paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ced1b479-59a2-47f8-a88e-7317da9d802e | multi-geometry-spatial-acoustic-modeling-for | 1903.06539 | null | http://arxiv.org/abs/1903.06539v2 | http://arxiv.org/pdf/1903.06539v2.pdf | Multi-Geometry Spatial Acoustic Modeling for Distant Speech Recognition | The use of spatial information with multiple microphones can improve
far-field automatic speech recognition (ASR) accuracy. However, conventional
microphone array techniques degrade speech enhancement performance when there
is an array geometry mismatch between design and test conditions. Moreover,
such speech enhancem... | [] | 2019-04-28 | null | null | null | null | ['distant-speech-recognition'] | ['speech'] | [ 3.37890208e-01 -3.11039448e-01 6.42952263e-01 -2.55299121e-01
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-8.17887336e-02 -3.14806044e-01 1.44003714e-02 -1.04172446... | [14.946783065795898, 5.949449062347412] |
20854e07-c327-48a7-b65c-6f0757c02f4d | dru-net-an-efficient-deep-convolutional | 2004.13453 | null | https://arxiv.org/abs/2004.13453v1 | https://arxiv.org/pdf/2004.13453v1.pdf | DRU-net: An Efficient Deep Convolutional Neural Network for Medical Image Segmentation | Residual network (ResNet) and densely connected network (DenseNet) have significantly improved the training efficiency and performance of deep convolutional neural networks (DCNNs) mainly for object classification tasks. In this paper, we propose an efficient network architecture by considering advantages of both netwo... | ['Jonathan Garibaldi', 'Susan Francis', 'Mina Jafari', 'Dorothee Auer', 'Xin Chen'] | 2020-04-28 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 2.26693064e-01 5.49697101e-01 -1.49590239e-01 -4.18358862e-01
-4.86378163e-01 -9.93189588e-02 2.26869613e-01 -1.02030382e-01
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8.29659700e-02 2.11919650e-01 3.74938935e-01 4.52302694... | [14.68786334991455, -2.588212251663208] |
a2c3330e-7136-4965-b5bb-643be4b2475f | foga-flag-optimization-with-genetic-algorithm | 2105.07202 | null | https://arxiv.org/abs/2105.07202v1 | https://arxiv.org/pdf/2105.07202v1.pdf | FOGA: Flag Optimization with Genetic Algorithm | Recently, program autotuning has become very popular especially in embedded systems, when we have limited resources such as computing power and memory where these systems run generally time-critical applications. Compiler optimization space gradually expands with the renewed compiler options and inclusion of new archit... | ['Mahiye Uluyağmur Öztürk', 'Mert Kutay Sezer', 'Berkan Höke', 'Burak Tağtekin'] | 2021-05-15 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [-2.13286549e-01 -2.90172458e-01 -2.79234231e-01 -5.92073239e-02
7.19344392e-02 -6.25630975e-01 2.71045655e-01 2.82717824e-01
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-3.52443568e-02 2.89280385e-01 3.16858202e-01 -5.71247280... | [5.901954174041748, 3.5982601642608643] |
79e83de1-5603-4ca9-bfcf-9b88fd984692 | lever-learning-to-verify-language-to-code | 2302.08468 | null | https://arxiv.org/abs/2302.08468v2 | https://arxiv.org/pdf/2302.08468v2.pdf | LEVER: Learning to Verify Language-to-Code Generation with Execution | The advent of large language models trained on code (code LLMs) has led to significant progress in language-to-code generation. State-of-the-art approaches in this area combine LLM decoding with sample pruning and reranking using test cases or heuristics based on the execution results. However, it is challenging to obt... | ['Xi Victoria Lin', 'Sida I. Wang', 'Wen-tau Yih', 'Ves Stoyanov', 'Dragomir Radev', 'Srini Iyer', 'Ansong Ni'] | 2023-02-16 | null | null | null | null | ['text-to-sql', 'semantic-parsing', 'arithmetic-reasoning'] | ['computer-code', 'natural-language-processing', 'reasoning'] | [ 1.52495429e-01 -1.94279134e-01 -7.07844615e-01 -5.19143641e-01
-1.35795236e+00 -7.56384134e-01 3.84216547e-01 5.62912166e-01
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-2.80991107e-01 2.99299300e-01 2.30709806e-01 9.76845697... | [7.8607892990112305, 7.712847709655762] |
e94e3294-c9c8-4038-a605-b91b7725c383 | animating-arbitrary-objects-via-deep-motion | 1812.08861 | null | https://arxiv.org/abs/1812.08861v3 | https://arxiv.org/pdf/1812.08861v3.pdf | Animating Arbitrary Objects via Deep Motion Transfer | This paper introduces a novel deep learning framework for image animation. Given an input image with a target object and a driving video sequence depicting a moving object, our framework generates a video in which the target object is animated according to the driving sequence. This is achieved through a deep architect... | ['Stéphane Lathuilière', 'Elisa Ricci', 'Aliaksandr Siarohin', 'Sergey Tulyakov', 'Nicu Sebe'] | 2018-12-20 | animating-arbitrary-objects-via-deep-motion-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Siarohin_Animating_Arbitrary_Objects_via_Deep_Motion_Transfer_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Siarohin_Animating_Arbitrary_Objects_via_Deep_Motion_Transfer_CVPR_2019_paper.pdf | cvpr-2019-6 | ['image-animation'] | ['computer-vision'] | [ 3.15487653e-01 -1.45186735e-02 -7.77130798e-02 -2.57891640e-02
-6.15850151e-01 -3.74586105e-01 7.80375838e-01 -5.49337149e-01
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-1.23344004e-01 4.06684995e-01 4.32045639e-01 -2.20482070... | [10.832925796508789, -0.7998821139335632] |
fbf2a185-42c6-4462-9647-4dda6ebee7cd | softmatch-addressing-the-quantity-quality | 2301.10921 | null | https://arxiv.org/abs/2301.10921v2 | https://arxiv.org/pdf/2301.10921v2.pdf | SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning | The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization performance. In this paper, we first revisit the popular pseudo-labeling methods via a unified sample weighting formulation and demonstrate th... | ['Marios Savvides', 'Bhiksha Raj', 'Xing Xie', 'Bernt Schiele', 'Jindong Wang', 'Yidong Wang', 'Yue Fan', 'Ran Tao', 'Hao Chen'] | 2023-01-26 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 3.73794109e-01 4.52518575e-02 -8.11250091e-01 -8.69087875e-01
-1.17480731e+00 -6.50759399e-01 4.61555153e-01 3.05449396e-01
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5.30155241e-01 3.08921009e-01 4.35027294e-02 2.20705077... | [9.413321495056152, 3.80349063873291] |
253bc2a1-02a6-4578-bd95-22591c5d6cc3 | advancing-3d-medical-image-analysis-with | 2201.01426 | null | https://arxiv.org/abs/2201.01426v1 | https://arxiv.org/pdf/2201.01426v1.pdf | Advancing 3D Medical Image Analysis with Variable Dimension Transform based Supervised 3D Pre-training | The difficulties in both data acquisition and annotation substantially restrict the sample sizes of training datasets for 3D medical imaging applications. As a result, constructing high-performance 3D convolutional neural networks from scratch remains a difficult task in the absence of a sufficient pre-training paramet... | ['Yizhou Yu', 'Jiechao Ma', 'Hong-Yu Zhou', 'Zihao Li', 'Shu Zhang'] | 2022-01-05 | null | null | null | null | ['medical-object-detection'] | ['computer-vision'] | [ 3.41202348e-01 2.21857280e-01 -2.44504258e-01 -5.31110644e-01
-9.85084236e-01 -3.62978131e-01 2.59573936e-01 1.89059138e-01
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-8.16293061e-03 4.64002818e-01 2.90372252e-01 2.75121201... | [14.681035995483398, -2.264152765274048] |
83c9df50-64fb-4aab-b51a-13190a78752d | in-context-learning-for-attention-scheme-from | 2307.02419 | null | https://arxiv.org/abs/2307.02419v1 | https://arxiv.org/pdf/2307.02419v1.pdf | In-Context Learning for Attention Scheme: from Single Softmax Regression to Multiple Softmax Regression via a Tensor Trick | Large language models (LLMs) have brought significant and transformative changes in human society. These models have demonstrated remarkable capabilities in natural language understanding and generation, leading to various advancements and impacts across several domains. We consider the in-context learning under two fo... | ['Shenghao Xie', 'Zhao Song', 'Yeqi Gao'] | 2023-07-05 | null | null | null | null | ['natural-language-understanding'] | ['natural-language-processing'] | [ 5.44836462e-01 6.29847273e-02 -2.38140702e-01 -5.38360536e-01
-8.94782662e-01 -2.89946377e-01 2.94436187e-01 -3.95042636e-03
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-6.46415949e-01 3.80490571e-02 -3.77693743e-01 -7.22687066... | [6.696108818054199, 4.502172946929932] |
02dc6632-471d-4432-adcf-b515fe3849ff | ericson-an-interactive-open-domain | 2304.02233 | null | https://arxiv.org/abs/2304.02233v1 | https://arxiv.org/pdf/2304.02233v1.pdf | Ericson: An Interactive Open-Domain Conversational Search Agent | Open-domain conversational search (ODCS) aims to provide valuable, up-to-date information, while maintaining natural conversations to help users refine and ultimately answer information needs. However, creating an effective and robust ODCS agent is challenging. In this paper, we present a fully functional ODCS system, ... | ['Eugene Agichtein', 'Payam Karisani', 'Jason Choi', 'Ali Ahmadvand', 'ZiHao Wang'] | 2023-04-05 | null | null | null | null | ['conversational-search', 'intent-classification', 'dialogue-management'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-5.45984268e-01 3.07320595e-01 -1.57127738e-01 -4.37240601e-01
-7.85990179e-01 -6.73083484e-01 7.69186795e-01 2.78499871e-01
-2.98123419e-01 5.81480742e-01 7.58206666e-01 -3.94253314e-01
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6.88303187e-02 7.96755970e-01 3.75738114e-01 -1.04165566... | [12.273353576660156, 7.785153865814209] |
f9c0b865-3a59-40f5-80ae-2cb4e647792d | digging-into-self-supervised-learning-of | 2110.04773 | null | https://arxiv.org/abs/2110.04773v1 | https://arxiv.org/pdf/2110.04773v1.pdf | Digging Into Self-Supervised Learning of Feature Descriptors | Fully-supervised CNN-based approaches for learning local image descriptors have shown remarkable results in a wide range of geometric tasks. However, most of them require per-pixel ground-truth keypoint correspondence data which is difficult to acquire at scale. To address this challenge, recent weakly- and self-superv... | ['Juho Kannala', 'Shuzhe Wang', 'Xiaotian Li', 'Zakaria Laskar', 'Iaroslav Melekhov'] | 2021-10-10 | null | null | null | null | ['image-based-localization', 'image-stylization'] | ['computer-vision', 'computer-vision'] | [ 2.73437172e-01 -3.78650904e-01 -6.27936244e-01 -5.15858114e-01
-1.17427719e+00 -6.17540121e-01 6.97489858e-01 7.29024708e-02
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-3.26803148e-01 -6.54617965e-01 -9.40062344e-01 -6.39296293e-01
1.86380997e-01 3.34170580e-01 4.77607697e-02 -2.93365657... | [8.07407283782959, -2.062380313873291] |
3d3a91fb-5619-457e-8ca0-5ced17f8f28d | interactivism-in-spoken-dialogue-systems | 2209.13547 | null | https://arxiv.org/abs/2209.13547v2 | https://arxiv.org/pdf/2209.13547v2.pdf | Interactivism in Spoken Dialogue Systems | The interactivism model introduces a dynamic approach to language, communication and cognition. In this work, we explore this fundamental theory in the context of dialogue modelling for spoken dialogue systems (SDS). To extend such a theoretical framework, we present a set of design principles which adhere to central p... | ['R. K. Moore', 'G. Huang', 'E. Ip', 'T. Rodríguez Muñoz'] | 2022-09-27 | null | null | null | null | ['spoken-dialogue-systems'] | ['speech'] | [-4.35784101e-01 9.96462822e-01 3.12895000e-01 -4.38103437e-01
4.43911105e-01 -6.11032426e-01 1.27102768e+00 1.66066647e-01
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-3.30214113e-01 -6.36764348e-01 1.35686725e-01 1.80538997e-01
-8.11689943e-02 2.35375106e-01 -7.68050104e-02 -1.07767820... | [12.948294639587402, 7.931819915771484] |
6f939c37-a5e2-451c-83a9-762e40a81876 | from-explanation-to-synthesis-compositional | 1902.10657 | null | https://arxiv.org/abs/1902.10657v2 | https://arxiv.org/pdf/1902.10657v2.pdf | From explanation to synthesis: Compositional program induction for learning from demonstration | Hybrid systems are a compact and natural mechanism with which to address problems in robotics. This work introduces an approach to learning hybrid systems from demonstrations, with an emphasis on extracting models that are explicitly verifiable and easily interpreted by robot operators. We fit a sequence of controllers... | ['Svetlin Penkov', 'Michael Burke', 'Subramanian Ramamoorthy'] | 2019-02-27 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 5.51859796e-01 6.93930387e-01 1.69581264e-01 -1.75490588e-01
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-8.57394189e-02 6.54428542e-01 3.18725824e-01 -4.81733024... | [4.485483169555664, 0.7441084384918213] |
7e188f8c-4b1b-4af3-bea1-32060f905eb3 | intriguing-properties-of-text-guided | 2306.00974 | null | https://arxiv.org/abs/2306.00974v3 | https://arxiv.org/pdf/2306.00974v3.pdf | Intriguing Properties of Text-guided Diffusion Models | Text-guided diffusion models (TDMs) are widely applied but can fail unexpectedly. Common failures include: (i) natural-looking text prompts generating images with the wrong content, or (ii) different random samples of the latent variables that generate vastly different, and even unrelated, outputs despite being conditi... | ['Alan Yuille', 'Song Bai', 'Yutong Bai', 'Adam Kortylewski', 'Qihao Liu'] | 2023-06-01 | null | null | null | null | ['adversarial-attack'] | ['adversarial'] | [ 8.06831956e-01 1.39701784e-01 2.38443151e-01 -1.28767341e-01
-7.79841721e-01 -1.01516771e+00 1.12574935e+00 -3.69390339e-01
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2.10312698e-02 3.37742239e-01 3.61322127e-02 -2.66862437... | [11.588644027709961, -0.1167781725525856] |
