paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a2c9dc57-7d42-400c-8813-8bde31ff1ff8 | quantification-of-robotic-surgeries-with | 2205.03028 | null | https://arxiv.org/abs/2205.03028v1 | https://arxiv.org/pdf/2205.03028v1.pdf | Quantification of Robotic Surgeries with Vision-Based Deep Learning | Surgery is a high-stakes domain where surgeons must navigate critical anatomical structures and actively avoid potential complications while achieving the main task at hand. Such surgical activity has been shown to affect long-term patient outcomes. To better understand this relationship, whose mechanics remain unknown... | ['Andrew J. Hung', 'Animashree Anandkumar', 'Christian Wagner', 'Jessica Nguyen', 'Taseen F. Haque', 'Runzhuo Ma', 'Dani Kiyasseh'] | 2022-05-06 | null | null | null | null | ['skills-assessment', 'surgical-phase-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.85085171e-01 4.29202169e-01 -5.44251323e-01 -7.39065111e-02
-8.58813465e-01 -8.40208769e-01 2.48646393e-01 3.13588619e-01
-6.44629478e-01 3.32377881e-01 8.33366692e-01 -6.49632573e-01
-5.36619782e-01 -2.33575389e-01 -8.01251471e-01 -4.96858329e-01
-2.35709041e-01 -4.26079333e-02 -3.08788896e-01 1.04095796... | [14.055708885192871, -3.3703932762145996] |
35a33392-3327-4156-a0ea-2ceff3125172 | relative-density-ratio-estimation-for-robust | null | null | http://papers.nips.cc/paper/4254-relative-density-ratio-estimation-for-robust-distribution-comparison | http://papers.nips.cc/paper/4254-relative-density-ratio-estimation-for-robust-distribution-comparison.pdf | Relative Density-Ratio Estimation for Robust Distribution Comparison | Divergence estimators based on direct approximation of density-ratios without going through separate approximation of numerator and denominator densities have been successfully applied to machine learning tasks that involve distribution comparison such as outlier detection, transfer learning, and two-sample homogeneity... | ['Hirotaka Hachiya', 'Makoto Yamada', 'Taiji Suzuki', 'Masashi Sugiyama', 'Takafumi Kanamori'] | 2011-12-01 | null | null | null | neurips-2011-12 | ['density-ratio-estimation'] | ['methodology'] | [-1.86212957e-01 -2.39782229e-01 2.76208967e-02 -3.86910141e-01
-9.01868284e-01 -2.49713510e-01 4.93479401e-01 4.53140467e-01
-6.43860102e-01 1.06413102e+00 -5.23366034e-01 -3.17358017e-01
-2.97672629e-01 -6.75176859e-01 -7.05889225e-01 -8.22731972e-01
7.94613585e-02 4.60010618e-01 1.24527283e-01 2.14591935... | [7.305465221405029, 4.055818557739258] |
db9b89c2-5b40-47ed-a971-692fc159ac10 | knowledge-extraction-with-interval-temporal | 2305.16864 | null | https://arxiv.org/abs/2305.16864v1 | https://arxiv.org/pdf/2305.16864v1.pdf | Knowledge Extraction with Interval Temporal Logic Decision Trees | Multivariate temporal, or time, series classification is, in a way, the temporal generalization of (numeric) classification, as every instance is described by multiple time series instead of multiple values. Symbolic classification is the machine learning strategy to extract explicit knowledge from a data set, and the ... | ['Stan Ionel Eduard', 'Guido Sciavicco'] | 2023-05-26 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 6.65021315e-02 -1.97824121e-01 -3.34417582e-01 -3.52895319e-01
-8.63099769e-02 -8.71320128e-01 8.42349291e-01 6.68428242e-01
-2.75612980e-01 7.74850786e-01 -2.72099972e-01 -6.96180165e-01
-7.63333201e-01 -9.96057630e-01 -2.54801154e-01 -5.26283205e-01
-9.70139325e-01 5.15321672e-01 3.72754574e-01 -2.97255665... | [7.258142471313477, 3.3350372314453125] |
29af4675-1065-4069-863a-053c52e33293 | norppa-novel-ringed-seal-re-identification-by | 2206.02498 | null | https://arxiv.org/abs/2206.02498v3 | https://arxiv.org/pdf/2206.02498v3.pdf | NORPPA: NOvel Ringed seal re-identification by Pelage Pattern Aggregation | We propose a method for Saimaa ringed seal (Pusa hispida saimensis) re-identification. Access to large image volumes through camera trapping and crowdsourcing provides novel possibilities for animal monitoring and conservation and calls for automatic methods for analysis, in particular, when re-identifying individual a... | ['Heikki Kälviäinen', 'Tuomas Eerola', 'Ilia Chelak', 'Ekaterina Nepovinnykh'] | 2022-06-06 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 2.31380276e-02 -1.07670575e-01 1.92230746e-01 -5.86197555e-01
-5.95205307e-01 -6.76497161e-01 5.45600235e-01 5.95568717e-01
-1.30832374e+00 4.31393832e-01 -1.04177810e-01 3.80390137e-01
6.78777089e-03 -6.46922827e-01 -7.16502786e-01 -6.47854388e-01
-5.77106237e-01 4.03002173e-01 3.03709418e-01 -1.91447929... | [8.245160102844238, -1.1261447668075562] |
c8b95b87-ed1a-406b-a247-668536708d79 | infinite-dimensional-sparse-learning-in | 2203.14731 | null | https://arxiv.org/abs/2203.14731v2 | https://arxiv.org/pdf/2203.14731v2.pdf | Infinite-Dimensional Sparse Learning in Linear System Identification | Regularized methods have been widely applied to system identification problems without known model structures. This paper proposes an infinite-dimensional sparse learning algorithm based on atomic norm regularization. Atomic norm regularization decomposes the transfer function into first-order atomic models and solves ... | ['Roy S. Smith', 'Andrea Iannelli', 'Mehmet Tolga Akan', 'Mingzhou Yin'] | 2022-03-28 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 2.37949505e-01 1.74683183e-01 -4.15069014e-01 2.61731148e-02
-1.07900703e+00 -3.56880456e-01 -2.82505397e-02 -2.86190480e-01
3.51476610e-01 1.00480485e+00 -6.88032806e-02 4.67822775e-02
-7.21590996e-01 -1.01802379e-01 -7.07875371e-01 -9.31199968e-01
-1.79926932e-01 5.36218286e-01 -3.56426388e-01 -1.69274002... | [7.135342121124268, 4.314519882202148] |
4d19e8ef-25b5-4667-ac70-4615cd70d70c | medical-image-retrieval-using-deep | 1703.08472 | null | http://arxiv.org/abs/1703.08472v1 | http://arxiv.org/pdf/1703.08472v1.pdf | Medical Image Retrieval using Deep Convolutional Neural Network | With a widespread use of digital imaging data in hospitals, the size of
medical image repositories is increasing rapidly. This causes difficulty in
managing and querying these large databases leading to the need of content
based medical image retrieval (CBMIR) systems. A major challenge in CBMIR
systems is the semantic... | ['Adnan Qayyum', 'Muhammad Awais', 'Syed Muhammad Anwar', 'Muhammad Majid'] | 2017-03-24 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [ 7.16250166e-02 -1.94390699e-01 -2.40253344e-01 -2.97153950e-01
-1.08484304e+00 -2.47157708e-01 5.24676442e-01 6.46755874e-01
-5.22125423e-01 4.11956996e-01 2.98023432e-01 5.71770258e-02
-2.48557597e-01 -8.91559899e-01 -2.36436978e-01 -7.06884086e-01
1.28415868e-01 4.00402874e-01 1.84860095e-01 -6.55268654... | [14.352544784545898, -1.575683832168579] |
c5df04bb-297c-4c5d-be63-c9975fd5edf0 | deep-recurrent-spiking-neural-networks | 2306.01354 | null | https://arxiv.org/abs/2306.01354v1 | https://arxiv.org/pdf/2306.01354v1.pdf | Deep recurrent spiking neural networks capture both static and dynamic representations of the visual cortex under movie stimuli | In the real world, visual stimuli received by the biological visual system are predominantly dynamic rather than static. A better understanding of how the visual cortex represents movie stimuli could provide deeper insight into the information processing mechanisms of the visual system. Although some progress has been ... | ['Yonghong Tian', 'Huihui Zhou', 'Zhengyu Ma', 'Liwei Huang'] | 2023-06-02 | null | null | null | null | ['action-recognition-in-videos', 'action-recognition'] | ['computer-vision', 'computer-vision'] | [ 5.21485746e-01 -4.41333890e-01 7.58105591e-02 -1.88125432e-01
4.15601730e-01 -5.15644670e-01 7.12546170e-01 -2.45950833e-01
-4.35545921e-01 4.34502326e-02 1.17757812e-01 8.07183050e-03
2.46670712e-02 -5.42473018e-01 -7.39596188e-01 -9.21403110e-01
-5.40584065e-02 -4.20606107e-01 5.66181958e-01 -2.58266658... | [9.592422485351562, 2.5118184089660645] |
35ba26a3-20da-477b-9844-665214fc06d5 | iso-timeml-event-extraction-in-persian-text | null | null | https://aclanthology.org/C12-1179 | https://aclanthology.org/C12-1179.pdf | ISO-TimeML Event Extraction in Persian Text | null | ['Gholamreza Ghassem-Sani', 'Mirrosh', 'Yadollah Yaghoobzadeh', 'Seyed Abolghasem el', 'Mahbaneh Eshaghzadeh'] | 2012-12-01 | iso-timeml-event-extraction-in-persian-text-1 | https://aclanthology.org/C12-1179 | https://aclanthology.org/C12-1179.pdf | coling-2012-12 | ['temporal-information-extraction'] | ['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... | [-7.239124774932861, 3.7641868591308594] |
45aa66df-ada2-43e3-80ec-3b2ea9e9920b | towards-speech-only-opinion-level-sentiment | null | null | https://aclanthology.org/2022.lrec-1.215 | https://aclanthology.org/2022.lrec-1.215.pdf | Towards Speech-only Opinion-level Sentiment Analysis | The growing popularity of various forms of Spoken Dialogue Systems (SDS) raises the demand for their capability of implicitly assessing the speaker’s sentiment from speech only. Mapping the latter on user preferences enables to adapt to the user and individualize the requested information while increasing user satisfac... | ['Wolfgang Minker', 'Yuri Matveev', 'Aleksei Gusev', 'Alisa Gazizullina', 'Annalena Aicher'] | null | null | null | null | lrec-2022-6 | ['spoken-dialogue-systems'] | ['speech'] | [ 9.12193805e-02 4.27529097e-01 -2.31374115e-01 -1.11073375e+00
-9.51641679e-01 -4.71687019e-01 8.15499485e-01 3.06000799e-01
-7.91879654e-01 5.13428628e-01 9.23768163e-01 3.69115919e-02
2.44290158e-01 -1.44018084e-01 -1.05264589e-01 -5.10470450e-01
4.35689315e-02 6.76517069e-01 -2.61946470e-01 -8.11732590... | [13.362642288208008, 5.5572638511657715] |
38baf180-881d-4e1e-bd77-f7929c5cfc6c | twistbytes-hierarchical-classification-at | 1908.06493 | null | https://arxiv.org/abs/1908.06493v1 | https://arxiv.org/pdf/1908.06493v1.pdf | TwistBytes -- Hierarchical Classification at GermEval 2019: walking the fine line (of recall and precision) | We present here our approach to the GermEval 2019 Task 1 - Shared Task on hierarchical classification of German blurbs. We achieved first place in the hierarchical subtask B and second place on the root node, flat classification subtask A. In subtask A, we applied a simple multi-feature TF-IDF extraction method using d... | ['Fernando Benites'] | 2019-08-18 | null | null | null | null | ['hierarchical-text-classification-of-blurbs'] | ['natural-language-processing'] | [ 4.37516004e-01 2.22465649e-01 1.78830624e-01 -3.30975950e-01
-7.96991229e-01 -6.38848066e-01 8.18328857e-01 6.73183322e-01
-8.76319706e-01 9.13496137e-01 3.23180050e-01 -2.88927168e-01
-3.91946614e-01 -5.49035311e-01 -1.91693544e-01 -7.69737780e-01
1.10075407e-01 3.64886999e-01 7.12406278e-01 -2.79911429... | [10.54720401763916, 10.387114524841309] |
04e1002b-b44e-4d6c-bd99-2ffac277f447 | event-representation-learning-enhanced-with | 1909.0519 | null | https://arxiv.org/abs/1909.05190v2 | https://arxiv.org/pdf/1909.05190v2.pdf | Event Representation Learning Enhanced with External Commonsense Knowledge | Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as script event prediction. On the other hand, events extracted from raw texts lacks of commonsense knowledge, such a... | ['Zhongyang Li', 'Kuo Liao', 'Ting Liu', 'Junwen Duan', 'Xiao Ding'] | 2019-09-09 | event-representation-learning-enhanced-with-1 | https://aclanthology.org/D19-1495 | https://aclanthology.org/D19-1495.pdf | ijcnlp-2019-11 | ['stock-market-prediction'] | ['time-series'] | [-1.17729604e-01 -1.58283144e-01 -3.13790858e-01 -7.16832280e-01
-4.09143150e-01 -7.96447754e-01 1.00834024e+00 7.64276385e-01
-3.94015163e-01 6.53914869e-01 9.46709752e-01 -2.90024102e-01
1.34012386e-01 -1.02253735e+00 -4.96924609e-01 -3.19774151e-01
-1.92601413e-01 1.34988859e-01 1.99467093e-01 -2.97910780... | [4.459316730499268, 4.406608581542969] |
3ff30e7c-3993-45db-9f40-8cd5ba039017 | rotational-crossed-slit-light-field | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Li_Rotational_Crossed-Slit_Light_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Li_Rotational_Crossed-Slit_Light_CVPR_2016_paper.pdf | Rotational Crossed-Slit Light Field | Light fields (LFs) are image-based representation that records the radiance along all rays along every direction through every point in space. Traditionally LFs are acquired by using a 2D grid of evenly spaced pinhole cameras or by translating a pinhole camera along the 2D grid using a robot arm. In this paper, we p... | ['Jingyi Yu', 'Haiting Lin', 'Nianyi Li', 'Mingyuan Zhou', 'Bilin Sun'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['stereo-matching'] | ['computer-vision'] | [ 5.09666681e-01 -3.49058270e-01 -2.71165036e-02 -8.46835151e-02
-2.20205501e-01 -6.74940884e-01 7.07953334e-01 -4.64562535e-01
-5.36534667e-01 7.21659303e-01 -4.12067473e-02 -2.62719244e-01
1.65659431e-02 -7.92828381e-01 -8.36271584e-01 -6.80342913e-01
7.72069693e-01 4.91534203e-01 3.53907228e-01 1.30062312... | [9.53967571258545, -2.714120626449585] |
66b9bf30-8f91-46ef-8c43-1103c3d82f64 | a-fully-convolutional-network-for-mr | 1911.09846 | null | http://arxiv.org/abs/1911.09846v1 | http://arxiv.org/pdf/1911.09846v1.pdf | A Fully Convolutional Network for MR Fingerprinting | Magnetic Resonance Fingerprinting (MRF) methods typically rely on dictionary
matching to map the temporal MRF signals to quantitative tissue parameters.
