paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2db962d5-9f8b-4780-b1a2-307236557f5d | bayesian-subspace-hidden-markov-model-for | 1904.03876 | null | https://arxiv.org/abs/1904.03876v2 | https://arxiv.org/pdf/1904.03876v2.pdf | Bayesian Subspace Hidden Markov Model for Acoustic Unit Discovery | This work tackles the problem of learning a set of language specific acoustic units from unlabeled speech recordings given a set of labeled recordings from other languages. Our approach may be described by the following two steps procedure: first the model learns the notion of acoustic units from the labelled data and ... | ['Jan Černocký', 'Lukáš Burget', 'Hari Krishna Vydana', 'Lucas Ondel'] | 2019-04-08 | null | null | null | null | ['acoustic-unit-discovery'] | ['speech'] | [ 1.49846405e-01 4.80112821e-01 -9.59198028e-02 -5.62697291e-01
-1.18955493e+00 -5.11051834e-01 1.00423145e+00 -5.21510661e-01
-4.08407450e-01 6.07869029e-01 6.01151526e-01 -2.69385129e-01
5.92044652e-01 -2.39744931e-01 -6.46555066e-01 -9.34552550e-01
4.89135981e-02 1.01421416e+00 3.07318777e-01 3.31050783... | [14.507821083068848, 6.629617691040039] |
35a3412d-18c2-444b-94b5-7a1eabdc92c6 | no-blind-spots-full-surround-multi-object | 1802.08755 | null | http://arxiv.org/abs/1802.08755v4 | http://arxiv.org/pdf/1802.08755v4.pdf | No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs | Online multi-object tracking (MOT) is extremely important for high-level
spatial reasoning and path planning for autonomous and highly-automated
vehicles. In this paper, we present a modular framework for tracking multiple
objects (vehicles), capable of accepting object proposals from different sensor
modalities (visio... | ['Akshay Rangesh', 'Mohan M. Trivedi'] | 2018-02-23 | null | null | null | null | ['online-multi-object-tracking'] | ['computer-vision'] | [-6.48000687e-02 -9.00203586e-02 -6.15092292e-02 -2.21411407e-01
-4.76995617e-01 -1.08075786e+00 8.43893468e-01 5.50690629e-02
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-2.32754052e-01 8.00982118e-01 1.22619724e+00 -2.18494087... | [6.795936584472656, -2.117668390274048] |
495adf42-b0b0-40b6-a7af-521cd472c4ba | auto-regressive-image-synthesis-with | 2207.10776 | null | https://arxiv.org/abs/2207.10776v1 | https://arxiv.org/pdf/2207.10776v1.pdf | Auto-regressive Image Synthesis with Integrated Quantization | Deep generative models have achieved conspicuous progress in realistic image synthesis with multifarious conditional inputs, while generating diverse yet high-fidelity images remains a grand challenge in conditional image generation. This paper presents a versatile framework for conditional image generation which incor... | ['Shijian Lu', 'Changgong Zhang', 'Kaiwen Cui', 'Jiahui Zhang', 'Rongliang Wu', 'Yingchen Yu', 'Fangneng Zhan'] | 2022-07-21 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 4.04918075e-01 -1.03214569e-02 5.70937991e-02 -3.05421859e-01
-1.03564012e+00 -4.13645327e-01 9.59796131e-01 -5.90354681e-01
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4.78847951e-01 2.51020610e-01 -2.75588274e-01 8.23440030... | [11.47536849975586, -0.28315988183021545] |
8eb8a97f-b985-43a0-b637-290e8bca7140 | interior-attention-aware-network-for-infrared | null | null | https://ieeexplore.ieee.org/document/9745054 | https://ieeexplore.ieee.org/document/9745054 | Interior Attention-Aware Network for Infrared Small Target Detection | Infrared small target detection plays an important role in target warning, ground monitoring, and flight guidance. Existing methods typically utilize local-contrast information of each pixel to detect infrared small targets, neglecting the interior relation between target pixels or background pixels. The mere use of th... | ['Zhiguo Cao', 'Chengxin Liu', 'Shuaiyuan Du', 'Kewei Wang'] | 2022-03-30 | null | null | null | ieee-transactions-on-geoscience-and-remote-8 | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 5.80631614e-01 -3.09925616e-01 -2.47799084e-01 -2.53628671e-01
-6.83217287e-01 -4.65966344e-01 3.18449229e-01 1.68939829e-01
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2.71894336e-01 -9.31079686e-02 5.36936343e-01 -7.30984136... | [8.831396102905273, -0.8595350980758667] |
30371005-77ca-4215-ae99-885c9f0ca500 | dont-take-it-literally-an-edit-invariant | null | null | https://aclanthology.org/2022.naacl-main.150 | https://aclanthology.org/2022.naacl-main.150.pdf | Don’t Take It Literally: An Edit-Invariant Sequence Loss for Text Generation | Neural text generation models are typically trained by maximizing log-likelihood with the sequence cross entropy (CE) loss, which encourages an exact token-by-token match between a target sequence with a generated sequence. Such training objective is sub-optimal when the target sequence is not perfect, e.g., when the t... | ['Zhiting Hu', 'Shuguang Cui', 'Xiaodong He', 'Zhen Li', 'Junwei Bao', 'Xiaodan Liang', 'Tianhua Tao', 'Zichao Yang', 'Guangyi Liu'] | null | null | null | null | naacl-2022-7 | ['text-style-transfoer'] | ['natural-language-processing'] | [ 5.93326211e-01 -1.35633528e-01 -3.31998952e-02 -3.10906321e-01
-1.09010458e+00 -6.44510746e-01 7.48525679e-01 -9.63646770e-02
-4.86935437e-01 9.66876209e-01 2.17233792e-01 -4.16968107e-01
6.45209551e-01 -7.93796182e-01 -1.05272722e+00 -6.54202104e-01
2.16610596e-01 2.85729706e-01 -1.28619909e-01 -3.85312319... | [11.694085121154785, 9.635796546936035] |
e04af4e6-a9f5-4645-9db7-33e611bbed3b | explainable-graph-pyramid-autoformer-for-long | 2209.13123 | null | https://arxiv.org/abs/2209.13123v1 | https://arxiv.org/pdf/2209.13123v1.pdf | Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting | Accurate traffic forecasting is vital to an intelligent transportation system. Although many deep learning models have achieved state-of-art performance for short-term traffic forecasting of up to 1 hour, long-term traffic forecasting that spans multiple hours remains a major challenge. Moreover, most of the existing d... | ['Prasanna Balaprakash', 'Jane Macfarlane', 'Hadi Meidani', 'Tanwi Mallick', 'Weiheng Zhong'] | 2022-09-27 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [-4.13219303e-01 4.36527655e-02 -5.33536136e-01 -6.11261785e-01
-2.85303205e-01 1.70595154e-01 6.45311236e-01 -4.17175382e-01
2.97267795e-01 7.59464622e-01 4.83056575e-01 -1.09231770e+00
-4.16452259e-01 -8.52949381e-01 -7.99024642e-01 -3.38853806e-01
-2.88656592e-01 8.06496978e-01 4.94578809e-01 -6.35416687... | [6.440960884094238, 2.0610263347625732] |
0afed527-0f6d-4b42-a7c3-bb06d76127e1 | covid-cxnet-detecting-covid-19-in-frontal | 2006.13807 | null | https://arxiv.org/abs/2006.13807v2 | https://arxiv.org/pdf/2006.13807v2.pdf | COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep Learning | One of the primary clinical observations for screening the infectious by the novel coronavirus is capturing a chest x-ray image. In most of the patients, a chest x-ray contains abnormalities, such as consolidation, which are the results of COVID-19 viral pneumonia. In this study, research is conducted on efficiently de... | ['Mahdiyar Molahasani Majdabadi', 'Younhee Choi', 'Arman Haghanifar', 'Seokbum Ko', 'S. Deivalakshmi'] | 2020-06-16 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 3.19433033e-01 -6.59598053e-01 1.14889313e-02 -3.07306737e-01
-6.35134518e-01 -5.59835196e-01 9.38718095e-02 2.36764461e-01
-4.94341969e-01 5.08584380e-01 -1.08460607e-02 -4.46859837e-01
-1.21854015e-01 -6.93120599e-01 -6.01383984e-01 -6.11055374e-01
-1.43858567e-01 6.67524695e-01 6.14050515e-02 3.44845504... | [15.542783737182617, -1.7303651571273804] |
c116c467-9120-444e-a388-a49e1f18db8c | composing-rnns-and-fsts-for-small-data-1 | 2208.10248 | null | https://arxiv.org/abs/2208.10248v1 | https://arxiv.org/pdf/2208.10248v1.pdf | Composing RNNs and FSTs for Small Data: Recovering Missing Characters in Old Hawaiian Text | In contrast to the older writing system of the 19th century, modern Hawaiian orthography employs characters for long vowels and glottal stops. These extra characters account for about one-third of the phonemes in Hawaiian, so including them makes a big difference to reading comprehension and pronunciation. However, tra... | ['Brendan Shillingford', 'Oiwi Parker Jones'] | 2022-07-24 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [ 1.97107419e-01 2.75468528e-01 1.18425146e-01 -2.88443863e-01
-9.75048959e-01 -8.70604336e-01 4.80814487e-01 -1.61696747e-01
-8.46946299e-01 6.51105523e-01 4.71076012e-01 -1.11924613e+00
3.28993618e-01 -6.31650805e-01 -6.69218123e-01 -2.66063988e-01
6.27172768e-01 6.73758328e-01 1.25496462e-01 -3.44358772... | [11.003872871398926, 10.198781967163086] |
b07cc47e-aa2f-41bd-9ed2-5dd7090a132d | extending-the-service-composition-formalism | 1909.04393 | null | https://arxiv.org/abs/1909.04393v1 | https://arxiv.org/pdf/1909.04393v1.pdf | Extending the Service Composition Formalism with Relational Parameters | Web Service Composition deals with the (re)use of Web Services to provide complex functionality, inexistent in any single service. Over the state-of-the-art, we introduce a new type of modeling, based on ontologies and relations between objects, which allows us to extend the expressiveness of problems that can be solve... | ['Radu Mereuta', 'Liana Tucar', 'Paul Diac'] | 2019-09-10 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [-1.21064804e-01 4.55037892e-01 2.12189376e-01 -4.52639610e-01
1.39153764e-01 -6.90011263e-01 9.45941448e-01 1.57818705e-01
-8.38944688e-02 5.26457965e-01 1.75287768e-01 -2.58731127e-01
-5.27912199e-01 -1.32171631e+00 -1.93961352e-01 -3.16850692e-01
-1.15675136e-01 5.84812582e-01 1.08425069e+00 -6.95045531... | [8.683406829833984, 7.036570072174072] |
3190b31b-f9f2-4e28-87be-1482a0bb9dcc | speak-memory-an-archaeology-of-books-known-to | 2305.00118 | null | https://arxiv.org/abs/2305.00118v1 | https://arxiv.org/pdf/2305.00118v1.pdf | Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4 | In this work, we carry out a data archaeology to infer books that are known to ChatGPT and GPT-4 using a name cloze membership inference query. We find that OpenAI models have memorized a wide collection of copyrighted materials, and that the degree of memorization is tied to the frequency with which passages of those ... | ['David Bamman', 'Sandeep Soni', 'Mackenzie Cramer', 'Kent K. Chang'] | 2023-04-28 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-4.16168571e-01 3.19434136e-01 -2.92217880e-01 -1.23073392e-01
-7.13342249e-01 -1.02498949e+00 1.00990379e+00 1.86376542e-01
-4.78644848e-01 9.54322100e-01 5.93698144e-01 -5.54624796e-01
-2.50572652e-01 -1.13420045e+00 -1.16237152e+00 -1.05082527e-01
2.15285912e-01 9.96023417e-01 2.27345303e-01 -3.69786978... | [10.793330192565918, 8.178717613220215] |
ef713d66-3999-461a-8390-16fd090334c5 | differentiable-signal-processing-with-black | 2105.04752 | null | https://arxiv.org/abs/2105.04752v1 | https://arxiv.org/pdf/2105.04752v1.pdf | Differentiable Signal Processing With Black-Box Audio Effects | We present a data-driven approach to automate audio signal processing by incorporating stateful third-party, audio effects as layers within a deep neural network. We then train a deep encoder to analyze input audio and control effect parameters to perform the desired signal manipulation, requiring only input-target pai... | ['Nicholas J. Bryan', 'Paris Smaragdis', 'Oliver Wang', 'Marco A. Martínez Ramírez'] | 2021-05-11 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 3.89566690e-01 -5.14567457e-02 4.92914617e-01 -9.83779803e-02
-1.17306244e+00 -8.27045143e-01 1.37833923e-01 -1.96832925e-01
-3.45292836e-01 3.34949970e-01 8.43267217e-02 -2.83784598e-01
-1.55156448e-01 -3.44049543e-01 -8.30731213e-01 -4.03322637e-01
-2.97163993e-01 1.80331588e-01 1.89758033e-01 -3.95810068... | [15.65436840057373, 5.8256659507751465] |
71932c6c-936c-4c40-8752-06133317ec35 | liveseg-unsupervised-multimodal-temporal | 2210.05840 | null | https://arxiv.org/abs/2210.05840v1 | https://arxiv.org/pdf/2210.05840v1.pdf | LiveSeg: Unsupervised Multimodal Temporal Segmentation of Long Livestream Videos | Livestream videos have become a significant part of online learning, where design, digital marketing, creative painting, and other skills are taught by experienced experts in the sessions, making them valuable materials. However, Livestream tutorial videos are usually hours long, recorded, and uploaded to the Internet ... | ['Hailin Jin', 'Ding Zhao', 'Zhaowen Wang', 'Trung Bui', 'Franck Dernoncourt', 'JieLin Qiu'] | 2022-10-12 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 1.20754123e-01 -3.70783538e-01 -3.90563548e-01 -4.52854007e-01
-7.49683619e-01 -6.60350084e-01 3.71532291e-01 -1.32140964e-01
-2.70438313e-01 3.17458749e-01 4.88055833e-02 2.84490939e-02
-3.30463260e-01 -6.00808680e-01 -9.17716146e-01 -6.43156826e-01
-5.70382252e-02 1.51849017e-01 4.01132226e-01 -1.24328084... | [9.687840461730957, 0.5063785314559937] |
