paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
de172a39-7111-4c7f-ab2d-0fcc8416f0c5 | weakly-supervised-data-augmentation-through | 2210.14169 | null | https://arxiv.org/abs/2210.14169v3 | https://arxiv.org/pdf/2210.14169v3.pdf | Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding | Dialogue understanding tasks often necessitate abundant annotated data to achieve good performance and that presents challenges in low-resource settings. To alleviate this barrier, we explore few-shot data augmentation for dialogue understanding by prompting large pre-trained language models and present a novel approac... | ['Dilek Hakkani-Tur', 'Zhou Yu', 'Yang Liu', 'Seokhwan Kim', 'Andy Rosenbaum', 'Chenyang Tao', 'Alexandros Papangelis', 'Maximillian Chen'] | 2022-10-25 | null | null | null | null | ['dialogue-understanding', 'intent-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.74474339e-02 9.67059493e-01 -1.89269349e-01 -7.18342602e-01
-9.25861776e-01 -2.85177946e-01 1.16408229e+00 2.46166140e-01
-8.80182922e-01 1.04412818e+00 1.02533054e+00 -6.47668466e-02
5.22365272e-01 -3.14372420e-01 -9.51137692e-02 1.85871631e-01
1.30540669e-01 9.03658032e-01 -3.21279824e-01 -8.64760458... | [12.793781280517578, 7.971930503845215] |
c890778b-0273-4d4b-bf75-2fa5de4fcd71 | open-arms-open-source-arms-hands-control | 2205.12992 | null | https://arxiv.org/abs/2205.12992v2 | https://arxiv.org/pdf/2205.12992v2.pdf | Open Arms: Open-Source Arms, Hands & Control | Open Arms is a novel open-source platform of realistic human-like robotic hands and arms hardware with 28 Degree-of-Freedom (DoF), designed to extend the capabilities and accessibility of humanoid robotic grasping and manipulation. The Open Arms framework includes an open SDK and development environment, simulation too... | ['Raviteja Upadrashta', 'Rushali Mohbe', 'Aman Malali', 'Aditya Sagi', 'Vytas Krisciunas', 'Gerardo Morales', 'Alishba Imran', 'David Hanson'] | 2022-05-20 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [-3.73067021e-01 3.63917470e-01 3.54811817e-01 -6.89676180e-02
1.19348466e-01 -6.65254235e-01 -1.72532141e-01 -1.22044528e+00
8.86619017e-02 3.43634278e-01 -1.08421646e-01 -1.60685167e-01
-4.62121338e-01 -6.38642848e-01 -8.78298283e-01 -6.09557331e-01
-3.89837712e-01 6.88408673e-01 -1.06030650e-01 -5.38689733... | [5.722343444824219, -0.8222242593765259] |
bd1cdf96-a46d-4dd5-ac18-845d39d0fea1 | rare-words-degenerate-all-words | 2109.03127 | null | https://arxiv.org/abs/2109.03127v3 | https://arxiv.org/pdf/2109.03127v3.pdf | Rare Tokens Degenerate All Tokens: Improving Neural Text Generation via Adaptive Gradient Gating for Rare Token Embeddings | Recent studies have determined that the learned token embeddings of large-scale neural language models are degenerated to be anisotropic with a narrow-cone shape. This phenomenon, called the representation degeneration problem, facilitates an increase in the overall similarity between token embeddings that negatively a... | ['Sungroh Yoon', 'Woo-Jong Ryu', 'Seong-min Lee', 'Heeseung Kim', 'Jongyoon Song', 'Sangwon Yu'] | 2021-09-07 | null | https://aclanthology.org/2022.acl-long.3 | https://aclanthology.org/2022.acl-long.3.pdf | acl-2022-5 | ['word-similarity'] | ['natural-language-processing'] | [-2.14802548e-01 2.00823154e-02 -3.87304932e-01 2.34049372e-02
-2.00892940e-01 -3.21237534e-01 7.17676997e-01 1.29122376e-01
-4.42841172e-01 5.38734853e-01 5.80180585e-01 -2.80313015e-01
1.64486617e-01 -5.86777687e-01 -7.27615356e-01 -8.66492510e-01
2.51932472e-01 2.08608985e-01 9.60838944e-02 -3.08931112... | [10.841097831726074, 8.640522003173828] |
7f83561c-6a15-44b4-8b96-54c06d348adf | neural-network-kalman-filtering-for-3d-object | 2111.09631 | null | https://arxiv.org/abs/2111.09631v3 | https://arxiv.org/pdf/2111.09631v3.pdf | Neural Network Kalman filtering for 3D object tracking from linear array ultrasound data | Many interventional surgical procedures rely on medical imaging to visualise and track instruments. Such imaging methods not only need to be real-time capable, but also provide accurate and robust positional information. In ultrasound applications, typically only two-dimensional data from a linear array are available, ... | ['Andreas Hauptmann', 'Mikko J. Sillanpää', 'Adrien Desjardins', 'Simon Arridge', 'Efthymios Maneas', 'Erwin J. Alles', 'Arttu Arjas'] | 2021-11-18 | null | null | null | null | ['3d-object-tracking'] | ['computer-vision'] | [ 3.97193164e-01 2.78850406e-01 5.14351904e-01 -1.65388640e-02
-8.35506141e-01 -6.05190575e-01 3.02473009e-01 4.55348700e-01
-8.24880362e-01 6.19520843e-01 -9.92121547e-02 -3.32962960e-01
-8.46498013e-01 -1.63964987e-01 -6.03313327e-01 -9.59014356e-01
-4.98054624e-01 4.13241088e-01 1.15897939e-01 3.87482345... | [13.668266296386719, -2.9699528217315674] |
ba6ad127-7145-4f79-8b79-0022c82050cd | active-self-training-for-weakly-supervised-3d | 2209.07069 | null | https://arxiv.org/abs/2209.07069v1 | https://arxiv.org/pdf/2209.07069v1.pdf | Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentation | Since the preparation of labeled data for training semantic segmentation networks of point clouds is a time-consuming process, weakly supervised approaches have been introduced to learn from only a small fraction of data. These methods are typically based on learning with contrastive losses while automatically deriving... | ['Ruizhen Hu', 'Hui Huang', 'Oliver van Kaick', 'Gengxin Liu'] | 2022-09-15 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 4.07742888e-01 5.54894567e-01 -4.58113611e-01 -7.83674657e-01
-1.25600386e+00 -7.17149854e-01 4.38880920e-01 6.26288652e-01
-7.30132163e-01 4.41745937e-01 -2.40501240e-01 -1.74519762e-01
2.82735735e-01 -7.05774009e-01 -1.05343497e+00 -6.40500188e-01
-2.37603858e-02 1.06398666e+00 6.55841589e-01 2.19686657... | [9.469837188720703, 0.5569085478782654] |
6c5e25c0-324a-4953-8ba4-daf46e5e95a0 | why-should-i-trust-you-bellman-evaluating-the | null | null | https://openreview.net/forum?id=MUpxS9vDbZr | https://openreview.net/pdf?id=MUpxS9vDbZr | Why Should I Trust You, Bellman? Evaluating the Bellman Objective with Off-Policy Data | In this work, we analyze the effectiveness of the Bellman equation as a proxy objective for value prediction accuracy in off-policy evaluation. While the Bellman equation is uniquely solved by the true value function over all state-action pairs, we show that in the finite data regime, the Bellman equation can be satisf... | ['Shixiang Shane Gu', 'Ofir Nachum', 'Doina Precup', 'David Meger', 'Scott Fujimoto'] | 2021-09-29 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [-1.43046156e-01 1.57232642e-01 -8.39569330e-01 -8.94240141e-02
-9.20815945e-01 -8.36720109e-01 4.40440774e-01 1.07177988e-01
-7.97405601e-01 1.32410586e+00 -8.82480368e-02 -6.65229261e-01
-5.36654353e-01 -4.79385465e-01 -6.10406697e-01 -7.46099234e-01
-1.36044413e-01 3.45402390e-01 -3.13466117e-02 -2.42925629... | [4.164836406707764, 2.4360311031341553] |
a20b436e-ec92-4481-bbfe-724e9b7ba3b4 | interpretable-image-clustering-via | 2012.09743 | null | https://arxiv.org/abs/2012.09743v1 | https://arxiv.org/pdf/2012.09743v1.pdf | Interpretable Image Clustering via Diffeomorphism-Aware K-Means | We design an interpretable clustering algorithm aware of the nonlinear structure of image manifolds. Our approach leverages the interpretability of $K$-means applied in the image space while addressing its clustering performance issues. Specifically, we develop a measure of similarity between images and centroids that ... | ['Behnaam Aazhang', 'Richard Baraniuk', 'Anirvan Sengupta', 'Yanis Bahroun', 'Randall Balestriero', 'Romain Cosentino'] | 2020-12-16 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [-4.13262576e-01 1.20635390e-01 -1.42620802e-01 -4.39626604e-01
-5.14903128e-01 -8.41297090e-01 6.12166524e-01 -3.60027909e-01
-5.50827831e-02 -7.36034811e-02 2.20697701e-01 6.25739470e-02
-5.27946413e-01 -3.12516749e-01 -6.83211505e-01 -7.41542518e-01
-5.53249180e-01 3.41626048e-01 -1.63305894e-01 5.73133677... | [8.618050575256348, 3.4356656074523926] |
bfb262fb-fb54-41d0-9dc0-f01fd80fc833 | generative-or-contrastive-phrase | 2204.09358 | null | https://arxiv.org/abs/2204.09358v2 | https://arxiv.org/pdf/2204.09358v2.pdf | Generative or Contrastive? Phrase Reconstruction for Better Sentence Representation Learning | Though offering amazing contextualized token-level representations, current pre-trained language models actually take less attention on acquiring sentence-level representation during its self-supervised pre-training. If self-supervised learning can be distinguished into two subcategories, generative and contrastive, th... | ['Hai Zhao', 'Bohong Wu'] | 2022-04-20 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [ 3.60353589e-01 2.88989127e-01 -3.23310703e-01 -5.25060594e-01
-1.09813809e+00 -3.74909997e-01 1.03664303e+00 4.33911800e-01
-4.04087931e-01 6.00163877e-01 6.63100421e-01 -3.33424181e-01
-1.33641526e-01 -1.00712025e+00 -4.90670085e-01 -5.98869145e-01
4.01951313e-01 8.37475836e-01 1.12314813e-01 -5.59453547... | [10.944986343383789, 8.632025718688965] |
de92a52e-49c2-4601-8914-b40879f5ee35 | can-current-nli-systems-handle-german-word | 2306.04523 | null | https://arxiv.org/abs/2306.04523v1 | https://arxiv.org/pdf/2306.04523v1.pdf | Can current NLI systems handle German word order? Investigating language model performance on a new German challenge set of minimal pairs | Compared to English, German word order is freer and therefore poses additional challenges for natural language inference (NLI). We create WOGLI (Word Order in German Language Inference), the first adversarial NLI dataset for German word order that has the following properties: (i) each premise has an entailed and a non... | ['Katja Markert', 'Ines Reinig'] | 2023-06-07 | null | null | null | null | ['natural-language-inference'] | ['natural-language-processing'] | [ 5.37302375e-01 3.49307775e-01 -2.14165032e-01 -3.39619577e-01
-5.29757679e-01 -1.15561104e+00 8.17866862e-01 1.19584687e-01
-5.46429813e-01 1.02445900e+00 3.70955020e-01 -1.03543007e+00
1.11618578e-01 -1.04573441e+00 -1.05347919e+00 -9.09541771e-02
6.18488677e-02 7.06619263e-01 -2.77592957e-01 -5.18790483... | [10.843408584594727, 9.438857078552246] |
78d53825-16ec-4839-a36e-b9a078d5886d | the-generalized-laplacian-distance-and-its | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Elboer_The_Generalized_Laplacian_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Elboer_The_Generalized_Laplacian_2013_CVPR_paper.pdf | The Generalized Laplacian Distance and Its Applications for Visual Matching | The graph Laplacian operator, which originated in spectral graph theory, is commonly used for learning applications such as spectral clustering and embedding. In this paper we explore the Laplacian distance, a distance function related to the graph Laplacian, and use it for visual search. We show that previous techniqu... | ['Yacov Hel-Or', 'Michael Werman', 'Elhanan Elboer'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['tone-mapping'] | ['computer-vision'] | [ 3.34264606e-01 -2.03002542e-01 -8.09558667e-03 -1.26255065e-01
-5.92047095e-01 -7.03686893e-01 4.17858839e-01 2.76458979e-01
-2.64955670e-01 2.58850157e-01 1.11532211e-02 -1.50545672e-01
-5.71935833e-01 -6.75030291e-01 -3.18279445e-01 -5.77739418e-01
-3.88567120e-01 2.63428718e-01 3.52126718e-01 -1.69528648... | [7.777602195739746, 4.456758499145508] |
18cee694-5a2e-4997-ae5a-8d121895f5df | the-power-of-motifs-as-inductive-bias-for | 2306.17246 | null | https://arxiv.org/abs/2306.17246v1 | https://arxiv.org/pdf/2306.17246v1.pdf | The power of motifs as inductive bias for learning molecular distributions | Machine learning for molecules holds great potential for efficiently exploring the vast chemical space and thus streamlining the drug discovery process by facilitating the design of new therapeutic molecules. Deep generative models have shown promising results for molecule generation, but the benefits of specific induc... | ['Stephan Günnemann', 'Fabian Theis', 'David Lüdke', 'Leon Hetzel', 'Johanna Sommer'] | 2023-04-04 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 3.90779108e-01 2.14666709e-01 -5.53013086e-01 1.42950052e-02
-8.42691422e-01 -7.27361262e-01 7.32319295e-01 4.49707806e-01
-9.67496932e-02 1.13027608e+00 3.18970472e-01 -7.44959891e-01
-1.25112291e-02 -9.02852297e-01 -8.34024847e-01 -9.58703458e-01
5.47882216e-03 6.04450643e-01 -1.04831979e-01 -6.03641709... | [5.009668350219727, 5.754953861236572] |
49419a5b-e06f-4e1c-95a1-4781c67333fd | a-volumetric-transformer-for-accurate-3d | 2111.13300 | null | https://arxiv.org/abs/2111.13300v2 | https://arxiv.org/pdf/2111.13300v2.pdf | A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation | We propose a Transformer architecture for volumetric segmentation, a challenging task that requires keeping a complex balance in encoding local and global spatial cues, and preserving information along all axes of the volume. Encoder of the proposed design benefits from self-attention mechanism to simultaneously encode... | ['Mehrtash Harandi', 'Gary Egan', 'Zhaolin Chen', 'Munawar Hayat', 'Himashi Peiris'] | 2021-11-26 | null | null | null | null | ['volumetric-medical-image-segmentation'] | ['medical'] | [ 8.34903568e-02 4.54072207e-01 -2.22674802e-01 -4.64176863e-01
-1.03782272e+00 -1.11719064e-01 3.40304375e-01 1.40885890e-01
-2.89601952e-01 6.27167583e-01 5.47441542e-01 -2.32559428e-01
-1.63429882e-02 -6.58125520e-01 -7.34931886e-01 -7.93340504e-01
-5.18816523e-02 4.54123199e-01 2.29265973e-01 -2.86598988... | [14.571266174316406, -2.5321221351623535] |
