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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7993b938-aed0-4508-b987-5f6520fc4ae8 | towards-improving-topic-models-with-the-bert | null | null | https://openreview.net/forum?id=qrCCfJ6uR1m | https://openreview.net/pdf?id=qrCCfJ6uR1m | Towards Improving Topic Models with the BERT-based Neural Topic Encoder | Neural Topic Models (NTMs) have been popular for mining a set of topics from a collection of corpora. Recently, there is an emerging direction of combining NTMs with pre-trained language models such as BERT, which aims to use the contextual information to of BERT to help train better NTMs.
However, existing works in th... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['topic-models'] | ['natural-language-processing'] | [-1.46990746e-01 3.19792688e-01 -1.96555629e-01 -5.80295622e-01
-6.22035146e-01 3.34581174e-02 8.76851201e-01 7.73321539e-02
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1.62140220e-01 -1.02677441e+00 -7.71397710e-01 -7.79631853e-01
1.94796249e-02 5.80710828e-01 5.15360057e-01 -3.35059762... | [10.4259033203125, 6.949657917022705] |
c9732944-dfd5-4fcb-9ffc-2c96b3315449 | distributed-compressed-sparse-row-format-for | 2304.05587 | null | https://arxiv.org/abs/2304.05587v1 | https://arxiv.org/pdf/2304.05587v1.pdf | Distributed Compressed Sparse Row Format for Spiking Neural Network Simulation, Serialization, and Interoperability | With the increasing development of neuromorphic platforms and their related software tools as well as the increasing scale of spiking neural network (SNN) models, there is a pressure for interoperable and scalable representations of network state. In response to this, we discuss a parallel extension of a widely used fo... | ['Felix Wang'] | 2023-04-12 | null | null | null | null | ['neural-network-simulation', 'graph-partitioning'] | ['computer-code', 'graphs'] | [ 1.94796324e-01 -2.91600108e-01 3.35137397e-01 -1.19810961e-01
4.54902090e-02 -7.52315700e-01 2.61418641e-01 3.78143877e-01
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-6.01570368e-01 6.69633925e-01 5.90555847e-01 -6.32864088... | [8.141459465026855, 2.6423532962799072] |
334ef880-eb5b-4469-892c-bb2beb7ebe06 | heavy-rain-face-image-restoration-integrating | 2204.08307 | null | https://arxiv.org/abs/2204.08307v1 | https://arxiv.org/pdf/2204.08307v1.pdf | Heavy Rain Face Image Restoration: Integrating Physical Degradation Model and Facial Component Guided Adversarial Learning | With the recent increase in intelligent CCTVs for visual surveillance, a new image degradation that integrates resolution conversion and synthetic rain models is required. For example, in heavy rain, face images captured by CCTV from a distance have significant deterioration in both visibility and resolution. Unlike tr... | ['Da-Hee Jeong', 'Chang-Hwan Son'] | 2022-04-18 | null | null | null | null | ['face-parsing'] | ['computer-vision'] | [ 4.08550590e-01 3.88493724e-02 2.40427181e-01 -4.70542192e-01
-3.81311238e-01 -1.06237419e-01 1.04283705e-01 -1.05783844e+00
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3.20461035e-01 -2.73260802e-01 -3.55132014e-01 -3.40584248... | [12.80179500579834, -0.06582092493772507] |
60730cb4-022c-4ac1-9091-58a3d0c863cb | video-action-transformer-network | 1812.02707 | null | https://arxiv.org/abs/1812.02707v2 | https://arxiv.org/pdf/1812.02707v2.pdf | Video Action Transformer Network | We introduce the Action Transformer model for recognizing and localizing human actions in video clips. We repurpose a Transformer-style architecture to aggregate features from the spatiotemporal context around the person whose actions we are trying to classify. We show that by using high-resolution, person-specific, cl... | ['Andrew Zisserman', 'João Carreira', 'Rohit Girdhar', 'Carl Doersch'] | 2018-12-06 | video-action-transformer-network-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Girdhar_Video_Action_Transformer_Network_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Girdhar_Video_Action_Transformer_Network_CVPR_2019_paper.pdf | cvpr-2019-6 | ['recognizing-and-localizing-human-actions'] | ['computer-vision'] | [ 2.34897882e-01 -1.18047677e-01 -6.90592155e-02 -5.21443725e-01
-4.90451962e-01 -6.43725336e-01 9.51115668e-01 -2.27749154e-01
-6.75642848e-01 6.42393470e-01 6.45494699e-01 4.09295291e-01
1.01493545e-01 -6.13285124e-01 -6.97937667e-01 -6.04769289e-01
-2.63318747e-01 6.32624686e-01 4.76467371e-01 1.92623958... | [8.154754638671875, 0.44541487097740173] |
bf69a12a-c981-4af9-9243-b95b1f67f487 | mind-the-backbone-minimizing-backbone | 2303.14744 | null | https://arxiv.org/abs/2303.14744v2 | https://arxiv.org/pdf/2303.14744v2.pdf | Mind the Backbone: Minimizing Backbone Distortion for Robust Object Detection | Building object detectors that are robust to domain shifts is critical for real-world applications. Prior approaches fine-tune a pre-trained backbone and risk overfitting it to in-distribution (ID) data and distorting features useful for out-of-distribution (OOD) generalization. We propose to use Relative Gradient Norm... | ['Kate Saenko', 'Rogerio Feris', 'Piotr Teterwak', 'Donghyun Kim', 'Kuniaki Saito'] | 2023-03-26 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [-2.38196645e-02 6.01993017e-02 -1.47773370e-01 -4.14620131e-01
-6.56233370e-01 -8.90601814e-01 5.79161465e-01 -4.01884951e-02
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1.41747221e-01 1.16465583e-01 6.00321054e-01 -1.12699941... | [9.871485710144043, 2.5215015411376953] |
175a4355-d0a8-4ae0-b51a-7239d54e8cc3 | deep-online-fused-video-stabilization | 2102.01279 | null | https://arxiv.org/abs/2102.01279v2 | https://arxiv.org/pdf/2102.01279v2.pdf | Deep Online Fused Video Stabilization | We present a deep neural network (DNN) that uses both sensor data (gyroscope) and image content (optical flow) to stabilize videos through unsupervised learning. The network fuses optical flow with real/virtual camera pose histories into a joint motion representation. Next, the LSTM block infers the new virtual camera ... | ['YIngyu Liang', 'Chia-Kai Liang', 'Wei-Sheng Lai', 'Fuhao Shi', 'Zhenmei Shi'] | 2021-02-02 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [ 6.48675561e-02 -3.72615494e-02 -3.66925120e-01 -1.58409223e-01
-2.00553983e-01 -5.85876822e-01 7.69981682e-01 -4.15177792e-01
-5.41651130e-01 6.80196345e-01 2.79286414e-01 -1.62981022e-02
3.50423455e-01 -3.06277841e-01 -1.03042793e+00 -5.51222086e-01
6.73757195e-02 -2.58936852e-01 1.02604121e-01 -1.42258286... | [10.61355972290039, -1.400499701499939] |
88a5541e-66ba-4f36-93ed-9e8c65d7a0bf | improving-natural-language-processing-tasks | 2010.07891 | null | https://arxiv.org/abs/2010.07891v2 | https://arxiv.org/pdf/2010.07891v2.pdf | Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention | A lack of corpora has so far limited advances in integrating human gaze data as a supervisory signal in neural attention mechanisms for natural language processing(NLP). We propose a novel hybrid text saliency model(TSM) that, for the first time, combines a cognitive model of reading with explicit human gaze supervisio... | ['Andreas Bulling', 'Philipp Mueller', 'Simon Tannert', 'Ekta Sood'] | 2020-10-15 | null | http://proceedings.neurips.cc/paper/2020/hash/460191c72f67e90150a093b4585e7eb4-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/460191c72f67e90150a093b4585e7eb4-Paper.pdf | neurips-2020-12 | ['sentence-compression'] | ['natural-language-processing'] | [ 5.20355821e-01 7.02739596e-01 -5.01367226e-02 -5.24630845e-01
-9.57976282e-01 -2.02771634e-01 8.86187136e-01 3.09563130e-01
-6.37846947e-01 3.93089563e-01 6.20748818e-01 -4.78898764e-01
3.23412381e-02 -2.62911826e-01 -8.99918973e-01 -1.51793167e-01
7.10051537e-01 4.34314698e-01 5.03876388e-01 -4.86187518... | [12.087431907653809, 9.210600852966309] |
aefe648e-493e-4c19-9b15-452b514abfa3 | fine-grained-3d-object-recognition-an | 2306.15919 | null | https://arxiv.org/abs/2306.15919v1 | https://arxiv.org/pdf/2306.15919v1.pdf | Fine-grained 3D object recognition: an approach and experiments | Three-dimensional (3D) object recognition technology is being used as a core technology in advanced technologies such as autonomous driving of automobiles. There are two sets of approaches for 3D object recognition: (i) hand-crafted approaches like Global Orthographic Object Descriptor (GOOD), and (ii) deep learning-ba... | ['Hamidreza Kasaei', 'Junhyung Jo'] | 2023-06-28 | null | null | null | null | ['3d-object-recognition', 'object-recognition'] | ['computer-vision', 'computer-vision'] | [-1.91092402e-01 2.17879098e-02 -9.35195312e-02 -6.42283082e-01
-3.82592469e-01 -5.48195243e-01 8.13832462e-01 5.84541261e-02
-5.42573631e-01 2.99896628e-01 -4.34946656e-01 -6.82963550e-01
-2.08385199e-01 -9.49255943e-01 -7.96560526e-01 -4.67553943e-01
6.71007335e-02 5.83850920e-01 5.53921044e-01 -2.99674738... | [7.775771617889404, -0.9194885492324829] |
97f7f9f8-aa08-4f63-b945-5309472354be | residual-dense-network-for-image-restoration | 1812.10477 | null | https://arxiv.org/abs/1812.10477v2 | https://arxiv.org/pdf/1812.10477v2.pdf | Residual Dense Network for Image Restoration | Convolutional neural network has recently achieved great success for image restoration (IR) and also offered hierarchical features. However, most deep CNN based IR models do not make full use of the hierarchical features from the original low-quality images, thereby achieving relatively-low performance. In this paper, ... | ['Yu Kong', 'Yulun Zhang', 'Yapeng Tian', 'Bineng Zhong', 'Yun Fu'] | 2018-12-25 | null | null | null | null | ['jpeg-artifact-correction', 'image-compression-artifact-reduction'] | ['computer-vision', 'computer-vision'] | [ 1.11259177e-01 -3.69735062e-01 -7.12429285e-02 -1.49712861e-01
-7.74177194e-01 5.37190661e-02 1.92322552e-01 -3.64007145e-01
-1.18715562e-01 4.76820588e-01 7.04505503e-01 1.35403812e-01
-3.16021860e-01 -8.42558086e-01 -6.95865691e-01 -8.84139597e-01
-4.22172323e-02 -4.42386210e-01 1.46578148e-01 -3.34125906... | [11.052374839782715, -2.0975723266601562] |
7e5c8eea-773e-47d2-9060-a40a0a947c2b | keypointnerf-generalizing-image-based | 2205.04992 | null | https://arxiv.org/abs/2205.04992v2 | https://arxiv.org/pdf/2205.04992v2.pdf | KeypointNeRF: Generalizing Image-based Volumetric Avatars using Relative Spatial Encoding of Keypoints | Image-based volumetric humans using pixel-aligned features promise generalization to unseen poses and identities. Prior work leverages global spatial encodings and multi-view geometric consistency to reduce spatial ambiguity. However, global encodings often suffer from overfitting to the distribution of the training da... | ['Shunsuke Saito', 'Siyu Tang', 'Michael Zollhoefer', 'Aayush Bansal', 'Marko Mihajlovic'] | 2022-05-10 | null | null | null | null | ['3d-face-reconstruction', '3d-human-reconstruction'] | ['computer-vision', 'computer-vision'] | [-2.18785718e-01 -1.15148397e-02 -2.04841465e-01 -5.64043999e-01
-9.39766884e-01 -2.36022994e-01 4.00368094e-01 -1.94097608e-01
4.76678051e-02 6.36482596e-01 7.70210385e-01 5.60763776e-01
-4.22843397e-02 -5.35769820e-01 -8.43473256e-01 -2.61802435e-01
3.06617636e-02 7.94350207e-01 2.07487509e-01 -1.92435637... | [7.12256383895874, -1.1635595560073853] |
40bdeb88-f713-44d4-8312-3ebf2293c506 | scene-text-detection-with-selected-anchor | 2008.08523 | null | https://arxiv.org/abs/2008.08523v1 | https://arxiv.org/pdf/2008.08523v1.pdf | Scene Text Detection with Selected Anchor | Object proposal technique with dense anchoring scheme for scene text detection were applied frequently to achieve high recall. It results in the significant improvement in accuracy but waste of computational searching, regression and classification. In this paper, we propose an anchor selection-based region proposal ne... | ['Shengwu Xiong', 'Anna Zhu', 'Hang Du'] | 2020-08-19 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [-5.91419376e-02 -2.68427223e-01 -1.04216062e-01 -7.30668530e-02
-8.70718181e-01 -3.05386543e-01 6.25780463e-01 3.84122849e-01
-6.30514205e-01 4.41013306e-01 4.31728423e-01 6.84056282e-02
2.98243940e-01 -7.26041079e-01 -5.89255810e-01 -4.61445451e-01
1.63434193e-01 6.85674489e-01 1.14096570e+00 -1.46050183... | [12.081991195678711, 2.287993907928467] |
3bd19f81-1b1b-45f4-bd10-efa8692306a7 | spikili-a-spiking-simulation-of-lidar-based | 2206.02876 | null | https://arxiv.org/abs/2206.02876v1 | https://arxiv.org/pdf/2206.02876v1.pdf | SpikiLi: A Spiking Simulation of LiDAR based Real-time Object Detection for Autonomous Driving | Spiking Neural Networks are a recent and new neural network design approach that promises tremendous improvements in power efficiency, computation efficiency, and processing latency. They do so by using asynchronous spike-based data flow, event-based signal generation, processing, and modifying the neuron model to rese... | ['Patrick Mader', 'Heinrich Gotzig', 'Senthil Yogamani', 'Mona Hodaei', 'Thomas Mesquida', 'Sambit Mohapatra'] | 2022-06-06 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [ 3.63029569e-01 -3.33650649e-01 7.44969487e-01 -1.35649920e-01
2.97160130e-02 -4.77929741e-01 6.00107133e-01 5.28290309e-03
-8.03245068e-01 6.79729223e-01 -5.18205106e-01 -2.15494618e-01
2.77532071e-01 -8.16250384e-01 -7.46482074e-01 -7.48248637e-01
-2.46198744e-01 2.09415123e-01 8.53621721e-01 -1.74010724... | [8.215608596801758, 2.415956974029541] |