0bbf9650-a818-4bfb-b064-12ac967b9a67 | geometric-algebra-attention-networks-for | 2110.02393 | null | https://arxiv.org/abs/2110.02393v2 | https://arxiv.org/pdf/2110.02393v2.pdf | Geometric Algebra Attention Networks for Small Point Clouds | Much of the success of deep learning is drawn from building architectures that properly respect underlying symmetry and structure in the data on which they operate - a set of considerations that have been united under the banner of geometric deep learning. Often problems in the physical sciences deal with relatively sm... | ['Matthew Spellings'] | 2021-10-05 | geometric-algebra-attention-networks-for-1 | https://openreview.net/forum?id=nLb60uXd6Np | https://openreview.net/pdf?id=nLb60uXd6Np | null | ['classify-3d-point-clouds', 'generating-3d-point-clouds'] | ['computer-vision', 'computer-vision'] | [ 1.32860705e-01 -1.03876159e-01 5.93118854e-02 -4.50286776e-01
-2.83256829e-01 -6.77500129e-01 1.13179100e+00 5.41258976e-02
-2.63621837e-01 3.71144533e-01 2.20461264e-01 -4.66249049e-01
-4.43991721e-01 -8.44316959e-01 -9.70202208e-01 -8.03849459e-01
-2.08117723e-01 6.61753953e-01 -3.26556593e-01 -5.03426731... | [8.840508460998535, 2.461977005004883] |
f01840f5-f1d7-4bfc-ab5d-3cbbe4f1dfe1 | vision-transformer-with-convolutional-encoder | 2209.05032 | null | https://arxiv.org/abs/2209.05032v1 | https://arxiv.org/pdf/2209.05032v1.pdf | Vision Transformer with Convolutional Encoder-Decoder for Hand Gesture Recognition using 24 GHz Doppler Radar | Transformers combined with convolutional encoders have been recently used for hand gesture recognition (HGR) using micro-Doppler signatures. We propose a vision-transformer-based architecture for HGR with multi-antenna continuous-wave Doppler radar receivers. The proposed architecture consists of three modules: a convo... | ['Chamira U. S. Edussooriya', 'Ranga Rodrigo', 'Arjuna Madanayake', 'Viduneth Ariyarathna', 'Nisal Kariyawasam', 'Dhanuka Marasinghe', 'Gayangana Leelarathne', 'Kavinda Kehelella'] | 2022-09-12 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.17373818e-01 5.33084050e-02 2.89955616e-01 -1.61109775e-01
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-4.23709273e-01 4.85258579e-01 -5.48195234e-03 -4.79833260... | [6.775153160095215, 0.19853460788726807] |
ec4da963-f521-48af-b34b-a43ce5555319 | face2ppg-an-unsupervised-pipeline-for-blood | 2202.04101 | null | https://arxiv.org/abs/2202.04101v3 | https://arxiv.org/pdf/2202.04101v3.pdf | Face2PPG: An unsupervised pipeline for blood volume pulse extraction from faces | Photoplethysmography (PPG) signals have become a key technology in many fields, such as medicine, well-being, or sports. Our work proposes a set of pipelines to extract remote PPG signals (rPPG) from the face robustly, reliably, and configurable. We identify and evaluate the possible choices in the critical steps of un... | ['Miguel Bordallo López', 'Constantino Álvarez Casado'] | 2022-02-08 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 4.57895041e-01 1.62094146e-01 2.74603188e-01 -3.70566428e-01
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-1.05408102e-01 2.30793327e-01 4.49272580e-02 -1.50006011... | [13.835180282592773, 2.634943723678589] |
206fff5c-70ea-4cc3-9a56-3495e63a154a | tugas-exploiting-unlabelled-data-for-twitter | null | null | https://aclanthology.org/S14-2120 | https://aclanthology.org/S14-2120.pdf | TUGAS: Exploiting unlabelled data for Twitter sentiment analysis | null | ["M{\\'a}rio J. Silva", 'Jo{\\~a}o Filgueiras', 'Miguel B. Almeida', 'Silvio Amir', 'Bruno Martins'] | 2014-08-01 | null | null | null | semeval-2014-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.206260681152344, 3.6059210300445557] |
ab8d473d-2fbc-4a48-9203-b7f845493666 | back-to-patterns-efficient-japanese | 2305.19045 | null | https://arxiv.org/abs/2305.19045v1 | https://arxiv.org/pdf/2305.19045v1.pdf | Back to Patterns: Efficient Japanese Morphological Analysis with Feature-Sequence Trie | Accurate neural models are much less efficient than non-neural models and are useless for processing billions of social media posts or handling user queries in real time with a limited budget. This study revisits the fastest pattern-based NLP methods to make them as accurate as possible, thus yielding a strikingly simp... | ['Naoki Yoshinaga'] | 2023-05-30 | null | null | null | null | ['morphological-analysis'] | ['natural-language-processing'] | [ 9.28008035e-02 -8.67479667e-02 -1.62371039e-01 -2.45558560e-01
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3.06347013e-01 7.59364843e-01 2.81796098e-01 -2.88894605... | [10.445728302001953, 10.078166007995605] |
71858018-562f-43f9-b84f-37b752a3c639 | pixelsteganalysis-pixel-wise-hidden | 1902.10905 | null | https://arxiv.org/abs/1902.10905v3 | https://arxiv.org/pdf/1902.10905v3.pdf | PixelSteganalysis: Pixel-wise Hidden Information Removal with Low Visual Degradation | Recently, the field of steganography has experienced rapid developments based on deep learning (DL). DL based steganography distributes secret information over all the available bits of the cover image, thereby posing difficulties in using conventional steganalysis methods to detect, extract or remove hidden secret ima... | ['Hyun-Soo Choi', 'Dahuin Jung', 'Sungroh Yoon', 'Ho Bae'] | 2019-02-28 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 1.07006311e+00 1.72187150e-01 1.63652584e-01 2.72885650e-01
-3.15668017e-01 -3.07841957e-01 4.59900290e-01 -3.17425728e-01
-3.36560130e-01 6.70429468e-01 -1.72184572e-01 -5.59430361e-01
4.08333927e-01 -1.14559460e+00 -8.53063941e-01 -1.33145308e+00
-2.17722610e-01 9.52630788e-02 2.37562731e-01 -7.12005615... | [4.31631326675415, 8.057793617248535] |
0b899e9d-7d09-4841-857e-b3572e8884aa | usip-unsupervised-stable-interest-point | 1904.00229 | null | http://arxiv.org/abs/1904.00229v1 | http://arxiv.org/pdf/1904.00229v1.pdf | USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds | In this paper, we propose the USIP detector: an Unsupervised Stable Interest
Point detector that can detect highly repeatable and accurately localized
keypoints from 3D point clouds under arbitrary transformations without the need
for any ground truth training data. Our USIP detector consists of a feature
proposal netw... | ['Gim Hee Lee', 'Jiaxin Li'] | 2019-03-30 | usip-unsupervised-stable-interest-point-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Li_USIP_Unsupervised_Stable_Interest_Point_Detection_From_3D_Point_Clouds_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Li_USIP_Unsupervised_Stable_Interest_Point_Detection_From_3D_Point_Clouds_ICCV_2019_paper.pdf | iccv-2019-10 | ['interest-point-detection'] | ['computer-vision'] | [-4.87953484e-01 -7.47875273e-02 -5.54160140e-02 -3.39528859e-01
-8.55186582e-01 -6.73156857e-01 7.58503854e-01 4.21805345e-02
-2.20830098e-01 1.54939547e-01 -4.97017026e-01 -1.82212964e-01
-2.15171337e-01 -5.95720768e-01 -1.18147910e+00 -1.88985854e-01
-2.58395612e-01 9.87207890e-01 4.95531946e-01 -2.90427450... | [7.595898151397705, -2.5901124477386475] |
ba9a7ec3-b811-45bb-960e-060f5b941930 | a-multidimensional-graph-fourier | 2305.07416 | null | https://arxiv.org/abs/2305.07416v1 | https://arxiv.org/pdf/2305.07416v1.pdf | A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction | This work introduces the multidimensional Graph Fourier Transformation Neural Network (GFTNN) for long-term trajectory predictions on highways. Similar to Graph Neural Networks (GNNs), the GFTNN is a novel network architecture that operates on graph structures. While several GNNs lack discriminative power due to subopt... | ['Wolfgang Utschick', 'Michael Botsch', 'Andreas Tollkühn', 'Marion Neumeier'] | 2023-05-12 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [ 1.74051464e-01 3.16380382e-01 -4.25173402e-01 -3.73249799e-01
-1.32571980e-01 -1.21080369e-01 9.49405193e-01 8.15969110e-02
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-2.35008031e-01 -1.26140702e+00 -9.86694694e-01 -6.55828834e-01
-4.89765763e-01 4.65521783e-01 2.75960684e-01 -3.59848499... | [6.476225852966309, 2.067124366760254] |
eaccd41d-9f39-45e2-9d2a-983d246a960c | implicit-function-theorem-estimates-on-the | 2205.12661 | null | https://arxiv.org/abs/2205.12661v3 | https://arxiv.org/pdf/2205.12661v3.pdf | Implicit Function Theorem: Estimates on the size of the domain | In this article, we present explicit estimates of the size of the domain on which the Implicit Function Theorem and the Inverse Function Theorem are valid. For maps that are twice continuously differentiable, these estimates depend upon the magnitude of the first-order derivatives evaluated at the point of interest, an... | ['Ashutosh Jindal', 'Ravi Banavar', 'Debasish Chatterjee'] | 2022-05-25 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [ 5.98866343e-02 5.31816065e-01 -3.01687330e-01 3.29868108e-01
-3.92190605e-01 -9.95763898e-01 6.57769889e-02 1.48499608e-01
-1.45357999e-03 1.07465792e+00 -6.28082514e-01 -4.44719106e-01
-6.51721835e-01 -3.66898984e-01 -8.52489531e-01 -9.09214973e-01
-4.92883205e-01 -1.05106518e-01 -1.70244008e-01 -6.90795004... | [5.447600364685059, 2.619988441467285] |
987caf40-4b60-47a9-a399-f85f805f211c | selective-supervised-contrastive-learning | 2203.04181 | null | https://arxiv.org/abs/2203.04181v1 | https://arxiv.org/pdf/2203.04181v1.pdf | Selective-Supervised Contrastive Learning with Noisy Labels | Deep networks have strong capacities of embedding data into latent representations and finishing following tasks. However, the capacities largely come from high-quality annotated labels, which are expensive to collect. Noisy labels are more affordable, but result in corrupted representations, leading to poor generaliza... | ['Tongliang Liu', 'Shiming Ge', 'Xiaobo Xia', 'Shikun Li'] | 2022-03-08 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Selective-Supervised_Contrastive_Learning_With_Noisy_Labels_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Selective-Supervised_Contrastive_Learning_With_Noisy_Labels_CVPR_2022_paper.pdf | cvpr-2022-1 | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 1.72016546e-01 -3.63816842e-02 -3.02042961e-01 -5.04866958e-01
-1.35800517e+00 -4.38669115e-01 3.76361758e-01 3.46304834e-01
-3.46967787e-01 7.56666183e-01 2.50149280e-01 2.16287434e-01
-2.75026560e-01 -8.01885605e-01 -6.18839622e-01 -1.01692235e+00
2.00974196e-01 3.49418074e-01 -2.01528937e-01 5.28699681... | [9.406689643859863, 3.842633008956909] |
42a0c403-90a6-4fdb-8b0d-d951f19db2d0 | g4-grounding-guided-goal-oriented-dialogues | null | null | https://aclanthology.org/2022.dialdoc-1.11 | https://aclanthology.org/2022.dialdoc-1.11.pdf | G4: Grounding-guided Goal-oriented Dialogues Generation with Multiple Documents | Goal-oriented dialogues generation grounded in multiple documents(MultiDoc2Dial) is a challenging and realistic task. Unlike previous works which treat document-grounded dialogue modeling as a machine reading comprehension task from single document, MultiDoc2Dial task faces challenges of both seeking information from m... | ['Yunbo Cao', 'Zhao Yan', 'Guanzhong Liu', 'Yiyang Du', 'Shiwei Zhang'] | null | null | null | null | dialdoc-acl-2022-5 | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 1.51814267e-01 9.45063889e-01 2.14427829e-01 -3.37408364e-01
-1.61088276e+00 -5.56259334e-01 1.22752190e+00 4.41796072e-02
-5.78049161e-02 1.41031921e+00 9.81378913e-01 -8.62498507e-02
3.03424239e-01 -7.71037519e-01 -7.58877993e-02 -3.17126870e-01
2.46787101e-01 1.23946083e+00 -1.10863730e-01 -1.03872836... | [12.51427173614502, 8.157846450805664] |
e8fd3a5b-d94d-4f56-937f-9aaa4e1f7988 | full-frame-scene-coordinate-regression-for | 1802.03237 | null | http://arxiv.org/abs/1802.03237v2 | http://arxiv.org/pdf/1802.03237v2.pdf | Full-Frame Scene Coordinate Regression for Image-Based Localization | Image-based localization, or camera relocalization, is a fundamental problem
in computer vision and robotics, and it refers to estimating camera pose from
an image. Recent state-of-the-art approaches use learning based methods, such
as Random Forests (RFs) and Convolutional Neural Networks (CNNs), to regress
for each p... | ['Juha Ylioinas', 'Xiaotian Li', 'Juho Kannala'] | 2018-02-09 | null | null | null | null | ['image-based-localization', 'camera-relocalization'] | ['computer-vision', 'computer-vision'] | [ 2.73196995e-01 -2.17314571e-01 -9.52986255e-02 -4.52427506e-01
-6.91618264e-01 -4.65418369e-01 4.11108643e-01 -2.36770228e-01
-6.26943290e-01 4.79930848e-01 5.87552264e-02 -2.60201871e-01
2.05469057e-01 -6.89534485e-01 -1.15898180e+00 -7.28326499e-01
4.62354302e-01 1.00857526e-01 3.34792465e-01 6.20370694... | [7.863763809204102, -2.225550413131714] |
e995ddcb-72b4-4161-a798-99c4e4cb8513 | exploring-intra-class-variation-factors-with | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Exploring_Intra-Class_Variation_Factors_With_Learnable_Cluster_Prompts_for_Semi-Supervised_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Exploring_Intra-Class_Variation_Factors_With_Learnable_Cluster_Prompts_for_Semi-Supervised_CVPR_2023_paper.pdf | Exploring Intra-Class Variation Factors With Learnable Cluster Prompts for Semi-Supervised Image Synthesis | Semi-supervised class-conditional image synthesis is typically performed by inferring and injecting class labels into a conditional Generative Adversarial Network (GAN). The supervision in the form of class identity may be inadequate to model classes with diverse visual appearances. In this paper, we propose a Lear... | ['Hau San Wong', 'Si Wu', 'Tianyi Chen', 'Xiaoyang Huo', 'Yunfei Zhang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['conditional-image-generation'] | ['computer-vision'] | [ 5.20251393e-01 3.79093528e-01 -2.16909081e-01 -5.17222703e-01