28a9141d-1f4d-4aac-944f-d80416516be4 | towards-more-discriminative-and-robust-iris | null | null | https://ieeexplore.ieee.org/abstract/document/9722888 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9722888 | Towards More Discriminative and Robust Iris Recognition by Learning Uncertain Factors | The uncontrollable acquisition process limits the performance of iris recognition. In the acquisition process, various inevitable factors, including eyes, devices, and environment, hinder the iris recognition system from learning a discriminative identity representation. This leads to severe performance degradation. In... | ['Jianze Wei; Huaibo Huang; Yunlong Wang; Ran He; Zhenan Sun'] | 2022-02-28 | null | null | null | ieee-transactions-on-information-forensics-9 | ['iris-recognition'] | ['computer-vision'] | [ 4.12226051e-01 -2.56589085e-01 -3.02755088e-01 -4.78375793e-01
-6.26201093e-01 -1.95295379e-01 1.72837839e-01 -3.69795978e-01
-2.89869696e-01 5.21076500e-01 -2.60971915e-02 3.60581428e-02
-6.88331366e-01 -4.91278619e-01 -6.52236521e-01 -1.11789513e+00
3.39510441e-01 -1.85559839e-02 -3.81087124e-01 4.50548142... | [13.102278709411621, 0.707775890827179] |
ee573ea4-42d4-42a5-a383-da91a5ac5d5b | rda-reciprocal-distribution-alignment-for | 2208.04619 | null | https://arxiv.org/abs/2208.04619v2 | https://arxiv.org/pdf/2208.04619v2.pdf | RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning | In this work, we propose Reciprocal Distribution Alignment (RDA) to address semi-supervised learning (SSL), which is a hyperparameter-free framework that is independent of confidence threshold and works with both the matched (conventionally) and the mismatched class distributions. Distribution mismatch is an often over... | ['Yinghuan Shi', 'Luping Zhou', 'Lei Wang', 'Lei Qi', 'Yue Duan'] | 2022-08-09 | null | null | null | null | ['semi-supervised-image-classification'] | ['computer-vision'] | [ 3.30053687e-01 3.11527669e-01 -4.91257280e-01 -5.47383249e-01
-1.03021026e+00 -8.83427799e-01 5.07548988e-01 1.00977175e-01
-1.07784815e-01 9.85616267e-01 -2.50990212e-01 -4.28450167e-01
-1.25423625e-01 -4.36204314e-01 -6.18347287e-01 -1.04004133e+00
5.95134795e-01 8.01375747e-01 1.80295065e-01 2.30003089... | [9.356354713439941, 3.916848659515381] |
2ea39dd1-05ef-48a4-98a8-d6aa3f26dd88 | distributed-filtered-hyperinterpolation-for | 1910.02434 | null | https://arxiv.org/abs/1910.02434v1 | https://arxiv.org/pdf/1910.02434v1.pdf | Distributed filtered hyperinterpolation for noisy data on the sphere | Problems in astrophysics, space weather research and geophysics usually need to analyze noisy big data on the sphere. This paper develops distributed filtered hyperinterpolation for noisy data on the sphere, which assigns the data fitting task to multiple servers to find a good approximation of the mapping of input and... | ['Ding-Xuan Zhou', 'Yu Guang Wang', 'Shao-Bo Lin'] | 2019-10-06 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [-1.10321176e+00 -1.27001375e-01 1.01081347e+00 -1.32319510e-01
-6.88652456e-01 -4.82538402e-01 3.80583048e-01 1.80488929e-01
-4.80893672e-01 8.18848431e-01 -1.16388880e-01 -1.40430093e-01
-3.58537018e-01 -1.18336511e+00 -7.87928760e-01 -8.12189460e-01
-2.10758179e-01 9.44127738e-01 5.62380373e-01 -1.19733825... | [6.656850814819336, 4.481255054473877] |
7fb362f7-8cb0-4106-8525-2a9fef04e15b | piqi-perceptual-image-quality-index-based-on | 2305.09214 | null | https://arxiv.org/abs/2305.09214v1 | https://arxiv.org/pdf/2305.09214v1.pdf | PIQI: Perceptual Image Quality Index based on Ensemble of Gaussian Process Regression | Digital images contain a lot of redundancies, therefore, compression techniques are applied to reduce the image size without loss of reasonable image quality. Same become more prominent in the case of videos which contains image sequences and higher compression ratios are achieved in low throughput networks. Assessment... | ['Hassan Khalid', 'Hafiz Muhammad Shahzad Asif', 'Nisar Ahmed'] | 2023-05-16 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 4.10278469e-01 -5.02319396e-01 2.97488034e-01 -3.79298210e-01
-7.37850428e-01 -1.86448544e-01 3.55126500e-01 3.09436500e-01
-5.13941526e-01 8.68849516e-01 6.57254085e-03 6.29255101e-02
-5.92799067e-01 -8.93507421e-01 -3.20396066e-01 -8.80583048e-01
-4.26104695e-01 -2.32693464e-01 3.98943186e-01 -3.51768732... | [11.719757080078125, -1.9838731288909912] |
a6b125e5-b4e3-4b74-b83e-5ff0e3333efb | text-conditional-alt-text-generation-for | 2305.14779 | null | https://arxiv.org/abs/2305.14779v1 | https://arxiv.org/pdf/2305.14779v1.pdf | Text Conditional Alt-Text Generation for Twitter Images | In this work we present an approach for generating alternative text (or alt-text) descriptions for images shared on social media, specifically Twitter. This task is more than just a special case of image captioning, as alt-text is both more literally descriptive and context-specific. Also critically, images posted to T... | ['Taylor Berg-Kirkpatrick', 'Omar Florez', 'Sofia Samaniego', 'Nikita Srivatsan'] | 2023-05-24 | null | null | null | null | ['image-captioning'] | ['computer-vision'] | [ 6.23431265e-01 4.10796881e-01 -3.83972526e-02 -5.37850976e-01
-9.67459083e-01 -8.69287968e-01 1.25523973e+00 3.49714160e-01
-6.15539551e-01 5.95553041e-01 6.60258770e-01 -3.53870898e-01
5.37849367e-01 -4.72361982e-01 -9.98960972e-01 -4.70996857e-01
1.43775657e-01 4.60222423e-01 -1.99645571e-02 -1.95615634... | [11.030924797058105, 0.9951381683349609] |
88c789f0-4b12-46ff-9cf3-6aa88594f1cf | neural-network-based-automatic-liver-tumor | 1706.00842 | null | http://arxiv.org/abs/1706.00842v3 | http://arxiv.org/pdf/1706.00842v3.pdf | Neural Network-Based Automatic Liver Tumor Segmentation With Random Forest-Based Candidate Filtering | We present a fully automatic method employing convolutional neural networks
based on the 2D U-net architecture and random forest classifier to solve the
automatic liver lesion segmentation problem of the ISBI 2017 Liver Tumor
Segmentation Challenge (LiTS). In order to constrain the ROI in which the
tumors could be loca... | ['Andrea Schenk', 'Grzegorz Chlebus', 'Jan Hendrik Moltz', 'Hans Meine'] | 2017-06-02 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-2.69225100e-03 5.37395000e-01 -4.07152742e-01 -4.13207620e-01
-5.51574409e-01 -5.67880988e-01 5.41779935e-01 1.21121481e-01
-3.36608350e-01 5.20061970e-01 4.29439843e-01 -6.62755072e-01
6.93507046e-02 -5.64738095e-01 -4.73080546e-01 -7.38776743e-01
-4.79251802e-01 8.92999291e-01 4.01768796e-02 5.19700825... | [14.498236656188965, -2.641587495803833] |
1d3d750b-11bb-46dd-a602-f6525f567116 | distinguish-before-answer-generating | 2305.08135 | null | https://arxiv.org/abs/2305.08135v2 | https://arxiv.org/pdf/2305.08135v2.pdf | Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering | Existing knowledge-enhanced methods have achieved remarkable results in certain QA tasks via obtaining diverse knowledge from different knowledge bases. However, limited by the properties of retrieved knowledge, they still have trouble benefiting from both the knowledge relevance and distinguishment simultaneously. To ... | ['Yin Zhang', 'Luo Si', 'Fei Huang', 'Ji Zhang', 'Ming Yan', 'Guohai Xu', 'Qianglong Chen'] | 2023-05-14 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [ 3.04939896e-01 4.95614141e-01 -1.91313043e-01 -4.36344564e-01
-1.04355276e+00 -6.39791727e-01 7.22555995e-01 1.51846185e-01
-1.94219261e-01 8.67927253e-01 2.63164401e-01 -4.04721171e-01
-4.96913999e-01 -8.92243981e-01 -7.29931235e-01 -3.05851609e-01
4.23528254e-01 7.49782741e-01 4.99709785e-01 -6.08435750... | [10.7037353515625, 7.969479560852051] |
aa7ad4f1-d8ec-4c88-a5d5-daf667e725d3 | are-neural-architecture-search-benchmarks | 2303.16938 | null | https://arxiv.org/abs/2303.16938v1 | https://arxiv.org/pdf/2303.16938v1.pdf | Are Neural Architecture Search Benchmarks Well Designed? A Deeper Look Into Operation Importance | Neural Architecture Search (NAS) benchmarks significantly improved the capability of developing and comparing NAS methods while at the same time drastically reduced the computational overhead by providing meta-information about thousands of trained neural networks. However, tabular benchmarks have several drawbacks tha... | ['Luís A. Alexandre', 'Bruno Degardin', 'Vasco Lopes'] | 2023-03-29 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-4.01582032e-01 -4.50537473e-01 -1.59688979e-01 -3.63044411e-01
-8.37021530e-01 -7.48362601e-01 4.15220708e-01 -2.42027845e-02
-7.63858378e-01 5.74868083e-01 6.08087070e-02 -6.49849296e-01
-3.42115074e-01 -7.39729881e-01 -7.66752839e-01 -5.23127496e-01
-1.41319573e-01 4.98026580e-01 7.69461840e-02 -2.52989799... | [8.521553039550781, 3.3130156993865967] |
88963ac7-0d2d-4b0b-a639-1f7f7fa23d7f | beyond-lexical-a-semantic-retrieval-framework | 2008.03917 | null | https://arxiv.org/abs/2008.03917v1 | https://arxiv.org/pdf/2008.03917v1.pdf | Beyond Lexical: A Semantic Retrieval Framework for Textual SearchEngine | Search engine has become a fundamental component in various web and mobile applications. Retrieving relevant documents from the massive datasets is challenging for a search engine system, especially when faced with verbose or tail queries. In this paper, we explore a vector space search framework for document retrieval... | ['Kuan Fang', 'RiKang Zhour', 'RuiXing Wang', 'Long Zhao', 'Zhan Shen', 'LiWen Fan'] | 2020-08-10 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [-2.37929776e-01 -6.61346436e-01 -6.14102066e-01 -2.86210001e-01
-1.11778319e+00 -7.35269129e-01 8.22649479e-01 9.30610020e-03
-4.75440383e-01 9.51443091e-02 4.49773431e-01 -2.34714374e-01
-8.41102183e-01 -8.58957350e-01 -2.97531635e-01 -2.09354296e-01
-1.07920105e-02 8.22038114e-01 3.45283121e-01 -4.31144774... | [11.350605964660645, 7.439302444458008] |
a8b874f8-ac18-416b-a8ba-66efb2b9a602 | is-summary-useful-or-not-an-extrinsic-human | 2305.15044 | null | https://arxiv.org/abs/2305.15044v1 | https://arxiv.org/pdf/2305.15044v1.pdf | Is Summary Useful or Not? An Extrinsic Human Evaluation of Text Summaries on Downstream Tasks | Research on automated text summarization relies heavily on human and automatic evaluation. While recent work on human evaluation mainly adopted intrinsic evaluation methods, judging the generic quality of text summaries, e.g. informativeness and coherence, our work focuses on evaluating the usefulness of text summaries... | ['Xiaojun Wan', 'Mingqi Gao', 'Xiao Pu'] | 2023-05-24 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [ 1.86555088e-01 3.69350821e-01 -7.32987598e-02 -3.19273502e-01
-1.20790768e+00 -6.38655782e-01 1.05337369e+00 1.01343751e+00
-7.23044574e-01 6.80895686e-01 9.22192574e-01 -7.39539936e-02
-2.73494184e-01 -4.43709552e-01 2.06576604e-02 -3.05092633e-01
4.34096575e-01 4.45565701e-01 3.04430485e-01 -2.59657502... | [12.079402923583984, 9.205304145812988] |
1c37283f-7526-46ad-871e-716b344d5276 | investigating-monolingual-and-multilingual | 2103.09519 | null | https://arxiv.org/abs/2103.09519v1 | https://arxiv.org/pdf/2103.09519v1.pdf | Investigating Monolingual and Multilingual BERTModels for Vietnamese Aspect Category Detection | Aspect category detection (ACD) is one of the challenging tasks in the Aspect-based sentiment Analysis problem. The purpose of this task is to identify the aspect categories mentioned in user-generated reviews from a set of pre-defined categories. In this paper, we investigate the performance of various monolingual pre... | ['Ngan Luu-Thuy Nguyen', 'Vu Xuan Hoang', 'Lac Si Le', 'Dang Van Thin'] | 2021-03-17 | null | null | null | null | ['aspect-category-detection'] | ['natural-language-processing'] | [-2.34267786e-01 -2.83478498e-01 -1.69038489e-01 -3.82842898e-01
-1.06984591e+00 -7.03539729e-01 1.01719475e+00 5.38308024e-01
-8.26706767e-01 3.94685060e-01 3.22545290e-01 -4.22042996e-01
4.96763527e-01 -5.89329958e-01 -1.73079312e-01 -3.46770763e-01
1.95910379e-01 5.89664102e-01 -6.15006164e-02 -5.94022810... | [11.295464515686035, 6.819601535797119] |
17322890-0052-4951-8231-9cc94de98dd9 | direct-image-to-point-cloud-descriptors | 1906.06064 | null | https://arxiv.org/abs/1906.06064v1 | https://arxiv.org/pdf/1906.06064v1.pdf | Direct Image to Point Cloud Descriptors Matching for 6-DOF Camera Localization in Dense 3D Point Cloud | We propose a novel concept to directly match feature descriptors extracted from RGB images, with feature descriptors extracted from 3D point clouds. We use this concept to localize the position and orientation (pose) of the camera of a query image in dense point clouds. We generate a dataset of matching 2D and 3D descr... | ['Mohammed Bennamoun', 'Mohammad A. A. K. Jalwana', 'Ferdous Sohel', 'Uzair Nadeem', 'Roberto Togneri'] | 2019-06-14 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-8.74336883e-02 -5.01419008e-01 -1.89140335e-01 -1.21184371e-01
-9.24480140e-01 -8.14743817e-01 5.77726960e-01 2.45527819e-01
-4.95251954e-01 -2.91473538e-01 -2.46720597e-01 2.96102256e-01
-1.46535069e-01 -6.09730959e-01 -7.80123830e-01 -4.39920813e-01
1.51774548e-02 7.58643866e-01 6.53456092e-01 4.67645042... | [7.644735813140869, -2.3655097484588623] |
354e00de-fe4b-45d0-aef7-a56b35508aa7 | class-specific-variational-auto-encoder-for | 2304.11734 | null | https://arxiv.org/abs/2304.11734v1 | https://arxiv.org/pdf/2304.11734v1.pdf | Class-Specific Variational Auto-Encoder for Content-Based Image Retrieval | Using a discriminative representation obtained by supervised deep learning methods showed promising results on diverse Content-Based Image Retrieval (CBIR) problems. However, existing methods exploiting labels during training try to discriminate all available classes, which is not ideal in cases where the retrieval pro... | ['Alexandros Iosifidis', 'Mehdi Rafiei'] | 2023-04-23 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 3.45087200e-02 -4.84810024e-01 -4.77340609e-01 -4.27082032e-01
-1.42286551e+00 -3.92748594e-01 9.03777480e-01 1.84932221e-02
-4.70679373e-01 5.96585333e-01 6.86930865e-02 2.45293751e-01
-4.36689556e-01 -6.48348749e-01 -5.53601265e-01 -9.99609947e-01
3.17896336e-01 1.02243507e+00 -3.56936753e-02 -2.92321946... | [11.129494667053223, 1.0602718591690063] |
aa89f729-4f69-43dc-bd92-92d07a8669f3 | towards-open-intent-discovery-for | 1904.08524 | null | http://arxiv.org/abs/1904.08524v1 | http://arxiv.org/pdf/1904.08524v1.pdf | Towards Open Intent Discovery for Conversational Text | Detecting and identifying user intent from text, both written and spoken,