73475f23-3361-4b26-b3ad-617eebd11d75 | keep-calm-and-switch-on-preserving-sentiment | 1909.00088 | null | https://arxiv.org/abs/1909.00088v2 | https://arxiv.org/pdf/1909.00088v2.pdf | Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange | In this paper, we present a novel method for measurably adjusting the semantics of text while preserving its sentiment and fluency, a task we call semantic text exchange. This is useful for text data augmentation and the semantic correction of text generated by chatbots and virtual assistants. We introduce a pipeline c... | ['Steven Y. Feng', 'Aaron W. Li', 'Jesse Hoey'] | 2019-08-30 | keep-calm-and-switch-on-preserving-sentiment-1 | https://aclanthology.org/D19-1272 | https://aclanthology.org/D19-1272.pdf | ijcnlp-2019-11 | ['text-infilling'] | ['natural-language-processing'] | [ 2.06348449e-01 3.68869781e-01 3.86684835e-02 -5.56087911e-01
-4.00704175e-01 -6.08055294e-01 8.10336649e-01 3.83614093e-01
-8.14265132e-01 6.22094214e-01 7.04838872e-01 -5.01857996e-02
4.12745059e-01 -3.33443433e-01 -5.57039976e-01 -9.13754385e-03
7.45291233e-01 2.97762483e-01 4.55505848e-01 -4.79190111... | [11.258702278137207, 9.191130638122559] |
4b38ad0f-ce1a-45ea-b133-afdb62b9ae18 | interpretable-time-series-clustering-using | 2208.01152 | null | https://arxiv.org/abs/2208.01152v1 | https://arxiv.org/pdf/2208.01152v1.pdf | Interpretable Time Series Clustering Using Local Explanations | This study focuses on exploring the use of local interpretability methods for explaining time series clustering models. Many of the state-of-the-art clustering models are not directly explainable. To provide explanations for these clustering algorithms, we train classification models to estimate the cluster labels. The... | ['Ayse Basar', 'Mucahit Cevik', 'Nicholas Prayogo', 'Ozan Ozyegen'] | 2022-08-01 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 9.82440487e-02 2.07957700e-01 -3.77299428e-01 -5.26383936e-01
-2.29437172e-01 -5.44873238e-01 5.95785022e-01 2.29916394e-01
4.43618685e-01 4.36601639e-01 -1.02497935e-02 -7.68840790e-01
-7.35393286e-01 -3.42144817e-01 -3.22202921e-01 -7.91399717e-01
-5.24154305e-01 6.53860211e-01 -3.08175266e-01 1.56785637... | [7.382131099700928, 3.4463863372802734] |
77b417b2-fcf5-4312-8890-782ce1a9675c | document-level-event-based-extraction-using | 2008.09249 | null | https://arxiv.org/abs/2008.09249v2 | https://arxiv.org/pdf/2008.09249v2.pdf | GRIT: Generative Role-filler Transformers for Document-level Event Entity Extraction | We revisit the classic problem of document-level role-filler entity extraction (REE) for template filling. We argue that sentence-level approaches are ill-suited to the task and introduce a generative transformer-based encoder-decoder framework (GRIT) that is designed to model context at the document level: it can make... | ['Alexander M. Rush', 'Xinya Du', 'Claire Cardie'] | 2020-08-21 | null | https://aclanthology.org/2021.eacl-main.52 | https://aclanthology.org/2021.eacl-main.52.pdf | eacl-2021-2 | ['role-filler-entity-extraction'] | ['natural-language-processing'] | [ 4.97609705e-01 8.57991993e-01 -6.17906451e-01 -4.22781199e-01
-1.49871325e+00 -1.04507983e+00 6.63705468e-01 2.50487417e-01
-3.16422760e-01 9.09128666e-01 9.28536057e-01 -5.43815613e-01
2.98143122e-02 -7.69221783e-01 -7.16251671e-01 -1.66388333e-01
2.08319753e-01 8.80483449e-01 1.95551798e-01 -4.76966709... | [10.159344673156738, 9.025223731994629] |
3b8e73f7-2c9c-4793-a566-197304e60eda | semi-supervised-clustering-with-contrastive | 2201.07604 | null | https://arxiv.org/abs/2201.07604v1 | https://arxiv.org/pdf/2201.07604v1.pdf | Semi-Supervised Clustering with Contrastive Learning for Discovering New Intents | Most dialogue systems in real world rely on predefined intents and answers for QA service, so discovering potential intents from large corpus previously is really important for building such dialogue services. Considering that most scenarios have few intents known already and most intents waiting to be discovered, we f... | ['Sheng Guo', 'Bing Han', 'Hua Wei', 'Fengxin Yang', 'Zhenghong Hao', 'Zhenbo Chen', 'Feng Wei'] | 2022-01-07 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [-1.17161341e-01 1.18536539e-01 -1.91354789e-02 -8.81754339e-01
-6.40740931e-01 -3.64863068e-01 8.26768339e-01 -1.03933878e-01
-4.06931549e-01 4.74512905e-01 8.50827813e-01 -1.67091146e-01
3.74438427e-02 -3.60388726e-01 1.52022734e-01 -6.58005118e-01
1.52021321e-02 1.03554022e+00 1.67731404e-01 -4.00608748... | [12.557271957397461, 7.5999064445495605] |
db8b0864-2d75-4bba-a143-3587050d1f26 | exploring-uncertainty-in-conditional-multi | 1901.07702 | null | http://arxiv.org/abs/1901.07702v1 | http://arxiv.org/pdf/1901.07702v1.pdf | Exploring Uncertainty in Conditional Multi-Modal Retrieval Systems | We cast visual retrieval as a regression problem by posing triplet loss as a
regression loss. This enables epistemic uncertainty estimation using dropout as
a Bayesian approximation framework in retrieval. Accordingly, Monte Carlo (MC)
sampling is leveraged to boost retrieval performance. Our approach is evaluated
on t... | ['Larry Davis', 'Teruhisa Misu', 'Yi-Ting Chen', 'Xitong Yang', 'Ahmed Taha'] | 2019-01-23 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [-2.41363585e-01 -2.15239942e-01 -5.66203713e-01 -4.19107705e-01
-1.50311172e+00 -4.68397766e-01 1.19763911e+00 -1.68917865e-01
-8.19520175e-01 6.62389755e-01 3.37492555e-01 -5.62887341e-02
6.40118793e-02 -5.74293137e-01 -8.65861356e-01 -5.48240602e-01
1.90749183e-01 4.83878523e-01 5.61663993e-02 1.13800056... | [10.64255428314209, 1.249658226966858] |
1c88640a-0e39-4772-8881-3167c7eca3cc | evidence-of-meaning-in-language-models | 2305.11169 | null | https://arxiv.org/abs/2305.11169v2 | https://arxiv.org/pdf/2305.11169v2.pdf | Evidence of Meaning in Language Models Trained on Programs | We present evidence that language models can learn meaning despite being trained only to perform next token prediction on text, specifically a corpus of programs. Each program is preceded by a specification in the form of (textual) input-output examples. Working with programs enables us to precisely define concepts rel... | ['Martin Rinard', 'Charles Jin'] | 2023-05-18 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 4.71670032e-01 4.34392512e-01 -5.34215748e-01 -5.54574370e-01
-5.22105932e-01 -8.60963047e-01 7.83597171e-01 3.85338366e-01
4.63160351e-02 2.67760932e-01 3.48942615e-02 -1.02635503e+00
4.63158876e-01 -1.25248921e+00 -1.07928193e+00 -1.20360352e-01
-1.73758805e-01 2.44290248e-01 2.54421115e-01 -2.39602607... | [8.355894088745117, 7.322004318237305] |
5e0ee62a-b0e2-485d-9710-97d5ec2bd3e2 | cross-resolution-face-recognition-via-prior | 1905.10777 | null | https://arxiv.org/abs/1905.10777v1 | https://arxiv.org/pdf/1905.10777v1.pdf | Cross-Resolution Face Recognition via Prior-Aided Face Hallucination and Residual Knowledge Distillation | Recent deep learning based face recognition methods have achieved great performance, but it still remains challenging to recognize very low-resolution query face like 28x28 pixels when CCTV camera is far from the captured subject. Such face with very low-resolution is totally out of detail information of the face ident... | ['ShengMei Shen', 'Hanyang Kong', 'Junliang Xing', 'Jian Zhao', 'Xiaoguang Tu', 'Jiashi Feng'] | 2019-05-26 | null | null | null | null | ['heterogeneous-face-recognition', 'face-hallucination'] | ['computer-vision', 'computer-vision'] | [ 3.43705177e-01 3.10204208e-01 2.10848719e-01 -4.56250072e-01
-1.07155740e+00 -4.70357805e-01 4.08417732e-01 -1.27823961e+00
2.13430822e-01 6.79614663e-01 2.35311538e-01 2.60627389e-01
-7.55141303e-02 -9.77470934e-01 -9.08522487e-01 -8.08335423e-01
3.08601260e-01 4.71309930e-01 -3.08987945e-01 -2.32031003... | [12.876225471496582, 0.05283329263329506] |
063210fd-8842-4d5a-b063-e72d019acc08 | dual-moving-average-pseudo-labeling-for | 2212.08187 | null | https://arxiv.org/abs/2212.08187v1 | https://arxiv.org/pdf/2212.08187v1.pdf | Dual Moving Average Pseudo-Labeling for Source-Free Inductive Domain Adaptation | Unsupervised domain adaptation reduces the reliance on data annotation in deep learning by adapting knowledge from a source to a target domain. For privacy and efficiency concerns, source-free domain adaptation extends unsupervised domain adaptation by adapting a pre-trained source model to an unlabeled target domain w... | ['Yuhong Guo', 'Hao Yan'] | 2022-12-15 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 5.38128495e-01 2.58998662e-01 -7.44957387e-01 -8.24546576e-01
-1.17344677e+00 -8.43757331e-01 4.41242188e-01 1.16618961e-01
-6.28940582e-01 1.27960742e+00 -8.12985972e-02 -3.61125283e-02
1.09858260e-01 -7.76113391e-01 -7.60426819e-01 -7.92829633e-01
4.62719411e-01 8.99307609e-01 3.08183730e-01 4.70289737... | [10.377721786499023, 3.1240086555480957] |
b10e8a56-7da7-43de-97c8-9f69748ce32a | multi-objective-reinforcement-learning-based-1 | 2103.06380 | null | https://arxiv.org/abs/2103.06380v1 | https://arxiv.org/pdf/2103.06380v1.pdf | Multi-Objective Reinforcement Learning based Multi-Microgrid System Optimisation Problem | Microgrids with energy storage systems and distributed renewable energy sources play a crucial role in reducing the consumption from traditional power sources and the emission of $CO_2$. Connecting multi microgrid to a distribution power grid can facilitate a more robust and reliable operation to increase the security ... | ['Mohammad Abusara', 'Ke Li', 'Jiangjiao Xu'] | 2021-03-10 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-5.99963665e-01 -9.88669544e-02 -3.33425254e-02 -1.15680555e-02
-3.08517426e-01 -5.54122388e-01 3.43853056e-01 2.01020345e-01
-2.27408171e-01 1.49243617e+00 -9.26210061e-02 5.15574403e-02
-4.76877809e-01 -9.38126683e-01 -1.22010499e-01 -1.32021105e+00
-2.94972509e-01 3.88396561e-01 -2.92201281e-01 1.55995876... | [5.631618022918701, 2.5714428424835205] |
f0ebfa1f-5a1d-46b4-8c8c-f861b863e274 | online-meta-learning-for-model-update | 2209.00629 | null | https://arxiv.org/abs/2209.00629v1 | https://arxiv.org/pdf/2209.00629v1.pdf | Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction | In Federated Learning (FL) of click-through rate (CTR) prediction, users' data is not shared for privacy protection. The learning is performed by training locally on client devices and communicating only model changes to the server. There are two main challenges: (i) the client heterogeneity, making FL algorithms that ... | ['Tri Kurniawan Wijaya', 'Bartłomiej Twardowski', 'Xianghang Liu'] | 2022-08-30 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-7.73104429e-02 -3.69941473e-01 -4.77871031e-01 -6.40271842e-01
-9.75290418e-01 -5.23490250e-01 3.91333550e-01 2.75371503e-02
-5.50929070e-01 7.48533845e-01 1.28257886e-01 -3.53974074e-01
-1.19027309e-01 -6.36496365e-01 -6.21128380e-01 -7.59464264e-01
-1.52959451e-02 4.59488153e-01 4.80670869e-01 2.12819561... | [5.851167678833008, 6.326186656951904] |
c0aee31c-438a-4b9a-ae8c-657741346c91 | cvb-a-video-dataset-of-cattle-visual | 2305.16555 | null | https://arxiv.org/abs/2305.16555v2 | https://arxiv.org/pdf/2305.16555v2.pdf | CVB: A Video Dataset of Cattle Visual Behaviors | Existing image/video datasets for cattle behavior recognition are mostly small, lack well-defined labels, or are collected in unrealistic controlled environments. This limits the utility of machine learning (ML) models learned from them. Therefore, we introduce a new dataset, called Cattle Visual Behaviors (CVB), that ... | ['Aaron Ingham', 'Lars Petersson', 'Brano Kusy', 'Vivien Rolland', 'Neil Bagnall', 'Jody McNally', 'Greg Bishop-hurley', 'Reza Arablouei', 'Renuka Sharma', 'Ali Zia'] | 2023-05-26 | null | null | null | null | ['action-recognition-in-videos'] | ['computer-vision'] | [ 2.20793009e-01 -2.67578334e-01 -3.01061362e-01 -9.49670315e-01
2.40118764e-02 -4.67515379e-01 2.12547541e-01 4.04612511e-01
-4.17223930e-01 4.27757919e-01 -1.11637764e-01 -9.55484882e-02
3.15666869e-02 -2.32777566e-01 -9.86606658e-01 -6.07338190e-01
-6.11971080e-01 5.24567246e-01 5.22752643e-01 5.72919697... | [7.753662109375, -0.8043919801712036] |
07a66fa7-3fd9-48cd-bfb0-8f8fdcbb239f | language-guided-3d-object-detection-in-point | 2305.15765 | null | https://arxiv.org/abs/2305.15765v1 | https://arxiv.org/pdf/2305.15765v1.pdf | Language-Guided 3D Object Detection in Point Cloud for Autonomous Driving | This paper addresses the problem of 3D referring expression comprehension (REC) in autonomous driving scenario, which aims to ground a natural language to the targeted region in LiDAR point clouds. Previous approaches for REC usually focus on the 2D or 3D-indoor domain, which is not suitable for accurately predicting t... | ['Jianbing Shen', 'Ruigang Yang', 'Wei Li', 'Junbo Yin', 'Wenhao Cheng'] | 2023-05-25 | null | null | null | null | ['visual-grounding', 'referring-expression'] | ['computer-vision', 'computer-vision'] | [ 4.69544753e-02 1.39438301e-01 -2.20040292e-01 -6.42434716e-01
-1.05087340e+00 -3.31118077e-01 4.57674056e-01 3.15679103e-01
-2.53207356e-01 3.36556971e-01 -3.03629756e-01 -3.53368461e-01
-9.39419791e-02 -1.15799367e+00 -8.92050505e-01 -6.79164886e-01
2.13726312e-01 5.65061390e-01 4.01194364e-01 -3.55554283... | [8.17110538482666, -2.553750514984131] |
28901b9b-a5ee-4161-b5af-c6f1b125cd80 | deep-attention-based-semi-supervised-2d-pose | 1912.04618 | null | https://arxiv.org/abs/1912.04618v2 | https://arxiv.org/pdf/1912.04618v2.pdf | Deep Attention Based Semi-Supervised 2D-Pose Estimation for Surgical Instruments | For many practical problems and applications, it is not feasible to create a vast and accurately labeled dataset, which restricts the application of deep learning in many areas. Semi-supervised learning algorithms intend to improve performance by also leveraging unlabeled data. This is very valuable for 2D-pose estimat... | ['Okan Köpüklü', 'Abouzar Eslami', 'Mert Kayhan', 'Mehmet Yigitsoy', 'Gerhard Rigoll', 'Mhd Hasan Sarhan'] | 2019-12-10 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 3.73442411e-01 8.72080445e-01 -3.69789094e-01 -4.46711302e-01