-8.16205978e-01 -9.54050720e-01 9.38733101e-01 -5.22409081e-01
8.34214017e-02 7.22678900e-01 5.02572954e-03 -7.40847588e-02
4.85888809e-01 -9.57723737e-01 -1.13680089e+00 -1.03012633e+00
4.99863535e-01 4.50942814e-01 -2.28094041e-01 4.38450128... | [11.599396705627441, -0.23775401711463928] |
24d14c17-7450-4c59-b62c-594c71d4b56e | rule-augmented-unsupervised-constituency | 2105.10193 | null | https://arxiv.org/abs/2105.10193v1 | https://arxiv.org/pdf/2105.10193v1.pdf | Rule Augmented Unsupervised Constituency Parsing | Recently, unsupervised parsing of syntactic trees has gained considerable attention. A prototypical approach to such unsupervised parsing employs reinforcement learning and auto-encoders. However, no mechanism ensures that the learnt model leverages the well-understood language grammar. We propose an approach that util... | ['Rishabh Iyer', 'Ganesh Ramakrishnan', 'Ayush Maheshwari', 'Anshul Nasery', 'Atul Sahay'] | 2021-05-21 | null | https://aclanthology.org/2021.findings-acl.436 | https://aclanthology.org/2021.findings-acl.436.pdf | findings-acl-2021-8 | ['constituency-parsing'] | ['natural-language-processing'] | [-1.43994940e-02 7.25981534e-01 -4.07650232e-01 -7.12913752e-01
-7.45779812e-01 -8.02127957e-01 3.44389260e-01 1.09068811e-01
-2.83757597e-01 6.88131571e-01 3.71293694e-01 -6.50843203e-01
1.00946926e-01 -1.00687718e+00 -8.88784170e-01 -3.81874532e-01
-1.33171873e-02 2.86220640e-01 1.73441634e-01 -2.85097957... | [10.38149642944336, 9.476972579956055] |
3ca47812-96cc-445e-abdb-90eb65e133ce | accurate-and-scalable-version-identification | 1910.12551 | null | https://arxiv.org/abs/1910.12551v2 | https://arxiv.org/pdf/1910.12551v2.pdf | Accurate and Scalable Version Identification Using Musically-Motivated Embeddings | The version identification (VI) task deals with the automatic detection of recordings that correspond to the same underlying musical piece. Despite many efforts, VI is still an open problem, with much room for improvement, specially with regard to combining accuracy and scalability. In this paper, we present MOVE, a mu... | ['Emilia Gómez', 'Joan Serrà', 'Furkan Yesiler'] | 2019-10-28 | null | null | null | null | ['cover-song-identification'] | ['music'] | [ 5.12678087e-01 -2.00021252e-01 -1.34996086e-01 1.64914921e-01
-1.13195658e+00 -1.06175876e+00 5.26317418e-01 3.58128011e-01
-3.69509846e-01 1.90569937e-01 7.28126705e-01 2.00803354e-01
-4.74891663e-01 -2.52919078e-01 -2.94587374e-01 -6.95156932e-01
-2.13395670e-01 2.65802205e-01 5.22549683e-03 -6.93196878... | [15.79300594329834, 5.316997528076172] |
f44c23ac-9e2e-427e-bdb6-e4ba7615f473 | towards-an-approach-based-on-knowledge-graph | null | null | https://ceur-ws.org/Vol-3320/paper12.pdf | https://ceur-ws.org/Vol-3320/paper12.pdf | Towards an Approach based on Knowledge Graph Refinement for Tabular Data to Knowledge Graph Matching | This paper presents our contribution to the Accuracy Track of Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab). This contribution consists of the proposition of an approach based on knowledge graph refinement for tabular data annotation. Internal methods were used to predict the links between... | ['Brice Foko', 'Azanzi Jiomekong'] | 2022-10-25 | null | null | null | semtab-iswc-2022-10 | ['graph-matching', 'column-type-annotation', 'cell-entity-annotation'] | ['graphs', 'natural-language-processing', 'natural-language-processing'] | [-7.14726567e-01 1.18965292e+00 -1.99944898e-01 -4.09163088e-02
-4.90948081e-01 -7.75421500e-01 6.33362234e-01 9.47526932e-01
3.10877291e-03 1.22548771e+00 3.60307842e-01 1.38142332e-02
-9.14793134e-01 -1.22290814e+00 -7.07344115e-01 3.10905516e-01
-2.01568067e-01 1.49080729e+00 8.51769686e-01 -7.71541536... | [9.286625862121582, 8.046356201171875] |
a6a08782-a4c5-4292-a817-465197a95cec | spatio-temporal-self-supervised-learning-for | 2212.04475 | null | https://arxiv.org/abs/2212.04475v1 | https://arxiv.org/pdf/2212.04475v1.pdf | Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction | Robust prediction of citywide traffic flows at different time periods plays a crucial role in intelligent transportation systems. While previous work has made great efforts to model spatio-temporal correlations, existing methods still suffer from two key limitations: i) Most models collectively predict all regions' flo... | ['Yu Zheng', 'Junbo Zhang', 'Zhenhe Wu', 'Boren Xu', 'Junjie Wu', 'Chao Huang', 'Jingyuan Wang', 'Jiahao Ji'] | 2022-12-07 | null | null | null | null | ['robust-traffic-prediction', 'spatio-temporal-forecasting'] | ['time-series', 'time-series'] | [-9.50992629e-02 -5.35702229e-01 -8.48461449e-01 -5.67623615e-01
-4.18675780e-01 -2.50114471e-01 7.44706154e-01 7.89657235e-02
2.02854238e-02 7.76438177e-01 4.16795820e-01 -6.78223848e-01
-3.63033712e-01 -1.19758594e+00 -6.64770782e-01 -4.38959897e-01
-4.00131375e-01 4.29557115e-01 5.88605046e-01 -2.58670539... | [6.482376575469971, 2.048733711242676] |
1ae15080-94e4-4283-8491-64a7afdbe0b7 | 3d-point-positional-encoding-for-multi-camera | 2211.14710 | null | https://arxiv.org/abs/2211.14710v2 | https://arxiv.org/pdf/2211.14710v2.pdf | 3DPPE: 3D Point Positional Encoding for Multi-Camera 3D Object Detection Transformers | Transformer-based methods have swept the benchmarks on 2D and 3D detection on images. Because tokenization before the attention mechanism drops the spatial information, positional encoding becomes critical for those methods. Recent works found that encodings based on samples of the 3D viewing rays can significantly imp... | ['Yifan Liu', 'Jiajun Deng', 'Fisher Yu', 'Changyong Shu'] | 2022-11-27 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [ 2.55608469e-01 -9.89159346e-02 -3.98933113e-01 -3.09647083e-01
-1.09983253e+00 -7.43213594e-01 6.19756341e-01 9.16193873e-02
-3.98106575e-01 6.94640949e-02 1.62735879e-01 -3.66976231e-01
5.21920502e-01 -9.85173583e-01 -1.25626111e+00 -5.58072329e-01
5.02112582e-02 5.33722103e-01 9.44639146e-01 1.50383338... | [7.853899002075195, -2.6714186668395996] |
651f84c2-1dea-4df9-b1cf-afa0348447ca | copymtl-copy-mechanism-for-joint-extraction | 1911.10438 | null | https://arxiv.org/abs/1911.10438v2 | https://arxiv.org/pdf/1911.10438v2.pdf | CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning | Joint extraction of entities and relations has received significant attention due to its potential of providing higher performance for both tasks. Among existing methods, CopyRE is effective and novel, which uses a sequence-to-sequence framework and copy mechanism to directly generate the relation triplets. However, it... | ['Daojian Zeng', 'Ranran Haoran Zhang', 'Qianying Liu'] | 2019-11-24 | null | null | null | null | ['entity-extraction'] | ['natural-language-processing'] | [-3.29130143e-02 2.81061172e-01 -2.15073690e-01 -2.89407104e-01
-1.12105644e+00 -4.78880107e-01 4.33079392e-01 1.06989928e-01
-4.06569541e-01 1.06963491e+00 1.20376684e-01 -3.64388525e-01
-5.14130481e-03 -8.47587168e-01 -9.03365552e-01 -3.75697196e-01
6.78678136e-03 7.41102278e-01 2.06793547e-01 -2.64126986... | [9.429718971252441, 8.76710319519043] |
7c0cf51a-917b-4b86-b10e-828f2cd29d8f | weakly-supervised-fine-grained-image-1 | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Weakly_Supervised_Fine-Grained_Image_Classification_via_Guassian_Mixture_Model_Oriented_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Weakly_Supervised_Fine-Grained_Image_Classification_via_Guassian_Mixture_Model_Oriented_CVPR_2020_paper.pdf | Weakly Supervised Fine-Grained Image Classification via Guassian Mixture Model Oriented Discriminative Learning | Existing weakly supervised fine-grained image recognition (WFGIR) methods usually pick out the discriminative regions from the high-level feature maps directly. We discover that due to the operation of stacking local receptive filed, Convolutional Neural Network causes the discriminative region diffusion in high-level ... | [' Zezhou Li', ' Jianjun Li', ' Haojie Li', ' Shuhui Yang', ' Shijie Wang', 'Zhihui Wang'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['fine-grained-image-recognition'] | ['computer-vision'] | [-4.11216795e-01 -4.73838240e-01 -4.16532308e-01 -3.63854468e-01
-9.43772972e-01 -5.71109235e-01 5.33688903e-01 -4.76903558e-01
-1.69525698e-01 3.84921521e-01 5.40994346e-01 2.20974430e-01
-2.87489146e-01 -6.98481023e-01 -7.13191330e-01 -8.10886741e-01
8.17755163e-02 2.66023189e-01 5.35331666e-01 -6.24837242... | [9.657719612121582, 2.0094854831695557] |
cd725a91-fd0a-42f7-b415-460d719658d7 | translating-radiology-reports-into-plain | 2303.09038 | null | https://arxiv.org/abs/2303.09038v3 | https://arxiv.org/pdf/2303.09038v3.pdf | Translating Radiology Reports into Plain Language using ChatGPT and GPT-4 with Prompt Learning: Promising Results, Limitations, and Potential | The large language model called ChatGPT has drawn extensively attention because of its human-like expression and reasoning abilities. In this study, we investigate the feasibility of using ChatGPT in experiments on using ChatGPT to translate radiology reports into plain language for patients and healthcare providers so... | ['Kyle J. Myers', 'Janardhana Ponnatapura', 'Michael E. Zapadka', 'Christopher T. Whitlow', 'Ge Wang', 'Chuang Niu', 'Josh Tan', 'Qing Lyu'] | 2023-03-16 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [-3.57379347e-01 7.66138673e-01 -4.16059822e-01 -4.89020556e-01
-1.45302343e+00 -4.46044564e-01 -1.67073030e-02 6.76070631e-01
-4.82606918e-01 8.70562732e-01 8.06147933e-01 -9.07536745e-01
-3.26075941e-01 -6.34386063e-01 -5.46689332e-01 -1.98114231e-01
1.53217822e-01 6.86421216e-01 4.53193858e-02 -1.81131735... | [8.749312400817871, 8.329926490783691] |
ef556f23-a592-40a5-9211-8442011857c2 | multifit-efficient-multi-lingual-language | 1909.04761 | null | https://arxiv.org/abs/1909.04761v2 | https://arxiv.org/pdf/1909.04761v2.pdf | MultiFiT: Efficient Multi-lingual Language Model Fine-tuning | Pretrained language models are promising particularly for low-resource languages as they only require unlabelled data. However, training existing models requires huge amounts of compute, while pretrained cross-lingual models often underperform on low-resource languages. We propose Multi-lingual language model Fine-Tuni... | ['Julian Martin Eisenschlos', 'Jeremy Howard', 'Sebastian Ruder', 'Piotr Czapla', 'Sylvain Gugger', 'Marcin Kardas'] | 2019-09-10 | multifit-efficient-multi-lingual-language-1 | https://aclanthology.org/D19-1572 | https://aclanthology.org/D19-1572.pdf | ijcnlp-2019-11 | ['cross-lingual-document-classification'] | ['natural-language-processing'] | [-4.83286470e-01 -4.57328439e-01 -7.97929108e-01 -5.44890583e-01
-1.55313301e+00 -8.21792364e-01 4.77632582e-01 -9.06383768e-02
-8.64388108e-01 7.81196535e-01 1.92976613e-02 -5.06148517e-01
5.33703446e-01 -5.50098419e-01 -7.60032356e-01 -9.19917375e-02
2.17828840e-01 8.59866023e-01 -8.54957104e-02 -2.76223719... | [10.958582878112793, 9.908451080322266] |
da93894b-14c0-4165-9a34-be2fa28d1948 | rethinking-log-odds-linear-probability | 2211.06360 | null | https://arxiv.org/abs/2211.06360v1 | https://arxiv.org/pdf/2211.06360v1.pdf | Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning | We introduce a family of interpretable machine learning models, with two broad additions: Linearised Additive Models (LAMs) which replace the ubiquitous logistic link function in General Additive Models (GAMs); and SubscaleHedge, an expert advice algorithm for combining base models trained on subsets of features called... | ['Daniele Magazzeni', 'Freddy Lecue', 'Nicolas Marchesotti', 'Danial Dervovic'] | 2022-11-11 | null | null | null | null | ['additive-models', 'interpretable-machine-learning'] | ['methodology', 'methodology'] | [ 1.04618430e-01 6.77583635e-01 -8.35504457e-02 -6.29794598e-01
-6.09539032e-01 -6.32534981e-01 7.56155789e-01 1.68061882e-01
-1.67508870e-01 8.22989285e-01 -1.20714299e-01 -8.89202893e-01
-6.89232707e-01 -7.73528039e-01 -8.95039141e-01 -4.14689600e-01
-1.73461884e-01 4.44330513e-01 -9.17586982e-02 4.84798290... | [8.624741554260254, 5.446321964263916] |
e8689a19-1483-4ff5-939f-a50cd197f268 | the-2021-nist-speaker-recognition-evaluation | 2204.10242 | null | https://arxiv.org/abs/2204.10242v1 | https://arxiv.org/pdf/2204.10242v1.pdf | The 2021 NIST Speaker Recognition Evaluation | The 2021 Speaker Recognition Evaluation (SRE21) was the latest cycle of the ongoing evaluation series conducted by the U.S. National Institute of Standards and Technology (NIST) since 1996. It was the second large-scale multimodal speaker/person recognition evaluation organized by NIST (the first one being SRE19). Simi... | ['Douglas Reynolds', 'Lisa Mason', 'Elliot Singer', 'Craig Greenberg', 'Seyed Omid Sadjadi'] | 2022-04-21 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 1.67265624e-01 -2.15634599e-01 3.75612192e-02 -6.81829333e-01
-1.48680639e+00 -7.04264343e-01 7.84452677e-01 -3.06684285e-01
-5.31799018e-01 4.73912597e-01 3.91312689e-01 -3.57166529e-01
1.59468025e-01 -1.03869967e-01 -4.69044387e-01 -6.05282009e-01
-5.41810878e-02 4.31278199e-01 -2.42064089e-01 -1.46136612... | [14.285845756530762, 6.119856834411621] |
74219203-403c-4e37-ac4b-9e8f0d337d4d | a-fully-first-order-method-for-stochastic | 2301.10945 | null | https://arxiv.org/abs/2301.10945v1 | https://arxiv.org/pdf/2301.10945v1.pdf | A Fully First-Order Method for Stochastic Bilevel Optimization | We consider stochastic unconstrained bilevel optimization problems when only the first-order gradient oracles are available. While numerous optimization methods have been proposed for tackling bilevel problems, existing methods either tend to require possibly expensive calculations regarding Hessians of lower-level obj... | ['Robert Nowak', 'Stephen Wright', 'Dohyun Kwon', 'Jeongyeol Kwon'] | 2023-01-26 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-2.13184599e-02 4.43891808e-02 4.22571376e-02 -1.88874483e-01
