01 -2.09657758e-01 -6.40908539e-01 -4.25864458e-01
-5.15169799e-01 -2.63086818e-02 -6.92450628e-02 -4.67894748e-02
-4.43107545e-01 3.29543114e-01 2.72776544e-01 -4.02257055e-01
-1.14896081e-01 -8.84051561e-01 -6.64260685e-01 -5.61001122e-01
-3.70549828e-01 -2.67560452e-01 3.46281618e-01 1.17990367... | [11.27991008758545, -1.6960477828979492] |
3ca535c5-2c6c-4000-9822-f4a741c57f40 | learning-3d-human-dynamics-from-video | 1812.01601 | null | https://arxiv.org/abs/1812.01601v4 | https://arxiv.org/pdf/1812.01601v4.pdf | Learning 3D Human Dynamics from Video | From an image of a person in action, we can easily guess the 3D motion of the person in the immediate past and future. This is because we have a mental model of 3D human dynamics that we have acquired from observing visual sequences of humans in motion. We present a framework that can similarly learn a representation o... | ['Jason Y. Zhang', 'Panna Felsen', 'Angjoo Kanazawa', 'Jitendra Malik'] | 2018-12-04 | learning-3d-human-dynamics-from-video-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Kanazawa_Learning_3D_Human_Dynamics_From_Video_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Kanazawa_Learning_3D_Human_Dynamics_From_Video_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-human-dynamics', 'human-dynamics'] | ['computer-vision', 'computer-vision'] | [-1.41466379e-01 -3.04975417e-02 -2.52058744e-01 -1.46095827e-01
-6.66310608e-01 -4.89398092e-01 5.41473985e-01 -4.37040538e-01
-4.61634874e-01 3.27200502e-01 4.44862574e-01 2.80681729e-01
6.15074396e-01 -3.74570042e-01 -1.02955747e+00 -2.59821117e-01
-3.13678682e-01 9.90068614e-01 3.63061935e-01 -1.18874490... | [7.199005603790283, -0.66729736328125] |
bf67765a-2b39-4043-8446-023a5d39530f | knowledge-graph-augmented-network-towards | 2201.04831 | null | https://arxiv.org/abs/2201.04831v2 | https://arxiv.org/pdf/2201.04831v2.pdf | Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis | Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information, linguistic and commonsense knowledge are generally required in this task. However, most current methods employ complicated and inefficient... | ['DaCheng Tao', 'Hua Jin', 'Bo Du', 'Juhua Liu', 'Liang Ding', 'Qihuang Zhong'] | 2022-01-13 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-3.78831699e-02 -1.30170599e-01 -2.18565613e-01 -6.30289376e-01
-5.81210434e-01 -6.22179508e-01 4.57063407e-01 3.28239918e-01
-2.69213885e-01 2.68236995e-01 4.99747932e-01 -1.03088811e-01
-6.02112040e-02 -1.12164736e+00 -4.39524323e-01 -5.91908157e-01
6.65061712e-01 3.63035649e-02 -1.63018972e-01 -5.27330995... | [11.487586975097656, 6.580418586730957] |
d6772124-93f3-4a3f-8420-aa4f32279a6a | a-multi-modal-transformer-network-for-action | 2305.19624 | null | https://arxiv.org/abs/2305.19624v1 | https://arxiv.org/pdf/2305.19624v1.pdf | A Multi-Modal Transformer Network for Action Detection | This paper proposes a novel multi-modal transformer network for detecting actions in untrimmed videos. To enrich the action features, our transformer network utilizes a new multi-modal attention mechanism that computes the correlations between different spatial and motion modalities combinations. Exploring such correla... | ['Peter Youngs', 'Scott T. Acton', 'Matthew Korban'] | 2023-05-31 | null | null | null | null | ['action-detection'] | ['computer-vision'] | [ 1.43561333e-01 -4.62324828e-01 -4.64158177e-01 6.41245511e-04
-3.80863547e-01 -5.86952567e-01 5.55701315e-01 -3.49345386e-01
-3.67872596e-01 4.76603895e-01 7.70340562e-01 -1.00813299e-01
-3.39510471e-01 -4.96817142e-01 -7.53940523e-01 -8.18238974e-01
-1.93670318e-01 -4.43249077e-01 5.77165663e-01 -2.22811140... | [8.577457427978516, 0.5342345833778381] |
ba8afa8b-bc82-4307-b9a0-0fd80913183d | unihcp-a-unified-model-for-human-centric | 2303.02936 | null | https://arxiv.org/abs/2303.02936v4 | https://arxiv.org/pdf/2303.02936v4.pdf | UniHCP: A Unified Model for Human-Centric Perceptions | Human-centric perceptions (e.g., pose estimation, human parsing, pedestrian detection, person re-identification, etc.) play a key role in industrial applications of visual models. While specific human-centric tasks have their own relevant semantic aspect to focus on, they also share the same underlying semantic structu... | ['Wanli Ouyang', 'Donglian Qi', 'Fengwei Yu', 'Rui Zhao', 'Feng Zhu', 'Lei Bai', 'Shixiang Tang', 'Meilin Chen', 'Yizhou Wang', 'Yuanzheng Ci'] | 2023-03-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ci_UniHCP_A_Unified_Model_for_Human-Centric_Perceptions_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ci_UniHCP_A_Unified_Model_for_Human-Centric_Perceptions_CVPR_2023_paper.pdf | cvpr-2023-1 | ['person-re-identification', 'pedestrian-attribute-recognition', 'pedestrian-detection', 'human-part-segmentation', 'human-parsing'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-2.27171004e-01 8.93400386e-02 -1.14981942e-01 -4.06441659e-01
-7.72918940e-01 -4.29316312e-01 7.19030440e-01 -1.79764092e-01
-6.09903455e-01 5.06502390e-01 2.01966837e-01 -1.54851362e-01
4.54574943e-01 -4.22469497e-01 -9.09408152e-01 -4.81756955e-01
2.36685336e-01 8.09728324e-01 6.04068518e-01 -3.17901880... | [7.745123863220215, -0.6099827289581299] |
5ba89db4-9d9c-47e7-b99f-7aaac8b840bc | explaining-away-results-in-accurate-and | 1911.04169 | null | https://arxiv.org/abs/1911.04169v1 | https://arxiv.org/pdf/1911.04169v1.pdf | Explaining Away Results in Accurate and Tolerant Template Matching | Recognising and locating image patches or sets of image features is an important task underlying much work in computer vision. Traditionally this has been accomplished using template matching. However, template matching is notoriously brittle in the face of changes in appearance caused by, for example, variations in vi... | ['M. W. Spratling'] | 2019-11-11 | null | null | null | null | ['patch-matching', 'contour-detection'] | ['computer-vision', 'computer-vision'] | [ 7.06886530e-01 1.78752854e-01 1.21689662e-01 -2.85854071e-01
-2.46443123e-01 -3.98873687e-01 6.92208648e-01 9.03734416e-02
-2.23575369e-01 3.51983845e-01 -3.02380234e-01 -1.23744290e-02
-2.51668960e-01 -7.20210850e-01 -6.14286125e-01 -8.41193199e-01
2.92374730e-01 4.57125336e-01 5.94152510e-01 1.33323535... | [10.298619270324707, -1.5794965028762817] |
f0e51ff1-b4a6-4156-be05-ceebda62c36b | domain-translation-via-latent-space-mapping | 2212.03361 | null | https://arxiv.org/abs/2212.03361v1 | https://arxiv.org/pdf/2212.03361v1.pdf | Domain Translation via Latent Space Mapping | In this paper, we investigate the problem of multi-domain translation: given an element $a$ of domain $A$, we would like to generate a corresponding $b$ sample in another domain $B$, and vice versa. Acquiring supervision in multiple domains can be a tedious task, also we propose to learn this translation from one domai... | ['Romain Herault', 'Clement Chatelain', 'Simon Bernard', 'Tsiry Mayet'] | 2022-12-06 | null | null | null | null | ['facial-landmark-detection'] | ['computer-vision'] | [ 6.85469508e-01 2.24303499e-01 -2.12143511e-01 -7.21849740e-01
-1.04225016e+00 -4.89142835e-01 5.90950131e-01 -9.10337418e-02
-5.30388594e-01 8.75619471e-01 -3.43186349e-01 -1.26218587e-01
9.39795673e-02 -9.02000725e-01 -1.00389040e+00 -7.09967136e-01
2.00611293e-01 7.78197229e-01 -1.47253145e-02 -5.54768890... | [11.811151504516602, -0.22455760836601257] |
437d64ce-dac8-4e16-a896-3b2e059568ec | rec-mv-reconstructing-3d-dynamic-cloth-from-1 | 2305.14236 | null | https://arxiv.org/abs/2305.14236v2 | https://arxiv.org/pdf/2305.14236v2.pdf | REC-MV: REconstructing 3D Dynamic Cloth from Monocular Videos | Reconstructing dynamic 3D garment surfaces with open boundaries from monocular videos is an important problem as it provides a practical and low-cost solution for clothes digitization. Recent neural rendering methods achieve high-quality dynamic clothed human reconstruction results from monocular video, but these metho... | ['Xiaoguang Han', 'Junle Wang', 'Mutian Xu', 'Jiapeng Zhou', 'GuanYing Chen', 'Lingteng Qiu'] | 2023-05-23 | rec-mv-reconstructing-3d-dynamic-cloth-from | http://openaccess.thecvf.com//content/CVPR2023/html/Qiu_REC-MV_REconstructing_3D_Dynamic_Cloth_From_Monocular_Videos_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qiu_REC-MV_REconstructing_3D_Dynamic_Cloth_From_Monocular_Videos_CVPR_2023_paper.pdf | cvpr-2023-1 | ['neural-rendering'] | ['computer-vision'] | [ 3.12327206e-01 -3.60245258e-01 1.33068278e-01 -1.69510618e-01
-6.10733509e-01 -5.09207487e-01 1.50661156e-01 -8.33425164e-01
8.65916610e-02 6.81797206e-01 -7.56378612e-03 2.09042996e-01
1.58311442e-01 -6.71709836e-01 -9.99104083e-01 -5.75525641e-01
1.37933403e-01 2.87517846e-01 7.99439922e-02 -1.20289035... | [7.202999114990234, -1.2906861305236816] |
87fb446f-cbe2-48c5-b2bf-430ddac4b302 | controllable-mixed-initiative-dialogue | 2305.04147 | null | https://arxiv.org/abs/2305.04147v1 | https://arxiv.org/pdf/2305.04147v1.pdf | Controllable Mixed-Initiative Dialogue Generation through Prompting | Mixed-initiative dialogue tasks involve repeated exchanges of information and conversational control. Conversational agents gain control by generating responses that follow particular dialogue intents or strategies, prescribed by a policy planner. The standard approach has been fine-tuning pre-trained language models t... | ['Zhou Yu', 'Urvi Awasthi', 'Weiyan Shi', 'Xiao Yu', 'Maximillian Chen'] | 2023-05-06 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [ 3.11662614e-01 1.16099858e+00 -1.70433715e-01 -8.88644218e-01
-1.16786647e+00 -8.03205788e-01 1.32246184e+00 1.21135153e-01
-4.94459361e-01 1.44483674e+00 1.02062929e+00 -1.88346937e-01
2.63142079e-01 -6.21713936e-01 2.70842668e-02 -1.29415572e-01
3.44928503e-01 1.00804794e+00 -2.94044971e-01 -6.38281167... | [12.846514701843262, 8.066627502441406] |
d337a078-33b9-40bb-ba29-77d8b9d49ea5 | bounding-information-leakage-in-machine | 2105.03875 | null | https://arxiv.org/abs/2105.03875v2 | https://arxiv.org/pdf/2105.03875v2.pdf | Bounding Information Leakage in Machine Learning | Recently, it has been shown that Machine Learning models can leak sensitive information about their training data. This information leakage is exposed through membership and attribute inference attacks. Although many attack strategies have been proposed, little effort has been made to formalize these problems. We prese... | ['Pablo Piantanida', 'Catuscia Palamidessi', 'Georg Pichler', 'Ganesh Del Grosso'] | 2021-05-09 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 6.75024450e-01 2.80951947e-01 -5.54668903e-02 -2.91983873e-01
-6.27009332e-01 -1.11123383e+00 7.14683652e-01 4.61655915e-01
-4.59878176e-01 7.30116785e-01 -3.56670231e-01 -3.32608998e-01
-1.95002854e-01 -9.91951942e-01 -7.62049198e-01 -8.63265157e-01
-1.23695411e-01 3.28339100e-01 2.18916103e-01 -1.82822391... | [5.918097019195557, 7.2443132400512695] |
d83ac179-ac16-4dae-abc2-ca30dc45d1ba | person-detection-using-an-ultra-low | 2212.08415 | null | https://arxiv.org/abs/2212.08415v1 | https://arxiv.org/pdf/2212.08415v1.pdf | Person Detection Using an Ultra Low-resolution Thermal Imager on a Low-cost MCU | Detecting persons in images or video with neural networks is a well-studied subject in literature. However, such works usually assume the availability of a camera of decent resolution and a high-performance processor or GPU to run the detection algorithm, which significantly increases the cost of a complete detection s... | ['Toon Goedemé', 'Kristof Van Beeck', 'Wouter Reusen', 'Maarten Vandersteegen'] | 2022-12-16 | null | null | null | null | ['human-detection'] | ['computer-vision'] | [ 2.55037695e-01 -2.90301979e-01 3.01256984e-01 -2.24845439e-01
-9.49543491e-02 -2.77525485e-01 2.01697916e-01 -1.15693547e-01
-1.02521491e+00 2.87789792e-01 -6.10070288e-01 -1.98328823e-01
5.54197013e-01 -8.92435968e-01 -5.51606297e-01 -4.51505065e-01
5.88225052e-02 -1.23704327e-02 4.54494148e-01 2.14158878... | [8.6336088180542, -0.8244243264198303] |
58e32eb3-cfd7-4cc9-9592-45ce56092db9 | how-to-plant-trees-in-language-models-data | 2305.19905 | null | https://arxiv.org/abs/2305.19905v1 | https://arxiv.org/pdf/2305.19905v1.pdf | How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases | Accurate syntactic representations are essential for robust generalization in natural language. Recent work has found that pre-training can teach language models to rely on hierarchical syntactic features - as opposed to incorrect linear features - when performing tasks after fine-tuning. We test what aspects of pre-tr... | ['Tal Linzen', 'Aaron Mueller'] | 2023-05-31 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 1.99665442e-01 7.32217133e-01 2.40199678e-02 -7.63740301e-01
-3.13381523e-01 -5.89565396e-01 6.33933723e-01 5.63812733e-01
-6.89466536e-01 4.16399449e-01 8.27654660e-01 -8.03655505e-01
-2.59934226e-03 -1.00595093e+00 -9.35173154e-01 -1.79154381e-01
-9.13858600e-03 4.22085971e-01 3.00172716e-01 -4.22917008... | [10.577780723571777, 8.897904396057129] |