-1.09894979e+00 -5.33761561e-01 4.49575543e-01 1.05070308e-01
-6.11820519e-01 7.62151599e-01 6.09758310e-02 -4.85426158e-01
-6.79529756e-02 -4.02557999e-01 -8.65117311e-01 -8.51018131e-01
4.56477180e-02 6.49862528e-01 -3.12169250e-02 7.51825944... | [14.458160400390625, -2.4673678874969482] |
2e744c65-e487-418e-b778-dbc0a34f7e33 | multiple-instance-fuzzy-inference-neural | 1610.04973 | null | http://arxiv.org/abs/1610.04973v1 | http://arxiv.org/pdf/1610.04973v1.pdf | Multiple Instance Fuzzy Inference Neural Networks | Fuzzy logic is a powerful tool to model knowledge uncertainty, measurements
imprecision, and vagueness. However, there is another type of vagueness that
arises when data have multiple forms of expression that fuzzy logic does not
address quite well. This is the case for multiple instance learning problems
(MIL). In MIL... | ['Hichem Frigui', 'Amine Ben Khalifa'] | 2016-10-17 | null | null | null | null | ['landmine'] | ['computer-vision'] | [-1.88178252e-02 3.36034968e-02 -1.08301401e-01 -7.46252596e-01
-2.17351876e-02 -6.03568256e-01 1.80924907e-01 1.52530238e-01
-2.79519200e-01 1.19970953e+00 -4.78137970e-01 -4.19367611e-01
-9.16221082e-01 -1.38355935e+00 -5.81518352e-01 -4.94998336e-01
-2.00630262e-01 8.64174128e-01 1.27739370e-01 -7.55172670... | [5.9184370040893555, 3.691338300704956] |
9d2dceca-7c1d-46dc-9a63-8209bcb921e5 | on-automatic-parsing-of-log-records | 2102.06320 | null | https://arxiv.org/abs/2102.06320v1 | https://arxiv.org/pdf/2102.06320v1.pdf | On Automatic Parsing of Log Records | Software log analysis helps to maintain the health of software solutions and ensure compliance and security. Existing software systems consist of heterogeneous components emitting logs in various formats. A typical solution is to unify the logs using manually built parsers, which is laborious. Instead, we explore the p... | ['Andriy Miranskyy', 'Jared Rand'] | 2021-02-12 | null | null | null | null | ['log-parsing'] | ['computer-code'] | [ 2.38190100e-01 2.34652236e-01 -4.27762046e-02 -4.46851403e-01
-1.04970288e+00 -5.94482541e-01 3.83155525e-01 2.76248693e-01
-1.25572652e-01 2.11198479e-01 6.34126067e-02 -1.06693947e+00
5.09211063e-01 -8.02596867e-01 -8.50627542e-01 1.31230161e-01
-1.21155106e-01 4.09099966e-01 4.27047163e-01 -1.44886330... | [7.88694953918457, 7.181585788726807] |
c0dffa46-6993-4eb9-bb6c-c0de76cc9006 | understanding-the-effect-of-textual | null | null | https://aclanthology.org/D19-6406 | https://aclanthology.org/D19-6406.pdf | Understanding the Effect of Textual Adversaries in Multimodal Machine Translation | It is assumed that multimodal machine translation systems are better than text-only systems at translating phrases that have a direct correspondence in the image. This assumption has been challenged in experiments demonstrating that state-of-the-art multimodal systems perform equally well in the presence of randomly se... | ['Koel Dutta Chowdhury', 'Desmond Elliott'] | 2019-11-01 | null | null | null | ws-2019-11 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 5.80375731e-01 1.69917822e-01 2.83797681e-02 4.78307754e-02
-1.15373707e+00 -1.14941406e+00 9.42138195e-01 -3.44016761e-01
-5.50511181e-01 6.77625954e-01 9.86540616e-02 -6.56285107e-01
7.83822358e-01 -3.82094592e-01 -1.13180041e+00 -5.87550640e-01
4.54082519e-01 4.31691259e-01 -2.88900472e-02 -2.80811340... | [11.408702850341797, 1.390946626663208] |
5454b700-124d-4b97-9139-01ba5bd2223f | kepler-keypoint-and-pose-estimation-of | 1702.05085 | null | http://arxiv.org/abs/1702.05085v1 | http://arxiv.org/pdf/1702.05085v1.pdf | KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors | Keypoint detection is one of the most important pre-processing steps in tasks
such as face modeling, recognition and verification. In this paper, we present
an iterative method for Keypoint Estimation and Pose prediction of
unconstrained faces by Learning Efficient H-CNN Regressors (KEPLER) for
addressing the face alig... | ['Azadeh Alavi', 'Rama Chellappa', 'Amit Kumar'] | 2017-02-16 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-3.94848824e-01 1.60555348e-01 -1.21140078e-01 -5.78392625e-01
-3.51979345e-01 -3.76604617e-01 7.28720129e-01 -2.00232968e-01
-2.77373552e-01 1.49764955e-01 9.37414635e-03 3.90448086e-02
-5.11505757e-04 -3.34160745e-01 -7.77846158e-01 -5.84277213e-01
-2.30628580e-01 5.33090174e-01 -2.99260557e-01 -1.22159772... | [13.478434562683105, 0.32869037985801697] |
c7f0a87a-462e-431a-ad90-f399d0d7702c | neural-3d-mesh-renderer | 1711.07566 | null | http://arxiv.org/abs/1711.07566v1 | http://arxiv.org/pdf/1711.07566v1.pdf | Neural 3D Mesh Renderer | For modeling the 3D world behind 2D images, which 3D representation is most
appropriate? A polygon mesh is a promising candidate for its compactness and
geometric properties. However, it is not straightforward to model a polygon
mesh from 2D images using neural networks because the conversion from a mesh to
an image, o... | ['Hiroharu Kato', 'Yoshitaka Ushiku', 'Tatsuya Harada'] | 2017-11-20 | neural-3d-mesh-renderer-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Kato_Neural_3D_Mesh_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Kato_Neural_3D_Mesh_CVPR_2018_paper.pdf | cvpr-2018-6 | ['3d-object-reconstruction'] | ['computer-vision'] | [ 4.19521928e-01 2.05383316e-01 2.36109033e-01 -5.00840962e-01
-2.60642678e-01 -2.44631082e-01 6.45866811e-01 7.57333785e-02
-4.45925236e-01 3.53776842e-01 -3.47886056e-01 -5.03076971e-01
4.01239753e-01 -1.31756258e+00 -1.26714325e+00 -1.54087394e-01
9.18759182e-02 5.86308420e-01 3.45226109e-01 -1.23266019... | [9.022353172302246, -3.3757004737854004] |
a6afb6ad-55cb-4cb5-a10b-75e404a9738e | step-by-step-a-hierarchical-framework-for | null | null | https://doi.org/10.1016/j.knosys.2022.108843 | https://doi.org/10.1016/j.knosys.2022.108843 | Step by step: a hierarchical framework for multi-hop knowledge graph reasoning with reinforcement learning | Recently, knowledge graph reasoning has sparked great interest in research community, which aims at inferring missing information in triples and provides critical support to various tasks (e.g., question answering and recommendation). To date, multi-hop reasoning is a dominant approach which infers the target answer by... | ['Jie Shao', 'Shuang Liang', 'Deqiang Ouyang', 'Anjie Zhu'] | 2022-07-19 | null | null | null | knowledge-based-systems-2022-7 | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 6.55100623e-04 6.31392598e-01 -4.92731541e-01 -2.28215590e-01
-1.42638966e-01 -3.94741327e-01 5.23908138e-01 5.55519164e-01
-3.35957289e-01 8.14815640e-01 3.56665939e-01 -3.87293220e-01
-5.34387052e-01 -1.30496168e+00 -7.83160806e-01 -4.46934283e-01
1.55076534e-01 6.13402009e-01 5.83104491e-01 -2.91111887... | [9.436222076416016, 7.8449907302856445] |
d56653c1-de75-4cdf-b786-86134fcd1fab | predicate-argument-structure-analysis-with | null | null | https://aclanthology.org/C14-1077 | https://aclanthology.org/C14-1077.pdf | Predicate-Argument Structure Analysis with Zero-Anaphora Resolution for Dialogue Systems | null | ['Tomoko Izumi', 'Ryuichiro Higashinaka', 'Kenji Imamura'] | 2014-08-01 | predicate-argument-structure-analysis-with-1 | https://aclanthology.org/C14-1077 | https://aclanthology.org/C14-1077.pdf | coling-2014-8 | ['dialogue-management'] | ['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... | [-7.2496747970581055, 3.5673441886901855] |
b3632b6d-3ce1-48a1-aa1f-45065d4efb31 | membership-inference-attacks-on-knowledge | 2104.08273 | null | https://arxiv.org/abs/2104.08273v2 | https://arxiv.org/pdf/2104.08273v2.pdf | Membership Inference Attacks on Knowledge Graphs | Membership inference attacks (MIAs) infer whether a specific data record is used for target model training. MIAs have provoked many discussions in the information security community since they give rise to severe data privacy issues, especially for private and sensitive datasets. Knowledge Graphs (KGs), which describe ... | ['Philip S. Yu', 'Lifu Huang', 'Lichao Sun', 'Yu Wang'] | 2021-04-16 | null | null | null | null | ['membership-inference-attack', 'triple-classification'] | ['computer-vision', 'graphs'] | [ 1.98853776e-01 5.00869989e-01 -1.72976136e-01 -4.40815389e-01
-3.30259025e-01 -6.94158077e-01 2.10655183e-01 7.69068241e-01
-1.26265064e-01 7.23948479e-01 6.52944371e-02 -4.50999856e-01
-6.26044512e-01 -1.32860541e+00 -7.21093535e-01 -6.37126088e-01
-5.71098924e-01 2.23868057e-01 3.35105509e-01 6.06926642... | [5.9710774421691895, 7.157015323638916] |
95cbaa71-77bf-487a-b5c3-96a176f1d3bb | model-based-metrics-sample-efficient | 2104.12231 | null | https://arxiv.org/abs/2104.12231v1 | https://arxiv.org/pdf/2104.12231v1.pdf | Model-based metrics: Sample-efficient estimates of predictive model subpopulation performance | Machine learning models $-$ now commonly developed to screen, diagnose, or predict health conditions $-$ are evaluated with a variety of performance metrics. An important first step in assessing the practical utility of a model is to evaluate its average performance over an entire population of interest. In many settin... | ['Emily B. Fox', 'Joseph Futoma', 'Leon A. Gatys', 'Andrew C. Miller'] | 2021-04-25 | null | null | null | null | ['readmission-prediction'] | ['medical'] | [-4.10743169e-02 3.29766609e-02 -5.07509470e-01 -5.42005420e-01
-1.36822653e+00 -2.23955929e-01 4.59159762e-01 8.30012441e-01
-4.50910509e-01 1.09051514e+00 8.95446464e-02 -5.66426098e-01
-2.84898490e-01 -8.87905657e-01 -6.64285839e-01 -3.93238276e-01
-2.38867000e-01 7.52958536e-01 -4.53025028e-02 4.09961075... | [8.060553550720215, 5.097592353820801] |
3aecb8a1-c103-4424-9cca-3ec7784ef938 | bigvideo-a-large-scale-video-subtitle | 2305.18326 | null | https://arxiv.org/abs/2305.18326v3 | https://arxiv.org/pdf/2305.18326v3.pdf | BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation | We present a large-scale video subtitle translation dataset, BigVideo, to facilitate the study of multi-modality machine translation. Compared with the widely used How2 and VaTeX datasets, BigVideo is more than 10 times larger, consisting of 4.5 million sentence pairs and 9,981 hours of videos. We also introduce two de... | ['Jinsong Su', 'Degen Huang', 'Mingxuan Wang', 'Shanbo Cheng', 'Zewei Sun', 'Peihao Zhu', 'Ningxin Peng', 'Luyang Huang', 'Liyan Kang'] | 2023-05-23 | null | null | null | null | ['nmt', 'multimodal-machine-translation'] | ['computer-code', 'natural-language-processing'] | [ 7.19579160e-02 -3.27715784e-01 -4.74992812e-01 -2.18672425e-01
-1.32209849e+00 -1.02055860e+00 1.02518427e+00 -3.08717906e-01
-4.91881102e-01 7.74235725e-01 6.85060024e-01 -4.10518467e-01
4.85391259e-01 -1.18810236e-01 -8.60210180e-01 -3.87556136e-01
5.03970802e-01 5.69847524e-01 -1.87649682e-01 -1.58057362... | [11.363565444946289, 1.4958096742630005] |
079fab77-31c3-47c4-a4ef-e0364b7b15a8 | shisrcnet-super-resolution-and-classification | 2306.14119 | null | https://arxiv.org/abs/2306.14119v1 | https://arxiv.org/pdf/2306.14119v1.pdf | SHISRCNet: Super-resolution And Classification Network For Low-resolution Breast Cancer Histopathology Image | The rapid identification and accurate diagnosis of breast cancer, known as the killer of women, have become greatly significant for those patients. Numerous breast cancer histopathological image classification methods have been proposed. But they still suffer from two problems. (1) These methods can only hand high-reso... | ['Zhonghai Wu', 'Qingni Shen', 'Boyan Chen', 'Xin Zhang', 'ZiRui Wang', 'Cong Li', 'Luyuan Xie'] | 2023-06-25 | null | null | null | null | ['super-resolution', 'histopathological-image-classification'] | ['computer-vision', 'medical'] | [ 4.91538256e-01 9.60708484e-02 -3.42933446e-01 -2.66216248e-01
-9.65314925e-01 -7.79463584e-03 2.31604934e-01 -1.14034779e-01
-3.33968461e-01 7.36335158e-01 -7.23499283e-02 -1.82890460e-01
-1.39004499e-01 -9.30387318e-01 -2.32407883e-01 -1.15343559e+00
3.31525087e-01 2.08811238e-02 4.91065711e-01 -2.76013464... | [14.808938980102539, -2.7295620441436768] |
f4c78cfc-24e0-4599-9591-5ff855427c9b | geometry-aware-face-completion-and-editing | 1809.02967 | null | http://arxiv.org/abs/1809.02967v2 | http://arxiv.org/pdf/1809.02967v2.pdf | Geometry-Aware Face Completion and Editing | Face completion is a challenging generation task because it requires
generating visually pleasing new pixels that are semantically consistent with
the unmasked face region. This paper proposes a geometry-aware Face Completion
and Editing NETwork (FCENet) by systematically studying facial geometry from
the unmasked regi... | ['Yibo Hu', 'Linxiao Song', 'Jie Cao', 'Ran He', 'Linsen Song'] | 2018-09-09 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 3.72145236e-01 6.16666913e-01 3.12531084e-01 -8.28179538e-01
-6.48659945e-01 -6.29361808e-01 3.77948672e-01 -8.42407107e-01
1.49388984e-02 4.86547172e-01 1.54151917e-01 3.63132209e-01
1.88780993e-01 -7.21848428e-01 -9.38123941e-01 -5.99529088e-01
3.22967887e-01 4.62171346e-01 -6.62524581e-01 7.27967173... | [12.717059135437012, -0.08988640457391739] |