These methods suffer from heavy storage and computation requirements as the
dictionary size grows. To address these issues, we proposed an end to end fully
convolution... | [] | 2019-11-22 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 5.35833389e-02 -2.44924173e-01 -1.60779580e-01 -5.85982025e-01
-6.35316849e-01 -8.81613344e-02 2.00378180e-01 -4.42545444e-01
-3.89061421e-01 5.51947951e-01 3.24141055e-01 -1.66324794e-01
-1.65736467e-01 -5.47320843e-01 -5.73206961e-01 -6.50547326e-01
-3.69674236e-01 2.77688563e-01 1.45392558e-02 1.46933377... | [13.513011932373047, -2.35417103767395] |
6675d136-765a-4875-a207-26110303996f | bringing-alive-blurred-moments | 1804.02913 | null | http://arxiv.org/abs/1804.02913v2 | http://arxiv.org/pdf/1804.02913v2.pdf | Bringing Alive Blurred Moments | We present a solution for the goal of extracting a video from a single motion
blurred image to sequentially reconstruct the clear views of a scene as beheld
by the camera during the time of exposure. We first learn motion representation
from sharp videos in an unsupervised manner through training of a convolutional
rec... | ['Anshul Shah', 'Kuldeep Purohit', 'A. N. Rajagopalan'] | 2018-04-09 | bringing-alive-blurred-moments-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Purohit_Bringing_Alive_Blurred_Moments_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Purohit_Bringing_Alive_Blurred_Moments_CVPR_2019_paper.pdf | cvpr-2019-6 | ['video-reconstruction'] | ['computer-vision'] | [ 4.95139152e-01 -2.67249765e-03 1.55776799e-01 -1.67395800e-01
-6.41577840e-01 -5.50927758e-01 6.03739738e-01 -1.03770983e+00
-1.91944003e-01 6.71303511e-01 6.61640823e-01 -3.13636959e-02
1.51889250e-01 -2.41320580e-01 -1.19722617e+00 -6.71494246e-01
1.73439950e-01 -1.71307176e-01 4.20043245e-02 1.92908555... | [11.374125480651855, -2.428046941757202] |
4881a1eb-e6b4-424d-9f56-fa6e625920ed | multi-scanner-canine-cutaneous-squamous-cell | 2301.04423 | null | https://arxiv.org/abs/2301.04423v2 | https://arxiv.org/pdf/2301.04423v2.pdf | Multi-Scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset | In histopathology, scanner-induced domain shifts are known to impede the performance of trained neural networks when tested on unseen data. Multi-domain pre-training or dedicated domain-generalization techniques can help to develop domain-agnostic algorithms. For this, multi-scanner datasets with a high variety of slid... | ['Marc Aubreville', 'Katharina Breininger', 'Andreas Maier', 'Robert Klopfleisch', 'Jingna Qiu', 'Mathias Öttl', 'Nikolas Stathonikos', 'Christof A. Bertram', 'Marco Fragoso', 'Frauke Wilm'] | 2023-01-11 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 6.30251348e-01 -1.25394166e-01 -5.06896451e-02 -3.38798463e-01
-1.28616893e+00 -9.41834927e-01 4.24435705e-01 4.73538935e-01
-5.92824221e-01 5.87581515e-01 -4.93927568e-01 -2.96354115e-01
-1.09756850e-01 -5.37550509e-01 -6.88037276e-01 -1.23569286e+00
9.34382826e-02 4.90928560e-01 5.04684329e-01 -1.38156787... | [15.103414535522461, -3.06050181388855] |
fa7a043b-4ed3-42ee-a13d-006f7dd09e38 | an-landcover-fuzzy-logic-classification-by | 1407.4739 | null | http://arxiv.org/abs/1407.4739v1 | http://arxiv.org/pdf/1407.4739v1.pdf | An landcover fuzzy logic classification by maximumlikelihood | In present days remote sensing is most used application in many sectors. This
remote sensing uses different images like multispectral, hyper spectral or
ultra spectral. The remote sensing image classification is one of the
significant method to classify image. In this state we classify the maximum
likelihood classifica... | ['G. Nagalakshmi', 'T. Sarath'] | 2014-07-17 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 3.71483952e-01 -4.93382305e-01 -5.32027073e-02 -6.66576385e-01
-1.40931547e-01 -8.09578121e-01 3.79216164e-01 8.86216015e-02
-3.92375886e-01 9.55818534e-01 -4.01743978e-01 -4.96954679e-01
-5.39344668e-01 -1.26461601e+00 3.64548713e-02 -3.29387248e-01
2.21351102e-01 4.50723827e-01 2.67660409e-01 -5.00003755... | [9.706321716308594, -1.7889801263809204] |
46d36aea-d901-4cf8-bd06-167783800e55 | towards-universal-representation-for-unseen | 1803.0846 | null | http://arxiv.org/abs/1803.08460v1 | http://arxiv.org/pdf/1803.08460v1.pdf | Towards Universal Representation for Unseen Action Recognition | Unseen Action Recognition (UAR) aims to recognise novel action categories
without training examples. While previous methods focus on inner-dataset
seen/unseen splits, this paper proposes a pipeline using a large-scale training
source to achieve a Universal Representation (UR) that can generalise to a more
realistic Cro... | ['Ling Shao', 'Yang Long', 'Yu Guan', 'Shawn Newsam', 'Yi Zhu'] | 2018-03-22 | towards-universal-representation-for-unseen-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhu_Towards_Universal_Representation_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhu_Towards_Universal_Representation_CVPR_2018_paper.pdf | cvpr-2018-6 | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 9.49283957e-01 8.46547037e-02 -2.72302508e-01 -4.29018676e-01
-1.07331777e+00 -4.35531944e-01 7.13522196e-01 -3.96781355e-01
-2.81971723e-01 9.01879847e-01 3.09725314e-01 1.88940048e-01
-1.22804001e-01 -5.04248500e-01 -8.79584491e-01 -7.77151048e-01
7.37567693e-02 5.77147663e-01 5.90743899e-01 -1.02755673... | [8.501482009887695, 0.821831464767456] |
20bfbcd2-d12b-468b-8818-dbc0f17354de | presenting-an-approach-based-on-weighted | 2306.17068 | null | https://arxiv.org/abs/2306.17068v2 | https://arxiv.org/pdf/2306.17068v2.pdf | weighted CapsuleNet networks for Persian multi-domain sentiment analysis | Sentiment classification is a fundamental task in natural language processing, assigning one of the three classes, positive, negative, or neutral, to free texts. However, sentiment classification models are highly domain dependent; the classifier may perform classification with reasonable accuracy in one domain but not... | ['Ramin Mousa', 'Benyamin Pourhosseini', 'Nima Karimi', 'Mahboobeh Sadat Kobari'] | 2023-06-12 | null | null | null | null | ['classification-1', 'sentiment-analysis'] | ['methodology', 'natural-language-processing'] | [ 1.13897324e-01 -1.42551437e-01 -2.32350349e-01 -3.64361107e-01
-2.30719447e-01 -7.75246441e-01 4.77250636e-01 7.02253997e-01
-5.42003810e-01 8.71671021e-01 1.14558032e-02 -1.66090056e-02
-3.25105578e-01 -9.61102664e-01 -7.22797364e-02 -8.28763962e-01
1.34567454e-01 3.79223764e-01 5.69613278e-02 -5.54504693... | [11.066542625427246, 6.9027581214904785] |
77f890bb-edff-48bf-8c63-917ab0249beb | a-novel-pipeline-for-improving-optical | 2307.04245 | null | https://arxiv.org/abs/2307.04245v1 | https://arxiv.org/pdf/2307.04245v1.pdf | A Novel Pipeline for Improving Optical Character Recognition through Post-processing Using Natural Language Processing | Optical Character Recognition (OCR) technology finds applications in digitizing books and unstructured documents, along with applications in other domains such as mobility statistics, law enforcement, traffic, security systems, etc. The state-of-the-art methods work well with the OCR with printed text on license plates... | ['Anirban Dasgupta', 'Samyak Mehta', 'Aishik Rakshit'] | 2023-07-09 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 3.52555603e-01 -6.00782931e-01 -1.86547637e-01 -1.59326017e-01
-4.77581590e-01 -8.95245612e-01 7.11849451e-01 1.69659153e-01
-5.68414330e-01 6.19377196e-01 -1.55348048e-01 -4.95805234e-01
-1.31434396e-01 -5.76021850e-01 -3.15216064e-01 -3.04548681e-01
3.04801494e-01 5.45688748e-01 5.64085066e-01 -5.38262390... | [11.828479766845703, 2.609433889389038] |
dd50ba40-a13a-4b17-b6ac-06c5c159b362 | clip-driven-fine-grained-text-image-person-re | 2210.10276 | null | https://arxiv.org/abs/2210.10276v1 | https://arxiv.org/pdf/2210.10276v1.pdf | CLIP-Driven Fine-grained Text-Image Person Re-identification | TIReID aims to retrieve the image corresponding to the given text query from a pool of candidate images. Existing methods employ prior knowledge from single-modality pre-training to facilitate learning, but lack multi-modal correspondences. Besides, due to the substantial gap between modalities, existing methods embed ... | ['Jinhui Tang', 'Liyan Zhang', 'Neng Dong', 'Shuanglin Yan'] | 2022-10-19 | null | null | null | null | ['nlp-based-person-retrival'] | ['computer-vision'] | [ 2.12139547e-01 -3.91046256e-01 -3.84793788e-01 -2.69463748e-01
-1.13167369e+00 -5.06624937e-01 5.23376703e-01 -1.93101257e-01
-2.12903872e-01 3.54821771e-01 5.98763704e-01 2.62738496e-01
-5.06102443e-01 -6.51540816e-01 -5.22794962e-01 -8.79628897e-01
3.50881100e-01 1.06821209e-01 2.29452714e-01 -7.03518093... | [10.86642074584961, 1.2582930326461792] |
6155f332-d090-4978-8a41-868cf7911e33 | generalized-spectral-clustering-for-directed | 2203.03221 | null | https://arxiv.org/abs/2203.03221v2 | https://arxiv.org/pdf/2203.03221v2.pdf | Generalized Spectral Clustering for Directed and Undirected Graphs | Spectral clustering is a popular approach for clustering undirected graphs, but its extension to directed graphs (digraphs) is much more challenging. A typical workaround is to naively symmetrize the adjacency matrix of the directed graph, which can however lead to discarding valuable information carried by edge direct... | ['Matthieu Jonckheere', 'Argyris Kalogeratos', 'Harry Sevi'] | 2022-03-07 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [ 3.15377325e-01 4.06812757e-01 -6.93248361e-02 -2.76203364e-01
-3.01604956e-01 -8.61229718e-01 4.15230215e-01 2.50145555e-01
-8.01557302e-02 3.97156417e-01 1.04488850e-01 -4.14454788e-01
-5.31445086e-01 -9.20182407e-01 -3.63866806e-01 -1.09579241e+00
-2.44981334e-01 8.06220055e-01 3.45460802e-01 -4.39060517... | [7.042876720428467, 5.240075588226318] |
f5ef241d-7261-4d11-b967-ec6922918400 | dominance-based-rough-set-approach-basic | 2210.03233 | null | https://arxiv.org/abs/2210.03233v1 | https://arxiv.org/pdf/2210.03233v1.pdf | Dominance-based Rough Set Approach, basic ideas and main trends | Dominance-based Rough Approach (DRSA) has been proposed as a machine learning and knowledge discovery methodology to handle Multiple Criteria Decision Aiding (MCDA). Due to its capacity of asking the decision maker (DM) for simple preference information and supplying easily understandable and explainable recommendation... | ['Marcin Szeląg', 'Benedetto Matarazzo', 'Salvatore Greco', 'Jerzy Błaszczyński'] | 2022-10-06 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [-1.10614389e-01 3.04086745e-01 -3.12432408e-01 -8.48135471e-01
-1.61024034e-01 -3.84947270e-01 3.86608094e-01 7.07525373e-01
-2.10893631e-01 1.02354908e+00 1.09696187e-01 -6.51625872e-01
-1.27956617e+00 -8.39382410e-01 6.55673519e-02 -3.72507632e-01
-2.35864446e-01 1.12874174e+00 9.54837576e-02 -5.56648731... | [8.438192367553711, 5.856001377105713] |
627462fa-0f97-4e40-bf7d-620c71521526 | weighted-concordance-index-loss-based | 2206.11458 | null | https://arxiv.org/abs/2206.11458v1 | https://arxiv.org/pdf/2206.11458v1.pdf | Weighted Concordance Index Loss-based Multimodal Survival Modeling for Radiation Encephalopathy Assessment in Nasopharyngeal Carcinoma Radiotherapy | Radiation encephalopathy (REP) is the most common complication for nasopharyngeal carcinoma (NPC) radiotherapy. It is highly desirable to assist clinicians in optimizing the NPC radiotherapy regimen to reduce radiotherapy-induced temporal lobe injury (RTLI) according to the probability of REP onset. To the best of our ... | ['Jiang Liu', 'Fang-Yun Xie', 'Hongbo Liu', 'Jingwen Wang', 'Jiajian Li', 'Pu-Yun OuYang', 'Anwei Li', 'Jiansheng Fang'] | 2022-06-23 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 1.96899265e-01 -3.44979674e-01 -5.31995356e-01 -2.95884132e-01
-1.31808925e+00 -4.20075059e-01 6.64604306e-01 -1.50487527e-01
-7.45833278e-01 6.50520325e-01 7.84884155e-01 -5.43684065e-01
-4.66247231e-01 -6.07821822e-01 -5.67531526e-01 -1.39718866e+00
9.33283865e-02 2.47012660e-01 -8.94233584e-02 3.35435346... | [14.9246244430542, -2.680715322494507] |
275de59e-c2be-4f06-8045-177f761260c4 | siamese-infrared-and-visible-light-fusion | 2103.07302 | null | https://arxiv.org/abs/2103.07302v1 | https://arxiv.org/pdf/2103.07302v1.pdf | Siamese Infrared and Visible Light Fusion Network for RGB-T Tracking | Due to the different photosensitive properties of infrared and visible light, the registered RGB-T image pairs shot in the same scene exhibit quite different characteristics. This paper proposes a siamese infrared and visible light fusion Network (SiamIVFN) for RBG-T image-based tracking. SiamIVFN contains two main sub... | ['Wang Bofan', 'Zhuang Yi', 'Hu Zhengwei', 'Zhao Haitao', 'Peng Jingchao'] | 2021-03-12 | null | null | null | null | ['rgb-t-tracking'] | ['computer-vision'] | [-1.21387601e-01 -6.09261215e-01 -5.51089644e-02 -1.64654240e-01
-3.66975963e-01 -4.91283506e-01 5.69113553e-01 -6.04449928e-01
-5.05886793e-01 1.09656654e-01 -1.62452504e-01 -7.63213411e-02
3.44800770e-01 -4.52350050e-01 -6.76879823e-01 -9.49666619e-01
4.81728494e-01 -3.15781951e-01 3.38547438e-01 -2.48644054... | [6.333939075469971, -2.2018253803253174] |
1e3021d1-ae8f-41c3-adb5-b1d437639e4c | face-generation-and-editing-with-stylegan-a | 2212.09102 | null | https://arxiv.org/abs/2212.09102v2 | https://arxiv.org/pdf/2212.09102v2.pdf | Face Generation and Editing with StyleGAN: A Survey | Our goal with this survey is to provide an overview of the state of the art deep learning technologies for face generation and editing. We will cover popular latest architectures and discuss key ideas that make them work, such as inversion, latent representation, loss functions, training procedures, editing methods, an... | ['Helge Ritter', 'Gustav Reichert', 'Tarek Renusch', 'Dennis Holzmann', 'Eren Akbulut', 'Dzianis Pirshtuk', 'Dzianis Makarovets', 'Maksim Miasayedzenkau', 'Andrew Melnik'] | 2022-12-18 | null | null | null | null | ['face-generation'] | ['computer-vision'] | [ 4.22879338e-01 5.58983445e-01 -8.50463100e-03 -5.74559569e-01