14c81493-7912-4144-9786-b0f44449d284 | a-multibranch-convolutional-neural-network | 2208.02361 | null | https://arxiv.org/abs/2208.02361v1 | https://arxiv.org/pdf/2208.02361v1.pdf | A Multibranch Convolutional Neural Network for Hyperspectral Unmixing | Hyperspectral unmixing remains one of the most challenging tasks in the analysis of such data. Deep learning has been blooming in the field and proved to outperform other classic unmixing techniques, and can be effectively deployed onboard Earth observation satellites equipped with hyperspectral imagers. In this letter... | ['Jakub Nalepa', 'Bertrand Le Saux', 'Nicolas Longépé', 'Michal Kawulok', 'Lukasz Tulczyjew'] | 2022-08-03 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 5.38504064e-01 -4.89873350e-01 -1.08938493e-01 7.50207677e-02
-3.98603976e-01 -7.18025267e-01 5.69795787e-01 -1.95536703e-01
-3.72839391e-01 7.02470839e-01 1.04070343e-01 -4.21282530e-01
-5.30465961e-01 -7.99050808e-01 -4.82905447e-01 -1.06429684e+00
-2.20937133e-01 2.36333266e-01 -4.93541926e-01 -2.94191480... | [10.052787780761719, -1.990002989768982] |
c3cb40f1-92f4-4424-9190-264c81587948 | usaar-at-semeval-2016-task-11-complex-word | null | null | https://aclanthology.org/S16-1147 | https://aclanthology.org/S16-1147.pdf | USAAR at SemEval-2016 Task 11: Complex Word Identification with Sense Entropy and Sentence Perplexity | null | ["Jos{\\'e} Manuel Mart{\\'\\i}nez Mart{\\'\\i}nez", 'Liling Tan'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['complex-word-identification'] | ['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... | [-7.220684051513672, 3.80130934715271] |
b4155b2d-4011-451f-9ebb-577506a68726 | zyj-lt-edi-eacl2021-xlm-roberta-based-model | null | null | https://aclanthology.org/2021.ltedi-1.16 | https://aclanthology.org/2021.ltedi-1.16.pdf | ZYJ@LT-EDI-EACL2021:XLM-RoBERTa-Based Model with Attention for Hope Speech Detection | Due to the development of modern computer technology and the increase in the number of online media users, we can see all kinds of posts and comments everywhere on the internet. Hope speech can not only inspire the creators but also make other viewers pleasant. It is necessary to effectively and automatically detect ho... | ['Xin Tao', 'Yingjia Zhao'] | null | null | null | null | eacl-ltedi-2021-4 | ['hope-speech-detection'] | ['natural-language-processing'] | [-1.79868624e-01 1.64969578e-01 -2.19510674e-01 -1.92130759e-01
-6.17841065e-01 -3.26924562e-01 5.15514672e-01 2.96856046e-01
-4.04345989e-01 3.83073717e-01 6.41187727e-01 -3.86647522e-01
1.57543689e-01 -6.32712901e-01 -9.45782736e-02 -3.53340715e-01
2.65610904e-01 -1.75183192e-02 1.59480423e-01 -5.18829763... | [9.077641487121582, 10.695947647094727] |
66cded31-8691-4a9f-8c56-256d10491836 | vietnamese-multi-document-summary-using | 2306.14827 | null | https://arxiv.org/abs/2306.14827v1 | https://arxiv.org/pdf/2306.14827v1.pdf | Vietnamese multi-document summary using subgraph selection approach -- VLSP 2022 AbMuSu Shared Task | Document summarization is a task to generate afluent, condensed summary for a document, andkeep important information. A cluster of documents serves as the input for multi-document summarizing (MDS), while the cluster summary serves as the output. In this paper, we focus on transforming the extractive MDS problem into ... | ['Cam-Van Thi Nguyen', 'Tam Doan Thanh', 'Huu-Thin Nguyen'] | 2023-06-26 | null | null | null | null | ['document-summarization'] | ['natural-language-processing'] | [ 3.58461529e-01 6.13140345e-01 -1.39957100e-01 -2.07926989e-01
-1.18335307e+00 -5.10330439e-01 6.65934741e-01 7.91091740e-01
9.24907774e-02 9.38660681e-01 1.04827809e+00 2.30098501e-01
-3.09401333e-01 -6.02120221e-01 -2.91501105e-01 -6.16744280e-01
-1.22307658e-01 5.76289833e-01 -5.86843193e-02 -1.13440260... | [12.5348482131958, 9.582038879394531] |
e2fa2a54-6cde-4f1c-956d-91b6347f1693 | context-aware-chart-element-detection | 2305.04151 | null | https://arxiv.org/abs/2305.04151v1 | https://arxiv.org/pdf/2305.04151v1.pdf | Context-Aware Chart Element Detection | As a prerequisite of chart data extraction, the accurate detection of chart basic elements is essential and mandatory. In contrast to object detection in the general image domain, chart element detection relies heavily on context information as charts are highly structured data visualization formats. To address this, w... | ['David Doermann', 'Saleem Ahmed', 'Pengyu Yan'] | 2023-05-07 | null | null | null | null | ['data-visualization', 'data-visualization', 'document-ai'] | ['methodology', 'miscellaneous', 'natural-language-processing'] | [ 3.52089971e-01 -4.40492719e-01 2.22519562e-02 -2.69850433e-01
-5.42332292e-01 -6.17088139e-01 5.30932069e-01 7.50956416e-01
-1.89191550e-01 2.09839866e-01 2.83635378e-01 -5.67379177e-01
-1.41293183e-01 -6.74146950e-01 -4.72673088e-01 -5.11775315e-01
-8.51325691e-02 -2.19375446e-01 4.00850534e-01 -1.10294588... | [11.481364250183105, 2.341392993927002] |
660cdc30-101e-4b14-a751-7e68042974c9 | content-based-models-of-quotation | null | null | https://aclanthology.org/2021.eacl-main.195 | https://aclanthology.org/2021.eacl-main.195.pdf | Content-based Models of Quotation | We explore the task of quotability identification, in which, given a document, we aim to identify which of its passages are the most quotable, i.e. the most likely to be directly quoted by later derived documents. We approach quotability identification as a passage ranking problem and evaluate how well both feature-bas... | ['David Smith', 'Ansel MacLaughlin'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['passage-ranking'] | ['natural-language-processing'] | [-2.23554969e-01 -2.63608247e-01 -4.74579483e-01 5.08320145e-02
-1.26193178e+00 -1.30702031e+00 1.08302796e+00 5.25550127e-01
-5.21163404e-01 9.10899997e-01 8.38770807e-01 -5.60081899e-01
-4.21703130e-01 -5.77764213e-01 -5.96367002e-01 -1.87491596e-01
2.89123803e-01 3.44569474e-01 3.98206860e-01 -4.71746385... | [12.12655258178711, 9.4329252243042] |
896ef78d-1b32-4e68-92bd-2f49e084e656 | dorsal-diffusion-for-object-centric | 2306.08068 | null | https://arxiv.org/abs/2306.08068v1 | https://arxiv.org/pdf/2306.08068v1.pdf | DORSal: Diffusion for Object-centric Representations of Scenes $\textit{et al.}$ | Recent progress in 3D scene understanding enables scalable learning of representations across large datasets of diverse scenes. As a consequence, generalization to unseen scenes and objects, rendering novel views from just a single or a handful of input images, and controllable scene generation that supports editing, i... | ['Thomas Kipf', 'Mehdi S. M. Sajjadi', 'Emiel Hoogeboom', 'Sjoerd van Steenkiste', 'Allan Jabri'] | 2023-06-13 | null | null | null | null | ['neural-rendering', 'scene-generation', 'scene-understanding'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.73324203e-01 7.58767053e-02 3.11948121e-01 -4.02784079e-01
-7.57427514e-01 -7.08600283e-01 8.72790337e-01 -1.63902029e-01
-4.11538854e-02 3.72858524e-01 4.22717601e-01 -5.13941348e-02
1.94865316e-02 -9.31093156e-01 -1.01082265e+00 -2.07401857e-01
3.08221187e-02 6.04194164e-01 2.40705758e-01 -1.78556994... | [9.188611030578613, -3.121476173400879] |
79fd80c1-4536-487d-af81-2ba62e373bb7 | boost-ctr-prediction-for-new-advertisements | 2209.11727 | null | https://arxiv.org/abs/2209.11727v1 | https://arxiv.org/pdf/2209.11727v1.pdf | Boost CTR Prediction for New Advertisements via Modeling Visual Content | Existing advertisements click-through rate (CTR) prediction models are mainly dependent on behavior ID features, which are learned based on the historical user-ad interactions. Nevertheless, behavior ID features relying on historical user behaviors are not feasible to describe new ads without previous interactions with... | ['Ping Li', 'Hongliang Fei', 'Yi Yang', 'Jie Liu', 'Zhipeng Jin', 'Tan Yu'] | 2022-09-23 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-1.15236349e-01 -1.70129776e-01 -6.14252031e-01 -6.47924662e-01
-5.09059548e-01 -5.65905094e-01 5.17468870e-01 1.04116397e-02
-3.62413973e-01 3.12326342e-01 1.89790130e-01 -4.39210147e-01
2.16385633e-01 -9.02686954e-01 -7.50419855e-01 -5.16137958e-01
-8.56202990e-02 6.17446452e-02 1.04339845e-01 -1.78253070... | [10.189990043640137, 5.35856294631958] |
c2fa7621-e8c1-428f-bd62-b65aa64c5f8b | human-trajectory-forecasting-with-explainable | 2307.01817 | null | https://arxiv.org/abs/2307.01817v1 | https://arxiv.org/pdf/2307.01817v1.pdf | Human Trajectory Forecasting with Explainable Behavioral Uncertainty | Human trajectory forecasting helps to understand and predict human behaviors, enabling applications from social robots to self-driving cars, and therefore has been heavily investigated. Most existing methods can be divided into model-free and model-based methods. Model-free methods offer superior prediction accuracy bu... | ['He Wang', 'Dinesh Manocha', 'Jiangbei Yue'] | 2023-07-04 | null | null | null | null | ['self-driving-cars', 'trajectory-forecasting'] | ['computer-vision', 'computer-vision'] | [-1.15266152e-01 2.37757474e-01 -2.41296053e-01 -7.37232327e-01
3.23195010e-03 1.01164885e-01 5.89891791e-01 -1.86554804e-01
-8.17253962e-02 8.66160393e-01 1.88147873e-01 -3.17063600e-01
-3.61586004e-01 -6.90898597e-01 -6.01711929e-01 -6.09862983e-01
-1.68123290e-01 8.18347454e-01 6.51484370e-01 -2.69358337... | [6.095353126525879, 0.8813832998275757] |
fcd59f58-f68c-4348-a8c3-c96127c2503e | semi-supervised-medical-image-classification-1 | 2005.11217 | null | https://arxiv.org/abs/2005.11217v1 | https://arxiv.org/pdf/2005.11217v1.pdf | Semi-supervised Medical Image Classification with Global Latent Mixing | Computer-aided diagnosis via deep learning relies on large-scale annotated data sets, which can be costly when involving expert knowledge. Semi-supervised learning (SSL) mitigates this challenge by leveraging unlabeled data. One effective SSL approach is to regularize the local smoothness of neural functions via pertur... | ['Linwei Wang', 'Sandesh Ghimire', 'Zhiyuan Li', 'Prashnna Kumar Gyawali', 'Pradeep Bajracharya'] | 2020-05-22 | null | null | null | null | ['semi-supervised-medical-image-classification'] | ['medical'] | [ 3.26054275e-01 6.75788283e-01 -3.14616144e-01 -5.66386759e-01
-1.06906784e+00 -4.77870375e-01 3.87929380e-01 2.64445245e-01
-4.08641368e-01 5.62975228e-01 2.53486127e-01 -3.92267972e-01
-9.27140862e-02 -2.80082881e-01 -7.10532248e-01 -9.23913836e-01
2.25348681e-01 4.72493887e-01 -1.75427616e-01 1.58771694... | [14.632733345031738, -2.182114362716675] |
6619d74b-27ed-4d79-ae20-e3ac95c3f66f | clip-pae-projection-augmentation-embedding-to | 2210.03919 | null | https://arxiv.org/abs/2210.03919v4 | https://arxiv.org/pdf/2210.03919v4.pdf | CLIP-PAE: Projection-Augmentation Embedding to Extract Relevant Features for a Disentangled, Interpretable, and Controllable Text-Guided Face Manipulation | Recently introduced Contrastive Language-Image Pre-Training (CLIP) bridges images and text by embedding them into a joint latent space. This opens the door to ample literature that aims to manipulate an input image by providing a textual explanation. However, due to the discrepancy between image and text embeddings in ... | ['Fangcheng Zhong', 'Cengiz Oztireli', 'Chenliang Zhou'] | 2022-10-08 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 6.60434186e-01 1.32625312e-01 -1.72310874e-01 -3.38590771e-01
-5.15100241e-01 -7.78555810e-01 8.35236728e-01 -4.52570260e-01
-1.30952716e-01 3.83340150e-01 4.23708022e-01 -3.11410725e-02
-1.79666117e-01 -4.51791883e-01 -8.84962976e-01 -6.45795107e-01
4.22830254e-01 1.24397449e-01 -5.69908857e-01 9.99050122... | [11.734407424926758, -0.29560407996177673] |
23b12b20-eae1-4c79-b306-ae0535d273c0 | deep-spectral-clustering-using-dual | 1904.13113 | null | http://arxiv.org/abs/1904.13113v1 | http://arxiv.org/pdf/1904.13113v1.pdf | Deep Spectral Clustering using Dual Autoencoder Network | The clustering methods have recently absorbed even-increasing attention in
learning and vision. Deep clustering combines embedding and clustering together
to obtain optimal embedding subspace for clustering, which can be more
effective compared with conventional clustering methods. In this paper, we
propose a joint lea... | ['Feng Zheng', 'Wei Liu', 'Junchi Yan', 'Xu Yang', 'Cheng Deng'] | 2019-04-30 | deep-spectral-clustering-using-dual-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Yang_Deep_Spectral_Clustering_Using_Dual_Autoencoder_Network_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_Deep_Spectral_Clustering_Using_Dual_Autoencoder_Network_CVPR_2019_paper.pdf | cvpr-2019-6 | ['mutual-information-estimation'] | ['methodology'] | [-4.55867857e-01 -4.93847251e-01 -8.09977427e-02 -3.49728942e-01
-6.72769308e-01 -3.80041331e-01 3.65818739e-01 -3.52274418e-01
-2.13453099e-01 -5.41873612e-02 4.26685482e-01 3.46226037e-01
-3.50516319e-01 -6.00548387e-01 -3.61281097e-01 -1.27948999e+00