-1.16839349e+00 -5.87880611e-01 3.45588736e-02 3.04675907e-01
-8.26747358e-01 9.15216744e-01 -3.96070272e-01 -5.78915894e-01
-8.78209949e-01 -6.25453234e-01 -9.37523961e-01 -9.77695048e-01
-3.14100832e-01 4.46566463e-01 -7.48636248e-03 -6.94716498... | [6.5250701904296875, 4.529043674468994] |
1ce30cb9-1ad7-4451-8eaa-070c9d9ae2ab | a-self-supervised-learning-based-approach-to | 2302.13457 | null | https://arxiv.org/abs/2302.13457v2 | https://arxiv.org/pdf/2302.13457v2.pdf | A Self-Supervised Learning-based Approach to Clustering Multivariate Time-Series Data with Missing Values (SLAC-Time): An Application to TBI Phenotyping | Self-supervised learning approaches provide a promising direction for clustering multivariate time-series data. However, real-world time-series data often include missing values, and the existing approaches require imputing missing values before clustering, which may cause extensive computations and noise and result in... | ['Vignesh Subbian', 'Chandan K. Reddy', 'Sindhu Tipirneni', 'Amin Nayebi', 'Brandon Foreman', 'Hamid Ghaderi'] | 2023-02-27 | null | null | null | null | ['clinical-knowledge', 'clustering-multivariate-time-series'] | ['miscellaneous', 'time-series'] | [ 2.74210684e-02 -5.07717967e-01 -1.99155480e-01 -5.48877597e-01
-9.33528960e-01 -3.42246681e-01 7.34841730e-03 5.24385989e-01
-3.93249840e-01 6.43676460e-01 3.81557345e-01 -3.37748498e-01
-7.66712725e-01 -2.29092285e-01 -2.83091992e-01 -1.23449087e+00
-6.22184396e-01 8.04755211e-01 -2.30793640e-01 2.67912418... | [7.887799263000488, 5.897399425506592] |
47446515-194c-447c-8382-483197e339d4 | domain-adaptation-for-visual-applications-a | 1702.05374 | null | http://arxiv.org/abs/1702.05374v2 | http://arxiv.org/pdf/1702.05374v2.pdf | Domain Adaptation for Visual Applications: A Comprehensive Survey | The aim of this paper is to give an overview of domain adaptation and
transfer learning with a specific view on visual applications. After a general
motivation, we first position domain adaptation in the larger transfer learning
problem. Second, we try to address and analyze briefly the state-of-the-art
methods for dif... | ['Gabriela Csurka'] | 2017-02-17 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 2.14477867e-01 6.47574663e-02 -4.41738814e-01 -5.37178457e-01
-4.89018172e-01 -6.61276877e-01 8.20506811e-01 -3.90199050e-02
-4.71838742e-01 7.09249794e-01 -3.95710431e-02 -9.25386325e-02
-8.34249146e-03 -7.33117104e-01 -5.63016295e-01 -6.48693621e-01
1.52276817e-03 5.34244776e-01 3.15699786e-01 -2.46914372... | [9.952799797058105, 2.4618685245513916] |
b48678b3-0d04-43b2-9f04-e6db1e4f61a8 | characterization-of-neighborhood-behaviours | 1603.06459 | null | http://arxiv.org/abs/1603.06459v1 | http://arxiv.org/pdf/1603.06459v1.pdf | Characterization of neighborhood behaviours in a multi-neighborhood local search algorithm | We consider a multi-neighborhood local search algorithm with a large number
of possible neighborhoods. Each neighborhood is accompanied by a weight value
which represents the probability of being chosen at each iteration. These
weights are fixed before the algorithm runs, and are considered as parameters
of the algorit... | ['Nguyen Thi Thanh Dang', 'Patrick De Causmaecker'] | 2016-03-12 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [ 1.53119579e-01 -2.87935287e-01 -2.98492640e-01 -8.90404284e-02
-5.04345715e-01 -9.82129633e-01 3.41195583e-01 5.44437349e-01
-3.19826156e-01 6.07691109e-01 -8.37348551e-02 -4.24443007e-01
-7.13678002e-01 -1.11646175e+00 -4.65249717e-01 -1.01086974e+00
3.39520499e-02 7.81997561e-01 5.30150115e-01 -3.05099636... | [7.15424108505249, 4.378809452056885] |
7805e6cc-f321-4f34-be65-cc77ae89c836 | brainclip-bridging-brain-and-visual | 2302.12971 | null | https://arxiv.org/abs/2302.12971v3 | https://arxiv.org/pdf/2302.12971v3.pdf | BrainCLIP: Bridging Brain and Visual-Linguistic Representation Via CLIP for Generic Natural Visual Stimulus Decoding | Due to the lack of paired samples and the low signal-to-noise ratio of functional MRI (fMRI) signals, reconstructing perceived natural images or decoding their semantic contents from fMRI data are challenging tasks. In this work, we propose, for the first time, a task-agnostic fMRI-based brain decoding model, BrainCLIP... | ['Nanning Zheng', 'Guibo Zhu', 'Wei Zhou', 'Yongqiang Ma', 'Yulong Liu'] | 2023-02-25 | null | null | null | null | ['image-reconstruction', 'brain-decoding', 'brain-decoding', 'text-matching'] | ['computer-vision', 'medical', 'miscellaneous', 'natural-language-processing'] | [ 5.59907913e-01 3.84194553e-02 -1.20010629e-01 -6.32790744e-01
-9.46528971e-01 -3.46984833e-01 7.87471294e-01 -2.09451482e-01
-1.62694633e-01 3.88163924e-01 5.41708231e-01 -1.35165183e-02
6.60659298e-02 -4.18880612e-01 -9.93743539e-01 -4.25631344e-01
1.73540398e-01 2.91752130e-01 -5.40343821e-02 4.32105437... | [10.777883529663086, 2.467440366744995] |
2e964072-e9dd-44f8-b797-f7880fbc2450 | memetic-eda-based-approaches-to-comprehensive | 1906.07900 | null | https://arxiv.org/abs/1906.07900v1 | https://arxiv.org/pdf/1906.07900v1.pdf | Memetic EDA-Based Approaches to Comprehensive Quality-Aware Automated Semantic Web Service Composition | Comprehensive quality-aware automated semantic web service composition is an NP-hard problem, where service composition workflows are unknown, and comprehensive quality, i.e., Quality of services (QoS) and Quality of semantic matchmaking (QoSM) are simultaneously optimized. The objective of this problem is to find a so... | ['Sven Hartmann', 'Gang Chen', 'Hui Ma', 'Chen Wang'] | 2019-06-19 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 1.53623521e-01 -3.65787745e-01 9.42179263e-02 -3.90304148e-01
-6.23156190e-01 -4.91669863e-01 2.60597497e-01 9.38874856e-03
-2.27800608e-01 6.29637599e-01 -7.47211054e-02 -9.40943658e-02
-8.44082355e-01 -8.37707579e-01 -5.24841666e-01 -7.93581247e-01
-2.10261047e-01 7.28691578e-01 4.93720770e-01 -4.97185588... | [8.573087692260742, 6.961820125579834] |
7d438ae5-0c9c-47f3-a3bd-8a7e92843cf5 | snap-self-supervised-neural-maps-for-visual | 2306.05407 | null | https://arxiv.org/abs/2306.05407v1 | https://arxiv.org/pdf/2306.05407v1.pdf | SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding | Semantic 2D maps are commonly used by humans and machines for navigation purposes, whether it's walking or driving. However, these maps have limitations: they lack detail, often contain inaccuracies, and are difficult to create and maintain, especially in an automated fashion. Can we use raw imagery to automatically cr... | ['Simon Lynen', 'Jan Hosang', 'Marc Pollefeys', 'Eduard Trulls', 'Paul-Edouard Sarlin'] | 2023-06-08 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [ 1.57934695e-01 2.92636901e-01 9.59431380e-02 -7.13625908e-01
-7.38348424e-01 -9.77665901e-01 5.78709900e-01 2.98157990e-01
-5.00786424e-01 5.24126649e-01 1.49110585e-01 -2.37500235e-01
5.26066422e-02 -1.06113338e+00 -1.06757796e+00 8.61463894e-04
-2.65003163e-02 9.02770996e-01 6.54191017e-01 -4.63739038... | [7.720203399658203, -2.0476114749908447] |
30811b6b-67bb-41a6-a976-44451fe986db | personalized-entity-resolution-with-dynamic-1 | null | null | https://aclanthology.org/2021.ecnlp-1.6 | https://aclanthology.org/2021.ecnlp-1.6.pdf | Personalized Entity Resolution with Dynamic Heterogeneous KnowledgeGraph Representations | The growing popularity of Virtual Assistants poses new challenges for Entity Resolution, the task of linking mentions in text to their referent entities in a knowledge base. Specifically, in the shopping domain, customers tend to mention the entities implicitly (e.g., “organic milk”) rather than use the entity names ex... | ['Premkumar Natarajan', 'Yang Liu', 'Heng Ji', 'Yue Liu', 'Tong Wang', 'Jiangning Chen', 'Han Wang', 'Ying Lin'] | null | null | null | null | acl-ecnlp-2021-8 | ['entity-resolution'] | ['natural-language-processing'] | [-3.48630905e-01 1.89518303e-01 -7.52082705e-01 -6.13721669e-01
-4.22311246e-01 -6.62389398e-01 2.97551244e-01 7.32584715e-01
-4.15504605e-01 5.62497437e-01 4.47005242e-01 4.36480530e-02
-1.54363409e-01 -1.17958176e+00 -7.13390768e-01 -1.92058235e-02
7.03412816e-02 9.16576922e-01 1.85394678e-02 -3.50847363... | [10.068492889404297, 6.163257122039795] |
80042760-c24c-4e03-864c-54fd5b294fe7 | unveiling-transformers-with-lego-a-synthetic | 2206.04301 | null | https://arxiv.org/abs/2206.04301v3 | https://arxiv.org/pdf/2206.04301v3.pdf | Unveiling Transformers with LEGO: a synthetic reasoning task | We propose a synthetic reasoning task, LEGO (Learning Equality and Group Operations), that encapsulates the problem of following a chain of reasoning, and we study how the Transformer architectures learn this task. We pay special attention to data effects such as pretraining (on seemingly unrelated NLP tasks) and datas... | ['Tal Wagner', 'Suriya Gunasekar', 'Ronen Eldan', 'Sébastien Bubeck', 'Arturs Backurs', 'Yi Zhang'] | 2022-06-09 | null | null | null | null | ['learning-to-execute'] | ['computer-code'] | [ 1.80820882e-01 6.93687260e-01 9.86850560e-02 -2.78461546e-01
-4.28122282e-01 -8.34060311e-01 6.52992964e-01 1.50863037e-01
-3.60061526e-01 4.19397086e-01 4.57462609e-01 -6.31916761e-01
-3.61945540e-01 -5.87146223e-01 -1.08550549e+00 -5.69262981e-01
7.18525276e-02 7.08627343e-01 3.58689278e-01 -4.69826132... | [9.695064544677734, 7.4947590827941895] |
69470ecc-97c6-4edf-8e7c-b71b4c62340f | evaluating-coherence-in-dialogue-systems | 1904.03371 | null | https://arxiv.org/abs/1904.03371v2 | https://arxiv.org/pdf/1904.03371v2.pdf | Evaluating Coherence in Dialogue Systems using Entailment | Evaluating open-domain dialogue systems is difficult due to the diversity of possible correct answers. Automatic metrics such as BLEU correlate weakly with human annotations, resulting in a significant bias across different models and datasets. Some researchers resort to human judgment experimentation for assessing res... | ['Osmar Zaiane', 'Nouha Dziri', 'Ehsan Kamalloo', 'Kory W. Mathewson'] | 2019-04-06 | evaluating-coherence-in-dialogue-systems-2 | https://aclanthology.org/N19-1381 | https://aclanthology.org/N19-1381.pdf | naacl-2019-6 | ['dialogue-evaluation', 'open-domain-dialog'] | ['natural-language-processing', 'natural-language-processing'] | [-9.94235054e-02 3.71767253e-01 5.49756251e-02 -6.92432642e-01
-1.12974679e+00 -9.77677941e-01 8.31793070e-01 5.01046896e-01
-6.30243480e-01 9.86669898e-01 8.19732487e-01 -2.69189537e-01
9.71767008e-02 -6.86619043e-01 7.58197382e-02 -2.40971267e-01
3.62695307e-01 7.01812565e-01 2.86401063e-01 -4.17661160... | [12.664900779724121, 8.2196683883667] |
7ceff17c-9228-4b8c-a918-82be53283ded | learning-debiased-representations-via | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Learning_Debiased_Representations_via_Conditional_Attribute_Interpolation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Learning_Debiased_Representations_via_Conditional_Attribute_Interpolation_CVPR_2023_paper.pdf | Learning Debiased Representations via Conditional Attribute Interpolation | An image is usually described by more than one attribute like "shape" and "color". When a dataset is biased, i.e., most samples have attributes spuriously correlated with the target label, a Deep Neural Network (DNN) is prone to make predictions by the "unintended" attribute, especially if it is easier to learn. To... | ['Han-Jia Ye', 'De-Chuan Zhan', 'Qi-Wei Wang', 'Yi-Kai Zhang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 2.47213423e-01 1.70287564e-02 -2.49586180e-01 -9.59578335e-01
-4.51417804e-01 -4.58013564e-01 4.30670321e-01 -1.05567239e-01
-8.33284706e-02 7.95182586e-01 1.70427782e-03 6.85373843e-02
-1.30873799e-01 -7.97125280e-01 -8.96171093e-01 -1.06799996e+00
1.79656167e-02 6.07260287e-01 1.48883676e-02 1.13232344... | [9.551665306091309, 3.269925355911255] |
45351456-10e5-4635-9f58-526684a5fa3c | sub-graph-learning-for-spatiotemporal | 2211.09740 | null | https://arxiv.org/abs/2211.09740v1 | https://arxiv.org/pdf/2211.09740v1.pdf | Sub-Graph Learning for Spatiotemporal Forecasting via Knowledge Distillation | One of the challenges in studying the interactions in large graphs is to learn their diverse pattern and various interaction types. Hence, considering only one distribution and model to study all nodes and ignoring their diversity and local features in their neighborhoods, might severely affect the overall performance.... | ['Yingxue Zhang', 'Mehrtash Mehrabi'] | 2022-11-17 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [-3.11407119e-01 -5.49249575e-02 -2.17527479e-01 -9.69047025e-02
6.67930115e-03 -6.80003941e-01 6.75559640e-01 4.16755795e-01
7.66240656e-02 6.94482505e-01 1.40241116e-01 -2.03627706e-01
-5.98854780e-01 -1.15420401e+00 -8.40857148e-01 -1.00825572e+00
-6.39838278e-01 4.69492972e-01 6.84987068e-01 -1.51580617... | [7.305108547210693, 6.103133678436279] |
143ea740-8f2a-4e1e-945f-f02403df0fe6 | metafusion-infrared-and-visible-image-fusion | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhao_MetaFusion_Infrared_and_Visible_Image_Fusion_via_Meta-Feature_Embedding_From_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhao_MetaFusion_Infrared_and_Visible_Image_Fusion_via_Meta-Feature_Embedding_From_CVPR_2023_paper.pdf | MetaFusion: Infrared and Visible Image Fusion via Meta-Feature Embedding From Object Detection | Fusing infrared and visible images can provide more texture details for subsequent object detection task. Conversely, detection task furnishes object semantic information to improve the infrared and visible image fusion. Thus, a joint fusion and detection learning to use their mutual promotion is attracting more at... | ['Huchuan Lu', 'You He', 'Fan Zhao', 'Shigeng Xie', 'Wenda Zhao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 1.49115101e-02 -3.13575089e-01 -2.84295499e-01 -3.26511174e-01
