paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
f4651457-4c87-4aea-b22f-133937a582e9 | time-dependent-entity-embedding-is-not-all | null | null | https://aclanthology.org/2021.emnlp-main.639 | https://aclanthology.org/2021.emnlp-main.639.pdf | Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework | Various temporal knowledge graph (KG) completion models have been proposed in the recent literature. The models usually contain two parts, a temporal embedding layer and a score function derived from existing static KG modeling approaches. Since the approaches differ along several dimensions, including different score ... | ['Volker Tresp', 'Yunpu Ma', 'Gengyuan Zhang', 'Zhen Han'] | null | null | null | null | emnlp-2021-11 | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-4.72682387e-01 3.06727011e-02 -5.69812715e-01 -9.11633596e-02
-3.73761147e-01 -5.37437856e-01 8.58007967e-01 4.57242221e-01
-5.02723336e-01 4.16165948e-01 3.91614825e-01 -3.26584727e-01
-4.74650621e-01 -9.69241023e-01 -5.98978281e-01 -5.23319721e-01
-5.25772631e-01 5.27478755e-01 4.83719617e-01 -2.23384023... | [8.625533103942871, 7.841413497924805] |
c25667a7-5283-49c0-930c-819479e0122a | multi-agent-path-finding-with-deadlines-1 | 1805.04961 | null | http://arxiv.org/abs/1805.04961v1 | http://arxiv.org/pdf/1805.04961v1.pdf | Multi-Agent Path Finding with Deadlines: Preliminary Results | We formalize the problem of multi-agent path finding with deadlines
(MAPF-DL). The objective is to maximize the number of agents that can reach
their given goal vertices from their given start vertices within a given
deadline, without colliding with each other. We first show that the MAPF-DL
problem is NP-hard to solve... | ['Ariel Felner', 'Sven Koenig', 'Glenn Wagner', 'T. K. Satish Kumar', 'Jiaoyang Li', 'Hang Ma'] | 2018-05-13 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-2.01405719e-01 4.68276232e-01 -3.32372934e-01 -2.31215566e-01
-8.02541599e-02 -9.88752604e-01 -7.26347510e-03 5.24365246e-01
-3.98503929e-01 1.22921419e+00 -3.14065963e-01 -2.06663549e-01
-9.44784880e-01 -1.09798932e+00 -5.90433061e-01 -3.62805098e-01
-1.03599393e+00 1.27935123e+00 3.91414553e-01 -2.41011307... | [4.977317810058594, 1.8222259283065796] |
518666c7-e8be-484e-b56d-653a98faffea | online-and-offline-handwritten-chinese | 1606.05763 | null | http://arxiv.org/abs/1606.05763v1 | http://arxiv.org/pdf/1606.05763v1.pdf | Online and Offline Handwritten Chinese Character Recognition: A Comprehensive Study and New Benchmark | Recent deep learning based methods have achieved the state-of-the-art
performance for handwritten Chinese character recognition (HCCR) by learning
discriminative representations directly from raw data. Nevertheless, we believe
that the long-and-well investigated domain-specific knowledge should still help
to boost the ... | ['Cheng-Lin Liu', 'Xu-Yao Zhang', 'Yoshua Bengio'] | 2016-06-18 | null | null | null | null | ['offline-handwritten-chinese-character', 'offline-handwritten-chinese-character'] | ['computer-vision', 'natural-language-processing'] | [-2.77073495e-02 -4.83661681e-01 6.30026981e-02 -6.21979833e-01
-5.64553916e-01 -4.42948043e-01 6.59698427e-01 -1.67247444e-01
-7.27442503e-01 5.53078413e-01 -5.59156835e-02 -6.87476844e-02
4.50640917e-02 -7.16501176e-01 -5.29355109e-01 -7.82556117e-01
3.18651736e-01 5.73580444e-01 2.87259132e-01 -3.74302298... | [11.823775291442871, 2.591913938522339] |
aa3216ba-3f78-4f51-a3e5-89dafedb231d | a-comparison-of-data-augmentation-techniques | 2003.13502 | null | https://arxiv.org/abs/2003.13502v2 | https://arxiv.org/pdf/2003.13502v2.pdf | An Open-source Tool for Hyperspectral Image Augmentation in Tensorflow | Satellite imagery allows a plethora of applications ranging from weather forecasting to land surveying. The rapid development of computer vision systems could open new horizons to the utilization of satellite data due to the abundance of large volumes of data. However, current state-of-the-art computer vision systems m... | ['Mohamed Abdelhack'] | 2020-03-30 | null | null | null | null | ['satellite-image-classification'] | ['computer-vision'] | [ 2.29550168e-01 -2.76543438e-01 6.79909736e-02 -4.31917161e-01
-1.60126597e-01 -4.82638448e-01 4.22300667e-01 8.57309066e-03
-4.93013293e-01 4.77174163e-01 -1.77078187e-01 -6.42747819e-01
-1.38681531e-01 -1.04189932e+00 -3.38020563e-01 -8.61503720e-01
-5.46164811e-01 2.63313174e-01 -1.38642922e-01 -5.09610116... | [9.609736442565918, -1.5363012552261353] |
8e06f89c-b667-4776-8d94-6c2debb41e7c | iterative-potts-minimization-for-the-recovery | 1812.00862 | null | https://arxiv.org/abs/1812.00862v2 | https://arxiv.org/pdf/1812.00862v2.pdf | Iterative Potts minimization for the recovery of signals with discontinuities from indirect measurements -- the multivariate case | Signals and images with discontinuities appear in many problems in such diverse areas as biology, medicine, mechanics, and electrical engineering. The concrete data are often discrete, indirect and noisy measurements of some quantities describing the signal under consideration. A frequent task is to find the segments o... | ['Lukas Kiefer', 'Andreas Weinmann', 'Martin Storath'] | 2018-12-03 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [ 4.10178483e-01 -1.95866019e-01 2.04186052e-01 -2.50533074e-01
-7.58388221e-01 -1.20595604e-01 9.11582112e-02 3.39652240e-01
-5.52010953e-01 1.00149500e+00 -2.63906986e-01 5.22022061e-02
-3.06346178e-01 -6.38067842e-01 -7.39272892e-01 -7.80315757e-01
-1.92495912e-01 2.75517493e-01 -2.23174952e-02 -6.89209392... | [7.233721733093262, 3.973675489425659] |
03987820-2690-4789-8fc3-8a50ae860aa7 | beyond-the-camera-neural-networks-in-world | 2003.05614 | null | https://arxiv.org/abs/2003.05614v1 | https://arxiv.org/pdf/2003.05614v1.pdf | Beyond the Camera: Neural Networks in World Coordinates | Eye movement and strategic placement of the visual field onto the retina, gives animals increased resolution of the scene and suppresses distracting information. This fundamental system has been missing from video understanding with deep networks, typically limited to 224 by 224 pixel content locked to the camera frame... | ['Karteek Alahari', 'Cordelia Schmid', 'Gunnar A. Sigurdsson', 'Abhinav Gupta'] | 2020-03-12 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [-5.91493025e-02 -2.72950321e-01 1.27750263e-01 -3.54307562e-01
3.74582827e-01 -4.63590950e-01 3.07241350e-01 -3.54813367e-01
-7.46619284e-01 5.45309663e-01 7.37347975e-02 1.60141125e-01
-8.26818645e-02 -3.80768865e-01 -1.07000613e+00 -6.01540923e-01
-1.10292487e-01 -3.64584565e-01 6.56452060e-01 -1.32051840... | [9.2272367477417, 1.5494073629379272] |
485cdace-4642-4dc3-a6c5-0dfa9dcd499f | beyond-instance-level-image-retrieval | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Gordo_Beyond_Instance-Level_Image_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Gordo_Beyond_Instance-Level_Image_CVPR_2017_paper.pdf | Beyond Instance-Level Image Retrieval: Leveraging Captions to Learn a Global Visual Representation for Semantic Retrieval | Querying with an example image is a simple and intuitive interface to retrieve information from a visual database. Most of the research in image retrieval has focused on the task of instance-level image retrieval, where the goal is to retrieve images that contain the same object instance as the query image. In this wor... | ['Diane Larlus', 'Albert Gordo'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['semantic-retrieval'] | ['natural-language-processing'] | [ 2.21927404e-01 6.14571348e-02 -3.14235777e-01 -5.38071036e-01
-9.39176202e-01 -7.84264088e-01 7.94905603e-01 7.15576768e-01
-4.50246632e-01 1.12865552e-01 3.97768974e-01 1.29029542e-01
-1.69608295e-01 -6.38314068e-01 -8.84322822e-01 -5.21012127e-01
1.65957913e-01 4.03119147e-01 2.81439602e-01 -2.22645864... | [10.777669906616211, 1.3182439804077148] |
f5aef0f3-fdb1-46a5-827d-092e308d61e6 | rethinking-the-objectives-of-vector-quantized | 2212.03185 | null | https://arxiv.org/abs/2212.03185v2 | https://arxiv.org/pdf/2212.03185v2.pdf | Rethinking the Objectives of Vector-Quantized Tokenizers for Image Synthesis | Vector-Quantized (VQ-based) generative models usually consist of two basic components, i.e., VQ tokenizers and generative transformers. Prior research focuses on improving the reconstruction fidelity of VQ tokenizers but rarely examines how the improvement in reconstruction affects the generation ability of generative ... | ['Mike Zheng Shou', 'XiaoHu Qie', 'Ying Shan', 'Yixiao Ge', 'Xintao Wang', 'YuChao Gu'] | 2022-12-06 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 3.58518273e-01 1.81023985e-01 -9.93053019e-02 -2.24417225e-01
-8.31070781e-01 -3.54793370e-01 7.40762830e-01 -3.90200078e-01
2.65032314e-02 7.18793273e-01 5.66641033e-01 -2.34331876e-01
1.03926353e-01 -1.19018197e+00 -8.34316194e-01 -8.22274148e-01
2.64954001e-01 7.90493861e-02 1.18810050e-01 -2.06968099... | [11.515533447265625, -0.6174464225769043] |
eeb3903a-e9a0-447a-92c8-4208fbab6e75 | text-based-sentiment-analysis-and-music | 1810.03031 | null | http://arxiv.org/abs/1810.03031v1 | http://arxiv.org/pdf/1810.03031v1.pdf | Text-based Sentiment Analysis and Music Emotion Recognition | Sentiment polarity of tweets, blog posts or product reviews has become highly
attractive and is utilized in recommender systems, market predictions, business
intelligence and more. Deep learning techniques are becoming top performers on
analyzing such texts. There are however several problems that need to be solved
for... | ['Erion Çano'] | 2018-10-06 | null | null | null | null | ['music-emotion-recognition'] | ['music'] | [-1.20324880e-01 -6.23658262e-02 -1.49553716e-01 -6.16936862e-01
-9.30848271e-02 -5.24076521e-01 4.97711748e-01 4.53980744e-01
-6.87583208e-01 5.75352013e-01 3.77531886e-01 -1.59169674e-01
1.41170949e-01 -8.80604029e-01 -2.99400747e-01 -5.51015377e-01
1.76377445e-01 3.76977146e-01 8.58217925e-02 -8.46937597... | [11.138564109802246, 7.050936222076416] |
dd948ead-2b61-43e7-a14a-53e31fb6ba5b | classification-of-goods-using-text | 2111.01663 | null | https://arxiv.org/abs/2111.01663v1 | https://arxiv.org/pdf/2111.01663v1.pdf | Classification of Goods Using Text Descriptions With Sentences Retrieval | The task of assigning and validating internationally accepted commodity code (HS code) to traded goods is one of the critical functions at the customs office. This decision is crucial to importers and exporters, as it determines the tariff rate. However, similar to court decisions made by judges, the task can be non-tr... | ['Heeja Kim', 'Minsoo Song', 'Sungdae Ji', 'Yeonsoo Choi', 'Suyoung Yang', 'Soyeon Jung', 'Meeyoung Cha', 'Sungwon Park', 'Sihyun Kim', 'Sundong Kim', 'Eunji Lee'] | 2021-11-02 | null | null | null | null | ['code-classification'] | ['computer-code'] | [-4.56723660e-01 -2.21290246e-01 -5.06721139e-01 -6.56495154e-01
-7.00094223e-01 -1.22227335e+00 2.29111925e-01 3.00272971e-01
-3.25175494e-01 3.68339717e-01 1.35621905e-01 -1.22112930e+00
-1.78289145e-01 -6.66636407e-01 -5.61939657e-01 -3.65740776e-01
-3.30508575e-02 5.50133884e-01 -4.15644765e-01 -2.15720013... | [9.647439002990723, 6.143615245819092] |
a4deac45-5d35-4632-ad24-96468087ab28 | fully-autonomous-programming-with-large | 2304.10423 | null | https://arxiv.org/abs/2304.10423v1 | https://arxiv.org/pdf/2304.10423v1.pdf | Fully Autonomous Programming with Large Language Models | Current approaches to program synthesis with Large Language Models (LLMs) exhibit a "near miss syndrome": they tend to generate programs that semantically resemble the correct answer (as measured by text similarity metrics or human evaluation), but achieve a low or even zero accuracy as measured by unit tests due to sm... | ['Leon Moonen', 'Aki Härmä', 'Anastasiia Grishina', 'Vadim Liventsev'] | 2023-04-20 | null | null | null | null | ['program-repair', 'program-synthesis', 'program-repair'] | ['computer-code', 'computer-code', 'reasoning'] | [ 1.80865616e-01 5.51621206e-02 -1.77276522e-01 -3.39247197e-01
-1.08969128e+00 -7.52846718e-01 4.88834500e-01 4.75984871e-01
-4.86961491e-02 6.07680082e-01 3.04525848e-02 -8.71869385e-01
9.63801518e-02 -8.44498158e-01 -7.09639251e-01 -1.61531687e-01
1.43289611e-01 5.32683611e-01 4.38830078e-01 -2.60299712... | [7.861971378326416, 7.576777458190918] |
0d16ccf9-269b-47b3-b888-2cd4048dcf59 | enhancing-the-robustness-of-qmix-against | 2307.00907 | null | https://arxiv.org/abs/2307.00907v1 | https://arxiv.org/pdf/2307.00907v1.pdf | Enhancing the Robustness of QMIX against State-adversarial Attacks | Deep reinforcement learning (DRL) performance is generally impacted by state-adversarial attacks, a perturbation applied to an agent's observation. Most recent research has concentrated on robust single-agent reinforcement learning (SARL) algorithms against state-adversarial attacks. Still, there has yet to be much wor... | ['Jiacun Wang', 'Ling Wang', 'Ziyuan Zhou', 'Guanjun Liu', 'Weiran Guo'] | 2023-07-03 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-1.49856955e-01 9.26576704e-02 -1.58338830e-01 1.85789570e-01
-9.48436975e-01 -8.03587854e-01 8.65449011e-01 2.02077776e-01
-5.87694287e-01 1.06155491e+00 -1.13889799e-01 -2.81056434e-01
-5.10678068e-02 -9.74067748e-01 -8.52979779e-01 -9.86156225e-01
-6.93379402e-01 4.63769913e-01 5.96294820e-01 -6.72227144... | [3.848306655883789, 2.2773351669311523] |