1.75521791e-01 1.90421969e-01 -1.44806221e-01 5.39313376... | [8.830306053161621, 3.7732431888580322] |
6b222717-6128-481a-a3cd-97cab517a349 | can-humans-fly-action-understanding-with | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Xu_Can_Humans_Fly_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Xu_Can_Humans_Fly_2015_CVPR_paper.pdf | Can Humans Fly? Action Understanding With Multiple Classes of Actors | Can humans fly? Emphatically no. Can cars eat? Again, absolutely not. Yet, these absurd inferences result from the current disregard for particular types of actors in action understanding. There is no work we know of on simultaneously inferring actors and actions in the video, not to mention a dataset to experiment wit... | ['Jason J. Corso', 'Shao-Hang Hsieh', 'Chenliang Xu', 'Caiming Xiong'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['action-understanding'] | ['computer-vision'] | [ 7.84174562e-01 3.27514738e-01 -6.03154242e-01 -6.54419422e-01
-6.27020121e-01 -7.29676783e-01 8.54346573e-01 -3.01109999e-01
-3.17727923e-01 6.42027020e-01 4.79775727e-01 -1.64764032e-01
2.24133991e-02 -2.89341420e-01 -8.10960650e-01 -7.43183732e-01
3.29127699e-01 4.41991419e-01 3.22785914e-01 2.07965568... | [8.465335845947266, 0.5606163144111633] |
9d48fcd7-2ada-44f3-be03-d5f73dcda16c | contextually-enhanced-es-drnn-with-dynamic | 2212.09030 | null | https://arxiv.org/abs/2212.09030v1 | https://arxiv.org/pdf/2212.09030v1.pdf | Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting | In this paper, we propose a new short-term load forecasting (STLF) model based on contextually enhanced hybrid and hierarchical architecture combining exponential smoothing (ES) and a recurrent neural network (RNN). The model is composed of two simultaneously trained tracks: the context track and the main track. The co... | ['Paweł Pełka', 'Grzegorz Dudek', 'Slawek Smyl'] | 2022-12-18 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [ 1.28335819e-01 3.54694836e-02 3.51407006e-02 -4.65412378e-01
-1.49972826e-01 -1.68989018e-01 8.36324334e-01 2.27426127e-01
-1.75821170e-01 9.27260876e-01 5.12414694e-01 -4.69482571e-01
-1.17056079e-01 -7.24900186e-01 -4.14906025e-01 -8.05550277e-01
-4.84149545e-01 6.04967594e-01 4.18478668e-01 -6.14672005... | [6.627400875091553, 2.9908320903778076] |
f94bc2ae-5dad-4a92-a18f-9a0c0da5d866 | mobasa-corpus-for-aspect-based-sentiment | null | null | https://aclanthology.org/2022.csrnlp-1.5 | https://aclanthology.org/2022.csrnlp-1.5.pdf | MobASA: Corpus for Aspect-based Sentiment Analysis and Social Inclusion in the Mobility Domain | In this paper we show how aspect-based sentiment analysis might help public transport companies to improve their social responsibility for accessible travel. We present MobASA: a novel German-language corpus of tweets annotated with their relevance for public transportation, and with sentiment towards aspects related t... | ['Philippe Thomas', 'Aleksandra Gabryszak'] | null | null | null | null | csrnlp-lrec-2022-6 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-3.61728460e-01 3.65598798e-01 -6.51942611e-01 -5.40194690e-01
-1.13611484e+00 -5.31711459e-01 4.26635295e-01 7.20903993e-01
-5.94545960e-01 6.53834105e-01 1.07093108e+00 -4.71990347e-01
-5.78313768e-02 -1.01740241e+00 -4.37340498e-01 -4.73963797e-01
4.73438650e-01 4.58573997e-01 3.90250862e-01 -9.77209866... | [10.625622749328613, 6.945745944976807] |
f3d6c375-da3b-4081-aaf4-94251c3ce1a7 | what-knowledge-is-needed-towards-explainable | 2211.04052 | null | https://arxiv.org/abs/2211.04052v2 | https://arxiv.org/pdf/2211.04052v2.pdf | What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation | kNN-MT presents a new paradigm for domain adaptation by building an external datastore, which usually saves all target language token occurrences in the parallel corpus. As a result, the constructed datastore is usually large and possibly redundant. In this paper, we investigate the interpretability issue of this appro... | ['Jiajun Chen', 'Xin Zheng', 'Yunzhe Lv', 'ShuJian Huang', 'Wenhao Zhu'] | 2022-11-08 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 1.86057955e-01 2.29420111e-01 -5.39557993e-01 -4.13902372e-01
-5.96388936e-01 -6.36567593e-01 4.97617841e-01 3.15847754e-01
-4.92488801e-01 1.21362519e+00 1.03109926e-01 -6.36266470e-01
-1.43884987e-01 -7.01088309e-01 -8.36084425e-01 -3.40667218e-01
2.71790743e-01 9.19373214e-01 4.34983045e-01 -4.38292891... | [11.276944160461426, 9.934761047363281] |
ba4b975d-3121-480d-9826-d7f60e7d3f52 | using-adaptive-gradient-for-texture-learning | 2104.14169 | null | https://arxiv.org/abs/2104.14169v1 | https://arxiv.org/pdf/2104.14169v1.pdf | Using Adaptive Gradient for Texture Learning in Single-View 3D Reconstruction | Recently, learning-based approaches for 3D model reconstruction have attracted attention owing to its modern applications such as Extended Reality(XR), robotics and self-driving cars. Several approaches presented good performance on reconstructing 3D shapes by learning solely from images, i.e., without using 3D models ... | ['Dihong Tian', 'Luoyang Lin'] | 2021-04-29 | null | null | null | null | ['texture-synthesis', 'single-view-3d-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 1.53409109e-01 1.13843761e-01 -2.61904933e-02 -2.97829598e-01
-6.02260828e-01 -1.48728594e-01 4.69338804e-01 -5.85707545e-01
-1.54376589e-02 4.21182245e-01 -2.98416346e-01 -1.74303144e-01
-2.26883125e-02 -1.02810144e+00 -1.02944803e+00 -5.92649162e-01
4.40389365e-01 4.99309421e-01 2.69921511e-01 -3.59273478... | [8.665996551513672, -3.1342320442199707] |
5f55ffd6-76df-49f2-a02d-440763899569 | deepstruct-pretraining-of-language-models-for-1 | 2205.10475 | null | https://arxiv.org/abs/2205.10475v2 | https://arxiv.org/pdf/2205.10475v2.pdf | DeepStruct: Pretraining of Language Models for Structure Prediction | We introduce a method for improving the structural understanding abilities of language models. Unlike previous approaches that finetune the models with task-specific augmentation, we pretrain language models on a collection of task-agnostic corpora to generate structures from text. Our structure pretraining enables zer... | ['Dawn Song', 'Jie Tang', 'Haoyun Hong', 'Zui Chen', 'Xiao Liu', 'Chenguang Wang'] | 2022-05-21 | deepstruct-pretraining-of-language-models-for | https://aclanthology.org/2022.findings-acl.67 | https://aclanthology.org/2022.findings-acl.67.pdf | findings-acl-2022-5 | ['open-information-extraction', 'dialogue-state-tracking', 'semantic-role-labeling', 'relation-classification', 'joint-entity-and-relation-extraction'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 4.91098821e-01 1.10269701e+00 -4.52351511e-01 -6.40811145e-01
-1.04531598e+00 -8.16722810e-01 9.13917482e-01 3.33436847e-01
-7.02593923e-01 1.14218521e+00 8.96123052e-01 -3.71905476e-01
1.65595278e-01 -5.94388068e-01 -6.92571044e-01 5.48292324e-02
-2.13064581e-01 1.23197281e+00 3.47233772e-01 -4.56258267... | [9.952609062194824, 8.90596866607666] |
87b94dc6-025d-441e-a78b-69443ff00f4c | learning-to-discover-reflection-symmetry-via | 2108.12952 | null | https://arxiv.org/abs/2108.12952v2 | https://arxiv.org/pdf/2108.12952v2.pdf | Learning to Discover Reflection Symmetry via Polar Matching Convolution | The task of reflection symmetry detection remains challenging due to significant variations and ambiguities of symmetry patterns in the wild. Furthermore, since the local regions are required to match in reflection for detecting a symmetry pattern, it is hard for standard convolutional networks, which are not equivaria... | ['Minsu Cho', 'Woohyeon Shim', 'Ahyun Seo'] | 2021-08-30 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Seo_Learning_To_Discover_Reflection_Symmetry_via_Polar_Matching_Convolution_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Seo_Learning_To_Discover_Reflection_Symmetry_via_Polar_Matching_Convolution_ICCV_2021_paper.pdf | iccv-2021-1 | ['symmetry-detection'] | ['computer-vision'] | [ 3.49642634e-01 -1.64282814e-01 1.14455596e-01 -4.78858083e-01
-4.30613965e-01 -7.53254294e-01 7.24390149e-01 -5.22684574e-01
-5.89457303e-02 2.35175565e-01 2.83790708e-01 -2.31738299e-01
-2.90494442e-01 -8.34806681e-01 -9.06692266e-01 -6.12958550e-01
-1.56780571e-01 -2.29190346e-02 1.60099745e-01 -3.30376685... | [8.5647554397583, -2.0755774974823] |
4b347892-0f8a-4cd8-a822-fdfed883e6d2 | direction-aware-joint-adaptation-of-neural | 2207.07273 | null | https://arxiv.org/abs/2207.07273v1 | https://arxiv.org/pdf/2207.07273v1.pdf | Direction-Aware Joint Adaptation of Neural Speech Enhancement and Recognition in Real Multiparty Conversational Environments | This paper describes noisy speech recognition for an augmented reality headset that helps verbal communication within real multiparty conversational environments. A major approach that has actively been studied in simulated environments is to sequentially perform speech enhancement and automatic speech recognition (ASR... | ['Kazuyoshi Yoshii', 'Mathieu Fontaine', 'Yoshiaki Bando', 'Kouhei Sekiguchi', 'Aditya Arie Nugraha', 'Yicheng Du'] | 2022-07-15 | null | null | null | null | ['noisy-speech-recognition', 'distant-speech-recognition'] | ['speech', 'speech'] | [ 3.52369279e-01 3.55831921e-01 6.15370810e-01 -7.45052934e-01
-1.28615189e+00 -2.07518473e-01 5.22334218e-01 -6.16425991e-01
-4.06733185e-01 4.45953429e-01 8.30770969e-01 -1.83738887e-01
2.22931325e-01 -4.40193526e-02 -4.66870546e-01 -8.99951339e-01
3.51312011e-01 3.18838716e-01 -1.26163796e-01 -3.93702686... | [14.806835174560547, 5.963128089904785] |
c9e1868b-ad5c-4ef6-b840-2db750634c1b | improving-low-resource-cross-lingual-parsing | 2210.09428 | null | https://arxiv.org/abs/2210.09428v1 | https://arxiv.org/pdf/2210.09428v1.pdf | Improving Low-Resource Cross-lingual Parsing with Expected Statistic Regularization | We present Expected Statistic Regularization (ESR), a novel regularization technique that utilizes low-order multi-task structural statistics to shape model distributions for semi-supervised learning on low-resource datasets. We study ESR in the context of cross-lingual transfer for syntactic analysis (POS tagging and ... | ['Michael Collins', 'Thomas Effland'] | 2022-10-17 | null | null | null | null | ['dependency-parsing'] | ['natural-language-processing'] | [-8.67264438e-03 2.74554610e-01 -4.79819030e-01 -9.67422962e-01
-1.97006750e+00 -7.83187330e-01 3.17025006e-01 2.31970772e-01
-8.42782199e-01 8.60701919e-01 3.20305645e-01 -5.02518654e-01
1.93538979e-01 1.71862764e-03 -1.11115086e+00 -5.73816419e-01
-2.12691918e-01 7.46812582e-01 3.47198784e-01 2.56880205... | [10.628233909606934, 9.797281265258789] |
2353862c-0032-4e4e-b21f-e4f4ebbcc593 | amd-hooknet-for-glacier-front-segmentation | 2302.02744 | null | https://arxiv.org/abs/2302.02744v1 | https://arxiv.org/pdf/2302.02744v1.pdf | AMD-HookNet for Glacier Front Segmentation | Knowledge on changes in glacier calving front positions is important for assessing the status of glaciers. Remote sensing imagery provides the ideal database for monitoring calving front positions, however, it is not feasible to perform this task manually for all calving glaciers globally due to time-constraints. Deep ... | ['Vincent Christlein', 'Andreas Maier', 'Matthias Braun', 'Jianlin Zhang', 'Thorsten Seehaus', 'Nora Gourmelon', 'Fei Wu'] | 2023-02-06 | null | null | null | null | ['calving-front-delineation-in-synthetic'] | ['computer-vision'] | [ 2.96919376e-01 -1.98592111e-01 1.47255242e-01 -5.42565048e-01
-5.26600659e-01 -5.62399745e-01 4.51235682e-01 -3.49232517e-02
-4.15169358e-01 5.80616653e-01 -5.26836552e-02 -5.06142080e-01
-7.19251186e-02 -1.00492108e+00 -5.03566444e-01 -8.98045003e-01
-5.86513102e-01 5.27890980e-01 8.86099115e-02 -6.33008957... | [9.557239532470703, -1.5536545515060425] |
d27f6002-d2ba-42d3-8994-e519e4ac96e9 | scc-automatic-classification-of-code-snippets | 1809.07945 | null | http://arxiv.org/abs/1809.07945v1 | http://arxiv.org/pdf/1809.07945v1.pdf | SCC: Automatic Classification of Code Snippets | Determining the programming language of a source code file has been
considered in the research community; it has been shown that Machine Learning
(ML) and Natural Language Processing (NLP) algorithms can be effective in
identifying the programming language of source code files. However, determining
the programming lang... | ['Daniel M. German', 'Venkatesh Srinivasan', 'T. Aaron Gulliver', 'Kamel Alreshedy', 'Dhanush Dharmaretnam'] | 2018-09-21 | null | null | null | null | ['code-classification'] | ['computer-code'] | [-3.88741761e-01 -2.24189475e-01 -5.50845563e-01 -5.91059364e-02
-7.01021373e-01 -1.00403368e+00 3.01881135e-01 7.71165729e-01
-8.12321976e-02 1.63947329e-01 -8.14851299e-02 -8.51912856e-01
4.90363799e-02 -7.06900537e-01 -4.35821921e-01 -5.76270781e-02
-1.38209820e-01 -8.58564153e-02 5.33402860e-01 1.57106251... | [7.618051052093506, 7.900876998901367] |