e4717504-43d6-4040-ad9a-c3ded9076b37 | deep-reinforcement-learning-for-time-1 | null | null | https://doi.org/10.1109/WCNC51071.2022.9771580 | https://hdl.handle.net/10356/155437 | Deep Reinforcement Learning for Time Allocation and Directional Transmission in Joint Radar-Communication | Current strategies for joint radar-communication (JRC) rely on prior knowledge of the communication and radar systems within the vehicle network. In this paper, we propose a framework for intelligent vehicles to conduct JRC, with minimal prior knowledge, in an environment where surrounding vehicles execute radar detect... | ['G David González', 'Yong Liang Guan', 'Dusit Niyato', 'Yanyu Cheng', 'Joash Lee'] | 2022-05-19 | null | null | null | ieee-wireless-communications-and-networking | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty', 'joint-radar-communication', 'intelligent-communication'] | ['medical', 'reasoning', 'robots', 'time-series'] | [ 7.83460960e-02 2.23944336e-01 -3.51182520e-01 -4.12651867e-01
-5.30885339e-01 -3.62256587e-01 9.54907715e-01 -1.55431271e-01
-7.23285675e-01 9.48048830e-01 -2.79193729e-01 -7.92638063e-01
-7.27037191e-02 -1.08006501e+00 -8.06014180e-01 -8.52917850e-01
-5.46935141e-01 6.64727449e-01 2.01582178e-01 -1.91345870... | [5.203249931335449, 1.4240344762802124] |
841755da-985e-4c90-9e55-8a9bc3847505 | semi-supervised-clustering-of-sparse-graphs | 2205.11677 | null | https://arxiv.org/abs/2205.11677v2 | https://arxiv.org/pdf/2205.11677v2.pdf | Semi-Supervised Clustering of Sparse Graphs: Crossing the Information-Theoretic Threshold | The stochastic block model is a canonical random graph model for clustering and community detection on network-structured data. Decades of extensive study on the problem have established many profound results, among which the phase transition at the Kesten-Stigum threshold is particularly interesting both from a mathem... | ['Thomas Strohmer', 'JunDa Sheng'] | 2022-05-24 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 5.12643576e-01 5.40879905e-01 -7.36362875e-01 -4.91981842e-02
-5.36298513e-01 -8.44699264e-01 4.06007499e-01 2.64150232e-01
-1.39888436e-01 6.74706161e-01 -1.19084075e-01 -2.29758561e-01
-6.86730802e-01 -7.56820738e-01 -5.13213873e-01 -1.00341117e+00
-4.70753163e-01 8.31319571e-01 1.22345805e-01 1.31785467... | [6.900477409362793, 5.115313529968262] |
187a0e08-1546-4b20-819b-dfb40c875c33 | segmentation-from-natural-language | 1603.06180 | null | http://arxiv.org/abs/1603.06180v1 | http://arxiv.org/pdf/1603.06180v1.pdf | Segmentation from Natural Language Expressions | In this paper we approach the novel problem of segmenting an image based on a
natural language expression. This is different from traditional semantic
segmentation over a predefined set of semantic classes, as e.g., the phrase
"two men sitting on the right bench" requires segmenting only the two people on
the right ben... | ['Trevor Darrell', 'Ronghang Hu', 'Marcus Rohrbach'] | 2016-03-20 | null | null | null | null | ['referring-expression-segmentation'] | ['computer-vision'] | [ 7.77849257e-01 4.22500879e-01 -2.72538483e-01 -7.96985090e-01
-8.26180100e-01 -7.29339898e-01 3.53311568e-01 -1.69605970e-01
-4.65074271e-01 3.89806271e-01 -1.80582508e-01 -2.43304282e-01
2.56076247e-01 -7.44633019e-01 -9.67707753e-01 -5.62685132e-01
3.64495426e-01 5.63403547e-01 3.16090584e-01 -7.99121559... | [10.202125549316406, 1.164605975151062] |
153f333e-5811-4083-88c8-9a2cc3759127 | swin-unetr-swin-transformers-for-semantic | 2201.01266 | null | https://arxiv.org/abs/2201.01266v1 | https://arxiv.org/pdf/2201.01266v1.pdf | Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images | Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient and successively studying the progression of the malignant entity. In recent years, Fully Convolutional Neural Networks (FCNNs) approaches hav... | ['Daguang Xu', 'Holger Roth', 'Dong Yang', 'Yucheng Tang', 'Vishwesh Nath', 'Ali Hatamizadeh'] | 2022-01-04 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 4.52057242e-01 2.56449908e-01 -2.37427667e-01 -2.77134925e-01
-7.87036538e-01 -1.92670152e-01 3.95710796e-01 -6.82475492e-02
-7.00864017e-01 2.81656176e-01 1.38930127e-01 -4.59244668e-01
4.44045151e-03 -5.61994970e-01 -4.98125434e-01 -7.08660007e-01
1.04626402e-01 6.00614130e-01 4.56936121e-01 -1.46470740... | [14.611905097961426, -2.440640687942505] |
80c98f0e-0602-414b-b3e7-0c60ab8bca05 | improving-fairness-in-facial-albedo | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ren_Improving_Fairness_in_Facial_Albedo_Estimation_via_Visual-Textual_Cues_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ren_Improving_Fairness_in_Facial_Albedo_Estimation_via_Visual-Textual_Cues_CVPR_2023_paper.pdf | Improving Fairness in Facial Albedo Estimation via Visual-Textual Cues | Recent 3D face reconstruction methods have made significant advances in geometry prediction, yet further cosmetic improvements are limited by lagged albedo because inferring albedo from appearance is an ill-posed problem. Although some existing methods consider prior knowledge from illumination to improve albedo es... | ['Xiaokang Yang', 'Yichao Yan', 'Chao Ma', 'Jiankang Deng', 'Xingyu Ren'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-face-reconstruction', 'face-reconstruction', 'pseudo-label'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 2.57519364e-01 4.16928940e-02 -1.19818568e-01 -7.92473912e-01
-1.82796210e-01 -3.85404676e-01 4.31258112e-01 -3.22351068e-01
6.41698688e-02 4.53972936e-01 1.20697036e-01 1.81321964e-01
3.23599488e-01 -9.18280840e-01 -6.12484515e-01 -8.77156615e-01
3.81721556e-01 1.65849552e-01 -5.63228607e-01 -1.83895960... | [12.829290390014648, -0.153189554810524] |
667619db-99ea-4901-8239-8f5ad38e70d7 | designing-an-encoder-for-fast-personalization | 2302.12228 | null | https://arxiv.org/abs/2302.12228v3 | https://arxiv.org/pdf/2302.12228v3.pdf | Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models | Text-to-image personalization aims to teach a pre-trained diffusion model to reason about novel, user provided concepts, embedding them into new scenes guided by natural language prompts. However, current personalization approaches struggle with lengthy training times, high storage requirements or loss of identity. To ... | ['Daniel Cohen-Or', 'Gal Chechik', 'Amit H. Bermano', 'Yuval Atzmon', 'Moab Arar', 'Rinon Gal'] | 2023-02-23 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 5.12058854e-01 1.85781792e-01 -7.35341534e-02 -4.61907029e-01
-5.54110765e-01 -6.54714406e-01 6.79021597e-01 3.02611381e-01
-7.31012821e-01 5.61590850e-01 2.95952946e-01 -8.79199505e-02
2.15831041e-01 -7.89459169e-01 -8.13967109e-01 -5.00740647e-01
1.41531035e-01 6.38174772e-01 1.30737588e-01 -9.17466581... | [10.22412395477295, 2.142333984375] |
404ad6ab-f00c-4ea9-8d51-f79c7f2a271b | spatio-focal-bidirectional-disparity | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kim_Spatio-Focal_Bidirectional_Disparity_Estimation_From_a_Dual-Pixel_Image_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kim_Spatio-Focal_Bidirectional_Disparity_Estimation_From_a_Dual-Pixel_Image_CVPR_2023_paper.pdf | Spatio-Focal Bidirectional Disparity Estimation From a Dual-Pixel Image | Dual-pixel photography is monocular RGB-D photography with an ultra-high resolution, enabling many applications in computational photography. However, there are still several challenges to fully utilizing dual-pixel photography. Unlike the conventional stereo pair, the dual pixel exhibits a bidirectional disparity ... | ['Min H. Kim', 'Inchul Kim', 'Hyeonjoong Jang', 'Donggun Kim'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['disparity-estimation'] | ['computer-vision'] | [ 5.93155563e-01 -1.33219168e-01 6.43159524e-02 -4.04188484e-01
-4.70575720e-01 -3.38524193e-01 4.60467666e-01 -5.53670764e-01
-3.58137012e-01 8.67432058e-01 1.61907822e-01 -1.74939111e-01
8.67975503e-02 -8.60413492e-01 -6.80735588e-01 -1.00891006e+00
5.03960252e-01 -2.14742109e-01 3.50207835e-01 1.24174803... | [9.739019393920898, -2.643693685531616] |
5a3b7c65-3cd5-4fcf-a3d2-f20cb3d71411 | napss-paragraph-level-medical-text | 2302.05574 | null | https://arxiv.org/abs/2302.05574v1 | https://arxiv.org/pdf/2302.05574v1.pdf | NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization | Accessing medical literature is difficult for laypeople as the content is written for specialists and contains medical jargon. Automated text simplification methods offer a potential means to address this issue. In this work, we propose a summarize-then-simplify two-stage strategy, which we call NapSS, identifying the ... | ['Gabriele Pergola', 'Yulan He', 'Byron C. Wallace', 'Jiazheng Li', 'Junru Lu'] | 2023-02-11 | null | null | null | null | ['semantic-textual-similarity'] | ['natural-language-processing'] | [ 5.22993743e-01 3.64532471e-01 -3.59808981e-01 -3.43399286e-01
-1.55011094e+00 -6.40690386e-01 3.71058673e-01 7.65611589e-01
-6.05106652e-01 8.07232141e-01 9.16310728e-01 -9.05677825e-02
-1.15161292e-01 -4.80833888e-01 -4.13168669e-01 -4.03839439e-01
4.76924777e-01 2.46803001e-01 -6.10609613e-02 -1.73054576... | [12.209528923034668, 9.463177680969238] |
260d041e-ea2c-4ae8-9dba-f1ea91d17ce3 | semeval-2018-task-9-hypernym-discovery | null | null | https://aclanthology.org/S18-1115 | https://aclanthology.org/S18-1115.pdf | SemEval-2018 Task 9: Hypernym Discovery | This paper describes the SemEval 2018 Shared Task on Hypernym Discovery. We put forward this task as a complementary benchmark for modeling hypernymy, a problem which has traditionally been cast as a binary classification task, taking a pair of candidate words as input. Instead, our reformulated task is defined as foll... | ['Tommaso Pasini', 'Luis Espinosa-Anke', 'Jose Camacho-Collados', 'Sergio Oramas', 'Claudio Delli Bovi', 'Roberto Navigli', 'Enrico Santus', 'Vered Shwartz', 'Horacio Saggion'] | 2018-06-01 | null | null | null | semeval-2018-6 | ['hypernym-discovery'] | ['natural-language-processing'] | [ 8.42478126e-02 3.48578513e-01 -3.70785296e-01 -2.18959048e-01
-6.06690884e-01 -5.75226963e-01 7.93973684e-01 4.18320060e-01
-9.47649658e-01 8.03968430e-01 1.15851112e-01 -3.16222370e-01
-1.81768492e-01 -5.94441056e-01 -3.70785028e-01 -3.42374384e-01
2.57870376e-01 1.08958662e+00 9.03394371e-02 -4.55208153... | [9.802184104919434, 8.753582000732422] |
211934ac-5b12-4098-9ce7-a49f746df991 | don-t-memorize-mimic-the-past-federated-class | 2307.00497 | null | https://arxiv.org/abs/2307.00497v1 | https://arxiv.org/pdf/2307.00497v1.pdf | Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory | Deep learning models are prone to forgetting information learned in the past when trained on new data. This problem becomes even more pronounced in the context of federated learning (FL), where data is decentralized and subject to independent changes for each user. Continual Learning (CL) studies this so-called \textit... | ['Salman Avestimehr', 'Mahdi Soltanolkotabi', 'Chaoyang He', 'Zalan Fabian', 'Sara Babakniya'] | 2023-07-02 | null | null | null | null | ['class-incremental-learning', 'continual-learning', 'incremental-learning'] | ['computer-vision', 'methodology', 'methodology'] | [-2.21092120e-01 1.86614275e-01 -1.98670104e-01 -5.76000154e-01
-8.15558851e-01 -7.31302261e-01 2.85909235e-01 1.59536287e-01
-6.31738782e-01 1.10389924e+00 1.91330448e-01 -3.73196125e-01
3.06099981e-01 -7.60228097e-01 -1.16985404e+00 -9.09243703e-01
-5.85495085e-02 3.91303211e-01 9.91022214e-03 4.03496146... | [5.84539794921875, 6.359963417053223] |
8c611faf-033a-4d6b-b248-bbfba960a379 | machine-learning-based-detection-of-parkinson | 2303.01389 | null | https://arxiv.org/abs/2303.01389v1 | https://arxiv.org/pdf/2303.01389v1.pdf | Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study | Resting-state EEG (rs-EEG) has been demonstrated to aid in Parkinson's disease (PD) diagnosis. In particular, the power spectral density (PSD) of low-frequency bands ({\delta} and {\theta}) and high-frequency bands ({\alpha} and \b{eta}) has been shown to be significantly different in patients with PD as compared to su... | ['Alvaro Fernandez-Quilez', 'Kolbjørn Brønnick', 'John Fredy Ochoa-Gomez', 'Alberto Jaramillo-Jimenez', 'Anna Kurbatskaya'] | 2023-03-02 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 3.41967009e-02 -1.83208540e-01 2.19549745e-01 -2.22829342e-01
-1.26008689e+00 -3.01313967e-01 1.79119796e-01 4.09570575e-01
-4.75149572e-01 1.01744819e+00 2.61662751e-01 1.33437857e-01
-4.30523515e-01 -4.57754433e-01 -1.25271410e-01 -5.95935047e-01
-5.86748302e-01 3.41519147e-01 1.57429814e-01 5.52089140... | [13.336831092834473, 3.3580124378204346] |
7f015680-af19-4954-823e-dc243c119355 | few-shot-learning-of-compact-models-via-task | 2210.09922 | null | https://arxiv.org/abs/2210.09922v1 | https://arxiv.org/pdf/2210.09922v1.pdf | Few-Shot Learning of Compact Models via Task-Specific Meta Distillation | We consider a new problem of few-shot learning of compact models. Meta-learning is a popular approach for few-shot learning. Previous work in meta-learning typically assumes that the model architecture during meta-training is the same as the model architecture used for final deployment. In this paper, we challenge this... | ['Yang Wang', 'Zhi Liu', 'Mehrdad Hosseinzadeh', 'Shekhor Chanda', 'Yong Wu'] | 2022-10-18 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 2.01204404e-01 -3.63397747e-02 -2.38060385e-01 -3.90399903e-01