d5fef5ec-ef20-4a59-a326-ed246c1ec5ae | fast-interactive-search-with-a-scale-free | 2306.01814 | null | https://arxiv.org/abs/2306.01814v1 | https://arxiv.org/pdf/2306.01814v1.pdf | Fast Interactive Search with a Scale-Free Comparison Oracle | A comparison-based search algorithm lets a user find a target item $t$ in a database by answering queries of the form, ``Which of items $i$ and $j$ is closer to $t$?'' Instead of formulating an explicit query (such as one or several keywords), the user navigates towards the target via a sequence of such (typically nois... | ['Matthias Grossglauser', 'Lucas Maystre', 'Lars Klein', 'Daniyar Chumbalov'] | 2023-06-02 | null | null | null | null | ['navigate'] | ['reasoning'] | [ 1.14128321e-01 -1.37628198e-01 -2.88012564e-01 -5.07649541e-01
-1.47459221e+00 -9.52238619e-01 9.81680304e-02 3.66836250e-01
-6.32697523e-01 4.95390505e-01 -4.30120200e-01 -2.35611320e-01
-7.01385379e-01 -7.55015373e-01 -8.17232192e-01 -5.54315507e-01
-2.21425727e-01 1.05963969e+00 2.62869269e-01 -2.33111128... | [6.59250020980835, 4.523097991943359] |
b4c12f78-b24b-4778-83d4-b2d2c59d3c02 | selective-encoding-for-abstractive-sentence | 1704.07073 | null | http://arxiv.org/abs/1704.07073v1 | http://arxiv.org/pdf/1704.07073v1.pdf | Selective Encoding for Abstractive Sentence Summarization | We propose a selective encoding model to extend the sequence-to-sequence
framework for abstractive sentence summarization. It consists of a sentence
encoder, a selective gate network, and an attention equipped decoder. The
sentence encoder and decoder are built with recurrent neural networks. The
selective gate network... | ['Nan Yang', 'Furu Wei', 'Qingyu Zhou', 'Ming Zhou'] | 2017-04-24 | selective-encoding-for-abstractive-sentence-1 | https://aclanthology.org/P17-1101 | https://aclanthology.org/P17-1101.pdf | acl-2017-7 | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 5.81673622e-01 4.88968730e-01 -1.06973149e-01 -3.29451174e-01
-9.96989906e-01 -1.56736210e-01 4.49060827e-01 4.12228823e-01
-4.13293362e-01 9.09245372e-01 1.27322519e+00 -8.72599036e-02
5.62140465e-01 -5.96128762e-01 -6.86096430e-01 -3.14817607e-01
2.10028633e-01 1.32566333e-01 1.39580190e-01 -5.18792927... | [12.502543449401855, 9.530150413513184] |
a38f4621-7f74-4227-ad75-f5095eb9ca43 | neutron-induced-single-event-effects-on | 2102.00112 | null | https://arxiv.org/abs/2102.00112v1 | https://arxiv.org/pdf/2102.00112v1.pdf | Neutron-Induced, Single-Event Effects on Neuromorphic Event-based Vision Sensor: A First Step Towards Space Applications | This paper studies the suitability of neuromorphic event-based vision cameras for spaceflight, and the effects of neutron radiation on their performance. Neuromorphic event-based vision cameras are novel sensors that implement asynchronous, clockless data acquisition, providing information about the change in illuminan... | ['Ryad Benosman', 'Bernabé Linares-Barranco', 'Alan D. George', 'Himanshu Akolkar', 'Seth Roffe'] | 2021-01-29 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 2.40106031e-01 -8.15933764e-01 7.37107754e-01 -1.86823651e-01
-8.16639513e-03 -4.02029216e-01 7.80912161e-01 1.76300243e-01
-1.11919570e+00 6.90766633e-01 4.18519266e-02 -4.72489558e-02
-1.59391806e-01 -7.62737036e-01 -6.98078930e-01 -8.04426253e-01
-1.16176829e-01 2.08730161e-01 7.77905643e-01 -9.81952772... | [8.756023406982422, -1.2848315238952637] |
acae04e4-2227-46ec-a3b4-ce239421970b | on-the-privacy-utility-trade-off-in | 2103.02895 | null | https://arxiv.org/abs/2103.02895v2 | https://arxiv.org/pdf/2103.02895v2.pdf | On the privacy-utility trade-off in differentially private hierarchical text classification | Hierarchical text classification consists in classifying text documents into a hierarchy of classes and sub-classes. Although artificial neural networks have proved useful to perform this task, unfortunately they can leak training data information to adversaries due to training data memorization. Using differential pri... | ['Thorsten Strufe', 'Javier Parra-Arnau', 'Francesco Aldà', 'Daniel Bernau', 'Dominik Wunderlich'] | 2021-03-04 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 4.65076715e-01 3.63756716e-01 6.82514831e-02 -5.80767512e-01
-9.71159875e-01 -1.12148035e+00 6.57337368e-01 4.94541436e-01
-8.40509951e-01 4.13123012e-01 -4.18117344e-02 -8.48448396e-01
9.61845964e-02 -7.76841760e-01 -6.32851958e-01 -6.77231371e-01
-1.86240867e-01 2.39148408e-01 -1.06056377e-01 1.14387840... | [5.94254732131958, 6.951089859008789] |
f69aca99-9984-4e64-85e0-2c7cc875fe82 | dfnet-enhance-aboslute-pose-regression-with | 2204.00559 | null | https://arxiv.org/abs/2204.00559v4 | https://arxiv.org/pdf/2204.00559v4.pdf | DFNet: Enhance Absolute Pose Regression with Direct Feature Matching | We introduce a camera relocalization pipeline that combines absolute pose regression (APR) and direct feature matching. By incorporating exposure-adaptive novel view synthesis, our method successfully addresses photometric distortions in outdoor environments that existing photometric-based methods fail to handle. With ... | ['Victor Adrian Prisacariu', 'ZiRui Wang', 'Xinghui Li', 'Shuai Chen'] | 2022-04-01 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [ 4.55697805e-01 -4.68446910e-02 1.37939200e-01 -3.83611888e-01
-1.20569956e+00 -1.15562892e+00 7.05894172e-01 -4.86906111e-01
-2.74824709e-01 5.36938906e-01 1.92844763e-01 1.33672714e-01
2.77322680e-01 -5.80223620e-01 -1.05734336e+00 -4.24591452e-01
6.99350953e-01 4.33251113e-01 4.18334812e-01 -1.13141052... | [8.282886505126953, -2.4643445014953613] |
71f94576-29f7-4f3a-b3c1-4ee0c6b6742a | biometric-face-presentation-attack-detection-1 | 1909.08848 | null | https://arxiv.org/abs/1909.08848v1 | https://arxiv.org/pdf/1909.08848v1.pdf | Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network | Face recognition is a mainstream biometric authentication method. However, vulnerability to presentation attacks (a.k.a spoofing) limits its usability in unsupervised applications. Even though there are many methods available for tackling presentation attacks (PA), most of them fail to detect sophisticated attacks such... | ['Andre Anjos', 'Olegs Nikisins', 'Anjith George', 'Zohreh Mostaani', 'David Geissenbuhler', 'Sebastien Marcel'] | 2019-09-19 | biometric-face-presentation-attack-detection | http://publications.idiap.ch/downloads/papers/2019/George_TIFS_2019.pdf | http://publications.idiap.ch/downloads/papers/2019/George_TIFS_2019.pdf | ieee-transactions-on-information-forensics-1 | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 4.49684650e-01 -4.92668033e-01 2.38812447e-01 -7.10190386e-02
-7.35401809e-01 -1.02918541e+00 7.02323794e-01 -4.76908460e-02
-2.06115365e-01 3.19840521e-01 -1.18010469e-01 -5.11963308e-01
-9.12812278e-02 -5.60818613e-01 -2.94459850e-01 -9.73009884e-01
-3.44135225e-01 -3.73507351e-01 -8.55136812e-02 -2.85330057... | [13.074593544006348, 1.0952194929122925] |
8d9c1ddb-324f-41fa-b456-e81ce629a659 | alterfactual-explanations-the-relevance-of | 2207.09374 | null | https://arxiv.org/abs/2207.09374v1 | https://arxiv.org/pdf/2207.09374v1.pdf | Alterfactual Explanations -- The Relevance of Irrelevance for Explaining AI Systems | Explanation mechanisms from the field of Counterfactual Thinking are a widely-used paradigm for Explainable Artificial Intelligence (XAI), as they follow a natural way of reasoning that humans are familiar with. However, all common approaches from this field are based on communicating information about features or char... | ['Elisabeth André', 'Ruben Schlagowski', 'Katharina Weitz', 'Tobias Huber', 'Christina Karle', 'Silvan Mertes'] | 2022-07-19 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 5.91827512e-01 8.86094868e-01 1.09263450e-01 -3.86196703e-01
1.86884090e-01 -6.06072664e-01 1.00364840e+00 4.29280907e-01
-2.46542349e-01 6.05026662e-01 4.07252550e-01 -7.68573165e-01
-1.54025748e-01 -9.83557642e-01 -7.31586814e-01 -1.18325315e-01
1.81323066e-01 3.95743221e-01 1.00428708e-01 -6.43609345... | [8.885815620422363, 6.092493534088135] |
c4a21854-0b92-4a67-8c88-922e94a50a24 | evaluation-of-noise-reduction-methods-for | 2303.17829 | null | https://arxiv.org/abs/2303.17829v3 | https://arxiv.org/pdf/2303.17829v3.pdf | Evaluation of Noise Reduction Methods for Sentence Recognition by Sinhala Speaking Listeners | Noise reduction is a crucial aspect of hearing aids, which researchers have been striving to address over the years. However, most existing noise reduction algorithms have primarily been evaluated using English. Considering the linguistic differences between English and Sinhala languages, including variation in syllabl... | ['Anjula De Silva', 'Nipuna Upeksha', 'Dinithi Fernando', 'Chathuki Navanjana', 'Malitha Gunawardhana'] | 2023-03-31 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [-3.70551869e-02 -5.71626127e-01 3.61118585e-01 -5.53031676e-02
-1.09549582e+00 -4.05615151e-01 -2.87321638e-02 2.50433296e-01
-6.94095790e-01 6.44598663e-01 5.72980940e-01 -3.51353347e-01
-4.23001766e-01 -6.06913626e-01 1.90250814e-01 -8.33347499e-01
6.08608779e-03 -2.54952222e-01 3.38955700e-01 -4.80073214... | [15.057908058166504, 5.74766731262207] |
a8522f73-213c-4fc7-b49b-454c38d4a078 | generalisation-and-sharing-in-triplet | 1611.05301 | null | http://arxiv.org/abs/1611.05301v1 | http://arxiv.org/pdf/1611.05301v1.pdf | Generalisation and Sharing in Triplet Convnets for Sketch based Visual Search | We propose and evaluate several triplet CNN architectures for measuring the
similarity between sketches and photographs, within the context of the sketch
based image retrieval (SBIR) task. In contrast to recent fine-grained SBIR
work, we study the ability of our networks to generalise across diverse object
categories f... | ['Leonardo Ribeiro', 'John Collomosse', 'Moacir Ponti', 'Tu Bui'] | 2016-11-16 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 1.97847828e-01 -5.63043237e-01 6.38961568e-02 -5.10942876e-01
-7.05887020e-01 -8.65089715e-01 8.36666822e-01 -5.10494858e-02
-5.69587708e-01 4.17991549e-01 3.35710645e-01 -1.44249186e-01
-8.64785194e-01 -7.46569216e-01 -4.03988123e-01 -1.83915198e-01
-2.80000623e-02 3.36471349e-01 -1.53650627e-01 -2.97508329... | [11.62424087524414, 0.5299427509307861] |
612a8cad-c197-4698-9802-29fd9e6aa6e5 | synthesize-extremely-high-dimensional | 2304.02169 | null | https://arxiv.org/abs/2304.02169v1 | https://arxiv.org/pdf/2304.02169v1.pdf | Synthesize Extremely High-dimensional Longitudinal Electronic Health Records via Hierarchical Autoregressive Language Model | Synthetic electronic health records (EHRs) that are both realistic and preserve privacy can serve as an alternative to real EHRs for machine learning (ML) modeling and statistical analysis. However, generating high-fidelity and granular electronic health record (EHR) data in its original, highly-dimensional form poses ... | ['Jimeng Sun', 'Cao Xiao', 'Brandon Theodorou'] | 2023-04-04 | null | null | null | null | ['variable-selection'] | ['methodology'] | [-8.98079425e-02 4.43535268e-01 7.93018192e-02 -5.68742394e-01
-1.34570014e+00 -3.58634621e-01 1.12008214e-01 6.56905055e-01
-1.80552423e-01 1.02724755e+00 4.87947732e-01 -5.11214674e-01
-9.41149816e-02 -8.69010270e-01 -8.80313873e-01 -3.41999143e-01
-4.37208354e-01 5.71154952e-01 -6.43154442e-01 4.34231877... | [6.415262699127197, 6.680747985839844] |
01f6abe7-d6d1-4bd0-8851-1cbfbe48454a | the-runner-up-solution-for-youtube-vis-long | 2211.09973 | null | https://arxiv.org/abs/2211.09973v1 | https://arxiv.org/pdf/2211.09973v1.pdf | The Runner-up Solution for YouTube-VIS Long Video Challenge 2022 | This technical report describes our 2nd-place solution for the ECCV 2022 YouTube-VIS Long Video Challenge. We adopt the previously proposed online video instance segmentation method IDOL for this challenge. In addition, we use pseudo labels to further help contrastive learning, so as to obtain more temporally consisten... | ['Song Bai', 'Xiang Bai', 'Qihao Liu', 'Yi Jiang', 'Junfeng Wu'] | 2022-11-18 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [-4.01785374e-01 -2.55342185e-01 -7.26709068e-01 -1.39101878e-01
-9.04145896e-01 -7.66570628e-01 1.85079336e-01 -3.43620539e-01
-6.35813057e-01 7.92158186e-01 -1.05234999e-02 -2.58649141e-01
2.51201928e-01 -1.95118353e-01 -6.64510250e-01 -3.20530355e-01
-3.81234884e-01 -1.98573604e-01 8.32392156e-01 1.96010917... | [9.171192169189453, 0.016688993200659752] |
9f172c26-aec2-4972-9b41-bfa653e753c5 | improved-low-resource-somali-speech | 1907.03064 | null | https://arxiv.org/abs/1907.03064v1 | https://arxiv.org/pdf/1907.03064v1.pdf | Improved low-resource Somali speech recognition by semi-supervised acoustic and language model training | We present improvements in automatic speech recognition (ASR) for Somali, a currently extremely under-resourced language. This forms part of a continuing United Nations (UN) effort to employ ASR-based keyword spotting systems to support humanitarian relief programmes in rural Africa. Using just 1.57 hours of annotated ... | ['Thomas Niesler', 'Raghav Menon', 'Astik Biswas', 'Ewald van der Westhuizen'] | 2019-07-06 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 5.59239030e-01 1.53274670e-01 2.16030017e-01 -4.48257864e-01
-1.47572494e+00 -4.82936710e-01 5.52095532e-01 1.59865230e-01
-9.44802284e-01 5.98375916e-01 6.36739314e-01 -8.87938857e-01