-4.68779445e-01 -3.60168099e-01 6.08103156e-01 -8.80105615e-01
-3.16869356e-02 8.91090453e-01 4.05977845e-01 2.59866863e-01
1.84528455e-01 -9.05647337e-01 -6.27848744e-01 -5.80047607e-01
6.12848848e-02 3.06626767e-01 -6.97996795e-01 -3.05729389... | [12.124787330627441, -0.31589004397392273] |
ca3f5f75-0c7b-46dc-90f7-9286bcf68478 | squant-on-the-fly-data-free-quantization-via-1 | 2202.07471 | null | https://arxiv.org/abs/2202.07471v1 | https://arxiv.org/pdf/2202.07471v1.pdf | SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation | Quantization of deep neural networks (DNN) has been proven effective for compressing and accelerating DNN models. Data-free quantization (DFQ) is a promising approach without the original datasets under privacy-sensitive and confidential scenarios. However, current DFQ solutions degrade accuracy, need synthetic data to... | ['Minyi Guo', 'Yuhao Zhu', 'Fan Yang', 'Yunxin Liu', 'Chen Zhang', 'Xiaotian Gao', 'Jingwen Leng', 'Yuxian Qiu', 'Cong Guo'] | 2022-02-14 | squant-on-the-fly-data-free-quantization-via | https://openreview.net/forum?id=JXhROKNZzOc | https://openreview.net/pdf?id=JXhROKNZzOc | iclr-2022-4 | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 3.03539215e-03 5.93691505e-03 -1.32985324e-01 -7.35568106e-01
-7.88150966e-01 -4.65938270e-01 1.79816008e-01 1.84189066e-01
-8.81595910e-01 8.22701275e-01 -1.25599578e-01 -5.62511921e-01
-1.35703743e-01 -9.44988489e-01 -9.73281920e-01 -6.21940136e-01
2.04562768e-02 2.88205057e-01 -6.74374402e-02 1.30371138... | [8.685030937194824, 3.0503571033477783] |
9793c22c-4778-4410-b915-7e46853e42d4 | towards-training-billion-parameter-graph-1 | 2203.09697 | null | https://arxiv.org/abs/2203.09697v1 | https://arxiv.org/pdf/2203.09697v1.pdf | Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations | Recent progress in Graph Neural Networks (GNNs) for modeling atomic simulations has the potential to revolutionize catalyst discovery, which is a key step in making progress towards the energy breakthroughs needed to combat climate change. However, the GNNs that have proven most effective for this task are memory inten... | ['C. Lawrence Zitnick', 'Siddharth Goyal', 'Brandon M. Wood', 'Abhishek Das', 'Anuroop Sriram'] | 2022-03-18 | towards-training-billion-parameter-graph | https://openreview.net/forum?id=0jP2n0YFmKG | https://openreview.net/pdf?id=0jP2n0YFmKG | iclr-2022-4 | ['initial-structure-to-relaxed-energy-is2re'] | ['graphs'] | [ 9.51527730e-02 5.39981760e-02 -1.48659155e-01 2.66988128e-01
-3.76729757e-01 -5.50679743e-01 9.24008131e-01 5.40176630e-01
-5.15118062e-01 7.68415093e-01 -1.09861344e-01 -1.10766923e+00
1.89452976e-01 -1.21061587e+00 -9.66666818e-01 -6.99191213e-01
-2.50279278e-01 8.01965892e-01 4.88445520e-01 -5.07795930... | [5.41118860244751, 5.6596574783325195] |
ce45ac54-7943-4a55-b1cd-252ca68ab60a | prediction-of-video-game-development-problems | null | null | https://aclanthology.org/2021.icon-main.56 | https://aclanthology.org/2021.icon-main.56.pdf | Prediction of Video Game Development Problems Based on Postmortems using Different Word Embedding Techniques | The interactive entertainment industry is being actively involved with the development, marketing and sale of video games in the past decade. The increasing interest in video games has led to an increase in video game development techniques and methods. It has emerged as an immensely large sector, and now it has grown ... | ['N L Bhanu Murthy', 'Lov Kumar', 'Anjali Goyal', 'Aman RAJ Singh', 'Anirudh A'] | null | null | null | null | icon-2021-12 | ['marketing'] | ['miscellaneous'] | [-1.10359862e-01 1.48178846e-01 2.41080046e-01 -4.16140594e-02
-1.01122297e-01 -6.12142324e-01 2.31682003e-01 3.39609981e-01
-1.66776642e-01 4.09975350e-01 2.29806211e-02 -4.84793812e-01
-2.58457363e-01 -7.69394755e-01 -2.02745318e-01 -2.73948491e-01
1.28774434e-01 2.52870172e-01 6.57746434e-01 -4.35788631... | [9.065300941467285, 6.536074161529541] |
e2927cb3-46e6-4baf-a1b8-75c56a960885 | mechanical-models-of-pattern-and-form-in | 2009.10953 | null | https://arxiv.org/abs/2009.10953v6 | https://arxiv.org/pdf/2009.10953v6.pdf | Mechanical models of pattern and form in biological tissues: the role of stress-strain constitutive equations | Mechanochemical models of pattern formation in biological tissues have been used to study a variety of biomedical systems and describe the physical interactions between cells and their local surroundings. These models generally consist of a balance equation for the cell density, one for the density of the extracellular... | ['Tommaso Lorenzi', 'Alf Gerisch', 'Mark A. J. Chaplain', 'Chiara Villa'] | 2020-09-23 | null | null | null | null | ['stress-strain-relation'] | ['miscellaneous'] | [ 3.90585475e-02 -7.88459778e-02 -1.01943433e-01 2.86477894e-01
5.65870702e-01 -3.95075411e-01 4.67797905e-01 4.10174340e-01
-2.31666148e-01 8.36780012e-01 -6.34815171e-02 -8.23366493e-02
-5.29162824e-01 -7.71629453e-01 -3.11743140e-01 -1.20276749e+00
-2.67339557e-01 5.78647435e-01 6.41303658e-01 -3.61883432... | [13.565567970275879, -3.027515172958374] |
7dcb5d02-4fe2-4381-8911-c0cd3b4b8073 | fault-diagnosis-for-pv-arrays-considering | 2304.06493 | null | https://arxiv.org/abs/2304.06493v1 | https://arxiv.org/pdf/2304.06493v1.pdf | Fault diagnosis for PV arrays considering dust impact based on transformed graphical feature of characteristic curves and convolutional neural network with CBAM modules | Various faults can occur during the operation of PV arrays, and both the dust-affected operating conditions and various diode configurations make the faults more complicated. However, current methods for fault diagnosis based on I-V characteristic curves only utilize partial feature information and often rely on calibr... | ['Zheng Qian', 'Hamidreza Zareipour', 'Qiang Sun', 'Lu Wei', 'Jiaqi Qu'] | 2023-03-24 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [ 3.88020128e-02 -9.21532273e-01 5.62275946e-01 -1.90934300e-01
-8.86487737e-02 -7.08715081e-01 2.56897420e-01 -9.20285359e-02
5.76622605e-01 5.88006139e-01 -3.53622228e-01 -5.20576000e-01
-5.99667847e-01 -9.65514541e-01 -6.87924862e-01 -1.12191391e+00
1.84260234e-02 4.90818545e-02 -2.68878877e-01 2.36273054... | [7.090937614440918, 2.080533266067505] |
ab4d9ff1-eaf2-4180-8d85-644c95c842c5 | a-multi-task-bert-model-for-schema-guided | 2207.00828 | null | https://arxiv.org/abs/2207.00828v1 | https://arxiv.org/pdf/2207.00828v1.pdf | A Multi-Task BERT Model for Schema-Guided Dialogue State Tracking | Task-oriented dialogue systems often employ a Dialogue State Tracker (DST) to successfully complete conversations. Recent state-of-the-art DST implementations rely on schemata of diverse services to improve model robustness and handle zero-shot generalization to new domains [1], however such methods [2, 3] typically re... | ['Alexandros Potamianos', 'Efthymios Georgiou', 'Eleftherios Kapelonis'] | 2022-07-02 | null | null | null | null | ['dialogue-state-tracking', 'zero-shot-slot-filling', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.08579481e-01 5.75939834e-01 -2.40521535e-01 -5.70898414e-01
-9.92195249e-01 -6.62579238e-01 1.08917844e+00 -1.42188057e-01
-2.30702743e-01 7.76575863e-01 4.96202528e-01 -4.45819259e-01
1.86851427e-01 -1.65522054e-01 -8.29010978e-02 -9.04952660e-02
7.47841001e-02 1.29966414e+00 6.96217239e-01 -7.55655050... | [12.847508430480957, 7.907779216766357] |
e8baf834-df85-4038-b748-aa1453db1002 | a-mixture-of-expert-approach-to-rl-based | 2206.00059 | null | https://arxiv.org/abs/2206.00059v1 | https://arxiv.org/pdf/2206.00059v1.pdf | A Mixture-of-Expert Approach to RL-based Dialogue Management | Despite recent advancements in language models (LMs), their application to dialogue management (DM) problems and ability to carry on rich conversations remain a challenge. We use reinforcement learning (RL) to develop a dialogue agent that avoids being short-sighted (outputting generic utterances) and maximizes overall... | ['Craig Boutilier', 'Mohammad Ghavamzadeh', 'MoonKyung Ryu', 'Ofir Nachum', 'Aza Tulepbergenov', 'Yinlam Chow'] | 2022-05-31 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [ 4.96041588e-02 9.31947827e-01 1.00346357e-01 -4.74901140e-01
-8.45795870e-01 -6.76195323e-01 9.54366326e-01 -2.78975517e-01
-2.85044968e-01 9.45168197e-01 5.50476015e-01 -4.11862105e-01
3.96213531e-01 -7.27173805e-01 -1.52565107e-01 -3.05858672e-01
2.00341940e-01 1.18569994e+00 -1.05181627e-01 -9.44267154... | [12.917275428771973, 8.066884994506836] |
82845cee-a335-4faf-8e5d-dcee45e126d4 | rethinking-the-protein-folding-problem-from-a | 2210.05004 | null | https://arxiv.org/abs/2210.05004v1 | https://arxiv.org/pdf/2210.05004v1.pdf | Rethinking the protein folding problem from a new perspective | One of the main concerns of Anfinsen was to reveal the connection between the amino acid sequence and their biologically active conformation. This search gave rise to two crucial questions in structural biology, namely, why the proteins fold and how a sequence encodes its folding. As to the why, he proposes a plausible... | ['Jorge A. Vila'] | 2022-10-10 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [ 2.83493102e-01 3.63150477e-01 -2.25967035e-01 -2.64676243e-01
-4.55910824e-02 -6.93361819e-01 2.48568147e-01 3.25956821e-01
-2.49833807e-01 9.12625492e-01 1.26834720e-01 -8.10860872e-01
-1.17641672e-01 -5.11918485e-01 -6.60135508e-01 -9.56269324e-01
1.05533861e-01 3.23478460e-01 1.11066535e-01 -5.22212207... | [4.781989097595215, 5.2525835037231445] |
0aff533d-c7bb-4310-8a38-b4f0935a35cb | sequence-learning-in-a-spiking-neuronal | 2211.16592 | null | https://arxiv.org/abs/2211.16592v1 | https://arxiv.org/pdf/2211.16592v1.pdf | Sequence learning in a spiking neuronal network with memristive synapses | Brain-inspired computing proposes a set of algorithmic principles that hold promise for advancing artificial intelligence. They endow systems with self learning capabilities, efficient energy usage, and high storage capacity. A core concept that lies at the heart of brain computation is sequence learning and prediction... | ['Dirk J. Wouters', 'Rainer Waser', 'Markus Diesmann', 'Tom Tetzlaff', 'Sebastian Siegel', 'Younes Bouhadjar'] | 2022-11-29 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 5.47559619e-01 -3.75339836e-01 1.43278256e-01 3.78537327e-02
6.25608504e-01 -3.68603468e-01 7.56729126e-01 2.43863434e-01
-6.40423119e-01 8.61005664e-01 -1.34451360e-01 -2.44280234e-01
-7.90721998e-02 -1.06782842e+00 -8.62464070e-01 -1.05145609e+00
-2.70915311e-02 1.28378689e-01 9.68267441e-01 -4.73954618... | [8.181904792785645, 2.5354785919189453] |
e1930f7b-eaea-4253-acbe-af26a027163c | d-2lv-a-data-driven-and-local-verification | 2111.0709 | null | https://arxiv.org/abs/2111.07090v2 | https://arxiv.org/pdf/2111.07090v2.pdf | D$^2$LV: A Data-Driven and Local-Verification Approach for Image Copy Detection | Image copy detection is of great importance in real-life social media. In this paper, a data-driven and local-verification (D$^2$LV) approach is proposed to compete for Image Similarity Challenge: Matching Track at NeurIPS'21. In D$^2$LV, unsupervised pre-training substitutes the commonly-used supervised one. When trai... | ['Yi Yang', 'Weipu Zhang', 'Yifan Sun', 'Wenhao Wang'] | 2021-11-13 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 3.34190398e-01 -1.79165632e-01 -3.26188177e-01 -6.60744488e-01
-1.07830870e+00 -2.52026826e-01 8.73731077e-01 1.60626024e-01
-7.28512645e-01 2.23367199e-01 1.11811168e-01 -3.18284519e-03
1.05912089e-01 -4.43585753e-01 -8.84097159e-01 -3.44602287e-01
-9.89267379e-02 2.59973526e-01 3.10682863e-01 -2.30999261... | [10.599766731262207, 0.8624497056007385] |
e5f12b88-f56c-41e3-9cca-7729ac79c494 | pixel-wise-deep-image-stitching | 2112.06171 | null | https://arxiv.org/abs/2112.06171v1 | https://arxiv.org/pdf/2112.06171v1.pdf | Pixel-wise Deep Image Stitching | Image stitching aims at stitching the images taken from different viewpoints into an image with a wider field of view. Existing methods warp the target image to the reference image using the estimated warp function, and a homography is one of the most commonly used warping functions. However, when images have large par... | ['Kuk-Jin Yoon', 'Wooseong Jeong', 'Youngho Yoon', 'Yoonsu Kang', 'Hyeonseong Kim', 'Hyeokjun Kweon'] | 2021-12-12 | null | null | null | null | ['image-stitching', 'homography-estimation'] | ['computer-vision', 'computer-vision'] | [ 4.69589710e-01 -2.71001041e-01 1.50110871e-02 1.04033418e-01
-3.26903611e-01 -5.78096807e-01 5.49881041e-01 -4.83486623e-01
-2.31261030e-01 5.72635710e-01 1.95983112e-01 1.24065645e-01
-1.96196139e-03 -6.67597055e-01 -7.78062582e-01 -9.49368894e-01
4.39108253e-01 1.42078057e-01 3.98392677e-01 -1.05667278... | [9.373757362365723, -2.3346121311187744] |
c1c2a842-41dc-4438-bd61-07fc71e3e5a7 | a-new-dataset-and-model-for-learning-to | 1805.07952 | null | http://arxiv.org/abs/1805.07952v1 | http://arxiv.org/pdf/1805.07952v1.pdf | A new dataset and model for learning to understand navigational instructions | In this paper, we present a state-of-the-art model and introduce a new
dataset for grounded language learning. Our goal is to develop a model that can
learn to follow new instructions given prior instruction-perception-action
examples. We based our work on the SAIL dataset which consists of navigational
instructions an... | ['Ozan Arkan Can', 'Deniz Yuret'] | 2018-05-21 | null | null | null | null | ['grounded-language-learning'] | ['natural-language-processing'] | [ 1.92895293e-01 3.20001155e-01 1.34733677e-01 -5.76852798e-01