282a55bb-f7f8-4b4e-aace-bc8f29fc988e | quality-control-for-more-reliable-integration | 2112.03277 | null | https://arxiv.org/abs/2112.03277v2 | https://arxiv.org/pdf/2112.03277v2.pdf | Automatic quality control framework for more reliable integration of machine learning-based image segmentation into medical workflows | Machine learning algorithms underpin modern diagnostic-aiding software, which has proved valuable in clinical practice, particularly in radiology. However, inaccuracies, mainly due to the limited availability of clinical samples for training these algorithms, hamper their wider applicability, acceptance, and recognitio... | ['Ingo Roeder', 'Daniel Lichterfeld', 'Evelyn Medawar', 'Konstantin Thierbach', 'Kersten Villringer', 'Maria del C. Valdés Hernández', 'Nico Scherf', 'Paul Glad Mihai', 'Alberto Merola', 'Janis Reinelt', 'Sebastian Niehaus', 'Elena Williams'] | 2021-12-06 | null | null | null | null | ['brain-image-segmentation'] | ['medical'] | [ 1.12616077e-01 3.07321046e-02 -1.01435967e-01 -4.18589860e-01
-9.77937043e-01 -4.73094404e-01 3.32613260e-01 5.63864529e-01
-5.91933370e-01 7.85012245e-01 1.70187637e-01 -5.09623826e-01
-6.08090758e-01 -2.69506454e-01 -1.89308167e-01 -7.40974963e-01
-4.70302731e-01 9.65585589e-01 4.00510728e-01 3.76634508... | [14.167407035827637, -2.2663323879241943] |
2dee8993-e318-48c5-9804-fd66fdcf4252 | dvdnet-a-fast-network-for-deep-video | 1906.11890 | null | https://arxiv.org/abs/1906.11890v1 | https://arxiv.org/pdf/1906.11890v1.pdf | DVDnet: A Fast Network for Deep Video Denoising | In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture. Previous neural network based approaches to video denoising have been unsuccessful as their performance cannot compete with the performance of patch-based methods. However, our approach outperfor... | ['Julie Delon', 'Thomas Veit', 'Matias Tassano'] | 2019-06-04 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [-3.75340618e-02 -4.29240197e-01 2.80456066e-01 -2.07401350e-01
-8.37647855e-01 -2.28016272e-01 3.16735446e-01 -1.73901945e-01
-4.34793204e-01 4.58924174e-01 3.40372622e-01 -1.30804539e-01
5.89968190e-02 -8.39903295e-01 -7.70731211e-01 -8.16377699e-01
-1.31708130e-01 -1.18108578e-01 3.40555191e-01 -4.89680409... | [11.487622261047363, -2.2775521278381348] |
7a25abf8-4ddc-42c6-b731-b0f0014c820e | an-end-to-end-reinforcement-learning-approach | 2306.05747 | null | https://arxiv.org/abs/2306.05747v1 | https://arxiv.org/pdf/2306.05747v1.pdf | An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint Programming | Constraint Programming (CP) is a declarative programming paradigm that allows for modeling and solving combinatorial optimization problems, such as the Job-Shop Scheduling Problem (JSSP). While CP solvers manage to find optimal or near-optimal solutions for small instances, they do not scale well to large ones, i.e., t... | ['Konstantin Schekotihin', 'Martin Gebser', 'Pierre Tassel'] | 2023-06-09 | null | null | null | null | ['combinatorial-optimization', 'feature-engineering'] | ['methodology', 'methodology'] | [ 1.05887130e-01 2.79372215e-01 -5.89369178e-01 -8.37578624e-02
-7.23552942e-01 -6.77213967e-01 2.59392798e-01 3.73264998e-01
-2.49936149e-01 9.92321491e-01 -1.69168636e-01 -5.64049363e-01
-4.72267002e-01 -9.54605401e-01 -9.74198699e-01 -5.33305287e-01
-3.54635090e-01 1.24917090e+00 1.29931405e-01 -4.25865263... | [5.133552074432373, 2.8853683471679688] |
43922334-dabb-4204-a187-b921b93de537 | constraint-based-graph-network-simulator-1 | 2112.09161 | null | https://arxiv.org/abs/2112.09161v2 | https://arxiv.org/pdf/2112.09161v2.pdf | Constraint-based graph network simulator | In the area of physical simulations, nearly all neural-network-based methods directly predict future states from the input states. However, many traditional simulation engines instead model the constraints of the system and select the state which satisfies them. Here we present a framework for constraint-based learned ... | ['Peter Battaglia', 'Tobias Pfaff', 'Alvaro Sanchez-Gonzalez', 'Yulia Rubanova'] | 2021-12-16 | constraint-based-graph-network-simulator | https://openreview.net/forum?id=Uxppuphg5ZL | https://openreview.net/pdf?id=Uxppuphg5ZL | null | ['physical-simulations'] | ['miscellaneous'] | [-1.09432362e-01 9.28783417e-02 -5.56558788e-01 -1.82186887e-01
-2.44226366e-01 -4.37157184e-01 5.45887053e-01 -1.05437323e-01
-2.38353983e-01 1.17478359e+00 -1.32903069e-01 -8.01981151e-01
-9.14808139e-02 -8.89197409e-01 -8.68362844e-01 -3.84850949e-01
-6.69868231e-01 6.07143581e-01 1.09122522e-01 -5.08917332... | [6.364625930786133, 3.30678129196167] |
1b79a8d7-80fe-4153-8a68-57b05886b159 | attention-link-an-efficient-attention-based | 2302.00340 | null | https://arxiv.org/abs/2302.00340v1 | https://arxiv.org/pdf/2302.00340v1.pdf | Attention Link: An Efficient Attention-Based Low Resource Machine Translation Architecture | Transformers have achieved great success in machine translation, but transformer-based NMT models often require millions of bilingual parallel corpus for training. In this paper, we propose a novel architecture named as attention link (AL) to help improve transformer models' performance, especially in low training reso... | ['Zeping Min'] | 2023-02-01 | null | null | null | null | ['nmt'] | ['computer-code'] | [-1.37381762e-01 -2.16231182e-01 -4.64561760e-01 -2.10927129e-01
-1.21695423e+00 -4.23118591e-01 7.59961665e-01 -6.56230450e-01
-3.83397847e-01 1.13488781e+00 4.67774540e-01 -1.01118767e+00
3.43517184e-01 -5.15644968e-01 -9.37660754e-01 -1.50926009e-01
5.55831671e-01 1.08517885e+00 -1.46183595e-01 -6.77706063... | [11.639888763427734, 10.218076705932617] |
42d29779-cf23-4375-a391-663646a6e1d9 | toward-an-amazigh-language-processing | null | null | https://aclanthology.org/W12-5015 | https://aclanthology.org/W12-5015.pdf | Toward an amazigh language processing | null | ['Fatima Zahra Nejme', 'Driss Aboutajdine', 'Siham Boulaknadel'] | 2012-12-01 | toward-an-amazigh-language-processing-1 | https://aclanthology.org/W12-5015 | https://aclanthology.org/W12-5015.pdf | ws-2012-12 | ['lexical-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.44087553024292, 3.73960280418396] |
41ace27c-7317-4cfb-8765-cd6181adcd63 | incsql-training-incremental-text-to-sql | 1809.05054 | null | http://arxiv.org/abs/1809.05054v2 | http://arxiv.org/pdf/1809.05054v2.pdf | IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles | We present a sequence-to-action parsing approach for the natural language to
SQL task that incrementally fills the slots of a SQL query with feasible
actions from a pre-defined inventory. To account for the fact that typically
there are multiple correct SQL queries with the same or very similar semantics,
we draw inspi... | ['Kaushik Chakrabarti', 'Weizhu Chen', 'Tianze Shi', 'Oleksandr Polozov', 'Kedar Tatwawadi', 'Yi Mao'] | 2018-09-13 | null | null | null | null | ['action-parsing'] | ['natural-language-processing'] | [ 2.91128725e-01 4.35358882e-01 -3.81736428e-01 -9.67453182e-01
-1.32069850e+00 -8.63507867e-01 4.82327610e-01 2.32111171e-01
-2.45129064e-01 3.87498081e-01 2.08404750e-01 -8.09276223e-01
2.03400701e-01 -9.73228872e-01 -1.16588104e+00 2.31443852e-01
7.47034773e-02 9.73609328e-01 4.91931796e-01 -6.87638670... | [9.863419532775879, 7.824802398681641] |
c6100ee9-b358-4eaf-a998-ddd8b7d6a09d | argument-linking-a-survey-and-forecast | 2107.08523 | null | https://arxiv.org/abs/2107.08523v1 | https://arxiv.org/pdf/2107.08523v1.pdf | Argument Linking: A Survey and Forecast | Semantic role labeling (SRL) -- identifying the semantic relationships between a predicate and other constituents in the same sentence -- is a well-studied task in natural language understanding (NLU). However, many of these relationships are evident only at the level of the document, as a role for a predicate in one s... | ['William Gantt'] | 2021-07-18 | null | null | null | null | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 7.60872185e-01 6.96490109e-01 -6.68704987e-01 -4.40624833e-01
-5.19363225e-01 -1.10653567e+00 7.64478385e-01 1.21943617e+00
-2.35579818e-01 1.11687958e+00 8.66778910e-01 -5.44480920e-01
-3.10462952e-01 -8.43366027e-01 -4.90668267e-01 -2.97115088e-01
1.19774543e-01 3.43076915e-01 3.62995863e-01 -5.40309429... | [10.189889907836914, 9.245351791381836] |
3d8effc6-7af2-41d8-b4cc-6555589f2f5e | omnizart-a-general-toolbox-for-automatic | 2106.00497 | null | https://arxiv.org/abs/2106.00497v1 | https://arxiv.org/pdf/2106.00497v1.pdf | Omnizart: A General Toolbox for Automatic Music Transcription | We present and release Omnizart, a new Python library that provides a streamlined solution to automatic music transcription (AMT). Omnizart encompasses modules that construct the life-cycle of deep learning-based AMT, and is designed for ease of use with a compact command-line interface. To the best of our knowledge, O... | ['Li Su', 'Yi-Chin Chuang', 'Jui-Yang Hsu', 'I-Chieh Wei', 'Tsung-Ping Chen', 'Yin-Jyun Luo', 'Yu-Te Wu'] | 2021-06-01 | null | null | null | null | ['chord-recognition', 'music-transcription', 'music-information-retrieval'] | ['audio', 'music', 'music'] | [-2.20077500e-01 -5.65136373e-01 -6.43354133e-02 1.45222083e-01
-9.39549506e-01 -1.11928427e+00 1.63580939e-01 -1.71194039e-02
-1.12920873e-01 5.97878173e-02 4.25608426e-01 -6.02015145e-02
-5.12345314e-01 -2.82850355e-01 -1.17039591e-01 -3.30738068e-01
-2.37016410e-01 5.65449536e-01 -3.20899278e-01 -4.03856039... | [15.949211120605469, 5.392426490783691] |
c7986680-bb06-4c50-a8ef-d870cfc6c91b | learning-the-change-for-automatic-image | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Yan_Learning_the_Change_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Yan_Learning_the_Change_2013_CVPR_paper.pdf | Learning the Change for Automatic Image Cropping | Image cropping is a common operation used to improve the visual quality of photographs. In this paper, we present an automatic cropping technique that accounts for the two primary considerations of people when they crop: removal of distracting content, and enhancement of overall composition. Our approach utilizes a lar... | ['Xiaoou Tang', 'Stephen Lin', 'Jianzhou Yan', 'Sing Bing Kang'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['image-cropping'] | ['computer-vision'] | [ 5.38560867e-01 -3.40685874e-01 1.54123574e-01 -1.79423034e-01
-4.90787178e-01 -7.05568552e-01 2.28840351e-01 -7.53601342e-02
-1.10112712e-01 5.41273057e-01 1.27230182e-01 6.03475682e-02
3.80271196e-01 -7.33380377e-01 -6.74107254e-01 -7.27028131e-01
3.88220131e-01 -5.81678033e-01 1.42584607e-01 -1.45953253... | [11.199662208557129, -1.0982877016067505] |
8fce07d8-ae89-48ac-b0a0-f9e97aa0c89d | blind-identification-of-state-space-models-in | 2108.08498 | null | https://arxiv.org/abs/2108.08498v1 | https://arxiv.org/pdf/2108.08498v1.pdf | Blind Identification of State-Space Models in Physical Coordinates | Blind identification is popular for modeling a system without the input information, such as in the research areas of structural health monitoring and audio signal processing. Existing blind identification methods have both advantages and disadvantages, in this paper, we briefly outline current methods and propose a no... | ['Georg Bauer', 'Christian Bohn', 'Runzhe Han'] | 2021-08-19 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 2.95550376e-01 -4.87683892e-01 1.03206776e-01 4.00101811e-01
-1.97886050e-01 -5.81961453e-01 2.04570115e-01 -5.30484855e-01
-1.66843072e-01 4.32464391e-01 4.46978398e-02 -5.04648864e-01
-5.08539319e-01 -1.95431799e-01 -1.34766653e-01 -9.68239069e-01
1.31980866e-01 -1.44069836e-01 -2.60681231e-02 -1.89104855... | [15.11059856414795, 5.703856468200684] |
f717dad1-8318-4ec4-9bcd-fe6556625a8f | blind-face-restoration-benchmark-datasets-and | 2206.03697 | null | https://arxiv.org/abs/2206.03697v1 | https://arxiv.org/pdf/2206.03697v1.pdf | Blind Face Restoration: Benchmark Datasets and a Baseline Model | Blind Face Restoration (BFR) aims to construct a high-quality (HQ) face image from its corresponding low-quality (LQ) input. Recently, many BFR methods have been proposed and they have achieved remarkable success. However, these methods are trained or evaluated on privately synthesized datasets, which makes it infeasib... | ['Guoren Wang', 'Changsheng Li', 'Wenhan Luo', 'Kaihao Zhang', 'Puyang Zhang'] | 2022-06-08 | null | null | null | null | ['blind-face-restoration'] | ['computer-vision'] | [ 1.86937973e-01 -4.55413282e-01 4.60812673e-02 -4.36745673e-01
-8.34043384e-01 -4.51597720e-02 6.86701536e-01 -6.43000543e-01
-1.19062208e-01 7.08892941e-01 5.28784692e-01 1.83040779e-02
-1.05695598e-01 -5.05423546e-01 -5.92438161e-01 -7.69661665e-01
-8.23374614e-02 -1.25625908e-01 -1.72238529e-01 -2.68509984... | [12.910399436950684, 0.06776900589466095] |
7604467e-f752-4598-a375-331e0634b792 | wildmix-dataset-and-spectro-temporal | 1911.09783 | null | https://arxiv.org/abs/1911.09783v1 | https://arxiv.org/pdf/1911.09783v1.pdf | WildMix Dataset and Spectro-Temporal Transformer Model for Monoaural Audio Source Separation | Monoaural audio source separation is a challenging research area in machine learning. In this area, a mixture containing multiple audio sources is given, and a model is expected to disentangle the mixture into isolated atomic sources. In this paper, we first introduce a challenging new dataset for monoaural source sepa... | ['Louis-Philippe Morency', 'Tianjun Ma', 'Soujanya Poria', 'Amir Zadeh'] | 2019-11-21 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 2.85493881e-01 -4.26967293e-01 1.47921532e-01 -1.14635654e-01
-1.44563317e+00 -8.04073095e-01 3.12753081e-01 -1.79542243e-01
5.35838194e-02 5.83313763e-01 6.19989634e-01 8.71006772e-03
1.87961198e-02 -1.37952015e-01 -7.10656941e-01 -8.61650646e-01
-1.36688903e-01 6.68783560e-02 9.95707437e-02 -1.73486527... | [15.294941902160645, 5.549594402313232] |