-6.82883680e-01 -2.91413628e-02 7.02254832e-01 -2.74611443e-01
-2.47396290e-01 2.42049485e-01 2.21595213e-01 1.24598918e-02
2.97845639e-02 -1.05891025e+00 -4.56407219e-01 -8.82916629e-01
4.81958091e-01 -4.91292328e-01 1.77017838e-01 -2.42913589... | [10.286189079284668, -1.6819655895233154] |
b2ae4711-7823-4fe6-be29-d60bd6f63433 | low-light-image-and-video-enhancement-a | 2212.10772 | null | https://arxiv.org/abs/2212.10772v4 | https://arxiv.org/pdf/2212.10772v4.pdf | Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond | This paper presents a comprehensive survey of low-light image and video enhancement. We begin with the challenging mixed over-/under-exposed images, which are under-performed by existing methods. To this end, we propose two variants of the SICE dataset named SICE\_Grad and SICE\_Mix. Next, we introduce Night Wenzhou, a... | ['Gaurav Gupta', 'Changjie Lu', 'Jinqian Pan', 'Yiling Ma', 'Shen Zheng'] | 2022-12-21 | null | null | null | null | ['low-light-image-enhancement', 'video-enhancement'] | ['computer-vision', 'computer-vision'] | [ 4.37970847e-01 -8.87727797e-01 1.43629059e-01 -1.10733844e-01
-7.82761574e-01 -5.48131168e-01 4.45453554e-01 -5.00986040e-01
-1.72709420e-01 7.52375364e-01 1.69032127e-01 -1.32812887e-01
-5.40309399e-02 -7.12067902e-01 -5.45291185e-01 -9.97466922e-01
4.15683649e-02 -8.62788737e-01 1.39567494e-01 -3.29862863... | [10.7743558883667, -2.2242298126220703] |
0f83d7b8-de1e-4562-adf5-3b5bd210580f | false-sense-of-security-leveraging-xai-to | 2307.04358 | null | https://arxiv.org/abs/2307.04358v1 | https://arxiv.org/pdf/2307.04358v1.pdf | False Sense of Security: Leveraging XAI to Analyze the Reasoning and True Performance of Context-less DGA Classifiers | The problem of revealing botnet activity through Domain Generation Algorithm (DGA) detection seems to be solved, considering that available deep learning classifiers achieve accuracies of over 99.9%. However, these classifiers provide a false sense of security as they are heavily biased and allow for trivial detection ... | ['Ulrike Meyer', 'Arthur Drichel'] | 2023-07-10 | null | null | null | null | ['explainable-artificial-intelligence', 'decision-making'] | ['computer-vision', 'reasoning'] | [ 1.22166857e-01 5.41340649e-01 -1.48809224e-01 -4.55261804e-02
1.22275941e-01 -7.00595796e-01 8.25229824e-01 5.38896695e-02
-2.73277402e-01 6.67037010e-01 -1.86435759e-01 -9.44330096e-01
7.77301118e-02 -9.94264841e-01 -4.59039450e-01 -5.11530817e-01
1.58742517e-02 2.19943315e-01 5.72552323e-01 -8.64812359... | [5.310491561889648, 7.228697776794434] |
ae65831c-da00-4b68-b1dc-12a15257dbc5 | action-concept-grounding-network-for | null | null | https://openreview.net/forum?id=4_57x7xhymn | https://openreview.net/pdf?id=4_57x7xhymn | Action Concept Grounding Network for Semantically-Consistent Video Generation | Recent works in self-supervised video prediction have mainly focused on passive forecasting and low-level action-conditional prediction, which sidesteps the problem of semantic learning. We introduce the task of semantic action-conditional video prediction, which can be regarded as an inverse problem of action recognit... | ['Animesh Garg', 'Wenxin Chen', 'Wei Yu'] | 2020-09-28 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 3.89779717e-01 9.65589285e-02 -4.95808005e-01 -3.34553838e-01
-4.22515064e-01 -2.56889462e-01 5.89258850e-01 -3.29625487e-01
-9.56095532e-02 5.80019951e-01 4.76646692e-01 -1.16696060e-01
2.31477439e-01 -5.58955848e-01 -1.02589893e+00 -4.26277846e-01
1.24128591e-02 2.49143511e-01 5.64642787e-01 7.12336972... | [8.600412368774414, 0.5971205234527588] |
fb319644-b748-4165-affd-2c593e927f66 | coordinated-cyber-attack-detection-model-of | 2103.00133 | null | https://arxiv.org/abs/2103.00133v1 | https://arxiv.org/pdf/2103.00133v1.pdf | Coordinated Cyber-Attack Detection Model of Cyber-Physical Power System Based on the Operating State Data Link | Existing coordinated cyber-attack detection methods have low detection accuracy and efficiency and poor generalization ability due to difficulties dealing with unbalanced attack data samples, high data dimensionality, and noisy data sets. This paper proposes a model for cyber and physical data fusion using a data link ... | ['Yang Li', 'Zhenming Zhang', 'Yunchang Dong', 'Xiaoyong Bo', 'Zhaoyang Qu', 'Pengcheng Xu', 'Lei Wang'] | 2021-02-27 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [-1.74133643e-03 -6.59957051e-01 -3.11612070e-01 -1.28182739e-01
-4.26366299e-01 -4.19423640e-01 1.56988487e-01 6.50291085e-01
4.91249524e-02 5.28925538e-01 -1.88447252e-01 -4.35293496e-01
-4.33704615e-01 -9.63016629e-01 2.51727611e-01 -9.35924768e-01
-2.41843298e-01 1.39579907e-01 3.68219048e-01 -9.12429020... | [6.157215118408203, 2.5664052963256836] |
fd197b49-b5f7-4d43-b1be-4b2e16da4c01 | longitudinal-performance-of-iris-recognition | 2303.12720 | null | https://arxiv.org/abs/2303.12720v1 | https://arxiv.org/pdf/2303.12720v1.pdf | Longitudinal Performance of Iris Recognition in Children: Time Intervals up to Six years | The temporal stability of iris recognition performance is core to its success as a biometric modality. With the expanding horizon of applications for children, gaps in the knowledge base on the temporal stability of iris recognition performance in children have impacted decision-making during applications at the global... | ['Stephanie Schuckers', 'Michael Schuckers', 'Masudul H Imtiaz', 'Laura Holsopple', 'Naveen G Venkataswamy', 'Priyanka Das'] | 2023-03-10 | null | null | null | null | ['iris-recognition'] | ['computer-vision'] | [ 2.1563347e-01 -2.3512866e-01 -6.0090286e-01 -4.1510874e-01
-2.1302201e-01 -3.7097827e-01 2.2846647e-01 6.2738466e-01
-4.4306427e-01 4.2136005e-01 4.9929547e-01 -7.8978467e-01
-5.4897106e-01 -4.9106306e-01 -5.2191126e-01 -4.5884138e-01
-2.1902658e-01 6.3571453e-02 -3.0574816e-01 3.1568873e-01
3.7158775e-01... | [3.748286247253418, -3.6258020401000977] |
ea0b0874-9889-444f-a283-efce6d2c9ded | molecular-docking-and-binding-mode-analysis | 2004.06447 | null | http://arxiv.org/abs/2004.06447v1 | http://arxiv.org/pdf/2004.06447v1.pdf | Molecular docking and binding mode analysis of selected FDA approved drugs against COVID-19 selected key protein targets: An effort towards drug repurposing to identify the combination therapy to combat COVID-19 | The emergence of COVID-19 has severely compromised the arsenal of antiviral
and antibiotic drugs. Drug discovery is a multistep process with a high failure
rate, high cost and it takes approximately 10-12 years for the development of
new molecules into the clinical candidate. On the other side, drug repurposing
also ca... | [] | 2020-04-14 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 1.93461210e-01 -6.03354871e-01 -1.10868707e-01 7.68125728e-02
1.09552278e-03 -1.00880504e+00 1.98085561e-01 4.64286834e-01
-3.20148498e-01 1.23223007e+00 -1.69246960e-02 -6.98642135e-01
2.95836311e-02 -3.89958918e-01 -1.94822341e-01 -8.12073946e-01
-2.39381716e-01 7.21535802e-01 -4.92250286e-02 -1.78513736... | [4.695308685302734, 5.105510711669922] |
1dedb041-9e81-485f-8d94-647153ffd13a | a-discourse-aware-attention-model-for | 1804.05685 | null | http://arxiv.org/abs/1804.05685v2 | http://arxiv.org/pdf/1804.05685v2.pdf | A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents | Neural abstractive summarization models have led to promising results in
summarizing relatively short documents. We propose the first model for
abstractive summarization of single, longer-form documents (e.g., research
papers). Our approach consists of a new hierarchical encoder that models the
discourse structure of a... | ['Walter Chang', 'Seokhwan Kim', 'Nazli Goharian', 'Franck Dernoncourt', 'Trung Bui', 'Doo Soon Kim', 'Arman Cohan'] | 2018-04-16 | a-discourse-aware-attention-model-for-1 | https://aclanthology.org/N18-2097 | https://aclanthology.org/N18-2097.pdf | naacl-2018-6 | ['unsupervised-extractive-summarization'] | ['natural-language-processing'] | [ 3.66248190e-01 8.41426015e-01 -4.33018744e-01 -2.96779960e-01
-1.17490232e+00 -4.51598883e-01 8.68189037e-01 6.25048995e-01
-1.61933526e-01 1.23618579e+00 1.12853909e+00 -2.71019340e-01
1.68142229e-01 -4.25638229e-01 -8.67650032e-01 -1.23980559e-01
1.77152410e-01 4.88497406e-01 1.37462825e-01 3.30456980... | [12.575874328613281, 9.555704116821289] |
39c5cb88-264f-4b48-a767-4665da7fc4c0 | provable-multi-objective-reinforcement | 2011.10134 | null | https://arxiv.org/abs/2011.10134v2 | https://arxiv.org/pdf/2011.10134v2.pdf | Provable Multi-Objective Reinforcement Learning with Generative Models | Multi-objective reinforcement learning (MORL) is an extension of ordinary, single-objective reinforcement learning (RL) that is applicable to many real-world tasks where multiple objectives exist without known relative costs. We study the problem of single policy MORL, which learns an optimal policy given the preferenc... | ['Quanquan Gu', 'Jiahao Chen', 'Dongruo Zhou'] | 2020-11-19 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-5.74670397e-02 2.91509740e-02 -8.00356209e-01 -1.73073009e-01
-1.26787138e+00 -5.26681721e-01 9.58207995e-02 5.73139414e-02
-9.58028853e-01 1.47416687e+00 -2.75910765e-01 -3.78375530e-01
-7.66967773e-01 -4.37225342e-01 -7.45932758e-01 -6.74410105e-01
-3.77851903e-01 8.81067753e-01 -7.20850602e-02 1.05149969... | [4.217244625091553, 2.399397373199463] |
8e4dfb2f-70b6-47e6-a0fe-541971fec5e1 | 181201711 | 1812.01711 | null | http://arxiv.org/abs/1812.01711v1 | http://arxiv.org/pdf/1812.01711v1.pdf | A Graph-CNN for 3D Point Cloud Classification | Graph convolutional neural networks (Graph-CNNs) extend traditional CNNs to
handle data that is supported on a graph. Major challenges when working with
data on graphs are that the support set (the vertices of the graph) do not
typically have a natural ordering, and in general, the topology of the graph is
not regular ... | ['Yingxue Zhang', 'Michael Rabbat'] | 2018-11-28 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [-3.72254819e-01 2.78010756e-01 -4.46012914e-02 -2.46889800e-01
3.15568805e-01 -4.98843908e-01 4.42820489e-01 4.23139066e-01
-1.00756630e-01 1.81956127e-01 -2.56502390e-01 -4.51628715e-01
8.21289346e-02 -1.27288556e+00 -1.07435048e+00 -4.01439607e-01
-4.15353537e-01 5.37020445e-01 2.32021853e-01 -2.17135623... | [7.952322483062744, -3.691884756088257] |
7e2b3027-d047-45ab-9363-ee2bcbfe1728 | estimating-soft-labels-for-out-of-domain | 2211.05561 | null | https://arxiv.org/abs/2211.05561v1 | https://arxiv.org/pdf/2211.05561v1.pdf | Estimating Soft Labels for Out-of-Domain Intent Detection | Out-of-Domain (OOD) intent detection is important for practical dialog systems. To alleviate the issue of lacking OOD training samples, some works propose synthesizing pseudo OOD samples and directly assigning one-hot OOD labels to these pseudo samples. However, these one-hot labels introduce noises to the training pro... | ['Yongbin Li', 'Luo Si', 'Fei Huang', 'Jian Sun', 'Yinhe Zheng', 'Hao Lang'] | 2022-11-10 | null | null | null | null | ['intent-detection'] | ['natural-language-processing'] | [ 1.19590849e-01 5.26410758e-01 -5.96865356e-01 -8.96487415e-01
-4.20049906e-01 -5.12721062e-01 8.75047863e-01 -7.20385090e-03
3.84204462e-03 3.40613246e-01 5.42282701e-01 3.75160836e-02
4.12319183e-01 -4.04683203e-01 -2.19780445e-01 -1.80723250e-01
3.99395823e-01 7.24643528e-01 3.49002212e-01 -2.37004217... | [12.406412124633789, 7.543406009674072] |
8f6ec9ab-6251-4b1c-86e1-56c8ce481a23 | convolutional-neural-network-based-partial | 2206.14350 | null | https://arxiv.org/abs/2206.14350v1 | https://arxiv.org/pdf/2206.14350v1.pdf | Convolutional Neural Network Based Partial Face Detection | Due to the massive explanation of artificial intelligence, machine learning technology is being used in various areas of our day-to-day life. In the world, there are a lot of scenarios where a simple crime can be prevented before it may even happen or find the person responsible for it. A face is one distinctive featur... | ['Md. Tarek Habib', 'Md. Sadekur Rahman', 'Taminul Islam', 'A. B. M. Raihanur Rashid', 'Tanzim Ahmed', 'Md. Towfiqul Islam'] | 2022-06-29 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [-1.43840447e-01 -3.66775006e-01 1.46418095e-01 -3.36227149e-01
1.27368748e-01 -3.59587133e-01 2.64664263e-01 -1.80608347e-01
-4.56373632e-01 6.52976573e-01 -2.00612277e-01 -1.74028784e-01
7.71163180e-02 -9.24182117e-01 -3.46901417e-01 -5.85544169e-01
2.39585921e-01 3.63503665e-01 1.56987220e-01 -3.88587266... | [13.317132949829102, 0.8842295408248901] |
0f173c1c-d06d-4ed4-868d-2bd37623127a | joint-graph-learning-from-gaussian | 2212.01816 | null | https://arxiv.org/abs/2212.01816v1 | https://arxiv.org/pdf/2212.01816v1.pdf | Joint graph learning from Gaussian observations in the presence of hidden nodes | Graph learning problems are typically approached by focusing on learning the topology of a single graph when signals from all nodes are available. However, many contemporary setups involve multiple related networks and, moreover, it is often the case that only a subset of nodes is observed while the rest remain hidden.... | ['Antonio G. Marques', 'Santiago Segarra', 'Andrei Buciulea', 'Madeline Navarro', 'Samuel Rey'] | 2022-12-04 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 2.69574493e-01 4.86972213e-01 -2.18004629e-01 -8.29444006e-02