396cab59-be5e-4b9a-8660-e75c45222a4f | pointr-diverse-point-cloud-completion-with | 2108.08839 | null | https://arxiv.org/abs/2108.08839v1 | https://arxiv.org/pdf/2108.08839v1.pdf | PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers | Point clouds captured in real-world applications are often incomplete due to the limited sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point clouds from partial ones becomes an indispensable task in many practical applications. In this paper, we present a new method that reformu... | ['Jie zhou', 'Jiwen Lu', 'Zuyan Liu', 'Ziyi Wang', 'Yongming Rao', 'Xumin Yu'] | 2021-08-19 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Yu_PoinTr_Diverse_Point_Cloud_Completion_With_Geometry-Aware_Transformers_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Yu_PoinTr_Diverse_Point_Cloud_Completion_With_Geometry-Aware_Transformers_ICCV_2021_paper.pdf | iccv-2021-1 | ['point-cloud-completion', 'point-cloud-generation'] | ['computer-vision', 'computer-vision'] | [-1.08750746e-01 -2.64141738e-01 -1.76474094e-01 -4.61403579e-01
-9.56283867e-01 -7.30363429e-01 5.71019769e-01 -1.63748533e-01
1.85587674e-01 3.73424709e-01 1.55519336e-01 -2.20822468e-01
1.04094997e-01 -1.09806252e+00 -1.30579102e+00 -4.29171890e-01
1.32073462e-01 6.38944983e-01 1.88543439e-01 -1.82614684... | [8.250967979431152, -3.5897674560546875] |
17cb98d8-9dff-431a-9ab2-b2e6e1fd7258 | towards-holistic-scene-understanding-semantic | 2201.07734 | null | https://arxiv.org/abs/2201.07734v1 | https://arxiv.org/pdf/2201.07734v1.pdf | Towards holistic scene understanding: Semantic segmentation and beyond | This dissertation addresses visual scene understanding and enhances segmentation performance and generalization, training efficiency of networks, and holistic understanding. First, we investigate semantic segmentation in the context of street scenes and train semantic segmentation networks on combinations of various da... | ['Panagiotis Meletis'] | 2022-01-16 | null | null | null | null | ['part-level-panoptic-segmentation'] | ['computer-vision'] | [ 6.93879604e-01 1.14430062e-01 -5.84747732e-01 -6.62543952e-01
-4.95769829e-01 -6.99103534e-01 1.73243165e-01 5.51220924e-02
-3.47743422e-01 4.32945371e-01 -1.09384693e-01 -2.39714414e-01
-1.39531121e-01 -1.13124263e+00 -7.92106390e-01 -5.74871719e-01
6.05072156e-02 4.61214066e-01 6.21130526e-01 -6.69993162... | [9.61483097076416, 0.4726540744304657] |
cb06d5c7-ca86-478f-931c-24c8c74bdaf9 | high-sensitivity-electric-potential-sensors | 2110.12313 | null | https://arxiv.org/abs/2110.12313v1 | https://arxiv.org/pdf/2110.12313v1.pdf | High-Sensitivity Electric Potential Sensors for Non-Contact Monitoring of Physiological Signals | The paper describes highly-sensitive passive electric potential sensors (EPS) for non-contact detection of multiple biophysical signals, including electrocardiogram (ECG), respiration cycle (RC), and electroencephalogram (EEG). The proposed EPS uses an optimized transimpedance amplifier (TIA), a single guarded sensing ... | ['Tayfun Ozdemir', 'Kevin Bi', 'Soumyajit Mandal', 'Wangbo Chen', 'Xinyao Tang'] | 2021-10-23 | null | null | null | null | ['contact-detection'] | ['robots'] | [ 4.58876401e-01 -2.15659976e-01 6.54647708e-01 -1.67944357e-01
-2.09823102e-01 -4.97361630e-01 -3.38535160e-01 3.94648403e-01
-6.85469270e-01 1.06982887e+00 -1.77306518e-01 1.92280132e-02
-6.82389885e-02 -3.00930113e-01 -2.48678640e-01 -5.58928847e-01
-4.06997293e-01 -3.85241061e-01 3.58404554e-02 1.51254505... | [13.781713485717773, 3.1267917156219482] |
85045de7-7b73-4134-aba9-2746371e96db | paraphrase-generation-a-survey-of-the-state | null | null | https://aclanthology.org/2021.emnlp-main.414 | https://aclanthology.org/2021.emnlp-main.414.pdf | Paraphrase Generation: A Survey of the State of the Art | This paper focuses on paraphrase generation,which is a widely studied natural language generation task in NLP. With the development of neural models, paraphrase generation research has exhibited a gradual shift to neural methods in the recent years. This has provided architectures for contextualized representation of a... | ['Suma Bhat', 'Jianing Zhou'] | null | null | null | null | emnlp-2021-11 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 3.96626621e-01 4.03930843e-01 -3.32355261e-01 -3.68865222e-01
-5.45634627e-01 -5.67325473e-01 1.16430926e+00 6.59142807e-02
-4.44028899e-02 1.16621256e+00 1.02696717e+00 -1.49899274e-01
8.78019631e-02 -9.29871619e-01 -5.08686602e-01 -1.21731050e-01
7.09162712e-01 6.11256242e-01 -4.93110836e-01 -6.04360223... | [11.813814163208008, 9.22635555267334] |
d874e994-ff1d-48eb-9b30-fc15e0c3daff | implicit-sample-extension-for-unsupervised | 2204.06892 | null | https://arxiv.org/abs/2204.06892v1 | https://arxiv.org/pdf/2204.06892v1.pdf | Implicit Sample Extension for Unsupervised Person Re-Identification | Most existing unsupervised person re-identification (Re-ID) methods use clustering to generate pseudo labels for model training. Unfortunately, clustering sometimes mixes different true identities together or splits the same identity into two or more sub clusters. Training on these noisy clusters substantially hampers ... | ['Jingdong Wang', 'Zhaoxiang Zhang', 'Javen Qinfeng Shi', 'Errui Ding', 'Jian Wang', 'Zhigang Wang', 'Dongdong Li', 'Xinyu Zhang'] | 2022-04-14 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhang_Implicit_Sample_Extension_for_Unsupervised_Person_Re-Identification_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhang_Implicit_Sample_Extension_for_Unsupervised_Person_Re-Identification_CVPR_2022_paper.pdf | cvpr-2022-1 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-1.47947356e-01 -1.01908475e-01 -3.10685158e-01 -3.97821844e-01
-5.06714463e-01 -4.67981130e-01 6.01761758e-01 1.86454266e-01
-3.79840970e-01 5.25631726e-01 3.11779380e-01 2.11286411e-01
-6.09088689e-02 -7.09427953e-01 -3.60135645e-01 -9.06993270e-01
1.25541225e-01 7.19651401e-01 -3.77693884e-02 2.00966060... | [14.850799560546875, 1.115143895149231] |
956cfa72-89cf-4609-9689-654a52e3b4e6 | online-knowledge-distillation-via-mutual | 2207.11518 | null | https://arxiv.org/abs/2207.11518v2 | https://arxiv.org/pdf/2207.11518v2.pdf | Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition | The teacher-free online Knowledge Distillation (KD) aims to train an ensemble of multiple student models collaboratively and distill knowledge from each other. Although existing online KD methods achieve desirable performance, they often focus on class probabilities as the core knowledge type, ignoring the valuable fea... | ['Yongjun Xu', 'Fuzhen Zhuang', 'Qian Zhan', 'Helong Zhou', 'Zhulin An', 'Chuanguang Yang'] | 2022-07-23 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-3.97653162e-01 4.79928292e-02 -2.95203984e-01 -5.08371413e-01
-4.31421757e-01 -5.15198231e-01 5.58950663e-01 1.54341817e-01
-3.76017272e-01 4.19740051e-01 1.91027410e-02 -1.88754648e-01
-3.47655118e-01 -8.35534990e-01 -8.62745702e-01 -6.39716268e-01
-1.27232477e-01 2.78280079e-01 3.63627911e-01 1.20302521... | [9.473777770996094, 3.2986254692077637] |
04ea7318-8a94-4ad1-b5e9-a9154990f298 | dynamic-collaborative-filtering-thompson | 2208.11926 | null | https://arxiv.org/abs/2208.11926v2 | https://arxiv.org/pdf/2208.11926v2.pdf | Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation | Recently online advertisers utilize Recommender systems (RSs) for display advertising to improve users' engagement. The contextual bandit model is a widely used RS to exploit and explore users' engagement and maximize the long-term rewards such as clicks or conversions. However, the current models aim to optimize a set... | ['Yu Hirate', 'Young-joo Chung', 'Shion Ishikawa'] | 2022-08-25 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [-3.19296449e-01 -3.86586457e-01 -1.01452339e+00 -7.47470319e-01
-9.87464607e-01 -6.37915611e-01 6.76505983e-01 -4.09719914e-01
-3.63057315e-01 6.23097301e-01 4.39546466e-01 -3.12865645e-01
-4.39237595e-01 -6.51529908e-01 -8.90139341e-01 -2.89245754e-01
-3.05933744e-01 4.54984456e-01 6.23755634e-01 -4.29491401... | [9.940383911132812, 5.567312717437744] |
0a081487-06a9-48a5-a2df-38232e15c93f | an-unsupervised-approach-to-discover-media | null | null | https://aclanthology.org/2022.politicalnlp-1.4 | https://aclanthology.org/2022.politicalnlp-1.4.pdf | An Unsupervised Approach to Discover Media Frames | Media framing refers to highlighting certain aspect of an issue in the news to promote a particular interpretation to the audience. Supervised learning has often been used to recognize frames in news articles, requiring a known pool of frames for a particular issue, which must be identified by communication researchers... | ['Derry Tanti Wijaya', 'Prakash Ishwar', 'Margrit Betke', 'Lei Guo', 'Yanru Jiang', 'Sha Lai'] | null | null | null | null | politicalnlp-lrec-2022-6 | ['community-detection'] | ['graphs'] | [ 6.22516930e-01 5.28092742e-01 -5.93992472e-01 -1.56701252e-01
-9.68995333e-01 -6.44569337e-01 1.05804634e+00 8.43380332e-01
-2.43841529e-01 7.95561731e-01 9.03368592e-01 -4.25671905e-01
3.75046879e-02 -1.03029239e+00 -7.66436756e-01 -3.60494316e-01
1.88083649e-01 1.22309364e-01 4.67719644e-01 -1.32819518... | [8.968777656555176, 9.576081275939941] |
60fc0030-621a-4560-a8af-5cbb5ab957d3 | online-multi-target-regression-trees-with | 1903.12483 | null | https://arxiv.org/abs/1903.12483v4 | https://arxiv.org/pdf/1903.12483v4.pdf | Online Multi-target regression trees with stacked leaf models | One of the current challenges in machine learning is how to deal with data coming at increasing rates in data streams. New predictive learning strategies are needed to cope with the high throughput data and concept drift. One of the data stream mining tasks where new learning strategies are needed is multi-target regre... | ['André Carlos Ponce de Leon Ferreira de Carvalho', 'Sylvio Barbon Jr.', 'Saulo Martiello Mastelini'] | 2019-03-29 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 3.91719609e-01 -5.11638224e-01 -6.34838045e-01 -3.63342732e-01
-8.05718958e-01 1.15080900e-01 2.79477924e-01 8.25941920e-01
-2.25538656e-01 6.92773521e-01 -3.50991726e-01 -1.99028283e-01
-5.58443904e-01 -7.33229399e-01 -4.56723660e-01 -7.12590039e-01
-4.64141130e-01 5.24911225e-01 6.93463683e-01 -1.72550127... | [7.364394187927246, 2.9133403301239014] |
82478890-8e6f-4659-988f-20b5e9529e44 | wavenet-a-generative-model-for-raw-audio | 1609.03499 | null | http://arxiv.org/abs/1609.03499v2 | http://arxiv.org/pdf/1609.03499v2.pdf | WaveNet: A Generative Model for Raw Audio | This paper introduces WaveNet, a deep neural network for generating raw audio
waveforms. The model is fully probabilistic and autoregressive, with the
predictive distribution for each audio sample conditioned on all previous ones;
nonetheless we show that it can be efficiently trained on data with tens of
thousands of ... | ['Sander Dieleman', 'Koray Kavukcuoglu', 'Nal Kalchbrenner', 'Karen Simonyan', 'Oriol Vinyals', 'Heiga Zen', 'Andrew Senior', 'Alex Graves', 'Aaron van den Oord'] | 2016-09-12 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 2.66465575e-01 -4.31145765e-02 1.59772456e-01 -1.80019945e-01
-1.28561747e+00 -8.66272151e-01 5.78848660e-01 -3.88565838e-01
-3.50875640e-03 5.84939301e-01 5.58826923e-01 -3.83368433e-02
-4.02360000e-02 -6.31774962e-01 -8.62984240e-01 -7.95607209e-01
-4.55038935e-01 7.48299956e-01 -4.15313616e-02 -2.33234122... | [15.592066764831543, 5.764960289001465] |
ac3e741e-9b37-463f-ae16-8f6051721ea1 | robust-lexicalized-native-language | null | null | https://aclanthology.org/C12-1025 | https://aclanthology.org/C12-1025.pdf | Robust, Lexicalized Native Language Identification | null | ['Julian Brooke', 'Graeme Hirst'] | 2012-12-01 | robust-lexicalized-native-language-1 | https://aclanthology.org/C12-1025 | https://aclanthology.org/C12-1025.pdf | coling-2012-12 | ['native-language-identification'] | ['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... | [-7.240152835845947, 3.7220027446746826] |
63764976-cc2a-40cd-87c2-6d6e76121ed7 | learning-elementary-structures-for-3d-shape | 1908.04725 | null | https://arxiv.org/abs/1908.04725v2 | https://arxiv.org/pdf/1908.04725v2.pdf | Learning elementary structures for 3D shape generation and matching | We propose to represent shapes as the deformation and combination of learnable elementary 3D structures, which are primitives resulting from training over a collection of shape. We demonstrate that the learned elementary 3D structures lead to clear improvements in 3D shape generation and matching. More precisely, we pr... | ['Thibault Groueix', 'Theo Deprelle', 'Bryan C. Russell', 'Matthew Fisher', 'Mathieu Aubry', 'Vladimir G. Kim'] | 2019-08-13 | learning-elementary-structures-for-3d-shape-1 | http://papers.nips.cc/paper/8962-learning-elementary-structures-for-3d-shape-generation-and-matching | http://papers.nips.cc/paper/8962-learning-elementary-structures-for-3d-shape-generation-and-matching.pdf | neurips-2019-12 | ['3d-dense-shape-correspondence', '3d-shape-generation'] | ['computer-vision', 'computer-vision'] | [ 1.65672049e-01 5.46813607e-01 1.04779296e-01 -4.13004756e-01