plays an important role in modelling and understand dialogs. Existing research
for intent discovery model it as a classification task with a predefined set of
known categories. To generailze beyond these preexisting classes, we define a
new task ... | ['Srinivasan Parthasarathy', 'Nikhita Vedula', 'Pranav Maneriker', 'Nedim Lipka'] | 2019-04-17 | null | null | null | null | ['open-intent-discovery', 'intent-discovery'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.96500951e-01 3.18924248e-01 -9.74889845e-02 -1.01526010e+00
-7.14023113e-01 -8.09109330e-01 8.45397472e-01 -1.16461933e-01
-4.83577460e-01 6.90130234e-01 6.47426486e-01 -3.58280599e-01
5.00945389e-01 -2.53242552e-01 -4.98336762e-01 -2.43487567e-01
1.08395524e-01 7.82570422e-01 1.23851858e-01 -3.70233655... | [12.548897743225098, 7.620955944061279] |
92245db0-7640-4dee-b40b-9d648aea3bb7 | improving-performance-insensitivity-of-large | 2304.04071 | null | https://arxiv.org/abs/2304.04071v2 | https://arxiv.org/pdf/2304.04071v2.pdf | Improving Performance Insensitivity of Large-scale Multiobjective Optimization via Monte Carlo Tree Search | The large-scale multiobjective optimization problem (LSMOP) is characterized by simultaneously optimizing multiple conflicting objectives and involving hundreds of decision variables. Many real-world applications in engineering fields can be modeled as LSMOPs; simultaneously, engineering applications require insensitiv... | ['Gary G. Yen', 'Min Jiang', 'Haokai Hong'] | 2023-04-08 | null | null | null | null | ['multiobjective-optimization'] | ['methodology'] | [ 3.64026916e-03 -4.74879205e-01 4.66030613e-02 -7.33411387e-02
-3.61964971e-01 -3.69864732e-01 -1.69324040e-01 1.25561401e-01
-6.95229992e-02 1.02109826e+00 -1.80900097e-01 -1.17885232e-01
-8.83891284e-01 -1.01980543e+00 -5.54637432e-01 -1.02290750e+00
-8.61572847e-02 4.43164170e-01 1.95198223e-01 -4.09184605... | [5.730721950531006, 3.5296080112457275] |
883fbc79-4634-496c-a49d-76d41d04b42c | quaternion-matrix-completion-using-untrained | 2305.00416 | null | https://arxiv.org/abs/2305.00416v1 | https://arxiv.org/pdf/2305.00416v1.pdf | Quaternion Matrix Completion Using Untrained Quaternion Convolutional Neural Network for Color Image Inpainting | The use of quaternions as a novel tool for color image representation has yielded impressive results in color image processing. By considering the color image as a unified entity rather than separate color space components, quaternions can effectively exploit the strong correlation among the RGB channels, leading to en... | ['Juan Han', 'Liqiao Yang', 'Kit Ian Kou', 'Jifei Miao'] | 2023-04-30 | null | null | null | null | ['image-inpainting', 'matrix-completion'] | ['computer-vision', 'methodology'] | [-9.81498435e-02 -4.39703941e-01 2.13496368e-02 6.79339394e-02
-4.33400154e-01 -2.14561913e-03 2.92377293e-01 -1.23505116e-01
-7.29930639e-01 8.19588423e-01 -1.05341882e-01 -6.74589127e-02
1.67950943e-01 -6.25680685e-01 -5.77602863e-01 -7.98269510e-01
8.95089731e-02 -7.53836408e-02 -1.10737860e-01 -5.55691242... | [10.83678913116455, -1.7185115814208984] |
54e238bd-efd7-4611-9d25-db8f1e5d5e5f | robust-point-cloud-registration-framework-1 | 2211.04696 | null | https://arxiv.org/abs/2211.04696v1 | https://arxiv.org/pdf/2211.04696v1.pdf | Robust Point Cloud Registration Framework Based on Deep Graph Matching(TPAMI Version) | 3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matching-... | ['Manning Wang', 'Chenxi Zhang', 'Shaolei Liu', 'Xiaoyuan Luo', 'Jiazheng Luo', 'Kexue Fu'] | 2022-11-09 | null | null | null | null | ['point-cloud-registration', 'graph-matching'] | ['computer-vision', 'graphs'] | [-2.84982800e-01 -1.75742254e-01 1.90524876e-01 -3.87626499e-01
-6.89569354e-01 -2.31219366e-01 3.23799193e-01 2.45487317e-01
-1.89890459e-01 1.83630347e-01 -2.87673801e-01 -7.52178282e-02
1.25057446e-02 -1.03109276e+00 -1.00412083e+00 -5.56216896e-01
1.86618745e-01 6.48590207e-01 2.34501705e-01 -1.11763343... | [7.718042373657227, -3.111527919769287] |
dfb8bd41-6845-4375-8fd6-6dc45816e90a | attention-based-writer-independent | 2009.04532 | null | https://arxiv.org/abs/2009.04532v3 | https://arxiv.org/pdf/2009.04532v3.pdf | Attention based Writer Independent Handwriting Verification | The task of writer verification is to provide a likelihood score for whether the queried and known handwritten image samples belong to the same writer or not. Such a task calls for the neural network to make it's outcome interpretable, i.e. provide a view into the network's decision making process. We implement and int... | ['Mihir Chauhan', 'Mohammad Abuzar Shaikh', 'Tiehang Duan', 'Sargur Srihari'] | 2020-09-07 | null | null | null | null | ['handwriting-verification'] | ['computer-vision'] | [ 1.85462058e-01 6.49765730e-02 -5.88378161e-02 -8.24939787e-01
-5.91888070e-01 -6.10684335e-01 3.41206104e-01 -1.40423074e-01
-2.63157710e-02 4.01082724e-01 2.48453707e-01 -1.33887053e-01
-1.81145072e-01 -4.90442902e-01 -4.91894662e-01 -6.95689976e-01
3.69201988e-01 2.23982170e-01 -1.48672640e-01 1.13766223... | [11.079718589782715, 2.1311075687408447] |
a64b16c4-9c29-48b6-8b6e-a1c5a5cce45f | on-recoverability-of-graph-neural-network | 2201.12843 | null | https://arxiv.org/abs/2201.12843v4 | https://arxiv.org/pdf/2201.12843v4.pdf | Graph Representation Learning via Aggregation Enhancement | Graph neural networks (GNNs) have become a powerful tool for processing graph-structured data but still face challenges in effectively aggregating and propagating information between layers, which limits their performance. We tackle this problem with the kernel regression (KR) approach, using KR loss as the primary los... | ['Avi Mendelson', 'Ron Banner', 'Almog David', 'Evgenii Zheltonozhskii', 'Chaim Baskin', 'Maxim Fishman'] | 2022-01-30 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 2.42654979e-01 3.60822201e-01 -3.71534258e-01 -3.29789370e-01
-5.60195923e-01 -6.10762775e-01 4.97427791e-01 6.10400259e-01
-4.92326349e-01 7.12231159e-01 1.27151474e-01 -6.34733975e-01
-3.75083774e-01 -1.16451550e+00 -7.89502561e-01 -6.10826254e-01
-5.25317013e-01 4.01268989e-01 -7.94824064e-02 -1.22919671... | [7.074853897094727, 6.234947681427002] |
d211f2b3-dd99-4322-b59d-a6f8cfae4862 | co-mining-self-supervised-learning-for | 2012.01950 | null | https://arxiv.org/abs/2012.01950v2 | https://arxiv.org/pdf/2012.01950v2.pdf | Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection | Object detectors usually achieve promising results with the supervision of complete instance annotations. However, their performance is far from satisfactory with sparse instance annotations. Most existing methods for sparsely annotated object detection either re-weight the loss of hard negative samples or convert the ... | ['Xiangyu Zhang', 'Jiale Cao', 'Tong Yang', 'Tiancai Wang'] | 2020-12-03 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-1.86679326e-02 1.51204944e-01 -6.22244120e-01 -4.01211530e-01
-7.61552989e-01 -1.68072134e-01 1.75045729e-01 -1.74737364e-01
-4.10577178e-01 5.79590380e-01 -9.63353813e-02 2.04934001e-01
3.83713573e-01 -4.12267029e-01 -7.80290902e-01 -7.43335605e-01
1.29560336e-01 4.50004429e-01 7.52370417e-01 6.41307756... | [9.17454719543457, 1.347322702407837] |
1f8aaa40-4157-4c3f-9434-f7688158f622 | mgfn-magnitude-contrastive-glance-and-focus | 2211.15098 | null | https://arxiv.org/abs/2211.15098v1 | https://arxiv.org/pdf/2211.15098v1.pdf | MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection | Weakly supervised detection of anomalies in surveillance videos is a challenging task. Going beyond existing works that have deficient capabilities to localize anomalies in long videos, we propose a novel glance and focus network to effectively integrate spatial-temporal information for accurate anomaly detection. In a... | ['Yik-Chung Wu', 'Xiaojuan Qi', 'Wilton Fok', 'Baoheng Zhang', 'Zhengzhe Liu', 'Yingxian Chen'] | 2022-11-28 | null | null | null | null | ['video-anomaly-detection', 'anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 1.48010075e-01 -4.77691352e-01 -4.34608199e-02 -6.16089880e-01
-4.67338502e-01 -4.94651437e-01 6.59285724e-01 2.10197404e-01
-2.76614219e-01 1.90395594e-01 3.24318290e-01 -1.73432633e-01
2.03411989e-02 -3.83229792e-01 -7.42060661e-01 -4.98638123e-01
-5.86201727e-01 -5.16714752e-01 4.88879502e-01 -4.45446149... | [7.864446640014648, 1.528595209121704] |
b457b544-f3c0-4b16-8a73-c24733f1d1c0 | phonetic-and-visual-priors-for-decipherment | 2005.02517 | null | https://arxiv.org/abs/2005.02517v1 | https://arxiv.org/pdf/2005.02517v1.pdf | Phonetic and Visual Priors for Decipherment of Informal Romanization | Informal romanization is an idiosyncratic process used by humans in informal digital communication to encode non-Latin script languages into Latin character sets found on common keyboards. Character substitution choices differ between users but have been shown to be governed by the same main principles observed across ... | ['Taylor Berg-Kirkpatrick', 'Maria Ryskina', 'Matthew R. Gormley'] | 2020-05-05 | phonetic-and-visual-priors-for-decipherment-1 | https://aclanthology.org/2020.acl-main.737 | https://aclanthology.org/2020.acl-main.737.pdf | acl-2020-6 | ['decipherment'] | ['natural-language-processing'] | [ 4.14344579e-01 -3.52846347e-02 -2.78894752e-01 -5.29524744e-01
-5.27442753e-01 -1.15956295e+00 7.97115445e-01 -3.12992066e-01
-6.23849511e-01 6.58878446e-01 4.91930604e-01 -5.11209548e-01
2.34465554e-01 -3.65239948e-01 -6.57025933e-01 -7.23923445e-02
5.44726312e-01 8.84612858e-01 -2.21054345e-01 -3.35672677... | [11.28786563873291, 9.94622802734375] |
e2782b31-9402-4382-b6dc-624e989eef93 | a-study-of-transfer-learning-in-music-source | 2010.12650 | null | https://arxiv.org/abs/2010.12650v1 | https://arxiv.org/pdf/2010.12650v1.pdf | A Study of Transfer Learning in Music Source Separation | Supervised deep learning methods for performing audio source separation can be very effective in domains where there is a large amount of training data. While some music domains have enough data suitable for training a separation system, such as rock and pop genres, many musical domains do not, such as classical music,... | ['Prem Seetharaman', 'Bryan Pardo', 'Andreas Bugler'] | 2020-10-23 | null | null | null | null | ['audio-source-separation', 'music-source-separation'] | ['audio', 'music'] | [ 1.70825496e-01 -7.33867586e-02 3.37479301e-02 -2.92222857e-01
-6.97645783e-01 -6.59986496e-01 3.23379278e-01 1.39887750e-01
-5.30767322e-01 5.62378049e-01 5.32520294e-01 -1.12163544e-01
-2.21261635e-01 -5.91969728e-01 -5.81919789e-01 -6.61310971e-01
-1.42855734e-01 5.77914178e-01 1.97952911e-01 -3.47284138... | [15.775272369384766, 5.294350624084473] |
5c1d7079-25f1-48f4-b97c-340154e0b8e1 | improving-gans-for-long-tailed-data-through | 2208.09932 | null | https://arxiv.org/abs/2208.09932v1 | https://arxiv.org/pdf/2208.09932v1.pdf | Improving GANs for Long-Tailed Data through Group Spectral Regularization | Deep long-tailed learning aims to train useful deep networks on practical, real-world imbalanced distributions, wherein most labels of the tail classes are associated with a few samples. There has been a large body of work to train discriminative models for visual recognition on long-tailed distribution. In contrast, w... | ['R. Venkatesh Babu', 'Varun Jampani', 'Tejan Karmali', 'Naman Jaswani', 'Harsh Rangwani'] | 2022-08-21 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 2.50587672e-01 -2.65340388e-01 -1.77921951e-01 -3.96355659e-01
-9.08985019e-01 -3.86570275e-01 6.94381952e-01 -3.12886387e-01
-4.13037129e-02 7.39481926e-01 1.44465908e-01 -1.74744368e-01
2.45497227e-01 -6.56370163e-01 -8.67085636e-01 -9.84296858e-01
2.40272939e-01 6.68561220e-01 -7.49684200e-02 -5.09223789... | [9.595014572143555, 2.9111335277557373] |
c615e08b-f6c6-433a-89a5-a22c97b2f735 | bag-of-tricks-for-natural-policy-gradient | 2201.09104 | null | https://arxiv.org/abs/2201.09104v2 | https://arxiv.org/pdf/2201.09104v2.pdf | Understanding the Effects of Second-Order Approximations in Natural Policy Gradient Reinforcement Learning | Natural policy gradient methods are popular reinforcement learning methods that improve the stability of policy gradient methods by utilizing second-order approximations to precondition the gradient with the inverse of the Fisher-information matrix. However, to the best of the authors' knowledge, there has not been a s... | ['David A. Clausi', 'Alexander Wong', 'Brennan Gebotys'] | 2022-01-22 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-6.25455022e-01 -6.34755343e-02 -5.10973394e-01 -1.12085462e-01
-6.43961608e-01 -4.66959536e-01 7.33735383e-01 -3.16772424e-02
-7.75003374e-01 9.82601523e-01 3.68379980e-01 -6.47797942e-01
-1.29157737e-01 -3.39786381e-01 -6.53955579e-01 -5.69449425e-01
-1.07477650e-01 1.57317415e-01 3.53712738e-01 -2.26019278... | [4.113807201385498, 2.3905954360961914] |
2596f4b8-09dc-4476-b92b-9937a3f22adc | k-plug-knowledge-injected-pre-trained | null | null | https://openreview.net/forum?id=5WcLI0e3cAY | https://openreview.net/pdf?id=5WcLI0e3cAY | K-PLUG: KNOWLEDGE-INJECTED PRE-TRAINED LANGUAGE MODEL FOR NATURAL LANGUAGE UNDERSTANDING AND GENERATION | Existing pre-trained language models (PLMs) have demonstrated the effectiveness of self-supervised learning for a broad range of natural language processing (NLP) tasks. However, most of them are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domains, such as tasks in... | ['BoWen Zhou', 'Ying Liu', 'Xiaodong He', 'Youzheng Wu', 'Yujia Wang', 'Peng Yuan', 'Haoran Li', 'Song Xu'] | 2021-01-01 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [ 1.97346047e-01 5.55848777e-01 -6.52657092e-01 -5.75440109e-01
-1.21357131e+00 -9.78552997e-01 6.60371482e-01 2.48056874e-01
-1.08460568e-01 8.82100463e-01 6.64616168e-01 -2.99866527e-01
6.81061372e-02 -8.52850199e-01 -1.10138857e+00 -1.07290208e-01