-5.50495088e-01 -4.30629998e-01 6.43158376e-01 2.45118901e-01
-3.41031514e-02 6.92251027e-01 3.83433923e-02 -6.22809641e-02
-1.92988530e-01 -7.12198138e-01 -9.01972771e-01 -9.03782308e-01
-4.12777483e-01 5.55597544e-01 -7.87368715e-02 4.62648243... | [7.05478572845459, 5.1385698318481445] |
3be13f1f-8fbb-4ad7-9ead-e36cc987de37 | deep-attention-spatio-temporal-point | 2002.07281 | null | https://arxiv.org/abs/2002.07281v5 | https://arxiv.org/pdf/2002.07281v5.pdf | Deep Fourier Kernel for Self-Attentive Point Processes | We present a novel attention-based model for discrete event data to capture complex non-linear temporal dependence structures. We borrow the idea from the attention mechanism and incorporate it into the point processes' conditional intensity function. We further introduce a novel score function using Fourier kernel emb... | ['Minghe Zhang', 'Yao Xie', 'Shixiang Zhu', 'Ruyi Ding'] | 2020-02-17 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-4.23544347e-02 -4.30290610e-01 -1.13970600e-01 -3.51112783e-01
-5.03387034e-01 -9.29079875e-02 8.51637185e-01 4.75003988e-01
-5.03755391e-01 5.65478921e-01 2.21970394e-01 -1.84125617e-01
-4.45718855e-01 -8.00800264e-01 -4.81647074e-01 -6.25143826e-01
-7.32908368e-01 3.71392429e-01 5.79427063e-01 -2.13248417... | [7.005598068237305, 3.4268219470977783] |
9af88905-9eb5-4a9e-9de4-5483dda7e27a | continual-learning-for-on-device | 2207.07429 | null | https://arxiv.org/abs/2207.07429v2 | https://arxiv.org/pdf/2207.07429v2.pdf | Continual Learning For On-Device Environmental Sound Classification | Continuously learning new classes without catastrophic forgetting is a challenging problem for on-device environmental sound classification given the restrictions on computation resources (e.g., model size, running memory). To address this issue, we propose a simple and efficient continual learning method. Our method s... | ['Arshdeep Singh', 'Wenwu Wang', 'Mark D. Plumbley', 'Eng Siong Chng', 'James King', 'Xubo Liu', 'Yang Xiao'] | 2022-07-15 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 3.06973636e-01 -2.74631888e-01 -1.18652560e-01 -2.28946090e-01
-9.58687007e-01 -5.02294540e-01 1.19105160e-01 1.44548357e-01
-5.73219538e-01 7.48001277e-01 -2.36993730e-01 -3.43229115e-01
-1.44668341e-01 -6.40290022e-01 -8.43386889e-01 -9.34268594e-01
-1.55300815e-02 1.72147766e-01 4.91662949e-01 4.42597300... | [9.971440315246582, 3.6311111450195312] |
4a426ee4-c0b7-4380-bdef-c23474c79b50 | bootstrapping-text-anonymization-models-with | null | null | https://openreview.net/forum?id=-MoY6seu_x | https://openreview.net/pdf?id=-MoY6seu_x | Bootstrapping Text Anonymization Models with Distant Supervision | We propose a novel method to bootstrap text anonymization models based on distant supervision. Instead of requiring manually labeled training data, the approach relies on a knowledge graph expressing the background information assumed to be publicly available about various individuals. This knowledge graph is employed... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['text-anonymization'] | ['natural-language-processing'] | [ 3.07335228e-01 6.87133551e-01 -2.83969402e-01 -5.36046147e-01
-7.53578305e-01 -9.79503036e-01 6.51750326e-01 6.24403238e-01
-6.04925215e-01 1.13171434e+00 4.77202713e-01 -8.41651931e-02
-7.84346238e-02 -8.27146888e-01 -6.37579918e-01 -5.32180130e-01
1.82181582e-01 8.21028471e-01 -1.44131109e-01 -2.77057127... | [6.159811019897461, 7.039047718048096] |
356991e2-f333-4e7b-afb8-84154c74e96a | lightweight-self-knowledge-distillation-with | 2305.09183 | null | https://arxiv.org/abs/2305.09183v1 | https://arxiv.org/pdf/2305.09183v1.pdf | Lightweight Self-Knowledge Distillation with Multi-source Information Fusion | Knowledge Distillation (KD) is a powerful technique for transferring knowledge between neural network models, where a pre-trained teacher model is used to facilitate the training of the target student model. However, the availability of a suitable teacher model is not always guaranteed. To address this challenge, Self-... | ['Lei Guo', 'Pengchao Han', 'Xucong Wang'] | 2023-05-16 | null | null | null | null | ['self-knowledge-distillation'] | ['computer-vision'] | [ 4.07809988e-02 1.99076794e-02 -2.18083173e-01 -3.45028222e-01
-7.12509334e-01 -6.85957849e-01 5.84148884e-01 1.26558572e-01
-4.30832416e-01 5.63566506e-01 -4.08373214e-02 -3.28280061e-01
-6.88089728e-02 -7.69403100e-01 -8.04984510e-01 -8.13099742e-01
4.23575848e-01 3.42377961e-01 4.41121578e-01 -3.02352989... | [9.50477123260498, 3.3220789432525635] |
255d9154-b11b-4683-a61c-b4cfca77e816 | textit-facialfilmroll-high-resolution-multi | 2110.02124 | null | https://arxiv.org/abs/2110.02124v2 | https://arxiv.org/pdf/2110.02124v2.pdf | FacialFilmroll: High-resolution multi-shot video editing | We present FacialFilmroll, a solution for spatially and temporally consistent editing of faces in one or multiple shots. We build upon unwrap mosaic [Rav-Acha et al. 2008] by specializing it to faces. We leverage recent techniques to fit a 3D face model on monocular videos to (i) improve the quality of the mosaic for e... | ['Pierre Hellier', 'Paul Ghezzo', 'Tim Christensen', 'Junghyun Ahn', 'Cédric Thébault', 'Philippe Henri Gosselin', 'Gilles Puy', 'Emmanuel Jolly', 'Bharath Bhushan Damodaran'] | 2021-10-05 | null | null | null | null | ['face-model'] | ['computer-vision'] | [ 3.35579872e-01 -1.95924640e-01 4.04804409e-01 -3.67550045e-01
-6.95235133e-02 -5.90346456e-01 6.07913375e-01 -5.79702735e-01
1.78898931e-01 4.64232683e-01 2.78530717e-01 1.77025124e-01
7.63885975e-02 -2.23137707e-01 -6.56244040e-01 -1.28295720e-01
-2.74382472e-01 4.92212363e-03 2.50364184e-01 5.02206832... | [12.91562271118164, -0.4365653693675995] |
931d6e9c-3bc5-45d8-a921-ef75a2b33012 | progress-and-summary-of-reinforcement | 2211.04001 | null | https://arxiv.org/abs/2211.04001v1 | https://arxiv.org/pdf/2211.04001v1.pdf | Progress and summary of reinforcement learning on energy management of MPS-EV | The high emission and low energy efficiency caused by internal combustion engines (ICE) have become unacceptable under environmental regulations and the energy crisis. As a promising alternative solution, multi-power source electric vehicles (MPS-EVs) introduce different clean energy systems to improve powertrain effic... | ['Yuanjian Zhang', 'Jingjing Jiang', 'Jihan Li', 'Zhuoran Hou', 'Liang Chu', 'Yang Lin', 'Jincheng Hu'] | 2022-11-08 | null | null | null | null | ['energy-management'] | ['time-series'] | [-1.30834743e-01 6.31874725e-02 -6.33572280e-01 2.51607895e-01
-1.36530235e-01 -4.98571664e-01 3.72037590e-01 -3.93767297e-01
-3.25107843e-01 1.01016700e+00 -4.54584181e-01 -2.85674661e-01
-5.25167823e-01 -9.02080536e-01 -3.82550418e-01 -1.11244845e+00
2.25199968e-01 1.26854688e-01 -2.25587323e-01 -3.19630235... | [5.533316135406494, 2.324204683303833] |
4b57eb14-1823-485e-b6fa-4e26235a43ab | performance-evaluation-of-3d-correspondence | 1804.02085 | null | http://arxiv.org/abs/1804.02085v1 | http://arxiv.org/pdf/1804.02085v1.pdf | Performance Evaluation of 3D Correspondence Grouping Algorithms | This paper presents a thorough evaluation of several widely-used 3D
correspondence grouping algorithms, motived by their significance in vision
tasks relying on correct feature correspondences. A good correspondence
grouping algorithm is desired to retrieve as many as inliers from initial
feature matches, giving a rise... | ['Zhiguo Cao', 'Ke Xian', 'Jiaqi Yang', 'Yang Xiao'] | 2018-04-06 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-8.45235959e-02 -5.19590318e-01 7.55670220e-02 -3.62343848e-01
-8.37797940e-01 -6.50920212e-01 1.09226036e+00 4.61100399e-01
-1.50005028e-01 3.74061048e-01 -6.99034333e-02 3.44196558e-02
-4.96165127e-01 -4.79727656e-01 -3.62906128e-01 -7.33150363e-01
-8.71146470e-02 8.20867419e-01 1.69261098e-01 -5.99843785... | [7.916311264038086, -2.6116199493408203] |
d0388f99-4c87-4045-978d-2a4f0dbd9041 | evaluating-end-to-end-entity-linking-on | 2305.14588 | null | https://arxiv.org/abs/2305.14588v1 | https://arxiv.org/pdf/2305.14588v1.pdf | Evaluating end-to-end entity linking on domain-specific knowledge bases: Learning about ancient technologies from museum collections | To study social, economic, and historical questions, researchers in the social sciences and humanities have started to use increasingly large unstructured textual datasets. While recent advances in NLP provide many tools to efficiently process such data, most existing approaches rely on generic solutions whose performa... | ['Daniel Simig', 'Danial Lashkari', 'Thomas Chaney', 'Johannes Boehm', 'Rafael Aparecido Martins Frade', 'Khalil Kacem', 'Sebastian Cadavid-Sanchez'] | 2023-05-23 | null | null | null | null | ['entity-linking'] | ['natural-language-processing'] | [-2.44487673e-01 3.75368506e-01 -1.86893284e-01 -3.83046657e-01
-1.00465679e+00 -9.04738784e-01 8.19180489e-01 7.35269666e-01
-1.20057917e+00 1.02854776e+00 6.48559928e-01 -1.85220137e-01
-3.43777776e-01 -5.66234112e-01 -5.07930517e-01 4.14284766e-02
-9.40083042e-02 1.17895162e+00 4.85349834e-01 -4.53561991... | [9.556353569030762, 8.99084758758545] |
17d50b54-e0ae-47ca-91d8-b540758942a7 | a-dataset-free-self-supervised-disentangled | 2112.02869 | null | https://arxiv.org/abs/2112.02869v4 | https://arxiv.org/pdf/2112.02869v4.pdf | Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image Fusion | Convolutional neural networks have turned into an illustrious tool for image fusion and super-resolution. However, their excellent performance cannot work without large fixed-paired datasets; and additionally, these high-demanded ground truth data always cannot be obtained easily in fusion tasks. In this study, we show... | ['Liang Xue', 'Cheng Liu', 'Haoran Dai', 'Yinghan Guan', 'Shouyu Wang', 'Zhibo Xiao', 'Yuanjie Gu'] | 2021-12-06 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 1.40360788e-01 -3.88051897e-01 1.02044351e-01 -8.33996087e-02
-8.11640799e-01 -2.60655701e-01 6.15970075e-01 -6.21729136e-01
-2.95639969e-02 9.83174622e-01 3.94538969e-01 2.51167446e-01
-4.42519456e-01 -1.14227438e+00 -7.23886073e-01 -1.02882409e+00
3.67803782e-01 1.73601359e-02 5.72083779e-02 -4.97666329... | [10.883707046508789, -2.0423500537872314] |
c1820e3f-15fa-44ce-8ba4-74f4e65f8b32 | investigating-the-lombard-effect-influence-on | 1906.02112 | null | https://arxiv.org/abs/1906.02112v4 | https://arxiv.org/pdf/1906.02112v4.pdf | Investigating the Lombard Effect Influence on End-to-End Audio-Visual Speech Recognition | Several audio-visual speech recognition models have been recently proposed which aim to improve the robustness over audio-only models in the presence of noise. However, almost all of them ignore the impact of the Lombard effect, i.e., the change in speaking style in noisy environments which aims to make speech more int... | ['Pingchuan Ma', 'Maja Pantic', 'Stavros Petridis'] | 2019-06-05 | null | null | null | null | ['audio-visual-speech-recognition'] | ['speech'] | [ 5.01932502e-02 7.00928718e-02 3.29032511e-01 -7.78438747e-02
-7.24820435e-01 -3.97058249e-01 6.11642420e-01 7.37701654e-02
-4.52867001e-01 5.09294271e-01 3.92442644e-01 -3.63351226e-01
1.51154429e-01 -2.86610335e-01 -8.70824695e-01 -8.77928138e-01
1.40284076e-01 3.09931934e-02 4.37815756e-01 -1.33077770... | [14.527108192443848, 5.374457359313965] |
6b78e66e-8a82-4203-978b-de50bb596fa0 | reduced-label-complexity-for-tight-ell-2 | 2305.07486 | null | https://arxiv.org/abs/2305.07486v1 | https://arxiv.org/pdf/2305.07486v1.pdf | Reduced Label Complexity For Tight $\ell_2$ Regression | Given data ${\rm X}\in\mathbb{R}^{n\times d}$ and labels $\mathbf{y}\in\mathbb{R}^{n}$ the goal is find $\mathbf{w}\in\mathbb{R}^d$ to minimize $\Vert{\rm X}\mathbf{w}-\mathbf{y}\Vert^2$. We give a polynomial algorithm that, \emph{oblivious to $\mathbf{y}$}, throws out $n/(d+\sqrt{n})$ data points and is a $(1+d/n)$-ap... | ['Malik Magdon-Ismail', 'Alex Gittens'] | 2023-05-12 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 2.46558473e-01 4.40516442e-01 -9.75028500e-02 -5.76040268e-01
-1.43607867e+00 -9.32081342e-01 -7.92674482e-01 3.20473701e-01
-7.85922825e-01 7.05028653e-01 -8.31235647e-01 -7.58433878e-01
-7.56428063e-01 -1.08893311e+00 -9.47579265e-01 -8.06127429e-01
-6.61797225e-01 7.88955569e-01 2.13821810e-02 -1.49809718... | [6.379638195037842, 4.614198684692383] |
5fc29dfd-ce55-44bc-b8c4-d0fd5ab196d2 | explainable-data-poison-attacks-on-human | 2301.06923 | null | https://arxiv.org/abs/2301.06923v1 | https://arxiv.org/pdf/2301.06923v1.pdf | Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG Signals | The major aim of this paper is to explain the data poisoning attacks using label-flipping during the training stage of the electroencephalogram (EEG) signal-based human emotion evaluation systems deploying Machine Learning models from the attackers' perspective. Human emotion evaluation using EEG signals has consistent... | ['Chan Yeob Yeun', 'Nicola Bena', 'Claudio Agostino Ardagna', 'Ernesto Damiani', 'Sangyoung Yoon', 'Ahmed Y. Al Hammadi', 'Sani Umar', 'Zhibo Zhang'] | 2023-01-17 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 4.26645763e-02 1.88056365e-01 4.26057279e-01 -4.50672865e-01
-2.22174630e-01 -7.58102357e-01 2.96045929e-01 1.05279364e-01
-1.25790790e-01 1.04909742e+00 -1.27367690e-01 -3.29787403e-01
-4.23608035e-01 -2.75413871e-01 -5.03747880e-01 -7.09584236e-01
-6.82802439e-01 -2.98706796e-02 -6.78830802e-01 -6.66807070... | [13.294084548950195, 3.028852939605713] |