-1.23113847e+00 -7.58247852e-01 8.02057445e-01 -7.28726014e-02
2.11978048e-01 1.87866554e-01 2.43285507e-01 -5.84767200e-02
8.43153428e-03 -9.13876295e-01 -1.33094633e+00 -3.22573215e-01
-1.16800435e-01 1.35594523e+00 3.59165579e-01 -1.10357307... | [8.760934829711914, -3.6422674655914307] |
a5a8d6bc-4bff-476a-8985-f6f533b71d25 | ambiguity-resistant-semi-supervised-learning | 2303.14960 | null | https://arxiv.org/abs/2303.14960v1 | https://arxiv.org/pdf/2303.14960v1.pdf | Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection | With basic Semi-Supervised Object Detection (SSOD) techniques, one-stage detectors generally obtain limited promotions compared with two-stage clusters. We experimentally find that the root lies in two kinds of ambiguities: (1) Selection ambiguity that selected pseudo labels are less accurate, since classification scor... | ['Jingdong Wang', 'Errui Ding', 'Xiaomao Li', 'Junyu Han', 'Xiao Tan', 'Wei zhang', 'Xiangru Lin', 'Weiming Zhang', 'Chang Liu'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_Ambiguity-Resistant_Semi-Supervised_Learning_for_Dense_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_Ambiguity-Resistant_Semi-Supervised_Learning_for_Dense_Object_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['dense-object-detection', 'semi-supervised-object-detection', 'pseudo-label'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 2.63934433e-01 -1.73949644e-01 -2.76223868e-01 -4.41982210e-01
-1.21556866e+00 -4.89549220e-01 3.58918518e-01 7.17933476e-02
-4.67376500e-01 6.17679536e-01 -3.97933006e-01 -1.09318942e-01
1.17234692e-01 -2.20927194e-01 -4.91553068e-01 -9.91826415e-01
3.94690871e-01 2.94441372e-01 8.68799269e-01 3.09212565... | [9.145699501037598, 1.2767778635025024] |
92908929-944b-4ffd-9818-21ed76cb3d27 | a-mask-based-adversarial-defense-scheme | 2204.11837 | null | https://arxiv.org/abs/2204.11837v1 | https://arxiv.org/pdf/2204.11837v1.pdf | A Mask-Based Adversarial Defense Scheme | Adversarial attacks hamper the functionality and accuracy of Deep Neural Networks (DNNs) by meddling with subtle perturbations to their inputs.In this work, we propose a new Mask-based Adversarial Defense scheme (MAD) for DNNs to mitigate the negative effect from adversarial attacks. To be precise, our method promotes ... | ['Liangda Fang', 'Fangzhen Zhao', 'Chenyi Zhang', 'Weizhen Xu'] | 2022-04-21 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 2.49660343e-01 1.22683026e-01 5.07921934e-01 -2.91559905e-01
-2.68732071e-01 -1.27563572e+00 5.99004447e-01 -4.44084316e-01
-4.63300914e-01 7.69115090e-01 -2.42029056e-01 -4.19934839e-01
3.00953597e-01 -9.35623646e-01 -9.57795978e-01 -1.08111978e+00
1.14608213e-01 -2.49973640e-01 5.15016377e-01 -3.60824645... | [5.564206600189209, 7.917988300323486] |
f5b6ae77-3a55-4752-bae6-1ddaeb1aa184 | correlated-quantization-for-distributed-mean | 2203.04925 | null | https://arxiv.org/abs/2203.04925v2 | https://arxiv.org/pdf/2203.04925v2.pdf | Correlated quantization for distributed mean estimation and optimization | We study the problem of distributed mean estimation and optimization under communication constraints. We propose a correlated quantization protocol whose leading term in the error guarantee depends on the mean deviation of data points rather than only their absolute range. The design doesn't need any prior knowledge on... | ['Felix Yu', 'Jae Hun Ro', 'Ziteng Sun', 'Ananda Theertha Suresh'] | 2022-03-09 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [ 1.63417414e-01 -1.21081188e-01 -1.60263464e-01 -5.29302657e-01
-1.07887518e+00 -5.08685231e-01 2.44260862e-01 4.39437360e-01
-8.24035406e-01 1.04232538e+00 -1.21776033e-02 -1.05154417e-01
-3.81653160e-01 -4.75826442e-01 -9.69424069e-01 -1.15608954e+00
-3.39557886e-01 5.07271171e-01 9.10167694e-02 7.03211278... | [6.686005592346191, 4.544431686401367] |
b12272cb-b53e-4ec5-94ad-c6cab491e124 | qurantree-jl-a-julia-package-for-quranic | null | null | https://aclanthology.org/2021.wanlp-1.22 | https://aclanthology.org/2021.wanlp-1.22.pdf | QuranTree.jl: A Julia Package for Quranic Arabic Corpus | QuranTree.jl is an open-source package for working with the Quranic Arabic Corpus (Dukes and Habash, 2010). It aims to provide Julia APIs as an alternative to the Java APIs of JQuranTree. QuranTree.jl currently offers functionalities for intuitive indexing of chapters, verses, words and parts of words of the Qur’an; fo... | ['Al-Ahmadgaid Asaad'] | null | null | null | null | eacl-wanlp-2021-4 | ['transliteration'] | ['natural-language-processing'] | [-6.15601897e-01 -3.15928131e-01 1.44257382e-01 6.92553632e-03
-9.19348359e-01 -1.35664964e+00 4.60481316e-01 2.22589910e-01
-2.08016187e-01 6.05528116e-01 4.04205620e-01 -6.66360140e-01
2.16862276e-01 -9.12387431e-01 1.84383065e-01 -6.14940345e-01
2.77923435e-01 4.10819769e-01 -2.32687220e-02 -7.97611177... | [10.377262115478516, 10.364002227783203] |
e39e97ba-b134-452f-ad26-6b75e4a0caf0 | gaia-search-hugging-face-and-pyserini | 2306.01481 | null | https://arxiv.org/abs/2306.01481v1 | https://arxiv.org/pdf/2306.01481v1.pdf | GAIA Search: Hugging Face and Pyserini Interoperability for NLP Training Data Exploration | Noticing the urgent need to provide tools for fast and user-friendly qualitative analysis of large-scale textual corpora of the modern NLP, we propose to turn to the mature and well-tested methods from the domain of Information Retrieval (IR) - a research field with a long history of tackling TB-scale document collecti... | ['Jimmy Lin', 'Martin Potthast', 'Stella Biderman', 'Hailey Schoelkopf', 'Xinyu Zhang', 'Akintunde Oladipo', 'Christopher Akiki', 'Odunayo Ogundepo', 'Aleksandra Piktus'] | 2023-06-02 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [-3.18106800e-01 2.97641810e-02 -3.89286309e-01 -2.76549011e-01
-1.19840753e+00 -9.56492662e-01 7.65219808e-01 2.26485297e-01
-2.92320520e-01 4.43224519e-01 4.28166896e-01 -6.81519985e-01
-4.80891615e-01 -5.42717218e-01 -4.14847314e-01 -3.72372329e-01
-1.46657825e-01 1.02215886e+00 -3.75547647e-01 -4.51505214... | [10.70208740234375, 8.456792831420898] |
7bcb3c55-846d-4c19-8c6b-849f9777ce48 | self-supervision-by-prediction-for-object | 2103.05669 | null | https://arxiv.org/abs/2103.05669v1 | https://arxiv.org/pdf/2103.05669v1.pdf | Self-Supervision by Prediction for Object Discovery in Videos | Despite their irresistible success, deep learning algorithms still heavily rely on annotated data. On the other hand, unsupervised settings pose many challenges, especially about determining the right inductive bias in diverse scenarios. One scalable solution is to make the model generate the supervision for itself by ... | ['Pascal Frossard', 'Beril Besbinar'] | 2021-03-09 | null | null | null | null | ['object-discovery-in-videos'] | ['computer-vision'] | [ 2.84378171e-01 2.71956533e-01 -4.22878802e-01 -4.89552468e-01
-3.33225697e-01 -4.86534864e-01 8.42441678e-01 5.42270578e-02
-3.38022351e-01 5.73427439e-01 3.21052909e-01 2.88019553e-02
3.74007463e-01 -5.18444121e-01 -9.73967373e-01 -7.25087702e-01
2.54152924e-01 5.36913037e-01 4.03751791e-01 -3.49268578... | [8.987251281738281, 0.07016721367835999] |
73f27709-e374-4f50-a26e-2ada8e06ee8f | potential-of-deep-features-for-opinion | 1911.11903 | null | https://arxiv.org/abs/1911.11903v1 | https://arxiv.org/pdf/1911.11903v1.pdf | Potential of deep features for opinion-unaware, distortion-unaware, no-reference image quality assessment | Image Quality Assessment algorithms predict a quality score for a pristine or distorted input image, such that it correlates with human opinion. Traditional methods required a non-distorted "reference" version of the input image to compare with, in order to predict this score. However, recent "No-reference" methods cir... | ['Giuseppe Valenzise', 'Irene Cheng', 'Subhayan Mukherjee'] | 2019-11-27 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 3.22644979e-01 -3.04872781e-01 2.16323093e-01 -7.18073189e-01
-5.63068807e-01 -5.68861723e-01 7.11987317e-01 1.55881792e-01
-3.55428308e-01 5.76007247e-01 3.27389508e-01 -1.78195029e-01
-5.20792663e-01 -8.66202414e-01 -4.45014805e-01 -7.49518633e-01
-1.91173047e-01 -3.20454687e-03 6.97706267e-02 -1.68978676... | [11.780197143554688, -1.8565599918365479] |
339cda65-0944-4170-9430-014233cb1ce8 | assessing-the-applicability-of-authorship | 1906.10551 | null | https://arxiv.org/abs/1906.10551v1 | https://arxiv.org/pdf/1906.10551v1.pdf | Assessing the Applicability of Authorship Verification Methods | Authorship verification (AV) is a research subject in the field of digital text forensics that concerns itself with the question, whether two documents have been written by the same person. During the past two decades, an increasing number of proposed AV approaches can be observed. However, a closer look at the respect... | ['Lukas Graner', 'Christian Winter', 'Oren Halvani'] | 2019-06-24 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [ 2.71404833e-01 1.25021011e-01 8.85441378e-02 -1.65499244e-02
-6.23147249e-01 -8.26303422e-01 1.19505441e+00 5.28438151e-01
-5.27399004e-01 9.53634977e-01 -2.52437741e-01 -4.20093030e-01
-2.36018121e-01 -3.99644643e-01 -3.18205655e-01 -5.50758600e-01
1.39660299e-01 6.36229992e-01 3.23861688e-01 1.66769579... | [9.52212905883789, 10.623369216918945] |
4922c59d-1674-4aa0-ad2e-b52c5064138d | predicting-customers-gender-and-age-depending | 1903.06756 | null | http://arxiv.org/abs/1903.06756v1 | http://arxiv.org/pdf/1903.06756v1.pdf | Predicting customer's gender and age depending on mobile phone data | In the age of data driven solution, the customer demographic attributes, such
as gender and age, play a core role that may enable companies to enhance the
offers of their services and target the right customer in the right time and
place. In the marketing campaign, the companies want to target the real user of
the GSM ... | ['Kadan Aljoumaa', 'Assef Jafar', 'Ibrahim Mousa AlZuabi'] | 2019-02-20 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [-3.70113343e-01 2.51874179e-01 -1.97101906e-01 -9.11889195e-01
-1.13416091e-01 -3.21313083e-01 2.60925978e-01 5.52283943e-01
-4.94779646e-01 6.47180617e-01 1.84122592e-01 -5.16498387e-01
-3.63729179e-01 -1.22279096e+00 1.67589337e-01 -5.70534110e-01
2.09741712e-01 1.42837965e+00 -2.71278322e-01 -5.88089168... | [9.587124824523926, 5.9800543785095215] |
3fcc25bb-2b43-4c45-bdcd-2dae29ef3525 | pcrlv2-a-unified-visual-information | 2301.00772 | null | https://arxiv.org/abs/2301.00772v1 | https://arxiv.org/pdf/2301.00772v1.pdf | PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis | Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views. However, the preserved high-level semantics do not contain enough local information, which is vital... | ['Yizhou Yu', 'Sibei Yang', 'Chaoqi Chen', 'Chixiang Lu', 'Hong-Yu Zhou'] | 2023-01-02 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 5.06194413e-01 6.50353655e-02 -5.20679355e-01 -2.62748122e-01
-1.09609473e+00 -3.89638543e-01 4.61234093e-01 4.47577924e-01
-4.69879270e-01 4.45920765e-01 2.25538477e-01 -1.42682090e-01
-1.96286082e-01 -4.59237486e-01 -6.39798939e-01 -9.76333439e-01
2.65800327e-01 1.53663144e-01 3.74182045e-01 -6.88728243... | [14.7022066116333, -2.151801347732544] |
abad8cbd-0da8-4bb4-beec-ff2b008e3fd8 | a-dataset-and-benchmark-for-automatically | 2206.05442 | null | https://arxiv.org/abs/2206.05442v7 | https://arxiv.org/pdf/2206.05442v7.pdf | From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams | A final exam in machine learning at a top institution such as MIT, Harvard, or Cornell typically takes faculty days to write, and students hours to solve. We demonstrate that large language models pass machine learning finals at a human level, on finals available online after the models were trained, and automatically ... | ['Madeleine Udell', 'Sage Simhon', 'Gregory Hunter', 'Saisamrit Surbehera', 'Keith Tyser', 'Reece Shuttleworth', 'Sarah J. Zhang', 'Iddo Drori', 'Pedro Lantigua', 'Zad Chin', 'Darnell Granberry', 'Sathwik Karnik', 'Leonard Tang', 'Yann Hicke', 'Derek Austin', 'Sarah Zhang'] | 2022-06-11 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 1.32623553e-01 2.05226481e-01 6.90133944e-02 -1.72555357e-01
-1.38689983e+00 -1.17112327e+00 -6.99751079e-02 6.08879626e-01
-2.45592594e-01 6.42229140e-01 -1.06741920e-01 -1.41026115e+00
-3.55301350e-01 -1.23906505e+00 -6.41519547e-01 3.36458862e-01
4.57817107e-01 1.01074845e-01 4.74255919e-01 -6.59869850... | [9.844291687011719, 7.370213031768799] |
1b6bc44e-612e-4183-aae4-29e8c4d77376 | scientific-table-search-using-keyword-queries | 1707.03423 | null | http://arxiv.org/abs/1707.03423v1 | http://arxiv.org/pdf/1707.03423v1.pdf | Scientific Table Search Using Keyword Queries | Tables are common and important in scientific documents, yet most text-based
document search systems do not capture structures and semantics specific to
tables. How to bridge different types of mismatch between keywords queries and