1.90486908e-01 8.26408446e-01 -2.90300101e-01 -6.55382454... | [11.151171684265137, 8.366984367370605] |
e397da90-e2bb-4869-9e2f-c8a8bec45863 | joint-community-detection-and-rotational | 2105.06031 | null | https://arxiv.org/abs/2105.06031v1 | https://arxiv.org/pdf/2105.06031v1.pdf | Joint Community Detection and Rotational Synchronization via Semidefinite Programming | In the presence of heterogeneous data, where randomly rotated objects fall into multiple underlying categories, it is challenging to simultaneously classify them into clusters and synchronize them based on pairwise relations. This gives rise to the joint problem of community detection and synchronization. We propose a ... | ['Zhizhen Zhao', 'Yuehaw Khoo', 'Yifeng Fan'] | 2021-05-13 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 1.32316247e-01 -1.34366721e-01 -3.31807107e-01 1.61060810e-01
-6.65554881e-01 -8.81775022e-01 3.15540284e-01 3.02816957e-01
-2.21474677e-01 6.78245127e-01 1.36508167e-01 -1.13838822e-01
-4.89769310e-01 -3.18332583e-01 -5.56535125e-01 -1.15784538e+00
-4.48412925e-01 8.52865577e-01 -3.57110240e-02 2.83448547... | [6.919951915740967, 5.0616631507873535] |
5099fe6f-09dd-4ced-ac40-ce31f42364c3 | acq-improving-generative-data-free | 2301.07266 | null | https://arxiv.org/abs/2301.07266v1 | https://arxiv.org/pdf/2301.07266v1.pdf | ACQ: Improving Generative Data-free Quantization Via Attention Correction | Data-free quantization aims to achieve model quantization without accessing any authentic sample. It is significant in an application-oriented context involving data privacy. Converting noise vectors into synthetic samples through a generator is a popular data-free quantization method, which is called generative data-f... | ['Huaxiang Lu', 'Wenyu Mao', 'Gang Chen', 'Min Jin', 'Guoliang Gong', 'Benzhe Dai', 'Xiaozhou Guo', 'Jixing Li'] | 2023-01-18 | null | null | null | null | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 1.21616744e-01 -1.89904839e-01 -2.88132608e-01 -4.70986366e-01
-8.74550760e-01 -4.14681107e-01 3.68366182e-01 -8.38019699e-02
-5.74060500e-01 7.86258280e-01 -5.30477799e-02 -1.01608172e-01
2.31197476e-01 -9.57583964e-01 -6.61510468e-01 -9.13360596e-01
5.64267159e-01 -6.96410006e-03 -1.19372867e-02 -5.29124700... | [8.791245460510254, 3.0163979530334473] |
264da34e-314c-4930-8b1e-2c3ab0e74371 | hand-gesture-recognition-through-reflected | 2301.05955 | null | https://arxiv.org/abs/2301.05955v2 | https://arxiv.org/pdf/2301.05955v2.pdf | Hand Gesture Recognition through Reflected Infrared Light Wave Signals | In this study, we present a wireless (non-contact) gesture recognition method using only incoherent light wave signals reflected from a human subject. In comparison to existing radar, light shadow, sound and camera-based sensing systems, this technology uses a low-cost ubiquitous light source (e.g., infrared LED) to se... | ['Li Yu', 'Md Zobaer Islam', 'Sabit Ekin', 'Christopher Crick', "John F. O'Hara", 'Hisham Abuella'] | 2023-01-14 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 8.68340969e-01 -6.15928471e-01 2.77281851e-01 -1.46390989e-01
-1.58895981e-02 -5.93235314e-01 3.75315487e-01 -8.49599600e-01
-7.81467080e-01 6.64892972e-01 -1.64907381e-01 -1.13291509e-01
2.43726850e-01 -9.78846908e-01 5.01293465e-02 -1.10347319e+00
5.43826640e-01 -7.51766264e-02 3.03511053e-01 1.29413769... | [6.561287879943848, 0.1249130591750145] |
5f0490f9-1166-4cf5-8e2e-b654db8bfb78 | a-joint-intensity-and-depth-co-sparse | 1304.5319 | null | http://arxiv.org/abs/1304.5319v1 | http://arxiv.org/pdf/1304.5319v1.pdf | A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution | High-resolution depth maps can be inferred from low-resolution depth
measurements and an additional high-resolution intensity image of the same
scene. To that end, we introduce a bimodal co-sparse analysis model, which is
able to capture the interdependency of registered intensity and depth
information. This model is b... | ['Simon Hawe', 'Martin Kiechle', 'Martin Kleinsteuber'] | 2013-04-19 | null | null | null | null | ['depth-map-super-resolution'] | ['computer-vision'] | [ 7.18359411e-01 1.24187909e-01 1.12751098e-02 -4.49854702e-01
-7.46964395e-01 -1.91940904e-01 6.49132609e-01 -2.79606832e-03
-1.90715045e-01 5.89312315e-01 1.84094459e-01 3.41763020e-01
-4.84729767e-01 -1.06404638e+00 -4.56291109e-01 -7.63411641e-01
4.31276746e-02 6.54003680e-01 3.35486472e-01 -3.01457345... | [9.528389930725098, -2.630035638809204] |
420d5bcf-0190-4fba-b89b-f755d3e6a97f | ernie-unix2-a-unified-cross-lingual-cross | 2211.04861 | null | https://arxiv.org/abs/2211.04861v1 | https://arxiv.org/pdf/2211.04861v1.pdf | ERNIE-UniX2: A Unified Cross-lingual Cross-modal Framework for Understanding and Generation | Recent cross-lingual cross-modal works attempt to extend Vision-Language Pre-training (VLP) models to non-English inputs and achieve impressive performance. However, these models focus only on understanding tasks utilizing encoder-only architecture. In this paper, we propose ERNIE-UniX2, a unified cross-lingual cross-m... | ['Haifeng Wang', 'Hua Wu', 'Hao Tian', 'Yu Sun', 'Shuohuan Wang', 'Weichong Yin', 'Yaqian Han', 'Bin Shan'] | 2022-11-09 | null | null | null | null | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 2.17719346e-01 8.00567269e-02 -3.09182703e-01 -4.69142467e-01
-1.65590680e+00 -7.17924654e-01 1.05004084e+00 -2.34448537e-01
-3.92221242e-01 7.00544477e-01 3.03157121e-01 -5.17314017e-01
5.87322712e-01 -6.39635801e-01 -1.02109873e+00 1.94272362e-02
5.85355222e-01 7.65921116e-01 -5.45182765e-01 -3.81031066... | [11.175018310546875, 1.610640287399292] |
96968888-1b76-4f97-826c-19152d2701b2 | learning-from-imperfect-training-data-using-a | 2208.04941 | null | https://arxiv.org/abs/2208.04941v1 | https://arxiv.org/pdf/2208.04941v1.pdf | Learning from imperfect training data using a robust loss function: application to brain image segmentation | Segmentation is one of the most important tasks in MRI medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, head segmentation is commonly used for measuring and visualizing the brain's anatomical structures and is also a necessary step for other ... | ['Richard M. Leahy', 'Anand A Joshi', 'Wenhui Cui', 'Haleh Akrami'] | 2022-08-08 | null | null | null | null | ['brain-image-segmentation'] | ['medical'] | [ 2.31213838e-01 -5.97912595e-02 2.24809900e-01 -5.67417800e-01
-3.58394712e-01 -1.24167286e-01 2.22511590e-01 2.77120143e-01
-7.46685624e-01 6.45331860e-01 5.22883656e-03 -3.73088419e-01
-1.03723437e-01 -4.37413782e-01 -5.34594595e-01 -7.63763905e-01
-1.17997132e-01 6.76186323e-01 2.22444698e-01 2.90034920... | [14.27866268157959, -2.2535173892974854] |
69fc128e-01d8-463e-975d-0b9b8cc1bc3f | multiresolution-attention-extractor-for-small | 2006.05941 | null | https://arxiv.org/abs/2006.05941v1 | https://arxiv.org/pdf/2006.05941v1.pdf | MultiResolution Attention Extractor for Small Object Detection | Small objects are difficult to detect because of their low resolution and small size. The existing small object detection methods mainly focus on data preprocessing or narrowing the differences between large and small objects. Inspired by human vision "attention" mechanism, we exploit two feature extraction methods to ... | ['Xu Liu', 'Licheng Jiao', 'Lingling Li', 'Fang Liu', 'Fan Zhang'] | 2020-06-10 | null | null | null | null | ['small-object-detection', 'hard-attention'] | ['computer-vision', 'methodology'] | [ 2.25766540e-01 7.48661608e-02 2.43759230e-01 -8.47068802e-03
-7.57712662e-01 -3.18867683e-01 5.38882613e-01 8.47767442e-02
-5.99819362e-01 2.36530498e-01 1.06436461e-01 6.23097196e-02
-6.15181699e-02 -7.67124414e-01 -7.32025445e-01 -7.31976926e-01
-7.82524943e-02 1.97372675e-01 8.06362808e-01 -2.84948915... | [8.985608100891113, 0.12181495130062103] |
594baaf7-4f68-480e-b1c6-e3fa341aabef | what-the-daam-interpreting-stable-diffusion | 2210.04885 | null | https://arxiv.org/abs/2210.04885v5 | https://arxiv.org/pdf/2210.04885v5.pdf | What the DAAM: Interpreting Stable Diffusion Using Cross Attention | Large-scale diffusion neural networks represent a substantial milestone in text-to-image generation, but they remain poorly understood, lacking interpretability analyses. In this paper, we perform a text-image attribution analysis on Stable Diffusion, a recently open-sourced model. To produce pixel-level attribution ma... | ['Pontus Stenetorp', 'Gefei Yang', 'Zhiying Jiang', 'Linqing Liu', 'Ferhan Ture', 'Jimmy Lin', 'Karun Kumar', 'Akshat Pandey', 'Raphael Tang'] | 2022-10-10 | null | null | null | null | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 2.11686701e-01 1.17049754e-01 -7.05978647e-02 -3.42708707e-01
-5.42821825e-01 -7.92587936e-01 1.13742220e+00 3.08769971e-01
-5.25863111e-01 3.07139963e-01 9.71859992e-01 -2.06762508e-01
-1.89891130e-01 -7.46375084e-01 -4.92012411e-01 -5.98524809e-01
2.52194732e-01 5.13361990e-01 -1.03198618e-01 -2.70601660... | [11.24045467376709, 0.2080158293247223] |
41df5ab0-dc66-4940-9bc8-671fbd352fed | a-chinese-math-word-problem-solving-system | null | null | https://aclanthology.org/2020.rocling-1.21 | https://aclanthology.org/2020.rocling-1.21.pdf | A Chinese Math Word Problem Solving System Based on Linguistic Theory and Non-statistical Approach | null | ['Hsin-Hung Lin', 'Chia-Ming Lee', 'Chien-yu Lai', 'Chia-Jung Chen', 'Wen-jet Wang'] | null | null | null | null | rocling-2020-9 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [-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... | [-7.423674583435059, 3.553525686264038] |
5453d730-b784-4f7e-84ea-9f7dbfb770ae | cross-lingual-alignment-of-contextual-word | 1902.09492 | null | http://arxiv.org/abs/1902.09492v2 | http://arxiv.org/pdf/1902.09492v2.pdf | Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing | We introduce a novel method for multilingual transfer that utilizes deep
contextual embeddings, pretrained in an unsupervised fashion. While contextual
embeddings have been shown to yield richer representations of meaning compared
to their static counterparts, aligning them poses a challenge due to their
dynamic nature... | ['Ori Ram', 'Regina Barzilay', 'Amir Globerson', 'Tal Schuster'] | 2019-02-25 | cross-lingual-alignment-of-contextual-word-1 | https://aclanthology.org/N19-1162 | https://aclanthology.org/N19-1162.pdf | naacl-2019-6 | ['cross-lingual-zero-shot-dependency-parsing'] | ['natural-language-processing'] | [-1.46698800e-03 -6.94220290e-02 -3.28060597e-01 -5.98462880e-01
-1.28673923e+00 -8.02974045e-01 7.57138431e-01 4.03073698e-01
-7.43523836e-01 6.54087663e-01 5.52944958e-01 -5.21996558e-01
4.25388932e-01 -5.82256854e-01 -7.65715897e-01 -4.92050260e-01
-2.03534812e-02 3.57272387e-01 8.52349401e-02 -3.42810392... | [10.697012901306152, 9.7193021774292] |
f4f11e78-cf87-4522-8cbf-17bb3b89b646 | galaxy-morphology-classification-using | 2008.13611 | null | https://arxiv.org/abs/2008.13611v2 | https://arxiv.org/pdf/2008.13611v2.pdf | Galaxy Morphology Classification using EfficientNet Architectures | We study the usage of EfficientNets and their applications to Galaxy Morphology Classification. We explore the usage of EfficientNets into predicting the vote fractions of the 79,975 testing images from the Galaxy Zoo 2 challenge on Kaggle. We evaluate this model using the standard competition metric i.e. rmse score an... | ['Pranav Parwate', 'Hrushikesh Pandit', 'Shreyas Kalvankar'] | 2020-08-31 | null | null | null | null | ['morphology-classification'] | ['computer-vision'] | [-6.29994750e-01 -1.75703347e-01 1.63594574e-01 -2.31800109e-01
-2.30939016e-01 -9.75923955e-01 7.66736031e-01 -2.02527538e-01
-5.26375830e-01 6.70021594e-01 1.50411308e-01 -4.84197438e-01
-4.58262593e-01 -9.32016730e-01 -4.95065451e-01 -6.70901597e-01
-2.98516333e-01 4.93570805e-01 9.14302886e-01 -1.63992271... | [7.967302322387695, 2.961817741394043] |
652fa91c-c3ae-48e5-9b2c-074b6178da80 | adversarial-image-alignment-and-interpolation | 1707.00067 | null | http://arxiv.org/abs/1707.00067v1 | http://arxiv.org/pdf/1707.00067v1.pdf | Adversarial Image Alignment and Interpolation | Volumetric (3d) images are acquired for many scientific and biomedical
purposes using imaging methods such as serial section microscopy, CT scans, and
MRI. A frequent step in the analysis and reconstruction of such data is the
alignment and registration of images that were acquired in succession along a
spatial or temp... | ['Viren Jain'] | 2017-06-30 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 6.42936707e-01 3.27605195e-02 5.39096653e-01 -4.83137280e-01
-7.81371772e-01 -4.57664073e-01 4.55515325e-01 7.76304305e-02
-7.91703045e-01 7.05775678e-01 -3.20392966e-01 4.43272926e-02
8.26275535e-03 -6.30360425e-01 -8.35179269e-01 -8.25096071e-01
-6.17566369e-02 1.05183399e+00 3.42727810e-01 7.84829110... | [13.687034606933594, -2.730955123901367] |
06bc4c8a-dfa4-4b14-b308-b3cf5bef7477 | argument-novelty-and-validity-assessment-via | null | null | https://aclanthology.org/2022.argmining-1.10 | https://aclanthology.org/2022.argmining-1.10.pdf | Argument Novelty and Validity Assessment via Multitask and Transfer Learning | An argument is a constellation of premises reasoning towards a certain conclusion. The automatic generation of conclusions is becoming a very prominent task, raising the need for automatic measures to assess the quality of these generated conclusions. The SharedTask at the 9th Workshop on Argument Mining proposes a new... | ['Maja Stahl', 'Milad Alshomary'] | null | null | null | null | argmining-acl-2022-10 | ['argument-mining'] | ['natural-language-processing'] | [ 2.09458604e-01 7.40761280e-01 -4.40992713e-01 -3.92771572e-01
-1.19246519e+00 -5.69028080e-01 1.43499768e+00 7.96523809e-01
-3.48474711e-01 1.07201731e+00 5.95998764e-01 -6.82846487e-01
-4.04767662e-01 -7.14591384e-01 -9.50740933e-01 -3.63037288e-01
2.28127271e-01 6.02326632e-01 3.25843871e-01 -2.05905467... | [9.581153869628906, 9.58338737487793] |