2.12712884e-01 -2.64956594e-01 -1.98849007e-01 -6.61847234e-01
-1.41501039e-01 6.02342963e-01 -1.28694579e-01 -4.23533112... | [14.375754356384277, 6.857481479644775] |
1e163303-6466-496c-82f1-e483ac8a1dd5 | diverse-instance-discovery-vision-transformer | 2204.10731 | null | https://arxiv.org/abs/2204.10731v1 | https://arxiv.org/pdf/2204.10731v1.pdf | Diverse Instance Discovery: Vision-Transformer for Instance-Aware Multi-Label Image Recognition | Previous works on multi-label image recognition (MLIR) usually use CNNs as a starting point for research. In this paper, we take pure Vision Transformer (ViT) as the research base and make full use of the advantages of Transformer with long-range dependency modeling to circumvent the disadvantages of CNNs limited to lo... | ['Hui Xue', 'Yuan He', 'Feihu Yan', 'Jingfeng Zhang', 'Haiwen Hong', 'Yin Zhang', 'Xuan Jin', 'Yunqing Hu'] | 2022-04-22 | null | null | null | null | ['weakly-supervised-object-localization'] | ['computer-vision'] | [ 2.26220593e-01 -3.03305775e-01 -2.62527525e-01 -6.28679812e-01
-8.30838263e-01 -4.81901407e-01 5.19168198e-01 -3.81575227e-02
-3.64544004e-01 4.75942135e-01 -9.32434872e-02 1.71349812e-02
-9.83603075e-02 -6.80330634e-01 -7.79339910e-01 -7.60955572e-01
6.10953212e-01 2.43230760e-01 4.92488891e-01 5.81374429... | [9.793025970458984, 3.877256393432617] |
2670277f-ac16-4677-bbaf-6c212a379224 | language-guided-image-clustering | null | null | https://openreview.net/forum?id=-JW-1Fg-v2 | https://openreview.net/pdf?id=-JW-1Fg-v2 | Language-Guided Image Clustering | Image clustering methods have rapidly improved their ability to discover object categories. However, unsupervised clustering methods struggle on other image attributes, e.g. age or activity. The reason is that most recent clustering methods learn deep features that are designed to be sensitive to object category, but l... | ['Yedid Hoshen', 'Niv Cohen'] | 2021-09-29 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 5.77851459e-02 -1.82780743e-01 -2.77371287e-01 -6.55471683e-01
-6.78573549e-01 -6.45322919e-01 7.27394700e-01 5.86020887e-01
-6.33954287e-01 9.20788050e-02 2.45290026e-01 1.80358559e-01
-2.99505174e-01 -7.05375314e-01 -3.78717691e-01 -1.14139223e+00
2.68279146e-02 7.69052863e-01 -1.34963363e-01 3.78972858... | [9.288463592529297, 3.008788585662842] |
c4e88a07-ddec-4753-9232-f0ccbdec0ea7 | self-supervised-learning-of-a-tailored | 2303.11837 | null | https://arxiv.org/abs/2303.11837v1 | https://arxiv.org/pdf/2303.11837v1.pdf | Self-supervised learning of a tailored Convolutional Auto Encoder for histopathological prostate grading | According to GLOBOCAN 2020, prostate cancer is the second most common cancer in men worldwide and the fourth most prevalent cancer overall. For pathologists, grading prostate cancer is challenging, especially when discriminating between Grade 3 (G3) and Grade 4 (G4). This paper proposes a Self-Supervised Learning (SSL)... | ['Valery Naranjo', 'Javier Oliver', 'Kjersti Engan', 'Adrian colomer', 'Zahra Tabatabaei'] | 2023-03-21 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 3.05709302e-01 5.88649869e-01 -1.19441107e-01 -4.19829041e-01
-1.05857265e+00 -5.73520482e-01 7.03826487e-01 6.68219090e-01
-4.85742956e-01 6.24100208e-01 -1.06710248e-01 -2.36402959e-01
-7.81344846e-02 -8.13890874e-01 -3.21696550e-01 -9.73982036e-01
-1.51150912e-01 6.46984041e-01 -6.61252439e-02 4.21459526... | [15.052544593811035, -2.895550012588501] |
45392ec9-eb73-43cc-839a-444aaf5ddf1a | advest-adversarial-perturbation-estimation-to | 2204.03848 | null | https://arxiv.org/abs/2204.03848v1 | https://arxiv.org/pdf/2204.03848v1.pdf | AdvEst: Adversarial Perturbation Estimation to Classify and Detect Adversarial Attacks against Speaker Identification | Adversarial attacks pose a severe security threat to the state-of-the-art speaker identification systems, thereby making it vital to propose countermeasures against them. Building on our previous work that used representation learning to classify and detect adversarial attacks, we propose an improvement to it using Adv... | ['Najim Dehak', 'Jesus Villalba', 'Saurabh Kataria', 'Sonal Joshi'] | 2022-04-08 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 4.37180161e-01 1.20358638e-01 2.38050863e-01 -3.26220877e-02
-1.09474659e+00 -1.11299717e+00 7.01683283e-01 1.90022904e-02
-1.49133757e-01 5.37847757e-01 3.91711533e-01 -7.43556857e-01
7.01707182e-03 -5.18464088e-01 -6.99173212e-01 -7.67633736e-01
-5.11212051e-01 1.46664202e-01 6.93576527e-04 -6.89220130... | [13.976191520690918, 5.820969581604004] |
e50ff3a9-e3c2-45c1-afee-f126256bb488 | pretraining-boosts-out-of-domain-robustness | 1909.11229 | null | https://arxiv.org/abs/1909.11229v2 | https://arxiv.org/pdf/1909.11229v2.pdf | Pretraining boosts out-of-domain robustness for pose estimation | Neural networks are highly effective tools for pose estimation. However, as in other computer vision tasks, robustness to out-of-domain data remains a challenge, especially for small training sets that are common for real-world applications. Here, we probe the generalization ability with three architecture classes (Mob... | ['Steffen Schneider', 'Thomas Biasi', 'Mert Yüksekgönül', 'Alexander Mathis', 'Matthias Bethge', 'Mackenzie W. Mathis', 'Byron Rogers'] | 2019-09-24 | pretraining-boosts-out-of-domain-robustness-1 | https://openaccess.thecvf.com/content/WACV2021/papers/Mathis_Pretraining_Boosts_Out-of-Domain_Robustness_for_Pose_Estimation_WACV_2021_paper.pdf | https://openaccess.thecvf.com/content/WACV2021/papers/Mathis_Pretraining_Boosts_Out-of-Domain_Robustness_for_Pose_Estimation_WACV_2021_paper.pdf | null | ['animal-pose-estimation'] | ['computer-vision'] | [-2.24781632e-02 -6.62568361e-02 9.67935622e-02 -4.60376680e-01
-7.14401364e-01 -6.11807466e-01 3.17328066e-01 -3.01265776e-01
-1.00946021e+00 7.21851766e-01 2.84761935e-02 1.32194445e-01
-8.88048112e-02 -5.23788154e-01 -1.24153006e+00 -3.84788811e-01
-3.50173414e-01 4.75810498e-01 5.28541565e-01 -8.29845071... | [7.466031074523926, -0.9637004137039185] |
807f2394-3bfb-45fa-abb6-3018653f313f | alpha-matte-generation-from-single-input-for | 2106.03210 | null | https://arxiv.org/abs/2106.03210v3 | https://arxiv.org/pdf/2106.03210v3.pdf | Alpha Matte Generation from Single Input for Portrait Matting | In the portrait matting, the goal is to predict an alpha matte that identifies the effect of each pixel on the foreground subject. Traditional approaches and most of the existing works utilized an additional input, e.g., trimap, background image, to predict alpha matte. However, (1) providing additional input is not al... | ['Alexander Waibel', 'Hazim Kemal Ekenel', 'Dogucan Yaman'] | 2021-06-06 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 6.86459839e-01 2.36090440e-02 -1.07150577e-01 -3.39982301e-01
-3.48906964e-01 -3.75665069e-01 5.17680705e-01 -1.68012291e-01
-1.04583569e-01 7.11563349e-01 -8.10273886e-02 -1.77581355e-01
3.74182492e-01 -1.09441113e+00 -9.79460597e-01 -8.11275423e-01
4.90919918e-01 2.99022108e-01 6.50527775e-01 2.63414234... | [10.659375190734863, -0.9397047162055969] |
502b34aa-570f-4f1a-a7b4-4d957661c76e | temporal-relational-crosstransformers-for-few | 2101.06184 | null | https://arxiv.org/abs/2101.06184v3 | https://arxiv.org/pdf/2101.06184v3.pdf | Temporal-Relational CrossTransformers for Few-Shot Action Recognition | We propose a novel approach to few-shot action recognition, finding temporally-corresponding frame tuples between the query and videos in the support set. Distinct from previous few-shot works, we construct class prototypes using the CrossTransformer attention mechanism to observe relevant sub-sequences of all support ... | ['Dima Damen', 'Majid Mirmehdi', 'Tilo Burghardt', 'Alessandro Masullo', 'Toby Perrett'] | 2021-01-15 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Perrett_Temporal-Relational_CrossTransformers_for_Few-Shot_Action_Recognition_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Perrett_Temporal-Relational_CrossTransformers_for_Few-Shot_Action_Recognition_CVPR_2021_paper.pdf | cvpr-2021-1 | ['few-shot-action-recognition'] | ['computer-vision'] | [ 2.25696906e-01 -3.58361185e-01 -6.83155656e-01 -3.11489522e-01
-9.77971852e-01 -4.63441133e-01 9.06897247e-01 3.79037336e-02
-3.53876024e-01 5.25090039e-01 5.49655318e-01 2.75814623e-01
-2.09682345e-01 -2.32389569e-01 -9.48234856e-01 -3.90954942e-01
-5.74127555e-01 3.95128965e-01 9.01728690e-01 -3.99937257... | [8.630788803100586, 0.7941852807998657] |
9d84e679-412a-443f-96c0-56a88e979ed0 | joint-spatio-temporal-modeling-for-semantic | 2212.05245 | null | https://arxiv.org/abs/2212.05245v4 | https://arxiv.org/pdf/2212.05245v4.pdf | Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Images | Semantic Change Detection (SCD) refers to the task of simultaneously extracting the changed areas and the semantic categories (before and after the changes) in Remote Sensing Images (RSIs). This is more meaningful than Binary Change Detection (BCD) since it enables detailed change analysis in the observed areas. Previo... | ['Lorenzo Bruzzone', 'Bing Liu', 'Haitao Guo', 'Kai Zhang', 'Jing Zhang', 'Lei Ding'] | 2022-12-10 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 6.57178283e-01 -4.73022312e-01 3.31582613e-02 -6.48576915e-01
-4.19404984e-01 -5.83814502e-01 9.08869147e-01 3.03652465e-01
-3.45072836e-01 4.94879037e-01 2.67792553e-01 -3.12423110e-01
-2.83829629e-01 -1.04466617e+00 -7.17263997e-01 -7.16425359e-01
-2.38501444e-01 -1.36004239e-01 4.77157176e-01 -2.46718392... | [9.683335304260254, -1.3023813962936401] |
f125ffb0-d6b4-423d-9ada-88568c543ebd | automated-essay-scoring-using-transformers | 2210.12809 | null | https://arxiv.org/abs/2210.12809v5 | https://arxiv.org/pdf/2210.12809v5.pdf | Data Augmentation for Automated Essay Scoring using Transformer Models | Automated essay scoring is one of the most important problem in Natural Language Processing. It has been explored for a number of years, and it remains partially solved. In addition to its economic and educational usefulness, it presents research problems. Transfer learning has proved to be beneficial in NLP. Data augm... | ['Kshitij Gupta'] | 2022-10-23 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [-2.49173135e-01 3.99430394e-02 -2.85308003e-01 -5.01988769e-01
-5.90989053e-01 -2.62118042e-01 5.20003021e-01 3.52801263e-01
-4.71503764e-01 1.03774476e+00 4.85966474e-01 -4.77260560e-01
-3.84556621e-01 -7.67641902e-01 -8.21621716e-02 -3.43229115e-01
3.12323749e-01 6.58867478e-01 2.26768598e-01 -6.55570924... | [11.293671607971191, 9.318516731262207] |
cfb8697e-ee80-4363-a951-3d22c1adecf2 | mutual-learning-for-domain-adaptation-self | 2203.09430 | null | https://arxiv.org/abs/2203.09430v1 | https://arxiv.org/pdf/2203.09430v1.pdf | Mutual Learning for Domain Adaptation: Self-distillation Image Dehazing Network with Sample-cycle | Deep learning-based methods have made significant achievements for image dehazing. However, most of existing dehazing networks are concentrated on training models using simulated hazy images, resulting in generalization performance degradation when applied on real-world hazy images because of domain shift. In this pape... | ['ErKang Chen', 'Sixiang Chen', 'Yunchen Zhang', 'Yun Liu', 'Tian Ye'] | 2022-03-17 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 2.40392298e-01 -2.51713395e-01 1.94248900e-01 -3.29810679e-01
-5.29431403e-01 7.39191100e-02 4.28575486e-01 -7.61147067e-02
-6.66484118e-01 8.22809935e-01 -1.30955487e-01 -4.15088870e-02
-1.10188015e-01 -1.17858875e+00 -7.59164333e-01 -1.03533792e+00
2.63321429e-01 1.27044842e-01 4.85317856e-01 -1.84025168... | [10.934354782104492, -3.080382823944092] |
809922a6-bf61-429a-8477-61f742f8cd70 | connective-cognition-network-for-directional | null | null | http://papers.nips.cc/paper/8804-connective-cognition-network-for-directional-visual-commonsense-reasoning | http://papers.nips.cc/paper/8804-connective-cognition-network-for-directional-visual-commonsense-reasoning.pdf | Connective Cognition Network for Directional Visual Commonsense Reasoning | Visual commonsense reasoning (VCR) has been introduced to boost research of cognition-level visual understanding, i.e., a thorough understanding of correlated details of the scene plus an inference with related commonsense knowledge. Recent studies on neuroscience have suggested that brain function or cognition can be ... | ['Yahong Han', 'Linchao Zhu', 'Aming Wu', 'Yi Yang'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 7.54970163e-02 4.33334373e-02 2.41117895e-01 -4.25319850e-01
2.27799132e-01 -4.56017405e-01 6.84906423e-01 1.94500640e-01
-1.30138263e-01 4.05987918e-01 5.89284778e-01 -3.57303590e-01
-9.54045132e-02 -9.31643486e-01 -5.20068228e-01 -3.09690475e-01
4.28048939e-01 -2.39995122e-01 1.95086703e-01 -4.45963621... | [10.761784553527832, 1.799759864807129] |
52d35329-26f0-4d51-ab73-7bbc8bb8cd17 | classification-of-12-lead-ecg-signals-with-bi | 1811.02090 | null | http://arxiv.org/abs/1811.02090v1 | http://arxiv.org/pdf/1811.02090v1.pdf | Classification of 12-Lead ECG Signals with Bi-directional LSTM Network | We propose a recurrent neural network classifier to detect pathologies in
12-lead ECG signals and train and validate the classifier with the Chinese
physiological signal challenge dataset (http://www.icbeb.org/Challenge.html).