a625f4e8-89ca-4a82-9207-75e09e6fe42a | midas-multi-integrated-domain-adaptive | 2205.09817 | null | https://arxiv.org/abs/2205.09817v1 | https://arxiv.org/pdf/2205.09817v1.pdf | MiDAS: Multi-integrated Domain Adaptive Supervision for Fake News Detection | COVID-19 related misinformation and fake news, coined an 'infodemic', has dramatically increased over the past few years. This misinformation exhibits concept drift, where the distribution of fake news changes over time, reducing effectiveness of previously trained models for fake news detection. Given a set of fake ne... | ['Calton Pu', 'Abhijit Suprem'] | 2022-05-19 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [-2.42769361e-01 -2.61522740e-01 -8.45619380e-01 -4.91765797e-01
-9.90580618e-01 -7.50921667e-01 1.00675440e+00 3.60305943e-02
-2.16806456e-01 8.30762923e-01 4.88954514e-01 1.40204340e-01
4.33947712e-01 -5.97997427e-01 -9.34050560e-01 -4.18913484e-01
1.54679984e-01 7.58906484e-01 4.89404023e-01 -5.02936184... | [8.127957344055176, 10.27217960357666] |
07669a40-de17-425d-8a43-678098ffbd59 | walt-watch-and-learn-2d-amodal-representation | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Reddy_WALT_Watch_and_Learn_2D_Amodal_Representation_From_Time-Lapse_Imagery_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Reddy_WALT_Watch_and_Learn_2D_Amodal_Representation_From_Time-Lapse_Imagery_CVPR_2022_paper.pdf | WALT: Watch and Learn 2D Amodal Representation From Time-Lapse Imagery | Current methods for object detection, segmentation, and tracking fail in the presence of severe occlusions in busy urban environments. Labeled real data of occlusions is scarce (even in large datasets) and synthetic data leaves a domain gap, making it hard to explicitly model and learn occlusions. In this work, we ... | ['Srinivasa G. Narasimhan', 'Robert Tamburo', 'N. Dinesh Reddy'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['occlusion-estimation', 'amodal-instance-segmentation', 'amodal-tracking', 'occlusion-handling'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.24547142e-01 3.12593907e-01 -3.59620452e-01 -5.32415092e-01
-9.64419127e-01 -5.58529556e-01 7.22558498e-01 -1.12524934e-01
-2.28280872e-01 8.24692965e-01 -1.20409079e-01 -2.70938426e-01
2.08092541e-01 -6.77361369e-01 -1.21436465e+00 -5.17450273e-01
-1.50339037e-01 8.90641212e-01 6.63174093e-01 1.36646628... | [8.488896369934082, -0.7836561799049377] |
3b0a9815-08e0-4a90-9928-142eb467eb59 | enabling-embodied-analogies-in-intelligent | 1712.00334 | null | http://arxiv.org/abs/1712.00334v1 | http://arxiv.org/pdf/1712.00334v1.pdf | Enabling Embodied Analogies in Intelligent Music Systems | The present methodology is aimed at cross-modal machine learning and uses
multidisciplinary tools and methods drawn from a broad range of areas and
disciplines, including music, systematic musicology, dance, motion capture,
human-computer interaction, computational linguistics and audio signal
processing. Main tasks in... | ['Fabio Paolizzo'] | 2017-11-30 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 5.27704658e-04 -4.36608881e-01 -1.55461192e-01 2.30062991e-01
-8.71495903e-01 -6.96578443e-01 3.63474458e-01 -2.84203202e-01
-1.07682139e-01 1.44954026e-01 9.53458309e-01 5.37920177e-01
-5.86833060e-01 -3.10017198e-01 -2.55879350e-02 -3.41431379e-01
-1.44632787e-01 2.94531554e-01 -3.48232687e-01 -3.83703262... | [15.916789054870605, 5.190313816070557] |
3df13610-d18a-4b55-bb05-99a0da2a0a8a | strong-consistency-and-optimality-of-spectral | 2306.06845 | null | https://arxiv.org/abs/2306.06845v1 | https://arxiv.org/pdf/2306.06845v1.pdf | Strong consistency and optimality of spectral clustering in symmetric binary non-uniform Hypergraph Stochastic Block Model | Consider the unsupervised classification problem in random hypergraphs under the non-uniform \emph{Hypergraph Stochastic Block Model} (HSBM) with two equal-sized communities ($n/2$), where each edge appears independently with some probability depending only on the labels of its vertices. In this paper, an \emph{informa... | ['Haixiao Wang'] | 2023-06-12 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 3.03348929e-01 8.50770175e-01 -1.56516925e-01 2.12586328e-01
-5.10176480e-01 -7.98675656e-01 -1.65557548e-01 4.13318098e-01
-3.31576407e-01 6.84907377e-01 -4.30306971e-01 -2.18270883e-01
-5.48627257e-01 -8.66571426e-01 -7.05401063e-01 -1.12626016e+00
-3.73262435e-01 9.96648192e-01 7.20896432e-03 3.50569665... | [6.91060209274292, 5.085941791534424] |
20ca0d6a-0da6-4183-ab08-37ea60bb39ac | 190412577 | 1904.12577 | null | https://arxiv.org/abs/1904.12577v2 | https://arxiv.org/pdf/1904.12577v2.pdf | Table understanding in structured documents | Abstract--- Table detection and extraction has been studied in the context of documents like reports, where tables are clearly outlined and stand out from the document structure visually. We study this topic in a rather more challenging domain of layout-heavy business documents, particularly invoices. Invoices present ... | ['Petr Baudiš', 'Antonín Hoskovec', 'Martin Holeček', 'Pavel Klinger'] | 2019-03-22 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [ 2.21849188e-01 3.26703340e-01 -4.47222497e-03 -3.07847887e-01
-6.45374298e-01 -1.11866093e+00 5.84364891e-01 6.72081649e-01
-8.92583281e-02 5.23704946e-01 4.64020878e-01 -7.99127936e-01
-2.04668626e-01 -8.21387410e-01 -9.19304311e-01 -4.15704846e-02
-2.25403577e-01 7.42947161e-01 1.17100559e-01 -1.76228836... | [11.680990219116211, 2.972585439682007] |
416b5a71-c68a-40b2-91a1-a0817dd97200 | hierarchical-multi-label-classification-of-1 | 2211.02810 | null | https://arxiv.org/abs/2211.02810v1 | https://arxiv.org/pdf/2211.02810v1.pdf | Hierarchical Multi-Label Classification of Scientific Documents | Automatic topic classification has been studied extensively to assist managing and indexing scientific documents in a digital collection. With the large number of topics being available in recent years, it has become necessary to arrange them in a hierarchy. Therefore, the automatic classification systems need to be ab... | ['Cornelia Caragea', 'Mobashir Sadat'] | 2022-11-05 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [-1.58248574e-01 -6.25406206e-02 -4.33663845e-01 -4.25443619e-01
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4.70068567e-02 7.92512834e-01 3.17298710e-01 6.03345811... | [10.214076042175293, 7.261381149291992] |
ae8a8b51-5371-4556-ab20-b833fdca4c35 | on-the-finite-time-behavior-of-suboptimal | 2305.10085 | null | https://arxiv.org/abs/2305.10085v1 | https://arxiv.org/pdf/2305.10085v1.pdf | On the Finite-Time Behavior of Suboptimal Linear Model Predictive Control | Inexact methods for model predictive control (MPC), such as real-time iterative schemes or time-distributed optimization, alleviate the computational burden of exact MPC by providing suboptimal solutions. While the asymptotic stability of such algorithms is well studied, their finite-time performance has not received m... | ['John Lygeros', 'Andrea Iannelli', 'Efe C. Balta', 'Aren Karapetyan'] | 2023-05-17 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [ 1.61465660e-01 4.09750879e-01 -6.58050418e-01 2.31453508e-01
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-1.53142959e-01 4.17298675e-02 -1.48745045e-01 2.80378819... | [4.920691967010498, 2.554887056350708] |
6f48ebff-c917-401b-a219-db436a5f853a | spaceevo-hardware-friendly-search-space | 2303.08308 | null | https://arxiv.org/abs/2303.08308v1 | https://arxiv.org/pdf/2303.08308v1.pdf | SpaceEvo: Hardware-Friendly Search Space Design for Efficient INT8 Inference | The combination of Neural Architecture Search (NAS) and quantization has proven successful in automatically designing low-FLOPs INT8 quantized neural networks (QNN). However, directly applying NAS to design accurate QNN models that achieve low latency on real-world devices leads to inferior performance. In this work, w... | ['Mao Yang', 'Ting Cao', 'Ningxin Zheng', 'Yuqing Yang', 'Yujing Wang', 'Quanlu Zhang', 'Jiahang Xu', 'Xudong Wang', 'Li Lyna Zhang'] | 2023-03-15 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 7.81734884e-02 -3.41486633e-01 -6.34806395e-01 -3.01144749e-01
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-2.06691086e-01 -6.98783815e-01 -9.78672266e-01 -4.17541593e-01
2.10539788e-01 2.55712569e-01 3.88841540e-01 1.85018256... | [8.533658027648926, 2.977543592453003] |
48e1fdf1-b66f-419b-a609-c81cf7a9d36c | conversational-search-a-report-from-dagstuhl | 2005.08658 | null | https://arxiv.org/abs/2005.08658v1 | https://arxiv.org/pdf/2005.08658v1.pdf | Conversational Search -- A Report from Dagstuhl Seminar 19461 | Dagstuhl Seminar 19461 "Conversational Search" was held on 10-15 November 2019. 44~researchers in Information Retrieval and Web Search, Natural Language Processing, Human Computer Interaction, and Dialogue Systems were invited to share the latest development in the area of Conversational Search and discuss its research... | ['Mark Sanderson', 'Avishek Anand', 'Hideo Joho', 'Benno Stein', 'Matthias Hagen', 'Lawrence Cavedon'] | 2020-05-18 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 1.22275818e-02 4.04676408e-01 -4.05517042e-01 -4.27645266e-01
-5.37009060e-01 -7.88404942e-01 1.13865447e+00 3.59451383e-01
-2.89481401e-01 6.76850736e-01 5.36033869e-01 -6.57422006e-01
-3.40026766e-01 -3.41097236e-01 3.40855300e-01 -2.29926318e-01
9.79213119e-02 7.37992764e-01 1.30210638e-01 -6.17215812... | [12.30894660949707, 7.8241167068481445] |
e5dcc301-863e-48e2-969a-71bcb104078a | elements-of-effective-machine-learning | 2211.14401 | null | https://arxiv.org/abs/2211.14401v2 | https://arxiv.org/pdf/2211.14401v2.pdf | Elements of effective machine learning datasets in astronomy | In this work, we identify elements of effective machine learning datasets in astronomy and present suggestions for their design and creation. Machine learning has become an increasingly important tool for analyzing and understanding the large-scale flood of data in astronomy. To take advantage of these tools, datasets ... | ['Christy Ma', 'Kevin Alfaro', 'Yunqi Li', 'Evan Jones', 'Tuan Do', 'Bernie Boscoe'] | 2022-11-25 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [ 7.26461112e-02 -1.23416610e-01 -3.41183156e-01 -4.33708519e-01
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1.58973098e-01 7.70877182e-01 -2.34113075e-02 1.14138179... | [7.959734916687012, 3.3011462688446045] |
b80f54ca-a3e5-4a83-be12-df659442667c | contrastner-contrastive-based-prompt-tuning | 2305.17951 | null | https://arxiv.org/abs/2305.17951v1 | https://arxiv.org/pdf/2305.17951v1.pdf | ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER | Prompt-based language models have produced encouraging results in numerous applications, including Named Entity Recognition (NER) tasks. NER aims to identify entities in a sentence and provide their types. However, the strong performance of most available NER approaches is heavily dependent on the design of discrete pr... | ['Mihhail Matskin', 'Dumitru Roman', 'Ahmet Soylu', 'Amir H. Payberah', 'Amirhossein Layegh'] | 2023-05-29 | null | null | null | null | ['prompt-engineering', 'few-shot-ner', 'named-entity-recognition-ner'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-2.96177804e-01 1.33450096e-03 -1.74703702e-01 -5.35012066e-01
-9.89832938e-01 -9.15549636e-01 7.25838125e-01 4.49842364e-01
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1.71616271e-01 6.65687263e-01 7.13513196e-02 -2.92985350... | [9.728391647338867, 9.419586181640625] |
e496aeca-1e4c-4699-b6ba-f71054eaa73c | ban-jian-du-kua-ling-yu-yu-yi-yi-cun-fen-xi | null | null | https://aclanthology.org/2020.ccl-1.73 | https://aclanthology.org/2020.ccl-1.73.pdf | 半监督跨领域语义依存分析技术研究(Semi-supervised Domain Adaptation for Semantic Dependency Parsing) | 近年来,尽管深度学习给语义依存分析带来了长足的进步,但由于语义依存分析数据标注代价非常高昂,并且在单领域上性能较好的依存分析器迁移到其他领域时,其性能会大幅度下降。因此为了使其走向实用,就必须解决领域适应问题。本文提出一个新的基于对抗学习的领域适应依存分析模型,我们提出了基于对抗学习的共享双编码器结构,并引入领域私有辅助任务和正交约束,同时也探究了多种预训练模型在跨领域依存分析任务上的效果和性能。 | ['Yanqiu Shao', 'Huayong Li', 'Dazhan Mao'] | null | null | null | null | ccl-2020-10 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-5.88519394e-01 -8.64324749e-01 4.91214335e-01 2.56608993e-01
8.75649691e-01 -1.25613153e+00 2.79896528e-01 7.23184347e-01
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-1.47517592e-01 -1.10090876e+00 2.12255642e-01 -1.31574678e+00
-4.56909895e-01 1.58194470e+00 6.22203588e-01 -5.28015792... | [-3.3159289360046387, 6.907735347747803] |
dc690895-2779-4d9e-98fb-edc5286cbeb2 | attribute-based-progressive-fusion-network | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/20187 | https://www.aaai.org/AAAI22Papers/AAAI-7747.XiaoY.pdf | Attribute-Based Progressive Fusion Network for RGBT Tracking | RGBT tracking usually suffers from various challenging factors of fast motion, scale variation, illumination variation,thermal crossover and occlusion, to name a few. Existing works often study fusion models to solve all challenges simultaneously, which requires fusion models complex enough and training data large enou... | ['Jin Tang', 'Lei Liu', 'Chenglong Li', 'Mengmeng Yang', 'Yun Xiao'] | 2022-01-26 | null | null | null | aaai2022-2022-1 | ['rgb-t-tracking'] | ['computer-vision'] | [-1.70849822e-02 -6.86857104e-01 -3.04993056e-02 -3.26667041e-01
-6.07727945e-01 -4.57073808e-01 3.88447940e-01 -3.25397998e-01
-3.17828536e-01 5.31737685e-01 -1.12168142e-03 8.70946944e-02
-6.59090951e-02 -6.11941040e-01 -5.60282767e-01 -1.00594497e+00
4.36770022e-01 3.46759856e-02 5.48686445e-01 -1.71397895... | [6.372369766235352, -2.200573682785034] |