d7e24f4b-be59-47c2-a8a8-949316b16dcf | ptde-personalized-training-with-distillated | 2210.08872 | null | https://arxiv.org/abs/2210.08872v1 | https://arxiv.org/pdf/2210.08872v1.pdf | PTDE: Personalized Training with Distillated Execution for Multi-Agent Reinforcement Learning | Centralized Training with Decentralized Execution (CTDE) has been a very popular paradigm for multi-agent reinforcement learning. One of its main features is making full use of the global information to learn a better joint $Q$-function or centralized critic. In this paper, we in turn explore how to leverage the global... | ['Hongxing Chang', 'Bin Wang', 'Dong Li', 'Jianye Hao', 'Bin Zhang', 'Shiguang Wu', 'Tianle Zhang', 'Hangyu Mao', 'Yiqun Chen'] | 2022-10-17 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-6.08325839e-01 -1.28288642e-01 -5.16897500e-01 -2.25256667e-01
-8.07328284e-01 -4.16599005e-01 3.96199077e-01 6.91332370e-02
-7.38604844e-01 1.06845427e+00 -5.46576902e-02 -1.77855000e-01
-1.99718848e-01 -8.17068696e-01 -6.38105690e-01 -9.31208491e-01
-2.47262776e-01 7.13205218e-01 3.23464334e-01 -5.35030782... | [3.7430310249328613, 2.058781385421753] |
f5655ba5-58b8-4a6a-92e9-1bc82cc58580 | fast-sparse-classification-for-generalized | 2202.11389 | null | https://arxiv.org/abs/2202.11389v2 | https://arxiv.org/pdf/2202.11389v2.pdf | Fast Sparse Classification for Generalized Linear and Additive Models | We present fast classification techniques for sparse generalized linear and additive models. These techniques can handle thousands of features and thousands of observations in minutes, even in the presence of many highly correlated features. For fast sparse logistic regression, our computational speed-up over other bes... | ['Cynthia Rudin', 'Margo Seltzer', 'Chudi Zhong', 'Jiachang Liu'] | 2022-02-23 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 7.91556165e-02 -1.89763576e-01 -3.70522857e-01 -6.82301104e-01
-1.16471910e+00 -3.83383423e-01 3.37058485e-01 3.98824990e-01
-4.61293101e-01 9.57037926e-01 -7.27672055e-02 -3.63500655e-01
-5.12895346e-01 -6.73744977e-01 -5.17604947e-01 -6.08052254e-01
-4.48861003e-01 8.82193148e-01 4.98651601e-02 -1.39312353... | [7.655708312988281, 4.410341262817383] |
8dad28ae-7373-4f4c-be24-455131284713 | dataset-bias-in-human-activity-recognition | 2301.10161 | null | https://arxiv.org/abs/2301.10161v1 | https://arxiv.org/pdf/2301.10161v1.pdf | Dataset Bias in Human Activity Recognition | When creating multi-channel time-series datasets for Human Activity Recognition (HAR), researchers are faced with the issue of subject selection criteria. It is unknown what physical characteristics and/or soft-biometrics, such as age, height, and weight, need to be taken into account to train a classifier to achieve r... | ['Christopher Reining', 'Gernot A. Fink', 'Markus Pauly', 'Fernando Moya Rueda', 'Lena Schmid', 'Nilah Ravi Nair'] | 2023-01-19 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 2.43678421e-01 -2.28471816e-01 -8.68904293e-02 -2.63478100e-01
-4.00948852e-01 -1.75216660e-01 4.02044326e-01 2.40395084e-01
-6.85423791e-01 5.38607478e-01 4.92706925e-01 -5.09663559e-02
-3.35132480e-01 -6.56349957e-01 -7.60923803e-01 -8.37650597e-01
-4.68267471e-01 -3.36268283e-02 -1.66976348e-01 -1.43480048... | [13.725889205932617, 2.1219069957733154] |
c82b3ea6-c650-4f8d-bbfc-ddf4bcc9d619 | towards-speech-emotion-recognition-in-the | 1708.03920 | null | http://arxiv.org/abs/1708.03920v1 | http://arxiv.org/pdf/1708.03920v1.pdf | Towards Speech Emotion Recognition "in the wild" using Aggregated Corpora and Deep Multi-Task Learning | One of the challenges in Speech Emotion Recognition (SER) "in the wild" is
the large mismatch between training and test data (e.g. speakers and tasks). In
order to improve the generalisation capabilities of the emotion models, we
propose to use Multi-Task Learning (MTL) and use gender and naturalness as
auxiliary tasks... | ['Khiet P. Truong', 'Jaebok Kim', 'Gwenn Englebienne', 'Vanessa Evers'] | 2017-08-13 | null | null | null | null | ['cross-corpus'] | ['computer-vision'] | [-4.38053906e-03 1.06396288e-01 4.27002907e-01 -6.63371146e-01
-7.50886500e-01 -4.08972025e-01 7.70519733e-01 2.12725811e-02
-6.41300619e-01 5.88030517e-01 1.43896058e-01 5.59204109e-02
8.99656340e-02 -4.16330881e-02 -4.48043704e-01 -7.57122338e-01
-1.04053475e-01 3.78101885e-01 -2.56060839e-01 -2.87632555... | [13.580784797668457, 5.819431304931641] |
a2e38de8-44c4-4d4b-b0d0-6b9b3558df45 | improving-astrobert-using-semantic-textual | 2212.00744 | null | https://arxiv.org/abs/2212.00744v1 | https://arxiv.org/pdf/2212.00744v1.pdf | Improving astroBERT using Semantic Textual Similarity | The NASA Astrophysics Data System (ADS) is an essential tool for researchers that allows them to explore the astronomy and astrophysics scientific literature, but it has yet to exploit recent advances in natural language processing. At ADASS 2021, we introduced astroBERT, a machine learning language model tailored to t... | ['Pavlos Protopapas', 'Taylor Jacovich', 'Jennifer Koch', 'Shinyi Chen', 'Kelly E. Lockhart', 'Matthew R. Templeton', 'Timothy W. Hostetler', 'Donna M. Thompson', 'Carolyn S. Grant', 'Edwin Henneken', 'Golnaz Shapurian', 'Michael J. Kurtz', 'Alberto Accomazzi', 'Sergi Blanco-Cuaresma', 'Thomas Allen', 'Felix Grezes'] | 2022-11-29 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [-8.33014607e-01 -2.97344536e-01 -4.83667523e-01 6.92101270e-02
-6.37470424e-01 -1.11830461e+00 1.11521101e+00 7.59531796e-01
-2.36603156e-01 6.33910000e-01 4.99842644e-01 -1.09218645e+00
-4.19896930e-01 -8.09476793e-01 -5.62393963e-01 2.37202328e-02
-2.38655031e-01 4.64990526e-01 -1.39231920e-01 1.36435658... | [9.70857048034668, 8.276421546936035] |
a0446e61-5369-4a39-866a-5cf2d6af6c30 | gibbs-duhem-informed-neural-networks-for | 2306.07937 | null | https://arxiv.org/abs/2306.07937v1 | https://arxiv.org/pdf/2306.07937v1.pdf | Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Prediction | We propose Gibbs-Duhem-informed neural networks for the prediction of binary activity coefficients at varying compositions. That is, we include the Gibbs-Duhem equation explicitly in the loss function for training neural networks, which is straightforward in standard machine learning (ML) frameworks enabling automatic ... | ['Alexander Mitsos', 'Alexei A. Lapkin', 'Kobi C. Felton', 'Jan G. Rittig'] | 2023-05-31 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 2.14190423e-01 1.65010810e-01 -3.74984384e-01 -4.18212056e-01
-2.18836322e-01 -1.60541832e-01 5.49226165e-01 6.61844090e-02
-3.46867234e-01 1.08689511e+00 -1.35395870e-01 -2.98994333e-01
-3.02709699e-01 -7.98315346e-01 -8.34042907e-01 -1.02630389e+00
-8.78657866e-03 5.08809566e-01 5.08245043e-02 -3.72071028... | [5.307944297790527, 5.344221591949463] |
9938e331-c847-4405-81cd-9fb926863a56 | an-empirical-evaluation-of-zero-resource | 1702.01360 | null | http://arxiv.org/abs/1702.01360v1 | http://arxiv.org/pdf/1702.01360v1.pdf | An Empirical Evaluation of Zero Resource Acoustic Unit Discovery | Acoustic unit discovery (AUD) is a process of automatically identifying a
categorical acoustic unit inventory from speech and producing corresponding
acoustic unit tokenizations. AUD provides an important avenue for unsupervised
acoustic model training in a zero resource setting where expert-provided
linguistic knowled... | ['Sanjeev Khudanpur', 'Santosh Kesiraju', 'Pegah Ghahremani', 'Jinyi Yang', 'Chunxi Liu', 'Najim Dehak', 'Lucas Ondel', 'Ming Sun', 'Lukas Burget', 'Alena Rott'] | 2017-02-05 | null | null | null | null | ['acoustic-unit-discovery'] | ['speech'] | [ 2.10270643e-01 8.94732680e-03 -2.86826432e-01 -3.04968357e-01
-1.55204296e+00 -7.20739603e-01 3.64050150e-01 3.78966071e-02
-5.99065304e-01 4.64563310e-01 6.33910120e-01 -6.88422620e-01
3.50771993e-01 -3.97872537e-01 -5.52708626e-01 -2.19138369e-01
-1.06089212e-01 4.15701926e-01 -1.68577164e-01 -1.76170971... | [14.424230575561523, 6.800588130950928] |
9529c1b6-f13f-486e-a1d8-09ce9a29700b | a-survey-of-video-based-action-quality | 2204.09271 | null | https://arxiv.org/abs/2204.09271v1 | https://arxiv.org/pdf/2204.09271v1.pdf | A Survey of Video-based Action Quality Assessment | Human action recognition and analysis have great demand and important application significance in video surveillance, video retrieval, and human-computer interaction. The task of human action quality evaluation requires the intelligent system to automatically and objectively evaluate the action completed by the human. ... | ['Lihua Zhang', 'Ka Li', 'Zhan Sun', 'Tao Suo', 'Qing Yu', 'Peng Zhai', 'Dingkang Yang', 'Shunli Wang'] | 2022-04-20 | null | null | null | null | ['action-quality-assessment'] | ['computer-vision'] | [ 3.70305628e-01 -1.84929013e-01 -7.60567009e-01 -1.60286248e-01
-6.11772597e-01 -2.67090321e-01 3.01263154e-01 -6.70125633e-02
-6.56584322e-01 4.75034475e-01 6.96223915e-01 2.09137067e-01
-2.27316618e-01 -7.30511010e-01 -2.65183579e-02 -6.17111862e-01
1.70547605e-01 -1.51409313e-01 3.23709637e-01 1.34238914... | [7.965075969696045, 0.4201321005821228] |
3a365f30-9419-4a65-a0d0-1b1265de74f4 | norwegian-native-language-identification | null | null | https://aclanthology.org/R15-1053 | https://aclanthology.org/R15-1053.pdf | Norwegian Native Language Identification | null | ['Mark Dras', 'Irina Temnikova', 'Shervin Malmasi'] | 2015-09-01 | norwegian-native-language-identification-1 | https://aclanthology.org/R15-1053 | https://aclanthology.org/R15-1053.pdf | ranlp-2015-9 | ['native-language-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.423096179962158, 3.8073649406433105] |
f8f9cc21-21c9-4347-be19-922538affff8 | r3-refined-retriever-reader-pipeline-for | null | null | https://aclanthology.org/2022.dialdoc-1.17 | https://aclanthology.org/2022.dialdoc-1.17.pdf | R3 : Refined Retriever-Reader pipeline for Multidoc2dial | In this paper, we present our submission to the DialDoc shared task based on the MultiDoc2Dial dataset. MultiDoc2Dial is a conversational question answering dataset that grounds dialogues in multiple documents. The task involves grounding a user’s query in a document followed by generating an appropriate response. We p... | ['Eric Nyberg', 'Teruko Mitamura', 'Ritam Dutt', 'Aditya Srikanth Veerubhotla', 'Sireesh Gururaja', 'Sumit Agarwal', 'Suraj Tripathi', 'Srijan Bansal'] | null | null | null | null | dialdoc-acl-2022-5 | ['passage-retrieval'] | ['natural-language-processing'] | [ 1.14046149e-01 7.69432068e-01 2.59910643e-01 -4.18682784e-01
-1.93710327e+00 -5.64168215e-01 1.24493575e+00 4.12753999e-01
-2.83512533e-01 1.08440042e+00 1.26367223e+00 -3.45435232e-01
3.97274680e-02 -6.55161858e-01 -6.24671280e-01 -1.49036214e-01
2.16319263e-01 1.14275861e+00 3.25855613e-01 -1.05072761... | [12.400299072265625, 8.090709686279297] |
8ad921a6-335e-4fbb-b596-6f44912e4b59 | shot-in-the-dark-few-shot-learning-with-no-1 | 2010.02430 | null | https://arxiv.org/abs/2010.02430v2 | https://arxiv.org/pdf/2010.02430v2.pdf | Shot in the Dark: Few-Shot Learning with No Base-Class Labels | Few-shot learning aims to build classifiers for new classes from a small number of labeled examples and is commonly facilitated by access to examples from a distinct set of 'base classes'. The difference in data distribution between the test set (novel classes) and the base classes used to learn an inductive bias often... | ['Erik Learned-Miller', 'Subhransu Maji', 'Zitian Chen'] | 2020-10-06 | shot-in-the-dark-few-shot-learning-with-no | null | null | null | ['unsupervised-few-shot-learning', 'unsupervised-few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 3.93922031e-01 2.98857093e-01 -3.99555355e-01 -7.35311806e-01
-6.13422990e-01 -2.80809075e-01 6.54783845e-01 3.02291363e-01
-5.05457520e-01 9.13099229e-01 7.23101720e-02 2.63839245e-01
3.37421261e-02 -8.98877382e-01 -6.24504507e-01 -8.54731202e-01
1.52833670e-01 4.89379674e-01 4.93651062e-01 -2.66951144... | [9.978893280029297, 2.990468740463257] |
c0718dea-69c5-4368-83e4-2c4c01eb0609 | multiple-sequence-alignment-is-not-a-solved | 1808.07717 | null | http://arxiv.org/abs/1808.07717v1 | http://arxiv.org/pdf/1808.07717v1.pdf | Multiple Sequence Alignment is not a Solved Problem | Multiple sequence alignment is a basic procedure in molecular biology, and it
is often treated as being essentially a solved computational problem. However,
this is not so, and here I review the evidence for this claim, and outline the
requirements for a solution. The goal of alignment is often stated to be to
juxtapos... | [] | 2018-08-23 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 5.16969621e-01 -2.89172024e-01 -9.95266214e-02 -2.74601668e-01
-2.43715525e-01 -8.22349429e-01 3.67984176e-01 3.72089356e-01
-4.07481283e-01 9.73270774e-01 -8.39188695e-02 -7.15796053e-01
-2.30900109e-01 -4.51120943e-01 -4.40498143e-01 -1.02930558e+00
7.70471320e-02 6.72281861e-01 4.52155232e-01 -4.04775232... | [4.8786492347717285, 5.230973720550537] |
5d895066-ca2c-428c-bef9-de941eac07e9 | vint-a-foundation-model-for-visual-navigation | 2306.14846 | null | https://arxiv.org/abs/2306.14846v1 | https://arxiv.org/pdf/2306.14846v1.pdf | ViNT: A Foundation Model for Visual Navigation | General-purpose pre-trained models ("foundation models") have enabled practitioners to produce generalizable solutions for individual machine learning problems with datasets that are significantly smaller than those required for learning from scratch. Such models are typically trained on large and diverse datasets with... | ['Sergey Levine', 'Noriaki Hirose', 'Kevin Black', 'Kyle Stachowicz', 'Nitish Dashora', 'Ajay Sridhar', 'Dhruv Shah'] | 2023-06-26 | null | null | null | null | ['visual-navigation'] | ['robots'] | [-1.67778596e-01 2.82182634e-01 -2.48837277e-01 -2.24595010e-01
-6.27635181e-01 -9.73650575e-01 5.06780386e-01 -3.43818069e-01
-4.63983506e-01 4.38961565e-01 1.97340116e-01 -6.95013165e-01
-3.35066408e-01 -4.79578793e-01 -1.01449573e+00 -3.26597244e-01
-2.96674103e-01 5.95880151e-01 2.71474510e-01 -9.24238741... | [4.504004955291748, 0.6575742959976196] |