scientific tables and what influences ranking quality needs to be carefully
investigated... | ['Callan Jamie', 'Gao Kyle Yingkai'] | 2017-07-11 | null | null | null | null | ['table-search'] | ['natural-language-processing'] | [-1.62207276e-01 4.19623181e-02 -5.92179775e-01 -2.82328427e-01
-1.16579378e+00 -1.14314294e+00 6.98063195e-01 9.62486982e-01
-4.31158215e-01 9.17057157e-01 5.55110276e-01 -2.80944496e-01
-6.65035725e-01 -1.03642726e+00 -6.72301233e-01 -1.60562903e-01
4.13799971e-01 9.75819290e-01 6.88029766e-01 -2.26341516... | [9.751143455505371, 7.953092098236084] |
1476f97f-9458-4e67-b126-63a56c65a68e | multi-objective-population-based-training | 2306.01436 | null | https://arxiv.org/abs/2306.01436v1 | https://arxiv.org/pdf/2306.01436v1.pdf | Multi-Objective Population Based Training | Population Based Training (PBT) is an efficient hyperparameter optimization algorithm. PBT is a single-objective algorithm, but many real-world hyperparameter optimization problems involve two or more conflicting objectives. In this work, we therefore introduce a multi-objective version of PBT, MO-PBT. Our experiments ... | ['Peter A. N. Bosman', 'Tanja Alderliesten', 'Alexander Chebykin', 'Arkadiy Dushatskiy'] | 2023-06-02 | null | null | null | null | ['adversarial-robustness', 'hyperparameter-optimization'] | ['adversarial', 'methodology'] | [-2.73143500e-01 -4.46139276e-01 -6.77574873e-01 1.09796889e-01
-8.70390773e-01 -3.48799199e-01 5.95660135e-02 7.51154795e-02
-7.02632844e-01 1.57624364e+00 -2.13276580e-01 -8.10003281e-02
-9.32066441e-01 -7.19386160e-01 -4.00567442e-01 -9.35989141e-01
-7.40307420e-02 1.22720695e+00 -1.44479536e-02 -2.45770350... | [6.513205051422119, 3.935822010040283] |
82ea32fb-4b92-4c74-8003-ce2d3b4996d1 | avasag-a-german-sign-language-translation | null | null | https://aclanthology.org/2021.mtsummit-at4ssl.5 | https://aclanthology.org/2021.mtsummit-at4ssl.5.pdf | AVASAG: A German Sign Language Translation System for Public Services (short paper) | This paper presents an overview of AVASAG; an ongoing applied-research project developing a text-to-sign-language translation system for public services. We describe the scientific innovation points (geometry-based SL-description, 3D animation and video corpus, simplified annotation scheme, motion capture strategy) and... | ['Alexander Stricker', 'Dieter Wallach', 'Martin Misiak', 'Yvonne Kossel', 'Marcel Hauck', 'Yasser Hamidullah', 'Patrick Gebhard', 'Arnulph Fuhrmann', 'Christian Dold', 'Stephan Busemann', 'Elisabeth André', 'Sonja Wecker', 'Kristoffer Waldow', 'Amelie Unger', 'Corinna Jäger', 'Cristina España-Bonet', 'Lucas Bernhard',... | null | null | null | null | mtsummit-2021-8 | ['sign-language-translation'] | ['computer-vision'] | [ 3.27894509e-01 -1.06336646e-01 -5.81077337e-01 -2.51522005e-01
-9.34767246e-01 -7.19497323e-01 7.47974873e-01 -7.73984373e-01
-2.46271014e-01 5.91523528e-01 5.70577383e-01 -6.77394211e-01
4.80642825e-01 1.03287488e-01 -2.20684111e-01 -2.92803377e-01
4.26337361e-01 6.88849747e-01 3.30915272e-01 -4.03848141... | [9.152815818786621, -6.476027011871338] |
2dcb3689-1860-4389-be0c-168a243a85b4 | llm-pruner-on-the-structural-pruning-of-large | 2305.11627 | null | https://arxiv.org/abs/2305.11627v2 | https://arxiv.org/pdf/2305.11627v2.pdf | LLM-Pruner: On the Structural Pruning of Large Language Models | Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. However, such impressive capability typically comes with a substantial model size, which presents significant challenges in both the deployment, inference, and training stages. With LLM being a general-purpose task... | ['Xinchao Wang', 'Gongfan Fang', 'Xinyin Ma'] | 2023-05-19 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 2.72456348e-01 2.42638469e-01 -2.25552827e-01 -1.56806976e-01
-9.01263952e-01 -4.36872602e-01 5.31853795e-01 -1.16853729e-01
-2.37336829e-01 6.53246403e-01 -6.12866729e-02 -5.47556162e-01
4.72856238e-02 -7.12724030e-01 -6.36787474e-01 -3.75345975e-01
1.01098031e-01 6.71968758e-01 -8.02952275e-02 -3.07058156... | [8.754240036010742, 3.648272752761841] |
079876be-2d63-4e35-934a-8a5d9a4ae90c | a-robust-classifier-under-missing-not-at | 2305.15641 | null | https://arxiv.org/abs/2305.15641v1 | https://arxiv.org/pdf/2305.15641v1.pdf | A Robust Classifier Under Missing-Not-At-Random Sample Selection Bias | The shift between the training and testing distributions is commonly due to sample selection bias, a type of bias caused by non-random sampling of examples to be included in the training set. Although there are many approaches proposed to learn a classifier under sample selection bias, few address the case where a subs... | ['Xintao Wu', 'Wei Du', 'Wen Huang', 'Huy Mai'] | 2023-05-25 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 4.39537853e-01 -2.52740481e-03 -8.65314901e-01 -1.01668608e+00
-7.42735088e-01 -4.14926320e-01 3.84042948e-01 -8.76847133e-02
-5.25610924e-01 1.09851599e+00 -3.43808532e-01 -3.09293479e-01
-1.40506729e-01 -9.24283504e-01 -8.09424937e-01 -8.21427524e-01
2.55508482e-01 4.35993046e-01 1.01387754e-01 1.90286949... | [8.82880973815918, 4.262908935546875] |
a0d5d65e-ba01-4e29-bc9d-25e013bd55fc | personalized-prediction-of-recurrent-stress | 2307.03337 | null | https://arxiv.org/abs/2307.03337v1 | https://arxiv.org/pdf/2307.03337v1.pdf | Personalized Prediction of Recurrent Stress Events Using Self-Supervised Learning on Multimodal Time-Series Data | Chronic stress can significantly affect physical and mental health. The advent of wearable technology allows for the tracking of physiological signals, potentially leading to innovative stress prediction and intervention methods. However, challenges such as label scarcity and data heterogeneity render stress prediction... | ['Peter Washington', 'Tanvir Islam'] | 2023-07-07 | null | null | null | null | ['self-supervised-learning'] | ['computer-vision'] | [ 5.54599226e-01 1.65123388e-01 -3.15307021e-01 -8.51638973e-01
-4.13484603e-01 -3.32119256e-01 -1.49867637e-02 7.56406486e-01
-3.48991692e-01 4.53754693e-01 4.54895705e-01 1.28376067e-01
1.61511481e-01 -2.08268136e-01 -1.70134589e-01 -2.41149068e-01
-5.21601319e-01 -1.39508724e-01 -2.01173171e-01 -1.38734549... | [13.693700790405273, 3.1817402839660645] |
ae6e41b2-c0ba-43c3-8282-04b5d1c087fd | a-neuromorphic-vision-based-measurement-for | 2206.11541 | null | https://arxiv.org/abs/2206.11541v2 | https://arxiv.org/pdf/2206.11541v2.pdf | A Neuromorphic Vision-Based Measurement for Robust Relative Localization in Future Space Exploration Missions | Space exploration has witnessed revolutionary changes upon landing of the Perseverance Rover on the Martian surface and demonstrating the first flight beyond Earth by the Mars helicopter, Ingenuity. During their mission on Mars, Perseverance Rover and Ingenuity collaboratively explore the Martian surface, where Ingenui... | ['Lakmal Seneviratne', 'Yahya Zweiri', 'Rana Azzam', 'Abdulla Ayyad', 'Mohammed Wahbah', 'Muhammed Humais', 'Mohammed Chehadah', 'Mohammed Salah'] | 2022-06-23 | null | null | null | null | ['landmark-tracking'] | ['computer-vision'] | [ 6.14427850e-02 -5.27331054e-01 5.93152046e-02 1.24407122e-02
-1.83783293e-01 -6.66425824e-01 8.13614011e-01 -1.77228078e-01
-8.17100883e-01 8.66956413e-01 -2.88400412e-01 5.87918833e-02
-3.88789535e-01 -6.26502633e-01 -6.57754004e-01 -6.60003364e-01
-3.25155526e-01 1.72446474e-01 1.74024254e-01 -2.86442846... | [7.354689121246338, -1.8871830701828003] |
f6ea4127-806e-4f3a-bd2d-d1eaa9273872 | conditioning-diffusion-models-via-attributes | 2306.00914 | null | https://arxiv.org/abs/2306.00914v1 | https://arxiv.org/pdf/2306.00914v1.pdf | Conditioning Diffusion Models via Attributes and Semantic Masks for Face Generation | Deep generative models have shown impressive results in generating realistic images of faces. GANs managed to generate high-quality, high-fidelity images when conditioned on semantic masks, but they still lack the ability to diversify their output. Diffusion models partially solve this problem and are able to generate ... | ['Giuseppe Lisanti', 'Nico Giambi'] | 2023-06-01 | null | null | null | null | ['face-generation'] | ['computer-vision'] | [ 2.75863647e-01 1.68686450e-01 3.23506355e-01 -3.67665976e-01
-7.59504080e-01 -5.12334287e-01 8.62467289e-01 -3.14978480e-01
-3.17040622e-01 8.08631957e-01 2.51009792e-01 3.43182921e-01
-3.56762186e-02 -1.05157673e+00 -8.38389456e-01 -8.58258426e-01
1.91077620e-01 4.86639380e-01 -4.42324243e-02 3.12680416... | [11.820842742919922, -0.31220537424087524] |
eaee080b-7589-4bf0-a86d-ab1b60312784 | page-a-position-aware-graph-based-model-for | 2303.01795 | null | https://arxiv.org/abs/2303.01795v1 | https://arxiv.org/pdf/2303.01795v1.pdf | PAGE: A Position-Aware Graph-Based Model for Emotion Cause Entailment in Conversation | Conversational Causal Emotion Entailment (C2E2) is a task that aims at recognizing the causes corresponding to a target emotion in a conversation. The order of utterances in the conversation affects the causal inference. However, most current position encoding strategies ignore the order relation among utterances and s... | ['Shangxin Li', 'Lin Sun', 'Renze Lou', 'Xiaojie Gu'] | 2023-03-03 | null | null | null | null | ['causal-emotion-entailment'] | ['natural-language-processing'] | [ 9.97836739e-02 5.03446996e-01 -4.48543131e-01 -6.83009267e-01
-5.51942468e-01 -5.26452422e-01 9.23815429e-01 -1.07577354e-01
2.65211850e-01 7.89439797e-01 1.08858943e+00 -3.15868646e-01
7.70259723e-02 -5.50816536e-01 -6.83386266e-01 -3.51230264e-01
-4.01858002e-01 9.46877003e-02 -1.25893503e-01 -4.25047696... | [12.80821704864502, 6.3720831871032715] |
b2a13487-bf7d-4b15-b71a-e22aedb7036e | measuring-human-perception-to-improve-open | 2209.03519 | null | https://arxiv.org/abs/2209.03519v4 | https://arxiv.org/pdf/2209.03519v4.pdf | Measuring Human Perception to Improve Open Set Recognition | The human ability to recognize when an object belongs or does not belong to a particular vision task outperforms all open set recognition algorithms. Human perception as measured by the methods and procedures of visual psychophysics from psychology provides an additional data stream for algorithms that need to manage n... | ['Justin Dulay', 'Walter Scheirer', 'Derek Prijatelj', 'Jin Huang'] | 2022-09-08 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 2.93752939e-01 -1.65597931e-01 2.21048534e-01 -4.62415278e-01
-4.53369051e-01 -6.91162765e-01 4.98743266e-01 1.75103679e-01
-8.78116310e-01 7.50383198e-01 -6.18521035e-01 -1.55731505e-02
-2.45624974e-01 -5.24785340e-01 -8.76085222e-01 -6.87491059e-01
-1.74083307e-01 2.96310693e-01 1.05364658e-01 9.48199630... | [9.769561767578125, 2.271914005279541] |
63eedcd0-8c5b-4a23-b166-70bb41bfeb16 | on-aligning-openie-extractions-with-knowledge | null | null | https://aclanthology.org/2020.eval4nlp-1.14 | https://aclanthology.org/2020.eval4nlp-1.14.pdf | On Aligning OpenIE Extractions with Knowledge Bases: A Case Study | Open information extraction (OIE) is the task of extracting relations and their corresponding arguments from a natural language text in un- supervised manner. Outputs of such systems are used for downstream tasks such as ques- tion answering and automatic knowledge base (KB) construction. Many of these downstream tasks... | ['Christian Meilicke', 'Sven Hertling', 'Bhushan Kotnis', 'Rainer Gemulla', 'Kiril Gashteovski'] | null | null | null | null | emnlp-eval4nlp-2020-11 | ['open-information-extraction'] | ['natural-language-processing'] | [-1.83885708e-01 8.92357528e-01 -2.61726975e-01 -3.64572942e-01
-7.66060114e-01 -8.49880874e-01 7.30952919e-01 7.56340444e-01
-3.89967412e-01 1.49611509e+00 3.18795234e-01 -4.99466777e-01
-4.47586834e-01 -1.23372245e+00 -1.15478408e+00 -4.77674156e-02
-6.53377688e-03 1.04668915e+00 6.00468516e-01 -6.59701765... | [9.382051467895508, 8.48138427734375] |
2b5d3182-7791-4dda-94d7-97161d9122ee | generative-adversarial-networks-based-on-1 | null | null | https://doi.org/10.3390/rs14143426 | https://www.mdpi.com/2072-4292/14/14/3426/pdf?version=1658469150 | Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification | Nowadays, HSI classification can reach a high classification accuracy when given sufficient labeled samples as training set. However, the performances of existing methods decrease sharply when trained on few labeled samples. Existing methods in few-shot problems usually require another dataset in order to improve the c... | ['Licheng Jiao', 'Zheng Chen', 'Zhu Xiao', 'Jiawei Lu', 'Jing Bai'] | 2022-07-16 | null | null | null | remote-sensing-2022-7 | ['classification-of-hyperspectral-images', 'few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 5.27720392e-01 -5.19452751e-01 8.82039890e-02 -3.02595347e-01
-7.93305159e-01 -4.27103996e-01 4.65033293e-01 -2.89618790e-01
-1.37508929e-01 1.03375661e+00 -1.62436873e-01 8.57151598e-02
-1.44351795e-01 -1.16939247e+00 -5.83960235e-01 -1.00987661e+00