The recurrent neural network consists of two bi-directional LSTM layers and can
train on arbi... | ['William Wee', 'Junye Luo', 'Ahmed Mostayed', 'Xingliang Shu'] | 2018-11-05 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 2.53182948e-01 -1.39038056e-01 8.76531973e-02 -3.59075963e-01
-9.90897298e-01 -1.50458470e-01 -6.16768837e-01 -2.17856869e-01
-3.40017378e-01 8.30683053e-01 1.10684916e-01 -5.91619968e-01
-3.59740742e-02 -1.91831976e-01 -2.90403754e-01 -6.02173030e-01
-6.08811855e-01 -1.24363795e-01 -3.76892656e-01 -6.67685494... | [14.341415405273438, 3.3117568492889404] |
841eae4f-75e8-40fd-82f7-35e821a86fac | deep-learning-for-automated-medical-image | 1903.04711 | null | http://arxiv.org/abs/1903.04711v1 | http://arxiv.org/pdf/1903.04711v1.pdf | Deep Learning for Automated Medical Image Analysis | Medical imaging is an essential tool in many areas of medical applications,
used for both diagnosis and treatment. However, reading medical images and
making diagnosis or treatment recommendations require specially trained medical
specialists. The current practice of reading medical images is labor-intensive,
time-cons... | ['Wentao Zhu'] | 2019-03-12 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 4.38319266e-01 6.50713861e-01 -1.89690530e-01 -3.36503386e-01
-9.41148162e-01 -3.90743345e-01 2.23843623e-02 -5.93555346e-02
-2.21722350e-01 6.72311008e-01 1.09733166e-02 -1.10910881e+00
-2.21968368e-02 -1.09433448e+00 -5.94013453e-01 -7.79854059e-01
3.55495252e-02 9.30884838e-01 3.34777534e-01 -1.23224622... | [15.234183311462402, -2.1379740238189697] |
e890a60f-84c8-4b5b-8b37-d52aedad828e | around-the-world-in-60-words-a-generative | 2302.01614 | null | https://arxiv.org/abs/2302.01614v1 | https://arxiv.org/pdf/2302.01614v1.pdf | Around the world in 60 words: A generative vocabulary test for online research | Conducting experiments with diverse participants in their native languages can uncover insights into culture, cognition, and language that may not be revealed otherwise. However, conducting these experiments online makes it difficult to validate self-reported language proficiency. Furthermore, existing proficiency test... | ['Nori Jacoby', 'Elisabeth André', 'Francesca Lanzarini', 'Ilia Sucholutsky', 'Raja Marjieh', 'Harin Lee', 'Yue Sun', 'Pol van Rijn'] | 2023-02-03 | null | null | null | null | ['culture'] | ['speech'] | [-5.30868292e-01 -2.57010847e-01 -3.22818637e-01 -2.93261766e-01
-8.17141652e-01 -1.28790152e+00 6.91039562e-01 4.18368489e-01
-8.46344769e-01 9.97865021e-01 5.05665004e-01 -5.12789190e-01
7.33362436e-02 -8.32015038e-01 -6.79996252e-01 1.31937131e-01
1.72843382e-01 4.65468109e-01 1.01457469e-01 -1.90784276... | [10.823559761047363, 10.026910781860352] |
c04360a0-2a51-452b-b0c9-049082b8d93a | distributed-optimization-for-reactive-power | 2302.09241 | null | https://arxiv.org/abs/2302.09241v2 | https://arxiv.org/pdf/2302.09241v2.pdf | Distributed Optimization for Reactive Power Sharing and Stability of Inverter-Based Resources Under Voltage Limits | Reactive power sharing and containment of voltages within limits for inverter-based resources (IBRs) are two important, yet coupled objectives in ac networks. In this article, we propose a distributed control technique to simultaneously achieve these objectives. Our controller consists of two components: a purely local... | ['Gilbert Bergna-Diaz', 'John W. Simpson-Porco', 'Babak Abdolmaleki'] | 2023-02-18 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-0.25744477 0.14964168 -0.2829394 0.41682202 -0.3621028 -1.0959083
0.12021749 0.334474 0.2453997 1.2202216 -0.3710196 -0.1913228
-0.7440739 -0.7278242 -0.333259 -1.2754232 -0.35326436 -0.02269368
-0.1902037 -0.5852471 0.02223462 0.6491808 -0.95982563 -0.6689218
1.1515671 1.0767632 -0.270... | [5.649630069732666, 2.580583095550537] |
5ae2b46a-00c5-4be3-8ad3-4232c2dc4813 | using-k-way-co-occurrences-for-learning-word | 1709.01199 | null | http://arxiv.org/abs/1709.01199v1 | http://arxiv.org/pdf/1709.01199v1.pdf | Using $k$-way Co-occurrences for Learning Word Embeddings | Co-occurrences between two words provide useful insights into the semantics
of those words. Consequently, numerous prior work on word embedding learning
have used co-occurrences between two words as the training signal for learning
word embeddings. However, in natural language texts it is common for multiple
words to b... | ['Ken-ichi Kawarabayashi', 'Yuichi Yoshida', 'Danushka Bollegala'] | 2017-09-05 | null | null | null | null | ['learning-word-embeddings'] | ['methodology'] | [-2.60803878e-01 -2.58251578e-02 -4.93519783e-01 -3.12355548e-01
-5.91046929e-01 -3.17696899e-01 3.46033543e-01 7.90912092e-01
-8.73628199e-01 2.86302477e-01 2.35785559e-01 -4.50395793e-01
-3.55279863e-01 -1.02083969e+00 -5.10118186e-01 -6.93084121e-01
-7.25878596e-01 -2.95011327e-02 -9.32512805e-02 -2.09137350... | [10.441325187683105, 8.772536277770996] |
98be4784-b7ef-4d6b-b2b4-d6e2d6d5e6a9 | openp5-benchmarking-foundation-models-for | 2306.11134 | null | https://arxiv.org/abs/2306.11134v1 | https://arxiv.org/pdf/2306.11134v1.pdf | OpenP5: Benchmarking Foundation Models for Recommendation | This paper presents OpenP5, an open-source library for benchmarking foundation models for recommendation under the Pre-train, Personalized Prompt and Predict Paradigm (P5). We consider the implementation of P5 on three dimensions: 1) downstream task, 2) recommendation dataset, and 3) item indexing method. For 1), we pr... | ['Yongfeng Zhang', 'Wenyue Hua', 'Shuyuan Xu'] | 2023-06-19 | null | null | null | null | ['sequential-recommendation', 'benchmarking', 'benchmarking'] | ['miscellaneous', 'miscellaneous', 'robots'] | [-2.44413957e-01 -3.80631626e-01 -6.26338005e-01 -5.00081718e-01
-9.82532561e-01 -8.08798075e-01 5.83961785e-01 -1.85174957e-01
3.42704728e-02 4.16700155e-01 6.56736612e-01 -4.38951761e-01
-7.81437337e-01 -5.41648507e-01 -6.00486577e-01 -3.95509928e-01
-2.00145021e-02 8.01997185e-01 3.64808887e-01 -3.64904970... | [10.172045707702637, 5.733040809631348] |
27a95049-1376-474c-8a66-adef8298efaf | error-correction-for-dense-semantic-image | 1712.03812 | null | http://arxiv.org/abs/1712.03812v1 | http://arxiv.org/pdf/1712.03812v1.pdf | Error Correction for Dense Semantic Image Labeling | Pixelwise semantic image labeling is an important, yet challenging, task with
many applications. Typical approaches to tackle this problem involve either the
training of deep networks on vast amounts of images to directly infer the
labels or the use of probabilistic graphical models to jointly model the
dependencies of... | ['Luc van Gool', 'Tinne Tuytelaars', 'Yu-Hui Huang', 'Xu Jia', 'Stamatios Georgoulis'] | 2017-12-11 | null | null | null | null | ['face-parsing'] | ['computer-vision'] | [ 6.90667808e-01 4.05879080e-01 1.08393587e-01 -7.90825307e-01
-7.61616886e-01 -5.54653823e-01 5.26010573e-01 2.12441266e-01
-4.76294041e-01 5.91106355e-01 -1.73411548e-01 -1.02499865e-01
4.55474220e-02 -7.43231356e-01 -9.59628582e-01 -7.73050725e-01
3.81812602e-01 6.81971014e-01 5.46036661e-01 2.83593953... | [9.548188209533691, 0.5539113879203796] |
bf24e74f-351d-44f1-b375-104f5980c8c0 | cutting-through-the-noise-an-empirical | 2211.01704 | null | https://arxiv.org/abs/2211.01704v2 | https://arxiv.org/pdf/2211.01704v2.pdf | Cutting Through the Noise: An Empirical Comparison of Psychoacoustic and Envelope-based Features for Machinery Fault Detection | Acoustic-based fault detection has a high potential to monitor the health condition of mechanical parts. However, the background noise of an industrial environment may negatively influence the performance of fault detection. Limited attention has been paid to improving the robustness of fault detection against industri... | ['Gregory Palmer', 'Zhao Ren', 'David Pelkmann', 'Yvonne Richter', 'Peter Wißbrock'] | 2022-11-03 | null | null | null | null | ['one-class-classifier', 'fault-detection'] | ['methodology', 'miscellaneous'] | [ 2.45257050e-01 -1.47440061e-01 8.02564383e-01 -3.97937596e-02
-8.43640327e-01 -2.62222350e-01 9.40332636e-02 4.78943624e-02
-1.18775643e-01 2.52454370e-01 -5.63461304e-01 -1.43811852e-01
-6.03611887e-01 -5.52925348e-01 -5.16847730e-01 -9.47588861e-01
-1.92315474e-01 5.75622581e-02 7.48256505e-01 -2.12980911... | [6.756795883178711, 2.3490638732910156] |
06d157f4-f5d2-4c6c-8e1f-696aeb7c4a50 | dialogps-dialogue-path-sampling-in-continuous | 2306.16770 | null | https://arxiv.org/abs/2306.16770v1 | https://arxiv.org/pdf/2306.16770v1.pdf | DialoGPS: Dialogue Path Sampling in Continuous Semantic Space for Data Augmentation in Multi-Turn Conversations | In open-domain dialogue generation tasks, contexts and responses in most datasets are one-to-one mapped, violating an important many-to-many characteristic: a context leads to various responses, and a response answers multiple contexts. Without such patterns, models poorly generalize and prefer responding safely. Many ... | ['Rui Yan', 'Ji Zhang', 'Xing Gao', 'Yuhan Chen', 'Jinpeng Li', 'Ang Lv'] | 2023-06-29 | null | null | null | null | ['dialogue-generation', 'semantic-textual-similarity', 'semantic-similarity', 'dialogue-generation'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 3.40086073e-01 4.41314161e-01 1.89581718e-02 -7.57068336e-01
-1.11826563e+00 -7.97068655e-01 9.62708712e-01 -1.34454876e-01
-2.72580773e-01 1.11328161e+00 6.22727096e-01 -1.66502655e-01
7.88713153e-03 -8.55552077e-01 -1.82393059e-01 -5.84034204e-01
4.31868076e-01 1.13711143e+00 2.25150943e-01 -6.99768364... | [12.800802230834961, 8.141160011291504] |
33129f9e-707b-4ec9-9342-491f6ac0b4b1 | logical-reasoning-over-natural-language-as | 2303.12023 | null | https://arxiv.org/abs/2303.12023v1 | https://arxiv.org/pdf/2303.12023v1.pdf | Logical Reasoning over Natural Language as Knowledge Representation: A Survey | Logical reasoning is central to human cognition and intelligence. Past research of logical reasoning within AI uses formal language as knowledge representation~(and symbolic reasoners). However, reasoning with formal language has proved challenging~(e.g., brittleness and knowledge-acquisition bottleneck). This paper pr... | ['Erik Cambria', 'Jinjie Ni', 'Rui Mao', 'Xinya Du', 'Zonglin Yang'] | 2023-03-21 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [-3.42956521e-02 5.59156179e-01 -1.15210429e-01 -5.60128093e-01
-1.89164713e-01 -7.02925026e-01 5.80400825e-01 -1.62902087e-01
-3.58260363e-01 9.91606176e-01 1.04881734e-01 -7.59516537e-01
-4.76687551e-01 -1.04077315e+00 -7.26416528e-01 -1.23920932e-01
5.04580326e-02 8.32197309e-01 3.07678767e-02 -4.52453792... | [9.232184410095215, 7.175995349884033] |
88da34af-a446-47d2-9cc9-808a9107b897 | multi-module-based-cvae-to-predict-hvcm | 2304.10639 | null | https://arxiv.org/abs/2304.10639v1 | https://arxiv.org/pdf/2304.10639v1.pdf | Multi-module based CVAE to predict HVCM faults in the SNS accelerator | We present a multi-module framework based on Conditional Variational Autoencoder (CVAE) to detect anomalies in the power signals coming from multiple High Voltage Converter Modulators (HVCMs). We condition the model with the specific modulator type to capture different representations of the normal waveforms and to imp... | ['Sarah Cousineau', 'Pradeep Ramuhalli', 'Dan Lu', 'Majdi I. Radaideh', 'Chris Pappas', 'Lasitha Vidyaratne', 'Steven Goldenberg', 'Kishansingh Rajput', 'Malachi Schram', 'Yasir Alanazi'] | 2023-04-20 | null | null | null | null | ['type'] | ['speech'] | [-4.67915146e-04 -7.06004277e-02 1.71461344e-01 -1.30560637e-01
-6.23622656e-01 -1.89829186e-01 5.55383265e-01 4.13832515e-02
8.34815726e-02 7.48291731e-01 -1.78269416e-01 -2.15528920e-01
-2.92915761e-01 -8.76937151e-01 -6.97734833e-01 -1.03155863e+00
-1.91676632e-01 7.43784964e-01 3.17400128e-01 -2.84396529... | [6.72311544418335, 2.4261562824249268] |
63d701d5-cda1-4208-984d-efa5e36d49d3 | evaluating-coreference-resolvers-on-community | null | null | https://aclanthology.org/2022.crac-1.7 | https://aclanthology.org/2022.crac-1.7.pdf | Evaluating Coreference Resolvers on Community-based Question Answering: From Rule-based to State of the Art | Coreference resolution is a key step in natural language understanding. Developments in coreference resolution are mainly focused on improving the performance on standard datasets annotated for coreference resolution. However, coreference resolution is an intermediate step for text understanding and it is not clear how... | ['Michael Strube', 'Iryna Gurevych', 'Nafise Sadat Moosavi', 'Haixia Chai'] | null | null | null | null | coling-crac-2022-10 | ['coreference-resolution', 'answer-selection'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.42925006e-01 4.49064583e-01 -2.16840670e-01 -3.87511164e-01
-1.08658755e+00 -9.52335238e-01 6.59620047e-01 3.75191629e-01
-7.19442725e-01 9.10295367e-01 8.15908551e-01 -3.57253999e-01
-6.20347977e-01 -6.05069101e-01 -4.82344091e-01 -2.41019890e-01
2.75192231e-01 1.19457483e+00 4.49262530e-01 -7.67543435... | [9.338930130004883, 9.501349449157715] |
8b6a613d-e8fb-4777-a89d-ce055a7e272e | nnqs-transformer-an-efficient-and-scalable | 2306.16705 | null | https://arxiv.org/abs/2306.16705v2 | https://arxiv.org/pdf/2306.16705v2.pdf | NNQS-Transformer: an Efficient and Scalable Neural Network Quantum States Approach for Ab initio Quantum Chemistry | Neural network quantum state (NNQS) has emerged as a promising candidate for quantum many-body problems, but its practical applications are often hindered by the high cost of sampling and local energy calculation. We develop a high-performance NNQS method for \textit{ab initio} electronic structure calculations. The ma... | ['Honghui Shang', 'Pengyu Zhou', 'Yi Fan', 'Chu Guo', 'Yangjun Wu'] | 2023-06-29 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 8.59963223e-02 -5.75458229e-01 -1.62330583e-01 -2.78960466e-01
-1.04254782e+00 -6.54466450e-02 3.86866242e-01 3.54532242e-01
-6.41502380e-01 1.20387518e+00 -5.11038378e-02 -5.61321259e-01
-2.08920136e-01 -1.13707316e+00 -6.64975345e-01 -1.24561679e+00
-2.15268061e-02 6.78530991e-01 3.30569863e-01 -5.17884552... | [5.39016580581665, 5.124839782714844] |
004fba26-5f98-4ac8-abd3-092cf3bc4b1a | automatic-generation-of-multiple-choice | 2303.14576 | null | https://arxiv.org/abs/2303.14576v1 | https://arxiv.org/pdf/2303.14576v1.pdf | Automatic Generation of Multiple-Choice Questions | Creating multiple-choice questions to assess reading comprehension of a given article involves generating question-answer pairs (QAPs) and adequate distractors. We present two methods to tackle the challenge of QAP generations: (1) A deep-learning-based end-to-end question generation system based on T5 Transformer with... | ['Cheng Zhang'] | 2023-03-25 | null | null | null | null | ['semantic-role-labeling', 'part-of-speech-tagging', 'reading-comprehension', 'question-generation'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 6.25406802e-01 5.76566279e-01 6.45061910e-01 -6.78288758e-01