-8.30204070e-01 -6.68950915e-01 8.08390141e-01 3.86361003e-01
-7.65679717e-01 7.26931691e-01 2.70929128e-01 -5.13239622e-01
-9.33439285e-02 -8.36236298e-01 -1.13452518e+00 -3.30274343e-01
-3.88401419e-01 8.28940213e-01 6.00613594e-01 -6.27222717... | [4.317961692810059, 0.8304154872894287] |
6b79a8d2-c2d2-4c0a-ac4e-5d43acdf04b8 | planning-with-large-language-models-via | 2211.09935 | null | https://arxiv.org/abs/2211.09935v1 | https://arxiv.org/pdf/2211.09935v1.pdf | Planning with Large Language Models via Corrective Re-prompting | Extracting the common sense knowledge present in Large Language Models (LLMs) offers a path to designing intelligent, embodied agents. Related works have queried LLMs with a wide-range of contextual information, such as goals, sensor observations and scene descriptions, to generate high-level action plans for specific ... | ['Stefanie Tellex', 'David Paulius', 'Ifrah Idrees', 'Eric Rosen', 'Vanya Cohen', 'Shreyas Sundara Raman'] | 2022-11-17 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 8.63897324e-01 4.38469082e-01 2.42146645e-02 -2.59070724e-01
-6.21972263e-01 -8.37687612e-01 7.91601062e-01 3.64243299e-01
-4.23234552e-01 7.22923458e-01 3.83423686e-01 -5.23690522e-01
-1.27315789e-01 -8.60244632e-01 -7.41394758e-01 -1.55000165e-01
8.54583606e-02 2.50396430e-01 4.73899066e-01 -1.76347271... | [4.4188337326049805, 0.9729396104812622] |
5faf8479-767b-4d39-84d7-7a1d3d3834f8 | on-the-impact-of-speech-recognition-errors-in | 2209.12944 | null | https://arxiv.org/abs/2209.12944v1 | https://arxiv.org/pdf/2209.12944v1.pdf | On the Impact of Speech Recognition Errors in Passage Retrieval for Spoken Question Answering | Interacting with a speech interface to query a Question Answering (QA) system is becoming increasingly popular. Typically, QA systems rely on passage retrieval to select candidate contexts and reading comprehension to extract the final answer. While there has been some attention to improving the reading comprehension p... | ['Evangelos Kanoulas', 'Svitlana Vakulenko', 'Georgios Sidiropoulos'] | 2022-09-26 | null | null | null | null | ['passage-ranking', 'passage-retrieval'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.04147416e-01 1.43782347e-01 3.43882024e-01 -3.71998042e-01
-1.73364365e+00 -8.12135100e-01 5.94527483e-01 2.70898432e-01
-5.18570125e-01 4.32987481e-01 8.04181576e-01 -5.78425586e-01
7.30565609e-03 -4.74778682e-01 -5.54423034e-01 -8.81589800e-02
4.45399553e-01 5.08431137e-01 4.84551787e-01 -7.87925065... | [11.551532745361328, 7.998539447784424] |
9b6b1756-b83b-4e71-8337-f957ac4a9dab | shape-illumination-and-reflectance-from | 2010.03592 | null | https://arxiv.org/abs/2010.03592v1 | https://arxiv.org/pdf/2010.03592v1.pdf | Shape, Illumination, and Reflectance from Shading | A fundamental problem in computer vision is that of inferring the intrinsic, 3D structure of the world from flat, 2D images of that world. Traditional methods for recovering scene properties such as shape, reflectance, or illumination rely on multiple observations of the same scene to overconstrain the problem. Recover... | ['Jitendra Malik', 'Jonathan T. Barron'] | 2020-10-07 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 7.06005156e-01 2.04263619e-04 2.49923348e-01 -5.95421970e-01
-2.73510993e-01 -5.33429384e-01 6.52731717e-01 -2.40890354e-01
-1.45771028e-02 5.83556771e-01 -1.58431739e-01 -2.14680076e-01
3.08615360e-02 -4.19684380e-01 -6.60532713e-01 -7.47181237e-01
3.77166867e-01 5.01517773e-01 1.97852284e-01 -1.14291020... | [9.86068344116211, -2.9506654739379883] |
84a1272e-32fc-4692-90ef-71a70b70d0ed | text-visual-prompting-for-efficient-2d | 2303.04995 | null | https://arxiv.org/abs/2303.04995v2 | https://arxiv.org/pdf/2303.04995v2.pdf | Text-Visual Prompting for Efficient 2D Temporal Video Grounding | In this paper, we study the problem of temporal video grounding (TVG), which aims to predict the starting/ending time points of moments described by a text sentence within a long untrimmed video. Benefiting from fine-grained 3D visual features, the TVG techniques have achieved remarkable progress in recent years. Howev... | ['Ke Ding', 'Sijia Liu', 'Jinghan Jia', 'Xin Chen', 'Yimeng Zhang'] | 2023-03-09 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Text-Visual_Prompting_for_Efficient_2D_Temporal_Video_Grounding_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Text-Visual_Prompting_for_Efficient_2D_Temporal_Video_Grounding_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-grounding', 'visual-prompting'] | ['computer-vision', 'computer-vision'] | [ 1.09195277e-01 -2.61329859e-01 -2.52433836e-01 -2.69087762e-01
-8.25669587e-01 -5.09526610e-01 6.94059491e-01 -6.60959259e-02
-4.35316771e-01 2.40870625e-01 4.07585084e-01 -3.83419752e-01
1.92096859e-01 -4.04889762e-01 -1.22178888e+00 -6.17274523e-01
-2.94765413e-01 -2.67352629e-02 2.48592421e-02 3.56880762... | [9.881659507751465, 0.7329277992248535] |
6aa38136-2522-4b42-b0ae-39c35dfb0f37 | incorporating-background-knowledge-into-video | null | null | https://aclanthology.org/D18-1433 | https://aclanthology.org/D18-1433.pdf | Incorporating Background Knowledge into Video Description Generation | Most previous efforts toward video captioning focus on generating generic descriptions, such as, {``}A man is talking.{''} We collect a news video dataset to generate enriched descriptions that include important background knowledge, such as named entities and related events, which allows the user to fully understand t... | ['Shih-Fu Chang', 'Heng Ji', 'Clare Voss', 'Mohit Bansal', 'Spencer Whitehead'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['video-description'] | ['computer-vision'] | [ 3.09696466e-01 2.85241634e-01 -5.38825214e-01 -6.33692682e-01
-1.03536534e+00 -6.31007433e-01 7.69762158e-01 1.96052656e-01
-3.75069141e-01 8.67171288e-01 1.01317942e+00 3.58008415e-01
1.55771285e-01 -5.52073896e-01 -1.18161786e+00 -4.54959810e-01
-3.12037021e-01 3.40941638e-01 4.68733460e-01 -5.19390106... | [10.515061378479004, 0.6590191721916199] |
b546e1c7-528a-4374-9d48-3add488a8ce6 | reliability-check-an-analysis-of-gpt-3-s | 2306.06199 | null | https://arxiv.org/abs/2306.06199v1 | https://arxiv.org/pdf/2306.06199v1.pdf | Reliability Check: An Analysis of GPT-3's Response to Sensitive Topics and Prompt Wording | Large language models (LLMs) have become mainstream technology with their versatile use cases and impressive performance. Despite the countless out-of-the-box applications, LLMs are still not reliable. A lot of work is being done to improve the factual accuracy, consistency, and ethical standards of these models throug... | ['Daniel G. Brown', 'Aisha Khatun'] | 2023-06-09 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [-3.07799578e-01 3.53950441e-01 -4.62005973e-01 -6.64112985e-01
-8.46241355e-01 -7.18562543e-01 4.65178430e-01 2.89739162e-01
-2.39491627e-01 5.56372702e-01 5.82247913e-01 -8.69151890e-01
3.11033577e-02 -2.76521027e-01 -7.01567411e-01 -1.30626902e-01
5.57852864e-01 3.20267916e-01 8.07637647e-02 -2.63020128... | [9.943741798400879, 7.889563083648682] |
b5983da4-1266-4e11-ba0f-5c337ddadc71 | learning-to-re-weight-examples-with-optimal | 2208.02951 | null | https://arxiv.org/abs/2208.02951v1 | https://arxiv.org/pdf/2208.02951v1.pdf | Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification | Imbalanced data pose challenges for deep learning based classification models. One of the most widely-used approaches for tackling imbalanced data is re-weighting, where training samples are associated with different weights in the loss function. Most of existing re-weighting approaches treat the example weights as the... | ['Hongyuan Zha', 'Mingyuan Zhou', 'He Zhao', 'Meixi Zheng', 'Zhuo Li', 'Dandan Guo'] | 2022-08-05 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [-1.00973636e-01 -2.08542094e-01 -3.91592801e-01 -5.98462939e-01
-7.05665350e-01 5.20333694e-03 2.35518292e-01 5.27062535e-01
-6.14287913e-01 5.24781942e-01 -1.61559820e-01 1.40524775e-01
-4.21365768e-01 -9.59860384e-01 -6.39607906e-01 -1.05438483e+00
1.41324759e-01 8.24885786e-01 -1.13669727e-02 -1.57747209... | [9.121203422546387, 3.8666651248931885] |
d9a54b1f-e363-4cd3-b6ef-aed6d020decd | audio-text-sentiment-analysis-using-deep | 1904.08138 | null | https://arxiv.org/abs/1904.08138v5 | https://arxiv.org/pdf/1904.08138v5.pdf | Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis | Sentiment analysis, mostly based on text, has been rapidly developing in the last decade and has attracted widespread attention in both academia and industry. However, the information in the real world usually comes from multiple modalities, such as audio and text. Therefore, in this paper, based on audio and text, we ... | ['Ziqian Luo', 'Feiyang Chen', 'Dengfeng Ke', 'Yanyan Xu'] | 2019-04-17 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-1.29962703e-02 -4.94085550e-01 1.16759382e-01 -5.11688471e-01
-1.09031856e+00 -2.99459904e-01 5.01644671e-01 1.89610898e-01
-3.99956435e-01 4.61691022e-01 5.24020493e-01 1.17894344e-01
2.74764970e-02 -3.33043545e-01 -4.63865519e-01 -7.65453756e-01
3.66313279e-01 -1.35811970e-01 -3.57904620e-02 -5.14840305... | [13.231444358825684, 5.130100250244141] |
d96ca787-6393-43e3-ac7b-b1925b670e5b | material-classification-using-neural-networks | 1710.06854 | null | http://arxiv.org/abs/1710.06854v1 | http://arxiv.org/pdf/1710.06854v1.pdf | Material Classification using Neural Networks | The recognition and classification of the diversity of materials that exist
in the environment around us are a key visual competence that computer vision
systems focus on in recent years. Understanding the identification of materials
in distinct images involves a deep process that has made usage of the recent
progress ... | ['Anca Sticlaru'] | 2017-10-17 | null | null | null | null | ['material-classification'] | ['computer-vision'] | [ 3.14196587e-01 -2.63540089e-01 1.51648611e-01 -2.85923660e-01
-9.75955650e-02 -2.97341406e-01 6.17435336e-01 3.57099771e-02
-3.37213188e-01 4.88836974e-01 -2.28081524e-01 -2.01216966e-01
-3.66095543e-01 -1.01055467e+00 -5.74680090e-01 -9.73841250e-01
4.40710783e-02 2.17229530e-01 3.69902849e-01 -4.88229185... | [10.131781578063965, -0.19076623022556305] |
d3058ca7-847c-48eb-a4c0-ce578aca06c8 | what-food-do-we-tweet-about-on-a-rainy-day | 2304.05041 | null | https://arxiv.org/abs/2304.05041v1 | https://arxiv.org/pdf/2304.05041v1.pdf | What Food Do We Tweet about on a Rainy Day? | Food choice is a complex phenomenon shaped by factors such as taste, ambience, culture or weather. In this paper, we explore food-related tweeting in different weather conditions. We inspect a Latvian food tweet dataset spanning the past decade in conjunction with a weather observation dataset consisting of average tem... | ['Matīss Rikters', 'Maija Kāle'] | 2023-04-11 | null | null | null | null | ['culture'] | ['speech'] | [-1.92576408e-01 -3.74427021e-01 -8.90714943e-01 -6.74121857e-01
4.50736731e-01 -7.26450920e-01 3.38155627e-01 1.29757988e+00
-2.93718517e-01 2.04492763e-01 9.68509555e-01 -2.07992330e-01
5.35717420e-02 -1.34453583e+00 -4.91697639e-01 -4.38084990e-01
-3.34227920e-01 -2.04706609e-01 -1.62978649e-01 -7.60568857... | [11.532064437866211, 4.517716407775879] |
287c55ed-8ada-4e6c-9d64-86453c834388 | snlp-at-textgraphs-2022-shared-task | null | null | https://aclanthology.org/2022.textgraphs-1.13 | https://aclanthology.org/2022.textgraphs-1.13.pdf | SNLP at TextGraphs 2022 Shared Task: Unsupervised Natural Language Premise Selection in Mathematical Texts Using Sentence-MPNet | This paper describes our system for the submission to the TextGraphs 2022 shared task at COLING 2022: Natural Language Premise Selection (NLPS) from mathematical texts. The task of NLPS is about selecting mathematical statements called premises in a knowledge base written in natural language and mathematical formulae t... | ['Ahmed Zahran', 'Evangelos Milios', 'Rosane Minghim', 'Haseeb Younis', 'Provia Kadusabe', 'Paul Trust'] | null | null | null | null | coling-textgraphs-2022-10 | ['mathematical-proofs'] | ['miscellaneous'] | [ 2.43914530e-01 1.91352323e-01 1.56760097e-01 -3.04000467e-01
-9.44250762e-01 -8.86023045e-01 1.16643119e+00 8.72691810e-01
-5.39017797e-01 8.29983830e-01 2.68826038e-01 -8.39305580e-01
-4.84190702e-01 -9.95227754e-01 -1.10788143e+00 -1.65139586e-01
-7.36193806e-02 3.52706492e-01 2.19689265e-01 -2.03634501... | [9.42333984375, 7.302882194519043] |
00817363-a830-482b-be46-0e394b1083bd | summ-n-a-multi-stage-summarization-framework | 2110.1015 | null | https://arxiv.org/abs/2110.10150v2 | https://arxiv.org/pdf/2110.10150v2.pdf | Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents | Text summarization helps readers capture salient information from documents, news, interviews, and meetings. However, most state-of-the-art pretrained language models (LM) are unable to efficiently process long text for many summarization tasks. In this paper, we propose Summ$^N$, a simple, flexible, and effective mult... | ['Rui Zhang', 'Dragomir Radev', 'Ahmed H. Awadallah', 'Budhaditya Deb', 'Chenguang Zhu', 'Chen Henry Wu', 'Ziming Mao', 'Ansong Ni', 'Yusen Zhang'] | 2021-10-16 | null | https://aclanthology.org/2022.acl-long.112 | https://aclanthology.org/2022.acl-long.112.pdf | acl-2022-5 | ['meeting-summarization'] | ['natural-language-processing'] | [ 2.8204560e-01 1.3943373e-01 -3.5472575e-01 -2.4079941e-01
-1.5353580e+00 -6.6102844e-01 4.6960136e-01 3.6317137e-01
-4.2390350e-01 9.4018483e-01 8.7537348e-01 -1.9978207e-01
3.6475915e-02 -4.1530064e-01 -5.3248632e-01 -2.0450053e-01
2.5521982e-01 6.2785906e-01 2.1064240e-01 -2.8288296e-01
5.3335166e-01... | [12.539617538452148, 9.435843467712402] |