-7.63216853e-01 -6.25927076e-02 4.31277663e-01 3.92524242e-01
-5.86676598e-01 4.72236454e-01 -2.55119920e-01 -7.76151021e-04
1.42294735e-01 -9.03427064e-01 -6.70929670e-01 -5.78134716e-01
1.00723654e-01 7.72131503e-01 7.62044311e-01 -2.84699857... | [9.958539962768555, 3.184420108795166] |
4320d656-12d0-4e62-9c0d-58bd437bff2e | knodle-modular-weakly-supervised-learning | 2104.11557 | null | https://arxiv.org/abs/2104.11557v3 | https://arxiv.org/pdf/2104.11557v3.pdf | Knodle: Modular Weakly Supervised Learning with PyTorch | Strategies for improving the training and prediction quality of weakly supervised machine learning models vary in how much they are tailored to a specific task or integrated with a specific model architecture. In this work, we introduce Knodle, a software framework that treats weak data annotations, deep learning model... | ['Benjamin Roth', 'Marina Speranskaya', 'Andreas Stephan', 'Anastasiia Sedova'] | 2021-04-23 | null | https://aclanthology.org/2021.repl4nlp-1.12 | https://aclanthology.org/2021.repl4nlp-1.12.pdf | acl-repl4nlp-2021-8 | ['weakly-supervised-data-denoising'] | ['natural-language-processing'] | [ 1.13607079e-01 3.90196443e-01 -6.42156005e-01 -6.79502130e-01
-4.95565832e-01 -8.70743513e-01 6.52935088e-01 3.05614740e-01
-3.19337398e-01 7.14526892e-01 2.91939765e-01 -3.46092790e-01
-1.66812912e-01 -6.01596057e-01 -8.06114614e-01 -5.57449162e-01
3.73271525e-01 7.28240907e-01 3.12526792e-01 -1.56370759... | [9.490022659301758, 4.006651401519775] |
b82fb1d4-fbc3-4348-acc1-7699448a20e5 | self-score-self-supervised-learning-on-score | 2209.00835 | null | https://arxiv.org/abs/2209.00835v1 | https://arxiv.org/pdf/2209.00835v1.pdf | Self-Score: Self-Supervised Learning on Score-Based Models for MRI Reconstruction | Recently, score-based diffusion models have shown satisfactory performance in MRI reconstruction. Most of these methods require a large amount of fully sampled MRI data as a training set, which, sometimes, is difficult to acquire in practice. This paper proposes a fully-sampled-data-free score-based diffusion model for... | ['Dong Liang', 'Yanjie Zhu', 'Haifeng Wang', 'Jing Cheng', 'Qingyong Zhu', 'Shaonan Liu', 'Chentao Cao', 'Zhuo-Xu Cui'] | 2022-09-02 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 3.03982645e-01 6.99421614e-02 -2.81281114e-01 -8.43212128e-01
-1.36093628e+00 -4.17756774e-02 4.45633799e-01 -1.71209425e-01
-6.90899312e-01 8.69504452e-01 5.50516725e-01 2.08161548e-02
-1.56070545e-01 -5.76588333e-01 -7.22217321e-01 -1.06861603e+00
-2.55055219e-01 1.23485959e+00 4.93691474e-01 6.42517567... | [13.490068435668945, -2.381375789642334] |
9aa5b432-0435-4c68-9dd6-c47f034689fe | deep-learning-based-cloud-detection-for | 1810.05801 | null | http://arxiv.org/abs/1810.05801v3 | http://arxiv.org/pdf/1810.05801v3.pdf | Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors | Cloud detection is an important preprocessing step for the precise
application of optical satellite imagery. In this paper, we propose a deep
learning based cloud detection method named multi-scale convolutional feature
fusion (MSCFF) for remote sensing images of different sensors. In the network
architecture of MSCFF,... | ['Yuhao Liu', 'Qing Cheng', 'Zhiwei Li', 'Shucheng You', 'Zongyi He', 'Huanfeng Shen'] | 2018-10-13 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 8.99702031e-03 -1.05257845e+00 1.37690395e-01 -3.59218478e-01
-7.87609339e-01 -4.11562055e-01 3.78424078e-01 -2.47059837e-01
-3.38819534e-01 6.48088157e-01 -3.75663847e-01 -2.88663954e-01
-2.03109518e-01 -1.24994385e+00 -4.17951912e-01 -9.73011136e-01
-4.75095510e-01 -1.38655141e-01 1.11508906e-01 -1.56822413... | [9.78941535949707, -1.7129042148590088] |
66d9bb1c-1855-4018-97a4-4808b5aa1002 | dualcf-efficient-model-extraction-attack-from | 2205.06504 | null | https://arxiv.org/abs/2205.06504v1 | https://arxiv.org/pdf/2205.06504v1.pdf | DualCF: Efficient Model Extraction Attack from Counterfactual Explanations | Cloud service providers have launched Machine-Learning-as-a-Service (MLaaS) platforms to allow users to access large-scale cloudbased models via APIs. In addition to prediction outputs, these APIs can also provide other information in a more human-understandable way, such as counterfactual explanations (CF). However, s... | ['Chunyan Miao', 'Hangwei Qian', 'Yongjie Wang'] | 2022-05-13 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 3.83137614e-02 -4.60759178e-02 -4.06859070e-01 -2.56021380e-01
-1.11691082e+00 -8.89148593e-01 7.11628377e-01 -1.14982508e-01
1.39285371e-01 6.80495560e-01 -2.65553772e-01 -9.35158610e-01
-7.98560083e-02 -9.34494376e-01 -9.17240322e-01 -4.33604747e-01
1.88768536e-01 2.77539551e-01 2.65135497e-01 2.19262466... | [5.860490798950195, 7.309166431427002] |
19185e38-f72f-4181-aeff-bd57a61db369 | latent-relational-metric-learning-via-memory | 1707.05176 | null | http://arxiv.org/abs/1707.05176v3 | http://arxiv.org/pdf/1707.05176v3.pdf | Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking | This paper proposes a new neural architecture for collaborative ranking with
implicit feedback. Our model, LRML (\textit{Latent Relational Metric Learning})
is a novel metric learning approach for recommendation. More specifically,
instead of simple push-pull mechanisms between user and item pairs, we propose
to learn ... | ['Anh Tuan Luu', 'Yi Tay', 'Siu Cheung Hui'] | 2017-07-17 | null | null | null | null | ['collaborative-ranking'] | ['graphs'] | [-1.09371930e-01 6.29771799e-02 -6.24388576e-01 -6.02861583e-01
-4.12422866e-01 -4.54487920e-01 5.90913296e-01 1.97717831e-01
-4.47305918e-01 5.34221411e-01 4.43343550e-01 -3.22174370e-01
-6.57667935e-01 -8.88442457e-01 -6.34093821e-01 -3.88312042e-01
-2.21380383e-01 4.72727954e-01 -1.81332856e-01 -1.90867007... | [10.118928909301758, 5.66462516784668] |
73b95177-23d9-4c42-bb0c-8cffd7a4bda6 | joint-learning-of-blind-super-resolution-and | 2302.12491 | null | https://arxiv.org/abs/2302.12491v2 | https://arxiv.org/pdf/2302.12491v2.pdf | Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images | This paper proposes crack segmentation augmented by super resolution (SR) with deep neural networks. In the proposed method, a SR network is jointly trained with a binary segmentation network in an end-to-end manner. This joint learning allows the SR network to be optimized for improving segmentation results. For reali... | ['Yuki Kondo', 'Norimichi Ukita'] | 2023-02-24 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [ 6.15606129e-01 1.82032093e-01 -1.04738146e-01 -2.40104303e-01
-1.21474266e+00 -3.02237004e-01 6.99973991e-03 -8.49912763e-01
-4.71194953e-01 6.98011637e-01 3.92935902e-01 9.98164266e-02
1.07405156e-01 -6.28320396e-01 -6.38809979e-01 -6.99806154e-01
1.68167546e-01 4.08084057e-02 3.65317613e-01 1.23518176... | [11.179614067077637, -2.305121660232544] |
7e5307b3-b12d-4cd2-9ca4-136b50e55aef | resolving-semantic-confusions-for-improved-1 | 2212.06097 | null | https://arxiv.org/abs/2212.06097v1 | https://arxiv.org/pdf/2212.06097v1.pdf | Resolving Semantic Confusions for Improved Zero-Shot Detection | Zero-shot detection (ZSD) is a challenging task where we aim to recognize and localize objects simultaneously, even when our model has not been trained with visual samples of a few target ("unseen") classes. Recently, methods employing generative models like GANs have shown some of the best results, where unseen-class ... | ['Arijit Sur', 'Sushil Kumar', 'Sandipan Sarma'] | 2022-12-12 | resolving-semantic-confusions-for-improved | https://bmvc2022.mpi-inf.mpg.de/347/ | https://bmvc2022.mpi-inf.mpg.de/0347.pdf | british-machine-vision-conference-2022-11 | ['zero-shot-object-detection'] | ['computer-vision'] | [ 5.08757532e-01 1.44919366e-01 5.11897802e-02 -4.20252413e-01
-7.05064356e-01 -3.60040247e-01 9.06551123e-01 -9.39538032e-02
-1.70862406e-01 5.86366415e-01 -8.71963128e-02 2.78363407e-01
4.50445294e-01 -8.45398784e-01 -7.33954012e-01 -7.95335591e-01
4.49918032e-01 6.05455935e-01 4.87335831e-01 9.35035944... | [9.742593765258789, 2.045947313308716] |
72710783-823a-4014-91a9-99be9fae55f2 | post-retrieval-clustering-using-third-order | null | null | https://aclanthology.org/P13-2028 | https://aclanthology.org/P13-2028.pdf | Post-Retrieval Clustering Using Third-Order Similarity Measures | null | ['Ga{\\"e}l Dias', "Jos{\\'e} G. Moreno", 'Guillaume Cleuziou'] | 2013-08-01 | null | null | null | acl-2013-8 | ['text-clustering'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.256807327270508, 3.8103299140930176] |
677ebc0f-d616-43b6-9ce2-ce2fafdece3a | feature-generation-for-long-tail | 2111.05956 | null | https://arxiv.org/abs/2111.05956v1 | https://arxiv.org/pdf/2111.05956v1.pdf | Feature Generation for Long-tail Classification | The visual world naturally exhibits an imbalance in the number of object or scene instances resulting in a \emph{long-tailed distribution}. This imbalance poses significant challenges for classification models based on deep learning. Oversampling instances of the tail classes attempts to solve this imbalance. However, ... | ['Makarand Tapaswi', 'Vineeth N. Balasubramanian', 'Marc T. Law', 'Rahul Vigneswaran'] | 2021-11-10 | null | null | null | null | ['classification'] | ['methodology'] | [-2.83994563e-02 -2.01208398e-01 -1.25237003e-01 -6.09938443e-01
-5.09981930e-01 -4.70031530e-01 6.63756430e-01 1.44256115e-01
-4.22380209e-01 7.39381671e-01 1.51364893e-01 -1.74364209e-01
-2.48956963e-01 -7.39799082e-01 -7.51551211e-01 -7.35724568e-01
-9.77910087e-02 4.46990848e-01 2.11587071e-01 -3.33569460... | [9.629480361938477, 2.726771116256714] |
a57cd7f2-c490-47ce-a56a-03d41cc90cad | nict-naist-system-for-wmt17-multimodal | null | null | https://aclanthology.org/W17-4753 | https://aclanthology.org/W17-4753.pdf | NICT-NAIST System for WMT17 Multimodal Translation Task | null | ['Masao Utiyama', 'Eiichro Sumita', 'Jingyi Zhang', 'Satoshi Nakamura', 'Graham Neubig'] | 2017-09-01 | null | null | null | ws-2017-9 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.245724678039551, 3.7562527656555176] |
7aee98b6-c39f-419b-a85c-5a6932a590a9 | strongly-incremental-constituency-parsing | 2010.14568 | null | https://arxiv.org/abs/2010.14568v1 | https://arxiv.org/pdf/2010.14568v1.pdf | Strongly Incremental Constituency Parsing with Graph Neural Networks | Parsing sentences into syntax trees can benefit downstream applications in NLP. Transition-based parsers build trees by executing actions in a state transition system. They are computationally efficient, and can leverage machine learning to predict actions based on partial trees. However, existing transition-based pars... | ['Jia Deng', 'Kaiyu Yang'] | 2020-10-27 | null | http://proceedings.neurips.cc/paper/2020/hash/f7177163c833dff4b38fc8d2872f1ec6-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/f7177163c833dff4b38fc8d2872f1ec6-Paper.pdf | neurips-2020-12 | ['constituency-parsing'] | ['natural-language-processing'] | [ 2.19900578e-01 7.90421188e-01 -3.46650660e-01 -7.48048782e-01
-9.45063531e-01 -8.23639154e-01 4.51725394e-01 3.76002908e-01
-1.77381814e-01 5.29647648e-01 4.84988034e-01 -9.67793643e-01
6.65360987e-01 -1.06110954e+00 -8.48720610e-01 3.07820253e-02
9.86673906e-02 4.66311663e-01 5.01332581e-01 -3.98809195... | [10.38431167602539, 9.5847806930542] |
0584a919-819e-4e83-98b8-eee6ac5d3404 | toward-trusted-and-swift-uav-communication | 2306.17350 | null | https://arxiv.org/abs/2306.17350v1 | https://arxiv.org/pdf/2306.17350v1.pdf | Toward Trusted and Swift UAV Communication: ISAC-Enabled Dual Identity Mapping | The UAV network has recently emerged as a capable carrier for ubiquitous wireless intelligent communication in the B5G/6G era. Nevertheless, the separation of dual identity raises challenges from the perspective of communication efficiency and security, including tedious communication feedback and malicious Sybil attac... | ['Ping Zhang', 'Chenlong Xu', 'Zhiqing Wei', 'Qixun Zhang', 'Zhiyong Feng', 'Yanpeng Cui'] | 2023-06-30 | null | null | null | null | ['management', 'intelligent-communication'] | ['miscellaneous', 'time-series'] | [ 2.60360569e-01 -5.63259274e-02 -4.04108167e-01 2.17240751e-01
3.47430669e-02 -1.23475182e+00 3.45866948e-01 -4.28188354e-01
4.29510288e-02 8.25973392e-01 -6.16951287e-01 -6.34419024e-01
-5.55357993e-01 -8.74184966e-01 -5.85772358e-02 -8.00464213e-01
-5.76030850e-01 -4.07545604e-02 -2.48513415e-01 -6.00174427... | [6.196676254272461, 1.2764744758605957] |
dccb1bbf-6799-4ecc-a5c6-1f9dbef10aa8 | bootstrapping-single-channel-source | 1811.02130 | null | http://arxiv.org/abs/1811.02130v1 | http://arxiv.org/pdf/1811.02130v1.pdf | Bootstrapping single-channel source separation via unsupervised spatial clustering on stereo mixtures | Separating an audio scene into isolated sources is a fundamental problem in
computer audition, analogous to image segmentation in visual scene analysis.