-1.48126864e+00 -9.07613218e-01 5.37376940e-01 4.83408660e-01
-5.28900981e-01 8.86337578e-01 5.74287236e-01 -5.38852632e-01
-1.47628665e-01 -1.07795548e+00 -6.21639431e-01 -5.10161296e-02
4.94716585e-01 7.86996841e-01 4.91503596e-01 -9.77787316... | [11.493325233459473, 8.209541320800781] |
f0fe872f-43c5-4c4b-abd0-f539055753cd | a-generic-approach-to-integrating-time-into | 2305.06827 | null | https://arxiv.org/abs/2305.06827v2 | https://arxiv.org/pdf/2305.06827v2.pdf | A Generic Approach to Integrating Time into Spatial-Temporal Forecasting via Conditional Neural Fields | Self-awareness is the key capability of autonomous systems, e.g., autonomous driving network, which relies on highly efficient time series forecasting algorithm to enable the system to reason about the future state of the environment, as well as its effect on the system behavior as time progresses. Recently, a large nu... | ['Demin Lu', 'Zonghua Zhang', 'Duc-Thinh Ngo', 'Minh-Thanh Bui'] | 2023-05-11 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 6.89497069e-02 -3.27032894e-01 1.29108531e-02 -5.91898859e-01
-4.94327992e-02 -1.19113766e-01 8.86339724e-01 3.11879851e-02
-6.70930594e-02 8.11888576e-01 1.35859445e-01 -3.59131098e-01
-2.04409495e-01 -1.00888467e+00 -4.18497056e-01 -1.03197646e+00
-2.23595232e-01 -2.31868133e-01 3.71513724e-01 -6.07981503... | [6.685892105102539, 2.7856557369232178] |
d2222cd0-7e7d-4a1e-829a-a619e91d8d8a | multimodal-analogical-reasoning-over | 2210.00312 | null | https://arxiv.org/abs/2210.00312v4 | https://arxiv.org/pdf/2210.00312v4.pdf | Multimodal Analogical Reasoning over Knowledge Graphs | Analogical reasoning is fundamental to human cognition and holds an important place in various fields. However, previous studies mainly focus on single-modal analogical reasoning and ignore taking advantage of structure knowledge. Notably, the research in cognitive psychology has demonstrated that information from mult... | ['Huajun Chen', 'Shumin Deng', 'Xiaozhuan Liang', 'Xiang Chen', 'Lei LI', 'Ningyu Zhang'] | 2022-10-01 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-3.78541276e-02 2.01023161e-01 -2.71222770e-01 -2.67703950e-01
-3.86046052e-01 -7.91598260e-01 9.05148327e-01 3.14023286e-01
-2.23705202e-01 4.75894213e-01 4.39511299e-01 -6.23814821e-01
-4.62541848e-01 -1.02928972e+00 -7.57626593e-01 -2.42031157e-01
4.46531087e-01 5.70573807e-01 -1.06236435e-01 -6.74735308... | [10.659477233886719, 1.80960214138031] |
25bcfc98-3bcc-4448-bdd6-626d7c2ffc76 | convolutional-fine-grained-classification | 2208.01997 | null | https://arxiv.org/abs/2208.01997v1 | https://arxiv.org/pdf/2208.01997v1.pdf | Convolutional Fine-Grained Classification with Self-Supervised Target Relation Regularization | Fine-grained visual classification can be addressed by deep representation learning under supervision of manually pre-defined targets (e.g., one-hot or the Hadamard codes). Such target coding schemes are less flexible to model inter-class correlation and are sensitive to sparse and imbalanced data distribution as well.... | ['Kui Jia', 'Ke Chen', 'KangJun Liu'] | 2022-08-03 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 2.91292936e-01 -5.65822646e-02 -5.21675408e-01 -4.87627774e-01
-6.94890738e-01 -5.94188869e-01 6.55110121e-01 9.89453867e-02
7.39196539e-02 5.22859693e-01 2.83199936e-01 1.26915902e-01
-4.23466414e-01 -8.00192475e-01 -8.89462948e-01 -8.96603823e-01
8.76124948e-03 2.03031838e-01 -1.41057268e-01 2.71286233... | [9.631376266479492, 2.1402688026428223] |
3b19b719-6d92-4193-8ac5-b4fc28d710a0 | netket-3-machine-learning-toolbox-for-many | 2112.10526 | null | https://arxiv.org/abs/2112.10526v2 | https://arxiv.org/pdf/2112.10526v2.pdf | NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems | We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics. NetKet is built around neural-network quantum states and provides efficient algorithms for their evaluation and optimization. This new version is built on top of JAX, a differentiable programming and accelerated linear algebra... | ['Giuseppe Carleo', 'Nikita Astrakhantsev', 'Vladimir Vargas-Calderon', 'Jannes Nys', 'Gabriel Pescia', 'Clemens Giuliani', 'Christopher Roth', 'Dian Wu', 'Attila Szabó', 'Damian Hofmann', 'Filippo Vicentini'] | 2021-12-20 | null | null | null | null | ['quantum-state-tomography', 'variational-monte-carlo'] | ['medical', 'miscellaneous'] | [-5.74423909e-01 -4.36039641e-02 7.70332366e-02 -3.31207603e-01
-1.17741622e-01 -4.78671074e-01 4.60767388e-01 1.33685648e-01
-5.25203228e-01 8.37628007e-01 -3.53394747e-01 -5.93827069e-01
1.40515780e-02 -1.23261023e+00 -4.11087126e-01 -8.34415138e-01
-5.45624495e-01 4.28797781e-01 1.50225744e-01 -7.61285543... | [5.468382358551025, 4.978702068328857] |
9a932829-21aa-4bf4-8028-bcb0d7477418 | large-scale-pre-training-for-person-re | 2203.16533 | null | https://arxiv.org/abs/2203.16533v2 | https://arxiv.org/pdf/2203.16533v2.pdf | Large-Scale Pre-training for Person Re-identification with Noisy Labels | This paper aims to address the problem of pre-training for person re-identification (Re-ID) with noisy labels. To setup the pre-training task, we apply a simple online multi-object tracking system on raw videos of an existing unlabeled Re-ID dataset "LUPerson" nd build the Noisy Labeled variant called "LUPerson-NL". Si... | ['Dong Chen', 'Fang Wen', 'Houqiang Li', 'Lei Zhang', 'Lu Yuan', 'Jianmin Bao', 'Hao Yang', 'Dongdong Chen', 'Dengpan Fu'] | 2022-03-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Fu_Large-Scale_Pre-Training_for_Person_Re-Identification_With_Noisy_Labels_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Fu_Large-Scale_Pre-Training_for_Person_Re-Identification_With_Noisy_Labels_CVPR_2022_paper.pdf | cvpr-2022-1 | ['online-multi-object-tracking', 'unsupervised-pre-training'] | ['computer-vision', 'methodology'] | [-8.28403607e-02 -3.10389042e-01 -1.53582171e-01 -3.38613749e-01
-9.64187324e-01 -4.82304513e-01 5.62869668e-01 -2.22300649e-01
-6.35757148e-01 8.06452096e-01 2.44194075e-01 2.27653250e-01
2.70895641e-02 -3.94377589e-01 -7.90266037e-01 -6.00177586e-01
1.52638763e-01 5.90688050e-01 3.01590860e-02 2.63867807... | [14.774704933166504, 1.1300017833709717] |
43e267b4-2c89-412a-86b2-793112ea173f | spacephish-the-evasion-space-of-adversarial | 2210.13660 | null | https://arxiv.org/abs/2210.13660v1 | https://arxiv.org/pdf/2210.13660v1.pdf | SpacePhish: The Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning | Existing literature on adversarial Machine Learning (ML) focuses either on showing attacks that break every ML model, or defenses that withstand most attacks. Unfortunately, little consideration is given to the actual \textit{cost} of the attack or the defense. Moreover, adversarial samples are often crafted in the "fe... | ['Ying Yuan', 'Mauro Conti', 'Giovanni Apruzzese'] | 2022-10-24 | null | null | null | null | ['phishing-website-detection'] | ['adversarial'] | [ 3.02109092e-01 5.30762896e-02 9.78473797e-02 1.49127632e-01
-7.74720132e-01 -1.29544187e+00 8.88998508e-01 1.43241420e-01
-3.15538317e-01 5.83766162e-01 -2.40327269e-01 -1.08413053e+00
-5.25438376e-02 -9.71037984e-01 -9.23693299e-01 -7.72083163e-01
-4.76765960e-01 7.81431347e-02 1.36216134e-01 -6.04707003... | [5.826358318328857, 7.687694072723389] |
602e6c9a-867e-4380-a7c9-491127ba27ac | rethinking-of-pedestrian-attribute | 2005.11909 | null | https://arxiv.org/abs/2005.11909v2 | https://arxiv.org/pdf/2005.11909v2.pdf | Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method | Despite various methods are proposed to make progress in pedestrian attribute recognition, a crucial problem on existing datasets is often neglected, namely, a large number of identical pedestrian identities in train and test set, which is not consistent with practical application. Thus, images of the same pedestrian i... | ['Houjing Huang', 'Xiaotang Chen', 'Wenjie Yang', 'Kaiqi Huang', 'Jian Jia'] | 2020-05-25 | null | null | null | null | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [-4.95733730e-02 -4.36872691e-01 5.04719242e-02 -7.14409947e-01
-5.36881745e-01 -3.06921691e-01 6.27407610e-01 -2.92897839e-02
-4.71674949e-01 1.07850885e+00 -2.01487705e-01 -1.39321297e-01
1.47194728e-01 -1.01046979e+00 -6.93788886e-01 -7.71305978e-01
1.37521446e-01 6.52231276e-01 2.29107022e-01 -2.30294272... | [14.538678169250488, 0.9516131281852722] |
4e3d356a-b778-4306-bb31-5c11537abe51 | machine-vision-based-sample-tube-localization | 2103.09942 | null | https://arxiv.org/abs/2103.09942v1 | https://arxiv.org/pdf/2103.09942v1.pdf | Machine Vision based Sample-Tube Localization for Mars Sample Return | A potential Mars Sample Return (MSR) architecture is being jointly studied by NASA and ESA. As currently envisioned, the MSR campaign consists of a series of 3 missions: sample cache, fetch and return to Earth. In this paper, we focus on the fetch part of the MSR, and more specifically the problem of autonomously detec... | ['Renaud Detry', 'Eric Kulczyski', 'John Mayo', 'Alex Brinkman', 'Mark Van der Merwe', 'Gerard Maggiolino', 'Peter Ilhardt', 'Tu-Hoa Pham', 'William Seto', 'Barry Ridge', 'Shreyansh Daftry'] | 2021-03-17 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 4.52500105e-01 9.90857556e-02 3.34776610e-01 -4.93901342e-01
-3.96665752e-01 -6.34104490e-01 7.48940229e-01 2.07412332e-01
-5.90408325e-01 2.94999778e-01 -3.85550678e-01 -3.66378576e-01
-8.55734050e-02 -9.44252133e-01 -7.38406360e-01 -3.21558297e-01
-2.22997800e-01 8.88041973e-01 4.05992746e-01 -6.64605856... | [8.279158592224121, -1.7710552215576172] |
278902c8-f018-4e21-bc05-36f61ab78ad3 | robust-speech-recognition-via-large-scale-1 | 2212.04356 | null | https://arxiv.org/abs/2212.04356v1 | https://arxiv.org/pdf/2212.04356v1.pdf | Robust Speech Recognition via Large-Scale Weak Supervision | We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervise... | ['Ilya Sutskever', 'Christine McLeavey', 'Greg Brockman', 'Tao Xu', 'Jong Wook Kim', 'Alec Radford'] | 2022-12-06 | robust-speech-recognition-via-large-scale | https://openai.com/blog/whisper/ | https://cdn.openai.com/papers/whisper.pdf | preprint-2022-9 | ['robust-speech-recognition'] | ['speech'] | [ 1.89316079e-01 3.46412271e-01 -3.64692211e-02 -8.60218525e-01
-1.57089770e+00 -5.64942062e-01 7.43004918e-01 -1.29344538e-02
-5.31343162e-01 8.30924273e-01 5.36706388e-01 -4.40955788e-01
2.50562191e-01 -1.39786974e-01 -8.33222032e-01 -2.84623116e-01
-2.85290480e-01 8.15334201e-01 2.15128317e-01 -3.51479977... | [14.298524856567383, 6.9539947509765625] |
14930df2-af6c-4751-81f0-3013c36ce66f | towards-semi-supervised-universal-graph | 2305.19598 | null | https://arxiv.org/abs/2305.19598v1 | https://arxiv.org/pdf/2305.19598v1.pdf | Towards Semi-supervised Universal Graph Classification | Graph neural networks have pushed state-of-the-arts in graph classifications recently. Typically, these methods are studied within the context of supervised end-to-end training, which necessities copious task-specific labels. However, in real-world circumstances, labeled data could be limited, and there could be a mass... | ['Ming Zhang', 'Wei Ju', 'Yifang Qin', 'Yusheng Zhao', 'Xiao Luo'] | 2023-05-31 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 2.44523793e-01 6.39773130e-01 -5.39137959e-01 -4.00249600e-01
-5.24873614e-01 -6.86335504e-01 3.66691262e-01 1.90612376e-01
7.90959373e-02 7.51483917e-01 -1.22800879e-01 -1.17358007e-01
-7.42586181e-02 -9.88185823e-01 -5.87834060e-01 -8.21753502e-01
2.15660051e-01 7.38116384e-01 1.45756751e-01 2.05193818... | [7.379183769226074, 6.151130676269531] |
615ae3aa-fa7c-48a3-8cf6-f59ba3149c94 | automatic-gaze-analysis-a-survey-of | 2108.05479 | null | https://arxiv.org/abs/2108.05479v3 | https://arxiv.org/pdf/2108.05479v3.pdf | Automatic Gaze Analysis: A Survey of Deep Learning based Approaches | Eye gaze analysis is an important research problem in the field of Computer Vision and Human-Computer Interaction. Even with notable progress in the last 10 years, automatic gaze analysis still remains challenging due to the uniqueness of eye appearance, eye-head interplay, occlusion, image quality, and illumination co... | ['Qiang Ji', 'Jarrod Knibbe', 'Munawar Hayat', 'Abhinav Dhall', 'Shreya Ghosh'] | 2021-08-12 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [ 2.07050040e-01 -4.43109125e-02 -1.62424520e-01 -3.65402848e-01
-2.16741681e-01 -4.86799896e-01 9.28016379e-02 -3.16144288e-01
-2.20636383e-01 4.52521384e-01 1.27980923e-02 -4.19528246e-01
-1.65742978e-01 2.06231385e-01 -3.13592911e-01 -5.80959678e-01
1.66241691e-01 -1.90263197e-01 3.91030125e-02 -2.42406338... | [14.103230476379395, 0.10896507650613785] |
c38e8d34-e11c-41ee-8a38-e2dc92c7b4fe | medical-code-prediction-from-discharge | 2106.07932 | null | https://arxiv.org/abs/2106.07932v4 | https://arxiv.org/pdf/2106.07932v4.pdf | Medical Code Prediction from Discharge Summary: Document to Sequence BERT using Sequence Attention | Clinical notes are unstructured text generated by clinicians during patient encounters. Clinical notes are usually accompanied by a set of metadata codes from the International Classification of Diseases(ICD). ICD code is an important code used in various operations, including insurance, reimbursement, medical diagnosi... | ['Kyungsun Kim', 'Byeong-Cheol Jo', 'Yeongjoon Park', 'Yongmin Yoo', 'Tak-Sung Heo'] | 2021-06-15 | null | null | null | null | ['medical-code-prediction'] | ['medical'] | [ 3.14255834e-01 6.58707172e-02 -8.31868052e-02 -3.41971755e-01
-7.98466027e-01 -3.99056226e-01 1.80666670e-01 8.43506873e-01
-3.42172146e-01 6.53440237e-01 7.94054210e-01 -5.84614515e-01
-2.10346773e-01 -5.58015823e-01 -4.64942545e-01 -3.45881730e-01
-7.48709217e-02 8.56091976e-01 -1.53481647e-01 1.40122682... | [8.000174522399902, 6.841665744781494] |
71347f7a-bc9e-486c-8399-047891f4e6f7 | knowledge-distillation-from-ensemble-of | 2108.09183 | null | https://arxiv.org/abs/2108.09183v2 | https://arxiv.org/pdf/2108.09183v2.pdf | Boosting of Head Pose Estimation by Knowledge Distillation | We propose a response-based method of knowledge distillation (KD) for the head pose estimation problem. A student model trained by the proposed KD achieves results better than a teacher model, which is atypical for the response-based method. Our method consists of two stages. In the first stage, we trained the base neu... | ['Victor Samun', 'Andrey Sheka'] | 2021-08-20 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-4.45908725e-01 3.91437590e-01 -1.24176286e-01 -7.18727827e-01