f705cbc6-d6bf-4219-b997-c9a7387010b0 | deep-dyna-q-integrating-planning-for-task | 1801.06176 | null | http://arxiv.org/abs/1801.06176v3 | http://arxiv.org/pdf/1801.06176v3.pdf | Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning | Training a task-completion dialogue agent via reinforcement learning (RL) is
costly because it requires many interactions with real users. One common
alternative is to use a user simulator. However, a user simulator usually lacks
the language complexity of human interlocutors and the biases in its design may
tend to de... | ['Shang-Yu Su', 'Kam-Fai Wong', 'Jingjing Liu', 'Jianfeng Gao', 'Xiujun Li', 'Baolin Peng'] | 2018-01-18 | deep-dyna-q-integrating-planning-for-task-1 | https://aclanthology.org/P18-1203 | https://aclanthology.org/P18-1203.pdf | acl-2018-7 | ['task-completion-dialogue-policy-learning'] | ['natural-language-processing'] | [-2.18229905e-01 6.37192965e-01 7.39067867e-02 -1.73694491e-01
-5.69357812e-01 -8.23587418e-01 7.93236852e-01 -1.37868628e-01
-6.90566838e-01 9.45299387e-01 2.36809134e-01 -1.96521133e-01
3.84923160e-01 -4.84848350e-01 -3.71471763e-01 -2.87153304e-01
8.36539492e-02 9.73138452e-01 4.58774306e-02 -6.94294274... | [13.064281463623047, 8.043342590332031] |
1aa0bbf3-c744-4846-bc7b-c10a0869b95b | concad-contrastive-learning-based-cross | 2105.03037 | null | https://arxiv.org/abs/2105.03037v1 | https://arxiv.org/pdf/2105.03037v1.pdf | ConCAD: Contrastive Learning-based Cross Attention for Sleep Apnea Detection | With recent advancements in deep learning methods, automatically learning deep features from the original data is becoming an effective and widespread approach. However, the hand-crafted expert knowledge-based features are still insightful. These expert-curated features can increase the model's generalization and remin... | ['Fenglong Ma', 'Guanjie Huang'] | 2021-05-07 | null | null | null | null | ['sleep-apnea-detection'] | ['medical'] | [ 6.52392954e-02 -1.35535598e-01 -2.07495540e-01 -6.15018904e-01
-6.62302196e-01 -7.33430237e-02 3.09258848e-02 4.66282070e-01
-7.07092166e-01 6.83274806e-01 1.23663045e-01 -7.80801624e-02
-4.24785644e-01 -6.60721779e-01 -3.46376657e-01 -7.73776412e-01
-1.06807061e-01 1.56189039e-01 1.28649577e-01 -6.18288890... | [14.876240730285645, -2.1502602100372314] |
6258ec40-dc80-4268-98b7-6665abf080ea | a-composite-t60-regression-and-classification | 2302.04932 | null | https://arxiv.org/abs/2302.04932v1 | https://arxiv.org/pdf/2302.04932v1.pdf | A Composite T60 Regression and Classification Approach for Speech Dereverberation | Dereverberation is often performed directly on the reverberant audio signal, without knowledge of the acoustic environment. Reverberation time, T60, however, is an essential acoustic factor that reflects how reverberation may impact a signal. In this work, we propose to perform dereverberation while leveraging key acou... | ['Donald S. Williamson', 'Yuchen Liu', 'Yuying Li'] | 2023-02-09 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [-4.26875018e-02 -9.00230169e-01 7.48068750e-01 -1.22618586e-01
-1.16917157e+00 -6.69042468e-01 1.28644735e-01 1.39778838e-01
-2.64752626e-01 5.31040549e-01 3.89225990e-01 -2.98593432e-01
-1.62267312e-01 -4.31512207e-01 -5.30742168e-01 -9.15837049e-01
-4.08893377e-01 -4.72955704e-01 -9.25006270e-02 -2.15784922... | [15.076435089111328, 5.964961051940918] |
393b9a15-cdc9-43d6-b570-3f1265591c01 | using-convolution-neural-network-with-bert | null | null | https://aclanthology.org/2022.lrec-1.783 | https://aclanthology.org/2022.lrec-1.783.pdf | Using Convolution Neural Network with BERT for Stance Detection in Vietnamese | Stance detection is the task of automatically eliciting stance information towards a specific claim made by a primary author. While most studies have been done for high-resource languages, this work is dedicated to a low-resource language, namely Vietnamese. In this paper, we propose an architecture using transformers ... | ['Bach Xuan Ngo', 'Anh Cong Phung', 'Oanh Tran'] | null | null | null | null | lrec-2022-6 | ['stance-detection'] | ['natural-language-processing'] | [ 1.23400711e-01 7.16655701e-02 -5.54491222e-01 -1.74718738e-01
-1.07993269e+00 -4.88166332e-01 9.54133034e-01 1.39370471e-01
-8.44082952e-01 7.48061419e-01 7.34183490e-01 -5.00822663e-01
4.93966758e-01 -8.29405665e-01 -3.73154342e-01 -6.60984099e-01
3.50546181e-01 4.74622011e-01 2.54776776e-01 -4.54928905... | [8.794142723083496, 10.147875785827637] |
dd5651b4-e6b2-4b5b-8b14-1a771e85d852 | pastnet-introducing-physical-inductive-biases | 2305.11421 | null | https://arxiv.org/abs/2305.11421v2 | https://arxiv.org/pdf/2305.11421v2.pdf | PastNet: Introducing Physical Inductive Biases for Spatio-temporal Video Prediction | In this paper, we investigate the challenge of spatio-temporal video prediction, which involves generating future videos based on historical data streams. Existing approaches typically utilize external information such as semantic maps to enhance video prediction, which often neglect the inherent physical knowledge emb... | ['Wei Xiong', 'Haixin Wang', 'Xian-Sheng Hua', 'Chong Chen', 'Xiao Luo', 'Fan Xu', 'Hao Wu'] | 2023-05-19 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 9.59923416e-02 -2.41235688e-01 -8.44007134e-02 -1.16280213e-01
-3.34769249e-01 -8.90259724e-03 5.22199214e-01 -3.69046241e-01
-1.93514735e-01 7.50298142e-01 3.11021954e-01 -5.63836321e-02
-1.75279096e-01 -9.53104138e-01 -9.34571803e-01 -6.45736933e-01
-2.78290898e-01 -3.23318183e-01 4.52592641e-01 3.12547758... | [10.566471099853516, -1.0261040925979614] |
124bd220-5b36-4532-8170-27b536190237 | data-resources-for-structural-bioinformatics | 2307.02171 | null | https://arxiv.org/abs/2307.02171v2 | https://arxiv.org/pdf/2307.02171v2.pdf | Data Resources for Structural Bioinformatics | While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previo... | ['Halima Mouhib', 'K. Anton Feenstra', 'Sanne Abeln', 'Olga Ivanova', 'Bas Stringer', 'Jose Gavaldá-Garciá'] | 2023-07-05 | null | null | null | null | ['protein-structure-prediction'] | ['miscellaneous'] | [ 4.33339119e-01 -2.10471183e-01 -2.35107571e-01 -2.45717421e-01
-4.40404803e-01 -6.18286431e-01 2.74709873e-02 5.04735231e-01
-2.38900453e-01 1.34694052e+00 -2.96744734e-01 -7.41364598e-01
7.02822057e-04 -3.92796814e-01 -5.63364863e-01 -1.21921206e+00
-1.00749008e-01 6.65700912e-01 3.13056916e-01 -4.92557824... | [4.738345623016357, 5.319107532501221] |
b6260780-1607-49de-915e-3308e755cf01 | speaker-change-detection-for-transformer | 2302.08549 | null | https://arxiv.org/abs/2302.08549v1 | https://arxiv.org/pdf/2302.08549v1.pdf | Speaker Change Detection for Transformer Transducer ASR | Speaker change detection (SCD) is an important feature that improves the readability of the recognized words from an automatic speech recognition (ASR) system by breaking the word sequence into paragraphs at speaker change points. Existing SCD solutions either require additional ensemble for the time based decisions an... | ['Jinyu Li', 'Xiong Xiao', 'Min Hu', 'Zhuo Chen', 'Jian Wu'] | 2023-02-16 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 4.08724666e-01 -2.34521143e-02 -8.68658870e-02 -5.52613556e-01
-1.26899946e+00 -6.34566188e-01 6.11567378e-01 -7.80447274e-02
-3.05842638e-01 1.89483896e-01 4.12929028e-01 -6.34363651e-01
4.46444094e-01 -1.91185489e-01 -4.90224689e-01 -6.18016124e-01
3.10964972e-01 2.44068317e-02 3.92357826e-01 -3.09868634... | [14.562073707580566, 6.361776828765869] |
8d56dc9f-e473-4414-865a-2dd0682deb89 | euclidnet-deep-visual-reasoning-for | 2301.13007 | null | https://arxiv.org/abs/2301.13007v1 | https://arxiv.org/pdf/2301.13007v1.pdf | EuclidNet: Deep Visual Reasoning for Constructible Problems in Geometry | In this paper, we present a deep learning-based framework for solving geometric construction problems through visual reasoning, which is useful for automated geometry theorem proving. Constructible problems in geometry often ask for the sequence of straightedge-and-compass constructions to construct a given goal given ... | ['Chee Wei Tan', 'Xintong Qi', 'Man Fai Wong'] | 2022-12-27 | null | null | null | null | ['visual-reasoning', 'automated-theorem-proving', 'automated-theorem-proving', 'visual-reasoning'] | ['computer-vision', 'miscellaneous', 'reasoning', 'reasoning'] | [-6.22245558e-02 5.22125542e-01 3.18868518e-01 3.54706636e-03
-5.97680449e-01 -1.09137309e+00 3.48745048e-01 3.69296908e-01
1.71397805e-01 4.22213525e-01 -7.45780915e-02 -1.13140249e+00
-2.28496492e-01 -1.18861473e+00 -1.07293963e+00 -3.05103838e-01
-2.67490178e-01 6.52740240e-01 -2.51053832e-02 -4.32148606... | [9.094038009643555, 7.32992696762085] |
abb80976-1028-45bb-ab8f-28cd10fdbafd | some-of-the-variables-some-of-the-parameters | 2304.14214 | null | https://arxiv.org/abs/2304.14214v1 | https://arxiv.org/pdf/2304.14214v1.pdf | Some of the variables, some of the parameters, some of the times, with some physics known: Identification with partial information | Experimental data is often comprised of variables measured independently, at different sampling rates (non-uniform ${\Delta}$t between successive measurements); and at a specific time point only a subset of all variables may be sampled. Approaches to identifying dynamical systems from such data typically use interpolat... | ['Ioannis G. Kevrekidis', 'Michael Betenbaugh', 'Jose L. Avalos', 'Tianqi Cui', 'Tom S. Bertalan', 'Saurabh Malani'] | 2023-04-27 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [ 3.29438001e-01 -1.43756688e-01 -7.24014118e-02 -9.80496630e-02
-4.73347932e-01 -4.68938202e-01 3.77360493e-01 4.41352874e-01
-6.50765538e-01 1.50018847e+00 -7.01923013e-01 -5.17445803e-01
-5.36912978e-01 -9.14291799e-01 -1.13519394e+00 -9.75439250e-01
-3.02809328e-01 6.08099341e-01 -3.91326040e-01 1.94056556... | [6.469693660736084, 3.5164337158203125] |
0cecaaaa-7811-427a-8c6b-29b9e6a5411f | 190807644 | 1908.07644 | null | https://arxiv.org/abs/1908.07644v3 | https://arxiv.org/pdf/1908.07644v3.pdf | Saccader: Improving Accuracy of Hard Attention Models for Vision | Although deep convolutional neural networks achieve state-of-the-art performance across nearly all image classification tasks, their decisions are difficult to interpret. One approach that offers some level of interpretability by design is \textit{hard attention}, which uses only relevant portions of the image. However... | ['Gamaleldin F. Elsayed', 'Simon Kornblith', 'Quoc V. Le'] | 2019-08-20 | saccader-improving-accuracy-of-hard-attention | http://papers.nips.cc/paper/8359-saccader-improving-accuracy-of-hard-attention-models-for-vision | http://papers.nips.cc/paper/8359-saccader-improving-accuracy-of-hard-attention-models-for-vision.pdf | neurips-2019-12 | ['hard-attention'] | ['methodology'] | [ 1.26299649e-01 5.32632530e-01 -5.47145724e-01 -5.10185242e-01
-6.01248145e-01 -4.39085960e-01 6.00004494e-01 -1.61352716e-02
-8.25009465e-01 5.81239164e-01 2.05026548e-02 -5.97373605e-01
7.10736290e-02 -3.65223557e-01 -9.31310773e-01 -2.29696482e-01
3.61063153e-01 3.86333287e-01 5.26066795e-02 -1.49634749... | [9.645176887512207, 1.7174293994903564] |
cc879adc-8359-4733-a4b5-9b288368d34f | reconstructing-spectral-functions-via | 2111.1476 | null | https://arxiv.org/abs/2111.14760v3 | https://arxiv.org/pdf/2111.14760v3.pdf | Reconstructing spectral functions via automatic differentiation | Reconstructing spectral functions from Euclidean Green's functions is an important inverse problem in many-body physics. However, the inversion is proved to be ill-posed in the realistic systems with noisy Green's functions. In this Letter, we propose an automatic differentiation(AD) framework as a generic tool for the... | ['Kai Zhou', 'Shuzhe Shi', 'Lingxiao Wang'] | 2021-11-29 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 2.81288683e-01 1.84682012e-01 2.73640543e-01 -2.64857531e-01
-4.84220892e-01 5.54068536e-02 1.33351341e-01 -2.55097300e-01
-6.66941941e-01 1.10984778e+00 2.88512278e-02 2.78758556e-02
-4.75962073e-01 -7.02634573e-01 -5.14820039e-01 -1.32509077e+00
-4.98136654e-02 2.62389392e-01 -1.12188295e-01 -3.01053315... | [12.054479598999023, -2.4909660816192627] |
73868239-cad9-4c46-8a98-a534026250b3 | generalizable-prediction-of-academic | 1912.00463 | null | https://arxiv.org/abs/1912.00463v1 | https://arxiv.org/pdf/1912.00463v1.pdf | Generalizable prediction of academic performance from short texts on social media | It has already been established that digital traces can be used to predict various human attributes. In most cases, however, predictive models rely on features that are specific to a particular source of digital trace data. In contrast, short texts written by users $-$ tweets, posts, or comments $-$ are ubiquitous acro... | ['Ivan Smirnov'] | 2019-12-01 | null | null | null | null | ['small-data'] | ['computer-vision'] | [-2.57079959e-01 2.15144902e-01 -3.08473468e-01 -3.30920517e-01
-7.97381997e-01 -4.68146682e-01 8.86552036e-01 1.09331703e+00
-5.75461388e-01 5.80860436e-01 7.11505771e-01 -4.90308732e-01
-3.27867329e-01 -1.21668875e+00 -5.95162690e-01 -3.02541554e-02
2.99413055e-01 2.00311244e-01 2.71347433e-01 -3.65174443... | [9.322514533996582, 9.989459037780762] |
71febb9d-b483-4fa0-9e29-5e9288b96f80 | lat-latent-translation-with-cycle-consistency | 2207.04858 | null | https://arxiv.org/abs/2207.04858v2 | https://arxiv.org/pdf/2207.04858v2.pdf | LaT: Latent Translation with Cycle-Consistency for Video-Text Retrieval | Video-text retrieval is a class of cross-modal representation learning problems, where the goal is to select the video which corresponds to the text query between a given text query and a pool of candidate videos. The contrastive paradigm of vision-language pretraining has shown promising success with large-scale datas... | ['Lele Cheng', 'Xiaofeng Guo', 'Mengying Hu', 'Haofan Wang', 'Feiyue Ni', 'Chunhui Liu', 'Jinbin Bai'] | 2022-07-11 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [ 2.27551684e-01 -4.26985711e-01 -2.72626698e-01 -3.33983839e-01