Source separation systems based on deep learning are currently the most
successful approaches for solving the underdetermined separation problem, where
there are more ... | ['Prem Seetharaman', 'Bryan Pardo', 'Jonathan Le Roux', 'Gordon Wichern'] | 2018-11-06 | null | null | null | null | ['unsupervised-spatial-clustering'] | ['time-series'] | [ 3.13048780e-01 -3.61012787e-01 -2.95921769e-02 -1.26048267e-01
-1.08956420e+00 -8.39376867e-01 2.36813188e-01 1.10889114e-01
-3.89517635e-01 7.28316307e-01 2.59879112e-01 -2.62746274e-01
-1.14266373e-01 -3.62465352e-01 -5.91926336e-01 -9.29983735e-01
1.76645711e-01 1.37765199e-01 2.04921082e-01 -1.94283780... | [15.266724586486816, 5.564388751983643] |
9f7cb0e3-c5bf-4937-adc9-6b3cb65b8d4c | cat-gen-improving-robustness-in-nlp-models | 2010.02338 | null | https://arxiv.org/abs/2010.02338v1 | https://arxiv.org/pdf/2010.02338v1.pdf | CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation | NLP models are shown to suffer from robustness issues, i.e., a model's prediction can be easily changed under small perturbations to the input. In this work, we present a Controlled Adversarial Text Generation (CAT-Gen) model that, given an input text, generates adversarial texts through controllable attributes that ar... | ['Ed Chi', 'Alex Beutel', 'Jilin Chen', 'Kang Li', 'Ben Packer', 'Yao Qin', 'Xuezhi Wang', 'Tianlu Wang'] | 2020-10-05 | null | https://aclanthology.org/2020.emnlp-main.417 | https://aclanthology.org/2020.emnlp-main.417.pdf | emnlp-2020-11 | ['adversarial-text'] | ['adversarial'] | [ 6.16846263e-01 7.44338930e-01 1.12965807e-01 -3.92309457e-01
-7.56545842e-01 -1.35027945e+00 8.98790240e-01 -1.30216122e-01
6.77652806e-02 9.53465641e-01 1.34444296e-01 -3.56558532e-01
6.54614210e-01 -1.01782513e+00 -1.03636503e+00 -4.39723402e-01
4.59653229e-01 5.36572635e-01 -1.88833669e-01 -6.29751325... | [5.99533748626709, 8.115008354187012] |
717bc3cb-3e30-48cb-a7f9-2f3cb0a61022 | scalable-attentive-sentence-pair-modeling-via | 1908.05161 | null | https://arxiv.org/abs/1908.05161v3 | https://arxiv.org/pdf/1908.05161v3.pdf | Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding | Recent state-of-the-art natural language understanding models, such as BERT and XLNet, score a pair of sentences (A and B) using multiple cross-attention operations - a process in which each word in sentence A attends to all words in sentence B and vice versa. As a result, computing the similarity between a query sente... | ['Itzik Malkiel', 'Avi Caciularu', 'Ori Katz', 'Noam Koenigstein', 'Oren Barkan', 'Noam Razin'] | 2019-08-14 | null | null | null | null | ['sentence-pair-modeling'] | ['natural-language-processing'] | [ 3.01216006e-01 1.33790672e-01 -1.84207428e-02 -7.03030109e-01
-1.13508832e+00 -4.59575713e-01 4.82458740e-01 7.69272566e-01
-7.28302419e-01 4.16943252e-01 3.96938562e-01 -5.11270821e-01
6.78525046e-02 -9.08881366e-01 -7.18851924e-01 -3.04197907e-01
3.37996962e-03 5.32787502e-01 7.46079832e-02 -5.62418401... | [10.972858428955078, 8.641406059265137] |
b466abbb-6e05-4803-b7a3-9d72abe45368 | a-novel-framework-for-multimodal-named-entity | 2305.08372 | null | https://arxiv.org/abs/2305.08372v1 | https://arxiv.org/pdf/2305.08372v1.pdf | A Novel Framework for Multimodal Named Entity Recognition with Multi-level Alignments | Mining structured knowledge from tweets using named entity recognition (NER) can be beneficial for many downstream applications such as recommendation and intention under standing. With tweet posts tending to be multimodal, multimodal named entity recognition (MNER) has attracted more attention. In this paper, we propo... | ['Limin Sun', 'Hongsong Zhu', 'Shuaizong Si', 'Jie Liu', 'Yimo Ren', 'Hong Li', 'Peipei Liu'] | 2023-05-15 | null | null | null | null | ['named-entity-recognition-ner'] | ['natural-language-processing'] | [ 3.15800846e-01 -2.87676863e-02 -3.50985974e-01 -4.33693081e-01
-8.34331870e-01 -2.97585577e-01 6.73795223e-01 1.47513390e-01
-8.57845902e-01 5.55776834e-01 8.21616411e-01 1.25138938e-01
-1.53396428e-01 -4.32300180e-01 -3.38222682e-01 -4.48642641e-01
2.80485362e-01 2.48579785e-01 2.05779985e-01 -1.84406698... | [10.785778999328613, 1.5681601762771606] |
7dd726cb-a590-4a12-aef6-d96e7f748df6 | algorithmic-trading-using-continuous-action | 2210.03469 | null | https://arxiv.org/abs/2210.03469v1 | https://arxiv.org/pdf/2210.03469v1.pdf | Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning | Price movement prediction has always been one of the traders' concerns in financial market trading. In order to increase their profit, they can analyze the historical data and predict the price movement. The large size of the data and complex relations between them lead us to use algorithmic trading and artificial inte... | ['Farokh Marvasti', 'Mahdi Shamsi', 'Naseh Majidi'] | 2022-10-07 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-6.39176667e-01 -1.31109744e-01 2.81123491e-03 3.22172098e-04
-1.92101687e-01 -9.02731776e-01 8.88638377e-01 1.67181306e-02
-5.11965036e-01 1.02154374e+00 -8.75554383e-02 -4.46426332e-01
-6.08667612e-01 -9.89436030e-01 -2.97763735e-01 -6.51164651e-01
-4.29431409e-01 8.59366059e-01 2.29122683e-01 -5.01492739... | [4.56176233291626, 3.989213466644287] |
14fc4b1d-e79b-4ec4-8af5-7b3df742fd73 | bi-vldoc-bidirectional-vision-language | 2206.13155 | null | https://arxiv.org/abs/2206.13155v1 | https://arxiv.org/pdf/2206.13155v1.pdf | Bi-VLDoc: Bidirectional Vision-Language Modeling for Visually-Rich Document Understanding | Multi-modal document pre-trained models have proven to be very effective in a variety of visually-rich document understanding (VrDU) tasks. Though existing document pre-trained models have achieved excellent performance on standard benchmarks for VrDU, the way they model and exploit the interactions between vision and ... | ['Luo Si', 'Yang Xue', 'Chenliang Li', 'Lianwen Jin', 'Cong Yao', 'Qi Zheng', 'Guozhi Tang', 'Chuwei Luo'] | 2022-06-27 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [ 1.82246432e-01 -8.28037187e-02 -3.99565786e-01 -2.54469693e-01
-1.15155602e+00 -5.68133891e-01 1.18704951e+00 9.22132581e-02
-6.99489564e-02 3.04448456e-01 3.71155173e-01 -3.33020926e-01
1.80766329e-01 -5.84918559e-01 -8.14908504e-01 -5.08458436e-01
4.81975913e-01 5.88687897e-01 -9.90098789e-02 -1.93874449... | [11.340306282043457, 2.067417860031128] |
c2c2300b-06c2-462e-8fbe-7d94f0103b2a | shapetalk-a-language-dataset-and-framework | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Achlioptas_ShapeTalk_A_Language_Dataset_and_Framework_for_3D_Shape_Edits_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Achlioptas_ShapeTalk_A_Language_Dataset_and_Framework_for_3D_Shape_Edits_CVPR_2023_paper.pdf | ShapeTalk: A Language Dataset and Framework for 3D Shape Edits and Deformations | Editing 3D geometry is a challenging task requiring specialized skills. In this work, we aim to facilitate the task of editing the geometry of 3D models through the use of natural language. For example, we may want to modify a 3D chair model to "make its legs thinner" or to "open a hole in its back". To tackle this... | ['Leonidas Guibas', 'Sergey Tulyakov', 'Minhyuk Sung', 'IAn Huang', 'Panos Achlioptas'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['neural-rendering'] | ['computer-vision'] | [ 1.69573903e-01 4.00148541e-01 4.70504403e-01 -6.40523911e-01
-6.10831201e-01 -1.15118027e+00 9.35084879e-01 3.19041163e-02
1.40308058e-02 6.24359250e-02 4.19753909e-01 -3.36953163e-01
2.58171707e-01 -8.39957774e-01 -6.88980758e-01 -1.79232582e-01
3.03166151e-01 6.99660122e-01 -1.27982572e-01 -4.82935488... | [9.107109069824219, -3.5173540115356445] |
942cf291-67a8-43f8-9eab-88394bf29c73 | playing-against-the-board-rolling-horizon | 2103.15090 | null | https://arxiv.org/abs/2103.15090v1 | https://arxiv.org/pdf/2103.15090v1.pdf | Playing Against the Board: Rolling Horizon Evolutionary Algorithms Against Pandemic | Competitive board games have provided a rich and diverse testbed for artificial intelligence. This paper contends that collaborative board games pose a different challenge to artificial intelligence as it must balance short-term risk mitigation with long-term winning strategies. Collaborative board games task all playe... | ['Antonios Liapis', 'Konstantinos Sfikas'] | 2021-03-28 | null | null | null | null | ['board-games'] | ['playing-games'] | [-6.14979723e-03 4.11890537e-01 1.69671446e-01 2.50647753e-01
-7.89980739e-02 -8.05618405e-01 5.84213555e-01 1.22162461e-01
-5.53657234e-01 9.43250418e-01 1.66209731e-02 -6.62064016e-01
-8.91144872e-01 -1.00369227e+00 -1.68832764e-01 -5.64236343e-01
-4.33986902e-01 1.13712072e+00 2.93815404e-01 -9.44914222... | [3.498680353164673, 1.5553375482559204] |
0403bbd2-16a9-4fc2-b27e-c2e6bc9e6311 | robust-ante-hoc-graph-explainer-using-bilevel | 2305.15745 | null | https://arxiv.org/abs/2305.15745v1 | https://arxiv.org/pdf/2305.15745v1.pdf | Robust Ante-hoc Graph Explainer using Bilevel Optimization | Explaining the decisions made by machine learning models for high-stakes applications is critical for increasing transparency and guiding improvements to these decisions. This is particularly true in the case of models for graphs, where decisions often depend on complex patterns combining rich structural and attribute ... | ['Ambuj Singh', 'Arlei Silva', 'Mert Kosan'] | 2023-05-25 | null | null | null | null | ['graph-classification', 'bilevel-optimization'] | ['graphs', 'methodology'] | [ 2.98593342e-01 1.06239545e+00 -5.95664144e-01 -6.03496075e-01
-1.26675308e-01 -3.73221308e-01 7.31687248e-01 5.26803732e-01
9.03452858e-02 6.59350097e-01 3.93215507e-01 -9.37127769e-01
-7.01210260e-01 -6.41597629e-01 -7.63432503e-01 -6.13692738e-02
-1.40395969e-01 7.67776549e-01 -1.66949674e-01 -1.05682857... | [8.583536148071289, 6.002323627471924] |
19899ceb-4776-42c9-84ed-f0bd86b3799d | hashtagwars-learning-a-sense-of-humor | 1612.03216 | null | http://arxiv.org/abs/1612.03216v2 | http://arxiv.org/pdf/1612.03216v2.pdf | #HashtagWars: Learning a Sense of Humor | In this work, we present a new dataset for computational humor, specifically
comparative humor ranking, which attempts to eschew the ubiquitous binary
approach to humor detection. The dataset consists of tweets that are humorous
responses to a given hashtag. We describe the motivation for this new dataset,
as well as t... | ['Anna Rumshisky', 'Peter Potash', 'Alexey Romanov'] | 2016-12-09 | null | null | null | null | ['humor-detection'] | ['natural-language-processing'] | [-2.65361875e-01 -1.10306337e-01 -1.58734500e-01 -3.85105647e-02
-2.97689587e-01 -5.23460209e-01 8.35491478e-01 3.49702448e-01
-4.06963021e-01 7.84202397e-01 9.87987995e-01 -2.42560267e-01
4.72577840e-01 -7.19882429e-01 9.30635259e-02 -2.79959470e-01
2.35167354e-01 4.99277502e-01 1.73537746e-01 -8.10661674... | [8.88158130645752, 11.048486709594727] |
b3e813b0-b1da-4511-b16d-a8cbafb929d3 | deephuman-3d-human-reconstruction-from-a | 1903.06473 | null | http://arxiv.org/abs/1903.06473v2 | http://arxiv.org/pdf/1903.06473v2.pdf | DeepHuman: 3D Human Reconstruction from a Single Image | We propose DeepHuman, an image-guided volume-to-volume translation CNN for 3D
human reconstruction from a single RGB image. To reduce the ambiguities
associated with the surface geometry reconstruction, even for the
reconstruction of invisible areas, we propose and leverage a dense semantic
representation generated fro... | ['Yebin Liu', 'Zerong Zheng', 'Yixuan Wei', 'Tao Yu', 'Qionghai Dai'] | 2019-03-15 | deephuman-3d-human-reconstruction-from-a-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Zheng_DeepHuman_3D_Human_Reconstruction_From_a_Single_Image_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zheng_DeepHuman_3D_Human_Reconstruction_From_a_Single_Image_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-human-reconstruction'] | ['computer-vision'] | [ 2.34341174e-01 4.99650389e-01 2.79761165e-01 -3.74578327e-01
-4.63100314e-01 -2.27364689e-01 4.70176131e-01 -1.94391087e-01
-3.04208606e-01 3.07459950e-01 1.89501420e-01 1.36488080e-01
4.98499572e-01 -9.17927980e-01 -9.48586047e-01 -9.29809585e-02
3.48907977e-01 9.07845557e-01 1.65023670e-01 -2.68861771... | [7.179253101348877, -1.3820263147354126] |
00e16d20-7591-4ef7-b4e9-e3c4e44a48de | monet-tackle-state-momentum-via-noise | 2211.05503 | null | https://arxiv.org/abs/2211.05503v3 | https://arxiv.org/pdf/2211.05503v3.pdf | MoNET: Tackle State Momentum via Noise-Enhanced Training for Dialogue State Tracking | Dialogue state tracking (DST) aims to convert the dialogue history into dialogue states which consist of slot-value pairs. As condensed structural information memorizing all history information, the dialogue state in the last turn is typically adopted as the input for predicting the current state by DST models. However... | ['Xiaodong He', 'Shuguang Cui', 'Wenye Li', 'Youzheng Wu', 'Haipeng Sun', 'Junwei Bao', 'Haoning Zhang'] | 2022-11-10 | null | null | null | null | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 2.76430875e-01 2.75563955e-01 -4.32543427e-01 -3.62007618e-01
-2.42325470e-01 -5.45319617e-01 6.17101014e-01 4.10457253e-01
-5.95795810e-01 8.24800551e-01 4.99489188e-01 -3.30994070e-01
1.92295045e-01 -7.47336745e-01 -2.16030806e-01 -4.68457967e-01
3.25151920e-01 4.22842652e-01 3.98521066e-01 -7.61516988... | [12.800678253173828, 7.888428211212158] |
188b138c-0629-454d-9195-6cf1a25264aa | a-deep-neural-networks-approach-for-pixel | 2001.03257 | null | https://arxiv.org/abs/2001.03257v1 | https://arxiv.org/pdf/2001.03257v1.pdf | A Deep Neural Networks Approach for Pixel-Level Runway Pavement Crack Segmentation Using Drone-Captured Images | Pavement conditions are a critical aspect of asset management and directly affect safety. This study introduces a deep neural network method called U-Net for pavement crack segmentation based on drone-captured images to reduce the cost and time needed for airport runway inspection. The proposed approach can also be use... | ['Tianzhu Ren', 'Yuanchang Xie', 'Liming Jiang'] | 2020-01-09 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [-1.81769356e-01 -3.48007739e-01 1.09523669e-01 -3.06008875e-01