5.82060933e-01 -7.18160942e-02 3.35510910e-01 -1.94530129... | [9.93130874633789, -1.4988118410110474] |
550d58f7-882d-4fde-b55a-104c40ec2913 | rethinking-the-compositionality-of-point | 2209.10318 | null | https://arxiv.org/abs/2209.10318v1 | https://arxiv.org/pdf/2209.10318v1.pdf | Rethinking the compositionality of point clouds through regularization in the hyperbolic space | Point clouds of 3D objects exhibit an inherent compositional nature where simple parts can be assembled into progressively more complex shapes to form whole objects. Explicitly capturing such part-whole hierarchy is a long-sought objective in order to build effective models, but its tree-like nature has made the task e... | ['Enrico Magli', 'Diego Valsesia', 'Antonio Montanaro'] | 2022-09-21 | null | null | null | null | ['3d-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [-1.56628609e-01 3.73858511e-02 -8.65556523e-02 -4.28633749e-01
-3.55972648e-01 -7.64593363e-01 8.92976642e-01 3.40121239e-01
2.03462914e-01 1.76386476e-01 -1.59914017e-01 -4.40323561e-01
-3.50034535e-01 -8.47663105e-01 -6.10063016e-01 -7.17481136e-01
-2.08010331e-01 8.08366477e-01 4.96471792e-01 -3.11845571... | [8.002555847167969, -3.2389867305755615] |
42df28dd-1696-47fd-995d-85d6af910572 | tackling-data-heterogeneity-in-federated | 2212.02758 | null | https://arxiv.org/abs/2212.02758v1 | https://arxiv.org/pdf/2212.02758v1.pdf | Tackling Data Heterogeneity in Federated Learning with Class Prototypes | Data heterogeneity across clients in federated learning (FL) settings is a widely acknowledged challenge. In response, personalized federated learning (PFL) emerged as a framework to curate local models for clients' tasks. In PFL, a common strategy is to develop local and global models jointly - the global model (for g... | ['ran Xu', 'Lichao Sun', 'Shelby Heinecke', 'Junnan Li', 'Zeyuan Chen', 'Yutong Dai'] | 2022-12-06 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-2.66958684e-01 -1.53621361e-01 -6.66811764e-01 -6.83818758e-01
-7.80430257e-01 -6.50929630e-01 3.46603155e-01 -3.44571322e-02
1.03032939e-01 5.40911913e-01 3.32658291e-01 -1.24817602e-01
-4.58755285e-01 -6.06306612e-01 -6.86734378e-01 -8.70583415e-01
1.05166644e-01 5.51075280e-01 1.98321760e-01 8.66714865... | [5.8162078857421875, 6.276295185089111] |
fe24bd25-9cc6-4ad0-bfa3-ac67a1a78f1d | distinct-label-representations-for-few-shot | null | null | https://aclanthology.org/2021.acl-short.105 | https://aclanthology.org/2021.acl-short.105.pdf | Distinct Label Representations for Few-Shot Text Classification | Few-shot text classification aims to classify inputs whose label has only a few examples. Previous studies overlooked the semantic relevance between label representations. Therefore, they are easily confused by labels that are relevant. To address this problem, we propose a method that generates distinct label represen... | ['Yuki Arase', 'Tomoyuki Kajiwara', 'Junya Takayama', 'Sora Ohashi'] | 2021-08-01 | null | null | null | acl-2021-5 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 4.62091357e-01 -2.72573275e-03 -6.13875866e-01 -7.09066868e-01
-6.87137604e-01 -2.18063951e-01 7.83049643e-01 5.95457911e-01
-3.27182323e-01 5.47938585e-01 2.41141140e-01 2.14162722e-01
-3.66181359e-02 -9.10977900e-01 1.07444942e-01 -4.41624731e-01
5.07779658e-01 3.46253961e-01 3.48148108e-01 -2.35609367... | [10.225857734680176, 3.6087822914123535] |
d7f6c923-fa0f-48ca-8899-d7e8fb9c246f | optimal-energy-rationing-for-prepaid | 2304.09251 | null | https://arxiv.org/abs/2304.09251v1 | https://arxiv.org/pdf/2304.09251v1.pdf | Optimal Energy Rationing for Prepaid Electricity Customers | For a large (and recently increasing) number of households, affordability is a major hurdle in accessing sufficient electricity and avoiding service disconnections. For such households, in-home energy rationing, i.e. the need to actively prioritize how to use a limited amount of electricity, is an everyday reality. In ... | ['Line A. Roald', 'Maitreyee Marathe'] | 2023-04-18 | null | null | null | null | ['energy-management'] | ['time-series'] | [-7.75283799e-02 3.08546633e-01 -3.38464975e-01 -2.38314778e-01
-1.37479112e-01 -6.60100520e-01 1.18630119e-01 4.81583893e-01
5.84397055e-02 8.55312407e-01 1.63577601e-01 -4.49035078e-01
-6.39361978e-01 -1.36666894e+00 7.97514766e-02 -6.91815734e-01
3.19442935e-02 6.97487593e-01 -2.66377658e-01 -2.53032595... | [5.7290120124816895, 2.5024521350860596] |
2364308e-8998-45f5-b499-0dc363b62816 | a-mention-ranking-model-for-abstract-anaphora | 1706.02256 | null | http://arxiv.org/abs/1706.02256v2 | http://arxiv.org/pdf/1706.02256v2.pdf | A Mention-Ranking Model for Abstract Anaphora Resolution | Resolving abstract anaphora is an important, but difficult task for text
understanding. Yet, with recent advances in representation learning this task
becomes a more tangible aim. A central property of abstract anaphora is that it
establishes a relation between the anaphor embedded in the anaphoric sentence
and its (ty... | ['Ana Marasović', 'Juri Opitz', 'Anette Frank', 'Leo Born'] | 2017-06-07 | a-mention-ranking-model-for-abstract-anaphora-1 | https://aclanthology.org/D17-1021 | https://aclanthology.org/D17-1021.pdf | emnlp-2017-9 | ['abstract-anaphora-resolution'] | ['natural-language-processing'] | [ 1.21925250e-01 4.34803963e-01 -6.36471391e-01 -5.46329260e-01
-9.92024302e-01 -7.79970229e-01 8.36668730e-01 4.25046116e-01
-7.03082919e-01 1.08212125e+00 1.13451302e+00 5.05184047e-02
-4.87291873e-01 -7.51323164e-01 -8.21834743e-01 -2.22296372e-01
-1.92918051e-02 1.32973886e+00 5.07430770e-02 -6.82434499... | [9.352425575256348, 9.518139839172363] |
47a69feb-bcca-49e5-9f1e-283f0c2d856e | on-using-the-two-way-cluster-robust-standard | 2301.13775 | null | https://arxiv.org/abs/2301.13775v1 | https://arxiv.org/pdf/2301.13775v1.pdf | On Using The Two-Way Cluster-Robust Standard Errors | Thousands of papers have reported two-way cluster-robust (TWCR) standard errors. However, the recent econometrics literature points out the potential non-gaussianity of two-way cluster sample means, and thus invalidity of the inference based on the TWCR standard errors. Fortunately, simulation studies nonetheless show ... | ['Yuya Sasaki', 'Harold D Chiang'] | 2023-01-31 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-1.53778255e-01 -2.25352064e-01 -1.26722589e-01 -3.58435899e-01
-7.66774595e-01 -7.37996697e-01 5.61777174e-01 2.18369201e-01
-3.44032109e-01 6.74327135e-01 1.44602612e-01 -1.16881227e+00
-6.16041362e-01 -4.58244681e-01 -5.99812448e-01 -9.79673028e-01
-7.22473189e-02 2.01969847e-01 -2.73078412e-01 4.82938081... | [6.877150058746338, 4.450325965881348] |
2558ceeb-9249-4d3c-86d9-cd2d50349ed5 | understanding-compositional-data-augmentation | 2305.13658 | null | https://arxiv.org/abs/2305.13658v1 | https://arxiv.org/pdf/2305.13658v1.pdf | Understanding compositional data augmentation in automatic morphological inflection | Data augmentation techniques are widely used in low-resource automatic morphological inflection to address the issue of data sparsity. However, the full implications of these techniques remain poorly understood. In this study, we aim to shed light on the theoretical aspects of the data augmentation strategy StemCorrupt... | ['Miikka Silfverberg', 'Farhan Samir'] | 2023-05-23 | null | null | null | null | ['morphological-inflection'] | ['natural-language-processing'] | [ 5.20484388e-01 1.30035818e-01 -3.97645772e-01 -1.94994614e-01
-7.08735704e-01 -8.84064019e-01 5.84391117e-01 2.36870036e-01
-5.93050122e-01 5.29099643e-01 8.98733735e-01 -6.19525731e-01
2.71398664e-01 -5.67763567e-01 -7.38072872e-01 -4.05775428e-01
2.74239540e-01 3.56771052e-01 -4.12222207e-01 -2.46306121... | [10.707789421081543, 9.714288711547852] |
340f428d-51ae-4a01-9e97-e703f5aac5e1 | enhanced-multi-channel-graph-convolutional | null | null | https://aclanthology.org/2022.acl-long.212 | https://aclanthology.org/2022.acl-long.212.pdf | Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction | Aspect Sentiment Triplet Extraction (ASTE) is an emerging sentiment analysis task. Most of the existing studies focus on devising a new tagging scheme that enables the model to extract the sentiment triplets in an end-to-end fashion. However, these methods ignore the relations between words for ASTE task. In this paper... | ['Xiaojie Wang', 'Ruifan Li', 'Fangxiang Feng', 'Zepeng Zhai', 'Hao Chen'] | null | null | null | null | acl-2022-5 | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [-4.94325720e-02 -4.24159989e-02 -9.25077498e-02 -5.80501437e-01
-4.38712776e-01 -3.97419184e-01 3.49429816e-01 1.09334379e-01
-3.30281824e-01 1.22186035e-01 4.06128854e-01 -3.69691104e-01
7.73966834e-02 -8.89441729e-01 -4.55742508e-01 -5.11432648e-01
2.38933429e-01 1.56972945e-01 -1.26242995e-01 -5.53873003... | [11.499410629272461, 6.605653285980225] |
ac444dc6-9f3c-4f40-a86d-31e6284c55a7 | uv-based-3d-hand-object-reconstruction-with | 2211.13429 | null | https://arxiv.org/abs/2211.13429v1 | https://arxiv.org/pdf/2211.13429v1.pdf | UV-Based 3D Hand-Object Reconstruction with Grasp Optimization | We propose a novel framework for 3D hand shape reconstruction and hand-object grasp optimization from a single RGB image. The representation of hand-object contact regions is critical for accurate reconstructions. Instead of approximating the contact regions with sparse points, as in previous works, we propose a dense ... | ['Angela Yao', 'Ping Chen', 'You Xie', 'Linlin Yang', 'Ziwei Yu'] | 2022-11-24 | null | null | null | null | ['object-reconstruction'] | ['computer-vision'] | [-2.38071665e-01 -2.46652395e-01 5.40868640e-02 -8.85326639e-02
-4.46367711e-01 -6.57051027e-01 7.20085055e-02 -3.60044569e-01
1.38771161e-01 3.33367556e-01 5.35423160e-02 1.09411418e-01
-9.98026654e-02 -7.59902894e-01 -9.53561604e-01 -4.81709659e-01
3.39983165e-01 1.08234894e+00 3.28682274e-01 -1.84978485... | [6.3319549560546875, -0.9886971712112427] |
112424ff-dffe-42ab-aa44-16a3b5e775ca | adults-as-augmentations-for-children-in | 2202.05187 | null | https://arxiv.org/abs/2202.05187v1 | https://arxiv.org/pdf/2202.05187v1.pdf | Adults as Augmentations for Children in Facial Emotion Recognition with Contrastive Learning | Emotion recognition in children can help the early identification of, and intervention on, psychological complications that arise in stressful situations such as cancer treatment. Though deep learning models are increasingly being adopted, data scarcity is often an issue in pediatric medicine, including for facial emot... | ['Peter A. N. Bosman', 'Tanja Alderliesten', 'Andrea De Lorenzo', 'Marco Virgolin'] | 2022-02-10 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 3.83990288e-01 4.31079954e-01 -2.76306838e-01 -8.58632326e-01
-2.94749290e-01 -1.45156354e-01 1.28060803e-01 4.31797028e-01
-7.97683477e-01 5.28190196e-01 1.25198677e-01 9.30285007e-02
1.83589950e-01 -6.04599476e-01 -4.76364195e-01 -8.79416406e-01
-1.59634829e-01 1.44106790e-01 -7.13196576e-01 -1.37370527... | [13.538864135742188, 1.815516471862793] |
94a7bd97-f0ad-48f2-b4db-4dfeffc52b3e | fastdvdnet-towards-real-time-video-denoising | 1907.01361 | null | https://arxiv.org/abs/1907.01361v2 | https://arxiv.org/pdf/1907.01361v2.pdf | FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation | In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture. Until recently, video denoising with neural networks had been a largely under explored domain, and existing methods could not compete with the performance of the best patch-based methods. The app... | ['Julie Delon', 'Thomas Veit', 'Matias Tassano'] | 2019-07-01 | fastdvdnet-towards-real-time-deep-video | http://openaccess.thecvf.com/content_CVPR_2020/html/Tassano_FastDVDnet_Towards_Real-Time_Deep_Video_Denoising_Without_Flow_Estimation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Tassano_FastDVDnet_Towards_Real-Time_Deep_Video_Denoising_Without_Flow_Estimation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['video-denoising'] | ['computer-vision'] | [ 1.08190961e-01 -4.74173933e-01 3.44119608e-01 -2.33128428e-01
-6.53593719e-01 -2.37434238e-01 4.03064579e-01 -1.17490470e-01
-6.33276224e-01 4.78601933e-01 2.37543046e-01 -2.83026192e-02
6.32433295e-02 -7.12634563e-01 -6.57666504e-01 -9.56367731e-01
-1.78976487e-02 -1.24006771e-01 4.37954694e-01 -5.15676618... | [11.450555801391602, -2.2519302368164062] |
b753483f-4cfa-48d1-849e-26f04d79c914 | on-rank-energy-statistics-via-optimal | 2302.07964 | null | https://arxiv.org/abs/2302.07964v1 | https://arxiv.org/pdf/2302.07964v1.pdf | On Rank Energy Statistics via Optimal Transport: Continuity, Convergence, and Change Point Detection | This paper considers the use of recently proposed optimal transport-based multivariate test statistics, namely rank energy and its variant the soft rank energy derived from entropically regularized optimal transport, for the unsupervised nonparametric change point detection (CPD) problem. We show that the soft rank ene... | ['Shuchin Aeron', 'James M. Murphy', 'Shoaib Bin Masud', 'Matthew Werenski'] | 2023-02-15 | null | null | null | null | ['change-point-detection'] | ['time-series'] | [ 4.25745174e-02 -1.77872837e-01 7.12718442e-02 -7.17768585e-03