-9.58016753e-01 -5.17692983e-01 6.85645282e-01 -2.96206713e-01
-4.40935552e-01 3.86797518e-01 2.21722826e-01 -1.70841873e-01
-6.93596676e-02 -5.58589697e-01 -9.25715506e-01 -7.09012628e-01
1.89556614e-01 3.51511896e-01 1.80938199e-01 -2.08097789... | [10.282781600952148, 0.9867562055587769] |
6caed314-3df7-4c01-9a32-d189ffa1dc5e | deep-learning-serves-traffic-safety-analysis | 2203.10939 | null | https://arxiv.org/abs/2203.10939v2 | https://arxiv.org/pdf/2203.10939v2.pdf | Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review | This paper explores Deep Learning (DL) methods that are used or have the potential to be used for traffic video analysis, emphasizing driving safety for both Autonomous Vehicles (AVs) and human-operated vehicles. We present a typical processing pipeline, which can be used to understand and interpret traffic videos by e... | ['Hongbin Yu', 'Yan Chen', 'Brendan Russo', 'Hao Wang', 'Huayu Li', 'Xiwen Chen', 'Abolfazl Razi'] | 2022-03-07 | null | null | null | null | ['video-stabilization', 'video-enhancement'] | ['computer-vision', 'computer-vision'] | [-2.09648892e-01 -2.80391991e-01 -2.61267245e-01 -5.41301250e-01
-3.97742510e-01 -2.44209096e-01 3.88322622e-01 9.43532437e-02
-4.76571202e-01 3.50592077e-01 -2.21185222e-01 -8.43213737e-01
-2.84003559e-02 -7.77803183e-01 -5.18642187e-01 -6.38497770e-01
-9.75063816e-02 3.57448280e-01 7.94388473e-01 -3.94797713... | [7.702057838439941, -0.5402663946151733] |
cd13c24e-756d-4f16-900a-5e4de2b780c3 | real-time-visual-tracking-and-identification | 1810.06411 | null | http://arxiv.org/abs/1810.06411v2 | http://arxiv.org/pdf/1810.06411v2.pdf | Real-Time Visual Tracking and Identification for a Team of Homogeneous Humanoid Robots | The use of a team of humanoid robots to collaborate in completing a task is
an increasingly important field of research. One of the challenges in achieving
collaboration, is mutual identification and tracking of the robots. This work
presents a real-time vision-based approach to the detection and tracking of
robots of ... | ['Hafez Farazi', 'Sven Behnke'] | 2018-10-15 | null | null | null | null | ['real-time-visual-tracking'] | ['computer-vision'] | [-4.46986258e-01 -1.84639338e-02 3.07875544e-01 -1.86175570e-01
4.47973050e-02 -5.06451249e-01 3.49803030e-01 1.33082047e-01
-8.13687861e-01 6.63006127e-01 -7.56527007e-01 3.72920573e-01
-2.91595399e-01 -1.61705196e-01 -5.31432450e-01 -7.15694785e-01
-4.21586722e-01 1.22491872e+00 6.30571425e-01 -2.96408623... | [7.254027366638184, -2.0572972297668457] |
162f6acd-e083-4e40-89f6-66b2e8ec40f5 | do-we-need-online-nlu-tools | 2011.09825 | null | https://arxiv.org/abs/2011.09825v1 | https://arxiv.org/pdf/2011.09825v1.pdf | Do We Need Online NLU Tools? | The intent recognition is an essential algorithm of any conversational AI application. It is responsible for the classification of an input message into meaningful classes. In many bot development platforms, we can configure the NLU pipeline. Several intent recognition services are currently available as an API, or we ... | ['Jan Šedivý', 'Jakub Konrád', 'Jan Pichl', 'Petr Marek', 'Petr Lorenc'] | 2020-11-19 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [ 2.25623652e-01 -3.68741572e-01 -2.63523787e-01 -5.15140891e-01
-2.92789012e-01 -8.51064444e-01 9.17805970e-01 -3.56021881e-01
-3.12378794e-01 2.69875109e-01 3.59601736e-01 -4.90056694e-01
3.25545780e-02 -5.07209957e-01 2.97852308e-01 -5.39982736e-01
3.50261740e-02 8.38095486e-01 5.91110528e-01 -2.84765750... | [12.605376243591309, 7.683715343475342] |
f257d4ac-db8c-417c-9c48-2f1b5d19d0de | paparazzi-a-deep-dive-into-the-capabilities | 2302.10282 | null | https://arxiv.org/abs/2302.10282v1 | https://arxiv.org/pdf/2302.10282v1.pdf | Paparazzi: A Deep Dive into the Capabilities of Language and Vision Models for Grounding Viewpoint Descriptions | Existing language and vision models achieve impressive performance in image-text understanding. Yet, it is an open question to what extent they can be used for language understanding in 3D environments and whether they implicitly acquire 3D object knowledge, e.g. about different views of an object. In this paper, we in... | ['Sina Zarrieß', 'Kai Lawonn', 'Monique Meuschke', 'Jan Hombeck', 'Henrik Voigt'] | 2023-02-13 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 1.79150492e-01 1.56056702e-01 -7.92094171e-02 -5.01909435e-01
-7.01875865e-01 -9.54593956e-01 1.05423164e+00 -3.18020999e-01
-1.42376274e-01 1.61654830e-01 2.67038971e-01 -2.80361861e-01
3.85360837e-01 -4.94310290e-01 -8.96304011e-01 -2.22096696e-01
5.09728730e-01 1.11246371e+00 2.51482129e-01 -1.24030367... | [8.598396301269531, -3.0563087463378906] |
a2f463eb-ebdf-458c-9e13-3c3ec5648d10 | causal-bandits-for-linear-structural-equation | 2208.12764 | null | https://arxiv.org/abs/2208.12764v3 | https://arxiv.org/pdf/2208.12764v3.pdf | Causal Bandits for Linear Structural Equation Models | This paper studies the problem of designing an optimal sequence of interventions in a causal graphical model to minimize cumulative regret with respect to the best intervention in hindsight. This is, naturally, posed as a causal bandit problem. The focus is on causal bandits for linear structural equation models (SEMs)... | ['Ali Tajer', 'Prasanna Sattigeri', 'Karthikeyan Shanmugam', 'Burak Varici'] | 2022-08-26 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 3.80137861e-01 6.23724461e-01 -7.58632541e-01 -1.50131807e-01
-5.02747118e-01 -5.50709486e-01 2.08660647e-01 1.47889838e-01
-5.19342005e-01 1.01035690e+00 -1.17205614e-02 -8.17218542e-01
-9.10270870e-01 -1.01349211e+00 -1.00738740e+00 -7.32455373e-01
-5.47111690e-01 4.37077194e-01 -1.40617922e-01 1.88738391... | [4.719142913818359, 3.462061643600464] |
796d11e9-5f90-4958-8a64-704cb54fdb87 | composition-aware-image-aesthetics-assessment | 1907.10801 | null | https://arxiv.org/abs/1907.10801v1 | https://arxiv.org/pdf/1907.10801v1.pdf | Composition-Aware Image Aesthetics Assessment | Automatic image aesthetics assessment is important for a wide variety of applications such as on-line photo suggestion, photo album management and image retrieval. Previous methods have focused on mapping the holistic image content to a high or low aesthetics rating. However, the composition information of an image cha... | ['Nagendra Kamath', 'Subhabrata Bhattachary', 'Rohit Puri', 'Dong Liu'] | 2019-07-25 | null | null | null | null | ['aesthetics-quality-assessment'] | ['computer-vision'] | [-4.36062217e-02 -2.11547688e-02 -2.53809541e-01 -4.97112215e-01
-1.18652798e-01 -4.38707530e-01 3.97637635e-01 3.76773745e-01
5.75750396e-02 -1.37134060e-01 5.47197163e-01 1.05341792e-01
-2.84545243e-01 -1.01978683e+00 -3.72467160e-01 -7.41179287e-01
1.31544501e-01 -1.04903728e-01 -1.49226978e-01 -3.74997348... | [11.516581535339355, -1.0237187147140503] |
eb3a5118-9281-4047-9b78-a4bb6e8ebd6f | adversarially-trained-actor-critic-for | 2202.02446 | null | https://arxiv.org/abs/2202.02446v2 | https://arxiv.org/pdf/2202.02446v2.pdf | Adversarially Trained Actor Critic for Offline Reinforcement Learning | We propose Adversarially Trained Actor Critic (ATAC), a new model-free algorithm for offline reinforcement learning (RL) under insufficient data coverage, based on the concept of relative pessimism. ATAC is designed as a two-player Stackelberg game: A policy actor competes against an adversarially trained value critic,... | ['Alekh Agarwal', 'Nan Jiang', 'Tengyang Xie', 'Ching-An Cheng'] | 2022-02-05 | null | null | null | null | ['d4rl'] | ['robots'] | [-2.45245054e-01 5.45383990e-01 -6.18817985e-01 9.66433063e-02
-1.28800356e+00 -9.28318083e-01 3.15640539e-01 3.48133408e-02
-8.43577564e-01 1.04887080e+00 4.40602414e-02 -5.63023627e-01
-1.12883531e-01 -5.14474332e-01 -1.17297876e+00 -7.75306284e-01
-3.30026597e-01 8.92931938e-01 -6.61463439e-02 -3.14679384... | [4.096884727478027, 2.342595100402832] |
5dc5c406-e8f3-43af-b53f-10a8227407dd | time-efficient-and-high-quality-graph | 2101.07026 | null | https://arxiv.org/abs/2101.07026v1 | https://arxiv.org/pdf/2101.07026v1.pdf | Time-Efficient and High-Quality Graph Partitioning for Graph Dynamic Scaling | The dynamic scaling of distributed computations plays an important role in the utilization of elastic computational resources, such as the cloud. It enables the provisioning and de-provisioning of resources to match dynamic resource availability and demands. In the case of distributed graph processing, changing the num... | ['Georgios Theodoropoulos', 'Wentong Cai', 'Toyotaro Suzumura', 'Nikos Tziritas', 'Masatoshi Hanai'] | 2021-01-18 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [-3.86466771e-01 -1.34659737e-01 1.14299543e-03 -7.96549767e-03
-5.86088449e-02 -6.90799057e-01 -5.23127913e-02 7.21074283e-01
-3.09431642e-01 5.43893516e-01 -1.29917175e-01 -4.91639018e-01
-4.19752568e-01 -1.47531080e+00 -2.42524117e-01 -7.73085773e-01
-4.00245339e-01 1.03056419e+00 8.96119416e-01 -1.68339093... | [7.046477794647217, 5.16167688369751] |
cf2a7326-e9c1-46df-9d38-66e364dee45a | nearly-optimal-hierarchical-clustering-for | 2306.0995 | null | https://arxiv.org/abs/2306.09950v1 | https://arxiv.org/pdf/2306.09950v1.pdf | Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs | This paper presents two efficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function. For any input graph $G$ with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of $G$, and return an $O(1)$-approximate HC tree with respect to Dasgupta's cost... | ['He Sun', 'Bogdan-Adrian Manghiuc', 'Steinar Laenen'] | 2023-06-16 | null | null | null | null | ['clustering'] | ['methodology'] | [-5.72314821e-02 2.77121514e-01 8.59858692e-02 -2.69943535e-01
-8.88940334e-01 -5.04569769e-01 7.44835958e-02 5.91386974e-01
-3.69120747e-01 3.78751516e-01 -2.54023671e-01 -5.65243602e-01
-4.58782256e-01 -1.25548947e+00 -6.28989577e-01 -7.27903664e-01
-8.27000916e-01 8.26182783e-01 7.17736959e-01 2.14062944... | [6.9443359375, 5.176884651184082] |
8e1070e0-dd8d-473c-9214-cb8aab2c8cfe | human-pose-transfer-with-disentangled-feature | 2107.10984 | null | https://arxiv.org/abs/2107.10984v3 | https://arxiv.org/pdf/2107.10984v3.pdf | Human Pose Transfer with Augmented Disentangled Feature Consistency | Deep generative models have made great progress in synthesizing images with arbitrary human poses and transferring poses of one person to others. Though many different methods have been proposed to generate images with high visual fidelity, the main challenge remains and comes from two fundamental issues: pose ambiguit... | ['Gangyi Ding', 'Zheng Guan', 'Jian Tang', 'Bo Jiang', 'Zhengping Che', 'Chengxiang Yin', 'Kun Wu'] | 2021-07-23 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 1.79177955e-01 1.79858327e-01 1.66573063e-01 -2.05732629e-01
-5.92680395e-01 -4.93076235e-01 5.52481890e-01 -8.85426581e-01
-9.12446156e-02 7.14973271e-01 3.12168002e-01 3.85454774e-01
6.94658048e-03 -5.72543442e-01 -8.62568557e-01 -7.64932573e-01
2.75603622e-01 3.34445357e-01 -1.71095729e-01 -3.79766792... | [11.97868537902832, -0.8345183730125427] |
3d4cccc6-0849-4f38-a554-1ca4165a08b1 | dapr-a-benchmark-on-document-aware-passage | 2305.13915 | null | https://arxiv.org/abs/2305.13915v1 | https://arxiv.org/pdf/2305.13915v1.pdf | DAPR: A Benchmark on Document-Aware Passage Retrieval | Recent neural retrieval mainly focuses on ranking short texts and is challenged with long documents. Existing work mainly evaluates either ranking passages or whole documents. However, there are many cases where the users want to find a relevant passage within a long document from a huge corpus, e.g. legal cases, resea... | ['Iryna Gurevych', 'Nils Reimers', 'Kexin Wang'] | 2023-05-23 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-4.83216345e-02 -7.99344242e-01 -4.05700237e-01 -2.34103948e-02
-1.81326056e+00 -6.81518137e-01 9.09718275e-01 6.48898780e-01
-6.55382156e-01 7.63774633e-01 6.64597213e-01 -1.76523194e-01
-5.51602483e-01 -7.18317151e-01 -3.91265243e-01 -4.37211186e-01
2.20628425e-01 5.65659404e-01 5.21036804e-01 -5.32024384... | [11.501471519470215, 7.667174816131592] |
5e4ea4fa-80b8-44c2-bb33-1824ac7e1269 | is-ai-the-better-programming-partner-human | 2306.05153 | null | https://arxiv.org/abs/2306.05153v2 | https://arxiv.org/pdf/2306.05153v2.pdf | Is AI the better programming partner? Human-Human Pair Programming vs. Human-AI pAIr Programming | The emergence of large-language models (LLMs) that excel at code generation and commercial products such as GitHub's Copilot has sparked interest in human-AI pair programming (referred to as "pAIr programming") where an AI system collaborates with a human programmer. While traditional pair programming between humans ha... | ['Tongshuang Wu', 'Qianou Ma', 'Kenneth Koedinger'] | 2023-06-08 | null | null | null | null | ['code-generation'] | ['computer-code'] | [-3.88789624e-01 5.97694218e-01 -2.11184565e-02 -3.91037792e-01
-3.56251925e-01 -8.11834335e-01 6.62446976e-01 6.24519348e-01
-2.15795591e-01 -1.71978891e-01 9.82136726e-02 -6.29048765e-01
-1.25637472e-01 -5.17726421e-01 -6.84510648e-01 -9.95576382e-02
2.40936384e-01 5.60532749e-01 -2.17667997e-01 -4.41061109... | [8.189321517944336, 7.615159034729004] |