-7.06985414e-01 -3.40400517e-01 -2.95683563e-01 1.85973600e-01
-3.54069650e-01 5.98993599e-01 -6.90852880e-01 -6.27957761e-01
-2.60836631e-01 -1.60092628e+00 -9.20142591e-01 -6.82978868e-01
-1.09230742e-01 3.30956727e-01 4.33212548e-01 -6.74992383... | [7.4384636878967285, 1.2626267671585083] |
bab61a8f-1ecf-460a-9d18-271ad2a7f285 | rumour-detection-and-analysis-on-twitter | 2304.01712 | null | https://arxiv.org/abs/2304.01712v1 | https://arxiv.org/pdf/2304.01712v1.pdf | Rumour Detection and Analysis on Twitter | In recent years people have become increasingly reliant on social media to read news and get information, and some social media users post unsubstantiated information to gain attention. Such information is known as rumours. Nowadays, rumour detection is receiving a growing amount of attention because of the pandemic of... | ['Yaohou Fan'] | 2023-04-04 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-4.52929884e-01 1.71890289e-01 -5.26420176e-01 -2.10169688e-01
1.56223178e-01 -2.26404205e-01 1.09332919e+00 1.00432706e+00
-3.88725311e-01 7.13783979e-01 8.72049987e-01 -2.49878347e-01
2.32175022e-01 -1.03522813e+00 -1.18348680e-01 -2.00463608e-01
-1.00957461e-01 3.77618253e-01 1.92442626e-01 -9.88971591... | [8.281567573547363, 10.105876922607422] |
fc38a839-47ce-44bf-a7a9-ac2817e7d410 | common-knowledge-concept-recognition-for-seva | 2003.11687 | null | https://arxiv.org/abs/2003.11687v1 | https://arxiv.org/pdf/2003.11687v1.pdf | Common-Knowledge Concept Recognition for SEVA | We build a common-knowledge concept recognition system for a Systems Engineer's Virtual Assistant (SEVA) which can be used for downstream tasks such as relation extraction, knowledge graph construction, and question-answering. The problem is formulated as a token classification task similar to named entity extraction. ... | ['Hemant Purohit', 'Patrick Coronado', 'Jitin Krishnan', 'Huzefa Rangwala'] | 2020-03-26 | null | null | null | null | ['entity-extraction'] | ['natural-language-processing'] | [ 2.80877173e-01 5.49814820e-01 -9.48116109e-02 -4.53360170e-01
-2.25440115e-01 -7.88837373e-01 5.14724374e-01 7.08755970e-01
-3.56160253e-01 6.11021638e-01 -5.57813458e-02 -9.68620121e-01
-2.07742244e-01 -1.21374321e+00 -3.63484025e-01 1.03745937e-01
2.20441490e-01 5.52044749e-01 1.28699034e-01 -4.51892674... | [9.27766227722168, 8.383500099182129] |
60b18796-26f8-4a74-be93-ff23ac0ced3d | generative-long-form-question-answering | 2211.08386 | null | https://arxiv.org/abs/2211.08386v1 | https://arxiv.org/pdf/2211.08386v1.pdf | Generative Long-form Question Answering: Relevance, Faithfulness and Succinctness | In this thesis, we investigated the relevance, faithfulness, and succinctness aspects of Long Form Question Answering (LFQA). LFQA aims to generate an in-depth, paragraph-length answer for a given question, to help bridge the gap between real scenarios and the existing open-domain QA models which can only extract short... | ['Dan Su'] | 2022-11-15 | null | null | null | null | ['long-form-question-answering'] | ['natural-language-processing'] | [-5.85839339e-02 5.22741914e-01 4.99330498e-02 -4.35718328e-01
-1.19588161e+00 -8.04410815e-01 5.12297213e-01 2.43596017e-01
9.38616768e-02 1.09394729e+00 5.67822337e-01 -3.85144591e-01
-4.12469655e-01 -9.45786178e-01 -3.48590404e-01 6.19786493e-02
5.32921612e-01 8.15313518e-01 5.95655859e-01 -8.41032624... | [11.336626052856445, 8.0873441696167] |
821bb2dc-296b-4a93-9e93-ddc1b0be2215 | u-gat-it-unsupervised-generative-attentional | 1907.10830 | null | https://arxiv.org/abs/1907.10830v4 | https://arxiv.org/pdf/1907.10830v4.pdf | U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation | We propose a novel method for unsupervised image-to-image translation, which incorporates a new attention module and a new learnable normalization function in an end-to-end manner. The attention module guides our model to focus on more important regions distinguishing between source and target domains based on the atte... | ['Junho Kim', 'Kwanghee Lee', 'Hyeonwoo Kang', 'Minjae Kim'] | 2019-07-25 | null | https://openreview.net/forum?id=BJlZ5ySKPH | https://openreview.net/pdf?id=BJlZ5ySKPH | iclr-2020-1 | ['fundus-to-angiography-generation'] | ['computer-vision'] | [ 1.86243325e-01 3.88757209e-03 -2.01817945e-01 -3.31424743e-01
-6.33869231e-01 -4.66026366e-01 5.05286574e-01 -3.19107056e-01
-4.14667785e-01 4.45379674e-01 9.40192416e-02 -9.97786671e-02
1.36940911e-01 -7.41851509e-01 -8.23311806e-01 -7.00807214e-01
3.76678854e-01 5.39388776e-01 3.46092910e-01 -2.80963957... | [11.267745018005371, -0.9083166718482971] |
b2c3eab1-1528-4044-bc2f-e1eecefa7b1e | simulating-2-1d-lattice-quantum | 2212.06835 | null | https://arxiv.org/abs/2212.06835v1 | https://arxiv.org/pdf/2212.06835v1.pdf | Simulating 2+1D Lattice Quantum Electrodynamics at Finite Density with Neural Flow Wavefunctions | We present a neural flow wavefunction, Gauge-Fermion FlowNet, and use it to simulate 2+1D lattice compact quantum electrodynamics with finite density dynamical fermions. The gauge field is represented by a neural network which parameterizes a discretized flow-based transformation of the amplitude while the fermionic si... | ['Bryan K. Clark', 'Kaiwen Hu', 'Di Luo', 'Zhuo Chen'] | 2022-12-14 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [-3.84301436e-03 -2.33380020e-01 -1.16438791e-01 1.37451261e-01
1.61078334e-01 -3.84072691e-01 9.67203557e-01 -5.62099755e-01
-3.98271739e-01 1.07357895e+00 -1.02620885e-01 -3.74798685e-01
-1.85407087e-01 -1.41473413e+00 -7.04844177e-01 -1.42679691e+00
-1.90305516e-01 8.84255111e-01 -9.98865254e-03 -8.23934138... | [5.467747688293457, 5.0423150062561035] |
91d38a0f-69d7-4fa5-bf5a-e5524c5d0cd1 | a-summary-of-adaptation-of-techniques-from | 1812.10851 | null | http://arxiv.org/abs/1812.10851v1 | http://arxiv.org/pdf/1812.10851v1.pdf | A Summary of Adaptation of Techniques from Search-based Optimal Multi-Agent Path Finding Solvers to Compilation-based Approach | In the multi-agent path finding problem (MAPF) we are given a set of agents
each with respective start and goal positions. The task is to find paths for
all agents while avoiding collisions aiming to minimize an objective function.
Two such common objective functions is the sum-of-costs and the makespan. Many
optimal s... | ['Pavel Surynek'] | 2018-12-28 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 7.98684433e-02 4.50773537e-01 -1.12377346e-01 -4.97772507e-02
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-6.05291069e-01 1.13316035e+00 7.39353776e-01 -6.27193868... | [4.999000549316406, 1.9737447500228882] |
58d938c8-34a7-40d7-aac6-20934eac8bf6 | daedalus-at-semeval-2014-task-9-comparing | null | null | https://aclanthology.org/S14-2035 | https://aclanthology.org/S14-2035.pdf | DAEDALUS at SemEval-2014 Task 9: Comparing Approaches for Sentiment Analysis in Twitter | null | ["Jos{\\'e} Carlos Gonz{\\'a}lez-Crist{\\'o}bal", "Janine Garc{\\'\\i}a-Morera", "Julio Villena-Rom{\\'a}n"] | 2014-08-01 | null | null | null | semeval-2014-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.168032646179199, 3.786731004714966] |
3d7520cc-7050-44f1-809d-5a31a11b4b8a | even-your-teacher-needs-guidance-ground-truth | 2102.13088 | null | https://arxiv.org/abs/2102.13088v2 | https://arxiv.org/pdf/2102.13088v2.pdf | Even your Teacher Needs Guidance: Ground-Truth Targets Dampen Regularization Imposed by Self-Distillation | Knowledge distillation is classically a procedure where a neural network is trained on the output of another network along with the original targets in order to transfer knowledge between the architectures. The special case of self-distillation, where the network architectures are identical, has been observed to improv... | ['Lars N. Andersen', 'Kenneth Borup'] | 2021-02-25 | null | http://proceedings.neurips.cc/paper/2021/hash/2adcefe38fbcd3dcd45908fbab1bf628-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/2adcefe38fbcd3dcd45908fbab1bf628-Paper.pdf | neurips-2021-12 | ['self-knowledge-distillation'] | ['computer-vision'] | [ 1.22466534e-01 5.38989067e-01 7.81304017e-02 -2.85697281e-01
-6.36488020e-01 -3.21770191e-01 4.60500389e-01 -8.03599879e-02
-8.71750295e-01 9.71412182e-01 -2.75947988e-01 -2.13466793e-01
-1.06645003e-01 -7.74232030e-01 -9.12603319e-01 -9.71730828e-01
1.05452940e-01 5.01764715e-01 1.10075347e-01 -1.25663623... | [7.9166154861450195, 3.582828998565674] |
141553ec-5031-4b5c-a1c3-4e03ac04a363 | inductive-link-prediction-for-nodes-having | 2007.08053 | null | https://arxiv.org/abs/2007.08053v1 | https://arxiv.org/pdf/2007.08053v1.pdf | Inductive Link Prediction for Nodes Having Only Attribute Information | Predicting the link between two nodes is a fundamental problem for graph data analytics. In attributed graphs, both the structure and attribute information can be utilized for link prediction. Most existing studies focus on transductive link prediction where both nodes are already in the graph. However, many real-world... | ['Yixiang Fang', 'Xin Cao', 'Yu Hao', 'Xike Xie', 'Sibo Wang'] | 2020-07-16 | null | null | null | null | ['inductive-link-prediction'] | ['graphs'] | [-8.54798034e-02 6.37720466e-01 -8.60651135e-01 -4.22436863e-01
3.40665430e-02 -2.33497486e-01 5.44464469e-01 6.90741539e-01
1.20669030e-01 6.79751337e-01 2.08881542e-01 -4.17913139e-01
-2.41830766e-01 -1.43586779e+00 -8.01948249e-01 -4.49419439e-01
-4.57275867e-01 7.82806039e-01 5.19666672e-01 -2.52359778... | [7.385988235473633, 6.376517295837402] |
5909d3dc-13b7-4cfc-a6b2-6600b6344179 | multi-task-zero-shot-action-recognition-with | 1611.08663 | null | http://arxiv.org/abs/1611.08663v1 | http://arxiv.org/pdf/1611.08663v1.pdf | Multi-Task Zero-Shot Action Recognition with Prioritised Data Augmentation | Zero-Shot Learning (ZSL) promises to scale visual recognition by bypassing
the conventional model training requirement of annotated examples for every
category. This is achieved by establishing a mapping connecting low-level
features and a semantic description of the label space, referred as
visual-semantic mapping, on... | ['Xun Xu', 'Timothy M. Hospedales', 'Shaogang Gong'] | 2016-11-26 | null | null | null | null | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 7.58691311e-01 3.09978127e-01 -3.65301669e-01 -4.26590949e-01
-5.12741208e-01 -4.18856382e-01 8.95499349e-01 1.47659257e-01
-4.77467507e-01 5.00329077e-01 3.57855409e-01 2.99768955e-01
-6.14115953e-01 -5.30909300e-01 -5.40672421e-01 -7.49975801e-01
1.35175541e-01 3.96072835e-01 5.21449506e-01 -1.81283280... | [9.830107688903809, 2.3601205348968506] |
00acb6e1-5748-48d0-8b6b-63a155d0b5d9 | rrnet-relational-reasoning-network-with | 2110.14223 | null | https://arxiv.org/abs/2110.14223v2 | https://arxiv.org/pdf/2110.14223v2.pdf | RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images | Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. Despite some saliency models were proposed to solve the intrinsic problem of optical RSIs (such as complex background and scale-variant objects), the accura... | ['Sam Kwong', 'Yao Zhao', 'Jun Li', 'Leyuan Fang', 'Yumo Zhang', 'Runmin Cong'] | 2021-10-27 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 2.80909866e-01 -6.61989525e-02 -1.19984867e-02 -1.13641247e-01
-5.83595753e-01 -1.03970535e-01 2.49249220e-01 -1.11049883e-01
-1.43845260e-01 5.28708041e-01 4.17132765e-01 3.84205654e-02
-3.88798892e-01 -7.21146166e-01 -5.83649814e-01 -7.15441108e-01
1.30271316e-01 -3.73454750e-01 8.27915430e-01 -3.96494836... | [9.6395902633667, -0.5436125993728638] |
d216e965-4dff-4226-9f9b-5ab4e8d66b0f | full-explicit-consistency-constraints-in | 1805.02352 | null | https://arxiv.org/abs/1805.02352v7 | https://arxiv.org/pdf/1805.02352v7.pdf | Full explicit consistency constraints in uncalibrated multiple homography estimation | We reveal a complete set of constraints that need to be imposed on a set of 3-by-3 matrices to ensure that the matrices represent genuine homographies associated with multiple planes between two views. We also show how to exploit the constraints to obtain more accurate estimates of homography matrices between two views... | ['Zygmunt L. Szpak', 'Wojciech Chojnacki'] | 2018-05-07 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 4.34968024e-01 7.99458008e-03 -1.74096555e-01 -3.38509053e-01
-5.93593121e-01 -8.98704588e-01 3.35962296e-01 -5.73165476e-01
2.82714784e-01 2.61740923e-01 2.01659247e-01 -1.56312734e-01
-2.57755190e-01 -3.93783987e-01 -7.38228738e-01 -4.06061083e-01
-1.89391315e-01 3.69785011e-01 1.91889152e-01 -1.59368992... | [7.945791244506836, -2.3283660411834717] |
834e42a4-48a8-403d-bcc4-bd89ec393a9c | improved-temporal-relation-classification | null | null | https://aclanthology.org/C12-1129 | https://aclanthology.org/C12-1129.pdf | Improved Temporal Relation Classification using Dependency Parses and Selective Crowdsourced Annotations | null | ['Min-Yen Kan', 'Jun-Ping Ng'] | 2012-12-01 | improved-temporal-relation-classification-1 | https://aclanthology.org/C12-1129 | https://aclanthology.org/C12-1129.pdf | coling-2012-12 | ['temporal-relation-classification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.316402912139893, 3.7623863220214844] |
0c7a8952-2d9c-4c85-9543-9cc1064f249e | multilingual-and-cross-lingual-complex-word | null | null | https://aclanthology.org/R17-1104 | https://aclanthology.org/R17-1104.pdf | Multilingual and Cross-Lingual Complex Word Identification | Complex Word Identification (CWI) is an important task in lexical simplification and text accessibility. Due to the lack of CWI datasets, previous works largely depend on Simple English Wikipedia and edit histories for obtaining {`}gold standard{'} annotations, which are of doubtable quality, and limited only to Englis... | ['Sanja {\\v{S}}tajner', 'Martin Riedl', 'Chris Biemann', 'Seid Muhie Yimam'] | 2017-09-01 | null | null | null | ranlp-2017-9 | ['complex-word-identification'] | ['natural-language-processing'] | [ 7.87803531e-03 8.34698752e-02 -3.23144704e-01 -7.19874650e-02