3969186a-eeb7-4900-8ed9-80b15437c6c7 | a-simple-and-general-graph-neural-network | 2009.02562 | null | https://arxiv.org/abs/2009.02562v2 | https://arxiv.org/pdf/2009.02562v2.pdf | Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing | Graph neural networks (GNNs) are emerging machine learning models on graphs. Permutation-equivariance and proximity-awareness are two important properties highly desirable for GNNs. Both properties are needed to tackle some challenging graph problems, such as finding communities and leaders. In this paper, we first ana... | ['Jian Pei', 'Peng Cui', 'Ziwei Zhang', 'Bo Zhang', 'Wenwu Zhu', 'Chenhao Niu'] | 2020-09-05 | null | https://openreview.net/forum?id=fhcMwjavKEZ | https://openreview.net/pdf?id=fhcMwjavKEZ | null | ['graph-reconstruction'] | ['graphs'] | [ 1.02290206e-01 2.02106059e-01 -4.28309470e-01 -1.54364526e-01
-1.12018555e-01 -3.31799954e-01 4.52752173e-01 4.67199773e-01
8.85626599e-02 6.93180025e-01 -1.37449317e-02 -5.33891737e-01
-7.06601501e-01 -1.20891285e+00 -8.86715651e-01 -8.47407460e-01
-8.60792398e-01 4.48959261e-01 2.10274190e-01 -4.99844849... | [7.064744472503662, 6.124591827392578] |
dc42423c-e2f5-4f6c-9317-6ee70b85be22 | bias-reducing-multitask-learning-on-mental | 2208.03621 | null | https://arxiv.org/abs/2208.03621v1 | https://arxiv.org/pdf/2208.03621v1.pdf | Bias Reducing Multitask Learning on Mental Health Prediction | There has been an increase in research in developing machine learning models for mental health detection or prediction in recent years due to increased mental health issues in society. Effective use of mental health prediction or detection models can help mental health practitioners re-define mental illnesses more obje... | ['Akane Sano', 'Han Yu', 'Kusha Sridhar', 'Khadija Zanna'] | 2022-08-07 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 3.23814243e-01 2.12495834e-01 -4.36354935e-01 -5.14070690e-01
-6.02211475e-01 6.47544712e-02 1.22688547e-01 6.33823335e-01
-4.94118035e-01 7.55290449e-01 6.52926922e-01 -2.39606321e-01
-5.99075794e-01 -6.66240513e-01 -2.22658813e-01 -4.52737629e-01
-2.15285689e-01 1.83959708e-01 -4.27111208e-01 -1.39998659... | [13.556145668029785, 3.3938655853271484] |
60dc86e9-92c4-4f30-98ca-d502c19fccd1 | an-algorithm-with-optimal-dimension | 2307.04504 | null | https://arxiv.org/abs/2307.04504v1 | https://arxiv.org/pdf/2307.04504v1.pdf | An Algorithm with Optimal Dimension-Dependence for Zero-Order Nonsmooth Nonconvex Stochastic Optimization | We study the complexity of producing $(\delta,\epsilon)$-stationary points of Lipschitz objectives which are possibly neither smooth nor convex, using only noisy function evaluations. Recent works proposed several stochastic zero-order algorithms that solve this task, all of which suffer from a dimension-dependence of ... | ['Ohad Shamir', 'Guy Kornowski'] | 2023-07-10 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-1.68956071e-01 3.65072370e-01 1.44058257e-01 8.41630176e-02
-1.21593356e+00 -5.35494685e-01 -3.13883483e-01 7.18452707e-02
-5.86638570e-01 1.15353596e+00 -1.36537999e-01 -2.06127942e-01
-6.30044401e-01 -6.01042092e-01 -1.06246483e+00 -1.17545605e+00
-5.32986760e-01 4.46529120e-01 3.25897895e-02 -4.95618373... | [6.536753177642822, 4.4715752601623535] |
2ee0d30e-11e8-4953-a2ad-fe01dc31fd4f | robustswap-a-simple-yet-robust-face-swapping | 2303.15768 | null | https://arxiv.org/abs/2303.15768v1 | https://arxiv.org/pdf/2303.15768v1.pdf | RobustSwap: A Simple yet Robust Face Swapping Model against Attribute Leakage | Face swapping aims at injecting a source image's identity (i.e., facial features) into a target image, while strictly preserving the target's attributes, which are irrelevant to identity. However, we observed that previous approaches still suffer from source attribute leakage, where the source image's attributes interf... | ['Jaegul Choo', 'Younggun Lee', 'Sunghyun Park', 'Taewoo Kim', 'Jaeseong Lee'] | 2023-03-28 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 3.89406025e-01 2.98407584e-01 -2.39362568e-01 -2.66860515e-01
-6.63596809e-01 -9.24324989e-01 6.12690687e-01 -8.09284866e-01
1.06037989e-01 5.96291780e-01 3.79487842e-01 2.87956707e-02
2.36648381e-01 -5.27313232e-01 -8.52879226e-01 -8.32111001e-01
2.56414294e-01 -1.36066020e-01 -3.89354914e-01 -1.19971670... | [12.752466201782227, 0.030547354370355606] |
2035c1cc-ba85-4a7a-a4ce-338ca38bdfc9 | accented-speech-recognition-benchmarking-pre | 2205.08014 | null | https://arxiv.org/abs/2205.08014v1 | https://arxiv.org/pdf/2205.08014v1.pdf | Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Data | Building inclusive speech recognition systems is a crucial step towards developing technologies that speakers of all language varieties can use. Therefore, ASR systems must work for everybody independently of the way they speak. To accomplish this goal, there should be available data sets representing language varietie... | ['Gary Wang', 'Suzan Schwartz', 'Andrew Rosenberg', 'Bhuvana Ramabhadran', 'Levi King', 'Wei Han', 'Pavel Golik', 'Daan van Esch', 'Chung-Cheng Chiu', 'Zhehuai Chen', 'Alëna Aksënova'] | 2022-05-16 | null | null | null | null | ['accented-speech-recognition'] | ['speech'] | [-6.58181161e-02 -7.67123029e-02 5.24374135e-02 -7.88705826e-01
-9.08666432e-01 -7.76017606e-01 3.58582616e-01 -1.22214794e-01
-4.78563935e-01 2.88851440e-01 6.10393286e-01 -6.78481698e-01
2.63508558e-01 -4.93703365e-01 -5.23322225e-02 -5.81687033e-01
3.76518279e-01 6.44368887e-01 -6.19130395e-02 -9.24309850... | [14.257556915283203, 6.75278377532959] |
2e3cc3af-62ab-431d-ae5e-578603c02309 | links-a-high-dimensional-online-clustering | 1801.10123 | null | http://arxiv.org/abs/1801.10123v1 | http://arxiv.org/pdf/1801.10123v1.pdf | Links: A High-Dimensional Online Clustering Method | We present a novel algorithm, called Links, designed to perform online
clustering on unit vectors in a high-dimensional Euclidean space. The algorithm
is appropriate when it is necessary to cluster data efficiently as it streams
in, and is to be contrasted with traditional batch clustering algorithms that
have access t... | ['Carlton Downey', 'Philip Andrew Mansfield', 'Li Wan', 'Ignacio Lopez Moreno', 'Quan Wang'] | 2018-01-30 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [ 9.93392542e-02 -2.34225124e-01 -2.10306030e-02 -4.88117248e-01
-3.24667931e-01 -4.57437128e-01 5.20650089e-01 5.38611591e-01
-5.26932240e-01 -3.21869925e-02 1.40523076e-01 -2.90742844e-01
-3.49920005e-01 -6.21470392e-01 -7.34407008e-02 -7.30862260e-01
-6.92445457e-01 7.06748664e-01 -1.58040568e-01 2.21877903... | [13.432961463928223, 1.1367790699005127] |
3cd78eb1-b36f-46cf-af1e-575778559832 | heterogeneous-graph-transformer | 2003.01332 | null | https://arxiv.org/abs/2003.01332v1 | https://arxiv.org/pdf/2003.01332v1.pdf | Heterogeneous Graph Transformer | Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data. However, most GNNs are designed for homogeneous graphs, in which all nodes and edges belong to the same types, making them infeasible to represent heterogeneous structures. In this paper, we present the Hetero... | ['Ziniu Hu', 'Kuansan Wang', 'Yuxiao Dong', 'Yizhou Sun'] | 2020-03-03 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [-5.59644103e-02 4.96994495e-01 -4.51667517e-01 -1.81123689e-01
-2.94801027e-01 -5.96461177e-01 3.63867372e-01 2.04017296e-01
9.64530036e-02 4.49410468e-01 2.29200765e-01 -5.86918950e-01
2.10876793e-01 -1.25465608e+00 -8.35702360e-01 -3.74602675e-01
-6.04568362e-01 5.37304640e-01 4.01228100e-01 -1.95329249... | [6.989778995513916, 6.274105072021484] |
f1e45a7e-a061-452b-ae14-c124c270d174 | reproducible-evaluation-of-classification | 1808.06452 | null | http://arxiv.org/abs/1808.06452v1 | http://arxiv.org/pdf/1808.06452v1.pdf | Reproducible evaluation of classification methods in Alzheimer's disease: framework and application to MRI and PET data | A large number of papers have introduced novel machine learning and feature
extraction methods for automatic classification of AD. However, they are
difficult to reproduce because key components of the validation are often not
readily available. These components include selected participants and input
data, image prepr... | ['the Australian Imaging Biomarkers', "for the Alzheimer's Disease Neuroimaging Initiative", 'Marie-Odile Habert', 'Junhao Wen', 'Jérémy Guillon', 'Alexandre Routier', 'Simona Bottani', 'Jorge Samper-González', 'Pascal Lu', 'Lifestyle flagship study of ageing', 'Sabrina Fontanella', 'Stanley Durrleman', 'Olivier Collio... | 2018-08-20 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 6.29805177e-02 -2.64476240e-01 -2.25636140e-01 -6.47441506e-01
-1.04140747e+00 -5.47150195e-01 7.59861469e-01 3.72343600e-01
-8.52334976e-01 9.31113839e-01 -1.86270792e-02 -9.93866101e-02
-1.96988285e-01 -4.98461813e-01 -3.89910638e-01 -6.15708351e-01
-2.25288898e-01 8.60534728e-01 4.79669958e-01 2.11852744... | [14.22966194152832, -1.8495522737503052] |
816a4200-8694-4d94-a312-74f9def6fa65 | galaxy-classification-using-transfer-learning | 2305.00002 | null | https://arxiv.org/abs/2305.00002v1 | https://arxiv.org/pdf/2305.00002v1.pdf | Galaxy Classification Using Transfer Learning and Ensemble of CNNs With Multiple Colour Spaces | Big data has become the norm in astronomy, making it an ideal domain for computer science research. Astronomers typically classify galaxies based on their morphologies, a practice that dates back to Hubble (1936). With small datasets, classification could be performed by individuals or small teams, but the exponential ... | ['Yevonnael Andrew'] | 2023-03-26 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [-2.26788253e-01 -3.93306971e-01 4.90578771e-01 -5.35363816e-02
2.36714259e-01 -8.15220177e-01 1.13572991e+00 -3.06376278e-01
-8.00389469e-01 4.13427234e-01 -5.01541942e-02 -7.39776552e-01
-8.44605193e-02 -1.06028640e+00 -3.46758395e-01 -7.14667797e-01
1.83960181e-02 2.75435746e-01 2.39072993e-01 -1.65796936... | [7.918581008911133, 2.969149589538574] |
6cee4325-d403-4ec9-ba9a-2d33b9b5adfd | class-incremental-learning-for-video-action | null | null | https://ieeexplore.ieee.org/document/9506788 | https://ieeexplore.ieee.org/document/9506788 | Class incremental learning for video action classification | Class Incremental Learning (CIL) is a hot topic in machine learning for CNN models to learn new classes incrementally. However, most of the CIL studies are for image classification and object recognition tasks and few CIL studies are available for video action classification. To mitigate this problem, in this paper, we... | ['Yihong Gong', 'Xiaopeng Hong', 'Jianxing Ma', 'Xiaoyu Tao', 'Jiawei Ma'] | 2021-09-19 | null | null | null | ieee-international-conference-on-image-9 | ['class-incremental-learning', 'action-classification', 'action-recognition-in-videos-2'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.31249952e-01 -2.75333613e-01 -8.01228046e-01 -2.60536104e-01
-6.29859269e-02 -3.23485315e-01 4.91489857e-01 -8.42896476e-02
-3.53281736e-01 7.37849772e-01 1.22977823e-01 -5.01472466e-02
-2.46643826e-01 -6.17250323e-01 -7.26033330e-01 -5.31403184e-01
-3.57832670e-01 -2.67947257e-01 5.51062942e-01 3.62340868... | [8.681632041931152, 0.8974113464355469] |
e7bae880-81a5-4fa6-9d1b-377e89b8ddd4 | bolt-an-automated-deep-learning-framework-for | 2303.17727 | null | https://arxiv.org/abs/2303.17727v3 | https://arxiv.org/pdf/2303.17727v3.pdf | BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Search and Recommendation Models on Commodity CPU Hardware | Efficient large-scale neural network training and inference on commodity CPU hardware is of immense practical significance in democratizing deep learning (DL) capabilities. Presently, the process of training massive models consisting of hundreds of millions to billions of parameters requires the extensive use of specia... | ['Anshumali Shrivastava', 'Tharun Medini', 'Yashwanth Adunukota', 'Shubh Gupta', 'Benjamin Meisburger', 'Benjamin Coleman', 'Pratik Pranav', 'David Torres Ramos', 'Joshua Engels', 'Benito Geordie', 'Vihan Lakshman', 'Nicholas Meisburger'] | 2023-03-30 | null | null | null | null | ['fraud-detection'] | ['miscellaneous'] | [-2.22557515e-01 -9.42469090e-02 -5.85839748e-01 -3.57130349e-01
-3.08396667e-01 -3.50776970e-01 3.54046196e-01 2.65214026e-01
-3.41257006e-01 2.36233249e-01 -7.81009495e-02 -8.86910737e-01
-1.71173006e-01 -1.06499195e+00 -7.67069995e-01 -5.29470742e-01
-1.49236217e-01 7.72821784e-01 -2.07946658e-01 -1.86342224... | [7.13943338394165, 5.4696879386901855] |
fdd8556c-245a-4193-912f-ab93317b4566 | graph-neural-networks-provably-benefit-from | 2306.13926 | null | https://arxiv.org/abs/2306.13926v1 | https://arxiv.org/pdf/2306.13926v1.pdf | Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective | Graph neural networks (GNNs) have pioneered advancements in graph representation learning, exhibiting superior feature learning and performance over multilayer perceptrons (MLPs) when handling graph inputs. However, understanding the feature learning aspect of GNNs is still in its initial stage. This study aims to brid... | ['Taiji Suzuki', 'Xin Cao', 'Haonan Wang', 'Yuan Cao', 'Wei Huang'] | 2023-06-24 | null | null | null | null | ['graph-representation-learning', 'memorization'] | ['methodology', 'natural-language-processing'] | [ 3.81011248e-01 4.32739675e-01 4.09007929e-02 -2.64357060e-01
7.04653934e-02 -3.15024287e-01 4.86996293e-01 5.48783720e-01
-5.87313235e-01 4.82742310e-01 -2.03762025e-01 -7.68891156e-01
-3.92426193e-01 -1.15478873e+00 -8.31632435e-01 -6.81891739e-01
-7.97148049e-01 -1.33582175e-01 -7.41109475e-02 -4.66082424... | [6.860459804534912, 6.098934173583984] |