-1.28737199e+00 -5.18187284e-01 5.87794244e-01 4.39830571e-01
-6.28336251e-01 1.32064068e+00 -2.55905837e-01 -6.05500340e-02
-5.37116110e-01 -3.10132056e-01 -8.11172962e-01 -1.00387907e+00
-5.77297926e-01 2.83846229e-01 4.61588383e-01 1.00435287... | [7.103859901428223, 4.142900466918945] |
923d712b-ce0d-4efb-954f-6be75b5954fe | panoramic-annular-localizer-tackling-the | 1905.05425 | null | https://arxiv.org/abs/1905.05425v2 | https://arxiv.org/pdf/1905.05425v2.pdf | Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors | Visual localization is an attractive problem that estimates the camera localization from database images based on the query image. It is a crucial task for various applications, such as autonomous vehicles, assistive navigation and augmented reality. The challenging issues of the task lie in various appearance variatio... | ['Huabing Li', 'Xiao Huang', 'Weijian Hu', 'Jian Bai', 'Shufei Lin', 'Kailun Yang', 'Ruiqi Cheng', 'Kaiwei Wang', 'Dongming Sun'] | 2019-05-14 | null | null | null | null | ['camera-localization', 'outdoor-localization'] | ['computer-vision', 'robots'] | [-2.01699361e-01 -9.16935205e-01 -2.43528441e-01 -3.95086974e-01
-4.98375982e-01 -7.94144988e-01 6.55427992e-01 -3.96170586e-01
-6.50109768e-01 5.82202673e-01 -1.30636752e-01 2.58851230e-01
-1.60329774e-01 -5.02156377e-01 -6.79603696e-01 -7.22892880e-01
2.83954561e-01 1.00837529e-01 3.63136172e-01 1.96193270... | [7.665925025939941, -2.0319056510925293] |
f825036e-b779-4330-9e39-1b79fbe1056d | integrated-replay-spoofing-aware-text | 2006.05599 | null | https://arxiv.org/abs/2006.05599v2 | https://arxiv.org/pdf/2006.05599v2.pdf | Integrated Replay Spoofing-aware Text-independent Speaker Verification | A number of studies have successfully developed speaker verification or presentation attack detection systems. However, studies integrating the two tasks remain in the preliminary stages. In this paper, we propose two approaches for building an integrated system of speaker verification and presentation attack detection... | ['Ha-Jin Yu', 'Seung-bin Kim', 'Ju-ho Kim', 'Jee-weon Jung', 'Hye-jin Shim'] | 2020-06-10 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 4.65447120e-02 -2.59790778e-01 1.03059866e-01 -5.53978145e-01
-1.36750853e+00 -5.89536011e-01 6.15705192e-01 1.86041862e-01
-2.90670216e-01 4.00355831e-03 1.00362837e-01 -6.51341677e-01
1.50744706e-01 -1.59159422e-01 -3.70902568e-01 -7.12800384e-01
1.00551009e-01 2.09285915e-01 1.12951815e-01 -1.60652041... | [14.312671661376953, 6.035068988800049] |
dcfd9baf-78b3-46ec-ba4f-1d4a08b548f0 | stereovoxelnet-real-time-obstacle-detection | 2209.08459 | null | https://arxiv.org/abs/2209.08459v2 | https://arxiv.org/pdf/2209.08459v2.pdf | StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks | Obstacle detection is a safety-critical problem in robot navigation, where stereo matching is a popular vision-based approach. While deep neural networks have shown impressive results in computer vision, most of the previous obstacle detection works only leverage traditional stereo matching techniques to meet the compu... | ['Taskin Padir', 'Yanzhi Wang', 'Huaizu Jiang', 'Neset Unver Akmandor', 'Zhengang Li', 'Hongyu Li'] | 2022-09-18 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 1.26520827e-01 -9.41658467e-02 2.92399585e-01 -3.72173280e-01
-6.47619128e-01 -4.63101774e-01 4.84818697e-01 1.61188647e-01
-8.06322217e-01 5.35031855e-01 -1.43968701e-01 -6.71226203e-01
6.99886680e-02 -1.12317121e+00 -9.65062261e-01 -3.98422182e-01
6.22326247e-02 6.60509884e-01 6.06134593e-01 -5.18036962... | [8.36783218383789, -2.2708497047424316] |
970f087f-0c13-4562-8b1a-e3562832b82b | adversarial-dropout-for-recurrent-neural | 1904.09816 | null | http://arxiv.org/abs/1904.09816v1 | http://arxiv.org/pdf/1904.09816v1.pdf | Adversarial Dropout for Recurrent Neural Networks | Successful application processing sequential data, such as text and speech,
requires an improved generalization performance of recurrent neural networks
(RNNs). Dropout techniques for RNNs were introduced to respond to these
demands, but we conjecture that the dropout on RNNs could have been improved by
adopting the ad... | ['Il-Chul Moon', 'Mingi Ji', 'Kyungwoo Song', 'Wonsung Lee', 'Sungrae Park'] | 2019-04-22 | null | null | null | null | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [ 4.32347506e-01 3.89204681e-01 1.09820075e-01 -5.36779642e-01
-3.49029005e-01 -4.84746963e-01 3.64642024e-01 -3.30601126e-01
-7.19167113e-01 7.09744513e-01 1.90969333e-01 -5.12232721e-01
3.32416326e-01 -7.70799875e-01 -9.57580328e-01 -7.14762807e-01
2.13105157e-01 2.04882711e-01 2.12785095e-01 -8.82016197... | [10.900960922241211, 6.470926761627197] |
9babd006-f099-4fb0-b2d6-c032eb3c5dfd | array-configuration-agnostic-personal-voice | 2304.08887 | null | https://arxiv.org/abs/2304.08887v1 | https://arxiv.org/pdf/2304.08887v1.pdf | Array Configuration-Agnostic Personal Voice Activity Detection Based on Spatial Coherence | Personal voice activity detection has received increased attention due to the growing popularity of personal mobile devices and smart speakers. PVAD is often an integral element to speech enhancement and recognition for these applications in which lightweight signal processing is only enabled for the target user. Howev... | ['Mingsian R. Bai', 'Yicheng Hsu'] | 2023-04-18 | null | null | null | null | ['activity-detection', 'speech-enhancement'] | ['computer-vision', 'speech'] | [ 3.29407662e-01 -1.94555566e-01 2.54745901e-01 -1.99824989e-01
-8.38808000e-01 -3.42324018e-01 3.50207388e-01 -5.99879920e-02
-3.33637089e-01 2.66807586e-01 3.43480974e-01 -4.08342034e-01
-5.07824272e-02 -2.16696665e-01 -4.00192924e-02 -8.91295195e-01
-2.65049636e-01 -4.34097052e-01 1.11974496e-02 1.92914605... | [14.87462329864502, 5.923952579498291] |
a49af599-ee55-4b14-bfa0-505fd6554a79 | hierarchical-attention-transfer-network-for | null | null | https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/16873 | https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16873/16149 | Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification | Cross-domain sentiment classification aims to leverage useful information in a source domain to help do sentiment classifi- cation in a target domain that has no or little supervised infor- mation. Existing cross-domain sentiment classification meth- ods cannot automatically capture non-pivots, i.e., the domain- specif... | ['Ying WEI', 'Zheng Li', 'Qiang Yang', 'Yu Zhang'] | 2018-04-26 | null | null | null | thirty-second-aaai-conference-on-artificial | ['cross-domain-text-classification'] | ['natural-language-processing'] | [-4.73376274e-01 -1.54922634e-01 -4.18859184e-01 -4.66352552e-01
-5.58202565e-01 -5.17634809e-01 1.54261842e-01 1.53678238e-01
-1.13112636e-01 4.90247995e-01 3.37879241e-01 -4.22753431e-02
-1.51978746e-01 -7.11053729e-01 -4.57093179e-01 -8.55284572e-01
3.11352760e-01 5.40432274e-01 -2.37873048e-02 -6.57349229... | [11.43284797668457, 6.613969326019287] |
25410f91-b244-462a-a680-47965c6f2556 | in-context-learning-unlocked-for-diffusion | 2305.01115 | null | https://arxiv.org/abs/2305.01115v1 | https://arxiv.org/pdf/2305.01115v1.pdf | In-Context Learning Unlocked for Diffusion Models | We present Prompt Diffusion, a framework for enabling in-context learning in diffusion-based generative models. Given a pair of task-specific example images, such as depth from/to image and scribble from/to image, and a text guidance, our model automatically understands the underlying task and performs the same task on... | ['Mingyuan Zhou', 'Zhangyang Wang', 'Weizhu Chen', 'Pengcheng He', 'Yelong Shen', 'Yadong Lu', 'Yifan Jiang', 'Zhendong Wang'] | 2023-05-01 | null | null | null | null | ['text-guided-image-editing'] | ['computer-vision'] | [ 5.32282591e-01 8.96708295e-02 1.26141950e-01 -7.25643754e-01
-8.17995489e-01 -7.37386405e-01 1.22739446e+00 -1.13564655e-01
-3.96671146e-01 2.55882621e-01 2.75204480e-01 -5.45174956e-01
2.56717771e-01 -6.43205881e-01 -8.78117919e-01 -5.38129628e-01
4.07451659e-01 4.75952327e-01 2.01056868e-01 3.08204684... | [11.246649742126465, 0.07388786971569061] |
f05da29f-d5ea-4259-acb9-cee677ba24cf | street-view-change-detection-with | null | null | https://link.springer.com/article/10.1007/s10514-018-9734-5 | https://link.springer.com/article/10.1007/s10514-018-9734-5 | Street-view change detection with deconvolutional networks | We propose a system for performing structural change detection in street-view videos captured by a vehicle-mounted monocular camera over time. Our approach is motivated by the need for more frequent and efficient updates in the large-scale maps used in autonomous vehicle navigation. Our method chains a multi-sensor fus... | ['Riccardo Gherardi', 'Roberto Arroyo', 'Germán Ros', 'Simon Stent', 'Pablo F. Alcantarilla'] | 2018-05-15 | null | null | null | autonomous-robots-2018-5 | ['change-detection', 'change-detection-for-remote-sensing-images'] | ['computer-vision', 'miscellaneous'] | [ 3.52617055e-01 -4.42252755e-01 6.43335702e-03 -5.50741017e-01
-3.41893286e-01 -5.98948956e-01 8.87665272e-01 -4.85007584e-01
-5.60704768e-01 4.81238484e-01 2.44317457e-01 -1.46922901e-01
1.33079946e-01 -8.61058474e-01 -1.04101861e+00 -5.04178345e-01
1.51680764e-02 2.31462657e-01 5.15269995e-01 -5.64982533... | [8.059664726257324, -2.2262401580810547] |
335b6b09-8e8d-49ae-8815-36b86c9fa9c4 | extract-and-edit-an-alternative-to-back | 1904.02331 | null | http://arxiv.org/abs/1904.02331v1 | http://arxiv.org/pdf/1904.02331v1.pdf | Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation | The overreliance on large parallel corpora significantly limits the
applicability of machine translation systems to the majority of language pairs.
Back-translation has been dominantly used in previous approaches for
unsupervised neural machine translation, where pseudo sentence pairs are
generated to train the models ... | ['William Yang Wang', 'Xin Wang', 'Jiawei Wu'] | 2019-04-04 | extract-and-edit-an-alternative-to-back-1 | https://aclanthology.org/N19-1120 | https://aclanthology.org/N19-1120.pdf | naacl-2019-6 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 5.37906945e-01 8.01247284e-02 -3.11058402e-01 -4.72431302e-01
-1.36169243e+00 -6.43325150e-01 8.47958922e-01 -8.28182474e-02
-7.08746850e-01 1.32310748e+00 -8.19814801e-02 -6.53715372e-01
5.29680729e-01 -6.28516078e-01 -9.76791680e-01 -4.45873857e-01
6.19299650e-01 8.67981136e-01 -2.07162440e-01 -4.23173606... | [11.625495910644531, 10.290610313415527] |
a252bdec-af09-4e05-a945-0cdd08b1ebf6 | stock-market-prediction-via-deep-learning | 2212.12717 | null | https://arxiv.org/abs/2212.12717v2 | https://arxiv.org/pdf/2212.12717v2.pdf | Stock Market Prediction via Deep Learning Techniques: A Survey | Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction. We present four elaborated subtasks of stock market prediction and propose a ... | ['Javen Qinfeng Shi', 'Lingqiao Liu', 'Ehsan Abbasnejad', 'Qingsen Yan', 'Yanxi Liu', 'Haiyao Cao', 'Yang Jiao', 'Qingying Zhao', 'Jinan Zou'] | 2022-12-24 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-9.95818973e-01 -3.39012831e-01 -8.20327222e-01 -2.07276583e-01
-1.64894193e-01 -4.73697752e-01 7.68910944e-01 -9.58814397e-02
-2.93423235e-01 8.49892378e-01 2.76704311e-01 -5.09297729e-01
2.37336326e-02 -1.21256495e+00 -4.06208783e-01 -1.73686668e-01
-3.59747082e-01 4.68084663e-01 2.32297778e-01 -7.21222401... | [4.3964996337890625, 4.289519309997559] |
98715a15-8b11-4217-bdeb-ab3a5ab2d915 | density-based-clustering-for-3d-object | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Ahmed_Density-Based_Clustering_for_3D_Object_Detection_in_Point_Clouds_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Ahmed_Density-Based_Clustering_for_3D_Object_Detection_in_Point_Clouds_CVPR_2020_paper.pdf | Density-Based Clustering for 3D Object Detection in Point Clouds | Current 3D detection networks either rely on 2D object proposals or try to directly predict bounding box parameters from each point in a scene. While former methods are dependent on performance of 2D detectors, latter approaches are challenging due to the sparsity and occlusion in point clouds, making it difficult to r... | [' Chee Meng Chew', 'Syeda Mariam Ahmed'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['foreground-segmentation'] | ['computer-vision'] | [ 1.25822529e-01 2.31408134e-01 1.91306490e-02 -4.16242123e-01
-6.14593625e-01 -6.54098153e-01 6.81996286e-01 3.39446574e-01
-4.75642532e-01 5.36336340e-02 -6.31382585e-01 -1.43422142e-01
4.37655374e-02 -5.54927588e-01 -9.21820819e-01 -6.04336500e-01
-6.01300225e-02 1.03948915e+00 9.40037429e-01 3.62419635... | [7.682411193847656, -2.8162500858306885] |
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