-8.59164834e-01 -8.43949676e-01 6.24556720e-01 4.93387014e-01
-1.09521401e+00 9.97546434e-01 3.93879831e-01 -4.61330354e-01
5.49109019e-02 -4.30277228e-01 -4.01099175e-01 5.40268645e-02
3.15966815e-01 8.77853394e-01 1.81131199e-01 -6.17874444... | [10.89577865600586, 10.412236213684082] |
df14dbf5-2c81-4e87-9946-03a30cf6fba7 | spatio-temporal-attention-based-neural | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/5371 | https://ojs.aaai.org/index.php/AAAI/article/download/5371/5227 | Spatio-Temporal Attention-Based Neural Network for Credit Card Fraud Detection | Credit card fraud is an important issue and incurs a considerable cost for both cardholders and issuing institutions. Contemporary methods apply machine learning-based approaches to detect fraudulent behavior from transaction records. But manually generating features needs domain knowledge and may lay behind the modus ... | ['Liqing Zhang', 'Fangzhou Yang', 'Yiyi Zhang', 'Chencheng Shang', 'Sheng Xiang', 'Dawei Cheng'] | 2020-04-03 | null | null | null | aaai-2020-4 | ['fraud-detection'] | ['miscellaneous'] | [-2.65003026e-01 -6.96110487e-01 2.56939679e-02 -5.35432458e-01
-2.61107534e-01 -1.61188066e-01 5.60091026e-02 3.09514910e-01
-6.04485393e-01 2.59827852e-01 9.28571448e-02 -3.16335231e-01
4.85512279e-02 -9.58512068e-01 -4.83547390e-01 -2.50756025e-01
-3.95575315e-01 2.45156705e-01 1.78498719e-02 -2.84187824... | [7.373605251312256, 5.8074421882629395] |
11ac510e-858a-425c-a083-eb91b8d194bf | tuning-legged-locomotion-controllers-via-safe | 2306.07092 | null | https://arxiv.org/abs/2306.07092v1 | https://arxiv.org/pdf/2306.07092v1.pdf | Tuning Legged Locomotion Controllers via Safe Bayesian Optimization | In this paper, we present a data-driven strategy to simplify the deployment of model-based controllers in legged robotic hardware platforms. Our approach leverages a model-free safe learning algorithm to automate the tuning of control gains, addressing the mismatch between the simplified model used in the control formu... | ['Stelian Coros', 'Andreas Krause', 'Jonas Hübotter', 'Bhavya Sukhija', 'Dongho Kang', 'Daniel Widmer'] | 2023-06-12 | null | null | null | null | ['efficient-exploration', 'bayesian-optimization'] | ['methodology', 'methodology'] | [-8.85070637e-02 3.14939290e-01 -5.71003377e-01 2.44589642e-01
-6.60975337e-01 -6.12354219e-01 4.13124084e-01 -3.20604473e-01
-2.69444764e-01 8.74706447e-01 -2.08983913e-01 -3.67877156e-01
-4.24731582e-01 -6.46043062e-01 -6.66025102e-01 -6.48848355e-01
-4.74329531e-01 5.65577388e-01 2.42026061e-01 -5.17647266... | [4.6471943855285645, 1.4234298467636108] |
16b7c669-c0bb-4e9f-b813-4f2e8a903546 | cell-abundance-aware-deep-learning-for-cell | 2102.11677 | null | https://arxiv.org/abs/2102.11677v1 | https://arxiv.org/pdf/2102.11677v1.pdf | Cell abundance aware deep learning for cell detection on highly imbalanced pathological data | Automated analysis of tissue sections allows a better understanding of disease biology and may reveal biomarkers that could guide prognosis or treatment selection. In digital pathology, less abundant cell types can be of biological significance, but their scarcity can result in biased and sub-optimal cell detection mod... | ['Yinyin Yuan', 'Kwee Yong', 'Manuel Rodriguez- Justo', 'Thien-An Tran', 'Lydia Lee', 'Dominic Patel', 'Catherine SY Lecat', 'Yeman Brhane Hagos'] | 2021-02-23 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [-8.75973329e-03 -3.38042602e-02 -3.65563691e-01 -1.49649933e-01
-9.43013251e-01 -3.95204395e-01 2.89214641e-01 8.35063815e-01
-7.34245121e-01 8.41039479e-01 2.20622256e-01 -4.24968004e-02
3.33671451e-01 -9.54511166e-01 -3.30844015e-01 -1.10917485e+00
-4.28388603e-02 8.04761887e-01 -8.56321305e-03 2.36072272... | [15.062432289123535, -3.100487232208252] |
ff7973b5-6dd4-43a4-aafd-9fd7d70310f2 | cross-modal-representation-learning-for-zero | 2205.01657 | null | https://arxiv.org/abs/2205.01657v1 | https://arxiv.org/pdf/2205.01657v1.pdf | Cross-modal Representation Learning for Zero-shot Action Recognition | We present a cross-modal Transformer-based framework, which jointly encodes video data and text labels for zero-shot action recognition (ZSAR). Our model employs a conceptually new pipeline by which visual representations are learned in conjunction with visual-semantic associations in an end-to-end manner. The model de... | ['Zicheng Liu', 'Lijuan Wang', 'Linjie Li', 'Kevin Lin', 'Chung-Ching Lin'] | 2022-05-03 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lin_Cross-Modal_Representation_Learning_for_Zero-Shot_Action_Recognition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lin_Cross-Modal_Representation_Learning_for_Zero-Shot_Action_Recognition_CVPR_2022_paper.pdf | cvpr-2022-1 | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 2.15742201e-01 1.51650861e-01 -5.27238667e-01 -4.40076649e-01
-7.51491845e-01 -1.93941355e-01 7.31463671e-01 -1.62972122e-01
-1.51030213e-01 4.51016456e-01 6.79808497e-01 2.15985328e-01
-7.29221385e-03 -5.05026460e-01 -8.45658720e-01 -5.96955776e-01
1.88952550e-01 1.91332996e-01 3.01854849e-01 -7.18993321... | [10.02674674987793, 1.911839246749878] |
cb3e79c0-c1de-4f1a-9b68-647e2723e850 | learning-to-dehaze-with-polarization | null | null | http://proceedings.neurips.cc/paper/2021/hash/5fd0b37cd7dbbb00f97ba6ce92bf5add-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/5fd0b37cd7dbbb00f97ba6ce92bf5add-Paper.pdf | Learning to dehaze with polarization | Haze, a common kind of bad weather caused by atmospheric scattering, decreases the visibility of scenes and degenerates the performance of computer vision algorithms. Single-image dehazing methods have shown their effectiveness in a large variety of scenes, however, they are based on handcrafted priors or learned featu... | ['Boxin Shi', 'Chao Xu', 'Yufei Han', 'Minggui Teng', 'Chu Zhou'] | 2021-12-01 | null | https://openreview.net/forum?id=Ua9Vi0QqwD4 | https://openreview.net/pdf?id=Ua9Vi0QqwD4 | neurips-2021-12 | ['image-dehazing'] | ['computer-vision'] | [ 2.62164861e-01 -3.23283792e-01 3.78301471e-01 -3.00827473e-01
-3.18683296e-01 -2.76099831e-01 5.69206953e-01 -4.71250951e-01
-1.63840890e-01 7.31330454e-01 -1.00264020e-01 -1.75469041e-01
9.81058925e-03 -8.80466878e-01 -7.04415679e-01 -1.42297852e+00
2.23577634e-01 2.13064745e-01 6.59620702e-01 -5.13472736... | [10.886259078979492, -3.170865535736084] |
4ed3f4e4-332b-40d0-921e-f4c24898acd1 | metaage-meta-learning-personalized-age | 2207.05288 | null | https://arxiv.org/abs/2207.05288v1 | https://arxiv.org/pdf/2207.05288v1.pdf | MetaAge: Meta-Learning Personalized Age Estimators | Different people age in different ways. Learning a personalized age estimator for each person is a promising direction for age estimation given that it better models the personalization of aging processes. However, most existing personalized methods suffer from the lack of large-scale datasets due to the high-level req... | ['Jie zhou', 'Jianjiang Feng', 'Abudukelimu Wuerkaixi', 'Jiwen Lu', 'Wanhua Li'] | 2022-07-12 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-2.48369321e-01 -1.73584759e-01 -5.02372384e-01 -5.51878989e-01
-4.70856011e-01 -2.04201117e-02 3.99380982e-01 -1.70773976e-02
-5.14531970e-01 9.25868094e-01 4.03912097e-01 3.29772562e-01
2.81754825e-02 -8.28873694e-01 -6.37628853e-01 -6.68251455e-01
1.23195551e-01 6.60114050e-01 -5.14445417e-02 -2.79067433... | [13.767585754394531, 0.8819168210029602] |
a665d1aa-f400-4c62-b71d-fb6f0f455468 | learning-to-rank-retargeted-images | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Chen_Learning_to_Rank_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Chen_Learning_to_Rank_CVPR_2017_paper.pdf | Learning to Rank Retargeted Images | Image retargeting techniques that adjust images into different sizes have attracted much attention recently. Objective quality assessment (OQA) of image retargeting results is often desired to automatically select the best results. Existing OQA methods output an absolute score for each retargeted image and use these sc... | ['Yu-Kun Lai', 'Yong-Jin Liu', 'Yang Chen'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['image-retargeting'] | ['computer-vision'] | [ 3.42529267e-01 -3.47483367e-01 -2.43732408e-01 -5.88440776e-01
-1.07506025e+00 -6.05203569e-01 3.37651044e-01 2.03935206e-01
-4.74460155e-01 5.20539582e-01 2.19284251e-01 -4.59309556e-02
-1.04285270e-01 -5.34511805e-01 -5.09237111e-01 -5.67625940e-01
2.45617762e-01 1.94583684e-01 5.69066882e-01 -4.05406952... | [11.288063049316406, -1.0853406190872192] |
cae082a4-dfe7-4149-921b-01a485bab9f5 | spatial-temporal-space-hand-in-hand-spatial | 2205.05264 | null | https://arxiv.org/abs/2205.05264v1 | https://arxiv.org/pdf/2205.05264v1.pdf | Spatial-Temporal Space Hand-in-Hand: Spatial-Temporal Video Super-Resolution via Cycle-Projected Mutual Learning | Spatial-Temporal Video Super-Resolution (ST-VSR) aims to generate super-resolved videos with higher resolution(HR) and higher frame rate (HFR). Quite intuitively, pioneering two-stage based methods complete ST-VSR by directly combining two sub-tasks: Spatial Video Super-Resolution (S-VSR) and Temporal Video Super-Resol... | ['Zheng Wang', 'Junjun Jiang', 'Jing Xiao', 'Liang Liao', 'Kui Jiang', 'Mengshun Hu'] | 2022-05-11 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Hu_Spatial-Temporal_Space_Hand-in-Hand_Spatial-Temporal_Video_Super-Resolution_via_Cycle-Projected_Mutual_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Hu_Spatial-Temporal_Space_Hand-in-Hand_Spatial-Temporal_Video_Super-Resolution_via_Cycle-Projected_Mutual_Learning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-super-resolution', 'video-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 2.11334482e-01 -1.80672020e-01 -3.45204920e-01 -1.41404271e-01
-8.87170434e-01 -1.74114898e-01 4.52359587e-01 -4.97787982e-01
8.04411396e-02 8.62241864e-01 5.55602729e-01 5.00435829e-02
-3.03791583e-01 -7.48601794e-01 -7.68054426e-01 -6.03858531e-01
-1.51874989e-01 -2.32543454e-01 6.29856765e-01 -3.72660875... | [11.047208786010742, -1.8535751104354858] |
15b81e5f-146a-47c0-b2b3-fda11f8fe62d | htnet-anchor-free-temporal-action | 2207.09662 | null | https://arxiv.org/abs/2207.09662v2 | https://arxiv.org/pdf/2207.09662v2.pdf | HTNet: Anchor-free Temporal Action Localization with Hierarchical Transformers | Temporal action localization (TAL) is a task of identifying a set of actions in a video, which involves localizing the start and end frames and classifying each action instance. Existing methods have addressed this task by using predefined anchor windows or heuristic bottom-up boundary-matching strategies, which are ma... | ['Seong-Whan Lee', 'Gun-Hee Lee', 'Tae-Kyung Kang'] | 2022-07-20 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 4.96684849e-01 -3.03191721e-01 -5.42003572e-01 -2.75375217e-01
-7.75386214e-01 -3.74719560e-01 6.12108886e-01 2.87400745e-02
-2.40280241e-01 4.17714566e-01 5.01720250e-01 1.18809760e-01
-9.90532488e-02 -4.94543493e-01 -6.54592872e-01 -5.11944294e-01
-2.19091430e-01 1.56329364e-01 1.02506089e+00 8.43146071... | [8.35991382598877, 0.5167893767356873] |
a2f3c207-a108-43e7-a52a-993d42b031ad | dialogue-evaluation-with-offline | 2209.00876 | null | https://arxiv.org/abs/2209.00876v1 | https://arxiv.org/pdf/2209.00876v1.pdf | Dialogue Evaluation with Offline Reinforcement Learning | Task-oriented dialogue systems aim to fulfill user goals through natural language interactions. They are ideally evaluated with human users, which however is unattainable to do at every iteration of the development phase. Simulated users could be an alternative, however their development is nontrivial. Therefore, resea... | ['Milica Gašić', 'Shutong Feng', 'Michael Heck', 'Carel van Niekerk', 'Hsien-Chin Lin', 'Christian Geishauser', 'Nurul Lubis'] | 2022-09-02 | null | https://aclanthology.org/2022.sigdial-1.46 | https://aclanthology.org/2022.sigdial-1.46.pdf | sigdial-acl-2022-9 | ['dialogue-evaluation', 'task-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [-7.46009648e-02 3.23625177e-01 9.06199962e-02 -3.85527164e-01
-8.40950847e-01 -9.92893219e-01 9.30935740e-01 4.50523168e-01
-8.29194427e-01 9.76618826e-01 -2.53980421e-02 -5.49971998e-01
1.65237695e-01 -4.26379144e-01 9.53668915e-03 -3.90010893e-01
7.69600123e-02 8.41955960e-01 3.05295229e-01 -7.33219147... | [12.964242935180664, 8.034671783447266] |
d8e25c88-ae0b-4d8b-8932-cb0548f570f5 | a-deep-learning-based-global-and-segmentation | 2302.06432 | null | https://arxiv.org/abs/2302.06432v2 | https://arxiv.org/pdf/2302.06432v2.pdf | A Deep Learning-based Global and Segmentation-based Semantic Feature Fusion Approach for Indoor Scene Classification | Indoor scene classification has become an important task in perception modules and has been widely used in various applications. However, problems such as intra-category variability and inter-category similarity have been holding back the models' performance, which leads to the need for new types of features to obtain ... | ['Urbano J. Nunes', 'Ana Lopes', 'Luís Garrote', 'Tiago Barros', 'Ricardo Pereira'] | 2023-02-13 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 4.21931893e-01 -4.37325627e-01 1.70858443e-01 -8.53803575e-01
-3.30733836e-01 -3.31551820e-01 4.57248449e-01 6.70301795e-01
-4.76429403e-01 5.23984194e-01 -1.62233591e-01 2.25196898e-01
-4.10560012e-01 -1.13983011e+00 -5.71960568e-01 -9.21103835e-01
1.01010986e-01 -1.84005797e-01 5.34762383e-01 -2.66099013... | [9.475067138671875, -0.8457909822463989] |