-7.19917059e-01 -1.91654816e-01 4.59438980e-01 -3.69553030e-01
-6.96249783e-01 8.20001781e-01 3.94437462e-01 -1.22211330e-01
2.16368988e-01 -9.70808804e-01 -9.31254089e-01 -6.83211923e-01
8.39079469e-02 7.83472002e-01 6.42816126e-01 -3.97361130... | [13.630918502807617, 0.34267160296440125] |
88e044a7-bc6b-4fa6-938e-a6fb8cabf56d | building-accurate-low-latency-asr-for | 2305.18596 | null | https://arxiv.org/abs/2305.18596v1 | https://arxiv.org/pdf/2305.18596v1.pdf | Building Accurate Low Latency ASR for Streaming Voice Search | Automatic Speech Recognition (ASR) plays a crucial role in voice-based applications. For applications requiring real-time feedback like Voice Search, streaming capability becomes vital. While LSTM/RNN and CTC based ASR systems are commonly employed for low-latency streaming applications, they often exhibit lower accura... | ['Nikesh Garera', 'Abhinav Goyal'] | 2023-05-29 | null | null | null | null | ['action-detection', 'activity-detection', 'automatic-speech-recognition'] | ['computer-vision', 'computer-vision', 'speech'] | [ 1.46071807e-01 -3.90408307e-01 -8.57475176e-02 -1.63499296e-01
-1.68515885e+00 -4.38474625e-01 3.84567380e-01 -7.34639540e-02
-7.13438988e-01 2.28313193e-01 3.99437785e-01 -8.63520205e-01
2.64098287e-01 -5.87011203e-02 -3.74913961e-01 -4.13320780e-01
3.29203159e-02 3.32090229e-01 6.01004958e-01 -2.58726090... | [14.497220039367676, 6.55946159362793] |
6144584d-8487-4c0b-abc5-dadb3126848d | regression-bugs-are-in-your-model-measuring | 2105.03048 | null | https://arxiv.org/abs/2105.03048v1 | https://arxiv.org/pdf/2105.03048v1.pdf | Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In NLP Model Updates | Behavior of deep neural networks can be inconsistent between different versions. Regressions during model update are a common cause of concern that often over-weigh the benefits in accuracy or efficiency gain. This work focuses on quantifying, reducing and analyzing regression errors in the NLP model updates. Using neg... | ['Stefano Soatto', 'Yi Zhang', 'Yuanjun Xiong', 'Yi-An Lai', 'Yuqing Xie'] | 2021-05-07 | null | https://aclanthology.org/2021.acl-long.515 | https://aclanthology.org/2021.acl-long.515.pdf | acl-2021-5 | ['negative-flip-rate'] | ['computer-vision'] | [-1.04618192e-01 2.15030294e-02 -3.63170922e-01 -6.13311231e-01
-6.26992583e-01 -5.27183712e-01 5.13251722e-01 -3.12664032e-01
-8.31131876e-01 1.01581967e+00 1.95258513e-01 -5.23301363e-01
-6.60658181e-02 -3.86932969e-01 -1.02062309e+00 -3.94402415e-01
4.40182745e-01 1.25401407e-01 -8.45656544e-02 -1.62425086... | [10.660789489746094, 8.232978820800781] |
faeaec64-63d5-4305-b335-8848d74410ad | compositional-sketch-search | 2106.08009 | null | https://arxiv.org/abs/2106.08009v1 | https://arxiv.org/pdf/2106.08009v1.pdf | Compositional Sketch Search | We present an algorithm for searching image collections using free-hand sketches that describe the appearance and relative positions of multiple objects. Sketch based image retrieval (SBIR) methods predominantly match queries containing a single, dominant object invariant to its position within an image. Our work explo... | ['John Collomosse', 'Hailin Jin', 'Long Mai', 'Tu Bui', 'Alexander Black'] | 2021-06-15 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 3.26830029e-01 -5.21694481e-01 -4.91982907e-01 -2.42035940e-01
-7.65499115e-01 -8.96279275e-01 9.09853876e-01 2.57774681e-01
-1.60255462e-01 5.20559102e-02 1.93520218e-01 7.15751797e-02
-2.72203952e-01 -7.09140420e-01 -7.04830110e-01 -4.77225065e-01
-1.20843187e-01 4.91156071e-01 1.21637478e-01 -2.32917387... | [11.640660285949707, 0.5519562363624573] |
142ecea9-0db3-4a48-a17b-628549f8a990 | multivariate-pair-trading-by-volatility-model | 2106.09132 | null | https://arxiv.org/abs/2106.09132v1 | https://arxiv.org/pdf/2106.09132v1.pdf | Multivariate Pair Trading by Volatility & Model Adaption Trade-off | Pair trading is one of the most discussed topics among financial researches. Despite a growing base of work, portfolio management for multivariate time series is rarely discussed. On the other hand, most researches focus on refining strategy rules instead of finding the optimal portfolio weight. In this paper, we broug... | ['Tianyang Xie', 'Chenyanzi Yu'] | 2021-06-11 | null | null | null | null | ['pair-trading'] | ['time-series'] | [-4.72742468e-01 -3.61981720e-01 -1.85304210e-01 -3.90304655e-01
-4.57973704e-02 -6.85167074e-01 5.82495570e-01 -1.48612261e-01
-2.08251134e-01 7.92955875e-01 -2.43613094e-01 -5.46159923e-01
-5.09657919e-01 -8.36286783e-01 -4.41200696e-02 -6.99279130e-01
8.84918571e-02 3.58809859e-01 3.62531304e-01 -2.89713621... | [4.700611591339111, 4.03399133682251] |
3ef62ba5-e1fe-487a-8ddb-88658da579bb | sg-translate-together-uplifting-singapores | null | null | https://aclanthology.org/2022.amta-upg.28 | https://aclanthology.org/2022.amta-upg.28.pdf | SG Translate Together - Uplifting Singapore’s translation standards with the community through technology | The Singapore’s Ministry of Communications and Information (MCI) has officially launched the SG Translate Together (SGTT) web portal on 27 June 2022, with the aim of partnering its citizens to improve translation standards in Singapore. This web portal houses the Singapore Government’s first neural machine translation ... | ['Nabilah Binte Md Johan', 'Tarun Kumar Vangani', 'Ding Yang', 'Zheng Weihua', 'Wu Kui', 'Aw Ai Ti', 'Sarina Mohamed Rasol', 'Gayathri Ayathorai', 'Foo Yong Xiang', 'Siti Amirah', 'Gowri Kanagarajah', 'Adeline Sim', 'Lee Siew Li'] | null | null | null | null | amta-2022-9 | ['culture'] | ['speech'] | [ 2.42525131e-01 1.85683742e-01 -3.24869096e-01 -2.27554604e-01
-1.46729195e+00 -9.62751269e-01 1.00077748e+00 -1.26045108e-01
-4.97342318e-01 9.31373894e-01 1.02584243e+00 -1.22629833e+00
3.83321941e-01 -6.80850446e-01 -3.55255127e-01 -3.63302648e-01
6.38744771e-01 3.97191286e-01 -6.65668726e-01 -4.12409186... | [11.485623359680176, 10.419071197509766] |
5ec57a14-949e-4617-bc96-9e3d48054a43 | residual-dense-network-for-image-super | 1802.08797 | null | http://arxiv.org/abs/1802.08797v2 | http://arxiv.org/pdf/1802.08797v2.pdf | Residual Dense Network for Image Super-Resolution | A very deep convolutional neural network (CNN) has recently achieved great
success for image super-resolution (SR) and offered hierarchical features as
well. However, most deep CNN based SR models do not make full use of the
hierarchical features from the original low-resolution (LR) images, thereby
achieving relativel... | ['Yulun Zhang', 'Yu Kong', 'Yapeng Tian', 'Bineng Zhong', 'Yun Fu'] | 2018-02-24 | residual-dense-network-for-image-super-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Residual_Dense_Network_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Residual_Dense_Network_CVPR_2018_paper.pdf | cvpr-2018-6 | ['color-image-denoising'] | ['computer-vision'] | [ 1.50672391e-01 -1.71705544e-01 -3.02283943e-01 -3.14555049e-01
-7.08581448e-01 2.25219011e-01 3.00587535e-01 -3.50731283e-01
-1.46173224e-01 7.08957434e-01 5.91958106e-01 2.47176394e-01
-8.44146609e-02 -9.69148040e-01 -7.06413031e-01 -8.41368556e-01
-3.72182727e-02 -3.71740252e-01 5.98341823e-01 -3.40232909... | [11.00069808959961, -1.989983081817627] |
a2d1d375-8e76-4f6c-8c28-31a2adff3b27 | a-unified-framework-of-predicting-binary | 1910.05996 | null | https://arxiv.org/abs/1910.05996v1 | https://arxiv.org/pdf/1910.05996v1.pdf | A unified framework of predicting binary interestingness of images based on discriminant correlation analysis and multiple kernel learning | In the modern content-based image retrieval systems, there is an increasingly interest in constructing a computationally effective model to predict the interestingness of images since the measure of image interestingness could improve the human-centered search satisfaction and the user experience in different applicati... | ['Yuxiang Yang', 'Maohui Li', 'Liting Wang', 'Longtao Zhang', 'Qiang Sun'] | 2019-10-14 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 5.83192594e-02 -3.30852330e-01 -4.89309192e-01 -4.12973255e-01
-6.39726043e-01 -2.47060269e-01 5.05728424e-01 5.73733807e-01
-3.79577786e-01 2.89738744e-01 1.59617841e-01 1.69403642e-01
-9.36294079e-01 -4.69517857e-01 -1.40780494e-01 -9.71831143e-01
-1.92237601e-01 -4.11880054e-02 4.73165214e-02 -2.25081947... | [10.691283226013184, 0.9112085700035095] |
297127f2-bb46-4dad-b2b3-2cb2e6a43ac8 | infinite-dimensional-optimization-and | 2205.15368 | null | https://arxiv.org/abs/2205.15368v1 | https://arxiv.org/pdf/2205.15368v1.pdf | Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations | The paper has two major themes. The first part of the paper establishes certain general results for infinite-dimensional optimization problems on Hilbert spaces. These results cover the classical representer theorem and many of its variants as special cases and offer a wider scope of applications. The second part of th... | ['Jinpu Zhou', 'Riten Mitra', 'Arnab Ganguly'] | 2022-05-30 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [-2.12157801e-01 2.91155457e-01 3.40206847e-02 -6.70672208e-02
-1.02172089e+00 -3.64706516e-01 3.84772003e-01 -2.82510459e-01
-1.72631681e-01 1.12391245e+00 1.40653387e-01 1.35378480e-01
-5.34160554e-01 -3.47653449e-01 -3.85056466e-01 -1.16578794e+00
-3.71209145e-01 2.16411501e-01 -1.53721690e-01 -1.15119100... | [6.976744651794434, 4.144832134246826] |
65a22bce-72ef-4128-8330-3d5bd6539f1e | unimodal-concentrated-loss-fully-adaptive | 2204.00309 | null | https://arxiv.org/abs/2204.00309v1 | https://arxiv.org/pdf/2204.00309v1.pdf | Unimodal-Concentrated Loss: Fully Adaptive Label Distribution Learning for Ordinal Regression | Learning from a label distribution has achieved promising results on ordinal regression tasks such as facial age and head pose estimation wherein, the concept of adaptive label distribution learning (ALDL) has drawn lots of attention recently for its superiority in theory. However, compared with the methods assuming fi... | ['ShiLiang Pu', 'Chunmao Wang', 'Jingwei Yan', 'Pengju Yang', 'Yachun Li', 'Zhaoliang Yao', 'Jingjing Wang', 'Qiang Li'] | 2022-04-01 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Unimodal-Concentrated_Loss_Fully_Adaptive_Label_Distribution_Learning_for_Ordinal_Regression_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Unimodal-Concentrated_Loss_Fully_Adaptive_Label_Distribution_Learning_for_Ordinal_Regression_CVPR_2022_paper.pdf | cvpr-2022-1 | ['head-pose-estimation'] | ['computer-vision'] | [ 4.81716841e-02 1.72456279e-01 -3.95992339e-01 -7.85144269e-01
-6.70577884e-01 -2.18334675e-01 1.37405038e-01 1.74189970e-01
-2.74410099e-01 8.75651002e-01 1.56850249e-01 3.41515481e-01
-4.54363614e-01 -4.12523955e-01 -6.00606263e-01 -1.03386927e+00
1.08133316e-01 5.04898965e-01 -3.09219062e-02 8.47623646... | [9.769686698913574, 3.666600465774536] |
71f09953-77a8-47c3-993a-585eb64d6b8c | 3dmm-rf-convolutional-radiance-fields-for-3d | 2209.07366 | null | https://arxiv.org/abs/2209.07366v1 | https://arxiv.org/pdf/2209.07366v1.pdf | 3DMM-RF: Convolutional Radiance Fields for 3D Face Modeling | Facial 3D Morphable Models are a main computer vision subject with countless applications and have been highly optimized in the last two decades. The tremendous improvements of deep generative networks have created various possibilities for improving such models and have attracted wide interest. Moreover, the recent ad... | ['Stefanos Zafeiriou', 'Alexandros Lattas', 'Baris Gecer', 'Stathis Galanakis'] | 2022-09-15 | null | null | null | null | ['3d-face-modeling'] | ['computer-vision'] | [ 3.01818818e-01 7.81714693e-02 3.07952404e-01 -5.88092506e-01
-3.08223009e-01 -4.59734261e-01 6.96561098e-01 -8.93397808e-01
2.15350285e-01 6.00702167e-01 -1.33132905e-01 2.17025578e-01
2.30320916e-01 -9.42371190e-01 -5.97464800e-01 -7.21077144e-01
2.65482426e-01 3.41035694e-01 -2.39516973e-01 -4.88955319... | [12.760024070739746, -0.22108587622642517] |
f2670a99-f923-4052-8226-6dbf944ad127 | 3d-neural-embedding-likelihood-for-robust-sim | 2302.03744 | null | https://arxiv.org/abs/2302.03744v2 | https://arxiv.org/pdf/2302.03744v2.pdf | 3D Neural Embedding Likelihood for Robust Probabilistic Inverse Graphics | The ability to perceive and understand 3D scenes is crucial for many applications in computer vision and robotics. Inverse graphics is an appealing approach to 3D scene understanding that aims to infer the 3D scene structure from 2D images. In this paper, we introduce probabilistic modeling to the inverse graphics fram... | ['Vikash K. Mansinghka', 'Dileep George', 'Miguel Lázaro-Gredilla', 'Dan Gutfreund', 'Joshua B. Tenenbaum', 'Lirui Wang', 'Nishad Gothoskar', 'Guangyao Zhou'] | 2023-02-07 | null | null | null | null | ['pose-tracking', '6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.94664717e-02 1.40309677e-01 -5.39942682e-02 -4.29176420e-01
-7.14133203e-01 -7.01319575e-01 8.06689203e-01 -2.15566531e-01
-2.97091573e-01 9.75237116e-02 2.31851131e-01 -1.03106149e-01
-2.14078963e-01 -5.31263590e-01 -9.34920013e-01 -5.31986415e-01
2.28333488e-01 8.53168547e-01 2.13183120e-01 2.81301200... | [7.811493873596191, -2.713285446166992] |
ba2be0bd-2a2c-49a4-b295-e84f4f635fcb | flexmatch-boosting-semi-supervised-learning | 2110.08263 | null | https://arxiv.org/abs/2110.08263v3 | https://arxiv.org/pdf/2110.08263v3.pdf | FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling | The recently proposed FixMatch achieved state-of-the-art results on most semi-supervised learning (SSL) benchmarks. However, like other modern SSL algorithms, FixMatch uses a pre-defined constant threshold for all classes to select unlabeled data that contribute to the training, thus failing to consider different learn... | ['Takahiro Shinozaki', 'Manabu Okumura', 'Jindong Wang', 'Hao Wu', 'Wenxin Hou', 'Yidong Wang', 'BoWen Zhang'] | 2021-10-15 | null | http://proceedings.neurips.cc/paper/2021/hash/995693c15f439e3d189b06e89d145dd5-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/995693c15f439e3d189b06e89d145dd5-Paper.pdf | neurips-2021-12 | ['semi-supervised-image-classification'] | ['computer-vision'] | [ 3.47755700e-02 1.49895802e-01 -5.72318733e-01 -6.92774057e-01
-1.02012563e+00 -9.21601176e-01 4.26761061e-01 3.28965992e-01
-5.53791046e-01 7.33334184e-01 -1.07533596e-01 -5.04191399e-01
-4.61879633e-02 -5.42054415e-01 -7.82545686e-01 -7.15825617e-01
7.58208036e-02 6.34087622e-01 4.36694533e-01 2.14026004... | [9.493011474609375, 3.5035293102264404] |