2e309c82-e39a-43de-aae2-e9ef7f73cb7f | molweni-a-challenge-multiparty-dialogues | 2004.05080 | null | https://arxiv.org/abs/2004.05080v3 | https://arxiv.org/pdf/2004.05080v3.pdf | Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure | Research into the area of multiparty dialog has grown considerably over recent years. We present the Molweni dataset, a machine reading comprehension (MRC) dataset with discourse structure built over multiparty dialog. Molweni's source samples from the Ubuntu Chat Corpus, including 10,000 dialogs comprising 88,303 utte... | ['Ting Liu', 'Zekun Wang', 'Zihao Zheng', 'Min-Yen Kan', 'Jiaqi Li', 'Bing Qin', 'Wenqiang Lei', 'Ming Liu'] | 2020-04-10 | null | https://aclanthology.org/2020.coling-main.238 | https://aclanthology.org/2020.coling-main.238.pdf | coling-2020-8 | ['dialogue-understanding'] | ['natural-language-processing'] | [ 1.03413045e-01 1.15257311e+00 1.04655838e-02 -5.73141932e-01
-1.26222610e+00 -1.09310687e+00 7.67784655e-01 2.42492765e-01
-2.17193022e-01 1.04549444e+00 1.07012141e+00 -6.65505290e-01
1.76543176e-01 -3.87188584e-01 -3.51537913e-01 -2.03950480e-02
1.79758787e-01 1.22843552e+00 4.34813499e-01 -8.16800892... | [12.28313159942627, 8.05882453918457] |
f3ae4eb1-8584-4a83-87b0-476b7347022a | energy-efficient-wearable-to-mobile-offload | 2306.06129 | null | https://arxiv.org/abs/2306.06129v1 | https://arxiv.org/pdf/2306.06129v1.pdf | Energy-efficient Wearable-to-Mobile Offload of ML Inference for PPG-based Heart-Rate Estimation | Modern smartwatches often include photoplethysmographic (PPG) sensors to measure heartbeats or blood pressure through complex algorithms that fuse PPG data with other signals. In this work, we propose a collaborative inference approach that uses both a smartwatch and a connected smartphone to maximize the performance o... | ['Daniele Jahier Pagliari', 'Massimo Poncino', 'Enrico Macii', 'Luca Benini', 'Yukai Chen', 'Noemi Tomasello', 'Matteo Risso', 'Alessio Burrello'] | 2023-06-08 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 1.42632112e-01 3.54431391e-01 -4.02996317e-02 -2.36966178e-01
-5.56595683e-01 -4.41974014e-01 -2.48743519e-01 -3.02546378e-02
-2.55414635e-01 8.17967892e-01 -1.90265104e-01 -5.15324652e-01
-6.35605380e-02 -9.21695411e-01 -3.84150684e-01 -6.65467620e-01
1.01261608e-01 1.62321374e-01 -1.30361840e-01 3.91289353... | [13.92483901977539, 3.038970947265625] |
8033067f-24b0-4f21-bdb6-8896b0a8ba4b | modeling-variable-space-with-residual-tensor | null | null | https://openreview.net/forum?id=Qx0EswNY_bW | https://openreview.net/pdf?id=Qx0EswNY_bW | Modeling Variable Space with Residual Tensor Networks for Multivariate Time Series | Multivariate time series involve a series of valuable applications in the real world, and the basic premise of which is that multiple variables are interdependent. However, the relationship between variables in the latent space is dynamic and complex, and as the time window increases, the size of the space also increas... | ['Guangjian Tian', 'Jun Wang', 'Siwei Rao', 'Yupeng He', 'Peng Zhang', 'Jing Zhang'] | 2021-09-29 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-2.17058033e-01 -5.38605452e-01 -2.15590999e-01 -2.00825214e-01
1.58187628e-01 -5.31537294e-01 3.04846376e-01 -3.14751983e-01
1.70317199e-02 2.67491043e-01 3.06827337e-01 -2.59186536e-01
-4.00096059e-01 -6.91776335e-01 -5.35850644e-01 -7.80466557e-01
-4.13088143e-01 -6.07365035e-02 -9.25525427e-02 -3.53971392... | [6.900774002075195, 2.9222614765167236] |
1b67de87-724a-4211-b7c0-7b5b201e1fed | webformer-the-web-page-transformer-for | 2202.00217 | null | https://arxiv.org/abs/2202.00217v1 | https://arxiv.org/pdf/2202.00217v1.pdf | WebFormer: The Web-page Transformer for Structure Information Extraction | Structure information extraction refers to the task of extracting structured text fields from web pages, such as extracting a product offer from a shopping page including product title, description, brand and price. It is an important research topic which has been widely studied in document understanding and web search... | ['Dongfang Liu', 'Xiaojun Quan', 'Fuli Feng', 'Anirudh Ravula', 'Yi Fang', 'Qifan Wang'] | 2022-02-01 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 4.27552611e-01 1.11795664e-01 -6.39777601e-01 -1.68000117e-01
-8.91543567e-01 -8.82044911e-01 4.67996478e-01 4.58158910e-01
-2.09053472e-01 2.82492250e-01 5.02914369e-01 -5.98471284e-01
-1.84333801e-01 -7.52477050e-01 -7.92150915e-01 -4.14648205e-01
-1.36064351e-01 2.08995864e-01 1.92125708e-01 4.82289866... | [9.8545560836792, 7.919076919555664] |
255419cb-b2f2-4eff-a154-4c91429747c5 | prompt-based-multi-modal-image-segmentation | 2112.10003 | null | https://arxiv.org/abs/2112.10003v2 | https://arxiv.org/pdf/2112.10003v2.pdf | Image Segmentation Using Text and Image Prompts | Image segmentation is usually addressed by training a model for a fixed set of object classes. Incorporating additional classes or more complex queries later is expensive as it requires re-training the model on a dataset that encompasses these expressions. Here we propose a system that can generate image segmentations ... | ['Alexander S. Ecker', 'Timo Lüddecke'] | 2021-12-18 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Luddecke_Image_Segmentation_Using_Text_and_Image_Prompts_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Luddecke_Image_Segmentation_Using_Text_and_Image_Prompts_CVPR_2022_paper.pdf | cvpr-2022-1 | ['referring-image-matting-refmatte-rw100', 'zero-shot-segmentation', 'referring-image-matting-keyword-based', 'one-shot-segmentation', 'referring-expression-segmentation', 'referring-image-matting-expression-based', 'multi-modal-image-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 7.54994094e-01 4.07535642e-01 -3.46878283e-02 -6.27208292e-01
-1.16423500e+00 -9.83787000e-01 6.01904809e-01 1.67668402e-01
-4.47768033e-01 6.88975677e-02 -3.29280108e-01 -3.40813965e-01
1.59635738e-01 -7.92633474e-01 -9.31962430e-01 -3.84107053e-01
6.16319478e-01 8.73087049e-01 8.37315440e-01 -1.49677724... | [10.201324462890625, 1.1208386421203613] |
338ef2a1-081d-43fe-b4d5-f1d7926ee566 | escnet-gaze-target-detection-with-the | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Bao_ESCNet_Gaze_Target_Detection_With_the_Understanding_of_3D_Scenes_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Bao_ESCNet_Gaze_Target_Detection_With_the_Understanding_of_3D_Scenes_CVPR_2022_paper.pdf | ESCNet: Gaze Target Detection With the Understanding of 3D Scenes | This paper aims to address the single image gaze target detection problem. Conventional methods either focus on 2D visual cues or exploit additional depth information in a very coarse manner. In this work, we propose to explicitly and effectively model 3D geometry under challenging scenario where only 2D annotation... | ['Jun Yu', 'Buyu Liu', 'Jun Bao'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['scene-parsing'] | ['computer-vision'] | [ 1.63787171e-01 1.46018550e-01 -9.06143114e-02 -5.35639942e-01
-5.34286201e-01 -6.27253771e-01 5.65948546e-01 6.72484338e-02
-4.24129725e-01 2.59372741e-01 2.72783488e-01 -1.51148677e-01
2.04966769e-01 -4.52783585e-01 -8.20350170e-01 -4.46589351e-01
1.84780642e-01 2.75590986e-01 4.39386725e-01 2.14327797... | [14.098119735717773, 0.05728166177868843] |
f9651b31-486d-4e4d-bf8e-0b9da1b728c7 | labeling-cutting-grouping-an-efficient-text | 1906.11894 | null | https://arxiv.org/abs/1906.11894v2 | https://arxiv.org/pdf/1906.11894v2.pdf | Labeling, Cutting, Grouping: an Efficient Text Line Segmentation Method for Medieval Manuscripts | This paper introduces a new way for text-line extraction by integrating deep-learning based pre-classification and state-of-the-art segmentation methods. Text-line extraction in complex handwritten documents poses a significant challenge, even to the most modern computer vision algorithms. Historical manuscripts are a ... | ['Rolf Ingold', 'Lars Vögtlin', 'Vinaychandran Pondenkandath', 'Michele Alberti', 'Mathias Seuret', 'Marcus Liwicki'] | 2019-06-11 | null | null | null | null | ['text-line-extraction'] | ['computer-vision'] | [ 4.44983274e-01 -4.25775260e-01 2.40526661e-01 -2.86338806e-01
-1.15206957e+00 -6.37750268e-01 7.47266829e-01 2.28652596e-01
-5.88783026e-01 6.89019442e-01 -2.39562839e-01 -1.23020247e-01
2.21419483e-01 -8.53435636e-01 -8.00424695e-01 -5.75499296e-01
4.03434008e-01 4.22054112e-01 5.13396263e-01 -8.43399316... | [11.808948516845703, 2.6025524139404297] |
a405aab6-bd99-4ac7-905e-2e41785177c8 | infrared-and-visible-image-fusion-with-resnet | 1806.07119 | null | https://arxiv.org/abs/1806.07119v7 | https://arxiv.org/pdf/1806.07119v7.pdf | Infrared and Visible Image Fusion with ResNet and zero-phase component analysis | Feature extraction and processing tasks play a key role in Image Fusion, and the fusion performance is directly affected by the different features and processing methods undertaken. By contrast, most of deep learning-based methods use deep features directly without feature extraction or processing. This leads to the fu... | ['Xiao-Jun Wu', 'Hui Li', 'Tariq S. Durrani'] | 2018-06-19 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 1.38651207e-01 -5.24155617e-01 1.82670295e-01 -2.72267163e-01
-5.51049531e-01 8.08094963e-02 5.79318762e-01 1.76087081e-01
-5.04809737e-01 5.09939313e-01 2.31367752e-01 2.51767278e-01
-1.41859069e-01 -8.56137395e-01 -2.51765043e-01 -1.06067133e+00
3.03864866e-01 -4.11821842e-01 1.71809897e-01 -1.84548661... | [10.548544883728027, -1.8562434911727905] |
b3852995-6b96-4a84-9c5e-bbf4f0d5818c | nslf-ol-online-learning-of-neural-surface | 2305.00282 | null | https://arxiv.org/abs/2305.00282v1 | https://arxiv.org/pdf/2305.00282v1.pdf | NSLF-OL: Online Learning of Neural Surface Light Fields alongside Real-time Incremental 3D Reconstruction | Immersive novel view generation is an important technology in the field of graphics and has recently also received attention for operator-based human-robot interaction. However, the involved training is time-consuming, and thus the current test scope is majorly on object capturing. This limits the usage of related mode... | ['Andreas Nuchter', 'Yijun Yuan'] | 2023-04-29 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [ 3.35021198e-01 3.11113417e-01 4.09683347e-01 -2.34573722e-01
-2.03960449e-01 -4.02058065e-01 3.99038881e-01 -2.73854703e-01
-3.33417267e-01 4.47895378e-01 -2.90379167e-01 -3.49910408e-01
1.05156638e-01 -1.22466087e+00 -1.11571622e+00 -5.52002788e-01
-2.24368498e-01 7.27804661e-01 3.76300365e-01 -4.00649399... | [8.763050079345703, -2.8462026119232178] |
ef59883c-10d8-4ee7-ab3b-c0d138023899 | tracing-semantic-variation-in-slang | 2210.08635 | null | https://arxiv.org/abs/2210.08635v2 | https://arxiv.org/pdf/2210.08635v2.pdf | Tracing Semantic Variation in Slang | The meaning of a slang term can vary in different communities. However, slang semantic variation is not well understood and under-explored in the natural language processing of slang. One existing view argues that slang semantic variation is driven by culture-dependent communicative needs. An alternative view focuses o... | ['Yang Xu', 'Zhewei Sun'] | 2022-10-16 | null | null | null | null | ['culture'] | ['speech'] | [-5.77000016e-03 -4.34240371e-01 -2.83098817e-01 -5.45722246e-01
-5.62464558e-02 -5.62851250e-01 9.61687744e-01 4.24439490e-01
-5.88050365e-01 2.09073246e-01 1.25860476e+00 -3.36755872e-01
-4.67465490e-01 -7.06276357e-01 -1.89749613e-01 -3.19291174e-01
2.81417221e-01 3.72144550e-01 1.49692353e-02 -8.13998699... | [10.348642349243164, 9.698309898376465] |
4830e219-51a7-48cf-887c-0cfa33aee62e | apr-online-distant-point-cloud-registration | 2305.02893 | null | https://arxiv.org/abs/2305.02893v2 | https://arxiv.org/pdf/2305.02893v2.pdf | APR: Online Distant Point Cloud Registration Through Aggregated Point Cloud Reconstruction | For many driving safety applications, it is of great importance to accurately register LiDAR point clouds generated on distant moving vehicles. However, such point clouds have extremely different point density and sensor perspective on the same object, making registration on such point clouds very hard. In this paper, ... | ['Minyi Guo', 'Shan Chang', 'Hongzi Zhu', 'Yunsong Zhou', 'Quan Liu'] | 2023-05-04 | null | null | null | null | ['point-cloud-reconstruction', 'point-cloud-registration'] | ['computer-vision', 'computer-vision'] | [-4.38111931e-01 -2.70181507e-01 -1.53578982e-01 -6.02115989e-01
-9.88992929e-01 -5.25535583e-01 6.72859490e-01 7.59146968e-03
-4.24753040e-01 1.67759657e-01 -5.83671965e-02 -1.19960427e-01
-7.30681941e-02 -1.05757952e+00 -1.14342713e+00 -5.14880061e-01
3.72018069e-02 6.66378260e-01 1.89132422e-01 -3.53340745... | [7.672710418701172, -2.942967414855957] |
3faa38f6-5535-4dd8-a41b-5096ee9a76d6 | the-challenge-of-imputation-in-explainable | 1907.12669 | null | https://arxiv.org/abs/1907.12669v1 | https://arxiv.org/pdf/1907.12669v1.pdf | The Challenge of Imputation in Explainable Artificial Intelligence Models | Explainable models in Artificial Intelligence are often employed to ensure transparency and accountability of AI systems. The fidelity of the explanations are dependent upon the algorithms used as well as on the fidelity of the data. Many real world datasets have missing values that can greatly influence explanation fi... | ['Ankur Teredesai', 'Muhammad Aurangzeb Ahmad', 'Carly Eckert'] | 2019-07-29 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 5.54552674e-01 6.43718302e-01 -1.89865515e-01 -6.54569566e-01
-3.65574658e-01 -4.61754471e-01 5.27242780e-01 -6.36186497e-03
-1.24623232e-01 1.44351840e+00 5.84501684e-01 -5.54532409e-01
-4.47831184e-01 -8.33915830e-01 -9.30830896e-01 -4.82818842e-01
4.70166028e-01 5.70245862e-01 -7.19343901e-01 2.62425154... | [8.714235305786133, 5.6551513671875] |
66fba441-fbe8-410f-8ee3-16f202c9aa33 | spectral-feature-mapping-with-mimic-loss-for | 1803.09816 | null | http://arxiv.org/abs/1803.09816v1 | http://arxiv.org/pdf/1803.09816v1.pdf | Spectral feature mapping with mimic loss for robust speech recognition | For the task of speech enhancement, local learning objectives are agnostic to
phonetic structures helpful for speech recognition. We propose to add a global
criterion to ensure de-noised speech is useful for downstream tasks like ASR.