ad06c659-e46f-478b-b28f-b4fcb768962a | language-identification-and-normalization-of | null | null | https://aclanthology.org/2020.icon-demos.12 | https://aclanthology.org/2020.icon-demos.12.pdf | Language Identification and Normalization of Code Mixed English and Punjabi Text | Code mixing is prevalent when users use two or more languages while communicating. It becomes more complex when users prefer romanized text to Unicode typing. The automatic processing of social media data has become one of popular areas of interest. Especially since COVID period the involvement of youngsters has attain... | ['Dr. Simpel Rani', 'Dr. Vishal Goyal', 'Neetika Bansal'] | null | null | null | null | icon-2020-12 | ['transliteration'] | ['natural-language-processing'] | [ 1.74756959e-01 -1.12795196e-01 -8.83160904e-02 -2.23611012e-01
-6.10406876e-01 -7.83800960e-01 5.77874184e-01 5.99248946e-01
-7.94277728e-01 6.34341717e-01 2.98609763e-01 -6.46195769e-01
2.50400662e-01 -6.09321654e-01 -8.76218826e-02 -2.02259690e-01
3.09352666e-01 6.20697737e-01 6.47913516e-02 -3.43789786... | [9.904030799865723, 10.227667808532715] |
97334287-fcb4-4b0a-b916-eac8e3b7ac0d | beyond-3d-siamese-tracking-a-motion-centric | 2203.01730 | null | https://arxiv.org/abs/2203.01730v1 | https://arxiv.org/pdf/2203.01730v1.pdf | Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds | 3D single object tracking (3D SOT) in LiDAR point clouds plays a crucial role in autonomous driving. Current approaches all follow the Siamese paradigm based on appearance matching. However, LiDAR point clouds are usually textureless and incomplete, which hinders effective appearance matching. Besides, previous methods... | ['Zhen Li', 'Shuguang Cui', 'Shenghui Cheng', 'Baoyuan Wang', 'Haiming Zhang', 'Xu Yan', 'Chaoda Zheng'] | 2022-03-03 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zheng_Beyond_3D_Siamese_Tracking_A_Motion-Centric_Paradigm_for_3D_Single_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zheng_Beyond_3D_Siamese_Tracking_A_Motion-Centric_Paradigm_for_3D_Single_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-single-object-tracking'] | ['computer-vision'] | [-1.16723396e-01 -4.15459752e-01 -2.69821376e-01 -3.08009647e-02
-5.78448176e-01 -5.64743459e-01 5.76583683e-01 -1.89803895e-02
-5.08392811e-01 3.00995469e-01 -4.38760787e-01 -3.31606984e-01
1.04697004e-01 -4.56745237e-01 -8.15627694e-01 -4.83220518e-01
1.61839828e-01 5.25770783e-01 1.09125447e+00 -2.91642070... | [6.659002780914307, -2.2641286849975586] |
de5a03ce-cfed-46f1-abbc-0ac02c126ba8 | leveraging-pre-trained-bert-for-audio | 2203.02838 | null | https://arxiv.org/abs/2203.02838v2 | https://arxiv.org/pdf/2203.02838v2.pdf | Leveraging Pre-trained BERT for Audio Captioning | Audio captioning aims at using natural language to describe the content of an audio clip. Existing audio captioning systems are generally based on an encoder-decoder architecture, in which acoustic information is extracted by an audio encoder and then a language decoder is used to generate the captions. Training an aud... | ['Wenwu Wang', 'Volkan Kılıç', 'Mark D. Plumbley', 'Haohe Liu', 'Jinzheng Zhao', 'Jianyuan Sun', 'Qiushi Huang', 'Xinhao Mei', 'Xubo Liu'] | 2022-03-06 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 5.03208518e-01 4.53666717e-01 9.95571613e-02 -2.77826905e-01
-1.40815043e+00 -4.10130650e-01 4.64196146e-01 5.44682220e-02
-3.15959901e-01 6.27653301e-01 7.84118176e-01 -7.94708356e-02
5.04988790e-01 -4.12615895e-01 -1.20332348e+00 -1.85037032e-01
8.64182860e-02 5.88876665e-01 4.24958169e-02 -2.03102455... | [15.285037994384766, 4.910487651824951] |
1dc5609b-6f14-487c-a538-e4005d06cc46 | graph-cnn-for-moving-object-detection-in | 2207.06440 | null | https://arxiv.org/abs/2207.06440v1 | https://arxiv.org/pdf/2207.06440v1.pdf | Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos | Moving Object Detection (MOD) is a fundamental step for many computer vision applications. MOD becomes very challenging when a video sequence captured from a static or moving camera suffers from the challenges: camouflage, shadow, dynamic backgrounds, and lighting variations, to name a few. Deep learning methods have b... | ['Thierry Bouwmans', 'Naoufel Werghi', 'Sajid Javed', 'Jhony H. Giraldo'] | 2022-07-13 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 5.59078157e-01 -5.01925528e-01 1.40932918e-01 -1.02845468e-01
-2.84788758e-01 -5.28131664e-01 3.97993296e-01 1.58009492e-02
-6.09837115e-01 6.90323710e-01 -3.07142049e-01 -2.98383266e-01
4.14811611e-01 -5.94746113e-01 -9.23931181e-01 -8.67981434e-01
1.19532108e-01 2.13403478e-01 7.48859406e-01 -2.03493889... | [9.00193977355957, -0.6215392351150513] |
a6106e98-2aa0-4aab-a4e4-db6340255c56 | f-coref-fast-accurate-and-easy-to-use | 2209.04280 | null | https://arxiv.org/abs/2209.04280v4 | https://arxiv.org/pdf/2209.04280v4.pdf | F-coref: Fast, Accurate and Easy to Use Coreference Resolution | We introduce fastcoref, a python package for fast, accurate, and easy-to-use English coreference resolution. The package is pip-installable, and allows two modes: an accurate mode based on the LingMess architecture, providing state-of-the-art coreference accuracy, and a substantially faster model, F-coref, which is the... | ['Yoav Goldberg', 'Arie Cattan', 'Shon Otmazgin'] | 2022-09-09 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [-3.76360774e-01 1.00751065e-01 -2.24629447e-01 -5.09775996e-01
-1.71827638e+00 -9.44110870e-01 6.25583649e-01 1.07654437e-01
-6.07733905e-01 6.62843645e-01 7.82062769e-01 -5.35541296e-01
-9.20503587e-02 -3.46982181e-01 -4.41552401e-01 -3.56784433e-01
1.82577550e-01 1.05427182e+00 1.22098848e-01 -2.56492019... | [9.343511581420898, 9.548988342285156] |
c07ac1a4-a170-4a63-b225-f625aafaa668 | smart-filter-aided-domain-adversarial-neural | 2307.01429 | null | https://arxiv.org/abs/2307.01429v1 | https://arxiv.org/pdf/2307.01429v1.pdf | Smart filter aided domain adversarial neural network: An unsupervised domain adaptation method for fault diagnosis in noisy industrial scenarios | The application of unsupervised domain adaptation (UDA)-based fault diagnosis methods has shown significant efficacy in industrial settings, facilitating the transfer of operational experience and fault signatures between different operating conditions, different units of a fleet or between simulated and real data. How... | ['Olga Fink', 'Qi Li', 'Tianfu Li', 'Gaëtan Frusque', 'Baorui Dai'] | 2023-07-04 | null | null | null | null | ['unsupervised-domain-adaptation'] | ['methodology'] | [ 2.67626911e-01 -2.41172418e-01 5.19821882e-01 2.01488435e-02
-7.20355093e-01 -4.97698247e-01 3.96627933e-01 -2.76811957e-01
1.04454450e-01 6.27541542e-01 -2.41678983e-01 -2.50432700e-01
-7.36562490e-01 -8.05823565e-01 -6.28660262e-01 -1.04906380e+00
-1.76002696e-01 4.71830308e-01 1.02912530e-01 -3.70906919... | [6.822889804840088, 2.3591582775115967] |
4cb582e9-0285-42f4-a13b-70ea3fa540ba | discovering-nonlinear-resonances-through | 2104.13471 | null | https://arxiv.org/abs/2104.13471v2 | https://arxiv.org/pdf/2104.13471v2.pdf | Discovering nonlinear resonances through physics-informed machine learning | For an ensemble of nonlinear systems that model, for instance, molecules or photonic systems, we propose a method that finds efficiently the configuration that has prescribed transfer properties. Specifically, we use physics-informed machine-learning (PIML) techniques to find the parameters for the efficient transfer o... | ['G. P. Tsironis', 'G. D. Barmparis'] | 2021-04-27 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 5.06467402e-01 -7.25010633e-02 -3.09234500e-01 -1.12951867e-01
-7.16114342e-01 -5.06677151e-01 3.58262777e-01 8.33807737e-02
-4.30121571e-01 1.17856228e+00 -2.90244222e-01 -2.96001136e-01
-2.65254229e-01 -1.04545712e+00 -1.02993071e+00 -1.44189286e+00
-1.12136967e-01 5.15113294e-01 -1.55859426e-01 -7.45484382... | [5.257725715637207, 5.199962139129639] |
1d509d89-d913-4485-ade3-b1a104d025fa | youmakeup-vqa-challenge-towards-fine-grained | 2004.05573 | null | https://arxiv.org/abs/2004.05573v1 | https://arxiv.org/pdf/2004.05573v1.pdf | YouMakeup VQA Challenge: Towards Fine-grained Action Understanding in Domain-Specific Videos | The goal of the YouMakeup VQA Challenge 2020 is to provide a common benchmark for fine-grained action understanding in domain-specific videos e.g. makeup instructional videos. We propose two novel question-answering tasks to evaluate models' fine-grained action understanding abilities. The first task is \textbf{Facial ... | ['Qin Jin', 'Weiying Wang', 'Shizhe Chen', 'Ludan Ruan', 'Linli Yao'] | 2020-04-12 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [ 1.17327444e-01 4.58120980e-04 -1.43919840e-01 -6.39278948e-01
-9.69476402e-01 -6.53987825e-01 6.79046273e-01 -4.98267770e-01
-2.74049550e-01 5.70594668e-01 6.94683015e-01 3.17224227e-02
1.03298962e-01 -2.95492619e-01 -1.22202945e+00 -4.28618968e-01
3.04688603e-01 2.43566975e-01 -5.39064668e-02 -2.77419508... | [10.433368682861328, 1.0382299423217773] |
9469fffa-6415-458b-9d51-7118316f8ed8 | skill-disentanglement-for-imitation-learning | 2306.07919 | null | https://arxiv.org/abs/2306.07919v1 | https://arxiv.org/pdf/2306.07919v1.pdf | Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations | Imitation learning has achieved great success in many sequential decision-making tasks, in which a neural agent is learned by imitating collected human demonstrations. However, existing algorithms typically require a large number of high-quality demonstrations that are difficult and expensive to collect. Usually, a tra... | ['Haifeng Chen', 'Wei Cheng', 'Yanchi Liu', 'Yuncong Chen', 'Xiang Zhang', 'Lu Wang', 'Suhang Wang', 'Wenchao Yu', 'Tianxiang Zhao'] | 2023-06-13 | null | null | null | null | ['imitation-learning', 'disentanglement'] | ['methodology', 'methodology'] | [ 4.28369492e-01 8.76131281e-02 -8.50279033e-02 -3.49752992e-01
-6.77753091e-01 -4.36423987e-01 3.91493946e-01 -1.25348076e-01
-7.38175511e-01 8.40725064e-01 7.19616264e-02 -5.78549840e-02
-3.08465600e-01 -4.02325660e-01 -1.00764143e+00 -6.94976211e-01
-2.76817650e-01 5.51944077e-01 -5.85869234e-03 -5.78885302... | [4.317742347717285, 1.2922570705413818] |
f1cc639c-b5b7-4f9a-bcb2-de604e5e6305 | gan-control-explicitly-controllable-gans | 2101.02477 | null | https://arxiv.org/abs/2101.02477v2 | https://arxiv.org/pdf/2101.02477v2.pdf | GAN-Control: Explicitly Controllable GANs | We present a framework for training GANs with explicit control over generated images. We are able to control the generated image by settings exact attributes such as age, pose, expression, etc. Most approaches for editing GAN-generated images achieve partial control by leveraging the latent space disentanglement proper... | ['Gerard Medioni', 'Igor Kviatkovsky', 'Nadav Bhonker', 'Alon Shoshan'] | 2021-01-07 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Shoshan_GAN-Control_Explicitly_Controllable_GANs_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Shoshan_GAN-Control_Explicitly_Controllable_GANs_ICCV_2021_paper.pdf | iccv-2021-1 | ['face-model'] | ['computer-vision'] | [ 4.86788392e-01 5.12730300e-01 -3.13329734e-02 -4.88662541e-01
-4.70362723e-01 -8.23884845e-01 1.13504243e+00 -5.87639034e-01
-9.65342522e-02 7.23080695e-01 2.57973969e-01 3.62338543e-01
1.92595378e-01 -9.67626333e-01 -8.63854527e-01 -8.45663309e-01
1.93632841e-01 5.63006401e-01 -5.76222539e-01 -3.21224958... | [12.356611251831055, -0.3038036525249481] |
aa0ba184-bcd0-47e8-ad91-9981860e0fb8 | simcrosstrans-a-simple-cross-modality | 2203.10456 | null | https://arxiv.org/abs/2203.10456v1 | https://arxiv.org/pdf/2203.10456v1.pdf | simCrossTrans: A Simple Cross-Modality Transfer Learning for Object Detection with ConvNets or Vision Transformers | Transfer learning is widely used in computer vision (CV), natural language processing (NLP) and achieves great success. Most transfer learning systems are based on the same modality (e.g. RGB image in CV and text in NLP). However, the cross-modality transfer learning (CMTL) systems are scarce. In this work, we study CM... | ['Ioannis Stamos', 'Xiaoke Shen'] | 2022-03-20 | null | null | null | null | ['object-detection-in-indoor-scenes'] | ['computer-vision'] | [-1.92632571e-01 1.23977114e-03 1.19752049e-01 -2.52630889e-01
-7.56367803e-01 -6.43092990e-01 6.49355352e-01 -5.94078958e-01
-6.79452479e-01 4.49765056e-01 -4.12998229e-01 -7.02380240e-01
2.71413147e-01 -9.70689893e-01 -1.25353551e+00 -7.18583882e-01
1.84674501e-01 2.86153048e-01 4.56727654e-01 -1.71050251... | [8.753670692443848, -1.9696396589279175] |
9da797a0-c5f6-429e-a459-fdf6c43fad87 | learning-to-define-terms-in-the-software | null | null | https://aclanthology.org/W18-6122 | https://aclanthology.org/W18-6122.pdf | Learning to Define Terms in the Software Domain | One way to test a person{'}s knowledge of a domain is to ask them to define domain-specific terms. Here, we investigate the task of automatically generating definitions of technical terms by reading text from the technical domain. Specifically, we learn definitions of software entities from a large corpus built from th... | ['Rose Catherine Kanjirathinkal', 'Vidhisha ran', 'Dheeraj Rajagopal', 'William Cohen', 'Balach'] | 2018-11-01 | null | null | null | ws-2018-11 | ['relationship-extraction-distant-supervised'] | ['natural-language-processing'] | [ 3.87727261e-01 6.20301366e-01 1.06318235e-01 -6.83889151e-01
-1.00427103e+00 -9.51969087e-01 8.98989618e-01 1.68716952e-01
-3.51028323e-01 9.22240973e-01 1.63020521e-01 -7.08798766e-01
1.01897471e-01 -9.78546262e-01 -7.43665397e-01 3.11254889e-01
2.88120747e-01 4.49041426e-01 1.54203773e-01 -5.07510841... | [10.508587837219238, 8.772260665893555] |