d49421e7-3806-40dd-86c1-92567533eba9 | scaling-up-open-tagging-from-tens-to | null | null | https://aclanthology.org/P19-1514 | https://aclanthology.org/P19-1514.pdf | Scaling up Open Tagging from Tens to Thousands: Comprehension Empowered Attribute Value Extraction from Product Title | Supplementing product information by extracting attribute values from title is a crucial task in e-Commerce domain. Previous studies treat each attribute only as an entity type and build one set of NER tags (e.g., BIO) for each of them, leading to a scalability issue which unfits to the large sized attribute system in ... | ['Man Lan', 'Wenting Wang', 'Huimin Xu', 'Xinyu Jiang', 'Xin Mao'] | 2019-07-01 | null | null | null | acl-2019-7 | ['attribute-value-extraction'] | ['natural-language-processing'] | [-3.73306498e-03 2.28206292e-01 -5.42733431e-01 -7.61875749e-01
-7.68420756e-01 -9.32187736e-01 3.26717854e-01 5.70947468e-01
-5.07224202e-01 6.55929506e-01 2.61405915e-01 -1.98237851e-01
-7.38714784e-02 -1.18285584e+00 -5.84021688e-01 -3.84474993e-01
9.77824703e-02 7.81195164e-01 2.74268031e-01 -3.79104733... | [9.979758262634277, 6.301032543182373] |
35552e89-f02b-4beb-af6b-0669e4fa9b7f | a-practitioner-s-guide-to-bayesian-inference | 2304.04752 | null | https://arxiv.org/abs/2304.04752v1 | https://arxiv.org/pdf/2304.04752v1.pdf | A Practitioner's Guide to Bayesian Inference in Pharmacometrics using Pumas | This paper provides a comprehensive tutorial for Bayesian practitioners in pharmacometrics using Pumas workflows. We start by giving a brief motivation of Bayesian inference for pharmacometrics highlighting limitations in existing software that Pumas addresses. We then follow by a description of all the steps of a stan... | ['Vijay Ivaturi', 'Chris Rackauckas', 'Julius Krumbiegel', 'Chris Elrod', 'Casey Davis', 'Jose Storopoli', 'Mohamed Tarek'] | 2023-03-31 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 2.68414587e-01 2.11573089e-03 -2.64285654e-01 -5.01551390e-01
-5.67598462e-01 -2.69352704e-01 3.48664433e-01 5.39045632e-01
-2.51738936e-01 1.12263060e+00 -3.37717608e-02 -9.08003449e-01
-7.04261124e-01 -2.18965188e-01 -4.12902772e-01 -9.68455255e-01
-1.14992909e-01 1.03949320e+00 -1.11713797e-01 4.40413296... | [6.508890151977539, 4.065654277801514] |
dab25d95-af1a-4ad9-a0f4-8f967aa3639a | fedgrad-mitigating-backdoor-attacks-in | 2305.00328 | null | https://arxiv.org/abs/2305.00328v1 | https://arxiv.org/pdf/2305.00328v1.pdf | FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients Inspection | Federated learning (FL) enables multiple clients to train a model without compromising sensitive data. The decentralized nature of FL makes it susceptible to adversarial attacks, especially backdoor insertion during training. Recently, the edge-case backdoor attack employing the tail of the data distribution has been p... | ['Truong Thao Nguyen', 'Phi Le Nguyen', 'Thanh Hung Nguyen', 'Huy Hieu Pham', 'Kok-Seng Wong', 'Anh Duy Nguyen', 'Thuy Dung Nguyen'] | 2023-04-29 | null | null | null | null | ['backdoor-attack'] | ['adversarial'] | [-2.60750860e-01 -2.33094037e-01 -3.08999687e-01 -7.87893757e-02
-1.11333776e+00 -1.26878226e+00 6.52031958e-01 -5.34204952e-02
-3.22878987e-01 3.41026932e-01 -1.97969630e-01 -7.93323994e-01
-1.57312810e-01 -7.08038092e-01 -7.59951651e-01 -9.10567284e-01
-3.58366907e-01 1.53041974e-01 4.36683506e-01 -2.45217964... | [5.6993937492370605, 7.255173206329346] |
abc278ff-309c-40f0-8205-dd6a94cc96d7 | wearing-masks-implies-refuting-trump-towards | 2303.12029 | null | https://arxiv.org/abs/2303.12029v1 | https://arxiv.org/pdf/2303.12029v1.pdf | Wearing Masks Implies Refuting Trump?: Towards Target-specific User Stance Prediction across Events in COVID-19 and US Election 2020 | People who share similar opinions towards controversial topics could form an echo chamber and may share similar political views toward other topics as well. The existence of such connections, which we call connected behavior, gives researchers a unique opportunity to predict how one would behave for a future event give... | ['Jisun An', 'Wei Gao', 'Haewoon Kwak', 'Hong Zhang'] | 2023-03-21 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [-4.50509399e-01 8.09714124e-02 -7.07924306e-01 -8.74449492e-01
-9.31686983e-02 -5.63998699e-01 9.92181540e-01 4.07482117e-01
-2.27221519e-01 6.79762959e-01 9.06784177e-01 -3.73074710e-01
4.66148376e-01 -1.15457678e+00 -4.42321181e-01 -2.07100362e-01
9.26198810e-02 2.04541329e-02 -1.20037273e-02 -3.81508917... | [8.843643188476562, 10.114853858947754] |
c60fb7de-667f-446b-a346-37c9d36d9e46 | cross-lingual-genre-classification | null | null | https://aclanthology.org/E12-3002 | https://aclanthology.org/E12-3002.pdf | Cross-Lingual Genre Classification | null | ['Philipp Petrenz'] | 2012-04-01 | null | null | null | eacl-2012-4 | ['genre-classification'] | ['computer-vision'] | [-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... | [-7.281436920166016, 3.826171636581421] |
25e8de21-7c9e-4fcd-b0d7-64d5b857a935 | efficiency-analysis-of-asp-encodings-for | 1711.05090 | null | http://arxiv.org/abs/1711.05090v1 | http://arxiv.org/pdf/1711.05090v1.pdf | Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks | This article presents the use of Answer Set Programming (ASP) to mine
sequential patterns. ASP is a high-level declarative logic programming paradigm
for high level encoding combinatorial and optimization problem solving as well
as knowledge representation and reasoning. Thus, ASP is a good candidate for
implementing p... | ['René Quiniou', 'Thomas Guyet', 'Torsten Schaub', 'Yves Moinard'] | 2017-11-14 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 0.0664973 0.42177644 -0.3062638 -0.51979774 0.00654218 -0.40099996
0.27425286 0.9580745 -0.3657355 0.6973146 -0.1684333 -0.5028159
-0.7327351 -1.6560713 -0.65034205 -0.11389408 -0.6009696 0.7660446
0.77655995 -0.14552438 0.41462007 0.5161084 -2.250572 0.7616262
0.7940897 1.1204172 0.118... | [8.369091987609863, 6.394146919250488] |
b0ec645f-e046-401c-94c0-e5f2b3cc1e5b | universal-face-restoration-with-memorized | 2110.01033 | null | https://arxiv.org/abs/2110.01033v1 | https://arxiv.org/pdf/2110.01033v1.pdf | Universal Face Restoration With Memorized Modulation | Blind face restoration (BFR) is a challenging problem because of the uncertainty of the degradation patterns. This paper proposes a Restoration with Memorized Modulation (RMM) framework for universal BFR in diverse degraded scenes and heterogeneous domains. We apply random noise as well as unsupervised wavelet memory t... | ['Ran He', 'Xiaofei Jia', 'Huaibo Huang', 'Jia Li'] | 2021-10-03 | null | null | null | null | ['blind-face-restoration'] | ['computer-vision'] | [ 6.19300246e-01 -2.70155400e-01 7.66859427e-02 -9.58885998e-02
-7.74612904e-01 1.36492223e-01 3.19844723e-01 -5.93762755e-01
-4.42263037e-01 7.11654186e-01 5.70948064e-01 3.50128680e-01
-4.29550499e-01 -7.93708742e-01 -6.36658251e-01 -1.45882869e+00
1.30932719e-01 -1.18990064e-01 -5.52330017e-02 -2.16776043... | [12.825699806213379, -0.06892905384302139] |
a4f5b13a-a20e-4c15-b8f5-75e6cee5567b | multiview-boosting-by-controlling-the | 1808.05784 | null | http://arxiv.org/abs/1808.05784v2 | http://arxiv.org/pdf/1808.05784v2.pdf | Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters | In this paper we propose a boosting based multiview learning algorithm,
referred to as PB-MVBoost, which iteratively learns i) weights over
view-specific voters capturing view-specific information; and ii) weights over
views by optimizing a PAC-Bayes multiview C-Bound that takes into account the
accuracy of view-specif... | ['Massih-Reza Amini', 'Pascal Germain', 'Emilie Morvant', 'Anil Goyal'] | 2018-08-17 | null | null | null | null | ['multiview-learning', 'multilingual-text-classification'] | ['computer-vision', 'miscellaneous'] | [-2.65870929e-01 -8.62352327e-02 -9.86545324e-01 -9.55847621e-01
-1.11829007e+00 -4.93412346e-01 1.09296978e+00 2.70588875e-01
-2.91564673e-01 5.22848010e-01 4.04374421e-01 -1.29150809e-03
-1.52187198e-01 -5.45458913e-01 -5.64685881e-01 -6.14816368e-01
1.59440324e-01 6.72424674e-01 2.72508502e-01 -6.83543161... | [8.506999015808105, 4.484723091125488] |
48b663a2-59bf-419a-80e1-61b700a2b8a4 | deid-gpt-zero-shot-medical-text-de | 2303.11032 | null | https://arxiv.org/abs/2303.11032v1 | https://arxiv.org/pdf/2303.11032v1.pdf | DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4 | The digitization of healthcare has facilitated the sharing and re-using of medical data but has also raised concerns about confidentiality and privacy. HIPAA (Health Insurance Portability and Accountability Act) mandates removing re-identifying information before the dissemination of medical records. Thus, effective an... | ['Xiang Li', 'Dajiang Zhu', 'Tianming Liu', 'Quanzheng Li', 'Dinggang Shen', 'Wei Liu', 'Lin Zhao', 'Haixing Dai', 'Chao Cao', 'Zihao Wu', 'Lu Zhang', 'Xiaowei Yu', 'Zhengliang Liu'] | 2023-03-20 | null | null | null | null | ['de-identification'] | ['natural-language-processing'] | [ 2.04747155e-01 1.44547895e-01 -2.78215617e-01 -3.49505305e-01
-1.04131222e+00 -5.19639015e-01 1.23169176e-01 8.63820672e-01
-6.59843922e-01 6.82732821e-01 4.69379097e-01 -6.80973351e-01
-3.55311602e-01 -6.81409001e-01 -2.31790558e-01 -5.56646585e-01
1.52371943e-01 5.89873910e-01 -1.79967299e-01 1.16972178... | [6.806180953979492, 6.99567985534668] |
15257be6-4ca7-4c59-8546-c1cf85208257 | scalable-mask-annotation-for-video-text | 2305.01443 | null | https://arxiv.org/abs/2305.01443v1 | https://arxiv.org/pdf/2305.01443v1.pdf | Scalable Mask Annotation for Video Text Spotting | Video text spotting refers to localizing, recognizing, and tracking textual elements such as captions, logos, license plates, signs, and other forms of text within consecutive video frames. However, current datasets available for this task rely on quadrilateral ground truth annotations, which may result in including ex... | ['DaCheng Tao', 'Bo Du', 'Juhua Liu', 'Mengyang Xu', 'Jing Zhang', 'Haibin He'] | 2023-05-02 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 5.29109776e-01 -3.00097734e-01 -1.49003237e-01 -2.84272671e-01
-8.95282149e-01 -8.52485180e-01 5.74778676e-01 -3.39856267e-01
-4.38456237e-02 2.88740367e-01 3.55937749e-01 -2.64299184e-01
5.02785563e-01 -3.35215807e-01 -7.84746230e-01 -2.04677612e-01
4.44574952e-01 2.35896498e-01 5.56774616e-01 2.14138344... | [11.941200256347656, 2.1904265880584717] |
cf3982ba-409b-4dbe-a085-2fd005d71233 | investigation-of-uncertainty-of-deep-learning | 2106.05870 | null | https://arxiv.org/abs/2106.05870v1 | https://arxiv.org/pdf/2106.05870v1.pdf | Investigation of Uncertainty of Deep Learning-based Object Classification on Radar Spectra | Deep learning (DL) has recently attracted increasing interest to improve object type classification for automotive radar.In addition to high accuracy, it is crucial for decision making in autonomous vehicles to evaluate the reliability of the predictions; however, decisions of DL networks are non-transparent. Current D... | ['Bin Yang', 'Michael Pfeiffer', 'Adriana-Eliza Cozma', 'Kilian Rambach', 'William Beluch', 'Kanil Patel'] | 2021-06-01 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 2.72502899e-01 1.05642572e-01 8.81322771e-02 -6.75246418e-01
-9.37119961e-01 -5.58647215e-01 6.28749073e-01 1.57085896e-01
-5.10141492e-01 1.14725411e+00 -1.68121159e-01 -6.73469365e-01
-3.94177854e-01 -9.75282848e-01 -8.14876974e-01 -8.17714572e-01
1.31008312e-01 5.75237274e-01 2.77440846e-01 5.40459901... | [7.530520915985107, 3.8346385955810547] |
3887619f-e820-42ae-a4fa-323df30171aa | end-to-end-semantics-based-summary-quality | 2005.06377 | null | https://arxiv.org/abs/2005.06377v3 | https://arxiv.org/pdf/2005.06377v3.pdf | SueNes: A Weakly Supervised Approach to Evaluating Single-Document Summarization via Negative Sampling | Canonical automatic summary evaluation metrics, such as ROUGE, focus on lexical similarity which cannot well capture semantics nor linguistic quality and require a reference summary which is costly to obtain. Recently, there have been a growing number of efforts to alleviate either or both of the two drawbacks. In this... | ['Cen Chen', 'Minghui Qiu', 'Youbiao He', 'Hebi Li', 'Yinfei Yang', 'Ge Luo', 'Forrest Sheng Bao'] | 2020-05-13 | null | https://aclanthology.org/2022.naacl-main.175 | https://aclanthology.org/2022.naacl-main.175.pdf | naacl-2022-7 | ['document-embedding'] | ['methodology'] | [ 1.16831802e-01 -5.52056804e-02 -4.58758742e-01 -4.19524789e-01
-1.64286184e+00 -7.50289023e-01 1.02190042e+00 9.18897331e-01
-4.82392490e-01 1.07925141e+00 1.08401239e+00 6.84198141e-02
-1.55167028e-01 -5.05904794e-01 -2.88006216e-01 -1.18842155e-01
2.98667371e-01 3.45035523e-01 2.73697764e-01 -3.65336746... | [12.136645317077637, 9.35391902923584] |
09a3d0cb-bf0f-476a-88f3-bdff64a48985 | fast-key-points-detection-and-matching-for | 2211.03242 | null | https://arxiv.org/abs/2211.03242v2 | https://arxiv.org/pdf/2211.03242v2.pdf | Fast Key Points Detection and Matching for Tree-Structured Images | This paper offers a new authentication algorithm based on image matching of nano-resolution visual identifiers with tree-shaped patterns. The algorithm includes image-to-tree conversion by greedy extraction of the fractal pattern skeleton along with a custom-built graph matching algorithm that is robust against imaging... | ['Rahul Amin', 'Abolfazl Razi', 'Xiwen Chen', 'Hao Wang'] | 2022-11-07 | null | null | null | null | ['graph-matching', 'key-point-matching'] | ['graphs', 'natural-language-processing'] | [ 5.78844726e-01 -2.35028133e-01 -3.43034491e-02 4.23231840e-01
-2.17639968e-01 -8.60523760e-01 4.45345700e-01 3.64941180e-01
-1.48052111e-01 1.59458533e-01 -3.58638525e-01 -5.16967475e-01
-3.33067060e-01 -9.47465777e-01 -4.39685673e-01 -6.17688179e-01
-4.17698883e-02 1.56545639e-01 4.52086806e-01 -4.95515056... | [8.511868476867676, -2.2341415882110596] |