91c9d8fd-98cd-4bb4-a277-39bd342ee258 | moral-machine-or-tyranny-of-the-majority | 2305.17319 | null | https://arxiv.org/abs/2305.17319v1 | https://arxiv.org/pdf/2305.17319v1.pdf | Moral Machine or Tyranny of the Majority? | With Artificial Intelligence systems increasingly applied in consequential domains, researchers have begun to ask how these systems ought to act in ethically charged situations where even humans lack consensus. In the Moral Machine project, researchers crowdsourced answers to "Trolley Problems" concerning autonomous ve... | ['Zachary C. Lipton', 'Hoda Heidari', 'Michael Feffer'] | 2023-05-27 | null | null | null | null | ['autonomous-vehicles'] | ['computer-vision'] | [-8.22340101e-02 5.02365649e-01 -4.57080126e-01 -1.27060741e-01
-3.13316703e-01 -1.12276161e+00 7.04618394e-01 2.99336389e-02
-8.69373143e-01 1.09595823e+00 5.13059258e-01 -5.60596108e-01
-1.65830851e-01 -6.02627933e-01 -4.57599401e-01 -8.30932796e-01
3.34844798e-01 5.42999208e-01 -3.02228779e-01 -2.32678279... | [8.812207221984863, 5.42784309387207] |
34ccb50a-a9ac-47c1-98b6-417f18c5b1d5 | structural-neural-additive-models-enhanced | 2302.09275 | null | https://arxiv.org/abs/2302.09275v1 | https://arxiv.org/pdf/2302.09275v1.pdf | Structural Neural Additive Models: Enhanced Interpretable Machine Learning | Deep neural networks (DNNs) have shown exceptional performances in a wide range of tasks and have become the go-to method for problems requiring high-level predictive power. There has been extensive research on how DNNs arrive at their decisions, however, the inherently uninterpretable networks remain up to this day mo... | ['Benjamin Säfken', 'Anton Thielmann', 'Mattias Luber'] | 2023-02-18 | null | null | null | null | ['additive-models', 'interpretable-machine-learning'] | ['methodology', 'methodology'] | [ 3.87269080e-01 7.21567452e-01 1.80220693e-01 -5.65024197e-01
2.46246322e-03 -4.41583395e-01 9.62765157e-01 -1.78032875e-01
-1.77020401e-01 7.34732687e-01 4.48899001e-01 -7.45676041e-01
-4.58203614e-01 -6.15579069e-01 -8.54211032e-01 -7.18569934e-01
-6.46812394e-02 3.55288237e-01 -1.69492453e-01 -1.18927658... | [8.880178451538086, 5.560168743133545] |
2bb08eaf-13a8-4832-9dfb-634e799ab7fe | jarvix-at-semeval-2022-task-2-it-takes-one-to | 2202.02394 | null | https://arxiv.org/abs/2202.02394v6 | https://arxiv.org/pdf/2202.02394v6.pdf | JARVix at SemEval-2022 Task 2: It Takes One to Know One? Idiomaticity Detection using Zero and One-Shot Learning | Large Language Models have been successful in a wide variety of Natural Language Processing tasks by capturing the compositionality of the text representations. In spite of their great success, these vector representations fail to capture meaning of idiomatic multi-word expressions (MWEs). In this paper, we focus on th... | ['Yash Jakhotiya', 'Vaibhav Kumar', 'Raj Shah', 'Ashwin Pathak'] | 2022-02-04 | null | https://aclanthology.org/2022.semeval-1.19 | https://aclanthology.org/2022.semeval-1.19.pdf | semeval-naacl-2022-7 | ['one-shot-learning'] | ['methodology'] | [ 6.70666248e-02 -2.23391697e-01 -5.88948429e-01 -2.75760055e-01
-6.24184549e-01 -5.29822946e-01 9.11463499e-01 1.24568924e-01
-4.33714598e-01 4.31255132e-01 4.55189586e-01 -2.38823071e-01
1.02167383e-01 -9.78371203e-01 -2.88231261e-02 -6.29155993e-01
3.32333952e-01 6.69831634e-01 1.45602068e-02 -5.37622273... | [10.586067199707031, 9.5941801071167] |
858b2710-9a9c-4284-929f-7012ff8aa0f5 | attentive-recurrent-tensor-model-for | 1801.06792 | null | http://arxiv.org/abs/1801.06792v1 | http://arxiv.org/pdf/1801.06792v1.pdf | Attentive Recurrent Tensor Model for Community Question Answering | A major challenge to the problem of community question answering is the
lexical and semantic gap between the sentence representations. Some solutions
to minimize this gap includes the introduction of extra parameters to deep
models or augmenting the external handcrafted features. In this paper, we
propose a novel atten... | ['Balasubramanian Raman', 'Shivam Sharma', 'Gaurav Bhatt'] | 2018-01-21 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 2.93753166e-02 -2.25957006e-01 1.56857058e-01 -6.10235393e-01
-1.29063606e+00 -7.31964111e-01 3.14532220e-01 3.47516328e-01
-6.48850620e-01 2.58575112e-01 4.55920488e-01 -3.79747152e-01
-2.16562569e-01 -5.41801989e-01 -3.62545997e-01 -2.32060730e-01
7.52016678e-02 5.94680429e-01 2.58606523e-01 -6.97077751... | [11.32333755493164, 8.053642272949219] |
a3f44305-cf82-46d9-bd70-c9454d7dd720 | unichart-a-universal-vision-language | 2305.14761 | null | https://arxiv.org/abs/2305.14761v1 | https://arxiv.org/pdf/2305.14761v1.pdf | UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning | Charts are very popular for analyzing data, visualizing key insights and answering complex reasoning questions about data. To facilitate chart-based data analysis using natural language, several downstream tasks have been introduced recently such as chart question answering and chart summarization. However, most of the... | ['Shafiq Joty', 'Enamul Hoque', 'Xuan Long Do', 'Parsa Kavehzadeh', 'Ahmed Masry'] | 2023-05-24 | null | null | null | null | ['chart-question-answering', 'chart-question-answering'] | ['computer-code', 'computer-vision'] | [ 3.00423384e-01 1.88755855e-01 9.64426771e-02 -4.27183777e-01
-7.97596753e-01 -8.53734374e-01 6.05049253e-01 7.79169202e-01
2.16850683e-01 1.11868240e-01 7.86553442e-01 -8.49216282e-01
3.68195683e-01 -7.07477748e-01 -9.65259910e-01 7.99923390e-02
-2.55417526e-01 4.17656302e-01 2.08138198e-01 -1.42046943... | [11.226428985595703, 2.065953254699707] |
2c132b4d-8714-4d06-b5db-3086af40ee81 | dialogue-act-classification-in-group-chats | 1908.01821 | null | https://arxiv.org/abs/1908.01821v1 | https://arxiv.org/pdf/1908.01821v1.pdf | Dialogue Act Classification in Group Chats with DAG-LSTMs | Dialogue act (DA) classification has been studied for the past two decades and has several key applications such as workflow automation and conversation analytics. Researchers have used, to address this problem, various traditional machine learning models, and more recently deep neural network models such as hierarchic... | ['Brendan Fahy', 'Mu-Hsin Wei', 'Ozan İrsoy', 'Haimin Zhang', 'Rakesh Gosangi', 'Peter Lund', 'Neophytos Nephytou', 'Duccio Pappadopulo', 'Camilo Ortiz'] | 2019-08-02 | null | null | null | null | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 1.79332793e-01 4.73376453e-01 2.18191966e-02 -5.20554602e-01
-1.22335128e-01 -3.22089612e-01 9.71240401e-01 3.80830973e-01
-2.86783338e-01 8.42808247e-01 6.25572562e-01 -4.87546802e-01
1.38019502e-01 -7.59371936e-01 -1.16290256e-01 -4.40510184e-01
-2.50404596e-01 5.23708761e-01 1.48573339e-01 -4.29781318... | [12.825575828552246, 7.767239570617676] |
17a5a936-1afe-4ba3-ab81-e7274bdcf143 | can-knowledge-graph-embeddings-tell-us-what | null | null | https://aclanthology.org/2020.insights-1.11 | https://aclanthology.org/2020.insights-1.11.pdf | Can Knowledge Graph Embeddings Tell Us What Fact-checked Claims Are About? | The web offers a wealth of discourse data that help researchers from various fields analyze debates about current societal issues and gauge the effects on society of important phenomena such as misinformation spread. Such analyses often revolve around claims made by people about a given topic of interest. Fact-checking... | ['Andon Tchechmedjiev', 'Konstantin Todorov', 'Luke Lo Seen', 'Katarina Boland', 'Sébastien Harispe', 'Valentina Beretta'] | null | null | null | null | emnlp-insights-2020-11 | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-1.10076979e-01 7.26893127e-01 -6.47665858e-01 9.74041373e-02
-4.35066134e-01 -7.48602450e-01 1.22434664e+00 1.36564827e+00
-1.67421088e-01 3.72538179e-01 1.26478386e+00 -9.53023672e-01
-2.26553738e-01 -1.15436745e+00 -4.96811748e-01 -2.15820774e-01
-3.29238653e-01 4.78570879e-01 4.37022954e-01 -5.28120518... | [8.706696510314941, 9.934659957885742] |
25f66e4f-f98d-45e8-b914-37ccd9d528c6 | leverage-financial-news-to-predict-stock | 1506.07220 | null | http://arxiv.org/abs/1506.07220v1 | http://arxiv.org/pdf/1506.07220v1.pdf | Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks | Financial news contains useful information on public companies and the
market. In this paper we apply the popular word embedding methods and deep
neural networks to leverage financial news to predict stock price movements in
the market. Experimental results have shown that our proposed methods are
simple but very effec... | ['Hui Jiang', 'Yangtuo Peng'] | 2015-06-24 | leverage-financial-news-to-predict-stock-1 | https://aclanthology.org/N16-1041 | https://aclanthology.org/N16-1041.pdf | naacl-2016-6 | ['stock-prediction'] | ['time-series'] | [-1.16539991e+00 -4.48227376e-01 -6.93705618e-01 -6.48424104e-02
-4.10363019e-01 -4.81809020e-01 8.20441127e-01 -2.16184542e-01
-4.53446507e-01 9.25735831e-01 8.69548738e-01 -3.96124691e-01
3.81362975e-01 -1.50836253e+00 -3.03978145e-01 -2.54685938e-01
6.07064692e-03 7.82482326e-02 4.53871757e-01 -6.33173764... | [4.434328079223633, 4.252329349517822] |
669befbb-efc7-4c24-915b-722d41fab3d1 | exploiting-negative-learning-for-implicit | 2106.12123 | null | https://arxiv.org/abs/2106.12123v1 | https://arxiv.org/pdf/2106.12123v1.pdf | Exploiting Negative Learning for Implicit Pseudo Label Rectification in Source-Free Domain Adaptive Semantic Segmentation | It is desirable to transfer the knowledge stored in a well-trained source model onto non-annotated target domain in the absence of source data. However, state-of-the-art methods for source free domain adaptation (SFDA) are subject to strict limits: 1) access to internal specifications of source models is a must; and 2)... | ['Xiaogang Jia', 'Yulin He', 'Chen Li', 'Yusong Tan', 'Wei Chen', 'Xin Luo'] | 2021-06-23 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 8.04808736e-01 4.26853478e-01 -4.70994502e-01 -5.56260765e-01
-1.04485810e+00 -5.60958028e-01 3.98396552e-01 -8.19238648e-02
-5.37771106e-01 8.72426867e-01 -3.34476948e-01 -2.32608616e-01
-1.06227577e-01 -4.89141345e-01 -7.75873005e-01 -7.76672184e-01
4.48871076e-01 7.59147108e-01 5.42179942e-01 -1.17499463... | [9.620694160461426, 1.3619030714035034] |
d2a59b7f-7ca8-4f8d-989a-4f0edc84aacf | doremi-grounding-language-model-by-detecting | 2307.00329 | null | https://arxiv.org/abs/2307.00329v1 | https://arxiv.org/pdf/2307.00329v1.pdf | DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment | Large language models encode a vast amount of semantic knowledge and possess remarkable understanding and reasoning capabilities. Previous research has explored how to ground language models in robotic tasks to ensure that the sequences generated by the language model are both logically correct and practically executab... | ['Jianyu Chen', 'Zheyuan Jiang', 'Lihan Zha', 'Yen-Jen Wang', 'Yanjiang Guo'] | 2023-07-01 | null | null | null | null | ['question-answering'] | ['natural-language-processing'] | [ 4.30119902e-01 3.49355489e-01 2.27178037e-02 -2.00678512e-01
-4.83481735e-01 -6.39161587e-01 5.51135242e-01 -3.05593442e-02
9.22425389e-02 4.76219684e-01 -2.75655203e-02 -3.64329636e-01
-1.67995319e-01 -5.18751204e-01 -8.48684669e-01 1.18704729e-01
1.27393782e-01 5.61652720e-01 3.63166690e-01 -3.33498895... | [4.430872440338135, 0.8886126279830933] |
9b28c874-9fcc-43f9-9ae9-6d8e62d67322 | e-lmc-extended-linear-model-of | 2203.00525 | null | https://arxiv.org/abs/2203.00525v2 | https://arxiv.org/pdf/2203.00525v2.pdf | E-LMC: Extended Linear Model of Coregionalization for Spatial Field Prediction | Physical simulations based on partial differential equations typically generate spatial fields results, which are utilized to calculate specific properties of a system for engineering design and optimization. Due to the intensive computational burden of the simulations, a surrogate model mapping the low-dimensional inp... | ['Wei W. Xing', 'Yichen Meng', 'Xueying Zhang', 'Shihong Wang'] | 2022-03-01 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [-1.01894408e-01 -3.74856710e-01 3.24871913e-02 -7.65094161e-02
-5.20014763e-01 -5.84028900e-01 5.44592857e-01 -1.97936967e-01
2.17254922e-01 9.57081914e-01 2.17482768e-04 -4.99603271e-01
-7.33595848e-01 -9.05120432e-01 -8.17768931e-01 -8.69042039e-01
-1.29976228e-01 1.57292351e-01 8.62940997e-02 -2.13677049... | [6.6085100173950195, 3.403987407684326] |
f558dada-8845-43c7-83fe-448ba8b78a09 | listening-to-chaotic-whispers-a-deep-learning | 1712.02136 | null | http://arxiv.org/abs/1712.02136v3 | http://arxiv.org/pdf/1712.02136v3.pdf | Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction | Stock trend prediction plays a critical role in seeking maximized profit from
stock investment. However, precise trend prediction is very difficult since the
highly volatile and non-stationary nature of stock market. Exploding
information on Internet together with advancing development of natural language
processing an... | ['Tie-Yan Liu', 'Weiqing Liu', 'Ziniu Hu', 'Xuanzhe Liu', 'Jiang Bian'] | 2017-12-06 | null | null | null | null | ['stock-trend-prediction'] | ['time-series'] | [-9.54227924e-01 -5.70160925e-01 -4.71166074e-01 -3.87764499e-02
2.28046209e-01 -5.07531524e-01 5.37496030e-01 1.32213548e-01
-2.83163518e-01 7.59367466e-01 5.76813579e-01 -1.94670871e-01
1.37328357e-01 -9.60871696e-01 -4.26571101e-01 -3.84300768e-01
-2.43984297e-01 8.86222944e-02 5.57628512e-01 -5.35639763... | [4.411365985870361, 4.271608829498291] |
60152d69-b4a7-4d93-8d59-a4fe5876776f | multimodal-fusion-with-deep-neural-networks | 1907.03196 | null | https://arxiv.org/abs/1907.03196v1 | https://arxiv.org/pdf/1907.03196v1.pdf | Multimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition | This paper presents a novel deep neural network (DNN) for multimodal fusion of audio, video and text modalities for emotion recognition. The proposed DNN architecture has independent and shared layers which aim to learn the representation for each modality, as well as the best combined representation to achieve the bes... | ['Marco Pedersoli', 'Patrick Cardinal', 'Mohammed Senoussaoui', 'Juan D. S. Ortega', 'Eric Granger', 'Alessandro L. Koerich'] | 2019-07-06 | null | null | null | null | ['video-emotion-recognition'] | ['computer-vision'] | [-7.30530769e-02 -3.81500661e-01 5.61759770e-02 -7.93375134e-01
-6.21946812e-01 -2.52445430e-01 4.01126236e-01 2.13534534e-01
-4.05239135e-01 5.69660008e-01 2.19369248e-01 4.25734669e-01
-1.38245121e-01 -4.00063843e-01 -2.26859361e-01 -7.49626279e-01
9.69109908e-02 -1.40981898e-01 -4.96977389e-01 -3.95638883... | [13.327082633972168, 5.132423400878906] |