67773d5b-a433-4c0a-93af-5ed2869e02db | face-deblurring-based-on-separable | 2112.09833 | null | https://arxiv.org/abs/2112.09833v1 | https://arxiv.org/pdf/2112.09833v1.pdf | Face Deblurring Based on Separable Normalization and Adaptive Denormalization | Face deblurring aims to restore a clear face image from a blurred input image with more explicit structure and facial details. However, most conventional image and face deblurring methods focus on the whole generated image resolution without consideration of special face part texture and generally produce unsufficient ... | ['Xiaojie Li', 'Jiancheng Lv', 'Hao Zhang', 'Xian Zhang'] | 2021-12-18 | null | null | null | null | ['face-parsing'] | ['computer-vision'] | [ 3.36303532e-01 -2.42889687e-01 2.09168360e-01 -4.15292114e-01
-3.90444875e-01 -3.44376981e-01 4.00292575e-01 -1.05348587e+00
9.50417891e-02 7.03099132e-01 6.51374936e-01 3.62021506e-01
-2.35465780e-01 -7.14563012e-01 -4.43779856e-01 -1.14461887e+00
3.81396681e-01 -4.80008684e-02 -2.38110855e-01 -2.24814042... | [12.839801788330078, 0.05429908633232117] |
94f2057c-93a0-4ff9-8ce4-1c30ccb5c6e1 | deep-learning-driven-natural-languages-text | 2208.04415 | null | https://arxiv.org/abs/2208.04415v1 | https://arxiv.org/pdf/2208.04415v1.pdf | Deep Learning Driven Natural Languages Text to SQL Query Conversion: A Survey | With the future striving toward data-centric decision-making, seamless access to databases is of utmost importance. There is extensive research on creating an efficient text-to-sql (TEXT2SQL) model to access data from the database. Using a Natural language is one of the best interfaces that can bridge the gap between t... | ['Sanjeev Vijayakumar', 'Prabhav Nalhe', 'Parth Nagarkar', 'Ayush Kumar'] | 2022-08-08 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [-4.65898246e-01 -8.19693599e-03 -3.69014218e-02 -5.65550506e-01
-3.82904530e-01 -2.50004768e-01 5.19102573e-01 3.58613908e-01
-4.31985676e-01 6.69225335e-01 1.30462684e-02 -4.12784249e-01
-3.43799412e-01 -1.24426961e+00 -6.14719331e-01 -2.33201444e-01
2.71188498e-01 9.73839283e-01 1.86142489e-01 -7.19585240... | [9.817914962768555, 7.820693492889404] |
61a7df5d-8fb8-45dc-b6eb-3423197cb19f | comparative-analysis-of-methods-for-cloud | 2012.0693 | null | https://arxiv.org/abs/2012.06930v3 | https://arxiv.org/pdf/2012.06930v3.pdf | Comparative Analysis of Methods for Cloud Segmentation in Ground-Based Infrared Images | The increasing penetration of photovoltaic systems in the power grid makes it vulnerable to cloud shadow projection. Real-time cloud segmentation in ground-based infrared images is important to reduce the noise in intra-hour global solar irradiance forecasting. We present a comparison between discriminative and generat... | ['Manel Martínez-Ramón', 'Guillermo Terrén-Serrano'] | 2020-12-13 | null | null | null | null | ['solar-irradiance-forecasting'] | ['time-series'] | [ 2.96788424e-01 -7.38728881e-01 1.10515833e-01 -3.17121327e-01
-6.94244087e-01 -8.34053814e-01 5.66984296e-01 -2.78732955e-01
-1.61178365e-01 8.24204028e-01 -2.47997403e-01 -2.36539826e-01
-1.99890181e-01 -8.91598105e-01 -1.70016423e-01 -1.57048404e+00
2.70725846e-01 3.07022095e-01 -2.13662282e-01 4.58004028... | [9.723876953125, -1.7698118686676025] |
9936aaba-6722-42de-b215-4975fa4ce3a6 | univariate-long-term-municipal-water-demand | 2105.08486 | null | https://arxiv.org/abs/2105.08486v1 | https://arxiv.org/pdf/2105.08486v1.pdf | Univariate Long-Term Municipal Water Demand Forecasting | This study describes an investigation into the modelling of citywide water consumption in London, Canada. Multiple modelling techniques were evaluated for the task of univariate time series forecasting with water consumption, including linear regression, Facebook's Prophet method, recurrent neural networks, and convolu... | ['Daniel Hsia', 'Matthew A. S. Ross', 'Blake VanBerlo'] | 2021-05-18 | null | null | null | null | ['univariate-time-series-forecasting'] | ['time-series'] | [-3.46207112e-01 3.34879637e-01 -2.24706963e-01 -1.68404296e-01
-5.65852642e-01 -2.14461342e-01 6.66486621e-01 4.11097229e-01
-2.82234490e-01 6.28206551e-01 8.99154365e-01 -8.35274458e-01
-2.68967450e-01 -1.03114998e+00 1.23708688e-01 -1.00008512e+00
-5.28830230e-01 -4.05054763e-02 -2.70306766e-01 -4.46861982... | [6.414000988006592, 2.998826503753662] |
8bccfeff-317f-409d-a998-81d4ed811dd7 | contrast-and-clustering-learning-neighborhood | 2301.13428 | null | https://arxiv.org/abs/2301.13428v3 | https://arxiv.org/pdf/2301.13428v3.pdf | Contrast and Clustering: Learning Neighborhood Pair Representation for Source-free Domain Adaptation | Unsupervised domain adaptation uses source data from different distributions to solve the problem of classifying data from unlabeled target domains. However, conventional methods require access to source data, which often raise concerns about data privacy. In this paper, we consider a more practical but challenging set... | ['Haojie Fang', 'Yingjian Li', 'Yonggang Li', 'Xiangbin Zhu', 'Yuqi Chen'] | 2023-01-31 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 2.21128508e-01 -1.68562576e-01 -4.75956708e-01 -7.10075974e-01
-1.08891261e+00 -8.38889003e-01 4.77520764e-01 7.02916086e-02
-5.30670702e-01 1.09168196e+00 -3.83629315e-02 -1.69488952e-01
-1.01581298e-01 -5.82053959e-01 -5.44451416e-01 -9.19645250e-01
2.38902807e-01 5.01967490e-01 3.57201286e-02 7.33323246... | [10.389314651489258, 3.1874606609344482] |
82e6b57f-72e4-4cca-b3b2-5dc98d04d803 | interpretable-sparsification-of-brain-graphs | 2306.14375 | null | https://arxiv.org/abs/2306.14375v1 | https://arxiv.org/pdf/2306.14375v1.pdf | Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks | Brain graphs, which model the structural and functional relationships between brain regions, are crucial in neuroscientific and clinical applications involving graph classification. However, dense brain graphs pose computational challenges including high runtime and memory usage and limited interpretability. In this pa... | ['Yujun Yan', 'Danai Koutra', 'Xiang Zhang', 'Marlena Duda', 'Gaotang Li'] | 2023-06-26 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 5.07858336e-01 5.27128756e-01 -1.98539436e-01 -3.68301839e-01
1.75909668e-01 -3.36140543e-01 2.37173513e-01 3.60913992e-01
-3.49896938e-01 7.85407722e-01 2.82266676e-01 -4.07578230e-01
-6.83199763e-01 -7.20766127e-01 -5.91850936e-01 -3.39205176e-01
-5.14502227e-01 4.34004754e-01 8.82110521e-02 -2.42657229... | [7.083847999572754, 6.042013645172119] |
619aa8b5-108b-4c1b-afd1-9d3c858f8c9f | automated-whole-slide-imaging-for-label-free | 2304.13736 | null | https://arxiv.org/abs/2304.13736v2 | https://arxiv.org/pdf/2304.13736v2.pdf | Automated Whole Slide Imaging for Label-Free Histology using Photon Absorption Remote Sensing Microscopy | The field of histology relies heavily on antiquated tissue processing and staining techniques that limit the efficiency of pathologic diagnoses of cancer and other diseases. Current staining and advanced labeling methods are often destructive and mutually incompatible, requiring new tissue sections for each stain. This... | ['Parsin Haji Reza', 'John R. Mackey', 'Deepak Dinakaran', 'Marian Boktor', 'Benjamin R. Ecclestone', 'James E. D. Tweel'] | 2023-04-26 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 5.16348958e-01 -1.24873705e-01 5.72099239e-02 1.11317346e-02
-9.68690038e-01 -8.26715171e-01 -5.89405261e-02 5.02086461e-01
-6.38322115e-01 7.14196205e-01 -5.83329380e-01 -3.90918523e-01
3.88207793e-01 -6.83277667e-01 1.46130070e-01 -1.41334510e+00
2.17334971e-01 7.38828421e-01 5.57552457e-01 4.29110751... | [14.818037986755371, -3.0811522006988525] |
b263b3eb-3143-48c4-b599-b8957073ebd0 | anorand-a-semi-supervised-deep-learning | 2305.18389 | null | https://arxiv.org/abs/2305.18389v1 | https://arxiv.org/pdf/2305.18389v1.pdf | AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling | Anomaly detection or more generally outliers detection is one of the most popular and challenging subject in theoretical and applied machine learning. The main challenge is that in general we have access to very few labeled data or no labels at all. In this paper, we present a new semi-supervised anomaly detection meth... | ['Michel Riveill', 'Mansour Zoubeirou A Mayaki'] | 2023-05-28 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 1.30508333e-01 6.13227002e-02 4.89051312e-01 -4.19861287e-01
-3.27679545e-01 -6.44132197e-02 6.03460789e-01 4.55601245e-01
-4.49232727e-01 6.99404895e-01 -2.26646319e-01 -2.22659528e-01
-9.31032654e-03 -9.05656099e-01 -8.49636674e-01 -9.06570137e-01
-1.48245275e-01 7.76772022e-01 2.83008248e-01 -1.27031520... | [7.664244651794434, 2.3190393447875977] |
ae7eb239-f4ba-4eb0-927a-2f364af15826 | a-joint-framework-for-coreference-resolution | null | null | https://aclanthology.org/K15-1002 | https://aclanthology.org/K15-1002.pdf | A Joint Framework for Coreference Resolution and Mention Head Detection | null | ['Kai-Wei Chang', 'Dan Roth', 'Haoruo Peng'] | 2015-07-01 | null | null | null | conll-2015-7 | ['head-detection'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.242028713226318, 3.7969932556152344] |
5c5e1c80-8504-445b-83aa-fc0257708329 | towards-harnessing-feature-embedding-for | 2206.13025 | null | https://arxiv.org/abs/2206.13025v1 | https://arxiv.org/pdf/2206.13025v1.pdf | Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels | The memorization effect of deep neural networks (DNNs) plays a pivotal role in recent label noise learning methods. To exploit this effect, the model prediction-based methods have been widely adopted, which aim to exploit the outputs of DNNs in the early stage of learning to correct noisy labels. However, we observe th... | ['Chen Gong', 'Jian Yang', 'Li Shen', 'Chuang Zhang'] | 2022-06-27 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 1.26452953e-01 -7.05911778e-03 2.09257126e-01 -3.50843668e-01
-4.59677994e-01 -2.57478833e-01 4.51394737e-01 1.85706109e-01
-4.23628688e-01 8.44515681e-01 1.24429807e-01 2.02085599e-01
-2.09398821e-01 -7.54595578e-01 -6.93015873e-01 -1.19833040e+00
4.01108503e-01 -1.42717600e-01 7.06986785e-02 -7.61415213... | [9.32751178741455, 3.8608596324920654] |
8d57d49a-d4e7-48a8-bd13-85313d79b679 | integrating-uncertainty-awareness-into | 2306.08693 | null | https://arxiv.org/abs/2306.08693v1 | https://arxiv.org/pdf/2306.08693v1.pdf | Integrating Uncertainty Awareness into Conformalized Quantile Regression | Conformalized Quantile Regression (CQR) is a recently proposed method for constructing prediction intervals for a response $Y$ given covariates $X$, without making distributional assumptions. However, as we demonstrate empirically, existing constructions of CQR can be ineffective for problems where the quantile regress... | ['Rebecca Willett', 'Rina Foygel Barber', 'Raphael Rossellini'] | 2023-06-14 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [-7.15692565e-02 2.36059487e-01 -4.44279373e-01 -5.33663273e-01
-1.33725357e+00 -6.87089622e-01 4.08855468e-01 5.02143443e-01
-1.46639705e-01 1.14050019e+00 2.31815964e-01 -4.04834896e-01
-5.69084108e-01 -1.19590199e+00 -9.50316191e-01 -6.89236045e-01
-9.78665575e-02 2.90795535e-01 -7.86556583e-03 3.25506441... | [7.527225971221924, 4.177443504333496] |
2dee8092-10e9-47cd-b9d2-0bd313d710a3 | invertible-low-dimensional-modelling-of-x-ray | 2307.04484 | null | https://arxiv.org/abs/2307.04484v1 | https://arxiv.org/pdf/2307.04484v1.pdf | Invertible Low-Dimensional Modelling of X-ray Absorption Spectra for Potential Applications in Spectral X-ray Imaging | X-ray interaction with matter is an energy-dependent process that is contingent on the atomic structure of the constituent material elements. The most advanced models to capture this relationship currently rely on Monte Carlo (MC) simulations. Whilst these very accurate models, in many problems in spectral X-ray imagin... | ['Thomas Blumensath', 'Raziye Kubra Kumrular'] | 2023-07-10 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 3.73758286e-01 -5.00482678e-01 1.26831517e-01 -2.85226941e-01
-6.20712936e-01 -1.48200681e-02 5.57568610e-01 2.70055681e-01
-6.95365489e-01 6.81275308e-01 4.84260917e-02 -1.05412222e-01
-5.15309989e-01 -1.04223275e+00 -7.56749690e-01 -1.12746060e+00
2.11341023e-01 8.73512089e-01 2.48996407e-01 -1.12037070... | [12.353226661682129, -2.5441386699676514] |
60493e9e-6b07-463b-9e33-b5c0761417a8 | web-api-based-chatbot-generation-with | null | null | https://aclanthology.org/2022.rocling-1.31 | https://aclanthology.org/2022.rocling-1.31.pdf | Web-API-Based Chatbot Generation with Analysis and Expansion for Training Sentences | With Web API technology becoming increasingly mature, how to integrate Web API and Chatbot technology has become an issue of great interest. This study plans to build a semi-automatic method and tool, BOTEN. This method allows application developers to build Chatbot interfaces with specified Web APIs quickly. To ensure... | ['Shang-Pin Ma', 'Wan-Lin You', 'Sheng-Kai Wang'] | null | null | null | null | rocling-2022-11 | ['intent-recognition'] | ['natural-language-processing'] | [-2.09876776e-01 -3.33730280e-02 -1.06833100e-01 -4.39494908e-01
-2.21245438e-01 -7.20124543e-01 2.98211217e-01 -4.30394560e-01
-1.33881122e-01 6.21056259e-01 3.46321553e-01 -5.09305716e-01
3.26035954e-02 -7.51905382e-01 1.42329887e-01 -1.26756448e-02
5.65411270e-01 1.09695457e-01 5.19334137e-01 -5.16878486... | [12.70343017578125, 7.733265399932861] |
29310ebe-68a5-4fbf-9339-4bfe8d4a951e | kepler-a-unified-model-for-knowledge | 1911.06136 | null | https://arxiv.org/abs/1911.06136v3 | https://arxiv.org/pdf/1911.06136v3.pdf | KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation | Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text. In contrast, knowledge embedding (KE) methods can effectively represent the relational facts in knowledge graphs (KGs) with informative entity embeddings, but conventional KE models cannot take full advantage of the abund... | ['Zhengyan Zhang', 'Zhaocheng Zhu', 'Jian Tang', 'Xiaozhi Wang', 'Tianyu Gao', 'Zhiyuan Liu', 'Juanzi Li'] | 2019-11-13 | null | null | null | null | ['inductive-knowledge-graph-completion'] | ['knowledge-base'] | [-7.89184451e-01 4.75434512e-01 -8.32643628e-01 -1.69716880e-01