We first train a spectral classifier on clean speech to predict senone labels.
Then, ... | ['Eric Fosler-Lussier', 'Deblin Bagchi', 'Peter Plantinga', 'Adam Stiff'] | 2018-03-26 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 5.20704269e-01 5.78796744e-01 2.20504925e-01 -5.65313637e-01
-1.43150830e+00 -5.04573703e-01 2.78650403e-01 -2.92776763e-01
-4.37806934e-01 3.43051195e-01 4.18457240e-01 -7.77427554e-01
1.85066059e-01 -3.50252151e-01 -5.65081835e-01 -8.69065762e-01
3.73754948e-01 7.12275803e-02 1.29776821e-01 -3.79288763... | [14.682263374328613, 6.289509296417236] |
a85c2c50-839c-4fd2-894f-06b5b453bd27 | videomae-masked-autoencoders-are-data-1 | 2203.12602 | null | https://arxiv.org/abs/2203.12602v3 | https://arxiv.org/pdf/2203.12602v3.pdf | VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training | Pre-training video transformers on extra large-scale datasets is generally required to achieve premier performance on relatively small datasets. In this paper, we show that video masked autoencoders (VideoMAE) are data-efficient learners for self-supervised video pre-training (SSVP). We are inspired by the recent Image... | ['LiMin Wang', 'Jue Wang', 'Yibing Song', 'Zhan Tong'] | 2022-03-23 | videomae-masked-autoencoders-are-data | https://arxiv.org/abs/2203.12602 | https://arxiv.org/pdf/2203.12602 | null | ['video-reconstruction', 'self-supervised-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.29563600e-01 -2.69148022e-01 -4.67317283e-01 -1.88978299e-01
-7.13064492e-01 -2.64460385e-01 4.00028378e-01 -6.53827608e-01
-7.20828354e-01 7.81979442e-01 3.30604881e-01 -3.49041373e-01
3.05547118e-01 -4.75717276e-01 -1.22439170e+00 -7.67003655e-01
-2.18998328e-01 -2.35616669e-01 3.34196061e-01 -2.53590912... | [9.43221664428711, 0.7815083861351013] |
46c53f40-94fd-41da-8803-532a3362e4fe | sug-single-dataset-unified-generalization-for | 2305.09160 | null | https://arxiv.org/abs/2305.09160v1 | https://arxiv.org/pdf/2305.09160v1.pdf | SUG: Single-dataset Unified Generalization for 3D Point Cloud Classification | Although Domain Generalization (DG) problem has been fast-growing in the 2D image tasks, its exploration on 3D point cloud data is still insufficient and challenged by more complex and uncertain cross-domain variances with uneven inter-class modality distribution. In this paper, different from previous 2D DG works, we ... | ['Hongsheng Li', 'Yikang Li', 'Peng Gao', 'Botian Shi', 'Bo Zhang', 'Siyuan Huang'] | 2023-05-16 | null | null | null | null | ['3d-point-cloud-classification', 'point-cloud-classification', 'unsupervised-domain-adaptation'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 3.09424758e-01 -1.50153860e-01 -2.80733913e-01 -5.25123298e-01
-8.42243195e-01 -7.27069497e-01 4.74437982e-01 -1.86943665e-01
-1.63003698e-01 6.67590141e-01 7.87110701e-02 -6.96693063e-02
-2.02003032e-01 -6.08729541e-01 -5.87294221e-01 -8.28671813e-01
2.84608543e-01 5.93051910e-01 2.40764827e-01 -1.24550432... | [10.343320846557617, 2.984407663345337] |
141a765c-0211-4946-b6fe-9a23b0c199fe | directional-mean-curvature-for-textured-image | 1611.08625 | null | http://arxiv.org/abs/1611.08625v1 | http://arxiv.org/pdf/1611.08625v1.pdf | Directional Mean Curvature for Textured Image Demixing | Approximation theory plays an important role in image processing, especially
image deconvolution and decomposition. For piecewise smooth images, there are
many methods that have been developed over the past thirty years. The goal of
this study is to devise similar and practical methodology for handling textured
images.... | ['David Banks', 'Duy Hoang Thai'] | 2016-11-25 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 4.06590998e-01 -2.48983502e-01 4.35578972e-01 -5.23063801e-02
-5.91812171e-02 -2.25855589e-01 1.78627729e-01 -2.81206012e-01
-2.22208813e-01 7.40436018e-01 -6.29991069e-02 7.24825487e-02
-2.43321285e-01 -4.44423378e-01 -4.15972739e-01 -9.51943099e-01
2.12309197e-01 -1.53003022e-01 2.62942553e-01 -7.56157190... | [11.6507568359375, -2.6204919815063477] |
efeb6739-3365-4d9c-9ac9-ab3c0965a518 | a-dynamic-window-neural-network-for-ccg | 1610.02749 | null | http://arxiv.org/abs/1610.02749v1 | http://arxiv.org/pdf/1610.02749v1.pdf | A Dynamic Window Neural Network for CCG Supertagging | Combinatory Category Grammar (CCG) supertagging is a task to assign lexical
categories to each word in a sentence. Almost all previous methods use fixed
context window sizes as input features. However, it is obvious that different
tags usually rely on different context window sizes. These motivate us to build
a superta... | ['Cheng-qing Zong', 'Jiajun Zhang', 'Huijia Wu'] | 2016-10-10 | null | null | null | null | ['ccg-supertagging'] | ['natural-language-processing'] | [-8.74482933e-03 2.61727095e-01 -1.19560063e-01 -6.86978042e-01
-7.09051609e-01 -5.87376654e-01 7.57596850e-01 1.31923109e-01
-7.80115306e-01 4.22849149e-01 4.56086457e-01 -6.17297292e-01
4.11281466e-01 -8.11702967e-01 -3.79346907e-01 -7.62420118e-01
-8.34765732e-02 3.16395372e-01 7.00981617e-01 -3.52178335... | [10.539226531982422, 9.240255355834961] |
f8873b85-d78d-44bd-a84a-9506a1002c94 | beyond-rule-based-named-entity-recognition | 2305.03960 | null | https://arxiv.org/abs/2305.03960v1 | https://arxiv.org/pdf/2305.03960v1.pdf | Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Text | Automated generation of business process models from natural language text is an emerging methodology for avoiding the manual creation of formal business process models. For this purpose, process entities like actors, activities, objects etc., and relations among them are extracted from textual process descriptions. A ... | ['Stefan Jablonski', 'Lars Ackermann', 'Julian Neuberger'] | 2023-05-06 | null | null | null | null | ['feature-engineering', 'relation-extraction', 'entity-resolution'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 3.99069101e-01 6.77597463e-01 7.71114305e-02 -1.15970396e-01
-4.59612101e-01 -7.17003107e-01 1.29925466e+00 7.90408134e-01
-4.10245329e-01 6.97045624e-01 1.87639058e-01 -3.80557150e-01
-3.55646372e-01 -1.12185931e+00 -4.75552738e-01 -2.50860751e-01
1.88560858e-01 1.03843224e+00 3.62808228e-01 2.58948445... | [9.249055862426758, 8.677992820739746] |
d6479e11-de20-46c4-a473-11f088232531 | 190600910 | 1906.00910 | null | https://arxiv.org/abs/1906.00910v2 | https://arxiv.org/pdf/1906.00910v2.pdf | Learning Representations by Maximizing Mutual Information Across Views | We propose an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context. For example, one could produce multiple views of a local spatio-temporal context by observing it from different locations (e.g., camera positions w... | ['R. Devon Hjelm', 'William Buchwalter', 'Philip Bachman'] | 2019-06-03 | learning-representations-by-maximizing-mutual | http://papers.nips.cc/paper/9686-learning-representations-by-maximizing-mutual-information-across-views | http://papers.nips.cc/paper/9686-learning-representations-by-maximizing-mutual-information-across-views.pdf | neurips-2019-12 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 5.40708959e-01 1.60975218e-01 -2.01739937e-01 -4.95528281e-01
-9.55488801e-01 -7.15469480e-01 8.56508732e-01 5.58392704e-02
-3.82342726e-01 5.44549704e-01 4.21194613e-01 5.74341081e-02
1.53125077e-01 -7.23723173e-01 -1.13228202e+00 -5.16022980e-01
1.00494316e-03 1.68507732e-02 -5.52306622e-02 4.01759088... | [9.94236946105957, 0.32377997040748596] |
92869db6-de7a-46ba-a58e-8757b61d0854 | towards-robustness-of-text-to-sql-models-3 | 2212.09994 | null | https://arxiv.org/abs/2212.09994v1 | https://arxiv.org/pdf/2212.09994v1.pdf | Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation | The robustness of Text-to-SQL parsers against adversarial perturbations plays a crucial role in delivering highly reliable applications. Previous studies along this line primarily focused on perturbations in the natural language question side, neglecting the variability of tables. Motivated by this, we propose the Adve... | ['Jian-Guang Lou', 'Zhoujun Li', 'Jiaqi Guo', 'Yan Gao', 'Bing Wang', 'Xinyu Pi'] | 2022-12-20 | towards-robustness-of-text-to-sql-models-2 | https://aclanthology.org/2022.acl-long.142 | https://aclanthology.org/2022.acl-long.142.pdf | acl-2022-5 | ['text-to-sql'] | ['computer-code'] | [ 4.28202078e-02 3.27492833e-01 -2.68149609e-03 -2.29088202e-01
-1.38521254e+00 -1.14549530e+00 5.57702899e-01 1.23995863e-01
1.01762772e-01 5.47508836e-01 1.96442783e-01 -8.74904752e-01
2.85256356e-01 -9.39905047e-01 -1.35319114e+00 -3.85085285e-01
6.34156987e-02 2.60433316e-01 4.55548257e-01 -6.00145400... | [6.085960388183594, 8.104360580444336] |
d9c9ca0c-69cc-4b4f-a90f-f9a999e930a3 | semantic-unfolding-of-stylegan-latent-space | 2206.14892 | null | https://arxiv.org/abs/2206.14892v1 | https://arxiv.org/pdf/2206.14892v1.pdf | Semantic Unfolding of StyleGAN Latent Space | Generative adversarial networks (GANs) have proven to be surprisingly efficient for image editing by inverting and manipulating the latent code corresponding to an input real image. This editing property emerges from the disentangled nature of the latent space. In this paper, we identify that the facial attribute disen... | ['Pierre Hellier', 'Bharath Bushan Damodaran', 'Xu Yao', 'Mustafa Shukor'] | 2022-06-29 | null | null | null | null | ['facial-editing'] | ['computer-vision'] | [ 7.59984791e-01 5.55845857e-01 -3.82871218e-02 -2.23890364e-01
-4.24660951e-01 -9.10281658e-01 8.79204154e-01 -6.59074664e-01
-1.55030310e-01 9.01027560e-01 2.52681524e-01 -9.62744504e-02
-1.16010882e-01 -7.54236758e-01 -7.02524841e-01 -8.15537691e-01
3.36356789e-01 3.59622419e-01 -7.73571074e-01 -6.63220510... | [11.8656005859375, -0.2794452905654907] |
370eb6f2-9926-43aa-a10e-99bd33bc794f | transformation-consistent-self-ensembling | 1903.00348 | null | https://arxiv.org/abs/1903.00348v3 | https://arxiv.org/pdf/1903.00348v3.pdf | Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation | Deep convolutional neural networks have achieved remarkable progress on a variety of medical image computing tasks. A common problem when applying supervised deep learning methods to medical images is the lack of labeled data, which is very expensive and time-consuming to be collected. In this paper, we present a novel... | ['Pheng-Ann Heng', 'Chi-Wing Fu', 'Lequan Yu', 'Lei Xing', 'Hao Chen', 'Xiaomeng Li'] | 2019-02-28 | null | null | null | null | ['semi-supervised-medical-image-segmentation', 'skin-lesion-segmentation', 'liver-segmentation'] | ['computer-vision', 'medical', 'medical'] | [ 5.97620547e-01 4.02317166e-01 -4.34891164e-01 -7.23151267e-01
-8.03207517e-01 -3.58554095e-01 1.82798207e-01 3.66008617e-02
-7.11159527e-01 5.21995723e-01 -1.28047958e-01 -4.69752222e-01
1.36743551e-02 -4.49405909e-01 -6.26681745e-01 -6.93660796e-01
1.14264593e-01 4.37375188e-01 1.18251435e-01 2.64690071... | [14.647855758666992, -2.3577706813812256] |
9eae4087-327f-40f8-966b-a506e83e4963 | explicit-shape-encoding-for-real-time | 1908.04067 | null | https://arxiv.org/abs/1908.04067v1 | https://arxiv.org/pdf/1908.04067v1.pdf | Explicit Shape Encoding for Real-Time Instance Segmentation | In this paper, we propose a novel top-down instance segmentation framework based on explicit shape encoding, named \textbf{ESE-Seg}. It largely reduces the computational consumption of the instance segmentation by explicitly decoding the multiple object shapes with tensor operations, thus performs the instance segmenta... | ['Cewu Lu', 'Haiyang Wang', 'Wenqiang Xu', 'Fubo Qi'] | 2019-08-12 | explicit-shape-encoding-for-real-time-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Xu_Explicit_Shape_Encoding_for_Real-Time_Instance_Segmentation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Xu_Explicit_Shape_Encoding_for_Real-Time_Instance_Segmentation_ICCV_2019_paper.pdf | iccv-2019-10 | ['real-time-instance-segmentation'] | ['computer-vision'] | [-1.13739066e-01 2.28617311e-01 1.68975338e-01 -3.70632291e-01
-8.31376851e-01 -7.70277798e-01 2.35448614e-01 1.15440693e-02
-4.91527855e-01 5.53756133e-02 -6.48299158e-01 -2.81782627e-01
7.70151392e-02 -7.06960857e-01 -9.92111862e-01 -5.05938530e-01
-1.04932729e-02 5.39271355e-01 7.82526255e-01 -1.33416191... | [9.49328899383545, 0.0800282210111618] |
53d43ba4-3588-403a-ad6d-de7781ab99d4 | enhancing-keyphrase-extraction-from-academic | 2111.14106 | null | https://arxiv.org/abs/2111.14106v2 | https://arxiv.org/pdf/2111.14106v2.pdf | Enhancing Keyphrase Extraction from Academic Articles with their Reference Information | With the development of Internet technology, the phenomenon of information overload is becoming more and more obvious. It takes a lot of time for users to obtain the information they need. However, keyphrases that summarize document information highly are helpful for users to quickly obtain and understand documents. Fo... | ['Yingyi Zhang', 'Mengyuan Zhao', 'Lei Zhao', 'Chengzhi Zhang'] | 2021-11-28 | null | null | null | null | ['keyphrase-extraction'] | ['natural-language-processing'] | [-1.16819784e-01 -2.88882345e-01 -7.09668994e-01 1.16529949e-01
-5.98837435e-01 -5.54561138e-01 9.10092592e-01 8.03606570e-01
-6.31944537e-01 1.00540924e+00 4.52980429e-01 -4.71643269e-01
-3.30559134e-01 -9.79739666e-01 -3.74038815e-01 -4.63270158e-01
1.98874816e-01 7.71398023e-02 1.57385692e-01 6.80639446... | [12.209250450134277, 8.923418045043945] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.