0177badc-0ce0-4e17-9e5d-bf21e54fd384 | structured-neural-summarization | 1811.01824 | null | https://arxiv.org/abs/1811.01824v4 | https://arxiv.org/pdf/1811.01824v4.pdf | Structured Neural Summarization | Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input. Based on the promising results of graph neural networks on highly structured data, we develop a framework to extend existing sequence encoders with a graph compone... | ['Marc Brockschmidt', 'Patrick Fernandes', 'Miltiadis Allamanis'] | 2018-11-05 | structured-neural-summarization-1 | https://openreview.net/forum?id=H1ersoRqtm | https://openreview.net/pdf?id=H1ersoRqtm | iclr-2019-5 | ['code-summarization'] | ['computer-code'] | [ 6.44831479e-01 7.45353043e-01 -3.20411474e-01 -4.83739167e-01
-4.63725716e-01 -6.09784067e-01 6.08679712e-01 8.00870836e-01
-2.58783728e-01 8.95051062e-01 1.20505488e+00 -6.40663981e-01
1.40377253e-01 -7.94932425e-01 -9.91687596e-01 -6.52282238e-02
-2.67892897e-01 5.21381259e-01 1.45238042e-01 -5.73747516... | [12.373994827270508, 9.435378074645996] |
3f572c86-b116-4759-9bb1-351c2036e643 | an-empirical-survey-of-data-augmentation-for-1 | 2106.07499 | null | https://arxiv.org/abs/2106.07499v1 | https://arxiv.org/pdf/2106.07499v1.pdf | An Empirical Survey of Data Augmentation for Limited Data Learning in NLP | NLP has achieved great progress in the past decade through the use of neural models and large labeled datasets. The dependence on abundant data prevents NLP models from being applied to low-resource settings or novel tasks where significant time, money, or expertise is required to label massive amounts of textual data.... | ['Diyi Yang', 'Mohit Bansal', 'Colin Raffel', 'Derek Tam', 'Jiaao Chen'] | 2021-06-14 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [ 6.05846882e-01 2.73870528e-01 -7.28715837e-01 -5.30770242e-01
-9.61687624e-01 -7.34906137e-01 6.25329852e-01 4.18790758e-01
-6.69875622e-01 9.64610338e-01 5.92269182e-01 -5.28123498e-01
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3.42235833e-01 7.64210165e-01 -4.53435302e-01 -3.31775546... | [10.78431510925293, 8.330680847167969] |
cfb4c829-4c64-4cfd-b241-b9a954721ba0 | gravitational-wave-detection-and-information | 2003.09995 | null | https://arxiv.org/abs/2003.09995v1 | https://arxiv.org/pdf/2003.09995v1.pdf | Gravitational Wave Detection and Information Extraction via Neural Networks | Laser Interferometer Gravitational-Wave Observatory (LIGO) was the first laboratory to measure the gravitational waves. It was needed an exceptional experimental design to measure distance changes much less than a radius of a proton. In the same way, the data analyses to confirm and extract information is a tremendousl... | ['Tiago A. E. Ferreira', 'Antonio de Pádua Santos', 'Marcela P. Figueiredo', 'Gerson R. Santos', 'Pavlos Protopapas'] | 2020-03-22 | null | null | null | null | ['gravitational-wave-detection'] | ['miscellaneous'] | [-3.40142578e-01 7.75172859e-02 1.85816467e-01 -2.22473279e-01
-9.35164467e-03 -4.03892756e-01 7.92601407e-01 -3.57824057e-01
-4.79763836e-01 9.34072316e-01 -1.96804181e-01 -5.36566734e-01
-2.27131784e-01 -1.20926487e+00 -3.04842979e-01 -1.19756079e+00
-1.31936833e-01 9.52979863e-01 1.63332924e-01 -1.17586605... | [7.3136444091796875, 3.2032761573791504] |
338331d2-9221-4ee3-8cb5-2f116cc6e359 | deformable-cross-attention-transformer-for | 2303.06179 | null | https://arxiv.org/abs/2303.06179v1 | https://arxiv.org/pdf/2303.06179v1.pdf | Deformable Cross-Attention Transformer for Medical Image Registration | Transformers have recently shown promise for medical image applications, leading to an increasing interest in developing such models for medical image registration. Recent advancements in designing registration Transformers have focused on using cross-attention (CA) to enable a more precise understanding of spatial cor... | ['Yong Du', 'Yufan He', 'Yihao Liu', 'Junyu Chen'] | 2023-03-10 | null | null | null | null | ['medical-image-registration'] | ['medical'] | [ 3.47820848e-01 3.67819541e-03 -2.01849312e-01 -4.85842168e-01
-1.27400827e+00 -1.57400459e-01 5.77220201e-01 3.36116731e-01
-6.59381986e-01 1.86396286e-01 2.97952414e-01 2.28348702e-01
-3.42253685e-01 -5.52859724e-01 -3.14139277e-01 -7.61800945e-01
-2.41634801e-01 5.84617138e-01 5.09678721e-01 -1.47247031... | [14.033514976501465, -2.6123087406158447] |
388ebac3-fbae-4baf-bf55-f2bf3976afc7 | coreference-for-discourse-parsing-a-neural | null | null | https://aclanthology.org/2020.codi-1.17 | https://aclanthology.org/2020.codi-1.17.pdf | Coreference for Discourse Parsing: A Neural Approach | We present preliminary results on investigating the benefits of coreference resolution features for neural RST discourse parsing by considering different levels of coupling of the discourse parser with the coreference resolver. In particular, starting with a strong baseline neural parser unaware of any coreference info... | ['Giuseppe Carenini', 'Grigorii Guz'] | null | null | null | null | emnlp-codi-2020-11 | ['discourse-parsing'] | ['natural-language-processing'] | [ 2.62787282e-01 1.15509951e+00 -9.72681791e-02 -4.08390850e-01
-1.04339683e+00 -7.40625441e-01 1.00415897e+00 2.34272212e-01
-5.75511754e-01 9.98631477e-01 1.12023294e+00 -4.06251341e-01
1.32333068e-02 -6.39013827e-01 -5.12390852e-01 -4.03273374e-01
-6.30422756e-02 8.64387214e-01 3.92508179e-01 -6.26250684... | [9.642701148986816, 9.503202438354492] |
d1adb2af-7b7b-4844-be70-b953b20d1e4f | a-simple-and-robust-correlation-filtering | null | null | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136950719.pdf | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136950719.pdf | A Simple and Robust Correlation Filtering Method for Text-based Person Search | Text-based person search aims to associate pedestrian images with natural language descriptions. In this task, extracting differentiated representations and aligning them among identities and descriptions is an essential yet challenging problem. Most of the previous methods depend on additional language parsers or visi... | ['Qi Wu', 'Yanning Zhang', 'Peng Wang', 'Yiqi Gao', 'Kai Niu', 'Mengyang Sun', 'Wei Suo'] | 2022-11-04 | null | null | null | eccv-2022-2022-11 | ['nlp-based-person-retrival', 'person-search'] | ['computer-vision', 'computer-vision'] | [-9.05068740e-02 -5.96670806e-01 -8.22658390e-02 -5.37595332e-01
-8.85276854e-01 -4.85329449e-01 5.83025455e-01 -7.78025836e-02
-6.21406376e-01 3.79202843e-01 3.87938082e-01 1.61936373e-01
-6.56753331e-02 -6.68736339e-01 -2.12501034e-01 -5.78930438e-01
5.79899132e-01 2.05218002e-01 4.91852194e-01 -1.61862001... | [14.701041221618652, 0.8367009162902832] |
61817757-f03d-4b22-8697-de38b54c024d | initialization-and-alignment-for-adversarial | 2207.14289 | null | https://arxiv.org/abs/2207.14289v1 | https://arxiv.org/pdf/2207.14289v1.pdf | Initialization and Alignment for Adversarial Texture Optimization | While recovery of geometry from image and video data has received a lot of attention in computer vision, methods to capture the texture for a given geometry are less mature. Specifically, classical methods for texture generation often assume clean geometry and reasonably well-aligned image data. While very recent metho... | ['Alexander G. Schwing', 'Zhizhen Zhao', 'Xiaoming Zhao'] | 2022-07-28 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 5.08516252e-01 8.42691585e-02 4.31717455e-01 -2.06784546e-01
-9.40148532e-01 -8.41850221e-01 5.19684792e-01 -1.24151126e-01
-2.32370034e-01 4.67477977e-01 -3.91725227e-02 1.81932151e-02
8.69992375e-02 -6.93592548e-01 -1.13423562e+00 -9.16646421e-01
1.92254364e-01 4.27859902e-01 1.33762211e-01 -2.22260490... | [9.2269868850708, -2.9874954223632812] |
8e5e82e2-f9c3-49d4-914b-9716c042f7ac | efficient-approximations-of-complete | 2306.10045 | null | https://arxiv.org/abs/2306.10045v4 | https://arxiv.org/pdf/2306.10045v4.pdf | Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction | We study property prediction for crystal materials. A crystal structure consists of a minimal unit cell that is repeated infinitely in 3D space. How to accurately represent such repetitive structures in machine learning models remains unresolved. Current methods construct graphs by establishing edges only between nearb... | ['Shuiwang Ji', 'Xiaoning Qian', 'Yi Liu', 'Youzhi Luo', 'Keqiang Yan', 'Yuchao Lin'] | 2023-06-12 | null | null | null | null | ['property-prediction', 'formation-energy'] | ['medical', 'miscellaneous'] | [ 1.8484822e-01 -3.8088579e-02 -1.2030058e-01 -1.9253570e-01
-7.6484609e-01 -3.7262738e-01 6.3686472e-01 6.2217343e-01
-2.7087766e-01 1.1211421e+00 1.5141882e-01 -5.4822731e-01
-5.6858912e-02 -1.2723883e+00 -1.2468115e+00 -8.6141843e-01
-5.0271869e-01 6.4446813e-01 3.2965338e-01 -2.4691322e-01
4.7301751e-01... | [5.248155117034912, 5.547995090484619] |
3866b4a9-264d-463e-9d1f-8853ee36ba5c | a-machine-learning-and-feature-engineering | 2303.10183 | null | https://arxiv.org/abs/2303.10183v1 | https://arxiv.org/pdf/2303.10183v1.pdf | A machine learning and feature engineering approach for the prediction of the uncontrolled re-entry of space objects | The continuously growing number of objects orbiting around the Earth is expected to be accompanied by an increasing frequency of objects re-entering the Earth's atmosphere. Many of these re-entries will be uncontrolled, making their prediction challenging and subject to several uncertainties. Traditionally, re-entry pr... | ['Camilla Colombo', 'Mirko Trisolini', 'Francesco Salmaso'] | 2023-03-17 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-1.44562706e-01 -7.46591687e-02 2.88069397e-01 -2.73626477e-01
1.94217876e-01 -2.97036171e-01 1.01101816e+00 3.41307133e-01
-5.10868967e-01 7.26491570e-01 -8.61528888e-02 -1.06410652e-01
-4.11411941e-01 -8.35201085e-01 -5.51106215e-01 -7.95034707e-01
-4.77073252e-01 9.84775126e-01 2.86227614e-01 -6.81252718... | [6.7578206062316895, 2.9223499298095703] |
8d7fdc91-f77e-48cf-8b54-9750ad34f4f0 | stock-price-prediction-based-on-natural | null | null | https://www.semanticscholar.org/paper/Stock-Price-Prediction-Based-on-Natural-Language-Tang-Lei/fd9760b7128943cf7632741136a042ad0c987605 | https://downloads.hindawi.com/journals/complexity/2022/9031900.pdf | Stock Price Prediction Based on Natural Language Processing | The keywords used in traditional stock price prediction are mainly based on literature and experience. This paper designs a new text mining method for keywords augmentation based on natural language processing models including Bidirectional Encoder Representation from Transformers (BERT) and Neural Contextualized Repre... | ['Dan Ma', 'Manru Dong', 'Nuo Lei', 'Xiaobin Tang'] | 2022-05-06 | null | null | null | complexity-2022-5 | ['stock-price-prediction'] | ['time-series'] | [-4.72082943e-01 -2.30732143e-01 -5.54712474e-01 -1.08506292e-01
-1.38144091e-01 -2.30474040e-01 7.35378385e-01 -5.95433377e-02
-6.38073325e-01 7.20827401e-01 8.63923848e-01 -5.76186121e-01
-1.03639707e-01 -1.12224746e+00 -3.95455450e-01 -4.52192873e-01
-2.25521281e-01 1.87142879e-01 -5.96509799e-02 -5.83122492... | [4.425688743591309, 4.269797325134277] |
e8390baa-6ebf-494a-ba4e-f78758935020 | guided-nonlocal-patch-regularization-and | 2210.04184 | null | https://arxiv.org/abs/2210.04184v1 | https://arxiv.org/pdf/2210.04184v1.pdf | Guided Nonlocal Patch Regularization and Efficient Filtering-Based Inversion for Multiband Fusion | In multiband fusion, an image with a high spatial and low spectral resolution is combined with an image with a low spatial but high spectral resolution to produce a single multiband image having high spatial and spectral resolutions. This comes up in remote sensing applications such as pansharpening~(MS+PAN), hyperspec... | ['Kunal N. Chaudhury', 'Pravin Nair', 'Unni V. S.'] | 2022-10-09 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 7.05698371e-01 -4.71988916e-01 2.84622282e-01 -3.41245532e-02
-1.07174587e+00 -2.16754660e-01 2.59472042e-01 -8.78964663e-02
-2.54096299e-01 8.40985119e-01 3.98649900e-05 -2.90775187e-02
-5.94960570e-01 -1.04020405e+00 -7.21031606e-01 -1.36401594e+00
2.13467792e-01 1.23953521e-01 -1.88478142e-01 -2.32572272... | [10.278660774230957, -2.141742706298828] |
8e856870-69ee-4611-a4e7-6b36d4419c10 | mono-sf-multi-view-geometry-meets-single-view | 1908.06316 | null | https://arxiv.org/abs/1908.06316v1 | https://arxiv.org/pdf/1908.06316v1.pdf | Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes | Existing 3D scene flow estimation methods provide the 3D geometry and 3D motion of a scene and gain a lot of interest, for example in the context of autonomous driving. These methods are traditionally based on a temporal series of stereo images. In this paper, we propose a novel monocular 3D scene flow estimation metho... | ['Fabian Brickwedde', 'Steffen Abraham', 'Rudolf Mester'] | 2019-08-17 | mono-sf-multi-view-geometry-meets-single-view-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Brickwedde_Mono-SF_Multi-View_Geometry_Meets_Single-View_Depth_for_Monocular_Scene_Flow_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Brickwedde_Mono-SF_Multi-View_Geometry_Meets_Single-View_Depth_for_Monocular_Scene_Flow_ICCV_2019_paper.pdf | iccv-2019-10 | ['scene-flow-estimation'] | ['computer-vision'] | [-1.09591633e-01 -4.80655432e-01 -5.35071827e-02 -4.79194552e-01
-1.49612144e-01 -5.83022237e-01 6.26772523e-01 -5.38286567e-01
-4.14921820e-01 5.19949317e-01 2.67096549e-01 -2.21420735e-01
1.63622439e-01 -8.55732977e-01 -6.16758108e-01 -7.11335242e-01
2.91121691e-01 2.74617244e-02 5.37907004e-01 -1.49226457... | [8.63717269897461, -2.1751766204833984] |