76ea162a-b211-4e4c-9813-9baa03af8837 | dynamic-and-context-dependent-stock-price | 2205.01639 | null | https://arxiv.org/abs/2205.01639v1 | https://arxiv.org/pdf/2205.01639v1.pdf | Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment | The growth of machine-readable data in finance, such as alternative data, requires new modeling techniques that can handle non-stationary and non-parametric data. Due to the underlying causal dependence and the size and complexity of the data, we propose a new modeling approach for financial time series data, the $\alp... | ['Nicole Koenigstein'] | 2022-03-13 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-5.76989412e-01 -3.92832875e-01 -7.96531364e-02 -5.02030253e-01
-2.46649489e-01 -4.75698978e-01 4.05547082e-01 -8.13000649e-02
-3.88276845e-01 6.20863318e-01 3.24587435e-01 -5.15475750e-01
-2.47380912e-01 -1.08960402e+00 -6.85270667e-01 -5.33765256e-01
-5.47891200e-01 2.69614786e-01 1.00751519e-01 -4.33157086... | [4.56151819229126, 4.179741382598877] |
be24987b-11b5-40c3-bbff-c0f639fc446e | code-switched-inspired-losses-for-spoken | null | null | https://aclanthology.org/2021.emnlp-main.656 | https://aclanthology.org/2021.emnlp-main.656.pdf | Code-switched inspired losses for spoken dialog representations | Spoken dialogue systems need to be able to handle both multiple languages and multilinguality inside a conversation (e.g in case of code-switching). In this work, we introduce new pretraining losses tailored to learn generic multilingual spoken dialogue representations. The goal of these losses is to expose the model t... | ['Chloé Clavel', 'Matthieu Labeau', 'Emile Chapuis', 'Pierre Colombo'] | null | null | null | null | emnlp-2021-11 | ['spoken-dialogue-systems'] | ['speech'] | [-2.14367777e-01 2.26452872e-01 2.73526385e-02 -5.21286368e-01
-1.12849331e+00 -9.18977439e-01 8.22675705e-01 -8.72814059e-02
-5.20862460e-01 1.03004050e+00 3.22081327e-01 -5.99966526e-01
4.85674500e-01 -3.09129506e-01 -5.29507101e-01 -2.92589724e-01
-1.12368025e-01 8.59419584e-01 -5.91968223e-02 -6.51853383... | [12.471441268920898, 8.364941596984863] |
2cd91399-0fd1-4e4c-bb9a-11a460211c06 | an-efficient-optical-flow-based-motion | 1811.08290 | null | http://arxiv.org/abs/1811.08290v2 | http://arxiv.org/pdf/1811.08290v2.pdf | An Efficient Optical Flow Based Motion Detection Method for Non-stationary Scenes | Real-time motion detection in non-stationary scenes is a difficult task due
to dynamic background, changing foreground appearance and limited computational
resource. These challenges degrade the performance of the existing methods in
practical applications. In this paper, an optical flow based framework is
proposed to ... | ['Jiagang Zhu', 'Wei Zou', 'Junjie Huang', 'Zheng Zhu'] | 2018-11-18 | null | null | null | null | ['motion-detection-in-non-stationary-scenes', 'motion-detection'] | ['computer-vision', 'computer-vision'] | [ 1.00859992e-01 -9.31075335e-01 -6.89371228e-02 -5.12670539e-02
9.51221809e-02 -4.35073107e-01 2.95605570e-01 -6.41364992e-01
-4.79240060e-01 8.02256107e-01 -1.80620179e-01 -6.15769625e-02
2.81359982e-02 -5.00156581e-01 -1.63467735e-01 -7.93040156e-01
-3.79728451e-02 -3.01264197e-01 1.10454953e+00 5.98165244... | [9.112645149230957, -0.7745100259780884] |
96a19b76-e3c1-4729-8b57-e1272146a8a9 | random-projections-of-mel-spectrograms-as-low | 1911.04660 | null | https://arxiv.org/abs/1911.04660v1 | https://arxiv.org/pdf/1911.04660v1.pdf | Random Projections of Mel-Spectrograms as Low-Level Features for Automatic Music Genre Classification | In this work, we analyse the random projections of Mel-spectrograms as low-level features for music genre classification. This approach was compared to handcrafted features, features learned using an auto-encoder and features obtained from a transfer learning setting. Tests in five different well-known, publicly availa... | ['Juliano Henrique Foleiss', 'Tiago Fernandes Tavares'] | 2019-11-12 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 0.3762427 -0.1359307 -0.25590622 -0.2766311 -0.94891334 -0.6885636
0.83655316 0.21433917 -0.55583316 0.8261658 0.48891982 0.14487836
-0.51580596 -0.8179626 -0.47489396 -0.7071298 -0.13563517 0.5350284
-0.04176512 -0.13669947 0.33525893 0.3236733 -1.7011527 0.72351396
0.11793058 1.111138 0.22... | [15.854598999023438, 5.273766994476318] |
70c6bd61-beb0-479a-9814-654eb064e4f4 | what-goes-on-inside-rumour-and-non-rumour | 2112.03003 | null | https://arxiv.org/abs/2112.03003v1 | https://arxiv.org/pdf/2112.03003v1.pdf | What goes on inside rumour and non-rumour tweets and their reactions: A Psycholinguistic Analyses | In recent years, the problem of rumours on online social media (OSM) has attracted lots of attention. Researchers have started investigating from two main directions. First is the descriptive analysis of rumours and secondly, proposing techniques to detect (or classify) rumours. In the descriptive line of works, where ... | ['Alexander Gelbukh', 'Grigori Sidorov', 'Rajesh Sharma', 'Shakshi Sharma', 'Sabur Butt'] | 2021-11-09 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-2.14703575e-01 3.68774295e-01 -1.84601709e-01 -2.19135448e-01
-2.14203000e-01 -1.15953170e-01 9.27587628e-01 8.28393579e-01
-2.06093803e-01 6.80623114e-01 6.88354194e-01 -5.05556345e-01
-8.28222334e-02 -8.20105493e-01 -2.47578219e-01 -3.19613218e-01
1.46451294e-02 2.85390437e-01 6.19573854e-02 -8.18690002... | [8.261858940124512, 10.136630058288574] |
c3a694cb-c09c-4b8f-94d9-6f2a1aa2081a | evaluation-of-taxonomic-and-neural-embedding | 2209.15197 | null | https://arxiv.org/abs/2209.15197v1 | https://arxiv.org/pdf/2209.15197v1.pdf | Evaluation of taxonomic and neural embedding methods for calculating semantic similarity | Modelling semantic similarity plays a fundamental role in lexical semantic applications. A natural way of calculating semantic similarity is to access handcrafted semantic networks, but similarity prediction can also be anticipated in a distributional vector space. Similarity calculation continues to be a challenging t... | ['Yanqin Yin', 'Dongqiang Yang'] | 2022-09-30 | null | null | null | null | ['word-similarity'] | ['natural-language-processing'] | [ 1.07360221e-01 -3.18444133e-01 -2.47000352e-01 -4.24643904e-01
-1.30868321e-02 -8.16856265e-01 8.97349238e-01 9.39113796e-01
-9.81157899e-01 2.79902995e-01 6.96322262e-01 -3.69217038e-01
-4.96032417e-01 -1.06870008e+00 1.03928350e-01 -2.32248008e-01
8.52479115e-02 3.57533336e-01 2.62778830e-02 -6.15230024... | [10.348360061645508, 8.886802673339844] |
fb32cd07-dca6-4b9f-bbfe-7369596beba3 | fairgen-fair-synthetic-data-generation | 2210.13023 | null | https://arxiv.org/abs/2210.13023v2 | https://arxiv.org/pdf/2210.13023v2.pdf | FairGen: Fair Synthetic Data Generation | With the rising adoption of Machine Learning across the domains like banking, pharmaceutical, ed-tech, etc, it has become utmost important to adopt responsible AI methods to ensure models are not unfairly discriminating against any group. Given the lack of clean training data, generative adversarial techniques are pref... | ['Himanshu Chaudhary', 'Tanmoy Bhowmik', 'Kamna Meena', 'Aakash Agarwal', 'Bhushan Chaudhari'] | 2022-10-24 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 4.74974036e-01 6.15864038e-01 3.15984921e-03 -6.00552619e-01
-8.73807371e-01 -5.18850088e-01 6.77736223e-01 1.31375398e-02
-2.78409839e-01 1.41824400e+00 2.23861039e-01 -1.25360876e-01
2.18209609e-01 -1.03942668e+00 -6.63160264e-01 -5.28774381e-01
4.60310340e-01 7.75422156e-01 -3.42587471e-01 -3.24126840... | [6.391613006591797, 6.752536296844482] |
236baa6a-4fdb-4a4a-80b5-cd392e31e0fa | extractive-text-summarization-using | 2212.10707 | null | https://arxiv.org/abs/2212.10707v1 | https://arxiv.org/pdf/2212.10707v1.pdf | Extractive Text Summarization Using Generalized Additive Models with Interactions for Sentence Selection | Automatic Text Summarization (ATS) is becoming relevant with the growth of textual data; however, with the popularization of public large-scale datasets, some recent machine learning approaches have focused on dense models and architectures that, despite producing notable results, usually turn out in models difficult t... | ['Kelton Augusto Pontara da Costa', 'João Paulo Papa', 'Vinícius Camargo da Silva'] | 2022-12-21 | null | null | null | null | ['additive-models', 'extractive-summarization', 'extractive-document-summarization'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 5.22926331e-01 7.88192272e-01 -3.21462274e-01 -4.76048410e-01
-1.03110468e+00 -8.77828896e-02 8.88599396e-01 7.58545756e-01
-2.09828496e-01 9.80751753e-01 1.24473190e+00 -4.31619078e-01
-1.36903882e-01 -3.57283652e-01 -8.45758796e-01 -2.48967171e-01
1.13355741e-01 7.08875418e-01 -3.95159245e-01 -3.65431458... | [12.449991226196289, 9.488152503967285] |
e7d6edb0-588e-4e04-ade9-914e2bc1c428 | active-learning-for-natural-language | 2305.15040 | null | https://arxiv.org/abs/2305.15040v1 | https://arxiv.org/pdf/2305.15040v1.pdf | Active Learning for Natural Language Generation | The field of text generation suffers from a severe shortage of labeled data due to the extremely expensive and time consuming process involved in manual annotation. A natural approach for coping with this problem is active learning (AL), a well-known machine learning technique for improving annotation efficiency by sel... | ['Liat Ein-Dor', 'Noam Slonim', 'Dafna Sheinwald', 'Michal Shmueli-Scheuer', 'Ariel Gera', 'Yotam Perlitz'] | 2023-05-24 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 9.27917302e-01 5.73074341e-01 -5.77291012e-01 -3.36495787e-01
-1.26392448e+00 -6.58929825e-01 1.09483445e+00 5.23316622e-01
-4.95804161e-01 1.15436661e+00 3.66468340e-01 -2.04792142e-01
-5.27731441e-02 -6.21603191e-01 -3.18916321e-01 -6.36019051e-01
2.49733046e-01 9.14427578e-01 -4.65904884e-02 -2.75419634... | [9.726007461547852, 4.488762855529785] |
e349ea3e-548c-47bc-ab54-7a09dc87b919 | punctuation-prediction-with-transition-based | null | null | https://aclanthology.org/P13-1074 | https://aclanthology.org/P13-1074.pdf | Punctuation Prediction with Transition-based Parsing | null | ['Dong-dong Zhang', 'Nan Yang', 'Mu Li', 'Shuangzhi Wu'] | 2013-08-01 | null | null | null | acl-2013-8 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.232165813446045, 3.766981840133667] |
ab300f0f-6ed4-45cf-a0b4-6eebae8dde79 | retrodiction-as-delayed-recurrence-the-case | null | null | https://aclanthology.org/2021.alta-1.17 | https://aclanthology.org/2021.alta-1.17.pdf | Retrodiction as Delayed Recurrence: the Case of Adjectives in Italian and English | We address the question of how to account for both forward and backward dependencies in an online processing account of human language acquisition. We focus on descriptive adjectives in English and Italian, and show that the acquisition of adjectives in these languages likely relies on tracking both forward and backwar... | ['Atiqah Khaliq', 'Francesca Zermiani', 'Raquel G. Alhama'] | null | null | null | null | alta-2021-12 | ['language-acquisition'] | ['natural-language-processing'] | [-1.01315416e-01 1.61306784e-01 3.27442661e-02 -3.96622181e-01
2.29296178e-01 -8.28942239e-01 6.52133346e-01 4.30790931e-01
-8.29984426e-01 3.52990419e-01 2.46262431e-01 -8.62605512e-01
-3.29260044e-02 -9.12772119e-01 -4.62786674e-01 1.27536766e-02
-2.84270287e-01 4.07360137e-01 1.97838247e-01 -6.67217195... | [10.500733375549316, 9.077664375305176] |
51dfc6bf-aa59-45c2-83cb-c4afc8f8ca5c | modular-interactive-video-object-segmentation | 2103.07941 | null | https://arxiv.org/abs/2103.07941v3 | https://arxiv.org/pdf/2103.07941v3.pdf | Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion | We present Modular interactive VOS (MiVOS) framework which decouples interaction-to-mask and mask propagation, allowing for higher generalizability and better performance. Trained separately, the interaction module converts user interactions to an object mask, which is then temporally propagated by our propagation modu... | ['Chi-Keung Tang', 'Yu-Wing Tai', 'Ho Kei Cheng'] | 2021-03-14 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Cheng_Modular_Interactive_Video_Object_Segmentation_Interaction-to-Mask_Propagation_and_Difference-Aware_Fusion_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Cheng_Modular_Interactive_Video_Object_Segmentation_Interaction-to-Mask_Propagation_and_Difference-Aware_Fusion_CVPR_2021_paper.pdf | cvpr-2021-1 | ['interactive-video-object-segmentation'] | ['computer-vision'] | [ 3.10377032e-01 -4.09464054e-02 1.64688826e-01 -4.66641575e-01
-5.76630831e-01 -6.98509097e-01 3.07751119e-01 -2.38747790e-01
-4.84028012e-01 2.77263612e-01 -2.48228423e-02 -1.53691277e-01
3.79434973e-01 -5.27258933e-01 -8.23825121e-01 -3.71725798e-01
9.70410258e-02 3.56101282e-02 8.65918517e-01 -6.75275177... | [9.282527923583984, -0.20281848311424255] |
fbaf1c69-8f3c-448d-a982-9883015587dd | named-entity-recognition-as-dependency | 2005.07150 | null | https://arxiv.org/abs/2005.07150v3 | https://arxiv.org/pdf/2005.07150v3.pdf | Named Entity Recognition as Dependency Parsing | Named Entity Recognition (NER) is a fundamental task in Natural Language Processing, concerned with identifying spans of text expressing references to entities. NER research is often focused on flat entities only (flat NER), ignoring the fact that entity references can be nested, as in [Bank of [China]] (Finkel and Man... | ['Juntao Yu', 'Bernd Bohnet', 'Massimo Poesio'] | 2020-05-14 | named-entity-recognition-as-dependency-1 | https://aclanthology.org/2020.acl-main.577 | https://aclanthology.org/2020.acl-main.577.pdf | acl-2020-6 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-4.54074353e-01 4.21432942e-01 -2.96453774e-01 -4.22253340e-01
-9.26243961e-01 -1.04016888e+00 4.82141852e-01 6.21389210e-01
-6.08396709e-01 7.18460917e-01 6.89573884e-01 -5.70157945e-01
1.46931946e-01 -7.77302384e-01 -5.31690180e-01 7.89049864e-02
-3.72520030e-01 2.43406996e-01 1.28138095e-01 -1.84340984... | [9.742833137512207, 9.5121488571167] |