a8e8f08f-adbe-416f-b45b-1b8769069f53 | semi-supervised-teacher-student-deep-neural | 2112.06142 | null | https://arxiv.org/abs/2112.06142v1 | https://arxiv.org/pdf/2112.06142v1.pdf | Semi-supervised teacher-student deep neural network for materials discovery | Data driven generative machine learning models have recently emerged as one of the most promising approaches for new materials discovery. While the generator models can generate millions of candidates, it is critical to train fast and accurate machine learning models to filter out stable, synthesizable materials with d... | ['Jianjun Hu', 'Nihang Fu', 'Yong Zhao', 'Edirisuriya M. Dilanga Siriwardane', 'Daniel Gleaves'] | 2021-12-12 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [ 4.31116730e-01 5.74693903e-02 -4.01967525e-01 1.87743045e-02
-1.27769065e+00 -2.78693199e-01 4.71234977e-01 1.97433736e-02
2.45221138e-01 1.20699596e+00 2.63115466e-02 -3.64398003e-01
6.54875860e-03 -1.35714436e+00 -9.66060281e-01 -1.19339120e+00
3.28850210e-01 8.65178227e-01 2.29011759e-01 -5.79014957... | [5.189000606536865, 5.372509956359863] |
eb9a12a1-e234-4ff3-b351-5901836558d9 | multi-grained-knowledge-retrieval-for-end-to | 2305.10149 | null | https://arxiv.org/abs/2305.10149v1 | https://arxiv.org/pdf/2305.10149v1.pdf | Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog | Retrieving proper domain knowledge from an external database lies at the heart of end-to-end task-oriented dialog systems to generate informative responses. Most existing systems blend knowledge retrieval with response generation and optimize them with direct supervision from reference responses, leading to suboptimal ... | ['Wei Bi', 'Xiaojun Quan', 'Ke Yang', 'Weizhou Shen', 'Fanqi Wan'] | 2023-05-17 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 6.61701709e-02 2.21516535e-01 -4.01279807e-01 -6.24917805e-01
-1.48105145e+00 -8.04631829e-01 5.84962249e-01 -8.91912207e-02
-5.84992707e-01 1.05921435e+00 5.32586217e-01 -3.80450711e-02
-6.87080547e-02 -7.87970960e-01 -5.64111054e-01 -2.00198457e-01
6.64793968e-01 1.08087373e+00 1.80728927e-01 -4.99376446... | [11.849284172058105, 8.032310485839844] |
a411622d-1f50-4a70-88ef-a972b6741d56 | motion-planning-for-parabolic-equations-using | 2305.12404 | null | https://arxiv.org/abs/2305.12404v1 | https://arxiv.org/pdf/2305.12404v1.pdf | Motion planning for parabolic equations using flatness and finite-difference approximations | We consider the problem of finding an input signal which transfers a linear boundary controlled 1D parabolic partial differential equation, with spatially-varying coefficients and a non-local term, from a given initial state to a desired final state. The initial and final states have certain smoothness and the transfer... | ['Vivek Natarajan', 'Soham Chatterjee'] | 2023-05-21 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 2.33146235e-01 3.28965247e-01 1.58213466e-01 4.81967330e-01
-4.57007170e-01 -4.62763101e-01 3.33041251e-01 -4.09756042e-03
-3.38376343e-01 9.62670922e-01 -3.26524168e-01 -3.21785539e-01
-1.81026652e-01 -7.87964344e-01 -8.18645656e-01 -1.00879025e+00
-3.02188396e-01 3.64822567e-01 6.20097995e-01 -4.31002229... | [6.204409599304199, 3.3252112865448] |
f208e44a-b953-41f2-b8cf-330d3340a956 | multi-step-greedy-policies-in-model-free-deep-1 | 1910.02919 | null | https://arxiv.org/abs/1910.02919v3 | https://arxiv.org/pdf/1910.02919v3.pdf | Multi-step Greedy Reinforcement Learning Algorithms | Multi-step greedy policies have been extensively used in model-based reinforcement learning (RL), both when a model of the environment is available (e.g.,~in the game of Go) and when it is learned. In this paper, we explore their benefits in model-free RL, when employed using multi-step dynamic programming algorithms: ... | ['Mohammad Ghavamzadeh', 'Yonathan Efroni', 'Manan Tomar'] | 2019-10-07 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/5786-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/5786-Paper.pdf | icml-2020-1 | ['game-of-go'] | ['playing-games'] | [-4.46224213e-03 1.32880583e-01 -5.44517875e-01 -1.19025388e-03
-7.21237540e-01 -5.47212362e-01 4.55776423e-01 4.00840975e-02
-1.09085596e+00 1.17135954e+00 -3.51577491e-01 -5.78209162e-01
-5.55644333e-01 -8.22154462e-01 -6.03425860e-01 -7.39515364e-01
-4.16539788e-01 7.21440196e-01 2.69449145e-01 -5.66515386... | [4.114576816558838, 2.1623358726501465] |
e983a3bd-96b4-49ec-8f8b-69b3c63108f7 | motionhint-self-supervised-monocular-visual | 2109.06768 | null | https://arxiv.org/abs/2109.06768v3 | https://arxiv.org/pdf/2109.06768v3.pdf | MotionHint: Self-Supervised Monocular Visual Odometry with Motion Constraints | We present a novel self-supervised algorithm named MotionHint for monocular visual odometry (VO) that takes motion constraints into account. A key aspect of our approach is to use an appropriate motion model that can help existing self-supervised monocular VO (SSM-VO) algorithms to overcome issues related to the local ... | ['Dinesh Manocha', 'Yu-Ping Wang', 'Cong Wang'] | 2021-09-14 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-3.32161725e-01 1.58991531e-01 -5.79445004e-01 -4.30939227e-01
-4.72394705e-01 -1.26615867e-01 6.19049489e-01 -5.72171450e-01
-4.39788967e-01 6.05782926e-01 3.40720385e-01 8.24156180e-02
2.53186047e-01 -3.84623289e-01 -8.70230973e-01 -4.52001214e-01
2.17424467e-01 6.77723467e-01 5.00909388e-01 2.81396024... | [8.150262832641602, -2.131906747817993] |
301e5a7b-f865-4a2b-a18c-86fc385444ed | working-with-a-small-dataset-semi-supervised | null | null | https://aclanthology.org/W13-4901 | https://aclanthology.org/W13-4901.pdf | Working with a small dataset - semi-supervised dependency parsing for Irish | null | ['Mark Dras', 'Jennifer Foster', 'Teresa Lynn'] | 2013-10-01 | null | null | null | ws-2013-10 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.462052822113037, 3.651705026626587] |
c0fbc35d-62c6-44d4-b1fa-cca2a989f44f | tag-boosting-text-vqa-via-text-aware-visual | 2208.01813 | null | https://arxiv.org/abs/2208.01813v3 | https://arxiv.org/pdf/2208.01813v3.pdf | TAG: Boosting Text-VQA via Text-aware Visual Question-answer Generation | Text-VQA aims at answering questions that require understanding the textual cues in an image. Despite the great progress of existing Text-VQA methods, their performance suffers from insufficient human-labeled question-answer (QA) pairs. However, we observe that, in general, the scene text is not fully exploited in the ... | ['Larry S. Davis', 'Joseph F. JaJa', 'ran Xu', 'Chetan Ramaiah', 'Ramprasaath R. Selvaraju', 'Yuqian Hu', 'Mingfei Gao', 'Jun Wang'] | 2022-08-03 | null | null | null | null | ['question-answer-generation'] | ['natural-language-processing'] | [ 3.06909889e-01 2.53452267e-02 1.80551112e-01 -6.13227129e-01
-1.53993189e+00 -7.62445450e-01 4.75522041e-01 -5.73991388e-02
-6.02963641e-02 4.79129106e-01 4.35364574e-01 -3.96495491e-01
4.22004342e-01 -7.32476175e-01 -7.81688750e-01 -5.33575118e-01
8.77214372e-01 6.98109090e-01 4.87964213e-01 -4.73356783... | [10.902643203735352, 1.5718796253204346] |
19cd9a63-d4a0-408a-be74-a2052deb5221 | qlib-an-ai-oriented-quantitative-investment | 2009.11189 | null | https://arxiv.org/abs/2009.11189v1 | https://arxiv.org/pdf/2009.11189v1.pdf | Qlib: An AI-oriented Quantitative Investment Platform | Quantitative investment aims to maximize the return and minimize the risk in a sequential trading period over a set of financial instruments. Recently, inspired by rapid development and great potential of AI technologies in generating remarkable innovation in quantitative investment, there has been increasing adoption ... | ['Tie-Yan Liu', 'Xiao Yang', 'Weiqing Liu', 'Jiang Bian', 'Dong Zhou'] | 2020-09-22 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-4.12748039e-01 7.01560453e-02 -2.80658202e-03 -3.90402317e-01
-1.72612444e-01 -5.56192279e-01 6.36960208e-01 -8.81617665e-02
-3.74677509e-01 4.29598898e-01 1.75470114e-02 -4.55164194e-01
-5.13331950e-01 -1.17463410e+00 -2.33745828e-01 -1.73124596e-01
-1.41120896e-01 7.40230560e-01 -3.41218114e-01 -3.56061220... | [4.533057689666748, 4.063995838165283] |
44bb3de1-17ae-4e6b-9f69-2750f3dad64e | representation-learning-on-large-and-small | 1707.09873 | null | http://arxiv.org/abs/1707.09873v1 | http://arxiv.org/pdf/1707.09873v1.pdf | Representation Learning on Large and Small Data | Deep learning owes its success to three key factors: scale of data, enhanced
models to learn representations from data, and scale of computation. This book
chapter presented the importance of the data-driven approach to learn good
representations from both big data and small data. In terms of big data, it has
been wide... | ['Fu-Chieh Chang', 'Chuen-Kai Shie', 'Chun-Nan Chou', 'Jocelyn Chang', 'Edward Y. Chang'] | 2017-07-25 | null | null | null | null | ['melanoma-diagnosis'] | ['computer-vision'] | [ 1.34900033e-01 3.84226382e-01 -2.85614431e-01 -4.83290255e-01
-1.04478717e+00 -3.96966264e-02 1.19948640e-01 2.06634507e-01
-3.29483569e-01 7.72156596e-01 4.82337892e-01 -6.56396970e-02
-3.87579590e-01 -1.02123809e+00 -5.49957573e-01 -4.64451998e-01
6.21503070e-02 7.20039964e-01 -4.55477834e-01 -4.91940826... | [15.091412544250488, -2.324871301651001] |
8cdac840-c162-48b4-bba8-3d8fc15fcb6a | spell-checking-for-chinese | null | null | https://aclanthology.org/L12-1423 | https://aclanthology.org/L12-1423.pdf | Spell Checking for Chinese | This paper presents some novel results on Chinese spell checking. In this paper, a concise algorithm based on minimized-path segmentation is proposed to reduce the cost and suit the needs of current Chinese input systems. The proposed algorithm is actually derived from a simple assumption that spelling errors often mak... | ['Bao-liang Lu', 'Xiaolin Wang', 'Shaohua Yang', 'Hai Zhao'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['chinese-spell-checking'] | ['natural-language-processing'] | [ 3.13883841e-01 -1.22056901e-01 -2.15596139e-01 -4.98146921e-01
-8.09000790e-01 -7.97698617e-01 1.64460987e-01 3.00235808e-01
-6.57855988e-01 7.34631956e-01 -7.51348883e-02 -9.54861939e-01
2.45689824e-01 -4.91256535e-01 -5.46259761e-01 -4.21854407e-01
2.17671975e-01 2.59893954e-01 6.72342539e-01 -4.55142468... | [10.917216300964355, 10.82865047454834] |
becddd8b-fbd1-42e7-9794-796c22bcad7a | a-decomposition-dynamic-graph-convolutional | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0031320323003710 | https://www.sciencedirect.com/science/article/abs/pii/S0031320323003710 | A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting | Our daily lives are greatly impacted by traffic conditions, making it essential to have accurate predictions of traffic flow within a road network. Traffic signals used for forecasting are usually generated by sensors along roads, which can be represented as nodes on a graph. These sensors typically produce normal sign... | ['Wenchao Weng; Jin Fan; Huifeng Wu; Yujie Hu; Hao Tian; Fu Zhu; Jia Wu'] | 2023-05-01 | null | null | null | pattern-recognition-2023-5 | ['traffic-prediction'] | ['time-series'] | [ 3.49939093e-02 -1.82675615e-01 -1.75209448e-01 -3.59505773e-01
-3.27258324e-03 -1.55645534e-01 4.44872528e-01 -5.39073832e-02
2.53783315e-01 3.56785685e-01 2.40885779e-01 -7.04705775e-01
-1.21255204e-01 -1.33837533e+00 -4.69527036e-01 -3.09703499e-01
-4.04144168e-01 3.18264872e-01 4.57391441e-01 -4.35980380... | [6.468679428100586, 2.046082019805908] |
660e2118-11cf-4a5c-8443-c7e2229b7771 | semantic-reinforced-attention-learning-for | 2108.08443 | null | https://arxiv.org/abs/2108.08443v1 | https://arxiv.org/pdf/2108.08443v1.pdf | Semantic Reinforced Attention Learning for Visual Place Recognition | Large-scale visual place recognition (VPR) is inherently challenging because not all visual cues in the image are beneficial to the task. In order to highlight the task-relevant visual cues in the feature embedding, the existing attention mechanisms are either based on artificial rules or trained in a thorough data-dri... | ['Danwei Wang', 'Xiaoyu Tang', 'Zhenyu Wu', 'Jun Zhang', 'Yufeng Yue', 'Guohao Peng'] | 2021-08-19 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 3.07099462e-01 1.15772873e-01 -3.44317108e-01 -4.47374284e-01
-5.37341118e-01 -3.20568591e-01 7.81528056e-01 8.68418217e-02
-2.51409888e-01 5.23721457e-01 7.17029810e-01 -1.21612595e-02
-2.95609385e-01 -6.90035164e-01 -7.69914210e-01 -7.36253977e-01
2.57555872e-01 -2.11833101e-02 3.97499591e-01 -4.90293741... | [9.833759307861328, 0.12119445204734802] |
272aa3fe-8b85-47b3-9818-e29774cfdaa6 | rgb-d-and-thermal-sensor-fusion-a-systematic | 2305.11427 | null | https://arxiv.org/abs/2305.11427v2 | https://arxiv.org/pdf/2305.11427v2.pdf | RGB-D And Thermal Sensor Fusion: A Systematic Literature Review | In the last decade, the computer vision field has seen significant progress in multimodal data fusion and learning, where multiple sensors, including depth, infrared, and visual, are used to capture the environment across diverse spectral ranges. Despite these advancements, there has been no systematic and comprehensiv... | ['Andre L. C. Barczak', 'Teo Susnjak', 'Napoleon H. Reyes', 'Martin Brenner'] | 2023-05-19 | null | null | null | null | ['3d-reconstruction', 'fault-detection'] | ['computer-vision', 'miscellaneous'] | [ 3.18330944e-01 -4.24055755e-01 6.54789954e-02 -4.18438882e-01
-7.83621430e-01 -3.65134686e-01 3.55574757e-01 2.11898070e-02
-5.76759815e-01 2.94415355e-01 -2.53461361e-01 -3.73675585e-01
-2.44034648e-01 -6.12196565e-01 -1.33865878e-01 -1.17517650e+00
1.98670730e-01 1.09383360e-01 4.99833673e-02 -1.79897472... | [8.435394287109375, -2.158364772796631] |
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