048b8144-e9ff-4120-a0a0-5fd68bea517f | ceasing-hate-withmoh-hate-speech-detection-in | 2110.09393 | null | https://arxiv.org/abs/2110.09393v1 | https://arxiv.org/pdf/2110.09393v1.pdf | Ceasing hate withMoH: Hate Speech Detection in Hindi-English Code-Switched Language | Social media has become a bedrock for people to voice their opinions worldwide. Due to the greater sense of freedom with the anonymity feature, it is possible to disregard social etiquette online and attack others without facing severe consequences, inevitably propagating hate speech. The current measures to sift the o... | ['Minni Jain', 'Anubha Kabra', 'Arushi Sharma'] | 2021-10-18 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [-3.61316979e-01 -5.17531186e-02 -5.21198139e-02 1.42457888e-01
-7.14154899e-01 -7.12173283e-01 8.51236761e-01 1.65686578e-01
-5.85122466e-01 7.21538126e-01 3.36216271e-01 -2.81152546e-01
1.61187127e-01 -5.26614428e-01 -2.72571266e-01 -4.74292040e-01
2.81060278e-01 4.00794566e-01 1.53134912e-01 -7.02714503... | [8.854646682739258, 10.582487106323242] |
4abc5b52-d7e9-48b8-ad57-0fe71350efeb | fast-and-efficient-scene-categorization-for | 2210.14981 | null | https://arxiv.org/abs/2210.14981v1 | https://arxiv.org/pdf/2210.14981v1.pdf | Fast and Efficient Scene Categorization for Autonomous Driving using VAEs | Scene categorization is a useful precursor task that provides prior knowledge for many advanced computer vision tasks with a broad range of applications in content-based image indexing and retrieval systems. Despite the success of data driven approaches in the field of computer vision such as object detection, semantic... | ['John McDonald', 'Ganesh Sistu', 'Jonathan Horgan', 'Saravanabalagi Ramachandran'] | 2022-10-26 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 4.10073370e-01 -3.93863976e-01 -4.89913344e-01 -6.42447948e-01
-4.94659573e-01 -4.36200321e-01 9.68605638e-01 5.15928090e-01
-5.81995249e-01 5.72418943e-02 7.53939524e-02 -1.00403823e-01
-4.71879482e-01 -9.97853637e-01 -3.23270589e-01 -9.61530685e-01
6.96571767e-02 4.70380902e-01 3.88225913e-01 1.70491692... | [9.87964916229248, 1.8992953300476074] |
65e52a0c-48ff-4120-8289-ce645afac668 | tifa-accurate-and-interpretable-text-to-image | 2303.11897 | null | https://arxiv.org/abs/2303.11897v2 | https://arxiv.org/pdf/2303.11897v2.pdf | TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering | Despite thousands of researchers, engineers, and artists actively working on improving text-to-image generation models, systems often fail to produce images that accurately align with the text inputs. We introduce TIFA (Text-to-Image Faithfulness evaluation with question Answering), an automatic evaluation metric that ... | ['Noah A. Smith', 'Ranjay Krishna', 'Mari Ostendorf', 'Yizhong Wang', 'Jungo Kasai', 'Benlin Liu', 'Yushi Hu'] | 2023-03-21 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 4.65513676e-01 -4.79293019e-02 1.71021089e-01 -3.82022083e-01
-1.02578151e+00 -9.33057249e-01 9.45098579e-01 5.93197048e-02
-8.11760575e-02 5.25930941e-01 2.85592437e-01 -3.71308118e-01
2.08845839e-01 -8.43030632e-01 -7.50628531e-01 -1.10417046e-01
7.61006832e-01 4.38864887e-01 3.47956657e-01 -1.78935483... | [11.092588424682617, 1.3046820163726807] |
5f7cbedd-3a8e-4917-9738-3816d5207527 | kaer-a-knowledge-augmented-pre-trained | 2301.04770 | null | https://arxiv.org/abs/2301.04770v1 | https://arxiv.org/pdf/2301.04770v1.pdf | KAER: A Knowledge Augmented Pre-Trained Language Model for Entity Resolution | Entity resolution has been an essential and well-studied task in data cleaning research for decades. Existing work has discussed the feasibility of utilizing pre-trained language models to perform entity resolution and achieved promising results. However, few works have discussed injecting domain knowledge to improve t... | ['Bertram Ludäscher', 'Vetle I. Torvik', 'Yiren Liu', 'Lan Li', 'Liri Fang'] | 2023-01-12 | null | null | null | null | ['general-knowledge', 'entity-resolution'] | ['miscellaneous', 'natural-language-processing'] | [-1.45975202e-01 4.17579800e-01 -7.36331046e-01 -2.39174172e-01
-1.11694431e+00 -3.95725429e-01 5.31133592e-01 3.11323225e-01
-5.59934616e-01 9.95572031e-01 4.00055617e-01 -2.31578364e-03
-1.37298286e-01 -7.93603063e-01 -9.31597233e-01 -5.38699217e-02
1.58322498e-01 6.32149398e-01 6.77651837e-02 -3.97393256... | [9.4421968460083, 8.698395729064941] |
a3af5cb8-83bc-476c-ab13-6dcdfbc5a861 | unsupervised-learning-of-camera-pose-with | 2001.06479 | null | https://arxiv.org/abs/2001.06479v1 | https://arxiv.org/pdf/2001.06479v1.pdf | Unsupervised Learning of Camera Pose with Compositional Re-estimation | We consider the problem of unsupervised camera pose estimation. Given an input video sequence, our goal is to estimate the camera pose (i.e. the camera motion) between consecutive frames. Traditionally, this problem is tackled by placing strict constraints on the transformation vector or by incorporating optical flow t... | ['Seyed Shahabeddin Nabavi', 'Ramin Fahimi', 'Mehrdad Hosseinzadeh', 'Yang Wang'] | 2020-01-17 | null | null | null | null | ['depth-and-camera-motion'] | ['computer-vision'] | [ 3.28820020e-01 -1.60653055e-01 7.29994848e-03 -1.80141613e-01
-6.18041039e-01 -9.13446665e-01 5.55387557e-01 4.40576524e-02
-6.37496889e-01 2.91648299e-01 2.22963616e-02 1.01358436e-01
3.52600932e-01 -4.49399382e-01 -8.27580690e-01 -6.47914708e-01
4.33330089e-01 3.71554375e-01 6.63121700e-01 1.20605588... | [8.055578231811523, -1.9251925945281982] |
f81b94d5-2fda-48ae-a3ff-4550da1e741d | on-efficient-reinforcement-learning-for-full | 2209.11553 | null | https://arxiv.org/abs/2209.11553v1 | https://arxiv.org/pdf/2209.11553v1.pdf | On Efficient Reinforcement Learning for Full-length Game of StarCraft II | StarCraft II (SC2) poses a grand challenge for reinforcement learning (RL), of which the main difficulties include huge state space, varying action space, and a long time horizon. In this work, we investigate a set of RL techniques for the full-length game of StarCraft II. We investigate a hierarchical RL approach invo... | ['Tong Lu', 'Yang Yu', 'Wenhai Wang', 'Zhou-Yu Meng', 'Zhen-Jia Pang', 'Ruo-Ze Liu'] | 2022-09-23 | null | null | null | null | ['starcraft-ii', 'starcraft'] | ['playing-games', 'playing-games'] | [-2.02564344e-01 -1.47067755e-02 -3.03628713e-01 2.22474307e-01
-7.51170337e-01 -7.20967650e-01 4.82420921e-01 -3.63890946e-01
-8.42461526e-01 9.85644460e-01 -5.06306477e-02 -6.04044497e-01
-1.57181686e-03 -9.06545997e-01 -9.04357195e-01 -8.35786223e-01
-4.39708978e-01 5.65500557e-01 6.17938042e-01 -9.96779084... | [3.6614513397216797, 1.5624895095825195] |
feeabe14-2efa-4ed6-a916-13c6b1c6fada | benchie-open-information-extraction | 2109.06850 | null | https://arxiv.org/abs/2109.06850v2 | https://arxiv.org/pdf/2109.06850v2.pdf | BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation | Intrinsic evaluations of OIE systems are carried out either manually -- with human evaluators judging the correctness of extractions -- or automatically, on standardized benchmarks. The latter, while much more cost-effective, is less reliable, primarily because of the incompleteness of the existing OIE benchmarks: the ... | ['Goran Glavaš', 'Mathias Niepert', 'Carolin Lawrence', 'Bhushan Kotnis', 'Mingying Yu', 'Kiril Gashteovski'] | 2021-09-14 | null | https://aclanthology.org/2022.acl-long.307 | https://aclanthology.org/2022.acl-long.307.pdf | acl-2022-5 | ['open-information-extraction'] | ['natural-language-processing'] | [-9.52677354e-02 4.17883039e-01 -3.20205748e-01 -1.96200199e-02
-7.98814714e-01 -9.62385833e-01 7.42269635e-01 3.31710756e-01
-4.40199435e-01 9.06447709e-01 3.81233960e-01 -2.68279970e-01
-2.43406892e-01 -9.39971626e-01 -6.88186467e-01 9.12010670e-03
2.50224680e-01 5.60043395e-01 2.28922844e-01 -3.12452435... | [9.427845001220703, 8.59784984588623] |
1303cd1d-ac40-4a4c-84a1-2c26debf18e7 | learning-to-brachiate-via-simplified-model | 2205.03943 | null | https://arxiv.org/abs/2205.03943v1 | https://arxiv.org/pdf/2205.03943v1.pdf | Learning to Brachiate via Simplified Model Imitation | Brachiation is the primary form of locomotion for gibbons and siamangs, in which these primates swing from tree limb to tree limb using only their arms. It is challenging to control because of the limited control authority, the required advance planning, and the precision of the required grasps. We present a novel appr... | ['Michiel Van de Panne', 'Hung Yu Ling', 'Daniele Reda'] | 2022-05-08 | null | null | null | null | ['humanoid-control'] | ['robots'] | [-6.12407140e-02 1.66112393e-01 -1.36952788e-01 2.45063245e-01
-2.50709772e-01 -1.04732990e+00 3.86952758e-01 -2.06618175e-01
-4.40474957e-01 9.70099747e-01 -2.51155674e-01 -1.85850456e-01
-3.75541955e-01 -4.40964043e-01 -6.36752248e-01 -6.85897291e-01
-8.26590180e-01 9.66227472e-01 3.77020419e-01 -7.61249781... | [4.6299967765808105, 1.011357307434082] |
72ff66b7-00ea-4e8b-b33e-16d556175769 | nonautoregressive-encoder-decoder-neural | null | null | https://ieeexplore.ieee.org/document/9634849/authors#authors | https://ieeexplore.ieee.org/document/9634849/authors#authors | Nonautoregressive Encoder-Decoder Neural Framework for End-to-End Aspect-Based Sentiment Triplet Extraction | Aspect-based sentiment triplet extraction (ASTE) aims at recognizing the joint triplets from texts, i.e., aspect terms, opinion expressions, and correlated sentiment polarities. As a newly proposed task, ASTE depicts the complete sentiment picture from different perspectives to better facilitate real-world applications... | ['Donghong Ji', 'Yue Zhang', 'Yafeng Ren', 'Hao Fei'] | 2021-12-03 | null | null | null | ieee-2021-12 | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [ 2.56402075e-01 -1.12693354e-01 -1.16415299e-01 -5.83107233e-01
-9.66260374e-01 -4.48692083e-01 3.15530926e-01 -2.64879931e-02
-1.60351694e-01 4.59879130e-01 4.36666042e-01 -1.60142705e-01
-8.17682520e-02 -4.64245111e-01 -5.55640996e-01 -8.36565018e-01
2.94806153e-01 2.43010953e-01 -2.77445763e-01 -4.58353102... | [11.515095710754395, 6.607597827911377] |
4df7895b-1c8e-4189-8e0e-bf55ed22aca2 | estimating-semantic-similarity-between-in | 2306.01206 | null | https://arxiv.org/abs/2306.01206v1 | https://arxiv.org/pdf/2306.01206v1.pdf | Estimating Semantic Similarity between In-Domain and Out-of-Domain Samples | Prior work typically describes out-of-domain (OOD) or out-of-distribution (OODist) samples as those that originate from dataset(s) or source(s) different from the training set but for the same task. When compared to in-domain (ID) samples, the models have been known to usually perform poorer on OOD samples, although th... | ['Ameeta Agrawal', 'Rhitabrat Pokharel'] | 2023-06-01 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.43812940e-01 1.59687251e-01 -3.50633919e-01 -4.37496334e-01
-8.42633605e-01 -5.57483673e-01 9.78496492e-01 4.15591598e-01
-6.24459423e-02 2.66724139e-01 1.97159961e-01 -2.83081215e-02
-1.98096499e-01 -5.01627445e-01 -3.89254928e-01 -5.55863976e-01
-3.95151675e-02 6.75526559e-01 1.24925591e-01 4.87617075... | [9.202067375183105, 3.151486396789551] |
3bd38ada-5a89-44d5-aedc-ab57f42d4fde | open-extraction-of-fine-grained-political | null | null | https://aclanthology.org/D15-1008 | https://aclanthology.org/D15-1008.pdf | Open Extraction of Fine-Grained Political Statements | null | ['Noah A. Smith', 'David Bamman'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.283380031585693, 3.650078535079956] |
db838f34-90e5-4984-8a05-66424ddd8f59 | compensating-supervision-incompleteness-with | 1910.00462 | null | https://arxiv.org/abs/1910.00462v1 | https://arxiv.org/pdf/1910.00462v1.pdf | Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image Interpretation | Semantic Image Interpretation is the task of extracting a structured semantic description from images. This requires the detection of visual relationships: triples (subject,relation,object) describing a semantic relation between a subject and an object. A pure supervised approach to visual relationship detection requir... | ['Luciano Serafini', 'Ivan Donadello'] | 2019-10-01 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 2.33280495e-01 2.79633820e-01 -4.16391701e-01 -5.93872070e-01
-5.76197989e-02 -2.58854389e-01 7.98542857e-01 5.12865782e-01
-3.62460315e-02 5.72866082e-01 9.24803242e-02 -1.86996490e-01
-3.12704325e-01 -1.01655924e+00 -4.52425390e-01 -3.53699625e-01
-8.66766647e-02 4.10604089e-01 6.40779734e-01 -3.51232380... | [10.336624145507812, 1.9752446413040161] |
2ec3fcfa-c1c1-46d0-a4f3-6670ff098aba | raw-multi-channel-audio-source-separation | 1803.00702 | null | http://arxiv.org/abs/1803.00702v1 | http://arxiv.org/pdf/1803.00702v1.pdf | Raw Multi-Channel Audio Source Separation using Multi-Resolution Convolutional Auto-Encoders | Supervised multi-channel audio source separation requires extracting useful
spectral, temporal, and spatial features from the mixed signals. The success of
many existing systems is therefore largely dependent on the choice of features
used for training. In this work, we introduce a novel multi-channel,
multi-resolution... | ['Emad M. Grais', 'Mark D. Plumbley', 'Dominic Ward'] | 2018-03-02 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 2.84045666e-01 -9.32939947e-01 3.17684919e-01 -1.55838266e-01
-1.40002370e+00 -6.08353198e-01 1.10561438e-01 -9.70358402e-02
-3.06859910e-01 5.83668113e-01 2.20873967e-01 6.50654361e-02
-5.35244584e-01 -3.77956778e-01 -2.80506730e-01 -6.96184278e-01
-2.49662608e-01 -2.25993037e-01 6.98712245e-02 -2.03396007... | [15.401833534240723, 5.5369062423706055] |
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