efaf5d55-376f-4586-8897-c7b20fa2c9c8 | trading-syntax-trees-for-wordpieces-target | 2305.11034 | null | https://arxiv.org/abs/2305.11034v1 | https://arxiv.org/pdf/2305.11034v1.pdf | Trading Syntax Trees for Wordpieces: Target-oriented Opinion Words Extraction with Wordpieces and Aspect Enhancement | State-of-the-art target-oriented opinion word extraction (TOWE) models typically use BERT-based text encoders that operate on the word level, along with graph convolutional networks (GCNs) that incorporate syntactic information extracted from syntax trees. These methods achieve limited gains with GCNs and have difficul... | ['Nikolaos Aletras', 'Kai Sun', 'Samuel Mensah'] | 2023-05-18 | null | null | null | null | ['target-oriented-opinion-words-extraction'] | ['natural-language-processing'] | [ 2.37694785e-01 2.30510250e-01 -4.62723613e-01 -5.05074084e-01
-7.03820884e-01 -3.61496896e-01 5.63783586e-01 4.06411976e-01
-5.61400771e-01 4.31841791e-01 3.27454478e-01 -7.22335994e-01
4.46619302e-01 -1.08105087e+00 -5.99891722e-01 -3.10053080e-01
-2.17729583e-04 2.26986930e-01 1.63601190e-01 -4.74843174... | [11.499053001403809, 6.667970657348633] |
01cc11a1-57af-44fa-a36f-3fcb900bf8aa | unsupervised-object-localization-observing | 2212.07834 | null | https://arxiv.org/abs/2212.07834v2 | https://arxiv.org/pdf/2212.07834v2.pdf | Unsupervised Object Localization: Observing the Background to Discover Objects | Recent advances in self-supervised visual representation learning have paved the way for unsupervised methods tackling tasks such as object discovery and instance segmentation. However, discovering objects in an image with no supervision is a very hard task; what are the desired objects, when to separate them into part... | ['Patrick Pérez', 'Éloi Zablocki', 'Antonin Vobecky', 'Gilles Puy', 'Chloé Sekkat', 'Oriane Siméoni'] | 2022-12-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Simeoni_Unsupervised_Object_Localization_Observing_the_Background_To_Discover_Objects_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Simeoni_Unsupervised_Object_Localization_Observing_the_Background_To_Discover_Objects_CVPR_2023_paper.pdf | cvpr-2023-1 | ['unsupervised-object-localization', 'object-discovery', 'saliency-detection', 'unsupervised-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 4.80500251e-01 2.20612094e-01 -2.31620431e-01 -3.23872149e-01
-7.05831826e-01 -4.61067468e-01 4.59928602e-01 5.85017622e-01
-4.59076524e-01 4.63485122e-01 -1.14218071e-01 9.75844115e-02
1.16676871e-04 -6.04185820e-01 -1.04196393e+00 -8.07089031e-01
2.94205453e-02 5.88906169e-01 8.43804002e-01 -1.83724687... | [9.493860244750977, 0.6307177543640137] |
4a93b5f3-a525-4d2b-a77d-684fdd2e0486 | facial-de-occlusion-network-for-virtual | 2210.12622 | null | https://arxiv.org/abs/2210.12622v1 | https://arxiv.org/pdf/2210.12622v1.pdf | Facial De-occlusion Network for Virtual Telepresence Systems | To see what is not in the image is one of the broader missions of computer vision. Technology to inpaint images has made significant progress with the coming of deep learning. This paper proposes a method to tackle occlusion specific to human faces. Virtual presence is a promising direction in communication and recreat... | ['Avinash Sharma', 'Ashwath Shetty', 'Surabhi Gupta'] | 2022-10-23 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 1.74126178e-01 4.21107948e-01 4.01541770e-01 -1.66977987e-01
-4.50914502e-02 -1.52686492e-01 3.71506453e-01 -7.52593279e-01
-2.27368951e-01 7.80430794e-01 -9.74736735e-02 -9.55043882e-02
3.95282209e-01 -6.09442949e-01 -7.62305140e-01 -5.18007636e-01
3.82175863e-01 -1.12810172e-01 -1.11577369e-01 -5.43825030... | [12.82144832611084, -0.27240830659866333] |
7acd8623-2a81-4fe0-a702-ff2fd0594435 | introduction-of-medical-imaging-modalities | 2306.01022 | null | https://arxiv.org/abs/2306.01022v2 | https://arxiv.org/pdf/2306.01022v2.pdf | Introduction of Medical Imaging Modalities | The diagnosis and treatment of various diseases had been expedited with the help of medical imaging. Different medical imaging modalities, including X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Nuclear Imaging, Ultrasound, Electrical Impedance Tomography (EIT), and Emerging Technologies for in viv... | ['Md Monjur Hossain Bhuiyan', 'Dr. Kishor Datta Gupta', 'Dr. Md Azim Ullah', 'Ismail Hossain', 'MD Abdullah Al Nasim', 'S. K. M Shadekul Islam'] | 2023-06-01 | null | null | null | null | ['computed-tomography-ct'] | ['methodology'] | [ 4.32516575e-01 -1.41406953e-01 -4.54196215e-01 2.93420833e-02
-4.43589896e-01 -1.10019527e-01 2.83024218e-02 3.49864155e-01
-5.83027959e-01 6.36666358e-01 1.83738485e-01 -4.49725300e-01
-6.52882040e-01 -4.88480061e-01 2.26025790e-01 -7.67626226e-01
-3.63664091e-01 8.52313936e-01 6.27643913e-02 2.27600530... | [14.848029136657715, -2.472247362136841] |
95cff414-6e5e-4a1b-8eed-2fc8c52ee0e5 | recognizing-birds-from-sound-the-2018 | 1804.07177 | null | http://arxiv.org/abs/1804.07177v1 | http://arxiv.org/pdf/1804.07177v1.pdf | Recognizing Birds from Sound - The 2018 BirdCLEF Baseline System | Reliable identification of bird species in recorded audio files would be a
transformative tool for researchers, conservation biologists, and birders. In
recent years, artificial neural networks have greatly improved the detection
quality of machine learning systems for bird species recognition. We present a
baseline sy... | ['Maximilian Eibl', 'Thomas Wilhelm-Stein', 'Danny Kowerko', 'Stefan Kahl', 'Holger Klinck'] | 2018-04-19 | null | null | null | null | ['bird-audio-detection'] | ['audio'] | [-7.02876970e-02 -6.24532878e-01 -1.46262556e-01 -3.99535954e-01
-1.82382941e-01 -9.50126290e-01 1.08512834e-01 3.30112815e-01
-1.04239619e+00 3.97258908e-01 3.16728562e-01 -5.82124256e-02
1.63086832e-01 -5.76514006e-01 -4.04122293e-01 -1.42316118e-01
-6.79127216e-01 -1.27867579e-01 -1.19206533e-01 -9.88442823... | [15.220392227172852, 5.250040054321289] |
1bda5e03-6b4b-412b-aa11-931c75aaa0ab | face-frontalization-for-alignment-and | 1502.00852 | null | http://arxiv.org/abs/1502.00852v1 | http://arxiv.org/pdf/1502.00852v1.pdf | Face frontalization for Alignment and Recognition | Recently, it was shown that excellent results can be achieved in both face
landmark localization and pose-invariant face recognition. These breakthroughs
are attributed to the efforts of the community to manually annotate facial
images in many different poses and to collect 3D faces data. In this paper, we
propose a no... | ['Stefanos Zafeiriou', 'Christos Sagonas', 'Yannis Panagakis', 'Maja Pantic'] | 2015-02-03 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 1.93545315e-02 -8.53264630e-02 2.92347688e-02 -7.84838200e-01
-8.97515893e-01 -4.32912588e-01 3.54044944e-01 -5.24568200e-01
-2.82887280e-01 3.54098350e-01 -8.42040554e-02 4.12934780e-01
-2.35780194e-01 -1.84378296e-01 -5.64638734e-01 -8.19482982e-01
7.55352005e-02 4.76839393e-01 -5.31541288e-01 1.61637977... | [13.176554679870605, 0.45960307121276855] |
3a615272-e8c9-44e4-950d-2ca2055fe840 | a-novel-framework-based-on-medical-concept-1 | null | null | https://aclanthology.org/2022.findings-acl.110 | https://aclanthology.org/2022.findings-acl.110.pdf | A Novel Framework Based on Medical Concept Driven Attention for Explainable Medical Code Prediction via External Knowledge | Medical code prediction from clinical notes aims at automatically associating medical codes with the clinical notes. Rare code problem, the medical codes with low occurrences, is prominent in medical code prediction. Recent studies employ deep neural networks and the external knowledge to tackle it. However, such appro... | ['Deyu Zhou', 'Junxi Liu', 'Chenchen Ye', 'Linhai Zhang', 'Tao Wang'] | null | null | null | null | findings-acl-2022-5 | ['medical-code-prediction'] | ['medical'] | [ 1.21991411e-01 4.15697157e-01 -2.65199006e-01 -4.38840538e-01
-8.17894220e-01 2.92750285e-03 -1.90929864e-02 6.75821066e-01
1.18189901e-02 6.13494754e-01 6.59908950e-01 -3.28577191e-01
-3.29912394e-01 -6.26712859e-01 -4.88319397e-01 -5.46793103e-01
-4.45491387e-05 5.87282956e-01 -1.98035195e-01 1.21911503... | [7.966622352600098, 6.777094841003418] |
266d4077-cf38-4455-a7e9-adaf692db892 | identity-aware-attribute-recognition-via-real | 2008.05255 | null | https://arxiv.org/abs/2008.05255v1 | https://arxiv.org/pdf/2008.05255v1.pdf | Identity-Aware Attribute Recognition via Real-Time Distributed Inference in Mobile Edge Clouds | With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time ... | ['HuiZhi Liang', 'Qiufen Xia', 'Pan Zhou', 'Zichuan Xu', 'Jiankang Ren', 'Jiangkai Wu'] | 2020-08-12 | null | null | null | null | ['pedestrian-attribute-recognition', 'person-identification'] | ['computer-vision', 'computer-vision'] | [-4.2736191e-01 -3.7676629e-01 2.3427966e-01 -4.8572138e-01
-1.4271633e-01 -3.7622708e-01 2.4697007e-01 -9.7773954e-02
-4.6653691e-01 7.4199963e-01 -2.8859985e-01 -7.9180561e-02
-5.2653176e-01 -9.8896039e-01 -8.0176067e-01 -8.0150884e-01
-2.8584281e-02 7.7210063e-01 9.1522016e-02 5.7605344e-01
-2.9179916e-01... | [5.912252902984619, 5.956698417663574] |
034202eb-5057-41f8-a8bb-2d115d8686d9 | exploration-of-reinforcement-learning-for | 2004.00801 | null | https://arxiv.org/abs/2004.00801v1 | https://arxiv.org/pdf/2004.00801v1.pdf | Exploration of Reinforcement Learning for Event Camera using Car-like Robots | We demonstrate the first reinforcement-learning application for robots equipped with an event camera. Because of the considerably lower latency of the event camera, it is possible to achieve much faster control of robots compared with the existing vision-based reinforcement-learning applications using standard cameras.... | ['Shintaro Shiba', 'Riku Arakawa'] | 2020-04-02 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [-2.03865439e-01 5.13005443e-02 3.07291806e-01 -1.63480788e-01
-3.36900741e-01 -5.36129713e-01 5.93805134e-01 1.92921311e-01
-1.15813076e+00 7.62832761e-01 -7.01996803e-01 -1.37011945e-01
3.07348706e-02 -8.63254905e-01 -1.23812139e+00 -5.52584648e-01
-6.81790829e-01 6.90291584e-01 9.64723051e-01 -3.79806161... | [4.685933589935303, 1.100114107131958] |
574bc250-3bf0-41cc-9dc8-c03dd357f03b | qlora-efficient-finetuning-of-quantized-llms | 2305.14314 | null | https://arxiv.org/abs/2305.14314v1 | https://arxiv.org/pdf/2305.14314v1.pdf | QLoRA: Efficient Finetuning of Quantized LLMs | We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance. QLoRA backpropagates gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters~(LoRA). O... | ['Luke Zettlemoyer', 'Ari Holtzman', 'Artidoro Pagnoni', 'Tim Dettmers'] | 2023-05-23 | null | null | null | null | ['chatbot', 'instruction-following', 'chatbot'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [-3.74370217e-01 -4.48304117e-01 -3.43655556e-01 -2.58754641e-01
-1.10452759e+00 -6.05496943e-01 2.08811462e-01 -1.09433047e-01
-7.12220311e-01 6.97117984e-01 1.44066006e-01 -9.61761236e-01
2.47574016e-01 -8.34599435e-01 -8.82386148e-01 -5.13275325e-01
-1.08076865e-02 4.23758715e-01 6.34559095e-01 -5.42236328... | [8.608209609985352, 3.4931795597076416] |
e6c7e89c-411f-4630-8569-e30d7f32b4cd | hardware-conditioned-policies-for-multi-robot | 1811.09864 | null | http://arxiv.org/abs/1811.09864v2 | http://arxiv.org/pdf/1811.09864v2.pdf | Hardware Conditioned Policies for Multi-Robot Transfer Learning | Deep reinforcement learning could be used to learn dexterous robotic policies
but it is challenging to transfer them to new robots with vastly different
hardware properties. It is also prohibitively expensive to learn a new policy
from scratch for each robot hardware due to the high sample complexity of
modern state-of... | ['Tao Chen', 'Abhinav Gupta', 'Adithyavairavan Murali'] | 2018-11-24 | hardware-conditioned-policies-for-multi-robot-1 | http://papers.nips.cc/paper/8145-hardware-conditioned-policies-for-multi-robot-transfer-learning | http://papers.nips.cc/paper/8145-hardware-conditioned-policies-for-multi-robot-transfer-learning.pdf | neurips-2018-12 | ['transfer-reinforcement-learning', 'industrial-robots'] | ['methodology', 'robots'] | [-7.96141177e-02 2.79468656e-01 -1.23457171e-01 -8.95364732e-02
-5.04096150e-01 -7.24045873e-01 3.34703445e-01 -2.37507492e-01
-6.36459291e-01 9.17387187e-01 -2.51025081e-01 -4.51262027e-01
6.29284009e-02 -5.39819658e-01 -1.64962423e+00 -6.70238316e-01
-3.96862030e-01 6.33317709e-01 1.24452591e-01 -3.97030801... | [4.517638206481934, 1.0305962562561035] |
146ec243-1537-46e9-9a45-73cffacbcfc2 | a-stacking-gated-neural-architecture-for | null | null | https://aclanthology.org/D16-1246 | https://aclanthology.org/D16-1246.pdf | A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification | null | ['Hai Zhao', 'Zhisong Zhang', 'Lianhui Qin'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.453622341156006, 3.749591588973999] |
56af8049-7c71-448c-9604-31098e9cf557 | cprune-compiler-informed-model-pruning-for | 2207.01260 | null | https://arxiv.org/abs/2207.01260v2 | https://arxiv.org/pdf/2207.01260v2.pdf | CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN Execution | Mobile devices run deep learning models for various purposes, such as image classification and speech recognition. Due to the resource constraints of mobile devices, researchers have focused on either making a lightweight deep neural network (DNN) model using model pruning or generating an efficient code using compiler... | ['TaeHo Kim', 'Sangtae Ha', 'Jemin Lee', 'Yongin Kwon'] | 2022-07-04 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [ 2.17021629e-01 1.04432650e-01 -6.93604469e-01 -6.16899133e-01
-4.85490471e-01 -5.63142411e-02 1.77641213e-01 -1.95197910e-01
-3.16570282e-01 2.61130303e-01 -6.38834089e-02 -1.04151702e+00
1.57010332e-01 -1.03458929e+00 -9.73079085e-01 -2.51465082e-01
3.60979289e-01 7.10716605e-01 1.03128806e-01 2.23165620... | [8.542539596557617, 3.0457961559295654] |
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