-3.23196560e-01 -3.04482788e-01 6.34845614e-01 2.82752782e-01
-3.31926525e-01 6.32872283e-01 6.56589210e-01 -1.86602056e-01
-4.53509808e-01 -1.22598398e+00 -8.20731342e-01 -3.97307009e-01
-1.38927191e-01 6.81605160e-01 1.77691415e-01 -2.54849970... | [8.914962768554688, 7.985876560211182] |
bf160070-24e3-4d9b-a911-c829dc08bede | adapterem-pre-trained-language-model | 2305.18725 | null | https://arxiv.org/abs/2305.18725v1 | https://arxiv.org/pdf/2305.18725v1.pdf | AdapterEM: Pre-trained Language Model Adaptation for Generalized Entity Matching using Adapter-tuning | Entity Matching (EM) involves identifying different data representations referring to the same entity from multiple data sources and is typically formulated as a binary classification problem. It is a challenging problem in data integration due to the heterogeneity of data representations. State-of-the-art solutions ha... | ['Akiyoshi Matono', 'Toshiyuki Amagasa', 'Steven Lynden', 'John Bosco Mugeni'] | 2023-05-30 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 1.52657837e-01 1.47650376e-01 -1.94360569e-01 -3.22165608e-01
-1.13403690e+00 -3.77246022e-01 5.60328841e-01 2.36055523e-01
-7.55104303e-01 6.39434397e-01 1.61815763e-01 -3.91658157e-01
-4.84865382e-02 -6.41371369e-01 -1.02919877e+00 -3.09343815e-01
1.82486162e-01 8.53430092e-01 4.41043340e-02 -3.40747058... | [9.837754249572754, 8.62270450592041] |
446f67f1-0a4c-40ce-a918-6f94a615111d | spatiotemporal-feature-learning-for-event | 1903.06923 | null | http://arxiv.org/abs/1903.06923v1 | http://arxiv.org/pdf/1903.06923v1.pdf | Spatiotemporal Feature Learning for Event-Based Vision | Unlike conventional frame-based sensors, event-based visual sensors output
information through spikes at a high temporal resolution. By only encoding
changes in pixel intensity, they showcase a low-power consuming, low-latency
approach to visual information sensing. To use this information for higher
sensory tasks like... | ['Anupam Gupta', 'Alcimar Soares', 'Rohan Ghosh', 'Siyi Tang', 'Nitish Thakor'] | 2019-03-16 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 5.13196588e-01 -8.42105985e-01 -4.42766361e-02 -4.61081862e-01
-5.05388677e-01 -7.50920057e-01 9.27647889e-01 3.00878435e-01
-5.36598861e-01 7.08331287e-01 -1.60167292e-01 3.54214549e-01
-2.53002673e-01 -5.91772676e-01 -1.08850396e+00 -8.23540330e-01
-2.72264808e-01 -2.77804047e-01 8.07346225e-01 1.09445408... | [8.419828414916992, -1.2683629989624023] |
2c95d5bc-5c13-41f2-9e2c-463a4aa929f3 | e-panns-sound-recognition-using-efficient-pre | 2305.18665 | null | https://arxiv.org/abs/2305.18665v1 | https://arxiv.org/pdf/2305.18665v1.pdf | E-PANNs: Sound Recognition Using Efficient Pre-trained Audio Neural Networks | Sounds carry an abundance of information about activities and events in our everyday environment, such as traffic noise, road works, music, or people talking. Recent machine learning methods, such as convolutional neural networks (CNNs), have been shown to be able to automatically recognize sound activities, a task kno... | ['Mark D. Plumbley', 'Haohe Liu', 'Arshdeep Singh'] | 2023-05-30 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 6.39576972e-01 -1.61990523e-02 -1.64723024e-02 -7.80946761e-02
-5.72658956e-01 -2.55856782e-01 1.33789271e-01 5.19357920e-02
-5.19456089e-01 3.93667370e-01 2.40459934e-01 -1.40425876e-01
-8.45388845e-02 -1.00830400e+00 -5.86818576e-01 -4.92613703e-01
-1.40340775e-01 -1.34939268e-01 5.88007450e-01 2.90323436... | [15.096927642822266, 5.222106456756592] |
5ad0035b-e2a3-4ea5-b5da-caee9d86e7f2 | communities-in-c-elegans-connectome-through | 2207.00767 | null | https://arxiv.org/abs/2207.00767v3 | https://arxiv.org/pdf/2207.00767v3.pdf | Perspectives and constraints on neural network models of neurobiological processes | Artificial and natural neural network models are a new toolkit which could be potentially have been used for clarifying of complex brain functions. To attend this goal, such models need to be neurobiologically realistic. However, although neural networks have advanced keenly in recent decades their strict similarity in... | ['Arsenii Onuchin'] | 2022-07-02 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 9.41850170e-02 1.00498810e-01 2.30899140e-01 3.41499001e-02
1.01277709e+00 -5.73374987e-01 8.29306901e-01 -1.69958428e-01
-4.15003806e-01 9.31832552e-01 3.44289728e-02 -2.78776228e-01
-6.89798474e-01 -6.89936578e-01 -4.77942050e-01 -7.60207534e-01
-4.94235367e-01 2.02259526e-01 3.06784004e-01 -5.51981449... | [8.042532920837402, 2.993739366531372] |
8be87062-55e5-4e81-913c-eb4b0505d591 | coarse3d-class-prototypes-for-contrastive | 2210.01784 | null | https://arxiv.org/abs/2210.01784v2 | https://arxiv.org/pdf/2210.01784v2.pdf | COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentation | Annotation of large-scale 3D data is notoriously cumbersome and costly. As an alternative, weakly-supervised learning alleviates such a need by reducing the annotation by several order of magnitudes. We propose COARSE3D, a novel architecture-agnostic contrastive learning strategy for 3D segmentation. Since contrastive ... | ['Raoul de Charette', 'Anh-Quan Cao', 'Rong Li'] | 2022-10-04 | null | null | null | null | ['lidar-semantic-segmentation', 'point-cloud-segmentation', 'weakly-supervised-3d-point-cloud-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.66401505e-01 2.95114785e-01 -5.65541804e-01 -4.73898470e-01
-1.23482668e+00 -9.75850224e-01 4.08351988e-01 2.48208076e-01
-5.43104708e-01 6.32276118e-01 7.12756291e-02 -2.79230297e-01
2.81837195e-01 -4.76133674e-01 -8.43331575e-01 -4.78611380e-01
-4.19651344e-02 8.94487441e-01 6.75500572e-01 2.13624567... | [8.176880836486816, -2.9404025077819824] |
4fbb3a0e-0fc7-4f18-a622-45e72c54693f | multi-task-self-supervised-pre-training-for | 2102.03229 | null | https://arxiv.org/abs/2102.03229v1 | https://arxiv.org/pdf/2102.03229v1.pdf | Multi-Task Self-Supervised Pre-Training for Music Classification | Deep learning is very data hungry, and supervised learning especially requires massive labeled data to work well. Machine listening research often suffers from limited labeled data problem, as human annotations are costly to acquire, and annotations for audio are time consuming and less intuitive. Besides, models learn... | ['Chao Wang', 'Juan Pablo Bello', 'Brian McFee', 'Ming Sun', 'Qingming Tang', 'Chieh-Chi Kao', 'Ho-Hsiang Wu'] | 2021-02-05 | null | null | null | null | ['music-classification'] | ['music'] | [ 4.38706756e-01 -2.38821898e-02 -2.54965425e-01 -7.00024843e-01
-1.01450622e+00 -4.66623664e-01 7.35413581e-02 5.66930287e-02
-6.97115064e-01 4.90345538e-01 3.53111267e-01 9.10736620e-02
-2.83204317e-01 -4.10880655e-01 -5.00452936e-01 -6.38376534e-01
2.10151553e-01 5.17203629e-01 -8.64103436e-02 -1.51827380... | [15.73707389831543, 5.234855651855469] |
83ae2554-8e26-4927-b759-d25b4f6f1395 | symmetric-exploration-in-combinatorial | 2306.01276 | null | https://arxiv.org/abs/2306.01276v1 | https://arxiv.org/pdf/2306.01276v1.pdf | Symmetric Exploration in Combinatorial Optimization is Free! | Recently, deep reinforcement learning (DRL) has shown promise in solving combinatorial optimization (CO) problems. However, they often require a large number of evaluations on the objective function, which can be time-consuming in real-world scenarios. To address this issue, we propose a "free" technique to enhance the... | ['Jinkyoo Park', 'Sungsoo Ahn', 'Minsu Kim', 'Hyeonah Kim'] | 2023-06-02 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [-1.21234670e-01 -1.06148906e-01 -4.54534203e-01 -2.70723812e-02
-8.70096684e-01 -7.70689249e-01 1.62341505e-01 2.63935894e-01
-3.87034923e-01 1.25003004e+00 -1.39678344e-01 -5.65341949e-01
-1.77237540e-01 -9.78447616e-01 -9.61345434e-01 -7.10812688e-01
1.42798265e-02 6.88507378e-01 -2.54963934e-01 -3.86987299... | [5.15932035446167, 2.993849515914917] |
3f271431-61c7-4176-af44-c3c7cca2d2dd | vudenc-vulnerability-detection-with-deep | 2201.08441 | null | https://arxiv.org/abs/2201.08441v1 | https://arxiv.org/pdf/2201.08441v1.pdf | VUDENC: Vulnerability Detection with Deep Learning on a Natural Codebase for Python | Context: Identifying potential vulnerable code is important to improve the security of our software systems. However, the manual detection of software vulnerabilities requires expert knowledge and is time-consuming, and must be supported by automated techniques. Objective: Such automated vulnerability detection techniq... | ['Lars Grunske', 'Timo Kehrer', 'Thomas Vogel', 'Yannic Noller', 'Laura Wartschinski'] | 2022-01-20 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-3.29747468e-01 -3.28294665e-01 -6.57493696e-02 -2.26096869e-01
-8.62807393e-01 -9.89292383e-01 2.20803879e-02 6.80377305e-01
1.50274783e-02 4.96546645e-03 2.38329276e-01 -9.13098633e-01
1.99169487e-01 -9.53008235e-01 -6.32236421e-01 -2.55196020e-02
-4.28929418e-01 -4.73999381e-01 3.32505941e-01 -1.81493759... | [7.072289943695068, 7.776976108551025] |
a3123bff-e1d6-4254-8bef-522fb2563cc8 | evaluation-of-gpt-3-5-and-gpt-4-for | 2304.13714 | null | https://arxiv.org/abs/2304.13714v3 | https://arxiv.org/pdf/2304.13714v3.pdf | Evaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare delivery | Despite growing interest in using large language models (LLMs) in healthcare, current explorations do not assess the real-world utility and safety of LLMs in clinical settings. Our objective was to determine whether two LLMs can serve information needs submitted by physicians as questions to an informatics consultation... | ['Nigam H. Shah', 'Eric Horvitz', 'Matthew P Lungren', 'Honor Magon', 'Garret Kenn Morris', 'Angel Arnaout', 'Ethan Goh', 'Rachel Pedreira', 'Lance Downing', 'Saurabh Gombar', 'Jonathan H. Chen', 'Nikesh Kotecha', 'Mehr Kashyap', 'Morgan Cheatham', 'Akshay Swaminathan', 'Juan M. Banda', 'Rahul Thapa', 'Debadutta Dash'] | 2023-04-26 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 5.75967208e-02 5.02275050e-01 -1.03652023e-01 -6.46079421e-01
-1.25547457e+00 -9.18955624e-01 1.56798363e-01 8.70616138e-01
-5.80138326e-01 6.05898142e-01 7.87799895e-01 -1.16532528e+00
-5.49452126e-01 -2.88441956e-01 -3.16462994e-01 -2.81385873e-02
6.99520469e-01 7.12235272e-01 -2.28192225e-01 4.86876294... | [8.734206199645996, 8.368429183959961] |
aa5e1108-80d7-4583-9b64-15764851db4a | narrative-xl-a-large-scale-dataset-for-long | 2305.13877 | null | https://arxiv.org/abs/2305.13877v1 | https://arxiv.org/pdf/2305.13877v1.pdf | Narrative XL: A Large-scale Dataset For Long-Term Memory Models | Despite their tremendous successes, most large language models do not have any long-term memory mechanisms, which restricts their applications. Overcoming this limitation would not only require changes to the typical transformer architectures or training procedures, but also a dataset on which these new models could be... | ['Ky-Vinh Mai', 'Arseny Moskvichev'] | 2023-05-23 | null | null | null | null | ['scene-recognition', 'reading-comprehension'] | ['computer-vision', 'natural-language-processing'] | [ 1.66568533e-01 1.01609722e-01 -1.77702844e-01 -2.26078525e-01
-1.15435588e+00 -9.90663052e-01 8.54890287e-01 3.13453823e-01
-4.52141911e-01 5.63349545e-01 5.90403497e-01 -7.60068238e-01
-2.49927372e-01 -9.79365587e-01 -7.88595200e-01 -1.21117011e-02
2.64584482e-01 7.06893682e-01 4.38189983e-01 -3.43871444... | [11.069449424743652, 7.968624591827393] |
363f7b35-06e8-4138-b8eb-88b0786755cb | discriminative-cross-domain-feature-learning | 2008.1136 | null | https://arxiv.org/abs/2008.11360v1 | https://arxiv.org/pdf/2008.11360v1.pdf | Discriminative Cross-Domain Feature Learning for Partial Domain Adaptation | Partial domain adaptation aims to adapt knowledge from a larger and more diverse source domain to a smaller target domain with less number of classes, which has attracted appealing attention. Recent practice on domain adaptation manages to extract effective features by incorporating the pseudo labels for the target dom... | ['Ming Shao', 'Zhengming Ding', 'Taotao Jing'] | 2020-08-26 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 2.90326506e-01 -2.33127698e-01 -5.16775727e-01 -7.37059236e-01
-9.82618213e-01 -7.03021049e-01 4.94168758e-01 2.45902874e-03
-2.15105116e-01 9.17423666e-01 7.34969750e-02 3.27604711e-01
-2.22772300e-01 -5.67086875e-01 -5.36580205e-01 -8.72825384e-01
3.35234880e-01 5.04619658e-01 4.63484257e-01 1.54805062... | [10.378421783447266, 3.0718345642089844] |
3b0c2db7-9403-4b04-aadd-4163dae01c0e | automated-radiology-report-generation-using | null | null | https://doi.org/10.1016/j.imu.2021.100557 | https://doi.org/10.1016/j.imu.2021.100557 | Automated radiology report generation using conditioned transformers | Radiology report writing in hospitals is a time-consuming task that also requires experience from the involved radiologists. This paper proposes a deep learning model to automatically generate radiology reports given a chest x-ray image from the public IU-Xray dataset. Our work consists of three stages: (1) Fine-tune a... | ['Aly Fahmy', 'Maha Helal', 'Abeer Elkorany', 'Rana Khaled', 'Omar Alfarghaly'] | 2021-03-26 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 4.75172281e-01 5.54400682e-01 2.64205784e-01 -6.01503074e-01
-1.34273434e+00 -3.01148862e-01 4.85787183e-01 4.83004063e-01
-2.89437830e-01 7.27794826e-01 5.63436687e-01 -5.28436899e-01
-3.30293119e-01 -8.28354657e-01 -5.19185007e-01 -5.93488872e-01
8.20489824e-02 7.02896833e-01 2.17059255e-01 1.07180215... | [15.041651725769043, -1.3747563362121582] |
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