c4cf9916-7d26-4b65-8239-1b50b532440a | mining-implicit-relevance-feedback-from-user | 2006.07581 | null | https://arxiv.org/abs/2006.07581v2 | https://arxiv.org/pdf/2006.07581v2.pdf | Mining Implicit Relevance Feedback from User Behavior for Web Question Answering | Training and refreshing a web-scale Question Answering (QA) system for a multi-lingual commercial search engine often requires a huge amount of training examples. One principled idea is to mine implicit relevance feedback from user behavior recorded in search engine logs. All previous works on mining implicit relevance... | ['Jian Pei', 'Feixiang Cheng', 'Ming Gong', 'Daxin Jiang', 'Shining Bo', 'Linjun Shou'] | 2020-06-13 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [ 7.27551430e-02 7.62378722e-02 -2.39241943e-01 -2.80528843e-01
-1.53248000e+00 -7.19729602e-01 6.16452217e-01 2.81186998e-01
-7.17742980e-01 7.32578516e-01 -4.80072722e-02 -8.65082085e-01
-2.70095080e-01 -6.00415885e-01 -2.91507363e-01 -8.69769230e-02
7.71931782e-02 8.70146930e-01 8.36651862e-01 -7.90046632... | [11.619684219360352, 7.950410842895508] |
0ebb491e-f863-4d28-9ab2-bd2c1ba2eba7 | heterogeneous-separation-consistency-training | 2204.11032 | null | https://arxiv.org/abs/2204.11032v3 | https://arxiv.org/pdf/2204.11032v3.pdf | Heterogeneous Separation Consistency Training for Adaptation of Unsupervised Speech Separation | Recently, supervised speech separation has made great progress. However, limited by the nature of supervised training, most existing separation methods require ground-truth sources and are trained on synthetic datasets. This ground-truth reliance is problematic, because the ground-truth signals are usually unavailable ... | ['Yanhua Long', 'Jiangyu Han'] | 2022-04-23 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 3.20870757e-01 -1.07142448e-01 -2.61499256e-01 -3.23489934e-01
-1.05308473e+00 -5.73166728e-01 3.66940558e-01 -3.24844956e-01
-1.96425319e-02 8.57092857e-01 1.05696999e-01 -1.35802701e-01
-1.62839890e-01 -1.41972393e-01 -5.30995607e-01 -1.06033671e+00
3.25241506e-01 5.47400296e-01 1.39542565e-01 2.67109200... | [14.790277481079102, 5.950778484344482] |
c376d664-c191-473b-a9bc-46c5de17e891 | take-a-prior-from-other-tasks-for-severe-blur | 2302.06898 | null | https://arxiv.org/abs/2302.06898v1 | https://arxiv.org/pdf/2302.06898v1.pdf | Take a Prior from Other Tasks for Severe Blur Removal | Recovering clear structures from severely blurry inputs is a challenging problem due to the large movements between the camera and the scene. Although some works apply segmentation maps on human face images for deblurring, they cannot handle natural scenes because objects and degradation are more complex, and inaccurat... | ['Yanning Zhang', 'Sung-Eui Yoon', 'Qingsen Yan', 'Jinqiu Sun', 'Yu Zhu', 'Danna Xue', 'Pei Wang'] | 2023-02-14 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 2.75058478e-01 -3.91076744e-01 5.21559119e-02 -3.71677577e-01
-3.03182989e-01 -3.57185543e-01 3.68466854e-01 -8.04636240e-01
-2.61960411e-03 6.65584981e-01 8.42639863e-01 2.07934558e-01
-7.24530816e-02 -2.98949897e-01 -6.65873110e-01 -8.91535759e-01
5.81488371e-01 -2.05374226e-01 2.54837751e-01 -4.86999042... | [11.524763107299805, -2.6930649280548096] |
737ff9a9-4af0-4d94-ba81-5895fecb3c01 | datasets-for-portuguese-legal-semantic | 2306.00007 | null | https://arxiv.org/abs/2306.00007v1 | https://arxiv.org/pdf/2306.00007v1.pdf | Datasets for Portuguese Legal Semantic Textual Similarity: Comparing weak supervision and an annotation process approaches | The Brazilian judiciary has a large workload, resulting in a long time to finish legal proceedings. Brazilian National Council of Justice has established in Resolution 469/2022 formal guidance for document and process digitalization opening up the possibility of using automatic techniques to help with everyday tasks in... | ['Daniel de Oliveira', 'Aline Paes', 'Paulo Roberto dos S. Corval', 'Daniel da Silva Junior'] | 2023-05-29 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.60801637e-01 6.04358017e-01 -2.20150575e-01 -2.60049820e-01
-9.51496780e-01 -9.40770984e-01 7.27254987e-01 8.04259002e-01
-6.32368684e-01 9.14378643e-01 3.80133271e-01 -4.13467675e-01
-6.13575995e-01 -7.05301285e-01 -1.92235932e-01 -3.07041198e-01
5.16807199e-01 9.63847637e-01 1.05886959e-01 -3.37192297... | [9.710095405578613, 9.19984245300293] |
274a18ab-61fb-4888-9459-c3bc0f328ed9 | pdnet-prior-model-guided-depth-enhanced | 1803.08636 | null | http://arxiv.org/abs/1803.08636v2 | http://arxiv.org/pdf/1803.08636v2.pdf | PDNet: Prior-model Guided Depth-enhanced Network for Salient Object Detection | Fully convolutional neural networks (FCNs) have shown outstanding performance
in many computer vision tasks including salient object detection. However,
there still remains two issues needed to be addressed in deep learning based
saliency detection. One is the lack of tremendous amount of annotated data to
train a netw... | ['Thomas H. Li', 'Ge Li', 'Chunbiao Zhu', 'Xing Cai', 'Kan Huang'] | 2018-03-23 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 5.24167061e-01 1.69502854e-01 -4.58883187e-05 -4.58480299e-01
-4.68349099e-01 5.04841171e-02 2.06129268e-01 -9.82470717e-03
-6.07982814e-01 6.37304604e-01 2.16485150e-02 -1.14630342e-01
3.00799280e-01 -6.35065854e-01 -7.17468143e-01 -7.53862202e-01
1.56166434e-01 -3.03736091e-01 1.02990878e+00 -3.96218300... | [9.695270538330078, -0.7330524325370789] |
eb5ec060-2f81-457c-b24f-24e871144f58 | gpu-activity-prediction-using-representation | 1703.09146 | null | http://arxiv.org/abs/1703.09146v1 | http://arxiv.org/pdf/1703.09146v1.pdf | GPU Activity Prediction using Representation Learning | GPU activity prediction is an important and complex problem. This is due to
the high level of contention among thousands of parallel threads. This problem
was mostly addressed using heuristics. We propose a representation learning
approach to address this problem. We model any performance metric as a temporal
function ... | ['Sek Chai', 'Mohamed Amer', 'David Zhang', 'Timothy Shields', 'Aswin Raghavan'] | 2017-03-27 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 2.06558462e-02 -4.99960572e-01 -6.93580568e-01 -2.03308105e-01
-5.35767794e-01 -5.08730650e-01 6.24965668e-01 4.79810178e-01
-7.79573917e-02 6.68936789e-01 6.17154717e-01 -4.63992625e-01
5.72896563e-02 -8.03108156e-01 -7.10502386e-01 -5.87337375e-01
-4.94203538e-01 3.51556569e-01 5.64404190e-01 -8.09521675... | [7.551483631134033, 7.512197971343994] |
13f1ee32-1856-45df-b516-550b30fbaa01 | legendre-memory-units-continuous-time | null | null | http://papers.nips.cc/paper/9689-legendre-memory-units-continuous-time-representation-in-recurrent-neural-networks | http://papers.nips.cc/paper/9689-legendre-memory-units-continuous-time-representation-in-recurrent-neural-networks.pdf | Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks | We propose a novel memory cell for recurrent neural networks that dynamically maintains information across long windows of time using relatively few resources. The Legendre Memory Unit~(LMU) is mathematically derived to orthogonalize its continuous-time history -- doing so by solving $d$ coupled ordinary differential e... | ['Ivana Kajić', 'Chris Eliasmith', 'Aaron Voelker'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['sequential-image-classification'] | ['computer-vision'] | [ 2.24143609e-01 -3.80594164e-01 3.36774617e-01 -4.97711040e-02
-3.18741947e-01 -5.35552025e-01 1.51207939e-01 -2.59814024e-01
-1.06824982e+00 9.95903254e-01 -4.49911922e-01 -2.98558205e-01
-1.37823313e-01 -8.18177342e-01 -9.05144334e-01 -8.69921029e-01
-6.17102265e-01 2.05347463e-01 3.30652714e-01 -1.87549815... | [8.109709739685059, 2.6685543060302734] |
e316463c-40b9-4498-ad40-74753edc644a | action-q-transformer-visual-explanation-in | 2306.13879 | null | https://arxiv.org/abs/2306.13879v1 | https://arxiv.org/pdf/2306.13879v1.pdf | Action Q-Transformer: Visual Explanation in Deep Reinforcement Learning with Encoder-Decoder Model using Action Query | The excellent performance of Transformer in supervised learning has led to growing interest in its potential application to deep reinforcement learning (DRL) to achieve high performance on a wide variety of problems. However, the decision making of a DRL agent is a black box, which greatly hinders the application of th... | ['Komei Sugiura', 'Hironobu Fujiyoshi', 'Takayoshi Yamashita', 'Tsubasa Hirakawa', 'Hidenori Itaya'] | 2023-06-24 | null | null | null | null | ['q-learning', 'atari-games', 'decision-making'] | ['methodology', 'playing-games', 'reasoning'] | [-3.32415313e-01 -1.20618485e-01 -5.47843166e-02 -2.60725051e-01
-2.85727620e-01 -3.48520637e-01 6.67998314e-01 5.89937009e-02
-5.14051199e-01 5.10010123e-01 1.65067211e-01 -4.39027995e-01
7.98338950e-02 -8.85021687e-01 -2.90923059e-01 -7.92583823e-01
-1.19884036e-01 4.09112722e-01 2.82631606e-01 -5.83950162... | [3.9414782524108887, 1.6065796613693237] |
88123ee2-e244-4f28-a7d6-2a657dcaac62 | does-generative-face-completion-help-face | 1906.02858 | null | https://arxiv.org/abs/1906.02858v1 | https://arxiv.org/pdf/1906.02858v1.pdf | Does Generative Face Completion Help Face Recognition? | Face occlusions, covering either the majority or discriminative parts of the face, can break facial perception and produce a drastic loss of information. Biometric systems such as recent deep face recognition models are not immune to obstructions or other objects covering parts of the face. While most of the current fa... | ['Wael Abd-Almageed', 'Joe Mathai', 'Iacopo Masi'] | 2019-06-07 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 4.62311894e-01 5.11682034e-01 2.62492508e-01 -7.41454124e-01
-3.09579343e-01 -3.00807834e-01 6.29140198e-01 -7.35633671e-01
-8.20188597e-02 3.46661448e-01 2.43034273e-01 -2.75563151e-02
7.85513520e-02 -5.17009795e-01 -1.04611969e+00 -7.71168411e-01
1.02658058e-02 2.71413803e-01 -3.05867136e-01 -1.46401450... | [13.070182800292969, 0.3230769634246826] |
5d3352e5-1a8d-4a7b-a847-9951c7334663 | discrete-optimization-for-unsupervised | 2005.01791 | null | https://arxiv.org/abs/2005.01791v1 | https://arxiv.org/pdf/2005.01791v1.pdf | Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction | Automatic sentence summarization produces a shorter version of a sentence, while preserving its most important information. A good summary is characterized by language fluency and high information overlap with the source sentence. We model these two aspects in an unsupervised objective function, consisting of language ... | ['Yao Lu', 'Lili Mou', 'Olga Vechtomova', 'Katja Markert', 'Raphael Schumann'] | 2020-05-04 | discrete-optimization-for-unsupervised-1 | https://aclanthology.org/2020.acl-main.452 | https://aclanthology.org/2020.acl-main.452.pdf | acl-2020-6 | ['abstractive-sentence-summarization', 'unsupervised-sentence-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.88009375e-01 4.86111552e-01 -4.47153866e-01 -3.50365907e-01
-1.13535464e+00 -4.93096888e-01 4.60020632e-01 9.64682579e-01
-3.96541178e-01 1.08706462e+00 1.13244951e+00 3.46514806e-02
-6.28189594e-02 -4.87583399e-01 -3.08196843e-01 -2.29656816e-01
8.32571536e-02 3.38606715e-01 -2.53708009e-02 -3.74488384... | [12.467926979064941, 9.500500679016113] |
29b86639-641d-458b-84a1-95a3fe9d87f9 | contrastive-learning-and-self-training-for | 2105.02001 | null | https://arxiv.org/abs/2105.02001v1 | https://arxiv.org/pdf/2105.02001v1.pdf | Contrastive Learning and Self-Training for Unsupervised Domain Adaptation in Semantic Segmentation | Deep convolutional neural networks have considerably improved state-of-the-art results for semantic segmentation. Nevertheless, even modern architectures lack the ability to generalize well to a test dataset that originates from a different domain. To avoid the costly annotation of training data for unseen domains, uns... | ['Bin Yang', 'Mario Döbler', 'Alexander Bartler', 'Robert A. Marsden'] | 2021-05-05 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 1.94318622e-01 8.96110460e-02 -5.53152561e-02 -5.62780261e-01
-7.22073197e-01 -8.92340064e-01 6.61578834e-01 -8.26165918e-03
-5.47497928e-01 8.51954579e-01 -3.75186265e-01 -1.41710237e-01
-4.63169366e-02 -9.19745088e-01 -8.06874096e-01 -6.05046034e-01
1.86089456e-01 7.78747976e-01 5.24703443e-01 -2.11468115... | [9.717194557189941, 1.4654121398925781] |
c513074a-f181-4d02-9a3a-9451c4abfa2c | enhancing-embedding-representations-of | 2303.13566 | null | https://arxiv.org/abs/2303.13566v1 | https://arxiv.org/pdf/2303.13566v1.pdf | Enhancing Embedding Representations of Biomedical Data using Logic Knowledge | Knowledge Graph Embeddings (KGE) have become a quite popular class of models specifically devised to deal with ontologies and graph structure data, as they can implicitly encode statistical dependencies between entities and relations in a latent space. KGE techniques are particularly effective for the biomedical domain... | ['Giuseppe Marra', 'Moreno Falaschi', 'Caterina Graziani', 'Stefano Fioravanti', 'Francesco Giannini', 'Michelangelo Diligenti'] | 2023-03-23 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-completion', 'knowledge-graph-embeddings', 'relational-reasoning'] | ['graphs', 'knowledge-base', 'methodology', 'natural-language-processing'] | [ 5.21756113e-02 5.84720671e-01 -2.73869723e-01 -2.27942288e-01
2.18800418e-02 -2.16260433e-01 7.29319513e-01 8.25918972e-01
-2.89540082e-01 7.50390708e-01 2.62761146e-01 -2.83151150e-01
-8.01607907e-01 -1.20993543e+00 -9.14126754e-01 -4.63183194e-01
-4.25638139e-01 7.82364249e-01 1.42801091e-01 -3.05748314... | [8.646493911743164, 7.738701820373535] |
8f303948-13bb-4773-9b20-d0a260937d0e | dosed-a-deep-learning-approach-to-detect | 1812.04079 | null | http://arxiv.org/abs/1812.04079v1 | http://arxiv.org/pdf/1812.04079v1.pdf | DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal | Background: Electroencephalography (EEG) monitors brain activity during sleep
and is used to identify sleep disorders. In sleep medicine, clinicians
interpret raw EEG signals in so-called sleep stages, which are assigned by
experts to every 30s window of signal. For diagnosis, they also rely on shorter
prototypical mic... | ['Alexandre Gramfort', 'Stanislas Chambon', 'Pierrick J. Arnal', 'Emmanuel Mignot', 'Valentin Thorey'] | 2018-12-07 | null | null | null | null | ['sleep-quality-prediction', 'k-complex-detection', 'spindle-detection', 'sleep-apnea-detection', 'sleep-micro-event-detection', 'sleep-arousal-detection'] | ['medical', 'medical', 'medical', 'medical', 'medical', 'medical'] | [ 1.26377076e-01 -3.39388669e-01 3.12543899e-01 -3.02703649e-01
-3.39933068e-01 -4.73264784e-01 3.99449021e-01 5.43445051e-01
-6.60300374e-01 8.67808163e-01 4.69663329e-02 -2.43333150e-02
-3.77173364e-01 -3.89523536e-01 -1.89003617e-01 -6.74327314e-01
-4.50164318e-01 2.50970513e-01 4.16273683e-01 -8.61304551... | [13.454808235168457, 3.4717748165130615] |
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